I like the decorator idea. Unfortunately, the version of Python I am using is pre-decorator, and there are various issues involved in upgrading on Mac OS X (due to the built-in Python 2.3 being used by the OS itself.) I'll have to look into how to upgrade without breaking too much...
Some further examples of what I am trying to do. First let me state what my general goal is: There are lots of inference engines out there, from Prolog to Yacas, but most of them rely on a custom interpreter. What I want to find out is if I can build a solver, not by creating a new language on top of Python, but rather by giving solver-like capabilities to a Python programmer. Needless to say, this involves a number of interesting hacks, and part of the motivation for my suggestion(s) is reducing the hack factor. So, at the risk of being visited by Social Services for my abuse of Python Operators, here's a sample of how the sovler works: # Define a function with multiple arities Simplify = Function() # Define some arities. We overload __setitem__ to define an arity. # Param is a class who'se metaclass defines __getattr__ to return a new instance # of Param with the given parameter name. Simplify[ ( add, Param.x, 0 ) ] = lamba x: return Simplify( x ) # x + 0 = x Simplify[ ( mul, Param.x, 1 ) ] = lamba x: return Simplify( x ) # x * 1 = x Simplify[ ( mul, Param.x, 0 ) ] = lamba x: return 0 # x * 0 = 0 Simplify[ Param.x ] = lamba x: return x # Fallback case # Invoke the function. Should print the value of x print Simplify( (add, x, 0) ) Of course, what I really want is not def or lambda, what I really want is to be able to define functions that take suites as arguments. But that would be crazy talk :) Define( "Simplify", args ): code -- http://mail.python.org/mailman/listinfo/python-list
