cmocean:turbid#

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

cmapmatplotlibplotlynapariCSSMore...

from cmap import Colormap

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

import matplotlib.pyplot as plt
from cmap import Colormap

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

import plotly.express as px
from cmap import Colormap

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

import napari
from cmap import Colormap

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

background: #E9F6AB;
background: linear-gradient(90deg, #E9F6AB 0%, #E4EC9F 3.22581%, #E0E393 6.45161%, #DCD988 9.67742%, #D8D07C 12.9032%, #D4C772 16.129%, #D0BE68 19.3548%, #CCB45E 22.5806%, #C9AC56 25.8065%, #C5A34F 29.0323%, #C19A49 32.2581%, #BC9244 35.4839%, #B78A41 38.7097%, #B1823E 41.9355%, #AB7B3D 45.1613%, #A5743C 48.3871%, #9E6D3B 51.6129%, #97673A 54.8387%, #8F613A 58.0645%, #875B39 61.2903%, #7F5639 64.5161%, #765138 67.7419%, #6E4C36 70.9677%, #654734 74.1935%, #5D4232 77.4194%, #543D30 80.6452%, #4C382D 83.871%, #43332A 87.0968%, #3B2E26 90.3226%, #332923 93.5484%, #2A241F 96.7742%, #221F1B 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:turbid").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:turbid").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:turbid").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:turbid").to_pygfx()
    ),
)
gfx.show(image)

import pyqtgraph as pg
from cmap import Colormap

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

Perceptual lightness#

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

cmocean:turbid#

Perceptual lightness#

RGB components#

Hue & saturation#