yorick:heat#

Aliases: matplotlib:gist_heat, gist_heat

collection	category	license	authors	source
yorick	Sequential	BSD-3-Clause	David Munro	https://github.com/LLNL/yorick

cmapmatplotlibplotlynapariCSSMore...

from cmap import Colormap

cm = Colormap("yorick:heat")  # case insensitive

import matplotlib.pyplot as plt
from cmap import Colormap

plt.imshow(data, cmap=Colormap("yorick:heat").to_mpl())

import plotly.express as px
from cmap import Colormap

px.imshow(data, color_continuous_scale=Colormap("yorick:heat").to_plotly())

import napari
from cmap import Colormap

napari.imshow(data, colormap=Colormap("yorick:heat").to_napari())
napari.run()

background: #000000;
background: linear-gradient(90deg, #000000 0%, #0C0000 3.22581%, #190000 6.45161%, #250000 9.67742%, #310000 12.9032%, #3E0000 16.129%, #4A0000 19.3548%, #560000 22.5806%, #630000 25.8065%, #6F0000 29.0323%, #7B0000 32.2581%, #880000 35.4839%, #940000 38.7097%, #A00000 41.9355%, #AD0000 45.1613%, #B90000 48.3871%, #C50800 51.6129%, #D21900 54.8387%, #DE2900 58.0645%, #EA3A00 61.2903%, #F74A00 64.5161%, #FF5A00 67.7419%, #FF6B00 70.9677%, #FF7B00 74.1935%, #FF8C19 77.4194%, #FF9C3A 80.6452%, #FFAD5A 83.871%, #FFBD7B 87.0968%, #FFCE9C 90.3226%, #FFDEBD 93.5484%, #FFEFDE 96.7742%, #FFFFFF 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("yorick:heat").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("yorick:heat").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("yorick:heat").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("yorick:heat").to_pygfx()
    ),
)
gfx.show(image)

import pyqtgraph as pg
from cmap import Colormap

view = pg.image(data)
view.setColorMap(Colormap("yorick:heat").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("yorick:heat").to_gee()
)

Perceptual lightness#

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

yorick:heat#

Perceptual lightness#

RGB components#

Hue & saturation#