def number_of_lp_lattice():
for D in [2]:
myfile = open(str(D)+'number_lp_lattice', 'w')
myfile.write('N_side' + '\t' + 'N'+ '\t'+ 'lp length' + '\t'+ 'no. lp'+ '\n')
start_time = time.time()
for N_side in range(10,16,1):
N = N_side**2
# model = models.COd(2, N)
model = models.square_lattice_model(D, N_side)
DAG = model[0]
extremes = model[1]
tr_DAG = tr.trans_red(DAG)
lp = dl.lpd(tr_DAG, extremes[1], extremes[0])
length_lp = lp[2]
j=0
paths_list = list(nx.all_simple_paths(tr_DAG, extremes[1], extremes[0], cutoff=length_lp+1))
for i in range(len(paths_list)):
if len(paths_list[i])==length_lp+1:
j+=1
myfile.write(str(N_side) + '\t' + str(N) +'\t' + str(length_lp)+ '\t' + str(j) + '\n')
print 'done', N_side
elapsed = time.time() - start_time
print 'finished.',D,'Dimension. Time elapsed = ',elapsed
return
def degree_distribution(trans_red = 'False', N=100, m=10, number_of_trials = 1, filename = 'degree_histogram'):
'''
Outputs node label (=integer time value) and in-degree before and after Transitive reduction.
n, int = final number of nodes, as usual for BA model.
m, int = number of edges to add with addition of each new node.
trials, int = number of realisations of model to be averaged over.
filename, string = txt file name for output
'''
f = open(filename, 'w')
f.write("<k_in>" + '\t' + "rank <k_in>"+ '\t' + "<k_out>"+ '\t' + "rank <k_out>"+ '\t' + "prob_of_in_degree"+ '\t' + "prob_of_out_degree"+ '\n')
in_freq=[]
out_freq = []
for i in range(N):
in_freq.append(0)
out_freq.append(0)
for trial in range(number_of_trials):
model = models.barabasi_albert_graph(N, m, None)
G = model[0]
if trans_red:
G = tr.trans_red(G)
in_list = degree_histogram(G, 'in')
out_list = degree_histogram(G, 'out')
for i in range(len(in_list)):
in_freq[i] += in_list[i]
for i in range(len(out_list)):
out_freq[i] += out_list[i]
print "done trial " , trial
prob_of_in_degree = []
prob_of_out_degree = []
for i in range(len(in_freq)):
in_freq[i] = float(in_freq[i])/float(number_of_trials)
out_freq[i] = float(out_freq[i])/float(number_of_trials)
prob_of_in_degree.append(float(in_freq[i])/float(N))
prob_of_out_degree.append(float(out_freq[i])/float(N))
f.write(str(i) + '\t' + str(in_freq[i])+ '\t' + str(i)+ '\t' + str(in_freq[i])+ '\t' + str(prob_of_in_degree[i])+ '\t' + str(prob_of_out_degree[i])+ '\n')
f.close()
return G
def create_TR_data(DAG, filename):
print 'Making TRed DAG'
TR = tr.trans_red(DAG)
print 'TR made'
# Write TR to data file
full_filename = './Data/%s.txt' % filename
TR_out = open(full_filename, 'w')
for edge in TR.edges():
TR_out.write(edge[0] + '\t' + edge[1] + '\n')
return TR
def test(N, D):
box = dl.box_generator(N, D, 'minkowski')
#box = mod2.COd(D,N)
dag = box[0]
ext = box[1]
tr_dag = tr.trans_red(dag)
good_dag = tr_dag
lp = dl.lpd(tr_dag, ext[1], ext[0])
length = lp[2]
print length
return length
def lpd_test(N, D):
box = dl.box_generator(N, D, 'minkowski')
dag = box[0]
ext = box[1]
tr_dag = tr.trans_red(dag)
print ext[0]
print ext[1]
lp = dl.lpd(tr_dag, ext[1], ext[0])
print lp[1]
return lp
def number_of_lp_BA(m_range=[1,5,10,50], n_max=50, n_step=5):
for m in m_range:
n_range=range(m+1,n_max,n_step)
# n_range=range(101,201,10)
myfile = open(str(m)+'C_for_BA', 'w')
myfile.write('n' + '\t' + 'lp_av'+'\t' + 'lp_err'+ '\t' + 'no._av' +'\t' + 'no._err'+ '\n')
start_time = time.time()
trials = 1000
for n in n_range:
lp_length_list = []
j_list = []
for _ in range(trials):
# model = models.COd(2, N)
model = models.barabasi_albert_graph(n, m)
DAG = model[0]
extremes = model[1]
tr_DAG = tr.trans_red(DAG)
lp = dl.lpd(tr_DAG, extremes[1], extremes[0])
length_lp = lp[2]
lp_length_list.append(length_lp)
j=0
paths_list = list(nx.all_simple_paths(tr_DAG, extremes[1], extremes[0], cutoff=length_lp+1))
for i in range(len(paths_list)):
if len(paths_list[i])==length_lp+1:
j+=1
j_list.append(j)
print j_list
lp_stats = stats.list_stats(lp_length_list)
lp_av = lp_stats[3]
lp_err = lp_stats[1]
j_stats = stats.list_stats(j_list)
j_av = j_stats[3]
j_err = j_stats[1]
print j_av
print j_err
myfile.write(str(n) + '\t' + str(lp_av)+'\t' + str(lp_err)+ '\t' + str(j_av) +'\t' + str(j_err)+ '\n')
print 'done', n
elapsed = time.time() - start_time
print 'finished.',m,'m. Time elapsed = ',elapsed
return
def tr_test(N):
box = dl.box_generator(N, 3)
dag = box[0]
ext = box[1]
'''for node in dag.nodes():
print '####################'
print node
print dag.successors(node)
print '####################'''
start_time = time.clock()
tr_dag = tr.trans_red(dag)
tr_time = time.clock() - start_time
#print tr_dag.successors(ext[1])
print 'N = %s and time for tr was %s' % (N, tr_time)
def test(N, D):
box = dl.box_generator(N, D)
dag = box[0]
ext = box[1]
tr_dag = tr.trans_red(dag)
good_dag = tr_dag
lp = dl.lpd(tr_dag, ext[1], ext[0])
path = lp[1]
tr_dag = good_dag
dim = mps.mpsd(tr_dag, path)
mm_dim = mmd.MM_dimension(dag)
print dim
print mm_dim
return [dim[0], mm_dim]
def number_of_lp():
for D in [2,3,4]:
myfile = open(str(D)+'how many longest paths?', 'w')
myfile.write('N' + '\t' + 'L_scale=N^1/D'+ '\t'+ 'average lp length'+ '\t'+'std err'+ '\t'+ 'average no. lp'+ '\t'+'std err' +'\n')
start_time = time.time()
trials = 100
for N in range(2,40,1):
lp_length_list = []
j_list = []
for _ in range(trials):
model = models.COd(2, N)
# model = models.barabasi_albert_graph(n, m, seed)
DAG = model[0]
extremes = model[1]
tr_DAG = tr.trans_red(DAG)
lp = dl.lpd(tr_DAG, extremes[1], extremes[0])
length_lp = lp[2]
lp_length_list.append(length_lp)
j=0
paths_list = list(nx.all_simple_paths(tr_DAG, extremes[1], extremes[0], cutoff=length_lp+1))
for i in range(len(paths_list)):
if len(paths_list[i])==length_lp+1:
j+=1
j_list.append(j)
lp_stats = stats.list_stats(lp_length_list)
lp_av = lp_stats[3]
lp_err = lp_stats[1]
j_stats = stats.list_stats(j_list)
j_av = j_stats[3]
j_err = j_stats[1]
l_scale = (N**(1.0/float(D)))
myfile.write(str(N) + '\t' + str(l_scale) +'\t' + str(lp_av)+'\t' + str(lp_err)+ '\t' + str(j_av) +'\t' + str(j_err)+ '\n')
print 'done', N
elapsed = time.time() - start_time
print 'finished.',D,'Dimension. Time elapsed = ',elapsed
return
def BA_node_degree_pre_post_TR(n=100, m=10, trials = 1000, filename = 'degree_before_after'):
'''
Outputs node label (=integer time value) and in-degree before and after Transitive reduction.
n, int = final number of nodes, as usual for BA model.
m, int = number of edges to add with addition of each new node.
trials, int = number of realisations of model to be averaged over.
filename, string = txt file name for output
'''
in_list = []
in_tr_list = []
node_list = []
for i in range(n):
in_list.append(0)
in_tr_list.append(0)
for trial in range(trials):
model = models.barabasi_albert_graph(n, m=10)
G = model[0]
H= copy.deepcopy(G)
G_tr = tr.trans_red(H)
f = open(filename, 'w')
f.write('node' + '\t' + 'degree'+ '\t'+'node_tr'+ '\t' + 'degree_tr'+'\n')
j=0
for node, node_tr in zip(G.nodes(), G_tr.nodes()):
j+=1
node_list.append(node)
in_degree = G.in_degree(node)
in_list[node] += in_degree
in_degree_tr = G_tr.in_degree(node_tr)
in_tr_list[node] += in_degree_tr
print "done trial no. ", trial
for i in range(i):
in_list[i] = in_list[i] / float(trials)
in_tr_list[i] = in_tr_list[i] / float(trials)
f.write(str(node_list[i]) + '\t' + str(in_list[i])+ '\t'+str(node_list[i])+ '\t' + str(in_tr_list[i])+'\n')
return
def lp_with_removal(number_of_trials, number_of_probabilities_considered, N, D, filename):
"""
arguments: number_of_trials for each probability, output data is averaged over this.
number_of_probabilities_considered = integer: number of data points
N = integer: number of nodes in network
D = integer: dimension of network
filename = string: output file path e.g. './testing.txt'
"""
start_time = time.time() #start clock
myfile = open(filename, 'w')
p_step = 10.0/number_of_probabilities_considered #define size of step in probability (prob = prob that edge is removed given it's causally allowed)
myfile.write(str(D)+"D box w/ random removal prob p, and longest path l. Number trials averaged over=" + str(number_of_trials) + " N="+ str(N) + '\n')
myfile.write('p' + '\t' + 'l(p)' +'\t' + 'std error' + '\n') #send column headings to output file
#initialize lists for each output variable
p_list = []
lp_average_list = []
lp_std_error_list = []
x_range = np.arange(1.0,10.0+p_step,p_step) #defines list of x values to loop over
for x in x_range: #Loop over x, where x=10^p, so we get more points around prob's closer to 1 and can see phase transition-y behavior more clearly.
#initialize lists for each quantity for which we require an individual value to be stored after every trial (e.g. for calculations stats)
lp_list = []
p = math.log10(x)
for _ in range(int(number_of_trials)):
model = models.box_model(D, N,1-p) #create model with prob of no edge given edge is causally allowed = p
DAG_with_removal = model[0] #networkx DAG object from above model
tr_DAG_with_removal = tr.trans_red(DAG_with_removal) #transitively reduce DAG - doesn't effect lp or MM dimension, DOES MIDPOINT SCALING
extremes = model[1] #returns list of extremes from model
if nx.has_path(DAG_with_removal, extremes[1], extremes[0]):
longest_path_between_extremes = dl.lpd(tr_DAG_with_removal, extremes[1],extremes[0]) #calculate longest path between extremes
length_of_longest_path = longest_path_between_extremes[2] #save length of longest path between extremes
else:
length_of_longest_path=0 #if no path between extremes, longest path = 0 is physical.
lp_list.append(length_of_longest_path)
statistics = stats(lp_list)
lp_average = statistics[3] #calculates average lp over all trials for the current probability
if p == 0.0:
lp_0 = lp_average #calculate average longest path for p==0 and use as normalization constant
lp_average = lp_average / lp_0 #normalize average longest paths so can compare results of different dimensions
for i in range(len(lp_list)):
lp_list[i] /= lp_0 #normalize longest paths (need to do this for std error calculation)
statistics = stats(lp_list)
lp_std_error = statistics[0]
p_list.append(p)
lp_average_list.append(lp_average)
lp_std_error_list.append(lp_std_error)
myfile.write(str(p)+ '\t' + str(lp_average) + '\t' + str(lp_std_error) + '\n')
print "finished ", p , "probability"
elapsed = (time.time() - start_time) #calculate time for method completion
print "finished. Time elapsed = " , elapsed
return [p_list, lp_average_list, lp_std_error_list]
def ms_dim_with_removal(number_of_trials, number_of_probabilities_considered, N, D, filename):
"""
arguments: number_of_trials for each probability, output data is averaged over this.
number_of_probabilities_considered = integer: number of data points
N = integer: number of nodes in network
D = integer: dimension of network
filename = string: output file path e.g. './testing.txt'
"""
start_time = time.time() #start clock
myfile = open(filename, 'w')
p_step = 1.0/number_of_probabilities_considered #define size of step in probability (prob = prob that edge is removed given it's causally allowed)
myfile.write(str(D)+"D box w/ random removal prob p, and longest path l. Number trials averaged over=" + str(number_of_trials) + " N="+ str(N) + '\n')
myfile.write('p' + '\t' + 'ms dimension (p)' +'\t' + 'std error' + '\n') #send column headings to output file
#initialize lists for each output variable
p_list_for_ms = []
ms_average_list = []
ms_std_error_list = []
p_range = np.arange(0.0,1.0+p_step,p_step)
for p in p_range: #Loop over x, where x=10^p, so we get more points around prob's closer to 1 and can see phase transition-y behavior more clearly.
#initialize lists for each quantity for which we require an individual value to be stored after every trial (e.g. for calculations stats)
ms_list = []
ms_diff_sq_list = []
ms_failed_trials = 0
ms_sum = ms_average = 0.0
for _ in range(int(number_of_trials)):
model = models.box_model(D, N,1-p) #create model with prob of no edge given edge is causally allowed = p
DAG_with_removal = model[0] #networkx DAG object from above model
tr_DAG_with_removal = tr.trans_red(DAG_with_removal) #transitively reduce DAG - doesn't effect lp or MM dimension, DOES MIDPOINT SCALING
extremes = model[1] #returns list of extremes from model
if nx.has_path(DAG_with_removal, extremes[1], extremes[0]):
longest_path_between_extremes = dl.lpd(tr_DAG_with_removal, extremes[1],extremes[0]) #calculate longest path between extremes
longest_path = longest_path_between_extremes[1]
length_of_longest_path = longest_path_between_extremes[2] #save length of longest path between extremes
else:
length_of_longest_path=0 #if no path between extremes, longest path = 0 is physical.
if length_of_longest_path > 2:
ms_dimension = ms.mpsd(DAG_with_removal, longest_path)[0] #need to trans red DAG first as otherwise lp method does not return the correct longest path. James said he'd sorted this?
else:
ms_dimension = 0
ms_failed_trials += 1
ms_list.append(ms_dimension)
if len(ms_list)<number_of_trials:
ms_list.append(None)
ms_sum = sum(ms_list)
if (number_of_trials - ms_failed_trials - 1)>0:
ms_average = ms_sum/float(number_of_trials-ms_failed_trials)
else: ms_average=None
ms_diff_sq_sum = 0.0
if (number_of_trials - ms_failed_trials - 1)>0:
for i in range(number_of_trials - ms_failed_trials):
ms_diff_sq_list.append((ms_list[i] - ms_average)**2)
ms_diff_sq_sum += ms_diff_sq_list[i]
ms_std_dev = math.sqrt(ms_diff_sq_sum/float(number_of_trials - ms_failed_trials -1))
ms_std_error = ms_std_dev/math.sqrt(float(number_of_trials - ms_failed_trials))
else:
ms_std_error = 0
p_list_for_ms.append(p)
ms_average_list.append(ms_average)
ms_std_error_list.append(ms_std_error)
myfile.write(str(p)+ '\t' + str(ms_average) + '\t' + str(ms_std_error) + '\n')
clean = clean_for_plotting(ms_average_list,p_list_for_ms, ms_std_error_list)
ms_average_list = clean[0]
p_list_for_ms = clean[1]
ms_std_error_list = clean[2]
print "finished ", p , "probability"
elapsed = (time.time() - start_time) #calculate time for method completion
print "finished. Time elapsed = " , elapsed
return [p_list_for_ms, ms_average_list, ms_std_error_list]
import models
import dag_lib as dl
import trans_red as tr
import matplotlib.pyplot as plt
import networkx as nx
import numpy as np
import time
start_time = time.time()
model = models.COd(2, 200)
DAG = model[0]
extremes = model[1]
print 'made model'
tr_DAG = tr.trans_red(DAG)
print 'done transitive reduction'
lp = dl.lpd(tr_DAG, extremes[1], extremes[0])
length_lp = lp[2]
path = lp[1]
print 'calculated lp'
t_list = []
x_list = []
path_t_list = []
path_x_list = []
#
for node in range(len(DAG.nodes())):
t_list.append(DAG.nodes()[node][0])
x_list.append(DAG.nodes()[node][1])
#
