Using System Fonts for Offline Work

DataMapPlot provides the use_system_fonts parameter to help users work in offline environments or behind restrictive firewalls. This feature allows you to create plots without downloading fonts from Google Fonts, instead using only the fonts installed on your system.

When to Use System Fonts

The use_system_fonts parameter is particularly useful when:

Working in environments without internet access

Operating behind firewalls that block Google Fonts

Working in regulated industries with strict network policies

Wanting to avoid font download warning messages

Ensuring consistent font rendering across different environments

How It Works

By default, DataMapPlot attempts to download and use Google Fonts for optimal typography. When you set use_system_fonts=True, the library:

Skips all attempts to download fonts from Google Fonts

Uses only fonts already installed on your system

Falls back gracefully if a requested font is not available

Basic Usage

Here’s a simple example of using system fonts:

import datamapplot
import numpy as np

# Create some sample data
data_coords = np.random.rand(1000, 2)
labels = np.random.choice(['Category A', 'Category B', 'Category C'], 1000)

# Create a plot using system fonts
fig, ax = datamapplot.create_plot(
    data_coords,
    labels,
    title="My Offline Plot",
    font_family="Arial",  # Use a common system font
    use_system_fonts=True
)

Common System Fonts

When using use_system_fonts=True, it’s recommended to use fonts that are commonly available across different operating systems:

Arial

Helvetica

Times New Roman

Courier New

Georgia

Verdana

Comic Sans MS

Impact

Trebuchet MS

Palatino

Checking Available Fonts

You can check which fonts are available on your system using matplotlib:

from matplotlib import font_manager

# Get all available font families
available_fonts = sorted(set([f.name for f in font_manager.fontManager.ttflist]))

print(f"Total available system fonts: {len(available_fonts)}")

# Check for common fonts
common_fonts = [
    "Arial", "Helvetica", "Times New Roman", "Georgia", "Verdana",
    "Courier New", "Comic Sans MS", "Impact", "Trebuchet MS", "Palatino"
]

for font in common_fonts:
    if font in available_fonts:
        print(f"✓ {font}")
    else:
        print(f"✗ {font} (not available)")

Advanced Usage

Custom Fonts for Different Elements

You can specify different system fonts for various plot elements:

fig, ax = datamapplot.create_plot(
    data_coords,
    labels,
    title="Mixed Font Example",
    sub_title="Using different fonts for different elements",
    font_family="Arial",  # Main font
    title_keywords={"fontfamily": "Times New Roman", "fontsize": 24},
    sub_title_keywords={"fontfamily": "Georgia", "fontsize": 16},
    use_system_fonts=True
)

Comparison with Offline Mode

DataMapPlot offers two different approaches for offline work:

Feature	`use_system_fonts`	Offline Mode (cached)
Use case	Static plots without internet	Interactive plots with cached resources
Setup required	None	Run `dmp_offline_cache` tool
Font selection	System fonts only	Cached Google Fonts
Works with	`create_plot()`	`create_interactive_plot()`
Storage needs	None	~10MB for cache files

Troubleshooting

Font Not Found

If you see warnings like “Font family ‘X’ not found”, it means the specified font is not installed on your system. Solutions:

Use a different font that is installed

Install the missing font on your system

Let matplotlib fall back to its default font

Performance Considerations

Using use_system_fonts=True can slightly improve performance by:

Skipping network checks for Google Fonts availability

Avoiding font download attempts

Reducing warning messages in restricted environments

Example: Working Behind a Firewall

Here’s a complete example for users working in restricted environments:

import datamapplot
import numpy as np
from sklearn.datasets import make_blobs

# Generate sample clustered data
X, y = make_blobs(n_samples=1000, centers=5, n_features=2, random_state=42)

# Map numeric labels to categories
label_names = ['Research', 'Development', 'Production', 'Testing', 'Documentation']
labels = np.array([label_names[i] for i in y])

# Create plot for offline use
fig, ax = datamapplot.create_plot(
    X,
    labels,
    title="Project Classification Map",
    sub_title="Internal data visualization - no external resources needed",
    font_family="Arial",  # Safe choice for most systems
    use_system_fonts=True,
    darkness=0.5,
    figsize=(12, 10)
)

# Save for sharing within the organization
fig.savefig("project_map.png", dpi=300, bbox_inches='tight')

Best Practices

Choose widely available fonts: Stick to common system fonts for maximum compatibility

Test on target systems: Verify your chosen fonts are available where the plots will be viewed

Provide fallbacks: Consider having alternative font choices for different platforms

Document requirements: If using specific fonts, document them for other users

Use for static plots: This feature is designed for create_plot(), not interactive plots

Further Examples

For more detailed examples and test cases demonstrating the use_system_fonts parameter, see the example notebook in the examples directory.

Note

For interactive plots that need to work offline, see the Offline Mode Interactive Plots documentation which covers caching JavaScript dependencies and fonts for fully self-contained HTML outputs.