def get_adhaar_sector_data(domain,
config,
loc_level,
location_id,
show_test=False,
beta=False):
group_by = ['%s_name' % loc_level]
config['month'] = datetime(*config['month'])
data = AggAwcMonthly.objects.filter(**config).values(*group_by).annotate(
in_month=Sum(person_has_aadhaar_column(beta)),
all=Sum(person_is_beneficiary_column(beta)),
).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})
for row in data:
valid = row['all']
name = row['%s_name' % loc_level]
in_month = row['in_month']
row_values = {'in_month': in_month or 0, 'all': valid or 0}
for prop, value in row_values.items():
tooltips_data[name][prop] += value
value = (in_month or 0) / float(valid or 1)
chart_data['blue'].append([name, value])
chart_data['blue'] = sorted(chart_data['blue'])
return {
"tooltips_data":
dict(tooltips_data),
"info":
percent_aadhaar_seeded_beneficiaries_help_text(),
"chart_data": [
{
"values": chart_data['blue'],
"key": "",
"strokeWidth": 2,
"classed": "dashed",
"color": MapColors.BLUE
},
]
}
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(
in_month=Sum(person_has_aadhaar_column(beta)),
all=Sum(person_is_beneficiary_column(beta)),
).order_by('%s_name' % loc_level,
'%s_map_location_name' % loc_level)
if not show_test:
queryset = apply_exclude(domain, queryset)
return queryset
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(
in_month=Sum(person_has_aadhaar_column(beta)),
all=Sum(person_is_beneficiary_column(beta)),
).order_by('%s_name' % loc_level, '%s_map_location_name' % loc_level)
if not show_test:
queryset = apply_exclude(domain, queryset)
return queryset
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
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
def person_has_aadhaar_column(self):
return person_has_aadhaar_column(self.beta)
def get_adhaar_data_chart(domain, config, loc_level, show_test=False, beta=False):
month = datetime(*config['month'])
three_before = datetime(*config['month']) - relativedelta(months=3)
config['month__range'] = (three_before, month)
del config['month']
chart_data = AggAwcMonthly.objects.filter(
**config
).values(
'month', '%s_name' % loc_level
).annotate(
in_month=Sum(person_has_aadhaar_column(beta)),
all=Sum(person_is_beneficiary_column(beta)),
).order_by('month')
if not show_test:
chart_data = apply_exclude(domain, chart_data)
data = {
'blue': OrderedDict(),
}
dates = [dt for dt in rrule(MONTHLY, dtstart=three_before, until=month)]
for date in dates:
miliseconds = int(date.strftime("%s")) * 1000
data['blue'][miliseconds] = {'y': 0, 'all': 0}
best_worst = defaultdict(lambda: {
'in_month': 0,
'all': 0
})
for row in chart_data:
date = row['month']
in_month = row['in_month']
location = row['%s_name' % loc_level]
valid = row['all']
best_worst[location]['in_month'] = in_month
best_worst[location]['all'] = (valid or 0)
date_in_miliseconds = int(date.strftime("%s")) * 1000
data['blue'][date_in_miliseconds]['y'] += in_month
data['blue'][date_in_miliseconds]['all'] += valid
all_locations = [
{
'loc_name': key,
'percent': (value['in_month'] * 100) / float(value['all'] or 1),
} for key, value in six.iteritems(best_worst)
]
all_locations_sorted_by_name = sorted(all_locations, key=lambda x: x['loc_name'])
all_locations_sorted_by_percent_and_name = sorted(
all_locations_sorted_by_name, key=lambda x: x['percent'], reverse=True)
return {
"chart_data": [
{
"values": [
{
'x': key,
'y': value['y'] / float(value['all'] or 1),
'all': value['all']
} for key, value in six.iteritems(data['blue'])
],
"key": "Percentage of beneficiaries with Aadhaar numbers",
"strokeWidth": 2,
"classed": "dashed",
"color": ChartColors.BLUE
}
],
"all_locations": all_locations_sorted_by_percent_and_name,
"top_five": all_locations_sorted_by_percent_and_name[:5],
"bottom_five": all_locations_sorted_by_percent_and_name[-5:],
"location_type": loc_level.title() if loc_level != LocationTypes.SUPERVISOR else 'Sector'
}
def person_has_aadhaar_column(self):
return person_has_aadhaar_column(self.beta)
def get_adhaar_sector_data(domain,
config,
loc_level,
location_id,
show_test=False,
beta=False):
group_by = ['%s_name' % loc_level]
config['month'] = datetime(*config['month'])
data = AggAwcMonthly.objects.filter(**config).values(*group_by).annotate(
in_month=Sum(person_has_aadhaar_column(beta)),
all=Sum(person_is_beneficiary_column(beta)),
).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 total number of ICDS beneficiaries, the percentage whose Adhaar "
"identification has been captured. ")),
"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, beta=False):
group_by = ['%s_name' % loc_level]
config['month'] = datetime(*config['month'])
data = AggAwcMonthly.objects.filter(
**config
).values(
*group_by
).annotate(
in_month=Sum(person_has_aadhaar_column(beta)),
all=Sum(person_is_beneficiary_column(beta)),
).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": percent_aadhaar_seeded_beneficiaries_help_text(),
"chart_data": [
{
"values": chart_data['blue'],
"key": "",
"strokeWidth": 2,
"classed": "dashed",
"color": MapColors.BLUE
},
]
}
def get_adhaar_data_chart(domain, config, loc_level, show_test=False, beta=False):
month = datetime(*config['month'])
three_before = datetime(*config['month']) - relativedelta(months=3)
config['month__range'] = (three_before, month)
del config['month']
chart_data = AggAwcMonthly.objects.filter(
**config
).values(
'month', '%s_name' % loc_level
).annotate(
in_month=Sum(person_has_aadhaar_column(beta)),
all=Sum(person_is_beneficiary_column(beta)),
).order_by('month')
if not show_test:
chart_data = apply_exclude(domain, chart_data)
data = {
'blue': OrderedDict(),
}
dates = [dt for dt in rrule(MONTHLY, dtstart=three_before, until=month)]
for date in dates:
miliseconds = int(date.strftime("%s")) * 1000
data['blue'][miliseconds] = {'y': 0, 'all': 0}
best_worst = defaultdict(lambda: {
'in_month': 0,
'all': 0
})
for row in chart_data:
date = row['month']
in_month = row['in_month']
location = row['%s_name' % loc_level]
valid = row['all']
best_worst[location]['in_month'] = in_month
best_worst[location]['all'] = (valid or 0)
date_in_miliseconds = int(date.strftime("%s")) * 1000
data['blue'][date_in_miliseconds]['y'] += in_month
data['blue'][date_in_miliseconds]['all'] += valid
top_locations = sorted(
[
dict(
loc_name=key,
percent=(value['in_month'] * 100) / float(value['all'] or 1)
) for key, value in six.iteritems(best_worst)
],
key=lambda x: x['percent'],
reverse=True
)
return {
"chart_data": [
{
"values": [
{
'x': key,
'y': value['y'] / float(value['all'] or 1),
'all': value['all']
} for key, value in six.iteritems(data['blue'])
],
"key": "Percentage of beneficiaries with Aadhaar numbers",
"strokeWidth": 2,
"classed": "dashed",
"color": ChartColors.BLUE
}
],
"all_locations": top_locations,
"top_five": top_locations[:5],
"bottom_five": top_locations[-5:],
"location_type": loc_level.title() if loc_level != LocationTypes.SUPERVISOR else 'Sector'
}
