def calculate(imTree):
trades = imTree.Crif.Sensitivities.keys()
standaloneTrees = {}
standaloneMatrix = imTree.FlatTree.copy(deep=True)
standaloneMatrix.set_index([
'Im Model', 'Silo', 'RiskClass', 'SensitivityType', 'Bucket',
'WeightedSensitivity'
],
inplace=True)
standaloneMatrix.drop(columns=['ExposureAmount'], inplace=True)
for t in trades:
standaloneFlatTree = GenerateCsvString.parseToFlatCsv(
Simm.calculateTreeStandard(imTree.Crif.Sensitivities[t],
imTree.CalculationCurrency))
standaloneFlatTree = StringIO(standaloneFlatTree)
standaloneFlatTree = ResultTrees.ImTreeUtil.read_csv(
standaloneFlatTree)
standaloneFlatTree.set_index([
'Im Model', 'Silo', 'RiskClass', 'SensitivityType', 'Bucket',
'WeightedSensitivity'
],
inplace=True)
standaloneFlatTree.drop(columns=['Level'], inplace=True)
standaloneFlatTree.rename(columns={'ExposureAmount': t},
inplace=True)
standaloneMatrix = pd.merge(standaloneMatrix,
standaloneFlatTree,
how='left',
on=[
'Im Model', 'Silo', 'RiskClass',
'SensitivityType', 'Bucket',
'WeightedSensitivity'
])
standaloneMatrix.drop(columns=['Level'], inplace=True)
standaloneMatrix.reset_index(drop=True, inplace=True)
for key, value in imTree.nodes.items():
standaloneAllocation = standaloneMatrix.iloc[value.data.rowNumber]
standaloneAllocation.dropna(inplace=True)
value.data.standaloneAllocation = standaloneAllocation
imTree.hasStandaloneAllocation = True
return imTree
print(text)
columns = [
"byr",# (Birth Year)",
"iyr",# (Issue Year)",
"eyr",# (Expiration Year)",
"hgt",# (Height)",
"hcl",# (Hair Color)",
"ecl",# (Eye Color)",
"pid",# (Passport ID)",
"cid"]# (Country ID)"
data=pd.read_csv(data, names= ["Raw", "byr","iyr","eyr","hgt","hcl","ecl","pid","cid"], header=None)
def datacheck(check):
if check.find(col) > 0:
#magic = r'\b'+col+'\S*'
magic = col+'\S+'
return str(re.search(magic, check)[0]).replace(col+":","")
else:
return
for col in columns:
data[col]=data.apply(lambda row: datacheck(row.Raw), axis=1)
data = data.drop(columns=["Raw"])
#print(data)
data['missing'] = data.apply(lambda x: 8-x.count(), axis=1)
print(data['missing'].value_counts())
print(data)
