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gh-k-dense-ai-claude-scient…/skills/scientific-visualization/scripts/style_presets.py
Zhongwei Li f0bd18fb4e Initial commit
2025-11-30 08:30:10 +08:00

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#!/usr/bin/env python3
"""
Matplotlib Style Presets for Publication-Ready Scientific Figures
This module provides pre-configured matplotlib styles optimized for
different journals and use cases.
"""
import matplotlib.pyplot as plt
import matplotlib as mpl
from typing import Optional, Dict, Any
# Okabe-Ito colorblind-friendly palette
OKABE_ITO_COLORS = [
'#E69F00', # Orange
'#56B4E9', # Sky Blue
'#009E73', # Bluish Green
'#F0E442', # Yellow
'#0072B2', # Blue
'#D55E00', # Vermillion
'#CC79A7', # Reddish Purple
'#000000' # Black
]
# Paul Tol palettes
TOL_BRIGHT = ['#4477AA', '#EE6677', '#228833', '#CCBB44', '#66CCEE', '#AA3377', '#BBBBBB']
TOL_MUTED = ['#332288', '#88CCEE', '#44AA99', '#117733', '#999933', '#DDCC77', '#CC6677', '#882255', '#AA4499']
TOL_HIGH_CONTRAST = ['#004488', '#DDAA33', '#BB5566']
# Wong palette
WONG_COLORS = ['#000000', '#E69F00', '#56B4E9', '#009E73', '#F0E442', '#0072B2', '#D55E00', '#CC79A7']
def get_base_style() -> Dict[str, Any]:
"""
Get base publication-quality style settings.
Returns
-------
dict
Dictionary of matplotlib rcParams
"""
return {
# Figure
'figure.dpi': 100, # Display DPI (changed on save)
'figure.facecolor': 'white',
'figure.autolayout': False,
'figure.constrained_layout.use': True,
# Font
'font.size': 8,
'font.family': 'sans-serif',
'font.sans-serif': ['Arial', 'Helvetica', 'DejaVu Sans'],
# Axes
'axes.linewidth': 0.5,
'axes.labelsize': 9,
'axes.titlesize': 9,
'axes.labelweight': 'normal',
'axes.spines.top': False,
'axes.spines.right': False,
'axes.spines.left': True,
'axes.spines.bottom': True,
'axes.edgecolor': 'black',
'axes.labelcolor': 'black',
'axes.axisbelow': True,
'axes.prop_cycle': mpl.cycler(color=OKABE_ITO_COLORS),
# Grid
'axes.grid': False,
# Ticks
'xtick.major.size': 3,
'xtick.minor.size': 2,
'xtick.major.width': 0.5,
'xtick.minor.width': 0.5,
'xtick.labelsize': 7,
'xtick.direction': 'out',
'ytick.major.size': 3,
'ytick.minor.size': 2,
'ytick.major.width': 0.5,
'ytick.minor.width': 0.5,
'ytick.labelsize': 7,
'ytick.direction': 'out',
# Lines
'lines.linewidth': 1.5,
'lines.markersize': 4,
'lines.markeredgewidth': 0.5,
# Legend
'legend.fontsize': 7,
'legend.frameon': False,
'legend.loc': 'best',
# Savefig
'savefig.dpi': 300,
'savefig.format': 'pdf',
'savefig.bbox': 'tight',
'savefig.pad_inches': 0.05,
'savefig.transparent': False,
'savefig.facecolor': 'white',
# Image
'image.cmap': 'viridis',
'image.aspect': 'auto',
}
def apply_publication_style(style_name: str = 'default') -> None:
"""
Apply a pre-configured publication style.
Parameters
----------
style_name : str, default 'default'
Name of the style to apply. Options:
- 'default': General publication style
- 'nature': Nature journal style
- 'science': Science journal style
- 'cell': Cell Press style
- 'minimal': Minimal clean style
- 'presentation': Larger fonts for presentations
Examples
--------
>>> apply_publication_style('nature')
>>> fig, ax = plt.subplots()
>>> ax.plot([1, 2, 3], [1, 4, 9])
"""
base_style = get_base_style()
# Style-specific modifications
if style_name == 'nature':
base_style.update({
'font.size': 7,
'axes.labelsize': 8,
'axes.titlesize': 8,
'xtick.labelsize': 6,
'ytick.labelsize': 6,
'legend.fontsize': 6,
'savefig.dpi': 600,
})
elif style_name == 'science':
base_style.update({
'font.size': 7,
'axes.labelsize': 8,
'xtick.labelsize': 6,
'ytick.labelsize': 6,
'legend.fontsize': 6,
'savefig.dpi': 600,
})
elif style_name == 'cell':
base_style.update({
'font.size': 8,
'axes.labelsize': 9,
'xtick.labelsize': 7,
'ytick.labelsize': 7,
'legend.fontsize': 7,
'savefig.dpi': 600,
})
elif style_name == 'minimal':
base_style.update({
'axes.linewidth': 0.8,
'xtick.major.width': 0.8,
'ytick.major.width': 0.8,
'lines.linewidth': 2,
})
elif style_name == 'presentation':
base_style.update({
'font.size': 14,
'axes.labelsize': 16,
'axes.titlesize': 18,
'xtick.labelsize': 12,
'ytick.labelsize': 12,
'legend.fontsize': 12,
'axes.linewidth': 1.5,
'lines.linewidth': 2.5,
'lines.markersize': 8,
})
elif style_name != 'default':
print(f"Warning: Style '{style_name}' not recognized. Using 'default'.")
# Apply the style
plt.rcParams.update(base_style)
print(f"✓ Applied '{style_name}' publication style")
def set_color_palette(palette_name: str = 'okabe_ito') -> None:
"""
Set a colorblind-friendly color palette.
Parameters
----------
palette_name : str, default 'okabe_ito'
Name of the palette. Options:
- 'okabe_ito': Okabe-Ito palette (8 colors)
- 'wong': Wong palette (8 colors)
- 'tol_bright': Paul Tol bright palette (7 colors)
- 'tol_muted': Paul Tol muted palette (9 colors)
- 'tol_high_contrast': Paul Tol high contrast (3 colors)
Examples
--------
>>> set_color_palette('tol_muted')
>>> fig, ax = plt.subplots()
>>> for i in range(5):
... ax.plot([1, 2, 3], [i, i+1, i+2])
"""
palettes = {
'okabe_ito': OKABE_ITO_COLORS,
'wong': WONG_COLORS,
'tol_bright': TOL_BRIGHT,
'tol_muted': TOL_MUTED,
'tol_high_contrast': TOL_HIGH_CONTRAST,
}
if palette_name not in palettes:
available = ', '.join(palettes.keys())
print(f"Warning: Palette '{palette_name}' not found. Available: {available}")
palette_name = 'okabe_ito'
colors = palettes[palette_name]
plt.rcParams['axes.prop_cycle'] = plt.cycler(color=colors)
print(f"✓ Applied '{palette_name}' color palette ({len(colors)} colors)")
def configure_for_journal(journal: str, figure_width: str = 'single') -> None:
"""
Configure matplotlib for a specific journal.
Parameters
----------
journal : str
Journal name: 'nature', 'science', 'cell', 'plos', 'acs', 'ieee'
figure_width : str, default 'single'
Figure width: 'single' or 'double' column
Examples
--------
>>> configure_for_journal('nature', figure_width='single')
>>> fig, ax = plt.subplots() # Will have correct size for Nature
"""
journal = journal.lower()
# Journal specifications
journal_configs = {
'nature': {
'single_width': 89, # mm
'double_width': 183,
'style': 'nature',
},
'science': {
'single_width': 55,
'double_width': 175,
'style': 'science',
},
'cell': {
'single_width': 85,
'double_width': 178,
'style': 'cell',
},
'plos': {
'single_width': 83,
'double_width': 173,
'style': 'default',
},
'acs': {
'single_width': 82.5,
'double_width': 178,
'style': 'default',
},
'ieee': {
'single_width': 89,
'double_width': 182,
'style': 'default',
},
}
if journal not in journal_configs:
available = ', '.join(journal_configs.keys())
raise ValueError(f"Journal '{journal}' not recognized. Available: {available}")
config = journal_configs[journal]
# Apply style
apply_publication_style(config['style'])
# Set default figure size
width_mm = config['single_width'] if figure_width == 'single' else config['double_width']
width_inches = width_mm / 25.4
plt.rcParams['figure.figsize'] = (width_inches, width_inches * 0.75) # 4:3 aspect ratio
print(f"✓ Configured for {journal.upper()} ({figure_width} column: {width_mm} mm)")
def create_style_template(output_file: str = 'publication.mplstyle') -> None:
"""
Create a matplotlib style file that can be used with plt.style.use().
Parameters
----------
output_file : str, default 'publication.mplstyle'
Output filename for the style file
Examples
--------
>>> create_style_template('my_style.mplstyle')
>>> plt.style.use('my_style.mplstyle')
"""
style = get_base_style()
with open(output_file, 'w') as f:
f.write("# Publication-quality matplotlib style\n")
f.write("# Usage: plt.style.use('publication.mplstyle')\n\n")
for key, value in style.items():
if isinstance(value, mpl.cycler):
# Handle cycler specially
colors = [c['color'] for c in value]
f.write(f"axes.prop_cycle : cycler('color', {colors})\n")
else:
f.write(f"{key} : {value}\n")
print(f"✓ Created style template: {output_file}")
print(f" Use with: plt.style.use('{output_file}')")
def show_color_palettes() -> None:
"""
Display available color palettes for visual inspection.
"""
palettes = {
'Okabe-Ito': OKABE_ITO_COLORS,
'Wong': WONG_COLORS,
'Tol Bright': TOL_BRIGHT,
'Tol Muted': TOL_MUTED,
'Tol High Contrast': TOL_HIGH_CONTRAST,
}
fig, axes = plt.subplots(len(palettes), 1, figsize=(8, len(palettes) * 0.5))
for ax, (name, colors) in zip(axes, palettes.items()):
ax.set_xlim(0, len(colors))
ax.set_ylim(0, 1)
ax.set_yticks([])
ax.set_xticks([])
ax.set_ylabel(name, fontsize=10)
for i, color in enumerate(colors):
ax.add_patch(plt.Rectangle((i, 0), 1, 1, facecolor=color, edgecolor='black', linewidth=0.5))
# Add hex code
ax.text(i + 0.5, 0.5, color, ha='center', va='center',
fontsize=7, color='white' if i >= len(colors) - 1 else 'black')
fig.suptitle('Colorblind-Friendly Palettes', fontsize=12, fontweight='bold')
plt.tight_layout()
plt.show()
def reset_to_default() -> None:
"""
Reset matplotlib to default settings.
"""
mpl.rcdefaults()
print("✓ Reset to matplotlib defaults")
if __name__ == "__main__":
print("Matplotlib Style Presets for Scientific Figures")
print("=" * 50)
# Show available styles
print("\nAvailable publication styles:")
print(" - default")
print(" - nature")
print(" - science")
print(" - cell")
print(" - minimal")
print(" - presentation")
print("\nAvailable color palettes:")
print(" - okabe_ito (recommended)")
print(" - wong")
print(" - tol_bright")
print(" - tol_muted")
print(" - tol_high_contrast")
print("\nExample usage:")
print(" from style_presets import apply_publication_style, set_color_palette")
print(" apply_publication_style('nature')")
print(" set_color_palette('okabe_ito')")
# Create example figure
print("\nGenerating example figure with 'default' style...")
apply_publication_style('default')
fig, ax = plt.subplots(figsize=(3.5, 2.5))
for i in range(5):
ax.plot([1, 2, 3, 4], [i, i+1, i+0.5, i+2], marker='o', label=f'Series {i+1}')
ax.set_xlabel('Time (hours)')
ax.set_ylabel('Response (AU)')
ax.legend()
fig.suptitle('Example with Publication Style')
plt.tight_layout()
plt.show()
# Show color palettes
print("\nDisplaying color palettes...")
show_color_palettes()
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