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colorcet:CET_CBTC1#

Aliases: colorcet:cyclic_tritanopic_cwrk_40_100_c20_n256

4-phase tritanopic cyclic map

collection category license authors source
colorcet Cyclic CC-BY-4.0 Peter Kovesi https://colorcet.com 
from cmap import Colormap

cm = Colormap("colorcet:CET_CBTC1")  # case insensitive

import matplotlib.pyplot as plt
from cmap import Colormap

plt.imshow(data, cmap=Colormap("colorcet:CET_CBTC1").to_mpl())

import plotly.express as px
from cmap import Colormap

px.imshow(data, color_continuous_scale=Colormap("colorcet:CET_CBTC1").to_plotly())

import napari
from cmap import Colormap

napari.imshow(data, colormap=Colormap("colorcet:CET_CBTC1").to_napari())
napari.run()

background: #26BBD7;
background: linear-gradient(90deg, #26BBD7 0%, #4DC4DE 3.22581%, #70CDE2 6.45161%, #8CD5E7 9.67742%, #A6DEEC 12.9032%, #BEE6F1 16.129%, #D5EFF6 19.3548%, #EBF7FA 22.5806%, #FBF7F6 25.8065%, #FEEBE8 29.0323%, #FDDED9 32.2581%, #FCD0C9 35.4839%, #F9C3BA 38.7097%, #F7B5AB 41.9355%, #F3A79C 45.1613%, #EF9A8F 48.3871%, #E39085 51.6129%, #D2897F 54.8387%, #BF827A 58.0645%, #AD7C75 61.2903%, #9B756F 64.5161%, #896E6A 67.7419%, #776865 70.9677%, #676463 74.1935%, #5F696C 77.4194%, #5D747A 80.6452%, #598089 83.871%, #548B99 87.0968%, #4D97A8 90.3226%, #41A3B8 93.5484%, #2FAFC8 96.7742%, #24BAD6 100%);

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("colorcet:CET_CBTC1").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("colorcet:CET_CBTC1").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("colorcet:CET_CBTC1").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("colorcet:CET_CBTC1").to_pygfx()
    ),
)
gfx.show(image)

import pyqtgraph as pg
from cmap import Colormap

view = pg.image(data)
view.setColorMap(Colormap("colorcet:CET_CBTC1").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("colorcet:CET_CBTC1").to_gee()
)

See also usage with external libraries.

colorcet:CET_CBTC1colorcet:CET_CBTC1 colormap
colorcet:CET_CBTC1colorcet:CET_CBTC1 colormap

Perceptual lightness#

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

RGB components#

Hue & saturation#