Plotting

A lightweight, high-level Python library for creating publication-ready scientific figures with consistent styling. Built on top of matplotlib, this package provides a simplified interface for generating plots that meet the standards of scientific journals.

Features

• Publication-ready styling: Clean, professional appearance with proper font embedding
• Consistent layout: Standardized spine positioning, grid styling, and tick formatting
• Flexible units: Support for both centimeters and inches for figure dimensions
• Cross-platform: Works on Linux, macOS, and Windows
• Minimal dependencies: Only requires matplotlib
• Easy customization: Simple font and size configuration

Installation

Install directly from GitHub:

pip install git+https://github.com/RDoerfel/plotting.git

Quick Start

from plotting import set_rc_params, get_figures
import numpy as np

# Configure publication-ready styling
set_rc_params(fontfamily="serif", small=8, medium=10, big=12)

# Create figure with subplots
fig, axs = get_figures(rows=2, cols=1, unit="cm", figwidth=10, figheight=15)

# Generate sample data
x = np.linspace(0, 10, 100)
y1 = np.sin(x)
y2 = np.cos(x)

# Plot data
axs[0].plot(x, y1, label='sin(x)')
axs[0].set_xlabel("x")
axs[0].set_ylabel("sin(x)")
axs[0].legend()

axs[1].plot(x, y2, 'r--', label='cos(x)')
axs[1].set_xlabel("x")
axs[1].set_ylabel("cos(x)")
axs[1].legend()

# Save with high quality
fig.savefig("scientific_plot.pdf", dpi=300, bbox_inches="tight")

API Reference

set_rc_params(fontfamily=None, small=8, medium=10, big=12)

Configure global matplotlib parameters for publication-ready plots.

Parameters:

• fontfamily (str, optional): Font family ('serif', 'sans-serif', 'monospace')
• small (int): Font size for ticks and legend (default: 8)
• medium (int): Font size for labels and default text (default: 10)
• big (int): Font size for titles (default: 12)

Example:

# Use serif fonts with larger text
set_rc_params(fontfamily="serif", small=10, medium=12, big=14)

get_figures(rows, cols, unit="cm", figwidth=10, figheight=8, sharex=False, sharey=False, **subplot_kw)

Create styled figure and axes with publication-ready formatting.

Parameters:

• rows (int): Number of subplot rows
• cols (int): Number of subplot columns
• unit (str): Units for dimensions ("cm" or "inch", default: "cm")
• figwidth (float): Figure width in specified units (default: 10)
• figheight (float): Figure height in specified units (default: 8)
• sharex (bool): Share x-axis across subplots (default: False)
• sharey (bool): Share y-axis across subplots (default: False)
• **subplot_kw: Additional arguments passed to plt.subplots()

Returns:

• fig (matplotlib.figure.Figure): Figure object
• axs (matplotlib.axes.Axes or array): Axes object(s)

Examples:

# Single plot
fig, ax = get_figures(1, 1, figwidth=8, figheight=6)

# Multiple subplots with shared x-axis
fig, axs = get_figures(3, 2, unit="inch", figwidth=8, figheight=10, sharex=True)

# Custom subplot parameters
fig, axs = get_figures(2, 2, subplot_kw={'facecolor': 'white'})

Styling Features

The library automatically applies several publication-ready styling features:

• Clean spines: Removes top and right spines, positions left and bottom spines with appropriate offsets
• Grid styling: Adds subtle horizontal grid lines for better readability
• Tick formatting: Consistent tick styling with proper direction and sizing
• Font embedding: Ensures fonts are properly embedded in PDF outputs (fonttype 42)
• High DPI: Default 300 DPI for crisp figures

Best Practices

1. Always call set_rc_params() first to ensure consistent styling across all figures
2. Use centimeters for dimensions when targeting specific journal requirements
3. Set explicit axis limits and ticks for precise control over the appearance
4. Save as PDF or PNG with bbox_inches="tight" for best quality

Requirements

• Python ≥ 3.10
• matplotlib ≥ 3.5.0

License

MIT License - see LICENSE file for details.

Development

This library is designed to be minimal and focused. For testing:

pytest test/ --cov=plotting

The package is tested across Python 3.10, 3.11, and 3.12 on Linux, macOS, and Windows.