Python 3.7.3 : Using the flask - part 003.

Another tutorial with python 3.7.3 and flask python module.
In the last tutorial, I speak about some tricks and tips.
Today, I will show some steps for fixing and run with flask-sqlalchemy.
The source code from my GitHub account can be updated with this source code.

app = Flask (__name__)
app.config['SECRET_KEY'] = 'abcdefg'
# set SQLAlchemy
basedir = os.path.abspath(os.path.dirname(__file__))
app.config['SQLALCHEMY_DATABASE_URI'] = 'sqlite:///' + os.path.join(basedir, 'server.sqlite')
db = SQLAlchemy(app)
ma = Marshmallow(app)
class User(db.Model):
    id = db.Column(db.Integer, primary_key=True)
    username = db.Column(db.String(80), unique=True)
    email = db.Column(db.String(120), unique=True)

    def __init__(self, username, email):
        self.username = username
        self.email = email

If you run it then you get this:

C:\Python373\my_flask>python
Python 3.7.3 (v3.7.3:ef4ec6ed12, Mar 25 2019, 22:22:05) [MSC v.1916 64 bit (AMD6
4)] on win32
Type "help", "copyright", "credits" or "license" for more information.
>>> from server import db
C:\Python373\lib\site-packages\flask_sqlalchemy\__init__.py:835: FSADeprecationW
arning: SQLALCHEMY_TRACK_MODIFICATIONS adds significant overhead and will be dis
abled by default in the future.  Set it to True or False to suppress this warnin
g.
  'SQLALCHEMY_TRACK_MODIFICATIONS adds significant overhead and '

You will have a database file named server.sqlite.

#app.config['SQLALCHEMY_TRACK_MODIFICATIONS'] = False

Let's create and show the database:

C:\Python373\my_flask>python
Python 3.7.3 (v3.7.3:ef4ec6ed12, Mar 25 2019, 22:22:05) [MSC v.1916 64 bit (AMD6
4)] on win32
Type "help", "copyright", "credits" or "license" for more information.
>>> from server import db
>>> db.create_all()
>>> db.engine.table_names()
['user']

A good approach is to create a config.py file and used into the application area.
The config.py file will have this source code:

SQLALCHEMY_TRACK_MODIFICATIONS = False
SQLALCHEMY_DATABASE_URI = 'sqlite:///' + os.path.join(basedir, 'server.sqlite')
DEBUG=True

The server.py has this source code:

...
app = Flask (__name__)
app.config.from_pyfile('config.py')
db = SQLAlchemy(app)
...

Python 3.7.3 : About pytweening python module.

This is a simple module of tweening and easing functions implemented in Python, see the GitHub webpage.

C:\Python373\Scripts>pip install pytweening
Collecting pytweening
Downloading https://files.pythonhosted.org/packages/b9/f8/c32a58d6e4dff8aa5c27
e907194d69f3b57e525c2e4af96f39c6e9c854d2/PyTweening-1.0.3.zip
Building wheels for collected packages: pytweening
Building wheel for pytweening (setup.py) ... done
Created wheel for pytweening: filename=PyTweening-1.0.3-cp37-none-any.whl size
=3821 sha256=6655c055d779982ff1259a5266bf1300c1cd02046e45e92cf18b20053a326531
Stored in directory: C:\Users\catafest\AppData\Local\pip\Cache\wheels\7b\92\30
\06e21159eed2709436bfb6d7c690959e578cf74f029643866e
Successfully built pytweening
Installing collected packages: pytweening
Successfully installed pytweening-1.0.3

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 (AMD6
4)] on win32
Type "help", "copyright", "credits" or "license" for more information.
>>> import pytweening
>>> dir(pytweening)
['__builtins__', '__cached__', '__doc__', '__file__', '__loader__', '__name__',
'__package__', '__path__', '__spec__', '__version__', '_checkRange', 'division',
 'easeInBack', 'easeInBounce', 'easeInCirc', 'easeInCubic', 'easeInElastic', 'ea
seInExpo', 'easeInOutBack', 'easeInOutBounce', 'easeInOutCirc', 'easeInOutCubic'
, 'easeInOutElastic', 'easeInOutExpo', 'easeInOutQuad', 'easeInOutQuart', 'easeI
nOutQuint', 'easeInOutSine', 'easeInQuad', 'easeInQuart', 'easeInQuint', 'easeIn
Sine', 'easeOutBack', 'easeOutBounce', 'easeOutCirc', 'easeOutCubic', 'easeOutEl
astic', 'easeOutExpo', 'easeOutQuad', 'easeOutQuart', 'easeOutQuint', 'easeOutSi
ne', 'getLine', 'getPointOnLine', 'linear', 'math']
>>> pytweening.linear(0.1)
0.1
>>> pytweening.easeInOutSine(0.1)
0.024471741852423234
>>> pytweening.easeInQuad(0.1)
0.010000000000000002
>>> pytweening.getLine(0, 0, 0.1, 0.1)
[(0, 0)]
>>> pytweening.getLine(0, 0, 1.1, 1.1)
[(0, 0), (1, 1)]

For example, this module can help with some math function:

• simple linear tweening - no easing, no acceleration;
• quadratic easing in/out - acceleration until halfway, then deceleration;
• cubic easing in/out - acceleration until halfway, then deceleration;
• quartic easing in/out - acceleration until halfway, then deceleration;
• sinusoidal easing in/out - accelerating until halfway, then decelerating;
• ...

Python 3.7.3 : Tonny I.D.E. for python programmers.

Today I tested the Thonny I.D.E. from thonny.org official webpage.
Yesterday I tried several editors for python programming language and did not work.
One of these is the spyder editor that does not work with python 3.7.3 - we have not discovered why.
The Mu is a simple Python editor for beginner programmers and has a strange working I.D.E. for good and fast development.
The PyCharm, it is developed by the Czech company JetBrains is not fully free ...

---

The Thonny I.D.E. is fast and comes with two good features:
Code completion and Highlights syntax errors.
The install steps are easy with a wizard application.
After that, you can test with any python source code.
The source code from the image is a simple example from this webpage.
Let's see a screenshot:

This is a video tutorial with this I.D.E. from Aivar Annamaa user:

Python 3.7.3 : Using the flask - part 002.

Let's see some tips for starting any project with flask python module.
Use these python modules to work with databases: flask-sqlalchemy and flask_marshmallow.
The Flask-SQLAlchemy is an extension for Flask that adds support for SQLAlchemy to your application.
The marshmallow is an ORM/ODM/framework-agnostic library for converting complex datatypes, such as objects, to and from native Python datatypes.
Let's install it:

C:\Python373>cd Scripts

C:\Python373\Scripts>pip install flask-sqlalchemy
Collecting flask_sqlalchemy
...
Successfully installed flask-sqlalchemy-2.4.0
C:\Python373\Scripts>pip install flask_marshmallow
Collecting flask_marshmallow
...
Successfully installed flask-marshmallow-0.10.1 marshmallow-2.19.5
C:\Python373\Scripts>pip install marshmallow-sqlalchemy
Collecting marshmallow-sqlalchemy
...
Successfully installed marshmallow-sqlalchemy-0.17.0

Use these python modules for login and authorization issues: flask-login
The Flask-Login provides user session management for Flask. It handles the common tasks of logging in, logging out, and remembering your users’ sessions over extended periods of time.
Flask-login doesn't actually have a user backend, it just handles the session machinery to help you to log in and logout users.
Flask-Login can work with user models that are based on any database system with four required items: is_authenticated, is_active, is_anonymous and get_id.
Let's install it:

C:\Python373\Scripts>pip install flask-login
Collecting flask-login
...
Successfully installed flask-login-0.4.1

This python module named python-dotenv let us to reads the key-value pair from .env file and adds them to an environment variable.

C:\Python373\Scripts>pip install python-dotenv
Collecting python-dotenv
...
Successfully installed python-dotenv-0.10.3

Flask-Login provides a UserMixin which makes it easy to create a user class, see example class User:

from flask_login import UserMixin
...
class User(UserMixin, db.Model):

Another tip is to use the get_or_404 and first_or_404 to avoid show errors on the webpage.
Reduce the passing of multiple variables to render_template, let's see the example:

from flask import render_template
...
return render_template('index.html', fn=name.first, ln=name.last, day=student.day, 
.... ')

This can de easy fix with this code to send value I want to pass:

context = {
'fn': name.first,
'ln': name.last,
'day': student.day,
...
}
return render_template('index.html', **context)

Use query to solve complex tasks.
Let's see a simple example of delete from a Student database class:

...
class Student(db.Model):
id=db.Column(db.Integer, primary_key=True)
...
country=db.Column(db.String(2))
...

Let's delete this with a query and return the number of deleted by delete_count:

del_ids= db.session.query(Student.id).filter(Student.country=='Ro')
delete_count=db.session.query(Student).filter(Student.id.in_(del_ids.subquery())).delete(synchronize_session=False)

I used two files in the main project named .env and .flaskenv:
The file .env comes with:

SQLALCHEMY_DATABASE_URI=sqlite:///db.sqlite3
SECRET_KEY = abcdef

The file .flaskenv has these lines of source code:

FLASK_ENV = development
FLASK_APP = my_flask_app

This can be used into a new file named settings.py:

import os 
SQLALCHEMY_DATABASE_URI = os.environ.get('SQLALCHEMY_DATABASE_URI')
SECRET_KEY = os.environ.get('SECRET_KEY')
SQLALCHEMY_TRACK_MODIFICATIONS = False

Using this the main flask application will start like this:

from flask import Flask
def create_my_app(config_file='settings.py'):
    app = Flask(__name__)
    app.config.from_pyfile(config_file)
    return app

There are plenty of tips and tricks that can be found based on the project in the bottle.
These are the most used.

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: