| name | scientific-visualization |
| description | Publication-quality figure guidelines for scientific papers — DPI, font sizes, colorblind-friendly palettes, plot type selection, multi-panel layout, uncertainty representation, and statistical annotation. Use when creating figures for papers or reports. |
| allowed_agents | ["experiment","native_coding","data"] |
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
| Use Case | Format | DPI | Notes |
|---|
| Primary figures | PDF or SVG | Vector (infinite) | Preferred for line plots, bar charts, scatter — scales losslessly |
| Photographs / microscopy | TIFF or PNG | ≥ 300 dpi | TIFF preferred for medical/bio journals |
| Web / preprint only | PNG | ≥ 150 dpi | Acceptable but not for camera-ready submission |
Rule: always produce a vector version (PDF/SVG) alongside any raster export.
Figure Width
Match journal column width exactly. Common standards:
| Layout | Width |
|---|
| Single column | 3.3–3.5 inches (84–89 mm) |
| 1.5 column | 5.0–5.5 inches |
| Double column / full width | 6.5–7.0 inches (165–178 mm) |
Check the author guide for the target journal; many specify mm.
Font Sizes (Minimum)
| Element | Minimum Size |
|---|
| Tick labels | 8 pt |
| Axis labels | 10 pt |
| Legend text | 8 pt |
| Subplot titles | 10 pt |
| Figure title | 12 pt |
| Annotations / significance stars | 8 pt |
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",
"#56B4E9",
"#009E73",
"#F0E442",
"#0072B2",
"#D55E00",
"#CC79A7",
"#000000",
]
Continuous / Sequential Data
import matplotlib.pyplot as plt
import matplotlib.cm as cm
plt.imshow(data, cmap='viridis')
plt.imshow(data, cmap='cividis')
Diverging Data (e.g., correlation matrices, residuals)
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
| Situation | Plot Type | Why |
|---|
| n > 30 per group, outliers matter | Box plot | Quartiles + outliers visible |
| n > 30, shape of distribution matters | Violin plot | Density visible; use cut=0 to avoid extending beyond data range |
| n ≤ 30 (small samples) | Strip / swarm plot | Show every data point; no hidden structure |
| Large n (> 1000), compare shapes | Ridge / KDE plot | Box plots obscure multimodality |
import matplotlib.pyplot as plt
import numpy as np
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)
colors = ["#56B4E9", "#E69F00", "#009E73"]
for patch, color in zip(bp['boxes'], colors):
patch.set_facecolor(color)
patch.set_alpha(0.7)
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)
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)
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)
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')
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)
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)
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.figsize": (5.5, 4.0),
"figure.dpi": 150,
"savefig.dpi": 300,
"savefig.bbox": "tight",
"savefig.facecolor": "white",
"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.linewidth": 1.5,
"lines.markersize": 5,
"patch.linewidth": 0.5,
"axes.spines.top": False,
"axes.spines.right": False,
"axes.prop_cycle": mpl.cycler(color=OKABE_ITO_COLORS),
"axes.grid": True,
"axes.axisbelow": True,
"grid.color": "#DDDDDD",
"grid.linewidth": 0.6,
"grid.linestyle": "--",
"grid.alpha": 0.7,
"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')
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, :])
ax_bl = fig.add_subplot(gs[1, 0])
ax_bm = fig.add_subplot(gs[1, 1])
ax_br = fig.add_subplot(gs[1, 2])
Saving Figures Correctly
import matplotlib.pyplot as plt
fig.savefig('figure1.pdf', dpi=300, bbox_inches='tight', facecolor='white')
fig.savefig('figure1.png', dpi=300, bbox_inches='tight', facecolor='white')
fig.savefig('figure1.tiff', dpi=600, bbox_inches='tight', facecolor='white')
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')
save_figure(fig, 'figure1', dpi=300, fmt='png')
Error Bars and Uncertainty Representation
Choose the right uncertainty measure for the claim you are making:
| Measure | Formula | Use When |
|---|
| ±SD | σ | Describing spread of raw data |
| ±SEM | σ / √n | Estimating precision of the sample mean (NOT data spread) |
| 95% CI | mean ± 1.96 × SEM | Inferential: range plausibly containing the true mean |
| Bootstrap 95% CI | percentile method | When distribution is non-normal or n is small |
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
| Symbol | p-value |
|---|
| ns | p > 0.05 |
| * | p ≤ 0.05 |
| ** | p ≤ 0.01 |
| *** | p ≤ 0.001 |
| **** | p ≤ 0.0001 |
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)
add_significance_bracket(ax, x1=0, x2=1, y=90, h=2, text='**')
Using statannotations (automated)
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
| Mistake | Why It's Wrong | Fix |
|---|
| Chartjunk (gridlines, 3D effects, shadows) | Distracts from data | Use minimal style; remove top/right spines |
| Truncated y-axis (not starting at zero for bar charts) | Exaggerates differences | Start bars at zero; use line plots if showing change |
| Missing axis labels | Reader cannot interpret units | Always label both axes with units in parentheses |
| Unlabeled axes | Same as above | All axes must have labels before saving |
| Pie charts | Angle/area comparisons are inaccurate | Use bar charts or stacked bars |
| Using red/green alone to encode categories | Invisible to deuteranopes | Use Okabe-Ito palette |
| Mismatched scales in multi-panel figures | Implies false relative differences | Use sharex / sharey when scales should match |
| Reporting SEM without saying so | Misleading — SEM is much smaller than SD | Always state "mean ± SEM" or "mean ± SD" in caption |
| Font below 8pt | Unreadable in print | Enforce minimum via rcParams |
| Saving at 72 dpi | Pixelated in print | Always use ≥ 300 dpi or vector format |
| No units | Ambiguous axes | "Accuracy" → "Accuracy (%)" or "Normalized Score" |
Figure Caption Checklist
Every figure must have a caption that includes:
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.
