Python 3.6.0 : Django 3.2 alpha 1 released.

Django is a high-level Python Web framework that encourages rapid development and clean, pragmatic design. Built by experienced developers, it takes care of much of the hassle of Web development, so you can focus on writing your app without needing to reinvent the wheel. It’s free and open source. 

I tested it and I was satisfied with the way it works with this framework and it has many good features for the web. 

Django 3.2 is designated as a long-term support release.

Today, Django 3.2 alpha 1 is available with new features.

A good roadmap towards Django 3.2. can be found on this schedule webpage.

As with all alpha and beta packages, this is not for production use.

Django 3.2 supports Python 3.6, 3.7, 3.8, and 3.9. The next release is expected in April 2021, see:

• January 14, 2021, Django 3.2 alpha; feature freeze;
• February 18 Django 3.2 beta; non-release blocking bug fix freeze;
• March 18 Django 3.2 RC 1; translation string freeze;
• April 6 Django 3.2 final release.

Python 3.9.1 : Testing the drawSvg python package.

The tutorial for today is about drawSvg python package.
This python package let you to create SVG images (vector drawings) and rendering them or displaying them in a Jupyter notebook.
Let's install it with pip3 tool:

[mythcat@desk ~]$ cd PythonProjects/
[mythcat@desk PythonProjects]$ pip3 install drawSvg
...
Successfully installed drawSvg-1.7.0 imageio-2.9.0

A simple source code test added to svg_001.py python script:

import drawSvg as draw

# Draw a frame of the animation
def draw_frame(t):
    #Create draw surface and add a geometric shapes ...
    out = draw.Drawing(1, 1, origin=(0,0))
    out.setRenderSize(h=460)
    out.append(draw.Rectangle(0, 0, 5, 5, fill='white'))
    y = t + 0.3
    out.append(draw.Circle(0.5, y, 0.5, fill='blue'))
    return out

with draw.animate_video('example.gif', draw_frame, duration=0.01) as anim:
    # Add each frame to the animation
    for i in range(20):
        anim.draw_frame(i/10)

You can change the source code and open the GIF animation file:

[mythcat@desk PythonProjects]$ vi svg_001.py
[mythcat@desk PythonProjects]$ python svg_001.py 
[mythcat@desk PythonProjects]$ google-chrome example.gif

The result of the source code is this:

Python 3.6.9 : Ursina python game engine - part 001 .

I wrote the tutorial a few days ago and today I completed it with a video clip ...
The official webpage comes with this intro:
Ursina makes it easier to develop games, visualizations and other kinds of software.
The concise API combined with the power of the Python programming language, makes life easier for the developer so they can focus on what they are making.
Ursina can be used on Windows and Linux
Today I tested on Fedora Linux and works great.
First, the install process is easy with pip3 tool.

[mythcat@desk ~]$ pip3 install ursina
Defaulting to user installation because normal site-packages is not writeable
Requirement already satisfied: ursina in ./.local/lib/python3.9/site-packages (3.2.2)
Requirement already satisfied: screeninfo in ./.local/lib/python3.9/site-packages (from ursina) (0.6.6)
Requirement already satisfied: numpy in /usr/lib64/python3.9/site-packages (from ursina) (1.19.4)
Requirement already satisfied: panda3d in ./.local/lib/python3.9/site-packages (from ursina) (1.10.7)
Requirement already satisfied: pyperclip in ./.local/lib/python3.9/site-packages (from ursina) (1.8.1)
Requirement already satisfied: pillow in /usr/lib64/python3.9/site-packages (from ursina) (7.2.0)

Let's create some examples and see how this python package works.
Create a python script file:

[mythcat@desk ~]$ cd PythonProjects/
[mythcat@desk PythonProjects]$ vi ursina_001.py

Add this simple example:

from ursina import *
# create the application
app = Ursina()
# set some data , like a cube
ground = Entity(
    model = 'cube',
    color = color.blue,
    z = -.1,
    y = -3,
    origin = (0, .5),
    scale = (50, 1, 10),
    collider = 'box'
    )
# run the application
app.run()

And the last pas, run it with python:

[mythcat@desk PythonProjects]$ python ursina_001.py 
ursina version: 3.2.2
package_folder: /home/mythcat/.local/lib/python3.9/site-packages/ursina
asset_folder: .
psd-tools not installed
which: no blender in (/usr/local/bin:/bin:/usr/bin:/usr/local/sbin:/usr/sbin:/sbin:
/home/mythcat/.composer/vendor/bin:/home/mythcat/.dotnet/tools:/var/lib/snapd/snap/bin)
blender_paths:
{}
OS: posix
screen resolution: (1280, 1024)
Known pipe types:
  glxGraphicsPipe
(all display modules loaded.)
render mode: default
no settings.py file
no settings.py file
development mode: True
application successfully started

You can read on web!:

Python 3.6.9 : My colab tutorials - part 010.

In this tutorial created with Colab online tool I used HTML and JavaScript source code.
The tutorial is easy to understand and use it.
You can see all my examples of my GitHub repo.

Python 3.6.9 : My colab tutorials - part 009.

I update may colab work and I add new notebooks.
You can see all of these on my GitHub account.
These are examples:

• catafest_009.ipynb - show you how to use %% colab features;
• catafest_010.ipynb - example with Detectron2 is Facebook AI Research's with state-of-the-art object detection algorithms;
• catafest_011.ipynb - test a sound classification with YAMNet from a web example - not very happy with the result;

Python 3.9.0 : Physics simulation with PyBullet .

I took a look at the documentation of this python packet to give you a brief introduction:
PyBullet is a fast and easy to use Python module for robotics simulation and machine learning, with a focus on sim-to-real transfer. With PyBullet you can load articulated bodies from URDF, SDF, MJCF and other file formats. PyBullet provides forward dynamics simulation, inverse dynamics computation, forward and inverse kinematics, collision detection and ray intersection queries. The Bullet Physics SDK includes PyBullet robotic examples such as a simulated Minitaur quadruped, humanoids running using TensorFlow inference and KUKA arms grasping objects.
...
PyBullet can be easily used with TensorFlow and OpenAI Gym.
...
The GUI connection will create a new graphical user interface (GUI) with 3D OpenGL rendering, within the same process space as PyBullet. On Linux and Windows this GUI runs in a separate thread, while on OSX it runs in the same thread due to operating system limitations.
...
For UDP networking, there is a App_PhysicsServerUDP that listens to a certain UDP port.
...
By default, there is no gravitational force enabled. setGravity lets you set the default gravity force for all objects. The loadURDF will send a command to the physics server to load a physics model from a Universal Robot Description File (URDF). The URDF file is used by the ROS project (Robot Operating System) to describe robots and other objects, it was created by the WillowGarage and the Open Source Robotics Foundation (OSRF).
I tested with Fedora 33 and Python version 3.9.0.
The first step was installation.

[mythcat@desk ~]$ pip3 install pybullet --upgrade --user
Collecting pybullet
  Downloading pybullet-3.0.6.tar.gz (89.8 MB)
     |████████████████████████████████| 89.8 MB 51 kB/s 
Using legacy 'setup.py install' for pybullet, since package 'wheel' is not installed.
Installing collected packages: pybullet
    Running setup.py install for pybullet ... done
Successfully installed pybullet-3.0.6

The next step was to test with examples already built using urdf file type.

import pybullet as p
# Can alternatively pass in p.DIRECT 
client = p.connect(p.GUI)
p.setGravity(0, 0, -10, physicsClientId=client) 

import pybullet_data
p.setAdditionalSearchPath(pybullet_data.getDataPath())
planeId = p.loadURDF("plane.urdf")

my_racecar = p.loadURDF("racecar/racecar.urdf", basePosition=[0,0,0.2])

position, orientation = p.getBasePositionAndOrientation(my_racecar)

for _ in range(10000): 
    p.stepSimulation()

I run the script and all works well.
It can be seen that in the cycle for which I have the simulation for 10000 steps of time.
A smaller number will quickly interrupt the simulation.
You can create your own URDF files because they are formatted in XML format.
A good area for learning is this webpage.

Python 3.9.0 : Testing twisted python module - part 001 .

Today I tested two python modules named: twisted and twisted[tls].
Twisted is an event-driven network programming framework written in Python and licensed under the MIT License. Twisted projects variously support TCP, UDP, SSL/TLS, IP multicast, Unix domain sockets, many protocols (including HTTP, XMPP, NNTP, IMAP, SSH, IRC, FTP, and others), and much more. Twisted is based on the event-driven programming paradigm, which means that users of Twisted write short callbacks which are called by the framework., see wikipedia webpage.
In this tutorial I will show you only some of these tests and how you can work with these python modules.
About twisted you can read more at the official webpage. In Fedora distro version 33 you can use the dnf tool to search for and install these python packages.

[root@desk mythcat]# dnf search twisted
...
python3-twisted.x86_64 : Twisted is a networking engine written in Python
python3-twisted+tls.x86_64 : Metapackage for python3-twisted: tls extras

You can also use the pip tool for installation:

[mythcat@desk ~]$ cd PythonProjects/
[mythcat@desk PythonProjects]$ pip3 install twisted
...
[mythcat@desk PythonProjects]$ pip3 install twisted[tls]
...

I used python 3.9.0 to test if this python package works:

[mythcat@desk PythonProjects]$ python3.9
Python 3.9.0 (default, Oct  6 2020, 00:00:00) 
[GCC 10.2.1 20200826 (Red Hat 10.2.1-3)] on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> from twisted.protocols import basic

Let's test with simple example using the reactor and protocol:

from twisted.internet import reactor, protocol

class ClientEcho(protocol.Protocol):
    def connectionMade(self):
        self.transport.write("Hello, world!".encode('utf-8'))

    def dataReceived(self, data):
        print ("Server: ", data)
        self.transport.loseConnection()

class FactoryEcho(protocol.ClientFactory):
    def buildProtocol(self, addr):
        return ClientEcho()

    def clientConnectionFailed(self, connector, reason):
        print ("Connection failed")
        reactor.stop()

    def clientConnectionLost(self, connector, reason):
        print ("Connection lost")
        reactor.stop()

reactor.connectTCP("localhost", 8080, FactoryEcho())
reactor.run()

Your protocol handling class will usually subclass twisted.internet.protocol.Protocol.
The default factory class twisted.internet.protocol.Factory just instantiates each Protocol and lets every Protocol access, and possibly modify, the persistent configuration.
This protocol responds to the initial connection with a well known quote, and then terminates the connection.
The protocol never waits for an event because handles data in an asynchronous manner.
The reactor interface lets many different loops handle the networking code.
The source code have two classes each is used to show a simple echo client on port 8080 - you can use any port.
This source code is the most simple example to understand the relation between factory , protocol and reactor.
The result is this:

[mythcat@desk PythonProjects]$ python3.9 echo_client_001.py 
Server:  b'Hello, world!'
Connection lost

Python 3.9.0 : Union and in-place union operators

Python introduces two new operators for dictionaries named union used in code with pipe operator | and in-place union used in python code with this |=.

I this tutorial I will show you how can be used:

[mythcat@desk ~]$ python3.9 
Python 3.9.0 (default, Oct  6 2020, 00:00:00) 
[GCC 10.2.1 20200723 (Red Hat 10.2.1-1)] on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> step_one = {"worker_one":"task_one", "worker_two":"task_two"}
>>> step_two = {"worker_three":"task_one", "worker_fouw":"task_two"}
>>> merged_step_one_step_two = {**step_one, **step_two}
>>> empty = {}
>>> empty |= step_one
>>> empty
{'worker_one': 'task_one', 'worker_two': 'task_two'}
>>> step_one 
{'worker_one': 'task_one', 'worker_two': 'task_two'}
>>> step_two
{'worker_three': 'task_one', 'worker_fouw': 'task_two'}
>>> all = step_one | step_two 
>>> all 
{'worker_one': 'task_one', 'worker_two': 'task_two', 'worker_three': 'task_one', 'worker_fouw': 'task_two'} 
>>> copy_step_one = empty | step_one
>>> copy_step_one 
{'worker_one': 'task_one', 'worker_two': 'task_two'} 
>>> copy_step_one |= [("manager", "steps")]
>>> copy_step_one 
{'worker_one': 'task_one', 'worker_two': 'task_two', 'manager': 'steps'}

You can easily see how to use these operators with the dictionaries created and how to add lists to dictionaries. Operations with these operators always result in a dictionary. The order of these operations is important, see example:

>>> numbers = {0: "zero", 1: "one"}
>>> numbers_one = {0: "zero", 1: "one"}
>>> numbers_two = {0: "zero", 2: "two"}
>>> numbers_one | numbers_two
{0: 'zero', 1: 'one', 2: 'two'}
>>> numbers_two | numbers_one
{0: 'zero', 2: 'two', 1: 'one'}
>>> numbers_three = {0: 'zero', 11: 'error 1', 22: 'error 2', 33:'error 3'}
>>> numbers_three | numbers_one
{0: 'zero', 11: 'error 1', 22: 'error 2', 33: 'error 3', 1: 'one'}
>>> numbers_one | numbers_three
{0: 'zero', 1: 'one', 11: 'error 1', 22: 'error 2', 33: 'error 3'}

You can see how the dictionaries are overwritten by the union operation. These operations are implemented through dunder operations for most the dictionary objects except abstract classes. Dunder or magic methods in Python are the methods having two prefix and suffix underscores in the method name. You can find on this page.

Python 3.9.0 : Introduction to release 3.9.0.

This is a short introduction to release 3.9.0. 

Five days ago, a new release of version 3.9 appeared with a series of improvements and new python packages, see the official website. 

You can install easily on Fedora 32 with dnf tool.

[root@desk mythcat]# dnf install  python39.x86_64
...
Installing:
 python39                x86_64                3.9.0-1.fc32
...
Installed:
  python39-3.9.0-1.fc32.x86_64                                                                             

Complete!

A new article written about this release shows some of the advantages of this programming language, you can read it here. 

You can run easily with this command;

[root@desk mythcat]# python3.9
Python 3.9.0 (default, Oct  6 2020, 00:00:00) 
[GCC 10.2.1 20200723 (Red Hat 10.2.1-1)] on linux
Type "help", "copyright", "credits" or "license" for more information. 

You can configure python with this command, see an example:

[root@desk mythcat]# python3.9-x86_64-config --libs 
 -lcrypt -lpthread -ldl  -lutil -lm -lm 

This release of Python 3.9 uses a new parser, based on parsing expression grammar (PEG) instead of LL(1) know as LL parser (Left-to-right, Leftmost derivation). PEG parsers are more powerful than LL(1) parsers and avoid the need for special hacks. This the same abstract syntax tree (AST) as the old LL(1) parser. The PEG parser is the default, but you can run your program using the old parser, see the next example:

[mythcat@desk ~]$ python3.9 -X oldparser my_script_name.py
...

One of the most important improvements for me is PEP 614 -- Relaxing Grammar Restrictions On Decorators. In Python 3.9, these restrictions are lifted and you can now use any expression. 

For example including one accessing items in a dictionary, like this simple example:

buttons = {
  "hello": QPushButton("hello word!"),
  "bye": QPushButton("bye word!"),
  "buy": QPushButton("buy!!"),
}
...
@buttons["hello"].clicked.connect
def speak_hello():
... 

Yes, comes with another Python Enhancement Proposals - PEP 615 with support for the IANA Time Zone Database in the Standard Library. This acronym is similar to I.A.N.A known as the Internet Assigned Numbers Authority, but it is not the same. 

In this case, the zoneinfo module provides a concrete time zone implementation to support the IANA time zone database. 

This support contains code and data that represent the history of local time for many representative locations around the globe. Many features for math, strings, union operator for the dictionary, HTTP codes, and more. 

Completion of variable and module names is automatically enabled at interpreter startup so that the Tab key invokes the completion function. 

You can see a simple example with zoneinfo python module and completion feature.

[mythcat@desk ~]$ python3.9
Python 3.9.0 (default, Oct  6 2020, 00:00:00) 
[GCC 10.2.1 20200723 (Red Hat 10.2.1-1)] on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> from zoneinfo import ZoneInfo
>>> import zoneinfo
>>> zoneinfo.available_timezones()
{'Hongkong', 'America/Iqaluit', 'America/Indianapolis', 'America/Louisville', 'America/New_York', 
'America/Mazatlan', 'Australia/Yancowinna', 'Africa/Ndjamena', 'Portugal', 'Africa/Bujumbura', 
'America/Rosario', 'America/Antigua','America/Indiana/Tell_City', 'America/Managua', 
'Europe/Paris', 'Europe/Oslo', 
...
>>> tz = ZoneInfo("Europe/Bucharest")
>>> tz.
tz.clear_cache(  tz.from_file(    tz.key           tz.tzname(       
tz.dst(          tz.fromutc(      tz.no_cache(     tz.utcoffset(    
>>> tz. 

On my desktop I got this result:

[mythcat@desk ~]$ python var_access_benchmark.py 
Variable and attribute read access:
   6.0 ns	read_local
   6.5 ns	read_nonlocal
  10.7 ns	read_global
  10.7 ns	read_builtin
  25.2 ns	read_classvar_from_class
  23.4 ns	read_classvar_from_instance
  34.3 ns	read_instancevar
  29.4 ns	read_instancevar_slots
  26.8 ns	read_namedtuple
  42.4 ns	read_boundmethod

Variable and attribute write access:
   6.6 ns	write_local
   7.0 ns	write_nonlocal
  23.3 ns	write_global
  54.1 ns	write_classvar
  46.4 ns	write_instancevar
  39.4 ns	write_instancevar_slots

Data structure read access:
  26.1 ns	read_list
  28.1 ns	read_deque
  27.5 ns	read_dict
  25.8 ns	read_strdict

Data structure write access:
  29.6 ns	write_list
  31.7 ns	write_deque
  34.4 ns	write_dict
  31.7 ns	write_strdict

Stack (or queue) operations:
  55.5 ns	list_append_pop
  49.7 ns	deque_append_pop
  51.0 ns	deque_append_popleft

Timing loop overhead:
   0.4 ns	loop_overhead

Python Qt5 : Use QStandardItem with Images.

This tutorial show you how to use QStandardItem with Images.

The source code is simple to understand.

import sys
from PyQt5.QtWidgets import QApplication, QMainWindow, QTreeView
from PyQt5.QtCore import  Qt
from PyQt5.QtGui import QFont, QColor, QImage, QStandardItemModel, QStandardItem

class ItemImage(QStandardItem):
    def __init__(self, txt='', image_path='', set_bold=False, color=QColor(0, 0, 0)):
        super().__init__()

        self.setEditable(False)
        self.setForeground(color)
        self.setText(txt)

        if image_path:
            image = QImage(image_path)
            self.setData(image, Qt.DecorationRole)

class MyApp(QMainWindow):
    def __init__(self):
        super().__init__()
        self.resize(1200, 1200)

        treeView = QTreeView()
        treeView.setHeaderHidden(True)
        treeView.header().setStretchLastSection(True)

        treeModel = QStandardItemModel()
        rootNode = treeModel.invisibleRootItem()

        robots = ItemImage('Robots', '', set_bold=True)

        aaa = ItemImage('aaa.jpg', 'aaa.jpg', 14)
        robots.appendRow(aaa)

        bbb = ItemImage('bbb.jpg', 'bbb.jpg', 16)
        robots.appendRow(bbb)

        robots2 = ItemImage('Robots 2', '', set_bold=False)

        aaa = ItemImage('ccc.png', 'ccc.png', 14)
        robots2.appendRow(aaa)

        bbb = ItemImage('ddd.jpg', 'ddd.jpg', 16)
        robots2.appendRow(bbb)

        rootNode.appendRow(robots)
        rootNode.appendRow(robots2)
        treeView.setModel(treeModel)
        treeView.expandAll()

        self.setCentralWidget(treeView)

app = QApplication(sys.argv)        
demo = MyApp()
demo.show()
sys.exit(app.exec_())

The result is this:

Python 3.8.5 : A sphere in Cartesian coordinates - part 001.

I like the equation of a sphere of radius R centered at the origin is given in Cartesian coordinates:

x*x + y*y + z*z = r*r

It is one of the first elements that helped me better understand mathematics and later the dynamics and theory of electromagnetic fields.

I did not find a graphical representation using python as accurately as possible without eliminating the discretion of the range from -1 and 1 and radius * radius = 1.

The main reason is the plot_surface from matplotlib python package.

This is output of my script:

[mythcat@desk ~]$ python sphere_xyz.py 
[-1.         -0.91666667 -0.83333333 -0.75       -0.66666667 -0.58333333
 -0.5        -0.41666667 -0.33333333 -0.25       -0.16666667 -0.08333333
  0.          0.08333333  0.16666667  0.25        0.33333333  0.41666667
  0.5         0.58333333  0.66666667  0.75        0.83333333  0.91666667
  1.        ]
sphere_xyz.py:7: RuntimeWarning: invalid value encountered in sqrt
  return np.sqrt(1-x**2 - y**2)
sphere_xyz.py:18: UserWarning: Z contains NaN values. This may result in rendering artifacts.
  surface1 = ax.plot_surface(X2, Y2, -Z2,rstride=1, cstride=1, linewidth=0,antialiased=True)
sphere_xyz.py:19: UserWarning: Z contains NaN values. This may result in rendering artifacts.
  surface2 = ax.plot_surface(X2, Y2, Z2,rstride=1, cstride=1, linewidth=0,antialiased=True)

The image result is this:

The source code is this:

import matplotlib.pyplot as plt
import numpy as np
from matplotlib.colors import LightSource

#@np.vectorize
def solve_Z2(x,y):
    return np.sqrt(1-x**2 - y**2)

fig = plt.figure()
ax = fig.gca(projection='3d')

xrange2 = np.linspace(-1.0, 1.0, 25)
yrange2 = np.linspace(-1.0, 1.0, 25)
print(xrange2)
X2, Y2 = np.meshgrid(xrange2, yrange2)
Z2 = solve_Z2(X2, Y2)

surface1 = ax.plot_surface(X2, Y2, -Z2,rstride=1, cstride=1, linewidth=0,antialiased=True)
surface2 = ax.plot_surface(X2, Y2, Z2,rstride=1, cstride=1, linewidth=0,antialiased=True)

plt.show()

Python 3.8.5 : Linked List - part 001.

In computer science, a linked list is a linear collection of data elements whose order is not given by their physical placement in memory. see wikipedia.org.

In this tutorial I will show you how these linked list works and how can build with python programming language.
Let's start with some basic elements:

• Linked List is a linear data structure;
• Linked List are not array;
• Linked List are not stored at a contiguous location because use pointes;
• Each element use a linked pointe;
• Linked List has a dynamic size;
• Linked List use operations like insertion and deletion for each element;
• The access to elements is sequentially;
• Using reference to pointer foe each element needs extra memory space and is not cache friendly;

The Linked List consists of at least two parts: data and pointer to the next node.
The first element of the list is called the head.

The last node has a reference to null.
The each element named node of the list has a similar structure like Linked List: data node and pointer to the next node.
The data from Linked list is represent like a sequence.

A number of operations are required to use the Linked List.

The basic operations supported by a list can be:

• insertion - adds an element at the beginning of the list;
• deletion - deletes an element at the beginning of the list;
• display - displays the complete list;
• search - searches an element using the given key;
• delete - deletes an element using the given key.

The Advantages of Linked List elements can be easily inserted or removed without reallocation or reorganization of the entire structure because the data items need not be stored contiguously in memory or on disk using operations.

The Linked List structure allows several ways to link nodes resulting in a number of types of Linked List:

• Simple linked list;
• Doubly linked list;
• Multiply linked list;
• Circular linked list;
• Sentinel nodes;
• Empty lists;
• Hash linking;
• List handles;
• Combining alternatives;

Let's start with first source code for the first type of Linked List named Simple linked list.

Python 3.8.5 (default, Aug 12 2020, 00:00:00) 
[GCC 10.2.1 20200723 (Red Hat 10.2.1-1)] on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> # create a node class 
>>> class Node:
...     # first initialise of the node object
...     def __init__(self, data):
...             # assign the node data 
...             self.data = data
...             # initialize next element as null
...             self.next = None
... 
>>> # create a simple linked list
>>> class Simple_LinkedList:
...     # first initialize of element named head
...     def __init__(self):
...             self.head = None
>>> # use the list 
>>> if __name__=='__main__': 
...     simple_list = Simple_LinkedList()
...     simple_list.head = Node(1)
...     second = Node(2)
...     third = Node(3)
...     #link first node with the second
...     simple_list.head.next = second
...     #link second  node with the third
...     second.next = third
...     # now the list is linked as a simple Linked List

This source code don't include any basic operations.

If you put this source code into python script and run it you will see this:

[mythcat@desk ~]$ python simple_LinkedList.py 
<__main__.Node object at 0x7f35163e1ac0>
<__main__.Node object at 0x7f351638b0a0>
<__main__.Node object at 0x7f351638b130>

I create a basic operation for this simple Linked List to display the content of this list:

# create a simple linked list with a basic diplay operation
class Simple_LinkedList:
    # first initialize of element named head
    def __init__(self):
            self.head = None
    # use the basic operation for display
    def display_Simple_LinkedList(self): 
        points_to_node = self.head
	#will traverse the list to the last node. 
        while (points_to_node): 
            # print data linked to points_to_node
            print (points_to_node.data) 
            # print next node linked to points_to_node
            points_to_node = points_to_node.next

I run with this new python source code and result is this:

[mythcat@desk ~]$ python simple_LinkedList_basic_display.py 
1
2
3 

Python 3.8.5 : Get Sentinel-3 satellite data from Eutelsat.

The tutorial for today is about Eutelsat satellites.
I used Sentinel-3 with these features:

• Instrument: SLSTR;
• Mode: EO;
• Satellite: Sentinel-3

You need to install xarray and netcdf4 python packages:

[mythcat@desk TLauncher]$ pip install xarray
...
[mythcat@desk ~]$ pip install netcdf4

Defaulting to user installation because normal site-packages is not writeable
Collecting netcdf4
  Downloading netCDF4-1.5.4-cp38-cp38-manylinux1_x86_64.whl (4.3 MB)
     |████████████████████████████████| 4.3 MB 649 kB/s 
Collecting cftime
  Downloading cftime-1.2.1-cp38-cp38-manylinux1_x86_64.whl (271 kB)
     |████████████████████████████████| 271 kB 30.5 MB/s 
Requirement already satisfied: numpy>=1.9 in /usr/lib64/python3.8/site-packages (from netcdf4) (1.18.4)
Installing collected packages: cftime, netcdf4
Successfully installed cftime-1.2.1 netcdf4-1.5.4 

You need to get data from the official webpage.

After I download the nc file with all data I used this source code:

Python 3.8.5 (default, Aug 12 2020, 00:00:00) 
[GCC 10.2.1 20200723 (Red Hat 10.2.1-1)] on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> import xarray as xr
>>> dir = '/home/mythcat/Downloads/'
>>> file_xr = xr.open_dataset(dir+'FRP_in.nc')
>>> file_xr
<xarray .dataset="">
Dimensions:                 (columns: 1500, fires: 23, rows: 2000)
Dimensions without coordinates: columns, fires, rows
Data variables:
    i                       (fires) int16 ...
    j                       (fires) int32 ...
    time                    (fires) datetime64[ns] ...
    latitude                (fires) float64 ...
    longitude               (fires) float64 ...
    FRP_MWIR                (fires) float64 ...
    FRP_uncertainty_MWIR    (fires) float64 ...
    transmittance_MWIR      (fires) float64 ...
    FRP_SWIR                (fires) float64 ...
    FRP_uncertainty_SWIR    (fires) float64 ...
    FLAG_SWIR_SAA           (fires) int16 ...
    transmittance_SWIR      (fires) float64 ...
    confidence              (fires) float64 ...
    classification          (fires) uint8 ...
    S7_Fire_pixel_radiance  (fires) float32 ...
    F1_Fire_pixel_radiance  (fires) float32 ...
    used_channel            (fires) uint8 ...
    Radiance_window         (fires) float32 ...
    Glint_angle             (fires) float64 ...
    IFOV_area               (fires) float64 ...
    TCWV                    (fires) float64 ...
    n_window                (fires) int16 ...
    n_water                 (fires) int16 ...
    n_cloud                 (fires) int16 ...
    n_SWIR_fire             (fires) float32 ...
    flags                   (rows, columns) uint32 ...
Attributes:
    title:                  SLSTR Level 2 Product, Fire Radiative Power measu...
    comment:                 
    netCDF_version:         4.2 of Jul  5 2012 17:07:43 $
    product_name:           S3B_SL_2_FRP____20200910T082906_20200910T083406_2...
    institution:            MAR
    source:                 IPF-SL-2-FRP 02.00
    history:                 
    references:             S3MPC ACR FRP 003 - i1r2 - SLSTR L2 Product Data ...
    contact:                ops@eumetsat.int
    creation_time:          2020-09-10T10:38:54Z
    resolution:             [ 1000 1000 ]
    absolute_orbit_number:  12385
    start_time:             2020-09-10T08:29:06.288252Z
    stop_time:              2020-09-10T08:34:06.277355Z
    track_offset:           998
    start_offset:           14032

Let's parse size by latitude and longitude:

>>> lat = file_xr['latitude']
>>> long = file_xr['longitude']
>>> lat , long 
(<xarray.DataArray 'latitude' (fires: 23)>
array([52.992509, 52.98967 , 48.447335, 44.00415 , 48.443263, 48.439204,
       48.438141, 48.434069, 48.430012, 48.430012, 43.99905 , 43.993966,
       43.996166, 43.991064, 43.991064, 43.985972, 43.986568, 43.981467,
       43.976379, 43.978613, 43.973495, 43.973495, 43.968419])
Dimensions without coordinates: fires
Attributes:
    long_name:      Latitude
    standard_name:  latitude
    units:          degrees_north
    valid_min:      -90.0
    valid_max:      90.0, 
array([38.424813, 38.441404, 29.594317, 26.774829, 29.607258, 29.620235,
       29.592402, 29.605285, 29.618236, 29.618236, 26.787083, 26.799272,
       26.77044 , 26.782656, 26.782656, 26.7949  , 26.769496, 26.781764,
       26.794036, 26.765122, 26.777358, 26.777358, 26.789596])
Dimensions without coordinates: fires
Attributes:
    long_name:      Longitude
    standard_name:  longitude
    units:          degrees_east
    valid_min:      -180.0
    valid_max:      180.0) 

Get FRP_SWIR and FRP_MWIR data from satelite:

>>> FRP_SWIR = file_xr['FRP_SWIR']
>>> FRP_SWIR 

array([-1., -1., -1., -1., -1., -1., -1., -1., -1., -1., -1., -1., -1., -1.,
       -1., -1., -1., -1., -1., -1., -1., -1., -1.])
Dimensions without coordinates: fires
Attributes:
    long_name:  Fire radiative power computed from SWIR channel (S6)
    units:      MW 
>>> FRP_MWIR = file_xr['FRP_MWIR']
>>> FRP_MWIR

array([ 10.290943,  11.447042, 179.555982,  84.84376 ,  48.277547,   9.320155,
        17.840467,  38.242334,  18.615514,  18.611452,  14.36118 ,   4.440371,
         3.06999 ,   5.008403,   5.005609,   4.452938,   8.228399,   8.442025,
         5.631591,   3.531036,   3.509205,   3.507225,   2.876292])
Dimensions without coordinates: fires
Attributes:
    long_name:  Fire radiative power computed from MWIR channels (S7 and F1)
    units:      MW