def main():
# Load in Mike's data
mikedewar = nx.read_graphml('mikedewar_rec.graphml')
# We need to relabel nodes as Twitter name if we want to show the names in the plot
label_dict = dict(map(lambda i : (mikedewar.nodes()[i], mikedewar.nodes(data=True)[i][1]['Label']), xrange(mikedewar.number_of_nodes())))
mikedewar_d3 = nx.relabel_nodes(mikedewar, label_dict)
# Export
d3_js.export_d3_js(mikedewar_d3, files_dir="mikedewar", graphname="mikedewar", group="REC", node_labels=False)
for node in retweet_network.nodes(data=True):
if retweet_network.degree(node[0]) == 0:
retweet_network.remove_node(node[0])
continue
# Builds Left-Right distribution
node_taglist = node[1]['tags']
for tag in node_taglist:
if node[1]['cluster'] == "left":
left_right_table[tag]['left'] += 1
else:
left_right_table[tag]['right'] += 1
print "############### PROBABILISTICALLY REDUCED GRAPH ################"
print "Number of edges: " + str(retweet_network.number_of_edges())
print "Number of nodes: " + str(retweet_network.number_of_nodes())
for tag in left_right_table:
print tag + " " + str(left_right_table[tag])
# Testing:
print "Structure of a node: +++++++++++++++"
print retweet_network.nodes(data=True)[0]
print "Structure of an edge: ++++++++++++++"
print retweet_network.edges(data=True)[0]
# Exports to D3.js
d3_js.export_d3_js(retweet_network, files_dir="visualization", graphname="retweet_network", group="cluster", node_labels=False)
for edge in edge_dict:
if edge_dict[
edge] > 3: # and cite_dict[edge[0]]>=8 and cite_dict[edge[1]]>=8 :
G.add_edge(edge[0], edge[1], weight=edge_dict[edge])
for node in G:
G.add_node(node) #adds list of nodes separately thru dict lookup
import community
partition = community.best_partition(G)
for node in G:
G.add_node(node, group=str(partition[node]))
d3_js.export_d3_js(G,
files_dir="netweb",
graphname="cites",
node_labels=True,
group="group")
fix = open('netweb/cites.json', 'rb').read()
# Not sure what this is fixing tbh
# for n in G:
# try:
# fix=re.sub(str(n)+'''"''',str(n)+'''" , "nodeSize":'''+str(cite_dict[n]),fix)
# except:
# print 'error with',n
f = open('netweb/cites.json', 'w+')
f.write(fix)
f.close()
from networkx.readwrite.d3_js import export_d3_js
from loaner import Loaner, LoanerSociety
loaner = Loaner(LoanerSociety.random_society(10))
def prep_graph(graph):
for i in range(graph.number_of_nodes()):
node = graph.nodes()[i]
graph.node[node]['group'] = int(node.pays_off) + 1
while True:
loaner.sim_n_rounds(20)
prep_graph(loaner.society)
export_d3_js(loaner.society,group='group',files_dir="small")
raw_input("Press Enter to continue...")
if cite_counter[cite_pair[0]] >= cite_threshold\
and cite_counter[cite_pair[1]] >= cite_threshold\
and co_cite_counter[cite_pair] >= co_cite_threshold:
graph.add_edge(cite_pair[0], cite_pair[1],
weight=co_cite_counter[cite_pair])
for cite in cite_pair:
if cite not in full_cites:
full_cites.append(cite)
num_edges += 1
partition = community.best_partition(graph)
for node in graph:
graph.add_node(node, freq=cite_counter[node], group=str(partition[node]))
d3_js.export_d3_js(graph, files_dir='results', graphname='graph',
node_labels=True, group='group')
with open('results/graph.json', 'rb') as graph_json:
fix = graph_json.read()
for node in graph:
try:
fix = re.sub(str(node) + '"',
str(node) + '" , "nodeSize":' +
str(cite_counter[node]),
fix)
except:
print 'Error with %s' % node
with open('results/graph.json', 'w+') as graph_json:
graph_json.write(fix)
import networkx as nx
from networkx.readwrite import d3_js
mikedewar = nx.read_graphml('mikedewar_rec.graphml')
# We need to relabel nodes as Twitter name if we want to show the names in the plot
label_dict = dict(
map(
lambda i:
(mikedewar.nodes()[i], mikedewar.nodes(data=True)[i][1]['Label']),
xrange(mikedewar.number_of_nodes())))
mikedewar_d3 = nx.relabel_nodes(mikedewar, label_dict)
# Export
d3_js.export_d3_js(mikedewar_d3,
files_dir="mikedewar",
graphname="mikedewar",
group="REC",
node_labels=False)
G=nx.Graph()
counter=0
for edge in edge_dict:
if edge_dict[edge]>3 and cite_dict[edge[0]]>=8 and cite_dict[edge[1]]>=8 :
G.add_edge(edge[0],edge[1],weight=edge_dict[edge])
counter=counter+1
print counter
for node in G:
G.add_node(node,freq=cite_dict[node])
import community
partition=community.best_partition(G)
print len(partition)
for node in G:
G.add_node(node,freq=cite_dict[node], group=str(partition[node]))
d3_js.export_d3_js(G,files_dir="netweb",graphname="cites",node_labels=True,group="group")
fix=open('netweb/cites.json','rb').read()
for n in G:
try:
fix=re.sub(str(n)+'''"''',str(n)+'''" , "nodeSize":'''+str(cite_dict[n]),fix)
except:
print 'error with',n
f = open('netweb/cites.json', 'w+')
f.write(fix)
f.close()
