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## Let's build a weighted random number generator!

Ever wondered how random loot in a dungeon is generated? Or how the rooms in a procedurally generated castle might be picked? Perhaps you need to skew the number of times an apple is picked by your game engine over a banana. If you've considered any of these things, then you want a weighted random number generator. In this post, I'll be showing you how I built one, and how you can build one too.

If you're interested in trying to build one for yourself first though, then look away now! Come back when you're done (or stuck) to see my solution.

To start with, let's consider what a weighted random number generator actually is. Let's say we've got 3 rewards for a treasure chest: a cool-looking shield, a health potion, and a fancy ring. We want to give the player 1 of the 3 when they option the chest, making sure that the health potion is more common than the others. We can represent that as a ratio: $3 : 4 : 3$.

(Above: The ratio between the different items. See below for the explanation of the math!).

In order to pick one of the 3 items using the ratio, we need to normalise the ratio so that it's between $0$ and $1$. That's rather easy, as far as maths goes: All we have to do is convert each part of the ratio into a fraction, and that into a decimal. Let's calculate the denominator of the fraction first. That's easy-peasy too - we just add up all the parts of the ratio, as we want to represent each part as a fraction of a whole: $3 + 4 + 3 = 10$. With our denominator sorted, we can convert each part into a fraction:

$$\frac{3}{10} + \frac{4}{10} + \frac{3}{10} = 1$$

Fractions are nice, but it's be better to have that as a decimal:

$$0.3 + 0.4 + 0.3 = 10$$

That's much better. Now, with the initial theory out of the way, let's start writing a class for it.

using System;
using System.Collections.Generic;
using System.Linq;

namespace SBRL.Algorithms
{
public class WeightedRandom<ItemType>
{
protected Random rand = new Random();

protected Dictionary<double, ItemType> weights = new Dictionary<double, ItemType>();

/// <summary>
/// Creates a new weighted random number generator.
/// </summary>
/// <param name="items">The dictionary of weights and their corresponding items.</param>
public WeightedRandom(IDictionary<double, ItemType> items)
{
if(items.Count == 0)
throw new ArgumentException("Error: The items dictionary provided is empty!");

double totalWeight = items.Keys.Aggregate((double a, double b) => a + b);
foreach(KeyValuePair<double, ItemType> itemData in items)
}
}
}

I've created a template class here, to allow the caller to provide us with any type of item (so long as they are all the same). That's what the <ItemType> bit is on the end of the class name - it's the same syntax behind the List class:

List<TreasureReward> rewards = new List<TreasureReward>() {
TreasureReward.FromFile("./treasure/coolsword.txt"),
TreasureReward.FromFile("./treasure/healthpotion.txt"),
TreasureReward.FromFile("./treasure/fancyring.txt"),
};

Next, let's go through that constructor bit by bit. First, we make sure that we actually have some weights in the first place:

if(items.Count == 0)
throw new ArgumentException("Error: The items dictionary provided is empty!");

Then, it's more Linq to the rescue in calculating the total of the weights we've been provided with:

double totalWeight = items.Keys.Aggregate((double a, double b) => a + b);

Finally, we loop over each of the items in the provided dictionary, dividing them by the sum of the weights and adding them to our internal dictionary of normalised weights.

foreach(KeyValuePair<double, ItemType> itemData in items)
weights.Add(itemData.Key / totalWeight, itemData.Value);

Now that we've got our items loaded and the weights normalised, we can start picking things from our dictionary. For this part, I devised a sort of 'sliding window' algorithm to work out which item to pick. It's best explained through a series of whiteboard images:

Basically, I have 2 variables: lower and higher. When I loop over each of the weights, I do the following things:

1. Add the current normalised weight to higher
2. Check if the target is between lower and higher a. If it is, then return the current item b. If not, then keep going
3. Bring lower up to the same value as higher
4. Loop around again until we find the weight in which the target lies.

With that in mind, here's the code I cooked up:

/// <summary>
/// Picks a new random item from the list provided at initialisation, based
/// on the weights assigned to them.
/// </summary>
/// <returns>A random item, picked according to the assigned weights.</returns>
public ItemType Next()
{
double target = rand.NextDouble();

double lower = 0;
double higher = 0;
foreach(KeyValuePair<double, ItemType> weightData in weights)
{
higher += weightData.Key;
if(target >= lower && target <= higher)
return weightData.Value;
lower += weightData.Key;
}

throw new Exception(\$"Error: Unable to find the weight that matches {target}");
}

That pretty much completes the class. While it seems daunting at first, it's actually quite easy once you get your head around it. Personally, I find whiteboards very useful in that regard! Here's the completed class:

Found this interesting? Got stuck? Have a suggestion for another cool algorithm I could implement? Comment below!

## Markov Chains Part 2: Unweighted Chains

Hello and welcome to the second part of this mini-series about markov chains. In the last part, I explained what an n-gram was, and how I went about generating them.

In this part, I'll get to the meat of the subject: The markov chain itself. To start with (to simplify matters) I'll be looking at unweighted markov chains.

A markov chain, in essence, takes the n-grams we generated last time, and picks one to start with. It then takes the all but the first character of the n-gram it chose, and finds all the n-grams in it's library that begin with that sequence of characters. After drawing up a list of suitable n-grams, it picks one at random, and tacks the last character in the n-gram it chose onto the end of the first n-gram.

Then, it starts the whole process all over again with the 2nd n-gram it chose, and then the 3rd, and so on until it either a) hits a brick wall and can't find any suitable n-grams to use next, or b) reaches the desired length of word it was asked to generate.

An unweighted markov chain, as I call it, does not take the frequency of the source n-grams in the original text into account - it just picks the next n-gram from the list randomly.

With explanations and introductions out of the way, let's get down to some code! Since the markov chain is slightly more complicated, I decided to write a class for it. Let's start with one of those, then:

using System;
using System.Collections.Generic;
using System.Linq;

namespace SBRL.Algorithms.MarkovGrams
{
/// <summary>
/// An unweighted character-based markov chain.
/// </summary>
public class UnweightedMarkovChain
{

}
}


I've also added a few using statements for later. Our new class is looking a bit bare. how about some methods to liven it up a bit?

/// <summary>
/// Creates a new character-based markov chain.
/// </summary>
/// <param name="inNgrams">The ngrams to populate the new markov chain with.</param>
public UnweightedMarkovChain(IEnumerable<string> inNgrams)
{

}

/// <summary>
/// Returns a random ngram that's currently loaded into this UnweightedMarkovChain.
/// </summary>
/// <returns>A random ngram from this UnweightMarkovChain's cache of ngrams.</returns>
public string RandomNgram()
{

}

/// <summary>
/// Generates a new random string from the currently stored ngrams.
/// </summary>
/// <param name="length">
/// The length of ngram to generate.
/// Note that this is a target, not a fixed value - e.g. passing 2 when the n-gram order is 3 will
/// result in a string of length 3. Also, depending on the current ngrams this markov chain contains,
/// it may end up being cut short.
/// </param>
/// <returns>A new random string.</returns>
public string Generate(int length)
{

}

That's much better. Let's keep going - this time with some member variables:

/// <summary>
/// The random number generator
/// </summary>
Random rand = new Random();

/// <summary>
/// The ngrams that this markov chain currently contains.
/// </summary>
List<string> ngrams;

We'll need that random number generator later! As for the List<string>, we'll be using that to store our n-grams - but you probably figured that one out for yourself :P

The class isn't looking completely bare anymore, but we can still do something about those methods. Let's start with that constructor:

public UnweightedMarkovChain(IEnumerable<string> inNgrams)
{
ngrams = new List<string>(inNgrams);
}

Easy peasy! It just turns the IEnumerable<string> into a List<string> and stores it. Let's do another one:

public string RandomNgram()
{
return ngrams[rand.Next(0, ngrams.Count)];
}

We're on a roll here! This is another fairly simple method - it just picks a random n-gram from the dictionary. We'll need this for our 3rd, and most important, method, Generate(). This one's a bit more complicated, so let's take it in a few stages. Firstly, we need an n-gram to start the whole thing off. We also need to return it at the end of the method.

string result = RandomNgram();

return result;

While we're at it, we'll also need a variable to keep track of the last n-gram in the chain, so we can find an appropriate match to come next.

string lastNgram = result;

Then we'll need a loop to keep adding n-grams to the chain. Since we're not entirely sure how long we'll be looping for (and we've got fairly complicated stop conditions, as far as that kind of thing goes), I decided to use a while loop here.

while(result.Length < length)
{

}

That's the first of our 2 stop conditions in place, too! We want to stop when the word we're working on reaches it's desired length. Now, we can write the bit that works out which n-gram should come next! This bit goes inside the while loop we created above (as you might suspect). First, let's fetch a list of n-grams that would actually make sense coming next.

// The substring that the next ngram in the chain needs to start with
string nextStartsWith = lastNgram.Substring(1);
// Get a list of possible n-grams we could choose from next
List<string> nextNgrams = ngrams.FindAll(gram => gram.StartsWith(nextStartsWith));

With a bit of Linq (Language-INtrgrated Query), that isn't too tough :-) If you haven't seen linq before, then I'd highly recommend you check it out! It makes sorting and searching datasets much easier. The above is quite simple - I just filter our list of n-grams through a function that extracts all the ones that start with the appropriate letter.

It's at this point that we can insert the second of our two stopping conditions. If there aren't any possible n-grams to pick from, then we can't continue.

// If there aren't any choices left, we can't exactly keep adding to the new string any more :-(
if(nextNgrams.Count == 0)
break;

With our list of possible n-grams, we're now in a position to pick one at random to add to the word. It's LINQ to the rescue again:

// Pick a random n-gram from the list
string nextNgram = nextNgrams.ElementAt(rand.Next(0, nextNgrams.Count));

This is another simple one - it just extract the element in the list at a random location in the list. In hindsight I could have used the array operator syntax here ([]), but it doesn't really matter :-)

Now that we've picked the next n-gram, we can add it to the word we're building:

// Add the last character from the n-gram to the string we're building
result += nextNgram[nextNgram.Length - 1];

and that's the markov chain practically done! Oh, we mustn't forget to update the lastNgram variable (I forgot this when building it :P):

lastNgram = nextNgram;

And that wraps up our unweighted markov chain. Here's the whole class in full:

using System;
using System.Collections.Generic;
using System.Linq;

namespace SBRL.Algorithms.MarkovGrams
{
/// <summary>
/// An unweighted character-based markov chain.
/// </summary>
public class UnweightedMarkovChain
{
/// <summary>
/// The random number generator
/// </summary>
Random rand = new Random();

/// <summary>
/// The ngrams that this markov chain currently contains.
/// </summary>
List<string> ngrams;

/// <summary>
/// Creates a new character-based markov chain.
/// </summary>
/// <param name="inNgrams">The ngrams to populate the new markov chain with.</param>
public UnweightedMarkovChain(IEnumerable<string> inNgrams)
{
ngrams = new List<string>(inNgrams);
}

/// <summary>
/// Returns a random ngram that's currently loaded into this UnweightedMarkovChain.
/// </summary>
/// <returns>A random ngram from this UnweightMarkovChain's cache of ngrams.</returns>
public string RandomNgram()
{
return ngrams[rand.Next(0, ngrams.Count)];
}

/// <summary>
/// Generates a new random string from the currently stored ngrams.
/// </summary>
/// <param name="length">
/// The length of ngram to generate.
/// Note that this is a target, not a fixed value - e.g. passing 2 when the n-gram order is 3 will
/// result in a string of length 3. Also, depending on the current ngrams this markov chain contains,
/// it may end up being cut short.
/// </param>
/// <returns>A new random string.</returns>
public string Generate(int length)
{
string result = RandomNgram();
string lastNgram = result;
while(result.Length < length)
{
// The substring that the next ngram in the chain needs to start with
string nextStartsWith = lastNgram.Substring(1);
// Get a list of possible n-grams we could choose from next
List<string> nextNgrams = ngrams.FindAll(gram => gram.StartsWith(nextStartsWith));
// If there aren't any choices left, we can't exactly keep adding to the new string any more :-(
if(nextNgrams.Count == 0)
break;
// Pick a random n-gram from the list
string nextNgram = nextNgrams.ElementAt(rand.Next(0, nextNgrams.Count));
// Add the last character from the n-gram to the string we're building
result += nextNgram[nextNgram.Length - 1];
lastNgram = nextNgram;
}

return result;
}
}
}

I've released the full code for my markov generator (with a complete command line interface!) on my personal git server. The repository can be found here: sbrl/MarkovGrams. To finish this post off, I'll leave you with a few more words that I've generated using it :D

1 2 3 4 5
mecuc uipes jeraq acrin nnvit
blerbopt drsacoqu yphortag roirrcai elurucon
pnsemophiqub omuayplisshi udaisponctec mocaltepraua rcyptheticys
eoigemmmpntartrc rattismemaxthotr hoaxtancurextudu rrgtryseumaqutrc hrpiniglucurutaj

## How to set up a WebDav share with Nginx

I've just been setting up a WebDav share on a raspberry pi 3 for my local network (long story), and since it was a bit of a pain to set up (and I had to combine a bunch of different tutorials out there to make mine work), I thought I'd share how I did it here.

I'll assume you have a raspberry pi all set up and up-to-date in headless mode, with a ufw for your firewall (if you need help with this, post in the comments below or check out the Raspberry Pi Stack Exchange). To start with, we need to install the nginx-full package:

sudo apt update
sudo apt install  nginx-full

Note that we need the nginx-full package here, because the nginx-extras or just simply nginx packages don't include the required additional webdav support modules. Next, we need to configure Nginx. Nginx's configuration files live at /etc/nginx/nginx.conf, and in /etc/nginx/conf.d. I did something like this for my nginx.conf:

user www-data;
worker_processes 4;
pid /run/nginx.pid;

events {
worker_connections 768;
# multi_accept on;
}

http {

##
# Basic Settings
##

sendfile on;
tcp_nopush on;
tcp_nodelay on;
keepalive_timeout 65;
types_hash_max_size 2048;
# server_tokens off;

# server_names_hash_bucket_size 64;
# server_name_in_redirect off;

include /etc/nginx/mime.types;
default_type application/octet-stream;

##
# SSL Settings
##

ssl_protocols TLSv1 TLSv1.1 TLSv1.2; # Dropping SSLv3, ref: POODLE
ssl_prefer_server_ciphers on;

##
# Logging Settings
##

access_log /var/log/nginx/access.log;
error_log /var/log/nginx/error.log;

##
# Gzip Settings
##

gzip on;

gzip_vary on;
gzip_proxied any;
gzip_comp_level 6;
gzip_buffers 16 8k;
gzip_http_version 1.1;
gzip_types text/plain text/css application/json application/javascript text/xml application/xml application/xml+rss text/javascript;

##
# Virtual Host Configs
##

include /etc/nginx/conf.d/*.conf;
include /etc/nginx/sites-enabled/*;
}

Not many changes here. Then, I created a file called 0-webdav.conf in the conf.d directory, and this is what I put in it:

server {
listen 80;
listen [::]:80;

server_name plans.helenshydrogen.be;

auth_basic              realm_name;

dav_methods     PUT DELETE MKCOL COPY MOVE;
dav_ext_methods PROPFIND OPTIONS;
dav_access      user:rw group:rw all:r

client_body_temp_path   /tmp/nginx/client-bodies;
client_max_body_size    0;
create_full_put_path    on;

root /mnt/hydroplans;
}

Now this is where the magic happens. The dav_access directive tells nginx to allow everyone to read, but only logged in users to write to the share. This isn't actually particularly relevant, because of the auth_basic and auth_basic_user_file directives, which tell nginx to require people to login to the share before they are allowed to access it.

It's also important to note that I found that Windows (10, at least), didn't like the basic authentication - even though Ubuntu's Nautilus accepted it just fine - so I had to comment that bit out :-(

If you do still want authentication (hey! May you'll have better luck than I :P), then you'll need to set up the passwords file. Here's how you create it:


echo -n 'helen:' | sudo tee /etc/nginx/.passwords.list
openssl passwd -apr1 | sudo tee -a /etc/nginx/.passwords.list


The above creates a user called helen, and asks you to type a password. If you're adding another user to the file, simply change the first tee to be tee -a to avoid overwriting the first one.

With that all configured, it's time to test the configuration file, and, if we're lucky, restart nginx!


sudo nginx -t
sudo systemctl restart nginx


That's all you should need to do to set up a simple WebDav share. Remember that this is a starting point, and not an ending point - there are a few big holes in the above that you'll need to address, depending on your use case (for example, I haven't included the setup of https / encryption - try letsencrypt for that).

Here are the connection details for the above for a few different clients:

• Ubuntu / Nautilus: (Go to "Other Locations" and paste this into the "Connect to Server" box) dav://plans.helenshydrogen.be/
• Windows: (Go to "Map Network Drive" and paste this in) http://plans.helenshydrogen.be/

Did this work for you? Have any problems? Got instructions for a WebDav client not listed here? Let me know in the comments!

## Markov Chains Part 1: N-Grams

After wanting to create a markov chain to generate random words for ages, I've recently had the time to actually write one :D Since I had a lot of fun writing it, I thought I'd share it here.

A markov chain, in simple terms, is an algorithm to take a bunch of input, and generate a virtually unlimited amount of output in the style of the input. If I put my 166 strong wordlist of sciencey words through a markov chain, I get a bunch of words like this:

a b c d
raccession bstrolaneu aticl lonicretiv
ssignatten attrotemic surspertiv tecommultr
ndui coiseceivi horinversp icreflerat
landargeog eograuxila omplecessu ginverceng
evertionde chartianua spliqui ydritangt
grajecubst ngintagorp ombintrepe mbithretec
trounicabl ombitagnai ccensorbit holialinai
cessurspec dui mperaneuma yptintivid
ectru llatividet imaccellat siondl
tru coo treptinver gnatiartia
nictrivide pneumagori entansplan uatellonic

Obviously, some of the above aren't particularly readable, but the majority are ok (I could do with a longer input wordlist, I think).

To create our very own markov chain that can output words like the above, we need 2 parts: An n-gram generator, to take in the word list and convert it into a form that we can feed into the second part - the markov chain itself. In this post, I'm going to just look at the n-gram generator - I'll cover the markov chain itself in the second part of this mini-series.

An n-gram is best explained by example. Take the word refractive, for example. Let's split it up into chunks:

ref
efr
fra
rac
act
cti
tiv
ive

See what I've done? I've taken the original word and split it into chunks of 3, but I've only moved along the word by 1 character at a time, so some characters have been duplicated. These are n-grams of order 3. The order, in the case of an n-gram, is the number of characters per chunk. We could use any order we like:

refra
efrac
fract
racti
activ
ctive

The order of the above is 5. If you're wondering how this could possibly be useful - don't worry: All will be explained in due time :-) For now though, writing all these n-grams out manually is rather annoying and tedious. Let's write some code!

Generating n-grams from a single word like we did above is actually pretty simple. Here's what I came up with:

/// <summary>
/// Generates a unique list of n-grams from the given string.
/// </summary>
/// <param name="str">The string to n-gram-ise.</param>
/// <param name="order">The order of n-gram to generate.</param>
/// <returns>A unique list of n-grams found in the specified string.</returns>
public static IEnumerable<string> GenerateFlat(string str, int order)
{
List<string> results = new List<string>();
for(int i = 0; i < str.Length - order; i++)
{
}
return results.Distinct();
}

I'm using C♯ here, but you can use whatever language you like. Basically, I enter a loop and crawl along the word, adding the n-grams I find to a list, which I then de-duplicate and return.

Generating n-grams for just one word is nice, but we need to process a whole bunch of words. Thankfully, that's easy to automate too with a sneaky overload:

/// <summary>
/// Generates a unique list of n-grams that the given list of words.
/// </summary>
/// <param name="words">The words to turn into n-grams.</param>
/// <param name="order">The order of n-gram to generate..</param>
/// <returns>A unique list of n-grams found in the given list of words.</returns>
public static IEnumerable<string> GenerateFlat(IEnumerable<string> words, int order)
{
List<string> results = new List<string>();
foreach(string word in words)
{
}
return results.Distinct();
}

All the above does is take a list of words, run them all through the n-gram generation method we wrote above and return the de-duplicated results. Here's a few that it generated from the same wordlist I used above in order 3:

1 2 3 4 5 6 7 8
hor sig ign gna str tre ren ngt
sol old lde oli sor sou oun tel
lla sub ubs bst tem emp mpe atu
tur err ert thr hre dim ime men
nsi ack cki kin raj aje jec tor
ans nsa sat nsf sfe nsl sla slu
luc uce nsm smi nsp are nsu tan

Next time, I'll show you my (unweighted) markov chain I've written that uses the n-grams generated by these methods.

## Website Integrations (Mini!) Series List

Since I ended up doing a mini-series on the various website integrations I implemented, I thought that you might find a (mini-)series list useful. Here it is:

That's just about all I've got to mention here. Do you have any suggestions and / or requests on what I should blog about? Let me know below in the comments!

## Website Integrations #3: Twitter cards

(The posts featured in the above images are this one about my new Raspberry Pi 3, and the latest coding conundrums evolved post).

You have arrived in the third of three parts in my mini-series on how I implemented rich snippets. In the last two parts I tackled open graph and becoming an oEmbed provider. In this part, I'll be talking a bit about twitter cards, and how I implemented them.

Twitter's take on the problem seems to be much simpler than Facebook's, which makes for easy implementing :D Like in the other two protocols too, they decided to have multiple different types of, well, in this case, cards. I decided to implement the summary card type. Like open graph, it adds a bunch of <meta /> tags to the header. Sigh. Anyway, here are the property names I needed to implement:

• twitter:card - The type of card. In my case this is set to summary
• twitter:site - This one's confusing. Although it's called 'site', it should actually be set to your own twitter handle - mine is @SBRLabs.
• twitter:title - The title of the content. Practically identical to open graph's og:title.
• twitter:description - The description of the content. The same as og:description.
• twitter:image - A url pointing to an image that should be displayed next to the title and description. Unlike Facebook's open graph, twitter appears to support https urls here with no problem at all.

Since after implementing open graph I already had 90% of the infrastructure and calculations in place already, throwing together values for the above wasn't too difficult. Here's an example set of twitter card <meta /> tags generated by the updated code:

<meta property="twitter:card" content="summary" />
<meta property="twitter:title" content="Running Prolog on Linux" />
<meta property="twitter:description" content="Hello! I hope you had a nice restful Easter. I've been a bit busy this last 6 months, but I've got a holiday at the moment, and I've just received a .... (click to read more)" />
<meta property="twitter:image" content="https://starbeamrainbowlabs.com/blog/images/20151015-learning-swi-prolog-banner.svg" />

Easy peasy. Next up was testing time. Thankfully, Twitter made this easy too by providing an official testing tool. Interestingly, they whitelist domains based on whether the webmaster has run a url through their tool - so if you want twitter cards to show up, make sure you plug at least one of your website's page urls through their tool.

After a few tweaks, I got this:

With that, my work was complete. This brings us to the end of my mini-series on rich-snippet integrations (unless I've missed a protocol O.o Comment below if I have)! I hope you've found it useful. If you have (or even if you haven't!) please let me know in the comments below :D

## Running Prolog on Linux

Hello! I hope you had a nice restful Easter. I've been a bit busy this last 6 months, but I've got a holiday at the moment, and I've just received a lovely email about my learning prolog series. It's reminded me about how much I actually rather like (swi) prolog (I'm a bit nuts, I know :P), and I've decided that it would be splendid if I could get back into it a bit.

I'm not sure whether it'll go anywhere, but I'm going to look into web crawling and parsing HTML. Anyway, since I'm re-discovering prolog, I had a teensy bit of trouble getting prolog to run one of my old scripts just now, so I thought I'd blog about it to save me the trouble next time :D

Running prolog scripts on linux isn't actually that hard. First, you need to install SWI-Prolog:

sudo add-apt-repository ppa:swi-prolog/stable
sudo apt install swi-prolog-nox


Then, you run a prolog script like this:

swipl -f ./Awesome-Prolog.pl
Welcome to SWI-Prolog (threaded, 64 bits, version 7.4.1)
SWI-Prolog comes with ABSOLUTELY NO WARRANTY. This is free software.

For built-in help, use ?- help(Topic). or ?- apropos(Word).

?-


...then you can interact with your Prolog knowledge bases as normal. When you want to exit, the easiest way I've found is if you press CTRL + D.

If, however, you're lazy and don't want to type prolog -f AwesomeSauce.pl out every time you want to run your script, you can prefix your file with the following shebang:

#!/usr/bin/prolog -f 

Make sure it's the very first line in the file. Prolog appears to ignore it and carry on regardless. Then you can run it like this (making sure you've chmod +x'd it first:

./CoolBot.pl

## Profiling PHP with XDebug

(This post is a fork of a draft version of a tutorial / guide originally written as a whilst at my internship.)

Since I've been looking into xdebug's profiling function recently, I've just been tasked with writing up a guide on how to set it up and use it, from start to finish - and I thought I'd share it here.

While I've written about xdebug before in my An easier way to debug PHP post, I didn't end up covering the profiling function - I had difficulty getting it to work properly. I've managed to get it working now - this post documents how I did it. While this is written for a standard Debian server, the instructions can easily be applied to other servers.

For the uninitiated, xdebug is an extension to PHP that aids in the debugging of PHP code. It consists of 2 parts: The php extension on the server, and a client built into your editor. With these 2 parts, you can create breakpoints, step through code and more - though these functions are not the focus of this post.

To start off, you need to install xdebug. SSH into your web server with a sudo-capable account (or just use root, though that's bad practice!), and run the following command:

sudo apt install php-debug


Windows users will need to download it from here and put it in their PHP extension direction. Users of other linux distributions and windows may need to enable xdebug in their php.ini file manually (windows users will need extension=xdebug.dll; linux systems use extension=xdebug.so instead).

Once done, xdebug should be loaded and working correctly. You can verify this by looking the php information page. To see this page, put the following in a php file and request it in your browser:

<?php
phpinfo();
?>

If it's been enabled correctly, you should see something like this somewhere on the resulting page:

With xdebug setup, we can now begin configuring it. Xdebug gets configured in php.ini, PHP's main configuration file. Under Virtualmin each user has their own php.ini because PHP is loaded via CGI, and it's usually located at ~/etc/php.ini. To find it on your system, check the php information page as described above - there should be a row with the name "Loaded Configuration File":

Once you've located your php.ini file, open it in your favourite editor (or type sensible-editor php.ini if you want to edit over SSH), and put something like this at the bottom:

[xdebug]
xdebug.remote_enable=1
xdebug.remote_connect_back=1
xdebug.remote_port=9000
xdebug.remote_handler=dbgp
xdebug.remote_mode=req
xdebug.remote_autostart=true

xdebug.profiler_enable=false
xdebug.profiler_enable_trigger=true
xdebug.profiler_enable_trigger_value=ZaoEtlWj50cWbBOCcbtlba04Fj
xdebug.profiler_output_dir=/tmp
xdebug.profiler_output_name=php.profile.%p-%u

Obviously, you'll want to customise the above. The xdebug.profiler_enable_trigger_value directive defines a secret key we'll use later to turn profiling on. If nothing else, make sure you change this! Profiling slows everything down a lot, and could easily bring your whole server down if this secret key falls into the wrong hands (that said, simply having xdebug loaded in the first place slows things down too, even if you're not using it - so you may want to set up a separate server for development work that has xdebug installed if you haven't already). If you're not sure on what to set it to, here's a bit of bash I used to generate my random password:

dd if=/dev/urandom bs=8 count=4 status=none | base64 |  tr -d '=' | tr '+/' '-_'


The xdebug.profiler_output_dir lets you change the folder that xdebug saves the profiling output files to - make sure that the folder you specify here is writable by the user that PHP is executing as. If you've got a lot of profiling to do, you may want to consider changing the output filename, since xdebug uses a rather unhelpful filename by default. The property you want to change here is xdebug.profiler_output_name - and it supports a number of special % substitutions, which are documented here. I can recommend something phpprofile.%t-%u.%p-%H.%R.%cachegrind - it includes a timestamp and the request uri for identification purposes, while still sorting chronologically. Remember that xdebug will overwrite the output file if you don't include something that differentiates it from request to request!

With the configuration done, we can now move on to actually profiling something :D This is actually quite simple. Simply add the XDEBUG_PROFILE GET (or POST!) parameter to the url that you want to test in your browser. Here are some examples:

https://localhost/carrots/moon-iter.php?XDEBUG_PROFILE=ZaoEtlWj50cWbBOCcbtlba04Fj
https://development.galacticaubergine.de/register?vegetable=yes&mode=plus&XDEBUG_PROFILE=ZaoEtlWj50cWbBOCcbtlba04Fj

Adding this parameter to a request will cause xdebug to profile that request, and spit out a cachegrind file according to the settings we configured above. This file can then be analysed in your favourite editor, or, if it doesn't have support, an external program like qcachegrind (Windows) or kcachegrind (Everyone else).

If you need to profile just a single AJAX request or similar, most browsers' developer tools let you copy a request as a curl or wget command (Chromium-based browsers, Firefox - has an 'edit and resend' option), allowing you to resend the request with the XDEBUG_PROFILE GET parameter.

If you need to profile everything - including all subrequests (only those that pass through PHP, of course) - then you can set the XDEBUG_PROFILE parameter as a cookie instead, and it will cause profiling to be enabled for everything on the domain you set it on. Here's a [bookmarklet]() that set the cookie:

javascript:(function(){document.cookie='XDEBUG_PROFILE='+'insert_secret_key_here'+';expires=Mon, 05 Jul 2100 00:00:00 GMT;path=/;';})();

(Source)

Replace insert_secret_key_here with the secret key you created for the xdebug.profiler_enable_trigger_value property in your php.ini file above, create a new bookmark in your browser, paste it in (making sure that your browser doesn't auto-remove the javascript: at the beginning), and then click on it when you want to enable profiling.

## My new Raspberry Pi 3!

I've got a little project in mind - I'd like to build a little storage server to back some things up to. It doesn't have to be anything terribly fancy, provide blisteringly fast speeds, or have store a huge number of files, so I've opted for a Raspberry Pi 3 to power the thing. It arrived just recently, and since the service I got from Pimoroni was excellent, I thought I'd post about it here. If you're after some bits for your raspberry pi, then they are a good reputable place to get them from.

In order to access the storage space on the server, I'll be configuring some samba shares (linux's implementation of Windows file shares, which is completely interoperable). Would anyone be interested in a tutorially kind of post on how you configure Samba? Let me know in the comments below.

## Website Integrations #2: oEmbed

Welcome to part 2 of this impromptu miniseries! In this second part of three, I'll be showing you a little about how I set up and tested a simple oEmbed provider for my blog posts - I've seen lots of oEmbed client information out there, but not much in the way of provider (or server) implementations.

If you haven't read part one about the open graph protocol yet, then you might find it interesting.

oEmbed is a bit different to open graph in that instead of throwing a bunch of meta tags into your <head />, you instead use a special <link /> element that points interested parties in the direction of some nice tasty json. Personally, I find this approach to be more sensible and easier to handle - the kind of thing you'd expect from an open standard.

To start with, I took a read of their specification, as I did with open graph. It doesn't have as many examples as I'd have liked, and I had to keep jumping around, but it's certainly not the worst I've seen.

oEmbed is built on the idea of providers (that's me!) and consumers (the programs and website you use). Providers, erm, provide machine-readable information about urls passed to them, and consumers take this information provided to them and display it to the user in a manner they think is appropriate.

To start with, I created a new PHP file to act as my provider over at https://starbeamrainbowlabs.com/blog/oembed.php and took a look at the different oEmbed types available - oEmbed has a type system of sorts, similar to open graph. I decided on link - while a rich would look cool, it would be almost impossible to test with every client out there, and I can't guarantee how the html would be rendered or what space it would have either.

With that decided, I made a list of the properties that I'd need to include in the json response:

• version - The version of oEmbed. Currently 1.0 as of the time of typing.
• type - The oEmbed type. I chose link here.
• title - The title of the page
• author_name - The name of the author
• author_url - A link to the author's homepage.
• provider_name - The provider's name.
• provider_url - A link to the provider's homepage. I chose my blog index, since this script will only serve my blog.
• cache_age - How long consumers should cache the response for. I put 1 hour (3600 seconds) here, since I usually correct mistakes after posting that I've missed, and I want them to go out fairly quickly.
• thumbnail_url - A link to a suitable thumbnail picture.
• thumbnail_width - The width of the thumbnail image, in pixels.
• thumbnail_height - The width of the thumbnail image, in pixels.

Then I looked at the data I'd be getting from the client. It all comes in the form of GET parameters:

• format - Either json or xml. Personally, I only support json.
• url - The url to send oEmbed information for.

With all the information close at hand, I spent a happy hour or so writing code, and ended up with a script that outputs something like this:

{
"version": "1.0",
"title": "Website Integrations #1: Open Graph",
"author_name": "Starbeamrainbowlabs",
"author_url": "https:\/\/starbeamrainbowlabs.com\/",
"provider_name": "Stardust | Starbeamrainbowlabs' Blog",
"provider_url": "https:\/\/starbeamrainbowlabs.com\/blog\/",
"cache_age": 3600,
"thumbnail_url": "https:\/\/starbeamrainbowlabs.com\/images\/logos\/open-graph.png",
"thumbnail_width": 300,
"thumbnail_height": 300
}

Though the specification includes requirements for satisfying 2 extra GET parameters, maxwidth and maxheight, I chose to ignore them since writing a dynamic thumbnail rescaling script is both rather complicated and requires a not insignificant amount of processing power every time it is used.

After finishing the oEmbed script, I turned my attention to one final detail: The special <link /> tag required for auto-discovery. A quick bit of PHP in the article page renderer adds something like this to the header:

<link rel="alternate" type="application/json+oembed" href="https://starbeamrainbowlabs.com/blog/oembed.php?format=json&url=https%3A%2F%2Fstarbeamrainbowlabs.com%2Fblog%2Farticle.php%3Farticle%3Dposts%252F229-Website-Integrations-1-Open-Graph.html" />

and with that, my oEmbed provider implementation is complete - but it still needs testing! Unfortunately, testing tool for oEmbed are few and far between, but I did manage to find a few:

• oEmbed Tester - A basic testing tool. Appears to work well for the most part - except the preview. Not sure why it says "Preview not available." all the time.
• Iframely URL Debugger - Actually a testing tool for some commercial tool or other, but it still appears to accurately test not only oEmbed, but open graph and twitter cards (more on them in the next post!) too!

After testing and fixing a few bugs, my oEmbed provider was complete! Next time, I'll be taking a look at twitter's take on the subject: Twitter cards.

Found this interesting? Comment below! Share it with a friend!

Art by Mythdael