def net_state_transition(G, nodes_list):
''':
Arguments:
G [networkx Graph object]
nodes_list [list of strs]
list of nodes in the network
Return:
G_transition_graph [networkx Graph object]
networkx graph directed graph showing how network configurations map to one another in a deterministic system
XXXTHIS TRANSITION GRAPH IS DEPENDENT ON THE STARTING STATE OF G BECAUSE IT ONLY MAPS THE STATE TRANSTITIONS
XXXFROM THAT STARTING STATE. IT SHOULD BE SHOWING THE ATTRACTOR LANDSCAPE REGARDLESS OF YOUR STARTING POSITION.
XXXI THINK IT IS MESSING UP BECAUSE I AM PASSING G THROUGH THE BOOLEAN UPDATING FUNCTION.
4/13/16 4:44pm- FIXED. now this should show the complete transition graph. Still need to check to make sure
that the attractors being output are accessible from the specified initial state though.
'''
n_nodes = len(G.nodes())
n_init_networks = n_states**n_nodes
G_transition_graph = nx.DiGraph()
for prev_network_ID in range(0, n_init_networks):
prev_network_bID = decimal_to_binary(nodes_list, prev_network_ID)
network_bID = ur.boolean_updating(G, prev_state=prev_network_bID)
network_ID = binary_to_decimal(nodes_list, network_bID)
G_transition_graph.add_edge(prev_network_ID, network_ID) #this is a directed graph showing you attractor landscape for how each of the states trasitions to each other
return G_transition_graph
def time_series_all(G, nodes_list, timesteps=20):
''':
Applies boolean updating rule to the network nodes for the specified number of timesteps.
AND
Applies boolean updating rule to the network nodes accross ALL possible initial network states.
Arguments:
G [networkx Graph object]
nodes_list [list of strs]
list of nodes in the network
timesteps [int]
number of timesteps for network to evolve
Return:
timeseriesdata [dict of (dict of list of ints)]
ex. {'gC': {0: [0, 1, 0, 0, 1, 0, 0, 1, 0, 0], 1: [1, 0, 0, 1, 0, 0, 1, 0, 0, 1],...}, 'gF': {...}}
innermost list (ex. [0, 1, 0, 0, 1, 0, 0, 1, 0, 0]) - timeseries of states of the node
innermost key (ex. 0)- network_ID specificing the initial state of all nodes in the network
outermost key (ex. 'gC')- node_ID specifing the node whose states are being listed
'''
n_nodes = len(G.nodes()) # number of nodes in network
n_init_networks = n_states**n_nodes # number of possible initial network states
timeseriesdata = {}
## Create empty time series list for every combination
## of node and initial network state
for node in G.nodes():
timeseriesdata[node] = {}
for network_ID in range(0, n_init_networks):
timeseriesdata[node][network_ID] = []
## Fill time series list with individual node states for
## every combination of node and initial network state
for network_ID in range(0, n_init_networks):
network_bID = decimal_to_binary(nodes_list, network_ID)
for step in range(0, timesteps):
prev_network_bID = network_bID.copy()
for node in nodes_list:
timeseriesdata[node][network_ID].append(prev_network_bID[node])
network_bID = ur.boolean_updating(G, prev_network_bID)
return timeseriesdata
