scientific-visualization

name	scientific-visualization
description	Create publication figures with matplotlib/seaborn/plotly. Multi-panel layouts, error bars, significance markers, colorblind-safe, export PDF/EPS/TIFF, for journal-ready scientific plots.

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:

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:

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:

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:

   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:

   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:

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))