News : JAX in 100 Seconds by Fireship!

JAX is a Python library similar to NumPy for scientific computing and linear algebra, but designed to run on accelerators like Cuda-based GPUs and Google's TPUs.

Blender 3D and python scripting - part 027.

Is more easy to create your player character based on the armature.

The next step is to add more animation and export to your game engine ...

Based on this issue - blender.stackexchange website, I tested to today and works very well:

Let's see the source code:

import bpy
import mathutils 
from mathutils import Vector 
from math import *

class ArmatureMenu(bpy.types.Menu):
    bl_label = "Mesh 2 Armature Menu"
    bl_idname = "OBJECT_MT_Mesh_From_Armature"

    def draw(self, context):
        layout = self.layout
        layout.operator("wm.mesh_from_armature", text="Pyramid").mesh_type = 'Pyramid' # from here
        layout.operator("wm.mesh_from_armature", text="Tapered").mesh_type = 'Tapered' # from here
        layout.operator("wm.mesh_from_armature", text="Box").mesh_type = 'Box' # from here

def CreateMesh(self, meshType):

    obj = bpy.context.active_object

    if obj == None:
        self.report({"ERROR"}, "No selection" )
    elif obj.type != 'ARMATURE':
        self.report({"ERROR"}, "Armature expected" )
    else:
        processArmature( bpy.context, obj, meshType = meshType )

#Create the base object from the armature
def meshFromArmature( arm ):
    name = arm.name + "_mesh"
    meshData = bpy.data.meshes.new( name + "Data" )
    meshObj = bpy.data.objects.new( name, meshData )
    meshObj.matrix_world = arm.matrix_world.copy()
    return meshObj

#Create the bone geometry (vertices and faces)
def boneGeometry( l1, l2, x, z, baseSize, l1Size, l2Size, base, meshType ):
    
    if meshType == 'Tapered':
        print(meshType)
        x1 = x * baseSize * l1Size 
        z1 = z * baseSize * l1Size

        x2 = x * baseSize * l2Size 
        z2 = z * baseSize * l2Size
    elif meshType == 'Box':
        print(meshType)
        lSize = (l1Size + l2Size) / 2
        x1 = x * baseSize * lSize 
        z1 = z * baseSize * lSize

        x2 = x * baseSize * lSize 
        z2 = z * baseSize * lSize

    else: # default to Pyramid
        print(meshType)
        x1 = x * baseSize * l1Size 
        z1 = z * baseSize * l1Size

        x2 = Vector( (0, 0, 0) )
        z2 = Vector( (0, 0, 0) )

    verts = [
        l1 - x1 + z1,
        l1 + x1 + z1,
        l1 - x1 - z1,
        l1 + x1 - z1,
        l2 - x2 + z2,
        l2 + x2 + z2,
        l2 - x2 - z2,
        l2 + x2 - z2
        ] 

    faces = [
        (base+3, base+1, base+0, base+2),
        (base+6, base+4, base+5, base+7),
        (base+4, base+0, base+1, base+5),
        (base+7, base+3, base+2, base+6),
        (base+5, base+1, base+3, base+7),
        (base+6, base+2, base+0, base+4)
        ]

    return verts, faces

#Process the armature, goes through its bones and creates the mesh
def processArmature(context, arm, genVertexGroups = True, meshType = 'Pyramid'):
    print("processing armature {0} {1}".format(arm.name, meshType) )

    #Creates the mesh object
    meshObj = meshFromArmature( arm )
    context.collection.objects.link( meshObj )

    verts = []
    edges = []
    faces = []
    vertexGroups = {}

    bpy.ops.object.mode_set(mode='EDIT')

    try:
        #Goes through each bone
        for editBone in [b for b in arm.data.edit_bones if b.use_deform]:
            boneName = editBone.name
            # print( boneName )
            poseBone = arm.pose.bones[boneName]

            #Gets edit bone informations
            editBoneHead = editBone.head
            editBoneTail = editBone.tail
            editBoneVector = editBoneTail - editBoneHead
            editBoneSize = editBoneVector.dot( editBoneVector )
            editBoneRoll = editBone.roll
            editBoneX = editBone.x_axis
            editBoneZ = editBone.z_axis
            editBoneHeadRadius = editBone.head_radius
            editBoneTailRadius = editBone.tail_radius

            #Creates the mesh data for the bone
            baseIndex = len(verts)
            baseSize = sqrt( editBoneSize )
            newVerts, newFaces = boneGeometry( editBoneHead, editBoneTail, editBoneX, editBoneZ, baseSize, editBoneHeadRadius, editBoneTailRadius, baseIndex, meshType )

            verts.extend( newVerts )
            faces.extend( newFaces )

            #Creates the weights for the vertex groups
            vertexGroups[boneName] = [(x, 1.0) for x in range(baseIndex, len(verts))]

        #Assigns the geometry to the mesh
        meshObj.data.from_pydata(verts, edges, faces)

    except:
        bpy.ops.object.mode_set(mode='OBJECT')
    else:
        bpy.ops.object.mode_set(mode='OBJECT')

    #Assigns the vertex groups
    if genVertexGroups:
        for name, vertexGroup in vertexGroups.items():
            groupObject = meshObj.vertex_groups.new(name=name)
            for (index, weight) in vertexGroup:
                groupObject.add([index], weight, 'REPLACE')

    #Creates the armature modifier
    modifier = meshObj.modifiers.new('ArmatureMod', 'ARMATURE')
    modifier.object = arm
    modifier.use_bone_envelopes = False
    modifier.use_vertex_groups = True

    meshObj.data.update()

    return meshObj

class MeshFromArmatureOperator(bpy.types.Operator):
    bl_idname = "wm.mesh_from_armature"
    bl_label  = "MeshFromArmatureOperator"

    mesh_type : bpy.props.StringProperty(name="mesh_type")

    def execute(self, context):
        print('The mesh type is', self.mesh_type)
        CreateMesh(self, self.mesh_type)
        return {'FINISHED'}

def register():
    bpy.utils.register_class( ArmatureMenu )
    bpy.utils.register_class( MeshFromArmatureOperator )


def unregister():
    bpy.utils.unregister_class( ArmatureMenu )
    bpy.utils.unregister_class( MeshFromArmatureOperator )


if __name__ == "__main__":
    register()

    # The menu can also be called from scripts
    bpy.ops.wm.call_menu(name='OBJECT_MT_Mesh_From_Armature')

The result of this running script using a mixamo animation is this:

Python 3.10.7 : Krita and python - part 001.

Here is a Python script for Krita that will help you read the Python functions of a document.

Open a new document, create a python script, and add the source code. Use the main menu Tools - Scripts - Ten Scripts to choose the place where the script exists and the combination of keys for execution.

This is the source code:

from PyQt5.QtWidgets import QWidget, QVBoxLayout, QTreeWidget, QTreeWidgetItem, QTextEdit
from krita import *

class LayerContentDialog(QWidget):
    def __init__(self, content):
        super().__init__()
        self.setWindowTitle("Conținutul preluat cu dir():")
        self.setGeometry(100, 100, 800, 600)

        main_layout = QVBoxLayout()

        tree_widget = QTreeWidget()
        tree_widget.setHeaderLabels(["Nume", "Tip"])
        self.add_items_recursive(tree_widget, content)
        main_layout.addWidget(tree_widget)

        close_button = QPushButton("Închide")
        close_button.clicked.connect(self.close)
        main_layout.addWidget(close_button)

        self.setLayout(main_layout)

    def add_items_recursive(self, tree_widget, items, parent_item=None):
        for item in items:
            if isinstance(item, str):
                if parent_item:
                    item_widget = QTreeWidgetItem(parent_item, [item, type(getattr(doc, item)).__name__])
                    help_text = "\n".join(dir(getattr(doc, item)))
                    item_widget.addChild(QTreeWidgetItem([help_text]))
                else:
                    item_widget = QTreeWidgetItem(tree_widget, [item, type(getattr(doc, item)).__name__])
                    help_text = "\n".join(dir(getattr(doc, item)))
                    item_widget.addChild(QTreeWidgetItem([help_text]))
            elif isinstance(item, type):
                parent = QTreeWidgetItem(tree_widget, [item.__name__, "Modul"])
                self.add_items_recursive(tree_widget, dir(item), parent)

doc = Krita.instance().activeDocument()
layerContents = dir(doc)

dialog = LayerContentDialog(layerContents)
dialog.show()

The result for this script is this:

You can use the main menu Tools - Scripts - Scripter to write, test, save, and run the source code.

News : New Django Builder online tool.

Django builder is free to use, and a personal project worked on in my spare time.

Any donations are very much appreciated.

If you want to, feel free to donate using the BitCoin address or PayPal link below.

Here is a new online tool that allows you to create projects with the Django framework and manage them more easily. It comes with different versions of Django, you can include channels and HTMX.

I don't see a command line to manage the project...

Python 3.12.1 : Using the subprocess python module - part 001.

The subprocess module allows you to spawn new processes, connect to their input/output/error pipes, and obtain their return codes. This module intends to replace several older modules and functions:

See the official webpage for Python verison 3.12.3 same like version 3.12.1 I used.

Let's see two examples with this python module:

First script will get output of net users command and will parse all into a list:

import subprocess

def find_users():
    try:
        result = subprocess.run(["net", "users"], capture_output=True, text=True)
        users = result.stdout.splitlines()      
        # define list content         
        user_list = []
        for line in users[4:-2]:  # inser only some rows 
            #print(line) #only users        
            user = line.split()
            user_list += user
        # print the right result
        print(user_list)
    except Exception as e:
        print(f"Error : {str(e)}")

# run find_users
find_users()

The result is this:

python test_001.py
['Administrator', 'catafest', 'DefaultAccount', 'Guest', 'WDAGUtilityAccount']

This source code will show data from tasklist and will print result of this ... :

import subprocess

def find_processes():
    try:
        result = subprocess.run(['tasklist', '/v'], capture_output=True, text=True)
        output_lines = result.stdout.splitlines()

        for line in output_lines[3:]:
            columns = line.split()
            #print(columns)
            #if len(columns) >= 8 and columns[9] == 'N/A':
            if len(columns) >= 8 and columns[0] == 'tasklist.exe':
                print(line)
    except Exception as e:
        print(f"A apărut o eroare la găsirea proceselor suspendate: {str(e)}")

find_processes()

The result will be like this:

python test_pid_003.py
tasklist.exe 8348 Console 1 10,304 K Unknown DESKTOP-mycomputer 0:00:00 N/A

Python 3.10.12 : Colab quantum circuits with qiskit - part 046.

I've added another introductory example to my GitHub repository with Google Colab notebooks on how to use quantum circuits with the Python package called qiskit and the IBM Quantum Platform.

I used the IBM Quantum Platform and it provides an A.P.I symbol so that it can be used with the source code.

You can find this notebook at this catafest_061.ipynb repo file.

Python 3.12.3 : python and Federated Message Bus in Fedora Linux Distro.

A few days ago I tested this functionality named Federated Message Bus of the Linux distribution called Fedora.

Federated Message Bus is a library built on ZeroMQ using the PyZMQ Python bindings. fedmsg aims to make it easy to connect services together using ZeroMQ publishers and subscribers.

You can use this python package named fedmsg to use this functionality ...

This is the source code :

import fedmsg
from fedmsg import *

import os
# Set the routing_nitpicky flag to True
os.environ['FEDMSG_ROUTING_NITPICKY'] = 'True'


config = fedmsg.config.load_config([],None)
config['mute'] = True
config['timeout'] = 0

for name, endpoint, topic, msg in fedmsg.tail_messages(**config):
    print ("name ", name)

This is the result :

[mythcat@fedora FedoraMessaging]$ python fedmsg_001.py
No routing policy defined for "org.fedoraproject.prod.copr.build.start" but routing_nitpicky is False so the message is being treated as authorized.
name  fedora-infrastructure
No routing policy defined for "org.fedoraproject.prod.copr.chroot.start" but routing_nitpicky is False so the message is being treated as authorized.
name  fedora-infrastructure
No routing policy defined for "org.fedoraproject.prod.github.check_run" but routing_nitpicky is False so the message is being treated as authorized.
name  fedora-infrastructure
No routing policy defined for "org.fedoraproject.prod.github.pull_request_review" but routing_nitpicky is False so the message is being treated as authorized.
name  fedora-infrastructure
No routing policy defined for "org.fedoraproject.prod.github.pull_request_review_comment" but routing_nitpicky is False so the message is being treated as authorized.
name  fedora-infrastructure ... 

Python 3.12.1 : aiohttp python package - part 001.

This python package named aiohttp provides asynchronous HTTP client and server functionality.

You can find more about this pytho package on the GitHub repo and the official page.

The last time I wrote about this python package was on Thursday, July 9, 2020 in this source code tutorial titled Python 3.8.3 : About aiohttp python package.

A few days ago I tested two python scripts that use this python packet.

One script makes a benchmark and the other uses cookie technology as a test.

Here is the script that makes the benchmark...

import timeit
from http import cookies

from yarl import URL

from aiohttp import CookieJar

def filter_large_cookie_jar():
    """Filter out large cookies from the cookie jar."""
    jar = CookieJar()
    c = cookies.SimpleCookie()
    domain_url = URL("http://maxagetest.com/")
    other_url = URL("http://otherurl.com/")

    for i in range(5000):
        cookie_name = f"max-age-cookie{i}"
        c[cookie_name] = "any"
        c[cookie_name]["max-age"] = 60
        c[cookie_name]["domain"] = "maxagetest.com"
    jar.update_cookies(c, domain_url)
    assert len(jar) == 5000
    assert len(jar.filter_cookies(domain_url)) == 5000
    assert len(jar.filter_cookies(other_url)) == 0

    filter_domain = timeit.timeit(lambda: jar.filter_cookies(domain_url), number=1000)
    filter_other_domain = timeit.timeit(
        lambda: jar.filter_cookies(other_url), number=1000
    )
    print(f"filter_domain: {filter_domain}")
    print(f"filter_other_domain: {filter_other_domain}")

filter_large_cookie_jar()

Here is the result obtained...

python test_bench_001.py
filter_domain: 59.85247729999901
filter_other_domain: 0.042927300000883406

Is more easier to understand code on how to use a cookie with this Python package using the httpbin website.

httpbin.org is a simple HTTP request and response service. It provides an easy way to test and inspect various aspects of HTTP communication.

Let's see the source code:

import asyncio
import aiohttp

async def main():
    urls = [
        'http://httpbin.org/cookies/set?test=ok',
    ]

    async with aiohttp.ClientSession(cookie_jar=aiohttp.CookieJar()) as s:
        for url in urls:
            async with s.get(url) as r:
                print('JSON:', await r.json())

        cookies = s.cookie_jar.filter_cookies('http://httpbin.org')
        for key, cookie in cookies.items():
            print(f'Key: "{cookie.key}", Value: "{cookie.value}"')

if __name__ == '__main__':
    loop = asyncio.new_event_loop()
    asyncio.set_event_loop(loop)
    try:
        asyncio.run(main())
    except KeyboardInterrupt:
        pass

The result of this running is this ...

python test_cookie_001.py
JSON: {'cookies': {'test': 'ok'}}
Key: "test", Value: "ok"

Python 3.12.1 : NiceGUI - UI framework over internet.

NiceGUI is an easy-to-use, Python-based UI framework, which shows up in your web browser. You can create buttons, dialogs, Markdown, 3D scenes, plots and much more.

The project can be found on this GitHub repo.

pip install nicegui
Collecting nicegui
  Downloading nicegui-1.4.21-py3-none-any.whl.metadata (9.4 kB)
...
Successfully installed aiofiles-23.2.1 aiohttp-3.9.4 aiosignal-1.3.1 bidict-0.23.1 docutils-0.19 fastapi-0.109.2 
frozenlist-1.4.1 httptools-0.6.1 ifaddr-0.2.0 markdown2-2.4.13 multidict-6.0.5 nicegui-1.4.21 orjson-3.10.0 
pscript-0.7.7 python-engineio-4.9.0 python-multipart-0.0.9 python-socketio-5.11.2 simple-websocket-1.0.0 
starlette-0.36.3 uvicorn-0.29.0 vbuild-0.8.2 watchfiles-0.21.0 websockets-12.0 yarl-1.9.4

The default demo example from the official webpage works fine.

This is the source code I used.

from nicegui import ui
from nicegui.events import ValueChangeEventArguments

def show(event: ValueChangeEventArguments):
    name = type(event.sender).__name__
    ui.notify(f'{name}: {event.value}')

ui.button('Button', on_click=lambda: ui.notify('Click'))
with ui.row():
    ui.checkbox('Checkbox', on_change=show)
    ui.switch('Switch', on_change=show)
ui.radio(['A', 'B', 'C'], value='A', on_change=show).props('inline')
with ui.row():
    ui.input('Text input', on_change=show)
    ui.select(['One', 'Two'], value='One', on_change=show)
ui.link('And many more...', '/documentation').classes('mt-8')

ui.run()

I run with the python and you can see is visible in many places:

python test_001.py
NiceGUI ready to go on http://localhost:8080, http://x.x.x.x:8080, and http://x.x.x.x:8080

The result is this:

You can find a lot of examples on the official GitHub repo.

News : SciPy 1.13.0 new release.

SciPy 1.13.0 is the culmination of 3 months of hard work. This out-of-band release aims to support NumPy 2.0.0, and is backwards compatible to NumPy 1.22.4. The version of OpenBLAS used to build the PyPI wheels has been increased to 0.3.26.dev.

This release requires Python 3.9+ and NumPy 1.22.4 or greater.

For running on PyPy, PyPy3 6.0+ is required.

This release can be found on the official GitHub repo.

python -m pip install --upgrade pip
Requirement already satisfied: pip in c:\python312\lib\site-packages (24.0)
...
python -m pip install --upgrade matplotlib
Collecting matplotlib
  Downloading matplotlib-3.8.4-cp312-cp312-win_amd64.whl.metadata (5.9 kB)
...
Successfully installed contourpy-1.2.1 cycler-0.12.1 fonttools-4.50.0 kiwisolver-1.4.5 matplotlib-3.8.4
...
python -m pip install --upgrade scipy
Collecting scipy
  Downloading scipy-1.13.0-cp312-cp312-win_amd64.whl.metadata (60 kB)
...
Successfully installed scipy-1.13.0

I tested the interpolate.Akima1DInterpolator changes with the default python script and works well:

import numpy as np
from scipy.interpolate import Akima1DInterpolator

import matplotlib.pyplot as plt
fig, ax = plt.subplots()

x = np.linspace(1, 7, 7)
y = np.array([-1, -1, -1, 0, 1, 1, 1])
xs = np.linspace(min(x), max(x), num=100)
y_akima = Akima1DInterpolator(x, y, method="akima")(xs)
y_makima = Akima1DInterpolator(x, y, method="makima")(xs)


ax.plot(x, y, "o", label="data")
ax.plot(xs, y_akima, label="akima")
ax.plot(xs, y_makima, label="makima")

ax.set_title('Fruit supply by kind and color')
ax.legend(title='Fruit color')

plt.show()

about Akima piecewise cubic Hermite interpolation.

Akima interpolator Fit piecewise cubic polynomials, given vectors x and y. The interpolation method by Akima uses a continuously differentiable sub-spline built from piecewise cubic polynomials. The resultant curve passes through the given data points and will appear smooth and natural.

The result of this source code is this:

Python 3.12.2 : Python and the Fedora Messaging Infrastructure - part 001.

I tried using the Fedora Messaging online tool with the python package of the same name on Python version 3.12.2.

You can find the documentation on the official page./div>

I created a working folder called FedoraMessaging:

[mythcat@fedora PythonProjects]$ mkdir FedoraMessaging
[mythcat@fedora PythonProjects]$ cd FedoraMessaging

You need to install the fedora-messaging and rabbitmq-server packages.

[root@fedora FedoraMessaging]# dnf5 install fedora-messaging
Updating and loading repositories:
Repositories loaded.
Package                             Arch    Version                       Repository         Size
Installing:                                                                                      
 fedora-messaging                   noarch  3.5.0-1.fc41                  rawhide        38.6 KiB
...
[root@fedora FedoraMessaging]# dnf install rabbitmq-server

At some point it will ask for a reboot.

You need to install the python package named fedora-messaging.

[root@fedora FedoraMessaging]# pip install --user fedora-messaging
Collecting fedora-messaging
...
Installing collected packages: pytz, incremental, wrapt, tomli, rpds-py, pyasn1, pika, hyperlink, constantly, attrs, 
referencing, pyasn1-modules, automat, twisted, jsonschema-specifications, service-identity, jsonschema, crochet, 
fedora-messaging
Successfully installed attrs-23.2.0 automat-22.10.0 constantly-23.10.4 crochet-2.1.1 fedora-messaging-3.5.0 
hyperlink-21.0.0 incremental-22.10.0 jsonschema-4.21.1 jsonschema-specifications-2023.12.1 pika-1.3.2 pyasn1-0.6.0 
pyasn1-modules-0.4.0 pytz-2024.1 referencing-0.34.0 rpds-py-0.18.0 service-identity-24.1.0 tomli-2.0.1 twisted-24.3.0 
wrapt-1.16.0

You need to start the broker:

[mythcat@fedora FedoraMessaging]$ sudo systemctl start rabbitmq-server

I used the source code from the documentation to test its functionality with a python script named hello_test.py.

from fedora_messaging import api, config

config.conf.setup_logging()
api.consume(lambda message: print(message))

from fedora_messaging import api, config

config.conf.setup_logging()
api.publish(api.Message(topic="hello by mythcat", body={"Hello": "world!"}))

I ran it and got this response:

[mythcat@fedora FedoraMessaging]$ python hello_test.py
[fedora_messaging.message INFO] Registering the 'base.message' key as the '<class 'fedora_messaging.message.Message'>' 
class in the Message class registry
[fedora_messaging.twisted.protocol INFO] Waiting for 0 consumer(s) to finish processing before halting
[fedora_messaging.twisted.protocol INFO] Finished canceling 0 consumers
[fedora_messaging.twisted.protocol INFO] Disconnect requested, but AMQP connection already gone

I created another python script named my_consumer.py, to check if this works:

from fedora_messaging import api, config
# Setup logging
config.conf.setup_logging()
# Define the callback function to process messages
def process_message(message):
    # Check if the message topic matches "hello by mythcat"
    if message.topic == "hello by mythcat":
        print(f"Received message: {message.body}")
    else:
        print(f"Ignoring message with topic: {message.topic}")
# Consume messages
api.consume(process_message)

I ran it and got this response:

[mythcat@fedora FedoraMessaging]$ python my_consumer.py
[fedora_messaging.twisted.protocol INFO] Successfully registered AMQP consumer Consumer(queue=amq.gen-9lKk7sGeYY5I40bdc5VrzQ,
callback=<function process_message at 0x7fdb0f5da160>)
[fedora_messaging.message INFO] Registering the 'base.message' key as the '<class 'fedora_messaging.message.Message'>'
class in the Message class registry
[fedora_messaging.twisted.consumer INFO] Consuming message from topic hello by mythcat 
(message id 800a1540-1e91-4b4a-a125-15e33eebb699)
Received message: {'Hello': 'world!'}
[fedora_messaging.twisted.consumer INFO] Successfully consumed message from topic hello by mythcat 
(message id 800a1540-1e91-4b4a-a125-15e33eebb699)

It can be seen that the answer is received and displayed correctly.

Python 3.12.1 : About multiprocessing performance.

Today I test a simple python script for Pool and ThreadPool python classes from multiprocessing python module.

The main goal was to test Python’s multiprocessing performance with my computer.

NumPy releases the GIL for many of its operations, which means you can use multiple CPU cores even with threads.

Processing large amounts of data with Pandas can be difficult, and with Polars dataframe library is a potential solution.

Sciagraph gives you both performance profiling and peak memory profiling information.

Let's teste only these class:

The multiprocessing.pool.Pool class provides a process pool in Python.

The multiprocessing.pool.ThreadPool class in Python provides a pool of reusable threads for executing spontaneous tasks.

This is the python script:

from time import time
import multiprocessing as mp
from multiprocessing.pool import ThreadPool
import numpy as np
import pickle

def main():
    arr = np.ones((1024, 1024, 1024), dtype=np.uint8)
    expected_sum = np.sum(arr)

    with ThreadPool(1) as threadpool:
        start = time()
        assert (
            threadpool.apply(np.sum, (arr,)) == expected_sum
        )
        print("Thread pool:", time() - start)

    with mp.get_context("spawn").Pool(1) as process_pool:
        start = time()
        assert (
            process_pool.apply(np.sum, (arr,))
            == expected_sum
        )
        print("Process pool:", time() - start)

if __name__ == "__main__":
    main()

This is the result:

python thread_process_pool_001.py
Thread pool: 1.6689703464508057
Process pool: 11.644825458526611

Python 2.7.12 : Django running on glitch website.

Django was design and developed by Lawrence journal world in 2003 and publicly released under BSD license in July 2005. Currently, DSF (Django Software Foundation) maintains its development and release cycle.

Django was released on 21, July 2005. Its current stable version is 2.2.5 which was released April 1, 2019.

It is maintained by the Django Software Foundation

Some time ago, the website bug allowed the use of Python language.

Today I looked at the core project in Django, fixed it and added a test user called test with the password password_test

You can test in the admin area.

I did not add new functionalities and pages because I wanted it to be a default start project.

Here's how I set permissions for this test user.