Interactive plots with holoviews in a jupyter notebook - needs to be in two separate code cells, see text for details

interactive_plot.py

# The following must be executed in a jupyter notebook rather than a shell environment.
# Use numpy to work with arrays
import numpy as np
# Use scipy.stats to do plot some statistical data
import scipy.stats as stats

import holoviews as hv
# Holoviews builds on top of either the Matplotlib or Bokeh plotting library. Choose to use Bokeh 
# in these plots as interactive plots work very well with Bokeh
hv.notebook_extension('bokeh')
# Create some fake, normally distributed data with mean = 2, standard deviation = 2 and 1000 elements
fake_data = np.random.normal(2,2,1000)
# Create a histogram of that data
freqs, edges = np.histogram(fake_data, bins=30, density=True)
# Set out the range of values for the x-axis for the line plots of the distribution below
x_range = np.arange(-10,10,1e-1)
###### NEW CELL IN NOTEBOOK ############################## 
%%opts Histogram (alpha=0.3)
# The line above starting with %%opts needs to be the first line in the new cell.
# It sets the transparency of the histogram in the plot. Setting this option is
# not necessary for the interactive plot, it just makes the plot a bit nicer.

# Plot the histogram of the data to compare with the interactive line plots below
normal_hist = hv.Histogram((freqs, edges), kdims=['x'], vdims=['Probability density'], group='Histogram of samples')

# Create a dictionary that maps from a tuple where the values are the mean and standard deviation to
# a line plot that is the normal distribution with this mean and standard deviation 
normal_curves = {(mu,sigma): hv.Curve( (x_range, stats.norm.pdf(x_range,mu,sigma)), 
         kdims=['x'],vdims=['Probability density']) 
                 for mu in np.arange(-6,6,0.5) 
                 for sigma in np.arange(0.5,4,0.5)}
# Create a list with the dimension names that will be shown on the plot
kdims = [('dim1', 'Mu'), ('dim2', 'Sigma')]
# Feed the dictionary that maps the tuple of mean and standard deviation to line plots into a HoloMap
interactive_plot = hv.HoloMap(normal_curves,kdims=kdims)

# Plot the histogram (a static plot) against the interactive Holomap 
normal_hist*interactive_plot
# You should have a plot of the histogram along with two sliders so that you can see which 
# values of the mean and standard deviation of the normal distribution match the data best.