crameri:tokyo#

collection	category	license	authors	source
crameri	Sequential	MIT	Fabio Crameri	https://zenodo.org/record/5501399

cmapmatplotlibplotlynapariCSSMore...

from cmap import Colormap

cm = Colormap("crameri:tokyo")  # case insensitive

import matplotlib.pyplot as plt
from cmap import Colormap

plt.imshow(data, cmap=Colormap("crameri:tokyo").to_mpl())

import plotly.express as px
from cmap import Colormap

px.imshow(data, color_continuous_scale=Colormap("crameri:tokyo").to_plotly())

import napari
from cmap import Colormap

napari.imshow(data, colormap=Colormap("crameri:tokyo").to_napari())
napari.run()

background: #1C0E34;
background: linear-gradient(90deg, #1C0E34 0%, #2A1238 3.22581%, #38173D 6.45161%, #451D42 9.67742%, #522546 12.9032%, #5C2F4A 16.129%, #64384D 19.3548%, #69414F 22.5806%, #6C4950 25.8065%, #6E4F51 29.0323%, #6F5551 32.2581%, #705A52 35.4839%, #715F52 38.7097%, #726352 41.9355%, #726853 45.1613%, #736D53 48.3871%, #747254 51.6129%, #757954 54.8387%, #778055 58.0645%, #788956 61.2903%, #7A9257 64.5161%, #7D9C5A 67.7419%, #80A75E 70.9677%, #85B364 74.1935%, #8DC06D 77.4194%, #97CE7A 80.6452%, #A5DB8B 83.871%, #B6E79F 87.0968%, #C8F0B3 90.3226%, #D7F7C4 93.5484%, #E5FAD2 96.7742%, #EFFCDD 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("crameri:tokyo").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("crameri:tokyo").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("crameri:tokyo").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("crameri:tokyo").to_pygfx()
    ),
)
gfx.show(image)

import pyqtgraph as pg
from cmap import Colormap

view = pg.image(data)
view.setColorMap(Colormap("crameri:tokyo").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("crameri:tokyo").to_gee()
)

Perceptual lightness#

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

crameri:tokyo#

Perceptual lightness#

RGB components#

Hue & saturation#