This page gives you access to lots of reusable pieces of Standard ML code. It is all distributed under GNU Lesser General Public License (LGPL) - except for one illustrative example which is under GPL. The code is released as small separate projects, but in a way that should make it easy to install several projects and use them together.
This document is a available in the following different file formats:
The following projects are available for download by clicking on the links:
If you want to use several projects together at the same time, just unpack them all in the same directory. An appropriate directory structure is used for this. Most, if not all, of the projects should work with SML/NJ, MLton and MLKit. It has also been used with MLWorks once upon a time, but that is not supported any more. The projects will be documented in the following.
This project gives a compatibility layer between various Standard ML compilers. Including this module in your own project will give a more uniform set of Standard ML Basis library modules. In particular it should give you the following modules:
It is implemented for the following ML compilers:
However there is no guarantee that I have had a use for all the listed modules for all the supported compilers - but then at least you should be able to add those modules quite easily by looking at the source code.
This project is an attempt to provide a general framework for handling errors in Standard ML. It provides both a simple signature for reporting errors and a framework of general exceptions.
The ERROR_REPORTER signature looks like the following:
signature ERROR_REPORTER = sig val bug : string -> unit val error : string -> unit val warning : string -> unit end
There is a structure called ErrorStdOut which implements this signature. In this structure the errors will just be shown as error messages on the standard output device. Further more the functions bug end error will also raise exceptions after the errormessage has been shown.
This structure attempts to be a general exception framework. The framework is probably best suited for a user interface toolkit where one can implement a general system for displaying errors as dialog boxes - but I'm not really sure if this is a good framework or not. The signature looks like this:
signature EXCN = sig datatype t = Generic | Bug | NotImplemented(* The excpetion data should be treated as being opaque - i.e.: DO NOT TOUCH! *) exception EGeneric of (t * string * string) list
val eRaise : (t * string * string) -> 'a val eRaiseBug : string -> 'a val eRaiseGeneric : string * string -> 'a val eRaiseNotImplemented : string -> 'a val reRaise : exn -> (t * string * string) -> 'a
val output : (string -> unit) -> exn -> unit
val checkedExecOutF : (string -> unit) -> ('a -> 'b) -> 'a -> 'b -> 'b val checkedExec : ('a -> 'b) -> 'a -> 'b -> 'b end
This framework classifies exceptions as either generic exceptions which could be anything, bugs which is errors that should never occur and an exception for when some part of a program is not implemented. It is intended that one does not raise exceptions other than through the supplied functions. All exceptions have 3 data elements:
The EGeneric exception (that you're not supposed to raise yourself...) contains a list of exceptions. This is intended to be a stack of raised exceptions when the exception reaches the point where it will be shown to the user. The stack will be built when using the reRaise function which can re-raise an exception caught at a lower level to give a higher level exception. The function eRaise is the general function for raising an exception. eRaiseBug, eRaiseGeneric and eRaiseNotImplemented are the specific versions. The functions eRaiseBug and eRaiseNotImplemented will give empty descriptive categories.
The function checkedExec is for automatically handling exceptions in this framework. It takes as arguments a function to be executed - of type 'a -> 'b, the argument to be supplied to the function and a default value to return in case an exception is raised. This function will display errors on the standard output device. The function checkedExecOutF is a more general version of this function. It takes as the first argument a function for displaying errors. So checkedExec is equivalent to checkedExecOutF print.
The function called output takes a function for displaying errors and an exception raised with this framework. The function will then show an appropriate error message. If the given exception is not the EGeneric exception used in this framework, then an error will be displayed indicating this.
The Arith project is a set of very useful arithmetic signatures. It is inspired by some of the ARITH signatures found in Larry Paulson's book ML for the Working Programmer. The following is some documentation for the signatures:
The ADD_ARITH signature is a basic set of functions for adding, subtracting negating and comparing values of some given type t. The signature is listed here:
signature ADD_ARITH = (* Addition arithmetic *) sig type t(* When zeroValue is added to t, the result is t *) val zeroValue : t val add : (t * t) -> t val subtract : (t * t) -> t
(* Inverse number for add *) val negate : t -> t
(* Comparison *) val equal : (t * t) -> bool end
The value called zeroValue is called zeroValue to be consistent with the value called unitValue in the MULT_ARITH signature.
signature SCALE_ARITH = (* Scaling arithmetic *) sig type t type scalarval scale : (t * scalar) -> t val inverseScale : (t * scalar) -> t end
The function scale will scale a value of type t with a value of type scalar. inverseScale will scale a value of type t by the multiplicative inverse of a value of type scalar. For instance if the type t is a coordinate vector of real valued coordinates and scalar have type real, the function scale would scale a vector by a real number and the function inverseScale would scale a vector by the reciprocal value of a real number - i.e. vector * 1 / scalar. You should look at the signature MULT_ARITH to see why the functions are not called multiply and divide.
signature MULT_ARITH = (* Multiplication arithmetic *) sig type t(* It's called unitValue and not unit to avoid conflicts with the SML type unit...*) (* When unitValue is multiplied by t, the result is t *) val unitValue : t val multiply : (t * t) -> t val divide : (t * t) -> t
(* Inverse number for multiply *) val invert : t -> t end
As can be seen from the comment in the code, it is not possible to call a value unit in Standard ML, because this is a builtin type.
This signature is the union of the ADD_ARITH and the SCALE_ARITH signatures. So it simply contains everthing that these two signatures contain.
This signature is the union of the ADD_ARITH and the MULT_ARITH signatures. So it simply contains everthing that these two signatures contain.
This signature is the union of the ADD_ARITH, SCALE_ARITH and the MULT_ARITH signatures. So it simply contains everthing that these 3 signatures contain.
The geometry project contains some basic geometry stuff. In particular it contains the following modules:
Each structure will be documented separately in the following.
The Point3D structure implements 3D points with real valued coordinates. It implements the ADD_SCALE_ARITH signature and 3 additional functions for projecting points to 2D along the principal axes. The signature looks like this:
signature POINT3D = (* 3D Point with addition arithmetics *) sig type coord (* This has type real in the Point3D structure *) include ADD_SCALE_ARITH where type t = {x : coord, y : coord, z : coord} and type scalar = real type point2d = {x : coord, y : coord}val xyCoordsAs2D : t -> point2d val xzCoordsAs2D : t -> point2d val yzCoordsAs2D : t -> point2d end
The Vector3D structure implements 3D vectors with real valued coordinates. It implements the ADD_SCALE_ARITH signature and some additional functions. The signature looks like this:
signature VECTOR3D = (* 3D Vector with addition arithmetics *) sig type coord (* This has type real in the Vector3D structure *) include ADD_SCALE_ARITH where type t = {x : coord, y : coord, z : coord} and type scalar = real type vertex = tval fromVertices : (vertex * vertex) -> t val dotProduct : (t * t) -> real val crossProduct : (t * t) -> t val length : t -> real val normalize : t -> t end
The function fromVertices creates a vector between 2 points in space, with the first point being the start of the vector and the second point being the end of the vector. dotProduct computes the dot product between 2 vectors and crossProduct computes the crossproduct between 2 vectors. length returns the length of a vector and normalize normalizes a given vector into a vector with the same direction and length 1.
The Point2D structure implements 2D points with real valued coordinates. It implements the ADD_SCALE_ARITH signature and an additional function called move. The signature looks like this:
signature POINT2D = sig type coord (* This has type real in the Point2D structure *) include ADD_SCALE_ARITH where type t = {x : coord, y : coord} and type scalar = real(* This is really the same as add, but it takes a pair of coords as the vector to move. So it's just for convenience. *) val move : (t * (coord * coord)) -> t end
The Point2D structure implements 2D points with integer valued coordinates. It implements the ADD_SCALE_ARITH signature, the move function from the POINT2D signature and and 2 additional functions for converting to and from points with real valued coordinats. The signature looks like this:
signature IPOINT2D = sig include POINT2D where type coord = int(* For converting to and from real-valued points. *) val toPoint2d : t -> Point2D.t val fromPoint2d : Point2D.t -> t end
The IRect2D structure implements 2D rectangles with integer valued coordinates. The signature looks like this:
signature IRECT2D = sig (* Public types and values *) type t = {x : int, y : int, w : int, h : int}val zeroValue : t
(* Public functions *) (* FIXME: Rename to hasNoArea? or isEmpty? or isDegenerate? *) val isZero : t -> bool
val equal : t * t -> bool
(* If the rectangle gets zero extent - zeroValue is returned *) val inset : t * int -> t val move : t * (int * int) -> t
(* A point on the boudary of a rectangle is also considered to be contained in the rectangle. *) val containsPoint : t * IPoint2D.t -> bool
(* Returns zeroValue for empty intersections i.e. intersections with zero or less height or width. *) val intersect : t * t -> t val union : t * t -> t end
The value zeroValue is a rectangle with (0, 0) as the corner vertex and width and hight 0. The function isZero checks if a rectangle does not have any extent or a negative extent (Notice: This function might be renamed in the future). inset shrinks the rectangle from all sides by some integer value. move will move the rectangle to another position. containsPoint returns true if the given point is inside or on the boundary of the given rectangle. intersect and union creates the intersection or the union, respectively, of two rectangles.
The structure ILine2D is very incomplete and only contains the a type for 2D lines with integer coordinates. This type is:
type t = {x0 : int, y0 : int, x1 : int, y1 : int}
The structure ISize2D is very incomplete and only contains the a type for 2D sizes with integer coordinates. This type is:
type t = {w : int, h : int}
This project implements 4x4 matrices. It is mostly intended for doing coordinate transformations on 3D points. The structure implements the ADD_SCALE_MULT_ARITH signature as well as some other stuff. The signature looks like this:
signature MATRIX_4X4 = sig include ADD_SCALE_MULT_ARITH where type scalar = real type tuples = Matrix4x4Type.tval fromTuples : tuples -> t val toTuples : t -> tuples val determinant : t -> real val transpose : t -> t val adjoint : t -> t structure Const : MATRIX_4X4_COMMON where type angle = real structure Trans : MATRIX_4X4_TRANSFORMATION where type t = t
sharing type t = Const.t end
It should be emphasized that matrix multiplication is not symmetric. So the result of multiplying two matrices with the function multiply will in general depend on which matrix is the left operand and which is the right operand. The Matrix4x4 structure assumes the mathematical standard representation of matrices - meaning that you will most likely want to multiply matrices on to the left of another matrix. Please read the section below called About Matrices for futher details on these issues.
The type tuples is a 4-tuple of 4-tuples of reals where the outer tuple is a tuple of rows and each of the inner tuples is a row. The functions fromTuples and toTuples converts to and from this representation. This is also the representation used internally in the implementation - but that could easily change without changing the signature.
There are also 2 nested structures. The structure Const is a collection of functions for creating some handy 4x4 matrices. Trans is for transforming 3D points and 3D vectors. Finally there are also 3 functions which are particular to matrices. These are determinant, transpose and adjoint which work as you would expect.
The structure Const has the following signature:
signature MATRIX_4X4_COMMON = sig type t (* This is the type of a matrix in the structure Const *) type angle (* This is type real in the structure Const *)val rotateSinCosX : real * real -> t val rotateSinCosY : real * real -> t val rotateSinCosZ : real * real -> t val rotateX : angle -> t val rotateY : angle -> t val rotateZ : angle -> t
(* FIXME: rotateXYZ not tested! *) val rotateXYZ : angle * real * real * real -> t val scaleXYZ : real * real * real -> t val translateXYZ : real * real * real -> t val shearXY : real * real -> t val shearXZ : real * real -> t val shearYZ : real * real -> t val perspective : real -> t (* FIXME: Not tested! *) val parallel : t (* FIXME: Not tested! *) val swapLRCoordsys : t (* FIXME: Not tested! *) end
If you know about 3D coordinate transformations this should be almost self explanatory. Otherwise you might want to read up on it anyway :) Notice that angle is expected to be in radians. The functions rotateSinCosX, rotateSinCosY and rotateSinCosZ can be used if you have the sine and cosine of a desired rotation angle and they are more efficient than using rotateX, rotateY and rotateZ.
The reason why this signature is called MATRIX_4X4_COMMON is that I have had plans for implementing a structure called Oper which has the same signature - except that the type t would be Matrix4x4.t -> Matrix4x4.t. The purpose of this would be to create optimized functions for multiplying a matrix with each of the common transformation matrices listed in the signature.
The signature for transforming 3D points and 3D vectors looks like this:
signature MATRIX_4X4_TRANSFORMATION = sig type t (* Matrix type *) val transformPoint : t -> {x : real, y : real, z : real} -> {x : real, y : real, z : real}val transformVector : t -> {x : real, y : real, z : real} -> {x : real, y : real, z : real} end
These points and vectors must have the type x : real, y : real, z : real which is also the type used in the Geometry project described above.
The functions will transform points and vectors by considering them as columns multiplied on to the right of the matrix - or put another way by multiplying a matrix on to the left of the point. This conforms to the mathematical standard way of transforming points.
First off it should be noted that some computer graphics textbooks use the mathematical standard way of transforming points while other books use a transposed notation for matrices.
Some books that follow the mathematical standard when describing coordinate transformations are listed here:
Some books that use the transposed notation are listed here:
You would typically transform a point by a series of matrices like this:
Ptransformed = (M5 * M4 * M3 * M2 * M1) * Poriginal
First you would mutiply the matrices to obtain a single matrix and then transform your bzillion points by multiplying with the resulting matrix. In ML you can multiply a list of matrices together with the following code:
val m = foldr Matrix4x4.multiply Matrix4x4.unitValue matrices
Or equivalently, since matrix multiplication is associative:
fun swap (a, b) = (b, a)val m = foldl (Matrix4x4.multiply o swap) Matrix4x4.unitValue matrices
However using foldr is probably slower and might use memory proportional to the length of the list matrices.
In case you want to generate the inverse of a list of matrix transformations, you could invert each matrix and multiply the matrices together in the opposite order. This can be done with the following code:
val mInverse = foldl Matrix4x4.multiply Matrix4x4.unitValue (map myInvert matrices)
This project implements a ternary search tree structure which is used in the Standard ML bindings for RenderMan - RI::ML. In my own CVS repository, the Collections project also contains other structures, such as dictionary, set implementations, hash table etc. However I'm still not content with the signatures for those structures, so it is not released here yet. However you can find some older versions of these structures as part of the 'AbstactUI::ML' project. Actually I would like to implement some very general signatures usable for many kinds of collection structures - even many of those found in the Standard ML Basis library! But, alas - time is in short supply.
But, returning to the ternary tree structure, the signature looks like this:
signature TERNARY_TREE = sig type 'a tval empty : 'a t val lookup : 'a t -> string -> 'a option
(* insert overwrites any previous data. *) val insert : 'a t -> string -> 'a -> 'a t end
It's like a functional dictionary structure with a string as the key value. Lookups and inserts are all done in practical constant time... actually linear time in the length of the key string, but never mind. It might even be more efficient than using a hash table! empty is an empty search tree, the lookup function will look up a string value in the search tree and insert will insert a new value with a key string into the tree - possibly overwriting existing data for that key. And, that's it! :)
This project currently only implements conversion of binary single precision IEEE floating point values into ML real values. The project has two structures IEEESingleFloat and IEEEDoubleFloat. However only IEEESingleFloat is implemented. The signature for both structures looks like this:
signature IEEE_FLOAT = sig type binary (* This is Word32.word in IEEESingleFloat and Word64.word in IEEEDoubleFloat *) val toLargeReal : binary -> LargeReal.real val fromLargeReal : LargeReal.real -> binary end
Only the function toLargeReal is implemented in IEEESingleFloat. Converting the other way seems more troublesome, and I have not had any need for it. The IEEESingleFloat structure is used in my commercial application called CeX3D Converter for reading binary float values in the LightWave 3D object file format, so it is production proven :)
I believe it would be very natural if a few functions for converting to and from binary IEEE float values were added to the REAL signature in the Standard ML Basis library. This is mostly because that these modules already use the IEEE binary representations internally.
It could be implemented by adding functions toBinary and fromBinary and implementing single precision for Real32 and double precision for Real64. But of course this might make the Real module inconsistent. Alternatively, all the Real modules could have 4 functions toBinarySingle, toBinaryDouble, fromBinarySingle and fromBinaryDouble, but this is less orthogonal, so I'm not sure what would be best.
This project implements a way of reading binary and text files in a functional way. It tries to match a subset of the the TextIO and BinIO modules of the Standard ML Basis library as closely as possible. It contains the structures FuncBinIO and FuncTextIO. The basic signature looks like this:
signature FUNCTIONAL_IO = sig include IO type vector (* This is Word8Vector.vector in FuncBinIO and CharVector.vector in FuncTextIO. *) type elem (* This is Word8.word in FuncBinIO and Char.char in FuncTextIO. *) type instream type outstreamval input : instream -> vector * instream val input1 : instream -> (elem * instream) option val inputN : (instream * int) -> vector * instream
(* Closes file for further input. New end of file becomes the furthest position in the file that has been read internally. *) val closeIn : instream -> unit end
However the FuncBinIO and FuncTextIO structures also implement the openIn function as can be seen in this signature:
signature FUNC_BIN_IO = sig include FUNCTIONAL_IOval openIn : string -> instream end
The functions input and inputN will return vectors of length zero if no more characters can be read from the stream. An important thing to notice is that closeIn will only close the underlying file, meaning that all the functional versions of the stream can continue to read as much data as has already been read into memory from the file.
This project implements combinator parsers as described in ML for the Working Programmer by Larry Paulson. However this implementation uses the FunctionalIO streams described above. The implementation is based on a combinator parser implementation by Fritz Henglein.
The main structures of this project are TextIOParserCombinators and BinIOParserCombinators. The signature for these looks like this:
signature PARSER_COMBINATORS = sig type instream (* This is FuncTextIO.instream in TextIOParserCombinators and FuncBinIO.instream in BinIOParserCombinators *) type vec (* This is string in TextIOParserCombinators and Word8Vector.vector in BinIOParserCombinators *) type elem (* This is char in TextIOParserCombinators and Word8.word in BinIOParserCombinators *)(* This is the type of a parser. A parser is a function taking a functional instream as argument. It returns a value that has been created by the parser during parsing, and a new functional instream with the stream position updated to where the parser stopped reading. *) type 'a parser = instream -> ('a * instream)
(* SyntaxError is raised when a parser fails to parse. *) exception SyntaxError of string * instream
(* Functions given to the >> combinator are expected to raise ValidityError on invalid arguments. *) exception ValidityError of string
(* The combinators *) (* The purpose of this combinator is to try parsing with 2 parser functions and return the result of the first function that succeeds. *) val || : ('a parser) * ('a parser) -> ('a parser)
(* This combinator will execute 2 parsers in sequence and return a pair of the results of the parsers. *) val -- : ('a parser) * ('b parser) -> (('a * 'b) parser)
(* This combinator executes 2 parsers in sequence and ignores the result of the first parser. *) val $-- : ('a parser) * ('b parser) -> ('b parser)
(* This combinator executes 2 parsers in sequence and ignores the result of the second parser. *) val --$ : ('a parser) * ('b parser) -> ('a parser)
(* Execute a parser and run the result through a function. *) val >> : ('a parser) * ('a -> 'b) -> ('b parser)
(* This combinator is for reading an verifying an expected keyword. *) val $$ : vec -> (vec parser)
(* Some handy built-in parsers. *)
(* Doesn't parse anything, just returns nil. *) val empty : ('a list) parser
(* Given a predicate, returns a parser that will read an element from the stream if the predicate is true. *) val getIf : (elem -> bool) -> (elem parser)
(* Given a parser, returns a parser that will read a list of values with the given parser. Parses as many values as possible. *) val repeat : ('a parser) -> (('a list) parser)
(* Given a predicate, returns a parser that will read a list of elements, until the predicate is false. *) val repeatIf : (elem -> bool) -> ((elem list) parser)
(* Given a number n and a parser, returns a parser that parses a list of n values with the given parser. *) val repeatN : int -> ('a parser) -> ('a list parser)
(* Same as repeatIf, except that this will read at least one value - or fail. *) val repeatOneIf : (elem -> bool) -> ((elem list) parser) end
A parser (of type 'a parser) is a function which reads data from a given input stream and returns a value created or read during parsing and the input stream updated with a new stream position. If a parser fails to read what it is supposed to read, it raises the exception SyntaxError.
Parsers operate on input streams of the type instream, which is from the text based or the binary FunctionalIO modules, depending on whether is is the binary parsers or the string based parsers. The data read from input streams are vectors of elements, where each element has type elem and the vector of elements has type vec.
Now we will go through all the combinators for constructing new parser functions from existing parser functions:
Some handy parsers are also supplied in these modules. It should be more or less clear what they do from the comments in the signature above. It should be noted that if the repeat parsers are implemented naively, they will use huge amounts of memory when reading a list of data values. However, the not-so-naive implementation supplied here works very well in practice.
There is also a structure called TextIOParserCombExtra. It gives a few more handy parser functions for use with text based parsing. Also, it can be see as a quick and very dirty example of writing parser functions. The signature looks like the following:
signature TEXT_IO_PARSER_COMB_EXTRA = sig type instream = TextIOParserCombinators.instream type elem = TextIOParserCombinators.elem type 'a parser = 'a TextIOParserCombinators.parserval isWhitespaceChar : char -> bool val isLetterChar : char -> bool val isDigitChar : char -> bool
val whitespaceForce : elem list parser val whitespace : elem list parser
val getReal : instream -> real * instream val getRealWS : real parser end
The functions whitespace and whitespaceForce are parsers that will read as many whitespace characters as possible. whitespaceForce will read at least one whitespace - or fail. The function getReal is a parser that tries to read a floating point value, as they look in many programming languages, and return it as an ML real value. getRealWS will also read a floating point value as a real, but will also read and skip any whitespaces that may be following the floating point value.
A small example is included for reading a small subset of
a RenderMan RIB file. There is no documentation for this, and
it is included only for instructive purposes of how to write a parser.
This particular example is released under GPL and not LGPL
as the rest of the code. There is a 4MB RenderMan RIB file called
test.rib available for download (it is packed with gzip),
so that you can see that the parser combinators and the functional IO modules
work well in practice even with relatively big files.