---
title: Ising model simulation in APL
date: 2018-03-05
tags: ising simulation montecarlo apl
toc: true
---

* The APL family of languages

** Why APL?

I recently got interested in [[https://en.wikipedia.org/wiki/APL_(programming_language)][APL]], an /array-based/ programming
language. In APL (and derivatives), we try to reason about programs as
series of transformations of multi-dimensional arrays. This is exactly
the kind of style I like in Haskell and other functional languages,
where I also try to use higher-order functions (map, fold, etc) on
lists or arrays. A developer only needs to understand these
abstractions once, instead of deconstructing each loop or each
recursive function encountered in a program.

APL also tries to be a really simple and /terse/ language. This
combined with strange Unicode characters for primitive functions and
operators, gives it a reputation of unreadability. However, there is
only a small number of functions to learn, and you get used really
quickly to read them and understand what they do. Some combinations
also occur so frequently that you can recognize them instantly (APL
programmers call them /idioms/).

** Implementations

APL is actually a family of languages. The classic APL, as created by
Ken Iverson, with strange symbols, has many implementations. I
initially tried [[https://www.gnu.org/software/apl/][GNU APL]], but due to the lack of documentation and
proper tooling, I went to [[https://www.dyalog.com/][Dyalog APL]] (which is proprietary, but free
for personal use). There are also APL derivatives, that often use
ASCII symbols: [[http://www.jsoftware.com/][J]] (free) and [[https://code.kx.com/q/][Q/kdb+]] (proprietary, but free for personal
use).

The advantage of Dyalog is that it comes with good tooling (which is
necessary for inserting all the symbols!), a large ecosystem, and
pretty good [[http://docs.dyalog.com/][documentation]]. If you want to start, look at [[http://www.dyalog.com/mastering-dyalog-apl.htm][/Mastering
Dyalog APL/]] by Bernard Legrand, freely available online.

* The Ising model in APL

I needed a small project to try APL while I was learning. Something
array-based, obviously. Since I already implemented a
Metropolis-Hastings simulation of the [[./ising-model.html][Ising
model]], which is based on a regular lattice, I decided to reimplement
it in Dyalog APL.

It is only a few lines long, but I will try to explain what it does
step by step.

The first function simply generates a random lattice filled by
elements of $\{-1,+1\}$.

#+BEGIN_SRC apl
L←{(2×?⍵ ⍵⍴2)-3}
#+END_SRC

Let's deconstruct what is done here:
- ⍵ is the argument of our function.
- We generate a ⍵×⍵ matrix filled with 2, using the ~⍴~ function: ~⍵ ⍵⍴2~
- ~?~ draws a random number between 1 and its argument. We give it our matrix to generate a random matrix of 1 and 2.
- We multiply everything by 2 and subtract 3, so that the result is in $\{-1,+1\}$.
- Finally, we assign the result to the name ~L~.

Sample output:
#+BEGIN_SRC apl
      ising.L 5
 1 ¯1  1 ¯1  1
 1  1  1 ¯1 ¯1
 1 ¯1 ¯1 ¯1 ¯1
 1  1  1 ¯1 ¯1
¯1 ¯1  1  1  1
#+END_SRC

Next, we compute the energy variation (for details on the Ising model,
see [[./ising-model.html][my previous post]]).

#+BEGIN_SRC apl
∆E←{
    ⎕IO←0
    (x y)←⍺
    N←⊃⍴⍵
    xn←N|((x-1)y)((x+1)y)
    yn←N|(x(y-1))(x(y+1))
    ⍵[x;y]×+/⍵[xn,yn]
}
#+END_SRC

- ⍺ is the left argument (coordinates of the site), ⍵ is the right argument (lattice).
- We extract the x and y coordinates of the site.
- ~N~ is the size of the lattice.
- ~xn~ and ~yn~ are respectively the vertical and lateral neighbours of the site. ~N|~ takes the coordinates modulo ~N~ (so the lattice is actually a torus). (Note: we used ~⎕IO←0~ to use 0-based array indexing.)
- ~+/~ sums over all neighbours of the site, and then we multiply by the value of the site itself to get $\Delta E$.

Sample output, for site $(3, 3)$ in a random $5\times 5$ lattice:

#+BEGIN_SRC apl
      3 3ising.∆E ising.L 5
¯4
#+END_SRC

Then comes the actual Metropolis-Hastings part:

#+BEGIN_SRC apl
U←{
    ⎕IO←0
    N←⊃⍴⍵
    (x y)←?N N
    new←⍵
    new[x;y]×←(2×(?0)>*-⍺×x y ∆E ⍵)-1
    new
}
#+END_SRC

- ⍺ is the $\beta$ parameter of the Ising model, ⍵ is the lattice.
- We draw a random site $(x,y)$ with the ~?~ function.
- ~new~ is the lattice but with the $(x,y)$ site flipped.
- We compute the probability $\alpha = \exp(-\beta\Delta E)$ using the ~*~ function (exponential) and our previous ~∆E~ function.
- ~?0~ returns a uniform random number in $[0,1)$. Based on this value, we decide whether to update the lattice, and we return it.

We can now bring everything together for display:

#+BEGIN_SRC apl
Ising←{' ⌹'[1+1=({10 U ⍵}⍣⍵)L ⍺]}
#+END_SRC

- We draw a random lattice of size ⍺ with ~L ⍺~.
- We apply to it our update function, with $\beta$=10, ⍵ times (using the ~⍣~ function, which applies a function $n$ times.
- Finally, we display -1 as a space and 1 as a domino ⌹.

Final output, with a $80\times 80$ random lattice, after 50000 update
steps:

#+BEGIN_SRC apl
      80ising.Ising 50000
   ⌹⌹⌹⌹ ⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹      ⌹⌹⌹⌹⌹⌹       ⌹⌹⌹⌹⌹      ⌹⌹⌹⌹       ⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹          
   ⌹⌹⌹⌹      ⌹⌹⌹⌹⌹⌹     ⌹⌹⌹⌹⌹        ⌹⌹⌹⌹⌹      ⌹⌹⌹⌹⌹⌹ ⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹           
⌹⌹⌹⌹⌹⌹⌹      ⌹⌹⌹⌹⌹⌹       ⌹⌹⌹     ⌹⌹⌹⌹⌹⌹⌹⌹       ⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹            ⌹
⌹⌹⌹⌹⌹⌹⌹      ⌹⌹⌹⌹⌹⌹       ⌹⌹⌹  ⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹         ⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹            ⌹
⌹⌹⌹⌹⌹⌹⌹⌹⌹      ⌹⌹⌹⌹            ⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹     ⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹             ⌹
⌹⌹⌹⌹⌹⌹⌹⌹⌹      ⌹⌹⌹⌹            ⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹   ⌹⌹⌹     ⌹⌹⌹⌹⌹⌹             ⌹
⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹ ⌹⌹⌹            ⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹   ⌹⌹       ⌹⌹⌹⌹⌹      ⌹       
  ⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹          ⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹   ⌹        ⌹⌹⌹⌹      ⌹⌹⌹      
 ⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹      ⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹  ⌹⌹⌹⌹⌹⌹⌹            ⌹⌹⌹       ⌹⌹⌹      
 ⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹      ⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹  ⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹         ⌹⌹⌹    ⌹⌹⌹⌹⌹⌹      
⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹      ⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹        ⌹⌹⌹⌹              ⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹
⌹⌹⌹⌹        ⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹      ⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹         ⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹       ⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹
⌹⌹⌹⌹           ⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹    ⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹         ⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹       ⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹
⌹⌹⌹⌹           ⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹            ⌹⌹⌹          ⌹⌹⌹⌹⌹⌹⌹⌹⌹       ⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹
⌹⌹⌹            ⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹            ⌹⌹⌹           ⌹⌹⌹⌹⌹⌹⌹⌹      ⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹
⌹⌹⌹⌹           ⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹            ⌹⌹⌹⌹           ⌹⌹⌹⌹⌹⌹⌹      ⌹⌹⌹⌹⌹⌹⌹  ⌹⌹⌹⌹⌹
⌹⌹⌹⌹ ⌹⌹⌹⌹⌹     ⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹            ⌹⌹⌹            ⌹⌹⌹⌹⌹⌹⌹     ⌹⌹⌹⌹⌹⌹⌹⌹  ⌹⌹⌹⌹⌹
⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹   ⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹           ⌹⌹⌹⌹                ⌹⌹⌹     ⌹⌹⌹⌹⌹⌹⌹    ⌹⌹⌹⌹
  ⌹ ⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹ ⌹⌹⌹⌹           ⌹⌹⌹⌹                ⌹⌹⌹      ⌹⌹⌹⌹⌹         
  ⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹    ⌹⌹⌹⌹⌹⌹       ⌹⌹⌹⌹⌹⌹⌹                ⌹⌹        ⌹           
  ⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹    ⌹⌹⌹⌹⌹⌹⌹      ⌹⌹⌹⌹⌹⌹⌹⌹                                     
  ⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹    ⌹⌹⌹⌹⌹⌹⌹⌹     ⌹⌹⌹⌹⌹⌹⌹⌹⌹                                    
  ⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹  ⌹⌹⌹⌹⌹⌹       ⌹⌹⌹⌹⌹⌹⌹⌹           ⌹                         
 ⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹  ⌹⌹⌹⌹⌹⌹        ⌹⌹⌹⌹⌹⌹⌹          ⌹                          
⌹⌹⌹⌹⌹⌹  ⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹     ⌹⌹⌹          ⌹⌹⌹⌹⌹⌹                          ⌹⌹     ⌹⌹⌹
⌹⌹⌹⌹⌹⌹  ⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹                  ⌹⌹⌹⌹⌹⌹         ⌹               ⌹⌹⌹     ⌹⌹⌹
⌹⌹⌹⌹⌹⌹  ⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹                   ⌹⌹⌹⌹⌹⌹                     ⌹⌹⌹⌹⌹⌹     ⌹⌹⌹
⌹⌹⌹⌹⌹⌹  ⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹       ⌹⌹⌹⌹⌹       ⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹            ⌹⌹⌹⌹⌹⌹    ⌹⌹⌹⌹
⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹     ⌹⌹⌹⌹⌹⌹⌹⌹      ⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹            ⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹
  ⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹    ⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹       ⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹             ⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹
⌹⌹⌹⌹⌹⌹⌹⌹⌹       ⌹⌹⌹⌹    ⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹     ⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹                ⌹⌹⌹⌹⌹⌹⌹⌹⌹
⌹⌹⌹⌹⌹⌹⌹⌹⌹       ⌹⌹⌹⌹    ⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹    ⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹                 ⌹⌹⌹⌹⌹⌹⌹⌹
⌹⌹⌹⌹⌹⌹⌹⌹⌹       ⌹              ⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹ ⌹⌹⌹⌹⌹⌹⌹⌹⌹              ⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹
⌹⌹⌹⌹⌹⌹⌹⌹⌹                            ⌹⌹⌹⌹⌹⌹⌹    ⌹⌹⌹⌹⌹⌹⌹⌹⌹          ⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹
⌹⌹⌹⌹⌹⌹⌹⌹⌹                               ⌹⌹⌹⌹      ⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹  ⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹
⌹⌹⌹⌹⌹⌹⌹⌹⌹                                ⌹⌹⌹        ⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹    ⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹
⌹⌹⌹⌹⌹⌹                   ⌹⌹⌹⌹             ⌹⌹        ⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹   ⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹
⌹⌹⌹⌹⌹⌹                   ⌹⌹⌹⌹                           ⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹
⌹⌹⌹⌹⌹⌹                  ⌹⌹⌹⌹⌹⌹⌹                ⌹⌹            ⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹ 
  ⌹⌹⌹⌹                  ⌹⌹⌹⌹⌹⌹⌹              ⌹⌹⌹⌹              ⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹  
  ⌹⌹⌹⌹                 ⌹⌹⌹⌹⌹⌹⌹⌹⌹             ⌹⌹⌹⌹              ⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹  
  ⌹⌹⌹⌹   ⌹         ⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹         ⌹⌹⌹⌹⌹⌹              ⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹  
 ⌹⌹⌹⌹⌹   ⌹ ⌹       ⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹      ⌹⌹⌹⌹⌹⌹⌹⌹           ⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹  
 ⌹⌹⌹⌹⌹   ⌹         ⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹      ⌹⌹⌹⌹⌹⌹⌹⌹           ⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹  
 ⌹⌹⌹⌹⌹             ⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹        ⌹⌹⌹⌹⌹           ⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹  
⌹⌹⌹⌹⌹              ⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹        ⌹⌹⌹⌹⌹⌹⌹          ⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹   ⌹⌹⌹⌹⌹
⌹⌹⌹⌹              ⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹       ⌹⌹⌹⌹⌹⌹⌹⌹⌹        ⌹⌹⌹⌹⌹⌹⌹⌹⌹    ⌹⌹⌹⌹⌹
⌹⌹⌹⌹              ⌹⌹⌹⌹⌹⌹⌹  ⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹        ⌹⌹⌹⌹⌹⌹⌹⌹        ⌹⌹⌹⌹⌹⌹⌹⌹⌹ ⌹⌹⌹⌹⌹⌹⌹⌹
⌹⌹⌹⌹⌹           ⌹⌹⌹⌹⌹⌹⌹⌹⌹    ⌹⌹⌹⌹⌹⌹⌹⌹⌹          ⌹⌹⌹⌹⌹⌹⌹       ⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹
⌹⌹⌹⌹⌹      ⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹   ⌹⌹⌹⌹⌹⌹⌹⌹⌹           ⌹⌹⌹⌹⌹⌹       ⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹
⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹   ⌹⌹⌹⌹⌹⌹⌹⌹⌹            ⌹⌹⌹         ⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹
⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹     ⌹⌹⌹⌹⌹⌹⌹⌹   ⌹⌹⌹⌹⌹⌹⌹⌹             ⌹          ⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹
⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹       ⌹⌹⌹⌹⌹⌹⌹⌹  ⌹⌹⌹⌹⌹⌹⌹⌹                       ⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹
⌹⌹⌹⌹⌹⌹⌹            ⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹         ⌹⌹            ⌹⌹⌹⌹⌹⌹⌹   ⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹
⌹⌹⌹⌹⌹⌹              ⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹       ⌹⌹⌹⌹         ⌹⌹⌹⌹⌹⌹⌹⌹     ⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹
                    ⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹       ⌹⌹⌹⌹⌹       ⌹⌹⌹⌹⌹⌹⌹⌹⌹     ⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹
                       ⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹       ⌹⌹⌹⌹⌹       ⌹⌹⌹⌹⌹⌹⌹⌹⌹    ⌹⌹⌹⌹⌹⌹⌹⌹⌹  
            ⌹⌹⌹⌹⌹⌹        ⌹⌹⌹⌹⌹⌹⌹⌹          ⌹⌹⌹⌹⌹       ⌹⌹⌹⌹⌹⌹⌹⌹⌹       ⌹⌹⌹⌹⌹⌹  
           ⌹⌹⌹⌹⌹⌹⌹⌹        ⌹⌹⌹⌹⌹⌹⌹          ⌹⌹⌹⌹⌹  ⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹     ⌹⌹⌹⌹⌹⌹  
   ⌹⌹⌹ ⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹        ⌹⌹⌹⌹⌹          ⌹⌹⌹⌹⌹  ⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹   ⌹⌹⌹⌹⌹⌹⌹⌹⌹
⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹        ⌹⌹⌹⌹           ⌹⌹⌹⌹⌹  ⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹   ⌹⌹⌹⌹⌹⌹⌹⌹⌹
⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹        ⌹⌹⌹             ⌹⌹⌹      ⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹  ⌹⌹⌹⌹   ⌹⌹⌹
⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹         ⌹⌹⌹        ⌹⌹⌹                       ⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹    ⌹⌹
⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹         ⌹⌹⌹         ⌹⌹⌹                          ⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹     ⌹⌹
⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹       ⌹⌹⌹         ⌹⌹⌹    ⌹⌹                      ⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹     ⌹⌹
⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹      ⌹⌹⌹         ⌹⌹⌹⌹⌹⌹⌹⌹⌹                      ⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹       
  ⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹   ⌹⌹          ⌹⌹⌹⌹⌹⌹⌹⌹                      ⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹       
  ⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹   ⌹⌹⌹⌹⌹       ⌹⌹⌹⌹⌹⌹⌹⌹                         ⌹⌹⌹⌹⌹⌹⌹⌹       
  ⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹   ⌹⌹⌹⌹⌹⌹      ⌹⌹⌹⌹⌹⌹⌹⌹                          ⌹⌹⌹⌹⌹⌹⌹       
  ⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹    ⌹⌹⌹⌹⌹         ⌹⌹⌹⌹⌹⌹                          ⌹⌹⌹          
 ⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹    ⌹⌹⌹⌹⌹⌹        ⌹⌹⌹⌹⌹⌹⌹⌹                        ⌹⌹⌹          
 ⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹      ⌹⌹⌹⌹⌹       ⌹⌹⌹⌹⌹⌹⌹⌹                        ⌹⌹⌹          
⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹         ⌹⌹⌹⌹⌹        ⌹⌹⌹⌹⌹⌹⌹⌹                      ⌹⌹⌹⌹          
⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹            ⌹⌹⌹⌹        ⌹⌹⌹⌹⌹⌹⌹⌹       ⌹             ⌹⌹⌹⌹⌹       ⌹⌹⌹
⌹⌹⌹⌹⌹⌹⌹⌹⌹ ⌹             ⌹⌹⌹⌹        ⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹   ⌹⌹⌹            ⌹⌹⌹⌹⌹⌹     ⌹⌹⌹⌹⌹
⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹             ⌹⌹⌹⌹        ⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹           ⌹⌹⌹⌹⌹⌹      ⌹⌹⌹⌹⌹
   ⌹⌹⌹⌹⌹⌹⌹⌹             ⌹⌹⌹⌹        ⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹        ⌹⌹⌹⌹⌹⌹⌹⌹        ⌹⌹ 
   ⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹         ⌹⌹⌹⌹⌹       ⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹         ⌹⌹⌹⌹⌹⌹⌹⌹           
   ⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹        ⌹⌹⌹⌹⌹       ⌹⌹⌹⌹⌹⌹⌹⌹   ⌹⌹⌹⌹⌹       ⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹         
   ⌹⌹⌹  ⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹      ⌹⌹⌹⌹⌹        ⌹⌹⌹⌹⌹⌹⌹    ⌹⌹⌹⌹       ⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹⌹         
#+END_SRC

Complete code, with the namespace:

#+BEGIN_SRC apl
:Namespace ising

        L←{(2×?⍵ ⍵⍴2)-3}

        ∆E←{
                ⎕IO←0
                (x y)←⍺
                N←⊃⍴⍵
                xn←N|((x-1)y)((x+1)y)
                yn←N|(x(y-1))(x(y+1))
                ⍵[x;y]×+/⍵[xn,yn]
        }

        U←{
                ⎕IO←0
                N←⊃⍴⍵
                (x y)←?N N
                new←⍵
                new[x;y]×←(2×(?0)>*-⍺×x y ∆E ⍵)-1
                new
        }

        Ising←{' ⌹'[1+1=({10 U ⍵}⍣⍵)L ⍺]}

:EndNamespace
#+END_SRC

* Conclusion

The algorithm is very fast (I think it can be optimized by the
interpreter because there is no branching), and is easy to reason
about. The whole program fits in a few lines, and you clearly see what
each function and each line does. It could probably be optimized
further (I don't know every APL function yet...), and also could
probably be golfed to a few lines (at the cost of readability?).

It took me some time to write this, but Dyalog's tools make it really
easy to insert symbols and to look up what they do. Next time, I will
look into some ASCII-based APL descendants. J seems to have a [[http://code.jsoftware.com/wiki/NuVoc][good
documentation]] and a tradition of /tacit definitions/, similar to the
point-free style in Haskell. Overall, J seems well-suited to modern
functional programming, while APL is still under the influence of its
early days when it was more procedural. Another interesting area is K,
Q, and their database engine kdb+, which seems to be extremely
performant and actually used in production.

Still, Unicode symbols make the code much more readable, mainly
because there is a one-to-one link between symbols and functions,
which cannot be maintained with only a few ASCII characters.
