Adam Kewley

Side Project: Rust: fo2dat

tl;dr: I used Rust to make this CLI utility for extracting Fallout 1+2 DAT files.

I love the occasional playthrough of Fallout 1 and 2. They were some of the the first “serious” games I played. Sure, DOOM/Quake/Command & Conquer were also “mature”—I played them around the age of ~10, which marked me as doomed (heh) by the kind of adults that would also think eating sweets is a surefire path to heroin addition or something—but F1+2 included prostitutes, drugs, slavery, and infanticide: irresistibly entertaining topics for a teenager.

You might think that, with Bethesda buying the rights to Fallout over 10 years ago, F1+2 would’ve had a commercial re-release by now, much like what happened with Baldur’s Gate 1/2, or that such a popular franchise with so many fans would create enough momentum to get an open-source engine ported, but those things haven’t really happened. Probably because of various historical hitches.

F1+2 were originally developed by Interplay, which had a meteoric rise in the 90s followed by a precipitous fall in the early 00s (details). Bethesda excavated Fallout from Interplay’s remains in 2007. However, Interplay’s zombie, adopting a 90s zombie movie strategy of having a character bitten right before the credits roll, decided to give away F1+2+Tactics for free just before the IP passed over. As a consequence, Bethesda probably sees a F1+2 reboot as unprofitable. This assumes that the source code is even available to Bethesda. Interplay may have only handed them the sales rights + binaries, which would be a big shame.

I’ve always wanted to make a F1+2 source port, but it’s an incredibly difficult task for several reasons: the literature for open-source RPG game engines is thin, which means an engine will need to be built from first-principles; F1+2 uses a bunch of bespoke file formats, which means deserializers will need to be coded for each of them; and the game logic—what characters do, actions, etc.—is held as a binary opcode language, which requires a small virtual machine to handle.

The Falltergeist developers are the closest to surmounting the F1+2 Everest. They’ve created something that is close to a complete engine, which is amazing. I decided to chip away at the smaller problem of F1+2’s file formats. The result was fo2dat, a CLI utility for unpacking F2 DAT files, which might help any developers wanting to view the game’s assets in a file explorer.

The micro-project was also a chance to try Rust, the new hot thing in systems programming languages. I enjoyed the experience quite a bit: it feels like the Rust developers really understood the strengths of languages like C (non-OO, simple procedures handling data arguments), C++ (powerful compilers), Scala (pattern matching and abstractions), and Lisp (code generation). They combined those ideas with an excellent build system and package manager, which has resulted in a very nice platform.

Cover Design: Core–Shell Crystals of Porous Organic Cages

A cover I designed for the Cooper Group’s latest breakthrough. Modelled in plain Blender 2.79b. Post processing done in GIMP 2.8.18.

[raw high-res render] , [cover official link], [journal article]


Core–shell crystals of porous materials allow multiple functionalities to be used synergistically, for example choosing the core material for capacity, and the shell for selectivity, or surface properties. The few previous examples have all been based on frameworks, but in this Communication (DOI: 10.1002/anie.201803244), T. Hasell, A. I. Cooper et al. present the first report of core–shell crystals formed from porous molecules, in this case porous organic cages—which are directed by chiral recognition. Depicted are octahedral crystals with a fluorescent cage as the core, and a non‐fluorescent cage shell layer growing on the surface.

Integrating Software

tl;dr: If you find you’re spending a lot of time integrating various pieces of software across multiple computers and are currently using a mixture of scripts, build systems, and manual methods to do that, look into configuration managers. They’re easy to pick up and automate the most common tasks. I’m using ansible, because it’s standard, simple, and written in python.

Research software typically requires integrating clusters, high-performance numerical libraries, 30-year-old Fortran applications by geniuses, and 30-minute-old python scripts written by PhD students.

A consistent thorn in my side is downloading, building, installing, and deploying all of that stuff. For example, on a recent project, I needed to:

  • Checkout a Java (Maven) project from svn
  • Build it with a particular build profile
  • Unzip the built binaries
  • Install the binaries at a specific location on the client machine
  • Install the binaries at specific location on a cluster
  • Reconfigure Luigi to run the application with the correct arguments
  • Copy some other binaries onto the cluster’s HDFS
  • (Sometimes) rebuild all the binaries from source, if the source was monkey-patched due to a runtime bug
  • (Sometimes) Nuke all of the above and start fresh

Each step is simple enough, but designing a clean architecture around doing slightly different permutations of those steps is a struggle between doing something the easy way (e.g. a directory containing scripts, hard-coded arguments in the Luigi task) and doing something the correct way.

The correct way (or so I thought) to handle these kinds of problems is to use a build system. However, there is no agreed-upon “one way” to download, build, and install software, which is why build systems are either extremely powerful/flexible (e.g. make, where anything is possible) and rigid/declarative (e.g. maven).

Because there’s so much choice out there, I concluded that researching each would obviously (ahem) be a poor use of my valuable time. So, over the years, I’ve been writing a set of scripts which have been gradually mutating:

  • Initially they were bash scripts
  • Then they were ruby scripts that mostly doing the same as the bash scripts
  • Then they were ruby scripts that integrated some build parts (e.g. pulling version numbers out of pom.xml files), but were mostly doing the same as the bash scripts
  • Then they were a mixture of structured YAML files containing some of the build steps and ruby filling in the gaps
  • Then they were a mixture of YAML files containing metadata (description strings, version numbers), YAML files containing build steps, and Python filling in the gaps because Python’s easier to integrate with the existing researcher/developer’s work

After many months of this, I decided “this sucks, I’ll develop a new, better, way of doing this”. So I spent an entire evening going through the weird, wonderful, and standard build systems out there, justifying why my solution would be better for this problem.

Well, it turns out this problem isn’t suitable for a build system, despite it having similar requirements (check inputs, run something, check outputs, transform files, etc.). Although my searches yielded a menagerie of weird software, what I actually needed was a configuration manager. Ansible being a particularly straightforward one.

This rollercoaster of “there probably isn’t a good solution already available to this problem”, “I’ll hack my own solution!”, “My hacks are a mess, I should build an actual system”, “oh, the system already exists” must be common among software developers. Maybe it’s because the problem isn’t actually about developing a solution: it’s about understanding the problem well enough. If the problem’s truly understood, it will be easier to identify which libraries/algorithms to use to solve it, which will make developing the solution a lot easier. Otherwise, you’ll end up like me: Keeper of the Mutant Scripts.

(Not so) Fancy-Pants new Website

So, I just spent an evening + morning refactoring the site into a, uh, “new” design.

I only ocassionally work on this site these days—I now see it as the sparse journal of a madman that also likes to distribute mobile-friendly versions of his CV—but I thought it would be a nice and easy blog post to reflect on how the site has changed over the last 3 years.

The original version of was launched in May 2015 and was the fanciest design:

It makes me feel a bit ill. The first version was modelled off of the kind of erudite landing pages you see 3-man startups use to try and sell IoT soap bars or something. By May 2016, I clearly had gotten sick enough of my own bullshit to remove a bunch of that cute stuff:

By May 2017, there were a few regressions:

And now, in March 2018, I’ve finally decided to just throw almost all fancy tricks out the window and use the simplest HTML + CSS solution I could create:

The previous versions of this site required a bunch of javascript libraries and jekyll gems to build. There were also subtle bugs that would pop up if you used it on a phone. Development/maintenance time was dedicated to fixing that - I also couldn’t help but tweak with the code.

This new site is HTML + a ~50 line long CSS file. It felt strangely liberating to make. Maybe because after working on ReactJS (e.g.) and angular sites I came across this absolute gem that satirically makes the point that barebones sites are: a) fast, b) easy to make, and c) responsive. I couldn’t argue with the logic and immediately wanted to just rip out all the complexity in my site, so here we are.

I wonder how quickly regressions will set in ;)

State Machines in ReactJS

I’m currently implementing job resubmission in Jobson UI and found that state machines greatly simplify the code needed to render a user workflow.


A large amount of Jobson UI’s codebase is dedicated to dynamically generating input forms at runtime.

Generating the relevant <input>, <select>, <textarea>s, etc. from a Jobson job spec is fairly easy (see createUiInput here) but became increasingly complex after adding job copying because extra checks needed to be made:

  • Is the job “fresh” or “based on an existing job”?
  • Was there a problem loading the existing job?
  • Did the existing job load OK but can’t be coerced into the live version of the job spec?
  • Did the user, on being informed of the coercion issue, decide to start a fresh spec or make a “best attempt” at coercion?
  • etc.

Each of these conditions are simple to check in isolation but, when combined, result in delicate state checks:

render() {
  if (this.state.isLoadingSpecs)
    return this.renderLoadingSpecsMessage();
  if (this.state.errorLoadingSpecs)
    return this.renderSpecsLoadingError();
  else if (this.state.isLoadingExistingJob)
    return this.renderLoadingExistingJob();
  else if (this.state.errorLoadingExistingJob)
    return this.renderErrorLoadingExistingJob();
  else if (this.state.isCoercingAnExistingJob)	
    // etc. etc.

These checks were cleaned up slightly by breaking things into smaller components. However, that didn’t remove the top-level rendering decisions altogether.

For example, the isLoadingSpecs and errorLoadingSpecs checks can put into a standalone <SpecsSelector /> component that emits selectedSpecs. However, the top level component (e.g. <JobSubmissionComponent />) still needs to decide what to render based on emissions from multiple child components (e.g. it would need to decide whether to even render <SpecsSelector /> at all).

State Machines to the Rescue

What ultimately gets rendered in these kind of workflows depends on a complex combination of flags because only state, rather than state and transitions are being modelled. The example above compensates for a lack of transition information by ordering the if statements: isLoadingSpecs is checked before isLoadingExistingJob because one “happens” before the other.

This problem—a lack of transition information—is quite common. Whenever you see code that contains a big block of if..else statements, or an ordered lookup table, or a switch on a step-like enum, that’s usually a sign that the code might be trying to model a set of transitions between states. Direct examples can be found in many network data parsers (e.g. websocket frame and HTTP parsers) because the entire payload (e.g. a frame) isn’t available in one read() call, so the parser has to handle intermediate parsing states (example from Java jetty).

State Machines (SMs) represent states and transitions. For example, here’s the Jobson UI job submission workflow represented by an SM:

From a simplistic point of view, SMs follow simple rules:

  • The system can only be in one state at a given time
  • There are a limited number of ways to transition to another state

I initially played with the idea of using SMs in ReactJs UIs after exploring SM implementations of network parsers. I later found the idea isn’t new. A similar (ish) post by Jeb Beich has been posted on cogninet here and contains some good ideas, but his approach is purer (it’s data-driven) and is implemented in ClojureScript (which I can’t use for JobsonUI). By comparison, this approach I used focuses on using callbacks to transition so that individual states can be implemented as standard ReactJS components. In the approach:

  • A state is represented by a component. Components can, as per the ReactJS approach, have their own internal state, events, etc. but the top-level state (e.g “editing job”) is represented by that sole component (e.g. EditingJobStateComponent)

  • A component transitions to another state by calling a callback with the next “state” (e.g. transitionTo(SubmittingJobStateComponent)).

  • A top-level “state machine renderer” is responsible for rendering the latest component emitted via the callback.

This slight implementation change means that each component only has to focus on doing its specific job (e.g. loading job specs) and transitioning to the next immediate step. There is no “top-level” component containing a big block of if..else statements.

Code Examples

A straightforward implementation involves a top-level renderer with no decision logic. Its only job is to render the latest component emitted via a callback:

export class StateMachineRenderer extends React.Component {

  constructor() {
    const initialComponent =
      React.createElement(InitialStateComponent, {transitionTo: this.handleTransition.bind(this)});

    this.state = {
      component: initialComponent,

  handleStateTransition(nextComponent) {
    this.setState({component: nextComponent});

  render() {
    return this.state.component;

A state is just a standard component that calls transitionTo when it wants to transition. Sometimes, that transition might occur immediately:

export class InitialState extends React.Component {
  componentWillMount() {
    const props = {transitionTo: this.props.transitionTo};

    let nextComponent;
    if (jobBasedOnExistingJob) {
      nextComponent = React.createElement(LoadExistingJobState, props, null);
    } else {
      nextComponent = React.createElement(StartFreshJobState, props, null);

Otherwise, it could be after a set of steps:

export class EditingJobState extends React.Component {
  // init etc.
  onUserClickedSubmit() {
  transitionToJobSubmittedState(jobIdFromApi) {
    const component = React.createElement(JobSubmittedState, {jobId: jobIdFromApi}, null);

Either way, this simple implementation seems to work fine for quite complex workflows, and means that each components only contains a limited amount of “transition” logic, resulting in a cleaner codebase.


This pattern could be useful to webdevs that find themselves tangled in state- and sequence-related complexity. I’ve found SMs can sometimes greatly reduce overall complexity (big blocks of if..else, many state flags) at the cost of a little local complexity (components need to handle transitions).

However, I don’t reccomend using this pattern everywhere: it’s usually easier to use the standard approaches up to the point of standard approaches being too complex. If your UI involves a large, spiralling, interconnected set of steps that pretty much require a mess of comparison logic though, give this approach a try.