# The HCL Color Space¶

The most well known color space is the Red-Green-Blue (RGB) color space. The RGB color space is based the technical demand of digital screens such as computer monitors or TVs. The color of each individual pixel on a screen is created by mixing intensities of red, green, and blue (additive color mixture) to create the picture we can see.

In contrast, the Hue-Chroma-Luminance color space is based on how human color perception works. In contrast to computer screens our visual system (eye-brain) is processing visual information in the dimension of luminance (the lightness of an object), chroma (the colorfulness) and hue (the actual color tone).

The Hue-Chroma-Luminance color space allows us to directly control each of these three dimensions. The swatch plot below illustrates how this works. Each of the three swatches varies only one specific dimension (hue, chroma, and luminance) while all others are held constant. Thus, all colors in the top swatch exhibit the same lightness and same color intensity while only the hue changes from left to right from reddish over greenish, blueish, back to red. The second swatch has constant luminance and hue, but varies from zero chroma (pure gray) on the left hand side to a vivid red. The last swatch goes from black (zero luminance) to white (full luminance) with zero chroma which yields pure gray scale colors.

## Path trough the HCL space¶

The properties of the HCL color space allows to draw well-defined and effective color palettes from the HCL color space. One example are the Diverging HCL palettes. The design principle of diverging color palettes is that both ends of the spectrum have the same luminance and chroma to not distort the data and add artificial weight to one side or the other. The only property which changes from one end to the other is the hue (e.g., from red to blue).

The animation below shows the HCL color space as a volume and the path of the default Diverging HCL palettes (“Blue-Red”) trough the space. The vertical axis shows the luminance dimension from L=0 (black) to L=100 (white), hue and chroma are shwon on the XY plane. The angle (from H=0 to H=360; cyclic) defines the hue, the radial distance to the center the chroma.

The solid line inside the volume shows the path of the “Blue-Red” color palette with 11 unique colors. Both sides of the palette proceed linearly from a neutral center point with 90% luminance (L=90) and no chroma to a dark blue (H=260; constant) and a dark red (H=0; constant) with equal luminance (L=30) and equal chroma (C=80).

This yields a well balanced diverging color map shown in the top left corner of the animation with equal weights on both ends which can easily be adjusted by adjusting the start and end point of the path without and preserve the well defined and monotonic behavior of the overall palette. Variations of the “Blue-Red” diverging color map can be found on the “default color palette page”.

## Why is the Luminance Important¶

The following non-technical example illustrates why it is beneficial to have direct control over the luminance dimension. The image below shows a juicy and delicious apple. Our visual system needs only the blink of an eye to identify the object as what it is.

The plot below shows the very same picture again but with modified color information. The left apple has no color at all (the chroma of all pixels is set to 0), the apple on the right hand side is blue instead of red - something which does not exist in nature. However, our visual system is still capable of identifying the object within a few hundreds of a second.

One reason is that our visual system is very efficient processing smallest differences in luminance. Our retina consists of about 5 to 6 times more cone than rod cells which are very sensitive the lightness of an object. The rod cells, which are responsible to distinguish between different colors, are much less common and only concentrated in a relatively small are in the center of the retina. Large parts of the visual information is gathered via luminance. Our brain converts the luminance information an impression of a shape of the object, in this case \u2018an apple\u2019. Even without color (left image) or with unnatural color attributes (right image) we can still easily identify the apple. The color on top is used as an additional attribute to check if the apple is mellow and sweet, bitter, or might even be poisonous, but is not really necessary identify the apple itself.

The next image shows the same apple again, but the luminance is artificially set constant. Thus, we have to classify the object solely based on the color and the colorfulness which is rather tricky.

This simple example illustrates how false or missing luminance information can quickly obscure the information of a figure or graph. Even with additional attributes on top (hue/chroma) the underlying luminance information is processed by our visual system and helps us to gather the information we are looking at. If used in a wrong way, colors can easily wreck the effectiveness of a (scientific) visualization and might, in a worst case, even mislead the reader in a way that he is perceiving something else or at least focussing on the wrong aspects of the image.

And here the python-colorspace package can help you out with designing effective color maps, investigate the properties of a new or existing color palette, and more.

## Effective Color Palettes¶

Todo

Introduction to the three basic principles of the diverging, sequential, and qualitative color maps.