Scientific Visualization

Overview

Scientific visualization transforms data into clear, accurate figures for publication. Create journal-ready plots with multi-panel layouts, error bars, significance markers, and colorblind-safe palettes. Export as PDF/EPS/TIFF using matplotlib, seaborn, and plotly for manuscripts.

When to Use This Skill

This skill should be used when:

• Creating plots or visualizations for scientific manuscripts
• Preparing figures for journal submission (Nature, Science, Cell, PLOS, etc.)
• Ensuring figures are colorblind-friendly and accessible
• Making multi-panel figures with consistent styling
• Exporting figures at correct resolution and format
• Following specific publication guidelines
• Improving existing figures to meet publication standards
• Creating figures that need to work in both color and grayscale

Does NOT Trigger

Quick Start Guide

Basic Publication-Quality Figure

import matplotlib.pyplot as plt
import numpy as np

# Apply publication style (from scripts/style_presets.py)
from style_presets import apply_publication_style
apply_publication_style('default')

# Create figure with appropriate size (single column = 3.5 inches)
fig, ax = plt.subplots(figsize=(3.5, 2.5))

# Plot data
x = np.linspace(0, 10, 100)
ax.plot(x, np.sin(x), label='sin(x)')
ax.plot(x, np.cos(x), label='cos(x)')

# Proper labeling with units
ax.set_xlabel('Time (seconds)')
ax.set_ylabel('Amplitude (mV)')
ax.legend(frameon=False)

# Remove unnecessary spines
ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)

# Save in publication formats (from scripts/figure_export.py)
from figure_export import save_publication_figure
save_publication_figure(fig, 'figure1', formats=['pdf', 'png'], dpi=300)

Using Pre-configured Styles

Apply journal-specific styles using the matplotlib style files in assets/:

import matplotlib.pyplot as plt

# Option 1: Use style file directly
plt.style.use('assets/nature.mplstyle')

# Option 2: Use style_presets.py helper
from style_presets import configure_for_journal
configure_for_journal('nature', figure_width='single')

# Now create figures - they'll automatically match Nature specifications
fig, ax = plt.subplots()
# ... your plotting code ...

Quick Start with Seaborn

For statistical plots, use seaborn with publication styling:

import seaborn as sns
import matplotlib.pyplot as plt
from style_presets import apply_publication_style

# Apply publication style
apply_publication_style('default')
sns.set_theme(style='ticks', context='paper', font_scale=1.1)
sns.set_palette('colorblind')

# Create statistical comparison figure
fig, ax = plt.subplots(figsize=(3.5, 3))
# seaborn >=0.13: pass hue + legend=False to color by category
# (a bare palette= without hue= is deprecated, slated for removal in v0.14)
sns.boxplot(data=df, x='treatment', y='response',
            order=['Control', 'Low', 'High'],
            hue='treatment', palette='Set2', legend=False, ax=ax)
sns.stripplot(data=df, x='treatment', y='response',
              order=['Control', 'Low', 'High'], 
              color='black', alpha=0.3, size=3, ax=ax)
ax.set_ylabel('Response (μM)')
sns.despine()

# Save figure
from figure_export import save_publication_figure
save_publication_figure(fig, 'treatment_comparison', formats=['pdf', 'png'], dpi=300)

Core Principles and Best Practices

1. Resolution and File Format

Critical requirements (detailed in references/publication_guidelines.md):

• Raster images (photos, microscopy): 300-600 DPI
• Line art (graphs, plots): 600-1200 DPI or vector format
• Vector formats (preferred): PDF, EPS, SVG
• Raster formats: TIFF, PNG (never JPEG for scientific data)

Implementation:

# Use the figure_export.py script for correct settings
from figure_export import save_publication_figure

# Saves in multiple formats with proper DPI
save_publication_figure(fig, 'myfigure', formats=['pdf', 'png'], dpi=300)

# Or save for specific journal requirements
from figure_export import save_for_journal
save_for_journal(fig, 'figure1', journal='nature', figure_type='combination')

2. Color Selection - Colorblind Accessibility

Always use colorblind-friendly palettes (detailed in references/color_palettes.md):

Recommended: Okabe-Ito palette (distinguishable by all types of color blindness):

# Option 1: Use assets/color_palettes.py
from color_palettes import OKABE_ITO_LIST, apply_palette
apply_palette('okabe_ito')

# Option 2: Manual specification
okabe_ito = ['#E69F00', '#56B4E9', '#009E73', '#F0E442',
             '#0072B2', '#D55E00', '#CC79A7', '#000000']
plt.rcParams['axes.prop_cycle'] = plt.cycler(color=okabe_ito)

For heatmaps/continuous data:

• Use perceptually uniform colormaps: viridis, plasma, cividis
• Avoid red-green diverging maps (use PuOr, RdBu, BrBG instead)
• Never use jet or rainbow colormaps

Always test figures in grayscale to ensure interpretability.

3. Typography and Text

Font guidelines (detailed in references/publication_guidelines.md):

• Sans-serif fonts: Arial, Helvetica, Calibri
• Minimum sizes at final print size:
• Axis labels: 7-9 pt
• Tick labels: 6-8 pt
• Panel labels: 8-12 pt (bold)
• Sentence case for labels: "Time (hours)" not "TIME (HOURS)"
• Always include units in parentheses

Implementation:

# Set fonts globally
import matplotlib as mpl
mpl.rcParams['font.family'] = 'sans-serif'
mpl.rcParams['font.sans-serif'] = ['Arial', 'Helvetica']
mpl.rcParams['font.size'] = 8
mpl.rcParams['axes.labelsize'] = 9
mpl.rcParams['xtick.labelsize'] = 7
mpl.rcParams['ytick.labelsize'] = 7

4. Figure Dimensions

Journal-specific widths (detailed in references/journal_requirements.md):

• Nature: Single 89 mm, Double 183 mm
• Science: Single 55 mm, Double 175 mm
• Cell: Single 85 mm, Double 178 mm

Check figure size compliance:

from figure_export import check_figure_size

fig = plt.figure(figsize=(3.5, 3))  # 89 mm for Nature
check_figure_size(fig, journal='nature')

5. Multi-Panel Figures

Best practices:

• Label panels with bold letters: A, B, C (uppercase for most journals, lowercase for Nature)
• Maintain consistent styling across all panels
• Align panels along edges where possible
• Use adequate white space between panels

Example implementation (see references/matplotlib_examples.md for complete code):

from string import ascii_uppercase

fig = plt.figure(figsize=(7, 4))
gs = fig.add_gridspec(2, 2, hspace=0.4, wspace=0.4)

ax1 = fig.add_subplot(gs[0, 0])
ax2 = fig.add_subplot(gs[0, 1])
# ... create other panels ...

# Add panel labels
for i, ax in enumerate([ax1, ax2, ...]):
    ax.text(-0.15, 1.05, ascii_uppercase[i], transform=ax.transAxes,
            fontsize=10, fontweight='bold', va='top')

Common Tasks

Step-by-step recipes — full code for each lives in references/common_tasks.md and references/matplotlib_examples.md:

1. Publication-ready line plot — style, journal size, colorblind colors, error bars, units, despine, vector export.
2. Multi-panel figure — GridSpec layout, consistent styling, bold panel labels.
3. Heatmap with proper colormap — perceptually uniform (viridis) or colorblind-safe diverging (RdBu_r), labeled colorbar, grayscale test.
4. Prepare for a specific journal — configure_for_journal(...) then save_for_journal(...).
5. Fix an existing figure — run the publication checklist (resolution, format, colors, fonts, labels, size, grayscale, chart junk).
6. Colorblind-friendly figures — approved palettes + redundant encoding (line styles, markers) + simulator test.

Statistical rigor (always): error bars (SD/SEM/CI — state which in caption), sample size n, significance markers, individual data points where possible.

Plotting Libraries — when to use which

• Matplotlib — most control, best for complex multi-panel figures. Examples: references/matplotlib_examples.md.
• Seaborn — high-level statistical graphics with automatic CIs and faceting. Full guide: references/seaborn_in_publications.md.
• Plotly — interactive exploration; export static via fig.write_image('figure.png', scale=3) (~300 DPI), which needs kaleido >= 1 plus Chrome (run plotly_get_chrome once). See matplotlib_examples.md Example 8.

Resources

References Directory

Load these as needed for detailed information:

• `publication_guidelines.md`: Comprehensive best practices
• Resolution and file format requirements
• Typography guidelines
• Layout and composition rules
• Statistical rigor requirements
• Complete publication checklist

• `color_palettes.md`: Color usage guide
• Colorblind-friendly palette specifications with RGB values
• Sequential and diverging colormap recommendations
• Testing procedures for accessibility
• Domain-specific palettes (genomics, microscopy)

• `journal_requirements.md`: Journal-specific specifications
• Technical requirements by publisher
• File format and DPI specifications
• Figure dimension requirements
• Quick reference table

• `matplotlib_examples.md`: Practical code examples
• 10 complete working examples
• Line plots, bar plots, heatmaps, multi-panel figures
• Journal-specific figure examples
• Tips for each library (matplotlib, seaborn, plotly)

Scripts Directory

Use these helper scripts for automation:

• `figure_export.py`: Export utilities
• save_publication_figure(): Save in multiple formats with correct DPI
• save_for_journal(): Use journal-specific requirements automatically
• check_figure_size(): Verify dimensions meet journal specs
• Run directly: python scripts/figure_export.py for examples

• `style_presets.py`: Pre-configured styles
• apply_publication_style(): Apply preset styles (default, nature, science, cell)
• set_color_palette(): Quick palette switching
• configure_for_journal(): One-command journal configuration
• Run directly: python scripts/style_presets.py to see examples

Assets Directory

Use these files in figures:

• `color_palettes.py`: Importable color definitions
• All recommended palettes as Python constants
• apply_palette() helper function
• Can be imported directly into notebooks/scripts

• Matplotlib style files: Use with plt.style.use()
• publication.mplstyle: General publication quality
• nature.mplstyle: Nature journal specifications
• presentation.mplstyle: Larger fonts for posters/slides

Workflow Summary

Recommended workflow for creating publication figures:

1. Plan: Determine target journal, figure type, and content
2. Configure: Apply appropriate style for journal

   from style_presets import configure_for_journal
   configure_for_journal('nature', 'single')

1. Create: Build figure with proper labels, colors, statistics
2. Verify: Check size, fonts, colors, accessibility

   from figure_export import check_figure_size
   check_figure_size(fig, journal='nature')

1. Export: Save in required formats

   from figure_export import save_for_journal
   save_for_journal(fig, 'figure1', 'nature', 'combination')

1. Review: View at final size in manuscript context

Common Pitfalls to Avoid

1. Font too small: Text unreadable when printed at final size
2. JPEG format: Never use JPEG for graphs/plots (creates artifacts)
3. Red-green colors: ~8% of males cannot distinguish
4. Low resolution: Pixelated figures in publication
5. Missing units: Always label axes with units
6. 3D effects: Distorts perception, avoid completely
7. Chart junk: Remove unnecessary gridlines, decorations
8. Truncated axes: Start bar charts at zero unless scientifically justified
9. Inconsistent styling: Different fonts/colors across figures in same manuscript
10. No error bars: Always show uncertainty

Final Checklist

Before submitting figures, verify:

• [ ] Resolution meets journal requirements (300+ DPI)
• [ ] File format is correct (vector for plots, TIFF for images)
• [ ] Figure size matches journal specifications
• [ ] All text readable at final size (≥6 pt)
• [ ] Colors are colorblind-friendly
• [ ] Figure works in grayscale
• [ ] All axes labeled with units
• [ ] Error bars present with definition in caption
• [ ] Panel labels present and consistent
• [ ] No chart junk or 3D effects
• [ ] Fonts consistent across all figures
• [ ] Statistical significance clearly marked
• [ ] Legend is clear and complete

Use this skill to ensure scientific figures meet the highest publication standards while remaining accessible to all readers.