DUNE Matplotlib Style Examples

Template repository for creating publication-quality plots for DUNE (Deep Underground Neutrino Experiment) using matplotlib with custom DUNE styling.

Overview

This repository provides examples and templates for creating consistent, publication-ready plots following DUNE collaboration standards. It includes:

• Custom DUNE matplotlib style - Professional styling for scientific plots
• Example scripts - Demonstrations of common plot types
• Template structure - Organized directory layout for your analysis

Quick Start

Installation

1 Clone this repository:

git clone https://github.com/DUNE/matplotlib_dunestyle_examles.git
cd matplotlib_dunestyle_examles

2 Installation of required dependencies:

pip install -r requirements.txt

Running Examples

After having installed the dune_plot_style repo. Run all examples at once:

python run_examples.py

Or run individual examples:

python styles/dune_plot_style*/examples/example_lineplot.py
python styles/dune_plot_style*/examples/example_histogram.py
python styles/dune_plot_style*/examples/example_errorbar.py
python styles/dune_plot_style*/examples/example_2dplot.py
python styles/dune_plot_style*/examples/example_multipanel.py

Output plots will be saved in the plots/ directory.

Using DUNE Style in Your Plots

To use the DUNE style in your own scripts:

import os
import matplotlib.pyplot as plt

# Use DUNE style
import dunestyle.matplotlib as dunestyle

# Create your plot
fig, ax = plt.subplots()
ax.plot([1, 2, 3], [1, 4, 9])
ax.set_xlabel('x [units]')
ax.set_ylabel('y [units]')
ax.set_title('My DUNE Plot')

# Save with high resolution
plt.savefig('plots/my_plot.png', dpi=300, bbox_inches='tight')

Data Directory

Place your data files here. Supported formats:

• CSV (.csv)
• NumPy arrays (.npy, .npz)
• JSON (.json)
• HDF5 (.h5, .hdf5)
• ROOT files (.root)

Example Data Structure

data/
├── raw/           # Raw experimental data
├── processed/     # Processed data ready for plotting
└── simulated/     # Simulated/MC data

Loading Data in Your Scripts

import numpy as np
import pandas as pd

# Load CSV data
data = pd.read_csv('data/mydata.csv')

# Load numpy array
data = np.load('data/mydata.npy')

Plots Output Directory

Generated plots will be saved here automatically when you run the example scripts.

All plots are saved with:

• High resolution (300 DPI) for publication quality
• PNG format with tight bounding boxes
• Consistent DUNE styling

You can also save plots in other formats by changing the extension:

• .pdf - Vector format, ideal for publications
• .svg - Scalable vector graphics
• .eps - Encapsulated PostScript
• .jpg - Compressed raster format

DUNE Style Features

The DUNE matplotlib style includes:

• High-quality output: 300 DPI for publications
• Professional fonts: Clear, readable sans-serif fonts
• Grid lines: Light grid for easy data reading
• Tick marks: Inward-pointing ticks on all sides
• Color scheme: Consistent, colorblind-friendly colors
• Legend styling: Clean, unobtrusive legends

Customization

You can customize the style by editing styles/dunestyle.mplstyle. Common modifications:

• Figure size: Change figure.figsize
• Font sizes: Adjust font.size, axes.labelsize, etc.
• Colors: Modify axes.prop_cycle
• Grid appearance: Edit grid.alpha, grid.linestyle

Contributing

To add new examples or improve existing ones:

1. Create your example script in the examples/ directory
2. Follow the naming convention: example_<description>.py
3. Include docstrings and comments
4. Make sure output is saved to plots/

References

• DUNE Plot Style Repository
• Matplotlib Documentation
• DUNE Collaboration

License

See LICENSE file for details.