I’ve been daily driving the PinePhone Pro with swmo for some times now, it’s not perfect but I still find it be one of the most enjoyable devices I’ve used. Probably only behind BlackBerry Q30/Passport which also has a decent keyboard and runs an unfortunately locked-down version of QNX. For me it’s less like a phone and more like a portable terminal for times when using a full size laptop is uncomfortable or impractical, and with the keyboard it’s possible to write lengthy articles on the go.

This isn’t the only portable Linux terminal I owned, before this I used a Nokia N900 which till this day is still being maintained by the maemo leste team, but the shutdown of 3G network in where I live made it significantly less usable as a phone and since it doesn’t have a proper USB port I cannot use it as a serial console easily.

The overall experience on the PPP now as of 2024 isn’t as polished as that of the BlackBerry Passport, and adhoc hacks are often required to get the system going, however as the ecosystem progress the experience will also improve with new revisions of hardware and better software.

Initial Setup #

I use sxmo and swmo interchangeably in this post, they refer to the same framework running under Xorg and wayland, the experience is pretty much the same.

Sxmo is packaged for Debian:

sudo apt install sway sxmo-util

Allow access to LED/brightness:

sudo usermod -aG feedbackd user

Scaling Under Wayland #

The default scaling of sxmo doesn’t allow the many desktop applications to display their window properly, especially when such application is written under the assumption of being used on a larger screen. To set the scaling to something more reasonable, add the following line to ~/.config/sxmo/sway:

exec wlr-randr --output DSI-1 --scale 1.3

When using swmo environment initialization is mostly done in ~/.config/sxmo/sway and ~/.config/sxmo/xinit is not used.

Scaling for Firefox needs to be adjusted separately by first enabling compact UI and then set settings -> default zoom to your liking.

Landscape Setup #

I used lightdm as my session manager, to launch lightdm in landscape mode, change the display-setup-script line in /etc/lightdm/lightdm.conf to:

display-setup-script=sh -c 'xrandr -o right; exit 0'

To rotate to swmo to landscape mode on start:

$ echo exec sxmo_rotate.sh >> ~/.config/sxmo/sway

To rotate Linux framebuffer, add fbcon=rotate:1 to the U_BOOT_PARAMETERS line in /usr/share/u-boot-menu/conf.d/mobian.conf and run u-boot-update to apply.

I also removed quiet splash from U_BOOT_PARAMETERS to disable polymouth animation as it isn’t very useful on landscape mode.

Password-Lockable Screen #

Swmo doesn’t come with a secure screen locker. but swaylock works fine and it can be bind to a key combination with sway’s configure file. To save some battery life, systemctl suspend can be triggered after swaylock, to bind that to Meta+L:

# .config/sxmo/sway
bindsym $mod+l exec 'swaylock -f -c 000000 && systemctl suspend'

In suspend mode, the battery discharge at a rate of about 1% per hour, I consider this to be more than acceptable.

To unlock from a shell, just kill swaylock.

Before you can suspend the system as a non-root user, the following polkit rule needs to be written to /etc/polkit-1/rules.d/85-suspend.rules:

polkit.addRule(function(action, subject) {
    if (action.id == "org.freedesktop.login1.suspend" &&
        subject.isInGroup("users")) {
        return polkit.Result.YES;
    }
});

It would be better if there can be a universal interactive user group which automatically grant such permission to the desktop/mobile user.

Extra Keymaps #

The default keymap for the PinePhone keyboard is missing a few useful keys, namely F11/F12 and PgUp/PgDown. To create those keys I used evremap(1) to make a custom keymap. Unfortunately the Fn key cannot be mapped as a layer switcher easily, so I opted to remap AltG and Esc as my primary modifiers.

I’m working on a Debian package for evremap and it will be made available for Debian/Mobian soon.

Isolate workload with incus containers #

Incus is a container/VM manager for Linux, it’s available for Debian from bookworm/backports and is a fork of LXD by the original maintainers behind LXD. It works well for creating isolated and unprivileged containers. I have multiple incus containers on the PinePhone Pro for Debian packaging and it’s a better experience than manually creating and managing chroots. In case there is a need for running another container inside an unprivileged incus container, it’s possible to configure incus to intercept certain safe system calls and forward them to the host, removing the need for using privileged container.

Convergence #

Sway is decently usable in convergence mode, in which the phone is connected to a dock that outputs to an external display and keyboard and mouse are used as primary controls instead of the touchscreen.

This isn’t surprising since sway always had great support for multi monitor, however another often overlooked convergence mode is with waypipe. In this mode another Linux machine (e.g. a laptop) can be used to interact with applications running on the phone and the phone will be kept charged by the laptop. This is particularly useful for debugging phone applications or for accessing resources on the phone (e.g. sending and receiving sms). One thing missing in this setup is that graphic applications cannot roam between the phone and the external system (e.g. move running applications from one machine to another). Xpra does this for Xorg but doesn’t work with wayland.

Security #

Due to the simplicity of the swmo environment it’s not too difficult to get the system running with SELinux in Enforcing mode, and I encourage everyone reading this to try it. If running debian/mobian a good starting point is the SELinux/Setup page on Debian wiki.

Note: selinux-activate won’t add the required security=selinux kernel option to u-boot (it only deals with GRUB) so you have to manually add it to the U_BOOT_PARAMETERS line in /usr/share/u-boot-menu/conf.d/mobian.conf and run u-boot-update after selinux-activate. The file labeling process can easily take 10 minutes and the progress won’t be displayed on the framebuffer (only visible via the serial console).

SELinux along with the reference policy aren’t enough for building a reasonably secure interactive system, but let’s leave that for a future post.

The April 2024 meeting is the first meeting after Everything Open 2024 and the discussions are primarily around talks and lectures people found interesting during the conference, including the n3n VPN and the challenges of running personal email server. At the start of the meeting Yifei Zhan demonstrated a development build of Maemo Leste, an active Maemo-like operating system running on a PinePhone Pro.

Other topics discussed including modern network protocol ossification, SIP and possible free and open source VoLTE implementation.

The melting plastic and the smoke #

The PinePhone keyboard contains a battery, which will be used to charge the PinePhone when the keyboard is attached. Althrough there are existing warnings on the pine64 wiki which sums up to ‘don’t charge or connect anything to your pinephone’s type C interface when the keyboard is attached’, my two pinephone keyboards still managed to fry themselves, with one releasing stinky magic smoke and the other melting the plastic around the pogo pins on the pinephone backplate.

This all happened while the pinephone’s type C interface being physically block when attached to the keyboard. In the first case, the keyboard’s controller PCB blew up when I tried to charge it, in the latter case the keyboard somehow overheated and melted the plastic near the pogo interface on the phone side.

Pine64 provided me a free replacement keyboard after multiple emails back and forth, but according to Pine64 there will be no more free replacement for me in future, and there is no guarantee that this will not happen to my replacement keyboard.

The cost for replacing all the fried parts with spare parts from the Pine64 store is about 40 USD (pogo pins + backplate + keyboard PCB), and considering this problem is likely to happen again, I don’t think purchasing those parts is a wise decision.

Mitigation #

Both the melting plastic and the magic smoke originated from the fact that charges are constantly shuffled around when the keyboard is attached to the pinephone, and since the keyboard can function independently from the battery, we can disconnect and remove the battery from the keyboard case to make sure it will not blow up again. After such precedure the keyboard will keep functioning althrough the keyboard-attached pinephone might flip over much more easily due to the lightened keyboard base. Be aware that the keyboard isn’t designed to be taken apart, and doing so will likely result in scratches on the case. As for me, I’d much rather have a keyboard case without builtin battery than have something that can overheat or blow up.

I didn’t take as much notes on day 2 and 3, so I merged them into a single article.

Wednesday, 17 Apr 2024 #

Keynote: How Adversaries Use AI #

• Adversaries:

  • Nation States
  • Ecrime
  • Hactivism
    • Not always clearly separated

• LLM can help eliminate common language mistakes, perform better social enginerring

• Many adversaries are trying to integrate LLMs into their workflow, with varying results

• Time frame from initial foothold to lateral movements is getting shorter, due to better toolings?

GoLang #

• IDE setup / difference with C and other common language
• Compile down to single binary for many arch/platforms

Rootless networking: From possible to practical #

• libslirp is too slow
• passt & pasta
  • much faster than libslirp
  • same binary, different command
  • translate between layer 2 network interface and native layer 4 sockets on a host
  • unprivileged, no capability needed, good fit for container & VM
  • https://passt.top/passt/about/

Running a Particle Accelerator on Open Source #

• One of the best talk!
• Software design & activity planning
• Synchrotron
  • https://en.wikipedia.org/wiki/Synchrotron
  • https://en.wikipedia.org/wiki/Australian_Synchrotron
• What’s happening there
  • https://www.ansto.gov.au/about/locations/visit-australian-synchrotron
• No more open days for now :(
  • maybe after mid 2024?

Thursday, 18 Apr 2024 #

Keynote: Intelligent Interfaces: Challenges and Opportunities #

• Another great talk, we don’t get HID talk often unfortunately
• Sensing: what can we sense more?
  • Eye tracking: figure out when the user is not paying attention and then when the user look back, show a diff/changelog
  • Change Blindness, proximity-based experience: change how detailed the UI is based on proximity
  • RadarCat, Radar and Categorization: better privacy than having camera everywhere
    • obtain infomation via wave reflection and absorption (can this be abused…?)
    • use ML trainning for better accuracy
  • MicroCam and SpeCam: placement based action: detect which surface is under/over the device

FOSS: From Building Websites to Changing Society #

• Echo chamber: FOSS run on different social/economic structure than commercial proprietary software, it takes effort to convince people

Adventures in fuzzing the kernel on Power #

• porting syzcaller to run on Power

• general fuzzinng engines

  • universal eginee: AFL++
  • domain specific fuzzer: syzkaller

• Unsupervised: no human input required

• Coveraged-guided: fuzz and measures which codepath is fuzzed

• Things to fuzz: syscalls/dxrivers/fs/ebpf/kvm/network stacks…

  • KVM: guest-host / host-guest

• Simple kernel fuzzers existed est. 1991

  • but not coverage based

• Hosted version on Google Cloud: https://syzkaller.appspot.com/upstream

• Sanitisers: print errors on memory corruption/UB/concurrency problems etc

• KMSAN isn’t on Power yet

• Hardware:

  • https://en.wikipedia.org/wiki/PowerPC
  • many more modern Power system

• New architecture enablement

  • Parse arch-specific details of kernel error
  • Enable kcov (but not everywhere)

• Stack traces are printed differently across archs

  • use regex, 2.5KLoC ;)

• instruction fuzzing

  • generate and mutate PPC64 PowerISA machine code
  • More coverage for KVM related pathways
  • Only for x86 and power at the moment

• QEMU/KVM on bare metal Open Power systems

• Bug found:

  • KVM guests can crash/hang the host, race conditions?
  • Bugs in KUAP

• PowerVM

  • Type 1 hypervisor
  • Runs Linux/AIX/IBM I VMs
  • Need a separate machine as management console

• PowerVC

  • Forked from openstack
  • Mostly OpenStack API
  • https://www.ibm.com/products/powervc

Lightning Talks #

• FileSender

  • https://github.com/filesender/filesender
  • https://filesender.org/
  • end-to-end encrypted data transter, from web.

• radio::console

  • https://github.com/gm-stack/radioconsole
  • control radios, remotely, good fit for stations in remote areas.

• AgOpenGPS

  • https://github.com/farmerbriantee/AgOpenGPS
  • self steering system (hardware, software, firmware) for tractors
  • only on Windows…?

sched_ext - Write your own Linux thread scheduler in BPF #

• BPF made creating new scheduler simpler

  • with strong safety guarantee to not break the system, the side effects of bad scheduler are confined.
  • run a binary to enable your scheduler, stop the binary to revert to default

• Scheduling problem is now more complicated due to increasing complexity of workload/CPU design

• BPF provides reliable access to critical data structures inside the kernel

Exploring mobile linux security with PinePhone Pro: OP-TEE sec enclave, Virtualization and beyond #

• This is my talk ;)
• See the readings page for slides/demos and more.

Presenting n3n - A simple Peer to Peer VPN #

• Forked from n2n to avoid CLA

  • Protocol level compatibility with n2n is maintained

• Peer-to-peer VPN at network layer, acting like a distributed virtual switch

  • Layer 2 over Layer 3
  • Only route packets through a server/supernode when required, p2p by default
  • Better latency due to being p2p

• NAT piecing

• Written in C, should have good cross-platform supports (more testing wanted on *BSD)

  • Relatively small codebase for a VPN

• TunTap interface support is expected from the OS side, shouldn’t be a problem for common Unix-likes

  • Modern macOS is dropping support for TunTap, need to use NetworkExtension?

• Packaging and distro submission are still WIP

  • Framework for a debian package exists but not in an upstreamable shape
  • OpenBSD?

• Future roadmap

  • n3n over IPv6
  • Code cleanup
  • Multiple network driver support (e.g. something other than TunTap)
  • Better NAR piecing
  • Mobile support?

• Useful for

  • LAN gaming with old/modern systems
  • Remote access

• Simpler than wireguard/openvpn but offers OK security (not for security-critical apps?)

• Easier to configure, use INI style config files

Running your own Mailserver #

• 90% of all incoming mails are low-effort spams.
• Setup DMARC/SPF records

Lions OS #

• seL4 is bad at usability, Lions OS intends to solve this

• Still in early stage of development

• Composable components for build custom OS for a single task

  • Runs on seL4 Microkernel
  • For things like IoT, embedded, cars etc…

• Focus on simplicity

• 0.1.0 just released, still in its early stage

• high performance

• Only for Arm64/aarch64 now, riscv64 in future?

• Device Driver Model

• Multi Language Support

• A reference system called Kitty exists

  • A Linux running inside VMM is used for framebuffer, but any OS should do

Here you can find a list of links related to my topic which I find useful or just interesting.

Meta #

Info page https://2024.everythingopen.au/schedule/presentation/24/

Slides EO2024.Slides.exploring.mobile.linux.security.odp

Recording XXX to be processed

VerityMobile GitHub :: ZhanYF/veritymobile

Demo #

Access Measurements from Linux Userland

Sign in to GitLab with fTPM-backed FIDO token

fTPM-backed SSH Identity

Disposable Web Session

OP-TEE #

Docs Index and high level introduction #

https://optee.readthedocs.io/en/latest/general/about.html

Secure Storage #

https://optee.readthedocs.io/en/latest/architecture/secure_storage.html

GlobalPlatform API #

https://optee.readthedocs.io/en/latest/architecture/globalplatform_api.html#globalplatform-api

Talks and Demos about OP-TEE #

https://optee.readthedocs.io/en/latest/general/presentations.html

Other TEEs #

Android Trusty #

https://source.android.com/docs/security/features/trusty

Apple Secure Enclave #

https://support.apple.com/en-sg/guide/security/sec59b0b31ff/web

TPM and Desktop/Mobile Linux #

What Can You Do with a TPM by Michael Peters #

This also covers Measured Boot and Secure Boot

https://next.redhat.com/2021/05/13/what-can-you-do-with-a-tpm/

A WebAuthn/U2F token protected by a TPM (Go/Linux) by Peter Sanford #

https://github.com/psanford/tpm-fido

Setup TPM-backed SSH identity #

https://www.ledger.com/blog/ssh-with-tpm

Secure Boot on embedded devices #

Secure boot in embedded Linux systems by Thomas Perrot #

https://bootlin.com/pub/conferences/2021/lee/perrot-secure-boot/perrot-secure-boot.pdf

Shadow-box #

Shadow-box for ARM using OP-TEE #

Highlevel description #

https://www.blackhat.com/asia-18/briefings.html#shadow-box-v2-the-practical-and-omnipotent-sandbox-for-arm

Source code and build instructions #

https://github.com/kkamagui/shadow-box-for-arm https://github.com/kkamagui/manifest

Older version of Shadow-box for x86 #

https://github.com/kkamagui/shadow-box-for-x86

RK3399 #

Enabling Secure Boot on RockChip SoCs by Artur Kowalski #

https://blog.3mdeb.com/2021/2021-12-03-rockchip-secure-boot/

RPMB #

RPMB, a secret place inside the eMMC by Sergio Prado #

https://sergioprado.blog/rpmb-a-secret-place-inside-the-emmc/

Virtualization #

Firecracker #

https://github.com/firecracker-microvm/firecracker

firectl(1) #

https://github.com/firecracker-microvm/firectl

Run general purpose arm64 VMs with KVM on RK3399 #

https://segments.zhan.science/posts/kvm_on_pinehone_pro/

I got a call yesterday from a guy who had looked at the Experia Bruce has at Zen and was considering buying one. I talked with him for about three quarters of an hour, going through my experience, and to sum it up simply I can just say: this is a fantastic motorbike.

Firstly, it handles exactly like a standard motorbike - it handles almost exactly like my previous Triumph Tiger Sport 1050. But it is so much easier to ride. You twist the throttle and you go. You wind it back and you slow down. If you want to, the bike will happily do nought to 100km/hr in under four seconds. But it will also happily and smoothly glide along in traffic. It says "you name the speed, I'm happy to go". It's not temperamental or impatient; it has no weird points where the throttle suddenly gets an extra boost or where the engine braking suddenly drops off. It is simple to ride.

As an aside, this makes it perfect for lane filtering. On my previous bike this would always be tinged with a frisson of danger - I had to rev it and ease the clutch in with a fair bit of power so I didn't accidentally stall it, but that always took some time. Now, I simply twist the throttle and I am ahead of the traffic - no danger of stalling, no delay in the clutch gripping, just power. It is much safer in that scenario.

I haven't done a lot of touring yet, but I've ridden up to Gosford once and up to Sydney several times. This is where Energica really is ahead of pretty much every other electric motorbike on the market now - they do DC fast charging. And by 'fast charger' here I mean anything from 50KW up; the Energica can only take 25KW maximum anyway :-) But this basically means I have to structure any stops we do around where I can charge up - no more stopping in at the local pub or a cafe on a whim for morning tea. That has to either offer DC fast charging or I'm moving on - the 3KW onboard AC charger means a 22KW AC charger is useless to me. In the hour or two we might stop for lunch I'd only get another 60 - 80 kilometres more range on AC; on DC I would be done in less than an hour.

But OTOH my experience so far is that structuring those breaks around where I can charge up is relatively easy. Most riders will furiously nod when I say that I can't sit in the seat for more than two hours before I really need to stretch the legs and massage the bum :-) So if that break is at a DC charger, no problems. I can stop at Sutton Forest or Pheasant's Nest or even Campbelltown and, in the time it takes for me to go to the toilet and have a bit of a coffee and snack break, the bike is basically charged and ready to go again.

The lesson I've learned, though, is to always give it that bit longer and charge as much as I can up to 80%. It's tempting sometimes when I'm standing around in a car park watching the bike charge to move on and charge up a bit more at the next stop. The problem is that, with chargers still relatively rare and there often only being one or two at each site, a single charger not working can mean another fifty or even a hundred kilometres more riding. That's a quarter to half my range, so I cannot afford to risk that. Charge up and take a good book (and a spare set of headphones).

In the future, of course, when there's a bank of a dozen DC fast chargers in every town, this won't be a problem. Charger anxiety only exists because they are still relatively rare. When charging is easy to find and always available, and there are electric forecourts like the UK is starting to get, charging stops will be easy and will fit in with my riding.

Anyway.

Other advantages of the Experia:

You can get it with a complete set of Givi MonoKey top box and panniers. This means you can buy your own much nicer and more streamlined top box and it fits right on.

Charging at home takes about six hours, so it's easy to do overnight. The Experia comes with an EVSE so you don't need any special charger at home. And really, since the onboard AC charger can only accept 3KW, there's hardly any point in spending much money on a home charger for the Experia.

Minor niggles:

The seat is a bit hard. I'm considering getting the EONE Canyon saddle, although I also just need to try to work out how to get underneath the seat to see if I can fit my existing sheepskin seat cover.

There are a few occasional glitches in the display in certain rare situations. I've mentioned them to Energica, hopefully they'll be addressed.

Overall rating:

5 stars. Already recommending.

Way back in the distant past, when the Apple ][ and the Commodore 64 were king, you could read the manual for a microprocessor and see how many CPU cycles each instruction took, and then do the math as to how long a sequence of instructions would take to execute. This cycle counting was used pretty effectively to do really neat things such as how you’d get anything on the screen from an Atari 2600. Modern CPUs are… complex. They can do several things at once, in a different order than what you wrote them in, and have an interesting arrangement of shared resources to allocate.

So, unlike with simpler hardware, if you have a sequence of instructions for a modern processor, it’s going to be pretty hard to work out how many cycles that could take by hand, and it’s going to differ for each micro-architecture available for the instruction set.

When designing a microprocessor, simulating what a series of existing instructions will take to execute compared to the previous generation of microprocessor is pretty important. The aim should be for it to take less time or energy or some other metric that means your new processor is better than the old one. It can be okay if processor generation to generation some sequence of instructions take more cycles, if your cycles are more frequent, or power efficient, or other positive metric you’re designing for.

Programmers may want this simulation too, as some code paths get rather performance critical for certain applications. Open Source tools for this aren’t as prolific as I’d like, but there is llvm-mca which I (relatively) recently learned about.

llvm-mca is a performance analysis tool that uses information available in LLVM (e.g. scheduling models) to statically measure the performance of machine code in a specific CPU.

the llvm-mca docs

So, when looking at an issue in the IPv6 address and connection hashing code in Linux last year, and being quite conscious of modern systems dealing with a LOT of network packets, and thus this can be quite CPU usage sensitive, I wanted to make sure that my suggested changes weren’t going to have a large impact on performance – across the variety of CPU generations in use.

There’s two ways to do this: run everything, throw a lot of packets at something, and measure it. That can be a long dev cycle, and sometimes just annoying to get going. It can be a lot quicker to simulate the small section of code in question and do some analysis of it before going through the trouble of spinning up multiple test environments to prove it in the real world.

So, enter llvm-mca and the ability to try and quickly evaluate possible changes before testing them. Seeing as the code in question was nicely self contained, I could easily get this to a point where I could easily get gcc (or llvm) to spit out assembler for it separately from the kernel tree. My preference was for gcc as that’s what most distros end up compiling Linux with, including the Linux distribution that’s my day job (Amazon Linux).

In order to share the results of the experiments as part of the discussion on where the code changes should end up, I published the code and results in a github project as things got way too large to throw on a mailing list post and retain sanity.

I used a container so that I could easily run it in a repeatable isolated environment, as well as have others reproduce my results if needed. Different compiler versions and optimization levels will very much produce different sequences of instructions, and thus possibly quite different results. This delta in compiler optimization levels is partially why the numbers don’t quite match on some of the mailing list messages, although the delta of the various options was all the same. The other reason is learning how to better use llvm-mca to isolate down the exact sequence of instructions I was caring about (and not including things like the guesswork that llvm-mca has to do for branches).

One thing I learned along the way is how to better use llvm-mca to get the results that I was looking for. One trick is to very much avoid branches, as that’s going to be near complete guesswork as there’s not a simulation of the branch predictor (at least in the version I was using.

The big thing I wanted to prove: is doing the extra work having a small or large impact on number of elapsed cycles. The answer was that doing a bunch of extra “work” was essentially near free. The CPU core could execute enough things in parallel that the incremental cost of doing extra work just… wasn’t relevant.

This helped getting a patch deployed without impact to performance, as well as get a patch upstream, fixing an issue that was partially fixed 10 years prior, and had existed since day 1 of the Linux IPv6 code.

Naturally, this wasn’t a solo effort, and that’s one of the joys of working with a bunch of smart people – both at the same company I work for, and in the broader open source community. It’s always humbling when you’re looking at code outside your usual area of expertise that was written (and then modified) by Really Smart People, and you’re then trying to fix a problem in it, while trying to learn all the implications of changing that bit of code.

Anyway, check out llvm-mca for your next adventure into premature optimization, as if you’re going to get started with evil, you may as well start with what’s at the root of all of it.

At this rate, there is no real blogging here, regardless of the lofty plans to starting writing more. Stats update from Hello 2023:

219 days on the road (less than 2022! -37, over a month, shocking), 376,961km travelled, 44 cities, 17 countries.

Can’t say why it was less, because it felt like I spent a long time away…

In Kuala Lumpur, I purchased a flat (just in time to see Malaysia go down), and I swapped cars (had a good 15 year run). I co-founded a company, and I think there is a lot more to come.

2024 is shaping up to be exciting, busy, and a year, where one must just do.

good read: 27 Years Ago, Steve Jobs Said the Best Employees Focus on Content, Not Process. Research Shows He Was Right. in simple terms, just do.

It’s time for a review of the second year of operation of our Redflow ZCell battery and Victron Energy inverter/charger system. To understand what follows it will help to read the earlier posts in this series:

• Go With The Flow (what all the pieces are, what they do, some teething problems)
• Hack Week 21: Keeping the Battery Full (an experiment in working around the limitations of a single ZCell)
• TANSTAAFL (review/analysis of the first year of operation)

In case ~12,000 words of background reading seem daunting, I’ll try to summarise the most important details here:

• We have a 5.94kW solar array hooked up to a Victron MPPT RS solar charge controller, two Victron 5kW Multi-Plus II inverter/chargers, a Victron Cerbo GX console, and a single 10kWh Redflow ZCell battery. It works really well. We’re using most of our generated power locally, and it’s enabled us to blissfully coast through several grid power outages and various other minor glitches. The Victron gear and the ZCell were installed by Lifestyle Electrical Services.
• Redflow batteries are excellent because you can 100% cycle them every day, and they aren’t a giant lump of lithium strapped to your house that’s impossible to put out if it bursts into flames. The catch is that they need to undergo periodic maintenance where they are completely discharged for a few hours at least every three days. If you have more than one, that’s fine because the maintenance cycles interleave (it’s all automatic). If you only have one, you can’t survive grid outages if you’re in a maintenance period, and you can’t ordinarily use the Cerbo’s Minimum State of Charge (MinSoC) setting to perpetually keep a small charge in the battery in case of emergencies. As we still only have one battery, I’ve spent a fair bit of time experimenting to mitigate this as much as I can.
• The system itself requires a certain amount of power to run. Think of the pumps and fans in the battery, and the power used directly by the inverters and the console. On top of that a certain amount of power is simply lost to AC/DC conversion and charge/discharge inefficiencies. That’s power that comes into your house from the grid and from the sun that your loads, i.e. the things you care about running, don’t get to use. This is true of all solar PV and battery storage systems to a greater or lesser degree, but it’s not something that people always think about.

With the background out of the way we can get on to the fun stuff, including a roof replacement, an unexpected fault after a power outage followed by some mains switchboard rewiring, a small electrolyte leak, further hackery to keep a bit of charge in the battery most of the time, and finally some numbers.

The big job we did this year was replacing our concrete tile roof with colorbond steel. When we bought the house – which is in a rural area and thus a bushfire risk – we thought: “concrete brick exterior, concrete tile roof – sweet, that’s not flammable”. Unfortunately it turns out that while a tile roof works just fine to keep water out, it won’t keep embers out. There’s a gadzillion little gaps where the tiles overlap each other, and in an ember attack, embers will get up in there and ignite the fantastic amount of dust and other stuff that’s accumulated inside the ceiling over several decades, and then your house will burn down. This could be avoided by installing roof blanket insulation under the tiles, but in order to do that you have to first remove all the tiles and put them down somewhere without breaking them, then later put them all back on again. It’s a lot of work. Alternately, you can just rip them all off and replace the whole lot with nice new steel, with roof blanket insulation underneath.

Of course, you need good weather to replace a roof, and you need to take your solar panels down while it’s happening. This meant we had twenty-two solar panels stacked on our back porch for three weeks of prime PV time from February 17 – March 9, 2023, which I suspect lost us a good 500kW of power generation. Also, the roof job meant we didn’t have the budget to get a second ZCell this year – for the cost of the roof replacement, we could have had three new ZCells installed – but as my wife rightly pointed out, all the battery storage in the world won’t do you any good if your house burns down.

We had at least five grid power outages during the year. A few were brief, the grid being down for only a couple of minutes, but there were two longer ones in September (one for 30 minutes, one for about an hour and half). We got through the long ones just fine with either the sun high in the sky, or charge in the battery, or both. One of the earlier short outages though uncovered a problem. On the morning of May 30, my wife woke up to discover there was no power, and thus no running water. Not a good thing to wake up to. This happened while I was away, because of course something like this would happen while I was away. It turns out there had been a grid outage at about 02:10, then the grid power had come back, but our system had not. The Multis ended up in some sort of fault state and were refusing to power our loads. On the console was an alarm message: “#8 – Ground relay test failed”.

Note the times in the console messages are about 08:00. I confirmed via the logs from the VRM portal that the grid really did go out some time between 02:10 and 02:15, but after that there was nothing in the logs until 07:59, which is when my wife used the manual changeover switch to shift all our loads back to direct grid power, bypassing the Victron kit. That brought our internet connection back, along with the running water. I contacted Murray Roberts from Lifestyle Electrical and Simon Hackett for assistance, Murray logged in remotely and reset the Multis, my wife flicked the changeover switch back and everything was fine. But the question remained, what had gone wrong?

The ground relay in the Multis is there to connect neutral to ground when the grid fails. Neutral and ground are already physically connected on the grid (AC input) side of the Multis in the main switchboard, but when the grid power goes out, the Multis disconnect their inputs, which means the loads on the AC output side no longer have that fixed connection from neutral to ground. The ground relay activates in this case to provide that connection, which is necessary for correct operation of the safety switches on the power circuits in the house.

The ground relay is tested automatically by the Multis. Looking up Error 8 – Ground relay test failed on Victron’s web site indicated that either the ground relay really was faulty, or possibly there was a wiring fault or an issue with one of the loads in our house. So I did some testing. First, with the battery at 50% State of Charge (SoC), I did the following:

1. Disconnected all loads (i.e. flipped the breaker on the output side of the Multis)
2. Killed the mains (i.e. flipped the breaker on the input side of the Multis)
3. Verified the system switched to inverting mode (i.e. running off the battery)
4. Restored mains power
5. Verified there was no error

This demonstrated that the ground relay and the Multis in general were fine. Had there been a problem at that level we would have seen an error when I restored mains power. I then reconnected the loads and repeated steps 2-5 above. Again, there was no error which indicated the problem wasn’t due to a wiring defect or short in any of the power or lighting circuits. I also re-tested with the heater on and the water pump running just in case there may have been an issue specifically with either of those devices. Again, there was no error.

The only difference between my test above and the power outage in the middle of the night was that in the middle of the night there was no charge in the battery (it was right after a maintenance cycle) and no power from the sun. So in the evening I turned off the DC isolators for the PV and deactivated my overnight scheduled grid charge so there’d be no backup power of any form in the morning. Then I repeated the test:

1. Disconnected all loads
2. Killed the mains.
3. Checked the console which showed the system as “off”, as opposed to “inverting”, as there was no battery power or solar generation
4. Restored mains power
5. Shortly thereafter, I got the ground relay test failed error

The underlying detailed error message was “PE2 Closed”, which meant that it was seeing the relay as closed when it’s meant to be open. Our best guess is that we’d somehow hit an edge case in the Multi’s ground relay test, where they maybe tried to switch to inverting mode and activated the ground relay, then just died in that state because there was no backup power, and got confused when mains power returned. I got things running again by simply power cycling the Multis.

So it kinda wasn’t a big deal, except that if the grid went out briefly with no backup power, our loads would remain without power until one of us manually reset the system. This was arguably worse than not having the system at all, especially if it happened in the middle of the night, or when we were away from home. The fact that we didn’t hit this problem in the first year of operation is a testament to how unlikely this event is, but the fact that it could happen at all remained a problem.

One fix would have been to get a second battery, because then we’d be able to keep at least a tiny bit of backup power at all times regardless of maintenance cycles, but we’re not there yet. Happily, Simon found another fix, which was to physically connect the neutral together between the AC input and AC output sides of the Multis, then reconfigure them to use the grid code “AS4777.2:2015 AC Neutral Path externally joined”. That physical link means the load (output) side picks up the ground connection from the grid (input) side in the swichboard, and changing the grid code setting in the Multis disables the ground relay and thus the test which isn’t necessary anymore.

Murray needed to come out anyway to replace the carbon sock in the ZCell (a small item of annual maintenance) and was able to do that little bit of rewriting and configuration at the same time. I repeated my tests both with and without backup power and everything worked perfectly, i.e. the system came back immediately by itself after a grid outage with no backup power, and of course switched over to inverting just fine when there was backup power available.

This leads to the next little bit of fun. The carbon sock is a thing that sits inside the zinc electrolyte tank and helps to keep the electrolyte pH in the correct operating range. Unfortunately I didn’t manage to get a photo of one, but they look a bit like door snakes. Replacing the carbon sock means opening the case, popping one side of the Gas Handling Unit (GHU) off the tank, pulling out the old sock and putting in a new one. Here’s a picture of the ZCell with the back of the case off, indicating where the carbon sock goes:

When Murray popped the GHU off, he noticed that one of the larger pipes on one side had perished slightly. Thankfully he happened to have a spare GHU with him so was able to replace the assembly immediately. All was well until later that afternoon, when the battery indicated hardware failure due to “Leak 1 Trip” and shut itself down out of an abundance of caution. Upon further investigation the next day, Murry and I discovered there was a tiny split in one of the little hoses going into the GHU which was letting the electrolyte drip out.

This small electrolyte leak was caught lower down in the battery, where the leak sensor is. Murray sucked the leaked electrolyte out of there, re-terminated that little hose and we were back in business. I was happy to learn that Redflow had obviously thought about the possibility of this type of failure and handled it. As I said to Murray at the time, we’d rather have a battery that leaks then turns itself off than a battery that catches fire!

Aside from those two interesting events, the rest of the year of operation was largely quite boring, which is exactly what one wants from a power system. As before I kept a small overnight scheduled charge and a larger late afternoon scheduled charge active on weekdays to ensure there was some power in the battery to use at peak (i.e. expensive) grid times. In spring and summer the afternoon charge is largely superfluous because the battery has usually been well filled up from the solar by then anyway, but there’s no harm in leaving it turned on. The one hack I did do during the year was to figure out a way to keep a small (I went with 15%) MinSoC in the battery at all times except for maintenance cycle evenings, and the morning after. This is more than enough to smooth out minor grid outages of a few minutes, and given our general load levels should be enough to run the house for more than an hour overnight if necessary, provided the hot water system and heating don’t decide to come on at the same time.

My earlier experiment along these lines involved a script that ran on the Cerbo twice a day to adjust scheduled charge settings in order to keep the battery at 100% SoC at all times except for peak electricity hours and maintenance cycle evenings. As mentioned in TANSTAAFL I ran that for all of July, August and most of September 2022. It worked fine, but ultimately I decided it was largely a waste of energy and money, especially when run during the winter months when there’s not much sun and you end up doing a lot of grid charging. This is a horribly inefficient way of getting power into the battery (AC to DC) versus charging the battery direct from solar PV. We did still use those scripts in the second year, but rather more judiciously, i.e. we kept an eye on the BOM forecasts as we always do, then occasionally activated the 100% charge when we knew severe weather and/or thunderstorms were on the way, those being the things most likely to cause extended grid outages. I also manually triggered maintenance on the battery earlier than strictly necessary several times when we expected severe weather in the coming days, to avoid having a maintenance cycle (and thus empty battery) coincide with potential outages. On most of those occasions this effort proved to be unnecessary. Bearing all that in mind, my general advice to anyone else with a single ZCell system (aside from maybe adding scheduled charges to time-shift expensive peak electricity) is to just leave it alone and let it do its thing. You’ll use most of your locally generated electricity onsite, you’ll save some money on your power bills, and you’ll avoid some, but not all, grid outages. This is a pretty good position to be in.

That said, I couldn’t resist messing around some more, hence my MinSoC experiment. Simon’s installation guide points out that “for correct system operation, the Settings->ESS menu ‘Min SoC’ value must be set to 0% in single-ZCell systems”. The issue here is that if MinSoC is greater than 0%, the Victron gear will try to charge the battery while the battery is simultaneously trying to empty itself during maintenance, which of course just isn’t going to work. My solution to this is the following script, which I run from a cron job on the Cerbo twice a day, once at midnight UTC and again at 06:00 UTC with the --check-maintenance flag set:

Midnight UTC corresponds to the end of our morning peak electricity time, and 06:00 UTC corresponds to the start of our afternoon peak. What this means is that after the morning peak finishes, the MinSoC setting will cause the system to automatically charge the battery to the value specified if it’s not up there already. Given it’s after the morning peak (10:00 AEST / 11:00 AEDT) this charge will likely come from solar PV, not the grid. When the script runs again just before the afternoon peak (16:00 AEST / 17:00 AEDT), MinSoC is set to either the value specified (effectively a no-op), or zero if it’s a maintenance day. This allows the battery to be discharged correctly in the evening on maintenance days, while keeping some charge every other day in case of emergencies. Unlike the script that tries for 100% SoC, this arrangement results in far less grid charging, while still giving protection from minor outages most of the time.

In case Simon is reading this now and is thinking “FFS, I wrote ‘MinSoC must be set to 0% in single-ZCell systems’ for a reason!” I should also add a note of caution. The script above detects ZCell maintenance cycles based solely on the configured maintenance time limit and the duration since last maintenance. It does not – and cannot – take into account occasions when the user manually forces maintenance, or situations in which a ZCell for whatever reason hypothetically decides to go into maintenance of its own accord. The latter shouldn’t generally happen, but it can. The point is, if you’re running this MinSoC script from a cron job, you really do still want to keep an eye on what the battery is doing each day, in case you need to turn that setting off and disable the cron job. If you’re not up for that I will reiterate my general advice from earlier: just leave the system alone – let it do its thing and you’ll (almost always) be perfectly fine. Or, get a second ZCell and you can ignore the last several paragraphs entirely.

Now, finally, let’s look at some numbers. The year periods here are a little sloppy for irritating historical reasons. 2018-2019, 2019-2020 and 2020-2021 are all August-based due to Aurora Energy’s previous quarterly billing cycle. The 2021-2022 year starts in late September partly because I had to wait until our new electricity meter was installed in September 2021, and partly because it let me include some nice screenshots when I started writing TANSTAAFL on September 25, 2022. I’ve chosen to make this year (2022-2023) mostly sane, in that it runs from October 1, 2022 through September 30, 2023 inclusive. This is only six days offset from the previous year, but notably makes it much easier to accurately correlate data from the VRM portal with our bills from Aurora. Overall we have five consecutive non-overlapping 12 month periods that are pretty close together. It’s not perfect, but I think it’s good enough to work with for our purposes here.

YeaR	Grid In	Solar In	Total In	Loads	Export
2018-2019	9,031	6,682	15,713	11,827	3,886
2019-2020	9,324	6,468	15,792	12,255	3,537
2020-2021	7,582	6,347	13,929	10,358	3,571
2021-2022	8,531	5,640	14,171	10,849	754
2022-2023	8,936	5,744	14,680	11,534	799

Overall, 2022-2023 had a similar shape to 2021-2022, including the fact that in both these years we missed three weeks of solar generation in late summer. In 2022 this was due to replacing the MPPT, and in 2023 it was because we replaced the roof. In both cases our PV generation was lower than it should have been by an estimated 500-600kW. Hopefully nothing like this happens again in future years.

All of our numbers in 2022-2023 were a bit higher than in 2021-2022. We pulled 4.75% more power from the grid, generated 1.84% more solar, the total power going into the system (grid + solar) was 3.59% higher, our loads used 6.31% more power, and we exported 5.97% more power than the previous year.

I honestly don’t know why our loads used more power this year. Here’s a table showing our consumption for both years, and the differences each month (note that September 2022 is only approximate because of how the years don’t quite line up):

Month	2022	2023	Diff
October	988	873	-115
November	866	805	-61
December	767	965	198
January	822	775	-47
February	638	721	83
March	813	911	98
April	775	1,115	340
May	953	1,098	145
June	1,073	1,149	76
July	1,118	1,103	-15
August	966	1,065	99
September	1,070	964	-116

Here’s a graph:

Did we use more cooling this December? Did we use more heating this April and May? I dug the nearest weather station’s monthly mean minimum and maximum temperatures out of the BOM Climate Data Online tool and found that there’s maybe a degree or so variance one way or the other each month year to year, so I don’t know what I can infer from that. All I can say is that something happened in December and April, but I don’t know what.

Another interesting thing is that what I referred to as “the energy cost of the system” in TANSTAAFL has gone down. That’s the kW figure below in the “what?” column, which is the difference between grid in + solar in – loads – export, i.e. the power consumed by the system itself. In 2021-2022, that was 2,568 kW, or about 18% of the total power that went into the system. In 2022-2023 it was down to 2,347kWh, or just under 16%:

Year	Grid In	Solar In	Total In	Loads	Export	Total Out	what?
2021-2022	8,531	5,640	14,171	10,849	754	11,603	2,568
2022-2023	8,936	5,744	14,680	11,534	799	12,333	2,347

I suspect the cause of this reduction is that we didn’t spend two and a half months doing lots of grid charging of the battery in 2022-2023. If that’s the case, this again points to the advisability of just letting the system do its thing and not messing with it too much unless you really know you need to.

The last set of numbers I have involve actual money. Here’s what our electricity bills looked like over the past five years:

Year	From Grid	Total Bill	Cost/kWh
2018-2019	9,031	$2,278.33	$0.25
2019-2020	9,324	$2,384.79	$0.26
2020-2021	7,582	$1,921.77	$0.25
2021-2022	8,531	$1,731.40	$0.20
2022-2023	8,936	$1,989.12	$0.22

Note that cost/kWh as I have it here is simply the total dollar amount of our bills divided by the total power drawn from the grid (I’m deliberately ignoring the additional power we use that comes from the sun in this calculation). The bills themselves say “peak power costs $X, off-peak costs $Y, you get $Z back for power exported and there’s a daily supply charge of $SUCKS_TO_BE_YOU”, but that’s all noise. What ultimately matters in my opinion is what I call the effective cost per kilowatt hour, which is why those things are all smooshed together here. The important point is that with our existing solar array we were previously effectively paying about $0.25 per kWh for grid power. After getting the battery and switching to Peak & Off-Peak billing, that went down to $0.20/kWh – a reduction of 20%. Now we’ve inched back up to $0.22/kWh, but it turns out that’s just because power prices have increased. As far as I can tell Aurora Energy don’t publish historical pricing data, so as a public service, I’ll include what I’ve been able to glean from our prior bills here:

• July 2023 onwards:
  • Daily supply charge: $1.26389
  • Peak: $0.36198/kWh
  • Off-Peak: $0.16855/kWh
  • Feed-In Tariff: $0.10869/kWh
• July 2022 – July 2023
  • Daily supply charge: $1.09903
  • Peak: $0.33399/kWh
  • Off-Peak: $0.15551/kWh
  • Feed-In Tariff: $0.08883/kWh
• Before July 2022:
  • Daily supply charge: $0.98
  • Peak: $0.29852
  • Off-Peak: $0.139
  • Feed-In Tariff: $0.06501

It’s nice that the feed-in tariff (i.e. what you get credited when you export power) has gone up quite a bit, but unless you’re somehow able to export 2-3x more power than you import, you’ll never get ahead of the ~20% increase in power prices over the last two years.

Having calculated the effective cost/kWh for grid power, I’m now going to do one more thing which I didn’t think to do during last year’s analysis, and that’s calculate the effective cost/kWh of running our loads, bearing in mind that they’re partially powered from the grid, and partially from the sun. I’ve managed to dig up some old Aurora bills from 2016-2017, back before we put the solar panels on. This should make for an interesting comparison.

Year	From Grid	Total Bill	Grid $/kWh	Loads	Loads $/kWh
2016-2017	17,026	$4,485.45	$0.26	17,026	$0.26
2018-2019	9,031	$2,278.33	$0.25	11,827	$0.19
2019-2020	9,324	$2,384.79	$0.26	12,255	$0.19
2020-2021	7,582	$1,921.77	$0.25	10,358	$0.19
2021-2022	8,531	$1,731.40	$0.20	10,849	$0.16
2022-2023	8,936	$1,989.12	$0.22	11,534	$0.17

The first thing to note is the horrifying 17 megawatts we pulled in 2016-2017. Given the hot water and lounge room heat pump were on a separate tariff, I was able to determine that four of those megawatts (i.e. about 24% of our power usage) went on heating that year. Replacing the crusty old conventional electric hot water system with a Sanden heat pump hot water service cut that in half – subsequent years showed the heating/hot water tariff using about 2MW/year. We obviously also somehow reduced our loads by another ~3MW/year on top of that, but I can’t find the Aurora bills for 2017-2018 so I’m not sure exactly when that drop happened. My best guess is that I probably got rid of some old, always-on computer equipment.

The second thing to note is how the cost of running the loads drops. In 2016-2017 the grid cost/kWh is the same as the loads cost/kWh, because grid power is all we had. From 2018-2021 though, the load cost/kWh drops to $0.19, a saving of about 26%. It remains there until 2021-2022 when we got the battery and it dropped again to $0.16 (another 15% or so). So the big win was certainly putting the solar panels on and swapping the hot water system, with the battery being a decent improvement on top of that.

Further wins are going to come from decreasing our power consumption. In previous posts I had mentioned the need to replace panel heaters with heat pumps, and also that some of our aging computer equipment needed upgrading. We did finally get a heat pump installed in the master bedroom this year, and we replaced the old undersized lounge room heat pump with a new correctly sized unit. This happened on June 30 though, so will have had minimal impact on this years’ figures. Likewise an always-on computer that previously pulled ~100W is now better, stronger and faster in all respects, while only pulling ~50W. That will save us ~438kW of power per year, but given the upgrade happened in mid August, again we won’t see the full effects until later.

I’m looking forward to doing another one of these posts in a year’s time. Hopefully I will have nothing at all interesting to report.

I (relatively) recently went down the rabbit hole of trying out personal finance apps to help get a better grip on, well, the things you’d expect (personal finances and planning around them).

In the past, I’ve had an off-again-on-again relationship with GNUCash. I did give it a solid go for a few months in 2004/2005 it seems (I found my old files) and I even had the OFX exports of transactions for a limited amount of time for a limited number of bank accounts! Amazingly, there’s a GNUCash port to macOS, and it’ll happily open up this file from what is alarmingly close to 20 years ago.

Back in those times, running Linux on the desktop was even more of an adventure than it has been since then, and I always found GNUCash to be strange (possibly a theme with me and personal finance software), but generally fine. It doesn’t seem to have changed a great deal in the years since. You still have to manually import data from your bank unless you happen to be lucky enough to live in the very limited number of places where there’s some kind of automation for it.

So, going back to GNUCash was an option. But I wanted to survey the land of what was available, and if it was possible to exchange money for convenience. I am not big on the motivation to go and spend a lot of time on this kind of thing anyway, so it had to be easy for me to do so.

For my requirements, I basically had:

• Support multiple currencies
• Be able to import data from my banks, even if manually
• Some kind of reporting and planning tools
• Be easy enough to use for me, and not leave me struggling with unknown concepts
• The ability to export data. No vendor lock-in

I viewed a mobile app (iOS) as a Nice to Have rather than essential. Given that, my shortlist was:

GNUCash

I’ve used it before, its web site at https://www.gnucash.org/ looks much the same as it always has. It’s Free and Open Source Software, and is thus well aligned with my values, and that’s a big step towards not having vendor lock-in.

I honestly could probably make it work. I wish it had the ability to import transactions from banks for anywhere I have ever lived or banked with. I also wish the UI got to be a bit more consistent and modern, and even remotely Mac like on the Mac version.

Honestly, if the deal was that a web service would pull bank transactions in exchange for ~$10/month and also fund GNUCash development… I’d struggle to say no.

Quicken

Here’s an option that has been around forever – https://www.quicken.com/ – and one that I figured I should solidly look at. It’s actually one I even spent money on…. before requesting a refund. It’s Import/Export is so broken it’s an insult to broken software everywhere.

Did you know that Quicken doesn’t import the Quicken Interchange Format (QIF), and hasn’t since 2005?

Me, incredulously, when trying out quicken

I don’t understand why you wouldn’t support as many as possible formats that banks export your transaction data as. It cannot possibly be that hard to parse these things, nor can it possibly be code that requires a lot of maintenance.

This basically meant that I couldn’t import data from my Australian Banks. Urgh. This alone ruled it out.

It really didn’t build confidence in ever getting my data out. At every turn it seemed to be really keen on locking you into Quicken rather than having a good experience all-up.

Moneywiz

This one was new to me – https://www.wiz.money/ – and had a fancy URL and everything. I spent a bunch of time trying MoneyWiz, and I concluded that it is pretty, but buggy. I had managed to create a report where it said I’d earned $0, but you click into it, and then it gives actual numbers. Not being self consistent and getting the numbers wrong, when this is literally the only function of said app (to get the numbers right), took this out of the running.

It did sync from my US and Australian banks though, so points there.

Intuit Mint

Intuit used to own Quicken until it sold it to H.I.G. Capital in 2016 (according to Wikipedia). I have no idea if that has had an impact as to the feature set / usability of Quicken, but they now have this Cloud-only product called Mint.

The big issue I had with Mint was that there didn’t seem to be any way to get your data out of it. It seemed to exemplify vendor lock-in. This seems to have changed a bit since I was originally looking, which is good (maybe I just couldn’t find it?). But with the cloud-only approach I wasn’t hugely comfortable with having everything there. It also seemed to be lacking a few features that I was begging to find useful in other places.

It is the only product that links with the Apple Card though. No idea why that is the case.

The price tag of $0 was pretty unbeatable, which does make me wonder where the money is made from to fund its development and maintenance. My guess is that it’s through commission on the various financial products advertised through it, and I dearly hope it is not through selling data on its users (I have no reason to believe it is, there’s just the popular habit of companies doing this).

Banktivity

This is what I’ve settled on. It seemed to be easy enough for me to figure out how to use, sync with an iPhone App, be a reasonable price, and be able to import and sync things from accounts that I have. Oddly enough, nothing can connect and pull things from the Apple Card – which is really weird. That isn’t a Banktivity thing though, that’s just universal (except for Intuit’s Mint).

I’ve been using it for a bit more than a year now, and am still pretty happy. I wish there was the ability to attach a PDF of a statement to the Statement that you reconcile. I wish I could better tune the auto match/classification rules, and a few other relatively minor things.

Periodically in life I’ve had the desire to be somewhat fit, or at least have the benefits that come with that such as not dying early and being able to navigate a mountain (or just the city of Seattle) on foot without collapsing. I have also found that holding myself accountable via data is pretty vital to me actually going and repeatedly doing something.

So, at some point I got myself a Garmin watch. The year was 2012 and it was a Garmin Forerunner 410. It had a standard black/grey LCD screen, GPS (where getting a GPS lock could be utterly infuriatingly slow), a sensor you attached to your foot, a sensor you strap to your chest for Heart Rate monitoring, and an ANT+ dongle for connecting to a PC to download your activities. There was even some open source software that someone wrote so I could actually get data off my watch on my Linux laptops. This wasn’t a smart watch – it was exclusively for wearing while exercising and tracking an activity, otherwise it was just a watch.

However, as I was ramping up to marathon distance running, one huge flaw emerged: I was not fast enough to run a marathon in the time that the battery in my Garmin lasted. IIRC it would end up dying around 3hr30min into something, which at the time was increasingly something I’d describe as “not going for too long of a run”. So, the search for a replacement began!

The year was 2017, and the Garmin fenix 5x attracted me for two big reasons: a battery life to be respected, and turn-by-turn navigation. At the time, I seldom went running with a phone, preferring a tiny SanDisk media play (RIP, they made a new version that completely sucked) and a watch. The attraction of being able to get better maps back to where I started (e.g. a hotel in some strange city where I didn’t speak the language) was very appealing. It also had (what I would now describe as) rudimentary smart-watch features. It didn’t have even remotely everything the Pebble had, but it was enough.

So, a (non-trivial) pile of money later (even with discounts), I had myself a shiny and virtually indestructible new Garmin. I didn’t even need a dongle to sync it anywhere – it could just upload via its own WiFi connection, or through Bluetooth to the Garmin Connect app to my phone. I could also (if I ever remembered to), plug in the USB cable to it and download the activities to my computer.

One problem: my skin rebelled against the Garmin fenix 5x after a while. Like, properly rebelled. If it wasn’t coming off, I wanted to rip it off. I tried all of the tricks that are posted anywhere online. Didn’t help. I even got tested for what was the most likely culprit (a Nickel allergy), and didn’t have one of them, so I (still) have no idea what I’m actually allergic to in it. It’s just that I cannot wear it constantly. Urgh. I was enjoying the daily smart watch uses too!

So, that’s one rather expensive watch that is special purpose only, and even then started to get to be a bit of an issue around longer activities. Urgh.

So the hunt began for a smart watch that I could wear constantly. This usually ends in frustration as anything I wanted was hundreds of $ and pretty much nobody listed what materials were in it apart from “stainless steel”, “may contain”, and some disclaimer about “other materials”, which wasn’t a particularly useful starting point for “it is one of these things that my skin doesn’t like”. As at least if the next one also turned out to cause me problems, I could at least have a list of things that I could then narrow down to what I needed to avoid.

So that was all annoying, with the end result being that I went a long time without really wearing a watch. Why? The search resumed periodically and ended up either with nothing, or totally nothing. That was except if I wanted to get further into some vendor lock-in.

Honestly, the only manufacturer of anything smartwatch like which actually listed everything and had some options was Apple. Bizarre. Well, since I already got on the iPhone bandwagon, this was possible. Rather annoyingly, they are very tied together and thus it makes it a bit of a vendor-lock-in if you alternate phone and watch replacement and at any point wish to switch platforms.

That being said though, it does work well and not irritate my skin. So that’s a bonus! If I get back into marathon level distance running, we’ll see how well it goes. But for more common distances that I’ve run or cycled with it… the accuracy seems decent, HR monitor never just sometimes decides I’m not exerting myself, and the GPS actually gets a lock in reasonable time. Plus it can pair with headphones and be the only thing I take out with me.

A few random notes about things that can make life on macOS (the modern one, as in, circa 2023) better for those coming from Linux.

For various reasons you may end up with Mac hardware with macOS on the metal rather than Linux. This could be anything from battery life of the Apple Silicon machines (and not quite being ready to jump on the Asahi Linux bandwagon), to being able to run the corporate suite of Enterprise Software (arguably a bug more than a feature), to some other reason that is also fine.

My approach to most of my development is to have a remote more powerful Linux machine to do the heavy lifting, or do Linux development on Linux, and not bank on messing around with a bunch of software on macOS that would approximate something on Linux. This also means I can move my GUI environment (the Mac) easily forward without worrying about whatever weird workarounds I needed to do in order to get things going for whatever development work I’m doing, and vice-versa.

Terminal emulator? iTerm2. The built in Terminal.app is fine, but there’s more than a few nice things in iTerm2, including tmux integration which can end up making it feel a lot more like a regular Linux machine. I should probably go read the tmux integration best practices before I complain about some random bugs I think I’ve hit, so let’s pretend I did that and everything is perfect.

I tend to use the Mac for SSHing to bigger Linux machines for most of my work. At work, that’s mostly to a Graviton 2 EC2 Instance running Amazon Linux with all my development environments on it. At home, it’s mostly a Raptor Blackbird POWER9 system running Fedora.

Running Linux locally? For all the use cases of containers, Podman Desktop or finch. There’s a GUI part of Podman which is nice, and finch I know about because of the relatively nearby team that works on it, and its relationship to lima. Lima positions itself as WSL2-like but for Mac. There’s UTM for a full virtual machine / qemu environment, although I rarely end up using this and am more commonly using a container or just SSHing to a bigger Linux box.

There’s XCode for any macOS development that may be needed (e.g. when you want that extra feature in UTM or something) I do use Homebrew to install a few things locally.

Have a read of Andrew‘s blog post on OpenBMC Development on an Apple M1 MacBook Pro too.

Last week I had occasion to test deploying ceph-csi on a k3s cluster, so that Kubernetes workloads could access block storage provided by an external Ceph cluster. I went with the upstream Ceph documentation, because assuming everything worked it’d then be really easy for me to say to others “just go do this”.

Everything did not work.

> kubectl get pods
NAME                                        READY   STATUS             RESTARTS          AGE
csi-rbdplugin-22zjr                         1/3     CrashLoopBackOff   107 (3m55s ago)   2d
csi-rbdplugin-pbtc2                         1/3     CrashLoopBackOff   104 (3m33s ago)   2d
csi-rbdplugin-provisioner-9dcfd56d7-c8s72   7/7     Running            28 (35m ago)      8d
csi-rbdplugin-provisioner-9dcfd56d7-hcztz   7/7     Running            28 (35m ago)      8d
csi-rbdplugin-provisioner-9dcfd56d7-w2ctc   7/7     Running            28 (35m ago)      8d
csi-rbdplugin-r2rzr                         1/3     CrashLoopBackOff   106 (3m39s ago)   2d

> kubectl logs csi-rbdplugin-22zjr --container csi-rbdplugin
I0726 10:25:12.862125    7628 cephcsi.go:199] Driver version: canary and Git version: d432421a88238a878a470d54cbf2c50f2e61cdda
I0726 10:25:12.862452    7628 cephcsi.go:231] Starting driver type: rbd with name: rbd.csi.ceph.com
I0726 10:25:12.865907    7628 mount_linux.go:284] Detected umount with safe 'not mounted' behavior
E0726 10:25:12.872477    7628 rbd_util.go:303] modprobe failed (an error (exit status 1) occurred while running modprobe args: [rbd]): "modprobe: ERROR: could not insert 'rbd': Key was rejected by service\n"
F0726 10:25:12.872702    7628 driver.go:150] an error (exit status 1) occurred while running modprobe args: [rbd] 

> kubectl run -it cephcsi --image=quay.io/cephcsi/cephcsi:canary --rm=true --command=true -- /bin/bash
If you don't see a command prompt, try pressing enter.
[root@cephcsi /]# modprobe rbd
modprobe: FATAL: Module rbd not found in directory /lib/modules/5.14.21-150400.24.66-default
[root@cephcsi /]# ls /lib/modules/
[root@cephcsi /]#  

> kubectl run -it cephcsi-test \
  --image=quay.io/cephcsi/cephcsi:canary --rm=true \
  --overrides='{
    "apiVersion": "v1",
    "spec": {
      "containers": [ {
        "name": "cephcsi",
        "command": ["/bin/bash"],
        "stdin": true, "tty": true,
        "image": "quay.io/cephcsi/cephcsi:canary",
        "volumeMounts": [ {
          "mountPath": "/lib/modules", "name": "lib-modules" }],
        "securityContext": {
          "allowPrivilegeEscalation": true,
          "capabilities": { "add": [ "SYS_ADMIN" ] },
          "privileged": true }
      } ],
      "volumes": [ {
        "name": "lib-modules",
        "hostPath": { "path": "/lib/modules", "type": "" }
      } ]
    } }'

> kubectl run -it cephcsi-test [honking great horrible chunk of JSON]
[root@cephcsi-test /]# ls /lib/modules/
5.14.21-150400.24.66-default
[root@cephcsi-test /]# modprobe rbd
modprobe: ERROR: could not insert 'rbd': Key was rejected by service

[root@cephcsi-test /]# ls /lib/modules/*/kernel/drivers/block/rbd*
/lib/modules/5.14.21-150400.24.66-default/kernel/drivers/block/rbd.ko.zst

# grep PRETTY /etc/os-release
PRETTY_NAME="SUSE Linux Enterprise Micro for Rancher 5.3"
# modprobe --version
kmod version 29
+ZSTD +XZ +ZLIB +LIBCRYPTO -EXPERIMENTAL

[root@cephcsi-test /]# grep PRETTY /etc/os-release 
PRETTY_NAME="CentOS Stream 8"
[root@cephcsi-test /]# modprobe --version
kmod version 25
+XZ +ZLIB +OPENSSL -EXPERIMENTAL

> kubectl debug csi-rbdplugin-22zjr -it \
    --copy-to=csi-debug --container=csi-rbdplugin -- /bin/bash
[root@... /]# modprobe rbd
modprobe: ERROR: could not insert 'rbd': Key was rejected by service

(...do whatever other messing around you need to do, then...)

[root@... /]# exit
Session ended, resume using 'kubectl attach csi-debug -c csi-rbdplugin -i -t' command when the pod is running
> kubectl delete pod csi-debug
pod "csi-debug" deleted 

Meeting Report

The July 2023 meeting sparked multiple new topics including Linux security architecture, Debian ports of LoongArch and Risc-V as well as hardware design of PinePhone backplates.

On the practical side, Russell Coker demonstrated running different applications in isolated environment with bubblewrap sandbox, as well as other hardening techniques and the way they interact with the host system. Russell also discussed some possible pathways of hardening desktop Linux to reach the security level of modern Android. Yifei Zhan demonstrated sending and receiving messages with the PineDio USB LoRa adapter and how to inspect LoRa signal with off-the-shelf software defined radio receiver, and discussed how the driver situation for LoRa on Linux might be improved. Yifei then gave a demonstration on utilizing KVM on PinePhone Pro to run NetBSD and OpenBSD virtual machines, more details on running VMs on the PinePhone Pro can be found on this blog post from Yifei.

We also had some discussion of the current state of Mobian and Debian ecosystem, along with how to contribute to different parts of Mobian with a Mobian developer who joined us.

Somewhat a while ago now, I wrote about how every time I return to write some software for the Mac, the preferred language has changed. The purpose of this adventure was to get my photos out of the aging Shotwell and onto my (then new) Mac and the Apple Photos App.

I’ve had a pretty varied experience with photo management on Linux over the past couple of decades. For a while I used f-spot as it was the new hotness. At some point this became…. slow and crashy enough that it was unusable. Today, it appears that the GitHub project warns that current bugs include “Not starting”.

At some point (and via a method I have long since forgotten), I did manage to finally get my photos over to Shotwell, which was the new hotness at the time. That data migration was so long ago now I actually forget what features I was missing from f-spot that I was grumbling about. I remember the import being annoying though. At some point in time Shotwell was no longer was the new hotness and now there is GNOME Photos. I remember looking at GNOME Photos, and seeing no method of importing photos from Shotwell, so put it aside. Hopefully that situation has improved somewhere.

At some point Shotwell was becoming rather stagnated, and I noticed more things stopping to work rather than getting added features and performance. The good news is that there has been some more development activity on Shotwell, so hopefully my issues with it end up being resolved.

One recommendation for Linux photo management was digiKam, and one that I never ended up using full time. One of the reasons behind that was that I couldn’t really see any non manual way to import photos from Shotwell into it.

With tens of thousands of photos (~58k at the time of writing), doing things manually didn’t seem like much fun at all.

As I postponed my decision, I ended up moving my main machine over to a Mac for a variety of random reasons, and one quite motivating thing was the ability to have Photos from my iPhone magically sync over to my photo library without having to plug it into my computer and copy things across.

So…. how to get photos across from Shotwell on Linux to Photos on a Mac/iPhone (and also keep a very keen eye on how to do it the other way around, because, well, vendor lock-in isn’t great).

It would be kind of neat if I could just run Shotwell on the Mac and have some kind of import button, but seeing as there wasn’t already a native Mac port, and that Shotwell is written in Vala rather than something I know has a working toolchain on macOS…. this seemed like more work than I’d really like to take on.

Luckily, I remembered that Shotwell’s database is actually just a SQLite database pointing to all the files on disk. So, if I could work out how to read it accurately, and how to import all the relevant metadata (such as what Albums a photo is in, tags, title, and description) into Apple Photos, I’d be able to make it work.

So… is there any useful documentation as to how the database is structured?

Semi annoyingly, Shotwell is written in Vala, a rather niche programming language that while integrating with all the GObject stuff that GNOME uses, is largely unheard of. Luckily, the database code in Shotwell isn’t too hard to read, so was a useful fallback for when the documentation proves inadequate.

So, I armed myself with the following resources:

• https://wiki.gnome.org/Apps/Shotwell/Architecture/Database
• and the Shotwell source code for the database

Programming the Mac side of things, it was a good excuse to start looking at Swift, so knowing I’d also need to read a SQLite database directly (rather than use any higher level abstraction), I armed myself with the following resources:

• https://docs.swift.org/swift-book/ (of course)
• https://www.raywenderlich.com/6620276-sqlite-with-swift-tutorial-getting-started

From here, I could work on getting the first half going, the ability to view my Shotwell database on the Mac (which is what I posted a screenshot of back in Feb 2022).

But also, I had to work out what I was doing on the other end of things, how would I import photos? It turns out there’s an API!

A bit of SwiftUI code:

import SwiftUI
import AppKit
import Photos

struct ContentView: View {
    @State var favorite_checked : Bool = false
    @State var hidden_checked : Bool = false
    var body: some View {
        VStack() {
            Text("Select a photo for import")
            Toggle("Favorite", isOn: $favorite_checked)
            Toggle("Hidden", isOn: $hidden_checked)
            Button("Import Photo")
            {
                let panel = NSOpenPanel()
                panel.allowsMultipleSelection = false
                panel.canChooseDirectories = false
                if panel.runModal() == .OK {
                    let photo_url = panel.url!
                    print("selected: " + String(photo_url.absoluteString))
                    addAsset(url: photo_url, isFavorite: favorite_checked, isHidden: hidden_checked)
                }
            }
            .padding()
        }
    }
}

struct ContentView_Previews: PreviewProvider {
    static var previews: some View {
        ContentView()
    }
}

Combined with a bit of code to do the import (which does look a bunch like the examples in the docs):

import SwiftUI
import Photos
import AppKit

@main
struct SinglePhotoImporterApp: App {
    var body: some Scene {
        WindowGroup {
            ContentView()
        }
    }
}

func addAsset(url: URL, isFavorite: Bool, isHidden: Bool) {
    // Add the asset to the photo library.
    let path = "/Users/stewart/Pictures/1970/01/01/1415446258647.jpg"
    let url = URL(fileURLWithPath: path)
    PHPhotoLibrary.shared().performChanges({
        let addedImage = PHAssetChangeRequest.creationRequestForAssetFromImage(atFileURL: url)
        addedImage?.isHidden = isHidden
        addedImage?.isFavorite = isFavorite
    }, completionHandler: {success, error in
        if !success { print("Error creating the asset: \(String(describing: error))") } else
        {
            print("Imported!")
        }
    })
}

This all meant I could import a single photo. However, there were some limitations.

There’s the PHAssetCollectionChangeRequest to do things to Albums, so it would solve that problem, but I couldn’t for the life of me work out how to add/edit Titles and Descriptions.

It was so close!

So what did I need to do in order to import Titles and Descriptions? It turns out you can do that via AppleScript. Yes, that thing that launched in 1993 and has somehow survived the transition of m68k based Macs to PowerPC based Macs to Intel based Macs to ARM based Macs.

So, just to make it easier to debug what was going on, I started adding code to my ShotwellImporter tool that would generate snippets of AppleScript I could run and check that it was doing the right thing…. but then very quickly ran into a problem…. it appears that the AppleScript language interpreter on modern macOS has limits that you’d be more familiar with in 1993 than 2023, and I very quickly hit limits where the script would just error out before running (I was out of dictionary size allegedly).

But there’s a new option! Everything you can do with AppleScript you can now do with JavaScript – it’s just even less documented than AppleScript is! But it does work! I got to the point where I could generate JavaScript that imported photos, into all the relevant albums, and set title and descriptions.

A useful write up of using JavaScript rather than AppleScript to do things with Photos: https://mudge.name/2019/11/13/scripting-photos-for-macos-with-javascript/

More recent than when I was doing my hacking, https://alexwlchan.net/2023/managing-albums-in-photos/ is a good read.

With luck I’ll find some time to write up a bit of a walkthrough of my code, and push it up somewhere.

In my last post, I wrote about how I taught sesdev (originally a tool for deploying Ceph clusters on virtual machines) to deploy k3s, because I wanted a little sandbox in which I could break learn more about Kubernetes. It’s nice to be able to do a toy deployment locally, on a bunch of VMs, on my own hardware, in my home office, rather than paying to do it on someone else’s computer. Given the k3s thing worked, I figured the next step was to teach sesdev how to deploy Longhorn so I could break that learn more about that too.

Teaching sesdev to deploy Longhorn meant asking it to:

• Install nfs-client, open-iscsi and e2fsprogs packages on all nodes.
• Make an ext4 filesystem on /dev/vdb on all the nodes that have extra disks, then mount that on /var/lib/longhorn.
• Use kubectl label node -l 'node-role.kubernetes.io/master!=true' node.longhorn.io/create-default-disk=true to ensure Longhorn does its storage thing only on the nodes that aren’t the k3s master.
• Install Longhorn with Helm, because that will install the latest version by default vs. using kubectl where you always explicitly need to specify the version.
• Create an ingress so the UI is exposed… from all nodes, via HTTP, with no authentication. Remember: this is a sandbox – please don’t do this sort of thing in production!

So, now I can do this:

> sesdev create k3s --deploy-longhorn
=== Creating deployment "k3s-longhorn" with the following configuration === 

Deployment-wide parameters (applicable to all VMs in deployment):

- deployment ID:    k3s-longhorn
- number of VMs:    5
- version:          k3s
- OS:               tumbleweed
- public network:   10.20.78.0/24 

Proceed with deployment (y=yes, n=no, d=show details) ? [y]: y

=== Running shell command ===
vagrant up --no-destroy-on-error --provision
Bringing machine 'master' up with 'libvirt' provider…
Bringing machine 'node1' up with 'libvirt' provider…
Bringing machine 'node2' up with 'libvirt' provider…
Bringing machine 'node3' up with 'libvirt' provider…
Bringing machine 'node4' up with 'libvirt' provider…

[... lots more log noise here - this takes several minutes... ]

=== Deployment Finished ===

You can login into the cluster with:

  $ sesdev ssh k3s-longhorn

Longhorn will now be deploying, which may take some time.
After logging into the cluster, try these:

  # kubectl get pods -n longhorn-system --watch
  # kubectl get pods -n longhorn-system

The Longhorn UI will be accessible via any cluster IP address
(see the kubectl -n longhorn-system get ingress output above).
Note that no authentication is required.

…and, after another minute or two, I can access the Longhorn UI and try creating some volumes. There’s a brief period while the UI pod is still starting where it just says “404 page not found”, and later after the UI is up, there’s still other pods coming online, so on the Volume screen in the Longhorn UI an error appears: “failed to get the parameters: failed to get target node ID: cannot find a node that is ready and has the default engine image longhornio/longhorn-engine:v1.4.1 deployed“. Rest assured this goes away in due course (it’s not impossible I’m suffering here from rural Tasmanian internet lag pulling container images). Anyway, with my five nodes – four of which have an 8GB virtual disk for use by Longhorn – I end up with a bit less than 22GB storage available:

Now for the fun part. Longhorn is a distributed storage solution, so I thought it would be interesting to see how it handled a couple of types of failure. The following tests are somewhat arbitrary (I’m really just kicking the tyres randomly at this stage) but Longhorn did, I think, behave pretty well given what I did to it.

Volumes in Longhorn consist of replicas stored as sparse files on a regular filesystem on each storage node. The Longhorn documentation recommends using a dedicated disk rather than just having /var/lib/longhorn backed by the root filesystem, so that’s what sesdev does: /var/lib/longhorn is an ext4 filesystem mounted on /dev/vdb. Now, what happens to Longhorn if that underlying block device suffers some kind of horrible failure? To test that, I used the Longhorn UI to create a 2GB volume, then attached that to the master node:

Then, I ssh’d to the master node and with my 2GB Longhorn volume attached, made a filesystem on it and created a little file:

> sesdev ssh k3s-longhorn
Have a lot of fun...

master:~ # cat /proc/partitions 
major minor  #blocks  name 
 253        0   44040192 vda
 253        1       2048 vda1
 253        2      20480 vda2
 253        3   44016623 vda3
   8        0    2097152 sda

master:~ # mkfs /dev/sda
mke2fs 1.46.5 (30-Dec-2021)
Discarding device blocks: done                            
Creating filesystem with 524288 4k blocks and 131072 inodes
Filesystem UUID: 3709b21c-b9a2-41c1-a6dd-e449bdeb275b
Superblock backups stored on blocks: 
        32768, 98304, 163840, 229376, 294912
Allocating group tables: done                            
Writing inode tables: done                            
Writing superblocks and filesystem accounting information: done 

master:~ # mount /dev/sda /mnt
master:~ # echo foo > /mnt/foo
master:~ # cat /mnt/foo
foo

Then I went and trashed the block device backing one of the replicas:

> sesdev ssh k3s-longhorn node3
Have a lot of fun...

node3:~ # ls /var/lib/longhorn
engine-binaries  longhorn-disk.cfg  lost+found  replicas  unix-domain-socket

node3:~ # dd if=/dev/urandom of=/dev/vdb bs=1M count=100
100+0 records in
100+0 records out
104857600 bytes (105 MB, 100 MiB) copied, 0.486205 s, 216 MB/s

node3:~ # ls /var/lib/longhorn

node3:~ # dmesg|tail -n1
[ 6544.197183] EXT4-fs error (device vdb): ext4_map_blocks:607: inode #393220: block 1607168: comm longhorn: lblock 0 mapped to illegal pblock 1607168 (length 1) 

At this point, the Longhorn UI still showed the volume as green (healthy, ready, scheduled). Then, back on the master node, I tried creating another file:

master:~ # echo bar > /mnt/bar
master:~ # cat /mnt/bar
bar

That’s fine so far, but suddenly the Longhorn UI noticed that something very bad had happened:

Ultimately node3 was rebooted and ended up stalled with the console requesting the root password for maintenance:

Meanwhile, Longhorn went and rebuilt a third replica on node2:

…and the volume remained usable the entire time:

master:~ # echo baz > /mnt/baz
master:~ # ls /mnt
bar  baz  foo  lost+found

That’s perfect!

Looking at the Node screen we could see that node3 was still down:

That’s OK, I was able to fix node3. I logged in on the console and ran mkfs.ext4 /dev/vdb then brought the node back up again.The disk remained unschedulable, because Longhorn was still expecting the ‘old’ disk to be there (I assume based on the UUID stored in /var/lib/longhorn/longhorn-disk.cfg) and of course the ‘new’ disk is empty. So I used the Longhorn UI to disable scheduling for that ‘old’ disk, then deleted it. Shortly after, Longhorn recognised the ‘new’ disk mounted at /var/lib/longhorn and everything was back to green across the board.

So Longhorn recovered well from the backing store of one replica going bad. Next I thought I’d try to break it from the other end by running a volume out of space. What follows is possibly not a fair test, because what I did was create a single Longhorn volume larger than the underlying disks, then filled that up. In normal usage, I assume one would ensure there’s plenty of backing storage available to service multiple volumes, that individual volumes wouldn’t generally be expected to get more than a certain percentage full, and that some sort of monitoring and/or alerting would be in place to warn of disk pressure.

With four nodes, each with a single 8GB disk, and Longhorn apparently reserving 2.33GB by default on each disk, that means no Longhorn volume can physically store more than a bit over 5.5GB of data (see the Size column in the previous screenshot). Given that the default setting for Storage Over Provisioning Percentage is 200, we’re actually allowed to allocate up to a bit under 11GB.

So I went and created a 10GB volume, attached that to the master node, created a filesystem on it, and wrote a whole lot of zeros to it:

master:~ # mkfs.ext4 /dev/sda
mke2fs 1.46.5 (30-Dec-2021)
[...]

master:~ # mount /dev/sda /mnt
master:~ # df -h /mnt
Filesystem      Size  Used Avail Use% Mounted on
/dev/sda        9.8G   24K  9.3G   1% /mnt

master:~ # dd if=/dev/zero of=/mnt/big-lot-of-zeros bs=1M status=progress
2357198848 bytes (2.4 GB, 2.2 GiB) copied, 107 s, 22.0 MB/s

While that dd was running, I was able to see the used space of the replicas increasing in the Longhorn UI:

After a few more minutes, the dd stalled…

master:~ # dd if=/dev/zero of=/mnt/big-lot-of-zeros bs=1M status=progress
9039773696 bytes (9.0 GB, 8.4 GiB) copied, 478 s, 18.9 MB/s

…there was a lot of unpleasantness on the master node’s console…

…the replicas became unschedulable due to lack of space…

…and finally the volume faulted:

Now what?

It turns out that Longhorn will actually recover if we’re able to somehow expand the disks that store the replicas. This is probably a good argument for backing Longhorn with an LVM volume on each node in real world deployments, because then you could just add another disk and extend the volume onto it. In my case though, given it’s all VMs and virtual block devices, I can actually just enlarge those devices. For each node then, I:

1. Shut it down
2. Ran qemu-img resize /var/lib/libvirt/images/k3s-longhorn_$NODE-vdb.qcow2 +8G
3. Started it back up again and ran resize2fs /dev/vdb to take advantage of the extra disk space.

After doing that to node1, Longhorn realised there was enough space there and brought node1’s replica of my 10GB volume back online. It also summarily discarded the other two replicas from the still-full disks on node2 and node3, which didn’t yet have enough free space to be useful:

As I repeated the virtual disk expansion on the other nodes, Longhorn happily went off and recreated the missing replicas:

Finally I could re-attach the volume to the master node, and have a look to see how many of my zeros were actually written to the volume:

master:~ # cat /proc/partitions 
major minor  #blocks  name
 254        0   44040192 vda
 254        1       2048 vda1
 254        2      20480 vda2
 254        3   44016623 vda3
   8        0   10485760 sda

master:~ # mount /dev/sda /mnt
master:~ # ls -l /mnt
total 7839764
-rw-r--r-- 1 root root 8027897856 May  3 04:41 big-lot-of-zeros
drwx------ 2 root root      16384 May  3 04:34 lost+found

Recall that dd claimed to have written 9039773696 bytes before it stalled when the volume faulted, so I guess that last gigabyte of zeros is lost in the aether. But, recall also that this isn’t really a fair test – one overprovisioned volume deliberately being quickly and deliberately filled to breaking point vs. a production deployment with (presumably) multiple volumes that don’t fill quite so fast, and where one is hopefully paying at least a little bit of attention to disk pressure as time goes by.

It’s worth noting that in a situation where there are multiple Longhorn volumes, assuming one disk or LVM volume per node, the replicas will all share the same underlying disks, and once those disks are full it seems all the Longhorn volumes backed by them will fault. Given multiple Longhorn volumes, one solution – rather than expanding the underlying disks – is simply to delete a volume or two if you can stand to lose the data, or maybe delete some snapshots (I didn’t try the latter yet). Once there’s enough free space, the remaining volumes will come back online. If you’re really worried about this failure mode, you could always just disable overprovisioning in the first place – whether this makes sense or not will really depend on your workloads and their data usage patterns.

All in all, like I said earlier, I think Longhorn behaved pretty well given what I did to it. Some more information in the event log could perhaps be beneficial though. In the UI I can see warnings from longhorn-node-controller e.g. “the disk default-disk-1cdbc4e904539d26(/var/lib/longhorn/) on the node node1 has 3879731200 available, but requires reserved 2505089433, minimal 25% to schedule more replicas” and warnings from longhorn-engine-controller e.g. “Detected replica overprovisioned-r-73d18ad6 (10.42.3.19:10000) in error“, but I couldn’t find anything really obvious like “Dude, your disks are totally full!”

Later, I found more detail in the engine manager logs after generating a support bundle ([…] level=error msg=”I/O error” error=”tcp://10.42.4.34:10000: write /host/var/lib/longhorn/replicas/overprovisioned-c3b9b547/volume-head-003.img: no space left on device”) so the error information is available – maybe it’s just a matter of learning where to look for it.

We – that is to say the storage team at SUSE – have a tool we’ve been using for the past few years to help with development and testing of Ceph on SUSE Linux. It’s called sesdev because it was created largely for SES (SUSE Enterprise Storage) development. It’s essentially a wrapper around vagrant and libvirt that will spin up clusters of VMs running openSUSE or SLES, then deploy Ceph on them. You would never use such clusters in production, but it’s really nice to be able to easily spin up a cluster for testing purposes that behaves something like a real cluster would, then throw it away when you’re done.

I’ve recently been trying to spend more time playing with Kubernetes, which means I wanted to be able to spin up clusters of VMs running openSUSE or SLES, then deploy Kubernetes on them, then throw the clusters away when I was done, or when I broke something horribly and wanted to start over. Yes, I know there’s a bunch of other tools for doing toy Kubernetes deployments (minikube comes to mind), but given I already had sesdev and was pretty familiar with it, I thought it’d be worthwhile seeing if I could teach it to deploy k3s, a particularly lightweight version of Kubernetes. Turns out that wasn’t too difficult, so now I can do this:

> sesdev create k3s
=== Creating deployment "k3s" with the following configuration === 
Deployment-wide parameters (applicable to all VMs in deployment):
deployment ID:    k3s
number of VMs:    5
version:          k3s
OS:               tumbleweed
public network:   10.20.190.0/24 
Proceed with deployment (y=yes, n=no, d=show details) ? [y]: y
=== Running shell command ===
vagrant up --no-destroy-on-error --provision
Bringing machine 'master' up with 'libvirt' provider...
Bringing machine 'node1' up with 'libvirt' provider...
Bringing machine 'node2' up with 'libvirt' provider...
Bringing machine 'node3' up with 'libvirt' provider...
Bringing machine 'node4' up with 'libvirt' provider...

[...
  wait a few minutes
  (there's lots more log information output here in real life)
...]

=== Deployment Finished ===
 You can login into the cluster with:
 $ sesdev ssh k3s

…and then I can do this:

> sesdev ssh k3s
Last login: Fri Mar 24 11:50:15 CET 2023 from 10.20.190.204 on ssh
Have a lot of fun…

master:~ # kubectl get nodes
NAME     STATUS   ROLES                  AGE     VERSION
master   Ready    control-plane,master   5m16s   v1.25.7+k3s1
node2    Ready                     2m17s   v1.25.7+k3s1
node1    Ready                     2m15s   v1.25.7+k3s1
node3    Ready                     2m16s   v1.25.7+k3s1
node4    Ready                     2m16s   v1.25.7+k3s1 

master:~ # kubectl get pods -A
NAMESPACE     NAME                                      READY   STATUS      RESTARTS   AGE
kube-system   local-path-provisioner-79f67d76f8-rpj4d   1/1     Running     0          5m9s
kube-system   metrics-server-5f9f776df5-rsqhb           1/1     Running     0          5m9s
kube-system   coredns-597584b69b-xh4p7                  1/1     Running     0          5m9s
kube-system   helm-install-traefik-crd-zz2ld            0/1     Completed   0          5m10s
kube-system   helm-install-traefik-ckdsr                0/1     Completed   1          5m10s
kube-system   svclb-traefik-952808e4-5txd7              2/2     Running     0          3m55s
kube-system   traefik-66c46d954f-pgnv8                  1/1     Running     0          3m55s
kube-system   svclb-traefik-952808e4-dkkp6              2/2     Running     0          2m25s
kube-system   svclb-traefik-952808e4-7wk6l              2/2     Running     0          2m13s
kube-system   svclb-traefik-952808e4-chmbx              2/2     Running     0          2m14s
kube-system   svclb-traefik-952808e4-k7hrw              2/2     Running     0          2m14s

…and then I can make a mess with kubectl apply, helm, etc.

One thing that sesdev knows how to do is deploy VMs with extra virtual disks. This functionality is there for Ceph deployments, but there’s no reason we can’t turn it on when deploying k3s:

> sesdev create k3s --num-disks=2
> sesdev ssh k3s
master:~ # for node in \
    $(kubectl get nodes -o 'jsonpath={.items[*].metadata.name}') ;
    do echo $node ; ssh $node cat /proc/partitions ; done
master
major minor  #blocks  name
 253        0   44040192 vda
 253        1       2048 vda1
 253        2      20480 vda2
 253        3   44016623 vda3
node3
major minor  #blocks  name
 253        0   44040192 vda
 253        1       2048 vda1
 253        2      20480 vda2
 253        3   44016623 vda3
 253       16    8388608 vdb
 253       32    8388608 vdc
node2
 major minor  #blocks  name
 253        0   44040192 vda
 253        1       2048 vda1
 253        2      20480 vda2
 253        3   44016623 vda3
 253       16    8388608 vdb
 253       32    8388608 vdc
node4
 major minor  #blocks  name
 253        0   44040192 vda
 253        1       2048 vda1
 253        2      20480 vda2
 253        3   44016623 vda3
 253       16    8388608 vdb
 253       32    8388608 vdc
node1
 major minor  #blocks  name
 253        0   44040192 vda
 253        1       2048 vda1
 253        2      20480 vda2
 253        3   44016623 vda3
 253       16    8388608 vdb
 253       32    8388608 vdc

As you can see this gives all the worker nodes an extra two 8GB virtual disks. I suspect this may make sesdev an interesting tool for testing other Kubernetes based storage systems such as Longhorn, but I haven’t tried that yet.

The decision to do this rather than build my own was complicated, and I'm going to mostly skip over the detail of that. At some time I might put it in another blog post. But for now it's enough to say that I'd accidentally cooked the motor in my Mark I, the work on the Mark II was going to take ages, and I was in the relatively fortunate situation of being able to afford the Experia if I sold my existing Triumph Tiger Sport and the parts for the Mark II.

For other complicated reasons I was planning to be in Sydney after the weekend that Bruce at Zen Motorcycles told me the bike would be arriving. Rather than have it freighted down, and since I would have room for my riding gear in our car, I decided to pick it up and ride it back on the Monday. In reconnoitering the route, we discovered that by pure coincidence Zen Motorcycles is on Euston Road in Alexandria, only 200 metres away from the entrance to WestConnex and the M8. So with one traffic light I could be out of Sydney.

I will admit to being more than a little excited that morning. Electric vehicles are still, in 2023, a rare enough commodity that waiting lists can be months long; I ordered this bike in October 2022 and it arrived in March 2023. So I'd had plenty of time to build my expectations. And likewise the thought of riding a brand new bike - literally one of the first of its kind in the country (it is the thirty-second Experia ever made!) - was a little daunting. I obtained PDF copies of the manual and familiarised myself with turning the cruise control on and off, as well as checking and setting the regen braking levels. Didn't want to stuff anything up on the way home.

There is that weird feeling in those situations of things being both very ordinary and completely unique. I met Bruce, we chatted, I saw the other Experia models in the store, met Ed - who had come down to chat with Bruce, and just happened to be the guy who rode a Harley Davidson Livewire from Perth to Sydney and then from Sydney to Cape Tribulation and back. He shared stories from his trip and tips on hypermiling. I signed paperwork, picked up the keys, put on my gear, prepared myself.

Even now I still get a bit choked up just thinking of that moment. Seeing that bike there, physically real, in front of me - after those months of anticipation - made the excitement real as well.

So finally, after making sure I wasn't floating, and making sure I had my ear plugs in and helmet on the right way round, I got on. Felt the bike's weight. Turned it on. Prepared myself. Took off. My partner followed behind, through the lights, onto the M8 toward Canberra. I gave her the thumbs up.

We planned to stop for lunch at Mittagong, while the NRMA still offers the free charger at the RSL there. One lady was charging her Nissan Leaf on the ChaDeMo side; shortly after I plugged in a guy arrived in his Volvo XC40 Recharge. He had the bigger battery and would take longer; I just needed a ten minute top up to get me to Marulan.

I got to Marulan and plugged in; a guy came thinking he needed to tell the petrol motorbike not to park in the electric vehicle bay, but then realised that the plug was going into my bike. Kate headed off, having charged up as well, and I waited another ten minutes or so to get a bit more charge. Then I rode back.

I stopped, only once more - at Mac's Reef Road. I turned off and did a U turn, then waited for the traffic to clear before trying the bike's acceleration. Believe me when I say this bike will absolutely do a 0-100km/hr in under four seconds! It is not a light bike, but when you pull on the power it gets up and goes.

Here is my basic review, given that experience and then having ridden it for about ten weeks around town.

The absolute best feature of the Energica Experia is that it is perfectly comfortable riding around town. Ease on the throttle and it gently takes off at the traffic lights and keeps pace with the traffic. Ease off, and it gently comes to rest with regenerative braking and a light touch on the rear brake after stopping to hold it still. If you want to take off faster, wind the throttle on more. It is not temperamental or twitchy, and you have no annoying gears and clutch to balance.

In fact, I feel much more confident lane filtering, because before I would have to have the clutch ready and be prepared to give the Tiger Sport lots of throttle lest I accidentally stall it in front of an irate line of traffic. With the Experia, I can simply wait peacefully - using no power - and then when the light goes green I simply twist on the throttle and I am away ahead of even the most aggressive car driver.

It is amazingly empowering.

I'm not going to bore you with the stats - you can probably look them up yourself if you care. The main thing to me is that it has DC fast charging, and watching 75KW go into a 22.5KWHr battery is just a little bit terrifying as well as incredibly cool. The stated range of 250km on a charge at highway speeds is absolutely correct, from my experience riding it down from Sydney. And that plus the fast charging means that I think it is going to be quite reasonable to tour on this bike, stopping off at fast or even mid-level chargers - even a boring 22KW charger can fill the battery up in an hour. The touring group I travel with stops often enough that if those stops can be top ups, I will not hold anyone up.

Some time in the near future I hope to have a nice fine day where I can take it out on the Cotter Loop. This is an 80km stretch of road that goes west of Canberra into the foothills of the Brindabella Ranges, out past the Deep Space Tracking Station and Tidbinbilla Nature Reserve. It's a great combination of curving country roads and hilly terrain, and reasonably well maintained as well. I did that on the Tiger Sport, with a GoPro, before I sold it - and if I can ever convince PiTiVi to actually compile the video from it I will put that hour's ride up on a platform somewhere.

I want to do that as much to show off Canberra's scenery as to show off the bike.

And if the CATL battery capacity improvement comes through to the rest of the industry, and we get bikes that can do 400km to 500km on a charge, then electric motorbike touring really will be no different to petrol motorbike touring. The Experia is definitely at the forefront of that change, but it is definitely possible on this bike.

Rustup (the community package manage for the Rust language) was starting to really suffer : CI times were up at ~ one hour.

We’ve made some strides in bringing this down.

Caching factory for test scenarios

The first thing, which achieved about a 30% reduction in test time was to stop recreating all the test context every time.

Rustup tests the download/installation/upgrade of distributions of Rust. To avoid downloading gigabytes in the test suite, the suite creates mocks of the published Rust artifacts. These mocks are GPG signed and compressed with multiple compression methods, both of which are quite heavyweight operations to perform – and not actually the interesting code under test to execute.

Previously, every test was entirely hermetic, and usually the server state was also unmodified.

There were two cases where the state was modified. One, a small number of tests testing error conditions such as GPG signature failures. And two, quite a number of tests that were testing temporal behaviour: for instance, install nightly at time A, then with a newer server state, perform a rustup update and check a new version is downloaded and installed.

We’re partway through this migration, but compare these two tests:

fn check_updates_some() {
    check_update_setup(&|config| {
        set_current_dist_date(config, "2015-01-01");
        config.expect_ok(&["rustup", "update", "stable"]);
        config.expect_ok(&["rustup", "update", "beta"]);
        config.expect_ok(&["rustup", "update", "nightly"]);
        set_current_dist_date(config, "2015-01-02");
        config.expect_stdout_ok(
            &["rustup", "check"],
            for_host!(
                r"stable-{0} - Update available : 1.0.0 (hash-stable-1.0.0) -> 1.1.0 (hash-stable-1.1.0)
beta-{0} - Update available : 1.1.0 (hash-beta-1.1.0) -> 1.2.0 (hash-beta-1.2.0)
nightly-{0} - Update available : 1.2.0 (hash-nightly-1) -> 1.3.0 (hash-nightly-2)
"
            ),
        );
    })
}

fn check_updates_some() {
    test(&|config| {
        config.with_scenario(Scenario::ArchivesV2_2015_01_01, &|config| {
            config.expect_ok(&["rustup", "toolchain", "add", "stable", "beta", "nightly"]);
        });
        config.with_scenario(Scenario::SimpleV2, &|config| {
        config.expect_stdout_ok(
            &["rustup", "check"],
            for_host!(
                r"stable-{0} - Update available : 1.0.0 (hash-stable-1.0.0) -> 1.1.0 (hash-stable-1.1.0)
beta-{0} - Update available : 1.1.0 (hash-beta-1.1.0) -> 1.2.0 (hash-beta-1.2.0)
nightly-{0} - Update available : 1.2.0 (hash-nightly-1) -> 1.3.0 (hash-nightly-2)
"
            ),
        );
            })
    })
}

The former version mutates the date with set_current_dist_date; the new version uses two scenarios, one for the earlier time, and one for the later time. This permits the server state to be constructed only once. On a per-test basis it can move as much as 50% of the time out of the test.

Single binary for the integration test suite

The next major gain was moving from having 14 separate integration test binaries to just one. This reduces the link cost of linking the test binaries, all of which link in the same library. It also permits us to see unused functions in our test support library, which helps with cleaning up cruft rather than having it accumulate.

Hard linking rather than copying ‘rustup-init’

Part of the test suite for each test is setting up an installed rustup environment. Why not start from scratch every time? Well, we obviously have tests that do that, but most tests are focused on steps beyond the new-user case. Setting up an installed rustup environment has a few steps, but particular ones are copying a binary of rustup into the test sandbox, and hard linking it under various names: cargo, rustc, rustup etc.

A debug build of rustup is ~20MB. Running 400 tests means about 8GB of IO; on some platforms most of that IO won’t hit disk, on others it will.

In review now is a PR that changes the initial copy to a hardlink: we hardlink the rustup-init built by cargo into each test, and then hardlink that to the various binaries. That saves 8GB of IO, which isn’t much from some perspectives, but it adds pressure on the page cache, and is wasted work. One wrinkle is a very low max-links limit on NTFS of 1023; to mitigate that we count the links made to rustup-init and generate a new inode for the original to avoid failures happening.

Future work

In GitHub actions this lowers our test time to 19m for Linux, 24m for Windows, which is a lot better but not great.

I plan on experimenting with separate actions for building release artifacts and doing CI tests – at the moment we have the same action do both, but they don’t share artifacts in the cache in any meaningful way, so we can probably gain parallelism there, as well as turning off release builds entirely for CI.

We should finish the cached test context work and use it everywhere.

Also we’re looking at having less integration tests and more narrow close to the code tests.

Back in 2012, I received a box of eight hundred openSUSE 12.1 promo DVDs, which I then set out to distribute to local Linux users’ groups, tech conferences, other SUSE crew in Australia, and so forth. I didn’t manage to shift all 800 DVDs at the time, and I recently rediscovered the remaining three hundred and eighty four while installing some new shelves. As openSUSE 12.1 went end of life in May 2013, it seemed likely the DVDs were now useless, but I couldn’t bring myself to toss them in landfill. Instead, given last week was Hack Week, I decided to use them for an art project. Here’s the end result:

Making that mosaic was extremely fiddly. It’s possibly the most annoying Hack Week project I’ve ever done, but I’m very happy with the outcome

The backing is a piece of 900mm x 600mm x 6mm plywood, primed with some leftover kitchen and bathroom undercoat, then spray pained black. I’d forgotten how bad spray paint smells, but it makes for a nice finish. To get the Geeko shape, I took the official openSUSE logo, then turned it into an outline in Inkscape, saved that as a PNG, opened it in GIMP, and cut it into nine 300mm x 200mm pieces which I then printed on A4 paper, stuck together with tape, and cut out to make a stencil. Of course, the first time I did that, nothing quite lined up, so I had to reprint it but with “Ignore page margins” turned off and “Draw crop marks” turned on, then cut the pages down along the crop marks before sticking them together the second time. Then I placed the stencil on the backing, glued the eye down (that just had to be made from the centre of a DVD!) and started laying out cut up DVD shards.

I initially tried cutting the DVDs with tin snips, which is easy on the hands, but had a tendency to sometimes warp the DVD pieces and/or cause them to delaminate, so I reverted to a large pair of scissors which was more effort but ultimately less problematic.

After placing the pieces that made up the head, tail, feet and spine, and deciding I was happy with how they looked, I glued each piece down with superglue. Think: carefully pick up DVD shard without moving too many other shards, turn over, dab on a few tiny globs of superglue, lower into place, press for a few seconds, move to next piece. Do not get any superglue on your fingers, or you’ll risk sticking your fingers together and/or make a gluey mess on the shiny visible side of the DVD shards.

It was another three sessions of layout-then-glue-down to fill in the body. I think I stuck my fingers together about six, or eight, or maybe twenty times. Also, despite my best efforts to get superglue absolutely nowhere near the stencil at all, when I removed the stencil, it had stuck to the backing in several places. I managed to scrape/cut that off with a combination of fingernails, tweezers, and the very sharp knife in my SLE 12 commemorative Leatherman tool, then touched up the remaining white bits with a fine point black Sharpie.

Judging from the leftover DVD centre pieces, this mosaic used about 12 DVDs in all, which isn’t very many considering my initial stash. I had a few other ideas for the remainder, mostly involving hanging them up somehow, which I messed around with earlier on while waiting for the paint to dry on the plywood.

One (failed) idea was to use a cutting wheel on my Dremel tool to slice half way through a few DVDs, then slot them into each other to make a hanging thingy that would spin in the wind. I was unable to make a smooth/straight enough cut for this to work, and superglue doesn’t bridge gaps. You can maybe get an idea of what I was aiming at from this photo:

My wife had an idea for a better way to do this, which is to take a piece of dowel, cut slots in the sides, and glue DVD halves into the slots using Araldite (that’s an epoxy resin, in case you didn’t grow up with that brand name). I didn’t get around to trying this, but I reckon she’s onto something. Next time I’m at the hardware store, I’ll try to remember to pick up some suitably sized dowel.

I did make one somewhat simpler hanging thingy, which I call “Geeko’s Tail (Uncurled)”. It’s just DVDs superglued together on the flat, hanging from fishing line, but I think it’s kinda cool:

Also, I’ve discovered that Officeworks has an e-waste recycling program, so any DVDs I don’t use in future projects needn’t go to landfill.

Update 2023-02-20: For photos of the mosaic, plus wallpapers made from the photos, see https://github.com/tserong/hackweek22

I have long said “Long Malaysians, Short Malaysia” in conversation to many. Maybe it took me a while to tweet it, but this was the first example: Dec 29, 2021. I’ve tweeted it a lot more since.

Malaysia has a 10th Prime Minister, but in general, it is a very precarious partnership. Consider it, same shit, different day?

5/n: Otherwise, there will be no change.

So change via “purported democracy” is never going to happen with a country like Malaysia, rotten to the core. It is a crazy dream.

You succeed, despite of. Davka.

Reboot, or bust.

Good luck, Malaysia.

— Colin Charles (@bytebot) August 18, 2021

I just have to get off the Malaysian news diet. Malaysians elsewhere, are generally very successful. Malaysians suffering by their daily doldrums, well, they just need to wake up, see the light, and succeed.

In the end, as much as people paraphrase, ask not what the country can do for you, legitimately, this is your life, and you should be taking good care of yourself and your loved ones. You succeed, despite of. Politics and the state happens, regardless of.

Me, personally? Ideas are abound for how to get Malaysians who see the light, to succeed elsewhere. And if I read, and get angry at something (tweet rage?), I’m going to pop RM50 into an investment account, which should help me get off this poor habit. I’ll probably also just cut subscriptions to Malaysian news things… Less exposure, is actually better for you. I can’t believe that it has taken me this long to realise this.

Time to build.

I did poorly blogging last year. Oops. I think to myself when I read, This Thing Still On?, I really have to do better in 2023. Maybe the catalyst is the fact that Twitter is becoming a shit show. I doubt people will leave the platform in droves, per se, but I think we are coming back to the need for decentralised blogs again.

I have 477 days to becoming 40. I ditched the Hobonich Techo sometime in 2022, and just focused on the Field Notes, and this year, I’ve got a Monocle x Leuchtturm1917 + Field Notes combo (though it seems my subscription lapsed Winter 2022, I should really burn down the existing collection, and resubscribe).

2022 was pretty amazing. Lots of work. Lots of fun. 256 days on the road (what a number), 339,551km travelled, 49 cities, 20 countries.

The getting back into doing, and not being afraid of experimenting in public is what 2023 is all about. The Year of The Rabbit is upon us tomorrow, hence why I don’t mind a little later Hello 2023 :)

Get back into the habit of doing. And publishing by learning and doing. No fear. Not that I wasn’t doing, but its time to be prolific with what’s been going on.

I better remember that.

I like using Catalyst Cloud to host some of my personal sites. In the past I used to use CAcert for my TLS certificates, but more recently I've been using Let's Encrypt for my TLS certificates as they're trusted in all browsers. Currently the LoadBalancer as a Service (LBaaS) in Catalyst Cloud doesn't have built in support for Let's Encrypt. I could use an apache2/nginx proxy and handle the TLS termination there and have that manage the Let's Encrypt lifecycle, but really, I'd rather use LBaaS.

So I thought I'd set about working out how to get Dehydrated (the Let's Encrypt client I've been using) to drive LBaaS (known as Octavia). I figured this would be of interest to other people using Octavia with OpenStack in general, not just Catalyst Cloud.

There's a few things you need to do. These instructions are specific to Debian:

1. Install and configure Dehydrated to create the certificates for the domain(s) you want.
   • apt install barbican
2. Create the LoadBalancer (use the API, ClickOps, whatever), just forward port 80 for now (see sample Apache configs below).
3. Save the sample hook.sh below to /etc/dehydrated/hook.sh, you'll probably need to customise it, mine is a bit more complicated!
4. Insert the UUID of your LoadBalancer in hook.sh where LB_LISTENER is set.
5. Create /etc/dehydrated/catalystcloud/password as described in hook.sh
6. Save OpenRC file from the Catalyst Cloud dashboard as /etc/dehydrated/catalystcloud/openrc.sh
7. Install jq, openssl and the openstack tools, on Debian this is:
   • apt install jq openssl python3-openstackclient python3-barbicanclient python3-octaviaclient
8. Add TLS termination to your LoadBalancer
9. You should be able to rename the latest certs /var/lib/dehydrated/certs/$DOMAIN and then run dehydrated -c to have it reissue and then deploy a cert.

As we're using HTTP-01 Challenge Type here, you need to have the LoadBalancer forwarding port 80 to your website to allow for the challenge response. It is good practice to have a redirect to HTTPS, here's an example virtual host for Apache:

<VirtualHost *:80>
    ServerName www.example.com
    ServerAlias example.com

    RewriteEngine On
    RewriteRule ^/.well-known/ - [L]
    RewriteRule ^/(.*)$ https://www.example.com/$1 [R=301,L]

    <Location />
        Require all granted
    </Location>
</VirtualHost>

Alias /.well-known/acme-challenge /var/lib/dehydrated/acme-challenges

<Directory /var/lib/dehydrated/acme-challenges>
        Options None
        AllowOverride None

        # Apache 2.x
        <IfModule !mod_authz_core.c>
                Order allow,deny
                Allow from all
        </IfModule>

        # Apache 2.4
        <IfModule mod_authz_core.c>
                Require all granted
        </IfModule>
</Directory>

And that should be all that you need to do. Now, when Dehydrated updates your certificate, it should update your LoadBalancer as well!

deploy_cert() {
    local DOMAIN="${1}" KEYFILE="${2}" CERTFILE="${3}" FULLCHAINFILE="${4}" \
          CHAINFILE="${5}" TIMESTAMP="${6}"
    shift 6

    # File contents should be:
    #   export OS_PASSWORD='your password in here'
    . /etc/dehydrated/catalystcloud/password

    # OpenRC file from the Catalyst Cloud dashboard
    . /etc/dehydrated/catalystcloud/openrc.sh --no-token

    # UUID of the LoadBalancer to be managed
    LB_LISTENER='xxxxxxxx-xxxx-xxxx-xxxx-xxxxxxxxxxxx'

    # Barbican uses P12 files, we need to make one.
    P12=$(readlink -f $KEYFILE \
        | sed -E 's/privkey-([0-9]+)\.pem/barbican-\1.p12/')
    openssl pkcs12 -export -inkey $KEYFILE -in $CERTFILE -certfile \
        $FULLCHAINFILE -passout pass: -out $P12

    # Keep track of existing certs for this domain (hopefully no more than 100)
    EXISTING_URIS=$(openstack secret list --limit 100 \
        -c Name -c 'Secret href' -f json \
        | jq -r ".[]|select(.Name | startswith(\"$DOMAIN\"))|.\"Secret href\"")

    # Upload the new cert
    NOW=$(date +"%s")
    openstack secret store --name $DOMAIN-$TIMESTAMP-$NOW -e base64 \
        -t "application/octet-stream" --payload="$(base64 < $P12)"

    NEW_URI=$(openstack secret list --name $DOMAIN-$TIMESTAMP-$NOW \
        -c 'Secret href' -f value) \
        || unset NEW_URI

    # Change LoadBalancer to use new cert - if the old one was the default,
    # change the default. If the old one was in the SNI list, update the
    # SNI list.
    if [ -n "$EXISTING_URIS" ]; then
        DEFAULT_CONTAINER=$(openstack loadbalancer listener show $LB_LISTENER \
            -c default_tls_container_ref -f value)

        for URI in $EXISTING_URIS; do
            if [ "x$URI" = "x$DEFAULT_CONTAINER" ]; then
                openstack loadbalancer listener set $LB_LISTENER \
                    --default-tls-container-ref $NEW_URI
            fi
        done

        SNI_CONTAINERS=$(openstack loadbalancer listener show $LB_LISTENER \
            -c sni_container_refs -f value | sed "s/'//g" | sed 's/^\[//' \
            | sed 's/\]$//' | sed "s/,//g")

        for URI in $EXISTING_URIS; do
            if echo $SNI_CONTAINERS | grep -q $URI; then
                SNI_CONTAINERS=$(echo $SNI_CONTAINERS | sed "s,$URI,$NEW_URI,")
                openstack loadbalancer listener set $LB_LISTENER \
                    --sni-container-refs $SNI_CONTAINERS
            fi
        done

        # Remove old certs
        for URI in $EXISTING_URIS; do
            openstack secret delete $URI
        done
    fi
}

HANDLER="$1"; shift
#if [[ "${HANDLER}" =~ ^(deploy_challenge|clean_challenge|sync_cert|deploy_cert|deploy_ocsp|unchanged_cert|invalid_challenge|request_failure|generate_csr|startup_hook|exit_hook)$ ]]; then
if [[ "${HANDLER}" =~ ^(deploy_cert)$ ]]; then
    "$HANDLER" "$@"
fi

It’s been a little over a year since our Redflow ZCell battery and Victron Energy inverter/charger kit were installed on our existing 5.94kW solar array. Now that we’re past the Southern Hemisphere spring equinox it seems like an opportune time to review the numbers and try to see exactly how the system has performed over its first full year. For background information on what all the pieces are and what they do, see my earlier post, Go With The Flow.

As we look at the figures for the year, it’s worth keeping in mind what we’re using the battery for, and how we’re doing it. Naturally we’re using it to store PV generated electricity for later use when the sun’s not shining. We are also charging the battery from the grid at certain times so it can be drawn down if necessary during peak times, for example I set up a small overnight charge to ensure there was power for the weekday morning peak, when the sun isn’t really happening yet, but grid power is more than twice as expensive. More recently in the winter months, I experimented with keeping the battery full with scheduled charges during most non-peak times. This involved quite a bit more grid charging, but got us through a couple of three hour grid outages without a hitch during some severe weather in August.

I spent some time going through data from the VRM portal for the last year, and correlating that with current bills from Aurora energy, and then I tried to compare our last year of usage with a battery, to the previous three years of usage without a battery. For reasons that will become apparent later, this turned out to be a massive pain in the ass, so I’m going to start by looking only at what we can see in the VRM portal for the past year.

The VRM portal has three summary views: System Overview, Consumption and Solar. System Overview tells us overall how much total power was pulled from the grid, how much was exported to the grid, how much was produced locally, and how much was consumed by our loads. The Consumption view (which I wish they’d named “Loads”, because I think that would be clearer) gives us the same consumption figure, but tells us how much of that came from the grid, vs. what came from the battery vs. what came from solar. The Solar view tells us how much PV generation went to the grid, how much went to the battery, and how much was used directly. There is some overlap in the figures from these three views, but there are also some interesting discrepancies, notably: the “From Grid” and “To Grid” figures shown under System Overview are higher than what’s shown in the Consumption and Solar views. But, let’s start by looking at the Consumption and Solar views, because those tell us what the system gives us, and what we’re using. I’ll come back after that to the System Overview, which is where things start to get weird and we discover what the system costs to run.

The VRM portal lets you chose any date range you like to get historical figures and bar charts. It also gives you pie charts of the last 24 hours, 7 days, 30 days and 365 days. To make the figures and bar charts match the pie charts, the year we’re analysing starts at 4pm on September 25, 2021 and ends at 4pm on September 25, 2022, because that’s exactly when I took the following screenshots. This means we get a partial September at each end of the bar chart. I’m sorry about that.

Here’s the Consumption view:

This shows us that in the last 12 months, our loads consumed 10,849kWh of electricity. Of that, 54% (5,848kWh) came from the grid, 23% (2,506kWh) came direct from solar PV and the final 23% (2,494kWh) came from the battery.

From the rough curve of the bar chart we can see that our consumption is lower in the summer months and higher in the winter months. I can’t say for certain, but I have to assume that’s largely due to heating. The low in February was 638kWh (an average of 22.8kWh/day). The high in July was 1,118kWh (average 36kWh/day).

Now let’s look at the Solar view:

In that same time period we generated 5,640kWh with our solar array, of which 44% (2,506kWh) was used directly by our loads, 43% (2,418kWh) went into the battery and 13% (716kWh) was exported to the grid.

Unsurprisingly our generation is significantly higher in summer than in winter. We got 956kWh (average 30kWh/day) in December but only 161kWh (5.3kWh/day) in June. Peak summer figures like that mean we’ll theoretically be able to do without grid power at all during that period once we get a second ZCell (note that we’re still exporting to the grid in December – that’s because we’ve got more generation capacity than storage). The winter figures clearly indicate that there’s no way we can provide anywhere near all our own power at that time of year with our current generation capacity and loads.

Now look closely at the summer months (December, January and February). There should be a nice curve evident there from December to March, but instead January and February form a weird dip. This is because we were without solar generation for three weeks from January 20 – February 11 due to replacing a faulty MPPT. Based on figures from previous years, I suspect we lost 500-600kWh of potential generation in that period.

Another interesting thing is that if we compare “To Battery” on the Solar view (2,418kWh) with “From Battery” on the Consumption view (2,494kWh), we see that our loads consumed 76kWh more from the battery than we actually put into it with solar generation. This discrepancy is due to the fact that in addition to charging the battery from solar, we’ve also been charging it from the grid at certain times, but the amount of power sent to the battery from the grid isn’t broken out explicitly anywhere in the VRM portal.

Now let’s look at the System Overview:

Here we see the same figures for “Production” (5,640kWh) and “Consumption” (10,849kWh) as were in the Consumption and Solar views, and the bar chart shows the same consumption and generation curves (ignore the blue overlay and line which indicate battery minimum/maximum and average state of charge – that information is largely meaningless at this scale, given we cycle the battery completely every day).

Now look at “To Grid” and “From Grid”. “To Grid” is 754 kWh, i.e. we somehow sent 38kWh more to the grid than came from solar. “From Grid”, at 8,531kWh, is a whopping 2,683kWh more than the 5,848kWh grid power consumed by our loads (i.e. close to half as much again).

So, what’s going on here?

One factor is that we’re charging the battery from the grid at certain times. Initially that was a few hours overnight and a few hours in the afternoon on weekdays, although the afternoon charge is obviously also provided by the solar if the sun is shining. For all of July, August and most of September though I was using a charge schedule to keep the battery full except for peak times and maintenance cycle nights, which meant quite a bit more grid charging overnight than earlier in the year, as well as grid charging most of the day during days with no or minimal sunshine. Grid power sent to the battery isn’t visible in the “From Grid” figure on the Consumption view – that view shows only our loads, i.e. the equipment the system is powering – but it is part of the “From Grid” figure in the System Overview.

Similarly, some of the power we export to the grid is actually exported from the battery, as opposed to being exported from solar generation. That usually only happens during maintenance cycles when our loads aren’t enough to draw the battery down at the desired discharge rate. But again, same thing, that figure is present here on the system overview page as part of “To Grid”, but of course is not part of the “To Grid” figure on the Solar view.

Another factor is that the system itself needs some amount of power to operate. The Victron kit (the MultiPlus II Inverter/Chargers, the Cerbo GX, the MPPT) use some small amount of power themselves. The ZCell battery also requires power to operate its pumps and fans. When the sun is out this power can of course come from solar. When solar power is not available, power to run the system needs to come from some combination of the remaining charge in the battery, and the grid.

On that note, I did a little experiment to see how much power the system uses just to operate. On July 9 (which happened to be a maintenance cycle day), I disabled all scheduled battery charges, and I shut off the DC isolators for the solar PV, so the battery would remain online (pumps and fans running) but empty for all of July 10. The following day I went and checked the figures on the System Overview, which showed we drew 35kWh, but that our consumption was 33kWh. So, together, the battery doing nothing other than running its pumps and fans, plus the Multis doing nothing other than passing grid power through, used 2kWh of power in 24 hours. Over a year, that’s 730kWh. As mentioned above, ordinarily some of that will be sourced from mains and some from solar, but if we look at the total power that came into the system as a whole (5,640kWh from solar + 8,531kWh from the grid = 14,171kWh), 730kWh is just slightly over 5% of that.

The final factor in play is that a certain amount of power is naturally lost due to conversion at various points. The ZCell has a maximum 80% DC-DC stack efficiency, meaning in the absolute best case if you want to get 10kW out of it, you have to put 12.5kW in. In reality you’ll never hit the best case: the lifetime charge and discharge figures the BMS currenly shows for our ZCell are 4,423 and 3,336kWh respectively, which is a bit over 75%. The Multis have a maximum efficiency of 96% when doing their invert/charge dance, so if we grid charge the battery, we lose at least 4% on the way in, and at least 4% on the way out as well, going to and from AC/DC. Again, in reality that loss will be higher than 4% each way, because 96% is the maximum efficiency.

A bunch of the stuff above just doesn’t apply to the previous system with the ABB inverter and no battery. I also don’t have anything like as much detailed data to go on for the old system, which makes comparing performance with the new system fiendishly difficult. The best comparison I’ve been able to come up with so far involves looking at total power input to the system (power from grid plus solar generation), total consumption by loads (i.e. actual locally usable power), and total power exported.

Prior to the Victron gear and Redflow battery installation, I had grid import and export figures from my Aurora Energy bills, and I had total generation figures from the ABB inverter. From this I can synthesise what are hopefully reasonably accurate load consumption figures by adding grid input to total PV generation minus grid export.

I had hoped to do this analysis on a quarterly basis to line up with Aurora bills, because then I would also be able to see how seasonal solar generation and usage went up and down. Unfortunately the billing for 2020 and 2021 was totally screwed up by the COVID-19 pandemic, because there were two quarters during which nobody was coming out to read the electricity meter. The bills for those quarters stated estimated usage (i.e. were wrong, especially given they estimated grid export as zero), with subsequent quarters correcting the figures. I have no way to reliably correlate that mess with my PV generation figures, except on an annual basis. Also, using billing periods from pre-battery years, the closest I can get to the September 25 based 2021-2022 year I’m looking at now is billing periods starting and ending in mid-August. But, that’s close enough. We’ve still got four pretty much back-to-back 12 month periods to look at.

Year	Grid In	Solar In	Total In	Loads	Export
2018-2019	9,031	6,682	15,713	11,827	3,886
2019-2020	9,324	6,468	15,792	12,255	3,537
2020-2021	7,582	6,347	13,929	10,358	3,571
2021-2022	8,531	5,640	14,171	10,849	754

One thing of note here is that in the 2018-2019 and 2019-2020 years, our annual consumption was pretty close to 12MWh, whereas in 2020-2021 and 2021-2022 it was closer to 10.5MWh. If I had to guess, I’d say that ~1.5MWh/year drop is due to a couple of pieces of computer equipment that were previously always on, now mostly running in standby mode except when actually needed. A couple of hundred watts constant draw is a fair whack of power over the course of a year. Another thing to note is the big drop in power exported in 2021-2022, because most of our solar generation is now used locally.

The thing that freaked me out when looking at these figures is that in the battery year, while our loads consumed 491kWh more than in the previous non-battery year, we pulled 949kWh more power in from the grid! This is the opposite of what I had expected to see, especially having previously written:

In the eight months the system has been running we’ve generated 4631kWh of electricity and “only” sent 588kWh to the grid, which means we’ve used 87% of what we generated locally – much better than the pre-battery figure of 45%. I suspect we’ve reduced the amount of power we pull from the grid by about 30% too, but I’ll have to wait until we have a full year’s worth of data to be sure.

– by me at the end of Go With The Flow

When I wrote that, I was looking at August 31, 2021 through April 27, 2022, and comparing that to the August 2020 to May 2021 grid power figures from my old Aurora bills. The mistake I must have made back then was to look at “From Grid” on the Consumption view, rather than “From Grid” on the System Overview. I’ve just done this exercise again, and the total grid draw from our Aurora bills from August 2020 to May 2021 is 4,980kWh. “From Grid” on the Consumption view for August 2021 to May 2022 is 3,575kWh, which is about 30% less, but “From Grid” on the System Overview is 4,754kWh, which is only about 5% less. So our loads pulled about 30% less from the grid than the same time the year before, but our system as a whole didn’t.

Now let’s break our ridiculous September-based year down further into months, to see if we can see more detail. I’ve highlighted some interesting periods in bold.

Month	Grid In	Solar In	Total In	Loads	Export
Sep 21 (part)	153	101	254	213	6
Oct 21	636	629	1,265	988	55
Nov 21	430	747	1,177	866	97
Dec 21	232	956	1,188	767	176
Jan 22	652	450	1,102	822	74
Feb 22	470	430	900	638	83
Mar 22	498	568	1,066	813	64
Apr 22	609	377	986	775	27
May 22	910	238	1,148	953	3
Jun 22	1,114	161	1,275	1073	2
Jul 22	1,163	223	1,386	1118	11
Aug 22	910	375	1,285	966	64
Sep 22 (part)	754	385	1,139	857	92
Total	8,531	5,640	14,171	10,849	754

December is great. We generated about 25% more power than our loads use (956/767=1.25), and our grid input was only about 30% of the total of our loads (232/767=0.30).

January and February show the effects of missing three weeks of potential generation. I mean, just look at December through February 2021-2022 versus the previous three summers.

	2018-2019	2019-2020	2020-2021	2021-2022
December	919	882	767	956
January	936	797	818	450
February	699	656	711	430

June and July are terrible. They’re our highest load months, with the lowest solar generation and we pulled 3-4% more power from the grid than our loads actually consumed. I’m going to attribute the latter largely to grid charging the battery.

If I dig a couple of interesting figures out for June and July I see “To Battery” on the Solar view shows 205kWh, and “From Battery” on the Consumption view shows 558kWh. Total consumption in that period was 2,191kWh, with the total “From Grid” reported in System Overview of 2,277kWh. Let’s mess with that a bit.

Bearing in mind the efficiency numbers mentioned earlier, if 205kWh went to the battery from PV, that means no more than 154kWh of what we got out of the battery was from PV generation (remember: real world DC-DC stack efficiency of about 75%). The remaining 404kWh out of the battery is power that went into it from the grid. And that means at least 538kWh in (404/0.75). Note that total from grid for these two months was 86kWh more than the 2,191kWh used by our loads. If I hadn’t been keeping the battery topped up from the grid, I’d’ve saved at least 134kWh of grid power, which would have brought our grid input figure back down below our consumption figure. Note also that this number will actually be higher in reality because I haven’t factored in AC/DC conversion losses from the Multis.

Now let’s look at some costs. When I started trying to compare the new system to the previous system, I went in thinking to look at in in terms of total power input to the system, total consumption by loads, and total power exported. There’s one piece missing there, so let’s add another couple of columns to an earlier table:

Year	Grid In	Solar In	Total In	Loads	Export	Total Out	what?
2021-2022	8,531	5,640	14,171	10,849	754	11,603	2,568

The total usable output of the system was 11,603kWh for 14,171kWh input. The difference between these two figures – 2,568kWh, or about 18% – went somewhere else. Per my earlier experiment, 5% is power that went to actually operate the system components, including the battery. That means about 13% of the power input to the system over the course of the year must have gone to some combination of charge/discharge and AC/DC conversion (in)efficiencies. We can consider this the energy cost of the system. To have the ability to time-shift expensive peak grid electricity, and to run the house without the grid if the sun is out, or from the battery when it has charge, costs us 18% of the total available energy input.

Grid power has energy costs too, but we’re not usually aware of this because it happens somewhere else. I haven’t yet found Tasmanian figures, but this 2021 Transmission Annual Planning Report PDF from Powerlink in Queensland has historical figures showing that about 7% of generation there went to auxiliaries, i.e. fans and pumps and things running at the power stations. And according to the Australian Energy Market Operator (AEMO), 10% of grid power generated is lost during transmission and distribution. Stanwell (a power company in Queensland) have a neat explainer of all this on their What’s Watt site.

Finally, speaking of expensive grid electricity, let’s look at how much we paid Aurora Energy over the past four years for our power. The bills are broken out into different tariffs, for which you’re charged different amounts per kilowatt hour and then there’s an additional daily supply charge, and also credits for power exported. We can simplify that by just taking the total dollar value of all the power bills and dividing that by the total power drawn from the grid to arrive at an effective cost per kilowatt hour for the entire year. Here it is:

Year	From Grid	Total Bill	Cost/kWh
2018-2019	9,031	$2,278.33	$0.25
2019-2020	9,324	$2,384.79	$0.26
2020-2021	7,582	$1,921.77	$0.25
2021-2022	8,531	$1,731.40	$0.20

So, the combination of the battery plus the switch from Flat Rate to Peak & Off-Peak billing has reduced the cost of our grid power by about 20%. I call that a win.

Going forwards it will be interesting to see how the next twelve months go, and, in particular, what we can do to reduce our power consumption. A significant portion of our power is used by a bunch of always-on computer equipment. Some of that I need for my work, and some of that provides internet access, file storage and email for us personally. Altogether, according to the UPSes, this kit pulls 200-250 watts continuously, but will pull more than that during the day when it’s being used interactively. If we call it 250W continuous, that’s a minimum of 6kWh/day, which is 2,190kWh/year, or about 20% of the 2021-2022 consumption. Some of that equipment should be replaced with newer, more power efficient kit. Some of it could possibly even be turned off or put into standby mode some of the time.

We still need to get a heat pump to replace the 2400W panel heater in our bedroom. That should save a huge amount of power in winter. We’re also slowly working our way through the house installing excellent double glazed windows from Elite Double Glazing, which will save on power for heating and cooling year round.

And of course, we still need to get that second ZCell.

My team at SUSE is working on a new S3-compatible storage solution for Kubernetes, based on Ceph’s RADOS Gateway (RGW), except without any of the RADOS bits. The idea is that you can deploy our s3gw container on top of Longhorn (which provides the underlying replicated storage), and all this is running in your Kubernetes cluster, along with your applications which thus have convenient access to a local S3-compatible object store.

We’ve done this by adding a new storage backend to RGW. The approach we’ve taken is to use SQLite for metadata, with object data stored as files in a regular filesystem. This works quite neatly in a Kubernetes cluster with Longhorn, because Longhorn can provide a persistent volume (think: an ext4 filesystem), on which s3gw can store its SQLite database and object data files. If you’d like to kick the tyres, check out Giuseppe’s deployment tutorial for the 0.2.0 release, but bear in mind that as I’m writing this we’re all the way up to 0.4.0 so some details may have changed.

While s3gw on Longhorn on Kubernetes remains our primary focus for this project, the fact that this thing only needs a filesystem for backing storage means it can be run on top of just about anything. Given “just about anything” includes an old school two node Pacemaker cluster with DRBD for replicated storage, why not give that a try? I kinda like the idea of a good solid highly available S3-compatible storage solution that you could shove into the bottom of a rack somewhere without too much difficulty.

# crm configure show
[...]
primitive drbd_s3 ocf:linbit:drbd \
        params drbd_resource=s3 drbdconf="/etc/drbd.conf" \
        op monitor interval=29s role=Master \
        op monitor interval=31s role=Slave
primitive fs_s3 Filesystem \
        params device="/dev/drbd0" directory="/data" fstype=ext4 \
        meta target-role=Started \
        op start timeout=60s interval=0 \
        op stop timeout=60s interval=0 \
        op monitor interval=20s timeout=40s
primitive https nginx \
        op start timeout=40s interval=0 \
        op stop timeout=60s interval=0 \
        op monitor timeout=30s interval=10s \
        op monitor timeout=30s interval=30s \
        op monitor timeout=60s interval=20s
primitive s3-ip IPaddr2 \
        params ip=192.168.100.50 \
        op monitor interval=10 timeout=20
primitive s3gw podman \
        params image="ghcr.io/aquarist-labs/s3gw:latest" run_opts="-p 7480:7480 -v/data:/data" \
        op start interval=0 timeout=90s \
        op stop interval=0 timeout=90s \
        op monitor interval=30s timeout=30s
primitive s3gw-ui podman \
        params image="ghcr.io/aquarist-labs/s3gw-ui:latest" run_opts="-p 8080:8080 -e RGW_SERVICE_URL=https://s3gw.sleha.test" \
        op start interval=0 timeout=90s \
        op stop interval=0 timeout=90s \
        op monitor interval=30s timeout=30s
group g-s3 fs_s3 s3gw s3gw-ui https s3-ip
ms ms-drbd_s3 drbd_s3 \
        meta master-max=1 master-node-max=1 clone-max=2 clone-node-max=1 notify=true
colocation col-s3_on_drbd inf: g-s3 ms-drbd_s3:Promoted
order o-drbd_before_fs Mandatory: ms-drbd_s3:promote g-s3:start
[...]

worker_processes  1;

events {
    worker_connections  1024;
    use epoll;
}

http {
    include       mime.types;
    default_type  application/octet-stream;

    sendfile        on;
    keepalive_timeout  65;

    server {
        listen       80;
        return       301 https://$host$request_uri; 
    }

    server {
        listen       443 ssl;
        server_name  s3gw.sleha.test;

        access_log /var/log/nginx/s3gw.access.log;

        location / {
            proxy_set_header        Host $host;
            proxy_set_header        X-Real-IP $remote_addr;
            proxy_set_header        X-Forwarded-For $proxy_add_x_forwarded_for;
            proxy_set_header        X-Forwarded-Proto $scheme;

            add_header Access-Control-Allow-Origin 'https://s3gw-ui.sleha.test';
            add_header Access-Control-Allow-Methods 'GET,HEAD,PUT,POST,DELETE';
            add_header Access-Control-Allow-Headers '*';
            add_header 'Access-Control-Allow-Credentials' 'true';

            if ($request_method = 'OPTIONS') {
                add_header Access-Control-Allow-Origin 'https://s3gw-ui.sleha.test';
                add_header Access-Control-Allow-Methods 'GET,HEAD,PUT,POST,DELETE';
                add_header Access-Control-Allow-Headers '*';
                add_header 'Access-Control-Allow-Credentials' 'true';
                add_header 'Content-Type' 'text/plain charset=UTF-8';
                add_header 'Content-Length' 0;
                return 204;
            }

            proxy_pass          http://localhost:7480;
            proxy_read_timeout  90;
            proxy_redirect      http://localhost:7480 https://s3gw.sleha.test;
        }

        ssl_certificate      cert.pem;
        ssl_certificate_key  cert.key;
        ssl_protocols        TLSv1.2;
        ssl_session_cache    shared:SSL:1m;
        ssl_session_timeout  5m;
        ssl_ciphers  HIGH:!aNULL:!MD5;
        ssl_prefer_server_ciphers  on;
    }

    server {
        listen       443 ssl;
        server_name  s3gw-ui.sleha.test;

        access_log /var/log/nginx/s3gw-ui.access.log;

        location / {
            proxy_set_header        Host $host;
            proxy_set_header        X-Real-IP $remote_addr;
            proxy_set_header        X-Forwarded-For $proxy_add_x_forwarded_for;
            proxy_set_header        X-Forwarded-Proto $scheme;

            proxy_pass          http://localhost:8080;
            proxy_read_timeout  90;

            proxy_redirect      http://localhost:8080 https://s3gw-ui.sleha.test;
        }

        ssl_certificate      cert-ui.pem;
        ssl_certificate_key  cert-ui.key;
        ssl_protocols        TLSv1.2;
        ssl_session_cache    shared:SSL:1m;
        ssl_session_timeout  5m;
        ssl_ciphers  HIGH:!aNULL:!MD5;
        ssl_prefer_server_ciphers  on;
    }
}

access_key = test
secret_key = test
host_base = https://s3gw.sleha.test/
host_bucket = htts://s3gw.sleha.test/%(bucket)

We’ve done this a number of times over the last decade, from OSDC to LCA. The idea is to provide a free psychologist or counsellor at an in-person conference. Attendees can do an anonymous booking by taking a stickynote (with the timeslot) from a signup sheet, and thus get a free appointment.

Many people find it difficult taking the first (very important) step towards getting professional help, and we’ve received good feedback that this approach indeed assists.

So far we’ve always focused on open source conferences. Now we’re moving into information security! First BrisSEC 2022 (Friday 29 April at the Hilton in Brisbane, QLD) and then AusCERT 2022 (10-13 May at the Star Hotel, Gold Coast QLD). The awesome and geek friendly Dr Carla Rogers will be at both events.

How does this get funded? Well, we’ve crowdfunded some, nudged sponsors, most mostly it gets picked up by the conference organisers (aka indirectly by the sponsors, mostly).

If you’re a conference organiser, or would like a particular upcoming conference to offer this service, do drop us a line and we’re happy to chase it up for you and help the organisers to make it happen. We know how to run that now.

In-person is best. But for virtual conferences, sure contact us as well.

The hack day didn’t go as well as I hoped, but didn’t go too badly. There was smaller attendance than hoped and the discussion was mostly about things other than FLOSS. But everyone who attended had fun and learned interesting things so generally I think it counts as a success. There was discussion on topics including military hardware, viruses (particularly Covid), rocketry, and literature. During the discussion one error in a Wikipedia page was discussed and hopefully we can get that fixed.

I think that everyone who attended will be interested in more such meetings. Overall I think this is a reasonable start to the Hack Day meetings, when I previously ran such meetings they often ended up being more social events than serious hacking events and that’s OK too.

One conclusion that we came to regarding meetings is that they should always be well announced in email and that the iCal file isn’t useful for everyone. Discussion continues on the best methods of announcing meetings but I anticipate that better email will get more attendance.

Meeting Report

The March 2022 meeting went reasonably well. Everyone seemed to have fun and learn useful things about computers. After 2 hours my Internet connection dropped out which stopped the people who were using VMs from doing the tutorial. Fortunately most people seemed ready for a break so we ended the meeting. The early and abrupt ending of the meeting was a disappointment but it wasn’t too bad, the meeting would probably only have gone for another half hour otherwise.

The BigBlueButton system was shown to be effective for training when one person got confused with the Debian package configuration options for Postfix and they were able to share the window with everyone else to get advice. I was also confused by that stage.

Future Meetings

The main feature of the meeting was training in setting up a mailserver with Postfix, here are the lecture notes for it [1]. The consensus at the end of the meeting was that people wanted more of that for the April meeting. So for the April meeting I will add to the Postfix Training to include SpamAssassin, SPF, DKIM, and DMARC. For the start of the next meeting instead of providing bare Debian installations for the VMs I’ll provide a basic Postfix/Dovecot setup so people can get straight into SpamAssassin etc.

For the May meeting training on SE Linux was requested.

Social Media

Towards the end of the meeting we discussed Matrix and federated social media. LUV has a Matrix server and I can give accounts to anyone who’s involved in FOSS in the Australia and New Zealand area. For Mastodon the NZOSS Mastodon server [2] seems like a good option. I have an account there to try Mastodon, my Mastodon address is @etbe@mastodon.nzoss.nz .

We are going to make Matrix a primary communication method for the Flounder group, the room is #flounder:luv.asn.au . My Matrix address is @etbe:luv.asn.au .

• [1] https://doc.coker.com.au/training/postfix-training/
• [2] https://mastodon.nzoss.nz/

We now have a mailing list see https://lists.linux.org.au/mailman/listinfo/flounder for information, the address to post to the list is flounder@lists.linux.org.au..

We also have a new URL for the blog and events. See the right sidebar for the link to the iCal file which can be connected to Google Calendar and most online calendaring systems.

We just had the first Flounder meeting which went well. Had some interesting discussion of storage technology, I learnt a few new things. Some people did the ZFS training and BTRFS training and we had lots of interesting discussion.

Andrew Pam gave a summary of new things in Linux and talked about the sites lwn.net, gamingonlinux.com, and cnx-software.com that he uses to find Linux news. One thing he talked about is the latest developments with SteamDeck which is driving Linux support in Steam games. The site protondb.com tracks Linux support in Steam games.

We had some discussion of BPF, for an introduction to that technology see the BPF lecture from LCA 2022.

Next Meeting

The next meeting (Saturday 5th of March 1PM Melbourne time) will focus on running your own mail server which is always of interest to people who are interested in system administration and which is probably of more interest than usual because of Google forcing companies with “a legacy G Suite subscription” to transition to a more expensive “Business family” offering.

I “recently” wrote about obtaining a new (to me, actually quite old) computer over in The Apple Power Macintosh 7200/120 PC Compatible (Part 1). This post is a bit of a detour, but may help others understand why some images they download from the internet don’t work.

Disk partitioning is (of course) a way to divide up a single disk into multiple volumes (partitions) for different uses. While the idea is similar, computer platforms over the ages have done this in a variety of different ways, with varying formats on disk, and varying limitations. The ones that you’re most likely to be familiar with are the MBR partitioning scheme (from the IBM PC), and the GPT partitioning scheme (common for UEFI systems such as the modern PC and Mac). One you’re less likely to be familiar with is the Apple Partition Map scheme.

The way all IBM PCs and compatibles worked from the introduction of MS-DOS 2.0 in 1983 until some time after 2005 was the Master Boot Record partitioning scheme. It was outrageously simple: of the first 512 byte sector of a disk, the first 446 bytes was for the bootstrapping code (the “boot sector”), the last 2 bytes were for the magic two bytes telling the BIOS this disk was bootable, and the other 64 bytes were four entries of 16 bytes, each describing a disk partition. The Wikipedia page is a good overview of what it all looks like. Since “four partitions should be enough for anybody” wasn’t going to last, DOS 3.2 introduced “extended partitions” which was just using one of those 4 partitions as another similar data structure that could point to more partitions.

In the 1980s (similar to today), the Macintosh was, of course, different. The Apple Partition Map is significantly more flexible than the MBR on PCs. For a start, you could have more than four partitions! You could actually have a lot more than four partitions, as the Apple Partition Map is a single 512-byte sector for each partition, and the partition map is itself a partition. Instead of being block 0 (like the MBR is), it actually starts at block 1, and is contiguous (The Driver Descriptor Record is what’s at block 0). So, once created, it’s hard to extend. Typically it’d be created as 64×512-byte entries, for 32kb… which turns out is actually about enough for anyone.

The Inside Macintosh reference on the SCSI Manager goes through more detail as to these structures. If you’re wondering what language all the coding examples are in, it’s Pascal – which was fairly popular for writing Macintosh applications in back in the day.

But the actual partition map isn’t the “interesting” part of all this (and yes, the quotation marks are significant here), because Macs are pretty darn finicky about what disks to boot off, which gets to be interesting if you’re trying to find a CD-ROM image on the internet from which to boot, and then use to install an Operating System from.

… the preferred programming language changes.

I never programmed a 1980s Macintosh actually in the 1980s. It was sometime in the early 1990s that I first experienced Microsoft Basic for the Macintosh. I’d previously (unknowingly at the time as it was branded Commodore) experienced Microsoft BASIC on the Commodore 16, Commodore 64, and even the Apple ][, but the Macintosh version was something else. It let you do some pretty neat things such as construct a GUI with largely the same amount of effort as it took to construct a Text based UI on the micros I was familiar with.

Okay, to be fair, I’d also dabbled in Microsoft QBasic that came bundled with MS-DOS of the era, which let you do a whole bunch of graphics – so you could theoretically construct a GUI with it. Something I did attempt to do. Programming on the Mac was so much easier to construct a GUI.

Of course, Microsoft Basic wasn’t the preferred way to program on the Macintosh. At that time it was largely Pascal, with C being something that also existed – but you were going to see Pascal in Inside Macintosh. It was probably somewhat fortuitous that I’d poked at Pascal a bit as something alternate to look at in the high school computing classes. I can only remember using TurboPascal on DOS systems and never actually writing Pascal on the Macintosh.

By the middle part of the 1990s though, I was firmly incompetently writing C on the Mac. No doubt the quality of my code increased after I’d done some university courses actually covering the language rather than the only practical way I had to attempt to write anything useful being looking at Inside Macintosh examples in Pascal and “C for Dummies” which was very not-Macintosh. Writing C on UNIX/Linux was a lot easier – everything was made for it, including Actual Documentation!

Anyway, in the early 2000s I ran MacOS X for a bit on my white iBook G3, and did a (very) small amount of any GUI / Project Builder (the precursor to Xcode) related development – instead largely focusing on command line / X11 things. The latest coolness being to use Objective-C to program applications (unless you were bringing over your Classic MacOS Carbon based application, then you could still write C). Enter some (incompetent) Objective-C coding!

Then Apple went to x86, so the hardware ceased being interesting, and I had no reason to poke at it even as a side effect of having hardware that could run the software stack. Enter a long-ass time of Debian, Ubuntu, and Fedora on laptops.

Come 2022 though, and (for reasons I should really write up), I’m poking at a Mac again and it’s now Swift as the preferred way to write apps. So, I’m (incompetently) hacking away at Swift code. I have to admit, it’s pretty nice. I’ve managed to be somewhat productive in a relative short amount of time, and all the affordances in the language gear towards the kind of safety that is a PITA when coding in C.

So this is my WIP utility to be able to import photos from a Shotwell database into the macOS Photos app:

There’s a lot of rough edges and unknowns left, including how to actually do the import (it looks like there’s going to be Swift code doing AppleScript things as the PhotoKit API is inadequate). But hey, some incompetent hacking in not too much time has a kind-of photo browser thing going on that feels pretty snappy.

Recently I read Michael Snoyman’s post on combining Axum, Hyper, Tonic and Tower. While his solution worked, it irked me – it seemed like there should be a much tighter solution possible.

I can deep dive into the code in a later post perhaps, but I think there are four points of difference. One, since the post was written Axum has started boxing its routes : so the enum dispatch approach taken, which delivers low overheads actually has no benefits today.

Two, while writing out the entire type by hand has some benefits, async code is much more pithy.

Thirdly, the code in the post is entirely generic, except the routing function itself.

And fourth, the outer Service<AddrStream> is an unnecessary layer to abstract over: given the similar constraints – the inner Service must take Request<..>, it is possible to just not use a couple of helpers and instead work directly with Service<Request...>.

So, onto a pithier version.

First, the app server code itself.

use std::{convert::Infallible, net::SocketAddr};

use axum::routing::get;
use hyper::{server::conn::AddrStream, service::make_service_fn};
use hyper::{Body, Request};
use tonic::async_trait;

use demo::echo_server::{Echo, EchoServer};
use demo::{EchoReply, EchoRequest};

struct MyEcho;

#[async_trait]
impl Echo for MyEcho {
    async fn echo(
        &self,
        request: tonic::Request<EchoRequest>,
    ) -> Result<tonic::Response<EchoReply>, tonic::Status> {
        Ok(tonic::Response::new(EchoReply {
            message: format!("Echoing back: {}", request.get_ref().message),
        }))
    }
}

#[tokio::main]
async fn main() {
    let addr = SocketAddr::from(([0, 0, 0, 0], 3000));

    let axum_service = axum::Router::new().route("/", get(|| async { "Hello world!" }));

    let grpc_service = tonic::transport::Server::builder()
        .add_service(EchoServer::new(MyEcho))
        .into_service();

    let both_service =
        demo_router::Router::new(axum_service, grpc_service, |req: &Request<Body>| {
            Ok::<bool, Infallible>(
                req.headers().get("content-type").map(|x| x.as_bytes())
                    == Some(b"application/grpc"),
            )
        });

    let make_service = make_service_fn(move |_conn: &AddrStream| {
        let both_service = both_service.clone();
        async { Ok::<_, Infallible>(both_service) }
    });

    let server = hyper::Server::bind(&addr).serve(make_service);

    if let Err(e) = server.await {
        eprintln!("server error: {}", e);
    }
}

Note the Router: it takes the two services and Fn to determine which to use on any given request. Then we just drop that composed service into make_service_fn and we’re done.

Next up we have the Router implementation. This is generic across any two Service<Request<...>> types as long as they are both Into<Bytes> for their Data, and Into<Box<dyn Error>> for errors.

use std::{future::Future, pin::Pin, task::Poll};

use http_body::combinators::UnsyncBoxBody;
use hyper::{body::HttpBody, Body, Request, Response};
use tower::Service;

#[derive(Clone)]
pub struct Router<First, Second, F> {
    first: First,
    second: Second,
    discriminator: F,
}

impl<First, Second, F> Router<First, Second, F> {
    pub fn new(first: First, second: Second, discriminator: F) -> Self {
        Self {
            first,
            second,
            discriminator,
        }
    }
}

impl<First, Second, FirstBody, FirstBodyError, SecondBody, SecondBodyError, F, FErr>
    Service<Request<Body>> for BinaryRouter<First, Second, F>
where
    First: Service<Request<Body>, Response = Response<FirstBody>>,
    First::Error: Into<Box<dyn std::error::Error + Send + Sync>> + 'static,
    First::Future: Send + 'static,
    First::Response: 'static,
    Second: Service<Request<Body>, Response = Response<SecondBody>>,
    Second::Error: Into<Box<dyn std::error::Error + Send + Sync>> + 'static,
    Second::Future: Send + 'static,
    Second::Response: 'static,
    F: Fn(&Request<Body>) -> Result<bool, FErr>,
    FErr: Into<Box<dyn std::error::Error + Send + Sync>> + Send + 'static,
    FirstBody: HttpBody<Error = FirstBodyError> + Send + 'static,
    FirstBody::Data: Into<bytes::Bytes>,
    FirstBodyError: Into<Box<dyn std::error::Error + Send + Sync>> + 'static,
    SecondBody: HttpBody<Error = SecondBodyError> + Send + 'static,
    SecondBody::Data: Into<bytes::Bytes>,
    SecondBodyError: Into<Box<dyn std::error::Error + Send + Sync>> + 'static,
{
    type Response = Response<
        UnsyncBoxBody<
            <hyper::Body as HttpBody>::Data,
            Box<dyn std::error::Error + Send + Sync + 'static>,
        >,
    >;
    type Error = Box<dyn std::error::Error + Send + Sync + 'static>;
    type Future =
        Pin<Box<dyn Future<Output = Result<Self::Response, Self::Error>> + Send + 'static>>;

    fn poll_ready(
        &mut self,
        cx: &mut std::task::Context<'_>,
    ) -> std::task::Poll<Result<(), Self::Error>> {
        match self.first.poll_ready(cx) {
            Poll::Ready(Ok(())) => match self.second.poll_ready(cx) {
                Poll::Ready(Ok(())) => Poll::Ready(Ok(())),
                Poll::Ready(Err(e)) => Poll::Ready(Err(e.into())),
                Poll::Pending => Poll::Pending,
            },
            Poll::Ready(Err(e)) => Poll::Ready(Err(e.into())),
            Poll::Pending => Poll::Pending,
        }
    }

    fn call(&mut self, req: Request<Body>) -> Self::Future {
        let discriminant = { (self.discriminator)(&req) };
        let (first, second) = if matches!(discriminant, Ok(false)) {
            (Some(self.first.call(req)), None)
        } else if matches!(discriminant, Ok(true)) {
            (None, Some(self.second.call(req)))
        } else {
            (None, None)
        };
        let f = async {
            Ok(match discriminant.map_err(Into::into)? {
                true => second
                    .unwrap()
                    .await
                    .map_err(Into::into)?
                    .map(|b| b.map_data(Into::into).map_err(Into::into).boxed_unsync()),
                false => first
                    .unwrap()
                    .await
                    .map_err(Into::into)?
                    .map(|b| b.map_data(Into::into).map_err(Into::into).boxed_unsync()),
            })
        };
        Box::pin(f)
    }
}

Interesting things here – I use boxed_unsync to abstract over the body concrete type, and I implement the future using async code rather than as a separate struct. It becomes much smaller even after a few bits of extra type constraining.

One thing that flummoxed me for a little was the need to capture the future for the underlying response outside of the async block. Failing to do so provokes a 'static requirement which was tricky to debug. Fortunately there is a bug on making this easier to diagnose in rustc already. The underlying problem is that if you create the async block, and then dereference self, the type for impl of .first has to live an arbitrary time. Whereas by capturing the future immediately, only the impl of the future has to live an arbitrary time, and that doesn’t then require changing the signature of the function.

This is almost worth turning into a crate – I couldn’t see an existing one when I looked, though it does end up rather small – < 100 lines. What do you all think?

The first meeting will start at 1PM Australian Eastern time (Melbourne/Sydney) which is +1100 on Saturday the 5th of February.

I will start the video chat an hour early in case someone makes a timezone mistake and gets there an hour before it starts. If anyone else joins early we will have random chat until the start time (deliberately avoiding topics worthy of the main meeting). The link http://b.coker.com.au will redirect to the meeting URL on the day.

The first scheduled talk is a summary and discussion of free software related news. Anyone who knows of something new that excites them is welcome to speak about it.

The main event is discussion of storage technology and hands-on training on BTRFS and ZFS for those who are interested. Here are the ZFS training notes and here are the BTRFS training notes. Feel free to do the training exercises on your own VM before the meeting if you wish.

Then discussion of the future of the group and the use of FOSS social media. While social media is never going to be compulsory some people will want to use it to communicate and we could run some servers for software that is considered good (lots of server capacity is available).

Finally we have to plan future meetings and decide on which communication methods are desired.

The BBB instance to be used for the video conference is sponsored by NZOSS and Catalyst Cloud.

Since PM Scott Morrison did not announce the federal election date last week, it will now be held somewhere between March and May (see the post from ABC’s Antony Green for details). Various aspects of elections are covered in the Civics & Citizenship Australian Curriculum in Years 4, 5 and 6. Students are interested in […]

Flounder is a new free software users group based in the Australia/NZ area. Flounder stands for FLOSS (Free Libre Open Source Software) down under.

Here is my blog post describing the initial idea, the comment from d3Xt3r suggested the name. Flounder is a group of fish that has species native to Australia and NZ.

The main aim is to provide educational benefits to free software users via an online meeting that can’t be obtained by watching YouTube videos etc in a scope that is larger than one country. When the pandemic ends we will keep running this as there are benefits to be obtained from a meeting of a wide geographic scope that can’t be obtained by meetings in a single city. People from other countries are welcome to attend but they aren’t the focus of the meeting.

Until we get a better DNS name the address http://b.coker.com.au will redirect to the BBB instance used for online meetings (the meeting address isn’t yet setup so it redirects to the blog). The aim is that there will always be a short URL for the meeting so anyone who has one device lose contact can quickly type the URL into their backup device.

The first meeting will be on the 5th of Feb 2022 at 1PM Melbourne time +1100. When we get a proper domain I’ll publish a URL for an iCal file with entries for all meetings. I will also find some suitable way for meeting times to be localised (I’m sure there’s a WordPress plugin for that).

For the hands-on part of the meetings there will be virtual machine images you can download to run on your own system (tested with KVM, should work with other VM systems) and the possibility of logging in to a running VM. The demonstration VMs will have public IPv6 addresses and will also be available through different ports on a single IPv4 address, having IPv6 on your workstation will be convenient for you but you can survive without it.

Linux Australia has a list of LUGs in Australia, is there a similar list for NZ? One thing I’d like to see is a list of links for iCal files for all the meetings and also an iCal aggregator that for all iCal feeds of online meetings. I’ll host it myself if necessary, but it’s probably best to do it via Linux Australia (Linux Australasia?) if possible.

I’m attending the https://linux.conf.au/ conference online this weekend, which is always a good opportunity for some sideline hacking.

I found something boneheaded doing that today.

There have been a few times while inventing the OpenHMD Rift driver where I’ve noticed something strange and followed the thread until it made sense. Sometimes that leads to improvements in the driver, sometimes not.

In this case, I wanted to generate a graph of how long the computer vision processing takes – from the moment each camera frame is captured until poses are generated for each device.

To do that, I have a some logging branches that output JSON events to log files and I write scripts to process those. I used that data and produced:

Two things caught my eye in this graph. The first is the way the baseline latency (pink lines) increases from ~20ms to ~58ms. The 2nd is the quantisation effect, where pose latencies are clearly moving in discrete steps.

Neither of those should be happening.

Camera frames are being captured from the CV1 sensors every 19.2ms, and it takes that 17-18ms for them to be delivered across the USB. Depending on how many IR sources the cameras can see, figuring out the device poses can take a different amount of time, but the baseline should always hover around 17-18ms because the fast “device tracking locked” case take as little as 1ms.

Did you see me mention 19.2ms as the interframe period? Guess what the spacing on those quantisation levels are in the graph? I recognised it as implying that something in the processing is tied to frame timing when it should not be.

This 2nd graph helped me pinpoint what exactly was going on. This graph is cut from the part of the session where the latency has jumped up. What it shows is a ~1 frame delay between when the frame is received (frame-arrival-finish-local-ts) before the initial analysis even starts!

That could imply that the analysis thread is just busy processing the previous frame and doesn’t get start working on the new one yet – but the graph says that fast analysis is typically done in 1-10ms at most. It should rarely be busy when the next frame arrives.

This is where I found the bone headed code – a rookie mistake I wrote when putting in place the image analysis threads early on in the driver development and never noticed.

There are 3 threads involved:

• USB service thread, reading video frame packets and assembling pixels in framebuffers
• Fast analysis thread, that checks tracking lock is still acquired
• Long analysis thread, which does brute-force pose searching to reacquire / match unknown IR sources to device LEDs

These 3 threads communicate using frame worker queues passing frames between each other. Each analysis thread does this pseudocode:

while driver_running:
    Pop a frame from the queue
    Process the frame
    Sleep for new frame notification

The problem is in the 3rd line. If the driver is ever still processing the frame in line 2 when a new frame arrives – say because the computer got really busy – the thread sleeps anyway and won’t wake up until the next frame arrives. At that point, there’ll be 2 frames in the queue, but it only still processes one – so the analysis gains a 1 frame latency from that point on. If it happens a second time, it gets later by another frame! Any further and it starts reclaiming frames from the queues to keep the video capture thread fed – but it only reclaims one frame at a time, so the latency remains!

The fix is simple:

while driver_running:
   Pop a frame
   Process the frame
   if queue_is_empty():
     sleep for new frame notification

Doing that for both the fast and long analysis threads changed the profile of the pose latency graph completely.

This is a massive win! To be clear, this has been causing problems in the driver for at least 18 months but was never obvious from the logs alone. A single good graph is worth a thousand logs.

What does this mean in practice?

The way the fusion filter I’ve built works, in between pose updates from the cameras, the position and orientation of each device are predicted / updated using the accelerometer and gyro readings. Particularly for position, using the IMU for prediction drifts fairly quickly. The longer the driver spends ‘coasting’ on the IMU, the less accurate the position tracking is. So, the sooner the driver can get a correction from the camera to the fusion filter the less drift we’ll get – especially under fast motion. Particularly for the hand controllers that get waved around.

Poses are now being updated up to 40ms earlier and the baseline is consistent with the USB transfer delay.

You can also visibly see the effect of the JPEG decoding support I added over Christmas. The ‘red’ camera is directly connected to USB3, while the ‘khaki’ camera is feeding JPEG frames over USB2 that then need to be decoded, adding a few ms delay.

The latency reduction is nicely visible in the pose graphs, where the ‘drop shadow’ effect of pose updates tailing fusion predictions largely disappears and there are fewer large gaps in the pose observations when long analysis happens (visible as straight lines jumping from point to point in the trace):

Yes, the blog is still on. January 2004 I moved to WordPress, and it is still here January 2022. I didn’t write much last year (neither here, not experimenting with the Hey blog). I didn’t post anything to Instagram last year either from what I can tell, just a lot of stories.

August 16 2021, I realised I was 1,000 days till May 12 2024, which is when I become 40. As of today, that leads 850 days. Did I squander the last 150 days? I’m back to writing almost daily in the Hobonichi Techo (I think last year and the year before were mostly washouts; I barely scribbled anything offline).

I got a new Apple Watch Series 7 yesterday. I can say I used the Series 4 well (79% battery life), purchased in the UK when I broke my Series 0 in Edinburgh airport.

TripIt stats for last year claimed 95 days on the road. This is of course, a massive joke, but I’m glad I did get to visit London, Lisbon, New York, San Francisco, Los Angeles without issue. I spent a lot of time in Kuantan, a bunch of Langkawi trips, and also, I stayed for many months at the Grand Hyatt Kuala Lumpur during the May lockdowns (I practically stayed there all lockdown).

With 850 days to go till I’m 40, I have plenty I would like to achieve. I think I’ll write a lot more here. And elsewhere. Get back into the habit of doing. And publishing by learning and doing. No fear. Not that I wasn’t doing, but its time to be prolific with what’s been going on.

Once again time has passed, and another update on Oculus Rift support feels due! As always, it feels like I’ve been busy with work and not found enough time for Rift CV1 hacking. Nevertheless, looking back over the history since I last wrote, there’s quite a lot to tell!

In general, the controller tracking is now really good most of the time. Like, wildly-swing-your-arms-and-not-lose-track levels (most of the time). The problems I’m hunting now are intermittent and hard to identify in the moment while using the headset – hence my enthusiasm over the last updates for implementing stream recording and a simulation setup. I’ll get back to that.

Outlier Detection

Since I last wrote, the tracking improvements have mostly come from identifying and rejecting incorrect measurements. That is, if I have 2 sensors active and 1 sensor says the left controller is in one place, but the 2nd sensor says it’s somewhere else, we’ll reject one of those – choosing the pose that best matches what we already know about the controller. The last known position, the gravity direction the IMU is detecting, and the last known orientation. The tracker will now also reject observations for a time if (for example) the reported orientation is outside the range we expect. The IMU gyroscope can track the orientation of a device for quite a while, so can be relied on to identify strong pose priors once we’ve integrated a few camera observations to get the yaw correct.

It works really well, but I think improving this area is still where most future refinements will come. That and avoiding incorrect pose extractions in the first place.

The above plot is a sample of headset tracking, showing the extracted poses from the computer vision vs the pose priors / tracking from the Kalman filter. As you can see, there are excursions in both position and orientation detected from the video, but these are largely ignored by the filter, producing a steadier result.

This plot shows the left controller being tracked during a Beat Saber session. The controller tracking plot is quite different, because controllers move a lot more than the headset, and have fewer LEDs to track against. There are larger gaps here in the timeline while the vision re-acquires the device – and in those gaps you can see the Kalman filter interpolating using IMU input only (sometimes well, sometimes less so).

Improved Pose Priors

Another nice thing I did is changes in the way the search for a tracked device is made in a video frame. Before starting looking for a particular device it always now gets the latest estimate of the previous device position from the fusion filter. Previously, it would use the estimate of the device pose as it was when the camera exposure happened – but between then and the moment we start analysis more IMU observations and other camera observations might arrive and be integrated into the filter, which will have updated the estimate of where the device was in the frame.

This is the bit where I think the Kalman filter is particularly clever: Estimates of the device position at an earlier or later exposure can improve and refine the filter’s estimate of where the device was when the camera captured the frame we’re currently analysing! So clever. That mechanism (lagged state tracking) is what allows the filter to integrate past tracking observations once the analysis is done – so even if the video frame search take 150ms (for example), it will correct the filter’s estimate of where the device was 150ms in the past, which ripples through and corrects the estimate of where the device is now.

LED visibility model

To improve the identification of devices better, I measured the actual angle from which LEDs are visible (about 75 degrees off axis) and measured the size. The pose matching now has a better idea of which LEDs should be visible for a proposed orientation and what pixel size we expect them to have at a particular distance.

Better Smoothing

I fixed a bug in the output pose smoothing filter where it would glitch as you turned completely around and crossed the point where the angle jumps from +pi to -pi or vice versa.

Improved Display Distortion Correction

I got a wide-angle hi-res webcam and took photos of a checkerboard pattern through the lens of my headset, then used OpenCV and panotools to calculate new distortion and chromatic aberration parameters for the display. For me, this has greatly improved. I’m waiting to hear if that’s true for everyone, or if I’ve just fixed it for my headset.

Persistent Config Cache

Config blocks! A long time ago, I prototyped code to create a persistent OpenHMD configuration file store in ~/.config/openhmd. The rift-kalman-filter branch now uses that to store the configuration blocks that it reads from the controllers. The first time a controller is seen, it will load the JSON calibration block as before, but it will now store it in that directory – removing a multiple second radio read process on every subsequent startup.

Persistent Room Configuration

To go along with that, I have an experimental rift-room-config branch that creates a rift-room-config.json file and stores the camera positions after the first startup. I haven’t pushed that to the rift-kalman-filter branch yet, because I’m a bit worried it’ll cause surprising problems for people. If the initial estimate of the headset pose is wrong, the code will back-project the wrong positions for the cameras, which will get written to the file and cause every subsequent run of OpenHMD to generate bad tracking until the file is removed. The goal is to have a loop that monitors whether the camera positions seem stable based on the tracking reports, and to use averaging and resetting to correct them if not – or at least to warn the user that they should re-run some (non-existent) setup utility.

Video Capture + Processing

The final big ticket item was a rewrite of how the USB video frame capture thread collects pixels and passes them to the analysis threads. This now does less work in the USB thread, so misses fewer frames, and also I made it so that every frame is now searched for LEDs and blob identities tracked with motion vectors, even when no further analysis will be done on that frame. That means that when we’re running late, it better preserves LED blob identities until the analysis threads can catch up – increasing the chances of having known LEDs to directly find device positions and avoid searching. This rewrite also opened up a path to easily support JPEG decode – which is needed to support Rift Sensors connected on USB 2.0 ports.

Session Simulator

I mentioned the recording simulator continues to progress. Since the tracking problems are now getting really tricky to figure out, this tool is becoming increasingly important. So far, I have code in OpenHMD to record all video and tracking data to a .mkv file. Then, there’s a simulator tool that loads those recordings. Currently it is capable of extracting the data back out of the recording, parsing the JSON and decoding the video, and presenting it to a partially implemented simulator that then runs the same blob analysis and tracking OpenHMD does. The end goal is a Godot based visualiser for this simulation, and to be able to step back and forth through time examining what happened at critical moments so I can improve the tracking for those situations.

To make recordings, there’s the rift-debug-gstreamer-record branch of OpenHMD. If you have GStreamer and the right plugins (gst-plugins-good) installed, and you set env vars like this, each run of OpenHMD will generate a recording in the target directory (make sure the target dir exists):

export OHMD_TRACE_DIR=/home/user/openhmd-traces/
export OHMD_FULL_RECORDING=1

Up Next

The next things that are calling to me are to improve the room configuration estimation and storage as mentioned above – to detect when the poses a camera is reporting don’t make sense because it’s been bumped or moved.

I’d also like to add back in tracking of the LEDS on the back of the headset headband, to support 360 tracking. I disabled those because they cause me trouble – the headband is adjustable relative to the headset, so the LEDs don’t appear where the 3D model says they should be and that causes jitter and pose mismatches. They need special handling.

One last thing I’m finding exciting is a new person taking an interest in Rift S and starting to look at inside-out tracking for that. That’s just happened in the last few days, so not much to report yet – but I’ll be happy to have someone looking at that while I’m still busy over here in CV1 land!

As always, if you have any questions, comments or testing feedback – hit me up at thaytan@noraisin.net or on @thaytan Twitter/IRC.

Thank you to the kind people signed up as Github Sponsors for this project!

For a long time computer manufacturers have tried to differentiate themselves and their products from their competitors with fancy names with odd capitalisation and spelling. But as an author, using these names does a disservice to the reader: how are they to know that DEC is pronounced as if it was written Dec ("deck").

It's time we pushed back, and wrote for our readers, not for corporations.

It's time to use standard English rules for these Corporate Fancy Names. Proper names begin with a capital, unlike "ciscoSystems®" (so bad that Cisco itself moved away from it). Words are separated by spaces, so "Cisco Systems". Abbreviations and acronyms are written in lower case if they are pronounced as a word, in upper case if each letter is pronounced: so "ram" and "IBM®".

So from here on in I'll be using the following:

• Face Book. Formerly, "Facebook®".
• Junos. Formerly JUNOS®.
• ram. Formerly RAM.
• Pan OS. Formerly PAN-OS®.
• Unix. Formerly UNIX®.

I'd encourage you to try this in your own writing. It does look odd for the first time, but the result is undeniably more readable. If we are not writing to be understood by our audience then we are nothing more than an unpaid member of some corporation's marketing team.

I gave the talk On The Use and Misuse of Decorators as part of PyConline AU 2021, the second in annoyingly long sequence of not-in-person PyCon AU events. Here’s some code samples that you might be interested in:

Simple @property implementation

This shows a demo of @property-style getters. Setters are left as an exercise :)

def demo_property(f):
    f.is_a_property = True
    return f


class HasProperties:

    def __getattribute__(self, name):
        ret = super().__getattribute__(name)
        if hasattr(ret, "is_a_property"):
            return ret()
        else:
            return ret

class Demo(HasProperties):

    @demo_property
    def is_a_property(self):
        return "I'm a property"

    def is_a_function(self):
        return "I'm a function"


a = Demo()
print(a.is_a_function())
print(a.is_a_property)

@run (The Scoped Block)

@run is a decorator that will run the body of the decorated function, and then store the result of that function in place of the function’s name. It makes it easier to assign the results of complex statements to a variable, and get the advantages of functions having less leaky scopes than if or loop blocks.

def run(f):

    return f()

@run
def hello_world():
    return "Hello, World!"

print(hello_world)

@apply (Multi-line stream transformers)

def apply(transformer, iterable_):

    def _applicator(f):

        return(transformer(f, iterable_))

    return _applicator

@apply(map, range(100)
def fizzbuzzed(i):
    if i % 3 == 0 and i % 5 == 0:
        return "fizzbuzz"

    if i % 3 == 0:
        return "fizz"
    elif i % 5 == 0:
        return "buzz"
    else:
        return str(i)

Builders

def html(f):
    builder = HtmlNodeBuilder("html")
    f(builder)
    return builder.build()


class HtmlNodeBuilder:
    def __init__(self, tag_name):
       self.tag_name = tag_name
       self.nodes = []

   def node(self, f):
        builder = HtmlNodeBuilder(f.__name__)
        f(builder)
        self.nodes.append(builder.build())

    def text(self, text):
        self.nodes.append(text)

    def build(self):
      nodes = "\n".join(self.nodes)
       return f"<{self.tag_name}>\n{nodes}\n</{self.tag_name}>"


@html
def document(b):
   @b.node
   def head(b):
       @b.node
       def title(b):
           b.text("Hello, World!")

   @b.node
   def body(b):
       for i in range(10, 0, -1):
           @b.node
           def p(b):
               b.text(f"{i}")

Code Registries

This is an incomplete implementation of a code registry for handling simple text processing tasks:

```python

def register(self, input, output):

def _register_code(f):
    self.registry[(input, output)] = f
    return f

return _register_code

in_type = (iterable[str], (WILDCARD, ) out_type = (Counter, (WILDCARD, frequency))

@registry.register(in_type, out_type) def count_strings(strings):

return Counter(strings)

@registry.register( (iterable[str], (WILDCARD, )), (iterable[str], (WILDCARD, lowercase)) ) def words_to_lowercase(words): …

@registry.register( (iterable[str], (WILDCARD, )), (iterable[str], (WILDCARD, no_punctuation)) ) def words_without_punctuation(words): …

def find_steps( self, input_type, input_attrs, output_type, output_attrs ):

hand_wave()

def give_me(self, input, output_type, output_attrs):

steps = self.find_steps(
    type(input), (), output_type, output_attrs
)

temp = input
for step in steps:
    temp = step(temp)

return temp

A while ago, I wrote a post about how to build and test my Oculus CV1 tracking code in SteamVR using the SteamVR-OpenHMD driver. I have updated those instructions and moved them to https://noraisin.net/diary/?page_id=1048 – so use those if you’d like to try things out.

The pandemic continues to sap my time for OpenHMD improvements. Since my last post, I have been working on various refinements. The biggest visible improvements are:

• Adding velocity and acceleration API to OpenHMD.
• Rewriting the pose transformation code that maps from the IMU-centric tracking space to the device pose needed by SteamVR / apps.

Adding velocity and acceleration reporting is needed in VR apps that support throwing things. It means that throwing objects and using gravity-grab to fetch objects works in Half-Life: Alyx, making it playable now.

The rewrite to the pose transformation code fixed problems where the rotation of controller models in VR didn’t match the rotation applied in the real world. Controllers would appear attached to the wrong part of the hand, and rotate around the wrong axis. Movements feel more natural now.

Ongoing work – record and replay

My focus going forward is on fixing glitches that are caused by tracking losses or outliers. Those problems happen when the computer vision code either fails to match what the cameras see to the device LED models, or when it matches incorrectly.

Tracking failure leads to the headset view or controllers ‘flying away’ suddenly. Incorrect matching leads to controllers jumping and jittering to the wrong pose, or swapping hands. Either condition is very annoying.

Unfortunately, as the tracking has improved the remaining problems get harder to understand and there is less low-hanging fruit for improvement. Further, when the computer vision runs at 52Hz, it’s impossible to diagnose the reasons for a glitch in real time.

I’ve built a branch of OpenHMD that uses GStreamer to record the CV1 camera video, plus IMU and tracking logs into a video file.

To go with those recordings, I’ve been working on a replay and simulation tool, that uses the Godot game engine to visualise the tracking session. The goal is to show, frame-by-frame, where OpenHMD thought the cameras, headset and controllers were at each point in the session, and to be able to step back and forth through the recording.

Right now, I’m working on the simulation portion of the replay, that will use the tracking logs to recreate all the poses.

I’ve been asked more than once what it was like at the beginning of Ubuntu, before it was a company, when an email from someone I’d never heard of came into my mailbox.

We’re coming up on 20 years now since Ubuntu was founded, and I had cause to do some spelunking into IMAP archives recently… while there I took the opportunity to grab the very first email I received.

The Ubuntu long shot succeeded wildly. Of course, we liked to joke about how spammy those emails where: cold-calling a raft of Debian developers with job offers, some of them were closer to phishing attacks :). This very early one – I was the second employee (though I started at 4 days a week to transition my clients gradually) – was less so.

I think its interesting though to note how explicit a gamble this was framed as: a time limited experiment, funded for a year. As the company scaled this very rapidly became a hiring problem and the horizon had to be pushed out to 2 years to get folk to join.

And of course, while we started with arch in earnest, we rapidly hit significant usability problems, some of which were solvable with porcelain and shallow non-architectural changes, and we built initially patches, and then the bazaar VCS project to tackle those. But others were not: for instance, I recall exceeding the 32K hard link limit on ext3 due to a single long history during a VCS conversion. The sum of these challenges led us to create the bzr project, a ground up rethink of our version control needs, architecture, implementation and user-experience. While ultimately git has conquered all, bzr had – still has in fact – extremely loyal advocates, due to its laser sharp focus on usability.

Anyhow, here it is: one of the original no-name-here-yet, aka Ubuntu, introductory emails (with permission from Mark, of course). When I clicked through to the website Mark provided there was a link there to a fantastical website about a space tourist… not what I had expected to be reading in Adelaide during LCA 2004.

From: Mark Shuttleworth <xxx@xxx>
To: Robert Collins <xxx@xxx>
Date: Thu, 15 Jan 2004, 04:30

Tom Lord gave me your email address, I believe he’s
already sent you the email that I sent him so I’m sure
you have some background.

In short, I am going to fund some open source
development for a year. This is part of a new project
that I will be getting off the ground in the coming
weeks. I don’t know where it will lead, it’s flying in
the face of a stiff breeze but I think at the end of
the day it will at least fund a few very good open
source developers for a full year to work on the
projects they like most.

One of the pieces of the puzzle is high end source
code management. I’ll be looking to build an
infrastructure that will manage source code for
between 100 and 8000 open source projects (yes,
there’s a big difference between the two, I don’t know
at which end of the spectrum we will be at the end of
the year but our infrastructure will have to at least
be capable of scaling to the latter within two years)
with upwards of 2000 developers, drawing code from a
variety of sources, playing with it and spitting it
out regularly in nice packages.

Arch and Subversion seem to be the two leading
contenders for “next generation open source sccm”. I’d
be interested in your thoughts on the two of them, and
how they stack up. I’m looking to hire one person who
will lead that part of the effort. They’ll work alone
from home, and be responsible for two things. First,
extending the tool (arch or svn) in ways that help the
project. Such extensions will be released under an
open source licence, and hopefully embraced by the
tools maintainers and included in the mainline code
for the tool. And second, they will be responsible for
our large-scale implementation of SCCM, using that
tool, and building the management scripts and other
infrastructure to support such a large, and hopefully
highly automated, set of repositories.

Would you be interested in this position? What
attributes and experience do you think would make you
a great person to have on the team? What would your
salary expectation be, as a monthly figure, for a one
year contract full time?

I’m currently on your continent, well, just off it. On
Lizard Island, up North. Am headed today for Brisbane,
then on the 17th to Launceston via Melbourne. If you
happen to be on any of those stops, would you be
interested in meeting up to discuss it further?

If you’re curious you can find out a bit more about me
at www.markshuttleworth.com. This project is much
lower key than some of what you’ll find there. It’s a
very long shot indeed. But if at worst all that
happens is a bunch of open source work gets funded at
my expense I’ll feel it was money well spent.

Cheers,
Mark

=====
—
“Good judgement comes from experience, and often experience
comes from bad judgement” – Rita Mae Brown

I have always liked cryptography, and public-key cryptography in particularly. When Pretty Good Privacy (PGP) first came out in 1991, I not only started using it, also but looking at the documentation and the code to see how it worked. I created my own implementation in C using very small keys, just to better understand.

Cryptography has been running a race against both faster and cheaper computing power. And these days, with banking and most other aspects of our lives entirely relying on secure communications, it’s a very juicy target for bad actors.

About 5 years ago, the National (USA) Institute for Science and Technology (NIST) initiated a search for cryptographic algorithmic that should withstand a near-future world where quantum computers with a significant number of qubits are a reality. There have been a number of rounds, which mid 2020 saw round 3 and the finalists.

This submission caught my eye some time ago: Classic McEliece, and out of the four finalists it’s the only one that is not lattice-based [wikipedia link].

For Public Key Encryption and Key Exchange Mechanism, Prof Bill Buchanan thinks that the winner will be lattice-based, but I am not convinced.

Tiny side-track, you may wonder where does the McEleice name come from? From mathematician Robert McEleice (1942-2019). McEleice developed his cryptosystem in 1978. So it’s not just named after him, he designed it. For various reasons that have nothing to do with the mathematical solidity of the ideas, it didn’t get used at the time. He’s done plenty cool other things, too. From his Caltech obituary:

He made fundamental contributions to the theory and design of channel codes for communication systems—including the interplanetary telecommunication systems that were used by the Voyager, Galileo, Mars Pathfinder, Cassini, and Mars Exploration Rover missions.

Back to lattices, there are both unknowns (aspects that have not been studied in exhaustive depth) and recent mathematical attacks, both of which create uncertainty – in the crypto sphere as well as for business and politics. Given how long it takes for crypto schemes to get widely adopted, the latter two are somewhat relevant, particularly since cyber security is a hot topic.

Lattices are definitely interesting, but given what we know so far, it is my feeling that systems based on lattices are more likely to be proven breakable than Classic McEleice, which come to this finalists’ table with 40+ years track record of in-depth analysis. Mind that all finalists are of course solid at this stage – but NIST’s thoughts on expected developments and breakthroughs is what is likely to decide the winner. NIST are not looking for shiny, they are looking for very very solid in all possible ways.

Prof Buchanan recently published implementations for the finalists, and did some benchmarks where we can directly compare them against each other.

We can see that Classic McEleice’s key generation is CPU intensive, but is that really a problem? The large size of its public key may be more of a factor (disadvantage), however the small ciphertext I think more than offsets that disadvantage.

As we’re nearing the end of the NIST process, in my opinion, fast encryption/decryption and small cyphertext, combined with the long track record of in-depth analysis, may still see Classic McEleice come out the winner.

The post Classic McEleice and the NIST search for post-quantum crypto first appeared on Lentz family blog.

Living in California, I’ve (sadly) grown accustomed to needing to keep track of our local air quality index (AQI) ratings, particularly as we live close to places where large wildfires happen every other year.

Last year, Josh and I bought a PurpleAir outdoor air quality meter, which has been great. We contribute our data to a collection of very local air quality meters, which is important, since the hilly nature of the North Bay means that the nearest government air quality ratings can be significantly different to what we experience here in Petaluma.

I recently went looking to pull my PurpleAir sensor data into my Home Assistant setup. Unfortunately, the PurpleAir API does not return the AQI metric for air quality, only the raw PM2.5/PM5/PM10 numbers. After some searching, I found a nice template sensor solution on the Home Assistant forums, which I’ve modernised by adding the AQI as a sub-sensor, and adding unique ID fields to each useful sensor, so that you can assign them to a location.

You’ll end up with sensors for raw PM2.5, the PM2.5 AQI value, the US EPA air quality category, air pressure, relative humidity and air pressure.

How to use this

First up, visit the PurpleAir Map, find the sensor you care about, click “get this widgetâ€�, and then “JSONâ€�. That will give you the URL to set as the resource key in purpleair.yaml.

Adding the configuration

In HomeAssistant, add the following line to your configuration.yaml:

sensor: !include purpleair.yaml

and then add the following contents to purpleair.yaml

 - platform: rest
   name: 'PurpleAir'

   # Substitute in the URL of the sensor you care about.  To find the URL, go
   # to purpleair.com/map, find your sensor, click on it, click on "Get This
   # Widget" then click on "JSON".
   resource: https://www.purpleair.com/json?key={KEY_GOES_HERE}&show={SENSOR_ID}

   # Only query once a minute to avoid rate limits:
   scan_interval: 60

   # Set this sensor to be the AQI value.
   #
   # Code translated from JavaScript found at:
   # https://docs.google.com/document/d/15ijz94dXJ-YAZLi9iZ_RaBwrZ4KtYeCy08goGBwnbCU/edit#
   value_template: >
     {{ value_json["results"][0]["Label"] }}
   unit_of_measurement: ""
   # The value of the sensor can't be longer than 255 characters, but the
   # attributes can.  Store away all the data for use by the templates below.
   json_attributes:
     - results

 - platform: template
   sensors:
     purpleair_aqi:
       unique_id: 'purpleair_SENSORID_aqi_pm25'
       friendly_name: 'PurpleAir PM2.5 AQI'
       value_template: >
         {% macro calcAQI(Cp, Ih, Il, BPh, BPl) -%}
           {{ (((Ih - Il)/(BPh - BPl)) * (Cp - BPl) + Il)|round|float }}
         {%- endmacro %}
         {% if (states('sensor.purpleair_pm25')|float) > 1000 %}
           invalid
         {% elif (states('sensor.purpleair_pm25')|float) > 350.5 %}
           {{ calcAQI((states('sensor.purpleair_pm25')|float), 500.0, 401.0, 500.0, 350.5) }}
         {% elif (states('sensor.purpleair_pm25')|float) > 250.5 %}
           {{ calcAQI((states('sensor.purpleair_pm25')|float), 400.0, 301.0, 350.4, 250.5) }}
         {% elif (states('sensor.purpleair_pm25')|float) > 150.5 %}
           {{ calcAQI((states('sensor.purpleair_pm25')|float), 300.0, 201.0, 250.4, 150.5) }}
         {% elif (states('sensor.purpleair_pm25')|float) > 55.5 %}
           {{ calcAQI((states('sensor.purpleair_pm25')|float), 200.0, 151.0, 150.4, 55.5) }}
         {% elif (states('sensor.purpleair_pm25')|float) > 35.5 %}
           {{ calcAQI((states('sensor.purpleair_pm25')|float), 150.0, 101.0, 55.4, 35.5) }}
         {% elif (states('sensor.purpleair_pm25')|float) > 12.1 %}
           {{ calcAQI((states('sensor.purpleair_pm25')|float), 100.0, 51.0, 35.4, 12.1) }}
         {% elif (states('sensor.purpleair_pm25')|float) >= 0.0 %}
           {{ calcAQI((states('sensor.purpleair_pm25')|float), 50.0, 0.0, 12.0, 0.0) }}
         {% else %}
           invalid
         {% endif %}
       unit_of_measurement: "bit"
     purpleair_description:
       unique_id: 'purpleair_SENSORID_description'
       friendly_name: 'PurpleAir AQI Description'
       value_template: >
         {% if (states('sensor.purpleair_aqi')|float) >= 401.0 %}
           Hazardous
         {% elif (states('sensor.purpleair_aqi')|float) >= 301.0 %}
           Hazardous
         {% elif (states('sensor.purpleair_aqi')|float) >= 201.0 %}
           Very Unhealthy
         {% elif (states('sensor.purpleair_aqi')|float) >= 151.0 %}
           Unhealthy
         {% elif (states('sensor.purpleair_aqi')|float) >= 101.0 %}
           Unhealthy for Sensitive Groups
         {% elif (states('sensor.purpleair_aqi')|float) >= 51.0 %}
           Moderate
         {% elif (states('sensor.purpleair_aqi')|float) >= 0.0 %}
           Good
         {% else %}
           undefined
         {% endif %}
       entity_id: sensor.purpleair
     purpleair_pm25:
       unique_id: 'purpleair_SENSORID_pm25'
       friendly_name: 'PurpleAir PM 2.5'
       value_template: "{{ state_attr('sensor.purpleair','results')[0]['PM2_5Value'] }}"
       unit_of_measurement: "μg/m3"
       entity_id: sensor.purpleair
     purpleair_temp:
       unique_id: 'purpleair_SENSORID_temperature'
       friendly_name: 'PurpleAir Temperature'
       value_template: "{{ state_attr('sensor.purpleair','results')[0]['temp_f'] }}"
       unit_of_measurement: "°F"
       entity_id: sensor.purpleair
     purpleair_humidity:
       unique_id: 'purpleair_SENSORID_humidity'
       friendly_name: 'PurpleAir Humidity'
       value_template: "{{ state_attr('sensor.purpleair','results')[0]['humidity'] }}"
       unit_of_measurement: "%"
       entity_id: sensor.purpleair
     purpleair_pressure:
       unique_id: 'purpleair_SENSORID_pressure'
       friendly_name: 'PurpleAir Pressure'
       value_template: "{{ state_attr('sensor.purpleair','results')[0]['pressure'] }}"
       unit_of_measurement: "hPa"
       entity_id: sensor.purpleair

Quirks

I had difficulty getting the AQI to display as a numeric graph when I didn’t set a unit. I went with bit, and that worked just fine. 🤷â€�♂ï¸�

So, this idea has been brewing for a while now… try and watch all of Doctor Who. All of it. All 38 seasons. Today(ish), we started. First up, from 1963 (first aired not quite when intended due to the Kennedy assassination): An Unearthly Child. The first episode of the first serial.

A lot of iconic things are there from the start: the music, the Police Box, embarrassing moments of not quite remembering what time one is in, and normal humans accidentally finding their way into the TARDIS.

I first saw this way back when a child, where they were repeated on ABC TV in Australia for some anniversary of Doctor Who (I forget which one). Well, I saw all but the first episode as the train home was delayed and stopped outside Caulfield for no reason for ages. Some things never change.

Of course, being a show from the early 1960s, there’s some rougher spots. We’re not about to have the picture of diversity, and there’s going to be casual racism and sexism. What will be interesting is noticing these things today, and contrasting with my memory of them at the time (at least for episodes I’ve seen before), and what I know of the attitudes of the time.

“This year-ometer is not calculating properly” is a very 2020 line though (technically from the second episode).

It’s been a while since my last post about tracking support for the Oculus Rift in February. There’s been big improvements since then – working really well a lot of the time. It’s gone from “If I don’t make any sudden moves, I can finish an easy Beat Saber level” to “You can’t hide from me!” quality.

Equally, there are still enough glitches and corner cases that I think I’ll still be at this a while.

Here’s a video from 3 weeks ago of (not me) playing Beat Saber on Expert+ setting showing just how good things can be now:

Strap in. Here’s what I’ve worked on in the last 6 weeks:

Pose Matching improvements

Most of the biggest improvements have come from improving the computer vision algorithm that’s matching the observed LEDs (blobs) in the camera frames to the 3D models of the devices.

I split the brute-force search algorithm into 2 phases. It now does a first pass looking for ‘obvious’ matches. In that pass, it does a shallow graph search of blobs and their nearest few neighbours against LEDs and their nearest neighbours, looking for a match using a “Strong” match metric. A match is considered strong if expected LEDs match observed blobs to within 1.5 pixels.

Coupled with checks on the expected orientation (matching the Gravity vector detected by the IMU) and the pose prior (expected position and orientation are within predicted error bounds) this short-circuit on the search is hit a lot of the time, and often completes within 1 frame duration.

In the remaining tricky cases, where a deeper graph search is required in order to recover the pose, the initial search reduces the number of LEDs and blobs under consideration, speeding up the remaining search.

I also added an LED size model to the mix – for a candidate pose, it tries to work out how large (in pixels) each LED should appear, and use that as a bound on matching blobs to LEDs. This helps reduce mismatches as devices move further from the camera.

LED labelling

When a brute-force search for pose recovery completes, the system now knows the identity of various blobs in the camera image. One way it avoids a search next time is to transfer the labels into future camera observations using optical-flow tracking on the visible blobs.

The problem is that even sped-up the search can still take a few frame-durations to complete. Previously LED labels would be transferred from frame to frame as they arrived, but there’s now a unique ID associated with each blob that allows the labels to be transferred even several frames later once their identity is known.

IMU Gyro scale

One of the problems with reverse engineering is the guesswork around exactly what different values mean. I was looking into why the controller movement felt “swimmy” under fast motions, and one thing I found was that the interpretation of the gyroscope readings from the IMU was incorrect.

The touch controllers report IMU angular velocity readings directly as a 16-bit signed integer. Previously the code would take the reading and divide by 1024 and use the value as radians/second.

From teardowns of the controller, I know the IMU is an Invensense MPU-6500. From the datasheet, the reported value is actually in degrees per second and appears to be configured for the +/- 2000 °/s range. That yields a calculation of Gyro-rad/s = Gyro-°/s * (2000 / 32768) * (?/180) – or a divisor of 938.734.

The 1024 divisor was under-estimating rotation speed by about 10% – close enough to work until you start moving quickly.

Limited interpolation

If we don’t find a device in the camera views, the fusion filter predicts motion using the IMU readings – but that quickly becomes inaccurate. In the worst case, the controllers fly off into the distance. To avoid that, I added a limit of 500ms for ‘coasting’. If we haven’t recovered the device pose by then, the position is frozen in place and only rotation is updated until the cameras find it again.

Exponential filtering

I implemented a 1-Euro exponential smoothing filter on the output poses for each device. This is an idea from the Project Esky driver for Project North Star/Deck-X AR headsets, and almost completely eliminates jitter in the headset view and hand controllers shown to the user. The tradeoff is against introducing lag when the user moves quickly – but there are some tunables in the exponential filter to play with for minimising that. For now I’ve picked some values that seem to work reasonably.

Non-blocking radio

Communications with the touch controllers happens through USB radio command packets sent to the headset. The main use of radio commands in OpenHMD is to read the JSON configuration block for each controller that is programmed in at the factory. The configuration block provides the 3D model of LED positions as well as initial IMU bias values.

Unfortunately, reading the configuration block takes a couple of seconds on startup, and blocks everything while it’s happening. Oculus saw that problem and added a checksum in the controller firmware. You can read the checksum first and if it hasn’t changed use a local cache of the configuration block. Eventually, I’ll implement that caching mechanism for OpenHMD but in the meantime it still reads the configuration blocks on each startup.

As an interim improvement I rewrote the radio communication logic to use a state machine that is checked in the update loop – allowing radio communications to be interleaved without blocking the regularly processing of events. It still interferes a bit, but no longer causes a full multi-second stall as each hand controller turns on.

Haptic feedback

The hand controllers have haptic feedback ‘rumble’ motors that really add to the immersiveness of VR by letting you sense collisions with objects. Until now, OpenHMD hasn’t had any support for applications to trigger haptic events. I spent a bit of time looking at USB packet traces with Philipp Zabel and we figured out the radio commands to turn the rumble motors on and off.

In the Rift CV1, the haptic motors have a mode where you schedule feedback events into a ringbuffer – effectively they operate like a low frequency audio device. However, that mode was removed for the Rift S (and presumably in the Quest devices) – and deprecated for the CV1.

With that in mind, I aimed for implementing the unbuffered mode, with explicit ‘motor on + frequency + amplitude’ and ‘motor off’ commands sent as needed. Thanks to already having rewritten the radio communications to use a state machine, adding haptic commands was fairly easy.

The big question mark is around what API OpenHMD should provide for haptic feedback. I’ve implemented something simple for now, to get some discussion going. It works really well and adds hugely to the experience. That code is in the https://github.com/thaytan/OpenHMD/tree/rift-haptics branch, with a SteamVR-OpenHMD branch that uses it in https://github.com/thaytan/SteamVR-OpenHMD/tree/controller-haptics-wip

Problem areas

Unexpected tracking losses

I’d say the biggest problem right now is unexpected tracking loss and incorrect pose extractions when I’m not expecting them. Especially my right controller will suddenly glitch and start jumping around. Looking at a video of the debug feed, it’s not obvious why that’s happening:

To fix cases like those, I plan to add code to log the raw video feed and the IMU information together so that I can replay the video analysis frame-by-frame and investigate glitches systematically. Those recordings will also work as a regression suite to test future changes.

Sensor fusion efficiency

The Kalman filter I have implemented works really nicely – it does the latency compensation, predicts motion and extracts sensor biases all in one place… but it has a big downside of being quite expensive in CPU. The Unscented Kalman Filter CPU cost grows at O(n^3) with the size of the state, and the state in this case is 43 dimensional – 22 base dimensions, and 7 per latency-compensation slot. Running 1000 updates per second for the HMD and 500 for each of the hand controllers adds up quickly.

At some point, I want to find a better / cheaper approach to the problem that still provides low-latency motion predictions for the user while still providing the same benefits around latency compensation and bias extraction.

Lens Distortion

To generate a convincing illusion of objects at a distance in a headset that’s only a few centimetres deep, VR headsets use some interesting optics. The LCD/OLED panels displaying the output get distorted heavily before they hit the users eyes. What the software generates needs to compensate by applying the right inverse distortion to the output video.

Everyone that tests the CV1 notices that the distortion is not quite correct. As you look around, the world warps and shifts annoyingly. Sooner or later that needs fixing. That’s done by taking photos of calibration patterns through the headset lenses and generating a distortion model.

Camera / USB failures

The camera feeds are captured using a custom user-space UVC driver implementation that knows how to set up the special synchronisation settings of the CV1 and DK2 cameras, and then repeatedly schedules isochronous USB packet transfers to receive the video.

Occasionally, some people experience failure to re-schedule those transfers. The kernel rejects them with an out-of-memory error failing to set aside DMA memory (even though it may have been running fine for quite some time). It’s not clear why that happens – but the end result at the moment is that the USB traffic for that camera dies completely and there’ll be no more tracking from that camera until the application is restarted.

Often once it starts happening, it will keep happening until the PC is rebooted and the kernel memory state is reset.

Occluded cases

Tracking generally works well when the cameras get a clear shot of each device, but there are cases like sighting down the barrel of a gun where we expect that the user will line up the controllers in front of one another, and in front of the headset. In that case, even though we probably have a good idea where each device is, it can be hard to figure out which LEDs belong to which device.

If we already have a good tracking lock on the devices, I think it should be possible to keep tracking even down to 1 or 2 LEDs being visible – but the pose assessment code will have to be aware that’s what is happening.

Upstreaming

April 14th marks 2 years since I first branched off OpenHMD master to start working on CV1 tracking. How hard can it be, I thought? I’ll knock this over in a few months.

Since then I’ve accumulated over 300 commits on top of OpenHMD master that eventually all need upstreaming in some way.

One thing people have expressed as a prerequisite for upstreaming is to try and remove the OpenCV dependency. The tracking relies on OpenCV to do camera distortion calculations, and for their PnP implementation. It should be possible to reimplement both of those directly in OpenHMD with a bit of work – possibly using the fast LambdaTwist P3P algorithm that Philipp Zabel wrote, that I’m already using for pose extraction in the brute-force search.

Others

I’ve picked the top issues to highlight here. https://github.com/thaytan/OpenHMD/issues has a list of all the other things that are still on the radar for fixing eventually.

Other Headsets

At some point soon, I plan to put a pin in the CV1 tracking and look at adapting it to more recent inside-out headsets like the Rift S and WMR headsets. I implemented 3DOF support for the Rift S last year, but getting to full positional tracking for that and other inside-out headsets means implementing a SLAM/VIO tracking algorithm to track the headset position.

Once the headset is tracking, the code I’m developing here for CV1 to find and track controllers will hopefully transfer across – the difference with inside-out tracking is that the cameras move around with the headset. Finding the controllers in the actual video feed should work much the same.

Sponsorship

This development happens mostly in my spare time and partly as open source contribution time at work at Centricular. I am accepting funding through Github Sponsorships to help me spend more time on it – I’d really like to keep helping Linux have top-notch support for VR/AR applications. Big thanks to the people that have helped get this far.

Today, 30 March, is World Bipolar Day.

Why that particular date? It’s Vincent van Gogh’s birthday (1853), and there is a fairly strong argument that the Dutch painter suffered from bipolar (among other things).

The image on the side is Vincent’s drawing “Worn Out” (from 1882), and it seems to capture the feeling rather well – whether (hypo)manic, depressed, or mixed. It’s exhausting.

Bipolar is complicated, often undiagnosed or misdiagnosed, and when only treated with anti-depressants, it can trigger the (hypo)mania – essentially dragging that person into that state near-permanently.

Have you heard of Bipolar II?

Hypo-mania is the “lesser” form of mania that distinguishes Bipolar I (the classic “manic depressive” syndrome) from Bipolar II. It’s “lesser” only in the sense that rather than someone going so hyper they may think they can fly (Bipolar I is often identified when someone in manic state gets admitted to hospital – good catch!) while with Bipolar II the hypo-mania may actually exhibit as anger. Anger in general, against nothing in particular but potentially everyone and everything around them. Or, if it’s a mixed episode, anger combined with strong negative thoughts. Either way, it does not look like classic mania. It is, however, exhausting and can be very debilitating.

Bipolar II people often present to a doctor while in depressed state, and GPs (not being psychiatrists) may not do a full diagnosis. Note that D.A.S. and similar test sheets are screening tools, they are not diagnostic. A proper diagnosis is more complex than filling in a form some questions (who would have thought!)

Call to action

If you have a diagnosis of depression, only from a GP, and are on medication for this, I would strongly recommend you also get a referral to a psychiatrist to confirm that diagnosis.

Our friends at the awesome Black Dog Institute have excellent information on bipolar, as well as a quick self-test – if that shows some likelihood of bipolar, go get that referral and follow up ASAP.

I will be writing more about the topic in the coming time.

Update:

This post documented an older method of building SteamVR-OpenHMD. I moved them to a page here. That version will be kept up to date for any future changes, so go there.

I’ve had a few people ask how to test my OpenHMD development branch of Rift CV1 positional tracking in SteamVR. Here’s what I do:

• Make sure Steam + SteamVR are already installed.
• Clone the SteamVR-OpenHMD repository:

git clone --recursive https://github.com/ChristophHaag/SteamVR-OpenHMD.git

• Switch the internal copy of OpenHMD to the right branch:

cd subprojects/openhmd
git remote add thaytan-github https://github.com/thaytan/OpenHMD.git
git fetch thaytan-github
git checkout -b rift-kalman-filter thaytan-github/rift-kalman-filter
cd ../../

• Use meson to build and register the SteamVR-OpenHMD binaries. You may need to install meson first (see below):

meson -Dbuildtype=release build
ninja -C build
./install_files_to_build.sh
./register.sh

• It is important to configure in release mode, as the kalman filtering code is generally too slow for real-time in debug mode (it has to run 2000 times per second)
• Make sure your USB devices are accessible to your user account by configuring udev. See the OpenHMD guide here: https://github.com/OpenHMD/OpenHMD/wiki/Udev-rules-list
• Please note – only Rift sensors on USB 3.0 ports will work right now. Supporting cameras on USB 2.0 requires someone implementing JPEG format streaming and decoding.
• It can be helpful to test OpenHMD is working by running the simple example. Check that it’s finding camera sensors at startup, and that the position seems to change when you move the headset:

./build/subprojects/openhmd/openhmd_simple_example

• Calibrate your expectations for how well tracking is working right now! Hint: It’s very experimental
• Start SteamVR. Hopefully it should detect your headset and the light(s) on your Rift Sensor(s) should power on.

Meson

I prefer the Meson build system here. There’s also a cmake build for SteamVR-OpenHMD you can use instead, but I haven’t tested it in a while and it sometimes breaks as I work on my development branch.

If you need to install meson, there are instructions here – https://mesonbuild.com/Getting-meson.html summarising the various methods.

I use a copy in my home directory, but you need to make sure ~/.local/bin is in your PATH

pip3 install --user meson

I spent some time this weekend implementing a couple of my ideas for improving the way the tracking code in OpenHMD filters and rejects (or accepts) possible poses when trying to match visible LEDs to the 3D models for each device.

In general, the tracking proceeds in several steps (in parallel for each of the 3 devices being tracked):

1. Do a brute-force search to match LEDs to 3D models, then (if matched)
   1. Assign labels to each LED blob in the video frame saying what LED they are.
   2. Send an update to the fusion filter about the position / orientation of the device
2. Then, as each video frame arrives:
   1. Use motion flow between video frames to track the movement of each visible LED
   2. Use the IMU + vision fusion filter to predict the position/orientation (pose) of each device, and calculate which LEDs are expected to be visible and where.
3. Try and match up and refine the poses using the predicted pose prior and labelled LEDs. In the best case, the LEDs are exactly where the fusion predicts they’ll be. More often, the orientation is mostly correct, but the position has drifted and needs correcting. In the worst case, we send the frame back to step 1 and do a brute-force search to reacquire an object.

The goal is to always assign the correct LEDs to the correct device (so you don’t end up with the right controller in your left hand), and to avoid going back to the expensive brute-force search to re-acquire devices as much as possible

What I’ve been working on this week is steps 1 and 3 – initial acquisition of correct poses, and fast validation / refinement of the pose in each video frame, and I’ve implemented two new strategies for that.

Gravity Vector matching

The first new strategy is to reject candidate poses that don’t closely match the known direction of gravity for each device. I had a previous implementation of that idea which turned out to be wrong, so I’ve re-worked it and it helps a lot with device acquisition.

The IMU accelerometer and gyro can usually tell us which way up the device is (roll and pitch) but not which way they are facing (yaw). The measure for ‘known gravity’ comes from the fusion Kalman filter covariance matrix – how certain the filter is about the orientation of the device. If that variance is small this new strategy is used to reject possible poses that don’t have the same idea of gravity (while permitting rotations around the Y axis), with the filter variance as a tolerance.

Partial tracking matches

The 2nd strategy is based around tracking with fewer LED correspondences once a tracking lock is acquired. Initial acquisition of the device pose relies on some heuristics for how many LEDs must match the 3D model. The general heuristic threshold I settled on for now is that 2/3rds of the expected LEDs must be visible to acquire a cold lock.

With the new strategy, if the pose prior has a good idea where the device is and which way it’s facing, it allows matching on far fewer LED correspondences. The idea is to keep tracking a device even down to just a couple of LEDs, and hope that more become visible soon.

While this definitely seems to help, I think the approach can use more work.

Status

With these two new approaches, tracking is improved but still quite erratic. Tracking of the headset itself is quite good now and for me rarely loses tracking lock. The controllers are better, but have a tendency to “fly off my hands” unexpectedly, especially after fast motions.

I have ideas for more tracking heuristics to implement, and I expect a continuous cycle of refinement on the existing strategies and new ones for some time to come.

For now, here’s a video of me playing Beat Saber using tonight’s code. The video shows the debug stream that OpenHMD can generate via Pipewire, showing the camera feed plus overlays of device predictions, LED device assignments and tracked device positions. Red is the headset, Green is the right controller, Blue is the left controller.

Initial tracking is completely wrong – I see some things to fix there. When the controllers go offline due to inactivity, the code keeps trying to match LEDs to them for example, and then there are some things wrong with how it’s relabelling LEDs when they get incorrect assignments.

After that, there are periods of good tracking with random tracking losses on the controllers – those show the problem cases to concentrate on.

These lack of updates are also likely because I’ve been quite caught up with stuff.

Monday I had a steak from Bay Leaf Steakhouse for dinner. It was kind of weird eating it from packs, but then I’m reminded you could do this in economy class. Tuesday I wanted to attempt to go vegetarian and by the time I was done with a workout, the only place was a chap fan shop (Leong Heng) where I had a mixture of Chinese and Indian chap fan. The Indian stall is run by an ex-Hyatt staff member who immediately recognised me! Wednesday, Alice came to visit, so we got to Hanks, got some alcohol, and managed a smorgasbord of food from Pickers/Sate Zul/Lila Wadi. Night ended very late, and on Thursday, visited Hai Tian for their famous salted egg squid and prawns in a coconut shell. Friday was back to being normal, so I grabbed a pizza from Mint Pizza (this time I tried their Aussie variant). Saturday, today, I hit up Rasa Sayang for some matcha latte, but grabbed food from Classic Pilot Cafe, which Faeeza owns! It was the famous salted egg chicken, double portion, half rice.

As for workouts, I did sign up for Mantas but found it pretty hard to do, timezone wise. I did spend a lot of time jogging on the beach (this has been almost a daily affair). Monday I also did 2 MD workouts, Tuesday 1 MD workout, Wednesday half a MD workout, Thursday I did a Ping workout at Pwrhouse (so good!), Friday 1 MD workout, and Saturday an Audrey workout at Pwrhouse and 1 MD workout.

Wednesday I also found out that Rasmus passed away. Frankly, there are no words.

Thursday, my Raspberry Pi 400 arrived. I set it up in under ten minutes, connecting it to the TV here. It “just works”. I made a video, which I should probably figure out how to upload to YouTube after I stitch it together. I have to work on using it a lot more.

COVID-19 cases are through the roof in Malaysia. This weekend we’ve seen two days of case breaking records, with today being 5,728 (yesterday was something close). Nutty. Singapore suspended the reciprocal green lane (RGL) agreement with Malaysia for the next 3 months.

I’ve managed to finish Bridgerton. I like the score. Finding something on Netflix is proving to be more difficult, regardless of having a VPN. Honestly, this is why Cable TV wins… linear programming that you’re just fed.

Stock market wise, I’ve been following the GameStop short squeeze, and even funnier is the Top Glove one, that they’re trying to repeat in Malaysia. Bitcoin seems to be doing “reasonably well” and I have to say, I think people are starting to realise decentralised services have a future. How do we get there?

What an interesting week, I look forward to more productive time. I’m still writing in my Hobonichi Techo, so at least that’s where most personal stuff ends up, I guess?

I hit an important OpenHMD milestone tonight – I completed a Beat Saber level using my Oculus Rift CV1!

I’ve been continuing to work on integrating Kalman filtering into OpenHMD, and on improving the computer vision that matches and tracks device LEDs. While I suspect noone will be completing Expert levels just yet, it’s working well enough that I was able to play through a complete level of Beat Saber. For a long time this has been my mental benchmark for tracking performance, and I’m really happy

Check it out:

I should admit at this point that completing this level took me multiple attempts. The tracking still has quite a tendency to lose track of controllers, or to get them confused and swap hands suddenly.

I have a list of more things to work on. See you at the next update!

What an unplanned day. I woke up in time to do an MD workout, despite feeling a little sore. So maybe I was about 10 minutes late and I missed the first set, but his workouts are so long, and I think there were seven sets anyway. Had a good brunch shortly thereafter.

Did a bit of reading, and then I decided to do a beach boardwalk walk… turns out they were policing the place, and you can’t hit the boardwalk. But the beach is fair game? So I went back to the hotel, dropped off my slippers, and went for a beach jog. Pretty nutty.

Came back to read a little more and figured I might as well do another MD workout. Then I headed out for dinner, trying out a new place — Mint Pizza. Opened 20.12.2020, and they’re empty, and their pizza is actually pretty good. Lamb and BBQ chicken, they did half-and-half.

Twitter was discussing Raspberry Pi’s, and all I could see is a lot of misinformation, which is truly shocking. The irony is that open source has been running the Internet for so long, and progressive web apps have come such a long way…

Back in the day when I did OpenOffice.org or Linux training even, we always did say you should learn concepts and not tools. From the time we ran Linux installfests in the late-90s in Sunway Pyramid (back then, yes, Linux was hard, and you had winmodems), but I had forgotten that I even did stuff for school teachers and NGOs back in 2002… I won’t forget PC Gemilang either…

Anyway, I placed an order again for another Raspberry Pi 400. I am certain that most people talk so much crap, without realising that Malaysia isn’t a developed nation and most people can’t afford a Mac let alone a PC. Laptops aren’t cheap. And there are so many other issues…. Saying Windows is still required in 2021 is the nuttiest thing I’ve heard in a long time. Easy to tweet, much harder to think about TCO, and realise where in the journey Malaysia is.

Maybe the best thing was that Malaysian Twitter learned about technology. I doubt many realised the difference between a Pi board vs the 400, but hey, the fact that they talked about tech is still a win (misinformed, but a win).

Today is the first day that in the state of Pahang, we have to encounter what many Malaysians are referring to as the Movement Control Order 2.0 (MCO 2.0). I think everyone finally agrees with the terminology that this is a lockdown now, because I remember back in the day when I was calling it that, I’d definitely offend a handful of journalists.

This is one interesting change for me compared to when I last wrote Life with Rona — Day 56 of being indoors and not even leaving my household, in Kuala Lumpur. I am now not in the state, I am living in a hotel, and I am obviously moving around a little more since we have access to the beach.

KL/Selangor and several other states have already been under the MCO 2.0 since January 13 2021, and while it was supposed to end on January 26, it seems like they’ve extended and harmonised the dates for Peninsular Malaysia to end on February 4 2021. I guess everyone got the “good news” yesterday. The Prime Minister announced some kind of aid last week, but it is still mostly a joke.

Today was the 2nd day I woke up at around 2.30pm because I went to bed at around 8am. First day I had a 23.5 hour uptime, and the today was less brutal, but working from 1-8am with the PST timezone is pretty brutal. Consequently, I barely got too much done, and had one meal, vegetarian, two packs that included rice. I did get to walk by the beach (between Teluk Cempedak and Teluk Cempedak 2), did quite a bit of exercise there and I think even the monkeys are getting hungry… lots of stray cats and monkeys. Starbucks closes at 7pm, and I rocked up at 7.10pm (this was just like yesterday, when I arrived at 9.55pm and was told they wouldn’t grant me a coffee!).

While writing this entry, I did manage to get into a long video call with some friends and I guess it was good catching up with people in various states. It also is what prevented me from publishing this entry!

Day 2

I did wake up reasonable early today because I had pre-ordered room service to arrive at 9am. There is a fixed menu at the hotel for various cuisines (RM48/pax, thankfully gratis for me) and I told them I prefer not having to waste, so just give me what I want which is off menu items anyway. Roti telur double telur (yes, I know it is a roti jantan) with some banjir dhal and sambal and a bit of fruit on the side with two teh tariks. They delivered as requested. I did forget to ask for a jar of honey but that is OK, there is always tomorrow.

I spent most of the day vacillating, and wouldn’t consider it productive by any measure. Just chit chats and napping. It did rain today after a long time, so the day seemed fairly dreary.

When I finally did awaken from my nap, I went for a run on the beach. I did it barefoot. I have no idea if this is how it is supposed to be done, or if you are to run nearer the water or further up above, but I did move around between the two quite often. The beach is still pretty dead, but it is expected since no one is allowed to go unless you’re a hotel guest.

The hotel has closed 3/4 of their villages (blocks) and moved everyone to the village I’m staying in (for long stay guests…). I’m thankful I have a pretty large suite, it is a little over 980sqft, and the ample space, while smaller than my home, is still welcome.

Post beach run, I did a workout with MD via Instagram. It was strength/HIIT based, and I burnt a tonne, because he gave us one of his signature 1.5h classes. It was longer than the 80 minute class he normally charges RM50 for (I still think this is undervaluing his service, but he really does care and does it for the love of seeing his students grow!).

Post-workout I decided to head downtown to find some dinner. Everything at the Teluk Cemepdak block of shops was closed, so they’re not even bothered with doing takeaway. Sg. Lembing steakhouse seemed to have cars parked, Vanggey was empty (Crocodile Rock was open, can’t say if there was a crowd, because the shared parking lot was empty), there was a modest queue at Sate Zul, and further down, Lena was closed, Pickers was open for takeaway but looked pretty closed, Tjantek was open surprisingly, and then I thought I’d give Nusantara a try again, this time for food, but their chef had just gone home at about 8pm. Oops. So I drove to LAN burger, initially ordering just one chicken double special; however they looked like they could use the business so I added on a beef double special. They now accept Boost payments so have joined the e-wallet era. One less place to use cash, which is also why I really like Kuantan. On the drive back, Classic Pilot Cafe was also open and I guess I’ll be heading there too during this lockdown.

Came back to the room to finish both burgers in probably under 15 minutes. While watching the first episode of Bridgerton on Netflix. I’m not sure what really captivates, but I will continue on (I still haven’t finished the first episode). I need to figure out how to use the 2 TVs that I have in this room — HDMI cable? Apple TV? Not normally using a TV, all this is clearly more complex than I care to admit.

I soaked longer than expected, ended up a prune, but I’m sure it will give me good rest!

One thought to leave with:

“Learn to enjoy every minute of your life. Be happy now. Don’t wait for something outside of yourself to make you happy in the future.” — Earl Nightingale

In my experience, the C programming language is still hard to beat, even 50 years after it was first developed (and I feel the same way about UNIX). When it comes to general-purpose utility, low-level systems programming, performance, and portability (even to tiny embedded systems), I would choose C over most modern or fashionable alternatives. In some cases, it is almost the only choice.

Many developers believe that it is difficult to write secure and reliable software in C, due to its free pointers, the lack of enforced memory integrity, and the lack of automatic memory management; however in my opinion it is possible to overcome these risks with discipline and a more secure system of libraries constructed on top of C and libc. Daniel J. Bernstein and Wietse Venema are two developers who have been able to write highly secure, stable, reliable software in C.

My other favourite language is Python. Although Python has numerous desirable features, my favourite is the light-weight syntax: in Python, block structure is indicated by indentation, and braces and semicolons are not required. Apart from the pleasure and relief of reading and writing such light and clear code, which almost appears to be executable pseudo-code, there are many other benefits. In C or JavaScript, if you omit a trailing brace somewhere in the code, or insert an extra brace somewhere, the compiler may tell you that there is a syntax error at the end of the file. These errors can be annoying to track down, and cannot occur in Python. Python not only looks better, the clear syntax helps to avoid errors.

The obvious disadvantage of Python, and other dynamic interpreted languages, is that most programs run extremely slower than C programs. This limits the scope and generality of Python. No AAA or performance-oriented video game engines are programmed in Python. The language is not suitable for low-level systems programming, such as operating system development, device drivers, filesystems, performance-critical networking servers, or real-time systems.

C is a great all-purpose language, but the code is uglier than Python code. Once upon a time, when I was experimenting with the Plan 9 operating system (which is built on C, but lacks Python), I missed Python’s syntax, so I decided to do something about it and write a little preprocessor for C. This converts from a “Pythonesque” indented syntax to regular C with the braces and semicolons. Having forked a little dialect of my own, I continued from there adding other modules and features (which might have been a mistake, but it has been fun and rewarding).

At first I called this translator Brace, because it added in the braces for me. I now call the language CZ. It sounds like “C-easy”. Ease-of-use for developers (DX) is the primary goal. CZ has all of the features of C, and translates cleanly into C, which is then compiled to machine code as normal (using any C compiler; I didn’t write one); and so CZ has the same features and performance as C, but enjoys a more pleasing syntax.

CZ is now self-hosted, in that the translator is written in the language CZ. I confess that originally I wrote most of it in Perl; I’m proficient at Perl, but I consider it to be a fairly ugly language, and overly complicated.

I intend for CZ’s new syntax to be “optional”, ideally a developer will be able to choose to use the normal C syntax when editing CZ, if they prefer it. For this, I need a tool to convert C back to CZ, which I have not fully implemented yet. I am aware that, in addition to traditionalists, some vision-impaired developers prefer to use braces and semicolons, as screen readers might not clearly indicate indentation. A C to CZ translator would of course also be valuable when porting an existing C program to CZ.

CZ has a number of useful features that are not found in standard C, but I did not go so far as C++, which language has been described as “an octopus made by nailing extra legs onto a dog”. I do not consider C to be a dog, at least not in a negative sense; but I think that C++ is not an improvement over plain C. I am creating CZ because I think that it is possible to improve on C, without losing any of its advantages or making it too complex.

One of the most interesting features I added is a simple syntax for fast, light coroutines. I based this on Simon Tatham’s approach to Coroutines in C, which may seem hacky at first glance, but is very efficient and can work very well in practice. I implemented a very fast web server with very clean code using these coroutines. The cost of switching coroutines with this method is little more than the cost of a function call.

CZ has hygienic macros. The regular cpp (C preprocessor) macros are not hygenic and many people consider them hacky and unsafe to use. My CZ macros are safe, and somewhat more powerful than standard C macros. They can be used to neatly add new program control structures. I have plans to further develop the macro system in interesting ways.

I added automatic prototype and header generation, as I do not like having to repeat myself when copying prototypes to separate header files. I added support for the UNIX #! scripting syntax, and for cached executables, which means that CZ can be used like a scripting language without having to use a separate compile or make command, but the programs are only recompiled when something has been changed.

For CZ, I invented a neat approach to portability without conditional compilation directives. Platform-specific library fragments are automatically included from directories having the name of that platform or platform-category. This can work very well in practice, and helps to avoid the nightmare of conditional compilation, feature detection, and Autotools. Using this method, I was able easily to implement portable interfaces to features such as asynchronous IO multiplexing (aka select / poll).

The CZ library includes flexible error handling wrappers, inspired by W. Richard Stevens’ wrappers in his books on Unix Network Programming. If these wrappers are used, there is no need to check return values for error codes, and this makes the code much safer, as an error cannot accidentally be ignored.

CZ has several major faults, which I intend to correct at some point. Some of the syntax is poorly thought out, and I need to revisit it. I developed a fairly rich library to go with the language, including safer data structures, IO, networking, graphics, and sound. There are many nice features, but my CZ library is more prototype than a finished product, there are major omissions, and some features are misconceived or poorly implemented. The misfeatures should be weeded out for the time-being, or moved to an experimental section of the library.

I think that a good software library should come in two parts, the essential low-level APIs with the minimum necessary functionality, and a rich set of high-level convenience functions built on top of the minimal API. I need to clearly separate these two parts in order to avoid polluting the namespaces with all sorts of nonsense!

CZ is lacking a good modern system of symbol namespaces. I can look to Python for a great example. I need to maintain compatibility with C, and avoid ugly symbol encodings. I think I can come up with something that will alleviate the need to type anything like gtk_window_set_default_size, and yet maintain compatibility with the library in question. I want all the power of C, but it should be easy to use, even for children. It should be as easy as BASIC or Processing, a child should be able to write short graphical demos and the like, without stumbling over tricky syntax or obscure compile errors.

Here is an example of a simple CZ program which plots the Mandelbrot set fractal. I think that the program is fairly clear and easy to understand, although there is still some potential to improve and clarify the code.

#!/usr/local/bin/cz --
use b
use ccomplex

Main:
	num outside = 16, ox = -0.5, oy = 0, r = 1.5
	long i, max_i = 50, rb_i = 30
	space()
	uint32_t *px = pixel()  # CONFIGURE!
	num d = 2*r/h, x0 = ox-d*w_2, y0 = oy+d*h_2
	for(y, 0, h):
		cmplx c = x0 + (y0-d*y)*I
		repeat(w):
			cmplx w = c
			for i=0; i < max_i && cabs(w) < outside; ++i
				w = w*w + c
			*px++ = i < max_i ? rainbow(i*359 / rb_i % 360) : black
			c += d

I wrote a more elaborate variant of this program, which generates images like the one shown below. There are a few tricks used: continuous colouring, rainbow colours, and plotting the logarithm of the iteration count, which makes the plot appear less busy close to the black fractal proper. I sell some T-shirts and other products with these fractal designs online.

I am interested in graph programming, and have been for three decades since I was a teenager. By graph programming, I mean programming and modelling based on mathematical graphs or diagrams. I avoid the term visual programming, because there is no necessary reason that vision impaired folks could not use a graph programming language; a graph or diagram may be perceived, understood, and manipulated without having to see it.

Mathematics is something that naturally exists, outside time and independent of our universe. We humans discover mathematics, we do not invent or create it. One of my main ideas for graph programming is to represent a mathematical (or software) model in the simplest and most natural way, using relational operators. Elementary mathematics can be reduced to just a few such operators:

I think that a language and notation based on these few operators (and similar) can be considerably simpler and more expressive than conventional math or programming languages.

CZ is for me a stepping-stone toward this goal of an expressive relational graph language. It is more pleasant for me to develop software tools in CZ than in C or another language.

Thanks for reading. I wrote this article during the process of applying to join Toptal, which appears to be a freelancing portal for top developers; and in response to this article on toptal: After All These Years, the World is Still Powered by C Programming.

My CZ project has been stalled for quite some time. I foolishly became discouraged after receiving some negative feedback. I now know that honest negative feedback should be valued as an opportunity to improve, and I intend to continue the project until it lacks glaring faults, and is useful for other people. If this project or this article interests you, please contact me and let me know. It is much more enjoyable to work on a project when other people are actively interested in it!

The uBITX uses an Arduino internally. This article describes how to update its software.

Required hardware

The connector on the back is a Mini-B USB connector, so you'll need a "Mini-B to A" USB cable. This is not the same cable as used with older Android smartphones. The Mini-B connector was used with a lot of cameras a decade ago.

You'll also need a computer. I use a laptop with Fedora Linux installed.

Required software for software development

In Fedora all the required software is installed with sudo dnf install arduino git. Add yourself to the users and lock groups with sudo usermod -a -G users,lock $USER (on Debian-style systems use sudo usermod -a -G dialout,lock $USER). You'll need to log out and log in again for that to have an effect (if you want to see which groups you are already in, then use the id command).

Run arduino as your ordinary non-root user to create the directories used by the Arduino IDE. You can quit the IDE once it starts.

Obtain the uBITX software

$ cd ~/Arduino
$ git clone https://github.com/afarhan/ubitxv6.git ubitx_v6.1_code

Connect the uBITX to your computer

Plug in the USB cable and turn on the radio. Running dmesg will show the Arduino appearing as a "USB serial" device:

usb 1-1: new full-speed USB device number 6 using xhci_hcd
usb 1-1: New USB device found, idVendor=1a86, idProduct=7523, bcdDevice= 2.64
usb 1-1: New USB device strings: Mfr=0, Product=2, SerialNumber=0
usb 1-1: Product: USB Serial
usbcore: registered new interface driver ch341
usbserial: USB Serial support registered for ch341-uart
ch341 1-1:1.0: ch341-uart converter detected
usb 1-1: ch341-uart converter now attached to ttyUSB1

If you want more information about the USB device then use:

$ lsusb -d 1a86:7523
Bus 001 Device 006: ID 1a86:7523 QinHeng Electronics CH340 serial converter

In the last post I had started implementing an Unscented Kalman Filter for position and orientation tracking in OpenHMD. Over the Christmas break, I continued that work.

A Quick Recap

When reading below, keep in mind that the goal of the filtering code I’m writing is to combine 2 sources of information for tracking the headset and controllers.

The first piece of information is acceleration and rotation data from the IMU on each device, and the second is observations of the device position and orientation from 1 or more camera sensors.

The IMU motion data drifts quickly (at least for position tracking) and can’t tell which way the device is facing (yaw, but can detect gravity and get pitch/roll).

The camera observations can tell exactly where each device is, but arrive at a much lower rate (52Hz vs 500/1000Hz) and can take a long time to process (hundreds of milliseconds) to analyse to acquire or re-acquire a lock on the tracked device(s).

The goal is to acquire tracking lock, then use the motion data to predict the motion closely enough that we always hit the ‘fast path’ of vision analysis. The key here is closely enough – the more closely the filter can track and predict the motion of devices between camera frames, the better.

Integration in OpenHMD

When I wrote the last post, I had the filter running as a standalone application, processing motion trace data collected by instrumenting a running OpenHMD app and moving my headset and controllers around. That’s a really good way to work, because it lets me run modifications on the same data set and see what changed.

However, the motion traces were captured using the current fusion/prediction code, which frequently loses tracking lock when the devices move – leading to big gaps in the camera observations and more interpolation for the filter.

By integrating the Kalman filter into OpenHMD, the predictions are improved leading to generally much better results. Here’s one trace of me moving the headset around reasonably vigourously with no tracking loss at all.

If it worked this well all the time, I’d be ecstatic! The predicted position matched the observed position closely enough for every frame for the computer vision to match poses and track perfectly. Unfortunately, this doesn’t happen every time yet, and definitely not with the controllers – although I think the latter largely comes down to the current computer vision having more troubler matching controller poses. They have fewer LEDs to match against compared to the headset, and the LEDs are generally more side-on to a front-facing camera.

Taking a closer look at a portion of that trace, the drift between camera frames when the position is interpolated using the IMU readings is clear.

This is really good. Most of the time, the drift between frames is within 1-2mm. The computer vision can only match the pose of the devices to within a pixel or two – so the observed jitter can also come from the pose extraction, not the filtering.

The worst tracking is again on the Z axis – distance from the camera in this case. Again, that makes sense – with a single camera matching LED blobs, distance is the most uncertain part of the extracted pose.

Losing Track

The trace above is good – the computer vision spots the headset and then the filtering + computer vision track it at all times. That isn’t always the case – the prediction goes wrong, or the computer vision fails to match (it’s definitely still far from perfect). When that happens, it needs to do a full pose search to reacquire the device, and there’s a big gap until the next pose report is available.

That looks more like this

This trace has 2 kinds of errors – gaps in the observed position timeline during full pose searches and erroneous position reports where the computer vision matched things incorrectly.

Fixing the errors in position reports will require improving the computer vision algorithm and would fix most of the plot above. Outlier rejection is one approach to investigate on that front.

Latency Compensation

There is inherent delay involved in processing of the camera observations. Every 19.2ms, the headset emits a radio signal that triggers each camera to capture a frame. At the same time, the headset and controller IR LEDS light up brightly to create the light constellation being tracked. After the frame is captured, it is delivered over USB over the next 18ms or so and then submitted for vision analysis. In the fast case where we’re already tracking the device the computer vision is complete in a millisecond or so. In the slow case, it’s much longer.

Overall, that means that there’s at least a 20ms offset between when the devices are observed and when the position information is available for use. In the plot above, this delay is ignored and position reports are fed into the filter when they are available. In the worst case, that means the filter is being told where the headset was hundreds of milliseconds earlier.

To compensate for that delay, I implemented a mechanism in the filter where it keeps extra position and orientation entries in the state that can be used to retroactively apply the position observations.

The way that works is to make a prediction of the position and orientation of the device at the moment the camera frame is captured and copy that prediction into the extra state variable. After that, it continues integrating IMU data as it becomes available while keeping the auxilliary state constant.

When a the camera frame analysis is complete, that delayed measurement is matched against the stored position and orientation prediction in the state and the error used to correct the overall filter. The cool thing is that in the intervening time, the filter covariance matrix has been building up the right correction terms to adjust the current position and orientation.

Here’s a good example of the difference:

Notice how most of the disconnected segments have now slotted back into position in the timeline. The ones that haven’t can either be attributed to incorrect pose extraction in the compute vision, or to not having enough auxilliary state slots for all the concurrent frames.

At any given moment, there can be a camera frame being analysed, one arriving over USB, and one awaiting “long term” analysis. The filter needs to track an auxilliary state variable for each frame that we expect to get pose information from later, so I implemented a slot allocation system and multiple slots.

The downside is that each slot adds 6 variables (3 position and 3 orientation) to the covariance matrix on top of the 18 base variables. Because the covariance matrix is square, the size grows quadratically with new variables. 5 new slots means 30 new variables – leading to a 48 x 48 covariance matrix instead of 18 x 18. That is a 7-fold increase in the size of the matrix (48 x 48 = 2304 vs 18 x 18 = 324) and unfortunately about a 10x slow-down in the filter run-time.

At that point, even after some optimisation and vectorisation on the matrix operations, the filter can only run about 3x real-time, which is too slow. Using fewer slots is quicker, but allows for fewer outstanding frames. With 3 slots, the slow-down is only about 2x.

There are some other possible approaches to this problem:

• Running the filtering delayed, only integrating IMU reports once the camera report is available. This has the disadvantage of not reporting the most up-to-date estimate of the user pose, which isn’t great for an interactive VR system.

• Keeping around IMU reports and rewinding / replaying the filter for late camera observations. This limits the overall increase in filter CPU usage to double (since we at most replay every observation twice), but potentially with large bursts when hundreds of IMU readings need replaying.
• It might be possible to only keep 2 “full” delayed measurement slots with both position and orientation, and to keep some position-only slots for others. The orientation of the headset tends to drift much more slowly than position does, so when there’s a big gap in the tracking it would be more important to be able to correct the position estimate. Orientation is likely to still be close to correct.
• Further optimisation in the filter implementation. I was hoping to keep everything dependency-free, so the filter implementation uses my own naive 2D matrix code, which only implements the features needed for the filter. A more sophisticated matrix library might perform better – but it’s hard to say without doing some testing on that front.

Controllers

So far in this post, I’ve only talked about the headset tracking and not mentioned controllers. The controllers are considerably harder to track right now, but most of the blame for that is in the computer vision part. Each controller has fewer LEDs than the headset, fewer are visible at any given moment, and they often aren’t pointing at the camera front-on.

This screenshot is a prime example. The controller is the cluster of lights at the top of the image, and the headset is lower left. The computer vision has gotten confused and thinks the controller is the ring of random blue crosses near the headset. It corrected itself a moment later, but those false readings make life very hard for the filtering.

Here’s a typical example of the controller tracking right now. There are some very promising portions of good tracking, but they are interspersed with bursts of tracking losses, and wild drifting from the computer vision giving wrong poses – leading to the filter predicting incorrect acceleration and hence cascaded tracking losses. Particularly (again) on the Z axis.

Timing Improvements

One of the problems I was looking at in my last post is variability in the arrival timing of the various USB streams (Headset reports, Controller reports, camera frames). I improved things in OpenHMD on that front, to use timestamps from the devices everywhere (removing USB timing jitter from the inter-sample time).

There are still potential problems in when IMU reports from controllers get updated in the filters vs the camera frames. That can be on the order of 2-4ms jitter. Time will tell how big a problem that will be – after the other bigger tracking problems are resolved.

Sponsorships

All the work that I’m doing implementing this positional tracking is a combination of my free time, hours contributed by my employer Centricular and contributions from people via Github Sponsorships. If you’d like to help me spend more hours on this and fewer on other paying work, I appreciate any contributions immensely!

Next Steps

The next things on my todo list are:

• Integrate the delayed-observation processing into OpenHMD (at the moment it is only in my standalone simulator).
• Improve the filter code structure – this is my first kalman filter and there are some implementation decisions I’d like to revisit.
• Publish the UKF branch for other people to try.
• Circle back to the computer vision and look at ways to improve the pose extraction and better reject outlying / erroneous poses, especially for the controllers.
• Think more about how to best handle / schedule analysis of frames from multiple cameras. At the moment each camera operates as a separate entity, capturing frames and analysing them in threads without considering what is happening in other cameras. That means any camera that can’t see a particular device starts doing full pose searches – which might be unnecessary if another camera still has a good view of the device. Coordinating those analyses across cameras could yield better CPU consumption, and let the filter retain fewer delayed observation slots.

udev can be used to block a USB device (or even an entire class of devices, such as USB storage). Add a file /etc/udev/rules.d/99-local-blacklist.rules containing:

SUBSYSTEM=="usb", ATTRS{idVendor}=="0123", ATTRS{idProduct}=="4567", ATTR{authorized}="0"

While I hope to update this site again soon, here’s a photo I captured over the weekend in my back yard. The red flowering plant is attracting wattlebirds and honey-eaters. This wattlebird stayed still long enough for me to take this shot. After a little bit of editing, I think it has turned out rather well.

Photo taken with: Canon 7D Mark II & Canon 55-250mm lens.

Edited in Lightroom and Photoshop (to remove a sun glare spot off the eye).

Digital TV uses MPEG Transport Stream, which is a container for video designed for lossy transmission, such as radio. To save CPU cycles, Personal Video Records often save the MPEG-TS stream directly to disk. The more usual MPEG is technically MPEG Program Stream, which is designed for lossless transmission, such as storage on a disk.

Since these are a container formats, it should be possible to losslessly and quickly re-code from MPEG-TS to MPEG-PS.

ffmpeg -ss "${STARTTIME}" -to "${DURATION}" -i "${FILENAME}" -ignore_unknown -map 0 -map -0:2 -c copy "${FILENAME}.mpeg"

I gave the talk Practicality Beats Purity: The Zen of Python’s Escape Hatch as part of PyConline AU 2020, the very online replacement for PyCon AU this year. In that talk, I included a few interesting links code samples which you may be interested in:

Links and code samples

• 20 Pythonic Fec^WTheses by Tim Peters, from comp.lang.python The Way of Python
• Fantastic Blocks and Where to Hide Them, from PyCon AU 2019
• PEP 8
• PEP 318 which introduce decorators

@apply

def apply(transform):

    def __decorator__(using_this):
        return transform(using_this)

    return __decorator__


numbers = [1, 2, 3, 4, 5]

@apply(lambda f: list(map(f, numbers)))
def squares(i):
  return i * i

print(list(squares))

# prints: [1, 4, 9, 16, 25]

Init.java

public class Init {
  public static void main(String[] args) {
    System.out.println("Hello, World!")
  }
}

@switch and @case

__NOT_A_MATCHER__ = object()
__MATCHER_SORT_KEY__ = 0

def switch(cls):

    inst = cls()
    methods = []

    for attr in dir(inst):
        method = getattr(inst, attr)
        matcher = getattr(method, "__matcher__", __NOT_A_MATCHER__)

        if matcher == __NOT_A_MATCHER__:
            continue

        methods.append(method)

    methods.sort(key = lambda i: i.__matcher_sort_key__)

    for method in methods:
        matches = method.__matcher__()
        if matches:
            return method()

    raise ValueError(f"No matcher matches value {test_value}")

def case(matcher):

    def __decorator__(f):
        global __MATCHER_SORT_KEY__

        f.__matcher__ = matcher
        f.__matcher_sort_key__ = __MATCHER_SORT_KEY__
        __MATCHER_SORT_KEY__ += 1
        return f

    return __decorator__



if __name__ == "__main__":
    for i in range(100):

        @switch
        class FizzBuzz:

            @case(lambda: i % 15 == 0)
            def fizzbuzz(self):
                return "fizzbuzz"

            @case(lambda: i % 3 == 0)
            def fizz(self):
                return "fizz"

            @case(lambda: i % 5 == 0)
            def buzz(self):
                return "buzz"

            @case(lambda: True)
            def default(self):
                return "-"

        print(f"{i} {FizzBuzz}")