def get_early_initiation_breastfeeding_data(domain,
config,
loc_level,
location_id,
show_test=False,
icds_features_flag=False):
group_by = ['%s_name' % loc_level]
config['month'] = datetime(*config['month'])
data = AggChildHealthMonthly.objects.filter(**config).values(
*group_by).annotate(
birth=Sum('bf_at_birth'),
in_month=Sum('born_in_month'),
).order_by('%s_name' % loc_level)
if not show_test:
data = apply_exclude(domain, data)
chart_data = {
'blue': [],
}
tooltips_data = defaultdict(lambda: {
'in_month': 0,
'birth': 0,
})
location_launched_status = get_location_launched_status(config, loc_level)
for row in data:
if location_launched_status:
launched_status = location_launched_status.get(row['%s_name' %
loc_level])
if launched_status is None or launched_status <= 0:
continue
in_month = row['in_month']
name = row['%s_name' % loc_level]
birth = row['birth']
value = (birth or 0) / float(in_month or 1)
tooltips_data[name]['birth'] += birth
tooltips_data[name]['in_month'] += (in_month or 0)
chart_data['blue'].append([name, value])
chart_data['blue'] = sorted(chart_data['blue'])
return {
"tooltips_data":
dict(tooltips_data),
"info":
early_initiation_breastfeeding_help_text(html=True),
"chart_data": [{
"values": chart_data['blue'],
"key": "",
"strokeWidth": 2,
"classed": "dashed",
"color": MapColors.BLUE
}]
}
def get_enrolled_children_sector_data(domain,
config,
loc_level,
location_id,
show_test=False,
icds_features_flag=False):
group_by = ['%s_name' % loc_level]
config['month'] = datetime(*config['month'])
data = AggChildHealthMonthly.objects.filter(**config).values(
*group_by).annotate(valid=Sum('valid_in_month'),
all=Sum('valid_all_registered_in_month')).order_by(
'%s_name' % loc_level)
if not show_test:
data = apply_exclude(domain, data)
chart_data = {'blue': []}
tooltips_data = defaultdict(lambda: {'valid': 0, 'all': 0})
if icds_features_flag:
location_launched_status = get_location_launched_status(
config, loc_level)
else:
location_launched_status = None
for row in data:
if location_launched_status:
launched_status = location_launched_status.get(row['%s_name' %
loc_level])
if launched_status is None or launched_status <= 0:
continue
valid = row['valid'] or 0
all_children = row['all'] or 0
name = row['%s_name' % loc_level]
row_values = {'valid': valid, 'all': all_children}
for prop, value in row_values.items():
tooltips_data[name][prop] += value
chart_data['blue'].append([name, valid])
chart_data['blue'] = sorted(chart_data['blue'])
return {
"tooltips_data":
dict(tooltips_data),
"format":
"number",
"info":
percent_children_enrolled_help_text(),
"chart_data": [{
"values": chart_data['blue'],
"key": "",
"strokeWidth": 2,
"classed": "dashed",
"color": MapColors.BLUE
}]
}
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_chart(domain, config, loc_level, show_test=False, icds_features_flag=False):
month = datetime(*config['month'])
three_before = datetime(*config['month']) - relativedelta(months=3)
config['month__range'] = (three_before, month)
del config['month']
# using child health monthly while querying for sector level due to performance issues
if icds_features_flag and config['aggregation_level'] >= AggregationLevels.SUPERVISOR:
chm_filter = get_filters_from_config_for_chart_view(config)
chm_queryset = ChildHealthMonthlyView.objects.filter(**chm_filter)
else:
chm_queryset = AggChildHealthMonthly.objects.filter(**config)
chart_data = chm_queryset.values(
'month', '%s_name' % loc_level
).annotate(
birth=Sum('bf_at_birth'),
in_month=Sum('born_in_month'),
).order_by('month')
if not show_test:
chart_data = apply_exclude(domain, chart_data)
data = {
'blue': OrderedDict()
}
dates = [dt for dt in rrule(MONTHLY, dtstart=three_before, until=month)]
for date in dates:
miliseconds = int(date.strftime("%s")) * 1000
data['blue'][miliseconds] = {'y': 0, 'all': 0, 'birth': 0}
best_worst = {}
if icds_features_flag:
if 'month' not in config:
config['month'] = month
location_launched_status = get_location_launched_status(config, loc_level)
else:
location_launched_status = None
for row in chart_data:
if location_launched_status:
launched_status = location_launched_status.get(row['%s_name' % loc_level])
if launched_status is None or launched_status <= 0:
continue
date = row['month']
in_month = row['in_month']
location = row['%s_name' % loc_level]
birth = row['birth']
best_worst[location] = (birth or 0) * 100 / float(in_month or 1)
date_in_miliseconds = int(date.strftime("%s")) * 1000
data_for_month = data['blue'][date_in_miliseconds]
data_for_month['all'] += in_month
data_for_month['birth'] += birth
data_for_month['y'] = data_for_month['birth'] / float(data_for_month['all'] or 1)
all_locations = [
{
'loc_name': key,
'percent': val
}
for key, val in best_worst.items()
]
all_locations_sorted_by_name = sorted(all_locations, key=lambda x: x['loc_name'])
all_locations_sorted_by_percent_and_name = sorted(
all_locations_sorted_by_name, key=lambda x: x['percent'], reverse=True)
return {
"chart_data": [
{
"values": [
{
'x': key,
'y': val['y'],
'all': val['all'],
'birth': val['birth']
} for key, val in data['blue'].items()
],
"key": "% Early Initiation of Breastfeeding",
"strokeWidth": 2,
"classed": "dashed",
"color": ChartColors.BLUE
}
],
"all_locations": all_locations_sorted_by_percent_and_name,
"top_five": all_locations_sorted_by_percent_and_name[:5],
"bottom_five": all_locations_sorted_by_percent_and_name[-5:],
"location_type": loc_level.title() if loc_level != LocationTypes.SUPERVISOR else 'Sector'
}
def get_exclusive_breastfeeding_sector_data(domain,
config,
loc_level,
location_id,
show_test=False,
icds_features_flag=False):
group_by = ['%s_name' % loc_level]
config['month'] = datetime(*config['month'])
data = AggChildHealthMonthly.objects.filter(**config).values(
*group_by).annotate(
in_month=Sum('ebf_in_month'),
eligible=Sum('ebf_eligible'),
).order_by('%s_name' % loc_level)
if not show_test:
data = apply_exclude(domain, data)
chart_data = {
'blue': [],
}
tooltips_data = defaultdict(lambda: {'children': 0, 'all': 0})
if icds_features_flag:
location_launched_status = get_location_launched_status(
config, loc_level)
else:
location_launched_status = None
for row in data:
if location_launched_status:
launched_status = location_launched_status.get(row['%s_name' %
loc_level])
if launched_status is None or launched_status <= 0:
continue
valid = row['eligible']
name = row['%s_name' % loc_level]
in_month = row['in_month']
row_values = {'children': in_month or 0, 'all': valid or 0}
for prop, value in row_values.items():
tooltips_data[name][prop] += value
in_month = row['in_month']
value = (in_month or 0) / float(valid or 1)
chart_data['blue'].append([name, value])
chart_data['blue'] = sorted(chart_data['blue'])
return {
"tooltips_data":
dict(tooltips_data),
"info":
exclusive_breastfeeding_help_text(html=True),
"chart_data": [
{
"values": chart_data['blue'],
"key": "",
"strokeWidth": 2,
"classed": "dashed",
"color": MapColors.BLUE
},
]
}
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_prevalence_of_undernutrition_sector_data(domain,
config,
loc_level,
location_id,
show_test=False,
icds_features_flag=False):
group_by = ['%s_name' % loc_level]
config['month'] = datetime(*config['month'])
data = AggChildHealthMonthly.objects.filter(**config).values(
*group_by).annotate(
moderately_underweight=Sum(
'nutrition_status_moderately_underweight'),
severely_underweight=Sum('nutrition_status_severely_underweight'),
weighed=Sum('nutrition_status_weighed'),
normal=Sum('nutrition_status_normal'),
total=Sum('wer_eligible'),
).order_by('%s_name' % loc_level)
if not show_test:
data = apply_exclude(domain, data)
if 'age_tranche' not in config:
data = data.filter(age_tranche__lt=72)
chart_data = {'blue': []}
tooltips_data = defaultdict(
lambda: {
'severely_underweight': 0,
'moderately_underweight': 0,
'weighed': 0,
'normal': 0,
'total': 0
})
location_launched_status = get_location_launched_status(config, loc_level)
for row in data:
if location_launched_status:
launched_status = location_launched_status.get(row['%s_name' %
loc_level])
if launched_status is None or launched_status <= 0:
continue
weighed = row['weighed']
total = row['total']
name = row['%s_name' % loc_level]
severely_underweight = row['severely_underweight']
moderately_underweight = row['moderately_underweight']
normal = row['normal']
tooltips_data[name]['severely_underweight'] += severely_underweight
tooltips_data[name]['moderately_underweight'] += moderately_underweight
tooltips_data[name]['weighed'] += (weighed or 0)
tooltips_data[name]['normal'] += normal
tooltips_data[name]['total'] += (total or 0)
chart_data['blue'].append([
name, ((moderately_underweight or 0) +
(severely_underweight or 0)) / float(weighed or 1)
])
chart_data['blue'] = sorted(chart_data['blue'],
key=lambda loc_and_value:
(loc_and_value[0] is not None, loc_and_value))
return {
"tooltips_data":
dict(tooltips_data),
"info":
underweight_children_help_text(age_label="0-5 years", html=True),
"chart_data": [{
"values": chart_data['blue'],
"key": "",
"strokeWidth": 2,
"classed": "dashed",
"color": MapColors.BLUE
}]
}
def get_prevalence_of_undernutrition_data_chart(domain,
config,
loc_level,
show_test=False,
icds_features_flag=False):
month = datetime(*config['month'])
three_before = datetime(*config['month']) - relativedelta(months=3)
config['month__range'] = (three_before, month)
del config['month']
if config['aggregation_level'] >= AggregationLevels.SUPERVISOR:
chm_filter = get_filters_from_config_for_chart_view(config)
chm_queryset = ChildHealthMonthlyView.objects.filter(**chm_filter)
else:
chm_queryset = AggChildHealthMonthly.objects.filter(**config)
chart_data = chm_queryset.values('month', '%s_name' % loc_level).annotate(
moderately_underweight=Sum('nutrition_status_moderately_underweight'),
normal=Sum('nutrition_status_normal'),
severely_underweight=Sum('nutrition_status_severely_underweight'),
weighed=Sum('nutrition_status_weighed'),
total=Sum('wer_eligible'),
).order_by('month')
if not show_test:
chart_data = apply_exclude(domain, chart_data)
if 'age_tranche' not in config:
chart_data = chart_data.filter(age_tranche__lt=72)
data = {
'peach': OrderedDict(),
'orange': OrderedDict(),
'red': OrderedDict()
}
dates = [dt for dt in rrule(MONTHLY, dtstart=three_before, until=month)]
for date in dates:
miliseconds = int(date.strftime("%s")) * 1000
data['peach'][miliseconds] = {'y': 0, 'weighed': 0, 'unweighed': 0}
data['orange'][miliseconds] = {'y': 0, 'weighed': 0, 'unweighed': 0}
data['red'][miliseconds] = {'y': 0, 'weighed': 0, 'unweighed': 0}
best_worst = {}
if 'month' not in config:
config['month'] = month
location_launched_status = get_location_launched_status(config, loc_level)
for row in chart_data:
if location_launched_status:
launched_status = location_launched_status.get(row['%s_name' %
loc_level])
if launched_status is None or launched_status <= 0:
continue
date = row['month']
weighed = row['weighed'] or 0
location = row['%s_name' % loc_level]
severely_underweight = row['severely_underweight'] or 0
moderately_underweight = row['moderately_underweight'] or 0
total = row['total'] or 0
normal = row['normal'] or 0
underweight = (moderately_underweight +
severely_underweight) * 100 / float(weighed or 1)
best_worst[location] = underweight
date_in_miliseconds = int(date.strftime("%s")) * 1000
data['peach'][date_in_miliseconds]['y'] += normal
data['peach'][date_in_miliseconds]['unweighed'] += (total - weighed)
data['peach'][date_in_miliseconds]['weighed'] += weighed
data['orange'][date_in_miliseconds]['y'] += moderately_underweight
data['orange'][date_in_miliseconds]['unweighed'] += (total - weighed)
data['orange'][date_in_miliseconds]['weighed'] += weighed
data['red'][date_in_miliseconds]['y'] += severely_underweight
data['red'][date_in_miliseconds]['unweighed'] += (total - weighed)
data['red'][date_in_miliseconds]['weighed'] += weighed
all_locations = [{
'loc_name': key,
'percent': value
} for key, value in best_worst.items()]
all_locations_sorted_by_name = sorted(all_locations,
key=lambda x: x['loc_name'])
all_locations_sorted_by_percent_and_name = sorted(
all_locations_sorted_by_name, key=lambda x: x['percent'])
return {
"chart_data": [{
"values": [{
'x': key,
'y': value['y'] / float(value['weighed'] or 1),
'weighed': value['weighed'],
'unweighed': value['unweighed'],
} for key, value in data['peach'].items()],
"key":
"% Normal",
"strokeWidth":
2,
"classed":
"dashed",
"color":
ChartColors.PINK
}, {
"values": [{
'x': key,
'y': value['y'] / float(value['weighed'] or 1),
'weighed': value['weighed'],
'unweighed': value['unweighed'],
} for key, value in data['orange'].items()],
"key":
"% Moderately Underweight (-2 SD)",
"strokeWidth":
2,
"classed":
"dashed",
"color":
ChartColors.ORANGE
}, {
"values": [{
'x': key,
'y': value['y'] / float(value['weighed'] or 1),
'weighed': value['weighed'],
'unweighed': value['unweighed'],
} for key, value in data['red'].items()],
"key":
"% Severely Underweight (-3 SD) ",
"strokeWidth":
2,
"classed":
"dashed",
"color":
ChartColors.RED
}],
"all_locations":
all_locations_sorted_by_percent_and_name,
"top_five":
all_locations_sorted_by_percent_and_name[:5],
"bottom_five":
all_locations_sorted_by_percent_and_name[-5:],
"location_type":
loc_level.title()
if loc_level != LocationTypes.SUPERVISOR else 'Sector'
}
def get_newborn_with_low_birth_weight_data(domain, config, loc_level, location_id,
show_test=False, icds_features_flag=False):
group_by = ['%s_name' % loc_level]
config['month'] = datetime(*config['month'])
data = AggChildHealthMonthly.objects.filter(
**config
).values(
*group_by
).annotate(
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)
if not show_test:
data = apply_exclude(domain, data)
chart_data = {
'blue': [],
}
tooltips_data = defaultdict(lambda: {
'in_month': 0,
'low_birth': 0,
'all': 0
})
if icds_features_flag:
location_launched_status = get_location_launched_status(config, loc_level)
else:
location_launched_status = None
for row in data:
if location_launched_status:
launched_status = location_launched_status.get(row['%s_name' % loc_level])
if launched_status is None or launched_status <= 0:
continue
in_month = row['in_month'] or 0
name = row['%s_name' % loc_level]
all_records = row['all'] or 0
low_birth = row['low_birth'] or 0
value = low_birth / float(in_month or 1)
tooltips_data[name]['low_birth'] += low_birth
tooltips_data[name]['in_month'] += in_month
tooltips_data[name]['all'] += all_records
chart_data['blue'].append([
name, value
])
chart_data['blue'] = sorted(chart_data['blue'])
return {
"tooltips_data": dict(tooltips_data),
"info": _((
new_born_with_low_weight_help_text(html=True)
)),
"chart_data": [
{
"values": chart_data['blue'],
"key": "",
"strokeWidth": 2,
"classed": "dashed",
"color": MapColors.BLUE
},
]
}
def get_newborn_with_low_birth_weight_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(
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
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, low_birth_total, average, total = generate_data_for_map(
get_data_for(config),
loc_level,
'low_birth',
'in_month',
20,
60,
'all',
location_launched_status=location_launched_status
)
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_severe_sector_data(domain,
config,
loc_level,
location_id,
show_test=False,
icds_feature_flag=False):
group_by = ['%s_name' % loc_level]
config['month'] = datetime(*config['month'])
data = AggChildHealthMonthly.objects.filter(**config).values(
*group_by).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)
if not show_test:
data = apply_exclude(domain, data)
if 'age_tranche' not in config:
data = data.filter(age_tranche__lt=72)
chart_data = {
'blue': [],
}
tooltips_data = defaultdict(
lambda: {
'severe': 0,
'moderate': 0,
'total_height_eligible': 0,
'normal': 0,
'total_weighed': 0,
'total_measured': 0
})
if icds_feature_flag:
location_launched_status = get_location_launched_status(
config, loc_level)
else:
location_launched_status = None
for row in data:
if location_launched_status:
launched_status = location_launched_status.get(row['%s_name' %
loc_level])
if launched_status is None or launched_status <= 0:
continue
total_weighed = row['total_weighed'] or 0
name = row['%s_name' % loc_level]
severe = row['severe'] or 0
moderate = row['moderate'] or 0
normal = row['normal'] or 0
total_measured = row['total_measured'] or 0
total_height_eligible = row['total_height_eligible'] or 0
tooltips_data[name]['severe'] += severe
tooltips_data[name]['moderate'] += moderate
tooltips_data[name]['total_weighed'] += total_weighed
tooltips_data[name]['normal'] += normal
tooltips_data[name]['total_measured'] += total_measured
tooltips_data[name]['total_height_eligible'] += total_height_eligible
value = (moderate + severe) / float(total_weighed or 1)
chart_data['blue'].append([name, value])
chart_data['blue'] = sorted(chart_data['blue'])
gender_label, age_label, chosen_filters = chosen_filters_to_labels(
config, default_interval=default_age_interval(icds_feature_flag))
return {
"tooltips_data":
dict(tooltips_data),
"info":
_(wasting_help_text(age_label)),
"chart_data": [
{
"values": chart_data['blue'],
"key": "",
"strokeWidth": 2,
"classed": "dashed",
"color": MapColors.BLUE
},
]
}
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_women_data_chart(domain,
config,
loc_level,
show_test=False,
icds_features_flag=False):
month = datetime(*config['month'])
three_before = datetime(*config['month']) - relativedelta(months=3)
config['month__range'] = (three_before, month)
del config['month']
chart_data = AggCcsRecordMonthly.objects.filter(**config).values(
'month', '%s_name' % loc_level).annotate(
valid=Sum('pregnant'),
all=Sum('pregnant_all'),
).order_by('month')
if not show_test:
chart_data = apply_exclude(domain, chart_data)
data = {'blue': OrderedDict()}
dates = [dt for dt in rrule(MONTHLY, dtstart=three_before, until=month)]
for date in dates:
miliseconds = int(date.strftime("%s")) * 1000
data['blue'][miliseconds] = {'y': 0, 'all': 0}
best_worst = {}
if 'month' not in config:
config['month'] = month
location_launched_status = get_location_launched_status(config, loc_level)
for row in chart_data:
if location_launched_status:
launched_status = location_launched_status.get(row['%s_name' %
loc_level])
if launched_status is None or launched_status <= 0:
continue
date = row['month']
valid = row['valid'] or 0
all_pregnant = row['all'] or 0
location = row['%s_name' % loc_level]
if date.month == month.month:
if location in best_worst:
best_worst[location].append(valid)
else:
best_worst[location] = [valid]
date_in_miliseconds = int(date.strftime("%s")) * 1000
data['blue'][date_in_miliseconds]['y'] += valid
data['blue'][date_in_miliseconds]['all'] += all_pregnant
all_locations = [{
'loc_name': key,
'value': sum(value) / len(value)
} for key, value in best_worst.items()]
all_locations_sorted_by_name = sorted(all_locations,
key=lambda x: x['loc_name'])
all_locations_sorted_by_value_and_name = sorted(
all_locations_sorted_by_name, key=lambda x: x['value'], reverse=True)
return {
"chart_data": [{
"values": [{
'x': key,
'y': value['y'],
'all': value['all']
} for key, value in data['blue'].items()],
"key":
"Total number of pregnant women who are enrolled for Anganwadi Services",
"strokeWidth":
2,
"classed":
"dashed",
"color":
ChartColors.BLUE
}],
"all_locations":
all_locations_sorted_by_value_and_name,
"top_five":
all_locations_sorted_by_value_and_name[:5],
"bottom_five":
all_locations_sorted_by_value_and_name[-5:],
"location_type":
loc_level.title()
if loc_level != LocationTypes.SUPERVISOR else 'Sector'
}
def get_immunization_coverage_data_chart(domain,
config,
loc_level,
show_test=False,
icds_features_flag=False):
month = datetime(*config['month'])
three_before = datetime(*config['month']) - relativedelta(months=3)
config['month__range'] = (three_before, month)
# Retrieving children of age 1-2 years
config['age_tranche__lte'] = '24'
del config['month']
# using child health monthly while querying for sector level due to performance issues
if config['aggregation_level'] >= AggregationLevels.SUPERVISOR:
chm_filter = get_filters_from_config_for_chart_view(config)
chm_queryset = ChildHealthMonthlyView.objects.filter(**chm_filter)
else:
chm_queryset = AggChildHealthMonthly.objects.filter(**config)
chart_data = chm_queryset.values('month', '%s_name' % loc_level).annotate(
in_month=Sum('fully_immunized_on_time') + Sum('fully_immunized_late'),
eligible=Sum('fully_immunized_eligible'),
).order_by('month')
if not show_test:
chart_data = apply_exclude(domain, chart_data)
data = {
'blue': OrderedDict(),
}
dates = [dt for dt in rrule(MONTHLY, dtstart=three_before, until=month)]
for date in dates:
miliseconds = int(date.strftime("%s")) * 1000
data['blue'][miliseconds] = {'y': 0, 'all': 0, 'in_month': 0}
best_worst = defaultdict(lambda: {'in_month': 0, 'all': 0})
if 'month' not in config:
config['month'] = month
location_launched_status = get_location_launched_status(config, loc_level)
for row in chart_data:
if location_launched_status:
launched_status = location_launched_status.get(row['%s_name' %
loc_level])
if launched_status is None or launched_status <= 0:
continue
date = row['month']
in_month = row['in_month']
location = row['%s_name' % loc_level]
valid = row['eligible']
best_worst[location]['in_month'] = in_month
best_worst[location]['all'] = (valid or 0)
date_in_miliseconds = int(date.strftime("%s")) * 1000
data_for_month = data['blue'][date_in_miliseconds]
data_for_month['all'] += valid
data_for_month['in_month'] += in_month
data_for_month['y'] = data_for_month['in_month'] / float(
data_for_month['all'] or 1)
all_locations = [{
'loc_name':
key,
'percent': (value['in_month'] * 100) / float(value['all'] or 1)
} for key, value in best_worst.items()]
all_locations_sorted_by_name = sorted(all_locations,
key=lambda x: x['loc_name'])
all_locations_sorted_by_percent_and_name = sorted(
all_locations_sorted_by_name, key=lambda x: x['percent'], reverse=True)
return {
"chart_data": [{
"values": [{
'x': key,
'y': value['y'],
'all': value['all'],
'in_month': value['in_month']
} for key, value in data['blue'].items()],
"key":
"% Children between 1-2 years old who received complete immunizations by 1 year",
"strokeWidth":
2,
"classed":
"dashed",
"color":
ChartColors.BLUE
}],
"all_locations":
all_locations_sorted_by_percent_and_name,
"top_five":
all_locations_sorted_by_percent_and_name[:5],
"bottom_five":
all_locations_sorted_by_percent_and_name[-5:],
"location_type":
loc_level.title()
if loc_level != LocationTypes.SUPERVISOR else 'Sector'
}
def get_immunization_coverage_sector_data(domain,
config,
loc_level,
location_id,
show_test=False,
icds_features_flag=False):
group_by = ['%s_name' % loc_level]
config['month'] = datetime(*config['month'])
data = AggChildHealthMonthly.objects.filter(**config).values(
*group_by).annotate(
in_month=Sum('fully_immunized_on_time') +
Sum('fully_immunized_late'),
eligible=Sum('fully_immunized_eligible'),
).order_by('%s_name' % loc_level)
if not show_test:
data = apply_exclude(domain, data)
chart_data = {
'blue': [],
}
tooltips_data = defaultdict(lambda: {'children': 0, 'all': 0})
if icds_features_flag:
location_launched_status = get_location_launched_status(
config, loc_level)
else:
location_launched_status = None
for row in data:
if location_launched_status:
launched_status = location_launched_status.get(row['%s_name' %
loc_level])
if launched_status is None or launched_status <= 0:
continue
valid = row['eligible']
name = row['%s_name' % loc_level]
in_month = row['in_month']
row_values = {'children': in_month or 0, 'all': valid or 0}
for prop, value in row_values.items():
tooltips_data[name][prop] += value
value = (in_month or 0) / float(valid or 1)
chart_data['blue'].append([name, value])
chart_data['blue'] = sorted(chart_data['blue'])
return {
"tooltips_data":
dict(tooltips_data),
"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"
)),
"chart_data": [{
"values": chart_data['blue'],
"key": "",
"strokeWidth": 2,
"classed": "dashed",
"color": MapColors.BLUE
}]
}
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_prevalence_of_stunting_data_chart(domain,
config,
loc_level,
show_test=False,
icds_feature_flag=False):
month = datetime(*config['month'])
three_before = datetime(*config['month']) - relativedelta(months=3)
config['month__range'] = (three_before, month)
del config['month']
chart_data = AggChildHealthMonthly.objects.filter(**config).values(
'month', '%s_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'),
measured=Sum(hfa_recorded_in_month_column(icds_feature_flag)),
).order_by('month')
if not show_test:
chart_data = apply_exclude(domain, chart_data)
if 'age_tranche' not in config:
chart_data = chart_data.filter(age_tranche__lt=72)
data = {
'red': OrderedDict(),
'orange': OrderedDict(),
'peach': OrderedDict()
}
dates = [dt for dt in rrule(MONTHLY, dtstart=three_before, until=month)]
for date in dates:
miliseconds = int(date.strftime("%s")) * 1000
data['red'][miliseconds] = {'y': 0, 'all': 0, 'measured': 0}
data['orange'][miliseconds] = {'y': 0, 'all': 0, 'measured': 0}
data['peach'][miliseconds] = {'y': 0, 'all': 0, 'measured': 0}
best_worst = {}
if icds_feature_flag:
if 'month' not in config:
config['month'] = month
location_launched_status = get_location_launched_status(
config, loc_level)
else:
location_launched_status = None
for row in chart_data:
if location_launched_status:
launched_status = location_launched_status.get(row['%s_name' %
loc_level])
if launched_status is None or launched_status <= 0:
continue
date = row['month']
total = row['total'] or 0
measured = row['measured'] or 0
location = row['%s_name' % loc_level]
severe = row['severe'] or 0
moderate = row['moderate'] or 0
normal = row['normal'] or 0
underweight = moderate + severe
best_worst[location] = underweight * 100 / float(measured or 1)
date_in_miliseconds = int(date.strftime("%s")) * 1000
data['peach'][date_in_miliseconds]['y'] += normal
data['peach'][date_in_miliseconds]['measured'] += measured
data['peach'][date_in_miliseconds]['all'] += total
data['orange'][date_in_miliseconds]['y'] += moderate
data['orange'][date_in_miliseconds]['measured'] += measured
data['orange'][date_in_miliseconds]['all'] += total
data['red'][date_in_miliseconds]['y'] += severe
data['red'][date_in_miliseconds]['measured'] += measured
data['red'][date_in_miliseconds]['all'] += total
top_locations = sorted([
dict(loc_name=key, percent=value) for key, value in best_worst.items()
],
key=lambda x: (x['percent'], x['loc_name']))
return {
"chart_data": [{
"values": [{
'x': key,
'y': value['y'] / float(value['measured'] or 1),
'all': value['all'],
'measured': value['measured']
} for key, value in data['peach'].items()],
"key":
"% normal",
"strokeWidth":
2,
"classed":
"dashed",
"color":
ChartColors.PINK
}, {
"values": [{
'x': key,
'y': value['y'] / float(value['measured'] or 1),
'all': value['all'],
'measured': value['measured']
} for key, value in data['orange'].items()],
"key":
"% moderately stunted",
"strokeWidth":
2,
"classed":
"dashed",
"color":
ChartColors.ORANGE
}, {
"values": [{
'x': key,
'y': value['y'] / float(value['measured'] or 1),
'all': value['all'],
'measured': value['measured']
} for key, value in data['red'].items()],
"key":
"% severely stunted",
"strokeWidth":
2,
"classed":
"dashed",
"color":
ChartColors.RED
}],
"all_locations":
top_locations,
"top_five":
top_locations[:5],
"bottom_five":
top_locations[-5:],
"location_type":
loc_level.title()
if loc_level != LocationTypes.SUPERVISOR else 'Sector'
}
def get_prevalence_of_undernutrition_data_map(domain,
config,
loc_level,
show_test=False,
icds_features_flag=False):
config['month'] = datetime(*config['month'])
def get_data_for(filters):
queryset = AggChildHealthMonthly.objects.filter(**filters).values(
'%s_name' % loc_level,
'%s_map_location_name' % loc_level).annotate(
moderately_underweight=Sum(
'nutrition_status_moderately_underweight'),
severely_underweight=Sum(
'nutrition_status_severely_underweight'),
normal=Sum('nutrition_status_normal'),
weighed=Sum('nutrition_status_weighed'),
total=Sum('wer_eligible'),
).order_by('%s_name' % loc_level,
'%s_map_location_name' % loc_level)
if not show_test:
queryset = apply_exclude(domain, queryset)
if 'age_tranche' not in config:
queryset = queryset.filter(age_tranche__lt=72)
return queryset
data_for_map = defaultdict(
lambda: {
'moderately_underweight': 0,
'severely_underweight': 0,
'normal': 0,
'weighed': 0,
'total': 0,
'original_name': []
})
moderately_underweight_total = 0
severely_underweight_total = 0
normal_total = 0
all_total = 0
weighed_total = 0
values_to_calculate_average = {'numerator': 0, 'denominator': 0}
location_launched_status = get_location_launched_status(config, loc_level)
for row in get_data_for(config):
if location_launched_status:
launched_status = location_launched_status.get(row['%s_name' %
loc_level])
if launched_status is None or launched_status <= 0:
continue
weighed = row['weighed'] or 0
total = row['total'] or 0
name = row['%s_name' % loc_level]
on_map_name = row['%s_map_location_name' % loc_level] or name
severely_underweight = row['severely_underweight'] or 0
moderately_underweight = row['moderately_underweight'] or 0
normal = row['normal'] or 0
values_to_calculate_average[
'numerator'] += moderately_underweight if moderately_underweight else 0
values_to_calculate_average[
'numerator'] += severely_underweight if severely_underweight else 0
values_to_calculate_average['denominator'] += weighed if weighed else 0
moderately_underweight_total += moderately_underweight
severely_underweight_total += severely_underweight
normal_total += normal
all_total += total
weighed_total += weighed
data_for_map[on_map_name][
'severely_underweight'] += severely_underweight
data_for_map[on_map_name][
'moderately_underweight'] += moderately_underweight
data_for_map[on_map_name]['normal'] += normal
data_for_map[on_map_name]['total'] += total
data_for_map[on_map_name]['weighed'] += weighed
data_for_map[on_map_name]['original_name'].append(name)
for data_for_location in data_for_map.values():
numerator = data_for_location[
'moderately_underweight'] + data_for_location[
'severely_underweight']
value = numerator * 100 / (data_for_location['weighed'] or 1)
if value < 20:
data_for_location.update({'fillKey': '0%-20%'})
elif 20 <= value < 35:
data_for_location.update({'fillKey': '20%-35%'})
elif value >= 35:
data_for_location.update({'fillKey': '35%-100%'})
fills = OrderedDict()
fills.update({'0%-20%': MapColors.PINK})
fills.update({'20%-35%': MapColors.ORANGE})
fills.update({'35%-100%': MapColors.RED})
fills.update({'Not Launched': MapColors.GREY})
fills.update({'defaultFill': MapColors.GREY})
average = ((values_to_calculate_average['numerator'] * 100) /
float(values_to_calculate_average['denominator'] or 1))
gender_label, age_label, chosen_filters = chosen_filters_to_labels(
config, default_interval='0 - 5 years')
return {
"slug":
"moderately_underweight",
"label":
"Percent of Children{gender} Underweight ({age})".format(
gender=gender_label, age=age_label),
"fills":
fills,
"rightLegend": {
"average":
format_decimal(average),
"info":
underweight_children_help_text(age_label=age_label, html=True),
"extended_info": [{
'indicator':
'Total Children{} weighed in given month:'.format(
chosen_filters),
'value':
indian_formatted_number(weighed_total)
}, {
'indicator':
'Number of children unweighed{}:'.format(chosen_filters),
'value':
indian_formatted_number(all_total - weighed_total)
}, {
'indicator':
'% Severely Underweight{}:'.format(chosen_filters),
'value':
'%.2f%%' %
(severely_underweight_total * 100 / float(weighed_total or 1))
}, {
'indicator':
'% Moderately Underweight{}:'.format(chosen_filters),
'value':
'%.2f%%' % (moderately_underweight_total * 100 /
float(weighed_total or 1))
}, {
'indicator':
'% Normal{}:'.format(chosen_filters),
'value':
'%.2f%%' % (normal_total * 100 / float(weighed_total or 1))
}]
},
"data":
dict(data_for_map)
}
def get_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 get_enrolled_children_data_chart(domain,
config,
loc_level,
show_test=False,
icds_features_flag=False):
config['month'] = datetime(*config['month'])
chart_data = AggChildHealthMonthly.objects.filter(**config).values(
'month', 'age_tranche', '%s_name' % loc_level).annotate(
valid=Sum('valid_in_month'), ).order_by('month')
if not show_test:
chart_data = apply_exclude(domain, chart_data)
chart = OrderedDict()
chart.update({'0-1 month': 0})
chart.update({'1-6 months': 0})
chart.update({'6-12 months': 0})
chart.update({'1-3 years': 0})
chart.update({'3-6 years': 0})
all = 0
best_worst = {}
location_launched_status = get_location_launched_status(config, loc_level)
for row in chart_data:
launched_status = location_launched_status.get(row['%s_name' %
loc_level])
if launched_status is None or launched_status <= 0:
continue
location = row['%s_name' % loc_level]
if not row['age_tranche']:
continue
age = int(row['age_tranche'])
valid = row['valid']
all += valid
chart[match_age(age)] += valid
if location in best_worst:
best_worst[location] += valid
else:
best_worst[location] = valid
return {
"chart_data": [{
"values": [{
'x': key,
'y': value,
'all': all
} for key, value in chart.items()],
"key":
"Children (0-6 years) who are enrolled",
"strokeWidth":
2,
"classed":
"dashed",
"color":
ChartColors.BLUE
}],
"location_type":
loc_level.title()
if loc_level != LocationTypes.SUPERVISOR else 'Sector'
}
def get_prevalence_of_stunting_sector_data(domain,
config,
loc_level,
location_id,
show_test=False,
icds_feature_flag=False):
group_by = ['%s_name' % loc_level]
config['month'] = datetime(*config['month'])
data = AggChildHealthMonthly.objects.filter(**config).values(
*group_by).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)
if not show_test:
data = apply_exclude(domain, data)
if 'age_tranche' not in config:
data = data.filter(age_tranche__lt=72)
chart_data = {
'blue': [],
}
tooltips_data = defaultdict(lambda: {
'severe': 0,
'moderate': 0,
'total': 0,
'normal': 0,
'total_measured': 0
})
if icds_feature_flag:
location_launched_status = get_location_launched_status(
config, loc_level)
else:
location_launched_status = None
for row in data:
if location_launched_status:
launched_status = location_launched_status.get(row['%s_name' %
loc_level])
if launched_status is None or launched_status <= 0:
continue
total = row['total'] or 0
name = row['%s_name' % loc_level]
severe = row['severe'] or 0
moderate = row['moderate'] or 0
normal = row['normal'] or 0
total_measured = row['total_measured'] or 0
row_values = {
'severe': severe,
'moderate': moderate,
'total': total,
'normal': normal,
'total_measured': total_measured,
}
for prop, value in row_values.items():
tooltips_data[name][prop] += value
value = (moderate + severe) / float(total_measured or 1)
chart_data['blue'].append([name, value])
chart_data['blue'] = sorted(chart_data['blue'])
__, __, chosen_filters = chosen_filters_to_labels(
config, default_interval=default_age_interval(icds_feature_flag))
return {
"tooltips_data":
dict(tooltips_data),
"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(chosen_filters))),
"chart_data": [
{
"values": chart_data['blue'],
"key": "",
"strokeWidth": 2,
"classed": "dashed",
"color": MapColors.BLUE
},
]
}
def get_exclusive_breastfeeding_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('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
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})
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": "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_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_exclusive_breastfeeding_data_chart(domain,
config,
loc_level,
show_test=False,
icds_features_flag=None):
month = datetime(*config['month'])
three_before = datetime(*config['month']) - relativedelta(months=3)
config['month__range'] = (three_before, month)
del config['month']
chart_data = AggChildHealthMonthly.objects.filter(**config).values(
'month', '%s_name' % loc_level).annotate(
in_month=Sum('ebf_in_month'),
eligible=Sum('ebf_eligible'),
).order_by('month')
if not show_test:
chart_data = apply_exclude(domain, chart_data)
data = {
'blue': OrderedDict(),
}
dates = [dt for dt in rrule(MONTHLY, dtstart=three_before, until=month)]
for date in dates:
miliseconds = int(date.strftime("%s")) * 1000
data['blue'][miliseconds] = {'y': 0, 'all': 0, 'in_month': 0}
best_worst = {}
if icds_features_flag:
if 'month' not in config:
config['month'] = month
location_launched_status = get_location_launched_status(
config, loc_level)
else:
location_launched_status = None
for row in chart_data:
if location_launched_status:
launched_status = location_launched_status.get(row['%s_name' %
loc_level])
if launched_status is None or launched_status <= 0:
continue
date = row['month']
in_month = row['in_month']
location = row['%s_name' % loc_level]
eligible = row['eligible']
best_worst[location] = in_month * 100 / float(eligible or 1)
date_in_miliseconds = int(date.strftime("%s")) * 1000
data_for_month = data['blue'][date_in_miliseconds]
data_for_month['in_month'] += in_month
data_for_month['all'] += eligible
data_for_month['y'] = data_for_month['in_month'] / float(
data_for_month['all'] or 1)
all_locations = [{
'loc_name': key,
'percent': value
} for key, value in best_worst.items()]
all_locations_sorted_by_name = sorted(all_locations,
key=lambda x: x['loc_name'])
all_locations_sorted_by_percent_and_name = sorted(
all_locations_sorted_by_name, key=lambda x: x['percent'], reverse=True)
return {
"chart_data": [{
"values": [{
'x': key,
'y': value['y'],
'all': value['all'],
'in_month': value['in_month']
} for key, value in data['blue'].items()],
"key":
"% children exclusively breastfed",
"strokeWidth":
2,
"classed":
"dashed",
"color":
ChartColors.BLUE
}],
"all_locations":
all_locations_sorted_by_percent_and_name,
"top_five":
all_locations_sorted_by_percent_and_name[:5],
"bottom_five":
all_locations_sorted_by_percent_and_name[-5:],
"location_type":
loc_level.title()
if loc_level != LocationTypes.SUPERVISOR else 'Sector'
}
def get_lactating_enrolled_women_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(
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
if icds_features_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
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),
}
