cmocean:rain#

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

cmapmatplotlibplotlynapariCSSMore...

from cmap import Colormap

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

import matplotlib.pyplot as plt
from cmap import Colormap

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

import plotly.express as px
from cmap import Colormap

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

import napari
from cmap import Colormap

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

background: #EEEDF3;
background: linear-gradient(90deg, #EEEDF3 0%, #E8E5E6 3.22581%, #E2DCD9 6.45161%, #DED3CA 9.67742%, #DACABA 12.9032%, #D5C2A9 16.129%, #CDBB9A 19.3548%, #C0B690 22.5806%, #B2B289 25.8065%, #A4AD84 29.0323%, #96A97F 32.2581%, #88A47A 35.4839%, #799F76 38.7097%, #6A9B72 41.9355%, #599670 45.1613%, #47916E 48.3871%, #348C6D 51.6129%, #23866E 54.8387%, #147F6E 58.0645%, #09786E 61.2903%, #04706E 64.5161%, #04696C 67.7419%, #0A616A 70.9677%, #125A67 74.1935%, #195263 77.4194%, #1F4A5E 80.6452%, #234258 83.871%, #253A51 87.0968%, #25324B 90.3226%, #252B44 93.5484%, #23233E 96.7742%, #221B38 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:rain").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:rain").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:rain").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:rain").to_pygfx()
    ),
)
gfx.show(image)

import pyqtgraph as pg
from cmap import Colormap

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

Perceptual lightness#

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

cmocean:rain#

Perceptual lightness#

RGB components#

Hue & saturation#