huez 0.0.5

A unified color scheme solution for Python visualization

Huez

The First Intelligent Color Management System for Python Visualization
Automatic • Consistent • Accessible • Smart

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💡 The Problem

Good science shouldn't be ruined by bad colors.

Yet creating publication-quality visualizations in Python is still tedious:

• ❌ Inconsistent colors across matplotlib, seaborn, plotly...
• ❌ Manual tweaking for every single plot
• ❌ No intelligent tools - colormaps, accessibility checks all manual
• ❌ 8% of readers (colorblind) may misinterpret your results

✨ The Solution

pip install huez

import huez as hz

# 🎨 One line for screen, print, and presentation
hz.use("scheme-1")  # Optimized colors for screen
hz.use("scheme-1", mode="print")  # Grayscale-friendly for printing
hz.use("scheme-1", mode="presentation")  # High contrast for projectors

# ✨ Huez automatically handles:
#   • Intelligent color expansion (LAB interpolation)
#   • Smart colormap detection (sequential/diverging)
#   • Cross-library consistency (matplotlib, seaborn, plotly, altair, plotnine)

✨ What Makes Huez Different?

Huez is the only tool that combines:

• 🧠 Intelligence - Smart colormap detection, LAB color interpolation, colorblind safety
• 🚀 Automation - One-line setup, automatic heatmap colormap injection
• 🎯 Unification - 5 libraries (matplotlib, seaborn, plotly, altair, plotnine) consistent
• 🖨️ Multi-Mode - Screen, print (grayscale-friendly), and presentation (high-contrast)
• ♿ Accessibility - Built-in colorblind simulation for 8% of population
• 🎨 Professional - Academic journal styles (Nature, Lancet, Science, JAMA, etc.)

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🎨 Visual Demonstrations

1️⃣ Intelligent Color Expansion (5 → 15 colors)

Problem: Default palettes have 5-10 colors → colors repeat when plotting 15+ categories Solution: Huez uses LAB space interpolation → generates 15 unique, perceptually distinct colors

Smooth color gradation with maximum distinguishability.

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2️⃣ Smart Colormap Detection (Correlation Heatmap)

Problem (Left): Sequential colormap (viridis) on diverging data → center value (0) not highlighted
Solution (Right): Auto-detected diverging colormap (coolwarm) → center at 0, symmetric red-blue colors

Critical for correlation matrices, gene expression, and any data centered at zero.

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3️⃣ Colorblind Accessibility (8% of Population)

Simulated in Deuteranopia (red-green colorblindness):

• Before (Left): Default colors → red/green bars become indistinguishable
• After (Right): Huez colorblind-safe palette → all 8 cell types remain distinct

8% of males have red-green colorblindness. Huez ensures your research is accessible to all.

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4️⃣ Print Mode: Grayscale Optimization

When printed in black & white:

• Before (Left): Screen colors → similar gray values (0.33-0.70) → lines merge together
• After (Right): mode="print" → optimized gray values (0.00-0.62) → clear separation

Perfect for journal submissions and B&W printing. Starting with pure black (0.00) ensures maximum contrast.

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🔧 Usage Guide

✅ Correct Usage (Fully Automatic)

import huez as hz
import matplotlib.pyplot as plt
import seaborn as sns

hz.use("scheme-1")  # One line setup

# ✅ Line plots - automatic colors
plt.plot(x, y1, label='Series 1')  
plt.plot(x, y2, label='Series 2')

# ✅ Heatmaps - automatic colormap detection
sns.heatmap(correlation_data)  # Diverging colormap (has negatives)
sns.heatmap(temperature_data)  # Sequential colormap (all positive)

❌ Incorrect Usage (Manual Override)

# ❌ WRONG: Explicit parameters override Huez
plt.plot(x, y1, color='red')       # Bypasses Huez
sns.heatmap(data, cmap='viridis')  # Bypasses auto-detection

Key Principle: Let Huez handle colors automatically for optimal results.

Why this works: Huez intelligently adapts to your data—detecting data types, expanding colors when needed, and ensuring accessibility—all without any manual intervention.

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🔍 Preview & Quality Checks

# Preview any scheme before using
hz.preview("scheme-1")  # Interactive color preview
hz.preview("scheme-1", mode="print")  # Preview in print mode

# List all available schemes
schemes = hz.list_schemes()
print(schemes)  # ['scheme-1', 'scheme-2', 'scheme-3', 'lancet', 'nejm', ...]

# Optional: Ensure colorblind accessibility
hz.use("scheme-1", ensure_accessible=True)

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📚 Supported Libraries (Click to expand)

Matplotlib

import matplotlib.pyplot as plt
plt.plot(x, y1, label='Data 1')  # Auto-colored
plt.plot(x, y2, label='Data 2')  # Auto-colored

Seaborn

import seaborn as sns
sns.scatterplot(data=df, x='x', y='y', hue='category')  # Auto-colored

plotnine (ggplot2 for Python)

from plotnine import *
(ggplot(df, aes('x', 'y', color='category')) + geom_point())  # Auto-colored

Altair

import altair as alt
alt.Chart(df).mark_circle().encode(
    x='x:Q', y='y:Q', color='category:N'  # Auto-colored
)

Plotly

import plotly.graph_objects as go
fig = go.Figure()
fig.add_trace(go.Scatter(x=x, y=y, name='Data'))  # Auto-colored

🎨 Custom Schemes (Click to expand)

Switch Between Built-in Schemes

hz.use("lancet")     # Lancet journal style
hz.use("scheme-1")   # Default scheme 1
hz.use("scheme-2")   # Default scheme 2

Create Custom Configuration

Create my_config.yaml:

version: 1
default_scheme: my_custom_scheme
schemes:
  my_custom_scheme:
    title: "My Custom Style"
    fonts: { family: "DejaVu Sans", size: 10 }
    palettes:
      discrete: "npg"
      sequential: "viridis"
      diverging: "coolwarm"
      cyclic: "twilight"
    figure: { dpi: 300 }
    style: { grid: "y", legend_loc: "best", spine_top_right_off: true }

Load and use:

hz.load_config("my_config.yaml")
hz.use("my_custom_scheme")

Available Built-in Palettes

• Journals: npg, aaas, nejm, lancet, jama, bmj
• Colorblind-safe: okabe-ito, paul-tol-bright, paul-tol-vibrant
• Scientific: viridis, plasma, inferno, cividis

📖 Complete Examples (Click to expand)

Example 1: Basic Multi-Library Workflow

import matplotlib.pyplot as plt
import seaborn as sns
import huez as hz

# One line setup
hz.use("lancet")

# All libraries automatically use consistent colors
fig, axes = plt.subplots(1, 2, figsize=(12, 5))

# Matplotlib
axes[0].plot(x, y1, label='Series 1')
axes[0].plot(x, y2, label='Series 2')
axes[0].legend()

# Seaborn
sns.scatterplot(data=df, x='x', y='y', hue='category', ax=axes[1])

plt.show()

Example 2: Automatic Intelligence Features

import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import huez as hz

# One line setup - auto_expand and smart_cmap are enabled by default
hz.use("scheme-1", ensure_accessible=True)

# ✅ Auto-expand: Plot 15 categories, Huez auto-generates 15 distinct colors
x = np.linspace(0, 10, 100)
for i in range(15):
    plt.plot(x, np.sin(x + i * 0.5), label=f'Series {i+1}')
plt.legend()
plt.show()

# ✅ Smart colormap: Correlation matrix auto-detects diverging colormap
correlation_data = np.corrcoef(np.random.randn(10, 100))
sns.heatmap(correlation_data)  # Automatically uses diverging colormap
plt.show()

Example 3: Context Manager

import huez as hz

# Temporarily use a different scheme
with hz.using("lancet"):
    plt.plot(x, y1)  # Uses lancet colors
    plt.show()

# Back to previous scheme automatically

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🆚 Comparison with Other Tools (Click to expand)

Feature	Huez	palettable	seaborn	plotly	colorcet
Cross-library unification	✅ 5 libraries	❌ None	❌ None	❌ None	❌ None
Intelligent color expansion	✅ LAB space	❌ None	❌ Simple cycle	❌ Simple cycle	❌ None
Smart colormap detection	✅ Auto-detect	❌ Manual	❌ Manual	❌ Manual	❌ Manual
Colorblind safety check	✅ 3 CVD types	❌ None	❌ None	❌ None	❌ None
One-line setup	✅hz.use()	❌ Per-plot	🟡 Partial	🟡 Partial	❌ Per-plot
Academic journal styles	✅ 6+ journals	🟡 Some	❌ None	❌ None	❌ None

Huez is the only tool with built-in intelligence for automatic adaptation.

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🤝 Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

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🎯 Scientific Visualization Made Better

⭐ Star us on GitHub if Huez saves your time!