crameri:vikO#

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

cmapmatplotlibplotlynapariCSSMore...

from cmap import Colormap

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

import matplotlib.pyplot as plt
from cmap import Colormap

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

import plotly.express as px
from cmap import Colormap

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

import napari
from cmap import Colormap

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

background: #4F1A3D;
background: linear-gradient(90deg, #4F1A3D 0%, #491F47 3.22581%, #432754 6.45161%, #3D3061 9.67742%, #373C6F 12.9032%, #334A7D 16.129%, #34598B 19.3548%, #3B6897 22.5806%, #4879A3 25.8065%, #5A89AF 29.0323%, #6E99B9 32.2581%, #84A8C1 35.4839%, #9BB5C7 38.7097%, #B0BEC8 41.9355%, #C3C2C4 45.1613%, #D0C1BA 48.3871%, #D7BBAC 51.6129%, #D9B19B 54.8387%, #D8A489 58.0645%, #D39676 61.2903%, #CB8763 64.5161%, #C17650 67.7419%, #B5643F 70.9677%, #A65130 74.1935%, #964025 77.4194%, #87301F 80.6452%, #79241E 83.871%, #6D1C20 87.0968%, #641725 90.3226%, #5D152B 93.5484%, #561633 96.7742%, #50193C 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:vikO").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:vikO").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:vikO").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:vikO").to_pygfx()
    ),
)
gfx.show(image)

import pyqtgraph as pg
from cmap import Colormap

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

Perceptual lightness#

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

crameri:vikO#

Perceptual lightness#

RGB components#

Hue & saturation#