cmasher:seasons#

The seasons colormap is a visual representation of the cycle of the four seasons; winter, spring, summer and fall.

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

cmapmatplotlibplotlynapariCSSMore...

from cmap import Colormap

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

import matplotlib.pyplot as plt
from cmap import Colormap

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

import plotly.express as px
from cmap import Colormap

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

import napari
from cmap import Colormap

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

background: #000000;
background: linear-gradient(90deg, #000000 0%, #0B0811 3.22581%, #1C172E 6.45161%, #27254F 9.67742%, #263769 12.9032%, #1F4C73 16.129%, #1D5F78 19.3548%, #21727C 22.5806%, #28857E 25.8065%, #32987E 29.0323%, #45AB7A 32.2581%, #65BC73 35.4839%, #94C87F 38.7097%, #B7D5A2 41.9355%, #D4E4C8 45.1613%, #F0F5ED 48.3871%, #F3F6EF 51.6129%, #DEE3C6 54.8387%, #CFCF99 58.0645%, #C9B86B 61.2903%, #C99F46 64.5161%, #C78434 67.7419%, #BF6C30 70.9677%, #B35533 74.1935%, #A44039 77.4194%, #922C3F 80.6452%, #7D1D45 83.871%, #621745 87.0968%, #451639 90.3226%, #2A1126 93.5484%, #10070F 96.7742%, #000000 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:seasons").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:seasons").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:seasons").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:seasons").to_pygfx()
    ),
)
gfx.show(image)

import pyqtgraph as pg
from cmap import Colormap

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

Perceptual lightness#

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

cmasher:seasons#

Perceptual lightness#

RGB components#

Hue & saturation#