def main(): print "time_evol 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 = inet.read_network_from_file(EDGE_FILE, NODE_FILE) nodes_list = inet.build_nodes_list(NODE_FILE) timeSeriesData = ensemble_time_series(net, nodes_list, 2, 8)#, Nbr_States=2, MAX_TimeStep=20) # initState = 1 # biStates = decimal_to_binary(nodes_list, initState) biStates = {'cdk-2/cyclinE':1, 'cki-1':1, 'cdc-14/fzy-1':1, 'fzr-1':1, 'cdk-1/cyclinB':0, 'lin-35/efl-1/dpl-1':1, 'cul-1/lin-23':0, 'cdc-25.1':0} dec_init = binary_to_decimal(nodes_list, biStates, Nbr_States=2) print 'initial state', biStates print 'cdk-2/cyclinE', timeSeriesData['cdk-2/cyclinE'][dec_init] print 'cki-1', timeSeriesData['cki-1'][dec_init] print 'cdc-14/fzy-1', timeSeriesData['cdc-14/fzy-1'][dec_init] print 'fzr-1', timeSeriesData['fzr-1'][dec_init] print 'cdk-1/cyclinB', timeSeriesData['cdk-1/cyclinB'][dec_init] print 'lin-35/efl-1/dpl-1', timeSeriesData['lin-35/efl-1/dpl-1'][dec_init] print 'cul-1/lin-23', timeSeriesData['cul-1/lin-23'][dec_init] print 'cdc-25.1', timeSeriesData['cdc-25.1'][dec_init] decStateTransMap = net_state_transition(net, nodes_list) # nx.write_graphml(decStateTransMap, '/Users/Kelle Dhein/C.-elegans/ElegansGraph.graphml') plt.show() attractors = find_attractor(decStateTransMap) print attractors
def main(args): ## 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 = '../data/fission-net/fission-net-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 = 5 result_ai = open('../results/fission-net/ai-step%d-trans0-h%d.dat'%(maxStep, historyLength),'w') result_te = open('../results/fission-net/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'