brycewang-stanford Publication Figures Guide
A skill for creating publication-quality scientific figures that meet journal standards for resolution, formatting, accessibility, and visual clarity. Covers matplotlib, seaborn, and ggplot2 workflows with journal-ready export settings.
Journal Figure Requirements
Common Standards
| Requirement | Typical Spec | Notes |
|---|---|---|
| Resolution | 300-600 DPI | 300 DPI minimum for print |
| File format | PDF, EPS, TIFF | Vector (PDF/EPS) preferred |
| Color mode | CMYK for print, RGB for online | Check journal spec |
| Max width | Single column: 3.3in / Double: 6.7in | Varies by journal |
| Font size | 6-8pt minimum | Must be legible at final print size |
| Line width | 0.5-1.5pt | Thin lines may not reproduce |
| File size | Varies (often <10MB per figure) | TIFF can be large |
Matplotlib Configuration for Publication
import matplotlib.pyplot as plt
import matplotlib as mpl
import numpy as np
def setup_publication_style(journal: str = 'nature'):
"""
Configure matplotlib for publication-quality figures.
"""
styles = {
'nature': {
'figure.figsize': (3.3, 2.5), # single column
'font.size': 7,
'font.family': 'sans-serif',
'font.sans-serif': ['Arial', 'Helvetica'],
'axes.linewidth': 0.5,
'axes.labelsize': 8,
'xtick.labelsize': 7,
'ytick.labelsize': 7,
'legend.fontsize': 6,
'lines.linewidth': 1.0,
'lines.markersize': 4,
'savefig.dpi': 300,
'savefig.bbox': 'tight',
'savefig.pad_inches': 0.05,
},
'ieee': {
'figure.figsize': (3.5, 2.6),
'font.size': 8,
'font.family': 'serif',
'font.serif': ['Times New Roman', 'Times'],
'axes.linewidth': 0.5,
'axes.labelsize': 9,
'xtick.labelsize': 8,
'ytick.labelsize': 8,
'legend.fontsize': 7,
'lines.linewidth': 1.0,
'savefig.dpi': 300,
},
'acs': {
'figure.figsize': (3.25, 2.5),
'font.size': 7,
'font.family': 'sans-serif',
'font.sans-serif': ['Arial'],
'axes.linewidth': 0.5,
'savefig.dpi': 600,
}
}
style = styles.get(journal, styles['nature'])
mpl.rcParams.update(style)
return style
setup_publication_style('nature')
Colorblind-Friendly Palettes
Recommended Color Schemes
def get_accessible_palette(n_colors: int = 8, style: str = 'categorical') -> list:
"""
Return colorblind-friendly palettes.
"""
palettes = {
'categorical': {
# Wong (2011) Nature Methods palette
3: ['#0072B2', '#D55E00', '#009E73'],
4: ['#0072B2', '#D55E00', '#009E73', '#CC79A7'],
5: ['#0072B2', '#D55E00', '#009E73', '#CC79A7', '#F0E442'],
8: ['#0072B2', '#D55E00', '#009E73', '#CC79A7',
'#F0E442', '#56B4E9', '#E69F00', '#000000']
},
'sequential': {
# Viridis-based (perceptually uniform)
'cmap': 'viridis' # Also: 'cividis', 'inferno', 'magma'
},
'diverging': {
'cmap': 'RdBu_r' # Also: 'coolwarm', 'BrBG'
}
}
if style == 'categorical':
n = min(n_colors, 8)
return palettes['categorical'].get(n, palettes['categorical'][8][:n])
else:
return palettes[style]
# Usage
colors = get_accessible_palette(4)
Common Figure Types
Bar Charts with Error Bars
def publication_barplot(data: dict, ylabel: str, title: str = '',
output: str = 'figure.pdf'):
"""
Create a publication-quality bar chart.
Args:
data: Dict mapping group names to (mean, std_error) tuples
"""
setup_publication_style('nature')
colors = get_accessible_palette(len(data))
fig, ax = plt.subplots()
x = np.arange(len(data))
names = list(data.keys())
means = [data[k][0] for k in names]
errors = [data[k][1] for k in names]
bars = ax.bar(x, means, yerr=errors, capsize=3, color=colors,
edgecolor='black', linewidth=0.5, width=0.6,
error_kw={'linewidth': 0.5})
ax.set_xticks(x)
ax.set_xticklabels(names, rotation=0)
ax.set_ylabel(ylabel)
if title:
ax.set_title(title)
# Remove top and right spines
ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)
fig.savefig(output, dpi=300, bbox_inches='tight')
plt.close()
return output
Scatter Plots with Regression Lines
from scipy import stats
def publication_scatter(x, y, xlabel, ylabel, output='scatter.pdf',
groups=None, group_labels=None):
"""Publication-quality scatter plot with optional regression line."""
setup_publication_style('nature')
fig, ax = plt.subplots()
if groups is None:
ax.scatter(x, y, s=15, alpha=0.7, color='#0072B2', edgecolors='none')
# Regression line
slope, intercept, r, p, se = stats.linregress(x, y)
x_fit = np.linspace(min(x), max(x), 100)
ax.plot(x_fit, slope*x_fit + intercept, '--', color='#D55E00', linewidth=0.8)
ax.text(0.05, 0.95, f'r = {r:.2f}, p = {p:.3f}',
transform=ax.transAxes, fontsize=6, va='top')
else:
colors = get_accessible_palette(len(set(groups)))
for i, label in enumerate(group_labels or sorted(set(groups))):
mask = np.array(groups) == label
ax.scatter(np.array(x)[mask], np.array(y)[mask],
s=15, alpha=0.7, color=colors[i], label=label)
ax.legend(frameon=False)
ax.set_xlabel(xlabel)
ax.set_ylabel(ylabel)
ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)
fig.savefig(output, dpi=300, bbox_inches='tight')
plt.close()
Multi-Panel Figures
def multi_panel_figure(n_rows, n_cols, panel_data, output='multipanel.pdf'):
"""Create a multi-panel figure with automatic panel labels."""
setup_publication_style('nature')
fig, axes = plt.subplots(n_rows, n_cols,
figsize=(3.3*n_cols, 2.5*n_rows))
if n_rows * n_cols == 1:
axes = np.array([axes])
axes = axes.flatten()
labels = 'abcdefghijklmnopqrstuvwxyz'
for i, ax in enumerate(axes[:len(panel_data)]):
# Add panel label
ax.text(-0.15, 1.05, labels[i], transform=ax.transAxes,
fontsize=10, fontweight='bold', va='bottom')
plt.tight_layout()
fig.savefig(output, dpi=300, bbox_inches='tight')
plt.close()
Export Best Practices
- Vector formats first: Use PDF or EPS for line art and charts; TIFF only for photographs
- Font embedding: Ensure all fonts are embedded (use
plt.rcParams['pdf.fonttype'] = 42) - Check at print size: View the figure at actual print size (3.3in wide) to verify readability
- CMYK conversion: For print journals, convert RGB to CMYK using ImageMagick or Photoshop
- Consistent styling: All figures in a paper should use the same fonts, colors, and styling
# Ensure fonts are embedded in PDF output
mpl.rcParams['pdf.fonttype'] = 42 # TrueType fonts
mpl.rcParams['ps.fonttype'] = 42