paraview:kindlmann#

A rainbow color map with the luminance adjusted such that it changes monotonically.

collection	category	license	authors	source
paraview	Sequential	BSD-3-Clause	Gordon Kindlmann, Erik Reinhard, Sarah Creem	https://github.com/kennethmoreland-com/kennethmoreland-com.github.io/tree/f0f50dcfda94c8e4c9c4927f78a98ff419fea450/content/color-advice/kindlmann

cmapmatplotlibplotlynapariCSSMore...

from cmap import Colormap

cm = Colormap("paraview:kindlmann")  # case insensitive

import matplotlib.pyplot as plt
from cmap import Colormap

plt.imshow(data, cmap=Colormap("paraview:kindlmann").to_mpl())

import plotly.express as px
from cmap import Colormap

px.imshow(data, color_continuous_scale=Colormap("paraview:kindlmann").to_plotly())

import napari
from cmap import Colormap

napari.imshow(data, colormap=Colormap("paraview:kindlmann").to_napari())
napari.run()

background: #000000;
background: linear-gradient(90deg, #000000 0%, #1B011D 3.22581%, #240337 6.45161%, #27044F 9.67742%, #260569 12.9032%, #1F0685 16.129%, #19089E 19.3548%, #0815AF 22.5806%, #082EA5 25.8065%, #074093 29.0323%, #064E83 32.2581%, #065A76 35.4839%, #05656D 38.7097%, #057064 41.9355%, #067A59 45.1613%, #06844B 48.3871%, #078E39 51.6129%, #079825 54.8387%, #08A20F 58.0645%, #14AC08 61.2903%, #2BB509 64.5161%, #4ABC09 67.7419%, #6BC309 70.9677%, #8EC80A 74.1935%, #B1CB0A 77.4194%, #D4CD0A 80.6452%, #F7CD52 83.871%, #FBD3A9 87.0968%, #FDDDCB 90.3226%, #FDE8E0 93.5484%, #FEF4F0 96.7742%, #FFFFFF 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("paraview:kindlmann").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("paraview:kindlmann").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("paraview:kindlmann").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("paraview:kindlmann").to_pygfx()
    ),
)
gfx.show(image)

import pyqtgraph as pg
from cmap import Colormap

view = pg.image(data)
view.setColorMap(Colormap("paraview:kindlmann").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("paraview:kindlmann").to_gee()
)

Perceptual lightness#

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

paraview:kindlmann#

Perceptual lightness#

RGB components#

Hue & saturation#