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references/color_palettes.md
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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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