Publication-Ready Figure Export

Python (matplotlib)

import matplotlib.pyplot as plt

Set publication defaults

plt.rcParams.update({ 'font.size': 8, 'font.family': 'Arial', 'axes.linewidth': 0.5, 'lines.linewidth': 1, 'figure.dpi': 300 })

fig, ax = plt.subplots(figsize=(3.5, 3)) # Single column width

... create plot ...

Save in multiple formats

fig.savefig('figure1.pdf', bbox_inches='tight', dpi=300) fig.savefig('figure1.png', bbox_inches='tight', dpi=300) fig.savefig('figure1.svg', bbox_inches='tight')

R (ggplot2)

library(ggplot2)

p <- ggplot(data, aes(x, y)) + geom_point() + theme_classic(base_size = 8) + theme(text = element_text(family = 'Arial'))

PDF for vector graphics

ggsave('figure1.pdf', p, width = 3.5, height = 3, units = 'in')

High-res PNG

ggsave('figure1.png', p, width = 3.5, height = 3, units = 'in', dpi = 300)

TIFF (some journals require)

ggsave('figure1.tiff', p, width = 3.5, height = 3, units = 'in', dpi = 300, compression = 'lzw')

Journal Requirements

Journal Type Format Resolution Width

Most journals PDF/EPS Vector 3.5" (1-col), 7" (2-col)

Online-only PNG 300 DPI Variable

Print TIFF 300-600 DPI Column width

Multi-panel Figures

import matplotlib.pyplot as plt from matplotlib.gridspec import GridSpec

fig = plt.figure(figsize=(7, 5)) # Two-column width gs = GridSpec(2, 3, figure=fig)

ax1 = fig.add_subplot(gs[0, 0]) ax2 = fig.add_subplot(gs[0, 1:]) ax3 = fig.add_subplot(gs[1, :])

Add panel labels

for ax, label in zip([ax1, ax2, ax3], ['A', 'B', 'C']): ax.text(-0.1, 1.1, label, transform=ax.transAxes, fontsize=10, fontweight='bold')

fig.savefig('figure_multipanel.pdf', bbox_inches='tight')

Color Considerations

• Use colorblind-friendly palettes (viridis, cividis)

• Ensure sufficient contrast for grayscale printing

• Maintain consistency across all figures

Related Skills

• data-visualization/ggplot2-fundamentals - Creating plots in R

• data-visualization/heatmaps-clustering - Complex visualizations

• data-visualization/multipanel-figures - Figure composition