def aggregate_scores(user, test, cycles, formulation, keys, count_values,
filters):
scores_filter = {}
if user:
access_level = user.access_level
access_area = user.access_area
if access_level and access_area:
scores_filter[access_level.lower()] = access_area
if user.is_superuser:
scores_filter = filters
score_objects = Score.objects.filter(**scores_filter).values(test, "cycle")
grouped_objects = pydash.group_by(score_objects, lambda x: x["cycle"])
def get_count_key(value):
value_as_dict = json.loads(value[test])
return value_as_dict.get(formulation,
None) if type(value_as_dict) is dict else None
def agg(value):
values = grouped_objects.get(value, [])
result = {'cycle': value}
total = len(values)
yes_count_value = count_values[YES]
no_count_value = count_values[NO]
not_reporting_count_value = count_values[NOT_REPORTING]
if total > 0:
counts = pydash.count_by(values, get_count_key)
yes_count = counts.get(yes_count_value, 0)
no_count = counts.get(no_count_value, 0)
not_reporting_count = counts.get(not_reporting_count_value, 0)
result[keys[YES]] = (yes_count * 100 / float(total))
result[keys[NO]] = (no_count * 100 / float(total))
result[keys[NOT_REPORTING]] = (not_reporting_count * 100 /
float(total))
else:
result[keys[YES]] = 0
result[keys[NO]] = 0
result[keys[NOT_REPORTING]] = 0
return result
return pydash.collect(cycles, agg)
def aggregate_scores(user, test, cycles, formulation, keys, count_values, filters):
scores_filter = {}
if user:
access_level = user.access_level
access_area = user.access_area
if access_level and access_area:
scores_filter[access_level.lower()] = access_area
if user.is_superuser:
scores_filter = filters
score_objects = Score.objects.filter(**scores_filter).values(test, "cycle")
grouped_objects = pydash.group_by(score_objects, lambda x: x["cycle"])
def get_count_key(value):
value_as_dict = json.loads(value[test])
return value_as_dict.get(formulation, None) if type(value_as_dict) is dict else None
def agg(value):
values = grouped_objects.get(value, [])
result = {'cycle': value}
total = len(values)
yes_count_value = count_values[YES]
no_count_value = count_values[NO]
not_reporting_count_value = count_values[NOT_REPORTING]
if total > 0:
counts = pydash.count_by(values, get_count_key)
yes_count = counts.get(yes_count_value, 0)
no_count = counts.get(no_count_value, 0)
not_reporting_count = counts.get(not_reporting_count_value, 0)
result[keys[YES]] = (yes_count * 100 / float(total))
result[keys[NO]] = (no_count * 100 / float(total))
result[keys[NOT_REPORTING]] = (not_reporting_count * 100 / float(total))
else:
result[keys[YES]] = 0
result[keys[NO]] = 0
result[keys[NOT_REPORTING]] = 0
return result
return pydash.collect(cycles, agg)
def get_patient_records(data, combinations, is_adult=True):
lower_case_combinations = pydash.collect(combinations, lambda x: x.lower())
records = data.get(A_RECORDS, []) if is_adult else data.get(P_RECORDS, [])
return pydash.chain(records).select(lambda x: x[FORMULATION].strip().lower(
) in lower_case_combinations).value()
def persist_multiple_order_records(report):
facilities_with_multiple_orders = pydash.reject(report.locs, lambda f: facility_has_single_order(f))
all = pydash.collect(facilities_with_multiple_orders, build_mof(report))
MultipleOrderFacility.objects.filter(cycle=report.cycle).delete()
MultipleOrderFacility.objects.bulk_create(all)
def get_patient_records(data, combinations, is_adult=True):
lower_case_combinations = pydash.collect(combinations, lambda x: x.lower())
records = data.get(A_RECORDS, []) if is_adult else data.get(P_RECORDS, [])
return pydash.chain(records).select(
lambda x: x[FORMULATION].strip().lower() in lower_case_combinations
).value()
