crameri:davos#

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

cmapmatplotlibplotlynapariCSSMore...

from cmap import Colormap

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

import matplotlib.pyplot as plt
from cmap import Colormap

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

import plotly.express as px
from cmap import Colormap

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

import napari
from cmap import Colormap

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

background: #00054A;
background: linear-gradient(90deg, #00054A 0%, #031256 3.22581%, #091D62 6.45161%, #0F286D 9.67742%, #153378 12.9032%, #1B3E82 16.129%, #22488A 19.3548%, #295392 22.5806%, #315C97 25.8065%, #39659B 29.0323%, #416D9D 32.2581%, #49749D 35.4839%, #507B9C 38.7097%, #58819A 41.9355%, #608698 45.1613%, #688B95 48.3871%, #6F9091 51.6129%, #77958E 54.8387%, #7F9A8B 58.0645%, #88A089 61.2903%, #92A787 64.5161%, #9DB088 67.7419%, #A9B98B 70.9677%, #B8C592 74.1935%, #C8D19D 77.4194%, #D7DDAB 80.6452%, #E4E8BA 83.871%, #EEF0C9 87.0968%, #F5F5D8 90.3226%, #FAF9E6 93.5484%, #FCFCF2 96.7742%, #FEFEFE 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:davos").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:davos").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:davos").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:davos").to_pygfx()
    ),
)
gfx.show(image)

import pyqtgraph as pg
from cmap import Colormap

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

Perceptual lightness#

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

crameri:davos#

Perceptual lightness#

RGB components#

Hue & saturation#