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Create journal-quality scientific figures with proper styling and accessibility
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Route empirical-research requests through the Auto-Empirical Research Skills catalog when this whole repository is installed as one skill in Codex, CodeBuddy, Claude Code, or another IDE. Use to choose and load the right vendored AERS skill for causal inference, econometrics, replication, data acquisition, manuscript writing, peer review and referee responses, citation checking, de-AIGC editing, or full empirical-paper workflows without reading the entire repository at once.
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Download, split, and deeply read academic PDFs. Use when asked to read, review, or summarize an academic paper. Splits PDFs into 4-page chunks, reads them in small batches, and produces structured reading notes — avoiding context window crashes and shallow comprehension.
This skill should be used when the user asks to "create a slash command", "add a command", "write a custom command", "define command arguments", "use command frontmatter", "organize commands", "create command with file references", "interactive command", "use AskUserQuestion in command", or needs guidance on slash command structure, YAML frontmatter fields, dynamic arguments, bash execution in commands, user interaction patterns, or command development best practices for Claude Code.
| name | publication-figures-guide |
| description | Create journal-quality scientific figures with proper styling and accessibility |
| metadata | {"openclaw":{"emoji":"🎨","category":"analysis","subcategory":"dataviz","keywords":["scientific figure creation","publication quality figure","figure standards","colorblind-friendly palette","data visualization"],"source":"wentor"}} |
A skill for creating publication-quality scientific figures that meet journal standards for resolution, formatting, accessibility, and visual clarity. Covers matplotlib, seaborn, and ggplot2 workflows with journal-ready export settings.
| Requirement | Typical Spec | Notes |
|---|---|---|
| Resolution | 300-600 DPI | 300 DPI minimum for print |
| File format | PDF, EPS, TIFF | Vector (PDF/EPS) preferred |
| Color mode | CMYK for print, RGB for online | Check journal spec |
| Max width | Single column: 3.3in / Double: 6.7in | Varies by journal |
| Font size | 6-8pt minimum | Must be legible at final print size |
| Line width | 0.5-1.5pt | Thin lines may not reproduce |
| File size | Varies (often <10MB per figure) | TIFF can be large |
import matplotlib.pyplot as plt
import matplotlib as mpl
import numpy as np
def setup_publication_style(journal: str = 'nature'):
"""
Configure matplotlib for publication-quality figures.
"""
styles = {
'nature': {
'figure.figsize': (3.3, 2.5), # single column
'font.size': 7,
'font.family': 'sans-serif',
'font.sans-serif': ['Arial', 'Helvetica'],
'axes.linewidth': 0.5,
'axes.labelsize': 8,
'xtick.labelsize': 7,
'ytick.labelsize': 7,
'legend.fontsize': 6,
'lines.linewidth': 1.0,
'lines.markersize': 4,
'savefig.dpi': 300,
'savefig.bbox': 'tight',
'savefig.pad_inches': 0.05,
},
'ieee': {
'figure.figsize': (3.5, 2.6),
'font.size': 8,
'font.family': 'serif',
'font.serif': ['Times New Roman', 'Times'],
'axes.linewidth': 0.5,
'axes.labelsize': 9,
'xtick.labelsize': 8,
'ytick.labelsize': 8,
'legend.fontsize': 7,
'lines.linewidth': 1.0,
'savefig.dpi': 300,
},
'acs': {
'figure.figsize': (3.25, 2.5),
'font.size': 7,
'font.family': 'sans-serif',
'font.sans-serif': ['Arial'],
'axes.linewidth': 0.5,
'savefig.dpi': 600,
}
}
style = styles.get(journal, styles['nature'])
mpl.rcParams.update(style)
return style
setup_publication_style('nature')
def get_accessible_palette(n_colors: int = 8, style: str = 'categorical') -> list:
"""
Return colorblind-friendly palettes.
"""
palettes = {
'categorical': {
# Wong (2011) Nature Methods palette
3: ['#0072B2', '#D55E00', '#009E73'],
4: ['#0072B2', '#D55E00', '#009E73', '#CC79A7'],
5: ['#0072B2', '#D55E00', '#009E73', '#CC79A7', '#F0E442'],
8: ['#0072B2', '#D55E00', '#009E73', '#CC79A7',
'#F0E442', '#56B4E9', '#E69F00', '#000000']
},
'sequential': {
# Viridis-based (perceptually uniform)
'cmap': 'viridis' # Also: 'cividis', 'inferno', 'magma'
},
'diverging': {
'cmap': 'RdBu_r' # Also: 'coolwarm', 'BrBG'
}
}
if style == 'categorical':
n = min(n_colors, 8)
return palettes['categorical'].get(n, palettes['categorical'][8][:n])
else:
return palettes[style]
# Usage
colors = get_accessible_palette(4)
def publication_barplot(data: dict, ylabel: str, title: str = '',
output: str = 'figure.pdf'):
"""
Create a publication-quality bar chart.
Args:
data: Dict mapping group names to (mean, std_error) tuples
"""
setup_publication_style('nature')
colors = get_accessible_palette(len(data))
fig, ax = plt.subplots()
x = np.arange(len(data))
names = list(data.keys())
means = [data[k][0] for k in names]
errors = [data[k][1] for k in names]
bars = ax.bar(x, means, yerr=errors, capsize=3, color=colors,
edgecolor='black', linewidth=0.5, width=0.6,
error_kw={'linewidth': 0.5})
ax.set_xticks(x)
ax.set_xticklabels(names, rotation=0)
ax.set_ylabel(ylabel)
if title:
ax.set_title(title)
# Remove top and right spines
ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)
fig.savefig(output, dpi=300, bbox_inches='tight')
plt.close()
return output
from scipy import stats
def publication_scatter(x, y, xlabel, ylabel, output='scatter.pdf',
groups=None, group_labels=None):
"""Publication-quality scatter plot with optional regression line."""
setup_publication_style('nature')
fig, ax = plt.subplots()
if groups is None:
ax.scatter(x, y, s=15, alpha=0.7, color='#0072B2', edgecolors='none')
# Regression line
slope, intercept, r, p, se = stats.linregress(x, y)
x_fit = np.linspace(min(x), max(x), 100)
ax.plot(x_fit, slope*x_fit + intercept, '--', color='#D55E00', linewidth=0.8)
ax.text(0.05, 0.95, f'r = {r:.2f}, p = {p:.3f}',
transform=ax.transAxes, fontsize=6, va='top')
else:
colors = get_accessible_palette(len(set(groups)))
for i, label in enumerate(group_labels or sorted(set(groups))):
mask = np.array(groups) == label
ax.scatter(np.array(x)[mask], np.array(y)[mask],
s=15, alpha=0.7, color=colors[i], label=label)
ax.legend(frameon=False)
ax.set_xlabel(xlabel)
ax.set_ylabel(ylabel)
ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)
fig.savefig(output, dpi=300, bbox_inches='tight')
plt.close()
def multi_panel_figure(n_rows, n_cols, panel_data, output='multipanel.pdf'):
"""Create a multi-panel figure with automatic panel labels."""
setup_publication_style('nature')
fig, axes = plt.subplots(n_rows, n_cols,
figsize=(3.3*n_cols, 2.5*n_rows))
if n_rows * n_cols == 1:
axes = np.array([axes])
axes = axes.flatten()
labels = 'abcdefghijklmnopqrstuvwxyz'
for i, ax in enumerate(axes[:len(panel_data)]):
# Add panel label
ax.text(-0.15, 1.05, labels[i], transform=ax.transAxes,
fontsize=10, fontweight='bold', va='bottom')
plt.tight_layout()
fig.savefig(output, dpi=300, bbox_inches='tight')
plt.close()
plt.rcParams['pdf.fonttype'] = 42)# Ensure fonts are embedded in PDF output
mpl.rcParams['pdf.fonttype'] = 42 # TrueType fonts
mpl.rcParams['ps.fonttype'] = 42