pltx - Enhanced Matplotlib for Scientific Visualization

A matplotlib wrapper with publication-quality styling and colorblind accessibility features.

---

I got bored to always create some custom formatting for my plots, run into reviewers asking for improving readability. I want just to import and use it with minor modifications. Here I made something one can install and have pretty looking plots ready to go (just some personal styling on top of matplotlib).

Credits to Mathieu Garrigues for Pasqal colormaps (pasqal, pasqal_contrast, pasqal_diverging).

Key Features

• Colorblind Accessible - Progressive line width variation distinguishes lines by thickness AND color
• Journal-Ready Presets - Nature, LaTeX, presentation, and poster styles in one function call
• Line Visibility - Optional outlines and centerlines for better contrast
• Drop-in Replacement - Works with all matplotlib plot types
• Auto Color Cycling - Intelligent palette management with intensity control

Example Gallery

Quick Start

For a new user, the fastest path is:

import pltx.pyplot as plt
plt.initialize_style()

Then use plt.plot_styled(...) (or regular matplotlib calls) and your figures get consistent defaults immediately.

import pltx.pyplot as plt
import numpy as np

# Enable colorblind-friendly progressive widths
plt.initialize_style(
    palette_name='pasqal',
    vary_linewidth=True,              # Lines get progressively thicker
    linewidth_progression_factor=1.3  # 30% increase per line
)

# Plot with automatic styling
x = np.linspace(0, 10, 100)
for i in range(5):
    plt.plot_styled(x, np.sin(x + i*0.5),
                   color_idx=i,
                   centerline=True,  # Add thin line on top
                   label=f'Line {i+1}')

plt.setup_axis(xlabel='x', ylabel='y', grid=True)
plt.legend()
plt.savefig('plot.png', dpi=300)

Pasqal Colormaps

Three custom Pasqal colormaps are auto-registered on import — no setup needed:

• pasqal — main brand colourmap (dark teal to bright green)
• pasqal_contrast — higher-contrast variant (purple to cyan)
• pasqal_diverging — diverging colourmap (blue-purple through white to teal-green)

# Use as palette for line plots
plt.initialize_style(palette_name='pasqal')

# Or use directly with any matplotlib function
ax.imshow(data, cmap='pasqal_diverging')

Regenerate these images with python examples/generate_readme_images.py.

Installation

pip install matplotlibx

# With optional seaborn support for extended palettes
pip install matplotlibx[all]

Key Features in Detail

1. Progressive Line Width (Colorblind Accessible)

Each line automatically gets thicker - distinguishable by both color and width:

plt.initialize_style(
    vary_linewidth=True,
    base_linewidth=2.0,
    linewidth_progression_factor=1.3  # 1.2=gentle, 1.3=moderate, 1.5=strong
)

Result: Line 0: 2.0pt -> Line 1: 2.6pt -> Line 2: 3.4pt -> Line 3: 4.4pt

2. Line Enhancements

# Outline (thick line behind)
plt.plot_styled(x, y, color_idx=0, outline=True)

# Centerline (thin line on top)
plt.plot_styled(x, y, color_idx=0, centerline=True)

# Both (maximum contrast)
plt.plot_styled(x, y, color_idx=0, outline=True, centerline=True)

3. Journal Presets

from pltx.rcparams import apply_style_preset

apply_style_preset('nature')        # Nature journal (Arial, 7-9pt, 3.5")
apply_style_preset('latex')         # LaTeX documents (Computer Modern, 10pt, usetex)
apply_style_preset('presentation')  # Slides (16-18pt, thick lines)
apply_style_preset('poster')        # Posters (24-28pt)

4. Works with All Plot Types

from pltx.colors import get_color

# Bar plots
colors = [get_color(i) for i in range(5)]
plt.bar(categories, values, color=colors)

# Scatter plots
plt.plot_styled(x, y, marker='o', linestyle='', color_idx=0)

# All matplotlib functions available!

Real-World Examples

Nature Journal Submission

from pltx.rcparams import apply_style_preset
import pltx.pyplot as plt

# Apply Nature style + accessibility
apply_style_preset('nature')
plt.initialize_style(
    vary_linewidth=True,
    base_linewidth=1.0,
    linewidth_progression_factor=1.3
)

# Single column figure
fig, ax = plt.subplots(figsize=(3.5, 2.6))

for i in range(4):
    plt.plot_styled(x, data[i],
                   color_idx=i,
                   centerline=True,  # Better in print
                   label=labels[i])

plt.setup_axis(xlabel='Time (s)', ylabel='Amplitude (a.u.)')
plt.legend()
plt.savefig('figure1.pdf', dpi=300)

LaTeX Document Figure

from pltx.rcparams import apply_style_preset
import pltx.pyplot as plt
import numpy as np

# Computer Modern fonts, usetex=True, amsmath loaded
apply_style_preset('latex')

fig, ax = plt.subplots()  # 5.5" x 3.4" (golden ratio)

x = np.linspace(0, 2 * np.pi, 200)
plt.plot_styled(x, np.sin(x), label=r'$\sin(x)$', ax=ax)
plt.plot_styled(x, np.cos(x), label=r'$\cos(x)$', ax=ax)

plt.setup_axis(ax, xlabel=r'$x$ [rad]', ylabel=r'$f(x)$',
               title=r'Trigonometric Functions')
ax.legend()
plt.savefig('figure.pdf')  # 300 DPI PDF, ready for \includegraphics

Documentation

• FULL_DOCUMENTATION.md - Complete API reference and detailed guide
• examples/ - Working examples (demo.py, showcase.py, simple_example.py)
• notebooks/new_user_mpl_vs_matplotlibx.ipynb - Side-by-side default matplotlib vs matplotlibx
• notebooks/showcase_pasqal.ipynb - Pasqal colormaps demo (heatmaps, swatches, line plots)
• notebooks/showcase_latex.ipynb - LaTeX preset demo (Computer Modern, math labels, multi-panel)

Quick Reference

Feature	Command
Progressive width	vary_linewidth=True
Outline	outline=True
Centerline	centerline=True
Nature style	apply_style_preset('nature')
LaTeX style	apply_style_preset('latex')
Pasqal colormaps	palette_name='pasqal' / 'pasqal_contrast' / 'pasqal_diverging'
Color cycling	color_idx=i
Axis setup	setup_axis(xlabel=..., ylabel=...)

Feature Comparison

Feature	matplotlib	pltx
Colorblind accessible	Manual	vary_linewidth=True
Line visibility	Complex code	outline=True
Journal styles	Research guidelines	One function call
Color palettes	Manual setup	Automatic

Progressive Width Factors

Factor	Increase	Best For
1.2	20%	8-10 lines
1.3	30%	4-6 lines (DEFAULT)
1.5	50%	2-4 lines

Style Presets

Preset	Fonts	Figure Size	Use Case
nature	Arial 7-9pt	3.5"x2.6"	Nature journal
latex	Computer Modern 9-11pt	5.5"x3.4"	LaTeX documents (usetex)
presentation	16-18pt	10"x6"	Slides
poster	24-28pt	12"x8"	Posters
default	12-13pt	6"x4"	General

Dependencies

Required:

• matplotlib >= 3.5.0
• numpy >= 1.20.0

Optional:

• seaborn >= 0.11.0 (for extended palettes; falls back to matplotlib colormaps)

Why pltx?

• Accessible - Works for colorblind viewers (~8% of males)
• Publication-Ready - Journal-specific presets
• Easy to Use - Drop-in replacement for matplotlib
• Flexible - Works with all plot types
• Well-Documented - Comprehensive guides and examples

Quick Tips

1. Always use vary_linewidth=True for multi-line plots
2. Choose factor based on number of lines (1.2 for many, 1.5 for few)
3. Test in grayscale to verify accessibility
4. Use presets to match your target medium
5. Combine features for maximum effect

Testing

The package includes a comprehensive test suite using pytest.

# Install test dependencies
pip install pytest

# Run all tests
pytest tests

The test suite covers color palette logic, style initialization, enhanced plotting functions, and style context management.

---

Version: 0.1.2 Python: 3.10+ Created: 2026-01-09 Author: Igor Sokolov

For complete documentation, see FULL_DOCUMENTATION.md