The result is:

Two-dimensional interactive contour plot with colorbar using Python and Bokeh

Add linear colorbar to the contourf using Python and Bokeh.

See also:

Python Matplotlib Tips: Interactive plot using Bokeh - first step -

I firstly thought that Bokeh uses matplotlib. We generate figure using matplotlib then convert the figure to the Bokeh compatible. However, it seems Bokeh works stand alone without matplotlib (is it true?) Anyway, let's check whether Bokeh works in my environment or not.

In [1]:

import platform
print('python: '+platform.python_version())
import numpy as np
print('numpy: '+np.__version__)

python: 3.6.3
numpy: 1.15.4

Load Bokeh

In [2]:

from bokeh.plotting import figure, show
from bokeh.io import output_notebook
from bokeh.models import LinearColorMapper, ColorBar
import bokeh
print('bokeh: '+bokeh.__version__)
output_notebook()

bokeh: 1.0.1

Loading BokehJS ...

Define function

In [3]:

def bivariate_normal(X, Y, sigmax=1.0, sigmay=1.0,
                     mux=0.0, muy=0.0, sigmaxy=0.0):
    """
    Bivariate Gaussian distribution for equal shape *X*, *Y*.
    See `bivariate normal
    <http://mathworld.wolfram.com/BivariateNormalDistribution.html>`_
    at mathworld.
    """
    Xmu = X-mux
    Ymu = Y-muy

    rho = sigmaxy/(sigmax*sigmay)
    z = Xmu**2/sigmax**2 + Ymu**2/sigmay**2 - 2*rho*Xmu*Ymu/(sigmax*sigmay)
    denom = 2*np.pi*sigmax*sigmay*np.sqrt(1-rho**2)
    return np.exp(-z/(2*(1-rho**2))) / denom

Calculate data

In [4]:

delta = 0.02
x = np.arange(-3.0, 3.0, delta)
y = np.arange(-2.0, 2.0, delta)
X, Y = np.meshgrid(x, y)
Z1 = bivariate_normal(X, Y, 1.0, 1.0, 0.0, 0.0)
Z2 = bivariate_normal(X, Y, 1.5, 0.5, 1, 1)
# difference of Gaussians
Z = 10.0 * (Z2 - Z1)

Check maximum and minimum value

In [5]:

Z.max(),Z.min()

Out[5]:

(1.7001094680556783, -1.4682881280424454)

Plot data using Bokeh

In [6]:

p = figure(x_range=(-3, 3), y_range=(-2, 2),
           tooltips=[("x", "$x"), ("y", "$y"), ("value", "@image")])

# define colormap used both of pl
color_mapper = LinearColorMapper(palette="RdBu11", low=-1.5, high=1.5)

# must give a vector of image data for image parameter
p.image(image=[Z], x=-3, y=-2, dw=6, dh=4, color_mapper=color_mapper)

# generate colorbar
color_bar = ColorBar(color_mapper=color_mapper, label_standoff=12, border_line_color=None, location=(0,0))

# add colorbar to the right side of the plot
p.add_layout(color_bar, 'right')

# show plot
show(p)

Two-dimensional interactive contour plot with colorbar using Python and Bokeh