Initial commit
This commit is contained in:
Zhongwei Li
773
skills/scientific-visualization/SKILL.md
Normal file
773
skills/scientific-visualization/SKILL.md
Normal file
@@ -0,0 +1,773 @@
|
||||
---
|
||||
name: scientific-visualization
|
||||
description: "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."
|
||||
---
|
||||
|
||||
# Scientific Visualization
|
||||
|
||||
## Overview
|
||||
|
||||
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.
|
||||
|
||||
## When to Use This Skill
|
||||
|
||||
This skill should be used when:
|
||||
- Creating plots or visualizations for scientific manuscripts
|
||||
- Preparing figures for journal submission (Nature, Science, Cell, PLOS, etc.)
|
||||
- Ensuring figures are colorblind-friendly and accessible
|
||||
- Making multi-panel figures with consistent styling
|
||||
- Exporting figures at correct resolution and format
|
||||
- Following specific publication guidelines
|
||||
- Improving existing figures to meet publication standards
|
||||
- Creating figures that need to work in both color and grayscale
|
||||
|
||||
## Quick Start Guide
|
||||
|
||||
### Basic Publication-Quality Figure
|
||||
|
||||
```python
|
||||
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)
|
||||
```
|
||||
|
||||
### Using Pre-configured Styles
|
||||
|
||||
Apply journal-specific styles using the matplotlib style files in `assets/`:
|
||||
|
||||
```python
|
||||
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 ...
|
||||
```
|
||||
|
||||
### Quick Start with Seaborn
|
||||
|
||||
For statistical plots, use seaborn with publication styling:
|
||||
|
||||
```python
|
||||
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)
|
||||
```
|
||||
|
||||
## Core Principles and Best Practices
|
||||
|
||||
### 1. Resolution and File Format
|
||||
|
||||
**Critical requirements** (detailed in `references/publication_guidelines.md`):
|
||||
- **Raster images** (photos, microscopy): 300-600 DPI
|
||||
- **Line art** (graphs, plots): 600-1200 DPI or vector format
|
||||
- **Vector formats** (preferred): PDF, EPS, SVG
|
||||
- **Raster formats**: TIFF, PNG (never JPEG for scientific data)
|
||||
|
||||
**Implementation:**
|
||||
```python
|
||||
# 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')
|
||||
```
|
||||
|
||||
### 2. Color Selection - Colorblind Accessibility
|
||||
|
||||
**Always use colorblind-friendly palettes** (detailed in `references/color_palettes.md`):
|
||||
|
||||
**Recommended: Okabe-Ito palette** (distinguishable by all types of color blindness):
|
||||
```python
|
||||
# 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:**
|
||||
- Use perceptually uniform colormaps: `viridis`, `plasma`, `cividis`
|
||||
- Avoid red-green diverging maps (use `PuOr`, `RdBu`, `BrBG` instead)
|
||||
- Never use `jet` or `rainbow` colormaps
|
||||
|
||||
**Always test figures in grayscale** to ensure interpretability.
|
||||
|
||||
### 3. Typography and Text
|
||||
|
||||
**Font guidelines** (detailed in `references/publication_guidelines.md`):
|
||||
- Sans-serif fonts: Arial, Helvetica, Calibri
|
||||
- Minimum sizes at **final print size**:
|
||||
- Axis labels: 7-9 pt
|
||||
- Tick labels: 6-8 pt
|
||||
- Panel labels: 8-12 pt (bold)
|
||||
- Sentence case for labels: "Time (hours)" not "TIME (HOURS)"
|
||||
- Always include units in parentheses
|
||||
|
||||
**Implementation:**
|
||||
```python
|
||||
# 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
|
||||
```
|
||||
|
||||
### 4. Figure Dimensions
|
||||
|
||||
**Journal-specific widths** (detailed in `references/journal_requirements.md`):
|
||||
- **Nature**: Single 89 mm, Double 183 mm
|
||||
- **Science**: Single 55 mm, Double 175 mm
|
||||
- **Cell**: Single 85 mm, Double 178 mm
|
||||
|
||||
**Check figure size compliance:**
|
||||
```python
|
||||
from figure_export import check_figure_size
|
||||
|
||||
fig = plt.figure(figsize=(3.5, 3)) # 89 mm for Nature
|
||||
check_figure_size(fig, journal='nature')
|
||||
```
|
||||
|
||||
### 5. Multi-Panel Figures
|
||||
|
||||
**Best practices:**
|
||||
- Label panels with bold letters: **A**, **B**, **C** (uppercase for most journals, lowercase for Nature)
|
||||
- Maintain consistent styling across all panels
|
||||
- Align panels along edges where possible
|
||||
- Use adequate white space between panels
|
||||
|
||||
**Example implementation** (see `references/matplotlib_examples.md` for complete code):
|
||||
```python
|
||||
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')
|
||||
```
|
||||
|
||||
## Common Tasks
|
||||
|
||||
### Task 1: Create a Publication-Ready Line Plot
|
||||
|
||||
See `references/matplotlib_examples.md` Example 1 for complete code.
|
||||
|
||||
**Key steps:**
|
||||
1. Apply publication style
|
||||
2. Set appropriate figure size for target journal
|
||||
3. Use colorblind-friendly colors
|
||||
4. Add error bars with correct representation (SEM, SD, or CI)
|
||||
5. Label axes with units
|
||||
6. Remove unnecessary spines
|
||||
7. Save in vector format
|
||||
|
||||
**Using seaborn for automatic confidence intervals:**
|
||||
```python
|
||||
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()
|
||||
```
|
||||
|
||||
### Task 2: Create a Multi-Panel Figure
|
||||
|
||||
See `references/matplotlib_examples.md` Example 2 for complete code.
|
||||
|
||||
**Key steps:**
|
||||
1. Use `GridSpec` for flexible layout
|
||||
2. Ensure consistent styling across panels
|
||||
3. Add bold panel labels (A, B, C, etc.)
|
||||
4. Align related panels
|
||||
5. Verify all text is readable at final size
|
||||
|
||||
### Task 3: Create a Heatmap with Proper Colormap
|
||||
|
||||
See `references/matplotlib_examples.md` Example 4 for complete code.
|
||||
|
||||
**Key steps:**
|
||||
1. Use perceptually uniform colormap (`viridis`, `plasma`, `cividis`)
|
||||
2. Include labeled colorbar
|
||||
3. For diverging data, use colorblind-safe diverging map (`RdBu_r`, `PuOr`)
|
||||
4. Set appropriate center value for diverging maps
|
||||
5. Test appearance in grayscale
|
||||
|
||||
**Using seaborn for correlation matrices:**
|
||||
```python
|
||||
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)
|
||||
```
|
||||
|
||||
### Task 4: Prepare Figure for Specific Journal
|
||||
|
||||
**Workflow:**
|
||||
1. Check journal requirements: `references/journal_requirements.md`
|
||||
2. Configure matplotlib for journal:
|
||||
```python
|
||||
from style_presets import configure_for_journal
|
||||
configure_for_journal('nature', figure_width='single')
|
||||
```
|
||||
3. Create figure (will auto-size correctly)
|
||||
4. Export with journal specifications:
|
||||
```python
|
||||
from figure_export import save_for_journal
|
||||
save_for_journal(fig, 'figure1', journal='nature', figure_type='line_art')
|
||||
```
|
||||
|
||||
### Task 5: Fix an Existing Figure to Meet Publication Standards
|
||||
|
||||
**Checklist approach** (full checklist in `references/publication_guidelines.md`):
|
||||
|
||||
1. **Check resolution**: Verify DPI meets journal requirements
|
||||
2. **Check file format**: Use vector for plots, TIFF/PNG for images
|
||||
3. **Check colors**: Ensure colorblind-friendly
|
||||
4. **Check fonts**: Minimum 6-7 pt at final size, sans-serif
|
||||
5. **Check labels**: All axes labeled with units
|
||||
6. **Check size**: Matches journal column width
|
||||
7. **Test grayscale**: Figure interpretable without color
|
||||
8. **Remove chart junk**: No unnecessary grids, 3D effects, shadows
|
||||
|
||||
### Task 6: Create Colorblind-Friendly Visualizations
|
||||
|
||||
**Strategy:**
|
||||
1. Use approved palettes from `assets/color_palettes.py`
|
||||
2. Add redundant encoding (line styles, markers, patterns)
|
||||
3. Test with colorblind simulator
|
||||
4. Ensure grayscale compatibility
|
||||
|
||||
**Example:**
|
||||
```python
|
||||
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)
|
||||
```
|
||||
|
||||
## Statistical Rigor
|
||||
|
||||
**Always include:**
|
||||
- Error bars (SD, SEM, or CI - specify which in caption)
|
||||
- Sample size (n) in figure or caption
|
||||
- Statistical significance markers (*, **, ***)
|
||||
- Individual data points when possible (not just summary statistics)
|
||||
|
||||
**Example with statistics:**
|
||||
```python
|
||||
# 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)
|
||||
```
|
||||
|
||||
## Working with Different Plotting Libraries
|
||||
|
||||
### Matplotlib
|
||||
- Most control over publication details
|
||||
- Best for complex multi-panel figures
|
||||
- Use provided style files for consistent formatting
|
||||
- See `references/matplotlib_examples.md` for extensive examples
|
||||
|
||||
### Seaborn
|
||||
|
||||
Seaborn 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:**
|
||||
- Automatic statistical estimation and confidence intervals
|
||||
- Built-in support for multi-panel figures (faceting)
|
||||
- Colorblind-friendly palettes by default
|
||||
- Dataset-oriented API using pandas DataFrames
|
||||
- Semantic mapping of variables to visual properties
|
||||
|
||||
#### Quick Start with Publication Style
|
||||
|
||||
Always apply matplotlib publication styles first, then configure seaborn:
|
||||
|
||||
```python
|
||||
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
|
||||
```
|
||||
|
||||
#### Common Plot Types for Publications
|
||||
|
||||
**Statistical comparisons:**
|
||||
```python
|
||||
# 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:**
|
||||
```python
|
||||
# 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:**
|
||||
```python
|
||||
# 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:**
|
||||
```python
|
||||
# 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()
|
||||
```
|
||||
|
||||
#### Multi-Panel Figures with Seaborn
|
||||
|
||||
**Using FacetGrid for automatic faceting:**
|
||||
```python
|
||||
# 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:**
|
||||
```python
|
||||
# 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()
|
||||
```
|
||||
|
||||
#### Color Palettes for Publications
|
||||
|
||||
Seaborn includes several colorblind-safe palettes:
|
||||
|
||||
```python
|
||||
# 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
|
||||
```
|
||||
|
||||
#### Choosing Between Axes-Level and Figure-Level Functions
|
||||
|
||||
**Axes-level functions** (e.g., `scatterplot`, `boxplot`, `heatmap`):
|
||||
- Use when building custom multi-panel layouts
|
||||
- Accept `ax=` parameter for precise placement
|
||||
- Better integration with matplotlib subplots
|
||||
- More control over figure composition
|
||||
|
||||
```python
|
||||
fig, 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`):
|
||||
- Use for automatic faceting by categorical variables
|
||||
- Create complete figures with consistent styling
|
||||
- Great for exploratory analysis
|
||||
- Use `height` and `aspect` for sizing
|
||||
|
||||
```python
|
||||
g = sns.relplot(data=df, x='x', y='y', col='category', kind='scatter')
|
||||
```
|
||||
|
||||
#### Statistical Rigor with Seaborn
|
||||
|
||||
Seaborn automatically computes and displays uncertainty:
|
||||
|
||||
```python
|
||||
# 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"
|
||||
```
|
||||
|
||||
#### Best Practices for Publication-Ready Seaborn Figures
|
||||
|
||||
1. **Always set publication theme first:**
|
||||
```python
|
||||
sns.set_theme(style='ticks', context='paper', font_scale=1.1)
|
||||
```
|
||||
|
||||
2. **Use colorblind-safe palettes:**
|
||||
```python
|
||||
sns.set_palette('colorblind')
|
||||
```
|
||||
|
||||
3. **Remove unnecessary elements:**
|
||||
```python
|
||||
sns.despine() # Remove top and right spines
|
||||
```
|
||||
|
||||
4. **Control figure size appropriately:**
|
||||
```python
|
||||
# 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)
|
||||
```
|
||||
|
||||
5. **Show individual data points when possible:**
|
||||
```python
|
||||
sns.boxplot(...) # Summary statistics
|
||||
sns.stripplot(..., alpha=0.3) # Individual points
|
||||
```
|
||||
|
||||
6. **Include proper labels with units:**
|
||||
```python
|
||||
ax.set_xlabel('Time (hours)')
|
||||
ax.set_ylabel('Expression (AU)')
|
||||
```
|
||||
|
||||
7. **Export at correct resolution:**
|
||||
```python
|
||||
from figure_export import save_publication_figure
|
||||
save_publication_figure(fig, 'figure_name',
|
||||
formats=['pdf', 'png'], dpi=300)
|
||||
```
|
||||
|
||||
#### Advanced Seaborn Techniques
|
||||
|
||||
**Pairwise relationships for exploratory analysis:**
|
||||
```python
|
||||
# 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:**
|
||||
```python
|
||||
# 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:**
|
||||
```python
|
||||
# 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})
|
||||
```
|
||||
|
||||
#### Common Seaborn Issues and Solutions
|
||||
|
||||
**Issue: Legend outside plot area**
|
||||
```python
|
||||
g = sns.relplot(...)
|
||||
g._legend.set_bbox_to_anchor((0.9, 0.5))
|
||||
```
|
||||
|
||||
**Issue: Overlapping labels**
|
||||
```python
|
||||
plt.xticks(rotation=45, ha='right')
|
||||
plt.tight_layout()
|
||||
```
|
||||
|
||||
**Issue: Text too small at final size**
|
||||
```python
|
||||
sns.set_context('paper', font_scale=1.2) # Increase if needed
|
||||
```
|
||||
|
||||
#### Additional Resources
|
||||
|
||||
For more detailed seaborn information, see:
|
||||
- `scientific-packages/seaborn/SKILL.md` - Comprehensive seaborn documentation
|
||||
- `scientific-packages/seaborn/references/examples.md` - Practical use cases
|
||||
- `scientific-packages/seaborn/references/function_reference.md` - Complete API reference
|
||||
- `scientific-packages/seaborn/references/objects_interface.md` - Modern declarative API
|
||||
|
||||
### Plotly
|
||||
- Interactive figures for exploration
|
||||
- Export static images for publication
|
||||
- Configure for publication quality:
|
||||
```python
|
||||
fig.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
|
||||
```
|
||||
|
||||
## Resources
|
||||
|
||||
### References Directory
|
||||
|
||||
**Load these as needed for detailed information:**
|
||||
|
||||
- **`publication_guidelines.md`**: Comprehensive best practices
|
||||
- Resolution and file format requirements
|
||||
- Typography guidelines
|
||||
- Layout and composition rules
|
||||
- Statistical rigor requirements
|
||||
- Complete publication checklist
|
||||
|
||||
- **`color_palettes.md`**: Color usage guide
|
||||
- Colorblind-friendly palette specifications with RGB values
|
||||
- Sequential and diverging colormap recommendations
|
||||
- Testing procedures for accessibility
|
||||
- Domain-specific palettes (genomics, microscopy)
|
||||
|
||||
- **`journal_requirements.md`**: Journal-specific specifications
|
||||
- Technical requirements by publisher
|
||||
- File format and DPI specifications
|
||||
- Figure dimension requirements
|
||||
- Quick reference table
|
||||
|
||||
- **`matplotlib_examples.md`**: Practical code examples
|
||||
- 10 complete working examples
|
||||
- Line plots, bar plots, heatmaps, multi-panel figures
|
||||
- Journal-specific figure examples
|
||||
- Tips for each library (matplotlib, seaborn, plotly)
|
||||
|
||||
### Scripts Directory
|
||||
|
||||
**Use these helper scripts for automation:**
|
||||
|
||||
- **`figure_export.py`**: Export utilities
|
||||
- `save_publication_figure()`: Save in multiple formats with correct DPI
|
||||
- `save_for_journal()`: Use journal-specific requirements automatically
|
||||
- `check_figure_size()`: Verify dimensions meet journal specs
|
||||
- Run directly: `python scripts/figure_export.py` for examples
|
||||
|
||||
- **`style_presets.py`**: Pre-configured styles
|
||||
- `apply_publication_style()`: Apply preset styles (default, nature, science, cell)
|
||||
- `set_color_palette()`: Quick palette switching
|
||||
- `configure_for_journal()`: One-command journal configuration
|
||||
- Run directly: `python scripts/style_presets.py` to see examples
|
||||
|
||||
### Assets Directory
|
||||
|
||||
**Use these files in figures:**
|
||||
|
||||
- **`color_palettes.py`**: Importable color definitions
|
||||
- All recommended palettes as Python constants
|
||||
- `apply_palette()` helper function
|
||||
- Can be imported directly into notebooks/scripts
|
||||
|
||||
- **Matplotlib style files**: Use with `plt.style.use()`
|
||||
- `publication.mplstyle`: General publication quality
|
||||
- `nature.mplstyle`: Nature journal specifications
|
||||
- `presentation.mplstyle`: Larger fonts for posters/slides
|
||||
|
||||
## Workflow Summary
|
||||
|
||||
**Recommended workflow for creating publication figures:**
|
||||
|
||||
1. **Plan**: Determine target journal, figure type, and content
|
||||
2. **Configure**: Apply appropriate style for journal
|
||||
```python
|
||||
from style_presets import configure_for_journal
|
||||
configure_for_journal('nature', 'single')
|
||||
```
|
||||
3. **Create**: Build figure with proper labels, colors, statistics
|
||||
4. **Verify**: Check size, fonts, colors, accessibility
|
||||
```python
|
||||
from figure_export import check_figure_size
|
||||
check_figure_size(fig, journal='nature')
|
||||
```
|
||||
5. **Export**: Save in required formats
|
||||
```python
|
||||
from figure_export import save_for_journal
|
||||
save_for_journal(fig, 'figure1', 'nature', 'combination')
|
||||
```
|
||||
6. **Review**: View at final size in manuscript context
|
||||
|
||||
## Common Pitfalls to Avoid
|
||||
|
||||
1. **Font too small**: Text unreadable when printed at final size
|
||||
2. **JPEG format**: Never use JPEG for graphs/plots (creates artifacts)
|
||||
3. **Red-green colors**: ~8% of males cannot distinguish
|
||||
4. **Low resolution**: Pixelated figures in publication
|
||||
5. **Missing units**: Always label axes with units
|
||||
6. **3D effects**: Distorts perception, avoid completely
|
||||
7. **Chart junk**: Remove unnecessary gridlines, decorations
|
||||
8. **Truncated axes**: Start bar charts at zero unless scientifically justified
|
||||
9. **Inconsistent styling**: Different fonts/colors across figures in same manuscript
|
||||
10. **No error bars**: Always show uncertainty
|
||||
|
||||
## Final Checklist
|
||||
|
||||
Before submitting figures, verify:
|
||||
|
||||
- [ ] Resolution meets journal requirements (300+ DPI)
|
||||
- [ ] File format is correct (vector for plots, TIFF for images)
|
||||
- [ ] Figure size matches journal specifications
|
||||
- [ ] All text readable at final size (≥6 pt)
|
||||
- [ ] Colors are colorblind-friendly
|
||||
- [ ] Figure works in grayscale
|
||||
- [ ] All axes labeled with units
|
||||
- [ ] Error bars present with definition in caption
|
||||
- [ ] Panel labels present and consistent
|
||||
- [ ] No chart junk or 3D effects
|
||||
- [ ] Fonts consistent across all figures
|
||||
- [ ] Statistical significance clearly marked
|
||||
- [ ] Legend is clear and complete
|
||||
|
||||
Use this skill to ensure scientific figures meet the highest publication standards while remaining accessible to all readers.
|
||||
Reference in New Issue
Block a user