# -*- coding: utf-8 -*-

"""

Created on Thu Jun 18 16:07:37 2020

@author: BALAMLAPTOP2

Input variables:

type_nx = ['bipartite', 'projected']

type_proj = ['icd', 'atc']

"""

import numpy as np

import networkx as nx

import sys

from networkx.algorithms import bipartite

import pandas as pd

if __name__ == '__main__':

type_nx = sys.argv[1]

type_proj = sys.argv[2]

print("Reading file ...")

vdmdata_reduce = pd.read_csv('vdmdata_reduce.csv')

print("Nodes lists ...")

nodes_0 = []

nodes_1 = []

for m in vdmdata_reduce.iterrows():

nodes_0.append(m[1][0]) #ICD

nodes_1.append(m[1][1]) #ATC

nodes_0 = list(dict.fromkeys(nodes_0))

nodes_1 = list(dict.fromkeys(nodes_1))

print("Build a bipartite graph ...")

# Build a bipartite graph:

G = nx.Graph()

G.add_nodes_from(nodes_0, bipartite=0) # disease

G.add_nodes_from(nodes_1, bipartite=1) # active substance

for m in vdmdata_reduce.iterrows():

enfermedad = m[1][0];

sustancia = m[1][1];

G.add_edge(enfermedad, sustancia)

if type_nx == 'projected' and type_proj == 'icd':

# Build Projected Graph Diseases

GP = bipartite.projected_graph(G, nodes_0)

print('Calculate Global properties for projected graph '+type_proj)

print("\n")

elif type_nx == 'projected' and type_proj == 'atc':

# Build Projected Graph Active Ingredients

GP = bipartite.projected_graph(G, nodes_1)

print('Calculate Global properties for projected graph '+type_proj)

print("\n")

else:

print('Calculate Global properties for bipartite network')

print("\n")

if type_nx == 'bipartite':

print("Nodes Number : "+str(G.number_of_nodes()))

print("\n")

print("Edges Number : "+str(G.number_of_edges()))

print("\n")

print('Calculating density ...')

print("\n")

print("Density ICD Nodes (Diseases): "+str(bipartite.density(G, nodes_0)))

print("\n")

print("Density ATC Nodes (Active Substances): "+str(bipartite.density(G, nodes_1)))

print("\n")

print('Calculating mean degree ...')

print("\n")

G_deg = nx.degree_histogram(G)

G_deg_sum = [a * b for a, b in zip(G_deg, range(0, len(G_deg)))]

print('average degree: {}'.format(sum(G_deg_sum) / G.number_of_nodes()))

print("\n")

print('Calculating mean clustering ...')

print("\n")

cluster_g = bipartite.clustering(G)

scg = 0;

for i in range(len(cluster_g)):

scg = scg + list(cluster_g.items())[i][1]

print("Average clustering %s" % str(scg/len(cluster_g)))

print("\n")

else:

print("Nodes Number : "+str(GP.number_of_nodes()))

print("\n")

print("Edges Number : "+str(GP.number_of_edges()))

print("\n")

print('Calculating density ...')

print("\n")

components = sorted(nx.connected_components(GP), key=len, reverse=True)

largest_component = components[0]

C = GP.subgraph(largest_component)

print("Density Largest Component: %s" % str(nx.density(C)))

print("\n")

print("Density projected graph: %s" % str(nx.density(GP)))

print("\n")

print('Calculating mean degree ...')

print("\n")

G_deg = nx.degree_histogram(GP)

G_deg_sum = [a * b for a, b in zip(G_deg, range(0, len(G_deg)))]

print('Average degree: {}'.format(sum(G_deg_sum) / GP.number_of_nodes()))

print("\n")

print('Calculating mean clustering ...')

print("\n")

print("Average: %s" % str(nx.average_clustering(C)))

print("\n")

print('Calculating mean shortest path lenght ...')

print("\n")

pathlengths = []

i = 0

m_pl_icd = np.zeros((GP.number_of_nodes(), GP.number_of_nodes()))

for v in GP.nodes():

spl = dict(nx.single_source_shortest_path_length(GP, v))

sorted_dict = {k: spl[k] for k in sorted(spl)}

j = 0

for p in sorted_dict:

#if v == p:

pathlengths.append(sorted_dict[p])

m_pl_icd[i][j] = sorted_dict[p]

j += 1

i += 1

if type_proj == 'icd':

df = pd.DataFrame(m_pl_icd, index=nodes_0, columns=nodes_0)

else:

df = pd.DataFrame(m_pl_icd, index=nodes_1, columns=nodes_1)

df.to_csv('path_length_nodes_'+type_proj+'.csv', index=True, header=True, sep=',', encoding = 'utf-8-sig')

print("Average Shortest Path Length %s" % str(sum(pathlengths) / len(pathlengths)))

print("\n")

print('Calculating max value diameter ...')

print("\n")

print("Diameter: %s" % str(np.amax(m_pl_icd)))

print("\n")

print('Calculating assortativity ...')

print("Assortativity: %s" % str(nx.degree_assortativity_coefficient(C)))

print("\n")

print('Calculating Betweenness Centrality')

print("\n")

b = nx.betweenness_centrality(C)

sb = 0;

for i in range(len(b)):

sb = sb + list(b.items())[i][1]

print("Betweenness: %s" % str(sb/len(b)))