Repository: numpy/numpy
File: numpy/ma/core.pyi (Python)

Code before:
    "sum",
    "swapaxes",
    "take",
    "tan",
    "tanh",
    "trace",
    "transpose",
    "true_divide",
    "var",
    "where",
    "zeros",
    "zeros_like",
]

_ShapeT = TypeVar("_ShapeT", bound=tuple[int, ...])
_ShapeT_co = TypeVar("_ShapeT_co", bound=tuple[int, ...], covariant=True)
_DTypeT = TypeVar("_DTypeT", bound=dtype)
_DTypeT_co = TypeVar("_DTypeT_co", bound=dtype, covariant=True)
_ArrayT = TypeVar("_ArrayT", bound=ndarray[Any, Any])
_ScalarT = TypeVar("_ScalarT", bound=generic)
_ScalarT_co = TypeVar("_ScalarT_co", bound=generic)
# A subset of `MaskedArray` that can be parametrized w.r.t. `np.generic`
_MaskedArray: TypeAlias = MaskedArray[_Shape, dtype[_ScalarT]]

MaskType: TypeAlias = bool_
nomask: bool_

class MaskedArrayFutureWarning(FutureWarning): ...
class MAError(Exception): ...
class MaskError(MAError): ...

def default_fill_value(obj): ...
def minimum_fill_value(obj): ...
def maximum_fill_value(obj): ...
def set_fill_value(a, fill_value): ...
def common_fill_value(a, b): ...
@overload
def filled(a: ndarray[_ShapeT_co, _DTypeT_co], fill_value: _ScalarLike_co | None = None) -> ndarray[_ShapeT_co, _DTypeT_co]: ...
@overload
def filled(a: _ArrayLike[_ScalarT_co], fill_value: _ScalarLike_co | None = None) -> NDArray[_ScalarT_co]: ...
@overload
def filled(a: ArrayLike, fill_value: _ScalarLike_co | None = None) -> NDArray[Any]: ...
def getdata(a, subok=...): ...
get_data = getdata

def fix_invalid(a, mask=..., copy=..., fill_value=...): ...

class _MaskedUFunc:
    f: Any
    __doc__: Any
    __name__: Any

Review comment (suggestion):
if you wanna be fancy about it, you could also write this as

```suggestion
nomask: bool_[Literal[False]]
```

(assuming that `MaskType` isn't only `np.False_` the case on my machine)

Code after:
    "sum",
    "swapaxes",
    "take",
    "tan",
    "tanh",
    "trace",
    "transpose",
    "true_divide",
    "var",
    "where",
    "zeros",
    "zeros_like",
]

_ShapeT = TypeVar("_ShapeT", bound=tuple[int, ...])
_ShapeT_co = TypeVar("_ShapeT_co", bound=tuple[int, ...], covariant=True)
_DTypeT = TypeVar("_DTypeT", bound=dtype)
_DTypeT_co = TypeVar("_DTypeT_co", bound=dtype, covariant=True)
_ArrayT = TypeVar("_ArrayT", bound=ndarray[Any, Any])
_ScalarT = TypeVar("_ScalarT", bound=generic)
_ScalarT_co = TypeVar("_ScalarT_co", bound=generic)
# A subset of `MaskedArray` that can be parametrized w.r.t. `np.generic`
_MaskedArray: TypeAlias = MaskedArray[_Shape, dtype[_ScalarT]]

MaskType = bool_
nomask: bool_[Literal[False]]

class MaskedArrayFutureWarning(FutureWarning): ...
class MAError(Exception): ...
class MaskError(MAError): ...

def default_fill_value(obj): ...
def minimum_fill_value(obj): ...
def maximum_fill_value(obj): ...
def set_fill_value(a, fill_value): ...
def common_fill_value(a, b): ...
@overload
def filled(a: ndarray[_ShapeT_co, _DTypeT_co], fill_value: _ScalarLike_co | None = None) -> ndarray[_ShapeT_co, _DTypeT_co]: ...
@overload
def filled(a: _ArrayLike[_ScalarT_co], fill_value: _ScalarLike_co | None = None) -> NDArray[_ScalarT_co]: ...
@overload
def filled(a: ArrayLike, fill_value: _ScalarLike_co | None = None) -> NDArray[Any]: ...
def getdata(a, subok=...): ...
get_data = getdata

def fix_invalid(a, mask=..., copy=..., fill_value=...): ...

class _MaskedUFunc:
    f: Any
    __doc__: Any
    __name__: Any