contrib:araucaria#

The araucaria colormap is a sequential colormap that transitions from deep blue to light pink, created with the viscm tool.

collection	category	license	authors	source
contrib	Sequential	CC0	Antonio Camargo	https://github.com/pyapp-kit/cmap

cmapmatplotlibplotlynapariCSSMore...

from cmap import Colormap

cm = Colormap("contrib:araucaria")  # case insensitive

import matplotlib.pyplot as plt
from cmap import Colormap

plt.imshow(data, cmap=Colormap("contrib:araucaria").to_mpl())

import plotly.express as px
from cmap import Colormap

px.imshow(data, color_continuous_scale=Colormap("contrib:araucaria").to_plotly())

import napari
from cmap import Colormap

napari.imshow(data, colormap=Colormap("contrib:araucaria").to_napari())
napari.run()

background: #2D417A;
background: linear-gradient(90deg, #2D417A 0%, #364383 3.22581%, #3F458C 6.45161%, #474796 9.67742%, #50499E 12.9032%, #5A4BA6 16.129%, #634DAB 19.3548%, #6C4FB0 22.5806%, #7551B5 25.8065%, #7F53B8 29.0323%, #8856BC 32.2581%, #9158BF 35.4839%, #9A5AC2 38.7097%, #A35DC3 41.9355%, #AB61C4 45.1613%, #B266C3 48.3871%, #B96BC2 51.6129%, #C070C0 54.8387%, #C577BF 58.0645%, #CA7DBD 61.2903%, #CF84BC 64.5161%, #D48ABC 67.7419%, #D991BB 70.9677%, #DE98BB 74.1935%, #E29FBB 77.4194%, #E5A6BC 80.6452%, #E8AEBF 83.871%, #EAB6C2 87.0968%, #EBBEC8 90.3226%, #ECC7CE 93.5484%, #EDCFD4 96.7742%, #EED7DA 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("contrib:araucaria").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("contrib:araucaria").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("contrib:araucaria").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("contrib:araucaria").to_pygfx()
    ),
)
gfx.show(image)

import pyqtgraph as pg
from cmap import Colormap

view = pg.image(data)
view.setColorMap(Colormap("contrib:araucaria").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("contrib:araucaria").to_gee()
)

Perceptual lightness#

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

contrib:araucaria#

Perceptual lightness#

RGB components#

Hue & saturation#