def test_scale(self):
seed = 1
# set param
average_student_teacher_ratio = 22
average_student_all_staff_ratio = 15
datadir = self.dataDir
location = 'seattle_metro'
state_location = 'Washington'
country_location = 'usa'
i = 0
for n in [2001, 10001]:
try:
pop = {}
sp.set_seed(seed)
print(seed)
pop = sp.generate_synthetic_population(n, datadir,average_student_teacher_ratio=average_student_teacher_ratio,
average_student_all_staff_ratio=average_student_all_staff_ratio,
return_popdict=True)
sc.savejson(os.path.join(self.resultdir, f"calltwice_{n}_{i}.json"), pop, indent=2)
result = utilities.check_teacher_staff_ratio(pop, self.dataDir, f"calltwice_{n}_{i}", average_student_teacher_ratio,
average_student_all_staff_ratio=average_student_all_staff_ratio, err_margin=2)
utilities_dist.check_enrollment_distribution(pop, n, datadir, location, state_location, country_location,
test_prefix=f"calltwice{n}_{i}", skip_stat_check=True, do_close=self.do_close)
utilities_dist.check_age_distribution(pop, n, datadir, self.resultdir, location, state_location, country_location,
test_prefix=f"calltwice{n}_{i}", do_close=self.do_close)
i += 1
except:
print("check failed, continue...")
return result
def test_with_ltcf(self):
"""
test with long term care facilities options
"""
rand_seed = 1
sp.set_seed(rand_seed)
# set param
n = self.n
datadir = self.dataDir
location = 'seattle_metro'
state_location = 'Washington'
country_location = 'usa'
sheet_name = 'United States of America'
use_two_group_reduction = True
average_LTCF_degree = 20
ltcf_staff_age_min = 20
ltcf_staff_age_max = 65
with_school_types = True
average_class_size = 20
inter_grade_mixing = 0.1
average_student_teacher_ratio = 20.0
average_teacher_teacher_degree = 3
teacher_age_min = 25
teacher_age_max = 70
with_non_teaching_staff = True
average_student_all_staff_ratio = 11
average_additional_staff_degree = 20
staff_age_min = 20
staff_age_max = 75
school_mixing_type = {'pk': 'age_and_class_clustered', 'es': 'random', 'ms': 'age_clustered', 'hs': 'random', 'uv': 'random'}
return_popdict = True
vals = locals()
test_prefix = sys._getframe().f_code.co_name
pop = utilities.runpop(resultdir=self.resultdir, testprefix=test_prefix, actual_vals=vals,
method=None)
if self.do_plot:
utilities.check_class_size(pop, average_class_size, average_student_teacher_ratio,
average_student_all_staff_ratio, 1)
result = utilities.check_teacher_staff_ratio(pop, datadir, test_prefix, average_student_teacher_ratio,
average_student_all_staff_ratio, err_margin=2)
utilities_dist.check_age_distribution(pop, self.n, datadir, self.resultdir, location, state_location, country_location, test_prefix=test_prefix, do_close=self.do_close)
utilities_dist.check_enrollment_distribution(pop, self.n, datadir, self.resultdir, location, state_location, country_location, test_prefix=test_prefix, do_close=self.do_close)
def test_staff_generate(self):
"""
generate a population and check if teacher/staff ratio match
"""
rand_seed = 1
sp.set_seed(rand_seed)
#set param
n = self.n
datadir = self.dataDir
location = 'seattle_metro'
state_location = 'Washington'
country_location = 'usa'
sheet_name = 'United States of America'
school_enrollment_counts_available = False
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
average_student_all_staff_ratio = 12
average_additional_staff_degree = 18
staff_age_min = 20
staff_age_max = 75
return_popdict = True
test_prefix = sys._getframe().f_code.co_name
vals = locals()
pop = utilities.runpop(resultdir=self.resultdir, testprefix=f"{test_prefix}", actual_vals=vals, method=sp.generate_synthetic_population)
if self.do_plot:
utilities.check_class_size(pop, average_class_size, average_student_teacher_ratio,
average_student_all_staff_ratio, 1)
result = utilities.check_teacher_staff_ratio(pop, self.dataDir, f"{test_prefix}", average_student_teacher_ratio, average_student_all_staff_ratio, err_margin=2)
utilities_dist.check_age_distribution(pop, self.n, datadir, self.resultdir, location, state_location, country_location, test_prefix=test_prefix, do_close=self.do_close)
utilities_dist.check_enrollment_distribution(pop, self.n, datadir, self.resultdir, location, state_location, country_location, test_prefix=f"{test_prefix}", do_close=self.do_close)
country_location=country_location,
setting_code=setting_code,
decimal=3)
utilities.plot_array(
average,
datadir=figdir,
testprefix=f"{setting_code} {test_prefix} contact by age bracket",
expect_label="contacts",
names=age_brackets_labels,
xlabel_rotation=50)
utilities_dist.check_age_distribution(pop,
n,
datadir=datadir,
figdir=figdir,
location=location,
state_location=state_location,
country_location=country_location,
test_prefix=test_prefix,
skip_stat_check=True)
utilities_dist.check_enrollment_distribution(
pop,
n,
datadir=datadir,
figdir=figdir,
location=location,
state_location=state_location,
country_location=country_location,
test_prefix=test_prefix,
skip_stat_check=True,