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
pythonimport 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
pythondef 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
pythondef 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
pythonfrom 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
pythondef 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
python# Ensure fonts are embedded in PDF output mpl.rcParams['pdf.fonttype'] = 42 # TrueType fonts mpl.rcParams['ps.fonttype'] = 42
