🏷️ ndimage: stub the remaining private modules

scipy-stubs/ndimage/_nd_image.pyi

@@ -0,0 +1,206 @@
+# defined in scipy/ndimage/src/nd_image.c
+
+from _typeshed import Incomplete
+from collections.abc import Callable, Mapping
+from typing import overload
+from typing_extensions import CapsuleType
+
+import numpy as np
+import optype.numpy as onp
+import optype.numpy.compat as npc
+
+from scipy._lib._ccallback import LowLevelCallable
+
+def correlate1d(
+    input: onp.ArrayND[npc.number],
+    weights: onp.ArrayND[np.float64],
+    axis: int,
+    output: onp.ArrayND[npc.number],
+    mode: int,
+    cval: float,
+    origin: int,
+    /,
+) -> None: ...
+def correlate(
+    input: onp.ArrayND[npc.number],
+    weights: onp.ArrayND[np.float64],
+    output: onp.ArrayND[npc.number],
+    mode: int,
+    cval: float,
+    origin: int | onp.ArrayND[np.intp],
+    /,
+) -> None: ...
+def uniform_filter1d(
+    input: onp.ArrayND[npc.number],
+    filter_size: int,
+    axis: int,
+    output: onp.ArrayND[npc.number],
+    mode: int,
+    cval: float,
+    origin: int,
+    /,
+) -> None: ...
+def min_or_max_filter1d(
+    input: onp.ArrayND[npc.number],
+    filter_size: int,
+    axis: int,
+    output: onp.ArrayND[npc.number],
+    mode: int,
+    cval: float,
+    origin: int,
+    minimum: int,
+    /,
+) -> None: ...
+def min_or_max_filter(
+    input: onp.ArrayND[npc.number],
+    footprint: onp.ArrayND[npc.number],
+    structure: onp.ArrayND[npc.number] | None,
+    output: onp.ArrayND[npc.number],
+    mode: int,
+    cval: float,
+    origin: int | onp.ArrayND[np.intp],
+    minimum: int,
+    /,
+) -> None: ...
+def rank_filter(
+    input: onp.ArrayND[npc.number],
+    rank: int,
+    footprint: onp.ArrayND[npc.number],
+    output: onp.ArrayND[npc.number],
+    mode: int,
+    cval: float,
+    origin: onp.ArrayND[np.intp],
+    /,
+) -> None: ...
+def generic_filter1d(
+    input: onp.ArrayND[npc.number],
+    fnc: Callable[..., Incomplete] | LowLevelCallable,
+    filter_size: int,
+    axis: int,
+    output: onp.ArrayND[npc.number],
+    mode: int,
+    cval: float,
+    origin: int,
+    extra_arguments: tuple[object, ...],
+    extra_keywords: Mapping[str, object],
+    /,
+) -> None: ...
+def generic_filter(
+    input: onp.ArrayND[npc.number],
+    fnc: Callable[..., Incomplete] | LowLevelCallable,
+    footprint: onp.ArrayND[npc.number],
+    output: onp.ArrayND[npc.number],
+    mode: int,
+    cval: float,
+    origin: int | onp.ArrayND[np.intp],
+    extra_arguments: tuple[object, ...],
+    extra_keywords: Mapping[str, object],
+    /,
+) -> None: ...
+def fourier_filter(
+    input: onp.ArrayND[npc.number],
+    parameters: onp.ArrayND[npc.number],
+    n: int,
+    axis: int,
+    output: onp.ArrayND[npc.number],
+    filter_type: int,
+    /,
+) -> None: ...
+def fourier_shift(
+    input: onp.ArrayND[npc.number], order: int, axis: int, output: onp.ArrayND[npc.number], mode: int, /
+) -> None: ...
+def spline_filter1d(
+    input: onp.ArrayND[npc.number], order: int, axis: int, output: onp.ArrayND[npc.number], mode: int, /
+) -> None: ...
+def geometric_transform(
+    input: onp.ArrayND[npc.number],
+    fnc: Callable[..., Incomplete] | LowLevelCallable,
+    coordinates: onp.ArrayND[npc.number] | None,
+    matrix: onp.ArrayND[npc.number] | None,
+    shift: onp.ArrayND[npc.number] | None,
+    output: onp.ArrayND[npc.number],
+    order: int,
+    mode: int,
+    cval: float,
+    nprepad: int,
+    extra_arguments: tuple[object, ...],
+    extra_keywords: Mapping[str, object],
+    /,
+) -> None: ...
+def zoom_shift(
+    input: onp.ArrayND[npc.number],
+    zoom: onp.ArrayND[npc.number] | None,
+    shift: onp.ArrayND[npc.number] | None,
+    output: onp.ArrayND[npc.number],
+    order: int,
+    mode: int,
+    cval: float,
+    nprepad: int,
+    grid_mode: int,
+    /,
+) -> None: ...
+
+#
+def find_objects(input: onp.ArrayND[npc.number], max_label: int) -> None: ...
+def value_indices(arr: onp.ArrayND[npc.integer], ignoreValIsNone: bool, ignorevalArr: onp.ArrayND, /) -> None: ...
+
+#
+def watershed_ift(
+    input: onp.ArrayND[npc.number],
+    markers: onp.ArrayND[npc.number],
+    strct: onp.ArrayND[npc.number],
+    output: onp.ArrayND[npc.number],
+) -> None: ...
+def distance_transform_bf(
+    input: onp.ArrayND[npc.number],
+    metric: int,
+    sampling: onp.ArrayND[npc.number] | None,
+    output: onp.ArrayND[npc.number] | None,
+    features: onp.ArrayND[npc.number] | None,
+) -> None: ...
+def distance_transform_op(
+    input: onp.ArrayND[npc.number],
+    strct: onp.ArrayND[npc.number],
+    distances: onp.ArrayND[npc.number],
+    features: onp.ArrayND[npc.number] | None,
+) -> None: ...
+def euclidean_feature_transform(
+    input: onp.ArrayND[npc.number], sampling: onp.ArrayND[npc.number] | None, features: onp.ArrayND[npc.number]
+) -> None: ...
+
+#
+@overload
+def binary_erosion(
+    input: onp.ArrayND[npc.number],
+    strct: onp.ArrayND[npc.number],
+    mask: onp.ArrayND[npc.number] | None,
+    output: onp.ArrayND[npc.number],
+    border_value: int,
+    origin: onp.ArrayND[np.intp],
+    invert: int,
+    center_is_true: int,
+    return_coordinates: onp.ToFalse,
+) -> int: ...
+@overload
+def binary_erosion(
+    input: onp.ArrayND[npc.number],
+    strct: onp.ArrayND[npc.number],
+    mask: onp.ArrayND[npc.number] | None,
+    output: onp.ArrayND[npc.number],
+    border_value: int,
+    origin: int | onp.ArrayND[np.intp],
+    invert: int,
+    center_is_true: int,
+    return_coordinates: onp.ToTrue,
+) -> tuple[int, CapsuleType]: ...
+
+#
+def binary_erosion2(
+    array: onp.ArrayND[npc.number],
+    strct: onp.ArrayND[npc.number],
+    mask: onp.ArrayND[npc.number] | None,
+    niter: int,
+    origin: int | onp.ArrayND[np.intp],
+    invert: int,
+    cobj: CapsuleType | None,
+) -> None: ...

scipy-stubs/ndimage/_ni_docstrings.pyi

@@ -0,0 +1,31 @@
+from collections.abc import Callable
+from typing import Final, Protocol, TypeVar, type_check_only
+
+__all__ = ["docfiller"]
+
+_FT = TypeVar("_FT", bound=Callable[..., object])
+
+@type_check_only
+class _Docfiller(Protocol):
+    def __call__(self, f: _FT, /) -> _FT: ...
+
+###
+
+_intput_doc: Final[str] = ...
+_axis_doc: Final[str] = ...
+_output_doc: Final[str] = ...
+_nan_doc: Final[str] = ...
+_size_foot_doc: Final[str] = ...
+_mode_reflect_doc: Final[str] = ...
+_mode_interp_constant_doc: Final[str] = ...
+_mode_interp_mirror_doc: Final[str] = ...
+_mode_multiple_doc: Final[str] = ...
+_cval_doc: Final[str] = ...
+_origin_doc: Final[str] = ...
+_origin_multiple_doc: Final[str] = ...
+_extra_arguments_doc: Final[str] = ...
+_extra_keywords_doc: Final[str] = ...
+_prefilter_doc: Final[str] = ...
+docdict: Final[dict[str, str]] = ...
+
+docfiller: Final[_Docfiller]

scipy-stubs/ndimage/_ni_label.pyi

@@ -0,0 +1,25 @@
+# defined in scipy/ndimage/src/_ni_label.pyx
+
+from typing import TypeAlias
+
+import numpy as np
+import optype.numpy as onp
+import optype.numpy.compat as npc
+
+# matches `ctypedef fused data_t`
+_data_t: TypeAlias = npc.integer | np.float32 | np.float64  # noqa: PYI042
+
+###
+
+class NeedMoreBits(Exception): ...  # undocumented
+
+def get_nonzero_line(a: onp.ArrayND[_data_t]) -> int: ...  # undocumented
+def get_read_line(a: onp.ArrayND[_data_t]) -> int: ...  # undocumented
+def get_write_line(a: onp.ArrayND[_data_t]) -> int: ...  # undocumented
+
+#
+def _label(
+    input: onp.ArrayND[_data_t | np.bool_],
+    structure: onp.ArrayND[np.bool_ | npc.integer],
+    output: onp.ArrayND[_data_t | np.bool_],
+) -> int: ...  # undocumented

scipy-stubs/ndimage/_ni_support.pyi

@@ -0,0 +1,55 @@
+from collections.abc import Iterable
+from typing import Any, Literal, TypeAlias, TypeVar, overload
+
+import numpy as np
+import optype as op
+import optype.numpy as onp
+import optype.numpy.compat as npc
+
+_T = TypeVar("_T")
+
+_Mode: TypeAlias = Literal["nearest", "wrap", "reflect", "grid-mirror", "mirror", "constant", "grid-wrap", "grid-constant"]
+_ModeCode: TypeAlias = Literal[0, 1, 2, 3, 4, 5, 6]
+
+def _extend_mode_to_code(mode: _Mode, is_filter: bool = False) -> _ModeCode: ...
+
+#
+@overload
+def _normalize_sequence(input: str, rank: int) -> list[str]: ...
+@overload
+def _normalize_sequence(input: Iterable[_T], rank: int) -> list[_T]: ...
+@overload
+def _normalize_sequence(input: _T, rank: int) -> list[_T]: ...
+
+#
+@overload
+def _get_output(
+    output: type[complex | npc.number] | np.dtype[npc.number] | str | onp.ToComplexND | None,
+    input: onp.ArrayND[npc.number],
+    shape: tuple[int, ...] | None = None,
+    complex_output: Literal[False] = False,
+) -> onp.ArrayND[npc.number]: ...
+@overload
+def _get_output(
+    output: type[complex | npc.complexfloating] | np.dtype[npc.complexfloating] | str | onp.ToJustComplexND | None,
+    input: onp.ArrayND[npc.complexfloating],
+    shape: tuple[int, ...] | None = None,
+    *,
+    complex_output: Literal[True],
+) -> onp.ArrayND[npc.complexfloating]: ...
+@overload
+def _get_output(
+    output: type[complex | npc.complexfloating] | np.dtype[npc.complexfloating] | str | onp.ToJustComplexND | None,
+    input: onp.ArrayND[npc.complexfloating],
+    shape: tuple[int, ...] | None,
+    complex_output: Literal[True],
+) -> onp.ArrayND[npc.complexfloating]: ...
+
+#
+def _check_axes(axes: op.CanIndex | Iterable[op.CanIndex], ndim: int) -> tuple[int, ...]: ...
+
+#
+@overload
+def _skip_if_dtype(arg: str | np.dtype[Any] | type[np.generic]) -> None: ...
+@overload
+def _skip_if_dtype(arg: _T) -> _T | None: ...

scipy-stubs/ndimage/_rank_filter_1d.pyi

@@ -0,0 +1,16 @@
+# defined in scipy/ndimage/src/_rank_filter_1d.cpp
+
+import numpy as np
+import optype.numpy as onp
+import optype.numpy.compat as npc
+
+def rank_filter(
+    in_arr_obj: onp.ArrayND[np.float32 | np.float64 | np.int64],
+    rank: int,
+    win_len: int,
+    out_arr_obj: onp.ArrayND[np.float32 | np.float64 | np.int64],
+    mode: int,
+    cval_obj: onp.ArrayND[npc.floating | npc.integer],
+    origin: int,
+    /,
+) -> None: ...  # undocumented