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_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_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_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_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_prevalence_of_stunting_data_map(domain, config, loc_level, show_test=False, icds_feature_flag=False):
def get_data_for(filters):
filters['month'] = datetime(*filters['month'])
queryset = AggChildHealthMonthly.objects.filter(
**filters
).values(
'%s_name' % loc_level, '%s_map_location_name' % loc_level
).annotate(
moderate=Sum(stunting_moderate_column(icds_feature_flag)),
severe=Sum(stunting_severe_column(icds_feature_flag)),
normal=Sum(stunting_normal_column(icds_feature_flag)),
total=Sum('height_eligible'),
total_measured=Sum(hfa_recorded_in_month_column(icds_feature_flag)),
).order_by('%s_name' % loc_level, '%s_map_location_name' % loc_level)
if not show_test:
queryset = apply_exclude(domain, queryset)
if 'age_tranche' not in config:
queryset = queryset.exclude(age_tranche=72)
return queryset
data_for_map = defaultdict(lambda: {
'moderate': 0,
'severe': 0,
'normal': 0,
'total': 0,
'total_measured': 0,
'original_name': []
})
moderate_total = 0
severe_total = 0
normal_total = 0
all_total = 0
measured_total = 0
values_to_calculate_average = {'numerator': 0, 'denominator': 0}
for row in get_data_for(config):
total = row['total'] or 0
name = row['%s_name' % loc_level]
on_map_name = row['%s_map_location_name' % loc_level] or name
severe = row['severe'] or 0
moderate = row['moderate'] or 0
normal = row['normal'] or 0
total_measured = row['total_measured'] or 0
values_to_calculate_average['numerator'] += moderate if moderate else 0
values_to_calculate_average['numerator'] += severe if severe else 0
values_to_calculate_average['denominator'] += total_measured if total_measured else 0
severe_total += severe
moderate_total += moderate
normal_total += normal
all_total += total
measured_total += total_measured
data_for_map[on_map_name]['severe'] += severe
data_for_map[on_map_name]['moderate'] += moderate
data_for_map[on_map_name]['normal'] += normal
data_for_map[on_map_name]['total'] += total
data_for_map[on_map_name]['total_measured'] += total_measured
data_for_map[on_map_name]['original_name'].append(name)
for data_for_location in six.itervalues(data_for_map):
numerator = data_for_location['moderate'] + data_for_location['severe']
value = numerator * 100 / (data_for_location['total_measured'] or 1)
if value < 25:
data_for_location.update({'fillKey': '0%-25%'})
elif 25 <= value < 38:
data_for_location.update({'fillKey': '25%-38%'})
elif value >= 38:
data_for_location.update({'fillKey': '38%-100%'})
fills = OrderedDict()
fills.update({'0%-25%': MapColors.PINK})
fills.update({'25%-38%': MapColors.ORANGE})
fills.update({'38%-100%': MapColors.RED})
fills.update({'defaultFill': MapColors.GREY})
gender_label, age_label, chosen_filters = chosen_filters_to_labels(
config,
default_interval=default_age_interval(icds_feature_flag)
)
average = (
(values_to_calculate_average['numerator'] * 100) /
float(values_to_calculate_average['denominator'] or 1)
)
return {
"slug": "severe",
"label": "Percent of Children{gender} Stunted ({age})".format(
gender=gender_label,
age=age_label
),
"fills": fills,
"rightLegend": {
"average": "%.2f" % average,
"info": _((
"Of the children enrolled for Anganwadi services, whose height was measured, the percentage of "
"children between {} who were moderately/severely stunted in the current month. "
"<br/><br/>"
"Stunting is a sign of chronic undernutrition and has long lasting harmful consequences on "
"the growth of a child".format(age_label)
)),
"extended_info": [
{
'indicator': 'Total Children{} eligible to have height measured:'.format(chosen_filters),
'value': indian_formatted_number(all_total)
},
{
'indicator': 'Total Children{} with height measured in given month:'
.format(chosen_filters),
'value': indian_formatted_number(measured_total)
},
{
'indicator': 'Number of Children{} unmeasured:'.format(chosen_filters),
'value': indian_formatted_number(all_total - measured_total)
},
{
'indicator': '% children{} with severely stunted growth:'.format(chosen_filters),
'value': '%.2f%%' % (severe_total * 100 / float(measured_total or 1))
},
{
'indicator': '% children{} with moderate stunted growth:'.format(chosen_filters),
'value': '%.2f%%' % (moderate_total * 100 / float(measured_total or 1))
},
{
'indicator': '% children{} with normal stunted growth:'.format(chosen_filters),
'value': '%.2f%%' % (normal_total * 100 / float(measured_total or 1))
}
]
},
"data": dict(data_for_map),
}
def get_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_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)
}
def get_awc_reports_maternal_child(domain,
config,
month,
prev_month,
show_test=False,
icds_feature_flag=False):
def get_data_for(date):
age_filters = {
'age_tranche': 72
} if icds_feature_flag else {
'age_tranche__in': [0, 6, 72]
}
moderately_underweight = exclude_records_by_age_for_column(
{'age_tranche': 72}, 'nutrition_status_moderately_underweight')
severely_underweight = exclude_records_by_age_for_column(
{'age_tranche': 72}, 'nutrition_status_severely_underweight')
wasting_moderate = exclude_records_by_age_for_column(
age_filters, wasting_moderate_column(icds_feature_flag))
wasting_severe = exclude_records_by_age_for_column(
age_filters, wasting_severe_column(icds_feature_flag))
stunting_moderate = exclude_records_by_age_for_column(
age_filters, stunting_moderate_column(icds_feature_flag))
stunting_severe = exclude_records_by_age_for_column(
age_filters, stunting_severe_column(icds_feature_flag))
nutrition_status_weighed = exclude_records_by_age_for_column(
{'age_tranche': 72}, 'nutrition_status_weighed')
height_measured_in_month = exclude_records_by_age_for_column(
age_filters, hfa_recorded_in_month_column(icds_feature_flag))
weighed_and_height_measured_in_month = exclude_records_by_age_for_column(
age_filters, wfh_recorded_in_month_column(icds_feature_flag))
queryset = AggChildHealthMonthly.objects.filter(
month=date,
**config).values('month', 'aggregation_level').annotate(
underweight=(Sum(moderately_underweight) +
Sum(severely_underweight)),
valid_weighed=Sum(nutrition_status_weighed),
immunized=(Sum('fully_immunized_on_time') +
Sum('fully_immunized_late')),
eligible=Sum('fully_immunized_eligible'),
wasting=Sum(wasting_moderate) + Sum(wasting_severe),
height_measured_in_month=Sum(height_measured_in_month),
weighed_and_height_measured_in_month=Sum(
weighed_and_height_measured_in_month),
stunting=Sum(stunting_moderate) + Sum(stunting_severe),
low_birth=Sum('low_birth_weight_in_month'),
birth=Sum('bf_at_birth'),
born=Sum('born_in_month'),
weighed_and_born_in_month=Sum('weighed_and_born_in_month'),
month_ebf=Sum('ebf_in_month'),
ebf=Sum('ebf_eligible'),
month_cf=Sum('cf_initiation_in_month'),
cf=Sum('cf_initiation_eligible'))
if not show_test:
queryset = apply_exclude(domain, queryset)
return queryset
def get_weight_efficiency(date):
queryset = AggAwcMonthly.objects.filter(month=date, **config).values(
'month', 'aggregation_level',
'awc_name').annotate(wer_weight=Sum('wer_weighed'),
wer_eli=Sum('wer_eligible'))
if not show_test:
queryset = apply_exclude(domain, queryset)
return queryset
def get_institutional_delivery_data(date):
queryset = AggCcsRecordMonthly.objects.filter(
month=date, **config).values(
'month', 'aggregation_level', 'awc_name').annotate(
institutional_delivery_in_month_sum=Sum(
'institutional_delivery_in_month'),
delivered_in_month_sum=Sum('delivered_in_month'))
if not show_test:
queryset = apply_exclude(domain, queryset)
return queryset
this_month_data = get_data_for(datetime(*month))
prev_month_data = get_data_for(datetime(*prev_month))
this_month_data_we = get_weight_efficiency(datetime(*month))
prev_month_data_we = get_weight_efficiency(datetime(*prev_month))
this_month_institutional_delivery_data = get_institutional_delivery_data(
datetime(*month))
prev_month_institutional_delivery_data = get_institutional_delivery_data(
datetime(*prev_month))
gender_label, age_label, chosen_filters = chosen_filters_to_labels(
config, default_interval=default_age_interval(icds_feature_flag))
return {
'kpi':
[[
{
'label':
_('Underweight (Weight-for-Age)'),
'help_text':
_(("Of the total children weighed, the percentage of children between 0-5 years who were "
"moderately/severely underweight in the current month. Children who are moderately or "
"severely underweight have a higher risk of mortality. ")),
'percent':
percent_diff('underweight', this_month_data, prev_month_data,
'valid_weighed'),
'color':
'red' if percent_diff('underweight', this_month_data,
prev_month_data, 'valid_weighed') > 0
else 'green',
'value':
get_value(this_month_data, 'underweight'),
'all':
get_value(this_month_data, 'valid_weighed'),
'format':
'percent_and_div',
'frequency':
'month'
},
{
'label':
_('Wasting (Weight-for-Height)'),
'help_text':
wasting_help_text(age_label),
'percent':
percent_diff('wasting', this_month_data, prev_month_data,
'weighed_and_height_measured_in_month'),
'color':
'red'
if percent_diff('wasting', this_month_data, prev_month_data,
'weighed_and_height_measured_in_month') > 0
else 'green',
'value':
get_value(this_month_data, 'wasting'),
'all':
get_value(this_month_data,
'weighed_and_height_measured_in_month'),
'format':
'percent_and_div',
'frequency':
'month'
},
],
[
{
'label':
_('Stunting (Height-for-Age)'),
'help_text':
stunting_help_text(age_label),
'percent':
percent_diff('stunting', this_month_data, prev_month_data,
'height_measured_in_month'),
'color':
'red'
if percent_diff('stunting', this_month_data, prev_month_data,
'height_measured_in_month') > 0 else 'green',
'value':
get_value(this_month_data, 'stunting'),
'all':
get_value(this_month_data, 'height_measured_in_month'),
'format':
'percent_and_div',
'frequency':
'month'
},
{
'label':
_('Weighing Efficiency'),
'help_text':
_("Of the children between the ages of 0-5 years who are enrolled for Anganwadi Services, "
"the percentage who were weighed in the given month. "),
'percent':
percent_diff('wer_weight', this_month_data_we,
prev_month_data_we, 'wer_eli'),
'color':
'green'
if percent_diff('wer_weight', this_month_data_we,
prev_month_data_we, 'wer_eli') > 0 else 'red',
'value':
get_value(this_month_data_we, 'wer_weight'),
'all':
get_value(this_month_data_we, 'wer_eli'),
'format':
'percent_and_div',
'frequency':
'month'
},
],
[
{
'label':
_('Newborns with Low Birth Weight'),
'help_text':
_("Of all the children born in the current month, the percentage that had a birth weight "
"less than 2500 grams. Newborns with Low Birth Weight are closely associated wtih foetal "
"and neonatal mortality and morbidity, inhibited growth and cognitive development, "
"and chronic diseases later in life."),
'percent':
percent_diff('low_birth', this_month_data, prev_month_data,
'weighed_and_born_in_month'),
'color':
'red'
if percent_diff('low_birth', this_month_data, prev_month_data,
'weighed_and_born_in_month') > 0 else 'green',
'value':
get_value(this_month_data, 'low_birth'),
'all':
get_value(this_month_data, 'weighed_and_born_in_month'),
'format':
'percent_and_div',
'frequency':
'month'
},
{
'label':
_('Early Initiation of Breastfeeding'),
'help_text':
_("Of the children born in the last month, the percentage whose "
"breastfeeding was initiated within 1 hour of delivery. Early initiation "
"of breastfeeding ensure the newborn recieves the \"first milk\" rich "
"in nutrients and encourages exclusive breastfeeding practice"
),
'percent':
percent_diff('birth', this_month_data, prev_month_data,
'born'),
'color':
'green'
if percent_diff('birth', this_month_data, prev_month_data,
'born') > 0 else 'red',
'value':
get_value(this_month_data, 'birth'),
'all':
get_value(this_month_data, 'born'),
'format':
'percent_and_div',
'frequency':
'month'
},
],
[
{
'label':
_('Exclusive breastfeeding'),
'help_text':
_("Of the total children between the ages of 0 to 6 months, the percentage that was "
"exclusively fed with breast milk. An infant is exclusively breastfed if they receive "
"only breastmilk with no additional food or liquids (even water), ensuring optimal "
"nutrition and growth between 0 - 6 months"),
'percent':
percent_diff('month_ebf', this_month_data, prev_month_data,
'ebf'),
'color':
'green'
if percent_diff('month_ebf', this_month_data, prev_month_data,
'ebf') > 0 else 'red',
'value':
get_value(this_month_data, 'month_ebf'),
'all':
get_value(this_month_data, 'ebf'),
'format':
'percent_and_div',
'frequency':
'month'
},
{
'label':
_('Children initiated appropriate Complementary Feeding'),
'help_text':
_("Of the total children between the ages of 6 to 8 months, the percentage that was "
"given a timely introduction to solid, semi-solid or soft food. Timely intiation of "
"complementary feeding in addition to breastmilk at 6 months of age is a key feeding "
"practice to reduce malnutrition"),
'percent':
percent_diff('month_cf', this_month_data, prev_month_data,
'cf'),
'color':
'green' if percent_diff('month_cf', this_month_data,
prev_month_data, 'cf') > 0 else 'red',
'value':
get_value(this_month_data, 'month_cf'),
'all':
get_value(this_month_data, 'cf'),
'format':
'percent_and_div',
'frequency':
'month'
},
],
[
{
'label':
_('Immunization Coverage (at age 1 year)'),
'help_text':
_(("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"
)),
'percent':
percent_diff('immunized', this_month_data, prev_month_data,
'eligible'),
'color':
'green'
if percent_diff('immunized', this_month_data, prev_month_data,
'eligible') > 0 else 'red',
'value':
get_value(this_month_data, 'immunized'),
'all':
get_value(this_month_data, 'eligible'),
'format':
'percent_and_div',
'frequency':
'month'
},
{
'label':
_('Institutional Deliveries'),
'help_text':
_(("Of the total number of women who gave birth in the last month, the percentage who "
"delivered in a public or private medical facility. Delivery in medical instituitions "
"is associated with a decrease in maternal mortality rate"
)),
'percent':
percent_diff('institutional_delivery_in_month_sum',
this_month_institutional_delivery_data,
prev_month_institutional_delivery_data,
'delivered_in_month_sum'),
'color':
'green'
if percent_diff('institutional_delivery_in_month_sum',
this_month_institutional_delivery_data,
prev_month_institutional_delivery_data,
'delivered_in_month_sum') > 0 else 'red',
'value':
get_value(this_month_institutional_delivery_data,
'institutional_delivery_in_month_sum'),
'all':
get_value(this_month_institutional_delivery_data,
'delivered_in_month_sum'),
'format':
'percent_and_div',
'frequency':
'month'
},
]]
}
def get_maternal_child_data(domain, config, show_test=False, icds_feature_flag=False):
def get_data_for_child_health_monthly(date, filters):
age_filters = {'age_tranche': 72} if icds_feature_flag else {'age_tranche__in': [0, 6, 72]}
moderately_underweight = exclude_records_by_age_for_column(
{'age_tranche': 72},
'nutrition_status_moderately_underweight'
)
severely_underweight = exclude_records_by_age_for_column(
{'age_tranche': 72},
'nutrition_status_severely_underweight'
)
wasting_moderate = exclude_records_by_age_for_column(
age_filters,
wasting_moderate_column(icds_feature_flag)
)
wasting_severe = exclude_records_by_age_for_column(
age_filters,
wasting_severe_column(icds_feature_flag)
)
stunting_moderate = exclude_records_by_age_for_column(
age_filters,
stunting_moderate_column(icds_feature_flag)
)
stunting_severe = exclude_records_by_age_for_column(
age_filters,
stunting_severe_column(icds_feature_flag)
)
nutrition_status_weighed = exclude_records_by_age_for_column(
{'age_tranche': 72},
'nutrition_status_weighed'
)
height_measured_in_month = exclude_records_by_age_for_column(
age_filters,
hfa_recorded_in_month_column(icds_feature_flag)
)
weighed_and_height_measured_in_month = exclude_records_by_age_for_column(
age_filters,
wfh_recorded_in_month_column(icds_feature_flag)
)
queryset = AggChildHealthMonthly.objects.filter(
month=date, **filters
).values(
'aggregation_level'
).annotate(
underweight=(
Sum(moderately_underweight) + Sum(severely_underweight)
),
valid=Sum(nutrition_status_weighed),
wasting=Sum(wasting_moderate) + Sum(wasting_severe),
stunting=Sum(stunting_moderate) + Sum(stunting_severe),
height_measured_in_month=Sum(height_measured_in_month),
weighed_and_height_measured_in_month=Sum(weighed_and_height_measured_in_month),
low_birth_weight=Sum('low_birth_weight_in_month'),
bf_birth=Sum('bf_at_birth'),
born=Sum('born_in_month'),
weighed_and_born_in_month=Sum('weighed_and_born_in_month'),
ebf=Sum('ebf_in_month'),
ebf_eli=Sum('ebf_eligible'),
cf_initiation=Sum('cf_initiation_in_month'),
cf_initiation_eli=Sum('cf_initiation_eligible')
)
if not show_test:
queryset = apply_exclude(domain, queryset)
return queryset
def get_data_for_deliveries(date, filters):
queryset = AggCcsRecordMonthly.objects.filter(
month=date, **filters
).values(
'aggregation_level'
).annotate(
institutional_delivery=Sum('institutional_delivery_in_month'),
delivered=Sum('delivered_in_month')
)
if not show_test:
queryset = apply_exclude(domain, queryset)
return queryset
current_month = datetime(*config['month'])
previous_month = datetime(*config['prev_month'])
del config['month']
del config['prev_month']
this_month_data = get_data_for_child_health_monthly(current_month, config)
prev_month_data = get_data_for_child_health_monthly(previous_month, config)
deliveries_this_month = get_data_for_deliveries(current_month, config)
deliveries_prev_month = get_data_for_deliveries(previous_month, config)
gender_label, age_label, chosen_filters = chosen_filters_to_labels(
config,
default_interval=default_age_interval(icds_feature_flag)
)
return {
'records': [
[
{
'label': _('Underweight (Weight-for-Age)'),
'help_text': _((
"Of the total children enrolled for Anganwadi services and weighed, the percentage "
"of children between 0-5 years who were moderately/severely underweight in the current "
"month. Children who are moderately or severely underweight have a higher risk "
"of mortality. "
)),
'percent': percent_diff(
'underweight',
this_month_data,
prev_month_data,
'valid'
),
'color': 'red' if percent_diff(
'underweight',
this_month_data,
prev_month_data,
'valid'
) > 0 else 'green',
'value': get_value(this_month_data, 'underweight'),
'all': get_value(this_month_data, 'valid'),
'format': 'percent_and_div',
'frequency': 'month',
'redirect': 'underweight_children'
},
{
'label': _('Wasting (Weight-for-Height)'),
'help_text': _(wasting_help_text(age_label)),
'percent': percent_diff(
'wasting',
this_month_data,
prev_month_data,
'weighed_and_height_measured_in_month'
),
'color': 'red' if percent_diff(
'wasting',
this_month_data,
prev_month_data,
'weighed_and_height_measured_in_month'
) > 0 else 'green',
'value': get_value(this_month_data, 'wasting'),
'all': get_value(this_month_data, 'weighed_and_height_measured_in_month'),
'format': 'percent_and_div',
'frequency': 'month',
'redirect': 'wasting'
}
],
[
{
'label': _('Stunting (Height-for-Age)'),
'help_text': _(stunting_help_text(age_label)),
'percent': percent_diff(
'stunting',
this_month_data,
prev_month_data,
'height_measured_in_month'
),
'color': 'red' if percent_diff(
'stunting',
this_month_data,
prev_month_data,
'height_measured_in_month'
) > 0 else 'green',
'value': get_value(this_month_data, 'stunting'),
'all': get_value(this_month_data, 'height_measured_in_month'),
'format': 'percent_and_div',
'frequency': 'month',
'redirect': 'stunting'
},
{
'label': _('Newborns with Low Birth Weight'),
'help_text': _((
"Of all the children born in the current month and enrolled for Anganwadi services, "
"the percentage that had a birth weight less than 2500 grams. Newborns with Low Birth "
"Weight are closely associated wtih foetal and neonatal mortality and morbidity, "
"inhibited growth and cognitive development, and chronic diseases later in life. ")),
'percent': percent_diff(
'low_birth_weight',
this_month_data,
prev_month_data,
'weighed_and_born_in_month'
),
'color': 'red' if percent_diff(
'low_birth_weight',
this_month_data,
prev_month_data,
'weighed_and_born_in_month'
) > 0 else 'green',
'value': get_value(this_month_data, 'low_birth_weight'),
'all': get_value(this_month_data, 'weighed_and_born_in_month'),
'format': 'percent_and_div',
'frequency': 'month',
'redirect': 'low_birth'
}
],
[
{
'label': _('Early Initiation of Breastfeeding'),
'help_text': _((
"Of the children born in the last month and enrolled for Anganwadi services, "
"the percentage whose breastfeeding was initiated within 1 hour of delivery. "
"Early initiation of breastfeeding ensure the newborn recieves the \"first milk\" "
"rich in nutrients and encourages exclusive breastfeeding practice")
),
'percent': percent_diff(
'bf_birth',
this_month_data,
prev_month_data,
'born'
),
'color': 'green' if percent_diff(
'bf_birth',
this_month_data,
prev_month_data,
'born'
) > 0 else 'red',
'value': get_value(this_month_data, 'bf_birth'),
'all': get_value(this_month_data, 'born'),
'format': 'percent_and_div',
'frequency': 'month',
'redirect': 'early_initiation'
},
{
'label': _('Exclusive Breastfeeding'),
'help_text': _((
"Of the total children enrolled for Anganwadi services between the ages of 0 to 6 months, "
"the percentage that was exclusively fed with breast milk. An infant is exclusively "
"breastfed if they receive only breastmilk with no additional food or liquids "
"(even water), ensuring optimal nutrition and growth between 0 - 6 months")
),
'percent': percent_diff(
'ebf',
this_month_data,
prev_month_data,
'ebf_eli'
),
'color': 'green' if percent_diff(
'ebf',
this_month_data,
prev_month_data,
'ebf_eli'
) > 0 else 'red',
'value': get_value(this_month_data, 'ebf'),
'all': get_value(this_month_data, 'ebf_eli'),
'format': 'percent_and_div',
'frequency': 'month',
'redirect': 'exclusive_breastfeeding'
}
],
[
{
'label': _('Children initiated appropriate Complementary Feeding'),
'help_text': _((
"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. "
"Timely intiation of complementary feeding in addition to breastmilk at 6 months of age "
"is a key feeding practice to reduce malnutrition")
),
'percent': percent_diff(
'cf_initiation',
this_month_data,
prev_month_data,
'cf_initiation_eli'
),
'color': 'green' if percent_diff(
'cf_initiation',
this_month_data,
prev_month_data,
'cf_initiation_eli'
) > 0 else 'red',
'value': get_value(this_month_data, 'cf_initiation'),
'all': get_value(this_month_data, 'cf_initiation_eli'),
'format': 'percent_and_div',
'frequency': 'month',
'redirect': 'children_initiated'
},
{
'label': _('Institutional Deliveries'),
'help_text': _((
"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. Delivery "
"in medical instituitions is associated with a decrease in maternal mortality rate")
),
'percent': percent_diff(
'institutional_delivery',
deliveries_this_month,
deliveries_prev_month,
'delivered'
),
'color': 'green' if percent_diff(
'institutional_delivery',
deliveries_this_month,
deliveries_prev_month,
'delivered'
) > 0 else 'red',
'value': get_value(deliveries_this_month, 'institutional_delivery'),
'all': get_value(deliveries_this_month, 'delivered'),
'format': 'percent_and_div',
'frequency': 'month',
'redirect': 'institutional_deliveries'
}
]
]
}
def get_prevalence_of_severe_data_map(domain,
config,
loc_level,
show_test=False,
icds_feature_flag=False):
def get_data_for(filters):
filters['month'] = datetime(*filters['month'])
queryset = AggChildHealthMonthly.objects.filter(**filters).values(
'%s_name' % loc_level,
'%s_map_location_name' % loc_level).annotate(
moderate=Sum(wasting_moderate_column(icds_feature_flag)),
severe=Sum(wasting_severe_column(icds_feature_flag)),
normal=Sum(wasting_normal_column(icds_feature_flag)),
total_height_eligible=Sum('height_eligible'),
total_weighed=Sum('nutrition_status_weighed'),
total_measured=Sum(
wfh_recorded_in_month_column(icds_feature_flag)),
).order_by('%s_name' % loc_level,
'%s_map_location_name' % loc_level)
if not show_test:
queryset = apply_exclude(domain, queryset)
if 'age_tranche' not in config:
if icds_feature_flag:
queryset = queryset.exclude(age_tranche=72)
else:
queryset = queryset.exclude(age_tranche__in=[0, 6, 72])
return queryset
data_for_map = defaultdict(
lambda: {
'moderate': 0,
'severe': 0,
'normal': 0,
'total_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 = []
for row in get_data_for(config):
total_weighed = row['total_weighed'] or 0
total_height_eligible = row['total_height_eligible'] or 0
name = row['%s_name' % loc_level]
on_map_name = row['%s_map_location_name' % loc_level] or name
severe = row['severe'] or 0
moderate = row['moderate'] or 0
normal = row['normal'] or 0
total_measured = row['total_measured'] or 0
numerator = moderate + severe
values_to_calculate_average.append(numerator * 100 /
(total_weighed or 1))
severe_for_all_locations += severe
moderate_for_all_locations += moderate
normal_for_all_locations += normal
weighed_for_all_locations += total_weighed
measured_for_all_locations += total_measured
height_eligible_for_all_locations += total_height_eligible
data_for_map[on_map_name]['severe'] += severe
data_for_map[on_map_name]['moderate'] += moderate
data_for_map[on_map_name]['normal'] += normal
data_for_map[on_map_name]['total_weighed'] += total_weighed
data_for_map[on_map_name]['total_measured'] += total_measured
data_for_map[on_map_name][
'total_height_eligible'] += total_height_eligible
data_for_map[on_map_name]['original_name'].append(name)
for data_for_location in six.itervalues(data_for_map):
numerator = data_for_location['moderate'] + data_for_location['severe']
value = numerator * 100 / (data_for_location['total_measured'] or 1)
if value < 5:
data_for_location.update({'fillKey': '0%-5%'})
elif 5 <= value <= 7:
data_for_location.update({'fillKey': '5%-7%'})
elif value > 7:
data_for_location.update({'fillKey': '7%-100%'})
fills = OrderedDict()
fills.update({'0%-5%': MapColors.PINK})
fills.update({'5%-7%': MapColors.ORANGE})
fills.update({'7%-100%': MapColors.RED})
fills.update({'defaultFill': MapColors.GREY})
gender_label, age_label, chosen_filters = chosen_filters_to_labels(
config, default_interval=default_age_interval(icds_feature_flag))
return {
"slug":
"severe",
"label":
"Percent of Children{gender} Wasted ({age})".format(
gender=gender_label, age=age_label),
"fills":
fills,
"rightLegend": {
"average":
"%.2f" % ((sum(values_to_calculate_average)) /
float(len(values_to_calculate_average) or 1)),
"info":
_(("Of the children enrolled for Anganwadi services, whose weight and height was measured, "
"the percentage of children between {} who were moderately/severely wasted in the current month. "
"<br/><br/>"
"Severe Acute Malnutrition (SAM) or wasting in children is a symptom of acute undernutrition "
"usually as a consequence of insufficient food intake or a high incidence of infectious diseases."
.format(age_label))),
"extended_info": [{
'indicator':
'Total Children{} weighed in given month:'.format(
chosen_filters),
'value':
indian_formatted_number(weighed_for_all_locations)
}, {
'indicator':
'Total Children{} with height measured in given month:'.format(
chosen_filters),
'value':
indian_formatted_number(measured_for_all_locations)
}, {
'indicator':
'Number of children{} unmeasured:'.format(chosen_filters),
'value':
indian_formatted_number(height_eligible_for_all_locations -
weighed_for_all_locations)
}, {
'indicator':
'% Severely Acute Malnutrition{}:'.format(chosen_filters),
'value':
'%.2f%%' % (severe_for_all_locations * 100 /
float(measured_for_all_locations or 1))
}, {
'indicator':
'% Moderately Acute Malnutrition{}:'.format(chosen_filters),
'value':
'%.2f%%' % (moderate_for_all_locations * 100 /
float(measured_for_all_locations or 1))
}, {
'indicator':
'% Normal{}:'.format(chosen_filters),
'value':
'%.2f%%' % (normal_for_all_locations * 100 /
float(measured_for_all_locations or 1))
}]
},
"data":
dict(data_for_map),
}
def get_maternal_child_data(domain, config, show_test=False, icds_feature_flag=False):
def get_data_for_child_health_monthly(date, filters):
age_filters = {'age_tranche': 72} if icds_feature_flag else {'age_tranche__in': [0, 6, 72]}
moderately_underweight = exclude_records_by_age_for_column(
{'age_tranche': 72},
'nutrition_status_moderately_underweight'
)
severely_underweight = exclude_records_by_age_for_column(
{'age_tranche': 72},
'nutrition_status_severely_underweight'
)
wasting_moderate = exclude_records_by_age_for_column(
age_filters,
wasting_moderate_column(icds_feature_flag)
)
wasting_severe = exclude_records_by_age_for_column(
age_filters,
wasting_severe_column(icds_feature_flag)
)
stunting_moderate = exclude_records_by_age_for_column(
age_filters,
stunting_moderate_column(icds_feature_flag)
)
stunting_severe = exclude_records_by_age_for_column(
age_filters,
stunting_severe_column(icds_feature_flag)
)
nutrition_status_weighed = exclude_records_by_age_for_column(
{'age_tranche': 72},
'nutrition_status_weighed'
)
height_measured_in_month = exclude_records_by_age_for_column(
age_filters,
hfa_recorded_in_month_column(icds_feature_flag)
)
weighed_and_height_measured_in_month = exclude_records_by_age_for_column(
age_filters,
wfh_recorded_in_month_column(icds_feature_flag)
)
queryset = AggChildHealthMonthly.objects.filter(
month=date, **filters
).values(
'aggregation_level'
).annotate(
underweight=(
Sum(moderately_underweight) + Sum(severely_underweight)
),
valid=Sum(nutrition_status_weighed),
wasting=Sum(wasting_moderate) + Sum(wasting_severe),
stunting=Sum(stunting_moderate) + Sum(stunting_severe),
height_measured_in_month=Sum(height_measured_in_month),
weighed_and_height_measured_in_month=Sum(weighed_and_height_measured_in_month),
low_birth_weight=Sum('low_birth_weight_in_month'),
bf_birth=Sum('bf_at_birth'),
born=Sum('born_in_month'),
weighed_and_born_in_month=Sum('weighed_and_born_in_month'),
ebf=Sum('ebf_in_month'),
ebf_eli=Sum('ebf_eligible'),
cf_initiation=Sum('cf_initiation_in_month'),
cf_initiation_eli=Sum('cf_initiation_eligible')
)
if not show_test:
queryset = apply_exclude(domain, queryset)
return queryset
def get_data_for_deliveries(date, filters):
queryset = AggCcsRecordMonthly.objects.filter(
month=date, **filters
).values(
'aggregation_level'
).annotate(
institutional_delivery=Sum('institutional_delivery_in_month'),
delivered=Sum('delivered_in_month')
)
if not show_test:
queryset = apply_exclude(domain, queryset)
return queryset
current_month = datetime(*config['month'])
previous_month = datetime(*config['prev_month'])
del config['month']
del config['prev_month']
this_month_data = get_data_for_child_health_monthly(current_month, config)
prev_month_data = get_data_for_child_health_monthly(previous_month, config)
deliveries_this_month = get_data_for_deliveries(current_month, config)
deliveries_prev_month = get_data_for_deliveries(previous_month, config)
gender_label, age_label, chosen_filters = chosen_filters_to_labels(
config,
default_interval=default_age_interval(icds_feature_flag)
)
return {
'records': [
[
{
'label': _('Underweight (Weight-for-Age)'),
'help_text': underweight_children_help_text(),
'percent': percent_diff(
'underweight',
this_month_data,
prev_month_data,
'valid'
),
'color': 'red' if percent_diff(
'underweight',
this_month_data,
prev_month_data,
'valid'
) > 0 else 'green',
'value': get_value(this_month_data, 'underweight'),
'all': get_value(this_month_data, 'valid'),
'format': 'percent_and_div',
'frequency': 'month',
'redirect': 'maternal_and_child/underweight_children'
},
{
'label': _('Wasting (Weight-for-Height)'),
'help_text': _(wasting_help_text(age_label)),
'percent': percent_diff(
'wasting',
this_month_data,
prev_month_data,
'weighed_and_height_measured_in_month'
),
'color': 'red' if percent_diff(
'wasting',
this_month_data,
prev_month_data,
'weighed_and_height_measured_in_month'
) > 0 else 'green',
'value': get_value(this_month_data, 'wasting'),
'all': get_value(this_month_data, 'weighed_and_height_measured_in_month'),
'format': 'percent_and_div',
'frequency': 'month',
'redirect': 'maternal_and_child/wasting'
}
],
[
{
'label': _('Stunting (Height-for-Age)'),
'help_text': _(stunting_help_text(age_label)),
'percent': percent_diff(
'stunting',
this_month_data,
prev_month_data,
'height_measured_in_month'
),
'color': 'red' if percent_diff(
'stunting',
this_month_data,
prev_month_data,
'height_measured_in_month'
) > 0 else 'green',
'value': get_value(this_month_data, 'stunting'),
'all': get_value(this_month_data, 'height_measured_in_month'),
'format': 'percent_and_div',
'frequency': 'month',
'redirect': 'maternal_and_child/stunting'
},
{
'label': _('Newborns with Low Birth Weight'),
'help_text': _((
new_born_with_low_weight_help_text(html=False)
)),
'percent': percent_diff(
'low_birth_weight',
this_month_data,
prev_month_data,
'weighed_and_born_in_month'
),
'color': 'red' if percent_diff(
'low_birth_weight',
this_month_data,
prev_month_data,
'weighed_and_born_in_month'
) > 0 else 'green',
'value': get_value(this_month_data, 'low_birth_weight'),
'all': get_value(this_month_data, 'weighed_and_born_in_month'),
'format': 'percent_and_div',
'frequency': 'month',
'redirect': 'maternal_and_child/low_birth'
}
],
[
{
'label': _('Early Initiation of Breastfeeding'),
'help_text': early_initiation_breastfeeding_help_text(),
'percent': percent_diff(
'bf_birth',
this_month_data,
prev_month_data,
'born'
),
'color': 'green' if percent_diff(
'bf_birth',
this_month_data,
prev_month_data,
'born'
) > 0 else 'red',
'value': get_value(this_month_data, 'bf_birth'),
'all': get_value(this_month_data, 'born'),
'format': 'percent_and_div',
'frequency': 'month',
'redirect': 'maternal_and_child/early_initiation'
},
{
'label': _('Exclusive Breastfeeding'),
'help_text': exclusive_breastfeeding_help_text(),
'percent': percent_diff(
'ebf',
this_month_data,
prev_month_data,
'ebf_eli'
),
'color': 'green' if percent_diff(
'ebf',
this_month_data,
prev_month_data,
'ebf_eli'
) > 0 else 'red',
'value': get_value(this_month_data, 'ebf'),
'all': get_value(this_month_data, 'ebf_eli'),
'format': 'percent_and_div',
'frequency': 'month',
'redirect': 'maternal_and_child/exclusive_breastfeeding'
}
],
[
{
'label': _('Children initiated appropriate Complementary Feeding'),
'help_text': children_initiated_appropriate_complementary_feeding_help_text(),
'percent': percent_diff(
'cf_initiation',
this_month_data,
prev_month_data,
'cf_initiation_eli'
),
'color': 'green' if percent_diff(
'cf_initiation',
this_month_data,
prev_month_data,
'cf_initiation_eli'
) > 0 else 'red',
'value': get_value(this_month_data, 'cf_initiation'),
'all': get_value(this_month_data, 'cf_initiation_eli'),
'format': 'percent_and_div',
'frequency': 'month',
'redirect': 'maternal_and_child/children_initiated'
},
{
'label': _('Institutional Deliveries'),
'help_text': institutional_deliveries_help_text(),
'percent': percent_diff(
'institutional_delivery',
deliveries_this_month,
deliveries_prev_month,
'delivered'
),
'color': 'green' if percent_diff(
'institutional_delivery',
deliveries_this_month,
deliveries_prev_month,
'delivered'
) > 0 else 'red',
'value': get_value(deliveries_this_month, 'institutional_delivery'),
'all': get_value(deliveries_this_month, 'delivered'),
'format': 'percent_and_div',
'frequency': 'month',
'redirect': 'maternal_and_child/institutional_deliveries'
}
]
]
}
def get_newborn_with_low_birth_weight_map(domain, config, loc_level, show_test=False):
def get_data_for(filters):
filters['month'] = datetime(*filters['month'])
queryset = AggChildHealthMonthly.objects.filter(
**filters
).values(
'%s_name' % loc_level, '%s_map_location_name' % loc_level
).annotate(
low_birth=Sum('low_birth_weight_in_month'),
in_month=Sum('weighed_and_born_in_month'),
all=Sum('born_in_month')
).order_by('%s_name' % loc_level, '%s_map_location_name' % loc_level)
if not show_test:
queryset = apply_exclude(domain, queryset)
return queryset
data_for_map, in_month_total, low_birth_total, average, total = generate_data_for_map(
get_data_for(config),
loc_level,
'low_birth',
'in_month',
20,
60,
'all'
)
fills = OrderedDict()
fills.update({'0%-20%': MapColors.PINK})
fills.update({'20%-60%': MapColors.ORANGE})
fills.update({'60%-100%': MapColors.RED})
fills.update({'defaultFill': MapColors.GREY})
gender_ignored, age_ignored, chosen_filters = chosen_filters_to_labels(config)
return {
"slug": "low_birth",
"label": "Percent Newborns with Low Birth Weight{}".format(chosen_filters),
"fills": fills,
"rightLegend": {
"average": average,
"info": _((
new_born_with_low_weight_help_text(html=True)
)),
"extended_info": [
{
'indicator': 'Total Number of Newborns born in given month{}:'.format(chosen_filters),
'value': indian_formatted_number(total)
},
{
'indicator': 'Number of Newborns with LBW in given month{}:'.format(chosen_filters),
'value': indian_formatted_number(low_birth_total)
},
{
'indicator': 'Total Number of children born and weight in given month{}:'.format(
chosen_filters
),
'value': indian_formatted_number(in_month_total)
},
{
'indicator': '% newborns with LBW in given month{}:'.format(chosen_filters),
'value': '%.2f%%' % (low_birth_total * 100 / float(in_month_total or 1))
},
{
'indicator': '% of children with weight in normal{}:'.format(chosen_filters),
'value': '%.2f%%' % ((in_month_total - low_birth_total) * 100 / float(in_month_total or 1))
},
{
'indicator': '% Unweighted{}:'.format(chosen_filters),
'value': '%.2f%%' % ((total - in_month_total) * 100 / float(total or 1))
}
]
},
"data": dict(data_for_map),
}
def get_prevalence_of_undernutrition_data_map(domain,
config,
loc_level,
show_test=False):
def get_data_for(filters):
filters['month'] = datetime(*filters['month'])
queryset = AggChildHealthMonthly.objects.filter(**filters).values(
'%s_name' % loc_level,
'%s_map_location_name' % loc_level).annotate(
moderately_underweight=Sum(
'nutrition_status_moderately_underweight'),
severely_underweight=Sum(
'nutrition_status_severely_underweight'),
normal=Sum('nutrition_status_normal'),
valid=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,
'total': 0,
'original_name': []
})
moderately_underweight_total = 0
severely_underweight_total = 0
normal_total = 0
valid_total = 0
for row in get_data_for(config):
valid = row['valid'] 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
moderately_underweight_total += moderately_underweight
severely_underweight_total += severely_underweight_total
normal_total += normal
valid_total += valid
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'] += valid
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['total'] 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 = (
(moderately_underweight_total or 0) +
(severely_underweight_total or 0)) * 100 / float(valid_total or 1)
sum_of_indicators = moderately_underweight_total + severely_underweight_total + normal_total
percent_unweighed = (valid_total - sum_of_indicators) * 100 / float(
valid_total or 1)
gender_label, age_label, chosen_filters = chosen_filters_to_labels(
config, default_interval='0 - 5 years')
return {
"slug":
"moderately_underweight",
"label":
"Percent of Children{gender} Underweight ({age})".format(
gender=gender_label, age=age_label),
"fills":
fills,
"rightLegend": {
"average":
average,
"info":
_(("Percentage of children between {} enrolled for ICDS services with weight-for-age "
"less than -2 standard deviations of the WHO Child Growth Standards median. "
"<br/><br/>"
"Children who are moderately or severely underweight have a higher risk of mortality"
.format(age_label))),
"extended_info": [{
'indicator':
'Total Children{} weighed in given month:'.format(
chosen_filters),
'value':
indian_formatted_number(valid_total)
}, {
'indicator':
'% Unweighed{}:'.format(chosen_filters),
'value':
'%.2f%%' % percent_unweighed
}, {
'indicator':
'% Severely Underweight{}:'.format(chosen_filters),
'value':
'%.2f%%' %
(severely_underweight_total * 100 / float(valid_total or 1))
}, {
'indicator':
'% Moderately Underweight{}:'.format(chosen_filters),
'value':
'%.2f%%' %
(moderately_underweight_total * 100 / float(valid_total or 1))
}, {
'indicator':
'% Normal{}:'.format(chosen_filters),
'value':
'%.2f%%' % (normal_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),
}
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('height_measured_in_month'),
).order_by('%s_name' % loc_level)
if not show_test:
data = apply_exclude(domain, data)
if 'age_tranche' not in config:
if icds_feature_flag:
data = data.exclude(age_tranche=72)
else:
data = data.exclude(age_tranche__in=[0, 6, 72])
chart_data = {
'blue': [],
}
tooltips_data = defaultdict(lambda: {
'severe': 0,
'moderate': 0,
'total': 0,
'normal': 0,
'total_measured': 0
})
loc_children = get_child_locations(domain, location_id, show_test)
result_set = set()
for row in data:
total = row['total'] or 0
name = row['%s_name' % loc_level]
result_set.add(name)
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 six.iteritems(row_values):
tooltips_data[name][prop] += value
value = (moderate + severe) / float(total_measured or 1)
chart_data['blue'].append([name, value])
for sql_location in loc_children:
if sql_location.name not in result_set:
chart_data['blue'].append([sql_location.name, 0])
chart_data['blue'] = sorted(chart_data['blue'])
__, __, chosen_filters = chosen_filters_to_labels(
config, default_interval=default_age_interval(icds_feature_flag))
return {
"tooltips_data":
dict(tooltips_data),
"info":
_(("Percentage of children{} enrolled for Anganwadi Services with height-for-age below "
"-2Z standard deviations of the WHO Child Growth Standards median."
"<br/><br/>"
"Stunting is a sign of chronic undernutrition and has long lasting harmful "
"consequences on the growth of a child".format(chosen_filters))),
"chart_data": [
{
"values": chart_data['blue'],
"key": "",
"strokeWidth": 2,
"classed": "dashed",
"color": MapColors.BLUE
},
]
}
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_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.exclude(age_tranche=72)
chart_data = {
'blue': [],
}
tooltips_data = defaultdict(lambda: {
'severe': 0,
'moderate': 0,
'total_height_eligible': 0,
'normal': 0,
'total_weighed': 0,
'total_measured': 0
})
loc_children = get_child_locations(domain, location_id, show_test)
result_set = set()
for row in data:
total_weighed = row['total_weighed'] or 0
name = row['%s_name' % loc_level]
result_set.add(name)
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
])
for sql_location in loc_children:
if sql_location.name not in result_set:
chart_data['blue'].append([sql_location.name, 0])
chart_data['blue'] = sorted(chart_data['blue'])
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):
def get_data_for(filters):
filters['month'] = datetime(*filters['month'])
queryset = AggChildHealthMonthly.objects.filter(
**filters
).values(
'%s_name' % loc_level, '%s_map_location_name' % loc_level
).annotate(
moderate=Sum('wasting_moderate'),
severe=Sum('wasting_severe'),
normal=Sum('wasting_normal'),
valid=Sum('height_eligible'),
total_measured=Sum('height_measured_in_month'),
).order_by('%s_name' % loc_level, '%s_map_location_name' % loc_level)
if not show_test:
queryset = apply_exclude(domain, queryset)
if 'age_tranche' not in config:
queryset = queryset.exclude(age_tranche__in=[0, 6, 72])
return queryset
data_for_map = defaultdict(lambda: {
'moderate': 0,
'severe': 0,
'normal': 0,
'total': 0,
'total_measured': 0,
'original_name': []
})
severe_total = 0
moderate_total = 0
normal_total = 0
valid_total = 0
measured_total = 0
for row in get_data_for(config):
valid = row['valid'] 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
severe_total += severe
moderate_total += moderate
normal_total += normal
valid_total += valid
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'] += valid
data_for_map[on_map_name]['total_measured'] += total_measured
data_for_map[on_map_name]['original_name'].append(name)
for data_for_location in six.itervalues(data_for_map):
numerator = data_for_location['moderate'] + data_for_location['severe']
value = numerator * 100 / (data_for_location['total'] or 1)
if value < 5:
data_for_location.update({'fillKey': '0%-5%'})
elif 5 <= value <= 7:
data_for_location.update({'fillKey': '5%-7%'})
elif value > 7:
data_for_location.update({'fillKey': '7%-100%'})
fills = OrderedDict()
fills.update({'0%-5%': MapColors.PINK})
fills.update({'5%-7%': MapColors.ORANGE})
fills.update({'7%-100%': MapColors.RED})
fills.update({'defaultFill': MapColors.GREY})
sum_of_indicators = moderate_total + severe_total + normal_total
percent_unmeasured = (valid_total - sum_of_indicators) * 100 / float(valid_total or 1)
gender_label, age_label, chosen_filters = chosen_filters_to_labels(config, default_interval='6 - 60 months')
return {
"slug": "severe",
"label": "Percent of Children{gender} Wasted ({age})".format(
gender=gender_label,
age=age_label
),
"fills": fills,
"rightLegend": {
"average": "%.2f" % (((severe_total + moderate_total) * 100) / float(valid_total or 1)),
"info": _((
"Percentage of children between {} enrolled for ICDS services with "
"weight-for-height below -2 standard deviations of the WHO Child Growth Standards median. "
"<br/><br/>"
"Wasting in children is a symptom of acute undernutrition usually as a consequence "
"of insufficient food intake or a high incidence of infectious diseases. Severe Acute "
"Malnutrition (SAM) is nutritional status for a child who has severe wasting "
"(weight-for-height) below -3 Z and Moderate Acute Malnutrition (MAM) is nutritional "
"status for a child that has moderate wasting (weight-for-height) below -2Z."
.format(age_label)
)),
"extended_info": [
{
'indicator': 'Total Children{} weighed in given month:'.format(chosen_filters),
'value': indian_formatted_number(valid_total)
},
{
'indicator': 'Total Children{} with height measured in given month:'
.format(chosen_filters),
'value': indian_formatted_number(measured_total)
},
{
'indicator': '% Unmeasured{}:'.format(chosen_filters),
'value': '%.2f%%' % percent_unmeasured
},
{
'indicator': '% Severely Acute Malnutrition{}:'.format(chosen_filters),
'value': '%.2f%%' % (severe_total * 100 / float(valid_total or 1))
},
{
'indicator': '% Moderately Acute Malnutrition{}:'.format(chosen_filters),
'value': '%.2f%%' % (moderate_total * 100 / float(valid_total or 1))
},
{
'indicator': '% Normal{}:'.format(chosen_filters),
'value': '%.2f%%' % (normal_total * 100 / float(valid_total or 1))
}
]
},
"data": dict(data_for_map),
}
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):
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_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.exclude(age_tranche=72)
chart_data = {
'blue': [],
}
tooltips_data = defaultdict(lambda: {
'severe': 0,
'moderate': 0,
'total': 0,
'normal': 0,
'total_measured': 0
})
loc_children = get_child_locations(domain, location_id, show_test)
result_set = set()
for row in data:
total = row['total'] or 0
name = row['%s_name' % loc_level]
result_set.add(name)
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 six.iteritems(row_values):
tooltips_data[name][prop] += value
value = (moderate + severe) / float(total_measured or 1)
chart_data['blue'].append([
name, value
])
for sql_location in loc_children:
if sql_location.name not in result_set:
chart_data['blue'].append([sql_location.name, 0])
chart_data['blue'] = sorted(chart_data['blue'])
__, __, 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_maternal_child_data(domain,
config,
show_test=False,
icds_feature_flag=False):
def get_data_for_child_health_monthly(date, filters):
age_filters = {
'age_tranche': 72
} if icds_feature_flag else {
'age_tranche__in': [0, 6, 72]
}
moderately_underweight = exclude_records_by_age_for_column(
{'age_tranche': 72}, 'nutrition_status_moderately_underweight')
severely_underweight = exclude_records_by_age_for_column(
{'age_tranche': 72}, 'nutrition_status_severely_underweight')
wasting_moderate = exclude_records_by_age_for_column(
age_filters, wasting_moderate_column(icds_feature_flag))
wasting_severe = exclude_records_by_age_for_column(
age_filters, wasting_severe_column(icds_feature_flag))
stunting_moderate = exclude_records_by_age_for_column(
age_filters, stunting_moderate_column(icds_feature_flag))
stunting_severe = exclude_records_by_age_for_column(
age_filters, stunting_severe_column(icds_feature_flag))
nutrition_status_weighed = exclude_records_by_age_for_column(
{'age_tranche': 72}, 'nutrition_status_weighed')
height_measured_in_month = exclude_records_by_age_for_column(
age_filters, hfa_recorded_in_month_column(icds_feature_flag))
weighed_and_height_measured_in_month = exclude_records_by_age_for_column(
age_filters, wfh_recorded_in_month_column(icds_feature_flag))
queryset = AggChildHealthMonthly.objects.filter(
month=date, **filters).values('aggregation_level').annotate(
underweight=(Sum(moderately_underweight) +
Sum(severely_underweight)),
valid=Sum(nutrition_status_weighed),
wasting=Sum(wasting_moderate) + Sum(wasting_severe),
stunting=Sum(stunting_moderate) + Sum(stunting_severe),
height_measured_in_month=Sum(height_measured_in_month),
weighed_and_height_measured_in_month=Sum(
weighed_and_height_measured_in_month),
low_birth_weight=Sum('low_birth_weight_in_month'),
bf_birth=Sum('bf_at_birth'),
born=Sum('born_in_month'),
weighed_and_born_in_month=Sum('weighed_and_born_in_month'),
ebf=Sum('ebf_in_month'),
ebf_eli=Sum('ebf_eligible'),
cf_initiation=Sum('cf_initiation_in_month'),
cf_initiation_eli=Sum('cf_initiation_eligible'))
if not show_test:
queryset = apply_exclude(domain, queryset)
return queryset
def get_data_for_deliveries(date, filters):
queryset = AggCcsRecordMonthly.objects.filter(
month=date, **filters).values('aggregation_level').annotate(
institutional_delivery=Sum('institutional_delivery_in_month'),
delivered=Sum('delivered_in_month'))
if not show_test:
queryset = apply_exclude(domain, queryset)
return queryset
current_month = datetime(*config['month'])
previous_month = datetime(*config['prev_month'])
del config['month']
del config['prev_month']
this_month_data = get_data_for_child_health_monthly(current_month, config)
prev_month_data = get_data_for_child_health_monthly(previous_month, config)
deliveries_this_month = get_data_for_deliveries(current_month, config)
deliveries_prev_month = get_data_for_deliveries(previous_month, config)
gender_label, age_label, chosen_filters = chosen_filters_to_labels(
config, default_interval=default_age_interval(icds_feature_flag))
return {
'records':
[[{
'label':
_('Underweight (Weight-for-Age)'),
'help_text':
underweight_children_help_text(),
'percent':
percent_diff('underweight', this_month_data, prev_month_data,
'valid'),
'color':
'red' if percent_diff('underweight', this_month_data,
prev_month_data, 'valid') > 0 else 'green',
'value':
get_value(this_month_data, 'underweight'),
'all':
get_value(this_month_data, 'valid'),
'format':
'percent_and_div',
'frequency':
'month',
'redirect':
'maternal_and_child/underweight_children'
}, {
'label':
_('Wasting (Weight-for-Height)'),
'help_text':
_(wasting_help_text(age_label)),
'percent':
percent_diff('wasting', this_month_data, prev_month_data,
'weighed_and_height_measured_in_month'),
'color':
'red' if percent_diff('wasting', this_month_data, prev_month_data,
'weighed_and_height_measured_in_month') > 0
else 'green',
'value':
get_value(this_month_data, 'wasting'),
'all':
get_value(this_month_data, 'weighed_and_height_measured_in_month'),
'format':
'percent_and_div',
'frequency':
'month',
'redirect':
'maternal_and_child/wasting'
}],
[{
'label':
_('Stunting (Height-for-Age)'),
'help_text':
_(stunting_help_text(age_label)),
'percent':
percent_diff('stunting', this_month_data, prev_month_data,
'height_measured_in_month'),
'color':
'red'
if percent_diff('stunting', this_month_data, prev_month_data,
'height_measured_in_month') > 0 else 'green',
'value':
get_value(this_month_data, 'stunting'),
'all':
get_value(this_month_data, 'height_measured_in_month'),
'format':
'percent_and_div',
'frequency':
'month',
'redirect':
'maternal_and_child/stunting'
}, {
'label':
_('Newborns with Low Birth Weight'),
'help_text':
_((new_born_with_low_weight_help_text(html=False))),
'percent':
percent_diff('low_birth_weight', this_month_data, prev_month_data,
'weighed_and_born_in_month'),
'color':
get_color_with_red_positive(
percent_diff('low_birth_weight', this_month_data,
prev_month_data, 'weighed_and_born_in_month')),
'value':
get_value(this_month_data, 'low_birth_weight'),
'all':
get_value(this_month_data, 'weighed_and_born_in_month'),
'format':
'percent_and_div',
'frequency':
'month',
'redirect':
'maternal_and_child/low_birth'
}],
[{
'label':
_('Early Initiation of Breastfeeding'),
'help_text':
early_initiation_breastfeeding_help_text(),
'percent':
percent_diff('bf_birth', this_month_data, prev_month_data,
'born'),
'color':
get_color_with_green_positive(
percent_diff('bf_birth', this_month_data, prev_month_data,
'born')),
'value':
get_value(this_month_data, 'bf_birth'),
'all':
get_value(this_month_data, 'born'),
'format':
'percent_and_div',
'frequency':
'month',
'redirect':
'maternal_and_child/early_initiation'
}, {
'label':
_('Exclusive Breastfeeding'),
'help_text':
exclusive_breastfeeding_help_text(),
'percent':
percent_diff('ebf', this_month_data, prev_month_data, 'ebf_eli'),
'color':
get_color_with_green_positive(
percent_diff('ebf', this_month_data, prev_month_data,
'ebf_eli')),
'value':
get_value(this_month_data, 'ebf'),
'all':
get_value(this_month_data, 'ebf_eli'),
'format':
'percent_and_div',
'frequency':
'month',
'redirect':
'maternal_and_child/exclusive_breastfeeding'
}],
[{
'label':
_('Children initiated appropriate Complementary Feeding'),
'help_text':
children_initiated_appropriate_complementary_feeding_help_text(),
'percent':
percent_diff('cf_initiation', this_month_data, prev_month_data,
'cf_initiation_eli'),
'color':
get_color_with_green_positive(
percent_diff('cf_initiation', this_month_data,
prev_month_data, 'cf_initiation_eli')),
'value':
get_value(this_month_data, 'cf_initiation'),
'all':
get_value(this_month_data, 'cf_initiation_eli'),
'format':
'percent_and_div',
'frequency':
'month',
'redirect':
'maternal_and_child/children_initiated'
}, {
'label':
_('Institutional Deliveries'),
'help_text':
institutional_deliveries_help_text(),
'percent':
percent_diff('institutional_delivery', deliveries_this_month,
deliveries_prev_month, 'delivered'),
'color':
get_color_with_green_positive(
percent_diff('institutional_delivery', deliveries_this_month,
deliveries_prev_month, 'delivered')),
'value':
get_value(deliveries_this_month, 'institutional_delivery'),
'all':
get_value(deliveries_this_month, 'delivered'),
'format':
'percent_and_div',
'frequency':
'month',
'redirect':
'maternal_and_child/institutional_deliveries'
}]]
}
def get_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_enrolled_children_data_map(domain, config, loc_level, show_test=False):
def get_data_for(filters):
filters['month'] = datetime(*filters['month'])
queryset = AggChildHealthMonthly.objects.filter(
**filters
).values(
'%s_name' % loc_level, '%s_map_location_name' % loc_level
).annotate(
valid=Sum('valid_in_month'),
all=Sum('valid_all_registered_in_month')
).order_by('%s_name' % loc_level, '%s_map_location_name' % loc_level)
if not show_test:
queryset = apply_exclude(domain, queryset)
return queryset
data_for_map = defaultdict(lambda: {
'valid': 0,
'all': 0,
'original_name': [],
'fillKey': 'Children'
})
average = []
total_valid = 0
total = 0
for row in get_data_for(config):
valid = row['valid'] or 0
name = row['%s_name' % loc_level]
all_children = row['all'] or 0
on_map_name = row['%s_map_location_name' % loc_level] or name
average.append(valid)
total_valid += valid
total += all_children
data_for_map[on_map_name]['valid'] += valid
data_for_map[on_map_name]['all'] += all_children
data_for_map[on_map_name]['original_name'].append(name)
fills = OrderedDict()
fills.update({'Children': MapColors.BLUE})
fills.update({'defaultFill': MapColors.GREY})
gender_ignored, age_label, chosen_filters = chosen_filters_to_labels(config, default_interval='0 - 6 years')
return {
"slug": "enrolled_children",
"label": "",
"fills": fills,
"rightLegend": {
"average": '%.2f' % (total_valid * 100 / float(total or 1)),
"info": percent_children_enrolled_help_text(age_label=age_label),
"extended_info": [
{
'indicator':
'Number of children{} who are enrolled for Anganwadi Services:'
.format(chosen_filters),
'value': indian_formatted_number(total_valid)
},
{
'indicator': (
'Total number of children{} who are registered: '
.format(chosen_filters)
),
'value': indian_formatted_number(total)
},
{
'indicator': (
'Percentage of registered children{} who are enrolled for Anganwadi Services:'
.format(chosen_filters)
),
'value': '%.2f%%' % (total_valid * 100 / float(total or 1))
}
]
},
"data": dict(data_for_map),
}
def get_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_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_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_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_stunting_data_map(domain,
config,
loc_level,
show_test=False,
icds_feature_flag=False):
def get_data_for(filters):
filters['month'] = datetime(*filters['month'])
queryset = AggChildHealthMonthly.objects.filter(**filters).values(
'%s_name' % loc_level,
'%s_map_location_name' % loc_level).annotate(
moderate=Sum(stunting_moderate_column(icds_feature_flag)),
severe=Sum(stunting_severe_column(icds_feature_flag)),
normal=Sum(stunting_normal_column(icds_feature_flag)),
total=Sum('height_eligible'),
total_measured=Sum('height_measured_in_month'),
).order_by('%s_name' % loc_level,
'%s_map_location_name' % loc_level)
if not show_test:
queryset = apply_exclude(domain, queryset)
if 'age_tranche' not in config:
if icds_feature_flag:
queryset = queryset.exclude(age_tranche=72)
else:
queryset = queryset.exclude(age_tranche__in=[0, 6, 72])
return queryset
data_for_map = defaultdict(
lambda: {
'moderate': 0,
'severe': 0,
'normal': 0,
'total': 0,
'total_measured': 0,
'original_name': []
})
moderate_total = 0
severe_total = 0
normal_total = 0
all_total = 0
measured_total = 0
values_to_calculate_average = []
for row in get_data_for(config):
total = row['total'] or 0
name = row['%s_name' % loc_level]
on_map_name = row['%s_map_location_name' % loc_level] or name
severe = row['severe'] or 0
moderate = row['moderate'] or 0
normal = row['normal'] or 0
total_measured = row['total_measured'] or 0
numerator = moderate + severe
values_to_calculate_average.append(numerator * 100 /
(total_measured or 1))
severe_total += severe
moderate_total += moderate
normal_total += normal
all_total += total
measured_total += total_measured
data_for_map[on_map_name]['severe'] += severe
data_for_map[on_map_name]['moderate'] += moderate
data_for_map[on_map_name]['normal'] += normal
data_for_map[on_map_name]['total'] += total
data_for_map[on_map_name]['total_measured'] += total_measured
data_for_map[on_map_name]['original_name'].append(name)
for data_for_location in six.itervalues(data_for_map):
numerator = data_for_location['moderate'] + data_for_location['severe']
value = numerator * 100 / (data_for_location['total_measured'] or 1)
if value < 25:
data_for_location.update({'fillKey': '0%-25%'})
elif 25 <= value < 38:
data_for_location.update({'fillKey': '25%-38%'})
elif value >= 38:
data_for_location.update({'fillKey': '38%-100%'})
fills = OrderedDict()
fills.update({'0%-25%': MapColors.PINK})
fills.update({'25%-38%': MapColors.ORANGE})
fills.update({'38%-100%': MapColors.RED})
fills.update({'defaultFill': MapColors.GREY})
gender_label, age_label, chosen_filters = chosen_filters_to_labels(
config, default_interval=default_age_interval(icds_feature_flag))
return {
"slug":
"severe",
"label":
"Percent of Children{gender} Stunted ({age})".format(
gender=gender_label, age=age_label),
"fills":
fills,
"rightLegend": {
"average":
"%.2f" % ((sum(values_to_calculate_average)) /
float(len(values_to_calculate_average) or 1)),
"info":
_(("Percentage of children ({}) enrolled for Anganwadi Services with height-for-age below "
"-2Z standard deviations of the WHO Child Growth Standards median."
"<br/><br/>"
"Stunting is a sign of chronic undernutrition and has long lasting harmful "
"consequences on the growth of a child".format(age_label))),
"extended_info": [{
'indicator':
'Total Children{} eligible to have height measured:'.format(
chosen_filters),
'value':
indian_formatted_number(all_total)
}, {
'indicator':
'Total Children{} with height measured in given month:'.format(
chosen_filters),
'value':
indian_formatted_number(measured_total)
}, {
'indicator':
'Number of Children{} unmeasured:'.format(chosen_filters),
'value':
indian_formatted_number(all_total - measured_total)
}, {
'indicator':
'% children{} with severely stunted growth:'.format(
chosen_filters),
'value':
'%.2f%%' % (severe_total * 100 / float(measured_total or 1))
}, {
'indicator':
'% children{} with moderate stunted growth:'.format(
chosen_filters),
'value':
'%.2f%%' % (moderate_total * 100 / float(measured_total or 1))
}, {
'indicator':
'% children{} with normal stunted growth:'.format(
chosen_filters),
'value':
'%.2f%%' % (normal_total * 100 / float(measured_total or 1))
}]
},
"data":
dict(data_for_map),
}
def get_prevalence_of_undernutrition_data_map(domain,
config,
loc_level,
show_test=False):
def get_data_for(filters):
filters['month'] = datetime(*filters['month'])
queryset = AggChildHealthMonthly.objects.filter(**filters).values(
'%s_name' % loc_level,
'%s_map_location_name' % loc_level).annotate(
moderately_underweight=Sum(
'nutrition_status_moderately_underweight'),
severely_underweight=Sum(
'nutrition_status_severely_underweight'),
normal=Sum('nutrition_status_normal'),
weighed=Sum('nutrition_status_weighed'),
total=Sum('wer_eligible'),
).order_by('%s_name' % loc_level,
'%s_map_location_name' % loc_level)
if not show_test:
queryset = apply_exclude(domain, queryset)
if 'age_tranche' not in config:
queryset = queryset.exclude(age_tranche=72)
return queryset
data_for_map = defaultdict(
lambda: {
'moderately_underweight': 0,
'severely_underweight': 0,
'normal': 0,
'weighed': 0,
'total': 0,
'original_name': []
})
moderately_underweight_total = 0
severely_underweight_total = 0
normal_total = 0
all_total = 0
weighed_total = 0
values_to_calculate_average = []
for row in get_data_for(config):
weighed = row['weighed'] or 0
total = row['total'] or 0
name = row['%s_name' % loc_level]
on_map_name = row['%s_map_location_name' % loc_level] or name
severely_underweight = row['severely_underweight'] or 0
moderately_underweight = row['moderately_underweight'] or 0
normal = row['normal'] or 0
numerator = moderately_underweight + severely_underweight
values_to_calculate_average.append(numerator * 100 / (weighed or 1))
moderately_underweight_total += moderately_underweight
severely_underweight_total += severely_underweight
normal_total += normal
all_total += total
weighed_total += weighed
data_for_map[on_map_name][
'severely_underweight'] += severely_underweight
data_for_map[on_map_name][
'moderately_underweight'] += moderately_underweight
data_for_map[on_map_name]['normal'] += normal
data_for_map[on_map_name]['total'] += total
data_for_map[on_map_name]['weighed'] += weighed
data_for_map[on_map_name]['original_name'].append(name)
for data_for_location in six.itervalues(data_for_map):
numerator = data_for_location[
'moderately_underweight'] + data_for_location[
'severely_underweight']
value = numerator * 100 / (data_for_location['weighed'] or 1)
if value < 20:
data_for_location.update({'fillKey': '0%-20%'})
elif 20 <= value < 35:
data_for_location.update({'fillKey': '20%-35%'})
elif value >= 35:
data_for_location.update({'fillKey': '35%-100%'})
fills = OrderedDict()
fills.update({'0%-20%': MapColors.PINK})
fills.update({'20%-35%': MapColors.ORANGE})
fills.update({'35%-100%': MapColors.RED})
fills.update({'defaultFill': MapColors.GREY})
average = ((sum(values_to_calculate_average)) /
float(len(values_to_calculate_average) or 1))
gender_label, age_label, chosen_filters = chosen_filters_to_labels(
config, default_interval='0 - 5 years')
return {
"slug":
"moderately_underweight",
"label":
"Percent of Children{gender} Underweight ({age})".format(
gender=gender_label, age=age_label),
"fills":
fills,
"rightLegend": {
"average":
average,
"info":
_(("Of the total children enrolled for Anganwadi services and weighed, the percentage of "
"children between {} years who were moderately/severely underweight in the current month. "
"<br/><br/>"
"Children who are moderately or severely underweight have a higher risk of mortality."
.format(age_label))),
"extended_info": [{
'indicator':
'Total Children{} weighed in given month:'.format(
chosen_filters),
'value':
indian_formatted_number(weighed_total)
}, {
'indicator':
'Number of children unweighed{}:'.format(chosen_filters),
'value':
indian_formatted_number(all_total - weighed_total)
}, {
'indicator':
'% Severely Underweight{}:'.format(chosen_filters),
'value':
'%.2f%%' %
(severely_underweight_total * 100 / float(weighed_total or 1))
}, {
'indicator':
'% Moderately Underweight{}:'.format(chosen_filters),
'value':
'%.2f%%' % (moderately_underweight_total * 100 /
float(weighed_total or 1))
}, {
'indicator':
'% Normal{}:'.format(chosen_filters),
'value':
'%.2f%%' % (normal_total * 100 / float(weighed_total or 1))
}]
},
"data":
dict(data_for_map)
}
def get_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_prevalence_of_severe_data_map(domain, config, loc_level, show_test=False, icds_feature_flag=False):
def get_data_for(filters):
filters['month'] = datetime(*filters['month'])
queryset = AggChildHealthMonthly.objects.filter(
**filters
).values(
'%s_name' % loc_level, '%s_map_location_name' % loc_level
).annotate(
moderate=Sum(wasting_moderate_column(icds_feature_flag)),
severe=Sum(wasting_severe_column(icds_feature_flag)),
normal=Sum(wasting_normal_column(icds_feature_flag)),
total_height_eligible=Sum('height_eligible'),
total_weighed=Sum('nutrition_status_weighed'),
total_measured=Sum(wfh_recorded_in_month_column(icds_feature_flag)),
).order_by('%s_name' % loc_level, '%s_map_location_name' % loc_level)
if not show_test:
queryset = apply_exclude(domain, queryset)
if 'age_tranche' not in config:
queryset = queryset.exclude(age_tranche=72)
return queryset
data_for_map = defaultdict(lambda: {
'moderate': 0,
'severe': 0,
'normal': 0,
'total_weighed': 0,
'total_measured': 0,
'total_height_eligible': 0,
'original_name': []
})
severe_for_all_locations = 0
moderate_for_all_locations = 0
normal_for_all_locations = 0
weighed_for_all_locations = 0
measured_for_all_locations = 0
height_eligible_for_all_locations = 0
values_to_calculate_average = {'numerator': 0, 'denominator': 0}
for row in get_data_for(config):
total_weighed = row['total_weighed'] or 0
total_height_eligible = row['total_height_eligible'] or 0
name = row['%s_name' % loc_level]
on_map_name = row['%s_map_location_name' % loc_level] or name
severe = row['severe'] or 0
moderate = row['moderate'] or 0
normal = row['normal'] or 0
total_measured = row['total_measured'] or 0
values_to_calculate_average['numerator'] += moderate if moderate else 0
values_to_calculate_average['numerator'] += severe if severe else 0
values_to_calculate_average['denominator'] += total_measured if total_measured else 0
severe_for_all_locations += severe
moderate_for_all_locations += moderate
normal_for_all_locations += normal
weighed_for_all_locations += total_weighed
measured_for_all_locations += total_measured
height_eligible_for_all_locations += total_height_eligible
data_for_map[on_map_name]['severe'] += severe
data_for_map[on_map_name]['moderate'] += moderate
data_for_map[on_map_name]['normal'] += normal
data_for_map[on_map_name]['total_weighed'] += total_weighed
data_for_map[on_map_name]['total_measured'] += total_measured
data_for_map[on_map_name]['total_height_eligible'] += total_height_eligible
data_for_map[on_map_name]['original_name'].append(name)
for data_for_location in six.itervalues(data_for_map):
numerator = data_for_location['moderate'] + data_for_location['severe']
value = numerator * 100 / (data_for_location['total_measured'] or 1)
if value < 5:
data_for_location.update({'fillKey': '0%-5%'})
elif 5 <= value <= 7:
data_for_location.update({'fillKey': '5%-7%'})
elif value > 7:
data_for_location.update({'fillKey': '7%-100%'})
fills = OrderedDict()
fills.update({'0%-5%': MapColors.PINK})
fills.update({'5%-7%': MapColors.ORANGE})
fills.update({'7%-100%': MapColors.RED})
fills.update({'defaultFill': MapColors.GREY})
gender_label, age_label, chosen_filters = chosen_filters_to_labels(
config,
default_interval=default_age_interval(icds_feature_flag)
)
average = (
(values_to_calculate_average['numerator'] * 100) /
float(values_to_calculate_average['denominator'] or 1)
)
return {
"slug": "severe",
"label": "Percent of Children{gender} Wasted ({age})".format(
gender=gender_label,
age=age_label
),
"fills": fills,
"rightLegend": {
"average": "%.2f" % average,
"info": wasting_help_text(age_label),
"extended_info": [
{
'indicator': 'Total Children{} weighed in given month:'.format(chosen_filters),
'value': indian_formatted_number(weighed_for_all_locations)
},
{
'indicator': 'Total Children{} with height measured in given month:'
.format(chosen_filters),
'value': indian_formatted_number(measured_for_all_locations)
},
{
'indicator': 'Number of children{} unmeasured:'.format(chosen_filters),
'value': indian_formatted_number(height_eligible_for_all_locations - weighed_for_all_locations)
},
{
'indicator': '% Severely Acute Malnutrition{}:'.format(chosen_filters),
'value': '%.2f%%' % (severe_for_all_locations * 100 / float(measured_for_all_locations or 1))
},
{
'indicator': '% Moderately Acute Malnutrition{}:'.format(chosen_filters),
'value': '%.2f%%' % (moderate_for_all_locations * 100 / float(measured_for_all_locations or 1))
},
{
'indicator': '% Normal{}:'.format(chosen_filters),
'value': '%.2f%%' % (normal_for_all_locations * 100 / float(measured_for_all_locations or 1))
}
]
},
"data": dict(data_for_map),
}
def get_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_enrolled_children_data_map(domain, config, loc_level, show_test=False):
def get_data_for(filters):
filters['month'] = datetime(*filters['month'])
queryset = AggChildHealthMonthly.objects.filter(**filters).values(
'%s_name' % loc_level,
'%s_map_location_name' % loc_level).annotate(
valid=Sum('valid_in_month'),
all=Sum('valid_all_registered_in_month')).order_by(
'%s_name' % loc_level, '%s_map_location_name' % loc_level)
if not show_test:
queryset = apply_exclude(domain, queryset)
return queryset
data_for_map = defaultdict(lambda: {
'valid': 0,
'all': 0,
'original_name': [],
'fillKey': 'Children'
})
average = []
total_valid = 0
total = 0
for row in get_data_for(config):
valid = row['valid'] or 0
name = row['%s_name' % loc_level]
all_children = row['all'] or 0
on_map_name = row['%s_map_location_name' % loc_level] or name
average.append(valid)
total_valid += valid
total += all_children
data_for_map[on_map_name]['valid'] += valid
data_for_map[on_map_name]['all'] += all_children
data_for_map[on_map_name]['original_name'].append(name)
fills = OrderedDict()
fills.update({'Children': MapColors.BLUE})
fills.update({'defaultFill': MapColors.GREY})
gender_ignored, age_label, chosen_filters = chosen_filters_to_labels(
config, default_interval='0 - 6 years')
return {
"slug": "enrolled_children",
"label": "",
"fills": fills,
"rightLegend": {
"average":
sum(average) / float(len(average) or 1),
"average_format":
'number',
"info":
_(("Total number of children between the age of ({}) who are enrolled for ICDS services"
.format(age_label))),
"extended_info": [{
'indicator':
'Number of children{} who are enrolled for ICDS 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 ICDS services:'
.format(chosen_filters)),
'value':
'%.2f%%' % (total_valid * 100 / float(total or 1))
}]
},
"data": dict(data_for_map),
}
