# This program implements a simple 2-node boolean network, and computes transitions from all possible states of the network (4 of them).
# The Boolean update equations for the 2 nodes (gene_0 and gene_1) are:
#   gene_0 = NOT gene_1
#   gene_1 = gene_0 AND gene_1

# Below is the set of all possible states of 2-gene boolen network
# For your 10-gene network, you have to create this list of all possible states programmatically. DO NOT hard-code them.

possible_states = [(False, False), (False, True), (True, False), (True, True)]

## One way to automatically generate the set of all possible states in as follows:
# possible_states = [(x, y) for x in [False, True] for y in [False, True]]
## Alternatively, you may also use the packages 'itertools': http://docs.python.org/2/library/itertools.html#itertools.product

for (i, j) in possible_states:
    genes = [i, j]
    updated_genes = [False, False] # initially set to some default value, which will then get updated in the loop below

    # Below, I have created my own simple boolean expressions. You have to use the expressions given in the paper
    # Gene0 = NOT Gene1
    # Gene1 = Gene0 AND Gene1
    # state of the network = [value of Gene 0, value of Gene 1]

    updated_genes[0] = not genes[1] # Boolean expression for updating gene 1
    updated_genes[1] = genes[0] and genes[1] # Boolean expression for updating gene 2

    print (genes, '===>', updated_genes)