def for_each_facility(self, data, combination, previous_cycle_data=None):
ratio = combination[RATIO]
df1_records = filter_consumption_records(data, combination[DF1])
df2_records = filter_consumption_records(data, combination[DF2])
df1_count = len(df1_records)
df2_count = len(df2_records)
df1_values = values_for_records(combination[FIELDS], df1_records)
df2_values = values_for_records(combination[FIELDS], df2_records)
sum_df1 = pydash.chain(df1_values).reject(lambda x: x is None).sum().value()
sum_df2 = pydash.chain(df2_values).reject(lambda x: x is None).sum().value()
all_df1_fields_are_blank = pydash.every(df1_values, lambda x: x is None)
all_df2_fields_are_blank = pydash.every(df2_values, lambda x: x is None)
return calculate_score(df1_count, df2_count, sum_df1, sum_df2, ratio, all_df1_fields_are_blank,
all_df2_fields_are_blank, facility_not_reporting(data))
def for_each_facility(self, data, combination, previous_cycle_data=None):
ratio = combination[RATIO]
df1_records = filter_consumption_records(data, combination[DF1])
df2_records = filter_consumption_records(data, combination[DF2])
df1_count = len(df1_records)
df2_count = len(df2_records)
df1_values = values_for_records(combination[FIELDS], df1_records)
df2_values = values_for_records(combination[FIELDS], df2_records)
sum_df1 = pydash.chain(df1_values).reject(
lambda x: x is None).sum().value()
sum_df2 = pydash.chain(df2_values).reject(
lambda x: x is None).sum().value()
all_df1_fields_are_blank = pydash.every(df1_values,
lambda x: x is None)
all_df2_fields_are_blank = pydash.every(df2_values,
lambda x: x is None)
return calculate_score(df1_count, df2_count, sum_df1, sum_df2, ratio,
all_df1_fields_are_blank,
all_df2_fields_are_blank,
facility_not_reporting(data))
def for_each_facility(self,
facility,
combination,
previous_cycle_data=None):
return NO if facility_not_reporting(facility) else YES
def for_each_facility(self, facility, combination, previous_cycle_data=None):
return NO if facility_not_reporting(facility) else YES
