Scientific Visualization

Scientific Visualization

Overview

Publication figures are permanent artifacts of a paper. A poorly formatted figure with unlabeled axes, inaccessible colors, or missing uncertainty estimates can cause rejection or mislead readers. This skill specifies the exact standards for producing figures that satisfy journal requirements, remain readable in black-and-white and for colorblind readers, and communicate uncertainty honestly.

When to Use This Skill

Use this skill when:

• Creating any figure that will appear in a paper, preprint, or technical report
• Choosing between plot types for a dataset
• Setting up matplotlib style for a project
• Adding error bars, confidence bands, or statistical significance annotations
• Saving figures in the correct format and resolution
• Designing multi-panel layouts for a results section
• Verifying that figures are accessible to colorblind readers

Figure Specifications for Publication

Resolution and Format

Rule: always produce a vector version (PDF/SVG) alongside any raster export.

Figure Width

Match journal column width exactly. Common standards:

Check the author guide for the target journal; many specify mm.

Font Sizes (Minimum)

These are minimums for print. Aim for 10–11 pt for body text elements.

Colorblind-Friendly Palettes

Approximately 8% of males have color vision deficiency. Use palettes that are distinguishable under deuteranopia and protanopia.

Okabe-Ito (Wong) 8-Color Palette — Recommended Default

This palette is the standard recommendation of Nature Methods and many scientific style guides.

OKABE_ITO_COLORS = [
    "#E69F00",  # orange
    "#56B4E9",  # sky blue
    "#009E73",  # bluish green
    "#F0E442",  # yellow
    "#0072B2",  # blue
    "#D55E00",  # vermillion
    "#CC79A7",  # reddish purple
    "#000000",  # black
]

Continuous / Sequential Data

import matplotlib.pyplot as plt
import matplotlib.cm as cm

# Good: perceptually uniform, works in grayscale, accessible
# viridis  — default recommendation
# plasma   — higher contrast
# cividis  — designed for color-vision deficiency

plt.imshow(data, cmap='viridis')   # sequential
plt.imshow(data, cmap='cividis')   # alternative, explicitly CVD-safe

Diverging Data (e.g., correlation matrices, residuals)

# RdBu or RdYlBu — both are CVD-safe
plt.imshow(correlation_matrix, cmap='RdBu', vmin=-1, vmax=1)

Applying Okabe-Ito as Default Color Cycle

import matplotlib.pyplot as plt

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

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

Plot Type Decision Guide

Choose the plot type that reveals the most information with the least chartjunk.

Distribution Comparisons

import matplotlib.pyplot as plt
import numpy as np

# Box plot with overlaid strip plot (best practice for moderate n)
fig, ax = plt.subplots(figsize=(5, 4))
groups = {'Control': np.random.normal(50, 10, 40),
          'Treatment A': np.random.normal(60, 12, 40),
          'Treatment B': np.random.normal(55, 8, 40)}

positions = range(len(groups))
bp = ax.boxplot(list(groups.values()), positions=positions,
                patch_artist=True, widths=0.4, showfliers=False)

# Okabe-Ito colors
colors = ["#56B4E9", "#E69F00", "#009E73"]
for patch, color in zip(bp['boxes'], colors):
    patch.set_facecolor(color)
    patch.set_alpha(0.7)

# Overlay individual points
for i, (name, data) in enumerate(groups.items()):
    jitter = np.random.uniform(-0.1, 0.1, len(data))
    ax.scatter(i + jitter, data, alpha=0.5, s=20, color=colors[i], zorder=3)

ax.set_xticks(positions)
ax.set_xticklabels(list(groups.keys()))
ax.set_ylabel('Score', fontsize=11)
ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)

Trends Over Time / Ordered Axis

import matplotlib.pyplot as plt
import numpy as np

x = np.linspace(0, 10, 100)
y_mean = np.sin(x)
y_std = 0.2 * np.abs(np.cos(x))

fig, ax = plt.subplots(figsize=(7, 4))
ax.plot(x, y_mean, color="#0072B2", linewidth=1.5, label='Model')
ax.fill_between(x, y_mean - 1.96 * y_std, y_mean + 1.96 * y_std,
                color="#0072B2", alpha=0.2, label='95% CI')
ax.set_xlabel('Epoch', fontsize=11)
ax.set_ylabel('Validation Loss', fontsize=11)
ax.legend(frameon=False, fontsize=9)
ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)

Relationships: Scatter

from scipy import stats

fig, ax = plt.subplots(figsize=(5, 5))
ax.scatter(x_vals, y_vals, alpha=0.6, s=30, color="#0072B2", edgecolors='white', linewidth=0.3)

# Regression line with CI
slope, intercept, r, p, se = stats.linregress(x_vals, y_vals)
x_line = np.linspace(x_vals.min(), x_vals.max(), 200)
y_line = slope * x_line + intercept
ax.plot(x_line, y_line, color="#D55E00", linewidth=1.5)
ax.text(0.05, 0.92, f'r = {r:.2f}, p = {p:.3f}', transform=ax.transAxes, fontsize=9)

ax.set_xlabel('X Variable (units)', fontsize=11)
ax.set_ylabel('Y Variable (units)', fontsize=11)
ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)

# Hexbin for large n (> 1000 points)
# ax.hexbin(x_vals, y_vals, gridsize=30, cmap='viridis')

Categorical Comparisons: Bar Charts

import matplotlib.pyplot as plt
import numpy as np

categories = ['Model A', 'Model B', 'Model C', 'Baseline']
means = [0.82, 0.78, 0.85, 0.65]
errors = [0.03, 0.04, 0.02, 0.01]
colors = ["#56B4E9", "#E69F00", "#009E73", "#999999"]

fig, ax = plt.subplots(figsize=(5, 4))
bars = ax.bar(categories, means, yerr=errors, capsize=4,
              color=colors, alpha=0.85, edgecolor='black', linewidth=0.5)

ax.set_ylabel('F1 Score (macro)', fontsize=11)
ax.set_ylim(0, 1.0)
ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)

# For many categories → horizontal bar chart
# ax.barh(categories, means, xerr=errors, ...)

# NEVER use 3D bar charts — they distort magnitude perception

Correlation Matrices: Heatmap

import matplotlib.pyplot as plt
import numpy as np

fig, ax = plt.subplots(figsize=(6, 5))
im = ax.imshow(corr_matrix, cmap='RdBu', vmin=-1, vmax=1, aspect='auto')
plt.colorbar(im, ax=ax, label='Pearson r')

# Annotate each cell
for i in range(corr_matrix.shape[0]):
    for j in range(corr_matrix.shape[1]):
        val = corr_matrix[i, j]
        color = 'white' if abs(val) > 0.6 else 'black'
        ax.text(j, i, f'{val:.2f}', ha='center', va='center',
                fontsize=8, color=color)

ax.set_xticks(range(len(feature_names)))
ax.set_yticks(range(len(feature_names)))
ax.set_xticklabels(feature_names, rotation=45, ha='right', fontsize=8)
ax.set_yticklabels(feature_names, fontsize=8)
ax.set_title('Feature Correlation Matrix', fontsize=11, pad=10)

Proportions: Stacked Bar (Never Pie)

# Pie charts make angle/area comparison difficult — use stacked bars
fig, ax = plt.subplots(figsize=(6, 3))
categories = ['Train', 'Validation', 'Test']
sizes = [0.70, 0.15, 0.15]
colors = ["#0072B2", "#56B4E9", "#E69F00"]

left = 0
for size, color, label in zip(sizes, colors, ['70% Train', '15% Val', '15% Test']):
    ax.barh(['Split'], [size], left=left, color=color, label=label, edgecolor='white')
    ax.text(left + size / 2, 0, label, ha='center', va='center', fontsize=9, color='white', fontweight='bold')
    left += size

ax.set_xlim(0, 1)
ax.set_xlabel('Proportion', fontsize=10)
ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)
ax.spines['left'].set_visible(False)
ax.set_yticks([])

Applying Paper Style with matplotlib rcParams

The apply_paper_style.py script (in scripts/) encapsulates these settings. Use it as follows:

from scripts.apply_paper_style import apply_paper_style, OKABE_ITO_COLORS, save_figure

apply_paper_style(font_size=10)
# All subsequent figures will use publication-quality defaults

To apply manually:

import matplotlib as mpl
import matplotlib.pyplot as plt

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

mpl.rcParams.update({
    # Figure
    "figure.figsize":        (5.5, 4.0),
    "figure.dpi":            150,
    "savefig.dpi":           300,
    "savefig.bbox":          "tight",
    "savefig.facecolor":     "white",

    # Font
    "font.family":           "sans-serif",
    "font.sans-serif":       ["Helvetica", "Arial", "DejaVu Sans"],
    "font.size":             10,
    "axes.titlesize":        11,
    "axes.labelsize":        10,
    "xtick.labelsize":       9,
    "ytick.labelsize":       9,
    "legend.fontsize":       9,

    # Lines and markers
    "lines.linewidth":       1.5,
    "lines.markersize":      5,
    "patch.linewidth":       0.5,

    # Axes
    "axes.spines.top":       False,
    "axes.spines.right":     False,
    "axes.prop_cycle":       mpl.cycler(color=OKABE_ITO_COLORS),
    "axes.grid":             True,
    "axes.axisbelow":        True,

    # Grid (subtle)
    "grid.color":            "#DDDDDD",
    "grid.linewidth":        0.6,
    "grid.linestyle":        "--",
    "grid.alpha":            0.7,

    # Ticks
    "xtick.direction":       "out",
    "ytick.direction":       "out",
    "xtick.major.size":      4,
    "ytick.major.size":      4,
    "xtick.minor.size":      2,
    "ytick.minor.size":      2,
})

Multi-Panel Layouts

2×2 Layout

import matplotlib.pyplot as plt

fig, axes = plt.subplots(2, 2, figsize=(7.0, 5.5))

axes[0, 0].plot([1, 2, 3], [1, 4, 9])
axes[0, 0].set_title('(A) Loss over epochs')
axes[0, 0].set_xlabel('Epoch')
axes[0, 0].set_ylabel('Loss')

# ... populate other panels ...

# Panel labels (A, B, C, D)
for ax, label in zip(axes.flat, ['A', 'B', 'C', 'D']):
    ax.text(-0.15, 1.05, label, transform=ax.transAxes,
            fontsize=12, fontweight='bold', va='top')

plt.tight_layout(pad=1.5)
plt.savefig('results_overview.pdf', dpi=300, bbox_inches='tight', facecolor='white')

1×3 Layout (horizontal)

fig, (ax1, ax2, ax3) = plt.subplots(1, 3, figsize=(7.0, 2.8))
# ...
fig.subplots_adjust(wspace=0.35)

GridSpec (unequal panels)

from matplotlib.gridspec import GridSpec

fig = plt.figure(figsize=(7.0, 5.0))
gs = GridSpec(2, 3, figure=fig, hspace=0.4, wspace=0.35)

ax_main  = fig.add_subplot(gs[0, :])   # full-width top row
ax_bl    = fig.add_subplot(gs[1, 0])   # bottom left
ax_bm    = fig.add_subplot(gs[1, 1])   # bottom middle
ax_br    = fig.add_subplot(gs[1, 2])   # bottom right

Saving Figures Correctly

import matplotlib.pyplot as plt

# Preferred: vector PDF
fig.savefig('figure1.pdf', dpi=300, bbox_inches='tight', facecolor='white')

# Raster fallback: PNG at 300 dpi
fig.savefig('figure1.png', dpi=300, bbox_inches='tight', facecolor='white')

# TIFF for bio/medical journals (large file)
fig.savefig('figure1.tiff', dpi=600, bbox_inches='tight', facecolor='white')

# SVG for web / editable vector
fig.savefig('figure1.svg', bbox_inches='tight', facecolor='white')

Use the helper from scripts/apply_paper_style.py:

from scripts.apply_paper_style import save_figure
save_figure(fig, 'figure1', dpi=300, fmt='pdf')   # saves figure1.pdf
save_figure(fig, 'figure1', dpi=300, fmt='png')   # also saves figure1.png

Error Bars and Uncertainty Representation

Choose the right uncertainty measure for the claim you are making:

import numpy as np
import matplotlib.pyplot as plt

n = 40
data_a = np.random.normal(75, 10, n)
data_b = np.random.normal(65, 12, n)

means = [data_a.mean(), data_b.mean()]
sems  = [data_a.std() / np.sqrt(n), data_b.std() / np.sqrt(n)]
cis   = [1.96 * sem for sem in sems]

fig, ax = plt.subplots(figsize=(4, 4))
ax.bar([0, 1], means, yerr=cis, capsize=5,
       color=["#56B4E9", "#E69F00"], alpha=0.85, edgecolor='black', linewidth=0.5)
ax.set_xticks([0, 1])
ax.set_xticklabels(['Group A', 'Group B'])
ax.set_ylabel('Score (mean ± 95% CI)', fontsize=10)
ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)

Important: If you use SEM as error bars, state that explicitly in the caption. Never label SEM bars as "error bars" without specifying the measure.

Statistical Significance Annotations

Standard Notation

Manual Bracket Annotation

def add_significance_bracket(ax, x1, x2, y, h, text, fontsize=10, linewidth=1.0):
    """
    Draw a significance bracket between positions x1 and x2.

    Parameters
    ----------
    ax      : matplotlib Axes
    x1, x2  : x-positions of the two groups being compared
    y       : y-position for the bracket (should be above the bars/data)
    h       : height of the bracket arms
    text    : annotation string, e.g. '**' or 'ns'
    """
    ax.plot([x1, x1, x2, x2], [y, y + h, y + h, y],
            lw=linewidth, color='black')
    ax.text((x1 + x2) / 2, y + h, text,
            ha='center', va='bottom', fontsize=fontsize)

# Example usage
add_significance_bracket(ax, x1=0, x2=1, y=90, h=2, text='**')

Using statannotations (automated)

# pip install statannotations
from statannotations.Annotator import Annotator
import seaborn as sns

ax = sns.boxplot(data=df, x='group', y='score')
pairs = [('Control', 'Treatment A'), ('Control', 'Treatment B')]
annotator = Annotator(ax, pairs, data=df, x='group', y='score')
annotator.configure(test='Mann-Whitney', text_format='star', loc='outside')
annotator.apply_and_annotate()

Common Mistakes to Avoid

Figure Caption Checklist

Every figure must have a caption that includes:

• What is shown (not just "Figure 1")
• What each panel shows (A, B, C, D)
• Definition of error bars (±SD, ±SEM, 95% CI, and n)
• Sample size per group
• Statistical test used for any significance annotation
• Color encoding explained if not obvious from axis labels

Example:

Figure 2. Model comparison across five datasets. (A) F1 score (macro) for each model; error bars represent 95% CI across 5-fold cross-validation (n = 5 folds). (B) Learning curves showing training and validation F1 as a function of training set size; shaded region = ±SD. *, p ≤ 0.05; **, p ≤ 0.01 by paired t-test across folds with Bonferroni correction.

Resources

Scripts Directory

• apply_paper_style.py: Import apply_paper_style(), OKABE_ITO_COLORS, save_figure(), and add_significance_annotation() to instantly apply publication-quality defaults to any matplotlib figure.