Style Module¶

The monet_plots.style module provides pre-configured styling options for creating publication-quality plots. All styles are designed to be consistent and professional.

Overview¶

MONET Plots includes several built-in styles that can be applied to your plots using matplotlib's style system. These styles ensure consistent appearance across different plot types and publications.

Available Styles¶

`wiley_style`¶

The default Wiley-compliant style for scientific publications.

from monet_plots import style

# Apply Wiley style
import matplotlib.pyplot as plt
plt.style.use(style.wiley_style)

Style Configuration:

wiley_style = {
    # Font settings
    'font.family': 'serif',
    'font.serif': 'Times New Roman',
    'font.size': 10,

    # Axes settings
    'axes.labelsize': 10,
    'axes.titlesize': 12,
    'axes.grid': True,
    'grid.linestyle': ':',
    'grid.color': 'gray',

    # Line settings
    'lines.linewidth': 1.5,
    'lines.markersize': 5,

    # Legend settings
    'legend.fontsize': 9,
    'legend.frameon': False,

    # Figure settings
    'figure.figsize': (6, 4),
    'figure.dpi': 300,
    'savefig.dpi': 300,
    'savefig.format': 'tiff',
    'savefig.bbox': 'tight',
}

`aps_style`¶

American Physical Society (APS) compliant style for physics journals.

import matplotlib.pyplot as plt
plt.style.use(style.aps_style)

Style Configuration:

aps_style = {
    # Font settings - APS uses Helvetica
    'font.family': 'sans-serif',
    'font.sans-serif': ['Helvetica', 'Arial', 'DejaVu Sans'],
    'font.size': 10,

    # Axes settings
    'axes.labelsize': 10,
    'axes.titlesize': 12,
    'axes.grid': False,

    # Line settings
    'lines.linewidth': 1.5,
    'lines.markersize': 6,

    # Legend settings
    'legend.fontsize': 9,
    'legend.framealpha': 0.8,

    # Figure settings
    'figure.figsize': (5, 4),
    'figure.dpi': 600,
    'savefig.dpi': 600,
    'savefig.format': 'pdf',
}

`nature_style`¶

Nature journal compliant style.

import matplotlib.pyplot as plt
plt.style.use(style.nature_style)

`science_style`¶

Science journal compliant style.

import matplotlib.pyplot as plt
plt.style.use(style.science_style)

Custom Style Creation¶

`custom_style(**kwargs)`¶

Create a custom style by modifying an existing style or creating a new one.

from monet_plots import style

# Create custom style from Wiley style
custom = style.custom_style(
    font_size=12,
    figure_size=(10, 8),
    grid_style='--',
    grid_alpha=0.3
)

import matplotlib.pyplot as plt
plt.style.use(custom)

Parameters:

Parameter	Type	Description
`base_style`	`str`	Base style to modify ('wiley', 'aps', 'nature', 'science')
`font_size`	`int`	Base font size
`figure_size`	`tuple`	Default figure size (width, height)
`font_family`	`str`	Font family ('serif', 'sans-serif', etc.)
`grid_style`	`str`	Grid line style ('-', '--', ':', etc.)
`grid_alpha`	`float`	Grid transparency (0-1)
`save_format`	`str`	Default save format ('png', 'pdf', 'tiff', etc.)
`dpi`	`int`	Default DPI for saving
`**kwargs`	`dict`	Additional style parameters

Example: Creating Custom Styles¶

from monet_plots import style

# Modify existing style
presentation_style = style.custom_style(
    base_style='wiley',
    font_size=14,
    figure_size=(12, 8),
    grid_alpha=0.5
)

# Create completely new style
dark_style = style.custom_style(
    font_family='sans-serif',
    figure.facecolor='#1a1a1a',
    axes.facecolor='#1a1a1a',
    text.color='white',
    axes.edgecolor='white',
    axes.labelcolor='white',
    xtick.color='white',
    ytick.color='white',
    grid_color='#666666'
)

Style Application¶

Global Style Application¶

import matplotlib.pyplot as plt
from monet_plots import style

# Apply style globally
plt.style.use(style.wiley_style)

# All subsequent plots will use this style
plot1 = SpatialPlot()
plot2 = TimeSeriesPlot()

Context Manager for Temporary Style¶

import matplotlib.pyplot as plt
from monet_plots import style

# Use style temporarily
with plt.style.context(style.aps_style):
    plot = SpatialPlot()
    plot.plot(data)
    plot.save('aps_style.png')

# Style is automatically reverted

Per-Plot Style Application¶

from monet_plots import SpatialPlot, style

# Create plot with custom style
plot = SpatialPlot()
plot.plot(data)
plt.style.use(style.nature_style)  # Affects only this plot
plot.save('nature_style.png')

Style Parameters Reference¶

Font Settings¶

Parameter	Default	Description
`font.family`	'serif'	Font family
`font.serif`	'Times New Roman'	Serif font family
`font.sans-serif`	['Arial', 'Helvetica']	Sans-serif font options
`font.monospace`	['Courier New']	Monospace font options
`font.size`	10	Base font size in points
`axes.labelsize`	10	Axis label font size
`axes.titlesize`	12	Axis title font size
`legend.fontsize`	9	Legend font size
`xtick.labelsize`	9	X-axis tick label size
`ytick.labelsize`	9	Y-axis tick label size

Axes Settings¶

Parameter	Default	Description
`axes.grid`	True	Whether to show grid
`grid.linestyle`	':'	Grid line style
`grid.color`	'gray'	Grid line color
`grid.alpha`	0.5	Grid transparency
`axes.facecolor`	'white'	Axis background color
`axes.edgecolor`	'black'	Axis edge color
`axes.spines.color`	'black'	Spine color

Line and Marker Settings¶

Parameter	Default	Description
`lines.linewidth`	1.5	Default line width
`lines.markersize`	5	Default marker size
`lines.markeredgewidth`	1	Marker edge width

Figure Settings¶

Parameter	Default	Description
`figure.figsize`	(6, 4)	Default figure size
`figure.dpi`	100	Display DPI
`savefig.dpi`	300	Save DPI
`savefig.format`	'tiff'	Default save format
`savefig.bbox`	'tight'	Save bounding box mode

Legend Settings¶

Parameter	Default	Description
`legend.frameon`	False	Whether to show legend frame
`legend.framealpha`	1.0	Frame transparency
`legend.fancybox`	False	Rounded legend frame
`legend.shadow`	False	Legend shadow

Style Best Practices¶

Choosing the Right Style¶

Publications: Use journal-specific styles when available
Presentations: Increase font sizes and figure dimensions
Web: Use high-contrast styles for better readability
Data Exploration: Use minimal grid and clear labels

Customization Guidelines¶

# Good: Modify existing style incrementally
custom_wiley = style.custom_style(
    base_style='wiley',
    font_size=11,  # Slight modification
    grid_alpha=0.3  # Subtle grid
)

# Avoid: Completely overriding styles
bad_style = {
    'font.size': 12,  # Missing many other parameters
    'figure.figsize': (8, 6)
}

Consistency Across Plots¶

# Set style at the beginning of your script
import matplotlib.pyplot as plt
from monet_plots import style

plt.style.use(style.wiley_style)

# Create all plots with consistent style
plot1 = SpatialPlot()
plot2 = TimeSeriesPlot()
plot3 = ScatterPlot()

Style Troubleshooting¶

Common Issues¶

Style Not Applied:

# Wrong: Style applied after plot creation
plot = SpatialPlot()
plt.style.use(style.wiley_style)  # Too late!
plot.plot(data)

# Correct: Apply style before creating plot
plt.style.use(style.wiley_style)
plot = SpatialPlot()
plot.plot(data)

Missing Fonts:

# Install missing fonts
import matplotlib.pyplot as plt
plt.rcParams['font.family'] = ['Arial', 'Helvetica', 'sans-serif']

Style Conflicts:

# Reset matplotlib style before applying new one
plt.style.use('default')  # Reset to defaults
plt.style.use(style.aps_style)  # Apply new style

Style Templates¶

Presentation Templates¶

# Large format presentation
presentation_style = style.custom_style(
    base_style='wiley',
    font_size=16,
    figure_size=(14, 10),
    grid_alpha=0.3
)

# Small format presentation
small_presentation = style.custom_style(
    base_style='wiley',
    font_size=12,
    figure_size=(8, 6),
    grid_alpha=0.2
)

Publication Templates¶

# Two-column journal
two_column_style = style.custom_style(
    base_style='wiley',
    figure_size=(3.5, 2.5),  # Two-column width
    font_size=9
)

# Full-page figure
full_page_style = style.custom_style(
    base_style='wiley',
    figure_size=(7, 9),  # Full page
    font_size=10
)

Related Resources:

Base Plot API - Core plotting functionality
Colorbars API - Colorbar customization
Examples - Style usage examples