cmasher:pride#
The pride colormap is a visual representation of the equality of all communities.
| collection | category | license | authors | source |
|---|---|---|---|---|
| cmasher | Diverging | BSD-3-Clause | Ellert van der Velden | https://github.com/1313e/CMasher |
from cmap import Colormap
cm = Colormap("cmasher:pride") # case insensitive
import matplotlib.pyplot as plt
from cmap import Colormap
plt.imshow(data, cmap=Colormap("cmasher:pride").to_mpl())
import plotly.express as px
from cmap import Colormap
px.imshow(data, color_continuous_scale=Colormap("cmasher:pride").to_plotly())
import napari
from cmap import Colormap
napari.imshow(data, colormap=Colormap("cmasher:pride").to_napari())
napari.run()
background: #160E28;
background: linear-gradient(90deg, #160E28 0%, #261643 3.22581%, #2D225F 6.45161%, #2B3372 9.67742%, #23457B 12.9032%, #1D567E 16.129%, #22657E 19.3548%, #30737E 22.5806%, #41817E 25.8065%, #528E7E 29.0323%, #649C7C 32.2581%, #79A977 35.4839%, #90B66F 38.7097%, #AAC264 41.9355%, #C8CE53 45.1613%, #E8D83B 48.3871%, #F9D120 51.6129%, #F8B912 54.8387%, #F5A20B 58.0645%, #EF8B10 61.2903%, #E8761A 64.5161%, #DE6124 67.7419%, #D24E2E 70.9677%, #C43B38 74.1935%, #B52A41 77.4194%, #A41949 80.6452%, #900750 83.871%, #7B0154 87.0968%, #630850 90.3226%, #4B0F42 93.5484%, #34102F 96.7742%, #1F0A1A 100%);
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:pride").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:pride").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:pride").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:pride").to_pygfx()
),
)
gfx.show(image)
import pyqtgraph as pg
from cmap import Colormap
view = pg.image(data)
view.setColorMap(Colormap("cmasher:pride").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:pride").to_gee()
)
See also usage with external libraries.
cmasher:pride
cmasher:pride
Perceptual lightness#
Lightness (J′) measured in CAM02 Uniform Color Space (CAM02-UCS)