What have I been up to?

Late last year, I launched a Patreon. Although not quite a “soft” launch — I did toot about it, after all — I didn’t promote it very much.

I started this way because I realized that if I didn’t just put something up I’d be dithering forever. I’d previously been writing a sprawling monster of an announcement post that went into way too much detail, and kept expanding to encompass more and more ideas until I came to understand that salvaging it was going to be an editing process just as brutal and interminable as the writing itself.

However, that post also included a section where I just wrote about what I was actually doing.

So, for lots of reasons¹, there are a diverse array of loosely related (or unrelated) projects below which may not get finished any time soon. Or, indeed, may go unfinished entirely. Some are “done enough” now, and just won’t receive much in the way of future polish.

That is an intentional choice.

The rationale, as briefly as I can manage, is: I want to lean into the my strength² of creative, divergent thinking, and see how these ideas pan out without committing to them particularly intensely. My habitual impulse, for many years, has been to lean extremely hard on strategies that compensate for my weaknesses in organization, planning, and continued focus, and attempt to commit to finishing every project to prove that I’ll never flake on anything.

While the reward tiers for the Patreon remain deliberately ambiguous³, I think it would be fair to say that patrons will have some level of influence in directing my focus by providing feedback on these projects, and requesting that I work more on some and less on others.

So, with no further ado: what have I been working on, and what work would you be supporting if you signed up? For each project, I’ll be answering 3 questions:

1. What is it?
2. What have I been doing with it recently?
3. What are my plans for it?

This. i.e. blog.glyph.im

What is it?

For starters, I write stuff here. I guess you’re reading this post for some reason, so you might like the stuff I write? I feel like this doesn’t require much explanation.

What have I done with it recently?

You might appreciate the explicitly patron-requested Potato Programming post, a screed about dataclass, or a deep dive on the difficulties of codesigning and notarization on macOS along with an announcement of a tool to remediate them.

What are my plans for it?

You can probably expect more of the same; just all the latest thoughts & ideas from Glyph.

Twisted

What is it?

If you know of me you probably know of me as “the Twisted guy” and yeah, I am still that. If, somehow, you’ve ended up here and you don’t know what it is, wow, that’s cool, thanks for coming, super interested to know what you do know me for.

Twisted is an event-driven networking engine written in Python, the precursor and inspiration for the asyncio module, and a suite of event-driven programming abstractions, network protocol implementations, and general utility code.

What have I done with it recently?

I’ve gotten a few things merged, including type annotations for getPrimes and making the bundled CLI OpenSSH server replacement work at all with public key authentication again, as well as some test cleanups that reduce the overall surface area of old-style Deferred-returning tests that can be flaky and slow.

I’ve also landed a posix_spawnp-based spawnProcess implementation which speed up process spawning significantly; this can be as much as 3x faster if you do a lot of spawning of short-running processes.

I have a bunch of PRs in flight, too, including better annotations for FilePath Deferred, and IReactorProcess, as well as a fix for the aforementioned posix_spawnp implementation.

What are my plans for it?

A lot of the projects below use Twisted in some way, and I continue to maintain it for my own uses. My particular focus is in quality-of-life improvements; issues that someone starting out with a Twisted project will bump into and find confusing or difficult. I want it to be really easy to write applications with Twisted and I want to use my own experiences with it.

I also do code reviews of other folks’ contributions; we do still have over 100 open PRs right now.

DateType

What is it?

DateType is a workaround for a very specific bug in the way that the datetime standard library module deals with type composition: to wit, that datetime is a subclass of date but is not Liskov-substitutable for it. There are even #type:ignore comments in the standard library type stubs to work around this problem, because if you did this in your own code, it simply wouldn’t type-check.

What have I done with it recently?

I updated it a few months ago to expose DateTime and Time directly (as opposed to AwareDateTime and NaiveDateTime), so that users could specialize their own functions that took either naive or aware times without ugly and slightly-incorrect unions.

What are my plans for it?

This library is mostly done for the time being, but if I had to polish it a bit I’d probably do two things:

1. a readthedocs page for nice documentation
2. write a PEP to get this integrated into the standard library

Although the compatibility problems are obviously very tricky and a PEP would probably be controversial, this is ultimately a bug in the stdlib, and should be fixed upstream there.

Automat

What is it?

It’s a library to make deterministic finite-state automata easier to create and work with.

What have I done with it recently?

Back in the middle of last year, I opened a PR to create a new, completely different front-end API for state machine definition. Instead of something like this:

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class MachineExample:
    machine = MethodicalMachine()

    @machine.state()
    def a_state(self): ...

    @machine.state()
    def other_state(self): ...

    @machine.input()
    def flip(self): ...

    @machine.output()
    def _do_flip(self): return ...

    on.upon(flip, enter=off, outputs=[_do_flip], collector=list)
    off.upon(flip, enter=on, outputs=[_do_flip], collector=list)

this branch lets you instead do something like this:

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class MachineProtocol(Protocol):
    def flip(self) -> None: ...

class MachineCore: ...

def buildCore() -> MachineCore: ...
machine = TypicalBuilder(MachineProtocol, buildCore)

@machine.state()
class _OffState:
    @machine.handle(MachineProtocol.flip, enter=lambda: _OnState)
    def flip(self) -> None: ...

@machine.state()
class _OnState:
    @machine.handle(MachineProtocol.flip, enter=lambda: _OffState)
    def flip(self) -> None: ...

MachineImplementation = machine.buildClass()

In other words, it creates a state for every type, and type safety that much more cleanly expresses what methods can be called and by whom; no need to make everything private with tons of underscore-prefixed methods and attributes, since all the caller can see is “an implementation of MachineProtocol”; your state classes can otherwise just be normal classes, which do not require special logic to be instantiated if you want to use them directly.

Also, by making a state for every type, it’s a lot cleaner to express that certain methods require certain attributes, by simply making them available as attributes on that state and then requiring an argument of that state type; you don’t need to plot your way through the outputs generated in your state graph.

What are my plans for it?

I want to finish up dealing with some issues with that branch - particularly the ugly patterns for communicating portions of the state core to the caller and also the documentation; there are a lot of magic signatures which make sense in heavy usage but are a bit mysterious to understand while you’re getting started.

I’d also like the visualizer to work on it, which it doesn’t yet, because the visualizer cribs a bunch of state from MethodicalMachine when it should be working purely on core objects.

Secretly

What is it?

This is an attempt at a holistic, end-to-end secret management wrapper around Keyring. Whereas Keyring handles password storage, this handles the whole lifecycle of looking up the secret to see if it’s there, displaying UI to prompt the user (leveraging a pinentry program from GPG if available)

What have I done with it recently?

It’s been a long time since I touched it.

What are my plans for it?

• Documentation. It’s totally undocumented.
• It could be written to be a bit more abstract. It dates from a time before asyncio, so its current Twisted requirement for Deferred could be made into a generic Awaitable one.
• Better platform support for Linux & Windows when GPG’s pinentry is not available.
• Support for multiple accounts so that when the user is prompted for the relevant credential, they can store it.
• Integration with 1Password via some of their many potentially relevant APIs.

Fritter

What is it?

Fritter is a frame-rate independent timer tree.

In the course of developing Twisted, I learned a lot about time and timers. LoopingCall encodes some of this knowledge, but it’s very tightly coupled to the somewhat limited IReactorTime API.

Also, LoopingCall was originally designed with the needs of media playback (particularly network streaming audio playback) in mind, but I have used it more for background maintenance tasks and for animations. Both of these things have requirements that LoopingCall makes awkward but FRITTer is designed to meet:

1. At higher loads, surprising interactions can occur with the underlying priority queue implementation, and different algorithms may make a significant difference to performance. Fritter has a pluggable implementation of a priority queue and is carefully minimally coupled to it.

2. Driver selection is a first-class part of the API, with an included, public “Memory” driver for testing, rather than LoopingCall’s “testing is at least possible” .reactor attribute. This means that out of the box it supports both Twisted and asyncio, and can easily have other things added.

3. The API is actually generic on what constitutes time itself, which means that you can use it for both short-term (i.e.: monotonic clock values as float-seconds) and long-term (civil times as timezone-aware datetime objects) recurring tasks. Recurrence rules can also be arbitrary functions.

4. There is a recursive driver (this is the “tree” part) which both allows for:

   a. groups of timers which can be suspended and resumed together, and

   b. scaling of time, so that you can e.g. speed up or slow down the ticks for AIs, groups of animations, and so on, also in groups.

5. The API is also generic on what constitutes work. This means that, for example, in a certain timer you can say “all work units scheduled on this scheduler, in addition to being callable, must also have an asJSON method”. And in fact that’s exactly what the longterm module in Fritter does.

I can neither confirm nor deny that this project was factored out of a game engine for a secret game project which does not appear on this list.

What have I done with it recently?

Besides realizing, in the course of writing this blog post, that its CI was failing its code quality static checks (oops), the last big change was the preliminary support for recursive timers and serialization.

What are my plans for it?

• These haven’t been tested in anger yet and I want to actually use them in a larger project to make sure that they don’t have any necessary missing pieces.

• Documentation.

Encrust

What is it?

I have written about Encrust quite recently so if you want to know about it, you should probably read that post. In brief, it is a code-shipping tool for py2app. It takes care of architecture-independence, code-signing, and notarization.

What have I done with it recently?

Wrote it. It’s brand new as of this month.

What are my plans for it?

I really want this project to go away as a tool with an independent existence. Either I want its lessons to be fully absorbed into Briefcase or perhaps py2app itself, or for it to become a library that those things call into to do its thing.

Various Small Mac Utilities

What is it?

• QuickMacApp is a very small library for creating status-item “menu bar apps” in Python which don’t have much of a UI but want to run some Python code in the background and occasionally pop up a notification or ask the user a question or something. The idea is that if you have a utility that needs a minimal UI to just ask the user one or two things, you should be able to give it a GUI immediately, without thinking about it too much.
• QuickMacHotkey this is a very minimal API to register hotkeys on macOS. this example is what comes up if you search the web for such a thing, but it hasn’t worked on a current Python for about 11 years. This isn’t the “right” way to do such a thing, since it provides no UI to set the shortcut, you’d have to hard-code it. But MASShortcut is now archived and I haven’t had the opportunity to investigate HotKey, so for the time being, it’s a handy thing, and totally adequate for the sort of quick-and-dirty applications you might make with QuickMacApp.
• VEnvDotApp is a way of giving a virtualenv its own Info.plist and bundle ID, so that command-line python tools that just need to pop up a little mac GUI, like an alert or a notification, can do so with cross-platform tools without looking like it’s an app called “Python”, or in some cases breaking entirely.
• MOPUp is a command-line updater for upstream Python.org macOS Python. For distributing third-party apps, Python.org’s version is really the one you want to use (it’s universal2, and it’s generally built with compiler options that make it a distributable thing itself) but updating it by downloading a .pkg file from a web browser is kind of annoying.

What have I done with it recently?

I’ve been releasing all these tools as they emerge and are factored out of other work, and they’re all fairly recent.

What are my plans for it?

I will continue to factor out any general-purpose tools from my platform-specific Python explorations — hopefully more Linux and Windows too, once I’ve got writing code for my own computer down, but most of the tools above are kind of “done” on their own, at the moment.

The two things that come to mind though are that QuickMacApp should have a way of owning the menubar sometimes (if you don’t have something like Bartender, menu-bar-status-item-only apps can look like they don’t do anything when you launch them), and that MOPUp should probably be upstreamed to python.org.

Pomodouroboros

What is it?

Pomodouroboros is a pomodoro timer with a highly opinionated take. It’s based on my own experience of ADHD time blindness, and is more like a therapeutic intervention for that specific condition than a typical “productivity” timer app.

In short, it has two important features that I have found lacking in other tools:

1. A gigantic, absolutely impossible to ignore visual timer that presents a HUD overlay over your entire desktop. It remains low-opacity and static most of the time but pulses every 30 seconds to remind you that time is passing.
2. Rather than requiring you to remember to set a timer before anything happens, it has an idea of “work hours” when you want to be time-sensitive and presents constant prompting to get started.

What have I done with it recently?

I’ve been working on it fairly consistently lately. The big things I’ve been doing have been:

1. factoring things out of the Pomodouroboros-specific code and into QuickMacApp and Encrust.
2. Porting the UI to the redesigned core of the application, which has been implemented and tested in platform-agnostic Python but does not have any UI yet.
3. fully productionizing the build process and ensuring that Encrust is producing binary app bundles that people can use.

What are my plans for it?

In brief, “finish the app”. I want this to have its own website and find a life beyond the Python community, with people who just want a timer app and don’t care how it’s written. The top priority is to replace the current data model, which is to say the parts of the UI that set and evaluate timers and edit the list of upcoming timers (the timer countdown HUD UI itself is fine).

I also want to port it to other platforms, particularly desktop Linux, where I know there are many users interested in such a thing. I also want to do a CLI version for folks who live on the command line.

Finally: Pomodouroboros serves as a test-bed for a larger goal, which is that I want to make it easier for Python programmers, particularly beginners who are just getting into coding at all, to write code that not only interacts with their own computer, but that they can share with other users in a real way. As you can see with Encrust and other projects above, as much as I can I want my bumpy ride to production code to serve as trailblazing so that future travelers of this path find it as easy as possible.

And Here Is Where The CTA Goes

If this stuff sounds compelling, you can obviously sign up, that would be great. But also, if you’re just curious, go ahead and give some of these projects some stars on GitHub or just share this post. I’d also love to hear from you about any of this!

If a lot of people find this compelling, then pursuing these ideas will become a full-time job, but I’m pretty far from that threshold right now. In the meanwhile, I will also be doing a bit of consulting work.

I believe much of my upcoming month will be spoken for with contracting, although quite a bit of that work will also be open source maintenance, for which I am very grateful to my generous clients. Please do get in touch if you have something more specific you’d like me to work on, and you’d like to become one of those clients as well.

Why Bother With All This?

In other words: if you want to run on an Apple platform, why not just write everything in an Apple programming language, like Swift? If you need to ship to multiple platforms, you might have to rewrite it all anyway, so why not give up?

Despite the significant investment that platform vendors make in their tools, I fundamentally believe that the core logic in any software application ought to be where its most important value lies. For small, independent developers, having portable logic that can be faithfully replicated on every platform without massive rework might be tricky to get started with, but if you can’t do it, it may not be cost effective to support multiple platforms at all.

So, it makes sense for me to write my applications in Python to achieve this sort of portability, even though on each platform it’s going to be a little bit more of a hassle to get it all built and shipped since the default tools don’t account for the use of Python.

But how much more is “a little bit” more of a hassle? I’ve been slowly learning about the pipeline to ship independently-distributed¹ macOS applications for the last few years, and I’ve encountered a ton of annoying roadblocks.

Didn’t You Do This Already? What’s New?

So nice of you to remember. Thanks for asking. While I’ve gotten this to mostly work in the past, some things have changed since then:

• the notarization toolchain has been updated (altool is now notarytool),
• I’ve had to ship libraries other than just PyGame,
• Apple Silicon launched, necessitating another dimension of build complexity to account for multiple architectures,
• Perhaps most significantly, I have written a tool that attempts to encode as much of this knowledge as possible, Encrust, which I have put on PyPI and GitHub. If this is of interest to you, I would encourage you to file bugs on it, and hopefully add in more corner cases which I have missed.

I’ve also recently shipped my first build of an end-user application that successfully launches on both Apple Silicon and Intel macs, so here is a brief summary of the hoops I needed to jump through, from the beginning, in order to make everything work.

Wait did you say you wrote a tool? Is this fixed, then?

Encrust is, I hope, a temporary stopgap on the way to a much better comprehensive solution.

Specifically, I believe that Briefcase is a much more holistic solution to the general problem being described here, but it doesn’t suit my very specific needs right now⁴, and it doesn’t address a couple of minor points that I was running into here.

It is mostly glue that is shelling out to other tools that already solve portions of the problem, even when better APIs exist. It addresses three very specific layers of complexity:

1. It enforces architecture independence, so that your app built on an M1 machine will still actually run on about half of the macs remaining out there².
2. It remembers tricky nuances of the notarization submission process, such as the highly specific way I need to generate my zip files to avoid mysterious notarization rejections³.
3. Providing a common and central way to store the configuration for these things across repositories so I don’t need to repeat this process and copy/paste a shell script every time I make a tiny new application.

It only works on Apple Silicon macs, because I didn’t bother to figure out how pip actually determines which architecture to download wheels for.

As such, unfortunately, Encrust is mostly a place for other people who have already solved this problem to collaborate to centralize this sort of knowledge and share ideas about where this code should ultimately go, rather than a tool for users trying to get started with shipping an app.

Open Offer

That said:

1. I want to help Python developers ship their Python apps to users who are not also Python developers.
2. macOS is an even trickier platform to do that on than most.
3. It’s now easy for me to sign, notarize, and release new applications reliably

Therefore:

If you have an open source Python application that runs on macOS⁵ but can’t ship to macOS — either because:

1. you’ve gotten stuck on one of the roadblocks that this post describes,
2. you don’t have $100 to give to Apple, or because
3. the app is using a cross-platform toolkit that should work just fine and you don’t have access to a mac at all, then

Send me an email and I’ll sign and post your releases.

What’s this post about, then?

People still frequently complain that “Python packaging” is really bad. And I’m on record that packaging Python (in the sense of “code”) for Python (in the sense of “deployment platform”) is actually kind of fine right now; if what you’re trying to get to is a package that can be pip installed, you can have a reasonably good experience modulo a few small onboarding hiccups that are well-understood in the community and fairly easy to overcome.

However, it’s still unfortunately hard to get Python code into the hands of users who are not also Python programmers with their own development environments.

My goal here is to document the difficulties themselves to try to provide a snapshot of what happens if you try to get started from scratch today. I think it is useful to record all the snags and inscrutable error messages that you will hit in a row, so we can see what the experience really feels like.

I hope that everyone will find it entertaining.

• Other Mac python programmers might find pieces of trivia useful, and
• Linux users will have fun making fun of the hoops we have to jump through on Apple platforms,

but the main audience is the maintainers of tools like Briefcase and py2app to evaluate the new-user experience holistically, and to see how much the use of their tools feels like this. This necessarily includes the parts of the process that are not actually packaging.

This is why I’m starting from the beginning again, and going through all the stuff that I’ve discussed in previous posts again, to present the whole experience.

Here Goes

So, with no further ado, here is a non-exhaustive list of frustrations that I have encountered in this process:

• Okay. Time to get started. How do I display a GUI at all? Nothing happens when I call some nominally GUI API. Oops: I need my app to exist in an app bundle, which means I need to have a framework build. Time to throw those partially-broken pyenv pythons in the trash; best to use the official python.org from here on out.
• Bonus Frustration since I’m using AppKit directly: why is my app segfaulting all the time? Oh, target is a weak reference in objective C, so if I make a window and put a button in it that points at a Python object, the Python interpreter deallocates it immediately because only the window (which is “nothing” as it’s a weakref) is referring to it. I need to start stuffing every Python object that talks to a UI element like a window or a button into a global list, or manually calling .retain() on all of them and hoping I don’t leak memory.
• Everything seems to be using the default Python Launcher icon, and the app menu says “Python”. That wouldn’t look too good to end users. I should probably have my own app.
• I’ll skip the part here where the author of a new application might have to investigate py2app, briefcase, pyoxidizer, and pyinstaller separately and try to figure out which one works the best right now. As I said above, I started with py2app and I’m stubborn to a fault, so that is the one I’m going to make work.
• Now I need to set up py2app. Oops, I can’t use pyproject.toml any more, time to go back to setup.py.
• Now I built it and the the app is crashing on startup when I click on it. I can’t see a traceback anywhere, so I guess I need to do something in the console.
  • Wow; the console is an unusable flood of useless garbage. Forget that.
  • I guess I need to run it in the terminal somehow. After some googling I figure out it’s ./dist/MyApp.app/Contents/Resources/MacOS/MyApp. Aha, okay, I can see the traceback now, and it’s … an import error?
  • Ugh, py2app isn’t actually including all of my code, it’s using some magic to figure out which modules are actually used, but it’s doing it by traversing import statements, which means I need to put a bunch of fake static import statements for everything that is used indirectly at the top of my app’s main script so that it gets found by the build. I experimentally discover a half a dozen things that are dynamically imported inside libraries that I use and jam them all in there.
• Okay. Now at least it starts up. The blank app icon is uninspiring, though, time to actually get my own icon in there. Cool, I’ll make an icon in my favorite image editor, and save it as... icons must be PNGs, right? Uhh... no, looks like they have to be .icns files. But luckily I can convert the PNG I saved with a simple 12-line shell script that invokes sips and iconutil⁶.

At this point I have an app bundle which kinda works. But in order to run on anyone else’s computer, I have to code-sign it.

• In order to code-sign anything, I have to have an account with Apple that costs $99 per year, on developer.apple.com.
• The easiest way to get these certificates is to log in to Xcode itself. There’s a web portal too but using it appears to involve a lot more manual management of key material, so, no thanks. This requires the full-fat Xcode.app though, not just the command-line tools that come down when I run xcode-select --install, so, time to wait for an 11GB download.
• Oops, I made the wrong certificate type. Apparently the only right answer here is a “Developer ID Application” certificate.
• Now that I’ve logged in to Xcode to get the certificate, I need to figure out how to tell my command-line tools about it (for starters, “codesign”). Looks like I need to run security find-identity -v -p codesigning.
• Time to sign the application’s code.
  • The codesign tool has a --deep option which can sign the whole bundle. Great!
  • Except, that doesn’t work, because Python ships shared libraries in locations that macOS doesn’t automatically expect, so I have to manually locate those files and sign them, invoking codesign once for each.
  • Also, --deep is deprecated. There’s no replacement.
  • Logically, it seems like I still need --deep, because it does some poorly-explained stuff with non-code resource files that maybe doesn’t happen properly if I don’t? Oh well. Let's drop the option and hope for the best.⁸
  • With a few heuristics I think we can find all the relevant files with a little script⁷.

Now my app bundle is signed! Hooray. 12 years ago, I’d be all set. But today I need some additional steps.

• After I sign my app, Apple needs to sign my app (to indicate they’ve checked it for malware), which is called “notarization”.
  • In order to be eligible for notarization, I can’t just code-sign my app. I have to code-sign it with entitlements.
  • Also I can’t just code sign it with entitlements, I have to sign it with the hardened runtime, or it fails notarization.
  • Oops, out of the box, the hardened runtime is incompatible with a bunch of stuff in Python, including cffi and ctypes, because nobody has implemented support for MAP_JIT yet, so it crashes at startup. After some thrashing around I discover that I need a legacy “allow unsigned executable memory” entitlement. I can’t avoid importing this because a bunch of things in py2app’s bootstrapping code import things that use ctypes, and probably a bunch of packages which I’m definitely going to need, like cryptography require cffi directly anyway.
  • In order to set up notarization external to Xcode, I need to create an App Password which is set up at appleid.apple.com, not the developer portal.
  • Bonus Frustration since I’ve been doing this for a few years: Originally this used to be even more annoying as I needed to wait for an email (with altool), and so I couldn’t script it directly. Now, at least, the new notarytool (which will shortly be mandatory) has a --wait flag.
  • Although the tool is documented under man notarytool, I actually have to run it as xcrun notarytool, even though codesign can be run either directly or via xcrun codesign.
  • Great, we’re ready to zip up our app and submit to Apple. Wait, they’re rejecting it? Why???
  • Aah, I need to manually copy and paste the UUID in the console output of xcrun notarytool submit into xcrun notarytool log to get some JSON that has some error messages embedded in it.
  • Oh. The bundle contains internal symlinks, so when I zipped it without the -y option, I got a corrupt archive.
  • Great, resubmitted with zip -y.
  • Oops, just kidding, that only works sometimes. Later, a different submission with a different hash will fail, and I’ll learn that the correct command line is actually ditto -c -k --sequesterRsrc --keepParent MyApp.app MyApp.app.zip.
    • Note that, for extra entertainment value, the position of the archive itself and directory are reversed on the command line from zip (and tar, and every other archive tool).
  • notarytool doesn’t record anything in my app though; it puts the “notarization ticket” on Apple's servers. Apparently, I still need to run stapler for users to be able to launch it while those servers are inaccessible, like, for example, if they’re offline.
  • Oops, not stapler. xcrun stapler. Whatever.
  • Except notarytool operates on a zip archive, but stapler operates on an app bundle. So we have to save the original app bundle, run stapler on it, then re-archive the whole thing into a new archive.

Hooray! Time to release my great app!

• Whoops, just got a bug report that it crashes immediately on every Intel mac. What’s going on?
• Turns out I’m using a library whose authors distribute both aarch64 and x86_64 wheels; pip will prefer single-architecture wheels even if universal2 wheels are also available, so I’ve got to somehow get fat binaries put together. Am I going to have to build a huge pile of C code by myself? I thought all these Python hassles would at least let me avoid the C hassles!

• Whew, okay, no need for that: there’s an amazing Swiss-army knife for macOS binary wheels, called delocate that includes a delocate-fuse tool that can fuse two wheels together. So I just need to figure out which binaries are the wrong architecture and somehow install my fixed/fused wheels before building my app with py2app.

  • except, oops, this tool just rewrites the file in-place without even changing its name, so I have to write some janky shell scripts to do the reinstallation. Ugh.

• OK now that all that is in place, I just need to re-do all the steps:

  • universal2-ize my virtualenv!
  • build!
  • sign!
  • archive!
  • notarize!
  • wait!!!
  • staple!
  • re-archive!
  • upload!

And we have an application bundle we can ship to users.

It’s just that easy.

As long as I don’t need sandboxing or Mac App Store distribution, of course. That’s a challenge for another day.

So, that was terrible. But what should be happening here?

Some of this is impossible to simplify beyond a certain point - many of the things above are not really about Python, but are about distribution requirements for macOS specifically, and we in the Python community can’t affect operating system vendors’ tooling.

What we can do is build tools that produce clear guidance on what step is required next, handle edge cases on their own, and generally guide users through these complex processes without requiring them to hit weird binary-format or cryptographic-signing errors on their own with no explanation of what to do next.

I do not think that documentation is the answer here. The necessary steps should be discoverable. If you need to go to a website, the tool should use the webbrowser module to open a website. If you need to launch an app, the tool should launch that app.

With Encrust, I am hoping to generalize the solutions that I found while working on this for this one specific slice of the app distribution pipeline — i.e. a macOS desktop application desktop, as distributed independently and not through the mac app store — but other platforms will need the same treatment.

However, even without really changing py2app or any of the existing tooling, we could imagine a tool that would interactively prompt the user for each manual step, automate as much of it as possible, verify that it was performed correctly, and give comprehensible error messages if it was not.

For a lot of users, this full code-signing journey may not be necessary; if you just want to run your code on one or two friends’ computers, telling them to right click, go to ‘open’ and enter their password is not too bad. But it may not even be clear to them what the trade-off is, exactly; it looks like the app is just broken when you download it. The app build pipeline should tell you what the limitations are.

Other parts of this just need bug-fixes to address. py2app specifically, for example, could have a better self-test for its module-collecting behavior, launching an app to make sure it didn’t leave anything out.

Interactive prompts to set up a Homebrew tap, or a Flatpak build, or a Microsoft Store Metro app, might be similarly useful. These all have outside-of-Python required manual steps, and all of them are also amenable to at least partial automation.

Thanks to my patrons on Patreon for supporting this sort of work, including development of Encrust, of Pomodouroboros, of posts like this one and of that offer to sign other people’s apps. If you think this sort of stuff is worthwhile, you might want to consider supporting me over there as well.

Is there a place for non-@dataclass classes in Python any more?

I have previously — and somewhat famously — written favorably about @dataclass’s venerable progenitor, attrs, and how you should use it for pretty much everything.

At the time, attrs was an additional dependency, a piece of technology that you could bolt on to your Python stack to make your particular code better. While I advocated for it strongly, there are all the usual implicit reasons against using a new thing. It was an additional dependency, it might not interoperate with other convenience mechanisms for type declarations that you were already using (i.e. NamedTuple), it might look weird to other Python programmers familiar with existing tools, and so on. I don’t think that any of these were good counterpoints, but there was nevertheless a robust discussion to be had in addressing them all.

But for many years now, dataclasses have been — and currently are — built in to the language. They are increasingly integrated to the toolchain at a deep level that is difficult for application code — or even other specialized tools — to replicate. Everybody knows what they are. Few or none of those reasons apply any longer.

For example, classes defined with @dataclass are now optimized as a C structure might be when you compile them with mypyc, a trick that is extremely useful in some circumstances, which even attrs itself now has trouble keeping up with.

This all raises the question for me: beyond backwards compatibility, is there any point to having non-@dataclass classes any more? Is there any remaining justification for writing them in new code?

Consider my original example, translated from attrs to dataclasses. First, the non-dataclass version:

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class Point3D:
    def __init__(self, x, y, z):
        self.x = x
        self.y = y
        self.z = z

And now the dataclass one:

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from dataclasses import dataclass

@dataclass
class Point3D:
    x: int
    y: int
    z: int

Many of my original points still stand. It’s still less repetitive. In fewer characters, we’ve expressed considerably more information, and we get more functionality (repr, sorting, hashing, etc). There doesn’t seem to be much of a downside besides the strictness of the types, and if typing.Any were a builtin, x: any would be fine for those who don’t want to unduly constrain their code.

The one real downside of the latter over the former right now is the need for an import. Which, at this point, just seems… confusing? Wouldn’t it be nicer to be able to just write this:

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class Point3D:
    x: int
    y: int
    z: int

and not need to faff around with decorator semantics and fudging the difference between Mypy (or Pyright or Pyre) type-check-time and Mypyc or Cython compile time? Or even better, to not need to explain the complexity of all these weird little distinctions to new learners of Python, and to have to cover import before class?

These tools all already treat the @dataclass decorator as a totally special language construct, not really like a decorator at all, so to really explore it you have to explain a special case and then a special case of a special case. The extension hook for this special case of the special case notwithstanding.

If we didn’t want any new syntax, we would need a from __future__ import dataclassification or some such for a while, but this doesn’t seem like an impossible bar to clear.

There are still some folks who don’t like type annotations at all, and there’s still the possibility of awkward implicit changes in meaning when transplanting code from a place with dataclassification enabled to one without, so perhaps an entirely new unambiguous syntax could be provided. One that more closely mirrors the meaning of parentheses in def, moving inheritance (a feature which, whether you like it or not, is clearly far less central to class definitions than ‘what fields do I have’) off to its own part of the syntax:

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data Point3D(x: int, y: int, z: int) from Vector:
    def method(self):
        ...

which, for the “I don’t like types” contingent, could reduce to this in the minimal case:

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data Point3D(x, y, z):
    pass

Just thinking pedagogically, I find it super compelling to imagine moving from teaching def foo(x, y, z):... to data Foo(x, y, z):... as opposed to @dataclass class Foo: x: int....

I don’t have any desire for semantic changes to accompany this, just to make it possible for newcomers to ignore the circuitous historical route of the @dataclass syntax and get straight into defining their own types with legible reprs from the very beginning of their Python journey.

(And make it possible for me to skip a couple of lines of boilerplate in short examples, as a bonus.)

I’m curious to know what y’all think, though. Shoot me an email or a toot and let me know.

In particular:

1. Do you think there’s some reason I’m missing why Python’s current method for defining classes via a bunch of dunder methods is still better than dataclasses, or should stick around into the future for reasons beyond “compatibility”?
2. Do you think “compatibility” is sufficient reason to keep the syntax the way it is forever, and I’m underestimating the cost of adding a keyword like this?
3. If you do think that a change should be made, would you prefer:
   1. changing the meaning of class itself via a __future__ import,
   2. a new data keyword like the one I’ve proposed,
   3. a new keyword that functions exactly like the one I have proposed but really want to bikeshed the word data a bunch,
   4. something more incremental like just putting dataclass and field in builtins,
   5. or an option I haven’t even contemplated here?

If I find I’m not alone in this perhaps I will wander over to the Python discussion boards to have a more substantive conversation...

Thank you to my patrons who are helping me while I try to turn… whatever this is… along with open source maintenance and application development, into a real job. Do you want to see me pursue ideas like this one further? If so, you can support me on Patreon as well!

One of the persistently lesser-known symptoms of ADHD is hyperfocus. It is sometimes quasi-accurately described as a “superpower”^{1 2}, which it can be. In the right conditions, hyperfocus is the ability to effortlessly maintain a singular locus of attention for far longer than a neurotypical person would be able to.

However, as a general rule, it would be more accurate to characterize hyperfocus not as an “ability to focus on X” but rather as “an inability to focus on anything other than X”. Sometimes hyperfocus comes on and it just digs its claws into you and won’t let go until you can achieve some kind of closure.

Recently, the X I could absolutely not stop focusing on — for days at a time — was this extremely annoying picture:

Which lead to me writing the silliest computer program I have written in quite some time.

You see, for some reason, macOS seems to prefer YUV422 chroma subsampling³ on external displays, even when the bitrate of the connection and selected refresh rate support RGB.⁴ Lots of people have been trying to address this for a literal decade^{5 6 7 8 9 10 11}, and the problem has gotten worse with Apple Silicon, where the operating system no longer even supports the EDID-override functionality available on every other PC operating system that supports plugging in a monitor.

In brief, this means that every time I unplug my MacBook from its dock and plug it back in more than 5 minutes later, its color accuracy is destroyed and red or blue text on certain backgrounds looks like that mangled mess in the picture above. Worse, while the color distinction is definitely noticeable, it’s so subtle that it’s like my display is constantly gaslighting me. I can almost hear it taunting me:

Magenta? Yeah, magenta always looked like this. Maybe it’s the ambient lighting in this room. You don’t even have a monitor hood. Remember how you had to use one of those for print design validation? Why would you expect it to always look the same without one?

Still, I’m one of the luckier people with this problem, because I can seem to force RGB / 444 color format on my display just by leaving the display at 120Hz rather than 144, then toggling HDR on and then off again. At least I don’t need to plug in the display via multiple HDMI and displayport cables and go into the OSD every time. However, there is no API to adjust, or even discover the chroma format of your connected display’s link, and even the accessibility features that supposedly let you drive GUIs are broken in the system settings “Displays” panel¹², so you have to do it by sending synthetic keystrokes and hoping you can tab-focus your way to the right place.

Anyway, this is a program which will be useless to anyone else as-is, but if someone else is struggling with the absolute inability to stop fiddling with the OS to try and get colors to look correct on a particular external display, by default, all the time, maybe you could do something to hack on this:

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import os
from Quartz import CGDisplayRegisterReconfigurationCallback, kCGDisplaySetMainFlag, kCGDisplayBeginConfigurationFlag
from ColorSync import CGDisplayCreateUUIDFromDisplayID
from CoreFoundation import CFUUIDCreateString
from AppKit import NSApplicationMain, NSApplicationActivationPolicyAccessory, NSApplication

NSApplication.sharedApplication().setActivationPolicy_(NSApplicationActivationPolicyAccessory)

CGDirectDisplayID = int
CGDisplayChangeSummaryFlags = int

MY_EXTERNAL_ULTRAWIDE = '48CEABD9-3824-4674-9269-60D1696F0916'
MY_INTERNAL_DISPLAY = '37D8832A-2D66-02CA-B9F7-8F30A301B230'

def cb(display: CGDirectDisplayID, flags: CGDisplayChangeSummaryFlags, userInfo: object) -> None:
    if flags & kCGDisplayBeginConfigurationFlag:
        return
    if flags & kCGDisplaySetMainFlag:
        displayUuid = CGDisplayCreateUUIDFromDisplayID(display)
        uuidString = CFUUIDCreateString(None, displayUuid)
        print(uuidString, "became the main display")
        if uuidString == MY_EXTERNAL_ULTRAWIDE:
            print("toggling HDR to attempt to clean up subsampling")
            os.system("/Users/glyph/.local/bin/desubsample")
            print("HDR toggled.")

print("registered", CGDisplayRegisterReconfigurationCallback(cb, None))

NSApplicationMain([])

and the linked desubsample is this atrocity, which I substantially cribbed from this helpful example:

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#!/usr/bin/osascript

use AppleScript version "2.4" -- Yosemite (10.10) or later
use framework "Foundation"
use framework "AppKit"
use scripting additions

tell application "System Settings"
    quit
    delay 1
    activate
    current application's NSWorkspace's sharedWorkspace()'s openURL:(current application's NSURL's URLWithString:"x-apple.systempreferences:com.apple.Displays-Settings.extension")
    delay 0.5

    tell application "System Events"
    tell process "System Settings"
        key code 48
        key code 48
        key code 48
            delay 0.5
        key code 49
        delay 0.5
        -- activate hdr on left monitor

        set hdr to checkbox 1 of group 3 of scroll area 2 of ¬
                group 1 of group 2 of splitter group 1 of group 1 of ¬
                window "Displays"
        tell hdr
                click it
                delay 1.0
                if value is 1
                    click it
                end if
        end tell

    end tell
    end tell
    quit
end tell

This ridiculous little pair of programs does it automatically, so whenever I reconnect my MacBook to my desktop dock at home, it faffs around with clicking the HDR button for me every time. I am leaving it running in a background tmux session so — hopefully — I can finally stop thinking about this.

One potato, two potato, three potato, four
Five potato, six potato, seven potato, more.

Traditional Children’s Counting Rhyme

Programmers waste enormous amounts of time thinking about, or worrying about, the speed of noncritical parts of their programs, and these attempts at efficiency actually have a strong negative impact when debugging and maintenance are considered. We should forget about small efficiencies, say about 97% of the time: premature optimization is the root of all evil. Yet we should not pass up our opportunities in that critical 3%.

Knuth, Donald
“Structured Programming with go to statements”
Computing Surveys, Vol. 6, No. 4, December 1974
(p. 268)
(Emphasis mine)

Knuth’s admonition about premature optimization is such a cliché among software developers at this point that even the correction to include the full context of the quote is itself a a cliché.

Still, it’s a cliché for a reason: the speed at which software can be written is in tension — if not necessarily in conflict — with the speed at which it executes. As Nelson Elhage has explained, software can be qualitatively worse when it is slow, but spending time optimizing an algorithm before getting any feedback from users or profiling the system as a whole can lead one down many blind alleys of wasted effort.

In that same essay, Nelson further elaborates that performant foundations simplify architecture¹. He then follows up with several bits of architectural advice that is highly specific to parsing—compilers and type-checkers specifically—which, while good, is hard to generalize beyond “optimizing performance early can also be good”.

So, here I will endeavor to generalize that advice. How does one provide a performant architectural foundation without necessarily wasting a lot of time on early micro-optimization?

Enter The Potato

Many years before Nelson wrote his excellent aforementioned essay, my father coined a related term: “Potato Programming”.

In modern vernacular, a potato is very slow hardware, and “potato programming” is the software equivalent of the same.

The term comes from the rhyme that opened this essay, and is meant to evoke a slow, childlike counting of individual elements as an algorithm operates upon them. it is an unfortunately quite common software-architectural idiom whereby interfaces are provided in terms of scalar values. In other words, APIs that require you to use for loops or other forms of explicit, individual, non-parallelized iteration. But this is all very abstract; an example might help.

For a generic business-logic example, let’s consider the problem of monthly recurring billing. Every month, we pull in the list of all of all subscriptions to our service, and we bill them.

Since our hypothetical company has an account-management team that owns the UI which updates subscriptions and a billing backend team that writes code to interface with 3rd-party payment providers, we’ll create 2 backends, here represented by some Protocols.

Finally, we’ll have an orchestration layer that puts them together to actually run the billing. I will use async to indicate which things require a network round trip:

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class SubscriptionService(Protocol):
    async def all_subscriptions(self) -> AsyncIterable[Subscription]:
        ...

class Subscription(Protocol):
    account_id: str
    to_charge_per_month: money

class BillingService(Protocol):
    async def bill_amount(self, account_id: str, amount: money) -> None:
        ...

To many readers, this may look like an entirely reasonable interface specification; indeed, it looks like a lot of real, public-facing “REST” APIs. An equally apparently-reasonable implementation of our orchestration between them might look like this:

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async def billing(s: SubscriptionService, b: BillingService) -> None:
    async for sub in s.all_subscriptions():
        await b.bill_amount(sub.account_id, sub.to_charge_per_month)

This is, however, just about the slowest implementation of this functionality that it’s possible to implement. So, this is the bad version. Let’s talk about the good version: no-tato programming, if you will. But first, some backstory.

Some Backstory

My father began his career as an APL programmer, and one of the key insights he took away from APL’s architecture is that, as he puts it:

Computers like to do things over and over again. They like to do things on arrays. They don’t want to do things on scalars. So, in fact, it’s not possible to write a program that only does things on a scalar. [...] You can’t have an ‘integer’ in APL, you can only have an ‘array of integers’. There’s no ‘loop’s, there’s no ‘map’s.

APL, like Python², is typically executed via an interpreter. Which means, like Python, execution of basic operations like calling functions can be quite slow. However, unlike Python, its pervasive reliance upon arrays meant that almost all of its operations could be safely parallelized, and would only get more and more efficient as more and more parallel hardware was developed.

I said ‘unlike Python’ there, but in fact, my father first related this concept to me regarding a part of the Python ecosystem which follows APL’s design idiom: NumPy. NumPy takes a similar approach: it cannot itself do anything to speed up Python’s fundamental interpreted execution speed³, but it can move the intensive numerical operations that it implements into operations on arrays, rather than operations on individual objects, whether numbers or not.

The performance difference involved in these two styles is not small. Consider this case study which shows a 5828% improvement⁴ when taking an algorithm from idiomatic pure Python to NumPy.

This idiom is also more or less how GPU programming works. GPUs cannot operate on individual values. You submit a program⁵ to the GPU, as well as a large array of data⁶, and the GPU executes the program on that data in parallel across hundreds of tiny cores. Submitting individual values for the GPU to work on would actually be much slower than just doing the work on the CPU directly, due to the bus latency involved to transfer the data back and forth.

Back from the Backstory

This is all interesting for a class of numerical software — and indeeed it works very well there — but it may seem a bit abstract to web backend developers just trying to glue together some internal microservice APIs, or indeed most app developers who aren’t working in those specialized fields. It’s not like Stripe is going to let you run their payment service on your GPU.

However, the lesson generalizes quite well: anywhere you see an API defined in terms of one-potato, two-potato iteration, ask yourself: “how can this be turned into an array”? Let’s go back to our example.

The simplest change that we can make, as a consumer of these potato-shaped APIs, is to submit them in parallel. So if we have to do the optimization in the orchestration layer, we might get something more like this:

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from asyncio import Semaphore, AbstractEventLoop

async def one_bill(
    loop: AbstractEventLoop,
    sem: Semaphore,
    sub: Subscription,
    b: BillingService,
) -> None:
    await sem.acquire()
    async def work() -> None:
        try:
            await b.bill_amount(sub.account_id, sub.to_charge_per_month)
        finally:
            sem.release()
    loop.create_task(work)

async def billing(
    loop: AbstractEventLoop,
    s: SubscriptionService,
    b: BillingService,
    batch_size: int,
) -> None:
    sem = Semaphore(batch_size)
    async for sub in s.all_subscriptions():
        await one_bill(loop, sem, sub, b)

This is an improvement, but it’s a bit of a brute-force solution; a multipotato, if you will. We’ve moved the work to the billing service faster, but it still has to do just as much work. Maybe even more work, because now it’s potentially got a lot more lock-contention on its end. And we’re still waiting for the Subscription objects to dribble out of the SubscriptionService potentially one request/response at a time.

In other words, we have used network concurrency as a hack to simulate a performant design. But the back end that we have been given here is not actually optimizable; we do not have a performant foundation. As you can see, we have even had to change our local architecture a little bit here, to include a loop parameter and a batch_size which we had not previously contemplated.

A better-designed interface in the first place would look like this:

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class SubscriptionService(Protocol):
    async def all_subscriptions(
        self, batch_size: int,
    ) -> AsyncIterable[Sequence[Subscription]]:
        ...

class Subscription(Protocol):
    account_id: str
    to_charge_per_month: money

@dataclass
class BillingRequest:
    account_id: str
    amount: money

class BillingService(Protocol):
    async def submit_bills(
        self,
        bills: Sequence[BillingRequest],
    ) -> None:
        ...

Superficially, the implementation here looks slightly more awkward than our naive first attempt:

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async def billing(s: SubscriptionService, b: BillingService, batch_size: int) -> None:
    async for sub_batch in s.all_subscriptions(batch_size):
        await b.submit_bills(
            [
                BillingRequest(sub.account_id, sub.to_charge_per_month)
                for sub in sub_batch
            ]
        )

However, while the implementation with batching in the backend is approximately as performant as our parallel orchestration implementation, backend batching has a number of advantages over parallel orchestration.

First, backend batching has less internal complexity; no need to have a Semaphore in the orchestration layer, or to create tasks on an event loop. There’s less surface area here for bugs.

Second, and more importantly: backend batching permits for future optimizations within the backend services, which are much closer to the relevant data and can achieve more substantial gains than we can as a client without knowledge of their implementation.

There are many ways this might manifest, but consider that each of these services has their own database, and have got to submit queries and execute transactions on those databases.

In the subscription service, it’s faster to run a single SELECT statement that returns a bunch of results than to select a single result at a time. On the billing service’s end, it’s much faster to issue a single INSERT or UPDATE and then COMMIT for N records at once than to concurrently issue a ton of potentially related modifications in separate transactions.

Potato No Mo

The initial implementation within each of these backends can be as naive and slow as necessary to achieve an MVP. You can do a SELECT … LIMIT 1 internally, if that’s easier, and performance is not important at first. There can be a mountain of potatoes hidden behind the veil of that batched list. In this way, you can avoid the potential trap of premature optimization. Maybe this is a terrible factoring of services for your application in the first place; best to have that prototype in place and functioning quickly so that you can throw it out faster!

However, by initially designing an interface based on lists of things rather than individual things, it’s much easier to hide irrelevant implementation details from the client, and to achieve meaningful improvements when optimizing.

Acknowledgements

This is the first post supported by my Patreon, with a topic suggested by a patron.