Reasonable Python is a module which adds F-Logic to Python
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README

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           Reasonable Python - README
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Reasonable Python is a module which adds F-Logic to Python.
This is an initial package and is still pretty unstable.
Any bug repport is very appriciated.

Here some introduction on how to use the package (it's a
copy/paste from a paper I wrote, but some basic information
is included). I'll try to write an easy to understand tutorial
as soon as possible.

To import Reasonable Python use:

   >>> from rp import *

   Most important logic programming concepts to introduce
into Python addressed here are logic variables, facts, rules and
queries. In FLORA-2 logic variables are denoted by any word
starting with an uppercase letter or the underscore (’_’). A
similar way is used in Python, by creating a class of objects
named Variable which behavior is shown in the following
Python interactive shell session.

   >>> X = f.Variable( ’X’ )
   >>> X
   <f.Variable instance at 0xb7dd854c>
   >>> print X
   X
   >>> X == 27
   >>> print X
   X = 27

   When analyzing facts, rules and queries we can conclude
that they are all instances of a construct which has the form:

                         Head : −Body.                      (1)

                            : −Body.                        (2)

                           Head : − .                       (3)

   (1) is a rule where Head is the rule head, and Body is
the rule body (denoted by Head :- Body.). (2) is a query
where Body is the query string (denoted by ?- Body.). (3)
is a fact where Head is the actual fact (denoted by Head.).
We can conclude that we need only one class when introducing
these concepts into Python which behavior is shown in the
following interactive shell session (where Bogus is a logical
class as argued further).

   >>> x = f.Construct()
   >>> print x
   >>> h, b1, b2 = f.Bogus(), f.Bogus(),
   f.Bogus()
   >>> x & h
   >>> x.__class__
   <class f.Fact at 0xb7ae356c>
   >>> del x
   >>> x = f.Construct()
   >>> x << b1 & b2
   >>> x.__class__
   <class f.Query at 0xb7ae365c>
   >>> del x
   >>> x = f.Construct()
   >>> ( x & h ) << b1 | b2
   >>> x.__class__
   <class f.Rule at 0xb7ac5dac>


   One can see that x which is an instance of the Construct
class dynamically changes its class to Fact, Rule or Query
according to its content (e. g. its head and body). The bitwise
shift (’<<’), the bitwise or (’|’) and the bitwise and (’&’) were
overridden in order to allow logic programming constructs,
and stand for ’:-’, ’;’ and ’,’ respectively. This means that
a logic programming construct like:

                          X :− (Y , Z) ; W.                     (4)

   in Python would be written like:

                         X << (Y & Z) | W.                        (5)

   Which is similar to both FLORA-2 and Python syntax. In
addition to these two classes some additional classes were
defined to ease such behavior.


   Another task to be accomplished is the translation of Python
objects into F-molecules in FLORA-2 syntax in order to allow
reasoning about them. Python has a feature which eases this
translation and can be seen in the following interactive shell
session.

   >>> class a:
   ... def __init__ ( self, b1 = 1, b2 = 2 ):
   ...       self.b1 = b1
   ...       self.b2 = b2
   ...
   >>> y = a()
   >>> dir( y )
   [’__doc__’, '__init__’, ’__module__’, ’b1’, ’b2’]
   >>> y. dict
   ’b1’: 1, ’b2’: 2
   >>> y.__class__
   <class main .a at 0xb7a27b9c>

   We can conclude that Python objects allow us to query their
internal structure and content. Using this feature a multiple
recursive algorithm was developed which translates Python
objects into FLORA-2 F-molecules. For example the y object
from the example when translated into FLORA-2 would be:

   >>> tr = py2f.py2f()
   >>> tr.f2obj( y )
   ’py0xb7db4e2cpyobj____main__py__ :
   pyapyclass____main__py__ ["b1"->1, "b2"->2]’

   To have unique object names in our knowledge base we
use the objects internal memory addresses and some additional
information about its module. When an object is translated it is
automatically stored in a ZOBD object base for later retrieval
and permanent storage. Python objects which are not logical
constructs as argued earlier, are considered to be facts.

   In order to use the FLORA-2 engine from Python an
interface had to be developed. FLORA-2 is build upon the
OpenSource XSB Prolog engine which is developed in C
and thus has a C interface [8]. The Simplified Wrapper and
Interface Generator (SWIG) was used to create an interface
between XSB and Python (but could be easily extended to
any other language supported by SWIG i. e. AllegroCL, C# -
Mono, C# - MS .NET, CFFI, CHICKEN, CLISP, Guile, Java,
Lua, MzScheme, Ocaml, Perl, PHP, Ruby, or Tcl/Tk).
   Through such an interface FLORA-2 is then loaded as a
module into XSB. Additional wrapper classes were devel-
oped to ease communication between XSB and Python, and
FLORA-2 and Python, as shown in the following interactive
shell session. This examples assumes that you are inside of 
…/flora2/demos/ where the file flogic_basics.flr can be found

   >>> f = interface.Flora2()
   [FLORA2 specific output]
   >>> f.consult('flogic_basics')
   [FLORA2 specific output]
   >>> f.query("?X:person[kids -> ?Y].", ['X','Y'])
   [{'X': 'mary', 'Y': 'betty'},
    {'X': 'mary', 'Y': 'leo'},
    {'X': 'mary', 'Y': 'tim'}]
   >>> f.close_query()

   We can conclude that such an interface allows for using
XSB and FLORA-2 engines from Python. Compiling and
loading of Prolog and FLORA-2 specific programs is also
supported. Queries can be issued directly whereas return
variables have to be provided in a Python list as the second
argument. The results of queries are Python lists which ele-
ments are Python dictionaries where each dictionary represents
one solution. Keys of each dictionary are the provided return
variables, and the matching values are the returned values from
the knowledge base.


   After describing the particular parts of this integration
it is possible to connect all together in order to facilitate
logic programming in Python. All previously described parts
constitute a Python module. In addition to theses parts a new
class was defined which integrates them. In particular this class
represents an F-Logic base with storing and querying facilities.
   Any object to be stored in to the knowledge base is first
stored in the ZODB in order to be persistent. Afterwards it is
translated into FLORA-2 syntax and loaded into the FLORA-2
engine. Additionally the FLORA-2 code is saved in an external
file so the state can be restored later. This behavior of the
system is transparent to the Python programmer.
   To query the knowledge base one can either use FLORA-2
syntax or create special query objects using logical variables
and logical constructs. In the following interactive shell session
we first create a construct and define a logical class which we
will use for querying the knowledge base.
   >>> x = Construct()
   >>> class query object( Logical ):
   ... def init ( self, a, b ):
   ...       Logical.__init__( self )
   ...       self.a = a
   ...       self.b = b

   Now we can create a query object and modify it to fit our
needs. Note how it is possible to use a logical object to query
for any object of any class by overriding its type and class
type attributes with logical variables. We can also override the
names of attributes by inserting a logical variable in the place
of the attributes name.

   >>> q = query_object( Variable( ’X’ ),
   Variable( ’Y’ ) )
   >>> x << q
   >>> print x
   ?- py0xb7d6cdacpyobj____main__py__ :
   pyquery__objectpyclass__main__py__ [
   "b"->Y, "a"->X].
   >>> q.__type__ = Variable( ’Z’ )
   >>> print x
   ?- Z : pyquery__objectpyclass__main__py__ [
   "b"->Y, "a"->X].
   >>> q.__classtype__ = Variable( ’W’ )
   >>> print x
   ?- Z:W[ "b"->Y, "a"->X].
   >>> q.__dict__[ Variable( ’V’ ) ] = q.__dict__[
   ’a’ ]
   >>> del q.__dict__[ ’a’ ]
   >>> print x
   ?- Z:W[ "b"->Y, V->X].
   >>> del q.__dict__[ ’b’ ]
   >>> print x
   ?- Z:W[ V->X ].

   To query the knowledge base some facts and/or rules have
to be stored in it. A simple data object class which subclasses
the Persistent class is created in the following.
   The subclassing allows all instances of the class to be stored
in the ZODB. Instances to be stored in the knowledge base
are also created in this interactive shell session.
   >>> class data object( Persistent ):
   ... def init ( self, a, b ):
   ...      self.a = a
   ...      self.b = b
   ...
   >>> d1 = data object( 1, 2 )
   >>> d2 = data object( 3, 4 )

   Now it is possible to create the knowledge base which is
just an Python object like any other and insert the data objects.

   >>> fb = FBase( ’my flbase’ )
   [FLORA-2 specific output]
   >>> fb.insert( d1 )
   [FLORA-2 specific output]
   >>> fb.insert( d2 )
   [FLORA-2 specific output]

   By using the previously created query object the following
results are obtained. Note that the W variable returned the
string ’class’ which is due to the impossibility of ZODB to
store class objects (e. g. they are not persistent).

   >>> fb.query( x )
   [{’X’: ’1’, ’Z’: < main .data object
object at 0xb7dde96c>, ’W’: ’class’},
{’X’: ’2’, ’Z’: < main .data object object
at 0xb7dde96c>, ’W’: ’class’}, {’X’:
’3’, ’Z’: < main .data object object at
0xb7c52aac>, ’W’: ’class’}, {’X’: ’4’, ’Z’:
< main .data object object at 0xb7c52aac>,
’W’: ’class’}]

   This simple example shows how transparent the knowledge
base is to a Python programmer. The programmer just has
to create an FBase object to store Python objects in the
knowledgde base.
   To query the knowledge base query objects have to be
created. Using logic objects and logical variables this task is
intuitive and fair easy to accomplish.

   To add rules to the knowledge base one has to use constructs
in order to create them and store them. These rules are similar
to their Prolog and FLORA-2 counterparts with some minor
syntax differences still preserving the flexibility of normal
Python code.