Matplotlib Clean Plots

Matplotlib Clean Plots

Create publication-quality matplotlib visualizations with clean, minimal styling optimized for technical blogs and learning notes.

Quick Start

Using Templates (Recommended)

Setup a new plot file:

# 2D plot
./scripts/setup_template.sh my_plot.py

# 3D plot
./scripts/setup_template.sh my_3d_plot.py 3d

# Windows PowerShell
.\scripts\setup_template.ps1 my_plot.py
.\scripts\setup_template.ps1 my_3d_plot.py 3d

Then edit and run: python3 my_plot.py

Template includes:

• Clean style configuration
• Standard color palette (Elegant Teal)
• Proper export settings
• Example structure

Manual Setup

import matplotlib.pyplot as plt

# Apply clean style
plt.style.use('seaborn-v0_8-white')
plt.rcParams['axes.spines.top'] = False
plt.rcParams['axes.spines.right'] = False
plt.rcParams['figure.dpi'] = 150

# Standard colors
COLOR_TEAL = '#4A9BAA'  # Primary
COLOR_CORAL = '#FF7B7B'  # Emphasis
COLOR_GRAY = '#95A5A6'   # Auxiliary

# Your plot
plt.plot(x, y, color=COLOR_TEAL, linewidth=2.5)
plt.grid(True, alpha=0.3, color=COLOR_GRAY)
plt.savefig('output.png', dpi=150, bbox_inches='tight')

Core Principles

1. English Labels - Use clear English for international audience
2. Clean Styling - Remove unnecessary elements (top/right spines, excessive decorations)
3. Math Plots: Axes Through Origin - For math/function plots, place spines at origin (see Common Patterns)
4. Preventing Overlap - Use semi-transparent bounding boxes (bbox) for annotations and legends to ensure legibility when crossing lines.
5. Standard Colors - Use Elegant Teal Palette for consistency
6. Proper Export - Always use dpi=150, bbox_inches='tight'
7. Verify Output - Check generated images before claiming success

Standard Colors

Elegant Teal Palette (copy from templates/colors.py or template files):

COLOR_TEAL = '#4A9BAA'      # Primary - main data, key elements
COLOR_AQUA = '#7ECFC0'      # Secondary - supporting data
COLOR_CREAM = '#FFD9A0'     # Tertiary - annotations, highlights
COLOR_CORAL = '#FF7B7B'     # Emphasis - important markers
COLOR_GRAY = '#95A5A6'      # Auxiliary - grids, reference lines

Usage: Import from template or copy color definitions. See Color Palettes Guide for detailed usage examples and alternatives.

Common Patterns

Use templates for full examples. Quick patterns:

# Main curve with emphasis points
ax.plot(x, y, color=COLOR_TEAL, linewidth=2.5)
ax.scatter(x_peaks, y_peaks, color=COLOR_CORAL, s=80, zorder=5)

# Filled confidence band
ax.fill_between(x, y_lower, y_upper, color=COLOR_AQUA, alpha=0.3)

# Reference line
ax.axhline(y=0, color=COLOR_GRAY, linestyle='--', alpha=0.5)

# Grid
ax.grid(True, alpha=0.3, color=COLOR_GRAY, linestyle=':')

Mathematical Function Plots (Axes Through Origin)

For math/function plots, use spines through the origin to match textbook conventions.

AXIS_COLOR = '#444444'

def setup_math_axes(ax, xticks, yticks):
    """Spines through origin — use for all math/function plots."""
    for spine in ['top', 'right']:
        ax.spines[spine].set_visible(False)
    ax.spines['left'].set_position('zero')
    ax.spines['bottom'].set_position('zero')
    for spine in ['left', 'bottom']:
        ax.spines[spine].set_color(AXIS_COLOR)
        ax.spines[spine].set_linewidth(1.0)
    ax.set_xticks(xticks)
    ax.set_yticks(yticks)
    ax.tick_params(axis='both', colors=AXIS_COLOR, labelsize=9,
                   direction='inout', length=5)

# Usage
setup_math_axes(ax, xticks=[-3, -2, -1, 1, 2, 3],
                yticks=[1, 2, 3])
ax.set_xlim(-3.2, 3.2)
ax.set_ylim(-0.5, 3.6)
ax.grid(True, alpha=0.15, color=COLOR_GRAY, linestyle=':')
ax.text(3.25, -0.15, '$x$', fontsize=11, ha='left', va='top', color=AXIS_COLOR)
ax.text(0.1, 3.55, '$y$', fontsize=11, ha='left', va='bottom', color=AXIS_COLOR)

Preventing Text & Line Overlap

When labels or legends cross plot lines, use bounding boxes to maintain legibility:

# For Annotations
ax.annotate('Important Point', 
            xy=(x_val, y_val), xytext=(20, 20),
            textcoords='offset points',
            arrowprops=dict(arrowstyle='->', color=COLOR_TEAL),
            # semi-transparent white box hides underlying lines
            bbox=dict(facecolor='white', edgecolor='none', alpha=0.8, pad=2))

# For Legends
ax.legend(frameon=True, framealpha=0.8, edgecolor='none')

Key rules:

• Skip 0 in tick lists to avoid clutter at the origin
• Use ax.text() for axis labels at tips, not set_xlabel/set_ylabel (those align to edges, not centered spines)
• Grid: dotted (':'), alpha 0.15, with AXIS_COLOR = '#444444' for spines (darker than COLOR_GRAY)

See Plot Templates Guide for complete examples.

Special Plot Types

• 3D Plots: See 3D Plotting Guide
• Polar Plots: See Polar Plots Guide for best practices and common pitfalls

Key Settings

# Base style
plt.style.use('seaborn-v0_8-white')
plt.rcParams['axes.spines.top'] = False
plt.rcParams['axes.spines.right'] = False
plt.rcParams['figure.dpi'] = 150

# Font sizes
plt.rcParams['font.size'] = 10
plt.rcParams['axes.labelsize'] = 11
plt.rcParams['axes.titlesize'] = 12

See Style Options Reference for complete configuration.

Files Structure

matplotlib-clean-plots/
├── templates/          # Copy these to start
│   ├── plot_template.py       - Basic 2D plot
│   ├── plot_3d_template.py    - 3D visualization
│   └── colors.py              - Color definitions
├── scripts/            # Setup utilities
│   ├── setup_template.sh      - Create from template (bash)
│   └── setup_template.ps1     - Create from template (PowerShell)
├── references/         # Detailed documentation
│   ├── color_palettes.md      - Color usage guide
│   ├── 3d_plotting_guide.md   - 3D visualization guide
│   ├── polar_plots_guide.md   - Polar coordinate plots guide
│   ├── plot_templates_guide.md - Complete examples
│   ├── style_options.md       - Style customization
│   └── export_guide.md        - Export best practices
└── examples/           # Demonstrations
    └── teal_palette_demo.py   - Color palette demo

Workflow

1. Create from template: ./scripts/setup_template.sh my_plot.py
2. Edit the file with your data and labels
3. Run: python3 my_plot.py
4. Verify the output image

Remember: Always verify output before claiming success!