Scientific Visualization

Create publication-ready figures that are clear, accurate, and accessible.

When to Use

• Creating plots or visualizations for manuscripts
• Preparing figures for journal submission
• Ensuring figures are colorblind-friendly and accessible
• Making multi-panel figures with consistent styling
• Exporting figures at correct resolution and format
• Creating figures in manuscript/figures/figN/
• During the ANALYSIS or WRITING phases

Quick Start: Publication Figure

import matplotlib.pyplot as plt
import numpy as np

# Publication settings
plt.rcParams.update({
    'font.family': 'sans-serif',
    'font.sans-serif': ['Arial', 'Helvetica'],
    'font.size': 8,
    'axes.labelsize': 9,
    'axes.titlesize': 10,
    'xtick.labelsize': 7,
    'ytick.labelsize': 7,
    'legend.fontsize': 7,
    'figure.dpi': 300,
    'savefig.dpi': 300,
    'savefig.bbox': 'tight',
})

# Create figure (single column = 3.5 inches / 89mm)
fig, ax = plt.subplots(figsize=(3.5, 2.5))

# Plot with colorblind-safe colors
x = np.linspace(0, 10, 100)
ax.plot(x, np.sin(x), color='#0072B2', label='sin(x)')
ax.plot(x, np.cos(x), color='#D55E00', label='cos(x)')

# Proper labeling with units
ax.set_xlabel('Time (s)')
ax.set_ylabel('Amplitude (mV)')
ax.legend(frameon=False)

# Clean style
ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)

# Save
fig.savefig('manuscript/figures/fig1/figure1.pdf')
fig.savefig('manuscript/figures/fig1/figure1.png', dpi=300)
plt.close()

---

Colorblind-Safe Palettes

Okabe-Ito Palette (Recommended)

# Best for categorical data - distinguishable by all color vision types
OKABE_ITO = [
    '#E69F00',  # Orange
    '#56B4E9',  # Sky Blue
    '#009E73',  # Bluish Green
    '#F0E442',  # Yellow
    '#0072B2',  # Blue
    '#D55E00',  # Vermillion
    '#CC79A7',  # Reddish Purple
    '#000000',  # Black
]

plt.rcParams['axes.prop_cycle'] = plt.cycler(color=OKABE_ITO)

For Continuous Data

# Use perceptually uniform colormaps
cmap = 'viridis'     # Default, good for most cases
cmap = 'plasma'      # High contrast
cmap = 'cividis'     # Colorblind-safe diverging

# AVOID: 'jet', 'rainbow', 'hsv' - these are NOT colorblind-safe

For Diverging Data

# Colorblind-safe diverging colormaps
cmap = 'RdBu_r'     # Red-Blue (reversed so blue = low)
cmap = 'PuOr'       # Purple-Orange
cmap = 'BrBG'       # Brown-Blue-Green

# Always center diverging maps at meaningful zero
sns.heatmap(data, cmap='RdBu_r', center=0)

---

Journal Figure Sizes

Common Widths (in inches)

Journal	Single Column	Double Column
Nature	3.5" (89mm)	7.2" (183mm)
Science	2.2" (55mm)	6.9" (175mm)
Cell	3.3" (85mm)	7.0" (178mm)
PLOS	5.2"	7.5"
Default	3.5"	7.0"

Resolution Requirements

Content Type	Minimum DPI
Line art (graphs)	600-1200 (or vector)
Photographs	300-600
Combination	600

File Formats

Format	Use For	Notes
PDF	Primary	Vector, preserves quality
EPS	Journals	Vector, legacy support
TIFF	Images	Lossless raster
PNG	Web/screen	Lossless, smaller than TIFF
JPEG	NEVER	Lossy compression artifacts

---

Multi-Panel Figures

Using GridSpec

import matplotlib.pyplot as plt
from matplotlib.gridspec import GridSpec
from string import ascii_uppercase

# Create figure with custom layout
fig = plt.figure(figsize=(7, 5))
gs = GridSpec(2, 3, figure=fig, hspace=0.4, wspace=0.4)

# Create panels
ax_a = fig.add_subplot(gs[0, 0])      # Top left
ax_b = fig.add_subplot(gs[0, 1:])     # Top right (spans 2 columns)
ax_c = fig.add_subplot(gs[1, 0])      # Bottom left
ax_d = fig.add_subplot(gs[1, 1])      # Bottom middle
ax_e = fig.add_subplot(gs[1, 2])      # Bottom right

# Add panel labels
axes = [ax_a, ax_b, ax_c, ax_d, ax_e]
for i, ax in enumerate(axes):
    ax.text(-0.15, 1.05, ascii_uppercase[i], transform=ax.transAxes,
            fontsize=12, fontweight='bold', va='top')
    
    # Example: clean up each axis
    ax.spines['top'].set_visible(False)
    ax.spines['right'].set_visible(False)

plt.savefig('manuscript/figures/fig1/figure1.pdf')

Using Subplots

fig, axes = plt.subplots(2, 2, figsize=(7, 6))
axes = axes.flatten()

for i, ax in enumerate(axes):
    ax.text(-0.1, 1.05, ascii_uppercase[i], transform=ax.transAxes,
            fontsize=12, fontweight='bold', va='top')

plt.tight_layout()

---

Common Plot Types

Bar Plot with Error Bars

import matplotlib.pyplot as plt
import numpy as np

groups = ['Control', 'Treatment A', 'Treatment B']
means = [10.2, 15.8, 18.3]
sems = [1.2, 1.5, 1.8]  # Standard error of mean
n = [20, 20, 20]

fig, ax = plt.subplots(figsize=(3.5, 3))

bars = ax.bar(groups, means, yerr=sems, capsize=4, 
              color=['#0072B2', '#E69F00', '#D55E00'],
              edgecolor='black', linewidth=0.5)

ax.set_ylabel('Response (AU)')
ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)

# Add individual data points (transparency)
for i, (group, n_i) in enumerate(zip(groups, n)):
    x_jitter = np.random.normal(i, 0.05, n_i)
    y_data = np.random.normal(means[i], sems[i]*np.sqrt(n_i), n_i)
    ax.scatter(x_jitter, y_data, alpha=0.4, s=15, c='black')

plt.tight_layout()

Line Plot with Confidence Bands

import matplotlib.pyplot as plt
import numpy as np

x = np.linspace(0, 10, 50)
y = np.sin(x) + np.random.normal(0, 0.2, 50)
y_smooth = np.sin(x)
ci = 0.3  # Confidence interval width

fig, ax = plt.subplots(figsize=(4, 3))

# Confidence band
ax.fill_between(x, y_smooth - ci, y_smooth + ci, alpha=0.3, color='#0072B2')
# Line
ax.plot(x, y_smooth, color='#0072B2', linewidth=2)
# Data points
ax.scatter(x, y, s=20, alpha=0.5, color='#0072B2')

ax.set_xlabel('Time (s)')
ax.set_ylabel('Signal (AU)')
ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)

Box Plot with Significance Markers

import matplotlib.pyplot as plt
import seaborn as sns

fig, ax = plt.subplots(figsize=(4, 4))

# Box plot
sns.boxplot(data=df, x='group', y='value', palette='colorblind', ax=ax)

# Add individual points
sns.stripplot(data=df, x='group', y='value', color='black', 
              alpha=0.3, size=4, ax=ax)

# Add significance bar
y_max = df['value'].max()
ax.plot([0, 1], [y_max*1.1, y_max*1.1], 'k-', linewidth=1)
ax.text(0.5, y_max*1.12, '***', ha='center', fontsize=10)

ax.set_ylabel('Response (AU)')
sns.despine()

Heatmap with Proper Colorbar

import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np

# Correlation matrix example
fig, ax = plt.subplots(figsize=(5, 4))

# Mask upper triangle
mask = np.triu(np.ones_like(corr_matrix, dtype=bool))

sns.heatmap(corr_matrix, mask=mask, annot=True, fmt='.2f',
            cmap='RdBu_r', center=0, square=True,
            linewidths=0.5, cbar_kws={'shrink': 0.8},
            vmin=-1, vmax=1, ax=ax)

ax.set_title('Correlation Matrix')
plt.tight_layout()

---

Seaborn for Statistical Plots

import seaborn as sns

# Apply publication style
sns.set_theme(style='ticks', context='paper', font_scale=1.1)
sns.set_palette('colorblind')

# Violin plot with split
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()

# Regression plot with CI
fig, ax = plt.subplots(figsize=(4, 3))
sns.regplot(data=df, x='predictor', y='outcome', 
            ci=95, scatter_kws={'alpha': 0.5}, ax=ax)
sns.despine()

---

Figure Export Workflow

For RA Projects

def save_figure(fig, fig_num, name):
    """Save figure in multiple formats following RA conventions."""
    import os
    
    # Create figure directory
    fig_dir = f'manuscript/figures/fig{fig_num}'
    os.makedirs(fig_dir, exist_ok=True)
    
    # Save in multiple formats
    fig.savefig(f'{fig_dir}/{name}.pdf', format='pdf', bbox_inches='tight')
    fig.savefig(f'{fig_dir}/{name}.png', format='png', dpi=300, bbox_inches='tight')
    fig.savefig(f'{fig_dir}/{name}.tiff', format='tiff', dpi=300, bbox_inches='tight')
    
    print(f"Saved: {fig_dir}/{name}.{{pdf,png,tiff}}")
    
# Usage
save_figure(fig, 1, 'treatment_comparison')

Caption Template

Create manuscript/figures/fig1/caption.md:

**Figure 1. Treatment comparison across experimental groups.**

(A) Response levels in control (n=20), Treatment A (n=20), and Treatment B (n=20) 
groups. Data shown as mean ± SEM with individual data points. ***p < 0.001, 
one-way ANOVA with Tukey's post-hoc test.

(B) Time course of response following treatment. Shaded regions indicate 95% 
confidence intervals. Treatment A (orange) shows significantly faster response 
than control (blue), p < 0.01.

---

Accessibility Checklist

• Colorblind-safe palette (Okabe-Ito or viridis)
• Works in grayscale (add patterns/markers if needed)
• Font size ≥ 6pt at final print size
• All axes labeled with units
• Error bars defined in caption (SEM, SD, or CI)
• No 3D effects or chartjunk
• Legend readable and positioned appropriately
• Resolution ≥ 300 DPI for raster, or vector format

---

Integration with RA Workflow

1. Create figure directory: manuscript/figures/figN/
2. Save figure files: figure.pdf, figure.png
3. Create caption: caption.md
4. Reference in manuscript/results.md
5. Log activity in .research/logs/activity.md