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

```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

**Be