bio-reporting-figure-export

name: bio-reporting-figure-export description: Exports publication-ready figures in various formats with proper resolution, sizing, and typography. Use when preparing figures for journal submission, creating vector graphics for presentations, or ensuring consistent figure styling across analyses. tool_type: mixed primary_tool: matplotlib measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools: - read_file - run_shell_command

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

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