Push numvrykvpnpl

docs/api/index.md

@@ -1,6 +1,8 @@
 # API
 
-::: plotfig.bar
+::: plotfig.single_bar
+
+::: plotfig.multi_bars
 
 ::: plotfig.correlation
 

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]

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",
 ]

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)
 

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

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])
 

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"]
 
@@ -24,26 +23,27 @@ 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 = "",
     title_fontsize: int = 12,
     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(

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