Python 3.7.3 : Testing firebase with Python 3.7.3 .

The tutorial for today consists of using the Firebase service with python version 3.7.3 .
As you know Firebase offers multiple free and paid services.
In order to use the Python programming language, we need to use the pip utility to enter the required modules.
If your installation requires other python modules then you will need to install them in the same way.

C:\Python373>pip install firebase-admin
C:\Python373\Scripts>pip install google-cloud-firestore

The next step is to log in with a firebase account and create or use a project with a database.
You must use this link to see your project data and create your JSON configuration file for access.
Here's a screenshot of an old project in another programming language that I used for this tutorial.

At point 1, you will find Project Settings - Service accounts and you will need to use the Generate new private key button.
This will generate a JSON file that we will use in the python script.
Be careful to define the path to the python script file.
At point 2, you will find the database or you will need to create it to use it.
The script I'm going to present has commented on the lines for a better understanding of how to use it.

import firebase_admin
from firebase_admin import credentials
from firebase_admin import firestore

# the go-test.json from my project settings 
cred = credentials.Certificate("go-test.json")

# start using firebase python modules 
firebase_admin.initialize_app(cred)

# access the database
database = firestore.client()

# access the collection with limit 
my_ref = database.collection("test").limit(2)

# show the values from database
try:
    docs = my_ref.stream()
    for doc in docs:
         print(u'Doc Data:{}'.format(doc.to_dict()))
except:
    print(u'Missing data')

# result running
#Doc Data:{'nrcrt': 1, 'string_value': 'this is a text'}
#Doc Data:{'name': 'test', 'added': 'just now'}

# add data to database
my_ref_add = database.collection("test")
my_ref_add.add({u'nickname': u'catafest', u'friend': u'Last Ocelot'})

# show the new database values
try:
    docs = my_ref_add.stream()
    for doc in docs:
         print(u'Doc Data:{}'.format(doc.to_dict()))
except:
    print(u'Missing data')

# result running
#Doc Data:{'nrcrt': 1, 'string_value': 'this is a text'}
#Doc Data:{'friend': 'Last Ocelot', 'nickname': 'catafest'}
#Doc Data:{'name': 'test', 'added': 'just now'}

# get just one 
doc = database.collection('test').document('doc_id').get()

# show create time 
print(doc.create_time)

# result running
#seconds: 1556014478
#nanos: 460439000

About psychopy tool.

A good definition for this tool can be found at the Wikipedia website:
2002: PsychoPy was originally written by Peirce as a proof of concept - that a high-level scripting language could generate experimental stimuli in real time (existing solutions, such as Psychtoolbox, had to pre-generate movies or use CLUT animation techniques).
The install of this python module is very simple:

C:\Python373\Scripts>pip install psychopy

Using this command to start this tool:

C:\Python373>psychopy

The tool starts with two graphical interfaces:

• one for Coder area;
• one for the project with an untitled.psyexp;

Let's see one screenshot with this tool:

This open source tool, written in the Python programming language, help you to run a wide range of neuroscience, psychology and psychophysics experiments.
I searched the internet for the capabilities of this tool and the obvious conclusion is a utility for building experiments with a wide range of applicability.
This tool lets you use the pavlovia website, see more:
Pavlovia is a place for the wide community of researchers in the behavioural sciences to run, share, and explore experiments online.
This tool used commonly-used components for linguistic experiments:

1. Text Component (display text on the screen);
2. Sound Component (play sounds);
3. Keyboard Component (receive input from the keyboard);
4. RatingScale Component (collect a numeric rating or a choice from a few alternatives, via the mouse, the keyboard or both);
5. Code Component (insert short pieces of python code into your experiments (e.g. time stamp for the production task);

The easy way is to use the Builder tool to generate a wide range of experiments easily from the Builder using its intuitive, graphical user interface (GUI).
Today I will use the Coder with programming a simple example.
To access demos, you need to create a python script in the Coder area (use File menu - New) then use the Demos from the application menu.
Everything in a PsychoPy experiment needs a unique name.
The name must contain only letters, numbers and underscores and not contain spaces, punctuation or mathematical symbols.
This tool saves several data files: a Microsoft Excel (spreadsheet) file, a psydat file, and a log file.
The example I used today for this tutorial is how to import from a CSV file named colors_001.csv and used with psychopy python module to create stimuli:

3.3;"red"
1.1;"green"
4.4;"red"
2.2;"green"

This python script named colors.py I used to import the CSV file and use it:

from psychopy import core, visual, event
import csv
  
## Setup section, read experiment variables from file
win = visual.Window([400,300], monitor="testMonitor", units="cm", fullscr=False)
stimuli = []
datafile = open("colors_001.csv", "r",encoding="utf8")
reader = csv.reader(datafile, delimiter=";")
for row in reader:
    if len(row)==2:         # ignore empty and incomplete lines
        size = float(row[0])  # the first element in the row converted to a floating point number
        color = row[1]        # the second element in the row
        stimulus = visual.Rect(win, width=size, height=size)
        stimulus.fillColor = color
        stimuli.append(stimulus)
datafile.close()
  
## Experiment Section, use experiment variables here
for stimulus in stimuli:
    stimulus.draw()
    win.flip()
    core.wait(1.000)

## Closing Section
win.close()
core.quit()

The result of this script will show you four squares colored by the CSV file.
If you don't like to use the Builder or Coder areas, then you can simply use the python:

C:\Python373>python.exe
Python 3.7.3 (v3.7.3:ef4ec6ed12, Mar 25 2019, 22:22:05) [MSC v.1916 64 bit (AMD64)]
 on win32
Type "help", "copyright", "credits" or "license" for more information.
>>> from psychopy import visual, core
pygame 1.9.5
Hello from the pygame community. https://www.pygame.org/contribute.html
>>> win = visual.Window()
>>> msg = visual.TextStim(win, text=u"Hello python users!")
>>> msg.draw()
>>> win.flip()
47.541564770566765

The output will be a window with a text message.

Update python modules of 3.73 version.

Today we tested an older tool with the new version of python 3.7.3.
This is a tool that will help you update your python modules.
Here's how to install:

C:\Python373\Scripts>pip install pip-review
Collecting pip-review
...
Requirement already satisfied: pyparsing>=2.0.2 in c:\python373\lib\site-package
s (from packaging->pip-review) (2.4.0)
Installing collected packages: packaging, pip-review
Successfully installed packaging-19.0 pip-review-1.0

Here is the complete overview of the options.

C:\Python373\Scripts>pip-review -h
usage: pip-review [-h] [--verbose] [--raw] [--interactive] [--auto]

Keeps your Python packages fresh.

optional arguments:
  -h, --help         show this help message and exit
  --verbose, -v      Show more output
  --raw, -r          Print raw lines (suitable for passing to pip install)
  --interactive, -i  Ask interactively to install updates
  --auto, -a         Automatically install every update found

Unrecognised arguments will be forwarded to pip list --outdated, so you can
pass things such as --user, --pre and --timeout and they will do exactly what
you expect. See pip list -h for a full overview of the options.

To update all python modules you can use:

C:\Python373\Scripts>pip-review --auto

To run interactively, you can ask to upgrade for each package:

C:\Python373\Scripts>pip-review --interactive

Using the ORB feature from OpenCV python module.

Today I will show you a simple script using the ORB (oriented BRIEF), see C++ documentation / OpenCV.
The algorithm uses FAST in pyramids to detect stable keypoints, selects the strongest features using FAST or Harris response, finds their orientation using first-order moments and computes the descriptors using BRIEF (where the coordinates of random point pairs (or k-tuples) are rotated according to the measured orientation).
One good feature of ORB is the is rotation invariant and resistant to noise.
The ORB descriptor use the Center of the mass of the patch of the Moment (sum of x,y), Centroid (the result of the matrix of all moment) and Orientation ( the atan2 of moment one and two).
One good article about ORB can be found here.
Let's see the script code of this python example:

import cv2
import numpy as np 

image_1 = cv2.imread("1.png", cv2.IMREAD_GRAYSCALE)
image_2 = cv2.imread("2.png", cv2.IMREAD_GRAYSCALE)

orb = cv2.ORB_create()
kp1, des1 = orb.detectAndCompute(image_1,None)
kp2, des2 = orb.detectAndCompute(image_2,None)

bf = cv2.BFMatcher(cv2.NORM_HAMMING, crossCheck=True)
matches = bf.match(des1, des2)
matches = sorted(matches, key = lambda x:x.distance)

matching_result = cv2.drawMatches(image_1, kp1, image_2, kp2, matches[:150], None, flags=2)

cv2.imshow("Image 1", image_1)
cv2.imshow("Image 2", image_2)
cv2.imshow("Matching result", matching_result)
cv2.waitKey(0)
cv2.destroyAllWindows()

The script use two file images 1.png and 2.png.
The result is an image composed of the two on which the areas of similitude are traced as detected by the mathematical algorithm.

Using the python module music21.

What is music21?
Music21 is a set of tools for helping scholars and other active listeners answer questions about music quickly and simply. If you’ve ever asked yourself a question like, “I wonder how often Bach does that” or “I wish I knew which band was the first to use these chords in this order,” or “I’ll bet we’d know more about Renaissance counterpoint (or Indian ragas or post-tonal pitch structures or the form of minuets) if I could write a program to automatically write more of them,” then music21 can help you with your work.
This toolkit for Computer-Aided Musical Analysis was developed at MIT by cuthbertLab. Michael Scott Cuthbert, Principal Investigator.
The development of music21 is supported by the generosity of the Seaver Institute and the NEH.
The tutorial today is about the python music21 module.
Let's start with the default pip tool to install this python module:

C:\Python364\Scripts>pip install --upgrade music21
Collecting music21
...
  Running setup.py install for music21 ... done
Successfully installed music21-5.5.0

The next step tells us to install some additional python modules:

C:\Python373>python.exe
Python 3.7.3 (v3.7.3:ef4ec6ed12, Mar 25 2019, 22:22:05) [MSC v.1916 64 bit (AMD64)]
 on win32
Type "help", "copyright", "credits" or "license" for more information.
>>> from music21 import * 
music21: Certain music21 functions might need these optional packages: matplotlib,
 scipy;
if you run into errors, install them by following the instructions at
http://mit.edu/music21/doc/installing/installAdditional.html

Let's install these python modules:

C:\Python373\Scripts>pip install matplotlib
C:\Python373\Scripts>pip install scipy

Let's test and play a simple example (Bach’s BWV 66.6):

>>> from music21 import *
>>> s = corpus.parse('bwv66.6')
>>> sChords = s.chordify()
>>> sChords
...
>>> sChords.show('midi')

If you want to save it, use:

>>> sChords.write("midi", "bach6.mid")
'bach6.mid'

Another example is to play a note on guitar (C#):

from music21 import stream, instrument
from music21.note import Note
cd = Note("C#", type='quarter')
test = stream.Part()
test.insert(0, instrument.AcousticGuitar())
test_measure = stream.Measure()
test_measure.append(cd)
test.append(test_measure)
test.show('midi')

You can see all the instruments here.
This python module is very complex and can be used with additional software, see here.
More details can be found in the documentation.

Using pytineye to automate searching for images.

The TinEye API is ideally suited for image and profile verification, UGC moderation, copyright compliance and fraud detection.
Read more about the TinEye API here.
You need to use authentication for this API, read here.
To use the TinEye API you must purchase a search bundle.
The documentation for Python can be found here.
Let's start with the installation.
You need to download the zip file from GitHub and install using the pip tool:

C:\Python373\Scripts>pip3.7.exe install pytineye-master.zip
...
Successfully installed pytineye-1.3

>>> import pytineye
>>> from pytineye import TinEyeAPIRequest
>>> myapi = TinEyeAPIRequest('http://api.tineye.com/rest/', 'your_public_key', 'your_private_key')
>>> myapi.remaining_searches()
{'bundles': [], 'total_remaining_searches': 0}

Because I don't have search the python code return an error:

>>> myapi.search_url(url='http://en.es-static.us/upl/2019/03/rogue-planet-CFBD-S
IR-J214947.2-040308.9-2012-800x450.jpg')
Traceback (most recent call last):
  File "", line 1, in 
  File "C:\Python373\lib\site-packages\pytineye\api.py", line 304, in search_url

    obj = self._request('search', params, **kwargs)
  File "C:\Python373\lib\site-packages\pytineye\api.py", line 276, in _request
    raise TinEyeAPIError(obj['code'], obj.get('messages'))
pytineye.exceptions.TinEyeAPIError: APIError:
                   code    = 401
                   message = ['AUTHORIZATION_ERROR', 'You have no more searches
available. Please purchase another bundle if you wish to make more searches.']

If you want to buy TinEye API searches then the prices start from:
5,000 searches $200.00 USD($0.04 per search)
10,000 searches $300.00 USD($0.03 per search)
50,000 searches $1,000.00 USD($0.02 per search)
1,000,000 searches $10,000.00 USD($0.01 per search)

First test with 3.7.3 and opencv-python module version 4.0.0 .

The Python 3.7.3 is the third maintenance release of Python 3.7 and is released at March 25, 2019.
More about this new released version can be found at official website.

C:\Python373\Scripts>pip install opencv-python
Collecting opencv-python
...
Installing collected packages: numpy, opencv-python
Successfully installed numpy-1.16.2 opencv-python-4.0.0.21

Let's test it:

C:\Python373>python.exe
Python 3.7.3 (v3.7.3:ef4ec6ed12, Mar 25 2019, 22:22:05) [MSC v.1916 64 bit (AMD64)]
 on win32
Type "help", "copyright", "credits" or "license" for more information.
>>> import cv2
>>> cv2.__version__
'4.0.0'

Let test a simple example that computes a dense optical flow using the Gunnar Farneback’s algorithm.

import cv2
import numpy as np
cap = cv2.VideoCapture(0)
ret, frame1 = cap.read()
prvs = cv2.cvtColor(frame1,cv2.COLOR_BGR2GRAY)
hsv = np.zeros_like(frame1)
hsv[...,1] = 255
while(1):
    ret, frame2 = cap.read()
    next = cv2.cvtColor(frame2,cv2.COLOR_BGR2GRAY)
    flow = cv2.calcOpticalFlowFarneback(prvs,next, None, 0.1, 1, 2, 3, 5, 11.2, 0)
    mag, ang = cv2.cartToPolar(flow[...,0], flow[...,1])
    hsv[...,0] = ang*180/np.pi/2
    hsv[...,2] = cv2.normalize(mag,None,0,255,cv2.NORM_MINMAX)
    bgr = cv2.cvtColor(hsv,cv2.COLOR_HSV2BGR)
    cv2.imshow('frame2',bgr)
    k = cv2.waitKey(30) & 0xff
    if k == 27:
        break
    prvs = next
cap.release()
cv2.destroyAllWindows()

About Ninja IDE for python programming.

This I.D.E. is a very good tool for python programming and development.

The version of this tool is 2.3.

I tested with my Django project on Windows OS and works great.

The development team comes with this info:

NINJA-IDE (from the recursive acronym: "Ninja-IDE Is Not Just Another IDE"), is a cross-platform integrated development environment (IDE). NINJA-IDE runs on Linux/X11, Mac OS X and Windows desktop operating systems, and allows developers to create applications for several purposes using all the tools and utilities of NINJA-IDE, making the task of writing software easier and more enjoyable.

The official webpage can be found here.

If you want to use it with Django the easy way is to install the plugin for this area.

You can see all about the development team about page.

Testing the python IMDbPY module with simple commands.

Today we tested a more innovative but useful method with python aaa mode.
The main reason I used this method is the lack of documentation.
Using this method, we have reached elements related to the use of reported methods and errors.
The test was done on a Fedora 29 Linux system with a classic install with the pip utility:

[mythcat@desk ~]$ pip install imdbpy --user
Collecting imdbpy
...
Successfully installed SQLAlchemy-1.3.1 imdbpy-6.6 

I used an example of a person's search in the IMDB database to test this method.

>>> from imdb import IMDb, IMDbError
>>> try:
...     im=IMDb()
...     people = im.search_person('Mel Gibson')
... except IMDbError as exc:
...     print(exc) 

Using the dir and print function will show the resulting output configuration and will have the following form:

>>> print(people)
[, , , , , , , , , , , , , , , , , , , ] 

I have used the dir function for a relative view of the options we have:

>>> print(dir(people))
['__add__', '__class__', '__contains__', '__delattr__', '__delitem__', '__delslice__', '__doc__', 
'__eq__', '__format__', '__ge__', '__getattribute__', '__getitem__', '__getslice__', 
'__gt__', '__hash__', '__iadd__', '__imul__', '__init__', '__iter__', '__le__', '__len__', 
'__lt__', '__mul__', '__ne__', '__new__', '__reduce__', '__reduce_ex__', '__repr__', '__reversed__', 
'__rmul__', '__setattr__', '__setitem__', '__setslice__', '__sizeof__', '__str__', '__subclasshook__', 
'append', 'count', 'extend', 'index', 'insert', 'pop', 'remove', 'reverse', 'sort']
>>> print(dir(people[0]))
['_Container__role', '__bool__', '__class__', '__contains__', '__deepcopy__', '__delattr__', 
'__delitem__', '__dict__', '__doc__', '__eq__', '__format__', '__ge__', '__getattribute__', 
'__getitem__', '__gt__', '__hash__', '__init__', '__le__', '__len__', '__lt__', '__module__', 
'__ne__', '__new__', '__reduce__', '__reduce_ex__', '__repr__', '__setattr__', '__setitem__', 
'__sizeof__', '__str__', '__subclasshook__', '__weakref__', '_additional_keys', '_clear', 
'_get_currentRole', '_get_roleID', '_getitem', '_image_key', '_init', '_reset', '_roleClass', 
'_roleIsPerson', '_set_currentRole', '_set_roleID', 'accessSystem', 'add_to_current_info', 
'append_item', 'asXML', 'billingPos', 'charactersRefs', 'clear', 'cmpFunct', 'copy', 'currentRole', 
'current_info', 'data', 'default_info', 'get', 'getAsXML', 'getID', 'get_charactersRefs', 
'get_current_info', 'get_fullsizeURL', 'get_namesRefs', 'get_titlesRefs', 'has_current_info', 
'has_key', 'infoset2keys', 'isSame', 'isSameName', 'isSamePerson', 'items', 'iteritems', 
'iterkeys', 'itervalues', 'key2infoset', 'keys', 'keys_alias', 'keys_tomodify', 'keys_tomodify_list', 
'modFunct', 'myID', 'myName', 'namesRefs', 'notes', 'personID', 'pop', 'popitem', 'reset', 'roleID', 
'set_current_info', 'set_data', 'set_item', 'set_mod_funct', 'set_name', 'setdefault', 'summary', 
'titlesRefs', 'update', 'update_charactersRefs', 'update_infoset_map', 'update_namesRefs', 
'update_titlesRefs', 'values']

Here are some simple examples of displaying using the print function to view content in output:

>>> print(people[0].values())
[u'Catalin', u'II', u'Catalin', u'Catalin (II)', u'Catalin (II)']
>>> print(people[0].data)
{u'name': u'Catalin', u'imdbIndex': u'II'}
>>> print(people[1].data.viewitems())
dict_items([(u'name', u'Moreno, Catalina Sandino')])
>>> print(people[1].data.values())
[u'Moreno, Catalina Sandino']
>>> print(people[0].getID())
2165704
>>> print(people[0].itervalues())

The built-in function iter takes an iterable object and returns an iterator.

>>> print(people[0].itervalues().next())
Catalin
>>> print(people[0].asXML()) 

The last line of code will return XML content.
This simple example simply illustrates how to access structured information through simple Python commands.

Fix errors with the python errors encyclopedia.

Today I will present a website that I find very useful in learning and developing with the Python programming language.
This very short tutorial it is very useful for newcomers to get rid of all sorts of common questions about certain errors.
I encounter these types of errors when using stackoverflow account and can be a time consuming for most people who use it.
Try to read all about these errors on the python errors encyclopedia.

Using multiprocessing - a simple introduction.

The multiprocessing module was added to Python in version 2.6 and can be used with new python versions.
It was originally defined in PEP 371 by Jesse Noller and Richard Oudkerk.
The multiprocessing package also includes some APIs that are not in the threading module at all.
Python introduced the multiprocessing module to let us write parallel code.
You can use three basic classes: Process, Queue, and Lock, which will help you build a parallel program.
For more details, you can check the documentation of this python module.
Today, I will present in this tutorial two examples that highlight how to work with this python module.
The most basic approach is probably to use the Process class.
The Process class is very similar to the threading module’s Thread class.
It is also necessary to use the Manager because is responsible for coordinating shared information.
The source code is simple to understand and is commented on to see the intermediate steps.
Let's start with the first example that uses a function of adding two numbers in two processes.

import multiprocessing

def sum(a,b):
 t=a+b
 print ("sum is:",t)

if __name__ == '__main__':
 # create process processing_001 with target function and args of the target function
 processing_001 = multiprocessing.Process(target=sum, args=(7,6))
 processing_002 = multiprocessing.Process(target=sum, args=(1,1))
 # starting processing_001
 processing_001.start()
 # starting processing_002
 processing_002.start()
 #processes are started
 # stop processing_001 until is complete with join
 processing_001.join()
 # stop processing_002 until is complete with join
 processing_001.join()
 # result is C:\Python364>python.exe multiprocessing_001.py
 #sum is: 13
 #sum is: 2

The second example solves the next problem of the engineer who checks the speed of accomplishment of the tasks performed by two robots.
Three processes are created for each: robots and engineer.
All these processes are completed in the same dictionary resource using the Manager.
For each task performed by the robot (start/join processes), the robot finds a random execution rate (function random.random) that is then added to the result (result) with the robot name and execution speed.
Let's see the source code and the result:

import multiprocessing 
from multiprocessing import Process, Manager
import os
import random

def robot1(result):
 print("robot1 has ID process: {}".format(os.getpid()))
 speed_robot1=random.random()
 result["robot1"] = speed_robot1
 print ("worker speed tasks:",speed_robot1)
 #print (result)
 
def robot2(result):
 print("robot2 has ID process: {}".format(os.getpid()))
 speed_robot2=random.random()
 result["robot2"] = speed_robot2
 print ("worker speed tasks:",speed_robot2)
 #print (result)
 
def engineer(result):
 print("engineer has ID process: {}".format(os.getpid()))
 for key, value in result.items():
  print("Result: {}".format((key, value)))
 # you can add code to sort the values and print 
 # for key, value in sorted(result.items()):
 # print("Result: {}".format((key, value)))
 
if __name__ == '__main__':
 # show the main process
 print("the main ID process: {}". format(os.getpid()))
 # create a share dictionary resouce 
 manager = multiprocessing.Manager()
 result = manager.dict()
 # the process starts for  robot1
 ro1 = multiprocessing.Process(target=robot1, args=(result,))
 ro1.start()
 # the process starts for  robot2
 ro2 = multiprocessing.Process(target=robot2, args=(result,))
 ro2.start()
 # stops the robot1 process
 ro1.join()
 # stops the robot2 process
 ro2.join()
 # create the engineer process
 en = multiprocessing.Process(target=engineer, args=(result,))
 # start the process for the engineer
 en.start()
 # stops the engineer's process
 en.join()

The result is:

C:\Python364>python.exe multiprocessing_002.py
the main ID process: 7816
robot1 has ID process: 6476
worker speed tasks: 0.48279764083908094
robot2 has ID process: 7560
worker speed tasks: 0.44408591959008503
engineer has ID process: 7000
Result: ('robot1', 0.48279764083908094)
Result: ('robot2', 0.44408591959008503)

Get bookmarks from your Firefox browser database.

This simple example tutorial is about reading the bookmarks from firefox database.
The database is an SQLite database.
You need to create a python file named: firefox_bookmarks.py.
Change your windows account on the bookmarks_path.
The script is simple to understand and comes with two functions: execute_query and get_bookmarks.
Follow the commented source code to understand how it's working the python script:

import os
import sqlite3

# execute a query on sqlite cursor
def execute_query(cursor, query):
    try:
        cursor.execute(query)
    except Exception as error:
        print(str(error) + "\n " + query)
# get bookmarks from firefox sqlite database file and print all
def get_bookmarks(cursor):
    bookmarks_query = """select url, moz_places.title, rev_host, frecency,
    last_visit_date from moz_places  join  \
    moz_bookmarks on moz_bookmarks.fk=moz_places.id where visit_count>0
    and moz_places.url  like 'http%'
    order by dateAdded desc;"""
    execute_query(cursor, bookmarks_query)
    for row in cursor:
        link = row[0]
        title = row[1]
        print(link,title)
# set the path of firefox folder with databases
bookmarks_path = "C:/Users/YOUR_WINDOWS_ACCOUNT/AppData/Roaming/Mozilla/Firefox/Profiles/"
# get firefox profile
profiles = [i for i in os.listdir(bookmarks_path) if i.endswith('.default')]
# get sqlite database of firefox bookmarks
sqlite_path = bookmarks_path+ profiles[0]+'/places.sqlite'
#
if os.path.exists(sqlite_path):
    firefox_connection = sqlite3.connect(sqlite_path)
cursor = firefox_connection.cursor()
get_bookmarks(cursor)
cursor.close()

The result of running the script comes with my bookmarks of Firefox:

C:\Python364>python.exe firefox_bookmarks.py
https://twitter.com/ Twitter. It's what's happening. 

Using Tornado - first steps...

About Tornado you can read at GitHub.
The basic info about this framework is this intro :
Tornado is a Python web framework and asynchronous networking library, originally developed at FriendFeed. By using non-blocking network I/O, Tornado can scale to tens of thousands of open connections, making it ideal for long polling, WebSockets, and other applications that require a long-lived connection to each user.

C:\Python364>git clone https://github.com/facebook/tornado.git
Cloning into 'tornado'...
remote: Enumerating objects: 51, done.
remote: Counting objects: 100% (51/51), done.
remote: Compressing objects: 100% (34/34), done.
remote: Total 22803 (delta 17), reused 51 (delta 17), pack-reused 22752
Receiving objects: 100% (22803/22803), 8.41 MiB | 2.18 MiB/s, done.
Resolving deltas: 100% (16735/16735), done.
Checking out files: 100% (302/302), done.

C:\Python364>cd tornado

C:\Python364\tornado>C:\Python364\python.exe setup.py install
running install
...
Processing dependencies for tornado==6.1.dev1
Finished processing dependencies for tornado==6.1.dev1

Use this demo chat to test it:

C:\Python364\tornado\demos\chat>C:\Python364\python.exe chatdemo.py
[I 190315 20:26:25 web:2162] 200 GET / (::1) 47.22ms

You can see the result into your browsers using http://localhost:8888
You can change port and address on this source code row with your IP address:

app.listen(options.port, '92.76.67.102')

The result is a chat example with Tornado framework.
The tornado comes with many demos for you, see all of this:

• blog
• chat
• facebook
• file_upload
• helloworld
• s3server
• tcpecho
• twitter
• websocket
• webspider