seaborn:vlag#

collection	category	license	authors	source
seaborn	Diverging	BSD-3-Clause	Michael Waskom	https://github.com/mwaskom/seaborn

cmapmatplotlibplotlynapariCSSMore...

from cmap import Colormap

cm = Colormap("seaborn:vlag")  # case insensitive

import matplotlib.pyplot as plt
from cmap import Colormap

plt.imshow(data, cmap=Colormap("seaborn:vlag").to_mpl())

import plotly.express as px
from cmap import Colormap

px.imshow(data, color_continuous_scale=Colormap("seaborn:vlag").to_plotly())

import napari
from cmap import Colormap

napari.imshow(data, colormap=Colormap("seaborn:vlag").to_napari())
napari.run()

background: #2369BD;
background: linear-gradient(90deg, #2369BD 0%, #3972BC 3.22581%, #4B7BBC 6.45161%, #5A84BD 9.67742%, #698CBE 12.9032%, #7695C1 16.129%, #849EC4 19.3548%, #91A7C7 22.5806%, #9EB0CB 25.8065%, #ABB9D0 29.0323%, #B9C3D6 32.2581%, #C6CDDB 35.4839%, #D3D7E2 38.7097%, #E1E2E9 41.9355%, #EEEDF0 45.1613%, #F8F4F5 48.3871%, #FAF3F2 51.6129%, #F6E8E7 54.8387%, #F0DBDA 58.0645%, #EACECC 61.2903%, #E4C1BF 64.5161%, #DFB4B2 67.7419%, #DAA8A5 70.9677%, #D59B99 74.1935%, #D08F8C 77.4194%, #CB8380 80.6452%, #C57674 83.871%, #C06A68 87.0968%, #BB5E5D 90.3226%, #B55151 93.5484%, #AF4446 96.7742%, #A9373B 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("seaborn:vlag").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("seaborn:vlag").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("seaborn:vlag").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("seaborn:vlag").to_pygfx()
    ),
)
gfx.show(image)

import pyqtgraph as pg
from cmap import Colormap

view = pg.image(data)
view.setColorMap(Colormap("seaborn:vlag").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("seaborn:vlag").to_gee()
)

Perceptual lightness#

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

seaborn:vlag#

Perceptual lightness#

RGB components#

Hue & saturation#