from functools import wraps
from types import TracebackType
from typing import (
TYPE_CHECKING,
Any,
Callable,
Dict,
List,
NewType,
Tuple,
Type,
Union,
)
import numpy as np
if TYPE_CHECKING:
import dask.array
import zarr
from magicgui.widgets import FunctionGui
from qtpy.QtWidgets import QWidget
# This is a WOEFULLY inadequate stub for a duck-array type.
# Mostly, just a placeholder for the concept of needing an ArrayLike type.
# Ultimately, this should come from https://github.com/napari/image-types
# and should probably be replaced by a typing.Protocol
# note, numpy.typing.ArrayLike (in v1.20) is not quite what we want either,
# since it includes all valid arguments for np.array() ( int, float, str...)
ArrayLike = Union[np.ndarray, 'dask.array.Array', 'zarr.Array']
# layer data may be: (data,) (data, meta), or (data, meta, layer_type)
# using "Any" for the data type until ArrayLike is more mature.
FullLayerData = Tuple[Any, Dict, str]
LayerData = Union[Tuple[Any], Tuple[Any, Dict], FullLayerData]
PathLike = Union[str, List[str]]
ReaderFunction = Callable[[PathLike], List[LayerData]]
WriterFunction = Callable[[str, List[FullLayerData]], List[str]]
ExcInfo = Union[
Tuple[Type[BaseException], BaseException, TracebackType],
Tuple[None, None, None],
]
# Types for GUI HookSpecs
WidgetCallable = Callable[..., Union['FunctionGui', 'QWidget']]
AugmentedWidget = Union[WidgetCallable, Tuple[WidgetCallable, dict]]
# these types are mostly "intentionality" placeholders. While it's still hard
# to use actual types to define what is acceptable data for a given layer,
# these types let us point to a concrete namespace to indicate "this data is
# intended to be (and is capable of) being turned into X layer type".
# while their names should not change (without deprecation), their typing
# implementations may... or may be rolled over to napari/image-types
if tuple(np.__version__.split('.')) < ('1', '20'):
# this hack is because NewType doesn't allow `Any` as a base type
# and numpy <=1.20 didn't provide type stubs for np.ndarray
# https://github.com/python/mypy/issues/6701#issuecomment-609638202
class ArrayBase(np.ndarray):
def __getattr__(self, name: str) -> Any:
return super().__getattr__(name)
else:
ArrayBase = np.ndarray # type: ignore
ImageData = NewType("ImageData", ArrayBase)
LabelsData = NewType("LabelsData", ArrayBase)
PointsData = NewType("PointsData", ArrayBase)
ShapesData = NewType("ShapesData", List[ArrayBase])
SurfaceData = NewType("SurfaceData", Tuple[ArrayBase, ArrayBase, ArrayBase])
TracksData = NewType("TracksData", ArrayBase)
VectorsData = NewType("VectorsData", ArrayBase)
LayerDataTuple = NewType("LayerDataTuple", tuple)
[docs]def image_reader_to_layerdata_reader(
func: Callable[[PathLike], ArrayLike]
) -> ReaderFunction:
"""Convert a PathLike -> ArrayLike function to a PathLike -> LayerData.
Parameters
----------
func : Callable[[PathLike], ArrayLike]
A function that accepts a string or list of strings, and returns an
ArrayLike.
Returns
-------
reader_function : Callable[[PathLike], List[LayerData]]
A function that accepts a string or list of strings, and returns data
as a list of LayerData: List[Tuple[ArrayLike]]
"""
@wraps(func)
def reader_function(*args, **kwargs) -> List[LayerData]:
result = func(*args, **kwargs)
return [(result,)]
return reader_function