News : The Python 2.7 no longer support from Python team.

The 1st of January 2020 will mark the sunset of Python 2.7.
It’s clear that Python 3 is more popular these days.
You can learn more about the popularity of both on Google Trends.
Python 3.0 was released in December 2008.
The main goal was to fix problems existing in Python 2.
Since the 1st January 2020, Python 2 will no longer receive any support whatsoever from the core Python team.
Migrating to Python 3 is recommended, including some of the top libraries, such as machine learning.

Python 3.7.5 : Fix to python language the GitHub project.

I created a GitHub project with Django and I saw is detect like tcl programming language:

You need to create a file named .gitattributes in the root folder of my repository.
Use this source code to tell GitHub is a python project:

* linguist-vendored
*.py linguist-vendored=false

Now the project will be target with python language.

Python 3.7.5 : Is Django the best web framework?

This is the question for today in order to lineup the Django features with any web framework from my point of view.
Let's start with a brief introduction to this framework:
Django was created in the fall of 2003, when the web programmers at the Lawrence Journal-World newspaper, Adrian Holovaty and Simon Willison, began using Python to build applications. Jacob Kaplan-Moss was hired early in Django’s development shortly before Simon Willison's internship ended.[16] It was released publicly under a BSD license in July 2005. The framework was named after guitarist Django Reinhardt.[17], see wikipedia.
The Python which is a high-level programming language interpreted with general-purpose and together with the Django web framework creates a solution in fulfilling the objectives of web programming.
The problem of most of those who do not know closely the programming of this framework remains unknown and can be difficult to understand only from examples and tutorials.
Here are some of the difficulties that I personally encountered and had to solve them step by step.

1. understand how to use the link system between the python files created by Django;
2. how to use the templates and how to update them with the Django specific syntax;
3. understanding the way of displaying and resolving specific errors in Django;
4. using and setting the settings.py file;
5. understanding of how the web framework interacts with web technologies;

After solving these problems you will see the true power of this framework:

1. the development is easier with good and lower development costs and so are the additions and upgrades;
2. security is very good, see security documentation and deployment checklist;
3. is an open-source framework and updated by developers who use it;
4. most used and crowd tested and used to develop DropBox, Quora, Google, and Reddit;
5. comes with extensive documentation;
6. a large and community;

The official page of this web framework can be found on this webpage.

Python 3.7.5 : About Django REST framework.

First, let's activate my Python virtual environment:

[mythcat@desk django]$ source env/bin/activate

I update my django version 3.0 up to 3.0.1.

(env) [mythcat@desk django]$ pip3 install --upgrade django --user
Collecting django
...
      Successfully uninstalled Django-3.0
Successfully installed django-3.0.1

The next step comes with installation of Python modules for Django and Django REST:

(env) [mythcat@desk django]$ pip3 install djangorestframework --user
Collecting djangorestframework
...
Installing collected packages: djangorestframework
Successfully installed djangorestframework-3.11.0

Into my folder mysite I run this commands

(env) [mythcat@desk django]$ cd mysite/
(env) [mythcat@desk mysite]$ python3 manage.py makemigrations
No changes detected
(env) [mythcat@desk mysite]$ python3 manage.py migrate
Operations to perform:
  Apply all migrations: admin, auth, contenttypes, sessions, test001
Running migrations:
  No migrations to apply.

To pass information over to an HTTP GET request, the information object must be translated into valid response data.
The Django implements serializers for this.
Serializers provide deserialization, allowing parsed data to be converted back into complex types and allow complex data such as querysets and model instances to be converted to native Python datatypes that can then be easily rendered into JSON, XML or other content types.
Let's create the mysite/serializers.py:

(env) [mythcat@desk mysite]$ cd mysite/
(env) [mythcat@desk mysite]$ vim serializers.py 

The code for this python script is this:

from django.contrib.auth.models import User, Group
from rest_framework import serializers

class UserSerializer(serializers.HyperlinkedModelSerializer):
    class Meta:
        model = User
        fields = ['url', 'username', 'email', 'groups']

class GroupSerializer(serializers.HyperlinkedModelSerializer):
    class Meta:
        model = Group
        fields = ['url', 'name']

The next changes will be on urls.py and views.py.
My urls.py file from the Django-chart project is this:

from django.contrib import admin
from django.urls import path
from test001.views import home_page
from test001.views import Test001ChartView
#
from django.urls import include, path
from rest_framework import routers
from test001 import views

router = routers.DefaultRouter()
router.register(r'users', views.UserViewSet)
router.register(r'groups', views.GroupViewSet)

app_name = 'test001'
urlpatterns = [
    path('admin/', admin.site.urls),
    #path('', home_page, name ='home'),
    path('', Test001ChartView.as_view(), name = 'home'), 
    # Use automatic URL routing
    # Can also include login URLs for the browsable API
    path('', include(router.urls)),
    path('api-auth/', include('rest_framework.urls', namespace='rest_framework'))
]

My views.py file is this:

from django.http import HttpResponse
from django.shortcuts import render
# snippet 
from django.shortcuts import get_object_or_404
# for chart 
from django.views.generic import TemplateView
from .models import Test001, Snippet
#
#def home_page(request):
#    return HttpResponse('Home page!')

# django framework
from django.contrib.auth.models import User, Group
from rest_framework import viewsets
from mysite.serializers import UserSerializer, GroupSerializer

def home_page(request):
    return render(request, 'test001/home.html',{
        'name':'CGF',
        'html_items': ['a','b','c','d','e']
    })


# define view for chart 
class Test001ChartView(TemplateView):
    template_name = 'test001/chart.html'

    def get_context_data(self, **kwargs):
        context = super().get_context_data(**kwargs)
        context["qs"] = Test001.objects.all()
        return context


def snippet_detail(request, id):
    snippet = get_object_or_404(Snippet, id=id)
    return render(request, 'test001/snippets_detail.html', {'snippet': snippet})

class UserViewSet(viewsets.ModelViewSet):
    """
    API endpoint  allows users to be viewed or edited.
    """
    queryset = User.objects.all().order_by('-date_joined')
    serializer_class = UserSerializer

class GroupViewSet(viewsets.ModelViewSet):
    """
    API endpoint  allows groups to be viewed or edited.
    """
    queryset = Group.objects.all()
    serializer_class = GroupSerializer

The settings module for this my project is stored in mysite/settings.py and I add this:

INSTALLED_APPS = [
    ...
    'rest_framework',
]

I run the django project and works well:

python3 manage.py runserver

The http://127.0.0.1:8000/users/ page come with this output:

Python 3.7.5 : The new PyQt5 released.

The PyQt v5.14.0 has been released with support for Qt v5.14.0.

[mythcat@desk ~]$ pip3 install --upgrade PyQt5 --user
Collecting PyQt5
...
Installing collected packages: PyQt5-sip, PyQt5
Successfully installed PyQt5-5.14.0 PyQt5-sip-12.7.0

Let's see how can see the version:

[mythcat@desk ~]$ python3 
Python 3.7.5 (default, Dec 15 2019, 17:54:26) 
[GCC 9.2.1 20190827 (Red Hat 9.2.1-1)] on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> from PyQt5.Qt import PYQT_VERSION_STR
>>> print("PyQt version:", PYQT_VERSION_STR)
PyQt version: 5.14.0

Python 3.7.5 : Simple web search with google python package.

This is a simple search on the web with python google package.

[mythcat@desk ~]$ pip3 install google --user
Collecting google
...
Installing collected packages: google
Successfully installed google-2.0.3

This is a simple example for search on web with this words: protv news 2019.
From the python package, I need to import just the search and used it.
The python package need a variable string named query.
The search wants to know the words from query and arguments, see the help:

Help on function search in module googlesearch:

search(query, tld='com', lang='en', tbs='0', safe='off', num=10, start=0, stop=None, 

domains=None, pause=2.0, tpe='', country='', extra_params=None, user_agent=None)
    Search the given query string using Google.
    
    :param str query: Query string. Must NOT be url-encoded.
    :param str tld: Top level domain.
    :param str lang: Language.
    :param str tbs: Time limits (i.e "qdr:h" => last hour,
        "qdr:d" => last 24 hours, "qdr:m" => last month).
    :param str safe: Safe search.
    :param int num: Number of results per page.
    :param int start: First result to retrieve.
    :param int stop: Last result to retrieve.
        Use None to keep searching forever.
    :param list domains: A list of web domains to constrain
        the search.
    :param float pause: Lapse to wait between HTTP requests.
        A lapse too long will make the search slow, but a lapse too short may
        cause Google to block your IP. Your mileage may vary!
    :param str tpe: Search type (images, videos, news, shopping, books, apps)
        Use the following values {videos: 'vid', images: 'isch',
        news: 'nws', shopping: 'shop', books: 'bks', applications: 'app'}
    :param str country: Country or region to focus the search on. Similar to
        changing the TLD, but does not yield exactly the same results.
        Only Google knows why...
    :param dict extra_params: A dictionary of extra HTTP GET
        parameters, which must be URL encoded. For example if you don't want
        Google to filter similar results you can set the extra_params to
        {'filter': '0'} which will append '&filter=0' to every query.
    :param str user_agent: User agent for the HTTP requests.
        Use None for the default.
    
    :rtype: generator of str
    :return: Generator (iterator) that yields found URLs.
        If the stop parameter is None the iterator will loop forever.

This is the script:

[mythcat@desk ~]$ python3
Python 3.7.5 (default, Dec 15 2019, 17:54:26) 
[GCC 9.2.1 20190827 (Red Hat 9.2.1-1)] on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> from googlesearch import search
>>> query = "protv news 2019"
>>> my_results_list = []
>>> for url in search(query,        
...     tld = 'com',  
...     lang = 'en',       
...     num = 10,     
...     start = 0,    
...     stop = None,  
...     pause = 2.0,):
...     my_results_list.append(url)
...     print(url)
... 
https://stirileprotv.ro/protvnews/
https://stirileprotv.ro/
https://stirileprotv.ro/superbun/protv-news.html
https://www.facebook.com/ProTvNews/
https://www.youtube.com/playlist?list=PLCJaU-QvLGR_FSZw6yeqBJHDe9LgFDRdy
https://www.youtube.com/watch?v=HaiQtDlaNic
https://www.youtube.com/watch?v=hxMEgAANSl4
https://www.youtube.com/channel/UCbbDChpDluLkdnH8QMwN6qA
https://www.youtube.com/watch?v=5zuN9uWFcTE
https://protvplus.ro/tv-live/1-pro-tv
https://en.wikipedia.org/wiki/Pro_TV
https://pro-tv.com/news/page/2/
https://m.youtube.cat/channel/UCbbDChpDluLkdnH8QMwN6qA
...

Python 3.7.5 : Simple intro in CSRF.

CSRF or Cross-Site Request Forgery is a technique used by cyber-criminals to force users into executing unwanted actions on a web application.
To protect against web form CSRF attacks, it's isn't sufficient for web applications to trust authenticated users, must be equipped with a unique identifier called a CSRF token similar to a session identifier.
Django 3.0 can be used with CSRF, see the documentation page.
The CSRF process is a simple one in Django framework.
However, it's highly recommended to use the CsrfViewMiddleware instead.
To activate the django.middleware.csrf.CsrfViewMiddleware in the settings.py file.
Basically, you can use the decorator method in the view.py file.
For example, see documentation:

@csrf_protect

However csrf_protect will check only POST requests.
This annotation CSRF tells Django I'm not handling CSRF properly and don't fail this.
If I don't use this annotation then the CSRF is on if the post request is on by default and I will receive a 403 error.
As the developer using templates you don't have to know anything about that and you can use this in your template HTML5 file.

{% csrf_token %}

I can use the token with my code if I want to send it to the website.

from django.middleware.csrf import get_token

Then this can be used in view.py with a hidden form field with the name csrfmiddlewaretoken present in all outgoing POST forms.
You can used in any forms tag area like: input ...

def my_csrf_form(request):
    response = """ ... type = "hidden" name = "csrfmiddlewaretoken" value = "__token__" ... """

This can be used with:

token = get_token(request)
response = response.replace('__token__', html.escape(token))
response += dumpdata('POST', request.POST)
return HttpResponse(response)

This will get a token for the current request and fill into value = "__token__".
Using my old example from GitHub.
First, I start the project:

[mythcat@desk projects]$ source django/env/bin/activate
(env) [mythcat@desk projects]$ ls
cache  django  kaggle  logs  OSMnx  pygal_ex  SantaClaus.py
(env) [mythcat@desk projects]$ cd django/
(env) [mythcat@desk django]$ ls
env  mysite  venv
(env) [mythcat@desk django]$ cd mysite/

I change the chart.html file with this before javascript script:

... {% csrf_token %} ... 

You can use it in javascript - jQuery like this, if you want:

var csrftoken = jQuery("[name=csrfmiddlewaretoken]").val();

The CSRF protection on Django on my form can bu used in view.py:

...
# use csrf_protect
from django.views.decorators.csrf import csrf_protect
from django.utils.decorators import method_decorator
...
# define view for chart 
class Test001ChartView(TemplateView):
    csrf_protected_method = method_decorator(csrf_protect)

I test it and works well.
The documentation tells us:
The CSRF protection cannot protect against man-in-the-middle attacks, so use HTTPS with HTTP Strict Transport Security. It also assumes validation of the HOST header and that there aren’t any cross-site scripting vulnerabilities on your site (because XSS vulnerabilities already let an attacker do anything a CSRF vulnerability allows and much worse).

Python 3.7.5 : Django admin shell by Grzegorz Tężycki.

Today I tested another python package for Django named django-admin-shell.
This package created by Grzegorz Tężycki can be found on GitHub and come with the intro:
Django application can execute python code in your project’s environment on django admin site. You can use similar as python manage shell without reloading the environment.

[mythcat@desk ~]$ cd projects/
[mythcat@desk projects]$ cd django/
...
[mythcat@desk projects]$ source django/env/bin/activate
(env) [mythcat@desk projects]$ 

(env) [mythcat@desk projects]$ pip3 install django-admin-shell --user
Collecting django-admin-shell
...
Installing collected packages: django-admin-shell
Successfully installed django-admin-shell-0.1 

First, I test my project to see if this working.

(env) [mythcat@desk projects]$ cd django/
(env) [mythcat@desk django]$ ls
env  mysite  venv
(env) [mythcat@desk django]$ cd mysite/
(env) [mythcat@desk mysite]$ ls
db.sqlite3  manage.py  mysite  test001
(env) [mythcat@desk mysite]$ python3 manage.py runserver
Watching for file changes with StatReloader
Performing system checks...

System check identified no issues (0 silenced).
December 14, 2019 - 18:23:47
Django version 3.0, using settings 'mysite.settings'
Starting development server at http://127.0.0.1:8000/
Quit the server with CONTROL-C. 

You need to add this package into the settings.py file:

INSTALLED_APPS = [
    ...
    'django_admin_shell',
    ...
]

I add this package into my url.py file:

...
from django.conf.urls import include, url
...
urlpatterns = [
...
    # admin shell 
    url(r'^admin/shell/', include('django_admin_shell.urls')),
]

After that, you can start the project and see if this working: http://127.0.0.1:8000/admin/shell/
This screenshot shows me is working well:

Python 3.7.5 : The Pygal python package.

Today's tutorial aims to get data from a URL and display it with the Pygal python package.
I believe that global warming is a very important topic for human evolution.
You can read more about this topic on this website.
About this python package you can learn more at the official website.

[mythcat@desk ~]$ pip3 install Pygal --user
Collecting Pygal
...
Installing collected packages: Pygal
Successfully installed Pygal-2.4.0

Let's test this python package with this script:

import pygal
import pandas as pd
import numpy as np
URL = 'https://data.giss.nasa.gov/gistemp/graphs/graph_data/
Global_Mean_Estimates_based_on_Land_and_Ocean_Data/graph.txt'

df = pd.read_fwf(
    URL,
    skiprows=(0,1,2,4),
    index_col=0,
)
print(df.head)

year_data = df['No_Smoothing']
print(year_data.head)
one_chart = pygal.Bar()
one_chart.title = "Land-Ocean Temperature Index (C)"
one_chart.add("",year_data)
one_chart.render_in_browser()

The result is this output:

Python 3.7.5 : The OSMnx python package.

About this python package named OSMnx, you can read on GitHub.
OSMnx is a Python package that lets you download spatial geometries and model, project, visualize, and analyze street networks from OpenStreetMap's APIs. Users can download and model walkable, drivable, or bikable urban networks with a single line of Python code, and then easily analyze and visualize them ...
You cannot install osmnx directly in Fedora 31 Linux distro because of the dependency with python packages.

[mythcat@desk projects]$ pip3 install osmnx --user

Installation depends by rtree python packages:

[root@desk projects]# dnf install python3-rtree.noarch
...
Installed:
  python3-rtree-0.9.1-1.fc31.noarch 

spatialindex-1.9.3-1.fc31.x86_64                             

Complete!

Now you can install with pip3 tool if you use the python3.

[mythcat@desk projects]$ pip3 install osmnx --user
Collecting osmnx
...
Installing collected packages: pyparsing, kiwisolver, cycler, matplotlib, descartes, networkx, Shapely,

 pyproj, click, click-plugins, cligj, attrs, munch, fiona, geopandas, osmnx
Successfully installed Shapely-1.6.4.post2 attrs-19.3.0 click-7.0 click-plugins-1.1.1 cligj-0.5.0 

cycler-0.10.0 descartes-1.1.0 fiona-1.8.13 geopandas-0.6.2 kiwisolver-1.1.0 matplotlib-3.1.2 

munch-2.5.0 networkx-2.4 osmnx-0.11 pyparsing-2.4.5 pyproj-2.4.2.post1

Let's test the default example:

[mythcat@desk projects]$ python3
Python 3.7.5 (default, Oct 17 2019, 12:16:48) 
[GCC 9.2.1 20190827 (Red Hat 9.2.1-1)] on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> import osmnx as ox
>>> ox.config(log_file=True, log_console=True, use_cache=True)
Configured osmnx
>>> my_loc  = ox.graph_from_address('Fălticeni, Suceava, 725200, Romania', network_type= 'all', 

retain_all =True)
Geocoded "Fa?lticeni, Suceava, 725200, Romania" to (47.4597637, 26.30255)
Projected the GeoDataFrame "geometry to project" to UTM-35 in 0.01 seconds
Projected the GeoDataFrame "geometry to project" to default_crs in 0.01 seconds
Created bounding box 1000 meters in each direction from (47.4597637, 26.30255): 47.46876106367192,

47.45076632191398,26.31581675508982,26.289283302659218
Projected the GeoDataFrame "geometry to project" to UTM-35 in 0.01 seconds
Projected the GeoDataFrame "geometry to project" to default_crs in 0.01 seconds
Projected the GeoDataFrame "geometry to project" to UTM-35 in 0.01 seconds
Projected the GeoDataFrame "geometry to project" to default_crs in 0.01 seconds
Requesting network data within bounding box from API in 1 request(s)
Retrieved response from cache file "cache/eb15fb167cf173e622992dbc2b7b7c77.json" for URL 

"http://overpass-api.de/api/interpreter?data=%5Bout%3Ajson%5D%5Btimeout%3A180%5D%3B%28way

%5B%22highway%22%5D%5B%22area%22%21~%22yes%22%5D%5B%22highway%22%21~%22proposed%7Cconstruction

%7Cabandoned%7Cplatform%7Craceway%22%5D%5B%22service%22%21~%22private%22%5D%5B%22access%22%21

~%22private%22%5D%2847.446267%2C26.282649%2C47.473260%2C26.322452%29%3B%3E%3B%29%3Bout%3B"
Got all network data within bounding box from API in 1 request(s) and 0.01 seconds
Creating networkx graph from downloaded OSM data...
Created graph with 2,003 nodes and 3,930 edges in 0.08 seconds
Added edge lengths to graph in 0.06 seconds
Truncated graph by bounding box in 0.05 seconds
Begin topologically simplifying the graph...
Identified 339 edge endpoints in 0.03 seconds
Constructed all paths to simplify in 0.01 seconds
Simplified graph (from 2,003 to 339 nodes and from 3,930 to 803 edges) in 0.22 seconds
Truncated graph by bounding box in 0.01 seconds
Got the counts of undirected street segments incident to each node (before 

removing peripheral edges) in 0.07 seconds
graph_from_bbox() returning graph with 235 nodes and 556 edges
graph_from_point() returning graph with 235 nodes and 556 edges
graph_from_address() returning graph with 235 nodes and 556 edges
>>> my_proj = ox.project_graph(my_loc)
Created a GeoDataFrame from graph in 0.05 seconds
Projected the GeoDataFrame "unnamed_nodes" to UTM-35 in 0.03 seconds
Projected the GeoDataFrame "unnamed_edges" to UTM-35 in 0.10 seconds
Extracted projected node geometries from GeoDataFrame in 0.01 seconds
Rebuilt projected graph in 0.94 seconds
>>> fig, ax = ox.plot_graph(my_proj)
Begin plotting the graph...
Created GeoDataFrame "unnamed_UTM_edges" from graph in 0.07 seconds
Drew the graph edges in 0.04 seconds

This python module comes with many features.
You can select a polygon area and show the map.
This is the result of source code from this tutorial:

Python 3.7.5 : Starting with kaggle platform.

Kaggle is the world's largest community of data scientists and the platform is the fastest way to get started on a new data science project.
A good choice to use Kaggle is this feature: Kaggle provides free access to NVidia K80 GPUs in kernels.
The tutorial for today is about kaggle and is new for me because I hear about this opportunity last year.
This platform that hosts data science and machine learning competitions can give the people a good area for development.
The official blog can tell you more about how can this platform works.
The kaggle A.P.I. can be found at GitHub.
Let's start the tutorial with the pip3 install tool:

[mythcat@desk kaggle]$ pip3 install kaggle --upgrade --user
Collecting kaggle
...
[mythcat@desk kaggle]$ mkdir ~/.kaggle/
[mythcat@desk kaggle]$ mv kaggle.json ~/.kaggle/kaggle.json
[mythcat@desk kaggle]$ kaggle 
Warning: Your Kaggle API key is readable by other users on this system! To fix this, you can

 run 'chmod 600 /home/mythcat/.kaggle/kaggle.json'
usage: kaggle [-h] [-v] {competitions,c,datasets,d,kernels,k,config} ...
kaggle: error: the following arguments are required: command
[mythcat@desk kaggle]$ chmod 600 /home/mythcat/.kaggle/kaggle.json

You can use the kaggle command to get informations from kaggle platform:

[mythcat@desk kaggle]$ kaggle competitions list 
ref                                            deadline             category            reward  teamCount
  userHasEntered  
---------------------------------------------  -------------------  ---------------  ---------  ---------
  --------------  
digit-recognizer                               2030-01-01 00:00:00  Getting Started  Knowledge       2305
           False  
titanic                                        2030-01-01 00:00:00  Getting Started  Knowledge      17135
           False  
house-prices-advanced-regression-techniques    2030-01-01 00:00:00  Getting Started  Knowledge       5532
           False  
imagenet-object-localization-challenge         2029-12-31 07:00:00  Research         Knowledge         57
           False  
google-quest-challenge                         2020-02-10 23:59:00  Featured           $25,000        410
           False  
tensorflow2-question-answering                 2020-01-22 23:59:00  Featured           $50,000        810
           False  
data-science-bowl-2019                         2020-01-22 23:59:00  Featured          $160,000       1762
           False  
pku-autonomous-driving   
...

The commands for this platform can be seen at GitHub:

kaggle competitions {list, files, download, submit, submissions, leaderboard}
kaggle datasets {list, files, download, create, version, init}
kaggle kernels {list, init, push, pull, output, status}
kaggle config {view, set, unset}

The kaggle platform use an online tool with notebooks similar with Jupyter notebook for process coding in Kernels.
The process of using models need the datasets and you can use the kaggle datasets.
You can use the New Notebook button from that page to start using the datasets.
The datasets can be load from kaggle or can be uploaded.
The next window let you to Select new notebook settings.
I used Python with a notebook and I got the default source code:

# This Python 3 environment comes with many helpful analytics libraries installed
# It is defined by the kaggle/python docker image: https://github.com/kaggle/docker-python
# For example, here's several helpful packages to load in 

import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)

# Input data files are available in the "../input/" directory.
# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input

 directory

import os
for dirname, _, filenames in os.walk('/kaggle/input'):
    for filename in filenames:
        print(os.path.join(dirname, filename))

# Any results you write to the current directory are saved as output.

Now, I can read the dataset shown in the right the Data with input
(read-only data) and output from kaggle with pandas module:

data = pd.read_csv("../input/lego-database/colors.csv")
data.head()

The Commit button let you to save your work for later.
You can see my online test I created with dataset Lego and python on my kaggle page.

Python 3.7.5 : Using the django with javascript.

The Django framework can work great with javascript.
I start this project a few days ago.
You can start a simple Django project, see my old tutorials.
I used a template for main page in my template folder named chart.html.
The chart.min.js is set on base.html.
The links work from the Django framework to HTML5 templates with javascript.
The full project can be found on my GitHub project.
See the screen output:

Python 3.7.5 : About PEP 8.

Today is December 5, 2019, and this is the reason I wrote about PEP 8.
The official webpage can be found at this webpage.
PEP 8 recommends using 4 spaces to show indentation and tabs should only be used to maintain consistency in the code.
The Python Library is conservative and 79 characters are the maximum required line limit as suggested by PEP 8.
The main goal is to avoid line wrapping.
Indentation allows you to differentiate between multiple lines of code and a single line of code that spans multiple lines.
The closing braces with the first white-space character of the last line.
The use of blank lines can greatly improve the readability of your code.
This rule allows the reader to understand the separation of the sections of code and the relation between them.

Type	Naming Conventions	Examples
Variable	Using short names with CapWords.	T, AnyString, My_First_Variable
Function	Using a lowercase word or words with underscores to improve readability.	function, my_first_function
Class	Using CapWords and do not use underscores between words.	Student, MyFirstClass
Method	Using lowercase words separated by underscores.	Student_method, method
Constants	Using all capital letters with underscores separating words	TOTAL, MY_CONSTANT, MAX_FLOW
Exceptions	Using CapWords without underscores.	IndexError, NameError
Module	Using short lower-case letters using underscores.	module.py, my_first_module.py
Package	Using short lowercase words and underscores are discouraged.	package, my_first_package

About comments, PEP 8 basically recommends using block comments for general-purpose coding.
Document strings or docstrings start at the first line of any function, class, file, module or method.
These type of comments are enclosed between single quotations ( ''') or double quotations ( """ ).
To improving the readability of expressions and statements use whitespace for any character or sequence of characters.
We need to avoiding whitespaces:

• inside parentheses, brackets, or braces;
• before a comma, a semicolon, or a colon;
• before open parenthesis that initiates the argument list of a function call;
• before an open bracket that begins an index or a slice;
• between a trailing comma and a closing parenthesis;
• to adjust assignment operators;

PEP 8 recommends using a clear development for implementation of source code, for example PEP recommends using .startswith() and .endswith() instead of list slicing.
You can check whether your code actually complies with the rules and regulations of PEP 8 or not.
The linters is a program that analyzes your code and detects program errors, syntax errors, bugs, and structural problems.
An autoformatted tool will format your code to adapt to PEP 8 automatically.
Both tools used to implement and check PEP 8 compliance.
To know more about PEP 8 and its book of guidelines, you can refer to pep8.org.
You can check it online at this webpage.

Python 3.7.5 : The new Django version 3.0 .

On December 2, 2019, comes with Django 3.0 Released.
This new release comes with many changes and features, let's see :

• Django 3.0 supports new versions of Python 3.6, 3.7, and 3.8 (the old Django 2.2.x series is the last to support Python 3.5);
• Django now officially supports MariaDB 10.1 and higher;
• Django is fully async-capable by providing support for running as an ASGI application;
• The exclusion class constraints on PostgreSQL;
• the output BooleanField may now be used directly in QuerySet filters;
• you can have custom enumeration types
• cache features: add_never_cache_headers() and never_cache() now add the private directive to Cache-Control headers;
• file storage, forms, internationalization, and logging features;
• the management commands are new options;
• the Requests and Responses, Models and Security has many changes and new features;
• the Tests and test cases have a good implementation;
• new minor features for django.contrib.admin, django.contrib.auth, django.contrib.gis, django.contrib.postgres, django.contrib.sessions, django.contrib.syndication and more

All these features can be read at the official webpage.

Python 3.7.5 : Script install and import python packages.

This script will try to import Python packages from a list.
If these packages are not installed then they will be installed on the system.

import sys
import subprocess

if __name__ == '__main__': 
    def ModuleInstall(package_name):
        try:
            subprocess.check_call(['python3', '-m', 'pip3', 'install', package_name, "--user"])
        except:
            subprocess.check_call(['python', '-m', 'pip', 'install', package_name, "--user"])
    def ModuleLoaded(package_name):
        __import__(package_name)
        print ("Successfully imported ", package_name, '.')

    required_libs = [
    "optparse",
    "socket",
    "select",
    "threading",
    "time",
    "re",
    "ssl",
    "textwrap",
    "sys",
    "random",
    "traceback",
    "binascii",
    "struct",
    "inspect"]
    for lib_name in required_libs:
        try:
            ModuleLoaded(lib_name)
        except:
            ModuleInstall(lib_name)

Python 3.7.5 : Python and SQL, a fast approach.

Today he had to solve a task that required declaring a series of consecutive days to read from several SQL tables.
As you know, this task would require a complex query to include statements.
I solved it quite simply by using a python script that would automatically generate the days and then concatenate them into a query.
It may not be the best solution but it is very fast and does not require very advanced SQL knowledge.
Let's see the source code:

from datetime import date, timedelta

start_date = date(2019, 01, 1)
end_date = date(2019, 11, 24)
delta = timedelta(days=1)
q1 = str("SELECT DATE_FORMAT(updated, '%Y-%m-%d') AS day, intern, COUNT(*) FROM `intern")
q2 = str("` WHERE intern = 0 GROUP BY intern, day UNION ALL ")
while start_date <= end_date:
    print (q1+start_date.strftime("%Y_%m_%d")+q2)
    start_date += delta

The result querry will be like this:

SELECT DATE_FORMAT(updated, "%Y-%m-%d") AS day, intern, COUNT(*) FROM `intern2019_01_01` GROUP BY intern, 
day UNION ALL 
SELECT DATE_FORMAT(updated, "%Y-%m-%d") AS day, intern, COUNT(*) FROM `intern2019_01_02` GROUP BY intern,
 day UNION ALL
...
SELECT DATE_FORMAT(updated, "%Y-%m-%d") AS day, intern, COUNT(*) FROM `intern2019_11_24` GROUP BY intern,
 day UNION ALL

The last step, I remove the UNION ALL from the last SELECT and I used with phpmyadmin.

Python 3.7.5 : Create GUI with npyscreen.

This python module solves the issue of creating easy GUI in the terminal.
The development team tells us:
Npyscreen is a python widget library and application framework for programming terminal or console applications.
The development of this python module is similar to the PyQt python module.
The npyscreen comes with many widgets and easy development.
The GitHub repo for this python module comes with many examples.
The documentation of this python module can be found at this

[mythcat@desk ~]$ pip3 install npyscreen --user
Collecting npyscreen
...
Installing collected packages: npyscreen
Successfully installed npyscreen-4.10.5

Let's see one simple example:

#!/usr/bin/env python3
import npyscreen

class ProductForm(npyscreen.Form):
     def create(self):
        self.product_name = self.add(npyscreen.TitleText, name='Name')
        self.category = self.add(npyscreen.TitleSelectOne, scroll_exit=True,\
     max_height=3, name='Category', values = ['Products 1', 'Products 2', 'Products 3'])
        self.myDate = self.add(npyscreen.TitleDateCombo, name='Date stocking')

class MyTest(npyscreen.NPSAppManaged):
    def onStart(self):
        self.addForm('MAIN', ProductForm, name='New product')

if __name__ == '__main__':
    TestApp = MyTest().run() 

The result of this python script can be seen on the next image.

Python 3.7.5 : Display a file in the hexadecimal and binary output.

This is an example with a few python3 modules that display a file in the hexadecimal and binary output:

import sys
import os.path
import argparse

parser = argparse.ArgumentParser()
parser.add_argument("FILE", help="the file that you wish to dump to hexadecimal", type=str)
parser.add_argument("-b", "--binary", help="display bytes in binary format instead of hexadecimal")
args = parser.parse_args()

try:
    with open(args.FILE, "rb") as f:
        n = 0
        b = f.read(16)
        while b:
            if not args.binary:
                s1 = " ".join([format(i,'02x') for i in b])
                s1 = s1[0:23] + " " + s1[23:]
                width = 48
            else:
                s1 = " ".join([format(i,'08b') for i in b])
                s1 = s1[0:71] + " " + s1[71:]
                width = 144
            s2 = "".join([chr(i) if 32 <= i <= 127 else "." for i in b])
            print(format(n * 16,'08x'), format(s1), format(s2))
            n += 1
            b = f.read(16)

    print(format(os.path.getsize(args.FILE),'08x'))

except Exception as e:
    print(__file__, ": ", type(e).__name__, " - ", e, sep="", file=sys.stderr)

The hexadecimal output of the sec.png file:

[mythcat@desk test]$ python3 hex.py sec.png 
00000000 89 50 4e 47 0d 0a 1a 0a  00 00 00 0d 49 48 44 52 .PNG........IHDR
00000010 00 00 01 0b 00 00 00 bd  08 03 00 00 00 93 6f a8 ..............o.
00000020 bc 00 00 00 5d 50 4c 54  45 ff ff ff ab ab ab fc ....]PLTE.......
00000030 fc fc a0 a0 a0 f5 f5 f5  a6 a6 a6 f9 f9 f9 9e 9e ................
00000040 9e f1 f1 f1 fa fa fa ea  ea ea ad ad ad a4 a4 a4 ................
00000050 d0 d0 d0 f6 f6 f6 e5 e5  e5 bc bc bc c3 c3 c3 97 ................
00000060 97 97 b4 b4 b4 d8 d8 d8  bf bf bf cb cb cb db db ................
00000070 db e1 e1 e1 93 93 93 7f  7f 7f 8d 8d 8d 85 85 85 .............
00000080 79 79 79 70 70 70 d9 a6  b1 29 00 00 08 fa 49 44 yyyppp...)....ID
00000090 41 54 78 9c e5 9d 89 92  a3 2a 14 40 45 45 09 a8 ATx......*.@EE..
000000a0 80 18 34 d3 3d ef ff 3f  f3 69 92 4e 67 71 45 90 ..4.=..?.i.NgqE.
000000b0 65 4e 55 4f a5 7b 52 c6  20 dc 9d 4b 14 f9 46 f7 eNUO.{R. ..K..F.
000000c0 87 36 79 8d 6d df 86 75  28 03 e4 4f c4 cb 28 ca .6y.m..u(..O..(.
000000d0 63 db f7 62 97 36 91 8f  e9 80 72 ce 6c de 8b 65 c..b.6....r.l..e
000000e0 c8 f9 f9 b7 14 53 68 eb  4e ac 73 a1 ef 7f 91 d2 .....Sh.N.s....
000000f0 c6 7d 6c 04 71 91 e9 be  66 f2 31 14 51 04 dd 97 .}l.q...f.1.Q...
00000100 a0 9c d0 8e e8 be 66 33  f6 d7 5a f3 a7 68 a7 e4 ......f3..Z..h..
00000110 91 f6 f9 8b c1 e8 9f 45  aa f5 53 34 93 45 71 7e .......E..S4.Eq~
00000120 7d 21 4a 9d 97 9d 98 00  ba 67 9f 4e b2 3a 61 a2 }!J......g.N.:a.
00000130 bb bd 96 10 f5 ff e2 b2  c3 d5 ee eb b6 13 b3 4c ...............L
00000140 a0 dd 97 36 06 96 51 c7  7f 7e 41 02 00 c8 da 86 ...6..Q.~A.....
00000150 13 00 bb 7d d7 9d 94 0b  f9 88 44 75 03 fc f5 f6 ...}......Du....
00000160 9c d2 d3 fd 45 d2 ff 34  cf 0f 17 89 75 aa 26 46 ....E..4....u.&F
00000170 b4 2d b9 98 fc 7f a0 75  29 ea 43 4e df b2 68 a2 .-....u).CN..h.
...

The binnary output of sec.png file:

[mythcat@desk test]$ python3 hex.py -b FILE sec.png 
00000000 10001001 01010000 01001110 01000111 00001101 00001010 00011010 00001010  
00000000 00000000 00000000 00001101 01001001 01001000 01000100 01010010 .PNG........IHDR
00000010 00000000 00000000 00000001 00001011 00000000 00000000 00000000 10111101
  00001000 00000011 00000000 00000000 00000000 10010011 01101111 10101000 ..............o.
00000020 10111100 00000000 00000000 00000000 01011101 01010000 01001100 01010100
  01000101 11111111 11111111 11111111 10101011 10101011 10101011 11111100 ....]PLTE.......
00000030 11111100 11111100 10100000 10100000 10100000 11110101 11110101 11110101
  10100110 10100110 10100110 11111001 11111001 11111001 10011110 10011110 ................
00000040 10011110 11110001 11110001 11110001 11111010 11111010 11111010 11101010
  11101010 11101010 10101101 10101101 10101101 10100100 10100100 10100100 ................
00000050 11010000 11010000 11010000 11110110 11110110 11110110 11100101 11100101
  11100101 10111100 10111100 10111100 11000011 11000011 11000011 10010111 ................
00000060 10010111 10010111 10110100 10110100 10110100 11011000 11011000 11011000
  10111111 10111111 10111111 11001011 11001011 11001011 11011011 11011011 ................
00000070 11011011 11100001 11100001 11100001 10010011 10010011 10010011 01111111
  01111111 01111111 10001101 10001101 10001101 10000101 10000101 10000101 .............
00000080 01111001 01111001 01111001 01110000 01110000 01110000 11011001 10100110
  10110001 00101001 00000000 00000000 00001000 11111010 01001001 01000100 yyyppp...)....ID
00000090 01000001 01010100 01111000 10011100 11100101 10011101 10001001 10010010
  10100011 00101010 00010100 01000000 01000101 01000101 00001001 10101000 ATx......*.@EE..
...

Python 3.7.5 : About PEP 8016.

Let's start with PEP 8012 which proposes a new model of Python governance based on consensus and voting, without the role of a centralized singular leader or a governing council and was rejected.
The PEP 8015 formalize the current organization of the Python community and proposes changes.
This PEP 8015 was rejected by a core developer vote described in PEP 8001 on Monday, December 17, 2018.
The next PEP 8016 proposes a model of Python governance based around a steering council.
All candidate PEPs are listed in PEP 8000 and consists of all PEPs numbered in the 801X range.
The PEP 8001 refers to the voting process and choose which governance PEP should be implemented by the Python project.
All these PEP's 2012, 2013, 2014, 2015 are rejected.
The last one PEP 2016 named The Steering Council Model was accepted.
The PEP 2016 comes with these new rules:
This PEP proposes a model of Python governance based around a steering council. The council has broad authority, which they seek to exercise as rarely as possible; instead, they use this power to establish standard processes, like those proposed in the other 801x-series PEPs. This follows the general philosophy that it's better to split up large changes into a series of small changes that can be reviewed independently: instead of trying to do everything in one PEP, we focus on providing a minimal-but-solid foundation for further governance decisions.
You can read more about this PEP on this webpage.

Python 3.7.5 : About PEP 3107.

The PEP 3107 introduces a syntax for adding arbitrary metadata annotations to Python functions.
The function annotations refer to syntax parameters with an expression.

def my_function(x: expression, y: expression = 5):
...

For example:

>>> def show(myvar:np.float64):
...     print(type(myvar))
...     print(myvar)
... 
>>> show(1.1)

1.1
>>> def files(filename: str, dot='.') -> list:
...     print(filename)
...     print(type(filename))
... 
>>> files('file.txt')
file.txt

>>> print(files.__annotations__)
{'filename': , 'return': }
>>> print(show.__annotations__)
{'myvar': }
...

You can see the annotation syntax with a dictionary called __annotations__ as an attribute on your functions.
This lets you rewrite Python 3 code with function annotations to be compatible with both Python 3 and Python 2.
Type hints are a specialization of function annotations, and they can also work side by side with other function annotations.
Annotations have no standard meaning or semantics.
There are several benefits to the annotations:

• if you rename an argument, the documentation docstring version may be out of date and is easier to see if an argument is not documented;
• is no need to come up with a special format of argument because the annotations attribute provides a direct, standard mechanism of access;
  

Let's see one example using type aliases:

>>> Temperature = float
>>> def forecast(local_temperature: Temperature) -> str:
...     print(local_temperature)
... 
>>> forecast(13.1)
13.1
...

I can create multiple annotations:

>>> def div(a: dict(type=float, help='the dividend'), b: dict(type=float, help='this <> 0)') ) -> 
dict(type=float, help='the result of dividing a by b'):
...     return a / b
... 
>>> div(3,4)
0.75
...

Annotations for excess parameters like *args and **kwargs, allow arbitrary number of arguments to be passed in a function call.
See example with my_func:

def my_func(*args: expression, *kwargs: expression):
...

Annotations combine well with decorators to provide input to a decorator, and decorator-generated wrappers are a good place to put code that gives meaning to annotations, but this is another issue.

Python 3.7.5 : About PEP 506.

Today I did a python evaluation and saw that there are many new aspects that should be kept in mind for a programmer.
So I decided to recall some necessary elements of PEP.
First, PEP stands for Python Enhancement Proposal.
A PEP is a design document providing information to the Python community, or describing a new feature for Python or its processes or environment.
My list will not follow a particular order and I will start with PEP 506.
This PEP 506 proposes the addition of a module for common security-related functions such as generating tokens to the Python standard library.
Python 3.6 added a new module called secrets that is designed to provide an obvious way to reliably generate cryptographically strong pseudo-random values suitable for managing secrets, such as account authentication, tokens, and similar.
Python’s random module was never designed for cryptographic but you can try to use it with urandom function:

[mythcat@desk ~]$ python3
Python 3.7.5 (default, Oct 17 2019, 12:09:47) 
[GCC 9.2.1 20190827 (Red Hat 9.2.1-1)] on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> import os
>>> os.urandom(8)

This module named secrets will contain a set of ready-to-use functions for dealing with anything which should remain secret (passwords, tokens, etc.).

>>> import secrets
>>> import string
>>> alphabet = string.ascii_letters + string.digits
>>> password = ''.join(secrets.choice(alphabet) for i in range(20)) 
>>> print(alphabet, password)
abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ0123456789 mwTKhSxGGBMU3voOV1Kf

The secrets module also provides several methods of generating tokens, see example:
As bytes, secrets.token_bytes;

>>> secrets.token_bytes()
b'I\xf9a\xd1j\xc6\xc9\xa0qV\x82\x07x\xc6\xe9\xbb\xd7<\xfb\xb2?\xe1\x94\xe9\xce\xbc\xaaF\xfc7\xfc='
>>> secrets.token_bytes(8)
b'\rl\xb1\xb9\x04i]d'
>>> secrets.token_bytes(16)
b'B!:G\x1c\xdd.\xacC\x7f\x95)\x1f^\xec\xb2'
>>> secrets.token_bytes(32)
b'\xfa\xa9\xff\x91y\x9e+z9\x88K\x95\xa8\xb0\x06\xc2b:\xf5]\xcf^%~\x0cJ\xdd\x80\xa2\xa0\xdc\xaa'
>>> secrets.token_bytes(64)
b"\xe4(\x80d7c6\\\xb2\xd5\xcb\x92\x8a'\x82\xcb\xfd\xcc\x9a\x8a\xd9jt\x84s\xb0\x8f]\x8cS\xdcP\n\xef\x14\xf6\
xe0+0\xaf\xcfL\xd3\xd0\xfe\x04\x98k\xc38\xf6\xad.~\xd1\xca\xd6\xc9\xf9\xbf\xff8O\xad"

As text, using hexadecimal digits, secrets.token_hex;

>>> secrets.token_hex()
'5a2eb8a0a89ecaf5a64e57215f359012eaaf8a3db51bd1ea171e922a24935183'
>>> secrets.token_hex(8)
'79e7582b72711af7'
>>> secrets.token_hex(16)
'9b274380935ae169ebd41159f7b85cf6'
>>> secrets.token_hex(32)
'0a2e5fde42c6578c3ba36501b69a9339e838d44c3240999a83d349d266bcb164'
>>> secrets.token_hex(64)
'fbd9ab627e9fe6c2b6d715b1438205321ac9139f5089fe6ca4ffece79aa0c08aa84a26fdbb984dc48a0489e1692b19d3f5fe40116be
60f1a1d7d61739718befe'

As text, using URL-safe base-64 encoding, secrets.token_urlsafe.

>>> secrets.token_urlsafe()
'L06rX6fIk1n-gpcLbsHq_w5SgkqgGcvnkjBRcOZqgXs'
>>> secrets.token_urlsafe(8)
'lhOw5llcgsQ'
>>> secrets.token_urlsafe(16)
'A493DgcDMiNx8WjlRswxBA'
>>> secrets.token_urlsafe(32)
'HSb5dqkaPrqFcdsQFYW5N_Fxb_Hxn0ESsT4VMfJcLYY'
>>> secrets.token_urlsafe(64)
'FKPC0LU7Sc_dsxm7m-VMA-vTEKgJeNcD2zpjKBEg0oLZlPBVVM0O5Vztp0ySLifyifok5009LByQUc5z8thCWQ'

Python 3.7.5 : Intro about scikit-learn python module.

This python module named scikit-learn used like sklearn is designed to interoperate with the Python numerical and scientific libraries NumPy and SciPy and comes with various classification, regression and clustering algorithms including support vector machines, random forests, gradient boosting, k-means and DBSCAN.
The official webpage can be found here
Let't install this on my Fedora 30 distro:

[mythcat@desk proiecte_github]$ mkdir sklearn_examples
[mythcat@desk proiecte_github]$ cd sklearn_examples/
[mythcat@desk sklearn_examples]$ pip3 install scikit-learn --user
Python 3.7.5 (default, Oct 17 2019, 12:09:47) 
[GCC 9.2.1 20190827 (Red Hat 9.2.1-1)] on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> import sklearn
>>> print('sklearn: %s' % sklearn.__version__)
sklearn: 0.21.3 

First, this is a complex python module with many examples on web.
You can learn much about how can use simple and efficient all data mining and data analysis.
You can learn a lot about how all data exploitation and data analysis can be used simply and efficiently.
Mathematical functions are simple and complex. How to use python programming and existing examples can be used in several learning points.
I would start with discovering the input and output data sets and then continue with clear examples used daily by us.
I tested today with SVC and sklearn python module.
The SVMs were introduced initially in the 1960s and were later refined in the 1990s.
The base of this algorithm is the decision boundary that maximizes the distance from the nearest data points of all the classes.
The wikipedia article show all informations about support-vector machines (named SVM).
As applications we can use this function in: medical field for cell counting or similar cell quantification, astronomy, etc.
This simple example use multiple kernels and gammas parameters to group the input data.

import numpy as np
from sklearn.datasets import make_blobs
from sklearn import svm
from sklearn.svm import SVC
# importing scikit learn with make_blobs 
from sklearn.datasets.samples_generator import make_blobs 
# 
import matplotlib.pyplot as plt

import numpy as np
import matplotlib.pyplot as plt
from sklearn import svm, datasets

# import some data to play with
iris = datasets.load_iris()
x = iris.data[:, :2]
y = iris.target

def plotSVC(title):
  # create a mesh to plot with dataset x and y
  x_min, x_max = x[:, 0].min() - 1, x[:, 0].max() + 1
  y_min, y_max = x[:, 1].min() - 1, x[:, 1].max() + 1
  # set the resolution by 100 
  h = (x_max / x_min)/100
  # create the meshgrid 
  xx, yy = np.meshgrid(np.arange(x_min, x_max, h),np.arange(y_min, y_max, h))
  # divides the current figure into an m-by-n grid and creates axes in the position specified by p
  plt.subplot(1, 1, 1)
  # the model can then be used to predict new values
  Z = svc.predict(np.c_[xx.ravel(), yy.ravel()])
  # reshape your test data because prediction needs an array that looks like your training data
  Z = Z.reshape(xx.shape)
  # use plt to show result 
  plt.contourf(xx, yy, Z, cmap=plt.cm.Paired, alpha=0.8) 
  plt.scatter(x[:, 0], x[:, 1], c=y, cmap=plt.cm.Paired)
  plt.xlabel('x length')
  plt.ylabel('y width')
  plt.xlim(xx.min(), xx.max())
  plt.title("Plot SVC")
  plt.show()

# create kernels for svg 
kernels = ['linear', 'rbf', 'poly']
# for each kernel show graphs
for kernel in kernels:
  svc = svm.SVC(kernel=kernel).fit(x, y)
  plotSVC('kernel=' + str(kernel))
# create gammas values
# the gamma parameter defines how far the influence of a single training example reaches
gammas = [0.1, 1, 10, 100, 1000]
# for each gammas and kernel rbf - fast processing,  show graphs
for gamma in gammas:
   svc = svm.SVC(kernel='rbf', gamma=gamma).fit(x, y)
   plotSVC('gamma=' + str(gamma))

See the last result for kernel rbf and gamma 1000.

Python 3.7.5 : The ani script with ascii.

ASCII, abbreviated from American Standard Code for Information Interchange, is a character encoding standard for electronic communication. ASCII codes represent text in computers, telecommunications equipment, and other devices. see Wikipedia.
This is a simple script named ani.py created by me to show an animation with ASCII ...

import os, time
os.system('cls')
filenames = ["0.txt","1.txt","2.txt","3.txt"]
frames = []
for name in filenames:
    with open (name, "r", encoding="utf8") as f:
        frames.append(f.readlines())
"""
for frame in frames:
    print("".join(frame))
    time.sleep(1)
    os.system('clear')
"""
for i in range (4):
    os.system('clear')
    for frame in frames:
        print("".join(frame))
        time.sleep(1)
        os.system('clear')

You need four text files with an 8X8 character matrix format: 0.txt , 1.txt , 2.txt and 3.txt.
The content of these files:

$ cat *.txt
        
 ###### 
        
        
        
        
 ###### 
                 
        
 ###### 
        
        
 ###### 
        
                 
        
        
  ####  
  ####  
        
        
                 
        
        
   ##   
   ##   
        
        

The end result is a square that shrinks to 4 characters #.

Python 3.7.4 : About with the PyOpenCL python module.

PyOpenCL lets you access GPUs and other massively parallel compute devices from Python.
It is important to note that OpenCL is not restricted to GPUs.
In fact, no special hardware is required to use OpenCL for computation–your existing CPU is enough.
The documentation of this project can be found at this website.
Let's install the python module for python 3 version:

[mythcat@desk ~]$ pip3 install pyopencl --user
Collecting pyopencl
...
Successfully built pytools
Installing collected packages: pytools, pyopencl
Successfully installed pyopencl-2019.1.1 pytools-2019.1.1

The install of OpenCL driver can be done with these commands:

# get OpenCL driver automated installer (installs kernel 4.7)
curl https://software.intel.com/sites/default/files/managed/f6/77/install_OCL_driver.sh_.txt > install_OCL\
_driver.sh
chmod +x install_OCL_driver.sh
# install OpenCL driver
sudo ./install_OCL_driver.sh install
# check
ls /boot/vmlinuz-*intel*

This is a simple python script to test the opencl context:

import pyopencl as cl
import numpy as np
ctx = cl.create_some_context()
# cet platforms, both CPU and GPU
my_plat= cl.get_platforms()
CPU = my_plat[0].get_devices()
try:
    GPU = my_plat[1].get_devices()
except IndexError:
    GPU = "none"
# create context for GPU/CPU
if GPU != "none":
    ctx = cl.Context(GPU)
else:
    ctx = cl.Context(CPU)
# create queue for each kernel execution
queue = cl.CommandQueue(ctx)
mf = cl.mem_flags

This is another simple python script:

# -*- coding: utf-8 -*-
import pyopencl as cl 
import numpy
a = numpy.random.rand(50000).astype(numpy.float32)
ctx = cl.create_some_context()
queue = cl.CommandQueue(ctx)
a_buf = cl.Buffer(ctx ,cl.mem_flags.READ_WRITE,size=a.nbytes)
cl.enqueue_write_buffer(queue, a_buf , a)

prg= cl.Program(ctx,
"""
__kernel void twice(__global float ∗a)
{
 int gid=get_global_id(0);
 a[gid] ∗= 2;
}"""
). build()

prg.twice(queue, a.shape, None,a_buf ).wait()

Python 3.7.4 : Usinge pytesseract for text recognition.

About this python module named tesseract, you can read here.
I tested with the tesseract tool install on my Fedora 30 distro and python module pytesseract version 0.3.0.

[root@desk mythcat]# dnf install tesseract
Last metadata expiration check: 0:24:18 ago on Sun 20 Oct 2019 10:56:23 AM EEST.
Package tesseract-4.1.0-1.fc30.x86_64 is already installed.
Dependencies resolved.
Nothing to do.
Complete!
[root@desk mythcat]# whereis tesseract
tesseract: /usr/bin/tesseract /usr/share/tesseract
[mythcat@desk ~]$ pip3 install pytesseract --user
Collecting pytesseract
...
Installing collected packages: pytesseract
Successfully installed pytesseract-0.3.0

I test with many images and texts and works very well.
Text images with a printed font are very well recognized.
This test with this image does not have very good accuracy.

The result of the handwriting image.

[mythcat@desk ~]$ python3 ocr_image.py 001.png 
rake Yous mnislakes,
take you chances,
look silby,

bul hep. mv going
dont freeze up

Python 3.7.4 : Test the DHCP handshakes.

First, the DHCP is based on the earlier BOOTP protocol which uses well-known port numbers for both server and client instead of an ephemeral port. The server and the client communicate via broadcast and the server broadcasts the offered IP address to the client on UDP port 68.
This python example has a learning purpose and does not harm anyone.

import subprocess as sub
import re

def find_word(w):
    return re.compile(r'\b({0})\b'.format(w), flags=re.IGNORECASE).search

p = sub.Popen(('sudo', 'tcpdump', '-l', '-s 0', '-vvv', '-n', '((udp port 67) and (udp[8:1] = 0x1))'),
 stdout=sub.PIPE)
for row in iter(p.stdout.readline, b''):
    if find_word(row):
        print (row.split(' ')[-1])
    elif find_word(row):
        print (row.split(' ')[-1])

The result of my script ( I don't have inputs on this port).

[mythcat@desk scripts]$ python3 dhcpreq.py 
tcpdump: listening on ___, link-type EN10MB (Ethernet), capture size 262144 bytes
^CTraceback (most recent call last):
  File "dhcpreq.py", line 10, in 
    for row in iter(p.stdout.readline, b''):
KeyboardInterrupt
0 packets captured
0 packets received by filter
0 packets dropped by kernel
[mythcat@desk scripts]$ vim dhcpreq.py 

Python 3.8.0 : New release of python development.

Good news from the python development area with the new release of python development:
Python 3.7.5 Oct. 15, 2019 and Python 3.8.0 Oct. 14, 2019

Now you can use the new python version 3.8.0 from the official webpage.

Major new features of the 3.8 series, compared to 3.7 - release Date: Oct. 14, 2019:

• PEP 572, Assignment expressions
• PEP 570, Positional-only arguments
• PEP 587, Python Initialization Configuration (improved embedding)
• PEP 590, Vectorcall: a fast calling protocol for CPython
• PEP 578, Runtime audit hooks
• PEP 574, Pickle protocol 5 with out-of-band data
• Typing-related: PEP 591 (Final qualifier), PEP 586 (Literal types), and PEP 589 (TypedDict)
• Parallel filesystem cache for compiled bytecode
• Debug builds share ABI as release builds
• f-strings support a handy = specifier for debugging
• continue is now legal in finally: blocks
• on Windows, the default asyncio event loop is now ProactorEventLoop
• on macOS, the spawn start method is now used by default in multiprocessing
• multiprocessing can now use shared memory segments to avoid pickling costs between processes
• typed_ast is merged back to CPython
• LOAD_GLOBAL is now 40% faster
• pickle now uses Protocol 4 by default, improving performance

Let's install on Fedora 30 Linux distro:

[mythcat@desk ~]$ cd Python-3.8.0/
[mythcat@desk Python-3.8.0]$ ls
aclocal.m4          Doc         m4               Parser         README.rst
CODE_OF_CONDUCT.md  Grammar     Mac              PC             setup.py
config.guess        Include     Makefile.pre.in  PCbuild        Tools
config.sub          install-sh  Misc             Programs
configure           Lib         Modules          pyconfig.h.in
configure.ac        LICENSE     Objects          Python
[mythcat@desk Python-3.8.0]$ ./configure 
checking build system type... x86_64-pc-linux-gnu
checking host system type... x86_64-pc-linux-gnu
checking for python3.8... no
...
creating Makefile

If you want a release build with all stable optimizations active (PGO, etc),
please run ./configure --enable-optimizations

If you want then you can run the tool to prepare the build with optimizations:

[mythcat@desk Python-3.8.0]$ ./configure --enable-optimizations --with-ensurepip=install
...
creating Modules/Setup.local
creating Makefile

Use the make with the -j option to use building into parallel steps to speed up the compilation.

[mythcat@desk Python-3.8.0]$ make -j 2
...
make[1]: Leaving directory '/home/mythcat/Python-3.8.0'

Since you’re installing Python into /usr/bin, you’ll need to run as root:

[mythcat@desk Python-3.8.0]$ sudo make altinstall
...
Collecting setuptools
Collecting pip
Installing collected packages: setuptools, pip
Successfully installed pip-19.2.3 setuptools-41.2.0

Let's test it in this folder and with new python3.8 :

[mythcat@desk Python-3.8.0]$ ./python 
Python 3.8.0 (default, Oct 15 2019, 23:45:20) 
[GCC 9.2.1 20190827 (Red Hat 9.2.1-1)] on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> exit()
[mythcat@desk Python-3.8.0]$ python3.8
Python 3.8.0 (default, Oct 15 2019, 23:45:20) 
[GCC 9.2.1 20190827 (Red Hat 9.2.1-1)] on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> 

Python 3.7.4 : Testing python source code with streamlit tool.

The official webpage for this python package can be found at streamlit.io.
Let's install it with pip3 tool:

[mythcat@desk proiecte_github]$ mkdir streamlit_examples
[mythcat@desk proiecte_github]$ cd streamlit_examples/
[mythcat@desk streamlit_examples]$ pip3 install streamlit --user

Let's try some examples.
Create a file named 001.py
This simple example will show a map with randoms spots:

import pandas as pd
import numpy as np
import streamlit as st    
df = pd.DataFrame(
    np.random.randn(100, 2) / [50, 50] + [47.45, 26.3],
    columns=['lat', 'lon'])
st.map(df)

Let's run it with this command:

[mythcat@desk streamlit_examples]$ streamlit run 001.py 

  You can now view your Streamlit app in your browser.

  Local URL: http://localhost:8501
...

The next source code will show just the map because the df variable is empty:

import streamlit as st
df = []
st.deck_gl_chart(
    viewport={
        'latitude': 47.45,
        'longitude': 26.3,
        'zoom': 13,
        'pitch': 50,
    },
    layers=[{
        'type': 'HexagonLayer',
        'data': df,
        'radius': 200,
        'elevationScale': 4,
        'elevationRange': [0, 1000],
        'pickable': True,
        'extruded': True,
    }, {
        'type': 'ScatterplotLayer',
        'data': df,
    }])

The source code is added into another file named 002.py and can be run with this command:

[mythcat@desk streamlit_examples]$ streamlit run 002.py 

  You can now view your Streamlit app in your browser.

  Local URL: http://localhost:8501
...

You can see more about this tool at the official youtube channel:

Python 3.7.4 : Testing the PyUSB python module.

This python module named PyUSB can be found at pypi website.

[mythcat@desk scripts]$ pip3 install pyusb --user
Collecting pyusb
...
Successfully installed pyusb-1.0.2

Let' see some usb device with lsusb command:

[mythcat@desk scripts]$ lsusb
Bus 002 Device 002: ID 8087:0024 Intel Corp. Integrated Rate Matching Hub
Bus 002 Device 001: ID 1d6b:0002 Linux Foundation 2.0 root hub
Bus 001 Device 004: ID 1a40:0101 Terminus Technology Inc. Hub
Bus 001 Device 003: ID 093a:2510 Pixart Imaging, Inc. Optical Mouse
Bus 001 Device 002: ID 8087:0024 Intel Corp. Integrated Rate Matching Hub
Bus 001 Device 001: ID 1d6b:0002 Linux Foundation 2.0 root hub

First you need to set this to avoid the error: Access denied (insufficient permissions.

[mythcat@desk scripts]$ ll  /dev/bus/usb/001/004
crw-rw-r--. 1 root root 189, 3 Oct 10 20:34 /dev/bus/usb/001/004
[mythcat@desk scripts]$ chmod a+rw /dev/bus/usb/001/004
chmod: changing permissions of '/dev/bus/usb/001/004': Operation not permitted
[mythcat@desk scripts]$ sudo chmod a+rw /dev/bus/usb/001/004
[sudo] password for mythcat: 
[mythcat@desk scripts]$ ll  /dev/bus/usb/001/004
crw-rw-rw-. 1 root root 189, 3 Oct 10 20:34 /dev/bus/usb/001/004

The script is simple:

import sys
  
import usb.core
import usb.util
print(usb.__version__)
busses = usb.busses()
for bus in busses:
    devices = bus.devices
    for dev in devices:
        if dev != None:
            try:
                usd_dev = usb.core.find(idVendor=dev.idVendor, idProduct=dev.idProduct)
                print(usb_dev)
            except:
                pass
# 1a40:0101
dev  = usb.core.find(idVendor=0x1a40, idProduct=0x0101)
print ("The 8087:0024 is : ", dev)
if dev is None:
    raise ValueError("Device not found!")
else:

    if dev.is_kernel_driver_active(0):
        try:
                dev.detach_kernel_driver(0)
                print ("kernel driver detached")
        except usb.core.USBError as e:
                sys.exit("Could not detach kernel driver: %s" % str(e))
    else:
        print ("no kernel driver attached")
    try:
        usb.util.claim_interface(dev, 0)
        print ("claimed device")
    except:
        sys.exit("Could not claim the device: %s" % str(e))
    try:
        dev.set_configuration()
        dev.reset()
    except usb.core.USBError as e:
        sys.exit("Could not set configuration: %s" % str(e))

usb.util.release_interface(dev,interface)
dev.attach_kernel(interface

The result of this python script is this:

[mythcat@desk scripts]$ python3 usb_test.py 
1.0.2
The 8087:0024 is :  DEVICE ID 1a40:0101 on Bus 001 Address 004 =================
 bLength                :   0x12 (18 bytes)
 bDescriptorType        :    0x1 Device
 bcdUSB                 :  0x200 USB 2.0
 bDeviceClass           :    0x9 Hub
 bDeviceSubClass        :    0x0
 bDeviceProtocol        :    0x1
 bMaxPacketSize0        :   0x40 (64 bytes)
 idVendor               : 0x1a40
 idProduct              : 0x0101
 bcdDevice              :  0x111 Device 1.11
 iManufacturer          :    0x0 
 iProduct               :    0x1 USB 2.0 Hub
 iSerialNumber          :    0x0 
 bNumConfigurations     :    0x1
  CONFIGURATION 1: 100 mA ==================================
   bLength              :    0x9 (9 bytes)
   bDescriptorType      :    0x2 Configuration
   wTotalLength         :   0x19 (25 bytes)
   bNumInterfaces       :    0x1
   bConfigurationValue  :    0x1
   iConfiguration       :    0x0 
   bmAttributes         :   0xe0 Self Powered, Remote Wakeup
   bMaxPower            :   0x32 (100 mA)
    INTERFACE 0: Hub =======================================
     bLength            :    0x9 (9 bytes)
     bDescriptorType    :    0x4 Interface
     bInterfaceNumber   :    0x0
     bAlternateSetting  :    0x0
     bNumEndpoints      :    0x1
     bInterfaceClass    :    0x9 Hub
     bInterfaceSubClass :    0x0
     bInterfaceProtocol :    0x0
     iInterface         :    0x0 
      ENDPOINT 0x81: Interrupt IN ==========================
       bLength          :    0x7 (7 bytes)
       bDescriptorType  :    0x5 Endpoint
       bEndpointAddress :   0x81 IN
       bmAttributes     :    0x3 Interrupt
       wMaxPacketSize   :    0x1 (1 bytes)
       bInterval        :    0xc
kernel driver detached
claimed device
Could not set configuration: [Errno 16] Resource busy

Set permisions for next usb:

[mythcat@desk scripts]$ ll /dev/bus/usb/001/003
crw-rw-rw-. 1 root root 189, 2 Oct 10 20:34 /dev/bus/usb/001/003

Te next source code will read the mouse device:

#!/usr/bin/python
import sys
import usb.core
import usb.util
# decimal vendor and product values
#dev = usb.core.find(idVendor=1118, idProduct=1917)
# or, uncomment the next line to search instead by the hexidecimal equivalent
# 093a:2510
dev = usb.core.find(idVendor=0x093a, idProduct=0x2510)
# first endpoint
interface = 0
endpoint = dev[0][(0,0)][0]
# if the OS kernel already claimed the device, which is most likely true
# thanks to http://stackoverflow.com/questions/8218683/pyusb-cannot-set-configuration
if dev.is_kernel_driver_active(interface) is True:
  # tell the kernel to detach
  dev.detach_kernel_driver(interface)
  # claim the device
  usb.util.claim_interface(dev, interface)
collected = 0
attempts = 50
while collected < attempts :
    try:
        data = dev.read(endpoint.bEndpointAddress,endpoint.wMaxPacketSize)
        collected += 1
        print (data)
    except usb.core.USBError as e:
        data = None
        if e.args == ('Operation timed out',):
            continue
# release the device
usb.util.release_interface(dev, interface)
# reattach the device to the OS kernel
dev.attach_kernel_driver(interface)

The output of mouse moves is this:

[mythcat@desk scripts]$ python3 usb_mouse.py 
[mythcat@desk scripts]$ python3 usb_mouse.py 
array('B', [0, 254, 255, 0])
array('B', [0, 253, 2, 0])
array('B', [0, 252, 3, 0])
array('B', [0, 251, 3, 0])
array('B', [0, 252, 3, 0])
array('B', [0, 254, 1, 0])
array('B', [0, 253, 2, 0])
array('B', [0, 255, 1, 0])
array('B', [0, 255, 4, 0])
array('B', [0, 0, 3, 0])
array('B', [0, 0, 3, 0])
array('B', [0, 0, 2, 0])
array('B', [0, 0, 2, 0])
array('B', [0, 2, 1, 0])
array('B', [0, 4, 1, 0])
array('B', [0, 3, 0, 0])
array('B', [0, 3, 0, 0])
array('B', [0, 1, 0, 0])

Python 3.7.4 : Example with subprocess - part 001.

This is a simple example with the python 3 subprocess package.
The source code is simple to understand.
The execute_proceess_with_communicate let run the ls command with the sudo user permissions:

import os
import sys
import string
import subprocess
import codecs

inp = ''
cmd = 'ls'
password = ''

def execute_proceess_with_communicate(inp):
    """Return a list of hops from traceroute command."""
    p = subprocess.Popen(
            ['sudo', cmd, inp],
            stdin=subprocess.PIPE,
            stdout=subprocess.PIPE,
            shell=False)
    text, _ = p.communicate(password)
    #print(type(text))
    outp = codecs.decode(text,'utf8')
    out_split=outp.split('\n')
    return out_split

def normalize_out(list_outp):
    """Extract information from traceroute line per line."""

    normalized_out = []
    for op in list_outp:
        # filer out if an empty line
        if len(op) is 0:
            continue
        op_split = op.split()
        normalized_out.append(op_split)
    return normalized_out

if __name__ == '__main__':
    inp = sys.argv[1]

    out = execute_proceess_with_communicate(inp)
    n_out = normalize_out(out)
    print(n_out)

The result is this:

[mythcat@desk scripts]$ python3 subprocess_001.py '/' 
[['bin'], ['boot'], ['dev'], ['etc'], ['home'], ['lib'], ['lib64'], ['media'], ['mnt'], ['opt'], ['proc'],
 ['root'], ['run'], ['sbin'], ['srv'], ['sys'], ['tmp'], ['usr'], ['var']]

Python Qt5 : the drag and drop feature.

Today I tested drag and drop feature with PyQt5.

Python 3.7.4 (default, Jul  9 2019, 16:32:37) 
[GCC 9.1.1 20190503 (Red Hat 9.1.1-1)] on linux

This is a simple example using setAcceptDrops and setDragEnabled:

import sys
from PyQt5.QtWidgets import QApplication, QWidget, QListWidget, QHBoxLayout,QListWidgetItem
from PyQt5.QtGui import QIcon

class Window(QWidget):
    def __init__(self):
        super().__init__()

        self.myListWidget1 = QListWidget()
        self.myListWidget2 = QListWidget()
        self.myListWidget2.setViewMode(QListWidget.IconMode)
        self.myListWidget1.setAcceptDrops(True)
        self.myListWidget1.setDragEnabled(True)
        self.myListWidget2.setAcceptDrops(True)
        self.myListWidget2.setDragEnabled(True)
        self.setGeometry(480, 400, 640, 480)
        self.myLayout = QHBoxLayout()
        self.myLayout.addWidget(self.myListWidget1)
        self.myLayout.addWidget(self.myListWidget2)

        l1 = QListWidgetItem(QIcon('house.png'), "House")
        l2 = QListWidgetItem(QIcon('cloud.png'), "Clouds ")
        l3 = QListWidgetItem(QIcon('user.png'), "User")
        l4 = QListWidgetItem(QIcon('save.png'), "Save")

        self.myListWidget1.insertItem(1, l1)
        self.myListWidget1.insertItem(2, l2)
        self.myListWidget1.insertItem(3, l3)
        self.myListWidget1.insertItem(4, l4)

        QListWidgetItem(QIcon('house.png'), "House", self.
                        myListWidget2)
        QListWidgetItem(QIcon('cloud.png'), "Clouds", self.
                        myListWidget2)
        QListWidgetItem(QIcon('save.png'), "Save", self.
                        myListWidget2)

        self.setWindowTitle('Example: Drag and Drop');
        self.setLayout(self.myLayout)

        self.show()

App = QApplication(sys.argv)
window = Window()
sys.exit(App.exec())

The result is a windows with two QListWidget with an drag and drop feature.

Python 3.7.4 : Using the paramiko pakage.

Today I tested the paramiko package.
First, I install and check the version of this package.

[mythcat@desk my_network_tools]$ pip3 install paramiko --user
Collecting paramiko
...
  Running setup.py install for pycparser ... done
Successfully installed asn1crypto-0.24.0 bcrypt-3.1.7 cffi-1.12.3 cryptography-2.7 paramiko-2.6.0

 pycparser-2.19 pynacl-1.3.0
[mythcat@desk my_network_tools]$ python3
Python 3.7.4 (default, Jul  9 2019, 16:32:37) 
[GCC 9.1.1 20190503 (Red Hat 9.1.1-1)] on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> import paramiko
>>> dir(paramiko)
['AUTH_FAILED', 'AUTH_PARTIALLY_SUCCESSFUL', 'AUTH_SUCCESSFUL', 'Agent', 'AgentKey', 'AuthHandler',

 'AuthenticationException', 'AutoAddPolicy', 'BadAuthenticationType', 'BadHostKeyException', 'BaseSFTP',

 'BufferedFile', 'Channel', 'ChannelException', 'ChannelFile', 'ChannelStderrFile', 'ChannelStdinFile',

 'DSSKey', 'ECDSAKey', 'Ed25519Key', 'GSSAuth', 'GSS_AUTH_AVAILABLE', 'GSS_EXCEPTIONS', 'HostKeys',

 'InteractiveQuery', 'Message', 'MissingHostKeyPolicy', 'OPEN_FAILED_ADMINISTRATIVELY_PROHIBITED',

 'OPEN_FAILED_CONNECT_FAILED', 'OPEN_FAILED_RESOURCE_SHORTAGE', 'OPEN_FAILED_UNKNOWN_CHANNEL_TYPE',

 'OPEN_SUCCEEDED', 'PKey', 'Packetizer', 'PasswordRequiredException', 'ProxyCommand', 'ProxyCommandFailure',

 'PublicBlob', 'RSAKey', 'RejectPolicy', 'SFTP', 'SFTPAttributes', 'SFTPClient', 'SFTPError', 'SFTPFile',

 'SFTPHandle', 'SFTPServer', 'SFTPServerInterface', 'SFTP_BAD_MESSAGE', 'SFTP_CONNECTION_LOST', 'SFTP_EOF',

 'SFTP_FAILURE', 'SFTP_NO_CONNECTION', 'SFTP_NO_SUCH_FILE', 'SFTP_OK', 'SFTP_OP_UNSUPPORTED', 

'SFTP_PERMISSION_DENIED', 'SSHClient', 'SSHConfig', 'SSHException', 'SecurityOptions', 'ServerInterface',

 'SubsystemHandler', 'Transport', 'WarningPolicy', '__all__', '__author__', '__builtins__', '__cached__', 

'__doc__', '__file__', '__license__', '__loader__', '__name__', '__package__', '__path__', '__spec__', 

'__version__', '__version_info__', '_version', 'agent', 'auth_handler', 'ber', 'buffered_pipe', 'channel',

 'client', 'common', 'compress', 'config', 'dsskey', 'ecdsakey', 'ed25519key', 'file', 'hostkeys', 

'io_sleep', 'kex_curve25519', 'kex_ecdh_nist', 'kex_gex', 'kex_group1', 'kex_group14', 'kex_group16',

 'kex_gss', 'message', 'packet', 'pipe', 'pkey', 'primes', 'proxy', 'py3compat', 'rsakey', 'server', 

'sftp', 'sftp_attr', 'sftp_client', 'sftp_file', 'sftp_handle', 'sftp_server', 'sftp_si', 'ssh_exception',

 'ssh_gss', 'sys', 'transport', 'util']
>>> paramiko.__version__
'2.6.0'

The documentation for this version can be found at this webpage.
A simple example with this python package and ssh connection to 192.168.0.143 on port 22 can be see on the next source code

#!/usr/bin/env python
"""Return the ssh output with password connection and Linux commands"""
import paramiko

client = paramiko.SSHClient()
client.set_missing_host_key_policy(paramiko.AutoAddPolicy())
conn= client.connect('192.168.0.143', port='22', username='mythcat', password='the_pass')
# Obtain session
session = client.get_transport().open_session()
print("| Retcode: "+str(session)+"|")

stdin, stdout, stderr=client.exec_command('sudo hostname;w')
save_stdout = stdout.readlines()
retcode = stdout.channel.recv_exit_status()

stdin, stdout, stderr=client.exec_command('ss -nap;')
save_stdout2 = stdout.readlines()
print(save_stdout,save_stdout2)
#for line in stdout:
#    print (line)

Python 3.7.4 : Install the protobuf from sources on Fedora distro.

Today I will show you how to build protobuf from sources using the Fedora distro.
The google team comes with this intro:
Protocol buffers are Google's language-neutral, platform-neutral, extensible mechanism for serializing structured data – think XML, but smaller, faster, and simpler...
This google project comes with these tutorials.
The GitHub project can be found here.
To install the compiler, download the package.

[mythcat@desk ~]$ cd Downloads/
[mythcat@desk Downloads]$ cp protobuf-python-3.9.2.tar.gz ~/
[mythcat@desk Downloads]$ cd ..
[mythcat@desk ~]$ tar xvzf protobuf-python-3.9.2.tar.gz 
...
protobuf-3.9.2/aclocal.m4
protobuf-3.9.2/install-sh
protobuf-3.9.2/generate_descriptor_proto.sh
protobuf-3.9.2/CHANGES.txt
protobuf-3.9.2/configure.ac
protobuf-3.9.2/configure

Let's see the content:

[mythcat@desk ~]$ cd protobuf-3.9.2/
[mythcat@desk protobuf-3.9.2]$ ls
aclocal.m4                   config.sub                    ltmain.sh            README.md
ar-lib                       configure                     m4                   six.BUILD
autogen.sh                   configure.ac                  Makefile.am          src
benchmarks                   conformance                   Makefile.in          test-driver
BUILD                        CONTRIBUTORS.txt              missing              third_party
CHANGES.txt                  depcomp                       objectivec           update_file_lists.sh
cmake                        editors                       protobuf.bzl         util
compile                      examples                      protobuf_deps.bzl    WORKSPACE
compiler_config_setting.bzl  generate_descriptor_proto.sh  protobuf-lite.pc.in
config.guess                 install-sh                    protobuf.pc.in
config.h.in                  LICENSE                       python

[mythcat@desk protobuf-3.9.2]$ ./configure
...
checking how to run the C preprocessor... gcc -E
checking how to run the C++ preprocessor... /lib/cpp
configure: error: in `/home/mythcat/protobuf-3.9.2':
configure: error: C++ preprocessor "/lib/cpp" fails sanity check
See `config.log' for more details
...
[root@desk protobuf-3.9.2]# dnf install g++
...
Installed:
  gcc-c++-9.2.1-1.fc30.x86_64                                                                               

Complete![root@desk protobuf-3.9.2]# exit
exit

Let's build again:

[mythcat@desk protobuf-3.9.2]$ ./configure
checking whether to enable maintainer-specific portions of Makefiles... yes
checking build system type... x86_64-pc-linux-gnu
...
checking that generated files are newer than configure... done
configure: creating ./config.status
config.status: creating Makefile
config.status: creating scripts/gmock-config
config.status: creating build-aux/config.h
config.status: executing depfiles commands
config.status: executing libtool commands
[mythcat@desk protobuf-3.9.2]$ nproc --all
2
[mythcat@desk protobuf-3.9.2]$ make -j2
...
  CXXLD    protoc
make[2]: Leaving directory '/home/mythcat/protobuf-3.9.2/src'
make[1]: Leaving directory '/home/mythcat/protobuf-3.9.2'
[mythcat@desk protobuf-3.9.2]$ make check -j2
...PASS: protobuf-lazy-descriptor-test
PASS: protobuf-lite-test
PASS: google/protobuf/compiler/zip_output_unittest.sh
PASS: google/protobuf/io/gzip_stream_unittest.sh
PASS: protobuf-lite-arena-test
PASS: no-warning-test
PASS: protobuf-test
============================================================================
Testsuite summary for Protocol Buffers 3.9.2
============================================================================
# TOTAL: 7
# PASS:  7
# SKIP:  0
# XFAIL: 0
# FAIL:  0
# XPASS: 0
# ERROR: 0
============================================================================
make[3]: Leaving directory '/home/mythcat/protobuf-3.9.2/src'
make[2]: Leaving directory '/home/mythcat/protobuf-3.9.2/src'
make[1]: Leaving directory '/home/mythcat/protobuf-3.9.2/src'

The last steps is for install:

[mythcat@desk protobuf-3.9.2]$ sudo make install
[sudo] password for mythcat: 
...
make[2]: Leaving directory '/home/mythcat/protobuf-3.9.2/src'
make[1]: Leaving directory '/home/mythcat/protobuf-3.9.2/src'
[mythcat@desk protobuf-3.9.2]$ sudo ldconfig 

Let's test it:

[mythcat@desk protobuf-3.9.2]$ protoc --version
libprotoc 3.9.2

Now the next step comes for python module:

[mythcat@desk protobuf-3.9.2]$ cd python/
[mythcat@desk python]$ ls
google  MANIFEST.in  mox.py  README.md  release  release.sh  setup.cfg  setup.py  stubout.py  tox.ini
[mythcat@desk python]$ python setup.py build
...
testSerialize (google.protobuf.internal.unknown_fields_test.UnknownFieldsTest) ... ok
testSerializeMessageSetWireFormatUnknownExtension 
(google.protobuf.internal.unknown_fields_test.UnknownFieldsTest) ... ok
testSerializeProto3 (google.protobuf.internal.unknown_fields_test.UnknownFieldsTest) ... ok
testByteSizeFunctions (google.protobuf.internal.wire_format_test.WireFormatTest) ... ok
testPackTag (google.protobuf.internal.wire_format_test.WireFormatTest) ... ok
testUnpackTag (google.protobuf.internal.wire_format_test.WireFormatTest) ... ok
testZigZagDecode (google.protobuf.internal.wire_format_test.WireFormatTest) ... ok
testZigZagEncode (google.protobuf.internal.wire_format_test.WireFormatTest) ... ok

----------------------------------------------------------------------
Ran 818 tests in 5.343s

OK (skipped=10)

Let's test the python module:

[mythcat@desk python]$ python3
Python 3.7.4 (default, Jul  9 2019, 16:32:37) 
[GCC 9.1.1 20190503 (Red Hat 9.1.1-1)] on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> import google
>>> from google.protobuf import descriptor as _descriptor
>>> from google.protobuf import message as _message
>>> from google.protobuf import reflection as _reflection
>>> from google.protobuf import symbol_database as _symbol_database
>>> from google.protobuf import descriptor_pb2
>>> from google.protobuf import text_format
>>> dir(_descriptor)
['Descriptor', 'DescriptorBase', 'DescriptorMetaclass', 'EnumDescriptor', 'EnumValueDescriptor', 
'Error', 'FieldDescriptor', 'FileDescriptor', 'MakeDescriptor', 'MethodDescriptor', 'OneofDescriptor', 
'ServiceDescriptor', 'TypeTransformationError', '_Lock', '_NestedDescriptorBase', '_OptionsOrNone', 
'_ParseOptions', '_ToCamelCase', '_ToJsonName', '_USE_C_DESCRIPTORS', '__author__', '__builtins__', 
'__cached__', '__doc__', '__file__', '__loader__', '__name__', '__package__', '__spec__', '_lock', 
'api_implementation', 'six', 'threading']

Now I can to define the structure for the data structured as messages
These message is a small logical record of information containing a series of name-value pairs called fields.
The protobuffers compiler (protoc) to generate the source code in the language you need (from the .proto file).
To generate a Python file, you need to execute:
protoc -I=$SRC_DIR --python_out=$DST_DIR $SRC_DIR/my_example.proto
Let's test with am example (you can use any proto file) in default folder (use . for default folder):

[mythcat@desk python]$ protoc -I=. --python_out=. my_example.proto
[mythcat@desk python]$ ls my_example*
my_example_pb2.py  my_example.proto
[mythcat@desk python]$ python3
Python 3.7.4 (default, Jul  9 2019, 16:32:37) 
[GCC 9.1.1 20190503 (Red Hat 9.1.1-1)] on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> import my_example_pb2
>>> dir(my_example_pb2)
['AddressBook', 'DESCRIPTOR', 'Person', '_ADDRESSBOOK', '_PERSON', '_PERSON_PHONENUMBER', '_PERSON_PHONETYPE',
 '__builtins__', '__cached__', '__doc__', '__file__', '__loader__', '__name__', '__package__', '__spec__',
 '_b', '_descriptor', '_message', '_reflection', '_sym_db', '_symbol_database', 'sys']

The tensorflow python module - part 004.

If you using the tensorflow then you can get some warnings.
You can use warnings python package to manage all of this:

[mythcat@desk ~]$ $ python3
Python 3.5.2 (default, Jul 10 2019, 11:58:48)
[GCC 5.4.0 20160609] on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> import warnings
>>> import tensorflow as tf
/home/mythcat/.local/lib/python3.5/site-packages/tensorflow/python/framework/dtypes.py:516: FutureWarning: 
Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be 
understood as (type, (1,)) / '(1,)type'.
  _np_qint8 = np.dtype([("qint8", np.int8, 1)])
/home/mythcat/.local/lib/python3.5/site-packages/tensorflow/python/framework/dtypes.py:517: FutureWarning: 
Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be 
understood as (type, (1,)) / '(1,)type'.
...

/home/mythcat/.local/lib/python3.5/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:550: 
FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of 
numpy, it will be understood as (type, (1,)) / '(1,)type'.
  np_resource = np.dtype([("resource", np.ubyte, 1)])
>>>
>>> warnings.filterwarnings('ignore')
>>>
[7]+  Stopped                 python3

Use it in this way to filter these warnings:

[mythcat@desk ~]$ $ python3
Python 3.5.2 (default, Jul 10 2019, 11:58:48)
[GCC 5.4.0 20160609] on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> import warnings
>>> warnings.filterwarnings('ignore',category=FutureWarning)
>>> import tensorflow as tf
>>> 

Python 3.7.4 : Print with random colors.

This is a simple example for custom print output.
The script detect the platform for color settings and then use print.
The first print will print with blue color the name of the script.
I used random to select a random color from colors array and used to print the -=RANDOM COLOR=- text.
The print (W+'') is used to set default white color for terminal

import sys
import random
if sys.platform == "linux" or sys.platform == "linux2":
        BB = "\033[34;1m" # Blue light
        YY = "\033[33;1m" # Yellow light
        GG = "\033[32;1m" # Green light
        WW = "\033[0;1m"  # White light
        RR = "\033[31;1m" # Red light
        CC = "\033[36;1m" # Cyan light
        B = "\033[34m"    # Blue
        Y = "\033[33m"    # Yellow
        G = "\033[32m"    # Green
        W = "\033[0m"     # White
        R = "\033[31m"    # Red
        C = "\033[36m"    # Cyan
colors = [BB,YY,GG,WW,RR,CC,B,Y,G,W,R,C]
print (B+"\033[2;2m "+sys.argv[0]+"\n"+B)

color=random.choice(colors)
print (color+"-=RANDOM COLOR=-"+color)
print (W+'')

For winodws platform you need to add this:

elif sys.platform == "win32":

 BB = '' # Blue light
 YY = '' # Yellow light
 GG = '' # Green light
 WW = '' # White light
 RR = '' # Red light
 CC = '' # Cyan light
 B = ''  # Blue
 Y = ''  # Yellow
 G = ''  # Green
 W = ''  # White
 R = ''  # Red
 C = ''  # Cyan
 P = ''  # Random color

Python 3.7.4 : Using the theano pakage.

If you want to test theano then you need to see this webpage.

[root@desk mythcat]# dnf search theano
======================== Name & Summary Matched: theano ========================
python-theano-doc.noarch : Theano documentation
============================= Name Matched: theano =============================
python3-theano.noarch : Mathematical expressions involving multidimensional
                      : arrays
=========================== Summary Matched: theano ============================
python3-lasagne.noarch : Lightweight library to build and train neural networks
                       : in Theano
[root@desk mythcat]# pip3 install Theano --user
WARNING: Running pip install with root privileges is generally not a good idea. 

Try `pip3 install --user` instead.
Collecting Theano
...
  Running setup.py install for Theano ... done
Successfully installed Theano-1.0.4 scipy-1.3.1

Let's see first example:

[mythcat@desk ~]$ python3 
Python 3.7.4 (default, Jul  9 2019, 16:32:37) 
[GCC 9.1.1 20190503 (Red Hat 9.1.1-1)] on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> import theano
/home/mythcat/.local/lib/python3.7/site-packages/theano/configdefaults.py:560: 

UserWarning: DeprecationWarning: there is no c++ compiler.This is deprecated and with 

Theano 0.11 a c++ compiler will be mandatory
  warnings.warn("DeprecationWarning: there is no c++ compiler."
WARNING (theano.configdefaults): g++ not detected ! Theano will be unable to execute 

optimized C-implementations (for both CPU and GPU) and will default to Python implementations.

 Performance will be severely degraded. To remove this warning, set Theano flags cxx to an empty

 string.
WARNING (theano.tensor.blas): Using NumPy C-API based implementation for BLAS functions.
>>> import theano
>>> import theano.tensor as T
>>> x = T.dmatrix('x')
>>> s = 1 / (1 + T.exp(-x))
>>> logistic = theano.function([x], s)
>>> logistic([[0, 1], [-1, -2]])
array([[0.5       , 0.73105858],
       [0.26894142, 0.11920292]])
>>> ...