Python 3.5.2 : Detect motion and save images with opencv.

This script is simple to use it with a webcam or to parse a video file.
The main goal of this script is to see the difference in various frames of a video or webcam output.
The first frame of our video file will contain no motion and just background and then is compute the absolute difference.
There is no need to process the large, raw images straight from the video stream and this is the reason I convert the image to grayscale.
Some text is put on the window to show us the status string to indicate it is detection.
With this script I detect cars and peoples from my window, see the screenshot with these files:

Let's see the python script:

import argparse
import datetime
import imutils

import cv2

import time
from time import sleep

def saveJpgImage(frame):
    #process image
    img_name = "opencv_frame_{}.jpg".format(time)
    cv2.imwrite(img_name, frame)

def savePngImage():
    #process image
    img_name = "opencv_frame_{}.png".format(time)
    cv2.imwrite(img_name, frame)

# get argument parse
ap = argparse.ArgumentParser()
ap.add_argument("-v", "--video", help="path to the video file")
ap.add_argument("-s", "--size", type=int, default=480, help="minimum area size , default 480")
args = vars(ap.parse_args())

# if no video use webcam
if args.get("video", None) is None:
    camera = cv2.VideoCapture(0)
    #time.sleep(1.5)

# use video file
else:
    camera = cv2.VideoCapture(args["video"])


# frame from video is none 
first_frame = None

# loop into frames of the video
while True:
    # grab the current frame 
    (grabbed, frame) = camera.read()
    text = "undetected"

    # is no frame grabbed the is end of video 
    if not grabbed:
        break

    # resize the frame 
    frame = imutils.resize(frame, width=640)
    gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
    gray = cv2.GaussianBlur(gray, (21, 21), 0)

    # is first frame is none , make gray 
    if first_frame is None:
        first_frame = gray
        continue


    # compute difference from current frame and first frame 
    frameDelta = cv2.absdiff(first_frame, gray)
    first_frame = gray
    thresh = cv2.threshold(frameDelta, 1, 255, cv2.THRESH_BINARY)[1]

    # dilate the thresholded image to fill in holes
    # then find contours on thresholded image
    thresh = cv2.dilate(thresh, None, iterations=2)
    (cnts, _) = cv2.findContours(thresh.copy(), cv2.RETR_EXTERNAL,
                                 cv2.CHAIN_APPROX_SIMPLE)

    # loop contours 
    for c in cnts:
        # if the contour is too small, ignore it
        if cv2.contourArea(c) < args["size"]:
            continue

        # compute the bounding box for the contour
        # draw it on the frame,
        # and update the text
        (x, y, w, h) = cv2.boundingRect(c)
        cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 255, 0), 0)
        roi = frame[y:y+h, x:x+w]
        ts = time.time()
        st = datetime.datetime.fromtimestamp(ts).strftime('%d-%m-%Y_%H-%M-%S')
        # if the detection is on sized then save the image 
        if (w > h ) and (y + h) > 50 and (y + h) < 550:
            cv2.imwrite(st+"opencv.jpg", roi)
        # set text to show on gui 
        text = "detected"
    
    # draw the text and timestamp on the frame
    cv2.putText(frame, "Detect: {}".format(text), (10, 20),
                cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255), 2)
    cv2.putText(frame, datetime.datetime.now().strftime("%A %d %B %Y %I:%M:%S%p"),
                (10, frame.shape[0] - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.35, (0, 0, 255), 1)

    #show frame , thresh and frame_Delta
    cv2.imshow("Security Feed", frame)
    cv2.imshow("Thresh", thresh)
    cv2.imshow("Frame Delta", frameDelta)
    key = cv2.waitKey(1) &  0xFF

    # break from loop with q key 
    if key == ord("q"):
        break

# close camera and windows 
camera.release()
cv2.destroyAllWindows()

Python 3.6.9 : My colab tutorials - part 002.

This is another notebook with the Altair python package.
The development team comes with this intro:
Altair is a declarative statistical visualization library for Python, based on Vega and Vega-Lite.

Altair offers a powerful and concise visualization grammar that enables you to build a wide range of statistical visualizations quickly. Here is an example of using the Altair API to quickly visualize a dataset with an interactive scatter plot:
See the notebook at my GitHub account.

Python 3.6.9 : My colab tutorials - part 001.

Today I start this tutorials series for the Colab tool.
To share my working with the Colab tool I created this GitHub project.
This project has two colab files :

catafest_001.ipynb  Created using Colaboratory  
catafest_002.ipynb  Created using Colaboratory 

First colab notebook come with a simple tutorial.
The next colab notebook is a little bit more complex and shares more information about how can deal with simples tasks on colab.
This is the table of contests for this colab notebook:

• Table of contents
• Select GPU for this notebook
• Check with nvidia-smi
• Check whether you have a visible GPU
• Check with tensoflow test
• Read information about hardware
• Check cpuinfo
• Check meminfo
• Use Linux commands
• Use python modules torch and fastai
• Use python modules
• Show and get information
• Enter credentials with Username and Password:
• Datatime fields
• Raw fields
• Number fields
• Boolean fields
• Pandas data fields
• Upload files
• Upload local files
• Use the Jupyter Widgets

Python Qt5 : Create a spectrum equalizer.

I haven't written much for a while on these issues about python and PyQt5.
Today I will show a complex example of QtMultimedia and how to create a spectrum equalizer.
First, the PyQt5 bindings come with this python module named QtMultimedia.
The main reason was the lack of time and focus of my effort on more stringent elements of my life.
Let's start with the few lines of source code that show us how can use this python module.

[mythcat@desk ~]$ python3 
Python 3.7.6 (default, Jan 30 2020, 09:44:41) 
[GCC 9.2.1 20190827 (Red Hat 9.2.1-1)] on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> import PyQt5
>>> from PyQt5.QtMultimedia import *
>>> dir(PyQt5.QtMultimedia)
['QAbstractVideoBuffer', 'QAbstractVideoFilter', 'QAbstractVideoSurface', 'QAudio', 'QAudioBuffer',
 'QAudioDecoder', 'QAudioDeviceInfo', 'QAudioEncoderSettings', 'QAudioFormat', 'QAudioInput', 'QAudioOutput',
 'QAudioProbe', 'QAudioRecorder', 'QCamera', 'QCameraExposure', 'QCameraFocus', 'QCameraFocusZone',
 'QCameraImageCapture', 'QCameraImageProcessing', 'QCameraInfo', 'QCameraViewfinderSettings', 
 'QImageEncoderSettings','QMediaBindableInterface', 'QMediaContent', 'QMediaControl', 'QMediaMetaData', 
 'QMediaObject', 'QMediaPlayer', 'QMediaPlaylist', 'QMediaRecorder', 'QMediaResource', 'QMediaService', 
 'QMediaTimeInterval', 'QMediaTimeRange', 'QMultimedia', 'QRadioData', 'QRadioTuner', 'QSound', 'QSoundEffect',
 'QVideoEncoderSettings', 'QVideoFilterRunnable', 'QVideoFrame', 'QVideoProbe', 'QVideoSurfaceFormat',
 '__doc__', '__file__', '__loader__', '__name__', '__package__', '__spec__']

The next issue is creating a random equalizer effect with bars.

This source doesn't use the QtMultimedia but can be implemented in the random area of equalizer set by update_values function:

import random
import sys
from PyQt5 import QtCore, QtGui, QtWidgets
from PyQt5.QtCore import Qt
from PyQt5.QtCore import pyqtSignal as Signal

print("load all modules!")

class Equalizer_Bars(QtWidgets.QWidget):

    def __init__(self, bars, steps, *args, **kwargs):
        super().__init__(*args, **kwargs)
        # this fit the size to equalizer bars area
        self.setSizePolicy(
            QtWidgets.QSizePolicy.MinimumExpanding,
            QtWidgets.QSizePolicy.MinimumExpanding
        )
        # set the size of the window
        self.setMinimumHeight(480)
        self.setMinimumWidth(640)
        
        if isinstance(steps, list):
            # list of colours.
            self.n_steps = len(steps)
            self.steps = steps

        elif isinstance(steps, int):
            # int number of bars, defaults to blue.
            self.n_steps = steps
            self.steps = ['blue'] * steps
        else:
            raise TypeError('steps variable set wrong, try with a list or int')
        
        # create bars
        self.set_bars = bars
        # define settings of bars
        self.set_space_between_bars = 1
        self.set_height_of_bars = 1
        # set color of background
        self.set_background_color = QtGui.QColor('white')
        # set padding by pixel .
        self.set_padding = 10  

        # create timer 
        self.set_timer = None
        # set decay for move bars 
        self.set_decay_frequency_ms(76)
        self.set_decay_value= 10

        # set values for minim and maxim 
        self.set_min_value = 0
        self.set_max_value = 100

        # store all current values in a list.
        self.set_all_values= [0.0] * bars


    def paintEvent(self, e):
        # create a painter
        painter = QtGui.QPainter(self)
        # create a brush for drawing 
        brush = QtGui.QBrush()
        brush.setColor(self.set_background_color)
        brush.setStyle(Qt.SolidPattern)
        rect = QtCore.QRect(0, 0, painter.device().width(), painter.device().height())
        painter.fillRect(rect, brush)
        
        # set the canvas.
        d_height = painter.device().height() - (self.set_padding * 2)
        d_width = painter.device().width() - (self.set_padding * 2)

        # set the bars.
        step_y = d_height / self.n_steps
        bar_height = step_y * self.set_height_of_bars
        bar_height_space = step_y * (1 - self.set_space_between_bars) / 2

        step_x = d_width / self.set_bars
        bar_width = step_x * self.set_space_between_bars
        bar_width_space = step_x * (1 - self.set_height_of_bars) / 2

        for i in range(self.set_bars):

            # calculating the y position for this bar for stop, from the value in range.
            c = (self.set_all_values[i] - self.set_min_value) / (self.set_max_value - self.set_min_value)
            n_steps_to_draw = int(c * self.n_steps)

            for n in range(n_steps_to_draw):
                brush.setColor(QtGui.QColor(self.steps[n]))
                rect = QtCore.QRect(
                    self.set_padding + (step_x * i) + bar_width_space,
                    self.set_padding + d_height - ((1 + n) * step_y) + bar_height_space,
                    bar_width,
                    bar_height
                )
                painter.fillRect(rect, brush)

        painter.end()

    def _trigger_refresh(self):
        self.update()

    def set_decay_trick(self, f):
        self.set_decay_value= float(f)

    def set_decay_frequency_ms(self, ms):
        if self.set_timer:
            self.set_timer.stop()

        if ms:
            self.set_timer = QtCore.QTimer()
            self.set_timer.setInterval(ms)
            self.set_timer.timeout.connect(self.set_decay_beat)
            self.set_timer.start()

    def set_decay_beat(self):
        self.set_all_values= [
            max(0, v - self.set_decay_value)
            for v in self.set_all_values
        ]
        self.update()  # Redraw new position.

    def size_values(self, v):
        self.set_all_values= v
        self.update()

    def values(self):
        return self.set_all_values

    def set_range(self, vmin, vmax):
        assert float(vmin) < float(vmax)
        self.set_min_value, self.set_max_value = float(vmin), float(vmax)
    # will be used with QBrush 
    def setColor(self, color):
        self.steps = [color] * self._bar.n_steps
        self.update()

    def set_color_bars(self, colors):
        self.n_steps = len(colors)
        self.steps = colors
        self.update()

    def set_bar_padding(self, i):
        self.set_padding = int(i)
        self.update()

    def set_bar_solid_percent(self, f):
        self._bar_solid_percent = float(f)
        self.update()

    def set_background_color(self, color):
        self.set_background_color = QtGui.QColor(color)
        self.update()


class Window(QtWidgets.QMainWindow):

    def __init__(self):
        super().__init__()

        self.equalizer = Equalizer_Bars(8, \
        ['#00405e', '#3350a1', '#386787','#0088ba','#3396e6', '#00b9d0','#c1f9f9', '#cce2f7'])

        self.setCentralWidget(self.equalizer)

        self.set_timer = QtCore.QTimer()
        self.set_timer.setInterval(100)
        self.set_timer.timeout.connect(self.update_values)
        self.set_timer.start()

    def update_values(self):
        self.equalizer.size_values([
            min(100, i+random.randint(0, 500) if random.randint(0, 10) > 2 else i)
            for i in self.equalizer.values()
            ])
            
# start the program 
app = QtWidgets.QApplication([])
w = Window()
w.show()
app.exec_()

Python 3.6.9 : Google give a new tool for python users.

Today I discovered a real surprise gift made by the team from Google for the evolution of programmers.
I say this because not everyone can afford hardware resources.
This gift is a new tool called Colab and uses these versions of python and sys:

Python version
3.6.9 (default, Nov  7 2019, 10:44:02) 
[GCC 8.3.0]
Version info.
sys.version_info(major=3, minor=6, micro=9, releaselevel='final', serial=0)

This utility allows you to run source code that requires online hardware resources using your google account.
Colab allows you to use and share Jupyter notebooks because is an open-source project on which Colab is based.
The types of GPUs that are available in Colab varies over time.
This is necessary for Colab to be able to provide access to these resources for free.
The GPUs available in Colab often include Nvidia K80s, T4s, P4s, and P100s.
This way you can test demanding modules like the python TensorFlow module.
The utility is free but you can pay extra for more hardware resources.
Colab notebooks are stored in Google Drive, or can be loaded from GitHub.
You can see a simple intro with a notebook on my GitHub account.

Python 3.7.6 : The new concepts of execution in python 3 - part 001.

The main goal of these tutorials series is learning to deal with python source code using the new concepts of execution in python 3.
When two or more events are concurrent it means that they are happening at the same time.
Concurrent programming is not equivalent to parallel execution.
In computing, concurrency is the execution of pieces of work or tasks by a computer at the same time.
Concurrency is a property which more than one operation can be run simultaneously.
When multiple computations or operations are carried out at the same time or in parallel with the goal of speeding up the computation process then this process is named parallelism.
Parallelism is a property which operations are actually being run simultaneously using the multiprocessing.
Multiprocessing, on the other hand, involves utilizing two or more processor units on a computer to achieve parallelism.
Multithreading is a property that refers to the ability of a CPU to execute multiple threads concurrently.
Python’s concurrency methods including threading, multiprocessing, and asyncio.
The difference between the threading and multiprocessing is this: the threading module uses threads, the multiprocessing module uses processes.
The threading is the package that provides API to create and manage threads.
With multiprocessing, Python creates new processes using an API similar to the threading module.
The asyncio is a library to write concurrent code using the async/await syntax.
The keyword async indicates that our function is a coroutine meaning they choose when to pause and let others execute and run coroutines multitask cooperatively.
The three fundamental advantages of async and await over threads are:

• cooperative multitasking - you can reasonably have millions of concurrent tasks;
• using await makes visible where the schedule points;
• if a task doesn’t yield then it can accidentally block all other tasks from running;
• tasks can support cancellation.

The next source of code show us how can deal with the execution code in Python 3 using the threading and multiprocessing python packages.
The timeit python package is used to benchmark the code write in the code_to_test variable:
Let's test the multi-threading execution with python:

[mythcat@desk ~]$ python3 
Python 3.7.6 (default, Jan 30 2020, 09:44:41) 
[GCC 9.2.1 20190827 (Red Hat 9.2.1-1)] on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> import timeit
>>> code_to_test = """
... import threading
... 
... text = "Hello World"
... 
... def print_text(text):
...     for char in text:
...             print (char)
... 
... # multi-threading execution
... def multi_threads():
...     thread_1 = threading.Thread(target=print_text(text))
...     thread_2 = threading.Thread(target=print_text(text))
...     thread_1.start()
...     thread_2.start()
...     thread_1.join()
...     thread_2.join()
... multi_threads()
... """
>>> 
>>> elapsed_time = timeit.timeit(code_to_test, number=1)
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>>> print(elapsed_time)
0.010613240000566293

Let's test the serially execution with python:

[mythcat@desk ~]$ python3 
Python 3.7.6 (default, Jan 30 2020, 09:44:41) 
[GCC 9.2.1 20190827 (Red Hat 9.2.1-1)] on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> import timeit
>>> code_to_test = """
... import threading
... 
... text = "Hello World"
... 
... def print_text(text):
...     for char in text:
...             print (char)
... # serially execution
... def serially():
...     print_text(text)
...     print_text(text)
... serially()
... """
>>> elapsed_time = timeit.timeit(code_to_test, number=1)
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>>> print(elapsed_time)
0.011771811000471644

Let's test the multiprocessing execution with python:

[mythcat@desk ~]$ python3 
Python 3.7.6 (default, Jan 30 2020, 09:44:41) 
[GCC 9.2.1 20190827 (Red Hat 9.2.1-1)] on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> import timeit
>>> code_to_test = """
... import multiprocessing
... 
... text = "Hello World"
... 
... def print_text(text):
...     for char in text:
...             print (char)
... 
... # multiprocessing execution
... def multiprocessing_test():
...      process_1 = multiprocessing.Process(target=print_text(text))
...      process_2 = multiprocessing.Process(target=print_text(text))
...      process_1.start()
...      process_2.start()
...      process_1.join()
...      process_2.join()
... multiprocessing_test()
... """
>>> elapsed_time = timeit.timeit(code_to_test, number=1)
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>>> print(elapsed_time)
0.3649730779998208

Since asyncio is a little complex, I will write about this in the next tutorial.

Python 3.7.6 : The SELinux python package.

The tutorial for today is about the SELinux python package.
The official webpage is this.
First, I update my pip tool and I used the python 3.7.6 version:
[mythcat@desk ~]$ pip install --upgrade pip --user
...
Successfully installed pip-20.0.2
Let's install the python package named selinux:
[mythcat@desk ~]$ pip3 install selinux --user
...
Requirement already satisfied: selinux in /usr/lib64/python3.7/site-packages (2.9)
Let's test it:

[mythcat@desk ~]$ python3 
Python 3.7.6 (default, Jan 30 2020, 09:44:41) 
[GCC 9.2.1 20190827 (Red Hat 9.2.1-1)] on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> import selinux 
>>> from selinux import * 
>>> print(dir(selinux))

Now, I can test my SELinux with this python package:

>>> selinux.is_selinux_enabled()
1
>>> selinux.selinux_getenforcemode()
[0, 0]
>>> mode = selinux.security_getenforce()
>>> mode
0
>>> selinux.selinux_getpolicytype()
[0, 'mls']
>>> selinux.is_selinux_enabled()
1
>>> selinux.is_selinux_mls_enabled()
1
>>> selinux.get_default_context('mythcat','user_u:object_r:user_home_t:s0')
[-1, None]
>>> context = selinux.matchpathcon(os.path.normpath(/), 0)[1]
>>> context
'system_u:object_r:root_t:s0'
>>> selinux.restorecon('/')
>>> context_t = selinux.getfilecon('/')[1].split(":")[2]
>>> context_t 
'root_t'
>>> context_t = selinux.getfilecon('/home/mythcat')[1].split(":")[2]
>>> context_t 
'user_home_dir_t'
>>> selinux.matchpathcon('/', mode)
[0, 'system_u:object_r:root_t:s0']
>>> selinux.matchpathcon('/home/mythcat', mode)
[0, 'user_u:object_r:user_home_dir_t:s0']
>>> file_context=selinux.lgetfilecon('/home/mythcat/sel_001.py')
>>> file_context
[31, 'user_u:object_r:user_home_t:s0']
>>> selinux.security_policyvers()
32

Python 3.7.5 : This python package can work with ArcGIS platform.

This python package is named like the ArcGIS platform and can be used for spatial analysis, mapping, and GIS.
The ArcGIS package uses the ArcGIS platform for organizations to create, manage, share, and analyze spatial data.
This platform has a server component, mobile and desktop applications, and developer tools.

[mythcat@desk projects]$ pip3 install arcgis --user
...

You can test it on your workstation or live using the notebooks esri.com, see the next screenshot:

Let's search all maps from world with the maps of disaster using the notebook from esri website.

from arcgis.gis import GIS
from arcgis.geocoding import geocode
from IPython.display import display
from arcgis.mapping import WebMap
from arcgis.geoprocessing import import_toolbox
gis = GIS()
map1 = gis.map()

webmap_search = gis.content.search("disaster", item_type="Web Map")
webmap_search

The result will be this list:

[<Item title:"Hurricane and Cyclone Web Map" type:Web Map owner:cfernerDRP>, 
<Item title:"Severe Weather Web Map" type:Web Map owner:cfernerDRP>, 
<Item title:"NYC OEM Hurricane Evacuation Zones" type:Web Map owner:nfurness>, 
<Item title:"County of San Diego Emergency Map (Live)" type:Web Map owner:oes_services>, 
<Item title:"Zombie US" type:Web Map owner:wjones_mt>, 
<Item title:"2009 Social Vulnerability in the United States (Mature Support)" type:Web Map owner:esri>, 
<Item title:"Waldo Canyon Fire - Consolidated Public Information" type:Web Map owner:jpfeffer>, 
<Item title:"US Wildfire Activity Web Map" type:Web Map owner:cfernerDRP>, 
<Item title:"Earthquake Web Map" type:Web Map owner:cfernerDRP>, 
<Item title:"Current Weather and Wind Station Information" type:Web Map owner:esri_livefeeds>]

Now you can show each map by usinh the index:

map_one = webmap_search[1]
map_one

Python 3.7.5 : The PyQtChart from python Qt5.

The PyQtChart is a set of Python bindings for The Qt Company’s Qt Charts library and is implemented as a single module.
Let's install this python package with the pip3 tool:

[mythcat@desk ~]$ pip3 install PyQtChart --user
...
Installing collected packages: PyQtChart
Successfully installed PyQtChart-5.14.0

Let's test with a simple example:

from PyQt5.QtWidgets import QApplication, QMainWindow
import sys
from PyQt5.QtChart import QChart, QChartView, QPieSeries, QPieSlice
from PyQt5.QtGui import QPainter, QPen
from PyQt5.QtCore import Qt

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

        self.setWindowTitle("testing Pie Chart")
        self.setGeometry(100,100, 640,480)
        self.show()
        self.create_piechart()

    def create_piechart(self):

        series = QPieSeries()
        #append all values with a sum of 360 
        series.append("size 5", 5)
        series.append("size 10", 10)
        series.append("size 30", 30)
        series.append("size 45", 45)
        series.append("size 90", 90)
        series.append("size 180", 180)

        #adding slice 
        slice = QPieSlice()
        slice = series.slices()[2]
        slice.setExploded(True)
        slice.setLabelVisible(True)
        slice.setPen(QPen(Qt.darkGreen, 1))
        slice.setBrush(Qt.green)
        slice = series.slices()[4]
        slice.setExploded(False)
        slice.setLabelVisible(True)
        slice.setPen(QPen(Qt.red, 1))
        #slice.setBrush(Qt.blue)

        #create chart 
        chart = QChart()
        #chart.legend().hide()
        chart.addSeries(series)
        chart.createDefaultAxes()
        chart.setAnimationOptions(QChart.SeriesAnimations)
        chart.setTitle("The all 360 on  chart .")

        chart.legend().setVisible(True)
        chart.legend().setAlignment(Qt.AlignBottom)

        chartview = QChartView(chart)
        chartview.setRenderHint(QPainter.Antialiasing)

        self.setCentralWidget(chartview)

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

The result can be seen in the next image:

Python 3.7.5 : The httpx python package.

Today I will present a new python packet that can help you in developing web applications.
This is the next generation HTTP client for Python and is named httpx.
This python package comes with a nice logo: a butterfly.
The official webpage can be found at this webpage.
The development team come with this intro:
HTTPX is a fully featured HTTP client for Python 3, which provides sync and async APIs, and support for both HTTP/1.1 and HTTP/2.
I install it on my Fedora 31 distro with the pip3 tool.

[mythcat@desk ~]$ pip3 install httpx --user
...
Successfully installed h11-0.9.0 h2-3.2.0 hpack-3.0.0 hstspreload-2020.2.15 httpx-0.11.1 
hyperframe-5.2.0 rfc3986-1.3.2 sniffio-1.1.0

With this python package, you can build a simple application with https API, migrate an application that uses web requests to make HTTP call.
use it as a test client for your web project, build a web spider and much more.
All HTTP methods get, post, patch, put, delete are implemented as coroutines in httpx python package and supports HTTP/2.
Let's start with few lines of source code:

[mythcat@desk ~]$ python3 
Python 3.7.6 (default, Jan 30 2020, 09:44:41) 
[GCC 9.2.1 20190827 (Red Hat 9.2.1-1)] on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> import httpx
>>> out = httpx.get('https://www.google.com')
>>> out.status_code
200
>>> out.headers['content-type']
'text/html; charset=ISO-8859-1'
>>> out.text[:76]
'<!doctype html><html itemscope="" itemtype="http://schema.org/WebPage" lang='
>>> out.http_version
'HTTP/1.1'
>>> with httpx.Client() as client:
...     out_client = client.get('https://www.google.com')
... 
>>> out_client.http_version
'HTTP/1.1'

You can use asynchronous call to bost your python application using httpx python package.
I will make a tutorial about asyncio in the future.
For example, the website named example.com can return json data and using the next source code I can get it using the asyncio package.

import httpx
import asyncio
from typing import Dict
async def get_web() -> Dict:
    resp = await httpx.get("https://example.com/")
    if resp.status_code == httpx.codes.OK:
        return resp.json()
if __name__ == '__main__':   
    comments = asyncio.run(get_web())

Use async and await when you have to execute HTTP calls with httpx because is fully Requests compatible async HTTP Client.
The HTTPX comes with a lot of features, see the official GitHub webpage.

Python 3.7.5 : Use Brython in web development to avoid javascript.

The tutorial for today is about how can avoid the javascript and use python script in webdevelopment using the Brython.
Brython's goal is to replace Javascript with Python, as the scripting language for web browsers. see the official webpage.
It is necessary to include brython.js and to run the brython() function upon page load using the onload attribute of the BODY tag.
You can use python language in the HTML file or you can write it in a separate file, and to load it using the src attribute of the script tag:

<html>
<head>
<script src="/brython.js"></script>
</head>
<body onload="brython()">
<script type="text/python" src="test.py"></script>
<input id="zone" autocomplete="off">
<button id="mybutton">click!</button>
</body>
</html>

Let's see one simple example with one edit and button bind

<html>
<head>
<script src="/brython.js"></script>
</head>
<body onload="brython()">
<script type="text/python">
from browser import document, alert
def echo(ev):
 alert(document["zone"].value)
document["mybutton"].bind("click", echo)
</script>
<input id="zone">
<button id="mybutton">click !</button>
</body>
</html>

When I click on the button, the onclick event calls and run the echo() function and gets the value of the INPUT element, through its id named zone and show the message.
Is fast and works great with javascripts libraries, see the example with THREE demo.

from browser import document, window

THREE = window.THREE

camera = THREE.PerspectiveCamera.new(75, 1, 1, 10000)
camera.position.z = 1000
scene = THREE.Scene.new()
geometry = THREE.CubeGeometry.new(200, 200, 200)
material = THREE.MeshBasicMaterial.new({"color": "#ff0000", "wireframe": True})
mesh = THREE.Mesh.new(geometry, material)
scene.add(mesh)

renderer = THREE.WebGLRenderer.new()
renderer.setSize(444, 444)

document <= renderer.domElement
renderer.render(scene, camera)

def animate(i):
    # note: three.js includes requestAnimationFrame shim
    window.requestAnimationFrame(animate)

    mesh.rotation.x += 0.01
    mesh.rotation.y += 0.02

    renderer.render(scene, camera)   

animate(0)

Python 3.7.5 : Using the hug framework - part 001.

Today I will come with another tutorial series about the hug framework.
The hug framework is on the top 3 performing web frameworks for Python and comes with the cleanest way to create HTTP REST APIs on Python 3.
The official webpage can be found hug web page with a good area for learn.
Let's install this python package.

[mythcat@desk projects]$ mkdir hug_001
[mythcat@desk projects]$ cd hug_001/
[mythcat@desk hug_001]$ pip3 install hug --user
...
Successfully installed hug-2.6.1

The Hug is a framework that allows you to expose a single code in several ways:

• a local package;
• as an A.P.I.;
• as a C.L.I.;

Let's create an app.py python script to see all of these options:

import hug

# local pkg 
@hug.local()
def get_products(product:hug.types.text):
        """Get product name"""
        return {"product":product.upper()}

# API
@hug.get()

# CLI
@hug.cli()

To test it I used this source code:

import hug

# local pkg 
@hug.get()
@hug.local()
def get_products(product:hug.types.text):
        """Get product name"""
        return {"product":product.upper()}

The rerver can be run with this command:

[mythcat@desk hug_001]$ hug -f app.py

/#######################################################################\
          `.----``..-------..``.----.
         :/:::::--:---------:--::::://.
        .+::::----##/-/oo+:-##----:::://
        `//::-------/oosoo-------::://.       ##    ##  ##    ##    #####
          .-:------./++o/o-.------::-`   ```  ##    ##  ##    ##  ##
             `----.-./+o+:..----.     `.:///. ########  ##    ## ##
   ```        `----.-::::::------  `.-:::://. ##    ##  ##    ## ##   ####
  ://::--.``` -:``...-----...` `:--::::::-.`  ##    ##  ##   ##   ##    ##
  :/:::::::::-:-     `````      .:::::-.`     ##    ##    ####     ######
   ``.--:::::::.                .:::.`
         ``..::.                .::         EMBRACE THE APIs OF THE FUTURE
             ::-                .:-
             -::`               ::-                   VERSION 2.6.1
             `::-              -::`
              -::-`           -::-
\########################################################################/

 Copyright (C) 2016 Timothy Edmund Crosley
 Under the MIT License


Serving on :8000...

OPen the browser with the http://localhost:8000/ or http://localhost:8000/products:
If you want to use the CLI then you need to make these changes in the source code:

import hug
# cli 
@hug.cli()
# local pkg 
@hug.get('/products')
@hug.local()
def get_products(product:hug.types.text):
        """Get product name"""
        return {"product":product.upper()}
# the main CLI
if __name__ == '__main__':
        get_products.interface.cli() 

I can see the help area from python:

[mythcat@desk hug_001]$ hug -f app.py -c help
app

Available Commands:

 - get_products: Get product name 

The hug help show the all arguments for use:

[mythcat@desk hug_001]$ hug --help
usage: hug [-h] [-v] [-f FILE] [-m MODULE] [-ho HOST] [-p PORT] [-n] [-ma]
           [-i INTERVAL] [-c COMMAND] [-s]

Hug API Development Server

optional arguments:
  -h, --help            show this help message and exit
  -v, --version         show program's version number and exit
  -f FILE, --file FILE  file
  -m MODULE, --module MODULE
                        module
  -ho HOST, --host HOST
                        host
  -p PORT, --port PORT  A whole number
  -n, --no_404_documentation
                        Providing any value will set this to true
  -ma, --manual_reload  Providing any value will set this to true
  -i INTERVAL, --interval INTERVAL
                        A whole number
  -c COMMAND, --command COMMAND
                        command
  -s, --silent          Providing any value will set this to true 

Python 3.7.5 : Django security issues - part 003.

Let's update this subject today with another new tutorial.
In the last tutorial about Django security I wrote about python package named django-axes.
First, let's fix an old issue about a URL pattern that matches anything and expects an integer that generates errors like:

...
  File "/home/mythcat/.local/lib/python3.7/site-packages/django/db/models/fields/__init__.py", line 1772, 
in get_prep_value
    ) from e
ValueError: Field 'id' expected a number but got 'favicon.ico'.
[22/Jan/2020 21:50:06] "GET /favicon.ico/ HTTP/1.1" 500 130547

Now, let's start my project:

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

Create a new folder named static in the test001 folder and add a icon file named favicon.ico.

(env) [mythcat@desk django]$ cd mysite/test001/
(env) [mythcat@desk test001]$ mkdir static 

In the settings.py file you need to have this source code:

STATIC_URL = '/static/'

STATICFILES_DIRS = [
    os.path.join(BASE_DIR, "static"),
]

Change in the urls.py this line of source code to fix the error:

path('<int:author_id>/',views.index_next, name = 'index_next'),

Let's run the Django project server with:

(env) [mythcat@desk django]$ cd mysite/
(env) [mythcat@desk mysite]$ python3 manage.py runserver

I login into my admin area with user catalin and password adminadmin.
If you try to login with a bad password then the account is locked by django-axes python package.
Use this command to reset all lockouts and access records.

(env) [mythcat@desk mysite]$ python3 manage.py axes_reset
No attempts found.

Into admin area you can see the AXES area with Access attempts and Access logs.
Axes listens to the following signals from django.contrib.auth.signals to log access attempts.
In this case Axes lockout responses on failed user authentication attempts from login views.
The Access logs shows access log, see examples:

Jan. 22, 2020, 8:46 p.m.	-	127.0.0.1	catalin	Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/79.0.3945.130 Safari/537.36	/admin/login/
Jan. 21, 2020, 6:42 p.m.	Jan. 22, 2020, 8:45 p.m.	127.0.0.1	catalin	Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/79.0.3945.130 Safari/537.36	/admin/login/

You can set the axes into settings.py file , see this link.