def main(): print "info_dyn_eleg module is the main code." EDGE_FILE = 'C:\Users\Kelle Dhein\C.-elegans\example\SES591_SampleCode\data\elegans\elegans-net-edges-new-names.dat' NODE_FILE = 'C:\Users\Kelle Dhein\C.-elegans\example\SES591_SampleCode\data\elegans\elegans-net-nodes-new-names.dat' net = read_network_from_file(EDGE_FILE, NODE_FILE) nodes_list = inet.build_nodes_list(NODE_FILE)
def main(): ''' print "time_evol module is the main code." ## to import a network of 3-node example EDGE_FILE = '../data/example/example-net-edges.dat' NODE_FILE = '../data/example/example-net-nodes.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.draw(decStateTransMap) plt.show() ## to find fixed point attractors and limited cycle attractors with given transition map. attractors = find_attractor(decStateTransMap) print attractors ''' ## to obtain biological sequence for the Fission Yeast Cell-Cycle Net starting from biological inital state EDGE_FILE = 'C:\Users\Kelle Dhein\C.-elegans\example\SES591_SampleCode\data\elegans\elegans-net-edges-new-names.dat' NODE_FILE = 'C:\Users\Kelle Dhein\C.-elegans\example\SES591_SampleCode\data\elegans\elegans-net-nodes-new-names.dat' BIO_INIT_FILE = 'C:\Users\Kelle Dhein\C.-elegans\example\SES591_SampleCode\data\elegans\elegans-bioSeq-initial.txt' net = inet.read_network_from_file(EDGE_FILE, NODE_FILE) nodes_list = inet.build_nodes_list(NODE_FILE) bio_initStates = inet.read_init_from_file(BIO_INIT_FILE) outputFile = 'C:\Users\Kelle Dhein\C.-elegans\example\SES591_SampleCode\data\elegans\elegans-results-bioSeq.txt' bioSeq = biological_sequence(net, nodes_list, bio_initStates, outputFile) ## to obtain and visulaize transition map in the network state space decStateTransMap = net_state_transition(net, nodes_list) nx.draw(decStateTransMap) plt.show() ## to find fixed point attractors and limited cycle attractors with given transition map. attractors = find_attractor(decStateTransMap) print attractors ## to save network graph as graph.ml file nx.write_graphml(decStateTransMap, '/Users/Kelle Dhein/C.-elegans/ControlKernalElegansGraph.graphml')
def main(args): ## to obtain biological sequence for the C. Elegans Early Embryonic Cell Cycle Net starting from biological inital state EDGE_FILE = 'C:\Users\Kelle Dhein\C.-elegans\example\SES591_SampleCode\data\elegans\elegans-net-edges-new-names.dat' NODE_FILE = 'C:\Users\Kelle Dhein\C.-elegans\example\SES591_SampleCode\data\elegans\elegans-net-nodes-new-names.dat' BIO_INIT_FILE = 'C:\Users\Kelle Dhein\C.-elegans\example\SES591_SampleCode\data\elegans\elegans-bioSeq-initial.txt' net = inet.read_network_from_file(EDGE_FILE, NODE_FILE) nodes_list = inet.build_nodes_list(NODE_FILE) #input_file_name1 = 'time-series/%s-step%d-trans0.dat'%(network_index, maxStep) #input_file1 = open( input_file_name1, 'r') Nbr_Initial_States = np.power(2,len(nodes_list)) maxStep = 20 Nbr_States = 2 historyLength = 2 #Specify data path where to write results result_ai = open('C:\Users\Kelle Dhein\C.-elegans\example\SES591_SampleCode\data\elegans\AI-step%d-trans0-h%d.dat'%(maxStep, historyLength),'w') result_te = open('C:\Users\Kelle Dhein\C.-elegans\example\SES591_SampleCode\data\elegans\TE-step%d-trans0-h%d.dat'%(maxStep, historyLength),'w') timeSeries = tev.time_series(net, nodes_list, Nbr_Initial_States, Nbr_States, MAX_TimeStep=20) print 'AI' AI = {} for n in nodes_list: AI[n] = info.compute_AI(timeSeries[n], historyLength, Nbr_Initial_States, Nbr_States) result_ai.write('%s\t%f\n'%(n, AI[n])) print n, AI[n] print 'done AI' print 'TE' TE = defaultdict(float) for v in nodes_list: for n in nodes_list: TE[(v, n)] = info.compute_TE(timeSeries[v], timeSeries[n], historyLength, Nbr_Initial_States, Nbr_States) result_te.write('%s\t%s\t%f\n'%(v, n,TE[(v, n)] )) print v, n, TE[(v, n)] print 'done TE'