scientific-visualization

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 (*, **, ***)

• 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:

• 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:

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

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