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# coding: utf-8
"""
VCell API
VCell API
The version of the OpenAPI document: 1.0.1
Contact: vcell_support@uchc.com
Generated by OpenAPI Generator (https://openapi-generator.tech)
Do not edit the class manually.
""" # noqa: E501
from __future__ import annotations
import pprint
import re # noqa: F401
import json
from typing import Any, ClassVar, Dict, List, Optional, Union
from pydantic import StrictBool, StrictInt, StrictStr
from pydantic import Field
from vcell_client.models.coordinate import Coordinate
from vcell_client.models.curve import Curve
try:
from typing import Self
except ImportError:
from typing_extensions import Self
class ControlPointCurve(Curve):
"""
ControlPointCurve
""" # noqa: E501
type: StrictStr
control_points: Optional[List[Coordinate]] = Field(default=None, alias="controlPoints")
control_point_count: Optional[StrictInt] = Field(default=None, alias="controlPointCount")
control_points_vector: Optional[List[Coordinate]] = Field(default=None, alias="controlPointsVector")
max_control_points: Optional[StrictInt] = Field(default=None, alias="maxControlPoints")
min_control_points: Optional[StrictInt] = Field(default=None, alias="minControlPoints")
control_point_addable: Optional[StrictBool] = Field(default=None, alias="controlPointAddable")
valid: Optional[StrictBool] = None
__properties: ClassVar[List[str]] = ["bClosed", "description", "type", "beginningCoordinate", "defaultNumSamples", "endingCoordinate", "numSamplePoints", "segmentCount", "spatialLength", "closed", "valid"]
model_config = {
"populate_by_name": True,
"validate_assignment": True
}
# JSON field name that stores the object type
__discriminator_property_name: ClassVar[List[str]] = 'type'
# discriminator mappings
__discriminator_value_class_map: ClassVar[Dict[str, str]] = {
'SampledCurve': 'SampledCurve','Spline': 'Spline'
}
@classmethod
def get_discriminator_value(cls, obj: Dict) -> str:
"""Returns the discriminator value (object type) of the data"""
discriminator_value = obj[cls.__discriminator_property_name]
if discriminator_value:
return cls.__discriminator_value_class_map.get(discriminator_value)
else:
return None
def to_str(self) -> str:
"""Returns the string representation of the model using alias"""
return pprint.pformat(self.model_dump(by_alias=True))
def to_json(self) -> str:
"""Returns the JSON representation of the model using alias"""
# TODO: pydantic v2: use .model_dump_json(by_alias=True, exclude_unset=True) instead
return json.dumps(self.to_dict())
@classmethod
def from_json(cls, json_str: str) -> Union[Self, Self]:
"""Create an instance of ControlPointCurve from a JSON string"""
return cls.from_dict(json.loads(json_str))
def to_dict(self) -> Dict[str, Any]:
"""Return the dictionary representation of the model using alias.
This has the following differences from calling pydantic's
`self.model_dump(by_alias=True)`:
* `None` is only added to the output dict for nullable fields that
were set at model initialization. Other fields with value `None`
are ignored.
"""
_dict = self.model_dump(
by_alias=True,
exclude={
},
exclude_none=True,
)
# override the default output from pydantic by calling `to_dict()` of beginning_coordinate
if self.beginning_coordinate:
_dict['beginningCoordinate'] = self.beginning_coordinate.to_dict()
# override the default output from pydantic by calling `to_dict()` of ending_coordinate
if self.ending_coordinate:
_dict['endingCoordinate'] = self.ending_coordinate.to_dict()
return _dict
@classmethod
def from_dict(cls, obj: Dict) -> Union[Self, Self]:
"""Create an instance of ControlPointCurve from a dict"""
# look up the object type based on discriminator mapping
object_type = cls.get_discriminator_value(obj)
if object_type:
klass = globals()[object_type]
return klass.from_dict(obj)
else:
raise ValueError("ControlPointCurve failed to lookup discriminator value from " +
json.dumps(obj) + ". Discriminator property name: " + cls.__discriminator_property_name +
", mapping: " + json.dumps(cls.__discriminator_value_class_map))
from vcell_client.models.sampled_curve import SampledCurve
from vcell_client.models.spline import Spline
# TODO: Rewrite to not use raise_errors
ControlPointCurve.model_rebuild(raise_errors=False)
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