def run_msim(self, new_deaths=False):
''' Run the simulation -- if new_deaths, fit to daily deaths rather than cumulative cases + deaths '''
if self.n_runs == 1:
sim = self.sim
sim.run()
else:
msim = cv.MultiSim(base_sim=self.sim)
msim.run(n_runs=self.n_runs)
sim = msim.reduce(output=True)
if new_deaths:
offset = cv.daydiff(sim['start_day'], sim.data['date'][0])
d_data = self.smooth(sim.data['new_deaths'].values)
d_sim = self.smooth(sim.results['new_deaths'].values[offset:])
minlen = min(len(d_data), len(d_sim))
d_data = d_data[:minlen]
d_sim = d_sim[:minlen]
deaths = {'deaths': dict(data=d_data, sim=d_sim, weights=1)}
sim.compute_fit(custom=deaths,
keys=['cum_diagnoses', 'cum_deaths'],
weights={
'cum_diagnoses': 0.2,
'cum_deaths': 0.2
},
output=False)
else:
sim.compute_fit(output=False)
self.sim = sim
self.mismatch = sim.results.fit.mismatch
return sim
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 create_sim(pars=None,
label=None,
use_safegraph=True,
show_intervs=False,
people=None,
school_reopening_pars=None,
teacher_test_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
if 'end_day' not in p:
end_day = '2020-05-30'
p['end_day'] = end_day
# Basic parameters and sim creation
pars = {
'pop_size': 225e3,
'pop_scale': 10,
'pop_type': 'synthpops',
'pop_infected': 400,
'beta': p.beta,
'start_day': '2020-01-27',
'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),
'beta_layer': dict(h=p.bl_h, s=p.bl_s, w=p.bl_w, c=p.bl_c, l=p.bl_l),
}
pars.update({'n_days': cv.daydiff(pars['start_day'], pars['end_day'])})
# 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
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.
# Define testing interventions
test_kwargs = dict(daily_tests=sim.data['new_tests'],
quar_test=1.0,
test_delay=2)
tn1 = cv.test_num(symp_test=p.tn1,
start_day='2020-01-27',
end_day='2020-03-23',
**test_kwargs,
label='tn1')
tn2 = cv.test_num(symp_test=p.tn2,
start_day='2020-03-24',
end_day='2020-04-14',
**test_kwargs,
label='tn2')
tn3 = cv.test_num(symp_test=p.tn3,
start_day='2020-04-15',
end_day=None,
**test_kwargs,
label='tn3')
interventions = [tn1, tn2, tn3]
ttq_scen = school_reopening_pars['ttq_scen']
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='2020-06-10', **tp),
cv.contact_tracing(start_day='2020-06-10', **ct)
]
# Define beta interventions (for school reopening)
b_ch = sc.objdict()
b_days = [
'2020-03-04', '2020-03-12', '2020-03-23', '2020-04-25', '2020-08-30'
]
b_ch.w = [1.00, p.bc_wc1, p.bc_wc2, p.bc_wc3, p.bc_wc3]
b_ch.c = [1.00, p.bc_wc1, p.bc_wc2, p.bc_wc3, p.bc_wc3]
NPI_schools = school_reopening_pars['NPI_schools']
if NPI_schools is None:
b_ch.s = [1.00, 1.00, 1.00, 1.00, 1.00]
else:
b_ch.s = [1.00, 1.00, 1.00, 1.00, NPI_schools]
# LTCF intervention
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=f'beta_l')
]
for lkey, ch in b_ch.items():
interventions += [
cv.change_beta(days=b_days,
changes=b_ch[lkey],
layers=lkey,
label=f'beta_{lkey}')
]
# Define school closure interventions
network_change = school_reopening_pars['network_change']
if network_change:
popfile_new = popfile_change
else:
popfile_new = None
school_start_day = school_reopening_pars['school_start_day']
intervention_start_day = school_reopening_pars['intervention_start_day']
num_pos = None
test_prob = teacher_test_scen['test_prob']
trace_prob = teacher_test_scen['trace_prob']
mobility_file = school_reopening_pars['mobility_file']
interventions += [
cv.close_schools(day_schools_closed='2020-03-12',
start_day=school_start_day,
pop_file=popfile_new,
label='close_schools')
]
test_freq = teacher_test_scen['test_freq']
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)
]
# SafeGraph intervention
interventions += make_safegraph(sim, mobility_file)
sim['interventions'] = interventions
analyzers = [cv.age_histogram(datafile=age_data_file)]
sim['analyzers'] += analyzers
# Don't show interventions in plots, there are too many
if show_intervs == False:
for interv in sim['interventions']:
interv.do_plot = False
# These are copied from parameters.py -- needed to get younger and 60-65 groups right
sim['prognoses']['age_cutoffs'] = np.array(
[0, 15, 20, 30, 40, 50, 65, 70, 80, 90]) # Age cutoffs (upper limits)
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 create_sim(pars=None,
label=None,
use_safegraph=True,
show_intervs=False,
people=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
if 'end_day' not in p:
end_day = '2020-07-19'
p['end_day'] = end_day
# Basic parameters and sim creation
pars = {
'pop_size': 225e3,
'pop_scale': 10,
'pop_type': 'synthpops',
'pop_infected': 400,
'beta': p.beta,
'start_day': '2020-01-27',
'end_day': '2020-07-19',
'rescale': True,
'rescale_factor': 1.1,
'verbose': 0,
'rand_seed': p.rand_seed,
'analyzers': cv.age_histogram(datafile=age_data_file),
'beta_layer': dict(h=p.bl_h, s=p.bl_s, w=p.bl_w, c=p.bl_c, l=p.bl_l),
}
pars.update({'n_days': cv.daydiff(pars['start_day'], pars['end_day'])})
# 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'
# 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
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.
# Define testing interventions
test_kwargs = dict(daily_tests=sim.data['new_tests'],
quar_test=1.0,
test_delay=2)
tn1 = cv.test_num(symp_test=p.tn1,
start_day='2020-01-27',
end_day='2020-03-23',
**test_kwargs,
label='tn1')
tn2 = cv.test_num(symp_test=p.tn2,
start_day='2020-03-24',
end_day='2020-04-14',
**test_kwargs,
label='tn2')
tn3 = cv.test_num(symp_test=p.tn3,
start_day='2020-04-15',
end_day=None,
**test_kwargs,
label='tn3')
interventions = [tn1, tn2, tn3]
# Define beta interventions (for calibration)
b_ch = sc.objdict()
b_days = ['2020-03-04', '2020-03-12', '2020-04-25']
b_ch.s = [1.00, 0.00, 0.00]
b_ch.w = [p.bc_wc1, p.bc_wc2, p.bc_wc3]
b_ch.c = [p.bc_wc1, p.bc_wc2, p.bc_wc3]
for lkey, ch in b_ch.items():
interventions += [
cv.change_beta(days=b_days,
changes=b_ch[lkey],
layers=lkey,
label=f'beta_{lkey}')
]
# LTCF intervention
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=f'beta_l')
]
# SafeGraph intervention & tidy up
interventions += make_safegraph(sim)
sim['interventions'] = interventions
# Don't show interventions in plots, there are too many
if show_intervs == False:
for interv in sim['interventions']:
interv.do_plot = False
# These are copied from parameters.py -- needed to get younger and 60-65 groups right
sim['prognoses']['age_cutoffs'] = np.array(
[0, 15, 20, 30, 40, 50, 65, 70, 80, 90]) # Age cutoffs (upper limits)
return sim
import os
import sciris as sc
import numpy as np
import covasim as cv
t = cv.daydiff('2020-01-21', '2020-09-01')
scenarios = [
'no_masks', 'low_comp', 'med_comp', 'low_comp_notschools',
'med_comp_notschools'
]
testcases = [
## (scenario, trace, test, infections)
(0, 0.47, 0.03, 110000),
(0, 0.47, 0.12, 17000),
(1, 0.47, 0.03, 70000),
(1, 0.47, 0.12, 5800),
]
for scenario, trace, test, expected in testcases:
## run the simulation
cmd = "python UK_tradeoffs.py --samples 12 --scenario %s --test %.02f --trace %.02f" % (
scenario, test, trace)
os.system(cmd)
## load the results
outfile = "%s/test%.02f-trace%.02f.obj" % (scenarios[scenario], test,
trace)
results = sc.loadobj(outfile)
