cmasher:emergency#

The emergency colormap is a visual representation of the emergency warning lights on emergency vehicles in many countries.

collection	category	license	authors	source
cmasher	Cyclic	BSD-3-Clause	Ellert van der Velden	https://github.com/1313e/CMasher

cmapmatplotlibplotlynapariCSSMore...

from cmap import Colormap

cm = Colormap("cmasher:emergency")  # case insensitive

import matplotlib.pyplot as plt
from cmap import Colormap

plt.imshow(data, cmap=Colormap("cmasher:emergency").to_mpl())

import plotly.express as px
from cmap import Colormap

px.imshow(data, color_continuous_scale=Colormap("cmasher:emergency").to_plotly())

import napari
from cmap import Colormap

napari.imshow(data, colormap=Colormap("cmasher:emergency").to_napari())
napari.run()

background: #000000;
background: linear-gradient(90deg, #000000 0%, #040B0D 3.22581%, #081E25 6.45161%, #062F40 9.67742%, #033E5F 12.9032%, #134B81 16.129%, #2F56A3 19.3548%, #4E5EC3 22.5806%, #6F65DC 25.8065%, #8E6FE8 29.0323%, #A87FE8 32.2581%, #BB94E2 35.4839%, #CAABE1 38.7097%, #D8C2E4 41.9355%, #E6DAEB 45.1613%, #F6F2F7 48.3871%, #F8F3F6 51.6129%, #ECDAE6 54.8387%, #E4C0D6 58.0645%, #E0A5C5 61.2903%, #DC89B0 64.5161%, #D76E95 67.7419%, #CE5773 70.9677%, #BE4650 74.1935%, #A93B31 77.4194%, #8F3518 80.6452%, #743006 83.871%, #592A02 87.0968%, #3F2203 90.3226%, #271704 93.5484%, #0F0903 96.7742%, #000000 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("cmasher:emergency").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("cmasher:emergency").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("cmasher:emergency").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("cmasher:emergency").to_pygfx()
    ),
)
gfx.show(image)

import pyqtgraph as pg
from cmap import Colormap

view = pg.image(data)
view.setColorMap(Colormap("cmasher:emergency").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("cmasher:emergency").to_gee()
)

Perceptual lightness#

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

cmasher:emergency#

Perceptual lightness#

RGB components#

Hue & saturation#