crameri:navia#

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

cmapmatplotlibplotlynapariCSSMore...

from cmap import Colormap

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

import matplotlib.pyplot as plt
from cmap import Colormap

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

import plotly.express as px
from cmap import Colormap

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

import napari
from cmap import Colormap

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

background: #031327;
background: linear-gradient(90deg, #031327 0%, #051C36 3.22581%, #052646 6.45161%, #063057 9.67742%, #083A67 12.9032%, #0B4476 16.129%, #0F4F83 19.3548%, #15598C 22.5806%, #1C6290 25.8065%, #226A91 29.0323%, #277090 32.2581%, #2C768D 35.4839%, #307A8A 38.7097%, #357F88 41.9355%, #398385 45.1613%, #3E8882 48.3871%, #438D7E 51.6129%, #49927B 54.8387%, #509877 58.0645%, #579E73 61.2903%, #5FA56F 64.5161%, #68AD6B 67.7419%, #74B668 70.9677%, #83C069 74.1935%, #96CA6E 77.4194%, #ABD37B 80.6452%, #BEDB8B 83.871%, #D0E29D 87.0968%, #DEE7AE 90.3226%, #EAECBF 93.5484%, #F3F1CD 96.7742%, #FCF4D9 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:navia").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:navia").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:navia").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:navia").to_pygfx()
    ),
)
gfx.show(image)

import pyqtgraph as pg
from cmap import Colormap

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

Perceptual lightness#

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

crameri:navia#

Perceptual lightness#

RGB components#

Hue & saturation#