I make use of PyCLIPS to integrate CLIPS into Python. Python methods are registered in CLIPS using clips.RegisterPythonFunction(method, optional-name). Since I have to register several functions and want to keep the code clear, I am looking for a decorator to do the registration.
This is how it is done now:
class CLIPS(object):
...
def __init__(self, data):
self.data = data
clips.RegisterPythonFunction(self.pyprint, "pyprint")
def pyprint(self, value):
print self.data, "".join(map(str, value))
and this is how I would like to do it:
class CLIPS(object):
...
def __init__(self, data):
self.data = data
#clips.RegisterPythonFunction(self.pyprint, "pyprint")
@clips_callable
def pyprint(self, value):
print self.data, "".join(map(str, value))
It keeps the coding of the methods and registering them in one place.
NB: I use this in a multiprocessor set-up in which the CLIPS process runs in a separate process like this:
import clips
import multiprocessing
class CLIPS(object):
def __init__(self, data):
self.environment = clips.Environment()
self.data = data
clips.RegisterPythonFunction(self.pyprint, "pyprint")
self.environment.Load("test.clp")
def Run(self, cycles=None):
self.environment.Reset()
self.environment.Run()
def pyprint(self, value):
print self.data, "".join(map(str, value))
class CLIPSProcess(multiprocessing.Process):
def run(self):
p = multiprocessing.current_process()
self.c = CLIPS("%s %s" % (p.name, p.pid))
self.c.Run()
if __name__ == "__main__":
p = multiprocessing.current_process()
c = CLIPS("%s %s" % (p.name, p.pid))
c.Run()
# Now run CLIPS from another process
cp = CLIPSProcess()
cp.start()