global g

g = nx.Graph(d)

nx.write_graphml(g, 'g.xml')

authorMap = from_networkx(g)

authorNet = requests.post(server + '/networks',

data=json.dumps(authorMap),

headers={'Content-Type': 'application/json'})

net_id = authorNet.json()['networkSUID']

requests.get('%s/apply/layouts/force-directed/%d' % (server, net_id))

degree_sequence = sorted([d for n, d in graph.degree()], reverse=True)

degreeCount = collections.Counter(degree_sequence)

deg, cnt = zip(*degreeCount.items())

# # plot node degrees

# fig, ax = plt.subplots()

# plt.bar(deg, cnt, width=0.80, color='b')

# plt.title("Degree Histogram")

# plt.ylabel("Count")

# plt.xlabel("Degree")

# ax.set_xticks([d + 0.4 for d in deg])

# ax.set_xticklabels(deg)

# plt.savefig('nodeDegrees')

np.seterr(divide='ignore', invalid='ignore')

fitgen = powerlaw.Fit(deg, discrete=True)

global RgenL

global PgenL

RgenL = []

PgenL = []

Rgen, pgen = fitgen.distribution_compare('power_law', 'lognormal', normalized_ratio=True)

print(Rgen, pgen)

RgenL.append(Rgen)

np.log(pgen)

plt.scatter(PL, RL)

plt.xlabel('log p-value')

plt.ylabel('maximum likelihood (R)')

plt.title('Graph Type Proof')

plt.savefig('HypoTesting.png')

#number of total nodes in a graph

global total

total = 0

total = nx.number_of_nodes(graph)

totalNodes.append(total)

print("Done with total node")

# Community counting

global parts

parts = 0

parts = community.best_partition(g)

max_value = max(parts.values())

if max_value > 0.00000000:

communities.append(max_value)

print("Done communities")

#Degree centrality

global deg_cen

prunedDegreeCent = []

deg_cen = {}

deg_cen.clear()

deg_cen = nx.degree_centrality(graph)

#getting average and median degree centrality

DCcount = 0

DCsum = 0

for key in deg_cen:

if deg_cen[key] > 0:

prunedDegreeCent.append(deg_cen[key])

DCcount += 1

DCsum += deg_cen[key]

DCavg = (DCsum / DCcount)

degreeCentAvg.append(DCavg)

median = statistics.median(prunedDegreeCent)

degreeCentMedian.append(median)

print("Done with Degree Centrality")

# Calculate cluster coefficent- measure of the degree to which nodes in a graph tend to cluster together.

global clusterCo

clusterCo = {}

clusterCo.clear()

clusterCo = nx.clustering(graph)

# getting average of all values in dictionary

CCcount = 0

CCsum = 0

for key in clusterCo:

if clusterCo[key] > 0:

CCcount += 1

CCsum += clusterCo[key]

CCavg = (CCsum / CCcount)

clusterCoAvg.append(CCavg)

print('Done w. ClusterCo')

#Betweenness centrality

global bet_cen

bet_cen = {}

bet_cen.clear()

bet_cen = nx.betweenness_centrality(graph)

betweennessCent.append(bet_cen)

print('Done w. Betweenness Cent')

# graph density

# global gDense

# gDense = 0

# gDense = nx.density(graph)

# if gDense > 0.00000000:

# densities.append(gDense)

# Eigenvector centrality

# global eig_cen

# eig_cen = {}

# eig_cen.clear()

# eig_cen = nx.eigenvector_centrality(graph)

#

# # getting average eigen vector centrality

# EVcount = 0

# EVsum = 0

# for key in eig_cen:

# EVcount += 1

# EVsum += eig_cen[key]

# EVavg = (EVsum / EVcount)

# eigenVectorAvg.append(EVavg)

# #Number of cliques and largest clique

# Qs = 0

# Qs = nx.graph_number_of_cliques(graph)

# if Qs > 0:

# numCliques.append(Qs)

#

# # Size of the largest clique

# size = 0

# size = nx.graph_clique_number(graph)

# if size > 0:

# largestCliqueSize.append(size)

#

# # is the graph connected?

# a = nx.is_connected(graph)

# isConnected.append(a)

#

# # how much of the graph is connected

# num = 0

# num = nx.number_connected_components(graph)

# numConnected.append(num)

# #Calculate the node centrality- measure of the influence of a node in a network

# Closeness centrality

# global clo_cen

# clo_cen = nx.closeness_centrality(graph)

# sorted_clo_cen = sorted(clo_cen.items(), key=operator.itemgetter(0), reverse=True)

# Function for graphing the various lists in matplotlib

#create graph for communities

plt.bar(l1, len(l1), align='center', alpha=0.5)

plt.xlabel('Number of Communities')

plt.title('Communities Detected')

plt.savefig('communities.png')

plt.close()

print(l1)

# create graph for densities

plt.bar(l2, len(l2), align='center', alpha= 0.5)

plt.xlabel('Density of Graph')

plt.ylabel('Graphs')

plt.title(' Graph Densities')

plt.savefig('densities.png')

plt.close()

# function to write any list to a CSV

with open(f'clusteringCsv{i}.csv', 'w+') as csv_file:

writer = csv.writer(csv_file)

for key, value in d1.items():

writer.writerow([key, value])

with open(f'eigenVectorCSV{i}.csv', 'w+') as csv_file:

writer = csv.writer(csv_file)

for key, value in d2.items():

writer.writerow([key, value])

with open(f'degreeCentCsv{i}.csv', 'w+') as csv_file:

writer = csv.writer(csv_file)

for key, value in d3.items():

# function just to print list to the Python console

print(l1)

print(l2)

print(l3)

print(l4)

print(l5)