def test_ltcf_resident_to_staff_ratios(do_show=False):
"""
Compare the ltcf resident to staff ratios generated to the data available for the location.
"""
sp.logger.info(f'Testing the ratios of ltcf residents to staff.')
# to actually match decently, you need to model a higher population size, but for regular
# test purposes, large sizes will be quite a lot to test on every merge
pop = sp.Pop(**pars)
popdict = pop.to_dict()
loc_pars = pop.loc_pars
expected_ltcf_ratio_distr = sp.get_long_term_care_facility_resident_to_staff_ratios_distr(
**loc_pars)
resident_to_staff_ratio_brackets = sp.get_long_term_care_facility_resident_to_staff_ratios_brackets(
**loc_pars)
bins = [
resident_to_staff_ratio_brackets[i][0] - 1
for i in range(len(resident_to_staff_ratio_brackets))
] + [
resident_to_staff_ratio_brackets[max(
resident_to_staff_ratio_brackets.keys())][0]
]
ltcfs = {}
for i, person in popdict.items():
if person['ltcfid'] is not None:
ltcfs.setdefault(person['ltcfid'], {'residents': [], 'staff': []})
if person['ltcf_res'] is not None:
ltcfs[person['ltcfid']]['residents'].append(i)
elif person['ltcf_staff']:
ltcfs[person['ltcfid']]['staff'].append(i)
gen_ratios = []
for l in ltcfs:
ratio = len(ltcfs[l]['residents']) / len(ltcfs[l]['staff'])
gen_ratios.append(ratio)
hist, bins = np.histogram(gen_ratios, bins=bins)
gen_ltcf_ratio_distr = {i: hist[i] / sum(hist) for i in range(len(hist))}
xlabels = [
f'{resident_to_staff_ratio_brackets[b][0]:.0f}'
for b in resident_to_staff_ratio_brackets
]
fig, ax = sppl.plot_array(list(expected_ltcf_ratio_distr.values()),
generated=list(gen_ltcf_ratio_distr.values()),
names=xlabels,
do_show=False,
binned=True)
ax.set_xlabel('LTCF Resident to Staff Ratio')
ax.set_title('Comparison of LTCF Resident to Staff Ratios')
ax.set_ylim(0., 1.)
if do_show:
plt.show()
def test_ltcf_resident_ages(do_show=False):
"""
Compare the ltcf resident ages generated with those expected for the location.
"""
sp.logger.info(f"Testing that ltcf resident ages align with the expected resident ages for the location.")
test_pars = sc.dcp(pars)
# to actually match decently, you need to model a higher population size, but for regular
# test purposes, large sizes will be quite a lot to test on every merge
test_pars['n'] = settings.pop_sizes.large
pop = sp.Pop(**test_pars)
pop_dict = pop.to_dict()
ltcf_resident_rates_by_age = sp.get_long_term_care_facility_use_rates(sp.settings.datadir,
country_location=pop.country_location,
state_location=pop.state_location,
)
expected_ltcf_rates = ltcf_resident_rates_by_age.values()
ltcf_resident_ages = dict.fromkeys(ltcf_resident_rates_by_age.keys(), 0)
age_count = pop.count_pop_ages()
for i, person in pop_dict.items():
if person['ltcf_res']:
ltcf_resident_ages[person['age']] += 1
gen_ltcf_rates = {a: ltcf_resident_ages[a] / age_count[a] for a in ltcf_resident_ages}
fig, ax = sppl.plot_array(expected_ltcf_rates, generated=gen_ltcf_rates.values(), do_show=False, binned=True)
ax.set_xlabel('LTCF Resident Ages')
ax.set_title('LTCF Resident Use Rates by Age')
ax.set_ylim(0., 1.)
ax.set_xlim(0., 100)
if do_show:
plt.show()
def plot_age_dist(datadir, pop, pars, do_show, testprefix):
sp.logger.info(
"Plot the expected age distribution and the generated age distribution."
)
age_brackets = sp.get_census_age_brackets(
datadir,
country_location=pars['country_location'],
state_location=pars['state_location'],
location=pars['location'])
age_by_brackets_dic = sp.get_age_by_brackets_dic(age_brackets)
if pars['smooth_ages']:
expected_age_distr = sp.get_smoothed_single_year_age_distr(
datadir,
location=pars['location'],
state_location=pars['state_location'],
country_location=pars['country_location'],
window_length=pars['window_length'])
else:
expected_age_distr = sp.get_smoothed_single_year_age_distr(
datadir,
location=pars['location'],
state_location=pars['state_location'],
country_location=pars['country_location'],
window_length=1)
gen_age_count = dict.fromkeys(expected_age_distr.keys(), 0)
for i, person in pop.items():
gen_age_count[person['age']] += 1
gen_age_distr = sp.norm_dic(gen_age_count)
fig, ax = sppl.plot_array(
[v * 100 for v in expected_age_distr.values()],
generated=[v * 100 for v in gen_age_distr.values()],
do_show=False,
binned=True,
testprefix=testprefix.replace('_', ' '))
ax.set_xlabel('Ages')
ax.set_ylabel('Distribution (%)')
ax.set_ylim(bottom=0)
ax.set_xlim(-1.5, max(age_by_brackets_dic.keys()) + 1.5)
ax.set_title(
f"Age Distribution of {pars['location'].replace('_', ' ')}: {pars['household_method'].replace('_', ' ')} method"
)
fig.set_figheight(4) # reset the figure size
fig.set_figwidth(7)
return fig, ax