Colormap Visualization

def view_cmap(
    cmap: str | Colormap,
    *,
    savefig: str | None = None,
    show_test: bool = False,
    show_grayscale: bool = False
) -> None:
    """
    Shows a simple plot of the provided colormap.

    Parameters:
    - cmap: Colormap name or object to visualize
    - savefig: Path to save plot (None to display)
    - show_test: Show colormap test data instead of gradient
    - show_grayscale: Also show grayscale version of colormap

    Returns:
    None
    """

import cmasher as cmr

# Basic colormap visualization
cmr.view_cmap('cmr.rainforest')

# View with grayscale comparison
cmr.view_cmap('cmr.iceburn', show_grayscale=True)

# View with test data pattern
cmr.view_cmap('cmr.ocean', show_test=True)

# Save visualization to file
cmr.view_cmap('cmr.wildfire', savefig='wildfire_cmap.png')

# View all three options together
cmr.view_cmap(
    'cmr.seasons',
    show_test=True,
    show_grayscale=True,
    savefig='seasons_analysis.png'
)

def create_cmap_overview(
    cmaps: list | dict | None = None,
    *,
    savefig: str | None = None,
    use_types: bool = True,
    sort: str | callable | None = "alphabetical",
    show_grayscale: bool = True,
    show_info: bool = False,
    plot_profile: bool | float = False,
    dark_mode: bool = False,
    title: str | None = "Colormap Overview",
    wscale: float = 1,
    hscale: float = 1
) -> None:
    """
    Creates an overview plot containing multiple colormaps.

    Parameters:
    - cmaps: Colormaps to include (None for all CMasher colormaps)
    - savefig: Path to save plot (None to display)
    - use_types: Categorize colormaps by type
    - sort: Sorting method - 'alphabetical', 'lightness', 'perceptual', function, or None
    - show_grayscale: Show grayscale versions alongside color versions
    - show_info: Show lightness and perceptual statistics
    - plot_profile: Plot lightness profiles (True, False, or alpha value)
    - dark_mode: Use dark background and light text
    - title: Plot title (None for no title)
    - wscale, hscale: Scale factors for subplot dimensions

    Returns:
    None
    """

import cmasher as cmr

# Basic overview of all CMasher colormaps
cmr.create_cmap_overview()

# Overview of specific colormap types
sequential_cmaps = cmr.get_cmap_list('sequential')
cmr.create_cmap_overview(
    [f'cmr.{name}' for name in sequential_cmaps[:10]],
    title="Sequential Colormaps",
    savefig='sequential_overview.png'
)

# Detailed analysis with lightness profiles
cmr.create_cmap_overview(
    show_info=True,
    plot_profile=0.3,
    sort='perceptual',
    title="CMasher Colormaps - Perceptual Analysis"
)

# Dark mode overview
cmr.create_cmap_overview(
    dark_mode=True,
    title="CMasher Colormaps (Dark Mode)",
    savefig='cmaps_dark.png'
)

# Custom colormap selection with categorical organization
custom_cmaps = {
    'Ocean Theme': ['cmr.ocean', 'cmr.tropical', 'cmr.arctic'],
    'Fire Theme': ['cmr.ember', 'cmr.torch', 'cmr.wildfire'],
    'Nature Theme': ['cmr.jungle', 'cmr.rainforest', 'cmr.savanna']
}
cmr.create_cmap_overview(
    custom_cmaps,
    show_grayscale=False,
    wscale=1.2,
    title="Themed Colormaps"
)

# Alphabetical sorting (default)
cmr.create_cmap_overview(sort='alphabetical')

# Sort by lightness profile
cmr.create_cmap_overview(sort='lightness')

# Sort by perceptual characteristics
cmr.create_cmap_overview(sort='perceptual')

# Custom sorting function
def custom_sort(cmap):
    return len(cmap.name)  # Sort by name length

cmr.create_cmap_overview(sort=custom_sort)

# No sorting (preserve input order)
cmr.create_cmap_overview(sort=None)

import cmasher as cmr

# Compare all diverging colormaps
diverging_names = cmr.get_cmap_list('diverging')
cmr.create_cmap_overview(
    [f'cmr.{name}' for name in diverging_names],
    title="Diverging Colormaps Comparison",
    show_info=True,
    plot_profile=True,
    savefig='diverging_comparison.png'
)

import cmasher as cmr

# Create publication-quality overview
cmr.create_cmap_overview(
    savefig='cmasher_overview_publication.png',
    show_grayscale=True,
    show_info=True,
    plot_profile=0.25,
    sort='perceptual',
    title="CMasher: Scientific Colormaps for Data Visualization",
    wscale=1.5,
    hscale=1.2
)

import cmasher as cmr

# Create focused comparison for selection
candidates = ['cmr.rainforest', 'cmr.ocean', 'cmr.tropical', 'cmr.jungle']
cmr.create_cmap_overview(
    candidates,
    show_grayscale=True,
    show_info=True,
    title="Sequential Colormap Candidates",
    use_types=False  # Don't group by type
)

import cmasher as cmr
import matplotlib.pyplot as plt

def explore_colormap(cmap_name):
    """Interactive colormap exploration."""
    print(f"\n=== {cmap_name} ===")
    print(f"Type: {cmr.get_cmap_type(cmap_name)}")
    
    # Show basic view
    cmr.view_cmap(cmap_name)
    
    # Show with test data
    cmr.view_cmap(cmap_name, show_test=True)
    
    # Extract sample colors
    colors = cmr.take_cmap_colors(cmap_name, 5, return_fmt='hex')
    print(f"Sample colors: {colors}")

# Explore different colormaps
for cmap in ['cmr.rainforest', 'cmr.iceburn', 'cmr.seasons']:
    explore_colormap(cmap)

import cmasher as cmr
import os

def create_all_cmap_views(output_dir='colormap_views'):
    """Create individual visualization for each CMasher colormap."""
    os.makedirs(output_dir, exist_ok=True)
    
    all_cmaps = cmr.get_cmap_list()
    for cmap_name in all_cmaps:
        filename = f"{output_dir}/{cmap_name}_view.png"
        cmr.view_cmap(
            f'cmr.{cmap_name}',
            show_grayscale=True,
            savefig=filename
        )
        print(f"Created: {filename}")

# Create all visualizations
create_all_cmap_views()