.. _man-metaprogramming:

*****************
 Metaprogramming  
*****************

The strongest legacy of Lisp in the Julia language is its metaprogramming
support. Like Lisp, Julia represents its own code as a data structure of
the language itself.
Since code is represented by objects that can be created and manipulated
from within the language, it is possible for a program to transform and
generate its own code. This allows sophisticated code generation without
extra build steps, and also allows true Lisp-style macros, as compared
to preprocessor "macro" systems, like that of C and C++, that perform
superficial textual manipulation as a separate pass before any real
parsing or interpretation occurs. Another aspect of metaprogramming is
reflection: the ability of a running program to dynamically discover
properties of itself. Reflection emerges naturally from the fact that
all data types and code are represented by normal Julia data structures,
so the structure of the program and its types can be explored
programmatically just like any other data.

Expressions and Eval
--------------------

Julia code is represented as a syntax tree built out of Julia data
structures of type ``Expr``. This makes it easy to construct and
manipulate Julia code from within Julia, without generating or parsing
source text. Here is the definition of the ``Expr`` type::

    type Expr
      head::Symbol
      args::Array{Any,1}
      typ
    end

The ``head`` is a symbol identifying the kind of expression, and
``args`` is an array of subexpressions, which may be symbols referencing
the values of variables at evaluation time, may be nested ``Expr``
objects, or may be actual values of objects. The ``typ`` field is used
by type inference to store type annotations, and can generally be
ignored.

There is special syntax for "quoting" code (analogous to quoting
strings) that makes it easy to create expression objects without
explicitly constructing ``Expr`` objects. There are two forms: a short
form for inline expressions using ``:`` followed by a single expression,
and a long form for blocks of code, enclosed in ``quote ... end``. Here
is an example of the short form used to quote an arithmetic expression:

.. doctest::

    julia> ex = :(a+b*c+1)
    :(a + b * c + 1)

    julia> typeof(ex)
    Expr

    julia> ex.head
    :call

    julia> typeof(ans)
    Symbol

    julia> ex.args
    4-element Array{Any,1}:
      :+
      :a
      :(b * c)
     1

    julia> typeof(ex.args[1])
    Symbol

    julia> typeof(ex.args[2])
    Symbol

    julia> typeof(ex.args[3])
    Expr

    julia> typeof(ex.args[4])
    Int64

Expressions provided by the parser generally only have symbols, other
expressions, and literal values as their args, whereas expressions
constructed by Julia code can easily have arbitrary run-time values
without literal forms as args. In this specific example, ``+`` and ``a``
are symbols, ``*(b,c)`` is a subexpression, and ``1`` is a literal
64-bit signed integer. Here's an example of the longer expression
quoting form:

.. doctest::

    julia> quote
             x = 1
             y = 2
             x + y
           end
    quote  # none, line 2:
        x = 1 # line 3:
        y = 2 # line 4:
        x + y
    end

Symbols
~~~~~~~

When the argument to ``:`` is just a symbol, a ``Symbol`` object results
instead of an ``Expr``:

.. doctest::

    julia> :foo
    :foo

    julia> typeof(ans)
    Symbol

In the context of an expression, symbols are used to indicate access to
variables, and when an expression is evaluated, a symbol evaluates to
the value bound to that symbol in the appropriate :ref:`scope
<man-variables-and-scoping>`.

Sometimes extra parentheses around the argument to ``:`` are needed to avoid
ambiguity in parsing.:

.. doctest::

    julia> :(:)
    :(:)

    julia> :(::)
    :(::)

``Symbol``\ s can also be created using the ``symbol`` function, which takes
a character or string as its argument:

.. doctest::

    julia> symbol('\'')
    :'

    julia> symbol("'")
    :'

``eval`` and Interpolation
~~~~~~~~~~~~~~~~~~~~~~~~~~

Given an expression object, one can cause Julia to evaluate (execute) it
at global scope using the ``eval`` function:

.. doctest::

    julia> :(1 + 2)
    :(1 + 2)

    julia> eval(ans)
    3

    julia> ex = :(a + b)
    :(a + b)

    julia> eval(ex)
    ERROR: a not defined

    julia> a = 1; b = 2;

    julia> eval(ex)
    3

Every :ref:`module <man-modules>` has its own ``eval`` function that
evaluates expressions in its global scope.
Expressions passed to ``eval`` are not limited to returning values
— they can also have side-effects that alter the state of the enclosing
module's environment:

.. doctest::

    julia> ex = :(x = 1)
    :(x = 1)

    julia> x
    ERROR: x not defined

    julia> eval(ex)
    1

    julia> x
    1

Here, the evaluation of an expression object causes a value to be
assigned to the global variable ``x``.

Since expressions are just ``Expr`` objects which can be constructed
programmatically and then evaluated, one can, from within Julia code,
dynamically generate arbitrary code which can then be run using
``eval``. Here is a simple example:

.. doctest::

    julia> a = 1;

    julia> ex = Expr(:call, :+,a,:b)
    :(1 + b)

    julia> a = 0; b = 2;

    julia> eval(ex)
    3

The value of ``a`` is used to construct the expression ``ex`` which
applies the ``+`` function to the value 1 and the variable ``b``. Note
the important distinction between the way ``a`` and ``b`` are used:

-  The value of the *variable* ``a`` at expression construction time is
   used as an immediate value in the expression. Thus, the value of
   ``a`` when the expression is evaluated no longer matters: the value
   in the expression is already ``1``, independent of whatever the value
   of ``a`` might be.
-  On the other hand, the *symbol* ``:b`` is used in the expression
   construction, so the value of the variable ``b`` at that time is
   irrelevant — ``:b`` is just a symbol and the variable ``b`` need not
   even be defined. At expression evaluation time, however, the value of
   the symbol ``:b`` is resolved by looking up the value of the variable
   ``b``.

Constructing ``Expr`` objects like this is powerful, but somewhat
tedious and ugly. Since the Julia parser is already excellent at
producing expression objects, Julia allows "splicing" or interpolation
of expression objects, prefixed with ``$``, into quoted expressions,
written using normal syntax. The above example can be written more
clearly and concisely using interpolation:

.. doctest::

    julia> a = 1;

    julia> ex = :($a + b)
    :(1 + b)

This syntax is automatically rewritten to the form above where we
explicitly called ``Expr``. The use of ``$`` for expression
interpolation is intentionally reminiscent of
:ref:`string interpolation <man-string-interpolation>` and
:ref:`command interpolation <man-command-interpolation>`.
Expression interpolation allows convenient, readable programmatic construction
of complex Julia expressions.

Code Generation
~~~~~~~~~~~~~~~

When a significant amount of repetitive boilerplate code is required, it
is common to generate it programmatically to avoid redundancy. In most
languages, this requires an extra build step, and a separate program to
generate the repetitive code. In Julia, expression interpolation and
eval allow such code generation to take place in the normal course of
program execution. For example, the following code defines a series of
operators on three arguments in terms of their 2-argument forms::

    for op = (:+, :*, :&, :|, :$)
      eval(quote
        ($op)(a,b,c) = ($op)(($op)(a,b),c)
      end)
    end

In this manner, Julia acts as its own preprocessor, and allows code
generation from inside the language. The above code could be written
slightly more tersely using the ``:`` prefix quoting form::

    for op = (:+, :*, :&, :|, :$)
      eval(:(($op)(a,b,c) = ($op)(($op)(a,b),c)))
    end

This sort of in-language code generation, however, using the
``eval(quote(...))`` pattern, is common enough that Julia comes with a
macro to abbreviate this pattern::

    for op = (:+, :*, :&, :|, :$)
      @eval ($op)(a,b,c) = ($op)(($op)(a,b),c)
    end

The ``@eval`` macro rewrites this call to be precisely equivalent to the
above longer versions. For longer blocks of generated code, the
expression argument given to ``@eval`` can be a block::

    @eval begin
      # multiple lines
    end

Interpolating into an unquoted expression is not supported and will
cause a compile-time error:

.. doctest::

    julia> $a + b
    ERROR: unsupported or misplaced expression $

.. _man-macros:

Macros
------

Macros are the analogue of functions for expression generation at
compile time. Just as functions map a tuple of argument values to a 
return value, macros map a tuple of argument *expressions* to a returned
*expression*. They allow the programmer to arbitrarily transform the
written code to a resulting expression, which then takes the place of
the macro call in the final syntax tree. Macros are invoked with the
following general syntax::

    @name expr1 expr2 ...
    @name(expr1, expr2, ...)

Note the distinguishing ``@`` before the macro name and the lack of
commas between the argument expressions in the first form, and the
lack of whitespace after ``@name`` in the second form. The two styles
should not be mixed. For example, the following syntax is different
from the examples above; it passes the tuple ``(expr1, expr2, ...)`` as
one argument to the macro::

    @name (expr1, expr2, ...)

Before the program runs, this statement will be replaced with the
returned result of calling an expander function for ``@name`` on the
expression arguments. Expanders are defined with the ``macro`` keyword::

    macro name(expr1, expr2, ...)
        ...
        return resulting_expr
    end

Here, for example, is a simplified definition of Julia's ``@assert`` macro::

    macro assert(ex)
        return :($ex ? nothing : error("Assertion failed: ", $(string(ex))))
    end

This macro can be used like this:

.. doctest::

    julia> @assert 1==1.0

    julia> @assert 1==0
    ERROR: assertion failed: 1 == 0
     in error at error.jl:21

In place of the written syntax, the macro call is expanded at parse time to
its returned result. This is equivalent to writing::

    1==1.0 ? nothing : error("Assertion failed: ", "1==1.0")
    1==0 ? nothing : error("Assertion failed: ", "1==0")

That is, in the first call, the expression ``:(1==1.0)`` is spliced into
the test condition slot, while the value of ``string(:(1==1.0))`` is
spliced into the assertion message slot. The entire expression, thus
constructed, is placed into the syntax tree where the ``@assert`` macro
call occurs. Then at execution time, if the test expression evaluates to
true, then ``nothing`` is returned, whereas if the test is false, an error 
is raised indicating the asserted expression that was false. Notice that 
it would not be possible to write this as a function, since only the 
*value* of the condition is available and it would be impossible to
display the expression that computed it in the error message.

The actual definition of ``@assert`` in the standard library is more
complicated. It allows the user to optionally specify their own error
message, instead of just printing the failed expression. Just like in
functions with a variable number of arguments, this is specified with an
ellipses following the last argument::

    macro assert(ex, msgs...)
        msg_body = isempty(msgs) ? ex : msgs[1]
        msg = string("assertion failed: ", msg_body)
        return :($ex ? nothing : error($msg))
    end

Now ``@assert`` has two modes of operation, depending upon the number of
arguments it receives! If there's only one argument, the tuple of expressions
captured by ``msgs`` will be empty and it will behave the same as the simpler
definition above. But now if the user specifies a second argument, it is
printed in the message body instead of the failing expression. You can inspect
the result of a macro expansion with the aptly named :func:`macroexpand`
function:

.. doctest::

    julia> macroexpand(:(@assert a==b))
    :(if a == b
            nothing
        else
            Base.error("assertion failed: a == b")
        end)

    julia> macroexpand(:(@assert a==b "a should equal b!"))
    :(if a == b
            nothing
        else
            Base.error("assertion failed: a should equal b!")
        end)

There is yet another case that the actual ``@assert`` macro handles: what
if, in addition to printing "a should equal b," we wanted to print their
values? One might naively try to use string interpolation in the custom
message, e.g., ``@assert a==b "a ($a) should equal b ($b)!"``, but this 
won't work as expected with the above macro. Can you see why? Recall
from :ref:`string interpolation <man-string-interpolation>` that an 
interpolated string is rewritten to a call to the ``string`` function.
Compare:

.. doctest::

    julia> typeof(:("a should equal b"))
    ASCIIString (constructor with 2 methods)

    julia> typeof(:("a ($a) should equal b ($b)!"))
    Expr

    julia> dump(:("a ($a) should equal b ($b)!"))
    Expr
      head: Symbol string
      args: Array(Any,(5,))
        1: ASCIIString "a ("
        2: Symbol a
        3: ASCIIString ") should equal b ("
        4: Symbol b
        5: ASCIIString ")!"
      typ: Any

So now instead of getting a plain string in ``msg_body``, the macro is
receiving a full expression that will need to be evaluated in order to
display as expected. This can be spliced directly into the returned expression
as an argument to the ``string`` call; see `error.jl
<https://github.com/JuliaLang/julia/blob/master/base/error.jl>`_ for
the complete implementation.

The ``@assert`` macro makes great use of splicing into quoted expressions
to simplify the manipulation of expressions inside the macro body.


Hygiene
~~~~~~~

An issue that arises in more complex macros is that of
`hygiene <http://en.wikipedia.org/wiki/Hygienic_macro>`_. In short, macros must
ensure that the variables they introduce in their returned expressions do not
accidentally clash with existing variables in the surrounding code they expand
into. Conversely, the expressions that are passed into a macro as arguments are
often *expected* to evaluate in the context of the surrounding code,
interacting with and modifying the existing variables. Another concern arises
from the fact that a macro may be called in a different module from where it
was defined. In this case we need to ensure that all global variables are
resolved to the correct module. Julia already has a major advantage over
languages with textual macro expansion (like C) in that it only needs to
consider the returned expression. All the other variables (such as ``msg`` in
``@assert`` above) follow the :ref:`normal scoping block behavior
<man-variables-and-scoping>`.

To demonstrate these issues,
let us consider writing a ``@time`` macro that takes an expression as
its argument, records the time, evaluates the expression, records the
time again, prints the difference between the before and after times,
and then has the value of the expression as its final value.
The macro might look like this::

    macro time(ex)
      return quote
        local t0 = time()
        local val = $ex
        local t1 = time()
        println("elapsed time: ", t1-t0, " seconds")
        val
      end
    end

Here, we want ``t0``, ``t1``, and ``val`` to be private temporary variables,
and we want ``time`` to refer to the ``time`` function in the standard library,
not to any ``time`` variable the user might have (the same applies to
``println``). Imagine the problems that could occur if the user expression
``ex`` also contained assignments to a variable called ``t0``, or defined
its own ``time`` variable. We might get errors, or mysteriously incorrect
behavior.

Julia's macro expander solves these problems in the following way. First,
variables within a macro result are classified as either local or global.
A variable is considered local if it is assigned to (and not declared
global), declared local, or used as a function argument name. Otherwise,
it is considered global. Local variables are then renamed to be unique
(using the ``gensym`` function, which generates new symbols), and global
variables are resolved within the macro definition environment. Therefore
both of the above concerns are handled; the macro's locals will not conflict
with any user variables, and ``time`` and ``println`` will refer to the
standard library definitions.

One problem remains however. Consider the following use of this macro::

    module MyModule
    import Base.@time

    time() = ... # compute something

    @time time()
    end

Here the user expression ``ex`` is a call to ``time``, but not the same
``time`` function that the macro uses. It clearly refers to ``MyModule.time``.
Therefore we must arrange for the code in ``ex`` to be resolved in the
macro call environment. This is done by "escaping" the expression with
the ``esc`` function::

    macro time(ex)
        ...
        local val = $(esc(ex))
        ...
    end

An expression wrapped in this manner is left alone by the macro expander
and simply pasted into the output verbatim. Therefore it will be
resolved in the macro call environment.

This escaping mechanism can be used to "violate" hygiene when necessary,
in order to introduce or manipulate user variables. For example, the
following macro sets ``x`` to zero in the call environment::

    macro zerox()
      return esc(:(x = 0))
    end

    function foo()
      x = 1
      @zerox
      x  # is zero
    end

This kind of manipulation of variables should be used judiciously, but
is occasionally quite handy.

.. _man-non-standard-string-literals2:

Non-Standard String Literals
~~~~~~~~~~~~~~~~~~~~~~~~~~~~

Recall from :ref:`Strings <man-non-standard-string-literals>` that
string literals prefixed by an identifier are called non-standard string
literals, and can have different semantics than un-prefixed string
literals. For example:

-  ``r"^\s*(?:#|$)"`` produces a regular expression object rather than a
   string
-  ``b"DATA\xff\u2200"`` is a byte array literal for
   ``[68,65,84,65,255,226,136,128]``.

Perhaps surprisingly, these behaviors are not hard-coded into the Julia
parser or compiler. Instead, they are custom behaviors provided by a
general mechanism that anyone can use: prefixed string literals are
parsed as calls to specially-named macros. For example, the regular
expression macro is just the following::

    macro r_str(p)
      Regex(p)
    end

That's all. This macro says that the literal contents of the string
literal ``r"^\s*(?:#|$)"`` should be passed to the ``@r_str`` macro and
the result of that expansion should be placed in the syntax tree where
the string literal occurs. In other words, the expression
``r"^\s*(?:#|$)"`` is equivalent to placing the following object
directly into the syntax tree::

    Regex("^\\s*(?:#|\$)")

Not only is the string literal form shorter and far more convenient, but
it is also more efficient: since the regular expression is compiled and
the ``Regex`` object is actually created *when the code is compiled*,
the compilation occurs only once, rather than every time the code is
executed. Consider if the regular expression occurs in a loop::

    for line = lines
      m = match(r"^\s*(?:#|$)", line)
      if m == nothing
        # non-comment
      else
        # comment
      end
    end

Since the regular expression ``r"^\s*(?:#|$)"`` is compiled and inserted
into the syntax tree when this code is parsed, the expression is only
compiled once instead of each time the loop is executed. In order to
accomplish this without macros, one would have to write this loop like
this::

    re = Regex("^\\s*(?:#|\$)")
    for line = lines
      m = match(re, line)
      if m == nothing
        # non-comment
      else
        # comment
      end
    end

Moreover, if the compiler could not determine that the regex object was
constant over all loops, certain optimizations might not be possible,
making this version still less efficient than the more convenient
literal form above. Of course, there are still situations where the
non-literal form is more convenient: if one needs to interpolate a
variable into the regular expression, one must take this more verbose
approach; in cases where the regular expression pattern itself is
dynamic, potentially changing upon each loop iteration, a new regular
expression object must be constructed on each iteration. In the vast
majority of use cases, however, regular expressions are not constructed based on run-time data. In this majority of
cases, the ability to write regular expressions as compile-time values
is invaluable.

The mechanism for user-defined string literals is deeply, profoundly
powerful. Not only are Julia's non-standard literals implemented using
it, but also the command literal syntax (```echo "Hello, $person"```)
is implemented with the following innocuous-looking macro::

    macro cmd(str)
      :(cmd_gen($shell_parse(str)))
    end

Of course, a large amount of complexity is hidden in the functions used
in this macro definition, but they are just functions, written
entirely in Julia. You can read their source and see precisely what they
do — and all they do is construct expression objects to be inserted into
your program's syntax tree.

Reflection
----------

In addition to the syntax-level introspection utilized in metaprogramming,
Julia provides several other runtime reflection capabilities.

**Type fields** The names of data type fields (or module members) may be interrogated
using the ``names`` function. For example, given the following type::

	type Point
	  x::FloatingPoint
	  y
	end

``names(Point)`` will return the array ``Any[:x, :y]``. The type of
each field in a ``Point`` is stored in the ``types`` field of the Point object::

	julia> typeof(Point)
	DataType
	julia> Point.types
	(FloatingPoint,Any)

**Subtypes** The *direct* subtypes of any DataType may be listed using
``subtypes(t::DataType)``. For example, the abstract DataType ``FloatingPoint``
has four (concrete) subtypes::
	
	julia> subtypes(FloatingPoint)
	4-element Array{Any,1}:
	 BigFloat
	 Float16
	 Float32
	 Float64

Any abstract subtype will also be included in this list, but further subtypes
thereof will not; recursive applications of ``subtypes`` allow to build the
full type tree.

**Type internals** The internal representation of types is critically important
when interfacing with C code. ``isbits(T::DataType)`` returns true if `T` is
stored with C-compatible aligment. The offsets of each field may be listed
using ``fieldoffsets(T::DataType)``.

**Function methods** The methods of any function may be listed using
``methods(f::Function)``. 

**Function representations** Functions may be introspected at several levels
of representation. The lowered form of a function is available
using ``code_lowered(f::Function, (Args...))``, and the type-inferred lowered form
is available using ``code_typed(f::Function, (Args...))``.

Closer to the machine, the LLVM Intermediate Representation of a function is
printed by ``code_llvm(f::Function, (Args...))``, and finally the resulting
assembly instructions (after JIT'ing step) are available using
``code_native(f::Function, (Args...)``.
