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skills/scientific-visualization/references/color_palettes.md
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skills/scientific-visualization/references/color_palettes.md
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# Scientific Color Palettes and Guidelines
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## Overview
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Color choice in scientific visualization is critical for accessibility, clarity, and accurate data representation. This reference provides colorblind-friendly palettes and best practices for color usage.
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## Colorblind-Friendly Palettes
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### Okabe-Ito Palette (Recommended for Categories)
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The Okabe-Ito palette is specifically designed to be distinguishable by people with all forms of color blindness.
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```python
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# Okabe-Ito colors (RGB values)
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okabe_ito = {
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'orange': '#E69F00', # RGB: (230, 159, 0)
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'sky_blue': '#56B4E9', # RGB: (86, 180, 233)
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'bluish_green': '#009E73', # RGB: (0, 158, 115)
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'yellow': '#F0E442', # RGB: (240, 228, 66)
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'blue': '#0072B2', # RGB: (0, 114, 178)
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'vermillion': '#D55E00', # RGB: (213, 94, 0)
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'reddish_purple': '#CC79A7', # RGB: (204, 121, 167)
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'black': '#000000' # RGB: (0, 0, 0)
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}
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```
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**Usage in Matplotlib:**
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```python
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import matplotlib.pyplot as plt
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colors = ['#E69F00', '#56B4E9', '#009E73', '#F0E442',
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'#0072B2', '#D55E00', '#CC79A7', '#000000']
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plt.rcParams['axes.prop_cycle'] = plt.cycler(color=colors)
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```
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**Usage in Seaborn:**
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```python
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import seaborn as sns
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okabe_ito_palette = ['#E69F00', '#56B4E9', '#009E73', '#F0E442',
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'#0072B2', '#D55E00', '#CC79A7']
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sns.set_palette(okabe_ito_palette)
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```
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**Usage in Plotly:**
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```python
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import plotly.graph_objects as go
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okabe_ito_plotly = ['#E69F00', '#56B4E9', '#009E73', '#F0E442',
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'#0072B2', '#D55E00', '#CC79A7']
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fig = go.Figure()
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# Apply to discrete color scale
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```
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### Wong Palette (Alternative for Categories)
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Another excellent colorblind-friendly palette by Bang Wong (Nature Methods).
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```python
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wong_palette = {
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'black': '#000000',
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'orange': '#E69F00',
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'sky_blue': '#56B4E9',
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'green': '#009E73',
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'yellow': '#F0E442',
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'blue': '#0072B2',
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'vermillion': '#D55E00',
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'purple': '#CC79A7'
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}
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```
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### Paul Tol Palettes
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Paul Tol has designed multiple scientifically-optimized palettes for different use cases.
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**Bright Palette (up to 7 categories):**
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```python
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tol_bright = ['#4477AA', '#EE6677', '#228833', '#CCBB44',
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'#66CCEE', '#AA3377', '#BBBBBB']
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```
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**Muted Palette (up to 9 categories):**
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```python
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tol_muted = ['#332288', '#88CCEE', '#44AA99', '#117733',
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'#999933', '#DDCC77', '#CC6677', '#882255', '#AA4499']
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```
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**High Contrast (3 categories only):**
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```python
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tol_high_contrast = ['#004488', '#DDAA33', '#BB5566']
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```
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## Sequential Colormaps (Continuous Data)
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Sequential colormaps represent data from low to high values with a single hue.
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### Perceptually Uniform Colormaps
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These colormaps have uniform perceptual change across the color scale.
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**Viridis (default in Matplotlib):**
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- Colorblind-friendly
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- Prints well in grayscale
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- Perceptually uniform
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```python
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plt.imshow(data, cmap='viridis')
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```
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**Cividis:**
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- Optimized for colorblind viewers
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- Designed specifically for deuteranopia/protanopia
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```python
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plt.imshow(data, cmap='cividis')
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```
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**Plasma, Inferno, Magma:**
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- Perceptually uniform alternatives to viridis
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- Good for different aesthetic preferences
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```python
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plt.imshow(data, cmap='plasma')
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```
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### When to Use Sequential Maps
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- Heatmaps showing intensity
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- Geographic elevation data
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- Probability distributions
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- Any single-variable continuous data (low → high)
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## Diverging Colormaps (Negative to Positive)
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Diverging colormaps have a neutral middle color with two contrasting colors at extremes.
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### Colorblind-Safe Diverging Maps
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**RdYlBu (Red-Yellow-Blue):**
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```python
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plt.imshow(data, cmap='RdYlBu_r') # _r reverses: blue (low) to red (high)
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```
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**PuOr (Purple-Orange):**
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- Excellent for colorblind viewers
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```python
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plt.imshow(data, cmap='PuOr')
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```
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**BrBG (Brown-Blue-Green):**
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- Good colorblind accessibility
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```python
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plt.imshow(data, cmap='BrBG')
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```
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### Avoid These Diverging Maps
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- **RdGn (Red-Green)**: Problematic for red-green colorblindness
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- **RdYlGn (Red-Yellow-Green)**: Same issue
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### When to Use Diverging Maps
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- Correlation matrices
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- Change/difference data (positive vs. negative)
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- Deviation from a central value
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- Temperature anomalies
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## Special Purpose Palettes
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### For Genomics/Bioinformatics
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**Sequence type identification:**
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```python
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# DNA/RNA bases
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nucleotide_colors = {
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'A': '#00CC00', # Green
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'C': '#0000CC', # Blue
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'G': '#FFB300', # Orange
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'T': '#CC0000', # Red
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'U': '#CC0000' # Red (RNA)
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}
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```
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**Gene expression:**
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- Use sequential colormaps (viridis, YlOrRd) for expression levels
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- Use diverging colormaps (RdBu) for log2 fold change
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### For Microscopy
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**Fluorescence channels:**
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```python
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# Traditional fluorophore colors (use with caution)
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fluorophore_colors = {
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'DAPI': '#0000FF', # Blue - DNA
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'GFP': '#00FF00', # Green (problematic for colorblind)
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'RFP': '#FF0000', # Red
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'Cy5': '#FF00FF' # Magenta
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}
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# Colorblind-friendly alternatives
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fluorophore_alt = {
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'Channel1': '#0072B2', # Blue
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'Channel2': '#E69F00', # Orange (instead of green)
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'Channel3': '#D55E00', # Vermillion
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'Channel4': '#CC79A7' # Magenta
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}
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```
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## Color Usage Best Practices
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### Categorical Data (Qualitative Color Schemes)
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**Do:**
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- Use distinct, saturated colors from Okabe-Ito or Wong palette
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- Limit to 7-8 categories max in one plot
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- Use consistent colors for same categories across figures
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- Add patterns/markers when colors alone might be insufficient
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**Don't:**
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- Use red/green combinations
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- Use rainbow (jet) colormap for categories
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- Use similar hues that are hard to distinguish
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### Continuous Data (Sequential/Diverging Schemes)
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**Do:**
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- Use perceptually uniform colormaps (viridis, plasma, cividis)
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- Choose diverging maps when data has meaningful center point
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- Include colorbar with labeled ticks
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- Test appearance in grayscale
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**Don't:**
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- Use rainbow (jet) colormap - not perceptually uniform
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- Use red-green diverging maps
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- Omit colorbar on heatmaps
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## Testing for Colorblind Accessibility
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### Online Simulators
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- **Coblis**: https://www.color-blindness.com/coblis-color-blindness-simulator/
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- **Color Oracle**: Free downloadable tool for Windows/Mac/Linux
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- **Sim Daltonism**: Mac application
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### Types of Color Vision Deficiency
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- **Deuteranopia** (~5% of males): Cannot distinguish green
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- **Protanopia** (~2% of males): Cannot distinguish red
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- **Tritanopia** (<1%): Cannot distinguish blue (rare)
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### Python Tools
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```python
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# Using colorspacious to simulate colorblind vision
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from colorspacious import cspace_convert
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def simulate_deuteranopia(image_rgb):
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from colorspacious import cspace_convert
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# Convert to colorblind simulation
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# (Implementation would require colorspacious library)
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pass
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```
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## Implementation Examples
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### Setting Global Matplotlib Style
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```python
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import matplotlib.pyplot as plt
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import matplotlib as mpl
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# Set Okabe-Ito as default color cycle
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okabe_ito_colors = ['#E69F00', '#56B4E9', '#009E73', '#F0E442',
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'#0072B2', '#D55E00', '#CC79A7']
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mpl.rcParams['axes.prop_cycle'] = mpl.cycler(color=okabe_ito_colors)
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# Set default colormap to viridis
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mpl.rcParams['image.cmap'] = 'viridis'
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```
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### Seaborn with Custom Palette
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```python
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import seaborn as sns
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# Set Paul Tol muted palette
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tol_muted = ['#332288', '#88CCEE', '#44AA99', '#117733',
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'#999933', '#DDCC77', '#CC6677', '#882255', '#AA4499']
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sns.set_palette(tol_muted)
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# For heatmaps
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sns.heatmap(data, cmap='viridis', annot=True)
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```
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### Plotly with Discrete Colors
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```python
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import plotly.express as px
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# Use Okabe-Ito for categorical data
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okabe_ito_plotly = ['#E69F00', '#56B4E9', '#009E73', '#F0E442',
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'#0072B2', '#D55E00', '#CC79A7']
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fig = px.scatter(df, x='x', y='y', color='category',
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color_discrete_sequence=okabe_ito_plotly)
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```
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## Grayscale Compatibility
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All figures should remain interpretable in grayscale. Test by converting to grayscale:
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```python
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# Convert figure to grayscale for testing
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fig.savefig('figure_gray.png', dpi=300, colormap='gray')
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```
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**Strategies for grayscale compatibility:**
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1. Use different line styles (solid, dashed, dotted)
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2. Use different marker shapes (circles, squares, triangles)
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3. Add hatching patterns to bars
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4. Ensure sufficient luminance contrast between colors
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## Color Spaces
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### RGB vs CMYK
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- **RGB** (Red, Green, Blue): For digital/screen display
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- **CMYK** (Cyan, Magenta, Yellow, Black): For print
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**Important:** Colors appear different in print vs. screen. When preparing for print:
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1. Convert to CMYK color space
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2. Check color appearance in CMYK preview
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3. Ensure sufficient contrast remains
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### Matplotlib Color Spaces
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```python
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# Save for print (CMYK)
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# Note: Direct CMYK support limited; use PDF and let publisher convert
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fig.savefig('figure.pdf', dpi=300)
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# For RGB (digital)
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fig.savefig('figure.png', dpi=300)
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```
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## Common Mistakes
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1. **Using jet/rainbow colormap**: Not perceptually uniform; avoid
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2. **Red-green combinations**: ~8% of males cannot distinguish
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3. **Too many colors**: More than 7-8 becomes difficult to distinguish
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4. **Inconsistent color meaning**: Same color should mean same thing across figures
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5. **Missing colorbar**: Always include for continuous data
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6. **Low contrast**: Ensure colors differ sufficiently
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7. **Relying solely on color**: Add texture, patterns, or markers
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## Resources
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- **ColorBrewer**: http://colorbrewer2.org/ - Choose palettes by colorblind-safe option
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- **Paul Tol's palettes**: https://personal.sron.nl/~pault/
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- **Okabe-Ito palette origin**: "Color Universal Design" (Okabe & Ito, 2008)
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- **Matplotlib colormaps**: https://matplotlib.org/stable/tutorials/colors/colormaps.html
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- **Seaborn palettes**: https://seaborn.pydata.org/tutorial/color_palettes.html
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# Journal-Specific Figure Requirements
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## Overview
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Different journals have specific technical requirements for figures. This reference compiles common requirements from major scientific publishers. **Always check the specific journal's author guidelines for the most current requirements.**
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## Nature Portfolio (Nature, Nature Methods, etc.)
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### Technical Specifications
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- **File formats**:
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- Vector: PDF, EPS, AI (preferred for graphs)
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- Raster: TIFF, PNG (for images)
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- Never: PowerPoint, Word, JPEG
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- **Resolution**:
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- Line art: 1000-1200 DPI
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- Combination (line art + images): 600 DPI
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- Photographs/microscopy: 300 DPI minimum
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- **Color space**: RGB (Nature is digital-first)
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- **Dimensions**:
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- Single column: 89 mm (3.5 inches)
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- 1.5 column: 120 mm (4.7 inches)
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- Double column: 183 mm (7.2 inches)
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- Maximum height: 247 mm (9.7 inches)
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- **Fonts**:
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- Arial or Helvetica (or similar sans-serif)
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- Minimum 5-7 pt at final size
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- Embed all fonts in PDF/EPS
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### Nature Specific Guidelines
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- Panel labels: a, b, c (lowercase, bold) in top-left corner
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- Scale bars required for microscopy images
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- Gel images: Include molecular weight markers
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- Cropping: Indicate with line breaks
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- Statistics: Mark significance; define symbols in legend
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- Source data: Required for all graphs
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### File Naming
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Format: `FirstAuthorLastName_FigureNumber.ext`
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Example: `Smith_Fig1.pdf`
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## Science (AAAS)
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### Technical Specifications
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- **File formats**:
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- Vector: EPS, PDF (preferred)
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- Raster: TIFF
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- Acceptable: AI, PSD (Photoshop)
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- **Resolution**:
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- Line art: 1000 DPI minimum
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- Photographs: 300 DPI minimum
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- Combination: 600 DPI minimum
|
||||
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- **Color space**: RGB
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||||
|
||||
- **Dimensions**:
|
||||
- Single column: 5.5 cm (2.17 inches)
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- 1.5 column: 12 cm (4.72 inches)
|
||||
- Full width: 17.5 cm (6.89 inches)
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||||
- Maximum height: 23.3 cm (9.17 inches)
|
||||
|
||||
- **Fonts**:
|
||||
- Helvetica (or Arial)
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- 6-8 pt minimum at final size
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||||
- Consistent across all figures
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|
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### Science Specific Guidelines
|
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- Panel labels: (A), (B), (C) in parentheses
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- Minimal text within figures (details in caption)
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- High contrast for web and print
|
||||
- Error bars required; define in caption
|
||||
- Avoid excessive whitespace
|
||||
|
||||
### File Naming
|
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Format: `Manuscript#_Fig#.ext`
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Example: `abn1234_Fig1.eps`
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|
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## Cell Press (Cell, Neuron, Molecular Cell, etc.)
|
||||
|
||||
### Technical Specifications
|
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- **File formats**:
|
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- Vector: PDF, EPS (preferred for graphs/diagrams)
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- Raster: TIFF (for photographs)
|
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|
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- **Resolution**:
|
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- Line art: 1000 DPI
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- Photographs: 300 DPI
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- Combination: 600 DPI
|
||||
|
||||
- **Color space**: RGB
|
||||
|
||||
- **Dimensions**:
|
||||
- Single column: 85 mm (3.35 inches)
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||||
- Double column: 178 mm (7.01 inches)
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||||
- Maximum height: 230 mm (9.06 inches)
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||||
|
||||
- **Fonts**:
|
||||
- Arial or Helvetica only
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||||
- 8-12 pt for axis labels
|
||||
- 6-8 pt for tick labels
|
||||
|
||||
### Cell Press Specific Guidelines
|
||||
- Panel labels: (A), (B), (C) or A, B, C in top-left
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- Related panels should match in size
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- Scale bars mandatory for microscopy
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- Western blots: Include molecular weight markers
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- Arrows/arrowheads: 2 pt minimum width
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- Line widths: 1-2 pt for data
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## PLOS (Public Library of Science)
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||||
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||||
### Technical Specifications
|
||||
- **File formats**:
|
||||
- Vector: EPS, PDF (preferred)
|
||||
- Raster: TIFF, PNG
|
||||
- TIFF with LZW compression acceptable
|
||||
|
||||
- **Resolution**:
|
||||
- Minimum 300 DPI at final size (all figure types)
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||||
- 600 DPI preferred for line art
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||||
|
||||
- **Color space**: RGB
|
||||
|
||||
- **Dimensions**:
|
||||
- Single column: 8.3 cm (3.27 inches)
|
||||
- 1.5 column: 11.4 cm (4.49 inches)
|
||||
- Double column: 17.3 cm (6.81 inches)
|
||||
- Maximum height: 23.3 cm (9.17 inches)
|
||||
|
||||
- **Fonts**:
|
||||
- Sans-serif preferred (Arial, Helvetica)
|
||||
- 8-12 pt for labels at final size
|
||||
|
||||
### PLOS Specific Guidelines
|
||||
- Figures should be understandable without caption
|
||||
- Color required only if adding information
|
||||
- All figures convertible to grayscale
|
||||
- Panel labels optional but recommended
|
||||
- Open access: Figures must be CC-BY licensed
|
||||
- Source data files encouraged
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||||
|
||||
## ACS (American Chemical Society)
|
||||
|
||||
### Technical Specifications
|
||||
- **File formats**:
|
||||
- Preferred: TIFF, PDF, EPS
|
||||
- Application files: AI, CDX (ChemDraw), CDL
|
||||
- Acceptable: PNG (not for publication)
|
||||
|
||||
- **Resolution**:
|
||||
- Minimum 300 DPI at final size
|
||||
- 600 DPI for line art and chemical structures
|
||||
- 1200 DPI for detailed structures
|
||||
|
||||
- **Color space**: RGB or CMYK (check specific journal)
|
||||
|
||||
- **Dimensions**:
|
||||
- Single column: 3.25 inches (8.25 cm)
|
||||
- Double column: 7 inches (17.78 cm)
|
||||
|
||||
- **Fonts**:
|
||||
- Embedded fonts required
|
||||
- Consistent sizing across figures
|
||||
|
||||
### ACS Specific Guidelines
|
||||
- Chemical structures: Use ChemDraw or equivalent
|
||||
- Atom labels: 10-12 pt
|
||||
- Bond thickness: 2 pt
|
||||
- Panel labels: Lowercase bold (a, b, c)
|
||||
- High contrast required (many ACS journals grayscale print)
|
||||
|
||||
## Elsevier Journals (varies by journal)
|
||||
|
||||
### Technical Specifications
|
||||
- **File formats**:
|
||||
- Vector: EPS, PDF
|
||||
- Raster: TIFF, JPEG (only for photographs)
|
||||
|
||||
- **Resolution**:
|
||||
- Line art: 1000 DPI minimum
|
||||
- Photographs: 300 DPI minimum
|
||||
- Combination: 600 DPI minimum
|
||||
|
||||
- **Color space**: RGB (for online); CMYK (for print journals)
|
||||
|
||||
- **Dimensions**: Vary by journal
|
||||
- Common single column: 90 mm
|
||||
- Common double column: 190 mm
|
||||
|
||||
- **Fonts**:
|
||||
- Preferred: Arial, Times, Symbol
|
||||
- Minimum 6 pt at final size
|
||||
|
||||
### Elsevier Specific Guidelines
|
||||
- Check individual journal guidelines (highly variable)
|
||||
- Some journals charge for color in print
|
||||
- Panel labels typically (A), (B), (C) or A, B, C
|
||||
- Graphical abstract often required (separate from figures)
|
||||
|
||||
## IEEE (Engineering/Computer Science)
|
||||
|
||||
### Technical Specifications
|
||||
- **File formats**:
|
||||
- Vector: PDF, EPS (preferred)
|
||||
- Raster: TIFF, PNG
|
||||
|
||||
- **Resolution**:
|
||||
- Photographs/graphics: 300 DPI minimum at final size
|
||||
- Line art: 600 DPI minimum
|
||||
|
||||
- **Color space**: RGB (online); CMYK (print)
|
||||
|
||||
- **Dimensions**:
|
||||
- Single column: 3.5 inches (8.9 cm)
|
||||
- Double column: 7.16 inches (18.2 cm)
|
||||
|
||||
- **Fonts**:
|
||||
- Sans-serif preferred
|
||||
- Minimum 8-10 pt at final size
|
||||
|
||||
### IEEE Specific Guidelines
|
||||
- Figures should be readable in black and white
|
||||
- Color figures incur no charge (online publication)
|
||||
- Panel labels: (a), (b), (c) in lowercase
|
||||
- Captions below figures (not on separate page)
|
||||
- Use IEEE graphics checker tool before submission
|
||||
|
||||
## BMC (BioMed Central) - Open Access
|
||||
|
||||
### Technical Specifications
|
||||
- **File formats**:
|
||||
- Any standard format accepted
|
||||
- Preferred: TIFF, PDF, EPS, PNG
|
||||
|
||||
- **Resolution**:
|
||||
- Minimum 600 DPI for line art
|
||||
- Minimum 300 DPI for photographs
|
||||
|
||||
- **Color space**: RGB
|
||||
|
||||
- **Dimensions**:
|
||||
- Flexible, but consider readability
|
||||
- Maximum width typically 140 mm
|
||||
|
||||
- **Fonts**:
|
||||
- Embedded and readable
|
||||
|
||||
### BMC Specific Guidelines
|
||||
- Open access: CC-BY license required
|
||||
- Figure files uploaded separately
|
||||
- Panel labels as appropriate for field
|
||||
- Source data encouraged
|
||||
- Accessibility important (colorblind-friendly)
|
||||
|
||||
## Common Requirements Across Journals
|
||||
|
||||
### Universal Best Practices
|
||||
1. **Never use JPEG for graphs/plots**: Compression artifacts
|
||||
2. **Embed all fonts**: In PDF/EPS files
|
||||
3. **Layer structure**: Flatten images (merge layers in Photoshop)
|
||||
4. **RGB vs CMYK**: Most journals now RGB (digital-first)
|
||||
5. **High resolution**: Always better to start high, reduce if needed
|
||||
6. **Consistency**: Same style across all figures in manuscript
|
||||
7. **File size**: Balance quality with reasonable file sizes (typically <10 MB per figure)
|
||||
|
||||
### Submitting Figures
|
||||
- **Initial submission**: Lower resolution often acceptable (for review)
|
||||
- **Revision/acceptance**: High-resolution required
|
||||
- **Separate files**: Each figure as separate file
|
||||
- **File naming**: Clear, systematic naming
|
||||
- **Supporting information**: May have different requirements
|
||||
|
||||
## Quick Reference Table
|
||||
|
||||
| Publisher | Single Column | Double Column | Min DPI (photos) | Min DPI (line art) | Preferred Format |
|
||||
|-----------|---------------|---------------|------------------|-------------------|------------------|
|
||||
| Nature | 89 mm | 183 mm | 300 | 1000 | EPS, PDF |
|
||||
| Science | 5.5 cm | 17.5 cm | 300 | 1000 | EPS, PDF |
|
||||
| Cell Press | 85 mm | 178 mm | 300 | 1000 | EPS, PDF |
|
||||
| PLOS | 8.3 cm | 17.3 cm | 300 | 600 | EPS, TIFF |
|
||||
| ACS | 3.25 in | 7 in | 300 | 600 | TIFF, EPS |
|
||||
|
||||
## Checking Requirements
|
||||
|
||||
### Before Submission Checklist
|
||||
1. Read journal's author guidelines (figure section)
|
||||
2. Check file format requirements
|
||||
3. Verify resolution requirements
|
||||
4. Confirm size specifications (width × height)
|
||||
5. Check font requirements
|
||||
6. Verify color space (RGB vs CMYK)
|
||||
7. Check panel labeling style
|
||||
8. Review supplementary materials requirements
|
||||
9. Confirm file naming conventions
|
||||
10. Check file size limits
|
||||
|
||||
### Useful Tools
|
||||
- **ImageJ/Fiji**: Check/adjust DPI
|
||||
- **Adobe Acrobat**: Verify embedded fonts, check PDF properties
|
||||
- **GIMP**: Free alternative to Photoshop for raster editing
|
||||
- **Inkscape**: Free vector graphics editor
|
||||
|
||||
## Resources
|
||||
|
||||
- **Journal websites**: Always check "Author Guidelines" or "Instructions for Authors"
|
||||
- **Publisher resources**: Many provide templates and tools
|
||||
- **Format conversion**: Use reputable tools; check output quality
|
||||
- **Help desks**: Contact journal staff if unclear
|
||||
|
||||
## Notes
|
||||
|
||||
- Requirements change periodically - always verify current guidelines
|
||||
- Preprint servers (bioRxiv, arXiv) often have different requirements
|
||||
- Conference proceedings may have separate requirements
|
||||
- Some journals offer figure preparation services (often paid)
|
||||
- Supplementary figures may have relaxed requirements compared to main text figures
|
||||
@@ -0,0 +1,620 @@
|
||||
# Publication-Ready Matplotlib Examples
|
||||
|
||||
## Overview
|
||||
|
||||
This reference provides practical code examples for creating publication-ready scientific figures using Matplotlib, Seaborn, and Plotly. All examples follow best practices from `publication_guidelines.md` and use colorblind-friendly palettes from `color_palettes.md`.
|
||||
|
||||
## Setup and Configuration
|
||||
|
||||
### Publication-Quality Matplotlib Configuration
|
||||
|
||||
```python
|
||||
import matplotlib.pyplot as plt
|
||||
import matplotlib as mpl
|
||||
import numpy as np
|
||||
|
||||
# Set publication quality parameters
|
||||
mpl.rcParams['figure.dpi'] = 300
|
||||
mpl.rcParams['savefig.dpi'] = 300
|
||||
mpl.rcParams['font.size'] = 8
|
||||
mpl.rcParams['font.family'] = 'sans-serif'
|
||||
mpl.rcParams['font.sans-serif'] = ['Arial', 'Helvetica']
|
||||
mpl.rcParams['axes.labelsize'] = 9
|
||||
mpl.rcParams['axes.titlesize'] = 9
|
||||
mpl.rcParams['xtick.labelsize'] = 7
|
||||
mpl.rcParams['ytick.labelsize'] = 7
|
||||
mpl.rcParams['legend.fontsize'] = 7
|
||||
mpl.rcParams['axes.linewidth'] = 0.5
|
||||
mpl.rcParams['xtick.major.width'] = 0.5
|
||||
mpl.rcParams['ytick.major.width'] = 0.5
|
||||
mpl.rcParams['lines.linewidth'] = 1.5
|
||||
|
||||
# Use colorblind-friendly colors (Okabe-Ito palette)
|
||||
okabe_ito = ['#E69F00', '#56B4E9', '#009E73', '#F0E442',
|
||||
'#0072B2', '#D55E00', '#CC79A7', '#000000']
|
||||
mpl.rcParams['axes.prop_cycle'] = mpl.cycler(color=okabe_ito)
|
||||
|
||||
# Use perceptually uniform colormap
|
||||
mpl.rcParams['image.cmap'] = 'viridis'
|
||||
```
|
||||
|
||||
### Helper Function for Saving
|
||||
|
||||
```python
|
||||
def save_publication_figure(fig, filename, formats=['pdf', 'png'], dpi=300):
|
||||
"""
|
||||
Save figure in multiple formats for publication.
|
||||
|
||||
Parameters:
|
||||
-----------
|
||||
fig : matplotlib.figure.Figure
|
||||
Figure to save
|
||||
filename : str
|
||||
Base filename (without extension)
|
||||
formats : list
|
||||
List of file formats to save ['pdf', 'png', 'eps', 'svg']
|
||||
dpi : int
|
||||
Resolution for raster formats
|
||||
"""
|
||||
for fmt in formats:
|
||||
output_file = f"{filename}.{fmt}"
|
||||
fig.savefig(output_file, dpi=dpi, bbox_inches='tight',
|
||||
facecolor='white', edgecolor='none',
|
||||
transparent=False, format=fmt)
|
||||
print(f"Saved: {output_file}")
|
||||
```
|
||||
|
||||
## Example 1: Line Plot with Error Bars
|
||||
|
||||
```python
|
||||
import matplotlib.pyplot as plt
|
||||
import numpy as np
|
||||
|
||||
# Generate sample data
|
||||
x = np.linspace(0, 10, 50)
|
||||
y1 = 2 * x + 1 + np.random.normal(0, 1, 50)
|
||||
y2 = 1.5 * x + 2 + np.random.normal(0, 1.2, 50)
|
||||
|
||||
# Calculate means and standard errors for binned data
|
||||
bins = np.linspace(0, 10, 11)
|
||||
y1_mean = [y1[(x >= bins[i]) & (x < bins[i+1])].mean() for i in range(len(bins)-1)]
|
||||
y1_sem = [y1[(x >= bins[i]) & (x < bins[i+1])].std() /
|
||||
np.sqrt(len(y1[(x >= bins[i]) & (x < bins[i+1])]))
|
||||
for i in range(len(bins)-1)]
|
||||
x_binned = (bins[:-1] + bins[1:]) / 2
|
||||
|
||||
# Create figure with appropriate size (single column width = 3.5 inches)
|
||||
fig, ax = plt.subplots(figsize=(3.5, 2.5))
|
||||
|
||||
# Plot with error bars
|
||||
ax.errorbar(x_binned, y1_mean, yerr=y1_sem,
|
||||
marker='o', markersize=4, capsize=3, capthick=0.5,
|
||||
label='Condition A', linewidth=1.5)
|
||||
|
||||
# Add labels with units
|
||||
ax.set_xlabel('Time (hours)')
|
||||
ax.set_ylabel('Fluorescence intensity (a.u.)')
|
||||
|
||||
# Add legend
|
||||
ax.legend(frameon=False, loc='upper left')
|
||||
|
||||
# Remove top and right spines
|
||||
ax.spines['top'].set_visible(False)
|
||||
ax.spines['right'].set_visible(False)
|
||||
|
||||
# Tight layout
|
||||
fig.tight_layout()
|
||||
|
||||
# Save
|
||||
save_publication_figure(fig, 'line_plot_with_errors')
|
||||
plt.show()
|
||||
```
|
||||
|
||||
## Example 2: Multi-Panel Figure
|
||||
|
||||
```python
|
||||
import matplotlib.pyplot as plt
|
||||
import numpy as np
|
||||
from string import ascii_uppercase
|
||||
|
||||
# Create figure with multiple panels (double column width = 7 inches)
|
||||
fig = plt.figure(figsize=(7, 4))
|
||||
|
||||
# Define grid for panels
|
||||
gs = fig.add_gridspec(2, 3, hspace=0.4, wspace=0.4,
|
||||
left=0.08, right=0.98, top=0.95, bottom=0.08)
|
||||
|
||||
# Panel A: Line plot
|
||||
ax_a = fig.add_subplot(gs[0, :2])
|
||||
x = np.linspace(0, 10, 100)
|
||||
for i, offset in enumerate([0, 0.5, 1.0]):
|
||||
ax_a.plot(x, np.sin(x) + offset, label=f'Dataset {i+1}')
|
||||
ax_a.set_xlabel('Time (s)')
|
||||
ax_a.set_ylabel('Amplitude (V)')
|
||||
ax_a.legend(frameon=False, fontsize=6)
|
||||
ax_a.spines['top'].set_visible(False)
|
||||
ax_a.spines['right'].set_visible(False)
|
||||
|
||||
# Panel B: Bar plot
|
||||
ax_b = fig.add_subplot(gs[0, 2])
|
||||
categories = ['Control', 'Treatment\nA', 'Treatment\nB']
|
||||
values = [100, 125, 140]
|
||||
errors = [5, 8, 6]
|
||||
ax_b.bar(categories, values, yerr=errors, capsize=3,
|
||||
color=['#0072B2', '#E69F00', '#009E73'], alpha=0.8)
|
||||
ax_b.set_ylabel('Response (%)')
|
||||
ax_b.spines['top'].set_visible(False)
|
||||
ax_b.spines['right'].set_visible(False)
|
||||
ax_b.set_ylim(0, 160)
|
||||
|
||||
# Panel C: Scatter plot
|
||||
ax_c = fig.add_subplot(gs[1, 0])
|
||||
x = np.random.randn(100)
|
||||
y = 2*x + np.random.randn(100)
|
||||
ax_c.scatter(x, y, s=10, alpha=0.6, color='#0072B2')
|
||||
ax_c.set_xlabel('Variable X')
|
||||
ax_c.set_ylabel('Variable Y')
|
||||
ax_c.spines['top'].set_visible(False)
|
||||
ax_c.spines['right'].set_visible(False)
|
||||
|
||||
# Panel D: Heatmap
|
||||
ax_d = fig.add_subplot(gs[1, 1:])
|
||||
data = np.random.randn(10, 20)
|
||||
im = ax_d.imshow(data, cmap='viridis', aspect='auto')
|
||||
ax_d.set_xlabel('Sample number')
|
||||
ax_d.set_ylabel('Feature')
|
||||
cbar = plt.colorbar(im, ax=ax_d, fraction=0.046, pad=0.04)
|
||||
cbar.set_label('Intensity (a.u.)', rotation=270, labelpad=12)
|
||||
|
||||
# Add panel labels
|
||||
panels = [ax_a, ax_b, ax_c, ax_d]
|
||||
for i, ax in enumerate(panels):
|
||||
ax.text(-0.15, 1.05, ascii_uppercase[i], transform=ax.transAxes,
|
||||
fontsize=10, fontweight='bold', va='top')
|
||||
|
||||
save_publication_figure(fig, 'multi_panel_figure')
|
||||
plt.show()
|
||||
```
|
||||
|
||||
## Example 3: Box Plot with Individual Points
|
||||
|
||||
```python
|
||||
import matplotlib.pyplot as plt
|
||||
import numpy as np
|
||||
|
||||
# Generate sample data
|
||||
np.random.seed(42)
|
||||
data = [np.random.normal(100, 15, 30),
|
||||
np.random.normal(120, 20, 30),
|
||||
np.random.normal(140, 18, 30),
|
||||
np.random.normal(110, 22, 30)]
|
||||
|
||||
fig, ax = plt.subplots(figsize=(3.5, 3))
|
||||
|
||||
# Create box plot
|
||||
bp = ax.boxplot(data, widths=0.5, patch_artist=True,
|
||||
showfliers=False, # We'll add points manually
|
||||
boxprops=dict(facecolor='lightgray', edgecolor='black', linewidth=0.8),
|
||||
medianprops=dict(color='black', linewidth=1.5),
|
||||
whiskerprops=dict(linewidth=0.8),
|
||||
capprops=dict(linewidth=0.8))
|
||||
|
||||
# Overlay individual points
|
||||
colors = ['#0072B2', '#E69F00', '#009E73', '#D55E00']
|
||||
for i, (d, color) in enumerate(zip(data, colors)):
|
||||
# Add jitter to x positions
|
||||
x = np.random.normal(i+1, 0.04, size=len(d))
|
||||
ax.scatter(x, d, alpha=0.4, s=8, color=color)
|
||||
|
||||
# Customize
|
||||
ax.set_xticklabels(['Control', 'Treatment A', 'Treatment B', 'Treatment C'])
|
||||
ax.set_ylabel('Cell count')
|
||||
ax.spines['top'].set_visible(False)
|
||||
ax.spines['right'].set_visible(False)
|
||||
ax.set_ylim(50, 200)
|
||||
|
||||
fig.tight_layout()
|
||||
save_publication_figure(fig, 'boxplot_with_points')
|
||||
plt.show()
|
||||
```
|
||||
|
||||
## Example 4: Heatmap with Colorbar
|
||||
|
||||
```python
|
||||
import matplotlib.pyplot as plt
|
||||
import numpy as np
|
||||
|
||||
# Generate correlation matrix
|
||||
np.random.seed(42)
|
||||
n = 10
|
||||
A = np.random.randn(n, n)
|
||||
corr_matrix = np.corrcoef(A)
|
||||
|
||||
# Create figure
|
||||
fig, ax = plt.subplots(figsize=(4, 3.5))
|
||||
|
||||
# Plot heatmap
|
||||
im = ax.imshow(corr_matrix, cmap='RdBu_r', vmin=-1, vmax=1, aspect='auto')
|
||||
|
||||
# Add colorbar
|
||||
cbar = plt.colorbar(im, ax=ax, fraction=0.046, pad=0.04)
|
||||
cbar.set_label('Correlation coefficient', rotation=270, labelpad=15)
|
||||
|
||||
# Set ticks and labels
|
||||
gene_names = [f'Gene{i+1}' for i in range(n)]
|
||||
ax.set_xticks(np.arange(n))
|
||||
ax.set_yticks(np.arange(n))
|
||||
ax.set_xticklabels(gene_names, rotation=45, ha='right')
|
||||
ax.set_yticklabels(gene_names)
|
||||
|
||||
# Add grid
|
||||
ax.set_xticks(np.arange(n)-.5, minor=True)
|
||||
ax.set_yticks(np.arange(n)-.5, minor=True)
|
||||
ax.grid(which='minor', color='white', linestyle='-', linewidth=0.5)
|
||||
|
||||
fig.tight_layout()
|
||||
save_publication_figure(fig, 'correlation_heatmap')
|
||||
plt.show()
|
||||
```
|
||||
|
||||
## Example 5: Seaborn Violin Plot
|
||||
|
||||
```python
|
||||
import matplotlib.pyplot as plt
|
||||
import seaborn as sns
|
||||
import pandas as pd
|
||||
import numpy as np
|
||||
|
||||
# Generate sample data
|
||||
np.random.seed(42)
|
||||
data = pd.DataFrame({
|
||||
'condition': np.repeat(['Control', 'Drug A', 'Drug B'], 50),
|
||||
'value': np.concatenate([
|
||||
np.random.normal(100, 15, 50),
|
||||
np.random.normal(120, 20, 50),
|
||||
np.random.normal(140, 18, 50)
|
||||
])
|
||||
})
|
||||
|
||||
# Set style
|
||||
sns.set_style('ticks')
|
||||
sns.set_palette(['#0072B2', '#E69F00', '#009E73'])
|
||||
|
||||
fig, ax = plt.subplots(figsize=(3.5, 3))
|
||||
|
||||
# Create violin plot
|
||||
sns.violinplot(data=data, x='condition', y='value', ax=ax,
|
||||
inner='box', linewidth=0.8)
|
||||
|
||||
# Add strip plot
|
||||
sns.stripplot(data=data, x='condition', y='value', ax=ax,
|
||||
size=2, alpha=0.3, color='black')
|
||||
|
||||
# Customize
|
||||
ax.set_xlabel('')
|
||||
ax.set_ylabel('Expression level (AU)')
|
||||
ax.spines['top'].set_visible(False)
|
||||
ax.spines['right'].set_visible(False)
|
||||
|
||||
fig.tight_layout()
|
||||
save_publication_figure(fig, 'violin_plot')
|
||||
plt.show()
|
||||
```
|
||||
|
||||
## Example 6: Scientific Scatter with Regression
|
||||
|
||||
```python
|
||||
import matplotlib.pyplot as plt
|
||||
import numpy as np
|
||||
from scipy import stats
|
||||
|
||||
# Generate data with correlation
|
||||
np.random.seed(42)
|
||||
x = np.random.randn(100)
|
||||
y = 2.5 * x + np.random.randn(100) * 0.8
|
||||
|
||||
# Calculate regression
|
||||
slope, intercept, r_value, p_value, std_err = stats.linregress(x, y)
|
||||
|
||||
# Create figure
|
||||
fig, ax = plt.subplots(figsize=(3.5, 3.5))
|
||||
|
||||
# Scatter plot
|
||||
ax.scatter(x, y, s=15, alpha=0.6, color='#0072B2', edgecolors='none')
|
||||
|
||||
# Regression line
|
||||
x_line = np.array([x.min(), x.max()])
|
||||
y_line = slope * x_line + intercept
|
||||
ax.plot(x_line, y_line, 'r-', linewidth=1.5, label=f'y = {slope:.2f}x + {intercept:.2f}')
|
||||
|
||||
# Add statistics text
|
||||
stats_text = f'$R^2$ = {r_value**2:.3f}\n$p$ < 0.001' if p_value < 0.001 else f'$R^2$ = {r_value**2:.3f}\n$p$ = {p_value:.3f}'
|
||||
ax.text(0.05, 0.95, stats_text, transform=ax.transAxes,
|
||||
verticalalignment='top', fontsize=7,
|
||||
bbox=dict(boxstyle='round', facecolor='white', alpha=0.8, edgecolor='gray', linewidth=0.5))
|
||||
|
||||
# Customize
|
||||
ax.set_xlabel('Predictor variable')
|
||||
ax.set_ylabel('Response variable')
|
||||
ax.spines['top'].set_visible(False)
|
||||
ax.spines['right'].set_visible(False)
|
||||
|
||||
fig.tight_layout()
|
||||
save_publication_figure(fig, 'scatter_regression')
|
||||
plt.show()
|
||||
```
|
||||
|
||||
## Example 7: Time Series with Shaded Error
|
||||
|
||||
```python
|
||||
import matplotlib.pyplot as plt
|
||||
import numpy as np
|
||||
|
||||
# Generate time series data
|
||||
np.random.seed(42)
|
||||
time = np.linspace(0, 24, 100)
|
||||
n_replicates = 5
|
||||
|
||||
# Simulate multiple replicates
|
||||
data = np.array([10 * np.exp(-time/10) + np.random.normal(0, 0.5, 100)
|
||||
for _ in range(n_replicates)])
|
||||
|
||||
# Calculate mean and SEM
|
||||
mean = data.mean(axis=0)
|
||||
sem = data.std(axis=0) / np.sqrt(n_replicates)
|
||||
|
||||
# Create figure
|
||||
fig, ax = plt.subplots(figsize=(4, 2.5))
|
||||
|
||||
# Plot mean line
|
||||
ax.plot(time, mean, linewidth=1.5, color='#0072B2', label='Mean ± SEM')
|
||||
|
||||
# Add shaded error region
|
||||
ax.fill_between(time, mean - sem, mean + sem,
|
||||
alpha=0.3, color='#0072B2', linewidth=0)
|
||||
|
||||
# Customize
|
||||
ax.set_xlabel('Time (hours)')
|
||||
ax.set_ylabel('Concentration (μM)')
|
||||
ax.legend(frameon=False, loc='upper right')
|
||||
ax.spines['top'].set_visible(False)
|
||||
ax.spines['right'].set_visible(False)
|
||||
ax.set_xlim(0, 24)
|
||||
ax.set_ylim(0, 12)
|
||||
|
||||
fig.tight_layout()
|
||||
save_publication_figure(fig, 'timeseries_shaded')
|
||||
plt.show()
|
||||
```
|
||||
|
||||
## Example 8: Plotly Interactive Figure
|
||||
|
||||
```python
|
||||
import plotly.graph_objects as go
|
||||
import numpy as np
|
||||
|
||||
# Generate data
|
||||
np.random.seed(42)
|
||||
x = np.random.randn(100)
|
||||
y = 2*x + np.random.randn(100)
|
||||
colors = np.random.choice(['Group A', 'Group B'], 100)
|
||||
|
||||
# Okabe-Ito colors for Plotly
|
||||
okabe_ito_plotly = ['#E69F00', '#56B4E9']
|
||||
|
||||
# Create figure
|
||||
fig = go.Figure()
|
||||
|
||||
for group, color in zip(['Group A', 'Group B'], okabe_ito_plotly):
|
||||
mask = colors == group
|
||||
fig.add_trace(go.Scatter(
|
||||
x=x[mask], y=y[mask],
|
||||
mode='markers',
|
||||
name=group,
|
||||
marker=dict(size=6, color=color, opacity=0.6)
|
||||
))
|
||||
|
||||
# Update layout for publication quality
|
||||
fig.update_layout(
|
||||
width=500,
|
||||
height=400,
|
||||
font=dict(family='Arial, sans-serif', size=10),
|
||||
plot_bgcolor='white',
|
||||
xaxis=dict(
|
||||
title='Variable X',
|
||||
showgrid=False,
|
||||
showline=True,
|
||||
linewidth=1,
|
||||
linecolor='black',
|
||||
mirror=False
|
||||
),
|
||||
yaxis=dict(
|
||||
title='Variable Y',
|
||||
showgrid=False,
|
||||
showline=True,
|
||||
linewidth=1,
|
||||
linecolor='black',
|
||||
mirror=False
|
||||
),
|
||||
legend=dict(
|
||||
x=0.02,
|
||||
y=0.98,
|
||||
bgcolor='rgba(255,255,255,0.8)',
|
||||
bordercolor='gray',
|
||||
borderwidth=0.5
|
||||
)
|
||||
)
|
||||
|
||||
# Save as static image (requires kaleido)
|
||||
fig.write_image('plotly_scatter.png', width=500, height=400, scale=3) # scale=3 gives ~300 DPI
|
||||
fig.write_html('plotly_scatter.html') # Interactive version
|
||||
|
||||
fig.show()
|
||||
```
|
||||
|
||||
## Example 9: Grouped Bar Plot with Significance
|
||||
|
||||
```python
|
||||
import matplotlib.pyplot as plt
|
||||
import numpy as np
|
||||
|
||||
# Data
|
||||
categories = ['WT', 'Mutant A', 'Mutant B']
|
||||
control_means = [100, 85, 70]
|
||||
control_sem = [5, 6, 5]
|
||||
treatment_means = [100, 120, 140]
|
||||
treatment_sem = [6, 8, 9]
|
||||
|
||||
x = np.arange(len(categories))
|
||||
width = 0.35
|
||||
|
||||
fig, ax = plt.subplots(figsize=(3.5, 3))
|
||||
|
||||
# Create bars
|
||||
bars1 = ax.bar(x - width/2, control_means, width, yerr=control_sem,
|
||||
capsize=3, label='Control', color='#0072B2', alpha=0.8)
|
||||
bars2 = ax.bar(x + width/2, treatment_means, width, yerr=treatment_sem,
|
||||
capsize=3, label='Treatment', color='#E69F00', alpha=0.8)
|
||||
|
||||
# Add significance markers
|
||||
def add_significance_bar(ax, x1, x2, y, h, text):
|
||||
"""Add significance bar between two bars"""
|
||||
ax.plot([x1, x1, x2, x2], [y, y+h, y+h, y], linewidth=0.8, c='black')
|
||||
ax.text((x1+x2)/2, y+h, text, ha='center', va='bottom', fontsize=7)
|
||||
|
||||
# Mark significant differences
|
||||
add_significance_bar(ax, x[1]-width/2, x[1]+width/2, 135, 3, '***')
|
||||
add_significance_bar(ax, x[2]-width/2, x[2]+width/2, 155, 3, '***')
|
||||
|
||||
# Customize
|
||||
ax.set_ylabel('Activity (% of WT control)')
|
||||
ax.set_xticks(x)
|
||||
ax.set_xticklabels(categories)
|
||||
ax.legend(frameon=False, loc='upper left')
|
||||
ax.spines['top'].set_visible(False)
|
||||
ax.spines['right'].set_visible(False)
|
||||
ax.set_ylim(0, 180)
|
||||
|
||||
# Add note about significance
|
||||
ax.text(0.98, 0.02, '*** p < 0.001', transform=ax.transAxes,
|
||||
ha='right', va='bottom', fontsize=6)
|
||||
|
||||
fig.tight_layout()
|
||||
save_publication_figure(fig, 'grouped_bar_significance')
|
||||
plt.show()
|
||||
```
|
||||
|
||||
## Example 10: Publication-Ready Figure for Nature
|
||||
|
||||
```python
|
||||
import matplotlib.pyplot as plt
|
||||
import numpy as np
|
||||
from string import ascii_lowercase
|
||||
|
||||
# Nature specifications: 89mm single column
|
||||
inch_per_mm = 0.0393701
|
||||
width_mm = 89
|
||||
height_mm = 110
|
||||
figsize = (width_mm * inch_per_mm, height_mm * inch_per_mm)
|
||||
|
||||
fig = plt.figure(figsize=figsize)
|
||||
gs = fig.add_gridspec(3, 2, hspace=0.5, wspace=0.4,
|
||||
left=0.12, right=0.95, top=0.96, bottom=0.08)
|
||||
|
||||
# Panel a: Time course
|
||||
ax_a = fig.add_subplot(gs[0, :])
|
||||
time = np.linspace(0, 48, 100)
|
||||
for i, label in enumerate(['Control', 'Treatment']):
|
||||
y = (1 + i*0.5) * np.exp(-time/20) * (1 + 0.3*np.sin(time/5))
|
||||
ax_a.plot(time, y, linewidth=1.2, label=label)
|
||||
ax_a.set_xlabel('Time (h)', fontsize=7)
|
||||
ax_a.set_ylabel('Growth (OD$_{600}$)', fontsize=7)
|
||||
ax_a.legend(frameon=False, fontsize=6)
|
||||
ax_a.tick_params(labelsize=6)
|
||||
ax_a.spines['top'].set_visible(False)
|
||||
ax_a.spines['right'].set_visible(False)
|
||||
|
||||
# Panel b: Bar plot
|
||||
ax_b = fig.add_subplot(gs[1, 0])
|
||||
categories = ['A', 'B', 'C']
|
||||
values = [1.0, 1.5, 2.2]
|
||||
errors = [0.1, 0.15, 0.2]
|
||||
ax_b.bar(categories, values, yerr=errors, capsize=2, width=0.6,
|
||||
color='#0072B2', alpha=0.8)
|
||||
ax_b.set_ylabel('Fold change', fontsize=7)
|
||||
ax_b.tick_params(labelsize=6)
|
||||
ax_b.spines['top'].set_visible(False)
|
||||
ax_b.spines['right'].set_visible(False)
|
||||
|
||||
# Panel c: Heatmap
|
||||
ax_c = fig.add_subplot(gs[1, 1])
|
||||
data = np.random.randn(8, 6)
|
||||
im = ax_c.imshow(data, cmap='viridis', aspect='auto')
|
||||
ax_c.set_xlabel('Sample', fontsize=7)
|
||||
ax_c.set_ylabel('Gene', fontsize=7)
|
||||
ax_c.tick_params(labelsize=6)
|
||||
|
||||
# Panel d: Scatter
|
||||
ax_d = fig.add_subplot(gs[2, :])
|
||||
x = np.random.randn(50)
|
||||
y = 2*x + np.random.randn(50)*0.5
|
||||
ax_d.scatter(x, y, s=8, alpha=0.6, color='#E69F00')
|
||||
ax_d.set_xlabel('Expression gene X', fontsize=7)
|
||||
ax_d.set_ylabel('Expression gene Y', fontsize=7)
|
||||
ax_d.tick_params(labelsize=6)
|
||||
ax_d.spines['top'].set_visible(False)
|
||||
ax_d.spines['right'].set_visible(False)
|
||||
|
||||
# Add lowercase panel labels (Nature style)
|
||||
for i, ax in enumerate([ax_a, ax_b, ax_c, ax_d]):
|
||||
ax.text(-0.2, 1.1, f'{ascii_lowercase[i]}', transform=ax.transAxes,
|
||||
fontsize=9, fontweight='bold', va='top')
|
||||
|
||||
# Save in Nature-preferred format
|
||||
fig.savefig('nature_figure.pdf', dpi=1000, bbox_inches='tight',
|
||||
facecolor='white', edgecolor='none')
|
||||
fig.savefig('nature_figure.png', dpi=300, bbox_inches='tight',
|
||||
facecolor='white', edgecolor='none')
|
||||
|
||||
plt.show()
|
||||
```
|
||||
|
||||
## Tips for Each Library
|
||||
|
||||
### Matplotlib
|
||||
- Use `fig.tight_layout()` or `constrained_layout=True` to prevent overlapping
|
||||
- Set DPI to 300-600 for publication
|
||||
- Use vector formats (PDF, EPS) for line plots
|
||||
- Embed fonts in PDF/EPS files
|
||||
|
||||
### Seaborn
|
||||
- Built on matplotlib, so all matplotlib customizations work
|
||||
- Use `sns.set_style('ticks')` or `'whitegrid'` for clean looks
|
||||
- `sns.despine()` removes top and right spines
|
||||
- Set custom palette with `sns.set_palette()`
|
||||
|
||||
### Plotly
|
||||
- Great for interactive exploratory analysis
|
||||
- Export static images with `fig.write_image()` (requires kaleido package)
|
||||
- Use `scale` parameter to control DPI (scale=3 ≈ 300 DPI)
|
||||
- Update layout extensively for publication quality
|
||||
|
||||
## Common Workflow
|
||||
|
||||
1. **Explore with default settings**
|
||||
2. **Apply publication configuration** (see Setup section)
|
||||
3. **Create plot with appropriate size** (check journal requirements)
|
||||
4. **Customize colors** (use colorblind-friendly palettes)
|
||||
5. **Adjust fonts and line widths** (readable at final size)
|
||||
6. **Remove chart junk** (top/right spines, excessive grid)
|
||||
7. **Add clear labels with units**
|
||||
8. **Test in grayscale**
|
||||
9. **Save in multiple formats** (PDF for vector, PNG for raster)
|
||||
10. **Verify in final context** (import into manuscript to check size)
|
||||
|
||||
## Resources
|
||||
|
||||
- Matplotlib documentation: https://matplotlib.org/
|
||||
- Seaborn gallery: https://seaborn.pydata.org/examples/index.html
|
||||
- Plotly documentation: https://plotly.com/python/
|
||||
- Nature Methods Points of View: Data visualization column archive
|
||||
@@ -0,0 +1,205 @@
|
||||
# Publication-Ready Figure Guidelines
|
||||
|
||||
## Core Principles
|
||||
|
||||
Scientific figures must be clear, accurate, and accessible. Publication-ready figures follow these fundamental principles:
|
||||
|
||||
1. **Clarity**: Information should be immediately understandable
|
||||
2. **Accuracy**: Data representation must be truthful and unmanipulated
|
||||
3. **Accessibility**: Figures should be interpretable by all readers, including those with visual impairments
|
||||
4. **Professional**: Clean, polished appearance suitable for peer-reviewed journals
|
||||
|
||||
## Resolution and File Format
|
||||
|
||||
### Resolution Requirements
|
||||
- **Raster images (photos, microscopy)**: 300-600 DPI at final print size
|
||||
- **Line art and graphs**: 600-1200 DPI (or vector format)
|
||||
- **Combined figures**: 300-600 DPI
|
||||
|
||||
### File Formats
|
||||
- **Vector formats (preferred for graphs/plots)**: PDF, EPS, SVG
|
||||
- Infinitely scalable without quality loss
|
||||
- Smaller file sizes for line art
|
||||
- Best for: plots, diagrams, schematics
|
||||
|
||||
- **Raster formats**: TIFF, PNG (never JPEG for scientific data)
|
||||
- Use for: photographs, microscopy, images with continuous tone
|
||||
- TIFF: Lossless, widely accepted
|
||||
- PNG: Lossless, good for web and supplementary materials
|
||||
- **Never use JPEG**: Lossy compression introduces artifacts
|
||||
|
||||
### Size Specifications
|
||||
- **Single column**: 85-90 mm (3.35-3.54 inches) width
|
||||
- **1.5 column**: 114-120 mm (4.49-4.72 inches) width
|
||||
- **Double column**: 174-180 mm (6.85-7.08 inches) width
|
||||
- **Maximum height**: Usually 230-240 mm (9-9.5 inches)
|
||||
|
||||
## Typography
|
||||
|
||||
### Font Guidelines
|
||||
- **Font family**: Sans-serif fonts (Arial, Helvetica, Calibri) for most journals
|
||||
- Some journals prefer specific fonts (check guidelines)
|
||||
- Consistency across all figures in manuscript
|
||||
|
||||
- **Font sizes at final print size**:
|
||||
- Axis labels: 7-9 pt minimum
|
||||
- Tick labels: 6-8 pt minimum
|
||||
- Legends: 6-8 pt
|
||||
- Panel labels (A, B, C): 8-12 pt, bold
|
||||
- Title: Generally avoided in multi-panel figures
|
||||
|
||||
- **Font weight**: Regular weight for most text; bold for panel labels only
|
||||
|
||||
### Text Best Practices
|
||||
- Use sentence case for axis labels ("Time (hours)" not "TIME (HOURS)")
|
||||
- Include units in parentheses
|
||||
- Avoid abbreviations unless space-constrained (define in caption)
|
||||
- No text smaller than 5-6 pt at final size
|
||||
|
||||
## Color Usage
|
||||
|
||||
### Color Selection Principles
|
||||
1. **Colorblind-friendly**: ~8% of males have color vision deficiency
|
||||
- Avoid red/green combinations
|
||||
- Use blue/orange, blue/yellow, or add texture/pattern
|
||||
- Test with colorblindness simulators
|
||||
|
||||
2. **Purposeful color**: Color should convey meaning, not just aesthetics
|
||||
- Use color to distinguish categories or highlight key data
|
||||
- Maintain consistency across figures (same treatment = same color)
|
||||
|
||||
3. **Print considerations**:
|
||||
- Colors may appear different in print vs. screen
|
||||
- Use CMYK color space for print, RGB for digital
|
||||
- Ensure sufficient contrast (especially for grayscale conversion)
|
||||
|
||||
### Recommended Color Palettes
|
||||
- **Qualitative (categories)**: ColorBrewer, Okabe-Ito palette
|
||||
- **Sequential (low to high)**: Viridis, Cividis, Blues, Oranges
|
||||
- **Diverging (negative to positive)**: RdBu, PuOr, BrBG (ensure colorblind-safe)
|
||||
|
||||
### Grayscale Compatibility
|
||||
- All figures should be interpretable in grayscale
|
||||
- Use different line styles (solid, dashed, dotted) and markers
|
||||
- Add patterns/hatching to bars and areas
|
||||
|
||||
## Layout and Composition
|
||||
|
||||
### Multi-Panel Figures
|
||||
- **Panel labels**: Use bold uppercase letters (A, B, C) in top-left corner
|
||||
- **Spacing**: Adequate white space between panels
|
||||
- **Alignment**: Align panels along edges or axes where possible
|
||||
- **Sizing**: Related panels should have consistent sizes
|
||||
- **Arrangement**: Logical flow (left-to-right, top-to-bottom)
|
||||
|
||||
### Plot Elements
|
||||
|
||||
#### Axes
|
||||
- **Axis lines**: 0.5-1 pt thickness
|
||||
- **Tick marks**: Point inward or outward consistently
|
||||
- **Tick frequency**: Enough to read values, not cluttered (typically 4-7 major ticks)
|
||||
- **Axis labels**: Required on all plots; state units
|
||||
- **Axis ranges**: Start from zero for bar charts (unless scientifically inappropriate)
|
||||
|
||||
#### Lines and Markers
|
||||
- **Line width**: 1-2 pt for data lines; 0.5-1 pt for reference lines
|
||||
- **Marker size**: 3-6 pt, larger than line width
|
||||
- **Marker types**: Differentiate when multiple series (circles, squares, triangles)
|
||||
- **Error bars**: 0.5-1 pt width; include caps if appropriate
|
||||
|
||||
#### Legends
|
||||
- **Position**: Inside plot area if space permits, outside otherwise
|
||||
- **Frame**: Optional; if used, thin line (0.5 pt)
|
||||
- **Order**: Match order of data appearance (top to bottom or left to right)
|
||||
- **Content**: Concise descriptions; full details in caption
|
||||
|
||||
### White Space and Margins
|
||||
- Remove unnecessary white space around plots
|
||||
- Maintain consistent margins
|
||||
- `tight_layout()` or `constrained_layout=True` in matplotlib
|
||||
|
||||
## Data Representation Best Practices
|
||||
|
||||
### Statistical Rigor
|
||||
- **Error bars**: Always show uncertainty (SD, SEM, CI) and state which in caption
|
||||
- **Sample size**: Indicate n in figure or caption
|
||||
- **Significance**: Mark statistical significance clearly (*, **, ***)
|
||||
- **Replicates**: Show individual data points when possible, not just summary statistics
|
||||
|
||||
### Appropriate Chart Types
|
||||
- **Bar plots**: Comparing discrete categories; always start y-axis at zero
|
||||
- **Line plots**: Time series or continuous relationships
|
||||
- **Scatter plots**: Correlation between variables; add regression line if appropriate
|
||||
- **Box plots**: Distribution comparisons; show outliers
|
||||
- **Heatmaps**: Matrix data, correlations, expression patterns
|
||||
- **Violin plots**: Distribution shape comparison (better than box plots for bimodal data)
|
||||
|
||||
### Avoiding Distortion
|
||||
- **No 3D effects**: Distorts perception of values
|
||||
- **No unnecessary decorations**: No gradients, shadows, or chart junk
|
||||
- **Consistent scales**: Use same scale for comparable panels
|
||||
- **No truncated axes**: Unless clearly indicated and scientifically justified
|
||||
- **Linear vs. log scales**: Choose appropriate scale; always label clearly
|
||||
|
||||
## Accessibility
|
||||
|
||||
### Colorblind Considerations
|
||||
- Test with online simulators (e.g., Coblis, Color Oracle)
|
||||
- Use patterns/textures in addition to color
|
||||
- Provide alternative representations in supplementary materials if needed
|
||||
|
||||
### Visual Impairment
|
||||
- High contrast between elements
|
||||
- Thick enough lines (minimum 0.5 pt)
|
||||
- Clear, uncluttered layouts
|
||||
|
||||
### Data Availability
|
||||
- Include data tables in supplementary materials
|
||||
- Provide source data files for graphs
|
||||
- Consider interactive figures for online supplementary materials
|
||||
|
||||
## Common Mistakes to Avoid
|
||||
|
||||
1. **Font too small**: Text unreadable at final print size
|
||||
2. **Low resolution**: Pixelated or blurry images
|
||||
3. **Chart junk**: Unnecessary grid lines, 3D effects, decorations
|
||||
4. **Poor color choices**: Red/green combinations, low contrast
|
||||
5. **Missing elements**: No axis labels, no units, no error bars
|
||||
6. **Inconsistent styling**: Different fonts/sizes within figure or between figures
|
||||
7. **Data distortion**: Truncated axes, inappropriate scales, 3D effects
|
||||
8. **JPEG compression**: Artifacts around text and lines
|
||||
9. **Too much information**: Cramming too many data series into one plot
|
||||
10. **Inaccessible legends**: Legends outside the figure boundary after export
|
||||
|
||||
## Figure Checklist
|
||||
|
||||
Before submission, verify:
|
||||
|
||||
- [ ] Resolution meets journal requirements (300+ DPI for raster)
|
||||
- [ ] File format is acceptable (vector for plots, TIFF/PNG for images)
|
||||
- [ ] Figure dimensions match journal specifications
|
||||
- [ ] All text is readable at final size (minimum 6-7 pt)
|
||||
- [ ] Fonts are consistent and embedded (for PDF/EPS)
|
||||
- [ ] Colors are colorblind-friendly
|
||||
- [ ] Figure is interpretable in grayscale
|
||||
- [ ] All axes are labeled with units
|
||||
- [ ] Error bars or uncertainty indicators are present
|
||||
- [ ] Statistical significance is marked if applicable
|
||||
- [ ] Panel labels are present and consistent (A, B, C)
|
||||
- [ ] Legend is clear and complete
|
||||
- [ ] No chart junk or unnecessary elements
|
||||
- [ ] File naming follows journal conventions
|
||||
- [ ] Figure caption is comprehensive
|
||||
- [ ] Source data is available
|
||||
|
||||
## Journal-Specific Considerations
|
||||
|
||||
Always consult the specific journal's author guidelines. Common variations include:
|
||||
|
||||
- **Nature journals**: RGB, 300 DPI minimum, specific size requirements
|
||||
- **Science**: EPS or high-res TIFF, specific font requirements
|
||||
- **Cell Press**: PDF or EPS preferred, Arial or Helvetica fonts
|
||||
- **PLOS**: TIFF or EPS, specific color space requirements
|
||||
- **ACS journals**: Application files (AI, EPS) or high-res TIFF
|
||||
|
||||
See `journal_requirements.md` for detailed specifications from major publishers.
|
||||
Reference in New Issue
Block a user