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cmocean:curl_pink#

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

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

import matplotlib.pyplot as plt
from cmap import Colormap

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

import plotly.express as px
from cmap import Colormap

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

import napari
from cmap import Colormap

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

background: #340D35;
background: linear-gradient(90deg, #340D35 0%, #3E103E 3.22581%, #491147 6.45161%, #53134E 9.67742%, #5E1455 12.9032%, #691659 16.129%, #74195D 19.3548%, #7E1D5F 22.5806%, #882260 25.8065%, #912860 29.0323%, #9A2E60 32.2581%, #A33560 35.4839%, #AB3D60 38.7097%, #B34460 41.9355%, #BA4D60 45.1613%, #C05561 48.3871%, #C75E62 51.6129%, #CC6864 54.8387%, #D17167 58.0645%, #D57B6C 61.2903%, #D98572 64.5161%, #DC907A 67.7419%, #DF9A83 70.9677%, #E2A58D 74.1935%, #E4AF98 77.4194%, #E7B9A4 80.6452%, #EAC3B1 83.871%, #EDCDBE 87.0968%, #F0D8CB 90.3226%, #F4E2D9 93.5484%, #F9ECE7 96.7742%, #FEF6F5 100%);

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:curl_pink").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:curl_pink").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:curl_pink").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:curl_pink").to_pygfx()
    ),
)
gfx.show(image)

import pyqtgraph as pg
from cmap import Colormap

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

See also usage with external libraries.

cmocean:curl_pinkcmocean:curl_pink colormap
cmocean:curl_pinkcmocean:curl_pink colormap

Perceptual lightness#

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

RGB components#

Hue & saturation#