dartwork-mpl: matplotlib utilities and assets library engineered by dartwork

dartwork-mpl is a sophisticated utility collection designed to elevate matplotlib visuals to publication-level elegance with added convenience features. It is being developed to resolve personal inconveniences in visualization using matplotlib and to provide more intuitive customization especially to beginners.

Features

dartwork-mpl enriches plotting experience with:

• Enhanced Aesthetics: Apply our curated themes to make your charts visually appealing.
• Easy Customization: Effortlessly adjust plot styles to fit your publication's needs.
• Advanced Color System: Use custom colors with simple prefixes (dm., tw.) and extended Tailwind CSS palette.
• Streamlined Workflow: Simplify your plotting code with our intuitive interface, saving time and reducing complexity.
• Publication-Ready Layout: Automatic layout optimization with simple_layout() for professional results.

Getting Started

Installation

Using uv (Recommended)

uv is a fast Python package installer and resolver. To install dartwork-mpl with uv:

# Add to your project
uv add git+https://github.com/dartwork-repo/dartwork-mpl

# Or install directly
uv pip install git+https://github.com/dartwork-repo/dartwork-mpl

Using pip

pip install git+https://github.com/dartwork-repo/dartwork-mpl

Quick Start

After installation, import and apply a style preset:

import matplotlib.pyplot as plt
import dartwork_mpl as dm

# Apply scientific publication style
dm.style.use('scientific')

# For Korean text support
dm.style.use('scientific-kr')

Core Features

Style Management

• Preset Styles: Ready-to-use presets for scientific papers, presentations, and reports
• Flexible Customization: Combine individual styles for fine-grained control
• Korean Language Support: Built-in Korean font support with -kr presets

Color System

• dartwork-mpl Colors: Custom color palette with oc.red5, oc.blue2, etc.
• Tailwind CSS Integration: Full Tailwind palette with tw.blue500, tw.gray200, etc.
• Color Utilities: Mix colors and apply pseudo-transparency

Layout & Utilities

• Smart Layout Optimization: simple_layout() replaces tight_layout() with better control
• Unit Conversion: cm2in() for precise figure sizing
• Multi-format Export: Save figures in SVG, PNG, PDF, and EPS simultaneously
• Font Utilities: Relative font size (fs()) and weight (fw()) adjustments

Visualization Tools

• Colormap Explorer: Preview and classify colormaps by type
• Color Palette Viewer: Display all available colors with names
• Font Gallery: Preview available fonts

Example Usage

import numpy as np
import matplotlib.pyplot as plt
import dartwork_mpl as dm

# Set style
dm.style.use('scientific')

# Create figure with precise sizing (single-column paper figure)
fig = plt.figure(figsize=(dm.cm2in(9), dm.cm2in(7)), dpi=200)

# Set up layout
gs = fig.add_gridspec(nrows=1, ncols=1,
                      left=0.17, right=0.95,
                      top=0.95, bottom=0.17)
ax = fig.add_subplot(gs[0, 0])

# Plot with custom colors
x = np.linspace(0, 10, 100)
ax.plot(x, np.sin(x), c='oc.red5', lw=0.7, label='Sin')
ax.plot(x, np.cos(x), c='tw.blue500', lw=0.7, label='Cos')

# Customize
ax.set_xlabel('X value')
ax.set_ylabel('Y value')
ax.legend(fontsize=dm.fs(-1))

# Optimize layout and save
dm.simple_layout(fig)
dm.save_and_show(fig)  # Display in Jupyter

# Export in multiple formats
dm.save_formats(fig, 'output/figure',
                formats=('svg', 'png', 'pdf', 'eps'),
                dpi=600)

Documentation

📚 Full Documentation - Complete Sphinx documentation with:

• Usage Guide - Comprehensive guide to all features
• Example Gallery - Interactive examples with code and plots
• Color System - Full-width Colors and Colormaps reference
• API Reference - Detailed API documentation

Example Gallery

Explore our comprehensive example gallery featuring:

• Basic Usage - Getting started with dartwork-mpl
• Color System - Custom colors and Tailwind CSS integration
• Layout Optimization - Advanced layout control with simple_layout()
• Scientific Figures - Multi-panel publication-ready plots
• Statistical Plots - Probability density, violin, and box plots
• Advanced Visualizations - Heatmaps, contours, streamplots, and 3D plots

Each example includes complete source code and rendered output.

Available Presets

Preset	Description
scientific	Small fonts for academic papers
presentation	Large fonts for presentations
investment	Style for investment reports
scientific-kr	Scientific style with Korean support
presentation-kr	Presentation style with Korean support
investment-kr	Investment style with Korean support

Reporting Issues

Encountered a bug or have a feature request? Please open an issue through our GitHub issue tracker. We appreciate your feedback and contributions.