Evaluate Palette Quality

Provides a comprehensive evaluation of a color palette's perceptual properties, including its distinguishability, CVD safety, and color distribution. Returns raw metrics without subjective scoring for post-hoc analysis.

Usage

evaluate_palette(colors, ...)

Arguments

colors

A character vector of hex colors, or a matrix of colors in OK LAB space.

...

Additional arguments reserved for future use.

Value

A list of evaluation metrics with class huerd_evaluation. Contains raw metrics including distances, CVD safety, and distribution for objective analysis without subjective heuristic scoring.

Examples

pal <- generate_palette(5, progress = FALSE)
metrics <- evaluate_palette(pal)
print(metrics) # Uses custom print method
#> 
#> -- huerd Palette Evaluation (5 colors) --
#> 
#> -- Perceptual Distances (OKLAB) --
#> * Min distance       : 0.2931
#> * Mean distance      : 0.4145
#> * Median distance    : 0.3780
#> * Std. Dev.          : 0.1133
#> * Estimated Max Min  : 0.4108 (for unconstrained palette of this size)
#> * Performance Ratio  : 71.4% (achieved min / estimated max)
#> 
#> -- CVD Safety (OKLAB distances under simulation) --
#> * Worst-case min dist: 0.2580
#>   Protanopia : min=0.258, preserved_ratio=0.88
#>   Deuteranopia: min=0.290, preserved_ratio=0.99
#>   Tritanopia : min=0.258, preserved_ratio=0.88
#> 
#> -- Color Distribution (OKLAB) --
#> * Lightness (L)    : range=[0.23, 0.81], mean=0.55
#> * Chroma (C)       : range=[0.051, 0.273], mean=0.157
#> * Hue (degrees)    : circular_variance=0.753

# The performance_ratio compares the achieved min distance to an
# estimated maximum
# metrics$distances$performance_ratio