// Use when creating data visualizations for papers - generates publication-quality plots with top-journal color schemes
| name | figures-python |
| description | Use when creating data visualizations for papers - generates publication-quality plots with top-journal color schemes |
本技能指导使用 Python 生成科研论文级别的数据图表。
默认环境名：research
激活命令：
conda activate research
必需库：
pip install matplotlib seaborn numpy pandas
如环境未配置，调用 environment-setup 技能。
任何数据图都必须先有数据文件和数据清单（data manifest）。默认路径：
figures/data-manifest.md
figures/data/<figure-name>.csv
figures/<section>/<figure-name>.py
figures/<section>/<figure-name>.png
figures/<section>/<figure-name>.svg
figures/data-manifest.md 至少记录：
| Figure | Data file | Real/mock | Source | Script | Outputs |
|---|
mock 或 synthetic 数据只允许用于规划版图表。文件名必须以 mock_ 或 synthetic_ 开头，并在图表、表格或章节草稿中保留 [待真实实验替换]。不得把 mock 数据写成“实验结果表明”。
| 用途 | DPI | 说明 |
|---|---|---|
| 期刊投稿 | 300-600 | 大多数期刊要求 |
| 顶刊投稿 | 450+ | Nature/Science等 |
| 屏幕展示 | 150 | PPT/网页 |
本技能默认使用 450 DPI
每张图同时输出两种格式：
| 类型 | 宽度（英寸） | 适用场景 |
|---|---|---|
| 单栏图 | 3.5 | 期刊单栏 |
| 双栏图 | 7.0 | 期刊双栏/全宽 |
| PPT图 | 10.0 | 演示文稿 |
NATURE_COLORS = ['#2E86AB', '#A23B72', '#F18F01', '#C73E1D', '#95C623']
CELL_COLORS = ['#4E79A7', '#F28E2B', '#E15759', '#76B7B2', '#59A14F', '#EDC948']
COLORBLIND_SAFE = ['#0077BB', '#33BBEE', '#009988', '#EE7733', '#CC3311', '#EE3377']
"""
Figure X: [图表标题]
论文章节: [所属章节]
"""
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.font_manager as fm
from pathlib import Path
# 中文字体配置
CHINESE_FONT = None
font_candidates = [
'/System/Library/Fonts/STHeiti Light.ttc',
'/System/Library/Fonts/PingFang.ttc',
]
for fp in font_candidates:
if Path(fp).exists():
CHINESE_FONT = fm.FontProperties(fname=fp)
break
plt.rcParams['axes.unicode_minus'] = False
# 顶刊配色
COLORS = ['#4E79A7', '#F28E2B', '#E15759', '#76B7B2', '#59A14F']
def setup_plot_style():
plt.rcParams.update({
'font.size': 10,
'axes.titlesize': 12,
'axes.labelsize': 10,
'axes.spines.top': False,
'axes.spines.right': False,
'axes.grid': True,
'grid.alpha': 0.3,
'legend.frameon': False,
'savefig.dpi': 450,
'savefig.bbox': 'tight',
})
def main():
setup_plot_style()
fig, ax = plt.subplots(figsize=(7, 5))
# === 绑定代码 ===
x = np.linspace(0, 10, 100)
ax.plot(x, np.sin(x), color=COLORS[0], label='Model A')
ax.plot(x, np.cos(x), color=COLORS[1], label='Model B')
if CHINESE_FONT:
ax.set_xlabel('时间 (s)', fontproperties=CHINESE_FONT)
ax.set_ylabel('幅值', fontproperties=CHINESE_FONT)
else:
ax.set_xlabel('Time (s)')
ax.set_ylabel('Amplitude')
ax.legend()
# === 绑定代码结束 ===
# 保存
output_dir = Path(__file__).parent
fig_name = Path(__file__).stem
plt.savefig(output_dir / f'{fig_name}.png', dpi=450)
plt.savefig(output_dir / f'{fig_name}.svg')
plt.show()
if __name__ == '__main__':
main()
ax.plot(x, y, color=COLORS[0], linewidth=1.5, marker='o', markersize=4)
ax.bar(x_pos, values, color=COLORS[:len(values)], edgecolor='white')
im = ax.imshow(matrix, cmap='RdBu_r', aspect='auto')
plt.colorbar(im, ax=ax)
bp = ax.boxplot(data_list, patch_artist=True)
for patch, color in zip(bp['boxes'], COLORS):
patch.set_facecolor(color)
ax.scatter(x, y, c=colors, s=sizes, alpha=0.6, cmap='viridis')
figures/
├── chapter1/
│ ├── fig1_overview.py
│ ├── fig1_overview.png
│ └── fig1_overview.svg
├── chapter2/
└── chapter3/
fig{序号}_{描述}.pyfig1_model_architecture.pyfrom matplotlib.font_manager import FontProperties
font = FontProperties(fname='/System/Library/Fonts/STHeiti Light.ttc')
ax.set_xlabel('中文标签', fontproperties=font)
plt.savefig('figure.png', dpi=450, bbox_inches='tight')
ax.legend(loc='upper left', bbox_to_anchor=(1.02, 1))
Use when writing academic papers, theses, or research articles - supports brainstorming, chapter writing, literature review, and LaTeX output
Use for translation, polishing, or de-AI-ification of academic text - provides ready-to-use prompt templates
Use when writing or revising academic papers, especially Chinese journal manuscripts, that need natural prose, de-AI-ification, Markdown formatting, or quality checks
Use when writing or revising Introduction, Related Work, background, literature synthesis, or any section where references must drive claims
Use when designing experiments, result tables, mock planning data, evaluation protocols, or results sections before real data are final
Use when writing literature review sections - guides searching, organizing, and synthesizing academic sources