scientific-visualization

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

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))
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:

# 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

Task 1: Create a Publication-Ready Line Plot

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

Key steps:

Apply publication style

Set appropriate figure size for target journal

Use colorblind-friendly colors

Add error bars with correct representation (SEM, SD, or CI)

Label axes with units

Remove unnecessary spines

Save in vector format

Using seaborn for automatic confidence intervals:

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:

Use GridSpec for flexible layout

Ensure consistent styling across panels

Add bold panel labels (A, B, C, etc.)

Align related panels

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:

Use perceptually uniform colormap (viridis, plasma, cividis)

Include labeled colorbar

For diverging data, use colorblind-safe diverging map (RdBu_r, PuOr)

Set appropriate center value for diverging maps

Test appearance in grayscale

Using seaborn for correlation matrices:

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:

Check journal requirements: references/journal_requirements.md

Configure matplotlib for journal:

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

Create figure (will auto-size correctly)

Export with journal specifications:

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):

Check resolution: Verify DPI meets journal requirements

Check file format: Use vector for plots, TIFF/PNG for images

Check colors: Ensure colorblind-friendly

Check fonts: Minimum 6-7 pt at final size, sans-serif

Check labels: All axes labeled with units

Check size: Matches journal column width

Test grayscale: Figure interpretable without color

Remove chart junk: No unnecessary grids, 3D effects, shadows

Task 6: Create Colorblind-Friendly Visualizations

Strategy:

Use approved palettes from assets/color_palettes.py

Add redundant encoding (line styles, markers, patterns)

Test with colorblind simulator

Ensure grayscale compatibility

Example:

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 (, <strong>, </strong>)

Individual data points when possible (not just summary statistics)

Example with statistics:

# 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:

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:

# 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:

# 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:

# 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:

# 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:

# 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:

# 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:

# 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

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

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

Statistical Rigor with Seaborn

Seaborn automatically computes and displays uncertainty:

# 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

Always set publication theme first:

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

Use colorblind-safe palettes:

sns.set_palette('colorblind')

Remove unnecessary elements:

sns.despine()  # Remove top and right spines

Control figure size appropriately:

# 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)

Show individual data points when possible:

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

Include proper labels with units:

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

Export at correct resolution:

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:

# 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:

# 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:

# 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

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

Issue: Overlapping labels

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

Issue: Text too small at final size

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

Additional Resources

For more detailed seaborn information, see:

skills/seaborn/SKILL.md - Comprehensive seaborn documentation

skills/seaborn/references/examples.md - Practical use cases

skills/seaborn/references/function_reference.md - Complete API reference

skills/seaborn/references/objects_interface.md - Modern declarative API

Plotly

Interactive figures for exploration

Export static images for publication

Configure for publication quality:

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:

Plan: Determine target journal, figure type, and content

Configure: Apply appropriate style for journal

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

Create: Build figure with proper labels, colors, statistics

Verify: Check size, fonts, colors, accessibility

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

Export: Save in required formats

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

Review: View at final size in manuscript context

Common Pitfalls to Avoid

Font too small: Text unreadable when printed at final size

JPEG format: Never use JPEG for graphs/plots (creates artifacts)

Red-green colors: ~8% of males cannot distinguish

Low resolution: Pixelated figures in publication

Missing units: Always label axes with units

3D effects: Distorts perception, avoid completely

Chart junk: Remove unnecessary gridlines, decorations

Truncated axes: Start bar charts at zero unless scientifically justified

Inconsistent styling: Different fonts/colors across figures in same manuscript

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.