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Tryton is a FOSS software suite which is highly modular and scalable. Tryton along with its standard modules can provide a complete ERP solution or it can be used for specific functions of a business like accounting, invoicing etc.

Debian packages for Tryton are being maintained by Mathias Behrle. You can follow him on Mastodon or get his help on Tryton related projects through MBSolutions (his own consulting company).

Freexian has been sponsoring Mathias’s packaging work on Tryton for a while, so that Debian gets all the quarterly bug fix releases as well as the security release in a timely manner.

About Tryton 7.0 LTS

Lately Mathias has been busy packaging Tryton 7.0 LTS. As the “LTS” tag implies, this release is recommended for production deployments since it will be supported until November 2028. This release brings numerous bug fixes, performance improvements and various new features.

As part of this work, 41 new Tryton modules and 3 dependency packages have been added to Debian, significantly broadening the options available to Debian users and improving integration with Tryton systems.

Running different versions of Tryton on different Debian releases

To provide extended compatibility, a dedicated Tryton mirror is being managed and is available at https://debian.m9s.biz/debian/. This mirror hosts backports for all supported Tryton series, ensuring availability for a variety of Debian releases and deployment scenarios.

These initiatives highlight MBSolutions’ technical contributions to the Tryton community, made possible by Freexian’s financial backing. Together, we are advancing the Tryton ecosystem for Debian users.

We received a grant from NLnet foundation to pay for the development of libeufin for regional- and event-currencies. NGI assists these projects by paying for independent security audits. Thus, we are happy that RadicallyOpenSecurity performed an external crystal-box security audit of the libeufin component of GNU Taler. You can find the final report here. We already addressed all significant findings and compiled a response detailing the changes. We thank RadicallyOpenSecurity for their work, and NLnet and the European Commission's Horizion 2020 NGI initiative for funding this work.

Today we are releasing GCompris version 4.3.

It contains bug fixes and graphics improvements on multiple activities.

It is fully translated in the following languages:

• Arabic
• Bulgarian
• Breton
• Catalan
• Catalan (Valencian)
• Greek
• UK English
• Esperanto
• Spanish
• Basque
• French
• Galician
• Croatian
• Hungarian
• Indonesian
• Italian
• Lithuanian
• Latvian
• Malayalam
• Dutch
• Norwegian Nynorsk
• Polish
• Brazilian Portuguese
• Romanian
• Russian
• Slovenian
• Albanian
• Swedish
• Swahili
• Turkish
• Ukrainian

It is also partially translated in the following languages:

• Azerbaijani (97%)
• Belarusian (87%)
• Czech (97%)
• German (96%)
• Estonian (96%)
• Finnish (95%)
• Hebrew (96%)
• Macedonian (90%)
• Portuguese (96%)
• Slovak (84%)
• Chinese Traditional (96%)

You can find packages of this new version for GNU/Linux, Windows, Android, Raspberry Pi and macOS on the download page. Also this update will soon be available in the Android Play store, the F-Droid repository and the Windows store.

Thank you all,
Timothée & Johnny

i686 users will probably be unable to upgrade, due to a problem with the latest archlinux32-keyring 20241114-1

the solution is posted on the bug tracker https://labs.parabola.nu/issues/3679

This week I fixed lots and lots of accessibility issues.

The following contributors got their Debian Developer accounts in the last two months:

• Joachim Bauch (fancycode)
• Alexander Kjäll (capitol)
• Jan Mojžíš (janmojzis)
• Xiao Sheng Wen (atzlinux)

The following contributors were added as Debian Maintainers in the last two months:

• Alberto Bertogli
• Alexis Murzeau
• David Heilderberg
• Xiyue Deng
• Kathara Sasikumar
• Philippe Swartvagher

Congratulations!

DrupalCon Vienna 2025 has onboarded The Drop Times as its official Media Partner. Join us in Austria's capital from October 14-17, 2025, for Europe's biggest Drupal event. Stay tuned to The Drop Times for exclusive updates, insights, and all things Drupal leading up to the conference.

https://arstechnica.com/gadgets/2024/11/smart-gadgets-failure-to-commit-to-software-support-could-be-illegal-ftc-warns/

Ready to boost your workflows with pre-trained ML models? 

PyCharm‘s integration with Hugging Face is designed to bring a new level of productivity to your machine learning and development workflows. 

This integration helps you seamlessly find and use the best-fit model from the Hugging Face library, access model documentation, and manage models – all in your IDE.

The integration is available starting with PyCharm 2024.2.

Read the blog post to learn more about the integration and how to use it.

What is Hugging Face?

Hugging Face is a platform where machine learning and data science developers share pre-trained AI models. The platform provides tools to build, deploy, and train machine learning models.

Key features of the Hugging Face integration

PyCharm’s integration with Hugging Face is designed to streamline workflows for developers, enabling seamless access to pre-trained machine learning models from the IDE. This integration allows you to:

• Simplify model selection and usage.
• Import models as easily as importing any other library, allowing you to stay focused on your code. 
• Eliminate distractions caused by switching between tools and browsers. 
• Maintain control over your machine storage.

Lysandre Debut, the Chief Open-Source Officer at Hugging Face, shares:

“As a Hugging Face user, even more so than as a Hugging Face team member, the HF integration in PyCharm has been instrumental in speeding up the machine learning workflows I’ve worked on. As a result of this feature, I find myself keeping my focus in the IDE, with much less context switching during development.” Easily find the best model for your task

Looking to use a Hugging Face model but unsure which one fits your needs? Whether you’re building a text classifier, working on image recognition, or exploring other areas, PyCharm simplifies the process for you.

With the Hugging Face integration, you can quickly access a vast library of models tailored to various tasks. Right-click in the editor, select Insert HF Model, and explore models categorized by task type. To make your search even easier, you can filter models by likes, licensing, or specific tags.

For each model, PyCharm provides a detailed model card, including essential information and sample code that you can use. Found the perfect fit? Just click Use Model to insert the necessary code snippet directly into your project and start building immediately.

Seamlessly access model documentation

When working with machine learning models, quick access to documentation is handy – and PyCharm ensures you stay focused. With the Hugging Face integration, you can instantly view detailed model information without leaving your IDE or interrupting your workflow.

Simply hover over a model name in your code, and PyCharm will display its full model card, including the tasks the model is designed for, date of its last update, its origin, licensing details, and other details.

No more jumping between your browser and IDE – everything you need is right next to your code.

Manage models stored on your machine

Hugging Face models can take up significant storage space, but PyCharm makes it easy to stay in control. With this integration, you can view the models you’ve downloaded, identify those you no longer need, and declutter with ease. 

Just head to the Hugging Face tool window to see and manage your models.

PyCharm helps you to keep your system optimized while ensuring you retain access to the tools you actually need – all from the comfort of your IDE! 

Get started with PyCharm integrated with Hugging Face

PyCharm is a powerful IDE designed for machine learning, data science, and web development. It has everything you need for productive coding, including intelligent coding assistance (both local and AI-powered), smart navigation, and project-wide refactorings. 

It also provides a full suite of integrations tailored to streamline machine learning and data science workflows, including support for Jupyter Notebooks, Databricks, and popular scientific libraries like pandas, Polars, NumPy, scikit-learn, and more.

Use the PyCharm4HF code on the redeem page below to get started with PyCharm for free.

Get a 3-month PyCharm subscription for free

Need more guidance? Head to the documentation for step-by-step instructions on using Hugging Face with PyCharm.

We’d love to hear your thoughts! Share your experience with the integration in the comments below.

Unleashing Flexibility: How Headless Drupal is Shaping the Future of Content Delivery bhavinhjoshi Thu, 11/28/2024 - 10:57

Headless Drupal is changing the course for content management systems by offering a fresh approach to how digital content is delivered across platforms.

At its core, headless Drupal separates the backend (where content is stored and managed) from the frontend (where content is displayed). This decoupling allows for a greater range of flexibility and customization in how content is presented to users, leading to faster, more dynamic websites that can easily integrate with a multitude of modern tech stacks.

Why choose headless? 

The benefits are abundant. By going headless, organizations can seamlessly deploy content across diverse platforms—from mobile apps and kiosks to IoT devices. This means a blog post, product description, or interactive experience can be shared consistently, regardless of where or how it is consumed.

However, embracing headless architecture is not without its challenges. Managing separate systems can introduce complexity. Multiple moving parts can sometimes lead to increased difficulty in synchronizing the backend with various frontends. Fortunately, Drupal's API-first architecture mitigates these challenges by providing a strong foundation for developers to build upon, ensuring a cohesive integration between systems.

The real excitement comes with the capability to pair Drupal's robust backend with cutting-edge front-end technologies like React, Vue.js, or Angular. This combination results in richer, more engaging user experiences that cater to modern audiences' expectations for speed and interactivity. Developers have the freedom to innovate and tailor the user interface to meet the unique needs of their audience, unencumbered by traditional constraints.

Moreover, businesses are positioned to benefit tremendously from headless Drupal. They gain the ability to bring new features to market rapidly, allowing them to stay ahead in today’s fast-paced digital landscape. Scalability becomes less of a concern, as the architecture easily accommodates growth and a wide variety of digital touchpoints.

Headless Drupal doesn't just promise flexibility; it delivers adaptability essential for future-proofing digital strategies. In an era where customer engagement is king, and technology continues to evolve at unprecedented speeds, having a versatile and powerful content management system is indispensable.

The question isn't just whether headless Drupal is the future—it's about understanding how this evolution in content management can redefine your digital strategy to better connect with users across every platform.

Headless Drupal Drupal Planet Add new comment

In this tutorial, we will learn about Python lists (creating lists, changing list items, removing items, and other list operations) with the help of examples.

If you’ve worked with statistical data, you’ve likely encountered z-scores. A z-score measures how far a data point is from the mean, expressed in terms of standard deviations. It helps identify outliers and compare data distributions, making it a vital tool in data science.

In this guide, we’ll show you how to calculate z-scores in Python using a custom function and built-in libraries like SciPy. You’ll also learn to visualize z-scores for better insights.

1. What is a z-score?

A z-score measures how many standard deviations a data point is from the mean. The formula for calculating the z-score of a data point X is:

\[Z_{X} = \frac{X - \overline{X}}{S}\]

Where:

• \(Z_{X}\) is the z score of the point \(X\);
• \(X\) is the value for which we want to calculate the Z score;
• \(\overline{X}\) is the mean of the sample;
• \(S\) is the standard deviation of the sample.

2. Python z score using a custom function

A custom function allows you to implement the z-score formula directly. Here’s how to define and use it in Python:

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def calculate_z(X, X_mean, X_sd): return (X - X_mean) / X_sd

The function takes three arguments:

• a vector X of values for which you want to calculate the z-scores, like a pandas dataframe column, for example;
• the mean of the values in X;
• the standard deviation of the values in X.

Finally, in the return clause, we apply the z-score formula explained above.

To test our function, we will use data from Playfair (1821). He collected data regarding the price of wheat and the typical weekly wage for a “good mechanic” in England from 1565 to 1821. His objective was to show how well-off working men were in the 19th century. This dataset is available in the HistData R package and also on the webpage of Professor Vincent Arel-Bundock, a great source of datasets. It consists of 3 variables: year, price of wheat (in Shillings) and weekly wages (in Shillings).

We will be calculating the z-scores for the weekly wages. First we load the dataset directly from the website, as indicated in the code below.

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import pandas as pd data = pd.read_csv("https://vincentarelbundock.github.io/Rdatasets/csv/HistData/Wheat.csv") print(data['Wages'].mean()) print(data['Wages'].std()) data["z-score_wages"] = calculate_z(data["Wages"], data["Wages"].mean(), data["Wages"].std())

The average weekly wage during the period was 11.58 Shillings, with a standard deviation of 7.34. With this information, we can calculate the Z score for each observation in the dataset. This is done and stored in a new column called “z-score_wages”.

If you check the first row of the data frame, you will find out that in 1565 the z score was around -0.9, that is, the wages were 0.9 standard deviations below the mean of the values for the whole period.

3. Python z score using SciPy

A second option to calculate z-scores in Python is to use the zscore method of the SciPy library as shown below. Ensure you set a policy for handling missing values if your dataset is incomplete.

In the code below, we calculate the z-scores for Wheat prices. If you look at the z-score summary statistics, you will see that the price of wheat varied between -1.13 and 3.65 standard deviations away from the mean in the observed period.

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from scipy import stats data["z-score_wheat"] = stats.zscore(data["Wheat"], nan_policy="omit") data["z-score_wheat"].describe()

3. Visualising z scores

Below you can better visualize the basic idea of z scores: to measure how far away a data point is from the mean in terms of standard deviations. This visualization was created in D3, a JavaScript library for interactive data visualization. Click “See average wage” to see the averave wage for the whole period. Then check out how far from the mean each data point is and finally note that the z-score consists of this distance in terms of standard deviation.

1. See Average Wage 2. See Distance to the Mean 3. See Z-Scores Reset

4. Visualizing z scores with Matplotlib

The code below plots the wage z scores over time and shows them as the distance from the point to the mean, as demonstrated in the D3 visualization above. Please consult the lesson ‘Storytelling with Matplotlib - Visualizing historical data’ to learn more about Matplotlib visualizations.

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# Calculate mean wage mean_wage = data["z-score_wages"].mean() # Create the plot fig, ax = plt.subplots(figsize=(10, 6)) # Scatter plot of wages over years ax.plot(data["Year"], data["z-score_wages"], 'o', color='#FF6885', label="Wage Z-scores", markeredgewidth=0.5) # Add a horizontal line for the mean wage ax.axhline(y=mean_wage, color='gray', linestyle='dashed', label=f"Mean Z-score = {mean_wage:.2f}") # Add gray lines connecting points to the mean for year, wage in zip(data["Year"], data["z-score_wages"]): ax.plot([year, year], [mean_wage, wage], color='gray', linestyle='dotted', linewidth=1) # Customize the plot ax.set_xlabel("Year") ax.set_ylabel("Z-scores") ax.set_title("Z-scores Over Time") ax.legend() # Show the plot plt.show()

Have questions or insights? Leave a comment below, and I’ll be happy to help.

Happy coding!

Conclusions

• A z score is a measure of how many standard deviations a data point is away from the mean. It can be easily calculated in Python;
• You can visualize z-scores using traditional python libraries like Matplotlib or Seaborn.

The end of Drupal 7 support is approaching (January 5, 2025), which means this system’s users need to migrate to a newer version. This article explains how to move smoothly from Drupal 7 to Drupal 11, the key steps, and what to pay attention to. I encourage you to read the article or watch the video in the “Nowoczesny Drupal” series.

Is your website the driving engine of your higher ed institution and a powerful catalyst for its goals? Is it effectively attracting prospective students, inspiring alumni, and building a vibrant community? There are always new strategies to boost your website's impact and ensure the answer is a resounding "yes"! As a team that specializes in higher education website design & development, we are passionate about sharing useful tips that can help those in this sector.

Craft Your Perfect Proposal: MidCamp 2025 Session Proposal Workshop

🚀 Ready to take your session ideas to the next level? Whether you're a seasoned speaker or a first-time presenter, the MidCamp 2025 Session Proposal Workshop is here to help you craft standout submissions.

📅 Date: December 12, 2024
🕒 Time: 3:00 PM - 4:00 PM CST
🌐 Location: Virtual via MidCamp Slack (#speakers channel)

Facilitated by Aaron Feledy

This workshop will be led by Aaron Feledy, a seasoned Drupal contributor and experienced speaker. Aaron brings years of expertise in proposal crafting and conference speaking, offering practical advice to help you refine and elevate your session submissions.

Why Attend?

Submitting a session proposal can be daunting—but it doesn't have to be! This workshop is designed to guide you through the process, from brainstorming topics to refining your submission. Our expert facilitators will share insider tips on what makes a proposal stand out to reviewers and resonate with attendees.

What You’ll Learn:

• How to choose and frame a compelling topic
• Crafting clear, concise, and engaging abstracts
• Tips for tailoring your proposal to different audiences
• Insight into the MidCamp review process

Session Submissions Now Open

Ready to submit? Session submissions for MidCamp 2025 are now open! Visit the MidCamp 2025 session submission page for guidelines and start your journey to the stage.

How to Join:

Simply join the MidCamp Slack and head over to the #speakers channel on December 12th at 3:00 PM CST. No registration required—just jump in and start collaborating!

Learn how to upgrade Drupal with Composer, ensuring seamless updates, compatibility, and enhanced security.

An Austrian petitioner succeeded in realizing what the US government failed to see: that free software is vital for the infrastructure of a free society.

An Austrian petitioner succeeded in realizing what the US government failed to see: that free software is vital for the infrastructure of a free society.

Creating software is an achievement worth celebrating. But software is never static. Bugs need to be fixed, features need to be added, and security demands regular updates. In today’s landscape, with agile methodologies dominating, robust DevOps systems are crucial for managing an evolving codebase. That’s where GitHub Actions shine, empowering Python developers to automate workflows and ensure their projects adapt seamlessly to change.

GitHub Actions for Python empowers developers to automate workflows efficiently. This enables teams to maintain software quality while adapting to constant change.

Continuous Integration and Continuous Deployment (CI/CD) systems help produce well-tested, high-quality software and streamline deployment. GitHub Actions makes CI/CD accessible to all, allowing automation and customization of workflows directly in your repository. This free service enables developers to execute their software development processes efficiently, improving productivity and code reliability.

In this tutorial, you’ll learn how to:

• Use GitHub Actions and workflows
• Automate linting, testing, and deployment of a Python project
• Secure credentials used for automation
• Automate security and dependency updates

This tutorial will use an existing codebase, Real Python Reader, as a starting point for which you’ll create a CI/CD pipeline. You can fork the Real Python Reader code on GitHub to follow along. Be sure to deselect the Copy the master branch only option when forking. Alternatively, if you prefer, you can build your own Real Python Reader using a previous tutorial.

In order to get the most out of this tutorial, you should be comfortable with pip, building Python packages, Git, and have some familiarity with YAML syntax.

Before you dig into GitHub Actions, it may be helpful to take a step back and learn about the benefits of CI/CD. This will help you understand the kinds of problems that GitHub Actions can solve.

Get Your Code: Click here to download the free sample code you’ll use to learn about CI/CD for Python With GitHub Actions.

Take the Quiz: Test your knowledge with our interactive “GitHub Actions for Python” quiz. You’ll receive a score upon completion to help you track your learning progress:

Interactive Quiz

GitHub Actions for Python

In this quiz, you'll test your understanding of GitHub Actions for Python. By working through this quiz, you'll revisit how to use GitHub Actions and workflows to automate linting, testing, and deployment of a Python project.

Unlocking the Benefits of CI/CD

Continuous Integration (CI) and Continuous Deployment (CD), commonly known as CI/CD, are essential practices in modern software development. These practices automate the integration of code changes, the execution of tests, and the deployment of applications. This helps teams and open-source contributors to deliver code changes more frequently in a reliable and structured way.

Moreover, when publishing open-source Python packages, CI/CD will ensure that all pull requests (PRs) and contributions to your package will meet the needs of the project while standardizing the code quality.

Note: To learn more about what a pull request is and how to create one, you can read GitHub’s official documentation.

More frequent deployments with smaller code changes reduce the risk of unintended breaking changes that can occur with larger, more complex releases. For example, even though developers can format all code using the same linting tools and rules, policy can automatically block PRs from being merged if the code’s tests don’t pass.

In the next section, you’ll learn how GitHub Workflows can help you implement CI/CD on a repository hosted on GitHub.

Exploring GitHub Workflows

GitHub Workflows are a powerful feature of GitHub Actions. They allow you to define custom automation workflows for your repositories. Whether you want to build, test, or deploy your code, GitHub Workflows provide a flexible and customizable solution that any project on GitHub can use for free, whether the repository is public or private.

Even though there are many CI/CD providers, GitHub Actions has become the default among open-source projects on GitHub because of its expansive ecosystem, flexibility, and low or no cost.

Anatomy of a Workflow File

Workflow files are declaratively written YAML files with a predefined structure that must be adhered to for a workflow to run successfully. Your YAML workflow files are stored and defined in a .github/workflows/ folder in your project’s root directory.

Your workflow folder can have multiple workflow files, each of which will perform a certain task. You can name these workflow files anything you’d like. However, for the sake of simplicity and readability, it’s common practice to name them after the tasks they achieve, such as test.yml.

Each file has a few elements that are required, but many, many more that are optional. The GitHub Actions documentation is thorough and well-written, so be sure to check it out after you’ve finished reading this tutorial.

There are three main parts that make up the bulk of a workflow file: triggers, jobs, and steps. You’ll cover these in the next sections.

Workflow Triggers Read the full article at https://realpython.com/github-actions-python/ »

[ 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 ]

Developed by Anand Toshniwal, a Python script now automates the creation of .component.yml files for Single Directory Components in Drupal. Simplifying workflows and improving accuracy, this tool supports projects like the Starshot Demo Design System, enhancing efficiency for developers.