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Copy pathcolors.pyi
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345 lines (318 loc) · 10.6 KB
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from collections.abc import Callable, Iterable, Iterator, Mapping, Sequence
from matplotlib import cbook, scale
import re
from typing import Any, Literal, overload
from .typing import ColorType
import numpy as np
from numpy.typing import ArrayLike
# Explicitly export colors dictionaries which are imported in the impl
BASE_COLORS: dict[str, ColorType]
CSS4_COLORS: dict[str, ColorType]
TABLEAU_COLORS: dict[str, ColorType]
XKCD_COLORS: dict[str, ColorType]
class _ColorMapping(dict[str, ColorType]):
cache: dict[tuple[ColorType, float | None], tuple[float, float, float, float]]
def __init__(self, mapping) -> None: ...
def __setitem__(self, key, value) -> None: ...
def __delitem__(self, key) -> None: ...
def get_named_colors_mapping() -> _ColorMapping: ...
class ColorSequenceRegistry(Mapping):
def __init__(self) -> None: ...
def __getitem__(self, item: str) -> list[ColorType]: ...
def __iter__(self) -> Iterator[str]: ...
def __len__(self) -> int: ...
def register(self, name: str, color_list: Iterable[ColorType]) -> None: ...
def unregister(self, name: str) -> None: ...
_color_sequences: ColorSequenceRegistry = ...
def is_color_like(c: Any) -> bool: ...
def same_color(c1: ColorType, c2: ColorType) -> bool: ...
def to_rgba(
c: ColorType, alpha: float | None = ...
) -> tuple[float, float, float, float]: ...
def to_rgba_array(
c: ColorType | ArrayLike, alpha: float | ArrayLike | None = ...
) -> np.ndarray: ...
def to_rgb(c: ColorType) -> tuple[float, float, float]: ...
def to_hex(c: ColorType, keep_alpha: bool = ...) -> str: ...
cnames: dict[str, ColorType]
hexColorPattern: re.Pattern
rgb2hex = to_hex
hex2color = to_rgb
class ColorConverter:
colors: _ColorMapping
cache: dict[tuple[ColorType, float | None], tuple[float, float, float, float]]
@staticmethod
def to_rgb(c: ColorType) -> tuple[float, float, float]: ...
@staticmethod
def to_rgba(
c: ColorType, alpha: float | None = ...
) -> tuple[float, float, float, float]: ...
@staticmethod
def to_rgba_array(
c: ColorType | ArrayLike, alpha: float | ArrayLike | None = ...
) -> np.ndarray: ...
colorConverter: ColorConverter
class Colormap:
name: str
N: int
colorbar_extend: bool
def __init__(self, name: str, N: int = ...) -> None: ...
def __call__(
self, X: ArrayLike, alpha: ArrayLike | None = ..., bytes: bool = ...
) -> tuple[float, float, float, float] | np.ndarray: ...
def __copy__(self) -> Colormap: ...
def __eq__(self, other: object) -> bool: ...
def get_bad(self) -> np.ndarray: ...
def set_bad(self, color: ColorType = ..., alpha: float | None = ...) -> None: ...
def get_under(self) -> np.ndarray: ...
def set_under(self, color: ColorType = ..., alpha: float | None = ...) -> None: ...
def get_over(self) -> np.ndarray: ...
def set_over(self, color: ColorType = ..., alpha: float | None = ...) -> None: ...
def set_extremes(
self,
*,
bad: ColorType | None = ...,
under: ColorType | None = ...,
over: ColorType | None = ...
) -> None: ...
def with_extremes(
self,
*,
bad: ColorType | None = ...,
under: ColorType | None = ...,
over: ColorType | None = ...
) -> Colormap: ...
def is_gray(self) -> bool: ...
def resampled(self, lutsize: int) -> Colormap: ...
def reversed(self, name: str | None = ...) -> Colormap: ...
def _repr_html_(self) -> str: ...
def _repr_png_(self) -> bytes: ...
def copy(self) -> Colormap: ...
class LinearSegmentedColormap(Colormap):
monochrome: bool
def __init__(
self,
name: str,
segmentdata: dict[
Literal["red", "green", "blue", "alpha"], Sequence[tuple[float, ...]]
],
N: int = ...,
gamma: float = ...,
) -> None: ...
def set_gamma(self, gamma: float) -> None: ...
@staticmethod
def from_list(
name: str, colors: ArrayLike, N: int = ..., gamma: float = ...
) -> LinearSegmentedColormap: ...
def resampled(self, lutsize: int) -> LinearSegmentedColormap: ...
def reversed(self, name: str | None = ...) -> LinearSegmentedColormap: ...
class ListedColormap(Colormap):
monochrome: bool
colors: ArrayLike | ColorType
def __init__(
self, colors: ArrayLike | ColorType, name: str = ..., N: int | None = ...
) -> None: ...
def resampled(self, lutsize: int) -> ListedColormap: ...
def reversed(self, name: str | None = ...) -> ListedColormap: ...
class Normalize:
callbacks: cbook.CallbackRegistry
def __init__(
self, vmin: float | None = ..., vmax: float | None = ..., clip: bool = ...
) -> None: ...
@property
def vmin(self) -> float | None: ...
@vmin.setter
def vmin(self, value: float | None) -> None: ...
@property
def vmax(self) -> float | None: ...
@vmax.setter
def vmax(self, value: float | None) -> None: ...
@property
def clip(self) -> bool: ...
@clip.setter
def clip(self, value: bool) -> None: ...
@staticmethod
def process_value(value: ArrayLike) -> tuple[np.ma.MaskedArray, bool]: ...
@overload
def __call__(self, value: float, clip: bool | None = ...) -> float: ...
@overload
def __call__(self, value: np.ndarray, clip: bool | None = ...) -> np.ma.MaskedArray: ...
@overload
def __call__(self, value: ArrayLike, clip: bool | None = ...) -> ArrayLike: ...
@overload
def inverse(self, value: float) -> float: ...
@overload
def inverse(self, value: np.ndarray) -> np.ma.MaskedArray: ...
@overload
def inverse(self, value: ArrayLike) -> ArrayLike: ...
def autoscale(self, A: ArrayLike) -> None: ...
def autoscale_None(self, A: ArrayLike) -> None: ...
def scaled(self) -> bool: ...
class TwoSlopeNorm(Normalize):
def __init__(
self, vcenter: float, vmin: float | None = ..., vmax: float | None = ...
) -> None: ...
@property
def vcenter(self) -> float: ...
@vcenter.setter
def vcenter(self, value: float) -> None: ...
def autoscale_None(self, A: ArrayLike) -> None: ...
class CenteredNorm(Normalize):
def __init__(
self, vcenter: float = ..., halfrange: float | None = ..., clip: bool = ...
) -> None: ...
@property
def vcenter(self) -> float: ...
@vcenter.setter
def vcenter(self, vcenter: float) -> None: ...
@property
def halfrange(self) -> float: ...
@halfrange.setter
def halfrange(self, halfrange: float) -> None: ...
@overload
def make_norm_from_scale(
scale_cls: type[scale.ScaleBase],
base_norm_cls: type[Normalize],
*,
init: Callable | None = ...
) -> type[Normalize]: ...
@overload
def make_norm_from_scale(
scale_cls: type[scale.ScaleBase],
base_norm_cls: None = ...,
*,
init: Callable | None = ...
) -> Callable[[type[Normalize]], type[Normalize]]: ...
class FuncNorm(Normalize):
def __init__(
self,
functions: tuple[Callable, Callable],
vmin: float | None = ...,
vmax: float | None = ...,
clip: bool = ...,
) -> None: ...
class LogNorm(Normalize): ...
class SymLogNorm(Normalize):
def __init__(
self,
linthresh: float,
linscale: float = ...,
vmin: float | None = ...,
vmax: float | None = ...,
clip: bool = ...,
*,
base: float = ...,
) -> None: ...
@property
def linthresh(self) -> float: ...
@linthresh.setter
def linthresh(self, value: float) -> None: ...
class AsinhNorm(Normalize):
def __init__(
self,
linear_width: float = ...,
vmin: float | None = ...,
vmax: float | None = ...,
clip: bool = ...,
) -> None: ...
@property
def linear_width(self) -> float: ...
@linear_width.setter
def linear_width(self, value: float) -> None: ...
class PowerNorm(Normalize):
gamma: float
def __init__(
self,
gamma: float,
vmin: float | None = ...,
vmax: float | None = ...,
clip: bool = ...,
) -> None: ...
class BoundaryNorm(Normalize):
boundaries: np.ndarray
N: int
Ncmap: int
extend: Literal["neither", "both", "min", "max"]
def __init__(
self,
boundaries: ArrayLike,
ncolors: int,
clip: bool = ...,
*,
extend: Literal["neither", "both", "min", "max"] = ...
) -> None: ...
class NoNorm(Normalize): ...
def rgb_to_hsv(arr: ArrayLike) -> np.ndarray: ...
def hsv_to_rgb(hsv: ArrayLike) -> np.ndarray: ...
class LightSource:
azdeg: float
altdeg: float
hsv_min_val: float
hsv_max_val: float
hsv_min_sat: float
hsv_max_sat: float
def __init__(
self,
azdeg: float = ...,
altdeg: float = ...,
hsv_min_val: float = ...,
hsv_max_val: float = ...,
hsv_min_sat: float = ...,
hsv_max_sat: float = ...,
) -> None: ...
@property
def direction(self) -> np.ndarray: ...
def hillshade(
self,
elevation: ArrayLike,
vert_exag: float = ...,
dx: float = ...,
dy: float = ...,
fraction: float = ...,
) -> np.ndarray: ...
def shade_normals(
self, normals: np.ndarray, fraction: float = ...
) -> np.ndarray: ...
def shade(
self,
data: ArrayLike,
cmap: Colormap,
norm: Normalize | None = ...,
blend_mode: Literal["hsv", "overlay", "soft"] | Callable = ...,
vmin: float | None = ...,
vmax: float | None = ...,
vert_exag: float = ...,
dx: float = ...,
dy: float = ...,
fraction: float = ...,
**kwargs
) -> np.ndarray: ...
def shade_rgb(
self,
rgb: ArrayLike,
elevation: ArrayLike,
fraction: float = ...,
blend_mode: Literal["hsv", "overlay", "soft"] | Callable = ...,
vert_exag: float = ...,
dx: float = ...,
dy: float = ...,
**kwargs
) -> np.ndarray: ...
def blend_hsv(
self,
rgb: ArrayLike,
intensity: ArrayLike,
hsv_max_sat: float | None = ...,
hsv_max_val: float | None = ...,
hsv_min_val: float | None = ...,
hsv_min_sat: float | None = ...,
) -> ArrayLike: ...
def blend_soft_light(
self, rgb: np.ndarray, intensity: np.ndarray
) -> np.ndarray: ...
def blend_overlay(self, rgb: np.ndarray, intensity: np.ndarray) -> np.ndarray: ...
def from_levels_and_colors(
levels: Sequence[float],
colors: Sequence[ColorType],
extend: Literal["neither", "min", "max", "both"] = ...,
) -> tuple[ListedColormap, BoundaryNorm]: ...