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matplotlib:coolwarm#

Generated from CoolWarmFloat33.csv of Diverging Color Maps for Scientific Visualization by Kenneth Moreland https://www.kennethmoreland.com/color-maps/. For paper reference, see https://doi.org/10.1007/978-3-642-10520-3_9

collection category license authors source
matplotlib Diverging PSF Kenneth Moreland https://github.com/matplotlib/matplotlib 
from cmap import Colormap

cm = Colormap("matplotlib:coolwarm")  # case insensitive

import matplotlib.pyplot as plt
from cmap import Colormap

plt.imshow(data, cmap=Colormap("matplotlib:coolwarm").to_mpl())

import plotly.express as px
from cmap import Colormap

px.imshow(data, color_continuous_scale=Colormap("matplotlib:coolwarm").to_plotly())

import napari
from cmap import Colormap

napari.imshow(data, colormap=Colormap("matplotlib:coolwarm").to_napari())
napari.run()

background: #3B4CC0;
background: linear-gradient(90deg, #3B4CC0 0%, #445ACD 3.22581%, #4E69D8 6.45161%, #5876E2 9.67742%, #6384EB 12.9032%, #6E90F2 16.129%, #799CF8 19.3548%, #84A8FC 22.5806%, #90B2FE 25.8065%, #9BBCFF 29.0323%, #A6C4FE 32.2581%, #B1CBFB 35.4839%, #BCD2F7 38.7097%, #C6D6F1 41.9355%, #D0DAEA 45.1613%, #D8DCE1 48.3871%, #E1DBD7 51.6129%, #E9D5CB 54.8387%, #EFCFBE 58.0645%, #F3C7B2 61.2903%, #F6BEA5 64.5161%, #F7B498 67.7419%, #F7A98B 70.9677%, #F59D7E 74.1935%, #F19071 77.4194%, #ED8265 80.6452%, #E67259 83.871%, #DF624E 87.0968%, #D65143 90.3226%, #CC3F39 93.5484%, #C0292F 96.7742%, #B40426 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("matplotlib:coolwarm").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("matplotlib:coolwarm").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("matplotlib:coolwarm").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("matplotlib:coolwarm").to_pygfx()
    ),
)
gfx.show(image)

import pyqtgraph as pg
from cmap import Colormap

view = pg.image(data)
view.setColorMap(Colormap("matplotlib:coolwarm").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("matplotlib:coolwarm").to_gee()
)

See also usage with external libraries.

matplotlib:coolwarmmatplotlib:coolwarm colormap
matplotlib:coolwarmmatplotlib:coolwarm colormap

Perceptual lightness#

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

RGB components#

Hue & saturation#