Simulate Color Vision Deficiency

Description

Transformation of colors by simulating color vision deficiencies, based on a CVD transform matrix. This function is an interface to the CVD object and returns simulated colors for deuteranope vision (green-yellow-red weakness).

See also protan, tritan, desaturate, and cvd_image.

Usage

deutan(cols, severity=1.0, linear=True)

Arguments

colslist, colorobject, matplotlib.colors.LinearSegmentedColormap: Single hex color, list of hex colors (str), a matoplotlib cmap, or a color color object (such as RGB, hexcols, CIELUV).
severityfloat: Severity in [0., 1.]. Zero means no deficiency, one maximum deficiency, defaults to 1..
linearbool: Should the color vision deficiency transformation be applied to the linearised RGB coordinates (default)? If False, the transformation is applied to the gamma-corrected sRGB coordinates (as in the Machado et al. 2009 supplementary materials).

Return

colorobject: Returns an object of the same type as the input object cols with modified colors as people with deuteranomaly see these colors (simulated).

Examples

from colorspace import rainbow_hcl, deutan, palette
from colorspace import specplot, swatchplot
# Drawing 100 colors along the HCL rainbow color palette
cols = rainbow_hcl()(100)
specplot(cols);

specplot(deutan(cols));

specplot(deutan(cols, 0.5));

# List of (hex) colors
cols = ["magenta", "red", "orange", "#F2F204", "#6BF204", "#4DA00D"]
deutan(cols);

# Visualize original and simulated color swatches
swatchplot([cols, deutan(cols)],
           show_names = False, figsize = (5, 1.5));

# From palette object
pal = palette(cols, name = "custom palette")
deutan(pal)

['#689BFA', '#A39000', '#D9C10E', '#FFED2F', '#E9D137', '#9B8B25']

# From cmap (returns cmap)
deutan(pal.cmap())

custom palette

under

bad

over