def main(args):
maxStep = int(raw_input("Type in the length of time steps (maxStep): ")) # the length of time steps starting from each initial state
Nbr_States = 2 # the number of all possible states of a node
historyLength = int(raw_input("Type in the length of history states (h): ")) # the length of history states
## 1. Network Information from Data File ##
random_network = str(raw_input("Type l for local random, g for global random: "))
NODE_FILE = 'net-nodes.dat'
# Asks user if network has been damaged.
damage = raw_input("Type in 1 for DNA damage and 0 for no damage: ")
# Asks user if there has been a node deletion.
node_delete = str(raw_input("Type in node for deletion or none for no deletion: "))
# Asks user if there has been a single edge link deletion.
u = str(raw_input("Type in upstream node of edge for deletion or none for no deletion: "))
if u != 'none':
v = str(raw_input("Type in downstream node of edge for deletion: "))
edge_delete = u,v
else:
edge_delete = u
for i in range(10):
ran_network_filename = i
if random_network == 'g':
EDGE_FILE = 'p53-gr%s.dat'%(ran_network_filename)
elif random_network == 'l':
EDGE_FILE = 'p53-lr%s.dat'%(ran_network_filename)
## 2. To Build net and nodes_list: Module 'input_net' is required ##
net = inet.read_network_from_file(EDGE_FILE, NODE_FILE, node_delete, edge_delete)
nodes_list = inet.build_nodes_list(NODE_FILE, node_delete)
## 5. To generate time series data from 1, 2 and 3: Modules 'time_evol' and 'updating_rule' are required ##
timeSeries_Type = ['all_initial'] # '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, damage, 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 = { 'SK': 1, 'Cdc2_Cdc13': 0, 'Ste9': 1, 'Rum1': 1, 'Slp1': 0, 'Cdc2_Cdc13_active': 0, 'Wee1_Mik1': 1, 'Cdc25': 0, 'PP': 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
if random_network == 'n':
result_ai_all = open('ai-all-step%d-h%d-%s.dat'%(maxStep, historyLength,damage),'w')
elif random_network == 'g':
result_ai_all = open('ai-all-step%d-h%d-gr%s-%s.dat'%(maxStep, historyLength, ran_network_filename,damage),'w')
elif random_network == 'l':
result_ai_all = open('ai-all-step%d-h%d-lr%s-%s.dat'%(maxStep, historyLength, ran_network_filename,damage),'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
if random_network == 'n':
result_te_all = open('te-all-step%d-h%d-%s.dat'%(maxStep, historyLength,damage),'w')
elif random_network == 'g':
result_te_all = open('te-all-step%d-h%d-gr%s-%s.dat'%(maxStep, historyLength, ran_network_filename,damage),'w')
elif random_network == 'l':
result_te_all = open('te-all-step%d-h%d-lr%s-%s.dat'%(maxStep, historyLength, ran_network_filename,damage),'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)]
result_file_name = 'ai-all-scale-step%d-h%d-%sr%s-%s.dat'%(maxStep, historyLength, random_network, ran_network_filename,damage)
viz_file_name = 'ai-all-scale-step%d-h%d-%sr%s-%s.pdf'%(maxStep, historyLength, random_network, ran_network_filename,damage)
draw_plots.plot_AI_scale(AI_all, result_file_name, viz_file_name)
result_file_name = 'te-all-scale-step%d-h%d-%sr%s-%s.dat'%(maxStep, historyLength, random_network, ran_network_filename,damage)
viz_file_name = 'te-all-scale-step%d-h%d-%sr%s-%s.pdf'%(maxStep, historyLength, random_network, ran_network_filename,damage)
draw_plots.plot_TE_scale(TE_all, result_file_name, viz_file_name)
## 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('../results/fission-net/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('../results/fission-net/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 = '../results/fission-net/ai-pa-scale-step%d-trans0-h%d.dat'%(maxStep, historyLength)
viz_file_name = '../viz/fission-net/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 = '../results/fission-net/te-pa-scale-step%d-trans0-h%d.dat'%(maxStep, historyLength)
viz_file_name = '../viz/fission-net/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('../results/fission-net/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('../results/fission-net/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 = '../results/fission-net/ai-one-scale-step%d-trans0-h%d.dat'%(maxStep, historyLength)
viz_file_name = '../viz/fission-net/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 = '../results/fission-net/te-one-scale-step%d-trans0-h%d.dat'%(maxStep, historyLength)
viz_file_name = '../viz/fission-net/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
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
def main(args):
maxStep = 20 # the length of time steps starting from each initail state
Nbr_States = 2 # the number of all possible states of a node
historyLength = 5 # the length of history states
## 1. Network Information from Data File ##
EDGE_FILE = '../data/fission-net/fission-net-edges.txt'
NODE_FILE = '../data/fission-net/fission-net-nodes.txt'
#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 = ['all_initial'] # '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 = { 'SK': 1, 'Cdc2_Cdc13': 0, 'Ste9': 1, 'Rum1': 1, 'Slp1': 0, 'Cdc2_Cdc13_active': 0, 'Wee1_Mik1': 1, 'Cdc25': 0, 'PP': 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('../results/fission-net/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('../results/fission-net/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 = '../results/fission-net/ai-all-scale-step%d-trans0-h%d.dat'%(maxStep, historyLength)
viz_file_name = '../viz/fission-net/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 = '../results/fission-net/te-all-scale-step%d-trans0-h%d.dat'%(maxStep, historyLength)
viz_file_name = '../viz/fission-net/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('../results/fission-net/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('../results/fission-net/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 = '../results/fission-net/ai-pa-scale-step%d-trans0-h%d.dat'%(maxStep, historyLength)
viz_file_name = '../viz/fission-net/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 = '../results/fission-net/te-pa-scale-step%d-trans0-h%d.dat'%(maxStep, historyLength)
viz_file_name = '../viz/fission-net/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('../results/fission-net/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('../results/fission-net/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 = '../results/fission-net/ai-one-scale-step%d-trans0-h%d.dat'%(maxStep, historyLength)
viz_file_name = '../viz/fission-net/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 = '../results/fission-net/te-one-scale-step%d-trans0-h%d.dat'%(maxStep, historyLength)
viz_file_name = '../viz/fission-net/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
## to obtain biological sequence for the Fission Yeast Cell-Cycle Net starting from biological inital state
EDGE_FILE = '../data/fission-net/fission-net-edges.txt'
NODE_FILE = '../data/fission-net/fission-net-nodes.txt'
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_all(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'
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 = '../results/fission-net/ai-all-scale-step%d-trans0-h%d.dat'%(maxStep, historyLength)
viz_file_name = '../viz/fission-net/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 = '../results/fission-net/te-all-scale-step%d-trans0-h%d.dat'%(maxStep, historyLength)
viz_file_name = '../viz/fission-net/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('../results/fission-net/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('../results/fission-net/te-pa-step%d-trans0-h%d.dat'%(maxStep, historyLength),'w')