Turbo colorscale is a Plotly version of the Google-AI Turbo colormap https://ai.googleblog.com/2019/08/turbo-improved-rainbow-colormap-for.html : turbo = [[0.0, '#30123b'], [0.071, '#4145ab'], [0.143, '#4675ed'], [0.214, '#39a2fc'], [0.286, '#1bcfd4'], [0.357, '#24eca6'], [0.429, '#61fc6c'], …

Cool! Should we add it to plotly.colors ?

@nicolaskruchten I think it should be included in the list of Plotly colorscales since it is an alternative to Jet. I didn’t notice Jet being used by Plotly users (probably because of its drawbacks) .

OK, it’ll be in 4.10: https://github.com/plotly/plotly.py/issues/2656

Sorry, didn’t make 4.10 … I’ll try for 4.11 :slight_smile:

@empet what do you think of adding some or all of these to Plotly? http://www.fabiocrameri.ch/colourmaps.php

Hah now I feel silly, seeing that your repo is listed in the official docs from that site! https://github.com/empet/scientific-colorscales/blob/master/scicolorscales.py

@nicolaskruchten The library scientific colormaps has at least two versions. My repo illustrates plots with colorscales derived from the first version. The colormaps there are defined following the Kovesi idea, to ensure lightness linearity in the space CIECAM. In 2019 when EHT Telescope releas…

Do you think this is an appropriate way of converting from 256 RGB tuples to our format? import hawaii import numpy as np import plotly.express as px px.bar(x=[1]*100, y=["hawaii"]*100, color=range(100), color_continuous_scale = [ '#%02x%02x%02x' % tuple(x) for x in …

@nicolaskruchten It’s OK, except the multilication np.array(...)*256 . I don’t understand why you don’t multiply by 255. The max element in the array of cm data is 1, and each r, g, b takes values within {0,1,2, ... 255}. With the factor 256 you can get r, g, or b=256. np.clip restricts to th…

NEW! Google-AI Turbo colorscale

📊 Plotly Python

nicolaskruchten November 9, 2020, 1:55am 9

OK, thanks! I’ll see if I have some time to convert the v6.0 maps into the format we need

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NEW! Google-AI Turbo colorscale

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