def test_calculate_average(self):
data_mock = [{
'all': 9,
'children': 9,
'state_name': 'State 1',
'state_map_location_name': 'test1'
}, {
'all': 0,
'children': 0,
'state_name': 'State 2',
'state_map_location_name': 'test2'
}, {
'all': 0,
'children': 0,
'state_name': 'State 3',
'state_map_location_name': 'test3'
}, {
'all': 0,
'children': 0,
'state_name': 'State 4',
'state_map_location_name': 'test4'
}, {
'all': 6,
'children': 0,
'state_name': 'State 5',
'state_map_location_name': 'test5'
}]
data_for_map, valid_total, in_month_total, average, total = generate_data_for_map(
data_mock, 'state', 'children', 'all', 20, 60)
self.assertEquals(average, 60.0)
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_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_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_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 test_calculate_average_if_divide_by_zero(self):
data_mock = [{
'all': 0,
'children': 0,
'state_name': 'State 1',
'state_map_location_name': 'test1'
}]
data_for_map, valid_total, in_month_total, average, total = generate_data_for_map(
data_mock, 'state', 'children', 'all', 20, 60)
self.assertEquals(average, 0.0)
def test_calculate_average_if_data_are_none(self):
data_mock = [{
'all': None,
'children': None,
'state_name': None,
'state_map_location_name': None
}]
data_for_map, valid_total, in_month_total, average, total = generate_data_for_map(
data_mock, 'state', 'children', 'all', 20, 60)
self.assertEquals(average, 0.0)
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 test_calculate_average_if_divide_by_zero(self):
data_mock = [{'all': 0, 'children': 0, 'state_name': 'State 1', 'state_map_location_name': 'test1'}]
data_for_map, valid_total, in_month_total, average, total = generate_data_for_map(
data_mock,
'state',
'children',
'all',
20,
60
)
self.assertEquals(average, 0.0)
def test_calculate_average_if_data_are_none(self):
data_mock = [{'all': None, 'children': None, 'state_name': None, 'state_map_location_name': None}]
data_for_map, valid_total, in_month_total, average, total = generate_data_for_map(
data_mock,
'state',
'children',
'all',
20,
60
)
self.assertEquals(average, 0.0)
def test_calculate_average(self):
data_mock = [{'all': 9, 'children': 9, 'state_name': 'State 1', 'state_map_location_name': 'test1'},
{'all': 0, 'children': 0, 'state_name': 'State 2', 'state_map_location_name': 'test2'},
{'all': 0, 'children': 0, 'state_name': 'State 3', 'state_map_location_name': 'test3'},
{'all': 0, 'children': 0, 'state_name': 'State 4', 'state_map_location_name': 'test4'},
{'all': 6, 'children': 0, 'state_name': 'State 5', 'state_map_location_name': 'test5'}]
data_for_map, valid_total, in_month_total, average, total = generate_data_for_map(
data_mock,
'state',
'children',
'all',
20,
60
)
self.assertEquals(average, 60.0)
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_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_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": _((
"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_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_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_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 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_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_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 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_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_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_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":
_(("Of the total children enrolled for Anganwadi services between the ages of 6 to 8 months, "
"the percentage that was given a timely introduction to solid, semi-solid or soft food."
"<br/><br/>"
"Timely intiation of complementary feeding in addition to breastmilk at 6 months of age "
"is a key feeding practice to reduce malnutrition")),
"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),
}
