google:turbo#

Turbo mimics the lightness profile of Jet, going from low to high back down to low, while also addressing some of its shortcomings, such as false detail, banding and color blindness ambiguity. Its lightness slope is generally double that of Viridis, allowing subtle changes to be more easily seen. This enhances detail perception when color can be used to disambiguate the low and high ends.

collection	category	license	authors	source
google	Miscellaneous	Apache-2.0	Anton Mikhailov	https://gist.github.com/mikhailov-work/ee72ba4191942acecc03fe6da94fc73f

cmapmatplotlibplotlynapariCSSMore...

from cmap import Colormap

cm = Colormap("google:turbo")  # case insensitive

import matplotlib.pyplot as plt
from cmap import Colormap

plt.imshow(data, cmap=Colormap("google:turbo").to_mpl())

import plotly.express as px
from cmap import Colormap

px.imshow(data, color_continuous_scale=Colormap("google:turbo").to_plotly())

import napari
from cmap import Colormap

napari.imshow(data, colormap=Colormap("google:turbo").to_napari())
napari.run()

background: #30123B;
background: linear-gradient(90deg, #30123B 0%, #3A2A74 3.22581%, #4042A4 6.45161%, #4558CA 9.67742%, #476EE6 12.9032%, #4683F8 16.129%, #4097FF 19.3548%, #34ACF8 22.5806%, #25C0E7 25.8065%, #1AD2D2 29.0323%, #18E0BD 32.2581%, #24ECA8 35.4839%, #3BF58F 38.7097%, #59FB73 41.9355%, #79FE59 45.1613%, #97FE44 48.3871%, #AEFA37 51.6129%, #C3F234 54.8387%, #D7E535 58.0645%, #E8D639 61.2903%, #F4C63A 64.5161%, #FCB437 67.7419%, #FE9E2F 70.9677%, #FC8624 74.1935%, #F76D1A 77.4194%, #EE5610 80.6452%, #E2430A 83.871%, #D43305 87.0968%, #C22403 90.3226%, #AD1801 93.5484%, #950D01 96.7742%, #7A0403 100%);

altairbokehvispypygfxpyqtgraphEarth Engine

import altair as alt
from cmap import Colormap

# df: a DataFrame with columns x, y and value
alt.Chart(df).mark_rect().encode(
    x="x:O",
    y="y:O",
    color=alt.Color(
        "value:Q", scale=alt.Scale(range=Colormap("google:turbo").to_altair())
    ),
)

from bokeh.plotting import figure, show
from cmap import Colormap

p = figure()
p.image(
    image=[data], x=0, y=0, dw=1, dh=1,
    color_mapper=Colormap("google:turbo").to_bokeh(),
)
show(p)

from vispy import app, scene
from cmap import Colormap

canvas = scene.SceneCanvas(show=True)
view = canvas.central_widget.add_view()
scene.visuals.Image(data, cmap=Colormap("google:turbo").to_vispy(), parent=view.scene)
view.camera = scene.PanZoomCamera(aspect=1)
view.camera.set_range()
app.run()

import pygfx as gfx
from cmap import Colormap

image = gfx.Image(
    gfx.Geometry(grid=gfx.Texture(data, dim=2)),
    gfx.ImageBasicMaterial(
        clim=(data.min(), data.max()), map=Colormap("google:turbo").to_pygfx()
    ),
)
gfx.show(image)

import pyqtgraph as pg
from cmap import Colormap

view = pg.image(data)
view.setColorMap(Colormap("google:turbo").to_pyqtgraph())
pg.exec()

import ee
from cmap import Colormap

ee.Initialize(project="my-project")  # your Google Cloud project
elevation = ee.Image("CGIAR/SRTM90_V4")
image = elevation.visualize(
    min=0, max=3000, palette=Colormap("google:turbo").to_gee()
)

Perceptual lightness#

Lightness (J′) measured in CAM02 Uniform Color Space (CAM02-UCS)

google:turbo#

Perceptual lightness#

RGB components#

Hue & saturation#