In this blog article we explore the meaning of zippers as well as higher order zippers. The idea for this blog article came while discussing with fasta on Freenode today about zippers for graphs. Out of the blue, the question popped to me, what does it mean to have higher order zippers. Not having thought it through completely, I hope this blog will offer some clarity, and perhaps some feedback.

[EDIT: This is a new version as of 2007-07-11 that takes some comments from reddit and the comments here into account, cleaning up a bit and adding some more content]

For those new to the concept of *Zippers*, I shall briefly explain what they are and how one can reason with them. The idea of a zipper is to provide an iterator that one can move through an arbitrary functor.

A functor is basically a container of elements. For instance, [] in Haskell is a functor. If you apply to it to a specific type, you get a container-type (in this case a list) of that type. Other typical examples include Maybe, Binary trees and the N-ary tree introduced in Flatten Benchmark.

Due to the fact that Haskell does not have mutability, at first it can seem inefficient to mutate an element inside such a Functor or container. That is why Huet invented the concept of a Zipper. A Zipper allows one to walk through such a container such that one specific element has O(1) access, namely the element that is currently in the ‘hole’. Then, typically, there will be functions that allow one to move the element we’re currently looking at.

## First Order Zippers

To illustrate, I will demonstrate the Zipper for a list first.

> {-# OPTIONS_GHC -fglasgow-exts #-}

>moduleZipperwhere

>importData.Maybe(fromJust)

>

> -- A zipper is the current element and a context that stores where this

> -- element is.

>dataZipper d a = Zipper a (d a)deriving(Eq, Ord, Show)

>

> -- The context for a list is a combination of the elements before and the

> -- elements after the current element.

>dataDList a = DList [a] [a]

>deriving(Eq, Ord, Show)

>typeZList a = Zipper DList a

The advantage of this zipper is that one can easily (with O(1) complexity) move the current hole we’re looking at back and forth. It also provides an O(1) complexity way of changing the current element. As last requirement is that it should be possible to reconstruct the entire data-type that has been ‘zipped’ from just the Zipper. This process is often referred to as ‘unzipping’ the data structure. The methods to perform these operations, as well as to create a zipper from a list are given here:

> enterList :: (Monad m) => [a] -> m (ZList a)

> enterList [] = fail "Can not zip an empty list"

> enterList (x:xs) = return $ Zipper x (DList [] xs)

>

> unzipList :: ZList a -> [a]

> unzipList (Zipper a (DList [] suf)) = a:suf

> unzipList z = unzipList . fromJust . prev $ z

>

> prev :: (Monad m) => ZList a -> m (ZList a)

> prev (Zipper a (DList [] tl)) = fail "Already at the first element"

> prev (Zipper a (DList (x:xs) tl)) = return $ Zipper x (DList xs (a:tl))

>

> next :: (Monad m) => ZList a -> m (ZList a)

> next (Zipper a (DList hd [])) = fail "Already at the last element"

> next (Zipper a (DList hd (x:xs))) = return $ Zipper x (DList (a:hd) xs)

>

> update :: (a -> a) -> Zipper d a -> Zipper d a

> update f (Zipper a d) = Zipper (f a) d

In The derivative of a

Regular Type is a Type of One-Hole Contests Conor McBride shows us how it is possible to determine the context for any data-type using algebra. Basically, the context for a data-type is it’s derivative. How can one derive a data-type, however? Well this is done by using the more algebraic notation of a data type. If one takes a list, then the actual data type is: ‘List a = Nil | Cons a (List a)’. In an algebraic notation, this would be: L = 1 + a·L. Namely, a list is a sumtype of a constant (1) and a tuple of an element ‘a’ and the list again. Well, this is not fully correct, a list is actually the least-fixed-type that satisfies this equation (namely: L = 1 + a·L μ L.1 + a·L). However I will gloss over this. The context of a list is then the derivative of this equation using standard derivation as taught in algebra/calculus:

∂_{a} L = ∂_{a} a · L + a·∂_{a} L = 1·L + a·∂_{a} L = L + a·∂_{a} L

=> data DList a = [a] | DCons a (DList a)

By factoring out the [a] (because any DList will contain exactly one), one gets “[a]*(mu dL.1 + a*dL)” or “[a]*[a]“. In general, I believe that in an equation “(mu x.S + Tx)” one can factor out the S (as long as it doesn’t contain a reference to x of course) to get “(S*(mu x.1 + Tx))”. For exercise’s sake, we will derive the context for a binary tree:

>dataTree a = Leaf | Node a (Tree a) (Tree a)

>deriving(Eq, Ord, Show)

> -- Tree a (or t for short)

∂_{a} t = t^{2} + a·t·∂_ t = t^{2} × (μ x. 1 + 2·a·t·x) = t^{2}·p where p = 1 + 2·a·t·p or p = 2·a·t

Looking at this from an intuitive point of view, this makes sense. The context of an element in a tree, are the left and right child-trees of the node this element is in, as well as the path that we took to get there by saving the trees that we skipped over along the way to the element.

>dataDTree a = DTree {leftChild :: (Tree a),

> rightChild :: (Tree a),

> path :: [SEither (Step a)]}

>deriving(Eq, Ord, Show)

>typeSEither a = Either a a

> -- The path through a btree to the current node, saving those trees we did not enter.

>dataStep a = Step a (Tree a)

>deriving(Eq, Ord, Show)

>typeZTree a = Zipper DTree a

>

> enterTree :: (Monad m) => Tree a -> m (ZTree a)

> enterTree Leaf = fail "Can not zip an empty btree"

> enterTree (Node x l r) = return $ Zipper x $ DTree l r []

>

> unzipTree :: (ZTree a) -> Tree a

> unzipTree (Zipper x (DTree l r [])) = Node x l r

> unzipTree dt = unzipTree . fromJust . up $ dt

>

> up :: (Monad m) => ZTree a -> m (ZTree a)

> up (Zipper x (DTree l r [])) = fail "Already at the top of a tree"

> up (Zipper x (DTree l r (p:pp))) =

>casepof

> Right (Step px pr) -> return $ Zipper px (DTree (Node x l r) pr pp)

> Left (Step px pl) -> return $ Zipper px (DTree pl (Node x l r) pp)

>

> left :: (Monad m) => ZTree a -> m (ZTree a)

> left (Zipper x (DTree Leaf pr pp)) = fail "Can not go further left"

> left (Zipper x (DTree (Node v l r) pr pp)) = return $ Zipper v $ DTree l r $ ((Right $ Step x pr):pp)

> right :: (Monad m) => ZTree a -> m (ZTree a)

> right (Zipper x (DTree pl Leaf pp)) = fail "Can not go further right"

> right (Zipper x (DTree pl (Node v l r) pp)) = return $ Zipper v $ DTree l r $ ((Left $ Step x pl):pp)

I will leave the implementation for an N-ary tree as an exercise for the reader.

## Second Order Zippers

Now nothing introduced so far is really new. The question that arose to me is: what would a higher-order zipper be? What does it mean to have a double derivative of a data-type. I will first show what the structure for the double derivative of a list looks like by working it out and then give an intuition of what the meaning of this data-type might mean. To make sure that this intuition makes any sense, we will then work out the double derivative for a binary tree, to ascertain that the intuition we had for the list is valid for other data-types. So first we define the generic meaning of a second-order zipper:

>typeZipper2 d2 a = Zipper (Zipper d2) a

>

> update2 :: (a -> a) -> Zipper2 d2 a -> Zipper2 d2 a

> update2 f (Zipper x (Zipper y d)) = Zipper x (Zipper (f y) d)

A second-order zipper is simply the zipper of the zipper of a datatype parametrized with the second-order derivative of that data-type and a the element-type. So we can already see that a second-order zipper will be containing two ‘a’s as well as this second-order derivative. So what is the second-order derivative of a list? Well we had: “[a]‘ = [a]_h*[a]_t”. Applying simple derivation in the algebraic sense we get: “[a]” = [a]_h*[a]_t’ + [a]_h’*[a]_t”.

### Second Order Zipper for a List

If we remember the meaning we gave to the two lists in the case of “[a]‘”, then we had the head part and the tail part of the list around the current element we’re looking at. Since we’re now either deriving one or the other, my intuition tells me that we’ll have either the prefix and suffix of the head part or the prefix and suffix of the tail part. If we take the definition of “[a]‘” and expand it in “[a]”” we get: “[a]*[a]*[a] + [a]*[a]*[a]“. Putting this in a Haskell data-type with names based on my intuition, we get:

> -- DDList a = Prev (reverse of prefix of prefix)

> -- (suffix of prefix)

> -- suffix

> -- | Next (rever se prefix)

> -- (rever se of prefix of suffix)

> -- (suffix of suffix)

>dataDDList a = Prev [a] [a] [a] | Next [a] [a] [a]

>deriving(Eq, Ord, Show)

>typeZZList a = Zipper2 DDList a

>

> openLeft :: (Monad m) => ZList a -> m (ZZList a)

> openLeft (Zipper r (DList [] tl)) = fail "Can not enter an empty list"

> openLeft (Zipper r (DList (l:hd) tl)) = return $ Zipper l (Zipper r (Prev hd [] tl))

>

> openRight :: (Monad m) => ZList a -> m (ZZList a)

> openRight (Zipper l (DList hd [])) = fail "Can not enter an empty list"

> openRight (Zipper l (DList hd (r:tl))) = return $ Zipper r (Zipper l (Next hd [] tl))

>

> unzipZList :: ZZList a -> ZList a

> unzipZList (Zipper l (Zipper r (Prev a [] c))) = Zipper r (DList (l:a) c)

> unzipZList z@(Zipper l (Zipper r (Prev a b c))) = unzipZList . fromJust . shrink $ z

> unzipZList (Zipper r (Zipper l (Next a [] c))) = Zipper l (DList a (r:c))

> unzipZList z@(Zipper r (Zipper l (Next a b c))) = unzipZList . fromJust . shrink $ z

>

> enlarge :: (Monad m) => ZZList a -> m (ZZList a)

> enlarge (Zipper l (Zipper r (Prev [] bs cs))) = fail "Can not move further to the left"

> enlarge (Zipper l (Zipper r (Prev (a:as) bs cs))) = return $ Zipper a (Zipper r (Prevas(l:bs) cs))

> enlarge (Zipper r (Zipper l (Nextasbs []))) = fail "Can not move further to the right"

> enlarge (Zipper r (Zipper l (Nextasbs (c:cs)))) = return $ Zipper c (Zipper l (Nextas(r:bs) cs))

>

> shrink :: (Monad m) => ZZList a -> m (ZZList a)

> shrink (Zipper l (Zipper r (Prevas[] cs))) = fail "Can not further shrink"

> shrink (Zipper l (Zipper r (Prevas(b:bs) cs))) = return $ Zipper b (Zipper r (Prev (l:as) bs cs))

> shrink (Zipper r (Zipper l (Nextas[] cs))) = fail "Can not further shrink"

> shrink (Zipper r (Zipper l (Nextas(b:bs) cs))) = return $ Zipper b (Zipper l (Nextasbs (r:cs)))

>

> switch :: ZZList a -> ZZList a

> switch (Zipper l (Zipper r (Prevasbs cs))) = Zipper r (Zipper l (Nextas(reverse bs) cs))

> switch (Zipper r (Zipper l (Nextasbs cs))) = Zipper l (Zipper r (Prevas(reverse bs) cs))

>

> splice :: ZZList a -> [a] -> ZZList a

> splice (Zipper l (Zipper r (Prevas_cs))) bs = Zipper l (Zipper r (Prevasbs cs))

> splice (Zipper r (Zipper l (Nextas_cs))) bs = Zipper r (Zipper l (Nextasbs cs))

>

> -- A more generic function (splice can be implemented in terms of this)

> modify :: ([a] -> [a]) -> ZZList a -> ZZList a

> modify f (Zipper l (Zipper r (Prevasbs cs))) = Zipper l (Zipper r (Prevas(f bs) cs))

> modify f (Zipper r (Zipper l (Nextasbs cs))) = Zipper r (Zipper l (Nextas(f bs) cs))

>

So what my intuitition tells me is that where a Zipper gives O(1) mutation of an element, a **Zipper2 gives O(1) splicing**. After all, we can directly access the part that is between the two singular elements stored in the Zipper2. Of course, to ascertain that this statement is valid for more than just lists, we should ascertain this is true for other data-types. In this case, we shall do the experiment for binary trees. Again, the exercise for N-ary trees is left as an exercise for the reader (feel free to put in a comment if you want to).

### Deriving the rules for a tree

*Note: I redid the math with a cleaner technique, and it seems I had made some mistakes in the original math, which is why my data-type was not as intuitive as I had hoped it to be. The correct version makes much more sense Namely: I was missing a “p” in the last term.*

## Higher Order Zippers

In this blog article we explore the meaning of zippers as well as higher order zippers. The idea for this blog article came while discussing with fasta on Freenode today about zippers for graphs. Out of the blue, the question popped to me, what does it mean to have higher order zippers. Not having thought it through completely, I hope this blog will offer some clarity, and perhaps some feedback.

[EDIT: This is a new version as of 2007-07-11 that takes some comments from reddit and the comments here into account, cleaning up a bit and adding some more content]

For those new to the concept of *Zippers*, I shall briefly explain what they are and how one can reason with them. The idea of a zipper is to provide an iterator that one can move through an arbitrary functor.

A functor is basically a container of elements. For instance, [] in Haskell is a functor. If you apply to it to a specific type, you get a container-type (in this case a list) of that type. Other typical examples include Maybe, Binary trees and the N-ary tree introduced in Flatten Benchmark.

Due to the fact that Haskell does not have mutability, at first it can seem inefficient to mutate an element inside such a Functor or container. That is why Huet invented the concept of a Zipper. A Zipper allows one to walk through such a container such that one specific element has O(1) access, namely the element that is currently in the ‘hole’. Then, typically, there will be functions that allow one to move the element we’re currently looking at.

## First Order Zippers

To illustrate, I will demonstrate the Zipper for a list first.

> {-# OPTIONS_GHC -fglasgow-exts #-}

>moduleZipperwhere

>importData.Maybe(fromJust)

>

> -- A zipper is the current element and a context that stores where this

> -- element is.

>dataZipper d a = Zipper a (d a)deriving(Eq, Ord, Show)

>

> -- The context for a list is a combination of the elements before and the

> -- elements after the current element.

>dataDList a = DList [a] [a]

>deriving(Eq, Ord, Show)

>typeZList a = Zipper DList a

The advantage of this zipper is that one can easily (with O(1) complexity) move the current hole we’re looking at back and forth. It also provides an O(1) complexity way of changing the current element. As last requirement is that it should be possible to reconstruct the entire data-type that has been ‘zipped’ from just the Zipper. This process is often referred to as ‘unzipping’ the data structure. The methods to perform these operations, as well as to create a zipper from a list are given here:

> enterList :: (Monad m) => [a] -> m (ZList a)

> enterList [] = fail "Can not zip an empty list"

> enterList (x:xs) = return $ Zipper x (DList [] xs)

>

> unzipList :: ZList a -> [a]

> unzipList (Zipper a (DList [] suf)) = a:suf

> unzipList z = unzipList . fromJust . prev $ z

>

> prev :: (Monad m) => ZList a -> m (ZList a)

> prev (Zipper a (DList [] tl)) = fail "Already at the first element"

> prev (Zipper a (DList (x:xs) tl)) = return $ Zipper x (DList xs (a:tl))

>

> next :: (Monad m) => ZList a -> m (ZList a)

> next (Zipper a (DList hd [])) = fail "Already at the last element"

> next (Zipper a (DList hd (x:xs))) = return $ Zipper x (DList (a:hd) xs)

>

> update :: (a -> a) -> Zipper d a -> Zipper d a

> update f (Zipper a d) = Zipper (f a) d

In The derivative of a Regular Type is a Type of One-Hole Contests Conor McBride shows us how it is possible to determine the context for any data-type using algebra. Basically, the context for a data-type is it’s derivative. How can one derive a data-type, however? Well this is done by using the more algebraic notation of a data type. If one takes a list, then the actual data type is: ‘List a = Nil | Cons a (List a)’. In an algebraic notation, this would be: L = 1 + a·L. Namely, a list is a sumtype of a constant (1) and a tuple of an element ‘a’ and the list again. Well, this is not fully correct, a list is actually the least-fixed-type that satisfies this equation (namely: L = 1 + a·L μ L.1 + a·L). However I will gloss over this. The context of a list is then the derivative of this equation using standard derivation as taught in algebra/calculus:

∂_{a} L = ∂_{a} a · L + a·∂_{a} L = 1·L + a·∂_{a} L = L + a·∂_{a} L

=> data DList a = [a] | DCons a (DList a)

By factoring out the [a] (because any DList will contain exactly one), one gets “[a]*(mu dL.1 + a*dL)” or “[a]*[a]“. In general, I believe that in an equation “(mu x.S + Tx)” one can factor out the S (as long as it doesn’t contain a reference to x of course) to get “(S*(mu x.1 + Tx))”. For exercise’s sake, we will derive the context for a binary tree:

>dataTree a = Leaf | Node a (Tree a) (Tree a)

>deriving(Eq, Ord, Show)

> -- Tree a (or t for short)

∂_{a} t = t^{2} + a·t·∂_ t = t^{2} × (μ x. 1 + 2·a·t·x) = t^{2}·p where p = 1 + 2·a·t·p or p = 2·a·t

Looking at this from an intuitive point of view, this makes sense. The context of an element in a tree, are the left and right child-trees of the node this element is in, as well as the path that we took to get there by saving the trees that we skipped over along the way to the element.

>dataDTree a = DTree {leftChild :: (Tree a),

> rightChild :: (Tree a),

> path :: [SEither (Step a)]}

>deriving(Eq, Ord, Show)

>typeSEither a = Either a a

> -- The path through a btree to the current node, saving those trees we did not enter.

>dataStep a = Step a (Tree a)

>deriving(Eq, Ord, Show)

>typeZTree a = Zipper DTree a

>

> enterTree :: (Monad m) => Tree a -> m (ZTree a)

> enterTree Leaf = fail "Can not zip an empty btree"

> enterTree (Node x l r) = return $ Zipper x $ DTree l r []

>

> unzipTree :: (ZTree a) -> Tree a

> unzipTree (Zipper x (DTree l r [])) = Node x l r

> unzipTree dt = unzipTree . fromJust . up $ dt

>

> up :: (Monad m) => ZTree a -> m (ZTree a)

> up (Zipper x (DTree l r [])) = fail "Already at the top of a tree"

> up (Zipper x (DTree l r (p:pp))) =

>casepof

> Right (Step px pr) -> return $ Zipper px (DTree (Node x l r) pr pp)

> Left (Step px pl) -> return $ Zipper px (DTree pl (Node x l r) pp)

>

> left :: (Monad m) => ZTree a -> m (ZTree a)

> left (Zipper x (DTree Leaf pr pp)) = fail "Can not go further left"

> left (Zipper x (DTree (Node v l r) pr pp)) = return $ Zipper v $ DTree l r $ ((Right $ Step x pr):pp)

> right :: (Monad m) => ZTree a -> m (ZTree a)

> right (Zipper x (DTree pl Leaf pp)) = fail "Can not go further right"

> right (Zipper x (DTree pl (Node v l r) pp)) = return $ Zipper v $ DTree l r $ ((Left $ Step x pl):pp)

I will leave the implementation for an N-ary tree as an exercise for the reader.

## Second Order Zippers

Now nothing introduced so far is really new. The question that arose to me is: what would a higher-order zipper be? What does it mean to have a double derivative of a data-type. I will first show what the structure for the double derivative of a list looks like by working it out and then give an intuition of what the meaning of this data-type might mean. To make sure that this intuition makes any sense, we will then work out the double derivative for a binary tree, to ascertain that the intuition we had for the list is valid for other data-types. So first we define the generic meaning of a second-order zipper:

>typeZipper2 d2 a = Zipper (Zipper d2) a

>

> update2 :: (a -> a) -> Zipper2 d2 a -> Zipper2 d2 a

> update2 f (Zipper x (Zipper y d)) = Zipper x (Zipper (f y) d)

A second-order zipper is simply the zipper of the zipper of a datatype parametrized with the second-order derivative of that data-type and a the element-type. So we can already see that a second-order zipper will be containing two ‘a’s as well as this second-order derivative. So what is the second-order derivative of a list? Well we had: “[a]‘ = [a]_h*[a]_t”. Applying simple derivation in the algebraic sense we get: “[a]” = [a]_h*[a]_t’ + [a]_h’*[a]_t”.

### Second Order Zipper for a List

If we remember the meaning we gave to the two lists in the case of “[a]‘”, then we had the head part and the tail part of the list around the current element we’re looking at. Since we’re now either deriving one or the other, my intuition tells me that we’ll have either the prefix and suffix of the head part or the prefix and suffix of the tail part. If we take the definition of “[a]‘” and expand it in “[a]”” we get: “[a]*[a]*[a] + [a]*[a]*[a]“. Putting this in a Haskell data-type with names based on my intuition, we get:

> -- DDList a = Prev (reverse of prefix of prefix)

> -- (suffix of prefix)

> -- suffix

> -- | Next (rever se prefix)

> -- (rever se of prefix of suffix)

> -- (suffix of suffix)

>dataDDList a = Prev [a] [a] [a] | Next [a] [a] [a]

>deriving(Eq, Ord, Show)

>typeZZList a = Zipper2 DDList a

>

> openLeft :: (Monad m) => ZList a -> m (ZZList a)

> openLeft (Zipper r (DList [] tl)) = fail "Can not enter an empty list"

> openLeft (Zipper r (DList (l:hd) tl)) = return $ Zipper l (Zipper r (Prev hd [] tl))

>

> openRight :: (Monad m) => ZList a -> m (ZZList a)

> openRight (Zipper l (DList hd [])) = fail "Can not enter an empty list"

> openRight (Zipper l (DList hd (r:tl))) = return $ Zipper r (Zipper l (Next hd [] tl))

>

> unzipZList :: ZZList a -> ZList a

> unzipZList (Zipper l (Zipper r (Prev a [] c))) = Zipper r (DList (l:a) c)

> unzipZList z@(Zipper l (Zipper r (Prev a b c))) = unzipZList . fromJust . shrink $ z

> unzipZList (Zipper r (Zipper l (Next a [] c))) = Zipper l (DList a (r:c))

> unzipZList z@(Zipper r (Zipper l (Next a b c))) = unzipZList . fromJust . shrink $ z

>

> enlarge :: (Monad m) => ZZList a -> m (ZZList a)

> enlarge (Zipper l (Zipper r (Prev [] bs cs))) = fail "Can not move further to the left"

> enlarge (Zipper l (Zipper r (Prev (a:as) bs cs))) = return $ Zipper a (Zipper r (Prevas(l:bs) cs))

> enlarge (Zipper r (Zipper l (Nextasbs []))) = fail "Can not move further to the right"

> enlarge (Zipper r (Zipper l (Nextasbs (c:cs)))) = return $ Zipper c (Zipper l (Nextas(r:bs) cs))

>

> shrink :: (Monad m) => ZZList a -> m (ZZList a)

> shrink (Zipper l (Zipper r (Prevas[] cs))) = fail "Can not further shrink"

> shrink (Zipper l (Zipper r (Prevas(b:bs) cs))) = return $ Zipper b (Zipper r (Prev (l:as) bs cs))

> shrink (Zipper r (Zipper l (Nextas[] cs))) = fail "Can not further shrink"

> shrink (Zipper r (Zipper l (Nextas(b:bs) cs))) = return $ Zipper b (Zipper l (Nextasbs (r:cs)))

>

> switch :: ZZList a -> ZZList a

> switch (Zipper l (Zipper r (Prevasbs cs))) = Zipper r (Zipper l (Nextas(reverse bs) cs))

> switch (Zipper r (Zipper l (Nextasbs cs))) = Zipper l (Zipper r (Prevas(reverse bs) cs))

>

> splice :: ZZList a -> [a] -> ZZList a

> splice (Zipper l (Zipper r (Prevas_cs))) bs = Zipper l (Zipper r (Prevasbs cs))

> splice (Z ipper r (Zipper l (Nextas_cs))) bs = Zipper r (Zipper l (Nextasbs cs))

>

> -- A more generic function (splice can be implemented in terms of this)

> modify :: ([a] -> [a]) -> ZZList a -> ZZList a

> modify f (Zipper l (Zipper r (Prevasbs cs))) = Zipper l (Zipper r (Prevas(f bs) cs))

> modify f (Zipper r (Zipper l (Nextasbs cs))) = Zipper r (Zipper l (Nextas(f bs) cs))

>

So what my intuitition tells me is that where a Zipper gives O(1) mutation of an element, a **Zipper2 gives O(1) splicing**. After all, we can directly access the part that is between the two singular elements stored in the Zipper2. Of course, to ascertain that this statement is valid for more than just lists, we should ascertain this is true for other data-types. In this case, we shall do the experiment for binary trees. Again, the exercise for N-ary trees is left as an exercise for the reader (feel free to put in a comment if you want to).

### Deriving the rules for a tree

*Note: I redid the math with a cleaner technique, and it seems I had made some mistakes in the original math, which is why my data-type was not as intuitive as I had hoped it to be. The correct version makes much more sense Namely: I was missing a “p” in the last term.*

<!–

$t = 1 + a * t^2 $

$\part_a t = t^2 + a*t*\part_ t = t^2 \x (\mu x. 1 + 2*a*t*x) = t^2*p$

where $p = 1 + 2*a*t*p$ or $p = [2*a*t]$

So we derive:

$\part_a^2 t = 2 * t * \part_a t * p + t^2 * \part_a = D + R$

where $D = 2 * t * \part_a t * p = 2 * t * (t^2) * p * p$

and $R = t^2 * \part_a p = t^2 * \part_x[x] |_{x=2*a*t} * \part_a(2*a*t) =…$

$… = t^2 * [2*a*t]*[2*a*t]*\part_a(2*a*t) = t^2 * p^2 * (2*t + 2*a*t’) = t^2 * p^2 *(2*t +2*a*t^2*p) = …$

$… = 2*t*t^2*p^2 + 2*a*t^2*t^2*p*p^2 = U + UD$

So finally we get (where we label the different parts to refer to them):

$\part_a^2 t = D + U + UD$ where

$D = 2 * t_1 * (t^2)_2 * p_3 * p_4$

$U = 2 * t_1 * (t^2)_2 * p_3 * p_4$

$UD = 2 * a_1 * (t^2)_2L * (t^2)_2R * p_3 * (p^2)_4$

–>

Returning to the definitions, we had:

t = 1 + a · t^{2}

∂_{a} t = t^{2} × (μ x. 1 + 2·a·t·x) = t^{2}·p

where p = 1 + 2·a·t·p or p = 2·a·t

So we derive:

∂2a t = 2 · t · ∂_{a} t · p + t^{2} · ∂_{a} = D + R

where D = 2 · t · ∂_{a} t · p = 2 · t · (t^{2}) · p · p

and R = t^{2} · ∂_{a} p = t^{2} · ∂_{x}[x] |_{x=2·a·t} · ∂_{a}(2·a·t) =…

… = t^{2} · 2·a·t · 2·a·t ·∂_{a}(2·a·t) = t^{2} · p^{2} · (2·t + 2·a·t’) = t^{2} · p^{2} ·(2·t +2·a·t^{2}·p) = …

… = 2·t·t^{2}·p^{2} + 2·a·t^{2}·t^{2}·p·p^{2} = U + UD

So finally we get (where we label the different parts to refer to them):

∂2a t = D + U + UD where

D = 2 · t_{1} · (t^{2})_{2} · p_{3} · p_{4}

U = 2 · t_{1} · (t^{2})_{2} · p_{3} · p_{4}

UD = 2 · a_{1} · (t^{2})_{2L} · (t^{2})_{2R} · p_{3} · (p^{2})_{4}

The first term, D, is basically a derivative of one or the left or right sub-trees of the tree we are currently looking at it with our first-order zipper. More specifically, if we go down the left-subtree, we save the right subtree in t_{1}. Then the second finger will be pointing at a tree further down, so we need to save it’s children, namely: (t^{2})_{2}. And obviously we have the two paths, one from the root of the tree to the first finger, p_{3}, and one from this finger to the second finger, p_{4}.

The second term, U, is analogous, except that the first finger will now be at the bottom and the second finger more at the top. This means that the path that goes from the first finger to the second finger, p_{4}, will be reversed. Additionally (at least I think this should be so for efficiency reasons), the path from the root, p_{3}, will only extend down to the second finger. Moving the second-finger around is then O(1) as we can just pop a path-element from one of the paths and put it on the other path or vice-versa.

The final term, UD, should then obviously be a path that walks up along the tree and then down from another node. In this case, we still have the path from the root of the tree, p_{3}, but instead of going down to the first finger or second finger, it will go down to the join-point of these two fingers (namely the place where the path stops going up and starts going down again. Because neith

er subtree of this node is being stored in this path (as we’re deriving both sides), we need to save the element at this point, namely in a_{1}. The two paths, (p^{2})_{4}, then simply consist of the path up from the first finger and then down to the second finger. We have two options for this one, as we might be going up from the left side of the join point or the right side of the join point. Finally, the two pairs of trees, (t^{2})_{2L} and (t^{2})_{2R} obviously are the subtrees of the two nodes where our fingers are pointing at.

>dataDDTree a = Down (Either (Mono a)) | Up (Either (Mono a)) | UpDown (Either (Duo a))

>deriving(Eq, Ord, Show)

>dataMono a = Mono {fromRoot :: [Path a],

> toSecond :: [Path a],

> otherChild :: Tree a,

> leftChild2 :: Tree a,

> rightChild2 :: Tree a,

> toBottom :: DTree a}

>deriving(Eq, Ord, Show)

>

>dataDuo a = Duo {fromRoot :: [Path a],

> joinValue :: a,

> leftleftChild :: Tree a,

> leftrightChild :: Tree a,

> rightleftChild :: Tree a,

> rightrightChild :: Tree a

> firstToRoot :: [Path a],

> rootToSecond :: [Path a]}

>deriving(Eq, Ord, Show)

The operations for this tree are left as an exercise for the reader (mostly for conciseness purposes and because I have other things to do, I might add them later). Suffice to say that intuitively (due to the way the lists are ordered (reverse path or not), moving the second finger will be easier than moving the first finger). Obviously, splicing will depend on whether we have a stricly unidirectional path, or an updown path.

I hope the above is clear, otherwise I can add figures, though if possible I’d prefer not to to say on time (and I don’t have a nice diagram editor :/).

## Generating higher-order zippers

By now the pattern for creating higher-order zippers should be clear. Given the definition of a data-type as an algebraic entity. The first-order zipper would be:

type FirstOrderZipper d a = Zipper d a

where ‘d’ is the first derivative of the data-type in question. To then go to higher-order zippers, we simply apply the meta-rule (note that this is a meta-rule as there are no haskell facilities to derive data-types that I am aware of).

Zipper d a -> Zipper (Zipper d') a

Where d’ obviously stands for the derivative of d. As a reader of reddit pointed out, this is “simply” having several pointers that point into a functional data-structure. However, due to the way that these ‘pointers’ work (for instance, you can not just get a second one from a data-type, you need to go higher-order to get more pointers) we get extra functionality, for instance the O(1) splicing which I mentioned previously. For completeness’ sake, I here show the rules that were introduced in McBride’s paper.

∂_{x} x = 1

∂_{x} y = 0 where x ≠ y

∂_{x} 0 = 0

∂_{x}(S + T) = ∂_{x} S + ∂_{x} T

∂_{x} 1 = 1

∂_{x}(S × T) = ∂_{x} S × T + S × ∂_{x} T

∂_{x}(μ y.F) = μ z.∂_{x} F|_{y=μ y.F} + ∂_{y} F|_{y=μ y.F} × z where z is fresh

∂_{x}(F|_{y = S}) = ∂_{x} F|_{y=S} + ∂_{y} F|_{y=S} × ∂_{x} S

∂_{x} T = 0 when T does not contain x

μ z.T + S × z = T × μ z. 1 + S × z when T and s do not contain z