def test_count_layer_degree(create_pop):
sp.logger.info(
"Testing degree_df and quantile stats calculating method for degree distribution by age."
)
pop = create_pop
layer = 'S'
ages = None
uids = None
degree_df = sp.count_layer_degree(pop, layer, ages, uids)
assert list(
degree_df.columns.values) == ['uid', 'age', 'degree',
'contact_ages'], 'Check failed.'
print('Check passed.')
desc = sp.compute_layer_degree_description(pop, degree_df=degree_df)
cols = list(desc.columns.values)
expected_cols = [
'count', 'mean', 'std', 'min', '5%', '25%', '50%', '75%', '95%', 'max'
]
for exc in expected_cols:
assert exc in cols, f'Check failed. {exc} not found in description columns.'
print('Check passed. Found all expected columns.')
return pop
def test_count_layer_degree_by_age(create_pop):
pop = create_pop
layer = 'W'
brackets = pop.age_brackets
ageindex = pop.age_by_brackets
total = np.zeros(len(brackets))
contacts = np.zeros(len(brackets))
# brute force check for contact count
for p in pop.popdict.values():
total[ageindex[p["age"]]] += 1
contacts[ageindex[p["age"]]] += len(p["contacts"][layer])
for b in brackets:
degree_df = sp.count_layer_degree(pop, layer, brackets[b])
expected = contacts[b]
actual = degree_df.sum(axis=0)['degree'] if len(degree_df) > 0 else 0
print(f"expected contacts for {brackets[b]} is {expected}")
assert expected == actual, f"expecred: {expected} actual:{actual}"
def get_pop_average_contacts_by_brackets(pop, layer, decimal=3):
"""
get population contact counts by age brackets and save the results as json files and plots
Args:
pop (pop object) : population, either synthpops.pop.Pop, covasim.people.People, or dict
layer (str) : name of the physical contact layer: H for households, S for schools, W for workplaces, C for community or other
decimal (int) : rounding precision for average contact calculation
Returns:
dict: a dictionary where index is the age bracket and value is the percentage of contact
"""
# 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(**pop.loc_pars)
average_contacts = []
for k in age_brackets:
degree_df = sp.count_layer_degree(pop, layer=layer, ages=age_brackets[k])
people_in_ages = sp.filter_people(pop, ages=age_brackets[k])
average_contacts.append(
0 if len(degree_df) == 0 else np.round(degree_df.sum(axis=0)['degree'] / len(people_in_ages),
decimals=decimal))
return dict(enumerate(average_contacts))
def test_plot_degree_by_age_methods(create_pop,
layer='S',
do_show=False,
do_save=False):
sp.logger.info(
"Testing the different plotting methods to show the degree distribution by age for a single population."
)
# age on x axis, degree distribution on y axis
pop = create_pop
kwargs = sc.objdict(do_show=do_show, do_save=do_save)
ages = None
uids = None
uids_included = None
degree_df = sp.count_layer_degree(pop,
layer=layer,
ages=ages,
uids=uids,
uids_included=uids_included)
if kwargs.do_show:
plt.switch_backend(mplt_org_backend)
# kde seaborn jointplot
gkde = sp.plotting.plot_degree_by_age(pop,
layer=layer,
ages=ages,
uids=uids,
uids_included=uids_included,
degree_df=degree_df,
kind='kde',
**kwargs)
# hist seaborn jointplot
ghist = sp.plotting.plot_degree_by_age(pop,
layer=layer,
ages=ages,
uids=uids,
uids_included=uids_included,
degree_df=degree_df,
kind='hist',
**kwargs)
# reg seaborn joint
greg = sp.plotting.plot_degree_by_age(pop,
layer=layer,
ages=ages,
uids=uids,
uids_included=uids_included,
degree_df=degree_df,
kind='reg',
**kwargs)
# hex seaborn joint
# extra features: can limit the ages or uids, then just don't include the degree_df and it will calculate a new one with those filters applied
ages = np.arange(3, 76)
kwargs.xlim = [3, 76]
ghex = sp.plotting.plot_degree_by_age(pop,
layer=layer,
ages=ages,
uids=uids,
uids_included=uids_included,
kind='hex',
**kwargs)
ages = np.arange(15, 76)
fig, axboxplot = sp.plotting.plot_degree_by_age_boxplot(pop,
layer='W',
ages=ages,
**kwargs)
return gkde, ghist, greg, ghex, axboxplot