Tag Archives: lighting

LED Matrix Cylinder — a blinkenlights tube

Post Syndicated from Liz Upton original https://www.raspberrypi.org/blog/led-matrix-cylinder-a-blinkenlights-tube/

We see lots of addressable LED projects, but there was something weirdly charming and very pretty about this cylinder of squares. It’d make for a lovely interactive nightlight in a kids’ room, or for a grown-up lighting feature that you could also use as a news ticker or something that monitors your in-home IoT devices. Once you’ve built something like this, you’re only limited by your imagination — and it’s nice enough to display in your home.

This project is from makeTVee on Instructables. The cleverness is in the layout and the really meticulous execution: vertical strips of LEDs form a cylinder in a laser-cut frame, with a very thin layer of wood veneer glued around the whole thing to act as a diffuser. It’s simple, but really rather beautiful and very effective.

diffuser, diffusing

In the case to the side is the Raspberry Pi Zero that’s driving the whole thing. Here it is doing its thing:

LED matrix cylinder WS2812 Raspberry Pi Zero

LED matrix cylinder based on WS2812 LEDs and some laser cutter parts. https://hackaday.io/project/162035-led-matrix-cylinder https://www.instructables.com/id/LED-Matrix-Cylinder/ #WS2812 #LEDcylinder

makeTVee has built a Pygame-based simulator of the whole matrix so you can program it to do exactly what you want: scroll marquee text, make pretty patterns, twinkle at random, display images: the world’s your (pixellated) oyster. The code’s available at GitHub.

GUI for programming cylinder

Thanks, makeTVee — if you’d like to leave your real name below, we’ll credit you properly here!

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Amazon Sumerian – Now Generally Available

Post Syndicated from Jeff Barr original https://aws.amazon.com/blogs/aws/amazon-sumerian-now-generally-available/

We announced Amazon Sumerian at AWS re:Invent 2017. As you can see from Tara‘s blog post (Presenting Amazon Sumerian: An Easy Way to Create VR, AR, and 3D Experiences), Sumerian does not require any specialized programming or 3D graphics expertise. You can build VR, AR, and 3D experiences for a wide variety of popular hardware platforms including mobile devices, head-mounted displays, digital signs, and web browsers.

I’m happy to announce that Sumerian is now generally available. You can create realistic virtual environments and scenes without having to acquire or master specialized tools for 3D modeling, animation, lighting, audio editing, or programming. Once built, you can deploy your finished creation across multiple platforms without having to write custom code or deal with specialized deployment systems and processes.

Sumerian gives you a web-based editor that you can use to quickly and easily create realistic, professional-quality scenes. There’s a visual scripting tool that lets you build logic to control how objects and characters (Sumerian Hosts) respond to user actions. Sumerian also lets you create rich, natural interactions powered by AWS services such as Amazon Lex, Polly, AWS Lambda, AWS IoT, and Amazon DynamoDB.

Sumerian was designed to work on multiple platforms. The VR and AR apps that you create in Sumerian will run in browsers that supports WebGL or WebVR and on popular devices such as the Oculus Rift, HTC Vive, and those powered by iOS or Android.

During the preview period, we have been working with a broad spectrum of customers to put Sumerian to the test and to create proof of concept (PoC) projects designed to highlight an equally broad spectrum of use cases, including employee education, training simulations, field service productivity, virtual concierge, design and creative, and brand engagement. Fidelity Labs (the internal R&D unit of Fidelity Investments), was the first to use a Sumerian host to create an engaging VR experience. Cora (the host) lives within a virtual chart room. She can display stock quotes, pull up company charts, and answer questions about a company’s performance. This PoC uses Amazon Polly to implement text to speech and Amazon Lex for conversational chatbot functionality. Read their blog post and watch the video inside to see Cora in action:

Now that Sumerian is generally available, you have the power to create engaging AR, VR, and 3D experiences of your own. To learn more, visit the Amazon Sumerian home page and then spend some quality time with our extensive collection of Sumerian Tutorials.

Jeff;

 

Augmented-reality projection lamp with Raspberry Pi and Android Things

Post Syndicated from Helen Lynn original https://www.raspberrypi.org/blog/augmented-reality-projector/

If your day has been a little fraught so far, watch this video. It opens with a tableau of methodically laid-out components and then shows them soldered, screwed, and slotted neatly into place. Everything fits perfectly; nothing needs percussive adjustment. Then it shows us glimpses of an AR future just like the one promised in the less dystopian comics and TV programmes of my 1980s childhood. It is all very soothing, and exactly what I needed.

Android Things – Lantern

Transform any surface into mixed-reality using Raspberry Pi, a laser projector, and Android Things. Android Experiments – http://experiments.withgoogle.com/android/lantern Lantern project site – http://nordprojects.co/lantern check below to make your own ↓↓↓ Get the code – https://github.com/nordprojects/lantern Build the lamp – https://www.hackster.io/nord-projects/lantern-9f0c28

Creating augmented reality with projection

We’ve seen plenty of Raspberry Pi IoT builds that are smart devices for the home; they add computing power to things like lights, door locks, or toasters to make these objects interact with humans and with their environment in new ways. Nord ProjectsLantern takes a different approach. In their words, it:

imagines a future where projections are used to present ambient information, and relevant UI within everyday objects. Point it at a clock to show your appointments, or point to speaker to display the currently playing song. Unlike a screen, when Lantern’s projections are no longer needed, they simply fade away.

Lantern is set up so that you can connect your wireless device to it using Google Nearby. This means there’s no need to create an account before you can dive into augmented reality.

Lantern Raspberry Pi powered projector lamp

Your own open-source AR lamp

Nord Projects collaborated on Lantern with Google’s Android Things team. They’ve made it fully open-source, so you can find the code on GitHub and also download their parts list, which includes a Pi, an IKEA lamp, an accelerometer, and a laser projector. Build instructions are at hackster.io and on GitHub.

This is a particularly clear tutorial, very well illustrated with photos and GIFs, and once you’ve sourced and 3D-printed all of the components, you shouldn’t need a whole lot of experience to put everything together successfully. Since everything is open-source, though, if you want to adapt it — for example, if you’d like to source a less costly projector than the snazzy one used here — you can do that too.

components of Lantern Raspberry Pi powered augmented reality projector lamp

The instructions walk you through the mechanical build and the wiring, as well as installing Android Things and Nord Projects’ custom software on the Raspberry Pi. Once you’ve set everything up, an accelerometer connected to the Pi’s GPIO pins lets the lamp know which surface it is pointing at. A companion app on your mobile device lets you choose from the mini apps that work on that surface to select the projection you want.

The designers are making several mini apps available for Lantern, including the charmingly named Space Porthole: this uses Processing and your local longitude and latitude to project onto your ceiling the stars you’d see if you punched a hole through to the sky, if it were night time, and clear weather. Wouldn’t you rather look at that than deal with the ant problem in your kitchen or tackle your GitHub notifications?

What would you like to project onto your living environment? Let us know in the comments!

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Pirate IPTV Blocking Case is No Slam Dunk Says Federal Court Judge

Post Syndicated from Andy original https://torrentfreak.com/pirate-iptv-blocking-case-is-no-slam-dunk-says-federal-court-judge-180502/

Last year, Hong Kong-based broadcaster Television Broadcasts Limited (TVB) applied for a blocking injunction against several unauthorized IPTV services.

Under the Copyright Act, the broadcaster asked the Federal Court to order ISPs including Telstra, Optus, Vocus, and TPG plus their subsidiaries to block access to seven Android-based services named as A1, BlueTV, EVPAD, FunTV, MoonBox, Unblock, and hTV5.

Unlike torrent site and streaming portal blocks granted earlier, it soon became clear that this case would present unique difficulties. TVB not only wants Internet locations (URLs, domains, IP addresses) related to the technical operation of the services blocked, but also hosting services akin to Google Play and Apple’s App Store that host the app.

Furthermore, it is far from clear whether China-focused live programming is eligible for copyright protection in Australia. If China had been a party to the 1961 Rome Convention for the Protection of Performers, Producers of Phonograms and Broadcasting Organisations, it would receive protection. As it stands, it does not.

That causes complications in respect of Section 115a of the Copyright Act which allows rightsholders to apply for an injunction to have “overseas online locations” blocked if they facilitate access to copyrighted content. Furthermore, the section requires that the “primary purpose” of the location is to infringe copyrights recognized in Australia. If it does not, then there’s no blocking option available.

“If most of what is occurring here is a reproduction of broadcasts that are not protected by copyright, then the primary purpose is not to facilitate copyright infringement,” Justice Nicholas said in April.

This morning TVB returned to Federal Court for a scheduled hearing. The ISPs were a no-show again, leaving the broadcaster’s legal team to battle it out with Justice Nicholas alone. According to details published by ComputerWorld, he isn’t making it easy for the overseas company.

The Judge put it to TVB that “the purpose of this system [the set-top boxes] is to make available a broadcast that’s not copyright protected in this country, in this country,” he said.

“If 10 per cent of the content was infringing content, how could you say the primary purpose is infringing copyright?” the Judge asked.

But despite the Judge’s reservations, TVB believes that the pirate IPTV services clearly infringe its rights, since alongside live programming, the devices also reproduce TVB movies which do receive protection in Australia. However, the company is also getting creative in an effort to sidestep the ‘live TV’ conundrum.

TVB counsel Julian Cooke told the Court that live TVB broadcasts are first reproduced on foreign servers from where they are communicated to set-top devices in Australia with a delay of between one and four minutes. This is a common feature of all pirate IPTV services which potentially calls into question the nature of the ‘live’ broadcasts. The same servers also carry recorded content too, he argued.

“Because the way the system is set up, it compounds itself … in a number of instances, a particular domain name, which we refer to as the portal target domain name, allows a communication path not just to live TV, but it’s also the communication path to other applications such as replay and video on demand,” Cooke said, as quoted by ZDNet.

Cooke told the Court that he wasn’t sure whether the threshold for “primary purpose” was set at 50% of infringing content but noted that the majority of the content available through the boxes is infringing and the nature of the servers is even more pronounced.

“It compounds the submission that the primary purpose of the online location which is the facilitating server is to facilitate the infringement of copyright using that communication path,” he said.

As TF predicted in our earlier coverage, TVB today got creative by highlighting other content that it does receive copyright protection for in Australia. Previously in the UK, the Premier League successfully stated that it owns copyright in the logos presented in a live broadcast.

This morning, Cooke told the court that TVB “literary works” – scripts used on news shows and subtitling services – receive copyright protection in Australia so urged the Court to consider the full package.

“If one had concerns about live TV, one shouldn’t based on the analysis we’ve done … if one adds that live TV infringements together with video on demand together with replay, there could be no doubt that the primary purpose of the online locations is to infringe copyright,” he said.

Due to the apparent complexity of the case, Justice Nicholas reserved his decision, telling TVB that his ruling could take a couple of months after receiving his “close attention.”

Last week, Village Roadshow and several major Hollywood studios won a blocking injunction against a different pirate IPTV service. HD Subs Plus delivers around 600 live premium channels plus hundreds of movies on demand, but the service will now be blocked by ISPs across Australia.

Source: TF, for the latest info on copyright, file-sharing, torrent sites and more. We also have VPN reviews, discounts, offers and coupons.

AIY Projects 2: Google’s AIY Projects Kits get an upgrade

Post Syndicated from Alex Bate original https://www.raspberrypi.org/blog/google-aiy-projects-2/

After the outstanding success of their AIY Projects Voice and Vision Kits, Google has announced the release of upgraded kits, complete with Raspberry Pi Zero WH, Camera Module, and preloaded SD card.

Google AIY Projects Vision Kit 2 Raspberry Pi

Google’s AIY Projects Kits

Google launched the AIY Projects Voice Kit last year, first as a cover gift with The MagPi magazine and later as a standalone product.

Makers needed to provide their own Raspberry Pi for the original kit. The new kits include everything you need, from Pi to SD card.

Within a DIY cardboard box, makers were able to assemble their own voice-activated AI assistant akin to the Amazon Alexa, Apple’s Siri, and Google’s own Google Home Assistant. The Voice Kit was an instant hit that spurred no end of maker videos and tutorials, including our own free tutorial for controlling a robot using voice commands.

Later in the year, the team followed up the success of the Voice Kit with the AIY Projects Vision Kit — the same cardboard box hosting a camera perfect for some pretty nifty image recognition projects.

For more on the AIY Voice Kit, here’s our release video hosted by the rather delightful Rob Zwetsloot.

AIY Projects adds natural human interaction to your Raspberry Pi

Check out the exclusive Google AIY Projects Kit that comes free with The MagPi 57! Grab yourself a copy in stores or online now: http://magpi.cc/2pI6IiQ This first AIY Projects kit taps into the Google Assistant SDK and Cloud Speech API using the AIY Projects Voice HAT (Hardware Accessory on Top) board, stereo microphone, and speaker (included free with the magazine).

AIY Projects 2

So what’s new with version 2 of the AIY Projects Voice Kit? The kit now includes the recently released Raspberry Pi Zero WH, our Zero W with added pre-soldered header pins for instant digital making accessibility. Purchasers of the kits will also get a micro SD card with preloaded OS to help them get started without having to set the card up themselves.

Google AIY Projects Vision Kit 2 Raspberry Pi

Everything you need to build your own Raspberry Pi-powered Google voice assistant

In the newly upgraded AIY Projects Vision Kit v1.2, makers are also treated to an official Raspberry Pi Camera Module v2, the latest model of our add-on camera.

Google AIY Projects Vision Kit 2 Raspberry Pi

“Everything you need to get started is right there in the box,” explains Billy Rutledge, Google’s Director of AIY Projects. “We knew from our research that even though makers are interested in AI, many felt that adding it to their projects was too difficult or required expensive hardware.”

Google AIY Projects Vision Kit 2 Raspberry Pi
Google AIY Projects Vision Kit 2 Raspberry Pi
Google AIY Projects Vision Kit 2 Raspberry Pi

Google is also hard at work producing AIY Projects companion apps for Android, iOS, and Chrome. The Android app is available now to coincide with the launch of the upgraded kits, with the other two due for release soon. The app supports wireless setup of the AIY Kit, though avid coders will still be able to hack theirs to better suit their projects.

Google has also updated the AIY Projects website with an AIY Models section highlighting a range of neural network projects for the kits.

Get your kit

The updated Voice and Vision Kits were announced last night, and in the US they are available now from Target. UK-based makers should be able to get their hands on them this summer — keep an eye on our social channels for updates and links.

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More power to your Pi

Post Syndicated from James Adams original https://www.raspberrypi.org/blog/pi-power-supply-chip/

It’s been just over three weeks since we launched the new Raspberry Pi 3 Model B+. Although the product is branded Raspberry Pi 3B+ and not Raspberry Pi 4, a serious amount of engineering was involved in creating it. The wireless networking, USB/Ethernet hub, on-board power supplies, and BCM2837 chip were all upgraded: together these represent almost all the circuitry on the board! Today, I’d like to tell you about the work that has gone into creating a custom power supply chip for our newest computer.

Raspberry Pi 3 Model B+, with custome power supply chip

The new Raspberry Pi 3B+, sporting a new, custom power supply chip (bottom left-hand corner)

Successful launch

The Raspberry Pi 3B+ has been well received, and we’ve enjoyed hearing feedback from the community as well as reading the various reviews and articles highlighting the solid improvements in wireless networking, Ethernet, CPU, and thermal performance of the new board. Gareth Halfacree’s post here has some particularly nice graphs showing the increased performance as well as how the Pi 3B+ keeps cool under load due to the new CPU package that incorporates a metal heat spreader. The Raspberry Pi production lines at the Sony UK Technology Centre are running at full speed, and it seems most people who want to get hold of the new board are able to find one in stock.

Powering your Pi

One of the most critical but often under-appreciated elements of any electronic product, particularly one such as Raspberry Pi with lots of complex on-board silicon (processor, networking, high-speed memory), is the power supply. In fact, the Raspberry Pi 3B+ has no fewer than six different voltage rails: two at 3.3V — one special ‘quiet’ one for audio, and one for everything else; 1.8V; 1.2V for the LPDDR2 memory; and 1.2V nominal for the CPU core. Note that the CPU voltage is actually raised and lowered on the fly as the speed of the CPU is increased and decreased depending on how hard the it is working. The sixth rail is 5V, which is the master supply that all the others are created from, and the output voltage for the four downstream USB ports; this is what the mains power adaptor is supplying through the micro USB power connector.

Power supply primer

There are two common classes of power supply circuits: linear regulators and switching regulators. Linear regulators work by creating a lower, regulated voltage from a higher one. In simple terms, they monitor the output voltage against an internally generated reference and continually change their own resistance to keep the output voltage constant. Switching regulators work in a different way: they ‘pump’ energy by first storing the energy coming from the source supply in a reactive component (usually an inductor, sometimes a capacitor) and then releasing it to the regulated output supply. The switches in switching regulators effect this energy transfer by first connecting the inductor (or capacitor) to store the source energy, and then switching the circuit so the energy is released to its destination.

Linear regulators produce smoother, less noisy output voltages, but they can only convert to a lower voltage, and have to dissipate energy to do so. The higher the output current and the voltage difference across them is, the more energy is lost as heat. On the other hand, switching supplies can, depending on their design, convert any voltage to any other voltage and can be much more efficient (efficiencies of 90% and above are not uncommon). However, they are more complex and generate noisier output voltages.

Designers use both types of regulators depending on the needs of the downstream circuit: for low-voltage drops, low current, or low noise, linear regulators are usually the right choice, while switching regulators are used for higher power or when efficiency of conversion is required. One of the simplest switching-mode power supply circuits is the buck converter, used to create a lower voltage from a higher one, and this is what we use on the Pi.

A history lesson

The BCM2835 processor chip (found on the original Raspberry Pi Model B and B+, as well as on the Zero products) has on-chip power supplies: one switch-mode regulator for the core voltage, as well as a linear one for the LPDDR2 memory supply. This meant that in addition to 5V, we only had to provide 3.3V and 1.8V on the board, which was relatively simple to do using cheap, off-the-shelf parts.

Pi Zero sporting a BCM2835 processor which only needs 2 external switchers (the components clustered behind the camera port)

When we moved to the BCM2836 for Raspberry Pi Model 2 (and subsequently to the BCM2837A1 and B0 for Raspberry Pi 3B and 3B+), the core supply and the on-chip LPDDR2 memory supply were not up to the job of supplying the extra processor cores and larger memory, so we removed them. (We also used the recovered chip area to help fit in the new quad-core ARM processors.) The upshot of this was that we had to supply these power rails externally for the Raspberry Pi 2 and models thereafter. Moreover, we also had to provide circuitry to sequence them correctly in order to control exactly when they power up compared to the other supplies on the board.

Power supply design is tricky (but critical)

Raspberry Pi boards take in 5V from the micro USB socket and have to generate the other required supplies from this. When 5V is first connected, each of these other supplies must ‘start up’, meaning go from ‘off’, or 0V, to their correct voltage in some short period of time. The order of the supplies starting up is often important: commonly, there are structures inside a chip that form diodes between supply rails, and bringing supplies up in the wrong order can sometimes ‘turn on’ these diodes, causing them to conduct, with undesirable consequences. Silicon chips come with a data sheet specifying what supplies (voltages and currents) are needed and whether they need to be low-noise, in what order they must power up (and in some cases down), and sometimes even the rate at which the voltages must power up and down.

A Pi3. Power supply components are clustered bottom left next to the micro USB, middle (above LPDDR2 chip which is on the bottom of the PCB) and above the A/V jack.

In designing the power chain for the Pi 2 and 3, the sequencing was fairly straightforward: power rails power up in order of voltage (5V, 3.3V, 1.8V, 1.2V). However, the supplies were all generated with individual, discrete devices. Therefore, I spent quite a lot of time designing circuitry to control the sequencing — even with some design tricks to reduce component count, quite a few sequencing components are required. More complex systems generally use a Power Management Integrated Circuit (PMIC) with multiple supplies on a single chip, and many different PMIC variants are made by various manufacturers. Since Raspberry Pi 2 days, I was looking for a suitable PMIC to simplify the Pi design, but invariably (and somewhat counter-intuitively) these were always too expensive compared to my discrete solution, usually because they came with more features than needed.

One device to rule them all

It was way back in May 2015 when I first chatted to Peter Coyle of Exar (Exar were bought by MaxLinear in 2017) about power supply products for Raspberry Pi. We didn’t find a product match then, but in June 2016 Peter, along with Tuomas Hollman and Trevor Latham, visited to pitch the possibility of building a custom power management solution for us.

I was initially sceptical that it could be made cheap enough. However, our discussion indicated that if we could tailor the solution to just what we needed, it could be cost-effective. Over the coming weeks and months, we honed a specification we agreed on from the initial sketches we’d made, and Exar thought they could build it for us at the target price.

The chip we designed would contain all the key supplies required for the Pi on one small device in a cheap QFN package, and it would also perform the required sequencing and voltage monitoring. Moreover, the chip would be flexible to allow adjustment of supply voltages from their default values via I2C; the largest supply would be capable of being adjusted quickly to perform the dynamic core voltage changes needed in order to reduce voltage to the processor when it is idling (to save power), and to boost voltage to the processor when running at maximum speed (1.4 GHz). The supplies on the chip would all be generously specified and could deliver significantly more power than those used on the Raspberry Pi 3. All in all, the chip would contain four switching-mode converters and one low-current linear regulator, this last one being low-noise for the audio circuitry.

The MXL7704 chip

The project was a great success: MaxLinear delivered working samples of first silicon at the end of May 2017 (almost exactly a year after we had kicked off the project), and followed through with production quantities in December 2017 in time for the Raspberry Pi 3B+ production ramp.

The team behind the power supply chip on the Raspberry Pi 3 Model B+ (group of six men, two of whom are holding Raspberry Pi boards)

Front row: Roger with the very first Pi 3B+ prototypes and James with a MXL7704 development board hacked to power a Pi 3. Back row left to right: Will Torgerson, Trevor Latham, Peter Coyle, Tuomas Hollman.

The MXL7704 device has been key to reducing Pi board complexity and therefore overall bill of materials cost. Furthermore, by being able to deliver more power when needed, it has also been essential to increasing the speed of the (newly packaged) BCM2837B0 processor on the 3B+ to 1.4GHz. The result is improvements to both the continuous output current to the CPU (from 3A to 4A) and to the transient performance (i.e. the chip has helped to reduce the ‘transient response’, which is the change in supply voltage due to a sudden current spike that occurs when the processor suddenly demands a large current in a few nanoseconds, as modern CPUs tend to do).

With the MXL7704, the power supply circuitry on the 3B+ is now a lot simpler than the Pi 3B design. This new supply also provides the LPDDR2 memory voltage directly from a switching regulator rather than using linear regulators like the Pi 3, thereby improving energy efficiency. This helps to somewhat offset the extra power that the faster Ethernet, wireless networking, and processor consume. A pleasing side effect of using the new chip is the symmetric board layout of the regulators — it’s easy to see the four switching-mode supplies, given away by four similar-looking blobs (three grey and one brownish), which are the inductors.

Close-up of the power supply chip on the Raspberry Pi 3 Model B+

The Pi 3B+ PMIC MXL7704 — pleasingly symmetric

Kudos

It takes a lot of effort to design a new chip from scratch and get it all the way through to production — we are very grateful to the team at MaxLinear for their hard work, dedication, and enthusiasm. We’re also proud to have created something that will not only power Raspberry Pis, but will also be useful for other product designs: it turns out when you have a low-cost and flexible device, it can be used for many things — something we’re fairly familiar with here at Raspberry Pi! For the curious, the product page (including the data sheet) for the MXL7704 chip is here. Particular thanks go to Peter Coyle, Tuomas Hollman, and Trevor Latham, and also to Jon Cronk, who has been our contact in the US and has had to get up early to attend all our conference calls!

The MXL7704 design team celebrating on Pi Day — it takes a lot of people to design a chip!

I hope you liked reading about some of the effort that has gone into creating the new Pi. It’s nice to finally have a chance to tell people about some of the (increasingly complex) technical work that makes building a $35 computer possible — we’re very pleased with the Raspberry Pi 3B+, and we hope you enjoy using it as much as we’ve enjoyed creating it!

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Barbot 4: the bartending Grandfather clock

Post Syndicated from Alex Bate original https://www.raspberrypi.org/blog/barbot-4/

Meet Barbot 4, the drink-dispensing Grandfather clock who knows when it’s time to party.

Barbot 4. Grandfather Time (first video of cocktail robot)

The first introduction to my latest barbot – this time made inside a grandfather clock. There is another video where I explain a bit about how it works, and am happy to give more explanations. https://youtu.be/hdxV_KKH5MA This can make cocktails with up to 4 spirits, and 4 mixers, and is controlled by voice, keyboard input, or a gui, depending which is easiest.

Barbot 4

Robert Prest’s Barbot 4 is a beverage dispenser loaded into an old Grandfather clock. There’s space in the back for your favourite spirits and mixers, and a Raspberry Pi controls servo motors that release the required measures of your favourite cocktail ingredients, according to preset recipes.

Barbot 4 Raspberry Pi drink-dispensing robot

The clock can hold four mixers and four spirits, and a human supervisor records these using Drinkydoodad, a friendly touchscreen interface. With information about its available ingredients and a library of recipes, Barbot 4 can create your chosen drink. Patrons control the system either with voice commands or with the touchscreen UI.

Barbot 4 Raspberry Pi drink-dispensing robot

Robert has experimented with various components as this project has progressed. He has switched out peristaltic pumps in order to increase the flow of liquid, and adjusted the motors so that they can handle carbonated beverages. In the video, he highlights other quirks he hopes to address, like the fact that drinks tend to splash during pouring.

Barbot 4 Raspberry Pi drink-dispensing robot

As well as a Raspberry Pi, the build uses Arduinos. These control the light show, which can be adjusted according to your party-time lighting preferences.

An explanation of the build accompanies Robert’s second video. We’re hoping he’ll also release more details of Barbot 3, his suitcase-sized, portable Barbot, and of Doom Shot Bot, a bottle topper that pours a shot every time you die in the game DoomZ.

Automated bartending

Barbot 4 isn’t the first cocktail-dispensing Raspberry Pi bartender we’ve seen, though we have to admit that fitting it into a grandfather clock definitely makes it one of the quirkiest.

If you’ve built a similar project using a Raspberry Pi, we’d love to see it. Share your project in the comments, or tell us what drinks you’d ask Barbot to mix if you had your own at home.

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Addressing Data Residency with AWS

Post Syndicated from Min Hyun original https://aws.amazon.com/blogs/security/addressing-data-residency-with-aws/

Whitepaper image

AWS has released a new whitepaper that has been requested by many AWS customers: AWS Policy Perspectives: Data Residency. Data residency is the requirement that all customer content processed and stored in an IT system must remain within a specific country’s borders, and it is one of the foremost concerns of governments that want to use commercial cloud services. General cybersecurity concerns and concerns about government requests for data have contributed to a continued focus on keeping data within countries’ borders. In fact, some governments have determined that mandating data residency provides an extra layer of security.

This approach, however, is counterproductive to the data protection objectives and the IT modernization and global economic growth goals that many governments have set as milestones. This new whitepaper addresses the real and perceived security risks expressed by governments when they demand in-country data residency by identifying the most likely and prevalent IT vulnerabilities and security risks, explaining the native security embedded in cloud services, and highlighting the roles and responsibilities of cloud service providers (CSPs), governments, and customers in protecting data.

Large-scale, multinational CSPs, often called hyperscale CSPs, represent a transformational disruption in technology because of how they support their customers with high degrees of efficiency, agility, and innovation as part of world-class security offerings. The whitepaper explains how hyperscale CSPs, such as AWS, that might be located out of country provide their customers the ability to achieve high levels of data protection through safeguards on their own platform and with turnkey tooling for their customers. They do this while at the same time preserving nation-state regulatory sovereignty.

The whitepaper also considers the commercial, public-sector, and economic effects of data residency policies and offers considerations for governments to evaluate before enforcing requirements that can unintentionally limit public-sector digital transformation goals, in turn possibly leading to increased cybersecurity risk.

AWS continues to engage with governments around the world to hear and address their top-of-mind security concerns. We take seriously our commitment to advocate for our customers’ interests and enforce security from “ground zero.” This means that when customers use AWS, they can have the confidence that their data is protected with a level of assurance that meets, if not exceeds, their needs, regardless of where the data resides.

– Min Hyun, Cloud Security Policy Strategist

Privacy expectations and the connected home

Post Syndicated from Matthew Garrett original https://mjg59.dreamwidth.org/50229.html

Traditionally, devices that were tied to logins tended to indicate that in some way – turn on someone’s xbox and it’ll show you their account name, run Netflix and it’ll ask which profile you want to use. The increasing prevalence of smart devices in the home changes that, in ways that may not be immediately obvious to the majority of people. You can configure a Philips Hue with wall-mounted dimmers, meaning that someone unfamiliar with the system may not recognise that it’s a smart lighting system at all. Without any actively malicious intent, you end up with a situation where the account holder is able to infer whether someone is home without that person necessarily having any idea that that’s possible. A visitor who uses an Amazon Echo is not necessarily going to know that it’s tied to somebody’s Amazon account, and even if they do they may not know that the log (and recorded audio!) of all interactions is available to the account holder. And someone grabbing an egg out of your fridge is almost certainly not going to think that your smart egg tray will trigger an immediate notification on the account owner’s phone that they need to buy new eggs.

Things get even more complicated when there’s multiple account support. Google Home supports multiple users on a single device, using voice recognition to determine which queries should be associated with which account. But the account that was used to initially configure the device remains as the fallback, with unrecognised voices ended up being logged to it. If a voice is misidentified, the query may end up being logged to an unexpected account.

There’s some interesting questions about consent and expectations of privacy here. If someone sets up a smart device in their home then at some point they’ll agree to the manufacturer’s privacy policy. But if someone else makes use of the system (by pressing a lightswitch, making a spoken query or, uh, picking up an egg), have they consented? Who has the social obligation to explain to them that the information they’re producing may be stored elsewhere and visible to someone else? If I use an Echo in a hotel room, who has access to the Amazon account it’s associated with? How do you explain to a teenager that there’s a chance that when they asked their Home for contact details for an abortion clinic, it ended up in their parent’s activity log? Who’s going to be the first person divorced for claiming that they were vegan but having been the only person home when an egg was taken out of the fridge?

To be clear, I’m not arguing against the design choices involved in the implementation of these devices. In many cases it’s hard to see how the desired functionality could be implemented without this sort of issue arising. But we’re gradually shifting to a place where the data we generate is not only available to corporations who probably don’t care about us as individuals, it’s also becoming available to people who own the more private spaces we inhabit. We have social norms against bugging our houseguests, but we have no social norms that require us to explain to them that there’ll be a record of every light that they turn on or off. This feels like it’s going to end badly.

(Thanks to Nikki Everett for conversations that inspired this post)

(Disclaimer: while I work for Google, I am not involved in any of the products or teams described in this post and my opinions are my own rather than those of my employer’s)

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Tamper-Detection App for Android

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2018/01/tamper-detectio.html

Edward Snowden and Nathan Freitas have created an Android app that detects when it’s being tampered with. The basic idea is to put the app on a second phone and put the app on or near something important, like your laptop. The app can then text you — and also record audio and video — when something happens around it: when it’s moved, when the lighting changes, and so on. This gives you some protection against the “evil maid attack” against laptops.

Micah Lee has a good article about the app, including some caveats about its use and security.

All the lights, all of the twinkly lights

Post Syndicated from Alex Bate original https://www.raspberrypi.org/blog/all-of-the-lights/

Twinkly lights are to Christmas what pumpkins are to Halloween. And when you add a Raspberry Pi to your light show, the result instantly goes from “Meh, yeah.” to “OMG, wow!”

Here are some cool light-based Christmas projects to inspire you this weekend.

Raspberry Pi Christmas Lights

App-based light control

Christmas Tree Lights Demo

Project Code – https://github.com/eidolonFIRE/Christmas-Lights Raspberry Pi A+ ws2812b – https://smile.amazon.com/gp/product/B01H04YAIQ/ref=od_aui_detailpages00?ie=UTF8&psc=1 200w 5V supply – https://smile.amazon.com/gp/product/B01LZRIWZD/ref=od_aui_detailpages01?ie=UTF8&psc=1

In his Christmas lights project, Caleb Johnson uses an app as a control panel to switch between predefined displays. The full code is available on his GitHub, and it connects a Raspberry Pi A+ to a strip of programmable LEDs that change their pattern at the touch of a phone screen.

What’s great about this project, aside from the simplicity of its design, is the scope for extending it. Why not share the app with friends and family, allowing them to control your lights remotely? Or link the lights to social media so they are triggered by a specific hashtag, like in Alex Ellis’ #cheerlights project below.

Worldwide holiday #cheerlights

Holiday lights hack – 1$ Snowman + Raspberry Pi

Here we have a smart holiday light which will only run when it detects your presence in the room through a passive infrared PIR sensor. I’ve used hot glue for the fixings and an 8-LED NeoPixel strip connected to port 18.

Cheerlights, an online service created by Hans Scharler, allows makers to incorporate hashtag-controlled lighting into the projects. By tweeting the hashtag #cheerlights, followed by a colour, you can control a network of lights so that they are all displaying the same colour.

For his holiday light hack using Cheerlights, Alex incorporated the Pimoroni Blinkt! and a collection of cheap Christmas decorations to create cute light-up ornaments for the festive season.

To make your own, check out Alex’s blog post, and head to your local £1/$1 store for hackable decor. You could even link your Christmas tree and the trees of your family, syncing them all in one glorious, Santa-pleasing spectacular.

Outdoor decorations

DIY musical Xmas lights for beginners with raspberry pi

With just a few bucks of extra material, I walk you through converting your regular Christmas lights into a whole-house light show. The goal here is to go from scratch. Although this guide is intended for people who don’t know how to use linux at all and those who do alike, the focus is for people for whom linux and the raspberry pi are a complete mystery.

Looking to outdo your neighbours with your Christmas light show this year? YouTuber Makin’Things has created a beginners guide to setting up a Raspberry Pi–based musical light show for your facade, complete with information on soldering, wiring, and coding.

Once you’ve wrapped your house in metres and metres of lights and boosted your speakers so they can be heard for miles around, why not incorporate #cheerlights to make your outdoor decor interactive?

Still not enough? How about controlling your lights using a drum kit? Christian Kratky’s MIDI-Based Christmas Lights Animation system (or as I like to call it, House Rock) does exactly that.

Eye Of The Tiger (MIDI based christmas lights animation system prototype)

Project documentation and source code: https://www.hackster.io/cyborg-titanium-14/light-pi-1c88b0 The song is taken from: https://www.youtube.com/watch?v=G6r1dAire0Y

Any more?

We know these projects are just the tip of the iceberg when it comes to the Raspberry Pi–powered Christmas projects out there, and as always, we’d love you to share yours with us. So post a link in the comments below, or tag us on social media when posting your build photos, videos, and/or blog links. ‘Tis the season for sharing after all.

The post All the lights, all of the twinkly lights appeared first on Raspberry Pi.

Object models

Post Syndicated from Eevee original https://eev.ee/blog/2017/11/28/object-models/

Anonymous asks, with dollars:

More about programming languages!

Well then!

I’ve written before about what I think objects are: state and behavior, which in practice mostly means method calls.

I suspect that the popular impression of what objects are, and also how they should work, comes from whatever C++ and Java happen to do. From that point of view, the whole post above is probably nonsense. If the baseline notion of “object” is a rigid definition woven tightly into the design of two massively popular languages, then it doesn’t even make sense to talk about what “object” should mean — it does mean the features of those languages, and cannot possibly mean anything else.

I think that’s a shame! It piles a lot of baggage onto a fairly simple idea. Polymorphism, for example, has nothing to do with objects — it’s an escape hatch for static type systems. Inheritance isn’t the only way to reuse code between objects, but it’s the easiest and fastest one, so it’s what we get. Frankly, it’s much closer to a speed tradeoff than a fundamental part of the concept.

We could do with more experimentation around how objects work, but that’s impossible in the languages most commonly thought of as object-oriented.

Here, then, is a (very) brief run through the inner workings of objects in four very dynamic languages. I don’t think I really appreciated objects until I’d spent some time with Python, and I hope this can help someone else whet their own appetite.

Python 3

Of the four languages I’m going to touch on, Python will look the most familiar to the Java and C++ crowd. For starters, it actually has a class construct.

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

    def __neg__(self):
        return Vector(-self.x, -self.y)

    def __div__(self, denom):
        return Vector(self.x / denom, self.y / denom)

    @property
    def magnitude(self):
        return (self.x ** 2 + self.y ** 2) ** 0.5

    def normalized(self):
        return self / self.magnitude

The __init__ method is an initializer, which is like a constructor but named differently (because the object already exists in a usable form by the time the initializer is called). Operator overloading is done by implementing methods with other special __dunder__ names. Properties can be created with @property, where the @ is syntax for applying a wrapper function to a function as it’s defined. You can do inheritance, even multiply:

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class Foo(A, B, C):
    def bar(self, x, y, z):
        # do some stuff
        super().bar(x, y, z)

Cool, a very traditional object model.

Except… for some details.

Some details

For one, Python objects don’t have a fixed layout. Code both inside and outside the class can add or remove whatever attributes they want from whatever object they want. The underlying storage is just a dict, Python’s mapping type. (Or, rather, something like one. Also, it’s possible to change, which will probably be the case for everything I say here.)

If you create some attributes at the class level, you’ll start to get a peek behind the curtains:

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class Foo:
    values = []

    def add_value(self, value):
        self.values.append(value)

a = Foo()
b = Foo()
a.add_value('a')
print(a.values)  # ['a']
b.add_value('b')
print(b.values)  # ['a', 'b']

The [] assigned to values isn’t a default assigned to each object. In fact, the individual objects don’t know about it at all! You can use vars(a) to get at the underlying storage dict, and you won’t see a values entry in there anywhere.

Instead, values lives on the class, which is a value (and thus an object) in its own right. When Python is asked for self.values, it checks to see if self has a values attribute; in this case, it doesn’t, so Python keeps going and asks the class for one.

Python’s object model is secretly prototypical — a class acts as a prototype, as a shared set of fallback values, for its objects.

In fact, this is also how method calls work! They aren’t syntactically special at all, which you can see by separating the attribute lookup from the call.

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print("abc".startswith("a"))  # True
meth = "abc".startswith
print(meth("a"))  # True

Reading obj.method looks for a method attribute; if there isn’t one on obj, Python checks the class. Here, it finds one: it’s a function from the class body.

Ah, but wait! In the code I just showed, meth seems to “know” the object it came from, so it can’t just be a plain function. If you inspect the resulting value, it claims to be a “bound method” or “built-in method” rather than a function, too. Something funny is going on here, and that funny something is the descriptor protocol.

Descriptors

Python allows attributes to implement their own custom behavior when read from or written to. Such an attribute is called a descriptor. I’ve written about them before, but here’s a quick overview.

If Python looks up an attribute, finds it in a class, and the value it gets has a __get__ method… then instead of using that value, Python will use the return value of its __get__ method.

The @property decorator works this way. The magnitude property in my original example was shorthand for doing this:

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class MagnitudeDescriptor:
    def __get__(self, instance, owner):
        if instance is None:
            return self
        return (instance.x ** 2 + instance.y ** 2) ** 0.5

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

    magnitude = MagnitudeDescriptor()

When you ask for somevec.magnitude, Python checks somevec but doesn’t find magnitude, so it consults the class instead. The class does have a magnitude, and it’s a value with a __get__ method, so Python calls that method and somevec.magnitude evaluates to its return value. (The instance is None check is because __get__ is called even if you get the descriptor directly from the class via Vector.magnitude. A descriptor intended to work on instances can’t do anything useful in that case, so the convention is to return the descriptor itself.)

You can also intercept attempts to write to or delete an attribute, and do absolutely whatever you want instead. But note that, similar to operating overloading in Python, the descriptor must be on a class; you can’t just slap one on an arbitrary object and have it work.

This brings me right around to how “bound methods” actually work. Functions are descriptors! The function type implements __get__, and when a function is retrieved from a class via an instance, that __get__ bundles the function and the instance together into a tiny bound method object. It’s essentially:

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class FunctionType:
    def __get__(self, instance, owner):
        if instance is None:
            return self
        return functools.partial(self, instance)

The self passed as the first argument to methods is not special or magical in any way. It’s built out of a few simple pieces that are also readily accessible to Python code.

Note also that because obj.method() is just an attribute lookup and a call, Python doesn’t actually care whether method is a method on the class or just some callable thing on the object. You won’t get the auto-self behavior if it’s on the object, but otherwise there’s no difference.

More attribute access, and the interesting part

Descriptors are one of several ways to customize attribute access. Classes can implement __getattr__ to intervene when an attribute isn’t found on an object; __setattr__ and __delattr__ to intervene when any attribute is set or deleted; and __getattribute__ to implement unconditional attribute access. (That last one is a fantastic way to create accidental recursion, since any attribute access you do within __getattribute__ will of course call __getattribute__ again.)

Here’s what I really love about Python. It might seem like a magical special case that descriptors only work on classes, but it really isn’t. You could implement exactly the same behavior yourself, in pure Python, using only the things I’ve just told you about. Classes are themselves objects, remember, and they are instances of type, so the reason descriptors only work on classes is that type effectively does this:

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class type:
    def __getattribute__(self, name):
        value = super().__getattribute__(name)
        # like all op overloads, __get__ must be on the type, not the instance
        ty = type(value)
        if hasattr(ty, '__get__'):
            # it's a descriptor!  this is a class access so there is no instance
            return ty.__get__(value, None, self)
        else:
            return value

You can even trivially prove to yourself that this is what’s going on by skipping over types behavior:

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class Descriptor:
    def __get__(self, instance, owner):
        print('called!')

class Foo:
    bar = Descriptor()

Foo.bar  # called!
type.__getattribute__(Foo, 'bar')  # called!
object.__getattribute__(Foo, 'bar')  # ...

And that’s not all! The mysterious super function, used to exhaustively traverse superclass method calls even in the face of diamond inheritance, can also be expressed in pure Python using these primitives. You could write your own superclass calling convention and use it exactly the same way as super.

This is one of the things I really like about Python. Very little of it is truly magical; virtually everything about the object model exists in the types rather than the language, which means virtually everything can be customized in pure Python.

Class creation and metaclasses

A very brief word on all of this stuff, since I could talk forever about Python and I have three other languages to get to.

The class block itself is fairly interesting. It looks like this:

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class Name(*bases, **kwargs):
    # code

I’ve said several times that classes are objects, and in fact the class block is one big pile of syntactic sugar for calling type(...) with some arguments to create a new type object.

The Python documentation has a remarkably detailed description of this process, but the gist is:

  • Python determines the type of the new class — the metaclass — by looking for a metaclass keyword argument. If there isn’t one, Python uses the “lowest” type among the provided base classes. (If you’re not doing anything special, that’ll just be type, since every class inherits from object and object is an instance of type.)

  • Python executes the class body. It gets its own local scope, and any assignments or method definitions go into that scope.

  • Python now calls type(name, bases, attrs, **kwargs). The name is whatever was right after class; the bases are position arguments; and attrs is the class body’s local scope. (This is how methods and other class attributes end up on the class.) The brand new type is then assigned to Name.

Of course, you can mess with most of this. You can implement __prepare__ on a metaclass, for example, to use a custom mapping as storage for the local scope — including any reads, which allows for some interesting shenanigans. The only part you can’t really implement in pure Python is the scoping bit, which has a couple extra rules that make sense for classes. (In particular, functions defined within a class block don’t close over the class body; that would be nonsense.)

Object creation

Finally, there’s what actually happens when you create an object — including a class, which remember is just an invocation of type(...).

Calling Foo(...) is implemented as, well, a call. Any type can implement calls with the __call__ special method, and you’ll find that type itself does so. It looks something like this:

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# oh, a fun wrinkle that's hard to express in pure python: type is a class, so
# it's an instance of itself
class type:
    def __call__(self, *args, **kwargs):
        # remember, here 'self' is a CLASS, an instance of type.
        # __new__ is a true constructor: object.__new__ allocates storage
        # for a new blank object
        instance = self.__new__(self, *args, **kwargs)
        # you can return whatever you want from __new__ (!), and __init__
        # is only called on it if it's of the right type
        if isinstance(instance, self):
            instance.__init__(*args, **kwargs)
        return instance

Again, you can trivially confirm this by asking any type for its __call__ method. Assuming that type doesn’t implement __call__ itself, you’ll get back a bound version of types implementation.

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>>> list.__call__
<method-wrapper '__call__' of type object at 0x7fafb831a400>

You can thus implement __call__ in your own metaclass to completely change how subclasses are created — including skipping the creation altogether, if you like.

And… there’s a bunch of stuff I haven’t even touched on.

The Python philosophy

Python offers something that, on the surface, looks like a “traditional” class/object model. Under the hood, it acts more like a prototypical system, where failed attribute lookups simply defer to a superclass or metaclass.

The language also goes to almost superhuman lengths to expose all of its moving parts. Even the prototypical behavior is an implementation of __getattribute__ somewhere, which you are free to completely replace in your own types. Proxying and delegation are easy.

Also very nice is that these features “bundle” well, by which I mean a library author can do all manner of convoluted hijinks, and a consumer of that library doesn’t have to see any of it or understand how it works. You only need to inherit from a particular class (which has a metaclass), or use some descriptor as a decorator, or even learn any new syntax.

This meshes well with Python culture, which is pretty big on the principle of least surprise. These super-advanced features tend to be tightly confined to single simple features (like “makes a weak attribute“) or cordoned with DSLs (e.g., defining a form/struct/database table with a class body). In particular, I’ve never seen a metaclass in the wild implement its own __call__.

I have mixed feelings about that. It’s probably a good thing overall that the Python world shows such restraint, but I wonder if there are some very interesting possibilities we’re missing out on. I implemented a metaclass __call__ myself, just once, in an entity/component system that strove to minimize fuss when communicating between components. It never saw the light of day, but I enjoyed seeing some new things Python could do with the same relatively simple syntax. I wouldn’t mind seeing, say, an object model based on composition (with no inheritance) built atop Python’s primitives.

Lua

Lua doesn’t have an object model. Instead, it gives you a handful of very small primitives for building your own object model. This is pretty typical of Lua — it’s a very powerful language, but has been carefully constructed to be very small at the same time. I’ve never encountered anything else quite like it, and “but it starts indexing at 1!” really doesn’t do it justice.

The best way to demonstrate how objects work in Lua is to build some from scratch. We need two key features. The first is metatables, which bear a passing resemblance to Python’s metaclasses.

Tables and metatables

The table is Lua’s mapping type and its primary data structure. Keys can be any value other than nil. Lists are implemented as tables whose keys are consecutive integers starting from 1. Nothing terribly surprising. The dot operator is sugar for indexing with a string key.

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local t = { a = 1, b = 2 }
print(t['a'])  -- 1
print(t.b)  -- 2
t.c = 3
print(t['c'])  -- 3

A metatable is a table that can be associated with another value (usually another table) to change its behavior. For example, operator overloading is implemented by assigning a function to a special key in a metatable.

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local t = { a = 1, b = 2 }
--print(t + 0)  -- error: attempt to perform arithmetic on a table value

local mt = {
    __add = function(left, right)
        return 12
    end,
}
setmetatable(t, mt)
print(t + 0)  -- 12

Now, the interesting part: one of the special keys is __index, which is consulted when the base table is indexed by a key it doesn’t contain. Here’s a table that claims every key maps to itself.

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local t = {}
local mt = {
    __index = function(table, key)
        return key
    end,
}
setmetatable(t, mt)
print(t.foo)  -- foo
print(t.bar)  -- bar
print(t[3])  -- 3

__index doesn’t have to be a function, either. It can be yet another table, in which case that table is simply indexed with the key. If the key still doesn’t exist and that table has a metatable with an __index, the process repeats.

With this, it’s easy to have several unrelated tables that act as a single table. Call the base table an object, fill the __index table with functions and call it a class, and you have half of an object system. You can even get prototypical inheritance by chaining __indexes together.

At this point things are a little confusing, since we have at least three tables going on, so here’s a diagram. Keep in mind that Lua doesn’t actually have anything called an “object”, “class”, or “method” — those are just convenient nicknames for a particular structure we might build with Lua’s primitives.

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                    ╔═══════════╗        ...
                    ║ metatable ║         ║
                    ╟───────────╢   ┌─────╨───────────────────────┐
                    ║ __index   ╫───┤ lookup table ("superclass") │
                    ╚═══╦═══════╝   ├─────────────────────────────┤
  ╔═══════════╗         ║           │ some other method           ┼─── function() ... end
  ║ metatable ║         ║           └─────────────────────────────┘
  ╟───────────╢   ┌─────╨──────────────────┐
  ║ __index   ╫───┤ lookup table ("class") │
  ╚═══╦═══════╝   ├────────────────────────┤
      ║           │ some method            ┼─── function() ... end
      ║           └────────────────────────┘
┌─────╨─────────────────┐
│ base table ("object") │
└───────────────────────┘

Note that a metatable is not the same as a class; it defines behavior, not methods. Conversely, if you try to use a class directly as a metatable, it will probably not do much. (This is pretty different from e.g. Python, where operator overloads are just methods with funny names. One nice thing about the Lua approach is that you can keep interface-like functionality separate from methods, and avoid clogging up arbitrary objects’ namespaces. You could even use a dummy table as a key and completely avoid name collisions.)

Anyway, code!

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local class = {
    foo = function(a)
        print("foo got", a)
    end,
}
local mt = { __index = class }
-- setmetatable returns its first argument, so this is nice shorthand
local obj1 = setmetatable({}, mt)
local obj2 = setmetatable({}, mt)
obj1.foo(7)  -- foo got 7
obj2.foo(9)  -- foo got 9

Wait, wait, hang on. Didn’t I call these methods? How do they get at the object? Maybe Lua has a magical this variable?

Methods, sort of

Not quite, but this is where the other key feature comes in: method-call syntax. It’s the lightest touch of sugar, just enough to have method invocation.

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-- note the colon!
a:b(c, d, ...)

-- exactly equivalent to this
-- (except that `a` is only evaluated once)
a.b(a, c, d, ...)

-- which of course is really this
a["b"](a, c, d, ...)

Now we can write methods that actually do something.

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local class = {
    bar = function(self)
        print("our score is", self.score)
    end,
}
local mt = { __index = class }
local obj1 = setmetatable({ score = 13 }, mt)
local obj2 = setmetatable({ score = 25 }, mt)
obj1:bar()  -- our score is 13
obj2:bar()  -- our score is 25

And that’s all you need. Much like Python, methods and data live in the same namespace, and Lua doesn’t care whether obj:method() finds a function on obj or gets one from the metatable’s __index. Unlike Python, the function will be passed self either way, because self comes from the use of : rather than from the lookup behavior.

(Aside: strictly speaking, any Lua value can have a metatable — and if you try to index a non-table, Lua will always consult the metatable’s __index. Strings all have the string library as a metatable, so you can call methods on them: try ("%s %s"):format(1, 2). I don’t think Lua lets user code set the metatable for non-tables, so this isn’t that interesting, but if you’re writing Lua bindings from C then you can wrap your pointers in metatables to give them methods implemented in C.)

Bringing it all together

Of course, writing all this stuff every time is a little tedious and error-prone, so instead you might want to wrap it all up inside a little function. No problem.

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local function make_object(body)
    -- create a metatable
    local mt = { __index = body }
    -- create a base table to serve as the object itself
    local obj = setmetatable({}, mt)
    -- and, done
    return obj
end

-- you can leave off parens if you're only passing in 
local Dog = {
    -- this acts as a "default" value; if obj.barks is missing, __index will
    -- kick in and find this value on the class.  but if obj.barks is assigned
    -- to, it'll go in the object and shadow the value here.
    barks = 0,

    bark = function(self)
        self.barks = self.barks + 1
        print("woof!")
    end,
}

local mydog = make_object(Dog)
mydog:bark()  -- woof!
mydog:bark()  -- woof!
mydog:bark()  -- woof!
print(mydog.barks)  -- 3
print(Dog.barks)  -- 0

It works, but it’s fairly barebones. The nice thing is that you can extend it pretty much however you want. I won’t reproduce an entire serious object system here — lord knows there are enough of them floating around — but the implementation I have for my LÖVE games lets me do this:

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local Animal = Object:extend{
    cries = 0,
}

-- called automatically by Object
function Animal:init()
    print("whoops i couldn't think of anything interesting to put here")
end

-- this is just nice syntax for adding a first argument called 'self', then
-- assigning this function to Animal.cry
function Animal:cry()
    self.cries = self.cries + 1
end

local Cat = Animal:extend{}

function Cat:cry()
    print("meow!")
    Cat.__super.cry(self)
end

local cat = Cat()
cat:cry()  -- meow!
cat:cry()  -- meow!
print(cat.cries)  -- 2

When I say you can extend it however you want, I mean that. I could’ve implemented Python (2)-style super(Cat, self):cry() syntax; I just never got around to it. I could even make it work with multiple inheritance if I really wanted to — or I could go the complete opposite direction and only implement composition. I could implement descriptors, customizing the behavior of individual table keys. I could add pretty decent syntax for composition/proxying. I am trying very hard to end this section now.

The Lua philosophy

Lua’s philosophy is to… not have a philosophy? It gives you the bare minimum to make objects work, and you can do absolutely whatever you want from there. Lua does have something resembling prototypical inheritance, but it’s not so much a first-class feature as an emergent property of some very simple tools. And since you can make __index be a function, you could avoid the prototypical behavior and do something different entirely.

The very severe downside, of course, is that you have to find or build your own object system — which can get pretty confusing very quickly, what with the multiple small moving parts. Third-party code may also have its own object system with subtly different behavior. (Though, in my experience, third-party code tries very hard to avoid needing an object system at all.)

It’s hard to say what the Lua “culture” is like, since Lua is an embedded language that’s often a little different in each environment. I imagine it has a thousand millicultures, instead. I can say that the tedium of building my own object model has led me into something very “traditional”, with prototypical inheritance and whatnot. It’s partly what I’m used to, but it’s also just really dang easy to get working.

Likewise, while I love properties in Python and use them all the dang time, I’ve yet to use a single one in Lua. They wouldn’t be particularly hard to add to my object model, but having to add them myself (or shop around for an object model with them and also port all my code to use it) adds a huge amount of friction. I’ve thought about designing an interesting ECS with custom object behavior, too, but… is it really worth the effort? For all the power and flexibility Lua offers, the cost is that by the time I have something working at all, I’m too exhausted to actually use any of it.

JavaScript

JavaScript is notable for being preposterously heavily used, yet not having a class block.

Well. Okay. Yes. It has one now. It didn’t for a very long time, and even the one it has now is sugar.

Here’s a vector class again:

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class Vector {
    constructor(x, y) {
        this.x = x;
        this.y = y;
    }

    get magnitude() {
        return Math.sqrt(this.x * this.x + this.y * this.y);
    }

    dot(other) {
        return this.x * other.x + this.y * other.y;
    }
}

In “classic” JavaScript, this would be written as:

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function Vector(x, y) {
    this.x = x;
    this.y = y;
}

Object.defineProperty(Vector.prototype, 'magnitude', {
    configurable: true,
    enumerable: true,
    get: function() {
        return Math.sqrt(this.x * this.x + this.y * this.y);
    },
});


Vector.prototype.dot = function(other) {
    return this.x * other.x + this.y * other.y;
};

Hm, yes. I can see why they added class.

The JavaScript model

In JavaScript, a new type is defined in terms of a function, which is its constructor.

Right away we get into trouble here. There is a very big difference between these two invocations, which I actually completely forgot about just now after spending four hours writing about Python and Lua:

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let vec = Vector(3, 4);
let vec = new Vector(3, 4);

The first calls the function Vector. It assigns some properties to this, which here is going to be window, so now you have a global x and y. It then returns nothing, so vec is undefined.

The second calls Vector with this set to a new empty object, then evaluates to that object. The result is what you’d actually expect.

(You can detect this situation with the strange new.target expression, but I have never once remembered to do so.)

From here, we have true, honest-to-god, first-class prototypical inheritance. The word “prototype” is even right there. When you write this:

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vec.dot(vec2)

JavaScript will look for dot on vec and (presumably) not find it. It then consults vecs prototype, an object you can see for yourself by using Object.getPrototypeOf(). Since vec is a Vector, its prototype is Vector.prototype.

I stress that Vector.prototype is not the prototype for Vector. It’s the prototype for instances of Vector.

(I say “instance”, but the true type of vec here is still just object. If you want to find Vector, it’s automatically assigned to the constructor property of its own prototype, so it’s available as vec.constructor.)

Of course, Vector.prototype can itself have a prototype, in which case the process would continue if dot were not found. A common (and, arguably, very bad) way to simulate single inheritance is to set Class.prototype to an instance of a superclass to get the prototype right, then tack on the methods for Class. Nowadays we can do Object.create(Superclass.prototype).

Now that I’ve been through Python and Lua, though, this isn’t particularly surprising. I kinda spoiled it.

I suppose one difference in JavaScript is that you can tack arbitrary attributes directly onto Vector all you like, and they will remain invisible to instances since they aren’t in the prototype chain. This is kind of backwards from Lua, where you can squirrel stuff away in the metatable.

Another difference is that every single object in JavaScript has a bunch of properties already tacked on — the ones in Object.prototype. Every object (and by “object” I mean any mapping) has a prototype, and that prototype defaults to Object.prototype, and it has a bunch of ancient junk like isPrototypeOf.

(Nit: it’s possible to explicitly create an object with no prototype via Object.create(null).)

Like Lua, and unlike Python, JavaScript doesn’t distinguish between keys found on an object and keys found via a prototype. Properties can be defined on prototypes with Object.defineProperty(), but that works just as well directly on an object, too. JavaScript doesn’t have a lot of operator overloading, but some things like Symbol.iterator also work on both objects and prototypes.

About this

You may, at this point, be wondering what this is. Unlike Lua and Python (and the last language below), this is a special built-in value — a context value, invisibly passed for every function call.

It’s determined by where the function came from. If the function was the result of an attribute lookup, then this is set to the object containing that attribute. Otherwise, this is set to the global object, window. (You can also set this to whatever you want via the call method on functions.)

This decision is made lexically, i.e. from the literal source code as written. There are no Python-style bound methods. In other words:

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// this = obj
obj.method()
// this = window
let meth = obj.method
meth()

Also, because this is reassigned on every function call, it cannot be meaningfully closed over, which makes using closures within methods incredibly annoying. The old approach was to assign this to some other regular name like self (which got syntax highlighting since it’s also a built-in name in browsers); then we got Function.bind, which produced a callable thing with a fixed context value, which was kind of nice; and now finally we have arrow functions, which explicitly close over the current this when they’re defined and don’t change it when called. Phew.

Class syntax

I already showed class syntax, and it’s really just one big macro for doing all the prototype stuff The Right Way. It even prevents you from calling the type without new. The underlying model is exactly the same, and you can inspect all the parts.

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class Vector { ... }

console.log(Vector.prototype);  // { dot: ..., magnitude: ..., ... }
let vec = new Vector(3, 4);
console.log(Object.getPrototypeOf(vec));  // same as Vector.prototype

// i don't know why you would subclass vector but let's roll with it
class Vectest extends Vector { ... }

console.log(Vectest.prototype);  // { ... }
console.log(Object.getPrototypeOf(Vectest.prototype))  // same as Vector.prototype

Alas, class syntax has a couple shortcomings. You can’t use the class block to assign arbitrary data to either the type object or the prototype — apparently it was deemed too confusing that mutations would be shared among instances. Which… is… how prototypes work. How Python works. How JavaScript itself, one of the most popular languages of all time, has worked for twenty-two years. Argh.

You can still do whatever assignment you want outside of the class block, of course. It’s just a little ugly, and not something I’d think to look for with a sugary class.

A more subtle result of this behavior is that a class block isn’t quite the same syntax as an object literal. The check for data isn’t a runtime thing; class Foo { x: 3 } fails to parse. So JavaScript now has two largely but not entirely identical styles of key/value block.

Attribute access

Here’s where things start to come apart at the seams, just a little bit.

JavaScript doesn’t really have an attribute protocol. Instead, it has two… extension points, I suppose.

One is Object.defineProperty, seen above. For common cases, there’s also the get syntax inside a property literal, which does the same thing. But unlike Python’s @property, these aren’t wrappers around some simple primitives; they are the primitives. JavaScript is the only language of these four to have “property that runs code on access” as a completely separate first-class concept.

If you want to intercept arbitrary attribute access (and some kinds of operators), there’s a completely different primitive: the Proxy type. It doesn’t let you intercept attribute access or operators; instead, it produces a wrapper object that supports interception and defers to the wrapped object by default.

It’s cool to see composition used in this way, but also, extremely weird. If you want to make your own type that overloads in or calling, you have to return a Proxy that wraps your own type, rather than actually returning your own type. And (unlike the other three languages in this post) you can’t return a different type from a constructor, so you have to throw that away and produce objects only from a factory. And instanceof would be broken, but you can at least fix that with Symbol.hasInstance — which is really operator overloading, implement yet another completely different way.

I know the design here is a result of legacy and speed — if any object could intercept all attribute access, then all attribute access would be slowed down everywhere. Fair enough. It still leaves the surface area of the language a bit… bumpy?

The JavaScript philosophy

It’s a little hard to tell. The original idea of prototypes was interesting, but it was hidden behind some very awkward syntax. Since then, we’ve gotten a bunch of extra features awkwardly bolted on to reflect the wildly varied things the built-in types and DOM API were already doing. We have class syntax, but it’s been explicitly designed to avoid exposing the prototype parts of the model.

I admit I don’t do a lot of heavy JavaScript, so I might just be overlooking it, but I’ve seen virtually no code that makes use of any of the recent advances in object capabilities. Forget about custom iterators or overloading call; I can’t remember seeing any JavaScript in the wild that even uses properties yet. I don’t know if everyone’s waiting for sufficient browser support, nobody knows about them, or nobody cares.

The model has advanced recently, but I suspect JavaScript is still shackled to its legacy of “something about prototypes, I don’t really get it, just copy the other code that’s there” as an object model. Alas! Prototypes are so good. Hopefully class syntax will make it a bit more accessible, as it has in Python.

Perl 5

Perl 5 also doesn’t have an object system and expects you to build your own. But where Lua gives you two simple, powerful tools for building one, Perl 5 feels more like a puzzle with half the pieces missing. Clearly they were going for something, but they only gave you half of it.

In brief, a Perl object is a reference that has been blessed with a package.

I need to explain a few things. Honestly, one of the biggest problems with the original Perl object setup was how many strange corners and unique jargon you had to understand just to get off the ground.

(If you want to try running any of this code, you should stick a use v5.26; as the first line. Perl is very big on backwards compatibility, so you need to opt into breaking changes, and even the mundane say builtin is behind a feature gate.)

References

A reference in Perl is sort of like a pointer, but its main use is very different. See, Perl has the strange property that its data structures try very hard to spill their contents all over the place. Despite having dedicated syntax for arrays — @foo is an array variable, distinct from the single scalar variable $foo — it’s actually impossible to nest arrays.

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my @foo = (1, 2, 3, 4);
my @bar = (@foo, @foo);
# @bar is now a flat list of eight items: 1, 2, 3, 4, 1, 2, 3, 4

The idea, I guess, is that an array is not one thing. It’s not a container, which happens to hold multiple things; it is multiple things. Anywhere that expects a single value, such as an array element, cannot contain an array, because an array fundamentally is not a single value.

And so we have “references”, which are a form of indirection, but also have the nice property that they’re single values. They add containment around arrays, and in general they make working with most of Perl’s primitive types much more sensible. A reference to a variable can be taken with the \ operator, or you can use [ ... ] and { ... } to directly create references to anonymous arrays or hashes.

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my @foo = (1, 2, 3, 4);
my @bar = (\@foo, \@foo);
# @bar is now a nested list of two items: [1, 2, 3, 4], [1, 2, 3, 4]

(Incidentally, this is the sole reason I initially abandoned Perl for Python. Non-trivial software kinda requires nesting a lot of data structures, so you end up with references everywhere, and the syntax for going back and forth between a reference and its contents is tedious and ugly.)

A Perl object must be a reference. Perl doesn’t care what kind of reference — it’s usually a hash reference, since hashes are a convenient place to store arbitrary properties, but it could just as well be a reference to an array, a scalar, or even a sub (i.e. function) or filehandle.

I’m getting a little ahead of myself. First, the other half: blessing and packages.

Packages and blessing

Perl packages are just namespaces. A package looks like this:

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package Foo::Bar;

sub quux {
    say "hi from quux!";
}

# now Foo::Bar::quux() can be called from anywhere

Nothing shocking, right? It’s just a named container. A lot of the details are kind of weird, like how a package exists in some liminal quasi-value space, but the basic idea is a Bag Of Stuff.

The final piece is “blessing,” which is Perl’s funny name for binding a package to a reference. A very basic class might look like this:

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package Vector;

# the name 'new' is convention, not special
sub new {
    # perl argument passing is weird, don't ask
    my ($class, $x, $y) = @_;

    # create the object itself -- here, unusually, an array reference makes sense
    my $self = [ $x, $y ];

    # associate the package with that reference
    # note that $class here is just the regular string, 'Vector'
    bless $self, $class;

    return $self;
}

sub x {
    my ($self) = @_;
    return $self->[0];
}

sub y {
    my ($self) = @_;
    return $self->[1];
}

sub magnitude {
    my ($self) = @_;
    return sqrt($self->x ** 2 + $self->y ** 2);
}

# switch back to the "default" package
package main;

# -> is method call syntax, which passes the invocant as the first argument;
# for a package, that's just the package name
my $vec = Vector->new(3, 4);
say $vec->magnitude;  # 5

A few things of note here. First, $self->[0] has nothing to do with objects; it’s normal syntax for getting the value of a index 0 out of an array reference called $self. (Most classes are based on hashrefs and would use $self->{value} instead.) A blessed reference is still a reference and can be treated like one.

In general, -> is Perl’s dereferencey operator, but its exact behavior depends on what follows. If it’s followed by brackets, then it’ll apply the brackets to the thing in the reference: ->{} to index a hash reference, ->[] to index an array reference, and ->() to call a function reference.

But if -> is followed by an identifier, then it’s a method call. For packages, that means calling a function in the package and passing the package name as the first argument. For objects — blessed references — that means calling a function in the associated package and passing the object as the first argument.

This is a little weird! A blessed reference is a superposition of two things: its normal reference behavior, and some completely orthogonal object behavior. Also, object behavior has no notion of methods vs data; it only knows about methods. Perl lets you omit parentheses in a lot of places, including when calling a method with no arguments, so $vec->magnitude is really $vec->magnitude().

Perl’s blessing bears some similarities to Lua’s metatables, but ultimately Perl is much closer to Ruby’s “message passing” approach than the above three languages’ approaches of “get me something and maybe it’ll be callable”. (But this is no surprise — Ruby is a spiritual successor to Perl 5.)

All of this leads to one little wrinkle: how do you actually expose data? Above, I had to write x and y methods. Am I supposed to do that for every single attribute on my type?

Yes! But don’t worry, there are third-party modules to help with this incredibly fundamental task. Take Class::Accessor::Fast, so named because it’s faster than Class::Accessor:

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package Foo;
use base qw(Class::Accessor::Fast);
__PACKAGE__->mk_accessors(qw(fred wilma barney));

(__PACKAGE__ is the lexical name of the current package; qw(...) is a list literal that splits its contents on whitespace.)

This assumes you’re using a hashref with keys of the same names as the attributes. $obj->fred will return the fred key from your hashref, and $obj->fred(4) will change it to 4.

You also, somewhat bizarrely, have to inherit from Class::Accessor::Fast. Speaking of which,

Inheritance

Inheritance is done by populating the package-global @ISA array with some number of (string) names of parent packages. Most code instead opts to write use base ...;, which does the same thing. Or, more commonly, use parent ...;, which… also… does the same thing.

Every package implicitly inherits from UNIVERSAL, which can be freely modified by Perl code.

A method can call its superclass method with the SUPER:: pseudo-package:

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sub foo {
    my ($self) = @_;
    $self->SUPER::foo;
}

However, this does a depth-first search, which means it almost certainly does the wrong thing when faced with multiple inheritance. For a while the accepted solution involved a third-party module, but Perl eventually grew an alternative you have to opt into: C3, which may be more familiar to you as the order Python uses.

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use mro 'c3';

sub foo {
    my ($self) = @_;
    $self->next::method;
}

Offhand, I’m not actually sure how next::method works, seeing as it was originally implemented in pure Perl code. I suspect it involves peeking at the caller’s stack frame. If so, then this is a very different style of customizability from e.g. Python — the MRO was never intended to be pluggable, and the use of a special pseudo-package means it isn’t really, but someone was determined enough to make it happen anyway.

Operator overloading and whatnot

Operator overloading looks a little weird, though really it’s pretty standard Perl.

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package MyClass;

use overload '+' => \&_add;

sub _add {
    my ($self, $other, $swap) = @_;
    ...
}

use overload here is a pragma, where “pragma” means “regular-ass module that does some wizardry when imported”.

\&_add is how you get a reference to the _add sub so you can pass it to the overload module. If you just said &_add or _add, that would call it.

And that’s it; you just pass a map of operators to functions to this built-in module. No worry about name clashes or pollution, which is pretty nice. You don’t even have to give references to functions that live in the package, if you don’t want them to clog your namespace; you could put them in another package, or even inline them anonymously.

One especially interesting thing is that Perl lets you overload every operator. Perl has a lot of operators. It considers some math builtins like sqrt and trig functions to be operators, or at least operator-y enough that you can overload them. You can also overload the “file text” operators, such as -e $path to test whether a file exists. You can overload conversions, including implicit conversion to a regex. And most fascinating to me, you can overload dereferencing — that is, the thing Perl does when you say $hashref->{key} to get at the underlying hash. So a single object could pretend to be references of multiple different types, including a subref to implement callability. Neat.

Somewhat related: you can overload basic operators (indexing, etc.) on basic types (not references!) with the tie function, which is designed completely differently and looks for methods with fixed names. Go figure.

You can intercept calls to nonexistent methods by implementing a function called AUTOLOAD, within which the $AUTOLOAD global will contain the name of the method being called. Originally this feature was, I think, intended for loading binary components or large libraries on-the-fly only when needed, hence the name. Offhand I’m not sure I ever saw it used the way __getattr__ is used in Python.

Is there a way to intercept all method calls? I don’t think so, but it is Perl, so I must be forgetting something.

Actually no one does this any more

Like a decade ago, a council of elder sages sat down and put together a whole whizbang system that covers all of it: Moose.

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package Vector;
use Moose;

has x => (is => 'rw', isa => 'Int');
has y => (is => 'rw', isa => 'Int');

sub magnitude {
    my ($self) = @_;
    return sqrt($self->x ** 2 + $self->y ** 2);
}

Moose has its own way to do pretty much everything, and it’s all built on the same primitives. Moose also adds metaclasses, somehow, despite that the underlying model doesn’t actually support them? I’m not entirely sure how they managed that, but I do remember doing some class introspection with Moose and it was much nicer than the built-in way.

(If you’re wondering, the built-in way begins with looking at the hash called %Vector::. No, that’s not a typo.)

I really cannot stress enough just how much stuff Moose does, but I don’t want to delve into it here since Moose itself is not actually the language model.

The Perl philosophy

I hope you can see what I meant with what I first said about Perl, now. It has multiple inheritance with an MRO, but uses the wrong one by default. It has extensive operator overloading, which looks nothing like how inheritance works, and also some of it uses a totally different mechanism with special method names instead. It only understands methods, not data, leaving you to figure out accessors by hand.

There’s 70% of an object system here with a clear general design it was gunning for, but none of the pieces really look anything like each other. It’s weird, in a distinctly Perl way.

The result is certainly flexible, at least! It’s especially cool that you can use whatever kind of reference you want for storage, though even as I say that, I acknowledge it’s no different from simply subclassing list or something in Python. It feels different in Perl, but maybe only because it looks so different.

I haven’t written much Perl in a long time, so I don’t know what the community is like any more. Moose was already ubiquitous when I left, which you’d think would let me say “the community mostly focuses on the stuff Moose can do” — but even a decade ago, Moose could already do far more than I had ever seen done by hand in Perl. It’s always made a big deal out of roles (read: interfaces), for instance, despite that I’d never seen anyone care about them in Perl before Moose came along. Maybe their presence in Moose has made them more popular? Who knows.

Also, I wrote Perl seriously, but in the intervening years I’ve only encountered people who only ever used Perl for one-offs. Maybe it’ll come as a surprise to a lot of readers that Perl has an object model at all.

End

Well, that was fun! I hope any of that made sense.

Special mention goes to Rust, which doesn’t have an object model you can fiddle with at runtime, but does do things a little differently.

It’s been really interesting thinking about how tiny differences make a huge impact on what people do in practice. Take the choice of storage in Perl versus Python. Perl’s massively common URI class uses a string as the storage, nothing else; I haven’t seen anything like that in Python aside from markupsafe, which is specifically designed as a string type. I would guess this is partly because Perl makes you choose — using a hashref is an obvious default, but you have to make that choice one way or the other. In Python (especially 3), inheriting from object and getting dict-based storage is the obvious thing to do; the ability to use another type isn’t quite so obvious, and doing it “right” involves a tiny bit of extra work.

Or, consider that Lua could have descriptors, but the extra bit of work (especially design work) has been enough of an impediment that I’ve never implemented them. I don’t think the object implementations I’ve looked at have included them, either. Super weird!

In that light, it’s only natural that objects would be so strongly associated with the features Java and C++ attach to them. I think that makes it all the more important to play around! Look at what Moose has done. No, really, you should bear in mind my description of how Perl does stuff and flip through the Moose documentation. It’s amazing what they’ve built.

Serverless Automated Cost Controls, Part1

Post Syndicated from Shankar Ramachandran original https://aws.amazon.com/blogs/compute/serverless-automated-cost-controls-part1/

This post courtesy of Shankar Ramachandran, Pubali Sen, and George Mao

In line with AWS’s continual efforts to reduce costs for customers, this series focuses on how customers can build serverless automated cost controls. This post provides an architecture blueprint and a sample implementation to prevent budget overruns.

This solution uses the following AWS products:

  • AWS Budgets – An AWS Cost Management tool that helps customers define and track budgets for AWS costs, and forecast for up to three months.
  • Amazon SNS – An AWS service that makes it easy to set up, operate, and send notifications from the cloud.
  • AWS Lambda – An AWS service that lets you run code without provisioning or managing servers.

You can fine-tune a budget for various parameters, for example filtering by service or tag. The Budgets tool lets you post notifications on an SNS topic. A Lambda function that subscribes to the SNS topic can act on the notification. Any programmatically implementable action can be taken.

The diagram below describes the architecture blueprint.

In this post, we describe how to use this blueprint with AWS Step Functions and IAM to effectively revoke the ability of a user to start new Amazon EC2 instances, after a budget amount is exceeded.

Freedom with guardrails

AWS lets you quickly spin up resources as you need them, deploying hundreds or even thousands of servers in minutes. This means you can quickly develop and roll out new applications. Teams can experiment and innovate more quickly and frequently. If an experiment fails, you can always de-provision those servers without risk.

This improved agility also brings in the need for effective cost controls. Your Finance and Accounting department must budget, monitor, and control the AWS spend. For example, this could be a budget per project. Further, Finance and Accounting must take appropriate actions if the budget for the project has been exceeded, for example. Call it “freedom with guardrails” – where Finance wants to give developers freedom, but with financial constraints.

Architecture

This section describes how to use the blueprint introduced earlier to implement a “freedom with guardrails” solution.

  1. The budget for “Project Beta” is set up in Budgets. In this example, we focus on EC2 usage and identify the instances that belong to this project by filtering on the tag Project with the value Beta. For more information, see Creating a Budget.
  2. The budget configuration also includes settings to send a notification on an SNS topic when the usage exceeds 100% of the budgeted amount. For more information, see Creating an Amazon SNS Topic for Budget Notifications.
  3. The master Lambda function receives the SNS notification.
  4. It triggers execution of a Step Functions state machine with the parameters for completing the configured action.
  5. The action Lambda function is triggered as a task in the state machine. The function interacts with IAM to effectively remove the user’s permissions to create an EC2 instance.

This decoupled modular design allows for extensibility.  New actions (serially or in parallel) can be added by simply adding new steps.

Implementing the solution

All the instructions and code needed to implement the architecture have been posted on the Serverless Automated Cost Controls GitHub repo. We recommend that you try this first in a Dev/Test environment.

This implementation description can be broken down into two parts:

  1. Create a solution stack for serverless automated cost controls.
  2. Verify the solution by testing the EC2 fleet.

To tie this back to the “freedom with guardrails” scenario, the Finance department performs a one-time implementation of the solution stack. To simulate resources for Project Beta, the developers spin up the test EC2 fleet.

Prerequisites

There are two prerequisites:

  • Make sure that you have the necessary IAM permissions. For more information, see the section titled “Required IAM permissions” in the README.
  • Define and activate a cost allocation tag with the key Project. For more information, see Using Cost Allocation Tags. It can take up to 12 hours for the tags to propagate to Budgets.

Create resources

The solution stack includes creating the following resources:

  • Three Lambda functions
  • One Step Functions state machine
  • One SNS topic
  • One IAM group
  • One IAM user
  • IAM policies as needed
  • One budget

Two of the Lambda functions were described in the previous section, to a) receive the SNS notification and b) trigger the Step Functions state machine. Another Lambda function is used to create the budget, as a custom AWS CloudFormation resource. The SNS topic connects Budgets with Lambda function A. Lambda function B is configured as a task in Step Functions. A budget for $2 is created which is filtered by Service: EC2 and Tag: Project, Beta. A test IAM group and user is created to enable you to validate this Cost Control Solution.

To create the serverless automated cost control solution stack, choose the button below. It takes few minutes to spin up the stack. You can monitor the progress in the CloudFormation console.

When you see the CREATE_COMPLETE status for the stack you had created, choose Outputs. Copy the following four values that you need later:

  • TemplateURL
  • UserName
  • SignInURL
  • Password

Verify the stack

The next step is to verify the serverless automated cost controls solution stack that you just created. To do this, spin up an EC2 fleet of t2.micro instances, representative of the resources needed for Project Beta, and tag them with Project, Beta.

  1. Browse to the SignInURL, and log in using the UserName and Password values copied on from the stack output.
  2. In the CloudFormation console, choose Create Stack.
  3. For Choose a template, select Choose an Amazon S3 template URL and paste the TemplateURL value from the preceding section. Choose Next.
  4. Give this stack a name, such as “testEc2FleetForProjectBeta”. Choose Next.
  5. On the Specify Details page, enter parameters such as the UserName and Password copied in the previous section. Choose Next.
  6. Ignore any errors related to listing IAM roles. The test user has a minimal set of permissions that is just sufficient to spin up this test stack (in line with security best practices).
  7. On the Options page, choose Next.
  8. On the Review page, choose Create. It takes a few minutes to spin up the stack, and you can monitor the progress in the CloudFormation console. 
  9. When you see the status “CREATE_COMPLETE”, open the EC2 console to verify that four t2.micro instances have been spun up, with the tag of Project, Beta.

The hourly cost for these instances depends on the region in which they are running. On the average (irrespective of the region), you can expect the aggregate cost for this EC2 fleet to exceed the set $2 budget in 48 hours.

Verify the solution

The first step is to identify the test IAM group that was created in the previous section. The group should have “projectBeta” in the name, prepended with the CloudFormation stack name and appended with an alphanumeric string. Verify that the managed policy associated is: “EC2FullAccess”, which indicates that the users in this group have unrestricted access to EC2.

There are two stages of verification for this serverless automated cost controls solution: simulating a notification and waiting for a breach.

Simulated notification

Because it takes at least a few hours for the aggregate cost of the EC2 fleet to breach the set budget, you can verify the solution by simulating the notification from Budgets.

  1. Log in to the SNS console (using your regular AWS credentials).
  2. Publish a message on the SNS topic that has “budgetNotificationTopic” in the name. The complete name is appended by the CloudFormation stack identifier.  
  3. Copy the following text as the body of the notification: “This is a mock notification”.
  4. Choose Publish.
  5. Open the IAM console to verify that the policy for the test group has been switched to “EC2ReadOnly”. This prevents users in this group from creating new instances.
  6. Verify that the test user created in the previous section cannot spin up new EC2 instances.  You can log in as the test user and try creating a new EC2 instance (via the same CloudFormation stack or the EC2 console). You should get an error message indicating that you do not have the necessary permissions.
  7. If you are proceeding to stage 2 of the verification, then you must switch the permissions back to “EC2FullAccess” for the test group, which can be done in the IAM console.

Automatic notification

Within 48 hours, the aggregate cost of the EC2 fleet spun up in the earlier section breaches the budget rule and triggers an automatic notification. This results in the permissions getting switched out, just as in the simulated notification.

Clean up

Use the following steps to delete your resources and stop incurring costs.

  1. Open the CloudFormation console.
  2. Delete the EC2 fleet by deleting the appropriate stack (for example, delete the stack named “testEc2FleetForProjectBeta”).                                               
  3. Next, delete the “costControlStack” stack.                                                                                                                                                    

Conclusion

Using Lambda in tandem with Budgets, you can build Serverless automated cost controls on AWS. Find all the resources (instructions, code) for implementing the solution discussed in this post on the Serverless Automated Cost Controls GitHub repo.

Stay tuned to this series for more tips about building serverless automated cost controls. In the next post, we discuss using smart lighting to influence developer behavior and describe a solution to encourage cost-aware development practices.

If you have questions or suggestions, please comment below.

 

Build a Flick-controlled marble maze

Post Syndicated from Alex Bate original https://www.raspberrypi.org/blog/flick-marble-maze/

Wiggle your fingers to guide a ball through a 3D-printed marble maze using the Pi Supply Flick board for Raspberry Pi!

Wiggle, wiggle, wiggle, wiggle, yeah

Using the Flick, previously seen in last week’s Hacker House’s gesture-controlled holographic visualiser, South Africa–based Tom Van den Bon has created a touch-free marble maze. He was motivated by, if his Twitter is any indication, his love for game-making and 3D printing.

Tom Van den Bon on Twitter

Day 172 of #3dprint365. #3dprinted Raspberry PI Controlled Maze Thingie Part 3 #3dprint #3dprinter #thingiverse #raspberrypi #pisupply

All non-electronic parts of this build are 3D printed. The marble maze sits atop a motorised structure which moves along two axes thanks to servo motors. Tom controls the movement using gestures which are picked up by the Flick Zero, a Pi Zero–sized 3D-tracking board that can detect movement up to 15cm away.

Find the code for the maze, which takes advantage of the Flick library, on Tom’s GitHub account.

Make your own games

Our free resources are a treasure trove of fun home-brew games that you can build with your friends and family.

If you like physical games such as Tom’s gesture-controlled maze, you should definitely check out our Python quick reaction game! In it, players are pitted against each other to react as quickly as possible to a randomly lighting up LED.

raspberry pi marble maze

You can also play solo with our Lights out game, where it’s you against four erratic lights eager to remain lit.

For games you can build on your computer with no need for any extra tech, Scratch games such as our button-smashing Olympic weightlifter and Hurdler projects are perfect — you can play them just using a keyboard and browser!

raspberry pi marble maze

And if you’d like to really get stuck into learning about game development, then you’re in luck! CoderDojo’s Make your own game book guides you through all the steps of building a game in JavaScript, from creating the world to designing characters.

Cover of CoderDojo Nano Make your own game

And because I just found this while searching for image content for today’s blog, here is a photo of Eben’s and Liz’s cat Mooncake with a Raspberry Pi on her head. Enjoy!

A cat with a Raspberry Pi pin on its head — raspberry pi marble maze

Ras-purry Pi?

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Using taxies to monitor air quality in Peru

Post Syndicated from Alex Bate original https://www.raspberrypi.org/blog/air-quality-peru/

When James Puderer moved to Lima, Peru, his roadside runs left a rather nasty taste in his mouth. Hit by the pollution from old diesel cars in the area, he decided to monitor the air quality in his new city using Raspberry Pis and the abundant taxies as his tech carriers.

Taxi Datalogger – Assembly

How to assemble the enclosure for my Taxi Datalogger project: https://www.hackster.io/james-puderer/distributed-air-quality-monitoring-using-taxis-69647e

Sensing air quality in Lima

Luckily for James, almost all taxies in Lima are equipped with the standard hollow vinyl roof sign seen in the video above, which makes them ideal for hacking.

Using a Raspberry Pi alongside various Adafuit tech including the BME280 Temperature/Humidity/Pressure Sensor and GPS Antenna, James created a battery-powered retrofit setup that fits snugly into the vinyl sign.

The schematic of the air quality monitor tech inside the taxi sign

With the onboard tech, the device collects data on longitude, latitude, humidity, temperature, pressure, and airborne particle count, feeding it back to an Android Things datalogger. This data is then pushed to Google IoT Core, where it can be remotely accessed.

Next, the data is processed by Google Dataflow and turned into a BigQuery table. Users can then visualize the collected measurements. And while James uses Google Maps to analyse his data, there are many tools online that will allow you to organise and study your figures depending on what final result you’re hoping to achieve.

A heat map of James' local area showing air quality

James hopped in a taxi and took his monitor on the road, collecting results throughout the journey

James has provided the complete build process, including all tech ingredients and code, on his Hackster.io project page, and urges makers to create their own air quality monitor for their local area. He also plans on building upon the existing design by adding a 12V power hookup for connecting to the taxi, functioning lights within the sign, and companion apps for drivers.

Sensing the world around you

We’ve seen a wide variety of Raspberry Pi projects using sensors to track the world around us, such as Kasia Molga’s Human Sensor costume series, which reacts to air pollution by lighting up, and Clodagh O’Mahony’s Social Interaction Dress, which she created to judge how conversation and physical human interaction can be scored and studied.

Human Sensor

Kasia Molga’s Human Sensor — a collection of hi-tech costumes that react to air pollution within the wearer’s environment.

Many people also build their own Pi-powered weather stations, or use the Raspberry Pi Oracle Weather Station, to measure and record conditions in their towns and cities from the roofs of schools, offices, and homes.

Have you incorporated sensors into your Raspberry Pi projects? Share your builds in the comments below or via social media by tagging us.

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Gladys Project: a Raspberry Pi home assistant

Post Syndicated from Alex Bate original https://www.raspberrypi.org/blog/gladys-project-home-assistant/

If, like me, you’re a pretty poor time-keeper with the uncanny ability to never get up when your alarm goes off and yet still somehow make it to work just in time — a little dishevelled, brushing your teeth in the office bathroom — then you too need Gladys.

Raspberry Pi home assistant

Over the last year, we’ve seen off-the-shelf home assistants make their way onto the Raspberry Pi. With the likes of Amazon Alexa, Google Home, and Siri, it’s becoming ever easier to tell the air around you to “Turn off the bathroom light” or “Resume my audiobook”, and it happens without you lifting a finger. It’s quite wonderful. And alongside these big names are several home-brew variants, such as Jarvis and Jasper, which were developed to run on a Pi in order to perform home automation tasks.

So do we need another such service? Sure! And here’s why…

A Romantic Mode with your Home Assistant Gladys !

A simple romantic mode in Gladys ! See https://gladysproject.com for more informations about the project 🙂 Devices used : – A 5$ Xiaomi Switch Button – A Raspberry Pi 3 with Gladys on it – Connected lights ( Works with Philips Hue, Milight lamp, etc..

Gladys Project

According to the Gladys creators’ website, Gladys Project is ‘an open-source program which runs on your Raspberry Pi. It communicates with all your devices and checks your calendar to help you in your everyday life’.

Gladys does the basic day-to-day life maintenance tasks that I need handled in order to exist without my mum there to remind me to wake up in time for work. And, as you can see from the video above, it also plays some mean George Michael.

A screenshot of a mobile phone showing the Gladys app - Gladys Project home assistant

Gladys can help run your day from start to finish, taking into consideration road conditions and travel time to ensure you’re never late, regardless of external influences. It takes you 30 minutes to get ready and another 30 minutes to drive to work for 9.00? OK, but today there’s a queue on the motorway, and now your drive time is looking to be closer to an hour. Thankfully, Gladys has woken you up a half hour earlier, so you’re still on time. Isn’t that nice of her? And while you’re showering and mourning those precious stolen minutes of sleep, she’s opening the blinds and brewing coffee for you. Thanks, mum!

A screenshot of the Gladys hub on the Raspberry Pi - Gladys Project home assistant

Set the parameters of your home(s) using the dedicated hub.

Detecting your return home at the end of the day, Gladys runs your pre-set evening routine. Then, once you place your phone on an NFC tag to indicate bedtime, she turns off the lights and, if your nighttime preferences dictate it, starts the whale music playlist, sending you into a deep, stressless slumber.

A screenshot of Etcher showing the install process of the Gladys image - Gladys Project home assistant

Gladys comes as a pre-built Raspbian image, ready to be cloned to an SD card.

Gladys is free to download from the Gladys Project website and is compatible with smart devices such as Philips Hue lightbulbs, WeMo Insight Switches, and the ever tricky to control without the official app Sonos speakers!

Automate and chill

Which tasks and devices in your home do you control with a home assistant? Do you love sensor-controlled lighting which helps you save on electricity? How about working your way through an audiobook as you do your housework, requesting a pause every time you turn on the vacuum cleaner?

Share your experiences with us in the comments below, and if you’ve built a home assistant for Raspberry Pi, or use an existing setup to run your household, share that too.

And, as ever, if you want to keep up to date with Raspberry Pi projects from across the globe, be sure to follow us on social media, sign up to our weekly newsletter, the Raspberry Pi Weekly, and check out The MagPi, the official magazine of the Raspberry Pi community, available in stores or as a free PDF download.

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Backing Up Linux to Backblaze B2 with Duplicity and Restic

Post Syndicated from Roderick Bauer original https://www.backblaze.com/blog/backing-linux-backblaze-b2-duplicity-restic/

Linux users have a variety of options for handling data backup. The choices range from free and open-source programs to paid commercial tools, and include applications that are purely command-line based (CLI) and others that have a graphical interface (GUI), or both.

If you take a look at our Backblaze B2 Cloud Storage Integrations page, you will see a number of offerings that enable you to back up your Linux desktops and servers to Backblaze B2. These include CloudBerry, Duplicity, Duplicacy, 45 Drives, GoodSync, HashBackup, QNAP, Restic, and Rclone, plus other choices for NAS and hybrid uses.

In this post, we’ll discuss two popular command line and open-source programs: one older, Duplicity, and a new player, Restic.

Old School vs. New School

We’re highlighting Duplicity and Restic today because they exemplify two different philosophical approaches to data backup: “Old School” (Duplicity) vs “New School” (Restic).

Old School (Duplicity)

In the old school model, data is written sequentially to the storage medium. Once a section of data is recorded, new data is written starting where that section of data ends. It’s not possible to go back and change the data that’s already been written.

This old-school model has long been associated with the use of magnetic tape, a prime example of which is the LTO (Linear Tape-Open) standard. In this “write once” model, files are always appended to the end of the tape. If a file is modified and overwritten or removed from the volume, the associated tape blocks used are not freed up: they are simply marked as unavailable, and the used volume capacity is not recovered. Data is deleted and capacity recovered only if the whole tape is reformatted. As a Linux/Unix user, you undoubtedly are familiar with the TAR archive format, which is an acronym for Tape ARchive. TAR has been around since 1979 and was originally developed to write data to sequential I/O devices with no file system of their own.

It is from the use of tape that we get the full backup/incremental backup approach to backups. A backup sequence beings with a full backup of data. Each incremental backup contains what’s been changed since the last full backup until the next full backup is made and the process starts over, filling more and more tape or whatever medium is being used.

This is the model used by Duplicity: full and incremental backups. Duplicity backs up files by producing encrypted, digitally signed, versioned, TAR-format volumes and uploading them to a remote location, including Backblaze B2 Cloud Storage. Released under the terms of the GNU General Public License (GPL), Duplicity is free software.

With Duplicity, the first archive is a complete (full) backup, and subsequent (incremental) backups only add differences from the latest full or incremental backup. Chains consisting of a full backup and a series of incremental backups can be recovered to the point in time that any of the incremental steps were taken. If any of the incremental backups are missing, then reconstructing a complete and current backup is much more difficult and sometimes impossible.

Duplicity is available under many Unix-like operating systems (such as Linux, BSD, and Mac OS X) and ships with many popular Linux distributions including Ubuntu, Debian, and Fedora. It also can be used with Windows under Cygwin.

We recently published a KB article on How to configure Backblaze B2 with Duplicity on Linux that demonstrates how to set up Duplicity with B2 and back up and restore a directory from Linux.

New School (Restic)

With the arrival of non-sequential storage medium, such as disk drives, and new ideas such as deduplication, comes the new school approach, which is used by Restic. Data can be written and changed anywhere on the storage medium. This efficiency comes largely through the use of deduplication. Deduplication is a process that eliminates redundant copies of data and reduces storage overhead. Data deduplication techniques ensure that only one unique instance of data is retained on storage media, greatly increasing storage efficiency and flexibility.

Restic is a recently available multi-platform command line backup software program that is designed to be fast, efficient, and secure. Restic supports a variety of backends for storing backups, including a local server, SFTP server, HTTP Rest server, and a number of cloud storage providers, including Backblaze B2.

Files are uploaded to a B2 bucket as deduplicated, encrypted chunks. Each time a backup runs, only changed data is backed up. On each backup run, a snapshot is created enabling restores to a specific date or time.

Restic assumes that the storage location for repository is shared, so it always encrypts the backed up data. This is in addition to any encryption and security from the storage provider.

Restic is open source and free software and licensed under the BSD 2-Clause License and actively developed on GitHub.

There’s a lot more you can do with Restic, including adding tags, mounting a repository locally, and scripting. To learn more, you can review the documentation at https://restic.readthedocs.io.

Coincidentally with this blog post, we published a KB article, How to configure Backblaze B2 with Restic on Linux, in which we show how to set up Restic for use with B2 and how to back up and restore a home directory from Linux to B2.

Which is Right for You?

While Duplicity is a popular, widely-available, and useful program, many users of cloud storage solutions such as B2 are moving to new-school solutions like Restic that take better advantage of the non-sequential access capabilities and speed of modern storage media used by cloud storage providers.

Tell us how you’re backing up Linux

Please let us know in the comments what you’re using for Linux backups, and if you have experience using Duplicity, Restic, or other backup software with Backblaze B2.

The post Backing Up Linux to Backblaze B2 with Duplicity and Restic appeared first on Backblaze Blog | Cloud Storage & Cloud Backup.

Sean Hodgins’ Haunted Jack in the Box

Post Syndicated from Janina Ander original https://www.raspberrypi.org/blog/sean-hodgins-haunted-jack-box/

After making a delightful Bitcoin lottery using a Raspberry Pi, Sean Hodgins brings us more Pi-powered goodness in time for every maker’s favourite holiday: Easter! Just kidding, it’s Halloween. Check out his hair-raising new build, the Haunted Jack in the Box.

Haunted Jack in the Box – DIY Raspberry Pi Project

This project uses a raspberry pi and face detection using the pi camera to determine when someone is looking at it. Plenty of opportunities to scare people with it. You can make your own!

Haunted jack-in-the-box?

Imagine yourself wandering around a dimly lit house. Your eyes idly scan a shelf. Suddenly, out of nowhere, a twangy melody! What was that? You take a closer look…there seems to be a box in jolly colours…with a handle that’s spinning by itself?!

Sidling up to Sean Hodgins' Haunted Jack in the Box

What’s…going on?

You freeze, unable to peel your eyes away, and BAM!, out pops a maniacally grinning clown. You promptly pee yourself. Happy Halloween, courtesy of Sean Hodgins.

Clip of Sean Hodgins' Haunted Jack in the Box

Eerie disembodied voice: You’re welco-o-o-ome!

How has Sean built this?

Sean purchased a jack-in-the-box toy and replaced its bottom side with one that would hold the necessary electronic components. He 3D-printed this part, but says you could also just build it by hand.

The bottom of the box houses a Raspberry Pi 3 Model B and a servomotor which can turn the windup handle. There’s also a magnetic reed switch which helps the Pi decide when to trigger the Jack. Sean hooked up the components to the Pi’s GPIO pins, and used an elastic band as a drive belt to connect the pulleys on the motor and the handle.

Film clip showing the inside of Sean Hodgin's Haunted Jack in the Box

Sean explains that he has used a lot of double-sided tape and superglue in this build. The bottom and top are held together with two screws, because, as he describes it, “the Jack coming out is a little violent.”

In addition to his video walk-through, he provides build instructions on Instructables, Hackaday, Hackster, and Imgur — pick your poison. And be sure to subscribe to Sean’s YouTube channel to see what he comes up with next.

Wait, how does the haunted part work?

But if I explain it, it won’t be scary anymore! OK, fiiiine.

With the help of a a Camera Module and OpenCV, Sean implemented facial recognition: Jack knows when someone is looking at his box, and responds by winding up and popping out.

View of command line output of the Python script for Sean Hodgins' Haunted Jack in the Box

Testing the haunting script

Sean’s Python script is available here, but as he points out, there are many ways in which you could adapt this code, and the build itself, to be even more frightening.

So very haunted

What would you do with this build? Add creepy laughter? Soundbites from It? Lighting effects? Maybe even infrared light and a NoIR Camera Module, so that you can scare people in total darkness? There are so many possibilities for this project — tell us your idea in the comments.

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The CoderDojo Girls Initiative

Post Syndicated from Nuala McHale original https://www.raspberrypi.org/blog/coderdojo-girls-initiative/

In March, the CoderDojo Foundation launched their Girls Initiative, which aims to increase the average proportion of girls attending CoderDojo clubs from 29% to at least 40% over the next three years.

The CoderDojo Girls Initiative

Six months on, we wanted to highlight what we’ve done so far and what’s next for our initiative.

What we’ve done so far

To date, we have focussed our efforts on four key areas:

  • Developing and improving content
  • Conducting and learning from research
  • Highlighting role models
  • Developing a guide of tried and tested best practices for encouraging and sustaining girls in a Dojo setting (Empowering the Future)

Content

We’ve taken measures to ensure our resources are as friendly to girls as well as boys, and we are improving them based on feedback from girls. For example, we have developed beginner-level content (Sushi Cards) for working with wearables and for building apps using App Inventor. In response to girls’ feedback, we are exploring more creative goal-orientated content.

The CoderDojo Girls Initiative

Moreover, as part of our Empowering the Future guide, we have developed three short ‘Mini-Sushi’ projects which provide a taster of different programming languages, such as Scratch, HTML, and App Inventor.

What’s next?

We are currently finalising our intermediate-level wearables Sushi Cards. These are resources for learners to further explore wearables and integrate them with other coding skills they are developing. The Cards will enable young people to program LEDs which can be sewn into clothing with conductive thread. We are also planning another series of Sushi Cards focused on using coding skills to solve problems Ninjas have reported as important to them.

Research

In June 2017 we conducted the first Ninja survey. It was sent to all young people registered on the CoderDojo community platform, Zen. Hundreds of young people involved in Dojos around the world responded and shared their experiences.

The CoderDojo Girls Initiative

We are currently examining these results to identify areas in which girls feel most or least confident, as well as the motivations and influencing factors that cause them to continue with coding.

What’s next?

Over the coming months we will delve deeper into the findings of this research, and decide how we can improve our content and Dojo support to adapt accordingly. Additionally, as part of sending out our Empowering the Future guide, we’re asking Dojos to provide insights into their current proportions of girls and female Mentors.

The CoderDojo Girls Initiative

We will follow up with recipients of the guide to document the impact of the recommended approaches they try at their Dojo. Thus, we will find out which approaches are most effective in different regional contexts, which will help us improve our support for Dojos wanting to increase their proportion of attending girls.

Role models

Many Dojos, Champions, and Mentors are doing amazing work to support and encourage girls at their Dojos. Female Mentors not only help by supporting attending girls, but they also act as vital role models in an environment which is often male-dominated. Blogs by female Mentors and Ninjas which have already featured on our website include:

What’s next?

We recognise the importance of female role models, and over the coming months we will continue to encourage community members to share their stories so that we bring them to the wider CoderDojo community. Do you know a female Mentor or Ninja you would like to shine a spotline on? Get in touch with us at [email protected] You can also use #CoderDojoGirls on social media.

The CoderDojo Girls Initiative

Empowering the Future guide

Ahead of Ada Lovelace Day and International Day of the Girl Child, the CoderDojo Foundation has released Empowering the Future, a comprehensive guide of practical approaches which Dojos have tested to engage and sustain girls.

Some topics covered in the guide are:

  • Approaches to improve the Dojo environment and layout
  • Language and images used to describe and promote Dojos
  • Content considerations, and suggested resources
  • The importance of female Mentors, and ways to increase access to role models

For the next month, Dojos that want to improve their proportion of girls can still sign up to have the guide book sent to them for free! From today, Dojos and anyone else can also download a PDF file of the guide.

The CoderDojo Girls Initiative

We would like to say a massive thank you to all community members who have shared their insights with us to make our Empowering the Future guide as comprehensive and beneficial as possible for other Dojos.

Tell us what you think

Have you found an approach, or used content, which girls find particularly engaging? Do you have questions about our Girls Initiative? We would love to hear your ideas, insights, and experiences in relation to supporting CoderDojo girls! Feel free to use our forums to share with the global CoderDojo community, and email us at [email protected]

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Grafana 4.5 Released

Post Syndicated from Blogs on Grafana Labs Blog original https://grafana.com/blog/2017/09/13/grafana-4.5-released/

Grafana v4.5 is now available for download. This release has some really significant improvements to Prometheus, Elasticsearch, MySQL and to the Table panel.

Prometheus Query Editor

The new query editor has full syntax highlighting. As well as auto complete for metrics, functions, and range vectors. There is also integrated function docs right from the query editor!

Elasticsearch: Add ad-hoc filters from the table panel

Create column styles that turn cells into links that use the value in the cell (or other other row values) to generate a url to another dashboard or system. Useful for
using the table panel as way to drilldown into dashboard with more detail or to ticket system for example.

Query Inspector

Query Inspector is a new feature that shows query requests and responses. This can be helpful if a graph is not shown or shows something very different than what you expected.
More information here.

Changelog

New Features

  • Table panel: Render cell values as links that can have an url template that uses variables from current table row. #3754
  • Elasticsearch: Add ad hoc filters directly by clicking values in table panel #8052.
  • MySQL: New rich query editor with syntax highlighting
  • Prometheus: New rich query editor with syntax highlighting, metric & range auto complete and integrated function docs. #5117

Enhancements

  • GitHub OAuth: Support for GitHub organizations with 100+ teams. #8846, thx @skwashd
  • Graphite: Calls to Graphite api /metrics/find now include panel or dashboad time range (from & until) in most cases, #8055
  • Graphite: Added new graphite 1.0 functions, available if you set version to 1.0.x in data source settings. New Functions: mapSeries, reduceSeries, isNonNull, groupByNodes, offsetToZero, grep, weightedAverage, removeEmptySeries, aggregateLine, averageOutsidePercentile, delay, exponentialMovingAverage, fallbackSeries, integralByInterval, interpolate, invert, linearRegression, movingMin, movingMax, movingSum, multiplySeriesWithWildcards, pow, powSeries, removeBetweenPercentile, squareRoot, timeSlice, closes #8261
  • Elasticsearch: Ad-hoc filters now use query phrase match filters instead of term filters, works on non keyword/raw fields #9095.

Breaking change

  • InfluxDB/Elasticsearch: The panel & data source option named “Group by time interval” is now named “Min time interval” and does now always define a lower limit for the auto group by time. Without having to use > prefix (that prefix still works). This should in theory have close to zero actual impact on existing dashboards. It does mean that if you used this setting to define a hard group by time interval of, say “1d”, if you zoomed to a time range wide enough the time range could increase above the “1d” range as the setting is now always considered a lower limit.

This option is now rennamed (and moved to Options sub section above your queries):
image|519x120

Datas source selection & options & help are now above your metric queries.
image|690x179

Minor Changes

  • InfluxDB: Change time range filter for absolute time ranges to be inclusive instead of exclusive #8319, thx @Oxydros
  • InfluxDB: Added paranthesis around tag filters in queries #9131

Bug Fixes

  • Modals: Maintain scroll position after opening/leaving modal #8800
  • Templating: You cannot select data source variables as data source for other template variables #7510
  • Security: Security fix for api vulnerability (in multiple org setups).

Download

Head to the v4.5 download page for download links & instructions.

Thanks

A big thanks to all the Grafana users who contribute by submitting PRs, bug reports, helping out on our community site and providing feedback!