Sources_For_Win/Python/Lib/site-packages/matplotlib/transforms.pyi

from .path import Path
from .patches import Patch
from .figure import Figure
import numpy as np
from numpy.typing import ArrayLike
from collections.abc import Iterable, Sequence
from typing import Literal

DEBUG: bool

class TransformNode:
    INVALID_NON_AFFINE: int
    INVALID_AFFINE: int
    INVALID: int
    is_bbox: bool
    # Implemented as a standard attr in base class, but functionally readonly and some subclasses implement as such
    @property
    def is_affine(self) -> bool: ...
    pass_through: bool
    def __init__(self, shorthand_name: str | None = ...) -> None: ...
    def __copy__(self) -> TransformNode: ...
    def invalidate(self) -> None: ...
    def set_children(self, *children: TransformNode) -> None: ...
    def frozen(self) -> TransformNode: ...

class BboxBase(TransformNode):
    is_bbox: bool
    is_affine: bool
    def frozen(self) -> Bbox: ...
    def __array__(self, *args, **kwargs): ...
    @property
    def x0(self) -> float: ...
    @property
    def y0(self) -> float: ...
    @property
    def x1(self) -> float: ...
    @property
    def y1(self) -> float: ...
    @property
    def p0(self) -> tuple[float, float]: ...
    @property
    def p1(self) -> tuple[float, float]: ...
    @property
    def xmin(self) -> float: ...
    @property
    def ymin(self) -> float: ...
    @property
    def xmax(self) -> float: ...
    @property
    def ymax(self) -> float: ...
    @property
    def min(self) -> tuple[float, float]: ...
    @property
    def max(self) -> tuple[float, float]: ...
    @property
    def intervalx(self) -> tuple[float, float]: ...
    @property
    def intervaly(self) -> tuple[float, float]: ...
    @property
    def width(self) -> float: ...
    @property
    def height(self) -> float: ...
    @property
    def size(self) -> tuple[float, float]: ...
    @property
    def bounds(self) -> tuple[float, float, float, float]: ...
    @property
    def extents(self) -> tuple[float, float, float, float]: ...
    def get_points(self) -> np.ndarray: ...
    def containsx(self, x: float) -> bool: ...
    def containsy(self, y: float) -> bool: ...
    def contains(self, x: float, y: float) -> bool: ...
    def overlaps(self, other: BboxBase) -> bool: ...
    def fully_containsx(self, x: float) -> bool: ...
    def fully_containsy(self, y: float) -> bool: ...
    def fully_contains(self, x: float, y: float) -> bool: ...
    def fully_overlaps(self, other: BboxBase) -> bool: ...
    def transformed(self, transform: Transform) -> Bbox: ...
    coefs: dict[str, tuple[float, float]]
    # anchored type can be s/str/Literal["C", "SW", "S", "SE", "E", "NE", "N", "NW", "W"]
    def anchored(
        self, c: tuple[float, float] | str, container: BboxBase | None = ...
    ) -> Bbox: ...
    def shrunk(self, mx: float, my: float) -> Bbox: ...
    def shrunk_to_aspect(
        self,
        box_aspect: float,
        container: BboxBase | None = ...,
        fig_aspect: float = ...,
    ) -> Bbox: ...
    def splitx(self, *args: float) -> list[Bbox]: ...
    def splity(self, *args: float) -> list[Bbox]: ...
    def count_contains(self, vertices: ArrayLike) -> int: ...
    def count_overlaps(self, bboxes: Iterable[BboxBase]) -> int: ...
    def expanded(self, sw: float, sh: float) -> Bbox: ...
    def padded(self, w_pad: float, h_pad: float | None = ...) -> Bbox: ...
    def translated(self, tx: float, ty: float) -> Bbox: ...
    def corners(self) -> np.ndarray: ...
    def rotated(self, radians: float) -> Bbox: ...
    @staticmethod
    def union(bboxes: Sequence[BboxBase]) -> Bbox: ...
    @staticmethod
    def intersection(bbox1: BboxBase, bbox2: BboxBase) -> Bbox | None: ...

class Bbox(BboxBase):
    def __init__(self, points: ArrayLike, **kwargs) -> None: ...
    @staticmethod
    def unit() -> Bbox: ...
    @staticmethod
    def null() -> Bbox: ...
    @staticmethod
    def from_bounds(x0: float, y0: float, width: float, height: float) -> Bbox: ...
    @staticmethod
    def from_extents(*args: float, minpos: float | None = ...) -> Bbox: ...
    def __format__(self, fmt: str) -> str: ...
    def ignore(self, value: bool) -> None: ...
    def update_from_path(
        self,
        path: Path,
        ignore: bool | None = ...,
        updatex: bool = ...,
        updatey: bool = ...,
    ) -> None: ...
    def update_from_data_x(self, x: ArrayLike, ignore: bool | None = ...) -> None: ...
    def update_from_data_y(self, y: ArrayLike, ignore: bool | None = ...) -> None: ...
    def update_from_data_xy(
        self,
        xy: ArrayLike,
        ignore: bool | None = ...,
        updatex: bool = ...,
        updatey: bool = ...,
    ) -> None: ...
    @property
    def minpos(self) -> float: ...
    @property
    def minposx(self) -> float: ...
    @property
    def minposy(self) -> float: ...
    def get_points(self) -> np.ndarray: ...
    def set_points(self, points: ArrayLike) -> None: ...
    def set(self, other: Bbox) -> None: ...
    def mutated(self) -> bool: ...
    def mutatedx(self) -> bool: ...
    def mutatedy(self) -> bool: ...

class TransformedBbox(BboxBase):
    def __init__(self, bbox: Bbox, transform: Transform, **kwargs) -> None: ...
    def get_points(self) -> np.ndarray: ...

class LockableBbox(BboxBase):
    def __init__(
        self,
        bbox: BboxBase,
        x0: float | None = ...,
        y0: float | None = ...,
        x1: float | None = ...,
        y1: float | None = ...,
        **kwargs
    ) -> None: ...
    @property
    def locked_x0(self) -> float | None: ...
    @locked_x0.setter
    def locked_x0(self, x0: float | None) -> None: ...
    @property
    def locked_y0(self) -> float | None: ...
    @locked_y0.setter
    def locked_y0(self, y0: float | None) -> None: ...
    @property
    def locked_x1(self) -> float | None: ...
    @locked_x1.setter
    def locked_x1(self, x1: float | None) -> None: ...
    @property
    def locked_y1(self) -> float | None: ...
    @locked_y1.setter
    def locked_y1(self, y1: float | None) -> None: ...

class Transform(TransformNode):

    # Implemented as a standard attrs in base class, but functionally readonly and some subclasses implement as such
    @property
    def input_dims(self) -> int | None: ...
    @property
    def output_dims(self) -> int | None: ...
    @property
    def is_separable(self) -> bool: ...
    @property
    def has_inverse(self) -> bool: ...

    def __add__(self, other: Transform) -> Transform: ...
    @property
    def depth(self) -> int: ...
    def contains_branch(self, other: Transform) -> bool: ...
    def contains_branch_seperately(
        self, other_transform: Transform
    ) -> Sequence[bool]: ...
    def __sub__(self, other: Transform) -> Transform: ...
    def __array__(self, *args, **kwargs) -> np.ndarray: ...
    def transform(self, values: ArrayLike) -> np.ndarray: ...
    def transform_affine(self, values: ArrayLike) -> np.ndarray: ...
    def transform_non_affine(self, values: ArrayLike) -> ArrayLike: ...
    def transform_bbox(self, bbox: BboxBase) -> Bbox: ...
    def get_affine(self) -> Transform: ...
    def get_matrix(self) -> np.ndarray: ...
    def transform_point(self, point: ArrayLike) -> np.ndarray: ...
    def transform_path(self, path: Path) -> Path: ...
    def transform_path_affine(self, path: Path) -> Path: ...
    def transform_path_non_affine(self, path: Path) -> Path: ...
    def transform_angles(
        self,
        angles: ArrayLike,
        pts: ArrayLike,
        radians: bool = ...,
        pushoff: float = ...,
    ) -> np.ndarray: ...
    def inverted(self) -> Transform: ...

class TransformWrapper(Transform):
    pass_through: bool
    def __init__(self, child: Transform) -> None: ...
    def __eq__(self, other: object) -> bool: ...
    def frozen(self) -> Transform: ...
    def set(self, child: Transform) -> None: ...

class AffineBase(Transform):
    is_affine: Literal[True]
    def __init__(self, *args, **kwargs) -> None: ...
    def __eq__(self, other: object) -> bool: ...

class Affine2DBase(AffineBase):
    input_dims: Literal[2]
    output_dims: Literal[2]
    def frozen(self) -> Affine2D: ...
    def to_values(self) -> tuple[float, float, float, float, float, float]: ...

class Affine2D(Affine2DBase):
    def __init__(self, matrix: ArrayLike | None = ..., **kwargs) -> None: ...
    @staticmethod
    def from_values(
        a: float, b: float, c: float, d: float, e: float, f: float
    ) -> Affine2D: ...
    def set_matrix(self, mtx: ArrayLike) -> None: ...
    def clear(self) -> Affine2D: ...
    def rotate(self, theta: float) -> Affine2D: ...
    def rotate_deg(self, degrees: float) -> Affine2D: ...
    def rotate_around(self, x: float, y: float, theta: float) -> Affine2D: ...
    def rotate_deg_around(self, x: float, y: float, degrees: float) -> Affine2D: ...
    def translate(self, tx: float, ty: float) -> Affine2D: ...
    def scale(self, sx: float, sy: float | None = ...) -> Affine2D: ...
    def skew(self, xShear: float, yShear: float) -> Affine2D: ...
    def skew_deg(self, xShear: float, yShear: float) -> Affine2D: ...

class IdentityTransform(Affine2DBase): ...

class _BlendedMixin:
    def __eq__(self, other: object) -> bool: ...
    def contains_branch_seperately(self, transform: Transform) -> Sequence[bool]: ...

class BlendedGenericTransform(_BlendedMixin, Transform):
    input_dims: Literal[2]
    output_dims: Literal[2]
    pass_through: bool
    def __init__(
        self, x_transform: Transform, y_transform: Transform, **kwargs
    ) -> None: ...
    @property
    def depth(self) -> int: ...
    def contains_branch(self, other: Transform) -> Literal[False]: ...
    @property
    def is_affine(self) -> bool: ...

class BlendedAffine2D(_BlendedMixin, Affine2DBase):
    def __init__(
        self, x_transform: Transform, y_transform: Transform, **kwargs
    ) -> None: ...

def blended_transform_factory(
    x_transform: Transform, y_transform: Transform
) -> BlendedGenericTransform | BlendedAffine2D: ...

class CompositeGenericTransform(Transform):
    pass_through: bool
    def __init__(self, a: Transform, b: Transform, **kwargs) -> None: ...

class CompositeAffine2D(Affine2DBase):
    def __init__(self, a: Affine2DBase, b: Affine2DBase, **kwargs) -> None: ...
    @property
    def depth(self) -> int: ...

def composite_transform_factory(a: Transform, b: Transform) -> Transform: ...

class BboxTransform(Affine2DBase):
    def __init__(self, boxin: BboxBase, boxout: BboxBase, **kwargs) -> None: ...

class BboxTransformTo(Affine2DBase):
    def __init__(self, boxout: BboxBase, **kwargs) -> None: ...

class BboxTransformToMaxOnly(BboxTransformTo): ...

class BboxTransformFrom(Affine2DBase):
    def __init__(self, boxin: BboxBase, **kwargs) -> None: ...

class ScaledTranslation(Affine2DBase):
    def __init__(
        self, xt: float, yt: float, scale_trans: Affine2DBase, **kwargs
    ) -> None: ...

class AffineDeltaTransform(Affine2DBase):
    def __init__(self, transform: Affine2DBase, **kwargs) -> None: ...

class TransformedPath(TransformNode):
    def __init__(self, path: Path, transform: Transform) -> None: ...
    def get_transformed_points_and_affine(self) -> tuple[Path, Transform]: ...
    def get_transformed_path_and_affine(self) -> tuple[Path, Transform]: ...
    def get_fully_transformed_path(self) -> Path: ...
    def get_affine(self) -> Transform: ...

class TransformedPatchPath(TransformedPath):
    def __init__(self, patch: Patch) -> None: ...

def nonsingular(
    vmin: float,
    vmax: float,
    expander: float = ...,
    tiny: float = ...,
    increasing: bool = ...,
) -> tuple[float, float]: ...
def interval_contains(interval: tuple[float, float], val: float) -> bool: ...
def interval_contains_open(interval: tuple[float, float], val: float) -> bool: ...
def offset_copy(
    trans: Transform,
    fig: Figure | None = ...,
    x: float = ...,
    y: float = ...,
    units: Literal["inches", "points", "dots"] = ...,
) -> Transform: ...