oxford-colors 1.2.0

Oxford University color palette for matplotlib

Oxford Colors

A Python library providing Oxford University's official color palette for matplotlib visualizations.

Installation

pip install oxford_colors

Quick Start

import matplotlib.pyplot as plt
from oxford_colors import oxford_style

# Apply Oxford colors with the context manager
with oxford_style():
    fig, ax = plt.subplots()
    ax.plot([1, 2, 3], [1, 4, 9])
    ax.plot([1, 2, 3], [1, 2, 3])
    plt.show()

Features

• Official Oxford Colors: Complete palette including primary, secondary, neutral, and metallic colors
• Context Manager: Temporary color styling that automatically restores previous rcParams
• Custom Color Selection: Choose specific colors from the palette
• Multiple Plot Types: Works with line plots, bar charts, scatter plots, histograms, and more
• Matplotlib Integration: Seamless integration with matplotlib's color cycle
• Color Metadata: Access hex, RGB, CMYK, and Pantone values for every color

Available Colors

Primary

Key	Name	Hex
oxford_blue	Oxford blue	#002147

Secondary

Key	Name	Hex
oxford_mauve	Oxford mauve	#776885
oxford_peach	Oxford peach	#E08D79
oxford_potters_pink	Oxford potters pink	#ED9390
oxford_dusk	Oxford dusk	#C4A29E
oxford_lilac	Oxford lilac	#D1BDD5
oxford_sienna	Oxford sienna	#994636
oxford_ccb_red	Oxford Red	#AA1A2D
oxford_plum	Oxford plum	#7F055F
oxford_coral	Oxford coral	#FE615A
oxford_lavender	Oxford lavender	#D4CDF4
oxford_orange	Oxford orange	#FB5607
oxford_pink	Oxford pink	#E6007E
oxford_green	Oxford green	#426A5A
oxford_ocean_grey	Oxford ocean grey	#789E9E
oxford_yellow_ochre	Oxford yellow ochre	#E2C044
oxford_cool_grey	Oxford cool grey	#E4F0EF
oxford_sky_blue	Oxford sky blue	#B9D6F2
oxford_sage_green	Oxford sage green	#A0AF84
oxford_viridian	Oxford viridian	#15616D
oxford_royal_blue	Oxford royal blue	#1D42A6
oxford_aqua	Oxford aqua	#00AAB4
oxford_vivid_green	Oxford vivid green	#65E5AE
oxford_lime_green	Oxford lime green	#95C11F
oxford_cerulean_blue	Oxford cerulean blue	#49B6FF
oxford_yellow	Oxford lemon yellow	#F7EF66

Neutrals

Key	Name	Hex
oxford_charcoal	Oxford charcoal	#211D1C
oxford_ash_grey	Oxford ash grey	#61615F
oxford_umber	Oxford umber	#89827A
oxford_stone_grey	Oxford stone grey	#D9D8D6
oxford_shell_grey	Oxford shell grey	#F1EEE9
oxford_off_white	Oxford off white	#F2F0F0

Metallic

Key	Name	Hex
oxford_gold	Oxford gold	#C9A14B
oxford_silver	Oxford silver	#BFC0C0

API Reference

Context Manager

oxford_style(colors=None)

Apply Oxford colors temporarily. Restores previous matplotlib rcParams on exit.

from oxford_colors import oxford_style

# Default Oxford palette
with oxford_style():
    plt.plot(...)

# Specific colors only
with oxford_style(colors=["oxford_blue", "oxford_pink", "oxford_green"]):
    plt.plot(...)

Also supports subscript access to look up hex values:

oxford_style["oxford_blue"]   # -> '#002147'
oxford_style["oxford_pink"]   # -> '#E6007E'

Automatic Graph Backups

If the GRAPH_BACKUP environment variable is set, any plt.savefig() call inside the context manager will automatically save a backup copy to that directory in addition to the original destination.

export GRAPH_BACKUP=~/graph_backups

with oxford_style():
    plt.plot([1, 2, 3], [1, 4, 9])
    plt.savefig("output/my_plot.png")
    # saves to: output/my_plot.png  (as normal)
    # saves to: ~/graph_backups/my_plot.png  (backup)

• The backup directory is created automatically if it does not exist.
• Backup uses the same format and options as the original savefig call.
• If GRAPH_BACKUP is not set, behaviour is unchanged.
• Backups are only made when the destination is a file path; passing a file object skips the backup.

Color Accessors

from oxford_colors import hex, rgb, cmyk, pantone, as_rgb_norm

hex("oxford_blue")        # -> '#002147'
rgb("oxford_blue")        # -> (0, 33, 71)
cmyk("oxford_blue")       # -> (100, 87, 42, 51)
pantone("oxford_blue")    # -> 'Pantone 282'
as_rgb_norm("oxford_blue")  # -> (0.0, 0.129, 0.278)  (normalized 0..1 for matplotlib)

Color Discovery

from oxford_colors import get_names, OXFORD_COLORS, COLORS_BY_GROUP

# All color keys (sorted)
get_names()

# Colors in a specific group: 'primary', 'secondary', 'neutrals', 'metallic'
get_names("secondary")

# Full Color objects (with .name, .hex, .rgb, .cmyk, .pantone attributes)
OXFORD_COLORS["oxford_blue"].name     # -> 'Oxford blue'
OXFORD_COLORS["oxford_blue"].pantone  # -> 'Pantone 282'

# Colors grouped by category
COLORS_BY_GROUP["primary"]   # -> ['oxford_blue']
COLORS_BY_GROUP["metallic"]  # -> ['oxford_gold', 'oxford_silver']

Matplotlib Helpers

from oxford_colors import mpl_palette, mpl_cycler, set_plt_colors, DEFAULT_PALETTE
import matplotlib.pyplot as plt

# List of hex strings for a set of colors
mpl_palette(["oxford_blue", "oxford_pink", "oxford_green"])

# Default palette (all colors ordered by "Oxford-ness")
DEFAULT_PALETTE

# matplotlib cycler object
plt.rcParams['axes.prop_cycle'] = mpl_cycler(["oxford_blue", "oxford_pink"])

# Convenience: set colors on an existing plt instance
set_plt_colors(plt, ["oxford_blue", "oxford_pink", "oxford_green"])

Usage Examples

Basic Line Plot

import matplotlib.pyplot as plt
from oxford_colors import oxford_style

with oxford_style():
    fig, ax = plt.subplots()
    ax.plot([1, 2, 3, 4], [1, 4, 2, 8], label='Series 1')
    ax.plot([1, 2, 3, 4], [2, 3, 4, 5], label='Series 2')
    ax.legend()
    plt.show()

Bar Chart

import matplotlib.pyplot as plt
from oxford_colors import oxford_style

with oxford_style():
    fig, ax = plt.subplots()
    ax.bar(['A', 'B', 'C', 'D'], [23, 45, 56, 78])
    plt.show()

Scatter Plot

import numpy as np
import matplotlib.pyplot as plt
from oxford_colors import oxford_style

with oxford_style():
    fig, ax = plt.subplots()
    ax.scatter(np.random.randn(50), np.random.randn(50), alpha=0.7)
    plt.show()

Multiple Subplots

import matplotlib.pyplot as plt
import numpy as np
from oxford_colors import oxford_style

with oxford_style():
    fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2, 2, figsize=(10, 8))

    x = np.linspace(0, 10, 100)
    ax1.plot(x, np.sin(x))
    ax1.set_title('Sine Wave')

    ax2.scatter(np.random.randn(50), np.random.randn(50))
    ax2.set_title('Scatter Plot')

    ax3.bar(['A', 'B', 'C'], [10, 20, 15])
    ax3.set_title('Bar Chart')

    ax4.hist(np.random.normal(0, 1, 1000), bins=30)
    ax4.set_title('Histogram')

    plt.tight_layout()
    plt.show()

Setting Colors Globally

import matplotlib.pyplot as plt
from oxford_colors import set_plt_colors

set_plt_colors(plt)  # applies default Oxford palette globally

fig, ax = plt.subplots()
ax.plot([1, 2, 3], [1, 4, 9])
plt.show()

Development

Setup

git clone https://github.com/HakamAtassi/oxford_colors.git
cd oxford_colors
pip install -e ".[dev]"

Running Tests

pytest

Building the Package

python -m build

Gallery

The examples/gallery.py script generates a comprehensive gallery of plots using Oxford colors:

python -m oxford_colors.examples.gallery

License

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

Contributing

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

Color References

The colors in this library are based on Oxford University's official brand guidelines and include:

• Official Pantone references where available
• CMYK values for print applications
• RGB values for digital use
• Hex codes for web applications

Changelog

Version 1.2.0

• oxford_style context manager now automatically backs up plots to the directory set in the GRAPH_BACKUP environment variable when plt.savefig() is called

Version 1.1.0

• Extended secondary palette (added oxford_cool_grey, oxford_sky_blue, oxford_sage_green, oxford_viridian, oxford_royal_blue, oxford_aqua, oxford_vivid_green, oxford_lime_green, oxford_cerulean_blue, oxford_yellow)
• Added cmyk, pantone, as_rgb_norm, get_names, mpl_cycler, set_plt_colors helpers
• Added COLORS_BY_GROUP for group-based color discovery
• oxford_style now supports subscript color lookup

Version 1.0.0

• Initial release
• Core Oxford color palette
• Context manager for temporary styling
• Comprehensive test suite
• PyPI distribution ready