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_plot_generated_trimmed_contact_matrix(setting_code='H', n=5000, aggregate_flag=True, logcolors_flag=True,
density_or_frequency='density', with_facilities=False, cmap='cmr.freeze_r', fontsize=16, rotation=50):
"""
Plot the age mixing matrix for a specific setting where the edges are trimmed.
Args:
setting_code (str) : name of the physial contact setting: H for households, S for schools, W for workplaces, C for community or other
n (int) : number of people in the population
aggregate_flag (book) : If True, plot the contact matrix for aggregate age brackets, else single year age contact matrix.
logcolors_flag (bool) : If True, plot heatmap in logscale
density_or_frequency (str) : If 'density', then each contact counts for 1/(group size -1) of a person's contact in a group, elif 'frequency' then count each contact. This means that more people in a group leads to higher rates of contact/exposure.
with_facilities (bool) : If True, create long term care facilities
cmap(str or matplotlib colormap) : colormap
fontsize (int) : base font size
rotation (int) : rotation for x axis labels
Returns:
A fig object.
"""
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)
population = sp.make_population(n, generate=True, with_facilities=with_facilities)
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 population:
ages.append(population[uid]['age'])
age_count = Counter(ages)
aggregate_age_count = sp.get_aggregate_ages(age_count, age_by_brackets_dic)
matrix = sp.calculate_contact_matrix(population, density_or_frequency, setting_code)
fig = sp.plot_contact_matrix(matrix, age_count, aggregate_age_count, age_brackets, age_by_brackets_dic,
setting_code, density_or_frequency, logcolors_flag, aggregate_flag, cmap, fontsize, rotation)
return fig
def plot_contact_matrix_after_intervention(n,
n_days,
interventions,
intervention_name,
location='seattle_metro',
state_location='Washington',
country_location='usa',
aggregate_flag=True,
logcolors_flag=True,
density_or_frequency='density',
setting_code='H',
cmap='cmr.freeze_r',
fontsize=16,
rotation=50):
"""
Args:
intervention (cv.intervention): a single intervention
"""
pars = sc.objdict(pop_size=n, n_days=n_days, pop_type='synthpops')
# sim = sc.objdict()
sim = cv.Sim(pars=pars, interventions=interventions)
sim.run()
age_brackets = sp.get_census_age_brackets(
sp.datadir,
state_location=state_location,
country_location=country_location)
age_by_brackets_dic = sp.get_age_by_brackets_dic(age_brackets)
ages = sim.people.age
ages = np.round(ages, 1)
ages = ages.astype(int)
max_age = max(ages)
age_count = Counter(ages)
age_count = dict(age_count)
for i in range(max_age + 1):
if i not in age_count:
age_count[i] = 0
aggregate_age_count = sp.get_aggregate_ages(age_count, age_by_brackets_dic)
matrix = calculate_contact_matrix(sim, density_or_frequency, setting_code)
fig = sp.plot_contact_matrix(matrix, age_count, aggregate_age_count,
age_brackets, age_by_brackets_dic,
setting_code, density_or_frequency,
logcolors_flag, aggregate_flag, cmap,
fontsize, rotation)
return fig
def process_age_tables():
"""Function to preprocess age tables."""
file_path = os.path.join(dir_path, 'Series A. Population Tables.xlsx')
df = pd.read_excel(file_path,
sheet_name='A5',
header=1,
skiprows=[2, 3],
skipfooter=303)
ages = df['Age in single Years'].values[1:]
age_count = np.array(df['National'].values[1:])
age_range = np.arange(len(ages))
age_dist = age_count / age_count.sum()
age_dist_mapping = dict(zip(age_range, age_dist))
data = dict(age_min=sc.dcp(age_range),
age_max=sc.dcp(age_range),
age_dist=age_dist)
data['age_max'][-1] = 100
new_df = pd.DataFrame.from_dict(data)
new_file_path = os.path.join(dir_path, 'Malawi_national_ages.csv')
new_df.to_csv(new_file_path, index=False)
census_age_brackets = sp.get_census_age_brackets(
sp.settings.datadir,
location='seattle-metro',
state_location='Washington',
country_location='usa',
nbrackets=16)
census_age_by_brackets = sp.get_age_by_brackets(census_age_brackets)
agg_ages = sp.get_aggregate_ages(age_dist_mapping, census_age_by_brackets)
agg_data = dict()
agg_data['age_min'] = np.array(
[census_age_brackets[b][0] for b in census_age_brackets])
agg_data['age_max'] = np.array(
[census_age_brackets[b][-1] for b in census_age_brackets])
agg_data['age_dist'] = np.array(
[agg_ages[b] for b in sorted(census_age_brackets.keys())])
agg_df = pd.DataFrame.from_dict(agg_data)
print(agg_df)
agg_path = os.path.join(dir_path, 'Malawi_national_ages_16.csv')
agg_df.to_csv(agg_path, index=False)
generate = True
population = sp.make_population(n,
generate=generate,
with_facilities=with_facilities)
# aggregate age brackets
age_brackets = sp.get_census_age_brackets(sp.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 population:
ages.append(population[uid]['age'])
age_count = Counter(ages)
aggregate_age_count = sp.get_aggregate_ages(age_count, age_by_brackets_dic)
matrix_dic = {}
density_or_frequency = 'density'
# density_or_frequency = 'frequency'
aggregate_flag = True
# aggregate_flag = False
logcolors_flag = True
logcolors_flag = False
# log color bounds
matrix_cmap = 'cmr.freeze_r'
vbounds = {}