Install , test and fix error of the jupyter-book into python 3.

Jupyter Books lets you build an online book using a collection of Jupyter Notebooks and Markdown files. Its output is similar to the excellent Bookdown tool, and adds extra functionality for people running a Jupyter stack.
Read more about this on the official webpage.
Today I start to test this python module named jupyter-book.
I find some errors and I fixed to running well a demo jupyter-book instance.

C:\Python364\Scripts>pip install jupyter-book
Collecting jupyter-book
...
Installing collected packages: ruamel.yaml, jupyter-book
Successfully installed jupyter-book-0.4.1 ruamel.yaml-0.15.88

First I try to create a jupyter-book named catafest but I got this error:

C:\Python364>jupyter-book create catafest --demo
Traceback (most recent call last):
...
from nbclean import NotebookCleaner
ModuleNotFoundError: No module named 'nbclean'

The next step was to fix the error by install the nbclean python module and see all dependencies of python modules:

C:\Python364\Scripts>pip3.6.exe install nbclean
Collecting nbclean
...
Collecting nbgrader (from nbclean)
...
Collecting sqlalchemy (from nbgrader->nbclean)
...
Collecting alembic (from nbgrader->nbclean)
...
Collecting ipython<=6.2.1 (from nbgrader->nbclean)
...
Collecting jupyter-console<=5.2.0 (from nbgrader->nbclean)
...
Collecting Mako (from alembic->nbgrader->nbclean)
...
Collecting prompt-toolkit<2 .0.0="">=1.0.4 (from ipython<=6.2.1->nbgrader->nbclean)
...
Collecting prompt-toolkit<2 .0.0="">=1.0.4 (from ipython<=6.2.1->nbgrader->nbclean)
...
Building wheels for collected packages: nbgrader, sqlalchemy, alembic, Mako
...
Successfully built nbgrader sqlalchemy alembic Mako
Installing collected packages: sqlalchemy, Mako, python-editor, alembic, prompt-toolkit, ipython, 
jupyter-console, nbgrader, nbclean
...
Successfully installed Mako-1.0.7 alembic-1.0.7 ipython-6.2.1 jupyter-console-5.2.0 nbclean-0.3.2 
nbgrader-0.5.5 prompt-toolkit-1.0.15 python-editor-1.0.4 sqlalchemy-1.2.17 

Using again the jupyter-book to create catafest I got another error:

C:\Python364>jupyter-book create catafest--demo
Copying new book to: .\catafest
Copying over demo repository content

This is an error when I start to buid first time:

C:\Python364>jupyter-book build catafest
Convert and copy notebook/md files...
  0%|                                                                       | 0/35 [00:00
  File "c:\python364\lib\site-packages\jupyter_book\main.py", line 31, in main
    commands[args.command]()
  File "c:\python364\lib\site-packages\jupyter_book\build.py", line 266, in build_book
    lines = ff.readlines()
  File "c:\python364\lib\encodings\cp1252.py", line 23, in decode
    return codecs.charmap_decode(input,self.errors,decoding_table)[0]
UnicodeDecodeError: 'charmap' codec can't decode byte 0x9d in position 183: character maps to  

The reason is the python 3.x and can be fixed by change the encoding into this file:
c:\python364\lib\site-packages\jupyter_book\build.py with this:

with open(path_new_file, 'r',encoding='utf-8') as ff:

The next step is to build the catafest jupyter-book:

C:\Python364>jupyter-book build catafest
Convert and copy notebook/md files...
... 

My GitHub account is catafest and I used this link to create a new repo named catafest_jupyter-book:

https://github.com/new

You need to use root folder use this commands:

C:\Python364\catafest>cd ..

C:\Python364>git clone https://github.com/catafest/catafest_jupyter-book
Cloning into 'catafest_jupyter-book'...
warning: You appear to have cloned an empty repository.

Copy all files and folders from catafest folder to catafest_jupyter-book folder and use GITHUB commands to upload to the web:

C:\Python364>cd catafest_jupyter-book

C:\Python364\catafest_jupyter-book>git add ./*

C:\Python364\catafest_jupyter-book>git commit -m "adding my first jupyter book!"

C:\Python364\catafest_jupyter-book>git push
Username for 'https://github.com': catafest
Password for 'https://catafest@github.com':
Enumerating objects: 347, done.
Counting objects: 100% (347/347), done.
Delta compression using up to 2 threads.
Compressing objects: 100% (304/304), done.
Writing objects: 100% (347/347), 1.40 MiB | 541.00 KiB/s, done.
Total 347 (delta 74), reused 0 (delta 0)
remote: Resolving deltas: 100% (74/74), done.
To https://github.com/catafest/catafest_jupyter-book
 * [new branch]      master -> master

You can make GITHUB settings with a new gh-pages branch to see into your browser.

Using python with documents files.

Today I tested with python version 3.6.4 two python modules: python-docx and openpyxl.
This python modules let us to deal with document files like: docx, xlsx, xlsm, xltx, xltm.
First python module named python-docx is a Python library for creating and updating Microsoft Word (.docx) files.
The documentation of this python module can be found here.
Let's start with a simple example.

C:\Python364>cd Scripts
C:\Python364\Scripts>pip3.6.exe install python-docx
Collecting python-docx
...
Successfully installed python-docx-0.8.10

Let's start with the import step:

C:\Python364>python.exe
Python 3.6.4 (v3.6.4:d48eceb, Dec 19 2017, 06:54:40) [MSC v.1900 64 bit (AMD64)] on win32
Type "help", "copyright", "credits" or "license" for more information.
>>> import docx
>>> dir(docx)
['Document', 'ImagePart', 'RT', '__builtins__', '__cached__', '__doc__', '__file__', '__loader__', '
__name__', '__package__', '__path__', '__spec__', '__version__', 'api', 'blkcntnr', 'compat', 'dml',
 'document', 'enum', 'exceptions', 'image', 'opc', 'oxml', 'package', 'parts', 'section', 'settings'
, 'shape', 'shared', 'styles', 'text']

Let's create a document with this python module and add some text and an image:

C:\Python364>python.exe
Python 3.6.4 (v3.6.4:d48eceb, Dec 19 2017, 06:54:40) [MSC v.1900 64 bit (AMD64)] on win32
Type "help", "copyright", "credits" or "license" for more information.
>>> import docx
>>> mydoc = docx.Document()
>>> mydoc.add_paragraph('This is a text')
>>> mydoc.add_picture('icon.png',width=docx.shared.Inches(1),height=docx.shared.Inches(1))
>>> mydoc.save('test.docx')

Another python module is openpyxl.
This python module let you to read/write Excel 2010 xlsx/xlsm/xltx/xltm files.

C:\Python364\Scripts>pip3.6.exe install openpyxl
Collecting openpyxl
...
Successfully installed et-xmlfile-1.0.1 jdcal-1.4 openpyxl-2.6.0

Let's test it:

C:\Python364>python.exe
Python 3.6.4 (v3.6.4:d48eceb, Dec 19 2017, 06:54:40) [MSC v.1900 64 bit (AMD64)] on win32
Type "help", "copyright", "credits" or "license" for more information.
>>> from openpyxl import load_workbook
>>> w = load_workbook(filename='test.xlsx',read_only=True)
>>> print(w.sheetnames)
['Sheet1', 'TestSheet2']
>>> s=w['Sheet1']
>>> for row in s.rows:
...     for c in row:
...             print(c.value)
...
A1
None
ABC
None
None
NOP

Using the asciimatics and pyfiglet python modules

This is a simple example how to use the asciimatics and pyfiglet python modules with python version 3.6.4.
First you need to install with the pip tool.
The source code is simple and start with the import it.
The Fire, Print and Screen is used to show the fire effect and print text with Figlet and FigletText.
Because the fire and text use the console application I used the default Screen Buffer Size set to 80.
The Screen.wrapper(my_work_web) show all effects from the my_work_web.
In this area is created variables for font type: banner_font and web_font.
The main reason I named the web_font was to show my web page but the size of the over the screensize.
I tested most of the fonts from pyfiglet python module but I cannot find one to show a web page link.
This is the source code I tested:

# -*- coding: utf-8 -*-
"""
@author: catafest
"""

from asciimatics.renderers import FigletText, Fire
from asciimatics.scene import Scene
from asciimatics.screen import Screen
from asciimatics.effects import Print
from asciimatics.exceptions import ResizeScreenError
from pyfiglet import Figlet
import sys

def my_work_web(screen):
    banner_font = "banner3"
    web_font = "block"
    scenes = []
    effects = [
        Print(screen,
              Fire(screen.height, 80, "*" * 70, 0.8, 60, screen.colours,
                   bg=screen.colours >= 256),
              0,
              speed=1,
              transparent=False),
        Print(screen,
              FigletText("Follow ", banner_font),
              (screen.height - 4) // 2,
              colour=Screen.COLOUR_BLUE,
              speed=1,
              stop_frame=30),
        Print(screen,
              FigletText("me", banner_font),
              (screen.height - 4) // 2,
              colour=Screen.COLOUR_BLUE,
              speed=1,
              start_frame=30,
              stop_frame=50),
        Print(screen,
              FigletText("on web", banner_font),
              (screen.height - 4) // 2,
              colour=Screen.COLOUR_BLUE,
              speed=1,
              start_frame=50,
              stop_frame=70),
        Print(screen,
              FigletText("catafest", banner_font),
              (screen.height - 4) // 2,
              colour=Screen.COLOUR_BLUE,
              speed=1,
              start_frame=70),
    ]
    scenes.append(Scene(effects, 100))

    text = Figlet(font=web_font, width=300).renderText("bye!")
    width = max([len(x) for x in text.split("\n")])

    effects = [
        Print(screen,
              Fire(screen.height, 80, "*" * 70, 0.8, 60, screen.colours),
              0,
              speed=1,
              transparent=False),

        Print(screen,
              FigletText("bye!", web_font),
              (screen.height - 2)  // 2,
              colour=Screen.COLOUR_WHITE,
              bg=Screen.COLOUR_BLUE,
              speed=1)
    ]
    scenes.append(Scene(effects, -1))
    screen.play(scenes, stop_on_resize=True)


if __name__ == "__main__":
    while True:
        try:
            Screen.wrapper(my_work_web)
            sys.exit(0)
        except ResizeScreenError:
            pass

The result of this source code is this:

Testing imageio python module.

This python module comes with this intro from pypi website:
Imageio is a Python library that provides an easy interface to read and write a wide range of image data, including animated images, volumetric data, and scientific formats. It is cross-platform, runs on Python 2.7 and 3.4+, and is easy to install.
Let's install this python module:

C:\>cd C:\Python364
C:\Python364>cd Scripts
C:\Python364\Scripts>pip3.6.exe install imageio
Collecting imageio
...
Successfully built imageio
Installing collected packages: imageio
Successfully installed imageio-2.4.1
You are using pip version 18.0, however version 18.1 is available.
You should consider upgrading via the 'python -m pip install --upgrade pip' comm
and.

I tested with a simple read medical data (DICOM - CT-MONO2-16-brain), see here.
The Digital Imaging and Communications in Medicine (DICOM) standard start with the basic idea is that patient and machine-readable information is embedded within a file (usually an image) as it’s created or converted.
This is a simple example without security.
Without encrypted connections between applications, anyone on the network could intercept the DICOM files and extract the patient information.
The Imageio provides a range of example images:

1. Read an image;
2. Iterate over frames in a movie;
3. Grab screenshot or image from the clipboard;
4. Convert a movie;
5. Writing videos with FFMPEG and vaapi;

All file formats (93 files type ) can be read by this python module, see webpage here.
The examples from the official webpage work well.
Only the example with the DICOM file cannot be tested.
The main reason: I try to find a DICOM file but I don't find one.

Testing the webpy python module.

Today I wrote about another python module named web.py.
The reasons I start this tutorial come from google page of SDK for App Engine.
The Google come with these options of the following frameworks can be used with Python programming language:

• Flask;
• Django;
• Pyramid;
• Bottle;
• web.py
• Tornado

I started in the past to learn and use Django and I tested with Flask and Bottle and today is web.py python module.
First, about this python module I can tell you is a simple web framework and comes with a web.py slogan:
Think about the ideal way to write a web app. Write the code to make it happen.

C:\Python364\Scripts>pip install web.py==0.40-dev1
Collecting web.py==0.40-dev1
  Downloading https://files.pythonhosted.org/packages/db/a5/8dfacc190908f9876632
69a92efa682175c377e3f7eab84ed0a89c963b47/web.py-0.40.dev1.tar.gz (117kB)
    100% |████████████████████████████████| 122kB 936kB/s
Building wheels for collected packages: web.py
  Building wheel for web.py (setup.py) ... done
  Stored in directory: C:\Users\catafest\AppData\Local\pip\Cache\wheels\1b\15\12
\4fd91f5ed7e3c8aae085050cce83f72b7ca4f463bf3e67d2b7
Successfully built web.py
Installing collected packages: web.py
Successfully installed web.py-0.40.dev1

Let's test the example from the official website:

C:\Python364>python.exe
Python 3.6.4 (v3.6.4:d48eceb, Dec 19 2017, 06:54:40) [MSC v.1900 64 bit (AMD64)]
 on win32
Type "help", "copyright", "credits" or "license" for more information.
>>> import web
>>>
... urls = (
...     '/(.*)', 'hello'
... )
>>> app = web.application(urls, globals())
>>>
>>> class hello:
...     def GET(self, name):
...         if not name:
...             name = 'World'
...         return 'Hello, ' + name + '!'
...
>>> if __name__ == "__main__":
...     app.run()
...
http://0.0.0.0:8080/
127.0.0.1:50542 - - [27/Jan/2019 07:30:28] "HTTP/1.1 GET /" - 200 OK
127.0.0.1:50542 - - [27/Jan/2019 07:30:28] "HTTP/1.1 GET /favicon.ico" - 200 OK

The server starts at 0.0.0.0 (invalid address) and can see the result at 127.0.0.1:8080.

Detect nudity with nudepy python module.

Today I tested another python module named nudepy.
You can find it here.
This python module is a port of nude.js to Python.
Let's start the tutorial with the installation:

C:\Python364\Scripts>cd ..

C:\Python364>cd Scripts

C:\Python364\Scripts>pip install nudepy
Requirement already satisfied: nudepy in c:\python364\lib\site-packages (0.4)
Requirement already satisfied: pillow in c:\python364\lib\site-packages (from nu
depy) (5.3.0)

To test this python module, I used four images with the idea of a nude image
This image is the result of all images of the test.

This image files are named:

• test_nude_001.jpg
• test_nude_002.jpg
• test_nude_003.jpg
• test_nude_004.jpg

Let's see the script:

# for select jpeg files
import os, fnmatch
# import nude python module
import nude
from nude import Nude
#
nude_jpegs=fnmatch.filter(os.listdir('.'), '*nude*.jpg')
print(nude_jpegs)
for found_file in nude_jpegs:
    print (found_file)
    print("Nude file: ",nude.is_nude(str(found_file)))
    n = Nude(str(found_file))
    n.parse()
    print("and test result: ", n.result, n.inspect())
    print("====================")

The result of the output script is this:

C:\Python364>python.exe test_nude.py
['test_nude_001.jpg', 'test_nude_002.jpg', 'test_nude_003.jpg', 'test_nude_004.j
pg']
test_nude_001.jpg
Nude file:  False
and test result:  False #
====================
test_nude_002.jpg
Nude file:  False
and test result:  False #
====================
test_nude_003.jpg
Nude file:  False
and test result:  False #
====================
test_nude_004.jpg
Nude file:  True
and test result:  True #
====================

Using LibROSA python module.

This python module named LibROSA is a python package for music and audio analysis and provides the building blocks necessary to create music information retrieval systems.

C:\Python364>cd Scripts
C:\Python364\Scripts>pip install librosa
Collecting librosa
...
Successfully installed audioread-2.1.6 joblib-0.13.0 librosa-0.6.2 llvmlite-0.26.0 numba-0.41.0 resampy-0.2.1 
scikit-learn-0.20.2

Let's create one waveform and a spectrogram with this python module.
The waveform (for sound) the term describes a depiction of the pattern of sound pressure variation (or amplitude) in the time domain.
A spectrogram (known also like sonographs, voiceprints, or voicegrams) is a visual representation of the spectrum of frequencies of sound or other signals as they vary with time.
I used a free WAV file sound from here.
The result of the waveform and spectrogram for that audio file is shown into next screenshots:


My example show first the waveform and you need to close the it to see the spectrogram.
Let's see the source code of this example:

import librosa
import librosa.display
import matplotlib.pyplot as plt
plt.figure(figsize=(14, 5))
path = "merry_christmas.wav"
out,samples = librosa.load(path)
print(out.shape, samples)
librosa.display.waveplot(out, sr=samples)
plt.show()
stft_array = librosa.stft(out)
stft_array_db = librosa.amplitude_to_db(abs(stft_array))
librosa.display.specshow(stft_array_db,sr=samples,x_axis='time', y_axis='hz')
plt.colorbar()
plt.show()

Using python modules: mayavi and moviepy - part 001.

This is a simple example with two modules named: mayavi and moviepy.
Let's see the introduction of these python modules:
Mayavi2 is a general purpose, cross-platform tool for 3-D scientific data visualization. Its features include:

• Visualization of scalar, vector and tensor data in 2 and 3 dimensions.
• Easy scriptability using Python.
• Easy extendibility via custom sources, modules, and data filters.
• Reading several file formats: VTK (legacy and XML), PLOT3D, etc.
• Saving of visualizations.
• Saving rendered visualization in a variety of image formats.
• Convenient functionality for rapid scientific plotting via mlab

MoviePy is a Python module for video editing, which can be used for basic operations (like cuts, concatenations, title insertions), video compositing (a.k.a. non-linear editing), video processing, or to create advanced effects. It can read and write the most common video formats, including GIF.
The installation with pip3.6 tool:

C:\Python364\Scripts>pip3.6.exe install mayavi
Requirement already satisfied: mayavi in c:\python364\lib\site-packages (4.6.2)
...
C:\Python364\Scripts>pip3.6.exe install moviepy
Collecting moviepy
...
Installing collected packages: tqdm, moviepy
Successfully installed moviepy-0.2.3.5 tqdm-4.28.1

Let's create a simple example with these python modules.
First example:

C:\Python364>python.exe
Python 3.6.4 (v3.6.4:d48eceb, Dec 19 2017, 06:54:40) [MSC v.1900 64 bit (AMD64)]
 on win32
Type "help", "copyright", "credits" or "license" for more information.
>>> import mayavi.mlab as mlab
>>> f = mlab.gcf()
>>> f.scene._lift()
>>>

I choose the most common filter math function: the sinc function, known as sine cardinal:
In signal processing, a sinc filter is an idealized filter that removes all frequency components above a given cutoff frequency, without affecting lower frequencies, and has linear phase response. The filter's impulse response is a sinc function in the time domain, and its frequency response is a rectangular function.
I create the example to show you a sinc function by time.
This is my output (is not the result of the frequency response of the Fourier transform of the rectangular function).

Let's see the source code:

# import python modules
import numpy as np
import mayavi.mlab as mlab
import moviepy.editor as mpy
# duration of the animation in seconds 
duration= 2
# create the grid of points for x and y
x, y = np.mgrid[-30:30:100j, -30:30:100j]
# create the size figure
fig = mlab.figure(size=(640,480), bgcolor=(1,1,1))
# create the plane surface
r = np.sqrt(x**2 + y**2)
# this fix issue https://github.com/enthought/mayavi/issues/702
fig = mlab.gcf()
fig.scene._lift()
# create all frames 
def make_frame(t):
    # clear the area 
    mlab.clf()
    #blend surface by z over time t step is 0.05
    z = np.sin(r*t)/r
    # create surface 
    mlab.surf(z, warp_scale='auto')
    return mlab.screenshot(antialiased=True)
# create animation movie clip
animation = mpy.VideoClip(make_frame,duration=duration)
# write file like a GIF 
animation.write_gif("sinc.gif", fps=20)

Python Qt5 : the most simple QTreeWidget - part 001.

The QTreeWidget is more complex in order to accomplish a simple development issue.
Today, I will show you how is the first step to start it.
This simple example will follow these goals:

• create a simple QTreeWidget;
• use the most simple way to do that;
• do not use the class object;
• show files and folders;

The example do not have any feature for and show my C drive:

• filter, sort and drag and drop;

The result of this example:

Using pytorch - the final of story.

Let's continue our story with the child and the gift.
The child saw the gift and his first thought was the desire to know.
The basic forming unit of a neural network is a perceptron.
He saw that he was not too big and his eyes lit up.
To compute the output will multiply input with respective weights and compare with a threshold value.
Each perceptron also has a bias which can be thought of as how much flexible the perceptron is.
This process is of evolving a perceptron to what a now called an artificial neuron.
The next step is the artificial network and is all artificial neuron and edges between.
He touched him in the corners and put his hand on his surface.
The activation function is mostly used to make a non-linear transformation which allows us to fit nonlinear hypotheses or to estimate the complex functions.
He began to understand that he had a special and complex form.
This artificial network is built from start to end from:

• Input Layer an X as an input matrix;
• Hidden Layers a matrix dot product of input and weights assigned to edges between the input and hidden layer, then add biases of the hidden layer neurons to respective inputs and use this to update all weights at the output and hidden layer to use update biases at the output and hidden layer.
• Output Layer an y as an output matrix;

Without too much thoughts he began to break out of the gift in the order in which he touched it.
This weight and bias of the updating process are known as back propagation.
To computed the output and this process is known as forward propagation.
Several moves were enough to complete the opening of the gift.
He looked and understood that the size of the gift is smaller, but the gift was thankful to him.
This forward and back propagation iteration is known as one training iteration named epoch.
The next example I created from an old example I saw on the internet and is the most simple way to show you the steps from this last part of the story:

##use an neural network in pytorch
import torch

#an input array
X = torch.Tensor([[1,0,1],[0,1,1],[0,1,0]])

#the output
y = torch.Tensor([[1],[1],[0]])

#the Sigmoid Function
def sigmoid (x):
  return 1/(1 + torch.exp(-x))

#the derivative of Sigmoid Function
def derivatives_sigmoid(x):
  return x * (1 - x)

#set the variable initialization
epoch=1000 #training iterations is epoch
lr=0.1 #learning rate value
inputlayer_neurons = X.shape[1] #number of features in data set
hiddenlayer_neurons = 3 #number of hidden layers neurons
output_neurons = 1 #number of neurons at output layer

#weight and bias initialization
wh=torch.randn(inputlayer_neurons, hiddenlayer_neurons).type(torch.FloatTensor)
print("weigt = ", wh)
bh=torch.randn(1, hiddenlayer_neurons).type(torch.FloatTensor)
print("bias = ", bh)
wout=torch.randn(hiddenlayer_neurons, output_neurons)
print("wout = ", wout)
bout=torch.randn(1, output_neurons)
print("bout = ", bout)

for i in range(epoch):

  #Forward Propogation
  hidden_layer_input1 = torch.mm(X, wh)
  hidden_layer_input = hidden_layer_input1 + bh
  hidden_layer_activations = sigmoid(hidden_layer_input)
 
  output_layer_input1 = torch.mm(hidden_layer_activations, wout)
  output_layer_input = output_layer_input1 + bout
  output = sigmoid(output_layer_input1)

  #Backpropagation
  E = y-output
  slope_output_layer = derivatives_sigmoid(output)
  slope_hidden_layer = derivatives_sigmoid(hidden_layer_activations)
  d_output = E * slope_output_layer
  Error_at_hidden_layer = torch.mm(d_output, wout.t())
  d_hiddenlayer = Error_at_hidden_layer * slope_hidden_layer
  wout += torch.mm(hidden_layer_activations.t(), d_output) *lr
  bout += d_output.sum() *lr
  wh += torch.mm(X.t(), d_hiddenlayer) *lr
  bh += d_output.sum() *lr
 
print('actual :\n', y, '\n')
print('predicted :\n', output)

The result is for 100 and 1000 epoch value and show us how close is the actual input (1,1,0) to the predicted results.
See also the weight and bias initialization of the artificial network is created random by torch.randn.
If I added this in my story it would sound like this:
The child's thoughts began to flinch in wanting to finish faster and find the gift.

C:\Python364>python.exe pytorch_test_002.py
weigt =  tensor([[-0.9364,  0.4214,  0.2473],
        [-1.0382,  2.0838, -1.2670],
        [ 1.2821, -0.7776, -1.8969]])
bias =  tensor([[-0.3604, -0.8943,  0.3786]])
wout =  tensor([[-0.5408],
        [ 1.3174],
        [-0.7556]])
bout =  tensor([[-0.4228]])
actual :
 tensor([[1.],
        [1.],
        [0.]])

predicted :
 tensor([[0.5903],
        [0.6910],
        [0.6168]])

C:\Python364>python.exe pytorch_test_002.py
weigt =  tensor([[ 1.2993,  1.5142, -1.6325],
        [ 0.0621, -0.5370,  0.1480],
        [ 1.5673, -0.2273, -0.3698]])
bias =  tensor([[-2.0730, -1.2494,  0.2484]])
wout =  tensor([[ 0.6642],
        [ 1.6692],
        [-0.4087]])
bout =  tensor([[0.3340]])
actual :
 tensor([[1.],
        [1.],
        [0.]])

predicted :
 tensor([[0.9417],
        [0.8510],
        [0.2364]])

Python Qt5 : simple draw with QPainter.

Using the QPainter is more complex than a simple example.
I try to create a simple example in order to have a good look at how can be used.
The main goal was to understand how can have the basic elements of QPainter.
The result of my example is this:

Here is my example with all commented lines for a good approach:

import sys 
from PyQt5 import QtGui, QtWidgets 
from PyQt5.QtGui import QPainter, QBrush, QColor
from PyQt5.QtCore import Qt, QPoint 
class My_QPainter(QtWidgets.QWidget): 
    def paintEvent(self, event): 
        # create custom QPainter
        my_painter = QtGui.QPainter() 
        # start and set my_painter
        my_painter.begin(self) 
        my_painter.setRenderHint(my_painter.TextAntialiasing, True)
        my_painter.setRenderHint(my_painter.Antialiasing, True)
        #set color for pen by RGB
        my_painter.setPen(QtGui.QColor(0,0,255)) 
        # draw a text on fixed coordinates
        my_painter.drawText(220,100, "Text at 220, 100 fixed coordinates") 
        # draw a text in the centre of my_painter   
        my_painter.drawText(event.rect(), Qt.AlignCenter, "Text centerd in the drawing area") 
        #set color for pen by Qt color  
        my_painter.setPen(QtGui.QPen(Qt.green, 1)) 
        # draw a ellipse
        my_painter.drawEllipse(QPoint(100,100),60,60) 
        # set color for pen by property
        my_painter.setPen(QtGui.QPen(Qt.blue, 3, join = Qt.MiterJoin)) 
        # draw a rectangle
        my_painter.drawRect(80,160,100,100) 
        # set color for pen by Qt color 
        my_painter.setPen(QtGui.QPen(Qt.red, 2))
        # set brush 
        my_brush = QBrush(QColor(33, 33, 100, 255), Qt.DiagCrossPattern)
        my_painter.setBrush(my_brush)
        # draw a rectangle and fill with the brush 
        my_painter.drawRect(300, 300,180, 180)
        my_painter.end() 
# create application  
app = QtWidgets.QApplication(sys.argv) 
# create the window application from class
window = My_QPainter() 
# show the window
window.show() 
# default exit 
sys.exit(app.exec_())

Python 3.6.4 : Learning OpenCV - centroids.

Today I was a little lazy.
I studied a little on the internet.
The last aspect was related to centroids.
An example I studied before TV news was from this webpage.
About centroid you can read here.
The result of the source code from a video with Simona Halep.

Python Qt5 : complex QML file.

Today, I will show you how to have a more complex custom application with PyQt5 and QML file.
You need to create into your python folder a new folder named QMLCustom.
Into this file create two python files named: __init__.py and QMLCustom.py.
The __init__ will be an empty file.
Into your python folder installation (where you create the QMLCustom folder), create a new QML_custom.qml file.
The QML_custom.qml file will have this:

import QtQuick 2.0
import SDK 1.0
import QtQuick.Layouts 1.1

Rectangle {
    id: appwnd
    visible: true
    width: 640
    height: 480

    property int columns : 2
    property int rows : 2

    Rectangle {
        anchors.fill: parent
        color: "#00f"
    }

    GridView {
        id: grid
        anchors.fill: parent
        cellWidth: Math.max(width/2, height/2);
        cellHeight: Math.max(width/2, height/2)
        model: dashModel
        delegate : Rectangle {
            Layout.alignment: Layout.Center
            width: grid.cellWidth
            height: grid.cellHeight
            color: "#0ff"
            border.color: "#fff"
            border.width: 10

            Text {
                id: name
                anchors.horizontalCenter: parent.horizontalCenter
                anchors.bottom: parent.bottom
                anchors.leftMargin:15
                anchors.topMargin: 15
                width: parent.width 
                height: parent.height
                textFont {
                    family: "Halvetica"
                    italic: false
                    pointSize:20
                }
                suffixText: suffix
            }

        }
        onWidthChanged: {
            grid.cellWidth = grid.width/appwnd.columns;
        }

        onHeightChanged: {
            grid.cellHeight = grid.height/appwnd.rows
        }
    }

    ListModel {
        id: dashModel
        ListElement {
            tagName: "Text"
            suffix: "First text"
        }
        ListElement {
            tagName: "Text"
            suffix: "Next text"
        }         
    }
} 

If you read this you will see the qml type file has two imports and a text.
The imports are used to load it and the text file is used to describe what we need.
In this case is created a Rectangle, GridView and one ListModel with two ListElement.
All of this part will be a link to the QMLCustom.py file.
For example: follow the suffixText from qml file suffixText: suffix into QMLCustom.py file (decorator def suffixText(self, text)).
Into the QMLCustom folder you need to fill the QMLCustom.py with this:

import PyQt5
from PyQt5.QtCore import *
from PyQt5.QtGui import *
from PyQt5.QtWidgets import *
from PyQt5.QtCore import pyqtProperty, pyqtSignal, pyqtSlot
from PyQt5.QtQuick import QQuickPaintedItem, QQuickItem
from PyQt5.QtGui import QPainter
from PyQt5 import QtCore

class QMLCustom(QQuickPaintedItem):
    #
    class DialType():
        FullDial = 0
        MinToMax = 1
        NoDial = 2
    #
    sizeChanged = pyqtSignal()
    valueChanged = pyqtSignal()
    #
    backgroundColorChanged = pyqtSignal()
    #
    textColorChanged = pyqtSignal()
    suffixTextChanged = pyqtSignal()
    showTextChanged = pyqtSignal()
    textFontChanged = pyqtSignal()

    def __init__(self, parent=None):
        super(QMLCustom, self).__init__(parent)

        self.setWidth(100)
        self.setHeight(100)
        self.setSmooth(True)
        self.setAntialiasing(True)

        self._Size = 100
        self._DialWidth = 15
        self._SuffixText = ""
        self._BackgroundColor = Qt.transparent
        self._TextColor = QColor(0, 0, 0)
        self._ShowText = True
        self._TextFont = QFont()

    def paint(self, painter):
        painter.save()

        size = min(self.width(), self.height())       
        self.setWidth(size)
        self.setHeight(size)
        rect = QRectF(0, 0, self.width(), self.height()) 
        painter.setRenderHint(QPainter.Antialiasing)
        
        painter.restore()

        painter.save()
        painter.setFont(self._TextFont)
        offset = self._DialWidth / 2
        if self._ShowText:
            painter.drawText(rect.adjusted(offset, offset, -offset, -offset), Qt.AlignCenter, self._SuffixText)
        else:
            painter.drawText(rect.adjusted(offset, offset, -offset, -offset), Qt.AlignCenter, self._SuffixText)
        painter.restore()

    @QtCore.pyqtProperty(str, notify=sizeChanged)
    def size(self):
        return self._Size

    @size.setter
    def size(self, size):
        if self._Size == size:
            return
        self._Size = size
        self.sizeChanged.emit()

    @QtCore.pyqtProperty(float, notify=valueChanged)
    def value(self):
        return self._Value

    @value.setter
    def value(self, value):
        if self._Value == value:
            return
        self._Value = value
        self.valueChanged.emit()


    @QtCore.pyqtProperty(QColor, notify=backgroundColorChanged)
    def backgroundColor(self):
        return self._BackgroundColor

    @backgroundColor.setter
    def backgroundColor(self, color):
        if self._BackgroundColor == color:
            return
        self._BackgroundColor = color
        self.backgroundColorChanged.emit()


    @QtCore.pyqtProperty(QColor, notify=textColorChanged)
    def textColor(self):
        return self._TextColor

    @textColor.setter
    def textColor(self, color):
        if self._TextColor == color:
            return
        self._TextColor = color
        self.textColorChanged.emit()  

    @QtCore.pyqtProperty(str, notify=suffixTextChanged)
    def suffixText(self):
        return self._SuffixText

    @suffixText.setter
    def suffixText(self, text):
        if self._SuffixText == text:
            return
        self._SuffixText = text
        self.suffixTextChanged.emit()

    @QtCore.pyqtProperty(str, notify=showTextChanged)
    def showText(self):
        return self._ShowText

    @showText.setter
    def showText(self, show):
        if self._ShowText == show:
            return
        self._ShowText = show


    @QtCore.pyqtProperty(QFont, notify=textFontChanged)
    def textFont(self):
        return self._TextFont

    @textFont.setter
    def textFont(self, font):
        if self._TextFont == font:
            return
        self._TextFont = font
        self.textFontChanged.emit()

This is a base python module that allows you to use the qml file and show it into your application.
The QMLCustom.py use a class (with pyqtSignal and paint to link all data with decorators) to be used into your application.
This can be a little difficult to follow but if you deal with a tool like QtCreator editor you will understand how this integrated GUI layout and forms designer with this script.
The last part is more simple and is the application.
This script uses both the custom python module QMLCustom and the qml file.
Create a python file into your folder python installation fill with the next script and run it:

import sys
import os
import subprocess

from QMLCustom.QMLCustom import QMLCustom

from PyQt5.QtCore import QUrl, Qt, QObject, pyqtSignal, pyqtSlot
from PyQt5.QtGui import QGuiApplication, QCursor
from PyQt5.QtQuick import QQuickView
from PyQt5.QtQml import qmlRegisterType
from OpenGL import GLU

class App(QGuiApplication):
 def __init__(self, argv):
  super(App, self).__init__(argv)

if __name__ == '__main__':
 try:
  app = App(sys.argv)
  
  qmlRegisterType(QMLCustom, "SDK", 1,0, "Text")

  view = QQuickView()
  ctxt = view.rootContext()
  view.setSource(QUrl("QML_custom.qml"))
  view.show()
  ret = app.exec_()

 except Exception as e:
  print (e)

The result is this: