If you ask a programmer for advice—a terrible idea—they might tell you something like the following: Don’t repeat yourself. Programs should do one thing and one thing well. Never rewrite your code from scratch, ever!.

Following “Don’t Repeat Yourself” might lead you to a function with four boolean flags, and a matrix of behaviours to carefully navigate when changing the code. Splitting things up into simple units can lead to awkward composition and struggling to coordinate cross cutting changes. Avoiding rewrites means they’re often left so late that they have no chance of succeeding.

The advice isn’t inherently bad—although there is good intent, following it to the letter can create more problems than it promises to solve.

Sometimes the best way to follow an adage is to do the exact opposite: embrace feature switches and constantly rewrite your code, pull things together to make coordination between them easier to manage, and repeat yourself to avoid implementing everything in one function..

This advice is much harder to follow, unfortunately.

Repeat yourself to find abstractions.

“Don’t Repeat Yourself” is almost a truism—if anything, the point of programming is to avoid work.

No-one enjoys writing boilerplate. The more straightforward it is to write, the duller it is to summon into a text editor. People are already tired of writing eight exact copies of the same code before even having to do so. You don’t need to convince programmers not to repeat themselves, but you do need to teach them how and when to avoid it.

“Don’t Repeat Yourself” often gets interpreted as “Don’t Copy Paste” or to avoid repeating code within the codebase, but the best form of avoiding repetition is in avoiding reimplementing what exists elsewhere—and thankfully most of us already do!

Almost every web application leans heavily on an operating system, a database, and a variety of other lumps of code to get the job done. A modern website reuses millions of lines of code without even trying. Unfortunately, programmers love to avoid repetition, and “Don’t Repeat Yourself” turns into “Always Use an Abstraction”.

By an abstraction, I mean two interlinked things: a idea we can think and reason about, and the way in which we model it inside our programming languages. Abstractions are way of repeating yourself, so that you can change multiple parts of your program in one place. Abstractions allow you to manage cross-cutting changes across your system, or sharing behaviors within it.

The problem with always using an abstraction is that you’re preemptively guessing which parts of the codebase need to change together. “Don’t Repeat Yourself” will lead to a rigid, tightly coupled mess of code. Repeating yourself is the best way to discover which abstractions, if any, you actually need.

As Sandi Metz put it, “duplication is far cheaper than the wrong abstraction”.

You can’t really write a re-usable abstraction up front. Most successful libraries or frameworks are extracted from a larger working system, rather than being created from scratch. If you haven’t built something useful with your library yet, it is unlikely anyone else will. Code reuse isn’t a good excuse to avoid duplicating code, and writing reusable code inside your project is often a form of preemptive optimization.

When it comes to repeating yourself inside your own project, the point isn’t to be able to reuse code, but rather to make coordinated changes. Use abstractions when you’re sure about coupling things together, rather than for opportunistic or accidental code reuse—it’s ok to repeat yourself to find out when.

Repeat yourself, but don’t repeat other people’s hard work. Repeat yourself: duplicate to find the right abstraction first, then deduplicate to implement it.

With “Don’t Repeat Yourself”, some insist that it isn’t about avoiding duplication of code, but about avoiding duplication of functionality or duplication of responsibility. This is more popularly known as the “Single Responsibility Principle”, and it’s just as easily mishandled.

Gather responsibilities to simplify interactions between them

When it comes to breaking a larger service into smaller pieces, one idea is that each piece should only do one thing within the system—do one thing, and do it well—and the hope is that by following this rule, changes and maintenance become easier.

It works out well in the small: reusing variables for different purposes is an ever-present source of bugs. It’s less successful elsewhere: although one class might do two things in a rather nasty way, disentangling it isn’t of much benefit when you end up with two nasty classes with a far more complex mess of wiring between them.

The only real difference between pushing something together and pulling something apart is that some changes become easier to perform than others.

The choice between a monolith and microservices is another example of this—the choice between developing and deploying a single service, or composing things out of smaller, independently developed services.

The big difference between them is that cross-cutting change is easier in one, and local changes are easier in the other. Which one works best for a team often depends more on environmental factors than on the specific changes being made.

Although a monolith can be painful when new features need to be added and microservices can be painful when co-ordination is required, a monolith can run smoothly with feature flags and short lived branches and microservices work well when deployment is easy and heavily automated.

Even a monolith can be decomposed internally into microservices, albeit in a single repository and deployed as a whole. Everything can be broken into smaller parts—the trick is knowing when it’s an advantage to do so.

Modularity is more than reducing things to their smallest parts.

Invoking the ‘single responsibility principle’, programmers have been known to brutally decompose software into a terrifyingly large number of small interlocking pieces—a craft rarely seen outside of obscenely expensive watches, or bash.

The traditional UNIX command line is a showcase of small components that do exactly one function, and it can be a challenge to discover which one you need and in which way to hold it to get the job done. Piping things into awk '{print $2}' is almost a rite of passage.

Another example of the single responsibility principle is git. Although you can use git checkout to do six different things to the repository, they all use similar operations internally. Despite having singular functionality, components can be used in very different ways.

A layer of small components with no shared features creates a need for a layer above where these features overlap, and if absent, the user will create one, with bash aliases, scripts, or even spreadsheets to copy-paste from.

Even adding this layer might not help you: git already has a notion of user-facing and automation-facing commands, and the UI is still a mess. It’s always easier to add a new flag to an existing command than to it is to duplicate it and maintain it in parallel.

Similarly, functions gain boolean flags and classes gain new methods as the needs of the codebase change. In trying to avoid duplication and keep code together, we end up entangling things.

Although components can be created with a single responsibility, over time their responsibilities will change and interact in new and unexpected ways. What a module is currently responsible for within a system does not necessarily correlate to how it will grow.

Modularity is about limiting the options for growth

A given module often gets changed because it is the easiest module to change, rather than the best place for the change to be made. In the end, what defines a module is what pieces of the system it will never responsible for, rather what it is currently responsible for.

When a unit has no rules about what code cannot be included, it will eventually contain larger and larger amounts of the system. This is eternally true of every module named ‘util’, and why almost everything in a Model-View-Controller system ends up in the controller.

In theory, Model-View-Controller is about three interlocking units of code. One for the database, another for the UI, and one for the glue between them. In practice, Model-View-Controller resembles a monolith with two distinct subsystems—one for the database code, another for the UI, both nestled inside the controller.

The purpose of MVC isn’t to just keep all the database code in one place, but also to keep it away from frontend code. The data we have and how we want to view it will change over time independent of the frontend code.

Although code reuse is good and smaller components are good, they should be the result of other desired changes. Both are tradeoffs, introducing coupling through a lack of redundancy, or complexity in how things are composed. Decomposing things into smaller parts or unifying them is neither universally good nor bad for the codebase, and largely depends on what changes come afterwards.

In the same way abstraction isn’t about code reuse, but coupling things for change, modularity isn’t about grouping similar things together by function, but working out how to keep things apart and limiting co-ordination across the codebase.

This means recognizing which bits are slightly more entangled than others, knowing which pieces need to talk to each other, which need to share resources, what shares responsibilities, and most importantly, what external constraints are in place and which way they are moving.

In the end, it’s about optimizing for those changes—and this is rarely achieved by aiming for reusable code, as sometimes handling changes means rewriting everything.

Rewrite Everything

Usually, a rewrite is only a practical option when it’s the only option left. Technical debt, or code the seniors wrote that we can’t be rude about, accrues until all change becomes hazardous. It is only when the system is at breaking point that a rewrite is even considered an option.

Sometimes the reasons can be less dramatic: an API is being switched off, a startup has taken a beautiful journey, or there’s a new fashion in town and orders from the top to chase it. Rewrites can happen to appease a programmer too—rewarding good teamwork with a solo project.

The reason rewrites are so risky in practice is that replacing one working system with another is rarely an overnight change. We rarely understand what the previous system did—many of its properties are accidental in nature. Documentation is scarce, tests are ornamental, and interfaces are organic in nature, stubbornly locking behaviors in place.

If migrating to the replacement depends on switching over everything at once, make sure you’ve booked a holiday during the transition, well in advance.

Successful rewrites plan for migration to and from the old system, plan to ease in the existing load, and plan to handle things being in one or both places at once. Both systems are continuously maintained until one of them can be decommissioned. A slow, careful migration is the only option that reliably works on larger systems.

To succeed, you have to start with the hard problems first—often performance related—but it can involve dealing with the most difficult customer, or biggest customer or user of the system too. Rewrites must be driven by triage, reducing the problem in scope into something that can be effectively improved while being guided by the larger problems at hand.

If a replacement isn’t doing something useful after three months, odds are it will never do anything useful.

The longer it takes to run a replacement system in production, the longer it takes to find bugs. Unfortunately, migrations get pushed back in the name of feature development. A new project has the most room for feature bloat—this is known as the second-system effect.

The second system effect is the name of the canonical doomed rewrite, one where numerous features are planned, not enough are implemented, and what has been written rarely works reliably. It’s a similar to writing a game engine without a game to implement to guide decisions, or a framework without a product inside. The resulting code is an unconstrained mess that is barely fit for its purpose.

The reason we say “Never Rewrite Code” is that we leave rewrites too late, demand too much, and expect them to work immediately. It’s more important to never rewrite in a hurry than to never rewrite at all.

null is true, everything is permitted

The problem with following advice to the letter is that it rarely works in practice. The problem with following it at all costs is that eventually we cannot afford to do so.

It isn’t “Don’t Repeat Yourself”, but “Some redundancy is healthy, some isn’t”, and using abstractions when you’re sure you want to couple things together.

It isn’t “Each thing has a unique component”, or other variants of the single responsibility principle, but “Decoupling parts into smaller pieces is often worth it if the interfaces are simple between them, and try to keep the fast changing and tricky to implement bits away from each other”.

It’s never “Don’t Rewrite!”, but “Don’t abandon what works”. Build a plan for migration, maintain in parallel, then decommission, eventually. In high-growth situations you can probably put off decommissioning, and possibly even migrations.

When you hear a piece of advice, you need to understand the structure and environment in place that made it true, because they can just as often make it false. Things like “Don’t Repeat Yourself” are about making a tradeoff, usually one that’s good in the small or for beginners to copy at first, but hazardous to invoke without question on larger systems.

In a larger system, it’s much harder to understand the consequences of our design choices—in many cases the consequences are only discovered far, far too late in the process and it is only by throwing more engineers into the pit that there is any hope of completion.

In the end, we call our good decisions ‘clean code’ and our bad decisions ‘technical debt’, despite following the same rules and practices to get there.

Introduction

In the last post we were left with some tests that exercised some very basic functionality of the Deck class. In this post, we will continue to add unit tests and write production code to make those tests pass, until we get a class which is able to produce a randomised deck of 52 cards.

Test Refactoring

You can, and should, refactor your tests where appropriate. For instance, on the last test in the last post, we only asserted that we could get all the cards for a particular suit. What about the other three? With most modern test frameworks, that is very easy.

Theory]
[InlineData(Suit.Clubs)]
[InlineData(Suit.Diamonds)]
[InlineData(Suit.Hearts)]
[InlineData(Suit.Spades)]
public void Should_BeAbleToSelectSuitOfCardsFromDeck(Suit suit)
{
    var deck = new Deck();

    var cards = deck.Where(x => x.Suit == suit);

    cards.Should().HaveCount(13);
}

More Cards

We are going to want actual cards with values to work with. And for the next test, we can literally copy and past the previous test to use as a starter.

[Theory]
[InlineData(Suit.Clubs)]
[InlineData(Suit.Diamonds)]
[InlineData(Suit.Hearts)]
[InlineData(Suit.Spades)]
public void Should_BuildAllCardsInDeck(Suit suit)
{
    var deck = new Deck();

    var cards = deck.Where(x => x.Suit == suit);

    cards.Should().Contain(new List<Card> 
    { 
        new Card(suit, "A"), new Card(suit, "2"), new Card(suit, "3"), new Card(suit, "4"),
        new Card(suit, "5"), new Card(suit, "6"), new Card(suit, "7"), new Card(suit, "8"),
        new Card(suit, "9"), new Card(suit, "10"), new Card(suit, "J"), new Card(suit, "Q"),
        new Card(suit, "K")
    });
}

Now that I’ve written this, when I compare it to the previous one, it’s testing the exact same thing, in slightly more detail. So we can delete the previous test, it’s just noise.

The test is currently failing because it can’t compile, due to there not being a constructor which takes a string. Lets fix that.

public struct Card
{
    private Suit _suit;
    private string _value;

    public Card(Suit suit, string value)
    {
        _suit = suit;
        _value = value;
    }

    public Suit Suit { get { return _suit; } }
    public string Value { get { return _value; } }

    public override string ToString()
    {
        return $"{Suit}";
    }
}

There are a couple of changes to this class. Firstly, I added the constructor, and private variables which hold the two defining variables, with properties with only public getters. I changed it from being a class to being a struct, and it’s now an immutable value type, which makes sense. In a deck of cards, there can, for example, only be one Ace of Spades.

These changes mean that are tests don’t work, as the Deck class is now broken, because the code which builds set of thirteen cards for a given suit is broken - it now doesn’t understand the Card constructor, or the fact that the .Suit property is now read-only.

Here is my first attempt at fixing the code, which I don’t currently think is all that bad:

private string _ranks = "A23456789XJQK";

private List<Card> BuildSuit(Suit suit)
{
    var cards = new List<Card>(_suitSize);

    for (var i = 1; i <= _suitSize; i++)
    {
        var rank = _ranks[i-1].ToString();
        var card = new Card(suit, rank);
        cards.Add(card);
    }

    return cards;
}

This now builds us four suites of thirteen cards. I realised as I was writing the production code that handling “10” as a value would be straightforward, so I opted for the simpler (and common) approach of using “X” to represent “10”. The test pass four times, once for each suit. This is probably unnecessary, but it protects us in future from inadvertantly adding any code which may affect the way that cards are generated for a particular suit.

Every day I’m (randomly) shuffling

It’s occured to me as I write this that the Deck class is funtionally complete, as it produces a deck of 52 cards when it is instantiated. You will however recall that we want a randomly shuffled deck of cards. If we consider, and invoke the Single Responsibility Principal, then we should add a Dealer class; we are modeling a real world event and a pack of cards cannot shuffle itself, that’s what the dealer does.

Conclusion

In this post I’ve completed the walk through of developing a class to create a deck of 52 cards using some basic TDD techniques. I realised adding the ability to shuffle the pack to the Deck class would be a violation of SRP, as the Deck class should not be concerned or have any knowledge about how it is shuffled. In the next post I will discuss how we can implement a Dealer class, and illustrate some techniques swapping the randomisation algorithim around.

Debuggable code is code that doesn’t outsmart you. Some code is a little to harder to debug than others: code with hidden behaviour, poor error handling, ambiguity, too little or too much structure, or code that’s in the middle of being changed. On a large enough project, you’ll eventually bump into code that you don’t understand.

On an old enough project, you’ll discover code you forgot about writing—and if it wasn’t for the commit logs, you’d swear it was someone else. As a project grows in size it becomes harder to remember what each piece of code does, harder still when the code doesn’t do what it is supposed to. When it comes to changing code you don’t understand, you’re forced to learn about it the hard way: Debugging.

Writing code that’s easy to debug begins with realising you won’t remember anything about the code later.

Rule 0: Good code has obvious faults.

Many used methodology salesmen have argued that the way to write understandable code is to write clean code. The problem is that “clean” is highly contextual in meaning. Clean code can be hardcoded into a system, and sometimes a dirty hack can written in a way that’s easy to turn off. Sometimes the code is clean because the filth has been pushed elsewhere. Good code isn’t necessarily clean code.

Code being clean or dirty is more about how much pride, or embarrassment the developer takes in the code, rather than how easy it has been to maintain or change. Instead of clean, we want boring code where change is obvious— I’ve found it easier to get people to contribute to a code base when the low hanging fruit has been left around for others to collect. The best code might be anything you can look at quickly learn things about it.

• Code that doesn’t try to make an ugly problem look good, or a boring problem look interesting.
• Code where the faults are obvious and the behaviour is clear, rather than code with no obvious faults and subtle behaviours.
• Code that documents where it falls short of perfect, rather than aiming to be perfect.
• Code with behaviour so obvious that any developer can imagine countless different ways to go about changing it.

Sometimes, code is just nasty as fuck, and any attempts to clean it up leaves you in a worse state. Writing clean code without understanding the consequences of your actions might as well be a summoning ritual for maintainable code.

It is not to say that clean code is bad, but sometimes the practice of clean coding is more akin to sweeping problems under the rug. Debuggable code isn’t necessarily clean, and code that’s littered with checks or error handling rarely makes for pleasant reading.

Rule 1: The computer is always on fire.

The computer is on fire, and the program crashed the last time it ran.

The first thing a program should do is ensure that it is starting out from a known, good, safe state before trying to get any work done. Sometimes there isn’t a clean copy of the state because the user deleted it, or upgraded their computer. The program crashed the last time it ran and, rather paradoxically, the program is being run for the first time too.

For example, when reading and writing program state to a file, a number of problems can happen:

• The file is missing
• The file is corrupt
• The file is an older version, or a newer one
• The last change to the file is unfinished
• The filesystem was lying to you

These are not new problems and databases have been dealing with them since the dawn of time (1970-01-01). Using something like SQLite will handle many of these problems for you, but If the program crashed the last time it ran, the code might be run with the wrong data, or in the wrong way too.

With scheduled programs, for example, you can guarantee that the following accidents will occur:

• It gets run twice in the same hour because of daylight savings time.
• It gets run twice because an operator forgot it had already been run.
• It will miss an hour, due to the machine running out of disk, or mysterious cloud networking issues.
• It will take longer than an hour to run and may delay subsequent invocations of the program.
• It will be run with the wrong time of day
• It will inevitably be run close to a boundary, like midnight, end of month, end of year and fail due to arithmetic error.

Writing robust software begins with writing software that assumed it crashed the last time it ran, and crashing whenever it doesn’t know the right thing to do. The best thing about throwing an exception over leaving a comment like “This Shouldn’t Happen”, is that when it inevitably does happen, you get a head-start on debugging your code.

You don’t have to be able to recover from these problems either—it’s enough to let the program give up and not make things any worse. Small checks that raise an exception can save weeks of tracing through logs, and a simple lock file can save hours of restoring from backup.

Code that’s easy to debug is code that checks to see if things are correct before doing what was asked of it, code that makes it easy to go back to a known good state and trying again, and code that has layers of defence to force errors to surface as early as possible.

Rule 2: Your program is at war with itself.

Google’s biggest DoS attacks come from ourselves—because we have really big systems—although every now and then someone will show up and try to give us a run for our money, but really we’re more capable of hammering ourselves into the ground than anybody else is.

This is true for all systems.

Astrid Atkinson, Engineering for the Long Game

The software always crashed the last time it ran, and now it is always out of cpu, out of memory, and out of disk too. All of the workers are hammering an empty queue, everyone is retrying a failed request that’s long expired, and all of the servers have paused for garbage collection at the same time. Not only is the system broken, it is constantly trying to break itself.

Even checking if the system is actually running can be quite difficult.

It can be quite easy to implement something that checks if the server is running, but not if it is handling requests. Unless you check the uptime, it is possible that the program is crashing in-between every check. Health checks can trigger bugs too: I have managed to write health checks that crashed the system it was meant to protect. On two separate occasions, three months apart.

In software, writing code to handle errors will inevitably lead to discovering more errors to handle, many of them caused by the error handling itself. Similarly, performance optimisations can often be the cause of bottlenecks in the system—Making an app that’s pleasant to use in one tab can make an app that’s painful to use when you have twenty copies of it running.

Another example is where a worker in a pipeline is running too fast, and exhausting the available memory before the next part has a chance to catch up. If you’d rather a car metaphor: traffic jams. Speeding up is what creates them, and can be seen in the way the congestion moves back through the traffic. Optimisations can create systems that fail under high or heavy load, often in mysterious ways.

In other words: the faster you make it, the harder it will be pushed, and if you don’t allow your system to push back even a little, don’t be surprised if it snaps.

Back-pressure is one form of feedback within a system, and a program that is easy to debug is one where the user is involved in the feedback loop, having insight into all behaviours of a system, the accidental, the intentional, the desired, and the unwanted too. Debuggable code is easy to inspect, where you can watch and understand the changes happening within.

Rule 3: What you don’t disambiguate now, you debug later.

In other words: it should not be hard to look at the variables in your program and work out what is happening. Give or take some terrifying linear algebra subroutines, you should strive to represent your program’s state as obviously as possible. This means things like not changing your mind about what a variable does halfway through a program, if there is one obvious cardinal sin it is using a single variable for two different purposes.

It also means carefully avoiding the semi-predicate problem, never using a single value (count) to represent a pair of values (boolean, count). Avoiding things like returning a positive number for a result, and returning -1 when nothing matches. The reason is that it’s easy to end up in the situation where you want something like "0, but true" (and notably, Perl 5 has this exact feature), or you create code that’s hard to compose with other parts of your system (-1 might be a valid input for the next part of the program, rather than an error).

Along with using a single variable for two purposes, it can be just as bad to use a pair of variables for a single purpose—especially if they are booleans. I don’t mean keeping a pair of numbers to store a range is bad, but using a number of booleans to indicate what state your program is in is often a state machine in disguise.

When state doesn’t flow from top to bottom, give or take the occasional loop, it’s best to give the state a variable of it’s own and clean the logic up. If you have a set of booleans inside an object, replace it with a variable called state and use an enum (or a string if it’s persisted somewhere). The if statements end up looking like if state == name and stop looking like if bad_name && !alternate_option.

Even when you do make the state machine explicit, you can still mess up: sometimes code has two state machines hidden inside. I had great difficulty writing an HTTP proxy until I had made each state machine explicit, tracing connection state and parsing state separately. When you merge two state machines into one, it can be hard to add new states, or know exactly what state something is meant to be in.

This is far more about creating things you won’t have to debug, than making things easy to debug. By working out the list of valid states, it’s far easier to reject the invalid ones outright, rather than accidentally letting one or two through.

Rule 4: Accidental Behaviour is Expected Behaviour.

When you’re less than clear about what a data structure does, users fill in the gaps—any behaviour of your code, intended or accidental, will eventually be relied upon somewhere else. Many mainstream programming languages had hash tables you could iterate through, which sort-of preserved insertion order, most of the time.

Some languages chose to make the hash table behave as many users expected them to, iterating through the keys in the order they were added, but others chose to make the hash table return keys in a different order, each time it was iterated through. In the latter case, some users then complained that the behaviour wasn’t random enough.

Tragically, any source of randomness in your program will eventually be used for statistical simulation purposes, or worse, cryptography, and any source of ordering will be used for sorting instead.

In a database, some identifiers carry a little bit more information than others. When creating a table, a developer can choose between different types of primary key. The correct answer is a UUID, or something that’s indistinguishable from a UUID. The problem with the other choices is that they can expose ordering information as well as identity, i.e. not just if a == b but if a <= b, and by other choices mean auto-incrementing keys.

With an auto-incrementing key, the database assigns a number to each row in the table, adding 1 when a new row is inserted. This creates an ambiguity of sorts: people do not know which part of the data is canonical. In other words: Do you sort by key, or by timestamp? Like with the hash-tables before, people will decide the right answer for themselves. The other problem is that users can easily guess the other keys records nearby, too.

Ultimately any attempt to be smarter than a UUID will backfire: we already tried with postcodes, telephone numbers, and IP Addresses, and we failed miserably each time. UUIDs might not make your code more debuggable, but less accidental behaviour tends to mean less accidents.

Ordering is not the only piece of information people will extract from a key: If you create database keys that are constructed from the other fields, then people will throw away the data and reconstruct it from the key instead. Now you have two problems: when a program’s state is kept in more than one place, it is all too easy for the copies to start disagreeing with each other. It’s even harder to keep them in sync if you aren’t sure which one you need to change, or which one you have changed.

Whatever you permit your users to do, they’ll implement. Writing debuggable code is thinking ahead about the ways in which it can be misused, and how other people might interact with it in general.

Rule 5: Debugging is social, before it is technical.

When a software project is split over multiple components and systems, it can be considerably harder to find bugs. Once you understand how the problem occurs, you might have to co-ordinate changes across several parts in order to fix the behaviour. Fixing bugs in a larger project is less about finding the bugs, and more about convincing the other people that they’re real, or even that a fix is possible.

Bugs stick around in software because no-one is entirely sure who is responsible for things. In other words, it’s harder to debug code when nothing is written down, everything must be asked in Slack, and nothing gets answered until the one person who knows logs-on.

Planning, tools, process, and documentation are the ways we can fix this.

Planning is how we can remove the stress of being on call, structures in place to manage incidents. Plans are how we keep customers informed, switch out people when they’ve been on call too long, and how we track the problems and introduce changes to reduce future risk. Tools are the way in which we deskill work and make it accessible to others. Process is the way in which can we remove control from the individual and give it to the team.

The people will change, the interactions too, but the processes and tools will be carried on as the team mutates over time. It isn’t so much valuing one more than the other but building one to support changes in the other.Process can also be used to remove control from the team too, so it isn’t always good or bad, but there is always some process at work, even when it isn’t written down, and the act of documenting it is the first step to letting other people change it.

Documentation means more than text files: documentation is how you handover responsibilities, how you bring new people up to speed, and how you communicate what’s changed to the people impacted by those changes. Writing documentation requires more empathy than writing code, and more skill too: there aren’t easy compiler flags or type checkers, and it’s easy to write a lot of words without documenting anything.

Without documentation, how can you expect people to make informed decisions, or even consent to the consequences of using the software? Without documentation, tools, or processes you cannot share the burden of maintenance, or even replace the people currently lumbered with the task.

Making things easy to debug applies just as much to the processes around code as the code itself, making it clear whose toes you will have to stand on to fix the code.

Code that’s easy to debug is easy to explain.

A common occurrence when debugging is realising the problem when explaining it to someone else. The other person doesn’t even have to exist but you do have to force yourself to start from scratch, explain the situation, the problem, the steps to reproduce it, and often that framing is enough to give us insight into the answer.

If only. Sometimes when we ask for help, we don’t ask for the right help, and I’m as guilty of this as anyone—it’s such a common affliction that it has a name: “The X-Y Problem”: How do I get the last three letters of a filename? Oh? No, I meant the file extension.

We talk about problems in terms of the solutions we understand, and we talk about the solutions in terms of the consequences we’re aware of. Debugging is learning the hard way about unexpected consequences, and alternative solutions, and involves one of the hardest things a programer can ever do: admit that they got something wrong.

It wasn’t a compiler bug, after all.

I once ate 10 mg LSD by accident. It was a dilution error. The peak lasted ~10 hr. At some point, I saw the top of my head. But hey, maybe it was just an hallucination ;)

maybe ;)

I know that last time I said I was going to start soldering, but I really wanted to play with the networking capabilities of the ESP8266 first.

There’s a bunch of example programs to do web requests, such as BasicHttpClient, HTTPSRequest, WifiClient, StreamHttpClient, etc. I had trouble getting these working because there’s no built in certificate store or TLS validation capabilities. That means you can’t do a “normal” HTTPS request to test services like RequestBin (since they’re HTTPS only). The example programs have you type in the server’s SHA1 thumbprint, but that didn’t seem to work for me. The certs I inspected were SHA256, which I assume is the problem.

Anyway, I’m not interested in doing HTTP in any of my project ideas right now, so I moved on to what I actually want to do, which is MQTT. Once again it was Hack-a-day that clued me in to this protocol, which is very popular for small devices & home automation. I started out looking for a “simplest possible MQTT example for ESP8266″ and didn’t find anything simple enough initially. Later I realized that there’s two great places to start looking for libraries & examples. First is the esp8266/Arduino repo on Github, which has a list of miscellaneous third party projects compatible with this specific chip. Second is in the Arduino IDE itself; the Library Manager is searchable:

Arduino Library Manager – Searching for “MQTT”

The problem here is that which (if any) are actually good, useful, or correct for the ESP8266. The first search result in that screenshot is only for the “Arduino Uno Wifi Developer Edition”, for example.

Another challenge here is working through all the company branding. The second library listed, “Adafruit MQTT Library”, is ESP8266 compatible and comes with a “simple” example program to get started. However, it’s oriented around the Adafruit IO IoT web service (which is apparently a thing). I did get it to work with the local MQTT broker I’m running here on my PC, but I had to guess if might and try to peel away their extra stuff just to get to the bones.

The ESP8266 Github linked to lmroy/pubsubclient, which itself is a fork of knolleary/pubsubclient which seems more up to date. I don’t know why they’re linking to an out of date fork, except that it appears to more easily support “large” messages. The original has a default max packet size of 128 bytes, which might be too small for real apps, but I’m looking for a simple example so it should be fine.

Here’s a link to the example program from that repo: https://github.com/knolleary/pubsubclient/blob/master/examples/mqtt_esp8266/mqtt_esp8266.ino

The example is easy to set up; just punch in your Wifi access info & MQTT broker host name. Interestingly it does apparently support DNS… from working with the HTTP examples earlier, some of them used IP addresses rather than host names, so it wasn’t clear if DNS was supported in some of these network libraries. This one does, apparently.

Here’s what the output looks like. I’m running mosquitto 1.4.8 on my PC, with mosquitto_sub running in the lower panel subscribed to # (a wildcard, so all topic messages are shown).

Basic MQTT Example on the ESP8266

Actual footage of me as this program was running

I thought it would be fun to give the messages a little personality, so I found a list of all the voice lines from the turrets in Portal 2, copied them into a giant array, and now instead of “Hello World #37″ it’ll send something like “So what am I, uh, supposed to do here?” or “Well, I tried. Best of Luck!” once every few seconds.

Additionally, I made it so that the power LED blinks as it’s sending a message, as a little visual chirp to let you know it’s alive.

via GIPHY

The code is here, and this version is a modification of the Adafruit MQTT example, rather than the other library linked above, because I wrote it before I discovered that simpler example. (Found the list of voice lines here, and removed a few dupes).

I thought it might be fun to play around with programmable microcontrollers, so I bought some to play with. One of the most popular chips right now is the ESP8266 which I first saw pop up on Hack-a-day in 2014. I had to search backwards through 47 pages of blog posts that have been made in the meantime — that might give you a sense of its popularity.

I played around with PIC16/PIC18s in the early 2000s but never actually made anything, but the interest has been there. It’s also been my long time desire to create an E-ink weather display (once again thanks to an old Hack-a-day post, this one from 2012. That’s how far behind on projects I am). Recently I noticed some inexpensive E-ink development boards on Aliexpress and decided to jump into a less shallow end of the pool. I had also been following the ESP8266 for Arduino repo on Github, so I vaguely knew where to begin.

This evening I received the hardware (specifically, three of these) and decided to see if I could get a basic program deployed, just to get started. I don’t really know what I’m doing but I’m pretty good at reading & following directions (that counts for a lot in life).

The basics are:

1. Follow the “Installing with Boards Manager” directions here: https://github.com/esp8266/Arduino/ (which includes grabbing the latest Arduino IDE software, then pulling in the ESP8266 chip configuration, which includes the WeMos D1 mini board configuration.

2. From WeMos’ website, grab the driver for the on-board USB/programmer chip, which for me was the CH240G (https://wiki.wemos.cc/tutorials:get_started:get_started_in_arduino). Nothing more “exciting” than installing Chinese device driver software!

3. I got a little tripped up here, but later figured it out- when you plug one of the D1 boards into your PC via USB, it’ll show up as a COM Port in Device Manager. You have to pick that same COM Port in the Arduino IDE or it can’t find your board to deploy to. This was a little confusing because it won’t show up until you’re plugged in.

4. The Arduino IDE comes with the ability to load in example programs from the board configuration, loaded in Step 1. I wanted the simplest possible thing to make sure everything was working, so picked the “Hello World” of microcontroller programs: “Blink”, which toggles the power LED in an infinite loop.

So far, so good! All told, this took about an hour from opening the package to getting the light to blink (which includes scrounging around for a good USB cable and trying to get a in-focus pictures).

As you can see, I didn’t even bother to solder on the headers yet. I will do that, but I think next I will look into getting some wifi code up and running.

Think of a team you work with closely. How strongly do you agree with these five statements?

1. If I take a chance and screw up, it will be held against me.
2. Our team has a strong sense of culture that can be hard for new people to join.
3. My team is slow to offer help to people who are struggling.
4. Using my unique skills and talents comes second to the objectives of the team.
5. It’s uncomfortable to have open, honest conversations about our team’s sensitive issues.

Teams that score high on questions like these can be deemed to be “unsafe.”

Introduction

In the previous post in this series, we had finished up with a very basic unit test, which didn’t really test much, which we had ran using dotnet xunit in a console, and saw some lovely output.

We’ll continue to write some more unit tests to try and understand what kind of API we need in a class (or classes) which can help us satisfy the first rule of our Freecell engine implementation. As a reminder, our first rule is: There is one standard deck of cards, shuffled.

I’m trying to write both the code and the blog posts as I go along, so I have no idea what the final code will look like when I’ve finished. This means I’ll probably make mistakes and make some poor design decisions, but the whole point of TDD is that you can get a feel for that as you go along, because the tests will tell you.

Don’t try to TDD without some sort of plan

Whilst we obey the 3 Laws of TDD, that doesn’t mean that we can’t or shouldn’t doodle a design and some notes on a whiteboard or a notebook about the way our API could look. I always find that having some idea of where you want to go and what you want to achieve aids the TDD process, because then the unit tests should kick in and you’ll get a feel for whether things are going well or the conceptual design you had is not working.

With that in mind, we know that we will want to define a Card object, and that there are going to be four suits of cards, so that gives us a hint that we’ll need an enum to define them. Unless we want to play the same game of Freecell over and over again, then we’ll need to randomly generate the cards in the deck. We also know that we will need to iterate over the deck when it comes to building the Cascades, but the Deck should not be concerned with that.

With that in mind, we can start writing some more tests.

To a functioning Deck class

First things first, I think that I really like the idea of having the Deck class enumerable, so I’ll start with testing that.

[Fact]
public void Should_BeAbleToEnumerateCards()
{
    foreach (var card in new Deck())
    {
    }
}

This is enough to make the test fail, because the Deck class doesn’t yet have a public definition for GetEnumerator, but it gives us a feel for how the class is going to be used. To make the test pass, we can do the simplest thing to make the compiler happy, and give the Deck class a GetEnumerator definition.

public IEnumerator<object> GetEnumerator()
{
    return Enumerable.Empty<object>().GetEnumerator();
}

I’m using the generic type of object in the method, because I haven’t yet decided on what that type is going to be, because to do so would violate the three rules of TDD, and it hasn’t yet been necessary.

Now that we can enumerate the Deck class, we can start making things a little more interesting. Given that it is a deck of cards, it should be reasonable to expect that we could expect to be able to select a suit of cards from the deck and get a collection which has 13 cards in it. Remember, we only need to write as much of this next test as is sufficient to get the test to fail.

[Fact]
public void Should_BeAbleToSelectSuitOfCardsFromDeck()
{
    var deck = new Deck();

    var hearts = deck.Where();
}

It turns out we can’t even get to the point in the test of asserting something because we get a compiler failure. The compiler can’t find a method or extension method for Where. But, the previous test where we enumerate the Deck in a foreach passes. Well, we only wrote as much code to make that test pass as we needed to, and that only involved adding the GetEnumerator method to the class. We need to write more code to get this current test to pass, such that we can keep the previous test passing too.

This is easy to do by implementing IEnumerable<> on the Deck class:

public class Deck : IEnumerable<object>
{
    public IEnumerator<object> GetEnumerator()
    {
        foreach (var card in _cards)
        {
            yield return card;
        }
    }

    IEnumerator IEnumerable.GetEnumerator() => GetEnumerator();
}

I’ve cut some of the other code out of the class so that you can see just the detail of the implementation. The second explicitly implemented IEnumerable.GetEnumerator is there because IEnumerable<> inherits from it, so it must be implemented, but as you can see, we can just fastward to the genericly implemented method. With that done, we can now add using System.Linq; to the Deck class so that we can use the Where method.

var deck = new Deck();

var hearts = deck.Where(x => x.Suit == Suit.Hearts);

This is where the implementation is going to start getting a little more complicated that the actual tests. Obviously in order to make the test pass, we need to add an actual Card class and give it a property which can use to select the correct suit of cards.

public enum Suit
{
    Clubs,
    Diamonds,
    Hearts,
    Spades
}

public class Card
{
    public Suit Suit { get; set; }
}

After writing this, we can then change the enumerable implementation in the Deck class to public class Deck : IEnumerable<Deck>, and the test will now compile. Now we can actually assert the intent of the test:

[Fact]
public void Should_BeAbleToSelectSuitOfCardsFromDeck()
{
    var deck = new Deck();

    var hearts = deck.Select(x => x.Suit == Suit.Hearts);

    hearts.Should().HaveCount(13);
}

Conclusion

In this post, I talked through several iterations of the TDD loop, based on the 3 Rules of TDD, in some detail. An interesting discussion that always rears its head at this point is: Do you need to follow the 3 rules so excruciatingly religously? I don’t really know the answer to that. Certainly I always had it in my head that I would need a Card class, and that would necessitate a Suit enum, as these are pretty obvious things when thinking about the concept of a class which models a deck of cards. Could I have taken a short cut, written everything and then wrote the tests to test the implementation (as it stands)? Probably, for something so trivial.

In the next post, I will write some more tests to continue building the Deck class.

Introduction

I thought Freecell would make a fine basis for talking about Test Driven Development. It is a game which I enjoy playing. I have an app for it on my phone, and it’s been available on Windows for as long as I can remember, although I’m writing this on a Mac, which does not by default have a Freecell game.

The rules are fairly simple:

• There is one standard deck of cards, shuffled.
• There are four “Free” Cell piles, which may each have any one card stored in it.
• There are four Foundation piles, one for each suit.
• The cards are dealt face-up left-to-right into eight cascades
  • The cards must alternate in colour.
  • The result of the deal is that the first four cascades will have seven cards, the final four will have six cards.
• The top most card of a cascade beings a tableau.
• A tableaux must be built down by alternating colours.
• A card in cell may be moved onto a tableau subject to the previous rule.
• A tableaux may be recursively moved onto another tableaux, or to an empty cascade only if there is enough free space in Cells or empty cascades to use as intermediate locations.
• The game is won when all four Foundation piles are built up in suit, Ace to King.

These rules will form the basis of a Frecell Rules Engine. Note that we’re not interested in a UI at the moment.

This post is a follow on from my previous post of how to setup a dotnet core environment for doing TDD.

red - first test

We know from the rules that we need a standard deck of cards to work with, so our initial test could assert that we can create an array, of some type that is yet to be determined, which has a length of 51.

[Fact]
public void Should_CreateAStandardDeckOfCards()
{
    var sut = new Deck();

}

There! Our first test. It fails (by not compiling). We’ve obeyed The 3 Laws of TDD: We’ve not written any production code and we’ve only written enough of the unit test to make it fail. We can make the test pass by creating a Deck class in the Freecell.Engine project. Time for another commit:

green - it passes

It is trivial to make our first test pass, as all we need to do is create a new class in our Freecell.Engine project, and our test passes as it now compiles. We can prove this by instructing dotnet to run our unit tests for us:

nostromo:Freecell.Engine.Tests stuart$ dotnet watch xunit
watch : Started
Detecting target frameworks in Freecell.Engine.Tests.csproj...
Building for framework netcoreapp2.0...
  Freecell.Engine -> /Users/stuart/dev/freecell/Freecell.Engine/bin/Debug/netstandard2.0/Freecell.Engine.dll
  Freecell.Engine.Tests -> /Users/stuart/dev/freecell/Freecell.Engine.Tests/bin/Debug/netcoreapp2.0/Freecell.Engine.Tests.dll
Running .NET Core 2.0.0 tests for framework netcoreapp2.0...
xUnit.net Console Runner (64-bit .NET Core 4.6.00001.0)
  Discovering: Freecell.Engine.Tests
  Discovered:  Freecell.Engine.Tests
  Starting:    Freecell.Engine.Tests
  Finished:    Freecell.Engine.Tests
=== TEST EXECUTION SUMMARY ===
   Freecell.Engine.Tests  Total: 1, Errors: 0, Failed: 0, Skipped: 0, Time: 0.142s
watch : Exited
watch : Waiting for a file to change before restarting dotnet...

It is important to make sure to run dotnet xunit from within the test project folder, you can’t pass the path to the test project like you can with dotnet test. As you can see, I’ve also started watching xunit, and the runner is now going to wait until I make and save a change before automatically compiling and running the tests.

red, green

This first unit test still doesn’t really test very much, and because we are obeying the 3 TDD rules, it forces us to think a little before we write any test code. When looking at the rules, I think we will probably want the ability to move through our deck of cards and have the ability to remove cards from the deck. So, with this in mind, the most logical thing to do is to make the Deck class enumerable. We could test that by checking a length property. Still in our first test, we can add this:

var sut = new Deck();

var length = sut.Length;

If I switch over to our dotnet watch window, we get the immediate feedback that this has failed:

Detecting target frameworks in Freecell.Engine.Tests.csproj...
Building for framework netcoreapp2.0...
  Freecell.Engine -> /Users/stuart/dev/freecell/Freecell.Engine/bin/Debug/netstandard2.0/Freecell.Engine.dll
DeckTests.cs(13,30): error CS1061: 'Deck' does not contain a definition for 'Length' and no extension method 'Length' accepting a first argument of type 'Deck' could be found (are you missing a using directive or an assembly reference?) [/Users/stuart/dev/freecell/Freecell.Engine.Tests/Freecell.Engine.Tests.csproj]
Build failed!
watch : Exited with error code 1
watch : Waiting for a file to change before restarting dotnet...

We know that we have a pretty good idea that we’re going to make the Deck class enumerable, and probably make it in implement IEnumerable<>, then we could add some sort of internal array to hold another type, probably a Card and then right a bunch more code that will make our test pass.

But that would violate the 3rd rule, so instead, we simply add a Length property to the Deck class:

public class Deck 
{
    public int Length {get;}
}

This makes our test happy, because it compiles again. But it still doesn’t assert anything. Let’s fix that, and assert that the Length property actually has a length that we would expect a deck of cards to have, namely 52:

var sut = new Deck();

var length = sut.Length;

length.Should().Be(51);

The last line of the test asserts through the use of FluentAssertions that the Length property should be 51. I like FluentAssertions, I think it looks a lot cleaner than writing something like Assert.True(sut.Length, 51), and it’s quite easy to read and understand: ‘Length’ should be 51. I love it. We can add it with the command dotnet add package FluentAssertions. Fix the using reference in the test class so that it compiles, and then check our watch window:

Detecting target frameworks in Freecell.Engine.Tests.csproj...
Building for framework netcoreapp2.0...
  Freecell.Engine -> /Users/stuart/dev/freecell/Freecell.Engine/bin/Debug/netstandard2.0/Freecell.Engine.dll
  Freecell.Engine.Tests -> /Users/stuart/dev/freecell/Freecell.Engine.Tests/bin/Debug/netcoreapp2.0/Freecell.Engine.Tests.dll
Running .NET Core 2.0.0 tests for framework netcoreapp2.0...
xUnit.net Console Runner (64-bit .NET Core 4.6.00001.0)
  Discovering: Freecell.Engine.Tests
  Discovered:  Freecell.Engine.Tests
  Starting:    Freecell.Engine.Tests
    Freecell.Engine.Tests.DeckTests.Should_CreateAStandardDeckOfCards [FAIL]
      Expected value to be 51, but found 0.
      Stack Trace:
           at FluentAssertions.Execution.XUnit2TestFramework.Throw(String message)
           at FluentAssertions.Execution.AssertionScope.FailWith(String message, Object[] args)
           at FluentAssertions.Numeric.NumericAssertions`1.Be(T expected, String because, Object[] becauseArgs)
        /Users/stuart/dev/freecell/Freecell.Engine.Tests/DeckTests.cs(16,0): at Freecell.Engine.Tests.DeckTests.Should_CreateAStandardDeckOfCards()
  Finished:    Freecell.Engine.Tests
=== TEST EXECUTION SUMMARY ===
   Freecell.Engine.Tests  Total: 1, Errors: 0, Failed: 1, Skipped: 0, Time: 0.201s
watch : Exited with error code 1
watch : Waiting for a file to change before restarting dotnet...

Now to make our test past, we could again just start implementing IEnumerable<>, but that’s not TDD, and Uncle Bob might get upset at me. Instead, we will do the simplest thing that will make the test pass:

public class Deck
{
    public int Length { get { return new string[51].Length; }}
}

refactor

Now that we have a full test with an assertion that passes, we can about the refactor stage of the red/gree/refactor TDD cycle. As it stands, our simple classes passes our test but we can see right away that newing up an array in the getter of the Length property is not going to be something that is going to serve our interests well in the long run, so we should do something about that. Making it a member variable seems to be the most logical thing to do at the moment, so we’ll do that. We don’t need to make any changes to our test on the refactor stage. If we do, that’s a design smell that would indicate that something is wrong.

ublic class Deck
{
    private const int _size = 51;
    private string[] _cards = new string[_size];
    public int Length { get { return _cards.Length; }}
}

Conclusion

In this post, we’ve fleshed out our Deck class a little more, and gone through the full red/green/refactor TDD cycle. I also introduced FluentAssertions, and showed the output from the watch window as it showed the test failing

A few months ago I left a busy startup job I’d had for over a year. The work was engrossing: I stopped blogging, but I was programming every day. I learned a completely new language, but got plenty of chances to use my existing knowledge. That is, after all, why they hired me.

I especially liked something that might seem boring: combing through logs of occasional server errors and modifying our code to avoid them. Maybe it was because I had setup the monitoring system. Or because I was manually deleting servers that had broken in new ways. The economist in me especially liked putting a dollar value on bugs of this nature: 20 useless servers cost an extra 500 dollars a week on AWS.

But, there’s only so much waste like this to clean up. I’d automated most of the manual work I was doing and taught a few interns how to do the rest. I spent two weeks openly wondering what I’d do after finishing my current project, even questioning whether I’d still be useful with the company’s new direction.

Career Tip: don’t do this.

That’s when we agreed to part ways. So, there I was, no “official” job but still a ton of things to keep me busy. I’d help run a chain of Hacker Hostels in Silicon Valley, I was still maintaining Wine as an Ubuntu developer, and I was still a “politician” on Ubuntu’s Community Council having weekly meetings with Mark Shuttleworth.

Politiking, business management, and even Ubuntu packaging, however, aren’t programming. I just wasn’t doing it anymore, until last week. I got curious about counting my users on Launchpad. Download counts are exposed by an API, but not viewable on any webpage. No one else had written a proper script to harvest that data. It was time to program.

And man, I went a little nuts. It was utterly engrossing, in the way that writing and video games used to be. I found myself up past 3am before I even noticed the time; I’d spent a whole day just testing and coding before finally putting it on github. I rationalized my need to make it good as a service to others who’d use it. But in truth I just liked doing it.

It didn’t stop there. I started looking around for programming puzzles. I wrote 4 lines of python that I thought were so neat they needed to be posted as a self-answered question on stack overflow. I literally thought they were beautiful, and using the new yield from feature in Python3 was making me inordinately happy.

And now, I’m writing again. And making terrible cartoons on my penboard. I missed this shit. It’s fucking awesome.

Lock’n'Roll, a Pidgin plugin for Windows designed to set an away status message when the PC is locked, has received its first update in three and a half years!

Daniel Laberge has forked the project and released a version 1.2 update which allows you to specify which status should be set when the workstation locks. Get it while it’s awesome (always)!

terVec3 lb = ti->points[1] - ti->points[0];
terVec3 lc = ti->points[2] - ti->points[0];
terVec2 lbt = ti->texCoords[1] - ti->texCoords[0];
terVec2 lct = ti->texCoords[2] - ti->texCoords[0];

// Generate local space for the triangle plane
terVec3 localX = lb.Normalize2();
terVec3 localZ = lb.Cross(lc).Normalize2();
terVec3 localY = localX.Cross(localZ).Normalize2();

// Determine X/Y vectors in local space
float plbx = lb.DotProduct(localX);
terVec2 plc = terVec2(lc.DotProduct(localX), lc.DotProduct(localY));

terVec2 tsvS, tsvT;

tsvS[0] = lbt[0] / plbx;
tsvS[1] = (lct[0] - tsvS[0]*plc[0]) / plc[1];
tsvT[0] = lbt[1] / plbx;
tsvT[1] = (lct[1] - tsvT[0]*plc[0]) / plc[1];

ti->svec = (localX*tsvS[0] + localY*tsvS[1]).Normalize2();
ti->tvec = (localX*tsvT[0] + localY*tsvT[1]).Normalize2();

#define SH_AMBIENT_FACTOR   (0.25f)
#define SH_LINEAR_FACTOR    (0.5f)
#define SH_QUADRATIC_FACTOR (0.3125f)

void LambertDiffuseToSHCoefs(const terVec3 &dir, float out[9])
{
  // Constant
  out[0] = 1.0f * SH_AMBIENT_FACTOR;

  // Linear
  out[1] = dir[1] * SH_LINEAR_FACTOR;
  out[2] = dir[2] * SH_LINEAR_FACTOR;
  out[3] = dir[0] * SH_LINEAR_FACTOR;

  // Quadratics
  out[4] = ( dir[0]*dir[1] ) * 3.0f*SH_QUADRATIC_FACTOR;
  out[5] = ( dir[1]*dir[2] ) * 3.0f*SH_QUADRATIC_FACTOR;
  out[6] = ( 1.5f*( dir[2]*dir[2] ) - 0.5f ) * SH_QUADRATIC_FACTOR;
  out[7] = ( dir[0]*dir[2] ) * 3.0f*SH_QUADRATIC_FACTOR;
  out[8] = 0.5f*( dir[0]*dir[0] - dir[1]*dir[1] ) * 3.0f*SH_QUADRATIC_FACTOR;
}


void RotateCoefsByMatrix(float outCoefs[9], const float pIn[9], const terMat3x3 &rMat)
{
  // DC
  outCoefs[0] = pIn[0];

  // Linear
  outCoefs[1] = rMat[1][0]*pIn[3] + rMat[1][1]*pIn[1] + rMat[1][2]*pIn[2];
  outCoefs[2] = rMat[2][0]*pIn[3] + rMat[2][1]*pIn[1] + rMat[2][2]*pIn[2];
  outCoefs[3] = rMat[0][0]*pIn[3] + rMat[0][1]*pIn[1] + rMat[0][2]*pIn[2];

  // Quadratics
  outCoefs[4] = (
        ( rMat[0][0]*rMat[1][1] + rMat[0][1]*rMat[1][0] ) * ( pIn[4] )
      + ( rMat[0][1]*rMat[1][2] + rMat[0][2]*rMat[1][1] ) * ( pIn[5] )
      + ( rMat[0][2]*rMat[1][0] + rMat[0][0]*rMat[1][2] ) * ( pIn[7] )
      + ( rMat[0][0]*rMat[1][0] ) * ( pIn[8] )
      + ( rMat[0][1]*rMat[1][1] ) * ( -pIn[8] )
      + ( rMat[0][2]*rMat[1][2] ) * ( 3.0f*pIn[6] )
      );

  outCoefs[5] =  (
         ( rMat[1][0]*rMat[2][1] + rMat[1][1]*rMat[2][0] ) * ( pIn[4] )
       + ( rMat[1][1]*rMat[2][2] + rMat[1][2]*rMat[2][1] ) * ( pIn[5] )
       + ( rMat[1][2]*rMat[2][0] + rMat[1][0]*rMat[2][2] ) * ( pIn[7] )
       + ( rMat[1][0]*rMat[2][0] ) * ( pIn[8] )
       + ( rMat[1][1]*rMat[2][1] ) * ( -pIn[8] )
       + ( rMat[1][2]*rMat[2][2] ) * ( 3.0f*pIn[6] )
       );

  outCoefs[6] = (
              ( rMat[2][1]*rMat[2][0] ) * ( pIn[4] )
       +      ( rMat[2][2]*rMat[2][1] ) * ( pIn[5] )
       +      ( rMat[2][0]*rMat[2][2] ) * ( pIn[7] )
       + 0.5f*( rMat[2][0]*rMat[2][0] ) * ( pIn[8])
       + 0.5f*( rMat[2][1]*rMat[2][1] ) * ( -pIn[8])
       + 1.5f*( rMat[2][2]*rMat[2][2] ) * ( pIn[6] )
       - 0.5f * ( pIn[6] )
          );

  outCoefs[7] =  (
         ( rMat[0][0]*rMat[2][1] + rMat[0][1]*rMat[2][0] ) * ( pIn[4] )
       + ( rMat[0][1]*rMat[2][2] + rMat[0][2]*rMat[2][1] ) * ( pIn[5] )
       + ( rMat[0][2]*rMat[2][0] + rMat[0][0]*rMat[2][2] ) * ( pIn[7] )
       + ( rMat[0][0]*rMat[2][0] ) * ( pIn[8] )
       + ( rMat[0][1]*rMat[2][1] ) * ( -pIn[8] )
       + ( rMat[0][2]*rMat[2][2] ) * ( 3.0f*pIn[6] )
       );

  outCoefs[8] = (
              ( rMat[0][1]*rMat[0][0] - rMat[1][1]*rMat[1][0] ) * ( pIn[4] )
        +     ( rMat[0][2]*rMat[0][1] - rMat[1][2]*rMat[1][1] ) * ( pIn[5] )
        +     ( rMat[0][0]*rMat[0][2] - rMat[1][0]*rMat[1][2] ) * ( pIn[7] )
        +0.5f*( rMat[0][0]*rMat[0][0] - rMat[1][0]*rMat[1][0] ) * ( pIn[8] )
        +0.5f*( rMat[0][1]*rMat[0][1] - rMat[1][1]*rMat[1][1] ) * ( -pIn[8] )
        +0.5f*( rMat[0][2]*rMat[0][2] - rMat[1][2]*rMat[1][2] ) * ( 3.0f*pIn[6] )
         );
}

float3 SampleSHQuadratic(float3 dir, float3 shVector[9])
{
  float3 ds1 = dir.xyz*dir.xyz;
  float3 ds2 = dir*dir.yzx;    // xy, zy, xz

  float3 v = shVector[0];

  v += dir.y * shVector[1];
  v += dir.z * shVector[2];
  v += dir.x * shVector[3];

  v += ds2.x * shVector[4];
  v += ds2.y * shVector[5];
  v += (ds1.z * 1.5 - 0.5) * shVector[6];
  v += ds2.z * shVector[7];
  v += (ds1.x - ds1.y) * 0.5 * shVector[8];

  return v;
}

void SHForDirection(const terVec3 &dir, float out[9])
{
  // Constant
  out[0] = 1.0f;

  // Linear
  out[1] = dir[1] * 3.0f;
  out[2] = dir[2] * 3.0f;
  out[3] = dir[0] * 3.0f;

  // Quadratics
  out[4] = ( dir[0]*dir[1] ) * 15.0f;
  out[5] = ( dir[1]*dir[2] ) * 15.0f;
  out[6] = ( 1.5f*( dir[2]*dir[2] ) - 0.5f ) * 5.0f;
  out[7] = ( dir[0]*dir[2] ) * 15.0f;
  out[8] = 0.5f*( dir[0]*dir[0] - dir[1]*dir[1] ) * 15.0f;
}

terVec3 RandomDirection(int (*randomFunc)(), int randMax)
{
   float u = (((float)randomFunc()) / (float)(randMax - 1))*2.0f - 1.0f;
   float n = sqrtf(1.0f - u*u);

   float theta = 2.0f * M_PI * (((float)randomFunc()) / (float)(randMax));

   return terVec3(n * cos(theta), n * sin(theta), u);
}

It was Thursday afternoon, a completely sensible hour, but for me I had been woken up by the call. In my sleepy haze I hadn’t realized that this quickly turned into a surprise job interview. I made the mistake of saying that, while I had worked plenty with Ubuntu packaging and scripted application tests, I hadn’t actually written any of Wine’s C code.

“Oh.”

I began to feel the consequences of the impression I’d given. “Well, we want a real developer.” Without thinking, I’d managed to frame my years of experience in precisely the wrong way. All that integration work, application testing, knowledge of scripting languages and the deep internals of Ubuntu suddenly counted for nothing. It didn’t matter how much work I’d done or how many developer summits I’d been sponsored to: in this moment I was someone who didn’t even write simple Wine conformance tests.

We talked some more, and I went back to bed to wake at midnight, technically Friday. Too late and too early to go out, everything was quiet enough to get some real work done. I thought about the earlier conversation, and while I hadn’t written C code since high school I decided to dive right back in and hack at Wine myself. Within minutes I found a bug, and four hours later I had code not only proving it, but also fixing it for good.

Test driven development

Today’s Wine consists of two equally important parts: a series of implementations pretending to be parts of Windows, and an ever-growing suite of unit tests. The implementations are straightforward once you know the right thing to do: if the waiter function in Windows’ restaurant.dll asks for a tip, then ours needs to as well. Similarly, the tests prove what the right thing actually is, on both Wine and Windows. They help us answer weird questions, like if the Windows waiter still demands a tip with a negative bill. Somewhere out there, there’s a Windows program that depends on this behavior, and it will be broken in Wine if we make the mistake of assuming Windows acts reasonably.

I asked a developer to recommend a DLL that needed better tests, picked a random C file in it, and started looking. I soon found my target, a documented and complete implementation of a function with only partial tests. This code is already written, and believed working, in Wine. I was supposed to write a bunch of tests that should pass in Wine. That’s when I learned the function is already broken.

The Wine Testbot

Wine owes a lot to the late Greg Geldorp, an employee of VMware who originally created a Windows testbot for us. Any developer can submit a test patch to it, and those tests will be run on multiple versions of Windows to make sure they pass. It saves us the trouble of having to reboot into 10 different versions of Windows when hacking on Wine.

When I used the testbot to run my new tests, however, I found that while they passed on Wine they actually failed on Windows. Since we’re supposed to do what Windows does, no matter how stupid, that meant two problems: my new tests were bad, and Wine had a bug. Fixing the tests is simple enough – you just change the definition of “pass” – but this kind of unexpected failure can also inspire even more tests. By the end of it I had 6 patches adding different tests to just one function, 3 of which were marked “todo_wine”.

Fixing Wine

While simply submitting tests would certainly be a useful contribution, I felt like I could do more. “You found the mess, you clean it up” is an annoying cliché, but here it has a ring of truth to it: my recent experience writing these tests meant that I had become the world expert on this function. At least, for the next few days, after which I planned on forgetting it forever. That’s what good tests are for – they let us confidently ignore the internals of done code. In the off chance we break it unintentionally, they tell us exactly what’s wrong.

And so I wrote what was supposed to be my final patch: one that fixed Wine and marked the tests as no longer todo. In true open source fashion, I sent it to a friend for review, where he promptly informed me that, while my new tests were passing, I’d created a place where Wine could crash. The solution is, unsurprisingly, yet more tests to see how Windows handles the situation (keeping in mind that sometimes Windows handles things by crashing). This is typical in Wine development: your first attempt at a patch often results in mere discovery that the problem is harder to solve than you thought.

The real world

None of this actually matters, of course, unless the bug I’d fixed was actually affecting a real application that someone would want to run. Did I actually fix anything useful? I don’t know. It’s not exactly easy to get a list of all Windows applications that rely on edge-case behavior of shlwapi.dll’s StrFromTimeInterval function, but at least Wine is more correct now.

Apparent correctness isn’t the end-all of software development, of course. It’s possible doing something seemingly correct in Wine can make things worse: if my initial version of a fix slipped in to the code, for instance, an application could go from displaying a slightly wrong string to flat-out crashing. That’s why unit tests are just one part of software QA – you still need peer review of code and actual application testing.

Part of something greater

Incidentally, the whole experience reminded me of a blog post I had written over a year ago about modeling Wine development. My model was telling me that what I had just done was a bit inefficient: I made a modest improvement to Wine, but it wasn’t directly inspired by a particular real world application. Perhaps it would have been better had I tackled a more salient bug in a big name application, rather than polishing off some random function in string.c. But it wasn’t random: another developer recommended this particular code section to me because it was missing tests, and he noticed this precisely because in the past some untested behavior in a similar function was breaking a real application for him.

This function is now done. The coding was relatively simple by Wine standards – no need for expertise in COM, Direct3D, OLE, or any number of Windows conventions that O’Reilly writes entire books about. Wine doesn’t need experts: it needs a lot of grunt work from people like me. People willing to tackle one function at a time until by sheer attrition we end up with a test suite so exhaustive that everything can be simply guaranteed to work. That’s how we win in the end. That’s how real developers do it.

The Problem

For those using chef to automate your server infrastructure you probably find managing third-party cookbooks to be a pain. Ideally I want to make custom changes to a cookbook while still being able to track for upstream enhancements.

A few techniques I see being used are:

no tracking: Manually download an archive from github or opscode community and drop it in your cookbooks/ directory. Easy to make custom changes but you have no automated way to check for updates.

git submodules: This tracks upstream well, but unless you own the repo you can’t make changes.

fork it: Since pretty much all cookbooks reside on github, so you can fork a copy. This works, but now you might have dozens of different repos to manage. And checking for updates means going into each repo and manually merging in enhancements from the upstream.

knife vendor: Now we are getting somewhere. Chef’s knife command has functionality for dealing with third-party cookbooks. It looks something like this:

knife cookbook site vendor nginx

This downloads the nginx cookbook from the opscode community site, puts an unmodified copy in a chef-vendor-nginx git branch, and then puts a copy in your cookbooks/nginx dir in your master branch. When you run the install again it will download the updated version into the chef-vendor-nginx branch, and then merge that into master.

This is a good start, but it has a number of problems. First you are restricted to using what is available on the opscode community site. Second, although this seems like a git-centric model, knife is actually downloading a .tar.gz file. In fact if you visited the nginx cookbook page you would see it only offers an archive download, no way to inspect what this cookbook actually provides before installing.

There is a sea of great high-quality cookbooks on github. Since we all know and love git it would be great if we could get the previous functionality but using git repositories as a source instead.

A Solution

Enter knife-github-cookbooks. This gem enhances the knife command and lets us get the above functionality by pulling from github rather than downloading from opscode. To use it just install the gem and run:

knife cookbook github install cookbooks/nginx

By default it assumes a username/repo from github. So for each cookbook you install you will have a chef-vendor-cookbookname branch storing the upstream copy and a cookbooks/cookbook-name directory in your master branch to make local changes to.

If you want to check for upstream changes:

knife cookbook github compare nginx

That will launch a github compare view. You can even pass this command a different user who has forked the repo and see or merge in changes from that fork! Read more about it on the github page.

One thing to keep in mind is this gem doesn’t pull in required dependencies automatically, so you will have to make sure you download any requirements a cookbook might have. You can check what dependencies a cookbook requires by inspecting the metadata files.

Bonus Tip!

Opscode has a github repository full of recipes you probably want to use (opscode/cookbooks). Unfortunately using this repository would mean pulling in *all* of those cookbooks. That just clutters up your chef project with cookbooks you don’t need. Luckily there is https://github.com/cookbooks! This repository get updated daily and separates each opscode/cookbook cookbook into a separate git repository.

Now you can cherry-pick the cookbooks you want, and manage them with knife-github-cookbooks!

Make sure you have the same path to the binaries you want to profile on your target device as your workstation.

On your profiling target as root:

export KREXP=dpkg -L kernel-debug | grep "vmlinux-2.6"
opcontrol --init
opcontrol --vmlinux=$KREXP
opcontrol --separate=kernel
opcontrol --status
opcontrol -c=8

start your application

as root again:

opcontrol --stop;  opcontrol --reset; opcontrol --start; sleep 5; opcontrol --stop

rm -rf /var/lib/oprofile && scp -r root@192.168.2.15:/var/lib/oprofile /var/lib/
this should give you some log info:
opreport -l -r

opreport -c /path/to/binary_to_profile > /tmp/opreport.txt
oprofile-callgraph-to-svg -e 1 -n 1 /tmp/opreport.txt

for(nsIntRegionRectIterator iter(mUpdateRegion); const nsIntRect* R=iter.Next();)
//do something with R

The problem:

Sometimes one version of Wine can run an application, but fail on others.  Maybe there’s a regression in the latest Wine version, or you’ve installed an optional patch that fixes one application but breaks another.

The solution to this is to have more than one version of Wine installed on the system, and have the system determine which version of Wine is best for the application.  This implies two different levels of usability – advanced users may want to configure and tweak which Wine runs which App, but mere humans won’t even want to know such a thing exists.

This is the reason why many people have created Wine front ends: they worry about things like patches and registry hacks and native DLLs so that users won’t have to.  You just click a button for the application you want to install, put in the requisite disc or password, and it does all the shaman dances for you.  Codeweaver’s, the chief sponsor of Wine, stays in business through sales of Crossover, which is basically just a front end to a special version of Wine.

So, these front ends exist to solve some very real and important problems. But now we have a new problem, in that we might have more than one front end — playonlinux, winetricks, and others all need to deal with this too.  In true open source fashion, we need to work together and come up with a standard.

My proposed solution:

Distribution packagers like me make a separate package, say, wine-hotfixes.  This package replaces (on Ubuntu via dpkg-diversions) the /usr/bin/wine binary, moving the existing one to /usr/bin/wine-default.

The new /usr/bin/wine will pass everything to wine-default, unless it detects the environment variable WINEHOTFIXES is set to something other than null. If it is set (to, say WINEHOTFIXES=”6971,12234″), then the script will look in /etc/wine/hotfixes.d/ and /etc/wine/hotfixes.conf for alternative wine versions that might make it happy.  In the case of a partial match it will prioritize bugs in the listed order.

hotfixes.d will contain a series of config files, one for each alternative version of wine.  These could be installed manually, but generally they’ll come from special packages — say, a version of Wine built with a workaround for that annoying mouse escape bug.  Each file will give a path (say, /opt/wine-hotfixes/yokos-tweaked-wine) and which bugs it hotfixes.  hotfixes.conf can specify a list of bugs that the default Wine already fixes, as well as which bugs to ignore (eg that are probably fixed by every hotfix).

Start menu items (.desktop entries) can then work exactly as they do now, except they will have the WINEHOTFIXES environment variable set, generally as created by a front end.  If the user has no alternative wine versions, or no wine-hotfixes package, nothing different will happen and everything will still use the wine-default.  If the user upgrades Wine to a version that fixes all the worked around bugs, the default will be used again (forward-compatible) — all that’s needed is for the newer Wine package to ship an updated hotfixes.conf.

The beauty of this is that a front end can specify a list of bugs to workaround without actually having a ready hotfix – if needed that can be handled by someone else.  Similarly, the hotfixed Wines don’t actually need to know about which app they’ll be running, as wine-hotfixes will do all the matchmaking.  We also keep /usr/bin free, except for one added binary.

Wrapping it all up

The real test of a design, of course, is if it can be any simpler.  With the above, we’ve got it down to a single configuration item depending on who’s using it — hotfixes.d for the packager, hotfixes.conf for a manual tweaker, and the WINEHOTFIXES environment variable for the front end author.

It is of course worth asking if we should be doing this at all.  Zero configurations are better than one, after all, and ideally one magic Wine package would run every application flawlessly.  But that’s what we’ve been trying for years now, and people keep feeling the need to write these front ends anyway — we’re clearly not doing well enough without them, so we might as well manage them and work together.  This way, at least everything is backwards (and forwards) compatible: environment variables mean nothing without wine-hotfixes, and if we ever do invent a perfect version of Wine all the applications installed by front ends will continue to work just fine.

That should wrap it up, unless I’ve missed something.

My last post detailed how to compile the Io language from source and install it in Ubuntu (10.10 Maverick). Io has a growing set of addons such as GUI’s, sound and image manipulation, OpenGL, and database support to name a few. However they will not be enabled if you don’t have the proper development libraries installed.

I’ll go through a couple of addons in this article, but if you just want to make sure you have as many dependencies as possible to run the addons here is a line you can paste:

$ sudo apt-get install build-essential cmake libreadline-dev libssl-dev ncurses-dev libffi-dev zlib1g-dev libpcre3-dev libpng-dev libtiff4-dev libjpeg62-dev python-dev libpng-dev libtiff4-dev libjpeg62-dev libmysqlclient-dev libmemcached-dev libtokyocabinet-dev libsqlite3-dev libdbi0-dev libpq-dev libgmp3-dev libogg-dev libvorbis-dev libtaglib-cil-dev libtag1-dev libtheora-dev libsamplerate0-dev libloudmouth1-dev libsndfile1-dev libflac-dev libgl1-mesa-dev libglu1-mesa-dev freeglut3-dev libxmu-dev libxi-dev libxml2-dev libyajl-dev uuid-dev liblzo2-dev zlib1g-dev

You will need to rebuild Io once these are all installed.

I would encourage you to browse the addons/* directory in the Io source tree. There are many good useful addons and samples, although unfortunately there are few that do not seem to currently work or are missing samples, so dust off that book on C

Sockets

sudo apt-get install libevent-dev

Here is a minimal webserver using sockets:

WebRequest := Object clone do(
    cache := Map clone
    handleSocket := method(socket, server,
        socket streamReadNextChunk
        if(socket isOpen == false, return)
        request := socket readBuffer betweenSeq("GET ", " HTTP")         
 
        data := cache atIfAbsentPut(request,
            writeln("caching ", request)
            f := File clone with(request)
            if(f exists, f contents, nil)
        )                                                                
 
        if(data,
            socket streamWrite("HTTP/1.0 200 OK\n\n")
            socket streamWrite(data)
        ,
            socket streamWrite("Not Found")
        )                                                                
 
        socket close
        server requests append(self)
    )
)                                                                        
 
WebServer := Server clone do(
    setPort(7777)
    socket setHost("127.0.0.1")
    requests := List clone
    handleSocket := method(socket,
        WebRequest handleSocket(socket, self)
    )
) start

Lots of other good socket based examples in addons/Socket/samples.

Regex

sudo apt-get install libpcre3-dev

That will install Perl Compatible Regular Expression support for Io. You can use it like:

regex := Regex with("(?\\d+)([ \t]+)?(?\\w+)")
match := "73noises" matchesOfRegex(regex) next

CFFI

During the configure process you might have noticed a message saying Could NOT find FFI  (missing:  FFI_INCLUDE_DIRS).  FFI (foreign function interface) is basically a system that lets us call functions in different programming languages. First make sure you have the development libraries:

$ sudo apt-get install libffi-dev

How FFI functions is very architecture and compiler dependent, and it seems debian places the includes in a location the cmake scripts aren’t looking. I’m not that familiar with cmake and couldn’t find a very elegant solution, so just place the following line in the modules/FindFFI.cmake script:

$ vim modules/FindFFI.cmake
 
# Add the following line
set(FFI_INCLUDE_DIRS /usr/include/x86_64-linux-gnu)
# Above these two
include(FindPackageHandleStandardArgs)
FIND_PACKAGE_HANDLE_STANDARD_ARGS(FFI DEFAULT_MSG FFI_INCLUDE_DIRS FFI_LIBRARIES)

Here is a small program that gets us direct access to libc’s puts(3) function:

CFFI
 
lib := Library clone setName("libc.so.6")
puts := Function with(Types CString) setLibrary(lib) setName("puts")
 
puts "Hello Io!"

Python

sudo apt-get install python-dev

Want to access Python from Io?

# Import a module
sys := Python import("sys")
 
"Which version of python are we running?" println
sys version println
 
"Split a string" println
str := "Brave brave Sir Robin"
str println
string split(str) println
 
"Load a C module (.so)" println
t := Python import("time")
 
writeln("Current time is: ", t time)

Databases

sudo apt-get install libmysqlclient-dev libmemcache-dev libtokyocabinet-dev libsqlite3-dev libdbi0-dev

Io has addons for MySQL, PostgresQL, memcached, Tokyo Cabinet, SQLite and a few others.

Sound

sudo apt-get install libgmp3-dev libogg-dev libvorbis-dev libtaglib-cil-dev libtag1-dev libtheora-dev libsamplerate0-dev libloudmouth1-dev libsndfile1-dev libflac-dev

Various sound processing libraries.

Images

$ sudo apt-get install libpng-dev libtiff4-dev libjpeg62-dev

Various image loading libraries.

GUI

$ sudo apt-get install x11proto-xf86misc-dev xutils-dev libxpm-dev libpango1.0-dev libcairo2-dev libfreetype6-dev 
 
$ sudo apt-get install libclutter-1.0-dev libatk1.0-dev

There is also a GUI called Flux that requires OpenGL support. I wasn’t able to get it working however.

OpenGL

$ sudo apt-get install libgl1-mesa-dev libglu1-mesa-dev freeglut3-dev libxmu-dev libxi-dev

Lots of great examples in addons/OpenGL/samples.

XML and JSON

$ sudo apt-get install libxml2-dev libyajl-dev

If you need to do any XML or JSON parsing.

UUID

$ sudo apt-get install uuid-dev

Support for UUID generator. Seems to be broken however.

Misc

$ sudo apt-get install libreadline-dev libssl-dev ncurses-dev libffi-dev zlib1g-dev liblzo2-dev zlib1g-dev

SSL, archives, REPL history, curses GUI.

I have recently begun reading through Bruce Tate’s fun Seven Languages In Seven Weeks book. One of the chapters focuses the Io language and it’s installation can be a little bit non-standard to get it to my liking.

Generally on my development machine when I compile from source I like to install locally to my home directory rather than system wide. This way sudo privileges are not needed plus I just like the idea of keeping these items close to home.

First Io requires the cmake build system so make sure that is available.

$ sudo apt-get install cmake

Next download and extract the source code.

$ wget --no-check-certificate http://github.com/stevedekorte/io/zipball/master -O io-lang.zip
$ unzip io-lang.zip
$ cd stevedekorte-io-[hash]

Io provides a build script, however it is setup to install the language to /usr/local. Since I want it to go in $HOME/local you just need to modify that file. Here is a quick one liner:

$ sed -i -e 's/^INSTALL_PREFIX="\/usr\/local/INSTALL_PREFIX="$HOME\/local/' build.sh

Now build and install.

$ ./build.sh
$ ./build.sh install

Since we are installing into a location our OS doesn’t really know about, we need to configure a few paths.

$ vim ~/.bashrc
export PATH="${HOME}/local/bin:${PATH}"
export LD_LIBRARY_PATH="${HOME}/local/lib:${LD_LIBRARY_PATH}"
 
# You might want these too
export LD_RUN_PATH=$LD_LIBRARY_PATH
export CPPFLAGS="-I${HOME}/local/include"
export CXXFLAGS=$CPPFLAGS
export CFLAGS=$CPPFLAGS
export MANPATH="${HOME}/local/share/man:${MANPATH}"

Lastly restart your shell and type ‘io’ and you should be dropped into Io’s REPL!

A side benefit to this method is you can install anything you build into $HOME/local. Usually you just need to pass the –prefix=$HOME/local parameter when you run a ./configure script.

int iY = px[0] + 2*px[1] + px[2];    // 0..1020
int iCo, iCg;

if (iY == 0)
{
    iCo = 0;
    iCg = 0;
}
else
{
    iCo = (px[0] + px[1]) * 255 / iY;
    iCg = (px[1] * 2) * 255 / iY;
}

px[0] = (unsigned char)iCo;
px[1] = (unsigned char)iCg;
px[2] = 0;
px[3] = (unsigned char)((iY + 2) / 4);

float3 DecodeYCoCgRel(float4 inColor)
{
    return (float3(4.0, 0.0, -4.0) * inColor.r
        + float3(-2.0, 2.0, -2.0) * inColor.g
        + float3(0.0, 0.0, 4.0)) * inColor.a;
}