plotting_utils

A Python package providing elegant wrappers around matplotlib and seaborn functions to make scientific plotting easier and more consistent.

Features

🎨 Color Palettes

• Custom color schemes: ten_godisnot, darjeeling, fantastic_fox
• Palette creation utilities: create_palette() for generating custom color schemes

📊 Plotting Functions

High-level plotting functions with sensible defaults:

• Basic plots: scatter(), bar(), box(), strip(), violin()
• Distribution plots: dist(), counts_plot()
• Specialized plots: heatmap(), dotplot(), volcano(), stem_plot(), rank_plot()
• Statistical plots: Built-in support for statistical annotations with add_wilcox()

🛠 Utility Functions

• Styling: set_rcParams() for Nature journal-style settings
• Plot enhancement: format_ax(), add_legend(), add_cbar()
• File operations: save_best_pdf_quality(), make_folder()
• Performance: Timer class for benchmarking

Installation

pip install plotting_utils

Quick Start

import plotting_utils as plu
import matplotlib.pyplot as plt
import pandas as pd

# Set journal-style parameters
plu.set_rcParams()

# Create a scatter plot with custom styling
fig, ax = plt.subplots(figsize=(6, 4))
plu.scatter(x, y, c=plu.darjeeling[0], ax=ax)
plu.format_ax(ax, xlabel='X values', ylabel='Y values')

# Save with best quality
plu.save_best_pdf_quality(fig, 'my_plot.pdf')

Dependencies

• matplotlib
• seaborn
• statannotations
• textalloc
• joblib
• scikit-learn

Release History

0.1.4 (2025-09-11)

• Enhanced README with comprehensive documentation
• Added proper package metadata (long_description, author, classifiers)
• Improved PyPI package presentation

0.1.3

• Previous stable release

License

See LICENSE file for details.

Version

Current version: 0.1.4