wehub-skill-sync
621 lines
18 KiB
Markdown
621 lines
18 KiB
Markdown
# 可发表级别的 Matplotlib 示例
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## 概述
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本参考文档提供了使用 Matplotlib、Seaborn 和 Plotly 创建可发表级别的科学示意图的实用代码示例。所有示例均遵循 `publication_guidelines.md` 中的最佳实践，并使用 `color_palettes.md` 中的色盲友好调色板。
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## 设置与配置
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### 发表级别 Matplotlib 配置
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```python
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import matplotlib.pyplot as plt
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import matplotlib as mpl
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import numpy as np
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# 设置发表质量参数
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mpl.rcParams['figure.dpi'] = 300
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mpl.rcParams['savefig.dpi'] = 300
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mpl.rcParams['font.size'] = 8
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mpl.rcParams['font.family'] = 'sans-serif'
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mpl.rcParams['font.sans-serif'] = ['Arial', 'Helvetica']
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mpl.rcParams['axes.labelsize'] = 9
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mpl.rcParams['axes.titlesize'] = 9
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mpl.rcParams['xtick.labelsize'] = 7
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mpl.rcParams['ytick.labelsize'] = 7
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mpl.rcParams['legend.fontsize'] = 7
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mpl.rcParams['axes.linewidth'] = 0.5
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mpl.rcParams['xtick.major.width'] = 0.5
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mpl.rcParams['ytick.major.width'] = 0.5
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mpl.rcParams['lines.linewidth'] = 1.5
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# 使用色盲友好颜色（Okabe-Ito 调色板）
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okabe_ito = ['#E69F00', '#56B4E9', '#009E73', '#F0E442',
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'#0072B2', '#D55E00', '#CC79A7', '#000000']
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mpl.rcParams['axes.prop_cycle'] = mpl.cycler(color=okabe_ito)
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# 使用感知均匀的色图
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mpl.rcParams['image.cmap'] = 'viridis'
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```
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### 保存辅助函数
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```python
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def save_publication_figure(fig, filename, formats=['pdf', 'png'], dpi=300):
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"""
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以多种格式保存图形以供发表。
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参数
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-----------
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fig : matplotlib.figure.Figure
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要保存的图形
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filename : str
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基础文件名（不含扩展名）
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formats : list
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要保存的文件格式列表 ['pdf', 'png', 'eps', 'svg']
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dpi : int
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光栅格式的分辨率
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"""
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for fmt in formats:
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output_file = f"{filename}.{fmt}"
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fig.savefig(output_file, dpi=dpi, bbox_inches='tight',
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facecolor='white', edgecolor='none',
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transparent=False, format=fmt)
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print(f"已保存: {output_file}")
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```
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## 示例 1：带误差线的折线图
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```python
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import matplotlib.pyplot as plt
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import numpy as np
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# 生成示例数据
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x = np.linspace(0, 10, 50)
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y1 = 2 * x + 1 + np.random.normal(0, 1, 50)
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y2 = 1.5 * x + 2 + np.random.normal(0, 1.2, 50)
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# 计算分箱数据的均值和标准误差
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bins = np.linspace(0, 10, 11)
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y1_mean = [y1[(x >= bins[i]) & (x < bins[i+1])].mean() for i in range(len(bins)-1)]
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y1_sem = [y1[(x >= bins[i]) & (x < bins[i+1])].std() /
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np.sqrt(len(y1[(x >= bins[i]) & (x < bins[i+1])]))
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for i in range(len(bins)-1)]
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x_binned = (bins[:-1] + bins[1:]) / 2
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# 创建合适大小的图形（单栏宽度 = 3.5 英寸）
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fig, ax = plt.subplots(figsize=(3.5, 2.5))
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# 绘制带误差线的图
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ax.errorbar(x_binned, y1_mean, yerr=y1_sem,
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marker='o', markersize=4, capsize=3, capthick=0.5,
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label='条件 A', linewidth=1.5)
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# 添加带单位的标签
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ax.set_xlabel('时间（小时）')
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ax.set_ylabel('荧光强度（a.u.）')
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# 添加图例
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ax.legend(frameon=False, loc='upper left')
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# 移除顶部和右侧的轴脊线
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ax.spines['top'].set_visible(False)
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ax.spines['right'].set_visible(False)
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# 紧凑布局
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fig.tight_layout()
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# 保存
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save_publication_figure(fig, 'line_plot_with_errors')
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plt.show()
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```
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## 示例 2：多面板图形
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```python
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import matplotlib.pyplot as plt
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import numpy as np
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from string import ascii_uppercase
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# 创建多面板图形（双栏宽度 = 7 英寸）
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fig = plt.figure(figsize=(7, 4))
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# 定义面板网格
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gs = fig.add_gridspec(2, 3, hspace=0.4, wspace=0.4,
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left=0.08, right=0.98, top=0.95, bottom=0.08)
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# 面板 A：折线图
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ax_a = fig.add_subplot(gs[0, :2])
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x = np.linspace(0, 10, 100)
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for i, offset in enumerate([0, 0.5, 1.0]):
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ax_a.plot(x, np.sin(x) + offset, label=f'数据集 {i+1}')
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ax_a.set_xlabel('时间（秒）')
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ax_a.set_ylabel('振幅（V）')
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ax_a.legend(frameon=False, fontsize=6)
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ax_a.spines['top'].set_visible(False)
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ax_a.spines['right'].set_visible(False)
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# 面板 B：柱状图
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ax_b = fig.add_subplot(gs[0, 2])
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categories = ['对照', '处理\nA', '处理\nB']
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values = [100, 125, 140]
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errors = [5, 8, 6]
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ax_b.bar(categories, values, yerr=errors, capsize=3,
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color=['#0072B2', '#E69F00', '#009E73'], alpha=0.8)
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ax_b.set_ylabel('响应（%）')
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ax_b.spines['top'].set_visible(False)
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ax_b.spines['right'].set_visible(False)
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ax_b.set_ylim(0, 160)
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# 面板 C：散点图
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ax_c = fig.add_subplot(gs[1, 0])
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x = np.random.randn(100)
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y = 2*x + np.random.randn(100)
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ax_c.scatter(x, y, s=10, alpha=0.6, color='#0072B2')
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ax_c.set_xlabel('变量 X')
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ax_c.set_ylabel('变量 Y')
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ax_c.spines['top'].set_visible(False)
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ax_c.spines['right'].set_visible(False)
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# 面板 D：热图
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ax_d = fig.add_subplot(gs[1, 1:])
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data = np.random.randn(10, 20)
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im = ax_d.imshow(data, cmap='viridis', aspect='auto')
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ax_d.set_xlabel('样本编号')
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ax_d.set_ylabel('特征')
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cbar = plt.colorbar(im, ax=ax_d, fraction=0.046, pad=0.04)
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cbar.set_label('强度（a.u.）', rotation=270, labelpad=12)
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# 添加面板标签
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panels = [ax_a, ax_b, ax_c, ax_d]
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for i, ax in enumerate(panels):
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ax.text(-0.15, 1.05, ascii_uppercase[i], transform=ax.transAxes,
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fontsize=10, fontweight='bold', va='top')
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save_publication_figure(fig, 'multi_panel_figure')
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plt.show()
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```
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## 示例 3：带散点的箱线图
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```python
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import matplotlib.pyplot as plt
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import numpy as np
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# 生成示例数据
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np.random.seed(42)
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data = [np.random.normal(100, 15, 30),
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np.random.normal(120, 20, 30),
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np.random.normal(140, 18, 30),
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np.random.normal(110, 22, 30)]
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fig, ax = plt.subplots(figsize=(3.5, 3))
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# 创建箱线图
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bp = ax.boxplot(data, widths=0.5, patch_artist=True,
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showfliers=False, # 我们将手动添加散点
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boxprops=dict(facecolor='lightgray', edgecolor='black', linewidth=0.8),
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medianprops=dict(color='black', linewidth=1.5),
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whiskerprops=dict(linewidth=0.8),
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capprops=dict(linewidth=0.8))
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# 叠加个体散点
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colors = ['#0072B2', '#E69F00', '#009E73', '#D55E00']
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for i, (d, color) in enumerate(zip(data, colors)):
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# 为 x 位置添加抖动
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x = np.random.normal(i+1, 0.04, size=len(d))
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ax.scatter(x, d, alpha=0.4, s=8, color=color)
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# 自定义
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ax.set_xticklabels(['对照', '处理 A', '处理 B', '处理 C'])
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ax.set_ylabel('细胞计数')
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ax.spines['top'].set_visible(False)
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ax.spines['right'].set_visible(False)
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ax.set_ylim(50, 200)
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fig.tight_layout()
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save_publication_figure(fig, 'boxplot_with_points')
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plt.show()
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```
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## 示例 4：带颜色条的热图
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```python
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import matplotlib.pyplot as plt
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import numpy as np
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# 生成相关矩阵
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np.random.seed(42)
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n = 10
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A = np.random.randn(n, n)
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corr_matrix = np.corrcoef(A)
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# 创建图形
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fig, ax = plt.subplots(figsize=(4, 3.5))
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# 绘制热图
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im = ax.imshow(corr_matrix, cmap='RdBu_r', vmin=-1, vmax=1, aspect='auto')
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# 添加颜色条
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cbar = plt.colorbar(im, ax=ax, fraction=0.046, pad=0.04)
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cbar.set_label('相关系数', rotation=270, labelpad=15)
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# 设置刻度和标签
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gene_names = [f'基因{i+1}' for i in range(n)]
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ax.set_xticks(np.arange(n))
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ax.set_yticks(np.arange(n))
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ax.set_xticklabels(gene_names, rotation=45, ha='right')
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ax.set_yticklabels(gene_names)
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# 添加网格
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ax.set_xticks(np.arange(n)-.5, minor=True)
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ax.set_yticks(np.arange(n)-.5, minor=True)
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ax.grid(which='minor', color='white', linestyle='-', linewidth=0.5)
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fig.tight_layout()
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save_publication_figure(fig, 'correlation_heatmap')
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plt.show()
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```
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## 示例 5：Seaborn 小提琴图
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```python
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import matplotlib.pyplot as plt
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import seaborn as sns
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import pandas as pd
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import numpy as np
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# 生成示例数据
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np.random.seed(42)
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data = pd.DataFrame({
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'condition': np.repeat(['对照', '药物 A', '药物 B'], 50),
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'value': np.concatenate([
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np.random.normal(100, 15, 50),
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np.random.normal(120, 20, 50),
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np.random.normal(140, 18, 50)
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])
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})
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# 设置样式
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sns.set_style('ticks')
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sns.set_palette(['#0072B2', '#E69F00', '#009E73'])
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fig, ax = plt.subplots(figsize=(3.5, 3))
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# 创建小提琴图
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sns.violinplot(data=data, x='condition', y='value', ax=ax,
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inner='box', linewidth=0.8)
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# 添加带状图
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sns.stripplot(data=data, x='condition', y='value', ax=ax,
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size=2, alpha=0.3, color='black')
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# 自定义
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ax.set_xlabel('')
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ax.set_ylabel('表达水平（AU）')
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ax.spines['top'].set_visible(False)
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ax.spines['right'].set_visible(False)
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fig.tight_layout()
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save_publication_figure(fig, 'violin_plot')
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plt.show()
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```
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## 示例 6：带回归线的科学散点图
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```python
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import matplotlib.pyplot as plt
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import numpy as np
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from scipy import stats
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# 生成具有相关性的数据
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np.random.seed(42)
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x = np.random.randn(100)
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y = 2.5 * x + np.random.randn(100) * 0.8
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# 计算回归
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slope, intercept, r_value, p_value, std_err = stats.linregress(x, y)
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# 创建图形
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fig, ax = plt.subplots(figsize=(3.5, 3.5))
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# 散点图
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ax.scatter(x, y, s=15, alpha=0.6, color='#0072B2', edgecolors='none')
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# 回归线
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x_line = np.array([x.min(), x.max()])
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y_line = slope * x_line + intercept
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ax.plot(x_line, y_line, 'r-', linewidth=1.5, label=f'y = {slope:.2f}x + {intercept:.2f}')
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# 添加统计文本
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stats_text = f'$R^2$ = {r_value**2:.3f}\n$p$ < 0.001' if p_value < 0.001 else f'$R^2$ = {r_value**2:.3f}\n$p$ = {p_value:.3f}'
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ax.text(0.05, 0.95, stats_text, transform=ax.transAxes,
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verticalalignment='top', fontsize=7,
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bbox=dict(boxstyle='round', facecolor='white', alpha=0.8, edgecolor='gray', linewidth=0.5))
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# 自定义
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ax.set_xlabel('预测变量')
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ax.set_ylabel('响应变量')
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ax.spines['top'].set_visible(False)
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ax.spines['right'].set_visible(False)
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fig.tight_layout()
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save_publication_figure(fig, 'scatter_regression')
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plt.show()
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```
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## 示例 7：带阴影误差的时间序列图
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```python
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import matplotlib.pyplot as plt
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import numpy as np
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# 生成时间序列数据
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np.random.seed(42)
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time = np.linspace(0, 24, 100)
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n_replicates = 5
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# 模拟多个重复
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data = np.array([10 * np.exp(-time/10) + np.random.normal(0, 0.5, 100)
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for _ in range(n_replicates)])
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# 计算均值和标准误差
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mean = data.mean(axis=0)
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sem = data.std(axis=0) / np.sqrt(n_replicates)
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# 创建图形
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fig, ax = plt.subplots(figsize=(4, 2.5))
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# 绘制均值线
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ax.plot(time, mean, linewidth=1.5, color='#0072B2', label='均值 ± SEM')
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# 添加阴影误差区域
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ax.fill_between(time, mean - sem, mean + sem,
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alpha=0.3, color='#0072B2', linewidth=0)
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# 自定义
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ax.set_xlabel('时间（小时）')
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ax.set_ylabel('浓度（μM）')
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ax.legend(frameon=False, loc='upper right')
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ax.spines['top'].set_visible(False)
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ax.spines['right'].set_visible(False)
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ax.set_xlim(0, 24)
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ax.set_ylim(0, 12)
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fig.tight_layout()
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save_publication_figure(fig, 'timeseries_shaded')
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plt.show()
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```
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## 示例 8：Plotly 交互式图形
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```python
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import plotly.graph_objects as go
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import numpy as np
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# 生成数据
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np.random.seed(42)
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x = np.random.randn(100)
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y = 2*x + np.random.randn(100)
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colors = np.random.choice(['组 A', '组 B'], 100)
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# Plotly 的 Okabe-Ito 颜色
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okabe_ito_plotly = ['#E69F00', '#56B4E9']
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# 创建图形
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fig = go.Figure()
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for group, color in zip(['组 A', '组 B'], okabe_ito_plotly):
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mask = colors == group
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fig.add_trace(go.Scatter(
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x=x[mask], y=y[mask],
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mode='markers',
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name=group,
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marker=dict(size=6, color=color, opacity=0.6)
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))
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# 更新布局以达到发表质量
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fig.update_layout(
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width=500,
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height=400,
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font=dict(family='Arial, sans-serif', size=10),
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plot_bgcolor='white',
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xaxis=dict(
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title='变量 X',
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showgrid=False,
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showline=True,
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linewidth=1,
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linecolor='black',
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mirror=False
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),
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yaxis=dict(
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title='变量 Y',
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showgrid=False,
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showline=True,
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linewidth=1,
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linecolor='black',
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mirror=False
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),
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legend=dict(
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x=0.02,
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y=0.98,
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bgcolor='rgba(255,255,255,0.8)',
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bordercolor='gray',
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borderwidth=0.5
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)
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)
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# 保存为静态图像（需要 kaleido）
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fig.write_image('plotly_scatter.png', width=500, height=400, scale=3) # scale=3 约等于 300 DPI
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fig.write_html('plotly_scatter.html') # 交互式版本
|
||
|
||
fig.show()
|
||
```
|
||
|
||
## 示例 9：带显著性标记的分组柱状图
|
||
|
||
```python
|
||
import matplotlib.pyplot as plt
|
||
import numpy as np
|
||
|
||
# 数据
|
||
categories = ['WT', '突变体 A', '突变体 B']
|
||
control_means = [100, 85, 70]
|
||
control_sem = [5, 6, 5]
|
||
treatment_means = [100, 120, 140]
|
||
treatment_sem = [6, 8, 9]
|
||
|
||
x = np.arange(len(categories))
|
||
width = 0.35
|
||
|
||
fig, ax = plt.subplots(figsize=(3.5, 3))
|
||
|
||
# 创建柱状条
|
||
bars1 = ax.bar(x - width/2, control_means, width, yerr=control_sem,
|
||
capsize=3, label='对照', color='#0072B2', alpha=0.8)
|
||
bars2 = ax.bar(x + width/2, treatment_means, width, yerr=treatment_sem,
|
||
capsize=3, label='处理', color='#E69F00', alpha=0.8)
|
||
|
||
# 添加显著性标记
|
||
def add_significance_bar(ax, x1, x2, y, h, text):
|
||
"""在两个柱状条之间添加显著性标记线"""
|
||
ax.plot([x1, x1, x2, x2], [y, y+h, y+h, y], linewidth=0.8, c='black')
|
||
ax.text((x1+x2)/2, y+h, text, ha='center', va='bottom', fontsize=7)
|
||
|
||
# 标记显著差异
|
||
add_significance_bar(ax, x[1]-width/2, x[1]+width/2, 135, 3, '***')
|
||
add_significance_bar(ax, x[2]-width/2, x[2]+width/2, 155, 3, '***')
|
||
|
||
# 自定义
|
||
ax.set_ylabel('活性（占 WT 对照的百分比）')
|
||
ax.set_xticks(x)
|
||
ax.set_xticklabels(categories)
|
||
ax.legend(frameon=False, loc='upper left')
|
||
ax.spines['top'].set_visible(False)
|
||
ax.spines['right'].set_visible(False)
|
||
ax.set_ylim(0, 180)
|
||
|
||
# 添加显著性注释
|
||
ax.text(0.98, 0.02, '*** p < 0.001', transform=ax.transAxes,
|
||
ha='right', va='bottom', fontsize=6)
|
||
|
||
fig.tight_layout()
|
||
save_publication_figure(fig, 'grouped_bar_significance')
|
||
plt.show()
|
||
```
|
||
|
||
## 示例 10：Nature 级别可发表图形
|
||
|
||
```python
|
||
import matplotlib.pyplot as plt
|
||
import numpy as np
|
||
from string import ascii_lowercase
|
||
|
||
# Nature 规格：89mm 单栏
|
||
inch_per_mm = 0.0393701
|
||
width_mm = 89
|
||
height_mm = 110
|
||
figsize = (width_mm * inch_per_mm, height_mm * inch_per_mm)
|
||
|
||
fig = plt.figure(figsize=figsize)
|
||
gs = fig.add_gridspec(3, 2, hspace=0.5, wspace=0.4,
|
||
left=0.12, right=0.95, top=0.96, bottom=0.08)
|
||
|
||
# 面板 a：时间过程
|
||
ax_a = fig.add_subplot(gs[0, :])
|
||
time = np.linspace(0, 48, 100)
|
||
for i, label in enumerate(['对照', '处理']):
|
||
y = (1 + i*0.5) * np.exp(-time/20) * (1 + 0.3*np.sin(time/5))
|
||
ax_a.plot(time, y, linewidth=1.2, label=label)
|
||
ax_a.set_xlabel('时间（小时）', fontsize=7)
|
||
ax_a.set_ylabel('生长（OD$_{600}$）', fontsize=7)
|
||
ax_a.legend(frameon=False, fontsize=6)
|
||
ax_a.tick_params(labelsize=6)
|
||
ax_a.spines['top'].set_visible(False)
|
||
ax_a.spines['right'].set_visible(False)
|
||
|
||
# 面板 b：柱状图
|
||
ax_b = fig.add_subplot(gs[1, 0])
|
||
categories = ['A', 'B', 'C']
|
||
values = [1.0, 1.5, 2.2]
|
||
errors = [0.1, 0.15, 0.2]
|
||
ax_b.bar(categories, values, yerr=errors, capsize=2, width=0.6,
|
||
color='#0072B2', alpha=0.8)
|
||
ax_b.set_ylabel('倍数变化', fontsize=7)
|
||
ax_b.tick_params(labelsize=6)
|
||
ax_b.spines['top'].set_visible(False)
|
||
ax_b.spines['right'].set_visible(False)
|
||
|
||
# 面板 c：热图
|
||
ax_c = fig.add_subplot(gs[1, 1])
|
||
data = np.random.randn(8, 6)
|
||
im = ax_c.imshow(data, cmap='viridis', aspect='auto')
|
||
ax_c.set_xlabel('样本', fontsize=7)
|
||
ax_c.set_ylabel('基因', fontsize=7)
|
||
ax_c.tick_params(labelsize=6)
|
||
|
||
# 面板 d：散点图
|
||
ax_d = fig.add_subplot(gs[2, :])
|
||
x = np.random.randn(50)
|
||
y = 2*x + np.random.randn(50)*0.5
|
||
ax_d.scatter(x, y, s=8, alpha=0.6, color='#E69F00')
|
||
ax_d.set_xlabel('基因 X 表达量', fontsize=7)
|
||
ax_d.set_ylabel('基因 Y 表达量', fontsize=7)
|
||
ax_d.tick_params(labelsize=6)
|
||
ax_d.spines['top'].set_visible(False)
|
||
ax_d.spines['right'].set_visible(False)
|
||
|
||
# 添加小写面板标签（Nature 风格）
|
||
for i, ax in enumerate([ax_a, ax_b, ax_c, ax_d]):
|
||
ax.text(-0.2, 1.1, f'{ascii_lowercase[i]}', transform=ax.transAxes,
|
||
fontsize=9, fontweight='bold', va='top')
|
||
|
||
# 以 Nature 偏好的格式保存
|
||
fig.savefig('nature_figure.pdf', dpi=1000, bbox_inches='tight',
|
||
facecolor='white', edgecolor='none')
|
||
fig.savefig('nature_figure.png', dpi=300, bbox_inches='tight',
|
||
facecolor='white', edgecolor='none')
|
||
|
||
plt.show()
|
||
```
|
||
|
||
## 各库的使用技巧
|
||
|
||
### Matplotlib
|
||
- 使用 `fig.tight_layout()` 或 `constrained_layout=True` 防止重叠
|
||
- 将 DPI 设置为 300–600 以用于发表
|
||
- 折线图使用矢量格式（PDF、EPS）
|
||
- 在 PDF/EPS 文件中嵌入字体
|
||
|
||
### Seaborn
|
||
- 基于 matplotlib 构建，因此所有 matplotlib 的自定义功能均适用
|
||
- 使用 `sns.set_style('ticks')` 或 `'whitegrid'` 以获得简洁外观
|
||
- `sns.despine()` 移除顶部和右侧的轴脊线
|
||
- 使用 `sns.set_palette()` 设置自定义调色板
|
||
|
||
### Plotly
|
||
- 非常适合交互式探索性分析
|
||
- 使用 `fig.write_image()` 导出静态图像（需要 kaleido 包）
|
||
- 使用 `scale` 参数控制 DPI（scale=3 ≈ 300 DPI）
|
||
- 大量调整布局以达到发表质量
|
||
|
||
## 通用工作流程
|
||
|
||
1. **使用默认设置进行探索**
|
||
2. **应用发表配置**（参见「设置」部分）
|
||
3. **创建合适大小的图形**（查看期刊要求）
|
||
4. **自定义颜色**（使用色盲友好调色板）
|
||
5. **调整字体和线宽**（在最终尺寸下可读）
|
||
6. **去除图表垃圾元素**（顶部/右侧轴脊线、过多网格线）
|
||
7. **添加清晰的标签及单位**
|
||
8. **用灰度测试**
|
||
9. **以多种格式保存**（矢量用 PDF，光栅用 PNG）
|
||
10. **在最终上下文中验证**（导入稿件中检查尺寸）
|
||
|
||
## 参考资料
|
||
|
||
- Matplotlib 文档：https://matplotlib.org/
|
||
- Seaborn 画廊：https://seaborn.pydata.org/examples/index.html
|
||
- Plotly 文档：https://plotly.com/python/
|
||
- Nature Methods Points of View：数据可视化专栏存档
|