scientific-visualization

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.

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

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)

```

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')

```

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')

```

1. Create figure (will auto-size correctly)
2. 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)

```

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)

```

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)

```

1. Use colorblind-safe palettes:

```python

sns.set_palette('colorblind')

```

1. Remove unnecessary elements:

```python

sns.despine() # Remove top and right spines

```

1. 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)

```

1. Show individual data points when possible:

```python

sns.boxplot(...) # Summary statistics

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

```

1. Include proper labels with units:

```python

ax.set_xlabel('Time (hours)')

ax.set_ylabel('Expression (AU)')

```

1. 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

```

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

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')

```

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

```python

from figure_export import check_figure_size

check_figure_size(fig, journal='nature')

```

1. Export: Save in required formats

```python

from figure_export import save_for_journal

save_for_journal(fig, 'figure1', 'nature', 'combination')

```

1. Review: View at final size in manuscript context

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

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.