cmocean:deep#

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

cmapmatplotlibplotlynapariCSSMore...

from cmap import Colormap

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

import matplotlib.pyplot as plt
from cmap import Colormap

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

import plotly.express as px
from cmap import Colormap

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

import napari
from cmap import Colormap

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

background: #FDFECC;
background: linear-gradient(90deg, #FDFECC 0%, #EDF7C2 3.22581%, #DCF1BA 6.45161%, #CBEBB2 9.67742%, #B9E5AC 12.9032%, #A7E0A7 16.129%, #95D9A4 19.3548%, #84D3A3 22.5806%, #75CCA3 25.8065%, #69C4A4 29.0323%, #5FBCA4 32.2581%, #59B4A4 35.4839%, #54ACA3 38.7097%, #50A3A2 41.9355%, #4D9BA1 45.1613%, #4A939F 48.3871%, #478A9D 51.6129%, #44829B 54.8387%, #427A99 58.0645%, #407297 61.2903%, #3E6A96 64.5161%, #3E6194 67.7419%, #3E5892 70.9677%, #404F8D 74.1935%, #414785 77.4194%, #413F79 80.6452%, #3F386B 83.871%, #3C325D 87.0968%, #372C50 90.3226%, #332643 93.5484%, #2D2037 96.7742%, #281A2C 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:deep").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:deep").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:deep").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:deep").to_pygfx()
    ),
)
gfx.show(image)

import pyqtgraph as pg
from cmap import Colormap

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

Perceptual lightness#

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

cmocean:deep#

Perceptual lightness#

RGB components#

Hue & saturation#