def test_map_data_right_legend_info(self):
data = get_prevalence_of_undernutrition_data_map('icds-cas',
config={
'month':
(2017, 5, 1),
},
loc_level='state')
expected = underweight_children_help_text(age_label="0 - 5 years",
html=True)
self.assertEquals(data['rightLegend']['info'], expected)
def test_map_data_right_legend_info(self):
data = get_prevalence_of_undernutrition_data_map(
'icds-cas',
config={
'month': (2017, 5, 1),
},
loc_level='state'
)
expected = underweight_children_help_text(age_label="0 - 5 years", html=True)
self.assertEquals(data['rightLegend']['info'], expected)
def test_sector_data_info(self):
data = get_prevalence_of_undernutrition_sector_data(
'icds-cas',
config={
'month': (2017, 5, 1),
'state_id': 'st1',
'district_id': 'd1',
'block_id': 'b1',
},
location_id='b1',
loc_level='supervisor')
self.assertEquals(
data['info'],
underweight_children_help_text(age_label="0-5 years", html=True))
def test_sector_data_info(self):
data = get_prevalence_of_undernutrition_sector_data(
'icds-cas',
config={
'month': (2017, 5, 1),
'state_id': 'st1',
'district_id': 'd1',
'block_id': 'b1',
},
location_id='b1',
loc_level='supervisor'
)
self.assertEquals(
data['info'],
underweight_children_help_text(age_label="0-5 years", html=True)
)
def test_data_underweight_weight_for_age(self):
self.assertDictEqual(
get_maternal_child_data(
'icds-cas', {
'month': (2017, 5, 1),
'prev_month': (2017, 4, 1),
'aggregation_level': 1
})['records'][0][0], {
"redirect": "maternal_and_child/underweight_children",
"color": "green",
"all": 696,
"frequency": "month",
"format": "percent_and_div",
"help_text": underweight_children_help_text(),
"percent": -14.901477832512326,
"value": 150,
"label": "Underweight (Weight-for-Age)"
})
def test_data_underweight_weight_for_age(self):
self.assertDictEqual(
get_maternal_child_data(
'icds-cas',
{
'month': (2017, 5, 1),
'prev_month': (2017, 4, 1),
'aggregation_level': 1
}
)['records'][0][0],
{
"redirect": "maternal_and_child/underweight_children",
"color": "green",
"all": 696,
"frequency": "month",
"format": "percent_and_div",
"help_text": underweight_children_help_text(),
"percent": -14.901477832512326,
"value": 150,
"label": "Underweight (Weight-for-Age)"
}
)
def get_prevalence_of_undernutrition_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 = AggChildHealthMonthly.objects.filter(**config).values(
*group_by).annotate(
moderately_underweight=Sum(
'nutrition_status_moderately_underweight'),
severely_underweight=Sum('nutrition_status_severely_underweight'),
weighed=Sum('nutrition_status_weighed'),
normal=Sum('nutrition_status_normal'),
total=Sum('wer_eligible'),
).order_by('%s_name' % loc_level)
if not show_test:
data = apply_exclude(domain, data)
if 'age_tranche' not in config:
data = data.filter(age_tranche__lt=72)
chart_data = {'blue': []}
tooltips_data = defaultdict(
lambda: {
'severely_underweight': 0,
'moderately_underweight': 0,
'weighed': 0,
'normal': 0,
'total': 0
})
location_launched_status = get_location_launched_status(config, loc_level)
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
weighed = row['weighed']
total = row['total']
name = row['%s_name' % loc_level]
severely_underweight = row['severely_underweight']
moderately_underweight = row['moderately_underweight']
normal = row['normal']
tooltips_data[name]['severely_underweight'] += severely_underweight
tooltips_data[name]['moderately_underweight'] += moderately_underweight
tooltips_data[name]['weighed'] += (weighed or 0)
tooltips_data[name]['normal'] += normal
tooltips_data[name]['total'] += (total or 0)
chart_data['blue'].append([
name, ((moderately_underweight or 0) +
(severely_underweight or 0)) / float(weighed or 1)
])
chart_data['blue'] = sorted(chart_data['blue'],
key=lambda loc_and_value:
(loc_and_value[0] is not None, loc_and_value))
return {
"tooltips_data":
dict(tooltips_data),
"info":
underweight_children_help_text(age_label="0-5 years", html=True),
"chart_data": [{
"values": chart_data['blue'],
"key": "",
"strokeWidth": 2,
"classed": "dashed",
"color": MapColors.BLUE
}]
}
def get_prevalence_of_undernutrition_data_map(domain,
config,
loc_level,
show_test=False,
icds_features_flag=False):
config['month'] = datetime(*config['month'])
def get_data_for(filters):
queryset = AggChildHealthMonthly.objects.filter(**filters).values(
'%s_name' % loc_level,
'%s_map_location_name' % loc_level).annotate(
moderately_underweight=Sum(
'nutrition_status_moderately_underweight'),
severely_underweight=Sum(
'nutrition_status_severely_underweight'),
normal=Sum('nutrition_status_normal'),
weighed=Sum('nutrition_status_weighed'),
total=Sum('wer_eligible'),
).order_by('%s_name' % loc_level,
'%s_map_location_name' % loc_level)
if not show_test:
queryset = apply_exclude(domain, queryset)
if 'age_tranche' not in config:
queryset = queryset.filter(age_tranche__lt=72)
return queryset
data_for_map = defaultdict(
lambda: {
'moderately_underweight': 0,
'severely_underweight': 0,
'normal': 0,
'weighed': 0,
'total': 0,
'original_name': []
})
moderately_underweight_total = 0
severely_underweight_total = 0
normal_total = 0
all_total = 0
weighed_total = 0
values_to_calculate_average = {'numerator': 0, 'denominator': 0}
location_launched_status = get_location_launched_status(config, loc_level)
for row in get_data_for(config):
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
weighed = row['weighed'] or 0
total = row['total'] or 0
name = row['%s_name' % loc_level]
on_map_name = row['%s_map_location_name' % loc_level] or name
severely_underweight = row['severely_underweight'] or 0
moderately_underweight = row['moderately_underweight'] or 0
normal = row['normal'] or 0
values_to_calculate_average[
'numerator'] += moderately_underweight if moderately_underweight else 0
values_to_calculate_average[
'numerator'] += severely_underweight if severely_underweight else 0
values_to_calculate_average['denominator'] += weighed if weighed else 0
moderately_underweight_total += moderately_underweight
severely_underweight_total += severely_underweight
normal_total += normal
all_total += total
weighed_total += weighed
data_for_map[on_map_name][
'severely_underweight'] += severely_underweight
data_for_map[on_map_name][
'moderately_underweight'] += moderately_underweight
data_for_map[on_map_name]['normal'] += normal
data_for_map[on_map_name]['total'] += total
data_for_map[on_map_name]['weighed'] += weighed
data_for_map[on_map_name]['original_name'].append(name)
for data_for_location in data_for_map.values():
numerator = data_for_location[
'moderately_underweight'] + data_for_location[
'severely_underweight']
value = numerator * 100 / (data_for_location['weighed'] or 1)
if value < 20:
data_for_location.update({'fillKey': '0%-20%'})
elif 20 <= value < 35:
data_for_location.update({'fillKey': '20%-35%'})
elif value >= 35:
data_for_location.update({'fillKey': '35%-100%'})
fills = OrderedDict()
fills.update({'0%-20%': MapColors.PINK})
fills.update({'20%-35%': MapColors.ORANGE})
fills.update({'35%-100%': MapColors.RED})
fills.update({'Not Launched': MapColors.GREY})
fills.update({'defaultFill': MapColors.GREY})
average = ((values_to_calculate_average['numerator'] * 100) /
float(values_to_calculate_average['denominator'] or 1))
gender_label, age_label, chosen_filters = chosen_filters_to_labels(
config, default_interval='0 - 5 years')
return {
"slug":
"moderately_underweight",
"label":
"Percent of Children{gender} Underweight ({age})".format(
gender=gender_label, age=age_label),
"fills":
fills,
"rightLegend": {
"average":
format_decimal(average),
"info":
underweight_children_help_text(age_label=age_label, html=True),
"extended_info": [{
'indicator':
'Total Children{} weighed in given month:'.format(
chosen_filters),
'value':
indian_formatted_number(weighed_total)
}, {
'indicator':
'Number of children unweighed{}:'.format(chosen_filters),
'value':
indian_formatted_number(all_total - weighed_total)
}, {
'indicator':
'% Severely Underweight{}:'.format(chosen_filters),
'value':
'%.2f%%' %
(severely_underweight_total * 100 / float(weighed_total or 1))
}, {
'indicator':
'% Moderately Underweight{}:'.format(chosen_filters),
'value':
'%.2f%%' % (moderately_underweight_total * 100 /
float(weighed_total or 1))
}, {
'indicator':
'% Normal{}:'.format(chosen_filters),
'value':
'%.2f%%' % (normal_total * 100 / float(weighed_total or 1))
}]
},
"data":
dict(data_for_map)
}
def get_maternal_child_data(domain,
config,
show_test=False,
icds_feature_flag=False):
def get_data_for_child_health_monthly(date, filters):
age_filters = {
'age_tranche': 72
} if icds_feature_flag else {
'age_tranche__in': [0, 6, 72]
}
moderately_underweight = exclude_records_by_age_for_column(
{'age_tranche': 72}, 'nutrition_status_moderately_underweight')
severely_underweight = exclude_records_by_age_for_column(
{'age_tranche': 72}, 'nutrition_status_severely_underweight')
wasting_moderate = exclude_records_by_age_for_column(
age_filters, wasting_moderate_column(icds_feature_flag))
wasting_severe = exclude_records_by_age_for_column(
age_filters, wasting_severe_column(icds_feature_flag))
stunting_moderate = exclude_records_by_age_for_column(
age_filters, stunting_moderate_column(icds_feature_flag))
stunting_severe = exclude_records_by_age_for_column(
age_filters, stunting_severe_column(icds_feature_flag))
nutrition_status_weighed = exclude_records_by_age_for_column(
{'age_tranche': 72}, 'nutrition_status_weighed')
height_measured_in_month = exclude_records_by_age_for_column(
age_filters, hfa_recorded_in_month_column(icds_feature_flag))
weighed_and_height_measured_in_month = exclude_records_by_age_for_column(
age_filters, wfh_recorded_in_month_column(icds_feature_flag))
queryset = AggChildHealthMonthly.objects.filter(
month=date, **filters).values('aggregation_level').annotate(
underweight=(Sum(moderately_underweight) +
Sum(severely_underweight)),
valid=Sum(nutrition_status_weighed),
wasting=Sum(wasting_moderate) + Sum(wasting_severe),
stunting=Sum(stunting_moderate) + Sum(stunting_severe),
height_measured_in_month=Sum(height_measured_in_month),
weighed_and_height_measured_in_month=Sum(
weighed_and_height_measured_in_month),
low_birth_weight=Sum('low_birth_weight_in_month'),
bf_birth=Sum('bf_at_birth'),
born=Sum('born_in_month'),
weighed_and_born_in_month=Sum('weighed_and_born_in_month'),
ebf=Sum('ebf_in_month'),
ebf_eli=Sum('ebf_eligible'),
cf_initiation=Sum('cf_initiation_in_month'),
cf_initiation_eli=Sum('cf_initiation_eligible'))
if not show_test:
queryset = apply_exclude(domain, queryset)
return queryset
def get_data_for_deliveries(date, filters):
queryset = AggCcsRecordMonthly.objects.filter(
month=date, **filters).values('aggregation_level').annotate(
institutional_delivery=Sum('institutional_delivery_in_month'),
delivered=Sum('delivered_in_month'))
if not show_test:
queryset = apply_exclude(domain, queryset)
return queryset
current_month = datetime(*config['month'])
previous_month = datetime(*config['prev_month'])
del config['month']
del config['prev_month']
this_month_data = get_data_for_child_health_monthly(current_month, config)
prev_month_data = get_data_for_child_health_monthly(previous_month, config)
deliveries_this_month = get_data_for_deliveries(current_month, config)
deliveries_prev_month = get_data_for_deliveries(previous_month, config)
gender_label, age_label, chosen_filters = chosen_filters_to_labels(
config, default_interval=default_age_interval(icds_feature_flag))
return {
'records':
[[{
'label':
_('Underweight (Weight-for-Age)'),
'help_text':
underweight_children_help_text(),
'percent':
percent_diff('underweight', this_month_data, prev_month_data,
'valid'),
'color':
'red' if percent_diff('underweight', this_month_data,
prev_month_data, 'valid') > 0 else 'green',
'value':
get_value(this_month_data, 'underweight'),
'all':
get_value(this_month_data, 'valid'),
'format':
'percent_and_div',
'frequency':
'month',
'redirect':
'maternal_and_child/underweight_children'
}, {
'label':
_('Wasting (Weight-for-Height)'),
'help_text':
_(wasting_help_text(age_label)),
'percent':
percent_diff('wasting', this_month_data, prev_month_data,
'weighed_and_height_measured_in_month'),
'color':
'red' if percent_diff('wasting', this_month_data, prev_month_data,
'weighed_and_height_measured_in_month') > 0
else 'green',
'value':
get_value(this_month_data, 'wasting'),
'all':
get_value(this_month_data, 'weighed_and_height_measured_in_month'),
'format':
'percent_and_div',
'frequency':
'month',
'redirect':
'maternal_and_child/wasting'
}],
[{
'label':
_('Stunting (Height-for-Age)'),
'help_text':
_(stunting_help_text(age_label)),
'percent':
percent_diff('stunting', this_month_data, prev_month_data,
'height_measured_in_month'),
'color':
'red'
if percent_diff('stunting', this_month_data, prev_month_data,
'height_measured_in_month') > 0 else 'green',
'value':
get_value(this_month_data, 'stunting'),
'all':
get_value(this_month_data, 'height_measured_in_month'),
'format':
'percent_and_div',
'frequency':
'month',
'redirect':
'maternal_and_child/stunting'
}, {
'label':
_('Newborns with Low Birth Weight'),
'help_text':
_((new_born_with_low_weight_help_text(html=False))),
'percent':
percent_diff('low_birth_weight', this_month_data, prev_month_data,
'weighed_and_born_in_month'),
'color':
get_color_with_red_positive(
percent_diff('low_birth_weight', this_month_data,
prev_month_data, 'weighed_and_born_in_month')),
'value':
get_value(this_month_data, 'low_birth_weight'),
'all':
get_value(this_month_data, 'weighed_and_born_in_month'),
'format':
'percent_and_div',
'frequency':
'month',
'redirect':
'maternal_and_child/low_birth'
}],
[{
'label':
_('Early Initiation of Breastfeeding'),
'help_text':
early_initiation_breastfeeding_help_text(),
'percent':
percent_diff('bf_birth', this_month_data, prev_month_data,
'born'),
'color':
get_color_with_green_positive(
percent_diff('bf_birth', this_month_data, prev_month_data,
'born')),
'value':
get_value(this_month_data, 'bf_birth'),
'all':
get_value(this_month_data, 'born'),
'format':
'percent_and_div',
'frequency':
'month',
'redirect':
'maternal_and_child/early_initiation'
}, {
'label':
_('Exclusive Breastfeeding'),
'help_text':
exclusive_breastfeeding_help_text(),
'percent':
percent_diff('ebf', this_month_data, prev_month_data, 'ebf_eli'),
'color':
get_color_with_green_positive(
percent_diff('ebf', this_month_data, prev_month_data,
'ebf_eli')),
'value':
get_value(this_month_data, 'ebf'),
'all':
get_value(this_month_data, 'ebf_eli'),
'format':
'percent_and_div',
'frequency':
'month',
'redirect':
'maternal_and_child/exclusive_breastfeeding'
}],
[{
'label':
_('Children initiated appropriate Complementary Feeding'),
'help_text':
children_initiated_appropriate_complementary_feeding_help_text(),
'percent':
percent_diff('cf_initiation', this_month_data, prev_month_data,
'cf_initiation_eli'),
'color':
get_color_with_green_positive(
percent_diff('cf_initiation', this_month_data,
prev_month_data, 'cf_initiation_eli')),
'value':
get_value(this_month_data, 'cf_initiation'),
'all':
get_value(this_month_data, 'cf_initiation_eli'),
'format':
'percent_and_div',
'frequency':
'month',
'redirect':
'maternal_and_child/children_initiated'
}, {
'label':
_('Institutional Deliveries'),
'help_text':
institutional_deliveries_help_text(),
'percent':
percent_diff('institutional_delivery', deliveries_this_month,
deliveries_prev_month, 'delivered'),
'color':
get_color_with_green_positive(
percent_diff('institutional_delivery', deliveries_this_month,
deliveries_prev_month, 'delivered')),
'value':
get_value(deliveries_this_month, 'institutional_delivery'),
'all':
get_value(deliveries_this_month, 'delivered'),
'format':
'percent_and_div',
'frequency':
'month',
'redirect':
'maternal_and_child/institutional_deliveries'
}]]
}
def get_prevalence_of_undernutrition_sector_data(domain,
config,
loc_level,
location_id,
show_test=False):
group_by = ['%s_name' % loc_level]
config['month'] = datetime(*config['month'])
data = AggChildHealthMonthly.objects.filter(**config).values(
*group_by).annotate(
moderately_underweight=Sum(
'nutrition_status_moderately_underweight'),
severely_underweight=Sum('nutrition_status_severely_underweight'),
weighed=Sum('nutrition_status_weighed'),
normal=Sum('nutrition_status_normal'),
total=Sum('wer_eligible'),
).order_by('%s_name' % loc_level)
if not show_test:
data = apply_exclude(domain, data)
if 'age_tranche' not in config:
data = data.exclude(age_tranche=72)
chart_data = {'blue': []}
tooltips_data = defaultdict(
lambda: {
'severely_underweight': 0,
'moderately_underweight': 0,
'weighed': 0,
'normal': 0,
'total': 0
})
loc_children = get_child_locations(domain, location_id, show_test)
result_set = set()
for row in data:
weighed = row['weighed']
total = row['total']
name = row['%s_name' % loc_level]
result_set.add(name)
severely_underweight = row['severely_underweight']
moderately_underweight = row['moderately_underweight']
normal = row['normal']
tooltips_data[name]['severely_underweight'] += severely_underweight
tooltips_data[name]['moderately_underweight'] += moderately_underweight
tooltips_data[name]['weighed'] += (weighed or 0)
tooltips_data[name]['normal'] += normal
tooltips_data[name]['total'] += (total or 0)
chart_data['blue'].append([
name, ((moderately_underweight or 0) +
(severely_underweight or 0)) / float(weighed or 1)
])
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),
"info":
underweight_children_help_text(age_label="0-5 years", html=True),
"chart_data": [{
"values": chart_data['blue'],
"key": "",
"strokeWidth": 2,
"classed": "dashed",
"color": MapColors.BLUE
}]
}
def get_maternal_child_data(domain, config, show_test=False, icds_feature_flag=False):
def get_data_for_child_health_monthly(date, filters):
age_filters = {'age_tranche': 72} if icds_feature_flag else {'age_tranche__in': [0, 6, 72]}
moderately_underweight = exclude_records_by_age_for_column(
{'age_tranche': 72},
'nutrition_status_moderately_underweight'
)
severely_underweight = exclude_records_by_age_for_column(
{'age_tranche': 72},
'nutrition_status_severely_underweight'
)
wasting_moderate = exclude_records_by_age_for_column(
age_filters,
wasting_moderate_column(icds_feature_flag)
)
wasting_severe = exclude_records_by_age_for_column(
age_filters,
wasting_severe_column(icds_feature_flag)
)
stunting_moderate = exclude_records_by_age_for_column(
age_filters,
stunting_moderate_column(icds_feature_flag)
)
stunting_severe = exclude_records_by_age_for_column(
age_filters,
stunting_severe_column(icds_feature_flag)
)
nutrition_status_weighed = exclude_records_by_age_for_column(
{'age_tranche': 72},
'nutrition_status_weighed'
)
height_measured_in_month = exclude_records_by_age_for_column(
age_filters,
hfa_recorded_in_month_column(icds_feature_flag)
)
weighed_and_height_measured_in_month = exclude_records_by_age_for_column(
age_filters,
wfh_recorded_in_month_column(icds_feature_flag)
)
queryset = AggChildHealthMonthly.objects.filter(
month=date, **filters
).values(
'aggregation_level'
).annotate(
underweight=(
Sum(moderately_underweight) + Sum(severely_underweight)
),
valid=Sum(nutrition_status_weighed),
wasting=Sum(wasting_moderate) + Sum(wasting_severe),
stunting=Sum(stunting_moderate) + Sum(stunting_severe),
height_measured_in_month=Sum(height_measured_in_month),
weighed_and_height_measured_in_month=Sum(weighed_and_height_measured_in_month),
low_birth_weight=Sum('low_birth_weight_in_month'),
bf_birth=Sum('bf_at_birth'),
born=Sum('born_in_month'),
weighed_and_born_in_month=Sum('weighed_and_born_in_month'),
ebf=Sum('ebf_in_month'),
ebf_eli=Sum('ebf_eligible'),
cf_initiation=Sum('cf_initiation_in_month'),
cf_initiation_eli=Sum('cf_initiation_eligible')
)
if not show_test:
queryset = apply_exclude(domain, queryset)
return queryset
def get_data_for_deliveries(date, filters):
queryset = AggCcsRecordMonthly.objects.filter(
month=date, **filters
).values(
'aggregation_level'
).annotate(
institutional_delivery=Sum('institutional_delivery_in_month'),
delivered=Sum('delivered_in_month')
)
if not show_test:
queryset = apply_exclude(domain, queryset)
return queryset
current_month = datetime(*config['month'])
previous_month = datetime(*config['prev_month'])
del config['month']
del config['prev_month']
this_month_data = get_data_for_child_health_monthly(current_month, config)
prev_month_data = get_data_for_child_health_monthly(previous_month, config)
deliveries_this_month = get_data_for_deliveries(current_month, config)
deliveries_prev_month = get_data_for_deliveries(previous_month, config)
gender_label, age_label, chosen_filters = chosen_filters_to_labels(
config,
default_interval=default_age_interval(icds_feature_flag)
)
return {
'records': [
[
{
'label': _('Underweight (Weight-for-Age)'),
'help_text': underweight_children_help_text(),
'percent': percent_diff(
'underweight',
this_month_data,
prev_month_data,
'valid'
),
'color': 'red' if percent_diff(
'underweight',
this_month_data,
prev_month_data,
'valid'
) > 0 else 'green',
'value': get_value(this_month_data, 'underweight'),
'all': get_value(this_month_data, 'valid'),
'format': 'percent_and_div',
'frequency': 'month',
'redirect': 'maternal_and_child/underweight_children'
},
{
'label': _('Wasting (Weight-for-Height)'),
'help_text': _(wasting_help_text(age_label)),
'percent': percent_diff(
'wasting',
this_month_data,
prev_month_data,
'weighed_and_height_measured_in_month'
),
'color': 'red' if percent_diff(
'wasting',
this_month_data,
prev_month_data,
'weighed_and_height_measured_in_month'
) > 0 else 'green',
'value': get_value(this_month_data, 'wasting'),
'all': get_value(this_month_data, 'weighed_and_height_measured_in_month'),
'format': 'percent_and_div',
'frequency': 'month',
'redirect': 'maternal_and_child/wasting'
}
],
[
{
'label': _('Stunting (Height-for-Age)'),
'help_text': _(stunting_help_text(age_label)),
'percent': percent_diff(
'stunting',
this_month_data,
prev_month_data,
'height_measured_in_month'
),
'color': 'red' if percent_diff(
'stunting',
this_month_data,
prev_month_data,
'height_measured_in_month'
) > 0 else 'green',
'value': get_value(this_month_data, 'stunting'),
'all': get_value(this_month_data, 'height_measured_in_month'),
'format': 'percent_and_div',
'frequency': 'month',
'redirect': 'maternal_and_child/stunting'
},
{
'label': _('Newborns with Low Birth Weight'),
'help_text': _((
new_born_with_low_weight_help_text(html=False)
)),
'percent': percent_diff(
'low_birth_weight',
this_month_data,
prev_month_data,
'weighed_and_born_in_month'
),
'color': 'red' if percent_diff(
'low_birth_weight',
this_month_data,
prev_month_data,
'weighed_and_born_in_month'
) > 0 else 'green',
'value': get_value(this_month_data, 'low_birth_weight'),
'all': get_value(this_month_data, 'weighed_and_born_in_month'),
'format': 'percent_and_div',
'frequency': 'month',
'redirect': 'maternal_and_child/low_birth'
}
],
[
{
'label': _('Early Initiation of Breastfeeding'),
'help_text': early_initiation_breastfeeding_help_text(),
'percent': percent_diff(
'bf_birth',
this_month_data,
prev_month_data,
'born'
),
'color': 'green' if percent_diff(
'bf_birth',
this_month_data,
prev_month_data,
'born'
) > 0 else 'red',
'value': get_value(this_month_data, 'bf_birth'),
'all': get_value(this_month_data, 'born'),
'format': 'percent_and_div',
'frequency': 'month',
'redirect': 'maternal_and_child/early_initiation'
},
{
'label': _('Exclusive Breastfeeding'),
'help_text': exclusive_breastfeeding_help_text(),
'percent': percent_diff(
'ebf',
this_month_data,
prev_month_data,
'ebf_eli'
),
'color': 'green' if percent_diff(
'ebf',
this_month_data,
prev_month_data,
'ebf_eli'
) > 0 else 'red',
'value': get_value(this_month_data, 'ebf'),
'all': get_value(this_month_data, 'ebf_eli'),
'format': 'percent_and_div',
'frequency': 'month',
'redirect': 'maternal_and_child/exclusive_breastfeeding'
}
],
[
{
'label': _('Children initiated appropriate Complementary Feeding'),
'help_text': children_initiated_appropriate_complementary_feeding_help_text(),
'percent': percent_diff(
'cf_initiation',
this_month_data,
prev_month_data,
'cf_initiation_eli'
),
'color': 'green' if percent_diff(
'cf_initiation',
this_month_data,
prev_month_data,
'cf_initiation_eli'
) > 0 else 'red',
'value': get_value(this_month_data, 'cf_initiation'),
'all': get_value(this_month_data, 'cf_initiation_eli'),
'format': 'percent_and_div',
'frequency': 'month',
'redirect': 'maternal_and_child/children_initiated'
},
{
'label': _('Institutional Deliveries'),
'help_text': institutional_deliveries_help_text(),
'percent': percent_diff(
'institutional_delivery',
deliveries_this_month,
deliveries_prev_month,
'delivered'
),
'color': 'green' if percent_diff(
'institutional_delivery',
deliveries_this_month,
deliveries_prev_month,
'delivered'
) > 0 else 'red',
'value': get_value(deliveries_this_month, 'institutional_delivery'),
'all': get_value(deliveries_this_month, 'delivered'),
'format': 'percent_and_div',
'frequency': 'month',
'redirect': 'maternal_and_child/institutional_deliveries'
}
]
]
}
def get_prevalence_of_undernutrition_sector_data(domain, config, loc_level, location_id, show_test=False):
group_by = ['%s_name' % loc_level]
config['month'] = datetime(*config['month'])
data = AggChildHealthMonthly.objects.filter(
**config
).values(
*group_by
).annotate(
moderately_underweight=Sum('nutrition_status_moderately_underweight'),
severely_underweight=Sum('nutrition_status_severely_underweight'),
weighed=Sum('nutrition_status_weighed'),
normal=Sum('nutrition_status_normal'),
total=Sum('wer_eligible'),
).order_by('%s_name' % loc_level)
if not show_test:
data = apply_exclude(domain, data)
if 'age_tranche' not in config:
data = data.exclude(age_tranche=72)
chart_data = {
'blue': []
}
tooltips_data = defaultdict(lambda: {
'severely_underweight': 0,
'moderately_underweight': 0,
'weighed': 0,
'normal': 0,
'total': 0
})
loc_children = get_child_locations(domain, location_id, show_test)
result_set = set()
for row in data:
weighed = row['weighed']
total = row['total']
name = row['%s_name' % loc_level]
result_set.add(name)
severely_underweight = row['severely_underweight']
moderately_underweight = row['moderately_underweight']
normal = row['normal']
tooltips_data[name]['severely_underweight'] += severely_underweight
tooltips_data[name]['moderately_underweight'] += moderately_underweight
tooltips_data[name]['weighed'] += (weighed or 0)
tooltips_data[name]['normal'] += normal
tooltips_data[name]['total'] += (total or 0)
chart_data['blue'].append([
name,
((moderately_underweight or 0) + (severely_underweight or 0)) / float(weighed or 1)
])
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),
"info": underweight_children_help_text(age_label="0-5 years", html=True),
"chart_data": [
{
"values": chart_data['blue'],
"key": "",
"strokeWidth": 2,
"classed": "dashed",
"color": MapColors.BLUE
}
]
}
def get_prevalence_of_undernutrition_data_map(domain, config, loc_level, show_test=False):
def get_data_for(filters):
filters['month'] = datetime(*filters['month'])
queryset = AggChildHealthMonthly.objects.filter(
**filters
).values(
'%s_name' % loc_level, '%s_map_location_name' % loc_level
).annotate(
moderately_underweight=Sum('nutrition_status_moderately_underweight'),
severely_underweight=Sum('nutrition_status_severely_underweight'),
normal=Sum('nutrition_status_normal'),
weighed=Sum('nutrition_status_weighed'),
total=Sum('wer_eligible'),
).order_by('%s_name' % loc_level, '%s_map_location_name' % loc_level)
if not show_test:
queryset = apply_exclude(domain, queryset)
if 'age_tranche' not in config:
queryset = queryset.exclude(age_tranche=72)
return queryset
data_for_map = defaultdict(lambda: {
'moderately_underweight': 0,
'severely_underweight': 0,
'normal': 0,
'weighed': 0,
'total': 0,
'original_name': []
})
moderately_underweight_total = 0
severely_underweight_total = 0
normal_total = 0
all_total = 0
weighed_total = 0
values_to_calculate_average = {'numerator': 0, 'denominator': 0}
for row in get_data_for(config):
weighed = row['weighed'] or 0
total = row['total'] or 0
name = row['%s_name' % loc_level]
on_map_name = row['%s_map_location_name' % loc_level] or name
severely_underweight = row['severely_underweight'] or 0
moderately_underweight = row['moderately_underweight'] or 0
normal = row['normal'] or 0
values_to_calculate_average['numerator'] += moderately_underweight if moderately_underweight else 0
values_to_calculate_average['numerator'] += severely_underweight if severely_underweight else 0
values_to_calculate_average['denominator'] += weighed if weighed else 0
moderately_underweight_total += moderately_underweight
severely_underweight_total += severely_underweight
normal_total += normal
all_total += total
weighed_total += weighed
data_for_map[on_map_name]['severely_underweight'] += severely_underweight
data_for_map[on_map_name]['moderately_underweight'] += moderately_underweight
data_for_map[on_map_name]['normal'] += normal
data_for_map[on_map_name]['total'] += total
data_for_map[on_map_name]['weighed'] += weighed
data_for_map[on_map_name]['original_name'].append(name)
for data_for_location in six.itervalues(data_for_map):
numerator = data_for_location['moderately_underweight'] + data_for_location['severely_underweight']
value = numerator * 100 / (data_for_location['weighed'] or 1)
if value < 20:
data_for_location.update({'fillKey': '0%-20%'})
elif 20 <= value < 35:
data_for_location.update({'fillKey': '20%-35%'})
elif value >= 35:
data_for_location.update({'fillKey': '35%-100%'})
fills = OrderedDict()
fills.update({'0%-20%': MapColors.PINK})
fills.update({'20%-35%': MapColors.ORANGE})
fills.update({'35%-100%': MapColors.RED})
fills.update({'defaultFill': MapColors.GREY})
average = (
(values_to_calculate_average['numerator'] * 100) /
float(values_to_calculate_average['denominator'] or 1)
)
gender_label, age_label, chosen_filters = chosen_filters_to_labels(config, default_interval='0 - 5 years')
return {
"slug": "moderately_underweight",
"label": "Percent of Children{gender} Underweight ({age})".format(
gender=gender_label,
age=age_label
),
"fills": fills,
"rightLegend": {
"average": format_decimal(average),
"info": underweight_children_help_text(age_label=age_label, html=True),
"extended_info": [
{
'indicator': 'Total Children{} weighed in given month:'.format(chosen_filters),
'value': indian_formatted_number(weighed_total)
},
{
'indicator': 'Number of children unweighed{}:'.format(chosen_filters),
'value': indian_formatted_number(all_total - weighed_total)
},
{
'indicator': '% Severely Underweight{}:'.format(chosen_filters),
'value': '%.2f%%' % (severely_underweight_total * 100 / float(weighed_total or 1))
},
{
'indicator': '% Moderately Underweight{}:'.format(chosen_filters),
'value': '%.2f%%' % (moderately_underweight_total * 100 / float(weighed_total or 1))
},
{
'indicator': '% Normal{}:'.format(chosen_filters),
'value': '%.2f%%' % (normal_total * 100 / float(weighed_total or 1))
}
]
},
"data": dict(data_for_map)
}
