bids:magma#

Aliases: matplotlib:magma

collection	category	license	authors	source
bids	Sequential	CC0	Nathaniel J. Smith, Stefan van der Walt	https://github.com/bids/colormap

cmapmatplotlibplotlynapariCSSMore...

from cmap import Colormap

cm = Colormap("bids:magma")  # case insensitive

import matplotlib.pyplot as plt
from cmap import Colormap

plt.imshow(data, cmap=Colormap("bids:magma").to_mpl())

import plotly.express as px
from cmap import Colormap

px.imshow(data, color_continuous_scale=Colormap("bids:magma").to_plotly())

import napari
from cmap import Colormap

napari.imshow(data, colormap=Colormap("bids:magma").to_napari())
napari.run()

background: #000004;
background: linear-gradient(90deg, #000004 0%, #040312 3.22581%, #0B0823 6.45161%, #140E35 9.67742%, #1E1149 12.9032%, #2A115D 16.129%, #38106D 19.3548%, #461077 22.5806%, #54137D 25.8065%, #601880 29.0323%, #6D1E81 32.2581%, #7A2382 35.4839%, #872781 38.7097%, #942C80 41.9355%, #A2307E 45.1613%, #AF347B 48.3871%, #BD3977 51.6129%, #CA3E72 54.8387%, #D6456C 58.0645%, #E24D66 61.2903%, #EC5860 64.5161%, #F3655C 67.7419%, #F8745C 70.9677%, #FB8360 74.1935%, #FD9366 77.4194%, #FEA26F 80.6452%, #FEB27A 83.871%, #FEC185 87.0968%, #FED093 90.3226%, #FDDFA1 93.5484%, #FCEEB0 96.7742%, #FCFDBF 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("bids:magma").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("bids:magma").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("bids:magma").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("bids:magma").to_pygfx()
    ),
)
gfx.show(image)

import pyqtgraph as pg
from cmap import Colormap

view = pg.image(data)
view.setColorMap(Colormap("bids:magma").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("bids:magma").to_gee()
)

Perceptual lightness#

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

bids:magma#

Perceptual lightness#

RGB components#

Hue & saturation#