Illustration Diagram
Chart overview
Illustration diagrams use basic shapes, lines, and text to create custom visualizations that explain concepts or structures.
Key points
- They are particularly valuable in education and science communication where standard chart types don't capture the subject matter.
Python Tutorial
How to create a illustration diagram in Python
Use the full tutorial for implementation details, troubleshooting, and chart variations in matplotlib, seaborn, and plotly.
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View example prompt
"Create a cross-sectional illustration diagram showing 'Earth's Internal Structure' with accurate proportions. Display 4 main layers as concentric circles: Inner Core (solid iron, radius 1,220 km, yellow/orange), Outer Core (liquid iron, to 3,400 km, orange/red), Mantle (silicate rock, to 2,900 km from surface, brown/tan gradient), Crust (5-70 km thick, gray). Add callout lines with labels for each layer including: composition, state (solid/liquid), temperature range, and approximate depth. Include a scale bar showing depths in km. Add the atmosphere as a thin blue rim. Mark the Moho discontinuity and core-mantle boundary. Use realistic Earth colors. Title: 'Internal Structure of Earth (Not to Scale)'."
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Python Code Example
# === IMPORTS ===
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
# === USER-EDITABLE PARAMETERS ===
title = "Earth's Internal Structure"
figsize = (12, 12)
# === LAYER DATA ===
# Layer: (name, outer_radius_km, inner_radius_km, color, properties)
layers = [
('Atmosphere', 6471, 6371, '#87CEEB', '0-100 km, Gas'),
('Crust', 6371, 6341, '#8B4513', '0-30 km, Solid rock'),
('Upper Mantle', 6341, 5711, '#CD853F', '30-660 km, Semi-solid'),
('Lower Mantle', 5711, 3481, '#B8860B', '660-2890 km, Solid'),
('Outer Core', 3481, 1221, '#FF8C00', '2890-5150 km, Liquid iron'),
('Inner Core', 1221, 0, '#FFD700', '5150-6371 km, Solid iron'),
]
# Print summary
print("=== Earth's Internal Structure ===")
print(f"\nTotal Earth Radius: 6,371 km")
print(f"\nLayer Details:")
for name, outer, inner, color, props in layers:
thickness = outer - inner
print(f" {name}: {props} (thickness: {thickness} km)")
# === CREATE ILLUSTRATION ===
fig, ax = plt.subplots(figsize=figsize)
# Draw layers from outside to inside
for name, outer_r, inner_r, color, props in layers:
# Normalize to plot coordinates (max radius = 1)
outer_norm = outer_r / 6471
circle = plt.Circle((0.5, 0.5), outer_norm * 0.4,
facecolor=color, edgecolor='white', linewidth=2)
ax.add_patch(circle)
# Add labels with callouts
label_positions = [
('Atmosphere', 0.95, 0.85, 0.82, 0.82),
('Crust', 0.15, 0.88, 0.25, 0.78),
('Upper Mantle', 0.1, 0.65, 0.22, 0.62),
('Lower Mantle', 0.08, 0.42, 0.28, 0.45),
('Outer Core', 0.75, 0.25, 0.62, 0.38),
('Inner Core', 0.75, 0.55, 0.55, 0.52),
]
for name, lx, ly, px, py in label_positions:
# Find layer info
layer_info = next((l for l in layers if l[0] == name), None)
if layer_info:
ax.annotate(
f'{name}\n{layer_info[4]}',
xy=(px, py),
xytext=(lx, ly),
fontsize=9,
fontweight='bold',
ha='center',
bbox=dict(boxstyle='round,pad=0.3', facecolor='white', alpha=0.9),
arrowprops=dict(arrowstyle='->', color='black', lw=1.5)
)
# Add scale bar
ax.plot([0.1, 0.35], [0.08, 0.08], 'k-', linewidth=3)
ax.text(0.225, 0.05, '~3,000 km', ha='center', fontsize=10)
# Title and labels
ax.set_title(title + '\n(Cross-section, not to scale)', fontsize=16, fontweight='bold', pad=20)
# Remove axes
ax.set_xlim(0, 1)
ax.set_ylim(0, 1)
ax.set_aspect('equal')
ax.axis('off')
plt.tight_layout()
plt.show()
# END-OF-CODE
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Console Output
=== Earth's Internal Structure === Total Earth Radius: 6,371 km Layer Details: Atmosphere: 0-100 km, Gas (thickness: 100 km) Crust: 0-30 km, Solid rock (thickness: 30 km) Upper Mantle: 30-660 km, Semi-solid (thickness: 630 km) Lower Mantle: 660-2890 km, Solid (thickness: 2230 km) Outer Core: 2890-5150 km, Liquid iron (thickness: 2260 km) Inner Core: 5150-6371 km, Solid iron (thickness: 1221 km)
Common Use Cases
- 1Educational content
- 2Scientific explanations
- 3Concept visualization
- 4Technical documentation
Pro Tips
Use consistent visual language
Add clear labels and legends
Maintain proper proportions
Long-tail keyword opportunities
High-intent chart variations
Library comparison for this chart
matplotlib
Best when you need full control over axis formatting, annotation placement, and journal-specific styling for illustration-diagram.
Scientific Chart Selection Cheat Sheet
Not sure whether to use a Violin Plot, Box Plot, or Ridge Plot? Download our single-page reference mapping the most-used scientific chart types, exactly when to use them, and the core Matplotlib/Seaborn functions.