journalplots 0.1.21

A package for consistent journal-ready matplotlib plotting styles

JournalPlots

A Python package for creating publication-ready matplotlib figures with consistent styling. Designed to make your scientific visualizations look professional and meet journal requirements.

Features

• 🎨 Colorblind-friendly color palettes
• 📏 Journal-appropriate font sizes and styles
• 🖼️ High-resolution output settings (300 DPI)
• 🎯 Easy-to-use styling functions
• 📐 Consistent figure dimensions

Installation

You can install the package directly from source:

git clone https://github.com/joemans3/journalplots.git
cd journalplots
pip install .

or use PyPI package:

pip install JournalPlots

Usage

Basic Usage

import matplotlib.pyplot as plt
from journalplots import set_style, apply_style, cb_colors

# Set the global style
set_style(font_scale=1.0)

# Create your plot
fig, ax = plt.subplots()
ax.plot([1, 2, 3], [1, 2, 3], color=cb_colors()['red'], label='Data')
apply_style(ax, title='My Plot', xlabel='X', ylabel='Y')
plt.show()

Available Colors

The package includes a colorblind-friendly palette:

from journalplots import cb_colors

red = cb_colors()['red']

# Available colors:
# - COLORBLIND_COLORS = {
# -    "blue": "#377eb8",
# -    "orange": "#ff7f00",
# -    "green": "#4daf4a",
# -    "pink": "#f781bf",
# -    "brown": "#a65628",
# -    "purple": "#984ea3",
# -    "gray": "#999999",
# -    "red": "#e41a1c",
# -    "yellow": "#dede00",
# }

# Using set_style, subsequent plots cycle through these colorblind colors rather than the Pyplot defaults.

Customizing Sizes

You can adjust various size parameters:

from journalplots import SIZES

# Available size presets:
# - SIZES['figure']       # Default figure size in inches
# - SIZES['font']         # Font sizes (tiny, small, medium, large, xlarge)
# - SIZES['linewidth']    # Default line width
# - SIZES['markersize']   # Default marker size
# - SIZES['tick_length']  # Length of axis ticks

Style Functions

set_style()

Sets global matplotlib parameters for consistent styling:

set_style(font_scale=1.0)  # Adjust font_scale to make all text larger or smaller

apply_style()

Apply styling to a specific axes object:

apply_style(ax,
           title='My Plot',     # Optional plot title
           xlabel='X',          # Optional x-axis label
           ylabel='Y',          # Optional y-axis label
           legend=True)         # Whether to show legend

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

License

This project is licensed under the MIT License - see the LICENSE file for details.