vispy:fire#

collection	category	license	authors	source
vispy	Sequential	BSD-3-Clause		https://github.com/vispy/

cmapmatplotlibplotlynapariCSSMore...

from cmap import Colormap

cm = Colormap("vispy:fire")  # case insensitive

import matplotlib.pyplot as plt
from cmap import Colormap

plt.imshow(data, cmap=Colormap("vispy:fire").to_mpl())

import plotly.express as px
from cmap import Colormap

px.imshow(data, color_continuous_scale=Colormap("vispy:fire").to_plotly())

import napari
from cmap import Colormap

napari.imshow(data, colormap=Colormap("vispy:fire").to_napari())
napari.run()

background: #FFFFFF;
background: linear-gradient(90deg, #FFFFFF 0%, #FFFFEF 3.22581%, #FFFEDF 6.45161%, #FFFDD0 9.67742%, #FFFBC1 12.9032%, #FFF8B3 16.129%, #FFF5A6 19.3548%, #FFF299 22.5806%, #FFEE8C 25.8065%, #FFEA80 29.0323%, #FFE475 32.2581%, #FFDF6A 35.4839%, #FFD960 38.7097%, #FFD256 41.9355%, #FFCB4D 45.1613%, #FFC344 48.3871%, #FFBB3C 51.6129%, #FFB234 54.8387%, #FFA92D 58.0645%, #FF9F26 61.2903%, #FF9520 64.5161%, #FF8A1B 67.7419%, #FF7F15 70.9677%, #FF7311 74.1935%, #FF660D 77.4194%, #FF590A 80.6452%, #FF4C07 83.871%, #FF3E04 87.0968%, #FF2F02 90.3226%, #FF2001 93.5484%, #FF1000 96.7742%, #FF0000 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("vispy:fire").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("vispy:fire").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("vispy:fire").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("vispy:fire").to_pygfx()
    ),
)
gfx.show(image)

import pyqtgraph as pg
from cmap import Colormap

view = pg.image(data)
view.setColorMap(Colormap("vispy:fire").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("vispy:fire").to_gee()
)

Perceptual lightness#

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

vispy:fire#

Perceptual lightness#

RGB components#

Hue & saturation#