def get_awc_daily_status_data_map(domain, config, loc_level, show_test=False):
date = datetime(*config['month'])
config['date'] = date
del config['month']
def get_data_for(filters):
queryset = AggAwcDailyView.objects.filter(**filters).values(
'%s_name' % loc_level,
'%s_map_location_name' % loc_level).annotate(
in_day=Sum('daily_attendance_open'),
all=Sum('num_launched_awcs'),
).order_by('%s_name' % loc_level,
'%s_map_location_name' % loc_level)
if not show_test:
queryset = apply_exclude(domain, queryset)
return queryset
data = get_data_for(config)
if not data:
config['date'] = (date - relativedelta(days=1)).date()
data = get_data_for(config)
data_for_map, valid_total, in_day_total, average, total = generate_data_for_map(
data, loc_level, 'in_day', 'all', 50, 75)
fills = OrderedDict()
fills.update({'0%-50%': MapColors.RED})
fills.update({'50%-75%': MapColors.ORANGE})
fills.update({'75%-100%': MapColors.PINK})
fills.update({'defaultFill': MapColors.GREY})
return {
"slug": "awc_daily_statuses",
"label": "Percent AWCs Open Yesterday",
"fills": fills,
"rightLegend": {
"average":
average,
"info":
_(("Of the total number of AWCs, the percentage of AWCs that were open yesterday."
)),
'period':
'Daily',
"extended_info": [{
'indicator': 'Total number of AWCs that were open yesterday:',
'value': indian_formatted_number(in_day_total)
}, {
'indicator': 'Total number of AWCs that have been launched:',
'value': indian_formatted_number(valid_total)
}, {
'indicator':
'% of AWCs open yesterday:',
'value':
'%.2f%%' % (in_day_total * 100 / float(valid_total or 1))
}]
},
"data": dict(data_for_map),
}
def get_exclusive_breastfeeding_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(
children=Sum('ebf_in_month'),
all=Sum('ebf_eligible'),
).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, valid_total, in_month_total, average, total = generate_data_for_map(
get_data_for(config), loc_level, 'children', 'all', 20, 60)
fills = OrderedDict()
fills.update({'0%-20%': MapColors.RED})
fills.update({'20%-60%': MapColors.ORANGE})
fills.update({'60%-100%': MapColors.PINK})
fills.update({'defaultFill': MapColors.GREY})
gender_ignored, age_ignored, chosen_filters = chosen_filters_to_labels(
config)
return {
"slug": "severe",
"label": "Percent Exclusive Breastfeeding{}".format(chosen_filters),
"fills": fills,
"rightLegend": {
"average":
average,
"info":
exclusive_breastfeeding_help_text(html=True),
"extended_info": [{
'indicator':
'Total number of children between ages 0 - 6 months{}:'.format(
chosen_filters),
'value':
indian_formatted_number(valid_total)
}, {
'indicator':
('Total number of children (0-6 months) exclusively breastfed in the given month{}:'
.format(chosen_filters)),
'value':
indian_formatted_number(in_month_total)
}, {
'indicator':
'% children (0-6 months) exclusively breastfed in the '
'given month{}:'.format(chosen_filters),
'value':
'%.2f%%' % (in_month_total * 100 / float(valid_total or 1))
}]
},
"data": dict(data_for_map),
}
def get_institutional_deliveries_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(
children=Sum('institutional_delivery_in_month'),
all=Sum('delivered_in_month'),
).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, valid_total, in_month_total, average, total = generate_data_for_map(
get_data_for(config), loc_level, 'children', 'all', 20, 60)
fills = OrderedDict()
fills.update({'0%-20%': MapColors.RED})
fills.update({'20%-60%': MapColors.ORANGE})
fills.update({'60%-100%': MapColors.PINK})
fills.update({'defaultFill': MapColors.GREY})
return {
"slug": "institutional_deliveries",
"label": "Percent Instituitional Deliveries",
"fills": fills,
"rightLegend": {
"average":
average,
"info":
_(("Of the total number of women enrolled for Anganwadi services who gave birth in the last month, "
"the percentage who delivered in a public or private medical facility. "
"<br/><br/>"
"Delivery in medical instituitions is associated with a decrease in maternal mortality rate"
)),
"extended_info": [{
'indicator':
'Total number of pregnant women who delivered in the last month:',
'value': indian_formatted_number(valid_total)
}, {
'indicator':
('Total number of pregnant women who delivered in a '
'public/private medical facilitiy in the last month:'),
'value':
indian_formatted_number(in_month_total)
}, {
'indicator':
('% pregnant women who delivered in a public or private medical '
'facility in the last month:'),
'value':
'%.2f%%' % (in_month_total * 100 / float(valid_total or 1))
}]
},
"data": dict(data_for_map),
}
def get_clean_water_data_map(domain, config, loc_level, show_test=False):
def get_data_for(filters):
filters['month'] = datetime(*filters['month'])
queryset = AggAwcMonthly.objects.filter(
**filters
).values(
'%s_name' % loc_level, '%s_map_location_name' % loc_level
).annotate(
in_month=Sum('infra_clean_water'),
all=Sum('num_awc_infra_last_update'),
).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, valid_total, in_month_total, average, total = generate_data_for_map(
get_data_for(config),
loc_level,
'in_month',
'all',
25,
75
)
fills = OrderedDict()
fills.update({'0%-25%': MapColors.RED})
fills.update({'25%-75%': MapColors.ORANGE})
fills.update({'75%-100%': MapColors.PINK})
fills.update({'defaultFill': MapColors.GREY})
return {
"slug": "clean_water",
"label": "Percentage of AWCs that reported having a source of clean drinking water",
"fills": fills,
"rightLegend": {
"average": average,
"info": _((
"Of the AWCs that have submitted an Infrastructure Details form, the percentage of AWCs that "
"reported having a source of clean drinking water. "
)),
"extended_info": [
{
'indicator': (
'Total number of AWCs with a source of clean drinking water:'
),
'value': indian_formatted_number(in_month_total)
},
{
'indicator': (
'% of AWCs with a source of clean drinking water:'
),
'value': '%.2f%%' % (in_month_total * 100 / float(valid_total or 1))
}
]
},
"data": dict(data_for_map),
}
def get_clean_water_data_map(domain, config, loc_level, show_test=False):
def get_data_for(filters):
filters['month'] = datetime(*filters['month'])
queryset = AggAwcMonthly.objects.filter(
**filters
).values(
'%s_name' % loc_level, '%s_map_location_name' % loc_level
).annotate(
in_month=Sum('infra_clean_water'),
all=Sum('num_awc_infra_last_update'),
).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, valid_total, in_month_total, average, total = generate_data_for_map(
get_data_for(config),
loc_level,
'in_month',
'all',
25,
75
)
fills = OrderedDict()
fills.update({'0%-25%': MapColors.RED})
fills.update({'25%-75%': MapColors.ORANGE})
fills.update({'75%-100%': MapColors.PINK})
fills.update({'defaultFill': MapColors.GREY})
return {
"slug": "clean_water",
"label": "Percentage of AWCs that reported having a source of clean drinking water",
"fills": fills,
"rightLegend": {
"average": average,
"info": _((
"Of the AWCs that have submitted an Infrastructure Details form, the percentage of AWCs that "
"reported having a source of clean drinking water. "
)),
"extended_info": [
{
'indicator': (
'Total number of AWCs with a source of clean drinking water:'
),
'value': indian_formatted_number(in_month_total)
},
{
'indicator': (
'% of AWCs with a source of clean drinking water:'
),
'value': '%.2f%%' % (in_month_total * 100 / float(valid_total or 1))
}
]
},
"data": dict(data_for_map),
}
def get_adult_weight_scale_data_map(domain, config, loc_level, show_test=False):
def get_data_for(filters):
filters['month'] = datetime(*filters['month'])
queryset = AggAwcMonthly.objects.filter(
**filters
).values(
'%s_name' % loc_level, '%s_map_location_name' % loc_level
).annotate(
in_month=Sum('infra_adult_weighing_scale'),
all=Sum('num_awc_infra_last_update'),
).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, valid_total, in_month_total, average, total = generate_data_for_map(
get_data_for(config),
loc_level,
'in_month',
'all',
25,
75
)
fills = OrderedDict()
fills.update({'0%-25%': MapColors.RED})
fills.update({'25%-75%': MapColors.ORANGE})
fills.update({'75%-100%': MapColors.PINK})
fills.update({'defaultFill': MapColors.GREY})
return {
"slug": "adult_weight_scale",
"label": "Percentage of AWCs that reported having a weighing scale for mother and child",
"fills": fills,
"rightLegend": {
"average": average,
"info": awcs_reported_weighing_scale_mother_and_child_help_text(),
"extended_info": [
{
'indicator': (
'Total number of AWCs with a weighing scale for mother and child:'
),
'value': indian_formatted_number(in_month_total)
},
{
'indicator': (
'% of AWCs with a weighing scale for mother and child:'
),
'value': '%.2f%%' % (in_month_total * 100 / float(valid_total or 1))
}
]
},
"data": dict(data_for_map),
}
def get_adhaar_data_map(domain, config, loc_level, show_test=False, beta=False):
def get_data_for(filters):
filters['month'] = datetime(*filters['month'])
queryset = AggAwcMonthly.objects.filter(
**filters
).values(
'%s_name' % loc_level, '%s_map_location_name' % loc_level
).annotate(
in_month=Sum(person_has_aadhaar_column(beta)),
all=Sum(person_is_beneficiary_column(beta)),
).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, valid_total, in_month_total, average, total = generate_data_for_map(
get_data_for(config),
loc_level,
'in_month',
'all',
25,
50
)
fills = OrderedDict()
fills.update({'0%-25%': MapColors.RED})
fills.update({'25%-50%': MapColors.ORANGE})
fills.update({'50%-100%': MapColors.PINK})
fills.update({'defaultFill': MapColors.GREY})
return {
"slug": "adhaar",
"label": "Percent Aadhaar-seeded Beneficiaries",
"fills": fills,
"rightLegend": {
"average": average,
"info": percent_aadhaar_seeded_beneficiaries_help_text(),
"extended_info": [
{
'indicator': (
'Total number of ICDS beneficiaries whose Aadhaar has been captured:'
),
'value': indian_formatted_number(in_month_total)
},
{
'indicator': (
'% of ICDS beneficiaries whose Aadhaar has been captured:'
),
'value': '%.2f%%' % (in_month_total * 100 / float(valid_total or 1))
}
]
},
"data": dict(data_for_map),
}
def get_adhaar_data_map(domain, config, loc_level, show_test=False, beta=False):
def get_data_for(filters):
filters['month'] = datetime(*filters['month'])
queryset = AggAwcMonthly.objects.filter(
**filters
).values(
'%s_name' % loc_level, '%s_map_location_name' % loc_level
).annotate(
in_month=Sum(person_has_aadhaar_column(beta)),
all=Sum(person_is_beneficiary_column(beta)),
).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, valid_total, in_month_total, average, total = generate_data_for_map(
get_data_for(config),
loc_level,
'in_month',
'all',
25,
50
)
fills = OrderedDict()
fills.update({'0%-25%': MapColors.RED})
fills.update({'25%-50%': MapColors.ORANGE})
fills.update({'50%-100%': MapColors.PINK})
fills.update({'defaultFill': MapColors.GREY})
return {
"slug": "adhaar",
"label": "Percent Aadhaar-seeded Beneficiaries",
"fills": fills,
"rightLegend": {
"average": average,
"info": percent_aadhaar_seeded_beneficiaries_help_text(),
"extended_info": [
{
'indicator': (
'Total number of ICDS beneficiaries whose Aadhaar has been captured:'
),
'value': indian_formatted_number(in_month_total)
},
{
'indicator': (
'% of ICDS beneficiaries whose Aadhaar has been captured:'
),
'value': '%.2f%%' % (in_month_total * 100 / float(valid_total or 1))
}
]
},
"data": dict(data_for_map),
}
def get_registered_household_data_map(domain,
config,
loc_level,
show_test=False,
beta=False):
def get_data_for(filters):
filters['month'] = datetime(*filters['month'])
queryset = AggAwcMonthly.objects.filter(**filters).values(
'%s_name' % loc_level, '%s_map_location_name' %
loc_level).annotate(household=Sum('cases_household'), ).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: {
'household': 0,
'original_name': [],
'fillKey': 'Household'
})
average = []
for row in get_data_for(config):
household = row['household'] or 0
name = row['%s_name' % loc_level]
on_map_name = row['%s_map_location_name' % loc_level] or name
average.append(household)
data_for_map[on_map_name]['household'] += household
data_for_map[on_map_name]['original_name'].append(name)
fills = OrderedDict()
fills.update({'Household': MapColors.BLUE})
if beta:
fills.update({'Not Launched': MapColors.GREY})
fills.update({'defaultFill': MapColors.GREY})
return {
"slug": "registered_household",
"label": "",
"fills": fills,
"rightLegend": {
"average":
sum(average) / float(len(average) or 1),
"average_format":
'number',
"info":
_("Total number of households registered: %s" %
indian_formatted_number(sum(average)))
},
"data": dict(data_for_map),
}
def get_registered_household_data_map(domain, config, loc_level, show_test=False):
def get_data_for(filters):
filters['month'] = datetime(*filters['month'])
queryset = AggAwcMonthly.objects.filter(
**filters
).values(
'%s_name' % loc_level, '%s_map_location_name' % loc_level
).annotate(
household=Sum('cases_household'),
).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: {
'household': 0,
'original_name': [],
'fillKey': 'Household'
})
average = []
for row in get_data_for(config):
household = row['household'] or 0
name = row['%s_name' % loc_level]
on_map_name = row['%s_map_location_name' % loc_level] or name
average.append(household)
data_for_map[on_map_name]['household'] += household
data_for_map[on_map_name]['original_name'].append(name)
fills = OrderedDict()
fills.update({'Household': MapColors.BLUE})
fills.update({'defaultFill': MapColors.GREY})
return {
"slug": "registered_household",
"label": "",
"fills": fills,
"rightLegend": {
"average": sum(average) / float(len(average) or 1),
"average_format": 'number',
"info": _("Total number of households registered: %s" % indian_formatted_number(sum(average)))
},
"data": dict(data_for_map),
}
def get_adolescent_girls_data_map(domain,
config,
loc_level,
show_test=False,
beta=False):
valid_col_name = 'cases_person_adolescent_girls_11_14_out_of_school'
all_col_name = 'cases_person_adolescent_girls_11_14_all_v2'
valid_num_title = 'Number of adolescent girls (11-14 years) who are out of school:'
all_num_title = 'Total Number of adolescent girls (11-14 years) who are registered:'
percent_title = 'Percentage of adolescent girls (11-14 years) who are out of school:'
info_text = percent_adolescent_girls_enrolled_help_text_v2()
def get_data_for(filters):
filters['month'] = datetime(*filters['month'])
queryset = AggAwcMonthly.objects.filter(**filters).values(
'%s_name' % loc_level,
'%s_map_location_name' % loc_level).annotate(
valid=Sum(valid_col_name),
all=Sum(all_col_name),
).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': 'Out of school Adolescent Girls'
})
average = []
total_valid = 0
total = 0
for row in get_data_for(config):
valid = row['valid'] or 0
all_adolescent = 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_adolescent
data_for_map[on_map_name]['valid'] += valid
data_for_map[on_map_name]['all'] += all_adolescent
data_for_map[on_map_name]['original_name'].append(name)
fills = OrderedDict()
fills.update({'Out of school Adolescent Girls': MapColors.BLUE})
if beta:
fills.update({'Not Launched': MapColors.GREY})
fills.update({'defaultFill': MapColors.GREY})
return {
"slug": "adolescent_girls",
"label": "",
"fills": fills,
"rightLegend": {
"average":
'%.2f' % (total_valid * 100 / float(total or 1)),
"info":
info_text,
"extended_info": [{
'indicator': valid_num_title,
'value': indian_formatted_number(total_valid)
}, {
'indicator': all_num_title,
'value': indian_formatted_number(total)
}, {
'indicator':
percent_title,
'value':
'%.2f%%' % (total_valid * 100 / float(total or 1))
}]
},
"data": dict(data_for_map),
}
def test_if_parameter_will_be_numeric_string(self):
number = "10000000"
india_format = indian_formatted_number(number)
self.assertEqual(india_format, "1,00,00,000")
def test_less_then_thousand(self):
number = 900
india_format = indian_formatted_number(number)
self.assertEqual(india_format, "900")
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 test_hundred_of_thousands(self):
number = 200000
india_format = indian_formatted_number(number)
self.assertEqual(india_format, "2,00,000")
def get_immunization_coverage_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(
children=Sum('fully_immunized_on_time') + Sum('fully_immunized_late'),
all=Sum('fully_immunized_eligible'),
).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, valid_total, in_month_total, average, total = generate_data_for_map(
get_data_for(config),
loc_level,
'children',
'all',
20,
60
)
fills = OrderedDict()
fills.update({'0%-20%': MapColors.RED})
fills.update({'20%-60%': MapColors.ORANGE})
fills.update({'60%-100%': MapColors.PINK})
fills.update({'defaultFill': MapColors.GREY})
gender_ignored, age_ignored, chosen_filters = chosen_filters_to_labels(config)
return {
"slug": "institutional_deliveries",
"label": "Percent Immunization Coverage at 1 year{}".format(chosen_filters),
"fills": fills,
"rightLegend": {
"average": average,
"info": _((
"Of the total number of children enrolled for Anganwadi Services who are over a year old, "
"the percentage of children who have received the complete immunization as per the National "
"Immunization Schedule of India that is required by age 1."
"<br/><br/>"
"This includes the following immunizations:<br/>"
"If Pentavalent path: Penta1/2/3, OPV1/2/3, BCG, Measles, VitA1<br/>"
"If DPT/HepB path: DPT1/2/3, HepB1/2/3, OPV1/2/3, BCG, Measles, VitA1"
)),
"extended_info": [
{
'indicator': 'Total number of ICDS Child beneficiaries older than '
'1 year{}:'.format(chosen_filters),
'value': indian_formatted_number(valid_total)
},
{
'indicator': (
'Total number of children who have recieved complete immunizations required '
'by age 1{}:'.format(chosen_filters)
),
'value': indian_formatted_number(in_month_total)
},
{
'indicator': (
'% of children who have recieved complete immunizations required by age 1{}:'
.format(chosen_filters)
),
'value': '%.2f%%' % (in_month_total * 100 / float(valid_total or 1))
}
]
},
"data": dict(data_for_map),
}
def test_if_parameter_will_be_numeric_string(self):
number = "10000000"
india_format = indian_formatted_number(number)
self.assertEquals(india_format, "1,00,00,000")
def get_prevalence_of_severe_data_map(domain, config, loc_level, show_test=False, icds_feature_flag=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(
moderate=Sum(wasting_moderate_column(icds_feature_flag)),
severe=Sum(wasting_severe_column(icds_feature_flag)),
normal=Sum(wasting_normal_column(icds_feature_flag)),
total_height_eligible=Sum('height_eligible'),
total_weighed=Sum('nutrition_status_weighed'),
total_measured=Sum(wfh_recorded_in_month_column(icds_feature_flag)),
).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: {
'moderate': 0,
'severe': 0,
'normal': 0,
'total_weighed': 0,
'total_measured': 0,
'total_height_eligible': 0,
'original_name': []
})
severe_for_all_locations = 0
moderate_for_all_locations = 0
normal_for_all_locations = 0
weighed_for_all_locations = 0
measured_for_all_locations = 0
height_eligible_for_all_locations = 0
values_to_calculate_average = {'numerator': 0, 'denominator': 0}
for row in get_data_for(config):
total_weighed = row['total_weighed'] or 0
total_height_eligible = row['total_height_eligible'] or 0
name = row['%s_name' % loc_level]
on_map_name = row['%s_map_location_name' % loc_level] or name
severe = row['severe'] or 0
moderate = row['moderate'] or 0
normal = row['normal'] or 0
total_measured = row['total_measured'] or 0
values_to_calculate_average['numerator'] += moderate if moderate else 0
values_to_calculate_average['numerator'] += severe if severe else 0
values_to_calculate_average['denominator'] += total_measured if total_measured else 0
severe_for_all_locations += severe
moderate_for_all_locations += moderate
normal_for_all_locations += normal
weighed_for_all_locations += total_weighed
measured_for_all_locations += total_measured
height_eligible_for_all_locations += total_height_eligible
data_for_map[on_map_name]['severe'] += severe
data_for_map[on_map_name]['moderate'] += moderate
data_for_map[on_map_name]['normal'] += normal
data_for_map[on_map_name]['total_weighed'] += total_weighed
data_for_map[on_map_name]['total_measured'] += total_measured
data_for_map[on_map_name]['total_height_eligible'] += total_height_eligible
data_for_map[on_map_name]['original_name'].append(name)
for data_for_location in six.itervalues(data_for_map):
numerator = data_for_location['moderate'] + data_for_location['severe']
value = numerator * 100 / (data_for_location['total_measured'] or 1)
if value < 5:
data_for_location.update({'fillKey': '0%-5%'})
elif 5 <= value <= 7:
data_for_location.update({'fillKey': '5%-7%'})
elif value > 7:
data_for_location.update({'fillKey': '7%-100%'})
fills = OrderedDict()
fills.update({'0%-5%': MapColors.PINK})
fills.update({'5%-7%': MapColors.ORANGE})
fills.update({'7%-100%': MapColors.RED})
fills.update({'defaultFill': MapColors.GREY})
gender_label, age_label, chosen_filters = chosen_filters_to_labels(
config,
default_interval=default_age_interval(icds_feature_flag)
)
average = (
(values_to_calculate_average['numerator'] * 100) /
float(values_to_calculate_average['denominator'] or 1)
)
return {
"slug": "severe",
"label": "Percent of Children{gender} Wasted ({age})".format(
gender=gender_label,
age=age_label
),
"fills": fills,
"rightLegend": {
"average": "%.2f" % average,
"info": wasting_help_text(age_label),
"extended_info": [
{
'indicator': 'Total Children{} weighed in given month:'.format(chosen_filters),
'value': indian_formatted_number(weighed_for_all_locations)
},
{
'indicator': 'Total Children{} with height measured in given month:'
.format(chosen_filters),
'value': indian_formatted_number(measured_for_all_locations)
},
{
'indicator': 'Number of children{} unmeasured:'.format(chosen_filters),
'value': indian_formatted_number(height_eligible_for_all_locations - weighed_for_all_locations)
},
{
'indicator': '% Severely Acute Malnutrition{}:'.format(chosen_filters),
'value': '%.2f%%' % (severe_for_all_locations * 100 / float(measured_for_all_locations or 1))
},
{
'indicator': '% Moderately Acute Malnutrition{}:'.format(chosen_filters),
'value': '%.2f%%' % (moderate_for_all_locations * 100 / float(measured_for_all_locations or 1))
},
{
'indicator': '% Normal{}:'.format(chosen_filters),
'value': '%.2f%%' % (normal_for_all_locations * 100 / float(measured_for_all_locations or 1))
}
]
},
"data": dict(data_for_map),
}
def get_awcs_covered_data_map(domain, config, loc_level, show_test=False):
level = config['aggregation_level']
def get_data_for(filters):
filters['month'] = datetime(*filters['month'])
queryset = AggAwcMonthly.objects.filter(**filters).values(
'%s_name' % loc_level,
'%s_map_location_name' % loc_level).annotate(
states=Sum('num_launched_states')
if level <= 1 else Max('num_launched_states'),
districts=Sum('num_launched_districts')
if level <= 2 else Max('num_launched_districts'),
blocks=Sum('num_launched_blocks')
if level <= 3 else Max('num_launched_blocks'),
supervisors=Sum('num_launched_supervisors')
if level <= 4 else Max('num_launched_supervisors'),
awcs=Sum('num_launched_awcs')
if level <= 5 else Max('num_launched_awcs'),
).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: {
'awcs': [],
'supervisors': [],
'blocks': [],
'districts': [],
'states': [],
'original_name': []
})
for row in get_data_for(config):
name = row['%s_name' % loc_level]
on_map_name = row['%s_map_location_name' % loc_level] or name
awcs = row['awcs'] or 0
supervisors = row['supervisors'] or 0
blocks = row['blocks'] or 0
districts = row['districts'] or 0
states = row['states'] or 0
data_for_map[on_map_name]['awcs'].append(awcs)
data_for_map[on_map_name]['supervisors'].append(supervisors)
data_for_map[on_map_name]['blocks'].append(blocks)
data_for_map[on_map_name]['districts'].append(districts)
data_for_map[on_map_name]['states'].append(states)
data_for_map[on_map_name]['original_name'].append(name)
for data_for_location in data_for_map.values():
data_for_location['awcs'] = (sum(data_for_location['awcs'])
if level <= 5 else max(
data_for_location['awcs']))
data_for_location['supervisors'] = (sum(
data_for_location['supervisors']) if level <= 4 else max(
data_for_location['supervisors']))
data_for_location['blocks'] = (sum(data_for_location['blocks'])
if level <= 3 else max(
data_for_location['blocks']))
data_for_location['districts'] = (sum(data_for_location['districts'])
if level <= 2 else max(
data_for_location['districts']))
data_for_location['states'] = (sum(data_for_location['states'])
if level <= 1 else max(
data_for_location['states']))
data_for_location.update({
'fillKey':
'Launched' if data_for_location['awcs'] > 0 else 'Not launched'
})
if level == 1:
prop = 'states'
elif level == 2:
prop = 'districts'
elif level == 3:
prop = 'blocks'
elif level == 4:
prop = 'supervisors'
else:
prop = 'awcs'
total_awcs = sum([(x['awcs'] or 0) for x in data_for_map.values()])
total = sum([(x[prop] or 0) for x in data_for_map.values()])
fills = OrderedDict()
fills.update({'Launched': MapColors.PINK})
fills.update({'Not launched': MapColors.GREY})
fills.update({'defaultFill': MapColors.GREY})
info = _("{:s}<br /><br />"
"Number of AWCs launched: {:s}".format(
awcs_launched_help_text(),
indian_formatted_number(total_awcs)))
if level != 5:
info = _("{:s}<br /><br />"
"Number of AWCs launched: {:s} <br />"
"Number of {:s} launched: {:s}".format(
awcs_launched_help_text(),
indian_formatted_number(total_awcs), prop.title(),
indian_formatted_number(total)))
return {
"slug": "awc_covered",
"label": "",
"fills": fills,
"rightLegend": {
"info": info
},
"data": dict(data_for_map),
}
def get_newborn_with_low_birth_weight_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(
low_birth=Sum('low_birth_weight_in_month'),
in_month=Sum('weighed_and_born_in_month'),
all=Sum('born_in_month')
).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, in_month_total, low_birth_total, average, total = generate_data_for_map(
get_data_for(config),
loc_level,
'low_birth',
'in_month',
20,
60,
'all'
)
fills = OrderedDict()
fills.update({'0%-20%': MapColors.PINK})
fills.update({'20%-60%': MapColors.ORANGE})
fills.update({'60%-100%': MapColors.RED})
fills.update({'defaultFill': MapColors.GREY})
gender_ignored, age_ignored, chosen_filters = chosen_filters_to_labels(config)
return {
"slug": "low_birth",
"label": "Percent Newborns with Low Birth Weight{}".format(chosen_filters),
"fills": fills,
"rightLegend": {
"average": average,
"info": _((
new_born_with_low_weight_help_text(html=True)
)),
"extended_info": [
{
'indicator': 'Total Number of Newborns born in given month{}:'.format(chosen_filters),
'value': indian_formatted_number(total)
},
{
'indicator': 'Number of Newborns with LBW in given month{}:'.format(chosen_filters),
'value': indian_formatted_number(low_birth_total)
},
{
'indicator': 'Total Number of children born and weight in given month{}:'.format(
chosen_filters
),
'value': indian_formatted_number(in_month_total)
},
{
'indicator': '% newborns with LBW in given month{}:'.format(chosen_filters),
'value': '%.2f%%' % (low_birth_total * 100 / float(in_month_total or 1))
},
{
'indicator': '% of children with weight in normal{}:'.format(chosen_filters),
'value': '%.2f%%' % ((in_month_total - low_birth_total) * 100 / float(in_month_total or 1))
},
{
'indicator': '% Unweighted{}:'.format(chosen_filters),
'value': '%.2f%%' % ((total - in_month_total) * 100 / float(total or 1))
}
]
},
"data": dict(data_for_map),
}
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 = []
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
numerator = moderately_underweight + severely_underweight
values_to_calculate_average.append(numerator * 100 / (weighed or 1))
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 = ((sum(values_to_calculate_average)) /
float(len(values_to_calculate_average) 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":
average,
"info":
_(("Of the total children enrolled for Anganwadi services and weighed, the percentage of "
"children between {} years who were moderately/severely underweight in the current month. "
"<br/><br/>"
"Children who are moderately or severely underweight have a higher risk of mortality."
.format(age_label))),
"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_prevalence_of_severe_data_map(domain,
config,
loc_level,
show_test=False,
icds_feature_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(
moderate=Sum(wasting_moderate_column(icds_feature_flag)),
severe=Sum(wasting_severe_column(icds_feature_flag)),
normal=Sum(wasting_normal_column(icds_feature_flag)),
total_height_eligible=Sum('height_eligible'),
total_weighed=Sum('nutrition_status_weighed'),
total_measured=Sum(
wfh_recorded_in_month_column(icds_feature_flag)),
).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: {
'moderate': 0,
'severe': 0,
'normal': 0,
'total_weighed': 0,
'total_measured': 0,
'total_height_eligible': 0,
'original_name': []
})
severe_for_all_locations = 0
moderate_for_all_locations = 0
normal_for_all_locations = 0
weighed_for_all_locations = 0
measured_for_all_locations = 0
height_eligible_for_all_locations = 0
values_to_calculate_average = {'numerator': 0, 'denominator': 0}
if icds_feature_flag:
location_launched_status = get_location_launched_status(
config, loc_level)
else:
location_launched_status = None
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
total_weighed = row['total_weighed'] or 0
total_height_eligible = row['total_height_eligible'] or 0
name = row['%s_name' % loc_level]
on_map_name = row['%s_map_location_name' % loc_level] or name
severe = row['severe'] or 0
moderate = row['moderate'] or 0
normal = row['normal'] or 0
total_measured = row['total_measured'] or 0
values_to_calculate_average['numerator'] += moderate if moderate else 0
values_to_calculate_average['numerator'] += severe if severe else 0
values_to_calculate_average[
'denominator'] += total_measured if total_measured else 0
severe_for_all_locations += severe
moderate_for_all_locations += moderate
normal_for_all_locations += normal
weighed_for_all_locations += total_weighed
measured_for_all_locations += total_measured
height_eligible_for_all_locations += total_height_eligible
data_for_map[on_map_name]['severe'] += severe
data_for_map[on_map_name]['moderate'] += moderate
data_for_map[on_map_name]['normal'] += normal
data_for_map[on_map_name]['total_weighed'] += total_weighed
data_for_map[on_map_name]['total_measured'] += total_measured
data_for_map[on_map_name][
'total_height_eligible'] += total_height_eligible
data_for_map[on_map_name]['original_name'].append(name)
for data_for_location in data_for_map.values():
numerator = data_for_location['moderate'] + data_for_location['severe']
value = numerator * 100 / (data_for_location['total_measured'] or 1)
if value < 5:
data_for_location.update({'fillKey': '0%-5%'})
elif 5 <= value <= 7:
data_for_location.update({'fillKey': '5%-7%'})
elif value > 7:
data_for_location.update({'fillKey': '7%-100%'})
fills = OrderedDict()
fills.update({'0%-5%': MapColors.PINK})
fills.update({'5%-7%': MapColors.ORANGE})
fills.update({'7%-100%': MapColors.RED})
if icds_feature_flag:
fills.update({'Not Launched': MapColors.GREY})
fills.update({'defaultFill': MapColors.GREY})
gender_label, age_label, chosen_filters = chosen_filters_to_labels(
config, default_interval=default_age_interval(icds_feature_flag))
average = ((values_to_calculate_average['numerator'] * 100) /
float(values_to_calculate_average['denominator'] or 1))
return {
"slug":
"severe",
"label":
"Percent of Children{gender} Wasted ({age})".format(
gender=gender_label, age=age_label),
"fills":
fills,
"rightLegend": {
"average":
"%.2f" % average,
"info":
wasting_help_text(age_label),
"extended_info": [{
'indicator':
'Total Children{} weighed in given month:'.format(
chosen_filters),
'value':
indian_formatted_number(weighed_for_all_locations)
}, {
'indicator':
'Total Children{} with weight and height measured in given month:'
.format(chosen_filters),
'value':
indian_formatted_number(measured_for_all_locations)
}, {
'indicator':
'Number of children{} unmeasured:'.format(chosen_filters),
'value':
indian_formatted_number(height_eligible_for_all_locations -
weighed_for_all_locations)
}, {
'indicator':
'% Severely Acute Malnutrition{}:'.format(chosen_filters),
'value':
'%.2f%%' % (severe_for_all_locations * 100 /
float(measured_for_all_locations or 1))
}, {
'indicator':
'% Moderately Acute Malnutrition{}:'.format(chosen_filters),
'value':
'%.2f%%' % (moderate_for_all_locations * 100 /
float(measured_for_all_locations or 1))
}, {
'indicator':
'% Normal{}:'.format(chosen_filters),
'value':
'%.2f%%' % (normal_for_all_locations * 100 /
float(measured_for_all_locations or 1))
}]
},
"data":
dict(data_for_map),
}
def get_enrolled_children_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(
valid=Sum('valid_in_month'),
all=Sum('valid_all_registered_in_month')
).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': 'Children'
})
average = []
total_valid = 0
total = 0
for row in get_data_for(config):
valid = row['valid'] or 0
name = row['%s_name' % loc_level]
all_children = row['all'] or 0
on_map_name = row['%s_map_location_name' % loc_level] or name
average.append(valid)
total_valid += valid
total += all_children
data_for_map[on_map_name]['valid'] += valid
data_for_map[on_map_name]['all'] += all_children
data_for_map[on_map_name]['original_name'].append(name)
fills = OrderedDict()
fills.update({'Children': MapColors.BLUE})
fills.update({'defaultFill': MapColors.GREY})
gender_ignored, age_label, chosen_filters = chosen_filters_to_labels(config, default_interval='0 - 6 years')
return {
"slug": "enrolled_children",
"label": "",
"fills": fills,
"rightLegend": {
"average": '%.2f' % (total_valid * 100 / float(total or 1)),
"info": percent_children_enrolled_help_text(age_label=age_label),
"extended_info": [
{
'indicator':
'Number of children{} who are enrolled for Anganwadi Services:'
.format(chosen_filters),
'value': indian_formatted_number(total_valid)
},
{
'indicator': (
'Total number of children{} who are registered: '
.format(chosen_filters)
),
'value': indian_formatted_number(total)
},
{
'indicator': (
'Percentage of registered children{} who are enrolled for Anganwadi Services:'
.format(chosen_filters)
),
'value': '%.2f%%' % (total_valid * 100 / float(total or 1))
}
]
},
"data": dict(data_for_map),
}
def get_exclusive_breastfeeding_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(
children=Sum('ebf_in_month'),
all=Sum('ebf_eligible'),
).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, valid_total, in_month_total = generate_data_for_map(
get_data_for(config),
loc_level,
'children',
'all',
20,
60
)
fills = OrderedDict()
fills.update({'0%-20%': MapColors.RED})
fills.update({'20%-60%': MapColors.ORANGE})
fills.update({'60%-100%': MapColors.PINK})
fills.update({'defaultFill': MapColors.GREY})
gender_ignored, age_ignored, chosen_filters = chosen_filters_to_labels(config)
return {
"slug": "severe",
"label": "Percent Exclusive Breastfeeding{}".format(chosen_filters),
"fills": fills,
"rightLegend": {
"average": (in_month_total * 100) / (float(valid_total) or 1),
"info": _((
"Percentage of infants 0-6 months of age who are fed exclusively with breast milk. "
"<br/><br/>"
"An infant is exclusively breastfed if they recieve only breastmilk with no additional food, "
"liquids (even water) ensuring optimal nutrition and growth between 0 - 6 months"
)),
"extended_info": [
{
'indicator': 'Total number of children between ages 0 - 6 months{}:'
.format(chosen_filters),
'value': indian_formatted_number(valid_total)
},
{
'indicator': (
'Total number of children (0-6 months) exclusively breastfed in the given month{}:'
.format(chosen_filters)
),
'value': indian_formatted_number(in_month_total)
},
{
'indicator': '% children (0-6 months) exclusively breastfed in the '
'given month{}:'.format(chosen_filters),
'value': '%.2f%%' % (in_month_total * 100 / float(valid_total or 1))
}
]
},
"data": dict(data_for_map),
}
def get_early_initiation_breastfeeding_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(
birth=Sum('bf_at_birth'),
in_month=Sum('born_in_month'),
).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, in_month_total, birth_total, average, total = generate_data_for_map(
get_data_for(config),
loc_level,
'birth',
'in_month',
20,
60
)
fills = OrderedDict()
fills.update({'0%-20%': MapColors.RED})
fills.update({'20%-60%': MapColors.ORANGE})
fills.update({'60%-100%': MapColors.PINK})
fills.update({'defaultFill': MapColors.GREY})
gender_ignored, age_ignored, chosen_filters = chosen_filters_to_labels(config)
return {
"slug": "early_initiation",
"label": "Percent Early Initiation of Breastfeeding{}".format(chosen_filters),
"fills": fills,
"rightLegend": {
"average": average,
"info": early_initiation_breastfeeding_help_text(html=True),
"extended_info": [
{
'indicator': 'Total Number of Children born in the given month{}:'.format(chosen_filters),
'value': indian_formatted_number(in_month_total)
},
{
'indicator': (
'Total Number of Children who were put to the breast within one hour of birth{}:'
.format(chosen_filters)
),
'value': indian_formatted_number(birth_total)
},
{
'indicator': '% children who were put to the breast within one hour of '
'birth{}:'.format(chosen_filters),
'value': '%.2f%%' % (birth_total * 100 / float(in_month_total or 1))
}
]
},
"data": dict(data_for_map),
}
def get_prevalence_of_stunting_data_map(domain,
config,
loc_level,
show_test=False,
icds_feature_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(
moderate=Sum(stunting_moderate_column(icds_feature_flag)),
severe=Sum(stunting_severe_column(icds_feature_flag)),
normal=Sum(stunting_normal_column(icds_feature_flag)),
total=Sum('height_eligible'),
total_measured=Sum(
hfa_recorded_in_month_column(icds_feature_flag)),
).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: {
'moderate': 0,
'severe': 0,
'normal': 0,
'total': 0,
'total_measured': 0,
'original_name': []
})
moderate_total = 0
severe_total = 0
normal_total = 0
all_total = 0
measured_total = 0
values_to_calculate_average = {'numerator': 0, 'denominator': 0}
if icds_feature_flag:
location_launched_status = get_location_launched_status(
config, loc_level)
else:
location_launched_status = None
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
total = row['total'] or 0
name = row['%s_name' % loc_level]
on_map_name = row['%s_map_location_name' % loc_level] or name
severe = row['severe'] or 0
moderate = row['moderate'] or 0
normal = row['normal'] or 0
total_measured = row['total_measured'] or 0
values_to_calculate_average['numerator'] += moderate if moderate else 0
values_to_calculate_average['numerator'] += severe if severe else 0
values_to_calculate_average[
'denominator'] += total_measured if total_measured else 0
severe_total += severe
moderate_total += moderate
normal_total += normal
all_total += total
measured_total += total_measured
data_for_map[on_map_name]['severe'] += severe
data_for_map[on_map_name]['moderate'] += moderate
data_for_map[on_map_name]['normal'] += normal
data_for_map[on_map_name]['total'] += total
data_for_map[on_map_name]['total_measured'] += total_measured
data_for_map[on_map_name]['original_name'].append(name)
for data_for_location in data_for_map.values():
numerator = data_for_location['moderate'] + data_for_location['severe']
value = numerator * 100 / (data_for_location['total_measured'] or 1)
if value < 25:
data_for_location.update({'fillKey': '0%-25%'})
elif 25 <= value < 38:
data_for_location.update({'fillKey': '25%-38%'})
elif value >= 38:
data_for_location.update({'fillKey': '38%-100%'})
fills = OrderedDict()
fills.update({'0%-25%': MapColors.PINK})
fills.update({'25%-38%': MapColors.ORANGE})
fills.update({'38%-100%': MapColors.RED})
fills.update({'defaultFill': MapColors.GREY})
gender_label, age_label, chosen_filters = chosen_filters_to_labels(
config, default_interval=default_age_interval(icds_feature_flag))
average = ((values_to_calculate_average['numerator'] * 100) /
float(values_to_calculate_average['denominator'] or 1))
return {
"slug":
"severe",
"label":
"Percent of Children{gender} Stunted ({age})".format(
gender=gender_label, age=age_label),
"fills":
fills,
"rightLegend": {
"average":
"%.2f" % average,
"info":
_(("Of the children enrolled for Anganwadi services, whose height was measured, the percentage of "
"children between {} who were moderately/severely stunted in the current month. "
"<br/><br/>"
"Stunting is a sign of chronic undernutrition and has long lasting harmful consequences on "
"the growth of a child".format(age_label))),
"extended_info": [{
'indicator':
'Total Children{} eligible to have height measured:'.format(
chosen_filters),
'value':
indian_formatted_number(all_total)
}, {
'indicator':
'Total Children{} with height measured in given month:'.format(
chosen_filters),
'value':
indian_formatted_number(measured_total)
}, {
'indicator':
'Number of Children{} unmeasured:'.format(chosen_filters),
'value':
indian_formatted_number(all_total - measured_total)
}, {
'indicator':
'% children{} with severely stunted growth:'.format(
chosen_filters),
'value':
'%.2f%%' % (severe_total * 100 / float(measured_total or 1))
}, {
'indicator':
'% children{} with moderate stunted growth:'.format(
chosen_filters),
'value':
'%.2f%%' % (moderate_total * 100 / float(measured_total or 1))
}, {
'indicator':
'% children{} with normal stunted growth:'.format(
chosen_filters),
'value':
'%.2f%%' % (normal_total * 100 / float(measured_total or 1))
}]
},
"data":
dict(data_for_map),
}
def test_milions(self):
number = 10000000
india_format = indian_formatted_number(number)
self.assertEquals(india_format, "1,00,00,000")
def get_awcs_covered_data_map(domain, config, loc_level, show_test=False):
level = config['aggregation_level']
def get_data_for(filters):
filters['month'] = datetime(*filters['month'])
queryset = AggAwcMonthly.objects.filter(
**filters
).values(
'%s_name' % loc_level, '%s_map_location_name' % loc_level
).annotate(
states=Sum('num_launched_states') if level <= 1 else Max('num_launched_states'),
districts=Sum('num_launched_districts') if level <= 2 else Max('num_launched_districts'),
blocks=Sum('num_launched_blocks') if level <= 3 else Max('num_launched_blocks'),
supervisors=Sum('num_launched_supervisors') if level <= 4 else Max('num_launched_supervisors'),
awcs=Sum('num_launched_awcs') if level <= 5 else Max('num_launched_awcs'),
).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: {
'awcs': [],
'supervisors': [],
'blocks': [],
'districts': [],
'states': [],
'original_name': []
})
for row in get_data_for(config):
name = row['%s_name' % loc_level]
on_map_name = row['%s_map_location_name' % loc_level] or name
awcs = row['awcs'] or 0
supervisors = row['supervisors'] or 0
blocks = row['blocks'] or 0
districts = row['districts'] or 0
states = row['states'] or 0
data_for_map[on_map_name]['awcs'].append(awcs)
data_for_map[on_map_name]['supervisors'].append(supervisors)
data_for_map[on_map_name]['blocks'].append(blocks)
data_for_map[on_map_name]['districts'].append(districts)
data_for_map[on_map_name]['states'].append(states)
data_for_map[on_map_name]['original_name'].append(name)
for data_for_location in six.itervalues(data_for_map):
data_for_location['awcs'] = (
sum(data_for_location['awcs']) if level <= 5 else max(data_for_location['awcs'])
)
data_for_location['supervisors'] = (
sum(data_for_location['supervisors']) if level <= 4 else max(data_for_location['supervisors'])
)
data_for_location['blocks'] = (
sum(data_for_location['blocks']) if level <= 3 else max(data_for_location['blocks'])
)
data_for_location['districts'] = (
sum(data_for_location['districts']) if level <= 2 else max(data_for_location['districts'])
)
data_for_location['states'] = (
sum(data_for_location['states']) if level <= 1 else max(data_for_location['states'])
)
data_for_location.update({'fillKey': 'Launched' if data_for_location['awcs'] > 0 else 'Not launched'})
if level == 1:
prop = 'states'
elif level == 2:
prop = 'districts'
elif level == 3:
prop = 'blocks'
elif level == 4:
prop = 'supervisors'
else:
prop = 'awcs'
total_awcs = sum([(x['awcs'] or 0) for x in six.itervalues(data_for_map)])
total = sum([(x[prop] or 0) for x in six.itervalues(data_for_map)])
fills = OrderedDict()
fills.update({'Launched': MapColors.PINK})
fills.update({'Not launched': MapColors.GREY})
fills.update({'defaultFill': MapColors.GREY})
info = _(
"{:s}<br /><br />"
"Number of AWCs launched: {:s}".format(
awcs_launched_help_text(),
indian_formatted_number(total_awcs)
)
)
if level != 5:
info = _(
"{:s}<br /><br />"
"Number of AWCs launched: {:s} <br />"
"Number of {:s} launched: {:s}".format(
awcs_launched_help_text(),
indian_formatted_number(total_awcs),
prop.title(),
indian_formatted_number(total)
)
)
return {
"slug": "awc_covered",
"label": "",
"fills": fills,
"rightLegend": {
"info": info
},
"data": dict(data_for_map),
}
def get_institutional_deliveries_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(
children=Sum('institutional_delivery_in_month'),
all=Sum('delivered_in_month'),
).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, valid_total, in_month_total, average, total = generate_data_for_map(
get_data_for(config),
loc_level,
'children',
'all',
20,
60
)
fills = OrderedDict()
fills.update({'0%-20%': MapColors.RED})
fills.update({'20%-60%': MapColors.ORANGE})
fills.update({'60%-100%': MapColors.PINK})
fills.update({'defaultFill': MapColors.GREY})
return {
"slug": "institutional_deliveries",
"label": "Percent Instituitional Deliveries",
"fills": fills,
"rightLegend": {
"average": average,
"info": institutional_deliveries_help_text(html=True),
"extended_info": [
{
'indicator': 'Total number of pregnant women who delivered in the last month:',
'value': indian_formatted_number(valid_total)
},
{
'indicator': (
'Total number of pregnant women who delivered in a '
'public/private medical facilitiy in the last month:'
),
'value': indian_formatted_number(in_month_total)
},
{
'indicator': (
'% pregnant women who delivered in a public or private medical '
'facility in the last month:'
),
'value': '%.2f%%' % (in_month_total * 100 / float(valid_total or 1))
}
]
},
"data": dict(data_for_map),
}
def get_prevalence_of_stunting_data_map(domain, config, loc_level, show_test=False, icds_feature_flag=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(
moderate=Sum(stunting_moderate_column(icds_feature_flag)),
severe=Sum(stunting_severe_column(icds_feature_flag)),
normal=Sum(stunting_normal_column(icds_feature_flag)),
total=Sum('height_eligible'),
total_measured=Sum(hfa_recorded_in_month_column(icds_feature_flag)),
).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: {
'moderate': 0,
'severe': 0,
'normal': 0,
'total': 0,
'total_measured': 0,
'original_name': []
})
moderate_total = 0
severe_total = 0
normal_total = 0
all_total = 0
measured_total = 0
values_to_calculate_average = {'numerator': 0, 'denominator': 0}
for row in get_data_for(config):
total = row['total'] or 0
name = row['%s_name' % loc_level]
on_map_name = row['%s_map_location_name' % loc_level] or name
severe = row['severe'] or 0
moderate = row['moderate'] or 0
normal = row['normal'] or 0
total_measured = row['total_measured'] or 0
values_to_calculate_average['numerator'] += moderate if moderate else 0
values_to_calculate_average['numerator'] += severe if severe else 0
values_to_calculate_average['denominator'] += total_measured if total_measured else 0
severe_total += severe
moderate_total += moderate
normal_total += normal
all_total += total
measured_total += total_measured
data_for_map[on_map_name]['severe'] += severe
data_for_map[on_map_name]['moderate'] += moderate
data_for_map[on_map_name]['normal'] += normal
data_for_map[on_map_name]['total'] += total
data_for_map[on_map_name]['total_measured'] += total_measured
data_for_map[on_map_name]['original_name'].append(name)
for data_for_location in six.itervalues(data_for_map):
numerator = data_for_location['moderate'] + data_for_location['severe']
value = numerator * 100 / (data_for_location['total_measured'] or 1)
if value < 25:
data_for_location.update({'fillKey': '0%-25%'})
elif 25 <= value < 38:
data_for_location.update({'fillKey': '25%-38%'})
elif value >= 38:
data_for_location.update({'fillKey': '38%-100%'})
fills = OrderedDict()
fills.update({'0%-25%': MapColors.PINK})
fills.update({'25%-38%': MapColors.ORANGE})
fills.update({'38%-100%': MapColors.RED})
fills.update({'defaultFill': MapColors.GREY})
gender_label, age_label, chosen_filters = chosen_filters_to_labels(
config,
default_interval=default_age_interval(icds_feature_flag)
)
average = (
(values_to_calculate_average['numerator'] * 100) /
float(values_to_calculate_average['denominator'] or 1)
)
return {
"slug": "severe",
"label": "Percent of Children{gender} Stunted ({age})".format(
gender=gender_label,
age=age_label
),
"fills": fills,
"rightLegend": {
"average": "%.2f" % average,
"info": _((
"Of the children enrolled for Anganwadi services, whose height was measured, the percentage of "
"children between {} who were moderately/severely stunted in the current month. "
"<br/><br/>"
"Stunting is a sign of chronic undernutrition and has long lasting harmful consequences on "
"the growth of a child".format(age_label)
)),
"extended_info": [
{
'indicator': 'Total Children{} eligible to have height measured:'.format(chosen_filters),
'value': indian_formatted_number(all_total)
},
{
'indicator': 'Total Children{} with height measured in given month:'
.format(chosen_filters),
'value': indian_formatted_number(measured_total)
},
{
'indicator': 'Number of Children{} unmeasured:'.format(chosen_filters),
'value': indian_formatted_number(all_total - measured_total)
},
{
'indicator': '% children{} with severely stunted growth:'.format(chosen_filters),
'value': '%.2f%%' % (severe_total * 100 / float(measured_total or 1))
},
{
'indicator': '% children{} with moderate stunted growth:'.format(chosen_filters),
'value': '%.2f%%' % (moderate_total * 100 / float(measured_total or 1))
},
{
'indicator': '% children{} with normal stunted growth:'.format(chosen_filters),
'value': '%.2f%%' % (normal_total * 100 / float(measured_total or 1))
}
]
},
"data": dict(data_for_map),
}
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_early_initiation_breastfeeding_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(
birth=Sum('bf_at_birth'),
in_month=Sum('born_in_month'),
).order_by('%s_name' % loc_level, '%s_map_location_name' % loc_level)
if not show_test:
queryset = apply_exclude(domain, queryset)
return queryset
if icds_features_flag:
location_launched_status = get_location_launched_status(config, loc_level)
else:
location_launched_status = None
data_for_map, in_month_total, birth_total, average, total = generate_data_for_map(
get_data_for(config),
loc_level,
'birth',
'in_month',
20,
60,
location_launched_status=location_launched_status
)
fills = OrderedDict()
fills.update({'0%-20%': MapColors.RED})
fills.update({'20%-60%': MapColors.ORANGE})
fills.update({'60%-100%': MapColors.PINK})
if icds_features_flag:
fills.update({'Not Launched': MapColors.GREY})
fills.update({'defaultFill': MapColors.GREY})
gender_ignored, age_ignored, chosen_filters = chosen_filters_to_labels(config)
return {
"slug": "early_initiation",
"label": "Percent Early Initiation of Breastfeeding{}".format(chosen_filters),
"fills": fills,
"rightLegend": {
"average": average,
"info": early_initiation_breastfeeding_help_text(html=True),
"extended_info": [
{
'indicator': 'Total Number of Children born in the current month{}:'.format(chosen_filters),
'value': indian_formatted_number(in_month_total)
},
{
'indicator': (
'Total Number of Children who were put to the breast within one hour of birth{}:'
.format(chosen_filters)
),
'value': indian_formatted_number(birth_total)
},
{
'indicator': '% children who were put to the breast within one hour of '
'birth{}:'.format(chosen_filters),
'value': '%.2f%%' % (birth_total * 100 / float(in_month_total or 1))
}
]
},
"data": dict(data_for_map),
}
def get_children_initiated_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(
children=Sum('cf_initiation_in_month'),
all=Sum('cf_initiation_eligible'),
).order_by('%s_name' % loc_level,
'%s_map_location_name' % loc_level)
if not show_test:
queryset = apply_exclude(domain, queryset)
return queryset
if icds_features_flag:
location_launched_status = get_location_launched_status(
config, loc_level)
else:
location_launched_status = None
data_for_map, valid_total, in_month_total, average, total = generate_data_for_map(
get_data_for(config),
loc_level,
'children',
'all',
20,
60,
location_launched_status=location_launched_status)
fills = OrderedDict()
fills.update({'0%-20%': MapColors.RED})
fills.update({'20%-60%': MapColors.ORANGE})
fills.update({'60%-100%': MapColors.PINK})
fills.update({'defaultFill': MapColors.GREY})
gender_ignored, age_ignored, chosen_filters = chosen_filters_to_labels(
config)
return {
"slug":
"severe",
"label":
"Percent Children (6-8 months) initiated Complementary Feeding{}".
format(chosen_filters),
"fills":
fills,
"rightLegend": {
"average":
average,
"info":
children_initiated_appropriate_complementary_feeding_help_text(
html=True),
"extended_info": [{
'indicator':
'Total number of children between age 6 - 8 months{}:'.format(
chosen_filters),
'value':
indian_formatted_number(valid_total)
}, {
'indicator':
('Total number of children (6-8 months) given timely introduction to sold or '
'semi-solid food in the given month{}:'.format(chosen_filters)
),
'value':
indian_formatted_number(in_month_total)
}, {
'indicator':
('% children (6-8 months) given timely introduction to solid or '
'semi-solid food in the given month{}:'.format(chosen_filters)
),
'value':
'%.2f%%' % (in_month_total * 100 / float(valid_total or 1))
}]
},
"data":
dict(data_for_map),
}
def get_institutional_deliveries_data_map(domain,
config,
loc_level,
show_test=False,
icds_features_flag=False):
config['month'] = datetime(*config['month'])
def get_data_for(filters):
queryset = AggCcsRecordMonthly.objects.filter(**filters).values(
'%s_name' % loc_level,
'%s_map_location_name' % loc_level).annotate(
children=Sum('institutional_delivery_in_month'),
all=Sum('delivered_in_month'),
).order_by('%s_name' % loc_level,
'%s_map_location_name' % loc_level)
if not show_test:
queryset = apply_exclude(domain, queryset)
return queryset
if icds_features_flag:
location_launched_status = get_location_launched_status(
config, loc_level)
else:
location_launched_status = None
data_for_map, valid_total, in_month_total, average, total = generate_data_for_map(
get_data_for(config),
loc_level,
'children',
'all',
20,
60,
location_launched_status=location_launched_status)
fills = OrderedDict()
fills.update({'0%-20%': MapColors.RED})
fills.update({'20%-60%': MapColors.ORANGE})
fills.update({'60%-100%': MapColors.PINK})
fills.update({'defaultFill': MapColors.GREY})
return {
"slug": "institutional_deliveries",
"label": "Percent Instituitional Deliveries",
"fills": fills,
"rightLegend": {
"average":
average,
"info":
institutional_deliveries_help_text(html=True),
"extended_info": [{
'indicator':
'Total number of pregnant women who delivered in the current month:',
'value': indian_formatted_number(valid_total)
}, {
'indicator':
('Total number of pregnant women who delivered in a '
'public/private medical facilitiy in the current month:'),
'value':
indian_formatted_number(in_month_total)
}, {
'indicator':
('% pregnant women who delivered in a public or private medical '
'facility in the current month:'),
'value':
'%.2f%%' % (in_month_total * 100 / float(valid_total or 1))
}]
},
"data": dict(data_for_map),
}
def test_milions(self):
number = 10000000
india_format = indian_formatted_number(number)
self.assertEqual(india_format, "1,00,00,000")
def test_wrong_data(self):
number = "some non numeric value"
india_format = indian_formatted_number(number)
self.assertEquals(india_format, 0)
def test_wrong_data(self):
number = "some non numeric value"
india_format = indian_formatted_number(number)
self.assertEqual(india_format, 0)
def get_early_initiation_breastfeeding_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(
birth=Sum('bf_at_birth'),
in_month=Sum('born_in_month'),
).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, in_month_total, birth_total, average, total = generate_data_for_map(
get_data_for(config),
loc_level,
'birth',
'in_month',
20,
60
)
fills = OrderedDict()
fills.update({'0%-20%': MapColors.RED})
fills.update({'20%-60%': MapColors.ORANGE})
fills.update({'60%-100%': MapColors.PINK})
fills.update({'defaultFill': MapColors.GREY})
gender_ignored, age_ignored, chosen_filters = chosen_filters_to_labels(config)
return {
"slug": "early_initiation",
"label": "Percent Early Initiation of Breastfeeding{}".format(chosen_filters),
"fills": fills,
"rightLegend": {
"average": average,
"info": _((
"Percentage of children who were put to the breast within one hour of birth."
"<br/><br/>"
"Early initiation of breastfeeding ensure the newborn recieves the 'first milk' rich in "
"nutrients and encourages exclusive breastfeeding practice"
)),
"extended_info": [
{
'indicator': 'Total Number of Children born in the given month{}:'.format(chosen_filters),
'value': indian_formatted_number(in_month_total)
},
{
'indicator': (
'Total Number of Children who were put to the breast within one hour of birth{}:'
.format(chosen_filters)
),
'value': indian_formatted_number(birth_total)
},
{
'indicator': '% children who were put to the breast within one hour of '
'birth{}:'.format(chosen_filters),
'value': '%.2f%%' % (birth_total * 100 / float(in_month_total or 1))
}
]
},
"data": dict(data_for_map),
}
def get_enrolled_children_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(
valid=Sum('valid_in_month'),
all=Sum('valid_all_registered_in_month')).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': 'Children'
})
average = []
total_valid = 0
total = 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
valid = row['valid'] or 0
name = row['%s_name' % loc_level]
all_children = row['all'] or 0
on_map_name = row['%s_map_location_name' % loc_level] or name
average.append(valid)
total_valid += valid
total += all_children
data_for_map[on_map_name]['valid'] += valid
data_for_map[on_map_name]['all'] += all_children
data_for_map[on_map_name]['original_name'].append(name)
fills = OrderedDict()
fills.update({'Children': MapColors.BLUE})
fills.update({'Not Launched': MapColors.GREY})
fills.update({'defaultFill': MapColors.GREY})
gender_ignored, age_label, chosen_filters = chosen_filters_to_labels(
config, default_interval='0 - 6 years')
return {
"slug": "enrolled_children",
"label": "",
"fills": fills,
"rightLegend": {
"average":
'%.2f' % (total_valid * 100 / float(total or 1)),
"info":
percent_children_enrolled_help_text(age_label=age_label),
"extended_info": [{
'indicator':
'Number of children{} who are enrolled for Anganwadi Services:'
.format(chosen_filters),
'value':
indian_formatted_number(total_valid)
}, {
'indicator':
('Total number of children{} who are registered: '.format(
chosen_filters)),
'value':
indian_formatted_number(total)
}, {
'indicator':
('Percentage of registered children{} who are enrolled for Anganwadi Services:'
.format(chosen_filters)),
'value':
'%.2f%%' % (total_valid * 100 / float(total or 1))
}]
},
"data": dict(data_for_map),
}
def get_children_initiated_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(
children=Sum('cf_initiation_in_month'),
all=Sum('cf_initiation_eligible'),
).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, valid_total, in_month_total, average, total = generate_data_for_map(
get_data_for(config),
loc_level,
'children',
'all',
20,
60
)
fills = OrderedDict()
fills.update({'0%-20%': MapColors.RED})
fills.update({'20%-60%': MapColors.ORANGE})
fills.update({'60%-100%': MapColors.PINK})
fills.update({'defaultFill': MapColors.GREY})
gender_ignored, age_ignored, chosen_filters = chosen_filters_to_labels(config)
return {
"slug": "severe",
"label": "Percent Children (6-8 months) initiated Complementary Feeding{}".format(chosen_filters),
"fills": fills,
"rightLegend": {
"average": average,
"info": children_initiated_appropriate_complementary_feeding_help_text(html=True),
"extended_info": [
{
'indicator': 'Total number of children between age 6 - 8 months{}:'.format(chosen_filters),
'value': indian_formatted_number(valid_total)
},
{
'indicator': (
'Total number of children (6-8 months) given timely introduction to sold or '
'semi-solid food in the given month{}:'.format(chosen_filters)
),
'value': indian_formatted_number(in_month_total)
},
{
'indicator': (
'% children (6-8 months) given timely introduction to solid or '
'semi-solid food in the given month{}:'.format(chosen_filters)
),
'value': '%.2f%%' % (in_month_total * 100 / float(valid_total or 1))
}
]
},
"data": dict(data_for_map),
}
def get_newborn_with_low_birth_weight_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(low_birth=Sum('low_birth_weight_in_month'),
in_month=Sum('weighed_and_born_in_month'),
all=Sum('born_in_month')).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, in_month_total, low_birth_total, average, total = generate_data_for_map(
get_data_for(config), loc_level, 'low_birth', 'in_month', 20, 60,
'all')
fills = OrderedDict()
fills.update({'0%-20%': MapColors.PINK})
fills.update({'20%-60%': MapColors.ORANGE})
fills.update({'60%-100%': MapColors.RED})
fills.update({'defaultFill': MapColors.GREY})
gender_ignored, age_ignored, chosen_filters = chosen_filters_to_labels(
config)
return {
"slug": "low_birth",
"label":
"Percent Newborns with Low Birth Weight{}".format(chosen_filters),
"fills": fills,
"rightLegend": {
"average":
average,
"info":
_((new_born_with_low_weight_help_text(html=True))),
"extended_info": [{
'indicator':
'Total Number of Newborns born in given month{}:'.format(
chosen_filters),
'value':
indian_formatted_number(total)
}, {
'indicator':
'Number of Newborns with LBW in given month{}:'.format(
chosen_filters),
'value':
indian_formatted_number(low_birth_total)
}, {
'indicator':
'Total Number of children born and weight in given month{}:'.
format(chosen_filters),
'value':
indian_formatted_number(in_month_total)
}, {
'indicator':
'% newborns with LBW in given month{}:'.format(chosen_filters),
'value':
'%.2f%%' % (low_birth_total * 100 / float(in_month_total or 1))
}, {
'indicator':
'% of children with weight in normal{}:'.format(
chosen_filters),
'value':
'%.2f%%' % ((in_month_total - low_birth_total) * 100 /
float(in_month_total or 1))
}, {
'indicator':
'% Unweighted{}:'.format(chosen_filters),
'value':
'%.2f%%' % ((total - in_month_total) * 100 / float(total or 1))
}]
},
"data": dict(data_for_map),
}
def test_less_then_thousand(self):
number = 900
india_format = indian_formatted_number(number)
self.assertEquals(india_format, "900")
def get_prevalence_of_stunting_data_map(domain,
config,
loc_level,
show_test=False,
icds_feature_flag=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(
moderate=Sum(stunting_moderate_column(icds_feature_flag)),
severe=Sum(stunting_severe_column(icds_feature_flag)),
normal=Sum(stunting_normal_column(icds_feature_flag)),
total=Sum('height_eligible'),
total_measured=Sum('height_measured_in_month'),
).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:
if icds_feature_flag:
queryset = queryset.exclude(age_tranche=72)
else:
queryset = queryset.exclude(age_tranche__in=[0, 6, 72])
return queryset
data_for_map = defaultdict(
lambda: {
'moderate': 0,
'severe': 0,
'normal': 0,
'total': 0,
'total_measured': 0,
'original_name': []
})
moderate_total = 0
severe_total = 0
normal_total = 0
all_total = 0
measured_total = 0
values_to_calculate_average = []
for row in get_data_for(config):
total = row['total'] or 0
name = row['%s_name' % loc_level]
on_map_name = row['%s_map_location_name' % loc_level] or name
severe = row['severe'] or 0
moderate = row['moderate'] or 0
normal = row['normal'] or 0
total_measured = row['total_measured'] or 0
numerator = moderate + severe
values_to_calculate_average.append(numerator * 100 /
(total_measured or 1))
severe_total += severe
moderate_total += moderate
normal_total += normal
all_total += total
measured_total += total_measured
data_for_map[on_map_name]['severe'] += severe
data_for_map[on_map_name]['moderate'] += moderate
data_for_map[on_map_name]['normal'] += normal
data_for_map[on_map_name]['total'] += total
data_for_map[on_map_name]['total_measured'] += total_measured
data_for_map[on_map_name]['original_name'].append(name)
for data_for_location in six.itervalues(data_for_map):
numerator = data_for_location['moderate'] + data_for_location['severe']
value = numerator * 100 / (data_for_location['total_measured'] or 1)
if value < 25:
data_for_location.update({'fillKey': '0%-25%'})
elif 25 <= value < 38:
data_for_location.update({'fillKey': '25%-38%'})
elif value >= 38:
data_for_location.update({'fillKey': '38%-100%'})
fills = OrderedDict()
fills.update({'0%-25%': MapColors.PINK})
fills.update({'25%-38%': MapColors.ORANGE})
fills.update({'38%-100%': MapColors.RED})
fills.update({'defaultFill': MapColors.GREY})
gender_label, age_label, chosen_filters = chosen_filters_to_labels(
config, default_interval=default_age_interval(icds_feature_flag))
return {
"slug":
"severe",
"label":
"Percent of Children{gender} Stunted ({age})".format(
gender=gender_label, age=age_label),
"fills":
fills,
"rightLegend": {
"average":
"%.2f" % ((sum(values_to_calculate_average)) /
float(len(values_to_calculate_average) or 1)),
"info":
_(("Percentage of children ({}) enrolled for Anganwadi Services with height-for-age below "
"-2Z standard deviations of the WHO Child Growth Standards median."
"<br/><br/>"
"Stunting is a sign of chronic undernutrition and has long lasting harmful "
"consequences on the growth of a child".format(age_label))),
"extended_info": [{
'indicator':
'Total Children{} eligible to have height measured:'.format(
chosen_filters),
'value':
indian_formatted_number(all_total)
}, {
'indicator':
'Total Children{} with height measured in given month:'.format(
chosen_filters),
'value':
indian_formatted_number(measured_total)
}, {
'indicator':
'Number of Children{} unmeasured:'.format(chosen_filters),
'value':
indian_formatted_number(all_total - measured_total)
}, {
'indicator':
'% children{} with severely stunted growth:'.format(
chosen_filters),
'value':
'%.2f%%' % (severe_total * 100 / float(measured_total or 1))
}, {
'indicator':
'% children{} with moderate stunted growth:'.format(
chosen_filters),
'value':
'%.2f%%' % (moderate_total * 100 / float(measured_total or 1))
}, {
'indicator':
'% children{} with normal stunted growth:'.format(
chosen_filters),
'value':
'%.2f%%' % (normal_total * 100 / float(measured_total or 1))
}]
},
"data":
dict(data_for_map),
}
def get_immunization_coverage_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(
children=Sum('fully_immunized_on_time') +
Sum('fully_immunized_late'),
all=Sum('fully_immunized_eligible'),
).order_by('%s_name' % loc_level,
'%s_map_location_name' % loc_level)
if not show_test:
queryset = apply_exclude(domain, queryset)
return queryset
if icds_features_flag:
location_launched_status = get_location_launched_status(
config, loc_level)
else:
location_launched_status = None
data_for_map, valid_total, in_month_total, average, total = generate_data_for_map(
get_data_for(config),
loc_level,
'children',
'all',
20,
60,
location_launched_status=location_launched_status)
fills = OrderedDict()
fills.update({'0%-20%': MapColors.RED})
fills.update({'20%-60%': MapColors.ORANGE})
fills.update({'60%-100%': MapColors.PINK})
fills.update({'defaultFill': MapColors.GREY})
gender_ignored, age_ignored, chosen_filters = chosen_filters_to_labels(
config)
return {
"slug": "institutional_deliveries",
"label":
"Percent Immunization Coverage at 1 year{}".format(chosen_filters),
"fills": fills,
"rightLegend": {
"average":
average,
"info":
_(("Of the total number of children enrolled for Anganwadi Services who are over a year old, "
"the percentage of children who have received the complete immunization as per the National "
"Immunization Schedule of India that is required by age 1."
"<br/><br/>"
"This includes the following immunizations:<br/>"
"If Pentavalent path: Penta1/2/3, OPV1/2/3, BCG, Measles, VitA1<br/>"
"If DPT/HepB path: DPT1/2/3, HepB1/2/3, OPV1/2/3, BCG, Measles, VitA1"
)),
"extended_info": [{
'indicator':
'Total number of ICDS Child beneficiaries older than '
'1 year{}:'.format(chosen_filters),
'value':
indian_formatted_number(valid_total)
}, {
'indicator':
('Total number of children who have recieved complete immunizations required '
'by age 1{}:'.format(chosen_filters)),
'value':
indian_formatted_number(in_month_total)
}, {
'indicator':
('% of children who have recieved complete immunizations required by age 1{}:'
.format(chosen_filters)),
'value':
'%.2f%%' % (in_month_total * 100 / float(valid_total or 1))
}]
},
"data": dict(data_for_map),
}
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_newborn_with_low_birth_weight_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(
low_birth=Sum('low_birth_weight_in_month'),
in_month=Sum('born_in_month'),
).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, in_month_total, low_birth_total, average = generate_data_for_map(
get_data_for(config), loc_level, 'low_birth', 'in_month', 20, 60)
fills = OrderedDict()
fills.update({'0%-20%': MapColors.PINK})
fills.update({'20%-60%': MapColors.ORANGE})
fills.update({'60%-100%': MapColors.RED})
fills.update({'defaultFill': MapColors.GREY})
gender_ignored, age_ignored, chosen_filters = chosen_filters_to_labels(
config)
return {
"slug": "low_birth",
"label":
"Percent Newborns with Low Birth Weight{}".format(chosen_filters),
"fills": fills,
"rightLegend": {
"average":
average,
"info":
_(("Percentage of newborns with born with birth weight less than 2500 grams."
"<br/><br/>"
"Newborns with Low Birth Weight are closely associated with foetal and neonatal "
"mortality and morbidity, inhibited growth and cognitive development, and chronic "
"diseases later in life")),
"extended_info": [{
'indicator':
'Total Number of Newborns born in given month{}:'.format(
chosen_filters),
'value':
indian_formatted_number(in_month_total)
}, {
'indicator':
'Number of Newborns with LBW in given month{}:'.format(
chosen_filters),
'value':
indian_formatted_number(low_birth_total)
}, {
'indicator':
'% newborns with LBW in given month{}:'.format(chosen_filters),
'value':
'%.2f%%' % (low_birth_total * 100 / float(in_month_total or 1))
}, {
'indicator':
'% Unweighed{}:'.format(chosen_filters),
'value':
'%.2f%%' % ((in_month_total - low_birth_total) * 100 /
float(in_month_total or 1))
}]
},
"data": dict(data_for_map),
}
def get_adolescent_girls_data_map(domain, config, loc_level, show_test=False):
def get_data_for(filters):
filters['month'] = datetime(*filters['month'])
queryset = AggAwcMonthly.objects.filter(**filters).values(
'%s_name' % loc_level,
'%s_map_location_name' % loc_level).annotate(
valid=Sum('cases_person_adolescent_girls_11_14'),
all=Sum('cases_person_adolescent_girls_11_14_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': 'Adolescent Girls'
})
average = []
total_valid = 0
total = 0
for row in get_data_for(config):
valid = row['valid'] or 0
all_adolescent = 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_adolescent
data_for_map[on_map_name]['valid'] += valid
data_for_map[on_map_name]['all'] += all_adolescent
data_for_map[on_map_name]['original_name'].append(name)
fills = OrderedDict()
fills.update({'Adolescent Girls': MapColors.BLUE})
fills.update({'defaultFill': MapColors.GREY})
return {
"slug": "adolescent_girls",
"label": "",
"fills": fills,
"rightLegend": {
"average":
sum(average) / float(len(average) or 1),
"average_format":
'number',
"info":
_(("Of the total number of adolescent girls (aged 11-14 years), the percentage of girls enrolled "
"for Anganwadi Services")),
"extended_info": [{
'indicator':
('Number of adolescent girls (11 - 14 years) who are enrolled for Anganwadi Services:'
),
'value':
indian_formatted_number(total_valid)
}, {
'indicator':
('Total number of adolescent girls (11 - 14 years) who are registered:'
),
'value':
indian_formatted_number(total)
}, {
'indicator':
('Percentage of registered adolescent girls (11 - 14 years) '
'who are enrolled for Anganwadi Services:'),
'value':
'%.2f%%' % (total_valid * 100 / float(total or 1))
}]
},
"data": dict(data_for_map),
}
def test_hundred_of_thousands(self):
number = 200000
india_format = indian_formatted_number(number)
self.assertEquals(india_format, "2,00,000")
def get_awc_daily_status_data_map(domain, config, loc_level, show_test=False):
date = datetime(*config['month'])
del config['month']
def get_data_for(filters):
queryset = AggAwcDailyView.objects.filter(
**filters
).values(
'%s_name' % loc_level, '%s_map_location_name' % loc_level
).annotate(
in_day=Sum('daily_attendance_open'),
all=Sum('num_launched_awcs'),
).order_by('%s_name' % loc_level, '%s_map_location_name' % loc_level)
if not show_test:
queryset = apply_exclude(domain, queryset)
return queryset
config['date'] = date.date()
data = None
while data is None or (not data and config['date'].day != 1):
data = get_data_for(config)
config['date'] -= relativedelta(days=1)
data_for_map, valid_total, in_day_total, average, total = generate_data_for_map(
data,
loc_level,
'in_day',
'all',
50,
75
)
fills = OrderedDict()
fills.update({'0%-50%': MapColors.RED})
fills.update({'50%-75%': MapColors.ORANGE})
fills.update({'75%-100%': MapColors.PINK})
fills.update({'defaultFill': MapColors.GREY})
return {
"slug": "awc_daily_statuses",
"label": "Percent AWCs Open Yesterday",
"fills": fills,
"rightLegend": {
"average": average,
"info": _((
"Of the total number of AWCs, the percentage of AWCs that were open yesterday."
)),
'period': 'Daily',
"extended_info": [
{
'indicator': 'Total number of AWCs that were open yesterday:',
'value': indian_formatted_number(in_day_total)
},
{
'indicator': 'Total number of AWCs that have been launched:',
'value': indian_formatted_number(valid_total)
},
{
'indicator': '% of AWCs open yesterday:',
'value': '%.2f%%' % (in_day_total * 100 / float(valid_total or 1))
}
]
},
"data": dict(data_for_map),
}
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)
}
