def for_each_facility(self, data, combination, previous_cycle_data=None):
df1_records = get_consumption_records(data, combination[CONSUMPTION_QUERY])
values = values_for_records(self.fields, df1_records)
all_cells_not_negative = pydash.every(values, lambda x: x is None or x >= 0)
if len(df1_records) == 0:
return NOT_REPORTING
return YES if all_cells_not_negative else NO
def test_consumption_records(self):
data = {}
data[C_RECORDS] = [{FORMULATION: "A", "openingBalance": 3},
{FORMULATION: "B", "openingBalance": 3},
{FORMULATION: "A", "openingBalance": 12}]
check = ConsumptionAndPatientsQualityCheck()
records = get_consumption_records(data, "A")
assert records == [{FORMULATION: "A", "openingBalance": 3}, {FORMULATION: "A", "openingBalance": 12}]
def for_each_facility(self, data, combination, previous_cycle_data=None):
df1_records = get_consumption_records(data,
combination[CONSUMPTION_QUERY])
values = values_for_records(self.fields, df1_records)
all_cells_not_negative = pydash.every(values,
lambda x: x is None or x >= 0)
if len(df1_records) == 0:
return NOT_REPORTING
return YES if all_cells_not_negative else NO
def for_each_facility(self, data, combination, previous_cycle_data=None):
df1_records = get_consumption_records(data, combination[CONSUMPTION_QUERY])
df2_records = get_patient_records(data, combination[PATIENT_QUERY],
combination[IS_ADULT])
df2_sum = get_patient_total(df2_records)
df1_sum = get_consumption_totals(combination[FIELDS], df1_records)
all_df1_blank = has_all_blanks(df1_records, combination[FIELDS])
all_df2_blank = has_all_blanks(df2_records, [NEW, EXISTING])
both_are_zero = (df1_sum == 0 and df2_sum == 0)
if len(df1_records) == 0 and len(df2_records) == 0:
return NOT_REPORTING
adjusted_df1_sum = df1_sum / combination[RATIO]
no_blanks = not all_df1_blank and not all_df2_blank
divisible = df2_sum != 0
if no_blanks and (both_are_zero or (divisible and 0.7 < float(adjusted_df1_sum) / float(df2_sum) < 1.429)):
return YES
else:
return NO
def for_each_facility(self, data, combination, previous_cycle_data=None):
df1_records = get_consumption_records(data,
combination[CONSUMPTION_QUERY])
df2_records = get_patient_records(data, combination[PATIENT_QUERY],
combination[IS_ADULT])
df2_sum = get_patient_total(df2_records)
df1_sum = get_consumption_totals(combination[FIELDS], df1_records)
all_df1_blank = has_all_blanks(df1_records, combination[FIELDS])
all_df2_blank = has_all_blanks(df2_records, [NEW, EXISTING])
both_are_zero = (df1_sum == 0 and df2_sum == 0)
if len(df1_records) == 0 and len(df2_records) == 0:
return NOT_REPORTING
adjusted_df1_sum = df1_sum / combination[RATIO]
no_blanks = not all_df1_blank and not all_df2_blank
divisible = df2_sum != 0
if no_blanks and (both_are_zero or
(divisible and 0.7 <
float(adjusted_df1_sum) / float(df2_sum) < 1.429)):
return YES
else:
return NO
def test_consumption_records(self):
data = {}
data[C_RECORDS] = [{
FORMULATION: "A",
"openingBalance": 3
}, {
FORMULATION: "B",
"openingBalance": 3
}, {
FORMULATION: "A",
"openingBalance": 12
}]
check = ConsumptionAndPatientsQualityCheck()
records = get_consumption_records(data, "A")
assert records == [{
FORMULATION: "A",
"openingBalance": 3
}, {
FORMULATION: "A",
"openingBalance": 12
}]