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engine.py
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engine.py
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"""Data types engine interface."""
# https://github.com/PyCQA/pylint/issues/3268
# pylint:disable=no-value-for-parameter
import functools
import inspect
from abc import ABCMeta
from dataclasses import dataclass
from typing import (
TYPE_CHECKING,
Any,
Callable,
Dict,
List,
Optional,
Set,
Tuple,
Type,
TypeVar,
get_type_hints,
)
import typing_inspect
from pandera.dtypes import DataType
_DataType = TypeVar("_DataType", bound=DataType)
_Engine = TypeVar("_Engine", bound="Engine")
_EngineType = Type[_Engine]
if TYPE_CHECKING: # pragma: no cover
class Dispatch:
"""Only used for type annotation."""
def __call__(self, data_type: Any, **kwds: Any) -> Any:
pass
@staticmethod
def register(
data_type: Any, func: Callable[[Any], DataType]
) -> Callable[[Any], DataType]:
"""Register a new implementation for the given cls."""
else:
Dispatch = Callable[[Any], DataType]
@dataclass
class _DtypeRegistry:
dispatch: Dispatch
equivalents: Dict[Any, DataType]
class Engine(ABCMeta):
"""Base Engine metaclass.
Keep a registry of concrete Engines.
"""
_registry: Dict["Engine", _DtypeRegistry] = {}
_registered_dtypes: Set[Type[DataType]]
_base_pandera_dtypes: Tuple[Type[DataType]]
def __new__(cls, name, bases, namespace, **kwargs):
base_pandera_dtypes = kwargs.pop("base_pandera_dtypes")
try:
namespace["_base_pandera_dtypes"] = tuple(base_pandera_dtypes)
except TypeError:
namespace["_base_pandera_dtypes"] = (base_pandera_dtypes,)
namespace["_registered_dtypes"] = set()
engine = super().__new__(cls, name, bases, namespace, **kwargs)
@functools.singledispatch
def dtype(data_type: Any) -> DataType:
raise ValueError(f"Data type '{data_type}' not understood")
cls._registry[engine] = _DtypeRegistry(dispatch=dtype, equivalents={})
return engine
def _check_source_dtype(cls, data_type: Any) -> None:
if isinstance(data_type, cls._base_pandera_dtypes) or (
inspect.isclass(data_type)
and issubclass(data_type, cls._base_pandera_dtypes)
):
base_names = [
f"{base.__module__}.{base.__qualname__}"
for base in cls._base_pandera_dtypes
]
raise ValueError(
f"Subclasses of {base_names} cannot be registered"
f" with {cls.__name__}."
)
def _register_from_parametrized_dtype(
cls,
pandera_dtype_cls: Type[DataType],
) -> None:
method = pandera_dtype_cls.__dict__["from_parametrized_dtype"]
if not isinstance(method, classmethod):
raise ValueError(
f"{pandera_dtype_cls.__name__}.from_parametrized_dtype "
+ "must be a classmethod."
)
func = method.__func__
annotations = get_type_hints(func).values()
dtype = next(iter(annotations)) # get 1st annotation
# parse typing.Union
dtypes = typing_inspect.get_args(dtype) or [dtype]
def _method(*args, **kwargs):
return func(pandera_dtype_cls, *args, **kwargs)
for source_dtype in dtypes:
cls._check_source_dtype(source_dtype)
cls._registry[cls].dispatch.register(source_dtype, _method)
def _register_equivalents(
cls, pandera_dtype_cls: Type[DataType], *source_dtypes: Any
) -> None:
pandera_dtype = pandera_dtype_cls() # type: ignore
for source_dtype in source_dtypes:
cls._check_source_dtype(source_dtype)
cls._registry[cls].equivalents[source_dtype] = pandera_dtype
def register_dtype(
cls: _EngineType,
pandera_dtype_cls: Type[_DataType] = None,
*,
equivalents: Optional[List[Any]] = None,
) -> Callable:
"""Register a Pandera :class:`~pandera.dtypes.DataType` with the engine,
as class decorator.
:param pandera_dtype: The DataType to register.
:param equivalents: Equivalent scalar data type classes or
non-parametrized data type instances.
.. note::
The classmethod ``from_parametrized_dtype`` will also be
registered. See :ref:`here<dtypes>` for more usage details.
:example:
>>> import pandera as pa
>>>
>>> class MyDataType(pa.DataType):
... pass
>>>
>>> class MyEngine(
... metaclass=pa.engines.engine.Engine,
... base_pandera_dtypes=MyDataType,
... ):
... pass
>>>
>>> @MyEngine.register_dtype(equivalents=[bool])
... class MyBool(MyDataType):
... pass
"""
def _wrapper(pandera_dtype_cls: Type[_DataType]) -> Type[_DataType]:
if not inspect.isclass(pandera_dtype_cls):
raise ValueError(
f"{cls.__name__}.register_dtype can only decorate a class,"
f" got {pandera_dtype_cls}"
)
if equivalents:
cls._register_equivalents(pandera_dtype_cls, *equivalents)
if "from_parametrized_dtype" in pandera_dtype_cls.__dict__:
cls._register_from_parametrized_dtype(pandera_dtype_cls)
cls._registered_dtypes.add(pandera_dtype_cls)
return pandera_dtype_cls
if pandera_dtype_cls:
return _wrapper(pandera_dtype_cls)
return _wrapper
def dtype(cls: _EngineType, data_type: Any) -> _DataType:
"""Convert input into a Pandera :class:`DataType` object."""
if isinstance(data_type, cls._base_pandera_dtypes):
return data_type
if inspect.isclass(data_type) and issubclass(
data_type, cls._base_pandera_dtypes
):
try:
return data_type()
except (TypeError, AttributeError) as err:
raise TypeError(
f"DataType '{data_type.__name__}' cannot be instantiated: "
f"{err}\n "
+ "Usage Tip: Use an instance or a string representation."
) from err
registry = cls._registry[cls]
equivalent_data_type = registry.equivalents.get(data_type)
if equivalent_data_type is not None:
return equivalent_data_type
elif isinstance(data_type, DataType):
# in the case where data_type is a parameterized dtypes.DataType instance that isn't
# in the equivalents registry, use its type to get the equivalent, and feed
# the parameters into the recognized data type class.
equivalent_data_type = registry.equivalents.get(type(data_type))
if equivalent_data_type is not None:
return type(equivalent_data_type)(**data_type.__dict__)
try:
return registry.dispatch(data_type)
except (KeyError, ValueError):
raise TypeError(
f"Data type '{data_type}' not understood by {cls.__name__}."
) from None
def get_registered_dtypes( # pylint:disable=W1401
cls,
) -> List[Type[DataType]]:
"""Return the :class:`pandera.dtypes.DataType`\s registered
with this engine."""
return list(cls._registered_dtypes)