Python 3.7.3 : Testing the timeit and Bokeh python module.

The tutorial today has several goals:

• testing the timeit function to measure the execution time of a line of print code;
• using python lists to record data;
• generate lists using the range function;
• multiplying a number with an entire list of numbers;
• how to use the bokeh module with CustomJSHover and HoverTool

Let's test with the timeit python module, see the official webpage.
This module provides a simple way to time small bits of Python code. It has both a Command-Line Interface as well as a callable one. It avoids a number of common traps for measuring execution times.

C:\Python373>python.exe
Python 3.7.3 (v3.7.3:ef4ec6ed12, Mar 25 2019, 21:26:53) [MSC v.1916 32 bit (Inte
l)] on win32
Type "help", "copyright", "credits" or "license" for more information.
>>> import timeit
>>> import random
>>> timeit.timeit('print("a")',number=1)
a
0.00013114200010022614
>>> timeit.timeit('print("a")',number=100)
...
a
0.007177434000027461
>>> timeit.timeit('print("a")',number=1000)
...
a
0.07585798000002342

Let make more visible the results with the bokeh python module:

import timeit
import random

# if you want to use numpy 
#import numpy as np

# import bokeh python module 
from bokeh.plotting import figure, show, output_file
# for show values
from bokeh.models.tools import CustomJSHover
from bokeh.models import HoverTool
print ('''
You can tests a number of times named l
with a number of print named i
and see the result value with bokeh python module
timeit.timeit('print("a")',number = i)
''')
i = int(input('The increment number i:'))
l = int(input('The number of test l:'))

# create the list with the output values
timit_list = []
# create the test function for l and i and return the list
def get_timeit(i,l):
 while i < l:
  i += 1
  out=timeit.timeit('print("a")',number = i)
  timit_list.append(out)
 return timit_list

# run the test function with l and i
# this will be coordinate y
yt=get_timeit(i,l)

# show result of the test 
print(yt)

# create the coordinate x
xt = [i for i in range(0, len(yt))]

#xt = np.linspace(0, l, l)
# print the coordinate x
#print(xt)

# create the output HTML file to see the result
output_file("test.html")

# create a figure with a timeit type y-axis
fig = figure(title='timit_list values for print("a")',
             plot_height=400, plot_width=700,
             x_axis_label='x has each incrementation of i', y_axis_label='value of timeit of print the char a',
             x_minor_ticks=3, 
             toolbar_location=None)

# create a circle for each value

# see new multiplication with a list 

# y=[i * 100 for i in yt]
fig.circle(x=xt, y=[i * 100 for i in yt], 
         color='blue', size=5,
         legend='Values')

x_custom = CustomJSHover(code="""
    return '' + special_vars.data_x""")

y_custom = CustomJSHover(code="""
    return '' + special_vars.data_y""")

fig.add_tools(
    HoverTool(
        show_arrow=True, 
        tooltips=[
 ('xt', '$data_x'),
 ('yt', '$data_y')
        ],
        formatters=dict(
            xt=x_custom,
            yt=y_custom
        )
    )
)

# Put the legend in the upper left corner
fig.legend.location = 'top_left'

# Let's check it out
show(fig)

The result of this python source code can be found on my YouTube channel:

Python 3.7.3 : Testing the Bokeh python module.

This python module has a beautiful website:
Bokeh is an interactive visualization library that targets modern web browsers for presentation. Its goal is to provide elegant, concise construction of versatile graphics, and to extend this capability with high-performance interactivity over very large or streaming datasets. Bokeh can help anyone who would like to quickly and easily create interactive plots, dashboards, and data applications.
Let's install this python module with the pip tool:

C:\Python373>cd Scripts

C:\Python373\Scripts>pip install bokeh
Collecting bokeh
...
Successfully built bokeh
Installing collected packages: PyYAML, tornado, bokeh
Successfully installed PyYAML-5.1.1 bokeh-1.2.0 tornado-6.0.3

Let's test it with a simple example:

from bokeh.plotting import figure, output_file, show

output_file("test.html")
plot = figure()
plot.line([1, 2, 3, 4, 5, 6, 7, 8, 9], [1, 7, 6, 1, 5, 6, 7, 9, 1], line_width=2)
show(plot)

This will create a file into my python folder C:/Python373/test.html.
The HTML webpage comes with the graph and additional tool like Pan, Box Zoom, Wheel Zoom, Save, Reset and help.
If you need sample data that is not included in the Bokeh GitHub repository or released packages then you need to download it.

>>> import bokeh.sampledata
>>> bokeh.sampledata.download()
Creating C:\Users\catafest\.bokeh directory
Creating C:\Users\catafest\.bokeh\data directory
Using data directory: C:\Users\catafest\.bokeh\data
Downloading: CGM.csv (1589982 bytes)
   1589982 [100.00%]
Downloading: US_Counties.zip (3171836 bytes)
    229376 [  7.23%]

Bokeh comes with support for working with Geographical data: Mercator, Google Maps, GeoJSON Data.
You can see all example into the webpage gallery.