More thinking skills

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+---
+name: scientific-visualization
+description: Create publication-ready scientific figures using best practices and guidelines for matplotlib, seaborn, and plotly. Use this skill when creating plots, charts, or visualizations for scientific papers, when figures need to meet journal requirements (Nature, Science, Cell, etc.), when ensuring colorblind accessibility, or when asked to make figures "publication-quality" or "publication-ready". Also use for multi-panel figures, data visualization with statistical rigor, and figures following specific style guidelines.
+---
+
+# Scientific Visualization
+
+## Overview
+
+This skill provides comprehensive guidance, tools, and best practices for creating publication-ready scientific figures. It covers proper figure composition, colorblind-friendly design, journal-specific requirements, and practical implementation using matplotlib, seaborn, and plotly.
+
+Publication-ready figures must be:
+- **Clear**: Immediately understandable with proper labeling
+- **Accurate**: Truthful data representation without distortion
+- **Accessible**: Interpretable by readers with color vision deficiencies
+- **Professional**: Polished appearance meeting journal standards
+
+## When to Use This Skill
+
+Activate this skill 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 ...
+```
+
+## 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
+
+### 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
+
+### 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
+- Built on matplotlib, inherits all matplotlib customizations
+- Good for statistical plots
+- Apply matplotlib styles first, then use seaborn
+```python
+import seaborn as sns
+from style_presets import apply_publication_style
+
+apply_publication_style('default')
+sns.set_palette(['#E69F00', '#56B4E9', '#009E73'])  # Okabe-Ito colors
+```
+
+### 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.