Feeds

As a member of the leadership team for Drupal CMS, the new product that makes Drupal more accessible to marketers and content teams, I’ve spent the last three months engaging with various teams about their CMS decisions. While I take notes on marketing tools, ease of use, benefits of open source, DXP capabilities, and SaaS options, the core of these conversations often revolves around people and culture.

Who Decides on a CMS?

The decision-maker for a CMS can vary: sometimes it’s a marketer or an IT professional, other times it’s the “head of digital,” or even an agency hired to handle the organization’s digital needs. Although many focus on features, decisions often hinge on feelings, prior experiences, and familiarity. Ultimately, the decisions reflect the experiences of those in the room.
This isn’t to say that the right technical fit isn’t important; rather, it often takes a backseat to personal experiences. it’s crucial to communicate why Drupal aligns with an organization’s digital strategy based on its goals.

Three Types of Websites: Why Drupal is a Great Fit

Let’s categorize websites into three types and discuss why Drupal suits each.

1. Cornerstone of Your Digital Strategy

Every organization needs a digital front door. For established brands, this digital presence serves as the foundation of their online strategy. A known brand must maintain consistent online expression, while an unknown brand needs to tell its story effectively, helping users recognize its voice and identity.
Users want to quickly understand if they’re in the right place and how to connect. They expect seamless integration with third-party tools and easy access to internal data.

Why Choose Drupal?

Drupal excels here because it goes beyond basic content management, offering flexibility for both internal and external users. It supports:

• Integration with third-party tools and data management.
• Enterprise-grade workflows and content management.
• Custom features and transactions.
• Tailored information architecture.
• A blend of structured content and marketing pages.

 Planned Parenthood Direct's mobile-first website serves as a cornerstone of its digital strategy, focusing on creating a distinct sub-brand that resonates with younger audiences while maintaining alignment with the larger Planned Parenthood identity. The site leverages bold colours, playful design elements, and clear messaging to connect with users, driving them to download the app and access reproductive healthcare services.
2. CMS Platform

Many organizations manage a complex ecosystem of websites, often hindered by internal politics and multiple CMSs that lead to inconsistent branding.
A successful CMS platform balances flexibility with guidelines, making site creation easy while adhering to the organization’s branding and content strategy. It often requires standardization of third-party tools.

Why Choose Drupal?

Drupal’s modularity simplifies standardization across websites. It supports:

• Configuration management to allow control over customization
• Flexibility that enables governance at both the platform and individual site levels
• As a widely adopted solution in enterprises, it benefits from optimized hosting tools designed for multi-site management (e.g., Pantheon Custom Upstreams, Acquia Site Factory).

 Evolving Web partnered with the Bibliothèque et Archives nationales du Québec (BAnQ) to redesign its website, building a platform where staff can curate content and users can search across multiple data sources.
3. Marketing Microsite Designed to Scale

Not every organization is large; some startups aim to create single-purpose websites quickly. These organizations need to build fast without sacrificing security or accessibility. Often, marketers seek easy drag-and-drop tools for rapid site creation.

While Drupal has traditionally been overlooked for quick projects, Drupal CMS provides a solution that fosters familiarity among a broader audience, because it lowers the barrier to entry and speeds up the timeline to launch a website. When marketers can create a website quickly, it enhances creativity and ownership, and frees up more time to focus on content and marketing strategy. Drupal CMS will be especially important for making the case for using Drupal for these types of projects.

Why Choose Drupal?

Drupal allows for the rapid launch of marketing sites, which can later scale into a digital cornerstone for an established brand. In particular, Drupal CMS will support:

• Built-in AI tools for site building that free up time to focus on content strategy and handling the influx of feature requests and content decisions that they are often faced with
• Allowing small sites to leverage the same modules and recipes available to larger sites
• No limitations to scaling up a small website to accommodate more content, authors, or functionality

Conclusion: If Drupal Wins, We All Win

Increased usage of Drupal leads to a better experience for everyone involved—developers, site builders, marketers, and content teams. As an open-source platform, Drupal's growth benefits the broader community, including government and non-profit organizations. Improving Drupal enhances a public good rather than enriching proprietary solutions. 

If you're looking to talk more about Drupal and Drupal CMS don’t hesitate to get in touch. 

If Drupal wins, we all win.

+ more awesome articles by Evolving Web

In Python, a closure is typically a function defined inside another function. This inner function grabs the objects defined in its enclosing scope and associates them with the inner function object itself. The resulting combination is called a closure.

Closures are a common feature in functional programming languages. In Python, closures can be pretty useful because they allow you to create function-based decorators, which are powerful tools.

In this tutorial, you’ll:

• Learn what closures are and how they work in Python
• Get to know common use cases of closures
• Explore alternatives to closures

To get the most out of this tutorial, you should be familiar with several Python topics, including functions, inner functions, decorators, classes, and callable instances.

Get Your Code: Click here to download the free sample code that shows you how to use closures in Python.

Take the Quiz: Test your knowledge with our interactive “Python Closures: Common Use Cases and Examples” quiz. You’ll receive a score upon completion to help you track your learning progress:

Interactive Quiz

Python Closures: Common Use Cases and Examples

In this quiz, you'll test your understanding of Python closures. Closures are a common feature in functional programming languages and are particularly popular in Python because they allow you to create function-based decorators.

Getting to Know Closures in Python

A closure is a function that retains access to its lexical scope, even when the function is executed outside that scope. When the enclosing function returns the inner function, then you get a function object with an extended scope.

In other words, closures are functions that capture the objects defined in their enclosing scope, allowing you to use them in their body. This feature allows you to use closures when you need to retain state information between consecutive calls.

Closures are common in programming languages that are focused on functional programming, and Python supports closures as part of its wide variety of features.

In Python, a closure is a function that you define in and return from another function. This inner function can retain the objects defined in the non-local scope right before the inner function’s definition.

To better understand closures in Python, you’ll first look at inner functions because closures are also inner functions.

Inner Functions

In Python, an inner function is a function that you define inside another function. This type of function can access and update names in their enclosing function, which is the non-local scope.

Here’s a quick example:

Python >>> def outer_func(): ... name = "Pythonista" ... def inner_func(): ... print(f"Hello, {name}!") ... inner_func() ... >>> outer_func() Hello, Pythonista! >>> greeter = outer_func() >>> print(greeter) None Copied!

In this example, you define outer_func() at the module level or global scope. Inside this function, you define the name local variable. Then, you define another function called inner_func(). Because this second function lives in the body of outer_func(), it’s an inner or nested function. Finally, you call the inner function, which uses the name variable defined in the enclosing function.

When you call outer_func(), inner_func() interpolates name into the greeting string and prints the result to your screen.

Note: To learn more about inner functions, check out the Python Inner Functions: What Are They Good For? tutorial.

In the above example, you defined an inner function that can use the names in the enclosing scope. However, when you call the outer function, you don’t get a reference to the inner function. The inner function and the local names won’t be available outside the outer function.

In the following section, you’ll learn how to turn an inner function into a closure, which makes the inner function and the retained variables available to you.

Function Closures

All closures are inner functions, but not all inner functions are closures. To turn an inner function into a closure, you must return the inner function object from the outer function. This may sound like a tongue twister, but here’s how you can make outer_func() return a closure object:

Python >>> def outer_func(): ... name = "Pythonista" ... def inner_func(): ... print(f"Hello, {name}!") ... return inner_func ... >>> outer_func() <function outer_func.<locals>.inner_func at 0x1066d16c0> >>> greeter = outer_func() >>> greeter() Hello, Pythonista! Copied! Read the full article at https://realpython.com/python-closure/ »

[ 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 ]

Zizmor is a new tool to check your GitHub action workflows for security concerns. I found it really helpful to lock down actions.

Action workflows can be esoteric, and continuous integration is not everyone’s top concern, so it’s easy for them to have subtle flaws. A tool like zizmor is great for drawing attention to them.

When I ran it, I had a few issues to fix:

• Some data available to actions is manipulable by unknown people, so you have to avoid interpolating it directly into shell commands. For example, you might want to add the branch name to the action summary: - name: "Summarize"
    run: |
      echo "### From branch ${{ github.ref }}" >> $GITHUB_STEP_SUMMARY
  But github.ref is a branch name chosen by the author of the pull request. It could have a shell injection which could let an attacker exfiltrate secrets. Instead, put the value into an environment variable, then use it to interpolate: - name: "Summarize"
    env:
      REF: ${{ github.ref }}
    run: |
      echo "### From branch ${REF}" >> $GITHUB_STEP_SUMMARY
  
• The actions/checkout step should avoid persisting credentials: - name: "Check out the repo"
    uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2
    with:
      persist-credentials: false
  
• In steps where I was pushing to GitHub, this meant I needed to explicitly set a remote URL with credentials: - name: "Push digests to pages"
    env:
      GITHUB_TOKEN: ${{ secrets.token }}
    run: |
      git config user.name nedbat
      git config user.email ned@nedbatchelder.com
      git remote set-url origin https://x-access-token:${GITHUB_TOKEN}@github.com/${GITHUB_REPOSITORY}.git
  

There were some other things that were easy to fix, and of course, you might have other issues. One improvement to zizmor: it could link to explanations of how to fix the problems it finds, but it wasn’t hard to find resources, like GitHub’s Security hardening for GitHub Actions.

William Woodruff is zizmor’s author. He was incredibly responsive when I had problems or questions about using zizmor. If you hit a snag, write an issue. It will be a good experience.

If you are like me, you have repos lying around that you don’t think about much. These are a special concern, because their actions could be years old, and not well maintained. These dusty corners could be a good vector for an attack. So I wanted to check all of my repos.

With Claude’s help I wrote a shell script to find all git repos I own and run zizmor on them. It checks the owner of the repo because my drive is littered with git repos I have no control over:

#!/bin/bash
# zizmor-repos.sh

echo "Looking for workflows in repos owned by: $*"

# Find all git repositories in current directory and subdirectories
find . \
    -type d \( \
        -name "Library" \
        -o -name "node_modules" \
        -o -name "venv" \
        -o -name ".venv" \
        -o -name "__pycache__" \
    \) -prune \
    -o -type d -name ".git" -print 2>/dev/null \
| while read gitdir; do
    # Get the repository directory (parent of .git)
    repo_dir="$(dirname "$gitdir")"

    # Check if .github/workflows exists
    if [ -d "${repo_dir}/.github/workflows" ]; then
        # Get the GitHub remote URL
        remote_url=$(git -C "$repo_dir" remote get-url origin)

        # Check if it's our repository
        # Handle both HTTPS and SSH URL formats
        for owner in $*; do
            if echo "$remote_url" | grep -q "github.com[/:]$owner/"; then
                echo ""
                echo "Found workflows in $owner repository: $repo_dir"
                ~/.cargo/bin/zizmor $repo_dir/.github/workflows
            fi
        done
    fi
done

After fixing issues, it’s very satisfying to see:

% zizmor-repos.sh nedbat BostonPython
Looking for workflows in repos owned by: nedbat BostonPython

Found workflows in nedbat repository: ./web/stellated
🌈 completed ping-nedbat.yml
No findings to report. Good job!

Found workflows in nedbat repository: ./web/nedbat_nedbat
🌈 completed build.yml
No findings to report. Good job!

Found workflows in nedbat repository: ./scriv
🌈 completed tests.yml
No findings to report. Good job!

Found workflows in nedbat repository: ./lab/gh-action-tests
🌈 completed matrix-play.yml
No findings to report. Good job!

Found workflows in nedbat repository: ./aptus/trunk
🌈 completed kit.yml
No findings to report. Good job!

Found workflows in nedbat repository: ./cog
🌈 completed ci.yml
No findings to report. Good job!

Found workflows in nedbat repository: ./dinghy/nedbat
🌈 completed test.yml
🌈 completed daily-digest.yml
🌈 completed docs.yml
No findings to report. Good job!

Found workflows in nedbat repository: ./dinghy/sample
🌈 completed daily-digest.yml
No findings to report. Good job!

Found workflows in nedbat repository: ./coverage/badge-samples
🌈 completed samples.yml
No findings to report. Good job!

Found workflows in nedbat repository: ./coverage/django_coverage_plugin
🌈 completed tests.yml
No findings to report. Good job!

Found workflows in nedbat repository: ./coverage/trunk
🌈 completed dependency-review.yml
🌈 completed publish.yml
🌈 completed codeql-analysis.yml
🌈 completed quality.yml
🌈 completed kit.yml
🌈 completed python-nightly.yml
🌈 completed coverage.yml
🌈 completed testsuite.yml
No findings to report. Good job!

Found workflows in BostonPython repository: ./bospy/about
🌈 completed past-events.yml
No findings to report. Good job!

Nice.

Dacid Brin wrote an interesting article about AI ecosystems and how humans might work with machines on creative projects [1]. Also he’s right about “influencers” being like funghi.

Cory Doctorow wrote an interesting post about DRM, coalitions, and cheating [2]. It seems that people like me who want “trusted computing” to secure their own computers don’t fit well in any of the coalitions.

The CHERI capability system for using extra hardware to validate jump addresses is an interesting advance in computer science [3]. The lecture is froim the seL4 Summit, this sort of advance in security goes well with a formally proven microkernel. I hope that this becomes a checkbox when ordering a custom RISC-V design.

Bunnie wrote an insightful blog post about how the Mossad might have gone about implementing the exploding pager attack [4]. I guess we will see a lot more of this in future, it seems easy to do.

Interesting blog post about Control Flow Integrity in the V8 engine of Chrome [5].

Interesting blog post about the new mseal() syscall which can be used by CFI among other things [6].

This is the Linux kernel documentation about the Control-flow Enforcement Technology (CET) Shadow Stack [7]. Unfortunately not enabled in Debian/Unstable yet.

ARM added support for Branch Target Identification in version 8.5 of the architecture [8].

The CEO of Automatic has taken his dispute with WPEngine to an epic level, this video catalogues it, I wonder what is wrong with him [9].

NuShell is an interesting development in shell technology which runs on Linux and Windows [10].

Interesting article about making a computer game without coding using ML [11]. I doubt that it would be a good game, but maybe educational for kids.

Krebs has an insightful article about location tracking by phones which is surprisingly accurate [12]. He has provided information on how to opt out of some of it on Android, but we need legislative action!

Interesting YouTube video about how to make a 20kW microwave oven and what it can do [13]. Don’t do this at home, or anywhere else!

The Void editor is an interesting project, a fork of VSCode that supports DIRECT connections to LLM systems where you don’t have their server acting as a middle-man and potentially snooping [14].

• [1] https://tinyurl.com/26geg4az
• [2] https://tinyurl.com/29n4hyvv
• [3] https://www.youtube.com/watch?v=VcufX8hZ5-o
• [4] https://tinyurl.com/2beb7ala
• [5] https://v8.dev/blog/control-flow-integrity
• [6] https://tinyurl.com/26p8hlcr
• [7] https://docs.kernel.org/next/x86/shstk.html
• [8] https://tinyurl.com/26ffcrho
• [9] https://www.youtube.com/watch?v=v-imW24OXLY
• [10] https://www.youtube.com/watch?v=zoX_S6d-XU4
• [11] https://tinyurl.com/ysqe79q7
• [12] https://tinyurl.com/258r74cl
• [13] https://www.youtube.com/watch?v=mg79n_ndR68
• [14] https://tinyurl.com/2xtu7kvv

Related posts:

1. Links August 2024 Bruce Schneier and Kim Córdova wrote an insightful article about...
2. Links September 2024 CNA Insider has an insightful documentary series about Chinese illegal...
3. Links June 2024 Modos Labs have released the design of an e-ink display...

Takeaway: October is Cybersecurity Awareness Month. For 21 years, the U.S. government has set aside this month to help organizations strengthen their online security and protect sensitive information. The 2024 theme is "Secure Our World." This reminds us that protecting sensitive information is a shared responsibility across all departments and roles.

Another pure maintenance release 0.2.7 of the gcbd package is now on CRAN. The gcbd proposes a benchmarking framework for LAPACK and BLAS operations (as the library can exchanged in a plug-and-play sense on suitable OSs) and records result in local database. Its original motivation was to also compare to GPU-based operations. However, as it is both challenging to keep CUDA working packages on CRAN providing the basic functionality appear to come and go so testing the GPU feature can be challenging. The main point of gcbd is now to actually demonstrate that ‘yes indeed’ we can just swap BLAS/LAPACK libraries without any change to R, or R packages. The ‘configure / rebuild R for xyz’ often seen with ‘xyz’ being Goto or MKL is simply plain wrong: you really can just swap them (on proper operating systems, and R configs – see the package vignette for more). But nomatter how often we aim to correct this record, it invariably raises its head another time.

This release accommodates a CRAN change request as we were referencing the (now only suggested) package gputools. As hinted in the previous paragraph, it was once on CRAN but is not right now so we adjusted our reference.

CRANberries also provides a diffstat report for the latest release.

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.

How to export OPML from Omnivore About • Blog • Cool URLs How to export OPML from Omnivore

Published 2024-10-30 by Seth Larson
Reading time: minutes

Omnivore recently announced they were bought by ElevenLabs, which is an AI company funded by Trump-supporting VC firm Andreessen Horowitz. As a part of this deal, they are shuttering the service on an extremely tight deadline.

This is very disappointing to me, I've previously recommended Omnivore to others and have donated for the past year that I've used the service. It goes without saying that I want nothing to do with Omnivore.

At the recommendation of a few friends I am going to try out Feedbin, which costs $5/month (the same price that I was willing to donate to Omnivore) and has a generous 30-day trial. This service has been around for much longer and appears to not be adding AI garbage to their app (thank you, Feedbin!)

The Omnivore team has unhelpfully given an extremely tight deadline to migrate your content out before they shutter the service: November 15th. Exporting your content (links, tags, rendered HTML pages) worked fine for me but I had to restart the export process once. Please do this ASAP to avoid losing your data.

I will need to write a few scripts to import the content into Feedbin. But your data export doesn't give you your RSS or newsletter subscriptions (again, screw you Omnivore).

So I wrote a short Python script to do that. First install the dependencies (OmnivoreQL and PyOPML):

$ python -m pip install omnivoreql pyopml

You'll also need to create an Omnivore API token and place the value in the script:

import opml from omnivoreql import OmnivoreQL omnivore_api_token = "<YOUR API TOKEN GOES HERE>" omnivore = OmnivoreQL(api_token=omnivore_api_token) subscriptions = omnivore.get_subscriptions() with open("newsletters.txt", mode="w") as newsletters_fileobj: newsletters_fileobj.truncate() feeds_opml = opml.OpmlDocument( title="Omnivore Feeds Export" ) for subscription in subscriptions["subscriptions"]["subscriptions"]: if subscription["newsletterEmail"] is not None: newsletters_fileobj.write(subscription["name"] + "\n") else: feeds_opml.add_rss( text=subscription["name"], title=subscription["name"], xml_url=subscription["url"], description=subscription["description"], ) with open("feeds.opml", mode="wb") as feeds_fileobj: feeds_fileobj.truncate() feeds_opml.dump(feeds_fileobj, pretty=True)

After running the script you'll have two files: feeds.opml and newsletters.txt.

The feeds.opml file can be imported into RSS readers that support the OPML format (Feedbin and many other services do).

The newsletters.txt file is mostly to remind you which newsletters you've subscribed to. Because these readers use custom email addresses to handle newsletters you'll need to manually set these subscriptions up on the new reader service you choose to use.

If there's any issues with the script above, get in contact and I'll try to fix any issues.

Have thoughts or questions? Let's chat over email or social:

sethmichaellarson@gmail.com
@sethmlarson@fosstodon.org

Want more articles like this one? Get notified of new posts by subscribing to the RSS feed or the email newsletter. I won't share your email or send spam, only whatever this is!

Want more content now? This blog's archive has ready-to-read articles. I also curate a list of cool URLs I find on the internet.

Find a typo? This blog is open source, pull requests are appreciated.

Thanks for reading! ♡ This work is licensed under CC BY-SA 4.0

︎

Most software that exists today does not forget. Creating software that remembers is easy, but designing software that deliberately “forgets” is a bit more complex. By “forgetting,” I don't mean losing data because it wasn’t saved or losing it randomly due to bugs. I'm referring to making a deliberate design decision to discard data at a later time. This ability to forget can be an incredibly benefitial property for many applications. Most importantly software that forgets enables different user experiences.

I'm willing to bet that your cloud storage or SaaS applications likely serve as dumping grounds for outdated, forgotten files and artifacts. This doesn’t have to be the case.

Older computer software often aimed to replicate physical objects and experiences. This approach (skeuomorphism) was about making digital interfaces feel familiar to older physical objects. They resembled the appearance and behavior even though they didn't need to. Ironically though skeuomorphism despite focusing on look and feel, rarely considers some of the hidden affordances of the physical world. Critically, rarely does digial software feature degradation. Yes, the trash bin was created as an appoximation of this, but the bin seemingly did not make it farther than file or email management software. It also does not go far enough.

In the physical world, much of what we create has a natural tendency to decay and that is really useful information. A sticky note on a monitor gathers dust and fades. A notebook fills with notes and random scribbles, becomes worn, and eventually ends up in a cabinet to finally end its life discarded in a bin. We probably all clear out our desk every couple of months, tossing outdated items to keep the space manageable. When I do that, a key part of this is quickly judging how “old” some paper looks. But even without regular cleaning, things are naturally lost or discarded over time on my desk. Yet software rarely behaves this way. I think that’s a problem.

When data is kept indefinitely by default, it changes our relationship with that software. People sometimes may hesitate to create anything in shared spaces for fear of cluttering them, while others might indiscriminately litter them. In file-based systems, this may be manageable, but in shared SaaS applications, everything created (dashboards, notebooks, diagrams) lingers indefinitely and remains searchable and discoverable. This persistence seems advantageous but can quickly lead to more and more clutter.

Adding new data to software is easy. Scheduling it for automatic deletion is a bit harder. Simulating any kind of “visual decay” to hint at age or relevance is rarely seen in today's software though it wouldn't be all that hard to add. I'm not convinced that the work required to implement any of those things is why it does not exist, I think it's more likely that there is a belief that keeping stuff around forever is a benefit over the limitations of the real world.

The reality is that even though the entities we create are sticking around forever, the information contained within them ages badly. Of the 30 odd "test" dashboards that are in our Datadog installation, most of them don't show data any more. The same is true for hundreds of notebooks. We have a few thousand notebooks and quite a few of them at this point are anchored to data that is past the retention period or are referencing metrics that are gone.

In shared spaces with lots of users, few things are intended to last forever. I hope that it will become more popular for software to take age more intentional into account. For instance one can start fading out old documents that are rarely maintained or refreshed. I want software to hide old documents, dashboards etc. and that includes most critically not showing up in search. I don't want to accidentally navigate to old and unused dashboards in the mids of an incident.

Sorting by frequency of use is insufficient to me. Ideally software embraced an “ephemeral by default” approach. While there’s some risk of data loss, you can make the deletion purely virtual (at least for a while). Imagine dashboard software with built-in “garbage collection”: everything created starts with a short time-to-live (say, 30 days), after which it moves to a “to sort” folder. If it’s not actively sorted and saved within six months, it's moved to a trash and eventually deleted.

This idea extends far beyond dashboards! Wiki and innformation management software like Notion could benefit from decaying notes, as the information they hold often becomes outdated quickly. I routinely encounter more outdated pages than current ones. While outright deletion may not be the solution, irrelevant notes and documents showing up in searches add to the clutter and make finding useful information harder. “But I need my data sometimes years later” I hear you say. What about making it intentional? Archive them in year books. Make me intentionally “dig into the archives” if I really have to. There are many very intentional ways of dealing with this problem.

And even if software does not want to go down that path I would at least wish for scheduled deletion. I will forget to delete, and I'm lazy and given the tools available I rarely clean up. Yet many of the things I create I already know I really only need for a week or to. So give me a button I can press to schedule deletion. Then I don't have to remember to clean up after myself a few months later, but I can make that call already today when I create my thing.

Fedora 41 has been released! 🎉 So let’s see what comes in this new release for the Fedora Atomic Desktops variants (Silverblue, Kinoite, Sway Atomic and Budgie Atomic).

bootupd enabled by default for UEFI systems (BIOS coming soon)

After a long wait and a lot of work and testing, bootloader updates are finally enabled by default for Atomic Desktops.

For now, only UEFI systems will see their bootloader automatically updated on boot as it is the safest option. Automatic updates for classic BIOS systems will be enabled in the upcoming weeks.

If you encounter issues when updating old systems, take a look at the Manual action needed to resolve boot failure for Fedora Atomic Desktops and Fedora IoT Fedora Magazine article which includes instructions to manually update UEFI systems.

Once you are on Fedora 41, there is nothing more to do.

See the Enable bootupd for Fedora Atomic Desktops and Fedora IoT change request and the tracking issue atomic-desktops-sig#1.

First step towards Bootable Containers: dnf5 and bootc

The next major evolution for the Atomic Desktops will be to transition to Bootable Containers.

We have established a roadmap (atomic-desktops-sig#26) and for Fedora 41, we added dnf5 and bootc to the Bootable Container images of Atomic Desktops.

Those images are currently built in the Fedora infrastructure (example) but not pushed to the container registry.

The images currently available on quay.io/fedora (Silverblue, Kinoite, etc.) are mirrored from the ostree repository and thus do not yet include dnf5 and bootc.

Once releng#12142 has been completed, they will be replaced by the Bootable Container images.

In the mean time, you can take a look at the unofficial images (see the Changes in unofficial images section below).

See the DNF and bootc in Image Mode Fedora variants change request and the tracking issue atomic-desktops-sig#48

What’s new in Silverblue GNOME 47

Fedora Silverblue comes with the latest GNOME 47 release.

For more details about the changes that alongside GNOME 47, see What’s new in Fedora Workstation 41 on the Fedora Magazine and Fedora Workstation development update – Artificial Intelligence edition from Christian F.K. Schaller.

Ptyxis as default terminal application

Ptyxis is a terminal for GNOME with first-class support for containers, and thus works really well with Toolbx (and Distrobox). This is now the default terminal app and it brings features such as native support for light/dark mode and user-customizable keyboard shortcuts.

See Ptyxis’ website.

Wayland only

Fedora Silverblue is now Wayland only by default. The packages needed for the X11 session will remain available in the repositories maintained by the GNOME SIG and may be overlayed on Silverblue systems that require them.

See the Wayland-only GNOME Workstation Media change request and the tracking issue: atomic-desktops-sig#41.

What’s new in Kinoite KDE Plasma 6.2

Fedora Kinoite ships with Plasma 6.2, Frameworks 6.7 and Gear 24.08.

See also What’s New in Fedora KDE 41? on the Fedora Magazine.

Kinoite Mobile

Kinoite Mobile is currently only provided as unofficial container images. See the Changes in unofficial images section below.

See the KDE Plasma Mobile Spin and Fedora Kinoite Mobile change request.

What’s new in Sway Atomic

Fedora Sway Atomic comes with the latest 1.10 Sway release.

What’s new in Budgie Atomic

Nothing specific this release. The team is working on Wayland support.

Changes in unofficial images

Until we complete the work needed in the Fedora infrastructure to build and push official container images for the Atomic Desktops (see releng#12142), I am providing unofficial builds of those. They are built on GitLab.com CI runners, use the official Fedora packages and the same sources as the official images.

You can find the configuration and list on gitlab.com/fedora/ostree/ci-test and the container images at quay.io/organization/fedora-ostree-desktops.

New unofficial images: Kinoite Mobile & COSMIC Atomic

With Fedora 41, we are now building two new unofficial images: Kinoite Mobile and COSMIC Atomic. They join our other unofficial images: XFCE Atomic and LXQt Atomic.

See How to make a new rpm-ostree desktop variant in Fedora? if you are interested in making those images official Fedora ones.

See the KDE Plasma Mobile Spin and Fedora Kinoite Mobile change request and the Fedora COSMIC Desktop Environment Special Interest Group (SIG) page.

Renaming the Sericea and Onyx unofficial images to Sway Atomic and Budgie Atomic

If you are using the Sericea or Onyx container images, please migrate to the new Atomic names for Sericea & Onyx (sway-atomic and budgie-atomic) as I will remove the images published under the old name soon, likely before Fedora 42.

We will likely rename the official container images as well.

Smaller changes common to all desktops Unprivileged updates

The polkit policy controlling access to the rpm-ostree daemon has been updated to:

• Enable users to update the system without having elevated privileges or typing a password. Note that this change only applies to system updates and repository meta updates; no other operations.
• Reduce access to the most privileged operations (such as changing the kernel arguments, or rebasing to another image) of rpm-ostree for administrators to avoid mistakes. Only the following operations will remain password-less to match the behavior of package mode Fedora with the dnf command:
  • install and uninstall packages
  • upgrade the image
  • rollback the image
  • cancel transactions
  • cleanup deployment

See the Unprivileged updates for Fedora Atomic Desktops change request and the tracking issue atomic-desktops-sig#7.

“Alternatives” work again

The alternatives command (alternatives(8)) is now working on Atomic Desktops.

See the tracking issue atomic-desktops-sig#51 for more details and documentation.

Fixes for LUKS unlock via TPM

Support for unlokcing a LUKS partition with the TPM is now included in the initramfs.

See the tracking issue atomic-desktops-sig#33 and the in progress documentation silverblue-docs#176.

Universal Blue, Bluefin, Bazzite and Aurora

Our friends in the Universal Blue project have prepared the update to Fedora 41 already. For Bazzite, you can find all the details in Bazzite F41 Update: New Kernel, MSI Claw Improvements, VRR Fixes, Better Changelogs, GNOME 47 & More.

For Bluefin (and similarly for Aurora), see Bluefin GTS is now based on Fedora 40.

I heavily recommend checking them out, especially if you feel like some things are missing from the Fedora Atomic Desktops and you depend on them (NVIDIA proprietary drivers, extra media codec, etc.).

What’s next

We have made lot of progress since the last time, thus this section is going to be more exciting!

Roadmap to Bootable Containers

As I mentionned in First step towards Bootable Containers: dnf5 and bootc, the next major evolution for the Atomic Desktops will be to transition to Bootable Containers. See also the Fedora bootc documentation.

We have established a roadmap (atomic-desktops-sig#26) and we need your help to make this a smooth transition for all of our existing users.

composefs

Moving to composefs is one of the items on the roadmap to Bootable Containers. composefs is the next step for ostree based systems and will enable us to provide better integrity and security in the future.

For Fedora 41, we moved Fedora CoreOS and Fedora IoT to composefs.

For the Atomic Desktops, this is planned for Fedora 42 as we still have a few issues to resolve. See the Enabling composefs by default for Atomic Desktops change request and the tracking issue atomic-desktops-sig#35.

Custom keyboard layout set on installation

This fix is important for setups where the root disk is encryptd with LUKS and the user is asked a passphrase on boot. Right now, the keyboard layout is not remembered and defaults to the US QWERTY layout. Unfortunately this fix did not land in time for Fedora 41 but this will be part of the Fedora 42 installations ISOs. Help us test this by installing systems from a Rawhide ISO to confirm that this issue is fixed.

If you are impacted by this issue, see the tracking issue atomic-desktops-sig#6 for the manual workarounds.

Unifying the Atomic Desktops documentation

We would like to unify the documentation for the Fedora Atomic Desktops into a single one instead of having per desktop environments docs which are mostly duplicate of one another and need to be constantly synced.

See the tracking issue atomic-desktops-sig#10.

Where to reach us

We are looking for contributors to help us make the Fedora Atomic Desktops the best experience for Fedora users.

• Atomic Desktops SIG: Issue tracker (gitlab.com/fedora/ostree/sig), #atomic-desktops:fedoraproject.org
• Silverblue: Workstation Working Group, #silverblue:fedoraproject.org
• Kinoite: KDE SIG, #kinoite:fedoraproject.org
• Sway Atomic: Sway SIG, #sway:fedoraproject.org
• Budgie Atomic: Budgie SIG, #budgie:fedoraproject.org

59th issue of Andrei Neagoie's must-read monthly Python Newsletter: Python 3.13, Django Project Ideas, GPU Bubble is Bursting, and much more. Read the full newsletter to get up-to-date with everything you need to know from last month.

Join the FSF and friends on Friday, November 1 from 12:00 to 15:00 EDT (16:00 to 19:00 UTC) to help improve the Free Software Directory.

#653 – OCTOBER 29, 2024
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Sudoku in Python Packaging

Simon writes about a Soduku solver written by Konstin that uses the Python packaging mechanisms to do Soduku puzzles. The results are output using a requirements.txt file, where soduku-0-3==5 represents the (0,3) cell’s answer of 5.
SIMON WILLISON

Python Thread Safety: Using a Lock and Other Techniques

In this tutorial, you’ll learn about the issues that can occur when your code is run in a multithreaded environment. Then you’ll explore the various synchronization primitives available in Python’s threading module, such as locks, which help you make your code safe.
REAL PYTHON

OpenAPI Python SDK Creation: Speakeasy vs Open Source

Open-source OpenAPI generators are great for experimentation and hobby projects but lack the reliability, performance, and intuitive developer experience required for critical applications. Speakeasy creates idiomatic SDKs that meet the bar for enterprise use. Check out this comparison guide →
SPEAKEASY sponsor

Python and Sigstore

PEP 761 proposes removing PGP signatures from CPython artifacts and solely relying on Sigstore. But just what is Sigstore? This post explains how CPython gets signed and why Sigstore is a good choice.
SETH LARSON

PSF Fellow Members for Q2 2024!

PYTHON SOFTWARE FOUNDATION

Quiz: Thread Safety: Using a Lock and Other Techniques

REAL PYTHON

Quiz: Python Closures: Common Use Cases and Examples

REAL PYTHON

Discussions Ideas: Multi-String Replacement Using a Dictionary

PYTHON.ORG

Python LTS and Maintenance Cycles

PYTHON.ORG

Articles & Tutorials Introducing the New Starter Kit for Wagtail CMS

Currently, if you want to play around with Wagtail, most people try the Bakery Demo test site, but it is not meant to be a starter site. They’ve created a new starter template that provides you with a high-performance, production-grade Wagtail site. This introduces you to it.
JAKE HOWARD • Shared by Meagen Voss

Understanding Python’s Global Interpreter Lock (GIL)

Python’s Global Interpreter Lock or GIL, in simple words, is a mutex (or a lock) that allows only one thread to hold the control of the Python interpreter at any one time. In this video course you’ll learn how the GIL affects the performance of your Python programs.
REAL PYTHON course

Write Code as if Failure Doesn’t Exist

“With Temporal, we…regularly reduced lines of code from 300 to 30.” -DigitalOcean. Ever wish you could eliminate recovery logic, callbacks, and timers? Temporal’s open source programming model allows you to stop plumbing and start focusing on what matters: building awesome features. Check it out now →
TEMPORAL TECHNOLOGIES sponsor

Python and SysV Shared Memory

At work, Adriaan deals with code that interfaces with Unix SysV’s shared memory components. For convenience he wanted to get at this from Python, and “because work”, from Python 3.7. This post talks about how he solved the problem.
ADRIAAN DE GROOT

Python 3.13, What Didn’t Make the Headlines

Bite Code summarizes some of the lesser covered changes to Python in the 3.13 release, including how some of the REPL improvements made it into pdb, improvements to shutil, and small additions to the asyncio library.
BITE CODE!

Django and HTMX Tutorial: Easier Web Development

This tutorial explores how the htmx library can bring dynamic functionality, like lazy loading, search-as-you-type, and infinite scroll to your Django project with almost no JavaScript necessary.
PYCHARM CHANNEL video

Python 3.12 vs 3.13: Performance Testing

This post shows the results of a performance comparison between Python 3.12 and 3.13 on two different processors. TL;DR: Python 3.13 is faster, but there are a couple of hairy edge cases.
WŁODZIMIERZ LEWONIEWSKI

Software Engineer Titles Have (Almost) Lost All Their Meaning

This post examines the devaluation of software engineer titles and its impact on the integrity of the tech industry.
TREVOR LASN

PyData Amsterdam 2024 Talks

This is a listing of the recorded talks from PyData Amsterdam.
YOUTUBE video

Projects & Code Encode Numbers Into Words and Decode It Back

GITHUB.COM/NIKDEDOV • Shared by Nikolai

Scrapling: Lightning-Fast, Adaptive Web Scraping for Python

GITHUB.COM/D4VINCI • Shared by Karim Shoair

otterwiki: A Minimalistic Wiki Powered by Python

GITHUB.COM/REDIMP

msgspec: Fast Serialization and Validation Library

JCRISTHARIF.COM

beartype: Bare Metal Type Checker

BEARTYPE.READTHEDOCS.IO

Events Weekly Real Python Office Hours Q&A (Virtual)

October 30, 2024
REALPYTHON.COM

PyCon FR 2024

October 31 to November 3, 2024
PYCON.FR

PyCon Zimbabwe

October 31 to November 3, 2024
PYCON.ORG

SPb Python Drinkup

October 31, 2024
MEETUP.COM

PyDelhi User Group Meetup

November 2, 2024
MEETUP.COM

Melbourne Python Users Group, Australia

November 4, 2024
J.MP

Happy Pythoning!
This was PyCoder’s Weekly Issue #653.
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Maybe you’ve heard complicated-sounding phrases such as ‘“Students t-test”, “regression models”, “support vector machines”, and so on. You might think there’s so much you need to learn before you can explore and understand your data, but I am going to show you two tools to help you go faster. These are summary statistics and graphs.

Summary statistics and graphs/plots are used by new and experienced data scientists alike, making them the perfect building blocks for exploring data.

We will be working with this dataset available from Kaggle if you’d like to follow along. I chose this dataset because it has several interesting properties, such as multiple continuous and categorical variables, missing data, and a variety of distributions and skews. I’ll explain each variable I work with and why I chose each one to show you the tools you can apply to your chosen data set.

In our previous blog posts, we looked at where to get data from and bring that data into PyCharm. You can look at steps 1 and 2 from our blog post entitled 7 ways to use Jupyter notebooks in PyCharm to create a new Jupyter notebook and import your data as a CSV file if you need a reminder. You can use the dataset I linked above or pick your own for this walkthrough.

We’re going to be using the pandas library in this blog post, so to ensure we’re all on the same page, your code should look something like the following block in a Jupyter notebook –  you’ll need to change the spreadsheet name and location to yours, though. Make sure you’ve imported matplotlib, too, as we will be using that library to explore our data.

import pandas as pd import matplotlib as plt df = pd.read_csv('../data/AmesHousing.csv') df

When you run that cell, PyCharm will show you your DataFrame, and we can get started.

Try PyCharm Professional for free

Summary statistics

When we looked at where to get data from, we discussed continuous and categorical variables. We can use Jupyter notebooks inside PyCharm to generate different summary statistics for these, and, as you might have already guessed, the summary statistics differ depending on whether the variables are continuous or categorical.

Continuous variables summary statistics

First, let’s see how we can view our summary statistics. Click on the small bar graph icon on the right-hand side of your DataFrame and select Compact:

Let me give you a little tip here if you’re unsure which variables are continuous and which are categorical, PyCharm shows different summary statistics for each one. The ones with the mini graphs (blue in this screenshot) are continuous, and those without are categorical.

This data set has several continuous variables, such as Order, PID, MS SubClass, and more, but we will focus on Lot Frontage first. That is the amount of space at the front of the property.

The summary statistics already give us some clues:

There’s a lot of data here, so let’s break it down and explore it to understand it better. Immediately, we can see that we have missing data for this variable; that’s something we want to note, as it might mean we have some issues with the dataset, although we won’t go into that in this blog post!

First, you can see the little histogram in blue in my screenshot, which tells us that we have a positive skew in our data because the data tails off to the right. We can further confirm this with the data because the mean is slightly larger than the median. That’s not entirely surprising, given we’d expect the majority of lot frontages to be of a similar size, but perhaps there are a small number of luxury properties with much bigger lot frontages that are skewing our data. Given this skew, we would be well advised not to use the standard deviation as a measure of dispersion because that is calculated by using all data points, so it’s affected by outliers, which we know we have on one side of our distribution.

Next, we can calculate our interquartile range as the difference between our 25th percentile of 58.0 and our 75th percentile of 80.0, giving us an interquartile range of 22.0. Alongside the interquartile range, it’s helpful to consider the median, the middle value in our data, and unlike the mean, it is not based on every data point. The median is more appropriate for Lot Frontage than the mean because it’s not affected by the outliers we know we have.

Since we’re talking about the median and interquartile range, it is worth saying that box plots are a great way to represent these values visually. We can ask JetBrains AI Assistant to create one for us with a prompt such as this:

Create code using matplotlib for a box plot for  ‘Lot Frontage’. Assume we have all necessary imports and the data exists.

Here’s the code that was generated:

plt.figure(figsize=(10, 6)) plt.boxplot(df['Lot Frontage'].dropna(), vert=False) plt.title('Box Plot of Lot Frontage') plt.xlabel('Lot Frontage') plt.show()

When I click Accept and run, we get our box plot:

The median is the line inside the box, which, as you can see, is slightly to the left, confirming the presence of the positive or right-hand skew. The box plot also makes it very easy to see a noticeable number of outliers to the right of the box, known as “the tail”. That’s the small number of likely luxury properties that we suspect we have.

It’s important to note that coupling the mean and standard deviation or the median and IQR gives you two pieces of information for that data: a central tendency and the variance. For determining the central tendency, the mean is more prone to being affected by outliers, so it is best when there is no skew in your data, whereas the median is more robust in that regard. Likewise, for the variation, the standard deviation can be affected by outliers in your data. In contrast, the interquartile range will always tell you the distribution of the middle 50% of your data. Your goals determine which measurements you want to use. 

Categorical variables summary statistics

When it comes to categorical variables in your data, you can use the summary statistics in PyCharm to find patterns. At this point, we need to be clear that we’re talking about descriptive rather than inferential statistics. That means we can see patterns, but we don’t know if they are significant.

Some examples of categorical data in this data set include MS Zoning, Lot Shape, and House Style. You can gain lots of insights just by looking through your data set. For example, looking at the categorical variable Neighborhood, the majority are stated as Other in the summary statistics with 75.8%. This tells you that there might well be a lot of categories in Neighborhood, which is something to bear in mind when we move on to graphs. 

As another example, the categorical variable House Style states that about 50% of the houses are one-story, while 30% are two-story, leaving 20% that fall into some other category that you might want to explore in more detail. You can ask JetBrains AI for help here with a prompt like:

Write pandas code that tells me all the categories for ‘House Style’ in my DataFrame ‘df’, which already exists. Assume we have all the necessary imports and that the data exists.

Here’s the resulting code:

unique_house_styles = df['House Style'].unique() print("Unique categories for 'House Style':") print(unique_house_styles)

When we run that we can see that the remaining 20% is split between various codes that we might want to research more to understand what they mean:

Unique categories for ‘House Style’:

['1Story' '2Story' '1.5Fin' 'SFoyer' 'SLvl' '2.5Unf' '1.5Unf' '2.5Fin']

Have a look through the data set at your categorical variables and see what insights you can gain!

Before we move on to graphs, I want to touch on one more piece of functionality inside PyCharm that you can use to access your summary statistics called Explain DataFrame. You can access it by clicking on the purple AI icon on the top-right of the DataFrame and then choosing AI Actions | Explain DataFrame.

JetBrains AI lists out your summary statistics but may also add some code snippets that are helpful for you to get your data journey started, such as how to drop missing values, filter rows based on a condition, select specific columns, as well as group and aggregate data. 

Graphs

Graphs or plots are a way of quickly getting patterns to pop out at you that might not be obvious when you’re looking at the numbers in the summary statistics. We’re going to look at some of the plots you can get PyCharm to generate to help you explore your data.

First, let’s revisit our continuous variable, Lot Frontage. We already learned that we have a positive or right-hand skew from the mini histogram in the summary statistics, but we want to know more! 

In your DataFrame in PyCharm, click the Chart View icon on the left-hand side:

Now click the cog on the right-hand side of the chart that says Show series settings and select the Histogram plot icon on the far right-hand side. Click x to clear the values in the X axis and Y axis and then select Lot Frontage with group and sort for the X axis and Lot Frontage with count for the Y axis:

PyCharm generates the same histogram as you see in the summary settings, but we didn’t have to write a single line of code. We can also explore the histogram and mouse over data points to learn more. 

Let’s take it to the next level while we’re here. Perhaps we want to see if the condition of the property, as captured by the Overall Cond variable, predicts the sale price.

Change your X axis SalePrice group and sort and your Y axis to SalePrice count and then add the group Overall Cond:

Looking at this chart, we can hypothesize that the overall condition of the property is indeed a predictor of the sale price, as the distribution and skew are remarkably similar. One small note is that grouping histograms like this works best when you have a smaller number of categories. If you change Groups to Neighborhood, which we know has many more categories, it becomes much harder to view! 

Moving on, let’s stick with PyCharm’s plotting capabilities and explore bar graphs. These are a companion to frequency charts such as histograms, but can also be used for categorical data. Perhaps you are interested in Neighbourhood (a categorical variable) in relation to SalesPrice.

Click the Bar [chart] icon on the left-hand side of your series setting, then select Neighbourhood as Categories and SalesPrice with the median as the Values:

This helps us understand the neighborhoods with the most expensive and cheapest housing. I chose the median for the SalesPrice as it’s less susceptible to outliers in the data. For example, I can see that housing in Mitchel is likely to be substantially cheaper than in NoRidge. 

Line plots are another useful plot for your toolkit. You can use these to demonstrate trends between continuous variables over a period of time. For example, select the Line [graph] icon and then choose Year Built as the X axis and SalePrice with the mean as the Y axis:

This suggests a small positive correlation between the year the house was built and the price of the house, especially after 1950. If you’re feeling adventurous, remove the mean from SalePrice and see how your graph changes when it has to plot every single price! 

The last plot I’d like to draw your attention to is scatter plots. These are a great way to see a relationship between two continuous variables and any correlation between them. A correlation shows the strength of a relationship between two variables. To dig deeper, check out this beginner-friendly overview from Real Python.

For example, if we set our X axis to SalePrice and our Y axis to Gr LivArea, we can see that there is a positive correlation between the two variables, and we can also easily spot some outliers in our data, including a couple of houses with a lower sale price but a huge living area!

Summary

Here’s a reminder of what we’ve covered today. You can access your summary statistics in PyCharm either through Explain DataFrame with JetBrains AI or by clicking on the small graph icon on the right-hand side of a DataFrame called Column statistics and then selecting Compact. You can also use Detailed to get even more information than we’ve covered in this blog post. 

You can get PyCharm to create graphs to explore your data and create hypotheses for further investigation. Some more commonly used ones are histograms, bar charts, line graphs, and scatter plots.

Finally, you can use JetBrains AI Assistant to generate code with natural language prompts in the AI tool window. This is a quick way to learn more about your data and start thinking about the insights on offer.

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

Try PyCharm Professional for free

Using both summary statistics and graphs in PyCharm, we can learn a lot about our data, giving us a solid foundation for our next step – cleaning our data, which we will talk about in the next blog post in this series.

As a Python developer who wants to harness the power of object-oriented programming, you’ll love to learn how to customize your classes using special methods, also known as magic methods or dunder methods. A special method is a method whose name starts and ends with a double underscore. These methods have special meanings in Python.

Python automatically calls magic methods as a response to certain operations, such as instantiation, sequence indexing, attribute managing, and much more. Magic methods support core object-oriented features in Python, so learning about them is fundamental for you as a Python programmer.

In this video course, you’ll:

• Learn what Python’s special or magic methods are
• Understand the magic behind magic methods in Python
• Customize different behaviors of your custom classes with special methods

[ 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 ]

In this quiz, you’ll test your understanding of Python’s .__repr__() and .__str__() special methods. These methods allow you to control how a program displays an object, making your classes more readable and easier to debug and maintain.

[ 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 ]

I’ve had my Creality CR-6 SE for quite some while now and it’s worked very well. I’ve even moved with it a couple of times. However, it seems that now was the time for it to give up the ghost, as the extruder casing developed a crack. Apparently something not completely uncommon.

The extruder casing removed. The spring is quite powerful. The crack.

So I searched the internet for spare parts before I realized that this is a common failure mode and that there are all-metal replacements. A few clicks later, I had one ordered from Amazon. I took a chance and ordered one for CR-10, as it looked like it would fit from the photos, and it did (phew). Here is a link to the one I got: link.

The replacement extruder installed.

The installation went smooth. The only tricky part was getting the threads of the screw being pushed by the spring right. The spring is quite strong, so it is really a three hand operation in the area where my fingers have a hard time fitting. Having done that, it seems like it just work straight out of the box.

First print.

The print has been going on for a couple of hours now, and there has been no hickups. Big shout-out to OctoPrint while I’m at it. Being able to keep an eye on things without having to sit next to the printer is just great (and not having to fiddle around with SD-cards is also really nice).

After a break of 5 years DrupalCamp Scotland is back! A small event with huge appeal. By all measures there’s no doubt the community welcomed its return and are already asking about next year. So what did you miss?

I was on a french speaking IRC chan bragging a tad about how I was doing a guitar tuner and paying attention to not fall into the pit of confusing precise and exact figures as a random computer engineer.

Science he was a patented CS engineer he wanted to prove me that my new guitar tuner was useless since AI could come with a better less convoluted exact example in less lines of code than mine (mine is adapted from a blog on audio processing and fast fourier transform because it was commented and was refreshing me the basics of signal processing).

And I asked him, have you ever heard of the Nyquist frequency ? or the tradeoff an oscilloscope have to do between time locality and accuracy ?

Of course he didn't. And was proud that a wannabee coder would be proven useless thanks to the dumbest AI.



So I actually made this guitar tuner because this time I wanted to have an exact figure around the Hertz.
The problem stated by FFT/Nyquist formula is that if I want an exact number around 1Hz (1 period per second) I should sample at least half a period (hence .5 second), and should not expect a good resolution.

The quoted chatgpt code takes 1024 samples out of 44100/sec, giving it a nice reactivity of 1/44th of a second with an accuracy of 44100/1024/2 => 21Hz.

I know chatgpt does not tune a guitar, but shouldn't the chatgpt user bragging about the superiority of pro as him remember that when we tune we may tune not only at A = 440Hz but maybe A = 432Hz or other ?

A note is defined as a racine 12th of 2 compared to an arbitrary reference (remember an octave is doubling => 12 half tones = 2) ; what makes a temperate scale is not the reference but the relationship between notes and this enable bands of instrument to tune themselves according to the most unreliable but also nice instrument which is the human voice.

Giving the user 3 decimal after the comma is called being precise : it makes you look like a genius in the eye of the crowd. Giving the user 0 decimal but accurate frequency is what makes you look dumb in the eyes of the computer science engineer, but it way more useful in real life.

Here I took the liberty with pysine to generate A on 6 octaves (ref A=440) and use the recommanded chatgpt buffer size acknowledged by a pro CS engineer for tuning your guitar and my default choices. for i in 55.0 110.0 220.0 440.0 880.0 1760.0 ; do python -m pysine $i 3; done Here is the result with a chunk size of 1024 : And here is the result with a chunk size corresponding to half a second of sampling :
I may not be a computer engineer, I am dumb, but checking with easy to use tools that your final result is in sync with your goal is for me more important than diplomas and professional formations.



The code is yet another basic animation in matplotlib with a nice arrow pointing the frequency best fitting item. It is not the best algorithm, but it does the job.

Showing the harmonics as well as the tonal has another benefit it answers the questions : why would I tune my string on the note of the upper string ?
Like tuning E on A ?

Well because -at least for my half broken guitar- it ensures to me that I will tune on the tonal note.

Here is a sample of tuning E on the empty string : And this is how tuning the E string on the A note looks like : And don't pay attention to the 56Hz residual noise triggered by my fans/appliance turning and making a constant noise :D Here is the code import pyaudio import matplotlib.pyplot as plt import matplotlib.animation as animation import numpy as np import time from sys import argv A = 440.0 try: A=float(argv[1]) except IndexError: pass form_1 = pyaudio.paInt16 # 16-bit resolution chans = 1 # 1 channel samp_rate = 44100 # 44.1kHz sampling rate chunk = 44100//2# .5 seconds of sampling for 1Hz accuracy audio = pyaudio.PyAudio() # create pyaudio instantiation # create pyaudio stream stream = audio.open( format = form_1,rate = samp_rate,channels = chans, input = True , frames_per_buffer=chunk ) fig = plt.figure(figsize=(13,8)) ax = fig.add_subplot(111) plt.grid(True) def compute_freq(ref, half_tones): return [ 1.0*ref*(2**((half_tones+12*i )/12)) for i in range(-4,4) ] print(compute_freq(A,0)) note_2_freq = dict( E = compute_freq(A,-5), A = compute_freq(A, 0), D = compute_freq(A, 5), G = compute_freq(A,-2), B = compute_freq(A, 2), ) resolution = samp_rate/(2*chunk) def closest_to(freq): res = dict() for note, freqs in note_2_freq.items(): res[note]=max(freqs) for f in freqs: res[note]= min(res[note], abs(freq -f)) note,diff_freq = sorted(res.items(), key = lambda item : item[1])[0] for f in note_2_freq[note]: if abs(freq-f) == diff_freq: return "%s %s %2.1f %d" % ( note, abs(freq - f ) < resolution and "=" or ( freq > f and "+" or "-"), abs(freq-f), freq ) def init_func(): plt.rcParams['font.size']=18 plt.xlabel('Frequency [Hz]') plt.ylabel('Amplitude [Arbitry Unit]') plt.grid(True) ax.set_xscale('log') ax.set_yscale('log') ax.set_xticks( note_2_freq["E"] + note_2_freq["A"]+ note_2_freq["D"]+ note_2_freq["G"]+ note_2_freq["B"] , labels = ( [ "E" ] * len(note_2_freq["E"]) + [ "A" ] * len(note_2_freq["A"]) + [ "D" ] * len(note_2_freq["D"]) + [ "G" ] * len(note_2_freq["G"]) + [ "B" ] * len(note_2_freq["B"]) ) ) ax.set_xlim(40, 4000) return ax def data_gen(): stream.start_stream() data = np.frombuffer(stream.read(chunk),dtype=np.int16) stream.stop_stream() yield data i=0 def animate(data): global i i+=1 ax.cla() init_func() # compute FFT parameters f_vec = samp_rate*np.arange(chunk/2)/chunk # frequency vector based on window # size and sample rate mic_low_freq = 50 # low frequency response of the mic low_freq_loc = np.argmin(np.abs(f_vec-mic_low_freq)) fft_data = (np.abs(np.fft.fft(data))[0:int(np.floor(chunk/2))])/chunk fft_data[1:] = 2*fft_data[1:] plt.plot(f_vec,fft_data) max_loc = np.argmax(fft_data[low_freq_loc:])+low_freq_loc # max frequency resolution plt.annotate(r'$\Delta f_{max}$: %2.1f Hz, A = %2.1f Hz' % ( resolution, A), xy=(0.7,0.92), xycoords='figure fraction' ) ax.set_ylim([0,2*np.max(fft_data)]) # annotate peak frequency annot = ax.annotate( 'Freq: %s'%(closest_to(f_vec[max_loc])), xy=(f_vec[max_loc], fft_data[max_loc]),\ xycoords='data', xytext=(0,30), textcoords='offset points', arrowprops=dict(arrowstyle="->"), ha='center',va='bottom') #fig.savefig('full_figure-%04d.png' % i) return ax, ani = animation.FuncAnimation( fig, animate, data_gen, init_func, interval=.15, cache_frame_data=False, repeat=True, blit=False ) plt.show()

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Let's say you want to create a web app and you know Python really well. Your first thought might be Flask or Django or even FastAPI? All good choices but there is a lot to get a full web app into production. The framework we'll talk about today, Reflex, allows you to just write Python code and it turns it into a full web app running FastAPI, NextJS, React and more plus it handles the deployment for you. It's a cool idea. Let's talk to Elvis Kahoro and Nikhil Rao from Reflex.dev.<br/> <br/> <strong>Episode sponsors</strong><br/> <br/> <a href='https://talkpython.fm/posit'>Posit</a><br> <a href='https://talkpython.fm/bluehost'>Bluehost</a><br> <a href='https://talkpython.fm/training'>Talk Python Courses</a><br/> <br/> <strong>Links from the show</strong><br/> <br/> <div><b>Elvis</b>: <a href="https://github.com/elviskahoro?featured_on=talkpython" target="_blank" >github.com</a><br/> <b>Nikhil</b>: <a href="https://github.com/picklelo?featured_on=talkpython" target="_blank" >github.com</a><br/> <br/> <b>Reflex Framework</b>: <a href="https://reflex.dev?featured_on=talkpython" target="_blank" >reflex.dev</a><br/> <b>Reflex source</b>: <a href="https://github.com/reflex-dev/reflex?featured_on=talkpython" target="_blank" >github.com</a><br/> <b>Reflex docs</b>: <a href="https://reflex.dev/docs/library/?featured_on=talkpython" target="_blank" >reflex.dev</a><br/> <b>Reflex Roadmap</b>: <a href="https://github.com/reflex-dev/reflex/issues/2727?featured_on=talkpython" target="_blank" >github.com</a><br/> <b>AG Grid</b>: <a href="https://www.ag-grid.com?featured_on=talkpython" target="_blank" >ag-grid.com</a><br/> <br/> <b>Warp terminal</b>: <a href="https://www.warp.dev?featured_on=talkpython" target="_blank" >warp.dev</a><br/> <b>A Stroll Down Startup Lane episode</b>: <a href="https://talkpython.fm/episodes/show/414/a-stroll-down-startup-lane" target="_blank" >talkpython.fm</a><br/> <b>PuePy: Reactive frontend framework in Python episode</b>: <a href="https://talkpython.fm/episodes/show/469/puepy-reactive-frontend-framework-in-python" target="_blank" >talkpython.fm</a><br/> <b>Watch this episode on YouTube</b>: <a href="https://www.youtube.com/watch?v=5Mkq5gWx_gA" target="_blank" >youtube.com</a><br/> <b>Episode transcripts</b>: <a href="https://talkpython.fm/episodes/transcript/483/reflex-framework-frontend-backend-pure-python" target="_blank" >talkpython.fm</a><br/> <br/> <b>--- Stay in touch with us ---</b><br/> <b>Subscribe to us on YouTube</b>: <a href="https://talkpython.fm/youtube" target="_blank" >youtube.com</a><br/> <b>Follow Talk Python on Mastodon</b>: <a href="https://fosstodon.org/web/@talkpython" target="_blank" ><i class="fa-brands fa-mastodon"></i>talkpython</a><br/> <b>Follow Michael on Mastodon</b>: <a href="https://fosstodon.org/web/@mkennedy" target="_blank" ><i class="fa-brands fa-mastodon"></i>mkennedy</a><br/></div>