def main():
print "updating_rule 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)
# prevState = {'a':0.0, 'b':0.0, 'c':1.0}
prevState = {}
prevState["a"] = float(sys.argv[1])
prevState["b"] = float(sys.argv[2])
prevState["c"] = float(sys.argv[3])
print "network state @ previous step", OrderedDict(sorted(prevState.items(), key=lambda t: t[0]))
currState = sigmoid_updating(net, prevState)
print "network state @ current step", OrderedDict(sorted(currState.items(), key=lambda t: t[0]))
def main():
'''
print "time_evol module is the main code."
## to import a network of 3-node example
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)
## 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 = '../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)
bio_initStates = inet.read_init_from_file(BIO_INIT_FILE)
outputFile = 'C:\Users\Kelle Dhein\C.-elegans\FINAL_CODE\data\Fission-net\elegans-net-bioSeq.txt'
bioSeq = biological_sequence(net, nodes_list, bio_initStates, outputFile)
def main(args):
maxStep = 10 # the length of time steps starting from each initail state
Nbr_States = 2 # the number of all possible states of a node
historyLength = 1 # the length of history states
## 1. Network Information from Data File ##
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'
#EDGE_FILE = '../data/budding-net/budding-net-edges.txt'
#NODE_FILE = '../data/budding-net/budding-net-nodes.txt'
## 2. To Build net and nodes_list: Module 'input_net' is required ##
net = inet.read_network_from_file(EDGE_FILE, NODE_FILE)
nodes_list = inet.build_nodes_list(NODE_FILE)
## 5. To generate time series data from 1, 2 and 3: Modules 'time_evol' and 'updating_rule' are required ##
timeSeries_Type = ['one_trajectory'] # 'all_initial', 'primary_attractor', 'one_trajectory'
if 'all_initial' in timeSeries_Type:
## 5-1. time series for all possible initial network states.
Nbr_All_Initial_States = np.power(Nbr_States, len(nodes_list)) # the number of initial states of the network
timeSeriesAll = tev.time_series_all(net, nodes_list, Nbr_All_Initial_States, Nbr_States, maxStep) # To generate time series data over all possible initial states
if 'primary_attractor' in timeSeries_Type:
## 5-2. time series for initial network states that converge to primary (or biological) attractor
decStateTransMap = tev.net_state_transition(net, nodes_list, Nbr_States) # To build transition map between network states over network state space
attractors = tev.find_attractor(decStateTransMap) # To find attractors ==> attractors = {attractor state1: {'type': 'fixed', 'basin-size': 2, 'basin': [network state a, network state b]}, attractor state2: {}, ... }
primary_attractor = attractors.keys()[0] #attractors is ordered with basin size from the function "find_attractor" and hence primary attractor is the first one.
Initial_States_List = list(attractors[primary_attractor]['basin']) # To assign all initial states in the basin of primary attractor to the list of initail states
timeSeriesPa = tev.time_series_pa(net, nodes_list, Initial_States_List, Nbr_States, maxStep) # To generate time series data over all initial states that converge to the primary attractor
if 'one_trajectory' in timeSeries_Type:
## 5-3. time sereis for one initial network state
InitBiState = { '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 } # initial state for fission yeast
#InitBiState = {'Cln3': 1, 'MBF':0, 'SBF': 0, 'Cln1_2':0, 'Cdh1':1, 'Swi5':0, 'Cdc20_Cdc14':0, 'Clb5_6':0, 'Sic1': 1, 'Clb1_2':0, 'Mcm1_SFF':0} # initial state for budding yeast
timeSeriesOne = tev.time_series_one(net, nodes_list, InitBiState, Nbr_States, maxStep) # To generate time series data for one particular initial state
'''
The format for timeSeries
==>
timeSeries = { node1: {1st initial_state: [0,1,1, ......, 0], ...... , Nth initial_state: [1,0,1, ......, 1]},
node 2: {1st initial_state: [1,1,1, ......, 0], ...... , Nth initial_state: [1,0,0, ......, 0]}, .......}
==>
timeSeries[node2][initial_state 1] = [1,1,1, ......, 0]
'''
## 4. Output files ##
#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')
## 6. To measure active information (AI) and transfer entropy (TE): using 'compute_AI' and 'compute_TE' from Module 'info_dyn" ##
## 6-1. For all possible initial network states
if 'all_initial' in timeSeries_Type:
## 6-1-a. To compute AI
result_ai_all = open('C:\Users\Kelle Dhein\C.-elegans\FinalCodes_Hyunju\Results\All_Initial\Ai-all-step%d-trans0-h%d.dat'%(maxStep, historyLength),'w')
AI_all = {}
for n in nodes_list:
AI_all[n] = info.compute_AI(timeSeriesAll[n], historyLength, Nbr_All_Initial_States, Nbr_States)
result_ai_all.write('%s\t%f\n'%(n, AI_all[n]))
print n, AI_all[n]
## 6-1-b. To compute TE
result_te_all = open('C:\Users\Kelle Dhein\C.-elegans\FinalCodes_Hyunju\Results\All_Initial\Te-all-step%d-trans0-h%d.dat'%(maxStep, historyLength),'w')
TE_all = defaultdict(float)
for v in nodes_list:
for n in nodes_list:
TE_all[(v, n)] = info.compute_TE(timeSeriesAll[v], timeSeriesAll[n], historyLength, Nbr_All_Initial_States, Nbr_States)
result_te_all.write('%s\t%s\t%f\n'%(v, n,TE_all[(v, n)] ))
print v, n,TE_all[(v, n)]
## 6-1-c. Scale behavior for AI (optional)
result_file_name = 'C:\Users\Kelle Dhein\C.-elegans\FinalCodes_Hyunju\Results\All_Initial\Ai-all-scale-step%d-trans0-h%d.dat'%(maxStep, historyLength)
viz_file_name = 'C:\Users\Kelle Dhein\C.-elegans\FinalCodes_Hyunju\Results\All_Initial\Ai-all-scale-step%d-trans0-h%d.pdf'%(maxStep, historyLength)
draw_plots.plot_AI_scale(AI_all, result_file_name, viz_file_name) ### plot and result file for AI scale
## 6-1-d. Scale behavior for TE (optional)
result_file_name = 'C:\Users\Kelle Dhein\C.-elegans\FinalCodes_Hyunju\Results\All_Initial\Te-all-scale-step%d-trans0-h%d.dat'%(maxStep, historyLength)
viz_file_name = 'C:\Users\Kelle Dhein\C.-elegans\FinalCodes_Hyunju\Results\All_Initial\Te-all-scale-step%d-trans0-h%d.pdf'%(maxStep, historyLength)
draw_plots.plot_TE_scale(TE_all, result_file_name, viz_file_name) ### plot and result file for TE scale
## 6-2. For initial network states that converge to the primary attractor
if 'primary_attractor' in timeSeries_Type:
## 6-2-a. To compute AI
result_ai_pa = open('C:\Users\Kelle Dhein\C.-elegans\FinalCodes_Hyunju\Results\Primary_Attractor\Ai-pa-step%d-trans0-h%d.dat'%(maxStep, historyLength),'w')
AI_pa = {}
for n in nodes_list:
AI_pa[n] = info.compute_AI(timeSeriesPa[n], historyLength, len(Initial_States_List), Nbr_States)
result_ai_pa.write('%s\t%f\n'%(n, AI_pa[n]))
## 6-2-b. To compute TE
result_te_pa = open('C:\Users\Kelle Dhein\C.-elegans\FinalCodes_Hyunju\Results\Primary_Attractor\Te-pa-step%d-trans0-h%d.dat'%(maxStep, historyLength),'w')
TE_pa = defaultdict(float)
for v in nodes_list:
for n in nodes_list:
TE_pa[(v, n)] = info.compute_TE(timeSeriesPa[v], timeSeriesPa[n], historyLength, len(Initial_States_List), Nbr_States)
result_te_pa.write('%s\t%s\t%f\n'%(v, n,TE_pa[(v, n)] ))
## 6-2-c. Scale behavior for AI (optional)
result_file_name = 'C:\Users\Kelle Dhein\C.-elegans\FinalCodes_Hyunju\Results\Primary_Attractor\Ai-pa-scale-step%d-trans0-h%d.dat'%(maxStep, historyLength)
viz_file_name = 'C:\Users\Kelle Dhein\C.-elegans\FinalCodes_Hyunju\Results\Primary_Attractor\Ai-pa-scale-step%d-trans0-h%d.pdf'%(maxStep, historyLength)
draw_plots.plot_AI_scale(AI_pa, result_file_name, viz_file_name) ### plot and result file for AI scale
## 6-2-d. Scale behavior for TE (optional)
result_file_name = 'C:\Users\Kelle Dhein\C.-elegans\FinalCodes_Hyunju\Results\Primary_Attractor\Te-pa-scale-step%d-trans0-h%d.dat'%(maxStep, historyLength)
viz_file_name = 'C:\Users\Kelle Dhein\C.-elegans\FinalCodes_Hyunju\Results\Primary_Attractor\Te-pa-scale-step%d-trans0-h%d.pdf'%(maxStep, historyLength)
draw_plots.plot_TE_scale(TE_pa, result_file_name, viz_file_name) ### plot and result file for TE scale
## 6-3. For a particular initial network state
if 'one_trajectory' in timeSeries_Type:
## 6-3-a. To compute AI
result_ai_one = open('C:\Users\Kelle Dhein\C.-elegans\FinalCodes_Hyunju\Results\Node_73_to_AltBio\Ai-one-step%d-trans0-h%d.dat'%(maxStep, historyLength),'w')
AI_one = {}
for n in nodes_list:
AI_one[n] = info.compute_AI(timeSeriesOne[n], historyLength, 1, Nbr_States)
result_ai_one.write('%s\t%f\n'%(n, AI_one[n]))
## 6-3-b. To compute TE
result_te_one = open('C:\Users\Kelle Dhein\C.-elegans\FinalCodes_Hyunju\Results\Node_73_to_AltBio\Te-one-step%d-trans0-h%d.dat'%(maxStep, historyLength),'w')
TE_one = defaultdict(float)
for v in nodes_list:
for n in nodes_list:
TE_one[(v, n)] = info.compute_TE(timeSeriesOne[v], timeSeriesOne[n], historyLength, 1, Nbr_States)
result_te_one.write('%s\t%s\t%f\n'%(v, n,TE_one[(v, n)] ))
## 6-3-c. Scale behavior for AI (optional)
result_file_name = 'C:\Users\Kelle Dhein\C.-elegans\FinalCodes_Hyunju\Results\One_Trajectory\Ai-one-scale-step%d-trans0-h%d.dat'%(maxStep, historyLength)
viz_file_name = 'C:\Users\Kelle Dhein\C.-elegans\FinalCodes_Hyunju\Results\One_Trajectory\Ai-one-scale-step%d-trans0-h%d.pdf'%(maxStep, historyLength)
draw_plots.plot_AI_scale(AI_one, result_file_name, viz_file_name) ### plot and result file for AI scale
## 6-3-d. Scale behavior for TE (optional)
result_file_name = 'C:\Users\Kelle Dhein\C.-elegans\FinalCodes_Hyunju\Results\One_Trajectory\Te-one-scale-step%d-trans0-h%d.dat'%(maxStep, historyLength)
viz_file_name = 'C:\Users\Kelle Dhein\C.-elegans\FinalCodes_Hyunju\Results\One_Trajectory\Te-one-scale-step%d-trans0-h%d.pdf'%(maxStep, historyLength)
draw_plots.plot_TE_scale(TE_one, result_file_name, viz_file_name) ### plot and result file for TE scale
