Color Maps

Important

Note that not every color map is perceptually uniform. Always check the documentation of the corresponding ColorMap enum value when selecting a suitable false color schema.

Be also aware that choosing a color map is more intricate than most people anticipate. For more details, I highly recommend Peter Kovesi’s excellent article about the caveats with color maps and how to avoid them.

Sequential

Sequential (aka linear) color maps linearly increase/decrease the lightness and/or saturation over the color map range. They are the most generic color maps and should be used to represent data which has an intrinsic ordering.

Name

Gradient
cividis Rendered color gradient
cvd-sequential Rendered color gradient
cvd-sequential-vivid Rendered color gradient
gouldian Rendered color gradient
viridis Rendered color gradient
inferno Rendered color gradient
hell Rendered color gradient
thermal Rendered color gradient
black-body Rendered color gradient
hot Rendered color gradient
cold Rendered color gradient
water Rendered color gradient
copper Rendered color gradient
spring Rendered color gradient
summer Rendered color gradient
autumn Rendered color gradient
winter Rendered color gradient
gray Rendered color gradient
yarg Rendered color gradient

Categorical

Categorical or quantitative maps are color palettes with distinct colors, suitable to visualize categorical data.

Name

Gradient
categories-10 Rendered color gradient
categories-12 Rendered color gradient
categories-20 Rendered color gradient
glasbey-dark Rendered color gradient
glasbey-light Rendered color gradient

Cyclic

Cyclic color maps begin and end at the same color. They are useful to represent data that wraps around, e.g. phase angles or orientation values.

Name

Gradient
cvd-orientation Rendered color gradient
orientation-4 Rendered color gradient
orientation-6 Rendered color gradient
hsv Rendered color gradient
optical-flow Rendered color gradient
twilight Rendered color gradient
twilight-shifted Rendered color gradient

Color Vision Deficiency

These color maps are suitable for viewers with color vision deficiency (CVD). The colors are chosen such that people who are color blind should share a common perceptual interpretation of data with people who have normal color vision.

Name

Gradient
cividis Rendered color gradient
cvd-sequential Rendered color gradient
cvd-sequential-vivid Rendered color gradient
cvd-diverging Rendered color gradient
cvd-orientation Rendered color gradient

Diverging

Diverging color maps are suitable to represent values above or below a defined reference value. Note that these maps usually have a small perceptual flat spot at the center.

Name

Gradient
cvd-diverging Rendered color gradient
seismic Rendered color gradient
temperature Rendered color gradient
temperature-dark Rendered color gradient
spectral-diverging Rendered color gradient

Rainbow

Rainbow color maps are widely used but also too often misused. They require special care as they can easily lead to perception of features that are not present in the data and also suffer from blind spots (where the viewer cannot distinguish actually present features).

These issues can be seen for example at the jet color gradient below, which shows banding effects. For further details, refer to the summary provided on Peter Kovesi’s website.

Name

Gradient
jet Rendered color gradient
rainbow Rendered color gradient
turbo Rendered color gradient

Special Purpose

These color maps are often designed for a particular purpose, such as representing thermographic (thermal), topographic (earth, ocean, etc.), or multi-spectral (spectral) data.

Name

Gradient
ocean Rendered color gradient
water Rendered color gradient
earth Rendered color gradient
terrain Rendered color gradient
relief Rendered color gradient
relief-low-contrast Rendered color gradient
thermal Rendered color gradient
spectral Rendered color gradient
disparity Rendered color gradient
optical-flow Rendered color gradient

User-Defined Colors

If you need other than the provided color maps, you can define your custom color maps via set_custom_colormap(). These can then be used analogously to the predefined color maps via the enumeration values ColorMap.Custom1, ColorMap.Custom2, etc. or their string representation. For example:

 1# Exemplary categorical data for visualization
 2import numpy as np
 3labels = np.array(
 4    [[0, 1, 2], [-3, -4, -6], [20000, 20001, 20003]],
 5    dtype=np.int32)
 6
 7# Register a custom color map. This is a usage example and by
 8# no means a useful color map!
 9viren2d.set_custom_colormap(
10   'custom1', ['#800000', (0.94, 0.13, 0.15), 'rose-red'])
11
12# Apply the custom map for label colorization:
13vis = viren2d.colorize_labels(labels=labels, colormap='custom1')