def test_get_uids_in_school(location='seattle_metro',
state_location='Washington',
country_location='usa',
folder_name='contact_networks'):
Npeople = 10000
homes = sprw.read_setting_groups(datadir,
location,
state_location,
country_location,
folder_name,
'households',
Npeople,
with_ages=True)
homes_by_uids, age_by_uid_dic = sp.assign_uids_by_homes(homes)
uids_in_school, uids_in_school_by_age, ages_in_school_count = sp.get_uids_in_school(
datadir,
Npeople,
location,
state_location,
country_location,
age_by_uid_dic,
homes_by_uids,
use_default=False)
assert uids_in_school is not None
def test_get_uids_potential_workers(location='seattle_metro',
state_location='Washington',
country_location='usa'):
n = 10000
homes = sp.get_head_age_by_size_distr(datadir,
state_location,
country_location,
file_path=None,
household_size_1_included=False,
use_default=True)
homes_by_uids, age_by_uid_dic = sp.assign_uids_by_homes(homes, id_len=16)
uids_in_school, uids_in_school_by_age, ages_in_school_count = sp.get_uids_in_school(
datadir,
n,
location,
state_location,
country_location,
age_by_uid_dic,
homes_by_uids,
use_default=False)
employment_rates = sp.get_employment_rates(
datadir,
location=location,
state_location=state_location,
country_location=country_location,
use_default=True)
potential_worker_uids, potential_worker_uids_by_age, potential_worker_ages_left_count = sp.get_uids_potential_workers(
uids_in_school, employment_rates, age_by_uid_dic)
assert potential_worker_ages_left_count is not None
return potential_worker_uids, potential_worker_uids_by_age, employment_rates, age_by_uid_dic
def test_assign_uids_by_homes(state_location='Washington', country_location='usa'):
homes = sp.get_head_age_by_size_distr(datadir, state_location, country_location, file_path=None,
household_size_1_included=False, use_default=True)
homes_by_uids, age_by_uid_dic = sp.assign_uids_by_homes(homes, id_len=16)
assert homes_by_uids, age_by_uid_dic is not None
def test_send_students_to_school(n=10000,
location='seattle_metro',
state_location='Washington',
country_location='usa',
folder_name='contact_networks'):
homes = sprw.read_setting_groups(datadir,
location,
state_location,
country_location,
folder_name,
'households',
n,
with_ages=True)
homes_by_uids, age_by_uid_dic = sp.assign_uids_by_homes(homes)
uids_in_school, uids_in_school_by_age, ages_in_school_count = sp.get_uids_in_school(
datadir,
n,
location,
state_location,
country_location,
age_by_uid_dic,
homes_by_uids,
use_default=False)
school_size_distr_by_bracket = sp.get_school_size_distr_by_brackets(
datadir, location, state_location, country_location)
school_size_brackets = sp.get_school_size_brackets(datadir, location,
state_location,
country_location)
school_sizes = sp.generate_school_sizes(school_size_distr_by_bracket,
school_size_brackets,
uids_in_school)
age_brackets_filepath = sp.get_census_age_brackets_path(
datadir, state_location, country_location)
age_brackets = sp.get_age_brackets_from_df(age_brackets_filepath)
age_by_brackets_dic = sp.get_age_by_brackets_dic(age_brackets)
contact_matrix_dic = sp.get_contact_matrix_dic(
datadir, sheet_name='United States of America')
syn_schools, syn_school_uids, syn_school_types = sp.send_students_to_school(
school_sizes,
uids_in_school,
uids_in_school_by_age,
ages_in_school_count,
age_brackets,
age_by_brackets_dic,
contact_matrix_dic,
verbose=False)
assert syn_schools, syn_school_uids is not None
return syn_schools, syn_school_uids
def test_send_students_to_school(n=10000, location='seattle_metro', state_location='Washington',
country_location='usa', folder_name='contact_networks'):
homes = sp.read_setting_groups(datadir, location, state_location, country_location, 'households', folder_name, n, with_ages=True)
homes_by_uids, age_by_uid_dic = sp.assign_uids_by_homes(homes)
uids_in_school, uids_in_school_by_age, ages_in_school_count = sp.get_uids_in_school(datadir, n, location,
state_location,
country_location,
age_by_uid_dic,
homes_by_uids,
use_default=False)
# assert uids_in_school is not None
# def test_send_students_to_school(n=1000, location='seattle_metro', state_location='Washington',
# country_location='usa'):
# homes = sp.get_head_age_by_size_distr(datadir, state_location, country_location, file_path=None,
# household_size_1_included=False, use_default=True)
# homes_by_uids, age_by_uid_dic = sp.assign_uids_by_homes(homes, id_len=16)
# uids_in_school, uids_in_school_by_age, ages_in_school_count = sp.get_uids_in_school(datadir, n, location,
# state_location,
# country_location,
# age_by_uid_dic,
# homes_by_uids,
# use_default=False)
# >>>>>>> origin/mf/update-saved-pop-fixes
school_size_distr_by_bracket = sp.get_school_size_distr_by_brackets(datadir, location, state_location,
country_location)
school_size_brackets = sp.get_school_size_brackets(datadir, location, state_location, country_location)
school_sizes = sp.generate_school_sizes(school_size_distr_by_bracket, school_size_brackets, uids_in_school)
age_brackets_filepath = sp.get_census_age_brackets_path(datadir, state_location, country_location)
age_brackets = sp.get_age_brackets_from_df(age_brackets_filepath)
age_by_brackets_dic = sp.get_age_by_brackets_dic(age_brackets)
contact_matrix_dic = sp.get_contact_matrix_dic(datadir, sheet_name='United States of America')
syn_schools, syn_school_uids = sp.send_students_to_school(school_sizes, uids_in_school, uids_in_school_by_age,
ages_in_school_count, age_brackets, age_by_brackets_dic,
contact_matrix_dic, verbose=False)
assert syn_schools, syn_school_uids is not None
return syn_schools, syn_school_uids
use_bayesian)
hha_by_size = sp.get_head_age_by_size_distr(datadir, country_location,
use_bayesian)
homes_dic, homes = sp.generate_all_households(hh_sizes, hha_by_size,
hha_brackets, age_brackets,
age_by_brackets_dic,
contact_matrix_dic,
syn_age_distr)
c = 0
for s in homes_dic:
c += s * len(homes_dic[s])
print('c', c)
homes_by_uids, age_by_uid_dic = sp.assign_uids_by_homes(homes)
new_ages_count = Counter(age_by_uid_dic.values())
print('a', len(age_by_uid_dic))
fig = plt.figure(figsize=(6, 4))
ax = fig.add_subplot(111)
x = np.arange(100)
y_exp = np.zeros(100)
y_sim = np.zeros(100)
for a in range(100):
expected = int(syn_age_distr[a] * totalpop)
# print(a, expected, new_ages_count[a], '%.2f' % (-1*(expected - new_ages_count[a])/float(expected) * 100 ))
y_exp[a] = expected
y_sim[a] = new_ages_count[a]