# coding: utf-8
"""
LUSID API
FINBOURNE Technology # noqa: E501
Contact: info@finbourne.com
Generated by OpenAPI Generator (https://openapi-generator.tech)
Do not edit the class manually.
"""
from __future__ import annotations
import pprint
import re # noqa: F401
import json
from typing import Any, Dict, Optional
from pydantic.v1 import Field, StrictStr, constr, validator
from lusid.models.market_data_options import MarketDataOptions
[docs]
class CurveOptions(MarketDataOptions):
"""
Options for configuring how ComplexMarketData representing a 'curve' is interpreted. # noqa: E501
"""
day_count_convention: Optional[constr(strict=True, max_length=50, min_length=0)] = Field(None, alias="dayCountConvention", description="Day count convention of the curve. Defaults to \"Act360\".")
front_extrapolation_type: Optional[constr(strict=True, max_length=50, min_length=0)] = Field(None, alias="frontExtrapolationType", description="What type of extrapolation is used to build the curve Imagine that the curve is facing the observer(you), then the \"front\" direction is the closest point on the curve onward. <br /> example: 0D tenor to past Defaults to \"Flat\". Supported string (enumeration) values are: [None, Flat, Linear].")
back_extrapolation_type: Optional[constr(strict=True, max_length=50, min_length=0)] = Field(None, alias="backExtrapolationType", description="What type of extrapolation is used to build the curve. <br /> Imagine that the curve is facing the observer(you), then the \"back\" direction is the furthest point on the curve onward. <br /> example: 30Y tenor to infinity Defaults to \"Flat\". Supported string (enumeration) values are: [None, Flat, Linear].")
market_data_options_type: StrictStr = Field(..., alias="marketDataOptionsType", description="The available values are: CurveOptions")
additional_properties: Dict[str, Any] = {}
__properties = ["marketDataOptionsType", "dayCountConvention", "frontExtrapolationType", "backExtrapolationType"]
[docs]
@validator('market_data_options_type')
def market_data_options_type_validate_enum(cls, value):
"""Validates the enum"""
if value not in ('CurveOptions'):
raise ValueError("must be one of enum values ('CurveOptions')")
return value
[docs]
class Config:
"""Pydantic configuration"""
allow_population_by_field_name = True
validate_assignment = True
[docs]
def to_str(self) -> str:
"""Returns the string representation of the model using alias"""
return pprint.pformat(self.dict(by_alias=True))
[docs]
def to_json(self) -> str:
"""Returns the JSON representation of the model using alias"""
return json.dumps(self.to_dict())
[docs]
@classmethod
def from_json(cls, json_str: str) -> CurveOptions:
"""Create an instance of CurveOptions from a JSON string"""
return cls.from_dict(json.loads(json_str))
[docs]
def to_dict(self):
"""Returns the dictionary representation of the model using alias"""
_dict = self.dict(by_alias=True,
exclude={
"additional_properties"
},
exclude_none=True)
# puts key-value pairs in additional_properties in the top level
if self.additional_properties is not None:
for _key, _value in self.additional_properties.items():
_dict[_key] = _value
# set to None if day_count_convention (nullable) is None
# and __fields_set__ contains the field
if self.day_count_convention is None and "day_count_convention" in self.__fields_set__:
_dict['dayCountConvention'] = None
# set to None if front_extrapolation_type (nullable) is None
# and __fields_set__ contains the field
if self.front_extrapolation_type is None and "front_extrapolation_type" in self.__fields_set__:
_dict['frontExtrapolationType'] = None
# set to None if back_extrapolation_type (nullable) is None
# and __fields_set__ contains the field
if self.back_extrapolation_type is None and "back_extrapolation_type" in self.__fields_set__:
_dict['backExtrapolationType'] = None
return _dict
[docs]
@classmethod
def from_dict(cls, obj: dict) -> CurveOptions:
"""Create an instance of CurveOptions from a dict"""
if obj is None:
return None
if not isinstance(obj, dict):
return CurveOptions.parse_obj(obj)
_obj = CurveOptions.parse_obj({
"market_data_options_type": obj.get("marketDataOptionsType"),
"day_count_convention": obj.get("dayCountConvention"),
"front_extrapolation_type": obj.get("frontExtrapolationType"),
"back_extrapolation_type": obj.get("backExtrapolationType")
})
# store additional fields in additional_properties
for _key in obj.keys():
if _key not in cls.__properties:
_obj.additional_properties[_key] = obj.get(_key)
return _obj