crameri:corkO#

collection	category	license	authors	source
crameri	Cyclic	MIT	Fabio Crameri	https://zenodo.org/record/5501399

cmapmatplotlibplotlynapariCSSMore...

from cmap import Colormap

cm = Colormap("crameri:corkO")  # case insensitive

import matplotlib.pyplot as plt
from cmap import Colormap

plt.imshow(data, cmap=Colormap("crameri:corkO").to_mpl())

import plotly.express as px
from cmap import Colormap

px.imshow(data, color_continuous_scale=Colormap("crameri:corkO").to_plotly())

import napari
from cmap import Colormap

napari.imshow(data, colormap=Colormap("crameri:corkO").to_napari())
napari.run()

background: #3F3E3A;
background: linear-gradient(90deg, #3F3E3A 0%, #3E3D42 3.22581%, #3E3D4B 6.45161%, #3E4157 9.67742%, #404966 12.9032%, #445376 16.129%, #4B6186 19.3548%, #556F95 22.5806%, #627DA2 25.8065%, #708CAD 29.0323%, #7F99B7 32.2581%, #8EA7BF 35.4839%, #9CB3C5 38.7097%, #A7BEC8 41.9355%, #AEC6C7 45.1613%, #B0CAC1 48.3871%, #ACCAB7 51.6129%, #A4C6AA 54.8387%, #98BF9B 58.0645%, #8AB68B 61.2903%, #7CAB7A 64.5161%, #6E9E69 67.7419%, #629058 70.9677%, #578248 74.1935%, #4F743C 77.4194%, #496733 80.6452%, #465C2E 83.871%, #44532C 87.0968%, #424C2D 90.3226%, #414630 93.5484%, #404234 96.7742%, #3F3E3A 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("crameri:corkO").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("crameri:corkO").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("crameri:corkO").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("crameri:corkO").to_pygfx()
    ),
)
gfx.show(image)

import pyqtgraph as pg
from cmap import Colormap

view = pg.image(data)
view.setColorMap(Colormap("crameri:corkO").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("crameri:corkO").to_gee()
)

Perceptual lightness#

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

crameri:corkO#

Perceptual lightness#

RGB components#

Hue & saturation#