cmasher:sepia#

The sepia colormap is a visual representation of the sepia toning often used in photography.

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

cmapmatplotlibplotlynapariCSSMore...

from cmap import Colormap

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

import matplotlib.pyplot as plt
from cmap import Colormap

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

import plotly.express as px
from cmap import Colormap

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

import napari
from cmap import Colormap

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

background: #000000;
background: linear-gradient(90deg, #000000 0%, #030203 3.22581%, #0B070B 6.45161%, #140F15 9.67742%, #1E151F 12.9032%, #281B28 16.129%, #322131 19.3548%, #3C2639 22.5806%, #462B41 25.8065%, #513047 29.0323%, #5C364C 32.2581%, #663B50 35.4839%, #704153 38.7097%, #794856 41.9355%, #824F58 45.1613%, #8B565A 48.3871%, #935D5C 51.6129%, #9B655F 54.8387%, #A26E62 58.0645%, #A97665 61.2903%, #AF8069 64.5161%, #B5896F 67.7419%, #BA9375 70.9677%, #BE9D7D 74.1935%, #C2A886 77.4194%, #C6B290 80.6452%, #CABD9B 83.871%, #CFC8A6 87.0968%, #D3D3B2 90.3226%, #D8DEBF 93.5484%, #DEE9CC 96.7742%, #E4F4DA 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:sepia").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:sepia").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:sepia").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:sepia").to_pygfx()
    ),
)
gfx.show(image)

import pyqtgraph as pg
from cmap import Colormap

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

Perceptual lightness#

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

cmasher:sepia#

Perceptual lightness#

RGB components#

Hue & saturation#