diff --git a/docs/api/index.md b/docs/api/index.md
index d968baa..e91b06f 100644
--- a/docs/api/index.md
+++ b/docs/api/index.md
@@ -1,6 +1,8 @@
 # API
 
-::: plotfig.bar
+::: plotfig.single_bar
+
+::: plotfig.multi_bars
 
 ::: plotfig.correlation
 
diff --git a/pyproject.toml b/pyproject.toml
index 6925440..a8be42d 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -26,7 +26,7 @@ dependencies = [
 dev = [
     "ipykernel>=6.30.1",
     "mkdocstrings-python>=2.0.1",
-    "zensical>=0.0.11",
+    "zensical==0.0.13",
 ]
 
 [build-system]
diff --git a/src/plotfig/__init__.py b/src/plotfig/__init__.py
index d938b1c..e0a5001 100644
--- a/src/plotfig/__init__.py
+++ b/src/plotfig/__init__.py
@@ -1,29 +1,30 @@
-from importlib.metadata import version, PackageNotFoundError
+from importlib.metadata import PackageNotFoundError, version
 
-from .bar import (
-    plot_one_group_bar_figure,
-    plot_one_group_violin_figure,
-    plot_multi_group_bar_figure,
-)
-from .correlation import plot_correlation_figure
-from .matrix import plot_matrix_figure
-from .brain_surface import plot_brain_surface_figure
-from .circos import plot_circos_figure
 from .brain_connection import (
-    plot_brain_connection_figure,
-    save_brain_connection_frames,
     batch_crop_images,
     create_gif_from_images,
+    plot_brain_connection_figure,
+    save_brain_connection_frames,
+)
+from .brain_surface import plot_brain_surface_figure
+from .circos import plot_circos_figure
+from .correlation import plot_correlation_figure
+from .matrix import plot_matrix_figure
+from .multi_bars import (
+    plot_multi_group_bar_figure,
+)
+from .single_bar import (
+    plot_one_group_bar_figure,
+    plot_one_group_violin_figure,
 )
 from .utils import (
     gen_hex_colors,
     gen_symmetric_matrix,
-    gen_cmap,
-    value_to_hex,
+    gen_white_to_color_cmap,
     is_symmetric_square,
+    value_to_hex,
 )
 
-
 __all__ = [
     # bar
     "plot_one_group_bar_figure",
@@ -45,7 +46,7 @@
     # utils
     "gen_hex_colors",
     "gen_symmetric_matrix",
-    "gen_cmap",
+    "gen_white_to_color_cmap",
     "value_to_hex",
     "is_symmetric_square",
 ]
diff --git a/src/plotfig/brain_connection.py b/src/plotfig/brain_connection.py
index 317f16d..8ca0546 100644
--- a/src/plotfig/brain_connection.py
+++ b/src/plotfig/brain_connection.py
@@ -5,16 +5,16 @@
 from typing import Literal
 
 import imageio
-from PIL import Image
 import nibabel as nib
 import numpy as np
 import numpy.typing as npt
-from scipy.ndimage import center_of_mass
-from tqdm import tqdm
 import plotly.graph_objects as go
 import plotly.io as pio
-from matplotlib.colors import LinearSegmentedColormap, to_hex
 from loguru import logger
+from matplotlib.colors import LinearSegmentedColormap, to_hex
+from PIL import Image
+from scipy.ndimage import center_of_mass
+from tqdm import tqdm
 
 warnings.filterwarnings("ignore", category=DeprecationWarning)
 
diff --git a/src/plotfig/brain_surface.py b/src/plotfig/brain_surface.py
index 0b24011..b984c3f 100644
--- a/src/plotfig/brain_surface.py
+++ b/src/plotfig/brain_surface.py
@@ -1,29 +1,26 @@
+from collections.abc import Mapping
 from pathlib import Path
 from typing import TypeAlias
-from collections.abc import Mapping
 
-import numpy as np
-import nibabel as nib
 import matplotlib.pyplot as plt
+import nibabel as nib
+import numpy as np
 from matplotlib.axes import Axes
-
 from surfplot import Plot
 
-# 类型别名定义
 Num: TypeAlias = float | int
 
 __all__ = [
     "plot_brain_surface_figure",
 ]
 
-# 路径常量
 NEURODATA = Path(__file__).resolve().parent / "data" / "neurodata"
 
 
 def _map_labels_to_values(data, gifti_file):
     gifti = nib.load(gifti_file)
     # 获取顶点标签编号数组，shape=(顶点数,)
-    labels = gifti.darrays[0].data  
+    labels = gifti.darrays[0].data
     # 构建标签编号到脑区名称的映射字典
     key_to_label = {label.key: label.label for label in gifti.labeltable.labels}
     # 检查数据中是否有在图集中找不到的脑区标签
@@ -64,7 +61,7 @@ def plot_brain_surface_figure(
     title_name: str = "",
     title_fontsize: int = 12,
     as_outline: bool = False,
-) -> Axes: 
+) -> Axes:
     """在大脑皮层表面绘制数值数据的函数。
 
     Args:
@@ -134,7 +131,7 @@ def plot_brain_surface_figure(
             "sulc": {
                 "lh": "surfaces/human_fsLR/100206.L.sulc.32k_fs_LR.shape.gii",
                 "rh": "surfaces/human_fsLR/100206.R.sulc.32k_fs_LR.shape.gii",
-            }
+            },
         },
         "chimpanzee": {
             "surf": {
@@ -146,7 +143,7 @@ def plot_brain_surface_figure(
                     "lh": "atlases/chimpanzee_BNA/ChimpBNA.L.32k_fs_LR.label.gii",
                     "rh": "atlases/chimpanzee_BNA/ChimpBNA.R.32k_fs_LR.label.gii",
                 },
-            }
+            },
         },
         "macaque": {
             "surf": {
@@ -174,14 +171,15 @@ def plot_brain_surface_figure(
             "sulc": {
                 "lh": "surfaces/macaque_BNA/SC_06018.L.sulc.32k_fs_LR.shape.gii",
                 "rh": "surfaces/macaque_BNA/SC_06018.R.sulc.32k_fs_LR.shape.gii",
-            }
-
-        }
+            },
+        },
     }
 
     # 检查物种是否支持
     if species not in atlas_info:
-        raise ValueError(f"Unsupported species: {species}. Supported species are: {list(atlas_info.keys())}")
+        raise ValueError(
+            f"Unsupported species: {species}. Supported species are: {list(atlas_info.keys())}"
+        )
     else:
         # 检查指定物种的图集是否支持
         if atlas not in atlas_info[species]["atlas"]:
@@ -199,7 +197,7 @@ def plot_brain_surface_figure(
             NEURODATA / atlas_info[species]["surf"]["lh"],
             NEURODATA / atlas_info[species]["surf"]["rh"],
             views="dorsal",
-            zoom = 1.2,
+            zoom=1.2,
         )
         lh_sulc_file = NEURODATA / atlas_info[species]["sulc"]["lh"]
         rh_sulc_file = NEURODATA / atlas_info[species]["sulc"]["rh"]
@@ -209,7 +207,7 @@ def plot_brain_surface_figure(
                 "right": nib.load(rh_sulc_file).darrays[0].data,
             },
             cmap="Grays_r",
-            cbar=False
+            cbar=False,
         )
 
     # 分离左半球和右半球的数据
diff --git a/src/plotfig/circos.py b/src/plotfig/circos.py
index 6bdd3d1..7d20ba1 100644
--- a/src/plotfig/circos.py
+++ b/src/plotfig/circos.py
@@ -1,27 +1,23 @@
-# 标准库
-from typing import Literal, Any
+from typing import Any, Literal
 
-# 第三方库
-import numpy as np
-from numpy.typing import NDArray
 import matplotlib.colors as mcolors
 import matplotlib.pyplot as plt
+import numpy as np
+from loguru import logger
+from matplotlib.axes import Axes
 from matplotlib.figure import Figure
 from matplotlib.projections.polar import PolarAxes
-from matplotlib.axes import Axes
+from numpy.typing import NDArray
 from pycirclize import Circos
-from loguru import logger
 
-# 项目内模块
-from plotfig.utils.matrix import (
-    is_symmetric_square,
-)
 from plotfig.utils.color import (
     gen_hex_colors,
-    gen_cmap,
+    gen_white_to_color_cmap,
     value_to_hex,
 )
-
+from plotfig.utils.matrix import (
+    is_symmetric_square,
+)
 
 __all__ = ["plot_circos_figure"]
 
@@ -141,7 +137,9 @@ def plot_circos_figure(
         logger.warning("connectome 矩阵所有元素均为0，可能没有有效连接数据")
         vmax = float(0 if vmax is None else vmax)
         vmin = float(0 if vmin is None else vmin)
-        colormap = gen_cmap(edge_color) if cmap is None else plt.get_cmap(cmap)
+        colormap = (
+            gen_white_to_color_cmap(edge_color) if cmap is None else plt.get_cmap(cmap)
+        )
     elif np.any(connectome < 0):
         logger.warning(
             "由于 connectome 存在负值，连线颜色无法自定义，只能正值显示红色，负值显示蓝色"
@@ -153,7 +151,9 @@ def plot_circos_figure(
     else:
         vmin = float(connectome.min() if vmin is None else vmin)
         vmax = float(connectome.max() if vmax is None else vmax)
-        colormap = gen_cmap(edge_color) if cmap is None else plt.get_cmap(cmap)
+        colormap = (
+            gen_white_to_color_cmap(edge_color) if cmap is None else plt.get_cmap(cmap)
+        )
     if vmin > vmax:
         raise ValueError(f"目前{vmin=}，而{vmax=}。但是vmin不得大于vmax，请检查数据")
     norm = mcolors.Normalize(vmin=vmin, vmax=vmax)
@@ -192,7 +192,9 @@ def plot_circos_figure(
     for sector in circos.sectors:
         if sector.name.startswith("_gap"):
             continue
-        sector.text(sector.name, size=node_label_fontsize, orientation=node_label_orientation)
+        sector.text(
+            sector.name, size=node_label_fontsize, orientation=node_label_orientation
+        )
         track = sector.add_track((95, 100))
         track.axis(fc=name2color[sector.name])
 
diff --git a/src/plotfig/correlation.py b/src/plotfig/correlation.py
index d8c0d9a..433bd23 100644
--- a/src/plotfig/correlation.py
+++ b/src/plotfig/correlation.py
@@ -1,19 +1,18 @@
-import numpy as np
+from typing import TypeAlias
+
 import matplotlib.pyplot as plt
+import numpy as np
 from matplotlib.axes import Axes
+from matplotlib.colors import LinearSegmentedColormap
 from matplotlib.ticker import (
-    ScalarFormatter,
+    FormatStrFormatter,
     FuncFormatter,
     MultipleLocator,
-    FormatStrFormatter,
+    ScalarFormatter,
 )
-from matplotlib.colors import LinearSegmentedColormap
 from scipy import stats
 
-from typing import TypeAlias
-
-# 类型别名定义
-Num: TypeAlias = float | int  # 可同时接受int和float的类型
+Num: TypeAlias = float | int
 
 __all__ = ["plot_correlation_figure"]
 
@@ -24,7 +23,8 @@ def plot_correlation_figure(
     ax: Axes | None = None,
     stats_method: str = "spearman",
     ci: bool = False,
-    dots_color: str = "steelblue",
+    ci_color: str = "gray",
+    dots_color: str | list[str] = "steelblue",
     dots_size: int | float = 10,
     line_color: str = "r",
     title_name: str = "",
@@ -32,18 +32,18 @@ def plot_correlation_figure(
     title_pad: int = 10,
     x_label_name: str = "",
     x_label_fontsize: int = 10,
-    x_tick_fontsize: int = 10,
+    x_tick_fontsize: int = 8,
     x_tick_rotation: int = 0,
     x_major_locator: float | None = None,
     x_max_tick_to_value: float | None = None,
     x_format: str = "normal",  # 支持 "normal", "sci", "1f", "percent"
     y_label_name: str = "",
     y_label_fontsize: int = 10,
-    y_tick_fontsize: int = 10,
+    y_tick_fontsize: int = 8,
     y_tick_rotation: int = 0,
     y_major_locator: float | None = None,
     y_max_tick_to_value: float | None = None,
-    y_format: str = "normal",  # 支持 "normal", "sci", "1f", "percent"
+    y_format: str = "sci",  # 支持 "normal", "sci", "1f", "percent"
     asterisk_fontsize: int = 10,
     show_p_value: bool = False,
     hexbin: bool = False,
@@ -61,6 +61,7 @@ def plot_correlation_figure(
         ax (plt.Axes | None, optional): matplotlib 的 Axes 对象，用于绘图。默认为 None，使用当前 Axes。
         stats_method (str, optional): 相关性统计方法，支持 "spearman" 和 "pearson"。默认为 "spearman"。
         ci (bool, optional): 是否绘制置信区间带。默认为 False。
+        ci_color (str, optional): 置信区间带颜色。默认为 "salmon"。
         dots_color (str, optional): 散点的颜色。默认为 "steelblue"。
         dots_size (int | float, optional): 散点的大小。默认为 1。
         line_color (str, optional): 回归线的颜色。默认为 "r"（红色）。
@@ -94,7 +95,16 @@ def plot_correlation_figure(
     """
 
     def set_axis(
-        ax, axis, label, labelsize, ticksize, rotation, locator, max_tick_value, fmt, lim
+        ax,
+        axis,
+        label,
+        labelsize,
+        ticksize,
+        rotation,
+        locator,
+        max_tick_value,
+        fmt,
+        lim,
     ):
         if axis == "x":
             set_label = ax.set_xlabel
@@ -149,7 +159,7 @@ def set_axis(
             )
         hb = ax.hexbin(A, B, gridsize=hexbin_gridsize, cmap=hexbin_cmap)
     else:
-        ax.scatter(A, B, c=dots_color, s=dots_size, alpha=0.8)
+        ax.scatter(A, B, c=dots_color, s=dots_size)
     ax.plot(x_seq, y_pred, line_color, lw=1)
 
     if ci:
@@ -165,7 +175,7 @@ def set_axis(
             x_seq,
             y_pred - conf_interval,
             y_pred + conf_interval,
-            color="salmon",
+            color=ci_color,
             alpha=0.3,
         )
 
@@ -210,9 +220,11 @@ def set_axis(
         label = r"$\rho$"
 
     if show_p_value:
-        asterisk = f" p={p:.4f}"
+        asterisk = f" p={p:.3f}"
     else:
-        asterisk = " ***" if p < 0.001 else " **" if p < 0.01 else " *" if p < 0.05 else ""
+        asterisk = (
+            " ***" if p < 0.001 else " **" if p < 0.01 else " *" if p < 0.05 else ""
+        )
     x_start, x_end = ax.get_xlim()
     y_start, y_end = ax.get_ylim()
     ax.text(
diff --git a/src/plotfig/matrix.py b/src/plotfig/matrix.py
index ba33709..96e7bbc 100644
--- a/src/plotfig/matrix.py
+++ b/src/plotfig/matrix.py
@@ -1,10 +1,10 @@
-import numpy as np
-import matplotlib.pyplot as plt
-from mpl_toolkits.axes_grid1 import make_axes_locatable
+from typing import Any, Sequence
 
+import matplotlib.pyplot as plt
+import numpy as np
 from matplotlib.axes import Axes
 from matplotlib.image import AxesImage
-from typing import Sequence, Any
+from mpl_toolkits.axes_grid1 import make_axes_locatable
 
 Num = int | float
 
diff --git a/src/plotfig/multi_bars.py b/src/plotfig/multi_bars.py
new file mode 100644
index 0000000..0329235
--- /dev/null
+++ b/src/plotfig/multi_bars.py
@@ -0,0 +1,329 @@
+import warnings
+from typing import Sequence
+
+import matplotlib.pyplot as plt
+import numpy as np
+from matplotlib.axes import Axes
+
+from .utils.bar import (
+    _annotate_significance,
+    _compute_summary,
+    _set_yaxis,
+)
+
+warnings.simplefilter("always")
+
+Num = int | float | np.integer | np.floating
+
+__all__ = [
+    "plot_multi_group_bar_figure",
+]
+
+
+def _data_valiation(data):
+    # 验证数据结构：必须是三层嵌套
+    if not isinstance(data, (Sequence, np.ndarray)):
+        raise ValueError(
+            f"data 必须是序列类型（list, tuple）或 numpy 数组，"
+            f"当前类型为 {type(data).__name__}"
+        )
+    if len(data) == 0:
+        raise ValueError("data 不能为空，至少需要一个组")
+    # 收集每个组的 bar 数量，用于后续检查一致性
+    n_bars = []
+    # 验证第一层和第二层
+    for i, group in enumerate(data):
+        if not isinstance(group, (Sequence, np.ndarray)):
+            raise ValueError(
+                f"data[{i}] 必须是序列或数组类型，当前类型为 {type(group).__name__}"
+            )
+        if len(group) == 0:
+            raise ValueError(f"data[{i}] 不能为空，每个组至少需要一个柱子")
+        # 记录当前组的 bar 数量
+        n_bars.append(len(group))
+        # 验证第二层和第三层
+        for j, bar in enumerate(group):
+            if not isinstance(bar, (Sequence, np.ndarray)):
+                raise ValueError(
+                    f"data[{i}][{j}] 必须是序列或数组类型，当前类型为 {type(bar).__name__}"
+                )
+            if len(bar) == 0:
+                raise ValueError(f"data[{i}][{j}] 不能为空，每个柱子至少需要一个数据点")
+            # 验证第三层：检查是否为数值类型
+            for k, value in enumerate(bar):
+                if not isinstance(value, (int, float, np.integer, np.floating)):
+                    raise ValueError(
+                        f"data[{i}][{j}][{k}] 必须是数值类型，"
+                        f"当前值为 {value!r}，类型为 {type(value).__name__}"
+                    )
+    # 检查所有组的 bar 数量是否一致
+    if len(set(n_bars)) > 1:
+        # 构建详细的错误信息
+        error_details = [
+            f"data[{i}] 有 {count} 个柱子" for i, count in enumerate(n_bars)
+        ]
+        raise ValueError(
+            "所有组的柱子数量必须一致，但发现不同的数量：\n" + "\n".join(error_details)
+        )
+
+
+def plot_multi_group_bar_figure(
+    data: Sequence[Sequence[Sequence[float]]],
+    ax: Axes | None = None,
+    group_labels: list[str] | None = None,
+    bar_labels: list[str] | None = None,
+    bar_width: Num = 0.2,
+    bar_gap: Num = 0.1,
+    bar_color: list[str] | None = None,
+    errorbar_type: str = "sd",
+    dots_color: str = "gray",
+    dots_size: int = 35,
+    legend: bool = True,
+    legend_position: tuple[Num, Num] = (1.2, 1),
+    title_name: str = "",
+    title_fontsize=12,
+    title_pad=10,
+    x_label_name: str = "",
+    x_label_ha="center",
+    x_label_fontsize=10,
+    x_tick_fontsize=8,
+    x_tick_rotation=0,
+    y_label_name: str = "",
+    y_label_fontsize=10,
+    y_tick_fontsize=8,
+    y_tick_rotation=0,
+    y_lim: tuple[float, float] | None = None,
+    statistic: bool = False,
+    test_method: str = "external",
+    p_list: list[list[Num]] | None = None,
+    line_color="0.5",
+    asterisk_fontsize=10,
+    asterisk_color="k",
+    y_base: float | None = None,
+    interval: float | None = None,
+    ax_bottom_is_0: bool = False,
+    y_max_tick_is_1: bool = False,
+    math_text: bool = True,
+    one_decimal_place: bool = False,
+    percentage: bool = False,
+) -> Axes:
+    """绘制多组分组条形图，支持误差线、散点叠加和统计显著性标注。
+
+    该函数用于可视化多组数据的比较，每组包含多个柱子，每个柱子显示均值、误差线
+    和原始数据点。特别适用于认知神经科学中的组间比较分析。
+
+    Args:
+        data (Sequence[Sequence[Sequence[float]]]): 三层嵌套的数据结构
+            - 第一层：组 (groups)
+            - 第二层：每组内的柱子 (bars)，所有组的柱子数量必须一致
+            - 第三层：每个柱子内的数据点 (points)，数量可以不同
+        ax (Axes | None): matplotlib 的 Axes 对象。如果为 None，使用当前活动的 Axes。
+        group_labels (list[str] | None): 每个组的标签。如果为 None，自动生成 "Group 1", "Group 2" 等。
+        bar_labels (list[str] | None): 每个柱子的标签，用于图例。如果为 None，自动生成 "Bar 1", "Bar 2" 等。
+        bar_width (Num): 柱子的宽度。默认为 0.2。
+        bar_gap (Num): 同一组内柱子之间的间隔。默认为 0.1。
+        bar_color (list[str] | None): 每个柱子的颜色列表。如果为 None，所有柱子使用灰色。
+        errorbar_type (str): 误差线类型，'sd' 表示标准差，'se' 表示标准误。默认为 'sd'。
+        dots_color (str): 散点的颜色。默认为 'gray'。
+        dots_size (int): 散点的大小。默认为 35。
+        legend (bool): 是否显示图例。默认为 True。
+        legend_position (tuple[Num, Num]): 图例位置，使用 bbox_to_anchor 坐标。默认为 (1.2, 1)。
+        title_name (str): 图表标题。默认为空字符串。
+        title_fontsize (int): 标题字体大小。默认为 12。
+        title_pad (int): 标题与图表的间距。默认为 10。
+        x_label_name (str): x 轴标签文本。默认为空字符串。
+        x_label_ha (str): x 轴刻度标签的水平对齐方式。默认为 'center'。
+        x_label_fontsize (int): x 轴标签字体大小。默认为 10。
+        x_tick_fontsize (int): x 轴刻度字体大小。默认为 8。
+        x_tick_rotation (int): x 轴刻度旋转角度。默认为 0。
+        y_label_name (str): y 轴标签文本。默认为空字符串。
+        y_label_fontsize (int): y 轴标签字体大小。默认为 10。
+        y_tick_fontsize (int): y 轴刻度字体大小。默认为 8。
+        y_tick_rotation (int): y 轴刻度旋转角度。默认为 0。
+        y_lim (tuple[float, float] | None): 手动指定的 y 轴范围 (y_min, y_max)。
+            如果为 None，根据数据自动计算。
+        statistic (bool): 是否添加统计显著性标注。默认为 False。
+        test_method (str): 统计检验方法。当前仅支持 'external'（使用外部提供的 p 值）。
+        p_list (list[list[Num]] | None): 外部提供的 p 值列表。
+            结构为 [组1的p值列表, 组2的p值列表, ...]，每个组的 p 值列表对应该组内所有两两比较。
+            当 statistic=True 且 test_method='external' 时必须提供。
+        line_color (str): 显著性标注连线的颜色。默认为 '0.5'（中灰色）。
+        asterisk_fontsize (int): 显著性星号的字体大小。默认为 10。
+        asterisk_color (str): 显著性星号的颜色。默认为 'k'（黑色）。
+        y_base (float | None): 显著性标注的起始 y 坐标。如果为 None，自动计算为数据最大值。
+        interval (float | None): 多个显著性标注之间的垂直间隔。
+            如果为 None，自动计算为 (y_max - 数据最大值) / (比较数量 + 1)。
+        ax_bottom_is_0 (bool): 是否将 y 轴底部固定为 0。默认为 False。
+        y_max_tick_is_1 (bool): 是否将最大刻度限制为 1。默认为 False。
+        math_text (bool): 是否使用科学计数法格式。默认为 True。
+        one_decimal_place (bool): 是否将刻度格式化为一位小数。默认为 False。
+        percentage (bool): 是否将刻度格式化为百分比形式。默认为 False。
+
+    Returns:
+        Axes: 包含绘制内容的 matplotlib Axes 对象。
+
+    Raises:
+        ValueError: 当 data 不是三层嵌套结构时抛出。
+        ValueError: 当所有组的柱子数量不一致时抛出。
+        ValueError: 当 errorbar_type 不是 'sd' 或 'se' 时抛出。
+        ValueError: 当 statistic=True 且 test_method='external' 但 p_list 为 None 时抛出。
+
+    Examples:
+        >>> import numpy as np
+        >>> import matplotlib.pyplot as plt
+        >>> from plotfig import plot_multi_group_bar_figure
+        >>>
+        >>> # 创建示例数据：2 组，每组 3 个柱子
+        >>> data = [
+        ...     [[1, 2, 3], [4, 5, 6], [7, 8, 9]],
+        ...     [[2, 3, 4], [5, 6, 7], [8, 9, 10]]
+        ... ]
+        >>>
+        >>> # 绘制基本图表
+        >>> fig, ax = plt.subplots(figsize=(8, 6))
+        >>> ax = plot_multi_group_bar_figure(
+        ...     data,
+        ...     ax=ax,
+        ...     group_labels=['Control', 'Treatment'],
+        ...     bar_labels=['Condition A', 'Condition B', 'Condition C'],
+        ...     title_name='Multi-Group Comparison'
+        ... )
+        >>> fig.show()
+
+    Notes:
+        - 显著性星号规则：* (p≤0.05), ** (p≤0.01), *** (p≤0.001)
+    """
+    _data_valiation(data)
+
+    n_groups = len(data)
+    n_bars = len(data[0])
+
+    ax = ax or plt.gca()
+    group_labels = group_labels or [f"Group {i + 1}" for i in range(len(data))]
+    bar_labels = bar_labels or [f"Bar {i + 1}" for i in range(n_bars)]
+    bar_color = bar_color or ["gray"] * n_bars
+
+    # 把所有子列表展开成一个大列表
+    all_values = [x for sublist1 in data for sublist2 in sublist1 for x in sublist2]
+
+    x_positions_all = []
+    for index_group, group_data in enumerate(data):
+        x_positions = (
+            np.arange(n_bars) * (bar_width + bar_gap)
+            + bar_width / 2
+            + index_group
+            - (n_bars * bar_width + (n_bars - 1) * bar_gap) / 2
+        )
+        x_positions_all.append(x_positions)
+
+        # 计算均值、标准差、标准误
+        means = [_compute_summary(group_data[i])[0] for i in range(n_bars)]
+        sds = [_compute_summary(group_data[i])[1] for i in range(n_bars)]
+        ses = [_compute_summary(group_data[i])[2] for i in range(n_bars)]
+        if errorbar_type == "sd":
+            error_values = sds
+        elif errorbar_type == "se":
+            error_values = ses
+        else:
+            raise ValueError("errorbar_type 只能是 'sd' 或者 'se'")
+        # 绘制柱子
+        bars = ax.bar(
+            x_positions, means, width=bar_width, color=bar_color, alpha=1, edgecolor="k"
+        )
+        ax.errorbar(
+            x_positions,
+            means,
+            error_values,
+            fmt="none",
+            linewidth=1,
+            capsize=3,
+            color="black",
+        )
+        # 绘制散点
+        for index_bar, dot in enumerate(group_data):
+            # 创建随机数生成器
+            rng = np.random.default_rng(seed=42)
+            dot_x_pos = rng.normal(
+                x_positions[index_bar], scale=bar_width / 7, size=len(dot)
+            )
+            ax.scatter(
+                dot_x_pos,
+                dot,
+                c=dots_color,
+                s=dots_size,
+                edgecolors="white",
+                linewidths=1,
+                alpha=0.5,
+            )
+
+    if legend:
+        ax.legend(bars, bar_labels, bbox_to_anchor=legend_position)
+
+    # 美化
+    ax.spines[["top", "right"]].set_visible(False)
+    ax.set_title(
+        title_name,
+        fontsize=title_fontsize,
+        pad=title_pad,
+    )
+    # x轴
+    ax.set_xlabel(x_label_name, fontsize=x_label_fontsize)
+    ax.set_xticks(np.arange(n_groups))
+    ax.set_xticklabels(
+        group_labels,
+        ha=x_label_ha,
+        rotation_mode="anchor",
+        fontsize=x_tick_fontsize,
+        rotation=x_tick_rotation,
+    )
+    # y轴
+    ax.tick_params(
+        axis="y",
+        labelsize=y_tick_fontsize,
+        rotation=y_tick_rotation,
+    )
+    ax.set_ylabel(y_label_name, fontsize=y_label_fontsize)
+    _set_yaxis(
+        ax,
+        all_values,
+        y_lim,
+        ax_bottom_is_0,
+        y_max_tick_is_1,
+        math_text,
+        one_decimal_place,
+        percentage,
+    )
+
+    # 添加统计显著性标记
+    if statistic:
+        for index_group, group_data in enumerate(data):
+            x_positions = x_positions_all[index_group]
+            comparisons = []
+            idx = 0
+            for i in range(len(group_data)):
+                for j in range(i + 1, len(group_data)):
+                    if test_method == "external":
+                        if p_list is None:
+                            raise ValueError("p_list不能为空")
+                        p = p_list[index_group][idx]
+                        idx += 1
+                    else:
+                        raise ValueError("多组数据统计测试方法暂时仅支持 external方法")
+                    if p <= 0.05:
+                        comparisons.append((x_positions[i], x_positions[j], p))
+            y_max = ax.get_ylim()[1]
+            y_base = y_base or np.max(all_values)
+            interval = interval or (y_max - np.max(all_values)) / (len(comparisons) + 1)
+
+            _annotate_significance(
+                ax,
+                comparisons,
+                y_base,
+                interval,
+                line_color=line_color,
+                star_offset=interval / 5,
+                fontsize=asterisk_fontsize,
+                color=asterisk_color,
+            )
+
+    return ax
diff --git a/src/plotfig/bar.py b/src/plotfig/single_bar.py
similarity index 66%
rename from src/plotfig/bar.py
rename to src/plotfig/single_bar.py
index 3e6302a..d20d082 100644
--- a/src/plotfig/bar.py
+++ b/src/plotfig/single_bar.py
@@ -3,17 +3,20 @@
 
 import matplotlib.pyplot as plt
 import numpy as np
-from numpy.typing import NDArray
 from matplotlib.axes import Axes
-from matplotlib.ticker import FuncFormatter, ScalarFormatter, FormatStrFormatter
 from matplotlib.colors import LinearSegmentedColormap, to_rgba
 from matplotlib.patches import Polygon, Rectangle
+from numpy.typing import NDArray
 from scipy import stats
 
-# 设置警告过滤器，显示所有警告
+from .utils.bar import (
+    _annotate_significance,
+    _compute_summary,
+    _set_yaxis,
+)
+
 warnings.simplefilter("always")
 
-# 类型别名
 Num = int | float | np.integer | np.floating
 DataType = (
     np.ndarray  # 三维 ndarray
@@ -24,7 +27,6 @@
 __all__ = [
     "plot_one_group_bar_figure",
     "plot_one_group_violin_figure",
-    "plot_multi_group_bar_figure",
 ]
 
 
@@ -45,14 +47,6 @@ def _is_valid_data(data):
     return False
 
 
-def _compute_summary(data):
-    """计算均值、标准差、标准误"""
-    mean = np.mean(data)
-    sd = np.std(data, ddof=1)
-    se = sd / np.sqrt(len(data))
-    return mean, sd, se
-
-
 def _add_scatter(
     ax,
     x_pos,
@@ -72,57 +66,6 @@ def _add_scatter(
     )
 
 
-def _set_yaxis(
-    ax,
-    data,
-    y_lim,
-    ax_bottom_is_0,
-    y_max_tick_is_1,
-    math_text,
-    one_decimal_place,
-    percentage,
-):
-    """设置Y轴格式"""
-    if y_lim:
-        ax.set_ylim(y_lim)
-    else:
-        y_min, y_max = np.min(data), np.max(data)
-        y_range = y_max - y_min
-        golden_ratio = 5**0.5 - 1
-        ax_min = 0 if ax_bottom_is_0 else y_min - (y_range / golden_ratio - y_range / 2)
-        ax_max = y_max + (y_range / golden_ratio - y_range / 2)
-        ax.set_ylim(ax_min, ax_max)
-
-    if y_max_tick_is_1:
-        ticks = [tick for tick in ax.get_yticks() if tick <= 1]
-        ax.set_yticks(ticks)
-
-    if math_text and (np.min(data) < 0.1 or np.max(data) > 100):
-        formatter = ScalarFormatter(useMathText=True)
-        formatter.set_powerlimits((-2, 2))
-        ax.yaxis.set_major_formatter(formatter)
-
-    if one_decimal_place:
-        if math_text:
-            warnings.warn(
-                "“one_decimal_place”会与“math_text”冲突，请关闭“math_text”后再开启！",
-                UserWarning,
-            )
-        else:
-            ax.yaxis.set_major_formatter(FormatStrFormatter("%.1f"))
-
-    if percentage:
-        if math_text:
-            warnings.warn(
-                "“percentage”会与“math_text”冲突，请关闭“math_text”后再开启！",
-                UserWarning,
-                stacklevel=2,
-            )
-        else:
-            ax.yaxis.set_major_formatter(FuncFormatter(lambda x, pos: f"{x:.0%}"))
-
-
-# 统计相关
 def _perform_stat_test(
     data1=None,
     data2=None,
@@ -172,48 +115,6 @@ def _determine_test_modle(data, method, p_list=None, popmean=0):
     return comparisons
 
 
-def _annotate_significance(
-    ax,
-    comparisons,
-    y_base,
-    interval,
-    line_color,
-    star_offset,
-    fontsize,
-    color,
-):
-    """添加显著性星号和连线"""
-
-    def _stars(pval, i, y, color, fontsize):
-        stars = "*" if pval > 0.01 else "**" if pval > 0.001 else "***"
-        ax.text(
-            i,
-            y,
-            stars,
-            ha="center",
-            va="center",
-            color=color,
-            fontsize=fontsize,
-        )
-
-    if len(comparisons[0]) == 3:
-        for (i, j, pval), count in zip(comparisons, range(1, len(comparisons) + 1)):
-            y = y_base + count * interval
-            ax.annotate(
-                "",
-                xy=(i, y),
-                xytext=(j, y),
-                arrowprops=dict(
-                    color=line_color, width=0.5, headwidth=0.1, headlength=0.1
-                ),
-            )
-            _stars(pval, (i + j) / 2, y + star_offset, color, fontsize)
-    elif len(comparisons[0]) == 2:
-        for i, pval in comparisons:
-            y = y_base
-            _stars(pval, i, y + star_offset, color, fontsize)
-
-
 def _statistics(
     data,
     test_method,
@@ -306,14 +207,14 @@ def plot_one_group_bar_figure(
     title_pad: Num = 10,
     x_label_name: str = "",
     x_label_ha: str = "center",
-    x_label_fontsize: Num = 10,
-    x_tick_fontsize: Num = 8,
+    x_label_fontsize: Num = 12,
+    x_tick_fontsize: Num = 12,
     x_tick_rotation: Num = 0,
     y_label_name: str = "",
-    y_label_fontsize: Num = 10,
+    y_label_fontsize: Num = 12,
     y_tick_fontsize: Num = 8,
     y_tick_rotation: Num = 0,
-    y_lim: list[Num] | tuple[Num, Num] | None = None,
+    y_lim: tuple[float, float] | None = None,
     statistic: bool = False,
     test_method: list[str] = ["ttest_ind"],
     p_list: list[float] | None = None,
@@ -371,20 +272,20 @@ def plot_one_group_bar_figure(
         x_label_ha (str, optional):
             X轴标签的水平对齐方式. Defaults to "center".
         x_label_fontsize (Num, optional):
-            X轴标签字体大小. Defaults to 10.
+            X轴标签字体大小. Defaults to 12.
         x_tick_fontsize (Num, optional):
-            X轴刻度字体大小. Defaults to 8.
+            X轴刻度字体大小. Defaults to 12.
         x_tick_rotation (Num, optional):
             X轴刻度旋转角度. Defaults to 0.
         y_label_name (str, optional):
             Y轴标签名称. Defaults to "".
         y_label_fontsize (Num, optional):
-            Y轴标签字体大小. Defaults to 10.
+            Y轴标签字体大小. Defaults to 12.
         y_tick_fontsize (Num, optional):
             Y轴刻度字体大小. Defaults to 8.
         y_tick_rotation (Num, optional):
             Y轴刻度旋转角度. Defaults to 0.
-        y_lim (list[Num] | tuple[Num, Num] | None, optional):
+        y_lim (tuple[Num, Num] | None, optional):
             Y轴的范围限制. Defaults to None.
         statistic (bool, optional):
             是否进行统计显著性分析. Defaults to False.
@@ -589,7 +490,7 @@ def plot_one_group_violin_figure(
     y_label_fontsize: Num = 10,
     y_tick_fontsize: Num = 8,
     y_tick_rotation: Num = 0,
-    y_lim: list[Num] | tuple[Num, Num] | None = None,
+    y_lim: tuple[float, float] | None = None,
     statistic: bool = False,
     test_method: list[str] = ["ttest_ind"],
     popmean: Num = 0,
@@ -654,7 +555,7 @@ def plot_one_group_violin_figure(
             Y轴刻度字体大小. Defaults to 8.
         y_tick_rotation (Num, optional):
             Y轴刻度旋转角度. Defaults to 0.
-        y_lim (list[Num] | tuple[Num, Num] | None, optional):
+        y_lim (tuple[Num, Num] | None, optional):
             Y轴的范围限制. Defaults to None.
         statistic (bool, optional):
             是否进行统计显著性分析. Defaults to False.
@@ -853,258 +754,3 @@ def _draw_gradient_violin(ax, data, pos, width, c1, c2, color_alpha):
         )
 
     return ax
-
-
-def plot_multi_group_bar_figure(
-    data: DataType,
-    ax: Axes | None = None,
-    group_labels: list[str] | None = None,
-    bar_labels: list[str] | None = None,
-    bar_width: Num = 0.2,
-    bar_gap: Num = 0.1,
-    bar_color: list[str] | None = None,
-    errorbar_type: str = "sd",
-    dots_color: str = "gray",
-    dots_size: int = 35,
-    legend: bool = True,
-    legend_position: tuple[Num, Num] = (1.2, 1),
-    title_name: str = "",
-    title_fontsize=12,
-    title_pad=10,
-    x_label_name: str = "",
-    x_label_ha="center",
-    x_label_fontsize=10,
-    x_tick_fontsize=8,
-    x_tick_rotation=0,
-    y_label_name: str = "",
-    y_label_fontsize=10,
-    y_tick_fontsize=8,
-    y_tick_rotation=0,
-    y_lim: list[Num] | tuple[Num, Num] | None = None,
-    statistic: bool = False,
-    test_method: str = "external",
-    p_list: list[list[Num]] | None = None,
-    line_color="0.5",
-    asterisk_fontsize=10,
-    asterisk_color="k",
-    y_base: float | None = None,
-    interval: float | None = None,
-    ax_bottom_is_0: bool = False,
-    y_max_tick_is_1: bool = False,
-    math_text: bool = True,
-    one_decimal_place: bool = False,
-    percentage: bool = False,
-) -> Axes:
-    """绘制多组柱状图，包含散点、误差条、显著性标注和图例等。
-
-    Args:
-        data (DataType):
-            输入数据，可以是三维numpy数组、二维numpy数组列表或嵌套序列
-        ax (Axes | None, optional):
-            matplotlib的坐标轴对象，如果为None则使用当前坐标轴. Defaults to None.
-        group_labels (list[str] | None, optional):
-            组标签名称列表. Defaults to None.
-        bar_labels (list[str] | None, optional):
-            柱状图标签名称列表. Defaults to None.
-        bar_width (Num, optional):
-            柱状图的宽度. Defaults to 0.2.
-        bar_gap (Num, optional):
-            柱状图之间的间隔. Defaults to 0.1.
-        bar_color (list[str] | None, optional):
-            柱状图的颜色列表. Defaults to None.
-        errorbar_type (str, optional):
-            误差条类型，可选 "sd"(标准差) 或 "se"(标准误). Defaults to "sd".
-        dots_color (str, optional):
-            散点的颜色. Defaults to "gray".
-        dots_size (int, optional):
-            散点的大小. Defaults to 35.
-        legend (bool, optional):
-            是否显示图例. Defaults to True.
-        legend_position (tuple[Num, Num], optional):
-            图例位置坐标. Defaults to (1.2, 1).
-        title_name (str, optional):
-            图表标题. Defaults to "".
-        title_fontsize (int, optional):
-            标题字体大小. Defaults to 12.
-        title_pad (int, optional):
-            标题与图表的间距. Defaults to 10.
-        x_label_name (str, optional):
-            X轴标签名称. Defaults to "".
-        x_label_ha (str, optional):
-            X轴标签的水平对齐方式. Defaults to "center".
-        x_label_fontsize (int, optional):
-            X轴标签字体大小. Defaults to 10.
-        x_tick_fontsize (int, optional):
-            X轴刻度字体大小. Defaults to 8.
-        x_tick_rotation (int, optional):
-            X轴刻度旋转角度. Defaults to 0.
-        y_label_name (str, optional):
-            Y轴标签名称. Defaults to "".
-        y_label_fontsize (int, optional):
-            Y轴标签字体大小. Defaults to 10.
-        y_tick_fontsize (int, optional):
-            Y轴刻度字体大小. Defaults to 8.
-        y_tick_rotation (int, optional):
-            Y轴刻度旋转角度. Defaults to 0.
-        y_lim (list[Num] | tuple[Num, Num] | None, optional):
-            Y轴的范围限制. Defaults to None.
-        statistic (bool, optional):
-            是否进行统计显著性分析. Defaults to False.
-        test_method (str, optional):
-            统计检验方法，目前仅支持"external". Defaults to "external".
-        p_list (list[list[Num]] | None, optional):
-            预计算的p值列表，用于显著性标记. Defaults to None.
-        line_color (str, optional):
-            显著性标记线的颜色. Defaults to "0.5".
-        asterisk_fontsize (int, optional):
-            显著性星号的字体大小. Defaults to 10.
-        asterisk_color (str, optional):
-            显著性星号的颜色. Defaults to "k".
-        y_base (float | None, optional):
-            显著性连线的起始Y轴位置（高度）。如果为None，则使用内部算法自动计算一个合适的位置。Defaults to None.
-        interval (float | None, optional):
-            相邻显著性连线之间的垂直距离（Y轴增量）。如果为None，则使用内部算法根据图表范围和比较对数自动计算。Defaults to None.
-        ax_bottom_is_0 (bool, optional):
-            Y轴是否从0开始. Defaults to False.
-        y_max_tick_is_1 (bool, optional):
-            Y轴最大刻度是否限制为1. Defaults to False.
-        math_text (bool, optional):
-            是否将Y轴显示为科学计数法格式. Defaults to True.
-        one_decimal_place (bool, optional):
-            Y轴刻度是否只保留一位小数. Defaults to False.
-        percentage (bool, optional):
-            是否将Y轴显示为百分比格式. Defaults to False.
-
-    Raises:
-        ValueError: 当data数据格式无效时抛出
-        ValueError: 当test_method不是"external"时抛出（多组数据统计测试方法暂时仅支持external方法）
-
-    Returns:
-        Axes: 返回matplotlib的坐标轴对象
-    """
-
-    ax = ax or plt.gca()
-    group_labels = group_labels or [f"Group {i + 1}" for i in range(len(data))]
-    n_groups = len(data)
-
-    # 把所有子列表展开成一个大列表
-    all_values = [x for sublist1 in data for sublist2 in sublist1 for x in sublist2]
-
-    x_positions_all = []
-    for index_group, group_data in enumerate(data):
-        n_bars = len(group_data)
-        if bar_labels is None:
-            bar_labels = [f"Bar {i + 1}" for i in range(n_bars)]
-        if bar_color is None:
-            bar_color = ["gray"] * n_bars
-
-        x_positions = (
-            np.arange(n_bars) * (bar_width + bar_gap)
-            + bar_width / 2
-            + index_group
-            - (n_bars * bar_width + (n_bars - 1) * bar_gap) / 2
-        )
-        x_positions_all.append(x_positions)
-
-        # 计算均值、标准差、标准误
-        means = [_compute_summary(group_data[i])[0] for i in range(n_bars)]
-        sds = [_compute_summary(group_data[i])[1] for i in range(n_bars)]
-        ses = [_compute_summary(group_data[i])[2] for i in range(n_bars)]
-        if errorbar_type == "sd":
-            error_values = sds
-        elif errorbar_type == "se":
-            error_values = ses
-        else:
-            raise ValueError("errorbar_type 只能是 'sd' 或者 'se'")
-        # 绘制柱子
-        bars = ax.bar(
-            x_positions, means, width=bar_width, color=bar_color, alpha=1, edgecolor="k"
-        )
-        ax.errorbar(
-            x_positions,
-            means,
-            error_values,
-            fmt="none",
-            linewidth=1,
-            capsize=3,
-            color="black",
-        )
-        # 绘制散点
-        for index_bar, dot in enumerate(group_data):
-            # 创建随机数生成器
-            rng = np.random.default_rng(seed=42)
-            dot_x_pos = rng.normal(
-                x_positions[index_bar], scale=bar_width / 7, size=len(dot)
-            )
-            _add_scatter(ax, dot_x_pos, dot, dots_color, dots_size=dots_size)
-    if legend:
-        ax.legend(bars, bar_labels, bbox_to_anchor=legend_position)
-
-    # 美化
-    ax.spines[["top", "right"]].set_visible(False)
-    ax.set_title(
-        title_name,
-        fontsize=title_fontsize,
-        pad=title_pad,
-    )
-    # x轴
-    ax.set_xlabel(x_label_name, fontsize=x_label_fontsize)
-    ax.set_xticks(np.arange(n_groups))
-    ax.set_xticklabels(
-        group_labels,
-        ha=x_label_ha,
-        rotation_mode="anchor",
-        fontsize=x_tick_fontsize,
-        rotation=x_tick_rotation,
-    )
-    # y轴
-    ax.tick_params(
-        axis="y",
-        labelsize=y_tick_fontsize,
-        rotation=y_tick_rotation,
-    )
-    ax.set_ylabel(y_label_name, fontsize=y_label_fontsize)
-    _set_yaxis(
-        ax,
-        all_values,
-        y_lim,
-        ax_bottom_is_0,
-        y_max_tick_is_1,
-        math_text,
-        one_decimal_place,
-        percentage,
-    )
-
-    # 添加统计显著性标记
-    if statistic:
-        for index_group, group_data in enumerate(data):
-            x_positions = x_positions_all[index_group]
-            comparisons = []
-            idx = 0
-            for i in range(len(group_data)):
-                for j in range(i + 1, len(group_data)):
-                    if test_method == "external":
-                        if p_list is None:
-                            raise ValueError("p_list不能为空")
-                        p = p_list[index_group][idx]
-                        idx += 1
-                    else:
-                        raise ValueError("多组数据统计测试方法暂时仅支持 external方法")
-                    if p <= 0.05:
-                        comparisons.append((x_positions[i], x_positions[j], p))
-            y_max = ax.get_ylim()[1]
-            y_base = y_base or np.max(all_values)
-            interval = interval or (y_max - np.max(all_values)) / (len(comparisons) + 1)
-
-            _annotate_significance(
-                ax,
-                comparisons,
-                y_base,
-                interval,
-                line_color=line_color,
-                star_offset=interval / 5,
-                fontsize=asterisk_fontsize,
-                color=asterisk_color,
-            )
-
-    return ax
diff --git a/src/plotfig/utils/__init__.py b/src/plotfig/utils/__init__.py
index 3cb02e7..93f0796 100644
--- a/src/plotfig/utils/__init__.py
+++ b/src/plotfig/utils/__init__.py
@@ -1,11 +1,10 @@
-from .matrix import *
-from .color import *
-
+from .color import gen_hex_colors, gen_white_to_color_cmap, value_to_hex
+from .matrix import gen_symmetric_matrix, is_symmetric_square
 
 __all__ = [
     "gen_hex_colors",
     "gen_symmetric_matrix",
-    "gen_cmap",
+    "gen_white_to_color_cmap",
     "value_to_hex",
     "is_symmetric_square",
 ]
diff --git a/src/plotfig/utils/bar.py b/src/plotfig/utils/bar.py
new file mode 100644
index 0000000..e4d474e
--- /dev/null
+++ b/src/plotfig/utils/bar.py
@@ -0,0 +1,203 @@
+import warnings
+from typing import Sequence
+
+import numpy as np
+from matplotlib.axes import Axes
+from matplotlib.ticker import FormatStrFormatter, FuncFormatter, ScalarFormatter
+from numpy.typing import ArrayLike
+
+Num = int | float | np.integer | np.floating
+
+
+def _compute_summary(data: ArrayLike) -> tuple[float, float, float]:
+    """计算数据的统计摘要信息，包括均值、标准差和标准误。
+
+    该函数用于为条形图和小提琴图等可视化提供统计度量，特别是用于绘制误差线。
+    标准差使用贝塞尔校正(ddof=1)计算，适用于样本数据的无偏估计。
+
+    Args:
+        data (array-like): 一维数值数据数组，用于计算统计摘要。
+
+    Returns:
+        tuple: 包含三个元素的元组 (mean, sd, se)
+            - mean (float): 数据的算术平均值
+            - sd (float): 样本标准差（使用贝塞尔校正）
+            - se (float): 标准误（标准差除以样本量的平方根）
+
+    Examples:
+        >>> data = [1, 2, 3, 4, 5]
+        >>> mean, sd, se = _compute_summary(data)
+        >>> print(f"Mean: {mean:.2f}, SD: {sd:.2f}, SE: {se:.2f}")
+        Mean: 3.00, SD: 1.58, SE: 0.71
+    """
+    data = np.asarray(data)
+    mean = np.mean(data)
+    sd = np.std(data, ddof=1)
+    se = sd / np.sqrt(len(data))
+    return float(mean), float(sd), float(se)
+
+
+def _set_yaxis(
+    ax: Axes,
+    data: ArrayLike,
+    y_lim: tuple[float, float] | None,
+    ax_bottom_is_0: bool,
+    y_max_tick_is_1: bool,
+    math_text: bool,
+    one_decimal_place: bool,
+    percentage: bool,
+) -> None:
+    """设置图表的 y 轴范围和格式。
+
+    该函数提供灵活的 y 轴配置选项，包括自动计算美观的轴范围（使用黄金比例）、
+    多种数值格式化方式（科学计数法、小数、百分比）以及刻度限制功能。
+
+    Args:
+        ax (Axes): matplotlib 的 Axes 对象，用于设置 y 轴属性。
+        data (ArrayLike): 数值数据数组，用于计算 y 轴的自动范围。
+        y_lim (tuple[float, float] | None): 手动指定的 y 轴范围 (y_min, y_max)。
+            如果为 None，则根据数据自动计算范围。
+        ax_bottom_is_0 (bool): 是否将 y 轴底部固定为 0。
+            当为 True 时，y 轴最小值始终为 0，适用于不包含负值的数据。
+        y_max_tick_is_1 (bool): 是否将最大刻度限制为 1。
+            当为 True 时，移除所有大于 1 的刻度，适用于比例或概率数据。
+        math_text (bool): 是否使用科学计数法格式。
+            当数据范围超出 [0.1, 100] 时启用，使用 10^n 的形式显示。
+        one_decimal_place (bool): 是否将刻度格式化为一位小数。
+            与 math_text 互斥，同时启用会发出警告。
+        percentage (bool): 是否将刻度格式化为百分比形式。
+            与 math_text 互斥，同时启用会发出警告。
+
+    Notes:
+        - 自动范围计算使用黄金比例（φ-1 ≈ 0.618）来确定上下边距，使图表更加美观
+        - math_text、one_decimal_place 和 percentage 三个格式化选项互斥，
+          同时启用多个会导致冲突并发出警告
+        - 函数直接修改传入的 ax 对象，不返回任何值
+
+    Examples:
+        >>> import matplotlib.pyplot as plt
+        >>> import numpy as np
+        >>> fig, ax = plt.subplots()
+        >>> data = np.random.randn(100)
+        >>> _set_yaxis(ax, data, None, False, False, True, False, False)
+    """
+    data = np.asarray(data)
+
+    if y_lim:
+        ax.set_ylim(y_lim)
+    else:
+        y_min, y_max = np.min(data), np.max(data)
+        y_range = y_max - y_min
+        golden_ratio = 5**0.5 - 1
+        ax_min = 0 if ax_bottom_is_0 else y_min - (y_range / golden_ratio - y_range / 2)
+        ax_max = y_max + (y_range / golden_ratio - y_range / 2)
+        ax.set_ylim(ax_min, ax_max)
+
+    if y_max_tick_is_1:
+        ticks = [tick for tick in ax.get_yticks() if tick <= 1]
+        ax.set_yticks(ticks)
+
+    if math_text and (np.min(data) < 0.1 or np.max(data) > 100):
+        formatter = ScalarFormatter(useMathText=True)
+        formatter.set_powerlimits((-2, 2))
+        ax.yaxis.set_major_formatter(formatter)
+
+    if one_decimal_place:
+        if math_text:
+            warnings.warn(
+                "“one_decimal_place”会与“math_text”冲突，请关闭“math_text”后再开启！",
+                UserWarning,
+            )
+        else:
+            ax.yaxis.set_major_formatter(FormatStrFormatter("%.1f"))
+
+    if percentage:
+        if math_text:
+            warnings.warn(
+                "“percentage”会与“math_text”冲突，请关闭“math_text”后再开启！",
+                UserWarning,
+                stacklevel=2,
+            )
+        else:
+            ax.yaxis.set_major_formatter(FuncFormatter(lambda x, pos: f"{x:.0%}"))
+
+
+def _annotate_significance(
+    ax: Axes,
+    comparisons: Sequence[tuple[int, int, float]] | Sequence[tuple[int, float]],
+    y_base: float,
+    interval: float,
+    line_color: str,
+    star_offset: float,
+    fontsize: int | float,
+    color: str,
+) -> None:
+    """在图表上添加统计显著性标注（星号和连线）。
+
+    该函数支持两种显著性标注模式：
+    1. 组间比较模式：在两组之间绘制连线，并在连线上方标注显著性星号
+    2. 单样本模式：直接在数据点上方标注显著性星号
+
+    显著性星号规则：
+    - * : 0.001 < p ≤ 0.01
+    - ** : 0.001 < p ≤ 0.01
+    - *** : p ≤ 0.001
+
+    Args:
+        ax (Axes): matplotlib 的 Axes 对象，用于绘制标注。
+        comparisons (Sequence[tuple[int, int, float]] | Sequence[tuple[int, float]]):
+            显著性比较数据，支持两种格式：
+            - 三元组序列 [(i, j, p_value), ...]：组间比较，i 和 j 是组索引，p_value 是 p 值
+            - 二元组序列 [(i, p_value), ...]：单样本检验，i 是组索引，p_value 是 p 值
+        y_base (float): 显著性标注的基准 y 坐标位置。
+        interval (float): 多个显著性标注之间的垂直间隔，用于避免重叠。
+        line_color (str): 连线的颜色（仅用于组间比较模式）。
+        star_offset (float): 星号相对于连线的垂直偏移量。
+        fontsize (int | float): 星号的字体大小。
+        color (str): 星号的颜色。
+
+    Notes:
+        - 组间比较模式会自动堆叠多个比较，每个比较占据一个 interval 的高度
+        - 星号位置在组间比较模式中位于两组的中点，单样本模式中位于组的正上方
+        - 函数直接修改传入的 ax 对象，不返回任何值
+
+    Examples:
+        >>> import matplotlib.pyplot as plt
+        >>> fig, ax = plt.subplots()
+        >>> # 组间比较模式
+        >>> comparisons = [(0, 1, 0.001), (1, 2, 0.03)]
+        >>> _annotate_significance(ax, comparisons, 5.0, 0.5, 'black', 0.1, 12, 'red')
+        >>> # 单样本模式
+        >>> comparisons = [(0, 0.02), (1, 0.005)]
+        >>> _annotate_significance(ax, comparisons, 5.0, 0.5, 'black', 0.1, 12, 'red')
+    """
+
+    def _stars(pval, i, y, color, fontsize):
+        stars = "*" if pval > 0.01 else "**" if pval > 0.001 else "***"
+        ax.text(
+            i,
+            y,
+            stars,
+            ha="center",
+            va="center",
+            color=color,
+            fontsize=fontsize,
+        )
+
+    if len(comparisons[0]) == 3:
+        for (i, j, pval), count in zip(comparisons, range(1, len(comparisons) + 1)):
+            y = y_base + count * interval
+            ax.annotate(
+                "",
+                xy=(i, y),
+                xytext=(j, y),
+                arrowprops=dict(
+                    color=line_color, width=0.5, headwidth=0.1, headlength=0.1
+                ),
+            )
+            _stars(pval, (i + j) / 2, y + star_offset, color, fontsize)
+    elif len(comparisons[0]) == 2:
+        for i_pval in comparisons:
+            i, pval = i_pval[0], i_pval[1]
+            y = y_base
+            _stars(pval, i, y + star_offset, color, fontsize)
diff --git a/src/plotfig/utils/color.py b/src/plotfig/utils/color.py
index 9f022c0..c86bf02 100644
--- a/src/plotfig/utils/color.py
+++ b/src/plotfig/utils/color.py
@@ -1,18 +1,42 @@
-import numpy as np
 import matplotlib.colors as mcolors
-from matplotlib.colors import LinearSegmentedColormap, Colormap, Normalize
+import numpy as np
+from matplotlib.colors import Colormap, LinearSegmentedColormap, Normalize
 
 
 def gen_hex_colors(n: int, seed: int = 42) -> list[str]:
-    """
-    生成 n 个随机的十六进制颜色字符串。
+    """生成指定数量的随机十六进制颜色代码。
+
+    该函数使用 NumPy 的随机数生成器创建随机 RGB 颜色值，并转换为十六进制格式。
+    通过固定随机种子确保结果可重复，适用于需要一致配色方案的科学可视化。
 
     Args:
-        n (int): 需要生成的颜色数量。
-        seed (int, optional): 随机种子，默认为 42。
+        n (int): 需要生成的颜色数量，必须为正整数。
+        seed (int): 随机种子，用于确保结果可重复。默认为 42。
 
     Returns:
-        list[str]: 包含 n 个十六进制颜色字符串的列表。
+        list[str]: 包含 n 个十六进制颜色字符串的列表，格式为 "#RRGGBB"。
+
+    Examples:
+        >>> # 生成 3 个随机颜色
+        >>> colors = gen_hex_colors(3)
+        >>> print(colors)
+        ['#66a0a9', '#8b7d3a', '#c94f6d']
+
+        >>> # 使用不同的随机种子
+        >>> colors = gen_hex_colors(5, seed=123)
+        >>> len(colors)
+        5
+
+        >>> # 在绘图中使用
+        >>> import matplotlib.pyplot as plt
+        >>> colors = gen_hex_colors(10)
+        >>> for i, color in enumerate(colors):
+        ...     plt.bar(i, i+1, color=color)
+
+    Notes:
+        - RGB 值范围为 [0, 255]
+        - 相同的 n 和 seed 参数总是生成相同的颜色序列
+        - 生成的颜色是完全随机的，可能包含对比度较低的颜色
     """
 
     RNG = np.random.default_rng(seed=seed)
@@ -21,15 +45,48 @@ def gen_hex_colors(n: int, seed: int = 42) -> list[str]:
     return colors
 
 
-def gen_cmap(color: str = "red") -> Colormap:
-    """
-    生成一个从白色到指定颜色的线性渐变色图。
+def gen_white_to_color_cmap(color: str = "red") -> Colormap:
+    """生成从白色到指定颜色的线性渐变色图。
+
+    该函数创建一个双色线性渐变色图，从白色（最小值）平滑过渡到指定颜色（最大值）。
+    适用于热力图、相关矩阵、脑连接图等需要表示连续数值强度的可视化场景。
 
     Args:
-        color (str, optional): 渐变的目标颜色，默认为 "red"。
+        color (str): 渐变的目标颜色，支持 matplotlib 颜色名称（如 "red", "blue"）、
+            十六进制格式（如 "#FF0000"）或 RGB 元组。默认为 "red"。
 
     Returns:
-        Colormap: 线性渐变的色图对象。
+        Colormap: matplotlib 的线性渐变色图对象，可直接用于绘图函数。
+
+    Examples:
+        >>> # 生成红色渐变色图
+        >>> cmap = gen_cmap("red")
+        >>> 
+        >>> # 在热力图中使用
+        >>> import matplotlib.pyplot as plt
+        >>> import numpy as np
+        >>> data = np.random.rand(10, 10)
+        >>> plt.imshow(data, cmap=gen_cmap("blue"))
+        >>> plt.colorbar()
+        >>> 
+        >>> # 使用十六进制颜色
+        >>> cmap = gen_cmap("#FF5733")
+        >>> 
+        >>> # 在散点图中使用
+        >>> x = np.random.rand(100)
+        >>> y = np.random.rand(100)
+        >>> c = np.random.rand(100)
+        >>> plt.scatter(x, y, c=c, cmap=gen_cmap("purple"))
+
+    Notes:
+        - 色图名称固定为 "white_to_color"
+        - 渐变是线性的，白色对应数值范围的最小值，指定颜色对应最大值
+        - 支持所有 matplotlib 认可的颜色格式
+        - 常用于表示正值数据，如相关系数的绝对值、连接强度等
+
+    See Also:
+        value_to_hex : 将数值映射为十六进制颜色
+        matplotlib.colors.LinearSegmentedColormap : 底层实现类
     """
 
     cmap = LinearSegmentedColormap.from_list("white_to_color", ["white", color])
@@ -37,16 +94,55 @@ def gen_cmap(color: str = "red") -> Colormap:
 
 
 def value_to_hex(value: float, cmap: Colormap, norm: Normalize) -> str:
-    """
-    根据数值、色图和归一化对象，将数值映射为十六进制颜色字符串。
+    """将数值通过色图和归一化映射为十六进制颜色字符串。
+
+    该函数实现了从数值到颜色的完整映射流程：首先使用归一化对象将数值映射到 [0, 1] 区间，
+    然后通过色图将归一化值转换为 RGBA 颜色，最后转换为十六进制格式。常用于根据数据强度
+    动态生成颜色，如脑连接图中根据连接强度着色连线。
 
     Args:
-        value (float): 需要映射的数值。
-        cmap (Colormap): 用于映射的色图对象。
-        norm (Normalize): 用于归一化的对象。
+        value (float): 需要映射的原始数值，可以是任意范围的浮点数。
+        cmap (Colormap): matplotlib 色图对象，定义了归一化值到颜色的映射关系。
+        norm (Normalize): matplotlib 归一化对象，定义了原始数值到 [0, 1] 的映射规则。
+            常用类型包括 Normalize（线性）、LogNorm（对数）等。
 
     Returns:
-        str: 映射得到的十六进制颜色字符串。
+        str: 十六进制颜色字符串，格式为 "#RRGGBB"。
+
+    Examples:
+        >>> import matplotlib.pyplot as plt
+        >>> from matplotlib.colors import Normalize
+        >>> 
+        >>> # 创建色图和归一化对象
+        >>> cmap = gen_cmap("red")
+        >>> norm = Normalize(vmin=0, vmax=100)
+        >>> 
+        >>> # 将数值映射为颜色
+        >>> color1 = value_to_hex(25, cmap, norm)  # 浅红色
+        >>> color2 = value_to_hex(75, cmap, norm)  # 深红色
+        >>> print(color1, color2)
+        '#ffbfbf' '#bf4040'
+        >>> 
+        >>> # 在脑连接图中使用
+        >>> connection_strengths = [0.1, 0.5, 0.9]
+        >>> norm = Normalize(vmin=0, vmax=1)
+        >>> colors = [value_to_hex(s, cmap, norm) for s in connection_strengths]
+        >>> 
+        >>> # 使用对数归一化
+        >>> from matplotlib.colors import LogNorm
+        >>> norm_log = LogNorm(vmin=1, vmax=1000)
+        >>> color = value_to_hex(100, cmap, norm_log)
+
+    Notes:
+        - 归一化对象决定了数值如何映射到 [0, 1] 区间
+        - 色图决定了 [0, 1] 区间的值如何映射到具体颜色
+        - 返回的十六进制颜色可直接用于 matplotlib 或 plotly 绘图
+        - 如果 value 超出归一化对象的范围，会被裁剪到边界值
+
+    See Also:
+        gen_cmap : 生成线性渐变色图
+        matplotlib.colors.Normalize : 线性归一化
+        matplotlib.colors.LogNorm : 对数归一化
     """
 
     rgba = cmap(norm(value))  # 得到 RGBA
diff --git a/uv.lock b/uv.lock
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--- a/uv.lock
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