def get_prevalence_of_undernutrition_data_map(domain,
config,
loc_level,
show_test=False,
icds_features_flag=False):
config['month'] = datetime(*config['month'])
def get_data_for(filters):
queryset = AggChildHealthMonthly.objects.filter(**filters).values(
'%s_name' % loc_level,
'%s_map_location_name' % loc_level).annotate(
moderately_underweight=Sum(
'nutrition_status_moderately_underweight'),
severely_underweight=Sum(
'nutrition_status_severely_underweight'),
normal=Sum('nutrition_status_normal'),
weighed=Sum('nutrition_status_weighed'),
total=Sum('wer_eligible'),
).order_by('%s_name' % loc_level,
'%s_map_location_name' % loc_level)
if not show_test:
queryset = apply_exclude(domain, queryset)
if 'age_tranche' not in config:
queryset = queryset.filter(age_tranche__lt=72)
return queryset
data_for_map = defaultdict(
lambda: {
'moderately_underweight': 0,
'severely_underweight': 0,
'normal': 0,
'weighed': 0,
'total': 0,
'original_name': []
})
moderately_underweight_total = 0
severely_underweight_total = 0
normal_total = 0
all_total = 0
weighed_total = 0
values_to_calculate_average = {'numerator': 0, 'denominator': 0}
location_launched_status = get_location_launched_status(config, loc_level)
for row in get_data_for(config):
if location_launched_status:
launched_status = location_launched_status.get(row['%s_name' %
loc_level])
if launched_status is None or launched_status <= 0:
continue
weighed = row['weighed'] or 0
total = row['total'] or 0
name = row['%s_name' % loc_level]
on_map_name = row['%s_map_location_name' % loc_level] or name
severely_underweight = row['severely_underweight'] or 0
moderately_underweight = row['moderately_underweight'] or 0
normal = row['normal'] or 0
values_to_calculate_average[
'numerator'] += moderately_underweight if moderately_underweight else 0
values_to_calculate_average[
'numerator'] += severely_underweight if severely_underweight else 0
values_to_calculate_average['denominator'] += weighed if weighed else 0
moderately_underweight_total += moderately_underweight
severely_underweight_total += severely_underweight
normal_total += normal
all_total += total
weighed_total += weighed
data_for_map[on_map_name][
'severely_underweight'] += severely_underweight
data_for_map[on_map_name][
'moderately_underweight'] += moderately_underweight
data_for_map[on_map_name]['normal'] += normal
data_for_map[on_map_name]['total'] += total
data_for_map[on_map_name]['weighed'] += weighed
data_for_map[on_map_name]['original_name'].append(name)
for data_for_location in data_for_map.values():
numerator = data_for_location[
'moderately_underweight'] + data_for_location[
'severely_underweight']
value = numerator * 100 / (data_for_location['weighed'] or 1)
if value < 20:
data_for_location.update({'fillKey': '0%-20%'})
elif 20 <= value < 35:
data_for_location.update({'fillKey': '20%-35%'})
elif value >= 35:
data_for_location.update({'fillKey': '35%-100%'})
fills = OrderedDict()
fills.update({'0%-20%': MapColors.PINK})
fills.update({'20%-35%': MapColors.ORANGE})
fills.update({'35%-100%': MapColors.RED})
fills.update({'Not Launched': MapColors.GREY})
fills.update({'defaultFill': MapColors.GREY})
average = ((values_to_calculate_average['numerator'] * 100) /
float(values_to_calculate_average['denominator'] or 1))
gender_label, age_label, chosen_filters = chosen_filters_to_labels(
config, default_interval='0 - 5 years')
return {
"slug":
"moderately_underweight",
"label":
"Percent of Children{gender} Underweight ({age})".format(
gender=gender_label, age=age_label),
"fills":
fills,
"rightLegend": {
"average":
format_decimal(average),
"info":
underweight_children_help_text(age_label=age_label, html=True),
"extended_info": [{
'indicator':
'Total Children{} weighed in given month:'.format(
chosen_filters),
'value':
indian_formatted_number(weighed_total)
}, {
'indicator':
'Number of children unweighed{}:'.format(chosen_filters),
'value':
indian_formatted_number(all_total - weighed_total)
}, {
'indicator':
'% Severely Underweight{}:'.format(chosen_filters),
'value':
'%.2f%%' %
(severely_underweight_total * 100 / float(weighed_total or 1))
}, {
'indicator':
'% Moderately Underweight{}:'.format(chosen_filters),
'value':
'%.2f%%' % (moderately_underweight_total * 100 /
float(weighed_total or 1))
}, {
'indicator':
'% Normal{}:'.format(chosen_filters),
'value':
'%.2f%%' % (normal_total * 100 / float(weighed_total or 1))
}]
},
"data":
dict(data_for_map)
}
def test_fucntion(x):
return format_decimal(x) if x else DATA_NOT_ENTERED
def test_fucntion(x):
return format_decimal(x) if x else DATA_NOT_ENTERED
def get_prevalence_of_undernutrition_data_map(domain, config, loc_level, show_test=False):
def get_data_for(filters):
filters['month'] = datetime(*filters['month'])
queryset = AggChildHealthMonthly.objects.filter(
**filters
).values(
'%s_name' % loc_level, '%s_map_location_name' % loc_level
).annotate(
moderately_underweight=Sum('nutrition_status_moderately_underweight'),
severely_underweight=Sum('nutrition_status_severely_underweight'),
normal=Sum('nutrition_status_normal'),
weighed=Sum('nutrition_status_weighed'),
total=Sum('wer_eligible'),
).order_by('%s_name' % loc_level, '%s_map_location_name' % loc_level)
if not show_test:
queryset = apply_exclude(domain, queryset)
if 'age_tranche' not in config:
queryset = queryset.exclude(age_tranche=72)
return queryset
data_for_map = defaultdict(lambda: {
'moderately_underweight': 0,
'severely_underweight': 0,
'normal': 0,
'weighed': 0,
'total': 0,
'original_name': []
})
moderately_underweight_total = 0
severely_underweight_total = 0
normal_total = 0
all_total = 0
weighed_total = 0
values_to_calculate_average = {'numerator': 0, 'denominator': 0}
for row in get_data_for(config):
weighed = row['weighed'] or 0
total = row['total'] or 0
name = row['%s_name' % loc_level]
on_map_name = row['%s_map_location_name' % loc_level] or name
severely_underweight = row['severely_underweight'] or 0
moderately_underweight = row['moderately_underweight'] or 0
normal = row['normal'] or 0
values_to_calculate_average['numerator'] += moderately_underweight if moderately_underweight else 0
values_to_calculate_average['numerator'] += severely_underweight if severely_underweight else 0
values_to_calculate_average['denominator'] += weighed if weighed else 0
moderately_underweight_total += moderately_underweight
severely_underweight_total += severely_underweight
normal_total += normal
all_total += total
weighed_total += weighed
data_for_map[on_map_name]['severely_underweight'] += severely_underweight
data_for_map[on_map_name]['moderately_underweight'] += moderately_underweight
data_for_map[on_map_name]['normal'] += normal
data_for_map[on_map_name]['total'] += total
data_for_map[on_map_name]['weighed'] += weighed
data_for_map[on_map_name]['original_name'].append(name)
for data_for_location in six.itervalues(data_for_map):
numerator = data_for_location['moderately_underweight'] + data_for_location['severely_underweight']
value = numerator * 100 / (data_for_location['weighed'] or 1)
if value < 20:
data_for_location.update({'fillKey': '0%-20%'})
elif 20 <= value < 35:
data_for_location.update({'fillKey': '20%-35%'})
elif value >= 35:
data_for_location.update({'fillKey': '35%-100%'})
fills = OrderedDict()
fills.update({'0%-20%': MapColors.PINK})
fills.update({'20%-35%': MapColors.ORANGE})
fills.update({'35%-100%': MapColors.RED})
fills.update({'defaultFill': MapColors.GREY})
average = (
(values_to_calculate_average['numerator'] * 100) /
float(values_to_calculate_average['denominator'] or 1)
)
gender_label, age_label, chosen_filters = chosen_filters_to_labels(config, default_interval='0 - 5 years')
return {
"slug": "moderately_underweight",
"label": "Percent of Children{gender} Underweight ({age})".format(
gender=gender_label,
age=age_label
),
"fills": fills,
"rightLegend": {
"average": format_decimal(average),
"info": underweight_children_help_text(age_label=age_label, html=True),
"extended_info": [
{
'indicator': 'Total Children{} weighed in given month:'.format(chosen_filters),
'value': indian_formatted_number(weighed_total)
},
{
'indicator': 'Number of children unweighed{}:'.format(chosen_filters),
'value': indian_formatted_number(all_total - weighed_total)
},
{
'indicator': '% Severely Underweight{}:'.format(chosen_filters),
'value': '%.2f%%' % (severely_underweight_total * 100 / float(weighed_total or 1))
},
{
'indicator': '% Moderately Underweight{}:'.format(chosen_filters),
'value': '%.2f%%' % (moderately_underweight_total * 100 / float(weighed_total or 1))
},
{
'indicator': '% Normal{}:'.format(chosen_filters),
'value': '%.2f%%' % (normal_total * 100 / float(weighed_total or 1))
}
]
},
"data": dict(data_for_map)
}