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I had great fun today expanding what was a proof-of-concept module from last year into a very usable live preview module for LGD Microsites.

Back from the fun of Akademy in Würzburg we can now get to the important task of testing Plasma 6.2 beta. It’s now in KDE neon testing edition which we build from the Git branches which will be used to make the 6.2 final release in 2.5 weeks time. Grab it now or if you don’t have a machine to install it on you can try the Docker images using the simple command `neondocker -p -e testing`.

The standard package manager for Python is pip. It allows you to install and manage packages that aren’t part of the Python standard library. If you’re looking for an introduction to pip, then you’ve come to the right place!

In this tutorial, you’ll learn how to:

• Set up pip in your working environment
• Fix common errors related to working with pip
• Install and uninstall packages with pip
• Manage projects’ dependencies using requirements files

You can do a lot with pip, but the Python community is very active and has created some neat alternatives to pip. You’ll learn about those later in this tutorial.

Get Your Cheat Sheet: Click here to download a free pip cheat sheet that summarizes the most important pip commands.

Getting Started With pip

So, what exactly does pip do? pip is a package manager for Python. That means it’s a tool that allows you to install and manage libraries and dependencies that aren’t distributed as part of the standard library. The name pip was introduced by Ian Bicking in 2008:

I’ve finished renaming pyinstall to its new name: pip. The name pip is [an] acronym and declaration: pip installs packages. (Source)

Package management is so important that Python’s installers have included pip since versions 3.4 and 2.7.9, for Python 3 and Python 2, respectively. Many Python projects use pip, which makes it an essential tool for every Pythonista.

The concept of a package manager might be familiar to you if you’re coming from another programming language. JavaScript uses npm for package management, Ruby uses gem, and the .NET platform uses NuGet. In Python, pip has become the standard package manager.

Finding pip on Your System

The Python installer gives you the option to install pip when installing Python on your system. In fact, the option to install pip with Python is checked by default, so pip should be ready for you to use after installing Python.

Note: On some Linux (Unix) systems like Ubuntu, pip comes in a separate package called python3-pip, which you need to install with sudo apt install python3-pip. It’s not installed by default with the interpreter.

You can verify that pip is available by looking for the pip3 executable on your system. Select your operating system below and use your platform-specific command accordingly:

• Windows
• Linux + macOS

Windows PowerShell PS> where pip3 Copied!

The where command on Windows will show you where you can find the executable of pip3. If Windows can’t find an executable named pip3, then you can also try looking for pip without the three (3) at the end.

Shell $ which pip3 Copied!

The which command on Linux systems and macOS shows you where the pip3 binary file is located.

On Windows and Unix systems, pip3 may be found in more than one location. This can happen when you have multiple Python versions installed. If you can’t find pip in any location on your system, then you may consider reinstalling pip.

Instead of running your system pip directly, you can also run it as a Python module. In the next section, you’ll learn how.

Running pip as a Module

When you run your system pip directly, the command itself doesn’t reveal which Python version pip belongs to. This unfortunately means that you could use pip to install a package into the site-packages of an old Python version without noticing. To prevent this from happening, you should run pip as a Python module:

Shell $ python -m pip Copied!

Notice that you use python -m to run pip. The -m switch tells Python to run a module as an executable of the python interpreter. This way, you can ensure that your system default Python version runs the pip command. If you want to learn more about this way of running pip, then you can read Brett Cannon’s insightful article about the advantages of using python -m pip.

Note: Depending on how you installed Python, your Python executable may have a different name than python. You’ll see python used in this tutorial, but you may have to adapt the commands to use something like py or python3 instead.

Sometimes you may want to be more explicit and limit packages to a specific project. In situations like this, you should run pip inside a virtual environment.

Using pip in a Python Virtual Environment Read the full article at https://realpython.com/what-is-pip/ »

[ Improve Your Python With 🐍 Python Tricks 💌 – Get a short & sweet Python Trick delivered to your inbox every couple of days. >> Click here to learn more and see examples ]

Reflections on OpenDevShop and the hidden costs of open source maintainership. Jon Pugh Mon, 09/16/2024 - 09:34

#OpenDevShop failed because it tried to solve too many problems at the same time.

This directed the energy away from designing for the future. When the future arrived, it was wholly unprepared.

I saw the potential to make #Aegir an all-in-one management console for all your web tech, so I created server management things, and local CLI things, and other silly, not so useful things.

DevShop became a huge burden. Unmaintainable. Un-upgradable. Working untold unpaid hours, self-funded travel and speaking took a major toll on my life, financially and personally.

We are happy to announce the release of Qt Tools for Android Studio 3.0. It can be downloaded from the JetBrains marketplace.  

Discover how QED42 is shaping the future of digital experiences! In this exclusive interview, Piyuesh Kumar, Director of Technology at QED42, shares insights on their journey with Drupal, their groundbreaking contributions, and the role of AI in transforming content management systems. Get a sneak peek into the upcoming advancements in Drupal and what to expect at DrupalCon Barcelona 2024. Don't miss this deep dive into QED42's vision and impact!

This tutorial guides you on how to create dynamic QR codes in Python. It involves a bit more than just generating the QR code itself. Before reading this, you must know how a QR code generator works. Steps to Create Dynamic QR Codes Dynamic QR codes require the ability to track and update the information […]

The post How to Create Dynamic QR Code in Python appeared first on TechBeamers.

Jupyter notebooks allow you to tell stories by creating and sharing data, equations, and visualizations sequentially, with a supporting narrative as you go through the notebook.

Jupyter notebooks in PyCharm Professional provide functionality above and beyond that of browser-based Jupyter notebooks, such as code completion, dynamic plots, and quick statistics, to help you explore and work with your data quickly and effectively.  

Let’s take a look at 7 ways you can use Jupyter notebooks in PyCharm to achieve your goals. They are:

• Creating or connecting to an existing notebook
• Importing your data
• Getting acquainted with your data
• Using JetBrains AI Assistant 
• Exploring your code with PyCharm
• Getting insights from your code
• Sharing your insights and charts

The Jupyter notebook that we used in this demo is available on GitHub.

1. Creating or connecting to an existing notebook

You can create and work on your Jupyter notebooks locally or connect to one remotely with PyCharm. Let’s take a look at both options so you can decide for yourself.

Creating a new Jupyter notebook

To work with a Jupyter notebook locally, you need to go to the Project tool window inside PyCharm, navigate to the location where you want to add the notebook, and invoke a new file. You can do this by using either your keyboard shortcuts ⌘N (macOS) / Alt+Ins (Windows/Linux) or by right-clicking and selecting New | Jupyter Notebook.

Give your new notebook a name, and PyCharm will open it ready for you to start work. You can also drag local Jupyter notebooks into PyCharm, and the IDE will automatically recognise them for you. 

Connecting to a remote Jupyter notebook

Alternatively, you can connect to a remote Jupyter notebook by selecting Tools | Add Jupyter Connection. You can then choose to start a local Jupyter server, connect to an existing running local Jupyter server, or connect to a Jupyter server using a URL – all of these options are supported.

Now you have your Jupyter notebook, you need some data!

2. Importing your data

Data generally comes in two formats, CSV or database. Let’s look at importing data from a CSV file first.

Importing from a CSV file

Polars and pandas are the two most commonly used libraries for importing data into Jupyter notebooks. I’ll give you code for both in this section, and you can check out the documentation for both Polars and pandas and learn how Polars is different to pandas. 

You need to ensure your CSV is somewhere in your PyCharm project, perhaps in a folder called `data`. Then, you can invoke import pandas and subsequently use it to read the code in:

import pandas as pd df = pd.read_csv("../data/airlines.csv")

In this example, airlines.csv is the file containing the data we want to manipulate. To run this and any code cell in PyCharm, use ⇧⏎ (macOS) / Shift+Enter (Windows/Linux). You can also use the green run arrows on the toolbar at the top.

If you prefer to use Polars, you can use this code:

import polars as pl df = pl.read_csv("../data/airlines.csv") Importing from a database

If your data is in a database, as is often the case for internal projects, importing it into a Jupyter notebook will require just a few more lines of code. First, you need to set up your database connection. In this example, we’re using postgreSQL. 

For pandas, you need to use this code to read the data in:

import pandas as pd engine = create_engine("postgresql://jetbrains:jetbrains@localhost/demo") df = pd.read_sql(sql=text("SELECT * FROM airlines"), con=engine.connect())

And for Polars, it’s this code:

import polars as pl engine = create_engine("postgresql://jetbrains:jetbrains@localhost/demo") connection = engine.connect() query = "SELECT * FROM airlines" df = pl.read_database(query, connection) 3. Getting acquainted with your data

Now we’ve read our data in, we can take a look at the DataFrame or `df` as we will refer to it in our code. To print out the DataFrame, you only need a single line of code, regardless of which method you used to read the data in:

df DataFrames

PyCharm displays your DataFrame as a table firstly so you can explore it. You can scroll horizontally through the DataFrame and click on any column header to order the data by that column. You can click on the Show Column Statistics icon on the right-hand side and select Compact or Detailed to get some helpful statistics on each column of data.   

Dynamic charts

You can use PyCharm to get a dynamic chart of your DataFrame by clicking on the Chart View icon on the left-hand side. We’re using pandas in this example, but Polars DataFrames also have the same option. 

Click on the Show Series Settings icon (a cog) on the right-hand side to configure your plot to meet your needs:

In this view, you can hover your mouse over your data to learn more about it and easily spot outliers:

You can do all of this with Polars, too. 

4. Using JetBrains AI Assistant

JetBrains AI Assistant has several offerings that can make you more productive when you’re working with Jupyter notebooks inside PyCharm. Let’s take a closer look at how you can use JetBrains AI Assistant to explain a DataFrame, write code, and even explain errors. 

Explaining DataFrames

If you’ve got a DataFrame but are unsure where to start, you can click the purple AI icon on the right-hand side of the DataFrame and select Explain DataFrame. JetBrains AI Assistant will use its context to give you an overview of the DataFrame:

You can use the generated explanation to aid your understanding.

Writing Code 

You can also get JetBrains AI Assistant to help you write code. Perhaps you know what kind of plot you want, but you’re not 100% sure what the code should look like. Well, now you can use JetBrains AI Assistant to help you. Let’s say you want to use ‘matplotlib’ to create a chart that finds the relationship between ‘TimeMonthName’ and ‘MinutesDelayedWeather’. By specifying the column names, we’re giving more context to the request which improves the reliability of the generated code. Try it with the following prompt:

Give me code using matplotlib to create a chart which finds the relationship between ‘TimeMonthName’ and ‘MinutesDelayedWeather’ for my dataframe df

If you like the resulting code, you can use the Insert Snippet at Caret button to insert the code and then run it:

import matplotlib.pyplot as plt # Assuming your data is in a DataFrame named 'df' # Replace 'df' with the actual name of your DataFrame if different # Plotting plt.figure(figsize=(10, 6)) plt.bar(df['TimeMonthName'], df['MinutesDelayedWeather'], color='skyblue') plt.xlabel('Month') plt.ylabel('Minutes Delayed due to Weather') plt.title('Relationship between TimeMonthName and MinutesDelayedWeather') plt.xticks(rotation=45) plt.grid(axis='y', linestyle='--', alpha=0.7) plt.tight_layout() plt.show()

If you don’t want to open the AI Assistant tool window, you can use the AI cell prompt to ask your questions. For example, we can ask the same question here and get the code we need:

Explaining errors

You can also get JetBrains AI Assistant to explain errors for you. When you get an error, click Explain with AI: 

You can use the resulting output to further your understanding of the problem and perhaps even get some code to fix it!

5. Exploring your code

PyCharm can help you get an overview of your Jupyter notebook, complete parts of your code to save your fingers, refactor it as required, debug it, and even add integrations to help you take it to the next level.

Tips for navigating and optimizing your code

Our Jupyter notebooks can grow large quite quickly, but thankfully you can use PyCharm’s Structure view to see all your notebook’s headings by clicking ⌘7 (macOS) / Alt+7 (Windows/Linux).

Code completion

Another helpful feature that you can take advantage of when using Jupyter notebooks inside PyCharm is code completion. You get both basic and type-based code completion out of the box with PyCharm, but you can also enable Full Line Code Completion in PyCharm Professional, which uses a local AI model to provide suggestions. Lastly, JetBrains AI Assistant can also help you write code and discover new libraries and frameworks. 

Refactoring

Sometimes you need to refactor your code, and in that case, you only need to know one keyboard shortcut ⌃T (macOS) / Shift+Ctrl+Alt+T (Windows/Linux) then you can choose the refactoring you want to invoke. Pick from popular options such as Rename, Change Signature, and Introduce Variable, or lesser-known options such as Extract Method, to change your code without changing the semantics: 

As your Jupyter notebook grows, it’s likely that your import statements will also grow. Sometimes you might import a package such as polars and numpy, but forget that numpy is a transitive dependency of the Polars library and as such, we don’t need to import it separately.  

To catch these cases and keep your code tidy, you can invoke Optimize Imports ⌃⌥O (macOS) / Ctrl+Alt+O (Windows/Linux) and PyCharm will remove the ones you don’t need. 

Debugging your code

You might not have used the debugger in PyCharm yet, and that’s okay. Just know that it’s there and ready to support you when you need to better understand some behavior in your Jupyter notebook. 

Place a breakpoint on the line you’re interested in by clicking in the gutter or by using ⌘F8 (macOS) / Ctrl+F8 (Windows/Linux), and then run your code with the debugger attached with the debug icon on the top toolbar:

You can also invoke PyCharm’s debugger in your Jupyter notebook with ⌥⇧⏎ (macOS) / Shift+Alt+Enter (Windows/Linux). There are some restrictions when it comes to debugging your code in a Jupyter notebook, but please try this out for yourself and share your feedback with us. 

Adding integrations into PyCharm 

IDEs wouldn’t be complete without the integrations you need. PyCharm Professional 2024.2 brings two new integrations to your workflow: DataBricks and HuggingFace.

You can enable the integrations with both Databricks and HuggingFace by going to your Settings <kbd>⌘</kbd> (macOS) / <kbd>Ctrl+Alt+S</kbd> (Windows/Linux), selecting Plugins and searching for the plugin with the corresponding name on the Marketplace tab.

6. Getting insights from your code

When analyzing your data, there’s a difference between categorical and continuous variables. Categorical data has a finite number of discrete groups or categories, whereas continuous data is one continuous measurement. Let’s look at how we can extract different insights from both the categorical and continuous variables in our airlines dataset.

Continuous variables

We can get a sense of how continuous data is distributed by looking at measures of the average value in that data and the spread of the data around the average. In normally distributed data, we can use the mean to measure the average and the standard deviation to measure the spread. However, when data is not distributed normally, we can get more accurate information using the median and the interquartile range (this is the difference between the seventy-fifth and twenty-fifth percentiles). Let’s look at one of our continuous variables to understand the difference between these measurements.

In our dataset, we have lots of continuous variables, but we’ll work with `NumDelaysLateAircraft` to see what we can learn. Let’s use the following code to get some summary statistics for just that column:

df['NumDelaysLateAircraft'].describe()

Looking at this data, we can see that there is a big difference between the `mean` of ~789 and the ‘median’ (our fiftieth percentile, indicated by “50%” in the table below) of ~618.

This indicates a skew in our variable’s distribution, so let’s use PyCharm to explore it further. Click on the Chart View icon at the top left. Once the chart has been rendered, we’ll change the series settings represented by the cog on the right-hand side of the screen. Change your x-axis to `NumDelaysLateAircraft` and your y-axis to `NumDelaysLateAircraft`. 

Now drop down the y-axis using the little arrow and select `count`. The final step is to change the chart type to Histogram using the icons in the top-right corner:

Now that we can see the skew laid out visually, we can see that most of the time, the delays are not too excessive. However, we have a number of more extreme delays – one aircraft is an outlier on the right and it was delayed by 4,509 minutes, which is just over three days!

In statistics, the mean is very sensitive to outliers because it’s a geometric average, unlike the median, which, if you ordered all observations in your variable, would sit exactly in the middle of these values. When the mean is higher than the median, it’s because you have outliers on the right-hand side of the data, the higher side, as we had here. In such cases, the median is a better indicator of the true average delay, as you can see if you look at the histogram.

Categorical variables

Let’s take a look at how we can use code to get some insights from our categorical variables. In order to get something that’s a little more interesting than just `AirportCode`, we’ll analyze how many aircraft were delayed by weather, `NumDelaysWeather`, in the different months of the year, `TimeMonthName`.

Use this code to group `NumDelaysWeather` with `TimeMonthName`:

result = df[['TimeMonthName', 'NumDelaysWeather']].groupby('TimeMonthName').sum() result

This gives us the DataFrame again in table format, but click the Chart View icon on the left-hand side of the  PyCharm UI to see what we can learn:

This is okay, but it would be helpful to have the months ordered according to the Gregorian calendar. Let’s first create a variable for the months that we expect:

month_order = [ "January", "February", "March", "April", "May", "June", "July", "August", "September", "October", "November", "December" ]

Now we can ask PyCharm to use the order that we’ve just defined in `month_order`:

# Convert the 'TimeMonthName' column to a categorical type with the specified order df["TimeMonthName"] = pd.Categorical(df["TimeMonthName"], categories=month_order, ordered=True) # Now you can group by 'TimeMonthName' and perform sum operation, specifying observed=False result = df[['TimeMonthName', 'NumDelaysWeather']].groupby('TimeMonthName', observed=False).sum() result

We then click on the Chart View icon once more, but something’s wrong!

Are we really saying that there were no flights delayed in February? That can’t be right. Let’s check our assumption with some more code:

df['TimeMonthName'].value_counts()

Aha! Now we can see that `Febuary` has been misspelt in our data set, so the correct spelling in our variable name does not match. Let’s update the spelling in our dataset with this code:

df["TimeMonthName"] = df["TimeMonthName"].replace("Febuary", "February") df['TimeMonthName'].value_counts()

Great, that looks right. Now we should be able to re-run our earlier code and get a chart view that we can interpret:

From this view, we can see that there is a higher number of delays during the months of December, January, and February, and then again in June, July, and August. However, we have not standardized this data against the total number of flights, so there may just be more flights in those months, which would cause these results along with an increased number of delays in those summer and winter months.

7. Sharing your insights and charts

When your masterpiece is complete, you’ll probably want to export data, and you can do that in various ways with Jupyter notebooks in PyCharm. 

Exporting a DataFrame

You can export a DataFrame by clicking on the down arrow on the right-hand side:

You have lots of helpful formats to choose from, including SQL, CSV, and JSON:

Exporting charts

If you prefer to export the interactive plot, you can do that too by clicking on the Export to PNG icon on the right-hand side:

Viewing your notebook as a browser

You can view your whole Jupyter notebook at any time in a browser by clicking the icon in the top-right corner of your notebook:

Finally, if you want to export your Jupyter notebook to a Python file, 2024.2 lets you do that too! Right-click on your Jupyter notebook in the Project tool window and select Convert to Python File. Follow the instructions, and you’re done!

Summary

Using Jupyter notebooks inside PyCharm Professional provides extensive functionality, enabling you to create code faster, explore data easily, and export your projects in the formats that matter to you. 

Download PyCharm Professional to try it out for yourself! Get an extended trial today and experience the difference PyCharm Professional can make in your data science endeavors.

Use the promo code “PyCharmNotebooks” at checkout to activate your free 60-day subscription to PyCharm Professional. The free subscription is available for individual users only.

Activate your 60-day trial

Smart IoT integrations with Akenza and Python 2024-09-16, by Dariusz Suchojad Overview

The Akenza IoT platform, on its own, excels in collecting and managing data from a myriad of IoT devices. However, it is integrations with other systems, such as enterprise resource planning (ERP), customer relationship management (CRM) platforms, workflow management or environmental monitoring tools that enable a complete view of the entire organizational landscape.

Complementing Akenza's capabilities, and enabling the smooth integrations, is the versatility of Python programming. Given how flexible Python is, the language is a natural choice when looking for a bridge between Akenza and the unique requirements of an organization looking to connect its intelligent infrastructure.

This article is about combining the two, Akenza and Python. At the end of it, you will have:

• A bi-directional connection to Akenza using Python and WebSockets
• A Python service subscribed to and receiving events from IoT devices through Akenza
• A Python service that will be sending data to IoT devices through Akenza

Since WebSocket connections are persistent, their usage enhances the responsiveness of IoT applications which in turn helps to exchange occurs in real-time, thus fostering a dynamic and agile integrated ecosystem.

Python and Akenza WebSocket connections

First, let's have a look at full Python code - to be discussed later.

# -*- coding: utf-8 -*- # Zato from zato.server.service import WSXAdapter # ############################################################################################### # ############################################################################################### if 0: from zato.server.generic.api.outconn.wsx.common import OnClosed, \ OnConnected, OnMessageReceived # ############################################################################################### # ############################################################################################### class DemoAkenza(WSXAdapter): # Our name name = 'demo.akenza' def on_connected(self, ctx:'OnConnected') -> 'None': self.logger.info('Akenza OnConnected -> %s', ctx) # ############################################################################################### def on_message_received(self, ctx:'OnMessageReceived') -> 'None': # Confirm what we received self.logger.info('Akenza OnMessageReceived -> %s', ctx.data) # This is an indication that we are connected .. if ctx.data['type'] == 'connected': # .. for testing purposes, use a fixed asset ID .. asset_id:'str' = 'abc123' # .. build our subscription message .. data = {'type': 'subscribe', 'subscriptions': [{'assetId': asset_id, 'topic': '*'}]} ctx.conn.send(data) else: # .. if we are here, it means that we received a message other than type "connected". self.logger.info('Akenza message (other than "connected") -> %s', ctx.data) # ############################################################################################## def on_closed(self, ctx:'OnClosed') -> 'None': self.logger.info('Akenza OnClosed -> %s', ctx) # ############################################################################################## # ##############################################################################################

Now, deploy the code to Zato and create a new outgoing WebSocket connection. Replace the API key with your own and make sure to set the data format to JSON.

Receiving messages from WebSockets

The WebSocket Python services that you author have three methods of interest, each reacting to specific events:

• on_connected - Invoked as soon as a WebSocket connection has been opened. Note that this is a low-level event and, in the case of Akenza, it does not mean yet that you are able to send or receive messages from it.

• on_message_received - The main method that you will be spending most time with. Invoked each time a remote WebSocket sends, or pushes, an event to your service. With Akenza, this method will be invoked each time Akenza has something to inform you about, e.g. that you subscribed to messages, that

• on_closed - Invoked when a WebSocket has been closed. It is no longer possible to use a WebSocket once it has been closed.

Let's focus on on_message_received, which is where the majority of action takes place. It receives a single parameter of type OnMessageReceived which describes the context of the received message. That is, it is in the "ctx" that you will both the current request as well as a handle to the WebSocket connection through which you can reply to the message.

The two important attributes of the context object are:

• ctx.data - A dictionary of data that Akenza sent to you

• ctx.conn - The underlying WebSocket connection through which the data was sent and through you can send a response

Now, the logic from lines 30-40 is clear:

• First, we check if Akenza confirmed that we are connected (type=='connected'). You need to check the type of a message each time Akenza sends something to you and react to it accordingly.

• Next, because we know that we are already connected (e.g. our API key was valid) we can subscribe to events from a given IoT asset. For testing purposes, the asset ID is given directly in the source code but, in practice, this information would be read from a configuration file or database.

• Finally, for messages of any other type we simply log their details. Naturally, a full integration would handle them per what is required in given circumstances, e.g. by transforming and pushing them to other applications or management systems.

A sample message from Akenza will look like this:

INFO - WebSocketClient - Akenza message (other than "connected") -> {'type': 'subscribed', 'replyTo': None, 'timeStamp': '2023-11-20T13:32:50.028Z', 'subscriptions': [{'assetId': 'abc123', 'topic': '*', 'tagId': None, 'valid': True}], 'message': None} How to send messages to WebSockets

An aspect not to be overlooked is communication in the other direction, that is, sending of messages to WebSockets. For instance, you may have services invoked through REST APIs, or perhaps from a scheduler, and their job will be to transform such calls into configuration commands for IoT devices.

Here is the core part of such a service, reusing the same Akenza WebSocket connection:

# -*- coding: utf-8 -*- # Zato from zato.server.service import Service # ############################################################################################## # ############################################################################################## class DemoAkenzaSend(Service): # Our name name = 'demo.akenza.send' def handle(self) -> 'None': # The connection to use conn_name = 'Akenza' # Get a connection .. with self.out.wsx[conn_name].conn.client() as client: # .. and send data through it. client.send('Hello') # ############################################################################################## # ##############################################################################################

Note that responses to the messages sent to Akenza will be received using your first service's on_message_received method - WebSockets-based messaging is inherently asynchronous and the channels are independent.

Now, we have a complete picture of real-time, IoT connectivity with Akenza and WebSockets. We are able to establish persistent, responsive connections to assets, we can subscribe to and send messages to devices, and that lets us build intelligent automation and integration architectures that make use of powerful, emerging technologies.

More resources

➤ Python API integration tutorial
➤ What is an integration platform?
➤ Python Integration platform as a Service (iPaaS)
➤ What is an Enterprise Service Bus (ESB)? What is SOA?

More blog posts➤

Hello Everyone 👋 It is that time of year again when we recognize someone from our community in memory of our friend Malcolm.

Malcolm was an early core contributor to Django and had both a huge influence and impact on Django as we know it today. Besides being knowledgeable he was also especially friendly to new users and contributors. He exemplified what it means to be an amazing Open Source contributor. We still miss him to this day.

The prize

The Django Software Foundation Prizes page summarizes it nicely:

The Malcolm Tredinnick Memorial Prize is a monetary prize, awarded annually, to the person who best exemplifies the spirit of Malcolm’s work - someone who welcomes, supports, and nurtures newcomers; freely gives feedback and assistance to others, and helps to grow the community. The hope is that the recipient of the award will use the award stipend as a contribution to travel to a community event -- a DjangoCon, a PyCon, a sprint -- and continue in Malcolm’s footsteps.

Please make your nominations using our form: 2024 Malcolm Tredinnick Memorial Prize.

We will take nominations until Monday, September 30th, 2024, Anywhere on Earth, and will announce the winner(s) soon after the next DSF Board meeting in October. If you have any questions please reach out to the DSF Board at foundation@djangoproject.com.

Submit a nomination

Review: The Wings Upon Her Back, by Samantha Mills

Publisher: Tachyon Copyright: 2024 ISBN: 1-61696-415-4 Format: Kindle Pages: 394

The Wings Upon Her Back is a political steampunk science fantasy novel. If the author's name sounds familiar, it may be because Samantha Mills's short story "Rabbit Test" won Nebula, Locus, Hugo, and Sturgeon awards. This is her first novel.

Winged Zemolai is a soldier of the mecha god and the protege of Mecha Vodaya, the Voice. She has served the city-state of Radezhda by defending it against all enemies, foreign and domestic, for twenty-six years. Despite that, it takes only a moment of errant mercy for her entire life to come crashing down. On a whim, she spares a kitchen worker who was concealing a statue of the scholar god, meaning that he was only pretending to worship the worker god like all workers should. Vodaya is unforgiving and uncompromising, as is the sleeping mecha god. Zemolai's wings are ripped from her back and crushed in the hand of the god, and she's left on the ground to die of mechalin withdrawal.

The Wings Upon Her Back is told in two alternating timelines. The main one follows Zemolai after her exile as she is rescued by a young group of revolutionaries who think she may be useful in their plans. The other thread starts with Zemolai's childhood and shows the reader how she became Winged Zemolai: her scholar family, her obsession with flying, her true devotion to the mecha god, and the critical early years when she became Vodaya's protege. Mills maintains the separate timelines through the book and wraps them up in a rather neat piece of symbolic parallelism in the epilogue.

I picked up this book on a recommendation from C.L. Clark, and yes, indeed, I can see why she liked this book. It's a story about a political awakening, in which Zemolai slowly realizes that she has been manipulated and lied to and that she may, in fact, be one of the baddies. The Wings Upon Her Back is more personal than some other books with that theme, since Zemolai was specifically (and abusively) groomed for her role by Vodaya. Much of the book is Zemolai trying to pull out the hooks that Vodaya put in her or, in the flashback timeline, the reader watching Vodaya install those hooks.

The flashback timeline is difficult reading. I don't think Mills could have left it out, but she says in the afterword that it was the hardest part of the book to write and it was also the hardest part of the book to read. It fills in some interesting bits of world-building and backstory, and Mills does a great job pacing the story revelations so that both threads contribute equally, but mostly it's a story of manipulative abuse. We know from the main storyline that Vodaya's tactics work, which gives those scenes the feel of a slow-motion train wreck. You know what's going to happen, you know it will be bad, and yet you can't look away.

It occurred to me while reading this that Emily Tesh's Some Desperate Glory told a similar type of story without the flashback structure, which eliminates the stifling feeling of inevitability. I don't think that would not have worked for this story. If you simply rearranged the chapters of The Wings Upon Her Back into a linear narrative, I would have bailed on the book. Watching Zemolai being manipulated would have been too depressing and awful for me to make it to the payoff without the forward-looking hope of the main timeline. It gave me new appreciation for the difficulty of what Tesh pulled off.

Mills uses this interwoven structure well, though. At about 90% through this book I had no idea how it could end in the space remaining, but it reaches a surprising and satisfying conclusion. Mills uses a type of ending that normally bothers me, but she does it by handling the psychological impact so well that I couldn't help but admire it. I'm avoiding specifics because I think it worked better when I wasn't expecting it, but it ties beautifully into the thematic point of the book.

I do have one structural objection, though. It's one of those problems I didn't notice while reading, but that started bothering me when I thought back through the story from a political lens. The Wings Upon Her Back is Zemolai's story, her redemption arc, and that means she drives the plot. The band of revolutionaries are great characters (particularly Galiana), but they're supporting characters. Zemolai is older, more experienced, and knows critical information they don't have, and she uses it to effectively take over. As setup for her character arc, I see why Mills did this. As political praxis, I have issues.

There is a tendency in politics to believe that political skill is portable and repurposable. Converting opposing operatives to the cause is welcomed not only because they indicate added support, but also because they can use their political skill to help you win instead. To an extent this is not wrong, and is probably the most true of combat skills (which Zemolai has in abundance). But there's an underlying assumption that politics is symmetric, and a critical reason why I hold many of the political positions that I do hold is that I don't think politics is symmetric.

If someone has been successfully stoking resentment and xenophobia in support of authoritarians, converts to an anti-authoritarian cause, and then produces propaganda stoking resentment and xenophobia against authoritarians, this is in some sense an improvement. But if one believes that resentment and xenophobia are inherently wrong, if one's politics are aimed at reducing the resentment and xenophobia in the world, then in a way this person has not truly converted. Worse, because this is an effective manipulation tactic, there is a strong tendency to put this type of political convert into a leadership position, where they will, intentionally or not, start turning the anti-authoritarian movement into a copy of the authoritarian movement they left. They haven't actually changed their politics because they haven't understood (or simply don't believe in) the fundamental asymmetry in the positions. It's the same criticism that I have of realpolitik: the ends do not justify the means because the means corrupt the ends.

Nothing that happens in this book is as egregious as my example, but the more I thought about the plot structure, the more it bothered me that Zemolai never listens to the revolutionaries she joins long enough to wrestle with why she became an agent of an authoritarian state and they didn't. They got something fundamentally right that she got wrong, and perhaps that should have been reflected in who got to make future decisions. Zemolai made very poor choices and yet continues to be the sole main character of the story, the one whose decisions and actions truly matter. Maybe being wrong about everything should be disqualifying for being the main character, at least for a while, even if you think you've understood why you were wrong.

That problem aside, I enjoyed this. Both timelines were compelling and quite difficult to put down, even when they got rather dark. I could have done with less body horror and a few fewer fight scenes, but I'm glad I read it.

Science fiction readers should be warned that the world-building, despite having an intricate and fascinating surface, is mostly vibes. I started the book wondering how people with giant metal wings on their back can literally fly, and thought the mentions of neural ports, high-tech materials, and immune-suppressing drugs might mean that we'd get some sort of explanation. We do not: heavier-than-air flight works because it looks really cool and serves some thematic purposes. There are enough hints of technology indistinguishable from magic that you could make up your own explanations if you wanted to, but that's not something this book is interested in. There's not a thing wrong with that, but don't get caught by surprise if you were in the mood for a neat scientific explanation of apparent magic.

Recommended if you like somewhat-harrowing character development with a heavy political lens and steampunk vibes, although it's not the sort of book that I'd press into the hands of everyone I know. The Wings Upon Her Back is a complete story in a single novel.

Content warning: the main character is a victim of physical and emotional abuse, so some of that is a lot. Also surgical gore, some torture, and genocide.

Rating: 7 out of 10

I recently sent a database to a client whose new Drupal website I'm building.

I'd populated it with some default users, nodes and menu links that they'd be able to review after they import the database into their hosting.

That worked well, but I've also recently been using the Default Content module which exports entities into YAML and saves them as code alongside the configuration.

Now I can install the website from scratch using the exported configuration to re-add the content types, block types, etc, and by enabling a custom module, all the default content will also be recreated.

I can tear the site down now and rebuild it as often as I like and avoid contaminating my environment with any rogue configuration or content changes.

Everything is reproducible.

I also wouldn't have needed to send the database to the client. They could have installed Drupal and followed the same steps I would do locally and got exactly the same result.

I like this approach and can see me using it more on future projects.

If your Python code isn’t fast enough, you have many options for compiled languages to write a faster extension. In this article we’ll focus on Rust, which benefits from:

• Modern tooling, including a package repository called crates.io, and built-in build tool (cargo).
• Excellent Python integration and tooling. The Rust package (they’re known as “crates”) for Python support is PyO3. For packaging you can use setuptools-rust, for integration with existing setuptools projects, or for standalone extensions you can use Maturin.
• Memory- and thread-safe, so it’s much less prone to crashes or memory corruption compared to C and C++.

In particular, we’ll:

• Implement a small algorithm in Python.
• Re-implement it as a Rust extension.
• Optimize the Rust version so it runs faster.

Read more...

Back from Akademy

Welcome to the first post in our "This Week in KDE Apps" series! You may have noticed that Nate's "This Week in KDE" blog posts no longer cover updates about KDE applications. KDE has grown significantly over the years, making it increasingly difficult for just one person to keep track of all the changes that happen each week in Plasma, and to cover the rest of KDE as well.

After discussing this at Akademy, we decided to create a parallel blog series specifically focused on KDE applications, supported by a small team of editors. This team is initially constituted by Tobias Fella, Joshua Goins and Carl Schwan.

Our goal is to cover as much as possible of what's happening in the KDE world, but we also encourage KDE app developers to collaborate with us to ensure we don't miss anything important. This collaboration will take place on Invent and on Matrix #this-week-kde-apps:kde.org.

We plan to publish a new blog post every Sunday, bringing you a summary of the previous week's developments.

This week we look at news regarding NeoChat, KDE's Matrix chat client; Itinerary, the travel assistant that lets you plan all your trips; the Gwenview image viewer; our sleek music player Elisa; KleverNotes, KDE's new note-taking application; the KStars astronomy software; and Konsole, the classic KDE terminal emulator loaded with features and utilities.

We also look at how Android support has been subtly improved, and the effort to clean up our software catalogue, retiring unmaintained programs and getting rid of cruft.

Let's get started!

NeoChat

Emojis in NeoChat are now all correctly detected by using ICU instead of a simple regex. (Claire, NeoChat 24.08.2, Link)

On mobile, NeoChat doesn't open any room by default any more, offering instead a list rooms and users. (Bart Ribbers, NeoChat 24.08.02, Link)

Filtering the list of users is back! (Tobias Fella, NeoChat 24.08.02, Link)

Itinerary

The seat information on public transport is now displayed in a more compact layout. (Carl Schwan, Itinerary 24.12.0, Link)

Gwenview

Rendering previews for RAW images is now much faster as long as KDcraw is installed and available (Fabian Vogt, Gwenview 24.12.0, Link)

Elisa

We fixed playing tracks without metadata (Pedro Nishiyama, Elisa 24.08.2, Link)

KleverNotes

The KleverNotes editor now comes with a powerful highlighter. (Louis Schul, KleverNotes 1.1.0, Link)

KStars

The scheduler will now show a small window popup graphing the altitude of the target for that night. (Hy Murveit, KStars 3.7.0, Link)

Konsole

You can set the cursor's color in Konsole using the OSC 12 escape sequence (e.g., printf '\e]12;red\a'). (Matan Ziv-Av, Konsole 24.12.0, Link)

Android Support

The status bars on Android apps now follow the colors of the Kirigami applications (Volker Krause, Craft backports, Link)

Cleaning Up

We have archived multiple old applications with no dedicated maintainers and no activity. This applies to Kuickshow, Kopete and Trojita, among others. Link

...And Everything Else

This blog only covers the tip of the iceberg! If you’re hungry for more, check out Nate's blog and KDE's Planet, where you can find more news from other KDE contributors.

Get Involved

The KDE organization has become important in the world, and your time and contributions have helped achieve that status. As we grow, it’s going to be equally important that your support become sustainable.

We need you for this to happen. You can help KDE by becoming an active community member and getting involved. Each contributor makes a huge difference in KDE; you are not a number or a cog in a machine! You don’t have to be a programmer, either. There are many things you can do: you can help hunt and confirm bugs, even maybe solve them; contribute designs for wallpapers, web pages, icons and app interfaces; translate messages and menu items into your own language; promote KDE in your local community; and a ton more things.

You can also help us by donating. Any monetary contribution, however small, will help us cover operational costs, salaries, travel expenses for contributors and in general help KDE continue bringing Free Software to the world.

A new release 0.0.3 of the RcppFastAD package by James Yang and myself is now on CRAN.

RcppFastAD wraps the FastAD header-only C++ library by James which provides a C++ implementation of both forward and reverse mode of automatic differentiation. It offers an easy-to-use header library (which we wrapped here) that is both lightweight and performant. With a little of bit of Rcpp glue, it is also easy to use from R in simple C++ applications. This release turns compilation to the C++20 standard as newer clang++ versions complained about a particular statement (it took to be C++20) when compiled under C++17. So we obliged.

The NEWS file for these two initial releases follows.

Changes in version 0.0.3 (2024-09-15)

• The package now compiles under the C++20 standard to avoid a warning under clang++-18 (Dirk addressing #9)

• Minor updates to continuous integration and badges have been made as well

Courtesy of my CRANberries, there is also a diffstat report for the most recent release. More information is available at the repository or the package page.

If you like this or other open-source work I do, you can sponsor me at GitHub.

This post by Dirk Eddelbuettel originated on his Thinking inside the box blog. Please report excessive re-aggregation in third-party for-profit settings.

This is the third article in a series of articles about the Batch API in Drupal. The Batch API is a system in Drupal that allows data to be processed in small chunks in order to prevent timeout errors or memory problems.

So far in this series we have looked at creating a batch process using a form and then creating a batch class so that batches can be run through Drush. Both of these examples used the Batch API to run a set number of items through a set number of process function callbacks. When setting up the batch run we created a list of items that we wanted to process and then split this list up into chunks, each chunk being sent to a batch process callback.

There is another way to set up the Batch API that will run the same number of operations without defining how many times we want to run them first. This is possible by using the "finished" setting in the batch context.

Let's create a batch process that we can run and control using the finished setting.

Setting Up

First we need to create a batch process that will accept the array we want to process. This is the same array as we have processed in the last two articles, but in this case we are passing the entire array to a single callback via the addOperation() method of the BatchBuilder class.

Read more

~$ mkdir moin-test ~$ cd moin-test ~/d/moin-test►python3 -m venv . 00:04 ~/d/moin-test►ls 2.119s 00:04 bin/ include/ lib/ lib64@ pyvenv.cfg ~/d/moin-test►source bin/activate.fish 00:04 ~/d/moin-test►pip install --pre moin moin-test 00:04 Collecting moin Using cached moin-2.0.0b1-py3-none-any.whl.metadata (4.7 kB) Collecting Babel>=2.10.0 (from moin) Using cached babel-2.16.0-py3-none-any.whl.metadata (1.5 kB) Collecting blinker>=1.6.2 (from moin) Using cached blinker-1.8.2-py3-none-any.whl.metadata (1.6 kB) Collecting docutils>=0.18.1 (from moin) Using cached docutils-0.21.2-py3-none-any.whl.metadata (2.8 kB) Collecting Markdown>=3.4.1 (from moin) Using cached Markdown-3.7-py3-none-any.whl.metadata (7.0 kB) Collecting mdx-wikilink-plus>=1.4.1 (from moin) Using cached mdx_wikilink_plus-1.4.1-py3-none-any.whl.metadata (6.6 kB) Collecting Flask>=3.0.0 (from moin) Using cached flask-3.0.3-py3-none-any.whl.metadata (3.2 kB) Collecting Flask-Babel>=3.0.0 (from moin) Using cached 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greenlet-3.1.0-cp312-cp312-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl (626 kB) Using cached itsdangerous-2.2.0-py3-none-any.whl (16 kB) Using cached pytz-2024.2-py2.py3-none-any.whl (508 kB) Using cached typing_extensions-4.12.2-py3-none-any.whl (37 kB) Using cached lxml-5.3.0-cp312-cp312-manylinux_2_28_x86_64.whl (4.9 MB) Using cached python_dateutil-2.9.0.post0-py2.py3-none-any.whl (229 kB) Using cached cffi-1.17.1-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (479 kB) Using cached six-1.16.0-py2.py3-none-any.whl (11 kB) Using cached pycparser-2.22-py3-none-any.whl (117 kB) Installing collected packages: XStatic-svg-edit-moin, XStatic-Pygments, XStatic-JQuery.TableSorter, XStatic-jQuery-File-Upload, XStatic-jQuery, XStatic-Font-Awesome, XStatic-CKEditor, XStatic-Bootstrap, XStatic-autosize, XStatic, whoosh, pytz, passlib, typing-extensions, six, pygments, pycparser, markupsafe, Markdown, lxml, itsdangerous, greenlet, emeraldtree, docutils, click, charset-normalizer, cachelib, blinker, Babel, Werkzeug, sqlalchemy, python-dateutil, mdx-wikilink-plus, Jinja2, flatland, cffi, Flask, feedgen, cryptography, pdfminer.six, Flask-Theme, Flask-Caching, Flask-Babel, moin Successfully installed Babel-2.16.0 Flask-3.0.3 Flask-Babel-4.0.0 Flask-Caching-2.3.0 Flask-Theme-0.3.6 Jinja2-3.1.4 Markdown-3.7 Werkzeug-3.0.4 XStatic-1.0.3 XStatic-Bootstrap-3.1.1.2 XStatic-CKEditor-3.6.4.0 XStatic-Font-Awesome-6.2.1.1 XStatic-JQuery.TableSorter-2.14.5.2 XStatic-Pygments-2.9.0.1 XStatic-autosize-1.17.2.1 XStatic-jQuery-3.5.1.1 XStatic-jQuery-File-Upload-10.31.0.1 XStatic-svg-edit-moin-2012.11.27.1 blinker-1.8.2 cachelib-0.9.0 cffi-1.17.1 charset-normalizer-3.3.2 click-8.1.7 cryptography-43.0.1 docutils-0.21.2 emeraldtree-0.11.0 feedgen-1.0.0 flatland-0.9.1 greenlet-3.1.0 itsdangerous-2.2.0 lxml-5.3.0 markupsafe-2.1.5 mdx-wikilink-plus-1.4.1 moin-2.0.0b1 passlib-1.7.4 pdfminer.six-20240706 pycparser-2.22 pygments-2.18.0 python-dateutil-2.9.0.post0 pytz-2024.2 six-1.16.0 sqlalchemy-2.0.34 typing-extensions-4.12.2 whoosh-2.7.4 ~/d/moin-test[1]►pip install setuptools moin-test 0.241s 00:06 Collecting setuptools Using cached setuptools-75.0.0-py3-none-any.whl.metadata (6.9 kB) Using cached setuptools-75.0.0-py3-none-any.whl (1.2 MB) Installing collected packages: setuptools Successfully installed setuptools-75.0.0 ~/d/moin-test►moin create-instance --full moin-test 1.457s 00:06 2024-09-16 00:06:36,812 INFO moin.cli.maint.create_instance:76 Directory /home/raj/dev/moin-test already exists, using as wikiconfig dir. 2024-09-16 00:06:36,813 INFO moin.cli.maint.create_instance:93 Instance creation finished. 2024-09-16 00:06:37,303 INFO moin.cli.maint.create_instance:107 Build Instance started. 2024-09-16 00:06:37,304 INFO moin.cli.maint.index:51 Index creation started 2024-09-16 00:06:37,308 INFO moin.cli.maint.index:55 Index creation finished 2024-09-16 00:06:37,308 INFO moin.cli.maint.modify_item:166 Load help started Item loaded: Home Item loaded: docbook Item loaded: mediawiki Item loaded: OtherTextItems/Diff Item loaded: WikiDict Item loaded: moin Item loaded: moin/subitem Item loaded: html/SubItem Item loaded: moin/HighlighterList Item loaded: MoinWikiMacros/Icons Item loaded: InclusionForMoinWikiMacros Item loaded: TemplateSample Item loaded: MoinWikiMacros Item loaded: rst/subitem Item loaded: OtherTextItems/IRC Item loaded: rst Item loaded: creole/subitem Item loaded: Home/subitem Item loaded: OtherTextItems/CSV Item loaded: images Item loaded: Sibling Item loaded: html Item loaded: markdown Item loaded: creole Item loaded: OtherTextItems Item loaded: OtherTextItems/Python Item loaded: docbook/SubItem Item loaded: OtherTextItems/PlainText Item loaded: MoinWikiMacros/MonthCalendar Item loaded: markdown/Subitem Success: help namespace help-en loaded successfully with 30 items 2024-09-16 00:06:46,258 INFO moin.cli.maint.modify_item:166 Load help started Item loaded: video.mp4 Item loaded: archive.tar.gz Item loaded: audio.mp3 Item loaded: archive.zip Item loaded: logo.png Item loaded: cat.jpg Item loaded: logo.svg Success: help namespace help-common loaded successfully with 7 items 2024-09-16 00:06:49,685 INFO moin.cli.maint.modify_item:338 Load welcome page started 2024-09-16 00:06:49,801 INFO moin.cli.maint.modify_item:347 Load welcome finished 2024-09-16 00:06:49,801 INFO moin.cli.maint.index:124 Index optimization started 2024-09-16 00:06:51,383 INFO moin.cli.maint.index:126 Index optimization finished 2024-09-16 00:06:51,383 INFO moin.cli.maint.create_instance:114 Full instance setup finished. 2024-09-16 00:06:51,383 INFO moin.cli.maint.create_instance:115 You can now use "moin run" to start the builtin server. ~/d/moin-test►ls moin-test 15.295s 00:06 bin/ intermap.txt lib64@ wiki/ wikiconfig.py include/ lib/ pyvenv.cfg wiki_local/ ~/d/moin-test►MOINCFG=wikiconfig.py moin-test 00:07 fish: Unsupported use of &apos=&apos. In fish, please use &aposset MOINCFG wikiconfig.py&apos. ~/d/moin-test[123]►set MOINCFG wikiconfig.py moin-test 00:07 ~/d/moin-test[123]►moin account-create --name test --email test@test.tld --password test123 Password not acceptable: For a password a minimum length of 8 characters is required. 2024-09-16 00:08:19,106 WARNING moin.utils.clock:53 These timers have not been stopped: total ~/d/moin-test►moin account-create --name test --email test@test.tld --password this-is-a-password 2024-09-16 00:08:43,798 INFO moin.cli.account.create:49 User c3608cafec184bd6a7a1d69d83109ad0 [&apostest&apos] test@test.tld - created. 2024-09-16 00:08:43,798 WARNING moin.utils.clock:53 These timers have not been stopped: total ~/d/moin-test►moin run --host 0.0.0.0 --port 5000 --no-debugger --no-reload * Debug mode: off 2024-09-16 00:09:26,146 INFO werkzeug:97 WARNING: This is a development server. Do not use it in a production deployment. Use a production WSGI server instead. * Running on all addresses (0.0.0.0) * Running on http://127.0.0.1:5000 * Running on http://192.168.1.2:5000 2024-09-16 00:09:26,146 INFO werkzeug:97 Press CTRL+C to quit

Kdenlive 24.08.1 is out and we urge all to upgrade. This version fixes recent playback and render regressions while fixing a wide range of bugs.

Full changelog:

• Fix reassigning timecode to project clip. Commit. Fixes bug #492697.
• Fix possible crash on undo/redo single selection move. Commit.
• Fix dragging transitions to a clip cut to create a mix. Commit.
• Fix multiple selection broken. Commit.
• Fix clip offset not appearing on selection in timeline. Commit.
• Ensure bin clips with effects disabled keep their effects disabled when added to a new sequence. Commit.
• Fix keyframe at last frame prevents resizing clip on high zoom. Commit.
• Fix effects/compositions list size. Commit. Fixes bug #492586.
• Fix compositions cannot be easily selected in timeline. Commit.
• Replace : and ? chars in guides names for rendering. Commit. See bug #492595.
• Don’t trigger timeline scroll when mouse exits timeline on a clip drag, it caused incorrect droppings and ghost clips. Commit. See bug #492720.
• Fix scolling timeline with rubberband or when dragging from file manager can move last selected clip in timeline. Commit. Fixes bug #492635.
• Fix adding marker from project notes always adds it at 00:00. Commit. Fixes bug #492697.
• Fix blurry widgets on high DPI displays. Commit.
• Fix keyframe param not correctly enabled on first keyframe click. Commit.
• Fix curveeditor crash on empty track. Commit.
• Ensure rendering with separate file for each audio track keeps the correct audio tag in the file name. Commit.
• Fix render project folder sometimes lost, add proper enums instead of unreadable ints. Commit. See bug #492476.
• Fix MLT lumas not correctly recognized by archive feature. Commit. Fixes bug #492435.
• Fix configure toolbars messing UI layout. Commit.
• Effects List: ensure deprecated category is always listed last. Commit.
• Fix tabulations in Titler (requires latest MLT git). Commit.
• Titler: ensure only plain text can be pasted, prepare support for tabulations (needs MLT patch). Commit.
• Don’t accept empty whisper device. Commit.
• Fix ffmpeg path for Whisper on Mac. Commit.
• Fix archive doesn’t save the video assets when run multiple times. Commit.
• Fix document notes timecode links may be broken after project reload. Commit. See bug #443597.
• Fix broken qml font on AppImage. Commit.
• Remove incorrect taskmanager unlock. Commit.

The post Kdenlive 24.08.1 released appeared first on Kdenlive.

I previously blogged about the difficulties in getting a good Wifi mesh network setup [1].

I bought the Kogan AX1800 Wifi6 Mesh with 3 nodes for $140, the price has now dropped to $130. It’s only Wifi 6 (not 6E which has the extra 6GHz frequency) because all the 6E ones were more expensive than I felt like paying.

I’ve got it running and it’s working really well. One of my laptops has a damaged wire connecting to it’s Wifi device which decreased the signal to a degree that I could usually only connect to wifi when in the computer room (and then walk with it to another room once connected). Now I can connect that laptop to wifi in any part of my home. I can now get decent wifi access in my car in front of my home which covers the important corner case of walking to my car and then immediately asking Google maps for directions. Previously my phone would be deciding whether to switch away from wifi due to poor signal and that would delay getting directions, now I get directions quickly on Google Maps.

I’ve done tests with the Speedtest.net Android app and now get speeds of about 52Mbit/17Mbit in all parts of my home which is limited only by the speed of my NBN connection (one of the many reasons for hating conservatives is giving us expensive slow Internet). As my main reason for buying the devices is for Internet access they have clearly met my reason for purchase and probably meet the requirements for most people as well. Getting that speed is not trivial, my neighbours have lots of Wifi APs and bandwidth is congested. My Kogan 4K Android TV now plays 4K Netflix without pausing even though it only supports 2.4GHz wifi, so having a wifi mesh node next to the TV seems to help it.

I did some tests with the Olive Tree FTP server on a Galaxy Note 9 phone running the stock Samsung Android and got over 10MByte (80Mbit) upload and 8Mbyte (64Mbit) download speeds. This might be limited by the Android app or might be limited by the older version of Android. But it still gives higher speeds than my home Internet connection and much higher speeds than I need from an Android device.

Running iperf on Linux laptops talking to a Linux workstation that’s wired to the main mesh node I get speeds of 27.5Mbit from an old laptop on 2.4GHz wifi, 398Mbit from a new Wifi5 laptop when near the main mesh node, and 91Mbit from the same laptop when at the far end of my home. So not as fast as I’d like but still acceptable speeds.

The claims about Wifi 6 vs Wifi 5 speeds are that 6 will be about 3* faster. That would be 20% faster than the Gigabit ethernet ports on the wifi nodes. So while 2.5Gbit ethernet on Wifi 6 APs would be a good feature to have it seems that it might provide a 20% benefit at some future time when I have laptops with Wifi 6. At this time all the devices with 2.5Gbit ethernet cost more than I wanted to pay so I’m happy with this. It will probably be quite a while before laptops with Wifi 6 are in the price range I feel like paying.

For Wifi 6E it seems that anything less than 2.5Gbit ethernet will be a significant bottleneck. But I expect that by the time I buy a Wifi 6E mesh they will all have 2.5Gbit ethernet as standard.

The configuration of this device was quite easy via the built in web pages, everything worked pretty much as I expected and I hardly had to look at the manual. The mesh nodes are supposed to connect to each other when you press hardware buttons but that didn’t work for me so I used the web admin page to tell them to connect which worked perfectly. The admin of this seemed to be about as good as it gets.

Conclusion

The performance of this mesh hardware is quite decent. I can’t know for sure if it’s good or bad because performance really depends on what interference there is. But using this means that for me the Internet connection is now the main bottleneck for all parts of my home and I think it’s quite likely that most people in Australia who buy it will find the same result.

So for everyone in Australia who doesn’t have fiber to their home this seems like an ideal set of mesh hardware. It’s cheap, easy to setup, has no cloud stuff to break your configuration, gives quite adequate speed, and generally just does the job.

• [1] https://etbe.coker.com.au/2024/08/24/wifi-6e-mesh/

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