187 lines
6.3 KiB
Python
187 lines
6.3 KiB
Python
import collections.abc
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from collections.abc import Callable, Collection, Generator, Iterable, Iterator
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import contextlib
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import os
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from pathlib import Path
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from matplotlib.artist import Artist
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import numpy as np
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from numpy.typing import ArrayLike
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from typing import (
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Any,
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Generic,
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IO,
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Literal,
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TypeVar,
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overload,
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)
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_T = TypeVar("_T")
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def _get_running_interactive_framework() -> str | None: ...
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class CallbackRegistry:
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exception_handler: Callable[[Exception], Any]
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callbacks: dict[Any, dict[int, Any]]
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def __init__(
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self,
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exception_handler: Callable[[Exception], Any] | None = ...,
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*,
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signals: Iterable[Any] | None = ...,
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) -> None: ...
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def connect(self, signal: Any, func: Callable) -> int: ...
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def disconnect(self, cid: int) -> None: ...
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def process(self, s: Any, *args, **kwargs) -> None: ...
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def blocked(
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self, *, signal: Any | None = ...
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) -> contextlib.AbstractContextManager[None]: ...
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class silent_list(list[_T]):
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type: str | None
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def __init__(self, type: str | None, seq: Iterable[_T] | None = ...) -> None: ...
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def strip_math(s: str) -> str: ...
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def is_writable_file_like(obj: Any) -> bool: ...
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def file_requires_unicode(x: Any) -> bool: ...
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@overload
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def to_filehandle(
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fname: str | os.PathLike | IO,
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flag: str = ...,
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return_opened: Literal[False] = ...,
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encoding: str | None = ...,
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) -> IO: ...
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@overload
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def to_filehandle(
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fname: str | os.PathLike | IO,
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flag: str,
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return_opened: Literal[True],
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encoding: str | None = ...,
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) -> tuple[IO, bool]: ...
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@overload
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def to_filehandle(
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fname: str | os.PathLike | IO,
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*, # if flag given, will match previous sig
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return_opened: Literal[True],
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encoding: str | None = ...,
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) -> tuple[IO, bool]: ...
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def open_file_cm(
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path_or_file: str | os.PathLike | IO,
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mode: str = ...,
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encoding: str | None = ...,
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) -> contextlib.AbstractContextManager[IO]: ...
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def is_scalar_or_string(val: Any) -> bool: ...
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@overload
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def get_sample_data(
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fname: str | os.PathLike, asfileobj: Literal[True] = ..., *, np_load: Literal[True]
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) -> np.ndarray: ...
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@overload
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def get_sample_data(
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fname: str | os.PathLike,
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asfileobj: Literal[True] = ...,
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*,
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np_load: Literal[False] = ...,
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) -> IO: ...
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@overload
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def get_sample_data(
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fname: str | os.PathLike, asfileobj: Literal[False], *, np_load: bool = ...
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) -> str: ...
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def _get_data_path(*args: Path | str) -> Path: ...
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def flatten(
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seq: Iterable[Any], scalarp: Callable[[Any], bool] = ...
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) -> Generator[Any, None, None]: ...
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class Stack(Generic[_T]):
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def __init__(self, default: _T | None = ...) -> None: ...
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def __call__(self) -> _T: ...
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def __len__(self) -> int: ...
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def __getitem__(self, ind: int) -> _T: ...
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def forward(self) -> _T: ...
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def back(self) -> _T: ...
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def push(self, o: _T) -> _T: ...
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def home(self) -> _T: ...
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def empty(self) -> bool: ...
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def clear(self) -> None: ...
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def bubble(self, o: _T) -> _T: ...
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def remove(self, o: _T) -> None: ...
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def safe_masked_invalid(x: ArrayLike, copy: bool = ...) -> np.ndarray: ...
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def print_cycles(
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objects: Iterable[Any], outstream: IO = ..., show_progress: bool = ...
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) -> None: ...
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class Grouper(Generic[_T]):
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def __init__(self, init: Iterable[_T] = ...) -> None: ...
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def __contains__(self, item: _T) -> bool: ...
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def clean(self) -> None: ...
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def join(self, a: _T, *args: _T) -> None: ...
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def joined(self, a: _T, b: _T) -> bool: ...
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def remove(self, a: _T) -> None: ...
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def __iter__(self) -> Iterator[list[_T]]: ...
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def get_siblings(self, a: _T) -> list[_T]: ...
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class GrouperView(Generic[_T]):
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def __init__(self, grouper: Grouper[_T]) -> None: ...
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def __contains__(self, item: _T) -> bool: ...
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def __iter__(self) -> Iterator[list[_T]]: ...
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def joined(self, a: _T, b: _T) -> bool: ...
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def get_siblings(self, a: _T) -> list[_T]: ...
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def simple_linear_interpolation(a: ArrayLike, steps: int) -> np.ndarray: ...
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def delete_masked_points(*args): ...
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def _broadcast_with_masks(*args: ArrayLike, compress: bool = ...) -> list[ArrayLike]: ...
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def boxplot_stats(
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X: ArrayLike,
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whis: float | tuple[float, float] = ...,
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bootstrap: int | None = ...,
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labels: ArrayLike | None = ...,
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autorange: bool = ...,
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) -> list[dict[str, Any]]: ...
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ls_mapper: dict[str, str]
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ls_mapper_r: dict[str, str]
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def contiguous_regions(mask: ArrayLike) -> list[np.ndarray]: ...
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def is_math_text(s: str) -> bool: ...
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def violin_stats(
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X: ArrayLike, method: Callable, points: int = ..., quantiles: ArrayLike | None = ...
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) -> list[dict[str, Any]]: ...
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def pts_to_prestep(x: ArrayLike, *args: ArrayLike) -> np.ndarray: ...
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def pts_to_poststep(x: ArrayLike, *args: ArrayLike) -> np.ndarray: ...
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def pts_to_midstep(x: np.ndarray, *args: np.ndarray) -> np.ndarray: ...
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STEP_LOOKUP_MAP: dict[str, Callable]
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def index_of(y: float | ArrayLike) -> tuple[np.ndarray, np.ndarray]: ...
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def safe_first_element(obj: Collection[_T]) -> _T: ...
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def sanitize_sequence(data): ...
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def normalize_kwargs(
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kw: dict[str, Any],
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alias_mapping: dict[str, list[str]] | type[Artist] | Artist | None = ...,
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) -> dict[str, Any]: ...
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def _lock_path(path: str | os.PathLike) -> contextlib.AbstractContextManager[None]: ...
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def _str_equal(obj: Any, s: str) -> bool: ...
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def _str_lower_equal(obj: Any, s: str) -> bool: ...
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def _array_perimeter(arr: np.ndarray) -> np.ndarray: ...
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def _unfold(arr: np.ndarray, axis: int, size: int, step: int) -> np.ndarray: ...
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def _array_patch_perimeters(x: np.ndarray, rstride: int, cstride: int) -> np.ndarray: ...
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def _setattr_cm(obj: Any, **kwargs) -> contextlib.AbstractContextManager[None]: ...
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class _OrderedSet(collections.abc.MutableSet):
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def __init__(self) -> None: ...
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def __contains__(self, key) -> bool: ...
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def __iter__(self): ...
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def __len__(self) -> int: ...
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def add(self, key) -> None: ...
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def discard(self, key) -> None: ...
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def _setup_new_guiapp() -> None: ...
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def _format_approx(number: float, precision: int) -> str: ...
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def _g_sig_digits(value: float, delta: float) -> int: ...
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def _unikey_or_keysym_to_mplkey(unikey: str, keysym: str) -> str: ...
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def _is_torch_array(x: Any) -> bool: ...
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def _is_jax_array(x: Any) -> bool: ...
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def _unpack_to_numpy(x: Any) -> Any: ...
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def _auto_format_str(fmt: str, value: Any) -> str: ...
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