H = nx.read_gml(graph_name)

for node in H.nodes(): # i remove self loops

if node in H.neighbors(node):

if len(H.neighbors(node))>1:

H.remove_edge(node,node)

else:

H.remove_node(node)

# for node in H.nodes():

# if H.node[node]['weigh_ins'] <5: #Adherent filter

# H.remove_node(node)

# print node, "is going down"

G= nx.connected_component_subgraphs(H)[0] # Giant component

print "size of the GC:",len(G.nodes())#, "after filtering for adherence!!"

#dir=graph_name.split("full_")[0]

#dir=graph_name.split("master")[0]

#dir=graph_name.split("method3_")[0]

#dir=graph_name.split("method3_adh")[0]

dir=graph_name.split("friends")[0]

dir=dir+"roles/"

time_in_system=50 #minimum amount of time in the sytem for a user to be included in the statistics

#name=graph_name.split('data/')[1]

#name=graph_name.split('method3_50/interim/')[1]

#name=graph_name.split('network_all_users/')[1]

name=graph_name.split('5_points_network_2010/data/')[1]

name=name.split('.gml')[0]

name0=dir+name+"_overlap_R6s_averages_"+str(time_in_system)+"days_exclude_R6s.dat"

file0=open(name0, 'wt')

file0.close()

contador=0

name12=dir+name+"_slopes_for_the_fits_average_weight_change.dat"

file=open(name12, 'wt')

file.close()

####for the Isolated Clusters:

list_GC_nodes=[]

for n in G.nodes():

list_GC_nodes.append(n)

# print G.node[n]['percentage_weight_change']

# print "# users GC:",len(list_GC_nodes),"total:",len(H.nodes())

list_weight_changes_not_GC=[]

for n in H.nodes():

if n not in list_GC_nodes:

#print n,"not in GC"

list_weight_changes_not_GC.append(float(H.node[n]['percentage_weight_change']))

#print "# users not in GC:",len(list_weight_changes_not_GC)

# who="not_GC"

#Nbins=18

#histograma(list_weight_changes_not_GC,Nbins,dir,name,who)

###########################

list_R6s=[] # collect the R6 of the system

list_R6s_label=[]

list_R6s_percent_weight_change=[]

for node in G.nodes() :

if str(G.node[node]['role']) == "R6" :

list_R6s.append(node)

list_R6s_label.append(G.node[node]['label'])

list_R6s_percent_weight_change.append(float(G.node[node]['percentage_weight_change']))

name00=dir+name+"R6s_and_top_tens_averages_"+str(time_in_system)+"days_exclude_R6s.dat"

file0=open(name00, 'at')

print >> file0,"R6s",numpy.mean(list_R6s_percent_weight_change),numpy.std(list_R6s_percent_weight_change)

file0.close()

# print "\n\n R6s:\n"

# for i in list_R6s_label:

# print i

# studying the possible cumulative effect of more than one R6 on the population:

for node in G.nodes():

cont=0

for n in G.neighbors(node):

if str(G.node[n]['role']) == "R6" :

cont+=1

G.node[node]["R6_overlap"]=int(cont)

##### weight change for people not connected to any R6s:####

list_weight_changes_no_neighbors=[]

for node in G.nodes():

interseccion=list(set(G.neighbors(node)) & set(list_R6s))

# print node, "intersection:",intersection,len(intersection)

# print "because", list_R6s, "and ",G.neighbors(node)

# raw_input()

if len(interseccion)==0:

list_weight_changes_no_neighbors.append(G.node[node]['percentage_weight_change'])

# print len(list_weight_changes_no_neighbors),"no_neighbors"

who="no_neigbors_R6s"

Nbins=18

histograma(list_weight_changes_no_neighbors,Nbins,dir,name,who)

# mood test

mood=stats.mood(list_weight_changes_no_neighbors,list_weight_changes_not_GC)

print "mood test for",who, "against not_GC:",mood

########

# K-S test:

ks=stats.ks_2samp(list_weight_changes_no_neighbors,list_weight_changes_not_GC)

print "KS test for",who, "against not_GC:",ks

name00="ks_results.dat"

file0=open(dir+name00, 'at')

print >> file0, "KS test for",who,"of",graph_name, "against not_GC:",ks

file0.close()

#############################################

#average percentage weight change as a function of the size of the largest CLIQUE the node belongs to:

absolute_max=1

for i in G.nodes():

maximo=1

list2=nx.cliques_containing_node(G, i)

# print i, list2

for elem in list2:

# print elem,len(elem,)

if len(elem) > maximo:

maximo=len(elem)

# print "\n",maximo

G.node[i]['max_clique_size']=maximo

if absolute_max < maximo:

absolute_max = maximo

#print absolute_max

lista=list(nx.find_cliques(G)) # crea una lista de cliques (lista de listas)

max_clique=nx.graph_clique_number(G) #finds out max size clique

num_tot_clique=nx.graph_number_of_cliques(G) #finds out total number of cliques

# count number of 2, 3, 4, 5, 6 and 7cliques:

num_2cliques=0

num_3cliques=0

num_4cliques=0

num_5cliques=0

num_6cliques=0

num_7cliques=0

num_8cliques=0

num_9cliques=0

for element in lista:

if len(element)==2:

num_2cliques=num_2cliques +1

elif len(element)==3:

num_3cliques=num_3cliques+1

elif len(element)==4:

num_4cliques=num_4cliques+1

elif len(element)==5:

num_5cliques=num_5cliques+1

elif len(element)==6:

num_6cliques=num_6cliques+1

elif len(element)==7:

num_7cliques=num_7cliques+1

elif len(element)==8:

num_8cliques=num_8cliques+1

elif len(element)==9:

num_9cliques=num_9cliques+1

# print " 2: ",num_2cliques, " 3: ",num_3cliques, " 4: ",num_4cliques, " 5: ",num_5cliques, " 6: ",num_6cliques, " 7: ",num_7cliques, " 8: ",num_8cliques, " 9: ",num_9cliques, " max_clique_size:",max_clique, " num_tot_cliques:", num_tot_clique

name33=dir+name+"_percent_weight_change_vs_largest_clique_size.dat"

file11=open(name33, 'wt')

file11.close()

list_of_lists_for_bootstrap=[]

x_positions_fit=[]

y_positions_fit=[]

cum_size_set=float(len(G.nodes()))

tot_nodes=[]

for clique_size in range(1,max_clique):

clique_size=clique_size+1

print clique_size

num_users_set=cum_size_set

percent_weight_change_that_clique_size=[]

for n in G.nodes():

if G.node[n]['max_clique_size']==clique_size:

percent_weight_change_that_clique_size.append(float(G.node[n]['percentage_weight_change']))

tot_nodes.append(float(G.node[n]['percentage_weight_change']))

cum_size_set-=1.0

file11=open(name33, 'at')

print >> file11,clique_size,len(percent_weight_change_that_clique_size),num_users_set/float(len(G.nodes())),numpy.mean(percent_weight_change_that_clique_size),numpy.std(percent_weight_change_that_clique_size)

file11.close()

if len(x_positions_fit)<=7:

x_positions_fit.append(clique_size)

y_positions_fit.append(numpy.mean(percent_weight_change_that_clique_size))

list_of_lists_for_bootstrap.append(percent_weight_change_that_clique_size)

slope, intercept, Corr_coef, p_value, std_err =stats.linregress(x_positions_fit,y_positions_fit) # least squeares polinomial fit

print "result linear. fit for clique size dependency:"

print "slope:",slope, "intercept:", intercept, "Corr_coef:", Corr_coef, "p_value:", p_value, "std_err:", std_err

name11=dir+name+"_fits_clique_size.dat"

file11=open(name11, 'wt')

for i in range(len(x_positions_fit)):

print >> file11,x_positions_fit[i],intercept+x_positions_fit[i]*slope

print >> file11,"\n\n","y=",intercept,"+",slope,"*x",

print "Bootstrap for clique size:\n"

mean_slope, standard_dev = bootstrap(x_positions_fit[0],x_positions_fit[-1],list_of_lists_for_bootstrap)

zscore=(slope-mean_slope)/standard_dev

print >> file11, "bootstrap:\n","actual slope:",slope,"mean_slope:",mean_slope,"standard_dev:",standard_dev,"\n zscore:",zscore

print x_positions_fit[0],x_positions_fit[-1],"actual slope:",slope,"mean_slope:",mean_slope,"standard_dev:",standard_dev,"\n zscore:",zscore

file11.close()

contador+=1

file=open(name12, 'at')

print >> file,contador,mean_slope,standard_dev, "largest_clique_size"

file.close()

#######################################

#####dose effect of the R6s independently########

name11=dir+name+"_dose_eff_indepently_only_one_R6_"+str(time_in_system)+"days_exclude_R6s.dat"

file11=open(name11, 'at')

print >> file11,0,"average_no_neighbors","average_no_neighbors","average_no_neighbors",len(list_weight_changes_no_neighbors),numpy.mean(list_weight_changes_no_neighbors),numpy.std(list_weight_changes_no_neighbors) # the first line of the file is actually for no_neighbors, the rest, for one_and_only_one

file11.close()

file11=open(name11, 'wt')

file11.close()

cont=1

list_all=[]

list_all_nodes=[]

for R6 in list_R6s:

list_weight_changes=[]

for n in G.neighbors(R6):

if (G.node[n]['role'] != "R6") and ( G.node[n]["R6_overlap"]==1) :

list_weight_changes.append(float(G.node[n]['percentage_weight_change']))

if n not in list_all_nodes:

list_all_nodes.append(n)

list_all.append(float(G.node[n]['percentage_weight_change']))

if len(list_weight_changes)>0:

file11=open(name11, 'at')

print >> file11,cont,G.node[R6]['role'],G.node[R6]['label'],len(G.neighbors(R6)),len(list_weight_changes),numpy.mean(list_weight_changes),numpy.std(list_weight_changes)

file11.close()

# print cont,G.node[R6]['role'],G.node[R6]['label'], len(G.neighbors(R6)),len(list_weight_changes),numpy.mean(list_weight_changes),numpy.std(list_weight_changes)

cont=cont+1

else:

# file11=open(name11, 'at')

#print >> file11,cont,G.node[R6]['role'],G.node[R6]['label'],len(G.neighbors(R6)),len(list_weight_changes)

#file11.close()

# print cont,G.node[R6]['role'],G.node[R6]['label'],len(G.neighbors(R6)),len(list_weight_changes)

cont=cont+1

who="one_and_only_one_R6s"

Nbins=18

histograma(list_all,Nbins,dir,name,who)

####################################

print "\n\n"

list_of_lists_for_bootstrap=[]

x_positions_fit=[]

y_positions_fit=[]

averages_larger5_x=[]

averages_larger5_y=[]

norm=0.0

cum_size_set=float(len(G.nodes()))-float(len(list_R6s))

for r in range(len(list_R6s)+1):

# list_BMI_changes=[]

list_weight_changes=[]

list_percentage_weight_changes=[]

list_activities=[]

num_users_set=cum_size_set

for node in G.nodes():

if int(G.node[node]["R6_overlap"])==r:

if G.node[node]["role"]== "R6": # i exclude the R6s

pass

else:

if int(G.node[node]['time_in_system']) > time_in_system:

# list_BMI_changes.append(float(G.node[node]['final_BMI'])-float(G.node[node]['initial_BMI']))

list_weight_changes.append(float(G.node[node]['weight_change']))

list_percentage_weight_changes.append(float(G.node[node]['percentage_weight_change']))

list_activities.append(float(G.node[node]['activity'])/float(G.node[node]['time_in_system']))

cum_size_set-=1.0

if len(list_percentage_weight_changes)>0:

# average_BMI_change=numpy.mean(list_BMI_changes)

average_weight_change=numpy.mean(list_weight_changes)

average_percentage_weight_change=numpy.mean(list_percentage_weight_changes)

average_activity=numpy.mean(list_activities)

#deviation_BMI=numpy.std(list_BMI_changes)

deviation_weight=numpy.std(list_weight_changes)

deviation_percentage_weight=numpy.std(list_percentage_weight_changes)

deviation_activity=numpy.std(list_activities)

#print out

file0=open(name0, 'at')

print >> file0,r,len(list_percentage_weight_changes),num_users_set/float(len(G.nodes())),average_percentage_weight_change,deviation_percentage_weight,average_weight_change,deviation_weight,average_activity,deviation_activity

file0.close()

if r <=5:

x_positions_fit.append(r)

y_positions_fit.append(average_percentage_weight_change)

list_of_lists_for_bootstrap.append(list_percentage_weight_changes)

# else:

# aux_x=r*len(list_percentage_weight_changes)

# averages_larger5_x.append(aux_x)

# aux_y=average_percentage_weight_change*len(list_percentage_weight_changes)

# averages_larger5_y.append(aux_y)

#norm+=float(len(list_percentage_weight_changes))

# x_positions_fit.append(numpy.mean(averages_larger5_x)/norm)

# y_positions_fit.append(numpy.mean(averages_larger5_y)/norm)

slope, intercept, Corr_coef, p_value, std_err =stats.linregress(x_positions_fit,y_positions_fit) # least squeares polinomial fit

print "result linear. fit for dose eff.:"

print "slope:",slope, "intercept:", intercept, "Corr_coef:", Corr_coef, "p_value:", p_value, "std_err:", std_err

name11=dir+name+"_fits_dose_eff_R6.dat"

file11=open(name11, 'wt')

for i in range(len(x_positions_fit)):

print >> file11,x_positions_fit[i],intercept+x_positions_fit[i]*slope

print >> file11,"\n\n","y=",intercept,"+",slope,"*x",

print "Bootstrap for dose eff. R6s:\n"

mean_slope, standard_dev = bootstrap(x_positions_fit[0],x_positions_fit[-1],list_of_lists_for_bootstrap)

zscore=(slope-mean_slope)/standard_dev

print >> file11, "bootstrap:\n","actual slope:",slope,"mean_slope:",mean_slope,"standard_dev:",standard_dev,"\n zscore:",zscore

print x_positions_fit[0],x_positions_fit[-1],"actual slope:",slope,"mean_slope:",mean_slope,"standard_dev:",standard_dev,"\n zscore:",zscore

file11.close()

contador+=1

file=open(name12, 'at')

print >> file,contador,mean_slope,standard_dev, "dose_eff"

file.close()

#### averages for every R6's egonetwork:#########

cont=1

list_all_=[]

list_all_nodes_=[]

for node in list_R6s:

neighbors=G.neighbors(node)#a list of nodes

average_BMI_change=0.0

list_BMI_changes=[]

average_weight_change=0.0

list_weight_changes=[]

average_percentage_weight_change=0.0

list_percentage_weight_changes=[]

average_activity=0.0 # ojo! sera dividida por el numero de dias!!!!!

list_activities=[]

for n in G.neighbors(node):

if int(G.node[n]['time_in_system']) > time_in_system:

# list_BMI_changes.append(float(G.node[n]['final_BMI'])-float(G.node[n]['initial_BMI']))

list_weight_changes.append(float(G.node[n]['weight_change']))

list_percentage_weight_changes.append(float(G.node[n]['percentage_weight_change']))

list_activities.append(float(G.node[n]['activity'])/float(G.node[n]['time_in_system']))

if n not in list_all_nodes_:

list_all_nodes_.append(n)

list_all_.append(float(G.node[n]['percentage_weight_change']))

#averages

average_weight_change=numpy.mean(list_weight_changes)

# average_BMI_change=numpy.mean(list_BMI_changes)

average_activity=numpy.mean(list_activities)

average_percentage_weight_change=numpy.mean(list_percentage_weight_changes)

#standard deviation

#deviation_BMI=numpy.std(list_BMI_changes)

deviation_weight=numpy.std(list_weight_changes)

deviation_percentage_weight=numpy.std(list_percentage_weight_changes)

deviation_activity=numpy.std(list_activities)

#print out

name2=dir+name+"_ego_R6s_average_weight_change_"+str(time_in_system)+"days.dat"

file2=open(name2, 'at')

print >> file2,cont,G.node[node]['role'],G.node[node]['label'],len(G.neighbors(node)),average_weight_change,deviation_weight

file2.close()

name22=dir+name+"_ego_R6s_average_percentage_weight_change_"+str(time_in_system)+"days.dat"

file22=open(name22, 'at')

print >> file22,cont,G.node[node]['role'],G.node[node]['label'],len(G.neighbors(node)),average_percentage_weight_change,deviation_percentage_weight

file22.close()

name3=dir+name+"_ego_R6s_average_activity_"+str(time_in_system)+"days.dat"

file3=open(name3, 'at')

print >> file3,cont,G.node[node]['role'],G.node[node]['label'],len(G.neighbors(node)),average_activity,deviation_activity

file3.close()

cont=cont+1

who="R6s_egonetworks_all"

Nbins=18

histograma(list_all_,Nbins,dir,name,who)

# print "intersection:",len(set(list_all_)&set(list_all)),len(list_all_),len(list_all)

#############just checking what happens if we remove the 40155 guy

##### percent weight change vs. role:

list_roles=["R1","R2","R3","R4","R5","R6","R7"]

file = open(dir+name+"_percentage_weight_change_vs_role",'wt')

cont=1

for role in list_roles:

list_weight_changes_role=[]

for n in G.nodes():

if G.node[n]['role']==role:

list_weight_changes_role.append(G.node[n]['percentage_weight_change'])

print >> file, cont, role, len(list_weight_changes_role),numpy.mean(list_weight_changes_role),numpy.std(list_weight_changes_role)

cont+=1

file.close()

#############################

############## percentage weight change vs k

x_positions_fit=[]

y_positions_fit=[]

cum_size_set=float(len(G.nodes()))

list_of_lists_for_bootstrap=[]

list_k=[]

for n in G.nodes():

list_k.append(len(G.neighbors(n)))

max_k=max(list_k)

file = open(dir+name+"_percentage_weight_change_vs_k.dat",'wt')

max_k=max_k+1

for k in range(1,max_k):

num_users_set=cum_size_set

list_percent_weight_change_k=[]

for n in G.nodes():

if len(G.neighbors(n))==k:

list_percent_weight_change_k.append(G.node[n]['percentage_weight_change'])

cum_size_set-=1.0

if len(list_percent_weight_change_k)>0:

print >> file,k, len(list_percent_weight_change_k),num_users_set/float(len(G.nodes())),numpy.mean(list_percent_weight_change_k),numpy.std(list_percent_weight_change_k)

if len(x_positions_fit)<=7:

x_positions_fit.append(k)

y_positions_fit.append(numpy.mean(list_percent_weight_change_k))

list_of_lists_for_bootstrap.append(list_percent_weight_change_k)

slope, intercept, Corr_coef, p_value, std_err =stats.linregress(x_positions_fit,y_positions_fit) # least squeares polinomial fit

print "result linear. fit for degree dependency:"

print "slope:",slope, "intercept:", intercept, "Corr_coef:", Corr_coef, "p_value:", p_value, "std_err:", std_err

file.close()

name11=dir+name+"_fits_degree.dat"

file11=open(name11, 'wt')

for i in range(len(x_positions_fit)):

print >> file11,x_positions_fit[i],intercept+x_positions_fit[i]*slope

print >> file11,"\n\n","y=",intercept,"+",slope,"*x",

print "Bootstrap for degree:\n"

mean_slope, standard_dev = bootstrap(x_positions_fit[0],x_positions_fit[-1],list_of_lists_for_bootstrap)

zscore=(slope-mean_slope)/standard_dev

print >> file11, "bootstrap:\n","actual slope:",slope,"mean_slope:",mean_slope,"standard_dev:",standard_dev,"\n zscore:",zscore

print x_positions_fit[0],x_positions_fit[-1],"actual slope:",slope,"mean_slope:",mean_slope,"standard_dev:",standard_dev,"\n zscore:",zscore

file11.close()

contador+=1

file=open(name12, 'at')

print >> file,contador,mean_slope,standard_dev, "degree"

file.close()

########################################

new_name=graph_name.split(".gml")[0]

new_name=new_name+"_adherent_num_R6s_largest_clique.gml"

#who es una etiqueta para saber sobre que poblacion hago el histograma

hist, bin_edges= numpy.histogram(list, bins=Nbins,range=(-45.0,22.0))

#if i wanna compare several distrib. i MUST give same Nbins and max_min range too!!!

print who, list

print "max:",max(list),"min:",min(list)

print hist, bin_edges

area=0.0

origin=float(bin_edges[0])

file = open(dir+name+"_histogram_weight_change_"+who,'wt')

for b in range (len(bin_edges)-1):

print >> file,origin+(bin_edges[b+1]-bin_edges[b])/2.0, hist[b], float(hist[b])/float(len(list))

area=area+float(hist[b])/float(len(list))

origin=origin+(bin_edges[b+1]-bin_edges[b])

file.close()

"Chooses k random elements (with replacement) from a population"

n = len(population)

_random, _int = random.random, int # speed hack

result = [None] * k

for i in xrange(k):

j = _int(_random() * n)

result[i] = population[j]

last_x +=1

x_positions=[]

for x in range(first_x,last_x):

x_positions.append(x)

print x

list_slopes=[]

list_intersections=[]

for iter in range (100):

y_positions=[]

for list in list_of_lists_for_bootstrap:

if len(list)>1:

list_synth=sample_with_replacement(list,len(list))

y_positions.append(numpy.mean(list_synth))

else:

y_positions.append(numpy.mean(list_synth))

slope, intercept, Corr_coef, p_value, std_err =stats.linregress(x_positions,y_positions) # least squeares polinomial fit

list_slopes.append(slope)

list_intersections.append(intercept)