LogicCircuit

A Python module to simulate logical circuits/graphs.

Inspired by the computational graph behind TensorFlow. Granted, it might be overkill but it is very powerful.

The module builds graphs as variables are created. Then, the graphs can be evaluated to obtain an output.

Basic Usage

Start by cloning or downloading the project, then import it into Python. Only Python 2.7 has been tested at the moment.

import logiccircuit as lc

Then, build your circuit. In this example, C = A and B

A = lc.Variable()
B = lc.Variable()
C = lc.And(A, B)

Currently the following functional nodes are availiable:

Name	# of Arguments
ConstTrue	0
ConstFalse	0
Variable	0
And	2 or more
Or	2 or more
Not	1
Nand	2 or more
Nor	2 or more
Xor	2 or more
Xnor	2 or more

To evaluate a circuit for a node with certain inputs to the variables, use partial_eval.

lc.partial_eval(C, input_dict={A: True, B: True})

# Output:
# True

Pass a list of nodes instead of a single node to evaluate multiple nodes at once.

Advanced Usage

You can define your own functional blocks from the existing selection of functional nodes. These blocks can then be used like any other functional node. For example, you can define a half adder like this:

def HalfAdder(A, B):
	S = lc.Xor(A, B)
	C = lc.And(A, B)
	return S, C

If you want to separate your circuits into completely different graphs, you can explicity create a graph and use it when adding nodes. Then evaluate with respect to a graph. For example:

circuit1 = lc.Graph()
circuit2 = lc.Graph()

with circuit1:
	A = lc.Variable()
	B = lc.Variable()
	C = lc.And(A, B)

circuit1.partial_eval(C, input_dict={A: True, B: False})

# Output:
# False

with circuit2:
	X = lc.Variable()
	Y = lc.Variable()
	Z = lc.Or(X, Y)

circuit2.partial_eval(Z, input_dict={X: True, Y: False})

# Output:
# True

To-Do

• Implement efficient medium-scale integration circuits
• Automate the process of evaluating every possible input
• Add memoization for more efficient computation
• Ensure compatibility with Python 3.x