crameri:managua#

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

cmapmatplotlibplotlynapariCSSMore...

from cmap import Colormap

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

import matplotlib.pyplot as plt
from cmap import Colormap

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

import plotly.express as px
from cmap import Colormap

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

import napari
from cmap import Colormap

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

background: #FFCF67;
background: linear-gradient(90deg, #FFCF67 0%, #F5BF62 3.22581%, #EBAF5C 6.45161%, #E1A057 9.67742%, #D79152 12.9032%, #CD844E 16.129%, #C3774A 19.3548%, #B96B46 22.5806%, #AE5F42 25.8065%, #A3543F 29.0323%, #97493C 32.2581%, #8A3F3A 35.4839%, #7C3639 38.7097%, #702F3A 41.9355%, #642A3D 45.1613%, #5B2844 48.3871%, #542A4E 51.6129%, #4F305B 54.8387%, #4D386A 58.0645%, #4C427A 61.2903%, #4D4D89 64.5161%, #4F5998 67.7419%, #5266A5 70.9677%, #5672B1 74.1935%, #5B7FBB 77.4194%, #608CC5 80.6452%, #659ACF 83.871%, #6AA8D9 87.0968%, #70B7E2 90.3226%, #75C6EC 93.5484%, #7BD7F5 96.7742%, #81E7FF 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:managua").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:managua").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:managua").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:managua").to_pygfx()
    ),
)
gfx.show(image)

import pyqtgraph as pg
from cmap import Colormap

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

Perceptual lightness#

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

crameri:managua#

Perceptual lightness#

RGB components#

Hue & saturation#