Create publication-quality scientific figures and plots using Python (matplotlib, seaborn, plotly). Supports bar charts, scatter plots, heatmaps, box plots, violin plots, survival curves, network graphs, and more. Use when user asks to plot data, create figures, make charts, visualize results, or generate publication-ready graphics. Triggers on "plot", "chart", "figure", "graph", "visualize", "heatmap", "scatter plot", "bar chart", "histogram".
Publication-quality figures with Python. Use venv: source /Users/zhangmingda/clawd/.venv/bin/activate
import matplotlib.pyplot as plt
import matplotlib as mpl
import seaborn as sns
import numpy as np
# Publication style
plt.rcParams.update({
'font.size': 12,
'font.family': 'sans-serif',
'axes.linewidth': 1.2,
'axes.spines.top': False,
'axes.spines.right': False,
'figure.dpi': 150,
'savefig.dpi': 300,
'savefig.bbox': 'tight',
'savefig.transparent': True,
})
sns.set_palette("colorblind") # accessible colors
fig, ax = plt.subplots(figsize=(6, 4))
sns.histplot(data=df, x='value', hue='group', kde=True, ax=ax)
ax.set_xlabel('Value')
ax.set_ylabel('Count')
plt.savefig('dist.png', dpi=300)
fig, ax = plt.subplots(figsize=(6, 4))
sns.boxplot(data=df, x='group', y='value', ax=ax, width=0.5)
sns.stripplot(data=df, x='group', y='value', ax=ax, color='black', alpha=0.3, size=3)
ax.set_ylabel('Measurement (units)')
plt.savefig('comparison.png', dpi=300)
fig, ax = plt.subplots(figsize=(6, 5))
sns.regplot(data=df, x='x', y='y', ax=ax, scatter_kws={'alpha': 0.5})
r, p = stats.pearsonr(df['x'], df['y'])
ax.annotate(f'r = {r:.3f}, p = {p:.3g}', xy=(0.05, 0.95), xycoords='axes fraction', fontsize=10)
plt.savefig('scatter.png', dpi=300)
fig, ax = plt.subplots(figsize=(8, 6))
sns.heatmap(corr_matrix, annot=True, fmt='.2f', cmap='RdBu_r', center=0,
square=True, linewidths=0.5, ax=ax)
plt.savefig('heatmap.png', dpi=300)
fig, axes = plt.subplots(1, 3, figsize=(15, 4))
# Panel A
axes[0].plot(x, y)
axes[0].set_title('A', loc='left', fontweight='bold')
# Panel B
axes[1].bar(categories, values)
axes[1].set_title('B', loc='left', fontweight='bold')
# Panel C
axes[2].scatter(x2, y2)
axes[2].set_title('C', loc='left', fontweight='bold')
plt.tight_layout()
plt.savefig('figure1.png', dpi=300)
fig, ax = plt.subplots(figsize=(7, 5))
colors = np.where((df['padj'] < 0.05) & (abs(df['log2FC']) > 1), 'red',
np.where(df['padj'] < 0.05, 'blue', 'grey'))
ax.scatter(df['log2FC'], -np.log10(df['padj']), c=colors, alpha=0.5, s=10)
ax.axhline(-np.log10(0.05), ls='--', color='grey', lw=0.8)
ax.axvline(-1, ls='--', color='grey', lw=0.8)
ax.axvline(1, ls='--', color='grey', lw=0.8)
ax.set_xlabel('log₂ Fold Change')
ax.set_ylabel('-log₁₀ adjusted p-value')
plt.savefig('volcano.png', dpi=300)
import networkx as nx
G = nx.from_pandas_edgelist(df, 'source', 'target', 'weight')
pos = nx.spring_layout(G, seed=42)
fig, ax = plt.subplots(figsize=(8, 8))
nx.draw_networkx(G, pos, ax=ax, node_size=300, font_size=8, edge_color='grey', alpha=0.7)
plt.savefig('network.png', dpi=300)
import plotly.express as px
fig = px.scatter(df, x='x', y='y', color='group', hover_data=['label'],
title='Interactive Scatter')
fig.write_html('interactive.html')
fig.write_image('scatter.png', scale=2) # needs kaleido
| Journal | Width (single col) | Width (double col) | Format | Font min |
|---|---|---|---|---|
| Nature | 89mm | 183mm | PDF/EPS/TIFF | 5pt |
| Science | 85mm | 174mm | PDF/EPS | 6pt |
| PNAS | 87mm | 178mm | PDF/EPS/TIFF | 6pt |
| IEEE | 3.5in | 7.16in | PDF/EPS | 8pt |
| Elsevier | 90mm | 190mm | PDF/EPS/TIFF | 6pt |
# Nature single-column figure
fig, ax = plt.subplots(figsize=(3.5, 2.6)) # 89mm ≈ 3.5in
sns.set_palette("colorblind")