def betweenness(self, paremeters=None):
print "measuring btw"
#graph_btw = self.network.betweenness()
graph_btw = self.extra_network.betweenness()
graph_btw_w = self.network.betweenness(weights=self.network.es['weight'])
ranked_by_btw = reverseSortList(graph_btw)
ranked_by_btw_w = reverseSortList(graph_btw_w)
print ranked_by_btw
print ranked_by_btw_w
self.node_rankings['btw'] = ranked_by_btw
self.node_rankings['btw_w'] = ranked_by_btw_w
self.node_values['btw'] = graph_btw
self.node_values['btw_w'] = graph_btw_w
def clustering_coefficient(self, paremeters=None):
print "measuring cc"
#graph__cc = self.network.transitivity_local_undirected()
graph__cc = self.extra_network.transitivity_local_undirected()
graph__cc_w = self.network.transitivity_local_undirected(weights=self.network.es['weight'])
ranked_by_cc = reverseSortList(graph__cc)
ranked_by_cc_w = reverseSortList(graph__cc_w)
print ranked_by_cc
#print ranked_by_cc_w
self.node_rankings['cc'] = ranked_by_cc
self.node_rankings['cc_w'] = ranked_by_cc_w
self.node_values['cc'] = graph__cc
self.node_values['cc_w'] = graph__cc_w
def degree(self, paremeters=None):
#print "measuring degree"
#graph_degree = self.network.degree()
graph_degree = self.extra_network.degree()
graph_stg = self.network.strength(weights=self.network.es['weight'])
ranked_by_degree = reverseSortList(graph_degree)
ranked_by_stg = reverseSortList(graph_stg)
print ranked_by_degree
#save_vector_to_file(ranked_by_degree)
print ranked_by_stg
self.node_rankings['dg'] = ranked_by_degree
self.node_rankings['stg'] = ranked_by_stg
self.node_values['dg'] = graph_degree
self.node_values['stg'] = graph_stg
def page_rank(self, paremeters=None):
print "measuring pr"
#graph_pr = self.network.pagerank()
graph_pr = self.extra_network.pagerank()
graph_pr_w = self.network.pagerank(weights=self.network.es['weight'])
ranked_by_pr = reverseSortList(graph_pr)
ranked_by_pr_w = reverseSortList(graph_pr_w)
print ranked_by_pr
print ranked_by_pr_w
self.node_rankings['pr'] = ranked_by_pr
#save_vector_to_file(ranked_by_pr)
self.node_rankings['pr_w'] = ranked_by_pr_w
self.node_values['pr'] = graph_pr
self.node_values['pr_w'] = graph_pr_w
def get_accs_values(self, h):
command = "./CVAccessibility2 -l " + h + " " + self.location
print command
values = get_terminal_values(command)
values = values.split('\n')
values = values[:len(values) - 1]
values = [float(i) for i in values]
return (reverseSortList(values), values)
def sort_by_concentric(self, type, h):
results = read_dat_files()
measure_type = results[type]
if h == 2:
measure = measure_type[0]
else:
measure = measure_type[1]
return reverseSortList(measure)
def sort_by_symmetry(self, order, type, h):
#order : h - l
#type: b - m
#h: 2-3
if type == 'b':
nType = 0
else:
nType = 1
if h == '2':
nH = 0
else:
nH = 1
symmetries = read_csv_file()
measure = symmetries[nType][nH]
the_high = reverseSortList(measure)
if order == 'h':
return the_high
else:
return specialSortList(the_high)
def get_gaccs_values(self):
measures = self.calculate_accesibility()
measures = measures.tolist()
return (utils.reverseSortList(measures), measures)
def sort_by_accesibility(self):
measures = self.calculate_accesibility()
measures = measures.tolist()
return utils.reverseSortList(measures)