def hypothesis_test_paris():
THRESHOLDS = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 25, 30]
winter = Winter()
# if params[1] in [10, 12, 1, 3, 5]:
# last_day = 31
# elif params[1] in [9, 11, 4]:
# last_day = 30
winter.START = datetime.date(winter.START.year, 10, 1)
winter.END = datetime.date(winter.END.year, 3, 31)
city_resolver = get_city_resolver()
population = get_population(PARIS)
ah = get_ah(PARIS)
ah_mean = get_ah_mean(ah)
ah_dev = get_ah_deviation(ah, ah_mean)
morbidity = get_daily_morbidity(PARIS)
morbidity_mean = get_morbidity_mean(morbidity)
morbidity_excess = get_morbidity_excess(morbidity, morbidity_mean)
excess_data = get_relative_weekly_morbidity_excess(morbidity_excess,
population)
onsets = get_onsets_by_morbidity(excess_data, THRESHOLDS)
years = range(1986, 2015)
for threshold in THRESHOLDS:
generate_control_sample(
onsets,
threshold,
ah_dev,
winter,
PARIS,
city_resolver,
years,
filename=f'results/stats/paris/ah_sample.{threshold}.json')
generate_experimental_sample(
onsets,
threshold,
ah_dev,
winter,
PARIS,
city_resolver,
filename=f'results/stats/paris/epidemic_sample.{threshold}.json')
for threshold in THRESHOLDS:
print(f'threshold {threshold}')
with open(f'results/stats/paris/ah_sample.{threshold}.json', 'r') as f:
ah_sample = json.load(f)
with open(f'results/stats/paris/epidemic_sample.{threshold}.json',
'r') as f:
epidemic_sample = json.load(f)
print(f"AH' sample size = {len(ah_sample)}")
print(f"Epidemic sample size = {len(epidemic_sample)}")
t, prob = stats.ttest_ind(ah_sample, epidemic_sample)
print(f"Equal variance (Student's t-test): P-value = {prob}")
t, prob = stats.ttest_ind(ah_sample, epidemic_sample, equal_var=False)
print(f"Not equal variance (Welch’s t-test): P-value = {prob}")
print()
def onset_distribution():
# Params
THRESHOLDS = [10]
winter = Winter()
# if params[1] in [10, 12, 1, 3, 5]:
# last_day = 31
# elif params[1] in [9, 11, 4]:
# last_day = 30
winter.START = datetime.date(winter.START.year, 10, 1)
winter.END = datetime.date(winter.END.year, 3, 31)
# End params
population = get_population(CITIES)
morbidity = get_daily_morbidity(CITIES)
morbidity_mean = get_morbidity_mean(morbidity)
morbidity_excess = get_morbidity_excess(morbidity, morbidity_mean)
excess_data = get_relative_weekly_morbidity_excess(morbidity_excess,
population)
onsets = get_onsets_by_morbidity(excess_data, THRESHOLDS, winter=winter)
filename = f'results/onsets/russia' \
f'_winter{winter.START.month}-{winter.END.month}' \
f'_threshold{THRESHOLDS[0]}.png'
draw_onset_distribution_by_week(
onsets[THRESHOLDS[0]],
CITIES,
winter=winter,
title='Epidemic number distribution in Russia',
save_to_file=filename)
def onset_distribution_paris():
# Params
THRESHOLDS = [5]
winter = Winter()
# if params[1] in [10, 12, 1, 3, 5]:
# last_day = 31
# elif params[1] in [9, 11, 4]:
# last_day = 30
winter.START = datetime.date(winter.START.year, 10, 1)
winter.END = datetime.date(winter.END.year, 3, 31)
# End params
population = get_population(PARIS)
morbidity = get_daily_morbidity(PARIS)
morbidity_mean = get_morbidity_mean(morbidity)
morbidity_excess = get_morbidity_excess(morbidity, morbidity_mean)
excess_data = get_relative_weekly_morbidity_excess(morbidity_excess,
population)
onsets = get_onsets_by_morbidity(excess_data, THRESHOLDS, winter=winter)
filename = f'results/onsets/paris' \
f'_winter{winter.START.month}-{winter.END.month}' \
f'_threshold{THRESHOLDS[0]}.png'
draw_onset_distribution_by_week(
onsets[THRESHOLDS[0]],
PARIS,
winter=winter,
title='Epidemic number distribution in Paris,\n'
'determined with mordibity deviation (October — March)',
save_to_file=filename)
def main():
"""
Parameters
"""
THRESHOLDS = [30, 35, 40, 45] # [25, 30, 35, 40, 45, 50]
# CITIES = ['spb']
winter = Winter()
# if params[1] in [10, 12, 1, 3, 5]:
# last_day = 31
# elif params[1] in [9, 11, 4]:
# last_day = 30
winter.START = datetime.date(winter.START.year, 11, 1)
winter.END = datetime.date(winter.END.year, 3, 31)
city_resolver = get_city_resolver()
population = get_population(CITIES)
ah = get_ah(CITIES)
ah_mean = get_ah_mean(ah)
ah_dev = get_ah_deviation(ah, ah_mean)
morbidity = get_daily_morbidity(CITIES)
morbidity_mean = get_morbidity_mean(morbidity)
morbidity_excess = get_morbidity_excess(morbidity, morbidity_mean)
excess_data = get_relative_weekly_morbidity_excess(morbidity_excess,
population)
onsets = get_onsets_by_morbidity(excess_data, THRESHOLDS, winter=winter)
cases = [
(['msk'], 'Moscow', 'Moscow'),
(['spb'], 'SaintPetersburg', 'Saint Petersburg'),
(['nsk'], 'Novosibirsk', 'Novosibirsk'),
(['spb', 'msk', 'nsk'], 'spb,msk,nsk', 'All cities'),
]
for case in cases:
CUR_CITIES, name_suffix, title = case
average_ah_dev = get_average_ah_vs_onsets(ah_dev, onsets, CUR_CITIES,
THRESHOLDS, DATE_SHIFT_RANGE,
city_resolver)
filename = 'results/russia/morbidity/' \
'rf_m_%s_winter%d-%d_threshold%s-%s.pdf' % (
name_suffix, winter.START.month, winter.END.month,
min(average_ah_dev.keys()), max(average_ah_dev.keys()))
plot_average_ah_dev(average_ah_dev,
THRESHOLD_COLORS,
DATE_SHIFT_RANGE,
title=title,
save_to_file=filename)
def onset_distribution_epidemiologists():
winter = Winter()
winter.START = datetime.date(winter.START.year, 10, 1)
winter.END = datetime.date(winter.END.year, 3, 31)
onsets = get_onsets_by_epidemiologists(CITIES, AH_FILE_PATTERN, [0])
filename = 'results/onsets/russia_epidemiologists.png'
draw_onset_distribution_by_week(
onsets[0],
CITIES,
winter=winter,
title='Epidemic number distribution in Russia\n'
'given by Influenza Institute (October — March)',
save_to_file=filename)
def get_onsets_by_morbidity(excess_data, thresholds, winter=Winter()):
onsets = dict()
for threshold in thresholds:
onsets[threshold] = dict()
for city in excess_data.keys():
onsets[threshold][city] = list()
excess = sorted(excess_data[city].items(),
key=lambda x: int(x[0][0:2]) + 31 * int(x[0][3:5])
+ 366 * int(x[0][6:10]))
for idx in range(len(excess)):
if idx == 0 or idx == 1:
continue
prev2, prev1, current = excess[idx - 2], \
excess[idx - 1], excess[idx]
current_date = \
datetime.datetime.strptime(current[0], "%d.%m.%Y").date()
if current_date <= datetime.date(1986, winter.END.month, winter.END.day - 1) or \
current_date >= datetime.date(2015, winter.START.month, winter.START.day):
continue
if prev2[1] >= threshold and prev1[1] >= threshold \
and winter.is_winter(current_date):
# Cutoff second epidemic in the same winter-time
if onsets[threshold][city] and current_date - onsets[threshold][city][-1] < \
datetime.timedelta(days=winter.days_count):
continue
onsets[threshold][city].append(current_date)
continue
return onsets
def get_onsets(excess_data, thresholds, winter=Winter()):
onsets = dict()
for threshold in thresholds:
onsets[threshold] = dict()
for idx in range(52):
onsets[threshold][idx] = list()
for state in range(52):
for idx in range(len(excess_data[state])):
if idx == 0 or idx == 1:
continue
prev2, prev1, current = excess_data[state][idx - 2], \
excess_data[state][idx - 1], excess_data[state][idx]
if current['date'] <= datetime.date(1972, winter.END.month, winter.END.day) or \
current['date'] >= datetime.date(2002, winter.START.month, winter.START.day):
continue
if prev2['excess'] >= threshold and prev1['excess'] >= threshold \
and winter.is_winter(current['date']):
# Cutoff second epidemic in the same winter-time
if onsets[threshold][state] and current['date'] - onsets[threshold][state][-1] < \
datetime.timedelta(days=winter.days_count):
continue
onsets[threshold][state].append(current['date'])
continue
return onsets
def hypothesis_test_epidemiologists():
THRESHOLDS = [0]
threshold = 0
city_resolver = get_city_resolver()
ah = get_ah(CITIES)
ah_mean = get_ah_mean(ah)
ah_dev = get_ah_deviation(ah, ah_mean)
onsets = get_onsets_by_epidemiologists(CITIES, AH_FILE_PATTERN, THRESHOLDS)
years = range(1986, 2015)
from hypothesis import generate_control_sample, generate_experimental_sample
generate_control_sample(
onsets,
threshold,
ah_dev,
Winter(),
CITIES,
city_resolver,
years,
filename=f'results/stats/russia_epid/ah_sample.{threshold}.json')
generate_experimental_sample(
onsets,
threshold,
ah_dev,
Winter(),
CITIES,
city_resolver,
filename=f'results/stats/russia_epid/epidemic_sample.{threshold}.json')
print(f'threshold {threshold}')
with open(f'results/stats/russia_epid/ah_sample.{threshold}.json',
'r') as f:
ah_sample = json.load(f)
with open(f'results/stats/russia_epid/epidemic_sample.{threshold}.json',
'r') as f:
epidemic_sample = json.load(f)
print(f"AH' sample size = {len(ah_sample)}")
print(f"Epidemic sample size = {len(epidemic_sample)}")
t, prob = stats.ttest_ind(ah_sample, epidemic_sample)
print(f"Equal variance (Student's t-test): P-value = {prob}")
t, prob = stats.ttest_ind(ah_sample, epidemic_sample, equal_var=False)
print(f"Not equal variance (Welch’s t-test): P-value = {prob}")
print()
def main_paris():
state_resolver = get_city_resolver()
population = get_population(PARIS)
"""
Parameters
"""
# THRESHOLDS = [-1000, 5, 10, 50, 100, 500, 750, ]
# THRESHOLDS = [0, 5, 9, 25, 35, 40, 45, 50, ]
THRESHOLDS = [9, 10, 20, 30] # , 40, 50, ]
# THRESHOLDS = [9, 10, 20, 30, 40, 50]
# THRESHOLDS = [0, 25, 50, 75, 100, ]
# THRESHOLDS = [10, 20, 30, 40, 50, 60, 70, 80]
winter = Winter()
# if params[1] in [10, 12, 1, 3, 5]:
# last_day = 31
# elif params[1] in [9, 11, 4]:
# last_day = 30
winter.START = datetime.date(winter.START.year, 11, 1)
winter.END = datetime.date(winter.END.year, 3, 31)
ah = get_ah(PARIS)
ah_mean = get_ah_mean(ah)
ah_dev = get_ah_deviation(ah, ah_mean)
morbidity = get_daily_morbidity(PARIS)
morbidity_mean = get_morbidity_mean(morbidity)
morbidity_excess = get_morbidity_excess(morbidity, morbidity_mean)
excess_data = get_relative_weekly_morbidity_excess(morbidity_excess,
population)
onsets = get_onsets_by_morbidity(excess_data, THRESHOLDS, winter=winter)
average_ah_dev = get_average_ah_vs_onsets(ah_dev, onsets, PARIS,
THRESHOLDS, DATE_SHIFT_RANGE,
state_resolver)
title = f'Île-de-France: outbreaks in ' \
f'{winter.START.strftime("%B")} — {winter.END.strftime("%B")}'
filename = 'results/paris/paris_winter%d-%d_threshold%s.pdf' % (
winter.START.month, winter.END.month, max(average_ah_dev.keys()))
plot_average_ah_dev(average_ah_dev,
THRESHOLD_COLORS,
DATE_SHIFT_RANGE,
limits=(-11e-4, 15e-4),
title=title,
save_to_file=filename)
def winter_range_investigation():
state_resolver = get_state_resolver(STATE_CODES_FILE)
ah = get_ah(AH_CSV_FILE)
ah_mean = get_ah_mean(ah)
ah_dev = get_ah_deviation(ah, ah_mean)
excess_data = get_mortality_excess(MORTALITY_EXCESS_FILE)
for params in (
(12, 2),
(12, 3),
(11, 2),
(11, 3),
(11, 4),
(10, 3),
(10, 4),
(10, 5),
(9, 4),
(9, 5),
):
winter = Winter()
winter.START = datetime.date(winter.START.year, params[0], 1)
if params[1] in [10, 12, 1, 3, 5]:
last_day = 31
elif params[1] in [9, 11, 4]:
last_day = 30
else: # 2 (February 1972)
last_day = 29
winter.END = datetime.date(winter.END.year, params[1], last_day)
onsets = get_onsets(excess_data, THRESHOLDS, winter)
average_ah_dev = get_average_ah_vs_onsets(ah_dev, onsets,
CONTIGUOUS_STATES,
THRESHOLDS, DATE_SHIFT_RANGE,
state_resolver)
rng = f'{winter.START.strftime("%B")} — {winter.END.strftime("%B")}'
title = 'AH\' v. Onset Day: outbreaks in ' + rng
filename = 'results/winter_range_usa/usa_winter%d-%d.pdf' % (
winter.START.month, winter.END.month)
plot_average_ah_dev(average_ah_dev,
THRESHOLD_COLORS,
DATE_SHIFT_RANGE,
limits=(-3.3e-4, 2.2e-4),
title=title,
save_to_file=filename)
def main():
winter = Winter()
# if params[1] in [10, 12, 1, 3, 5]:
# last_day = 31
# elif params[1] in [9, 11, 4]:
# last_day = 30
winter.START = datetime.date(winter.START.year, 10, 1)
winter.END = datetime.date(winter.END.year, 4, 30)
state_resolver = get_state_resolver(STATE_CODES_FILE)
ah = get_ah(AH_CSV_FILE)
ah_mean = get_ah_mean(ah)
ah_dev = get_ah_deviation(ah, ah_mean)
excess_data = get_mortality_excess(MORTALITY_EXCESS_FILE)
onsets = get_onsets(excess_data, THRESHOLDS, winter)
regions = [
# ('all', 'Contiguous States', CONTIGUOUS_STATES),
('sw', 'Southwest States', SW_STATES),
('ne', 'Northeast States', NE_STATES),
('gulf', 'Gulf States', GULF_STATES),
('the_rest', 'The Remained States', REST_STATES)
]
for region in regions:
name_suffix, title_suffix, SITES = region
average_ah_dev = get_average_ah_vs_onsets(ah_dev, onsets, SITES,
THRESHOLDS, DATE_SHIFT_RANGE,
state_resolver)
plot_average_ah_dev(average_ah_dev,
THRESHOLD_COLORS,
DATE_SHIFT_RANGE,
limits=(-7e-4, 5e-4),
title='AH\' v. Onset Day: ' + title_suffix,
save_to_file='results/usa/usa_winter10-4_%s.pdf' %
name_suffix)
def stats_all_country():
# THRESHOLDS = [0.005]
winter = Winter()
# if params[1] in [10, 12, 1, 3, 5]:
# last_day = 31
# elif params[1] in [9, 11, 4]:
# last_day = 30
winter.START = datetime.date(winter.START.year, 10, 1)
winter.END = datetime.date(winter.END.year, 3, 31)
state_resolver = get_state_resolver(STATE_CODES_FILE)
ah = get_ah(AH_CSV_FILE)
ah_mean = get_ah_mean(ah)
ah_dev = get_ah_deviation(ah, ah_mean)
excess_data = get_mortality_excess(MORTALITY_EXCESS_FILE)
onsets = get_onsets(excess_data, THRESHOLDS, winter)
years = range(1972, 2002)
# Generate for all the country
for threshold in THRESHOLDS:
generate_control_sample(
onsets,
threshold,
ah_dev,
Winter(),
CONTIGUOUS_STATES,
state_resolver,
years,
filename=f'results/stats/usa/ah_sample.{threshold}.json')
generate_experimental_sample(
onsets,
threshold,
ah_dev,
Winter(),
CONTIGUOUS_STATES,
state_resolver,
filename=f'results/stats/usa/epidemic_sample.{threshold}.json')
for threshold in THRESHOLDS:
print(f'threshold {threshold}:')
with open(f'results/stats/usa/ah_sample.{threshold}.json', 'r') as f:
ah_sample = json.load(f)
with open(f'results/stats/usa/epidemic_sample.{threshold}.json',
'r') as f:
epidemic_sample = json.load(f)
t, prob = stats.ttest_ind(ah_sample, epidemic_sample)
print(t, prob)
t, prob = stats.ttest_ind(ah_sample, epidemic_sample, equal_var=False)
print(t, prob)
def stats_regions():
winter = Winter()
# if params[1] in [10, 12, 1, 3, 5]:
# last_day = 31
# elif params[1] in [9, 11, 4]:
# last_day = 30
winter.START = datetime.date(winter.START.year, 10, 1)
winter.END = datetime.date(winter.END.year, 3, 31)
state_resolver = get_state_resolver(STATE_CODES_FILE)
ah = get_ah(AH_CSV_FILE)
ah_mean = get_ah_mean(ah)
ah_dev = get_ah_deviation(ah, ah_mean)
excess_data = get_mortality_excess(MORTALITY_EXCESS_FILE)
onsets = get_onsets(excess_data, THRESHOLDS, winter)
years = range(1972, 2002)
# For regions
threshold = THRESHOLDS[-1] # The strongest 0.02
regions = {
'sw': SW_STATES,
'ne': NE_STATES,
'gulf': GULF_STATES,
'the_rest': REST_STATES
}
for region_name, region in regions.items():
generate_control_sample(
onsets,
threshold,
ah_dev,
winter,
region,
state_resolver,
years,
filename=
f'results/stats/usa/regions/control.{region_name}.{threshold}.json'
)
generate_experimental_sample(
onsets,
threshold,
ah_dev,
winter,
region,
state_resolver,
filename=
f'results/stats/usa/regions/experimental.{region_name}.{threshold}.json'
)
for region_name, region in regions.items():
try:
with open(
f'results/stats/usa/regions/control.{region_name}.{threshold}.json',
'r') as f:
ah_sample = json.load(f)
with open(
f'results/stats/usa/regions/experimental.{region_name}.{threshold}.json',
'r') as f:
epidemic_sample = json.load(f)
except:
continue
print(f'Region {region_name} ({len(region)} states)')
print(f"AH' sample size = {len(ah_sample)}")
print(f"Epidemic sample size = {len(epidemic_sample)}")
# t, prob = stats.ttest_ind(ah_sample, epidemic_sample)
# print(f"Equal variance (Student's t-test): P-value = {prob}")
t, prob = stats.ttest_ind(ah_sample, epidemic_sample, equal_var=False)
print(f"Not equal variance (Welch’s t-test): P-value = {prob}")
print()
def stats_joint():
# Assert stats_distinct_states been already performed for every state
winter = Winter()
# if params[1] in [10, 12, 1, 3, 5]:
# last_day = 31
# elif params[1] in [9, 11, 4]:
# last_day = 30
winter.START = datetime.date(winter.START.year, 10, 1)
winter.END = datetime.date(winter.END.year, 3, 31)
state_resolver = get_state_resolver(STATE_CODES_FILE)
ah = get_ah(AH_CSV_FILE)
ah_mean = get_ah_mean(ah)
ah_dev = get_ah_deviation(ah, ah_mean)
excess_data = get_mortality_excess(MORTALITY_EXCESS_FILE)
onsets = get_onsets(excess_data, THRESHOLDS, winter)
top_dip = distinct_states()
CONTIGUOUS_STATES = [1] + list(range(3, 12)) + list(range(13, 52))
NOT_TOP_STATES = list(set(CONTIGUOUS_STATES) -
set(top_dip[:24])) # exclude top 24 AH' lowest
average_ah_dev = get_average_ah_vs_onsets(ah_dev, onsets, NOT_TOP_STATES,
THRESHOLDS, DATE_SHIFT_RANGE,
state_resolver)
plot_average_ah_dev(average_ah_dev,
THRESHOLD_COLORS,
DATE_SHIFT_RANGE,
limits=(-7e-4, 5e-4),
title='AH\' v. Onset Day: The Remained States',
save_to_file='results/usa/usa_top.pdf')
# For joint states test
threshold = THRESHOLDS[-1] # The strongest
ah_sample = []
for i in range(len(top_dip)):
CONTIGUOUS_STATES = [1] + list(range(3, 12)) + list(range(13, 52))
CONTIGUOUS_STATES = list(set(CONTIGUOUS_STATES) -
set(top_dip[:i])) # exclude top 24 AH' lowest
for site in CONTIGUOUS_STATES:
try:
with open(
f'results/stats/usa/distinct/control.{site}.{threshold}.json',
'r') as f:
ah_sample += json.load(f)
except:
continue
generate_experimental_sample(
onsets,
threshold,
ah_dev,
Winter(),
CONTIGUOUS_STATES,
state_resolver,
filename=
f'results/stats/usa/joint/sites_cnt{len(CONTIGUOUS_STATES)}.{threshold}.json'
)
with open(
f'results/stats/usa/joint/sites_cnt{len(CONTIGUOUS_STATES)}.{threshold}.json',
'r') as f:
epidemic_sample = json.load(f)
print(f'Some {len(CONTIGUOUS_STATES)} states')
print(f"AH' sample size = {len(ah_sample)}")
print(f"Epidemic sample size = {len(epidemic_sample)}")
# t, prob = stats.ttest_ind(ah_sample, epidemic_sample)
# print(f"Equal variance (Student's t-test): P-value = {prob}")
t, prob = stats.ttest_ind(ah_sample, epidemic_sample, equal_var=False)
print(f"Not equal variance (Welch’s t-test): P-value = {prob}")
print()
def stats_distinct_states():
winter = Winter()
# if params[1] in [10, 12, 1, 3, 5]:
# last_day = 31
# elif params[1] in [9, 11, 4]:
# last_day = 30
winter.START = datetime.date(winter.START.year, 10, 1)
winter.END = datetime.date(winter.END.year, 3, 31)
state_resolver = get_state_resolver(STATE_CODES_FILE)
ah = get_ah(AH_CSV_FILE)
ah_mean = get_ah_mean(ah)
ah_dev = get_ah_deviation(ah, ah_mean)
excess_data = get_mortality_excess(MORTALITY_EXCESS_FILE)
onsets = get_onsets(excess_data, THRESHOLDS, winter)
years = range(1972, 2002)
# For distinct states
threshold = THRESHOLDS[-1] # The strongest 0.02
for site in CONTIGUOUS_STATES[1:]:
generate_control_sample(
onsets,
threshold,
ah_dev,
Winter(), [site],
state_resolver,
years,
filename=
f'results/stats/usa/distinct/control.{site}.{threshold}.json')
generate_experimental_sample(
onsets,
threshold,
ah_dev,
Winter(), [site],
state_resolver,
filename=
f'results/stats/usa/distinct/experimental.{site}.{threshold}.json')
different = []
equal = []
for site in CONTIGUOUS_STATES:
try:
with open(
f'results/stats/usa/distinct/control.{site}.{threshold}.json',
'r') as f:
ah_sample = json.load(f)
# ggg(onsets, threshold, ah_dev, Winter(), [site], state_resolver,
# filename=f'results/stats/usa/distinct/experimental.{site}.{threshold}.json')
with open(
f'results/stats/usa/distinct/experimental.{site}.{threshold}.json',
'r') as f:
epidemic_sample = json.load(f)
except:
continue
# print(state_resolver[site]['name'])
# print(f"AH' sample size = {len(ah_sample)}")
# print(f"Epidemic sample size = {len(epidemic_sample)}")
# t, prob = stats.ttest_ind(ah_sample, epidemic_sample)
# print(f"Equal variance (Student's t-test): P-value = {prob}")
t, prob = stats.ttest_ind(ah_sample, epidemic_sample, equal_var=False)
# print(f"Not equal variance (Welch’s t-test): P-value = {prob}")
# print()
prob_str = "\\textbf{" + str(prob)[:7] + "}" if prob < 0.05 else str(
prob)[:7]
print(f"{state_resolver[site]['name']} & {len(epidemic_sample)} & " +
prob_str + " \\\\\n\\hline")
if prob < 0.05:
different.append((
state_resolver[site]['name'],
prob,
))
else:
equal.append((
state_resolver[site]['name'],
prob,
))
print(f"\n\n\nOverall {len(different) + len(equal)} states:")
print(f'\t{len(different)} different avg: {[x[0] for x in different]}')
print(f'\t{len(equal)} equal avg: {[x[0] for x in equal]}')
print()
diff_prob, eq_prob = [x[1] for x in different], [x[1] for x in equal]
if diff_prob:
print(
f'Different P-value variance: [{min(diff_prob)} ... {max(diff_prob)}]'
)
if eq_prob:
print(f'Equal P-value variance: [{min(eq_prob)} ... {max(eq_prob)}]')