import os
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from matplotlib import ticker
from matplotlib.ticker import FuncFormatter, FixedLocator, NullLocator, NullFormatter
from boviz.config import GLOBAL_COLORS, set_default_dpi_figsize_savedir, set_residual_dpi_figsize_savedir
from boviz.style import set_default_style, set_cm_style, set_ax_style, apply_axis_scientific_format, apply_axis_limits_and_ticks, save_or_display_legend, plot_residual_curves, set_sans_style
from boviz.utils import generate_plot_filename, load_data_csv, save_figure
# -----------------------------------------------------------------------------
# 内部辅助函数 (Helper Functions)
# -----------------------------------------------------------------------------
[文档]
def update_curve_plotting_with_styles(ax, x_data, y_data, label, index):
"""
内部函数：使用循环的线型和标记样式绘制曲线。
用于 use_marker=True 的情况。
"""
line_styles = ['-', '--', '-.', ':']
markers = ['o', 's', 'D', '^', 'v', '*']
color = GLOBAL_COLORS[index % len(GLOBAL_COLORS)]
ax.plot(x_data, y_data,
label=label,
linestyle=line_styles[index % len(line_styles)],
marker=markers[index % len(markers)],
markevery=slice(index * 2, None, max(1, len(x_data) // 20)),
markersize=2,
linewidth=1,
color=color,
alpha=0.9)
[文档]
def plot_scatter_style(ax, x_data, y_data, label, index):
"""
内部函数：绘制散点图样式。
用于 use_scatter=True 的情况。
"""
markers = ['o', 's', 'D', '^', 'v', '*']
color = GLOBAL_COLORS[index % len(GLOBAL_COLORS)]
ax.scatter(x_data, y_data,
label=label,
s=5,
marker=markers[index % len(markers)],
edgecolors=color,
facecolors=color,
linewidths=1,
zorder=3)
def _apply_font_style(font_style, font_weight):
"""内部函数：统一应用字体设置"""
if not font_style:
if font_weight == 'bold':
set_cm_style(bold=True)
elif font_weight == 'normal':
set_cm_style(bold=False)
else:
raise ValueError("Invalid font_weight. Choose 'bold' or 'normal'.")
elif font_style == 'sans':
if font_weight == 'bold':
set_sans_style(bold=True)
elif font_weight == 'normal':
set_sans_style(bold=False)
else:
raise ValueError("Invalid font_weight. Choose 'bold' or 'normal'.")
elif font_style == 'times':
if font_weight == 'bold':
set_default_style(bold=True)
elif font_weight == 'normal':
set_default_style(bold=False)
else:
raise ValueError("Invalid font_weight. Choose 'bold' or 'normal'.")
else:
raise ValueError("Invalid font_style. Choose 'sans', 'times' or None.")
def _finalize_plot(
fig, ax_main, ax_res,
save_dir, title_figure, label_suffix,
xy_label, curves, label,
xlim, ylim, tick_interval_x, tick_interval_y,
sci, ylog,
legend_location, split_legend, show_legend, legend_ncol, legend_fontsize,
show_residual,
font_weight, figure_format,
save, show, dpi
):
"""
内部核心函数：统一处理图形的后处理（标签、刻度、保存等）。
提取此函数是为了避免 plot_curves_csv 和 plot_curves 代码大量重复。
"""
# 设置轴标签
ax_main.set_xlabel(xy_label[0], fontweight=font_weight, labelpad=2)
ax_main.set_ylabel(xy_label[1], fontweight=font_weight, labelpad=3)
# 科学计数法
# 这里为了保持原代码逻辑，直接内嵌 FixedOrderFormatter 类
class FixedOrderFormatter(ticker.ScalarFormatter):
def __init__(self, order=0, useOffset=True, useMathText=True):
super().__init__(useOffset=useOffset, useMathText=useMathText)
self._force_order = order
def _set_order_of_magnitude(self):
self.orderOfMagnitude = self._force_order
def apply_native_sci(ax, axis, scale):
if scale is None: return
order = int(np.floor(np.log10(abs(scale)))) if scale != 0 else 0
formatter = FixedOrderFormatter(order=order, useOffset=True, useMathText=True)
if axis == 'x': ax.xaxis.set_major_formatter(formatter)
elif axis == 'y': ax.yaxis.set_major_formatter(formatter)
apply_native_sci(ax_main, 'x', sci[0])
apply_native_sci(ax_main, 'y', sci[1])
# 刻度与范围
apply_axis_limits_and_ticks(
ax=ax_main, curves=curves, xlim=xlim, ylim=ylim,
tick_interval_x=tick_interval_x, tick_interval_y=tick_interval_y
)
# 对数坐标
if ylog:
ax_main.set_yscale('log')
if ylim:
if ylim[0] <= 0 or ylim[1] <= 0:
new_ylim = (max(ylim[0], 1e-6), max(ylim[1], 1e-6))
ax_main.set_ylim(new_ylim)
print(f"[Warning] 对数坐标轴 ylim 必须为正数，已自动调整为: {new_ylim}")
else:
ax_main.set_ylim(ylim)
ax_main.yaxis.set_major_formatter(ticker.LogFormatterSciNotation())
ax_main.yaxis.set_major_locator(ticker.LogLocator(base=10.0, numticks=10))
# 图例字体
if legend_fontsize is not None:
plt.rcParams['legend.fontsize'] = legend_fontsize
# 图例处理
save_or_display_legend(
ax=ax_main, save_dir=save_dir, figure_name_suffix=label_suffix,
split_legend=split_legend, legend_location=legend_location,
legend_ncol=legend_ncol or 1, dpi=dpi, xy_label=xy_label,
show_legend=show_legend
)
# 残差绘制
if show_residual and len(curves) >= 2 and ax_res is not None:
plot_residual_curves(
ax_res=ax_res, curves=curves, label=label,
xy_label=xy_label, x_title_fallback=xy_label[0]
)
# 布局与保存
plt.tight_layout(pad=0.1)
filename = generate_plot_filename(title=title_figure, file_format=figure_format, suffix=label_suffix)
save_path = os.path.join(save_dir, filename)
if save:
save_figure(save_path, dpi=dpi)
if show:
plt.show()
plt.close()
return save_path
# -----------------------------------------------------------------------------
# 公开绘图接口 (Public APIs)
# -----------------------------------------------------------------------------
[文档]
def plot_curves_csv(
path: list[str],
label: list[str],
x: list[int],
y: list[int],
information: str = None,
factor: list[tuple[tuple, tuple]] = None,
time_step: list[int | list | tuple] = None,
xy_label: tuple[str, str] = None,
use_marker: list[bool] = None,
use_scatter: list[bool] = None,
line_style: list[str] = None,
tick_interval_x: float = None,
tick_interval_y: float = None,
legend_location: str = None,
xlim: tuple[float, float] = None,
ylim: tuple[float, float] = None,
highlight_x: float = None,
split_legend: bool = False,
show_residual: bool = False,
show_legend: bool = True,
show_title: bool = True,
title_figure: str = None,
legend_ncol: int = None,
legend_fontsize: int | float | str = None,
ylog: bool = False,
sci: tuple[float, float] = [None, None],
color_group: list[int] = None,
show: bool = False,
save: bool = False,
font_style: str = None,
font_weight: str = "bold",
figure_format: str = 'png',
) -> str:
"""
从 CSV 文件读取数据并绘制科学曲线。支持多曲线对比、样式定制及残差分析。
Args:
path (list[str]): CSV 文件路径列表。
label (list[str]): 每条曲线的图例标签。
x (list[int]): X 轴数据所在的列索引（从0开始）。
y (list[int]): Y 轴数据所在的列索引（从0开始）。
information (str, optional): 附加信息，用于生成文件名的后缀。默认为 None。
factor (list[tuple[tuple, tuple]], optional): 数据缩放与平移因子。
格式为 `[((x_scale, x_offset), (y_scale, y_offset)), ...]`。
默认为 `(1.0, 0.0)`。
time_step (list[int | list | tuple], optional): 数据截取设置。
- 整数 N: 取前 N 行。
- 列表 [start, end]: 取 start 到 end 行。
xy_label (tuple[str, str], optional): (X轴名称, Y轴名称)。若不提供，将尝试使用 CSV 列名。
use_marker (list[bool], optional): 是否为每条曲线启用标记点样式。默认为 False。
use_scatter (list[bool], optional): 是否以散点图形式绘制。默认为 False。
line_style (list[str], optional): 指定每条曲线的线型（如 '-', '--'）。默认为 '-'。
tick_interval_x (float, optional): 强制 X 轴主刻度间隔。
tick_interval_y (float, optional): 强制 Y 轴主刻度间隔。
legend_location (str, optional): 图例位置，如 'upper right', 'best'。
xlim (tuple[float, float], optional): X 轴显示范围 (min, max)。
ylim (tuple[float, float], optional): Y 轴显示范围 (min, max)。
split_legend (bool, optional): 是否将图例单独保存为一张图片。默认为 False。
show_residual (bool, optional): 是否绘制残差子图（相对于第一条曲线）。默认为 False。
show_legend (bool, optional): 是否显示图例。默认为 True。
show_title (bool, optional): 是否显示标题。默认为 True。
title_figure (str, optional): 图像标题，同时用于文件命名。
legend_ncol (int, optional): 图例列数。
ylog (bool, optional): Y 轴是否使用对数坐标。
sci (tuple[float, float], optional): 科学计数法缩放因子 [x_scale, y_scale]。
color_group (list[int], optional): 指定颜色分组索引，强制多条曲线使用相同颜色。
show (bool, optional): 是否在窗口显示图像。默认为 False。
save (bool, optional): 是否保存图像到文件。默认为 False。
font_style (str, optional): 字体风格 ('times', 'sans' 或 None)。
font_weight (str, optional): 字体粗细 ('bold' 或 'normal')。
figure_format (str, optional): 保存格式 ('png', 'pdf', 'svg')。默认为 'png'。
Returns:
str: 保存的图像文件路径。
"""
# 1. 字体设置
_apply_font_style(font_style, font_weight)
# 2. 画布初始化
if show_residual:
dpi, figuresize, savedir = set_residual_dpi_figsize_savedir()
fig, (ax_main, ax_res) = plt.subplots(2, 1, figsize=figuresize, dpi=dpi,
gridspec_kw={'height_ratios': [3, 1]}, sharex=True)
else:
dpi, figuresize, savedir = set_default_dpi_figsize_savedir()
fig, ax_main = plt.subplots(figsize=figuresize, dpi=dpi)
ax_res = None
save_dir = os.path.join(savedir, "Curves")
# 3. 样式初始化
for ax in [ax_main] + ([ax_res] if ax_res is not None else []):
set_ax_style(ax)
ax.tick_params(axis='both', length=2, direction='in', width=0.75, which='both', pad=4)
# 4. 默认参数填充
time_step = time_step or [0] * len(path)
use_marker = use_marker or [False] * len(path)
use_scatter = use_scatter or [False] * len(path)
line_style = line_style or ['-'] * len(path)
factor = factor or [[(1.0, 0.0), (1.0, 0.0)] for _ in range(len(path))]
# 5. 数据读取与绘制循环
curves = [] # 用于存储绘制后的数据 (x, y)，供范围计算和残差使用
# 单曲线/多曲线逻辑统一处理
# 如果只有一条曲线且未提供 xy_label，尝试从 CSV 读取列名
need_auto_label = (len(path) == 1 and not xy_label)
if show_title:
default_title = f'Comparison' if len(path) > 1 else 'Curve'
ax_main.set_title(title_figure or default_title, pad=7.5, fontweight=font_weight)
for i in range(len(path)):
x_d, y_d, x_colname, y_colname = load_data_csv(
source=path[i],
x_index=x[i] if isinstance(path[i], str) else None,
y_index=y[i] if isinstance(path[i], str) else None,
factor=factor[i],
time_step=time_step[i]
)
curves.append((x_d, y_d))
# 自动标签处理
if need_auto_label:
if not x_colname or not y_colname:
raise ValueError("CSV data missing column names. Please specify 'xy_label'.")
xy_label = [x_colname, y_colname]
# 颜色索引
color_index = color_group[i] if color_group else (i if len(path) > 1 else 10)
# 绘制
if use_scatter[i]:
plot_scatter_style(ax_main, x_d, y_d, label[i], color_index)
elif use_marker[i]:
update_curve_plotting_with_styles(ax_main, x_d, y_d, label[i], color_index)
else:
ax_main.plot(x_d, y_d, label=label[i], linewidth=1,
linestyle=line_style[i],
color=GLOBAL_COLORS[color_index % len(GLOBAL_COLORS)])
if not xy_label:
raise ValueError("Please specify 'xy_label'.")
# 6. 调用统一的后处理函数
label_suffix = f"({information})" if information else None
return _finalize_plot(
fig, ax_main, ax_res, save_dir, title_figure, label_suffix,
xy_label, curves, label,
xlim, ylim, tick_interval_x, tick_interval_y,
sci, ylog,
legend_location, split_legend, show_legend, legend_ncol, legend_fontsize,
show_residual, font_weight, figure_format,
save, show, dpi
)
[文档]
def plot_curves(
data: list[tuple[np.ndarray, np.ndarray]],
label: list[str],
information: str = None,
factor: list[tuple[tuple, tuple]] = None,
time_step: list[int | list | tuple] = None,
xy_label: tuple[str, str] = None,
use_marker: list[bool] = None,
use_scatter: list[bool] = None,
line_style: list[str] = None,
tick_interval_x: float = None,
tick_interval_y: float = None,
legend_location: str = None,
xlim: tuple[float, float] = None,
ylim: tuple[float, float] = None,
highlight_x: float = None,
split_legend: bool = False,
show_legend: bool = True,
show_residual: bool = False,
show_title: bool = True,
title_figure: str = None,
legend_ncol: int = None,
ylog: bool = False,
sci: tuple[float, float] = (None, None),
color_group: list[int] = None,
show: bool = False,
save: bool = False,
font_style: str = None,
font_weight: str = "bold",
figure_format: str = 'png',
) -> str:
"""
直接绘制内存中的数据（NumPy 数组）。参数含义同 plot_curves_csv。
"""
# 1. 字体
_apply_font_style(font_style, font_weight)
# 2. 画布
if show_residual:
dpi, figuresize, savedir = set_residual_dpi_figsize_savedir()
fig, (ax_main, ax_res) = plt.subplots(2, 1, figsize=figuresize, dpi=dpi,
gridspec_kw={'height_ratios': [3, 1]}, sharex=True)
else:
dpi, figuresize, savedir = set_default_dpi_figsize_savedir()
fig, ax_main = plt.subplots(figsize=figuresize, dpi=dpi)
ax_res = None
save_dir = os.path.join(savedir, "Curves")
# 3. 样式
for ax in [ax_main] + ([ax_res] if ax_res is not None else []):
set_ax_style(ax)
ax.tick_params(axis='both', length=2, direction='in', width=0.75, which='both', pad=4)
# 4. 默认值
time_step = time_step or [0] * len(data)
use_marker = use_marker or [False] * len(data)
use_scatter = use_scatter or [False] * len(data)
line_style = line_style or ['-'] * len(data)
factor = factor or [[(1.0, 0.0), (1.0, 0.0)] for _ in range(len(data))]
# 5. 绘制循环
curves = []
if not xy_label:
raise ValueError("For plot_curves, 'xy_label' is required.")
if show_title:
default_title = f'Comparison of {xy_label[1]}' if len(data) > 1 else f'Curve of {xy_label[1]}'
ax_main.set_title(title_figure or default_title, pad=7.5, fontweight=font_weight)
for i in range(len(data)):
x_data, y_data = data[i]
# 截断处理
current_ts = time_step[i]
start_idx, end_idx = 0, None
if isinstance(current_ts, int) and current_ts != 0:
end_idx = current_ts
elif isinstance(current_ts, (list, tuple)):
if len(current_ts) >= 1: start_idx = current_ts[0]
if len(current_ts) >= 2: end_idx = current_ts[1] if current_ts[1] != 0 else None
if start_idx != 0 or end_idx is not None:
x_data = x_data[start_idx:end_idx]
y_data = y_data[start_idx:end_idx]
# 缩放处理
x_scale, x_offset = factor[i][0]
y_scale, y_offset = factor[i][1]
x_data = x_data * x_scale + x_offset
y_data = y_data * y_scale + y_offset
curves.append((x_data, y_data))
# 颜色
color_index = color_group[i] if color_group else (i if len(data) > 1 else 10)
# 绘图
if use_scatter[i]:
plot_scatter_style(ax_main, x_data, y_data, label[i], color_index)
elif use_marker[i]:
update_curve_plotting_with_styles(ax_main, x_data, y_data, label[i], color_index)
else:
ax_main.plot(x_data, y_data, label=label[i], linewidth=1, linestyle=line_style[i],
color=GLOBAL_COLORS[color_index % len(GLOBAL_COLORS)])
# 6. 后处理
label_suffix = f"({information})" if information else None
return _finalize_plot(
fig, ax_main, ax_res, save_dir, title_figure, label_suffix,
xy_label, curves, label,
xlim, ylim, tick_interval_x, tick_interval_y,
sci, ylog,
legend_location, split_legend, show_legend, legend_ncol, None,
show_residual, font_weight, figure_format,
save, show, dpi
)
[文档]
def plot_dual_curves_csv(
path: list[str],
label: list[str],
x: list[int],
y: list[int],
path_right: list[str],
label_right: list[str],
x_right: list[int],
y_right: list[int],
information: str = None,
factor: list[tuple[tuple, tuple]] = None,
factor_right: list[tuple[tuple, tuple]] = None,
time_step: list[int | list | tuple] = None,
time_step_right: list[int | list | tuple] = None,
xy_label: tuple[str, str] = None,
y_label_right: str = None,
use_marker: list[bool] = None,
use_marker_right: list[bool] = None,
use_scatter: list[bool] = None,
use_scatter_right: list[bool] = None,
tick_interval_x: float = None,
tick_interval_y: float = None,
tick_interval_y_right: float = None,
legend_location: str = None,
xlim: tuple[float, float] = None,
ylim: tuple[float, float] = None,
ylim_right: tuple[float, float] = None,
split_legend: bool = False,
show_legend: bool = False,
match_axis_color: bool = True,
add_axis_arrow: bool = False,
show_title: bool = True,
title_figure: str = None,
legend_ncol: int = None,
# --- 科学计数法设置 [X轴, 左Y轴, 右Y轴] ---
auto_sci: bool = True,
sci: list[float] = [None, None, None],
# ----------------------------------------
color_group: list[int] = None,
color_group_right: list[int] = None,
show: bool = False,
save: bool = False,
font_style: str = None,
font_weight: str = "bold",
figure_format: str = 'png',
) -> str:
"""
双轴绘图（风格统一版）：线宽为1，实线，完美复刻 plot_curves_csv 风格。
"""
# ... (字体设置保持不变) ...
if not font_style:
if font_weight == 'bold':
set_cm_style(bold=True)
elif font_weight == 'normal':
set_cm_style(bold=False)
elif font_style == 'sans':
if font_weight == 'bold':
set_sans_style(bold=True)
elif font_weight == 'normal':
set_sans_style(bold=False)
elif font_style == 'times':
if font_weight == 'bold':
set_default_style(bold=True)
elif font_weight == 'normal':
set_default_style(bold=False)
dpi, figuresize, savedir = set_default_dpi_figsize_savedir()
fig, ax_main = plt.subplots(figsize=figuresize, dpi=dpi)
ax_right = ax_main.twinx()
save_dir = os.path.join(savedir, "Curves")
# 样式基础设置
for ax in [ax_main, ax_right]:
set_ax_style(ax)
ax.tick_params(axis='both', length=2, direction='in', width=0.75, which='both', pad=4)
# ... (数据加载逻辑保持不变) ...
time_step = time_step or [0] * len(path)
time_step_right = time_step_right or [0] * len(path_right)
use_marker = use_marker or [False] * len(path)
use_marker_right = use_marker_right or [False] * len(path_right)
use_scatter = use_scatter or [False] * len(path)
use_scatter_right = use_scatter_right or [False] * len(path_right)
curves_left = []
if not xy_label: raise ValueError("需要 xy_label")
for i in range(len(path)):
x_d, y_d, _, _ = load_data_csv(path[i], x[i], y[i], factor[i] if factor else [(1,0),(1,0)], time_step[i])
curves_left.append((x_d, y_d))
color_idx = color_group[i] if color_group else i
if use_scatter[i]: plot_scatter_style(ax_main, x_d, y_d, label[i], color_idx)
elif use_marker[i]: update_curve_plotting_with_styles(ax_main, x_d, y_d, label[i], color_idx)
else:
# 【修改】显式指定 linewidth=1
ax_main.plot(x_d, y_d, label=label[i], linewidth=1,
color=GLOBAL_COLORS[color_idx%len(GLOBAL_COLORS)])
curves_right = []
color_offset = len(path) if not color_group_right else 0
for i in range(len(path_right)):
x_d, y_d, _, _ = load_data_csv(path_right[i], x_right[i], y_right[i], factor_right[i] if factor_right else [(1,0),(1,0)], time_step_right[i])
curves_right.append((x_d, y_d))
color_idx = color_group_right[i] if color_group_right else (i+color_offset)
if use_scatter_right[i]: plot_scatter_style(ax_right, x_d, y_d, label_right[i], color_idx)
elif use_marker_right[i]: update_curve_plotting_with_styles(ax_right, x_d, y_d, label_right[i], color_idx)
else:
# 【修改】显式指定 linewidth=1，去除 linestyle='--'
ax_right.plot(x_d, y_d, label=label_right[i], linewidth=1,
color=GLOBAL_COLORS[color_idx%len(GLOBAL_COLORS)])
# =========================================================================
# 科学计数法参数解析 & Formatter (之前讨论的完美方案)
# =========================================================================
sci_list = list(sci) if sci else []
while len(sci_list) < 3: sci_list.append(None)
scale_x, scale_y_left, scale_y_right = sci_list[0], sci_list[1], sci_list[2]
class FixedOrderFormatter(ticker.ScalarFormatter):
def __init__(self, order=0, useOffset=True, useMathText=True):
super().__init__(useOffset=useOffset, useMathText=useMathText)
self._force_order = order
def _set_order_of_magnitude(self):
self.orderOfMagnitude = self._force_order
def apply_sci_format(ax, axis, scale=None):
ax_obj = ax.yaxis if axis == 'y' else ax.xaxis
if scale is not None:
order = int(np.floor(np.log10(abs(scale)))) if scale != 0 else 0
formatter = FixedOrderFormatter(order=order, useOffset=True, useMathText=True)
ax_obj.set_major_formatter(formatter)
elif auto_sci:
formatter = ticker.ScalarFormatter(useOffset=True, useMathText=True)
formatter.set_powerlimits((-2, 3))
ax_obj.set_major_formatter(formatter)
apply_sci_format(ax_main, 'x', scale_x)
apply_sci_format(ax_main, 'y', scale_y_left)
apply_sci_format(ax_right, 'y', scale_y_right)
# =========================================================================
if show_title:
ax_main.set_title(title_figure or f'Comparison', pad=10, fontweight=font_weight)
ax_main.set_xlabel(xy_label[0], fontweight=font_weight, labelpad=2)
c_left = GLOBAL_COLORS[color_group[0]%len(GLOBAL_COLORS)] if color_group else GLOBAL_COLORS[0]
idx_right = color_group_right[0] if color_group_right else (0 + color_offset)
c_right = GLOBAL_COLORS[idx_right%len(GLOBAL_COLORS)]
label_color_left = c_left if match_axis_color else 'black'
label_color_right = c_right if match_axis_color else 'black'
ax_main.set_ylabel(xy_label[1], color=label_color_left, fontweight=font_weight, labelpad=3)
ax_right.set_ylabel(y_label_right, color=label_color_right, fontweight=font_weight, labelpad=3)
if match_axis_color:
ax_main.tick_params(axis='y', colors=c_left, which='both')
ax_main.yaxis.get_offset_text().set_color(c_left)
ax_main.spines['left'].set_color(c_left)
ax_main.spines['left'].set_linewidth(0.75)
ax_main.spines['right'].set_visible(False)
ax_right.tick_params(axis='y', colors=c_right, which='both')
ax_right.yaxis.get_offset_text().set_color(c_right)
ax_right.spines['right'].set_color(c_right)
ax_right.spines['right'].set_linewidth(0.75)
ax_right.spines['left'].set_visible(False)
if add_axis_arrow:
ax_main.plot(0, 1, "^", transform=ax_main.transAxes, color=label_color_left, clip_on=False, markersize=4, zorder=10)
ax_main.plot(1, 1, "^", transform=ax_main.transAxes, color=label_color_right, clip_on=False, markersize=4, zorder=10)
ax_main.plot(1, 0, ">", transform=ax_main.transAxes, color='black', clip_on=False, markersize=4, zorder=10)
apply_axis_limits_and_ticks(ax_main, curves_left, xlim, ylim, tick_interval_x, tick_interval_y)
if tick_interval_y_right:
y_min = min(min(c[1]) for c in curves_right)
y_max = max(max(c[1]) for c in curves_right)
start = ylim_right[0] if ylim_right else y_min
end = ylim_right[1] if ylim_right else y_max
ax_right.set_yticks(np.arange(start, end + 0.1 * tick_interval_y_right, tick_interval_y_right))
if ylim_right: ax_right.set_ylim(*ylim_right)
h1, l1 = ax_main.get_legend_handles_labels()
h2, l2 = ax_right.get_legend_handles_labels()
label_suffix = f"({information})" if information else None
if split_legend:
fig_leg = plt.figure(figsize=(4, 2))
fig_leg.legend(h1 + h2, l1 + l2, loc='center', ncol=legend_ncol or 1)
fig_leg.tight_layout()
legend_path = os.path.join(save_dir, f'Legend_Dual_{xy_label[1]}_{y_label_right}{label_suffix or ""}.png')
fig_leg.savefig(legend_path, dpi=dpi, bbox_inches='tight')
plt.close(fig_leg)
elif show_legend:
ax_main.legend(h1 + h2, l1 + l2, loc=legend_location or 'best', ncol=legend_ncol or 1, frameon=True).get_frame().set_linewidth(0.5)
plt.tight_layout(pad=0.1)
filename = generate_plot_filename(title=title_figure, file_format=figure_format, suffix=label_suffix)
save_path = os.path.join(save_dir, filename)
if save:
os.makedirs(os.path.dirname(save_path), exist_ok=True)
# 【关键】这里直接调用 savefig 并开启 bbox_inches='tight'
plt.savefig(save_path, dpi=dpi, bbox_inches='tight', pad_inches=0.1)
print(f"[SAVE] 图像已保存到: {save_path}\n")
if show:
plt.show()
plt.close()
return save_path