This module embeds a fully functional CLIPS engine into Python, and gives the developer a more Python-compliant interface to CLIPS without cutting down on functionalities. In fact CLIPS is compiled into the module in its entirety, and most API functions are bound to Python methods. However the direct bindings to the CLIPS library (implemented as the _clips submodule) are not described here: each function is described by an appropriate documentation string, and accessible by means of the help() function or through the pydoc tool. Each direct binding maps to an API provided function. For a detailed reference for these functions see Clips Reference Guide Vol. II: Advanced Programming Guide, available for download at the CLIPS website.

PyCLIPS is also capable of generating CLIPS text and binary files: this allows the user to interact with sessions of the CLIPS system itself. An important thing to know, is that PyCLIPS implements CLIPS as a separated engine: in the CLIPS module implementation, CLIPS ``lives'' in its own memory space, allocates its own objects. The module only provides a way to send information and commands to this engine and to retrieve results from it.

"A Distributed Agent-Based system with PyClips, RandomForests and, ThespianPy Actors over a shared Tuple-Space." - Charles Kosta, Sc.D.

We are ultimately trying to use NetWorkSpaces (NWS) as a way for Python, R, and Java agents running on different machines to
communicate and coordinate with one another.

To get started with Python NWS, we'll first have to install the NWS server, and the Python NWS client.

pip install nwsserver (https://pypi.org/project/nwsserver/)

pip install Twisted (https://pypi.org/project/Twisted/)

Next, an NWS server must be started. This can be done from a shell
using the nws command, as follows:

% twistd -ny .local/nws.tac

From another window, start an interactive Python session, and import
the NetWorkSpace class:

% python
>>> from nws.client import NetWorkSpace

Next, create a work space called 'cpe':

>>> ws = NetWorkSpace('cpe')

This is using the NWS server on the local machine. Additional arguments
can be used to specify the hostname and port used by the NWS server if
necessary.

Once we have a work space, we can write data into a variable using the
store method:

>>> ws.store('joe', 17)

The variable 'joe' now has a value of 17 in our work space.

To read that variable we use the find method:

>>> age = ws.find('joe')

which sets 'age' to 17.

Note that the find method will block until the variable 'joe' has a
value. That is important when we're trying to read that variable from a
different machine. If it didn't block, you might have to repeatedly try
to read the variable until it succeeded.

There are times when you don't want to block, but just want to see if

some variable has a value. That is done with the findTry method.

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From another window, start an interactive Python session, and import
the NetWorkSpace class:

% python
>>> from nws.client import NetWorkSpace

Next, connect to a work space called 'cpe':

>>> ws = NetWorkSpace('cpe')

This won't create the 'cpe' work space, since it already exists. Now you
can execute a operation such as:

>>> x = ws.fetch('joe')

>>> x

17

you should see that you got the value 17 from the previous w.store() function;

only this time you fetched it permanently. Now try to execute the the fetch again.

>>> x = ws.fetch('joe')

Your program will now be blocked! in this session, watch it block for a minute, and then execute a 'store' in

the other session:

>>> ws.store('joe', 18)

You should see the previous session return from the fetch command and x should now be 18.

That's a basic sample of this technology, which of course can become much more complex. Keep in mind

that these programs could have been on different machines; or hosted on Amazon.

More importantly, an R program and a python program

and even a Java program could be working cooperatively.