Sunburst Chart

NPM Package Dependencies

Dark-themed sunburst chart showing NPM package dependency tree from project to dependency categories to packages.

Output
NPM Package Dependencies
Python
import matplotlib.pyplot as plt
import numpy as np

fig, ax = plt.subplots(figsize=(12, 10), facecolor='#0a0a0f')
ax.set_facecolor('#0a0a0f')

# Inner ring - Dependency types
dep_sizes = [35, 30, 20, 15]
dep_labels = ['Runtime', 'Dev Tools', 'Types', 'Peer']
dep_colors = ['#6CF527', '#27D3F5', '#F5B027', '#F5276C']

# Outer ring - Packages
pkg_sizes = [12, 12, 11, 10, 10, 10, 10, 10, 8, 7]
pkg_colors = ['#6CF527', '#8FFF5C', '#B2FF8C',
              '#27D3F5', '#5CE8FF', '#8CF2FF',
              '#F5B027', '#FFCC5C',
              '#F5276C', '#FF5A94']

wedges_outer, _ = ax.pie(pkg_sizes, radius=1, colors=pkg_colors,
       wedgeprops=dict(width=0.3, edgecolor='#0a0a0f', linewidth=2))

wedges_inner, _ = ax.pie(dep_sizes, radius=0.7, colors=dep_colors,
       wedgeprops=dict(width=0.3, edgecolor='#0a0a0f', linewidth=2))

# Add dependency type labels
for i, (wedge, label) in enumerate(zip(wedges_inner, dep_labels)):
    ang = (wedge.theta2 - wedge.theta1) / 2 + wedge.theta1
    x = 0.55 * np.cos(np.radians(ang))
    y = 0.55 * np.sin(np.radians(ang))
    ax.text(x, y, label, ha='center', va='center', fontsize=10, 
            color='white', fontweight='bold')

centre_circle = plt.Circle((0, 0), 0.4, fc='#0a0a0f', ec='#334155', linewidth=2)
ax.add_artist(centre_circle)
ax.text(0, 0, 'package.json\n1,247 deps', ha='center', va='center',
        fontsize=11, color='white', fontweight='bold')

# Legend
pkg_labels = ['react', 'lodash', 'axios', 'webpack', 'eslint', 
              'jest', '@types/node', '@types/react', 'react-dom', 'react-router']
ax.legend(wedges_outer, pkg_labels, loc='center left', bbox_to_anchor=(1.05, 0.5),
          fontsize=9, frameon=True, facecolor='#1e293b', edgecolor='#334155',
          labelcolor='white', title='Packages', title_fontsize=10)

ax.set_title('NPM Dependencies', fontsize=18, color='#f8fafc',
             fontweight='bold', pad=20)

plt.tight_layout()
plt.show()
Library

Matplotlib

Category

Part-to-Whole

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