feat(models): add NonNegativeFloat class

birdxplorer/models.py

@@ -11,6 +11,7 @@
 IncEx: TypeAlias = "set[int] | set[str] | dict[int, IncEx] | dict[str, IncEx] | None"
 StrT = TypeVar("StrT", bound="BaseString")
 IntT = TypeVar("IntT", bound="BaseInt")
+FloatT = TypeVar("FloatT", bound="BaseFloat")
 
 
 class BaseString(str):
@@ -258,6 +259,107 @@ def min_value(cls) -> int:
         return int(datetime(2006, 7, 15, 0, 0, 0, 0, timezone.utc).timestamp() * 1000)
 
 
+class BaseFloat(float):
+    """
+    >>> BaseFloat(1.0)
+    BaseFloat(1.0)
+    >>> float(BaseFloat(1.0))
+    1.0
+    >>> ta = TypeAdapter(BaseFloat)
+    >>> ta.validate_python(1.0)
+    BaseFloat(1.0)
+    >>> ta.validate_python("1.0")
+    BaseFloat(1.0)
+    >>> ta.dump_json(BaseFloat(1.0))
+    b'1.0'
+    >>> BaseFloat.from_float(1.0)
+    BaseFloat(1.0)
+    >>> ta.validate_python(BaseFloat.from_float(1.0))
+    BaseFloat(1.0)
+    >>> hash(BaseFloat(1.0)) == hash(1.0)
+    True
+    """
+
+    @classmethod
+    def _proc_float(cls, f: float) -> float:
+        return f
+
+    def __repr__(self) -> str:
+        return f"{self.__class__.__name__}({super().__repr__()})"
+
+    def __float__(self) -> float:
+        return super(BaseFloat, self).__float__()
+
+    @classmethod
+    def __get_pydantic_core_schema__(cls, _source_type: Any, _handler: GetCoreSchemaHandler) -> core_schema.CoreSchema:
+        return core_schema.no_info_after_validator_function(
+            cls.validate,
+            core_schema.float_schema(**cls.__get_extra_constraint_dict__()),
+            serialization=core_schema.plain_serializer_function_ser_schema(cls.serialize, when_used="json"),
+        )
+
+    @classmethod
+    def validate(cls: Type[FloatT], v: Any) -> FloatT:
+        return cls(cls._proc_float(v))
+
+    def serialize(self) -> float:
+        return float(self)
+
+    @classmethod
+    def __get_extra_constraint_dict__(cls) -> dict[str, Any]:
+        return {}
+
+    def __hash__(self) -> int:
+        return super(BaseFloat, self).__hash__()
+
+    @classmethod
+    def from_float(cls: Type[FloatT], v: float) -> FloatT:
+        return TypeAdapter(cls).validate_python(v)
+
+
+class BaseLowerBoundedFloat(BaseFloat, ABC):
+    """
+    >>> class PositiveFloat(BaseLowerBoundedFloat):
+    ...   @classmethod
+    ...   def min_value(cls) -> float:
+    ...     return 0.0
+    >>> PositiveFloat.from_float(0.0)
+    PositiveFloat(0.0)
+    >>> PositiveFloat.from_float(-0.1)
+    Traceback (most recent call last):
+     ...
+    pydantic_core._pydantic_core.ValidationError: 1 validation error for function-after[validate(), constrained-float]
+      Input should be greater than or equal to 0 [type=greater_than_equal, input_value=-0.1, input_type=float]
+     ...
+    """
+
+    @classmethod
+    @abstractmethod
+    def min_value(cls) -> float:
+        raise NotImplementedError
+
+    @classmethod
+    def __get_extra_constraint_dict__(cls) -> dict[str, Any]:
+        return dict(super().__get_extra_constraint_dict__(), ge=cls.min_value())
+
+
+class NonNegativeFloat(BaseLowerBoundedFloat):
+    """
+    >>> NonNegativeFloat.from_float(0.0)
+    NonNegativeFloat(0.0)
+    >>> NonNegativeFloat.from_float(-0.1)
+    Traceback (most recent call last):
+     ...
+    pydantic_core._pydantic_core.ValidationError: 1 validation error for function-after[validate(), constrained-float]
+      Input should be greater than or equal to 0 [type=greater_than_equal, input_value=-0.1, input_type=float]
+     ...
+    """
+
+    @classmethod
+    def min_value(cls) -> float:
+        return 0.0
+
+
 class BaseModel(PydanticBaseModel):
     """
     >>> from unittest.mock import patch
@@ -342,7 +444,7 @@ class UserEnrollment(BaseModel):
     timestamp_of_last_state_change: UserEnrollmentLastStateChangeTimeStamp
     timestamp_of_last_earn_out: UserEnrollmentLastEarnOutTimestamp
     modeling_population: ModelingPopulation
-    modeling_group: str
+    modeling_group: NonNegativeFloat
 
 
 class NoteId(BaseString):