biStates = {}

x = len(nodes_list) -1

for u in nodes_list:

biStates[u] = decState / np.power(Nbr_States, x)

decState = decState % np.power(Nbr_States, x)

x = x - 1

decState = 0

x = len(nodes_list) -1

for u in nodes_list:

decState = decState + biStates[u] * np.power(Nbr_States, x)

x = x - 1

'''

Description:

-- compute TE for every pair of nodes using distribution from all possible initial conditions or an arbitrary set of initial conditions

Arguments:

-- 1. net

-- 2. nodes_list

-- 3. Initial_States_List

-- 4. Nbr_States

-- 5. MAX_TimeStep

Return:

-- 1. timeSeriesData

'''

#Nbr_Nodes = len(net.nodes())

#Nbr_All_Initial_States = np.power(Nbr_States, Nbr_Nodes)

timeSeriesData = {}

for n in net.nodes():

timeSeriesData[n] = {}

for initState in range(Nbr_Initial_States):

timeSeriesData[n][initState] = []

for initDecState in range(Nbr_Initial_States):

currBiState = decimal_to_binary(nodes_list, initDecState, Nbr_States)

for step in range(MAX_TimeStep):

prevBiState = currBiState.copy()

for n in nodes_list:

timeSeriesData[n][initDecState].append(prevBiState[n])

currBiState = ur.sigmoid_updating(net, prevBiState)

'''

Arguments:

1. net

2. Nbr_States

Return:

1. decStateTransMap

'''

Nbr_Nodes = len(net.nodes())

Nbr_All_Initial_States = np.power(Nbr_States, Nbr_Nodes)

decStateTransMap = nx.DiGraph()

for prevDecState in range(Nbr_All_Initial_States):

prevBiState = decimal_to_binary(nodes_list, prevDecState, Nbr_States)

currBiState = ur.sigmoid_updating(net, prevBiState)

currDecState = binary_to_decimal(nodes_list, currBiState, Nbr_States)

decStateTransMap.add_edge(prevDecState, currDecState)

'''

Arguments:

1. decStateTransMap

Return:

1. attractor

'''

attractor_list = nx.simple_cycles(decStateTransMap) #in case of deterministic system, any cycle without considering edge direction will be directed cycle.

attractors = {}

attractors['fixed'] = []

attractors['cycle'] = []

for u in attractor_list:

if len(u) == 1:

attractors['fixed'].append(u)

else:

attractors['cycle'].append(u)

'''

Arguments:

-- 1. decStateTransMap

Return:

-- attractor

'''

attractor_list = nx.simple_cycles(decStateTransMap) #in case of deterministic system, any cycle without considering edge direction will be directed cycle.

attractors = {}

#attractors['fixed'] = []

#attractors['cycle'] = []

undirectedMap = nx.DiGraph.to_undirected(decStateTransMap)

for u in attractor_list:

attractors[u[0]] = {}

if len(u) == 1:

attractors[u[0]]['type'] = 'fixed'

else:

attractors[u[0]]['type'] = 'cycle'

for v in attractors.iterkeys():

basin = nx.node_connected_component(undirectedMap, v)

attractors[v]['basin'] = basin

attractors[v]['basin-size'] = len(basin)

sorted_attractors = OrderedDict(sorted(attractors.items(), key=lambda kv: kv[1]['basin-size'], reverse=True))

'''

Description:

-- compute TE for every pair of nodes using distribution from all initial conditions that converge to the primary or biological attractor

Arguments:

-- 1. net

-- 2. nodes_list

-- 3. Initial_States_List

-- 4. Nbr_States

-- 5. MAX_TimeStep

Return:

-- 1. timeSeriesData (only for primary attractor)

'''

timeSeriesData = {}

for n in net.nodes():

timeSeriesData[n] = {}

for initState in range(len(Initial_States_List)):

timeSeriesData[n][initState] = []

for initState in range(len(Initial_States_List)):

initDecState = Initial_States_List[initState]

currBiState = decimal_to_binary(nodes_list, initDecState, Nbr_States)

for step in range(MAX_TimeStep):

prevBiState = currBiState.copy()

for n in nodes_list:

timeSeriesData[n][initState].append(prevBiState[n])

currBiState = ur.sigmoid_updating(net, prevBiState)

'''

Description:

-- compute TE for every pair of nodes using distribution from all initial conditions that converge to the primary or biological attractor

Arguments:

-- 1. net

-- 2. nodes_list

-- 3. Initial_States_List

-- 4. Nbr_States

-- 5. MAX_TimeStep

Return:

-- 1. timeSeriesData (only for primary attractor)

'''

timeSeriesData = {}

for n in net.nodes():

timeSeriesData[n] = {}

timeSeriesData[n][0] = []

currBiState = Initial_State

for step in range(MAX_TimeStep):

prevBiState = currBiState.copy()

for n in nodes_list:

timeSeriesData[n][0].append(prevBiState[n])

currBiState = ur.sigmoid_updating(net, prevBiState)

print "time_evol module is the main code."

## to import a network of 3-node example

EDGE_FILE = 'C:\Boolean_Delay_in_Economics\Manny\EDGE_FILE.dat'

NODE_FILE = 'C:\Boolean_Delay_in_Economics\Manny\NODE_FILE.dat'

net = inet.read_network_from_file(EDGE_FILE, NODE_FILE)

nodes_list = inet.build_nodes_list(NODE_FILE)

'''

## to obtain time series data for all possible initial conditions for 3-node example network

timeSeriesData = ensemble_time_series(net, nodes_list, 2, 10)#, Nbr_States=2, MAX_TimeStep=20)

initState = 1

biStates = decimal_to_binary(nodes_list, initState)

print 'initial state', biStates

## to print time series data for each node: a, b, c starting particualr decimal inital condition 1

print 'a', timeSeriesData['a'][1]

print 'b', timeSeriesData['b'][1]

print 'c', timeSeriesData['c'][1]

'''

## to obtain and visulaize transition map in the network state space

decStateTransMap = net_state_transition(net, nodes_list)

nx.write_graphml(decStateTransMap,'C:\Boolean_Delay_in_Economics\Manny\Results\BDE.graphml')

'''

nx.draw(decStateTransMap)

plt.show()

## to find fixed point attractors and limited cycle attractors with given transition map.

attractors = find_attractor(decStateTransMap)

print attractors