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Quick Start Guide

This guide will get you started with MSUthemes in just a few minutes.

Your First MSU Plot

Let's create a simple plot with MSU branding:

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

# Apply MSU theme
theme_msu()

# Create data
x = np.linspace(0, 10, 100)
y = np.sin(x)

# Create plot
fig, ax = plt.subplots(figsize=(8, 6))
ax.plot(x, y, color=colors.MSU_GREEN, linewidth=2)
ax.set_xlabel('X Axis')
ax.set_ylabel('Y Axis')
ax.set_title('My First MSU Plot')
plt.show()

That's it! Your plot now has MSU branding with:

  • Metropolis font
  • MSU color palette
  • Professional styling

Using MSU Colors

Access MSU's official colors directly:

from msuthemes import colors

# Primary colors
print(colors.MSU_GREEN)    # '#18453B'
print(colors.MSU_WHITE)    # '#FFFFFF'
print(colors.MSU_BLACK)    # '#000000'

# Secondary colors
print(colors.MSU_SILVER)   # '#C1C6C8'
print(colors.MSU_GRAY)     # '#909090'

# Accent colors
print(colors.MSU_ORANGE)   # '#D14D28'
print(colors.MSU_TEAL)     # '#005F83'
print(colors.MSU_GRELLOW)  # '#849E2A'

# Use in plots
plt.scatter(x, y, c=colors.MSU_TEAL, s=50)

Color Palettes

MSUthemes provides 11 carefully designed palettes:

Sequential Palettes

For continuous data from low to high:

from msuthemes import palettes

# Get 5 colors from MSU sequential palette
colors_seq = palettes.msu_seq.as_hex(n_colors=5)

# Use in a heatmap
import seaborn as sns
data = np.random.rand(10, 10)
sns.heatmap(data, cmap=palettes.msu_seq.as_matplotlib_cmap())

Diverging Palettes

For data with a meaningful midpoint:

# Get 7 colors from diverging palette
colors_div = palettes.msu_div.as_hex(n_colors=7)

# Use with seaborn
sns.heatmap(data - 0.5, cmap=palettes.msu_div.as_matplotlib_cmap())

Qualitative Palettes

For categorical data:

# Get qualitative palette
colors_qual = palettes.msu_qual1.as_hex()

# Use for categories
categories = ['A', 'B', 'C', 'D']
values = [3, 7, 2, 5]
plt.bar(categories, values, color=colors_qual[:4])

Big Ten Colors

Get colors for any Big Ten institution:

from msuthemes import get_bigten_colors

# Single institution
msu_color = get_bigten_colors("MSU")
print(msu_color)  # '#18453B'

# Multiple institutions
colors = get_bigten_colors(["MSU", "Michigan", "Ohio State"])
# Returns: {'MSU': '#18453B', 'Michigan': '#00274C', 'Ohio State': '#BB0000'}

# Works with nicknames!
color = get_bigten_colors("Spartans")  # Same as "MSU"
color = get_bigten_colors("Buckeyes")  # Same as "Ohio State"

Create a Big Ten comparison:

from msuthemes import bigten_palette

# Get all Big Ten colors
all_colors = bigten_palette()

# Get specific schools
rivalry_colors = bigten_palette(["MSU", "Michigan", "Ohio State"])

Applying Themes

Apply MSU theme with customization:

from msuthemes import theme_msu

# Basic theme
theme_msu()

# With grid
theme_msu(use_grid=True)

# Larger text
theme_msu(base_size=14)

# Custom color cycle
theme_msu(color_cycle=['#18453B', '#D14D28', '#005F83'])

Seaborn Integration

For seaborn plots:

from msuthemes import set_msu_style
import seaborn as sns

# Apply MSU style to seaborn
set_msu_style(style='whitegrid')

# Create seaborn plot
tips = sns.load_dataset('tips')
sns.scatterplot(data=tips, x='total_bill', y='tip', hue='time')

Resetting Theme

Return to matplotlib defaults:

from msuthemes import reset_theme

# Apply MSU theme
theme_msu()
# ... create plots ...

# Reset to defaults
reset_theme()

Working with Data

MSUthemes includes the BigTen dataset with historical institutional data:

from msuthemes import load_bigten_data

# Load all data (1996-2023, 18 institutions)
df = load_bigten_data()
print(df.shape)  # (504, 38)

# Filter for MSU
msu_data = load_bigten_data(institutions=['MSU'])

# Recent years only
recent = load_bigten_data(years=[2020, 2021, 2022, 2023])

# Multiple institutions and years
comparison = load_bigten_data(
    institutions=['MSU', 'Michigan', 'Ohio State'],
    years=[2020, 2021, 2022, 2023]
)

Create a visualization with the data:

import matplotlib.pyplot as plt
from msuthemes import theme_msu, get_bigten_colors, load_bigten_data

# Apply theme
theme_msu()

# Load data
data = load_bigten_data(
    institutions=['MSU', 'Michigan', 'Ohio State'],
    columns=['name', 'entry_term', 'UGDS', 'ADM_RATE']
)

# Get colors for schools
school_colors = get_bigten_colors(['MSU', 'Michigan', 'Ohio State'])

# Create plot
fig, ax = plt.subplots(figsize=(10, 6))

for school in ['MSU', 'Michigan', 'Ohio State']:
    school_data = data[data['name'] == school]
    ax.plot(
        school_data['entry_term'],
        school_data['ADM_RATE'] * 100,
        label=school,
        color=school_colors[school],
        linewidth=2
    )

ax.set_xlabel('Year')
ax.set_ylabel('Admission Rate (%)')
ax.set_title('Big Ten Admission Rates Over Time')
ax.legend()
plt.show()

Complete Example

Here's a complete example combining multiple features:

import matplotlib.pyplot as plt
import numpy as np
from msuthemes import (
    theme_msu,
    palettes,
    colors,
    load_bigten_data,
    get_bigten_summary
)

# Apply MSU theme with grid
theme_msu(use_grid=True, base_size=12)

# Load and summarize data
summary = get_bigten_summary()
top_5 = summary.nlargest(5, 'UGDS_mean')

# Create figure with subplots
fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(14, 6))

# Left plot: Bar chart
palette_colors = palettes.msu_qual1.as_hex(n_colors=5)
ax1.barh(top_5['name'], top_5['UGDS_mean'], color=palette_colors)
ax1.set_xlabel('Average Enrollment')
ax1.set_title('Top 5 Big Ten Schools by Enrollment')

# Right plot: Line plot
msu_data = load_bigten_data(
    institutions=['MSU'],
    columns=['entry_term', 'UGDS']
)
ax2.plot(
    msu_data['entry_term'],
    msu_data['UGDS'],
    color=colors.MSU_GREEN,
    linewidth=2,
    marker='o'
)
ax2.set_xlabel('Year')
ax2.set_ylabel('Enrollment')
ax2.set_title('MSU Enrollment Over Time')

plt.tight_layout()
plt.savefig('msu_analysis.png', dpi=300)
plt.show()

Tips and Best Practices

Apply theme first

Always call theme_msu() before creating plots to ensure consistent styling.

Use MSU colors consistently

Stick to MSU Green for primary elements to maintain brand consistency.

Choose appropriate palettes

  • Sequential: For continuous data (temperature, time series)
  • Diverging: For data with a midpoint (profit/loss, deviations)
  • Qualitative: For categories (departments, schools)

Font clarity

Metropolis is optimized for readability at all sizes.

Save high-resolution

Use dpi=300 for publication-quality figures: 

plt.savefig('figure.png', dpi=300, bbox_inches='tight')

Accessibility

Test your visualizations for colorblind accessibility using tools like Coblis.

Common Patterns

Create a standard MSU figure

def create_msu_figure(figsize=(8, 6)):
    """Create a figure with MSU theme."""
    theme_msu()
    fig, ax = plt.subplots(figsize=figsize)
    return fig, ax

Apply and save

def save_msu_plot(filename, dpi=300):
    """Save plot with MSU branding."""
    plt.savefig(filename, dpi=dpi, bbox_inches='tight', facecolor='white')

Create a reusable color function

def get_school_color(school_name):
    """Get color for a Big Ten school."""
    from msuthemes import get_bigten_colors
    return get_bigten_colors(school_name)

Next Steps

Now that you know the basics, explore:

  • Colors - Complete guide to colors and palettes
  • Themes - Advanced theme customization
  • Big Ten - Working with Big Ten institutional data
  • Gallery - More example visualizations
  • API Reference - Full API reference