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seaborn: improve the matrix module (#14464)

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Ali Hamdan
2025-07-26 17:42:14 +02:00
committed by GitHub
parent f15658bfe4
commit 7864edbc39
1 changed files with 81 additions and 58 deletions
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stubs/seaborn/seaborn/matrix.pyi
+81 -58
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@@ -1,26 +1,41 @@
from _typeshed import Incomplete
from collections.abc import Hashable, Iterable, Mapping, Sequence
from typing import Literal
from typing import Literal, TypedDict, type_check_only
from typing_extensions import Self, TypeAlias
import numpy as np
import pandas as pd
from matplotlib.axes import Axes
from matplotlib.colors import Colormap, ListedColormap
from matplotlib.colors import Colormap, ListedColormap, Normalize
from matplotlib.gridspec import GridSpec
from matplotlib.typing import ColorType
from numpy._typing import _ArrayLike, _ArrayLikeInt_co
from numpy._typing import _ArrayLikeInt_co
from numpy.typing import ArrayLike, NDArray
from pandas import DataFrame, Index, Series
from .axisgrid import Grid
# pandas._typing.ListLikeU is partially Unknown
_ListLikeU: TypeAlias = Sequence[Incomplete] | np.ndarray[Incomplete, Incomplete] | Series[Incomplete] | Index[Incomplete]
_ListLikeU: TypeAlias = Sequence[Incomplete] | NDArray[Incomplete] | pd.Series[Incomplete] | pd.Index[Incomplete]
_ConvertibleToDataFrame: TypeAlias = (
_ListLikeU
| pd.DataFrame
| dict[Incomplete, Incomplete]
| Iterable[_ListLikeU | tuple[Hashable, _ListLikeU] | dict[Incomplete, Incomplete]]
| None
)
_FlatOrNestedSequenceOfColors: TypeAlias = (
Sequence[ColorType]
| Sequence[Iterable[ColorType]]
| NDArray[Incomplete]
| pd.Index[Incomplete]
| pd.Series[Incomplete]
| pd.DataFrame
)
__all__ = ["heatmap", "clustermap"]
def heatmap(
data: DataFrame | _ArrayLike[Incomplete],
data: pd.DataFrame | ArrayLike,
*,
vmin: float | None = None,
vmax: float | None = None,
@@ -38,24 +53,37 @@ def heatmap(
square: bool = False,
xticklabels: Literal["auto"] | bool | int | Sequence[str] = "auto",
yticklabels: Literal["auto"] | bool | int | Sequence[str] = "auto",
mask: NDArray[np.bool_] | DataFrame | None = None,
mask: NDArray[np.bool_] | pd.DataFrame | None = None,
ax: Axes | None = None,
# Kwargs below passed to matplotlib.axes.Axes.pcolormesh
alpha: float | None = None,
norm: str | Normalize | None = None,
shading: Literal["flat", "nearest", "gouraud", "auto"] | None = None,
antialiased: bool = False,
**kwargs,
) -> Axes: ...
@type_check_only
class _Dendogram(TypedDict):
icoord: list[list[float]]
dcoord: list[list[float]]
ivl: list[str]
leaves: list[int]
color_list: list[str]
leaves_color_list: list[str]
class _DendrogramPlotter:
axis: int
array: NDArray[Incomplete]
data: DataFrame
array: NDArray[np.floating]
data: pd.DataFrame
shape: tuple[int, int]
metric: str
method: str
label: bool
rotate: bool
linkage: NDArray[Incomplete]
dendrogram: dict[str, list[Incomplete]]
xticks: list[float] | NDArray[Incomplete]
yticks: list[float] | NDArray[Incomplete]
linkage: NDArray[np.floating]
dendrogram: _Dendogram
xticks: list[float] | NDArray[np.floating]
yticks: list[float] | NDArray[np.floating]
xticklabels: list[str]
yticklabels: list[str]
ylabel: str
@@ -63,19 +91,26 @@ class _DendrogramPlotter:
dependent_coord: list[list[float]]
independent_coord: list[list[float]]
def __init__(
self, data: DataFrame, linkage: NDArray[Incomplete] | None, metric: str, method: str, axis: int, label: bool, rotate: bool
self,
data: pd.DataFrame,
linkage: NDArray[np.floating] | None,
metric: str,
method: str,
axis: int,
label: bool,
rotate: bool,
) -> None: ...
@property
def calculated_linkage(self) -> NDArray[Incomplete]: ...
def calculate_dendrogram(self) -> dict[str, list[Incomplete]]: ...
def calculated_linkage(self) -> NDArray[np.float64]: ...
def calculate_dendrogram(self) -> _Dendogram: ...
@property
def reordered_ind(self) -> list[int]: ...
def plot(self, ax: Axes, tree_kws: dict[str, Incomplete]) -> Self: ...
def dendrogram(
data: DataFrame,
data: pd.DataFrame,
*,
linkage: NDArray[Incomplete] | None = None,
linkage: NDArray[np.floating] | None = None,
axis: int = 1,
label: bool = True,
metric: str = "euclidean",
@@ -86,13 +121,13 @@ def dendrogram(
) -> _DendrogramPlotter: ...
class ClusterGrid(Grid):
data: DataFrame
data2d: DataFrame
mask: DataFrame
row_colors: Incomplete
row_color_labels: Incomplete
col_colors: Incomplete
col_color_labels: Incomplete
data: pd.DataFrame
data2d: pd.DataFrame
mask: pd.DataFrame
row_colors: list[list[tuple[float, float, float]]] | None
row_color_labels: list[str] | None
col_colors: list[list[tuple[float, float, float]]] | None
col_color_labels: list[str] | None
gs: GridSpec
ax_row_dendrogram: Axes
ax_col_dendrogram: Axes
@@ -101,44 +136,38 @@ class ClusterGrid(Grid):
ax_heatmap: Axes
ax_cbar: Axes | None
cax: Axes | None
cbar_pos: Incomplete
cbar_pos: tuple[float, float, float, float] | None
dendrogram_row: _DendrogramPlotter | None
dendrogram_col: _DendrogramPlotter | None
def __init__(
self,
data: (
_ListLikeU
| DataFrame
| dict[Incomplete, Incomplete]
| Iterable[_ListLikeU | tuple[Hashable, _ListLikeU] | dict[Incomplete, Incomplete]]
| None
),
data: _ConvertibleToDataFrame,
pivot_kws: Mapping[str, Incomplete] | None = None,
z_score: int | None = None,
standard_scale: int | None = None,
figsize: tuple[float, float] | None = None,
row_colors=None,
col_colors=None,
mask: NDArray[np.bool_] | DataFrame | None = None,
row_colors: _FlatOrNestedSequenceOfColors | None = None,
col_colors: _FlatOrNestedSequenceOfColors | None = None,
mask: NDArray[np.bool_] | pd.DataFrame | None = None,
dendrogram_ratio: float | tuple[float, float] | None = None,
colors_ratio: float | tuple[float, float] | None = None,
cbar_pos: tuple[float, float, float, float] | None = None,
) -> None: ...
def format_data(
self,
data: DataFrame,
data: pd.DataFrame,
pivot_kws: Mapping[str, Incomplete] | None,
z_score: int | None = None,
standard_scale: int | None = None,
) -> DataFrame: ...
) -> pd.DataFrame: ...
@staticmethod
def z_score(data2d: DataFrame, axis: int = 1) -> DataFrame: ...
def z_score(data2d: pd.DataFrame, axis: int = 1) -> pd.DataFrame: ...
@staticmethod
def standard_scale(data2d: DataFrame, axis: int = 1) -> DataFrame: ...
def dim_ratios(self, colors: Incomplete | None, dendrogram_ratio: float, colors_ratio: float) -> list[float]: ...
def standard_scale(data2d: pd.DataFrame, axis: int = 1) -> pd.DataFrame: ...
def dim_ratios(self, colors: ArrayLike | None, dendrogram_ratio: float, colors_ratio: float) -> list[float]: ...
@staticmethod
def color_list_to_matrix_and_cmap(
colors: Sequence[ColorType], ind: _ArrayLikeInt_co, axis: int = 0
colors: _FlatOrNestedSequenceOfColors, ind: _ArrayLikeInt_co, axis: int = 0
) -> tuple[NDArray[np.int_], ListedColormap]: ...
def plot_dendrograms(
self,
@@ -146,8 +175,8 @@ class ClusterGrid(Grid):
col_cluster: bool,
metric: str,
method: str,
row_linkage: NDArray[Incomplete] | None,
col_linkage: NDArray[Incomplete] | None,
row_linkage: NDArray[np.floating] | None,
col_linkage: NDArray[np.floating] | None,
tree_kws: dict[str, Incomplete] | None,
) -> None: ...
def plot_colors(self, xind: _ArrayLikeInt_co, yind: _ArrayLikeInt_co, **kws) -> None: ...
@@ -159,20 +188,14 @@ class ClusterGrid(Grid):
colorbar_kws: dict[str, Incomplete] | None,
row_cluster: bool,
col_cluster: bool,
row_linkage: NDArray[Incomplete] | None,
col_linkage: NDArray[Incomplete] | None,
row_linkage: NDArray[np.floating] | None,
col_linkage: NDArray[np.floating] | None,
tree_kws: dict[str, Incomplete] | None,
**kws,
) -> Self: ...
def clustermap(
data: (
_ListLikeU
| DataFrame
| dict[Incomplete, Incomplete]
| Iterable[_ListLikeU | tuple[Hashable, _ListLikeU] | dict[Incomplete, Incomplete]]
| None
),
data: _ConvertibleToDataFrame,
*,
pivot_kws: dict[str, Incomplete] | None = None,
method: str = "average",
@@ -183,11 +206,11 @@ def clustermap(
cbar_kws: dict[str, Incomplete] | None = None,
row_cluster: bool = True,
col_cluster: bool = True,
row_linkage: NDArray[Incomplete] | None = None,
col_linkage: NDArray[Incomplete] | None = None,
row_colors=None,
col_colors=None,
mask: NDArray[np.bool_] | DataFrame | None = None,
row_linkage: NDArray[np.floating] | None = None,
col_linkage: NDArray[np.floating] | None = None,
row_colors: _FlatOrNestedSequenceOfColors | None = None,
col_colors: _FlatOrNestedSequenceOfColors | None = None,
mask: NDArray[np.bool_] | pd.DataFrame | None = None,
dendrogram_ratio: float | tuple[float, float] = 0.2,
colors_ratio: float | tuple[float, float] = 0.03,
cbar_pos: tuple[float, float, float, float] | None = (0.02, 0.8, 0.05, 0.18),