Computed · Published Method

IS YOUR PALETTE
COLORBLIND-SAFE?

The default palettes in your charting library were mostly not designed for color-vision deficiency. Here is every common one, checked by the same published method behind our tools — with the exact conflicts, and a generated safe version of each one that fails.

CATEGORICAL PALETTES

Discrete categories — every color must stay tellable-apart from every other. Flagged when a pair that's distinct to normal vision collapses under simulated protan, deutan or tritan vision (CIEDE2000). Failing palettes show an auto-generated colorblind-safe fix.

COLORMAPS

Sequential and diverging ramps are read by lightness, not by telling swatches apart — so the right test is different: does perceived lightness stay monotonic under CVD? A big reversal means two data values map to the same brightness.

How this is computed. Every verdict on this page is produced live in your browser by opticquiz-cvd — the same engine as the checker, method published at DOI 10.5281/zenodo.21310578. Nothing is hand-entered. As a cross-check, a second independent model (Brettel 1997) agrees with the primary model (Machado 2009) on of the categorical palettes; the lone difference is a single borderline tritan pair in Okabe–Ito.
Honest limits. This is a screening measure on an uncalibrated screen at full worst-case severity, not a legal accessibility audit and not a statement about any individual's vision. "Fixed" palettes are a starting point — they preserve hue where possible and separate conflicts in lightness, but always sanity-check them for your context. Colormaps here are judged only on lightness monotonicity, one of several properties that matter.
Your palette isn't listed?
Paste it into the checker → to see its conflicts and get a fix, or check text contrast →.

QUESTIONS

Is viridis colorblind-safe?

Yes, used as intended. viridis is a sequential colormap read by lightness, and its lightness stays monotonic under all three CVD types (zero reversal here) — so order survives. Don't use it as a set of distinct categorical colors.

Is jet colorblind-safe?

No. jet's lightness is strongly non-monotonic under simulation (~37-point L* reversal), so different values collapse to the same brightness. That's the long-standing case against jet.

What should I use instead?

For categories: Okabe–Ito or Tableau Colorblind 10 (both pass here). For continuous data: viridis and its siblings. Or paste your brand colors into the checker and use the generated fix.