cividis:cividis#

Like viridis, cividis is perceptually uniform and colorblind friendly. However, cividis also goes a step further: not only is it usable by colorblind users, it should actually look effectively identical to colorblind and non-colorblind users. For more details, see Nuñez J, et al (2018).

collection	category	license	authors	source
cividis	Sequential	BSD-3-Clause	Jamie Nunez	https://github.com/pnnl/cmaputil

cmapmatplotlibplotlynapariCSSMore...

from cmap import Colormap

cm = Colormap("cividis:cividis")  # case insensitive

import matplotlib.pyplot as plt
from cmap import Colormap

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

import plotly.express as px
from cmap import Colormap

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

import napari
from cmap import Colormap

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

background: #00224E;
background: linear-gradient(90deg, #00224E 0%, #00285C 3.22581%, #002E6B 6.45161%, #073370 9.67742%, #1C396F 12.9032%, #293F6E 16.129%, #33446C 19.3548%, #3D4A6C 22.5806%, #45506C 25.8065%, #4D556C 29.0323%, #555B6D 32.2581%, #5C616E 35.4839%, #636770 38.7097%, #6B6D72 41.9355%, #727274 45.1613%, #797877 48.3871%, #807E78 51.6129%, #888578 54.8387%, #908B78 58.0645%, #989177 61.2903%, #A09775 64.5161%, #A89E73 67.7419%, #B0A471 70.9677%, #B9AB6D 74.1935%, #C1B26A 77.4194%, #CAB965 80.6452%, #D3C060 83.871%, #DCC859 87.0968%, #E5CF52 90.3226%, #EED748 93.5484%, #F8DF3C 96.7742%, #FEE838 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("cividis:cividis").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("cividis:cividis").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("cividis:cividis").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("cividis:cividis").to_pygfx()
    ),
)
gfx.show(image)

import pyqtgraph as pg
from cmap import Colormap

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

Perceptual lightness#

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

cividis:cividis#

Perceptual lightness#

RGB components#

Hue & saturation#