Scientific Visualization

import matplotlib.pyplot as plt
import numpy as np

# Set publication defaults
plt.rcParams.update({
    'font.family': 'sans-serif',
    'font.sans-serif': ['Arial', 'Helvetica'],
    'font.size': 8,
    'axes.labelsize': 9,
    'xtick.labelsize': 7,
    'ytick.labelsize': 7,
    'figure.dpi': 300,
})

# 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
fig.savefig('figure1.pdf', bbox_inches='tight', dpi=300)
fig.savefig('figure1.png', bbox_inches='tight', dpi=300)

Quick Start with Seaborn

For statistical plots, use seaborn with publication styling:

import seaborn as sns
import matplotlib.pyplot as plt

# Configure for publication
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 (uM)')
sns.despine()

fig.savefig('treatment_comparison.pdf', bbox_inches='tight', dpi=300)

Core Principles and Best Practices

1. Resolution and File Format

Critical requirements:

• 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)

2. Color Selection - Colorblind Accessibility

Always use colorblind-friendly palettes.

Recommended: Okabe-Ito palette (distinguishable by all types of color blindness):

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:

• 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:

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:

• Nature: Single 89 mm, Double 183 mm
• Science: Single 55 mm, Double 175 mm
• Cell: Single 85 mm, Double 178 mm

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:

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])
ax3 = fig.add_subplot(gs[1, 0])
ax4 = fig.add_subplot(gs[1, 1])

# Add panel labels
for i, ax in enumerate([ax1, ax2, ax3, ax4]):
    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

Key steps:

1. Set appropriate figure size for target journal
2. Use colorblind-friendly colors
3. Add error bars with correct representation (SEM, SD, or CI)
4. Label axes with units
5. Remove unnecessary spines
6. 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

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

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: Fix an Existing Figure to Meet Publication Standards

Checklist approach:

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 5: Create Colorblind-Friendly Visualizations

Strategy:

1. Use approved colorblind-safe palettes
2. Add redundant encoding (line styles, markers, patterns)
3. Test with colorblind simulator
4. Ensure grayscale compatibility

Example:

# Add redundant encoding beyond color
line_styles = ['-', '--', '-.', ':']
markers = ['o', 's', '^', 'v']

okabe_ito = ['#E69F00', '#56B4E9', '#009E73', '#F0E442',
             '#0072B2', '#D55E00', '#CC79A7', '#000000']

for i, (data, label) in enumerate(datasets):
    plt.plot(x, data, linestyle=line_styles[i % 4],
             marker=markers[i % 4], color=okabe_ito[i],
             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 style files for consistent formatting

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.

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

Common Plot Types for Publications

Statistical comparisons:

# 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 (uM)')
sns.despine()

Distribution analysis:

# 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()

Time series with confidence bands:

# 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:

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 (uM)', 'Response (AU)')
g.set_titles('{row_name} | {col_name}')
sns.despine()

Combining seaborn with matplotlib subplots:

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:

# 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

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

Statistical Rigor with Seaborn

Seaborn automatically computes and displays uncertainty:

# 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:

   sns.set_theme(style='ticks', context='paper', font_scale=1.1)
   

2. Use colorblind-safe palettes:

   sns.set_palette('colorblind')
   

3. Remove unnecessary elements:

   sns.despine()  # Remove top and right spines
   

4. Control figure size appropriately:

   # 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)
   

5. Show individual data points when possible:

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

6. Include proper labels with units:

   ax.set_xlabel('Time (hours)')
   ax.set_ylabel('Expression (AU)')
   

7. Export at correct resolution:

   fig.savefig('figure.pdf', bbox_inches='tight', dpi=300)
   

Advanced Seaborn Techniques

Pairwise relationships for exploratory analysis:

g = sns.pairplot(data=df, hue='condition',
                 vars=['gene1', 'gene2', 'gene3'],
                 corner=True, diag_kind='kde', height=2)

Hierarchical clustering heatmap:

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:

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

g = sns.relplot(...)
g._legend.set_bbox_to_anchor((0.9, 0.5))

Issue: Overlapping labels

plt.xticks(rotation=45, ha='right')
plt.tight_layout()

Issue: Text too small at final size

sns.set_context('paper', font_scale=1.2)  # Increase if needed

Plotly

• Interactive figures for exploration
• Export static images for publication
• Configure for publication quality:

fig.update_layout(
    font=dict(family='Arial, sans-serif', size=10),
    plot_bgcolor='white',
)
fig.write_image('figure.png', scale=3)  # scale=3 gives ~300 DPI

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.