def generate_network(n_nodes, mean_inters, std_dev, n_sick):
# Create a graph with selected attributes
n_ties = pd.Series(map(int, np.round(np.random.normal(mean_inters, std_dev, n_nodes))))
# Who distances themselves? Make them safer
distancers = random.sample(list(n_ties.index), round(prop_social_distancing * n_nodes))
n_ties[distancers] = n_ties[distancers] * effectiveness_social_distancing
# If you isolate, you dont get sick and dont get others sick
isolaters = random.sample(list(n_ties.index), round(prop_self_isolating * n_nodes))
n_ties[isolaters] = 0
G = expected_degree_graph(n_ties, selfloops=False)
# Decide who is sick
nx.set_node_attributes(G, 0, 'COVID-19')
nx.set_node_attributes(G, 0, 'Time Sick')
nx.set_node_attributes(G, 0, 'Symptomatic')
nx.set_node_attributes(G, 0, 'Time Showing')
sick_nodes = random.sample(list(G.nodes), n_sick)
for node in sick_nodes:
G.nodes[node]['COVID-19'] = 1
return(G, sick_nodes)
"""
========================
Expected Degree Sequence
========================
Random graph from given degree sequence.
"""
import networkx as nx
from networkx.generators.degree_seq import expected_degree_graph
# make a random graph of 500 nodes with expected degrees of 50
n = 500 # n nodes
p = 0.1
w = [p * n for i in range(n)] # w = p*n for all nodes
G = expected_degree_graph(w) # configuration model
print("Degree histogram")
print("degree (#nodes) ****")
dh = nx.degree_histogram(G)
for i, d in enumerate(dh):
print(f"{i:2} ({d:2}) {'*'*d}")
======================== Expected Degree Sequence ======================== Random graph from given degree sequence. """ # Author: Aric Hagberg ([email protected]) # Copyright (C) 2006-2018 by # Aric Hagberg <*****@*****.**> # Dan Schult <*****@*****.**> # Pieter Swart <*****@*****.**> # All rights reserved. # BSD license. import networkx as nx from networkx.generators.degree_seq import expected_degree_graph # make a random graph of 500 nodes with expected degrees of 50 n = 500 # n nodes p = 0.1 w = [p * n for i in range(n)] # w = p*n for all nodes G = expected_degree_graph(w) # configuration model print("Degree histogram") print("degree (#nodes) ****") dh = nx.degree_histogram(G) low = min(nx.degree(G)) for i in range(low, len(dh)): bar = ''.join(dh[i] * ['*']) print("%2s (%2s) %s" % (i, dh[i], bar))
def network_creation (path,t): #t number of nodes
lista_attributi = ['gender', 'age', 'state']
w = []
p_df= covid_statistics(path+"covid_gender_age.csv")
p_df1 = p_df[p_df.columns[-2:]]
p_df1 = p_df1.replace(p_df1, 0)
diz = p_df1.to_dict('index')
df_con = pd.read_csv(path+"contacts_agegroups.csv", index_col=0, names=['contacts'], header=None)
df_con = round(df_con.sort_values(by=['contacts']))
df_con.rename(index={'70-100': '70-105'}, inplace=True)
print('The number of daily contat for each age group is:\n',df_con)
table= gen_age(path+"Italy-2019.csv")
#print(diz)
nodi = {} # final dictionary will contain: node number: [sex,age,State]
ind= -1
for age in (list(table.index.values)):
e = re.findall(r'\d+', age)
età = randrange(int(e[0]), int(e[1]))
for sex in (list(table.columns)):
perc = table.loc[age][sex] # this is the fraction (percentage) of population that will have a specific sex and age
# rows= age , col= sex
n = int(round(perc * t / 100))
for i in range(len(df_con)):
N=df_con.index[i].split('-')
if int(N[0]) <= età <= int(N[1]):
w1 = [df_con.iloc[i][0] for k in range(n)]
for el in w1:
ind=ind+1
nodi[ind]=[sex,età, 'S']
for ages in diz:
ages1=ages.split('-')
if int(ages1[0]) <=età<= int(ages1[1]):
if diz[ages][sex]==0:
diz[ages][sex]=[ind]
else:
diz[ages][sex].append(ind)
w.extend(w1)
#HERE WE CREATED W THAT IS A LIST CONTAINING THE NUMBER OF CONTACTS FOR EACH NODE
G = expected_degree_graph(w) # configuration model
print("Degree histogram")
# dh = nx.degree_histogram(G)
#print(dh)
#print("degree (#nodes) ****")
#for i, d in enumerate(dh):
# print(f"{i:2} ({d:2}) {'*'*d}")
degree_sequence = sorted([d for n, d in G.degree()], reverse=True) # degree sequence
degreeCount = collections.Counter(degree_sequence)
deg, cnt = zip(*degreeCount.items())
#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.show()
#HERE WE ASSIGN ALL THE ATTRIBUTES (AGE, GENDER, AND STATE) FOR EACH NODE OF THE NETWORK
for key in nodi:
for j in range(len(lista_attributi)):
G.nodes[int(key)][lista_attributi[j]] = nodi[key][j] # per ogni attributo della lista, prende il valore dal dizionario
return(G,diz,p_df)