seaborn:mako#

collection	category	license	authors	source
seaborn	Sequential	BSD-3-Clause	Michael Waskom	https://github.com/mwaskom/seaborn

cmapmatplotlibplotlynapariCSSMore...

from cmap import Colormap

cm = Colormap("seaborn:mako")  # case insensitive

import matplotlib.pyplot as plt
from cmap import Colormap

plt.imshow(data, cmap=Colormap("seaborn:mako").to_mpl())

import plotly.express as px
from cmap import Colormap

px.imshow(data, color_continuous_scale=Colormap("seaborn:mako").to_plotly())

import napari
from cmap import Colormap

napari.imshow(data, colormap=Colormap("seaborn:mako").to_napari())
napari.run()

background: #0B0405;
background: linear-gradient(90deg, #0B0405 0%, #140A11 3.22581%, #1C101D 6.45161%, #251629 9.67742%, #2C1C36 12.9032%, #322243 16.129%, #382951 19.3548%, #3C2F60 22.5806%, #3F366E 25.8065%, #413E7D 29.0323%, #40468A 32.2581%, #3E4F94 35.4839%, #3A599A 38.7097%, #38639D 41.9355%, #366CA0 45.1613%, #3576A2 48.3871%, #357FA4 51.6129%, #3488A6 54.8387%, #3491A8 58.0645%, #349AAA 61.2903%, #36A4AB 64.5161%, #3AADAC 67.7419%, #3FB6AD 70.9677%, #47BFAD 74.1935%, #52C8AD 77.4194%, #64D0AD 80.6452%, #7CD6AF 83.871%, #94DCB5 87.0968%, #A9E2BD 90.3226%, #BDE8C9 93.5484%, #CEEED7 96.7742%, #DEF5E5 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("seaborn:mako").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("seaborn:mako").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("seaborn:mako").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("seaborn:mako").to_pygfx()
    ),
)
gfx.show(image)

import pyqtgraph as pg
from cmap import Colormap

view = pg.image(data)
view.setColorMap(Colormap("seaborn:mako").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("seaborn:mako").to_gee()
)

Perceptual lightness#

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

seaborn:mako#

Perceptual lightness#

RGB components#

Hue & saturation#