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

Quick Start Guide#

Basic Publication-Quality Figure#

python

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/:

python

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:

python

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))
sns.boxplot(data=df, x='treatment', y='response',
            order=['Control', 'Low', 'High'], palette='Set2', 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:

python

# 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):

python

# 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:

python

# 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:

python

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):

python

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#

Task 1: Create a Publication-Ready Line Plot#

See references/matplotlib_examples.md Example 1 for complete code.

Key steps:

1. Apply publication style
2. Set appropriate figure size for target journal
3. Use colorblind-friendly colors
4. Add error bars with correct representation (SEM, SD, or CI)
5. Label axes with units
6. Remove unnecessary spines
7. Save in vector format

Using seaborn for automatic confidence intervals:

python

import seaborn as sns
fig, ax = plt.subplots(figsize=(5, 3))
sns.lineplot(data=timeseries, x='time', y='measurement',
             hue='treatment', errorbar=('ci', 95),
             markers=True, ax=ax)
ax.set_xlabel('Time (hours)')
ax.set_ylabel('Measurement (AU)')
sns.despine()

Task 2: Create a Multi-Panel Figure#

See references/matplotlib_examples.md Example 2 for complete code.

Key steps:

1. Use GridSpec for flexible layout
2. Ensure consistent styling across panels
3. Add bold panel labels (A, B, C, etc.)
4. Align related panels
5. Verify all text is readable at final size

Task 3: Create a Heatmap with Proper Colormap#

See references/matplotlib_examples.md Example 4 for complete code.

Key steps:

1. Use perceptually uniform colormap ( viridis, plasma, cividis)
2. Include labeled colorbar
3. For diverging data, use colorblind-safe diverging map ( RdBu_r, PuOr)
4. Set appropriate center value for diverging maps
5. Test appearance in grayscale

Using seaborn for correlation matrices:

python

import seaborn as sns
fig, ax = plt.subplots(figsize=(5, 4))
corr = df.corr()
mask = np.triu(np.ones_like(corr, dtype=bool))
sns.heatmap(corr, mask=mask, annot=True, fmt='.2f',
            cmap='RdBu_r', center=0, square=True,
            linewidths=1, cbar_kws={'shrink': 0.8}, ax=ax)

Task 4: Prepare Figure for Specific Journal#

Workflow:

1. Check journal requirements: references/journal_requirements.md

2. Configure matplotlib for journal: python

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

3. Create figure (will auto-size correctly)

4. Export with journal specifications: python

   from figure_export import save_for_journal
   save_for_journal(fig, 'figure1', journal='nature', figure_type='line_art')
   
   

Task 5: Fix an Existing Figure to Meet Publication Standards#

Checklist approach (full checklist in references/publication_guidelines.md):

1. Check resolution: Verify DPI meets journal requirements
2. Check file format: Use vector for plots, TIFF/PNG for images
3. Check colors: Ensure colorblind-friendly
4. Check fonts: Minimum 6-7 pt at final size, sans-serif
5. Check labels: All axes labeled with units
6. Check size: Matches journal column width
7. Test grayscale: Figure interpretable without color
8. Remove chart junk: No unnecessary grids, 3D effects, shadows

Task 6: Create Colorblind-Friendly Visualizations#

Strategy:

1. Use approved palettes from assets/color_palettes.py
2. Add redundant encoding (line styles, markers, patterns)
3. Test with colorblind simulator
4. Ensure grayscale compatibility

Example:

python

from color_palettes import apply_palette
import matplotlib.pyplot as plt

apply_palette('okabe_ito')

# Add redundant encoding beyond color
line_styles = ['-', '--', '-.', ':']
markers = ['o', 's', '^', 'v']

for i, (data, label) in enumerate(datasets):
    plt.plot(x, data, linestyle=line_styles[i % 4],
             marker=markers[i % 4], label=label)

Statistical Rigor#

Always include:

• Error bars (SD, SEM, or CI - specify which in caption)
• Sample size (n) in figure or caption
• Statistical significance markers (*, **, ***)
• Individual data points when possible (not just summary statistics)

Example with statistics:

python

# Show individual points with summary statistics
ax.scatter(x_jittered, individual_points, alpha=0.4, s=8)
ax.errorbar(x, means, yerr=sems, fmt='o', capsize=3)

# Mark significance
ax.text(1.5, max_y * 1.1, '***', ha='center', fontsize=8)

Working with Different Plotting Libraries#

Matplotlib#

• Most control over publication details
• Best for complex multi-panel figures
• Use provided style files for consistent formatting
• See references/matplotlib_examples.md for extensive examples

Seaborn#

Seaborn provides a high-level, dataset-oriented interface for statistical graphics, built on matplotlib. It excels at creating publication-quality statistical visualizations with minimal code while maintaining full compatibility with matplotlib customization.

Key advantages for scientific visualization:

• Automatic statistical estimation and confidence intervals
• Built-in support for multi-panel figures (faceting)
• Colorblind-friendly palettes by default
• Dataset-oriented API using pandas DataFrames
• Semantic mapping of variables to visual properties

Quick Start with Publication Style

Always apply matplotlib publication styles first, then configure seaborn:

python

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

# Apply publication style
apply_publication_style('default')

# Configure seaborn for publication
sns.set_theme(style='ticks', context='paper', font_scale=1.1)
sns.set_palette('colorblind')  # Use colorblind-safe palette

# Create figure
fig, ax = plt.subplots(figsize=(3.5, 2.5))
sns.scatterplot(data=df, x='time', y='response',
                hue='treatment', style='condition', ax=ax)
sns.despine()  # Remove top and right spines

Common Plot Types for Publications

Statistical comparisons:

python

# Box plot with individual points for transparency
fig, ax = plt.subplots(figsize=(3.5, 3))
sns.boxplot(data=df, x='treatment', y='response',
            order=['Control', 'Low', 'High'], palette='Set2', 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()

Distribution analysis:

python

# Violin plot with split comparison
fig, ax = plt.subplots(figsize=(4, 3))
sns.violinplot(data=df, x='timepoint', y='expression',
               hue='treatment', split=True, inner='quartile', ax=ax)
ax.set_ylabel('Gene Expression (AU)')
sns.despine()

Correlation matrices:

python

# Heatmap with proper colormap and annotations
fig, ax = plt.subplots(figsize=(5, 4))
corr = df.corr()
mask = np.triu(np.ones_like(corr, dtype=bool))  # Show only lower triangle
sns.heatmap(corr, mask=mask, annot=True, fmt='.2f',
            cmap='RdBu_r', center=0, square=True,
            linewidths=1, cbar_kws={'shrink': 0.8}, ax=ax)
plt.tight_layout()

Time series with confidence bands:

python

# Line plot with automatic CI calculation
fig, ax = plt.subplots(figsize=(5, 3))
sns.lineplot(data=timeseries, x='time', y='measurement',
             hue='treatment', style='replicate',
             errorbar=('ci', 95), markers=True, dashes=False, ax=ax)
ax.set_xlabel('Time (hours)')
ax.set_ylabel('Measurement (AU)')
sns.despine()

Multi-Panel Figures with Seaborn

Using FacetGrid for automatic faceting:

python

# Create faceted plot
g = sns.relplot(data=df, x='dose', y='response',
                hue='treatment', col='cell_line', row='timepoint',
                kind='line', height=2.5, aspect=1.2,
                errorbar=('ci', 95), markers=True)
g.set_axis_labels('Dose (μM)', 'Response (AU)')
g.set_titles('{row_name} | {col_name}')
sns.despine()

# Save with correct DPI
from figure_export import save_publication_figure
save_publication_figure(g.figure, 'figure_facets',
                       formats=['pdf', 'png'], dpi=300)

Combining seaborn with matplotlib subplots:

python

# Create custom multi-panel layout
fig, axes = plt.subplots(2, 2, figsize=(7, 6))

# Panel A: Scatter with regression
sns.regplot(data=df, x='predictor', y='response', ax=axes[0, 0])
axes[0, 0].text(-0.15, 1.05, 'A', transform=axes[0, 0].transAxes,
                fontsize=10, fontweight='bold')

# Panel B: Distribution comparison
sns.violinplot(data=df, x='group', y='value', ax=axes[0, 1])
axes[0, 1].text(-0.15, 1.05, 'B', transform=axes[0, 1].transAxes,
                fontsize=10, fontweight='bold')

# Panel C: Heatmap
sns.heatmap(correlation_data, cmap='viridis', ax=axes[1, 0])
axes[1, 0].text(-0.15, 1.05, 'C', transform=axes[1, 0].transAxes,
                fontsize=10, fontweight='bold')

# Panel D: Time series
sns.lineplot(data=timeseries, x='time', y='signal',
             hue='condition', ax=axes[1, 1])
axes[1, 1].text(-0.15, 1.05, 'D', transform=axes[1, 1].transAxes,
                fontsize=10, fontweight='bold')

plt.tight_layout()
sns.despine()

Color Palettes for Publications

Seaborn includes several colorblind-safe palettes:

python

# Use built-in colorblind palette (recommended)
sns.set_palette('colorblind')

# Or specify custom colorblind-safe colors (Okabe-Ito)
okabe_ito = ['#E69F00', '#56B4E9', '#009E73', '#F0E442',
             '#0072B2', '#D55E00', '#CC79A7', '#000000']
sns.set_palette(okabe_ito)

# For heatmaps and continuous data
sns.heatmap(data, cmap='viridis')  # Perceptually uniform
sns.heatmap(corr, cmap='RdBu_r', center=0)  # Diverging, centered

Choosing Between Axes-Level and Figure-Level Functions

Axes-level functions (e.g., scatterplot, boxplot, heatmap):

• Use when building custom multi-panel layouts
• Accept ax= parameter for precise placement
• Better integration with matplotlib subplots
• More control over figure composition

python

fig, ax = plt.subplots(figsize=(3.5, 2.5))
sns.scatterplot(data=df, x='x', y='y', hue='group', ax=ax)

Figure-level functions (e.g., relplot, catplot, displot):

• Use for automatic faceting by categorical variables
• Create complete figures with consistent styling
• Great for exploratory analysis
• Use height and aspect for sizing

python

g = sns.relplot(data=df, x='x', y='y', col='category', kind='scatter')

Statistical Rigor with Seaborn

Seaborn automatically computes and displays uncertainty:

python

# Line plot: shows mean ± 95% CI by default
sns.lineplot(data=df, x='time', y='value', hue='treatment',
             errorbar=('ci', 95))  # Can change to 'sd', 'se', etc.

# Bar plot: shows mean with bootstrapped CI
sns.barplot(data=df, x='treatment', y='response',
            errorbar=('ci', 95), capsize=0.1)

# Always specify error type in figure caption:
# "Error bars represent 95% confidence intervals"

Best Practices for Publication-Ready Seaborn Figures

1. Always set publication theme first: python

   sns.set_theme(style='ticks', context='paper', font_scale=1.1)
   
   

2. Use colorblind-safe palettes: python

   sns.set_palette('colorblind')
   
   

3. Remove unnecessary elements: python

   sns.despine()  # Remove top and right spines
   
   

4. Control figure size appropriately: python

   # Axes-level: use matplotlib figsize
   fig, ax = plt.subplots(figsize=(3.5, 2.5))
   
   # Figure-level: use height and aspect
   g = sns.relplot(..., height=3, aspect=1.2)
   
   

5. Show individual data points when possible: python

   sns.boxplot(...)  # Summary statistics
   sns.stripplot(..., alpha=0.3)  # Individual points
   
   

6. Include proper labels with units: python

   ax.set_xlabel('Time (hours)')
   ax.set_ylabel('Expression (AU)')
   
   

7. Export at correct resolution: python

   from figure_export import save_publication_figure
   save_publication_figure(fig, 'figure_name',
                          formats=['pdf', 'png'], dpi=300)
   
   

Advanced Seaborn Techniques

Pairwise relationships for exploratory analysis:

python

# Quick overview of all relationships
g = sns.pairplot(data=df, hue='condition',
                 vars=['gene1', 'gene2', 'gene3'],
                 corner=True, diag_kind='kde', height=2)

Hierarchical clustering heatmap:

python

# Cluster samples and features
g = sns.clustermap(expression_data, method='ward',
                   metric='euclidean', z_score=0,
                   cmap='RdBu_r', center=0,
                   figsize=(10, 8),
                   row_colors=condition_colors,
                   cbar_kws={'label': 'Z-score'})

Joint distributions with marginals:

python

# Bivariate distribution with context
g = sns.jointplot(data=df, x='gene1', y='gene2',
                  hue='treatment', kind='scatter',
                  height=6, ratio=4, marginal_kws={'kde': True})

Common Seaborn Issues and Solutions

Issue: Legend outside plot area

python

g = sns.relplot(...)
g._legend.set_bbox_to_anchor((0.9, 0.5))

Issue: Overlapping labels

python

plt.xticks(rotation=45, ha='right')
plt.tight_layout()

Issue: Text too small at final size

python

sns.set_context('paper', font_scale=1.2)  # Increase if needed

Additional Resources

For more detailed seaborn information, see:

• scientific-packages/seaborn/SKILL.md - Comprehensive seaborn documentation
• scientific-packages/seaborn/references/examples.md - Practical use cases
• scientific-packages/seaborn/references/function_reference.md - Complete API reference
• scientific-packages/seaborn/references/objects_interface.md - Modern declarative API

Plotly#

• Interactive figures for exploration
• Export static images for publication
• Configure for publication quality:

python

fig.update_layout(
    font=dict(family='Arial, sans-serif', size=10),
    plot_bgcolor='white',
    # ... see matplotlib_examples.md Example 8
)
fig.write_image('figure.png', scale=3)  # scale=3 gives ~300 DPI

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 python

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

3. Create: Build figure with proper labels, colors, statistics

4. Verify: Check size, fonts, colors, accessibility python

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

5. Export: Save in required formats python

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

6. 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.