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?
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.