cmasher:wildfire#

The wildfire colormap is a visual representation of the natural disaster with the same name.

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

cmapmatplotlibplotlynapariCSSMore...

from cmap import Colormap

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

import matplotlib.pyplot as plt
from cmap import Colormap

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

import plotly.express as px
from cmap import Colormap

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

import napari
from cmap import Colormap

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

background: #93FD2A;
background: linear-gradient(90deg, #93FD2A 0%, #6DEC4F 3.22581%, #50DA65 6.45161%, #3AC774 9.67742%, #2CB57C 12.9032%, #24A281 16.129%, #1F9082 19.3548%, #1A7F81 22.5806%, #136E7F 25.8065%, #0D5D7D 29.0323%, #124C7A 32.2581%, #1F3872 35.4839%, #232857 38.7097%, #1B1C37 41.9355%, #0F0E1B 45.1613%, #020204 48.3871%, #030203 51.6129%, #180A15 54.8387%, #301228 58.0645%, #49153A 61.2903%, #631448 64.5161%, #7E0F4E 67.7419%, #96174A 70.9677%, #AA2843 74.1935%, #BB3B3B 77.4194%, #CA4F33 80.6452%, #D8632A 83.871%, #E47921 87.0968%, #EE9017 90.3226%, #F5A810 93.5484%, #FAC112 96.7742%, #FCDC1F 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:wildfire").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:wildfire").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:wildfire").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:wildfire").to_pygfx()
    ),
)
gfx.show(image)

import pyqtgraph as pg
from cmap import Colormap

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

Perceptual lightness#

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

cmasher:wildfire#

Perceptual lightness#

RGB components#

Hue & saturation#