scientific-visualization

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Scientific Visualization

科学可视化

Overview

概述

Scientific visualization transforms data into clear, accurate figures for publication. Create journal-ready plots with multi-panel layouts, error bars, significance markers, and colorblind-safe palettes. Export as PDF/EPS/TIFF using matplotlib, seaborn, and plotly for manuscripts.

科学可视化将数据转化为清晰、准确的可发表图表。使用matplotlib、seaborn和plotly创建符合期刊要求的图表，支持多面板布局、误差棒、显著性标记和色盲友好配色方案，并可导出为PDF/EPS/TIFF格式用于手稿提交。

When to Use This Skill

何时使用该技能

This skill should be used when:

Creating plots or visualizations for scientific manuscripts
Preparing figures for journal submission (Nature, Science, Cell, PLOS, etc.)
Ensuring figures are colorblind-friendly and accessible
Making multi-panel figures with consistent styling
Exporting figures at correct resolution and format
Following specific publication guidelines
Improving existing figures to meet publication standards
Creating figures that need to work in both color and grayscale

在以下场景中应使用该技能：

为科学手稿创建图表或可视化内容
准备用于期刊投稿的图表（如Nature、Science、Cell、PLOS等）
确保图表对色盲人群友好且具有可访问性
创建具有一致样式的多面板图表
以正确的分辨率和格式导出图表
遵循特定的期刊发表指南
改进现有图表使其达到发表标准
创建可同时在彩色和灰度模式下正常展示的图表

Quick Start Guide

快速入门指南

Basic Publication-Quality Figure

基础可发表质量图表

python

import matplotlib.pyplot as plt
import numpy as np

python

import matplotlib.pyplot as plt
import numpy as np

Apply publication style (from scripts/style_presets.py)

应用发表样式（来自scripts/style_presets.py）

from style_presets import apply_publication_style apply_publication_style('default')

Create figure with appropriate size (single column = 3.5 inches)

创建合适尺寸的图表（单栏 = 3.5英寸）

fig, ax = plt.subplots(figsize=(3.5, 2.5))

Plot data

绘制数据

x = np.linspace(0, 10, 100) ax.plot(x, np.sin(x), label='sin(x)') ax.plot(x, np.cos(x), label='cos(x)')

Proper labeling with units

带单位的规范标签

ax.set_xlabel('Time (seconds)') ax.set_ylabel('Amplitude (mV)') ax.legend(frameon=False)

ax.set_xlabel('时间（秒）') ax.set_ylabel('振幅（mV）') ax.legend(frameon=False)

Remove unnecessary spines

移除不必要的边框

ax.spines['top'].set_visible(False) ax.spines['right'].set_visible(False)

Save in publication formats (from scripts/figure_export.py)

保存为发表格式（来自scripts/figure_export.py）

from figure_export import save_publication_figure save_publication_figure(fig, 'figure1', formats=['pdf', 'png'], dpi=300)

undefined

from figure_export import save_publication_figure save_publication_figure(fig, 'figure1', formats=['pdf', 'png'], dpi=300)

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Using Pre-configured Styles

使用预配置样式

Apply journal-specific styles using the matplotlib style files in

assets/

python

import matplotlib.pyplot as plt

使用

assets/

目录中的matplotlib样式文件应用期刊特定样式：

python

import matplotlib.pyplot as plt

Option 1: Use style file directly

选项1：直接使用样式文件

plt.style.use('assets/nature.mplstyle')

Option 2: Use style_presets.py helper

选项2：使用style_presets.py辅助工具

from style_presets import configure_for_journal configure_for_journal('nature', figure_width='single')

Now create figures - they'll automatically match Nature specifications

现在创建图表 - 它们将自动匹配Nature的规范

fig, ax = plt.subplots()

... your plotting code ...

... 你的绘图代码 ...

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Quick Start with Seaborn

Seaborn快速入门

For statistical plots, use seaborn with publication styling:

python

import seaborn as sns
import matplotlib.pyplot as plt
from style_presets import apply_publication_style

对于统计图表，结合发表样式使用seaborn：

python

import seaborn as sns
import matplotlib.pyplot as plt
from style_presets import apply_publication_style

Apply publication style

应用发表样式

apply_publication_style('default') sns.set_theme(style='ticks', context='paper', font_scale=1.1) sns.set_palette('colorblind')

Create statistical comparison figure

创建统计对比图表

fig, ax = plt.subplots(figsize=(3.5, 3)) sns.boxplot(data=df, x='treatment', y='response', order=['Control', 'Low', 'High'], palette='Set2', ax=ax) sns.stripplot(data=df, x='treatment', y='response', order=['Control', 'Low', 'High'], color='black', alpha=0.3, size=3, ax=ax) ax.set_ylabel('Response (μM)') sns.despine()

Save figure

保存图表

from figure_export import save_publication_figure save_publication_figure(fig, 'treatment_comparison', formats=['pdf', 'png'], dpi=300)

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from figure_export import save_publication_figure save_publication_figure(fig, 'treatment_comparison', formats=['pdf', 'png'], dpi=300)

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Core Principles and Best Practices

核心原则与最佳实践

1. Resolution and File Format

1. 分辨率与文件格式

Critical requirements (detailed in

references/publication_guidelines.md

Raster images (photos, microscopy): 300-600 DPI
Line art (graphs, plots): 600-1200 DPI or vector format
Vector formats (preferred): PDF, EPS, SVG
Raster formats: TIFF, PNG (never JPEG for scientific data)

Implementation:

python

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关键要求（详情见

references/publication_guidelines.md

）：

光栅图像（照片、显微图像）：300-600 DPI
线稿（图表、曲线图）：600-1200 DPI或矢量格式
矢量格式（首选）：PDF、EPS、SVG
光栅格式：TIFF、PNG（科学数据绝对不要使用JPEG）

实现方式：

python

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Use the figure_export.py script for correct settings

使用figure_export.py脚本获取正确设置

from figure_export import save_publication_figure

Saves in multiple formats with proper DPI

以正确DPI保存为多种格式

save_publication_figure(fig, 'myfigure', formats=['pdf', 'png'], dpi=300)

Or save for specific journal requirements

或针对特定期刊要求保存

from figure_export import save_for_journal save_for_journal(fig, 'figure1', journal='nature', figure_type='combination')

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from figure_export import save_for_journal save_for_journal(fig, 'figure1', journal='nature', figure_type='combination')

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2. Color Selection - Colorblind Accessibility

2. 颜色选择 - 色盲可访问性

Always use colorblind-friendly palettes (detailed in

references/color_palettes.md

Recommended: Okabe-Ito palette (distinguishable by all types of color blindness):

python

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始终使用色盲友好的配色方案（详情见

references/color_palettes.md

）：

推荐：Okabe-Ito配色方案（所有类型色盲人群均可区分）：

python

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Option 1: Use assets/color_palettes.py

选项1：使用assets/color_palettes.py

from color_palettes import OKABE_ITO_LIST, apply_palette apply_palette('okabe_ito')

Option 2: Manual specification

选项2：手动指定

okabe_ito = ['#E69F00', '#56B4E9', '#009E73', '#F0E442', '#0072B2', '#D55E00', '#CC79A7', '#000000'] plt.rcParams['axes.prop_cycle'] = plt.cycler(color=okabe_ito)


**For heatmaps/continuous data:**
- Use perceptually uniform colormaps: `viridis`, `plasma`, `cividis`
- Avoid red-green diverging maps (use `PuOr`, `RdBu`, `BrBG` instead)
- Never use `jet` or `rainbow` colormaps

**Always test figures in grayscale** to ensure interpretability.

okabe_ito = ['#E69F00', '#56B4E9', '#009E73', '#F0E442', '#0072B2', '#D55E00', '#CC79A7', '#000000'] plt.rcParams['axes.prop_cycle'] = plt.cycler(color=okabe_ito)


**用于热图/连续数据：**
- 使用感知均匀的颜色映射：`viridis`、`plasma`、`cividis`
- 避免红绿发散映射（改用`PuOr`、`RdBu`、`BrBG`）
- 绝对不要使用`jet`或`rainbow`颜色映射

**始终在灰度模式下测试图表**以确保可解释性。

3. Typography and Text

3. 排版与文本

Font guidelines (detailed in

references/publication_guidelines.md

Sans-serif fonts: Arial, Helvetica, Calibri
Minimum sizes at final print size:
- Axis labels: 7-9 pt
- Tick labels: 6-8 pt
- Panel labels: 8-12 pt (bold)
Sentence case for labels: "Time (hours)" not "TIME (HOURS)"
Always include units in parentheses

Implementation:

python

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字体指南（详情见

references/publication_guidelines.md

）：

无衬线字体：Arial、Helvetica、Calibri
最终打印尺寸下的最小字号：
- 坐标轴标签：7-9磅
- 刻度标签：6-8磅
- 面板标签：8-12磅（加粗）
标签采用句首大写格式："时间（小时）"而非"TIME（HOURS）"
始终在括号中包含单位

实现方式：

python

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Set fonts globally

全局设置字体

import matplotlib as mpl mpl.rcParams['font.family'] = 'sans-serif' mpl.rcParams['font.sans-serif'] = ['Arial', 'Helvetica'] mpl.rcParams['font.size'] = 8 mpl.rcParams['axes.labelsize'] = 9 mpl.rcParams['xtick.labelsize'] = 7 mpl.rcParams['ytick.labelsize'] = 7

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4. Figure Dimensions

4. 图表尺寸

Journal-specific widths (detailed in

references/journal_requirements.md

Nature: Single 89 mm, Double 183 mm
Science: Single 55 mm, Double 175 mm
Cell: Single 85 mm, Double 178 mm

Check figure size compliance:

python

from figure_export import check_figure_size

fig = plt.figure(figsize=(3.5, 3))  # 89 mm for Nature
check_figure_size(fig, journal='nature')

期刊特定宽度（详情见

references/journal_requirements.md

）：

Nature：单栏89毫米，双栏183毫米
Science：单栏55毫米，双栏175毫米
Cell：单栏85毫米，双栏178毫米

检查图表尺寸合规性：

python

from figure_export import check_figure_size

fig = plt.figure(figsize=(3.5, 3))  # Nature的89毫米
check_figure_size(fig, journal='nature')

5. Multi-Panel Figures

5. 多面板图表

Best practices:

Label panels with bold letters: A, B, C (uppercase for most journals, lowercase for Nature)
Maintain consistent styling across all panels
Align panels along edges where possible
Use adequate white space between panels

Example implementation (see

references/matplotlib_examples.md

for complete code):

python

from string import ascii_uppercase

fig = plt.figure(figsize=(7, 4))
gs = fig.add_gridspec(2, 2, hspace=0.4, wspace=0.4)

ax1 = fig.add_subplot(gs[0, 0])
ax2 = fig.add_subplot(gs[0, 1])

最佳实践：

使用加粗字母标记面板：A、B、C（大多数期刊用大写，Nature用小写）
所有面板保持一致的样式
尽可能对齐面板边缘
面板间保留足够的空白

示例实现（完整代码见

references/matplotlib_examples.md

）：

python

from string import ascii_uppercase

fig = plt.figure(figsize=(7, 4))
gs = fig.add_gridspec(2, 2, hspace=0.4, wspace=0.4)

ax1 = fig.add_subplot(gs[0, 0])
ax2 = fig.add_subplot(gs[0, 1])

... create other panels ...

... 创建其他面板 ...

Add panel labels

添加面板标签

for i, ax in enumerate([ax1, ax2, ...]): ax.text(-0.15, 1.05, ascii_uppercase[i], transform=ax.transAxes, fontsize=10, fontweight='bold', va='top')

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for i, ax in enumerate([ax1, ax2, ...]): ax.text(-0.15, 1.05, ascii_uppercase[i], transform=ax.transAxes, fontsize=10, fontweight='bold', va='top')

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Common Tasks

常见任务

Task 1: Create a Publication-Ready Line Plot

任务1：创建可发表的折线图

See

references/matplotlib_examples.md

Example 1 for complete code.

Key steps:

Apply publication style
Set appropriate figure size for target journal
Use colorblind-friendly colors
Add error bars with correct representation (SEM, SD, or CI)
Label axes with units
Remove unnecessary spines
Save in vector format

Using seaborn for automatic confidence intervals:

python

import seaborn as sns
fig, ax = plt.subplots(figsize=(5, 3))
sns.lineplot(data=timeseries, x='time', y='measurement',
             hue='treatment', errorbar=('ci', 95), 
             markers=True, ax=ax)
ax.set_xlabel('Time (hours)')
ax.set_ylabel('Measurement (AU)')
sns.despine()

完整代码见

references/matplotlib_examples.md

示例1。

关键步骤：

应用发表样式
为目标期刊设置合适的图表尺寸
使用色盲友好的颜色
添加正确表示的误差棒（标准误、标准差或置信区间）
为坐标轴添加带单位的标签
移除不必要的边框
保存为矢量格式

使用seaborn自动生成置信区间：

python

import seaborn as sns
fig, ax = plt.subplots(figsize=(5, 3))
sns.lineplot(data=timeseries, x='time', y='measurement',
             hue='treatment', errorbar=('ci', 95), 
             markers=True, ax=ax)
ax.set_xlabel('时间（小时）')
ax.set_ylabel('测量值（AU）')
sns.despine()

Task 2: Create a Multi-Panel Figure

任务2：创建多面板图表

See

references/matplotlib_examples.md

Example 2 for complete code.

Key steps:

Use
```
GridSpec
```
for flexible layout
Ensure consistent styling across panels
Add bold panel labels (A, B, C, etc.)
Align related panels
Verify all text is readable at final size

完整代码见

references/matplotlib_examples.md

示例2。

关键步骤：

使用
```
GridSpec
```
实现灵活布局
确保所有面板样式一致
添加加粗的面板标签（A、B、C等）
对齐相关面板
验证所有文本在最终尺寸下可读

Task 3: Create a Heatmap with Proper Colormap

任务3：创建带合适颜色映射的热图

See

references/matplotlib_examples.md

Example 4 for complete code.

Key steps:

Use perceptually uniform colormap (
```
viridis
```
,
```
plasma
```
,
```
cividis
```
)
Include labeled colorbar
For diverging data, use colorblind-safe diverging map (
```
RdBu_r
```
,
```
PuOr
```
)
Set appropriate center value for diverging maps
Test appearance in grayscale

Using seaborn for correlation matrices:

python

import seaborn as sns
fig, ax = plt.subplots(figsize=(5, 4))
corr = df.corr()
mask = np.triu(np.ones_like(corr, dtype=bool))
sns.heatmap(corr, mask=mask, annot=True, fmt='.2f',
            cmap='RdBu_r', center=0, square=True,
            linewidths=1, cbar_kws={'shrink': 0.8}, ax=ax)

完整代码见

references/matplotlib_examples.md

示例4。

关键步骤：

使用感知均匀的颜色映射（
```
viridis
```
、
```
plasma
```
、
```
cividis
```
）
包含带标签的颜色条
对于发散数据，使用色盲友好的发散映射（
```
RdBu_r
```
、
```
PuOr
```
）
为发散映射设置合适的中心值
在灰度模式下测试显示效果

使用seaborn创建相关矩阵：

python

import seaborn as sns
fig, ax = plt.subplots(figsize=(5, 4))
corr = df.corr()
mask = np.triu(np.ones_like(corr, dtype=bool))
sns.heatmap(corr, mask=mask, annot=True, fmt='.2f',
            cmap='RdBu_r', center=0, square=True,
            linewidths=1, cbar_kws={'shrink': 0.8}, ax=ax)

Task 4: Prepare Figure for Specific Journal

任务4：为特定期刊准备图表

Workflow:

Check journal requirements:
```
references/journal_requirements.md
```

Configure matplotlib for journal:

python

from style_presets import configure_for_journal
configure_for_journal('nature', figure_width='single')

Create figure (will auto-size correctly)

Export with journal specifications:

python

from figure_export import save_for_journal
save_for_journal(fig, 'figure1', journal='nature', figure_type='line_art')

工作流程：

查看期刊要求：
```
references/journal_requirements.md
```

为期刊配置matplotlib：

python

from style_presets import configure_for_journal
configure_for_journal('nature', figure_width='single')

创建图表（将自动调整为正确尺寸）

按期刊规范导出：

python

from figure_export import save_for_journal
save_for_journal(fig, 'figure1', journal='nature', figure_type='line_art')

Task 5: Fix an Existing Figure to Meet Publication Standards

任务5：修改现有图表使其达到发表标准

Checklist approach (full checklist in

references/publication_guidelines.md

Check resolution: Verify DPI meets journal requirements
Check file format: Use vector for plots, TIFF/PNG for images
Check colors: Ensure colorblind-friendly
Check fonts: Minimum 6-7 pt at final size, sans-serif
Check labels: All axes labeled with units
Check size: Matches journal column width
Test grayscale: Figure interpretable without color
Remove chart junk: No unnecessary grids, 3D effects, shadows

清单法（完整清单见

references/publication_guidelines.md

）：

检查分辨率：验证DPI符合期刊要求
检查文件格式：图表使用矢量格式，图像使用TIFF/PNG
检查颜色：确保色盲友好
检查字体：最终尺寸下最小6-7磅，使用无衬线字体
检查标签：所有坐标轴均带单位标签
检查尺寸：匹配期刊栏宽
灰度测试：图表在无颜色时仍可解释
移除冗余元素：移除不必要的网格、3D效果、阴影

Task 6: Create Colorblind-Friendly Visualizations

任务6：创建色盲友好的可视化内容

Strategy:

Use approved palettes from
```
assets/color_palettes.py
```
Add redundant encoding (line styles, markers, patterns)
Test with colorblind simulator
Ensure grayscale compatibility

Example:

python

from color_palettes import apply_palette
import matplotlib.pyplot as plt

apply_palette('okabe_ito')

策略：

使用
```
assets/color_palettes.py
```
中的已批准配色方案
添加冗余编码（线型、标记、图案）
使用色盲模拟器测试
确保灰度兼容性

示例：

python

from color_palettes import apply_palette
import matplotlib.pyplot as plt

apply_palette('okabe_ito')

Add redundant encoding beyond color

添加颜色之外的冗余编码

line_styles = ['-', '--', '-.', ':'] markers = ['o', 's', '^', 'v']

for i, (data, label) in enumerate(datasets): plt.plot(x, data, linestyle=line_styles[i % 4], marker=markers[i % 4], label=label)

undefined

line_styles = ['-', '--', '-.', ':'] markers = ['o', 's', '^', 'v']

for i, (data, label) in enumerate(datasets): plt.plot(x, data, linestyle=line_styles[i % 4], marker=markers[i % 4], label=label)

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Statistical Rigor

统计严谨性

Always include:

Error bars (SD, SEM, or CI - specify which in caption)
Sample size (n) in figure or caption
Statistical significance markers (*, **, ***)
Individual data points when possible (not just summary statistics)

Example with statistics:

python

undefined

必须包含：

误差棒（标准差、标准误或置信区间 - 在图注中说明类型）
样本量（n），可在图表或图注中体现
显著性标记（、、）
尽可能展示单个数据点（而非仅展示汇总统计）

带统计信息的示例：

python

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Show individual points with summary statistics

展示单个数据点与汇总统计

ax.scatter(x_jittered, individual_points, alpha=0.4, s=8) ax.errorbar(x, means, yerr=sems, fmt='o', capsize=3)

Mark significance

标记显著性

ax.text(1.5, max_y * 1.1, '***', ha='center', fontsize=8)

undefined

ax.text(1.5, max_y * 1.1, '***', ha='center', fontsize=8)

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Working with Different Plotting Libraries

不同绘图库的使用

Matplotlib

Most control over publication details
Best for complex multi-panel figures
Use provided style files for consistent formatting
See
```
references/matplotlib_examples.md
```
for extensive examples

对发表细节的控制程度最高
最适合复杂的多面板图表
使用提供的样式文件保持格式一致
详细示例见
```
references/matplotlib_examples.md
```

Seaborn

Seaborn provides a high-level, dataset-oriented interface for statistical graphics, built on matplotlib. It excels at creating publication-quality statistical visualizations with minimal code while maintaining full compatibility with matplotlib customization.

Key advantages for scientific visualization:

Automatic statistical estimation and confidence intervals
Built-in support for multi-panel figures (faceting)
Colorblind-friendly palettes by default
Dataset-oriented API using pandas DataFrames
Semantic mapping of variables to visual properties

Seaborn基于matplotlib提供了面向数据集的高级统计图形接口。它能以最少的代码创建符合发表质量的统计可视化内容，同时保持与matplotlib自定义功能的完全兼容性。

科学可视化的核心优势：

自动统计估计与置信区间计算
内置多面板图表支持（分面）
默认使用色盲友好的配色方案
基于pandas DataFrame的数据集导向API
变量到视觉属性的语义映射

Quick Start with Publication Style

Seaborn发表样式快速入门

Always apply matplotlib publication styles first, then configure seaborn:

python

import seaborn as sns
import matplotlib.pyplot as plt
from style_presets import apply_publication_style

始终先应用matplotlib发表样式，再配置seaborn：

python

import seaborn as sns
import matplotlib.pyplot as plt
from style_presets import apply_publication_style

Apply publication style

应用发表样式

apply_publication_style('default')

Configure seaborn for publication

配置seaborn以适配发表需求

sns.set_theme(style='ticks', context='paper', font_scale=1.1) sns.set_palette('colorblind') # Use colorblind-safe palette

sns.set_theme(style='ticks', context='paper', font_scale=1.1) sns.set_palette('colorblind') # 使用色盲友好的配色方案

Create figure

创建图表

fig, ax = plt.subplots(figsize=(3.5, 2.5)) sns.scatterplot(data=df, x='time', y='response', hue='treatment', style='condition', ax=ax) sns.despine() # Remove top and right spines

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fig, ax = plt.subplots(figsize=(3.5, 2.5)) sns.scatterplot(data=df, x='time', y='response', hue='treatment', style='condition', ax=ax) sns.despine() # 移除顶部和右侧边框

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Common Plot Types for Publications

适用于发表的常见图表类型

Statistical comparisons:

python

undefined

统计对比：

python

undefined

Box plot with individual points for transparency

带单个数据点的箱线图以保证透明度


**Distribution analysis:**
```python


**分布分析：**
```python

Violin plot with split comparison

带拆分对比的小提琴图

fig, ax = plt.subplots(figsize=(4, 3)) sns.violinplot(data=df, x='timepoint', y='expression', hue='treatment', split=True, inner='quartile', ax=ax) ax.set_ylabel('Gene Expression (AU)') sns.despine()


**Correlation matrices:**
```python

fig, ax = plt.subplots(figsize=(4, 3)) sns.violinplot(data=df, x='timepoint', y='expression', hue='treatment', split=True, inner='quartile', ax=ax) ax.set_ylabel('基因表达量（AU）') sns.despine()


**相关矩阵：**
```python

Heatmap with proper colormap and annotations

带合适颜色映射和注释的热图

fig, ax = plt.subplots(figsize=(5, 4)) corr = df.corr() mask = np.triu(np.ones_like(corr, dtype=bool)) # Show only lower triangle sns.heatmap(corr, mask=mask, annot=True, fmt='.2f', cmap='RdBu_r', center=0, square=True, linewidths=1, cbar_kws={'shrink': 0.8}, ax=ax) plt.tight_layout()


**Time series with confidence bands:**
```python

fig, ax = plt.subplots(figsize=(5, 4)) corr = df.corr() mask = np.triu(np.ones_like(corr, dtype=bool)) # 仅显示下三角 sns.heatmap(corr, mask=mask, annot=True, fmt='.2f', cmap='RdBu_r', center=0, square=True, linewidths=1, cbar_kws={'shrink': 0.8}, ax=ax) plt.tight_layout()


**带置信带的时间序列：**
```python

Line plot with automatic CI calculation

带自动置信区间计算的折线图

fig, ax = plt.subplots(figsize=(5, 3)) sns.lineplot(data=timeseries, x='time', y='measurement', hue='treatment', style='replicate', errorbar=('ci', 95), markers=True, dashes=False, ax=ax) ax.set_xlabel('Time (hours)') ax.set_ylabel('Measurement (AU)') sns.despine()

undefined

fig, ax = plt.subplots(figsize=(5, 3)) sns.lineplot(data=timeseries, x='time', y='measurement', hue='treatment', style='replicate', errorbar=('ci', 95), markers=True, dashes=False, ax=ax) ax.set_xlabel('时间（小时）') ax.set_ylabel('测量值（AU）') sns.despine()

undefined

Multi-Panel Figures with Seaborn

使用Seaborn创建多面板图表

Using FacetGrid for automatic faceting:

python

undefined

使用FacetGrid自动分面：

python

undefined

Create faceted plot

创建分面图

g = sns.relplot(data=df, x='dose', y='response', hue='treatment', col='cell_line', row='timepoint', kind='line', height=2.5, aspect=1.2, errorbar=('ci', 95), markers=True) g.set_axis_labels('Dose (μM)', 'Response (AU)') g.set_titles('{row_name} | {col_name}') sns.despine()

g = sns.relplot(data=df, x='dose', y='response', hue='treatment', col='cell_line', row='timepoint', kind='line', height=2.5, aspect=1.2, errorbar=('ci', 95), markers=True) g.set_axis_labels('剂量（μM）', '响应值（AU）') g.set_titles('{row_name} | {col_name}') sns.despine()

Save with correct DPI

以正确DPI保存

from figure_export import save_publication_figure save_publication_figure(g.figure, 'figure_facets', formats=['pdf', 'png'], dpi=300)


**Combining seaborn with matplotlib subplots:**
```python

from figure_export import save_publication_figure save_publication_figure(g.figure, 'figure_facets', formats=['pdf', 'png'], dpi=300)


**结合Seaborn与matplotlib子图：**
```python

Create custom multi-panel layout

创建自定义多面板布局

fig, axes = plt.subplots(2, 2, figsize=(7, 6))

Panel A: Scatter with regression

面板A：带回归线的散点图

sns.regplot(data=df, x='predictor', y='response', ax=axes[0, 0]) axes[0, 0].text(-0.15, 1.05, 'A', transform=axes[0, 0].transAxes, fontsize=10, fontweight='bold')

Panel B: Distribution comparison

面板B：分布对比

sns.violinplot(data=df, x='group', y='value', ax=axes[0, 1]) axes[0, 1].text(-0.15, 1.05, 'B', transform=axes[0, 1].transAxes, fontsize=10, fontweight='bold')

Panel C: Heatmap

面板C：热图

sns.heatmap(correlation_data, cmap='viridis', ax=axes[1, 0]) axes[1, 0].text(-0.15, 1.05, 'C', transform=axes[1, 0].transAxes, fontsize=10, fontweight='bold')

Panel D: Time series

面板D：时间序列

sns.lineplot(data=timeseries, x='time', y='signal', hue='condition', ax=axes[1, 1]) axes[1, 1].text(-0.15, 1.05, 'D', transform=axes[1, 1].transAxes, fontsize=10, fontweight='bold')

plt.tight_layout() sns.despine()

undefined

sns.lineplot(data=timeseries, x='time', y='signal', hue='condition', ax=axes[1, 1]) axes[1, 1].text(-0.15, 1.05, 'D', transform=axes[1, 1].transAxes, fontsize=10, fontweight='bold')

plt.tight_layout() sns.despine()

undefined

Color Palettes for Publications

适用于发表的Seaborn配色方案

Seaborn includes several colorblind-safe palettes:

python

undefined

Seaborn包含多种色盲友好的配色方案：

python

undefined

Use built-in colorblind palette (recommended)

使用内置色盲配色方案（推荐）

sns.set_palette('colorblind')

Or specify custom colorblind-safe colors (Okabe-Ito)

或指定自定义色盲友好颜色（Okabe-Ito）

okabe_ito = ['#E69F00', '#56B4E9', '#009E73', '#F0E442', '#0072B2', '#D55E00', '#CC79A7', '#000000'] sns.set_palette(okabe_ito)

For heatmaps and continuous data

用于热图和连续数据

sns.heatmap(data, cmap='viridis') # Perceptually uniform sns.heatmap(corr, cmap='RdBu_r', center=0) # Diverging, centered

undefined

sns.heatmap(data, cmap='viridis') # 感知均匀 sns.heatmap(corr, cmap='RdBu_r', center=0) # 发散型，居中

undefined

Choosing Between Axes-Level and Figure-Level Functions

选择轴级与图级函数

Axes-level functions (e.g.,

scatterplot

boxplot

heatmap

Use when building custom multi-panel layouts
Accept
```
ax=
```
parameter for precise placement
Better integration with matplotlib subplots
More control over figure composition

python

fig, ax = plt.subplots(figsize=(3.5, 2.5))
sns.scatterplot(data=df, x='x', y='y', hue='group', ax=ax)

Figure-level functions (e.g.,

relplot

catplot

displot

Use for automatic faceting by categorical variables
Create complete figures with consistent styling
Great for exploratory analysis
Use
```
height
```
and
```
aspect
```
for sizing

python

g = sns.relplot(data=df, x='x', y='y', col='category', kind='scatter')

轴级函数（如

scatterplot

、

boxplot

、

heatmap

）：

构建自定义多面板布局时使用
接受
```
ax=
```
参数进行精确定位
与matplotlib子图更好集成
对图表组合有更多控制

python

fig, ax = plt.subplots(figsize=(3.5, 2.5))
sns.scatterplot(data=df, x='x', y='y', hue='group', ax=ax)

图级函数（如

relplot

、

catplot

、

displot

）：

按分类变量自动分面时使用
创建样式一致的完整图表
非常适合探索性分析
使用
```
height
```
和
```
aspect
```
调整尺寸

python

g = sns.relplot(data=df, x='x', y='y', col='category', kind='scatter')

Statistical Rigor with Seaborn

Seaborn的统计严谨性

Seaborn automatically computes and displays uncertainty:

python

undefined

Seaborn自动计算并展示不确定性：

python

undefined

Line plot: shows mean ± 95% CI by default

折线图：默认显示均值±95%置信区间

sns.lineplot(data=df, x='time', y='value', hue='treatment', errorbar=('ci', 95)) # Can change to 'sd', 'se', etc.

sns.lineplot(data=df, x='time', y='value', hue='treatment', errorbar=('ci', 95)) # 可改为'sd'、'se'等

Bar plot: shows mean with bootstrapped CI

柱状图：显示均值与自助法置信区间

sns.barplot(data=df, x='treatment', y='response', errorbar=('ci', 95), capsize=0.1)

Always specify error type in figure caption:

始终在图注中说明误差类型：

"Error bars represent 95% confidence intervals"

"误差棒代表95%置信区间"

undefined

undefined

Best Practices for Publication-Ready Seaborn Figures

可发表Seaborn图表的最佳实践

Always set publication theme first:

python

sns.set_theme(style='ticks', context='paper', font_scale=1.1)

Use colorblind-safe palettes:
python
```
sns.set_palette('colorblind')
```

Remove unnecessary elements:

python

sns.despine()  # Remove top and right spines

Control figure size appropriately:

python

# Axes-level: use matplotlib figsize
fig, ax = plt.subplots(figsize=(3.5, 2.5))

# Figure-level: use height and aspect
g = sns.relplot(..., height=3, aspect=1.2)

Show individual data points when possible:

python

sns.boxplot(...)  # Summary statistics
sns.stripplot(..., alpha=0.3)  # Individual points

Include proper labels with units:

python

ax.set_xlabel('Time (hours)')
ax.set_ylabel('Expression (AU)')

Export at correct resolution:

python

from figure_export import save_publication_figure
save_publication_figure(fig, 'figure_name', 
                       formats=['pdf', 'png'], dpi=300)

始终先设置发表主题：

python

sns.set_theme(style='ticks', context='paper', font_scale=1.1)

使用色盲友好的配色方案：
python
```
sns.set_palette('colorblind')
```

移除不必要的元素：

python

sns.despine()  # 移除顶部和右侧边框

适当控制图表尺寸：

python

# 轴级：使用matplotlib的figsize
fig, ax = plt.subplots(figsize=(3.5, 2.5))

# 图级：使用height和aspect
g = sns.relplot(..., height=3, aspect=1.2)

尽可能展示单个数据点：

python

sns.boxplot(...)  # 汇总统计
sns.stripplot(..., alpha=0.3)  # 单个数据点

添加带单位的规范标签：

python

ax.set_xlabel('时间（小时）')
ax.set_ylabel('表达量（AU）')

以正确分辨率导出：

python

from figure_export import save_publication_figure
save_publication_figure(fig, 'figure_name', 
                       formats=['pdf', 'png'], dpi=300)

Advanced Seaborn Techniques

高级Seaborn技巧

Pairwise relationships for exploratory analysis:

python

undefined

用于探索性分析的成对关系：

python

undefined

Quick overview of all relationships

快速查看所有变量关系

g = sns.pairplot(data=df, hue='condition', vars=['gene1', 'gene2', 'gene3'], corner=True, diag_kind='kde', height=2)


**Hierarchical clustering heatmap:**
```python

g = sns.pairplot(data=df, hue='condition', vars=['gene1', 'gene2', 'gene3'], corner=True, diag_kind='kde', height=2)


**层次聚类热图：**
```python

Cluster samples and features

对样本和特征进行聚类

g = sns.clustermap(expression_data, method='ward', metric='euclidean', z_score=0, cmap='RdBu_r', center=0, figsize=(10, 8), row_colors=condition_colors, cbar_kws={'label': 'Z-score'})


**Joint distributions with marginals:**
```python

g = sns.clustermap(expression_data, method='ward', metric='euclidean', z_score=0, cmap='RdBu_r', center=0, figsize=(10, 8), row_colors=condition_colors, cbar_kws={'label': 'Z值'})


**带边缘分布的联合分布：**
```python

Bivariate distribution with context

带上下文的双变量分布

g = sns.jointplot(data=df, x='gene1', y='gene2', hue='treatment', kind='scatter', height=6, ratio=4, marginal_kws={'kde': True})

undefined

g = sns.jointplot(data=df, x='gene1', y='gene2', hue='treatment', kind='scatter', height=6, ratio=4, marginal_kws={'kde': True})

undefined

Common Seaborn Issues and Solutions

Seaborn常见问题与解决方案

Issue: Legend outside plot area

python

g = sns.relplot(...)
g._legend.set_bbox_to_anchor((0.9, 0.5))

Issue: Overlapping labels

python

plt.xticks(rotation=45, ha='right')
plt.tight_layout()

Issue: Text too small at final size

python

sns.set_context('paper', font_scale=1.2)  # Increase if needed

问题：图例超出图表区域

python

g = sns.relplot(...)
g._legend.set_bbox_to_anchor((0.9, 0.5))

问题：标签重叠

python

plt.xticks(rotation=45, ha='right')
plt.tight_layout()

问题：最终尺寸下文本过小

python

sns.set_context('paper', font_scale=1.2)  # 必要时增大

Additional Resources

Plotly

Interactive figures for exploration
Export static images for publication
Configure for publication quality:

python

fig.update_layout(
    font=dict(family='Arial, sans-serif', size=10),
    plot_bgcolor='white',
    # ... see matplotlib_examples.md Example 8
)
fig.write_image('figure.png', scale=3)  # scale=3 gives ~300 DPI

用于探索性分析的交互式图表
可导出静态图像用于发表
配置为发表质量：

python

fig.update_layout(
    font=dict(family='Arial, sans-serif', size=10),
    plot_bgcolor='white',
    # ... 详见matplotlib_examples.md示例8
)
fig.write_image('figure.png', scale=3)  # scale=3约等于300 DPI

Resources

资源

References Directory

参考文档目录

Load these as needed for detailed information:

publication_guidelines.md
: Comprehensive best practices
- Resolution and file format requirements
- Typography guidelines
- Layout and composition rules
- Statistical rigor requirements
- Complete publication checklist
color_palettes.md
: Color usage guide
- Colorblind-friendly palette specifications with RGB values
- Sequential and diverging colormap recommendations
- Testing procedures for accessibility
- Domain-specific palettes (genomics, microscopy)
journal_requirements.md
: Journal-specific specifications
- Technical requirements by publisher
- File format and DPI specifications
- Figure dimension requirements
- Quick reference table
matplotlib_examples.md
: Practical code examples
- 10 complete working examples
- Line plots, bar plots, heatmaps, multi-panel figures
- Journal-specific figure examples
- Tips for each library (matplotlib, seaborn, plotly)

按需加载以获取详细信息：

publication_guidelines.md
：全面的最佳实践
- 分辨率与文件格式要求
- 排版指南
- 布局与构图规则
- 统计严谨性要求
- 完整的发表检查清单
color_palettes.md
：颜色使用指南
- 带RGB值的色盲友好配色方案规范
- 连续型和发散型颜色映射推荐
- 可访问性测试流程
- 领域特定配色方案（基因组学、显微成像）
journal_requirements.md
：期刊特定规范
- 各出版商的技术要求
- 文件格式与DPI规范
- 图表尺寸要求
- 快速参考表格
matplotlib_examples.md
：实用代码示例
- 10个完整的可运行示例
- 折线图、柱状图、热图、多面板图表
- 期刊特定图表示例
- 各库（matplotlib、seaborn、plotly）使用技巧

Scripts Directory

脚本目录

Use these helper scripts for automation:

figure_export.py
: Export utilities
- ```
save_publication_figure()
```
  : Save in multiple formats with correct DPI
- ```
save_for_journal()
```
  : Use journal-specific requirements automatically
- ```
check_figure_size()
```
  : Verify dimensions meet journal specs
- Run directly:
```
python scripts/figure_export.py
```
  for examples
style_presets.py
: Pre-configured styles
- ```
apply_publication_style()
```
  : Apply preset styles (default, nature, science, cell)
- ```
set_color_palette()
```
  : Quick palette switching
- ```
configure_for_journal()
```
  : One-command journal configuration
- Run directly:
```
python scripts/style_presets.py
```
  to see examples

使用这些辅助脚本实现自动化：

figure_export.py
：导出工具
- ```
save_publication_figure()
```
  ：以正确DPI保存为多种格式
- ```
save_for_journal()
```
  ：自动使用期刊特定要求
- ```
check_figure_size()
```
  ：验证尺寸是否符合期刊规范
- 直接运行：
```
python scripts/figure_export.py
```
  查看示例
style_presets.py
：预配置样式
- ```
apply_publication_style()
```
  ：应用预设样式（默认、Nature、Science、Cell）
- ```
set_color_palette()
```
  ：快速切换配色方案
- ```
configure_for_journal()
```
  ：一键配置期刊样式
- 直接运行：
```
python scripts/style_presets.py
```
  查看示例

Assets Directory

资源文件目录

Use these files in figures:

color_palettes.py
: Importable color definitions
- All recommended palettes as Python constants
- ```
apply_palette()
```
  helper function
- Can be imported directly into notebooks/scripts
Matplotlib style files: Use with
```
plt.style.use()
```
- ```
publication.mplstyle
```
  : General publication quality
- ```
nature.mplstyle
```
  : Nature journal specifications
- ```
presentation.mplstyle
```
  : Larger fonts for posters/slides

在图表中使用这些文件：

color_palettes.py
：可导入的颜色定义
- 所有推荐配色方案的Python常量
- ```
apply_palette()
```
  辅助函数
- 可直接导入到笔记本/脚本中
Matplotlib样式文件：与
```
plt.style.use()
```
配合使用
- ```
publication.mplstyle
```
  ：通用发表质量样式
- ```
nature.mplstyle
```
  ：Nature期刊规范
- ```
presentation.mplstyle
```
  ：用于海报/幻灯片的大字体样式

Workflow Summary

工作流程总结

Recommended workflow for creating publication figures:

Plan: Determine target journal, figure type, and content

Configure: Apply appropriate style for journal

python

from style_presets import configure_for_journal
configure_for_journal('nature', 'single')

Create: Build figure with proper labels, colors, statistics

Verify: Check size, fonts, colors, accessibility

python

from figure_export import check_figure_size
check_figure_size(fig, journal='nature')

Export: Save in required formats

python

from figure_export import save_for_journal
save_for_journal(fig, 'figure1', 'nature', 'combination')

Review: View at final size in manuscript context

创建发表图表的推荐工作流程：

规划：确定目标期刊、图表类型和内容

配置：为期刊应用合适的样式

python

from style_presets import configure_for_journal
configure_for_journal('nature', 'single')

创建：构建带正确标签、颜色和统计信息的图表

验证：检查尺寸、字体、颜色、可访问性

python

from figure_export import check_figure_size
check_figure_size(fig, journal='nature')

导出：保存为所需格式

python

from figure_export import save_for_journal
save_for_journal(fig, 'figure1', 'nature', 'combination')

审核：在手稿语境下以最终尺寸查看图表

Common Pitfalls to Avoid

需避免的常见陷阱

Font too small: Text unreadable when printed at final size
JPEG format: Never use JPEG for graphs/plots (creates artifacts)
Red-green colors: ~8% of males cannot distinguish
Low resolution: Pixelated figures in publication
Missing units: Always label axes with units
3D effects: Distorts perception, avoid completely
Chart junk: Remove unnecessary gridlines, decorations
Truncated axes: Start bar charts at zero unless scientifically justified
Inconsistent styling: Different fonts/colors across figures in same manuscript
No error bars: Always show uncertainty

字体过小：最终打印时文本无法读取
JPEG格式：绝对不要将JPEG用于图表/曲线图（会产生伪影）
红绿配色：约8%的男性无法区分
低分辨率：发表时图表出现像素化
缺少单位：始终为坐标轴添加单位
3D效果：扭曲感知，完全避免使用
冗余元素：移除不必要的网格线、装饰
坐标轴截断：除非有科学依据，否则柱状图从0开始
样式不一致：同一手稿中不同图表的字体/颜色不一致
无误差棒：始终展示不确定性