def main():
print "updating_rule module is the main code."
edge_file = '../data/inputs/edges-init.dat'
#############################
## Read in anterior nodes
#############################
node_file = '../data/inputs/ant-nodes-init.dat'
desired_node_file = '../data/inputs/ant-nodes-final.dat'
G = inet.read_network_from_file(edge_file, node_file)
nodes_list = G.nodes()
node_dict = nx.get_node_attributes(G,'state')
init_dID = time_evol.binary_to_decimal(nodes_list, node_dict)
#############################
## Read in posterior nodes
#############################
post_node_file = '../data/inputs/post-nodes-init.dat'
post_desired_node_file = '../data/inputs/post-nodes-final.dat'
G_post = inet.read_network_from_file(edge_file, post_node_file)
post_nodes_list = G_post.nodes()
post_node_dict = nx.get_node_attributes(G_post,'state')
post_init_dID = time_evol.binary_to_decimal(post_nodes_list, post_node_dict)
#############################
# print G.nodes()
# print G.edges()
# prev_state = nx.get_node_attributes(G,'state') # dict of key=node, value=state of network G
# print "network state @ prev step", OrderedDict(sorted(prev_state.items(), key=lambda t: t[0]))
# curr_state = boolean_updating(G)
# print "network state @ curr step", OrderedDict(sorted(curr_state.items(), key=lambda t: t[0]))
# print prev_state
# print curr_state
###### calc steady state probabilities #####################################################
# init_dID,desired_dID = 0, 43
init_dID,desired_dID = 20, 980
attractor_dict, prob_desired_dID = full_steady_state_calc(G,nodes_list,init_dID,desired_dID)
print attractor_dict
print prob_desired_dID
exit()
#############################################################################################
## Create deterministic transition graph
state_trans_dict = create_state_transitions_dict(G)
G_state_trans = create_graph_from_transitions(state_trans_dict) # stochastic transition graph
## Calculate in and out degrees of transition graph
out_deg_dict = G_state_trans.out_degree(G_state_trans.nodes())
sorted_out_deg_dict = sorted(out_deg_dict.items(), key=operator.itemgetter(1))
in_deg_dict = G_state_trans.in_degree(G_state_trans.nodes())
sorted_in_deg_dict = sorted(in_deg_dict.items(), key=operator.itemgetter(1))
largest = max(nx.strongly_connected_components(G_state_trans), key=len)
# print len(largest) # this shows that graph of stochastic state transitions is strongly connected
# attractors = time_evol.find_attractor_old(G_state_trans)
create_transition_matrix(state_trans_dict)
deterministic_transition_graph = time_evol.net_state_transition(G, nodes_list) # deterministic transition graph
attractors = time_evol.find_attractor_old(deterministic_transition_graph) # this must only print out cycles for a subset of the graph...
#############################
## Find control kernals
#############################
attractor_ID = 980
# print find_len_1_control_kernals(deterministic_transition_graph, nodes_list, attractor_ID)
# print find_len_2_control_kernals(deterministic_transition_graph, nodes_list, attractor_ID)
print find_len_2_control_kernals_fo_real(deterministic_transition_graph, nodes_list, attractor_ID)
exit()
#############################
## Desired node states
## Anterior
G_desired = inet.read_network_from_file(edge_file, desired_node_file)
desired_nodes_list = G_desired.nodes()
desired_node_dict = nx.get_node_attributes(G_desired,'state')
desired_dID = time_evol.binary_to_decimal(desired_nodes_list,desired_node_dict)
print "desired_node_dict:", desired_node_dict
print "desired dID:", desired_dID
## Posterior
G_desired_post = inet.read_network_from_file(edge_file, post_desired_node_file)
post_desired_nodes_list = G_desired_post.nodes()
post_desired_node_dict = nx.get_node_attributes(G_desired_post,'state')
post_desired_dID = time_evol.binary_to_decimal(post_desired_nodes_list,post_desired_node_dict)
print "post_desired_node_dict:", post_desired_node_dict
print "desired dID:", post_desired_dID
## Check if path between desired init state and final state
print nx.has_path(deterministic_transition_graph,init_dID,desired_dID)
## Print binary version of attractors
print "attractors:"
print attractors
## Network 980 is the steady state network, let's see if this is correct based on rules
# steady_state_network =
print "end_ant:", desired_node_dict
print "beg_ant:", node_dict
print "end_ant_dID:", desired_dID
print "beg_ant_dID:", init_dID
print "end_post:", post_desired_node_dict
print "beg_post:", post_node_dict
print "end_post_dID:", post_desired_dID
print "beg_post_dID:", post_init_dID
# print attractors
# print G_state_trans.edges()
# for line in nx.generate_edgelist(G_state_trans, data=False):
# print line
# nx.draw(G_state_trans)
# nx.write_graphml(G_state_trans,'g_state_trans.xml')
# plt.show()
# for graph in list(nx.weakly_connected_component_subgraphs(deterministic_transition_graph)):
# if 43 in graph.nodes():
# print [time_evol.decimal_to_binary(nodes_list, dID) for dID in graph.nodes()]
# print " 0:", time_evol.decimal_to_binary(nodes_list, 0)
# print "683:", time_evol.decimal_to_binary(nodes_list, 683)
# nx.draw(deterministic_transition_graph)
# nx.write_graphml(deterministic_transition_graph,'deterministic_transition_graph.xml')
# plt.show()
exit()
def create_state_transitions_dict(G):
"""
output a dictionary of key=network_ID, value=list of possible next network_IDs
each of the next possible network_IDs is exactly 1 value different than the previous network ID
this is working correctly- trust past harrison 4/13/16
"""
state_trans_dict = {} # key=network_ID, value=list of next network IDs
n_nodes = len(G.nodes())
n_init_networks = time_evol.n_states**n_nodes
for prev_network_ID in range(0, n_init_networks):
nodes_list = G.nodes()
prev_network_bID = time_evol.decimal_to_binary(nodes_list, prev_network_ID) #same as 'prev_state' within boolean_updating
curr_network_bID = boolean_updating(G, prev_state=prev_network_bID)
# get list of nodes that change when applying update rule
altered_nodes = []
for node in curr_network_bID:
if prev_network_bID[node] != curr_network_bID[node]:
altered_nodes.append(node)
# print prev_network_ID, altered_nodes
# look through altered nodes, and make list of all networks with nodes being altered 1 at a time
state_trans_dict[prev_network_ID] = []
for node in altered_nodes:
possible_bID = copy.deepcopy(prev_network_bID)
# print possible_bID #why is this not changing???
not_flipped = True
# print possible_bID[node]
# if node was updated, flip it
if prev_network_bID[node] == 0:
possible_bID[node] = 1
not_flipped = False
# print "0, flipping"
# print possible_bID[node]
# if node was updated, flip it
elif (prev_network_bID[node] == 1) and (not_flipped == True):
possible_bID[node] = 0
# print "1, flipping"
# print possible_bID[node]
else:
raise ValueError("Node %s of prev_state must be 0 or 1. Something bad happened."%node)
# print possible_bID
# nodes_list = [k for k in possible_bID]
network_ID = time_evol.binary_to_decimal(nodes_list,possible_bID) #get network_ID of this possible next state
state_trans_dict[prev_network_ID].append(network_ID)
# if no nodes are altered, you're in a fixed stable state-- make state_trans_dict[stable_ID]=stable_ID
if len(altered_nodes) == 0:
state_trans_dict[prev_network_ID].append(prev_network_ID)
# print 'prev network ID:',prev_network_ID
return state_trans_dict
