/scientific-visualization

name: scientific-visualization
description: Meta-skill for publication-ready figures. Use when creating journal submission figures requiring multi-panel layouts, significance annotations, error bars, colorblind-safe palettes, and specific journal formatting (Nature, Science, Cell). Orchestrates matplotlib/seaborn/plotly with publication styles. For quick exploration use seaborn or plotly directly.
license: MIT license
metadata: {"version": "1.0", "skill-author": "K-Dense Inc."}

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.