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colorcet:CET_C4#

Aliases: colorcet:cyclic_wrwbw_40_90_c42_n256, colorcet:phase2

Cyclic: white - red - white - blue. Good if you just want to visualise +ve and -ve phase

collection category license authors source
colorcet Cyclic CC-BY-4.0 Peter Kovesi https://colorcet.com 
from cmap import Colormap

cm = Colormap("colorcet:CET_C4")  # case insensitive

import matplotlib.pyplot as plt
from cmap import Colormap

plt.imshow(data, cmap=Colormap("colorcet:CET_C4").to_mpl())

import plotly.express as px
from cmap import Colormap

px.imshow(data, color_continuous_scale=Colormap("colorcet:CET_C4").to_plotly())

import napari
from cmap import Colormap

napari.imshow(data, colormap=Colormap("colorcet:CET_C4").to_napari())
napari.run()

background: #DFD5D8;
background: linear-gradient(90deg, #DFD5D8 0%, #E5C9C3 3.22581%, #E6B4A6 6.45161%, #E59D89 9.67742%, #E1856D 12.9032%, #DB6D52 16.129%, #D45437 19.3548%, #CC3B20 22.5806%, #C93117 25.8065%, #CF4327 29.0323%, #D65D40 32.2581%, #DD765B 35.4839%, #E28E77 38.7097%, #E6A593 41.9355%, #E6BCB0 45.1613%, #E2CECC 48.3871%, #D6D0DE 51.6129%, #C4C3E4 54.8387%, #AFB1E5 58.0645%, #999FE5 61.2903%, #818EE5 64.5161%, #657CE5 67.7419%, #416DE5 70.9677%, #1C64E5 74.1935%, #3068E5 77.4194%, #5776E5 80.6452%, #7686E5 83.871%, #9098E5 87.0968%, #A6AAE5 90.3226%, #BBBCE4 93.5484%, #CECDE2 96.7742%, #DDD5DA 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("colorcet:CET_C4").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("colorcet:CET_C4").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("colorcet:CET_C4").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("colorcet:CET_C4").to_pygfx()
    ),
)
gfx.show(image)

import pyqtgraph as pg
from cmap import Colormap

view = pg.image(data)
view.setColorMap(Colormap("colorcet:CET_C4").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("colorcet:CET_C4").to_gee()
)

See also usage with external libraries.

colorcet:CET_C4colorcet:CET_C4 colormap
colorcet:CET_C4colorcet:CET_C4 colormap

Perceptual lightness#

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

RGB components#

Hue & saturation#