def check_similarity(self, actual, expected):
"""
Compare two population dictionaries using contact matrix
Assuming the canberra distance should be small
"""
passed = True
checked = False
for code in ['H', 'W', 'S']:
# check average contacts per age, if difference is small enough to tolerate
expected_contacts = utilities.get_average_contact_by_age(
expected, self.datadir, code=code)
print("expected contacts by age:\n", expected_contacts)
np.savetxt(os.path.join(
self.pdfDir,
f"pop{self.n}_seed_{self.seed}_{code}_average_contact.csv"),
expected_contacts,
delimiter=",")
actual_contacts = utilities.get_average_contact_by_age(
actual, self.datadir, code=code)
print("actual contacts by age:\n", actual_contacts)
max_difference = np.abs(expected_contacts - actual_contacts).max()
checked = True
print(
f"max_difference for contacts {code} in each age bracket:{max_difference}\n"
)
if max_difference > 1:
for option in ['density', 'frequency']:
print(f"\ncheck:{code} with {option}")
actual_matrix = sp.calculate_contact_matrix(
actual, density_or_frequency=option, setting_code=code)
# expected_matrix = sp.calculate_contact_matrix(expected, density_or_frequency=option, setting_code=code)
expected_matrix = np.loadtxt(os.path.join(
self.expectedDir,
f"pop{self.n}_seed_{self.seed}_{code}_{option}_contact_matrix.csv"
),
unpack=True,
delimiter=",")
np.savetxt(os.path.join(
self.pdfDir,
f"pop{self.n}_seed_{self.seed}_{code}_{option}_contact_matrix.csv"
),
expected_matrix,
delimiter=",")
# calculate Canberra distance
# assuming they should round to 0
d = np.abs(expected_matrix - actual_matrix).mean()
# d = distance.canberra(actual_matrix.flatten(), expected_matrix.flatten())
print(
f"mean absolute difference between actual/expected contact matrix for {code}/{option} is {str(round(d, 3))}"
)
if d > 1:
passed = passed & False
return checked & passed
def get_pop_details(self,
pop,
dir,
title_prefix,
location,
state_location,
country_location,
decimal=3):
os.makedirs(dir, exist_ok=True)
# want default age brackets and to see that they line up with the average contacts by age bracket created
age_brackets = spdd.get_census_age_brackets(self.datadir, location,
state_location,
country_location)
age_brackets_labels = [
str(age_brackets[b][0]) + '-' + str(age_brackets[b][-1])
for b in sorted(age_brackets.keys())
]
for setting_code in ['H', 'W', 'S']:
average_contacts = utilities.get_average_contact_by_age(
pop, self.datadir, setting_code=setting_code, decimal=decimal)
fmt = f'%.{str(decimal)}f'
# print(f"expected contacts by age for {code}:\n", average_contacts)
utilities.plot_array(
average_contacts,
datadir=self.figDir,
testprefix=
f"{self.n}_seed_{self.seed}_{setting_code}_average_contacts",
expect_label='Expected' if self.generateBaseline else 'Test',
names=age_brackets_labels,
xlabel_rotation=50)
sc.savejson(os.path.join(
dir,
f"{self.n}_seed_{self.seed}_{setting_code}_average_contact.json"
),
dict(enumerate(average_contacts.tolist())),
indent=2)
for method in ['density', 'frequency']:
matrix = sp.calculate_contact_matrix(pop, method, setting_code)
brackets = spdd.get_census_age_brackets(
self.datadir, location, state_location, country_location)
ageindex = spb.get_age_by_brackets_dic(brackets)
agg_matrix = spb.get_aggregate_matrix(matrix, ageindex)
textfile = os.path.join(
dir,
f"{self.n}_seed_{self.seed}_{setting_code}_{method}_contact_matrix.csv"
)
np.savetxt(textfile, agg_matrix, delimiter=",", fmt=fmt)
fig = sp.plot_contacts(pop,
setting_code=setting_code,
density_or_frequency=method,
do_show=False)
fig.savefig(
os.path.join(
self.figDir,
f"{self.n}_seed_{self.seed}_{setting_code}_{method}_contact_matrix.png"
))
def get_pop_details(self,
pop,
dir,
title_prefix,
location,
state_location,
country_location,
decimal=3):
os.makedirs(dir, exist_ok=True)
for setting_code in ['H', 'W', 'S']:
average_contacts = utilities.get_average_contact_by_age(
pop, self.datadir, setting_code=setting_code, decimal=decimal)
fmt = f'%.{str(decimal)}f'
# print(f"expected contacts by age for {code}:\n", average_contacts)
utilities.plot_array(
average_contacts,
datadir=self.figDir,
testprefix=
f"{self.n}_seed_{self.seed}_{setting_code}_average_contacts",
expect_label='Expected' if self.generateBaseline else 'Test')
sc.savejson(os.path.join(
dir,
f"{self.n}_seed_{self.seed}_{setting_code}_average_contact.json"
),
dict(enumerate(average_contacts.tolist())),
indent=2)
for type in ['density', 'frequency']:
matrix = sp.calculate_contact_matrix(pop, type, setting_code)
brackets = sp.get_census_age_brackets(self.datadir,
state_location,
country_location)
ageindex = sp.get_age_by_brackets_dic(brackets)
agg_matrix = sp.get_aggregate_matrix(matrix, ageindex)
np.savetxt(os.path.join(
dir,
f"{self.n}_seed_{self.seed}_{setting_code}_{type}_contact_matrix.csv"
),
agg_matrix,
delimiter=",",
fmt=fmt)
fig = plot_age_mixing_matrices.test_plot_generated_contact_matrix(
setting_code=setting_code,
population=pop,
title_prefix=" Expected "
if self.generateBaseline else " Test ",
density_or_frequency=type)
# fig.show()
fig.savefig(
os.path.join(
self.figDir,
f"{self.n}_seed_{self.seed}_{setting_code}_{type}_contact_matrix.png"
))
location=location,
state_location=state_location,
country_location=country_location,
generate=True,
rand_seed=seed,
with_school_types=with_school_types,
school_mixing_type=school_mixing_type)
sc.tic()
pop = sp.make_population(**params)
sc.toc()
for setting_code in ['H', 'S', 'W']:
average = utilities.get_average_contact_by_age(
pop,
datadir=datadir,
state_location=state_location,
country_location=country_location,
setting_code=setting_code,
decimal=3)
utilities.plot_array(
average,
datadir=figdir,
testprefix=f"{setting_code} {test_prefix} contact by age bracket",
expect_label="contacts",
names=age_brackets_labels,
xlabel_rotation=50)
utilities_dist.check_age_distribution(pop,
n,
datadir=datadir,
figdir=figdir,