def get_infants_weight_scale_sector_data(domain,
config,
loc_level,
location_id,
show_test=False):
group_by = ['%s_name' % loc_level]
config['month'] = datetime(*config['month'])
data = AggAwcMonthly.objects.filter(**config).values(*group_by).annotate(
in_month=Sum('infra_infant_weighing_scale'),
all=Sum('num_awc_infra_last_update'),
).order_by('%s_name' % loc_level)
if not show_test:
data = apply_exclude(domain, data)
chart_data = {
'blue': [],
}
tooltips_data = defaultdict(lambda: {'in_month': 0, 'all': 0})
loc_children = get_child_locations(domain, location_id, show_test)
result_set = set()
for row in data:
valid = row['all']
name = row['%s_name' % loc_level]
result_set.add(name)
in_month = row['in_month']
row_values = {'in_month': in_month or 0, 'all': valid or 0}
for prop, value in six.iteritems(row_values):
tooltips_data[name][prop] += value
value = (in_month or 0) / float(valid 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'])
return {
"tooltips_data":
dict(tooltips_data),
"info":
awcs_reported_weighing_scale_infants_help_text(),
"chart_data": [
{
"values": chart_data['blue'],
"key": "",
"strokeWidth": 2,
"classed": "dashed",
"color": MapColors.BLUE
},
]
}
def get_adolescent_girls_sector_data(domain,
config,
loc_level,
location_id,
show_test=False):
group_by = ['%s_name' % loc_level]
config['month'] = datetime(*config['month'])
data = AggAwcMonthly.objects.filter(**config).values(*group_by).annotate(
valid=Sum('cases_person_adolescent_girls_11_14') +
Sum('cases_person_adolescent_girls_15_18'),
all=Sum('cases_person_adolescent_girls_11_14_all') +
Sum('cases_person_adolescent_girls_15_18_all'),
).order_by('%s_name' % loc_level)
if not show_test:
data = apply_exclude(domain, data)
chart_data = {'blue': []}
tooltips_data = defaultdict(lambda: {'valid': 0, 'all': 0})
loc_children = get_child_locations(domain, location_id, show_test)
result_set = set()
for row in data:
valid = row['valid'] or 0
all_adolescent = row['all'] or 0
name = row['%s_name' % loc_level]
result_set.add(name)
row_values = {'valid': valid, 'all': all_adolescent}
for prop, value in six.iteritems(row_values):
tooltips_data[name][prop] += value
chart_data['blue'].append([name, valid])
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'])
return {
"tooltips_data":
dict(tooltips_data),
"format":
"number",
"info":
_(("Total number of adolescent girls who are enrolled for ICDS services"
)),
"chart_data": [{
"values": chart_data['blue'],
"key": "Number Of Girls",
"strokeWidth": 2,
"classed": "dashed",
"color": MapColors.BLUE
}]
}
def get_enrolled_children_sector_data(domain,
config,
loc_level,
location_id,
show_test=False):
group_by = ['%s_name' % loc_level]
config['month'] = datetime(*config['month'])
data = AggChildHealthMonthly.objects.filter(**config).values(
*group_by).annotate(valid=Sum('valid_in_month'),
all=Sum('valid_all_registered_in_month')).order_by(
'%s_name' % loc_level)
if not show_test:
data = apply_exclude(domain, data)
chart_data = {'blue': []}
tooltips_data = defaultdict(lambda: {'valid': 0, 'all': 0})
loc_children = get_child_locations(domain, location_id, show_test)
result_set = set()
for row in data:
valid = row['valid'] or 0
all_children = row['all'] or 0
name = row['%s_name' % loc_level]
result_set.add(name)
row_values = {'valid': valid, 'all': all_children}
for prop, value in six.iteritems(row_values):
tooltips_data[name][prop] += value
chart_data['blue'].append([name, valid])
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'])
return {
"tooltips_data":
dict(tooltips_data),
"format":
"number",
"info":
_(("Total number of children between the age of 0 - 6 years who are enrolled for Anganwadi Services"
)),
"chart_data": [{
"values": chart_data['blue'],
"key": "",
"strokeWidth": 2,
"classed": "dashed",
"color": MapColors.BLUE
}]
}
def get_lactating_enrolled_women_sector_data(domain,
config,
loc_level,
location_id,
show_test=False):
group_by = ['%s_name' % loc_level]
config['month'] = datetime(*config['month'])
data = AggCcsRecordMonthly.objects.filter(**config).values(
*group_by).annotate(valid=Sum('lactating'),
all=Sum('lactating_all')).order_by('%s_name' %
loc_level)
if not show_test:
data = apply_exclude(domain, data)
chart_data = {'blue': []}
tooltips_data = defaultdict(lambda: {'valid': 0, 'all': 0})
loc_children = get_child_locations(domain, location_id, show_test)
result_set = set()
for row in data:
valid = row['valid'] or 0
all_lactating = row['all'] or 0
name = row['%s_name' % loc_level]
result_set.add(name)
row_values = {'valid': valid, 'all': all_lactating}
for prop, value in six.iteritems(row_values):
tooltips_data[name][prop] += value
chart_data['blue'].append([name, valid])
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'])
return {
"tooltips_data":
dict(tooltips_data),
"format":
"number",
"info":
percent_lactating_women_enrolled_help_text(),
"chart_data": [{
"values": chart_data['blue'],
"key": "",
"strokeWidth": 2,
"classed": "dashed",
"color": MapColors.BLUE
}]
}
def get_registered_household_sector_data(domain,
config,
loc_level,
location_id,
show_test=False):
group_by = ['%s_name' % loc_level]
config['month'] = datetime(*config['month'])
data = AggAwcMonthly.objects.filter(**config).values(*group_by).annotate(
household=Sum('cases_household'), ).order_by('%s_name' % loc_level)
if not show_test:
data = apply_exclude(domain, data)
chart_data = {'blue': []}
tooltips_data = defaultdict(lambda: {'household': 0})
loc_children = get_child_locations(domain, location_id, show_test)
result_set = set()
for row in data:
name = row['%s_name' % loc_level]
household = row['household']
result_set.add(name)
row_values = {'household': household}
for prop, value in six.iteritems(row_values):
tooltips_data[name][prop] += value
for name, value_dict in six.iteritems(tooltips_data):
chart_data['blue'].append([name, value_dict['household'] or 0])
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'])
return {
"tooltips_data":
dict(tooltips_data),
"format":
"number",
"info":
_("Total number of households registered"),
"chart_data": [{
"values": chart_data['blue'],
"key": "",
"strokeWidth": 2,
"classed": "dashed",
"color": MapColors.BLUE
}]
}
def get_registered_household_sector_data(domain, config, loc_level, location_id, show_test=False):
group_by = ['%s_name' % loc_level]
config['month'] = datetime(*config['month'])
data = AggAwcMonthly.objects.filter(
**config
).values(
*group_by
).annotate(
household=Sum('cases_household'),
).order_by('%s_name' % loc_level)
if not show_test:
data = apply_exclude(domain, data)
chart_data = {
'blue': []
}
tooltips_data = defaultdict(lambda: {
'household': 0
})
loc_children = get_child_locations(domain, location_id, show_test)
result_set = set()
for row in data:
name = row['%s_name' % loc_level]
household = row['household']
result_set.add(name)
row_values = {
'household': household
}
for prop, value in six.iteritems(row_values):
tooltips_data[name][prop] += value
for name, value_dict in six.iteritems(tooltips_data):
chart_data['blue'].append([name, value_dict['household'] or 0])
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'])
return {
"tooltips_data": dict(tooltips_data),
"format": "number",
"info": _("Total number of households registered"),
"chart_data": [
{
"values": chart_data['blue'],
"key": "",
"strokeWidth": 2,
"classed": "dashed",
"color": MapColors.BLUE
}
]
}
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_awcs_covered_sector_data(domain, config, loc_level, location_id, show_test=False):
group_by = ['%s_name' % loc_level]
config['month'] = datetime(*config['month'])
level = config['aggregation_level']
data = AggAwcMonthly.objects.filter(
**config
).values(
*group_by
).annotate(
states=Sum('num_launched_states') if level <= 1 else Max('num_launched_states'),
districts=Sum('num_launched_districts') if level <= 2 else Max('num_launched_districts'),
blocks=Sum('num_launched_blocks') if level <= 3 else Max('num_launched_blocks'),
supervisors=Sum('num_launched_supervisors') if level <= 4 else Max('num_launched_supervisors'),
awcs=Sum('num_launched_awcs') if level <= 5 else Max('num_launched_awcs'),
).order_by('%s_name' % loc_level)
if not show_test:
data = apply_exclude(domain, data)
chart_data = {
'blue': [],
}
tooltips_data = defaultdict(lambda: {
'districts': 0,
'blocks': 0,
'states': 0,
'supervisors': 0,
'awcs': 0
})
loc_children = get_child_locations(domain, location_id, show_test)
result_set = set()
for row in data:
name = row['%s_name' % loc_level]
awcs = row['awcs'] or 0
supervisors = row['supervisors'] or 0
blocks = row['blocks'] or 0
districts = row['districts'] or 0
states = row['states'] or 0
result_set.add(name)
row_values = {
'awcs': awcs,
'supervisors': supervisors,
'blocks': blocks,
'districts': districts,
'states': states,
}
for prop, value in six.iteritems(row_values):
tooltips_data[name][prop] += (value or 0)
for name, value_dict in six.iteritems(tooltips_data):
chart_data['blue'].append([name, value_dict['awcs']])
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'])
if level == 1:
prop = 'states'
elif level == 2:
prop = 'districts'
elif level == 3:
prop = 'blocks'
elif level == 4:
prop = 'supervisors'
else:
prop = 'awcs'
total_awcs = sum([(x['awcs'] or 0) for x in six.itervalues(tooltips_data)])
total = sum([(x[prop] or 0) for x in six.itervalues(tooltips_data)])
info = _(
"{:s}<br /><br />"
"Number of AWCs launched: {:d}".format(awcs_launched_help_text(), total_awcs)
)
if level != 5:
info = _(
"{:s}<br /><br />"
"Number of AWCs launched: {:d} <br />"
"Number of {:s} launched: {:d}".format(awcs_launched_help_text(), total_awcs, prop.title(), total)
)
return {
"tooltips_data": dict(tooltips_data),
"format": "number",
"info": info,
"chart_data": [
{
"values": chart_data['blue'],
"key": "",
"strokeWidth": 2,
"classed": "dashed",
"color": MapColors.BLUE
}
]
}
def get_newborn_with_low_birth_weight_data(domain,
config,
loc_level,
location_id,
show_test=False):
group_by = ['%s_name' % loc_level]
config['month'] = datetime(*config['month'])
data = AggChildHealthMonthly.objects.filter(**config).values(
*group_by).annotate(low_birth=Sum('low_birth_weight_in_month'),
in_month=Sum('weighed_and_born_in_month'),
all=Sum('born_in_month')).order_by('%s_name' %
loc_level)
if not show_test:
data = apply_exclude(domain, data)
chart_data = {
'blue': [],
}
tooltips_data = defaultdict(lambda: {
'in_month': 0,
'low_birth': 0,
'all': 0
})
loc_children = get_child_locations(domain, location_id, show_test)
result_set = set()
for row in data:
in_month = row['in_month'] or 0
name = row['%s_name' % loc_level]
result_set.add(name)
all_records = row['all'] or 0
low_birth = row['low_birth'] or 0
value = low_birth / float(in_month or 1)
tooltips_data[name]['low_birth'] += low_birth
tooltips_data[name]['in_month'] += in_month
tooltips_data[name]['all'] += all_records
chart_data['blue'].append([name, value])
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'])
return {
"tooltips_data":
dict(tooltips_data),
"info":
_((new_born_with_low_weight_help_text(html=True))),
"chart_data": [
{
"values": chart_data['blue'],
"key": "",
"strokeWidth": 2,
"classed": "dashed",
"color": MapColors.BLUE
},
]
}
def get_immunization_coverage_sector_data(domain, config, loc_level, location_id, show_test=False):
group_by = ['%s_name' % loc_level]
config['month'] = datetime(*config['month'])
data = AggChildHealthMonthly.objects.filter(
**config
).values(
*group_by
).annotate(
in_month=Sum('fully_immunized_on_time') + Sum('fully_immunized_late'),
eligible=Sum('fully_immunized_eligible'),
).order_by('%s_name' % loc_level)
if not show_test:
data = apply_exclude(domain, data)
chart_data = {
'blue': [],
}
tooltips_data = defaultdict(lambda: {
'children': 0,
'all': 0
})
loc_children = get_child_locations(domain, location_id, show_test)
result_set = set()
for row in data:
valid = row['eligible']
name = row['%s_name' % loc_level]
result_set.add(name)
in_month = row['in_month']
row_values = {
'children': in_month or 0,
'all': valid or 0
}
for prop, value in six.iteritems(row_values):
tooltips_data[name][prop] += value
value = (in_month or 0) / float(valid 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'])
return {
"tooltips_data": dict(tooltips_data),
"info": _((
"Of the total number of children enrolled for Anganwadi Services who are over a year old, the "
"percentage of children who have received the complete immunization as per the National Immunization "
"Schedule of India that is required by age 1."
"<br/><br/>"
"This includes the following immunizations:<br/>"
"If Pentavalent path: Penta1/2/3, OPV1/2/3, BCG, Measles, VitA1<br/>"
"If DPT/HepB path: DPT1/2/3, HepB1/2/3, OPV1/2/3, BCG, Measles, VitA1"
)),
"chart_data": [
{
"values": chart_data['blue'],
"key": "",
"strokeWidth": 2,
"classed": "dashed",
"color": MapColors.BLUE
}
]
}
def get_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)),
weighed_and_height_measured=Sum('weighed_and_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:
data = data.exclude(age_tranche=72)
chart_data = {
'blue': [],
}
tooltips_data = defaultdict(lambda: {
'severe': 0,
'moderate': 0,
'total_height_eligible': 0,
'weighed_and_height_measured': 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
weighed_and_height_measured = row['weighed_and_height_measured'] 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
tooltips_data[name]['weighed_and_height_measured'] += weighed_and_height_measured
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_awcs_covered_sector_data(domain, config, loc_level, location_id, show_test=False):
group_by = ['%s_name' % loc_level]
config['month'] = datetime(*config['month'])
level = config['aggregation_level']
data = AggAwcMonthly.objects.filter(
**config
).values(
*group_by
).annotate(
states=Sum('num_launched_states') if level <= 1 else Max('num_launched_states'),
districts=Sum('num_launched_districts') if level <= 2 else Max('num_launched_districts'),
blocks=Sum('num_launched_blocks') if level <= 3 else Max('num_launched_blocks'),
supervisors=Sum('num_launched_supervisors') if level <= 4 else Max('num_launched_supervisors'),
awcs=Sum('num_launched_awcs') if level <= 5 else Max('num_launched_awcs'),
).order_by('%s_name' % loc_level)
if not show_test:
data = apply_exclude(domain, data)
chart_data = {
'blue': [],
}
tooltips_data = defaultdict(lambda: {
'districts': 0,
'blocks': 0,
'states': 0,
'supervisors': 0,
'awcs': 0
})
loc_children = get_child_locations(domain, location_id, show_test)
result_set = set()
for row in data:
name = row['%s_name' % loc_level]
awcs = row['awcs'] or 0
supervisors = row['supervisors'] or 0
blocks = row['blocks'] or 0
districts = row['districts'] or 0
states = row['states'] or 0
result_set.add(name)
row_values = {
'awcs': awcs,
'supervisors': supervisors,
'blocks': blocks,
'districts': districts,
'states': states,
}
for prop, value in row_values.items():
tooltips_data[name][prop] += (value or 0)
for name, value_dict in tooltips_data.items():
chart_data['blue'].append([name, value_dict['awcs']])
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'])
if level == 1:
prop = 'states'
elif level == 2:
prop = 'districts'
elif level == 3:
prop = 'blocks'
elif level == 4:
prop = 'supervisors'
else:
prop = 'awcs'
total_awcs = sum([(x['awcs'] or 0) for x in tooltips_data.values()])
total = sum([(x[prop] or 0) for x in tooltips_data.values()])
info = _(
"{:s}<br /><br />"
"Number of AWCs launched: {:d}".format(awcs_launched_help_text(), total_awcs)
)
if level != 5:
info = _(
"{:s}<br /><br />"
"Number of AWCs launched: {:d} <br />"
"Number of {:s} launched: {:d}".format(awcs_launched_help_text(), total_awcs, prop.title(), total)
)
return {
"tooltips_data": dict(tooltips_data),
"format": "number",
"info": info,
"chart_data": [
{
"values": chart_data['blue'],
"key": "",
"strokeWidth": 2,
"classed": "dashed",
"color": MapColors.BLUE
}
]
}
def get_institutional_deliveries_sector_data(domain,
config,
loc_level,
location_id,
show_test=False):
group_by = ['%s_name' % loc_level]
config['month'] = datetime(*config['month'])
data = AggCcsRecordMonthly.objects.filter(**config).values(
*group_by).annotate(
in_month=Sum('institutional_delivery_in_month'),
eligible=Sum('delivered_in_month'),
).order_by('%s_name' % loc_level)
if not show_test:
data = apply_exclude(domain, data)
chart_data = {
'blue': [],
}
tooltips_data = defaultdict(lambda: {'children': 0, 'all': 0})
loc_children = get_child_locations(domain, location_id, show_test)
result_set = set()
for row in data:
valid = row['eligible']
name = row['%s_name' % loc_level]
result_set.add(name)
in_month = row['in_month']
row_values = {'children': in_month or 0, 'all': valid or 0}
for prop, value in six.iteritems(row_values):
tooltips_data[name][prop] += value
value = (in_month or 0) / float(valid 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'])
return {
"tooltips_data":
dict(tooltips_data),
"info":
_(("Of the total number of women enrolled for Anganwadi services who gave birth in the last month, "
"the percentage who delivered in a public or private medical facility. "
"<br/><br/>"
"Delivery in medical instituitions is associated with a decrease in maternal mortality rate"
)),
"chart_data": [
{
"values": chart_data['blue'],
"key": "",
"strokeWidth": 2,
"classed": "dashed",
"color": MapColors.BLUE
},
]
}
def get_adhaar_sector_data(domain,
config,
loc_level,
location_id,
show_test=False):
group_by = ['%s_name' % loc_level]
config['month'] = datetime(*config['month'])
data = AggAwcMonthly.objects.filter(**config).values(*group_by).annotate(
in_month=Sum('cases_person_has_aadhaar'),
all=Sum('cases_person_beneficiary'),
).order_by('%s_name' % loc_level)
if not show_test:
data = apply_exclude(domain, data)
chart_data = {
'blue': [],
}
tooltips_data = defaultdict(lambda: {'in_month': 0, 'all': 0})
loc_children = get_child_locations(domain, location_id, show_test)
result_set = set()
for row in data:
valid = row['all']
name = row['%s_name' % loc_level]
result_set.add(name)
in_month = row['in_month']
row_values = {'in_month': in_month or 0, 'all': valid or 0}
for prop, value in six.iteritems(row_values):
tooltips_data[name][prop] += value
value = (in_month or 0) / float(valid 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'])
return {
"tooltips_data":
dict(tooltips_data),
"info":
_(("Percentage of individuals registered using CAS whose Aadhaar identification has been captured"
)),
"chart_data": [
{
"values": chart_data['blue'],
"key": "",
"strokeWidth": 2,
"classed": "dashed",
"color": MapColors.BLUE
},
]
}
def get_newborn_with_low_birth_weight_data(domain, config, loc_level, location_id, show_test=False):
group_by = ['%s_name' % loc_level]
config['month'] = datetime(*config['month'])
data = AggChildHealthMonthly.objects.filter(
**config
).values(
*group_by
).annotate(
low_birth=Sum('low_birth_weight_in_month'),
in_month=Sum('weighed_and_born_in_month'),
all=Sum('born_in_month')
).order_by('%s_name' % loc_level)
if not show_test:
data = apply_exclude(domain, data)
chart_data = {
'blue': [],
}
tooltips_data = defaultdict(lambda: {
'in_month': 0,
'low_birth': 0,
'all': 0
})
loc_children = get_child_locations(domain, location_id, show_test)
result_set = set()
for row in data:
in_month = row['in_month'] or 0
name = row['%s_name' % loc_level]
result_set.add(name)
all_records = row['all'] or 0
low_birth = row['low_birth'] or 0
value = low_birth / float(in_month or 1)
tooltips_data[name]['low_birth'] += low_birth
tooltips_data[name]['in_month'] += in_month
tooltips_data[name]['all'] += all_records
chart_data['blue'].append([
name, value
])
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'])
return {
"tooltips_data": dict(tooltips_data),
"info": _((
new_born_with_low_weight_help_text(html=True)
)),
"chart_data": [
{
"values": chart_data['blue'],
"key": "",
"strokeWidth": 2,
"classed": "dashed",
"color": MapColors.BLUE
},
]
}
def get_exclusive_breastfeeding_sector_data(domain,
config,
loc_level,
location_id,
show_test=False):
group_by = ['%s_name' % loc_level]
config['month'] = datetime(*config['month'])
data = AggChildHealthMonthly.objects.filter(**config).values(
*group_by).annotate(
in_month=Sum('ebf_in_month'),
eligible=Sum('ebf_eligible'),
).order_by('%s_name' % loc_level)
if not show_test:
data = apply_exclude(domain, data)
chart_data = {
'blue': [],
}
tooltips_data = defaultdict(lambda: {'children': 0, 'all': 0})
loc_children = get_child_locations(domain, location_id, show_test)
result_set = set()
for row in data:
valid = row['eligible']
name = row['%s_name' % loc_level]
result_set.add(name)
in_month = row['in_month']
row_values = {'children': in_month or 0, 'all': valid or 0}
for prop, value in row_values.items():
tooltips_data[name][prop] += value
in_month = row['in_month']
value = (in_month or 0) / float(valid or 1)
chart_data['blue'].append([name, value])
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'])
return {
"tooltips_data":
dict(tooltips_data),
"info":
exclusive_breastfeeding_help_text(html=True),
"chart_data": [
{
"values": chart_data['blue'],
"key": "",
"strokeWidth": 2,
"classed": "dashed",
"color": MapColors.BLUE
},
]
}
def get_prevalence_of_undernutrition_sector_data(domain,
config,
loc_level,
location_id,
show_test=False):
group_by = ['%s_name' % loc_level]
config['month'] = datetime(*config['month'])
data = AggChildHealthMonthly.objects.filter(**config).values(
*group_by).annotate(
moderately_underweight=Sum(
'nutrition_status_moderately_underweight'),
severely_underweight=Sum('nutrition_status_severely_underweight'),
weighed=Sum('nutrition_status_weighed'),
normal=Sum('nutrition_status_normal'),
total=Sum('wer_eligible'),
).order_by('%s_name' % loc_level)
if not show_test:
data = apply_exclude(domain, data)
if 'age_tranche' not in config:
data = data.exclude(age_tranche=72)
chart_data = {'blue': []}
tooltips_data = defaultdict(
lambda: {
'severely_underweight': 0,
'moderately_underweight': 0,
'weighed': 0,
'normal': 0,
'total': 0
})
loc_children = get_child_locations(domain, location_id, show_test)
result_set = set()
for row in data:
weighed = row['weighed']
total = row['total']
name = row['%s_name' % loc_level]
result_set.add(name)
severely_underweight = row['severely_underweight']
moderately_underweight = row['moderately_underweight']
normal = row['normal']
tooltips_data[name]['severely_underweight'] += severely_underweight
tooltips_data[name]['moderately_underweight'] += moderately_underweight
tooltips_data[name]['weighed'] += (weighed or 0)
tooltips_data[name]['normal'] += normal
tooltips_data[name]['total'] += (total or 0)
chart_data['blue'].append([
name, ((moderately_underweight or 0) +
(severely_underweight or 0)) / float(weighed or 1)
])
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'])
return {
"tooltips_data":
dict(tooltips_data),
"info":
underweight_children_help_text(age_label="0-5 years", html=True),
"chart_data": [{
"values": chart_data['blue'],
"key": "",
"strokeWidth": 2,
"classed": "dashed",
"color": MapColors.BLUE
}]
}
def get_prevalence_of_severe_sector_data(domain, config, loc_level, location_id, show_test=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'),
severe=Sum('wasting_severe'),
valid=Sum('height_eligible'),
normal=Sum('wasting_normal'),
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:
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:
valid = row['valid']
name = row['%s_name' % loc_level]
result_set.add(name)
severe = row['severe']
moderate = row['moderate']
normal = row['normal']
total_measured = row['total_measured']
tooltips_data[name]['severe'] += (severe or 0)
tooltips_data[name]['moderate'] += (moderate or 0)
tooltips_data[name]['total'] += (valid or 0)
tooltips_data[name]['normal'] += normal
tooltips_data[name]['total_measured'] += total_measured
value = ((moderate or 0) + (severe or 0)) / float(valid 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'])
return {
"tooltips_data": dict(tooltips_data),
"info": _((
"Percentage of children between 6 - 60 months enrolled for ICDS services with "
"weight-for-height below -3 standard deviations of the WHO Child Growth Standards median."
"<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."
)),
"chart_data": [
{
"values": chart_data['blue'],
"key": "",
"strokeWidth": 2,
"classed": "dashed",
"color": MapColors.BLUE
},
]
}
def get_adult_weight_scale_sector_data(domain, config, loc_level, location_id, show_test=False):
group_by = ['%s_name' % loc_level]
config['month'] = datetime(*config['month'])
data = AggAwcMonthly.objects.filter(
**config
).values(
*group_by
).annotate(
in_month=Sum('infra_adult_weighing_scale'),
all=Sum('num_awc_infra_last_update'),
).order_by('%s_name' % loc_level)
if not show_test:
data = apply_exclude(domain, data)
chart_data = {
'blue': [],
}
tooltips_data = defaultdict(lambda: {
'in_month': 0,
'all': 0
})
loc_children = get_child_locations(domain, location_id, show_test)
result_set = set()
for row in data:
valid = row['all']
name = row['%s_name' % loc_level]
result_set.add(name)
in_month = row['in_month']
row_values = {
'in_month': in_month or 0,
'all': valid or 0
}
for prop, value in six.iteritems(row_values):
tooltips_data[name][prop] += value
value = (in_month or 0) / float(valid 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'])
return {
"tooltips_data": dict(tooltips_data),
"info": awcs_reported_weighing_scale_mother_and_child_help_text(),
"chart_data": [
{
"values": chart_data['blue'],
"key": "",
"strokeWidth": 2,
"classed": "dashed",
"color": MapColors.BLUE
}
]
}
def get_clean_water_sector_data(domain, config, loc_level, location_id, show_test=False):
group_by = ['%s_name' % loc_level]
config['month'] = datetime(*config['month'])
data = AggAwcMonthly.objects.filter(
**config
).values(
*group_by
).annotate(
in_month=Sum('infra_clean_water'),
all=Sum('num_awc_infra_last_update')
).order_by('%s_name' % loc_level)
if not show_test:
data = apply_exclude(domain, data)
chart_data = {
'blue': [],
}
tooltips_data = defaultdict(lambda: {
'in_month': 0,
'all': 0
})
loc_children = get_child_locations(domain, location_id, show_test)
result_set = set()
for row in data:
valid = row['all']
name = row['%s_name' % loc_level]
result_set.add(name)
in_month = row['in_month']
row_values = {
'in_month': in_month or 0,
'all': valid or 0
}
for prop, value in six.iteritems(row_values):
tooltips_data[name][prop] += value
value = (in_month or 0) / float(valid 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'])
return {
"tooltips_data": dict(tooltips_data),
"info": _((
"Of the AWCs that have submitted an Infrastructure Details form, the percentage of AWCs that "
"reported having a source of clean drinking water. "
)),
"chart_data": [
{
"values": chart_data['blue'],
"key": "",
"strokeWidth": 2,
"classed": "dashed",
"color": MapColors.BLUE
},
]
}
def get_exclusive_breastfeeding_sector_data(domain,
config,
loc_level,
location_id,
show_test=False):
group_by = ['%s_name' % loc_level]
config['month'] = datetime(*config['month'])
data = AggChildHealthMonthly.objects.filter(**config).values(
*group_by).annotate(
in_month=Sum('ebf_in_month'),
eligible=Sum('ebf_eligible'),
).order_by('%s_name' % loc_level)
if not show_test:
data = apply_exclude(domain, data)
chart_data = {
'blue': [],
}
tooltips_data = defaultdict(lambda: {'children': 0, 'all': 0})
loc_children = get_child_locations(domain, location_id, show_test)
result_set = set()
for row in data:
valid = row['eligible']
name = row['%s_name' % loc_level]
result_set.add(name)
in_month = row['in_month']
row_values = {'children': in_month or 0, 'all': valid or 0}
for prop, value in six.iteritems(row_values):
tooltips_data[name][prop] += value
in_month = row['in_month']
value = (in_month or 0) / float(valid or 1)
chart_data['blue'].append([name, value])
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'])
return {
"tooltips_data":
dict(tooltips_data),
"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"
)),
"chart_data": [
{
"values": chart_data['blue'],
"key": "",
"strokeWidth": 2,
"classed": "dashed",
"color": MapColors.BLUE
},
]
}
def get_awc_daily_status_sector_data(domain, config, loc_level, location_id, show_test=False):
group_by = ['%s_name' % loc_level]
date = datetime(*config['month'])
del config['month']
def get_data_for(filters):
queryset = AggAwcDailyView.objects.filter(
**filters
).values(
*group_by
).annotate(
in_day=Sum('daily_attendance_open'),
all=Sum('num_launched_awcs'),
).order_by('%s_name' % loc_level)
if not show_test:
queryset = apply_exclude(domain, queryset)
return queryset
chart_data = {
'blue': [],
}
tooltips_data = defaultdict(lambda: {
'in_day': 0,
'all': 0
})
loc_children = get_child_locations(domain, location_id, show_test)
result_set = set()
config['date'] = date.date()
sector_data = None
while sector_data is None or (not sector_data and config['date'].day != 1):
sector_data = get_data_for(config)
config['date'] -= relativedelta(days=1)
for row in sector_data:
valid = row['all']
name = row['%s_name' % loc_level]
result_set.add(name)
in_day = row['in_day']
row_values = {
'in_day': in_day or 0,
'all': valid or 0
}
for prop, value in six.iteritems(row_values):
tooltips_data[name][prop] += value
value = (in_day or 0) / float(valid 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'])
return {
"tooltips_data": dict(tooltips_data),
"info": _((
"Of the total number of AWCs, the percentage of AWCs that were open yesterday."
)),
"chart_data": [
{
"values": chart_data['blue'],
"key": "",
"strokeWidth": 2,
"classed": "dashed",
"color": MapColors.BLUE
}
]
}
def get_newborn_with_low_birth_weight_data(domain,
config,
loc_level,
location_id,
show_test=False):
group_by = ['%s_name' % loc_level]
config['month'] = datetime(*config['month'])
data = AggChildHealthMonthly.objects.filter(**config).values(
*group_by).annotate(
low_birth=Sum('low_birth_weight_in_month'),
in_month=Sum('weighed_and_born_in_month'),
).order_by('%s_name' % loc_level)
if not show_test:
data = apply_exclude(domain, data)
chart_data = {
'blue': [],
}
tooltips_data = defaultdict(lambda: {
'in_month': 0,
'low_birth': 0,
})
loc_children = get_child_locations(domain, location_id, show_test)
result_set = set()
for row in data:
in_month = row['in_month']
name = row['%s_name' % loc_level]
result_set.add(name)
low_birth = row['low_birth'] or 0
value = low_birth / float(in_month or 1)
tooltips_data[name]['low_birth'] += low_birth
tooltips_data[name]['in_month'] += (in_month or 0)
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'])
return {
"tooltips_data":
dict(tooltips_data),
"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")),
"chart_data": [
{
"values": chart_data['blue'],
"key": "",
"strokeWidth": 2,
"classed": "dashed",
"color": MapColors.BLUE
},
]
}
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_immunization_coverage_sector_data(domain,
config,
loc_level,
location_id,
show_test=False):
group_by = ['%s_name' % loc_level]
config['month'] = datetime(*config['month'])
data = AggChildHealthMonthly.objects.filter(**config).values(
*group_by).annotate(
in_month=Sum('fully_immunized_on_time') +
Sum('fully_immunized_late'),
eligible=Sum('fully_immunized_eligible'),
).order_by('%s_name' % loc_level)
if not show_test:
data = apply_exclude(domain, data)
chart_data = {
'blue': [],
}
tooltips_data = defaultdict(lambda: {'children': 0, 'all': 0})
loc_children = get_child_locations(domain, location_id, show_test)
result_set = set()
for row in data:
valid = row['eligible']
name = row['%s_name' % loc_level]
result_set.add(name)
in_month = row['in_month']
row_values = {'children': in_month or 0, 'all': valid or 0}
for prop, value in six.iteritems(row_values):
tooltips_data[name][prop] += value
value = (in_month or 0) / float(valid 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'])
return {
"tooltips_data":
dict(tooltips_data),
"info":
_(("Of the total number of children enrolled for Anganwadi Services who are over a year old, the "
"percentage of children who have received the complete immunization as per the National Immunization "
"Schedule of India that is required by age 1."
"<br/><br/>"
"This includes the following immunizations:<br/>"
"If Pentavalent path: Penta1/2/3, OPV1/2/3, BCG, Measles, VitA1<br/>"
"If DPT/HepB path: DPT1/2/3, HepB1/2/3, OPV1/2/3, BCG, Measles, VitA1"
)),
"chart_data": [{
"values": chart_data['blue'],
"key": "",
"strokeWidth": 2,
"classed": "dashed",
"color": MapColors.BLUE
}]
}
def get_early_initiation_breastfeeding_data(domain, config, loc_level, location_id, show_test=False):
group_by = ['%s_name' % loc_level]
config['month'] = datetime(*config['month'])
data = AggChildHealthMonthly.objects.filter(
**config
).values(
*group_by
).annotate(
birth=Sum('bf_at_birth'),
in_month=Sum('born_in_month'),
).order_by('%s_name' % loc_level)
if not show_test:
data = apply_exclude(domain, data)
chart_data = {
'blue': [],
}
tooltips_data = defaultdict(lambda: {
'in_month': 0,
'birth': 0,
})
loc_children = get_child_locations(domain, location_id, show_test)
result_set = set()
for row in data:
in_month = row['in_month']
name = row['%s_name' % loc_level]
result_set.add(name)
birth = row['birth']
value = (birth or 0) / float(in_month or 1)
tooltips_data[name]['birth'] += birth
tooltips_data[name]['in_month'] += (in_month or 0)
chart_data['blue'].append([
name, value
])
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'])
return {
"tooltips_data": dict(tooltips_data),
"info": early_initiation_breastfeeding_help_text(html=True),
"chart_data": [
{
"values": chart_data['blue'],
"key": "",
"strokeWidth": 2,
"classed": "dashed",
"color": MapColors.BLUE
}
]
}
def get_lactating_enrolled_women_sector_data(domain, config, loc_level, location_id, show_test=False):
group_by = ['%s_name' % loc_level]
config['month'] = datetime(*config['month'])
data = AggCcsRecordMonthly.objects.filter(
**config
).values(
*group_by
).annotate(
valid=Sum('lactating'),
all=Sum('lactating_all')
).order_by('%s_name' % loc_level)
if not show_test:
data = apply_exclude(domain, data)
chart_data = {
'blue': []
}
tooltips_data = defaultdict(lambda: {
'valid': 0,
'all': 0
})
loc_children = get_child_locations(domain, location_id, show_test)
result_set = set()
for row in data:
valid = row['valid'] or 0
all_lactating = row['all'] or 0
name = row['%s_name' % loc_level]
result_set.add(name)
row_values = {
'valid': valid,
'all': all_lactating
}
for prop, value in six.iteritems(row_values):
tooltips_data[name][prop] += value
chart_data['blue'].append([
name, valid
])
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'])
return {
"tooltips_data": dict(tooltips_data),
"format": "number",
"info": percent_lactating_women_enrolled_help_text(),
"chart_data": [
{
"values": chart_data['blue'],
"key": "",
"strokeWidth": 2,
"classed": "dashed",
"color": MapColors.BLUE
}
]
}
def get_early_initiation_breastfeeding_data(domain,
config,
loc_level,
location_id,
show_test=False):
group_by = ['%s_name' % loc_level]
config['month'] = datetime(*config['month'])
data = AggChildHealthMonthly.objects.filter(**config).values(
*group_by).annotate(
birth=Sum('bf_at_birth'),
in_month=Sum('born_in_month'),
).order_by('%s_name' % loc_level)
if not show_test:
data = apply_exclude(domain, data)
chart_data = {
'blue': [],
}
tooltips_data = defaultdict(lambda: {
'in_month': 0,
'birth': 0,
})
loc_children = get_child_locations(domain, location_id, show_test)
result_set = set()
for row in data:
in_month = row['in_month']
name = row['%s_name' % loc_level]
result_set.add(name)
birth = row['birth']
value = (birth or 0) / float(in_month or 1)
tooltips_data[name]['birth'] += birth
tooltips_data[name]['in_month'] += (in_month or 0)
chart_data['blue'].append([name, value])
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'])
return {
"tooltips_data":
dict(tooltips_data),
"info":
early_initiation_breastfeeding_help_text(html=True),
"chart_data": [{
"values": chart_data['blue'],
"key": "",
"strokeWidth": 2,
"classed": "dashed",
"color": MapColors.BLUE
}]
}
def get_children_initiated_sector_data(domain,
config,
loc_level,
location_id,
show_test=False):
group_by = ['%s_name' % loc_level]
config['month'] = datetime(*config['month'])
data = AggChildHealthMonthly.objects.filter(**config).values(
*group_by).annotate(
in_month=Sum('cf_initiation_in_month'),
eligible=Sum('cf_initiation_eligible'),
).order_by('%s_name' % loc_level)
if not show_test:
data = apply_exclude(domain, data)
chart_data = {
'blue': [],
}
tooltips_data = defaultdict(lambda: {'children': 0, 'all': 0})
loc_children = get_child_locations(domain, location_id, show_test)
result_set = set()
for row in data:
valid = row['eligible']
name = row['%s_name' % loc_level]
result_set.add(name)
in_month = row['in_month']
row_values = {'children': in_month or 0, 'all': valid or 0}
for prop, value in six.iteritems(row_values):
tooltips_data[name][prop] += value
value = (in_month or 0) / float(valid 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'])
return {
"tooltips_data":
dict(tooltips_data),
"info":
_(("Percentage of children between 6 - 8 months given timely introduction to solid, "
"semi-solid or soft food.")),
"chart_data": [
{
"values": chart_data['blue'],
"key": "",
"strokeWidth": 2,
"classed": "dashed",
"color": MapColors.BLUE
},
]
}
def get_prevalence_of_undernutrition_sector_data(domain,
config,
loc_level,
location_id,
show_test=False):
group_by = ['%s_name' % loc_level]
config['month'] = datetime(*config['month'])
data = AggChildHealthMonthly.objects.filter(**config).values(
*group_by).annotate(
moderately_underweight=Sum(
'nutrition_status_moderately_underweight'),
severely_underweight=Sum('nutrition_status_severely_underweight'),
weighed=Sum('nutrition_status_weighed'),
normal=Sum('nutrition_status_normal'),
total=Sum('wer_eligible'),
).order_by('%s_name' % loc_level)
if not show_test:
data = apply_exclude(domain, data)
if 'age_tranche' not in config:
data = data.exclude(age_tranche=72)
chart_data = {'blue': []}
tooltips_data = defaultdict(
lambda: {
'severely_underweight': 0,
'moderately_underweight': 0,
'weighed': 0,
'normal': 0,
'total': 0
})
loc_children = get_child_locations(domain, location_id, show_test)
result_set = set()
for row in data:
weighed = row['weighed']
total = row['total']
name = row['%s_name' % loc_level]
result_set.add(name)
severely_underweight = row['severely_underweight']
moderately_underweight = row['moderately_underweight']
normal = row['normal']
tooltips_data[name]['severely_underweight'] += severely_underweight
tooltips_data[name]['moderately_underweight'] += moderately_underweight
tooltips_data[name]['weighed'] += (weighed or 0)
tooltips_data[name]['normal'] += normal
tooltips_data[name]['total'] += (total or 0)
chart_data['blue'].append([
name, ((moderately_underweight or 0) +
(severely_underweight or 0)) / float(weighed or 1)
])
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'])
return {
"tooltips_data":
dict(tooltips_data),
"info":
_(("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. "
"<br/><br/>"
"Children who are moderately or severely underweight have a higher risk of mortality. "
)),
"chart_data": [{
"values": chart_data['blue'],
"key": "",
"strokeWidth": 2,
"classed": "dashed",
"color": MapColors.BLUE
}]
}
def get_early_initiation_breastfeeding_data(domain, config, loc_level, location_id, show_test=False):
group_by = ['%s_name' % loc_level]
config['month'] = datetime(*config['month'])
data = AggChildHealthMonthly.objects.filter(
**config
).values(
*group_by
).annotate(
birth=Sum('bf_at_birth'),
in_month=Sum('born_in_month'),
).order_by('%s_name' % loc_level)
if not show_test:
data = apply_exclude(domain, data)
chart_data = {
'blue': [],
}
tooltips_data = defaultdict(lambda: {
'in_month': 0,
'birth': 0,
})
loc_children = get_child_locations(domain, location_id, show_test)
result_set = set()
for row in data:
in_month = row['in_month']
name = row['%s_name' % loc_level]
result_set.add(name)
birth = row['birth']
value = (birth or 0) / float(in_month or 1)
tooltips_data[name]['birth'] += birth
tooltips_data[name]['in_month'] += (in_month or 0)
chart_data['blue'].append([
name, value
])
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'])
return {
"tooltips_data": dict(tooltips_data),
"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"
)),
"chart_data": [
{
"values": chart_data['blue'],
"key": "",
"strokeWidth": 2,
"classed": "dashed",
"color": MapColors.BLUE
}
]
}
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_children_initiated_sector_data(domain, config, loc_level, location_id, show_test=False):
group_by = ['%s_name' % loc_level]
config['month'] = datetime(*config['month'])
data = AggChildHealthMonthly.objects.filter(
**config
).values(
*group_by
).annotate(
in_month=Sum('cf_initiation_in_month'),
eligible=Sum('cf_initiation_eligible'),
).order_by('%s_name' % loc_level)
if not show_test:
data = apply_exclude(domain, data)
chart_data = {
'blue': [],
}
tooltips_data = defaultdict(lambda: {
'children': 0,
'all': 0
})
loc_children = get_child_locations(domain, location_id, show_test)
result_set = set()
for row in data:
valid = row['eligible']
name = row['%s_name' % loc_level]
result_set.add(name)
in_month = row['in_month']
row_values = {
'children': in_month or 0,
'all': valid or 0
}
for prop, value in six.iteritems(row_values):
tooltips_data[name][prop] += value
value = (in_month or 0) / float(valid 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'])
return {
"tooltips_data": dict(tooltips_data),
"info": children_initiated_appropriate_complementary_feeding_help_text(html=True),
"chart_data": [
{
"values": chart_data['blue'],
"key": "",
"strokeWidth": 2,
"classed": "dashed",
"color": MapColors.BLUE
},
]
}
def get_clean_water_sector_data(domain, config, loc_level, location_id, show_test=False):
group_by = ['%s_name' % loc_level]
config['month'] = datetime(*config['month'])
data = AggAwcMonthly.objects.filter(
**config
).values(
*group_by
).annotate(
in_month=Sum('infra_clean_water'),
all=Sum('num_awc_infra_last_update')
).order_by('%s_name' % loc_level)
if not show_test:
data = apply_exclude(domain, data)
chart_data = {
'blue': [],
}
tooltips_data = defaultdict(lambda: {
'in_month': 0,
'all': 0
})
loc_children = get_child_locations(domain, location_id, show_test)
result_set = set()
for row in data:
valid = row['all']
name = row['%s_name' % loc_level]
result_set.add(name)
in_month = row['in_month']
row_values = {
'in_month': in_month or 0,
'all': valid or 0
}
for prop, value in six.iteritems(row_values):
tooltips_data[name][prop] += value
value = (in_month or 0) / float(valid 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'])
return {
"tooltips_data": dict(tooltips_data),
"info": _((
"Of the AWCs that have submitted an Infrastructure Details form, the percentage of AWCs that "
"reported having a source of clean drinking water. "
)),
"chart_data": [
{
"values": chart_data['blue'],
"key": "",
"strokeWidth": 2,
"classed": "dashed",
"color": MapColors.BLUE
},
]
}
def get_awc_daily_status_sector_data(domain, config, loc_level, location_id, show_test=False):
group_by = ['%s_name' % loc_level]
date = datetime(*config['month'])
del config['month']
def get_data_for(filters):
queryset = AggAwcDailyView.objects.filter(
**filters
).values(
*group_by
).annotate(
in_day=Sum('daily_attendance_open'),
all=Sum('num_launched_awcs'),
).order_by('%s_name' % loc_level)
if not show_test:
queryset = apply_exclude(domain, queryset)
return queryset
chart_data = {
'blue': [],
}
tooltips_data = defaultdict(lambda: {
'in_day': 0,
'all': 0
})
loc_children = get_child_locations(domain, location_id, show_test)
result_set = set()
config['date'] = date.date()
sector_data = None
while sector_data is None or (not sector_data and config['date'].day != 1):
sector_data = get_data_for(config)
config['date'] -= relativedelta(days=1)
for row in sector_data:
valid = row['all']
name = row['%s_name' % loc_level]
result_set.add(name)
in_day = row['in_day']
row_values = {
'in_day': in_day or 0,
'all': valid or 0
}
for prop, value in six.iteritems(row_values):
tooltips_data[name][prop] += value
value = (in_day or 0) / float(valid 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'])
return {
"tooltips_data": dict(tooltips_data),
"info": _((
"Of the total number of AWCs, the percentage of AWCs that were open yesterday."
)),
"chart_data": [
{
"values": chart_data['blue'],
"key": "",
"strokeWidth": 2,
"classed": "dashed",
"color": MapColors.BLUE
}
]
}
def get_prevalence_of_undernutrition_sector_data(domain, config, loc_level, location_id, show_test=False):
group_by = ['%s_name' % loc_level]
config['month'] = datetime(*config['month'])
data = AggChildHealthMonthly.objects.filter(
**config
).values(
*group_by
).annotate(
moderately_underweight=Sum('nutrition_status_moderately_underweight'),
severely_underweight=Sum('nutrition_status_severely_underweight'),
weighed=Sum('nutrition_status_weighed'),
normal=Sum('nutrition_status_normal'),
total=Sum('wer_eligible'),
).order_by('%s_name' % loc_level)
if not show_test:
data = apply_exclude(domain, data)
if 'age_tranche' not in config:
data = data.exclude(age_tranche=72)
chart_data = {
'blue': []
}
tooltips_data = defaultdict(lambda: {
'severely_underweight': 0,
'moderately_underweight': 0,
'weighed': 0,
'normal': 0,
'total': 0
})
loc_children = get_child_locations(domain, location_id, show_test)
result_set = set()
for row in data:
weighed = row['weighed']
total = row['total']
name = row['%s_name' % loc_level]
result_set.add(name)
severely_underweight = row['severely_underweight']
moderately_underweight = row['moderately_underweight']
normal = row['normal']
tooltips_data[name]['severely_underweight'] += severely_underweight
tooltips_data[name]['moderately_underweight'] += moderately_underweight
tooltips_data[name]['weighed'] += (weighed or 0)
tooltips_data[name]['normal'] += normal
tooltips_data[name]['total'] += (total or 0)
chart_data['blue'].append([
name,
((moderately_underweight or 0) + (severely_underweight or 0)) / float(weighed or 1)
])
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'])
return {
"tooltips_data": dict(tooltips_data),
"info": underweight_children_help_text(age_label="0-5 years", html=True),
"chart_data": [
{
"values": chart_data['blue'],
"key": "",
"strokeWidth": 2,
"classed": "dashed",
"color": MapColors.BLUE
}
]
}
