crameri:devon#

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

cmapmatplotlibplotlynapariCSSMore...

from cmap import Colormap

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

import matplotlib.pyplot as plt
from cmap import Colormap

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

import plotly.express as px
from cmap import Colormap

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

import napari
from cmap import Colormap

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

background: #2C1A4C;
background: linear-gradient(90deg, #2C1A4C 0%, #2B2154 3.22581%, #2A295C 6.45161%, #293163 9.67742%, #28386B 12.9032%, #284074 16.129%, #27497C 19.3548%, #275186 22.5806%, #295A92 25.8065%, #2E619D 29.0323%, #3367A9 32.2581%, #3B6DB4 35.4839%, #4674C0 38.7097%, #557BCA 41.9355%, #6583D2 45.1613%, #758BD9 48.3871%, #8592E0 51.6129%, #9499E5 54.8387%, #A2A0EA 58.0645%, #ACA7ED 61.2903%, #B5AFF0 64.5161%, #BCB6F1 67.7419%, #C2BDF3 70.9677%, #C9C4F4 74.1935%, #CFCBF5 77.4194%, #D6D2F7 80.6452%, #DDDAF8 83.871%, #E4E1F9 87.0968%, #EAE8FB 90.3226%, #F1F0FC 93.5484%, #F8F7FE 96.7742%, #FFFFFF 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:devon").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:devon").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:devon").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:devon").to_pygfx()
    ),
)
gfx.show(image)

import pyqtgraph as pg
from cmap import Colormap

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

Perceptual lightness#

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

crameri:devon#

Perceptual lightness#

RGB components#

Hue & saturation#