path: root/posts/2024-07-03-frp-with-propagators.md

title: Functional reactive user interfaces with propagators
date: 2024-07-08 08:30:00
tags: lisp, scheme, guile, frp
summary: A look at a prototype functional reactive web UI using the propagator paradigm
---

I’ve been interested in functional reactive programming (FRP) for
about a decade now.  I even wrote a couple of
[blog](/posts/functional-reactive-programming-in-scheme-with-guile-2d.html)
[posts](/posts/live-asset-reloading-with-guile-2d.html) back in 2014
describing my experiments.  My initial source of inspiration was
[Elm](https://elm-lang.org/), the Haskell-like language for the web
that [once had FRP](https://elm-lang.org/news/farewell-to-frp) as a
core part of the language.  From there, I explored the academic
literature on the subject.

(Sidenote: The Elm of 10 years ago looks quite different than the Elm
of today and I don’t know where to find that time-traveling Mario demo
that sent me down this path in the first place.  That demo was really
cool!  I’ll update this post later if I can find an archive of it.)

Ultimately, I created and then abandoned a library that focused on
[FRP for games](/projects/sly.html).  It was a neat idea, but the
performance was terrible.  The overhead of my kinda-sorta FRP system
was part of the problem, but mostly it was my own inexperience.  I
didn’t know how to optimize effectively and my implementation
language, [Guile](https://gnu.org/s/guile), did not have as many
optimization passes as it does now.  Also, realtime simulations like
games require much more careful use of heap allocation.

I found that, overhead aside, FRP is a bad fit for things like
scripting sequences of actions in a game.  I don’t want to give up
things like coroutines that make it easy.  I’ve learned how different
layers of a program may call for different programming paradigms.
Functional layers rest upon imperative foundations.  Events are built
on top of polling.  Languages with expression trees run on machines
that only understand linear sequences.  You get the idea.  A good
general-purpose language will allow you to compose many paradigms in
the same program.  I’m still a big fan of functional programming, but
single paradigm languages do not appeal to me.

Fast forward 10 years, I find myself thinking about FRP again in a new
context.  I now work for the [Spritely
Institute](https://spritely.institute) where we’re researching and
building the next generation of [secure, distributed application
infrastructure](https://spritely.institute/goblins).  We want to demo
our tech through easy-to-use web applications, which means we need to
do some UI programming.  So, the back burner of my brain has been
mulling over the possibilities.  What’s the least painful way to build
web UIs?  Is this FRP thing worth revisiting?

The reason why FRP is so appealing to me (on paper, at least) is that
it allows for writing interactive programs *declaratively*.  With FRP,
I can simply describe the *relationships* between the various
time-varying components, and the system wires it all together for me.
I’m spared from *callback hell*, one of the more frightening layers of
hell that forces programs to be written in a kind of
[continuation-passing
style](https://en.wikipedia.org/wiki/Continuation-passing_style) where
timing and state bugs consume more development time as the project
grows.

## What about React?

In the time during and since I last experimented with FRP, a different
approach to declarative UI programming has swept the web development
world: [React](https://react.dev/).  From React, many other similar
libraries emerged.  On the minimalist side there are things like
[Mithril](https://mithril.js.org/) (a favorite of mine), and then
there are bigger players like [Vue](https://vuejs.org/).  The term
“reactive” has become overloaded, but in the mainstream software world
it is associated with React and friends.  FRP is quite different,
despite sharing the declarative and reactive traits.  Both help free
programmers from callback hell, but they achieve their results
differently.

The React model describes an application as a tree of “components”.
Each component represents a subset of the complete UI element tree.
For each component, there is a template function that takes some
inputs and returns the new desired state of the UI.  This function is
called whenever an event occurs that might change the state of the UI.
The template produces a data structure known as a “virtual
[DOM](https://developer.mozilla.org/en-US/docs/Web/API/Document_Object_Model)”.
To realize this new state in the actual DOM, React diffs the previous
tree with the new one and updates, creates, and deletes elements as
necessary.

With FRP, you describe your program as an acyclic graph of nodes that
contain time-varying values.  The actual value of any given node is
determined by a function that maps the current values of some input
nodes into an output value.  The system is bootstrapped by handling a
UI event and updating the appropriate root node, which kicks off a
cascade of updates throughout the graph.  At the leaf nodes,
side-effects occur that realize the desired state of the application.
Racket’s [FrTime](https://docs.racket-lang.org/frtime/) is one example
of such a system, which is based on Greg Cooper’s 2008 PhD
dissertation [“Integrating Dataflow Evaluation into a Practical
Higher-Order Call-by-Value
Language”](https://cs.brown.edu/people/ghcooper/thesis.pdf).  In
FrTime, time-varying values are called “signals”.  Elm borrowed this
language, too, and there’s currently a [proposal to add signals to
JavaScript](https://github.com/tc39/proposal-signals).  Research into
FRP goes back quite a bit further.  Notably, Conal Elliot and Paul
Hudak wrote [“Functional Reactive
Animation”](http://conal.net/papers/icfp97/icfp97.pdf) in 1997.

## On jank

The scope of potential change for any given event is larger in React
than FRP.  An FRP system flows data through an acyclic graph, updating
only the nodes affected by the event.  React requires re-evaluating
the template for each component that needs refreshing and applying a
diff algorithm to determine what needs changing in the currently
rendered UI.  The virtual DOM diffing process can be quite wasteful in
terms of both memory usage and processing time, leading to jank on
systems with limited resources like phones.  Andy Wingo has done some
interesting analyses of things like [React
Native](https://wingolog.org/archives/2023/04/21/structure-and-interpretation-of-react-native)
and
[Flutter](https://wingolog.org/archives/2023/04/26/structure-and-interpretation-of-flutter)
and covers the subject of jank well.  So, while I appreciate the
greatly improved developer experience of React-likes (I wrote my fair
share of frontend code in the jQuery days), I’m less pleased by the
overhead that it pushes onto each user’s computer.  React feels like
an important step forward on the declarative UI trail, but it’s not
the destination.

FRP has the potential for less jank because side-effects (the UI
widget state updates) can be more precise.  For example, if a web page
has a text node that displays the number of times the user has clicked
a mouse button, an FRP system could produce a program that would do
the natural thing: Register a click event handler that replaces the
text node with a new one containing the updated count.  We don’t need
to diff the whole page, nor do we need to create a wrapper component
to update a subset of the page.  The scope is narrow, so we can apply
smaller updates.  No virtual DOM necessary.  There is, of course,
overhead to maintaining the graph of time-varying values.  The
underlying runtime is free to use mutable state, but the user layer
must take care to use pure functions and persistent, immutable data
structures.  This has a cost, but the per-event cost to refresh the UI
feels much more reasonable when compared to React.  From here on, I
will stop talking about React and start exploring if FRP might really
offer a more expressive way to do declarative UI without too much
overhead.  But first, we need to talk about a serious problem.

## FRP is acyclic

FRP is no silver bullet.  As mentioned earlier, FRP graphs are
typically of the acyclic flavor.  This limits the set of UIs that are
possible to create with FRP.  Is this the cost of declarativeness?  To
demonstrate the problem, consider a color picker tool that has sliders
for both the red-green-blue and hue-saturation-value representations
of color:

![Network diagram of RGB/HSV color picker](/images/propagators/rgb-hsv-color-diagram.png)

In this program, updating the sliders on the RGB side should change
the values of the sliders on the HSV side, and vice versa.  This forms
a cycle between the two sets of sliders.  It’s possible to express
cycles like this with event callbacks, though it’s messy and
error-prone to do manually.  We’d like a system built on top of event
callbacks that can do the right thing without strange glitches or
worse, unbounded loops.

## Propagators to the rescue

Fortunately, I didn’t create that diagram above.  It’s from Alexey
Radul’s 2009 PhD dissertation: [“Propagation Networks: A Flexible and
Expressive Substrate for
Computation”](https://dspace.mit.edu/bitstream/handle/1721.1/49525/MIT-CSAIL-TR-2009-053.pdf).
This paper dedicates a section to explaining how FRP can be built on
top a more general paradigm called *propagation networks*, or just
*propagators* for short.  The paper is lengthy, naturally, but it is
written in an approachable style.  There isn’t any terse math notation
and there are plenty of code examples.  As far as PhD dissertations
go, this one is a real page turner!

Here is a quote from section 5.5 about FrTime (with emphasis added by
me):

> FrTime is built around a custom propagation infrastructure; it
> nicely achieves both non-recomputation and glitch avoidance, but
> unfortunately, the propagation system is **nontrivially
> complicated**, and **specialized for the purpose of supporting
> functional reactivity**. In particular, the FrTime system **imposes
> the invariant that the propagation graph be acyclic**, and
> guarantees that it will **execute the propagators in
> topological-sort order**. This simplifies the propagators
> themselves, but greatly complicates the runtime system, especially
> because it has to **dynamically recompute the sort order** when the
> structure of some portion of the graph changes (as when the
> predicate of a conditional changes from true to false, and the other
> branch must now be computed). That complexity, in turn, makes that
> runtime system **unsuitable for other kinds of propagation**, and
> even makes it **difficult for other kinds of propagation to
> interoperate** with it.

So, the claim is that FRP-on-propagators can remove the acyclic
restriction, reduce complexity, and improve interoperability.  But
what are propagators?  I like how the book [“Software Design for
Flexibility”](https://mitpress.mit.edu/9780262045490/software-design-for-flexibility/)
(2021) defines them (again, with emphasis added by me):

> “The propagator model is built on the idea that the basic
> computational elements are propagators, **autonomous independent
> machines interconnected by shared cells through which they
> communicate**.  Each propagator machine continuously examines the
> cells is is connected to, and adds information to some cells based
> on computations it can make from information it can get from others.
> **Cells accumulate information and propagators produce
> information**.”

Research on propagators goes back a long way (you’ll even find a form
of propagators in the all-time classic [“Structure and Interpretation
of Computer
Programs”](https://sarabander.github.io/sicp/html/3_002e3.xhtml#g_t3_002e3_002e5)),
but it was Alexey Radul that discovered how to unify many different
types of special-purpose propagation systems so that they could share
a generic substrate and interoperate.

Perhaps the most exciting application of the propagator model is AI,
where it can be used to create “explainable AI” that keeps track of
how a particular output was computed.  This type of AI stands in stark
contrast to the much hyped mainstream machine learning models that
hoover up our planet’s precious natural resources to produce black
boxes that generate [impressive
bullshit](https://link.springer.com/article/10.1007/s10676-024-09775-5).
But anyway!

The diagram above can also be found in section 5.5 of the
dissertation.  Here’s the description:

> “A network for a widget for RGB and HSV color selection. Traditional
> functional reactive systems have qualms about the circularity, but
> general-purpose propagation handles it automatically.”

This color picker felt like a good, achievable target for a prototype.
The propagator network is small and there are only a handful of UI
elements, yet it will test if the FRP system is working correctly.

## The prototype

I read Alexey Radul’s dissertation, and then read chapter 7 of
Software Design for Flexibility, which is all about propagators.  Both
use Scheme as the implementation language.  The latter makes no
mention of FRP, and while the former explains *how* FRP can be
implemented in terms of propagators, there is (understandably) no code
included.  So, I had to implement it for myself to test my
understanding.  Unsurprisingly, I had misunderstood many things along
the way and my iterations of broken code let me know that.
Implementation is the best teacher.

After much code fiddling, I was able to create a working prototype of
the color picker.  Here it is below:

![(iframe (@ (src "/embeds/frp-color-picker/") (height "600px")))](sxml)

This prototype is written in Scheme and uses
[Hoot](https://spritely.institute/hoot) to compile it to WebAssembly
so I can embed it right here in my blog.  Sure beats a screenshot or
video!  This prototype contains a minimal propagator implementation
that is sufficient to bootstrap a similarly minimal FRP
implementation.

## Propagator implementation

Let’s take a look at the code and see how it all works, starting with
propagation.  There are two essential data types: Cells and
propagators.  Cells accumulate information about a value, ranging from
*nothing*, some form of *partial information*, or a complete value.
The concept of partial information is Alexey Radul’s major
contribution to the propagator model.  It is through partial
information structures that general-purpose propagators can be used to
implement logic programming, probabilistic programming, type
inference, and FRP, among others.  I’m going to keep things as simple
as possible in this post (it’s a big enough infodump already), but do
read the propagator literature if phrases like “dependency directed
backtracking” and “truth maintenance system” sound like a good time to
you.

Cells start out knowing *nothing*, so we need a special, unique value
to represent nothing:

```scheme
(define-record-type <nothing>
  (make-nothing)
  %nothing?)
(define nothing (make-nothing))
(define (nothing? x) (eq? x nothing))
```

Any unique (as in `eq?`) object would do, such as `(list ’nothing)`,
but I chose to use a record type because I like the way it prints.

In addition to nothing, the propagator model also has a notion of
*contradictions*.  If one source of information says there are four
lights, but another says there are five, then we have a contradiction.
Propagation networks do not fall apart in the presence of
contradictory information.  There’s a data type that captures
information about them and they can be resolved in a context-specific
manner.  I mention contradictions only for the sake of completeness,
as a general-purpose propagator system needs to handle them.  This
prototype does not create any contradictions, so I won’t mention them
again.

Now we can define a cell type:

```scheme
(define-record-type <cell>
  (%make-cell relations neighbors content strongest
              equivalent? merge find-strongest handle-contradiction)
  cell?
  (relations cell-relations)
  (neighbors cell-neighbors set-cell-neighbors!)
  (content cell-content set-cell-content!)
  (strongest cell-strongest set-cell-strongest!)
  ;; Dispatch table:
  (equivalent? cell-equivalent?)
  (merge cell-merge)
  (find-strongest cell-find-strongest)
  (handle-contradiction cell-handle-contradiction))
```

The details of *how* a cell does things like merge old information
with new information is left intentionally unanswered at this level of
abstraction.  Different systems built on propagators will want to
handle things in different ways.  In the propagator literature, you’ll
see generic procedures used extensively for this purpose.  For the
sake of simplicity, I use a dispatch table instead.  It would be easy
to layer generic merge on top later, if we wanted.

The constructor for cells sets the default contents to nothing:

```scheme
(define default-equivalent? equal?)
;; But what about partial information???
(define (default-merge old new) new)
(define (default-find-strongest content) content)
(define (default-handle-contradiction cell) (values))

(define* (make-cell name #:key
                    (equivalent? default-equivalent?)
                    (merge default-merge)
                    (find-strongest default-find-strongest)
                    (handle-contradiction default-handle-contradiction))
  (let ((cell (%make-cell (make-relations name) '() nothing nothing
                          equivalent? merge find-strongest
                          handle-contradiction)))
    (add-child! (current-parent) cell)
    cell))
```

The default procedures used for the dispatch table are either no-ops
or trivial.  The default `merge` doesn’t merge at all, it just
clobbers the old with the new.  It’s up to the layers on top to
provide more useful implementations.

Cells can have neighbors (which will be propagators):

```scheme
(define (add-cell-neighbor! cell neighbor)
  (set-cell-neighbors! cell (lset-adjoin eq? (cell-neighbors cell) neighbor)))
```

Since cells accumulate information, there are procedures for adding
new content and finding the current strongest value contained within:

```scheme
(define (add-cell-content! cell new)
  (match cell
    (($ <cell> _ neighbors content strongest equivalent? merge
               find-strongest handle-contradiction)
     (let ((content* (merge content new)))
       (set-cell-content! cell content*)
       (let ((strongest* (find-strongest content*)))
         (cond
          ;; New strongest value is equivalent to the old one.  No need
          ;; to alert propagators.
          ((equivalent? strongest strongest*)
           (set-cell-strongest! cell strongest*))
          ;; Uh oh, a contradiction!  Call handler.
          ((contradiction? strongest*)
           (set-cell-strongest! cell strongest*)
           (handle-contradiction cell))
          ;; Strongest value has changed.  Alert the propagators!
          (else
           (set-cell-strongest! cell strongest*)
           (for-each alert-propagator! neighbors))))))))
```

Next up is the propagator type.  Propagators can be *activated* to
create information using content stored in cells and store their
results in some other cells, forming a graph.  Data flow is not forced
to be directional.  Cycles are not only permitted, but very common in
practice.  So, propagators keep track of both their input and output
cells:

```scheme
(define-record-type <propagator>
  (%make-propagator relations inputs outputs activate)
  propagator?
  (relations propagator-relations)
  (inputs propagator-inputs)
  (outputs propagator-outputs)
  (activate propagator-activate))
```

Propagators can be *alerted* to schedule themselves to be re-evaluted:

```scheme
(define (alert-propagator! propagator)
  (queue-task! (propagator-activate propagator)))
```

The constructor for propagators adds the new propagator as a neighbor
to all input cells and then calls `alert-propagator!` to bootstrap it:

```scheme
(define (make-propagator name inputs outputs activate)
  (let ((propagator (%make-propagator (make-relations name)
                                      inputs outputs activate)))
    (add-child! (current-parent) propagator)
    (for-each (lambda (cell)
                (add-cell-neighbor! cell propagator))
              inputs)
    (alert-propagator! propagator)
    propagator))
```

There are two main classes of propagators that will be used:
**primitive propagators** and **constraint propagators**.  Primitive
propagators are directional; they apply a function to the values of
some input cells and write the result to an output cell:

```scheme
(define (unusable-value? x)
  (or (nothing? x) (contradiction? x)))

(define (primitive-propagator name f)
  (match-lambda*
    ((inputs ... output)
     (define (activate)
       (let ((args (map cell-strongest inputs)))
         (unless (any unusable-value? args)
           (add-cell-content! output (apply f args)))))
     (make-propagator name inputs (list output) activate))))
```

We can use `primitive-propagator` to lift standard Scheme procedures
into the realm of propagators.  Here’s how we’d make and use an
addition propagator:

```scheme
(define p:+ (primitive-propagator +))
(define a (make-cell))
(define b (make-cell))
(define c (make-cell))
(p:+ a b c)
(add-cell-content! a 1)
(add-cell-content! b 3)
;; After the scheduler runs…
(cell-strongest c) ;; => 4
```

It is from these primitive propagators that we can build more
complicated, compound propagators.  Compound propagators compose
primitive propagators (or other compound propagators) and lazily
construct their section of the network upon first activation:

```scheme
(define (compound-propagator name inputs outputs build)
  (let ((built? #f))
    (define (maybe-build)
      (unless (or built?
                  (and (not (null? inputs))
                       (every unusable-value? (map cell-strongest inputs))))
        (parameterize ((current-parent (propagator-relations propagator)))
          (build)
          (set! built? #t))))
    (define propagator (make-propagator name inputs outputs maybe-build))
    propagator))
```

By this point you may be wondering what all the references to
`current-parent` are about.  It is for tracking the parent/child
relationships of the cells and propagators in the network.  This could
be helpful for things like visualizing the network, but we aren’t
going to do anything with it today.  I’ve omitted all of the other
relation code for this reason.

Constraint propagators are compound propagators whose inputs and
outputs are the same, which results in bidirectional propagation:

```scheme
(define (constraint-propagator name cells build)
  (compound-propagator name cells cells build))
```

Using primitive propagators for addition and subtraction, we can build
a constraint propagator for the equation `a + b = c`:

```scheme
(define p:+ (primitive-propagator +))
(define p:- (primitive-propagator -))
(define (c:sum a b c)
  (define (build)
    (p:+ a b c)
    (p:- c a b)
    (p:- c b a))
  (constraint-propagator 'sum (list a b c) build))
(define a (make-cell))
(define b (make-cell))
(define c (make-cell))
(c:sum a b c)
(add-cell-content! a 1)
(add-cell-content! c 4)
;; After the scheduler runs…
(cell-strongest b) ;; => 3
```

With a constraint, we can populate any two cells and the propagation
system will figure out the value of the third.  Pretty cool!

This is a good enough propagation system for the FRP prototype.

## FRP implementation

If you’re familiar with terminology from other FRP systems like
“signals” and “behaviors” then set that knowledge aside for now.  We
need some new nouns.  But first, a bit about the problems that need
solving in order to implement FRP on top of general-purpose
propagators:

* The propagator model does not enforce any ordering of *when*
  propagators will be re-activated in relation to each other.  If
  we’re not careful, something in the network could compute a value
  using a mix of fresh and stale data, resulting in a momentary
  “glitch” that could be noticeable in the UI.

* The presence of cycles introduce a crisis of identity.  It’s not
  sufficient for every time-varying value to be treated as its own
  self.  In the color picker, the RGB values and the HSV values are
  two representations of *the same thing*.  We need a new notion of
  identity to capture this and prevent unnecessary churn and glitches
  in the network.

For starters, we will create a “reactive identifier” (needs a better
name) data type that serves two purposes:

1) To create shared identity between different information sources
that are *logically* part of the same thing

2) To create localized timestamps for values associated with this identity

```scheme
(define-record-type <reactive-id>
  (%make-reactive-id name clock)
  reactive-id?
  (name reactive-id-name)
  (clock reactive-id-clock set-reactive-id-clock!))

(define (make-reactive-id name)
  (%make-reactive-id name 0))

(define (reactive-id-tick! id)
  (let ((t (1+ (reactive-id-clock id))))
    (set-reactive-id-clock! id t)
    `((,id . ,t))))
```

Giving each logical identity in the FRP system its own clock
eliminates the need for a global clock, avoiding a potentially
troublesome source of centralization.  This is kind of like how
[Lamport timestamps](https://en.wikipedia.org/wiki/Lamport_timestamp)
are used in distributed systems.

We also need a data type that captures the value of something at a
particular point in time.  Since the cruel march of time is unceasing,
these are *ephemeral* values:

```scheme
(define-record-type <ephemeral>
  (make-ephemeral value timestamps)
  ephemeral?
  (value ephemeral-value)
  ;; Association list mapping identity -> time
  (timestamps ephemeral-timestamps))
```

Ephemerals are boxes that contain some arbitrary data with a bunch of
shipping labels slapped onto the outside explaining from whence they
came.  This is the partial information structure that our propagators
will manipulate and add to cells.

Here’s how to say “the mouse position was (1, 2) at time 3” in code:

```scheme
(define mouse-position (make-reactive-id ’mouse-position))
(make-ephemeral #(1 2) `((,mouse-position . 3)))
```

We need to perform a few operations with ephemerals:

1) Test if one ephemeral is *fresher* (more recent) than another

2) Compose the timestamps from several inputs to form an aggregate
timestamp for an output, but only if all timestamps for each distinct
identifier match (no mixing of fresh and stale values)

3) Merge two ephemerals when cell content is added

```scheme
(define (ephemeral-fresher? a b)
  (let ((b-inputs (ephemeral-timestamps b)))
    (let lp ((a-inputs (ephemeral-timestamps a)))
      (match a-inputs
        (() #t)
        (((key . a-time) . rest)
         (match (assq-ref b-inputs key)
           (#f (lp rest))
           (b-time
            (and (> a-time b-time)
                 (lp rest)))))))))

(define (merge-ephemerals old new)
  (cond
   ((nothing? old) new)
   ((nothing? new) old)
   (else (if (ephemeral-fresher? new old) new old))))

(define (merge-ephemeral-timestamps ephemerals)
  (define (adjoin-keys alist keys)
    (fold (lambda (key+value keys)
            (match key+value
              ((key . _)
               (lset-adjoin eq? keys key))))
          keys alist))
  (define (check-timestamps id)
    (let lp ((ephemerals ephemerals) (t #f))
      (match ephemerals
        (() t)
        ((($ <ephemeral> _ timestamps) . rest)
         (match (assq-ref timestamps id)
           ;; No timestamp for this id in this ephemeral.  Continue.
           (#f (lp rest t))
           (t*
            (if t
                ;; If timestamps don't match then we have a mix of
                ;; fresh and stale values, so return #f.  Otherwise,
                ;; continue.
                (and (= t t*) (lp rest t))
                ;; Initialize timestamp and continue.
                (lp rest t*))))))))
  ;; Build a set of all reactive identifiers across all ephemerals.
  (let ((ids (fold (lambda (ephemeral ids)
                     (adjoin-keys (ephemeral-timestamps ephemeral) ids))
                   '() ephemerals)))
    (let lp ((ids ids) (timestamps '()))
      (match ids
        (() timestamps)
        ((id . rest)
         ;; Check for consistent timestamps.  If they are consistent
         ;; then add it to the alist and continue.  Otherwise, return
         ;; #f.
         (let ((t (check-timestamps id)))
           (and t (lp rest (cons (cons id t) timestamps)))))))))
```

Example usage:

```scheme
(define e1 (make-ephemeral #(3 4) `((,mouse-position . 4))))
(define e2 (make-ephemeral #(1 2) `((,mouse-position . 3))))

(ephemeral-fresher? e1 e2) ;; => #t
(merge-ephemerals e1 e2) ;; => e1

(merge-ephemeral-timestamps (list e1 e2)) ;; => #f

(define (mouse-max-coordinate e)
  (match e
    (($ <ephemeral> #(x y) timestamps)
     (make-ephemeral (max x y) timestamps))))
(define e3 (mouse-max-coordinate e1))
(merge-ephemeral-timestamps (list e1 e3)) ;; => ((mouse-position . 4))
```

Now we can build a primitive propagator constructor that lifts
ordinary Scheme procedures so that they work with ephemerals:

```scheme
(define (ephemeral-wrap proc)
  (match-lambda*
    ((and ephemerals (($ <ephemeral> args) ...))
     (match (merge-ephemeral-timestamps ephemerals)
       (#f nothing)
       (timestamps (make-ephemeral (apply proc args) timestamps))))))

(define* (primitive-reactive-propagator name proc)
  (primitive-propagator name (ephemeral-wrap proc)))
```

## Reactive UI implementation

Now we need some propagators that live at the edges of our network
that know how to interact with the DOM and can do the following:

1) Sync a DOM element attribute with the value of a cell

2) Create a two-way data binding between an element’s `value` attribute
and a cell

3) Render the markup in a cell and place it into the DOM tree in the
right location

Syncing an element attribute is a directional operation and the
easiest to implement:

```scheme
(define (r:attribute input elem attr)
  (let ((attr (symbol->string attr)))
    (define (activate)
      (match (cell-strongest input)
        (($ <ephemeral> val)
         (attribute-set! elem attr (obj->string val)))
        ;; Ignore unusable values.
        (_ (values))))
    (make-propagator 'r:attribute (list input) '() activate)))
```

Two-way data binding is more involved.  First, a new data type is used
to capture the necessary information:

```scheme
(define-record-type <binding>
  (make-binding id cell default group)
  binding?
  (id binding-id)
  (cell binding-cell)
  (default binding-default)
  (group binding-group))

(define* (binding id cell #:key (default nothing) (group '()))
  (make-binding id cell default group))
```

And then a reactive propagator applies that binding to a specific DOM
element:

```scheme
(define* (r:binding binding elem)
  (match binding
    (($ <binding> id cell default group)
     (define (update new)
       (unless (nothing? new)
         (let ((timestamp (reactive-id-tick! id)))
           (add-cell-content! cell (make-ephemeral new timestamp))
           ;; Freshen timestamps for all cells in the same group.
           (for-each (lambda (other)
                       (unless (eq? other cell)
                         (match (cell-strongest other)
                           (($ <ephemeral> val)
                            (add-cell-content! other (make-ephemeral val timestamp)))
                           (_ #f))))
                     group))))
     ;; Sync the element's value with the cell's value.
     (define (activate)
       (match (cell-strongest cell)
         (($ <ephemeral> val)
          (set-value! elem (obj->string val)))
         (_ (values))))
     ;; Initialize element value with the default value.
     (update default)
     ;; Sync the cell's value with the element's value.
     (add-event-listener! elem "input"
                          (procedure->external
                           (lambda (event)
                             (update (string->obj (value elem))))))
     (make-propagator 'r:binding (list cell) '() activate))))
```

A simple method for rendering to the DOM is to replace some element
with a newly created element based on the current ephemeral value of a
cell:

```scheme
(define (r:dom input elem)
  (define (activate)
    (match (cell-strongest input)
      (($ <ephemeral> exp)
       (let ((new (sxml->dom exp)))
         (replace-with! elem new)
         (set! elem new)))
      (_ (values))))
  (make-propagator 'dom (list input) '() activate))
```

The `sxml->dom` procedure deserves some further explanation.  To
create a subtree of new elements, we have two options:

1) Use something like the
[`innerHTML`](https://developer.mozilla.org/en-US/docs/Web/API/Element/innerHTML)
element property to insert arbitrary HTML as a string and let the
browser parse and build the elements.

2) Use a Scheme data structure that we can iterate over and make the
relevant `document.createTextNode`, `document.createElement`,
etc. calls.

Option 1 might be a shortcut and would be fine for a quick prototype,
but it would mean that to generate the HTML we’d be stuck using raw
strings.  While string-based templating is commonplace, we can
certainly [do
better](https://www.more-magic.net/posts/structurally-fixing-injection-bugs.html)
in Scheme.  Option 2 is actually not too much work and we get to use
Lisp’s universal templating system,
[`quasiquote`](https://www.gnu.org/software/guile/manual/r5rs/Quasiquotation.html),
to write our markup.

Thankfully, [SXML](https://en.wikipedia.org/wiki/SXML) already exists
for this purpose.  SXML is an alternative XML syntax that uses
s-expressions.  Since Scheme uses s-expression syntax, it’s a natural
fit.

Example:

```scheme
'(article
  (h1 "SXML is neat")
  (img (@ (src "cool.jpg") (alt "cool image")))
  (p "Yeah, SXML is " (em "pretty neato!")))
```

Instead of using it to generate HTML text, we’ll instead generate a
tree of DOM elements.  Furthermore, because we’re now in full control
of how the element tree is built, we can build in support for reactive
propagators!

Check it out:

```scheme
(define (sxml->dom exp)
  (match exp
    ;; The simple case: a string representing a text node.
    ((? string? str)
     (make-text-node str))
    ((? number? num)
     (make-text-node (number->string num)))
    ;; A cell containing SXML (or nothing)
    ((? cell? cell)
     (let ((elem (cell->elem cell)))
       (r:dom cell elem)
       elem))
    ;; An element tree.  The first item is the HTML tag.
    (((? symbol? tag) . body)
     ;; Create a new element with the given tag.
     (let ((elem (make-element (symbol->string tag))))
       (define (add-children children)
         ;; Recursively call sxml->dom for each child node and
         ;; append it to elem.
         (for-each (lambda (child)
                     (append-child! elem (sxml->dom child)))
                   children))
       (match body
         ((('@ . attrs) . children)
          (for-each (lambda (attr)
                      (match attr
                        (((? symbol? name) (? string? val))
                         (attribute-set! elem
                                         (symbol->string name)
                                         val))
                        (((? symbol? name) (? number? val))
                         (attribute-set! elem
                                         (symbol->string name)
                                         (number->string val)))
                        (((? symbol? name) (? cell? cell))
                         (r:attribute cell elem name))
                        ;; The value attribute is special and can be
                        ;; used to setup a 2-way data binding.
                        (('value (? binding? binding))
                         (r:binding binding elem))))
                    attrs)
          (add-children children))
         (children (add-children children)))
       elem))))
```

Notice the calls to `r:dom`, `r:attribute`, and `r:binding`.  A cell
can be used in either the context of an element (`r:dom`) or an
attribute (`r:attribute`).  The `value` attribute gets the additional
superpower of `r:binding`.  We will make use of this when it is time
to render the color picker UI!

## Color picker implementation

Alright, I’ve spent a lot of time explaining how I built a minimal
propagator and FRP system from first principles on top of
Hoot-flavored Scheme.  Let’s *finally* write the dang color picker!

First we need some data types to represent RGB and HSV colors:

```scheme
(define-record-type <rgb-color>
  (rgb-color r g b)
  rgb-color?
  (r rgb-color-r)
  (g rgb-color-g)
  (b rgb-color-b))

(define-record-type <hsv-color>
  (hsv-color h s v)
  hsv-color?
  (h hsv-color-h)
  (s hsv-color-s)
  (v hsv-color-v))
```

And procedures to convert RGB to HSV and vice versa:

```scheme
(define (rgb->hsv rgb)
  (match rgb
    (($ <rgb-color> r g b)
     (let* ((cmax (max r g b))
            (cmin (min r g b))
            (delta (- cmax cmin)))
       (hsv-color (cond
                   ((= delta 0.0) 0.0)
                   ((= cmax r)
                    (let ((h (* 60.0 (fmod (/ (- g b) delta) 6.0))))
                      (if (< h 0.0) (+ h 360.0) h)))
                   ((= cmax g) (* 60.0 (+ (/ (- b r) delta) 2.0)))
                   ((= cmax b) (* 60.0 (+ (/ (- r g) delta) 4.0))))
                  (if (= cmax 0.0)
                      0.0
                      (/ delta cmax))
                  cmax)))))

(define (hsv->rgb hsv)
  (match hsv
    (($ <hsv-color> h s v)
     (let* ((h' (/ h 60.0))
            (c (* v s))
            (x (* c (- 1.0 (abs (- (fmod h' 2.0) 1.0)))))
            (m (- v c)))
       (define-values (r' g' b')
         (cond
          ((<= 0.0 h 60.0) (values c x 0.0))
          ((<= h 120.0) (values x c 0.0))
          ((<= h 180.0) (values 0.0 c x))
          ((<= h 240.0) (values 0.0 x c))
          ((<= h 300.0) (values x 0.0 c))
          ((<= h 360.0) (values c 0.0 x))))
       (rgb-color (+ r' m) (+ g' m) (+ b' m))))))
```

We also need some procedures to convert colors into the hexadecimal
representations we’re used to seeing:

```scheme
(define (uniform->byte x)
  (inexact->exact (round (* x 255.0))))

(define (rgb->int rgb)
  (match rgb
    (($ <rgb-color> r g b)
     (+ (* (uniform->byte r) (ash 1 16))
        (* (uniform->byte g) (ash 1 8))
        (uniform->byte b)))))

(define (rgb->hex-string rgb)
  (list->string
   (cons #\#
         (let lp ((i 0) (n (rgb->int rgb)) (out '()))
           (if (= i 6)
               out
               (lp (1+ i) (ash n -4)
                   (cons (integer->char
                          (let ((digit (logand n 15)))
                            (+ (if (< digit 10)
                                   (char->integer #\0)
                                   (- (char->integer #\a) 10))
                               digit)))
                         out)))))))

(define (rgb-hex->style hex)
  (string-append "background-color: " hex ";"))
```

Now we can lift the color API into primitive reactive propagator
constructors:

```scheme
(define-syntax-rule (define-primitive-reactive-propagator name proc)
  (define name (primitive-reactive-propagator 'name proc)))

(define-primitive-reactive-propagator r:rgb-color rgb-color)
(define-primitive-reactive-propagator r:rgb-color-r rgb-color-r)
(define-primitive-reactive-propagator r:rgb-color-g rgb-color-g)
(define-primitive-reactive-propagator r:rgb-color-b rgb-color-b)
(define-primitive-reactive-propagator r:hsv-color hsv-color)
(define-primitive-reactive-propagator r:hsv-color-h hsv-color-h)
(define-primitive-reactive-propagator r:hsv-color-s hsv-color-s)
(define-primitive-reactive-propagator r:hsv-color-v hsv-color-v)
(define-primitive-reactive-propagator r:rgb->hsv rgb->hsv)
(define-primitive-reactive-propagator r:hsv->rgb hsv->rgb)
(define-primitive-reactive-propagator r:rgb->hex-string rgb->hex-string)
(define-primitive-reactive-propagator r:rgb-hex->style rgb-hex->style)
```

From those primitive propagators, we can build the necessary
constraint propagators:

```scheme
(define (r:components<->rgb r g b rgb)
  (define (build)
    (r:rgb-color r g b rgb)
    (r:rgb-color-r rgb r)
    (r:rgb-color-g rgb g)
    (r:rgb-color-b rgb b))
  (constraint-propagator 'r:components<->rgb (list r g b rgb) build))

(define (r:components<->hsv h s v hsv)
  (define (build)
    (r:hsv-color h s v hsv)
    (r:hsv-color-h hsv h)
    (r:hsv-color-s hsv s)
    (r:hsv-color-v hsv v))
  (constraint-propagator 'r:components<->hsv (list h s v hsv) build))

(define (r:rgb<->hsv rgb hsv)
  (define (build)
    (r:rgb->hsv rgb hsv)
    (r:hsv->rgb hsv rgb))
  (constraint-propagator 'r:components<->hsv (list rgb hsv) build))
```

At long last, we are ready to define the UI! Here it is:

```scheme
(define (render exp)
  (append-child! (document-body) (sxml->dom exp)))

(define* (slider id name min max default #:optional (step "any"))
  `(div (@ (class "slider"))
        (label (@ (for ,id)) ,name)
        (input (@ (id ,id) (type "range")
                  (min ,min) (max ,max) (step ,step)
                  (value ,default)))))

(define (uslider id name default) ; [0,1] slider
  (slider id name 0 1 default))

(define-syntax-rule (with-cells (name ...) body . body*)
  (let ((name (make-cell 'name #:merge merge-ephemerals)) ...) body . body*))

(with-cells (r g b rgb h s v hsv hex style)
  (define color (make-reactive-id 'color))
  (define rgb-group (list r g b))
  (define hsv-group (list h s v))
  (r:components<->rgb r g b rgb)
  (r:components<->hsv h s v hsv)
  (r:rgb<->hsv rgb hsv)
  (r:rgb->hex-string rgb hex)
  (r:rgb-hex->style hex style)
  (render
   `(div
     (h1 "Color Picker")
     (div (@ (class "preview"))
          (div (@ (class "color-block") (style ,style)))
          (div (@ (class "hex")) ,hex))
     (fieldset
      (legend "RGB")
      ,(uslider "red" "Red"
                (binding color r #:default 1.0 #:group rgb-group))
      ,(uslider "green" "Green"
                (binding color g #:default 0.0 #:group rgb-group))
      ,(uslider "blue" "Blue"
                (binding color b #:default 1.0 #:group rgb-group)))
     (fieldset
      (legend "HSV")
      ,(slider "hue" "Hue" 0 360 (binding color h #:group hsv-group))
      ,(uslider "saturation" "Saturation" (binding color s #:group hsv-group))
      ,(uslider "value" "Value" (binding color v #:group hsv-group))))))
```

Each color channel (R, G, B, H, S, and V) has a cell which is bound to
a slider (`<input type="range">`).  All six sliders are identified as
`color`, so adjusting *any of them* increments `color`’s timestamp.
The R, G, and B sliders form one input group, and the H, S, and V
sliders form another.  By grouping the related sliders together,
whenever one of the sliders is moved, *all members* of the group will
have their ephemeral value refreshed with the latest timestamp.  This
behavior is *crucial* because otherwise the `r:components<->rgb` and
`r:components<->hsv` propagators would see that one color channel has
a fresher value than the other two and *do nothing*.  Since the
underlying propagator infrastructure does not enforce activation
order, reactive propagators *must* wait until their inputs reach a
consistent state where the timestamps for a given reactive identifier
are all the same.

With this setup, changing a slider on the RGB side will cause a new
color value to propagate over to the HSV side.  Because the
relationship is cyclical, the HSV side will then attempt to propagate
an equivalent color value back to the RGB side!  This could be bad
news, but since the current RGB value is equally fresh (same
timestamp), the propagation stops right there.  Redundant work is
minimized and an unbounded loop is avoided.

And that’s it!  Phew!

Complete source code can be found
[here](https://git.dthompson.us/software-design-for-flexibility/tree/chapter-7/propagators.scm).

## Reflections

I think the results of this prototype are promising.  I’d like to try
building some larger demos to see what new problems arise.  Since
propagation networks include cycles, they cannot be garbage collected
until there are no references to any part of the network from the
outside.  Is this acceptable?  I didn’t optimize, either.  A more
serious implementation would want to do things like use `case-lambda`
for all n-ary procedures to avoid consing an argument list in the
common cases of 1, 2, 3, etc. arguments.  There is also a need for a
more pleasing domain-specific language, using Scheme’s macro system,
for describing FRP graphs.

Alexey Radul’s dissertation was published in 2009.  Has anyone made a
FRP system based on propagators since then that’s used in real
software?  I don’t know of anything but it’s a big information
superhighway out there.

I wish I had read Alexey Radul's disseration 10 years ago when I was
first exploring FRP.  It would have saved me a lot of time spent
running into problems that have already been solved that I was not
equipped to solve on my own.  I have even talked to Gerald Sussman (a
key figure in propagator research) *in person* about the propagator
model.  That conversation was focused on AI, though, and I didn’t
realize that propagators could also be used for FRP.  It wasn’t until
more recently that friend and colleague [Christine
Lemmer-Webber](https://dustycloud.org/), who was present for the
aforementioned conversation with Sussman, told me about it.  There are
so many interesting things to learn out there, but I am also so tired.
Better late than never, I guess!

Anyway, if you made it this far then I hope you have enjoyed reading
about propagators and FRP.  ’Til next time!