Basic Styling Guide¶

Learn how to quickly customize the appearance of your plots using MONET Plots' built-in styling system and basic customization options.

Overview¶

This guide covers fundamental styling techniques to make your plots look professional and consistent. We'll explore the default styles, quick customizations, and basic plot modifications.

Learning Objectives¶

Apply built-in style presets
Modify basic plot elements
Change colors and fonts
Adjust plot dimensions and layout
Create simple custom styles

Built-in Style Presets¶

MONET Plots comes with professionally designed style presets that follow publication standards.

Wiley Style¶

The default Wiley-compliant style provides a clean, professional appearance suitable for scientific publications.

import matplotlib.pyplot as plt
from monet_plots import style, TimeSeriesPlot

# Apply Wiley style (default)
style.set_style("wiley")

# Create a plot
plot = TimeSeriesPlot(figsize=(12, 6))
plot.plot(df, x='time', y='value', title="Wiley Style Plot")
plot.save("wiley_style.png")

Wiley Style Characteristics: - Font: Times New Roman serif font - Size: 10pt for body text, 12pt for titles - Grid: Light gray dotted lines - Lines: 1.5pt width - Colors: Professional color palette - Save Format: TIFF by default, 300 DPI

APS Style¶

For American Physical Society publications, use the paper style context:

from monet_plots import style
style.set_style("paper")

Nature Style¶

For Nature journal publications, use the paper style context (or customize further if needed):

from monet_plots import style
style.set_style("paper")
# Further customizations can be applied after setting the base style
# plt.rcParams.update({'font.size': 7, 'figure.figsize': (3.5, 2)})

Quick Customizations¶

Changing Colors¶

import matplotlib.pyplot as plt
from monet_plots import TimeSeriesPlot

# Use a custom color palette
custom_colors = ['#1f77b4', '#ff7f0e', '#2ca02c', '#d62728', '#9467bd']

# Apply custom style
plt.style.use({
    'axes.prop_cycle': plt.cycler('color', custom_colors),
    'axes.labelsize': 12,
    'axes.titlesize': 14,
    'legend.fontsize': 11
})

plot = TimeSeriesPlot()
plot.plot(df, x='time', y='value', color=custom_colors[0])
plot.save("custom_colors.png")

Font Customization¶

# Custom font configuration
font_style = {
    'font.family': 'serif',
    'font.serif': ['Georgia', 'Times New Roman', 'serif'],
    'font.size': 11,
    'axes.labelsize': 11,
    'axes.titlesize': 14,
    'legend.fontsize': 10,
    'xtick.labelsize': 9,
    'ytick.labelsize': 9
}

plt.style.use(font_style)

Size and Layout¶

# Figure size and layout customization
layout_style = {
    'figure.figsize': (14, 10),  # Large figure
    'figure.dpi': 150,           # Lower DPI for web
    'figure.autolayout': True,   # Automatic layout
    'axes.titlesize': 16,        # Larger titles
    'axes.labelsize': 14,        # Larger labels
    'savefig.dpi': 300,          # High quality saves
    'savefig.bbox': 'tight'      # Tight bounding box
}

plt.style.use(layout_style)

Basic Plot Customization¶

Title and Labels¶

from monet_plots import SpatialPlot

plot = SpatialPlot(figsize=(12, 8))

# Plot data
plot.plot(data, title="Custom Title with Subtitle")

# Add custom labels
plot.xlabel("Longitude (degrees)")
plot.ylabel("Latitude (degrees)")

# Add subtitle
plot.ax.text(0.5, 0.95, "Regional Analysis",
            transform=plot.ax.transAxes,
            ha='center', fontsize=11, style='italic')

plot.save("custom_labels.png")

Grid and Ticks¶

from monet_plots import TimeSeriesPlot

plot = TimeSeriesPlot(figsize=(12, 6))

# Plot data
plot.plot(df, x='time', y='value')

# Customize grid
plot.ax.grid(True, linestyle='--', alpha=0.7, color='gray')

# Customize ticks
plot.ax.tick_params(axis='both', which='major', labelsize=10, length=6, width=1.5)
plot.ax.tick_params(axis='both', which='minor', length=3, width=1)

# Add minor grid
plot.ax.grid(True, axis='both', which='minor', linestyle=':', alpha=0.3)

plot.save("custom_grid.png")

Legend Customization¶

from monet_plots import TimeSeriesPlot

plot = TimeSeriesPlot(figsize=(12, 6))

# Plot with custom legend
plot.plot(
    df,
    x='time',
    y='value',
    title="Custom Legend Example",
    label="Main Dataset"
)

# Customize legend
legend = plot.ax.legend(
    loc='upper right',
    frameon=True,
    fancybox=True,
    shadow=True,
    framealpha=0.9,
    fontsize=10,
    markerscale=1.2,
    edgecolor='black',
    facecolor='white'
)

# Add legend title
legend.set_title("Data Sources", prop={'size': 11, 'weight': 'bold'})

plot.save("custom_legend.png")

Color Schemes¶

Sequential Color Schemes¶

from monet_plots import SpatialPlot

# Use sequential colormap for continuous data
plot = SpatialPlot(figsize=(12, 8))

# Sequential data
sequential_data = np.random.random((20, 30)) * 100

plot.plot(
    sequential_data,
    cmap='viridis',  # Sequential colormap
    title="Sequential Color Scheme"
)

plot.save("sequential_colors.png")

Diverging Color Schemes¶

from monet_plots import SpatialPlot

# Use diverging colormap for data with center point
plot = SpatialPlot(figsize=(12, 8))

# Diverging data (positive and negative values)
diverging_data = np.random.normal(0, 50, (20, 30))

plot.plot(
    diverging_data,
    cmap='RdBu_r',  # Red-Blue diverging
    title="Diverging Color Scheme"
)

plot.save("diverging_colors.png")

Qualitative Color Schemes¶

from monet_plots import ScatterPlot

# Use qualitative colormap for categorical data
plot = ScatterPlot(figsize=(12, 8))

# Create categorical data
categories = ['A', 'B', 'C', 'D', 'E']
colors = plt.cm.Set3(np.linspace(0, 1, len(categories)))

for i, (category, color) in enumerate(zip(categories, colors)):
    subset = df[df['category'] == category]
    plot.ax.scatter(subset['x'], subset['y'],
                   color=color, label=category, s=50, alpha=0.7)

plot.ax.legend(title="Categories")
plot.title("Qualitative Color Scheme")
plot.save("qualitative_colors.png")

Colorblind-Friendly Practices¶

Ensuring your plots are accessible to individuals with color vision deficiencies is crucial for effective communication. MONET Plots encourages the use of colorblind-friendly palettes and practices.

Recommended Colormaps¶

Matplotlib offers several perceptually uniform colormaps that are suitable for colorblind individuals.

For Sequential Data (ordered data, e.g., temperature, elevation):

viridis
plasma
inferno
magma
cividis

Example (Sequential):

import matplotlib.pyplot as plt
from monet_plots import SpatialPlot
import numpy as np

plot = SpatialPlot(figsize=(12, 8))
data = np.random.random((20, 30)) * 100

plot.plot(
    data,
    cmap='viridis',  # Colorblind-friendly sequential colormap
    title="Sequential Data with Viridis Colormap"
)
plot.save("colorblind_sequential.png")

For Diverging Data (data with a critical central value, e.g., anomalies, differences):

BrBG (Brown-BlueGreen)
PiYG (Pink-YellowGreen)
PRGn (Purple-Green)
PuOr (Purple-Orange)
RdGy (Red-Gray)
RdBu (Red-Blue)

(Note: Adding _r to the end of a colormap name reverses it, e.g., RdBu_r)

Example (Diverging):

import matplotlib.pyplot as plt
from monet_plots import SpatialPlot
import numpy as np

plot = SpatialPlot(figsize=(12, 8))
diverging_data = np.random.normal(0, 50, (20, 30))

plot.plot(
    diverging_data,
    cmap='RdBu_r',  # Colorblind-friendly diverging colormap
    title="Diverging Data with RdBu_r Colormap"
)
plot.save("colorblind_diverging.png")

For Qualitative/Categorical Data (distinct categories, no inherent order):

When plotting categorical data, avoid using too many distinct colors that might be hard to differentiate. Consider using:

Colorblind-friendly palettes: Matplotlib's tab10, tab20, Paired are often good starting points.
Varying line styles or markers: In addition to color, use different line styles (e.g., solid, dashed, dotted) or marker shapes (e.g., circles, squares, triangles) to distinguish categories.
Direct labeling: Label categories directly on the plot rather than relying solely on a legend.

Example (Qualitative):

import matplotlib.pyplot as plt
from monet_plots import ScatterPlot
import pandas as pd
import numpy as np

plot = ScatterPlot(figsize=(12, 8))

# Create categorical data
categories = ['Group A', 'Group B', 'Group C', 'Group D']
data = {
    'x': np.random.rand(100) * 10,
    'y': np.random.rand(100) * 10,
    'category': np.random.choice(categories, 100)
}
df = pd.DataFrame(data)

# Use a colorblind-friendly qualitative palette (tab10)
colors = plt.cm.get_cmap('tab10', len(categories))

for i, category in enumerate(categories):
    subset = df[df['category'] == category]
    plot.ax.scatter(subset['x'], subset['y'],
                   color=colors(i), label=category, s=50, alpha=0.7)

plot.ax.legend(title="Categories")
plot.title("Qualitative Data with Colorblind-Friendly Palette")
plot.save("colorblind_qualitative.png")

General Best Practices¶

Avoid red-green combinations: These are particularly problematic for the most common forms of colorblindness (deuteranomaly and protanomaly).
Use sufficient contrast: Ensure there is enough contrast between colors and between colors and the background.
Provide redundant coding: Whenever possible, use other visual cues in addition to color, such as different line styles, marker shapes, patterns, or direct labels.
Test your plots: Use online simulators or tools (e.g., Color Oracle) to check how your plots appear to individuals with different types of colorblindness.

Quick Style Templates¶

MONET Plots provides convenient set_style contexts for common use cases:

Presentation Style¶

Apply a style optimized for presentations:

from monet_plots import style
style.set_style("presentation")

Paper Publication Style¶

Apply a style suitable for academic papers:

from monet_plots import style
style.set_style("paper")

Web Publication Style¶

Apply a style designed for web content:

from monet_plots import style
style.set_style("web")

Common Styling Patterns¶

Pattern 1: Quick Style Application¶

# Apply style before creating plot
plt.style.use('seaborn-v0_8-whitegrid')

plot = TimeSeriesPlot()
plot.plot(df, x='time', y='value')
plot.save("quick_style.png")

Pattern 2: Style Context Manager¶

# Apply style temporarily
with plt.style.context('seaborn-v0_8-darkgrid'):
    plot = TimeSeriesPlot()
    plot.plot(df, x='time', y='value')
    plot.save("temp_style.png")

Pattern 3: Style Combination¶

# Combine multiple styles
base_style = plt.style.library['seaborn-v0_8-whitegrid']
custom_colors = {'axes.prop_cycle': plt.cycler('color', ['#e74c3c', '#3498db'])}

combined_style = {**base_style, **custom_colors}
plt.style.use(combined_style)

plot = TimeSeriesPlot()
plot.plot(df, x='time', y='value')
plot.save("combined_style.png")

Practice Exercises¶

Exercise 1: Wiley Style Modification¶

Create a modified Wiley style with larger fonts and a different color scheme.

Exercise 2: Custom Presentation Style¶

Design a style optimized for academic presentations with readable fonts and high contrast.

Exercise 3: Journal-Specific Style¶

Create a style for a specific journal of your choice based on their submission guidelines.

Exercise 4: Color Scheme Experimentation¶

Test different colormaps (viridis, plasma, inferno, magma) on your data and compare.

Exercise 5: Layout Optimization¶

Create a style that optimizes plot layout for multi-panel figures.

Troubleshooting¶

Issue 1: Style Not Applying¶

# Reset matplotlib defaults before applying new style
plt.rcParams.update(plt.rcParamsDefault)
plt.style.use('your_style')

Issue 2: Font Not Available¶

# Check available fonts
import matplotlib.font_manager as fm
print([f.name for f in fm.fontManager.ttflist])

# Use fallback fonts
font_style = {
    'font.family': 'serif',
    'font.serif': ['Georgia', 'Times New Roman', 'serif']  # Fallback chain
}

Issue 3: Poor Text Readability¶

# Improve text readability
readable_style = {
    'font.size': 12,
    'axes.labelsize': 14,
    'axes.titlesize': 16,
    'legend.fontsize': 12,
    'axes.titlesize': 16,
    'axes.labelweight': 'bold'
}

Next Steps¶

After mastering basic styling, explore:

Custom Styles - Create your own comprehensive style themes
Advanced Customization - Fine-tune individual plot elements
Color Management - Advanced color palette management
Theming Guide - Create consistent branded styles

Quick Reference¶

Style Element	Key	Common Values
Preset Style	`style.set_style(context)`	`"wiley"`, `"presentation"`, `"paper"`, `"web"`, `"default"`
Font Family	`font.family`	`serif`, `sans-serif`, `monospace`
Font Size	`font.size`	8-16pt for most uses
Figure Size	`figure.figsize`	(8, 6), (12, 8), (10, 6)
Save Format	`savefig.format`	`png`, `tiff`, `pdf`, `eps`
DPI	`savefig.dpi`	300 for print, 150 for web
Grid Style	`grid.linestyle`	`:`, `--`, `-`, `None`

Navigation:

Configuration Index - All configuration guides
Custom Styles - Advanced style creation
Advanced Customization - Full plot control
Color Management - Color schemes and palettes