claude-scientific-skills/scientific-thinking/scientific-visualization/SKILL.md

---
name: scientific-visualization
description: "Create publication-ready scientific figures, plots, charts, and visualizations using matplotlib, seaborn, and plotly. Use this skill for any scientific data visualization task including: creating figures for research papers and manuscripts; preparing plots for journal submission (Nature, Science, Cell, PLOS, PNAS, etc.); making publication-quality figures with proper resolution, fonts, and formatting; ensuring colorblind accessibility and accessibility compliance; creating multi-panel figures with consistent styling; visualizing statistical data with error bars, significance markers, and proper statistical representation; exporting figures in correct formats (PDF, EPS, TIFF, PNG) with appropriate DPI; following journal-specific requirements and style guidelines; improving existing figures to meet publication standards; creating figures that work in both color and grayscale; visualizing experimental results, data analysis outputs, statistical comparisons, time series, distributions, correlations, heatmaps, scatter plots, bar charts, line plots, box plots, violin plots, and other scientific plot types; ensuring figures are clear, accurate, accessible, and professional; applying proper typography, color palettes, and layout principles; creating figures for presentations, posters, and scientific communication; visualizing genomics data, microscopy images, experimental measurements, and research findings."
---

# Scientific Visualization

## Overview

This skill provides comprehensive guidance, tools, and best practices for creating publication-ready scientific figures. It covers proper figure composition, colorblind-friendly design, journal-specific requirements, and practical implementation using matplotlib, seaborn, and plotly.

Publication-ready figures must be:
- **Clear**: Immediately understandable with proper labeling
- **Accurate**: Truthful data representation without distortion
- **Accessible**: Interpretable by readers with color vision deficiencies
- **Professional**: Polished appearance meeting journal standards

## When to Use This Skill

Activate this skill 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 ...
```

## 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

### 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

### 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
- Built on matplotlib, inherits all matplotlib customizations
- Good for statistical plots
- Apply matplotlib styles first, then use seaborn
```python
import seaborn as sns
from style_presets import apply_publication_style

apply_publication_style('default')
sns.set_palette(['#E69F00', '#56B4E9', '#009E73'])  # Okabe-Ito colors
```

### 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.