Palettes¶

MSUthemes provides carefully designed color palettes based on Michigan State University's brand colors. These palettes are optimized for data visualization and work seamlessly with matplotlib, seaborn, and plotly.

Available Palettes¶

MSUthemes includes four built-in palettes:

Palette	Type	Colors	Best For
`msu_seq`	Sequential	9	Continuous data, heatmaps
`msu_div`	Diverging	11	Data with a meaningful midpoint
`msu_qual1`	Qualitative	5	Categorical data (main)
`msu_qual2`	Qualitative	5	Categorical data (alternative)

Using Palettes¶

Getting a Palette¶

from msuthemes import get_palette

# Get the sequential palette
seq_palette = get_palette('msu_seq')

# Get colors as hex codes
colors = seq_palette.as_hex()
print(colors)
# ['#18453b', '#1f5a4d', '#277060', ..., '#b8e6dd', '#d9f2ed']

Listing Available Palettes¶

from msuthemes import list_palettes

palettes = list_palettes()
print(palettes)
# ['msu_seq', 'msu_div', 'msu_qual1', 'msu_qual2']

Direct Palette Access¶

You can also import palettes directly:

from msuthemes import msu_seq, msu_div, msu_qual1, msu_qual2

# Use directly
colors = msu_qual1.as_hex()

Palette Formats¶

Hex Colors¶

Get colors as hex strings (default):

from msuthemes import msu_qual1

colors = msu_qual1.as_hex()
print(colors)
# ['#18453b', '#ff6e1b', '#008183', '#ffb81c', '#6e005f']

RGB Colors¶

Get colors as RGB tuples:

from msuthemes import msu_qual1

colors = msu_qual1.as_rgb()
print(colors)
# [(24, 69, 59), (255, 110, 27), (0, 129, 131), (255, 184, 28), (110, 0, 95)]

Matplotlib Colormap¶

Convert to matplotlib colormap for use with continuous data:

import matplotlib.pyplot as plt
import numpy as np
from msuthemes import msu_seq

# Create colormap
cmap = msu_seq.as_matplotlib_cmap()

# Use in a plot
data = np.random.rand(10, 10)
plt.imshow(data, cmap=cmap)
plt.colorbar()
plt.show()

Seaborn Palette¶

Convert to seaborn palette:

import seaborn as sns
from msuthemes import msu_qual1

# Create seaborn palette
palette = msu_qual1.as_seaborn_palette()

# Use in a plot
sns.boxplot(data=data, palette=palette)
plt.show()

Sequential Palette¶

The sequential palette (msu_seq) is ideal for continuous data that progresses from low to high values.

import matplotlib.pyplot as plt
import numpy as np
from msuthemes import msu_seq, theme_msu

# Apply MSU theme
theme_msu()

# Create heatmap
data = np.random.rand(10, 10)
plt.imshow(data, cmap=msu_seq.as_matplotlib_cmap())
plt.colorbar(label='Value')
plt.title('Sequential Palette Example')
plt.show()

Reversed Sequential¶

from msuthemes import get_palette

# Get reversed sequential palette
seq_rev = get_palette('msu_seq', reverse=True)
colors = seq_rev.as_hex()

Diverging Palette¶

The diverging palette (msu_div) is ideal for data with a meaningful midpoint (e.g., correlation coefficients, differences from a baseline).

import matplotlib.pyplot as plt
import numpy as np
from msuthemes import msu_div, theme_msu

# Apply MSU theme
theme_msu()

# Create diverging heatmap
data = np.random.randn(10, 10)  # Data centered around 0
plt.imshow(data, cmap=msu_div.as_matplotlib_cmap(),
           vmin=-2, vmax=2, aspect='auto')
plt.colorbar(label='Deviation')
plt.title('Diverging Palette Example')
plt.show()

Qualitative Palettes¶

Qualitative palettes (msu_qual1, msu_qual2) are ideal for categorical data with no inherent order.

Main Qualitative Palette (msu_qual1)¶

import matplotlib.pyplot as plt
from msuthemes import msu_qual1, theme_msu

# Apply MSU theme
theme_msu()

# Create bar chart
categories = ['A', 'B', 'C', 'D', 'E']
values = [23, 45, 56, 78, 32]

plt.bar(categories, values, color=msu_qual1.as_hex())
plt.title('Qualitative Palette Example')
plt.show()

Alternative Qualitative Palette (msu_qual2)¶

import matplotlib.pyplot as plt
from msuthemes import msu_qual2, theme_msu

# Apply MSU theme
theme_msu()

# Create line chart
x = range(5)
for i, color in enumerate(msu_qual2.as_hex()):
    y = [val + i * 2 for val in x]
    plt.plot(x, y, color=color, linewidth=2,
             label=f'Series {i+1}')

plt.legend()
plt.title('Alternative Qualitative Palette')
plt.show()

Palette Customization¶

Specifying Number of Colors¶

For continuous palettes (sequential and diverging), you can specify the number of colors through interpolation:

from msuthemes import get_palette

# Get 5 colors from sequential palette
seq_5 = get_palette('msu_seq', n_colors=5)
print(seq_5.as_hex())

# Get 20 colors for finer gradation
seq_20 = get_palette('msu_seq', n_colors=20)
print(len(seq_20.as_hex()))  # 20

Reversing Palettes¶

All palettes can be reversed:

from msuthemes import get_palette

# Normal palette
normal = get_palette('msu_seq')

# Reversed palette
reversed_pal = get_palette('msu_seq', reverse=True)

Integration with Visualization Libraries¶

Matplotlib¶

import matplotlib.pyplot as plt
from msuthemes import msu_seq

# As colormap
cmap = msu_seq.as_matplotlib_cmap()
plt.imshow(data, cmap=cmap)

# As discrete colors
colors = msu_seq.as_hex()
plt.bar(x, y, color=colors)

Seaborn¶

import seaborn as sns
from msuthemes import msu_qual1

# As palette
palette = msu_qual1.as_seaborn_palette()
sns.boxplot(data=data, palette=palette)

# Set as default palette
sns.set_palette(palette)

Plotly¶

import plotly.graph_objects as go
from msuthemes import msu_qual1

# Use hex colors directly
colors = msu_qual1.as_hex()
fig = go.Figure(data=[
    go.Bar(x=['A', 'B', 'C'],
           y=[1, 2, 3],
           marker_color=colors)
])
fig.show()

Advanced Usage¶

Creating Custom Palettes¶

You can create your own palettes using the MSUPalette class:

from msuthemes.palettes import MSUPalette

# Create a custom qualitative palette
custom_colors = ['#18453b', '#ff6e1b', '#008183']
custom_palette = MSUPalette(
    name='my_custom',
    colors=custom_colors,
    palette_type='qual'
)

# Use it
hex_colors = custom_palette.as_hex()

Accessing Palette Objects¶

All palettes are stored in a dictionary:

from msuthemes.palettes import MSU_PALETTES

# Access any palette
seq_palette = MSU_PALETTES['msu_seq']
print(seq_palette.name)
print(seq_palette.palette_type)
print(seq_palette.as_hex())

Best Practices¶

Sequential palettes: Use for ordered data (e.g., temperature, population)
Diverging palettes: Use for data with a meaningful midpoint (e.g., profit/loss, above/below average)
Qualitative palettes: Use for categorical data (e.g., species, departments, products)
Color count: Use 5-7 colors for qualitative data; humans struggle to distinguish more
Accessibility: Test your visualizations for colorblind accessibility