cmocean:dense#

collection	category	license	authors	source
cmocean	Sequential	MIT	Kristen M. Thyng	https://github.com/matplotlib/cmocean

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from cmap import Colormap

cm = Colormap("cmocean:dense")  # case insensitive

import matplotlib.pyplot as plt
from cmap import Colormap

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

import plotly.express as px
from cmap import Colormap

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

import napari
from cmap import Colormap

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

background: #E6F1F1;
background: linear-gradient(90deg, #E6F1F1 0%, #D8EAEC 3.22581%, #CAE3E8 6.45161%, #BCDCE5 9.67742%, #AFD5E3 12.9032%, #A3CEE2 16.129%, #98C7E2 19.3548%, #8DBFE2 22.5806%, #84B7E3 25.8065%, #7DAFE4 29.0323%, #78A7E4 32.2581%, #749EE5 35.4839%, #7394E4 38.7097%, #748AE2 41.9355%, #7580DE 45.1613%, #7777D9 48.3871%, #786DD2 51.6129%, #7963CA 54.8387%, #795AC1 58.0645%, #7951B7 61.2903%, #7848AC 64.5161%, #7640A1 67.7419%, #733895 70.9677%, #703088 74.1935%, #6C297B 77.4194%, #67226E 80.6452%, #611D60 83.871%, #5A1853 87.0968%, #531545 90.3226%, #4A1239 93.5484%, #40102E 96.7742%, #360E24 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("cmocean:dense").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("cmocean:dense").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("cmocean:dense").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("cmocean:dense").to_pygfx()
    ),
)
gfx.show(image)

import pyqtgraph as pg
from cmap import Colormap

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

Perceptual lightness#

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

cmocean:dense#

Perceptual lightness#

RGB components#

Hue & saturation#