scientific-visualization

Scientific Visualization

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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_max1.1, y_max1.1], 'k-', linewidth=1) ax.text(0.5, y_max1.12, '**', 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

  • Create figure directory: manuscript/figures/figN/

  • Save figure files: figure.pdf , figure.png

  • Create caption: caption.md

  • Reference in manuscript/results.md

  • Log activity in .research/logs/activity.md

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