Perceptually-Uniform, Colorblind-Friendly Colormaps

Avoid rainbow/jet¶

• Not perceptually uniform: equal numeric steps don’t look like equal visual steps; non-monotonic lightness introduces false “edges” that can mislead interpretation.

• Accessibility issues: rainbow palettes map distinct hues that many viewers with color-vision deficiencies (approx. 1 in 12 men) cannot reliably differentiate.

• Context switching: abrupt hue jumps make reading gradients and subtle trends harder, especially when printed or viewed on projectors.

What to use instead¶

Choose colormaps designed for uniform perceived change (usually monotonic lightness) and CVD robustness:

• Sequential (low > high): e.g., Matplotlib’s viridis, magma, plasma, inferno; cmocean’s domain-aware sequences (e.g., cmo.thermal for temperature, cmo.haline for salinity).

• Diverging (midpoint emphasis): use when values deviate around a meaningful center (0, climatology, etc.). Examples: Matplotlib’s seismic-style but perceptually tuned options like coolwarm (still imperfect) or cmocean’s balance, delta, curl, which are engineered for symmetry and lightness control.

• Cyclic (wrap-around variables): for phase/aspect (0°≡360°). Use cyclic maps such as cmocean’s phase.

• Categorical (discrete classes): use distinct, desaturated palettes with good lightness separation; avoid “rainbow” categories for quantitative data.

Picking the right map for your data¶

• Monotonic data: sequential.

• Signed anomalies around a reference: diverging with a clearly defined, perceptually central midpoint.

• Angles/orientations: cyclic so the endpoints match.

• Dynamic range: ensure the lightness ramp spans the range where your audience needs discrimination (you can trim/clip the colormap range if needed).

• Background: pick a map whose lightness contrasts with the figure background (dark maps on dark backgrounds obscure low values).

Quick recipes¶

Matplotlib + cmocean¶

Install cmocean (Matplotlib port):

pip install cmocean

Set a global default and plot:

import matplotlib.pyplot as plt
import numpy as np
import cmocean

# Set a perceptually-uniform default
plt.rcParams["image.cmap"] = "viridis"

# Example data
x = np.linspace(-3, 3, 400)
y = np.linspace(-3, 3, 400)
X, Y = np.meshgrid(x, y)
Z = np.hypot(X, Y)

# Sequential (distance field)
plt.imshow(Z, origin="lower", cmap=cmocean.cm.thermal)
plt.colorbar(label="Temperature-like quantity")
plt.title("Sequential, perceptually-uniform")
plt.show()

# Diverging (positive/negative anomaly)
Z_anom = np.sin(X) * np.cos(Y)
plt.imshow(Z_anom, origin="lower", cmap=cmocean.cm.balance, vmin=-1, vmax=1)
plt.colorbar(label="Anomaly")
plt.title("Diverging around 0")
plt.show()

# Cyclic (phase)
Z_phase = np.angle(np.exp(1j*(X)))
plt.imshow(Z_phase, origin="lower", cmap=cmocean.cm.phase)
plt.colorbar(label="Phase [rad]")
plt.title("Cyclic for wrap-around variables")
plt.show()

More cmocean info: matplotlib.org/cmocean

Fabio Crameri’s cm tools¶

Fabio Crameri developed a sophisticated toolset for scientific color maps that are universally readable by color-vision deficient and color-blind individuals, and when printed in black and white. For background information, refer to Crameri et al. (2020) (direct link) and Fabio Crameri’s EGU blogpost. The Python package cmcrameri is hosted at https://pypi.org/project/cmcrameri .

Here is a quick way to use the cmcrameri scientific colormaps in Python:

1. Install

pip install cmcrameri

2. Basic usage with Matplotlib

import matplotlib.pyplot as plt
import numpy as np
import cmcrameri.cm as cm

x = np.linspace(-3, 3, 400)
y = np.linspace(-3, 3, 400)
X, Y = np.meshgrid(x, y)
Z = np.hypot(X, Y)

plt.imshow(Z, cmap=cm.batlow, origin="lower")
plt.colorbar(label="value")
plt.title("cmcrameri: batlow")
plt.tight_layout()
plt.show()

All colormaps are available under cmcrameri.cm.<name>.

3. Reversed and categorical variants

plt.imshow(Z, cmap=cm.batlow_r)

Categorical (discrete) versions use an “S” suffix, for example cm.batlowS.

4. Quickly browse available maps

from cmcrameri import show_cmaps
show_cmaps()

This displays all installed cmcrameri colormaps in the Python session.

5. Good defaults and tips

   • Sequential data: start with batlow or oslo.

   • Diverging data centered on zero: try vik or broc.

These palettes are designed to be perceptually ordered and fair, and to remain readable for many forms of color-vision deficiency.

6. Set a project-wide default (optional)

import matplotlib as mpl
import cmcrameri.cm as cm
mpl.rcParams["image.cmap"] = cm.batlow

Matplotlib accepts a Colormap object for image.cmap. See Matplotlib’s colormap docs for general behavior. ([Matplotlib][4])

Use cm.<name>, add _r for reversed, S for categorical, and show_cmaps() to explore.

Make figures colorblind-safe (Linux + GNOME)¶

A handy way to simulate common color-vision deficiencies directly on your screen is the GNOME Shell extension Colorblind Filters. It applies real-time filters so you can preview how your plots look under Deuteranopia, Protanopia, Tritanopia, etc.

• Extension: G-dH/gnome-colorblind-filters

• Workflow: generate your plots > toggle the relevant filter > adjust colormap/range/line styles until the figure remains readable.

Best-practice checklist¶

• Use perceptually uniform maps with monotonic lightness for quantitative gradients.

• Match map type to data type (sequential/diverging/cyclic).

• Ensure contrast and legible colorbar ticks/labels; set meaningful limits (vmin/vmax, centered diverging scales).

• Verify accessibility with CVD simulation; don’t rely on color alone—add contours, annotations, or varying line styles where helpful.

• Be consistent across panels and publications; document the colormap choice in captions (e.g., “cmocean ‘balance’”).