def build_graph(self, data, score):
# import biopython, that is needed for the pairwise alignment
# http://biopython.org/DIST/docs/api/Bio.pairwise2-module.html
from Bio import pairwise2
# import a library required to produce gexf graphs
# https://networkx.github.io/documentation/networkx-1.10/reference/generated/networkx.readwrite.gexf.write_gexf.html#networkx.readwrite.gexf.write_gexf
from networkx import nx
###Similarity graph
self.graph = nx.Graph()
### pairwise alignement with biopython
k = 0
for key in data: #These loops are made in order to avoid redundant alignement
k += 1
for i in range(k, len(data.keys())):
key_2 = list(data.keys())
if key != key_2[
i]: # Check with we don't align two identical sequences.
print(key_2[i])
seq1 = data.get(key)
seq2 = data.get(key_2[i])
pairwise_result = pairwise2.align.globalxx(seq1, seq2)
# Add the edge if score is greater than minimal score
#We take only the first ten characters of the identifier.
#It could need to be adjusted regarding the identifier length.
if pairwise_result[0][2] > score:
self.graph.add_edge(key[0:10],
key_2[i][0:10],
weight=int(pairwise_result[0][2]))
# Alignement score display
print("score = ", pairwise_result[0][2])
return self.graph
def draw_small_graph(graph):
"""Draw the graph showing edge weight
:param graph: graph object to draw
"""
graph_nx = nx.Graph()
graph_nx.add_weighted_edges_from(graph.weighted_edges)
labels = nx.get_edge_attributes(graph_nx, 'weight')
pos = nx.spring_layout(graph_nx)
nx.draw_networkx_edge_labels(graph_nx, pos=pos, edge_labels=labels)
nx.draw(graph_nx,
pos=pos,
with_labels=True,
node_size=10,
node_color="skyblue",
node_shape="o",
alpha=0.5,
linewidths=30)
plt.title(graph.name)
plt.show()
def betweenness_centrality_labeling(self, graph, approx=None):
result = {}
labeled_graph = nx.Graph(graph)
if approx is None:
centrality = list(nx.betweenness_centrality(graph).items())
else:
centrality = list(
nx.betweenness_centrality(graph, k=approx).items())
sorted_centrality = sorted(centrality,
key=lambda n: n[1],
reverse=True)
dict_ = {}
label = 0
for t in sorted_centrality:
dict_[t[0]] = label
label += 1
nx.set_node_attributes(labeled_graph, dict_, 'labeling')
ordered_nodes = list(zip(*sorted_centrality))[0]
result['labeled_graph'] = labeled_graph
result['sorted_centrality'] = sorted_centrality
result['ordered_nodes'] = ordered_nodes
return result
def receptive_field_padding(self, normalized_graph):
"""
Method that ensures uniformity across receptive fields when width or rf_size are too big
:param normalized_graph: rf_transformed graph to which we add dummy nodes
:return: uniformized graph
"""
graph = nx.Graph(normalized_graph)
keys = [key for key, v in dict(normalized_graph.nodes()).items()]
labels = [value for key, value in dict(nx.get_node_attributes(normalized_graph, 'labeling')).items()]
# add extra dummy nodes as long as rf_size is not reached
#########################################################
counter = 1
while len(graph.nodes()) < self.rf_size:
graph.add_node(max(keys) + counter,
attr_name=self.dummy_value,
labeling=max(labels) + counter)
counter += 1
#########################################################
return graph
def compute_graph_ranking(graph: nx.Graph,
vertex: int,
original_node_order: dict):
"""
Method that relabels a graph w.r.t. nodes distances to given root
:param graph: subgraph to rank
:param vertex: landmark vertex for the ranking
:param original_node_order: original ranking
:return: graph labeled by the new ranking
"""
labeled_graph = nx.Graph(graph)
ordered_graph = compute_ranking_distance(graph, vertex)
labels = nx.get_node_attributes(ordered_graph, 'labeling')
new_order = relabel_graph(graph=ordered_graph,
original_labeling=labels,
new_labeling=original_node_order)
nx.set_node_attributes(labeled_graph, new_order, 'labeling')
return labeled_graph
def nauty_graph_automorphism(graph: nx.Graph):
"""
Graph canonicalization funtion, meant to break timebreakers of the non-injective ranking function
:param graph: subgraph to be canonicalized
:return: canonicalized subgraph
"""
# convert labels to integers to give nauty the node partitions required
graph_int_labeled = convert_node_labels_to_integers(graph)
canonicalized_graph = nx.Graph(graph_int_labeled)
# get canonicalized graph using nauty
nauty = Graph(len(graph_int_labeled.nodes()), directed=False)
nauty.set_adjacency_dict({node: list(nbr) for node, nbr in graph_int_labeled.adjacency()})
labels_dict = nx.get_node_attributes(graph_int_labeled, 'labeling')
canonical_labeling_order = {k: canonical_labeling(nauty)[k] for k in
range(len(graph_int_labeled.nodes()))}
canonical_order = relabel_graph(graph_int_labeled, labels_dict, canonical_labeling_order)
nx.set_node_attributes(canonicalized_graph, canonical_order, 'labeling')
return canonicalized_graph
def canonicalizes(self,subgraph):
st=time.time()
#wl_subgraph_normalized=self.wl_normalization(subgraph)['labeled_graph']
#g_relabel=convert_node_labels_to_integers(wl_subgraph_normalized)
g_relabel=convert_node_labels_to_integers(subgraph)
labeled_graph=nx.Graph(g_relabel)
nauty_graph=Graph(len(g_relabel.nodes()),directed=False)
nauty_graph.set_adjacency_dict({n:list(nbrdict) for n,nbrdict in g_relabel.adjacency()})
labels_dict=nx.get_node_attributes(g_relabel,'labeling')
canonical_labeling_dict={k:canonical_labeling(nauty_graph)[k] for k in range(len(g_relabel.nodes()))}
new_ordered_dict=self.rank_label_wrt_dict(g_relabel,labels_dict,canonical_labeling_dict)
nx.set_node_attributes(labeled_graph,new_ordered_dict,'labeling')
ed=time.time()
self.all_times['canonicalizes'].append(ed-st)
return labeled_graph
"""
Created on Sun Aug 2 22:41:48 2020
@author: eric
"""
#---------------------------------------------------------------------
# Code qui permet de tracer un graphe pour simuler un réeau social
# On ajoute des sommets (node)
# On dessine les arêtes (edge) entre les sommets voulus
#---------------------------------------------------------------------
from networkx import nx, diameter, radius, center
import matplotlib.pyplot as plt
reseau_social = nx.Graph()
reseau_social.add_node('laurent')
reseau_social.add_node('pierre')
reseau_social.add_node('lucie')
reseau_social.add_node('sophie')
reseau_social.add_node('martin')
reseau_social.add_node('jacques')
reseau_social.add_edge('laurent', 'pierre')
reseau_social.add_edge('lucie', 'pierre')
#reseau_social.add_edge('laurent','lucie')
reseau_social.add_edge('sophie', 'lucie')
reseau_social.add_edge('sophie', 'pierre')
reseau_social.add_edge('sophie', 'martin')
reseau_social.add_edge('martin', 'laurent')
def test_cycle_undirected_unweighted(self):
G = nx.Graph()
G.add_edge(1, 2)
assert_equal(nx.global_reaching_centrality(G), 0)
def test_undirected_weighted_star(self):
G = nx.Graph()
G.add_edge(1, 2, weight=1)
G.add_edge(1, 3, weight=2)
assert_equal(nx.global_reaching_centrality(G, normalized=False), 0.25)
def test_negatively_weighted(self):
G = nx.Graph()
G.add_weighted_edges_from([(0, 1, -2), (1, 2, +1)])
nx.global_reaching_centrality(G, weight='weight')
from networkx import nx
mapa = nx.Graph()
# ------------------------------------------ CLASSE PARA CRIACAO DE ARESTA
class Aresta:
def __init__(self, origem, destino, custo):
self._origem = origem
self._destino = destino
self._custo = custo
self._feromonio = 0.1
def getOrigem(self):
return self._origem
def getDestino(self):
return self._destino
def getCusto(self):
return self._custo
def getFeromonio(self):
return self._feromonio
def setFeromonio(self, feromonio):
self._feromonio = feromonio
# ------------------------------------------ CLASSE PARA CRIACAO DE GRAFO
class Grafo:
10: "ber",
11: "mun",
12: "mil",
13: "pra",
14: "vie",
15: "zag",
16: "rom"
}
switch_link_matrix = [(1, 2), (1, 4), (2, 3), (2, 5), (3, 4),
(3, 6), (4, 7), (4, 9), (5, 6), (5, 10), (6, 7),
(6, 11), (7, 8), (8, 9), (8, 12), (10, 11), (10, 13),
(11, 12), (11, 14), (12, 16), (13, 14), (14, 15),
(15, 16)]
host_count_per_switch = 1
topology = nx.Graph()
nodes = list(switch_names.keys())
topology.add_nodes_from(nodes)
topology.add_edges_from(switch_link_matrix)
result = minimum_spanning_tree(topology)
no_flood_links = list(set(switch_link_matrix) - set(result.edges))
# ---------- initialize network -----------------------------
#dpid = DPID_BASE
OpenFlow14Switch = partial(OVSKernelSwitch, protocols=OPENFLOW_PROTOCOL)
#STPEnabledSwitch = partial(OVSKernelSwitch, protocols=OPENFLOW_PROTOCOL, failMode="standalone", stp=True)
net = Containernet(ipBase=IP_BASE)
net.addController("c0",
controller=RemoteController,
def __init__(self, g_dict=None):
if g_dict is None:
g_dict = []
self._g_dict = g_dict
self.g_networkx = nx.Graph(g_dict)
def test_cycle_undirected_weighted(self):
G = nx.Graph()
G.add_edge(1, 2, weight=1)
grc = nx.global_reaching_centrality
assert grc(G, normalized=False) == 0
def test_undirected_weighted_star(self):
G = nx.Graph()
G.add_weighted_edges_from([(1, 2, 1), (1, 3, 2)])
grc = nx.global_reaching_centrality
assert grc(G, normalized=False, weight='weight') == 0.375
def test_convert_to_integers_raise():
G = nx.Graph()
with pytest.raises(nx.NetworkXError) as excinfo:
H = convert_node_labels_to_integers(G, ordering="increasing age")
def labeling_to_root(self,graph,vertex):
labeled_graph=nx.Graph(graph)
source_path_lengths = nx.single_source_dijkstra_path_length(graph, vertex)
nx.set_node_attributes(labeled_graph,source_path_lengths,'labeling')
return labeled_graph
def gnp_random_graph_Renana(n ,rand_num, p_1, p_2, p_12, SH_per, seed=None, directed=False):
"""Returns a random graph with two populations
Parameters
----------
n : int
The number of nodes.
rand_num : float
Location in the parameter space
p_1 : float
Probability for edge creation in the SH group // More correct is the distance among close friends.
p_2 : float
Probability for edge creation in the NORMAL group.
p_12 : float
Probability for edge creation in between the NORMAL and the SH groups.
SH_per : float
The amount of SH in the network
seed : int, optional
Seed for random number generator (default=None).
directed : bool, optional (default=False)
If ``True``, this function returns a directed graph.
Notes
-----
This algorithm runs in `O(n^2)` time.
References
----------
.. [1] P. Erdős and A. Rényi, On Random Graphs, Publ. Math. 6, 290 (1959).
.. [2] E. N. Gilbert, Random Graphs, Ann. Math. Stat., 30, 1141 (1959).
.. [3] R. Peres, The impact of network characteristics on the diffusion
of innovations, Physica A, (2014)
"""
if directed:
G=nx.DiGraph()
else:
G=nx.Graph()
G.name="gnp_random_graph_Renana(%s,%s,%s,%s)"%(n,p_1,p_2,p_12)
SH_size = int(SH_per*n)
G.add_nodes_from(range(SH_size), group ='SH')
G.add_nodes_from(range(SH_size,n), group ='Normal')
color_map = {'SH':'#9B0029', 'Normal':'#003366'}
colors = [color_map[G._node[node]['group']] for node in G]
if not seed is None:
np.random.seed(seed)
if G.is_directed():
edges=itertools.permutations(range(n),2)
else:
edges=itertools.combinations(range(n),2)
for e in edges:
if (e[0] < SH_size) and (e[1] < SH_size):
if np.abs(rand_num[e[0]] - rand_num[e[1]])< p_1:
G.add_edge(*e)
if (e[0] >= SH_size) and (e[1] >= SH_size):
if np.abs(rand_num[e[0]] - rand_num[e[1]]) < p_2:
G.add_edge(*e)
if ((e[0] < SH_size) and (e[1] >= SH_size)) or ((e[0] >= SH_size) and (e[1] < SH_size)):
if np.random.uniform() < p_12:
G.add_edge(*e)
return G, colors
x = x ^ l[i*16 + j]
denseHash.append(x)
s = ""
for c in denseHash:
s += "{0:02x}".format(c)
return s
grid = []
for i in xrange(128):
kh = knotHash("%s-%d" % (inpt, i))
gridline = []
for c in kh:
gridline.extend([int(c) for c in "{0:04b}".format(int(c, 16))])
grid.append(gridline)
graph = nx.Graph()
for y in xrange(128):
for x in xrange(128):
if grid[y][x]:
graph.add_node((y,x))
for y in xrange(128):
for x in xrange(128):
if y > 0:
if grid[y][x] and grid[y-1][x]:
graph.add_edge((y,x), (y-1,x))
if x > 0:
if grid[y][x] and grid[y][x-1]:
graph.add_edge((y,x), (y,x-1))
# part 1
print sum(sum(gridline) for gridline in grid)
def main():
df = pd.read_csv('metrosp_stations.csv')
df = df.drop(columns=['Unnamed: 0'])
df.neigh = df.neigh.str[1:-1].str.split(',').tolist()
df = df.neigh.apply(pd.Series) \
.merge(df, left_index = True, right_index = True) \
.drop(["neigh"], axis = 1) \
.melt(id_vars = ['name','station','lat', 'lon', 'line'], value_name = "onlyNeigh") \
.drop("variable", axis = 1) \
.dropna()
df.to_json('metroSP.json')
with open('metroSPNotEdited.json') as json_file:
dataNotEdited = json.load(json_file)
with open('metroSP.json') as json_file:
data = json.load(json_file)
listaDeAdjacencia = {}
listaEstacoes = []
# Montando lista de adjacência
for (key, estacao) in data['station'].items():
if not listaDeAdjacencia.get(estacao):
listaDeAdjacencia[estacao] = []
listaEstacoes.append(estacao)
listaDeAdjacencia[estacao].append(data['onlyNeigh'][key])
# Salvando lista de adjacência
with open('listaAdjacencia.json', 'w') as json_file:
json.dump(listaDeAdjacencia, json_file)
while True:
os.system("clear")
opcao = menuPrincipal()
if opcao == '1':
origem = input("Estação de Origem: ")
destino = input("Estação de Destino: ")
menor_caminho = BFS(listaDeAdjacencia, origem, destino)
print('Esse é o menor caminho para chegar no seu destino:')
for item in menor_caminho:
print(
recupera_nome(dataNotEdited, item) + ' - ' +
recupera_linha(dataNotEdited, item))
G = nx.Graph()
labels = {}
mapaDeCores = []
for estacao, arestas in listaDeAdjacencia.items():
corNo = recupera_linha(dataNotEdited, estacao)
labels[estacao] = recupera_nome(dataNotEdited, estacao)
G.add_node(estacao)
# corNo = nx.get_node_attributes(G,'color')
# corNo = recupera_linha(dataNotEdited, estacao)
if corNo == '[lilas]':
mapaDeCores.append('#8B008B')
elif corNo == '[verde]':
mapaDeCores.append('#006400')
elif corNo == '[azul]':
mapaDeCores.append('#000080')
elif corNo == '[vermelha]':
mapaDeCores.append('#FF0000')
elif corNo == '[amarela]':
mapaDeCores.append('#FF8C00')
elif corNo == '[prata]':
mapaDeCores.append('#1C1C1C')
else:
mapaDeCores.append('#8B4513')
for aresta in arestas:
G.add_edge(estacao, aresta)
listaNos = G.nodes()
listaNos = sorted((set(listaNos)))
posicoes = get_posicoes()
fig1 = plt.figure('Grafo Principal')
nx.draw(G,
pos=posicoes,
nodelist=listaNos,
with_labels=True,
labels=labels,
node_color=mapaDeCores,
font_size=6,
font_color='white',
edge_color='#A0522D')
fig1.set_facecolor("#00000F")
fig2 = plt.figure('Subgrafo')
fig2.set_facecolor("#00000F")
ax = plt.gca()
ax.set_facecolor('#00000F')
subgraph = G.subgraph(menor_caminho)
nx.draw_networkx(subgraph,
pos=posicoes,
font_size=8,
edge_color='#00CED1',
node_color='#00CED1',
font_color='white')
plt.show()
else:
os.system("clear")
print('Encerrando Programa!')
break
[i, data["Address Receiver"][k], data["Transaction_value"][k]])
connections[i] = {value for value in temp}
for k in temp:
temp2 = []
for p in range(len(data["Address Sender"])):
if data["Address Sender"][p] == k:
temp2.append(data["Address Receiver"][p])
num.append([
k, data["Address Receiver"][p],
data["Transaction_value"][p]
])
connections[k] = {value for value in temp2}
print("Building Succeeds!")
G = nx.Graph(connections)
pos = nx.spring_layout(G)
colormap = []
for node in G:
for k in num:
if k[1] == i:
if k[2] < 1:
colormap.append('blue')
elif k[2] > 20:
colormap.append('red')
else:
colormap.append('green')
nx.draw(G, node_color=colormap, with_labels=True)
plt.show()
"""Graph partioning program from slide 89"""
from dimod import DiscreteQuadraticModel
from dwave.system import LeapHybridDQMSampler
from networkx import nx
lagrange = 10
num_colors = 4
colors = range(num_colors)
dqm = DiscreteQuadraticModel()
G = nx.Graph()
G.add_edges_from([(0, 1), (1, 2), (2, 3), (3, 4), (4, 5), (5, 6), (0, 6)])
n_edges = len(G.edges)
for p in G.nodes:
dqm.add_variable(num_colors, label=p)
for p in G.nodes:
dqm.set_linear(p, colors)
for p0, p1 in G.edges:
dqm.set_quadratic(p0, p1, {(c, c): lagrange for c in colors})
for p0, p1 in G.edges:
dqm.set_quadratic(p0, p1, {(c, c): lagrange for c in colors})
sampler = LeapHybridDQMSampler()
sampleset = sampler.sample_dqm(dqm)
sample = sampleset.first.sample
energy = sampleset.first.energy
valid = True
for edge in G.edges:
i, j = edge
if sample[i] == sample[j]:
valid = False
def wl_normalization(self, graph):
result = {}
labeled_graph = nx.Graph(graph)
relabel_dict_ = {}
graph_node_list = list(graph.nodes())
for i in range(len(graph_node_list)):
relabel_dict_[graph_node_list[i]] = i
i += 1
inv_relabel_dict_ = {v: k for k, v in relabel_dict_.items()}
graph_relabel = nx.relabel_nodes(graph, relabel_dict_)
label_lookup = {}
label_counter = 0
l_aux = list(
nx.get_node_attributes(graph_relabel, 'attr_name').values())
labels = np.zeros(len(l_aux), dtype=np.int32)
adjency_list = list([
list(x[1].keys()) for x in graph_relabel.adjacency()
]) #adjency list à l'ancienne comme version 1.0 de networkx
for j in range(len(l_aux)):
if not (l_aux[j] in label_lookup):
label_lookup[l_aux[j]] = label_counter
labels[j] = label_counter
label_counter += 1
else:
labels[j] = label_lookup[l_aux[j]]
# labels are associated to a natural number
# starting with 0.
new_labels = copy.deepcopy(labels)
# create an empty lookup table
label_lookup = {}
label_counter = 0
for v in range(len(adjency_list)):
# form a multiset label of the node v of the i'th graph
# and convert it to a string
long_label = np.concatenate(
(np.array([labels[v]]), np.sort(labels[adjency_list[v]])))
long_label_string = str(long_label)
# if the multiset label has not yet occurred, add it to the
# lookup table and assign a number to it
if not (long_label_string in label_lookup):
label_lookup[long_label_string] = label_counter
new_labels[v] = label_counter
label_counter += 1
else:
new_labels[v] = label_lookup[long_label_string]
# fill the column for i'th graph in phi
labels = copy.deepcopy(new_labels)
dict_ = {inv_relabel_dict_[i]: labels[i] for i in range(len(labels))}
nx.set_node_attributes(labeled_graph, dict_, 'labeling')
result['labeled_graph'] = labeled_graph
result['ordered_nodes'] = [
x[0] for x in sorted(dict_.items(), key=lambda x: x[1])
]
return result
def test_undirected_weighted_star(self):
G = nx.Graph()
G.add_weighted_edges_from([(1, 2, 1), (1, 3, 2)])
centrality = nx.local_reaching_centrality(G, 1, normalized=False, weight='weight')
assert centrality == 1.5
def __init__(self, directed=True):
self.graph = nx.DiGraph()
# self.sc_authors = sc_authors
# self.rc_authors = rc_authors
if not directed:
self.graph = nx.Graph()
def test_cycle_undirected_unweighted(self):
G = nx.Graph()
G.add_edge(1, 2)
assert nx.global_reaching_centrality(G, weight=None) == 0
def convert_network(network): G = nx.Graph() for user, friends in network.items(): for friend in friends: G.add_edge(user, friend) return G
def test_negatively_weighted(self):
with pytest.raises(nx.NetworkXError):
G = nx.Graph()
G.add_weighted_edges_from([(0, 1, -2), (1, 2, +1)])
nx.local_reaching_centrality(G, 0, weight='weight')
def buildGraphGexf(root, title, data, flt=[]):
"""Convert supplied raw data into GEXF format (e.g. for Gephi)
GEXF produced by PyGEXF doesn't work with SigmaJS because
SJS needs coordinates for each node.
flt is a list of event types to include, if not set everything is
included.
Args:
root (str): TBD
title (str): unused
data (list): scan result as list
flt (list): TBD
Returns:
str: TBD
"""
mapping = SpiderFootHelpers.buildGraphData(data, flt)
graph = nx.Graph()
nodelist = dict()
ncounter = 0
for pair in mapping:
(dst, src) = pair
col = ["0", "0", "0"]
# Leave out this special case
if dst == "ROOT" or src == "ROOT":
continue
if dst not in nodelist:
ncounter = ncounter + 1
if dst in root:
col = ["255", "0", "0"]
graph.node[dst]['viz'] = {
'color': {
'r': col[0],
'g': col[1],
'b': col[2]
}
}
nodelist[dst] = ncounter
if src not in nodelist:
ncounter = ncounter + 1
if src in root:
col = ["255", "0", "0"]
graph.add_node(src)
graph.node[src]['viz'] = {
'color': {
'r': col[0],
'g': col[1],
'b': col[2]
}
}
nodelist[src] = ncounter
graph.add_edge(src, dst)
gexf = GEXFWriter(graph=graph)
return str(gexf).encode('utf-8')
