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C2HS example: To save other people frustration

Friday, July 10th, 2009

C2Hs is a wonderful little tool.  It generates a lot of the boilerplate code for binding C libraries to Haskell and saves wrists and frustration. However, the documentation I’ve manage to find on it has at times been buggy and incomplete.  I can’t guarantee the following tutorial is idiomatic c2hs — actually I can guarantee that it isn’t — but I can guarantee that it works.

Today I bound libshapefile to Haskell using c2hs.  The following code is not at all Haskell-ish, but I find that it is best to write a true-to-C interface to C code first and then write Haskellish interfaces on top of that.  The following bindings are generated, along with getters and setters for the items in the SHPObject structure:

open :: String -> String -> IO (SHPHandle)
getInfo :: SHPHandle -> IO (Int, Int, [Double], [Double])
readObject :: SHPHandle -> Int -> IO (SHPObject)
close :: SHPHandle -> IO ()
create :: String -> Int -> IO (SHPHandle)
createSimpleObject :: Int -> Int -> [Double] -> [Double] -> [Double] -> IO (SHPObject)
createObject :: Int -> Int -> Int -> [Int] -> [Int] -> Int -> [Double] -> [Double] -> [Double] -> [Double] -> IO (SHPObject)
computeExtents :: SHPObject -> IO ()
writeObject :: SHPHandle -> Int -> SHPObject -> IO (Int)
destroyObject :: SHPObject -> IO ()
rewindObject :: SHPHandle -> SHPObject -> IO (Int)

I’m going to intersperse the c2hs code that I wrote with some explanatory text in hopes that someone finds it helpful.  If the authors of c2hs or someone who is more of an expert in this than me has comments on the code, I’ll be happy to include them in the tutorial.

{-# LANGUAGE ForeignFunctionInterface #-}
{-# LANGUAGE TypeSynonymInstances #-}
module Gis.Shapefile.Internal where

#include <shapefil.h>

import C2HS
import Foreign.Ptr
import System.IO.Unsafe
import Foreign.C
import Control.Monad
import Control.Applicative ((<$>))

The library that you’re binding to needs to be enclosed in a {#context #} tag first.  There are other parameters that can go into context, but these weren’t needed for what I was doing, and don’t seem to be needed super-commonly.  The C2HS import can be found in $HOME/.cabal/share if you’ve installed c2hs with Cabal.  Noe that the #include is left bare up top.  That’s correct even though it’s invalid Haskell code.

For many enumerations, c2hs provides the handy-dandy {#enum #} construct, but for #define-d constants, we have to wrap our own.  I could make these instances of Enum, but it seems like too much work for an internal interface.

{#context lib="shapefile" #}

-- these are defined constants, not an enum, so we can't just use the #enum hook
shptNull = 0
shptPoint = 1
shptArc = 3
shptPolygon = 5
shptMultipoint = 8
shptPointZ = 11
shptArcZ = 13
shptPolygonZ = 15
shptMultipointZ = 18
shptPointM = 21
shptArcM = 23
shptPolygonM = 25
shptMultipointM = 28
shptMultiPatch = 35

data SHPType =
  | TPoint
  | TArc
  | TPolygon
  | TMultipoint
  | TPointZ
  | TArcZ
  | TPolygonZ
  | TMultipointZ
  | TPointM
  | TArcM
  | TPolygonM
  | TMultipointM
  | TMultipatch

shpTypeToIntegral TNull = 0
shpTypeToIntegral TPoint= 1
shpTypeToIntegral TArc=3
shpTypeToIntegral TPolygon=5
shpTypeToIntegral TMultipoint=8
shpTypeToIntegral TPointZ=11
shpTypeToIntegral TArcZ=13
shpTypeToIntegral TPolygonZ=15
shpTypeToIntegral TMultipointZ=18
shpTypeToIntegral TPointM=21
shpTypeToIntegral TArcM=23
shpTypeToIntegral TPolygonM=25
shpTypeToIntegral TMultipointM=28
shpTypeToIntegral TMultipatch=35

integralToSHPType :: (Integral a) => a -> SHPType
integralToSHPType 0=TNull
integralToSHPType 1=TPoint
integralToSHPType 3=TArc
integralToSHPType 5=TPolygon
integralToSHPType 8=TMultipoint
integralToSHPType 11=TPointZ
integralToSHPType 13=TArcZ
integralToSHPType 15=TPolygonZ
integralToSHPType 18=TMultipointZ
integralToSHPType 21=TPointM
integralToSHPType 23=TArcM
integralToSHPType 25=TPolygonM
integralToSHPType 28=TMultipointM
integralToSHPType 35=TMultipatch

Note that I declare opaque types two different ways below.  I couldn’t get c2hs to understand the SHPHandle declaration, because it was itself an opaque type in C.  Well, because of something anyway.  Anyway, because of that, I wrote out the opaque type by hand.  Yes it looks recursive, but GHC handles it just fine, and if you use the -XEmptyDataDecls extension this is what the code actually reduces to.

newtype SHPHandle = SHPHandle (Ptr (SHPHandle))
{#pointer *SHPObject newtype #}

Now to define a bunch of getters and setters for the SHPObject type.  The c2hs documentation doesn’t seem to require that you unwrap the opaque type yourself into the Ptr type, but I had to.  It compiles fine if you don’t, but then you have to unwrap it in your other code, and I like to keep all my foreign data in one module where possible.

getSHPType (SHPObject x) = integralToSHPType <$> {#get SHPObject->nSHPType #} x
getShapeId (SHPObject x) = {#get SHPObject->nShapeId #} x
getParts (SHPObject x) = {#get SHPObject->nParts #} x
getPartStart (SHPObject x) = {#get SHPObject->panPartStart #} x
getPartType (SHPObject x) = {#get SHPObject->panPartType #} x
getVertices (SHPObject x) = {#get SHPObject->nVertices #} x
getX (SHPObject x) = {#get SHPObject->padfX #} x
getY (SHPObject x) = {#get SHPObject->padfY #} x
getZ (SHPObject x) = {#get SHPObject->padfZ #} x
getM (SHPObject x) = {#get SHPObject->padfM #} x
getXMin (SHPObject x) = {#get SHPObject->dfXMin #} x
getYMin (SHPObject x) = {#get SHPObject->dfYMin #} x
getZMin (SHPObject x) = {#get SHPObject->dfZMin #} x
getMMin (SHPObject x) = {#get SHPObject->dfMMin #} x
getXMax (SHPObject x) = {#get SHPObject->dfXMax #} x
getYMax (SHPObject x) = {#get SHPObject->dfYMax #} x
getZMax (SHPObject x) = {#get SHPObject->dfZMax #} x
getMMax (SHPObject x) = {#get SHPObject->dfMMax #} x

setSHPType (SHPObject p) v = {#set SHPObject->nSHPType #} p (shpTypeToIntegral v)
setShapeId (SHPObject p) = {#set SHPObject->nShapeId #} p
setParts (SHPObject p) = {#set SHPObject->nParts #} p

In the next line we see some Haskell code being obviously mixed with c2hs markup.  This is standard practice and it’s safer than it seems like it might be.  c2hs parenthesizes most things and seems to handle Haskell syntax perfectly.  Here we have to marshal an list of doubles to a pointer.  The newArray is correct here, because the array needs to live on the C heap and not be garbage collected.  The new- as opposed to alloca- means that it is our responsibility to deallocate it.  Fortunately, destroyObject (bound later) releases that memory for us.

setPartStart (SHPObject p) v = newArray v >>= {#set SHPObject->panPartStart #} p
setPartType (SHPObject p) v = newArray v >>= {#set SHPObject->panPartType #} p
setVertices (SHPObject p) = {#set SHPObject->nVertices #} p
setX (SHPObject p) v = newArray v >>= {#set SHPObject->padfX #} p
setY (SHPObject p) v = newArray v >>= {#set SHPObject->padfY #} p
setZ (SHPObject p) v = newArray v >>= {#set SHPObject->padfZ #} p
setM (SHPObject p) v = newArray v >>= {#set SHPObject->padfM #} p
setXMin (SHPObject p) = {#set SHPObject->dfXMin #} p
setYMin (SHPObject p) = {#set SHPObject->dfYMin #} p
setZMin (SHPObject p) = {#set SHPObject->dfZMin #} p
setMMin (SHPObject p) = {#set SHPObject->dfMMin #} p
setXMax (SHPObject p) = {#set SHPObject->dfXMax #} p
setYMax (SHPObject p) = {#set SHPObject->dfYMax #} p
setZMax (SHPObject p) = {#set SHPObject->dfZMax #} p
setMMax (SHPObject p) = {#set SHPObject->dfMMax #} p

Now for a few utility functions: to/fromSHPHandle, which marshals and unmarshals a bare pointer, allocate/peek4, and peekInt.  We’ll use these soon in our {#fun #} bindings, but it appears from working with c2hs that you can’t curry arguments into marshallers or use lambda expressions; I’m not sure if this is me or if this is c2hs, but the solution of writing utility functions works.

toSHPHandle = SHPHandle . castPtr
fromSHPHandle (SHPHandle x) = castPtr x

arrDouble x = ((newArray . map realToFrac $ x) >>=)
arrInt x = ((newArray . map fromIntegral $ x) >>=)

allocate4 = allocaArray 4 

peek4 d = do
    lst <- (peekArray 4 d :: IO [CDouble])
    return . map cFloatConv $ lst

peekInt i = peek i >>= return . cIntConv    

{#fun SHPOpen as open
    { `String'
    , `String'
    } -> `SHPHandle' toSHPHandle #}

Right.  So I’ll explain the two marshallers on either side of the paragraph here.  Argument and return types are outlined in c2hs using a backtick followed by the type followed by a forward tick.  That is not a formatting mistake.  To the left of the arrow are the arguments, and to the right of the arrow is the return type. On the left side of each type can be an “in-marshaller” and on the right, an “out-marshaller”, which translates between Haskell types and C types. There are also two signifiers that can come at the end of the marshaller: - and *.  the ‘-’ on the in-marshaller signifies that the argument is to be handled entirely within the function def and not passed in as a parameter.  The * signifier says that the result will be within the IO monad and to handle it specially.  A good rule seems to be that if you’re working with pointers, you’ll need it.  Out marshallers in the arg list imply that the parameters are “out” parameters, meant to be returned as part of the function return.  c2hs generally handles this as a tuple.  So the following function will have type SHPHandle -> IO (Int,Int,[Double],[Double]).

-- void SHPGetInfo(SHPHandle, int*, int*, double*, double*)
{#fun SHPGetInfo as getInfo
    { fromSHPHandle `SHPHandle'
    , alloca- `Int' peekInt*
    , alloca- `Int' peekInt*
    , allocate4- `[Double]' peek4*
    , allocate4- `[Double]' peek4*
    } -> `()' #}

Note the use of “id” as a marshaller below.  It seemed like c2hs should have a default marshaller for opaque types, but compiling it told me otherwise, so I added it and it worked.  Experimentation, experimentation!

{#fun SHPReadObject as readObject
    { fromSHPHandle `SHPHandle'
    , `Int'
    } -> `SHPObject' id #}

{#fun SHPClose as close
    { fromSHPHandle `SHPHandle' } -> `()' #}

{#fun SHPCreate as create
    { `String'
    , `Int'
    } -> `SHPHandle' toSHPHandle  #}

{#fun SHPCreateSimpleObject as createSimpleObject
    { `Int'
    , `Int'
    , arrDouble* `[Double]'
    , arrDouble* `[Double]'
    , arrDouble* `[Double]'
    } -> `SHPObject' id #}

{#fun SHPCreateObject as createObject
    { `Int'
    , `Int'
    , `Int'
    , arrInt* `[Int]'
    , arrInt* `[Int]'
    , `Int'
    , arrDouble* `[Double]'
    , arrDouble* `[Double]'
    , arrDouble* `[Double]'
    , arrDouble* `[Double]'
    } -> `SHPObject' id #}

{#fun SHPComputeExtents as computeExtents
    { id `SHPObject' } -> `()' #}

{#fun SHPWriteObject as writeObject
    { fromSHPHandle `SHPHandle'
    , `Int'
    , id `SHPObject' } -> `Int' #}

{#fun SHPDestroyObject as destroyObject
    { id `SHPObject' } -> `()' #}

{#fun SHPRewindObject as rewindObject
    { fromSHPHandle `SHPHandle'
    , id `SHPObject' } -> `Int'  #}

This compiled and ran on my machine. Note that you do have to have libshp and shapfil.h installed on your machine to compile this example.

Anyway, I hope this helps someone.  My first c2hs wrapper was a heck of an experience, but the code seems easy to maintain and the results of it not much different than what I would have written by hand except that it’s incredibly shorter.


A quick buster example

Thursday, July 9th, 2009

Some people have asked of late for examples of Buster.  What follows is the (annotated) code that I used to generate the FDL video of the particles swarming around the lambda in the last post:

module Main where

import qualified Data.Array.IO as A
import Graphics.Rendering.Hieroglyph
import Graphics.Rendering.Hieroglyph.OpenGL
import App.EventBus
import IsoFDL
import Random
import Data.Colour
import Data.Colour.Names
import System.Random
import Control.Concurrent

dim = 480
minSeedCoord = fromIntegral $ quot dim 2 - 80
maxSeedCoord = fromIntegral $ quot dim 2 + 80
minIdealDist = (-100)
maxIdealDist = 100
npts = 100
basestrength = 4 

randomRIOs :: Double -> Double -> IO [Double]
randomRIOs a b = do
    g <- getStdGen
    return $ randomRs (a, b) g

This is the only behaviour we define, adjustLayout, and it runs a single layout iteration over the data.  We take the old locales of points, compute an FDL iteration, take the new locales of points, and produce the event, which is “Visible” (this is important, as Hieroglyph looks for all items in the Visible group, assumes they are o the class Visual, and attempts to render them.  We also produce a re-rendering request, and return the list of rerender and our layout as a future to be used at the appropriate time by the buster framework.  One final thing to note about our event is that it should live for 10 iterations.  Often geometry is persistent, but this was used to give the “contrail” effect to the particles as they move, effectively layering slightly transparent geometry over the top of older slightly transparent geometry to create the effect.

The Hieroglyph code is also fairly straightforward.  We apply linewidth and line color combinators (using the colours in Russell O’Connor’s excellent Data.Colour library) to a list of paths which are slightly exaggerated (that’s the +/-(x1-x0) and +/-(y1-y0) operations on the Points in the path) vectors along the path that was most recently traveled.

adjustLayout :: [Attractor] -> (Arr (Int,Int) Double) -> (Arr Int Double) -> (Arr Int Double) -> Behaviour [EData HieroglyphGLRuntime]
adjustLayout poles dists xs ys b = do
    pts <- externalizeLayout xs ys
    layoutIteration basestrength poles xs ys dists
    pts' <- externalizeLayout xs ys
    event' <- produce "Visible" "" (show . head $ pts') (Iterations 10)
                . (:[])
                . EOther
                . Geometry
                . linewidth 1
                . strokecolour (dissolve 0.3 (opaque darkblue))
                . name "points"
                $ zipWith (\(x0,y0) (x1,y1) -> path{ begin=Point (x0-(x1-x0)) (y0-(y1-y0)), segments=[Line $ Point (x1+(x1-x0)) (y1+(y1-y0))] }) pts pts'
    rerender <- produce "Hieroglyph" "" "Rerender" once []
    future b . return $ [event',rerender]

The next function, behaviour would normally be pure, but because we have some data that is internal to the adjustLayout behaviour, it’s got a do.  One of the things that I will change in the near future about Hieroglyph and OpenGL is to make the events it can respond to more polymorphic.  Currently I require that to use Hieroglyph in OpenGL that the event data type is [EData HieroglyphGLRuntime].  That’s pretty inane if I do say so myself, and in the future, there will be a constraint more along the lines of (HieroglyphInteractiveEventData a) => a rather than a static type to be determined at compile time.  However, with the current state of things, this is our behaviour, and it’s still pretty straightforward.  renderBehaviour depends on the result of adjustLayout.  That’s it.  The <~< establishes the dependency relationship and buster takes care of the rest.  The “poles” are a series of attractors (defined in IsoFDL.hs), which are static fields attached to a fixed point in space, attracting or repelling depending on the constant given them.

behaviour = do
    randomDistances <- randomRIOs minIdealDist maxIdealDist >>= A.newListArray ((0,0),(npts-1,npts-1)) . take (npts^2) . drop (2*npts)
    randomXs <- randomRIOs minSeedCoord maxSeedCoord >>= A.newListArray (0,npts-1) . take npts
    randomYs <- randomRIOs minSeedCoord maxSeedCoord >>= A.newListArray (0,npts-1) . take npts . drop npts

    poles <- mapM (\(x,y) -> compileAttractor . Attractor x y . take npts $ repeat 150)
        [(100,(440-100)), (170,(440-160)), (220,(440-220)), (160,(440-280)),
         (100,(440-320)), (280,(440-280)), (350,(440-320)), (380,(440-300))]

    return $ renderBehaviour <~< adjustLayout poles randomDistances randomXs randomYs

main = behaviour >>= boilerplateOpenGLMain  [initializeBus "Testing Force Directed layouts" dim dim]

Finally, our main function is incredibly simple.  We simply bind the behaviour to the boilerplate buster OpenGL main.  The other argument to boilerplateOpenGLMain is a list of “widgets” to be bound to the bus at the beginning of the application.

An ugly force-directed layout implementation

Thursday, July 9th, 2009

Updated 7.9.2009: I’ve added another video showing the effect of attractors on the layout

Unalbe to show flash video

I’ve been working on visualizations silently of late.  Today, though, I threw together a quick FDL (force-directed layout) algorithm based on POV-Ray’s static-field isosurface or “blob” equation, which is (sorry, but I’m using plaintext)

density = strength * (1 - (distance / radius)^2)^2

The documented equation has a problem, in that if distance > radius, the density approaches infinity fast, so I change it to be:

density = strength * (1 - (min(distance, radius) / radius)^2)^2

That solves the infinity problem, and now all there is to the algorithm is to apply it:

adjust :: Double -> Double -> Double -> Double -> Double -> Double -> (Double,Double)
adjust sx sy ss sr dx dy = if isNaN vx || isNaN vy then (dx,dy) else (dx+force * vx, dy+force * vy)
    where force = density ss sr dist
          dist = sqrt ((dx-sx)^^2 + (dy-sy)^^2)
          vx = (dx-sx) / dist
          vy = (dy-sy) / dist

Note that it is possible for dist to be NaN for a single point-to-point interaction, so we correct for that.  Basically, this function is a single adjustment from point set to a point set that fits our force constraint a little better.  In the following code, we take a static vector, <sx,sy> and a movable vector <dx,dy> and take the distance.  We apply the density function to the distance using ss as our strength and sr as the radius or distance at which the density function falls off to zero.  We return the translated point P’ which is translated along the normal vector <vx,vy> by the variable “force”. Simple and straightforward.

Now I know… by my title, you’re thinking, “What’s ugly about that?” Well, when I code fast, I use shortcuts.  The following code, which uses this adjustment function as a kernel for the actual force-directed layout algorithm is imperative.  I’ve tried FDL and MDS (multi-dimensional scaling) algorithms before using lists and tries and always ran into enough overhead that it significantly diminished the number of points that are viable to run a single iteration in real-time.  I’m sure there’s a way to do it, but I solicit the community’s help in suggesting a faster way than this.  Yes, I could have used STUArray and avoided IO, but that doesn’t really eliminate the imperative nature of things.

layoutIteration :: Double -> [Attractor] -> (Arr Int Double) -> (Arr Int Double) -> (Arr (Int,Int) Double) -> IO ()
layoutIteration alpha attractors xsArray ysArray radii = do
    bounds <- A.getBounds radii
    forM_ (A.range bounds) $ \(r,c) -> when (r /= c) $ do
        radius <- radii -| (r,c)
        x0 <- xsArray -| c
        y0 <- ysArray -| c
        x1 <- xsArray -| r
        y1 <- ysArray -| r
        let (x',y') = adjust x0 y0 alpha radius x1 y1

        xsArray =| r $ x'
        ysArray =| r $ y'

    forM_ attractors $ \(CAttractor ax ay arArray) -> do
        bounds <- A.getBounds arArray
        forM_ (A.range bounds) $ \ix -> do
            x0 <- xsArray -| ix
            y0 <- ysArray -| ix
            ar <- arArray -| ix
            let (x',y') = adjust ax ay (-alpha) ar x0 y0
            xsArray =| ix $ x'
            ysArray =| ix $ y'

As you can see, this is pretty ugly, even with the aliased (-|) readArray and (=|) writeArray binary operators cleaning things up a bit.  It’s straightforward, and random access to the points is O(1).  There are algorithms for which this is more important than this one, and I can see a list comprehension version coalescing even as I write this, but this kind of code is O(N^2). The constants are sometimes (depending on the adjustment function) quite high, so what I really need is the fastest version possible, probably taking a lot of advantage of fusion in the compiler.  The other problem, which is more subtle,  is that points are updated multiple times in one pass and we always want to use the latest point.  Any functional solution would have to take this into account (and storing all the update deltas and taking their centroid won’t work, because each update wouldn’t then be based on the point’s most current position).

This one seems to handle up to about 250 points quite well, which is decent for an FDL algorithm (keep in mind, that’s 10000 distance calculations per iteration, all of which have to happen before sending any points to the video card and other such matters — doing this 30-50 times a second along with other functionality is harder than it sounds).  Handling more than that would require one of two modifications to the algorithm: either select a random N moves to make per iteration or subdivide the point set into sqrt(N) size blocks, perform the FDL on each of those, and then on their centroid, translating them all by the delta.

So essentially, there were two purposes to this exercise.  One was coding an FDL algorithm in Haskell, but the other was trying out an FDL I hadn’t seen tried before.  This one in particular does not result in the usual circular pattern arising and also handles negative forces as easily as positive forces with a simple, low-overhead function. Oh, and of course the other was showing that I could get realtime performance of such an algorithm (that previously people insisted had to be done in C to be done properly) with a relatively naive implementation.

Force directed layout algorithms are useful for tasks like graph layouts and other dense datasets as well as for performing a sort of “visual” clustering.   Here’s an example showing the starting state, with all points clustered in a square in the middle of the screen:


And here is one after the layout has run for a few hundred iterations:

screenshot-testing-force-directed-layoutsThe evenness reflected here is because the ideal distances I selected are random.  If there is a system to the distances, there will be a system to the layout.  The neat thing about this algorithm is that given any set of input points and distances, the result of the algorithm at any point is deterministic. Follows is the code that I used to generate the above pictures and a short movie of the forces in action.

This is the code for the algorithm and a test program in Hieroglyph/Buster

A video of the algorithm in action

Unalbe to show flash video

Hieroglyph Haddock docs

Wednesday, February 4th, 2009

Since Hackage is building with GHC 6.10 now and we’re still waiting on a final release of Gtk for 6.10, it’s not generating the Haddock docs for Hieroglyph.  I’m posting them here for the time being. That’s all!

A random note on programming with Gtk2Hs.

Monday, January 19th, 2009

Gtk has quite a few conventions that are entirely unintuitive and make for some very interesting bugs in your app if you’re not aware of them.  For instance, calling a redraw function inside an event handler is wrong.  You wouldn’t know that, because no-one really says it anywhere, but let’s say you have a Gtk window that holds an OpenGL scene or a Cairo scene.  If you call your render function at the end of a Gtk mouse button handler, then you stand a small chance, especially if your app is multithreaded of the whole thing coming crashing down with an unintelligible error.  Instead, you should so something like this:

mouseMotionHandler :: UIState a => MVar a -> Gtk.Event -> IO Bool
mouseMotionHandler uistate_mvar event = do
    state <- takeMVar uistate_mvar
    putMVar uistate_mvar
        $ setMousePosition (Point (Gtk.eventX event) (Gtk.eventY event)) state
    dwin <- Gtk.widgetGetDrawWindow . fromJust . drawing $ state
    Gtk.drawWindowGetPointer dwin
    Gtk.drawWindowInvalidateRect dwin (Gtk.Rectangle 0 0 (round . sizeX $ state) (round . sizeY $ state)) False
    return False

The important code here is in the next to last line, Gtk.drawWindowInvalidateRect.  This code tells the Gtk main loop that the window area in dwin is dirty and that an “expose” event needs to occur.  The sizeX and sizeY of the rectangle should be the width and height of the window.  The dwin is obtained by calling Gtk.widgetGetDrawWindow on any widget with a drawing window (this is a Window, a DrawingArea, or a GLDrawingArea). Then your expose handler will look like this:

Gtk.onExpose canvas $ evt -> renderer state scene >> return True

where renderer is the name of the function that actually draws your scene on the drawing window.  Once you’ve setup your event handlers like this, you can switch to a multithreaded app, and at least your event handlers won’t cause your program to die.

Beautiful Code, Compelling Evidence

Friday, January 16th, 2009

Beautiful Code, Compelling Evidence. Purely Functional Information Visualization

This is a tutorial I presented at ICFP last year on visualization and graphics in Haskell.  It talks at length about why functional programming is a good idea for graphics, and then presents the basic tools that exist today.

I’m continuing that work right now with an interest in developing declarative systems that provide a graphics as a transformational layer from pure data.  Non basic tools that don’t yet exist today.  In other words, you don’t draw graphics based on data — the graphics are a function on the data and user interactions on the data.  Hopefully, this will lead to better, easier ways of building visualizations and will also lead to code that is directly understandable and predictable in terms of the data.