Category Archives: Julia

E-graphs in Julia (Part I)

By: Hey There Buddo!

Re-posted from: https://www.philipzucker.com/egraph-1/

E-graphs are the data structure that efficiently compute with equalities between terms. It can be used to prove equivalences for equational reasoning, but also to find equational rewrites, which is useful for program transformations or algebraic simplification.

I got a hot tip from Talia Ringer on twitter about a paper and library called egg. Egg implements E-graphs as a performant generic library in Rust. The paper explains the data structure, a efficiency twist about when to fix its invariants, and shows some compelling use case studies. The claimed speedups over a naive implementation of E-graphs are absolutely nuts (x1000?!).

E-graphs compactly contain all the terms that are generated by a set of equalities and make it fast to perform the operation of congruence closure. Congruence is the property that $a = b \rightarrow f(a) = f(b)$ for all functions $f$. Functions take equals to equals. Congruence closure is taking an equivalence relation and closing it under this congruence operation, saturating the relation.

I decided to try parallel tracks of attempting to rebuild a version in Julia vs trying to build bindings to the implementation in egg. It is very unlikely I’ll beat the performance of egg, but native language flexibility is awful nice. Regardless I figured it’s a good learning experience.

SMT solvers like Z3 use E-graphs to reason about equality of uninterpreted functions. Understanding the E-graph structure has given me some possible insight into where I went wrong. Equality reasoning like this has been on my mind since I failed getting off-the-shelf provers to prove equations produced by Catlab.jl. So that’s sort of where I’m trying to head with this.

The actual E-graph data structure looks something like if a Union-Find and a Hash-Cons has a baby, so let’s talk about about those a bit.

Union Find or Disjoint Set Data Structure

The union find data structure is a fast data structure for representing partitions of sets.

Roughly how it works is that every element points to a parent in the partition. When you want to know what partition your thing is in, you follow the parent pointers up to a canonical element. While doing this you trickle the pointers upwards so the query will be faster next time. Eventually all the pointers in the partition will point directly to the canonical element with no indirection.

To merge two sets, set the parent pointer of one canonical element to the other.

That’s the rough sketch. There’s more in the wikipedia article https://en.wikipedia.org/wiki/Disjoint-set_data_structure. Julia has an implementation in the DataStructures.jl library https://juliacollections.github.io/DataStructures.jl/latest/disjoint_sets/ which I’m just going to use.

To start to see how union find is useful for equations, let’s say we have a pile of atoms :a :b :c :d and some equations :a = :c, :b = :d, :d = :c. Question: is :a = :b? For this small example you can see that it is, but the disjoint set structure will do the same.

using DataStructures
s = DisjointSets(:a,:b,:c,:d)
union!(s, :a, :c)
union!(s, :b, :d)
union!(s, :c, :d)
in_same_set(s,:a,:c)
# >>> true

Now the question is, how can we extend this to compound terms that appear in syntax trees like :(g(f(a)))?
The trick is consider each subterm as an atom in it’s own right, :a, :(f(a)), :(g(f(a))) and place these things into the disjoint set. This trick is significantly aided by the technique of hash-consing, which brings the structure and performance differences between compound terms and atoms closer in alignment.

Hash-Consing

Hash-Consing is a programming technique to convert tree data structures into a DAG data structure where all identical subterms are shared rather than copied. This has the advantage of faster processing and decreased memory usage for trees with a lot of repetitive subterms and fast equality comparison since you can check the unique identity of a term immediately rather than recursing down it.
The way this is done by identifying each node with a unique id and maintaining a mapping of nodes to ids in a hash table.
Whenever you create new nodes, you use smart constructors that first check if the node is already defined in the hash table. If it is, this canonical node is returned, otherwise the node is added to the hash table.

You can recursively convert a tree into it’s hash consed from by destructing it and reconstructing it with the smart constructors.
The unique id may be the memory address where the node is stored or it may come from a counter being incremented every time you create a novel term.

The canonical reference on hash-consing is Type-Safe Modular Hash-Consing. Hash consing has been folklore for a very, very long time though. There is a reference in here to a paper by Ershov in 1958 for example. A really fun example in there is using HashConsing for a BDD implementation.

Another term sometimes used is “interned”. Julia’s Symbol is interned.

E-Graphs

When we’re talking about equations, it no longer makes sense to give each term an id as in the pure hash-cons. Instead we want to give each equivalence class of terms a unique id and maintain the equivalence classes in a union-find structure. As we put more equalities into the union-find structure and the equivalence classes grow, these canonical ids change under our feet, which is annoying but manageable.

The E-graph maintains:

  • a map from hash-consed terms to class ids
  • a map from class-ids to equivalence classes
  • a unionfind data structure on class ids

Each equivalence class needs to maintain

  • an array of terms in the class
  • an array of possible parent terms for efficiently propagating congruences

We will index into congruence classes with Id. Wrapping the Int64 in a struct should have no performance cost, and keeps things sane.

struct Id
    id::Int64
end

We need a structure to hold hash consed terms of our language. AutoHashEquals.jl makes it so hash and == use a structural definition.

using AutoHashEquals
@auto_hash_equals mutable struct Term
    head::Symbol
    args::Array{Id}
end

An equivalence class is a set of nodes. We also need to store parents of these nodes in order to make congruence closure and hash cons updating fast.

mutable struct EClass
    nodes::Array{Term}
    parents::Array{Tuple{Term,Id}}
end

Finally we have the actual EGraph data structure. It holds a unionfind that keeps equivalences of classes, a memo field to map Term to their equivalence class, a classes field to map equivalence class Id to the EClass. Maintaining all the appropriate invariants and connections between these pieces sucks.

using DataStructures

mutable struct EGraph
    unionfind::IntDisjointSets
    memo::Dict{Term,Id} # int32 UInt32?
    classes::Dict{Id,EClass} # Use array?
    dirty_unions::Array{Id}
end

# Build an empty EGraph
EGraph() = EGraph(IntDisjointSets(0), Dict(), Dict(), [])

Finding the root class or querying whether in_same_set for an Id just passes off the query to the union find.

# returns the canonical Id
find_root!(e::EGraph, id::Id) = Id(DataStructures.find_root!(e.unionfind, id.id))

in_same_class(e::EGraph, t1::Term, t2::Term) = in_same_set(e.unionfind, e.memo[t1] , e.memo[t2])

We can canonicalize terms so that the Id arguments only point to the root class.

function canonicalize!(e::EGraph, t::Term)
    t.args = [ find_root!(e, a) for a in t.args ]
end

We can lookup the class a term belongs to by canonicalizing and using the memo table

function find_class!(e::EGraph, t::Term)  # lookup
    canonicalize!(e, t) #t.args = [ find_root!(e, a) for a in t.args ]  # canonicalize the term
    if haskey(e.memo, t)
        id = e.memo[t]
        return find_root!(e, id)
    else
        return nothing
    end
end

We can add new terms by finding if they already exist in the hashcons. If so it, it does nothing.
But if not, we need to make a new EClass for this term to live in. This is the only way new EClasses are made.
It is important to maintain the hashcons correctly and canonicalize so that we don’t accidentally create a new EClass for a term that should already be in another EClass.

function Base.push!(e::EGraph, t::Term)
    id = find_class!(e,t) # also canonicalizes t
    if id == nothing # term not in egraph. Make new class and put in graph
        id = Id(push!(e.unionfind))
        cls = EClass( [t] , [] )
        for child in t.args # set parent pointers
            push!(e.classes[child].parents,  (t, id))
        end
        e.classes[id] = cls
        e.memo[t] = id
        return id
    else
        return id
    end
end

Alright. Here is where stuff gets hairy. We want to union two equivalence classes.

  1. Cananonize the Id.
  2. Check if already equivalent. If so, we’re done
  3. Ask unionfind to perform union
  4. Recanonize the arguments of nodes in memo
  5. Merge nodes and parents arrays of the two eclasses
  6. Mark equivalence class as dirty for rebuilding.
  7. Destroy the unneeded EClass in classes
function Base.union!(e::EGraph, id1::Id, id2::Id)
    id1 = find_root!(e, id1)
    id2 = find_root!(e, id2)
    # An invariant is that the EClass data should always keyed with the root of the union find
    # in the e.classes datastructure
    if id1 != id2 # if not already in same class
        id3 = Id(union!(e.unionfind, id1.id,id2.id)) # perform the union find
        if id3 == id1 # picked id1 as root
            to = id1
            from = id2
        elseif id3 == id2 # picked id2 as root
            to = id2
            from = id1
        else
            @assert false
        end
            
        push!(e.dirty_unions, id3) # id3 will need it's parents processed in rebuild!
        
        # we empty out the e.class[from] and put everything in e.class[to]
        for t in e.classes[from].nodes
            push!(e.classes[to].nodes, t) 
        end
        
        # recanonize all nodes in memo.
        for t in e.classes[to].nodes
            delete!(e.memo, t) # remove stale term
            canonicalize!(e, t) # update term in place in nodes array
            e.memo[t] = to # now this term is in the to eqauivalence class
        end
        
        # merge parents list
        for (p,id) in e.classes[from].parents
            push!(e.classes[to].parents, (p, find_root!(e,id)))
        end
        
        # destroy "from" from e.classes. It's information should now be
        # in e.classes[to] and it should never be accessed.
        delete!(e.classes, from) 
    end 
        
end

This code might be right. Lemme tell ya, I’ve had versions only subtly different that weren’t right. So I dunno. Buyer beware.

Finally the last big chunk is rebuild!. This function works through the dirty_unions array and fixes parent nodes and propagates any congruences.


function repair!(e::EGraph, id::Id)
    cls = e.classes[id]
    
    #  for every parent, update the hash cons. We need to repair that the term has possibly a wrong id in it
    for (t,t_id) in cls.parents
        delete!( e.memo, t) # the parent should be updated to use the updated class id
        canonicalize!(e, t) # canonicalize
        e.memo[t] = find_root!(e, t_id) # replace in hash cons  
    end
    
    # now we need to discover possible new congruence eqaulities in the parent nodes.
    # we do this by building up a parent hash to see if any new terms are generated.
    new_parents = Dict()
    for (t,t_id) in cls.parents
        canonicalize!(e,t) # canonicalize. Unnecessary by the above?
        if haskey(new_parents, t)
            union!(e, t_id, new_parents[t])
        end
        new_parents[t] = find_root!(e, t_id)
    end
    e.classes[id].parents = [ (p,id) for (p,id) in new_parents]
end

function rebuild!(e::EGraph)
    while length(e.dirty_unions) > 0
       todo = Set([ find_root!(e,id) for id in e.dirty_unions])
       e.dirty_unions = []
       for id in todo
            repair!(e, id)
        end
    end
end


Here’s some helper functions to build constants and terms

function constant!(e::EGraph, x::Symbol)
      t = Term(x, [])
      push!(e, t)
end

term!(e::EGraph, f::Symbol) = (args...) -> begin
    t = Term(f, collect(args))
    push!(e,  t)
end

and some other helper functions to help draw the EGraph, which is pretty crucial

using LightGraphs
using GraphPlot
using Colors

function graph(e::EGraph) 
    nverts = length(e.memo)
    g = SimpleDiGraph(nverts)
    vertmap = Dict([ t => n for (n, (t, id)) in enumerate(e.memo)])
    #=for (id, cls) in e.classes
        for t1 in cls.nodes
            for (t2,_) in cls.parents
                add_edge!(g, vertmap[t2], vertmap[t1])
            end
        end
    end =#
    for (n,(t,id)) in enumerate(e.memo)
        for n2 in t.args
            for t2 in e.classes[n2].nodes
                add_edge!(g, n, vertmap[t2])
            end
        end
    end
    nodelabel = [ t.head for (t, id) in e.memo]
    classmap = Dict([ (id,n)  for (n,id) in enumerate(Set([ id.id for (t, id) in e.memo]))])
    nodecolor = [classmap[id.id] for (t, id) in e.memo]
    
    return g, nodelabel, nodecolor
end

function graphplot(e::EGraph) 
    g, nodelabel,nodecolor = graph(e)


    # Generate n maximally distinguishable colors in LCHab space.
    nodefillc = distinguishable_colors(maximum(nodecolor), colorant"blue")
    gplot(g, nodelabel=nodelabel, nodefillc=nodefillc[nodecolor])
end

Example Usage

A simple toy problem is to use equalities like $f^n(a) == a$. What this does is put a loop in the EGraph.

e = EGraph()
a = constant!(e, :a)
f = term!(e, :f)
apply(n, f, x) = n == 0 ? x : apply(n-1,f,f(x))


union!(e, a, apply(6,f,a))
display(graphplot(e))

If you think about it, if we assert another equality $f^m(a)==a$, what we’re kind of doing is computing the $gcd(n,m)$. Here the gcd(6,9) = 3.

union!(e, a, apply(9,f,a))
rebuild!(e)
display(graphplot(e))

And then if additionally we assert a prime number of applications of f, we reduced the loop of nodes to a single equivalence class

union!(e, a, apply(11,f,a))
rebuild!(e)
display(graphplot(e))

We’ve only just begun. To do anything really fun, we need to implement pattern matching over the E-graph (EMatching).

We’ll see.

Bits and Bobbles

  • I should try just binding to egg. A combo of https://github.com/herbie-fp/egg-herbie and https://github.com/felipenoris/JuliaPackageWithRustDep.jl makes it seem like this shouldn’t be so bad. My implementation is trash compared to egg’s and I haven’t even done pattern matching yet
  • Factoring out the hashcons in the struct definition is like factoring out Fix in recursion schemes
  • Factoring out hash cons is like factoring out the fix for a recursive definition. When you do this, it also opens up the option of memoization, like in fib
  • Unification and union find. If you don’t congruence close, and instead merge recursively down terms, you get a kind of unification. Each equivalence class can only hold variables and terms of the same head for the unification to succeed. This can be checked after unioning everything. I think this is roughly what the MM algorithm does
  • Halfway House E-graph. For a loss of efficiency, you can store a simpler e graph structure that is really is basically a hash cons and union-find smashed together.
  • The models returns by Z3 for a bunch of equalities are basically a serialization of the E graph. Could this be a way to build a rewrite library? You might have to reimplement pattern matching.

References

Implementing Marching Squares

By: The Cedar Ledge

Re-posted from: http://jacobzelko.com/marching-squares/

Naive Implementation of the Marching Algorithm for topography and 2D Graphics!

One day, I was examining topoplots created from popular libraries like MATLAB’s EEGLAB and Python’s MNE program for processing and visualizing EEG data.
I think the plots are beautiful and can be quite informative as shown from Mikołaj Magnuski’s example in MNE-Python:

However, what was curious to me was how to make the boundaries one can see in the above image between the different color gradients.
I chatted with my friend and fellow programmer Ole Kröger about this and he explained this was accomplished via an algorithm called Marching Squares.
So, naturally, I had to figure out how to implement this for myself!

If you find this blog post useful, please consider citing it:

Jacob Zelko. Implementing Marching Squares. December 1st, 2020. http://jacobzelko.com

What Is the Marching Squares Algorithm?

A simplified explanation of the marching squares algorithm is that it can be thought of as a way of visually separating data based on a user defined threshold.
This data takes the form of a 2D array and the threshold is used to filter each value in this array to either a \(0\) if it does not reach the threshold or \(1\) if it meets or exceeds the threshold.
Each \(2 \times 2\) square in the provided grid creates a cell that will be used for creating the boundaries around thresholded values.

Following this simplified explanation, lets get to implementing the algorithm!
I always find explanations easier to understand after seeing the full implementation. :smiley:

Implementing Marching Squares!

All I See Are :one:’s and :zero:’s!

To start, we need some data!
I went ahead and created this \(10 \times 10\) grid that has a threshold applied to it to make it hold only \(1\)’s and \(0\)’s.
We will be using the following array of values in this post:

10 × 10 Array{Int64,2}:
 0  1  1  0  0  0  1  0  1  0
 0  1  1  0  1  0  0  1  0  0
 0  0  1  1  0  1  1  1  0  0
 0  0  0  1  1  0  1  1  1  0
 0  0  1  0  0  1  0  1  0  0
 0  0  1  1  1  1  0  1  1  0
 0  0  0  1  0  0  1  0  0  0
 1  1  1  0  0  1  0  0  0  0
 0  0  1  0  0  1  1  1  0  1
 0  1  1  0  1  1  0  0  0  1

Visualizing a Grid Using Luxor.jl :beetle:

To process this information and implement the algorithm, we need a way to actually visualize these values.
Thankfully, fellow Julia developer, Cormullion, created a wonderful visualization library called Luxor.jl that allows us to do just that!
One can add Luxor.jl to your Julia REPL by typing ]add Luxor.
After that, let’s start with making our script!

First, let’s use Luxor and create a function that defines our background:

using Luxor

function make_drawing(width, height, img_path, bkg_color, origin_p)
    d = Drawing(width, height, img_path)
    background(bkg_color)
    origin(Point(0, 0))
    return d
end

make_drawing creates a Drawing object that we will then draw our future lines and shapes!
It requires you to specify the dimensions of your drawing, where to save the image, the background color, and an origin.
Upon execution, it gives you back the Drawing object ready for additional shapes to be drawn on it.

NOTE: What is an “origin” in Luxor?

By default, Luxor assumes you want to draw everything based off the center of the Drawing’s given dimensions.
To change the origin, we supply a Point from Luxor to Luxor’s function, origin.

Perfect!
Now that we have our drawing created, let’s get to work actually showing our values.

I think the best way is to show \(1\)’s as black balls and \(0\)’s as white balls.
To do this, let’s create another function for our script:

function draw_balls(drawing, grid)
    nrows, ncols = size(grid)
    step_x = drawing.width / (ncols - 1)
    step_y = drawing.height / (nrows - 1)
    circ_scale = min(nrows, ncols) / max(nrows, ncols)
    points = Array{NamedTuple}(undef, nrows, ncols)
    for i = 1:nrows
        for j = 1:ncols
	    grid[i, j] == 0 ? sethue("white") : sethue("black")
            pos = Point(step_x * (j - 1), step_y * (i - 1))
            circle(pos, 7.5 * circ_scale, :fill)
	    points[i, j] = (x = pos.x, y = pos.y, val = grid[i, j])
        end
    end
    return points
end

There is a lot happening in this new function!
Let’s examine the draw_balls function step by step to understand it.

First, there is a bit of initialization that occurs before we get to actually drawing the balls – let’s start here!

  1. The function takes in a Drawing object and the binary valued grid.
  2. The number of rows and columns are determined from the grid.
  3. step_x and step_y are calculated to find how much space should be between each ball along the x and y axes. (We subtract one away from the denominator of the step calculations to draw along the edges of the Drawing).
  4. We create a scaling value, circ_scale, that scales our balls to an appropriate radius for the grid.
  5. Finally, points is initialized to an array of NamedTuple objects that we will use to store the position and associated value of each ball.

Now that everything is initialized, let’s get to drawing the balls!

  1. We use two loops to access our grid to get the value for each ball we are going to draw and once we get the value from the grid, we use a ternary statement to sethue the color of our ball.
    Remember, that the \(0\)’s are white and \(1\)’s are black!
  2. Next, we determine the position of each circle by using the steps we calculated.
    Here, we subtract away \(1\) from our indexing values so we are able to draw at the edges of our Drawing.
  3. The Luxor function, circle is actually what we use to draw the balls!
    This takes in the position of the circle we calculated in the last step and sets a radius based on our scaling factor.
    :fill allows us to color in the circle to create a ball.
  4. Finally, we store the x and y position of each ball, along with its value from the input grid, into our points array as a NamedTuple.
    The points array is then returned to us for later usage.

Whew!
That was a lot of work!
However, it was worth it because now we get this nice layout of our balls:

Now that we have our layout, we are ready to implement our algorithm!

In This Case… :briefcase:

If you look at our layout and how we drew it, all the points form the corners of rectangles.
With each of these rectangles, we can associate them with a value based on the values of the circles at each corner of these rectangles.
If that sounds confusing, here is a picture to better explain it:

In this image, the balls here represent four balls taken from our grid.
Together, based on our layout, they form a rectangle.
In a clockwise fashion, starting from the top left of the rectangle, I label each corner, a, b, c, and d accordingly.
To assign a rectangle a value, we count the values at each corner as part of a 4 bit structure.
To calculate the rectangle value with our corners, we would do the following calculation: \(a \times 8 + b \times 4 + c \times 2 + d \times 1\)
This determines the value, or isovalue as it is commonly referred to as, for a rectangle in this algorithm.

In this specific image, we have a = 0, b = 1, c = 1, and d = 1.
Using our previous formulation, we calculate the value as such: \(0 \times 8 + 1 \times 4 + 1 \times 2 + 1 \times 2 = 7\).
Now that we have our value, we can then determine how a line inside of the rectangle.
As part of the Marching Squares algorithm, there is a lookup table that defines 16 different cases based on the values calculated.
Here are the various cases:

In our example above, we would use Case 7.
We then draw a line from the center of the left edge to the middle of top edge of the rectangle like this:

Let’s go ahead and calculate each rectangle’s isovalue in the grid!
For convenience, lets define a small function that calculates the isovalue for each rectangle.

iso_value(a, b, c, d) = a * 8 + b * 4 + c * 2 + d * 1

Moving on from there, we can then use the following code snippet to index each value in our points array to determine the values for a, b, c, and d and calculate each rectangles’ isovalue:

nrows, ncols = size(points)
sethue("black")
for j = 1:(nrows - 1)
    for i = 1:(ncols - 1)
       a = points[j, i]
       b = points[j, i + 1]
       c = points[j + 1, i + 1]
       d = points[j + 1, i]

       case = iso_value(a.val, b.val, c.val, d.val)
       fontsize(14)
       textcentered(string(case), Point((a.x + c.x) / 2, (a.y + c.y) / 2))
    end
end
finish()

This snippet produces the following image:

Hooray!
We now know the case for each rectangle.
But… How do we actually draw the lines inside of our grid?
Thankfully, our draw_balls algorithm gives us the ability to do just that!

NOTE: What is finish()?

finish() is a Luxor function which tells Luxor that we are done drawing on the Drawing object and to save the Drawing as a file.

Using Cardinal Directions :bird:

Using our points array, we can calculate the center point of each edge on a rectangle.
Taking our previous code snippet, we can modify it slightly to calculate the center of each rectangular edge:

nrows, ncols = size(points)
sethue("black")
for j = 1:(nrows - 1)
    for i = 1:(ncols - 1)
       a = points[j, i]
       b = points[j, i + 1]
       c = points[j + 1, i + 1]
       d = points[j + 1, i]

       north = Point((b.x + a.x) / 2, a.y)
       east = Point(b.x, (b.y + c.y) / 2)
       south = Point((b.x + a.x) / 2, c.y)
       west = Point(a.x, (a.y + d.y) / 2)
            
       circle(north, 2, :fill)
       circle(east, 2, :fill)
       circle(south, 2, :fill)
       circle(west, 2, :fill)

       case = iso_value(a.val, b.val, c.val, d.val)
       fontsize(14)
       textcentered(string(case), Point((a.x + c.x) / 2, (a.y + c.y) / 2))
    end
end
finish()

In this situation, I call the top edge of the rectangle north, the right edge, east, the bottom edge, south, and the left edge, west.
The center of each of these edges are represented as a small circle shown in the following diagram:

Now that we have calculated each of the isovalues, let’s draw the lines in each of the rectangles to separate the black and white circles!

Creating Outlines :pencil:

Finally, we can draw the lines, technically called isolines, in each rectangle.
Based on the marching squares cases, let’s create a function that defines these cases for us and draws these lines:

function iso_line(case_value, north, east, south, west)
    if case_value == 0 || case_value == 15
    elseif case_value == 1
        line(west, south, :stroke)
    elseif case_value == 2
        line(south, east, :stroke)
    elseif case_value == 3 || case_value == 12
        line(east, west, :stroke)
    elseif case_value == 4 || case_value == 11
        line(north, east, :stroke)
    elseif case_value == 5
        line(north, west, :stroke)
        line(south, east, :stroke)
    elseif case_value == 6 || case_value == 9
        line(north, south, :stroke)
    elseif case_value == 7 || case_value == 8
        line(north, west, :stroke)
    elseif case_value == 10
        line(north, east, :stroke)
        line(south, west, :stroke)
    elseif case_value == 13
        line(east, south, :stroke)
    elseif case_value == 14
        line(west, south, :stroke)
    end
end

This takes in an isovalue and the points we want to draw lines between on each rectangle.
Furthermore, let’s finally turn our snippet that we have been building into a function as well to do this all for us:

function marching_squares(points)
    nrows, ncols = size(points)
    sethue("black")
    for j = 1:(nrows - 1)
        for i = 1:(ncols - 1)
            a = points[j, i]
            b = points[j, i + 1]
            c = points[j + 1, i + 1]
            d = points[j + 1, i]

            north = Point((b.x + a.x) / 2, a.y)
            east = Point(b.x, (b.y + c.y) / 2)
            south = Point((b.x + a.x) / 2, c.y)
            west = Point(a.x, (a.y + d.y) / 2)

            circle(north, 2, :fill)
            circle(east, 2, :fill)
            circle(south, 2, :fill)
            circle(west, 2, :fill)

            case = iso_value(a.val, b.val, c.val, d.val)

            fontsize(14)
            textcentered(string(case), Point((a.x + c.x) / 2, (a.y + c.y) / 2))

            iso_line(case, north, east, south, west)
        end
    end
end

The marching_squares function takes in the points we calculated from draw_balls and creates the boundaries between each white and black ball.
Let’s see the final product shall we?

The Finished Marching Squares Algorithm :tada:

The previous image was a little cluttered with all the text and additional circles.
I cleaned it up a bit and removed the text from the final image to give this final image:

Great work!
You have successfully implemented the marching squares algorithm! :tada: :tada: :tada:

Concluding Remarks

This algorithm has utility in multiple different places.
Originally, one of the primary usages of the algorithm was for land topography:

If you can see the faint contours in this image provided by OpenStreetMap, these represent different heights of mountain ranges in the Swiss Alps!

Furthermore, people now also use this algorithm for things like video game design as well.
In a post called “Squares Made for Marching” by Andrea Doimo, it is shown how a game map can be automated via this algorithm:

Finally, one can also use interpolation to make the edges of the boundaries better fit to the data you are processing.
Check out “Metaballs and Marching Squares” by Jamie Wong for more examples; here is what he made with his marching squares implementation:

I hope this tutorial was a fun introduction to marching squares and that you learned more about this algorithm and how to implement it in Julia!
Now, onto me trying to apply this algorithm to brain data!
Here we go!

Take care and all the best! ~ jz

If you spot any errors or have any questions, feel free to contact me about them!

Full Code

If at any point in this tutorial you got stuck, here is a copy of the fully operational code.
This code, however, creates a grid for you so you don’t have to give it a grid yourself.
If you want to provide your own grid, you just need to replace the create_grid with the draw_balls function from the tutorial.
Have fun and feel free to tweak this code however you want!

#=

Copyright 2020 Jacob Zelko (aka TheCedarPrince)

Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.

=#

using Luxor

function make_drawing(width, height, img_path, bkg_color, origin_p)
    d = Drawing(width, height, img_path)
    background(bkg_color)
    origin(Point(0, 0))
    return d
end

function iso_line(case_value, north, east, south, west)
    if case_value == 0 || case_value == 15
    elseif case_value == 1
        line(west, south, :stroke)
    elseif case_value == 2
        line(south, east, :stroke)
    elseif case_value == 3 || case_value == 12
        line(east, west, :stroke)
    elseif case_value == 4 || case_value == 11
        line(north, east, :stroke)
    elseif case_value == 5
        line(north, west, :stroke)
        line(south, east, :stroke)
    elseif case_value == 6 || case_value == 9
        line(north, south, :stroke)
    elseif case_value == 7 || case_value == 8
        line(north, west, :stroke)
    elseif case_value == 10
        line(north, east, :stroke)
        line(south, west, :stroke)
    elseif case_value == 13
        line(east, south, :stroke)
    elseif case_value == 14
        line(west, south, :stroke)
    end
end

iso_value(a, b, c, d) = a * 8 + b * 4 + c * 2 + d * 1

function create_grid(drawing, nrows, ncols)
    step_x = drawing.width / (ncols - 1)
    step_y = drawing.height / (nrows - 1)
    circ_scale = min(nrows, ncols) / max(nrows, ncols)
    points = Array{NamedTuple}(undef, nrows, ncols)
    for j = 1:nrows
        for i = 1:ncols
            cvalue = rand([0, 1])
            cvalue == 0 ? sethue("white") : sethue("black")
            pos = Point(step_x * (i - 1), step_y * (j - 1))
            circle(pos, 6 * circ_scale, :fill)
            points[j, i] = (x = pos.x, y = pos.y, val = cvalue)
        end
    end
    return points
end

function marching_squares(points)
    nrows, ncols = size(points)
    sethue("black")
    for j = 1:(nrows - 1)
        for i = 1:(ncols - 1)
            a = points[j, i]
            b = points[j, i + 1]
            c = points[j + 1, i + 1]
            d = points[j + 1, i]

            north = Point((b.x + a.x) / 2, a.y)
            east = Point(b.x, (b.y + c.y) / 2)
            south = Point((b.x + a.x) / 2, c.y)
            west = Point(a.x, (a.y + d.y) / 2)

            # circle(north, 2, :fill)
            # circle(east, 2, :fill)
            # circle(south, 2, :fill)
            # circle(west, 2, :fill)

            case = iso_value(a.val, b.val, c.val, d.val)

            # fontsize(14)
            # textcentered(string(case), Point((a.x + c.x) / 2, (a.y + c.y) / 2))

            iso_line(case, north, east, south, west)
        end
    end
end

width = 500
height = 500

nrows = 25
ncols = 25

my_draw = make_drawing(
    width,
    height,
    "squares.png"
    "gray",
    Point(0, 0),
)
grid = create_grid(my_draw, nrows, ncols)

marching_squares(grid)
finish();