def test_sector_data(self):
self.assertDictEqual(
get_lactating_enrolled_women_sector_data('icds-cas',
config={
'month': (2017, 5, 1),
'state_id': 'st1',
'district_id': 'd1',
'block_id': 'b1',
'aggregation_level': 4
},
location_id='b1',
loc_level='supervisor'),
{
"info":
percent_lactating_women_enrolled_help_text(),
"tooltips_data": {
"s2": {
"valid": 24,
"all": 24
},
"s1": {
"valid": 19,
"all": 19
}
},
"chart_data": [{
"color": MapColors.BLUE,
"classed": "dashed",
"strokeWidth": 2,
"values": [["s1", 19], ["s2", 24]],
"key": ""
}],
"format":
"number"
})
def get_lactating_enrolled_women_sector_data(domain,
config,
loc_level,
location_id,
show_test=False,
icds_features_flag=False):
group_by = ['%s_name' % loc_level]
config['month'] = datetime(*config['month'])
data = AggCcsRecordMonthly.objects.filter(**config).values(
*group_by).annotate(valid=Sum('lactating'),
all=Sum('lactating_all')).order_by('%s_name' %
loc_level)
if not show_test:
data = apply_exclude(domain, data)
chart_data = {'blue': []}
tooltips_data = defaultdict(lambda: {'valid': 0, 'all': 0})
if icds_features_flag:
location_launched_status = get_location_launched_status(
config, loc_level)
else:
location_launched_status = None
for row in data:
if location_launched_status:
launched_status = location_launched_status.get(row['%s_name' %
loc_level])
if launched_status is None or launched_status <= 0:
continue
valid = row['valid'] or 0
all_lactating = row['all'] or 0
name = row['%s_name' % loc_level]
row_values = {'valid': valid, 'all': all_lactating}
for prop, value in row_values.items():
tooltips_data[name][prop] += value
chart_data['blue'].append([name, valid])
chart_data['blue'] = sorted(chart_data['blue'])
return {
"tooltips_data":
dict(tooltips_data),
"format":
"number",
"info":
percent_lactating_women_enrolled_help_text(),
"chart_data": [{
"values": chart_data['blue'],
"key": "",
"strokeWidth": 2,
"classed": "dashed",
"color": MapColors.BLUE
}]
}
def get_lactating_enrolled_women_sector_data(domain,
config,
loc_level,
location_id,
show_test=False):
group_by = ['%s_name' % loc_level]
config['month'] = datetime(*config['month'])
data = AggCcsRecordMonthly.objects.filter(**config).values(
*group_by).annotate(valid=Sum('lactating'),
all=Sum('lactating_all')).order_by('%s_name' %
loc_level)
if not show_test:
data = apply_exclude(domain, data)
chart_data = {'blue': []}
tooltips_data = defaultdict(lambda: {'valid': 0, 'all': 0})
loc_children = get_child_locations(domain, location_id, show_test)
result_set = set()
for row in data:
valid = row['valid'] or 0
all_lactating = row['all'] or 0
name = row['%s_name' % loc_level]
result_set.add(name)
row_values = {'valid': valid, 'all': all_lactating}
for prop, value in six.iteritems(row_values):
tooltips_data[name][prop] += value
chart_data['blue'].append([name, valid])
for sql_location in loc_children:
if sql_location.name not in result_set:
chart_data['blue'].append([sql_location.name, 0])
chart_data['blue'] = sorted(chart_data['blue'])
return {
"tooltips_data":
dict(tooltips_data),
"format":
"number",
"info":
percent_lactating_women_enrolled_help_text(),
"chart_data": [{
"values": chart_data['blue'],
"key": "",
"strokeWidth": 2,
"classed": "dashed",
"color": MapColors.BLUE
}]
}
def test_map_name_is_different_data(self):
self.assertDictEqual(
get_lactating_enrolled_women_data_map(
'icds-cas',
config={
'month': (2017, 5, 1),
'state_id': 'st1',
'district_id': 'd1',
'aggregation_level': 3
},
loc_level='block',
), {
"rightLegend": {
"info":
percent_lactating_women_enrolled_help_text(),
"average":
'100.00',
'extended_info': [{
'indicator':
'Number of lactating women who are enrolled for Anganwadi Services:',
'value': "87"
}, {
'indicator':
'Total number of lactating women who are registered:',
'value': "87"
}, {
'indicator':
('Percentage of registered lactating women who are enrolled for Anganwadi Services:'
),
'value':
'100.00%'
}]
},
"fills": {
"Women": MapColors.BLUE,
"defaultFill": MapColors.GREY
},
"data": {
'block_map': {
'valid': 87,
'all': 87,
'original_name': ['b1', 'b2'],
'fillKey': 'Women'
}
},
"slug": "lactating_enrolled_women",
"label": ""
})
def test_sector_data(self):
self.assertDictEqual(
get_lactating_enrolled_women_sector_data(
'icds-cas',
config={
'month': (2017, 5, 1),
'state_id': 'st1',
'district_id': 'd1',
'block_id': 'b1',
'aggregation_level': 4
},
location_id='b1',
loc_level='supervisor'
),
{
"info": percent_lactating_women_enrolled_help_text(),
"tooltips_data": {
"s2": {
"valid": 24,
"all": 24
},
"s1": {
"valid": 19,
"all": 19
}
},
"chart_data": [
{
"color": MapColors.BLUE,
"classed": "dashed",
"strokeWidth": 2,
"values": [
[
"s1",
19
],
[
"s2",
24
]
],
"key": ""
}
],
"format": "number"
}
)
def test_map_name_is_different_data(self):
self.assertDictEqual(
get_lactating_enrolled_women_data_map(
'icds-cas',
config={
'month': (2017, 5, 1),
'state_id': 'st1',
'district_id': 'd1',
'aggregation_level': 3
},
loc_level='block',
),
{
"rightLegend": {
"info": percent_lactating_women_enrolled_help_text(),
"average": '100.00',
'extended_info': [
{
'indicator': 'Number of lactating women who are enrolled for Anganwadi Services:',
'value': "87"
},
{'indicator': 'Total number of lactating women who are registered:', 'value': "87"},
{
'indicator': (
'Percentage of registered lactating women who are enrolled for Anganwadi Services:'
),
'value': '100.00%'
}
]
},
"fills": {
"Women": MapColors.BLUE,
"defaultFill": MapColors.GREY
},
"data": {
'block_map': {
'valid': 87,
'all': 87,
'original_name': ['b1', 'b2'],
'fillKey': 'Women'
}
},
"slug": "lactating_enrolled_women",
"label": ""
}
)
def test_data_daily_lactating_enrolled_women(self):
data = get_demographics_data(
'icds-cas', (2017, 5, 29), {
'month': (2017, 5, 1),
'prev_month': (2017, 4, 1),
'aggregation_level': 1
})
expected = {
"redirect": "demographics/lactating_enrolled_women",
"all": 167,
"format": "percent_and_div",
"color": "red",
"percent": 0,
"value": 167,
"label": LACTATING_WOMEN_ENROLLED_FOR_ANGANWADI_SERVICES,
"frequency": "day",
"help_text": percent_lactating_women_enrolled_help_text()
}
self.assertDictEqual(expected, data['records'][2][0])
def test_data_daily_lactating_enrolled_women(self):
data = get_demographics_data(
'icds-cas', (2017, 5, 29), {
'month': (2017, 5, 1),
'prev_month': (2017, 4, 1),
'aggregation_level': 1
})
expected = {
"redirect": "demographics/lactating_enrolled_women",
"all": 167,
"format": "percent_and_div",
"color": "green",
"percent": "Data in the previous reporting period was 0",
"value": 167,
"label": "Percent lactating women enrolled for Anganwadi Services",
"frequency": "day",
"help_text": percent_lactating_women_enrolled_help_text()
}
self.assertDictEqual(expected, data['records'][2][0])
def test_data_monthly_lactating_enrolled_women(self):
data = get_demographics_data(
'icds-cas', (2017, 6, 1), {
'month': (2017, 5, 1),
'prev_month': (2017, 4, 1),
'aggregation_level': 1
})
expected = {
'redirect': 'demographics/lactating_enrolled_women',
"all": 167,
"format": "percent_and_div",
"color": "red",
"percent": 0.0,
"value": 167,
"label": "Percent lactating women enrolled for Anganwadi Services",
"frequency": "month",
"help_text": percent_lactating_women_enrolled_help_text()
}
self.assertDictEqual(expected, data['records'][2][0])
def test_data_daily_if_aggregation_script_fail_lactating_enrolled_women(self):
data = get_demographics_data(
'icds-cas',
(2017, 5, 30),
{
'month': (2017, 5, 1),
'prev_month': (2017, 4, 1),
'aggregation_level': 1
}
)
expected = {
"redirect": "demographics/lactating_enrolled_women",
"all": 167,
"format": "percent_and_div",
"color": "green",
"percent": "Data in the previous reporting period was 0",
"value": 167,
"label": "Percent lactating women enrolled for Anganwadi Services",
"frequency": "day",
"help_text": percent_lactating_women_enrolled_help_text()
}
self.assertDictEqual(expected, data['records'][2][0])
def test_data_monthly_lactating_enrolled_women(self):
data = get_demographics_data(
'icds-cas',
(2017, 6, 1),
{
'month': (2017, 5, 1),
'prev_month': (2017, 4, 1),
'aggregation_level': 1
}
)
expected = {
'redirect': 'demographics/lactating_enrolled_women',
"all": 167,
"format": "percent_and_div",
"color": "red",
"percent": 0.0,
"value": 167,
"label": "Percent lactating women enrolled for Anganwadi Services",
"frequency": "month",
"help_text": percent_lactating_women_enrolled_help_text()
}
self.assertDictEqual(expected, data['records'][2][0])
def get_demographics_data(domain,
now_date,
config,
show_test=False,
beta=False):
now_date = datetime(*now_date)
current_month = datetime(*config['month'])
previous_month = datetime(*config['prev_month'])
del config['month']
del config['prev_month']
def get_data_for(query_class, filters):
queryset = query_class.objects.filter(
**filters).values('aggregation_level').annotate(
household=Sum('cases_household'),
child_health=Sum('cases_child_health'),
child_health_all=Sum('cases_child_health_all'),
ccs_pregnant=Sum('cases_ccs_pregnant'),
ccs_pregnant_all=Sum('cases_ccs_pregnant_all'),
css_lactating=Sum('cases_ccs_lactating'),
css_lactating_all=Sum('cases_ccs_lactating_all'),
person_adolescent=Sum('cases_person_adolescent_girls_11_14'),
person_adolescent_all=Sum(
'cases_person_adolescent_girls_11_14_all'),
person_aadhaar=Sum(person_has_aadhaar_column(beta)),
all_persons=Sum(person_is_beneficiary_column(beta)))
if not show_test:
queryset = apply_exclude(domain, queryset)
return queryset
if current_month.month == now_date.month and current_month.year == now_date.year:
config['date'] = now_date.date()
data = None
# keep the record in searched - current - month
while data is None or (not data and config['date'].day != 1):
config['date'] -= relativedelta(days=1)
data = get_data_for(AggAwcDailyView, config)
prev_data = None
while prev_data is None or (not prev_data and config['date'].day != 1):
config['date'] -= relativedelta(days=1)
prev_data = get_data_for(AggAwcDailyView, config)
frequency = 'day'
else:
config['month'] = current_month
data = get_data_for(AggAwcMonthly, config)
config['month'] = previous_month
prev_data = get_data_for(AggAwcMonthly, config)
frequency = 'month'
return {
'records':
[[{
'label':
_('Registered Households'),
'help_text':
_('Total number of households registered'),
'percent':
percent_increase('household', data, prev_data),
'color':
get_color_with_green_positive(
percent_increase('household', data, prev_data)),
'value':
get_value(data, 'household'),
'all':
None,
'format':
'number',
'frequency':
frequency,
'redirect':
'demographics/registered_household'
}, {
'label':
_(AADHAR_SEEDED_BENEFICIARIES),
'help_text':
percent_aadhaar_seeded_beneficiaries_help_text(),
'percent':
percent_diff('person_aadhaar', data, prev_data, 'all_persons'),
'color':
get_color_with_green_positive(
percent_diff('person_aadhaar', data, prev_data,
'all_persons')),
'value':
get_value(data, 'person_aadhaar'),
'all':
get_value(data, 'all_persons'),
'format':
'percent_and_div',
'frequency':
frequency,
'redirect':
'demographics/adhaar'
}],
[{
'label':
_(CHILDREN_ENROLLED_FOR_ANGANWADI_SERVICES),
'help_text':
percent_children_enrolled_help_text(),
'percent':
percent_diff('child_health', data, prev_data, 'child_health_all'),
'color':
get_color_with_green_positive(
percent_diff('child_health', data, prev_data,
'child_health_all')),
'value':
get_value(data, 'child_health'),
'all':
get_value(data, 'child_health_all'),
'format':
'percent_and_div',
'frequency':
frequency,
'redirect':
'demographics/enrolled_children'
}, {
'label':
_(PREGNANT_WOMEN_ENROLLED_FOR_ANGANWADI_SERVICES),
'help_text':
percent_pregnant_women_enrolled_help_text(),
'percent':
percent_diff('ccs_pregnant', data, prev_data, 'ccs_pregnant_all'),
'color':
get_color_with_green_positive(
percent_diff('ccs_pregnant', data, prev_data,
'ccs_pregnant_all')),
'value':
get_value(data, 'ccs_pregnant'),
'all':
get_value(data, 'ccs_pregnant_all'),
'format':
'percent_and_div',
'frequency':
frequency,
'redirect':
'demographics/enrolled_women'
}],
[{
'label':
_(LACTATING_WOMEN_ENROLLED_FOR_ANGANWADI_SERVICES),
'help_text':
percent_lactating_women_enrolled_help_text(),
'percent':
percent_diff('css_lactating', data, prev_data,
'css_lactating_all'),
'color':
get_color_with_green_positive(
percent_diff('css_lactating', data, prev_data,
'css_lactating_all')),
'value':
get_value(data, 'css_lactating'),
'all':
get_value(data, 'css_lactating_all'),
'format':
'percent_and_div',
'frequency':
frequency,
'redirect':
'demographics/lactating_enrolled_women'
}, {
'label':
_(ADOLESCENT_GIRLS_ENROLLED_FOR_ANGANWADI_SERVICES),
'help_text':
percent_adolescent_girls_enrolled_help_text(),
'percent':
percent_diff('person_adolescent', data, prev_data,
'person_adolescent_all'),
'color':
get_color_with_green_positive(
percent_diff('person_adolescent', data, prev_data,
'person_adolescent_all')),
'value':
get_value(data, 'person_adolescent'),
'all':
get_value(data, 'person_adolescent_all'),
'format':
'percent_and_div',
'frequency':
frequency,
'redirect':
'demographics/adolescent_girls'
}]]
}
def get_lactating_enrolled_women_data_map(domain,
config,
loc_level,
show_test=False):
def get_data_for(filters):
filters['month'] = datetime(*filters['month'])
queryset = AggCcsRecordMonthly.objects.filter(**filters).values(
'%s_name' % loc_level,
'%s_map_location_name' % loc_level).annotate(
valid=Sum('lactating'),
all=Sum('lactating_all'),
).order_by('%s_name' % loc_level,
'%s_map_location_name' % loc_level)
if not show_test:
queryset = apply_exclude(domain, queryset)
return queryset
data_for_map = defaultdict(lambda: {
'valid': 0,
'all': 0,
'original_name': [],
'fillKey': 'Women'
})
average = []
total_valid = 0
total = 0
for row in get_data_for(config):
valid = row['valid'] or 0
all_lactating = row['all'] or 0
name = row['%s_name' % loc_level]
on_map_name = row['%s_map_location_name' % loc_level] or name
average.append(valid)
total_valid += valid
total += all_lactating
data_for_map[on_map_name]['valid'] += valid
data_for_map[on_map_name]['all'] += all_lactating
data_for_map[on_map_name]['original_name'].append(name)
fills = OrderedDict()
fills.update({'Women': MapColors.BLUE})
fills.update({'defaultFill': MapColors.GREY})
return {
"slug": "lactating_enrolled_women",
"label": "",
"fills": fills,
"rightLegend": {
"average":
'%.2f' % (total_valid * 100 / float(total or 1)),
"info":
percent_lactating_women_enrolled_help_text(),
"extended_info": [{
'indicator':
'Number of lactating women who are enrolled for Anganwadi Services:',
'value': indian_formatted_number(total_valid)
}, {
'indicator':
('Total number of lactating women who are registered:'),
'value':
indian_formatted_number(total)
}, {
'indicator':
('Percentage of registered lactating women who are enrolled for Anganwadi Services:'
),
'value':
'%.2f%%' % (total_valid * 100 / float(total or 1))
}]
},
"data": dict(data_for_map),
}
def get_lactating_enrolled_women_sector_data(domain, config, loc_level, location_id, show_test=False):
group_by = ['%s_name' % loc_level]
config['month'] = datetime(*config['month'])
data = AggCcsRecordMonthly.objects.filter(
**config
).values(
*group_by
).annotate(
valid=Sum('lactating'),
all=Sum('lactating_all')
).order_by('%s_name' % loc_level)
if not show_test:
data = apply_exclude(domain, data)
chart_data = {
'blue': []
}
tooltips_data = defaultdict(lambda: {
'valid': 0,
'all': 0
})
loc_children = get_child_locations(domain, location_id, show_test)
result_set = set()
for row in data:
valid = row['valid'] or 0
all_lactating = row['all'] or 0
name = row['%s_name' % loc_level]
result_set.add(name)
row_values = {
'valid': valid,
'all': all_lactating
}
for prop, value in six.iteritems(row_values):
tooltips_data[name][prop] += value
chart_data['blue'].append([
name, valid
])
for sql_location in loc_children:
if sql_location.name not in result_set:
chart_data['blue'].append([sql_location.name, 0])
chart_data['blue'] = sorted(chart_data['blue'])
return {
"tooltips_data": dict(tooltips_data),
"format": "number",
"info": percent_lactating_women_enrolled_help_text(),
"chart_data": [
{
"values": chart_data['blue'],
"key": "",
"strokeWidth": 2,
"classed": "dashed",
"color": MapColors.BLUE
}
]
}
def get_lactating_enrolled_women_data_map(domain, config, loc_level, show_test=False):
def get_data_for(filters):
filters['month'] = datetime(*filters['month'])
queryset = AggCcsRecordMonthly.objects.filter(
**filters
).values(
'%s_name' % loc_level, '%s_map_location_name' % loc_level
).annotate(
valid=Sum('lactating'),
all=Sum('lactating_all'),
).order_by('%s_name' % loc_level, '%s_map_location_name' % loc_level)
if not show_test:
queryset = apply_exclude(domain, queryset)
return queryset
data_for_map = defaultdict(lambda: {
'valid': 0,
'all': 0,
'original_name': [],
'fillKey': 'Women'
})
average = []
total_valid = 0
total = 0
for row in get_data_for(config):
valid = row['valid'] or 0
all_lactating = row['all'] or 0
name = row['%s_name' % loc_level]
on_map_name = row['%s_map_location_name' % loc_level] or name
average.append(valid)
total_valid += valid
total += all_lactating
data_for_map[on_map_name]['valid'] += valid
data_for_map[on_map_name]['all'] += all_lactating
data_for_map[on_map_name]['original_name'].append(name)
fills = OrderedDict()
fills.update({'Women': MapColors.BLUE})
fills.update({'defaultFill': MapColors.GREY})
return {
"slug": "lactating_enrolled_women",
"label": "",
"fills": fills,
"rightLegend": {
"average": '%.2f' % (total_valid * 100 / float(total or 1)),
"info": percent_lactating_women_enrolled_help_text(),
"extended_info": [
{
'indicator': 'Number of lactating women who are enrolled for Anganwadi Services:',
'value': indian_formatted_number(total_valid)
},
{
'indicator': (
'Total number of lactating women who are registered:'
),
'value': indian_formatted_number(total)
},
{
'indicator': (
'Percentage of registered lactating women who are enrolled for Anganwadi Services:'
),
'value': '%.2f%%' % (total_valid * 100 / float(total or 1))
}
]
},
"data": dict(data_for_map),
}
def get_demographics_data(domain, now_date, config, show_test=False, beta=False):
now_date = datetime(*now_date)
current_month = datetime(*config['month'])
previous_month = datetime(*config['prev_month'])
del config['month']
del config['prev_month']
def get_data_for(query_class, filters):
queryset = query_class.objects.filter(
**filters
).values(
'aggregation_level'
).annotate(
household=Sum('cases_household'),
child_health=Sum('cases_child_health'),
child_health_all=Sum('cases_child_health_all'),
ccs_pregnant=Sum('cases_ccs_pregnant'),
ccs_pregnant_all=Sum('cases_ccs_pregnant_all'),
css_lactating=Sum('cases_ccs_lactating'),
css_lactating_all=Sum('cases_ccs_lactating_all'),
person_adolescent=Sum('cases_person_adolescent_girls_11_14'),
person_adolescent_all=Sum('cases_person_adolescent_girls_11_14_all'),
person_aadhaar=Sum(person_has_aadhaar_column(beta)),
all_persons=Sum(person_is_beneficiary_column(beta))
)
if not show_test:
queryset = apply_exclude(domain, queryset)
return queryset
if current_month.month == now_date.month and current_month.year == now_date.year:
config['date'] = now_date.date()
data = None
# keep the record in searched - current - month
while data is None or (not data and config['date'].day != 1):
config['date'] -= relativedelta(days=1)
data = get_data_for(AggAwcDailyView, config)
prev_data = None
while prev_data is None or (not prev_data and config['date'].day != 1):
config['date'] -= relativedelta(days=1)
prev_data = get_data_for(AggAwcDailyView, config)
frequency = 'day'
else:
config['month'] = current_month
data = get_data_for(AggAwcMonthly, config)
config['month'] = previous_month
prev_data = get_data_for(AggAwcMonthly, config)
frequency = 'month'
return {
'records': [
[
{
'label': _('Registered Households'),
'help_text': _('Total number of households registered'),
'percent': percent_increase('household', data, prev_data),
'color': 'green' if percent_increase(
'household',
data,
prev_data) > 0 else 'red',
'value': get_value(data, 'household'),
'all': None,
'format': 'number',
'frequency': frequency,
'redirect': 'demographics/registered_household'
},
{
'label': _('Percent Aadhaar-seeded Beneficiaries'),
'help_text': percent_aadhaar_seeded_beneficiaries_help_text(),
'percent': percent_diff(
'person_aadhaar',
data,
prev_data,
'all_persons'
),
'color': 'green' if percent_diff(
'person_aadhaar',
data,
prev_data,
'all_persons') > 0 else 'red',
'value': get_value(data, 'person_aadhaar'),
'all': get_value(data, 'all_persons'),
'format': 'percent_and_div',
'frequency': frequency,
'redirect': 'demographics/adhaar'
}
],
[
{
'label': _('Percent children (0-6 years) enrolled for Anganwadi Services'),
'help_text': percent_children_enrolled_help_text(),
'percent': percent_diff('child_health', data, prev_data, 'child_health_all'),
'color': 'green' if percent_diff(
'child_health',
data,
prev_data, 'child_health_all') > 0 else 'red',
'value': get_value(data, 'child_health'),
'all': get_value(data, 'child_health_all'),
'format': 'percent_and_div',
'frequency': frequency,
'redirect': 'demographics/enrolled_children'
},
{
'label': _('Percent pregnant women enrolled for Anganwadi Services'),
'help_text': percent_pregnant_women_enrolled_help_text(),
'percent': percent_diff('ccs_pregnant', data, prev_data, 'ccs_pregnant_all'),
'color': 'green' if percent_diff(
'ccs_pregnant',
data,
prev_data,
'ccs_pregnant_all'
) > 0 else 'red',
'value': get_value(data, 'ccs_pregnant'),
'all': get_value(data, 'ccs_pregnant_all'),
'format': 'percent_and_div',
'frequency': frequency,
'redirect': 'demographics/enrolled_women'
}
],
[
{
'label': _('Percent lactating women enrolled for Anganwadi Services'),
'help_text': percent_lactating_women_enrolled_help_text(),
'percent': percent_diff('css_lactating', data, prev_data, 'css_lactating_all'),
'color': 'green' if percent_diff(
'css_lactating',
data,
prev_data,
'css_lactating_all'
) > 0 else 'red',
'value': get_value(data, 'css_lactating'),
'all': get_value(data, 'css_lactating_all'),
'format': 'percent_and_div',
'frequency': frequency,
'redirect': 'demographics/lactating_enrolled_women'
},
{
'label': _('Percent adolescent girls (11-14 years) enrolled for Anganwadi Services'),
'help_text': percent_adolescent_girls_enrolled_help_text(),
'percent': percent_diff(
'person_adolescent',
data,
prev_data,
'person_adolescent_all'
),
'color': 'green' if percent_diff(
'person_adolescent',
data,
prev_data,
'person_adolescent_all'
) > 0 else 'red',
'value': get_value(data, 'person_adolescent'),
'all': get_value(data, 'person_adolescent_all'),
'format': 'percent_and_div',
'frequency': frequency,
'redirect': 'demographics/adolescent_girls'
}
]
]
}
