import os
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.ticker import FuncFormatter, FixedLocator, NullLocator, NullFormatter
from boviz.config import GLOBAL_COLORS, set_default_dpi_figsize_savedir
from boviz.style import (
set_default_style,
set_sans_style,
set_cm_style,
set_ax_style,
apply_axis_scientific_format,
apply_axis_limits_and_ticks,
save_or_display_legend,
)
from boviz.utils import generate_plot_filename, save_figure
[文档]
def plot_histogram(
label: str,
x: list[float],
y: list[float],
xy_label: tuple[str, str],
information: str = None,
factor: list[(float, float)] = None,
title: str = "Histogram",
ylog: bool = False,
sci: tuple[float, float] = [None, None],
save: bool = False,
show: bool = False,
font_style: str = None,
font_weight: str = "bold",
categorical_x: bool = True,
show_values: bool = True,
) -> str:
"""
绘制柱状图（直方图）
Args:
x (list[int]): 核数列表
y (list[float]): 对应的运行时间列表
label (str, optional): 图例标签. Defaults to "Runtime".
xy_label (tuple[str, str], optional): x轴和y轴标签. Defaults to ("Number of Cores", "Runtime (s)").
title (str, optional): 图表标题. Defaults to "Multi-core Performance".
legend_location (str, optional): 图例位置. Defaults to "best".
ylog (bool, optional): 是否使用对数y轴. Defaults to False.
sci (tuple[float, float], optional): x轴和y轴的科学计数法阈值. Defaults to [None, None].
save (bool, optional): 是否保存图表. Defaults to True.
show (bool, optional): 是否显示图表. Defaults to True.
font_style (str, optional): 字体风格，"sans"表示无衬线字体，None表示默认字体. Defaults to None.
font_weight (str, optional): 字体粗细，"bold"表示粗体，"normal"表示正常字体. Defaults to "bold".
categorical_x (bool, optional): 是否将x轴作为类别处理（等间距）. Defaults to True.
Returns:
str: 保存的图表路径
"""
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.")
dpi, figuresize, savedir = set_default_dpi_figsize_savedir()
fig, ax = plt.subplots(figsize=figuresize, dpi=dpi)
save_dir = os.path.join(savedir, "Histograms")
set_ax_style(ax)
ax.tick_params(axis="both", length=2.5, direction="in", width=0.75, which="both", pad=4)
if factor:
y = np.asarray(y, dtype=float)
y = y * factor[0] + factor[1]
# 绘制柱状图
colors = plt.cm.Dark2.colors
if categorical_x:
width = 0.28
x_pos = np.arange(len(x))
bars = ax.bar(x_pos, y, color=colors, edgecolor="black", linewidth=0.5, width=width, label=label)
ax.set_xticks(x_pos)
ax.set_xticklabels([str(v) for v in x], fontweight=font_weight)
# ✅ 两端空白 = 条间距的一半
gap = 0.75
ax.set_xlim(-gap, len(x) - 1 + gap)
else:
# 数值轴：按数值比例排布（你原来的画法）
bars = ax.bar(x, y, color=colors, edgecolor="black", linewidth=0.5, width=0.6, label=label)
ax.set_xlim(min(x) - 0.6, max(x) + 0.6)
if show_values:
ymin, ymax = ax.get_ylim()
ax.set_ylim(ymin, ymax * 1.1) # 上限增加 5%
for bar, val in zip(bars, y):
ax.text(
bar.get_x() + bar.get_width() / 2,
bar.get_height(),
f"{val:.2f}", # 保留两位小数
ha="center", va="bottom",
fontweight=font_weight,
fontsize=6
)
ax.set_title(title, pad=7.5, fontweight=font_weight)
ax.set_xlabel(xy_label[0], fontweight=font_weight, labelpad=2)
ax.set_ylabel(xy_label[1], fontweight=font_weight, labelpad=3)
label_suffix = f"({information})" if information else None
if sci[0]:
apply_axis_scientific_format(ax, 'x', sci[0])
if sci[1]:
apply_axis_scientific_format(ax, 'y', sci[1])
if ylog:
ax.set_yscale('log')
ax.set_ylim(0.0, 0.6)
ax.yaxis.set_major_locator(FixedLocator([0.002, 0.01, 0.1, 0.6]))
ax.yaxis.set_major_formatter(FuncFormatter(lambda y, _: f'{y:g}'))
ax.yaxis.set_minor_locator(NullLocator())
ax.yaxis.set_minor_formatter(NullFormatter())
plt.tight_layout(pad=0.1)
filename = generate_plot_filename(title=title, 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