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Fix spurious 3.3+ failure; now tests run clean on 3.3/3.4, too

==================
Predicate Dispatch
==================

Here, we document and test the internals of PEAK-Rules' predicate dispatch
implementation.  Specifically, how it assembles the things we've already seen
in the `AST Building`_, `Code Generation`_, `Criteria`_ and `Indexing`_
documents, into a complete implementation.

.. _AST Building: http://peak.telecommunity.com/DevCenter/PEAK-Rules/AST-Builder
.. _Code Generation: http://peak.telecommunity.com/DevCenter/PEAK-Rules/Code-Generation
.. _Criteria: http://peak.telecommunity.com/DevCenter/PEAK-Rules/Criteria
.. _Indexing: http://peak.telecommunity.com/DevCenter/PEAK-Rules/Indexing

.. contents:: **Table of Contents**


Predicate Expression Types
==========================

Predicate expression types wrap expressions to specify what kind of dispatching
should be done on the base expression.  For example,  ``predicates.IsInstance``
indicates that an expression is to be looked up by what it's an instance of.

There are five built-in expression types::

    >>> from peak.rules.predicates import \
    ...     Truth, Identity, Comparison, IsSubclass, IsInstance

And we will test them using code objects:

    >>> from peak.util.assembler import Code, Const, dump
    >>> from dis import dis

Truth
-----

The ``Truth`` predicate tests whether its subject expression is true or false,
and selects the appropriate sub-node from a ``(true_node, false_node)`` tuple::

    >>> c = Code()
    >>> c(Truth(42))
    >>> dump(c.code())
                    LOAD_FAST                0 ($Arg)
                    UNPACK_SEQUENCE          2
                    LOAD_CONST               1 (42)
                    JUMP_IF_TRUE            L1
                    ROT_THREE
            L1:     POP_TOP
                    ROT_TWO
                    POP_TOP

The generated code unpacks the 2-tuple, and then does a bit of stack
manipulation to select the correct subnode.

The ``disjuncts()`` of a Truth Test is the Test itself::

    >>> from peak.rules.core import disjuncts
    >>> from peak.rules.criteria import Test, Signature, Value

    >>> disjuncts(Test(Truth(88), Value(True)))
    [Test(Truth(88), Value(True, True))]

    >>> disjuncts(Test(Truth(88), Value(True, False)))
    [Test(Truth(88), Value(True, False))]


Identity
--------

The ``Identity`` predicate looks up the ``id()`` of its subject expression in
a dictionary of sub-nodes.  If the id isn't found, the ``None`` entry is used::

    >>> c = Code()
    >>> c(Identity(99))
    >>> dump(c.code())
                    LOAD_CONST               1 (<built-in function id>)
                    LOAD_CONST               2 (99)
                    CALL_FUNCTION            1
                    DUP_TOP
                    LOAD_FAST                0 ($Arg)
                    COMPARE_OP               6 (in)
                    JUMP_IF_FALSE           L1
                    POP_TOP
                    LOAD_FAST                0 ($Arg)
                    ROT_TWO
                    BINARY_SUBSCR
                    JUMP_FORWARD            L2
            L1:     POP_TOP
                    POP_TOP
                    LOAD_FAST                0 ($Arg)
                    LOAD_CONST               0 (None)
                    BINARY_SUBSCR


Comparison
----------

The ``Comparison`` predicate expects its "arg" to be an ``(exact, ranges)``
pair, such as might be generated by the ``peak.rules.indexing.split_ranges``
function::

    >>> c = Code()
    >>> c(Comparison(555))
    >>> dump(c.code())
                    LOAD_CONST               1 (<function value_check at ...>)
                    LOAD_CONST               2 (555)
                    LOAD_FAST                0 ($Arg)
                    CALL_FUNCTION            2

The generated code simply calls a helper function, ``value_check``, with its
expression and argument.  The helper function looks up and returns the
appropriate subnode, first by trying for an exact match, and then looking for
a range match if no exact match is found::

    >>> from peak.rules.predicates import value_check
    >>> from peak.util.extremes import Min, Max

    >>> exact = {'x':1, 'y':2}
    >>> ranges = [((Min,'x'),42), (('x','y'),99), (('y',Max),88)]

    >>> for letter in 'wxyz':
    ...     print(value_check(letter, (exact, ranges)))
    42
    1
    2
    88
    >>> value_check('xx', (exact, ranges))
    99


IsSubclass
----------

The ``IsSubclass`` predicate uses a ``(cache, lookup)`` node pair, where
`cache` is a dictionary from classes to nodes, and `lookup` is a function to
call with the class, in the event that the target class isn't found in the
cache::

    >>> c = Code()
    >>> c(IsSubclass(Const(int)))
    >>> dump(c.code())
                    LOAD_CONST               1 (<... 'int'>)
                    SETUP_EXCEPT            L1
                    DUP_TOP
                    LOAD_FAST                0 ($Arg)
                    UNPACK_SEQUENCE          2
                    ROT_THREE
                    POP_TOP
                    BINARY_SUBSCR
                    ROT_TWO
                    POP_TOP
                    POP_BLOCK
                    JUMP_FORWARD            L3
            L1:     DUP_TOP
                    LOAD_CONST               2 (<...KeyError...>)
                    COMPARE_OP              10 (exception match)
                    JUMP_IF_FALSE           L2
                    POP_TOP
                    POP_TOP
                    POP_TOP
                    POP_TOP...
                    LOAD_FAST                0 ($Arg)
                    UNPACK_SEQUENCE          2
                    POP_TOP
                    ROT_TWO
                    CALL_FUNCTION            1
                    JUMP_FORWARD            L3
            L2:     POP_TOP
                    END_FINALLY


IsInstance
----------

The ``IsInstance`` predicate is virtually identical to ``IsSubclass``, except
that it first obtains the ``__class__`` or ``type()`` of its target::

    >>> c = Code()
    >>> c(IsInstance(Const(999)))
    >>> dump(c.code())
                    LOAD_CONST               1 (999)
                    SETUP_EXCEPT            L1
                    DUP_TOP
                    LOAD_ATTR                0 (__class__)
                    ROT_TWO
                    POP_TOP
                    POP_BLOCK
                    JUMP_FORWARD            L3
            L1:     DUP_TOP
                    LOAD_CONST               2 (<...AttributeError...>)
                    COMPARE_OP              10 (exception match)
                    JUMP_IF_FALSE           L2
                    POP_TOP
                    POP_TOP
                    POP_TOP
                    POP_TOP...
                    LOAD_CONST               3 (<... 'type'>)
                    ROT_TWO
                    CALL_FUNCTION            1
                    JUMP_FORWARD            L3
            L2:     POP_TOP
                    END_FINALLY
            L3:     SETUP_EXCEPT            L4
                    DUP_TOP
                    LOAD_FAST                0 ($Arg)
                    UNPACK_SEQUENCE          2
                    ROT_THREE
                    POP_TOP
                    BINARY_SUBSCR
                    ROT_TWO
                    POP_TOP
                    POP_BLOCK
                    JUMP_FORWARD            L6
            L4:     DUP_TOP
                    LOAD_CONST               4 (<...KeyError...>)
                    COMPARE_OP              10 (exception match)
                    JUMP_IF_FALSE           L5
                    POP_TOP
                    POP_TOP
                    POP_TOP
                    POP_TOP...
                    LOAD_FAST                0 ($Arg)
                    UNPACK_SEQUENCE          2
                    POP_TOP
                    ROT_TWO
                    CALL_FUNCTION            1
                    JUMP_FORWARD            L6
            L5:     POP_TOP
                    END_FINALLY


Defining New Predicate Types
-----------------------------

A predicate type must be a ``peak.util.assembler.nodetype``, capable of
generating its own lookup code.  The code will be used in a ``SMIGenerator``
context (see the `Code Generation`_ manual), so ``SMIGenerator.ARG`` will
contain a lookup node.

Each predicate type must be usable with the ``predicates.predicate_node_for``
function, and the ``predicates.always_testable`` function:

predicate_node_for(builder, expr, cases, remaining_exprs, memo)
    Return a dispatch tree node argument appropriate for the expr.  The return
    value(s) of this function will be in the ``SMIGenerator.ARG`` local
    variable when the predicate type's bytecode is executed.

always_testable(expr)
    Return true if the expression can always be tested, regardless of its
    position among the signature condition(s).  Most predicate types should
    just implement this by calling ``always_testable()`` recursively on their
    target expression, and in fact all of the built-in predicate types do this.
    For more details, see the section on `Order Independence`_ below.


Predicate Parsing
=================

The ``CriteriaBuilder`` class can be used to parse Python expressions into
tests and signatures.  It's initialized using the same arguments as the
``codegen.ExprBuilder`` class::

    >>> from peak.rules.predicates import CriteriaBuilder, Comparison, istype
    >>> from peak.rules.criteria import Disjunction, Value, Test, Range, Class, OrElse
    >>> from peak.util.assembler import Local

    >>> try:
    ...     unicode = unicode
    ... except NameError:
    ...     class unicode: """Python 3 placeholder"""

    >>> builder = CriteriaBuilder(
    ...     dict(x=Local('x'), y=Local('y')), locals(), globals(), __builtins__
    ... )

    >>> pe = builder.parse

    >>> pe('x+42 > 23*2')
    Test(Comparison(Add(Local('x'), Const(42))), Range((46, 1), (Max, 1)))


Iterable constants into or-ed equality tests::

    >>> pe('x in (1,2,3)') == Disjunction([
    ...     Test(Comparison(Local('x')), Value(2, True)),
    ...     Test(Comparison(Local('x')), Value(3, True)),
    ...     Test(Comparison(Local('x')), Value(1, True))
    ... ])
    True

    >>> pe('x not in (1,2,3)') == Test(
    ...     Comparison(Local('x')),
    ...     Disjunction([  
    ...         Range((Min, -1), (1, -1)), Range((1, 1), (2, -1)),
    ...         Range((2, 1), (3, -1)), Range((3, 1), (Max, 1))
    ...     ])
    ... )
    True


And non-iterable constants into plain expressions::

    >>> pe('x in 27')
    Test(Truth(Compare(Local('x'), (('in', Const(27)),))), Value(True, True))

    >>> pe('x not in 27')
    Test(Truth(Compare(Local('x'), (('not in', Const(27)),))), Value(True, True))


The ``is`` operator produces identity tests, if either side is a constant::

    >>> pe('x is 42')
    Test(Identity(Local('x')), IsObject(42, True))

    >>> pe('42 is not x')
    Test(Identity(Local('x')), IsObject(42, False))


And plain expressions when neither side is constant::

    >>> pe('x is y')
    Test(Truth(Compare(Local('x'), (('is', Local('y')),))), Value(True, True))

    >>> pe('x is not y')
    Test(Truth(Compare(Local('x'), (('is not', Local('y')),))), Value(True, True))

    >>> pe('not (x is y)')
    Test(Truth(Compare(Local('x'), (('is', Local('y')),))), Value(True, False))

    >>> pe('not (x is not y)')
    Test(Truth(Compare(Local('x'), (('is not', Local('y')),))), Value(True, False))


Complex logical expressions are always rendered in disjunctive normal form,
with negations simplified away or reduced to match flags on criteria objects::

    >>> pe('isinstance(x, int) and isinstance(y, str)')
    Signature([Test(IsInstance(Local('x')), Class(<... 'int'>, True)),
               Test(IsInstance(Local('y')), Class(<... 'str'>, True))])

    >>> pe('not(not isinstance(x, int) or not isinstance(y, str))')
    Signature([Test(IsInstance(Local('x')), Class(<... 'int'>, True)),
               Test(IsInstance(Local('y')), Class(<... 'str'>, True))])


    >>> pe('isinstance(x,int) and (isinstance(y, str) or isinstance(y, unicode))')
    OrElse([Signature([Test(IsInstance(Local('x')),
                            Class(<... 'int'>, True)),
                       Test(IsInstance(Local('y')),
                            Class(<... 'str'>, True))]),
            Signature([Test(IsInstance(Local('x')),
                            Class(<... 'int'>, True)),
                       Test(IsInstance(Local('y')),
                            Class(<... 'unicode'>, True))])])

    >>> pe('not (isinstance(x, int) or isinstance(y, str))')
    Signature([Test(IsInstance(Local('x')), Class(<... 'int'>, False)),
               Test(IsInstance(Local('y')), Class(<... 'str'>, False))])

    >>> pe('not(not isinstance(x, int) and not isinstance(y, str))') == OrElse([
    ...     Test(IsInstance(Local('x')), Class(int)),
    ...     Test(IsInstance(Local('y')), Class(str))
    ... ])
    True

    >>> pe('not( isinstance(x, int) and isinstance(y, str))')
    OrElse([Test(IsInstance(Local('x')), Class(<... 'int'>, False)),
            Test(IsInstance(Local('y')), Class(<... 'str'>, False))])


And arbitrary expressions are handled as truth tests::

    >>> pe('x')
    Test(Truth(Local('x')), Value(True, True))

    >>> pe('not x')
    Test(Truth(Local('x')), Value(True, False))


Note, by the way, that backquotes are not allowed in predicate expressions, as
they are reserved for use by macros or "meta functions" to create specialized
syntax::

    >>> pe('`x`')
    Traceback (most recent call last):
      ...
    SyntaxError: ...



Pattern Matching
----------------

Arbitrary expressions can be pattern matched for conversion into signatures.
At the moment, the only patterns matched are ``isinstance`` and ``issubclass``
calls where the second argument is a constant, and ``type(x) is y`` expressions
where `y` is a constant::

    >>> from peak.rules.criteria import Test, Signature, Conjunction

    >>> pe('isinstance(x,int)')
    Test(IsInstance(Local('x')), Class(<... 'int'>, True))

    >>> pe('isinstance(x,(str,unicode))') == Disjunction([
    ...     Test(IsInstance(Local('x')), Class(str)),
    ...     Test(IsInstance(Local('x')), Class(unicode))
    ... ])
    True

    >>> pe('type(x) is int')
    Test(IsInstance(Local('x')), istype(<... 'int'>, True))

    >>> pe('str is not type(x)')
    Test(IsInstance(Local('x')), istype(<... 'str'>, False))

    >>> pe('not isinstance(x,(int,(str,unicode)))') == Test(
    ...     IsInstance(Local('x')), Conjunction([
    ...         Class(unicode, False), Class(int, False), Class(str, False)])
    ... )
    True

    >>> pe('isinstance(x,(int,(str,unicode)))') == Disjunction([
    ...     Test(IsInstance(Local('x')), Class(str)),
    ...     Test(IsInstance(Local('x')), Class(int)),
    ...     Test(IsInstance(Local('x')), Class(unicode))
    ... ])
    True

    >>> pe('issubclass(x,int)')
    Test(IsSubclass(Local('x')), Class(<... 'int'>, True))

    >>> pe('issubclass(x,(str,unicode))') == Disjunction([
    ...     Test(IsSubclass(Local('x')), Class(str)),
    ...     Test(IsSubclass(Local('x')), Class(unicode))
    ... ])
    True

    >>> pe('issubclass(x,(int,(str,unicode)))') == Disjunction([
    ...     Test(IsSubclass(Local('x')), Class(str)),
    ...     Test(IsSubclass(Local('x')), Class(int)),
    ...     Test(IsSubclass(Local('x')), Class(unicode))
    ... ])
    True

    >>> pe('not issubclass(x,(int,(str,unicode)))') == Test(
    ...     IsSubclass(Local('x')), Conjunction([
    ...         Class(unicode, False), Class(int, False), Class(str, False)])
    ... )
    True

    >>> pe('issubclass(int, object)')
    True


"Meta Function" Expansion
-------------------------

To create special functions with the ability to manipulate the compile-time
representation of a rule, you can register "meta functions" with the
``meta_function`` decorator.  You begin by defining a stub function which
will be imported and used by the caller in their rules::

    >>> def let(**kw):
    ...     """This is a function that will have special behavior in rules"""
    ...     raise NotImplementedError("`let` can only be used in rules")

Then, you define a "meta function" for this function, that will be called at
compile time.  The signature of this function must match the signature with
which it will be called, except that it can have zero or more extra parameters
at the beginning named ``__builder__``, ``__star__`` and/or ``__dstar__``.
``__builder__``, for example, will be the active ``ExpressionBuilder``::

    >>> def compile_let(__builder__, **kw):
    ...     __builder__.bind(kw)
    ...     return True

(Note: the above is not the actual implementation of the ``peak.rules.let()``
pseudo-function; the actual implementation uses a lower-level interface that
allows the keywords to be seen in definition order, so this is just a demo
to illustrate the operation of the ``meta_function`` decorator.)

To register your "meta function", you use ``@meta_function(stub_function)``::

    >>> from peak.rules.predicates import meta_function
    >>> compile_let = meta_function(let)(compile_let)

Then, when the stub function is used in a rule, the meta function is called
with the PEAK-Rules AST objects resulting from compiling the invocation of
the stub function in the rule::

    >>> builder = CriteriaBuilder(
    ...     dict(x=Local('x'), y=Local('y')), locals(), globals(), __builtins__
    ... )
    >>> pe = builder.parse

    >>> pe('let(q=x*y) and q>42')
    Test(Comparison(Mul(Local('x'), Local('y'))), Range((42, 1), (Max, 1)))

As you can see, our ``compile_let`` meta-function bound ``q`` to
``Mul(Local('x'), Local('y'))``, which was the compiled form of the keyword
argument it received.

Notice, by the way, that our meta-function does NOT accept ``**`` arguments::

    >>> pe('let(**{"z":x*y}) and z>42')
    Traceback (most recent call last):
      ...
    TypeError: <function compile_let at ...> does not support parsing **kw

Or ``*`` arguments:::

    >>> pe('let(*[1,2]) and z>42')
    Traceback (most recent call last):
      ...
    TypeError: <function compile_let at ...> does not support parsing *args

This is because we didn't include ``__star__`` or ``__dstar__`` parameters
at the beginning of the ``compile_let()`` parameter list; if we had, the
function would have received either the compiled AST for the corresponding
part of the call, or ``None`` if no star or double-star arguments were
provided.


Dynamic Arguments
~~~~~~~~~~~~~~~~~

Notice, by the way, that ``__star__`` and ``__dstar__`` refer to the *caller's*
use of ``*`` and ``**`` to make dynamic calls.  The meta function can have
``*`` and ``**`` parameters, but these are passed any *static* positional or
keyword arguments used by the caller.  For example::

    >>> def dummy(*args, **kw):
    ...     """Just a dummy"""

    >>> def compile_dummy(__star__, __dstar__, p1, p2=None, *args, **kw):
    ...     print("p1        = "+str(p1))
    ...     print("p2        = "+str(p2))
    ...     print("args      = "+str(args))
    ...     print("kw        = "+str(sorted(kw.items())))
    ...     print("__star__  = "+str(__star__))
    ...     print("__dstar__ = "+str(__dstar__))
    ...     return True

    >>> compile_dummy = meta_function(dummy)(compile_dummy)
    
    >>> builder = CriteriaBuilder(
    ...     dict(x=Local('x'), y=Local('y')), locals(), globals(), __builtins__
    ... )
    >>> pe = builder.parse

    >>> pe('dummy(x, y, x*x, y*y, k1=x, k2=y, *x+1, **y*2)')
    p1        = Local('x')
    p2        = Local('y')
    args      = (Mul(Local('x'), Local('x')), Mul(Local('y'), Local('y')))
    kw        = [('k1', Local('x')), ('k2', Local('y'))]
    __star__  = Add(Local('x'), Const(1))
    __dstar__ = Mul(Local('y'), Const(2))
    True

    >>> pe('dummy(x)')
    p1        = Local('x')
    p2        = None
    args      = ()
    kw        = []
    __star__  = None
    __dstar__ = None
    True


Argument Errors
~~~~~~~~~~~~~~~

Static argument errors, such as failure to pass the right number of positional
arguments, and duplicate keyword arguments that occur in the source (as opposed
to runtime ``*`` or ``**`` problems), are detected at compile time::

    >>> pe('dummy(x, p1=y)')
    Traceback (most recent call last):
      ...
    TypeError: Duplicate keyword p1 for <... compile_dummy at ...>

    >>> pe('dummy(p2=x, p2=y)')
    Traceback (most recent call last):
      ...
    TypeError: Duplicate keyword p2 for <... compile_dummy at ...>

    >>> pe('dummy()')
    Traceback (most recent call last):
      ...
    TypeError: Missing positional argument p1 for <... compile_dummy at ...>

    >>> pe('let(x)')
    Traceback (most recent call last):
      ...
    TypeError: Too many arguments for <... compile_let at ...>

Also, note that meta functions cannot have packed-tuple arguments::

    >>> meta_function(lambda x,y:None)(lambda x,(y,z): True)
    Traceback (most recent call last):
      ...
    SyntaxError: ...


Custom Argument Compiling
~~~~~~~~~~~~~~~~~~~~~~~~~

On occasion, a meta function may wish to interpret one or more of its arguments
using a custom expression builder in place of the standard one, so that instead
of a PEAK-Rules AST, it gets some other data structure.  You can do this
by passing keyword arguments to ``@meta_function()`` that supply a builder
function for each argument that needs custom building.

A builder function is a 2-argument callable that will be passed the active
``ExpressionBuilder`` instance and the raw Python AST tuples of the argument
it is supposed to parse.  The function must then return whatever value should
be used as the parsed form of the argument supplied to the meta function.

For example::

    >>> def make_builder(text):
    ...     def builder_function(old_builder, arg_node):
    ...         return text
    ...     return builder_function

    >>> def dummy2(*args, **kw):
    ...     """Just another dummy"""

    >>> compile_dummy = meta_function(dummy2,
    ...     p1=make_builder('p1'), p2=make_builder('p2'),
    ...     args=make_builder('args'), kw=make_builder('kw'),
    ...     k2=make_builder('k2'),
    ...     __star__ = make_builder('*'), __dstar__=make_builder('**')
    ... )(compile_dummy)
    
    >>> builder = CriteriaBuilder(
    ...     dict(x=Local('x'), y=Local('y')), locals(), globals(), __builtins__
    ... )
    >>> pe = builder.parse

    >>> pe('dummy2(x, y, x*x, y*y, k1=x, k2=y, *x+1, **y*2)')
    p1        = p1
    p2        = p2
    args      = ('args', 'args')
    kw        = [('k1', 'kw'), ('k2', 'k2')]
    __star__  = *
    __dstar__ = **
    True

As you can see, build functions are selected on the basis of the argument
name they target.  If the meta function has a ``*`` parameter, each of the
overflow positional arguments is parsed with the builder function of the
corresponding name.  If a named keyword argument has a build function, that one
is used, otherwise any build function for the ``**`` parameter is used.


Binding Scope
~~~~~~~~~~~~~

Note that bindings defined by meta-functions (e.g. our ``let`` example) cannot
escape "or" or "not" clauses in an expression::

    >>> pe('let(q=1) or x>q')
    Traceback (most recent call last):
      ...
    NameError: q

    >>> pe('not let(q=1) and x<q')
    Traceback (most recent call last):
      ...
    NameError: q

But they can work within an overall ``not`` clause, as long as there are only
``and`` operators between the function call and the place where the bindings
are used::

    >>> pe('not (let(q=1) and x<q)')
    Test(Comparison(Local('x')), Range((1, -1), (Max, 1)))

Note that whenever an "or" is encountered or a "not" clause is completed, any
previous bindings in effect are restored.  So in this example ``x<q`` becomes
``x<2``, since that's the binding that was in effect before the ``or`` clause::

    >>> pe('let(q=2) and (not let(q=3) or x<q)')
    Test(Comparison(Local('x')), Range((Min, -1), (2, -1)))


Method Argument Binding
~~~~~~~~~~~~~~~~~~~~~~~

Any bindings defined in an expression can be converted into arguments for the
function associated with the rule that defined the bindings, by having its
first positional argument be a named argument tuple (Python 2.x only) or
an ``__args__`` argument that's unpacked to a tuple (Python 2.x and 3.x)::

    >>> from peak.rules import abstract, when, around, let
    >>> def f(x): pass
    >>> f = abstract(f)

    >>> def dummy(__args__, x):
    ...     (q,z) = __args__
    ...     print("Got q = "+str(q,)+" and z = "+str(z))
    ...     print("x was "+str(x))

    >>> when(f, "let(q=x*2, z='whatever') and True")(dummy)
    <function dummy ...>

    >>> f(42)
    Got q = 84 and z = whatever
    x was 42

This even works when you have a ``next_method`` argument after the tuple, and
even if your method is defined inside a closure::

    >>> def closure(y):
    ...     def dummy2(__args__, next_method, x):
    ...         (a,b,c) = __args__
    ...         print("a, b, c = "+str((a,b,c)))
    ...         print("y was "+str(y))
    ...         return next_method(x)
    ...     return dummy2

    >>> around(f, "let(a='a', b=x*3, c=b+23)")(closure(99))
    <function ...dummy2...>

    >>> f(42)
    a, b, c = ('a', 126, 149)
    y was 99
    Got q = 84 and z = whatever
    x was 42

At the moment, this actually works by recalculating the expressions in a
wrapper function that then invokes your original method, so it's more of a DRY
thing than an efficiency thing.  That is, it keeps you from accidentally
getting your rule and your function out of sync, and saves on retyping or
copy-pasting.

(Future versions of PEAK-Rules, however, may improve this so that the bindings
aren't recalculated at every method level, or perhaps aren't recalculated at
all.  It's tricky, though, because depending on the calculations involved, it
might be more efficient to redo them than to do the dynamic stack inspection
that would be needed to locate the active expression cache!  So, in that event,
the main value would be supporting at-most-once execution of expressions with
side-effects.)


Custom Predicate Functions
==========================

The ``@expand_as`` decorator lets you specify a string that will be used in
place of a function, when the function is referenced in a condition.

Here's a trivial example::

    >>> from peak.rules import expand_as, value

    >>> def just(arg): pass

    >>> expand_as("arg")(just)
    <function just ...>
    
    >>> def f(x): pass

    >>> when(f, "x==just(42)")(value(23))
    value(23)

    >>> f(42)
    23

In the above, the ``just(arg)`` function is defined as being the same as its
argument.  So, the ``"x==just(42)`` is treated as though you'd just said
``"x==42"``.

And, although we never defined an actual *implementation* of the ``just()``
function, it actually still works::

    >>> just(42)
    42

This is because if you decorate an empty function with ``@expand_as``, the
supplied condition will be compiled and attached to the existing function
object for you.  (This saves you having to actually write the body.)

Of course, if you decorate, say, an already-existing function that you want
to replace, then nothing happens to that function::

    >>> def isint(ob):
    ...     print("called!")
    ...     return isinstance(ob, int)

    >>> expand_as("isinstance(x, int)")(isint)
    <function isint ...>
    
    >>> isint(42)
    called!
    True

But, the correct expansion still happens when you use that function in a rule::

    >>> around(f, "isint(x)")(value(99))
    value(99)

    >>> f(42)
    99

Note that it's ok to use ``let()`` or other binding-creating expressions inside
an expansion string, and they won't interfere with the surrounding conditions::

    >>> def oddment(a, b): pass

    >>> expand_as("let(x=a*2, y=x+b+1) and y")(oddment)
    <function oddment ...>

    >>> oddment(27, 51)  # prove bindings work even in a function
    106

    >>> around(f, "x==oddment(27, 51) and x==106 and isint(x)")(value('yeah!'))
    value('yeah!')

    >>> f(106)  # prove that x doesn't get redefined after oddment
    'yeah!'

In the above, temporary variables ``x`` and ``y`` are created in the
expansion, but they don't affect the original value of x in the rule where
the function is expanded.

Of course, this also means that you can't implement something like a
pattern-matching feature or the ``let()`` function using ``@expand_as``.
It's just an easier way to handle the sort of common cases where meta-functions
would be overkill.


Expression to Predicate Conversion
==================================

Meta-functions are only one way to transform a Python expression into a
predicate.  It's also possible to register somewhat-arbitrary transformations
by registering methods with the ``expressionSignature()`` generic function.

In this section, we'll create a simple "priority" predicate that doesn't
influence method selection, but affects implication order between predicates.

The basic idea is that we'll create a ``priority`` type that's an integer
subclass, and use it in expressions of the form ``isisntance(foo, Bar) and
priority(3)``, that will then have precedence over an identical expression with
a lower priority::

    >>> from peak.rules import when
    >>> from peak.rules.core import implies
    
    >>> class priority(int):
    ...     """A simple priority"""
    
    >>> when(implies, (priority, priority))(lambda p1,p2: p1>p2)
    <function <lambda> ...>
    
    >>> implies(priority(3), priority(2))
    True
    
    >>> implies(priority(2), priority(3))
    False

To use our new type, we'll need to implement a conversion from a
``Const(somepriority)`` expression, to a ``Test(priority, somepriority)``
condition.

Normally, these conversions are handled by the ``expressionSignature()``
generic function in the ``peak.rules.predicates`` module.

By default, ``expressionSignature()`` simply takes the expression object it's
given, and returns ``Test(Truth(expr), Value(True))`` -- that is, a truth test
on the boolean value of the expression.  Or, if the value given is a constant,
it simply returns an immediate boolean value::

    >>> from peak.rules.predicates import expressionSignature

    >>> expressionSignature(Const(priority(3)))
    True

So, we need to register a method that handles priorities appropriately::

    >>> when(expressionSignature, "isinstance(expr, Const) and isinstance(expr.value, priority)")(
    ...     lambda expr: Test(None, expr.value)
    ... )
    <function <lambda> ...>    
    
Okay, let's try out our new condition::

    >>> from peak.rules import value

    >>> def dummy(arg): return "default"
    >>> when(dummy, "arg==1 and priority(1)")(value("1 @ 1"))
    value('1 @ 1')

    >>> dummy(1)
    '1 @ 1'
    >>> dummy(2)
    'default'

    >>> when(dummy, "arg==1 and priority(2)")(value("1 @ 2"))
    value('1 @ 2')

    >>> dummy(1)
    '1 @ 2'
    >>> dummy(2)
    'default'


    >>> when(dummy, "arg==2 and priority(2)")(value("2 @ 2"))
    value('2 @ 2')

    >>> dummy(1)
    '1 @ 2'
    >>> dummy(2)
    '2 @ 2'

    >>> when(dummy, "arg==2 and priority(1)")(value("2 @ 1"))
    value('2 @ 1')

    >>> dummy(1)
    '1 @ 2'
    >>> dummy(2)
    '2 @ 2'



Upgrading from ``TypeEngine``
=============================

In order to allow a function to safely upgrade from type-only dispatch to full
predicate dispatch, it's necessary for predicate engines to support using type
tuples as signatures (since such tuples may already be registered with the
function's ``RuleSet``).

To support this, the ``tests_for()`` function takes an optional second
parameter, representing the engine that "wants" the tests, and whose argument
names will be used to accomplish the conversion::

    >>> from peak.rules.core import Dispatching, implies
    >>> from peak.rules.criteria import tests_for
    >>> engine = Dispatching(implies).engine

    >>> list(tests_for((int,str), engine))
    [Test(IsInstance(Local('s1')), Class(<... 'int'>, True)),
     Test(IsInstance(Local('s2')), Class(<... 'str'>, True))]

    >>> list(tests_for((istype(tuple),), engine))
    [Test(IsInstance(Local('s1')), istype(<... 'tuple'>, True))]

Each element of the type tuple is converted using a second generic function,
``type_to_test``::

    >>> from peak.rules.predicates import type_to_test

    >>> type_to_test(int, Local('x'), engine)
    Test(IsInstance(Local('x')), Class(<... 'int'>, True))

    >>> type_to_test(istype(str), Local('x'), engine)
    Test(IsInstance(Local('x')), istype(<... 'str'>, True))

    >>> class x: pass
    >>> type_to_test(x, Local('x'), engine)
    Test(IsInstance(Local('x')), Class(<class ...x...>, True))

If you implement a new kind of class test for use in type tuples, you'll need
to add the appropriate method(s) to ``type_to_test`` if you want it to also
work with the predicate engine.

So, let's test the actual upgrade process, and also confirm that you can
still pass in type tuples (or precomputed tests, signatures, etc.) after
upgrading::

    >>> def demo(ob): pass
    >>> tmp = when(demo, (int,))(value('int'))
    >>> tmp = when(demo, (str,))(value('str'))

    >>> demo(42)
    'int'

    >>> demo('test')
    'str'

    >>> tmp = when(demo, "isinstance(ob, int) and ob==42")(value('Ultimate answer'))
    >>> tmp = when(demo, (list,))(value('list'))
    >>> tmp = when(demo, Test(IsInstance(Local('ob')), Class(tuple)))(
    ...     value('tuple')
    ... )

    >>> demo(42)
    'Ultimate answer'

    >>> demo([])
    'list'    

    >>> demo(())
    'tuple'    

    >>> demo('test'), demo(23)
    ('str', 'int')

And, just for the heck of it, let's make sure that you can upgrade to an
IndexedEngine by using any other values on a TypeEngine function::

    >>> def demo(ob): pass
    >>> tmp = when(demo, Test(IsInstance(Local('ob')), Class(tuple)))(
    ...     value('tuple')
    ... )

    >>> demo(())
    'tuple'    



Criterion Ordering
==================

Criterion ordering for a predicate dispatch engine is defined by the ordering
of the tests in its signatures.  Any test expression that is not defined as
``always_testable``, must not be computed until after any test expressions
to its left have been tested.  But tests whose expression is just a local
variable (i.e., a plain function argument), do not have such restrictions::

    >>> from peak.rules.predicates import IndexedEngine
    >>> from peak.rules import abstract, when
    >>> from peak.rules.indexing import Ordering
    >>> from peak.rules.codegen import Add

    >>> def f(a,b): pass
    >>> f = abstract(f)
    >>> m = when(f, "isinstance(a, int) and a+b==42")(value(None))
    >>> engine = Dispatching(f).engine
    >>> list(Ordering(engine, IsInstance(Local('a'))).constraints)
    [frozenset([])]
    >>> list(Ordering(engine, Comparison(Add(Local('a'),Local('b')))).constraints)
    [frozenset([IsInstance(Local('a'))])]


    >>> def f(a,b): pass
    >>> f = abstract(f)
    >>> m = when(f, "isinstance(b, str) and a+b==42 and isinstance(a, int)")(
    ...     value(None)
    ... )
    >>> engine = Dispatching(f).engine
    >>> list(Ordering(engine, IsInstance(Local('a'))).constraints)
    [frozenset([])]
    >>> list(Ordering(engine, IsInstance(Local('b'))).constraints)
    [frozenset([])]
    >>> list(Ordering(engine, Comparison(Add(Local('a'),Local('b')))).constraints)
    [frozenset([IsInstance(Local('b'))])]

    >>> def f(a,b): pass
    >>> f = abstract(f)
    >>> m = when(f, "isinstance(a, int) and isinstance(b, str) and a+b==42")(
    ...     value(None)
    ... )
    >>> engine = Dispatching(f).engine

    >>> list(Ordering(engine, IsInstance(Local('a'))).constraints)
    [frozenset([])]

    >>> list(Ordering(engine, IsInstance(Local('b'))).constraints)
    [frozenset([])]

    >>> try: frozenset and None
    ... except NameError: from peak.rules.core import frozenset
    
    >>> list(
    ...     Ordering(engine, Comparison(Add(Local('a'),Local('b')))).constraints
    ... ) == [frozenset([IsInstance(Local('a')), IsInstance(Local('b'))])]
    True


Order Independence
------------------

The determination of whether a test expression can be used in an order-
independent way, is via the ``always_testable()`` function::

    >>> from peak.rules.predicates import always_testable

In general, only locals and constants can have their tests applied independent
of signature ordering::

    >>> always_testable(Local('x'))
    True

    >>> always_testable(Const(99))
    True

    >>> always_testable(Add(Local('a'),Local('b')))
    False

And predicate test expressions are evaluated according to their tested
expression::

    >>> always_testable(IsInstance(Local('x')))
    True
    >>> always_testable(IsInstance(Add(Local('a'),Local('b'))))
    False

    >>> always_testable(Comparison(Local('x')))
    True
    >>> always_testable(Comparison(Add(Local('a'),Local('b'))))
    False

    >>> always_testable(Identity(Local('x')))
    True
    >>> always_testable(Identity(Add(Local('a'),Local('b'))))
    False

    >>> always_testable(Truth(Local('x')))
    True
    >>> always_testable(Truth(Add(Local('a'),Local('b'))))
    False

Except for ``IsSubclass()``, which may need to have other tests applied before
it::

    >>> always_testable(IsSubclass(Local('x')))
    False

If you create a new predicate type, be sure to define a method for
``always_testable`` that will recursively invoke ``always_testable`` on the
predicate's target expression.  If you don't do this, then your predicate
type will always be treated as order-dependent, even if its target expression
is a local or constant.