This manual describes the Pure programming language and how to invoke the Pure
interpreter program. To read the manual inside the interpreter, just type
help at the command prompt. See the Online Help section for details.
The Pure interpreter is invoked as follows:
pure [options ...] [script ...] [-- args ...]
pure [options ...] -x script [args ...]
Use pure -h to get help about the command line options. As already
mentioned, just the pure command without any command line parameters
invokes the interpreter in interactive mode, see Running Interactively
below for details. Some other important ways to invoke the interpreter are
summarized below.
- pure -g
- Runs the interpreter interactively, with debugging support.
- pure script ...
- Runs the given scripts in batch mode.
- pure -i script ...
- Runs the given scripts in batch mode as above, but then enters the
interactive command loop. (Add -g to also get debugging support,
and -q to suppress the sign-on message.)
- pure -x script [arg ...]
- Runs the given script with the given parameters. The script name and command
line arguments are available in the global argv variable.
- pure -c script [-o prog]
- Batch compilation: Runs the given script, compiling it to a native
executable prog (a.out by default).
Depending on your local setup, there may be additional ways to run the Pure
interpreter. In particular, if you have Emacs Pure mode installed, then you
can just open a script in Emacs and run it with the C-c C-k keyboard
command. For Emacs aficionados, this is probably the most convenient way to
execute a Pure script interactively in the interpreter. Pure mode actually
turns Emacs into an advanced IDE (integrated development environment) for
Pure, which offers a lot of convenient features such as syntax highlighting,
automatic indentation, online help and different ways to interact with the
Pure interpreter.
The interpreter accepts various options which are described in more detail
below.
-
-c
Batch compilation.
-
--ctags
-
--etags
Create a tags file in ctags (vi) or etags (emacs) format.
-
--disable optname
Disable source option (conditional compilation).
-
--eager-jit
Enable eager JIT compilation. This requires LLVM 2.7 or later, otherwise
this flag will be ignored.
-
--enable optname
Enable source option (conditional compilation).
-
-fPIC
-
-fpic
Create position-independent code (batch compilation).
-
-g
Enable symbolic debugging.
-
-h
-
--help
Print help message and exit.
-
-i
Force interactive mode (read commands from stdin).
-
-I directory
Add a directory to be searched for included source scripts.
-
-L directory
Add a directory to be searched for dynamic libraries.
-
-l libname
Library to be linked in batch compilation.
-
--main name
Name of main entry point in batch compilation.
-
--noediting
Disable command-line editing.
-
-n
-
--noprelude
Do not load the prelude.
-
--norc
Do not run the interactive startup files.
-
-o filename
Output filename for batch compilation.
-
-q
Quiet startup (suppresses sign-on message in interactive mode).
-
-T filename
Tags file to be written by --ctags or --etags.
-
-u
Do not strip unused functions in batch compilation.
-
-v[level]
Set verbosity level.
-
--version
Print version information and exit.
-
-w
Enable compiler warnings.
-
-x
Execute script with given command line arguments.
-
--
Stop option processing and pass the remaining command line arguments in
the argv variable.
Besides these, the interpreter also understands a number of other command line
switches for setting various compilation options; please see Compilation
Options below for details.
Note
Option parsing follows the usual (UNIX) conventions, but is somewhat
more rigid than the GNU getopt conventions. In particular, it is not
possible to combine short options, and there are no abbreviations for
“long” options. Mixing options and other command line parameters is
generally possible, but note that all option processing stops right after
-x and --, passing the remaining parameters to the executing script
in the Pure argv variable.
As usual, if an option takes a required argument, the argument may be
written either as a separate command line parameter immediately following
the option (as in -I directory or --enable optname), or
directly after the option (-Idirectory or --enable=optname;
note the equals sign in the case of a long option). Options with optional
arguments work in the same fashion, but in this case the argument, if
present, must be written directly behind the option.
If any source scripts are specified on the command line, they are loaded and
executed, after which the interpreter exits. Otherwise the interpreter enters
the interactive read-eval-print loop, see Running Interactively below. You
can also use the -i option to enter the interactive loop (continue
reading from stdin) even after processing some source scripts.
Options and source files are processed in the order in which they are given on
the command line. Processing of options and source files ends when either the
-- or the -x option is encountered. The -x
option must be followed by the name of a script to be executed, which becomes
the “main script” of the application. In either case, any remaining parameters
are passed to the executing script by means of the global argc and
argv variables, denoting the number of arguments and the list of the
actual parameter strings, respectively. In the case of -x this also
includes the script name as argv!0. The -x option is useful, in
particular, to turn Pure scripts into executable programs by including a
“shebang” like the following as the first line in your main script. (This
trick only works with Unix shells, though.)
The following variables are always predefined by the interpreter:
-
variable argc
-
variable argv
The number of extra command line arguments and the arguments themselves as
a list of strings; see above. These are useful if a script is usually run
non-interactively and takes its input from the command line.
-
variable compiling
A flag indicating whether the program is executed in a batch compilation
(-c option), see Compiling Scripts below.
-
variable version
-
variable sysinfo
The version string of the Pure interpreter and a string identifying the
host system. These are useful if parts of your script depend on the
particular version of the interpreter and the system it runs on. (An
alternative way to deal with version and system dependencies is to use
conditional compilation; see Conditional Compilation.)
If available, the prelude script prelude.pure is loaded by the interpreter
prior to any other definitions, unless the -n or
--noprelude option is specified. The prelude is searched for in the
directory specified with the PURELIB environment variable. If the
PURELIB variable is not set, a system-specific default is
used. Relative pathnames of other source scripts specified on the command line
are interpreted relative to the current working directory. In addition, the
executed program may load other scripts and libraries via a using
declaration in the source, which are searched for in a number of locations,
including the directories named with the -I and -L
options; see the Declarations and C Interface sections for details.
The interpreter compiles scripts, as well as definitions that you enter
interactively, automatically. This is done in an incremental fashion, as the
code is needed, and is therefore known as JIT (just in time) compilation.
Thus the interpreter never really “interprets” the source program or some
intermediate representation, it just acts as a frontend to the compiler,
taking care of compiling source code to native machine code before it gets
executed.
Pure’s LLVM backend does “lazy JIT compilation” by default, meaning that each
function (global or local) is compiled no sooner than it is run for the first
time. With the --eager-jit option, however, it will also compile all
other (global or local) functions that may be called by the compiled
function. (The PURE_EAGER_JIT environment variable, when set to any
value, has the same effect, so that you do not have to specify the
--eager-jit option each time you run the interpreter.) Eager JIT
compilation may be more efficient in some cases (since bigger chunks of
compilation work can be done in one go) and less efficient in others (e.g.,
eager JITing may compile large chunks of code which aren’t actually called
later, except in rare circumstances).
Note that the eager JIT mode is only available with LLVM 2.7 or later;
otherwise this option will be ignored.
It is also possible to compile your scripts to native code beforehand, using
the -c batch compilation option. This options forces the interpreter
to non-interactive mode (unless -i is specified as well, which
overrides -c). Any scripts specified on the command line are then
executed as usual, but after execution the interpreter takes a snapshot of the
program and compiles it to one of several supported output formats, LLVM
assembler (.ll) or bitcode (.bc), native assembler (.s) or object (.o), or a
native executable, depending on the output filename specified with
-o. If the output filename ends in the .ll extension, an LLVM
assembler file is created which can then be processed with the LLVM
toolchain. If the output filename is just ‘-‘, the assembler file is written
to standard output, which is useful if you want to pass the generated code to
the LLVM tools in a pipeline. If the output filename ends in the .bc
extension, an LLVM bitcode file is created instead.
The .ll and .bc formats are supported natively by the Pure interpreter, no
external tools are required to generate these. If the target is an .s, .o or
executable file, the Pure interpreter creates a temporary bitcode file on
which it invokes the LLVM tools opt and llc to create a
native assembler file, and then uses the C/C++ compiler (normally
gcc, but you can change this with the CC and
CXX environment variables) to assemble and link the resulting
program (if requested). You can also specify additional libraries to be linked
into the executable with the -l option. If the output filename is
omitted, it defaults to a.out (a.exe on Windows).
The -c option provides a convenient way to quickly turn a Pure
script into a standalone executable which can be invoked directly from the
shell. One advantage of compiling your script is that this eliminates the JIT
compilation time and thus considerably reduces the startup time of the
program. Another reason to prefer a standalone executable is that it lets you
deploy the program on systems without a full Pure installation (usually only
the runtime library is required on the target system). On the other hand,
compiled scripts also have some limitations, mostly concerning the use of the
built-in eval function. Please see the Batch Compilation section
for details.
The -v64 (or -v0100) verbosity option can be used to have the
interpreter print the commands it executes during compilation, see Verbosity
and Debugging Options below. When creating an object file, this also prints
the suggested linker command (including all the dynamic modules loaded by the
script, which also have to be linked in to create a working executable), to
which you only have to add the options describing the desired output file.
Pure programs often have declarations and definitions of global symbols
scattered out over many different source files. The --ctags and
--etags options let you create a tags file which allows you to
quickly locate these items in text editors such as vi and
emacs which support this feature.
If --ctags or --etags is specified, the interpreter enters
a special mode in which it only parses source files without executing them and
collects information about the locations of global symbol declarations and
definitions. The collected information is then written to a tags file in the
ctags or etags format used by vi and emacs,
respectively. The desired name of the tags file can be specified with the
-T option; it defaults to tags for --ctags and TAGS for
--etags (which matches the default tags file names used by
vi and emacs, respectively).
The tags file contains information about the global constant, variable, macro,
function and operator symbols of all scripts specified on the command line, as
well as the prelude and other scripts included via a using clause.
Tagged scripts which are located in the same directory as the tags file (or,
recursively, in one of its subdirectories) are specified using relative
pathnames, while scripts outside this hierarchy (such as included scripts from
the standard library) are denoted with absolute pathnames. This scheme makes
it possible to move an entire directory together with its tags file and have
the tags information still work in the new location.
If the interpreter runs in interactive mode, it repeatedly prompts you for
input (which may be any legal Pure code or some special interpreter commands
provided for interactive usage), and prints computed results. This is also
known as the read-eval-print loop and is described in much more detail in
the Interactive Usage section. To exit the interpreter, just type the
quit command or the end-of-file character (Ctrl-d on Unix) at the
beginning of the command line.
The interpreter may also source a few additional interactive startup files
immediately before entering the interactive loop, unless the --norc
option is specified. First .purerc in the user’s home directory is read, then
.purerc in the current working directory. These are ordinary Pure scripts
which can be used to provide additional definitions for interactive
usage. Finally, a .pure file in the current directory (containing a dump from
a previous interactive session) is loaded if it is present.
When the interpreter is in interactive mode and reads from a tty, unless the
--noediting option is specified, commands are usually read using
readline or some compatible replacement, providing completion for
all commands listed under Interactive Usage, as well as for symbols defined
in the running program. When exiting the interpreter, the command history is
stored in ~/.pure_history, from where it is restored the next time you run the
interpreter.
The interpreter also provides a simple source level debugger when run in
interactive mode, see Debugging for details. To enable the debugger, you need
to specify the -g option when invoking the interpreter. This option
causes your script to run much slower, so you should only use this option if
you want to run the debugger.
The -v option is useful for debugging the interpreter, or if you are
interested in the code your program gets compiled to. The level argument is
optional; it defaults to 1. Seven different levels are implemented at this
time (one more bit is reserved for future extensions). Only the first two
levels will be useful for the average Pure programmer; the remaining levels
are mostly intended for maintenance purposes.
- 1 (0x1, 001)
- denotes echoing of parsed definitions and expressions.
- 2 (0x2, 002)
- adds special annotations concerning local bindings (de Bruijn indices,
subterm paths; this can be helpful to debug tricky variable binding
issues).
- 4 (0x4, 004)
- adds descriptions of the matching automata for the left-hand sides of
equations (you probably want to see this only when working on the guts of
the interpreter).
- 8 (0x8, 010)
- dumps the “real” output code (LLVM assembler, which is as close to the
native machine code for your program as it gets; you definitely don’t want
to see this unless you have to inspect the generated code for bugs or
performance issues).
- 16 (0x10, 020)
- adds debugging messages from the bison(1) parser; useful for debugging the
parser.
- 32 (0x20, 040)
- adds debugging messages from the flex(1) lexer; useful for debugging the
lexer.
- 64 (0x40, 0100)
- turns on verbose batch compilation; this is useful if you want to see
exactly which commands get executed during batch compilation
(-c).
These values can be or’ed together, and, for convenience, can be specified in
either decimal, hexadecimal or octal. Thus 0xff or 0777 always gives you full
debugging output (which isn’t likely to be used by anyone but the Pure
developers). Some useful flag combinations for experts are (in octal) 007
(echo definitions along with de Bruijn indices and matching automata), 011
(definitions and assembler code) and 021 (parser debugging output along with
parsed definitions).
Note that the -v option is only applied after the prelude has been
loaded. If you want to debug the prelude, use the -n option and
specify the prelude.pure file explicitly on the command line. Verbose output
is also suppressed for modules imported through a using clause. As
a remedy, you can use the interactive show command (see the Interactive
Usage section) to list definitions along with additional debugging
information.
Besides the options listed above, the interpreter also understands some
additional command line switches and corresponding environment variables to
control various compilation options.
These options take the form --opt and --noopt, respectively, where
opt denotes the option name (see below for a list of supported
options). By default, these options are all enabled; --noopt disables the
option, --opt reenables it. In addition, for each option opt there is
also a corresponding environment variable PURE_NOOPT (with the option name
in uppercase) which, when set, disables the option by default. (Setting this
variable to any value will do, the interpreter only checks whether the
variable exists in the environment.)
For instance, the checks option controls stack and signal checks. Thus
--nochecks on the command line disables the option, and setting the
PURE_NOCHECKS environment variable makes this the default, in which case
you can use --checks on the command line to reenable the option.
Each code generation option can also be used as a pragma (compiler
directive) in source code so that you can control it on a per-rule basis. The
pragma must be on a line by itself, starting in column 1, and takes the
following form (using --nochecks as an example):
#! --nochecks // line-oriented comment may go here
Currently, the following code generation options are recognized:
-
--checks
-
--nochecks
Enable or disable various extra stack and signal checks. By default, the
interpreter checks for stack overflows (if the PURE_STACK
environment variable is set) and pending signals on entry to every
function, see Stack Size and Tail Recursion and Handling of
Asynchronous Signals for details. This is needed to catch these
conditions in a reliable way, so we recommend to leave this enabled.
However, these checks also make programs run a little slower (typically
some 5%, YMMV). If performance is critical then you can disable the checks
with the --nochecks option. (Even then, a minimal amount of
checking will be done, usually on entry to every global function.)
-
--const
-
--noconst
Enable or disable the precomputing of constant values in batch compilation
(cf. Compiling Scripts). If enabled (which is the default), the values
of constants in const definitions are precomputed at compile
time (if possible) and then stored in the generated executable. This
usually yields faster startup times but bigger executables. You can disable
this option with --noconst to get smaller executables at the
expense of slower startup times. Please see the Batch Compilation
section for an example.
-
--fold
-
--nofold
Enable or disable constant folding in the compiler frontend. This means
that constant expressions involving int and double values and the usual
arithmetic and logical operations on these are precomputed at compile
time. (This is mostly for cosmetic purposes; the LLVM backend will perform
this optimization anyway when generating machine code.) For instance:
> foo x = 2*3*x;
> show foo
foo x = 6*x;
Disabling constant folding in the frontend causes constant expressions to
be shown as you entered them:
> #! --nofold
> bar x = 2*3*x;
> show bar
bar x = 2*3*x;
The same option also determines the handling of type aliases at compile
time, see Type Rules.
-
--tc
-
--notc
Enable or disable tail call optimization (TCO). TCO is needed to make
tail-recursive functions execute in constant stack space, so we recommend
to leave this enabled. However, at the time of this writing LLVM’s TCO
support is still bug-ridden on some platforms, so the --notc
option allows you to disable it. (Note that TCO can also be disabled when
compiling the Pure interpreter, in which case these options have no effect;
see the installation instructions for details.)
Note
All of the options above also have a corresponding “option symbol”
so that they can be queried and set using the facilities described under
Conditional Compilation below. (The symbol is just the name of the
option, e.g., checks for the --checks, --nochecks
option and pragma.)
Besides these, there are the following special pragmas affecting the
evaluation of some global function or macro, which is specified in the
pragma. These pragmas can only be used in source code, they cannot be
controlled using command line options or environment variables. Note that the
given symbol fun may in fact be an arbitrary symbol (not just an
identifier), so that these pragmas can also be applied to special operator
symbols (cf. Lexical Matters). Also note that each of these pragmas also
implicitly declares the symbol, so if a symbol needs any special attributes
then it must be declared before any pragmas involving it (cf. Symbol
Declarations).
-
--eager fun
Instruct the interpreter to JIT-compile the given function eagerly. This
means that native code will be created for the function, as well as all
other (global or local) functions that may be called by the compiled
function, as soon as the function gets recompiled. This avoids the hiccups
you get when a function is compiled on the fly if it is run for the first
time, which is particularly useful for functions which are to be run in
realtime (typically in multimedia applications). Please note that, in
difference to the --eager-jit option, this feature is available
for all LLVM versions (it doesn’t require LLVM 2.7 or later).
-
--required fun
Inform the batch compiler (cf. Compiling Scripts) that the given
function symbol fun should never be stripped from the program. This is
useful, e.g., if a function is never called explicitly but only through
eval. Adding a --required pragma for the function then
makes sure that the function is always linked into the program. Please see
the Batch Compilation section for an example.
-
--defined fun
-
--nodefined fun
These pragmas change the behaviour of functions defined in a Pure program.
Pure’s default mode is to evaluate function applications in a symbolic
fashion using the equations (rewriting rules) supplied by the programmer,
cf. Definitions and Expression Evaluation. This means that it is not
normally an error if there is no equation which applies to the given
function application to be evaluated; rather, the application simply
becomes a “normal form” which stands for itself. E.g., here’s what you get
if you try to add an (undefined) symbol and a number:
The --defined pragma allows you to declare a function symbol as a
“defined” function, so that it will raise a proper exception when no
equation is applicable:
> #! --defined +
> a+1;
<stdin>, line 3: unhandled exception 'failed_match' while evaluating 'a+1'
The --defined status of a function can be changed at any time
(causing the function to be recompiled on the fly if necessary), and the
--nodefined pragma restores the default behaviour of returning a
normal form upon failure:
> #! --nodefined +
> a+1;
a+1
More information and examples for common uses of the --defined
pragma can be found under Defined Functions in the Caveats and Notes
section.
-
--quoteargs fun
This pragma tells the macro evaluator (cf. Macros) that the given macro
should receive its arguments unevaluated, i.e., in quoted form. This is
described in more detail in the Built-in Macros and Special Expressions
section.
As of version 0.49, Pure also provides a rudimentary facility for denoting
optional and alternative code paths. This is supposed to cover the most common
cases where conditional compilation is needed. (For more elaborate needs you
can always use real Pure code which enables you to configure your program at
runtime using, e.g., the eval function.)
Pure’s conditional compilation pragmas are based on the notion of user-defined
symbols (which can be really any text that does not contain whitespace or any
of the shell wildcard characters *?[]) called compilation options. By
default, all options are undefined and enabled. An option becomes
defined as soon as it is set explicitly, either with an environment variable
or one of the --enable and --disable pragmas, see below.
You can define the value of an option by setting a corresponding environment
variable PURE_OPTION_OPT, where OPT is the option symbol in uppercase.
The value of the environment variable should either be 0 (disabled) or
1 (enabled).
Options can be enabled and disabled in Pure scripts with the following
pragmas, which are also available as command line options when invoking the
Pure interpreter:
-
--enable option
-
--disable option
Enable or disable the given option, respectively. Note that an option
specified in the environment is overridden by a value specified with these
options on the command line, which in turn is overridden by a corresponding
pragma in source code.
The actual conditional compilation pragmas work in pretty much the same
fashion as the C preprocessor directives #if, #ifdef etc. (except
that, as already mentioned, an option is always enabled if it is undefined).
-
--ifdef option
-
--ifndef option
Begins a code section which should be included in the program if the given
option is defined or undefined, respectively.
-
--if option
-
--ifnot option
Begins a code section which should be included in the program if the given
option is enabled or disabled, respectively.
-
--else
Begins an alternative code section which is included in the program if the
corresponding --ifdef, --ifndef, --if or
--ifnot section was excluded, and vice versa.
-
--endif
Ends a conditional code section.
Conditional code sections may be nested to an arbitrary depth. Each
--ifdef, --ifndef, --if or --ifnot
pragma must be followed by a matching --endif. The --else
section is optional; if present, it applies to the most recent
--ifdef, --ifndef, --if or --ifnot
section not terminated by a matching --endif. Unmatched conditional
pragmas warrant an error message by the compiler.
Conditional code is handled at the level of the lexical analyzer. Excluded
code sections are treated like comments, i.e., the parser never gets to see
them.
The --ifdef and --ifndef pragmas are typically used to
change the default of an option without clobbering defaults set by the user
through an environment variable or a command line option. For instance:
#! --ifndef opt
#! --disable opt
#! --endif
Here’s a (rather contrived) example which shows all these pragmas in action.
You may want to type this in the interpreter to verify that the code sections
are indeed included and excluded from the Pure program as indicated:
// disable the 'bar' option
#! --disable bar
#! --ifdef foo
1/2; // excluded
#! --endif
#! --ifndef bar
1/3; // excluded
#! --endif
#! --if foo
foo x = x+1; // included
#! --if bar
bar x = x-1; // excluded
#! --else
bar x = x/2; // included
#! --endif // bar
#! --endif // foo
// reenable the 'bar' option
#! --enable bar
#! --if bar
bar 99; // included
#! --endif // bar
#! --ifnot foo
baz x = 2*x; // excluded
#! --endif // not foo
A few options are always predefined as “builtins” by the interpreter. This
includes all of the options described under Code Generation Options and
Warning Options, so that these can also be queried with --if,
--ifnot and set with --enable, --disable. For
instance:
#! --ifnot checks
puts "This program uses deep recursion, so we enable stack checks here!";
#! --enable checks
#! --endif // not checks
#! --if warn
puts "Beware of bugs in the above code.";
puts "I have only proved it correct, not tried it.";
#! --endif // warn
Moreover, the following options are provided as additional builtins which are
useful for handling special compilation requirements as well as system and
version dependencies.
The compiled option is enabled if a program is batch-compiled. This lets
you pick alternative code paths depending on whether a script is compiled to
a native executable or not. Please see the example at the end of the Batch
Compilation section for details.
The interactive and debugging options are enabled if a program runs
in interactive (-i) and/or debugging
(-g) mode, respectively. These options are read-only;
they cannot be changed with --enable, --disable.
Example:
#! --if interactive
puts "Usage: run 'main filename'";
#! --else
main (argv!1);
#! --endif
The version-x.y option indicates a check against the version of the host
Pure interpreter. x.y indicates the required (major/minor) version. You
can also use x.y+ to indicate version x.y or later, or x.y- for
version x.y or earlier. By combining these, you can pick code depending
on a particular range of Pure versions, or you can reverse the test to check
for anything later or earlier than a given version:
#! --if version-0.36+
#! --if version-0.48-
// code to be executed for Pure versions 0.36..0.48 (inclusive)
#! --endif
#! --endif
#! --ifnot version-0.48-
// code to be executed for Pure versions > 0.48
#! --endif
Last but not least, the interpreter always defines the target triplet of the
host system as an option symbol. This is the same as what sysinfo
returns, so you can check for a specific system like this:
#! --if x86_64-unknown-linux-gnu
// 64 bit Linux-specific code goes here
#! --endif
It goes without saying that this method isn’t very practical if you want to
check for a wide range of systems. As a remedy, the --if and
--ifnot pragmas treat shell glob patterns in tests for option
symbols in a special way, by matching the pattern against the host triplet
to see whether the condition holds. This allows you to write a generic test,
e.g., for Windows systems like this:
#! --if *-mingw32
// Windows-specific code goes here
#! --endif
The -w option enables some additional warnings which are
useful to check your scripts for possible errors. In particular, it will
report implicit declarations of function and type symbols, which might
indicate undefined or mistyped symbols that need to be fixed, see Symbol
Lookup and Creation for details.
This option can also be controlled on a per-rule basis by adding the following
pragmas to your script:
-
--warn
-
--nowarn
Enable or disable compiler warnings. The -w flag sets
the default for these pragmas.
-
--rewarn
Reset compiler warnings to the default, as set with the
-w flag (or not).
The latter pragma is useful to enable or disable warnings in a section of code
and reset it to the default afterwards:
#! --warn
// Code with warnings goes here.
#! --rewarn
(The same could also be achieved with conditional compilation, but only much
more clumsily. However, note that --rewarn only provides a single
level of “backup”, so nesting such sections is not supported.)
The interpreter may source various files during its startup. These are:
-
~/.pure_history
Interactive command history.
-
~/.purerc, .purerc, .pure
Interactive startup files. The latter is usually a dump from a previous
interactive session.
-
prelude.pure
Standard prelude. If available, this script is loaded before any other
definitions, unless -n was specified.
Various aspects of the interpreter can be configured through the following
shell environment variables:
-
CC
-
CXX
C and C++ compiler used by the Pure batch compiler (pure -c) to compile
and link native executables. Defaults to gcc and g++,
respectively.
-
BROWSER
If the PURE_HELP variable is not set (see below), this specifies
a colon-separated list of browsers to try for reading the online
documentation. See http://catb.org/~esr/BROWSER/.
-
PURELIB
Directory to search for library scripts, including the prelude. If
PURELIB is not set, it defaults to some location specified at
installation time.
-
PURE_EAGER_JIT
Enable eager JIT compilation (same as --eager-jit), see
Compiling Scripts for details.
-
PURE_HELP
Command used to browse the Pure manual. This must be a browser capable of
displaying html files. Default is w3m.
-
PURE_INCLUDE
Additional directories (in colon-separated format) to be searched for
included scripts.
-
PURE_LIBRARY
Additional directories (in colon-separated format) to be searched for
dynamic libraries.
-
PURE_MORE
Shell command to be used for paging through output of the show command,
when the interpreter runs in interactive mode. PURE_LESS does the
same for evaluation results printed by the interpreter.
-
PURE_PS
Command prompt used in the interactive command loop (“> ” by default).
-
PURE_STACK
Maximum stack size in kilobytes (default: 0 = unlimited).
Besides these, the interpreter also understands a number of other environment
variables for setting various compilation options (see Compilation Options
above) and commands to invoke different LLVM compilers on inline code (see
Inline Code).
Pure is a fairly simple yet powerful language. Programs are basically
collections of term rewriting rules, which are used to reduce expressions to
normal form in a symbolic fashion. For convenience, Pure also offers some
extensions to the basic term rewriting calculus, like global variables and
constants, nested scopes of local function and variable definitions, anonymous
functions (lambdas), exception handling and a built-in macro facility. These
are all described below and in the following sections.
Most basic operations are defined in the standard prelude.
This includes the usual arithmetic and logical operations, as well as the
basic string, list and matrix functions. The prelude is always loaded by the
interpreter, so that you can start using the interpreter as a sophisticated
kind of desktop calculator right away. Other useful operations are provided
through separate library modules. Some of these, like the system interface and
the container data structures, are distributed with the interpreter, others
are available as separate add-on packages from the Pure website. A (very)
brief overview of some of the modules distributed with the Pure interpreter
can be found in the Standard Library section.
In this section we first give a brief overview of the most important elements
of the Pure language. After starting out with a discussion of the lexical
syntax, we proceed by explaining definitions and expressions, which are the
major ingredients of Pure programs. After studying this section you should be
able to write simple Pure programs. Subsequent sections then describe the
concepts and notions introduced here in much greater detail and also cover the
more advanced language elements which we only gloss over here.
Pure is a free-format language, i.e., whitespace is insignificant (unless it
is used to delimit other symbols). Thus, in contrast to “layout-based”
languages like Haskell, you must use the proper delimiters (;) and
keywords (end) to terminate definitions and block structures. In
particular, as shown in the example above, definitions and expressions at the
toplevel have to be terminated with a semicolon, even in interactive mode.
Comments use the same syntax as in C++: // for line-oriented, and
/* ... */ for multiline comments. The latter must not be nested. Lines
beginning with #! are treated as comments, too; as already discussed
above, on Unix-like systems this allows you to add a “shebang” to your main
script in order to turn it into an executable program.
A few ASCII symbols are reserved for special uses, namely the semicolon, the
“at” symbol @, the equals sign =, the backslash \, the Unix pipe
symbol |, parentheses (), brackets [] and curly braces {}.
(Among these, only the semicolon is a “hard delimiter” which is always a
lexeme by itself; the other symbols can be used inside operator symbols.)
Moreover, there are some keywords which cannot be used as identifiers:
case const def else end extern if
infix infixl infixr interface let namespace nonfix
of otherwise outfix postfix prefix private public
then type using when with
Pure fully supports the Unicode character set or, more precisely, UTF-8.
This is an ASCII extension capable of representing all Unicode characters,
which provides you with thousands of characters from most of the languages of
the world, as well as an abundance of special symbols for almost any purpose.
If your text editor supports the UTF-8 encoding (most editors do nowadays),
you can use all Unicode characters in your Pure programs, not only inside
strings, but also for denoting identifiers and special operator symbols.
The customary notations for identifiers, numbers and strings are all
provided. In addition, Pure also allows you to define your own operator
symbols. Identifiers and other symbols are described by the following grammar
rules in EBNF format:
symbol ::= identifier | special
identifier ::= letter (letter | digit)*
special ::= punct+
letter ::= "A"|...|"Z"|"a"|...|"z"|"_"|...
digit ::= "0"|...|"9"
punct ::= "!"|"#"|"$"|"%"|"&"|...
Pure uses the following rules to distinguish “punctuation” (which may only
occur in declared operator symbols) and “letters” (identifier
constituents). In addition to the punctuation symbols in the 7 bit ASCII
range, the following code points in the Unicode repertoire are considered as
punctuation: U+00A1 through U+00BF, U+00D7, U+00F7, and U+20D0 through
U+2BFF. This comprises the special symbols in the Latin-1 repertoire, as well
as the Combining Diacritical Marks for Symbols, Letterlike Symbols, Number
Forms, Arrows, Mathematical Symbols, Miscellaneous Technical Symbols, Control
Pictures, OCR, Enclosed Alphanumerics, Box Drawing, Blocks, Geometric Shapes,
Miscellaneous Symbols, Dingbats, Miscellaneous Mathematical Symbols A,
Supplemental Arrows A, Supplemental Arrows B, Miscellaneous Mathematical
Symbols B, Supplemental Mathematical Operators, and Miscellaneous Symbols and
Arrows. This should cover almost everything you’d ever want to use in an
operator symbol. All other extended Unicode characters are effectively treated
as “letters” which can be used as identifier constituents. (Charts of all
Unicode symbols can be found at the Code Charts page of the Unicode
Consortium.)
The following are examples of valid identifiers: foo, foo_bar,
FooBar, BAR, bar99. Case is significant in identifiers, so Bar
and bar are distinct identifiers, but otherwise the case of letters
carries no meaning. Special symbols consist entirely of punctuation, such as
::=. These may be used as operator symbols, but have to be declared before
they can be used (see Symbol Declarations).
Pure also has a notation for qualified symbols which carry a namespace prefix.
These take the following format (note that no whitespace is permitted between
the namespace prefix and the symbol):
qualified_symbol ::= [qualifier] symbol
qualified_identifier ::= [qualifier] identifier
qualifier ::= [identifier] "::" (identifier "::")*
Example: foo::bar.
Number literals come in three flavours: integers, bigints (denoted with an
L suffix) and floating point numbers (indicated by the presence of the
decimal point and/or a base 10 scaling factor). Integers and bigints may be
written in different bases (decimal, binary, octal and hexadecimal), while
floating point numbers are always denoted in decimal.
number ::= integer | integer "L" | float
integer ::= digit+
| "0" ("X"|"x") hex_digit+
| "0" ("B"|"b") bin_digit+
| "0" oct_digit+
oct_digit ::= "0"|...|"7"
hex_digit ::= "0"|...|"9"|"A"|...|"F"|"a"|...|"f"
bin_digit ::= "0"|"1"
float ::= digit+ ["." digit+] exponent
| digit* "." digit+ [exponent]
exponent ::= ("E"|"e") ["+"|"-"] digit+
Examples: 4711, 4711L, 1.2e-3. Numbers in different bases:
1000 (decimal), 0x3e8 (hexadecimal), 01750 (octal),
0b1111101000 (binary).
String literals are arbitrary sequences of characters enclosed in double
quotes, such as "Hello, world!".
string ::= '"' char* '"'
Special escape sequences may be used to denote double quotes and backslashes
(\", \\), control characters (\b, \f, \n, \r, \t,
these have the same meaning as in C), and arbitrary Unicode characters given
by their number or XML entity name (e.g., \169, \0xa9 and
\© all denote the Unicode copyright character, code point U+00A9). As
indicated, numeric escapes can be specified in any of the supported bases for
integer literals. For disambiguating purposes, these can also be enclosed in
parentheses. E.g., "\(123)4" is a string consisting of the character
\123 followed by the digit 4. Strings can also be continued across
line ends by escaping the line end with a backslash. The escaped line end is
ignored (use \n if you need to embed a newline in a string). For instance,
denotes the same string literal as
The real meat of a Pure program is in its definitions. In Pure these generally
take the form of equations which tell the interpreter how expressions are to
be evaluated. For instance, the following two equations together define a
function fact which computes, for each given integer n, the factorial
of n:
fact 0 = 1;
fact n::int = n*fact (n-1) if n>0;
Note that the first equation covers the case that n is zero, in which case
the result is 1. The second equation handles the case of a positive
integer. Note the n::int on the left-hand side of the equation which
stipulates that the given value n must be a (machine) integer, and the
n>0 in the condition part of the equation which ensures that n is
positive. If these conditions hold, the equation becomes applicable and we
recursively compute fact (n-1) and multply by n to obtain the result.
The fact function thus computes the product of all positive integers up to
n, which is indeed just how the factorial is defined in mathematics.
To give this definition a try, you can just enter it at the command prompt of
the interpreter as follows:
> fact 0 = 1;
> fact n::int = n*fact (n-1) if n>0;
> let x = fact 10; x;
3628800
Note that the last command we typed computes the factorial of 10 and assigns
it to the global variable x. At the end of the line we also typed the
variable x again to have its value printed by the interpreter.
On the surface, Pure is quite similar to other modern functional languages
like Haskell and ML. But under the hood it is a much more dynamic language,
more akin to Lisp. In particular, Pure is dynamically typed, so functions can
process arguments of as many different types as you like (using FP parlance,
we say that functions are “polymorphic”). In fact, you can add to the
definition of an existing function at any time. For instance, we can extend
our example above to make the fact function work with floating point
numbers, too:
> fact 0.0 = 1.0;
> fact n::double = n*fact (n-1) if n>0;
> fact 10.0;
3628800.0
> fact 10;
3628800
Note the n::double construct on the left-hand side of the second equation,
which means that the equation is only to be applied for (double precision)
floating point values n. This construct is also called a “type tag” in
Pure parlance, which is actually a simple form of pattern matching (see
below). Similarly, our previous definition above employed the int tag to
indicate that the n parameter is an integer value. The int and
double types are built into the Pure language.
Expressions are generally evaluated from left to right, innermost expressions
first, i.e., using “call by value” semantics. E.g., in the above definition of
the factorial this means that in the second equation first n-1 (being the
argument of fact) is evaluated, then fact (n-1) (which is an argument
to the * operator), and finally fact (n-1) is multiplied by n to
give the value of fact n.
We mention in passing here that Pure also has a few built-in special forms
(most notably, conditional expressions, the short-circuit logical connectives
&& and ||, the sequencing operator $$,
the lazy evaluation operator &, and the quote) which take some
or all of their arguments unevaluated, using “call by name”. We’ll discuss
these later.
Like in Haskell and ML, functions are often defined by pattern matching, i.e.,
the left-hand side of a definition is matched against the target expression,
binding the variables in the pattern to their actual values accordingly. This
is frequently used in definitions involving aggregate arguments such as lists.
For instance, we might compute the product of the elements of a list as
follows:
> prod [] = 1;
> prod (x:xs) = x*prod xs;
> prod (1..10);
3628800
Note that 1..10 denotes the list of all positive integers up to 10 here.
This operation is provided in Pure’s prelude, i.e., it is part of the standard
library.
Due to its term rewriting semantics, Pure actually goes beyond most other
functional languages in that it can do symbolic evaluations just as well as
“normal” computations:
> square x = x*x;
> square 4;
16
> square (a+b);
(a+b)*(a+b)
In fact, leaving aside the built-in support for some common data structures
such as numbers and strings, all the Pure interpreter really does is evaluate
expressions in a symbolic fashion, rewriting expressions using the equations
supplied by the programmer, until no more equations are applicable. The result
of this process is called a normal form which represents the “value” of the
original expression. Keeping with the tradition of term rewriting, there’s no
distinction between “defined” and “constructor” function symbols in Pure.
Consequently, any function symbol or operator can be used anywhere on the
left-hand side of an equation, and may act as a constructor symbol if it
happens to occur in a normal form term. This enables you to work with
algebraic rules like associativity and distributivity in a direct fashion:
> (x+y)*z = x*z+y*z; x*(y+z) = x*y+x*z;
> x*(y*z) = (x*y)*z; x+(y+z) = (x+y)+z;
> square (a+b);
a*a+a*b+b*a+b*b
The above isn’t possible in languages like Haskell and ML which always enforce
the so-called “constructor discipline”, which stipulates that only pure
constructor symbols (without any defining equations) may occur as a subterm on
the left-hand side of a definition. Thus equational definitions like the above
are forbidden in these languages. In Pure they are just normal business.
This symbolic mode of evaluation is rather unusual outside of the realm of
symbolic algebra system, but it provides the programmer with a very flexible
model of computation and is one of Pure’s most distinguishing features. In
some cases, however, the unevaluated normal forms may also become a nuisance
since they may obscure possible programming errors. Therefore Pure provides a
special --defined pragma (cf. Code
Generation Options) which forces a function to be treated as a defined
function, so that it becomes more like functions in traditional untyped
languages such as Lisp and Python which raise an exception under such
conditions. This is described in more detail under Defined Functions in the
Caveats and Notes section.
Taking another look at the examples above, you might be wondering how the Pure
interpreter figures out what the parameters (a.k.a. “variables”) in an
equation are. This is quite obvious in rules involving just variables and
special operator symbols, such as (x+y)*z = x*z+y*z. However, what about
an equation like foo (foo bar) = bar? Since most of the time we don’t
declare any symbols in Pure, how does the interpreter know that foo is a
literal function symbol here, while bar is a variable?
The answer is that the interpreter considers the different positions in the
left-hand side expression of an equation. Basically, a Pure expression is just
a tree formed by applying expressions to other expressions, with the atomic
subexpressions like numbers and symbols at the leaves of the tree. (This is
true even for infix expressions like x+y, since in Pure these are always
equivalent to a function application of the form (+) x y which has the
atomic subterms (+), x and y at its leaves.)
Now the interpreter divides the leaves of the expression tree into “head” (or
“function”) and “parameter” (or “variable”) positions based on which leaves
are leftmost in a function application or not. Thus, in an expression like f
x y z, f is in the head or function position, while x, y and
z are in parameter or variable positions. (Note that in an infix
expression like x+y, (+) is the head symbol, not x, as the
expression is really parsed as (+) x y, see above.)
Identifiers in head positions are taken as literal function symbols by the
interpreter, while identifiers in variable positions denote, well,
variables. We also refer to this convention as the head = function rule. It
is quite intuitive and lets us get away without declaring the variables in
equations. (There are some corner cases not covered here, however. In
particular, Pure allows you to declare special “nonfix” symbols, if you need a
symbol to be recognized as a literal even if it occurs in a variable
position. This is done by means of a nonfix declaration, see
Symbol Declarations for details.)
Like in other functional languages, expressions are the central ingredient of
all Pure programs. All computation performed by a Pure program consists in the
evaluation of expressions, and expressions also form the building blocks of
the equational rules which are used to define the constants, variables,
functions and macros of a Pure program.
Pure’s expression syntax can be summarized in the following grammar rules:
expr ::= "\" prim_expr+ "->" expr
| "case" expr "of" rules "end"
| expr "when" simple_rules "end"
| expr "with" rules "end"
| "if" expr "then" expr "else" expr
| simple_expr
simple_expr ::= simple_expr op simple_expr
| op simple_expr
| simple_expr op
| application
application ::= application prim_expr
| prim_expr
rules ::= rule (";" rule)* [";"]
simple_rules ::= simple_rule (";" simple_rule)* [";"]
prim_expr ::= qualified_symbol
| number
| string
| "(" op ")"
| "(" left_op right_op ")"
| "(" simple_expr op ")"
| "(" op simple_expr ")"
| "(" expr ")"
| left_op expr right_op
| "[" exprs "]"
| "{" exprs (";" exprs)* [";"] "}"
| "[" expr "|" simple_rules "]"
| "{" expr "|" simple_rules "}"
exprs ::= expr ("," expr)*
op ::= qualified_symbol
left_op ::= qualified_symbol
right_op ::= qualified_symbol
(Note that the rule and simple_rule elements are part of the
definition syntax, which is explained in the Rule Syntax section.)
Typical examples of the different expression types are summarized in the
following table. Note that lambdas bind most weakly, followed by the special
case, when and with constructs, followed by
conditional expressions (if-then-else),
followed by the simple expressions. Operators are a part of the simple
expression syntax, and are parsed according to their declared precedences and
associativities (cf. Symbol Declarations). Function application binds
stronger than all operators. Parentheses can be used to group expressions and
override default precedences as usual.
Type |
Example |
Description |
Lambda |
\x->x+1 |
anonymous function |
Block |
case x of y = z; ... end |
pattern-matching conditional |
|
x when y = z; ... end |
local variable definition |
|
x with f y = z; ... end |
local function definition |
Conditional |
if x then y else z |
conditional expression |
Simple |
x+y, -x, x mod y |
operator application |
|
sin x, max a b |
function application |
Primary |
4711, 1.2e-3 |
number |
|
"Hello, world!\n" |
string |
|
foo, x, (+) |
function or variable symbol |
|
[1,2,3], {1,2;3,4} |
list and matrix |
|
[x,-y | x=1..n; y=1..m; x<y] |
list comprehension |
|
{i==j | i=1..n; j=1..m} |
matrix comprehension |
The Pure language provides built-in support for machine integers (32 bit),
bigints (implemented using GMP), floating point values (double precision IEEE
754) and character strings (UTF-8 encoded). These can all be denoted using the
corresponding literals described in Lexical Matters. Truth values are
encoded as machine integers; as you might expect, zero denotes false and any
non-zero value true, and the prelude also provides symbolic constants
false and true to denote these. Pure also supports generic C
pointers, but these don’t have a syntactic representation in Pure, except that
the predefined constant NULL may be used to denote a generic null
pointer; other pointer values need to be created with external C functions.
Finally, Pure also provides some built-in support for compound primaries in
the form of lists and matrices, although most of the corresponding operations
are actually defined in the prelude.
Together, these “atomic” types of expressions make up Pure’s primary
expression syntax. Here is a brief rundown of the primary expression types.
- Numbers: 4711, 4711L, 1.2e-3
- The usual C notations for integers (decimal: 1000, hexadecimal:
0x3e8, octal: 01750) and floating point values are all provided.
Integers can also be denoted in base 2 by using the 0b or 0B
prefix: 0b1111101000. Integer constants that are too large to fit into
machine integers are promoted to bigints automatically. Moreover, integer
literals immediately followed by the uppercase letter L are always
interpreted as bigint constants, even if they fit into machine integers.
This notation is also used when printing bigint constants, to distinguish
them from machine integers.
- Strings: "Hello, world!\n"
- String constants are double-quoted and terminated with a null character,
like in C. In contrast to C, strings are always encoded in UTF-8, and
character escapes in Pure strings have a more flexible syntax (borrowed
from the author’s Q language) which provides notations to specify any
Unicode character. Please refer to Lexical Matters for details.
- Function and variable symbols: foo, foo_bar, BAR, foo::bar
- These consist of the usual sequence of letters (including the underscore)
and digits, starting with a letter. Case is significant, thus foo,
Foo and FOO are distinct identifiers. The ‘_‘ symbol, when
occurring on the left-hand side of an equation, is special; it denotes the
anonymous variable which matches any value without actually binding a
variable. Identifiers can also be prefixed with a namespace identifier,
like in foo::bar. (This requires that the given namespace has already
been created, as explained under Namespaces in the Declarations section.)
- Operator symbols: +, ==, not
For convenience, Pure also provides you with a limited means to extend the
syntax of the language with special operator symbols by means of a
corresponding fixity declaration, as discussed in section Symbol
Declarations. Besides the usual infix, prefix and postfix operators,
Pure also provides outfix (bracket) and nonfix (nullary operator)
symbols. (Nonfix symbols actually work more or less like ordinary
identifiers, but the nonfix attribute tells the compiler that
when such a symbol occurs on the left-hand side of an equation, it is
always to be interpreted as a literal, cf. Variables in Equations.)
Operator (and nonfix) symbols may take the form of an identifier or a
sequence of punctuation characters, which may optionally be qualified with
a namespace prefix. These symbols must always be declared before use. Once
declared, they are always special, and can’t be used as ordinary
identifiers any more. However, like in Haskell, by enclosing an operator
in parentheses, such as (+) or (not), you can turn it into an
ordinary function symbol.
Note
The common operator symbols like +, -, *, / etc. are
all declared at the beginning of the prelude, see the Pure Library Manual
for a list of these. Arithmetic and relational operators mostly follow
C conventions. However, out of necessity (!, & and | are
used for other purposes in Pure) the logical and bitwise operations, as
well as the negated equality predicates are named a bit differently:
~, && and || denote logical negation, conjunction and
disjunction, while the corresponding bitwise operations are named
not, and and or. Moreover, following these conventions,
inequality is denoted ~=. Also note that && and || are
special forms which are evaluated in short-circuit mode (see Special
Forms below), whereas the bitwise connectives receive their arguments
using call-by-value, just like the other arithmetic operations.
- Lists: [x,y,z], x:xs
Pure’s basic list syntax is the same as in Haskell, thus [] is the
empty list and x:xs denotes a list with head element x and tail
list xs. The infix constructor symbol ‘:‘ is declared in
the prelude. The usual syntactic sugar for list values in brackets is
provided, thus [x,y,z] is exactly the same as x:y:z:[]. (This kind
of list value is also called a “proper” list. Pure also permits “improper”
list values such as 1:2:3 with a non-list value in the tail. These
aren’t of much use as ordinary list values, but are frequently used in
patterns or symbolic expressions such as x:xs where the tail usually
is a variable. Also, lists can be “lazy” in which case the tail is a
special kind of deferred value known as a “thunk”, see Lazy Evaluation
and Streams; technically, such lazy list values are improper lists,
too.)
There’s also a way to denote arithmetic sequences such as 1..5, which
denotes the list [1,2,3,4,5]. Haskell users should note the missing
brackets. In contrast to Haskell, Pure doesn’t use any special syntax for
arithmetic sequences, the ‘..‘ symbol is just an ordinary
infix operator declared and defined in the prelude. Sequences with
arbitrary stepsizes can be written by denoting the first two sequence
elements using the ‘:‘ operator, as in 1.0:1.2..3.0. To
prevent unwanted artifacts due to rounding errors, the upper bound in a
floating point sequence is always rounded to the nearest grid point. Thus,
e.g., 0.0:0.1..0.29 actually yields [0.0,0.1,0.2,0.3], as does
0.0:0.1..0.31.
- Tuples: (x,y,z)
Pure’s tuples are a bit unusual: They are constructed by just “pairing”
things using the ‘,‘ operator, for which the empty tuple
() acts as a neutral element (i.e., (),x is just x, as is
x,()). Pairs always associate to the right, meaning that x,y,z ==
x,(y,z) == (x,y),z, where x,(y,z) is the normalized representation.
This implies that tuples are always flat, i.e., there are no nested tuples
(tuples of tuples); if you need such constructs then you should use lists
instead.
Note that the parentheses are in fact not part of the tuple syntax in
Pure (they’re just used to group expressions). However, they will be
needed to include a tuple in a list or matrix. (E.g., [(1,2),3,(4,5)]
is a three element list consisting of the tuple 1,2, the integer
3, and another tuple 4,5.) Hence, tuples aren’t really primary
expressions at all, but we still include them here because they are often
used as a simpler replacement for lists, in particular in function
arguments and return values, when no elaborate hierarchical structure is
needed.
- Matrices: {1.0,2.0,3.0}, {1,2;3,4}, {1L,y+1;foo,bar}
Pure also offers matrices, a kind of two-dimensional arrays, as a built-in
data structure which provides efficient storage and element access. These
work more or less like their Octave/MATLAB equivalents, but using curly
braces instead of brackets. As indicated, commas are used to separate the
columns of a matrix, semicolons for its rows. In fact, the {...}
construct is rather general and allows you to construct new matrices from
any collection of individual elements (“scalars”) and submatrices,
provided that all dimensions match up. Here, any expression which doesn’t
yield a matrix denotes a scalar, which is considered to be a 1x1 matrix
for the purpose of matrix construction. The comma arranges submatrices in
columns, while the semicolon arranges them in rows. So, if both x and
y are nxm matrices, then {x,y} becomes an n x
2*m matrix consisting of all the columns of x followed by all the
columns of y. Likewise, {x;y} becomes a 2*n x m matrix
(all the rows of x above of all rows of y). In addition, {...}
constructs can be nested to an arbitrary depth. Thus {{1;3},{2;4}} is
another way to write the 2x2 matrix {1,2;3,4} in a kind of
“column-major” format (however, internally all matrices are stored in C’s
row-major format).
Note that {...} only behaves this way when constructing matrix
values. When used as a pattern on the left-hand side of equations, nested
matrices are matched literally, and variables can only match single
elements, not rows or columns. Thus the pattern {x,y} will only match
a 1x2 matrix and bind x and y to the two elements of the matrix.
Similarly, the pattern {{x,y},z} matches a (symbolic) 1x2 matrix which
has another matrix {x,y} as its first element.
Pure supports both numeric and symbolic matrices. The former are
homogeneous arrays of double, complex double or (machine) int matrices,
while the latter can contain any mixture of Pure expressions. Pure will
pick the appropriate type for the data at hand. If a matrix contains
values of different types, or Pure values which cannot be stored in a
numeric matrix, then a symbolic matrix is created instead (this also
includes the case of bigints, which are considered as symbolic values as
far as matrix construction is concerned). Numeric matrices use an internal
data layout that is fully compatible with the GNU Scientific Library
(GSL), and can readily be passed to GSL routines via the C interface. (The
Pure interpreter does not require GSL, however, so numeric matrices will
work even if GSL is not installed.)
More information about matrices and corresponding examples can be found in
the Examples section below.
- Comprehensions: [x,y | x=1..n; y=1..m; x<y], {f x | x=1..n}
Pure provides both list and matrix comprehensions as a convenient means to
construct list and matrix values from a “template” expression and one or
more “generator” and “filter” clauses. The former bind a pattern to values
drawn from a list or matrix, the latter are just predicates determining
which generated elements should actually be added to the result. Both list
and matrix comprehensions are in fact syntactic sugar for a combination of
nested lambdas, conditional expressions and “catmaps” (a collection of
operations which combine list or matrix construction and mapping a
function over a list or matrix, defined in the prelude), but they are
often much easier to write.
Matrix comprehensions work pretty much like list comprehensions, but
produce matrices instead of lists. List generators in matrix
comprehensions alternate between row and column generation so that most
common mathematical abbreviations carry over quite easily. Examples of
both kinds of comprehensions can be found in the Examples section below.
The rest of Pure’s expression syntax mostly revolves around the notion of
function applications. For convenience, Pure also allows you to declare pre-,
post-, out- and infix operator symbols, but these are in fact just syntactic
sugar for function applications; see Symbol Declarations for details.
Function and operator applications are used to combine primary expressions to
compound terms, also referred to as simple expressions; these are the data
elements which are manipulated by Pure programs.
As in other modern FPLs, function applications are written simply as
juxtaposition (i.e., in “curried” form) and associate to the left. This means
that in fact all functions only take a single argument. Multi-argument
functions are represented as chains of single-argument functions. For
instance, in f x y = (f x) y first the function f is applied to the
first argument x, yielding the function f x which in turn gets applied
to the second argument y. This makes it possible to derive new functions
from existing ones using partial applications which only specify some but
not all arguments of a function. For instance, taking the max function
from the prelude as an example, max 0 is the function which, for a given
x, returns x itself if it is nonnegative and zero otherwise. This
works because (max 0) x = max 0 x is the maximum of 0 and x.
Note
The major advantage of having curried function applications is that,
without any further ado, functions become first-class objects. That is,
they can be passed around freely both as parameters and as function return
values. Functions which take other functions as arguments and/or yield them
as results are also known as higher-order functions (HOFs). Much of the
power of functional programming languages stems from this feature, so the
treatment of functions as first-class values is generally considered as one
of the defining characteristics of functional languages.
Operator applications are written using prefix, postfix, outfix or infix
notation, as the declaration of the operator demands, but are just ordinary
function applications in disguise. As already mentioned, enclosing an operator
in parentheses turns it into an ordinary function symbol, thus x+y is
exactly the same as (+) x y. For convenience, partial applications of
infix operators can also be written using so-called operator sections. A
left section takes the form (x+) which is equivalent to the partial
application (+) x. A right section takes the form (+x) and is
equivalent to the term flip (+) x. (This uses the flip combinator
from the prelude which is defined as flip f x y = f y x.) Thus (x+) y
is equivalent to x+y, while (+x) y reduces to y+x. For instance,
(1/) denotes the reciprocal and (+1) the successor function. (Note
that, in contrast, (-x) always denotes an application of unary minus; the
section (+-x) can be used to indicate a function which subtracts x
from its argument.)
Some special notations are provided for conditional expressions as well as
anonymous functions (lambdas) and blocks of local function and variable
definitions.
- Conditional expressions: if x then y else z
- Evaluates to y or z depending on whether x is “true” (i.e., a
nonzero integer). An exception is raised if the condition is not an
integer.
- Lambdas: \x -> y
- These denote anonymous functions and work pretty much like in Haskell.
Pure supports multiple-argument lambdas (e.g, \x y -> x*y), as well as
pattern-matching lambda abstractions which match one or more patterns
against the lambda arguments, such as \(x,y) -> x*y. An exception is
raised if the actual lambda arguments do not match the given patterns.
- Case expressions: case x of rule; ... end
- Matches an expression, discriminating over a number of different cases,
similar to the Haskell case construct. The expression x is matched in
turn against each left-hand side pattern in the rule list, and the first
pattern which matches x gives the value of the entire expression, by
evaluating the corresponding right-hand side with the variables in the
pattern bound to their corresponding values. An exception is raised if the
target expression doesn’t match any of the patterns.
- When expressions: x when rule; ... end
- An alternative way to bind local variables by matching a collection of
subject terms against corresponding patterns, similar to Aardappel‘s
when construct. A single binding such as x when u = v end is
equivalent to case v of u = x end, but the former is often more
convenient to write. A when clause may contain multiple definitions,
which are processed from left to right, so that later definitions may
refer to the variables in earlier ones. This is exactly the same as
several nested single definitions, with the first binding being the
“outermost” one.
- With expressions: x with rule; ... end
- Defines local functions. Like Haskell’s where construct, but it can be
used anywhere inside an expression (just like Aardappel’s where, but
Pure uses the keyword with which better lines up with case and
when). Several functions can be defined in a single with clause,
and the definitions can be mutually recursive and consist of as many
equations as you want.
At the toplevel, a Pure program basically consists of rewriting rules (which
are used to define functions, macros and types), constant and variable
definitions, and expressions to be evaluated:
script ::= item*
item ::= "let" simple_rule ";"
| "const" simple_rule ";"
| "def" macro_rule ";"
| "type" type_rule ";"
| rule ";"
| expr ";"
These elements are discussed in more detail in the Rule Syntax section.
Also, a few additional toplevel elements are part of the declaration syntax,
see Declarations.
-
lhs = rhs;
Rewriting rules always combine a left-hand side pattern (which must be a
simple expression) and a right-hand side (which can be any kind of Pure
expression described above). The same format is also used in
with, when and case expressions. In
toplevel rules, with and case expressions, this basic
form can also be augmented with a condition if guard tacked on to the
end of the rule, where guard is an integer expression which determines
whether the rule is applicable. Moreover, the keyword otherwise
may be used to denote an empty guard which is always true (this is
syntactic sugar to point out the “default” case of a definition; the
interpreter just treats this as a comment). Pure also provides some
abbreviations for factoring out common left-hand or right-hand sides in
collections of rules; see the Rule Syntax section for details.
-
type lhs = rhs;
A rule starting with the keyword type defines a type predicate.
This works pretty much like an ordinary rewriting rule, except that only a
single right-hand side is permitted (which may also be omitted in some
cases) and the left-hand side may involve at most one argument expression;
see the Type Rules section for details. There’s also an alternative
syntax which lets you define types in a more abstract way and have the
compiler generate the type rules for you; this is described in the
Interface Types section.
-
def lhs = rhs;
A rule starting with the keyword def defines a macro
function. No guards or multiple right-hand sides are permitted here. Macro
rules are used to preprocess expressions on the right-hand side of other
definitions at compile time, and are typically employed to implement
user-defined special forms and simple kinds of optimization rules. See the
Macros section below for details and examples.
-
let lhs = rhs;
Binds every variable in the left-hand side pattern to the corresponding
subterm of the right-hand side (after evaluating it). This works like a
when clause, but serves to bind global variables occurring free
on the right-hand side of other function and variable definitions.
-
const lhs = rhs;
An alternative form of let which defines constants rather than
variables. (These are not to be confused with nonfix symbols which simply
stand for themselves!) Like let, this construct binds the
variable symbols on the left-hand side to the corresponding values on the
right-hand side (after evaluation). The difference is that const
symbols can only be defined once, and thus their values do not change
during program execution. This also allows the compiler to apply some
special optimizations such as constant folding.
-
expr;
A singleton expression at the toplevel, terminated with a semicolon, simply
causes the given value to be evaluated (and the result to be printed, when
running in interactive mode).
A few remarks about the scope of identifiers and other symbols are in order
here. Like most modern functional languages, Pure uses lexical or static
binding for local functions and variables. What this means is that the binding
of a local name is completely determined at compile time by the surrounding
program text, and does not change as the program is being executed. In
particular, if a function returns another (anonymous or local) function, the
returned function captures the environment it was created in, i.e., it becomes
a (lexical) closure. For instance, the following function, when invoked with
a single argument x, returns another function which adds x to its
argument:
> foo x = bar with bar y = x+y end;
> let f = foo 99; f;
bar
> f 10, f 20;
109,119
This works the same no matter what other bindings of x may be in effect
when the closure is invoked:
> let x = 77; f 10, (f 20 when x = 88 end);
109,119
Global bindings of variable and function symbols work a bit differently,
though. Like many languages which are to be used interactively, Pure binds
global symbols dynamically, so that the bindings can be changed easily at
any time during an interactive session. This is mainly a convenience for
interactive usage, but works the same no matter whether the source code is
entered interactively or being read from a script, in order to ensure
consistent behaviour between interactive and batch mode operation.
So, for instance, you can easily bind a global variable to a new value by just
entering a corresponding let command:
> foo x = c*x;
> foo 99;
c*99
> let c = 2; foo 99;
198
> let c = 3; foo 99;
297
This works pretty much like global variables in imperative languages, but note
that in Pure the value of a global variable can only be changed with a
let command at the toplevel. Thus referential transparency is
unimpaired; while the value of a global variable may change between different
toplevel expressions, it will always take the same value in a single
evaluation.
Similarly, you can also add new equations to an existing function at any
time:
> fact 0 = 1;
> fact n::int = n*fact (n-1) if n>0;
> fact 10;
3628800
> fact 10.0;
fact 10.0
> fact 1.0 = 1.0;
> fact n::double = n*fact (n-1) if n>1;
> fact 10.0;
3628800.0
> fact 10;
3628800
(In interactive mode, it is even possible to completely erase a definition,
see section Interactive Usage for details.)
So, while the meaning of a local symbol never changes once its definition has
been processed, toplevel definitions may well evolve while the program is
being processed, and the interpreter will always use the latest definitions at
a given point in the source when an expression is evaluated. This means that,
even in a script file, you have to define all symbols needed in an evaluation
before entering the expression to be evaluated.
Basically, the same rule syntax is used in all kinds of global and local
definitions. However, some constructs (specifically, when,
let, const, type and def) use a
variation of the basic rule syntax which does away with guards and/or multiple
left-hand or right-hand sides. The syntax of these elements is captured by the
following grammar rules:
rule ::= pattern ("|" pattern)* "=" expr [guard]
(";" "=" expr [guard])*
type_rule ::= pattern ("|" pattern)* [ "=" expr [guard] ]
macro_rule ::= pattern ("|" pattern)* "=" expr
simple_rule ::= pattern = expr | expr
pattern ::= simple_expr
guard ::= "if" simple_expr
| "otherwise"
| guard "when" simple_rules "end"
| guard "with" rules "end"
When matching against a function or macro call, or the subject term in a
case expression, the rules are always considered in the order in
which they are written, and the first matching rule (whose guard evaluates to
a nonzero value, if applicable) is picked. (Again, the when
construct is treated differently, because each rule is actually a separate
definition.)
The left-hand side of a rule is a special kind of simple expression, called a
pattern. The variables in a pattern serve as placeholders which are bound to
corresponding values when the rule is applied to a target expression. (As
already mentioned, the variables in a pattern are the identifiers in “variable
positions”, cf. Variables in Equations.) To these ends, the pattern is
matched against the target expression, i.e., the literal parts of the
pattern are compared against the target expression and, if everything matches
up, the variables in the pattern are bound to (set to the value of) the
corresponding subterms of the target expression.
Patterns are pervasive in Pure; they are used on the left-hand side of
function and macro definitions, just as well as in global and local variable
definitions. For instance, the following variable definition matches the
result of evaluating the right-hand side list expression against the pattern
x:y:xs and binds the variables x, y and xs to the first two
elements of the resulting list and xs to the list of remaining elements,
respectively.
> let x:y:xs = 1..10;
> x,y,xs;
1,2,[3,4,5,6,7,8,9,10]
The same works with local variable definitions:
> x,y,xs when x:y:xs = 1..10 end;
1,2,[3,4,5,6,7,8,9,10]
Or with case expressions:
> case 1..10 of x:y:xs = x,y,xs end;
1,2,[3,4,5,6,7,8,9,10]
The arguments of functions (and macros) are handled in the same fashion, too:
> swap [x,y] = [y,x];
> swap [1,2];
[2,1]
However, in this case you can keep adding more equations to make the function
work with different argument patterns:
> swap (x,y) = y,x;
> swap {x,y} = {y,x};
> swap (1,2); swap {1,2};
2,1
{2,1}
This doesn’t only work with the usual predefined aggregates (such as lists,
tuples and matrices, as shown in the above examples), but with any kind of
Pure expression:
> foo (bar x) = x+1;
> foo (bar 99);
100
If a pattern fails to match the target expression, the corresponding rule
isn’t applicable. In the case of global and local variable bindings, this
indicates an error which raises a corresponding exception:
> let x:y:xs = [1];
<stdin>, line 12: failed match while evaluating 'let x:y:xs = [1]'
However, for the rules in a function definition a match failure just means
that the corresponding rule will be bypassed and other rules will be tried
instead. Failing that, the target expression becomes a normal form which is
simply returned as is:
> swap [1,2,3];
swap [1,2,3]
This may come as a surprise (other functional languages will give you an error
in such cases), but is a crucial feature of term rewriting languages, as it
opens the door to symbolic evaluation techniques, see Definitions and
Expression Evaluation.
Any kind of legal Pure expression can be used as a pattern. Syntactically,
patterns are simple expressions, thus special expressions need to be
parenthesized if they occur in a pattern. (Special expressions in a pattern
are automatically translated to their quoted representations, see The
Quote. These are typically used in macro definitions, see the Macros
section for details.)
Also note that the pattern matching capabilities for matrices are somewhat
limited, as a matrix pattern can only match a matrix with exactly the same
dimensions as the pattern. To match a matrix of arbitrary dimensions, you’ll
have to use the built-in matrix type (or a user-defined type
derived from that), see Matrix Computations for some examples.
The ‘_‘ symbol is special in patterns; it denotes the anonymous variable
which matches an arbitrary value (independently for all occurrences) without
actually binding a variable. For instance:
This will match the application of foo to any combination of two
arguments (and just ignore the values of these arguments).
Constants in patterns must be matched literally. For instance:
This will only match an application of foo to the machine integer 0,
not 0.0 or 0L (even though these compare equal to 0 using the
‘==‘ operator).
In contrast to Haskell, patterns may contain repeated variables (other than
the anonymous variable), i.e., they may be non-linear. Thus rules like the
following are legal in Pure, and will only be matched if all occurrences of
the same variable in the left-hand side pattern are matched to the same
value:
> foo x x = x;
> foo 1 1;
1
> foo 1 2;
foo 1 2
Non-linear patterns are particularly useful for computer algebra where you
will frequently encounter rules such as the following:
> x*y+x*z = x*(y+z);
> a*(3*4)+a*5;
a*17
The notion of “sameness” employed here is that of syntactical identity, which
means that the matched subterms must be identical in structure and content.
The prelude provides syntactic equality as a function same and a
comparison predicate ‘===‘. Thus the above definition of foo
is roughly equivalent to the following:
It is important to note the differences between syntactic equality embodied by
same and ‘===‘, and the “semantic” equality operator
‘==‘. The former are always defined on all terms, whereas
‘==‘ is only available on data where it has been defined
explicitly, either in the prelude or by the programmer. Also note that
‘==‘ may assert that two terms are equal even if they are
syntactically different. Consider, e.g.:
This distinction is actually quite useful. It gives the programmer the
flexibility to define ‘==‘ in any way that he sees fit, which is
consistent with the way the other comparison operators like ‘<‘
and ‘>‘ are handled in Pure.
Patterns may also contain the following special elements which are not
permitted in right-hand side expressions:
- A Haskell-style “as” pattern of the form variable @ pattern
binds the given variable to the expression matched by the subpattern
pattern (in addition to the variables bound by pattern itself). This
is convenient if the value matched by the subpattern is to be used on the
right-hand side of an equation.
- A left-hand side variable (including the anonymous variable) may be followed
by a type tag of the form :: name, where name is either one of the
built-in type symbols int, bigint, double, string,
matrix, pointer, or an identifier denoting a user-defined data
type. The variable can then match only values of the designated type. Thus,
for instance, ‘x::int‘ only matches machine integers. See the Type
Tags section below for details.
To these ends, the expression syntax is augmented with the following grammar
rule (but note that this form of expression is in fact only allowed on the
left-hand side of a rule):
prim_expr ::= qualified_identifier
("::" qualified_identifier | "@" prim_expr)
As shown, both “as” patterns and type tags are primary expressions, and the
subpattern of an “as” pattern is a primary expression, too. Thus, if a
compound expression is to be used as the subpattern, it must be
parenthesized. For instance, the following function duplicates the head
element of a list:
Note that if you accidentally forget the parentheses around the subpattern
x:_, you still get a syntactically correct definition:
But this gets parsed as (foo xs@x):_ = x:xs, which is most certainly not
what you want. It is thus a good idea to just always enclose the subpattern
with parentheses in order to prevent such glitches.
Note
Another pitfall is that the notation foo::bar is also used to
denote “qualified symbols” in Pure, cf. Namespaces. Usually this will be
resolved correctly, but if foo happens to also be a valid namespace
then most likely you’ll get an error message about an undeclared symbol.
You can always work around this by adding spaces around the ‘::‘
symbol, as in foo :: bar. Spaces are never permitted in qualified
symbols, so this makes it clear that the construct denotes a type tag. The
same applies if the variable or the tag is a qualified identifier; in this
case they should always be separated by whitespace.
Like Lisp, Pure is essentially a typeless language and doesn’t really have a
built-in notion of “data types”. Rather, all data belongs to the same universe
of terms. However, for convenience it is possible to describe data domains by
means of (unary) type predicates which may denote arbitrary sets of
terms. The names of these type predicates can then be used as type tags on
variables, so that they can only be matched by values of the given type.
We have to emphasize here that Pure’s notion of types has nothing to do with
static typing. Type tags are merely used at runtime to restrict the kind of
data that can be matched by a rule (and by the compiler to generate better
code in some cases). But they will never cause the compiler to impose a static
typing discipline and spit out corresponding “type errors”. (This wouldn’t
make any sense in Pure anyway, as failure to match any of the rules given in
the definition of a function simply means that a function application is in
normal form.)
Some basic types are built into the language. The corresponding tags enable
you to match the built-in types of terms for which there is no way to spell
out all “constructors”, as there are infinitely many (or none, as in the case
of pointer values which are constructed and inspected using
special primitives, but are otherwise “opaque” at the Pure level).
Specifically, the following data types are built-in (in fact, the pattern
matcher has special knowledge about these so that they can be matched very
efficiently):
-
type int
The type of machine integers.
-
type bigint
The type of arbitrary precision integers (GMP bigints).
-
type double
The type of double precision floating point numbers.
-
type string
The type of character strings.
-
type matrix
The type of all numeric and symbolic matrix values.
-
type pointer
The type of C pointer values.
Pure’s standard library provides additional data types along with the
corresponding operations, such as rational and complex numbers, lists, tuples
and the container data types (sets, dictionaries, etc.). These are all
described in the Pure Library Manual.
You can define your own data types using a special kind of rule syntax which
is explained in Type Rules below. For instance, we might represent points
in the plane using a constructor symbol Point which gets applied to pairs
of coordinates. We can then define the point data type as follows:
This introduces the type symbol point and specifies that this type
consists of terms of the form Point x y. We can now equip this data type
with an operation point to construct a point from its coordinates, two
operations xcoord and ycoord to retrieve the coordinates, and an
operation move to change the coordinates to the given values:
point x y = Point x y;
xcoord (Point x y) = x;
ycoord (Point x y) = y;
move (Point _ _) x y = Point x y;
Next we might define a function translate which shifts the coordinates of
a point by a given amount in the x and y directions as follows:
translate x y p::point = move p (xcoord p+x) (ycoord p+y);
Note the use of point as a type tag on the p variable. By these means,
we can ensure that the argument is actually an instance of the point data type
we just defined. The type tag acts just like an extra guard of the equation
defining translate, but all the necessary type checking is done
automatically during pattern matching. This is often more convenient (and,
depending on the implementation, the compiler may generate more efficient code
for a type tag than for an ordinary guard).
The translate function can be invoked as follows:
> let p::point = point 3 3;
> p; translate 1 2 p;
Point 3 3
Point 4 5
One important point to note here is that translate can be defined without
knowing or assuming anything about the internal representation of the
point data type. We have defined point as a concrete data type in
this example, making its constructor and internal structure visible in the
rest of the program. This is often convenient, but the Point constructor
might just as well be hidden by making it a private member of some namespace
(cf. Namespaces), so that all accesses to the data structure would have to be
done through the provided operations. Such a data type is also known as an
abstract data type (ADT).
Note
As we’ve already seen, Pure has some powerful capabilities which
enable you to write functions to inspect and manipulate terms in a
completely generic fashion. Thus the internal structure of term data is
never truly opaque in Pure and it is always possible to break the
“abstraction barrier” provided by an ADT. But if the user of an ADT plays
such dirty tricks to wreak havoc on the internal representation of an ADT,
he gets what he deserves.
Pure provides some additional facilities to ease the handling of abstract data
types. Specifically, instead of defining point as a concrete data type
using a type rule, we might also specify it as an interface type
which merely lists the supported operations as follows:
interface point with
xcoord p::point;
ycoord p::point;
move p::point x y;
end;
We can implement this type the same way as before:
point x y = Point x y;
xcoord (Point x y) = x;
ycoord (Point x y) = y;
move (Point _ _) x y = Point x y;
The definition of the translate function is also unchanged:
translate x y p::point = move p (xcoord p+x) (ycoord p+y);
The difference is that now the structure of members of the type is not made
explicit anywhere in the definition of the type. Instead, the compiler
figures out which data matches the point tag on its own. We can check the
actual term patterns making up the point type with the show interface
command:
> show interface point
type point (Point x y);
As you can see, the compiler derived our previous definition of the type. But
in fact translate will now work with any data type which implements the
point interface (i.e., provides the xcoord, ycoord and move
operations), so we may swap out the underlying data structure on a whim. For
instance, if we’d like to use vectors instead of constructor terms, all we
have to do is to provide a corresponding construction function and implement
the interface operations:
vpoint x y = {x,y};
xcoord {x,y} = x;
ycoord {x,y} = y;
move {_,_} x y = {x,y};
After these definitions the new data representation works just fine with
existing point operations such as translate:
> show interface point
type point (Point x y);
type point {x,y};
> let p::point = vpoint 3 3;
> p; translate (1,2) p;
{3,3}
{4,5}
This separation of interface and implementation of a data structure is an
important ingredient of software engineering techniques. More examples and
detailed explanations of Pure’s notions of type predicates and interface types
can be found in the Type Rules and Interface Types sections below.
The most general type of rule, used in function definitions and
case expressions, consists of a left-hand side pattern, a
right-hand side expression and an optional guard. The left-hand side of a rule
can be omitted if it is the same as for the previous rule. This provides a
convenient means to write out a collection of equations for the same left-hand
side which discriminates over different conditions:
lhs = rhs if guard;
= rhs if guard;
...
= rhs otherwise;
For instance:
fact n = n*fact (n-1) if n>0;
= 1 otherwise;
Pure also allows a collection of rules with different left-hand sides but the
same right-hand side(s) to be abbreviated as follows:
This is useful if you need different specializations of the same rule which
use different type tags on the left-hand side variables. For instance:
fact n::int |
fact n::double |
fact n = n*fact(n-1) if n>0;
= 1 otherwise;
In fact, the left-hand sides don’t have to be related at all, so that you can
also write something like:
However, this construct is most useful when using an “as” pattern to bind a
common variable to a parameter value after checking that it matches one of
several possible argument patterns (which is slightly more efficient than
using an equivalent type-checking guard). E.g., the following definition binds
the xs variable to the parameter of foo, if it is either the empty
list or a list starting with an integer:
foo xs@[] | foo xs@(_::int:_) = ... xs ...;
The same construct also works in case expressions, which is
convenient if different cases should be mapped to the same value, e.g.:
case ans of "y" | "Y" = 1; _ = 0; end;
Sometimes it is useful if local definitions (when and
with) can be shared by the right-hand side and the guard of a
rule. This can be done by placing the local definitions behind the guard, as
follows (we only show the case of a single when clause here, but of
course there may be any number of when and with clauses
behind the guard):
lhs = rhs if guard when defns end;
Note that this is different from the following, which indicates that the
definitions only apply to the guard but not the right-hand side of the rule:
lhs = rhs if (guard when defns end);
Conversely, definitions placed before the guard only apply to the right-hand
side but not the guard (no parentheses are required in this case):
lhs = rhs when defns end if guard;
An example showing the use of a local variable binding spanning both the
right-hand side and the guard of a rule is the following quadratic equation
solver, which returns the (real) solutions of the equation x^2+p*x+q = 0
if the discriminant d = p^2/4-q is nonnegative:
> using math;
> solve p q = -p/2+sqrt d,-p/2-sqrt d if d>=0 when d = p^2/4-q end;
> solve 4 2; solve 2 4;
-0.585786437626905,-3.41421356237309
solve 2 4
Note that the above definition leaves the case of a negative discriminant
undefined.
As already mentioned, when, let and const use
a simplified kind of rule syntax which just consists of a left-hand and a
right-hand side separated by the equals sign. In this case the meaning of the
rule is to bind the variables in the left-hand side of the rule to the
corresponding subterms of the value of the right-hand side. This is also
called a pattern binding.
Guards or multiple left-hand or right-hand sides are not permitted in these
rules. However, it is possible to omit the left-hand side if it is just the
anonymous variable ‘_‘ by itself, indicating that you don’t care about the
result. The right-hand side is still evaluated, if only for its side-effects,
which is handy, e.g., for adding debugging statements to your code. For
instance, here is a variation of the quadratic equation solver which also
prints the discriminant after it has been computed:
> using math, system;
> solve p q = -p/2+sqrt d,-p/2-sqrt d if d>=0
> when d = p^2/4-q; printf "The discriminant is: %g\n" d; end;
> solve 4 2;
The discriminant is: 2
-0.585786437626905,-3.41421356237309
> solve 2 4;
The discriminant is: -3
solve 2 4
Note that simple rules of the same form lhs = rhs are also used in macro
definitions (def), to be discussed in the Macros section. In
this case, however, the rule denotes a real rewriting rule, not a pattern
binding, hence the left-hand side is mandatory in these rules.
In Pure the definition of a type takes a somewhat unusual form, since it is
not a static declaration of the structure of the type’s members, but rather an
arbitrary predicate which determines through a runtime check which terms
belong to the type. Thus the definition of a type looks more like an ordinary
function definition (and that’s essentially what it is, although types live in
their own space where they can’t be confused with functions of the same name).
The definition of a type thus consists of one or more type rules which
basically have the same format as the general rules, but with the keyword
type in front of each rule. Also, each left-hand side must have at
most one argument pattern and exactly one right-hand side. Hence, if the
definition of a type requires several right-hand sides, you normally have to
write a separate type rule for each of them. Multiple left-hand
sides work the same as in the general rule format, though.
The identifier in the head of the left-hand side of a type rule is the name of
the type which can then be used as a type tag in other equations, cf. Type
Tags. This is just a normal, possibly qualified identifier subject to the
same namespace mechanisms as other symbols; see Namespaces for details.
However, as the type symbol only gets used as a type tag, it can never collide
with function and variable symbols and hence the same symbol can be used both
as a type and as a function or variable name.
A collection of type rules specifies a predicate, i.e. a unary, truth-valued
function which denotes a set of terms. The type consists precisely of those
terms for which the type predicate yields true. For instance, the
following type defines the type triple as the set of all tuples with
exactly three elements:
type triple (x,y,z) = ~tuplep z;
Note that the type check consists of two parts here: The left-hand side
pattern (x,y,z) restricts the set to all tuples with at least three
elements. The right-hand side ~tuplep z then verifies that the last
component z is not a tuple itself, and thus the entire tuple consists of
exactly three elements.
Another important point here is that the definition of the triple
predicate is partial, as the given rule only applies to tuples with at least
three elements. A value will only match the triple type tag if the
predicate explicitly returns true; otherwise the match will fail, no
matter what the result is (and even if the predicates just fails, i.e.,
returns an unevaluated normal form). Thus there is no need to make the
predicate work on all terms (and in fact there are good reasons to not do
so, see below).
In general, you should try to make your type definitions as specific as
possible. This makes it possible to extend the predicate later, just like Pure
allows you to extend the definition of a function to new types of arguments.
For instance, if you later decide that lists with three elements should be
considered as triples, too, then you may add the following type rule:
type triple [x,y,z] = true;
This makes it possible to define a type in a piecemeal fashion. Each
subsequent rule enlarges the term set of the type. Conversely, consider a
definition like:
type pair x = tuplep x && #x==2;
In this case the type rule applies to all values x and thus the type
definition is complete; there is no way to extend it later. Whether to prefer
the former or latter kind of definition depends on the situation. If you want
to keep a type extensible, so that you can later make existing definitions of
operations on the type work with new data representations, then you should use
the former approach, otherwise the latter.
As an example for an extensible type definition, consider the following type
nat which denotes the type of positive (machine) integers:
This definition is complete for the case of machine integers, but allows the
type to be extended for other base types, and we’ll do that in a moment. But
first let’s define the factorial on nat values as follows:
fact n::nat = if n==1 then 1 else n * fact (n-1);
Because of the type tag on the left-hand side, this function works on positive
machine integers, but nothing else:
> map fact (0..10);
[fact 0,1,2,6,24,120,720,5040,40320,362880,3628800]
> fact 10L;
fact 10L
But if we later decide that positive bigints should be considered as members
of nat as well, we can simply add another rule for the nat type:
type nat x::bigint = x>0;
Et voila, our fact routine now magically works with bigints, too:
> map fact (0L..10L);
[fact 0L,1,2L,6L,24L,120L,720L,5040L,40320L,362880L,3628800L]
Note that we did all this without ever touching our original definition of
fact. This works because the bigint data type already provides all the
operations which we expect to use with the nat type. Pulling off this
trick with other, more exotic kinds of data requires more preparation, since
we’ll first have to provide the required operations. In this case, we need at
least multiplication, as well as comparisons with 1 and subtraction by 1. For
instance, and just for the fun of it, let’s implement our own variation of the
nat type using Peano arithmetic:
type nat (s x) = true;
// addition
x + 0 = x;
x + 1 = s x;
x + s y = s (x+y);
// multiplication
x * 0 = 0;
x * 1 = x;
x * s y = x + x*y;
// subtract 1
s x - 1 = x;
// comparison with 0 and 1
s x == 0 = false;
s x == 1 = x == 0;
This implements just the bare bones, but that should be enough to make
fact work. Let’s give it a try:
> fact (s (s (s 0)));
s (s (s (s (s (s 0)))))
So, counting the s‘s, the factorial of 3 is 6. Works! (It goes without
saying, though, that this implementation of nat is not very practical;
you’ll get mountains of s‘s for larger values of n.)
As you can see, a type definition may in general consist of many type rules
which may be scattered out over different parts of a program. This works
in exactly the same way as with ordinary functions.
There’s an additional convenience provided for type rules, namely that the
right-hand side may be omitted if it’s just true. For instance, the
rule
from above can also be written simply as:
This kind of notation is particularly convenient for “algebraic types” which
are usually given by a collection of constructors with different arities. For
instance, a binary tree data type might be defined as follows (here we employ
the | symbol to separate the different left-hand sides so that we can give
all the constructor patterns in one go):
type bintree (tip value) | bintree (bin left right);
This method is also useful if you define your own abstract data types. In this
case you’re free to choose any suitable representation, so you might just wrap
up all data objects of the type with a special constructor symbol, which makes
checking the type simple and efficient. This is also the approach taken in the
point example in Type Tags above, as well as by the container data
types in the standard library.
The same notation can also be used to quickly make one type a “subtype” of
another, or to create a type which is the union of several existing types. The
following example can be found in the standard library:
type integer x::int | integer x::bigint;
A type rule can also take the form of a function definition without arguments.
The corresponding right-hand side may either be another type symbol, or any
kind of closure denoting a (curried) type predicate. In this case the defined
type is simply an alias for the type denoted on the right-hand side. This is
often done, e.g., for numeric types, to document that they actually stand for
special kinds of quantities:
type speed = double;
type size = int;
Note that the definition of a type alias is always complete; there’s no way to
extend the corresponding type later. Therefore type aliases are normally
resolved at compile time, so that they incur no additional runtime cost. For
instance:
> half x::speed = x/2;
> show half
half x::double = x/2;
(If necessary, this “type folding” can also be disabled with the
--nofold pragma.)
Finally, it’s also possible to just specify the type name, without giving the
right-hand side:
This doesn’t have any effect other than just declaring the type symbol, so
that it can be used as a type tag in subsequent definitions. You then still
have to give a proper definition of the type later (either as an explicit
predicate or an alias).
Type aliases can also be used to quickly turn an existing predicate into a
“convenience” type which can be used as a tag on the left-hand side of
equations. The prelude defines a number of these, see Prelude Types.
For instance:
Conversely, you can turn any type tag into an ordinary predicate which can be
used on the right-hand side of other definitions. To these ends, the prelude
provides the typep predicate which takes a type symbol and the value
to be checked as arguments. For instance:
type odd x::int = x mod 2;
type even x::int = ~odd x;
odd x = typep odd x;
even x = typep even x;
With those definitions you get:
> map odd (0..10);
[0,1,0,1,0,1,0,1,0,1,0]
> map even (0..10);
[1,0,1,0,1,0,1,0,1,0,1]
There’s one caveat here. As the type symbol passed to typep gets
evaluated in normal code you have to be careful if the symbol is also defined
as a parameterless function or a variable; in such a case you’ll have to quote
the symbol, as described in section The Quote. For instance, we might
rewrite the above definitions as follows, giving “pointless” definitions of
the odd and even predicates in terms of typep:
type odd x::int = x mod 2;
type even x::int = ~odd x;
odd = typep ('odd);
even = typep ('even);
Note that the quotes on odd and even are really needed here to prevent
the predicate definitions from looping. If you need this a lot then you might
define a little helper macro (cf. Macros) which quotes the type symbol in an
automatic fashion:
def typep ty::symbol = typep ('ty);
(However, this gets in the way if you want to check for computed type symbols,
that’s why this macro isn’t defined in the prelude.)
Pure places no a priori restrictions on the rules defining a data type (other
than that they must either define a unary predicate or an alias for an
existing data type). As far as Pure is concerned, types are just subsets of
the universe of terms. Thus any type of relation between two data types is
possible; they might be unrelated (disjoint) term sets, one may be a subset of
another, or they might be related in some other way (some terms may be members
of both types, while others aren’t).
For instance, consider the types nat and odd from above. Both are
subtypes of the int type (assuming our original definition of nat as
the positive int values), but neither is a subtype of the other. It’s
sometimes useful to define the “intersection type” of two such types, which
can be done in a straightforward way using the logical conjunction of the two
type predicates:
type nat x::int = x>0;
type odd x::int = x mod 2;
type odd_nat x = typep nat x && typep odd x;
Similarly, a variation of the integer union type from above could be
defined using logical disjunction (this employs the intp and
bigintp predicates from the prelude):
type myinteger x = intp x || bigintp x;
(Note that this isn’t quite the same as the previous definition, which uses
explicit patterns in order to make the definition extensible.)
Since the right-hand side of a type definition may in general be any
predicate, it is up to the programmer to ensure that the definition of a type
is actually computable. In fact, you should strive for the best possible
efficiency in type predicates. A type definition which has worse than O(1)
complexity may well be a serious performance hog depending on the way in which
it is used, see Recursive Types in the Caveats and Notes section for
more information about this.
Finally, note that in general it may be hard or even impossible to predict
exactly when the code of a type definition will be executed at runtime. Thus,
as a general rule, a type definition should not rely on side effects such as
doing I/O (except maybe for debugging purposes), modifying references or
external data structures via C pointers, etc.
Besides the “concrete” types described in the previous section, Pure provides
another, more abstract way to characterize a type through the collection of
operations it supports. These interface types work pretty much like in
Google’s Go programming language. They provide a safe form of “Duck typing”
in which the operations available on a type are stated explicitly, and hence
members of the type are always known to provide all of the listed operations.
Syntactically, an interface in Pure is a toplevel element (cf. Toplevel)
which gives the type name along with a collection of patterns, the so-called
signature which specifies the manifest operations of the type:
item ::= "interface" qualified_identifier
"with" interface_item* "end" ";"
interface_item ::= pattern ";"
| "interface" qualified_identifier ";"
Interfaces thus consist of two kinds of items:
- The patterns, which indicate which operations are supported by the type, and
which arguments they expect. This may be anything that can occur as the
left-hand side of an ordinary function definition, cf. General Rules.
- The name of another interface type. This causes the signature of the named
interface type to be included in the interface type being defined, which
effectively turns the new interface type into a subtype of the existing one.
The gist of an interface is in its patterns, more precisely: in the pattern
variables which have the name of the interface as a type tag. The precise
meaning of the patterns is as follows:
- The patterns are matched against the left-hand sides of ordinary function
definitions. If a left-hand side matches, any argument pattern substituted
for a variable tagged with the interface type becomes a “candidate pattern”
of the type.
- The type consists of all candidate patterns which can be matched by some
candidate pattern of each interface function. That is, candidate patterns
which are only supported by some but not all of the interface functions, are
eliminated.
- Finally, all trivial candidate patterns (x where x is just a
variable without any type tag, which thus matches any value) are
eliminated as well.
Interface patterns often take a simple form like the following,
interface foo with foo x::foo y z; end;
specifying the number of arguments of the interface function along with the
position of the interface type argument. However, general patterns are
permitted, in order to further restrict the left-hand sides of the function
definitions to be taken into consideration. Specifically, note that type
tags other than the interface type must always be matched literally on
the left-hand sides of equations. Thus,
interface foo with foo x::foo y::int; end;
matches any rule of the form
but not:
foo x 0 = ...;
foo x y::bar = ...;
(unless bar happens to be an alias of the int type, of course). In
such cases it is necessary to explicitly add these patterns to the
interface if you want them to be included.
Interface patterns may contain the interface type tag any number of times,
yielding candidate patterns for each occurrence of the interface type tag
in the pattern. For instance, here is a quick way to determine the type of
all “addable” data structures in the prelude (this uses the interactive
show interface command to list the patterns actually matched by an
interface type, cf. The show Command):
> interface addable with x::addable + y::addable; end;
> show interface addable
type addable x::int;
type addable x::double;
type addable x::bigint;
type addable s::string;
type addable [];
type addable xs@(_:_);
On the other hand, interfaces may also contain “static” patterns which do
not include the interface type as a tag at all, such as:
interface foo with bar x::bar y; end;
These do not contribute anything to the candidate patterns of the type, but
do restrict the type just like the other patterns, in that the type will be
empty unless the static patterns are all “implemented”. In the example
above, this means that the foo type will be empty unless the bar
function is defined and takes an element of the bar type as its first
argument.
An interface may also be empty, in which case it matches any value. Thus,
is just a fancy way to define the type:
Interfaces can be composed in a piecemeal fashion, by adding more interface
patterns. Thus,
interface foo with foo x::foo; end;
interface foo with bar x::foo; end;
is equivalent to:
interface foo with foo x::foo; bar x::foo; end;
It is also possible to include one interface in another, which effectively
establishes a subtype relationship. For instance, here’s yet another way to
define the foo interface above:
interface bar with
bar x::bar;
end;
interface foo with
foo x::foo;
interface bar;
end;
This has the effect of including the signature of bar in foo (while
renaming the interface type tags in the bar signature accordingly):
> show foo
interface foo with
foo x::foo;
bar x::foo;
end;
Note
Including interfaces is a static operation. Only the interface
patterns known at the point of inclusion become part of the including
interface; refining the included interface later has no effect on the set
of included patterns. In particular, this also prevents circular interface
definitions.
When composing interfaces in this fashion, it is easy to end up with
duplicate interface patterns from various sources. The compiler removes
such duplicates, even if they only match up to the renaming of variables.
For instance:
> show bar foo
interface bar with
bar x::bar;
end;
interface foo with
foo x::foo;
bar x::foo;
end;
> interface baz with
> interface foo; interface bar;
> foo y::baz;
> end;
> show baz
interface baz with
foo x::baz;
bar x::baz;
end;
Also note that, despite the obvious similarities between interfaces and
classes in object-oriented programming, they are really different things.
The former are essentially just signatures of functions living elsewhere,
whereas the latter also include data layouts and method implementations.
More on the similarities and differences of interfaces and classes can be
found in the Go FAQ.
Let’s now take a look at the example of a stack data structure to see how this
all works in practice:
interface stack with
push s::stack x;
pop s::stack;
top s::stack;
end;
Note the use of the type tag stack in the operation patterns, which marks
the positions of stack arguments of the interface operations. The
interface tells us that a stack provides three operations push, pop
and top which each take a stack as their first argument; also, push
takes two arguments, while pop and top just take a single (stack)
argument.
This information is all that the compiler needs to figure out which terms are
members of the stack data type. To these ends, the compiler looks at
existing definitions of push, pop and top and extracts the
patterns for arguments marked with the stack tag in the interface. The
stack patterns implemented by all of the interface operations make up
the stack type; i.e., the members of the type are all the instances of
these patterns.
Right now our stack type doesn’t have any members, because we didn’t
implement the interface operations yet, so let’s do this now. For instance, to
implement stacks as lists, we might define:
push xs@[] x | push xs@(_:_) x = x:xs;
pop (x:xs) = xs;
top (x:xs) = x;
This is also known as “instantiating” the type. In addition, we will need an
operation to create an initial stack value. The following will do for our
purposes:
This yields a stack with the given initial contents. Let’s give it a go:
> top (push (stack []) 99);
99
Looks good so far. We can also check the actual definition of the type in
terms of its type rules using the show interface command:
> show interface stack
type stack xs@(_:_);
Wait, something seems to be wrong there. The empty list pattern of the
push function is missing, where did it go? Let’s restart the interpreter
with warnings enabled (-w) and retype the above definitions. The
compiler then tells us:
> show interface stack
warning: interface 'stack' may be incomplete
warning: function 'pop' might lack a rule for 'xs@[]'
warning: function 'top' might lack a rule for 'xs@[]'
type stack xs@(_:_);
See? A pattern is only considered part of the type if it is supported by all
the interface operations. Since the pop and top operations don’t have
any rules for empty list arguments, empty lists are excluded from the type. We
can fix this quite easily by adding the following “error rules” which handle
this case:
> pop [] = throw "empty stack";
> top [] = throw "empty stack";
> show interface stack
type stack xs@[];
type stack xs@(_:_);
This looks fine now, so let’s see how we can put our new stack data structure
to good use. Operations on the type are defined as usual, employing stack
as a type tag for stack arguments so that we can be sure that the push,
pop and top operations are all supported. For instance, let’s
implement a little RPN (“Reverse Polish Notation”) calculator:
rpn xs::stack ops::list = foldl (call []) xs ops with
call ys xs op = push xs (foldl ($) op ys) if nargs op<=#ys;
= call (top xs:ys) (pop xs) op otherwise;
end;
This takes an initial stack xs and a list ops of operands and
operations as inputs and returns the resulting stack after processing ops.
Examples:
> rpn (stack []) [10,4,3,(+),2,(*),(-)];
[-4]
> using math;
> rpn (stack []) [1,2,ln,(/)];
[1.44269504088896]
> rpn (stack []) [4,1,atan,(*)];
[3.14159265358979]
> rpn (stack []) [2,(*)];
<stdin>, line 5: unhandled exception '"empty stack"' while evaluating
'rpn (stack []) [2,(*)]'
Ok, this is all very nice, but it seems that so far we haven’t done much more
than we could have achieved just as easily with plain lists instead. So what
are the benefits of having an interface type?
First, an interface provides a fair amount of safety. As long as we stick to
the interface functions, we can be sure that the data is capable of carrying
out the requested operations. At the same time, the interface also serves as a
valuable piece of documentation, since it tells us at a glance exactly which
operations are supported by the type.
Second, an interface provides data abstraction. We don’t need to know how
the interface operations are implemented, and in fact functions coded against
the interface will work with any implementation of the interface. For
instance, suppose that we’d like to provide a “bounded stacks” data structure,
i.e., stacks which don’t grow beyond a certain limit. These can be implemented
as follows:
push (n,xs@[]) x | push (n,xs@(_:_)) x =
if n>0 then (n-1,x:xs) else throw "full stack";
pop (n,x:xs) = n+1,xs;
top (n,x:xs) = x;
pop (n,[]) = throw "empty stack";
top (n,[]) = throw "empty stack";
Note that we represent a bounded stack by a pair (n,xs) here, where xs
is the list of elements and n is the “free space” (number of elements we
still allow to be pushed). We also add a function to construct such values:
bstack n::int xs::list = (n-#xs,xs);
Without any further ado, our little RPN calculator works just fine with the
new variation of the data structure:
> rpn (bstack 3 []) [10,4,3,(+),2,(*),(-)];
2,[-4]
> rpn (bstack 2 []) [10,4,3,(+),2,(*),(-)];
<stdin>, line 7: unhandled exception '"full stack"' while evaluating
'rpn (bstack 2 []) [10,4,3,(+),2,(*),(-)]'
While they’re quite useful in general, Pure’s interface types also have their
limitations. In particular, the guarantees provided by an interface are of a
purely syntactic nature; the signature doesn’t tell us anything about the
actual meaning of the provided operations, so unit testing is still needed to
ensure certain semantic properties of the implementation. Some further issues
due to Pure’s dynamically typed nature are discussed under Interfaces in the
Caveats and Notes section.
Here are a few examples of simple Pure programs.
The factorial:
fact n = n*fact (n-1) if n>0;
= 1 otherwise;
let facts = map fact (1..10); facts;
The Fibonacci numbers:
fib n = a when a,b = fibs n end
with fibs n = 0,1 if n<=0;
= case fibs (n-1) of
a,b = b,a+b;
end;
end;
let fibs = map fib (1..30); fibs;
It is worth noting here that Pure performs tail call optimization so that
tail-recursive definitions like the following will be executed in constant
stack space (see Stack Size and Tail Recursion in the Caveats and Notes
section for more details on this):
// tail-recursive factorial using an "accumulating parameter"
fact n = loop 1 n with
loop p n = if n>0 then loop (p*n) (n-1) else p;
end;
Here is an example showing how constants are defined and used. Constant
definitions take pretty much the same form as variable definitions with
let (see above), but work more like the definition of a
parameterless function whose value is precomputed at compile time:
> extern double atan(double);
> const pi = 4*atan 1.0;
> pi;
3.14159265358979
> foo x = 2*pi*x;
> show foo
foo x = 6.28318530717959*x;
Note that the compiler normally computes constant subexpressions at compile
time, such as 2*pi in the foo function. This works with all simple
scalars (machine ints and doubles), see Constant Definitions for details.
List comprehensions are Pure’s main workhorse for generating and processing
all kinds of list values. Here’s a well-known example, a variation of
Erathosthenes’ classical prime sieve:
primes n = sieve (2..n) with
sieve [] = [];
sieve (p:qs) = p : sieve [q | q = qs; q mod p];
end;
(This definition is actually rather inefficient, there are much better albeit
more complicated implementations of this sieve.)
For instance:
> primes 100;
[2,3,5,7,11,13,17,19,23,29,31,37,41,43,47,53,59,61,67,71,73,79,83,89,97]
If you dare, you can actually have a look at the catmap-lambda-if-then-else
expression the comprehension expanded to:
> show primes
primes n = sieve (2..n) with sieve [] = []; sieve (p:qs) = p:sieve
(catmap (\q -> if q mod p then [q] else []) qs) end;
List comprehensions are also a useful device to organize backtracking
searches. For instance, here’s an algorithm for the n queens problem, which
returns the list of all placements of n queens on an n x n board (encoded as
lists of n pairs (i,j) with i = 1..n), so that no two queens hold each other
in check:
queens n = search n 1 [] with
search n i p = [reverse p] if i>n;
= cat [search n (i+1) ((i,j):p) | j = 1..n; safe (i,j) p];
safe (i,j) p = ~any (check (i,j)) p;
check (i1,j1) (i2,j2)
= i1==i2 || j1==j2 || i1+j1==i2+j2 || i1-j1==i2-j2;
end;
(Again, this algorithm is rather inefficient, see the examples included in the
Pure distribution for a much better algorithm by Libor Spacek.)
As already mentioned, lists can also be evaluated in a “lazy” fashion, by just
turning the tail of a list into a future. This special kind of list is also
called a stream. Streams enable you to work with infinite lists (or finite
lists which are so huge that you would never want to keep them in memory in
their entirety). E.g., here’s one way to define the infinite stream of all
Fibonacci numbers:
> let fibs = fibs 0L 1L with fibs a b = a : fibs b (a+b) & end;
> fibs;
0L:#<thunk 0xb5d54320>
Note the & on the tail of the list in the definition of the local
fibs function. This turns the result of fibs into a stream, which is
required to prevent the function from recursing into samadhi. Also note that
we work with bigints in this example because the Fibonacci numbers grow quite
rapidly, so with machine integers the values would soon start wrapping around
to negative integers.
Streams like these can be worked with in pretty much the same way as with
lists. Of course, care must be taken not to invoke “eager” operations such as
# (which computes the size of a list) on infinite streams, to prevent
infinite recursion. However, many list operations work with infinite streams
just fine, and return the appropriate stream results. E.g., the take
function (which retrieves a given number of elements from the front of a list)
works with streams just as well as with “eager” lists:
> take 10 fibs;
0L:#<thunk 0xb5d54350>
Hmm, not much progress there, but that’s just how streams work (or rather they
don’t, they’re lazy bums indeed!). Nevertheless, the stream computed with
take is in fact finite and we can readily convert it to an ordinary
list, forcing its evaluation:
> list (take 10 fibs);
[0L,1L,1L,2L,3L,5L,8L,13L,21L,34L]
An easier way to achieve this is to cut a “slice” from the stream:
> fibs!!(0..10);
[0L,1L,1L,2L,3L,5L,8L,13L,21L,34L,55L]
Also note that since we bound the stream to a variable, the already computed
prefix of the stream has been memoized, so that this portion of the stream is
now readily available in case we need to have another look at it later. By
these means, possibly costly reevaluations are avoided, trading memory for
execution speed:
> fibs;
0L:1L:1L:2L:3L:5L:8L:13L:21L:34L:55L:#<thunk 0xb5d54590>
Let’s take a look at some of the other convenience operations for generating
stream values. The prelude defines infinite arithmetic sequences, using
inf or -inf to denote an upper (or lower) infinite bound for the
sequence, e.g.:
> let u = 1..inf; let v = -1.0:-1.2..-inf;
> u!!(0..10); v!!(0..10);
[1,2,3,4,5,6,7,8,9,10,11]
[-1.0,-1.2,-1.4,-1.6,-1.8,-2.0,-2.2,-2.4,-2.6,-2.8,-3.0]
Other useful stream generator functions are iterate, which keeps
applying the same function over and over again, repeat, which just
repeats its argument forever, and cycle, which cycles through the
elements of the given list:
> iterate (*2) 1!!(0..10);
[1,2,4,8,16,32,64,128,256,512,1024]
> repeat 1!!(0..10);
[1,1,1,1,1,1,1,1,1,1,1]
> cycle [0,1]!!(0..10);
[0,1,0,1,0,1,0,1,0,1,0]
Moreover, list comprehensions can draw values from streams and return the
appropriate stream result:
> let rats = [m,n-m | n=2..inf; m=1..n-1; gcd m (n-m) == 1]; rats;
(1,1):#<thunk 0xb5d54950>
> rats!!(0..10);
[(1,1),(1,2),(2,1),(1,3),(3,1),(1,4),(2,3),(3,2),(4,1),(1,5),(5,1)]
Finally, let’s rewrite our prime sieve so that it generates the infinite
stream of all prime numbers:
all_primes = sieve (2..inf) with
sieve (p:qs) = p : sieve [q | q = qs; q mod p] &;
end;
Note that we can omit the empty list case of sieve here, since the sieve
now never becomes empty. Example:
> let P = all_primes;
> P!!(0..20);
[2,3,5,7,11,13,17,19,23,29,31,37,41,43,47,53,59,61,67,71,73]
> P!299;
1987
You can also just print the entire stream. This will run forever, so hit
Ctrl-c when you get bored:
> using system;
> do (printf "%d\n") all_primes;
2
3
5
...
(Make sure that you really use the all_primes function instead of the
P variable to print the stream. Otherwise, because of memoization the
stream stored in P will grow with the number of elements printed until
memory is exhausted. Calling do on a fresh instance of the stream of
primes allows do to get rid of each “cons” cell after having printed
the corresponding stream element.)
Pure offers a number of basic matrix operations, such as matrix construction,
pattern matching, indexing, slicing, as well as getting the size and
dimensions of a matrix. However, it does not supply built-in support for
matrix arithmetic and other linear algebra algorithms. The idea is that these
can and should be provided through separate libraries (please check the Pure
website for the pure-gsl module which is an ongoing project to provide a full
GSL interface for the Pure language).
But Pure’s facilities for matrix and list processing also make it easy to roll
your own, if desired. The prelude provides matrix versions of the common list
operations like map, foldl, zip etc., which provide a
way to implement common matrix operations. E.g., multiplying a matrix x
with a scalar a amounts to mapping the function (a*) to x, which can
be done as follows:
> a * x::matrix = map (a*) x if ~matrixp a;
> 2*{1,2,3;4,5,6};
{2,4,6;8,10,12}
Likewise, matrix addition and other element-wise operations can be realized
using zipwith, which combines corresponding elements of two matrices
using a given binary function:
> x::matrix + y::matrix = zipwith (+) x y;
> {1,2,3;4,5,6}+{1,2,1;3,2,3};
{2,4,4;7,7,9}
Note that, as shown in the examples above, the matrix tag can be
used on the left-hand side of an equation to restrict a variable to matrix
values. (The prelude provides a few other types for various specific kinds of
matrices, see the Pure Library Manual for details.) Another possibility is to
employ a matrix pattern. The Pure language has built-in support for these,
so that they work like the other kinds of patterns we’ve already encountered.
For instance, to compute the dot product of two 2D vectors, you may write
something like:
> {x1,y1}*{x2,y2} = x1*x2+y1*y2;
> {2,3}*{1,4};
14
Or, to compute the determinant of a 2x2 matrix:
> det {a,b;c,d} = a*d-b*c;
> det {1,2;3,4};
-2
This simplifies the definitions if the dimensions of the involved matrices are
small and known beforehand. If you need to go beyond this, matrix
comprehensions provide a means to express a variety of algorithms which would
typically be implemented using for loops in conventional programming
languages. To illustrate the use of matrix comprehensions, here is how we can
define an operation to create a square identity matrix of a given dimension:
> eye n = {i==j | i = 1..n; j = 1..n};
> eye 3;
{1,0,0;0,1,0;0,0,1}
Note that the i==j term is just a Pure idiom for the Kronecker
symbol. Another point worth mentioning here is that the generator clauses of
matrix comprehensions alternate between row and column generation
automatically, if values are drawn from lists as in the example above. (More
precisely, the last generator, which varies most quickly, yields a row, the
next-to-last one a column of these row vectors, and so on.) This makes matrix
comprehensions resemble customary mathematical notation very closely.
Of course, matrix comprehensions can also draw values from other matrices
instead of lists. In this case the block layout of the component matrices is
preserved. For instance:
> {x,y | x = {1,2}; y = {a,b;c,d}};
{(1,a),(1,b),(2,a),(2,b);(1,c),(1,d),(2,c),(2,d)}
Note that a matrix comprehension involving filters may fail because the
filtered result isn’t a rectangular matrix any more. E.g.,
{2*x|x={1,2,3,-4};x>0} works, as does {2*x|x={-1,2;3,-4};x>0}, but
{2*x|x={1,2;3,-4};x>0} doesn’t because the rows of the result matrix have
different lengths.
As a slightly more comprehensive example (no pun intended!), here is a
definition of matrix multiplication in Pure. The building block here is the
dot product of two vectors which can be defined for arbitrary dimensions as
follows:
> sum = foldl (+) 0;
> dot x::matrix y::matrix = sum $ zipwith (*) (rowvector x) (rowvector y);
> dot {1,2,3} {1,0,1};
4
The general matrix product now boils down to a simple matrix comprehension
which just computes the dot product of all rows of x with all columns of
y (the rows and cols functions are prelude operations
found in matrices.pure):
> x::matrix * y::matrix = {dot u v | u = rows x; v = cols y};
> {0,1;1,0;1,1}*{1,2,3;4,5,6};
{4,5,6;1,2,3;5,7,9}
(For the sake of simplicity, this doesn’t do much error checking. In
production code you’d check at least the conformance of matrix dimensions, of
course.)
Well, that was easy. So let’s take a look at a more challenging example,
Gaussian elimination, which can be used to solve systems of linear
equations. The algorithm brings a matrix into “row echelon” form, a
generalization of triangular matrices. The resulting system can then be solved
quite easily using back substitution.
Here is a Pure implementation of the algorithm. Note that the real meat is in
the pivoting and elimination step (step function) which is iterated over
all columns of the input matrix. In each step, x is the current matrix,
i the current row index, j the current column index, and p keeps
track of the current permutation of the row indices performed during
pivoting. The algorithm returns the updated matrix x, row index i and
row permutation p.
gauss_elimination x::matrix = p,x
when n,m = dim x; p,_,x = foldl step (0..n-1,0,x) (0..m-1) end;
// One pivoting and elimination step in column j of the matrix:
step (p,i,x) j
= if max_x==0 then p,i,x
else
// updated row permutation and index:
transp i max_i p, i+1,
{// the top rows of the matrix remain unchanged:
x!!(0..i-1,0..m-1);
// the pivot row, divided by the pivot element:
{x!(i,l)/x!(i,j) | l=0..m-1};
// subtract suitable multiples of the pivot row:
{x!(k,l)-x!(k,j)*x!(i,l)/x!(i,j) | k=i+1..n-1; l=0..m-1}}
when
n,m = dim x; max_i, max_x = pivot i (col x j);
x = if max_x>0 then swap x i max_i else x;
end with
pivot i x = foldl max (0,0) [j,abs (x!j)|j=i..#x-1];
max (i,x) (j,y) = if x<y then j,y else i,x;
end;
Please refer to any good textbook on numerical mathematics for a closer
description of the algorithm. But here is a brief rundown of what happens in
each elimination step: First we find the pivot element in column j of the
matrix. (We’re doing partial pivoting here, i.e., we only look for the element
with the largest absolute value in column j, starting at row i. That’s
usually good enough to achieve numerical stability.) If the pivot is zero then
we’re done (the rest of the pivot column is already zeroed out). Otherwise, we
bring it into the pivot position (swapping row i and the pivot row),
divide the pivot row by the pivot, and subtract suitable multiples of the
pivot row to eliminate the elements of the pivot column in all subsequent
rows. Finally we update i and p accordingly and return the result.
In order to complete the implementation, we still need the following little
helper functions to swap two rows of a matrix (this is used in the pivoting
step) and to apply a transposition to a permutation (represented as a list):
swap x i j = x!!(transp i j (0..n-1),0..m-1) when n,m = dim x end;
transp i j p = [p!tr k | k=0..#p-1]
with tr k = if k==i then j else if k==j then i else k end;
Finally, let us define a convenient print representation of double matrices a
la Octave (the meaning of the __show__ function is explained in
Pretty-Printing):
using system;
__show__ x::matrix
= strcat [printd j (x!(i,j))|i=0..n-1; j=0..m-1] + "\n"
with printd 0 = sprintf "\n%10.5f"; printd _ = sprintf "%10.5f" end
when n,m = dim x end if dmatrixp x;
Example:
> let x = dmatrix {2,1,-1,8; -3,-1,2,-11; -2,1,2,-3};
> x; gauss_elimination x;
2.00000 1.00000 -1.00000 8.00000
-3.00000 -1.00000 2.00000 -11.00000
-2.00000 1.00000 2.00000 -3.00000
[1,2,0],
1.00000 0.33333 -0.66667 3.66667
0.00000 1.00000 0.40000 2.60000
0.00000 0.00000 1.00000 -1.00000
As already mentioned, matrices may contain not just numbers but any kind of
Pure value, in which case they become symbolic matrices. Symbolic matrices
are a convenient data structure for storing arbitrary collections of values
which provides fast random access to its members. In particular, symbolic
matrices can also be nested, and thus arrays of arbitrary dimension can be
realized as nested symbolic vectors. However, you have to be careful when
constructing such values, as the {...} construct normally combines
submatrices to larger matrices. For instance:
> {{1,2},{3,4}};
{1,2,3,4}
One way to inhibit this “splicing” of the submatrices in a larger matrix is to
use the “quote” operator (cf. The Quote):
> '{{1,2},{3,4}};
{{1,2},{3,4}}
Note that this result is really different from {1,2;3,4}. The latter is a
2x2 integer matrix, while the former is a symbolic vector a.k.a. 1x2 matrix
whose elements happen to be two integer vectors. You can match these values
with a nested matrix pattern as usual, e.g.:
> let {{a,b},{c,d}} = '{{1,2},{3,4}};
> a,b,c,d;
1,2,3,4
Unfortunately, the quote operator in fact inhibits evaluation of all
embedded subterms which may be undesirable if the matrix expression contains
arithmetic (as in '{{1+1,2*3}}), so this method works best for constant
matrices. A more general way to create a symbolic vector of matrices is
provided by the vector function from the prelude, which is applied to
a list of the vector elements as follows:
> vector [{1,2},{3,4}];
{{1,2},{3,4}}
Calls to the vector function can be nested to an arbitrary depth to
obtain higher-dimensional “arrays”:
> vector [vector [{1,2}],vector [{3,4}]];
{{{1,2}},{{3,4}}}
This obviously becomes a bit unwieldy for higher dimensions, but in Pure you
can easily define yourself some more convenient notation if you like. For
instance, the following macro may be used to define a pair of “non-splicing”
vector brackets:
> outfix {: :};
> def {: xs@(_,_) :} = vector (__list__ xs);
> def {: x :} = vector [x];
> {:{:{1,2}:},{:{3,4}:}:};
{{{1,2}},{{3,4}}}
(Both macros and outfix symbol declarations are described later in
the appropriate sections, see Macros and Symbol Declarations. Please also
check the Pure Library Manual for a description of the built-in __list__
macro.)
Symbolic matrices also provide a means to represent simple record-like data,
by encoding records as symbolic vectors consisting of “hash pairs” of the form
key => value. This kind of data structure is very convenient to represent
aggregates with lots of different components. Since the components of records
can be accessed by indexing with key values, you don’t have to remember which
components are stored in which order, just knowing the keys of the required
members is enough. In contrast, tuples, lists and other kinds of constructor
terms quickly become unwieldy for such purposes.
The keys used for indexing the record data must be either symbols or strings,
while the corresponding values may be arbitrary Pure values. The prelude
provides some operations on these special kinds of matrices, which let you
retrieve vector elements by indexing and perform non-destructive updates, see
the Record Functions section in the Pure Library Manual for details. Here
are a few examples which illustrate how to create records and work with them:
> let r = {x=>5, y=>12};
> recordp r, member r x;
1,1
> r!y; r!![y,x];
12
{12,5}
> insert r (x=>99);
{x=>99,y=>12}
> insert ans (z=>77);
{x=>99,y=>12,z=>77}
> delete ans z;
{x=>99,y=>12}
Note the use of the “hash rocket” => which denotes the key=>value
associations in a record. The hash rocket is a constructor declared as an
infix operator in the prelude, see the Prelude section in the
Pure Library Manual. There’s one caveat here, however. Since neither ‘=>‘ nor
‘!‘ treat their key operand in a special way, you’ll have to take care
that the key symbols do not evaluate to something else, as might be the case
if they are bound to a global or local variable or parameterless function:
> let u = 99;
> {u=>u};
{99=>99}
In the case of global variables and function symbols, you might also protect
the symbol with a quote (see The Quote):
However, even the quote doesn’t save you from local variable substitution:
> {'u=>u} when u = 99 end;
{99=>99}
In such cases you’ll either have to rename the local variable, or use the
prelude function val to quote the symbol:
> {'u=>v} when v = 99 end;
{u=>99}
> {val "u"=>u} when u = 99 end;
{u=>99}
It’s also possible to directly use strings as keys instead, which may actually
be more convenient in some cases:
> let r = {"x"=>5, "y"=>12};
> keys r; vals r;
{"x","y"}
{5,12}
> update r "y" (r!"y"+1);
{"x"=>5,"y"=>13}
You can also mix strings and symbols as keys in the same record (but note that
strings and symbols are always distinct, so y and "y" are really two
different keys here):
> insert r (y=>99);
{"x"=>5,"y"=>12,y=>99}
As records are in fact just special kinds of matrices, the standard matrix
operations can be used on record values as well. For instance, the matrix
constructor provides an alternative way to quickly augment a record with a
collection of new key=>value associations:
> let r = {x=>5, y=>12};
> let r = {r, x=>7, z=>3}; r;
{x=>5,y=>12,x=>7,z=>3}
> r!x, r!z;
7,3
> delete r x;
{x=>5,y=>12,z=>3}
> ans!x;
5
As the example shows, this may produce duplicate keys, but these are handled
gracefully; indexing and updates will always work with the last association
for a given key in the record. If necessary, you can remove duplicate entries
from a record as follows; this will only keep the last association for each
key:
> record r;
{x=>7,y=>12,z=>3}
In fact, the record operation not only removes duplicates, but also
orders the record entries by keys. This produces a kind of normalized
representation which is useful if you want to compare or combine two record
values irrespective of the ordering of the fields. For instance:
> record {x=>5, y=>12} === record {y=>12, x=>5};
1
The record function can also be used to construct a normalized record
directly from a list or tuple of hash pairs:
> record [x=>5, x=>7, y=>12];
{x=>7,y=>12}
Other matrix operations such as map, foldl, etc., and matrix
comprehensions can be applied to records just as easily. This enables you to
perform bulk updates of record data in a straightforward way. For instance,
here’s how you can define a function maprec which applies a function to
all values stored in a record:
> maprec f = map (\(u=>v) -> u=>f v);
> maprec (*2) {x=>5,y=>12};
{x=>10,y=>24}
Another example: The following ziprec function collects pairs of values
stored under common keys in two records (we also normalize the result here so
that duplicate keys are always removed):
> ziprec x y = record {u=>(x!u,y!u) | u = keys x; member y u};
> ziprec {a=>3,x=>5,y=>12} {x=>10,y=>24,z=>7};
{x=>(5,10),y=>(12,24)}
Thus the full power of generic matrix operations is available for records,
which turns them into a very versatile data structure, much more powerful than
records in conventional programming languages which are usually limited to
constructing records and accessing or modifying their components. Note that
since the values stored in records can be arbitrary Pure values, you can also
have mutable records by making use of Pure’s expression references (see
Expression References in the library manual). And of course records can
be nested, too:
> let r = {a => {b=>1,c=>2}, b => 2};
> r!a, r!b, r!a!b;
{b=>1,c=>2},2,1
As already mentioned in Special Forms, the quote operation quotes
an expression, so that it can be passed around and manipulated freely until
its value is needed, in which case you can pass it to the eval
function to obtain its value. For instance:
> let x = '(2*42+2^12); x;
2*42+2^12
> eval x;
4180.0
Lisp programmers will be well familiar with this operation which enables some
powerful metaprogramming techniques. However, there are some notable
differences to Lisp’s quote. In particular, quote only inhibits the
evaluation of global variables, local variables are substituted as usual:
> (\x -> '(2*x+1)) 99;
2*99+1
> foo x = '(2*x+1);
> foo 99; foo $ '(7/y);
2*99+1
2*(7/y)+1
> '(x+1) when x = '(2*3) end;
2*3+1
> '(2*42+2^n) when n = 12 end;
2*42+2^12
Local parameterless functions are treated in the same fashion:
> '(2*42+2^n) with n = 12 end;
2*42+2^12
Note that, in contrast, for global variables (and functions) we have:
> let n = 12;
> '(2*42+2^n);
2*42+2^n
This discrepancy may come as a surprise (or even annoyance) to real Lisp
weenies, but it does have its advantages. As illustrated in the examples
above, local variable substitution makes it easy to fill in the variable parts
in a quoted “template” expression, without any need for an arguably complex
tool like Lisp’s “quasiquote”. (But note that it is quite easy to define the
quasiquote in Pure if you want it. See the Recursive Macros section for a
simplified version; a full implementation can be found in the Pure library.)
If you do need to quote a symbol which is already being used as a local
variable or function in the current context, you can do this by supplying the
symbol as a string to the prelude function val:
> val "x"+x when x = 99 end;
x+99
Also note that while local functions are always substituted in a quoted
expression, applications involving local functions can still be quoted:
> 'foo 99 with foo x = 2*x+1 end;
foo 99
> eval ans;
199
The quote also inhibits evaluation inside matrix expressions, including the
“splicing” of embedded submatrices:
> '{1,2+3,2*3};
{1,2+3,2*3}
> '{1,{2,3},4};
{1,{2,3},4}
Special expressions (conditionals, lambda and the case,
when and with constructs) can be quoted as well. But
since these constructs cannot be directly represented at runtime, the quote
actually produces some ordinary “placeholder” terms for these:
> '(x+1 when x = '(2*3) end);
x+1 __when__ [x-->'(2*3)]
> eval ans;
2*3+1
> '(2*42+(f 6 with f n = 2^(2*n) end));
2*42+(f 6 __with__ [f n-->2^(2*n)])
> eval ans;
4180.0
Note that these placeholders are in fact special built-in macros which
reconstruct the special expression when evaluated. Moreover, special
expressions are implicitly quoted when they occur on the left-hand side of an
equation or as an argument of a “quoteargs”
macro call. This is often used to implement macros which manipulate these
constructs as literals. For instance, the following macro swaps the arguments
in a lambda:
> #! --quoteargs bar
> def bar (\x y -> z) = __eval__ ('(\y x -> z));
> show bar
def bar (__lambda__ [x,y] z) = __eval__ ('__lambda__ [y,x] z);
> baz = bar (\a b -> a-b);
> show baz
baz = \b a -> a-b;
> baz 2 3;
1
The Macros section explains in detail how this meta programming works.
Pure is a very terse language by design. Usually you don’t declare much stuff,
you just define it and be done with it. However, there are a few constructs
which let you declare symbols with special attributes and manage programs
consisting of several source modules:
- symbol declarations determine “scope” and “fixity” of a symbol;
- extern declarations specify external C functions;
- using clauses let you include other scripts in a Pure script;
- namespace declarations let you avoid name clashes and thereby
make it easier to manage large programs consisting of many separate modules.
These are toplevel elements (cf. Toplevel):
item ::= symbol_decl | extern_decl | using_decl | namespace_decl
We defer the discussion of extern declarations to the C
Interface section. The other kinds of declarations are described in the
following subsections.
symbol_decl ::= scope symbol+ ";"
| [scope] fixity symbol+ ";"
scope ::= "public" | "private"
fixity ::= "nonfix" | "outfix"
| ("infix"|"infixl"|"infixr"|"prefix"|"postfix") precedence
precedence ::= integer | "(" op ")"
Scope declarations take the following form:
-
public symbol ...;
-
private symbol ...;
This declares the listed symbols as public or private, respectively. Each
symbol must either be an identifier or a sequence of punctuation
characters. The latter kind of symbols must always be declared before use,
whereas ordinary identifiers can be used without a prior declaration in which
case they are declared implicitly and default to public scope, meaning that
they are visible everywhere in a program. An explicit public declaration of
ordinary identifiers is thus rarely needed (unless you want to declare symbols
as members of a specific namespace, see Namespaces below). Symbols can also
be declared private, meaning that the symbol is visible only in the namespace
it belongs to. This is explained in more detail under Private Symbols in
the Namespaces section below.
Note that to declare several symbols in a single declaration, you can list
them all with whitespace in between. The same syntax applies to the other
types of symbol declarations discussed below. (Commas are not allowed as
delimiters here, as they may occur as legal symbol constituents in the list of
symbols.) The public and private keywords can also be
used as a prefix in any of the special symbol declarations discussed below, to
specify the scope of the declared symbols (if the scope prefix is omitted, it
defaults to public).
The following “fixity” declarations are available for introducing special
operator symbols. This changes the way that these symbols are parsed and thus
provides you with a limited means to extend the Pure language at the lexical
and syntactical level.
-
infix level symbol ...;
-
infixl level symbol ...;
-
infixr level symbol ...;
-
prefix level symbol ...;
-
postfix level symbol ...;
Pure provides you with a theoretically unlimited number of different
precedence levels for user-defined infix, prefix and postfix operators.
Precedence levels are numbered starting at 0; larger numbers indicate higher
precedence. (For practical reasons, the current implementation does require
that precedence numbers can be encoded as 24 bit unsigned machine integers,
giving you a range from 0 to 16777215, but this should be large enough to
incur no real limitations on applications. Also, the operator declarations in
the prelude have been set up to leave enough “space” between the “standard”
levels so that you can easily sneak in new operator symbols at low, high or
intermediate precedences.)
On each precedence level, you can declare (in order of increasing precedence)
infix (binary non-associative), infixl (binary
left-associative), infixr (binary right-associative),
prefix (unary prefix) and postfix (unary postfix)
operators. For instance, here is a typical excerpt from the prelude (the full
table can be found in the Prelude section of the Pure Library Manual):
infix 1800 < > <= >= == ~= ;
infixl 2200 + - ;
infixl 2300 * / div mod ;
infixr 2500 ^ ;
prefix 2600 # ;
Note
Unary minus plays a special role in the syntax. Like in Haskell and
following mathematical tradition, unary minus is the only prefix operator
symbol which is also used as an infix operator, and is always on the same
precedence level as binary minus, whose precedence may be chosen freely in
the prelude. (The minus operator is the only symbol which gets that special
treatment; all other operators must have distinct lexical representations.)
Thus, with the standard prelude, -x+y will be parsed as (-x)+y,
whereas -x*y is the same as -(x*y). Also note that the notation
(-) always denotes the binary minus operator; the unary minus operation
can be denoted using the built-in neg function.
Instead of denoting the precedence by an explicit integer value, you can also
specify an existing operator symbol enclosed in parentheses. Thus the
following declaration gives the ++ operator the same precedence as +:
The given symbol may be of a different fixity than the declaration, but it
must have a proper precedence level (i.e., it must be an infix, prefix or
postfix symbol). E.g., the following declaration gives ^^ the same
precedence level as the infix ^ symbol, but turns it into a postfix
operator:
Pure also provides unary outfix operators, which work like in Wm Leler’s
constraint programming language Bertrand. These can be declared as follows:
-
outfix left right ...;
Outfix operators let you define your own bracket structures. The operators
must be given as pairs of matching left and right symbols (which must be
distinct). For instance:
After this declaration you can write bracketed expressions like |:x:| or
BEGIN foo, bar END. These are always at the highest precedence level
(i.e., syntactically they work like parenthesized expressions). Just like
other operators, you can turn outfix symbols into ordinary functions by
enclosing them in parentheses, but you have to specify the symbols in matching
pairs, such as (BEGIN END).
Pure also has a notation for “nullary” operators, that is, “operators without
operands”. These are used to denote special literals which simply stand for
themselves. They are introduced using a nonfix declaration:
-
nonfix symbol ...;
For instance:
Semantically, nonfix symbols are a kind of “symbolic constants”. However, it
is important to note the difference to defined constants, which are symbols
bound to a constant value by means of a const definition. In fact,
there are some use cases where a symbol may be both a defined constant and a
nonfix symbol, see Constant Definitions in the Caveats and Notes section
for details.
Syntactically, nonfix symbols work just like ordinary identifiers, so they may
stand whereever an identifier is allowed (no parentheses are required to
“escape” them). However, just like other kinds of operators, they may also
consist of punctuation (which isn’t allowed in ordinary identifiers). The
other difference to ordinary identifiers is that nonfix symbols are always
interpreted as literals, even if they occur in a variable position on the
left-hand side of a rule. So, with the above declaration, you can write
something like:
> foo x = case x of red = green; green = blue; blue = red end;
> map foo [red,green,blue];
[green,blue,red]
Thus nonfix symbols are pretty much like nullary constructor symbols in
languages like Haskell. Non-fixity is just a syntactic attribute,
however. Pure doesn’t enforce that such values are irreducible, so you can
still write a “constructor equation” like the following:
> red = blue;
> map foo [red,green,blue];
[blue,blue,blue]
Examples for all types of symbol declarations can be found in the
prelude which declares a bunch of standard (arithmetic,
relational, logical) operator symbols as well as the list and pair
constructors ‘:‘ and ‘,‘, and a few nonfix symbols (mostly for
denoting different kinds of exceptions).
using_decl ::= "using" name ("," name)* ";"
name ::= qualified_identifier | string
While Pure doesn’t offer separate compilation, the using
declaration provides a simple but effective way to assemble a Pure program
from several source modules. It takes the following form (note that in
contrast to symbol declarations, the comma is used as a delimiter symbol
here):
-
using name, ...;
This causes each given script to be included in the Pure program at the given
point (if it wasn’t already included before), which makes available all the
definitions of the included script in your program. Note that each included
script is loaded only once, when the first using clause for the
script is encountered. Nested imports are allowed, i.e., an imported module
may itself import other modules, etc. A Pure program then basically is the
concatenation of all the source modules given as command line arguments, with
other modules listed in using clauses inserted at the corresponding
source locations.
(The using clause also has an alternative form which allows dynamic
libraries and LLVM bitcode modules to be loaded, this will be discussed in the
C Interface section.)
For instance, the following declaration causes the math.pure script from the
standard library to be included in your program:
You can also import multiple scripts in one go:
Moreover, Pure provides a notation for qualified module names which can be
used to denote scripts located in specific package directories, e.g.:
using examples::libor::bits;
In fact this is equivalent to the following using clause which
spells out the real filename of the script between double quotes (the
.pure suffix can also be omitted in which case it is added
automatically):
using "examples/libor/bits.pure";
Both notations can be used interchangeably; the former is usually more
convenient, but the latter allows you to denote scripts whose names aren’t
valid Pure identifiers.
Script identifiers are translated to the corresponding filenames by replacing
the ‘::‘ symbol with the pathname separator ‘/‘ and tacking on the
‘.pure‘ suffix. The following table illustrates this with a few examples.
Script identifier |
Filename |
math |
"math.pure" |
examples::libor::bits |
"examples/libor/bits.pure" |
::pure::examples::hello |
"/pure/examples/hello.pure" |
Note the last example, which shows how an absolute pathname can be denoted
using a qualifier starting with ‘::‘.
Unless an absolute pathname is given, the interpreter performs a search to
locate the script. The search algorithm considers the following directories in
the given order:
- the directory of the current script, which is the directory of the script
containing the using clause, or the current working directory if
the clause was read from standard input (as is the case, e.g., in an
interactive session);
- the directories named in -I options on the command line (in the
given order);
- the colon-separated list of directories in the PURE_INCLUDE
environment variable (in the given order);
- finally the directory named by the PURELIB environment variable.
Note that the current working directory is not searched by default (unless the
using clause is read from standard input), but of course you can
force this by adding the option -I. to the command line, or by
including ‘.’ in the PURE_INCLUDE variable.
The directory of the current script (the first item above) can be skipped by
specifying the script to be loaded as a filename in double quotes, prefixed
with the special sys: tag. The search then starts with the “system”
directories (-I, PURE_INCLUDE and PURELIB)
instead. This is useful, e.g., if you want to provide your own custom version
of a standard library script which in turn imports that library script. For
instance, a custom version of math.pure might employ the following
using clause to load the math.pure script from the Pure library:
using "sys:math";
// custom definitions go here
log2 x = ln x/ln 2;
The interpreter compares script names (to determine whether two scripts are
actually the same) by using the canonicalized full pathname of the script,
following symbolic links to the destination file (albeit only one level). Thus
different scripts with the same basename, such as foo/utils.pure and
bar/utils.pure can both be included in the same program (unless they link to
the same file).
More precisely, canonicalizing a pathname involves the following steps:
- relative pathnames are expanded to absolute ones, using the search rules
discussed above;
- the directory part of the pathname is normalized to the form returned by the
getcwd system call;
- the ”.pure” suffix is added if needed;
- if the resulting script name is actually a symbolic link, the interpreter
follows that link to its destination, albeit only one level. (This is only
done on Unix-like systems.)
The directory of the canonicalized pathname is also used when searching other
scripts included in a script. This makes it possible to have an executable
script with a shebang line in its own directory, which is then executed via a
symbolic link placed on the system PATH. In this case the script
search performed in using clauses will use the real script
directory and thus other required scripts can be located there. This is the
recommended practice for installing standalone Pure applications in source
form which are to be run directly from the shell.
namespace_decl ::= "namespace" [name] ";"
| "namespace" name "with" item+ "end" ";"
| "using" "namespace" [name_spec ("," name_spec)*] ";"
name_spec ::= name ["(" symbol+ ")"]
To facilitate modular development, Pure also provides namespaces as a means to
avoid name clashes between symbols, and to keep the global namespace tidy and
clean. Namespaces serve as containers holding groups of related identifiers
and other symbols. Inside each namespace, symbols must be unique, but the same
symbol may be used to denote different objects (variables, functions, etc.) in
different namespaces. (Pure’s namespace system was heavily inspired by C++ and
works in a very similar fashion. So if you know C++ you should feel right at
home and skimming this section to pick up Pure’s syntax of the namespace
constructs should be enough to start using it.)
The global namespace is always available. By default, new symbols are created
in this namespace, which is also called the default namespace. Additional
namespaces can be created with the namespace declaration, which
also switches to the given namespace (makes it the current namespace), so
that new symbols are then created in that namespace rather than the default
one. The current namespace also applies to all kinds of symbol declarations,
including operator and nonfix symbol declarations, as well as
extern declarations (the latter are described in the C Interface
section).
The basic form of the namespace declaration has the following
syntax (there’s also a “scoped” form of the namespace declaration
which will be discussed in Scoped Namespaces at the end of this section):
namespace name;
// declarations and definitions in namespace 'name'
namespace;
The second form switches back to the default namespace. For instance, in order
to define two symbols with the same print name foo in two different
namespaces foo and bar, you can write:
namespace foo;
foo x = x+1;
namespace bar;
foo x = x-1;
namespace;
We can now refer to the symbols we just defined using qualified symbols of
the form namespace::symbol:
> foo::foo 99;
100
> bar::foo 99;
98
This avoids any potential name clashes, since the qualified identifier
notation always makes it clear which namespace the given identifier belongs
to.
A namespace can be “reopened” at any time to add new symbols and definitions
to it. This allows namespaces to be created that span several source
modules. You can also create several different namespaces in the same module.
Similar to the using declaration, a namespace
declaration accepts either identifiers or double-quoted strings as namespace
names. E.g., the following two declarations are equivalent:
namespace foo;
namespace "foo";
The latter form also allows more descriptive labels which aren’t identifiers,
e.g.:
namespace "Private stuff, keep out!";
Note that the namespace prefix in a qualified identifier must be a legal
identifier, so it isn’t possible to access symbols in namespaces with such
descriptive labels in a direct fashion. The only way to get at the symbols in
this case is to use a namespace or using namespace
declaration (for the latter see Using Namespaces below).
Since it is rather inconvenient if you always have to write identifiers in
their qualified form outside of their “home” namespace, Pure allows you to
specify a list of search namespaces which are used to look up symbols not in
the default or the current namespace. This is done with the using
namespace declaration, which takes the following form:
using namespace name1, name2, ...;
// ...
using namespace;
(As with namespace declarations, the second form without any
namespace arguments gets you back to the default empty list of search
namespaces.)
For instance, consider this example:
namespace foo;
foo x = x+1;
namespace bar;
foo x = x-1;
bar x = x+1;
namespace;
The symbols in these namespaces can be accessed unqualified as follows:
> using namespace foo;
> foo 99;
100
> using namespace bar;
> foo 99;
98
> bar 99;
100
This method is often to be preferred over opening a namespace with the
namespace declaration, since using namespace only gives
you “read access” to the imported symbols, so you can’t accidentally mess up
the definitions of the namespace you’re using. Another advantage is that the
using namespace declaration also lets you search multiple
namespaces at once:
using namespace foo, bar;
Be warned, however, that this brings up the very same issue of name clashes
again:
> using namespace foo, bar;
> foo 99;
<stdin>, line 15: symbol 'foo' is ambiguous here
In such a case you’ll have to resort to using namespace qualifiers again, in
order to resolve the name clash:
To avoid this kind of mishap, you can also selectively import just a few
symbols from a namespace instead. This can be done with a declaration of the
following form:
using namespace name1 ( sym1 sym2 ... ), name2 ... ;
As indicated, the symbols to be imported can optionally be placed as a
whitespace-delimited list inside parentheses, following the corresponding
namespace name. For instance:
> using namespace foo, bar (bar);
> foo 99;
100
> bar 99;
100
> bar::foo 99;
98
Note that now we have no clash on the foo symbol any more, because we
restricted the import from the bar namespace to the bar symbol, so
that bar::foo has to be denoted with a qualified symbol now.
Pure’s rules for looking up and creating symbols are fairly straightforward
and akin to those in other languages featuring namespaces. However, there are
some intricacies involved, because the rewriting rule format of definitions
allows “referential” use of symbols not only in the “body” (right-hand side)
of a definition, but also in the left-hand side patterns. We discuss this in
detail below.
The compiler searches for symbols first in the current namespace (if any),
then in the currently active search namespaces (if any), and finally in the
default (i.e., the global) namespace, in that order. This automatic lookup can
be bypassed by using an absolute namespace qualifier of the form
::foo::bar. In particular, ::bar always denotes the symbol bar in
the default namespace, while ::foo::bar denotes the symbol bar in the
foo namespace. (Normally, the latter kind of notation is only needed if
you have to deal with nested namespaces, see Hierarchical Namespaces
below.)
If no existing symbol is found, a new symbol is created automatically, by
implicitly declaring a public symbol with default attributes. New
unqualified symbols are always created in the current namespace, while new
qualified symbols are created in the namespace given by the namespace prefix
of the symbol.
Note
Pure’s implicit symbol declarations are a mixed blessing. They are
convenient, especially in interactive usage, but they also let missing or
mistyped symbols go unnoticed much to easily. As a remedy, in the case of
qualified symbols the compiler checks that the given namespace prefix
matches the current namespace, in order to catch typos and other silly
mistakes and prevent you from accidentally clobbering the contents of other
namespaces. For instance:
> namespace foo;
> namespace;
> foo::bar x = 1/x;
<stdin>, line 3: undeclared symbol 'foo::bar'
To make these errors go away it’s enough to just declare the symbols in
their proper namespaces.
In addition, you can run the interpreter with the -w option (see
Invoking Pure) to check your scripts for (non-defining) uses of
undeclared unqualified function symbols. This is highly recommended. For
instance, in the following example we forgot to import the system
module which defines the puts function. Running the interpreter
with -w highlights such potential errors:
$ pure -w
> puts "bla"; // missing import of system module
<stdin>, line 1: warning: implicit declaration of 'puts'
puts "bla"
For legitimate uses (such as forward uses of a symbol which is defined
later), you can make these warnings go away by declaring the symbol before
using it.
New symbols are also created if a global unqualified (and yet undeclared)
symbol is being “defined” in a rewriting rule or
let/const definition, even if a symbol with the same
print name from another namespace is already visible in the current scope. To
distinguish “defining” from “referring” uses of a global symbol, Pure uses the
following (purely syntactic) notions:
- A defining occurrence of a global function, macro or type symbol is
any occurrence of the symbol as the head symbol on the left-hand side of a
rewriting rule.
- A defining occurrence of a global variable or constant symbol is any
occurrence of the symbol in a variable position (as given by the “head =
function” rule, cf. Variables in Equations) on the left-hand side of a
let or const definition.
- All other occurrences of global symbols on the left-hand side, as well as
all symbol occurrences on the right-hand side of a definition are
referring occurrences. (Note that this also subsumes all occurrences of
type tags on the left-hand side of an equation.)
The following example illustrates these notions:
namespace foo;
bar (bar x) = bar x;
let x,y = 1,2;
namespace;
Here, the first occurrence of bar on the left-hand side bar (bar x) of
the first rule is a defining occurrence, as are the occurrences of x and
y on the left-hand side of the let definition. Hence these
symbols are created as new symbols in the namespace foo. On the other
hand, the other occurrences of bar in the first rule, as well as the ‘,‘ symbol on the left-hand side of the let definition are
referring occurrences. In the former case, bar refers to the bar
symbol defined by the rule, while in the latter case the ‘,‘ operator
is actually declared in the prelude and thus imported from the global
namespace.
The same rules of lookup also apply to type tags on the left-hand side of an
equation, but in this case the interpreter will look specifically for type
symbols, avoiding any other kinds of symbols which might be visible in the
same context. Thus, in the following example, the type tag bar is
correctly resolved to bar::bar, even though the (function) symbol
foo::bar is visible at this point:
> namespace bar;
> type bar;
> namespace foo;
> public bar;
> using namespace bar;
> foo x::bar = bar x;
> show foo::foo
foo::foo x :: bar::bar = foo::bar x;
Note that special operator (and nonfix) symbols always require an explicit
declaration. This works as already discussed in the Symbol Declarations
section, except that you first switch to the appropriate namespace before
declaring the symbols. For instance, here is how you can create a new +
operation which multiplies its operands rather than adding them:
> namespace my;
> infixl 2200 +;
> x+y = x*y;
> 5+7;
35
Note that the new + operation really belongs to the namespace we
created. The + operation in the default namespace works as before, and in
fact you can use qualified symbols to pick the version that you need:
> namespace;
> 5+7;
12
> 5 ::+ 7;
12
> 5 my::+ 7;
35
Here’s what you get if you happen to forget the declaration of the +
operator:
> namespace my;
> x+y = x*y;
<stdin>, line 2: infixl symbol '+' was not declared in this namespace
Thus the compiler will never create a new instance of an operator symbol on
the fly, an explicit declaration is always needed in such cases.
Note that if you really wanted to redefine the global + operator, you
can do this even while the my namespace is current. You just have to use a
qualified identifier in this case, as follows:
> namespace my;
> x ::+ y = x*y;
> a+b;
a*b
This should rarely be necessary (in the above example you might just as well
enter this rule while in the global namespace), but it can be useful in some
circumstances. Specifically, you might want to “overload” a global function
or operator with a definition that makes use of private symbols of a namespace
(which are only visible inside that namespace; see Private Symbols
below). For instance:
> namespace my;
> private bar;
> bar x y = x*y;
> x ::+ y = bar x y;
> a+b;
a*b
(The above is a rather contrived example, since the very same functionality
can be accomplished much easier, but there are some situations where this
approach is necessary.)
Pure also allows you to have private symbols, as a means to hide away internal
operations which shouldn’t be accessed directly outside the namespace in which
they are declared. The scope of a private symbol is confined to its namespace,
i.e., the symbol is only visible when its “home” namespace is current. Symbols
are declared private by using the private keyword in the symbol
declaration:
> namespace secret;
> private baz;
> // 'baz' is a private symbol in namespace 'secret' here
> baz x = 2*x;
> // you can use 'baz' just like any other symbol here
> baz 99;
198
> namespace;
Note that, at this point, secret::baz is now invisible, even if you have
secret in the search namespace list:
> using namespace secret;
> // this actually creates a 'baz' symbol in the default namespace:
> baz 99;
baz 99
> secret::baz 99;
<stdin>, line 27: symbol 'secret::baz' is private here
The only way to bring the symbol back into scope is to make the secret
namespace current again:
> namespace secret;
> baz 99;
198
> secret::baz 99;
198
Namespace identifiers can themselves be qualified identifiers in Pure, which
enables you to introduce a hierarchy of namespaces. This is useful, e.g., to
group related namespaces together under a common “umbrella” namespace:
namespace my;
namespace my::old;
foo x = x+1;
namespace my::new;
foo x = x-1;
Note that the namespace my, which serves as the parent namespace, must be
created before the my::old and my::new namespaces, even if it does not
contain any symbols of its own. After these declarations, the my::old and
my::new namespaces are part of the my namespace and will be considered
in name lookup accordingly, so that you can write:
> using namespace my;
> old::foo 99;
100
> new::foo 99;
98
This works pretty much like a hierarchy of directories and files, where the
namespaces play the role of the directories (with the default namespace as the
root directory), the symbols in each namespace correspond to the files in a
directory, and the using namespace declaration functions similar to
the shell’s PATH variable.
Sometimes it is necessary to tell the compiler to use a symbol in a specific
namespace, bypassing the usual symbol lookup mechanism. For instance, suppose
that we introduce another global old namespace and define yet another
version of foo in that namespace:
namespace old;
foo x = 2*x;
namespace;
Now, if we want to access that function, with my still active as the
search namespace, we cannot simply refer to the new function as old::foo,
since this name will resolve to my::old::foo instead. As a remedy, the
compiler accepts an absolute qualified identifier of the form
::old::foo. This bypasses name lookup and thus always yields exactly the
symbol in the given namespace (if it exists; as mentioned previously, the
compiler will complain about an undeclared symbol otherwise):
> old::foo 99;
100
> ::old::foo 99;
198
Also note that, as a special case of the absolute qualifier notation,
::foo always denotes the symbol foo in the default namespace.
Pure also provides an alternative scoped namespace construct which
makes nested namespace definitions more convenient. This construct takes the
following form:
namespace name with ... end;
The part between with and end may contain arbitrary
declarations and definitions, using the same syntax as the toplevel. These are
processed in the context of the given namespace, as if you had written:
namespace name;
...
namespace;
However, the scoped namespace construct always returns you to the namespace
which was active before, and thus these declarations may be nested:
namespace foo with
// declarations and definitions in namespace foo
namespace bar with
// declarations and definitions in namespace bar
end;
// more declarations and definitions in namespace foo
end;
Note that this kind of nesting does not necessarily imply a namespace
hierarchy as discussed in Hierarchical Namespaces. However, you can achieve
this by using the appropriate qualified namespace names:
namespace foo with
// ...
namespace foo::bar with
// ...
end;
// ...
end;
Another special feature of the scoped namespace construct is that
using namespace declarations are always local to the current
namespace scope (and other nested namespace scopes inside it). Thus the
previous setting is restored at the end of each scope:
using namespace foo;
namespace foo with
// still using namespace foo here
using namespace bar;
// now using namespace bar
namespace bar with
// still using namespace bar here
using namespace foo;
// now using namespace foo
end;
// back to using namespace bar
end;
// back to using namespace foo at toplevel
Finally, here’s a more concrete example which shows how scoped namespaces
might be used to declare two namespaces and populate them with various
functions and operators:
namespace foo with
infixr (::^) ^;
foo x = x+1;
bar x = x-1;
x^y = 2*x+y;
end;
namespace bar with
outfix <: :>;
foo x = x+2;
bar x = x-2;
end;
using namespace foo(^ foo), bar(bar <: :>);
// namespace foo
foo x;
x^y;
// namespace bar
bar x;
<: x,y :>;
Pure’s namespaces can thus be used pretty much like “modules” or “packages” in
languages like Ada or Modula-2. They provide a structured way to describe
program components offering collections of related data and operations, which
can be brought into scope in a controlled way by making judicious use of
using namespace declarations. They also provide an abstraction
barrier, since internal operations and data structures can be hidden away
employing private symbols.
Please note that these facilities are not Pure’s main focus and thus they are
somewhat limited compared to programming languages specifically designed for
big projects and large teams of developers. Nevertheless they should be useful
if your programs grow beyond a small collection of simple source modules, and
enable you to manage most Pure projects with ease.
Macros are a special type of functions to be executed as a kind of
“preprocessing stage” at compile time. In Pure these are typically used to
define custom special forms and to perform inlining of function calls and
other kinds of source-level optimizations.
Whereas the macro facilities of most programming languages simply provide a
kind of textual substitution mechanism, Pure macros operate on symbolic
expressions and are implemented by the same kind of rewriting rules that are
also used to define ordinary functions in Pure. This makes them robust and
easy to use for most common preprocessing purposes.
Syntactically, a macro definition looks just like a function definition with
the def keyword in front of it. Only unconditional rewriting rules
are permitted here, i.e., rules without guards and multiple right-hand sides.
However, multiple left-hand sides can be employed as usual to abbreviate a
collection of rules with the same left-hand side, as described in the General
Rules section.
The major difference between function and macro definitions is that the latter
are processed at compile time rather than run time. To these ends, macro calls
on the right-hand sides of function, constant and variable definitions are
evaluated by reducing them to normal form using the available macro rules. The
resulting expressions are then substituted for the macro calls. All macro
substitution happens before constant substitutions and the actual compilation
step. Macros can be defined in terms of other macros (also recursively), and
are normally evaluated using call by value (i.e., macro calls in macro
arguments are expanded before the macro gets applied to its parameters).
Let’s begin with a simple example of an optimization rule from the prelude,
which eliminates saturated instances of the right-associative function
application operator (you can find this near the beginning of prelude.pure):
Like in Haskell, ‘$‘ in fact just denotes function application,
but it is a low-priority operator which is handy to write cascading function
calls. With the above macro rule, these will be “inlined” as ordinary function
applications automatically. Example:
> foo x = bar $ bar $ 2*x;
> show foo
foo x = bar (bar (2*x));
Note that a macro may have the same name as an ordinary Pure function, which
is essential if you want to inline calls to an existing function. (Just like
ordinary functions, the number of parameters in each rule for a given macro
must be the same, but a macro may have a different number of arguments than
the corresponding function.)
When running interactively, you can follow the reduction steps the compiler
performs during macro evaluation. To these ends, you have to set “tracepoints”
on the relevant macros, using the trace command with the -m option;
see Interactive Commands. (This works even if the interpreter is run in
non-debugging mode.) Note that since macro expansion is performed at compile
time, you’ll have to do this before entering the definitions in which the
macro is used. However, in many cases you can also just enter the right-hand
side of the equation at the interpreter prompt to see how it gets
expanded. For instance:
> trace -m $
> bar $ bar $ 2*x;
-- macro ($): bar$2*x --> bar (2*x)
-- macro ($): bar$bar (2*x) --> bar (bar (2*x))
bar (bar (2*x))
Now let’s see how we can add our own optimization rules. Suppose we’d like to
expand saturated calls of the succ function. This function is defined
in the prelude; it just adds 1 to its single argument. We can inline such
calls as follows:
> def succ (x+y) = x+(y+1);
> def succ x = x+1;
> foo x = succ (succ (succ x));
> show foo
foo x = x+3;
Again, let’s see exactly what’s going on there:
> trace -m succ
> succ (succ (succ x));
-- macro succ: succ x --> x+1
-- macro succ: succ (x+1) --> x+(1+1)
-- macro succ: succ (x+(1+1)) --> x+(1+1+1)
x+3
Note that the contraction of the subterm 1+1+1 to the integer constant
3 is actually done by the compiler after macro expansion has been
performed. This is also called “constant folding”, see Constant Definitions
in the Caveats and Notes section for details. It is also the reason that we
added the first rule for succ. This rule may seem superflous at first
sight, but actually it is needed to massage the sum into a form which enables
constant folding.
Rules like these can help the compiler generate better code. Of course, the
above examples are still rather elementary. Pure macros can do much more
elaborate optimizations, but for this we first need to discuss how to write
recursive macros, as well as macros which take apart special terms like
lambdas. After that we’ll return to the subject of optimization rules in
Advanced Optimization below.
Macros can be recursive, in which case they usually consist of multiple rules
and make use of pattern-matching like ordinary function definitions. As a
simple example, let’s implement a Pure version of Lisp’s quasiquote which
allows you to create a quoted expression from a “template” while substituting
variable parts of the template. (For the sake of brevity, our definition is
somewhat simplified and does not cover some corner cases. See the Pure
distribution for a full version of this example.)
def quasiquote (unquote x) = x;
def quasiquote (f@_ (splice x)) = foldl ($) (quasiquote f) x;
def quasiquote (f@_ x) = quasiquote f (quasiquote x);
def quasiquote x = quote x;
(Note the f@_, which is an anonymous “as” pattern forcing the compiler to
recognize f as a function variable, rather than a literal function
symbol. See “As” Patterns in the Caveats and Notes section for an
explanation of this trick.)
The first rule above takes care of “unquoting” embedded subterms. The second
rule “splices” an argument list into an enclosing function application. The
third rule recurses into subterms of a function application, and the fourth
and last rule takes care of quoting the “atomic” subterms. Note that
unquote and splice themselves are just passive constructor symbols,
the real work is done by quasiquote, using foldl at runtime to
actually perform the splicing. (Putting off the splicing until runtime makes
it possible to splice argument lists computed at runtime.)
If we want, we can also add some syntactic sugar for Lisp weenies. (Note that
we cannot have ‘,‘ for unquoting, so we use ‘,$‘ instead.)
prefix 9 ` ,$ ,@ ;
def `x = quasiquote x; def ,$x = unquote x; def ,@x = splice x;
Examples:
> `(2*42+2^12);
2*42+2^12
> `(2*42+,$(2^12));
2*42+4096.0
> `foo 1 2 (,@'[2/3,3/4]) (5/6);
foo 1 2 (2/3) (3/4) (5/6)
> `foo 1 2 (,@args) (5/6) when args = '[2/3,3/4] end;
foo 1 2 (2/3) (3/4) (5/6)
We mention in passing here that, technically, Pure macros are just as powerful
as (unconditional) term rewriting systems and thus they are Turing-complete.
This implies that a badly written macro may well send the Pure compiler into
an infinite recursion, which results in a stack overflow at compile time. See
Stack Size and Tail Recursion in the Caveats and Notes section for
information on how to deal with these by setting the PURE_STACK
environment variable.
Pure macros are lexically scoped, i.e., the binding of symbols in the
right-hand-side of a macro definition is determined statically by the text of
the definition, and macro parameter substitution also takes into account
binding constructs, such as with and when clauses, in
the right-hand side of the definition. Macro facilities with these pleasant
properties are also known as hygienic macros. They are not susceptible to
so-called “name capture,” which makes macros in less sophisticated languages
bug-ridden and hard to use.
Macro hygiene is a somewhat esoteric topic for most programmers, so let us
take a brief look at what it’s all about. The problem avoided by hygienic
macros is that of name capture. There are actually two kinds of name capture
which may occur in unhygienic macro systems:
- A free symbol in the macro body inadvertently becomes bound to the value
of a local symbol in the context in which the macro is called.
- A free symbol in the macro call inadvertently becomes bound to the value
of a local symbol in the macro body.
Pure’s hygienic macros avoid both pitfalls. Here is an example for the first
form of name capture:
> def G x = x+y;
> G 10 when y = 99 end;
10+y
Note that the expansion of the G macro correctly uses the global instance
of y, even though y is locally defined in the context of the macro
call. (In some languages this form of name capture is sometimes used
deliberately in order to make the macro use the binding of the symbol which is
active at the point of the macro call. Normally, this won’t work in Pure,
although there is a way to force this behaviour in Pure as well, see Name
Capture in the Caveats and Notes section.)
In contrast, the second form of name capture is usually not intended, and is
therefore more dangerous. Consider the following example:
> def F x = x+y when y = x+1 end;
> F y;
y+(y+1)
Pure again gives the correct result here. You’d have to be worried if you got
(y+1)+(y+1) instead, which would result from the literal expansion y+y
when y = y+1 end, where the (free) variable y passed to F gets
captured by the local binding of y. In fact, that’s exactly what you get
with C macros:
#define F(x) { int y = x+1; return x+y; }
Here F(y) expands to { int y = y+1; return y+y; } which is usually
not what you want.
As already mentioned in The Quote, special expressions such as
conditionals and lambdas cannot be directly represented as runtime data in
Pure. But they can be quoted in which case they are replaced by
corresponding “placeholder terms”. These placeholder terms are in fact
implemented as built-in macros which, when evaluated, construct the
corresponding specials.
-
macro __ifelse__ x y z
This macro expands to the conditional expression if x then y else z
during macro evaluation.
-
macro __lambda__ [x1,...,xn] y
Expands to the lambda expression \x1 ... xn -> y.
-
macro __case__ x [(x1 --> y1),...,(xn --> yn)]
Expands to the case expression case x of x1 = y1; ...; xn = yn
end. Note that the --> symbol is used to separate the left-hand
side and the right-hand side of each rule (see below).
-
macro x __when__ [(x1 --> y1),...,(xn --> yn)]
Expands to the when expression x when x1 = y1; ...; xn = yn
end. Here the left-hand side of a rule may be omitted if it is just the
anonymous variable; i.e., x __when__ [foo y] is the same as x
__when__ [_ --> foo y].
-
macro x __with__ [(x1 --> y1),...,(xn --> yn)]
Expands to the with expression x with x1 = y1; ...; xn = yn
end.
Note that the following low-priority infix operators are used to denote
equations in the __case__, __when__ and __with__
macros:
-
constructor x --> y
Denotes an equation x = y.
-
constructor x __if__ y
Attaches a guard to the right-hand side of an equation. That is, x --> y
__if__ z denotes the conditional equation x = y if z. This symbol is
only recognized in __case__ and __with__ calls.
In addition, patterns on the left-hand side of equations or in lambda
arguments may be decorated with the following constructor terms to indicate
“as” patterns and type tags (these are infix operators with a very high
priority):
-
constructor x __as__ y
Denotes an “as” pattern x @ y.
-
constructor x __type__ y
Denotes a type tag x :: y.
Note that all these symbols are in fact just constructors which are only
interpreted in the context of the built-in macros listed above; they aren’t
macros themselves.
It’s good to remember the above when you’re doing macro programming. However,
to see the placeholder term of a special, you can also just type a quoted
expression in the interpreter:
> '(\x->x+1);
__lambda__ [x] (x+1)
> '(f with f x = y when y = x+1 end end);
f __with__ [f x-->y __when__ [y-->x+1]]
List and matrix comprehensions can also be quoted. These are basically
syntactic sugar for lambda applications, cf. Primary Expressions. The
compiler expands them to their “unsugared” form already before macro
substitution, so no special kinds of built-in macros are needed to represent
them. When quoted, comprehensions are thus denoted in their unsugared form,
which consists of a pile of lambda expressions and list or matrix construction
functions for the generation clauses, and possibly some conditionals for the
filter clauses of the comprehension. For instance:
> '[2*x | x = 1..3];
listmap (__lambda__ [x] (2*x)) (1..3)
Here’s how type tags and “as” patterns in quoted specials look like:
> '(\x::int->x+1);
__lambda__ [x __type__ int] (x+1)
> '(dup (1..3) with dup xs@(x:_) = x:xs end);
dup (1..3) __with__ [dup (xs __as__ (x:_))-->x:xs]
Note that the placeholder terms for the specials are quoted here, and hence
they are not evaluated (quoting inhibits macro expansion, just like it
prevents the evaluation of ordinary function calls). Evaluating the
placeholder terms executes the corresponding specials:
> '(dup (1..3) with dup xs@(x:_) = x:xs end);
dup (1..3) __with__ [dup (xs __as__ (x:_))-->x:xs]
> eval ans;
[1,1,2,3]
Of course, you can also just enter the macros directly (without quoting) to
have them evaluated:
> dup (1..3) __with__ [dup (xs __as__ (x:_))-->x:xs];
[1,1,2,3]
> __lambda__ [x __type__ int] (x+1);
#<closure 0x7f1934158dc8>
> ans 99;
100
The __str__ function can be used to pretty-print quoted specials:
> __str__ ('__lambda__ [x __type__ int] (x+1));
"\\x::int -> x+1"
> __str__ ('(dup (1..3) __with__ [dup (xs __as__ (x:_))-->x:xs]));
"dup (1..3) with dup xs@(x:_) = x:xs end"
This is useful to see which expression a quoted special will expand to. Note
that __str__ can also be used to define print representations for
quoted specials with __show__ (described in Pretty-Printing) if you
always want to have them printed that way by the interpreter.
As quoted specials are just ordinary Pure expressions, they can be manipulated
by functions just like any other term. For instance, here’s how you can define
a function which takes a quoted lambda and swaps its two arguments:
> swap (__lambda__ [x,y] z) = '(__lambda__ [y,x] z);
> swap ('(\a b->a-b));
__lambda__ [b,a] (a-b)
> eval ans 2 3; // same as (\b a->a-b) 2 3
1
For convenience, a literal special expression can also be used on the
left-hand side of an equation, in which case it actually denotes the
corresponding placeholder term. So the swap function can also be defined
like this (note that we first scratch the previous definition of swap with
the clear command, see Interactive Commands):
> clear swap
> swap (\x y -> z) = '(\y x -> z);
> swap ('(\a b->a-b));
__lambda__ [b,a] (a-b)
This is usually easier to write and improves readability. However, there are
cases in which you want to work with the built-in macros in a direct fashion.
In particular, this becomes necessary when writing more generic rules which
deal, e.g., with lambdas involving a variable number of arguments, or if you
need real (i.e., unquoted) type tags or “as” patterns in a placeholder
pattern. We’ll see examples of these later.
Quoted specials can be manipulated with macros just as well as with functions.
In fact, this is quite common and thus the macro evaluator has some special
support to make this more convenient. Specifically, it is possible to make a
macro quote its arguments in an automatic fashion, by means of the
--quoteargs pragma. To illustrate this,
let’s redefine swap as a macro:
> clear swap
> #! --quoteargs swap
> def swap (\x y -> z) = '(\y x -> z);
> swap (\a b->a-b);
__lambda__ [b,a] (a-b)
The --quoteargs pragma makes the swap
macro receive its argument unevaluated, as if it was quoted (but without a
literal quote around it). Therefore the quote on the lambda argument of
swap can now be omitted. However, the result is still a quoted
lambda. It’s tempting to just omit the quote on the right-hand side of the
macro definition as well, in order to get a real lambda instead:
> clear swap
> def swap (\x y -> z) = \y x -> z;
> swap (\a b->a-b);
#<closure 0x7f1934156f00>
> ans 2 3;
a-b
We got a closure all right, but apparently it’s not the right one. Let’s use
trace -m to figure out what went wrong:
> trace -m swap
> swap (\a b->a-b);
-- macro swap: swap (\a b -> a-b) --> \y x -> a-b
#<closure 0x7f1934157248>
Ok, so the result is the lambda \y x -> a-b, not \b a -> a-b as we
expected. This happens because we used a literal (unquoted) lambda on the
right-hand side, which does its own variable binding; consequently, the
variables x and y are bound by the lambda in this context, not by the
left-hand side of the macro rule.
So just putting an unquoted lambda on the right-hand side doesn’t do the
job. One way to deal with the situation is to just employ the __lambda__
macro in a direct way, as we’ve seen before:
> clear swap
> def swap (__lambda__ [x,y] z) = __lambda__ [y,x] z;
> swap (\a b->a-b);
-- macro swap: swap (\a b -> a-b) --> __lambda__ [b,a] (a-b)
-- macro __lambda__: __lambda__ [b,a] (a-b) --> \b a -> a-b
#<closure 0x7f1934156f00>
> ans 2 3;
1
This works, but doesn’t look very nice. Often it’s more convenient to first
construct a quoted term involving the necessary specials and then have it
evaluated during macro evaluation. Pure provides yet another built-in macro
for this purpose:
-
macro __eval__ x
Evaluate x at macro expansion time. This works by stripping one level
of (outermost) quotes from x and performing macro expansion on the
resulting unquoted subexpressions.
Using __eval__, we can implement the swap macro as follows:
> clear swap
> def swap (\x y -> z) = __eval__ ('(\y x -> z));
> swap (\a b->a-b);
-- macro swap: swap (\a b -> a-b) --> __eval__ ('__lambda__ [b,a] (a-b))
-- macro __lambda__: __lambda__ [b,a] (a-b) --> \b a -> a-b
-- macro __eval__: __eval__ ('__lambda__ [b,a] (a-b)) --> \b a -> a-b
#<closure 0x7f7e1f867dc8>
> ans 2 3;
1
Lisp programmers should note the difference. In Lisp, macros usually yield a
quoted expression which is evaluated implicitly during macro expansion. This
is never done automatically in Pure, since many Pure macros work perfectly
well without it. Instead, quotes in a macro expansion are treated as literals,
and you’ll have to explicitly call __eval__ to remove them during macro
evaluation.
A final caveat: Placeholder terms for specials are just simple expressions;
they don’t do any variable binding by themselves. Thus the rules of macro
hygiene don’t apply to them, which makes it possible to manipulate lambdas
and local definitions in any desired way. On the other hand, this means that
it is the programmer’s responsibility to avoid accidental name capture when
using these facilities. Most macro code will work all right when written in a
straightforward way, but there are some corner cases which need special
attention (cf. Name Capture).
We are now in a position to have a look at some of the trickier optimization
macros defined in the prelude. Most of the following macros can be found near
the end of the prelude.pure module; they are used to optimize the case of
“throwaway” list and matrix comprehensions. This is useful if a comprehension
is evaluated solely for its side effects. To keep things simple, we discuss a
slightly abridged version here which only deals with list comprehensions.
Please check the actual prelude code for the full version.
#! --quoteargs __std__::__do__
def void [x] = void x;
def void (catmap f x) | void (listmap f x) = __do__ f x;
// Recurse into embedded generator clauses.
def __do__ (__lambda__ [x] y@(listmap _ _)) |
__do__ (__lambda__ [x] y@(catmap _ _)) =
__do__ $ (__lambda__ [x] (void y));
// Recurse into embedded filter clauses.
def __do__ (__lambda__ [x] (__ifelse__ y z [])) =
__do__ $ (__lambda__ [x] (__ifelse__ y (void z) ()));
// Eliminate extra calls to 'void' in generator clauses.
def __do__ (__lambda__ [x] (void y)) = __do__ (__lambda__ [x] y);
// Eliminate extra calls to 'void' in filter clauses.
def __do__ (__lambda__ [x] (__ifelse__ y (void z) ())) =
__do__ (__lambda__ [x] (__ifelse__ y z ()));
// Any remaining instances reduce to a plain 'do' (this must come last).
def __do__ f = do f;
First, note that the void function simply throws away its argument and
returns () instead. The do function applies a function to
every member of a list (like map), but throws away all intermediate
results and just returns (), which is much more efficient if you don’t
need those results anyway. These are both defined in the prelude. The
__do__ macro eventually reduces to just a plain do call, but applies
some optimizations along the way. (While the above rules for __do__ are
always valid optimizations for do, the prelude uses a separate macro here
instead of clobbering do itself, so that these optimizations do not
interfere with calls to do in ordinary user code. Also note that the
__do__ macro is not intended to be invoked directly by the programmer, so
the prelude puts it into the __std__ namespace, to keep the default
namespace clean.)
Before we further delve into this example, a few remarks are in order about
the way list comprehensions are implemented in Pure. As already mentioned,
list comprehensions are just syntactic sugar; the compiler immediately
transforms them to an equivalent expression involving only lambdas and a few
other list operations. The latter are essentially equivalent to piles of
nested filters and maps, but for various reasons they are actually implemented
using two special helper operations, catmap and listmap.
The catmap operation combines map and cat; this is
needed, in particular, to accumulate the results of nested generators, such as
[i,j | i = 1..n; j = 1..m]. The same operation is also used to implement
filter clauses, you can see this below in the examples. However, for
efficiency simple generators like [2*i | i = 1..n] are translated to a
listmap instead (which is basically just map, but works with
different aggregate types, so that list comprehensions can draw values from
aggregates other than lists, such as matrices).
Now let’s see how the rules above transform a list comprehension if we “void”
it:
> using system;
> f = [printf "%g\n" (2^x+1) | x=1..5; x mod 2];
> g = void [printf "%g\n" (2^x+1) | x=1..5; x mod 2];
> show f g
f = catmap (\x -> if x mod 2 then [printf "%g\n" (2^x+1)] else []) (1..5);
g = do (\x -> if x mod 2 then printf "%g\n" (2^x+1) else ()) (1..5);
As you can see, the catmap got replaced with a do, and the
list brackets inside the lambda were eliminated as well. These optimizations
are just what’s needed to make this code go essentially as fast as a for
loop in traditional programming languages (up to constant factors, of
course). Here’s how it looks like when we run the g function:
It’s also instructive to have a look at how the above macro rules work in
concert to rewrite a “voided” comprehension. To these ends, you can rerun the
right-hand side of g with some tracing enabled, as follows (we omit the
tracing output here for brevity):
> trace -m void
> void [printf "%g\n" (2^x+1) | x=1..5; x mod 2];
The above optimization rules also take care of nested list comprehensions,
since they recurse into the lambda bodies of generator and filter clauses. For
instance:
> h = void [puts $ str (x,y) | x=1..2; y=1..3];
> show h
h = do (\x -> do (\y -> puts (str (x,y))) (1..3)) (1..2);
Again, you should run this with macro tracing enabled to see how the
__do__ macro recurses into the outer lambda body of the list
comprehension. Here’s the rule which actually does this:
def __do__ (__lambda__ [x] y@(catmap _ _)) =
__do__ $ (__lambda__ [x] (void y));
Note that __do__ is actually implemented as a
“quoteargs” macro so that it can inspect
and recurse into the lambda terms in its argument. Also note the $ on
the right-hand side of this rule; this is also implemented as a macro in the
prelude. Here the $ operator is used to forcibly evaluate the macro
argument __lambda__ [x] (void y), so that the embedded call to the
void macro gets expanded. (Without the $ the argument to
__do__ would be quoted and thus not be evaluated.)
The meta representation of specials discussed in Built-in Macros and Special
Expressions is also useful to obtain information about the running program
and even modify it. Pure’s runtime provides some built-in operations to
implement these reflection capabilities, which are comparable in scope to what
the Lisp programming language offers.
Specifically, the get_fundef function allows you to retrieve the
definition of a global Pure function. Given the symbol denoting the function,
get_fundef returns the list of rewriting rules implementing the
functions, using the same lhs --> rhs format used by the
__case__, __when__ and __with__ macros discussed
above. For instance:
> fact n = 1 if n<=1;
> = n*fact (n-1) otherwise;
> get_fundef fact;
[(fact n-->1 __if__ n<=1),(fact n-->n*fact (n-1))]
Defining a new function or extending an existing function definition can be
done just as easily, using the add_fundef function:
> add_fundef $ '[(fib n-->1 __if__ n<=1),(fib n-->fib (n-2)+fib (n-1))];
()
> show fib
fib n = 1 if n<=1;
fib n = fib (n-2)+fib (n-1);
> map fib (0..10);
[1,1,2,3,5,8,13,21,34,55,89]
Note that, to be on the safe side, we quoted the rule list passed to
add_fundef to prevent premature evaluation of symbols used in the
rules. This is necessary because add_fundef is an ordinary function,
not a macro. (Of course, you could easily define a macro which would take care
of this, if you like. We leave this as an exercise to the reader.)
Also note that add_fundef doesn’t override existing function
definitions. It simply keeps on adding rules to the current program, just as
if you typed the equations at the command prompt of the interpreter. It is
possible to delete individual equations with del_fundef:
> del_fundef $ '(fib n-->fib (n-2)+fib (n-1));
()
> show fib
fib n = 1 if n<=1;
Moreover, the clearsym function allows you to completely get rid of an
existing function:
> clearsym fib 0;
()
> show fib
> fib 9;
fib 9
There’s also a companion function, globsym, which enables you to get
a list of defined symbols which match a given glob pattern:
> globsym "fact" 0;
[fact]
> globsym "*" 0;
[(!),(!!),(#),($),($$),...]
> #globsym "*" 0;
304
Note that globsym also returns symbols defined as types, macros,
variables or constants. But we can easily check for a given type of symbol by
using the appropriate function to retrieve the rules defining the symbol, and
filter out symbols with an empty rule list:
> #[sym | sym = globsym "*" 0; ~null (get_fundef sym)];
253
Pure also provides the operations get_typedef, get_macdef,
get_vardef and get_constdef, which are completely analogous to
get_fundef, but return the definitions of types, macros, (global)
variables and constants. Note that in the latter two cases the rule list takes
the form [var-->val] if the symbol is defined, [] if it isn’t.
For instance, let’s check the definition of the $ macro
(cf. Optimization Rules) and the list type (cf. Recursive
Types):
> get_macdef ($);
[f$x-->f x]
> get_typedef list;
[(list []-->1),(list (_:_)-->1)]
Or let’s lists all global variables along with their values:
> catmap get_vardef (globsym "*" 0);
[(argc-->0),(argv-->[]),(compiling-->0),
(sysinfo-->"x86_64-unknown-linux-gnu"),(version-->"0.54")]
The counterparts of add_fundef and del_fundef are provided as
well. Not very surprisingly, they are named add_typedef,
del_typedef, etc. For instance:
> add_vardef ['x-->3*33];
()
> show x
let x = 99;
> del_vardef ('x);
()
> show x
The above facilities should cover most metaprogramming needs. For even more
exotic requirements, you can also use the eval and evalcmd
primitives to execute arbitrary Pure code in text form; please see the
Pure Library Manual for details.
Finally, a word of caution: The use of add_fundef, del_fundef
and similar operations to modify a running program breaks referential
transparency and hence these functions should be used with care. Moreover, at
present the JIT compiler doesn’t support truly self-modifying code (i.e.,
functions modifying themselves while they’re executing); this results in
undefined behaviour. Also, note that none of the inspection and mutation
capabilities provided by these operations will work in batch-compiled
programs, please check the Batch Compilation section for details.
Pure makes it very easy to call C functions (as well as functions in a number
of other languages supported by the GNU compiler collection). To call an
existing C function, you just need an extern declaration of the
function, as described below. By these means, all functions in the standard C
library and the Pure runtime are readily available to Pure scripts. Functions
can also be loaded from dynamic libraries and LLVM bitcode files at
runtime. In the latter case, you don’t even need to write any
extern declarations, the interpreter will do that for you. As of
Pure 0.45, you can also add inline C/C++ and Fortran code to your Pure scripts
and have the Pure interpreter compile them on the fly, provided that you have
the corresponding compilers from the LLVM project installed.
In some cases you will still have to rely on big and complicated third-party
and system libraries which aren’t readily available in bitcode form. It goes
without saying that writing all the extern declarations for such
libraries can be a daunting task. Fortunately, there is a utility to help with
this, by extracting the extern declarations automatically from C
headers. Please see External C Functions in the Caveats and Notes
section for details.
To access an existing C function in Pure, you need an extern
declaration of the function, which is a simplified kind of C prototype. The
syntax of these declarations is described by the following grammar rules:
extern_decl ::= [scope] "extern" prototype ("," prototype) ";"
prototype ::= c_type identifier "(" [parameters | "..."] ")" ["=" identifier]
parameters ::= parameter ("," parameter)* ["," "..."]
parameter ::= c_type [identifier]
c_type ::= identifier "*"*
Extern functions can be called in Pure just like any other. For instance, the
following commands, entered interactively in the interpreter, let you use the
sin function from the C library (of course you could just as well put the
extern declaration into a script):
> extern double sin(double);
> sin 0.3;
0.29552020666134
An extern declaration can also be prefixed with a
public/private scope specifier:
private extern double sin(double);
Multiple prototypes can be given in one extern declaration,
separating them with commas:
extern double sin(double), double cos(double), double tan(double);
For clarity, the parameter types can also be annotated with parameter names
(these only serve informational purposes and are for the human reader; they
are effectively treated as comments by the compiler):
extern double sin(double x);
Pointer types are indicated by following the name of the element type with one
or more asterisks, as in C. For instance:
> extern char* strchr(char *s, int c);
> strchr "foo bar" (ord "b");
"bar"
As you can see in the previous example, some pointer types get special
treatment, allowing you to pass certain kinds of Pure data (such as Pure
strings as char* in this example). This is discussed in more detail in C
Types below.
The interpreter makes sure that the parameters in a call match; if not, then
by default the call is treated as a normal form expression:
> extern double sin(double);
> sin 0.3;
0.29552020666134
> sin 0;
sin 0
This gives you the opportunity to augment the external function with your own
Pure equations. To make this work, you have to make sure that the
extern declaration of the function comes first. For instance, we
might want to extend the sin function with a rule to handle integers:
> sin x::int = sin (double x);
> sin 0;
0.0
Sometimes it is preferable to replace a C function with a wrapper function
written in Pure. In such a case you can specify an alias under which the
original C function is known to the Pure program, so that you can still call
the C function from the wrapper. An alias is introduced by terminating the
extern declaration with a clause of the form = alias. For instance:
> extern double sin(double) = c_sin;
> sin x::double = c_sin x;
> sin x::int = c_sin (double x);
> sin 0.3; sin 0;
0.29552020666134
0.0
Aliases are just one way to declare a synonym of an external function. As
an alternative, you can also declare the C function in a special namespace
(cf. Namespaces in the Declarations section):
> namespace c;
> extern double sin(double);
> c::sin 0.3;
0.29552020666134
Note that the namespace qualification only affects the Pure side; the
underlying C function is still called under the unqualified name as usual. The
way in which such qualified externs are accessed is the same as for ordinary
qualified symbols. In particular, the using namespace declaration
applies as usual, and you can declare such symbols as private if
needed. It is also possible to combine a namespace qualifier with an alias:
> namespace c;
> extern double sin(double) = mysin;
> c::mysin 0.3;
0.29552020666134
In either case, different synonyms of the same external function can be
declared in slightly different ways, which makes it possible to adjust the
interpretation of pointer values on the Pure side. This is particularly useful
for string arguments which, as described below, may be passed both as
char* (which implies copying and conversion to or from the system
encoding) and as void* (which simply passes through the character
pointers). For instance:
> extern char *strchr(char *s, int c) = foo;
> extern void *strchr(void *s, int c) = bar;
> foo "foo bar" 98; bar "foo bar" 98;
"bar"
#<pointer 0x12c2f24>
Also note that, as far as Pure is concerned, different synonyms of an external
function are really different functions. In particular, they can each have
their own set of augmenting Pure equations. For instance:
> extern double sin(double);
> extern double sin(double) = mysin;
> sin === sin;
1
> sin === mysin;
0
> sin 1.0; mysin 1.0;
0.841470984807897
0.841470984807897
> sin x::int = sin (double x);
> sin 1; mysin 1;
0.841470984807897
mysin 1
Variadic C functions are declared as usual by terminating the parameter list
with an ellipsis (...):
> extern int printf(char*, ...);
> printf "Hello, world\n";
Hello, world
13
Note that the variadic prototype is mandatory here, since the compiler needs
to know about the optional arguments so that it can generate the proper code
to call the function. However, in Pure a function always has a fixed arity,
so, as far as Pure is concerned, the function is still treated as if it had no
extra arguments. Thus the above declaration only allows you to call printf
with a single argument.
To make it possible to pass optional arguments to a variadic function, you
must explicitly give the (non-variadic) prototypes with which the function is
to be called. To these ends, the additional prototypes are declared as
synonyms of the original variadic function. This works because the compiler
only checks the non-variadic parameters for conformance. For instance:
> extern int printf(char*, char*) = printf_s;
> printf_s "Hello, %s\n" "world";
Hello, world
13
> extern int printf(char*, int) = printf_d;
> printf_d "Hello, %d\n" 99;
Hello, 99
10
As indicated in the previous section, the data types in extern
declarations are either C type names or pointer types derived from these. The
special expr* pointer type is simply passed through; this provides a means
to deal with Pure data in C functions in a direct fashion. For all other C
types, Pure values are “marshalled” (converted) from Pure to C when passed as
arguments to C functions, and the result returned by the C function is then
converted back from C to Pure. All of this is handled by the runtime system in
a transparent way, of course.
Note that, to keep things simple, Pure does not provide any notations for C
structs or function types, although it is possible to represent pointers to
such objects using void* or some other appropriate pointer types. In
practice, this simplified system should cover most kinds of calls that need to
be done when interfacing to C libraries, but there are ways to work around
these limitations if you need to access C structs or call back from C to Pure,
see External C Functions in the Caveats and Notes section for details.
Pure supports the usual range of basic C types: void, bool, char,
short, int, long, float, double, and converts between
these and the corresponding Pure data types (machine ints, bigints and double
values) in a straightforward way.
The void type is only allowed in function results. It is converted to the
empty tuple ().
Both float and double are supported as floating point types. Single
precision float arguments and return values are converted from/to Pure’s
double precision floating point numbers.
A variety of C integer types (bool, char, short, int,
long) are provided which are converted from/to the available Pure integer
types in a straightforward way. In addition, the synonyms int8, int16
and int32 are provided for char, short and int, respectively,
and int64 denotes 64 bit integers (a.k.a. ISO C99 long long). Note
that long is equivalent to int32 on 32 bit systems, whereas it is the
same as int64 on most 64 bit systems. To make it easier to interface to
various system routines, there’s also a special size_t integer type which
usually is 4 bytes on 32 bit and 8 bytes on 64 bit systems.
All integer parameters take both Pure ints and bigints as actual arguments;
truncation or sign extension is performed as needed, so that the C interface
behaves as if the argument was “cast” to the C target type. Returned integers
use the smallest Pure type capable of holding the result, i.e., int for the C
char, short and int types, bigint for int64.
Pure considers all integers as signed quantities, but it is possible to pass
unsigned integers as well (if necessary, you can use a bigint to pass positive
values which are too big to fit into a machine int). Also note that when an
unsigned integer is returned by a C routine, which is too big to fit into the
corresponding signed integer type, it will “wrap around” and become
negative. In this case, depending on the target type, you can use the
ubyte, ushort, uint, ulong and uint64
functions provided by the prelude to convert the result back to an unsigned
quantity.
The use of pointer types is also fairly straightforward, but Pure has some
special rules for the conversion of certain pointer types which make it easy
to pass aggregate Pure data to and from C routines, while also following the
most common idioms for pointer usage in C. The following types of pointers are
recognized both as arguments and return values of C functions.
Bidirectional pointer conversions:
- char* is used for string arguments and return values which are converted
from Pure’s internal utf-8 based string representation to the system
encoding and vice versa. (Thus a C routine can never modify the raw Pure
string data in-place; if this is required then you’ll have to pass the
string argument as a void*, see below.)
- void* is for any generic pointer value, which is simply passed through
unchanged. When used as an argument, you can also pass Pure strings,
matrices and bigints. In this case the raw underlying data pointer
(char* in the case of strings, int*, double* or expr* in the
case of numeric and symbolic matrices, and the GMP type mpz_t in the
case of bigints) is passed, which allows the data to be modified in place
(with care). In particular, passing bigints as void* makes it possible
to call most GMP integer routines directly from Pure.
- dmatrix*, cmatrix* and imatrix* allow you to pass numeric Pure
matrices of the appropriate types (double, complex, int). Here a pointer to
the underlying GSL matrix structure is passed (not just the data itself).
This makes it possible to transfer GSL matrices between Pure and GSL
routines in a direct fashion without any overhead. (For convenience, there
are also some other pointer conversions for marshalling matrix arguments to
numeric C vectors, which are described in Pointers and Matrices below.)
- expr* is for any kind of Pure value. A pointer to the expression node is
passed to or from the C function. This type is to be used for C routines
which are prepared to deal with pristine Pure data, using the corresponding
functions provided by the runtime. You can find many examples of this in the
standard library.
All other pointer types are simply taken at face value, allowing you to pass
Pure pointer values as is, without any conversions. This also includes
pointers to arbitrary named types which don’t have a predefined meaning in
Pure, such as FILE*. As of Pure 0.45, the interpreter keeps track of the
actual names of all pointer types and checks (at runtime) that the types match
in an external call, so that you can’t accidentally get a core dump by
passing, say, a FILE* for a char*. (The call will then simply fail and
yield a normal form, which gives you the opportunity to hook into the function
with your own Pure definitions which may supply any desired data conversions.)
Typing information about pointer values is also available to Pure scripts by
means of corresponding library functions, please see the Tagged Pointers section in the Pure Library Manual for details.
The following additional pointer conversions are provided to deal with Pure
matrix values in arguments of C functions, i.e., on the input side. These
enable you to pass Pure matrices for certain kinds of C vectors. Note that in
any case, you can also simply pass a suitable plain pointer value instead.
Also, these types aren’t special in return values, where they will simply
yield a pointer value (with the exception of char* which gets special
treatment as explained in the previous subsection). Thus you will have to
decode such results manually if needed. The standard library provides various
routines to do this, please see the String Functions and Matrix Functions sections in the Pure Library Manual for details.
Numeric pointer conversions (input only):
- char*, short*, int*, int64*, float*, double* can be
used to pass numeric matrices as C vectors. This kind of conversion passes
just the matrix data (not the GSL matrix structure, as the dmatrix* et
al conversions do) and does conversions between integer or floating point
data of different sizes on the fly. You can either pass an int matrix as a
char*, short* int* or int64* argument, or a double or
complex matrix as a float* or double* argument (complex values are
then represented as two separate double numbers, first the real, then the
imaginary part, for each matrix element).
- char**, short**, int**, int64**, float**, double**
provide yet another way to pass numeric matrix arguments. This works
analogously to the numeric vector conversions above, but here a temporary C
vector of pointers is passed to the C function, whose elements point to the
rows of the matrix.
Argv-style conversions (input only):
- char** and void** can be used to pass argv-style vectors as
arguments to C functions. In this case, the Pure argument must be a symbolic
vector of strings or generic pointer values. char** converts the string
elements to the system encoding, whereas void** passes through character
string data and other pointers unchanged (and allows in-place modification
of the data). A temporary C vector of these elements is passed to the C
function, which is always NULL-terminated and can thus be used for
almost any purpose which requires such argv-style vectors.
Note that in the numeric pointer conversions, the matrix data is passed “per
reference” to C routines, i.e., the C function may modify the data “in
place”. This is true even for target data types such as short* or
float** which involve automatic conversions and hence need temporary
storage. In this case the data from the temporary storage is written back to
the original matrix when the function returns, to maintain the illusion of
in-place modification. Temporary storage is also needed when the GSL matrix
has the data in non-contiguous storage. You may want to avoid this if
performance is critical, by always using “packed” matrices (see pack
in Matrix Functions) of the appropriate types.
Let’s finally have a look at some instructive examples to explain some of the
trickier pointer types.
First, the matrix pointer types dmatrix*, cmatrix* and imatrix*
can be used to pass double, complex double and int matrices to GSL functions
taking pointers to the corresponding GSL types (gsl_matrix,
gsl_matrix_complex and gsl_matrix_int) as arguments or returning them
as results. (Note that there is no special marshalling of Pure’s symbolic
matrix type, as these aren’t supported by GSL anyway.) Also note that matrices
are always passed by reference. Thus, if you need to pass a matrix as an
output parameter of a GSL matrix routine, you should either create a zero
matrix or a copy of an existing matrix to hold the result. The prelude
provides various operations for that purpose (in particular, see the
dmatrix, cmatrix, imatrix and pack functions
in matrices.pure). For instance, here is how you can quickly wrap up GSL’s
double matrix addition function in a way that preserves value semantics:
> using "lib:gsl";
> extern int gsl_matrix_add(dmatrix*, dmatrix*);
> x::matrix + y::matrix = gsl_matrix_add x y $$ x when x = pack x end;
> let x = dmatrix {1,2,3}; let y = dmatrix {2,3,2}; x; y; x+y;
{1.0,2.0,3.0}
{2.0,3.0,2.0}
{3.0,5.0,5.0}
Most GSL matrix routines can be wrapped in this fashion quite easily. A
ready-made GSL interface providing access to all of GSL’s numeric functions is
in the works; please check the Pure website for details.
For convenience, it is also possible to pass any kind of numeric matrix for a
char*, short*, int*, int64*, float* or double*
parameter. This requires that the pointer and the matrix type match up;
conversions between char, short, int64 and int data and,
likewise, between float and double are handled automatically,
however. For instance, here is how you can call the puts routine from the
C library with an int matrix encoding the string "Hello, world!" as byte
values (ASCII codes):
> extern int puts(char*);
> puts {72,101,108,108,111,44,32,119,111,114,108,100,33,0};
Hello, world!
14
Pure 0.45 and later also support char**, short**, int**,
int64**, float** and double** parameters which encode a matrix as
a vector of row pointers instead. This kind of matrix representation is often
found in audio and video processing software (where the rows of the matrix
might denote different audio channels, display lines or video frames), but
it’s also fairly convenient to do any kind of matrix processing in C. For
instance, here’s how to do matrix multiplication (the naive algorithm):
void matmult(int n, int l, int m, double **x, double **y, double **z)
{
int i, j, k;
for (i = 0; i < n; i++)
for (j = 0; j < m; j++) {
z[i][j] = 0.0;
for (k = 0; k < l; k++)
z[i][j] += x[i][k]*y[k][j];
}
}
As you can see, this multiplies a n times l matrix x with a l
times m matrix y and puts the result into the n times m matrix
z:
> extern void matmult(int, int, int, double**, double**, double**);
> let x = {0.11,0.12,0.13;0.21,0.22,0.23};
> let y = {1011.0,1012.0;1021.0,1022.0;1031.0,1032.0};
> let z = dmatrix (2,2);
> matmult 2 3 2 x y z $$ z;
{367.76,368.12;674.06,674.72}
Also new in Pure 0.45 is the support for passing argv-style vectors as
arguments. For instance, here is how you can use fork and execvp to
implement a poor man’s version of the C system function. (This is
UNIX-specific and doesn’t do much error-checking, but you get the idea.)
extern int fork();
extern int execvp(char *path, char **argv);
extern int waitpid(int pid, int *status, int options);
system cmd::string = case fork of
// child: execute the program, bail out if error
0 = execvp "/bin/sh" {"/bin/sh","-c",cmd} $$ exit 1;
// parent: wait for the child and return its exit code
pid = waitpid pid status 0 $$ status!0 >> 8
when status = {0} end if pid>=0;
end;
system "echo Hello, world!";
system "ls -l *.pure";
system "exit 1";
By default, external C functions are resolved by the LLVM runtime, which first
looks for the symbol in the C library and Pure’s runtime library (or the
interpreter executable, if the interpreter was linked statically). Thus all C
library and Pure runtime functions are readily available in Pure programs.
Other functions can be provided by adding them to the runtime, or by linking
them into the runtime or the interpreter executable. Better yet, you can just
“dlopen” shared libraries at runtime with a special form of the
using clause:
using "lib:libname[.ext]";
For instance, if you want to call the functions from library libxyz directly
from Pure:
After this declaration the functions from the given library will be ready to
be imported into your Pure program by means of corresponding extern
declarations.
Shared libraries opened with using clauses are searched for in the same way as
source scripts (see section Modules and Imports above), using the
-L option and the PURE_LIBRARY environment variable in
place of -I and PURE_INCLUDE. If the library isn’t found
by these means, the interpreter will also consider other platform-specific
locations searched by the dynamic linker, such as the system library
directories and LD_LIBRARY_PATH on Linux. The necessary filename
suffix (e.g., .so on Linux or .dll on Windows) will be supplied automatically
when needed. Of course you can also specify a full pathname for the library if
you prefer that. If a library file cannot be found, or if an extern
declaration names a function symbol which cannot be resolved, an appropriate
error message is printed.
As of Pure 0.44, the interpreter also provides a direct way to import LLVM
bitcode modules in Pure scripts. The main advantage of this method over the
“plain” C interface explained above is that the bitcode loader knows all the
call interfaces and generates the necessary extern declarations
automatically. This is more than just a convenience, as it also eliminates at
least some of the mistakes in extern declarations that may arise
when importing functions manually from dynamic libraries.
Note
The facilities described below require that you have an LLVM-capable
C/C++ compiler installed. The available options right now are clang,
llvm-gcc and dragonegg. Please check the Pure installation
instructions on how to get one of these (or all of them) up and
running. Note that clang and llvm-gcc are standalone compilers, while
dragonegg is supplied as a gcc plugin which hooks into your existing system
compiler (gcc 4.5 or later is required for that). Any of these enable you
to compile C/C++ source to LLVM assembler or bitcode. The clang compiler is
recommended for C/C++ development, as it offers faster compilation times
and has much better diagnostics than gcc. On the other hand, llvm-gcc and
dragonegg have the advantage that they also support alternative frontends
so that you can compile Fortran and Ada code as well.
LLVM bitcode is loaded in a Pure script using the following special format of
the using clause:
(Here the bc tag indicates a bitcode file, and the default .bc bitcode
filename extension is supplied automatically. Also, the bitcode file is
searched for on the usual library search path.)
That’s it, no explicit extern declarations are required on the Pure
side. The Pure interpreter automatically creates extern
declarations (in the current namespace) for all the external functions defined
in the LLVM bitcode module, and generates the corresponding wrappers to make
the functions callable from Pure. (This also works when batch-compiling a Pure
script. In this case, the bitcode file actually gets linked into the output
code, so the loaded bitcode module only needs to be present at compile time.)
By default the imported symbols will be public. You can also specify the
desired scope of the symbols explicitly, by placing the public or
private keyword before the module name. For instance:
using private "bc:modname";
You can also import the same bitcode module several times, possibly in
different namespaces. This will not actually reload the module, but it will
create synonyms for the external functions in different namespaces:
namespace foo;
using "bc:modname";
namespace bar;
using private "bc:modname";
You can load any number of bitcode modules along with shared libraries in a
Pure script, in any order. The JIT will try to satisfy external references in
modules and libraries from other loaded libraries and bitcode modules. This is
deferred until the code is actually JIT-compiled, so that you can make sure
beforehand that all required libraries and bitcode modules have been loaded.
If the JIT fails to resolve a function, the interpreter will print its name
and also raise an exception at runtime when the function is being called from
other C code. (You can then run your script in the debugger to locate the
external visible in Pure from which the unresolved function is called.)
Let’s take a look at a concrete example to see how this actually
works. Consider the following C code which defines a little function to
compute the greatest common divisor of two (machine) integers:
int mygcd(int x, int y)
{
if (y == 0)
return x;
else
return mygcd(y, x%y);
}
Let’s say that this code is in the file mygcd.c, then you’d compile it to
a bitcode module using llvm-gcc as follows:
llvm-gcc -emit-llvm -c mygcd.c -o mygcd.bc
Or, if you prefer to use clang, the new LLVM-based C/C++ compiler:
clang -emit-llvm -c mygcd.c -o mygcd.bc
Note that the -emit-llvm -c options instruct llvm-gcc or clang to build an
LLVM bitcode module. Of course, you can also add optimizations and other
options to the compile command as desired.
Using dragonegg is somewhat more involved, as it doesn’t provide a direct way
to produce a bitcode file yet. However, you can create an LLVM assembler file
which can then be translated to bitcode using the llvm-as program as follows:
gcc -fplugin=dragonegg -flto -S mygcd.c -o mygcd.ll
llvm-as mygcd.ll -o mygcd.bc
(Note that the -fplugin option instructs gcc to use the dragonegg plugin,
which in conjunction with the -flto flag switches it to LLVM output.
Please check the dragonegg website for details.)
In either case, you can now load the resulting bitcode module and run the
mygcd function in the Pure interpreter simply as follows:
> using "bc:mygcd";
> mygcd 75 105;
15
To actually see the generated extern declaration of the imported
function, you can use the interactive show command:
> show mygcd
extern int mygcd(int, int);
Some more examples showing how to use the bitcode interface can be found in
the Pure sources. In particular, the interface also works with Fortran (using
llvm-gfortran or gfortran with dragonegg), and there is special support for
interfacing to Grame’s functional DSP programming language Faust (the latter
uses a special variant of the bitcode loader, which is selected with the
dsp tag in the using clause). Further details about these can
be found below.
Please note that at this time the LLVM bitcode interface is still somewhat
experimental, and there are some known limitations:
LLVM doesn’t distinguish between char* and void* in bitcode, so all
void* parameters and return values in C code will be promoted to
char* on the Pure side. Also, pointers to types which neither have a
predefined meaning in Pure nor a proper type name in the bitcode file, will
become a generic pointer type (void*, void**, etc.) in Pure. If this
is a problem then you can just redeclare the corresponding functions under a
synonym after loading the bitcode module, giving the proper argument and
result types (see Extern Declarations above). For instance:
> using "bc:foo";
> show foo
extern char* foo(char*);
> extern void *foo(void*) = myfoo;
> show myfoo
extern void* foo(void*) = myfoo;
The bitcode interface is limited to the same range of C types as Pure’s
plain C interface. In practice, this should cover most C code, but it’s
certainly possible that you run into unsupported types for arguments and
return values. The compiler will then print a warning; the affected
functions will still be linked in, but they will not be callable from Pure.
Also note that calling conventions for passing C structs by value depend
on the host ABI, so you should have a look at the resulting
extern declaration (using show) to determine how the function
is actually to be called from Pure.
Instead of manually compiling source files to bitcode modules, you can also
just place the source code into a Pure script, enclosing it in %< ... %>.
(Optionally, the opening brace may also be preceded with a public
or private scope specifier, which is used in the same way as the
scope specifier following the using keyword when importing bitcode
files.)
For instance, here is a little script showing inline code for the mygcd
function from the previous subsection:
%<
int mygcd(int x, int y)
{
if (y == 0)
return x;
else
return mygcd(y, x%y);
}
%>
mygcd 75 105;
The interpreter automatically compiles the inlined code to LLVM bitcode which
is then loaded as usual. (Of course, this will only work if you have the
corresponding LLVM compilers installed.) This method has the advantage that
you don’t have to write a Makefile and you can create self-contained Pure
scripts which include all required external functions. The downside is that
the inline code sections will have to be recompiled every time you run the
script with the interpreter which may considerably increase startup times. If
this is a problem then it’s usually better to import a separate bitcode module
instead (see Importing LLVM Bitcode), or batch-compile your script to an
executable (see Batch Compilation).
Note
At present, the default LLVM compiler used by the Pure interpreter is
llvm-gcc, unless you compiled Pure itself using clang, in which case clang
is used as the default for C/C++ compilation. You can easily change the
default with the corresponding environment variables, see below. In the
future dragonegg might be used as the new default instead, as soon as it is
widely supported. Note that llvm-gcc is currently being phased out in
favour of dragonegg, and will not be supported in the LLVM 3.x series any
more. Thus with LLVM 3.0 or later we recommend using dragonegg or clang
instead, although recent llvm-gcc versions should continue to work as well,
at least with LLVM 3.0.
Currently, C, C++, Fortran and Faust are supported as foreign source
languages, with llvm-gcc, llvm-g++, llvm-gfortran and faust as the
corresponding (default) compilers. C is the default language. The desired
source language can be selected by placing an appropriate tag into the inline
code section, immediately after the opening brace. (The tag is removed before
the code is submitted to compilation.) For instance:
%< -*- Fortran90 -*-
function fact(n) result(p)
integer n, p
p = 1
do i = 1, n
p = p*i
end do
end function fact
%>
fact n::int = fact_ {n};
map fact (1..10);
As indicated, the language tag takes the form -*- lang -*- where lang
can currently be any of c, c++, fortran and dsp (the latter
indicates the Faust language). Case is insignificant here, so you can also
write C, C++, Fortran, DSP etc. For the fortran tag, you
may also have to specify the appropriate language standard, such as
fortran90 which is used in the example above. The language tag can also be
followed by a module name, using the format -*- lang:name -*-. This is
optional for all languages except Faust (where the module name specifies the
namespace for the interface routines of the Faust module; see Interfacing to
Faust below). So, e.g., a Faust DSP named test would be specified with a
dsp:test tag. Case is significant in the module name.
The Pure interpreter has some built-in knowledge on how to invoke the LLVM
compilers to produce a working bitcode file ready to be loaded by the
interpreter, so the examples above should work out of the box if you have the
required compilers installed on your PATH. However, there are also
some environment variables you can set for customization purposes.
Specifically, PURE_CC is the command to invoke the C compiler. This
variable lets you specify the exact name of the executable along with any
debugging and optimization options that you may want to add. Likewise,
PURE_CXX, PURE_FC and PURE_FAUST are used for
the C++, Fortran and Faust compilers, respectively.
For instance, if you prefer to use clang as your C compiler, and you’d like
to invoke it with the -O3 optimization option, you would set
PURE_CC to "clang -O3". (To verify the settings you made, you
can have the interpreter echo the compilation commands which are actually
executed, by running Pure with the -v0100 option, see Verbosity and
Debugging Options. Also note that the options necessary to produce LLVM
bitcode will be added automatically, so you don’t have to specify these.)
Beginning with Pure 0.48, the dragonegg gcc plugin is also fully supported.
To make this work, you need to explicitly specify the name of the plugin in
the compilation command, so that the Pure interpreter can add the proper set
of options needed for bitcode compilation. For instance:
PURE_CC="gcc -fplugin=dragonegg -O3"
Some further details on the bitcode support for specific target languages can
be found in the subsections below.
Interfacing to C++ code requires additional preparations because of the name
mangling performed by C++ compilers. Usually, you won’t be able to call C++
functions and methods directly, so you’ll have to expose the required
functionality using functions with C binding (extern "C"). For instance,
the following example shows how to work with STL maps from Pure.
%< -*- C++ -*-
#include <pure/runtime.h>
#include <string>
#include <map>
// An STL map mapping strings to Pure expressions.
using namespace std;
typedef map<string,pure_expr*> exprmap;
// Since we can't directly deal with C++ classes in Pure, provide some C
// functions to create, destroy and manipulate these objects.
extern "C" exprmap *map_create()
{
return new exprmap;
}
extern "C" void map_add(exprmap *m, const char *key, pure_expr *x)
{
exprmap::iterator it = m->find(string(key));
if (it != m->end()) pure_free(it->second);
(*m)[key] = pure_new(x);
}
extern "C" void map_del(exprmap *m, const char *key)
{
exprmap::iterator it = m->find(key);
if (it != m->end()) {
pure_free(it->second);
m->erase(it);
}
}
extern "C" pure_expr *map_get(exprmap *m, const char *key)
{
exprmap::iterator it = m->find(key);
return (it != m->end())?it->second:0;
}
extern "C" pure_expr *map_keys(exprmap *m)
{
size_t i = 0, n = m->size();
pure_expr **xs = new pure_expr*[n];
for (exprmap::iterator it = m->begin(); it != m->end(); ++it)
xs[i++] = pure_string_dup(it->first.c_str());
pure_expr *x = pure_listv(n, xs);
delete[] xs;
return x;
}
extern "C" void map_destroy(exprmap *m)
{
for (exprmap::iterator it = m->begin(); it != m->end(); ++it)
pure_free(it->second);
delete m;
}
%>
// Create the STL map and add a sentry so that it garbage-collects itself.
let m = sentry map_destroy map_create;
// Populate the map with some arbitrary Pure data.
do (\(x=>y) -> map_add m x y) ["foo"=>99, "bar"=>bar 4711L, "baz"=>1..5];
// Query the map.
map_keys m; // => ["bar","baz","foo"]
map (map_get m) (map_keys m); // => [bar 4711L,[1,2,3,4,5],99]
// Delete an element.
map_del m "foo";
map_keys m; // => ["bar","baz"]
map (map_get m) (map_keys m); // => [bar 4711L,[1,2,3,4,5]]
Faust is a functional dsp (digital signal processing) programming language
developed at Grame, which is tailored to the task of generating and
transforming streams of numeric data at the sample level. It is typically used
to program sound synthesis and audio effect units, but can in fact be employed
to process any kind of numeric vector and matrix data. The Faust compiler is
capable of generating very efficient code for such tasks which is comparable
in performance with carefully handcrafted C routines. Pure’s Faust interface
lets you use these capabilities in order to process sample data stored in Pure
matrices.
Pure’s LLVM bitcode loader has some special knowledge about Faust built into
it, which makes interfacing to Faust programs simple and efficient. At
present, you’ll need a special LLVM-capable version of Faust to make this
work, which is available under the “faust2” branch in Faust’s git repository.
Some information on how to get this up and running can be found on the LLVM
backend for Faust website.
Note
There’s also an alternative interface to Faust which is available as
a separate package and works with either Faust2 or the stable Faust
version. Please check the pure-faust package for
details. This package also provides the faust2 compatibility module
which implements the pure-faust API on top of Pure’s built-in Faust
interface, so that you can also use the operations of this module instead.
(The pure-faust API can in fact be more convenient to use in some cases,
especially if you want to load a lot of different Faust modules dynamically
at runtime.)
The -lang llvm option instructs the Faust compiler to output LLVM
bitcode. Also, you want to add the -double option to make the compiled
Faust module use double precision floating point values for samples and
control values. So you’d compile an existing Faust module in the source file
example.dsp as follows:
faust -double -lang llvm example.dsp -o example.bc
The -double option isn’t strictly necessary, but it makes interfacing
between Pure and Faust easier and more efficient, since Pure uses double
as its native floating point format.
Alternatively, you can also use the Faust pure.c architecture (included in
recent Faust2 revisions and also in the pure-faust package)
to compile a Faust program to corresponding C source which can then be fed
into an LLVM-capable C compiler to produce bitcode which is compatible with
Pure’s Faust bitcode loader. This is useful, in particular, if you want to
make use of special optimization options provided by the C compiler, or if the
Faust module needs to be linked against additional C/C++ code. For instance:
faust -double -a pure.c -lang c example.dsp -o example.c
clang -emit-llvm -O3 -c example.c -o example.bc
A third possibility is to just inline Faust code in a Pure script, as
described in the Inline Code section. The compilation step is then handled
by the Pure compiler and the -double option is added automatically. The
PURE_FAUST environment variable can be used to specify a custom
Faust command to be invoked by the Pure interpreter. This is useful if you’d
like to invoke the Faust compiler with some special options, e.g.:
PURE_FAUST="faust -single -vec"
(Note that you do not have to include the -lang llvm option; the inline
compiler will supply it automatically.)
Moreover, you can also set the FAUST_OPT environment variable to
specify any needed postprocessing of the output of the Faust compiler; this is
typically used to invoke the LLVM opt utility in a pipeline, in order to
have some additional optimizations performed on the Faust-generated code:
After loading or inlining the Faust module, the Pure compiler makes the
interface routines of the Faust module available in its own namespace. Thus,
e.g., the interface routines for the example.dsp module will end up in the
example namespace.
Pure’s Faust interface offers another useful feature not provided by the
general bitcode interface, namely the ability to reload Faust modules on the
fly. If you repeat the import clause for a Faust module, the compiler checks
whether the module was modified and, if so, replaces the old module with the
new one. Retyping an inline Faust code section has the same effect. This is
mainly intended as a convenience for interactive usage, so that you can test
different versions of a Faust module without having to restart the Pure
interpreter. But it is also put to good use in addon packages like
pd-faust which allows Faust dsps to be reloaded at runtime.
For instance, consider the following little Faust program, which takes a
stereo audio signal as input, mixes the two channels and multiplies the
resulting mono signal with a gain value given by a corresponding Faust control
variable:
gain = nentry("gain", 0.3, 0, 10, 0.01);
process = + : *(gain);
The interface routines of this Faust module look as follows on the Pure side:
> show -g example::*
extern void buildUserInterface(struct_dsp_example*, struct_UIGlue*) = example::buildUserInterface;
extern void classInit(int) = example::classInit;
extern void compute(struct_dsp_example*, int, double**, double**) = example::compute;
extern void delete(struct_dsp_example*) = example::delete;
extern void destroy(struct_dsp_example*) = example::destroy;
extern int getNumInputs(struct_dsp_example*) = example::getNumInputs;
extern int getNumOutputs(struct_dsp_example*) = example::getNumOutputs;
extern int getSampleRate(struct_dsp_example*) = example::getSampleRate;
extern expr* info(struct_dsp_example*) = example::info;
extern void init(struct_dsp_example*, int) = example::init;
extern void instanceInit(struct_dsp_example*, int) = example::instanceInit;
extern expr* meta() = example::meta;
extern void metadata(struct_MetaGlue*) = example::metadata;
extern struct_dsp_example* new() = example::new;
extern struct_dsp_example* newinit(int) = example::newinit;
The most important interface routines are new, init and delete
(used to create, initialize and destroy an instance of the dsp) and
compute (used to apply the dsp to a given block of samples). Some useful
convenience functions are added by the Pure compiler:
- newinit combines new and init;
- info yields pertinent information about the dsp as a Pure tuple
containing the number of input and output channels and the Faust control
descriptions;
- meta yields metadata about the dsp, as declared in the Faust source.
The latter two are provided in a symbolic format ready to be used in Pure;
more about that below.
Note that there’s usually no need to explicitly invoke the delete routine
in Pure programs; the Pure compiler makes sure that this routine is added
automatically as a finalizer (see sentry) to all dsp pointers created
through the new and newinit routines so that dsp instances are
destroyed automatically when the corresponding Pure objects are
garbage-collected. (If you prefer to do the finalization manually then you
must also remove the sentry from the dsp object, so that it doesn’t get
deleted twice.)
Another point worth mentioning here is that the Pure compiler always generates
code that ensures that the Faust dsp instances (the struct_dsp pointers)
are fully typechecked at runtime. Thus it is only possible to pass a dsp
struct pointer to the interface routines of the Faust module it was created
with.
Let’s have a brief look at how we can actually run a Faust module in Pure to
process some audio samples.
Step 1: Load the Faust dsp. This assumes that the Faust source has already
been compiled to a bitcode file, as shown above. You can then load the module
in Pure as follows:
Note that the .bc bitcode extension is supplied automatically. Also note the
special dsp tag; this tells the compiler that this is a Faust-generated
module, so that it does some Faust-specific processing while linking the
module.
Alternatively, you can also just inline the code of the Faust module. For the
example above, the inline code section looks as follows:
%< -*- dsp:example -*-
gain = nentry("gain", 0.3, 0, 10, 0.01);
process = + : *(gain);
%>
You can either add this code to a Pure script, or just type it directly in
the Pure interpreter.
Finally, you may want to verify that the module has been properly loaded by
typing show -g example::*. The output should look like the listing above.
Step 2: Create and initialize a dsp instance. After importing the Faust
module you can now create an instance of the Faust signal processor using the
newinit routine, and assign it to a Pure variable as follows:
> let dsp = example::newinit 44100;
Note that the constant 44100 denotes the desired sample rate in Hz. This can
be an arbitrary integer value, which is available in the Faust program by
means of the SR variable. It’s completely up to the dsp whether it
actually uses this value in some way (our example doesn’t, but we need to
specify a value anyway).
The dsp is now fully initialized and we can use it to compute some samples.
But before we can do this, we’ll need to know how many channels of audio data
the dsp consumes and produces, and which control variables it provides. This
information can be extracted with the info function, and be assigned to
some Pure variables as follows:
> let k,l,ui = example::info dsp;
(We’ll have a closer look at the contents of the ui variable below.)
In a similar fashion, the meta function provides some “metadata” about the
Faust dsp, as a list of key=>val string pairs. This is static data which
doesn’t belong to any particular dsp instance, so it can be extracted without
actually creating an instance. In our case the metadata will be empty, since
we didn’t supply any in the Faust program. If needed, we can add some metadata
as follows:
declare descr "Faust Hello World";
declare author "Faust Guru";
declare version "1.0";
gain = nentry("gain", 0.3, 0, 10, 0.01);
process = + : *(gain);
If we now reload the Faust dsp, we’ll get:
> test::meta;
["descr"=>"Faust Hello World","author"=>"Faust Guru","version"=>"1.0"]
Step 3: Prepare input and output buffers. Pure’s Faust interface allows
you to pass Pure double matrices as sample buffers, which makes this step
quite convenient. For given numbers k and l of input and output channels,
respectively, we’ll need a k x n matrix for the input and a l x n matrix for
the output, where n is the desired block size (the number of samples to be
processed per channel in one go). Note that the matrices have one row per
input or output channel. Here’s how we can create some suitable input and
output matrices using a Pure matrix comprehension and the dmatrix function
available in the standard library:
> let n = 10; // the block size
> let in = {i*10.0+j | i = 1..k; j = 1..n};
> let out = dmatrix (l,n);
In our example, k=2 and l=1, thus we obtain the following matrices:
> in;
{11.0,12.0,13.0,14.0,15.0,16.0,17.0,18.0,19.0,20.0;
21.0,22.0,23.0,24.0,25.0,26.0,27.0,28.0,29.0,30.0}
> out;
{0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0}
Step 4: Apply the dsp to compute some samples. With the in and out
matrices as given above, we can now apply the dsp by invoking its compute
routine:
> example::compute dsp n in out;
This takes the input samples specified in the in matrix and stores the
resulting output in the out matrix. The output matrix now looks as
follows:
> out;
{9.6,10.2,10.8,11.4,12.0,12.6,13.2,13.8,14.4,15.0}
Note that the compute routine also modifies the internal state of the dsp
instance so that a subsequent call will continue with the output stream where
the previous call left off. (This isn’t relevant in this specific example, but
in general a Faust dsp may contain delays and similar constructions which need
a memory of past samples to be maintained between different invocations of
compute.) Thus we can now just keep on calling compute (possibly with
different in buffers) to compute as much of the output signal as we need.
Step 5: Inspecting and modifying control variables. Recall that our sample
dsp also has a Faust control variable gain which lets us change the
amplification of the output signal. We’ve already assigned the corresponding
information to the ui variable, let’s have a look at it now:
> ui;
vgroup [] ("test",[nentry #<pointer 0x1611f00> [] ("gain",0.3,0.0,10.0,0.01)])
In general, this data structure takes the form of a tree which corresponds to
the hierarchical layout of the control groups and values in the Faust program.
In this case, we just have one toplevel group containing a single gain
parameter, which is represented as a Pure term containing the relevant
information about the type, name, initial value, range and stepsize of the
control, along with a double pointer which can be used to inspect and modify
the control value. While it’s possible to access this information in a direct
fashion, there’s also a faustui.pure module in the standard library which
makes this easier. First we extract the mapping of control variable names to
the corresponding double pointers as follows:
> using faustui;
> let ui = control_map $ controls ui; ui;
{"gain"=>#<pointer 0xd81820>}
The result is a record value indexed by control names, thus the pointer which
belongs to our gain control can now be obtained with ui!"gain". The
faustui.pure module also provides convenience functions to inspect a control
and change its value:
> let gain = ui!"gain";
> get_control gain;
0.3
> put_control gain 1.0;
()
> get_control gain;
1.0
Let’s rerun compute to get another block of samples from the same input
data, using the new gain value:
> example::compute dsp n in out;
> out;
{32.0,34.0,36.0,38.0,40.0,42.0,44.0,46.0,48.0,50.0}
Faust also allows metadata to be attached to individual controls and control
groups, which is available in the same form of a list of key=>val string
pairs that we have seen already with the meta operation. This metadata is
used to provide auxiliary information about a control to specific
applications. It’s completely up to the application how to interpret this
metadata. Typical examples are style hints about GUI renderings of a control,
or the assignment of external “MIDI” controllers. (MIDI is the “Musical
Instruments Digital Interface”, a standardized hardware and software interface
for electronic music instruments and other digital multimedia equipment.)
In our example these metadata lists are all empty. Control metadata is
specified in a Faust program in the labels of the controls using the syntax
[key:val], please see the Faust documentation for details. For instance,
if we’d like to assign MIDI controller 7 (usually the “volume controller” on
MIDI keyboards) to our gain control, this might be done as follows:
gain = nentry("gain [midi:ctrl 7]", 0.3, 0, 10, 0.01);
After reloading the dsp and creating a new instance, this metadata is
available in the ui structure and can be extracted with the
control_meta function of the faustui module as follows:
> let dsp = test::newinit SR;
> let k,l,ui = example::info dsp;
> controls ui!0;
nentry #<pointer 0x1c97070> ["midi"=>"ctrl 7"] ("gain",0.3,0.0,10.0,0.01)
> control_meta ans;
["midi"=>"ctrl 7"]
As you can see, all these steps are rather straightforward. Of course, in a
real program we would probably run compute in a loop which reads some
samples from an audio device or sound file, applies the dsp, and writes back
the resulting samples to another audio device or file. We might also have to
process MIDI controller input and change the control variables accordingly.
This can all be done quite easily using the appropriate addon modules
available on the Pure website.
We barely scratched the surface here, but it should be apparent that the
programming techniques sketched out in this section open the door to the realm
of sophisticated multimedia and signal processing applications. More
Faust-related examples can be found in the Pure distribution. Also, have a
look at the pd-pure and pd-faust packages to
see how these facilities can be used in Pd modules written in Pure.
In interactive mode, the interpreter reads definitions and expressions and
processes them as usual. You can use the -i option to force
interactive mode when invoking the interpreter with some script files.
Additional scripts can be loaded interactively using either a using
declaration or the interactive run command (see the description of the
run command below for the differences between these). Or you can just
start typing away, entering your own definitions and expressions to be
evaluated.
The input language is mostly the same as for source scripts, and hence
individual definitions and expressions must be terminated with a semicolon
before they are processed. For instance, here is a simple interaction which
defines the factorial and then uses that definition in some evaluations. Input
lines begin with “> ”, which is the interpreter’s default command prompt:
> fact 1 = 1;
> fact n = n*fact (n-1) if n>1;
> let x = fact 10; x;
3628800
> map fact (1..10);
[1,2,6,24,120,720,5040,40320,362880,3628800]
As indicated, in interactive mode the normal forms of toplevel expressions are
printed after each expression is entered. This is also commonly known as the
read-eval-print loop. Normal form expressions are usually printed in the
same form as you’d enter them. However, there are a few special kinds of
objects like anonymous closures, thunks (“lazy” values to be evaluated when
needed) and pointers which don’t have a textual representation in the Pure
syntax and will be printed in the format #<object description>
by default. It is also possible to override the print representation of any
kind of expression by means of the __show__ function, see
Pretty-Printing below for details.
Besides Pure definitions and expressions, the interpreter also understands a
number of special interactive commands for performing basic maintenance tasks,
such as loading source scripts, exiting and restarting the interpreter,
changing the working directory, escaping to the shell, getting help and
displaying definitions. In contrast to the normal input language, the command
language is line-oriented; it consists of special command words to be typed at
the beginning of an input line, which may be followed by some parameters as
required by the command. The command language is intended solely for
interactive purposes and thus doesn’t offer any programming facilities of its
own. However, it can be extended with user-defined commands implemented as
ordinary Pure functions; this is described in the User-Defined Commands
section below.
Note
The interactive commands are only recognized at the very beginning
of the command line. As most of the commands look just like ordinary
identifiers, you may run into situations where the beginning of an
expression or definition to be typed at the prompt can be mistaken for a
command word. In such cases it will be necessary to “escape” the input by
inserting one or more spaces at the beginning of the line, so that the
interpreter reads the line as normal Pure code.
Online help is available in the interpreter with the interactive help
command, which gives you access to all the available documentation in html
format; this includes the present manual, the Pure Library Manual, as well as all
manuals of the addon modules available from the Pure website.
You need to have a html browser installed to make this work. By default, the
help command uses w3m, but you can change this by setting
either the PURE_HELP or the BROWSER environment variable
accordingly.
When invoked without arguments, the help command displays an overview of
the available documentation, from which you can follow the links to the
provided manuals:
(If the interpreter gives you an error message when you do this then you
haven’t installed the documentation yet. The complete set of manuals is
provided as a separate package at the Pure website, please see the Pure
installation instructions for details.)
The help command also accepts a parameter which lets you specify a search
term which is looked up in the global index, e.g.:
Besides Pure functions, macros, variables and constants described in the
manual you can also look up program options and environment variables, e.g.:
> help -x
> help pure-gen -x
> help PURE_STACK
(Note that you can specify the program name to disambiguate between options
for different utilities, such as the -x option which is accepted both by
the Pure interpreter and the pure-gen program.)
If the search term doesn’t appear in the index, it is assumed to be a topic (a
link target, usually a section title) in the Pure manual. Note that the
docutils tools used to generate the html source of the Pure documentation
mangle the section titles so that they are in lowercase and blanks are
replaced with hyphens. So to look up the present section in this manual you’d
have to type:
The help files are in html format and located in the docs subdirectory of the
Pure library directory (i.e., /usr/local/lib/pure/docs by default). You can
look up topics in any of the help files with a command like the following:
Here pure-gsl is the basename of the help file (library path and .html
suffix are supplied automatically), and matrices is a link target in that
document. To just read the pure-gsl.html file without specifying a target,
type the following:
(Note that just help pure-gsl won’t work, since it would look for a search
term in the index or a topic in the Pure manual.)
Last but not least, you can also point the help browser to any html document
(either a local file or some website) denoted by a proper URL, provided that
your browser program can handle these. For instance:
> help file:mydoc.html#foo
> help http://pure-lang.googlecode.com
The following built-in commands are always understood by the interpreter. (In
addition, you can define your own commands for frequently-used operations; see
User-Defined Commands below.)
-
! command
Shell escape.
-
break [symbol ...]
Sets breakpoints on the given function or operator symbols. All symbols
must be specified in fully qualified form, see the remarks below. If
invoked without arguments, prints all currently defined breakpoints. This
requires that the interpreter was invoked with the -g option to
enable debugging support. See Debugging below for details.
-
bt
Prints a full backtrace of the call sequence of the most recent evaluation,
if that evaluation ended with an unhandled exception. This requires that
the interpreter was invoked with the -g option to enable
debugging support. See Debugging below for details.
-
cd dir
Change the current working dir.
-
clear [option ...] [symbol ...]
Purge the definitions of the given symbols (functions, macros, constants or
global variables). All symbols must be specified in fully qualified form,
see the remarks below. If invoked as clear ans, clears the ans
value (see Last Result below). When invoked without any arguments,
clear purges all definitions at the current interactive “level” (after
confirmation) and returns you to the previous level, if any. (It might be a
good idea to first check your current definitions with show or back
them up with dump before you do that.) The desired level can be
specified with the -t option. See the description of the save
command and Definition Levels below for further details. A description
of the common options accepted by the clear, dump and show
commands can be found in Specifying Symbol Selections below.
-
del [-b|-m|-t] [symbol ...]
Deletes breakpoints and tracepoints on the given function or operator
symbols. If the -b option is specified then only breakpoints are
deleted; similarly, del -t only deletes tracepoints. If none of these
are specified then both breakpoints and tracepoints are deleted. All
symbols must be specified in fully qualified form, see the remarks
below. If invoked without non-option arguments, del clears all
currently defined breakpoints and/or tracepoints (after confirmation); see
Debugging below for details.
The -m option works similarly to -t, but deletes macro rather than
function tracepoints, see the description of the trace command below.
-
dump [-n filename] [option ...] [symbol ...]
Dump a snapshot of the current function, macro, constant and variable
definitions in Pure syntax to a text file. All symbols must be specified in
fully qualified form, see the remarks below. This works similar to the
show command (see below), but writes the definitions to a file. The
default output file is .pure in the current directory, which is then
reloaded automatically the next time the interpreter starts up in
interactive mode in the same directory. This provides a quick-and-dirty way
to save an interactive session and have it restored later, but note that
this isn’t perfect. In particular, declarations of extern
symbols won’t be saved unless they’re specified explicitly, and some
objects like closures, thunks and pointers don’t have a textual
representation from which they could be reconstructed. To handle these,
you’ll probably have to prepare a corresponding .purerc file yourself, see
Interactive Startup below.
A different filename can be specified with the -n option, which expects
the name of the script to be written in the next argument, e.g: dump -n
myscript.pure. You can then edit that file and use it as a starting point
for an ordinary script or a .purerc file, or you can just run the file with
the run command (see below) to restore the definitions in a subsequent
interpreter session.
-
help [topic]
Display online documentation. If a topic is given, it is looked up in the
index. Alternatively, you can also specify a link target in any of the
installed help files, or any other html document denoted by a proper URL.
Please see Online Help above for details.
-
ls [args]
List files (shell ls command).
-
mem
Print current memory usage. This reports the number of expression cells
currently in use by the program, along with the size of the freelist (the
number of allocated but currently unused expression cells). Note that the
actual size of the expression storage may be somewhat larger than this,
since the runtime always allocates expression memory in bigger chunks.
Also, this figure does not reflect other heap-allocated memory in use by
the program, such as strings or malloc’ed pointers.
-
override
Enter “override” mode. This allows you to add equations “above” existing
definitions in the source script, possibly overriding existing
equations. See Definition Levels below for details.
-
pwd
Print the current working dir (shell pwd command).
-
quit
Exits the interpreter.
-
run [-g|script]
When invoked without arguments or with the -g option, run does a
“cold” restart of the interpreter, with the scripts and options given on
the interpreter’s original command line. If just -g is specified as the
argument, the interpreter is run with debugging enabled. Otherwise the
interpreter is invoked without debugging support. (This overrides the
corresponding option from the interpreter’s command line.) This command
provides a quick way to rerun the interpreter after changes in some of the
loaded script files, or if you want to enable or disable debugging on the
fly (which requires a restart of the interpreter). You’ll also loose any
definitions that you entered interactively in the interpreter, so you may
want to back them up with dump beforehand.
When invoked with a script name as argument, run loads the given script
file and adds its definitions to the current environment. This works more
or less like a using clause, but only searches for the script in
the current directory and places the definitions in the script at the
current temporary level, so that clear can be used to remove them
again. Also note that namespace and pragma settings of scripts loaded with
run stick around after loading the script. This allows you to quickly
set up your environment by just running a script containing the necessary
namespace declarations and compiler directives. (Alternatively, you can
also use the interpreter’s startup files for that purpose, see Interactive
Startup below.)
-
save
Begin a new level of temporary definitions. A subsequent clear command
(see above) will purge the definitions made since the most recent save
command. See Definition Levels below for details.
-
show [option ...] [symbol ...]
Show the definitions of symbols in various formats. See The show Command
below for details. All symbols must be specified in fully qualified form,
see the remarks below. A description of the common options accepted by the
clear, dump and show commands can be found in Specifying
Symbol Selections below.
-
stats [-m] [on|off]
Enables (default) or disables “stats” mode, in which some statistics are
printed after an expression has been evaluated. Invoking just stats or
stats on only prints the cpu time in seconds for each evaluation. If
the -m option is specified, memory usage is printed along with the cpu
time, which indicates the maximum amount of expression memory (in terms of
expression cells) used during the computation. Invoking stats off
disables stats mode, while stats -m off just disables the printing of
the memory usage statistics.
-
trace [-a] [-m] [-r] [-s] [symbol ...]
Sets tracepoints on the given function or operator symbols. Without the
-m option, this works pretty much like the break command (see
above) but only prints rule invocations and reductions without actually
interrupting the evaluation; see Debugging below for details.
The -m option allows you to trace macro (rather than function)
calls. If this option is specified, the compiler prints reduction sequences
involving the given macro symbol, which is useful when debugging macros;
see the Macros section for details and examples. Note that macro tracing
works even if the interpreter was invoked without debugging mode.
If the -a option is specified, tracepoints are set on all global
function or macro symbols, respectively (in this case the symbol arguments
are ignored). This is convenient if you want to see any and all reductions
performed in a computation.
Tracing can actually be performed in two different modes, recursive mode
in which the trace is triggered by any of the active tracepoints and
continues until the corresponding call is finished, or skip mode in which
only calls by the active tracepoints are reported. The former is usually
more helpful and is the default. The -s option allows you to switch to
skip mode, while the -r option switches back to recursive mode.
Finally, if neither symbols nor any of the -a, -r and -s
options are specified then the currently defined tracepoints are printed.
Note that, as with the break command, existing tracepoints can be
deleted with the del command (see above).
-
underride
Exits “override” mode. This returns you to the normal mode of operation,
where new equations are added “below” previous rules of an existing
function. See Definition Levels below for details.
Note that symbols (identifiers, operators etc.) must always be specified in
fully qualified form. No form of namespace lookup is performed by commands
like break, clear, show etc. Thus the specified symbols always
work the same no matter what namespace and using namespace
declarations are currently in effect.
Besides the commands listed above, the interpreter also provides some special
commands for the benefit of other programs such as emacs driving
the interpreter; currently these are completion_matches, help_matches
and help_index. These aren’t supposed to be invoked directly by the user,
although they may sometimes be useful to implement custom functionality, see
User-Defined Commands.
The clear, dump and show commands all accept the following options
for specifying a subset of symbols and definitions on which to operate. All
symbols must be specified in fully qualified form. Options may be combined,
thus, e.g., show -mft is the same as show -m -f -t. Some options
specify optional numeric parameters; these must follow immediately behind the
option character if present, as in -t0.
-c |
Selects defined constants. |
-f |
Selects defined functions. |
-g |
Indicates that the following symbols are actually shell glob patterns and
that all matching symbols should be selected. |
-m |
Select defined macros. |
-pflag |
Select only private symbols if flag is nonzero (the default), otherwise
(flag is zero) select only public symbols. If this option is omitted
then both private and public symbols are selected. |
-tlevel |
Select symbols and definitions at the given “level” of definitions and
above. This is described in more detail below. Briefly, the executing
program and all imported modules (including the prelude) are at level 0,
while “temporary” definitions made interactively in the interpreter are at
level 1 and above. Thus a level of 1 restricts the selection to all
temporary definitions, whereas 0 indicates all definitions (i.e.,
everything, including the prelude). If level is omitted, it defaults to
the current definitions level. |
-v |
Select defined variables. |
-y |
Select defined types. |
In addition, the -h option prints a short help message describing all
available options of the command at hand.
If none of the -c, -f, -m, -v and -y options are
specified, then all kinds of symbols (constants, functions, macros, variables
and types) are selected, otherwise only the specified categories will be
considered.
A reasonable default is used if the -t option is omitted. By default, if
no symbols are specified, only temporary definitions are considered, which
corresponds to -t1. Otherwise the command applies to all corresponding
definitions, no matter whether they belong to the executing program, the
prelude, or some temporary level, which has the same effect as -t0. This
default choice can be overridden by specifying the desired level explicitly.
As a special case, just clear (without any other options or symbol
arguments) always backs out to the previous definitions level (instead of
level #1). This is inconsistent with the rules set out above, but is
implemented this way for convenience and backward compatibility. Thus, if you
really want to delete all your temporary definitions, use clear -t1
instead. When used in this way, the clear command will only remove
temporary definitions; if you need to remove definitions at level #0, you must
specify those symbols explicitly.
Note that clear -g * will have pretty much the same disastrous
consequences as the Unix command rm -rf *, so don’t do that. Also note
that a macro or function symbol may well have defining equations at different
levels, in which case a command like clear -tn foo might only affect some
part of foo‘s definition. The dump and show commands work
analogously (albeit less destructively). See Definition Levels below for
some examples.
The show command can be used to obtain information about defined symbols
in various formats. Besides the common selection options discussed above, this
command recognizes the following additional options for specifying the content
to be listed and the format to use.
-a |
Disassembles pattern matching automata. Works like the -v4 option of
the interpreter. |
-d |
Disassembles LLVM IR, showing the generated LLVM assembler code of a
function. Works like the -v8 option of the interpreter. |
-e |
Annotate printed definitions with lexical environment information (de
Bruijn indices, subterm paths). Works like the -v2 option of the
interpreter. |
-l |
Long format, prints definitions along with the summary symbol
information. This implies -s. |
-s |
Summary format, print just summary information about listed symbols. |
Symbols are always listed in lexicographic order. Note that some of the
options (in particular, -a and -d) may produce excessive amounts of
information. By setting the PURE_MORE environment variable, you can
specify a shell command to be used for paging, usually more or
less.
For instance, to list all temporary definitions made in an interactive
session, simply say:
You can also list a specific symbol, no matter whether it comes from the
interactive command line, the executing script or the prelude:
> show foldl
foldl f a x::matrix = foldl f a (list x);
foldl f a s::string = foldl f a (chars s);
foldl f a [] = a;
foldl f a (x:xs) = foldl f (f a x) xs;
Wildcards can be used with the -g option, which is useful if you want to
print an entire family of related functions, e.g.:
> show -g foldl*
foldl f a x::matrix = foldl f a (list x);
foldl f a s::string = foldl f a (chars s);
foldl f a [] = a;
foldl f a (x:xs) = foldl f (f a x) xs;
foldl1 f x::matrix = foldl1 f (list x);
foldl1 f s::string = foldl1 f (chars s);
foldl1 f (x:xs) = foldl f x xs;
Or you can just specify multiple symbols as follows (this also works with
multiple glob patterns when you add the -g option):
> show min max
max x y = if x>=y then x else y;
min x y = if x<=y then x else y;
You can also select symbols by category. E.g., the following command shows
summary information about all the variable symbols along with their current
values (using the “long” format):
> show -lvg *
argc var argc = 0;
argv var argv = [];
compiling var compiling = 0;
sysinfo var sysinfo = "x86_64-unknown-linux-gnu";
version var version = "0.54";
5 variables
Or you can list just private symbols of the namespace foo, as follows:
The following command will list each and every symbol that’s currently defined
(instead of -g * you can also use the -t0 option):
This usually produces a lot of output and is rarely needed, unless you’d like
to browse through an entire program including all library imports. (In that
case you might consider to use the dump command instead, which writes the
definitions to a file which can then be loaded into a text editor for easier
viewing. This may occasionally be useful for debugging purposes.)
The show command also has the following alternate forms which are used for
special purposes:
show interface lists the actual type rules for an interface
type. This is useful if you want to verify which patterns will be matched by
an interface type, see Interface Types for details. For instance:
> interface stack with
> push xs::stack x;
> pop xs::stack;
> top xs::stack;
> end;
> push xs@[] x |
> push xs@(_:_) x = x:xs;
> pop (x:xs) = xs;
> top (x:xs) = x;
> show interface stack
type stack xs@(_:_);
> pop [] = throw "empty stack";
> top [] = throw "empty stack";
> show interface stack
type stack xs@[];
type stack xs@(_:_);
show namespace lists the current and search namespaces, while show
namespaces lists all declared namespaces. These come in handy if you have
forgotten what namespaces are currently active and which other namespaces
are available in your program. For instance:
> show namespace
> show namespaces
namespace C;
namespace matrix;
> using namespace C;
> namespace my;
> show namespace
namespace my;
using namespace C;
To help with incremental development, the interpreter offers some commands to
manipulate the current set of definitions interactively. To these ends,
definitions are organized into different subsets called levels. As already
mentioned, the prelude, as well as other source programs specified when
invoking the interpreter, are always at level 0, while the interactive
environment starts at level 1. Each save command introduces a new
temporary level, and each subsequent clear command (without any arguments)
“pops” the definitions on the current level and returns you to the previous
one (if any). This gives you a “stack” of temporary environments which enables
you to “plug and play” in a (more or less) safe fashion, without affecting the
rest of your program.
For all practical purposes, this stack is unlimited, so that you can create as
many levels as you like. However, this facility also has its limitations. The
interpreter doesn’t really keep a full history of everything you entered
interactively, it only records the level a variable, constant, and function or
macro rule belongs to so that the corresponding definitions can be removed
again when the level is popped. On the other hand, intermediate changes in
variable values are not recorded anywhere and cannot be undone. Moreover,
global declarations (which encompasses using clauses,
extern declarations and special symbol declarations) always apply
to all levels, so they can’t be undone either.
That said, the temporary levels can still be pretty useful when you’re playing
around with the interpreter. Here’s a little example which shows how to use
clear to quickly get rid of a definition that you entered interactively:
> foo (x:xs) = x+foo xs;
> foo [] = 0;
> show
foo (x:xs) = x+foo xs;
foo [] = 0;
> foo (1..10);
55
> clear
This will clear all temporary definitions at level #1.
Continue (y/n)? y
> show
> foo (1..10);
foo [1,2,3,4,5,6,7,8,9,10]
We’ve seen already that normally, if you enter a sequence of equations, they
will be recorded in the order in which they were written. However, it is also
possible to override definitions in lower levels with the override
command:
> foo (x:xs) = x+foo xs;
> foo [] = 0;
> show
foo (x:xs) = x+foo xs;
foo [] = 0;
> foo (1..10);
55
> save
save: now at temporary definitions level #2
> override
> foo (x:xs) = x*foo xs;
> show
foo (x:xs) = x*foo xs;
foo (x:xs) = x+foo xs;
foo [] = 0;
> foo (1..10);
warning: rule never reduced: foo (x:xs) = x+foo xs;
0
Note that the equation foo (x:xs) = x*foo xs was inserted before the
previous rule foo (x:xs) = x+foo xs, which is at level #1. (The latter
equation is now “shadowed” by the rule we just entered, hence the compiler
warns us that this rule can’t be reduced any more.)
Even in override mode, new definitions will be added after other definitions
at the current level. This allows us to just continue adding more
high-priority definitions overriding lower-priority ones:
> foo [] = 1;
> show
foo (x:xs) = x*foo xs;
foo [] = 1;
foo (x:xs) = x+foo xs;
foo [] = 0;
> foo (1..10);
warning: rule never reduced: foo (x:xs) = x+foo xs;
warning: rule never reduced: foo [] = 0;
3628800
Again, the new equation was inserted above the existing lower-priority rules,
but below our previous equation foo (x:xs) = x*foo xs entered at the same
level. As you can see, we have now effectively replaced our original
definition of foo with a version that calculates list products instead of
sums, but of course we can easily go back one level to restore the previous
definition:
> clear
This will clear all temporary definitions at level #2.
Continue (y/n)? y
clear: now at temporary definitions level #1
clear: override mode is on
> show
foo (x:xs) = x+foo xs;
foo [] = 0;
> foo (1..10);
55
Note that clear reminded us that override mode is still enabled (save
will do the same if override mode is on while pushing a new definitions
level). To turn it off again, use the underride command. This will revert
to the normal behaviour of adding new equations below existing ones:
It’s also possible to use clear to back out multiple levels at once, if
you specify the target level to be cleared with the -t option. For instance:
> save
save: now at temporary definitions level #2
> let bar = 99;
> show
let bar = 99;
foo (x:xs) = x+foo xs;
foo [] = 0;
> // this scraps all our scribblings!
> clear -t1
This will clear all temporary definitions at level #1 and above.
Continue (y/n)? y
clear: now at temporary definitions level #1
> show
>
The facilities described above are also available to Pure programs, as the
save and clear commands can also be executed under program control
using the evalcmd primitive. Conversely, the library provides its own
functions for inspecting and manipulating the source program, which may also
be useful in custom command definitions; see the Pure Library Manual for details.
The interpreter provides a simple but reasonably convenient symbolic debugging
facility when running interactively. To make this work, you have to specify
the -g option when invoking the interpreter (pure -g). If you’re
already at the interpreter’s command line, you can also use the run -g
command to enable the debugger. The -g option disables tail call
optimization (see Stack Size and Tail Recursion) to make it easier to debug
programs. It also causes special debugging code to be generated which will
make your program run much slower. Therefore the -g option should
only be used if you actually need the debugger.
One common use of the debugger is “post mortem” debugging after an evaluation
ended with an unhandled exception. In such a case, the bt command of the
interpreter prints a backtrace of the call sequence which caused the
exception. Note that this only works if debugging mode was enabled. For
instance:
> [1,2]!3;
<stdin>, line 2: unhandled exception 'out_of_bounds' while evaluating '[1,2]!3'
> bt
[1] (!): (x:xs)!n::int = xs!(n-1) if n>0;
n = 3; x = 1; xs = [2]
[2] (!): (x:xs)!n::int = xs!(n-1) if n>0;
n = 2; x = 2; xs = []
[3] (!): []!n::int = throw out_of_bounds;
n = 1
>> [4] throw: extern void pure_throw(expr*) = throw;
x1 = out_of_bounds
The last call, which is also marked with the >> symbol, is the call that
raised the exception. The format is similar to the p command of the
debugger, see below, but bt always prints a full backtrace. (As with the
show command of the interpreter, you can set the PURE_MORE
environment variable to pipe the output through the corresponding command, or
use evalcmd to capture the output of bt in a string.)
The debugger can also be used interactively. To these ends, you can set
breakpoints on functions with the break command. The debugger then gets
invoked as soon as a rule for one of the given functions is
executed. Example:
> fact n::int = if n>0 then n*fact (n-1) else 1;
> break fact
> fact 1;
** [1] fact: fact n::int = if n>0 then n*fact (n-1) else 1;
n = 1
(Type 'h' for help.)
:
** [2] fact: fact n::int = if n>0 then n*fact (n-1) else 1;
n = 0
:
++ [2] fact: fact n::int = if n>0 then n*fact (n-1) else 1;
n = 0
--> 1
** [2] (*): x::int*y::int = x*y;
x = 1; y = 1
:
++ [2] (*): x::int*y::int = x*y;
x = 1; y = 1
--> 1
++ [1] fact: fact n::int = if n>0 then n*fact (n-1) else 1;
n = 1
--> 1
1
Lines beginning with ** indicate that the evaluation was interrupted to
show the rule (or external) which is currently being considered, along with
the current depth of the call stack, the invoked function and the values of
parameters and other local variables in the current lexical environment. In
contrast, the prefix ++ denotes reductions which were actually performed
during the evaluation and the results that were returned by the function call
(printed as --> return value).
Sometimes you might also see funny symbols like #<closure>, #<case> or
#<when> instead of the function name. These indicate lambdas and the
special variable-binding environments, which are all implemented as anonymous
closures in Pure. Also note that the debugger doesn’t know about the argument
names of external functions (which are optional in Pure and not recorded
anywhere), so it will display the generic names x1, x2 etc. instead.
At the debugger prompt ‘:‘ you can enter various special debugger
commands, or just keep on hitting the carriage return key to walk through an
evaluation step by step, as we did in the example above. (Command line editing
works as usual at the debugger prompt, if it is enabled.) The usual commands
are provided to walk through an evaluation, print and navigate the call stack,
step over the current call, or continue the evaluation unattended until you
hit another breakpoint. If you know other source level debuggers like
gdb then you should feel right at home. You can type h at the
debugger prompt to print the following list:
: h
Debugger commands:
a auto: step through the entire program, run unattended
c [f] continue until next breakpoint, or given function f
h help: print this list
n next step: step over reduction
p [n] print rule stack (n = number of frames)
r run: finish evaluation without debugger
s single step: step into reduction
t, b move to the top or bottom of the rule stack
u, d move up or down one level in the rule stack
x exit the interpreter (after confirmation)
. reprint current rule
! cmd execute interpreter command
? expr evaluate expression
<cr> single step (same as 's')
<eof> step through program, run unattended (same as 'a')
The command syntax is very simple. Besides the commands listed above you can
also enter comment lines (// comment text) which will just be
ignored. Extra arguments on commands which don’t expect any will generally be
ignored as well. The single letter commands all have to be separated from any
additional parameters with whitespace, whereas the ‘!‘, ‘?‘ and
‘.‘ commands count as word delimiters and can thus be followed immediately
by an argument. For convenience, the ‘?‘ command can also be omitted if
the expression to be evaluated doesn’t start with a single letter or one of
the special punctuation commands.
The debugger can be exited or suspended in the following ways:
- You can type c to continue the evaluation until the next breakpoint, or
c foo in order to proceed until the debugger hits an invocation of the
function foo.
- You can type r to run the rest of the evaluation without the debugger.
- The a (“auto”) command single-steps through the rest of the evaluation,
running unattended. This command can also be entered by just hitting the
end-of-file key (Ctrl-d on Unix systems) at the debugger prompt.
- You can also type x to exit from the debugger and the interpreter
immediately (after confirmation).
In addition, you can use the ! command to run any interpreter command
while in the debugger. This is particularly useful to invoke the break and
del commands to change breakpoints, or you can use !! to escape a
shell command. (However, you shouldn’t try to modify the program while you’re
debugging it. This may work in some cases, but will have nasty consequences if
you happen to change a function which is currently being executed.)
At the debugger prompt, you can use the u (“up”), d (“down”), t
(“top”) and b (“bottom”) commands to move around on the current call
stack. The p command prints a range of the call stack centered around the
currently selected stack frame, which is indicated with the >> tag,
whereas ** denotes the current bottom of the stack (which is the rule to
be executed with the single step command). The p command can also be
followed by a numeric argument which indicates the number of stack frames to
be printed (this will then become the default for subsequent invocations of
p). The n command steps over the call selected with the stack
navigation commands. For instance:
> fact 3;
** [1] fact: fact n::int = if n>0 then n*fact (n-1) else 1;
n = 3
: c *
** [4] (*): x::int*y::int = x*y;
x = 1; y = 1
: p
[1] fact: fact n::int = if n>0 then n*fact (n-1) else 1;
n = 3
[2] fact: fact n::int = if n>0 then n*fact (n-1) else 1;
n = 2
[3] fact: fact n::int = if n>0 then n*fact (n-1) else 1;
n = 1
** [4] (*): x::int*y::int = x*y;
x = 1; y = 1
: u
>> [3] fact: fact n::int = if n>0 then n*fact (n-1) else 1;
n = 1
: u
>> [2] fact: fact n::int = if n>0 then n*fact (n-1) else 1;
n = 2
: p
[1] fact: fact n::int = if n>0 then n*fact (n-1) else 1;
n = 3
>> [2] fact: fact n::int = if n>0 then n*fact (n-1) else 1;
n = 2
[3] fact: fact n::int = if n>0 then n*fact (n-1) else 1;
n = 1
** [4] (*): x::int*y::int = x*y;
x = 1; y = 1
: n
++ [2] fact: fact n::int = if n>0 then n*fact (n-1) else 1;
n = 2
--> 2
** [2] (*): x::int*y::int = x*y;
x = 3; y = 2
:
If you ever get lost, you can reprint the current rule with the ‘.‘
command:
: .
** [2] (*): x::int*y::int = x*y;
x = 3; y = 2
Another useful feature is the ? command which lets you evaluate any Pure
expression, with the local variables of the current rule bound to their
corresponding values. Like the n command, ? applies to the current
stack frame as selected with the stack navigation commands. The expression
must be entered on a single line, and the trailing semicolon is optional. For
instance:
> fact 3;
** [1] fact: fact n::int = if n>0 then n*fact (n-1) else 1;
n = 3
: c *
** [4] (*): x::int*y::int = x*y;
x = 1; y = 1
: ?x+y
2
: u
>> [3] fact: fact n::int = if n>0 then n*fact (n-1) else 1;
n = 1
: n>0, fact n
1,1
A third use of the debugger is to trace function calls. For that the
interpreter provides the trace command which works similarly to break,
but sets so-called “tracepoints” which only print rule invocations and
reductions instead of actually interrupting the evaluation. For instance,
assuming the same example as above, let’s first remove the breakpoint on
fact (using the del command) and then set it as a tracepoint instead:
> del fact
> trace fact
> fact 1;
** [1] fact: fact n::int = if n>0 then n*fact (n-1) else 1;
n = 1
** [2] fact: fact n::int = if n>0 then n*fact (n-1) else 1;
n = 0
++ [2] fact: fact n::int = if n>0 then n*fact (n-1) else 1;
n = 0
--> 1
** [2] (*): x::int*y::int = x*y;
x = 1; y = 1
++ [2] (*): x::int*y::int = x*y;
x = 1; y = 1
--> 1
++ [1] fact: fact n::int = if n>0 then n*fact (n-1) else 1;
n = 1
--> 1
1
The break and trace commands can also be used in concert if you want
to debug some functions while only tracing others.
Note that the trace command can actually be run in two different modes:
recursive mode in which the trace is triggered by any of the active
tracepoints and continues until the corresponding call is finished, or skip
mode in which only calls by the active tracepoints are reported. The former
is the default and is often preferable, because it gives you a complete
transcript of the reductions performed during a global function call,
including reductions of local and anonymous function applications.
If you don’t need that much detail, you can also switch to skip mode by
invoking the trace command with the -s option. This allows you to see
a quick summary of the computation which only shows reductions by rules
directly involving the active breakpoints. (Note that it’s only possible to
see reductions by global functions that way, since there’s no way to set
breakpoints or tracepoints on a local function.) For instance:
> trace -s
> fact 1;
** [1] fact: fact n::int = if n>0 then n*fact (n-1) else 1;
n = 1
** [2] fact: fact n::int = if n>0 then n*fact (n-1) else 1;
n = 0
++ [2] fact: fact n::int = if n>0 then n*fact (n-1) else 1;
n = 0
--> 1
++ [1] fact: fact n::int = if n>0 then n*fact (n-1) else 1;
n = 1
--> 1
1
Moreover, the trace command can also be invoked with the -a option to
trace all global function calls, which is convenient to quickly obtain a full
transcript of a reduction sequence. The same options also work in an analogous
fashion with macro calls, see the Macros section for some examples.
The current sets of breakpoints and tracepoints can be changed with the
break, trace and del commands, as shown above, and just break
or trace without any arguments lists the currently defined breakpoints or
tracepoints, respectively. Please see Interactive Commands above for
details. Also note that these are really interpreter commands, so to invoke
them in the debugger you have to escape them with the ! command.
Another convenience for interactive usage is the ans function, which
retrieves the most recent result printed in interactive mode. For instance:
> fact n = if n<=1 then 1 else n*fact (n-1);
> map fact (1..10);
[1,2,6,24,120,720,5040,40320,362880,3628800]
> scanl (+) 0 ans;
[0,1,3,9,33,153,873,5913,46233,409113,4037913]
Note that ans is just an ordinary function, defined in the prelude,
not a special command. However, there is a special clear ans command which
purges the ans value. This is useful, e.g., if you got a huge result which
you want to erase from memory before starting the next computation.
The interpreter provides the following “hook” to override the print
representations of expressions. This works in a fashion similar to Haskell’s
show function.
-
__show__ x
The programmer may define this function to supply custom print
representations for certain expressions.
__show__ is just an ordinary Pure function expected to return a string
with the desired custom representation of a normal form value given as the
function’s single argument. The interpreter prints the strings returned by
__show__ just as they are. It will not check whether they conform to
Pure syntax and/or semantics, or modify them in any way. Also, the library
doesn’t define this function anywhere, so you are free to add any rules that
you want.
Custom print representations are most useful for interactive purposes, if
you’re not happy with the default print syntax of some kinds of objects. One
particularly useful application of __show__ is to change the format of
numeric values. Here are some examples:
> using system;
> __show__ x::double = sprintf "%0.6f" x;
> 1/7;
0.142857
> __show__ x::int = sprintf "0x%0x" x;
> 1786;
0x6fa
> using math;
> __show__ (x::double +: y::double) = sprintf "%0.6f+%0.6fi" (x,y);
> cis (-pi/2);
0.000000+-1.000000i
The prelude function str, which returns the print representation of
any Pure expression, calls __show__ as well:
Conversely, you can call the str function from __show__, but
in this case it always returns the default representation of an
expression. This prevents the expression printer from going recursive, and
allows you to define your custom representation in terms of the default
one. E.g., the following rule removes the L suffixes from bigint values:
> __show__ x::bigint = init (str x);
> fact n = foldl (*) 1L (1..n);
> fact 30;
265252859812191058636308480000000
Of course, your definition of __show__ can also call __show__
itself recursively to determine the custom representation of an object.
One case which needs special consideration are thunks (futures). The printer
will never use __show__ for those, to prevent them from being forced
inadvertently. In fact, you can use __show__ to define custom
representations for thunks, but only in the context of a rule for other kinds
of objects, such as lists. For instance:
> nonfix ...;
> __show__ (x:xs) = str (x:...) if thunkp xs;
> 1:2:(3..inf);
1:2:3:...
Another case which needs special consideration are numeric matrices. For
efficiency, the expression printer will always use the default representation
for these, unless you override the representation of the matrix as a
whole. E.g., the following rule for double matrices mimics Octave’s default
output format (for the sake of simplicity, this isn’t perfect, but you get the
idea):
> __show__ x::matrix =
> strcat [printd j (x!(i,j))|i=0..n-1; j=0..m-1] + "\n"
> with printd 0 = sprintf "\n%10.5f"; printd _ = sprintf "%10.5f" end
> when n,m = dim x end if dmatrixp x;
> {1.0,1/2;1/3,4.0};
1.00000 0.50000
0.33333 4.00000
Finally, by just purging the definition of the __show__ function you
can easily go back to the standard print syntax:
> clear __show__
> 1/7; 1786; cis (-pi/2);
0.142857142857143
1786
6.12303176911189e-17+:-1.0
Note that if you have a set of definitions for the __show__ function
which should always be loaded at startup, you can put them into the
interpreter’s interactive startup files, see Interactive Startup below.
It is possible to extend the interpreter with your own interactive
commands. To these ends, all you have to do is provide some corresponding
public function definitions in the special __cmd__ namespace
(cf. Namespaces). These definitions are typically placed in one of the
interpreter’s startup files (see Interactive Startup below) so that they
are always available when running the interpreter interactively.
A command function is invoked with one string argument which contains the rest
of the command line (with leading and trailing whitespace stripped off). It
may return a string result which is printed on standard output (appending a
newline if needed). Thus a simple command which just prints its arguments as
is can be implemented as follows:
> namespace __cmd__;
> echo s = s;
> echo Hello, world!
Hello, world!
You can split arguments and do any required processing of the arguments with
the usual string processing functions. For instance, let’s change our echo
command so that it prints each whitespace-delimited token on a line of its
own:
> clear __cmd__::echo
> echo s = join "\n" args when
> args = [a | a = split " " s; ~null a];
> end;
> echo Hello, world!
Hello,
world!
A command function may in fact return any kind of value. However, only string
results are printed by the interpreter, other results are silently ignored.
Thus we might implement the echo command in a direct fashion, using the C
puts function:
> clear __cmd__::echo
> private extern int puts(char*);
> echo s = puts s;
> echo Hello, world!
Hello, world!
Note that we declared puts as a private symbol here. In general, the
interpreter only exposes public functions in the __cmd__ namespace as
commands, private symbols are hidden. On the other hand, we might also just
expose the external function puts itself under the (public) alias
echo, so here’s yet another possible implementation of the echo
command:
> clear __cmd__::echo
> extern int puts(char*) = echo;
warning: external 'echo' shadows previous undefined use of this symbol
> echo Hello, world!
Hello, world!
Instead of returning a result, a command function may also throw an
exception. If the exception value is a string, it will be printed as an error
message on standard error, using the same format as the built-in commands:
> error s = throw s;
> error Hello, world!
error: Hello, world!
You can also override a built-in command in order to provide custom
functionality. In this case, the original builtin can still be executed by
escaping the command name with a leading ‘^‘. The same syntax works
with the evalcmd function, so that a custom command can be defined in
terms of the builtin that it replaces. E.g., if we always want to invoke the
ls command with the -l option, we can redefine the ls command as
follows:
> ls examples/*.c
examples/poor.c examples/sort.c
> ls s = evalcmd $ "^ls -l "+s;
> ls examples/*.c
-rw-r--r-- 1 ag users 1883 2011-01-07 16:35 examples/poor.c
-rw-r--r-- 1 ag users 3885 2011-01-07 16:35 examples/sort.c
(Note that since we entered the definition of the ls function
interactively, we need to escape the second input line above with leading
whitespace, so that it’s not mistaken for an invocation of the built-in ls
command.)
To do more interesting things, you should take a look at the reflection
capabilities discussed in the Macros section, which open up endless
possibilities for commands to inspect and manipulate the running program in an
interactive fashion. For instance, let’s define a variation of the built-in
clear command which allows us to delete a specific rule rather than an
entire function definition:
namespace __cmd__;
clr s = case val $ "'(0 with "+s+" end)" of
'(0 __with__ [r]) = del_fundef r;
_ = throw "bad rule syntax";
end;
Note that we employ a little trick here to have val do all the hard
work of parsing the rule specified as argument to the command, in order to
translate the Pure rule syntax to the special meta representation used by
del_fundef. The following example shows our clr command in
action:
> namespace;
> fact n = 1 if n<=0;
> = n*fact (n-1) otherwise;
> show fact
fact n = 1 if n<=0;
fact n = n*fact (n-1);
> clr fact n = 1 if n<=0;
> show fact
fact n = n*fact (n-1);
Here’s another useful command apropos which quickly summarizes the
information available on a given symbol (as reported by the show and
help_index commands):
namespace __cmd__;
apropos s = case catmap descr $ split "\n" $ evalcmd $ "show -s "+s of
[] = throw $ "undefined symbol '"+s+"'";
info = s+" is a "+join " and a " info+". \
Type 'show "+s+"' for more information."+
(if null (evalcmd $ "help_index "+s) then "" else
"\nDocumentation for this symbol is available. Type 'help "+s+"'.");
end with
descr info = case [x | x = split " " info; ~null x] of
t:c:_ = [symtypes!c] if s==t when
symtypes = {"fun"=>"function","mac"=>"macro","var"=>"variable",
"cst"=>"constant"};
end;
_ = [];
end;
end;
This command can be used as follows:
> apropos foldl
foldl is a function. Type 'show foldl' for more information.
Documentation for this symbol is available. Type 'help foldl'.
> apropos $
$ is a macro and a function. Type 'show $' for more information.
Documentation for this symbol is available. Type 'help $'.
> let x = 11;
> apropos x
x is a variable. Type 'show x' for more information.
> apropos y
apropos: undefined symbol 'y'
More examples can be found in the sample.purerc file distributed with the Pure
interpreter.
In interactive mode, the interpreter runs some additional scripts at startup,
after loading the prelude and the scripts specified on the command line. This
lets you tailor the interactive environment to your liking.
The interpreter first looks for a .purerc file in the user’s home directory
(as given by the HOME environment variable) and then for a .purerc
file in the current working directory. These are just ordinary Pure scripts
which may contain any additional definitions (including command definitions,
as described in the previous section) that you need. The .purerc file in the
home directory is for global definitions which should always be available when
running interactively, while the .purerc file in the current directory can be
used for project-specific definitions.
Finally, you can also have a .pure initialization file in the current
directory, which is usually created with the dump command (see
above). This file is loaded after the .purerc files if it is present.
The interpreter processes all these files in the same way as with the run
command (see Interactive Commands above). When invoking the interpreter,
you can specify the --norc option on the command line if you wish to
skip these initializations.
The interpreter’s -c option provides a means to turn Pure scripts
into standalone executables. This feature is still a bit experimental. In
particular, note that the compiled executable is essentially a static
snapshot of your program which is executed on the “bare metal”, without a
hosting interpreter. Only a minimal runtime system is provided. This
considerably reduces startup times, but also implies the following quirks and
limitations:
- All toplevel expressions and let bindings are evaluated after
all functions have been defined. This might cause inconsistent behaviour
with an interpreted run of the same program, which executes expressions and
variable definitions immediately, as the program is being processed. To
avoid these semantic differences, you’ll have to make sure that expressions
are evaluated after all functions used in the evaluation have been defined
completely.
- Toplevel expressions won’t be of much use in a batch-compiled program,
unless, of course, they are evaluated for their side-effects. Usually your
program will have to include at least one of these to play the role of the
“main program” in your script. In most cases these expressions are best
placed after all the function and variable definitions, at the end of your
program.
- The eval function can only be used to evaluate plain toplevel
expressions. You can define local functions and variables in
with and when clauses inside an expression, but you
can’t use eval to define new global variables and functions. In
other words, anything which changes the executing program is “verboten”.
Moreover, the introspective capabilities provided by evalcmd and
similar operations (discussed under Reflection in the Macros section) are
all disabled. If you need any of these capabilities, you have to run your
program with the interpreter.
- Constant and macro definitions, being compile time features, aren’t
available in the compiled program. If you need to use these with
eval at run time, you have to provide them through variable and
function definitions instead. Also, the compiler usually strips unused
functions from the output code, so that only functions which are actually
called somewhere in the static program text are available to eval.
(The -u option and the --required
pragma can be used to avoid this, see Options Affecting Code
Size below.)
- Code which gets executed to compute constant values at compile time will
generally not be executed in the compiled executable, so your program
shouldn’t rely on side-effects of such computations (this would be bad
practice anyway). There is an exception to this rule, however, namely if a
constant value contains run time data such as pointers and local functions
which requires an initialization at run time, then the batch compiler will
generate code for that. (The same happens if the --noconst option
is used to force computation of constant values at run time, see Options
Affecting Code Size.)
What all this boils down to is that anything which requires the compile time
or interactive facilities of the interpreter, is unavailable. These
restrictions only apply at run time, of course. At compile time the program
is being executed by the interpreter so you can use eval and
evalcmd in any desired way. See the description of the
compiling variable below for how to distinguish these cases in your
script.
For most kinds of scripts, the above restrictions aren’t really that much of
an obstacle, or can easily be worked around. For the few scripts which
actually need the full dynamic capabilities of Pure you’ll just have to run
the script with the interpreter. This isn’t a big deal either, only the
startup will be somewhat slower because the script is compiled on the
fly. Once the JIT has done its thing the “interpreted” script will run every
bit as fast as the “compiled” one, since in fact both are compiled (only at
different times) to exactly the same code!
Also note that during a batch compilation, the compiled program is actually
executed as usual, i.e., the script is also run at compile time. This might
first seem to be a big annoyance, but it actually opens the door for some
powerful programming techniques like partial evaluation. It is also a
necessity because of Pure’s highly dynamic nature. For instance, Pure allows
you to define constants by evaluating an arbitrary expression (cf. Constant
Definitions), and using eval a program can easily modify itself in
even more unforeseeable ways. Therefore pretty much anything in your program
can actually depend on previous computations performed while the program is
being executed.
For the sake of a concrete example, consider the following little script:
using system;
fact n = if n>0 then n*fact (n-1) else 1;
main n = do puts ["Hello, world!", str (map fact (1..n))];
if argc<=1 then () else main (sscanf (argv!1) "%d");
When invoked from the command line, with the number n as the first
parameter, this program will print the string "Hello, world!" and the list
of the first n factorials:
$ pure -x hello.pure 10
Hello, world!
[1,2,6,24,120,720,5040,40320,362880,3628800]
Note the condition on argc in the last line of the script. This prevents
the program from producing an exception if no command line parameters are
specified, so that the program can also be run interactively:
$ pure -i -q hello.pure
> main 10;
Hello, world!
[1,2,6,24,120,720,5040,40320,362880,3628800]
()
> quit
To turn the script into an executable, we just invoke the Pure interpreter
with the -c option, using the -o option to specify the
desired output file name:
$ pure -c hello.pure -o hello
$ ./hello 10
Hello, world!
[1,2,6,24,120,720,5040,40320,362880,3628800]
Next suppose that we’d like to supply the value n at compile rather than
run time. To these ends we want to turn the value passed to the main
function into a compile time constant, which can be done as follows:
const n = if argc>1 then sscanf (argv!1) "%d" else 10;
(Note that we provide 10 as a default if n isn’t specified on the
command line.)
Moreover, in such a case we usually want to skip the execution of the main
function at compile time. To these ends, the predefined compiling
variable holds a truth value indicating whether the program is actually
running under the auspices of the batch compiler, so that it can adjust
accordingly. In our example, the evaluation of main becomes:
if compiling then () else main n;
Our program now looks as follows:
using system;
fact n = if n>0 then n*fact (n-1) else 1;
main n = do puts ["Hello, world!", str (map fact (1..n))];
const n = if argc>1 then sscanf (argv!1) "%d" else 10;
if compiling then () else main n;
This script “specializes” n to the first (compile time) parameter when
being batch-compiled, and it still works as before when we run it through the
interpreter in both batch and interactive mode, too:
$ pure -i -q hello.pure
Hello, world!
[1,2,6,24,120,720,5040,40320,362880,3628800]
> main 5;
Hello, world!
[1,2,6,24,120]
()
> quit
$ pure -x hello.pure 7
Hello, world!
[1,2,6,24,120,720,5040]
$ pure -o hello -c -x hello.pure 7
$ ./hello
Hello, world!
[1,2,6,24,120,720,5040]
In addition, there’s also a compile time check analogous to the
compiling variable, which indicates whether the source script is being
run normally or in a batch compilation; see Conditional Compilation. We
might employ this as follows, replacing the last line of the script with
this:
#! --if compiled
if compiling then () else main n;
#! --else
if argc>1 then main n else puts "Try 'main n' where n is a number.";
#! --endif
The code in the --if compiled section, which is the same as before, is now
only executed during batch compilation and in the compiled executable. If we
run the script normally, in the interpreter, the code in the --else
section, which just prints a welcome message if no arguments are given on the
command line, is executed instead. So we now actually have four different
code paths, depending on whether the script is run normally, with or without
arguments, or in a batch compilation, or as a native executable. This kind of
setup is useful if the script is to be run both interactively and
non-interactively in the interpreter while developing it, but once the script
is finished it gets compiled and installed as a native executable.
$ pure -i -q hello.pure
Try 'main n' where n is a number.
> main 5;
Hello, world!
[1,2,6,24,120]
()
> quit
$ pure -x hello.pure 7
Hello, world!
[1,2,6,24,120,720,5040]
$ pure -o hello -c -x hello.pure
$ ./hello
Hello, world!
[1,2,6,24,120,720,5040,40320,362880,3628800]
You’ll rarely need an elaborate setup like this, most of the time something
like our simple first example will do the trick. But, as you’ve seen, Pure can
easily do it.
By default, the batch compiler strips unused functions from the output code,
to keep the code size small. You can disable this with the -u
option, in which case the output code includes all functions defined in the
compiled program, the prelude and any other module imported with a
using clause, even if they don’t seem to be used anywhere. This
considerably increases compilation times and makes the compiled executable
much larger. For instance, on a 64 bit Linux systems with ELF binaries the
executable of our hello.pure example is about thrice as large:
$ pure -o hello -c -x hello.pure 7 && ls -l hello
-rwxr-xr-x 1 ag users 178484 2010-01-12 06:21 hello
$ pure -o hello -c -u -x hello.pure 7 && ls -l hello
-rwxr-xr-x 1 ag users 541941 2010-01-12 06:21 hello
(Note that even the stripped executable is fairly large when compared to
compiled C code, as it still contains the symbol table of the entire program,
which is needed by the runtime environment.)
Stripped executables should be fine for most purposes, but you have to be
careful when using eval in your compiled program. The compiler only
does a static analysis of which functions might be reached from the
initialization code (i.e., toplevel expressions and let
bindings). It does not take into account code run via the eval
routine. Thus, functions used only in evaled code will be stripped
from the executable, as if they were never defined at all. If such a function
is then being called using eval at runtime, it will evaluate to a
plain constructor symbol.
If this is a problem then you can either use the -u option to
produce an unstripped executable, or you can force specific functions to be
included in the stripped executable with the
--required pragma (cf. Code Generation
Options). For instance:
#! --required foo
foo x = bar (x-1);
eval "foo 99";
There is another code generation option which may have a substantial effect on
code size, namely the --noconst option. Normally, constant values
defined in a const definition are precomputed at compile time and
then stored in the generated executable; this reduces startup times but may
increase the code size considerably if your program contains big constant
values such as lists. If you prefer smaller executables then you can use the
--noconst option to force the value of the constant to be recomputed
at run time (which effectively turns the constant into a kind of read-only
variable). For instance:
#! --noconst
const xs = 1L..100000L;
sum = foldl (+) 0;
using system;
puts $ str $ sum xs;
On my 64 bit Linux system this produces a 187115 bytes executable. Without
--noconst the code becomes almost an order of magnitude larger in
this case (1788699 bytes). On the other hand, the smaller executable also
takes a little longer to run since it must first recompute the value of the
list constant at startup. So you have to consider the tradeoffs in a given
situation. Usually big executables aren’t much of a problem on modern
operating systems, but if your program contains a lot of big constants then
this may become an important consideration. However, if a constant value takes
a long time to compute then you’ll be better off with the default behaviour of
precomputing the value at compile time.
Note that while the batch compiler generates native executables by default, it
can just as well create object files which can be linked into other C/C++
programs and libraries:
$ pure -o hello.o -c -x hello.pure 7
The .o extension tells the compiler that you want an object file. When linking
the object module, you also need to supply an initialization routine which
calls the __pure_main__ function in hello.o to initialize the compiled
module. This routine is declared in C/C++ code as follows:
extern "C" void __pure_main__(int argc, char** argv);
As indicated, __pure_main__ is to be invoked with two parameters, the
argument count and NULL-terminated argument vector which become the
argc and the argv of the Pure program, respectively. (You can also
just pass 0 for both arguments if you don’t need to supply command line
parameters.) The purpose of __pure_main__ is to initialize a shell
instance of the Pure interpreter which provides the minimal runtime support
necessary to execute the Pure program, and to invoke all “initialization code”
(variable definitions and toplevel expressions) of the program itself.
A minimal C main function which does the job of initializing the Pure
module looks as follows:
extern void __pure_main__(int argc, char** argv);
int main(int argc, char** argv)
{
__pure_main__(argc, argv);
return 0;
}
If you link the main routine with the Pure module, don’t forget to also
pull in the Pure runtime library. Assuming that the above C code is in
pure_main.c:
$ gcc -c pure_main.c -o pure_main.o
$ g++ -o hello hello.o pure_main.o -lpure
$ ./hello
Hello, world!
[1,2,6,24,120,720,5040]
(The C++ compiler is used as the linker here so that the standard C++ library
gets linked in, too. This is necessary because Pure’s runtime library is
actually written in C++.)
In fact, this is pretty much what pure -c actually does for you when
creating an executable.
If your script loads dynamic libraries (using "lib:...";) then you’ll also
have to link with those; all external references have to be resolved at
compile time. This is taken care of automatically when creating
executables. Otherwise it is a good idea to run pure -c with the
-v0100 verbosity option so that it prints the libraries to be linked (in
addition to the commands which are invoked in the compilation process):
$ pure -v0100 -c hello.pure -o hello.o
opt -f -std-compile-opts hello.o.bc | llc -f -o hello.o.s
gcc -c hello.o.s -o hello.o
Link with: g++ hello.o -lpure
Well, we already knew that, so let’s consider a slightly more interesting
example from Pure’s ODBC module:
$ pure -v0100 -c pure-odbc/examples/menagerie.pure -o menagerie.o
opt -f -std-compile-opts menagerie.o.bc | llc -f -o menagerie.o.s
gcc -c menagerie.o.s -o menagerie.o
Link with: g++ menagerie.o /usr/local/lib/pure/odbc.so -lpure
$ g++ -shared -o menagerie.so menagerie.o /usr/local/lib/pure/odbc.so -lpure
Note that the listed link options are necessary but might not be sufficient;
pure -c just makes a best guess based on the Pure source. On most systems
this will be good enough, but if it isn’t, you can just add options to the
linker command as needed to pull in additional required libraries.
As this last example shows, you can also create shared libraries from Pure
modules. However, on some systems (most notably x86_64), this requires that
you pass the -fPIC option when batch-compiling the module, so that
position-independent code is generated:
$ pure -c -fPIC pure-odbc/examples/menagerie.pure -o menagerie.o
Note that even when building a shared module, you’ll have to supply an
initialization routine which calls __pure_main__ somewhere.
Also note that since Pure doesn’t support separate compilation in the present
implementation, if you create different shared modules like this, each will
contain their own copy all the required Pure functions from the prelude and
other imported Pure modules. This becomes a problem when trying to link
several separate batch-compiled modules into the same executable or library,
because you’ll get many name clashes for routines present in different modules
(including the __pure_main__ entry point). To prevent this, the batch
compiler can be invoked with the --main option to explicitly set a
name for the main entry point. For instance:
$ pure -c hello.pure -o hello.o --main __hello_main__
This has two effects. First, the main entry point will be called whatever you
specified with --main, so you have to call this function instead of
__pure_main__ to initialize the module. Second, if --main is
specified, then all Pure functions in the module will be changed to internal
linkage (like static functions in C) to prevent any possible name clashes
between different modules. (Alas, this also makes it impossible to employ
pure_funcall to call Pure functions directly from C, as described in the
following section, so you’ll have to use other runtime routines such as
pure_eval or pure_appl to achieve this in an indirect way.)
Last but not least, pure -c can also generate just plain LLVM assembler
code:
pure -c hello.pure -o hello.ll
Note the .ll extension; this tells the compiler that you want an LLVM
assembler file. An LLVM bitcode file can be created just as easily:
pure -c hello.pure -o hello.bc
In these cases you’ll have to have to handle the rest of the compilation
yourself. This gives you the opportunity, e.g., to play with special
optimization and code generation options provided by the LLVM
toolchain. Please refer to the LLVM documentation (in particular, the
description of the opt and llc programs) for details.
Another point worth mentioning here is that you can’t just call Pure functions
in a batch-compiled module directly. That’s because in order to call a Pure
function, at least in the current implementation, you have to set up a Pure
stack frame for the function. However, there’s a convenience function called
pure_funcall in the runtime API to handle this. This function takes a
pointer to the Pure function, the argument count and the arguments themselves
(as pure_expr* objects) as parameters. For instance, here is a pure_main.c
module which can be linked against the hello.pure program from above, which
calls the fact function from the Pure program:
#include <stdio.h>
#include <pure/runtime.h>
extern void __pure_main__(int argc, char** argv);
extern pure_expr *fact(pure_expr *x);
int main()
{
int n = 10, m;
__pure_main__(0, NULL);
if (pure_is_int(pure_funcall(fact, 1, pure_int(n)), &m))
printf("fact %d = %d\n", n, m);
return 0;
}
And here’s how you can compile, link and run this program:
$ pure -o hello.o -c -x hello.pure 7
$ gcc -o pure_main.o -c pure_main.c
$ g++ -o myhello hello.o pure_main.o -lpure
$ ./myhello
Hello, world!
[1,2,6,24,120,720,5040]
fact 10 = 3628800
Note that the first two lines are output from the Pure program; the last line
is what gets printed by the main routine in pure_main.c.
This section is a grab bag of casual remarks, useful tips and tricks, and
information on common pitfalls, quirks and limitations of the current
implementation and how to deal with them.
People keep asking me what’s so “pure” about Pure. The long and apologetic
answer is that Pure tries to stay as close as possible to the spirit of term
rewriting without sacrificing practicality. Pure’s term rewriting core is in
fact purely functional. It’s thus possible and in fact quite easy to write
purely functional programs in Pure, and you’re encouraged to do so whenever
this is reasonable. On the other hand, Pure doesn’t get in your way if you
want to call external operations with side effects; after all, it does allow
you to call any C function at any point in a Pure program.
The short answer is that I simply liked the name, and there wasn’t any
programming language named “Pure” yet (quite a feat nowadays), so there’s one
now. If you insist on a (recursive) backronym, just take “Pure” to stand for
the “Pure universal rewriting engine”.
Pure is based on the author’s earlier Q language, but it offers many new and
powerful features and programs run much faster than their Q equivalents. The
language also went through a thorough facelift in order to modernize the
syntax and make it more similar to other modern-style functional languages, in
particular Miranda and Haskell. Thus porting Q scripts to Pure often
involves a substantial amount of manual work, but it can (and has) been done.
Since its modest beginnings in April 2008, Pure has gone through a lot of
major and minor revisions which raise various backward compatibility issues.
We document these in the following, in order to facilitate the porting of
older Pure scripts.
Pure 0.7 introduced built-in matrix structures, which called for some minor
changes in the syntax of comprehensions and arithmetic sequences.
Specifically, the template expression and generator/filter clauses of a
comprehension are now separated with | instead of ;. Moreover,
arithmetic sequences with arbitrary stepsize are now written x:y..z
instead of x,y..z, and the ‘..‘ operator now has a higher precedence
than the ‘,‘ operator. This makes writing matrix slices like
x!!(i..j,k..l) much more convenient.
In Pure 0.13 the naming of the logical and bitwise operations was changed, so
that these are now called ~, &&, || and not/and/or,
respectively. (Previously, ~ was used for bitwise, not for logical
negation, which was rather inconsistent, albeit compatible with the naming of
the not operation in Haskell and ML.) Also, to stay in line with this
naming scheme, inequality was renamed to ~= (previously !=).
Pure 0.14 introduced the namespaces feature. Consequently, the scope of
private symbols is now confined to a namespace rather than a source module;
scripts making use of private symbols need to be adapted accordingly. Also
note that syntax like foo::int may now also denote a qualified symbol
rather than a tagged variable, if foo has been declared as a
namespace. You can work around such ambiguities by renaming the variable, or
by placing spaces around the ‘::‘ delimiter (these aren’t permitted in a
qualified symbol, so the construct foo :: int is always interpreted as a
tagged variable, no matter whether foo is also a valid namespace).
Pure 0.26 extended the namespaces feature to add support for hierarchical
namespaces. This means that name lookup works in a slightly different fashion
now (see Hierarchical Namespaces for details), but old code which doesn’t
use the new feature should continue to work unchanged.
Pure 0.26 also changed the nullary keyword to nonfix, which is
more consistent with the other kinds of fixity declarations. Moreover, the
parser was enhanced so that it can cope with a theoretically unbounded number
of precedence levels, and the system of standard operators in the prelude was
modified so that it becomes possible to sneak in new operator symbols with
ease; details can be found in the Symbol Declarations section.
Pure 0.41 added support for optimization of indirect tail calls, so that any
previous restrictions on the use of tail recursion in indirect function calls
and mutually recursive globals have been removed. Moreover, the logical
operators && and || are now tail-recursive in their second operand and
can also be extended with user-defined equations, just like the other
builtins. Note that this implies that the values returned by && and ||
aren’t normalized to the values 0 and 1 any more (this isn’t possible with
tail call semantics). If you need this then you’ll have to make sure that
either the operands are already normalized, or you’ll have to normalize the
result yourself.
Also, as of Pure 0.41 the batch compiler produces stripped executables by
default. To create unstripped executables you now have to use the -u
option, see Options Affecting Code Size for details. The -s
option to produce stripped executables is still provided for backward
compatibility, but it won’t have any effect unless you use it to override a
previous -u option.
Pure 0.43 changed the rules for looking up symbols in user-defined namespaces.
Unqualified symbols are now created in the current (rather than the global)
namespace by default, see Symbol Lookup and Creation for details. The
-w option can be used to get warnings about unqualified symbols
which are resolved to a different namespace than previously. It also provides
a means to check your scripts for implicit declarations which might indicate
missing or mistyped function symbols.
Pure 0.45 added support for checking arbitrary pointer types in the C
interface, so that you don’t have to worry about passing the wrong kinds of
pointers to system and library routines any more. Moreover, the interpretation
of numeric pointer arguments (int* etc.) was changed to bring them in line
with the other new numeric matrix conversions (int** etc.). In particular,
the matrix data can now be modified in-place and type checking is more strict
(int* requires an int matrix, etc.). Also, there’s now support for
argv-style vector arguments (char** and void**). Please see the C
Types section for details.
Pure 0.47 added a bunch of new features which have been on the wishlist for
the forthcoming 1.0 release:
- You can now define your own interactive commands by placing suitable
function definitions in the special __cmd__ namespace; see User-Defined
Commands for details.
- The syntax used to denote inline code sections was changed from
%{...%} to %<...%>. This resolves an ambiguity in the syntax (note
that %{ is legal Pure syntax; it could denote a % operator followed
by a matrix value), and also makes it easier to properly support this
construct in Emacs Pure mode.
- It is now possible to declare variadic externs, so that functions like
printf can be called without much ado; see Variadic C Functions.
- Support for simple kinds of matrix patterns like {x,y}, {x::int,y},
{x,y;z,t}, {{x,y},z} was added.
- The meaning of quoted specials such as lambdas and local definitions was
changed. Previously these would be evaluated even in the middle of a quoted
expression. Now they will produce a special meta representation in terms of
built-in macros, in order to support the advanced metaprogramming
capabilities discussed in Built-in Macros and Special Expressions and
Reflection.
- Last but not least, Pure 0.47 sports a new, more flexible type tag feature
which defines type tags as unary predicates implemented using normal
rewriting rules; cf. section Type Rules for details. To these ends, a new
keyword type was added (if you used this as an ordinary
identifier, you will have to rename these). Note that the old-style type
tags, which were just a syntactic shortcut for “as” patterns involving unary
constructor symbols, aren’t supported any more, so you’ll have to fix up
your old scripts accordingly. To assist with this, the Pure interpreter can
be run with the -w option in order to identify occurrences of
undefined (presumably old-style) type tags. You should either change these
to the corresponding “as” pattern (i.e., x::foo to x@(foo _)), or
just add a proper type definition for the tag (like type foo (foo _);).
Pure 0.48 moved pointer arithmetic and the regex functions into separate
pointers and regex modules, so you now have to import these
modules if you need this functionality. It also introduced the
--defined pragma which lets you have
“defined” functions in Pure which throw an exception if they can’t be applied,
e.g., because they are invoked with the wrong arguments.
Pure 0.49 introduced the conditional compilation pragmas, so that simple
version and system dependencies can now be handled in a more convenient way.
Pure 0.50 introduced the declaration of interface types, which make it
possible to create the definition of a type from a description of its
operations. To these ends, a new keyword interface was added to the
language.
The parser uses a fairly simplistic panic mode error recovery which tries to
catch syntax errors at the toplevel only. This seems to work reasonably well,
but might catch some errors much too late. Unfortunately, Pure’s terseness
makes it rather difficult to design a better scheme. As a remedy, the parser
accepts an empty definition (just ; by itself) at the toplevel only. Thus,
in interactive usage, if the parser seems to eat away your input without doing
anything, entering an extra semicolon or two should break the spell, putting
you back at the toplevel where you can start typing the definition again.
A common source of confusion is that Pure provides two different constructs to
bind local function and variable symbols, respectively. This distinction is
necessary because Pure does not segregate defined functions and constructors,
and thus there is no magic to figure out whether an equation like foo x =
y by itself is meant as a definition of a function foo with formal
parameter x and return value y, or a pattern binding defining the
local variable x by matching the pattern foo x against the value of
y. The with construct does the former, when the
latter.
Also note that the function definitions in a with clause are all
done simultaneously (and can thus be mutually recursive), while the individual
variable definitions and expressions in a when clause are executed
in order. This works in exactly the same fashion as letrec and let in
Scheme. (As a mnemonic, consider that when conveys a sense of time,
so its parts are “executed in sequence”.)
The sequential execution aspect of when is rather important in
Pure, because it enables you to do a series of “actions” (variable bindings
and expression evaluations) in sequence by simply enclosing it in a
when clause. This provides the Pure programmer with a useful and
familiar bit of imperative “look and feel” (even though the when
clause itself works in a purely functional way). For instance, suppose that
we’d like to define a function which opens a file, checks that the file was
opened successfully and throws an exception otherwise, outputs a message to
indicate which file was opened, and finally returns the contents of the file
as a string. The easiest way to do this in Pure is as follows:
using system;
read_file name::string = s when
fp = fopen name "r";
pointerp fp || throw (sprintf "%s: %s" (name,strerror errno));
printf "opened file %s\n" name;
s = fget fp;
end;
One of Pure’s major idiosyncrasies is that with and when
clauses are tacked on to the end of the expression they belong to. This mimics
mathematical language and makes it easy to read and understand a definition,
because you’re told right up front what is to be computed, before going into
the details of how the computation is performed. However, this style differs
considerably from other block-structured programming languages, which often
place local definitions in front of the code they apply to. Pure doesn’t offer
any special syntax for this, but note that you can always write a
when or with clause in the following style which places
the “body” at the bottom:
result when
y = foo (x+1);
z = bar y;
result = baz z;
end;
This can be read and written more or less like a let expression in Scheme
or ML, except that the name of the result is given explicitly at the beginning.
However, this style doesn’t really save you either if you need several
sections with both local functions and variables. In this case you’ll just
have to bite the bullet and arrange the with and when
clauses the way that Pure wants them. That is, first come the local variables
used in the right-hand side, then the local functions needed to compute those
variables, then maybe another section with local variables needed by those
functions, etc. When looking at a complicated definition like this, it
sometimes helps to read the with and when blocks “in
reverse”, i.e., from bottom to top, which is the order in which they will
actually be executed.
As explained in section Patterns, Pure allows multiple occurrences of the
same variable in a pattern (so-called non-linearities):
This rule will only be matched if both occurrences of x are bound to the
same value. More precisely, the two instances of x will checked for
syntactic equality during pattern matching, using the same primitive
provided by the prelude. This may need time proportional to the sizes of both
argument terms, and thus become quite costly for big terms. In fact,
same might not even terminate at all if the compared terms are both
infinite lazy data structures, such as in foo (1..inf) (1..inf). So you
have to be careful to avoid such uses.
When using non-linearities in conjunction with “as” patterns, you also have to
make sure that the “as” variable does not occur inside the corresponding
subpattern. Thus a definition like the following is illegal:
> foo xs@(x:xs) = x;
<stdin>, line 1: error in pattern (recursive variable 'xs')
The explanation is that such a pattern couldn’t possibly be matched by a
finite list anyway. Indeed, the only match for xs@(x:xs) would be an
infinite list of x‘s, and there’s no way that this condition could be
verified in a finite amount of time. Therefore the interpreter reports a
“recursive variable” error in such situations.
In the current implementation, “as” patterns cannot be placed on the “spine”
of a function definition. Thus rules like the following, which have the
pattern somewhere in the head of the left-hand side, will all provoke an error
message from the compiler:
a@foo x y = a,x,y;
a@(foo x) y = a,x,y;
a@(foo x y) = a,x,y;
This is because the spine of a function application is not available when the
function is called at runtime. “As” patterns in pattern bindings
(let, const, case, when) are not
affected by this restriction since the entire value to be matched is available
at runtime. For instance:
> case bar 99 of y@(bar x) = y,x+1; end;
bar 99,100
“As” patterns are also a useful device if you need to manipulate function
applications in a generic way. Note that the “head = function” rule means that
the head symbol f of an application f x1 ... xn occurring on (or
inside) the left-hand side of an equation, variable binding, or
pattern-matching lambda expression, is always interpreted as a literal
function symbol (not a variable). This implies that you cannot match the
“function” component of an application against a variable, at least not
directly. An anonymous “as” pattern like f@_ does the trick, however,
since the anonymous variable is always recognized, even if it occurs as the
head symbol of a function application. Here’s a little example which
demonstrates how you can convert a function application to a list containing
the function and all arguments:
> foo x = a [] x with a xs (x@_ y) = a (y:xs) x; a xs x = x:xs end;
> foo (a b c d);
[a,b,c,d]
This may seem a little awkward, but as a matter of fact the “head = function”
rule is quite useful the way that it is, since it covers the common cases
without forcing the programmer to declare “constructor” symbols (except nonfix
symbols). On the other hand, generic rules operating on arbitrary function
applications are not all that common, so having to “escape” a variable using
the anonymous “as” pattern trick is a small price to pay for that convenience.
Sometimes you may also run into the complementary problem, i.e., to match a
function argument against a given function. Consider this code fragment:
foo x = x+1;
foop f = case f of foo = 1; _ = 0 end;
You might expect foop to return true for foo, and false on all other
values. Better think again, because in reality foop will always return
true! In fact, the Pure compiler will warn you about the second rule of the
case expression not being used at all:
> foop 99;
warning: rule never reduced: _ = 0;
1
This happens because an identifier on the left-hand side of a rule, which is
neither the head symbol of a function application nor a nonfix
symbol, is always considered to be a variable (cf. Variables in Equations),
even if that symbol is defined as a global function elsewhere. So foo
isn’t a literal name in the above case expression, it’s a variable!
(As a matter of fact, this is rather useful, since otherwise a rule like f g
= g+1 would suddenly change meaning if you happen to add a definition like
g x = x-1 somewhere else in your program, which certainly isn’t
desirable.)
A possible workaround is to “escape” the function symbol using an empty
namespace qualifier:
foop f = case f of ::foo = 1; _ = 0 end;
This trick works in case expressions and function definitions, but
fails in circumstances in which qualified variable symbols are permitted
(i.e., in variable and constant definitions). A better solution is to employ
the syntactic equality operator === defined in the prelude to match the
target value against the function symbol. This allows you to define the
foop predicate as follows:
> foop f = f===foo;
> foop foo, foop 99;
1,0
Another way to deal with the situation would be to just declare foo as a
nonfix symbol. However, this makes the foo symbol “precious”, i.e., after
such a declaration it cannot be used as a local variable anymore. It’s usually
a good idea to avoid that kind of thing, at least for generic symbols, so the
above solution is preferred in this case.
As explained in Definitions and Expression Evaluation, Pure doesn’t really
distinguish “constructors” from “defined functions” and thus allows any
function symbol to become part of a normal form expression yielded by an
evaluation. This behaviour follows the usual semantics of (typeless) term
rewriting and is actually quite useful if you also want to evaluate
expressions symbolically.
However, this becomes a nuisance if you really expect the given function to
reduce to something else, and just accidentally supplied the wrong arguments
to the function. Especially annoying in this respect are functions involving
side effects:
> using system;
> puts 99;
puts 99
Here we accidentally specified a number (rather than a string) as the argument
of the puts function. This kind of error can easily be spotted if the
function is invoked interactively, but it may well go unnoticed if the call is
buried deeply in a big program which runs unattended (in batch mode).
As a remedy, Pure 0.48 introduces the
--defined pragma (cf. Code Generation
Options) which allows you to explicitly declare a function symbol as a
“defined” function, so that it will raise a proper exception when the defining
equations (or, as it were, the external definition) of the function are not
applicable to the subject expression:
> #! --defined puts
> puts 99;
<stdin>, line 4: unhandled exception 'failed_match' while evaluating 'puts 99'
This is the same kind of failed_match exception that you’ll get, e.g.,
if the subject term fails to match all patterns in a case
construct, cf. Exception Handling. However, note that the exception will
only be generated if the symbol actually has any defining equations, so a
“pure constructor” (i.e., a symbol without defining equations) will still
return a normal form even if it is also declared --defined:
> #! --defined foo
> foo bar;
foo bar
Nevertheless, the --defined pragma will be
recorded and take effect as soon as you add an equation for the function:
> foo x::int = x+1;
> foo bar;
<stdin>, line 4: unhandled exception 'failed_match' while evaluating 'foo bar'
There’s also a --nodefined pragma which
reverts the function to the default behaviour of returning normal forms:
> #! --nodefined foo
> foo bar;
foo bar
As indicated, the --defined and
--nodefined pragmas can be invoked freely
at any time, and the interpreter takes care that the affected function is
recompiled automatically as needed.
Please note that the --defined pragma is
still considered experimental. It interferes with Pure’s symbolic evaluation
capabilities, so the pragma isn’t currently used in the standard library and
we recommend that programmers shouldn’t use it in a careless fashion
either. However, while most error conditions stemming from unexpected normal
forms can also be caught with diligent unit testing, the pragma can sometimes
save you some time and trouble, especially when testing programs which are to
be executed mostly in batch mode. Future versions of the interpreter might
also make good use of this pragma for static checks and optimization purposes.
Pure programs may need a considerable amount of stack space to handle
recursive function and macro calls, and the interpreter itself also takes its
toll. So you should configure your system accordingly (8 MB of stack space is
recommended for 32 bit systems, systems with 64 bit pointers probably need
more). If the PURE_STACK environment variable is defined, the
interpreter performs advisory stack checks on function entry and raises a Pure
exception if the current stack size exceeds the given limit. The value of
PURE_STACK should be the maximum stack size in kilobytes. Please
note that this is only an advisory limit which does not change the program’s
physical stack size. Your operating system should supply you with a command
such as ulimit(1) to set the real process stack size. (The
PURE_STACK limit should be a little less than that, to account for
temporary stack usage by the interpreter itself.)
Like Scheme, Pure does proper tail calls (if LLVM provides that feature on the
platform at hand), so tail-recursive definitions should work fine in limited
stack space. For instance, the following little program will loop forever if
your platform supports the required optimizations:
This also works if your definition involves function parameters, guards and
multiple equations, of course. Moreover, conditional expressions
(if-then-else) are tail-recursive in both
branches, and the logical operators && and ||,
as well as the sequence operator $$, are tail-recursive in their
second operand.
In addition, the Pure compiler also does a specialized form of tail recursion
optimization for type definition rules. Due to the special way in which type
tags are processed, however, the amount of optimization performed in this case
is somewhat limited; see Recursive Types below.
Finally, note that tail call optimization is always disabled if the debugger
is enabled (-g). This makes it much easier to debug programs, but means that
you may run into stack overflows when debugging a program that does deep tail
recursion.
As described in section Exception Handling, signals delivered to the
process can be caught and handled with Pure’s exception handling facilities.
This has its limitations, however. Since Pure code cannot be executed directly
from a C signal handler, checks for pending signals are only done on function
entry. This means that in certain situations (such as the execution of an
external C routine), delivery of a signal may be delayed by an arbitrary
amount of time. Moreover, if more than one signal arrives between two
successive signal checks, only the last one will be reported in the current
implementation.
When delivering a signal which has been remapped to a Pure exception, the
corresponding exception handler (if any) will be invoked as usual. Further
signals are blocked while the exception handler is being executed.
A fairly typical case is that you have to handle signals in a tail-recursive
function. This can be done with code like the following:
using system;
// Remap some common POSIX signals.
do (trap SIG_TRAP) [SIGHUP, SIGINT, SIGTERM];
loop = catch handler process $$ loop
with handler (signal k) = printf "Hey, I got signal %d.\n" k end;
process = sleep 1; // do something
Running the above loop function enters an endless loop reporting all
signals delivered to the process. Note that to make this work, the
tail-recursive invocation of loop must immediately follow the
signal-handling code, so that signals don’t escape the exception handler.
Of course, in a real application you’d probably want the loop function to
carry around some data to be processed by the process routine, which then
returns an updated value for the next iteration. This can be implemented as
follows:
loop x = loop (catch handler (process x))
with handler (signal k) = printf "Hey, I got signal %d.\n" k $$ 0 end;
process x = printf "counting: %d\n" x $$ sleep 1 $$ x+1;
Using the facilities described in Type Rules, type tags can easily be
defined in a recursive fashion. In simple cases, the compiler can optimize
such definitions so that they are executed in constant stack space, just like
ordinary tail-recursive functions. The main difference here is that the
recursion already takes place during matching, i.e., on the left-hand side
of a rule, since this is where type predicates are normally invoked. This also
limits the amount of tail recursion optimization available on type rules, as
detailed below.
For instance, the following rlist type from the prelude is
defined in such a way that it only matches “proper” lists which have list
values in all their tails (and are thus terminated by the empty list).
type rlist [] | rlist (x : xs::rlist);
Note that this type definition recurses in the last rlist tag
of the last rule of the type. If tail calls are supported by the host
implementation (cf. Stack Size and Tail Recursion), the compiler makes sure
that such definitions are safe to use even if the recursion may go arbitrarily
deep. For instance:
> typep rlist (1..10000000);
1
The precise rules for tail-recursive type definitions are as follows:
- The last rule of the type must have a trivial right-hand side (either just
true or missing) and must be directly recursive in the last type
tag on the left-hand side of the rule.
- The rule may not contain any non-linearities. (That’s because these are
always checked after the type guards for efficiency.)
While these are rather strict requirements, they work reasonably well for
simple recursive types such as the recursive list type above. More general
recursion in types will not be optimized by the compiler, however, and may
thus be subject to stack overflows. For instance, consider the following
binary tree type:
nonfix nil;
type tree nil | tree (bin x l::tree r::tree);
This is a perfectly legal type definition, and the recursion in the last
tree tag of the second rule will indeed be optimized away. However, the
second rule also recurses on the first tree tag which will cause trouble
if there are long chains of left branches in a tree. For instance:
> mktree xs = foldr (\x t->bin x t nil) nil xs;
> mktree [];
nil
> mktree [1,2,3];
bin 1 (bin 2 (bin 3 nil nil) nil) nil
> typep tree (mktree []);
1
> typep tree (mktree [1,2,3]);
1
> typep tree (mktree (1..10000));
<stdin>, line 6: unhandled exception 'stack_fault' while evaluating
'typep tree (mktree (1..10000))'
To avoid deep recursion in such cases it is necessary to implement the type
using a general predicate, which handles the recursion by transforming it into
a tail-recursive form using a technique like continuation passing.
There’s yet another important issue with recursive type definitions, namely
the time it takes to check the definition. In the above example, checking
rlist takes O(n) time, where n is the size of the list. This will
have dire consequences if you do this check repeatedly while traversing a
list, as in the following sum function:
sum xs::rlist = if null xs then 0 else head xs+sum (tail xs);
As this function repeatedly checks its entire argument, the total time it
takes to compute the sum of a list this way becomes O(n^2). To see how slow
this function is, just try it on successively larger lists 1..1000,
1..2000, etc. One way to work around this is to write a “wrapper” function
which simply checks the type of its argument in advance and then invokes a
“worker” function to do the actual computation:
sum xs::rlist = sum xs with
sum xs = if null xs then 0 else head xs+sum (tail xs);
end;
This “wrapper-worker” design is quite common and useful in many situations,
but it is a bit cumbersome in this specific case. An easier way is to just do
the type checking in a piecemeal fashion, as the list is being traversed. To
these ends, the prelude also provides a basic list type which is
defined as follows:
type list [] | list (x:xs);
Note that the recursion is missing here and thus this type can always be
checked in O(1) time, performing just a single pattern match, which is
efficient. Hence, if we replace rlist with the list
type in our original definition then sum will now run in O(n) time, as
desired. On the other hand, this approach also has its drawbacks. For
instance, consider:
> sum xs::list = if null xs then 0 else head xs+sum (tail xs);
> sum (1:2:3);
1+(2+sum 3)
In contrast, our wrapper-worker definition of sum from above returns a
somewhat prettier normal form instead:
> clear sum
> sum xs::rlist = sum xs with
> sum xs = if null xs then 0 else head xs+sum (tail xs);
> end;
> sum (1:2:3);
sum (1:2:3)
Thus the wrapper-worker approach also has its merits, and whether to use one
or the other depends on the situation. Similar techniques and tradeoffs also
apply to other recursive types such as trees.
Pure’s implementation of interface types has some notable differences to
interfaces in a statically typed language like Go. These are mostly due to
Pure’s dynamically typed nature.
Nothing is known about the return type of an interface operation, but this
is no real impediment since Pure types are all about restricting the kind of
arguments which can be passed to a function, not their result types, so
return types are irrelevant to Pure’s interface types anyway.
Pure interfaces aren’t based on the notion of “methods” and therefore don’t
provide any kind of “method dispatch”. Interface operations are just
ordinary Pure functions which rely on Pure’s usual pattern-matching
mechanism to do the dynamic dispatch.
Membership in interface types is decided by considering the left-hand sides
of the definitions of the interface functions only. Guards are not taken
into account, and thus there’s no real guarantee that a member of an
interface type will always be valid input to an interface function.
Interface types work best if all interface operations are completely defined
on the target data domain. This may sometimes force you to add default or
error rules raising exceptions, as shown in the Interface Types section,
which may interfere with symbolic evaluation (cf. Exception Handling and
Defined Functions). If this is not desirable, you can also just include
the missing members manually. To these ends, Pure allows an interface type
to be augmented with ordinary type rules as described in Type Rules. For
instance, we might also have implemented the stack type discussed in the
Interface Types section as follows:
interface stack with
push s::stack x;
pop s::stack;
top s::stack;
end;
type stack [];
push xs@[] x | push xs@(_:_) x = x:xs;
pop (x:xs) = xs;
top (x:xs) = x;
Pure’s interface types are really a compromise between theoretical soundness
and practicality. From the theoretical point of view, we’d like an interface
type to be the intersection of the interface types for the individual
interface functions. Unfortunately, the pattern set for such an intersection
type might well be exponential in size. Hence the approach taken in Pure is to
eliminate those candidate patterns which aren’t supported by all interface
functions. This can be done much more efficiently, but will in general only
produce a subtype of the intersection type. (On the other hand, this method
also has the advantage that the compiler can warn about potentially missing
rules in some of the interface operations. We’ve seen in the Interface
Types section that this can be fairly useful at times.)
Another issue arises with interface operations which allow the interface type
in multiple arguments. A typical example are operators:
interface addable with x::addable + y::addable; end;
In the present implementation, the pattern set will be the union of the
pattern sets for each argument, so the above definition is in fact equivalent
to:
interface addable with x::addable + y; x + y::addable; end;
This makes sense in many situations, but of course this depends on the
particular operation. In some cases, you might have to decide on which
argument you want to place the interface type tag, or even have different
types for each possible argument position.
If possible, you should decorate numeric variables on the left-hand sides of
function definitions with the appropriate type tags, like int or
double. This often helps the compiler to generate better code and makes
your programs run faster. The | syntax makes it easy to add the necessary
specializations of existing rules to your program. E.g., taking the
polymorphic implementation of the factorial as an example, you only have to
add a left-hand side with the appropriate type tag to make that definition go
as fast as possible for the special case of machine integers:
fact n::int |
fact n = n*fact(n-1) if n>0;
= 1 otherwise;
(This obviously becomes unwieldy if you have to deal with several numeric
arguments of different types, however, so in this case it is usually better to
just use a polymorphic rule.)
Also note that int (the machine integers), bigint (the GMP “big” integers) and
double (floating point numbers) are all different kinds of objects. While they
can be used in mixed operations (such as multiplying an int with a bigint
which produces a bigint, or a bigint with a double which produces a double),
the int tag will only ever match a machine int, not a bigint or a
double. Likewise, bigint only matches bigints (never int or double
values), and double only doubles. Thus, if you want to define a function
operating on different kinds of numbers, you’ll also have to provide equations
for all the types that you need (or a polymorphic rule which catches them
all). This also applies to equations matching against constant values of these
types. In particular, a small integer constant like 0 only matches machine
integers, not bigints; for the latter you’ll have to use the “big L” notation
0L. Similarly, the constant 0.0 only matches doubles, but not ints or
bigints.
Constants differ from variables in that they cannot be redefined (that’s their
main purpose after all) so that their values, once defined, can be substituted
into other definitions which use them. For instance:
> const c = 2;
> foo x = c*x;
> show foo
foo x = 2*x;
> foo 99;
198
While a variable can be rebound to a new value at any time, you will get an
error message if you try to do this with a constant:
> const c = 3;
<stdin>, line 5: symbol 'c' is already defined as a constant
Note that in interactive mode you can work around this by purging the old
definition with the clear command. However, this won’t affect any earlier
uses of the symbol:
> clear c
> const c = 3;
> bar x = c*x;
> show foo bar
bar x = 3*x;
foo x = 2*x;
(You’ll also have to purge any existing definition of a variable if you want
to redefine it as a constant, or vice versa, since Pure won’t let you redefine
an existing constant or variable as a different kind of symbol. The same also
holds if a symbol is currently defined as a function or a macro.)
Constants can also be used in patterns (i.e., on the left-hand side of a rule
in a definition or a case expression), but only if you also declare
the corresponding symbol as nonfix. This is useful, e.g., if you’d
like to use constants such as true and false on the
left-hand side of a definition, just like other nonfix symbols:
> show false true
const false = 0;
const true = 1;
> nonfix false true;
> check false = "no"; check true = "yes";
> show check
check 0 = "no";
check 1 = "yes";
> check (5>0);
"yes"
Note that without the nonfix declaration, the above definition of
check wouldn’t work as intended, because the true and
false symbols on the left-hand side of the two equations would be
interpreted as local variables. Also note that the standard library never
declares any constant symbols as nonfix, since once a symbol is
nonfix there’s no going back. Thus the library leaves this to the
programmer to decide.
As the value of a constant is known at compile time, the compiler can apply
various optimizations to uses of such values. In particular, the Pure compiler
inlines constant scalars (numbers, strings and pointers) by literally
substituting their values into the output code. It also precomputes simple
constant expressions involving only (machine) integer and double values. (The
latter is called constant folding and can also be disabled, see the
description of the --fold and --nofold pragmas for
details.) Example:
> extern double atan(double);
> const pi = 4*atan 1.0;
> show pi
const pi = 3.14159265358979;
> foo x = 2*pi*x;
> show foo
foo x = 6.28318530717959*x;
Constant folding also works with conditional expressions. E.g., consider:
const win = index sysinfo "mingw32" >= 0;
check boy = if win then bad boy else good boy;
On a Linux system, this gives:
> show check
check boy = good boy;
By these means, you can employ a constant to configure your code for different
environments, without any runtime penalties. Note that this only works with
conditional expressions, not with guarded equations. However, in the latter
case the LLVM backend still eliminates dead code automatically, so the
check function from above could also be defined as follows:
check boy = bad boy if win;
= good boy otherwise;
In this case the code for one of the branches of check will be completely
eliminated, depending on the outcome of the configuration check. (The
interpreter will still print both equations if you type show check, but
only one of the branches will actually be present in the assembler code of the
function; you can verify this with show -d check.)
For efficiency, constant aggregates (lists, tuples, matrices and other kinds
of non-scalar terms) receive special treatment. Here, the constant is computed
once and stored in a read-only variable which then gets looked up at runtime,
just like an ordinary global variable. However, there’s an important
difference: If a script is batch-compiled (cf. Batch Compilation), the
constant value is normally computed at compile time only; when running the
compiled executable, the constant value is simply reconstructed, which is
often much more efficient than recomputing its value. For instance, you might
use this to precompute a large table whose computation may be costly or
involve functions with side effects:
const table = [foo x | x = 1..1000000];
process table;
Note that this only works with const values which are completely
determined at compile time. If a constant contains run time objects such as
(non-null) pointers and (local) functions, this is impossible, and the batch
compiler will instead create code to recompute the value of the constant at
run time. For instance, consider:
const p = malloc 100;
foo p;
Here, the value of the pointer p of course critically depends on its
computation (involving a side effect which sets aside a corresponding chunk of
memory). It would become unusable without actually executing the
initialization, so the compiler generates the appropriate run time
initialization code in this case. For all practical purposes, this turns the
constant into a read-only variable. (There’s also a code generation option to
force this behaviour even for “normal” constants for which it’s not strictly
necessary, in order to create smaller executables; see Options Affecting Code
Size for details.)
The interpreter always takes your extern declarations of C routines
at face value. It will not go and read any C header files to determine whether
you actually declared the function correctly! So you have to be careful to
give the proper declarations, otherwise your program might well give a
segfault when calling the function. This problem can to some extent be
alleviated by using the bitcode interface, see Importing LLVM Bitcode and
Inline Code in the C Interface section. However, you always have to be
careful when calling variadic C functions, as the compiler has no way of
checking which combinations of extra parameters a function like printf is
to be invoked with. (As a remedy, the standard library provides safe
implementations of printf and other commonly used variadic functions
from the C library, see the Pure Library Manual for details.)
Another limitation of the C interface is that it does not offer any special
support for C structs and C function parameters. However, an optional addon
module is available which interfaces to the libffi library to provide that
kind of functionality, please see pure-ffi for details.
Last but not least, to make it easier to create Pure interfaces to large C
libraries, there’s a separate pure-gen program available at the Pure website.
This program takes a C header (.h) file and creates a corresponding Pure
module with definitions and extern declarations for the constants
and functions declared in the header. Please refer to pure-gen: Pure interface generator for
details.
Pure does lazy evaluation in the same way as Alice ML, providing an
explicit operation (&) to defer evaluation and create a “future” which
is called by need. However, note that like any language with a basically eager
evaluation strategy, Pure cannot really support lazy evaluation in a fully
automatic way. That is, coding an operation so that it works with infinite
data structures usually requires additional thought, and sometimes special
code will be needed to recognize futures in the input and handle them
accordingly. This can be hard, but of course in the case of the prelude
operations this work has already been done for you, so as long as you stick to
these, you’ll never have to think about these issues. (It should be noted here
that lazy evaluation has its pitfalls even in fully lazy FPLs, such as hidden
memory leaks and other kinds of subtle inefficiencies or non-termination
issues resulting from definitions being too lazy or not lazy enough. You can
read about that in any good textbook on Haskell.)
The prelude goes to great lengths to implement all standard list operations in
a way that properly deals with streams (a.k.a. lazy lists). What this all
boils down to is that all list operations which can reasonably be expected to
operate in a lazy way on streams, will do so. (Exceptions are inherently eager
operations such as #, reverse and foldl.) Only those
portions of an input stream will be traversed which are strictly required to
produce the result. For most purposes, this works just like in fully lazy FPLs
such as Haskell. However, there are some notable differences:
- Since Pure uses dynamic typing, some of the list functions may have to peek
ahead one element in input streams to check their arguments for validity,
meaning that these functions will be slightly more eager than their Haskell
counterparts.
- Pure’s list functions never produce truly cyclic list structures such as the
ones you get, e.g., with Haskell’s cycle operation. (This is actually a
good thing, because the current implementation of the interpreter cannot
garbage-collect cyclic expression data.) Cyclic streams such as cycle
[1] or fix (1:) will of course work as expected, but, depending
on the algorithm, memory usage may increase linearly as they are traversed.
- Pattern matching is always refutable (and therefore eager) in Pure. If you
need something like Haskell’s irrefutable matches, you’ll have to code them
explicitly using futures. See the definition of the unzip function
in the prelude for an example showing how to do this.
There are two other pitfalls with lazy data structures that you should be
aware of:
Laziness and side effects don’t go well together, as most of the time you
can’t be sure when a given thunk will be executed. So as a general guideline
you should avoid side effects in thunked data structures. If you can’t avoid
them, then at least make sure that all accesses to the affected resources
are done through a single instance of the thunked data structure. E.g., the
following definition lets you create a stream of random numbers:
> using math;
> let xs = [random | _ = 1..inf];
This works as expected if only a single stream created with random
exists in your program. However, as the random function in the
math module modifies an internal data structure to produce a
sequence of pseudorandom numbers, using two or more such streams in your
program will in fact modify the same underlying data structure and thus
produce two disjoint subsequences of the same underlying pseudorandom
sequence which might not be distributed uniformly any more.
You should avoid keeping references to potentially big (or even infinite)
thunked data structures when traversing them (unless you specifically need
to memoize the entire data structure). In particular, if you assign such a
data structure to a local variable, the traversal of the data structure
should then be invoked as a tail call. If you fail to do this, it forces the
entire memoized part of the data structure to stay in main memory while it
is being traversed, leading to rather nasty memory leaks. Please see the
all_primes function in Lazy Evaluation and Streams for an example.
As explained in the Macro Hygiene section, Pure macros are lexically scoped
and thus “hygienic”. So in principle Pure macros are not susceptible to name
capture. However, this principle only applies to “real” block constructs, not
their quoted “placeholder” representations described in Built-in Macros and
Special Expressions. One (rather obscure) case which deserves special
attention is the case of macros involving free variables which are being
called inside quoted block constructs. Note that this corresponds to the
“mild” first form of name capture described in the Macro Hygiene
section. For instance, consider the following example:
> def G x = x+y;
> '(G 10 when y = 99 end);
G 10 __when__ [y-->99]
> eval ans;
109
Here the free y variable of the macro G got captured by the quoted
when clause when the quoted expression is evaluated. This happens
because, using call by value, the call G 10 gets evaluated before the
__when__ macro. So the behaviour of the macro evaluator in this case
is in fact correct; the only remedy here is to avoid macros involving free
variables inside a quoted block construct. The same applies to
“quoteargs” macros which quote their
arguments automatically, as described in Built-in Macros and Special
Expressions. On the other hand, the described behaviour might even be useful
at times, to forcibly rebind a free macro variable. The following little
helper macro illustrates this trick:
> #! --quoteargs invoke
> def invoke x = x;
> foo = invoke (G 10 when y = 99 end);
> show foo
foo = 10+y when y = 99 end;
> foo;
109
Besides the above form of real name capture in quoted specials, there’s also a
case of apparent name capture in the expression printer which isn’t actually
real name capture, but just looks like it was. The reason for this is that the
expression printer currently doesn’t check for different bindings of the same
variable identifier when it prints a (compile time) expression. For instance,
consider:
> def F x = x+y when y = x+1 end;
> foo y = F y;
> show foo
foo y = y+y when y = y+1 end;
This looks as if y got captured, but in fact it’s not, it’s just the
show command which displays the definition in an incorrect way. You can
add the -e option to show which prints the deBruijn indices of locally
bound symbols, then you see that the actual bindings are all right anyway:
> show -e foo
foo y/*0:1*/ = y/*1:1*/+y/*0:*/ when y/*0:*/ = y/*0:1*/+1 end;
Note that the number before the colon is the actual deBruijn index, the
sequence of bits behind it is the subterm path. Thus the first instance of
y in y+y (which has a deBruijn index of 1, indicating “one environment
up”) actually refers to the y in the left-hand side foo y, while the
second instance refers to the local binding y = y+1 in the when
clause.
Alas, this means that if you use dump to write such a definition to a text
file and read it back with run later, then the apparent name capture
becomes a real one and you’ll get the wrong definition. This is an outright
bug in the expression printer which will hopefully be fixed some time. But for
the time being you will have to correct such glitches manually.