Create publication figures with matplotlib/seaborn/plotly. Multi-panel layouts, error bars, significance markers, colorblind-safe, export PDF/EPS/TIFF, for journal-ready scientific plots.
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Scientific visualization transforms data into clear, accurate figures for publication. Create journal-ready plots with multi-panel layouts, error bars, significance markers, and colorblind-safe palettes. Export as PDF/EPS/TIFF using matplotlib, seaborn, and plotly for manuscripts.
This skill should be used when:
import matplotlib.pyplot as plt
import numpy as np
# Apply publication style (from scripts/style_presets.py)
from style_presets import apply_publication_style
apply_publication_style('default')
# Create figure with appropriate size (single column = 3.5 inches)
fig, ax = plt.subplots(figsize=(3.5, 2.5))
# Plot data
x = np.linspace(0, 10, 100)
ax.plot(x, np.sin(x), label='sin(x)')
ax.plot(x, np.cos(x), label='cos(x)')
# Proper labeling with units
ax.set_xlabel('Time (seconds)')
ax.set_ylabel('Amplitude (mV)')
ax.legend(frameon=False)
# Remove unnecessary spines
ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)
# Save in publication formats (from scripts/figure_export.py)
from figure_export import save_publication_figure
save_publication_figure(fig, 'figure1', formats=['pdf', 'png'], dpi=300)
Apply journal-specific styles using the matplotlib style files in assets/:
import matplotlib.pyplot as plt
# Option 1: Use style file directly
plt.style.use('assets/nature.mplstyle')
# Option 2: Use style_presets.py helper
from style_presets import configure_for_journal
configure_for_journal('nature', figure_width='single')
# Now create figures - they'll automatically match Nature specifications
fig, ax = plt.subplots()
# ... your plotting code ...
For statistical plots, use seaborn with publication styling:
import seaborn as sns
import matplotlib.pyplot as plt
from style_presets import apply_publication_style
# Apply publication style
apply_publication_style('default')
sns.set_theme(style='ticks', context='paper', font_scale=1.1)
sns.set_palette('colorblind')
# Create statistical comparison figure
fig, ax = plt.subplots(figsize=(3.5, 3))
sns.boxplot(data=df, x='treatment', y='response',
order=['Control', 'Low', 'High'], palette='Set2', ax=ax)
sns.stripplot(data=df, x='treatment', y='response',
order=['Control', 'Low', 'High'],
color='black', alpha=0.3, size=3, ax=ax)
ax.set_ylabel('Response (μM)')
sns.despine()
# Save figure
from figure_export import save_publication_figure
save_publication_figure(fig, 'treatment_comparison', formats=['pdf', 'png'], dpi=300)
Critical requirements (detailed in references/publication_guidelines.md):
Implementation:
# Use the figure_export.py script for correct settings
from figure_export import save_publication_figure
# Saves in multiple formats with proper DPI
save_publication_figure(fig, 'myfigure', formats=['pdf', 'png'], dpi=300)
# Or save for specific journal requirements
from figure_export import save_for_journal
save_for_journal(fig, 'figure1', journal='nature', figure_type='combination')
Always use colorblind-friendly palettes (detailed in references/color_palettes.md):
Recommended: Okabe-Ito palette (distinguishable by all types of color blindness):
# Option 1: Use assets/color_palettes.py
from color_palettes import OKABE_ITO_LIST, apply_palette
apply_palette('okabe_ito')
# Option 2: Manual specification
okabe_ito = ['#E69F00', '#56B4E9', '#009E73', '#F0E442',
'#0072B2', '#D55E00', '#CC79A7', '#000000']
plt.rcParams['axes.prop_cycle'] = plt.cycler(color=okabe_ito)
For heatmaps/continuous data:
viridis, plasma, cividisPuOr, RdBu, BrBG instead)jet or rainbow colormapsAlways test figures in grayscale to ensure interpretability.
Font guidelines (detailed in references/publication_guidelines.md):
Implementation:
# Set fonts globally
import matplotlib as mpl
mpl.rcParams['font.family'] = 'sans-serif'
mpl.rcParams['font.sans-serif'] = ['Arial', 'Helvetica']
mpl.rcParams['font.size'] = 8
mpl.rcParams['axes.labelsize'] = 9
mpl.rcParams['xtick.labelsize'] = 7
mpl.rcParams['ytick.labelsize'] = 7
Journal-specific widths (detailed in references/journal_requirements.md):
Check figure size compliance:
from figure_export import check_figure_size
fig = plt.figure(figsize=(3.5, 3)) # 89 mm for Nature
check_figure_size(fig, journal='nature')
Best practices:
Example implementation (see references/matplotlib_examples.md for complete code):
from string import ascii_uppercase
fig = plt.figure(figsize=(7, 4))
gs = fig.add_gridspec(2, 2, hspace=0.4, wspace=0.4)
ax1 = fig.add_subplot(gs[0, 0])
ax2 = fig.add_subplot(gs[0, 1])
# ... create other panels ...
# Add panel labels
for i, ax in enumerate([ax1, ax2, ...]):
ax.text(-0.15, 1.05, ascii_uppercase[i], transform=ax.transAxes,
fontsize=10, fontweight='bold', va='top')
See references/matplotlib_examples.md Example 1 for complete code.
Key steps:
Using seaborn for automatic confidence intervals:
import seaborn as sns
fig, ax = plt.subplots(figsize=(5, 3))
sns.lineplot(data=timeseries, x='time', y='measurement',
hue='treatment', errorbar=('ci', 95),
markers=True, ax=ax)
ax.set_xlabel('Time (hours)')
ax.set_ylabel('Measurement (AU)')
sns.despine()
See references/matplotlib_examples.md Example 2 for complete code.
Key steps:
GridSpec for flexible layoutSee references/matplotlib_examples.md Example 4 for complete code.
Key steps:
viridis, plasma, cividis)RdBu_r, PuOr)Using seaborn for correlation matrices:
import seaborn as sns
fig, ax = plt.subplots(figsize=(5, 4))
corr = df.corr()
mask = np.triu(np.ones_like(corr, dtype=bool))
sns.heatmap(corr, mask=mask, annot=True, fmt='.2f',
cmap='RdBu_r', center=0, square=True,
linewidths=1, cbar_kws={'shrink': 0.8}, ax=ax)
Workflow:
references/journal_requirements.mdfrom style_presets import configure_for_journal
configure_for_journal('nature', figure_width='single')
from figure_export import save_for_journal
save_for_journal(fig, 'figure1', journal='nature', figure_type='line_art')
Checklist approach (full checklist in references/publication_guidelines.md):
Strategy:
assets/color_palettes.pyExample:
from color_palettes import apply_palette
import matplotlib.pyplot as plt
apply_palette('okabe_ito')
# Add redundant encoding beyond color
line_styles = ['-', '--', '-.', ':']
markers = ['o', 's', '^', 'v']
for i, (data, label) in enumerate(datasets):
plt.plot(x, data, linestyle=line_styles[i % 4],
marker=markers[i % 4], label=label)
Always include:
Example with statistics:
# Show individual points with summary statistics
ax.scatter(x_jittered, individual_points, alpha=0.4, s=8)
ax.errorbar(x, means, yerr=sems, fmt='o', capsize=3)
# Mark significance
ax.text(1.5, max_y * 1.1, '***', ha='center', fontsize=8)
references/matplotlib_examples.md for extensive examplesSeaborn provides a high-level, dataset-oriented interface for statistical graphics, built on matplotlib. It excels at creating publication-quality statistical visualizations with minimal code while maintaining full compatibility with matplotlib customization.
Key advantages for scientific visualization:
Always apply matplotlib publication styles first, then configure seaborn:
import seaborn as sns
import matplotlib.pyplot as plt
from style_presets import apply_publication_style
# Apply publication style
apply_publication_style('default')
# Configure seaborn for publication
sns.set_theme(style='ticks', context='paper', font_scale=1.1)
sns.set_palette('colorblind') # Use colorblind-safe palette
# Create figure
fig, ax = plt.subplots(figsize=(3.5, 2.5))
sns.scatterplot(data=df, x='time', y='response',
hue='treatment', style='condition', ax=ax)
sns.despine() # Remove top and right spines
Statistical comparisons:
# Box plot with individual points for transparency
fig, ax = plt.subplots(figsize=(3.5, 3))
sns.boxplot(data=df, x='treatment', y='response',
order=['Control', 'Low', 'High'], palette='Set2', ax=ax)
sns.stripplot(data=df, x='treatment', y='response',
order=['Control', 'Low', 'High'],
color='black', alpha=0.3, size=3, ax=ax)
ax.set_ylabel('Response (μM)')
sns.despine()
Distribution analysis:
# Violin plot with split comparison
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()
Correlation matrices:
# Heatmap with proper colormap and annotations
fig, ax = plt.subplots(figsize=(5, 4))
corr = df.corr()
mask = np.triu(np.ones_like(corr, dtype=bool)) # Show only lower triangle
sns.heatmap(corr, mask=mask, annot=True, fmt='.2f',
cmap='RdBu_r', center=0, square=True,
linewidths=1, cbar_kws={'shrink': 0.8}, ax=ax)
plt.tight_layout()
Time series with confidence bands:
# Line plot with automatic CI calculation
fig, ax = plt.subplots(figsize=(5, 3))
sns.lineplot(data=timeseries, x='time', y='measurement',
hue='treatment', style='replicate',
errorbar=('ci', 95), markers=True, dashes=False, ax=ax)
ax.set_xlabel('Time (hours)')
ax.set_ylabel('Measurement (AU)')
sns.despine()
Using FacetGrid for automatic faceting:
# Create faceted plot
g = sns.relplot(data=df, x='dose', y='response',
hue='treatment', col='cell_line', row='timepoint',
kind='line', height=2.5, aspect=1.2,
errorbar=('ci', 95), markers=True)
g.set_axis_labels('Dose (μM)', 'Response (AU)')
g.set_titles('{row_name} | {col_name}')
sns.despine()
# Save with correct DPI
from figure_export import save_publication_figure
save_publication_figure(g.figure, 'figure_facets',
formats=['pdf', 'png'], dpi=300)
Combining seaborn with matplotlib subplots:
# Create custom multi-panel layout
fig, axes = plt.subplots(2, 2, figsize=(7, 6))
# Panel A: Scatter with regression
sns.regplot(data=df, x='predictor', y='response', ax=axes[0, 0])
axes[0, 0].text(-0.15, 1.05, 'A', transform=axes[0, 0].transAxes,
fontsize=10, fontweight='bold')
# Panel B: Distribution comparison
sns.violinplot(data=df, x='group', y='value', ax=axes[0, 1])
axes[0, 1].text(-0.15, 1.05, 'B', transform=axes[0, 1].transAxes,
fontsize=10, fontweight='bold')
# Panel C: Heatmap
sns.heatmap(correlation_data, cmap='viridis', ax=axes[1, 0])
axes[1, 0].text(-0.15, 1.05, 'C', transform=axes[1, 0].transAxes,
fontsize=10, fontweight='bold')
# Panel D: Time series
sns.lineplot(data=timeseries, x='time', y='signal',
hue='condition', ax=axes[1, 1])
axes[1, 1].text(-0.15, 1.05, 'D', transform=axes[1, 1].transAxes,
fontsize=10, fontweight='bold')
plt.tight_layout()
sns.despine()
Seaborn includes several colorblind-safe palettes:
# Use built-in colorblind palette (recommended)
sns.set_palette('colorblind')
# Or specify custom colorblind-safe colors (Okabe-Ito)
okabe_ito = ['#E69F00', '#56B4E9', '#009E73', '#F0E442',
'#0072B2', '#D55E00', '#CC79A7', '#000000']
sns.set_palette(okabe_ito)
# For heatmaps and continuous data
sns.heatmap(data, cmap='viridis') # Perceptually uniform
sns.heatmap(corr, cmap='RdBu_r', center=0) # Diverging, centered
Axes-level functions (e.g., scatterplot, boxplot, heatmap):
ax= parameter for precise placementfig, ax = plt.subplots(figsize=(3.5, 2.5))
sns.scatterplot(data=df, x='x', y='y', hue='group', ax=ax)
Figure-level functions (e.g., relplot, catplot, displot):
height and aspect for sizingg = sns.relplot(data=df, x='x', y='y', col='category', kind='scatter')
Seaborn automatically computes and displays uncertainty:
# Line plot: shows mean ± 95% CI by default
sns.lineplot(data=df, x='time', y='value', hue='treatment',
errorbar=('ci', 95)) # Can change to 'sd', 'se', etc.
# Bar plot: shows mean with bootstrapped CI
sns.barplot(data=df, x='treatment', y='response',
errorbar=('ci', 95), capsize=0.1)
# Always specify error type in figure caption:
# "Error bars represent 95% confidence intervals"
Always set publication theme first:
sns.set_theme(style='ticks', context='paper', font_scale=1.1)
Use colorblind-safe palettes:
sns.set_palette('colorblind')
Remove unnecessary elements:
sns.despine() # Remove top and right spines
Control figure size appropriately:
# Axes-level: use matplotlib figsize
fig, ax = plt.subplots(figsize=(3.5, 2.5))
# Figure-level: use height and aspect
g = sns.relplot(..., height=3, aspect=1.2)
Show individual data points when possible:
sns.boxplot(...) # Summary statistics
sns.stripplot(..., alpha=0.3) # Individual points
Pairwise relationships for exploratory analysis:
# Quick overview of all relationships
g = sns.pairplot(data=df, hue='condition',
vars=['gene1', 'gene2', 'gene3'],
corner=True, diag_kind='kde', height=2)
Hierarchical clustering heatmap:
# Cluster samples and features
g = sns.clustermap(expression_data, method='ward',
metric='euclidean', z_score=0,
cmap='RdBu_r', center=0,
figsize=(10, 8),
row_colors=condition_colors,
cbar_kws={'label': 'Z-score'})
Joint distributions with marginals:
# Bivariate distribution with context
g = sns.jointplot(data=df, x='gene1', y='gene2',
hue='treatment', kind='scatter',
height=6, ratio=4, marginal_kws={'kde': True})
Issue: Legend outside plot area
g = sns.relplot(...)
g._legend.set_bbox_to_anchor((0.9, 0.5))
Issue: Overlapping labels
plt.xticks(rotation=45, ha='right')
plt.tight_layout()
Issue: Text too small at final size
sns.set_context('paper', font_scale=1.2) # Increase if needed
For more detailed seaborn information, see:
scientific-packages/seaborn/SKILL.md - Comprehensive seaborn documentationscientific-packages/seaborn/references/examples.md - Practical use casesscientific-packages/seaborn/references/function_reference.md - Complete API referencescientific-packages/seaborn/references/objects_interface.md - Modern declarative APIfig.update_layout(
font=dict(family='Arial, sans-serif', size=10),
plot_bgcolor='white',
# ... see matplotlib_examples.md Example 8
)
fig.write_image('figure.png', scale=3) # scale=3 gives ~300 DPI
Load these as needed for detailed information:
publication_guidelines.md: Comprehensive best practices
color_palettes.md: Color usage guide
journal_requirements.md: Journal-specific specifications
matplotlib_examples.md: Practical code examples
Use these helper scripts for automation:
figure_export.py: Export utilities
save_publication_figure(): Save in multiple formats with correct DPIsave_for_journal(): Use journal-specific requirements automaticallycheck_figure_size(): Verify dimensions meet journal specspython scripts/figure_export.py for examplesstyle_presets.py: Pre-configured styles
apply_publication_style(): Apply preset styles (default, nature, science, cell)set_color_palette(): Quick palette switchingUse these files in figures:
color_palettes.py: Importable color definitions
apply_palette() helper functionMatplotlib style files: Use with plt.style.use()
publication.mplstyle: General publication qualitynature.mplstyle: Nature journal specificationspresentation.mplstyle: Larger fonts for posters/slidesRecommended workflow for creating publication figures:
from style_presets import configure_for_journal
configure_for_journal('nature', 'single')
from figure_export import check_figure_size
check_figure_size(fig, journal='nature')
from figure_export import save_for_journal
save_for_journal(fig, 'figure1', 'nature', 'combination')
Before submitting figures, verify:
Use this skill to ensure scientific figures meet the highest publication standards while remaining accessible to all readers.
ax.set_xlabel('Time (hours)')
ax.set_ylabel('Expression (AU)')
Export at correct resolution:
from figure_export import save_publication_figure
save_publication_figure(fig, 'figure_name',
formats=['pdf', 'png'], dpi=300)
configure_for_journal(): One-command journal configurationpython scripts/style_presets.py to see examples
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Create or update AgentSkills. Use when designing, structuring, or packaging skills with scripts, references, and assets.
Create or update AgentSkills. Use when designing, structuring, or packaging skills with scripts, references, and assets.
Set up and use 1Password CLI (op). Use when installing the CLI, enabling desktop app integration, signing in (single or multi-account), or reading/injecting/running secrets via op.
CLI to manage emails via IMAP/SMTP. Use `himalaya` to list, read, write, reply, forward, search, and organize emails from the terminal. Supports multiple accounts and message composition with MML (MIME Meta Language).