I Am Seb

It’s Saturday morning, I have a pot of fresh coffee in hand and a good night’s sleep behind me, so it’s time to get underway.

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Concept:

Hybrid race / collecting / combat game set inside derelict spacecraft. 2D overhead space ships in zero gravity; small arenas with environmental hazards. Single player against AI, possible multiplayer. (more…)

This weekend is the first I’ve had entirely free for a while, so to stave off the inevitable encroachment of boredom I’m going to be making a game with the new Unity 3, and because this is the internet: blogging about it in an almost-live way.

My goals for this weekend are to complete a functional prototype (no fancy graphics, entirely placeholder art and sound effects), to implement as many of the gameplay mechanisms from the design document as possible, and to have a lot of fun.

To get started, I’ve been rummaging through my ever-expanding collection of half-done game design documents, and decided on a concept I named “Scavenger Wars” as something that is both achievable and offers enough complexity to present a decent challenge.

At WooMe, we’ve been using Django and Postgresql in anger for coming up on three years, scaling up to tens of thousands of queries per second means we’ve had to look long and hard at Django’s database support to meet our needs.

Our performance tweaks have run the gamut from simple queryset changes, through changing Django’s transactional behaviour, right the way up to writing our own table partitioning and master/slave replication support long before multidb support hit Django trunk.

Of biggest impact so far was rewriting Django’s transactional layer to allow database transactions to commit automatically and run with PgBouncer as a connection pooler.

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The last few weeks I’ve been experimenting with different Django setups, which virtualenv makes trivial. (If you’re not using virtualenv and virtualenvwrapper for Python development, the secret is to bang the rocks together guys!) My goal has been to create a reusable project framework for quick social experiments that provides the following features:

• External authentication, to exploit as wide an audience as possible without the hurdle of requiring registration.
• Useful debugging information, both through logging and in-page debug.
• Database schema versioning and migration.
• Asynchronous task queuing and control.
• A lightweight WSGI HTTP server to sit between Django and Nginx.
• Straightforward deployment.
• Messaging, so that users can communicate with one another.
• Friends / social groups, so that users know who they want to communicate with.
• A rewards system, to reinforce and drive positive activity.

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PyPlants has come on in leaps and bounds over the past few days (well, evenings), and now from its new home as PyPlants on bitbucket sports a completely rewritten rendering backend which is more modular, should be really easy to plug into, and now supports POV-Ray out of the box.

What’s that you say? A 3-D ray-tracer? Yes indeed, as promised in the second part of this series of development diaries, I’ve now finished work on an update that turns this:

("A", "I+[A+O]-->>[--L]I[++L]-[AO]++AO")
("I", "FS[>>&&L][>>^^L]FS")
("S", "SFS")
("L", "['{+f-ff-f+|+f-ff-f}]")
("O", "[&&&C`>W>>>>W>>>>W>>>>W>>>>W]")
("C", "FF")
("W", "[`^F][{&&&&-f+f|-f+f}]")

Into this:

Rendering of an olive bush from pyplants povray renderer

Unfortunately it’s now 2am, so the write-up will have to wait for the weekend. Do feel free to grab the code and have a poke around. You’ll obviously need povray, pygame, and pycairo installed, but everything else should work with python’s included batteries.

In the previous part of this article we looked at the background to L-Systems, and how they could be used for describing self-similar biological systems. In this part we’ll look at a sample implementation of a very basic 2D L-System in Python, together with a basic PNG renderer using PyCairo.

This is an implementation of a deterministic, context-less L-System, which given the same initial conditions will always produce the same results. Whilst this won’t produce very naturalistic results for our plant system, it’s a useful first step to produce results like this:

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For a fledgling project idea, I’ve recently needed to work out how to draw plants procedurally, and of course Python is my language of choice for some rapid prototyping. Whilst some richly-featured professional applications exist for generating flora in a procedural fashion for high-end rendering, there are precious few systems available for the kind of bulk task that I require.

At first the task of generating plants might seem like a massive endeavour. The various permutations of stem widths, lengths, branch frequency, colour variation, leaf size, shape, frequency and mind-boggling. And that’s not even considering the problem space represented by flowers.

Fortunately, the problem of representing self-similar biological development is one that attracted Aristid Lindenmayer, who in 1968 introduced L-Systems:

… as a theoretical framework for studying the development of simple multicellular organisms, and subsequently applied to investigate higher plants and plant organs. After the incorporation of geometric features, plant models expressed using L-systems became detailed enough to allow the use of computer graphics for realistic visualization of plant structures and developmental processes.

(Lindenmayer’s book The Algorithmic Beauty of Plants is no longer in print, but is available for free in PDF.)

L-Systems are remarkably powerful, simple systems that express the development of a formal grammar through a parallel rewriting process. In effect, you start with a string that represents certain graphical operations, and a set of rules for rewriting the string. You iterate over the string a certain number of times, and are left with a final product.

For example in a 2D system, the symbol F represents drawing a straight line in a forward direction, the symbol + represents rotating left, and — represents rotating right. If you start with the string “F”, and a rule that says “(F → F+F−F−F+F)”, then you’d see the following iterations:

1. F+F-F-F+F
2. F+F-F-F+F+F+F-F-F+F-F+F-F-F+F-F+F-F-F+F+F+F-F-F+F
3. F+F-F-F+F+F+F-F-F+F-F+F-F-F+F-F+F-F-F+F+F+F-F-F+F+ F+F-F-F+F+F+F-F-F+F-F+F-F-F+F-F+F-F-F+F+F+F-F-F+F– F+F-F-F+F+F+F-F-F+F-F+F-F-F+F-F+F-F-F+F+F+F-F-F+F– F+F-F-F+F+F+F-F-F+F-F+F-F-F+F-F+F-F-F+F+F+F-F-F+F+ F+F-F-F+F+F+F-F-F+F-F+F-F-F+F-F+F-F-F+F+F+F-F-F+F

If you then plot those rules using a unit vector length and an angle of 90 degrees, you end up with the following iterations:

There are several more symbols that can be applied to the graphical operations, including pushing the current position and orientation onto a stack and popping it off again, rotations in 3D, and operations to define closed polygons and moving through colour and size lookup tables.

The most basic type of L-System is the deterministic context-less system, D0L, where each operation is independent and will always produce the same output. The Koch Square above is one of the most simplistic examples, but with a couple of variations much more complex patterns arise:

• Start: “X”
• Rule 1: “(X → F-[[X]+X]+F[+FX]-X)”
• Rule 2: “(F → FF)”
• Angle: 25 degrees
• Iterations: 5
• 

In Part 2 we’ll look at a first approach to implementing L-Systems in Python, and a rudimentary renderer for 2D systems using PyCairo.

Ten things I’ve learned as a developer at WooMe:

1. Be clear what your direction is. Identify your key proposition and focus on it. Don’t get distracted by unrelated features — somebody else is probably already doing those better than you.
2. Be merciless with features that don’t cut it. No matter how much you like it, if your audience doesn’t like it, either change it or kill it fast. Wasting time on your personal pet project when there’s no clear demand for it is time you should be spending on features users want.
3. Use what’s already there rather than inventing your own. Need a message queue? Video processing? Load balancer? Email distribution? Payment system? Use something that already exists rather than waste time you should be spending on feature development.
4. Quick and dirty is better than late to market. At the rate this industry moves, capturing your audience’s attention first is more important than having a well-engineered solution. For the most part, users don’t care about your code quality, as long as it works.
5. Don’t optimise until you need to. Sure, you may end up chasing your tail for a bit when you need to scale, but until you hit that point you’re wasting valuable time that should be spent on feature development.
6. Scaling is most often a data problem. When you do need to optimise, scaling won’t be about the performance of your application. Scaling web applications horizontally is easy. Scaling databases vertically is expensive. Scaling databases horizontally is very, very hard. Don’t worry about what web application framework you use, worry about what database you put behind it.
7. Abstraction is great, as long as you understand what you’re abstracting. This has been a constant pain with Django’s ORM. Yes, it’s a powerful and useful abstraction, but without a solid understanding of what the database is doing behind it, it’s easy to get into very deep trouble, very quickly.
8. Log everything. Nothing is harder than trying to find problems on a live environment without detailed logging.
9. Storage is cheap. It’s better to store everything than to throw away data. You never know when you might need something.
10. Communication is critical. Not just outside your organisation, but within it. Tell people what you’re doing, frequently. Ask them what they’re doing just as often. Not only do ideas percolate through this process much more effectively, but when things go wrong, clear and frequent communication will stop them from getting worse.

Every now and then someone at TED presents a technology or an idea that’s so utterly amazing, or ridiculously simple that it can’t help but change the world. David Merrill shows off an MIT project called Siftables in this talk, and even though I’ve been messing around with computing for 25 years my jaw is still dragging along the floor. Check it out.