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"""
Matplotlib style configuration for consistent plotting across scripts.
"""
import matplotlib.pyplot as plt
import matplotlib.colors as mcolors
from contextlib import contextmanager
import numpy as np
# Colorblind-friendly color palettes
COLORBLIND_PALETTES = {
# Tol's bright qualitative scheme (up to 7 colors)
'tol_bright': ['#EE6677', '#228833', '#4477AA', '#CCBB44', '#66CCEE', '#AA3377', '#BBBBBB'],
# Tol's muted qualitative scheme (up to 9 colors)
'tol_muted': ['#CC6677', '#332288', '#DDCC77', '#117733', '#88CCEE', '#882255', '#44AA99', '#999933', '#AA4499'],
# Tol's light qualitative scheme (up to 9 colors)
'tol_light': ['#BBCC33', '#AAAA00', '#77AADD', '#EE8866', '#EEDD88', '#FFAABB', '#99DDFF', '#44BB99', '#DDDDDD'],
# Okabe-Ito palette (most common colorblind-friendly palette)
'okabe_ito': ['#E69F00', '#56B4E9', '#009E73', '#F0E442', '#0072B2', '#D55E00', '#CC79A7', '#000000'],
# Viridis-like discrete colors
'viridis_discrete': ['#440154', '#31688e', '#35b779', '#fde725'],
# Wong palette (good for presentations)
'wong': ['#000000', '#E69F00', '#56B4E9', '#009E73', '#F0E442', '#0072B2', '#D55E00', '#CC79A7'],
}
default_palette = 'tol_bright'
def get_colorblind_palette(name=default_palette, n_colors=None):
"""
Get a colorblind-friendly color palette.
Parameters:
-----------
name : str
Name of the palette ('okabe_ito', 'tol_bright', 'tol_muted', 'tol_light', 'viridis_discrete', 'wong')
n_colors : int, optional
Number of colors to return. If None, returns all colors in palette.
Returns:
--------
list : List of hex color codes
"""
palette = COLORBLIND_PALETTES.get(name, COLORBLIND_PALETTES['okabe_ito'])
if n_colors is not None:
palette = palette[:n_colors]
return palette
def set_colorblind_cycle(name=default_palette):
"""Set the default color cycle to a colorblind-friendly palette."""
colors = get_colorblind_palette(name)
plt.rcParams['axes.prop_cycle'] = plt.cycler(color=colors)
# Physical Review style settings
PHYSREV_STYLE = {
'text.usetex': True,
'font.family': 'serif',
'font.serif': ['Computer Modern Roman'],
'font.size': 16,
'axes.labelsize': 16,
'axes.titlesize': 16,
'legend.fontsize': 14,
'xtick.labelsize': 16,
'ytick.labelsize': 16,
'figure.figsize': (6.5, 3.7),
'savefig.dpi': 300,
'lines.linewidth': 1.5,
'axes.linewidth': 0.8,
}
def apply_physrev_style(colorblind=True, palette=default_palette):
"""Apply Physical Review style to matplotlib."""
plt.rcParams.update(PHYSREV_STYLE)
if colorblind:
set_colorblind_cycle(palette)
def apply_style(style_dict):
"""Apply custom style dictionary to matplotlib."""
plt.rcParams.update(style_dict)
def reset_style():
"""Reset matplotlib to default style."""
plt.rcdefaults()
@contextmanager
def physrev_style(colorblind=True, palette=default_palette):
"""Context manager for temporary style application."""
old_params = plt.rcParams.copy()
try:
apply_physrev_style(colorblind=colorblind, palette=palette)
yield
finally:
plt.rcParams.update(old_params)
# Alternative style configurations
PRESENTATION_STYLE = {
'text.usetex': True,
'font.family': 'serif',
'font.serif': ['Computer Modern Roman'],
'font.size': 18,
'axes.labelsize': 20,
'axes.titlesize': 22,
'legend.fontsize': 16,
'xtick.labelsize': 18,
'ytick.labelsize': 18,
'figure.figsize': (10, 6),
'savefig.dpi': 300,
'lines.linewidth': 2.0,
'axes.linewidth': 1.2,
}
def apply_presentation_style(colorblind=True, palette=default_palette):
"""Apply presentation style to matplotlib."""
plt.rcParams.update(PRESENTATION_STYLE)
if colorblind:
set_colorblind_cycle(palette)
@contextmanager
def presentation_style(colorblind=True, palette=default_palette):
"""Context manager for temporary presentation style application."""
old_params = plt.rcParams.copy()
try:
apply_presentation_style(colorblind=colorblind, palette=palette)
yield
finally:
plt.rcParams.update(old_params)
# Utility functions for colorblind-friendly plotting
def create_colorblind_cmap(name=default_palette, n_colors=256):
"""
Create a colorblind-friendly colormap.
Parameters:
-----------
name : str
Base palette name
n_colors : int
Number of colors in the colormap
Returns:
--------
matplotlib.colors.LinearSegmentedColormap
"""
colors = get_colorblind_palette(name)
return mcolors.LinearSegmentedColormap.from_list(f"{name}_cmap", colors, N=n_colors)
def show_colorblind_palettes():
"""Display all available colorblind-friendly palettes."""
fig, axes = plt.subplots(len(COLORBLIND_PALETTES), 1, figsize=(10, 2*len(COLORBLIND_PALETTES)))
for i, (name, colors) in enumerate(COLORBLIND_PALETTES.items()):
ax = axes[i] if len(COLORBLIND_PALETTES) > 1 else axes
# Create color swatches
for j, color in enumerate(colors):
ax.add_patch(plt.Rectangle((j, 0), 1, 1, facecolor=color))
ax.set_xlim(0, len(colors))
ax.set_ylim(0, 1)
ax.set_title(f'{name} ({len(colors)} colors)')
ax.set_xticks([])
ax.set_yticks([])
# Add hex codes as text
for j, color in enumerate(colors):
ax.text(j+0.5, 0.5, color, ha='center', va='center',
color='white' if _is_dark_color(color) else 'black', fontsize=8)
plt.tight_layout()
plt.show()
def _is_dark_color(hex_color):
"""Check if a hex color is dark (for text color selection)."""
hex_color = hex_color.lstrip('#')
rgb = tuple(int(hex_color[i:i+2], 16) for i in (0, 2, 4))
luminance = (0.299 * rgb[0] + 0.587 * rgb[1] + 0.114 * rgb[2]) / 255
return luminance < 0.5 |