def for_each_facility(self, data, combination, other_cycle_data={}):
ratio = combination.get(RATIO)
df1_records = filter_consumption_records(data, combination[DF1])
df2_records = filter_consumption_records(data, combination[DF2])
other_records = filter_consumption_records(data, combination.get(OTHER, []))
df1_count = len(df1_records)
df2_count = len(df2_records) + len(other_records)
df1_values = values_for_records(combination[FIELDS], df1_records)
df2_values = values_for_records(combination[FIELDS], df2_records)
other_values = values_for_records(combination[FIELDS], other_records)
sum_df1 = pydash.chain(df1_values).reject(lambda x: x is None).map(float).sum().value()
sum_df2 = pydash.chain(df2_values).reject(lambda x: x is None).map(float).sum().value()
other_sum = pydash.chain(other_values).reject(lambda x: x is None).map(float).sum().value()
all_df1_fields_are_blank = pydash.every(df1_values, lambda x: x is None) and len(df1_values) > 0
b1 = pydash.every(df2_values, lambda x: x is None) and len(df2_values) > 0
b2 = pydash.every(other_values, lambda x: x is None) and len(other_values) > 0
all_df2_fields_are_blank = b1 and b2
adjusted_sum_df1 = sum_df1 / ratio
numerator = adjusted_sum_df1
denominator = (sum_df2 / ratio) + other_sum
if df1_count == 0 or df2_count == 0:
return NOT_REPORTING
if all_df1_fields_are_blank or all_df2_fields_are_blank:
result = NO
elif (sum_df2 == 0 and sum_df1 == 0) or (denominator != 0 and 0.7 < abs(numerator / denominator) < 1.429):
result = YES
else:
result = NO
return result
def for_each_facility(self, data, combination, previous_cycle_data=None):
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.get(FIELDS, []),
df1_records)
df2_values = values_for_records(combination.get(FIELDS, []),
df2_records)
all_df1_fields_are_blank = pydash.every(
df1_values, lambda x: x is None) and len(df1_values) > 0
all_df2_fields_are_blank = pydash.every(
df2_values, lambda x: x is None) and len(df2_values) > 0
sum_df1 = pydash.chain(df1_values).reject(lambda x: x is None).map(
float).sum().value()
sum_df2 = pydash.chain(df2_values).reject(lambda x: x is None).map(
float).sum().value()
if df1_count == 0 or df2_count == 0:
return NOT_REPORTING
if all_df1_fields_are_blank or all_df2_fields_are_blank:
result = NO
elif (sum_df2 == 0
and sum_df1 == 0) or (sum_df2 != 0
and 0.7 < abs(sum_df1 / sum_df2) < 1.429):
result = YES
else:
result = NO
return result
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, data, combination, other_cycle_data={}):
ratio = combination.get(RATIO)
df1_records = filter_consumption_records(data, combination[DF1])
df2_records = filter_consumption_records(data, combination[DF2])
other_records = filter_consumption_records(data,
combination.get(OTHER, []))
df1_count = len(df1_records)
df2_count = len(df2_records) + len(other_records)
df1_values = values_for_records(combination[FIELDS], df1_records)
df2_values = values_for_records(combination[FIELDS], df2_records)
other_values = values_for_records(combination[FIELDS], other_records)
sum_df1 = pydash.chain(df1_values).reject(lambda x: x is None).map(
float).sum().value()
sum_df2 = pydash.chain(df2_values).reject(lambda x: x is None).map(
float).sum().value()
other_sum = pydash.chain(other_values).reject(lambda x: x is None).map(
float).sum().value()
all_df1_fields_are_blank = pydash.every(
df1_values, lambda x: x is None) and len(df1_values) > 0
b1 = pydash.every(df2_values,
lambda x: x is None) and len(df2_values) > 0
b2 = pydash.every(other_values,
lambda x: x is None) and len(other_values) > 0
all_df2_fields_are_blank = b1 and b2
adjusted_sum_df1 = sum_df1 / ratio
numerator = adjusted_sum_df1
denominator = (sum_df2 / ratio) + other_sum
if df1_count == 0 or df2_count == 0:
return NOT_REPORTING
if all_df1_fields_are_blank or all_df2_fields_are_blank:
result = NO
elif (sum_df2 == 0 and sum_df1
== 0) or (denominator != 0
and 0.7 < abs(numerator / denominator) < 1.429):
result = YES
else:
result = NO
return result
def for_each_facility(self, data, combination, previous_cycle_data=None):
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.get(FIELDS, []), df1_records)
df2_values = values_for_records(combination.get(FIELDS, []), df2_records)
all_df1_fields_are_blank = pydash.every(df1_values, lambda x: x is None) and len(df1_values) > 0
all_df2_fields_are_blank = pydash.every(df2_values, lambda x: x is None) and len(df2_values) > 0
sum_df1 = pydash.chain(df1_values).reject(lambda x: x is None).map(float).sum().value()
sum_df2 = pydash.chain(df2_values).reject(lambda x: x is None).map(float).sum().value()
if df1_count == 0 or df2_count == 0:
return NOT_REPORTING
if all_df1_fields_are_blank or all_df2_fields_are_blank:
result = NO
elif (sum_df2 == 0 and sum_df1 == 0) or (sum_df2 != 0 and 0.7 < abs(sum_df1 / sum_df2) < 1.429):
result = YES
else:
result = NO
return result
