def test_plot_generated_trimmed_contact_matrix(setting_code='H', n=5000, aggregate_flag=True, logcolors_flag=True,
density_or_frequency='density'):
datadir = sp.datadir
state_location = 'Washington'
location = 'seattle_metro'
country_location = 'usa'
popdict = {}
options_args = {'use_microstructure': True}
network_distr_args = {'Npop': int(n)}
contacts = sp.make_contacts(popdict, state_location=state_location, location=location, options_args=options_args,
network_distr_args=network_distr_args)
contacts = sp.trim_contacts(contacts, trimmed_size_dic=None, use_clusters=False)
age_brackets = sp.get_census_age_brackets(datadir, state_location=state_location, country_location=country_location)
age_by_brackets_dic = sp.get_age_by_brackets_dic(age_brackets)
ages = []
for uid in contacts:
ages.append(contacts[uid]['age'])
age_count = Counter(ages)
aggregate_age_count = sp.get_aggregate_ages(age_count, age_by_brackets_dic)
freq_matrix_dic = sp.calculate_contact_matrix(contacts, density_or_frequency)
fig = sp.plot_contact_frequency(freq_matrix_dic, age_count, aggregate_age_count, age_brackets, age_by_brackets_dic,
setting_code, density_or_frequency, logcolors_flag, aggregate_flag)
return fig
def plot_generated_trimmed_contact_matrix(datadir,
n,
location='seattle_metro',
state_location='Washington',
country_location='usa',
setting_code='H',
aggregate_flag=True,
logcolors_flag=True,
density_or_frequency='density',
trimmed_size_dic=None):
popdict = {}
options_args = {'use_microstructure': True}
network_distr_args = {'Npop': int(n)}
contacts = sp.make_contacts(popdict,
country_location=country_location,
state_location=state_location,
location=location,
options_args=options_args,
network_distr_args=network_distr_args)
contacts = sp.trim_contacts(contacts,
trimmed_size_dic=trimmed_size_dic,
use_clusters=False)
age_brackets = sp.get_census_age_brackets(
datadir,
state_location=state_location,
country_location=country_location)
age_by_brackets_dic = sp.get_age_by_brackets_dic(age_brackets)
ages = []
for uid in contacts:
ages.append(contacts[uid]['age'])
num_agebrackets = len(age_brackets)
age_count = Counter(ages)
aggregate_age_count = sp.get_aggregate_ages(age_count, age_by_brackets_dic)
symmetric_matrix = calculate_contact_matrix(contacts, density_or_frequency,
setting_code)
fig = plot_contact_matrix(symmetric_matrix,
age_count,
aggregate_age_count,
age_brackets,
age_by_brackets_dic,
setting_code=setting_code,
density_or_frequency=density_or_frequency,
logcolors_flag=logcolors_flag,
aggregate_flag=aggregate_flag)
return fig
def test_trimmed_contacts_are_bidirectional():
num_people = 5
checked_people = []
last_index_checked = 0
close_contacts_numbers = {'S': 10, 'W': 10}
is_debugging = False
my_contacts = sp.make_contacts(location=location,
state_location=state_location,
country_location=country_location,
options_args=options_args,
network_distr_args=network_distr_args)
my_trim_contacts = sp.trim_contacts(
my_contacts, trimmed_size_dic=close_contacts_numbers)
popdict = my_trim_contacts
uids = popdict.keys()
uid_list = list(uids)
while len(checked_people) < num_people:
my_uid, last_index_checked, checked_people = \
find_fresh_uid(uid_list=uid_list,
last_index_checked=last_index_checked,
checked_people=checked_people)
first_person = popdict[my_uid]
person_json = make_person_json(popdict=popdict, target_uid=my_uid)
if is_debugging:
person_filename = f"DEBUG_popdict_person_{my_uid}.json"
print(f"TEST: {my_uid}")
if os.path.isfile(person_filename):
os.unlink(person_filename)
pass
with open(person_filename, "w") as outfile:
json.dump(person_json, outfile, indent=4, sort_keys=True)
pass
pass
# Now check that each person in each network has me in their network
check_bidirectionality_of_contacts(person_json=person_json,
popdict=popdict)
pass
pass
def make_population(n=None,
max_contacts=None,
as_objdict=False,
generate=False):
'''
Make a full population network including both people (ages, sexes) and contacts using Seattle, Washington cached data.
Args:
n (int) : The number of people to create.
max_contacts (dict) : A dictionary for maximum number of contacts per layer: keys must be "S" (school) and/or "W" (work).
as_objdict (bool) : If True, change popdict type to ``sc.objdict``.
generate (bool) : If True, first look for cached population files and if those are not available, generate new population
Returns:
network (dict): A dictionary of the full population with ages and connections.
'''
default_n = 10000
default_max_contacts = {
'S': 20,
'W': 10
} # this can be anything but should be based on relevant average number of contacts for the population under study
if n is None: n = default_n
n = int(n)
if n not in popsize_choices:
if not generate:
choicestr = ', '.join([str(choice) for choice in popsize_choices])
errormsg = f'Number of people must be one of {choicestr}, not {n}'
raise ValueError(errormsg)
# else:
# Let's start generating a new network shall we?
max_contacts = sc.mergedicts(default_max_contacts, max_contacts)
country_location = 'usa'
state_location = 'Washington'
location = 'seattle_metro'
sheet_name = 'United States of America'
options_args = {'use_microstructure': True}
network_distr_args = {'Npop': int(n)}
# Heavy lift 1: make the contacts and their connections
try:
# try to read in from file
population = sp.make_contacts(location=location,
state_location=state_location,
country_location=country_location,
options_args=options_args,
network_distr_args=network_distr_args)
except:
# make a new network on the fly
if generate:
population = sp.generate_synthetic_population(
n,
sp.datadir,
location=location,
state_location=state_location,
country_location=country_location,
sheet_name=sheet_name,
plot=False,
return_popdict=True)
else:
raise ValueError(errormsg)
# Semi-heavy-lift 2: trim them to the desired numbers
population = sp.trim_contacts(population,
trimmed_size_dic=max_contacts,
use_clusters=False)
# Change types
if as_objdict:
population = sc.objdict(population)
for key, person in population.items():
if as_objdict:
population[key] = sc.objdict(population[key])
population[key]['contacts'] = sc.objdict(
population[key]['contacts'])
for layerkey in population[key]['contacts'].keys():
population[key]['contacts'][layerkey] = list(
population[key]['contacts'][layerkey])
return population
def make_population(n=None, max_contacts=None, generate=None, with_industry_code=False, with_facilities=False,
use_two_group_reduction=True, average_LTCF_degree=20, ltcf_staff_age_min=20, ltcf_staff_age_max=60,
with_school_types=False, school_mixing_type='random', average_class_size=20, inter_grade_mixing=0.1,
average_student_teacher_ratio=20, average_teacher_teacher_degree=3, teacher_age_min=25, teacher_age_max=75,
with_non_teaching_staff=False,
average_student_all_staff_ratio=15, average_additional_staff_degree=20, staff_age_min=20, staff_age_max=75,
rand_seed=None):
'''
Make a full population network including both people (ages, sexes) and contacts using Seattle, Washington cached data.
Args:
n (int) : The number of people to create.
max_contacts (dict) : A dictionary for maximum number of contacts per layer: keys must be "W" (work).
generate (bool) : If True, generate a new population. Else, look for cached population and if those are not available, generate a new population.
with_industry_code (bool) : If True, assign industry codes for workplaces, currently only possible for cached files of populations in the US.
with_facilities (bool) : If True, create long term care facilities, currently only available for locations in the US.
use_two_group_reduction (bool) : If True, create long term care facilities with reduced contacts across both groups.
average_LTCF_degree (float) : default average degree in long term care facilities.
ltcf_staff_age_min (int) : Long term care facility staff minimum age.
ltcf_staff_age_max (int) : Long term care facility staff maximum age.
with_school_types (bool) : If True, creates explicit school types.
school_mixing_type (str or dict) : The mixing type for schools, 'random', 'age_clustered', or 'age_and_class_clustered' if string, and a dictionary of these by school type otherwise.
average_class_size (float) : The average classroom size.
inter_grade_mixing (float) : The average fraction of mixing between grades in the same school for clustered school mixing types.
average_student_teacher_ratio (float) : The average number of students per teacher.
average_teacher_teacher_degree (float) : The average number of contacts per teacher with other teachers.
teacher_age_min (int) : The minimum age for teachers.
teacher_age_max (int) : The maximum age for teachers.
with_non_teaching_staff (bool) : If True, includes non teaching staff.
average_student_all_staff_ratio (float) : The average number of students per staff members at school (including both teachers and non teachers).
average_additional_staff_degree (float) : The average number of contacts per additional non teaching staff in schools.
staff_age_min (int) : The minimum age for non teaching staff.
staff_age_max (int) : The maximum age for non teaching staff.
rand_seed (int) : Start point random sequence is generated from.
Returns:
network (dict): A dictionary of the full population with ages and connections.
'''
if rand_seed is not None:
sp.set_seed(rand_seed)
default_n = 10000
default_max_contacts = {'W': 20} # this can be anything but should be based on relevant average number of contacts for the population under study
if n is None:
n = default_n
n = int(n)
if n not in popsize_choices:
if generate is False:
choicestr = ', '.join([str(choice) for choice in popsize_choices])
errormsg = f'If generate=False, number of people must be one of {choicestr}, not {n}'
raise ValueError(errormsg)
else:
generate = True # If n not found in popsize_choices and generate was not False, generate a new population.
# Default to False, unless LTCF are requested
if generate is None:
if with_facilities:
generate = True
else:
generate = False
max_contacts = sc.mergedicts(default_max_contacts, max_contacts)
country_location = 'usa'
state_location = 'Washington'
location = 'seattle_metro'
sheet_name = 'United States of America'
options_args = {}
options_args['use_microstructure'] = True
options_args['use_industry_code'] = with_industry_code
options_args['use_long_term_care_facilities'] = with_facilities
options_args['use_two_group_reduction'] = use_two_group_reduction
options_args['with_school_types'] = with_school_types
options_args['with_non_teaching_staff'] = with_non_teaching_staff
network_distr_args = {}
network_distr_args['Npop'] = int(n)
network_distr_args['average_LTCF_degree'] = average_LTCF_degree
network_distr_args['average_class_size'] = average_class_size
network_distr_args['average_student_teacher_ratio'] = average_student_teacher_ratio
network_distr_args['average_teacher_teacher_degree'] = average_teacher_teacher_degree
network_distr_args['inter_grade_mixing'] = inter_grade_mixing
network_distr_args['average_student_all_staff_ratio'] = average_student_all_staff_ratio
network_distr_args['average_additional_staff_degree'] = average_additional_staff_degree
network_distr_args['school_mixing_type'] = school_mixing_type
# Heavy lift 1: make the contacts and their connections
if not generate:
# must read in from file, will fail if the data has not yet been generated
population = sp.make_contacts(location=location, state_location=state_location,
country_location=country_location, sheet_name=sheet_name,
options_args=options_args,
network_distr_args=network_distr_args)
else:
# make a new network on the fly
if with_facilities and with_industry_code:
errormsg = f'Requesting both long term care facilities and industries by code is not supported yet.'
raise ValueError(errormsg)
elif with_facilities:
population = sp.generate_microstructure_with_facilities(sp.datadir, location=location, state_location=state_location, country_location=country_location, n=n, sheet_name=sheet_name,
use_two_group_reduction=use_two_group_reduction, average_LTCF_degree=average_LTCF_degree, ltcf_staff_age_min=ltcf_staff_age_min, ltcf_staff_age_max=ltcf_staff_age_max,
with_school_types=with_school_types, school_mixing_type=school_mixing_type, average_class_size=average_class_size, inter_grade_mixing=inter_grade_mixing,
average_student_teacher_ratio=average_student_teacher_ratio, average_teacher_teacher_degree=average_teacher_teacher_degree, teacher_age_min=teacher_age_min, teacher_age_max=teacher_age_max,
average_student_all_staff_ratio=average_student_all_staff_ratio, average_additional_staff_degree=average_additional_staff_degree, staff_age_min=staff_age_min, staff_age_max=staff_age_max,
return_popdict=True )
else:
population = sp.generate_synthetic_population(n, sp.datadir, location=location, state_location=state_location, country_location=country_location, sheet_name=sheet_name,
with_school_types=with_school_types, school_mixing_type=school_mixing_type, average_class_size=average_class_size, inter_grade_mixing=inter_grade_mixing,
average_student_teacher_ratio=average_student_teacher_ratio, average_teacher_teacher_degree=average_teacher_teacher_degree, teacher_age_min=teacher_age_min, teacher_age_max=teacher_age_max,
average_student_all_staff_ratio=average_student_all_staff_ratio, average_additional_staff_degree=average_additional_staff_degree, staff_age_min=staff_age_min, staff_age_max=staff_age_max,
return_popdict=True,
)
# Semi-heavy-lift 2: trim them to the desired numbers
population = sp.trim_contacts(population, trimmed_size_dic=max_contacts, use_clusters=False)
# Change types
for key, person in population.items():
for layerkey in population[key]['contacts'].keys():
population[key]['contacts'][layerkey] = list(population[key]['contacts'][layerkey])
return population
def make_population(n=None,
max_contacts=None,
generate=None,
with_industry_code=False,
with_facilities=False,
use_two_group_reduction=True,
average_LTCF_degree=20,
rand_seed=None):
'''
Make a full population network including both people (ages, sexes) and contacts using Seattle, Washington cached data.
Args:
n (int) : The number of people to create.
max_contacts (dict) : A dictionary for maximum number of contacts per layer: keys must be "S" (school) and/or "W" (work).
generate (bool) : If True, first look for cached population files and if those are not available, generate new population
with_industry_code (bool) : If True, assign industry codes for workplaces, currently only possible for cached files of populations in the US
with_facilities (bool) : If True, create long term care facilities
use_two_group_reduction (bool) : If True, create long term care facilities with reduced contacts across both groups
average_LTCF_degree (int) : default average degree in long term care facilities
Returns:
network (dict): A dictionary of the full population with ages and connections.
'''
if rand_seed is not None:
sp.set_seed(rand_seed)
default_n = 10000
default_max_contacts = {
'S': 20,
'W': 20
} # this can be anything but should be based on relevant average number of contacts for the population under study
if n is None:
n = default_n
n = int(n)
if n not in popsize_choices:
if generate is False:
choicestr = ', '.join([str(choice) for choice in popsize_choices])
errormsg = f'If generate=False, number of people must be one of {choicestr}, not {n}'
raise ValueError(errormsg)
else:
generate = True # If not found, generate
# Default to False, unless LTCF are requested
if generate is None:
if with_facilities:
generate = True
else:
generate = False
max_contacts = sc.mergedicts(default_max_contacts, max_contacts)
country_location = 'usa'
state_location = 'Washington'
location = 'seattle_metro'
sheet_name = 'United States of America'
options_args = {
'use_microstructure': True,
'use_industry_code': with_industry_code,
'use_long_term_care_facilities': with_facilities,
'use_two_group_reduction': use_two_group_reduction,
'average_LTCF_degree': average_LTCF_degree
}
network_distr_args = {'Npop': int(n)}
# Heavy lift 1: make the contacts and their connections
if not generate:
# must read in from file, will fail if the data has not yet been generated
population = sp.make_contacts(location=location,
state_location=state_location,
country_location=country_location,
options_args=options_args,
network_distr_args=network_distr_args)
else:
# make a new network on the fly
if with_facilities and with_industry_code:
errormsg = f'Requesting both long term care facilities and industries by code is not supported yet.'
raise ValueError(errormsg)
elif with_facilities:
population = sp.generate_microstructure_with_facilities(
sp.datadir,
location=location,
state_location=state_location,
country_location=country_location,
n=n,
return_popdict=True,
use_two_group_reduction=use_two_group_reduction,
average_LTCF_degree=average_LTCF_degree)
else:
population = sp.generate_synthetic_population(
n,
sp.datadir,
location=location,
state_location=state_location,
country_location=country_location,
sheet_name=sheet_name,
plot=False,
return_popdict=True)
# Semi-heavy-lift 2: trim them to the desired numbers
population = sp.trim_contacts(population,
trimmed_size_dic=max_contacts,
use_clusters=False)
# Change types
for key, person in population.items():
for layerkey in population[key]['contacts'].keys():
population[key]['contacts'][layerkey] = list(
population[key]['contacts'][layerkey])
return population
country_location = 'usa'
sheet_name = 'United States of America'
level = 'county'
n = 10000
verbose = True
plot = True
# loads population with microstructure and age demographics that approximate those of the location selected
# files located in:
# datadir/demographics/contact_matrices_152_countries/state_location/
# load population into a dictionary of individuals who know who their contacts are
options_args = {'use_microstructure': True}
network_distr_args = {'Npop': n}
contacts = sp.make_contacts(location=location,
state_location=state_location,
country_location=country_location,
options_args=options_args,
network_distr_args=network_distr_args)
# not all school and workplace contacts are going to be close contacts so create 'closer' contacts for these settings
close_contacts_number = {'S': 20, 'W': 20}
contacts = sp.trim_contacts(contacts,
trimmed_size_dic=close_contacts_number)
verbose = True
# verbose = False
if verbose:
show_layers(contacts, show_ages=True)