From 1fdd20542df14a70fce213ec0a1a257fddb1a325 Mon Sep 17 00:00:00 2001 From: Ricardo_MI Date: Sat, 9 Aug 2025 02:02:12 +0800 Subject: [PATCH 1/6] refactor(bar): Remove the legacy `plot_one_group_violin_figure_old` function MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit The `plot_one_group_violin_figure_old` function is officially deprecated. It is replaced by the new `plot_one_group_violin_figure` function. --- 重构(bar): 移除旧版`plot_one_violin_figure_old`函数 正式弃用`plot_one_violin_figure_old`函数。 用新函数`plot_one_violin_figure`函数代替。 --- src/plotfig/__init__.py | 2 -- 1 file changed, 2 deletions(-) diff --git a/src/plotfig/__init__.py b/src/plotfig/__init__.py index 8587c4c..b76b6ab 100644 --- a/src/plotfig/__init__.py +++ b/src/plotfig/__init__.py @@ -1,7 +1,6 @@ from .bar import ( plot_one_group_bar_figure, plot_one_group_violin_figure, - plot_one_group_violin_figure_old, plot_multi_group_bar_figure, ) from .correlation import plot_correlation_figure @@ -23,7 +22,6 @@ __all__ = [ "plot_one_group_bar_figure", "plot_one_group_violin_figure", - "plot_one_group_violin_figure_old", "plot_multi_group_bar_figure", "plot_correlation_figure", "plot_matrix_figure", From 30802811422e5a9ddc0df38ae987d3f6811450c7 Mon Sep 17 00:00:00 2001 From: Ricardo_MI Date: Sat, 9 Aug 2025 02:08:18 +0800 Subject: [PATCH 2/6] docs(bar): Update function comments and fix static analysis errors MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Attempted to add type annotations for functions, but the task is not yet fully completed. --- 文档(bar): 更新函数注释，修复静态检查错误 尝试为函数添加类型注释，但是还没有完全完成 --- src/plotfig/bar.py | 925 +++++++++++++++++++++++++++------------------ 1 file changed, 550 insertions(+), 375 deletions(-) diff --git a/src/plotfig/bar.py b/src/plotfig/bar.py index e116166..559af37 100644 --- a/src/plotfig/bar.py +++ b/src/plotfig/bar.py @@ -1,26 +1,29 @@ import warnings -from typing import Any +from typing import Sequence import matplotlib.pyplot as plt import numpy as np -import numpy.typing as npt +from numpy.typing import NDArray from matplotlib.axes import Axes -from matplotlib.ticker import FuncFormatter, ScalarFormatter +from matplotlib.ticker import FuncFormatter, ScalarFormatter, FormatStrFormatter from matplotlib.colors import LinearSegmentedColormap, to_rgba -from matplotlib.patches import Polygon +from matplotlib.patches import Polygon, Rectangle from scipy import stats # 设置警告过滤器，显示所有警告 warnings.simplefilter("always") # 类型别名 -Num = int | float # 可同时接受int和float的类型 -NumArray = list[Num] | npt.NDArray[np.float64] # 数字数组类型 +Num = int | float | np.integer | np.floating +DataType = ( + np.ndarray # 三维 ndarray + | Sequence[np.ndarray] # list[二维 ndarray] + | Sequence[Sequence[Sequence[Num] | np.ndarray]] # 纯 list 嵌套数字 +) __all__ = [ "plot_one_group_bar_figure", "plot_one_group_violin_figure", - "plot_one_group_violin_figure_old", "plot_multi_group_bar_figure", ] @@ -28,7 +31,24 @@ RNG = np.random.default_rng(seed=1998) -def compute_summary(data: NumArray) -> tuple[float, float, float]: +def _is_valid_data(data): + if isinstance(data, np.ndarray): + return data.ndim == 2 + if isinstance(data, (list, tuple)): + for x in data: + if isinstance(x, np.ndarray): + if x.ndim != 1: + return False + elif isinstance(x, (list, tuple)): + if not all(isinstance(i, (int, float, np.floating)) for i in x): + return False + else: + return False + return True + 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) @@ -36,13 +56,13 @@ def compute_summary(data: NumArray) -> tuple[float, float, float]: return mean, sd, se -def add_scatter( - ax: Axes, - x_pos: Num, - data: NumArray, - color: str, - dots_size: Num = 35, -) -> None: +def _add_scatter( + ax, + x_pos, + data, + color, + dots_size, +): """添加散点""" ax.scatter( x_pos, @@ -55,51 +75,47 @@ def add_scatter( ) -def set_yaxis( - ax: Axes, - data: NumArray, - options: dict[str, Any] | None, -) -> None: +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 options.get("y_lim"): - ax.set_ylim(*options["y_lim"]) + 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 options.get("ax_bottom_is_0") - else y_min - (y_range / golden_ratio - y_range / 2) - ) + 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 options.get("y_max_tick_is_1"): - ticks = [ - tick - for tick in ax.get_yticks() - if tick <= options.get("y_max_tick_is_1", 1) - ] + if y_max_tick_is_1: + ticks = [tick for tick in ax.get_yticks() if tick <= 1] ax.set_yticks(ticks) - if options.get("math_text", True) and (np.min(data) < 0.1 or np.max(data) > 100): + 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 options.get("one_decimal_place"): - if options.get("math_text", True): + 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, - stacklevel=2, ) else: - ax.yaxis.set_major_formatter(plt.FormatStrFormatter("%.1f")) + ax.yaxis.set_major_formatter(FormatStrFormatter("%.1f")) - if options.get("percentage"): - if options.get("math_text", True): + if percentage: + if math_text: warnings.warn( "“percentage”会与“math_text”冲突，请关闭“math_text”后再开启！", UserWarning, @@ -110,12 +126,12 @@ def set_yaxis( # 统计相关 -def perform_stat_test( - data1: NumArray | None = None, - data2: NumArray | None = None, - popmean: NumArray | None = None, - method: str = "ttest_ind", -) -> tuple[float, float]: +def _perform_stat_test( + data1=None, + data2=None, + popmean=None, + method="ttest_ind", +): """执行统计检验""" # 使用字典映射替代多个elif分支，提高可读性和可扩展性 test_methods = { @@ -134,39 +150,41 @@ def perform_stat_test( return stat, p -def determine_test_modle(data, method, p_list=None, popmean=0): +def _determine_test_modle(data, method, p_list=None, popmean=0): comparisons = [] idx = 0 if method != "ttest_1samp": for i in range(len(data)): for j in range(i + 1, len(data)): if method == "external": + if p_list is None: + raise ValueError("p_list参数不能为空") p = p_list[idx] idx += 1 else: - _, p = perform_stat_test( + _, p = _perform_stat_test( data1=data[i], data2=data[j], method=method ) if p <= 0.05: comparisons.append((i, j, p)) else: for i in range(len(data)): - _, p = perform_stat_test(data1=data[i], popmean=popmean, method=method) + _, p = _perform_stat_test(data1=data[i], popmean=popmean, method=method) if p <= 0.05: comparisons.append((i, p)) return comparisons -def annotate_significance( - ax: Axes, - comparisons: list[tuple[int, int, float]], - y_base: Num, - interval: Num, - line_color: str, - star_offset: Num, - fontsize: Num, - color: str, -) -> None: +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): @@ -199,7 +217,7 @@ def _stars(pval, i, y, color, fontsize): _stars(pval, i, y + star_offset, color, fontsize) -def statistics( +def _statistics( data, test_method, p_list, @@ -219,7 +237,7 @@ def statistics( ) for method in test_method: - comparisons = determine_test_modle(data, method, p_list, popmean) + comparisons = _determine_test_modle(data, method, p_list, popmean) if not comparisons: return @@ -232,7 +250,7 @@ def statistics( else asterisk_color ) - annotate_significance( + _annotate_significance( ax, comparisons, np.max(all_values), @@ -248,13 +266,13 @@ def statistics( DeprecationWarning, stacklevel=1, ) - comparisons = determine_test_modle(data, test_method, p_list, popmean) + comparisons = _determine_test_modle(data, test_method, p_list, popmean) if not comparisons: return y_max = ax.get_ylim()[1] interval = (y_max - np.max(all_values)) / (len(comparisons) + 1) - annotate_significance( + _annotate_significance( ax, comparisons, np.max(all_values), @@ -267,87 +285,171 @@ def statistics( # 可调用接口函数 +# TODO: 增加不显示散点的flag def plot_one_group_bar_figure( - data: list[NumArray], + data: np.ndarray | Sequence[Sequence[Num] | np.ndarray], ax: Axes | None = None, labels_name: list[str] | None = None, - width: Num = 0.5, colors: list[str] | None = None, - color_alpha: Num = 1, edgecolor: str | None = None, gradient_color: bool = False, - colors_start=None, - colors_end=None, - dots_size: Num = 35, + colors_start: list[str] | None = None, + colors_end: list[str] | None = None, dots_color: list[list[str]] | None = None, + y_lim: list[Num] | tuple[Num, Num] | None = None, + width: Num = 0.5, + color_alpha: Num = 1, + dots_size: Num = 35, + errorbar_type: str = "sd", title_name: str = "", + title_fontsize: Num = 12, + title_pad: Num = 10, x_label_name: str = "", + x_label_ha: str = "center", + x_label_fontsize: Num = 10, + x_tick_fontsize: Num = 8, + x_tick_rotation: Num = 0, y_label_name: str = "", - errorbar_type: str = "sd", + y_label_fontsize: Num = 10, + y_tick_fontsize: Num = 8, + y_tick_rotation: Num = 0, statistic: bool = False, - test_method: str = "ttest_ind", - popmean: Num = 0, + test_method: list[str] = ["ttest_ind"], p_list: list[float] | None = None, + popmean: Num = 0, statistical_line_color: str = "0.5", asterisk_fontsize: Num = 10, asterisk_color: str = "k", - **kwargs: Any, -) -> 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 | None: """绘制单组柱状图，包含散点、误差条和统计显著性标记。 Args: - data (list[NumArray]): 包含多个数据集的列表，每个数据集是一个数字数组。 - ax (Axes | None, optional): matplotlib 的 Axes 对象，用于绘图。默认为 None，使用当前 Axes。 - labels_name (list[str] | None, optional): 每个数据集对应的标签。默认为 None，使用索引作为标签。 - width (Num, optional): 柱子的宽度。默认为 0.5。 - colors (list[str] | None, optional): 每个柱子的颜色列表。若为 None，使用默认灰色。 - color_alpha (Num, optional): 颜色透明度，取值范围为 0（完全透明）到 1（完全不透明）。使用 gradient_color 时该参数无效。默认为 1。 - edgecolor (str | None, optional): 柱子的边缘颜色。默认为 None，即不特别设置。 - gradient_color (bool, optional): 是否为柱子启用渐变色填充。默认为 False。 - colors_start (list[str] | None, optional): 渐变色的起始颜色列表。用于 gradient_color=True。 - colors_end (list[str] | None, optional): 渐变色的结束颜色列表。用于 gradient_color=True。 - dots_size (Num, optional): 每个散点的大小。默认为 35。 - dots_color (list[list[str]] | None, optional): 每组数据中每个散点的颜色（二维列表）。默认为 None，使用灰色。 - title_name (str, optional): 图表的标题文字。默认为空字符串。 - x_label_name (str, optional): X 轴的标签。默认为空字符串。 - y_label_name (str, optional): Y 轴的标签。默认为空字符串。 - errorbar_type (str, optional): 误差条类型。支持 "sd"（标准差）或 "se"（标准误）。默认为 "sd"。 - statistic (bool, optional): 是否进行统计检验并在柱状图上标记显著性。默认为 False。 - test_method (str, optional): 统计检验方法。支持 "ttest_ind"、"ttest_rel"、"ttest_1samp"、"mannwhitneyu" 或 "external"。默认为 "ttest_ind"。 - popmean (Num): 总体均值假设值，用于单样本t检验(ttest_1samp)。默认为 0。 - p_list (list[float] | None, optional): 提供的 p 值列表，用于 "external" 检验。默认为None。 - statistical_line_color (str, optional): 统计显著性标记连线的颜色。默认为 "0.5"（灰色）。 - asterisk_fontsize (Num, optional): 显著性星号的字体大小。默认为 10。 - asterisk_color (str, optional): 显著性星号的颜色。默认为 "k"（黑色）。 - **kwargs (Any): 其他 matplotlib 参数，用于进一步定制图表样式。 + data (np.ndarray | Sequence[Sequence[Num] | np.ndarray]): + 输入数据，可以是二维numpy数组或嵌套序列，每个子序列代表一个柱状图的数据点 + 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. + labels_name (list[str] | None, optional): + 柱状图的标签名称列表. Defaults to None. + colors (list[str] | None, optional): + 柱状图的颜色列表. Defaults to None. + edgecolor (str | None, optional): + 柱状图边缘颜色. Defaults to None. + gradient_color (bool, optional): + 是否使用渐变颜色填充柱状图. Defaults to False. + colors_start (list[str] | None, optional): + 渐变色的起始颜色列表. Defaults to None. + colors_end (list[str] | None, optional): + 渐变色的结束颜色列表. Defaults to None. + dots_color (list[list[str]] | None, optional): + 散点的颜色列表. Defaults to None. + y_lim (list[Num] | tuple[Num, Num] | None, optional): + Y轴的范围限制. Defaults to None. + width (Num, optional): + 柱状图的宽度. Defaults to 0.5. + color_alpha (Num, optional): + 柱状图颜色的透明度. Defaults to 1. + dots_size (Num, optional): + 散点的大小. Defaults to 35. + errorbar_type (str, optional): + 误差条类型，可选 "sd"(标准差) 或 "se"(标准误). Defaults to "sd". + title_name (str, optional): + 图表标题. Defaults to "". + title_fontsize (Num, optional): + 标题字体大小. Defaults to 12. + title_pad (Num, 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 (Num, optional): + X轴标签字体大小. Defaults to 10. + x_tick_fontsize (Num, optional): + X轴刻度字体大小. Defaults to 8. + 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_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. + statistic (bool, optional): + 是否进行统计显著性分析. Defaults to False. + test_method (list[str], optional): + 统计检验方法列表. Defaults to ["ttest_ind"]. + p_list (list[float] | None, optional): + 预计算的p值列表，用于显著性标记. Defaults to None. + popmean (Num, optional): + 单样本t检验的假设均值. Defaults to 0. + statistical_line_color (str, optional): + 显著性标记线的颜色. Defaults to "0.5". + asterisk_fontsize (Num, 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". + 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errorbar_type不是"sd"或"se"时抛出 Returns: - None + Axes | None: 返回matplotlib的坐标轴对象或None """ + # 处理None值 + if not _is_valid_data(data): + raise ValueError("无效的 data") + ax = ax or plt.gca() + labels_name = labels_name or [str(i) for i in range(len(data))] + colors = colors or ["gray"] * len(data) + # 统一参数型 + width = float(width) + color_alpha = float(color_alpha) + dots_size = float(dots_size) + title_fontsize = float(title_fontsize) + title_pad = float(title_pad) + x_label_fontsize = float(x_label_fontsize) + x_tick_fontsize = float(x_tick_fontsize) + x_tick_rotation = float(x_tick_rotation) + y_label_fontsize = float(y_label_fontsize) + y_tick_fontsize = float(y_tick_fontsize) + y_tick_rotation = float(y_tick_rotation) + popmean = float(popmean) + asterisk_fontsize = float(asterisk_fontsize) - if ax is None: - ax = plt.gca() - if labels_name is None: - labels_name = [str(i) for i in range(len(data))] - if colors is None: - colors = ["gray"] * len(data) - - means, sds, ses = [], [], [] x_positions = np.arange(len(labels_name)) + means, sds, ses = [], [], [] scatter_positions = [] - for i, d in enumerate(data): - mean, sd, se = compute_summary(d) + mean, sd, se = _compute_summary(d) means.append(mean) sds.append(sd) ses.append(se) scatter_x = RNG.normal(i, 0.1, len(d)) scatter_positions.append(scatter_x) - 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 if gradient_color: @@ -360,7 +462,7 @@ def plot_one_group_bar_figure( cmap = LinearSegmentedColormap.from_list("grad_cmap", [c1, "white", c2]) gradient = np.linspace(0, 1, 100).reshape(1, -1) # 横向渐变 # 计算渐变矩形位置：跟bar完全对齐 - extent = [x - width / 2, x + width / 2, 0, h] + extent = (float(x - width / 2), float(x + width / 2), 0, h) # 叠加渐变矩形（imshow） ax.imshow(gradient, aspect="auto", cmap=cmap, extent=extent, zorder=0) else: @@ -386,41 +488,50 @@ def plot_one_group_bar_figure( # 绘制散点 for i, d in enumerate(data): if dots_color is None: - add_scatter(ax, scatter_positions[i], d, ["gray"] * len(d), dots_size) + _add_scatter(ax, scatter_positions[i], d, ["gray"] * len(d), dots_size) else: - add_scatter(ax, scatter_positions[i], d, dots_color[i], dots_size) + _add_scatter(ax, scatter_positions[i], d, dots_color[i], dots_size) # 美化 ax.spines[["top", "right"]].set_visible(False) ax.set_title( title_name, - fontsize=kwargs.get("title_fontsize", 10), - pad=kwargs.get("title_pad", 10), + fontsize=title_fontsize, + pad=float(title_pad), ) # x轴 - ax.set_xlim(min(x_positions) - 0.5, max(x_positions) + 0.5) - ax.set_xlabel(x_label_name, fontsize=kwargs.get("x_label_fontsize", 10)) + ax.set_xlim(np.min(x_positions) - 0.5, np.max(x_positions) + 0.5) + ax.set_xlabel(x_label_name, fontsize=x_label_fontsize) ax.set_xticks(x_positions) ax.set_xticklabels( labels_name, - ha=kwargs.get("x_label_ha", "center"), + fontsize=x_tick_fontsize, + rotation=x_tick_rotation, + ha=x_label_ha, rotation_mode="anchor", - fontsize=kwargs.get("x_tick_fontsize", 10), - rotation=kwargs.get("x_tick_rotation", 0), ) # y轴 ax.tick_params( axis="y", - labelsize=kwargs.get("y_tick_fontsize", 10), - rotation=kwargs.get("y_tick_rotation", 0), + labelsize=y_tick_fontsize, + rotation=y_tick_rotation, + ) + ax.set_ylabel(y_label_name, fontsize=y_label_fontsize) + all_values = np.concatenate([np.asarray(x) for x in data]).ravel() + _set_yaxis( + ax, + all_values, + y_lim=y_lim, + ax_bottom_is_0=ax_bottom_is_0, + y_max_tick_is_1=y_max_tick_is_1, + math_text=math_text, + one_decimal_place=one_decimal_place, + percentage=percentage, ) - ax.set_ylabel(y_label_name, fontsize=kwargs.get("y_label_fontsize", 10)) - all_values = np.concatenate(data) - set_yaxis(ax, all_values, kwargs) # 添加统计显著性标记 if statistic: - statistics( + _statistics( data, test_method, p_list, @@ -431,71 +542,151 @@ def plot_one_group_bar_figure( asterisk_fontsize, asterisk_color, ) + return ax def plot_one_group_violin_figure( - data: list[NumArray], + data: Sequence[list[float] | NDArray[np.float64]], ax: Axes | None = None, + labels_name: list[str] | None = None, width: Num = 0.8, colors: list[str] | None = None, color_alpha: Num = 1, gradient_color: bool = False, colors_start: list[str] | None = None, colors_end: list[str] | None = None, - labels_name: list[str] | None = None, - x_label_name: str = "", - y_label_name: str = "", - title_name: str = "", - title_pad: Num = 10, show_dots: bool = False, dots_size: Num = 35, + title_name: str = "", + title_fontsize: Num = 12, + title_pad: Num = 10, + x_label_name: str = "", + x_label_ha: str = "center", + x_label_fontsize: Num = 10, + x_tick_fontsize: Num = 8, + x_tick_rotation: Num = 0, + y_label_name: str = "", + y_label_fontsize: Num = 10, + y_tick_fontsize: Num = 8, + y_tick_rotation: Num = 0, statistic: bool = False, - test_method: str = "ttest_ind", + test_method: list[str] = ["ttest_ind"], popmean: Num = 0, p_list: list[float] | None = None, statistical_line_color: str = "0.5", asterisk_fontsize: Num = 10, asterisk_color: str = "k", - **kwargs: Any, -) -> None: + y_lim: list[Num] | tuple[Num, Num] | 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 | None: """绘制单组小提琴图，可选散点叠加、渐变填色和统计显著性标注。 Args: - data (list[NumArray]): 包含多个数据集的列表，每个数据集是一个数值数组。 - ax (Axes | None, optional): matplotlib 的 Axes 对象，用于绘图。默认为 None，使用当前 Axes。 - width (Num, optional): 小提琴图的总宽度。默认为 0.8。 - colors (list[str] | None, optional): 每个小提琴的颜色。若为 None，使用默认灰色。 - color_alpha (Num, optional): 颜色透明度，取值范围为 0（完全透明）到 1（完全不透明）。使用 gradient_color 时该参数无效。默认为 1。 - gradient_color (bool, optional): 是否启用渐变色填充。默认为 False。 - colors_start (list[str] | None, optional): 渐变起始颜色列表，对应每组数据。 - colors_end (list[str] | None, optional): 渐变结束颜色列表，对应每组数据。 - labels_name (list[str] | None, optional): 每个数据集的标签名称。默认为 None，使用索引作为标签。 - x_label_name (str, optional): X 轴的标签。默认为空字符串。 - y_label_name (str, optional): Y 轴的标签。默认为空字符串。 - title_name (str, optional): 图表标题。默认为空字符串。 - title_pad (Num, optional): 标题与图之间的垂直距离。默认为 10。 - show_dots (bool, optional): 是否在小提琴图上叠加散点。默认为 False。 - dots_size (Num, optional): 散点大小。默认为 35。 - statistic (bool, optional): 是否进行统计检验并标注显著性。默认为 False。 - test_method (str, optional): 统计检验方法。支持 "ttest_ind"、"ttest_rel"、"mannwhitneyu" 或 "external"。默认为 "ttest_ind"。 - popmean (Num): 总体均值假设值，用于单样本t检验(ttest_1samp)。默认为 0。 - p_list (list[float] | None, optional): 外部提供的 p 值列表。默认为 None。 - statistical_line_color (str, optional): 统计显著性标记连线的颜色。默认为 "0.5"（灰色）。 - asterisk_fontsize (Num, optional): 显著性星号的字体大小。默认为 10。 - asterisk_color (str, optional): 显著性星号的颜色。默认为 "k"（黑色）。 - **kwargs (Any): 其他 matplotlib 参数，用于进一步定制图表样式。 + data (Sequence[list[float] | NDArray[np.float64]]): + 输入数据，可以是二维numpy数组或嵌套序列，每个子序列代表一个小提琴的数据点 + 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. + labels_name (list[str] | None, optional): + 小提琴图的标签名称列表. Defaults to None. + width (Num, optional): + 小提琴图的宽度. Defaults to 0.8. + colors (list[str] | None, optional): + 小提琴图的颜色列表. Defaults to None. + color_alpha (Num, optional): + 小提琴图颜色的透明度. Defaults to 1. + gradient_color (bool, optional): + 是否使用渐变颜色填充小提琴图. Defaults to False. + colors_start (list[str] | None, optional): + 渐变色的起始颜色列表. Defaults to None. + colors_end (list[str] | None, optional): + 渐变色的结束颜色列表. Defaults to None. + show_dots (bool, optional): + 是否显示散点. Defaults to False. + dots_size (Num, optional): + 散点的大小. Defaults to 35. + title_name (str, optional): + 图表标题. Defaults to "". + title_fontsize (Num, optional): + 标题字体大小. Defaults to 12. + title_pad (Num, 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 (Num, optional): + X轴标签字体大小. Defaults to 10. + x_tick_fontsize (Num, optional): + X轴刻度字体大小. Defaults to 8. + 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_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. + statistic (bool, optional): + 是否进行统计显著性分析. Defaults to False. + test_method (list[str], optional): + 统计检验方法列表. Defaults to ["ttest_ind"]. + popmean (Num, optional): + 单样本t检验的假设均值. Defaults to 0. + p_list (list[float] | None, optional): + 预计算的p值列表，用于显著性标记. Defaults to None. + statistical_line_color (str, optional): + 显著性标记线的颜色. Defaults to "0.5". + asterisk_fontsize (Num, 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_lim (list[Num] | tuple[Num, Num] | None, optional): + Y轴的范围限制. 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 Returns: - None + Axes | None: 返回matplotlib的坐标轴对象或None """ - + # 处理None值 + if not _is_valid_data(data): + raise ValueError("无效的 data") ax = ax or plt.gca() labels_name = labels_name or [str(i) for i in range(len(data))] colors = colors or ["gray"] * len(data) - - def _draw_gradient_violin( - ax, data, pos, width=width, c1="red", c2="blue", color_alpha=1 - ): + # 统一参数型 + width = float(width) + color_alpha = float(color_alpha) + dots_size = float(dots_size) + title_fontsize = float(title_fontsize) + title_pad = float(title_pad) + x_label_fontsize = float(x_label_fontsize) + x_tick_fontsize = float(x_tick_fontsize) + x_tick_rotation = float(x_tick_rotation) + y_label_fontsize = float(y_label_fontsize) + y_tick_fontsize = float(y_tick_fontsize) + y_tick_rotation = float(y_tick_rotation) + popmean = float(popmean) + asterisk_fontsize = float(asterisk_fontsize) + + def _draw_gradient_violin(ax, data, pos, width, c1, c2, color_alpha): # KDE估计 kde = stats.gaussian_kde(data) buffer = (max(data) - min(data)) / 5 @@ -548,9 +739,9 @@ def _draw_gradient_violin( median = np.median(data) # 添加 IQR box（黑色矩形） ax.add_patch( - plt.Rectangle( + Rectangle( (pos - width / 16, q1), # 左下角坐标 - width / 8, # 宽度 + float(width / 8), # 宽度 q3 - q1, # 高度 facecolor="black", alpha=0.7, @@ -563,13 +754,16 @@ def _draw_gradient_violin( ymax_lst, ymin_lst = [], [] for i, d in enumerate(data): if gradient_color: + if colors_start is None: + colors_start = ["#e38a48"] * len(data) + if colors_end is None: # 默认颜色 + colors_end = ["#4573a5"] * len(data) c1 = colors_start[i] c2 = colors_end[i] else: c1 = c2 = colors[i] - ymax, ymin = _draw_gradient_violin( - ax, d, pos=i, c1=c1, c2=c2, color_alpha=color_alpha - ) + ymax, ymin = _draw_gradient_violin(ax, d, i, width, c1, c2, color_alpha) + ymax_lst.append(ymax) ymin_lst.append(ymin) ymax = max(ymax_lst) @@ -579,33 +773,44 @@ def _draw_gradient_violin( if show_dots: scatter_positions = [RNG.normal(i, 0.1, len(d)) for i, d in enumerate(data)] for i, d in enumerate(data): - add_scatter(ax, scatter_positions[i], d, colors[i], dots_size) + _add_scatter(ax, scatter_positions[i], d, colors[i], dots_size) # 美化 - ax.set_title(title_name, fontsize=kwargs.get("title_fontsize", 10), pad=title_pad) ax.spines[["top", "right"]].set_visible(False) + ax.set_title(title_name, fontsize=title_fontsize, pad=title_pad) # x轴 ax.set_xlim(-0.5, len(data) - 0.5) - ax.set_xlabel(x_label_name, fontsize=kwargs.get("x_label_fontsize", 10)) + ax.set_xlabel(x_label_name, fontsize=x_label_fontsize) ax.set_xticks(np.arange(len(data))) ax.set_xticklabels( labels_name, - fontsize=kwargs.get("x_tick_fontsize", 10), - rotation=kwargs.get("x_tick_rotation", 0), + fontsize=x_tick_fontsize, + rotation=x_tick_rotation, + ha=x_label_ha, + rotation_mode="anchor", ) # y轴 ax.tick_params( axis="y", - labelsize=kwargs.get("y_tick_fontsize", 10), - rotation=kwargs.get("y_tick_rotation", 0), + labelsize=y_tick_fontsize, + rotation=y_tick_rotation, ) - ax.set_ylabel(y_label_name, fontsize=kwargs.get("y_label_fontsize", 10)) + ax.set_ylabel(y_label_name, fontsize=y_label_fontsize) all_values = [ymin, ymax] - set_yaxis(ax, all_values, kwargs) + _set_yaxis( + ax, + all_values, + y_lim=y_lim, + ax_bottom_is_0=ax_bottom_is_0, + y_max_tick_is_1=y_max_tick_is_1, + math_text=math_text, + one_decimal_place=one_decimal_place, + percentage=percentage, + ) # 添加统计标记（复用现有函数） if statistic: - statistics( + _statistics( data, test_method, p_list, @@ -617,152 +822,12 @@ def _draw_gradient_violin( asterisk_color, ) - return - - -def plot_one_group_violin_figure_old( - data: list[NumArray], - ax: Axes | None = None, - width: Num = 0.8, - show_extrema: bool = True, - colors: list[str] | None = None, - labels_name: list[str] | None = None, - x_label_name: str = "", - y_label_name: str = "", - title_name: str = "", - title_pad: Num = 10, - show_dots: bool = False, - dots_size: Num = 35, - statistic: bool = False, - test_method: str = "ttest_ind", - p_list: list[float] | None = None, - **kwargs: Any, -) -> None: - warnings.warn( - "plot_one_group_violin_figure_old 即将弃用，请使用 plot_one_group_violin_figure 替代。未来版本将移除本函数。", - DeprecationWarning, - stacklevel=2, - ) - """绘制单组小提琴图，包含散点和统计显著性标记。 - - Args: - data (list[NumArray]): 包含多个数据集的列表，每个数据集是一个数字数组。 - ax (Axes | None, optional): matplotlib 的 Axes 对象，用于绘图。默认为 None，使用当前的 Axes。 - width (Num, optional): 小提琴图的宽度。默认为 0.8。 - show_extrema (bool, optional): 是否显示极值线。默认为 True。 - colors (list[str] | None, optional): 每个小提琴图的颜色。默认为 None，使用灰色。 - labels_name (list[str] | None, optional): 每个数据集的标签名称。默认为 None，使用索引作为标签。 - x_label_name (str, optional): X 轴的标签。默认为空字符串。 - y_label_name (str, optional): Y 轴的标签。默认为空字符串。 - title_name (str, optional): 图表的标题。默认为空字符串。 - title_pad (Num, optional): 标题与图之间的垂直距离。默认为 10。 - show_dots (bool, optional): 是否显示散点。默认为 False。 - dots_size (Num, optional): 散点的大小。默认为 35。 - statistic (bool, optional): 是否进行统计检验并标注显著性标记。默认为 False。 - test_method (str, optional): 统计检验的方法，支持 "ttest_ind"、"ttest_rel" 和 "mannwhitneyu"。默认为 "ttest_ind"。 - p_list (list[float] | None, optional): 外部提供的 p 值列表，用于统计检验。默认为 None。 - **kwargs (Any): 其他可选参数，用于进一步定制图表样式。 - - Returns: - None - """ - - ax = ax or plt.gca() - labels_name = labels_name or [str(i) for i in range(len(data))] - colors = colors or ["gray"] * len(data) - - # 绘制小提琴图 - parts = ax.violinplot( - dataset=list(data), - positions=np.arange(len(data)), - widths=width, - showextrema=show_extrema, - ) - # 添加 box 元素 - for i, d in enumerate(data): - # 计算统计量 - q1 = np.percentile(d, 25) - q3 = np.percentile(d, 75) - median = np.median(d) - # 添加 IQR box（黑色矩形） - ax.add_patch( - plt.Rectangle( - (i - width / 16, q1), # 左下角坐标 - width / 8, # 宽度 - q3 - q1, # 高度 - facecolor="black", - alpha=0.7, - ) - ) - # 添加白色中位数点 - ax.plot(i, median, "o", color="white", markersize=3, zorder=3) - - # 设置小提琴颜色（修改默认样式） - for pc, color in zip(parts["bodies"], colors): - pc.set_facecolor(color) - pc.set_edgecolor("black") - pc.set_alpha(1) - - # 修改内部线条颜色 - if show_extrema: - parts["cmins"].set_color("black") # 最小值线 - parts["cmaxes"].set_color("black") # 最大值线 - parts["cbars"].set_color("black") # 中线（median） - - # 绘制散点（复用现有函数） - if show_dots: - scatter_positions = [RNG.normal(i, 0.1, len(d)) for i, d in enumerate(data)] - for i, d in enumerate(data): - add_scatter(ax, scatter_positions[i], d, colors[i], dots_size) - - # 美化坐标轴（复用现有函数） - ax.spines[["top", "right"]].set_visible(False) - ax.set_title(title_name, fontsize=kwargs.get("title_fontsize", 10), pad=title_pad) - ax.set_xlabel(x_label_name, fontsize=kwargs.get("x_label_fontsize", 10)) - ax.set_ylabel(y_label_name, fontsize=kwargs.get("y_label_fontsize", 10)) - ax.set_xticks(np.arange(len(data))) - ax.set_xticklabels( - labels_name, - fontsize=kwargs.get("x_tick_fontsize", 10), - rotation=kwargs.get("x_tick_rotation", 0), - ) - - # 设置Y轴（复用现有函数） - all_values = np.concatenate(data) - set_yaxis(ax, all_values, kwargs) - - # 添加统计标记（复用现有函数） - if statistic: - comparisons = [] - idx = 0 - for i in range(len(data)): - for j in range(i + 1, len(data)): - if test_method == "external": - p = p_list[idx] if p_list else 1.0 - idx += 1 - else: - _, p = perform_stat_test(data[i], data[j], test_method) - if p <= 0.05: - comparisons.append((i, j, p)) - - if comparisons: - y_max = ax.get_ylim()[1] - interval = (y_max - np.max(all_values)) / (len(comparisons) + 1) - annotate_significance( - ax, - comparisons, - np.max(all_values), - interval, - line_color=kwargs.get("line_color", "0.5"), - star_offset=interval / 5, - fontsize=kwargs.get("asterisk_fontsize", 10), - color=kwargs.get("asterisk_color", "k"), - ) + return ax def plot_multi_group_bar_figure( - data: list[list[NumArray]], - ax: plt.Axes | None = None, + data: DataType, + ax: Axes | None = None, group_labels: list[str] | None = None, bar_labels: list[str] | None = None, bar_width: Num = 0.2, @@ -771,45 +836,146 @@ def plot_multi_group_bar_figure( 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, statistic: bool = False, test_method: str = "external", p_list: list[list[Num]] | None = None, - legend: bool = True, - legend_position: tuple[Num, Num] = (1.2, 1), - **kwargs: Any, -) -> None: - """ - 绘制多组柱状图，包含散点、误差条、显著性标注和图例等。 + line_color="0.5", + asterisk_fontsize=10, + asterisk_color="k", + y_lim: list[Num] | tuple[Num, Num] | 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 (list[list[NumArray]]): 多组数据，每组是一个包含若干数值数组的列表。 - ax (plt.Axes | None, optional): matplotlib 的 Axes 对象。默认为 None，自动使用当前的 Axes。 - group_labels (list[str] | None, optional): 每组的标签名。默认为 None，使用 "Group i"。 - bar_labels (list[str] | None, optional): 每组内每个柱子的标签。默认为 None，使用 "Bar i"。 - bar_width (Num, optional): 单个柱子的宽度。默认为 0.2。 - bar_gap (Num, optional): 每组柱子之间的间距。默认为 0.1。 - bar_color (list[str] | None, optional): 每个柱子的颜色列表。默认为 None，使用灰色。 - errorbar_type (str, optional): 误差条类型，支持 "sd"（标准差）或 "se"（标准误）。默认为 "sd"。 - dots_color (str, optional): 散点的颜色。默认为 "gray"。 - dots_size (int, optional): 散点的大小。默认为 35。 - title_name (str, optional): 图标题。默认为空字符串。 - x_label_name (str, optional): X 轴标签。默认为空字符串。 - y_label_name (str, optional): Y 轴标签。默认为空字符串。 - statistic (bool, optional): 是否执行统计检验并显示显著性标注。默认为 False。 - test_method (str, optional): 统计检验方法。支持 "external"（外部 p 值）或其他方法。默认为 "external"。 - p_list (list[list[Num]] | None, optional): 外部提供的显著性 p 值列表。默认为 None。 - legend (bool, optional): 是否显示图例。默认为 True。 - legend_position (tuple[Num, Num], optional): 图例在坐标系中的位置。默认为 (1.2, 1)。 - **kwargs (Any): 其他可选参数，用于进一步定制图表样式。 + 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. + 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_lim (list[Num] | tuple[Num, Num] | None, optional): + Y轴的范围限制. 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: - None + Axes: 返回matplotlib的坐标轴对象 """ + def _is_valid_data_for_multi_group(data): + if not data: + raise ValueError("data 不能为空") + NumberTypes = (int, float, np.integer, np.floating) + # 1) 3D ndarray + if isinstance(data, np.ndarray): + return data.ndim == 3 + # 必须是外层序列 + if not isinstance(data, (list, tuple)): + return False + # 2) 平铺的 list，其中每个元素都是 2D ndarray + if all(isinstance(x, np.ndarray) and x.ndim == 2 for x in data): + return True + # 3) 外层是 groups：每个 group 是序列，group 内的 bar 要么 1D ndarray 要么数字序列 + for group in data: + if not isinstance(group, (list, tuple)): + return False + for bar in group: + if isinstance(bar, np.ndarray): + if bar.ndim != 1: + return False + elif isinstance(bar, (list, tuple)): + if not all(isinstance(v, NumberTypes) for v in bar): + return False + else: + return False + return True + if not _is_valid_data_for_multi_group(data): + raise ValueError("无效的 data") - # 动态参数 ax = ax or plt.gca() group_labels = group_labels or [f"Group {i + 1}" for i in range(len(data))] n_groups = len(data) @@ -834,13 +1000,15 @@ def plot_multi_group_bar_figure( 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)] + 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" @@ -859,7 +1027,7 @@ def plot_multi_group_bar_figure( 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) + _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) @@ -867,27 +1035,36 @@ def plot_multi_group_bar_figure( ax.spines[["top", "right"]].set_visible(False) ax.set_title( title_name, - fontsize=kwargs.get("title_fontsize", 15), - pad=kwargs.get("title_pad", 10), + fontsize=title_fontsize, + pad=title_pad, ) # x轴 - ax.set_xlabel(x_label_name, fontsize=kwargs.get("x_label_fontsize", 10)) + ax.set_xlabel(x_label_name, fontsize=x_label_fontsize) ax.set_xticks(np.arange(n_groups)) ax.set_xticklabels( group_labels, - ha=kwargs.get("x_label_ha", "center"), + ha=x_label_ha, rotation_mode="anchor", - fontsize=kwargs.get("x_tick_fontsize", 10), - rotation=kwargs.get("x_tick_rotation", 0), + fontsize=x_tick_fontsize, + rotation=x_tick_rotation, ) # y轴 ax.tick_params( axis="y", - labelsize=kwargs.get("y_tick_fontsize", 10), - rotation=kwargs.get("y_tick_rotation", 0), + 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, ) - ax.set_ylabel(y_label_name, fontsize=kwargs.get("y_label_fontsize", 10)) - set_yaxis(ax, all_values, kwargs) # 添加统计显著性标记 if statistic: @@ -898,27 +1075,25 @@ def plot_multi_group_bar_figure( 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] interval = (y_max - np.max(all_values)) / (len(comparisons) + 1) - annotate_significance( + _annotate_significance( ax, comparisons, np.max(all_values), interval, - line_color=kwargs.get("line_color", "0.5"), + line_color=line_color, star_offset=interval / 5, - fontsize=kwargs.get("asterisk_fontsize", 10), - color=kwargs.get("asterisk_color", "k"), + fontsize=asterisk_fontsize, + color=asterisk_color, ) - -def main(): - pass - - -if __name__ == "__main__": - main() + return ax From 54881e3615414c887351a6d2d0e128baee968660 Mon Sep 17 00:00:00 2001 From: Ricardo_T8 Date: Mon, 11 Aug 2025 16:16:08 +0800 Subject: [PATCH 3/6] style(bar): Format code and remove trailing whitespace MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- 风格(bar): 格式化代码，移除末尾空格 --- src/plotfig/bar.py | 224 +++++++++++++++++++++++---------------------- 1 file changed, 113 insertions(+), 111 deletions(-) diff --git a/src/plotfig/bar.py b/src/plotfig/bar.py index 559af37..005999b 100644 --- a/src/plotfig/bar.py +++ b/src/plotfig/bar.py @@ -329,81 +329,81 @@ def plot_one_group_bar_figure( """绘制单组柱状图，包含散点、误差条和统计显著性标记。 Args: - data (np.ndarray | Sequence[Sequence[Num] | np.ndarray]): + data (np.ndarray | Sequence[Sequence[Num] | np.ndarray]): 输入数据，可以是二维numpy数组或嵌套序列，每个子序列代表一个柱状图的数据点 - ax (Axes | None, optional): + 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. - labels_name (list[str] | None, optional): + labels_name (list[str] | None, optional): 柱状图的标签名称列表. Defaults to None. - colors (list[str] | None, optional): + colors (list[str] | None, optional): 柱状图的颜色列表. Defaults to None. - edgecolor (str | None, optional): + edgecolor (str | None, optional): 柱状图边缘颜色. Defaults to None. - gradient_color (bool, optional): + gradient_color (bool, optional): 是否使用渐变颜色填充柱状图. Defaults to False. - colors_start (list[str] | None, optional): + colors_start (list[str] | None, optional): 渐变色的起始颜色列表. Defaults to None. - colors_end (list[str] | None, optional): + colors_end (list[str] | None, optional): 渐变色的结束颜色列表. Defaults to None. - dots_color (list[list[str]] | None, optional): + dots_color (list[list[str]] | None, optional): 散点的颜色列表. Defaults to None. - y_lim (list[Num] | tuple[Num, Num] | None, optional): + y_lim (list[Num] | tuple[Num, Num] | None, optional): Y轴的范围限制. Defaults to None. - width (Num, optional): + width (Num, optional): 柱状图的宽度. Defaults to 0.5. - color_alpha (Num, optional): + color_alpha (Num, optional): 柱状图颜色的透明度. Defaults to 1. - dots_size (Num, optional): + dots_size (Num, optional): 散点的大小. Defaults to 35. - errorbar_type (str, optional): + errorbar_type (str, optional): 误差条类型，可选 "sd"(标准差) 或 "se"(标准误). Defaults to "sd". - title_name (str, optional): + title_name (str, optional): 图表标题. Defaults to "". - title_fontsize (Num, optional): + title_fontsize (Num, optional): 标题字体大小. Defaults to 12. - title_pad (Num, optional): + title_pad (Num, optional): 标题与图表的间距. Defaults to 10. - x_label_name (str, optional): + x_label_name (str, optional): X轴标签名称. Defaults to "". - x_label_ha (str, optional): + 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_label_fontsize (Num, optional): X轴标签字体大小. Defaults to 10. - x_tick_fontsize (Num, optional): + x_tick_fontsize (Num, optional): X轴刻度字体大小. Defaults to 8. - x_tick_rotation (Num, optional): + x_tick_rotation (Num, optional): X轴刻度旋转角度. Defaults to 0. - y_label_name (str, optional): + y_label_name (str, optional): Y轴标签名称. Defaults to "". - y_label_fontsize (Num, optional): + y_label_fontsize (Num, optional): Y轴标签字体大小. Defaults to 10. - y_tick_fontsize (Num, optional): + y_tick_fontsize (Num, optional): Y轴刻度字体大小. Defaults to 8. - y_tick_rotation (Num, optional): + y_tick_rotation (Num, optional): Y轴刻度旋转角度. Defaults to 0. - statistic (bool, optional): + statistic (bool, optional): 是否进行统计显著性分析. Defaults to False. - test_method (list[str], optional): + test_method (list[str], optional): 统计检验方法列表. Defaults to ["ttest_ind"]. - p_list (list[float] | None, optional): + p_list (list[float] | None, optional): 预计算的p值列表，用于显著性标记. Defaults to None. - popmean (Num, optional): + popmean (Num, optional): 单样本t检验的假设均值. Defaults to 0. - statistical_line_color (str, optional): + statistical_line_color (str, optional): 显著性标记线的颜色. Defaults to "0.5". - asterisk_fontsize (Num, optional): + asterisk_fontsize (Num, optional): 显著性星号的字体大小. Defaults to 10. - asterisk_color (str, optional): + asterisk_color (str, optional): 显著性星号的颜色. Defaults to "k". - ax_bottom_is_0 (bool, optional): + 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_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): + 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): + one_decimal_place (bool, optional): Y轴刻度是否只保留一位小数. Defaults to False. - percentage (bool, optional): + percentage (bool, optional): 是否将Y轴显示为百分比格式. Defaults to False. Raises: @@ -586,77 +586,77 @@ def plot_one_group_violin_figure( """绘制单组小提琴图，可选散点叠加、渐变填色和统计显著性标注。 Args: - data (Sequence[list[float] | NDArray[np.float64]]): + data (Sequence[list[float] | NDArray[np.float64]]): 输入数据，可以是二维numpy数组或嵌套序列，每个子序列代表一个小提琴的数据点 - ax (Axes | None, optional): + 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. - labels_name (list[str] | None, optional): + labels_name (list[str] | None, optional): 小提琴图的标签名称列表. Defaults to None. - width (Num, optional): + width (Num, optional): 小提琴图的宽度. Defaults to 0.8. - colors (list[str] | None, optional): + colors (list[str] | None, optional): 小提琴图的颜色列表. Defaults to None. - color_alpha (Num, optional): + color_alpha (Num, optional): 小提琴图颜色的透明度. Defaults to 1. - gradient_color (bool, optional): + gradient_color (bool, optional): 是否使用渐变颜色填充小提琴图. Defaults to False. - colors_start (list[str] | None, optional): + colors_start (list[str] | None, optional): 渐变色的起始颜色列表. Defaults to None. - colors_end (list[str] | None, optional): + colors_end (list[str] | None, optional): 渐变色的结束颜色列表. Defaults to None. - show_dots (bool, optional): + show_dots (bool, optional): 是否显示散点. Defaults to False. - dots_size (Num, optional): + dots_size (Num, optional): 散点的大小. Defaults to 35. - title_name (str, optional): + title_name (str, optional): 图表标题. Defaults to "". - title_fontsize (Num, optional): + title_fontsize (Num, optional): 标题字体大小. Defaults to 12. - title_pad (Num, optional): + title_pad (Num, optional): 标题与图表的间距. Defaults to 10. - x_label_name (str, optional): + x_label_name (str, optional): X轴标签名称. Defaults to "". - x_label_ha (str, optional): + 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_label_fontsize (Num, optional): X轴标签字体大小. Defaults to 10. - x_tick_fontsize (Num, optional): + x_tick_fontsize (Num, optional): X轴刻度字体大小. Defaults to 8. - x_tick_rotation (Num, optional): + x_tick_rotation (Num, optional): X轴刻度旋转角度. Defaults to 0. - y_label_name (str, optional): + y_label_name (str, optional): Y轴标签名称. Defaults to "". - y_label_fontsize (Num, optional): + y_label_fontsize (Num, optional): Y轴标签字体大小. Defaults to 10. - y_tick_fontsize (Num, optional): + y_tick_fontsize (Num, optional): Y轴刻度字体大小. Defaults to 8. - y_tick_rotation (Num, optional): + y_tick_rotation (Num, optional): Y轴刻度旋转角度. Defaults to 0. - statistic (bool, optional): + statistic (bool, optional): 是否进行统计显著性分析. Defaults to False. - test_method (list[str], optional): + test_method (list[str], optional): 统计检验方法列表. Defaults to ["ttest_ind"]. - popmean (Num, optional): + popmean (Num, optional): 单样本t检验的假设均值. Defaults to 0. - p_list (list[float] | None, optional): + p_list (list[float] | None, optional): 预计算的p值列表，用于显著性标记. Defaults to None. - statistical_line_color (str, optional): + statistical_line_color (str, optional): 显著性标记线的颜色. Defaults to "0.5". - asterisk_fontsize (Num, optional): + asterisk_fontsize (Num, optional): 显著性星号的字体大小. Defaults to 10. - asterisk_color (str, optional): + asterisk_color (str, optional): 显著性星号的颜色. Defaults to "k". - y_lim (list[Num] | tuple[Num, Num] | None, optional): + y_lim (list[Num] | tuple[Num, Num] | None, optional): Y轴的范围限制. Defaults to None. - ax_bottom_is_0 (bool, optional): + 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_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): + 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): + one_decimal_place (bool, optional): Y轴刻度是否只保留一位小数. Defaults to False. - percentage (bool, optional): + percentage (bool, optional): 是否将Y轴显示为百分比格式. Defaults to False. Raises: @@ -866,77 +866,77 @@ def plot_multi_group_bar_figure( """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): + 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): + 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): + group_labels (list[str] | None, optional): 组标签名称列表. Defaults to None. - bar_labels (list[str] | None, optional): + bar_labels (list[str] | None, optional): 柱状图标签名称列表. Defaults to None. - bar_width (Num, optional): + bar_width (Num, optional): 柱状图的宽度. Defaults to 0.2. - bar_gap (Num, optional): + bar_gap (Num, optional): 柱状图之间的间隔. Defaults to 0.1. - bar_color (list[str] | None, optional): + bar_color (list[str] | None, optional): 柱状图的颜色列表. Defaults to None. - errorbar_type (str, optional): + 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): + dots_color (str, optional): 散点的颜色. Defaults to "gray". - dots_size (int, optional): + dots_size (int, optional): 散点的大小. Defaults to 35. - legend (bool, optional): + legend (bool, optional): 是否显示图例. Defaults to True. - legend_position (tuple[Num, Num], optional): + legend_position (tuple[Num, Num], optional): 图例位置坐标. Defaults to (1.2, 1). - title_name (str, optional): + title_name (str, optional): 图表标题. Defaults to "". - title_fontsize (int, optional): + title_fontsize (int, optional): 标题字体大小. Defaults to 12. - title_pad (int, optional): + title_pad (int, optional): 标题与图表的间距. Defaults to 10. - x_label_name (str, optional): + x_label_name (str, optional): X轴标签名称. Defaults to "". - x_label_ha (str, optional): + 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_label_fontsize (int, optional): X轴标签字体大小. Defaults to 10. - x_tick_fontsize (int, optional): + x_tick_fontsize (int, optional): X轴刻度字体大小. Defaults to 8. - x_tick_rotation (int, optional): + x_tick_rotation (int, optional): X轴刻度旋转角度. Defaults to 0. - y_label_name (str, optional): + y_label_name (str, optional): Y轴标签名称. Defaults to "". - y_label_fontsize (int, optional): + y_label_fontsize (int, optional): Y轴标签字体大小. Defaults to 10. - y_tick_fontsize (int, optional): + y_tick_fontsize (int, optional): Y轴刻度字体大小. Defaults to 8. - y_tick_rotation (int, optional): + y_tick_rotation (int, optional): Y轴刻度旋转角度. Defaults to 0. - statistic (bool, optional): + statistic (bool, optional): 是否进行统计显著性分析. Defaults to False. - test_method (str, optional): + 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): + 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): + line_color (str, optional): 显著性标记线的颜色. Defaults to "0.5". - asterisk_fontsize (int, optional): + asterisk_fontsize (int, optional): 显著性星号的字体大小. Defaults to 10. - asterisk_color (str, optional): + asterisk_color (str, optional): 显著性星号的颜色. Defaults to "k". - y_lim (list[Num] | tuple[Num, Num] | None, optional): + y_lim (list[Num] | tuple[Num, Num] | None, optional): Y轴的范围限制. Defaults to None. - ax_bottom_is_0 (bool, optional): + 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_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): + 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): + one_decimal_place (bool, optional): Y轴刻度是否只保留一位小数. Defaults to False. - percentage (bool, optional): + percentage (bool, optional): 是否将Y轴显示为百分比格式. Defaults to False. Raises: @@ -946,8 +946,9 @@ def plot_multi_group_bar_figure( Returns: Axes: 返回matplotlib的坐标轴对象 """ + def _is_valid_data_for_multi_group(data): - if not data: + if not data.any(): raise ValueError("data 不能为空") NumberTypes = (int, float, np.integer, np.floating) # 1) 3D ndarray @@ -973,6 +974,7 @@ def _is_valid_data_for_multi_group(data): else: return False return True + if not _is_valid_data_for_multi_group(data): raise ValueError("无效的 data") From f20f4620fc25e22dc037f76be664b789aeb33118 Mon Sep 17 00:00:00 2001 From: Ricardo_T8 Date: Mon, 11 Aug 2025 22:55:17 +0800 Subject: [PATCH 4/6] test(connec): added some test scripts MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- 测试(connec): 增加一些测试用的脚本 --- .gitignore | 3 +- tests/test.ipynb | 174 +++++++++++++++++++++++++++++++++++++++++++++++ tests/test.py | 18 +++++ 3 files changed, 193 insertions(+), 2 deletions(-) create mode 100644 tests/test.ipynb create mode 100644 tests/test.py diff --git a/.gitignore b/.gitignore index e67cc6b..7e91538 100644 --- a/.gitignore +++ b/.gitignore @@ -18,8 +18,7 @@ notebooks/ # docs docs/changelog.md -# tests -tests/ +tests/figures/* # utils utils/ diff --git a/tests/test.ipynb b/tests/test.ipynb new file mode 100644 index 0000000..7234fcf --- /dev/null +++ b/tests/test.ipynb @@ -0,0 +1,174 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "84d45348", + "metadata": {}, + "source": [ + "# 测试brain_connection" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "ce1c6a22", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "\n", + "\n", + "def generate_sparse_connectome_v1(size: int = 31, sparsity: float = 0.9, seed=None):\n", + " \"\"\"\n", + " 生成稀疏对称矩阵\n", + "\n", + " Parameters:\n", + " size: 矩阵大小\n", + " sparsity: 稀疏度，0表示没有0元素，1表示全部为0元素\n", + " \"\"\"\n", + " if seed is not None:\n", + " np.random.seed(seed)\n", + " # 生成随机矩阵\n", + " connectome = np.random.rand(size, size)\n", + "\n", + " # 应用稀疏度 - 随机将一些元素设为0\n", + " mask = np.random.rand(size, size) < sparsity\n", + " connectome[mask] = 0\n", + "\n", + " # 确保矩阵对称\n", + " connectome = np.triu(connectome) + np.triu(connectome, 1).T\n", + "\n", + " # 确保对角线为0\n", + " np.fill_diagonal(connectome, 0)\n", + "\n", + " return connectome\n", + "\n", + "\n", + "def generate_sparse_connectome_v2(\n", + " size: int = 31, density: float = 0.1, seed: int | None = None\n", + ") -> np.ndarray:\n", + " \"\"\"\n", + " 生成一个对称的方阵，满足：\n", + " - 对角线为0\n", + " - 有正负值\n", + " - 稀疏程度由 density 控制（非零元素比例）\n", + "\n", + " 参数:\n", + " size: 方阵大小（行列数）\n", + " density: 非零元素比例，0~1，越小越稀疏\n", + " seed: 随机种子，方便复现\n", + "\n", + " 返回:\n", + " np.ndarray 方阵\n", + " \"\"\"\n", + " if seed is not None:\n", + " np.random.seed(seed)\n", + "\n", + " # 先生成上三角（不含对角线）随机稀疏矩阵\n", + " upper = np.random.choice(\n", + " [0, 1], size=(size, size), p=[1 - density, density]\n", + " ) * np.random.uniform(-1, 1, size=(size, size))\n", + "\n", + " # 让对角线为0\n", + " np.fill_diagonal(upper, 0)\n", + "\n", + " # 只保留上三角部分（含对角线0）\n", + " upper = np.triu(upper, k=1)\n", + "\n", + " # 下三角镜像上三角，保证对称\n", + " mat = upper + upper.T\n", + "\n", + " return mat\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "88082368", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[32m2025-08-11 22:50:10.596\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mplotfig.brain_connection\u001b[0m:\u001b[36mplot_brain_connection_figure\u001b[0m:\u001b[36m267\u001b[0m - \u001b[1m未指定保存路径，默认保存在当前文件夹下的2025-08-11_22-50-10.html中。\u001b[0m\n" + ] + } + ], + "source": [ + "import numpy as np\n", + "from plotfig import plot_brain_connection_figure\n", + "\n", + "lh_surfgii_file = r\"e:\\6_Self\\plot_self_brain_connectivity\\103818.L.midthickness.32k_fs_LR.surf.gii\"\n", + "rh_surfgii_file = r\"e:\\6_Self\\plot_self_brain_connectivity\\103818.R.midthickness.32k_fs_LR.surf.gii\"\n", + "niigz_file = r\"e:\\6_Self\\plot_self_brain_connectivity\\human_Self_processing_network.nii.gz\"\n", + "output_file = r\"E:\\git_repositories\\plotfig\\tests\\test.html\"\n", + "\n", + "connectome = generate_sparse_connectome_v1(seed=42)\n", + "# connectome = generate_sparse_connectome_v2(seed=42)\n", + "# print(np.max(connectome), np.min(connectome))\n", + "\n", + "fig = plot_brain_connection_figure(\n", + " connectome,\n", + " lh_surfgii_file=lh_surfgii_file,\n", + " rh_surfgii_file=rh_surfgii_file,\n", + " niigz_file=niigz_file,\n", + " # \n", + " line_color=\"green\",\n", + " scale_method=\"width_color\"\n", + "\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "36628f32", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 36/36 [02:34<00:00, 4.30s/it]\n", + "\u001b[32m2025-08-11 22:52:55.854\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mplotfig.brain_connection\u001b[0m:\u001b[36msave_brain_connection_frames\u001b[0m:\u001b[36m320\u001b[0m - \u001b[1m保存了 36 张图片在 E:/git_repositories/plotfig/tests/brain_connec_frames\u001b[0m\n" + ] + } + ], + "source": [ + "from plotfig import save_brain_connection_frames\n", + "\n", + "save_brain_connection_frames(fig, \"E:/git_repositories/plotfig/tests/figures/brain_connec_frames\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8b7c94cf", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "plotfig (3.11.11)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.11" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/tests/test.py b/tests/test.py new file mode 100644 index 0000000..363a4ba --- /dev/null +++ b/tests/test.py @@ -0,0 +1,18 @@ +import random +from plotfig import * + +input_file = r"e:\git_repositories\plotfig\src\plotfig\data\neurodata\atlases\macaque_D99\label.txt" +output_file = r"e:\git_repositories\plotfig\src\plotfig\data\neurodata\atlases\macaque_D99\label1.txt" + +with open(input_file, "r", encoding="utf-8") as fin, \ + open(output_file, "w", encoding="utf-8") as fout: + + for idx, line in enumerate(fin, start=1): + region_name = line.rstrip("\n") + fout.write(region_name + "\n") + + r = random.randint(0, 255) + g = random.randint(0, 255) + b = random.randint(0, 255) + # 写格式：序号, r, g, b, 255 + fout.write(f"{idx} {r} {g} {b} 255\n") From 4ed3e3a758bb9090425d6f349da2b2f761049fcf Mon Sep 17 00:00:00 2001 From: Ricardo_T8 Date: Mon, 11 Aug 2025 22:57:59 +0800 Subject: [PATCH 5/6] build(deps): update dependencies to ensure correct saving of brain connection frames MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit To guarantee proper operation of `save_brain_connection_frames`: - update plotly to >=6.1.1 - add kaleido >=1.0.0 - add loguru >=0.7.3 --- 构建(deps): 更新依赖使正确保存brain connec frames 为保证save_brain_connection_frames正确运行: - 更新plotly>=6.1.1 - 安装kaleido>=1.0.0 - 安装loguru>=0.7.3 --- README.md | 14 ++-- docs/installation.md | 4 +- pyproject.toml | 4 +- uv.lock | 185 +++++++++++++++++++++++++++++++++++++++++-- 4 files changed, 194 insertions(+), 13 deletions(-) diff --git a/README.md b/README.md index 24b54fd..24392e1 100644 --- a/README.md +++ b/README.md @@ -51,13 +51,15 @@ pip install . `plotfig` 依赖若干核心库，这些依赖将在安装过程中自动处理： -- [matplotlib](https://matplotlib.org/) ≥ 3.10. -- [mne-connectivity](https://mne.tools/mne-connectivity/stable/index.html) ≥ 0.7. -- [nibabel](https://nipy.org/nibabel/) ≥ 5.3. +- [matplotlib](https://matplotlib.org/) ≥ 3.10.1 +- [mne-connectivity](https://mne.tools/mne-connectivity/stable/index.html) ≥ 0.7.0 +- [nibabel](https://nipy.org/nibabel/) ≥ 5.3.2 - [numpy](https://numpy.org/) ≥ 2.2.4 -- [pandas](https://pandas.pydata.org/) ≥ 2.2. -- [plotly](https://plotly.com/) ≥ 6.0.1 -- [scipy](https://scipy.org/) ≥ 1.15. +- [pandas](https://pandas.pydata.org/) ≥ 2.2.3 +- [plotly](https://plotly.com/) ≥ 6.1.1 +- [kaleido](https://github.com/plotly/Kaleido) ≥ 1.0.0 +- [scipy](https://scipy.org/) ≥ 1.15.2 +- [loguru](https://loguru.readthedocs.io/en/stable/) ≥ 0.7.3 - [surfplot](https://github.com/danjgale/surfplot) 需使用其 GitHub 仓库中的最新版，而非 PyPI 上的版本，因后者尚未包含所需功能。 > ⚠️ **指定 `surfplot` 版本** diff --git a/docs/installation.md b/docs/installation.md index 7dfc446..080ff22 100644 --- a/docs/installation.md +++ b/docs/installation.md @@ -27,8 +27,10 @@ pip install . - [nibabel](https://nipy.org/nibabel/) ≥ 5.3.2 - [numpy](https://numpy.org/) ≥ 2.2.4 - [pandas](https://pandas.pydata.org/) ≥ 2.2.3 -- [plotly](https://plotly.com/) ≥ 6.0.1 +- [plotly](https://plotly.com/) ≥ 6.1.1 +- [kaleido](https://github.com/plotly/Kaleido) ≥ 1.0.0 - [scipy](https://scipy.org/) ≥ 1.15.2 +- [loguru](https://loguru.readthedocs.io/en/stable/) ≥ 0.7.3 - [surfplot](https://github.com/danjgale/surfplot) 需使用其 GitHub 仓库中的最新版，而非 PyPI 上的版本，因后者尚未包含所需功能。 !!! warning "指定 `surfplot` 版本" diff --git a/pyproject.toml b/pyproject.toml index 92b2833..e9bf621 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -9,12 +9,14 @@ authors = [ { name = "Ricardo Ryn", email = "ricardoRyn1317@gmail.com" } ] dependencies = [ + "kaleido>=1.0.0", + "loguru>=0.7.3", "matplotlib>=3.10.1", "mne-connectivity>=0.7.0", "nibabel>=5.3.2", "numpy>=2.2.4", "pandas>=2.2.3", - "plotly>=6.0.1", + "plotly>=6.1.1", "scipy>=1.15.2", "surfplot", ] diff --git a/uv.lock b/uv.lock index 254a5c6..f8f80cb 100644 --- a/uv.lock +++ b/uv.lock @@ -196,6 +196,19 @@ wheels = [ { url = "https://files.pythonhosted.org/packages/0e/f6/65ecc6878a89bb1c23a086ea335ad4bf21a588990c3f535a227b9eea9108/charset_normalizer-3.4.1-py3-none-any.whl", 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This fix ensures the color is displayed as intended. --- 修复(connec): 修复 color scale 下 line_color 显示异常问题 当 `scale_method` 设置为 `color` 或 `width_color` 且同时指定了 `line_color` 时， 颜色未能正确应用。该问题已修复，确保颜色显示符合预期。 --- src/plotfig/brain_connection.py | 237 +++++++++++++++++++++----------- 1 file changed, 156 insertions(+), 81 deletions(-) diff --git a/src/plotfig/brain_connection.py b/src/plotfig/brain_connection.py index bd3f39f..90c69a5 100644 --- a/src/plotfig/brain_connection.py +++ b/src/plotfig/brain_connection.py @@ -1,16 +1,18 @@ -import os -import os.path as op import datetime +from pathlib import Path +from typing import Literal +from collections.abc import Sequence + import numpy as np +import numpy.typing as npt import nibabel as nib -from scipy.ndimage import center_of_mass import plotly.graph_objects as go import plotly.io as pio -import matplotlib.colors as mcolors -import matplotlib.cm as cm +from matplotlib.colors import LinearSegmentedColormap, to_hex +from scipy.ndimage import center_of_mass from tqdm import tqdm -from pathlib import Path -import numpy.typing as npt + +from loguru import logger Num = int | float @@ -19,15 +21,30 @@ "save_brain_connection_frames", ] -def _load_surface(file: str | Path): - '''加载 .surf.gii 文件，提取顶点和面''' - gii = nib.load(file) - vertices = gii.darrays[0].data - faces = gii.darrays[1].data - return vertices, faces + +def _validate_connectome(connectome): + """检测数据是否为有效的对称且对角线为0的连接矩阵""" + # 1. 判断是否二维方阵 + if connectome.ndim != 2 or connectome.shape[0] != connectome.shape[1]: + raise ValueError("connectome 必须是二维方阵") + # 2. 判断是否对称矩阵 + if not np.allclose(connectome, connectome.T, atol=1e-8): + raise ValueError("connectome 必须是对称矩阵") + # 3. 判断对角线是否全为0 + if not np.allclose(np.diag(connectome), 0, atol=1e-8): + raise ValueError("connectome 对角线必须全部为0") + # 4. 判断是否全0矩阵，警告但不抛异常 + if np.allclose(connectome, 0, atol=1e-8): + logger.warning("connectome 矩阵所有元素均为0，可能没有有效连接数据") + + +def _load_surface(file): + """加载 .surf.gii 文件，提取顶点和面""" + return nib.load(file).darrays[0].data, nib.load(file).darrays[1].data + def _create_mesh(vertices, faces, name): - ''' 创建 plotly 的 Mesh3d 图层''' + """创建 plotly 的 Mesh3d 图层""" return go.Mesh3d( x=vertices[:, 0], y=vertices[:, 1], @@ -42,29 +59,33 @@ def _create_mesh(vertices, faces, name): name=name, ) + def _get_node_indices(connectome, show_all_nodes): - ''' 判断哪些节点需要显示''' + """判断是否显示无任何连接的节点""" if not show_all_nodes: row_is_zero = np.any(connectome != 0, axis=1) return np.where(row_is_zero)[0] else: return np.arange(connectome.shape[0]) -def _get_centroids_real(niigz_file: str | Path): - '''读取 NIfTI 图集并计算ROI质心''' - img = nib.load(niigz_file) - atlas_data = img.get_fdata() - affine = img.affine +def _get_centroids_real(niigz_file): + """读取 NIfTI 图集并计算ROI质心""" + atlas_data = nib.load(niigz_file).get_fdata() + affine = nib.load(niigz_file).affine roi_labels = np.unique(atlas_data) roi_labels = roi_labels[roi_labels != 0] - - centroids_voxel = [center_of_mass((atlas_data == label).astype(int)) for label in roi_labels] + centroids_voxel = [ + center_of_mass((atlas_data == label).astype(int)) for label in roi_labels + ] centroids_real = [np.dot(affine, [*coord, 1])[:3] for coord in centroids_voxel] return np.array(centroids_real) -def _add_nodes_to_fig(fig, centroids_real, node_indices, nodes_name, nodes_size, nodes_color): - '''将节点（球）添加到图中''' + +def _add_nodes_to_fig( + fig, centroids_real, node_indices, nodes_name, nodes_size, nodes_color +): + """将节点（球）添加到图中""" for i in node_indices: fig.add_trace( go.Scatter3d( @@ -74,7 +95,7 @@ def _add_nodes_to_fig(fig, centroids_real, node_indices, nodes_name, nodes_size, mode="markers+text", marker={ "size": nodes_size[i], - "color": nodes_color, + "color": nodes_color[i], "colorscale": "Rainbow", "opacity": 0.8, "line": {"width": 2, "color": "black"}, @@ -85,8 +106,25 @@ def _add_nodes_to_fig(fig, centroids_real, node_indices, nodes_name, nodes_size, ) ) -def _add_edges_to_fig(fig, connectome, centroids_real, nodes_name, scale_method, line_width, line_color="#ff0000"): - '''将连接线绘制到图中''' + +def _add_edges_to_fig( + fig, + connectome, + centroids_real, + nodes_name, + scale_method, + line_width, + line_color, +): + """将连接线绘制到图中""" + + def _get_gradient_color(value, color): + """获取渐变色""" + assert 0 <= value <= 1, "value 必须在0和1之间" + cmap = LinearSegmentedColormap.from_list("grad_cmap", ["white", color]) + rgba = cmap(value) + return to_hex(rgba[:3]) + nodes_num = connectome.shape[0] if np.all(connectome == 0): return @@ -100,24 +138,28 @@ def _add_edges_to_fig(fig, connectome, centroids_real, nodes_name, scale_method, continue match scale_method: + case "": + each_line_color = line_color if value > 0 else "#0000ff" + each_line_width = line_width case "width": each_line_color = line_color if value > 0 else "#0000ff" each_line_width = abs(value / max_strength) * line_width case "color": norm_value = value / max_strength - each_line_color = mcolors.to_hex(cm.bwr(mcolors.Normalize(vmin=-1, vmax=1)(norm_value))) + each_line_color = _get_gradient_color(norm_value, line_color) each_line_width = line_width case "width_color" | "color_width": norm_value = value / max_strength each_line_width = abs(norm_value) * line_width - each_line_color = mcolors.to_hex(cm.bwr(mcolors.Normalize(vmin=-1, vmax=1)(norm_value))) - case "": - each_line_color = "#ff0000" if value > 0 else "#0000ff" - each_line_width = line_width + each_line_color = _get_gradient_color(norm_value, line_color) case _: - raise ValueError("scale_method must be '', 'width', 'color', 'width_color', or 'color_width'") + raise ValueError( + "scale_method 必须为 '', 'width', 'color', 'width_color', or 'color_width'中的一种" + ) - connection_line = np.array([centroids_real[i], centroids_real[j], [None] * 3]) + connection_line = np.array( + [centroids_real[i], centroids_real[j], [None] * 3] + ) fig.add_trace( go.Scatter3d( x=connection_line[:, 0], @@ -125,16 +167,21 @@ def _add_edges_to_fig(fig, connectome, centroids_real, nodes_name, scale_method, z=connection_line[:, 2], mode="lines", line={"color": each_line_color, "width": each_line_width}, - hoverinfo="none", + hoverinfo="name", name=f"{nodes_name[i]}-{nodes_name[j]}", ) ) + def _finalize_figure(fig): - '''调整图形布局与视觉样式''' + """调整图形布局与视觉样式""" fig.update_traces( selector={"mode": "markers"}, - marker={"size": 10, "colorscale": "Viridis", "line": {"width": 3, "color": "black"}}, + marker={ + "size": 10, + "colorscale": "Viridis", + "line": {"width": 3, "color": "black"}, + }, ) fig.update_layout( title="Connection", @@ -147,48 +194,75 @@ def _finalize_figure(fig): margin={"l": 0, "r": 0, "b": 0, "t": 30}, ) + def plot_brain_connection_figure( connectome: npt.NDArray, lh_surfgii_file: str | Path, rh_surfgii_file: str | Path, niigz_file: str | Path, + output_file: str | Path | None = None, + show_all_nodes: bool = False, + nodes_size: Sequence[Num] | npt.NDArray | None = None, nodes_name: list[str] | None = None, - nodes_size=None, nodes_color: list[str] | None = None, - output_file: str | Path | None = None, - scale_method: str = "", + scale_method: Literal["", "width", "color", "width_color", "color_width"] = "", line_width: Num = 10, - show_all_nodes: bool = False, - line_color: str = "#ff0000", -) -> None: + line_color: str = "red", +) -> go.Figure: """绘制大脑连接图，保存在指定的html文件中 Args: - connectome (npt.NDArray): 连接矩阵 - lh_surfgii_file (str | Path): 左脑surf.gii文件. - rh_surfgii_file (str | Path): 右脑surf.gii文件. - niigz_file (str | Path): 图集nii文件. - nodes_name (List[str] | None, optional): 节点名称. Defaults to None. - nodes_size (Num, optional): 节点大小. Defaults to 5. - nodes_color (List[str] | None, optional): 节点颜色. Defaults to None. - output_file (str | Path | None, optional): 保存的完整路径及文件名. Defaults to None. - scale_method (str, optional): 连接scale的形式. Defaults to "". - line_width (Num, optional): 连接粗细. Defaults to 10. - - Raises: - ValueError: 参数参数取值不合法时抛出. + connectome (npt.NDArray): + 大脑连接矩阵，形状为 (n, n)，其中 n 是脑区数量。 + 矩阵中的值表示脑区之间的连接强度，正值表示正相关连接，负值表示负相关连接，0表示无连接。 + lh_surfgii_file (str | Path): + 左半脑表面几何文件路径 (.surf.gii 格式)，用于绘制左半脑表面 + rh_surfgii_file (str | Path): + 右半脑表面几何文件路径 (.surf.gii 格式)，用于绘制右半脑表面 + niigz_file (str | Path): + NIfTI格式的脑区图谱文件路径 (.nii.gz 格式)，用于定位脑区节点的三维坐标 + output_file (str | Path | None, optional): + 输出HTML文件路径。如果未指定，则使用当前时间戳生成文件名。默认为None + show_all_nodes (bool, optional): + 是否显示所有脑区节点。如果为False，则只显示有连接的节点。默认为False + nodes_size (Sequence[Num] | npt.NDArray | None, optional): + 每个节点的大小，长度应与脑区数量一致。默认为None，即所有节点大小为5 + nodes_name (list[str] | None, optional): + 每个节点的名称标签，长度应与脑区数量一致。默认为None，即不显示名称 + nodes_color (list[str] | None, optional): + 每个节点的颜色，长度应与脑区数量一致。默认为None，即所有节点为白色 + scale_method (Literal["", "width", "color", "width_color", "color_width"], optional): + 连接线的缩放方法: + - "" : 所有连接线宽度和颜色固定 + - "width" : 根据连接强度调整线宽，正连接为红色，负连接为蓝色 + - "color" : 根据连接强度调整颜色(使用蓝白红颜色映射)，线宽固定 + - "width_color" or "color_width" : 同时根据连接强度调整线宽和颜色 + 默认为 "" + line_width (Num, optional): + 连接线的基本宽度。当scale_method包含"width"时，此值作为最大宽度参考。默认为10 + line_color (str, optional): + 连接线的基本颜色。当scale_method不包含"color"时生效。默认为"#ff0000"(红色) + + Returns: + go.Figure: Plotly图形对象，包含绘制的大脑连接图 """ + _validate_connectome(connectome) + + if 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" + ) + line_color = "#ff0000" + nodes_num = connectome.shape[0] - if nodes_name is None: - nodes_name = [f"ROI-{i}" for i in range(nodes_num)] - if nodes_color is None: - nodes_color = ["white"] * nodes_num - if nodes_size is None: - nodes_size = [5] * nodes_num + nodes_name = nodes_name or [""] * nodes_num + nodes_color = nodes_color or ["white"] * nodes_num + nodes_size = nodes_size or [5] * nodes_num + if output_file is None: timestamp = datetime.datetime.now().strftime("%Y-%m-%d_%H-%M-%S") - output_file = op.join(f"./{timestamp}.html") - print(f"未指定保存路径，默认保存为：{output_file}") + output_file = Path(f"{timestamp}.html") + logger.info(f"未指定保存路径，默认保存在当前文件夹下的{output_file}中。") node_indices = _get_node_indices(connectome, show_all_nodes) vertices_L, faces_L = _load_surface(lh_surfgii_file) @@ -196,11 +270,22 @@ def plot_brain_connection_figure( mesh_L = _create_mesh(vertices_L, faces_L, "Left Hemisphere") mesh_R = _create_mesh(vertices_R, faces_R, "Right Hemisphere") + fig = go.Figure(data=[mesh_L, mesh_R]) centroids_real = _get_centroids_real(niigz_file) - _add_nodes_to_fig(fig, centroids_real, node_indices, nodes_name, nodes_size, nodes_color) - _add_edges_to_fig(fig, connectome, centroids_real, nodes_name, scale_method, line_width, line_color) + _add_nodes_to_fig( + fig, centroids_real, node_indices, nodes_name, nodes_size, nodes_color + ) + _add_edges_to_fig( + fig, + connectome, + centroids_real, + nodes_name, + scale_method, + line_width, + line_color, + ) _finalize_figure(fig) fig.write_html(output_file) @@ -208,19 +293,17 @@ def plot_brain_connection_figure( def save_brain_connection_frames( - fig: go.Figure, - output_dir: str, - n_frames: int = 36 + fig: go.Figure, output_dir: str | Path, n_frames: int = 36 ) -> None: """ - 生成不同角度的静态图片帧，用于制作旋转大脑连接图的 GIF 或视频。 + 生成不同角度的静态图片帧，可用于制作旋转大脑连接图的 GIF。 Args: fig (go.Figure): Plotly 的 Figure 对象，包含大脑表面和连接图。 - output_dir (str): 图片保存的文件夹路径，会自动创建文件夹。 + output_dir (str): 图片保存的文件夹路径，若文件夹不存在则自动创建。 n_frames (int, optional): 旋转帧的数量。默认 36，即每 10 度一帧。 """ - os.makedirs(output_dir, exist_ok=True) + Path(output_dir).mkdir(parents=True, exist_ok=True) angles = np.linspace(0, 360, n_frames, endpoint=False) for i, angle in tqdm(enumerate(angles), total=len(angles)): camera = dict( @@ -230,12 +313,4 @@ def save_brain_connection_frames( ) fig.update_layout(scene_camera=camera) pio.write_image(fig, f"{output_dir}/frame_{i:03d}.png", width=800, height=800) - print(f"保存了 {n_frames} 张图片在 {output_dir}") - - -def main(): - pass - - -if __name__ == "__main__": - main() \ No newline at end of file + logger.info(f"保存了 {n_frames} 张图片在 {output_dir}")