# Fix slow Nintendo Switch play with your Raspberry Pi

Post Syndicated from Ashley Whittaker original https://www.raspberrypi.org/blog/fix-slow-nintendo-switch-play-with-your-raspberry-pi/

Is your Nintendo Switch behaving more like a Nintendon’t due to poor connectivity? Well, TopSpec (hosted Chris Barlas) has shared a brilliant Raspberry Pi-powered hack on YouTube to help you fix that.

## Here’s the problem…

When you play Switch online, the servers are peer-to-peer. The Switches decide which Switch’s internet connection is more stable, and that player becomes the host.

However, some users have found that poor internet performance causes game play to lag. Why? It’s to do with the way data is shared between the Switches, as ‘packets’.

## What are packets?

Think of it like this: 200 postcards will fit through your letterbox a few at a time, but one big file wrapped as a parcel won’t. Even though it’s only one, it’s too big to fit. So instead, you could receive all the postcards through the letterbox and stitch them together once they’ve been delivered.

Similarly, a packet is a small unit of data sent over a network, and packets are reassembled into a whole file, or some other chunk of related data, by the computer that receives them.

Problems arise if any of the packets containing your Switch game’s data go missing, or arrive late. This will cause the game to pause.

#### Fix Nintendo Switch Online Lag with a Raspberry Pi! (Ethernet Bridge)

Want to increase the slow internet speed of your Nintendo Switch? Having lag in games like Smash, Mario Maker, and more? Well, we decided to try out a really…

Chris explains that games like Call of Duty have code built in to mitigate the problems around this, but that it seems to be missing from a lot of Switch titles.

## How can Raspberry Pi help?

The advantage of using Raspberry Pi is that it can handle wireless networking more reliably than Nintendo Switch on its own. Bring the two devices together using a LAN adapter, and you’ve got a perfect pairing. Chris reports speeds up to three times faster using this hack.

A Nintendo Switch > LAN adaptor > Raspberry Pi

He ran a download speed test using a Nintendo Switch by itself, and then using a Nintendo Switch with a LAN adapter plugged into a Raspberry Pi. He found the Switch connected to the Raspberry Pi was quicker than the Switch on its own.

At 02mins 50secs of Chris’ video, he walks through the steps you’ll need to take to get similar results.

We’ve handily linked to some of the things Chris mentions here:

To test his creation, Chris ran a speed test downloading a 10GB game, Pokémon Shield, using three different connection solutions. The Raspberry Pi hack came out “way ahead” of the wireless connection relying on the Switch alone. Of course, plugging your Switch directly into your internet router would get the fastest results of all, but routers have a habit of being miles away from where you want to sit and play.

Have a look at TopSpec on YouTube for more great videos.

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# MagPi 70: Home automation with Raspberry Pi

Post Syndicated from Rob Zwetsloot original https://www.raspberrypi.org/blog/magpi-70-home-automation/

Hey folks, Rob here! It’s the last Thursday of the month, and that means it’s time for a brand-new The MagPi. Issue 70 is all about home automation using your favourite microcomputer, the Raspberry Pi.

Home automation in this month’s The MagPi!

## Raspberry Pi home automation

We think home automation is an excellent use of the Raspberry Pi, hiding it around your house and letting it power your lights and doorbells and…fish tanks? We show you how to do all of that, and give you some excellent tips on how to add even more automation to your home in our ten-page cover feature.

Our other big feature this issue covers upcycling, the hot trend of taking old electronics and making them better than new with some custom code and a tactically placed Raspberry Pi. For this feature, we had a chat with Martin Mander, upcycler extraordinaire, to find out his top tips for hacking your old hardware.

Upcycling is a lot of fun

## But wait, there’s more!

If for some reason you want even more content, you’re in luck! We have some fun tutorials for you to try, like creating a theremin and turning a Babbage into an IoT nanny cam. We also continue our quest to make a video game in C++. Our project showcase is headlined by the Teslonda on page 28, a Honda/Tesla car hybrid that is just wonderful.

We review PiBorg’s latest robot

All this comes with our definitive reviews and the community section where we celebrate you, our amazing community! You’re all good beans

An amazing, and practical, Raspberry Pi project

## Get The MagPi 70

Issue 70 is available today from WHSmith, Tesco, Sainsbury’s, and Asda. If you live in the US, head over to your local Barnes & Noble or Micro Center in the next few days for a print copy. You can also get the new issue online from our store, or digitally via our Android and iOS apps. And don’t forget, there’s always the free PDF as well.

## New subscription offer!

Want to support the Raspberry Pi Foundation and the magazine? We’ve launched a new way to subscribe to the print version of The MagPi: you can now take out a monthly £4 subscription to the magazine, effectively creating a rolling pre-order system that saves you money on each issue.

You can also take out a twelve-month print subscription and get a Pi Zero W plus case and adapter cables absolutely free! This offer does not currently have an end date.

That’s it for today! See you next month.

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# Amazon Neptune Generally Available

Post Syndicated from Randall Hunt original https://aws.amazon.com/blogs/aws/amazon-neptune-generally-available/

Amazon Neptune is now Generally Available in US East (N. Virginia), US East (Ohio), US West (Oregon), and EU (Ireland). Amazon Neptune is a fast, reliable, fully-managed graph database service that makes it easy to build and run applications that work with highly connected datasets. At the core of Neptune is a purpose-built, high-performance graph database engine optimized for storing billions of relationships and querying the graph with millisecond latencies. Neptune supports two popular graph models, Property Graph and RDF, through Apache TinkerPop Gremlin and SPARQL, allowing you to easily build queries that efficiently navigate highly connected datasets. Neptune can be used to power everything from recommendation engines and knowledge graphs to drug discovery and network security. Neptune is fully-managed with automatic minor version upgrades, backups, encryption, and fail-over. I wrote about Neptune in detail for AWS re:Invent last year and customers have been using the preview and providing great feedback that the team has used to prepare the service for GA.

Now that Amazon Neptune is generally available there are a few changes from the preview:

### Launching an Amazon Neptune Cluster

Launching a Neptune cluster is as easy as navigating to the AWS Management Console and clicking create cluster. Of course you can also launch with CloudFormation, the CLI, or the SDKs.

You can monitor your cluster health and the health of individual instances through Amazon CloudWatch and the console.

We’ve created two repos with some additional tools and examples here. You can expect continuous development on these repos as we add additional tools and examples.

• Amazon Neptune Tools Repo
This repo has a useful tool for converting GraphML files into Neptune compatible CSVs for bulk loading from S3.
• Amazon Neptune Samples Repo
This repo has a really cool example of building a collaborative filtering recommendation engine for video game preferences.

### Purpose Built Databases

There’s an industry trend where we’re moving more and more onto purpose-built databases. Developers and businesses want to access their data in the format that makes the most sense for their applications. As cloud resources make transforming large datasets easier with tools like AWS Glue, we have a lot more options than we used to for accessing our data. With tools like Amazon Redshift, Amazon Athena, Amazon Aurora, Amazon DynamoDB, and more we get to choose the best database for the job or even enable entirely new use-cases. Amazon Neptune is perfect for workloads where the data is highly connected across data rich edges.

I’m really excited about graph databases and I see a huge number of applications. Looking for ideas of cool things to build? I’d love to build a web crawler in AWS Lambda that uses Neptune as the backing store. You could further enrich it by running Amazon Comprehend or Amazon Rekognition on the text and images found and creating a search engine on top of Neptune.

As always, feel free to reach out in the comments or on twitter to provide any feedback!

# Welcome Jack — Data Center Tech

Post Syndicated from Yev original https://www.backblaze.com/blog/welcome-jack-data-center-tech/

As we shoot way past 500 petabytes of data stored, we need a lot of helping hands in the data center to keep those hard drives spinning! We’ve been hiring quite a lot, and our latest addition is Jack. Lets learn a bit more about him, shall we?

Data Center Tech

Where are you originally from?
Walnut Creek, CA until 7th grade when the family moved to Durango, Colorado.

What attracted you to Backblaze?
I had heard about how cool the Backblaze community is and have always been fascinated by technology.

What do you expect to learn while being at Backblaze?
I expect to learn a lot about how our data centers run and all of the hardware behind it.

Where else have you worked?
Garrhs HVAC as an HVAC Installer and then Durango Electrical as a Low Volt Technician.

Where did you go to school?
Durango High School and then Montana State University.

I would love to be a driver for the Audi Sport. Race cars are so much fun!

Favorite place you’ve traveled?
Iceland has definitely been my favorite so far.

Favorite hobby?
Video games.

Of what achievement are you most proud?
Getting my Eagle Scout badge was a tough, but rewarding experience that I will always cherish.

Star Trek or Star Wars?
Star Wars.

Coke or Pepsi?

Favorite food?
Thai food.

Why do you like certain things?
I tend to warm up to things the more time I spend around them, although I never really know until it happens.

Anything else you’d like to tell us?
I’m a friendly car guy who will always be in love with my European cars and I really enjoy the Backblaze community!

We’re happy you joined us Out West! Welcome aboard Jack!

The post Welcome Jack — Data Center Tech appeared first on Backblaze Blog | Cloud Storage & Cloud Backup.

# Raspberry Jam Cameroon #PiParty

Post Syndicated from Ben Nuttall original https://www.raspberrypi.org/blog/raspberry-jam-cameroon-piparty/

Earlier this year on 3 and 4 March, communities around the world held Raspberry Jam events to celebrate Raspberry Pi’s sixth birthday. We sent out special birthday kits to participating Jams — it was amazing to know the kits would end up in the hands of people in parts of the world very far from Raspberry Pi HQ in Cambridge, UK.

The Raspberry Jam Camer team: Damien Doumer, Eyong Etta, Loïc Dessap and Lionel Sichom, aka Lionel Tellem

## Preparing for the #PiParty

One birthday kit went to Yaoundé, the capital of Cameroon. There, a team of four students in their twenties — Lionel Sichom (aka Lionel Tellem), Eyong Etta, Loïc Dessap, and Damien Doumer — were organising Yaoundé’s first Jam, called Raspberry Jam Camer, as part of the Raspberry Jam Big Birthday Weekend. The team knew one another through their shared interests and skills in electronics, robotics, and programming. Damien explains in his blog post about the Jam that they planned ahead for several activities for the Jam based on their own projects, so they could be confident of having a few things that would definitely be successful for attendees to do and see.

## Show-and-tell at Raspberry Jam Cameroon

Loïc presented a Raspberry Pi–based, Android app–controlled robot arm that he had built, and Lionel coded a small video game using Scratch on Raspberry Pi while the audience watched. Damien demonstrated the possibilities of Windows 10 IoT Core on Raspberry Pi, showing how to install it, how to use it remotely, and what you can do with it, including building a simple application.

Loïc showcases the prototype robot arm he built

There was lots more too, with others discussing their own Pi projects and talking about the possibilities Raspberry Pi offers, including a Pi-controlled drone and car. Cake was a prevailing theme of the Raspberry Jam Big Birthday Weekend around the world, and Raspberry Jam Camer made sure they didn’t miss out.

Yay, birthday cake!!

## A big success

Most visitors to the Jam were secondary school students, while others were university students and graduates. The majority were unfamiliar with Raspberry Pi, but all wanted to learn about Raspberry Pi and what they could do with it. Damien comments that the fact most people were new to Raspberry Pi made the event more interactive rather than creating any challenges, because the visitors were all interested in finding out about the little computer. The Jam was an all-round success, and the team was pleased with how it went:

What I liked the most was that we sensitized several people about the Raspberry Pi and what one can be capable of with such a small but powerful device. — Damien Doumer

The Jam team rounded off the event by announcing that this was the start of a Raspberry Pi community in Yaoundé. They hope that they and others will be able to organise more Jams and similar events in the area to spread the word about what people can do with Raspberry Pi, and to help them realise their ideas.

Raspberry Jam Camer gets the thumbs-up

## The Raspberry Pi community in Cameroon

In a French-language interview about their Jam, the team behind Raspberry Jam Camer said they’d like programming to become the third official language of Cameroon, after French and English; their aim is to to popularise programming and digital making across Cameroonian society. Neither of these fields is very familiar to most people in Cameroon, but both are very well aligned with the country’s ambitions for development. The team is conscious of the difficulties around the emergence of information and communication technologies in the Cameroonian context; in response, they are seizing the opportunities Raspberry Pi offers to give children and young people access to modern and constantly evolving technology at low cost.

Thanks to Lionel, Eyong, Damien, and Loïc, and to everyone who helped put on a Jam for the Big Birthday Weekend! Remember, anyone can start a Jam at any time — and we provide plenty of resources to get you started. Check out the Guidebook, the Jam branding pack, our specially-made Jam activities online (in multiple languages), printable worksheets, and more.

The post Raspberry Jam Cameroon #PiParty appeared first on Raspberry Pi.

# Brutus 2: the gaming PC case of your dreams

Post Syndicated from Janina Ander original https://www.raspberrypi.org/blog/brutus-2-gaming-pc-case/

Attention, case modders: take a look at the Brutus 2, an extremely snazzy computer case with a partly transparent, animated side panel that’s powered by a Pi. Daniel Otto and Carsten Lehman have a current crowdfunder for the case; their video is in German, but the looks of the build speak for themselves. There are some truly gorgeous effects here.

#### der BRUTUS 2 by 3nb Gaming

Vorbestellungen ab sofort auf https://www.startnext.com/brutus2 Weitere Infos zu uns auf: https://3nb.de https://www.facebook.com/3nb.de https://www.instagram.com/3nb.de Über 3nb: – GbR aus Leipzig, gegründet 2017 – wir kommen aus den Bereichen Elektronik und Informatik – erstes Produkt: der Brutus One ein Gaming PC mit transparentem Display in der Seite Kurzinfo Brutus 2: – Markencomputergehäuse für Gaming- /Casemoddingszene – Besonderheit: animiertes Seitenfenster angesteuert mit einem Raspberry Pi – Vorteile von unserem Case: o Case ist einzeln lieferbar und nicht nur als komplett-PC o kein Leistungsverbrauch der Grafikkarte dank integriertem Raspberry Pi o bessere Darstellung von Texten und Grafiken durch unscharfen Hintergrund

## What’s case modding?

Case modding just means modifying your computer or gaming console’s case, and it’s very popular in the gaming community. Some mods are functional, while others improve the way the case looks. Lots of dedicated gamers don’t only want a powerful computer, they also want it to look amazing — at home, or at LAN parties and games tournaments.

## The Brutus 2 case

The Brutus 2 case is made by Daniel and Carsten’s startup, 3nb electronics, and it’s a product that is officially Powered by Raspberry Pi. Its standout feature is the semi-transparent TFT screen, which lets you play any video clip you choose while keeping your gaming hardware on display. It looks incredibly cool. All the graphics for the case’s screen are handled by a Raspberry Pi, so it doesn’t use any of your main PC’s GPU power and your gaming won’t suffer.

## The software

To use Brutus 2, you just need to run a small desktop application on your PC to choose what you want to display on the case. A number of neat animations are included, and you can upload your own if you want.

So far, the app only runs on Windows, but 3nb electronics are planning to make the code open-source, so you can modify it for other operating systems, or to display other file types. This is true to the spirit of the case modding and Raspberry Pi communities, who love adapting, retrofitting, and overhauling projects and code to fit their needs.

Daniel and Carsten say that one of their campaign’s stretch goals is to implement more functionality in the Brutus 2 app. So in the future, the case could also show things like CPU temperature, gaming stats, and in-game messages. Of course, there’s nothing stopping you from integrating features like that yourself.

If you have any questions about the case, you can post them directly to Daniel and Carsten here.

## The crowdfunding campaign

The Brutus 2 campaign on Startnext is currently halfway to its first funding goal of €10000, with over three weeks to go until it closes. If you’re quick, you still be may be able to snatch one of the early-bird offers. And if your whole guild NEEDS this, that’s OK — there are discounts for bulk orders.

The post Brutus 2: the gaming PC case of your dreams appeared first on Raspberry Pi.

# Own your own working Pokémon Pokédex!

Post Syndicated from Alex Bate original https://www.raspberrypi.org/blog/deep-learning-pokedex/

Squeal with delight as your inner Pokémon trainer witnesses the wonder of Adrian Rosebrock’s deep learning Pokédex.

#### Creating a real-life Pokedex with a Raspberry Pi, Python, and Deep Learning

This video demos a real-like Pokedex, complete with visual recognition, that I created using a Raspberry Pi, Python, and Deep Learning. You can find the entire blog post, including code, using this link: https://www.pyimagesearch.com/2018/04/30/a-fun-hands-on-deep-learning-project-for-beginners-students-and-hobbyists/ Music credit to YouTube user “No Copyright” for providing royalty free music: https://www.youtube.com/watch?v=PXpjqURczn8

## The history of Pokémon in 30 seconds

The Pokémon franchise was created by video game designer Satoshi Tajiri in 1995. In the fictional world of Pokémon, Pokémon Trainers explore the vast landscape, catching and training small creatures called Pokémon. To date, there are 802 different types of Pokémon. They range from the ever recognisable Pikachu, a bright yellow electric Pokémon, to the highly sought-after Shiny Charizard, a metallic, playing-card-shaped Pokémon that your mate Alex claims she has in mint condition, but refuses to show you.

In the world of Pokémon, children as young as ten-year-old protagonist and all-round annoyance Ash Ketchum are allowed to leave home and wander the wilderness. There, they hunt vicious, deadly creatures in the hope of becoming a Pokémon Master.

Adrian is a bit of a deep learning pro, as demonstrated by his Santa/Not Santa detector, which we wrote about last year. For that project, he also provided a great explanation of what deep learning actually is. In a nutshell:

…a subfield of machine learning, which is, in turn, a subfield of artificial intelligence (AI).While AI embodies a large, diverse set of techniques and algorithms related to automatic reasoning (inference, planning, heuristics, etc), the machine learning subfields are specifically interested in pattern recognition and learning from data.

As with his earlier Raspberry Pi project, Adrian uses the Keras deep learning model and the TensorFlow backend, plus a few other packages such as Adrian’s own imutils functions and OpenCV.

Adrian trained a Convolutional Neural Network using Keras on a dataset of 1191 Pokémon images, obtaining 96.84% accuracy. As Adrian explains, this model is able to identify Pokémon via still image and video. It’s perfect for creating a Pokédex – an interactive Pokémon catalogue that should, according to the franchise, be able to identify and read out information on any known Pokémon when captured by camera. More information on model training can be found on Adrian’s blog.

For the physical build, a Raspberry Pi 3 with camera module is paired with the Raspberry Pi 7″ touch display to create a portable Pokédex. And while Adrian comments that the same result can be achieved using your home computer and a webcam, that’s not how Adrian rolls as a Raspberry Pi fan.

Plus, the smaller size of the Pi is perfect for one of you to incorporate this deep learning model into a 3D-printed Pokédex for ultimate Pokémon glory, pretty please, thank you.

Adrian has gone into impressive detail about how the project works and how you can create your own on his blog, pyimagesearch. So if you’re interested in learning more about deep learning, and making your own Pokédex, be sure to visit.

The post Own your own working Pokémon Pokédex! appeared first on Raspberry Pi.

# The robotic teapot from your nightmares

Post Syndicated from Alex Bate original https://www.raspberrypi.org/blog/robotic-teapot/

For those moments when you wish the cast of Disney’s Beauty and the Beast was real, only to realise what a nightmare that would be, here’s Paul-Louis Ageneau’s robotic teapot!

See what I mean?

## Tale as old as time…

It’s the classic story of guy meets digital killer teapot, digital killer teapot inspires him to 3D print his own. Loosely based on a boss level of the video game Alice: Madness Returns, Paul-Louis’s creation is a one-eyed walking teapot robot with a (possible) thirst for blood.

## Kill Build the beast

“My new robot is based on a Raspberry Pi Zero W with a camera.” Paul-Louis explains in his blog. “It is connected via a serial link to an Arduino Pro Mini board, which drives servos.”

Each leg has two points of articulation, one for the knee and one for the ankle. In order to move each of the joints, the teapot uses eight servo motor in total.

Paul-Louis designed and 3D printed the body of the teapot to fit the components needed. So if you’re considering this build as a means of acquiring tea on your laziest of days, I hate to be the bearer of bad news, but the most you’ll get from your pour will be jumper leads and Pi.

While the Arduino board controls the legs, it’s the Raspberry Pi’s job to receive user commands and tell the board how to direct the servos. The protocol for moving the servos is simple, with short lines of characters specifying instructions. First a digit from 0 to 7 selects a servo; next the angle of movement, such as 45 or 90, is input; and finally, the use of C commits the instruction.

Typing in commands is great for debugging, but you don’t want to be glued to a keyboard. Therefore, Paul-Louis continued to work on the code in order to string together several lines to create larger movements.

The final control system of the teapot runs on a web browser as a standard four-axis arrow pad, with two extra arrows for turning.

## Something there that wasn’t there before

Jean-Paul also included an ‘eye’ in the side of the pot to fit the Raspberry Pi Camera Module as another nod to the walking teapot from the video game, but with a purpose other than evil and wrong-doing. As you can see from the image above, the camera live-streams footage, allowing for remote control of the monster teapot regardless of your location.

## If you like it all that much, it’s yours

In case you fancy yourself as an inventor, Paul-Louis has provided the entire build process and the code on his blog, documenting how to bring your own teapot to life. And if you’ve created any robotic household items or any props from video games or movies, we’d love to see them, so leave a link in the comments or share it with us across social media using the hashtag #IBuiltThisAndNowIThinkItIsTryingToKillMe.

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# AWS Quest- a puzzling situation

Post Syndicated from Ana Visneski original https://aws.amazon.com/blogs/aws/aws-quest-a-puzzling-situation/

Starting on March 8th you might have seen AWS Quest popping up in different places. Now that we are a bit over halfway through the game, we thought it would be a great time give everyone a peek behind the curtain.

The whole idea started about a year ago during an casual conversation with Jeff when I first joined AWS. While we’re usually pretty good at staying focused in our meetings, he brought up that he had just finished a book he really enjoyed and asked me if I had read it. (A book that has since been made into a movie.) I don’t think there was a way for him to even imagine that as a huge fan of games, both table top and video games, how stoked I would be about the idea of bringing a game to our readers.

We got to talking about how great it would be to attempt a game that would involve the entire suite of AWS products and our various platforms. This idea might appear to be easy, but it has kept us busy with Lone Shark for about a year and we haven’t even scratched the surface of what we would like to do. Being able to finally share this first game with our customers has been an absolute delight.

From March 8-27th, each day we have been and will be releasing a new puzzle. The clues for the puzzles are hidden somewhere all over AWS, and once customers have found the clues they can figure out the puzzle which results in a word. That word is the name of a component to rebuild Ozz, Jeff’s robot buddy.

We wanted to try make sure that anyone could play and we tried to surround each puzzle with interesting Easter eggs. So far, it seems to be working and we are seeing some really cool collaborative effort between customers to solve the puzzles. From tech talks to women who code, posts both recent and well in the past, and to Twitter and podcasts, we wanted to hide the puzzles in places our customers might not have had a chance to really explore before. Given how much Jeff enjoyed doing a live Twitch stream so much I won’t be surprised when he tells me he wants to do a TV show next.

So far players have solved 8 of 13 puzzles!

The learnings we have already gathered as we are just a little past halfway in the quest are mind boggling. We have learned that there will be a guy who figures out how to build a chicken coop in 3D to solve a puzzle, or build a script to crawl a site looking for any reply to a blog post that might be a clue. There were puzzles we completely expected people to get stuck on that they have solved in a snap. They have really kept us on our toes, which isn’t a bad thing. It really doesn’t hurt that the players are incredibly adept at thinking outside the box, and we can’t wait to tell you how the puzzles were solved at the end.

We still have a little under a week of puzzles to go, before you can all join Jeff and special guests on a live Twitch stream to reassemble Ozz 2.0! And you don’t have to hold off for the next time we play, as there are still many puzzles to be solved and every player matters! Just keep an eye out for new puzzles to appear everyday until March 27th, join the Reddit, come to the AMA, or take a peek into the chat and get solving!

Time to wipe off your brow, and get back into solving the last of the puzzles! I am going to try to go explain to my mother and father what exactly I am doing with those two masters degrees and how much fun it really is…

# Welcome New Support Tech – Matt!

Post Syndicated from Yev original https://www.backblaze.com/blog/welcome-new-support-tech-matt/

Our hiring spree keeps rolling and we have a new addition to the support team, Matt! He joins the team as a Junior Technical Support Rep, and will be helping answer folks’ questions, guiding them through the product, and making sure that everyone’s taken care of! Lets learn a bit more about Matt shall we?

Junior Technical Support Representative

Where are you originally from?
San Francisco Bay Area

What attracted you to Backblaze?
Everyone is super chill and I like how transparent everyone is. The culture is very casual and not overbearing.

What do you expect to learn while being at Backblaze?
What the tech industry is like.

Where else have you worked?
The Chairman! Best bao ever.

Where did you go to school?
College of San Mateo.

Being a chef has always interested me. It’s so interesting that we’ve turned food into an art.

Favorite place you’ve traveled?
Japan. Holy crap Japan is cool. Everyone is so polite and the place is so clean. You haven’t had ramen like they serve, I literally couldn’t stop smiling after my first bite. The moment we arrived, I said, “I already miss Japan.”

Favorite hobby?
As much as I like video games, cooking is my favorite. Everyone eats, and it’s a good feeling to make food that people like. Currently trying to figure out how to make brussel sprouts taste better than brussel sprouts.

Of what achievement are you most proud?
Meeting my girlfriend. My life turned around when I met her. She’s taught me a lot of things.

Star Trek or Star Wars?
Star Wars!

Coke or Pepsi?
Good ol’ Cola. I quit drinking soda, though.

Favorite food?
As much as I love eating healthy, there’s nothing like spam.

Why do you like certain things?
Because certain things are either fun or delicious.

Anything else you’d like you’d like to tell us?
If you have any good recipes, I’ll probably cook it. Or try to.

You’re right Matt, certain things are either fun or delicious, like The Chairman’s bao! Welcome aboard!

The post Welcome New Support Tech – Matt! appeared first on Backblaze Blog | Cloud Storage & Cloud Backup.

# Welcome Lin – Our Newest Support Tech!

Post Syndicated from Yev original https://www.backblaze.com/blog/welcome-lin-newest-support-tech/

As Backblaze continues to grow a couple of our departments need to grow right along with it. One of the quickest-growing departments we have at Backblaze is Customer Support. We do all of our support in-house and the team grows to accommodate our growing customer base! We have a new person joining us in support, Lin! Lets take a moment to learn a bit more about her shall we?

Jr. Support Technician.

Where are you originally from?
Ventura, CA. It’s okay if you haven’t heard of it, it is very, very, small.

What attracted you to Backblaze?
The company culture, the delightful ads on Critical Role, and how immediately genuinely friendly everyone I met was.

Where else have you worked?
I previously did content management at Wish, and an awful lot of temp gigs. I did a few years at a coffee shop in the beginning of college, but my first job ever was a JoAnn’s Fabrics.

Where did you go to school?
San Francisco State University

Magical Girl!

Favorite place you’ve traveled?
Tokyo, but Disneyworld is a real close second.

Favorite hobby?
I spend an awful lot of time playing video games, and possibly even more making silly costumes.

Star Trek or Star Wars?
Truthfully I love both. But I was raised on original series and next generation Trek.

Coke or Pepsi?
Coke … definitely coke.

Favorite food?
Cupcakes. Especially funfetti cupcakes.

Anything else you’d like you’d like to tell us?
I discovered Sailor Moon as a child and it possibly influenced my life way too much. Like many people here I am a huge Disney fan; Anyone who spends longer than a few hours with me will probably tell you I can go on for hours about my cat (but in my defense he’s adorable and fluffy and I have the pictures to prove it).

We keep hiring folks that love Disney! It’s kind of amazing. It’s also nice to have folks in the office that can chat about the latest Critical Role episode! Welcome aboard Lin, we’ll try to get some funfetti stocked for the cupcakes that come in!

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# Weekly roundup: Re-emerging

Post Syndicated from Eevee original https://eev.ee/dev/2018/03/03/weekly-roundup-re-emerging/

Hi! It’s been three weeks again. But this time it’s because I was up to my eyeballs in making a video game, and I generally don’t have much to say during those spans beyond “I’m making a video game”.

• alice: We made a video game! Or, at least, a demo. Note: extremely NSFW (although that link is fine; it’s just a release post).

I hadn’t used Ren’Py a month ago, so there was a lot of scurrying around trying to figure out how to make it do what I want, and then there was a lot of fiction-writing which I have a tough time getting into, but I’m pretty happy with how it came out. Now we just need to make the other 80% of it.

• idchoppers: Oh, yeah, I got the geometry thing I was doing basically working. Next I gotta adjust the algorithm to work with an arbitrary number of input shapes, which is slightly more complicated.

• blog: Wrote about some tech wishes for 2018. Wrote a decent chunk of a post about my experience with idchoppers and Rust and porting weird C++ code, but it had to wait until I’d actually gotten the thing working, and then I just didn’t finish it yet haha.

Yep, that’s it, really busy, bye

# I am Beemo, a little living boy: Adventure Time prop build

Post Syndicated from Alex Bate original https://www.raspberrypi.org/blog/adventure-time-bmo/

Bob Herzberg, BMO builder and blogger at BYOBMO.com, fills us in on the whys and hows and even the Pen Wards of creating interactive Adventure Time BMO props with the Raspberry Pi.

#### A Conversation With BMO

A conversation with BMO showing off some voice recognition capabilities. There is no interaction for BMO’s responses other than voice commands. There is a small microphone inside BMO (right behind the blue dot) and the voice commands are processed by Google voice API over WiFi.

## Finding BMO

My first BMO began as a cosplay prop for my daughter. She and her friends are huge fans of Adventure Time and made their costumes for Princess Bubblegum, Marceline, and Finn. It was my job to come up with a BMO.

Bob as Banana Guard, daughter Laura as Princess Bubblegum, and son Steven as Finn

I wanted something electronic, and also interactive if possible. And it had to run on battery power. There was only one option that I found that would work: the Raspberry Pi.

## Building a living little boy

BMO’s basic internals consist of the Raspberry Pi, an 8” HDMI monitor, and a USB battery pack. The body is made from laser-cut MDF wood, which I sanded, sealed, and painted. I added 3D-printed arms and legs along with some vinyl lettering to complete the look. There is also a small wireless keyboard that works as a remote control.

To make the front panel button function, I created a custom PCB, mounted laser-cut acrylic buttons on it, and connected it to the Pi’s IO header.

Custom-made PCBs control BMO’s gaming buttons and USB input.

The USB jack is extended with another custom PCB, which gives BMO USB ports on the front panel. His battery life is an impressive 8 hours of continuous use.

## The main brain game frame

Most of BMO’s personality comes from custom animations that my daughter created and that were then turned into MP4 video files. The animations are triggered by the remote keyboard. Some versions of BMO have an internal microphone, and the Google Voice API is used to translate the user’s voice and map it to an appropriate response, so it’s possible to have a conversation with BMO.

The Raspberry Pi Camera Module was also put to use. Some BMOs have a servo that can pop up a camera, called GoMO, which takes pictures. Although some people mistake it for ghost detecting equipment, BMO just likes taking nice pictures.

## Who wants to play video games?

Playing games on BMO is as simple as loading one of the emulators supported by Raspbian.

I’m partial to the Atari 800 emulator, since I used to write games for that platform when I was just starting to learn programming. The front-panel USB ports are used for connecting gamepads, or his front-panel buttons and D-Pad can be used.

BMO has been a lot of fun to bring to conventions. He makes it to ComicCon San Diego each year and has been as far away as DragonCon in Atlanta, where he finally got to meet the voice of BMO, Niki Yang.

BMO’s back panel, autographed by Niki Yang

One day, I received an email from the producer of Adventure Time, Kelly Crews, with a very special request. Kelly was looking for a birthday present for the show’s creator, Pendleton Ward. It was either luck or coincidence that I just was finishing up the latest version of BMO. Niki Yang added some custom greetings just for Pen.

#### BMO Wishes Pendleton Ward a Happy Birthday!

Happy birthday to Pendleton Ward, the creator of, well, you know what. We were asked to build Pen his very own BMO and with help from Niki Yang and the Adventure Time crew here is the result.

We added a few more items inside, including a 3D-printed heart, a medal, and a certificate which come from the famous Be More episode that explains BMO’s origins.

BMO was quite a challenge to create. Fabricating the enclosure required several different techniques and materials. Fortunately, bringing him to life was quite simple once he had a Raspberry Pi inside!

## Find out more

Be sure to follow Bob’s adventures with BMO at the Build Your Own BMO blog. And if you’ve built your own prop from television or film using a Raspberry Pi, be sure to share it with us in the comments below or on our social media channels.

All images c/o Bob and Laura Herzberg

The post I am Beemo, a little living boy: Adventure Time prop build appeared first on Raspberry Pi.

# Physics cheats

Post Syndicated from Eevee original https://eev.ee/blog/2018/01/06/physics-cheats/

something about how we tweak physics to “work” better in games?

Ho ho! Work. Get it? Like in physics…?

## Hitboxes

Hitbox” is perhaps not the most accurate term, since the shape used for colliding with the environment and the shape used for detecting damage might be totally different. They’re usually the same in simple platformers, though, and that’s what most of my games have been.

The hitbox is the biggest physics fudge by far, and it exists because of a single massive approximation that (most) games make: you’re controlling a single entity in the abstract, not a physical body in great detail.

That is: when you walk with your real-world meat shell, you perform a complex dance of putting one foot in front of the other, a motion you spent years perfecting. When you walk in a video game, you press a single “walk” button. Your avatar may play an animation that moves its legs back and forth, but since you’re not actually controlling the legs independently (and since simulating them is way harder), the game just treats you like a simple shape. Fairly often, this is a box, or something very box-like.

Since the player has no direct control over the exact placement of their limbs, it would be slightly frustrating to have them collide with the world. This is especially true in cases like the above, where the tail and left ear protrude significantly out from the main body. If that Eevee wanted to stand against a real-world wall, she would simply tilt her ear or tail out of the way, so there’s no reason for the ear to block her from standing against a game wall. To compensate for this, the ear and tail are left out of the collision box entirely and will simply jut into a wall if necessary — a goofy affordance that’s so common it doesn’t even register as unusual. As a bonus (assuming this same box is used for combat), she won’t take damage from projectiles that merely graze past an ear.

(One extra consideration for sprite games in particular: the hitbox ought to be horizontally symmetric around the sprite’s pivot — i.e. the point where the entity is truly considered to be standing — so that the hitbox doesn’t abruptly move when the entity turns around!)

### Corners

Treating the player (and indeed most objects) as a box has one annoying side effect: boxes have corners. Corners can catch on other corners, even by a single pixel. Real-world bodies tend to be a bit rounder and squishier and this can tolerate grazing a corner; even real-world boxes will simply rotate a bit.

Ah, but in our faux physics world, we generally don’t want conscious actors (such as the player) to rotate, even with a realistic physics simulator! Real-world bodies are made of parts that will generally try to keep you upright, after all; you don’t tilt back and forth much.

One way to handle corners is to simply remove them from conscious actors. A hitbox doesn’t have to be a literal box, after all. A popular alternative — especially in Unity where it’s a standard asset — is the pill-shaped capsule, which has semicircles/hemispheres on the top and bottom and a cylindrical body in 3D. No corners, no problem.

Of course, that introduces a new problem: now the player can’t balance precariously on edges without their rounded bottom sliding them off. Alas.

If you’re stuck with corners, then, you may want to use a corner bump, a term I just made up. If the player would collide with a corner, but the collision is only by a few pixels, just nudge them to the side a bit and carry on.

When the corner is horizontal, this creates stairs! This is, more or less kinda, how steps work in Doom: when the player tries to cross from one sector into another, if the height difference is 24 units or less, the game simply bumps them upwards to the height of the new floor and lets them continue on.

Implementing this in a game without Doom’s notion of sectors is a little trickier. In fact, I still haven’t done it. Collision detection based on rejection gets it for free, kinda, but it’s not very deterministic and it breaks other things. But that’s a whole other post.

## Gravity

Gravity is pretty easy. Everything accelerates downwards all the time. What’s interesting are the exceptions.

### Jumping

Jumping is a giant hack.

Think about how actual jumping works: you tense your legs, which generally involves bending your knees first, and then spring upwards. In a platformer, you can just leap whenever you feel like it, which is nonsense. Also you go like twenty feet into the air?

Worse, most platformers allow variable-height jumping, where your jump is lower if you let go of the jump button while you’re in the air. Normally, one would expect to have to decide how much force to put into the jump beforehand.

But of course this is about convenience of controls: when jumping is your primary action, you want to be able to do it immediately, without any windup for how high you want to jump.

(And then there’s double jumping? Come on.)

Air control is a similar phenomenon: usually you’d jump in a particular direction by controlling how you push off the ground with your feet, but in a video game, you don’t have feet! You only have the box. The compromise is to let you control your horizontal movement to a limit degree in midair, even though that doesn’t make any sense. (It’s way more fun, though, and overall gives you more movement options, which are good to have in an interactive medium.)

Air control also exposes an obvious place that game physics collide with the realistic model of serious physics engines. I’ve mentioned this before, but: if you use Real Physics™ and air control yourself into a wall, you might find that you’ll simply stick to the wall until you let go of the movement buttons. Why? Remember, player movement acts as though an external force were pushing you around (and from the perspective of a Real™ physics engine, this is exactly how you’d implement it) — so air-controlling into a wall is equivalent to pushing a book against a wall with your hand, and the friction with the wall holds you in place. Oops.

### Ground sticking

Another place game physics conflict with physics engines is with running to the top of a slope. On a real hill, of course, you land on top of the slope and are probably glad of it; slopes are hard to climb!

In a video game, you go flying. Because you’re a box. With momentum. So you hit the peak and keep going in the same direction. Which is diagonally upwards.

### Projectiles

To make them more predictable, projectiles generally aren’t subject to gravity, at least as far as I’ve seen. The real world does not have such an exemption. The real world imposes gravity even on sniper rifles, which in a video game are often implemented as an instant trace unaffected by anything in the world because the bullet never actually exists in the world.

## Resistance

Ah. Welcome to hell.

### Water

Water is an interesting case, and offhand I don’t know the gritty details of how games implement it. In the real world, water applies a resistant drag force to movement — and that force is proportional to the square of velocity, which I’d completely forgotten until right now. I am almost positive that no game handles that correctly. But then, in real-world water, you can push against the water itself for movement, and games don’t simulate that either. What’s the rough equivalent?

The Sonic Physics Guide suggests that Sonic handles it by basically halving everything: acceleration, max speed, friction, etc. When Sonic enters water, his speed is cut; when Sonic exits water, his speed is increased.

That last bit feels validating — I could swear Metroid Prime did the same thing, and built my own solution around it, but couldn’t remember for sure. It makes no sense, of course, for a jump to become faster just because you happened to break the surface of the water, but it feels fantastic.

The thing I did was similar, except that I didn’t want to add a multiplier in a dozen places when you happen to be underwater (and remember which ones need it to be squared, etc.). So instead, I calculate everything completely as normal, so velocity is exactly the same as it would be on dry land — but the distance you would move gets halved. The effect seems to be pretty similar to most platformers with water, at least as far as I can tell. It hasn’t shown up in a published game and I only added this fairly recently, so I might be overlooking some reason this is a bad idea.

(One reason that comes to mind is that velocity is now a little white lie while underwater, so anything relying on velocity for interesting effects might be thrown off. Or maybe that’s correct, because velocity thresholds should be halved underwater too? Hm!)

Notably, air is also a fluid, so it should behave the same way (just with different constants). I definitely don’t think any games apply air drag that’s proportional to the square of velocity.

### Friction

Friction is, in my experience, a little handwaved. Probably because real-world friction is so darn complicated.

Consider that in the real world, we want very high friction on the surfaces we walk on — shoes and tires are explicitly designed to increase it, even. We move by bracing a back foot against the ground and using that to push ourselves forward, so we want the ground to resist our push as much as possible.

In a game world, we are a box. We move by being pushed by some invisible outside force, so if the friction between ourselves and the ground is too high, we won’t be able to move at all! That’s complete nonsense physically, but it turns out to be handy in some cases — for example, highish friction can simulate walking through deep mud, which should be difficult due to fluid drag and low friction.

But the best-known example of the fakeness of game friction is video game ice. Walking on real-world ice is difficult because the low friction means low grip; your feet are likely to slip out from under you, and you’ll simply fall down and have trouble moving at all. In a video game, you can’t fall down, so you have the opposite experience: you spend most of your time sliding around uncontrollably. Yet ice is so common in video games (and perhaps so uncommon in places I’ve lived) that I, at least, had never really thought about this disparity until an hour or so ago.

### Game friction vs real-world friction

Real-world friction is a force. It’s the normal force (which is the force exerted by the object on the surface) times some constant that depends on how the two materials interact.

Force is mass times acceleration, and platformers often ignore mass, so friction ought to be an acceleration — applied against the object’s movement, but never enough to push it backwards.

I haven’t made any games where variable friction plays a significant role, but my gut instinct is that low friction should mean the player accelerates more slowly but has a higher max speed, and high friction should mean the opposite. I see from my own source code that I didn’t even do what I just said, so let’s defer to some better-made and well-documented games: Sonic and Doom.

In Sonic, friction is a fixed value subtracted from the player’s velocity (regardless of direction) each tic. Sonic has a fixed framerate, so the units are really pixels per tic squared (i.e. acceleration), multiplied by an implicit 1 tic per tic. So far, so good.

But Sonic’s friction only applies if the player isn’t pressing or . Hang on, that isn’t friction at all; that’s just deceleration! That’s equivalent to jogging to a stop. If friction were lower, Sonic would take longer to stop, but otherwise this is only tangentially related to friction.

(In fairness, this approach would decently emulate friction for non-conscious sliding objects, which are never going to be pressing movement buttons. Also, we don’t have the Sonic source code, and the name “friction” is a fan invention; the Sonic Physics Guide already uses “deceleration” to describe the player’s acceleration when turning around.)

Okay, let’s try Doom. In Doom, the default friction is 90.625%.

Hang on, what?

Yes, in Doom, friction is a multiplier applied every tic. Doom runs at 35 tics per second, so this is a multiplier of 0.032 per second. Yikes!

This isn’t anything remotely like real friction, but it’s much easier to implement. With friction as acceleration, the game has to know both the direction of movement (so it can apply friction in the opposite direction) and the magnitude (so it doesn’t overshoot and launch the object in the other direction). That means taking a semi-costly square root and also writing extra code to cap the amount of friction. With a multiplier, neither is necessary; just multiply the whole velocity vector and you’re done.

There are some downsides. One is that objects will never actually stop, since multiplying by 3% repeatedly will never produce a result of zero — though eventually the speed will become small enough to either slip below a “minimum speed” threshold or simply no longer fit in a float representation. Another is that the units are fairly meaningless: with Doom’s default friction of 90.625%, about how long does it take for the player to stop? I have no idea, partly because “stop” is ambiguous here! If friction were an acceleration, I could divide it into the player’s max speed to get a time.

All that aside, what are the actual effects of changing Doom’s friction? What an excellent question that’s surprisingly tricky to answer. (Note that friction can’t be changed in original Doom, only in the Boom port and its derivatives.) Here’s what I’ve pieced together.

Doom’s “friction” is really two values. “Friction” itself is a multiplier applied to moving objects on every tic, but there’s also a move factor which defaults to $$\frac{1}{32} = 0.03125$$ and is derived from friction for custom values.

Every tic, the player’s velocity is multiplied by friction, and then increased by their speed times the move factor.

$$v(n) = v(n – 1) \times friction + speed \times move factor$$

Eventually, the reduction from friction will balance out the speed boost. That happens when $$v(n) = v(n – 1)$$, so we can rearrange it to find the player’s effective max speed:

$$v = v \times friction + speed \times move factor \\ v – v \times friction = speed \times move factor \\ v = speed \times \frac{move factor}{1 – friction}$$

For vanilla Doom’s move factor of 0.03125 and friction of 0.90625, that becomes:

$$v = speed \times \frac{\frac{1}{32}}{1 – \frac{29}{32}} = speed \times \frac{\frac{1}{32}}{\frac{3}{32}} = \frac{1}{3} \times speed$$

Curiously, “speed” is three times the maximum speed an actor can actually move. Doomguy’s run speed is 50, so in practice he moves a third of that, or 16⅔ units per tic. (Of course, this isn’t counting SR40, a bug that lets Doomguy run ~40% faster than intended diagonally.)

So now, what if you change friction? Even more curiously, the move factor is calculated completely differently depending on whether friction is higher or lower than the default Doom amount:

$$move factor = \begin{cases} \frac{133 – 128 \times friction}{544} &≈ 0.244 – 0.235 \times friction & \text{ if } friction \ge \frac{29}{32} \\ \frac{81920 \times friction – 70145}{1048576} &≈ 0.078 \times friction – 0.067 & \text{ otherwise } \end{cases}$$

That’s pretty weird? Complicating things further is that low friction (which means muddy terrain, remember) has an extra multiplier on its move factor, depending on how fast you’re already going — the idea is apparently that you have a hard time getting going, but it gets easier as you find your footing. The extra multiplier maxes out at 8, which makes the two halves of that function meet at the vanilla Doom value.

That very top point corresponds to the move factor from the original game. So no matter what you do to friction, the move factor becomes lower. At 0.85 and change, you can no longer move at all; below that, you move backwards.

From the formula above, it’s easy to see what changes to friction and move factor will do to Doomguy’s stable velocity. Move factor is in the numerator, so increasing it will increase stable velocity — but it can’t increase, so stable velocity can only ever decrease. Friction is in the denominator, but it’s subtracted from 1, so increasing friction will make the denominator a smaller value less than 1, i.e. increase stable velocity. Combined, we get this relationship between friction and stable velocity.

As friction approaches 1, stable velocity grows without bound. This makes sense, given the definition of $$v(n)$$ — if friction is 1, the velocity from the previous tic isn’t reduced at all, so we just keep accelerating freely.

All of this is why I’m wary of using multipliers.

Anyway, this leaves me with one last question about the effects of Doom’s friction: how long does it take to reach stable velocity? Barring precision errors, we’ll never truly reach stable velocity, but let’s say within 5%. First we need a closed formula for the velocity after some number of tics. This is a simple recurrence relation, and you can write a few terms out yourself if you want to be sure this is right.

$$v(n) = v_0 \times friction^n + speed \times move factor \times \frac{friction^n – 1}{friction – 1}$$

Our initial velocity is zero, so the first term disappears. Set this equal to the stable formula and solve for n:

$$speed \times move factor \times \frac{friction^n – 1}{friction – 1} = (1 – 5\%) \times speed \times \frac{move factor}{1 – friction} \\ friction^n – 1 = -(1 – 5\%) \\ n = \frac{\ln 5\%}{\ln friction}$$

Speed” and move factor disappear entirely, which makes sense, and this is purely a function of friction (and how close we want to get). For vanilla Doom, that comes out to 30.4, which is a little less than a second. For other values of friction:

As friction increases (which in Doom terms means the surface is more slippery), it takes longer and longer to reach stable speed, which is in turn greater and greater. For lesser friction (i.e. mud), stable speed is lower, but reached fairly quickly. (Of course, the extra “getting going” multiplier while in mud adds some extra time here, but including that in the graph is a bit more complicated.)

I think this matches with my instincts above. How fascinating!

What’s that? This is way too much math and you hate it? Then don’t use multipliers in game physics.

## Uh

That was a hell of a diversion!

I guess the goofiest stuff in basic game physics is really just about mapping player controls to in-game actions like jumping and deceleration; the rest consists of hacks to compensate for representing everything as a box.

# Random with care

Post Syndicated from Eevee original https://eev.ee/blog/2018/01/02/random-with-care/

Hi! Here are a few loose thoughts about picking random numbers.

This is all aimed at frivolous pursuits like video games. Hell, even video games where money is at stake should be deferring to someone who knows way more than I do. Otherwise you might find out that your deck shuffles in your poker game are woefully inadequate and some smartass is cheating you out of millions. (If your random number generator has fewer than 226 bits of state, it can’t even generate every possible shuffling of a deck of cards!)

## Use the right distribution

Most languages have a random number primitive that spits out a number uniformly in the range [0, 1), and you can go pretty far with just that. But beware a few traps!

### Random pitches

Say you want to pitch up a sound by a random amount, perhaps up to an octave. Your audio API probably has a way to do this that takes a pitch multiplier, where I say “probably” because that’s how the only audio API I’ve used works.

Easy peasy. If 1 is unchanged and 2 is pitched up by an octave, then all you need is rand() + 1. Right?

No! Pitch is exponential — within the same octave, the “gap” between C and C♯ is about half as big as the gap between B and the following C. If you pick a pitch multiplier uniformly, you’ll have a noticeable bias towards the higher pitches.

One octave corresponds to a doubling of pitch, so if you want to pick a random note, you want 2 ** rand().

### Random directions

For two dimensions, you can just pick a random angle with rand() * TAU.

If you want a vector rather than an angle, or if you want a random direction in three dimensions, it’s a little trickier. You might be tempted to just pick a random point where each component is rand() * 2 - 1 (ranging from −1 to 1), but that’s not quite right. A direction is a point on the surface (or, equivalently, within the volume) of a sphere, and picking each component independently produces a point within the volume of a cube; the result will be a bias towards the corners of the cube, where there’s much more extra volume beyond the sphere.

No? Well, just trust me. I don’t know how to make a diagram for this.

Anyway, you could use the Pythagorean theorem a few times and make a huge mess of things, or it turns out there’s a really easy way that even works for two or four or any number of dimensions. You pick each coordinate from a Gaussian (normal) distribution, then normalize the resulting vector. In other words, using Python’s random module:

 1 2 3 4 5 6 def random_direction(): x = random.gauss(0, 1) y = random.gauss(0, 1) z = random.gauss(0, 1) r = math.sqrt(x*x + y*y + z*z) return x/r, y/r, z/r 

Why does this work? I have no idea!

Note that it is possible to get zero (or close to it) for every component, in which case the result is nonsense. You can re-roll all the components if necessary; just check that the magnitude (or its square) is less than some epsilon, which is equivalent to throwing away a tiny sphere at the center and shouldn’t affect the distribution.

### Beware Gauss

Since I brought it up: the Gaussian distribution is a pretty nice one for choosing things in some range, where the middle is the common case and should appear more frequently.

That said, I never use it, because it has one annoying drawback: the Gaussian distribution has no minimum or maximum value, so you can’t really scale it down to the range you want. In theory, you might get any value out of it, with no limit on scale.

In practice, it’s astronomically rare to actually get such a value out. I did a hundred million trials just to see what would happen, and the largest value produced was 5.8.

But, still, I’d rather not knowingly put extremely rare corner cases in my code if I can at all avoid it. I could clamp the ends, but that would cause unnatural bunching at the endpoints. I could reroll if I got a value outside some desired range, but I prefer to avoid rerolling when I can, too; after all, it’s still (astronomically) possible to have to reroll for an indefinite amount of time. (Okay, it’s really not, since you’ll eventually hit the period of your PRNG. Still, though.) I don’t bend over backwards here — I did just say to reroll when picking a random direction, after all — but when there’s a nicer alternative I’ll gladly use it.

And lo, there is a nicer alternative! Enter the beta distribution. It always spits out a number in [0, 1], so you can easily swap it in for the standard normal function, but it takes two “shape” parameters α and β that alter its behavior fairly dramatically.

With α = β = 1, the beta distribution is uniform, i.e. no different from rand(). As α increases, the distribution skews towards the right, and as β increases, the distribution skews towards the left. If α = β, the whole thing is symmetric with a hump in the middle. The higher either one gets, the more extreme the hump (meaning that value is far more common than any other). With a little fiddling, you can get a number of interesting curves.

Screenshots don’t really do it justice, so here’s a little Wolfram widget that lets you play with α and β live:

Note that if α = 1, then 1 is a possible value; if β = 1, then 0 is a possible value. You probably want them both greater than 1, which clamps the endpoints to zero.

Also, it’s possible to have either α or β or both be less than 1, but this creates very different behavior: the corresponding endpoints become poles.

Anyway, something like α = β = 3 is probably close enough to normal for most purposes but already clamped for you. And you could easily replicate something like, say, NetHack’s incredibly bizarre rnz function.

### Random frequency

Say you want some event to have an 80% chance to happen every second. You (who am I kidding, I) might be tempted to do something like this:

 1 2 if random() < 0.8 * dt: do_thing() 

In an ideal world, dt is always the same and is equal to 1 / f, where f is the framerate. Replace that 80% with a variable, say P, and every tic you have a P / f chance to do the… whatever it is.

Each second, f tics pass, so you’ll make this check f times. The chance that any check succeeds is the inverse of the chance that every check fails, which is $$1 – \left(1 – \frac{P}{f}\right)^f$$.

For P of 80% and a framerate of 60, that’s a total probability of 55.3%. Wait, what?

Consider what happens if the framerate is 2. On the first tic, you roll 0.4 twice — but probabilities are combined by multiplying, and splitting work up by dt only works for additive quantities. You lose some accuracy along the way. If you’re dealing with something that multiplies, you need an exponent somewhere.

But in this case, maybe you don’t want that at all. Each separate roll you make might independently succeed, so it’s possible (but very unlikely) that the event will happen 60 times within a single second! Or 200 times, if that’s someone’s framerate.

If you explicitly want something to have a chance to happen on a specific interval, you have to check on that interval. If you don’t have a gizmo handy to run code on an interval, it’s easy to do yourself with a time buffer:

 1 2 3 4 5 6 timer += dt # here, 1 is the "every 1 seconds" while timer > 1: timer -= 1 if random() < 0.8: do_thing() 

Using while means rolls still happen even if you somehow skipped over an entire second.

(For the curious, and the nerds who already noticed: the expression $$1 – \left(1 – \frac{P}{f}\right)^f$$ converges to a specific value! As the framerate increases, it becomes a better and better approximation for $$1 – e^{-P}$$, which for the example above is 0.551. Hey, 60 fps is pretty accurate — it’s just accurately representing something nowhere near what I wanted. Er, you wanted.)

### Rolling your own

Of course, you can fuss with the classic [0, 1] uniform value however you want. If I want a bias towards zero, I’ll often just square it, or multiply two of them together. If I want a bias towards one, I’ll take a square root. If I want something like a Gaussian/normal distribution, but with clearly-defined endpoints, I might add together n rolls and divide by n. (The normal distribution is just what you get if you roll infinite dice and divide by infinity!)

It’d be nice to be able to understand exactly what this will do to the distribution. Unfortunately, that requires some calculus, which this post is too small to contain, and which I didn’t even know much about myself until I went down a deep rabbit hole while writing, and which in many cases is straight up impossible to express directly.

Here’s the non-calculus bit. A source of randomness is often graphed as a PDF — a probability density function. You’ve almost certainly seen a bell curve graphed, and that’s a PDF. They’re pretty nice, since they do exactly what they look like: they show the relative chance that any given value will pop out. On a bog standard bell curve, there’s a peak at zero, and of course zero is the most common result from a normal distribution.

(Okay, actually, since the results are continuous, it’s vanishingly unlikely that you’ll get exactly zero — but you’re much more likely to get a value near zero than near any other number.)

For the uniform distribution, which is what a classic rand() gives you, the PDF is just a straight horizontal line — every result is equally likely.

If there were a calculus bit, it would go here! Instead, we can cheat. Sometimes. Mathematica knows how to work with probability distributions in the abstract, and there’s a free web version you can use. For the example of squaring a uniform variable, try this out:

 1 PDF[TransformedDistribution[u^2, u \[Distributed] UniformDistribution[{0, 1}]], u] 

(The \[Distributed] is a funny tilde that doesn’t exist in Unicode, but which Mathematica uses as a first-class operator. Also, press shiftEnter to evaluate the line.)

This will tell you that the distribution is… $$\frac{1}{2\sqrt{u}}$$. Weird! You can plot it:

 1 Plot[%, {u, 0, 1}] 

(The % refers to the result of the last thing you did, so if you want to try several of these, you can just do Plot[PDF[…], u] directly.)

The resulting graph shows that numbers around zero are, in fact, vastly — infinitely — more likely than anything else.

What about multiplying two together? I can’t figure out how to get Mathematica to understand this, but a great amount of digging revealed that the answer is -ln x, and from there you can plot them both on Wolfram Alpha. They’re similar, though squaring has a much better chance of giving you high numbers than multiplying two separate rolls — which makes some sense, since if either of two rolls is a low number, the product will be even lower.

What if you know the graph you want, and you want to figure out how to play with a uniform roll to get it? Good news! That’s a whole thing called inverse transform sampling. All you have to do is take an integral. Good luck!

This is all extremely ridiculous. New tactic: Just Simulate The Damn Thing. You already have the code; run it a million times, make a histogram, and tada, there’s your PDF. That’s one of the great things about computers! Brute-force numerical answers are easy to come by, so there’s no excuse for producing something like rnz. (Though, be sure your histogram has sufficiently narrow buckets — I tried plotting one for rnz once and the weird stuff on the left side didn’t show up at all!)

By the way, I learned something from futzing with Mathematica here! Taking the square root (to bias towards 1) gives a PDF that’s a straight diagonal line, nothing like the hyperbola you get from squaring (to bias towards 0). How do you get a straight line the other way? Surprise: $$1 – \sqrt{1 – u}$$.

### Okay, okay, here’s the actual math

I don’t claim to have a very firm grasp on this, but I had a hell of a time finding it written out clearly, so I might as well write it down as best I can. This was a great excuse to finally set up MathJax, too.

Say $$u(x)$$ is the PDF of the original distribution and $$u$$ is a representative number you plucked from that distribution. For the uniform distribution, $$u(x) = 1$$. Or, more accurately,

$$u(x) = \begin{cases} 1 & \text{ if } 0 \le x \lt 1 \\ 0 & \text{ otherwise } \end{cases}$$

Remember that $$x$$ here is a possible outcome you want to know about, and the PDF tells you the relative probability that a roll will be near it. This PDF spits out 1 for every $$x$$, meaning every number between 0 and 1 is equally likely to appear.

We want to do something to that PDF, which creates a new distribution, whose PDF we want to know. I’ll use my original example of $$f(u) = u^2$$, which creates a new PDF $$v(x)$$.

The trick is that we need to work in terms of the cumulative distribution function for $$u$$. Where the PDF gives the relative chance that a roll will be (“near”) a specific value, the CDF gives the relative chance that a roll will be less than a specific value.

The conventions for this seem to be a bit fuzzy, and nobody bothers to explain which ones they’re using, which makes this all the more confusing to read about… but let’s write the CDF with a capital letter, so we have $$U(x)$$. In this case, $$U(x) = x$$, a straight 45° line (at least between 0 and 1). With the definition I gave, this should make sense. At some arbitrary point like 0.4, the value of the PDF is 1 (0.4 is just as likely as anything else), and the value of the CDF is 0.4 (you have a 40% chance of getting a number from 0 to 0.4).

Calculus ahoy: the PDF is the derivative of the CDF, which means it measures the slope of the CDF at any point. For $$U(x) = x$$, the slope is always 1, and indeed $$u(x) = 1$$. See, calculus is easy.

Okay, so, now we’re getting somewhere. What we want is the CDF of our new distribution, $$V(x)$$. The CDF is defined as the probability that a roll $$v$$ will be less than $$x$$, so we can literally write:

$$V(x) = P(v \le x)$$

(This is why we have to work with CDFs, rather than PDFs — a PDF gives the chance that a roll will be “nearby,” whatever that means. A CDF is much more concrete.)

What is $$v$$, exactly? We defined it ourselves; it’s the do something applied to a roll from the original distribution, or $$f(u)$$.

$$V(x) = P\!\left(f(u) \le x\right)$$

Now the first tricky part: we have to solve that inequality for $$u$$, which means we have to do something, backwards to $$x$$.

$$V(x) = P\!\left(u \le f^{-1}(x)\right)$$

Almost there! We now have a probability that $$u$$ is less than some value, and that’s the definition of a CDF!

$$V(x) = U\!\left(f^{-1}(x)\right)$$

Hooray! Now to turn these CDFs back into PDFs, all we need to do is differentiate both sides and use the chain rule. If you never took calculus, don’t worry too much about what that means!

$$v(x) = u\!\left(f^{-1}(x)\right)\left|\frac{d}{dx}f^{-1}(x)\right|$$

Wait! Where did that absolute value come from? It takes care of whether $$f(x)$$ increases or decreases. It’s the least interesting part here by far, so, whatever.

There’s one more magical part here when using the uniform distribution — $$u(\dots)$$ is always equal to 1, so that entire term disappears! (Note that this only works for a uniform distribution with a width of 1; PDFs are scaled so the entire area under them sums to 1, so if you had a rand() that could spit out a number between 0 and 2, the PDF would be $$u(x) = \frac{1}{2}$$.)

$$v(x) = \left|\frac{d}{dx}f^{-1}(x)\right|$$

So for the specific case of modifying the output of rand(), all we have to do is invert, then differentiate. The inverse of $$f(u) = u^2$$ is $$f^{-1}(x) = \sqrt{x}$$ (no need for a ± since we’re only dealing with positive numbers), and differentiating that gives $$v(x) = \frac{1}{2\sqrt{x}}$$. Done! This is also why square root comes out nicer; inverting it gives $$x^2$$, and differentiating that gives $$2x$$, a straight line.

Incidentally, that method for turning a uniform distribution into any distribution — inverse transform sampling — is pretty much the same thing in reverse: integrate, then invert. For example, when I saw that taking the square root gave $$v(x) = 2x$$, I naturally wondered how to get a straight line going the other way, $$v(x) = 2 – 2x$$. Integrating that gives $$2x – x^2$$, and then you can use the quadratic formula (or just ask Wolfram Alpha) to solve $$2x – x^2 = u$$ for $$x$$ and get $$f(u) = 1 – \sqrt{1 – u}$$.

Multiply two rolls is a bit more complicated; you have to write out the CDF as an integral and you end up doing a double integral and wow it’s a mess. The only thing I’ve retained is that you do a division somewhere, which then gets integrated, and that’s why it ends up as $$-\ln x$$.

And that’s quite enough of that! (Okay but having math in my blog is pretty cool and I will definitely be doing more of this, sorry, not sorry.)

## Random vs varied

Sometimes, random isn’t actually what you want. We tend to use the word “random” casually to mean something more like chaotic, i.e., with no discernible pattern. But that’s not really random. In fact, given how good humans can be at finding incidental patterns, they aren’t all that unlikely! Consider that when you roll two dice, they’ll come up either the same or only one apart almost half the time. Coincidence? Well, yes.

If you ask for randomness, you’re saying that any outcome — or series of outcomes — is acceptable, including five heads in a row or five tails in a row. Most of the time, that’s fine. Some of the time, it’s less fine, and what you really want is variety. Here are a couple examples and some fairly easy workarounds.

### NPC quips

The nature of games is such that NPCs will eventually run out of things to say, at which point further conversation will give the player a short brush-off quip — a slight nod from the designer to the player that, hey, you hit the end of the script.

Some NPCs have multiple possible quips and will give one at random. The trouble with this is that it’s very possible for an NPC to repeat the same quip several times in a row before abruptly switching to another one. With only a few options to choose from, getting the same option twice or thrice (especially across an entire game, which may have numerous NPCs) isn’t all that unlikely. The notion of an NPC quip isn’t very realistic to start with, but having someone repeat themselves and then abruptly switch to something else is especially jarring.

The easy fix is to show the quips in order! Paradoxically, this is more consistently varied than choosing at random — the original “order” is likely to be meaningless anyway, and it already has the property that the same quip can never appear twice in a row.

If you like, you can shuffle the list of quips every time you reach the end, but take care here — it’s possible that the last quip in the old order will be the same as the first quip in the new order, so you may still get a repeat. (Of course, you can just check for this case and swap the first quip somewhere else if it bothers you.)

That last behavior is, in fact, the canonical way that Tetris chooses pieces — the game simply shuffles a list of all 7 pieces, gives those to you in shuffled order, then shuffles them again to make a new list once it’s exhausted. There’s no avoidance of duplicates, though, so you can still get two S blocks in a row, or even two S and two Z all clumped together, but no more than that. Some Tetris variants take other approaches, such as actively avoiding repeats even several pieces apart or deliberately giving you the worst piece possible.

### Random drops

Random drops are often implemented as a flat chance each time. Maybe enemies have a 5% chance to drop health when they die. Legally speaking, over the long term, a player will see health drops for about 5% of enemy kills.

Over the short term, they may be desperate for health and not survive to see the long term. So you may want to put a thumb on the scale sometimes. Games in the Metroid series, for example, have a somewhat infamous bias towards whatever kind of drop they think you need — health if your health is low, missiles if your missiles are low.

I can’t give you an exact approach to use, since it depends on the game and the feeling you’re going for and the variables at your disposal. In extreme cases, you might want to guarantee a health drop from a tough enemy when the player is critically low on health. (Or if you’re feeling particularly evil, you could go the other way and deny the player health when they most need it…)

The problem becomes a little different, and worse, when the event that triggers the drop is relatively rare. The pathological case here would be something like a raid boss in World of Warcraft, which requires hours of effort from a coordinated group of people to defeat, and which has some tiny chance of dropping a good item that will go to only one of those people. This is why I stopped playing World of Warcraft at 60.

Dialing it back a little bit gives us Enter the Gungeon, a roguelike where each room is a set of encounters and each floor only has a dozen or so rooms. Initially, you have a 1% chance of getting a reward after completing a room — but every time you complete a room and don’t get a reward, the chance increases by 9%, up to a cap of 80%. Once you get a reward, the chance resets to 1%.

The natural question is: how frequently, exactly, can a player expect to get a reward? We could do math, or we could Just Simulate The Damn Thing.

  1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 from collections import Counter import random histogram = Counter() TRIALS = 1000000 chance = 1 rooms_cleared = 0 rewards_found = 0 while rewards_found < TRIALS: rooms_cleared += 1 if random.random() * 100 < chance: # Reward! rewards_found += 1 histogram[rooms_cleared] += 1 rooms_cleared = 0 chance = 1 else: chance = min(80, chance + 9) for gaps, count in sorted(histogram.items()): print(f"{gaps:3d} | {count / TRIALS * 100:6.2f}%", '#' * (count // (TRIALS // 100))) 
  1 2 3 4 5 6 7 8 9 10 11 12 13 14 15  1 | 0.98% 2 | 9.91% ######### 3 | 17.00% ################ 4 | 20.23% #################### 5 | 19.21% ################### 6 | 15.05% ############### 7 | 9.69% ######### 8 | 5.07% ##### 9 | 2.09% ## 10 | 0.63% 11 | 0.12% 12 | 0.03% 13 | 0.00% 14 | 0.00% 15 | 0.00% 

We’ve got kind of a hilly distribution, skewed to the left, which is up in this histogram. Most of the time, a player should see a reward every three to six rooms, which is maybe twice per floor. It’s vanishingly unlikely to go through a dozen rooms without ever seeing a reward, so a player should see at least one per floor.

Of course, this simulated a single continuous playthrough; when starting the game from scratch, your chance at a reward always starts fresh at 1%, the worst it can be. If you want to know about how many rewards a player will get on the first floor, hey, Just Simulate The Damn Thing.

 1 2 3 4 5 6 7  0 | 0.01% 1 | 13.01% ############# 2 | 56.28% ######################################################## 3 | 27.49% ########################### 4 | 3.10% ### 5 | 0.11% 6 | 0.00% 

Cool. Though, that’s assuming exactly 12 rooms; it might be worth changing that to pick at random in a way that matches the level generator.

(Enter the Gungeon does some other things to skew probability, which is very nice in a roguelike where blind luck can make or break you. For example, if you kill a boss without having gotten a new gun anywhere else on the floor, the boss is guaranteed to drop a gun.)

### Critical hits

I suppose this is the same problem as random drops, but backwards.

Say you have a battle sim where every attack has a 6% chance to land a devastating critical hit. Presumably the same rules apply to both the player and the AI opponents.

Consider, then, that the AI opponents have exactly the same 6% chance to ruin the player’s day. Consider also that this gives them an 0.4% chance to critical hit twice in a row. 0.4% doesn’t sound like much, but across an entire playthrough, it’s not unlikely that a player might see it happen and find it incredibly annoying.

Perhaps it would be worthwhile to explicitly forbid AI opponents from getting consecutive critical hits.

## In conclusion

An emerging theme here has been to Just Simulate The Damn Thing. So consider Just Simulating The Damn Thing. Even a simple change to a random value can do surprising things to the resulting distribution, so unless you feel like differentiating the inverse function of your code, maybe test out any non-trivial behavior and make sure it’s what you wanted. Probability is hard to reason about.

# Weekly roundup: Anise’s very own video game

Post Syndicated from Eevee original https://eev.ee/dev/2018/01/01/weekly-roundup-anises-very-own-video-game/

Happy new year! 🎆

In an unprecedented move, I did one thing for an entire calendar week. I say “unprecedented” but I guess the same thing happened with fox flux. And NEON PHASE. Hmm. Sensing a pattern. See if you can guess what the one thing was!

• anise!!: Wow! It’s Anise! The game has come so far that I can’t even believe that any of this was a recent change. I made monster AI vastly more sensible, added a boatload of mechanics, fleshed out more than half the map (and sketched out the rest), and drew and implemented most of a menu with a number of excellent goodies. Also, FINALLY (after a full year of daydreaming about it), eliminated the terrible “clock” structure I invented for collision detection, as well as cut down on a huge source of completely pointless allocations, which sped physics up in general by at least 10% and cut GC churn significantly. Hooray! And I’ve done even more just in the last day and a half. Still a good bit of work left, but this game is gonna be fantastic.

• art: Oh right I tried drawing a picture but I didn’t like it so I stopped.

I have some writing to catch up on — I have several things 80% written, but had to stop because I was just starting to get a cold and couldn’t even tell if my own writing was sensible any more. And then I had to work on a video game about my cat. Sorry. Actually, not sorry, video games about my cat are always top priority. You knew what you were signing up for.

# Simplify Querying Nested JSON with the AWS Glue Relationalize Transform

AWS Glue has a transform called Relationalize that simplifies the extract, transform, load (ETL) process by converting nested JSON into columns that you can easily import into relational databases. Relationalize transforms the nested JSON into key-value pairs at the outermost level of the JSON document. The transformed data maintains a list of the original keys from the nested JSON separated by periods.

Let’s look at how Relationalize can help you with a sample use case.

## An example of Relationalize in action

Suppose that the developers of a video game want to use a data warehouse like Amazon Redshift to run reports on player behavior based on data that is stored in JSON. Sample 1 shows example user data from the game. The player named “user1” has characteristics such as race, class, and location in nested JSON data. Further down, the player’s arsenal information includes additional nested JSON data. If the developers want to ETL this data into their data warehouse, they might have to resort to nested loops or recursive functions in their code.

### Sample 1: Nested JSON

{
"player": {
"characteristics": {
"race": "Human",
"class": "Warlock",
"power": 300,
"playercountry": "USA"
},
"arsenal": {
"kinetic": {
"type": "Auto Rifle",
"power": 300,
"element": "Kinetic"
},
"energy": {
"name": "MIDA Mini-Tool",
"type": "Submachine Gun",
"power": 300,
"element": "Solar"
},
"power": {
"name": "Play of the Game",
"power": 300,
"element": "Arc"
}
},
"armor": {
"arms": "Philomath Gloves",
"chest": "Philomath Robes",
"leg": "Philomath Boots",
"classitem": "Philomath Bond"
},
"location": {
"map": "Titan",
"waypoint": "The Rig"
}
}
}

Instead, the developers can use the Relationalize transform. Sample 2 shows what the transformed data looks like.

### Sample 2: Flattened JSON

{
"player.characteristics.race": "Human",
"player.characteristics.class": "Warlock",
"player.characteristics.power": 300,
"player.characteristics.playercountry": "USA",
"player.arsenal.kinetic.type": "Auto Rifle",
"player.arsenal.kinetic.power": 300,
"player.arsenal.kinetic.element": "Kinetic",
"player.arsenal.energy.name": "MIDA Mini-Tool",
"player.arsenal.energy.type": "Submachine Gun",
"player.arsenal.energy.power": 300,
"player.arsenal.energy.element": "Solar",
"player.arsenal.power.name": "Play of the Game",
"player.arsenal.power.power": 300,
"player.arsenal.power.element": "Arc",
"player.armor.arms": "Philomath Gloves",
"player.armor.chest": "Philomath Robes",
"player.armor.leg": "Philomath Boots",
"player.armor.classitem": "Philomath Bond",
"player.location.map": "Titan",
"player.location.waypoint": "The Rig"
}

You can then write the data to a database or to a data warehouse. You can also write it to delimited text files, such as in comma-separated value (CSV) format, or columnar file formats such as Optimized Row Columnar (ORC) format. You can use either of these format types for long-term storage in Amazon S3. Storing the transformed files in S3 provides the additional benefit of being able to query this data using Amazon Athena or Amazon Redshift Spectrum. You can further extend the usefulness of the data by performing joins between data stored in S3 and the data stored in an Amazon Redshift data warehouse.

## Before we get started…

In my example, I took two preparatory steps that save some time in your ETL code development:

1. I stored my data in an Amazon S3 bucket and used an AWS Glue crawler to make my data available in the AWS Glue data catalog. You can find instructions on how to do that in Cataloging Tables with a Crawler in the AWS Glue documentation. The AWS Glue database name I used was “blog,” and the table name was “players.” You can see these values in use in the sample code that follows.
2. I deployed a Zeppelin notebook using the automated deployment available within AWS Glue. If you already used an AWS Glue development endpoint to deploy a Zeppelin notebook, you can skip the deployment instructions. Otherwise, let’s quickly review how to deploy Zeppelin.

## Deploying a Zeppelin notebook with AWS Glue

The following steps are outlined in the AWS Glue documentation, and I include a few screenshots here for clarity.

First, create two IAM roles:

Next, in the AWS Glue Management Console, choose Dev endpoints, and then choose Add endpoint.

Specify a name for the endpoint and the AWS Glue IAM role that you created.

On the networking screen, choose Skip Networking because our code only communicates with S3.

Complete the development endpoint process by providing a Secure Shell (SSH) public key and confirming your settings.

When your new development endpoint’s Provisioning status changes from PROVISIONING to READY, choose your endpoint, and then for Actions choose Create notebook server.

Enter the notebook server details, including the role you previously created and a security group with inbound access allowed on TCP port 443.

Doing this automatically launches an AWS CloudFormation template. The output specifies the URL that you can use to access your Zeppelin notebook with the username and password you specified in the wizard.

## How do we flatten nested JSON?

With my data loaded and my notebook server ready, I accessed Zeppelin, created a new note, and set my interpreter to spark. I used some Python code that AWS Glue previously generated for another job that outputs to ORC. Then I added the Relationalize transform. You can see the resulting Python code in Sample 3.­

### Sample 3: Python code to transform the nested JSON and output it to ORC

import sys
from awsglue.transforms import *
from awsglue.utils import getResolvedOptions
from pyspark.context import SparkContext
from awsglue.context import GlueContext
from awsglue.job import Job
#from awsglue.transforms import Relationalize

# Begin variables to customize with your information
glue_source_database = "blog"
glue_source_table = "players"
glue_temp_storage = "s3://blog-example-edz/temp"
glue_relationalize_output_s3_path = "s3://blog-example-edz/output-flat"
dfc_root_table_name = "root" #default value is "roottable"
# End variables to customize with your information

glueContext = GlueContext(spark.sparkContext)
datasource0 = glueContext.create_dynamic_frame.from_catalog(database = glue_source_database, table_name = glue_source_table, transformation_ctx = "datasource0")
dfc = Relationalize.apply(frame = datasource0, staging_path = glue_temp_storage, name = dfc_root_table_name, transformation_ctx = "dfc")
blogdata = dfc.select(dfc_root_table_name)
blogdataoutput = glueContext.write_dynamic_frame.from_options(frame = blogdata, connection_type = "s3", connection_options = {"path": glue_relationalize_output_s3_path}, format = "orc", transformation_ctx = "blogdataoutput")

### What exactly is going on in this script?

After the import statements, we instantiate a GlueContext object, which allows us to work with the data in AWS Glue. Next, we create a DynamicFrame (datasource0) from the “players” table in the AWS Glue “blog” database. We use this DynamicFrame to perform any necessary operations on the data structure before it’s written to our desired output format. The source files remain unchanged.

We then run the Relationalize transform (Relationalize.apply()) with our datasource0 as one of the parameters. Another important parameter is the name parameter, which is a key that identifies our data after the transformation completes.

The Relationalize.apply() method returns a DynamicFrameCollection, and this is stored in the dfc variable. Before we can write our data to S3, we need to select the DynamicFrame from the DynamicFrameCollection object. We do this with the dfc.select() method. The correct DynamicFrame is stored in the blogdata variable.

You might be curious why a DynamicFrameCollection was returned when we started with a single DynamicFrame. This return value comes from the way Relationalize treats arrays in the JSON document: A DynamicFrame is created for each array. Together with the root data structure, each generated DynamicFrame is added to a DynamicFrameCollection when Relationalize completes its work. Although we didn’t have any arrays in our data, it’s good to keep this in mind. Finally, we output (blogdataoutput) the root DynamicFrame to ORC files in S3.

## Using the transformed data

One of the use cases we discussed earlier was using Amazon Athena or Amazon Redshift Spectrum to query the ORC files.

I used the following SQL DDL statements to create external tables in both services to enable queries of my data stored in Amazon S3.

### Sample 4: Amazon Athena DDL

CREATE EXTERNAL TABLE IF NOT EXISTS blog.blog_data_athena_test (
characteristics_race string,
characteristics_class string,
characteristics_subclass string,
characteristics_power int,
characteristics_playercountry string,
kinetic_name string,
kinetic_type string,
kinetic_power int,
kinetic_element string,
energy_name string,
energy_type string,
energy_power int,
energy_element string,
power_name string,
power_type string,
power_power int,
power_element string,
armor_head string,
armor_arms string,
armor_chest string,
armor_leg string,
armor_classitem string,
map string,
waypoint string
)
WITH SERDEPROPERTIES (
'serialization.format' = '1'
) LOCATION 's3://blog-example-edz/output-flat/'
TBLPROPERTIES ('has_encrypted_data'='false');


### Sample 5: Amazon Redshift Spectrum DDL

-- Create a Schema
-- A single schema can be used with multiple external tables.
-- This step is only required once for the external tables you create.
create external schema spectrum
from data catalog
database 'blog'
iam_role 'arn:aws:iam::0123456789:role/redshift-role'
create external database if not exists;

-- Create an external table in the schema
create external table spectrum.blog(
characteristics_race VARCHAR,
characteristics_class VARCHAR,
characteristics_subclass VARCHAR,
characteristics_power INTEGER,
characteristics_playercountry VARCHAR,
kinetic_name VARCHAR,
kinetic_type VARCHAR,
kinetic_power INTEGER,
kinetic_element VARCHAR,
energy_name VARCHAR,
energy_type VARCHAR,
energy_power INTEGER,
energy_element VARCHAR,
power_name VARCHAR,
power_type VARCHAR,
power_power INTEGER,
power_element VARCHAR,
armor_arms VARCHAR,
armor_chest VARCHAR,
armor_leg VARCHAR,
armor_classItem VARCHAR,
map VARCHAR,
waypoint VARCHAR)
stored as orc
location 's3://blog-example-edz/output-flat';

I even ran a query, shown in Sample 6, that joined my Redshift Spectrum table (spectrum.playerdata) with data in an Amazon Redshift table (public.raids) to generate advanced reports. In the where clause, I join the two tables based on the username values that are common to both data sources.

### Sample 6: Select statement with a join of Redshift Spectrum data with Amazon Redshift data

-- Get Total Raid Completions for the Hunter Class.
select spectrum.playerdata.characteristics_class as class, sum(public.raids."completions.val.raids.leviathan") as "Total Hunter Leviathan Raid Completions" from spectrum.playerdata, public.raids
and spectrum.playerdata.characteristics_class = 'Hunter'
group by spectrum.playerdata.characteristics_class;

## Summary

This post demonstrated how simple it can be to flatten nested JSON data with AWS Glue, using the Relationalize transform to automate the conversion of nested JSON. AWS Glue also automates the deployment of Zeppelin notebooks that you can use to develop your Python automation script. Finally, AWS Glue can output the transformed data directly to a relational database, or to files in Amazon S3 for further analysis with tools such as Amazon Athena and Amazon Redshift Spectrum.

As great as Relationalize is, it’s not the only transform available with AWS Glue. You can see a complete list of available transforms in Built-In Transforms in the AWS Glue documentation. Try them out today!

If you found this post useful, be sure to check out Using Amazon Redshift Spectrum, Amazon Athena and AWS Glue with Node.js in Production and Build a Data Lake Foundation with AWS Glue and Amazon S3.

Trevor Roberts Jr is a Solutions Architect with AWS. He provides architectural guidance to help customers achieve success in the cloud. In his spare time, Trevor enjoys traveling to new places and spending time with family.

# Weekly roundup: Odyssey, you see

Post Syndicated from Eevee original https://eev.ee/dev/2017/11/01/weekly-roundup-odyssey-you-see/

Dammit, another video game came out.

• fox flux: Some nitpicks to the landing frames, and copying them to every other form (augh). Finished up another form entirely, hallelujah. Very little left now. I think last week is also when I pixeled out a few more experimental characters.

• cc: More sprite animation UI work, which is incredibly tedious oh my goodness. I spent a day investigating Mecanim’s suitability for sprite animation again, and ultimately concluded… no. Good use of time.

• blog: I, ah, started on my final October post. Should be done shortly.

• art: The doodling continues! The best results are NSFW, alas, but I did make this quick relatable comic. Also this good face.

• writing: I have begun work on a Twine. Okay, well, last week I basically just wrote a bunch of custom JavaScript for it and zero actual prose, but it’s still work.

Post Syndicated from Rob Zwetsloot original https://www.raspberrypi.org/blog/magpi-63/

Hi folks, Rob from The MagPi here! Issue 63 is now available, and it’s a huge one: we finally show you how to create the ultimate Raspberry Pi arcade cabinet in our latest detailed tutorial, so get some quarters and your saw ready.

Totally awesome video game builds!

The 16-page-long arcade machine instructions cover everything from the tools you need and how to do the woodwork, to setting up the electronics. In my spare time, I pretend to be Street Fighter baddie M. Bison, so I’m no stranger to arcade machines. However, I had never actually built one — luckily, the excellent Bob Clagett of I Like To Make Stuff was generous enough to help out with this project. I hope you enjoy reading the article, and making your own cabinet, as much as I enjoyed writing and building them.

## Projects for kids

Retro gaming isn’t the only thing you’ll find in this issue of The MagPi though. We have a big feature called Junior Pi Projects, which we hope will inspire young people to make something really cool using Scratch or Python.

As usual, the new issue also includes a collection of other tutorials for you to follow, for example for building a hydroponic garden, or making a special MIDI box. There are also fantastic maker projects to read up on, and reviews to tempt your wallet.

The kids are alright

## Get The MagPi 63

You can grab The MagPi 63 right now from WH Smith, Tesco, Sainsbury’s, and Asda. If you live in the US, check out your local Barnes & Noble or Micro Center in the next few days. You can also get the new issue online from our store, or digitally via our Android or iOS apps. And don’t forget, there’s always the free PDF as well.

Want to support the Raspberry Pi Foundation, the magazine, and get some cool free stuff? If you take out a twelve-month print subscription to The MagPi, you’ll get a Pi Zero W, Pi Zero case, and adapter cables absolutely free! This offer does not currently have an end date.

That’s it for this month! We’re off to play some games.

The post MagPi 63: build the arcade cabinet of your dreams appeared first on Raspberry Pi.

# Weekly roundup: Slow start

Post Syndicated from Eevee original https://eev.ee/dev/2017/10/08/weekly-roundup-slow-start/

Getting back up to speed, finishing getting my computer back how it was, etc. Also we got a SNES Classic and Stardew Valley so, those have been things. But between all that, I somehow found time to do a microscopic amount of actual work!

• art: Sketched some stuff! It wasn’t very good. Need to do this more often.

• fox flux: Finally, after a great many attempts, I drew a pixel art bush I’m fairly happy with. And yet, I can already see ways to improve it! But hey I’m learning stuff and that’s really cool. I’ve been working on a much larger pixel art forest background, too, which is proving a little harder to figure out.

• blog: After a long period of silence, I wrote about how JavaScript has gotten a bit better lately. More words to come, probably!

I’ve got some high aspirations for the month, so I’m gonna get to it and definitely not go visit my video game chickens.