def validate_pars(self):
''' Some parameters can take multiple types; this makes them consistent '''
# Handle start day
start_day = self['start_day'] # Shorten
if start_day in [None, 0]: # Use default start day
start_day = dt.datetime(2020, 1, 1)
if not isinstance(start_day, dt.datetime):
start_day = sc.readdate(start_day)
self['start_day'] = start_day # Convert back
# Handle population data
popdata_choices = ['random', 'bayesian', 'data']
if sc.isnumber(self['usepopdata']) or isinstance(
self['usepopdata'],
bool): # Convert e.g. usepopdata=1 to 'bayesian'
self['usepopdata'] = popdata_choices[int(
self['usepopdata'])] # Choose one of these
if self['usepopdata'] not in popdata_choices:
choice = self['usepopdata']
choicestr = ', '.join(popdata_choices)
errormsg = f'Population data option "{choice}" not available; choices are: {choicestr}'
raise ValueError(errormsg)
# Handle interventions
self['interventions'] = sc.promotetolist(self['interventions'],
keepnone=False)
return
def make_safegraph(sim, mobility_file):
''' Create interventions representing SafeGraph data '''
# Load data.values
if mobility_file is None:
fn = safegraph_file
else:
fn = mobility_file
df = pd.read_csv(fn)
week = df['week']
w = c = df['p.tot'].values
# w = df['p.emp'].values
# c = df['p.cust'].values
# Do processing
npts = len(week)
start_date = sc.readdate(week[0], dateformat='%Y-%m-%d')
start_day = sim.day(start_date)
sg_days = start_day + 7 * np.arange(npts)
# Create interventions
interventions = [
cv.clip_edges(days=sg_days, changes=w, layers='w', label='clip_w'),
cv.clip_edges(days=sg_days, changes=c, layers='c', label='clip_c'),
]
return interventions
def validate_pars(self):
''' Some parameters can take multiple types; this makes them consistent '''
# Handle start day
start_day = self['start_day'] # Shorten
if start_day in [None, 0]: # Use default start day
start_day = dt.date(2020, 1, 1)
elif sc.isstring(start_day):
start_day = sc.readdate(start_day)
if isinstance(start_day,dt.datetime):
start_day = start_day.date()
self['start_day'] = start_day
# Handle contacts
contacts = self['contacts']
if sc.isnumber(contacts): # It's a scalar instead of a dict, assume it's all contacts
self['contacts'] = {'a':contacts}
self['beta_layers'] = {'a':1.0}
# Handle population data
popdata_choices = ['random', 'microstructure', 'synthpops']
if sc.isnumber(self['pop_type']) or isinstance(self['pop_type'], bool): # Convert e.g. pop_type=1 to 'microstructure'
self['pop_type'] = popdata_choices[int(self['pop_type'])] # Choose one of these
if self['pop_type'] not in popdata_choices:
choice = self['pop_type']
choicestr = ', '.join(popdata_choices)
errormsg = f'Population type "{choice}" not available; choices are: {choicestr}'
raise ValueError(errormsg)
# Handle interventions
self['interventions'] = sc.promotetolist(self['interventions'], keepnone=False)
return
def day(obj, *args, start_day=None):
'''
Convert a string, date/datetime object, or int to a day (int), the number of
days since the start day. See also date() and daydiff(). Used primarily via
sim.day() rather than directly.
Args:
obj (str, date, int, or list): convert any of these objects to a day relative to the start day
args (list): additional days
start_day (str or date): the start day; if none is supplied, return days since 2020-01-01.
Returns:
days (int or str): the day(s) in simulation time
**Example**::
sim.day('2020-04-05') # Returns 35
'''
# Do not process a day if it's not supplied
if obj is None:
return None
if start_day is None:
start_day = '2020-01-01'
# Convert to list
if sc.isstring(obj) or sc.isnumber(obj) or isinstance(
obj, (dt.date, dt.datetime)):
obj = sc.promotetolist(obj) # Ensure it's iterable
elif isinstance(obj, np.ndarray):
obj = obj.tolist() # Convert to list if it's an array
obj.extend(args)
days = []
for d in obj:
if d is None:
days.append(d)
elif sc.isnumber(d):
days.append(int(d)) # Just convert to an integer
else:
try:
if sc.isstring(d):
d = sc.readdate(d).date()
elif isinstance(d, dt.datetime):
d = d.date()
d_day = (d - date(start_day)
).days # Heavy lifting -- actually compute the day
days.append(d_day)
except Exception as E:
errormsg = f'Could not interpret "{d}" as a date: {str(E)}'
raise ValueError(errormsg)
# Return an integer rather than a list if only one provided
if len(days) == 1:
days = days[0]
return days
def validate_pars(self):
''' Some parameters can take multiple types; this makes them consistent '''
# Handle types
for key in ['pop_size', 'pop_infected', 'pop_size', 'n_days']:
self[key] = int(self[key])
# Handle start day
start_day = self['start_day'] # Shorten
if start_day in [None, 0]: # Use default start day
start_day = dt.date(2020, 1, 1)
elif sc.isstring(start_day):
start_day = sc.readdate(start_day)
if isinstance(start_day, dt.datetime):
start_day = start_day.date()
self['start_day'] = start_day
# Handle contacts
contacts = self['contacts']
if sc.isnumber(
contacts
): # It's a scalar instead of a dict, assume it's all contacts
self['contacts'] = {'a': contacts}
# Handle key mismaches
beta_layer_keys = set(self.pars['beta_layer'].keys())
contacts_keys = set(self.pars['contacts'].keys())
quar_eff_keys = set(self.pars['quar_eff'].keys())
if not (beta_layer_keys == contacts_keys == quar_eff_keys):
errormsg = f'Layer parameters beta={beta_layer_keys}, contacts={contacts_keys}, quar_eff={quar_eff_keys} have inconsistent keys'
raise cvm.KeyNotFoundError(errormsg)
if self.people is not None:
pop_keys = set(self.people.contacts.keys())
if pop_keys != beta_layer_keys:
errormsg = f'Please update your parameter keys {beta_layer_keys} to match population keys {pop_keys}. You may find sim.reset_layer_pars() helpful.'
raise cvm.KeyNotFoundError(errormsg)
# Handle population data
popdata_choices = ['random', 'hybrid', 'clustered', 'synthpops']
choice = self['pop_type']
if choice not in popdata_choices:
choicestr = ', '.join(popdata_choices)
errormsg = f'Population type "{choice}" not available; choices are: {choicestr}'
raise cvm.KeyNotFoundError(errormsg)
# Handle interventions
self['interventions'] = sc.promotetolist(self['interventions'],
keepnone=False)
return
def begin_day(self, date):
''' Called at the beginning of each day to configure the school layer '''
dayname = sc.readdate(date).strftime('%A')
group = self.schedule[dayname]
self.school_day = group == 'all'
# Could modify layer based on group
if group == 'all':
# Start with the original layer, will remove uids at home later
self.layer = sc.dcp(self.base_layer) # needed?
uids = self.uids
else:
self.layer = cv.Layer() # Empty
uids = np.empty(0, dtype='int64')
return uids # Everyone is scheduled for school today, unless it's a weekend
def date(obj, *args, **kwargs):
'''
Convert a string or a datetime object to a date object. To convert to an integer
from the start day, use sim.date() instead.
Args:
obj (str, date, datetime): the object to convert
args (str, date, datetime): additional objects to convert
Returns:
dates (date or list): either a single date object, or a list of them
**Examples**::
cv.date('2020-04-05') # Returns datetime.date(2020, 4, 5)
'''
# Convert to list
if sc.isstring(obj) or sc.isnumber(obj) or isinstance(
obj, (dt.date, dt.datetime)):
obj = sc.promotetolist(obj) # Ensure it's iterable
obj.extend(args)
dates = []
for d in obj:
try:
if type(
d
) == dt.date: # Do not use isinstance, since must be the exact type
pass
elif sc.isstring(d):
d = sc.readdate(d).date()
elif isinstance(d, dt.datetime):
d = d.date()
else:
errormsg = f'Could not interpret "{d}" of type {type(d)} as a date'
raise TypeError(errormsg)
dates.append(d)
except Exception as E:
errormsg = f'Conversion of "{d}" to a date failed: {str(E)}'
raise ValueError(errormsg)
# Return an integer rather than a list if only one provided
if len(dates) == 1:
dates = dates[0]
return dates
def day(self, day, *args):
'''
Convert a string, date/datetime object, or int to a day (int).
Args:
day (str, date, int, or list): convert any of these objects to a day relative to the simulation's start day
Returns:
days (int or str): the day(s) in simulation time
**Example**::
sim.day('2020-04-05') # Returns 35
'''
# Do not process a day if it's not supplied
if day is None:
return None
# Convert to list
if sc.isstring(day) or sc.isnumber(day) or isinstance(
day, (dt.date, dt.datetime)):
day = sc.promotetolist(day) # Ensure it's iterable
day.extend(args)
days = []
for d in day:
if sc.isnumber(d):
days.append(int(d)) # Just convert to an integer
else:
try:
if sc.isstring(d):
d = sc.readdate(d).date()
elif isinstance(d, dt.datetime):
d = d.date()
d_day = (d - self['start_day']).days
days.append(d_day)
except Exception as E:
errormsg = f'Could not interpret "{d}" as a date: {str(E)}'
raise ValueError(errormsg)
# Return an integer rather than a list if only one provided
if len(days) == 1:
days = days[0]
return days
def begin_day(self, date):
''' Called at the beginning of each day to configure the school layer '''
dayname = sc.readdate(date).strftime('%A')
group = self.schedule[dayname]
self.school_day = group in ['A', 'B']
# Could modify layer based on group
if group == 'A':
self.layer = sc.dcp(self.A_base_layer) # needed?
uids = self.A_group
elif group == 'B':
self.layer = sc.dcp(self.B_base_layer) # needed?
uids = self.B_group
else:
uids = np.empty(0, dtype='int64')
self.layer = cv.Layer() # Empty
return uids # Hybrid scheduling
def test_readdate():
sc.heading('Test string-to-date conversion')
string1 = '2020-Mar-21'
string2 = '2020-03-21'
string3 = 'Sat Mar 21 23:13:56 2020'
dateobj1 = sc.readdate(string1)
dateobj2 = sc.readdate(string2)
sc.readdate(string3)
assert dateobj1 == dateobj2
with pytest.raises(ValueError):
sc.readdate('Not a date')
# Automated tests
formats_to_try = sc.readdate(return_defaults=True)
for key, fmt in formats_to_try.items():
datestr = sc.getdate(dateformat=fmt)
dateobj = sc.readdate(datestr, dateformat=fmt)
print(f'{key:15s} {fmt:22s}: {dateobj}')
return dateobj1
def test_base():
sc.heading('Testing base.py sim...')
json_path = 'base_tests.json'
sim_path = 'base_tests.sim'
# Create a small sim for later use
sim = cv.Sim(pop_size=100, verbose=verbose)
sim.run()
# Check setting invalid key
with pytest.raises(sc.KeyNotFoundError):
po = cv.ParsObj(pars={'a': 2, 'b': 3})
po.update_pars({'c': 4})
# Printing result
r = cv.Result()
print(r)
print(r.npts)
# Day and date conversion
daystr = '2020-04-04'
sim.day(daystr)
sim.day(sc.readdate(daystr))
with pytest.raises(ValueError):
sim.day('not a date')
sim.date(34)
sim.date([34, 54])
sim.date(34, 54, as_date=True)
# BaseSim methods
sim.copy()
sim.export_results(filename=json_path)
sim.export_pars(filename=json_path)
sim.shrink(in_place=False)
for keep_people in [True, False]:
sim.save(filename=sim_path, keep_people=keep_people)
cv.Sim.load(sim_path)
# Tidy up
remove_files(json_path, sim_path)
return
def validate_pars(self):
''' Some parameters can take multiple types; this makes them consistent '''
# Handle start day
start_day = self['start_day'] # Shorten
if start_day in [None, 0]: # Use default start day
start_day = dt.date(2020, 1, 1)
elif sc.isstring(start_day):
start_day = sc.readdate(start_day)
if isinstance(start_day, dt.datetime):
start_day = start_day.date()
self['start_day'] = start_day
# Handle contacts
contacts = self['contacts']
if sc.isnumber(
contacts
): # It's a scalar instead of a dict, assume it's all contacts
self['contacts'] = {'a': contacts}
# Handle key mismaches
beta_layer_keys = set(self.pars['beta_layer'].keys())
contacts_keys = set(self.pars['contacts'].keys())
quar_eff_keys = set(self.pars['quar_eff'].keys())
if not (beta_layer_keys == contacts_keys == quar_eff_keys):
errormsg = f'Layer parameters beta={beta_layer_keys}, contacts={contacts_keys}, quar_eff={quar_eff_keys} are not consistent'
raise ValueError(errormsg)
# Handle population data
popdata_choices = ['random', 'hybrid', 'clustered', 'synthpops']
choice = self['pop_type']
if choice not in popdata_choices:
choicestr = ', '.join(popdata_choices)
errormsg = f'Population type "{choice}" not available; choices are: {choicestr}'
raise ValueError(errormsg)
# Handle interventions
self['interventions'] = sc.promotetolist(self['interventions'],
keepnone=False)
return
def make_safegraph(sim, use_interp=True):
''' Create interventions representing SafeGraph data '''
# Load data.values
fn = safegraph_file
df = pd.read_csv(fn)
week = df['week']
w = df["p.emp.no.schools"].values
c = df["p.cust.no.schools"].values
s = df["p.tot.schools"].values
# Do processing
npts = len(week)
start_date = sc.readdate(week[0], dateformat='%Y-%m-%d')
start_day = sim.day(start_date)
sg_days = start_day + 7 * np.arange(npts)
# Create interventions
interventions = [
cv.clip_edges(days=sg_days, changes=w, layers='w', label='clip_w'),
cv.clip_edges(days=sg_days, changes=c, layers='c', label='clip_c'),
cv.clip_edges(days=sg_days, changes=s, layers='s', label='clip_s'),
]
return interventions
def test_misc():
sc.heading('Testing miscellaneous functions')
sim_path = 'test_misc.sim'
json_path = 'test_misc.json'
gitinfo_path = 'test_misc.gitinfo'
fig_path = 'test_misc.png'
fig_comments = 'Test comment'
# Data loading
cv.load_data(csv_file)
cv.load_data(xlsx_file)
with pytest.raises(NotImplementedError):
cv.load_data('example_data.unsupported_extension')
with pytest.raises(ValueError):
cv.load_data(xlsx_file, columns=['missing_column'])
# Dates
d1 = cv.date('2020-04-04')
d2 = cv.date(sc.readdate('2020-04-04'))
ds = cv.date('2020-04-04', d2)
assert d1 == d2
assert d2 == ds[0]
with pytest.raises(ValueError):
cv.date([(2020, 4, 4)]) # Raises a TypeError which raises a ValueError
with pytest.raises(ValueError):
cv.date('Not a date')
cv.daydiff('2020-04-04')
# Run sim for more investigations
sim = cv.Sim(pop_size=500, verbose=0)
sim.run()
sim.plot(do_show=False)
# Saving and loading
cv.savefig(fig_path, comments=fig_comments)
cv.save(filename=sim_path, obj=sim)
cv.load(filename=sim_path)
# Version checks
cv.check_version('0.0.0') # Nonsense version
print('↑ Should complain about version')
with pytest.raises(ValueError):
cv.check_version('0.0.0', die=True)
# Git checks
cv.git_info(json_path)
cv.git_info(json_path, check=True)
# Poisson tests
c1 = 5
c2 = 8
for alternative in ['two-sided', 'larger', 'smaller']:
cv.poisson_test(c1, c2, alternative=alternative)
for method in ['score', 'wald', 'sqrt', 'exact-cond']:
cv.poisson_test(c1, c2, method=method)
with pytest.raises(ValueError):
cv.poisson_test(c1, c2, method='not a method')
# Test locations
for location in [None, 'viet-nam']:
cv.data.show_locations(location)
# Test versions
with pytest.raises(ValueError):
cv.check_save_version('1.3.2', die=True)
cv.check_save_version(cv.__version__,
filename=gitinfo_path,
comments='Test')
# Test PNG
try:
metadata = cv.get_png_metadata(fig_path, output=True)
assert metadata['Covasim version'] == cv.__version__
assert metadata['Covasim comments'] == fig_comments
except ImportError as E:
print(
f'Cannot test PNG function since pillow not installed ({str(E)}), skipping'
)
# Tidy up
remove_files(sim_path, json_path, fig_path, gitinfo_path)
return
def single_sim_new(end_day='2020-05-10',
rand_seed=1,
dist='lognormal',
par1=10,
par2=170):
pop_type = 'hybrid'
pop_size = 225000
pop_scale = 2.25e6 / pop_size
start_day = '2020-01-27'
# end_day = '2020-05-01' # for calibration plots
# end_day = '2020-06-30' # for projection plots
pars = {
'verbose': 0,
'pop_size': pop_size,
'pop_infected': 30, # 300
'pop_type': pop_type,
'start_day': start_day,
'n_days': (sc.readdate(end_day) - sc.readdate(start_day)).days,
'pop_scale': pop_scale,
'rescale': True,
'beta': 0.015,
'rand_seed': rand_seed,
}
sim = cv.Sim(pars, datafile=datafile)
# Define beta interventions
b_days = ['2020-03-04', '2020-03-12', '2020-03-23']
b_ch = sc.objdict()
b_ch.h = [1.00, 1.10, 1.20]
b_ch.s = [1.00, 0.00, 0.00]
b_ch.w = [0.60, 0.40, 0.25]
b_ch.c = [0.60, 0.40, 0.25]
# Define testing interventions
daily_tests = sim.data['new_tests']
test_kwargs = {
'quar_test': 0,
'sensitivity': 1.0,
'test_delay': 0,
'loss_prob': 0
}
interventions = [
cv.test_num(daily_tests=daily_tests,
symp_test=70,
start_day='2020-01-27',
end_day=None,
swab_delay_dist={
'dist': dist,
'par1': par1,
'par2': par2
},
**test_kwargs),
]
for lkey, ch in b_ch.items():
interventions.append(
cv.change_beta(days=b_days, changes=b_ch[lkey], layers=lkey))
sim.update_pars(interventions=interventions)
sim.initialize()
# Define age susceptibility
mapping = {
0: 0.2,
20: 0.9,
40: 1.0,
70: 2.5,
80: 5.0,
90: 10.0,
}
for age, val in mapping.items():
sim.people.rel_sus[sim.people.age > age] = val
return sim
90: 10.0,
}
for age, val in mapping.items():
sim.people.rel_sus[sim.people.age > age] = val
return sim
if __name__ == "__main__":
time_num_old = []
time_num_new = []
time_num_flat = []
# 2020-05-10 - 2020-01-27
days = (sc.readdate("2020-05-10") - sc.readdate("2020-01-27")).days + 1
yield_num_old = np.zeros(days)
yield_num_new = np.zeros(days)
yield_num_flat = np.zeros(days)
# Need a couple of sims to make sure we are not looking at noise
stage_old = {'mild': 0, 'sev': 0, 'crit': 0}
stage_new = {'mild': 0, 'sev': 0, 'crit': 0}
stage_flat = {'mild': 0, 'sev': 0, 'crit': 0}
start = 1
end = 2
n_run = end - start
for i in range(start, end):
sim = single_sim_old(rand_seed=i)
t = sc.tic()
sim.run()
def create_sim(pars=None, use_safegraph=True, label=None, show_intervs=False, people=None, adjust_ORs=False,
num_pos=None, test_prob=None,
trace_prob=None, NPI_schools=None, test_freq=None, network_change=False, schedule=None,
school_start_day=None,
intervention_start_day=None, ttq_scen=None
):
''' Create a single simulation for further use '''
p = sc.objdict(sc.mergedicts(define_pars(which='best', kind='both', use_safegraph=use_safegraph), pars))
if 'rand_seed' not in p:
seed = 1
print(f'Note, could not find random seed in {pars}! Setting to {seed}')
p['rand_seed'] = seed # Ensure this exists
# Basic parameters and sim creation
pars = {'pop_size': 225e3,
'pop_scale': 10,
'pop_type': 'synthpops',
'pop_infected': p.pop_infected,
'beta': p.beta,
'start_day': '2020-02-01',
'end_day': p['end_day'],
'rescale': True,
'rescale_factor': 1.1,
'verbose': 0.1,
'rand_seed': p.rand_seed,
'analyzers': [cv.age_histogram(datafile=age_data_file,
states=['exposed', 'dead', 'tested', 'diagnosed', 'severe'])],
'beta_layer': dict(h=p.bl_h, s=p.bl_s, w=p.bl_w, c=p.bl_c, l=p.bl_l),
}
# If supplied, use an existing people object
if people:
popfile = people
else:
# Generate the population filename
n_popfiles = 5
popfile = popfile_stem + str(pars['rand_seed'] % n_popfiles) + '.ppl'
popfile_change = popfile_stem_change + str(pars['rand_seed'] % n_popfiles) + '.ppl'
# Check that the population file exists
if not os.path.exists(popfile):
errormsg = f'WARNING: could not find population file {popfile}! Please regenerate first'
raise FileNotFoundError(errormsg)
# Create and initialize the sim
print(f'Creating sim! safegraph={use_safegraph}, seed={p.rand_seed}')
sim = cv.Sim(pars, label=label, popfile=popfile, load_pop=True,
datafile=epi_data_file) # Create this here so can be used for test numbers etc.
interventions = []
# Testing bins
df = pd.read_csv(age_series_file)
age_bins = [0, 20, 40, 60, 80, np.inf]
test_num_subtargs = [ftest_num_subtarg1, ftest_num_subtarg2, ftest_num_subtarg3, ftest_num_subtarg4,
ftest_num_subtarg5]
for ia in range(len(age_bins) - 1):
label = '{}-{}'.format(age_bins[ia], age_bins[ia + 1] - 1)
df_ = df[(df['age'] >= age_bins[ia]) & (df['age'] < age_bins[ia + 1])]
# sum for the dates
df_ = df_.groupby('date').sum()
df_['age'] = age_bins[ia]
df_['datetime'] = [sc.readdate(d) for d in df_.index.values]
df_ = df_.set_index('datetime')
# make sure we have all the days we care about
new_index = pd.date_range(df_.index[0], df_.index[-1], freq='1d')
df_ = df_.reindex(new_index, fill_value=0.0, method='nearest')
test_kwargs = dict(daily_tests=df_['new_tests'], quar_test=1.0, test_delay=2, subtarget=test_num_subtargs[ia])
interventions += [cv.test_num(symp_test=p.tn1, start_day='2020-01-27', end_day='2020-03-23', **test_kwargs,
label='tn1 ' + label)]
interventions += [cv.test_num(symp_test=p.tn2, start_day='2020-03-24', end_day='2020-04-14', **test_kwargs,
label='tn2 ' + label)]
interventions += [cv.test_num(symp_test=p.tn3, start_day='2020-04-15', end_day='2020-05-07', **test_kwargs,
label='tn3 ' + label)]
interventions += [cv.test_num(symp_test=p.tn4, start_day='2020-05-08', end_day='2020-06-04', **test_kwargs,
label='tn4 ' + label)]
interventions += [cv.test_num(symp_test=p.tn5, start_day='2020-06-17', end_day='2020-06-18', **test_kwargs,
label='tn5 ' + label)]
interventions += [cv.test_num(symp_test=p.tn6, start_day='2020-06-19', end_day=None, **test_kwargs,
label='tn6 ' + label)]
# Seed in LTCF
interventions += [seed_ltcf(days=[15], seeds=[p.lseeds])]
# Define beta interventions
b_days = ['2020-03-02',
'2020-03-12',
'2020-03-23',
'2020-04-15',
'2020-05-08',
'2020-06-05',
'2020-06-19'
]
# Home and school beta changes
interventions += [
cv.change_beta(days=b_days[1], changes=p.bc_s, layers='s', label="beta_s")
]
# Work and community beta changes
b_days_wc = np.arange(sim.day(b_days[0]), sim.day(b_days[1]) + 1).tolist() + b_days[2:]
b_ch_wc = np.linspace(1.0, p.bc_wc1, len(b_days_wc) - 5).tolist() + [p.bc_wc2, p.bc_wc3, p.bc_wc4, p.bc_wc5,
p.bc_wc6]
for lkey in ['w', 'c']:
interventions += [cv.change_beta(days=b_days_wc, changes=b_ch_wc, layers=lkey, label=f'beta_{lkey}')]
# LTCF beta change
b_days_l = np.arange(sim.day(b_days[0]), sim.day(b_days[2]) + 1)
b_ch_l = np.linspace(1.0, p.bc_lf, len(b_days_l))
interventions += [cv.change_beta(days=b_days_l, changes=b_ch_l, layers='l', label='beta_l')]
# sim.people.contacts['c'] = remove_ltcf_community(sim) # Remove community contacts from LTCF
# Age-based beta changes
b_age_days = ["2020-05-08", "2020-06-05", "2020-06-19"]
b_age_changes = [[p.bc_age_u10_a, # 0
p.bc_age_10_20_a, # 10
p.bc_age_20_30_a, # 20
p.bc_age_30_40_a, # 30
1.0, # 40
1.0, # 50
p.bc_age_65u_a, # 65
p.bc_age_65u_a, # 70
p.bc_age_65u_a, # 80
p.bc_age_65u_a, # 90
],
[p.bc_age_u10_b, # 0
p.bc_age_10_20_b, # 10
p.bc_age_20_30_b, # 20
p.bc_age_30_40_b, # 30
1.0, # 40
1.0, # 50
p.bc_age_65u_b, # 65
p.bc_age_65u_b, # 70
p.bc_age_65u_b, # 80
p.bc_age_65u_b, # 90
],
[p.bc_age_u10_c, # 0
p.bc_age_10_20_c, # 10
p.bc_age_20_30_c, # 20
p.bc_age_30_40_c, # 30
1.0, # 40
1.0, # 50
p.bc_age_65u_c, # 65
p.bc_age_65u_c, # 70
p.bc_age_65u_c, # 80
p.bc_age_65u_c, # 90
]
]
interventions += [beta_change_age.change_beta_age(b_age_days, b_age_changes)]
# SafeGraph intervention & tidy up
if use_safegraph:
interventions += make_safegraph(sim)
if ttq_scen == 'lower':
tp = sc.objdict(
symp_prob=0.08,
asymp_prob=0.001,
symp_quar_prob=0.8,
asymp_quar_prob=0.1,
test_delay=2.0,
)
ct = sc.objdict(
trace_probs=0.01,
trace_time=3.0,
)
elif ttq_scen == 'medium':
tp = sc.objdict(
symp_prob=0.12,
asymp_prob=0.0015,
symp_quar_prob=0.8,
asymp_quar_prob=0.1,
test_delay=2.0,
)
ct = sc.objdict(
trace_probs=0.25,
trace_time=3.0,
)
elif ttq_scen == 'upper':
tp = sc.objdict(
symp_prob=0.24,
asymp_prob=0.003,
symp_quar_prob=0.8,
asymp_quar_prob=0.1,
test_delay=2.0,
)
ct = sc.objdict(
trace_probs=0.5,
trace_time=3.0,
)
if ttq_scen is not None:
interventions += [cv.test_prob(start_day='2020-07-10', **tp), cv.contact_tracing(start_day='2020-07-10', **ct)]
if network_change:
popfile_new = popfile_change
else:
popfile_new = None
if NPI_schools is not None:
interventions += [cv.change_beta(days=sim.day('2020-09-01'), changes=NPI_schools, layers='s')]
interventions += [cv.close_schools(day_schools_closed='2020-03-12', start_day=school_start_day,
pop_file=popfile_new)]
interventions += [cv.reopen_schools(start_day=intervention_start_day, num_pos=num_pos, test=test_prob,
trace=trace_prob, ili_prev=0.002, test_freq=test_freq, schedule=schedule)]
sim['interventions'] = interventions
# Don't show interventions in plots, there are too many
for interv in sim['interventions']:
interv.do_plot = False
# Prognoses (particularly to severe/hospitalizations) need attention
prognoses = sc.dcp(cv.get_prognoses())
prognoses['severe_probs'] *= prognoses[
'symp_probs'] # Conditional probability of symptoms becoming severe, given symptomatic
prognoses['crit_probs'] *= prognoses[
'severe_probs'] # Conditional probability of symptoms becoming critical, given severe
prognoses['death_probs'] *= prognoses['crit_probs'] # Conditional probability of dying, given critical symptoms
prognoses.update({'age_cutoff': np.array([0, 10, 20, 30, 40, 50, 65, 70, 80, 90])})
prognoses.update({'severe_probs': np.array([1, 1, 1, p.rsp1, p.rsp1, p.rsp1, p.rsp1, p.rsp1, p.rsp2, p.rsp2]) *
prognoses['severe_probs']})
prognoses['death_probs'] /= prognoses['crit_probs'] # Conditional probability of dying, given critical symptoms
prognoses['crit_probs'] /= prognoses[
'severe_probs'] # Conditional probability of symptoms becoming critical, given severe
prognoses['severe_probs'] /= prognoses[
'symp_probs'] # Conditional probability of symptoms becoming severe, given symptomatic
sim.pars.update({'prognoses': prognoses})
return sim
def test_base():
sc.heading('Testing base.py...')
json_path = 'base_tests.json'
sim_path = 'base_tests.sim'
# Create a small sim for later use
sim = cv.Sim(pop_size=100, verbose=verbose)
sim.run()
# Check setting invalid key
with pytest.raises(sc.KeyNotFoundError):
po = cv.ParsObj(pars={'a': 2, 'b': 3})
po.update_pars({'c': 4})
# Printing result
r = cv.Result()
print(r)
print(r.npts)
# Day and date conversion
daystr = '2020-04-04'
sim.day(daystr)
sim.day(sc.readdate(daystr))
with pytest.raises(ValueError):
sim.day('not a date')
sim.date(34)
sim.date([34, 54])
sim.date(34, 54, as_date=True)
# BaseSim methods
sim.copy()
sim.export_results(filename=json_path)
sim.export_pars(filename=json_path)
sim.shrink(in_place=False)
for keep_people in [True, False]:
sim.save(filename=sim_path, keep_people=keep_people)
cv.Sim.load(sim_path)
# BasePeople methods
ppl = sim.people
ppl.get(['susceptible', 'infectious'])
ppl.keys(which='all_states')
ppl.index()
ppl.resize(pop_size=200)
ppl.to_df()
ppl.to_arr()
ppl.person(50)
people = ppl.to_people()
ppl.from_people(people)
with pytest.raises(sc.KeyNotFoundError):
ppl.make_edgelist([{'invalid_key': [0, 1, 2]}])
# Contacts methods
contacts = ppl.contacts
df = contacts['a'].to_df()
ppl.remove_duplicates(df)
with pytest.raises(sc.KeyNotFoundError):
contacts['invalid_key']
contacts.values()
len(contacts)
# Transmission tree methods
ppl.transtree.make_targets()
ppl.make_detailed_transtree()
ppl.transtree.plot()
ppl.transtree.animate(animate=False)
# Tidy up
remove_files(json_path, sim_path)
return
90: 10.0,
}
for age, val in mapping.items():
sim.people.rel_sus[sim.people.age > age] = val
return sim
if __name__ == "__main__":
time_prob_old = []
time_prob_new = []
time_prob_flat = []
# 2020-05-10 - 2020-01-27
days = (sc.readdate("2020-05-10") - sc.readdate("2020-01-27")).days + 1
yield_prob_old = np.zeros(days)
yield_prob_new = np.zeros(days)
yield_prob_flat = np.zeros(days)
# Need a couple of sims to make sure we are not looking at noise
stage_old = {'mild': 0, 'sev': 0, 'crit': 0}
stage_new = {'mild': 0, 'sev': 0, 'crit': 0}
stage_flat = {'mild': 0, 'sev': 0, 'crit': 0}
start = 1
end = 11
n_run = end - start
for i in range(start, end):
sim = single_sim_old(rand_seed=i)
t = sc.tic()
sim.run()
sim = msim.base_sim
tt = sim.make_transtree()
#tt = sc.loadobj(f'{resfolder}/tt.obj')
layer_keys = list(sim.people.layer_keys())
layer_mapping = {k:i for i,k in enumerate(layer_keys)}
n_layers = len(layer_keys)
colors = sc.gridcolors(n_layers)
layer_counts = np.zeros((sim.npts, n_layers))
for source_ind, target_ind in tt.transmissions:
dd = tt.detailed[target_ind]
date = dd['date']
layer_num = layer_mapping[dd['layer']]
layer_counts[date, layer_num] += sim.rescale_vec[date]
lockdown1 = [sc.readdate('2020-03-23'),sc.readdate('2020-05-31')]
lockdown2 = [sc.readdate('2020-11-05'),sc.readdate('2020-12-03')]
lockdown3 = [sc.readdate('2021-01-04'),sc.readdate('2021-02-08')]
labels = ['Household', 'School', 'Workplace', 'Community']
for l in range(n_layers):
ax.plot(sim.datevec, layer_counts[:,l], c=colors[l], lw=3, label=labels[l])
ax.axvspan(lockdown1[0], lockdown1[1], color='steelblue', alpha=0.2, lw=0)
ax.axvspan(lockdown2[0], lockdown2[1], color='steelblue', alpha=0.2, lw=0)
ax.axvspan(lockdown3[0], lockdown3[1], color='lightblue', alpha=0.2, lw=0)
sc.setylim(ax=ax)
sc.boxoff(ax=ax)
ax.set_ylabel('Transmissions per day')
ax.set_xlim([sc.readdate('2020-01-21'), sc.readdate('2021-03-01')])
ax.xaxis.set_major_formatter(mdates.DateFormatter('%b\n%y'))
do_plot = 1
do_save = 1
do_show = 1
verbose = 1
seed = 1
version = 'v1'
date = '2020may26'
folder = f'results_FINAL_{date}'
file_path = f'{folder}/phase_{version}' # Completed below
data_path = 'UK_Covid_cases_may21.xlsx'
pop_path = f'{file_path}.pop'
fig_path = f'{file_path}.png'
#ig_paths = [f'results/testing_scen_{i}.png' for i in range(3)]
start_day = sc.readdate('2020-01-21')
end_day = sc.readdate('2021-05-31')
n_days = (end_day - start_day).days
# Set the parameters
#quar_eff = 0.8
#quar_effs = {k:quar_eff for k in 'hwsc'}
total_pop = 67.86e6 # UK population size
pop_size = 100e3 # Actual simulated population
ratio = int(total_pop / pop_size)
pop_scale = ratio
pop_type = 'hybrid'
#100% transmissibility of kids also ps=0.0135 for May and June
pop_infected = 4000
#beta=0.00485
beta = 0.007661
'''
import sciris as sc
import covasim as cv
intervs1 = [
cv.change_beta(30, 0.5),
cv.test_prob(symp_prob=0.1, start_day=20),
]
s1 = cv.Sim(start_day='2020-03-01', interventions=intervs1)
s1.run()
intervs2 = [
cv.change_beta('2020-03-31', 0.5),
cv.test_prob(symp_prob=0.1, start_day='2020-03-21'),
]
s2 = cv.Sim(start_day='2020-03-01', interventions=intervs2)
s2.run()
assert s1.summary == s2.summary
intervs3 = [
cv.change_beta(days=['2020-03-31', sc.readdate('2020-04-07'), 50], changes=[0.5, 0.3, 0.0]),
]
s3 = cv.Sim(start_day='2020-03-01', interventions=intervs3)
s3.run()
s3.plot()
def single_sim_old(end_day='2020-05-10', rand_seed=1):
pop_type = 'hybrid'
pop_size = 225000
pop_scale = 2.25e6 / pop_size
start_day = '2020-01-27'
# end_day = '2020-05-01' # for calibration plots
# end_day = '2020-06-30' # for projection plots
pars = {
'verbose': 0,
'pop_size': pop_size,
'pop_infected': 30, # 300
'pop_type': pop_type,
'start_day': start_day,
'n_days': (sc.readdate(end_day) - sc.readdate(start_day)).days,
'pop_scale': pop_scale,
'beta': 0.015,
'rand_seed': rand_seed,
}
sim = cv.Sim(pars, datafile=datafile)
# Define beta interventions
b_days = ['2020-03-04', '2020-03-12', '2020-03-23']
b_ch = sc.objdict()
b_ch.h = [1.00, 1.10, 1.20]
b_ch.s = [1.00, 0.00, 0.00]
b_ch.w = [0.60, 0.40, 0.25]
b_ch.c = [0.60, 0.40, 0.25]
# Define testing interventions
test_kwargs = {
'symp_prob': 0.05,
'asymp_prob': 0.0008,
'symp_quar_prob': 0,
'asymp_quar_prob': 0,
'test_delay': 0,
'test_sensitivity': 1.0,
'loss_prob': 0.0
}
interventions = [
test_prob_old(start_day='2020-01-27', **test_kwargs),
]
for lkey, ch in b_ch.items():
interventions.append(
cv.change_beta(days=b_days, changes=b_ch[lkey], layers=lkey))
sim.update_pars(interventions=interventions)
sim.initialize()
# Define age susceptibility
mapping = {
0: 0.2,
20: 0.9,
40: 1.0,
70: 2.5,
80: 5.0,
90: 10.0,
}
for age, val in mapping.items():
sim.people.rel_sus[sim.people.age > age] = val
return sim
def test_misc():
sc.heading('Testing miscellaneous functions')
sim_path = 'test_misc.sim'
json_path = 'test_misc.json'
# Data loading
cv.load_data(csv_file)
cv.load_data(xlsx_file)
with pytest.raises(NotImplementedError):
cv.load_data('example_data.unsupported_extension')
with pytest.raises(ValueError):
cv.load_data(xlsx_file, columns=['missing_column'])
# Dates
d1 = cv.date('2020-04-04')
d2 = cv.date(sc.readdate('2020-04-04'))
ds = cv.date('2020-04-04', d2)
assert d1 == d2
assert d2 == ds[0]
with pytest.raises(ValueError):
cv.date([(2020, 4, 4)]) # Raises a TypeError which raises a ValueError
with pytest.raises(ValueError):
cv.date('Not a date')
cv.daydiff('2020-04-04')
# Saving and loading
sim = cv.Sim()
cv.save(filename=sim_path, obj=sim)
cv.load(filename=sim_path)
# Version checks
cv.check_version('0.0.0') # Nonsense version
print('↑ Should complain about version')
with pytest.raises(ValueError):
cv.check_version('0.0.0', die=True)
# Git checks
cv.git_info(json_path)
cv.git_info(json_path, check=True)
# Poisson tests
c1 = 5
c2 = 8
for alternative in ['two-sided', 'larger', 'smaller']:
cv.poisson_test(c1, c2, alternative=alternative)
for method in ['score', 'wald', 'sqrt', 'exact-cond']:
cv.poisson_test(c1, c2, method=method)
with pytest.raises(ValueError):
cv.poisson_test(c1, c2, method='not a method')
# Tidy up
remove_files(sim_path, json_path)
return
def covid_tracking_date_to_date(d):
''' Date is in format e.g. 20201031 '''
out = sc.readdate(str(d)) # Should be a supported format
return out
def plot():
fig = pl.figure(num='Fig. 2: Transmission dynamics', figsize=(20,14))
piey, tsy, r3y = 0.68, 0.50, 0.07
piedx, tsdx, r3dx = 0.2, 0.9, 0.25
piedy, tsdy, r3dy = 0.2, 0.47, 0.35
pie1x, pie2x = 0.12, 0.65
tsx = 0.07
dispx, cumx, sympx = tsx, 0.33+tsx, 0.66+tsx
ts_ax = pl.axes([tsx, tsy, tsdx, tsdy])
pie_ax1 = pl.axes([pie1x, piey, piedx, piedy])
pie_ax2 = pl.axes([pie2x, piey, piedx, piedy])
symp_ax = pl.axes([sympx, r3y, r3dx, r3dy])
disp_ax = pl.axes([dispx, r3y, r3dx, r3dy])
cum_ax = pl.axes([cumx, r3y, r3dx, r3dy])
off = 0.06
txtdispx, txtcumx, txtsympx = dispx-off, cumx-off, sympx-off+0.02
tsytxt = tsy+tsdy
r3ytxt = r3y+r3dy
labelsize = 40-wf
pl.figtext(txtdispx, tsytxt, 'a', fontsize=labelsize)
pl.figtext(txtdispx, r3ytxt, 'b', fontsize=labelsize)
pl.figtext(txtcumx, r3ytxt, 'c', fontsize=labelsize)
pl.figtext(txtsympx, r3ytxt, 'd', fontsize=labelsize)
#%% Fig. 2A -- Time series plot
layer_keys = list(sim.layer_keys())
layer_mapping = {k:i for i,k in enumerate(layer_keys)}
n_layers = len(layer_keys)
colors = sc.gridcolors(n_layers)
layer_counts = np.zeros((sim.npts, n_layers))
for source_ind, target_ind in tt.count_transmissions():
dd = tt.detailed[target_ind]
date = dd['date']
layer_num = layer_mapping[dd['layer']]
layer_counts[date, layer_num] += sim.rescale_vec[date]
mar12 = cv.date('2020-03-12')
mar23 = cv.date('2020-03-23')
mar12d = sim.day(mar12)
mar23d = sim.day(mar23)
labels = ['Household', 'School', 'Workplace', 'Community', 'LTCF']
for l in range(n_layers):
ts_ax.plot(sim.datevec, layer_counts[:,l], c=colors[l], lw=3, label=labels[l])
sc.setylim(ax=ts_ax)
sc.boxoff(ax=ts_ax)
ts_ax.set_ylabel('Transmissions per day')
ts_ax.set_xlim([sc.readdate('2020-01-18'), sc.readdate('2020-06-09')])
ts_ax.xaxis.set_major_formatter(mdates.DateFormatter('%b-%d'))
ts_ax.set_xticks([sim.date(d, as_date=True) for d in np.arange(0, sim.day('2020-06-09'), 14)])
ts_ax.legend(frameon=False, bbox_to_anchor=(0.85,0.1))
color = [0.2, 0.2, 0.2]
ts_ax.axvline(mar12, c=color, linestyle='--', alpha=0.4, lw=3)
ts_ax.axvline(mar23, c=color, linestyle='--', alpha=0.4, lw=3)
yl = ts_ax.get_ylim()
labely = yl[1]*1.015
ts_ax.text(mar12, labely, 'Schools close ', color=color, alpha=0.9, style='italic', horizontalalignment='center')
ts_ax.text(mar23, labely, ' Stay-at-home', color=color, alpha=0.9, style='italic', horizontalalignment='center')
#%% Fig. 2A inset -- Pie charts
pre_counts = layer_counts[0:mar12d, :].sum(axis=0)
post_counts = layer_counts[mar23d:, :].sum(axis=0)
pre_counts = pre_counts/pre_counts.sum()*100
post_counts = post_counts/post_counts.sum()*100
lpre = [
f'Household\n{pre_counts[0]:0.1f}%',
f'School\n{pre_counts[1]:0.1f}% ',
f'Workplace\n{pre_counts[2]:0.1f}% ',
f'Community\n{pre_counts[3]:0.1f}%',
f'LTCF\n{pre_counts[4]:0.1f}%',
]
lpost = [
f'Household\n{post_counts[0]:0.1f}%',
f'School\n{post_counts[1]:0.1f}%',
f'Workplace\n{post_counts[2]:0.1f}%',
f'Community\n{post_counts[3]:0.1f}%',
f'LTCF\n{post_counts[4]:0.1f}%',
]
pie_ax1.pie(pre_counts, colors=colors, labels=lpre, **pieargs)
pie_ax2.pie(post_counts, colors=colors, labels=lpost, **pieargs)
pie_ax1.text(0, 1.75, 'Transmissions by layer\nbefore schools closed', style='italic', horizontalalignment='center')
pie_ax2.text(0, 1.75, 'Transmissions by layer\nafter stay-at-home', style='italic', horizontalalignment='center')
#%% Fig. 2B -- histogram by overdispersion
# Process targets
n_targets = tt.count_targets(end_day=mar12)
# Handle bins
max_infections = n_targets.max()
edges = np.arange(0, max_infections+2)
# Analysis
counts = np.histogram(n_targets, edges)[0]
bins = edges[:-1] # Remove last bin since it's an edge
norm_counts = counts/counts.sum()
raw_counts = counts*bins
total_counts = raw_counts/raw_counts.sum()*100
n_bins = len(bins)
index = np.linspace(0, 100, len(n_targets))
sorted_arr = np.sort(n_targets)
sorted_sum = np.cumsum(sorted_arr)
sorted_sum = sorted_sum/sorted_sum.max()*100
change_inds = sc.findinds(np.diff(sorted_arr) != 0)
pl.set_cmap('Spectral_r')
sscolors = sc.vectocolor(n_bins)
width = 1.0
for i in range(n_bins):
disp_ax.bar(bins[i], total_counts[i], width=width, facecolor=sscolors[i])
disp_ax.set_xlabel('Number of transmissions per case')
disp_ax.set_ylabel('Proportion of transmissions (%)')
sc.boxoff()
disp_ax.set_xlim([0.5, 32.5])
disp_ax.set_xticks(np.arange(0, 32.5, 4))
sc.boxoff(ax=disp_ax)
dpie_ax = pl.axes([dispx+0.05, 0.20, 0.2, 0.2])
trans1 = total_counts[1:3].sum()
trans2 = total_counts[3:5].sum()
trans3 = total_counts[5:8].sum()
trans4 = total_counts[8:].sum()
labels = [
f'1-2:\n{trans1:0.0f}%',
f' 3-4:\n {trans2:0.0f}%',
f'5-7: \n{trans3:0.0f}%\n',
f'>7: \n{trans4:0.0f}%\n',
]
dpie_args = sc.mergedicts(pieargs, dict(labeldistance=1.2)) # Slightly smaller label distance
dpie_ax.pie([trans1, trans2, trans3, trans4], labels=labels, colors=sscolors[[0,4,7,12]], **dpie_args)
#%% Fig. 2C -- cumulative distribution function
rev_ind = 100 - index
n_change_inds = len(change_inds)
change_bins = bins[counts>0][1:]
for i in range(n_change_inds):
ib = int(change_bins[i])
ci = change_inds[i]
ici = index[ci]
sci = sorted_sum[ci]
color = sscolors[ib]
if i>0:
cim1 = change_inds[i-1]
icim1 = index[cim1]
scim1 = sorted_sum[cim1]
cum_ax.plot([icim1, ici], [scim1, sci], lw=4, c=color)
cum_ax.scatter([ici], [sci], s=150, zorder=50-i, c=[color], edgecolor='w', linewidth=0.2)
if ib<=6 or ib in [8, 10, 25]:
xoff = 5 - 2*(ib==1) + 3*(ib>=10) + 1*(ib>=20)
yoff = 2*(ib==1)
cum_ax.text(ici-xoff, sci+yoff, ib, fontsize=18-wf, color=color)
cum_ax.set_xlabel('Proportion of primary infections (%)')
cum_ax.set_ylabel('Proportion of transmissions (%)')
xmin = -2
ymin = -2
cum_ax.set_xlim([xmin, 102])
cum_ax.set_ylim([ymin, 102])
sc.boxoff(ax=cum_ax)
# Draw horizontal lines and annotations
ancol1 = [0.2, 0.2, 0.2]
ancol2 = sscolors[0]
ancol3 = sscolors[6]
i01 = sc.findlast(sorted_sum==0)
i20 = sc.findlast(sorted_sum<=20)
i50 = sc.findlast(sorted_sum<=50)
cum_ax.plot([xmin, index[i01]], [0, 0], '--', lw=2, c=ancol1)
cum_ax.plot([xmin, index[i20], index[i20]], [20, 20, ymin], '--', lw=2, c=ancol2)
cum_ax.plot([xmin, index[i50], index[i50]], [50, 50, ymin], '--', lw=2, c=ancol3)
# Compute mean number of transmissions for 80% and 50% thresholds
q80 = sc.findfirst(np.cumsum(total_counts)>20) # Count corresponding to 80% of cumulative infections (100-80)
q50 = sc.findfirst(np.cumsum(total_counts)>50) # Count corresponding to 50% of cumulative infections
n80, n50 = [sum(bins[q:]*norm_counts[q:]/norm_counts[q:].sum()) for q in [q80, q50]]
# Plot annotations
kw = dict(bbox=dict(facecolor='w', alpha=0.9, lw=0), fontsize=20-wf)
cum_ax.text(2, 3, f'{index[i01]:0.0f}% of infections\ndo not transmit', c=ancol1, **kw)
cum_ax.text(8, 23, f'{rev_ind[i20]:0.0f}% of infections cause\n80% of transmissions\n(mean: {n80:0.1f} per infection)', c=ancol2, **kw)
cum_ax.text(14, 53, f'{rev_ind[i50]:0.0f}% of infections cause\n50% of transmissions\n(mean: {n50:0.1f} per infection)', c=ancol3, **kw)
#%% Fig. 2D -- histogram by date of symptom onset
# Calculate
asymp_count = 0
symp_counts = {}
minind = -5
maxind = 15
for _, target_ind in tt.transmissions:
dd = tt.detailed[target_ind]
date = dd['date']
delta = sim.rescale_vec[date] # Increment counts by this much
if dd['s']:
if tt.detailed[dd['source']]['date'] <= date: # Skip dynamical scaling reinfections
sdate = dd['s']['date_symptomatic']
if np.isnan(sdate):
asymp_count += delta
else:
ind = int(date - sdate)
if ind not in symp_counts:
symp_counts[ind] = 0
symp_counts[ind] += delta
# Convert to an array
xax = np.arange(minind-1, maxind+1)
sympcounts = np.zeros(len(xax))
for i,val in symp_counts.items():
if i<minind:
ind = 0
elif i>maxind:
ind = -1
else:
ind = sc.findinds(xax==i)[0]
sympcounts[ind] += val
# Plot
total_count = asymp_count + sympcounts.sum()
sympcounts = sympcounts/total_count*100
presymp = sc.findinds(xax<=0)[-1]
colors = ['#eed15b', '#ee943a', '#c3211a']
asymp_frac = asymp_count/total_count*100
pre_frac = sympcounts[:presymp].sum()
symp_frac = sympcounts[presymp:].sum()
symp_ax.bar(xax[0]-2, asymp_frac, label='Asymptomatic', color=colors[0])
symp_ax.bar(xax[:presymp], sympcounts[:presymp], label='Presymptomatic', color=colors[1])
symp_ax.bar(xax[presymp:], sympcounts[presymp:], label='Symptomatic', color=colors[2])
symp_ax.set_xlabel('Days since symptom onset')
symp_ax.set_ylabel('Proportion of transmissions (%)')
symp_ax.set_xticks([minind-3, 0, 5, 10, maxind])
symp_ax.set_xticklabels(['Asymp.', '0', '5', '10', f'>{maxind}'])
sc.boxoff(ax=symp_ax)
spie_ax = pl.axes([sympx+0.05, 0.20, 0.2, 0.2])
labels = [f'Asymp-\ntomatic\n{asymp_frac:0.0f}%', f' Presymp-\n tomatic\n {pre_frac:0.0f}%', f'Symp-\ntomatic\n{symp_frac:0.0f}%']
spie_ax.pie([asymp_frac, pre_frac, symp_frac], labels=labels, colors=colors, **pieargs)
return fig
def date(obj, *args, start_date=None, dateformat=None, as_date=True):
'''
Convert a string or a datetime object to a date object. To convert to an integer
from the start day, it is recommended you supply a start date, or use sim.date()
instead; otherwise, it will calculate the date counting days from 2020-01-01.
This means that the output of cv.date() will not necessarily match the output
of sim.date() for an integer input.
Args:
obj (str, date, datetime, list, array): the object to convert
args (str, date, datetime): additional objects to convert
start_date (str, date, datetime): the starting date, if an integer is supplied
dateformat (str): the format to return the date in
as_date (bool): whether to return as a datetime date instead of a string
Returns:
dates (date or list): either a single date object, or a list of them
**Examples**::
cv.date('2020-04-05') # Returns datetime.date(2020, 4, 5)
cv.date('2020-04-14', start_date='2020-04-04', as_date=False) # Returns 10
cv.date([35,36,37], as_date=False) # Returns ['2020-02-05', '2020-02-06', '2020-02-07']
'''
if obj is None:
return None
# Convert to list and handle other inputs
if isinstance(obj, np.ndarray):
obj = obj.tolist() # If it's an array, convert to a list
obj = sc.promotetolist(obj) # Ensure it's iterable
obj.extend(args)
if dateformat is None:
dateformat = '%Y-%m-%d'
if start_date is None:
start_date = '2020-01-01'
dates = []
for d in obj:
if d is None:
dates.append(d)
continue
try:
if type(
d
) == dt.date: # Do not use isinstance, since must be the exact type
pass
elif sc.isstring(d):
d = sc.readdate(d).date()
elif isinstance(d, dt.datetime):
d = d.date()
elif sc.isnumber(d):
if start_date is None:
errormsg = f'To convert the number {d} to a date, you must supply start_date'
raise ValueError(errormsg)
d = date(start_date) + dt.timedelta(days=int(d))
else:
errormsg = f'Cannot interpret {type(d)} as a date, must be date, datetime, or string'
raise TypeError(errormsg)
if as_date:
dates.append(d)
else:
dates.append(d.strftime(dateformat))
except Exception as E:
errormsg = f'Conversion of "{d}" to a date failed: {str(E)}'
raise ValueError(errormsg)
# Return an integer rather than a list if only one provided
if len(dates) == 1:
dates = dates[0]
return dates
def test_base():
sc.heading('Testing base.py...')
json_path = 'base_tests.json'
sim_path = 'base_tests.sim'
# Create a small sim for later use
sim = cv.Sim(pop_size=100, verbose=verbose)
sim.run()
# Check setting invalid key
with pytest.raises(sc.KeyNotFoundError):
po = cv.ParsObj(pars={'a':2, 'b':3})
po.update_pars({'c':4})
# Printing result
r = cv.Result()
print(r)
print(r.npts)
# Day and date conversion
daystr = '2020-04-04'
sim.day(daystr)
sim.day(sc.readdate(daystr))
with pytest.raises(ValueError):
sim.day('not a date')
sim.date(34)
sim.date([34, 54])
sim.date(34, 54, as_date=True)
# BaseSim methods
sim.copy()
sim.export_results(filename=json_path)
sim.export_pars(filename=json_path)
sim.shrink(in_place=False)
for keep_people in [True, False]:
sim.save(filename=sim_path, keep_people=keep_people)
cv.Sim.load(sim_path)
# BasePeople methods
ppl = sim.people
ppl.get(['susceptible', 'infectious'])
ppl.keys()
ppl.person_keys()
ppl.state_keys()
ppl.date_keys()
ppl.dur_keys()
ppl.indices()
ppl._resize_arrays(pop_size=200) # This only resizes the arrays, not actually create new people
ppl._resize_arrays(pop_size=100) # Change back
ppl.to_df()
ppl.to_arr()
ppl.person(50)
people = ppl.to_people()
ppl.from_people(people)
ppl.make_edgelist([{'new_key':[0,1,2]}])
ppl.brief()
# Contacts methods
contacts = ppl.contacts
df = contacts['a'].to_df()
ppl.remove_duplicates(df)
with pytest.raises(sc.KeyNotFoundError):
contacts['invalid_key']
contacts.values()
len(contacts)
print(contacts)
print(contacts['a'])
# Layer methods
hospitals_layer = cv.Layer()
contacts.add_layer(hospitals=hospitals_layer)
contacts.pop_layer('hospitals')
df = hospitals_layer.to_df()
hospitals_layer.from_df(df)
# Tidy up
remove_files(json_path, sim_path)
return