Oxford Matplotlib Theme

Official Oxford University colors and styling for Matplotlib visualizations.

Create publication-quality figures with official Oxford University branding for academic papers, presentations, and reports.

Author: Sami Adnan Nuffield Department of Primary Care Health Sciences University of Oxford

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Features

Official Oxford Branding

• 56 official Oxford University brand colors (35 brand + 21 thesis colors)
• Compliant with Oxford Brand Guidelines
• Oxford Blue (#002147) throughout axes, labels, and text

13 Curated Color Palettes

• General purpose: PRIMARY, PROFESSIONAL, VIBRANT, PASTEL
• Data-specific: DIVERGING, SEQUENTIAL_BLUE
• Themed: HEALTH, TRADITIONAL, CONTEMPORARY, CELEBRATORY, CORPORATE, INNOVATIVE
• Academic: PHC_THESIS (27 colors for thesis work)

7 Pre-configured Presets

• default - Standard for academic papers
• presentation - Larger fonts for slides (1.4x)
• poster - Extra large fonts for posters (1.8x)
• colorblind - Accessible color palette
• minimal - Clean with grid background
• notebook - Optimized for Jupyter
• print - High contrast for printing

Journal-Ready Export

• Direct SVG/PNG/PDF export (no browser required!)
• Pre-configured settings for Nature, PLOS, BMJ, Lancet
• 300 DPI publication quality

Zero Browser Dependencies

• Unlike Plotly themes, no kaleido or Chrome required
• Faster rendering and export
• Smaller file sizes for vector formats

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Installation

# Clone the repository
git clone https://github.com/sami5001/oxford-matplotlib-theme.git
cd oxford-matplotlib-theme

# Install in editable mode
pip install -e .

# Or install with all dependencies
pip install -e ".[all]"

Dependencies

Core (required):

• matplotlib >= 3.5.0
• numpy >= 1.21.0

Examples (optional):

• pandas >= 1.3.0
• scikit-learn >= 1.0.0
• scipy >= 1.7.0

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Quick Start

Basic Usage

import matplotlib.pyplot as plt
from oxford_matplotlib_theme import apply_oxford_theme

# Apply Oxford theme globally
apply_oxford_theme()

# Create your plot
plt.plot([1, 2, 3], [1, 4, 9])
plt.title('My Oxford Plot')
plt.xlabel('X-axis')
plt.ylabel('Y-axis')
plt.show()

Using the Convenience Function

from oxford_matplotlib_theme import oxford_figure

# Create pre-styled figure
fig, ax = oxford_figure()
ax.plot([1, 2, 3], [1, 4, 9])
ax.set_title('My Oxford Plot')
plt.show()

Using Presets

from oxford_matplotlib_theme import apply_preset

# Larger fonts for presentations
apply_preset('presentation')

# Create plots - automatically uses larger fonts
fig, ax = plt.subplots()
ax.plot([1, 2, 3], [1, 4, 9])
plt.show()

Custom Colors

from oxford_matplotlib_theme import get_palette, OXFORD_COLORS

# Use specific Oxford colors
plt.plot(x, y, color=OXFORD_COLORS['coral'])

# Get a color palette
colors = get_palette('vibrant', n_colors=5)
for i, color in enumerate(colors):
    plt.plot(x, data[i], color=color, label=f'Series {i+1}')

Export for Journal Submission

from oxford_matplotlib_theme import save_oxford_figure, get_journal_preset

# Get Nature journal preset
preset = get_journal_preset('nature')

# Create figure with journal size
fig, ax = oxford_figure(figsize=preset['figsize'])
ax.plot(x, y)
ax.set_title('My Research')

# Save with journal settings
save_oxford_figure(fig, 'figure1', format=preset['format'], dpi=preset['dpi'])
# Creates: figure1.svg at 300 DPI

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Color Palettes

All palettes use official Oxford University brand colors:

Palette	Colors	Use Case
PRIMARY	10	General purpose, most versatile
PROFESSIONAL	6	Business/academic presentations
VIBRANT	7	Eye-catching visualizations
PASTEL	7	Softer, subtle visualizations
DIVERGING	5	Data with meaningful center point
SEQUENTIAL_BLUE	5	Continuous/ordered data
HEALTH	5	Medical/PHC department specific
TRADITIONAL	6	Heritage and stability
CONTEMPORARY	6	Modern and clean
CELEBRATORY	6	Festive and bright
CORPORATE	6	Professional and formal
INNOVATIVE	6	Tech-focused and forward-looking
PHC_THESIS	27	Comprehensive thesis palette

Access palettes:

from oxford_matplotlib_theme import get_palette, ColorPalettes

# Get palette as list
colors = get_palette('vibrant')

# Access palette class
all_professional = ColorPalettes.PROFESSIONAL

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Available Presets

Preset	Description	Font Scale
default	Standard for academic papers	1.0
presentation	Larger fonts for slides	1.4
poster	Extra large for conference posters	1.8
colorblind	Accessible color palette	1.0
minimal	Clean with grid background	1.0
notebook	Optimized for Jupyter	1.1
print	High contrast for printing	1.0

from oxford_matplotlib_theme import apply_preset, list_presets

# See all presets
list_presets()

# Use a preset
apply_preset('presentation')

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Journal Export Presets

Built-in settings for major journals:

Journal	Figure Size (inches)	DPI	Format
Nature (single)	3.5 × 2.5	300	SVG
Nature (double)	7.0 × 5.0	300	SVG
PLOS	6.83 × 5.0	300	TIFF
BMJ	3.27 × 2.5	300	EPS
Lancet	3.27 × 2.5	300	TIFF

from oxford_matplotlib_theme import get_journal_preset

preset = get_journal_preset('nature')
# Returns: {'figsize': (3.5, 2.5), 'dpi': 300, 'format': 'svg'}

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Examples

Line Plot with Multiple Series

import numpy as np
from oxford_matplotlib_theme import apply_oxford_theme

apply_oxford_theme()

x = np.linspace(0, 10, 100)
fig, ax = plt.subplots(figsize=(10, 6))

ax.plot(x, np.sin(x), label='sin(x)', linewidth=2)
ax.plot(x, np.cos(x), label='cos(x)', linewidth=2)

ax.set_xlabel('Time (s)')
ax.set_ylabel('Amplitude')
ax.set_title('Trigonometric Functions')
ax.legend()
ax.grid(True, alpha=0.3)
plt.show()

Bar Chart with Oxford Colors

from oxford_matplotlib_theme import get_palette

categories = ['A', 'B', 'C', 'D']
values = [23, 45, 56, 78]
colors = get_palette('professional', n_colors=4)

fig, ax = plt.subplots()
ax.bar(categories, values, color=colors[0])
ax.set_ylabel('Values')
ax.set_title('Category Comparison')
ax.grid(True, axis='y', alpha=0.3)
plt.show()

ROC Curve for Clinical ML

from sklearn.metrics import roc_curve, auc
from oxford_matplotlib_theme import apply_oxford_theme, get_palette

apply_oxford_theme()
colors = get_palette('primary', n_colors=3)

# Assuming you have y_true and y_pred
fpr, tpr, _ = roc_curve(y_true, y_pred)
roc_auc = auc(fpr, tpr)

fig, ax = plt.subplots(figsize=(8, 8))
ax.plot(fpr, tpr, color=colors[0], linewidth=2.5,
       label=f'ROC curve (AUC = {roc_auc:.3f})')
ax.plot([0, 1], [0, 1], 'k--', linewidth=1.5, alpha=0.5)

ax.set_xlabel('False Positive Rate')
ax.set_ylabel('True Positive Rate')
ax.set_title('ROC Curve - Clinical Prediction Model')
ax.legend(loc='lower right')
ax.grid(True, alpha=0.3)
plt.show()

More Examples

See the examples/ directory for complete runnable examples:

• basic_usage.py - 6 fundamental plot types
• clinical_ml.py - ROC, PR, calibration curves for healthcare research
• publication_figures.py - Multi-panel journal-ready figures
• migration_comparison.py - Plotly to Matplotlib migration guide

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Advanced Usage

Custom Color Cycles

from oxford_matplotlib_theme import apply_oxford_theme

# Use specific colors in custom order
custom_colors = ['coral', 'aqua', 'oxford_blue', 'vivid_green']
apply_oxford_theme(color_cycle=custom_colors)

Font Scaling

# Make all fonts 50% larger
apply_oxford_theme(font_scale=1.5)

# For presentations
apply_oxford_theme(font_scale=1.4)

# For posters
apply_oxford_theme(font_scale=1.8)

Adding Branding

from oxford_matplotlib_theme import add_oxford_branding

fig, ax = oxford_figure()
ax.plot([1, 2, 3], [1, 4, 9])

# Add watermark
add_oxford_branding(
    fig,
    add_watermark=True,
    watermark_text='University of Oxford',
    position='bottom_right',
    opacity=0.5
)
plt.show()

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Migration from oxford-plotly-theme

If you're migrating from the Plotly version:

Plotly	Matplotlib
create_oxford_figure()	oxford_figure() returns (fig, ax)
fig.add_trace(go.Scatter(...))	ax.plot(...)
get_color_palette()	get_palette()
fig.write_image() (needs kaleido)	fig.savefig() (native)
Manual sizing	get_journal_preset()

Key benefits of matplotlib version:

• No browser/kaleido dependency
• Faster export
• Built-in presets
• Better LaTeX integration
• Native format support

See examples/migration_comparison.py and docs/MIGRATION_GUIDE.md for detailed migration instructions.

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Documentation

• API Reference: See docs/API_REFERENCE.md for complete function documentation
• Migration Guide: See docs/MIGRATION_GUIDE.md for Plotly to Matplotlib migration
• Examples: See examples/ directory for runnable code

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Contributing

This theme is maintained for internal use at the University of Oxford. For bug reports or feature requests, please open an issue on GitHub.

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License

MIT License - Copyright (c) 2025 Sami Adnan

Based on Oxford University Brand Guidelines

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Acknowledgments

• Oxford University Communications Office for brand guidelines
• Inspired by oxford-plotly-theme
• Built for clinical prediction research at Oxford Primary Health Care

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Contact

Sami Adnan Nuffield Department of Primary Care Health Sciences University of Oxford

For questions about Oxford brand guidelines, contact the Oxford Communications Office.