# DnD: First Encounter at Lyst

It was everyone’s first time playing DnD, including mine so there will likely be rookie DM and player mistakes. We were all work colleagues from Lyst (@MakingLyst) and spent 3-4 hours in our canteen playing.

The whole night has fantastic fun and we will be continuing the quest to find the Mine of Phandelver in the future.

## Characters

@StevieBuckley as ‘Iron’ Mike Baggins – Fighter, Folk Hero
@Maciej_Kula as Blane Kinglyquartz – Cleric
@ejlbell as Artemis von Spiegelhorn the Chaoshadow – Wizard
@trepca as Ljubomir ljivkovic Sljucica – Rogue
@wolffan as Kosef Longsummer – Figher, Noble

@carlc75 as The Dungeon Master

### Setup

Projected maps,  Fog of War, Sound effects.

I used map resources from Mike Schley who has kindly put up the digital assets for a pittance. I made custom fog of war masks for the cragmaw cave map and used javascript to hide/show the different areas. The code and resources (minus Mike Schley’s maps) are up at GitHub.

## Encounter 1, Goblin Arrows

In the city of Neverwinter, a dwarf named Gundren Rockseeker has asked the party to escort a wagon of supplies to the rough and tumble settlement of Phandalin, which lies a couple of days travel southeast. Gundren was excited, and claimed he had found “something big”, so promised to pay the party 10gp each to get the supplies to Barthen’s Provisions in Phandalin. He then rode on ahead with a warrior escort named Sildar Hallwinter as he needed to “take care of business”.

The party has spent the last couple of days with the an ox wagon on the road. ‘Iron’ Mike is riding an ox, guiding the cart with his animal handling skills. Arty and Kosef are in the back of the cart with the supplies: Arty trying to forcefully converse about foreign tax law unsuccessfully. Blane walks beside the wagon with Ljubomir bringing up the rear.

As the turn a bend, they notice 2 dead horses peppered with arrows in front of them. ‘Iron’ Mike stops the cart, and an irritated ‘Driver, driver, why are you stopping?!’ floats over from Arty. Annoyed, ‘Iron’ Mike drags Arty along to the horses to investigate. As they draw closer, they recognise the horses as those that their friends rode on ahead.

Before they can investigate further, 2 goblins rush from the under brush and attack ‘Iron’ Mike. The first goblin misses his swing, but the other manages to get in a hit. He attempts to retaliate with his mighty sword, but fumbles the swing. Instead a surprise arrow from Ljubomir takes the goblin through the chest. Arty, suitable annoyed at the goblin in front of it, sends 3 Magic Missiles to ‘explode’ it. Suddenly, a goblin arrow streaks from the southern under brush, flies past Kosef’s head, and lands in the cart with a thunk. Kosef spots the offending goblin shooter, grabs his axe, and charges into the under brush. His battleaxe swings down with such power it splits the goblin (and some small trees) in two. Across the trail, another previously hidden goblin is heard panicking and running away through the undergrowth to the north.

The battle over, and with Blane mostly oblivious to what just happened, the survivors check the goblin bodies for loot. Kosef grabs some arrows for ‘Iron’ Mike and is disappointed at the lack of loot from the malnourished goblins. Arty pokes at the residue of his slain goblin and laments that he could not learn more about it. ‘Iron’ Mike, however, finds that the goblin with the arrow in its chest is still alive and, with the help of Blane, manages to stabilise it.

Arty is brought over to translate, and finds that the goblin is named Stinkblade. Arty attempts to persuade Stinkblade that they will not harm him if he talks, but the goblin does not believe him, even when donning a godlike illusion. ‘Iron’ Mike attempts to intimidate the goblin into submission by sitting on him, but this only aggravates it. Finally, Blane casts Command and forces the goblin to speak.

They learn that their friends were captured by Stinkblade’s goblin troupe and were taken to their hideout in a northward cave. Their troupe leader had received orders to capture the duo via courier from Cragmaw Castle. At the mention of this place, Arty flies in to a rage and stabs the goblin, ending the interrogation.

Worried about Gundren and Sildar, the party decide to investigate this cave to the north. However, first they take a short rest and engage in a heated argument about taking the cart with them. ‘Iron’ Mike ends up winning, taking the cart with them, but at the cost of making the journey time much longer.

With wounds healed and spell slots replenished, the party head out slowly with the cart along a difficult woodland trail. ‘Iron’ Mike is sitting upon the ox, guiding them with twitches of his hands. Arty and Blane are in the cart discussing religions affect on crop yields in winter, Kosef is taking the rear guard and Ljubomir is out in front — on the lookout for traps and things to snag the cart.

After an hour on the trail, Ljubomir fails to notice a snare trap that is draped in front of him but just manages to jump out before he is snapped up. Kosef decides to take the front, and another hour later fails to notice a pit trap in front of him.
He noticed, however, that he was falling 10ft to the bottom of it. Climbing out, with minor injuries to body, major injuries to pride, he helped the cart navigate around the pit. After a further hour travelling, the party sees a large cliff coming into view, so decide to tie up the cart and oxen, then investigate on foot.

The party reach the cliff, trying to stay as quiet as possible. They exit the trail and come upon a clearing, which edges up to a stream. On the other side of the stream lies thick brambles and trees, and the stream is bursting forth from a cave in the cliff. There is room on the other side to walk into the cave.

Ljubomir stealthily creeps up to the stream and spots two goblins in the under brush, not paying attention to him or anything else that isn’t their simple stones game. He slowly creeps back to inform the others of the enemy.

With a surprise attack, Blane channels Sacred Flames to burn the goblins, but miss and burn everything else around them. As the under brush turns to ash, Ljubomir and ‘Iron’ Mike unleash arrows, which both miss. Kosef charges with his great axe brandished and cleaves a goblin in twain. Arty sends a ray of frost at the second goblin, who freezes, shatters, and scatters into the wind.

Blane scouts ahead into the cave, using his Dwarfen sight to see in the dark. He stealthily notices the wolf kennel and assesses than they are very hungry, more so than caring about guard duty. Sneaking back, Blane informs the party and they get to work dragging the 2 pieces of goblin into the cave. ‘Iron’ Mike gains acceptance, but not leadership, of the pack, in order to drag the goblin meals inside. The wolves begin eating, but will not let anyone near them.

Blane scouts further into the cave and notices a wooden bridge, 20ft above the main passage – along with a goblin sentry. The goblin does not notice him. Blane sneaks back, and the party come up with a plan. Creeping forward, Ljubomir and ‘Iron’ Mike ready their bows as Arty creates a ball of light in front of the goblin. Blinded, it did not see the two arrows that sunk into its heart. The goblin toppled forward into the stream and floated out of the cave.

Noticing a small, fragile, trail to the left Blane and Arty scouted ahead. At the top of the trail they successfully find rooms at either end of the passage they find themselves in. At the western end: a barracks of sorts with 6 goblins; to the east: a room with 2 dammed pools of water and 3 goblins. The party decide a sneak attack on the larger group of goblins in the western room. Everyone climbs up the fragile trail in pairs until the party is in the upper corridor.

From the shadows, Arty casts sleep on 4 of the goblins, with ‘Iron’ Mike taking out a fifth with an arrow. Ljubomir creeps in to the room, and attacks a much larger goblin, scoring a critical hit with an arrow that perfectly hits the part of the goblin’s brain that makes it explode. Entering the room the party find Sildar who is badly beaten. They help him up and pass him a goblin short sword for protection. Sildar says he will help where he can and will answer questions once they deal with the goblins.

Seeing the sleeping goblins as a threat, ‘Iron’ Mike brings out his secret weapons. Ham fists, from his encounter with a greedy boat captain in Neverwinter, and punches a sleeping goblin as hard as he can. The goblin takes massive damage, but survives. The ham, now quite old, explodes sending rancid meat into the eyes and mouths of the sleeping dwarfs, instantly waking them.

Ham Fists: When working in the galley of a ship in Neverwinter, ‘Iron’ Mike led a workers revolt against a captain who was unlawfully exploiting his workers. Finding two large hams in the kitchen, he plunged his hands through the flesh to grab the sturdy ham bones within, creating his Ham Fists. Using his weapons, he beat the corrupt captain into submission, before taking him to the guards.

The party, thoroughly annoyed at having to engage the 4 groggy goblins send arrows, Rays of Frosts, and the business end of an axe into the goblins. 3 are killed but 1 managed to escape and run towards the other goblins at the pools in the eastern cave.

Giving chase, ‘Iron’ Mike, Ljubomir, and Kosef all send ranged attacks, but due to the darkness miss. Arty catches the fleeing goblin with a ray of frost, not killing it but slowing it. They could only watch in apprehension as the very cold goblin rounds a corner into the welcoming arms of reinforcements.

Blane chants a prayer to bless the party in preparation of their next fight. ‘Iron’ Mike, Ljubomir, and Kosef ready ranges attacks on the next goblin they see across the bridge. The other goblins across the bridge move to confront the party. Arty casts light onto the ceiling of  the pool room, flooding it with light. This fortunately illuminates a goblin on the other side of the bridge. Arrows and javelins pummel the goblin, instantly killing it. The other goblins move up to the bridge, one hiding behind a stalagmite. Meanwhile the ham fist survivor manages to run into a deeper cave on the southern wall, no doubt alerting those inside.

An arrow from Ljubomir takes out the goblin on the bridge, allowing ‘Iron’ Mike to charge at the stalagmite with his great sword drawn. As he stabs the goblin and lifts it above his head on the tip of his sword, Arty finishes him off with a quick Ray of Frost.

Regrouping outside of the next cave, the party try to perceive what is inside. Kosef peers in and sees a near empty room with crates and sacks piled up near the far wall. There is more of the cave to the right, but it hidden from the entrance. Just as Kosef is about to turn to the others, he notices a goblin foot stick out from behind a sack. The ambush is ruined as he notices 2 more limbs behind various items. Telling Arty the goblin positions, 3 Magic Missiles zip into the room to injure the goblins. The most injured goblin flies at Kosef in a rage, to only be split in two by his great axe.

Smelling the rancid ham that is emanating from the original goblin survivor, ‘Iron’ Mike bursts into the room and sets his sites on the poor bloodied beast — but not before being bitten by a wolf which was lying in wait in the shadows. ‘Iron’ Mike shrugs it off to finish the goblin who broke his Ham Fist. Only then does he notice how badly he is bleeding. Ljubomir enters the room and hides behind ‘Iron’ Mike, while trying to send an arrow into one of the remaining goblins — but he misses. Kosef enters the room to levy his axe upon the wolf, heavily wounding it. Arty attempts to freeze a goblin with Ray of Frost, but misses. The two remaining goblins move up to attack the intruders, one focusing on ‘Iron’ Mike, landing a crippling hit, and the other focusing on Kosef, but he misses.

Out from behind one of the stalagmites of the cave, a large bugbear appears, screaming ‘KLAARG WILL KILL THE INTRUDERS‘. Klaarg then throws a javelin at Kosef, heavily wounding him before moving closer. Blane enters the cave and lays his hand on Kosef to cure his wounds. ‘Iron’ Mike, fed up of goblins, brings his great sword down in a tight arc to finish off the goblin attacking him, then uses his Second Wind to make a slight recovery. Ljubomir sends an arrow to finish off the final goblin.

Kosef levels his axe on Klaarg, attempting to cleave him from the crotch up, but the axe bounces off his armour. Arty steps fully into the cave and tries to grab the wolf to use his Shocking Grasp, but the wolf sidesteps away. Retaliating, the wolf tries to get a bite out of Arty, but misses. Klaarg swings his morning star at Kosef, but the brute of a weapon swings above his head.

Blane attempts to finish the wolf off with his hammer, but looses his footing and fumbles the blow. ‘Iron’ Mike sheaths his great sword and brings out his longbow, firing an arrow into Klaarg. The arrow lodges into his shoulder, sending him into a rage. Ljubomir performs a ranged sneak attack and sends an arrow deep into Klaargs gut. Kosef swings with his axe, taking a chunk of Klaarg with it. Arty tries to send a Ray of Frost at Klaarg, but just succeeds in freezing a nearby wall. The wolf tries a second bite, but misses again, and Klaarg swings again at Kosef with his morning star, but his rage has effected his accuracy, and he misses by a clear foot. Finally regaining his composure, Blane smashes his hammer onto the wolfs skull, ending it there. ‘Iron’ Mike and Ljubomir send two arrows into Klaargs face, sending him reeling backwards onto the ground and into the afterlife.

The battle over, the party surveys their surroundings. Gundren is not here, but there are many branded crates in the cavern which look recoverable, and a chest in the corner of the room. Inside the chest they find 1200cp, 110sp, a small statue, and 2 potions. The party splits the cash, ‘Iron’ Mike pockets the statue, and after Arty identifies the potions as healing potions, they are given to the fighters.

Interrogating Sildar does not provide much more information than they got from Stinkblade, other than he will pay the party 50gp to escort him to Phandalin, payable there. The party agree and start hauling out the stolen crates to their parked wagon, intent on returning them to their owners, feeling more experienced from the whole encounter. ‘Iron’ Mike tries one last time to tame one of the tied up wolfs, and manages to gain the trust of the smaller wolf, which the party allow him to keep so long as it is chained up and doesn’t bite anyone.

They decide to camp for the night outside the cave, away from the stench of dead goblins and under the stars.

# Stripe CTF 3.0

Sadly, level 3 would not run for me, even with Stripe’s patch, so I could not continue with the competition. It was fun while it lasted though – C

Wednesday saw the beginning of another Stripe CTF! This time I was in London when it started so I went to the launch meeting with some old uni friends.

The theme this time was distributed computation, so with a tiny netbook, I dove in to the levels.

## Level 0

Level 0 was essentially an exercise in optimisation. A given text input was checked against a list of words. If an input word was in the preset dictionary, it had to be tagged. The preset dictionary was an ordered list, and as such was O(n) to search. By applying the following:

index 1558f2d..d07273f 100644
--- orig.rb
+++ mod.rb
@@ -1,4 +1,5 @@
#!/usr/bin/env ruby
+require 'set'

# Our test cases will always use the same dictionary file (with SHA1
# 6b898d7c48630be05b72b3ae07c5be6617f90d8e). Running test/harness
@@ -7,6 +8,7 @@

path = ARGV.length &gt; 0 ? ARGV[0] : '/usr/share/dict/words'
+entries = Set.new(entries)

contents = $stdin.read output = contents.gsub(/[^ \n]+/) do |word| The list is turned into a set with a O(1) lookup time. Significantly speeding up the operation. ## Level 1 This level was about cryptocurrencies, and to pass this level you had to mine a … ‘GitCoin’. Essentially, you were given a repo with an initial catalog of transactions. You had to successfully submit a transaction with gave your given use a gitcoin. Proof of work for a gitcoin was determined by ensuring that the git commit message had a SHA1 signature that was lexigraphically smaller than the difficulty. So add a nonce to your commit message and keep cycling though random numbers until the commit message had a valid signature. Stripe provided a very slow bash reference implementation, which I still used to pass the level. Instead of increasing the nonce in bash though, I wrote a python script to find a correct hash for me faster. import sys from hashlib import sha1 import random import string import Queue as queue import threading def work(diff, tree, parent, timestamp, q): diffl = len(diff) diff = ''.join('0' for x in range(diffl)) body = "tree %s\nparent %s\nauthor CTF user &lt;me@example.com&gt; %s +0000\ncommitter CTF user &lt;me@example.com&gt; %s +0000\n\nGive me a Gitcoin\n\nnonce: " % (tree, parent, timestamp, timestamp) while True: body_b = '%s%s' % (body, ''.join(random.choice(string.hexdigits) for x in range(8))) s = sha1('commit ' + str(len(body_b)) + '\0' + body_b) hex = s.hexdigest()[:diffl] if hex.startswith(diff): body = body_b break q.put(body) def main(): diff, tree, parent, timestamp = sys.argv[1:] q = queue.Queue() threads = [threading.Thread(target=work, args=(diff, tree, parent, timestamp, q)) for i in range(1)] for th in threads: th.daemon = True th.start() body = bytes(q.get()) with open('/home/carl/level1/test.txt', 'w') as f: f.write(body) for th in threads: th.join(0) if __name__ == '__main__': main() There were some hurdles I came across while solving this, which show in the code. The git hashing command git hash-object -t commit didn’t just take the SHA1 hash of its input, it would first prepend commit len(data)\0 before hashing. This was easy enough to find with a bit of searching, but a major issue I was having that I couldn’t replicate the SHA1 hash unless I first wrote the commit to a file, rather than streaming via stdout. So I just wrote to a file and modified the miner bash script to change: @@ -56,12 +56,12 @@$counter"

# See http://git-scm.com/book/en/Git-Internals-Git-Objects for
# details on Git objects.
-       sha1=$(git hash-object -t commit --stdin &lt;&lt;&lt; "$body")
+       sha1=$(git hash-object -t commit /home/carl/level1/test.txt) if [ "$sha1" "&lt;" "$difficulty" ]; then echo echo "Mined a Gitcoin with commit:$sha1"
-           git hash-object -t commit --stdin -w &lt;&lt;&lt; "$body" &gt; /dev/null + git hash-object -t commit --stdin -w /home/carl/level1/test.txt &gt; /dev/null git reset --hard "$sha1" &gt; /dev/null
break
fi

Which let me get the correct hashes and mine the coin.

## Level 2

Level 2 was all about DDOS attacks. The idea was that there were a number of back end servers, a reverse proxy (which you modified), and a number of clients, some malicious and others not. You had to modify the reverse proxy (called shield) to not let malicious traffic through, and to attempt to minimise back end idleness. Scores were determined by a test harness and also on the git push hook.

Stripe provided the attack code for reference, which made the level really easy. Malicious attackers basically spawned more connections more often, and the numbers that they spawned was defined in the file, as:

simulationParameters = {
'roundLength': 500, // In ms
'roundCount': 40,
'clientsPerRound': 5,
'pElephant': 0.4,
'mouseRequestsPerRound': 2,
'elephantRequestsPerRound': 50,
'backendCount': 2,
'backendInFlight': 2,
'backendProcessingTime': 75
};

So from this you can see that malicious clients send 50 requests per round, and normal clients send 2.  So my first solution was just to limit the number of connections from each client with a simple counter. My implementation looks like:

diff --git a/shield b/shield
index c67bd68..8ba87f2 100755
--- a/shield
+++ b/shield
@@ -7,6 +7,7 @@ var httpProxy = require('./network_simulation/lib/proxy');
var checkServer = require('./network_simulation/lib/check_server');
var nopt = require('nopt');
var url = require('url');
+var rcount = {};

var RequestData = function (request, response, buffer) {
this.request = request;
@@ -14,6 +15,16 @@ var RequestData = function (request, response, buffer) {
this.buffer = buffer;
};

+
+function checkRequest(ip){
+ if (rcount[ip] === undefined) {
+ rcount[ip] = 1;
+ } else {
+ rcount[ip]++;
+ }
+ return rcount[ip] &lt;= 4;
+}
+
function ipFromRequest(reqData) {
}
@@ -29,10 +40,10 @@ var Queue = function (proxies, parameters) {
};
Queue.prototype.takeRequest = function (reqData) {
// Reject traffic as necessary:
- // if (currently_blacklisted(ipFromRequest(reqData))) {
- // rejectRequest(reqData);
- // return;
- // }
+ if (!checkRequest(ipFromRequest(reqData))) {
+ rejectRequest(reqData);
+ return;
+ }
// Otherwise proxy it through:
this.proxies[0].proxyRequest(reqData.request, reqData.response, reqData.buffer);
};

I committed and pushed, and surprisingly, this gave me a passing score!

## Level 3

This is where the story gets sad. I checked out the code, and I could not get the ./test/harness to work correctly. The tak was a file indexing service, and it had to be optimised. It was written in Scala, which I have never used – so I could not work out how to debug it.  Stripe released a fix, but it still did not fix my issues. At which point I had to move on to other things and could not complete the CTF.

# Fashion Hackathon – London Startup Weekend

The weekend of the 14th December I attended the London Startup Weekend Fashion Hackathon. This was a much larger event than the previous hackathon I attended and was more geared towards creating a viable business as well as the tech to support it.

The format was fun, on the first day a number of people would pitch ideas, we would all vote for them, then form teams to begin on the Saturday morning. I attended in order to build something new and fun, so just stood back and listened for some interesting pitches.

There were two super interesting pitches: A smart bag which worked out what was in your bag and alerted you if things were missing; and an automatic garment detector which would allow you to take a picture, and then buy the clothes from the picture.

I ended up picking the image recognition project as it sounded the most fun and I didn’t think we would be able to source an RFID reader (or similar) over the weekend. (it turned out that this team didn’t pitch,  so maybe they pivoted or disbanded?)

The mini-startup we made was called LookSnap, and it was fun and quite gratifying to see that my business instincts were reinforced by the actions of the rest of the group. Over the day and a half that it was worked on,  I think the business model ended up fairly solid.

My main job for the weekend was getting the image recognition working. In terms of the technology and with the very short time-scale in mind I decided to limit the acceptable inputs as much as possible. As such, I designed an algorithm that would be able to extract the clothing (top, bottoms, shoes) from a picture of someone who was facing forward and had their arms down.

The algorithm works as follows:

1. Use OpenCV to detect a face
2. With the face position, composite a “clothing mask” (see images) onto the original photo using graphicsmagick
3. This than gives you a fairly decent cut out of just that persons clothes. Apply different masks for top, bottom, and shoes.

Once I had these images, the idea was to use reverse image search on the lyst.com domain to always return something relevant.

However, there was a slight hitch with this plan. Google reverse image search, which worked well manually, had no API in which to pass an image…

So the stopgap method was to extract the average colour from the garment by averaging all the pixel colours that were in the appropriate garment masks, and then mapping these colour to their more broader hue. This turned out to be incredibly hard and would have been impossible if not for reverse engineering a very good hue detector at http://www.color-blindness.com/color-name-hue/

Once this was working I packaged it all up in a FLASK api where an image file was posted to the endpoint, the above magic happened, and a json file was returned giving the X,Y of the garment in the photo, and information on the product name, description, image, and a buy link.

Unfortunately there was not enough time to integrate the service into our POC app, which would have made persuading the judges that we have actually done basic image detection much easier!

Overall, the team did an excellent job, and even though we didn’t win I feel the weekend was very well spent.

# Data Science London Hackathon

On the weekend of October 5th, I participated in the Data Science London Hackathon for Smart Cities. This involved having access to a number of datasets of city based data from London. These datasets included things such as:

• Car Parking Counts
• Oyster Journeys
• Incidents of Antisocial Behaviour

A couple of guys from work and myself made a team (TeamLYST) and decided to have a closer look at the antisocial behaviour dataset to see if we could make something interesting.

The data gave events that happen on a given day, for a given street for about a month. The events were lovingly given as:

• Dog Fouling
• Graffiti
• AntiSocials (public urination, vomit, etc)

So from this we decided to make a predictive application that would generate a number of likely events to happen for a Monday, Tuesday, etc.

The application was split into 3 parts:

1. Pre-processing the data into a format which was useful, adding in default values etc,
2. Creating a generative predictive model from this data
3. Visualising the data

There were three on our team, so I picked the visualisation. I did this using Python and PyGame to draw a PNG of London, which was generated by open streetmap. Event locations were translated to map locations, and the map could be translated and zoomed with the events staying where they were supposed to be. The visualiser allowed you to flip through different days and to access new generated events.

The generative model was trained by looking at each Monday, Tuesday, etc to work out a count of each event type per street, which was then normalised against the total events of that day. This gave a likelihood for each event in each street for each day in the week. Assuming that all events are equally likely to occur (a big assumption) we can sample a normal distribution and apply this to our likelihood map to generate an event. We do this the same amount as the average number of events for that day and we get a pseudo -typical event set.

The final product worked as intended, and with more accurate data could be extended into a nice predictive application to help with local law enforcement responses and distributions.

We didn’t win the hackathon, but it was a fun experience. We put up a video of our work too.

# Migrated fully to WordPress

I’ve moved my main domain to WordPress now, so carlellis.co.uk, www.carlellis.co.uk, and blog.carlellis.co.uk all point to here now!

The move has been slow and forthcoming with my originally semi-static site needing more dynamic content and then becoming stagnant as I focused on adding things to the WordPress. The only thing which was given attention on the old site was the literature review pages.

I didn’t want to lose the site completely, so currently it is parked at old.carlellis.co.uk .

I consider this a pre-emptive strike against my growing NIH syndrome.

# A Fractal Height Map Generator in Ruby

[I'm migrating my old articles to the blog in order to switch to it entirely]

Date: 25th March 2010

## Introduction

This article describes the theory behind, and how to implement, a basic fractal height map generator. It is based upon the algorithm defined at http://gameprogrammer.com/fractal.html. Height maps are used in many things: positioning software, graphical rendering, and procedural map generation. Given the right conditions, height maps can be used to create visually stunning images, and are frequently used with an alpha channel to simulate clouds.

All source code used in this article shall be made freely available, under a Creative Commons GPL Licence

The implementation which will be defined here outputs an array of numbers, which on the surface seems fairly mundane. However, with a bit of tweaking with VT100 colour codes, or passing to an image generation program, the algorithm can produce outputs such as this:

The ASCII renderer assigns a colour code to a range of numbers and then depending on your ranges, you can create rainbow-like maps like the one above. The grey scale and transparent images had their number arrays passed to an image generation program called RMagick.

## The Theory

I will go through the basic idea again as it was defined in the gameprogrammer link, to keep a consistency with terms used further in the article. So before we get on to the DiamondSquare algorithm, I shall implement the simpler 1 dimensional algorithm, which will describe the height of a line, similar to a horizon. The patterns created show some fractal behaviour, in that it is said to be self-similar. An object is said to be self-similar when magnified subsets of the object look like, or are identical to, the whole and to each other[1].

In context of this algorithm, it means that as we add more detail, the more rougher and major features will still hold true, but more detail will become apparent as we look closer. This makes the algorithm ideal for recursion or iterative methods. This implementation uses an iterative method.

### Midpoint Displacement in One Dimension

For the creation of the 1 dimensional height map, similar to a horizon, a very simple algorithm is used:

    def generateLine(length)

# Due to this algorithm being simple for
# the articles sake, length should be
# constrained to the powers of 2.
#
# As we need a midpoint, however, length
# should be defined as (2^n)+1.

# Create an array which describes a line.
# Set the default values to 0American Standard Code for Information Interchange
line = Array.new(length, 0)

# Define the range of the terrain values.
# For this example we shall take the range
# of values to be -63 to 63, with the midpoint
# at 0, out default value.
# Range is then 128.
range = 128;

# Work out the number of line segments
# levels there are in the line. As each line
# segment level is defined by deviding by two
# until length is 1, the number of segments
# is the log_2 of the length.
noSegments = Math.log(length-1, 2)

#Iterate through the levels
(1 .. noSegments).each{ |level|

# Work out the line segment length so you
# can properly address the offset.
segLength = (length/2**level)

# Work out the number of line segments
# within this level
noSegL = (2**level)-1

# Iterate through the line segments and
(1 .. noSegL).each{ |segOffset|

# If value is not zero, skip over it, done on a previous
# level
if( line[segLength*segOffset] == 0 )

# Make sure the current value of the line
# is directly midway between its two parents.
line[segLength*segOffset] = (line[segLength*(segOffset-1)] + line[segLength*(segOffset+1)])/2

# Apply random function to value
line[segLength*segOffset] = randomFunction(line[segLength*segOffset], level, range)

end
}
}

return line
end

Now as you can see the most important part of that algorithm is that which I have purposely missed, randomFunction. This is where you, quite obviously, define how you want your heights defined. A good way is to simply use a bounded random function, where the bounds are defined by the line segment level you are currently within. For example, a function like: 

    def randomFunction(value, level, range)

# Roughness constant 0 &lt; h &lt; 1
# As h approachs 0, the level dependent
# bounds grow and tend to 1 for all values
# of level
h = 0.8

# Define bounds in terms of level and a roughness
# function
multiplier = 2 ** (-h * level-1)

# Perform random function, bound it, and then apply
# multiplier
value += (rand() * (range) - (range / 2)) * multiplier

# Return
return value
end

  Would offset the value of the line a random amount which is bounded by a function dependent on the line segment level and a roughness coefficient. As this function is the heart of this algorithm and the 2 dimensional algorithm, I will go into some detail on the use of the roughness coefficient. If the roughness coefficient is set to 1, then the multiplier acts the same as : $2^{(-l-1)}; 0 < l < \infty ; l \in \mathbb{Z}^+$. Decreasing the value of h flattens out the function meaning the bounds are less constrictive and allowing for much rougher terrain. Here is a plot of the multiplier when h=0.8 and h=0.2, and a plot of the generated lines when those constraints are used. X axis is equal to line segment level.

As you can see, the roughness coefficient makes a massive difference to the outputted numbers. For those interested in recreating the plot, I piped the output of the above code into a text file, and then used GNUPlot to make the images.

### Extending into 2 dimensions - The diamond square algorithm

To extend this algorithm into the second dimension, we have to imagine the terrain data as a two dimensional array of numbers. Each number represents a height in this two dimensional field, and so each column and row can be treated similarly to above.

To split up a two dimensional array in a self-similar way we must use squares, or diamonds, which are analogous to the line segments of the 1 dimensional algorithm. Then, rather than using the ends of a line segment to work out a base height the corners of the square, or diamond, are used. For example:

Like the line segment algorithm, the mathematical 'meat' is the same random function as before. The complexity comes in managing which indexes are part of a diamond or a square. So, for example, here is a code segment which works out indexes of a square, depending on level and location, and applies the random function:

    # Get the corners of an arbitrary square and perform operation
# on center.
#
# @param  array           Array to use
# @param  topleftIndexX   X index of top left of square
# @param  topleftIndexY   Y index of top left of square
# @param  length          Length of the square
# @param  level           Level into the calculation
def processSquare(array, topleftIndexX, topleftIndexY, length, level, range, h)

# Get coordinates of the corners of the square
toprightIndexX    = topleftIndexX
toprightIndexY    = topleftIndexY + length + ((level == 0) ? -1 : 0)

bottomleftIndexX  = topleftIndexX + length + ((level == 0) ? -1 : 0)
bottomleftIndexY  = topleftIndexY

bottomrightIndexX = topleftIndexX + length + ((level == 0) ? -1 : 0)
bottomrightIndexY = topleftIndexY + length + ((level == 0) ? -1 : 0)

middleX           = topleftIndexX + (length)/2
middleY           = topleftIndexY + (length)/2

# Get values
topleftValue      = array[topleftIndexX][topleftIndexY]
toprightValue     = array[toprightIndexX][toprightIndexY]
bottomleftValue   = array[bottomleftIndexX][bottomleftIndexY]
bottomrightValue  = array[bottomrightIndexX][bottomrightIndexY]

# Get average
average = (topleftValue + toprightValue + bottomleftValue + bottomrightValue)/4

# Set new value
array[middleX][middleY] = average + calculateOffset(level, range, h)
end

Where calculateOffset is the random function in this application. The diamond calculation algorithm is very similar and looks like this:

    # Get the edges of an arbitrary diamond and perform operation
# on center
#
# @param  array           Array to use
# @param  topIndexX       X index of top of diamond
# @param  topIndexY       Y index of top of diamond
# @param  length          Length of diamond
# @param  level           Level into the calculation
def processDiamond(array, topIndexX, topIndexY, arraylength, level, range, h)

arraylength -= 1
length = arraylength/(2 ** level)

#Get coordinates of the diamond
rightIndexX   = topIndexX + length/2
rightIndexY   = (topIndexY == length) ? length/2 : topIndexY + length/2

leftIndexX    = topIndexX + length/2
leftIndexY    = (topIndexY == 0) ? arraylength - length/2 : topIndexY - length/2

bottomIndexX  = (topIndexX + length/2 == arraylength) ? length/2 : topIndexX + length
bottomIndexY  = topIndexY

middleX       = topIndexX + length/2
middleY       = topIndexY

# Get values
topValue      = array[topIndexX][topIndexY]
rightValue    = array[rightIndexX][rightIndexY]
bottomValue   = array[bottomIndexX][bottomIndexY]
leftValue     = array[leftIndexX][leftIndexY]

# Get average
average = (topValue + rightValue + bottomValue + leftValue)/4

# Set new value
array[middleX][middleY] = average + calculateOffset(level, range, h)

# Wraps
if(middleX == arraylength)
array[0][middleY] = array[middleX][middleY]
end
if(middleY == 0)
array[middleX][arraylength] = array[middleX][middleY]
end
end

The only difference with the above snippet is the different indices it retrieves, and that it must handle wrap around for some of the edges.

So currently, we can create arbitrary diamonds and squares within a 2-dimensional array and assign a fuzzy average of the edges according the h value. Now all we need is some code to manage traversing through the levels of iterations and through the diamonds and squares themselves. Here is my solution:

    # The main control loop for the algorithm.
#
# @param  lengthExp       Length exponent
# @param  range           Value range
# @param  h               Roughness constant
def generateTerrain(lengthExp, range, h)

length = (2 ** lengthExp) + 1

array = Array.new
array = createArray(array, length)

#Go through Levels (irerative recursion)
(0 .. lengthExp - 1).each{ |level|

# Iterator for the Square part of the algorithm
# Will go through the x-axis coords
(0 .. (2 ** level) -1 ).each { |sqx|

# Y axis coords
(0 .. (2 ** level) -1).each { |sqy|

gap = length/2 ** level
x = (0 + (gap*sqx))
y = (0 + (gap*sqy))

processSquare(array, x, y, gap, level, range, h)
}
}

# Iterator for the diamond part of the algorithm
(0 ... (2 ** (level+1))).each { |dix|

# Offset in the number of points on the y-axis. Dependant
# on if x iteration is even or odd.
offset = (dix.even?) ? 1 : 2
(0 ... (2 ** (level+1)/2)).each { |diy|

gap = (length/2 ** (level+1))
ygap = 2 * gap

x = (0 + (gap*dix))
if (dix.even?)

y = 0 + (ygap*diy)
else

y = gap + (ygap*diy)
end
processDiamond(array, x, y, length, level, range, h)
}
}
}
return array
end

And this gives us our array with its height map hidden inside. Using a library like RMagick we can output images like the ones shown above. To create the gray scale image, the following code was used:

  image = Image.new(array.length, array.length)

(0 ... array.length).each { |x|
(0 ... array[x].length).each { |y|
val = array[x][y] * (2**9)
# Create greyscale image
image.pixel_color(x, y, Pixel.new(val, val, val, val))
}
}
image.display

Which just takes the value in the array, and multiplies it by 512 which gives the values a range of $0 \ge v \ge 2^{15}, \; \frac{v}{512} \in \mathbb{Z}$ . This gives us the gaseous image that has been generated above.

## Code Listings

A library version of the ruby code found in this tutorial can be found at GitHub.

## References

1. Voss, Richard D., FRACTALS in NATURE: characterization, measurement, and simulation. SIGGRAPH 1987

Based on a work at gameprogrammer.com.

# Getting back into C

So I spend a lot of my time at my computer, it’s a fact of life as a computer science PhD student. However, while I may have a vim window open 90% of the time, more often than not there will be latex or matlab code in that vim window. Sometimes, if I get one of those rare lulls in deadlines, there may even be Ruby or Haskell code from when I’m learning or prototyping.

This, however, has made me incredibly lazy. I do no low level programming, know very limited assembler, and get spoiled rotten by higher level languages. How much work does my_hash["key"] = value do for me? Well, a lot.

To address this, I’m refreshing myself of my C knowledge and building basic abstract types. But, I’m doing it in a way which would make the most academically minded coder weep with glee in its abstraction and cleanliness – although possibly at the expense of speed. But, I’m an academic, I only need to be aware of speed, while making sure my code is clear enough for people to read and hopefully learn.

So far, I’ve blasted through stacks, queues, linked lists, and binary trees. There are still some utility functions to build with regards to the trees, but I’m engineering them to use function pointers and void * memory in order to not tie my data structures to types.

Currently the code is on GitHub and will be built up as I add more data structures. Hopefully I have time for tries, ropes, prefix trees, graphs, and example code of how to use them. Search algorithms and other classics are hopefully going in, with the aim to make a library not for performance, but for clarity and aid in teaching.

Feel free to email me suggestions and requests for algorithms.