<story><title>Turbo, an Improved Rainbow Colormap for Visualization</title><url>https://ai.googleblog.com/2019/08/turbo-improved-rainbow-colormap-for.html</url></story><parent_chain><item><author>krick</author><text>While arguments in the blogpost are mostly compelling (perhaps the most important to me is Jet&amp;#x2F;Turbo being far more high-contrast in the &amp;quot;colder&amp;quot; regions), I see Inferno as vastly superior still, mostly for the same reasons it was superior to Jet. All these colormaps are intended to represent one-dimensional gradient, as in &amp;quot;from hot to cold&amp;quot; and it is completely obvious when looking at Inferno. Jet &amp;amp; Turbo give a false impression of non-one-dimensionality. It &lt;i&gt;might&lt;/i&gt; be a matter of being used to the colormap in question, but Jet is as old and common as it gets, and it still is not self-evident to me that the dark red is hotter than the very light yellow. I mean, obviously I &lt;i&gt;know&lt;/i&gt; that, but I will tell you instantly without thinking what is the hottest and coldest regions on Inferno image, and I am significantly more likely to make a mistake when I&amp;#x27;m tired with the Jet (and, presumably Turbo — for the same reasons).</text></item></parent_chain><comment><author>crazygringo</author><text>I have to disagree.&lt;p&gt;If you want to represent a one-dimensional gradient where what is most important is &amp;quot;hot vs cold&amp;quot;, i.e. the extremes, then you shouldn&amp;#x27;t be using a colormap (rainbow) in the first place, you should be using a direct gradient instead, whether grayscale or interpolated between two nearby colors (e.g. red&amp;#x2F;yellow).&lt;p&gt;Colormaps are for where highlighting &lt;i&gt;local variation&lt;/i&gt; is important -- more important than indicating whether something is closer to one extreme or another. In the examples, this means being able to distinguish the patterns of the objects. In math plots, it means helping to see the shapes of curves. In weather maps, that means judging variation within your state, even while the map ranges from Alaska to Florida.&lt;p&gt;For this, it&amp;#x27;s most important to be able to maximize contrast in local differences but also have the perceptual rate of change be roughly comparable.&lt;p&gt;Rainbows maximize contrast using hue, because our eyes can simply distinguish so much more than with just brightness. Turbo makes the rate of change more constant than Jet. And Jet&amp;#x2F;Turbo most importantly &lt;i&gt;double&lt;/i&gt; the resolution that Inferno has, going from dark-bright-dark rather than just dark-bright.</text></comment>
<story><title>Turbo, an Improved Rainbow Colormap for Visualization</title><url>https://ai.googleblog.com/2019/08/turbo-improved-rainbow-colormap-for.html</url></story><parent_chain><item><author>krick</author><text>While arguments in the blogpost are mostly compelling (perhaps the most important to me is Jet&amp;#x2F;Turbo being far more high-contrast in the &amp;quot;colder&amp;quot; regions), I see Inferno as vastly superior still, mostly for the same reasons it was superior to Jet. All these colormaps are intended to represent one-dimensional gradient, as in &amp;quot;from hot to cold&amp;quot; and it is completely obvious when looking at Inferno. Jet &amp;amp; Turbo give a false impression of non-one-dimensionality. It &lt;i&gt;might&lt;/i&gt; be a matter of being used to the colormap in question, but Jet is as old and common as it gets, and it still is not self-evident to me that the dark red is hotter than the very light yellow. I mean, obviously I &lt;i&gt;know&lt;/i&gt; that, but I will tell you instantly without thinking what is the hottest and coldest regions on Inferno image, and I am significantly more likely to make a mistake when I&amp;#x27;m tired with the Jet (and, presumably Turbo — for the same reasons).</text></item></parent_chain><comment><author>kuzehanka</author><text>This is all true.&lt;p&gt;Where jet and other non-uniform maps are useful is in highlighting small fluctuations in the value. It&amp;#x27;s less about z1 &amp;gt; z2 and more about z1 != z2  across the image plane.&lt;p&gt;A similar problem happens with datashader in histogram normalisation mode. It&amp;#x27;s great for highlighting small variations in the underlying density and revealing detail that you&amp;#x27;d otherwise never see, but worthless for trying to compare one density to another by eye.&lt;p&gt;Horses for courses.</text></comment>