Using colormap with bokeh scatter

Question

In matplotlib the scatterplot offers the possibility of using the color of a plot to indicate value or magnitude like this plot:

For bokeh, similar examples seem to manually generate the rgb colors, which makes it difficult to produce plots with color scaled by magnitude, esp. wrt. diverging colormaps.

Is it possible to have similar functionality in bokeh, or to use matplotlib colormaps to set the color?

Answer 1

Another option if you want to use a field name, is to use a LinearColorMapper:

from bokeh.models import LinearColorMapper

color_mapper = LinearColorMapper(palette='Magma256', low=min(radii), high=max(radii))

p.scatter(x,y,color={'field': 'radii', 'transform': color_mapper})

Answer 2

It's easy enough to just use matplotlib's colormaps directly. For example, the following uses viridis in bokeh's example (note that I'm using a jupyter notebook):

import numpy as np

from bokeh.plotting import figure, show, output_notebook
import matplotlib as mpl

output_notebook()

N = 4000
x = np.random.random(size=N) * 100
y = np.random.random(size=N) * 100
radii = np.random.random(size=N) * 1.5
colors = [
    "#%02x%02x%02x" % (int(r), int(g), int(b)) for r, g, b, _ in 255*mpl.cm.viridis(mpl.colors.Normalize()(radii))
]

p = figure()

p.scatter(x, y, radius=radii,
          fill_color=colors, fill_alpha=0.6,
          line_color=None)

show(p)  

Essentially, for any matplotlib colormap in cm, initializing it with an array of values will return an array with each value replaced by [r,g,b,a] values in the range [0,1]. Note that this assumes all the values are between 0 and 1 as well; here I use matplot.colors.Normalize to ensure this.