Zipper Comonads, Generically

Question

Given any container type we can form the (element-focused) Zipper and know that this structure is a Comonad. This was recently explored in wonderful detail in another Stack Overflow question for the following type:

data Bin a = Branch (Bin a) a (Bin a) | Leaf a deriving Functor 

with the following zipper

data Dir = L | R data Step a = Step a Dir (Bin a)   deriving Functor data Zip  a = Zip [Step a] (Bin a) deriving Functor instance Comonad Zip where ... 

It is the case that Zip is a Comonad though the construction of its instance is a little hairy. That said, Zip can be completely mechanically derived from Tree and (I believe) any type derived this way is automatically a Comonad, so I feel it ought to be the case that we can construct these types and their comonads generically and automatically.

One method for achieving generality for zipper construction is the use the following class and type family

data Zipper t a = Zipper { diff :: D t a, here :: a }  deriving instance Diff t => Functor (Zipper t)  class (Functor t, Functor (D t)) => Diff t where   data D t :: * -> *   inTo  :: t a -> t (Zipper t a)   outOf :: Zipper t a -> t a 

which has (more or less) shown up in Haskell Cafe threads and on Conal Elliott's blog. This class can be instantiated for the various core algebraic types and thus provides a general framework for talking about the derivatives of ADTs.

So, ultimately, my question is whether or not we can write

instance Diff t => Comonad (Zipper t) where ... 

which could be used to subsume the specific Comonad instance described above:

instance Diff Bin where   data D Bin a = DBin { context :: [Step a], descend :: Maybe (Bin a, Bin a) }   ... 

Unfortunately, I've had no luck writing such an instance. Is the inTo/outOf signature sufficient? Is there something else needed to constrain the types? Is this instance even possible?

Answer 1

Like the childcatcher in Chitty-Chitty-Bang-Bang luring kids into captivity with sweets and toys, recruiters to undergraduate Physics like to fool about with soap bubbles and boomerangs, but when the door clangs shut, it's "Right, children, time to learn about partial differentiation!". Me too. Don't say I didn't warn you.

Here's another warning: the following code needs {-# LANGUAGE KitchenSink #-}, or rather

{-# LANGUAGE TypeFamilies, FlexibleContexts, TupleSections, GADTs, DataKinds,     TypeOperators, FlexibleInstances, RankNTypes, ScopedTypeVariables,     StandaloneDeriving, UndecidableInstances #-} 

in no particular order.

Differentiable functors give comonadic zippers

What is a differentiable functor, anyway?

class (Functor f, Functor (DF f)) => Diff1 f where   type DF f :: * -> *   upF      ::  ZF f x  ->  f x   downF    ::  f x     ->  f (ZF f x)   aroundF  ::  ZF f x  ->  ZF f (ZF f x)  data ZF f x = (:<-:) {cxF :: DF f x, elF :: x} 

It's a functor which has a derivative, which is also a functor. The derivative represents a one-hole context for an element. The zipper type ZF f x represents the pair of a one-hole context and the element in the hole.

The operations for Diff1 describe the kinds of navigation we can do on zippers (without any notion of "leftward" and "rightward", for which see my Clowns and Jokers paper). We can go "upward", reassembling the structure by plugging the element in its hole. We can go "downward", finding every way to visit an element in a give structure: we decorate every element with its context. We can go "around", taking an existing zipper and decorating each element with its context, so we find all the ways to refocus (and how to keep our current focus).

Now, the type of aroundF might remind some of you of

class Functor c => Comonad c where   extract    :: c x -> x   duplicate  :: c x -> c (c x) 

and you're right to be reminded! We have, with a hop and a skip,

instance Diff1 f => Functor (ZF f) where   fmap f (df :<-: x) = fmap f df :<-: f x  instance Diff1 f => Comonad (ZF f) where   extract    = elF   duplicate  = aroundF 

and we insist that

extract . duplicate == id fmap extract . duplicate == id duplicate . duplicate == fmap duplicate . duplicate 

We also need that

fmap extract (downF xs) == xs              -- downF decorates the element in position fmap upF (downF xs) = fmap (const xs) xs   -- downF gives the correct context 

Polynomial functors are differentiable

Constant functors are differentiable.

data KF a x = KF a instance Functor (KF a) where   fmap f (KF a) = KF a  instance Diff1 (KF a) where   type DF (KF a) = KF Void   upF (KF w :<-: _) = absurd w   downF (KF a) = KF a   aroundF (KF w :<-: _) = absurd w 

There's nowhere to put an element, so it's impossible to form a context. There's nowhere to go upF or downF from, and we easily find all none of the ways to go downF.

The identity functor is differentiable.

data IF x = IF x instance Functor IF where   fmap f (IF x) = IF (f x)  instance Diff1 IF where   type DF IF = KF ()   upF (KF () :<-: x) = IF x   downF (IF x) = IF (KF () :<-: x)   aroundF z@(KF () :<-: x) = KF () :<-: z 

There's one element in a trivial context, downF finds it, upF repacks it, and aroundF can only stay put.

Sum preserves differentiability.

data (f :+: g) x = LF (f x) | RF (g x) instance (Functor f, Functor g) => Functor (f :+: g) where   fmap h (LF f) = LF (fmap h f)   fmap h (RF g) = RF (fmap h g)  instance (Diff1 f, Diff1 g) => Diff1 (f :+: g) where   type DF (f :+: g) = DF f :+: DF g   upF (LF f' :<-: x) = LF (upF (f' :<-: x))   upF (RF g' :<-: x) = RF (upF (g' :<-: x)) 

The other bits and pieces are a bit more of a handful. To go downF, we must go downF inside the tagged component, then fix up the resulting zippers to show the tag in the context.

  downF (LF f) = LF (fmap (\ (f' :<-: x) -> LF f' :<-: x) (downF f))   downF (RF g) = RF (fmap (\ (g' :<-: x) -> RF g' :<-: x) (downF g)) 

To go aroundF, we strip the tag, figure out how to go around the untagged thing, then restore the tag in all the resulting zippers. The element in focus, x, is replaced by its entire zipper, z.

  aroundF z@(LF f' :<-: (x :: x)) =     LF (fmap (\ (f' :<-: x) -> LF f' :<-: x) . cxF $ aroundF (f' :<-: x :: ZF f x))     :<-: z   aroundF z@(RF g' :<-: (x :: x)) =     RF (fmap (\ (g' :<-: x) -> RF g' :<-: x) . cxF $ aroundF (g' :<-: x :: ZF g x))     :<-: z 

Note that I had to use ScopedTypeVariables to disambiguate the recursive calls to aroundF. As a type function, DF is not injective, so the fact that f' :: D f x is not enough to force f' :<-: x :: Z f x.

Product preserves differentiability.

data (f :*: g) x = f x :*: g x instance (Functor f, Functor g) => Functor (f :*: g) where   fmap h (f :*: g) = fmap h f :*: fmap h g 

To focus on an element in a pair, you either focus on the left and leave the right alone, or vice versa. Leibniz's famous product rule corresponds to a simple spatial intuition!

instance (Diff1 f, Diff1 g) => Diff1 (f :*: g) where   type DF (f :*: g) = (DF f :*: g) :+: (f :*: DF g)   upF (LF (f' :*: g) :<-: x) = upF (f' :<-: x) :*: g   upF (RF (f :*: g') :<-: x) = f :*: upF (g' :<-: x) 

Now, downF works similarly to the way it did for sums, except that we have to fix up the zipper context not only with a tag (to show which way we went) but also with the untouched other component.

  downF (f :*: g)     =    fmap (\ (f' :<-: x) -> LF (f' :*: g) :<-: x) (downF f)     :*:  fmap (\ (g' :<-: x) -> RF (f :*: g') :<-: x) (downF g) 

But aroundF is a massive bag of laughs. Whichever side we are currently visiting, we have two choices:

1. Move aroundF on that side.
2. Move upF out of that side and downF into the other side.

Each case requires us to make use of the operations for the substructure, then fix up contexts.

  aroundF z@(LF (f' :*: g) :<-: (x :: x)) =     LF (fmap (\ (f' :<-: x) -> LF (f' :*: g) :<-: x)           (cxF $ aroundF (f' :<-: x :: ZF f x))         :*: fmap (\ (g' :<-: x) -> RF (f :*: g') :<-: x) (downF g))     :<-: z     where f = upF (f' :<-: x)   aroundF z@(RF (f :*: g') :<-: (x :: x)) =     RF (fmap (\ (f' :<-: x) -> LF (f' :*: g) :<-: x) (downF f) :*:         fmap (\ (g' :<-: x) -> RF (f :*: g') :<-: x)           (cxF $ aroundF (g' :<-: x :: ZF g x)))     :<-: z     where g = upF (g' :<-: x) 

Phew! The polynomials are all differentiable, and thus give us comonads.

Hmm. It's all a bit abstract. So I added deriving Show everywhere I could, and threw in

deriving instance (Show (DF f x), Show x) => Show (ZF f x) 

which allowed the following interaction (tidied up by hand)

> downF (IF 1 :*: IF 2) IF (LF (KF () :*: IF 2) :<-: 1) :*: IF (RF (IF 1 :*: KF ()) :<-: 2)  > fmap aroundF it IF  (LF (KF () :*: IF (RF (IF 1 :*: KF ()) :<-: 2)) :<-: (LF (KF () :*: IF 2) :<-: 1)) :*: IF  (RF (IF (LF (KF () :*: IF 2) :<-: 1) :*: KF ()) :<-: (RF (IF 1 :*: KF ()) :<-: 2)) 

Exercise Show that the composition of differentiable functors is differentiable, using the chain rule.

Sweet! Can we go home now? Of course not. We haven't differentiated any recursive structures yet.

Making recursive functors from bifunctors

A Bifunctor, as the existing literature on datatype generic programming (see work by Patrik Jansson and Johan Jeuring, or excellent lecture notes by Jeremy Gibbons) explains at length is a type constructor with two parameters, corresponding to two sorts of substructure. We should be able to "map" both.

class Bifunctor b where   bimap :: (x -> x') -> (y -> y') -> b x y -> b x' y' 

We can use Bifunctors to give the node structure of recursive containers. Each node has subnodes and elements. These can just be the two sorts of substructure.

data Mu b y = In (b (Mu b y) y) 

See? We "tie the recursive knot" in b's first argument, and keep the parameter y in its second. Accordingly, we obtain once for all

instance Bifunctor b => Functor (Mu b) where   fmap f (In b) = In (bimap (fmap f) f b) 

To use this, we'll need a kit of Bifunctor instances.

The Bifunctor Kit

Constants are bifunctorial.

newtype K a x y = K a  instance Bifunctor (K a) where   bimap f g (K a) = K a 

You can tell I wrote this bit first, because the identifiers are shorter, but that's good because the code is longer.

Variables are bifunctorial.

We need the bifunctors corresponding to one parameter or the other, so I made a datatype to distinguish them, then defined a suitable GADT.

data Var = X | Y  data V :: Var -> * -> * -> * where   XX :: x -> V X x y   YY :: y -> V Y x y 

That makes V X x y a copy of x and V Y x y a copy of y. Accordingly

instance Bifunctor (V v) where   bimap f g (XX x) = XX (f x)   bimap f g (YY y) = YY (g y) 

Sums and Products of bifunctors are bifunctors

data (:++:) f g x y = L (f x y) | R (g x y) deriving Show  instance (Bifunctor b, Bifunctor c) => Bifunctor (b :++: c) where   bimap f g (L b) = L (bimap f g b)   bimap f g (R b) = R (bimap f g b)  data (:**:) f g x y = f x y :**: g x y deriving Show  instance (Bifunctor b, Bifunctor c) => Bifunctor (b :**: c) where   bimap f g (b :**: c) = bimap f g b :**: bimap f g c 

So far, so boilerplate, but now we can define things like

List = Mu (K () :++: (V Y :**: V X))  Bin = Mu (V Y :**: (K () :++: (V X :**: V X))) 

If you want to use these types for actual data and not go blind in the pointilliste tradition of Georges Seurat, use pattern synonyms.

But what of zippers? How shall we show that Mu b is differentiable? We shall need to show that b is differentiable in both variables. Clang! It's time to learn about partial differentiation.

Partial derivatives of bifunctors

Because we have two variables, we shall need to be able to talk about them collectively sometimes and individually at other times. We shall need the singleton family:

data Vary :: Var -> * where   VX :: Vary X   VY :: Vary Y 

Now we can say what it means for a Bifunctor to have partial derivatives at each variable, and give the corresponding notion of zipper.

class (Bifunctor b, Bifunctor (D b X), Bifunctor (D b Y)) => Diff2 b where   type D b (v :: Var) :: * -> * -> *   up      :: Vary v -> Z b v x y -> b x y   down    :: b x y -> b (Z b X x y) (Z b Y x y)   around  :: Vary v -> Z b v x y -> Z b v (Z b X x y) (Z b Y x y)  data Z b v x y = (:<-) {cxZ :: D b v x y, elZ :: V v x y} 

This D operation needs to know which variable to target. The corresponding zipper Z b v tells us which variable v must be in focus. When we "decorate with context", we have to decorate x-elements with X-contexts and y-elements with Y-contexts. But otherwise, it's the same story.

We have two remaining tasks: firstly, to show that our bifunctor kit is differentiable; secondly, to show that Diff2 b allows us to establish Diff1 (Mu b).

Differentiating the Bifunctor kit

I'm afraid this bit is fiddly rather than edifying. Feel free to skip along.

The constants are as before.

instance Diff2 (K a) where   type D (K a) v = K Void   up _ (K q :<- _) = absurd q   down (K a) = K a   around _ (K q :<- _) = absurd q 

On this occasion, life is too short to develop the theory of the type level Kronecker-delta, so I just treated the variables separately.

instance Diff2 (V X) where   type D (V X) X = K ()   type D (V X) Y = K Void   up VX (K () :<- XX x)  = XX x   up VY (K q :<- _)      = absurd q   down (XX x) = XX (K () :<- XX x)   around VX z@(K () :<- XX x)  = K () :<- XX z   around VY (K q :<- _)        = absurd q  instance Diff2 (V Y) where   type D (V Y) X = K Void   type D (V Y) Y = K ()   up VX (K q :<- _)      = absurd q   up VY (K () :<- YY y)  = YY y   down (YY y) = YY (K () :<- YY y)   around VX (K q :<- _)        = absurd q   around VY z@(K () :<- YY y)  = K () :<- YY z 

For the structural cases, I found it useful to introduce a helper allowing me to treat variables uniformly.

vV :: Vary v -> Z b v x y -> V v (Z b X x y) (Z b Y x y) vV VX z = XX z vV VY z = YY z 

I then built gadgets to facilitate the kind of "retagging" we need for down and around. (Of course, I saw which gadgets I needed as I was working.)

zimap :: (Bifunctor c) => (forall v. Vary v -> D b v x y -> D b' v x y) ->          c (Z b X x y) (Z b Y x y) -> c (Z b' X x y) (Z b' Y x y) zimap f = bimap   (\ (d :<- XX x) -> f VX d :<- XX x)   (\ (d :<- YY y) -> f VY d :<- YY y)  dzimap :: (Bifunctor (D c X), Bifunctor (D c Y)) =>          (forall v. Vary v -> D b v x y -> D b' v x y) ->          Vary v -> Z c v (Z b X x y) (Z b Y x y) -> D c v (Z b' X x y) (Z b' Y x y) dzimap f VX (d :<- _) = bimap   (\ (d :<- XX x) -> f VX d :<- XX x)   (\ (d :<- YY y) -> f VY d :<- YY y)   d dzimap f VY (d :<- _) = bimap   (\ (d :<- XX x) -> f VX d :<- XX x)   (\ (d :<- YY y) -> f VY d :<- YY y)   d 

And with that lot ready to go, we can grind out the details. Sums are easy.

instance (Diff2 b, Diff2 c) => Diff2 (b :++: c) where   type D (b :++: c) v = D b v :++: D c v   up v (L b' :<- vv) = L (up v (b' :<- vv))   down (L b) = L (zimap (const L) (down b))   down (R c) = R (zimap (const R) (down c))   around v z@(L b' :<- vv :: Z (b :++: c) v x y)     = L (dzimap (const L) v ba) :<- vV v z     where ba = around v (b' :<- vv :: Z b v x y)   around v z@(R c' :<- vv :: Z (b :++: c) v x y)     = R (dzimap (const R) v ca) :<- vV v z     where ca = around v (c' :<- vv :: Z c v x y) 

Products are hard work, which is why I'm a mathematician rather than an engineer.

instance (Diff2 b, Diff2 c) => Diff2 (b :**: c) where   type D (b :**: c) v = (D b v :**: c) :++: (b :**: D c v)   up v (L (b' :**: c) :<- vv) = up v (b' :<- vv) :**: c   up v (R (b :**: c') :<- vv) = b :**: up v (c' :<- vv)   down (b :**: c) =     zimap (const (L . (:**: c))) (down b) :**: zimap (const (R . (b :**:))) (down c)   around v z@(L (b' :**: c) :<- vv :: Z (b :**: c) v x y)     = L (dzimap (const (L . (:**: c))) v ba :**:         zimap (const (R . (b :**:))) (down c))       :<- vV v z where       b = up v (b' :<- vv :: Z b v x y)       ba = around v (b' :<- vv :: Z b v x y)   around v z@(R (b :**: c') :<- vv :: Z (b :**: c) v x y)     = R (zimap (const (L . (:**: c))) (down b):**:         dzimap (const (R . (b :**:))) v ca)       :<- vV v z where       c = up v (c' :<- vv :: Z c v x y)       ca = around v (c' :<- vv :: Z c v x y) 

Conceptually, it's just as before, but with more bureaucracy. I built these using pre-type-hole technology, using undefined as a stub in places I wasn't ready to work, and introducing a deliberate type error in the one place (at any given time) where I wanted a useful hint from the typechecker. You too can have the typechecking as videogame experience, even in Haskell.

Subnode zippers for recursive containers

The partial derivative of b with respect to X tells us how to find a subnode one step inside a node, so we get the conventional notion of zipper.

data MuZpr b y = MuZpr   {  aboveMu  :: [D b X (Mu b y) y]   ,  hereMu   :: Mu b y   } 

We can zoom all the way up to the root by repeated plugging in X positions.

muUp :: Diff2 b => MuZpr b y -> Mu b y muUp (MuZpr {aboveMu = [], hereMu = t}) = t muUp (MuZpr {aboveMu = (dX : dXs), hereMu = t}) =   muUp (MuZpr {aboveMu = dXs, hereMu = In (up VX (dX :<- XX t))}) 

But we need element-zippers.

Element-zippers for fixpoints of bifunctors

Each element is somewhere inside a node. That node is sitting under a stack of X-derivatives. But the position of the element in that node is given by a Y-derivative. We get

data MuCx b y = MuCx   {  aboveY  :: [D b X (Mu b y) y]   ,  belowY  :: D b Y (Mu b y) y   }  instance Diff2 b => Functor (MuCx b) where   fmap f (MuCx { aboveY = dXs, belowY = dY }) = MuCx     {  aboveY  = map (bimap (fmap f) f) dXs     ,  belowY  = bimap (fmap f) f dY     } 

Boldly, I claim

instance Diff2 b => Diff1 (Mu b) where   type DF (Mu b) = MuCx b 

but before I develop the operations, I'll need some bits and pieces.

I can trade data between functor-zippers and bifunctor-zippers as follows:

zAboveY :: ZF (Mu b) y -> [D b X (Mu b y) y]  -- the stack of `X`-derivatives above me zAboveY (d :<-: y) = aboveY d  zZipY :: ZF (Mu b) y -> Z b Y (Mu b y) y      -- the `Y`-zipper where I am zZipY (d :<-: y) = belowY d :<- YY y 

That's enough to let me define:

  upF z  = muUp (MuZpr {aboveMu = zAboveY z, hereMu = In (up VY (zZipY z))}) 

That is, we go up by first reassembling the node where the element is, turning an element-zipper into a subnode-zipper, then zooming all the way out, as above.

Next, I say

  downF  = yOnDown [] 

to go down starting with the empty stack, and define the helper function which goes down repeatedly from below any stack:

yOnDown :: Diff2 b => [D b X (Mu b y) y] -> Mu b y -> Mu b (ZF (Mu b) y) yOnDown dXs (In b) = In (contextualize dXs (down b)) 

Now, down b only takes us inside the node. The zippers we need must also carry the node's context. That's what contextualise does:

contextualize :: (Bifunctor c, Diff2 b) =>   [D b X (Mu b y) y] ->   c (Z b X (Mu b y) y) (Z b Y (Mu b y) y) ->   c (Mu b (ZF (Mu b) y)) (ZF (Mu b) y) contextualize dXs = bimap   (\ (dX :<- XX t) -> yOnDown (dX : dXs) t)   (\ (dY :<- YY y) -> MuCx {aboveY = dXs, belowY = dY} :<-: y) 

For every Y-position, we must give an element-zipper, so it is good we know the whole context dXs back to the root, as well as the dY which describes how the element sits in its node. For every X-position, there is a further subtree to explore, so we grow the stack and keep going!

That leaves only the business of shifting focus. We might stay put, or go down from where we are, or go up, or go up and then down some other path. Here goes.

  aroundF z@(MuCx {aboveY = dXs, belowY = dY} :<-: _) = MuCx     {  aboveY = yOnUp dXs (In (up VY (zZipY z)))     ,  belowY = contextualize dXs (cxZ $ around VY (zZipY z))     }  :<-: z 

As ever, the existing element is replaced by its entire zipper. For the belowY part, we look where else we can go in the existing node: we will find either alternative element Y-positions or further X-subnodes to explore, so we contextualise them. For the aboveY part, we must work our way back up the stack of X-derivatives after reassembling the node we were visiting.

yOnUp :: Diff2 b => [D b X (Mu b y) y] -> Mu b y ->          [D b X (Mu b (ZF (Mu b) y)) (ZF (Mu b) y)] yOnUp [] t = [] yOnUp (dX : dXs) (t :: Mu b y)   =  contextualize dXs (cxZ $ around VX (dX :<- XX t))   :  yOnUp dXs (In (up VX (dX :<- XX t))) 

At each step of the way, we can either turn somewhere else that's around, or keep going up.

And that's it! I haven't given a formal proof of the laws, but it looks to me as if the operations carefully maintain the context correctly as they crawl the structure.

What have we learned?

Differentiability induces notions of thing-in-its-context, inducing a comonadic structure where extract gives you the thing and duplicate explores the context looking for other things to contextualise. If we have the appropriate differential structure for nodes, we can develop differential structure for whole trees.

Oh, and treating each individual arity of type constructor separately is blatantly horrendous. The better way is to work with functors between indexed sets

f :: (i -> *) -> (o -> *) 

where we make o different sorts of structure storing i different sorts of element. These are closed under the Jacobian construction

J f :: (i -> *) -> ((o, i) -> *) 

where each of the resulting (o, i)-structures is a partial derivative, telling you how to make an i-element-hole in an o-structure. But that's dependently typed fun, for another time.

Answer 2

The Comonad instance for zippers is not

instance (Diff t, Diff (D t)) => Comonad (Zipper t) where     extract = here     duplicate = fmap outOf . inTo 

where outOf and inTo come from the Diff instance for Zipper t itself. The above instance violates the Comonad law fmap extract . duplicate == id. Instead it behaves like:

fmap extract . duplicate == \z -> fmap (const (here z)) z 

Diff (Zipper t)

The Diff instance for Zipper is provided by identifying them as products and reusing the code for products (below).

-- Zippers are themselves products toZipper :: (D t :*: Identity) a -> Zipper t a toZipper (d :*: (Identity h)) = Zipper d h  fromZipper :: Zipper t a -> (D t :*: Identity) a fromZipper (Zipper d h) = (d :*: (Identity h)) 

Given an isomorphism between data types, and an isomorphism between their derivatives, we can reuse one type's inTo and outOf for the other.

inToFor' :: (Diff r) =>             (forall a.   r a ->   t a) ->             (forall a.   t a ->   r a) ->             (forall a. D r a -> D t a) ->             (forall a. D t a -> D r a) ->             t a -> t (Zipper t a) inToFor' to from toD fromD = to . fmap (onDiff toD) . inTo . from  outOfFor' :: (Diff r) =>             (forall a.   r a ->   t a) ->             (forall a.   t a ->   r a) ->             (forall a. D r a -> D t a) ->             (forall a. D t a -> D r a) ->             Zipper t a -> t a outOfFor' to from toD fromD = to . outOf . onDiff fromD 

For types that are just newTypes for an existing Diff instance, their derivatives are the same type. If we tell the type checker about that type equality D r ~ D t, we can take advantage of that instead of providing an isomorphism for the derivatives.

inToFor :: (Diff r, D r ~ D t) =>            (forall a. r a -> t a) ->            (forall a. t a -> r a) ->            t a -> t (Zipper t a) inToFor to from = inToFor' to from id id  outOfFor :: (Diff r, D r ~ D t) =>             (forall a. r a -> t a) ->             (forall a. t a -> r a) ->             Zipper t a -> t a outOfFor to from = outOfFor' to from id id 

Equipped with these tools, we can reuse the Diff instance for products to implement Diff (Zipper t)

-- This requires undecidable instances, due to the need to take D (D t) instance (Diff t, Diff (D t)) => Diff (Zipper t) where     type D (Zipper t) = D ((D t) :*: Identity)     -- inTo :: t        a -> t        (Zipper  t         a)     -- inTo :: Zipper t a -> Zipper t (Zipper (Zipper t) a)     inTo = inToFor toZipper fromZipper     -- outOf :: Zipper  t         a -> t        a     -- outOf :: Zipper (Zipper t) a -> Zipper t a     outOf = outOfFor toZipper fromZipper 

Boilerplate

In order to actually use the code presented here, we need some language extensions, imports, and a restatement of the proposed problem.

{-# LANGUAGE StandaloneDeriving #-} {-# LANGUAGE TypeFamilies #-} {-# LANGUAGE FlexibleContexts #-} {-# LANGUAGE DeriveFunctor #-} {-# LANGUAGE TypeOperators #-} {-# LANGUAGE UndecidableInstances #-} {-# LANGUAGE RankNTypes #-}  import Control.Monad.Identity import Data.Proxy import Control.Comonad  data Zipper t a = Zipper { diff :: D t a, here :: a }  onDiff :: (D t a -> D u a) -> Zipper t a -> Zipper u a onDiff f (Zipper d a) = Zipper (f d) a  deriving instance Diff t => Functor (Zipper t) deriving instance (Eq (D t a), Eq a) => Eq (Zipper t a) deriving instance (Show (D t a), Show a) => Show (Zipper t a)  class (Functor t, Functor (D t)) => Diff t where   type D t :: * -> *   inTo  :: t a -> t (Zipper t a)   outOf :: Zipper t a -> t a 

Products, Sums, and Constants

The Diff (Zipper t) instance relies on implementations of Diff for products :*:, sums :+:, constants Identity, and zero Proxy.

data (:+:) a b x = InL (a x) | InR (b x)     deriving (Eq, Show) data (:*:) a b x = a x :*: b x     deriving (Eq, Show)  infixl 7 :*: infixl 6 :+:  deriving instance (Functor a, Functor b) => Functor (a :*: b)  instance (Functor a, Functor b) => Functor (a :+: b) where     fmap f (InL a) = InL . fmap f $ a     fmap f (InR b) = InR . fmap f $ b   instance (Diff a, Diff b) => Diff (a :*: b) where     type D (a :*: b) = D a :*: b :+: a :*: D b     inTo (a :*: b) =          (fmap (onDiff (InL . (:*: b))) . inTo) a :*:         (fmap (onDiff (InR . (a :*:))) . inTo) b     outOf (Zipper (InL (a :*: b)) x) = (:*: b) . outOf . Zipper a $ x     outOf (Zipper (InR (a :*: b)) x) = (a :*:) . outOf . Zipper b $ x  instance (Diff a, Diff b) => Diff (a :+: b) where     type D (a :+: b) = D a :+: D b     inTo (InL a) = InL . fmap (onDiff InL) . inTo $ a     inTo (InR b) = InR . fmap (onDiff InR) . inTo $ b     outOf (Zipper (InL a) x) = InL . outOf . Zipper a $ x     outOf (Zipper (InR a) x) = InR . outOf . Zipper a $ x  instance Diff (Identity) where     type D (Identity) = Proxy     inTo = Identity . (Zipper Proxy) . runIdentity     outOf = Identity . here  instance Diff (Proxy) where     type D (Proxy) = Proxy     inTo = const Proxy     outOf = const Proxy 

Bin Example

I posed the Bin example as an isomorphism to a sum of products. We need not only its derivative but its second derivative as well

newtype Bin   a = Bin   {unBin   ::      (Bin :*: Identity :*: Bin :+: Identity)  a}     deriving (Functor, Eq, Show) newtype DBin  a = DBin  {unDBin  ::    D (Bin :*: Identity :*: Bin :+: Identity)  a}     deriving (Functor, Eq, Show) newtype DDBin a = DDBin {unDDBin :: D (D (Bin :*: Identity :*: Bin :+: Identity)) a}     deriving (Functor, Eq, Show)  instance Diff Bin where     type D Bin = DBin     inTo  = inToFor'  Bin unBin DBin unDBin     outOf = outOfFor' Bin unBin DBin unDBin  instance Diff DBin where     type D DBin = DDBin     inTo  = inToFor'  DBin unDBin DDBin unDDBin     outOf = outOfFor' DBin unDBin DDBin unDDBin 

The example data from the previous answer is

aTree :: Bin Int     aTree =     (Bin . InL) (         (Bin . InL) (             (Bin . InR) (Identity 2)             :*: (Identity 1) :*:             (Bin . InR) (Identity 3)         )         :*: (Identity 0) :*:         (Bin . InR) (Identity 4)     ) 

Not the Comonad instance

The Bin example above provides a counter-example to fmap outOf . inTo being the correct implementation of duplicate for Zipper t. In particular, it provides a counter-example to the fmap extract . duplicate = id law:

fmap ( \z -> (fmap extract . duplicate) z == z) . inTo $ aTree 

Which evaluates to (notice how it is full of Falses everywhere, any False would be enough to disprove the law)

Bin {unBin = InL ((Bin {unBin = InL ((Bin {unBin = InR (Identity False)} :*: Identity False) :*: Bin {unBin = InR (Identity False)})} :*: Identity False) :*: Bin {unBin = InR (Identity False)})} 

inTo aTree is a tree with the same structure as aTree, but everywhere there was a value there is instead a zipper with the value, and the remainder of the tree with all of the original values intact. fmap (fmap extract . duplicate) . inTo $ aTree is also a tree with the same structure as aTree, but everywere there was a value there is instead a zipper with the value, and the remainder of the tree with all of the values replaced with that same value. In other words:

fmap extract . duplicate == \z -> fmap (const (here z)) z 

The complete test-suite for all three Comonad laws, extract . duplicate == id, fmap extract . duplicate == id, and duplicate . duplicate == fmap duplicate . duplicate is

main = do     putStrLn "fmap (\\z -> (extract . duplicate) z == z) . inTo $ aTree"     print   . fmap ( \z -> (extract . duplicate) z == z) . inTo $ aTree         putStrLn ""     putStrLn  "fmap (\\z -> (fmap extract . duplicate) z == z) . inTo $ aTree"     print    . fmap ( \z -> (fmap extract . duplicate) z == z) . inTo $ aTree         putStrLn ""     putStrLn "fmap (\\z -> (duplicate . duplicate) z) == (fmap duplicate . duplicate) z) . inTo $ aTree"     print   . fmap ( \z -> (duplicate . duplicate) z == (fmap duplicate . duplicate) z) . inTo $ aTree