def test_data_monthly_adolescent_girls(self):
data = get_demographics_data(
'icds-cas', (2017, 6, 1), {
'month': (2017, 5, 1),
'prev_month': (2017, 4, 1),
'aggregation_level': 1
})
expected = {
"redirect": "demographics/adolescent_girls",
"all": 24,
"format": "percent_and_div",
"color": "red",
"percent": 37.49999999999998,
"value": 2,
"label": OUT_OF_SCHOOL_ADOLESCENT_GIRLS_11_14_YEARS,
"frequency": "month",
"help_text": percent_adolescent_girls_enrolled_help_text_v2()
}
self.assertDictEqual(expected, data['records'][2][1])
def get_adolescent_girls_data_map(domain,
config,
loc_level,
show_test=False,
beta=False):
valid_col_name = 'cases_person_adolescent_girls_11_14_out_of_school'
all_col_name = 'cases_person_adolescent_girls_11_14_all_v2'
valid_num_title = 'Number of adolescent girls (11-14 years) who are out of school:'
all_num_title = 'Total Number of adolescent girls (11-14 years) who are registered:'
percent_title = 'Percentage of adolescent girls (11-14 years) who are out of school:'
info_text = percent_adolescent_girls_enrolled_help_text_v2()
def get_data_for(filters):
filters['month'] = datetime(*filters['month'])
queryset = AggAwcMonthly.objects.filter(**filters).values(
'%s_name' % loc_level,
'%s_map_location_name' % loc_level).annotate(
valid=Sum(valid_col_name),
all=Sum(all_col_name),
).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': 'Out of school Adolescent Girls'
})
average = []
total_valid = 0
total = 0
for row in get_data_for(config):
valid = row['valid'] or 0
all_adolescent = row['all'] or 0
name = row['%s_name' % loc_level]
on_map_name = row['%s_map_location_name' % loc_level] or name
average.append(valid)
total_valid += valid
total += all_adolescent
data_for_map[on_map_name]['valid'] += valid
data_for_map[on_map_name]['all'] += all_adolescent
data_for_map[on_map_name]['original_name'].append(name)
fills = OrderedDict()
fills.update({'Out of school Adolescent Girls': MapColors.BLUE})
if beta:
fills.update({'Not Launched': MapColors.GREY})
fills.update({'defaultFill': MapColors.GREY})
return {
"slug": "adolescent_girls",
"label": "",
"fills": fills,
"rightLegend": {
"average":
'%.2f' % (total_valid * 100 / float(total or 1)),
"info":
info_text,
"extended_info": [{
'indicator': valid_num_title,
'value': indian_formatted_number(total_valid)
}, {
'indicator': all_num_title,
'value': indian_formatted_number(total)
}, {
'indicator':
percent_title,
'value':
'%.2f%%' % (total_valid * 100 / float(total or 1))
}]
},
"data": dict(data_for_map),
}
def get_demographics_data(domain,
now_date,
config,
show_test=False,
beta=False):
now_date = datetime(*now_date)
current_month = datetime(*config['month'])
previous_month = datetime(*config['prev_month'])
del config['month']
del config['prev_month']
def get_data_for(query_class, filters):
queryset = query_class.objects.filter(
**filters).values('aggregation_level').annotate(
household=Sum('cases_household'),
child_health=Sum('cases_child_health'),
child_health_all=Sum('cases_child_health_all'),
ccs_pregnant=Sum('cases_ccs_pregnant'),
ccs_pregnant_all=Sum('cases_ccs_pregnant_all'),
css_lactating=Sum('cases_ccs_lactating'),
css_lactating_all=Sum('cases_ccs_lactating_all'),
person_adolescent=Sum('cases_person_adolescent_girls_11_14'),
person_adolescent_all=Sum(
'cases_person_adolescent_girls_11_14_all'),
person_aadhaar=Sum(person_has_aadhaar_column(beta)),
all_persons=Sum(person_is_beneficiary_column(beta)))
if not show_test:
queryset = apply_exclude(domain, queryset)
return queryset
if beta:
config['month'] = current_month
data = get_data_for(AggAwcMonthly, config)
config['month'] = previous_month
prev_data = get_data_for(AggAwcMonthly, config)
frequency = 'month'
else:
if current_month.month == now_date.month and current_month.year == now_date.year:
config['date'] = now_date.date()
data = None
# keep the record in searched - current - month
while data is None or (not data and config['date'].day != 1):
config['date'] -= relativedelta(days=1)
data = get_data_for(AggAwcDailyView, config)
prev_data = None
while prev_data is None or (not prev_data
and config['date'].day != 1):
config['date'] -= relativedelta(days=1)
prev_data = get_data_for(AggAwcDailyView, config)
frequency = 'day'
else:
config['month'] = current_month
data = get_data_for(AggAwcMonthly, config)
config['month'] = previous_month
prev_data = get_data_for(AggAwcMonthly, config)
frequency = 'month'
if 'date' in config:
del config['date']
config['month'] = current_month
ag_data = get_adolescent_girls_data(domain, config, show_test)
config['month'] = previous_month
ag_data_prev_data = get_adolescent_girls_data(domain, config, show_test)
demographics_data = {
'records':
[[{
'label':
_('Registered Households'),
'help_text':
_('Total number of households registered'),
'percent':
percent_increase('household', data, prev_data),
'color':
get_color_with_green_positive(
percent_increase('household', data, prev_data)),
'value':
get_value(data, 'household'),
'all':
None,
'format':
'number',
'frequency':
frequency,
'redirect':
'demographics/registered_household'
}, {
'label':
_(AADHAR_SEEDED_BENEFICIARIES),
'help_text':
percent_aadhaar_seeded_beneficiaries_help_text(),
'percent':
percent_diff('person_aadhaar', data, prev_data, 'all_persons'),
'color':
get_color_with_green_positive(
percent_diff('person_aadhaar', data, prev_data,
'all_persons')),
'value':
get_value(data, 'person_aadhaar'),
'all':
get_value(data, 'all_persons'),
'format':
'percent_and_div',
'frequency':
frequency,
'redirect':
'demographics/adhaar'
}],
[{
'label':
_(CHILDREN_ENROLLED_FOR_ANGANWADI_SERVICES),
'help_text':
percent_children_enrolled_help_text(),
'percent':
percent_diff('child_health', data, prev_data, 'child_health_all'),
'color':
get_color_with_green_positive(
percent_diff('child_health', data, prev_data,
'child_health_all')),
'value':
get_value(data, 'child_health'),
'all':
get_value(data, 'child_health_all'),
'format':
'percent_and_div',
'frequency':
frequency,
'redirect':
'demographics/enrolled_children'
}, {
'label':
_(PREGNANT_WOMEN_ENROLLED_FOR_ANGANWADI_SERVICES),
'help_text':
percent_pregnant_women_enrolled_help_text(),
'percent':
percent_diff('ccs_pregnant', data, prev_data, 'ccs_pregnant_all'),
'color':
get_color_with_green_positive(
percent_diff('ccs_pregnant', data, prev_data,
'ccs_pregnant_all')),
'value':
get_value(data, 'ccs_pregnant'),
'all':
get_value(data, 'ccs_pregnant_all'),
'format':
'percent_and_div',
'frequency':
frequency,
'redirect':
'demographics/enrolled_women'
}],
[{
'label':
_(LACTATING_WOMEN_ENROLLED_FOR_ANGANWADI_SERVICES),
'help_text':
percent_lactating_women_enrolled_help_text(),
'percent':
percent_diff('css_lactating', data, prev_data,
'css_lactating_all'),
'color':
get_color_with_green_positive(
percent_diff('css_lactating', data, prev_data,
'css_lactating_all')),
'value':
get_value(data, 'css_lactating'),
'all':
get_value(data, 'css_lactating_all'),
'format':
'percent_and_div',
'frequency':
frequency,
'redirect':
'demographics/lactating_enrolled_women'
}, {
'label':
_(OUT_OF_SCHOOL_ADOLESCENT_GIRLS_11_14_YEARS),
'help_text':
percent_adolescent_girls_enrolled_help_text_v2(),
'percent':
percent_diff('person_adolescent', ag_data, ag_data_prev_data,
'person_adolescent_all'),
'color':
get_color_with_red_positive(
percent_diff('person_adolescent', ag_data, ag_data_prev_data,
'person_adolescent_all')),
'value':
get_value(ag_data, 'person_adolescent'),
'all':
get_value(ag_data, 'person_adolescent_all'),
'format':
'percent_and_div',
'frequency':
frequency,
'redirect':
'demographics/adolescent_girls'
}]]
}
return demographics_data
def get_adolescent_girls_sector_data(domain, config, loc_level, location_id, show_test=False, beta=False):
valid_col_name = 'cases_person_adolescent_girls_11_14_out_of_school'
all_col_name = 'cases_person_adolescent_girls_11_14_all_v2'
info_text = percent_adolescent_girls_enrolled_help_text_v2()
group_by = ['%s_name' % loc_level]
config['month'] = datetime(*config['month'])
data = AggAwcMonthly.objects.filter(
**config
).values(
*group_by
).annotate(
valid=Sum(valid_col_name),
all=Sum(all_col_name),
).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
})
for row in data:
valid = row['valid'] or 0
all_adolescent = row['all'] or 0
name = row['%s_name' % loc_level]
row_values = {
'valid': valid,
'all': all_adolescent
}
for prop, value in row_values.items():
tooltips_data[name][prop] += value
chart_data['blue'].append([
name,
valid
])
chart_data['blue'] = sorted(chart_data['blue'])
return {
"tooltips_data": dict(tooltips_data),
"format": "number",
"info": info_text,
"chart_data": [
{
"values": chart_data['blue'],
"key": "Number Of Girls",
"strokeWidth": 2,
"classed": "dashed",
"color": MapColors.BLUE
}
]
}
