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jorenhamjorenham
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🏷️ ndimage: stub the remaining private modules (#720)
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‎scipy-stubs/ndimage/_nd_image.pyi

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

‎scipy-stubs/ndimage/_ni_docstrings.pyi

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

‎scipy-stubs/ndimage/_ni_label.pyi

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

‎scipy-stubs/ndimage/_ni_support.pyi

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

‎scipy-stubs/ndimage/_rank_filter_1d.pyi

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

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