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