def _create_and_save_remixgroup(sg, remixgroup):
# print ' ========================================= '
# add to list the subgraphs(connected components) with the extra data
node_list = sg.nodes(data=True)
# pp(node_list)
# sort by date (holds all subgraph nodes sorted by date)
# we need this since we go forward in time (source older than remix)
node_list.sort(
key=lambda x: x[1]['date']) # I think ['date'] is not necessary
# print ' ========== SORTED NODE_LIST ========= '
# pp(node_list)
# dict with key=sound_id, value=index, nodeName=original_filname
# in the previous sorted by date list
# FIXME: no need for all this data, can be simple dict, key=value
container = dict((val[0], {
'index': idx,
'nodeName': val[1]['nodeName']
}) for (idx, val) in enumerate(node_list))
# print ' ========== CONTAINER ========= '
# pp(container)
links = []
remixgroup.save() # need to save to have primary key before ManyToMany
# FIXME: no idea why nx.weakly_connected_components(sg) return list in list...
remixgroup.sounds = set(nx.weakly_connected_components(sg)[0])
remixgroup.group_size = len(node_list)
# FIXME: seems like double work here, maybe convert container to list and sort?
nodes = [{
'id': val[0],
'username': val[1]['username'],
'nodeName': val[1]['nodeName'],
'sound_url_mp3': val[1]['sound_url_mp3'],
'sound_url_ogg': val[1]['sound_url_ogg'],
'waveform_url': val[1]['waveform_url'],
'group': 1
} for (idx, val) in enumerate(node_list)]
for line in nx.generate_adjlist(sg):
# print line
if len(line.split()) > 1:
for i, l in enumerate(line.strip().split(" ")):
# index 0 is the source, which we already know
if i > 0:
link = {
'target': container[int(line.split(" ")[0])]['index'],
'source': container[int(l)]['index']
}
links.append(link)
#print link
remixgroup.protovis_data = "{\"color\": \"#F1D9FF\"," \
"\"length\":" + str(len(node_list)) + "," \
"\"nodes\": " + json.dumps(nodes) + "," \
"\"links\": " + json.dumps(links) + "}"
remixgroup.networkx_data = json.dumps(
dict(nodes=sg.nodes(), edges=sg.edges()))
remixgroup.save()
print remixgroup.id
print remixgroup.networkx_data
def load_graph(ifilename=None):
"""Load graph from file
"""
G = None
# Testing mode
if ifilename is None:
n = 10 # 10 nodes
m = 20 # 20 edges
G = nx.gnm_random_graph(n, m)
# some properties
print("node degree clustering")
for v in nx.nodes(G):
print(f"{v} {nx.degree(G, v)} {nx.clustering(G, v)}")
print()
print("the adjacency list")
for line in nx.generate_adjlist(G):
print(line)
else:
try:
G = networkx.read_graphml(open(ifilename, 'r'))
except Exception as ifile_err:
print(f"Unable to load graph from {ifilename}: {ifile_err}")
return G
def _create_and_save_remixgroup(sg, remixgroup):
# print ' ========================================= '
# add to list the subgraphs(connected components) with the extra data
node_list = sg.nodes(data=True)
# pp(node_list)
# sort by date (holds all subgraph nodes sorted by date)
# we need this since we go forward in time (source older than remix)
node_list.sort(key=lambda x: x[1]["date"]) # I think ['date'] is not necessary
# print ' ========== SORTED NODE_LIST ========= '
# pp(node_list)
# dict with key=sound_id, value=index, nodeName=original_filname
# in the previous sorted by date list
# FIXME: no need for all this data, can be simple dict, key=value
container = dict((val[0], {"index": idx, "nodeName": val[1]["nodeName"]}) for (idx, val) in enumerate(node_list))
# print ' ========== CONTAINER ========= '
# pp(container)
links = []
remixgroup.save() # need to save to have primary key before ManyToMany
# FIXME: no idea why nx.weakly_connected_components(sg) return list in list...
remixgroup.sounds = set(nx.weakly_connected_components(sg)[0])
remixgroup.group_size = len(node_list)
# FIXME: seems like double work here, maybe convert container to list and sort?
nodes = [
{
"id": val[0],
"username": val[1]["username"],
"nodeName": val[1]["nodeName"],
"sound_url_mp3": val[1]["sound_url_mp3"],
"sound_url_ogg": val[1]["sound_url_ogg"],
"waveform_url": val[1]["waveform_url"],
"group": 1,
}
for (idx, val) in enumerate(node_list)
]
for line in nx.generate_adjlist(sg):
# print line
if len(line.split()) > 1:
for i, l in enumerate(line.strip().split(" ")):
# index 0 is the source, which we already know
if i > 0:
link = {"target": container[int(line.split(" ")[0])]["index"], "source": container[int(l)]["index"]}
links.append(link)
remixgroup.protovis_data = (
'{"color": "#F1D9FF",'
'"length":' + str(len(node_list)) + ","
'"nodes": ' + json.dumps(nodes) + ","
'"links": ' + json.dumps(links) + "}"
)
remixgroup.networkx_data = json.dumps(dict(nodes=sg.nodes(), edges=sg.edges()))
remixgroup.save()
def _create_and_save_remixgroup(sg, remixgroup):
# print ' ========================================= '
# add to list the subgraphs(connected components) with the extra data
node_list = sg.nodes(data=True)
# pp(node_list)
# sort by date (holds all subgraph nodes sorted by date)
# we need this since we go forward in time (source older than remix)
node_list.sort(key=lambda x: x[1]['date']) # I think ['date'] is not necessary
# print ' ========== SORTED NODE_LIST ========= '
# pp(node_list)
# dict with key=sound_id, value=index, nodeName=original_filname
# in the previous sorted by date list
# FIXME: no need for all this data, can be simple dict, key=value
container = dict((val[0], {'index': idx, 'nodeName': val[1]['nodeName']}) for (idx, val) in enumerate(node_list))
# print ' ========== CONTAINER ========= '
# pp(container)
links = []
remixgroup.save() # need to save to have primary key before ManyToMany
# FIXME: no idea why nx.weakly_connected_components(sg) return list in list...
remixgroup.sounds = set(nx.weakly_connected_components(sg)[0])
for sound in remixgroup.sounds.all():
sound.invalidate_template_caches()
remixgroup.group_size = len(node_list)
# FIXME: seems like double work here, maybe convert container to list and sort?
nodes = [{'id': val[0],
'username': val[1]['username'],
'nodeName': val[1]['nodeName'],
'sound_url_mp3': val[1]['sound_url_mp3'],
'sound_url_ogg': val[1]['sound_url_ogg'],
'waveform_url': val[1]['waveform_url'],
'group': 1} for (idx, val) in enumerate(node_list)]
for line in nx.generate_adjlist(sg):
# print line
if len(line.split()) > 1:
for i, l in enumerate(line.strip().split(" ")):
# index 0 is the source, which we already know
if i > 0:
link = {'target': container[int(line.split(" ")[0])]['index'],
'source': container[int(l)]['index']}
links.append(link)
remixgroup.protovis_data = "{\"color\": \"#F1D9FF\"," \
"\"length\":" + str(len(node_list)) + "," \
"\"nodes\": " + json.dumps(nodes) + "," \
"\"links\": " + json.dumps(links) + "}"
remixgroup.networkx_data = json.dumps(dict(nodes=sg.nodes(), edges=sg.edges()))
remixgroup.save()
import matplotlib.pyplot as plt
from networkx import nx
a, b, c, d, e, f = ('a', 'b', 'c', 'd', 'e', 'f')
G = nx.Graph()
G.add_edge(a, b)
G.add_edge(b, c)
G.add_edge(c, b)
G.add_edge(c, a)
G.add_edge(c, d)
G.add_edge(d, e)
G.add_edge(e, f)
G.add_edge(f, d)
for line in nx.generate_adjlist(G):
print(line)
nx.draw(G, with_labels=True)
plt.show()
args = parser.parse_args() # realiza o parse
print ("### Gerando Topologia Genérica com %s switches e %s links ###" %
(args.s, args.l))
switches = args.s # é a quantidade de switches
# é quantidade de enlaces ou o dobro de switches
links = args.l if args.l else 2*switches
output = args.output # arquivo de saida
view = args.view # indica se quer visualizar a rede
# manipulando o grafo
G = nx.gnm_random_graph(switches, links) # grapho aleatório
edges = [] # lista de arestas
for line in nx.generate_adjlist(G): # para cada linha das adjascentes
nodes = line.split() # quebra por espaços
first = int(nodes[0]) # pega o primeiro
for node in nodes[1:]: # para cada node restante
edges.append([first, int(node)]) # adiciona na lista o parte de node
# manipulando arquivo
with open(output, "w") as f: # abre o arquivo para escrita
dump(edges, f) # escreve o arquivo
if view:
nx.draw(G, with_labels=True, font_weight='bold')
plt.show()
__author__ = 'Andrei Bastos'
__email__ = '*****@*****.**'
sometimes called the Erdős-Rényi graph. """ # Author: Aric Hagberg ([email protected]) # Copyright (C) 2004-2019 by # Aric Hagberg <*****@*****.**> # Dan Schult <*****@*****.**> # Pieter Swart <*****@*****.**> # All rights reserved. # BSD license. import matplotlib.pyplot as plt from networkx import nx n = 10 # 10 nodes m = 20 # 20 edges G = nx.gnm_random_graph(n, m) # some properties print("node degree clustering") for v in nx.nodes(G): print('%s %d %f' % (v, nx.degree(G, v), nx.clustering(G, v))) # print the adjacency list for line in nx.generate_adjlist(G): print(line) nx.draw(G) plt.show()
