def test_check_dist_normal():
sc.heading(
'Testing test_check_dist_normal() (statistical tests for a continuous distribution)...'
)
# Normal tests
np.random.seed(0) # set random seed for test
n = 100
expected = 5
invalid = 15
std = 3
valid_ndata = np.random.randn(n) * std + expected
invalid_ndata = np.random.randn(n) * std + invalid
alpha = 1e-3
print(
f"If any of test_check_dist_normal() tests fail, try to rerun this by resetting the random seed. With alpha: {alpha} we expect 1 out of {1/alpha:.0f} tests to fail."
)
sp.check_normal(actual=valid_ndata,
expected=expected,
alpha=alpha,
die=True)
with pytest.raises(ValueError):
sp.check_normal(actual=invalid_ndata,
expected=expected,
alpha=alpha,
die=True)
def test_student_all_staff_ratio(average_student_all_staff_ratio,
do_show,
do_save,
get_fig_dir,
threshold=2):
"""
Test case to check average_student_all_staff_ratio by taking average of students contacts from teachers and staff
Args:
average_student_all_staff_ratio: The average number of students per staff members at school
(including both teachers and non teachers)
Returns:
None
"""
testpars = dict(
average_student_all_staff_ratio=average_student_all_staff_ratio, )
pop = sp.Pop(**pars, **testpars)
ratios = get_teacher_staff_ratio(pop.popdict,
"average_student_all_staff_ratio",
average_student_all_staff_ratio, do_show,
do_save, get_fig_dir)
sp.check_normal(actual=ratios,
expected=average_student_all_staff_ratio,
label='average_student_all_staff_ratio',
check='mean')
return
def test_average_student_teacher_ratio(average_student_teacher_ratio,
do_show,
do_save,
get_fig_dir,
threshold=2):
"""
Test case for average_student_teacher_ratio by taking average of student contacts per teacher
Args:
average_student_teacher_ratio: The average number of students per teacher
Returns:
None
"""
testpars = dict(
average_student_teacher_ratio=average_student_teacher_ratio, )
pop = sp.Pop(**pars, **testpars)
ratios = get_teacher_staff_ratio(pop.popdict,
"average_student_teacher_ratio",
average_student_teacher_ratio, do_show,
do_save, get_fig_dir)
sp.check_normal(actual=ratios,
expected=average_student_teacher_ratio,
label='average_student_teacher_ratio',
check='mean')
return
def test_average_teacher_teacher_degree(average_teacher_teacher_degree,
do_show,
do_save,
get_fig_dir,
threshold=2):
"""
Test case for average_teacher_teacher_degree by taking average of teachers' contacts per teacher
Args:
average_teacher_teacher_degree: The average number of contacts per teacher with other teachers
Returns:
None
"""
testpars = dict(
average_teacher_teacher_degree=average_teacher_teacher_degree,
with_school_types=1,
school_mixing_type={
'pk':
'age_and_class_clustered', # average_teacher_teacher_degree will not be used in school_mixing_type == 'random' scenario
'es': 'age_and_class_clustered',
'ms': 'age_and_class_clustered',
'hs': 'age_clustered',
'uv': 'age_clustered'
})
pop = sp.Pop(**pars, **testpars)
plotting_kwargs = sc.objdict(do_show=do_show,
do_save=do_save,
figdir=get_fig_dir)
contacts = sp.get_contact_counts_by_layer(pop.popdict, with_layer_ids=1)[0]
plotting_kwargs.append(
"title_prefix",
f"Average Teacher-Teacher Degree = {average_teacher_teacher_degree}")
plotting_kwargs.append(
"figname",
f"contact_average_teacher_teacher_degree_{average_teacher_teacher_degree}"
)
sp.plot_contact_counts(contacts, **plotting_kwargs)
counts = contacts['sc_teacher']['sc_teacher']
sp.check_normal(actual=counts,
expected=average_teacher_teacher_degree,
label='teacher degree',
check='mean')
return
def test_average_additional_staff_degree(average_additional_staff_degree,
do_show,
do_save,
get_fig_dir,
threshold=2):
"""
Test case to check average_additional_staff_degree by taking average of all contacts per staff
Args:
average_additional_staff_degree: The average number of contacts per additional non teaching staff in schools
Returns:
None
"""
# note this must be greater than default average_student_all_staff_ratio (20)
testpars = dict(
average_additional_staff_degree=average_additional_staff_degree,
with_school_types=1,
)
pop = sp.Pop(**pars, **testpars)
plotting_kwargs = sc.objdict(do_show=do_show,
do_save=do_save,
figdir=get_fig_dir)
contacts = sp.get_contact_counts_by_layer(pop.popdict, with_layer_ids=1)[0]
plotting_kwargs.append(
"title_prefix",
f"Average Additional Staff Degree = {average_additional_staff_degree}")
plotting_kwargs.append(
"figname",
f"contact_average_additional_staff_degree_{average_additional_staff_degree}"
)
sp.plot_contact_counts(contacts, **plotting_kwargs)
counts = contacts['sc_staff']['all']
sp.check_normal(actual=counts,
expected=average_additional_staff_degree,
label='staff degree',
check='mean')
return