def setUp(self):
G1 = cnlti(nx.grid_2d_graph(2, 2), first_label=0, ordering="sorted")
G2 = cnlti(nx.lollipop_graph(3, 3), first_label=4, ordering="sorted")
G3 = cnlti(nx.house_graph(), first_label=10, ordering="sorted")
self.G = nx.union(G1, G2)
self.G = nx.union(self.G, G3)
self.DG = nx.DiGraph([(1, 2), (1, 3), (2, 3)])
self.grid = cnlti(nx.grid_2d_graph(4, 4), first_label=1)
self.gc = []
G = nx.DiGraph()
G.add_edges_from([(1, 2), (2, 3), (2, 8), (3, 4), (3, 7), (4, 5),
(5, 3), (5, 6), (7, 4), (7, 6), (8, 1), (8, 7)])
C = [[3, 4, 5, 7], [1, 2, 8], [6]]
self.gc.append((G, C))
G = nx.DiGraph()
G.add_edges_from([(1, 2), (1, 3), (1, 4), (4, 2), (3, 4), (2, 3)])
C = [[2, 3, 4],[1]]
self.gc.append((G, C))
G = nx.DiGraph()
G.add_edges_from([(1, 2), (2, 3), (3, 2), (2, 1)])
C = [[1, 2, 3]]
self.gc.append((G,C))
# Eppstein's tests
G = nx.DiGraph({0:[1], 1:[2, 3], 2:[4, 5], 3:[4, 5], 4:[6], 5:[], 6:[]})
C = [[0], [1], [2],[ 3], [4], [5], [6]]
self.gc.append((G,C))
G = nx.DiGraph({0:[1], 1:[2, 3, 4], 2:[0, 3], 3:[4], 4:[3]})
C = [[0, 1, 2], [3, 4]]
self.gc.append((G, C))
def setUp(self):
G1=cnlti(nx.grid_2d_graph(2,2),first_label=0,ordering="sorted")
G2=cnlti(nx.lollipop_graph(3,3),first_label=4,ordering="sorted")
G3=cnlti(nx.house_graph(),first_label=10,ordering="sorted")
self.G=nx.union(G1,G2)
self.G=nx.union(self.G,G3)
self.DG=nx.DiGraph([(1,2),(1,3),(2,3)])
self.grid=cnlti(nx.grid_2d_graph(4,4),first_label=1)
def spingraph_from_graph(graph):
# even_graph = nx.relabel_nodes(graph, lambda x:x*2)
# odd_graph = nx.relabel_nodes(graph, lambda x:2*x+1)
# union_graph = nx.union(even_graph, odd_graph)
# on the fly union saves about 20% memory, ugly but more efficient
union_graph = nx.union(nx.relabel_nodes(graph, lambda x:x*2),nx.relabel_nodes(graph, lambda x:2*x+1))
# from pudb import set_trace; set_trace()
for spin_down_node in xrange(1,union_graph.order(),2):
spin_up_node = spin_down_node -1
for spin_down_node_neighbour in union_graph[spin_down_node].keys():
if spin_down_node_neighbour % 2 ==0:
continue
if spin_down_node_neighbour < spin_down_node: # is either top or left neighbour
if spin_down_node_neighbour == spin_down_node-2: # is left neighbour
union_graph.add_edge(spin_up_node,spin_down_node_neighbour,weight=-p.tso)
union_graph.add_edge(spin_down_node_neighbour,spin_up_node,weight=-p.tso)
else:
union_graph.add_edge(spin_up_node,spin_down_node_neighbour,weight=+1j*p.tso)
union_graph.add_edge(spin_down_node_neighbour,spin_up_node,weight=-1j*p.tso)
if spin_down_node_neighbour > spin_down_node: # is either right or bottom neighbour
if spin_down_node_neighbour == spin_down_node+2: # is right neighbour
union_graph.add_edge(spin_up_node,spin_down_node_neighbour,weight=p.tso)
union_graph.add_edge(spin_down_node_neighbour,spin_up_node,weight=p.tso)
else:
union_graph.add_edge(spin_up_node,spin_down_node_neighbour,weight=-1j*p.tso)
union_graph.add_edge(spin_down_node_neighbour,spin_up_node,weight=+1j*p.tso)
return union_graph
def patternsets2(MotifG1, MotifG2):
#enumerate all possible permutations of node labels,
#minimum is sharing one edge, all the way to max is the smaller number of edges, complexity 2^edgenum_max
#return a set of possibly isomorphic collapses
patternset = set()
edgenum_max = min(MotifG1.number_of_edges(), MotifG2.number_of_edges())
#select L (two+) edges to overlap
for L in range(1, edgenum_max + 1):
print L
L_subsets = list(itertools.combinations(MotifG1.edges(),L))
L_subsets2 = list(itertools.combinations(MotifG2.edges(),L))
for subset1 in L_subsets:
for subset2 in L_subsets2:
print "already chose these" +str(L)+" edges in Motif2"
print subset2
permutations = list(itertools.permutations(subset1))
i = 0
for permutation in permutations:
print "this permutation is"
print permutation
print "in this particular order" + str(i)
if MotifG1 == MotifG2:
print "waring!!!same motif non-relabled"
G = nx.disjoint_union(MotifG1, MotifG2)
else:
G = nx.union(MotifG1, MotifG2)
if len(G) != 0:
G2 = nx.Graph()
G22 = nx.Graph()
Motif2merged_nodes = set()
for j in range(0, len(permutation)):
edge_1 = permutation[j]
edge_2 = subset2[j]
print "edge 1"
print edge_1
print "edge 2"
print edge_2
if edge_2[0] not in Motif2merged_nodes:
G1 = merge_nodes(G, edge_1[0], edge_2[0])
Motif2merged_nodes.add(edge_2[0])
if edge_2[1] not in Motif2merged_nodes:
G2 = merge_nodes(G1, edge_1[1], edge_2[1])
Motif2merged_nodes.add(edge_2[1])
if edge_2[0] not in Motif2merged_nodes:
G11 = merge_nodes(G, edge_1[1], edge_2[0])
if edge_2[1] not in Motif2merged_nodes:
G22 = merge_nodes(G11, edge_1[0], edge_2[1])
patternset.add(G2)
patternset.add(G22)
print G2.nodes()
i += 1
return patternset
def __call__(self, code, charge_type):
errors = {}
for type in code.types:
errors[type] = code.Syndrome(type, charge_type)
shrunk_errs, shrunk_exts, matches = {}, {}, {}
loops_graph = nx.Graph()
for t1 in code.types:
[t2, t3] = code.complementaryTypes(t1)
shrunk_errs[t1] = nx.union(errors[t2], errors[t3])
shrunk_exts[t1] = code.External[t2] + code.External[t3]
alt_ext = code.External[t1][0]
matches[t1] = DSP_Matching(shrunk_errs[t1], shrunk_exts[t1], 2, alt_ext)
for start in matches[t1]:
end = matches[t1][start]
chain = DSP_Path(code.Dual[t1], start, end)
links = len(chain) -1
for i in range(links):
node1, node2 = chain[i], chain[i+1]
edge = (node1, node2)
if edge in loops_graph.edges():
loops_graph.remove_edge(*edge)
else:
loops_graph.add_edge(*edge)
Exts = code.External['red']+code.External['blue']+code.External['green']
code, loops_graph = correctLoops(code, loops_graph, charge_type)
while hasConnectedBoundaries(code, loops_graph, Exts):
ext1, ext2 = connectedBoundaries(loops_graph, Exts)
code, loops_graph = makeBoundLoop(code, loops_graph, ext1, ext2)
code, loops_graph = correctLoops(code, loops_graph, charge_type)
return code
def union_all(graphs, rename=(None,) , name=None):
graphs_names = zip_longest(graphs,rename)
U, gname = next(graphs_names)
for H,hname in graphs_names:
U = nx.union(U, H, (gname,hname),name=name)
gname = None
return U
def _and_gate(num, bp1, idx1, bp2, idx2):
#
# AND gates are constructed as follows:
#
# Given BP_1 and BP_2, we merge the acc node of BP_1 with the src
# node of BP_2 and the rej node of BP_1 with the rej node of BP_2.
#
t1 = bp1.nlayers
t2 = bp2.nlayers
relabel_layers(bp2.graph, t1)
oldlayer = bp2.graph.node[('src', idx2)]['layer']
newnode = ('node-%d' % num, num)
g = nx.union(bp1.graph, bp2.graph)
g = contract(g, ('acc', idx1), ('src', idx2), newnode)
g = contract(g, ('rej', idx1), ('rej', idx2), ('rej', num))
g = relabel(g, num)
g.node[newnode]['layer'] = oldlayer
def eval(inp):
if inp <= t1 - 1:
return bp1.inp(inp)
elif inp <= t1 + t2 - 1:
return bp2.inp(inp - t1)
else:
raise Exception("andgate eval failed on %s!" % inp)
return _Graph(eval, g, t1 + t2, num)
def crearSubGrafo(self,key,key2,key3):
temp = nx.Graph()
for i in range(0,6):
if i == 0:
temp.add_node(key+str(i)+key3,extremo=True)
temp.add_node(key2+str(i)+key3,extremo=True)
elif i == 5:
temp.add_node(key+str(i)+key3,extremo=True)
temp.add_node(key2+str(i)+key3,extremo=True)
else:
temp.add_node(key+str(i)+key3)
temp.add_node(key2+str(i)+key3)
if (i != 0):
temp.add_edge(key + str(i-1) +key3 , key+str(i)+key3)
temp.add_edge(key2 + str(i-1)+key3 , key2+str(i)+key3)
if (i == 2):
temp.add_edge(key+'2'+key3 , key2+'0'+key3)
temp.add_edge(key+'0'+key3 , key2+'2'+key3)
if (i == 5):
temp.add_edge(key+'3'+key3 , key2+'5'+key3)
temp.add_edge(key+'5' +key3, key2+'3'+key3)
self.grafo = nx.union(self.grafo,temp)
def combine_graphs(self, true_class_bias=1,
multi_class_bias=0, multi_class_threshold=0,
class_graph=None, instance_graph=None):
"""Combine graphs."""
probs = np.array([instance_graph.node[v]['prob']
for v in instance_graph.nodes()])
id_offset = max(instance_graph.nodes()) + 1
offset_pred_graph = \
nx.relabel_nodes(class_graph, lambda x: x + id_offset)
union_graph = nx.union(instance_graph, offset_pred_graph)
mcb = multi_class_bias
if mcb != 0:
# add links from each instance to the class nodes
# with a length inversely proportional to the prob
for u in instance_graph.nodes():
# find k-th largest prob value (k=multi_class_threshold)
# and instantiate only the k most probable edges
th = sorted(probs[u], reverse=True)[multi_class_threshold]
for group, prob in enumerate(probs[u]):
if prob >= th:
group_id = group + id_offset
length = (1 - mcb) * self._prob_to_len(prob)
union_graph.add_edge(u, group_id, len=length)
if true_class_bias != 0:
# add links from each instance to its assigned class
for u in instance_graph.nodes():
group_id = instance_graph.node[u]['group'] + id_offset
union_graph.add_edge(u, group_id,
len=1 - true_class_bias)
return union_graph
def setUp(self):
# G is the example graph in Figure 1 from Batagelj and
# Zaversnik's paper titled An O(m) Algorithm for Cores
# Decomposition of Networks, 2003,
# http://arXiv.org/abs/cs/0310049. With nodes labeled as
# shown, the 3-core is given by nodes 1-8, the 2-core by nodes
# 9-16, the 1-core by nodes 17-20 and node 21 is in the
# 0-core.
t1 = nx.convert_node_labels_to_integers(nx.tetrahedral_graph(), 1)
t2 = nx.convert_node_labels_to_integers(t1, 5)
G = nx.union(t1, t2)
G.add_edges_from([(3, 7), (2, 11), (11, 5), (11, 12), (5, 12),
(12, 19), (12, 18), (3, 9), (7, 9), (7, 10),
(9, 10), (9, 20), (17, 13), (13, 14), (14, 15),
(15, 16), (16, 13)])
G.add_node(21)
self.G = G
# Create the graph H resulting from the degree sequence
# [0, 1, 2, 2, 2, 2, 3] when using the Havel-Hakimi algorithm.
degseq = [0, 1, 2, 2, 2, 2, 3]
H = nx.havel_hakimi_graph(degseq)
mapping = {6: 0, 0: 1, 4: 3, 5: 6, 3: 4, 1: 2, 2: 5}
self.H = nx.relabel_nodes(H, mapping)
def copy_and_offset_with_mirror(self, original, offset_val, reflect=False):
"""Add a copy of the graph, offsetting all nodes by a given
vector. For nodes with the "rung" attribute, add an edge
between existing node and its offset copy."""
# make an unchanged copy and an offset/mirrored copy
orig_copy = original.copy()
offset_copy = original.copy()
for nodeid in offset_copy.node:
# perform an offset
xyz = offset_copy.node[nodeid]["xyz"]
xyz = pt_plus_pt(xyz, offset_val)
if reflect:
## also perform a mirror in the y axis
xyz = [xyz[0], - xyz[1], xyz[2]]
offset_copy.node[nodeid]["xyz"] = xyz
# make a union of the original and copy, renaming nodes
# note that this requires nx to be updated to svn 1520 or above
# which fixes a bug where union discards node attributes
new_graph = nx.union(orig_copy, offset_copy, rename=("G-", "H-"))
# make edges between nodes in original and copy depending on label
for nodeid in new_graph.node:
if nodeid.startswith("G-"):
h_node_id = nodeid.replace("G", "H")
#connect nodes labelled walkway or join
if new_graph.node[nodeid]['label'] == 'walkway':
new_graph.node[h_node_id]['label'] = 'walkway'
new_graph.add_edge(nodeid, h_node_id, label='walkway')
if new_graph.node[nodeid]['label'] == 'join':
new_graph.node[h_node_id]['label'] = 'join'
new_graph.add_edge(nodeid, h_node_id, label='join')
new_graph.frame_count = original.frame_count
return new_graph
def combine_graphs(self, true_class_bias=1,
multi_class_bias=0, multi_class_threshold=0,
class_graph=None, instance_graph=None):
"""Combine graphs."""
probs = np.array([instance_graph.node[v]['prob']
for v in instance_graph.nodes()])
id_offset = max(instance_graph.nodes()) + 1
offset_pred_graph = \
nx.relabel_nodes(class_graph, lambda x: x + id_offset)
union_graph = nx.union(instance_graph, offset_pred_graph)
if multi_class_bias != 0:
for u in instance_graph.nodes():
for group, prob in enumerate(probs[u]):
if prob >= multi_class_threshold:
group_id = group + id_offset
weight = prob * multi_class_bias
union_graph.add_edge(
u, group_id,
weight=weight,
len=self._weigth_to_len(weight))
if true_class_bias != 0:
for u in instance_graph.nodes():
group_id = instance_graph.node[u]['group'] + id_offset
union_graph.add_edge(u, group_id,
weight=true_class_bias,
len=self._weigth_to_len(true_class_bias))
return union_graph
def computeDocumentGraph(self, verbose=False):
"""Create a single document graph from the union of the graphs created
for each sentence in the archive. Note that the algorithm in NetworkX
is different based on whether the Python version is greater than or
equal to 2.6"""
# Note that this as written does not include the currentGraph in the DocumentGraph
# Maybe this should be changed
self.__documentGraph = ConTextMarkup()
if verbose:
print "Document markup has %d edges" % self.__document.number_of_edges()
markups = [e[1] for e in self.__document.edges(data=True) if e[2].get("category") == "markup"]
if verbose:
print "Document markup has %d conTextMarkup objects" % len(markups)
ic = 0
for i in range(len(markups)):
# for m in markups:
m = markups[i]
if verbose:
print "markup %d has %d total items including %d targets" % (
i,
m.number_of_nodes(),
m.getNumMarkedTargets(),
)
self.__documentGraph = nx.union(m, self.__documentGraph)
if verbose:
print "documentGraph now has %d nodes" % self.__documentGraph.number_of_nodes()
def _get_best_graph_cost_pair(semantic_forest, head_key, semantic_weight):
assert isinstance(semantic_forest, SemanticForest)
assert isinstance(semantic_weight, SemanticWeight)
basic_ontology = semantic_forest.basic_ontology
obj = semantic_forest.graph_nodes[head_key]
if isinstance(obj, GroundedToken):
function = obj.function
else:
raise Exception
graph = nx.MultiDiGraph()
graph.add_node(head_key)
if function.valence == 0:
cost = get_semantic_tree_graph_cost(semantic_forest, graph, semantic_weight)
return GraphCostPair(graph, cost)
else:
all_pairs = [[] for _ in range(function.valence)]
for u, v, edge_key, data in semantic_forest.forest_graph.edges(keys=True, data=True):
v_graph, v_cost = _get_best_graph_cost_pair(semantic_forest, v, semantic_weight)
arg_idx = data['arg_idx']
pair = GraphHeadKeyCostPair(v_graph, v, edge_key, v_cost)
all_pairs[arg_idx].append(pair)
for arg_idx, pairs in enumerate(all_pairs):
best_pair = min(pairs, key=lambda p: _get_cost(semantic_forest, head_key, p, semantic_weight))
graph = nx.union(graph, best_pair.graph)
graph.add_edge(head_key, best_pair.head, arg_idx=arg_idx, key=best_pair.key)
cost = get_semantic_tree_graph_cost(semantic_forest, graph, semantic_weight)
return GraphCostPair(graph, cost)
def draw_graph(label_flag=True, remove_isolated=True, different_size=True, iso_level=10, node_size=40):
G=build_graph(fb.get_friends_network())
betweenness=nx.betweenness_centrality(G)
degree=nx.degree_centrality(G)
degree_num=[ degree[v] for v in G]
maxdegree=max(degree_num);mindegree=min(degree_num);
print maxdegree,mindegree
clustering=nx.clustering(G)
print nx.transitivity(G)
# Judge whether remove the isolated point from graph
if remove_isolated is True:
H = nx.empty_graph()
for SG in nx.connected_component_subgraphs(G):
if SG.number_of_nodes() > iso_level:
H = nx.union(SG, H)
G = H
# Ajust graph for better presentation
if different_size is True:
L = nx.degree(G)
G.dot_size = {}
for k, v in L.items():
G.dot_size[k] = v
#node_size = [betweenness[v] *1000 for v in G]
node_size = [G.dot_size[v] * 10 for v in G]
node_color= [((degree[v]-mindegree))/(maxdegree-mindegree) for v in G]
#edge_width = [getcommonfriends(u,v) for u,v in G.edges()]
pos = nx.spring_layout(G, iterations=15)
nx.draw_networkx_edges(G, pos, alpha=0.05)
nx.draw_networkx_nodes(G, pos, node_size=node_size, node_color=node_color, vmin=0.0,vmax=1.0, alpha=0.3)
# Judge whether shows label
if label_flag is True:
nx.draw_networkx_labels(G, pos, font_size=6,alpha=0.1)
#nx.draw_graphviz(G)
plt.show()
return G
def core_substitution(graph, orig_cip, new_cip):
"""
graph is the whole graph..
subgraph is the interface region in that we will transplant
new_cip_graph which is the interface and the new core
"""
# preprocess
graph = _edge_to_vertex(graph)
assert (
set(orig_cip.graph.nodes()) - set(graph.nodes()) == set([])), 'lsgg_compose_util orig_cip_graph not in graph'
# get isomorphism
iso = find_all_isomorphisms(orig_cip.interface_graph, new_cip.interface_graph).next()
if len(iso) != len(orig_cip.interface_graph):
logger.log(5, "lsgg_compose_util grammar hash collision, discovered in 'core_substution' ")
return None
# make graph union (the old graph and the new cip are now floating side by side)
graph = nx.union(graph, new_cip.graph, rename=('', '-'))
graph.remove_nodes_from(map(str, orig_cip.core_nodes))
# merge interface nodes
for k, v in iso.iteritems():
merge(graph, str(k), '-' + str(v))
graph = eg._revert_edge_to_vertex_transform(graph)
re = nx.convert_node_labels_to_integers(graph)
return re
def draw_graph(username, password, filename='graph.txt', label_flag=True, remove_isolated=True, different_size=True, iso_level=10, node_size=40):
"""Reading data from file and draw the graph.If not exists, create the file and re-scratch data from net"""
print "Generating graph..."
try:
with open(filename, 'r') as f:
G = p.load(f)
except:
G = getgraph(username, password)
with open(filename, 'w') as f:
p.dump(G, f)
#nx.draw(G)
# Judge whether remove the isolated point from graph
if remove_isolated is True:
H = nx.empty_graph()
for SG in nx.connected_component_subgraphs(G):
if SG.number_of_nodes() > iso_level:
H = nx.union(SG, H)
G = H
# Ajust graph for better presentation
if different_size is True:
L = nx.degree(G)
G.dot_size = {}
for k, v in L.items():
G.dot_size[k] = v
node_size = [G.dot_size[v] * 10 for v in G]
pos = nx.spring_layout(G, iterations=50)
nx.draw_networkx_edges(G, pos, alpha=0.2)
nx.draw_networkx_nodes(G, pos, node_size=node_size, node_color='r', alpha=0.3)
# Judge whether shows label
if label_flag is True:
nx.draw_networkx_labels(G, pos, alpha=0.5)
#nx.draw_graphviz(G)
plt.show()
return G
def mergeNFA(self, nfa1, nfa2):
nfa1.graph = nx.union(nfa1.graph, nfa2.graph)
nfa1.graph.add_edge(nfa1.first, nfa2.first, label="epsilon")
# nfa1.graph.add_edge(nfa2.last, nfa1.last, label='epsilon')
nfa1.lastArr[nfa2.last] = nfa2.property
nfa1.refresh()
return nfa1
def load_graph(data_dir):
import pickle
aminer = pickle.load(open("D:\\Users\\chenwei\\experiment\\aminer_two_3.pickle"))
linkedin = pickle.load(open("D:\\Users\\chenwei\\experiment\\linkedin_two_filter_3.pickle"))
id = 0
for n in aminer.nodes():
n
merge = nx.union(linkedin, aminer)
return merge
def __call__(self, code, charge_type):
l,d = code.depth, code.dimension
s = {}
for type in code.types:
s[type] = code.Syndrome(type, charge_type)
uc = nx.union(s['green'], nx.union(s['red'], s['blue']))
for edge in code.Dual['red'].edges():
break
scale = common.euclidean_dist(edge[0], edge[1])+.1
i = 1
while uc.nodes() != []:
clusters = GCC_Partition(uc, i*scale)
for cluster in clusters:
code, uc = GCC_Annihilate(cluster, code, uc, charge_type, i*scale)
i += 1
return code
def _xor_gate(num, bp1, idx1, bp2, idx2):
#
# XOR gates are constructed as follows:
#
# Given BP_1 and BP_2 (where BP_2 is the "smaller" of the two BPs),
# produce NOT BP_2, merge the acc node of BP_1 with the src node of
# NOT BP_2, and merge the rej node of BP_1 with the src node of
# BP_2.
#
assert num > idx1 and num > idx2
if len(bp1.graph) < len(bp2.graph):
bp1, bp2 = bp2, bp1
# choose a temporary idx outside the range of possible indices
tmpidx = len(bp1.graph) + len(bp2.graph)
t1 = bp1.nlayers
t2 = bp2.nlayers
relabel_layers(bp2.graph, t1)
oldlayer = bp2.graph.node[('src', idx2)]['layer']
# construct (G_2, not(G_2))
bp2not = _not_gate(tmpidx, bp2, idx2)
# Need to relabel the internal wires as bp2not so we don't end up
# with duplicate node names when we merge the graphs
bp2not.graph = relabel_internal(bp2not.graph, tmpidx)
g = nx.union(bp2.graph, bp2not.graph)
g = contract(g, ('acc', tmpidx), ('acc', idx2), ('acc', num))
g = contract(g, ('rej', tmpidx), ('rej', idx2), ('rej', num))
# construct XOR(G_1, G_2)
g = nx.union(bp1.graph, g)
accnode = ('acc-%d' % num, num)
rejnode = ('rej-%d' % num, num)
g = contract(g, ('acc', idx1), ('src', tmpidx), accnode)
g = contract(g, ('rej', idx1), ('src', idx2), rejnode)
g = relabel(g, num)
g.node[accnode]['layer'] = oldlayer
g.node[rejnode]['layer'] = oldlayer
def eval(inp):
if inp <= t1 - 1:
return bp1.inp(inp)
elif inp <= t1 + t2 - 1:
return bp2.inp(inp - t1)
else:
raise Exception("xorgate eval failed on %s!" % inp)
return _Graph(eval, g, t1 + t2, num)
def test_union_multigraph():
G = nx.MultiGraph()
G.add_edge(1, 2, key=0)
G.add_edge(1, 2, key=1)
H = nx.MultiGraph()
H.add_edge(3, 4, key=0)
H.add_edge(3, 4, key=1)
GH = nx.union(G, H)
assert_equal(set(GH), set(G) | set(H))
assert_equal(set(GH.edges(keys=True)), set(G.edges(keys=True)) | set(H.edges(keys=True)))
def create_topology(self, PoP, k, h):
topology = fnss.Topology()
for core in range(PoP):
tmp = fnss.k_ary_tree_topology(k, h)
for node in tmp.node:
if tmp.node[node]['type']<>'root':
tmp.node[node]['server']=core*(k**(h+1)-1)
tmp_tree = nx.relabel_nodes(tmp, {node:node+core*(k**(h+1)-1) for node in tmp.node})
topology = nx.union(topology, tmp_tree)
# Full mesh in the core of network
for i in range(core):
topology.edge[i*(k**(h+1)-1)][core*(k**(h+1)-1)] = {}
topology.edge[core*(k**(h+1)-1)][i*(k**(h+1)-1)] = {}
return topology
#class Topology(fnss.Topology):
# def __init__(self, core, k, h, cache_budget):
# cache_size = cache_budget/float(core*(k**h-1))
# self.topology = self._create_topology(core, k, h)
# self.clients = {node:self.topology.node[node] for node in self.topology.node \
# if self.topology.node[node]['type']=='leaf'}
# self.pops = {node:self.topology.node[node] for node in self.topology.node \
# if self.topology.node[node]['type']=='root'}
# self.routers = {node:self.topology.node[node] for node in self.topology.node \
# if self.topology.node[node]['type'] in ['leaf','intermediate']}
## props = open('properties', 'r').readlines()
#
#
# self.content_store = {node:cache(cache_size) for node in self.topology.nodes_iter()}
# self.informations = {node:{} for node in self.topology.nodes_iter()}
#
#
# def _create_topology(self, PoP, k, h):
# topology = fnss.Topology()
# for core in range(PoP):
# tmp = fnss.k_ary_tree_topology(k, h)
# for node in tmp.node:
# if tmp.node[node]['type']<>'leaf':
# tmp.node[node]['server']=core*(k**(h+1)-1)
# tmp_tree = nx.relabel_nodes(tmp, {node:node+core*(k**(h+1)-1) for node in tmp.node})
# topology = nx.union(topology, tmp_tree)
# # Full mesh in the core of network
# for i in range(core):
# topology.edge[i*(k**(h+1)-1)][core*(k**(h+1)-1)] = {}
# topology.edge[core*(k**(h+1)-1)][i*(k**(h+1)-1)] = {}
#
# return topology
#
#
def test_wf_improved(self):
G = nx.union(self.P4, nx.path_graph([4, 5, 6]))
c = nx.closeness_centrality(G)
cwf = nx.closeness_centrality(G, wf_improved=False)
res = {0: 0.25, 1: 0.375, 2: 0.375, 3: 0.25,
4: 0.222, 5: 0.333, 6: 0.222}
wf_res = {0: 0.5, 1: 0.75, 2: 0.75, 3: 0.5,
4: 0.667, 5: 1.0, 6: 0.667}
for n in G:
assert_almost_equal(c[n], res[n], places=3)
assert_almost_equal(cwf[n], wf_res[n], places=3)
def compute_documentGraph(cd):
"""Create a single document graph from the union of the graphs created
for each sentence in the archive. Note that the algorithm in NetworkX
is different based on whether the Python version is greater than or
equal to 2.6"""
# Note that this as written does not include the currentGraph in the DocumentGraph
documentGraph = nx.DiGraph()
markups = [e[1] for e in cd.edges(data=True) if e[2].get('category') == 'markup']
for i in range(len(markups)):
m = markups[i]
documentGraph = nx.union(m,documentGraph)
return documentGraph
def copy_and_rotate_around_xy_plane(self, original, tangent):
#tangent = int(360 / sectors)
orig_copy = original.copy()
offset_copy = original.copy()
for nodeid in offset_copy.node:
xyz = offset_copy.node[nodeid]["xyz"]
xyz = xy_rotate(xyz, tangent)
xyz = [int(round(xyz[0], 0)), int(round(xyz[1], 0)),
int(round(xyz[2], 0))]
offset_copy.node[nodeid]["xyz"] = xyz
new_graph = nx.union(orig_copy, offset_copy, rename=("G-", "H-"))
new_graph.frame_count = original.frame_count
return new_graph
def build_fault_tree_for_app(self):
recordCloudList = []
nonOverlapping = 1
for cloudItem in self.cloudList:
if self.faultTree.number_of_nodes() == 0:
self.faultTree = cloudItem.topology
else:
for i in range(len(cloudItem.dataCenterID)):
if cloudItem.dataCenterID[i] in recordCloudList:
nonOverlapping = 0
break
if nonOverlapping == 1:
self.faultTree \
= nx.union(self.faultTree, cloudItem.topology)
for i in range(len(cloudItem.dataCenterID)):
recordCloudList.append(cloudItem.dataCenterID[i])
for appItem in self.appList:
self.faultTree = nx.union(self.faultTree, appItem.topology)
for dcItem in self.faultTree.node:
if self.faultTree.node[dcItem].keys()[0] == 'DATACENTER'\
and dcItem in appItem.dcList:
self.faultTree.add_edge(dcItem, appItem.jobID)
def core_substitution(graph, orig_cip_graph, new_cip_graph):
"""
graph is the whole graph..
subgraph is the interfaceregrion in that we will transplant
new_cip_graph which is the interface and the new core
"""
assert( set(orig_cip_graph.nodes()) - set(graph.nodes()) == set([]) ), 'orig_cip_graph not in graph'
# select only the interfaces of the cips
new_graph_interface_nodes = [n for n, d in new_cip_graph.nodes(data=True) if 'core' not in d]
new_cip_interface_graph = nx.subgraph(new_cip_graph, new_graph_interface_nodes)
original_graph_interface_nodes = [n for n, d in orig_cip_graph.nodes(data=True) if 'core' not in d]
original_interface_graph = nx.subgraph(orig_cip_graph, original_graph_interface_nodes)
# get isomorphism between interfaces, if none is found we return an empty graph
iso = get_good_isomorphism(graph,
orig_cip_graph,
new_cip_graph,
original_interface_graph,
new_cip_interface_graph)
if len(iso) != len(original_interface_graph):
# print iso
# draw.display(orig_cip_graph)
# draw.display(new_cip_graph)
#draw.graphlearn([orig_cip_graph, new_cip_graph],size=10)
logger.log(5,"grammar hash collision, discovered in 'core_substution' ")
return nx.Graph()
# ok we got an isomorphism so lets do the merging
graph = nx.union(graph, new_cip_graph, rename=('', '-'))
# removing old core
# original_graph_core_nodes = [n for n, d in orig_cip_graph.nodes(data=True) if 'core' in d]
original_graph_core_nodes = [n for n, d in orig_cip_graph.nodes(data=True) if 'core' in d]
for n in original_graph_core_nodes:
graph.remove_node(str(n))
# merge interfaces
for k, v in iso.iteritems():
graph.node[str(k)][
'interface'] = True # i am marking the interface only for the backflow probability calculation in graphlearn, this is probably deleteable because we also do this in merge, also this line is superlong Ooo
merge(graph, str(k), '-' + str(v))
# unionizing killed my labels so we need to relabel
return nx.convert_node_labels_to_integers(graph)
def test_disconnected_graph_root_node(self):
"""Test for a single component of a disconnected graph."""
G = nx.barbell_graph(3, 0)
H = nx.barbell_graph(3, 0)
mapping = dict(zip(range(6), 'abcdef'))
nx.relabel_nodes(H, mapping, copy=False)
G = nx.union(G, H)
chains = list(nx.chain_decomposition(G, root='a'))
expected = [
[('a', 'b'), ('b', 'c'), ('c', 'a')],
[('d', 'e'), ('e', 'f'), ('f', 'd')],
]
self.assertEqual(len(chains), len(expected))
for chain in chains:
self.assertContainsChain(chain, expected)
def _create_topology(self, PoP, k, h):
topology = fnss.Topology()
for core in range(PoP):
tmp = fnss.k_ary_tree_topology(k, h)
for node in tmp.node:
if tmp.node[node]['type'] <> 'root':
tmp.node[node]['server'] = core * (k ** (h + 1) - 1)
tmp_tree = nx.relabel_nodes(tmp, {node: node + core * (k ** (h + 1) - 1) for node in tmp.node})
topology = nx.union(topology, tmp_tree)
# Full mesh in the core of network
for i in range(core):
topology.edge[i * (k ** (h + 1) - 1)][core * (k ** (h + 1) - 1)] = {}
topology.edge[core * (k ** (h + 1) - 1)][i * (k ** (h + 1) - 1)] = {}
return topology
def test_disconnected_graph(self):
"""Test for a graph with multiple connected components."""
G = nx.barbell_graph(3, 0)
H = nx.barbell_graph(3, 0)
mapping = dict(zip(range(6), 'abcdef'))
nx.relabel_nodes(H, mapping, copy=False)
G = nx.union(G, H)
chains = list(nx.chain_decomposition(G))
expected = [
[(0, 1), (1, 2), (2, 0)],
[(3, 4), (4, 5), (5, 3)],
[('a', 'b'), ('b', 'c'), ('c', 'a')],
[('d', 'e'), ('e', 'f'), ('f', 'd')],
]
self.assertEqual(len(chains), len(expected))
for chain in chains:
self.assertContainsChain(chain, expected)
def test_wf_improved(self):
G = nx.union(self.P4, nx.path_graph([4, 5, 6]))
c = nx.closeness_centrality(G)
cwf = nx.closeness_centrality(G, wf_improved=False)
res = {
0: 0.25,
1: 0.375,
2: 0.375,
3: 0.25,
4: 0.222,
5: 0.333,
6: 0.222
}
wf_res = {0: 0.5, 1: 0.75, 2: 0.75, 3: 0.5, 4: 0.667, 5: 1.0, 6: 0.667}
for n in G:
assert_almost_equal(c[n], res[n], places=3)
assert_almost_equal(cwf[n], wf_res[n], places=3)
def test_disjoint_clique(self):
""""
A group of num_clique of size size_clique disjoint, should maximize
the modularity and have a modularity of 1 - 1/ num_clique
"""
for _ in range(self.number_of_tests):
size_clique = random.randint(5, 20)
num_clique = random.randint(5, 20)
graph = nx.Graph()
for i in range(num_clique):
clique_i = nx.complete_graph(size_clique)
graph = nx.union(graph, clique_i, rename=("", str(i) + "_"))
part = dict([])
for node in graph:
part[node] = node.split("_")[0].strip()
mod = co.modularity(part, graph)
self.assertAlmostEqual(mod, 1. - 1. / float(num_clique))
def generateFatTree(k):
''' A fat tree is composed by k PODs. Each POD is connected to all the core switches by
means of the aggregate-switches: each one of those is connected to k/2 core switches '''
fatTree = nx.Graph()
cores = map(lambda z: ("c"+str(z),{"type":"core"}),range(0,k^2/4))
fatTree.add_nodes_from(cores)
pods = [generatePod(k, z) for z in range(0,k)]
for pod in pods:
fatTree = nx.union(fatTree, pod)
for node in pod.nodes(data=True):
if node[1]["type"] == "aggregate1":
fatTree.add_edges_from([(node[0],core[0]) for core in cores[0:k/2]])
elif node[1]["type"] == "aggregate2":
fatTree.add_edges_from([(node[0],core[0]) for core in cores[k/2:k]])
return fatTree
def disabling(disabling_term, right_operand, gt_edges=None):
if disabling_term is None or right_operand is None:
return None
disabling_term.graph = networkx.union(disabling_term.graph,
right_operand.graph)
l_end = HiddenMarkovModelTopology.__find_end_states(disabling_term)
r_start = HiddenMarkovModelTopology.__find_start_states(right_operand)
r_end = HiddenMarkovModelTopology.__find_end_states(right_operand)
seq_edges = []
if gt_edges is not None:
seq_edges[:] = gt_edges[:]
# add a transition to the disabling term from all the ground terms in the left operand
for edge in gt_edges:
print('edge ({}, {})'.format(edge[0].name, edge[1].name))
print(disabling_term.graph[edge[0]][edge[1]])
prob = numpy.exp(
disabling_term.graph[edge[0]][edge[1]]['probability'])
#print('edge ({} {}) prob: {}, updated: {}'.format(edge[0].name, edge[1].name, prob, prob / (len(r_start) + 1)))
prob = prob / (len(r_start) + 1)
disabling_term.graph[edge[0]][
edge[1]]['probability'] = numpy.log(prob)
for i in range(0, len(r_start)):
disabling_term.add_transition(edge[0], r_start[i], prob)
seq_edges.append((edge[0], r_start[i]))
# add a transition to the disabling term for all terms that end the left operand
for i in range(0, len(l_end)):
prob = numpy.exp(disabling_term.graph[l_end[i]][disabling_term.end]
['probability']) / len(r_start)
for j in range(0, len(r_start)):
disabling_term.add_transition(l_end[i], r_start[j], prob)
seq_edges.append((l_end[i], r_start[i]))
disabling_term.graph.remove_edge(l_end[i], disabling_term.end)
# add a transition from all final states in the right operand to the end state
for i in range(0, len(r_end)):
prob = numpy.exp(disabling_term.graph[r_end[i]][right_operand.end]
['probability'])
disabling_term.add_transition(r_end[i], disabling_term.end, prob)
disabling_term.bake()
return disabling_term, seq_edges
def merge_all_subgraphs(self):
"""Generates a single networkx graph object from the subgraphs that have
been processed
Returns
-------
finalGraph : NetworkX graph obj
the final graph produced merging all the subgraphs. The produced
graph may have disconneted parts
"""
finalGraph = nx.Graph()
for subgraph in self.workingSubgraphsList:
finalGraph = nx.union(finalGraph, subgraph)
return finalGraph
def _read_graph(self):
"""
Reads or initializes a graph.
:return:
"""
args = self._args
if self.train_graph is None or self.test_graph is None:
helper.log(f'Reading graph from {args.input}')
self._reader = nx.read_adjlist if args.fmt == 'adjlist' else nx.read_edgelist
self._creator = nx.DiGraph if args.directed else nx.Graph
self.graph = self._reader(path=args.input,
create_using=self._creator,
nodetype=int)
self.num_nodes = self.graph.number_of_nodes()
self.num_edges = self.graph.number_of_edges()
else:
self.graph = nx.union(self.train_graph, self.test_graph)
def musketeer_on_subgraphs(original, params=None):
components = nx.connected_component_subgraphs(original)
merged_G = nx.Graph()
component_is_edited = params.get('component_is_edited',
[True] * len(components))
for G_num, G in enumerate(components):
if component_is_edited[G_num]:
replica = generate_graph(original=G, params=params)
else:
replica = G
merged_G = nx.union(merged_G, replica)
merged_G.name = getattr(original, 'name',
'graph') + '_replica_' + timeNow()
return merged_G
def computeDocumentGraph(self):
"""Create a single document graph from the union of the graphs created
for each sentence in the archive. Note that the algorithm in NetworkX
is different based on whether the Python version is greater than or
equal to 2.6"""
self.__documentGraph = nx.DiGraph()
for key in self.__archive.keys():
if (platform.python_version() <= '2.6'):
g = self.__archive[key]["graph"]
self.__documentGraph = nx.union(g, self.__documentGraph)
# this should work but doesn't preseve the node data attributes
else:
nds = g.nodes(
data=True) # for python < 2.6 need to use code below
for n in nds:
self.__documentGraph.add_node(n[0],
category=n[1]['category'])
self.__documentGraph.add_edges_from(g.edges())
def _add_application_seperate_method(self, app, tc) -> DiGraph:
# Seperate method: All applications are seperate
appDAG = DiGraph()
# Add all tasks as nodes to the DAG
for t in app.verticies.values():
tgn_id = t.id
tgn = TaskGraphNode_Task(tgn_id, t)
appDAG.add_node(tgn_id)
self.nodes[tgn_id] = tgn
# For each stream
for s_id in app.edges.keys():
s = tc.F[s_id]
if s.is_self_stream():
# Simply add edges for self-streams
appDAG.add_edge(s.sender_task_id, list(s.receiver_task_ids)[0])
else:
t_sender_id = s.sender_task_id
tgn_sender = self.nodes[t_sender_id]
# Add a node for the stream
tgn_id = f"{s_id}"
tgn = TaskGraphNode_Stream(tgn_id, s)
appDAG.add_node(tgn_id)
self.nodes[tgn_id] = tgn
# Connect it to sender task
appDAG.add_edge(tgn_sender.id, tgn.id)
# Connect stream node with receiver task nodes
for reciever_t_id in s.receiver_task_ids:
tgn_receiver = self.nodes[reciever_t_id]
appDAG.add_edge(tgn.id, tgn_receiver.id)
self.DAG = nx.union(self.DAG, appDAG)
if app.id in tc.A_sec:
self.keyDAGs.append((appDAG, app.id))
elif app.id in tc.A_app:
self.normalDAGs.append((appDAG, app.id))
else:
raise ValueError
def setup_class(cls):
# G is the example graph in Figure 1 from Batagelj and
# Zaversnik's paper titled An O(m) Algorithm for Cores
# Decomposition of Networks, 2003,
# http://arXiv.org/abs/cs/0310049. With nodes labeled as
# shown, the 3-core is given by nodes 1-8, the 2-core by nodes
# 9-16, the 1-core by nodes 17-20 and node 21 is in the
# 0-core.
t1 = nx.convert_node_labels_to_integers(nx.tetrahedral_graph(), 1)
t2 = nx.convert_node_labels_to_integers(t1, 5)
G = nx.union(t1, t2)
G.add_edges_from(
[
(3, 7),
(2, 11),
(11, 5),
(11, 12),
(5, 12),
(12, 19),
(12, 18),
(3, 9),
(7, 9),
(7, 10),
(9, 10),
(9, 20),
(17, 13),
(13, 14),
(14, 15),
(15, 16),
(16, 13),
]
)
G.add_node(21)
cls.G = G
# Create the graph H resulting from the degree sequence
# [0, 1, 2, 2, 2, 2, 3] when using the Havel-Hakimi algorithm.
degseq = [0, 1, 2, 2, 2, 2, 3]
H = nx.havel_hakimi_graph(degseq)
mapping = {6: 0, 0: 1, 4: 3, 5: 6, 3: 4, 1: 2, 2: 5}
cls.H = nx.relabel_nodes(H, mapping)
def decompose_all_sccs(g,
score_function=score_subgraph,
min_loop_size=2,
max_loop_size=12,
modes=("pos", "neg"),
undirected=False):
'''
run decomposition on each SCC in g
'''
h = nx.DiGraph()
roots = []
if undirected:
component_iter = nx.connected_components(g.to_undirected())
else:
component_iter = nx.strongly_connected_components(g)
for cc in component_iter:
print ("processing CC", cc)
cc = g.subgraph(cc)
cc_tree = bottom_up_partition(cc,
score_function=score_function,
min_loop_size=min_loop_size,
max_loop_size=max_loop_size,
modes=modes,
undirected=undirected)
degrees = dict(cc_tree.out_degree())
root = min(degrees, key=degrees.get)
roots.append(root)
h = nx.union(h, cc_tree)
print ()
print (roots)
if len(roots) > 1:
# add final root to represent whole network
all_nodes = frozenset(roots)
for root in roots:
h.add_edge(root, all_nodes)
return h
def union_all(graphs, rename=(None, ), name=None):
"""Return the union of all graphs.
The graphs must be disjoint, otherwise an exception is raised.
Parameters
----------
graphs : list of graphs
List of NetworkX graphs
rename : bool , default=(None, None)
Node names of G and H can be changed by specifying the tuple
rename=('G-','H-') (for example). Node "u" in G is then renamed
"G-u" and "v" in H is renamed "H-v".
name : string
Specify the name for the union graph@not_implemnted_for('direct
Returns
-------
U : a graph with the same type as the first graph in list
Notes
-----
To force a disjoint union with node relabeling, use
disjoint_union_all(G,H) or convert_node_labels_to integers().
Graph, edge, and node attributes are propagated to the union graph.
If a graph attribute is present in multiple graphs, then the value
from the last graph in the list with that attribute is used.
See Also
--------
union
disjoint_union_all
"""
graphs_names = zip_longest(graphs, rename)
U, gname = next(graphs_names)
for H, hname in graphs_names:
U = nx.union(U, H, (gname, hname), name=name)
gname = None
return U
def copy_and_offset_with_mirror(self, offset, mirror=False):
# make an unchanged copy and an offset/mirrored copy
orig_copy = self.copy()
offset_copy = self.copy()
for nodeid in offset_copy.node:
# perform an offset
xyz = offset_copy.node[nodeid]["xyz"]
xyz = pt_plus_pt(xyz, offset)
if mirror:
## also perform a mirror in the y axis
xyz = [xyz[0], -xyz[1], xyz[2]]
offset_copy.node[nodeid]["xyz"] = xyz
# make a union of the original and copy, renaming nodes
# note that this requires nx to be updated to svn 1520 or above
# which fixes a bug where union discards node attributes
new_graph = nx.union(orig_copy, offset_copy, rename=("G-", "H-"))
# make edges between corresponding nodes in original and copy where needed
for nodeid in new_graph.node:
if nodeid.startswith("G-"):
# print "looking at", nodeid
if "rung" in new_graph.node[nodeid]:
h_node_id = nodeid.replace("G", "H")
h_node = new_graph.node[h_node_id]
new_graph.add_edge(nodeid, h_node_id)
# print("making edge between " +
# str(new_graph.node[nodeid]["xyz"]) +
# " : " +
# str(new_graph.node[h_node_id]["xyz"]))
# rename nodes back to integers: FIXME doesn't work because bug in nx
# strips node attributes.
# new_graph = nx.convert_node_labels_to_integers(new_graph)
# clear self and add edges from new graph
self.clear()
for edge in new_graph.edges_iter():
self.add_node(edge[0], xyz=new_graph.node[edge[0]]["xyz"])
self.add_node(edge[1], xyz=new_graph.node[edge[1]]["xyz"])
self.add_edge(edge[0], edge[1])
def gen_hierarchical_net(n, level):
"""Generate hiearchical graph using method proposed of:
Ravasz E, Barabasi AL, Hierarchical organization in complex networks, PRE, 2003.
Parameters
----------
n : int
Number of nodes in the lowest level.
level : int
Number of hierarchical levels to create
Returns
-------
networkx graph
The hierchically-structured network
"""
if level == 0:
return nx.complete_graph(n)
else:
fullG = nx.Graph()
#get lower-order graphs
for i in range(n):
fullG = nx.union(gen_hierarchical_net(n, level - 1),
fullG,
rename=(str(i) + '.', ''))
edges = []
suffix = ''
for l in range(level - 1):
suffix += '.0'
#connect outer nodes to the center
center = '0.0' + suffix
for node in fullG.nodes():
if not '0' in node:
edges.append((node, center))
fullG.add_edges_from(edges)
return fullG
def test_union_attributes():
g = nx.Graph()
g.add_node(0, x=4)
g.add_node(1, x=5)
g.add_edge(0, 1, size=5)
g.graph["name"] = "g"
h = g.copy()
h.graph["name"] = "h"
h.graph["attr"] = "attr"
h.nodes[0]["x"] = 7
gh = nx.union(g, h, rename=("g", "h"))
assert set(gh.nodes()) == {"h0", "h1", "g0", "g1"}
for n in gh:
graph, node = n
assert gh.nodes[n] == eval(graph).nodes[int(node)]
assert gh.graph["attr"] == "attr"
assert gh.graph["name"] == "h" # h graph attributes take precendent
def union(self, other, rename=False):
'''
Union/add two topologies together to form a larger topology.
If rename is False, the method assumes that node names
don't clash (i.e., you've called addNodeLabelPrefix or
you've explicitly chosen names to avoid clashes).
If rename is True, nodes/links are relabeled such that the
new "prefix" for each node is the graph name (i.e., for graph
name A, node h1 is renamed A_h1).
This method returns a new Topology object and does not modify
either topology used for unioning.
'''
if rename:
self.nxgraph = Topology.__relabel_graph(self.__nxgraph, self.name)
other.nxgraph = Topology.__relabel_graph(other.__nxgraph, other.name)
nxgraph = nx.union(self.nxgraph, other.nxgraph, name="{}_{}".format(self.name, other.name))
newtopo = Topology(nxgraph=nxgraph, name="{}_{}".format(self.name, other.name))
return newtopo
def test_ring_clique(self) :
""""
then, a group of num_clique of size size_clique connected with only two links to other in a ring
have a modularity of 1 - 1/ num_clique - num_clique / num_links
"""
for num_test in range(self.numtest) :
size_clique = random.randint(5, 20)
num_clique = random.randint(5, 20)
g = nx.Graph()
for i in range(num_clique) :
clique_i = nx.complete_graph(size_clique)
g = nx.union(g, clique_i, rename=("",str(i)+"_"))
if i > 0 :
g.add_edge(str(i)+"_0", str(i-1)+"_1")
g.add_edge("0_0", str(num_clique-1)+"_1")
part = dict([])
for node in g :
part[node] = node.split("_")[0].strip()
mod = co.modularity(part, g)
self.assertAlmostEqual(mod, 1. - 1./float(num_clique) - float(num_clique) / float(g.number_of_edges()), msg = "Num clique: " + str(num_clique) + " size_clique: " + str(size_clique) )
def merge_graph(g1, g2, merge_type): # 聚合图
"""
:param g1: 第一个图对象
:param g2: 第二个图对象
:param merge_type: 聚合方式（"subgraph","union","dis_union", "cartesian", "compose"）
:return:
"""
if merge_type == "subgraph":
new_g = nx.subgraph(g1, g2) # g2为 list,
elif merge_type == "union": # 不相交的拼接
new_g = nx.union(g1, g2)
elif merge_type == "dis_union": # 所有节点都不同的不相交拼接
new_g = nx.disjoint_union(g1, g2)
elif merge_type == "cartesian": # 笛卡尔乘积图
new_g = nx.cartesian_product(g1, g2)
elif merge_type == "compose":
new_g = nx.compose(g1, g2) # 与g1 一样新的图
else:
raise ValueError("error merge_type")
return new_g
def test_union_attributes():
g = nx.Graph()
g.add_node(0, x=4)
g.add_node(1, x=5)
g.add_edge(0, 1, size=5)
g.graph['name'] = 'g'
h = g.copy()
h.graph['name'] = 'h'
h.graph['attr'] = 'attr'
h.nodes[0]['x'] = 7
gh = nx.union(g, h, rename=('g', 'h'))
assert_equal(set(gh.nodes()), set(['h0', 'h1', 'g0', 'g1']))
for n in gh:
graph, node = n
assert_equal(gh.nodes[n], eval(graph).nodes[int(node)])
assert_equal(gh.graph['attr'], 'attr')
assert_equal(gh.graph['name'], 'h') # h graph attributes take precendent
def test_ring(self):
"""
Test that community found are good using a ring of cliques
"""
for num_test in range(self.numtest):
size_clique = random.randint(5, 20)
num_clique = random.randint(5, 20)
g = nx.Graph()
for i in range(num_clique):
clique_i = nx.complete_graph(size_clique)
g = nx.union(g, clique_i, rename=("", str(i) + "_"))
if i > 0:
g.add_edge(str(i) + "_0", str(i - 1) + "_1")
g.add_edge("0_0", str(num_clique - 1) + "_1")
part = co.best_partition(g)
for clique in range(num_clique):
p = part[str(clique) + "_0"]
for node in range(size_clique):
self.assertEqual(p, part[str(clique) + "_" + str(node)])
def _get_best_graph_cost_pair(semantic_forest, head_key, semantic_weight):
assert isinstance(semantic_forest, SemanticForest)
assert isinstance(semantic_weight, SemanticWeight)
basic_ontology = semantic_forest.basic_ontology
obj = semantic_forest.graph_nodes[head_key]
if isinstance(obj, GroundedToken):
function = obj.function
else:
raise Exception
graph = nx.MultiDiGraph()
graph.add_node(head_key)
if function.valence == 0:
cost = get_semantic_tree_graph_cost(semantic_forest, graph,
semantic_weight)
return GraphCostPair(graph, cost)
else:
all_pairs = [[] for _ in range(function.valence)]
for u, v, edge_key, data in semantic_forest.forest_graph.edges(
keys=True, data=True):
v_graph, v_cost = _get_best_graph_cost_pair(
semantic_forest, v, semantic_weight)
arg_idx = data['arg_idx']
pair = GraphHeadKeyCostPair(v_graph, v, edge_key, v_cost)
all_pairs[arg_idx].append(pair)
for arg_idx, pairs in enumerate(all_pairs):
best_pair = min(pairs,
key=lambda p: _get_cost(semantic_forest, head_key,
p, semantic_weight))
graph = nx.union(graph, best_pair.graph)
graph.add_edge(head_key,
best_pair.head,
arg_idx=arg_idx,
key=best_pair.key)
cost = get_semantic_tree_graph_cost(semantic_forest, graph,
semantic_weight)
return GraphCostPair(graph, cost)
def two_reg_edge_rewiring(n1, k1, n2, k2, rat):
G1 = nx.random_regular_graph(k1, n1)
G2 = nx.random_regular_graph(k2, n2)
H = nx.union(G1, G2, rename=['G1-', 'G2-'])
mix_mat = nx.degree_mixing_matrix(H)
mix_mat[k1, k2] = rat
mix_mat[k2, k1] = mix_mat[k1, k2]
mix_mat[k1, k1] -= rat
mix_mat[k2, k2] -= rat
print mix_mat
for i in range((n1 * k1 + n2 * k2)):
selected_H1_edge = H.edges()[rd.randint(0,
(n1 * k1 + n2 * k2) / 2 - 1)]
H1_edge_ele_1 = selected_H1_edge[0]
H1_edge_ele_2 = selected_H1_edge[1]
while 1:
selected_H2_edge = H.edges()[rd.randint(
0, (n1 * k1 + n2 * k2) / 2 - 1)]
H2_edge_ele_1 = selected_H2_edge[0]
H2_edge_ele_2 = selected_H2_edge[1]
if H1_edge_ele_1 != H2_edge_ele_1 and H1_edge_ele_2 != H2_edge_ele_2 and H1_edge_ele_1 != H2_edge_ele_2 and H1_edge_ele_2 != H2_edge_ele_1 and H1_edge_ele_1 not in H[
H2_edge_ele_1] and H1_edge_ele_2 not in H[H2_edge_ele_2]:
break
if (mix_mat[len(H[H1_edge_ele_1]),
len(H[H2_edge_ele_1])] * mix_mat[len(H[H1_edge_ele_2]),
len(H[H2_edge_ele_2])]
) / (mix_mat[len(H[H1_edge_ele_1]),
len(H[H1_edge_ele_2])] *
mix_mat[len(H[H2_edge_ele_1]),
len(H[H2_edge_ele_2])]) > rd.random():
H.add_edge(H1_edge_ele_1, H2_edge_ele_1)
H.add_edge(H1_edge_ele_2, H2_edge_ele_2)
H.remove_edge(H1_edge_ele_1, H1_edge_ele_2)
H.remove_edge(H2_edge_ele_1, H2_edge_ele_2)
return H
def merge_graphs(g, h, attributes=list(), merge_parents=False):
"""
:param g: a networkx graph
:param h: a networkx graph (preferably the smaller graph being added to g)
:param attributes: A list of attributes to match on. If present, all other attributes will be Don't Care's
:param merge_parents: If True, nodes that have children will still be merged. Default is 'False'
:return: returns the union of the graphs per the CAGS schema
"""
# TODO: ADD the ability to pass in a set of edges which will also be created manually. (link graph roots, etc)
# Alternately, users could be expected to create edges by retrieving nodes by attribute
# create a union graph
G = nx.union(g, h, rename=('g-', 'h-'))
# Look through graph for duplicates
for n1 in h.nodes(data=True):
# The None:None node is likely to be a root node in a nested dictionary so don't merge it
if not ("" in n1[1] and n1[1][""] == "") and \
(merge_parents == True or len(g.successors(n1[0])) == 0): # Was 'None' instead of "" but that doesn't work in graphs
for n2 in g.nodes(data=True):
# Match nodes
match = True
if len(attributes) == 0:
if n1[1] != n2[1]:
match = False
else:
# for each attribute to match...
for attribute in attributes:
# check if node 1 has the attribute
if attribute in n1[1]:
# if it does, match is false if n2 doesn't have the attribute or has a different value
if attribute not in n2[
1] or n1[1][attribute] != n2[1][attribute]:
match = False
if match:
# if there is a duplicate based on attributes, merge the nodes
G = nx.relabel_nodes(G, {"h-" + n1[0]: "g-" + n2[0]})
print "matched, {0} and {1}".format(n1[0], n2[0])
return G
def test_ring(self):
"""
Test that community found are good using a ring of cliques
"""
for _ in range(self.number_of_tests):
size_clique = random.randint(5, 20)
num_clique = random.randint(5, 20)
graph = nx.Graph()
for i in range(num_clique):
clique_i = nx.complete_graph(size_clique)
graph = nx.union(graph, clique_i, rename=("", str(i) + "_"))
if i > 0:
graph.add_edge(str(i) + "_0", str(i - 1) + "_1")
graph.add_edge("0_0", str(num_clique - 1) + "_1")
part = co.best_partition(graph)
for clique in range(num_clique):
part_name = part[str(clique) + "_0"]
for node in range(size_clique):
expected = part[str(clique) + "_" + str(node)]
self.assertEqual(part_name, expected)
def FourClusterGraph(n1, n2, n3, n4):
G = ThreeClusterGraph(n1, n2, n3)
H = nx.complete_graph(n4)
mapping = {}
for i in range(n4):
mapping[i] = i + n1 + n2 + n3
H = nx.relabel_nodes(H, mapping=mapping)
I = nx.union(G, H)
I.add_edge(n1 + n2 + n3 - 1, n1 + n2 + n3)
I.weighted = False
#set weight to 1
for e in I.edges_iter():
I.add_edge(e[0], e[1], weight=1)
print(I.number_of_edges())
print(I.number_of_nodes())
print(I.edges())
#Draw(I);
return I
def __generate_cliques(self, min_size, max_size, k):
cliques = {}
for i in range(k):
cliques[i] = []
clique = self.__generate_clique(random.randint(min_size, max_size))
for node in nx.nodes(clique):
clique.nodes[node]['clique'] = i
self.__G = nx.union(self.__G, clique, rename=('A-', 'B-'))
self.__G = nx.convert_node_labels_to_integers(self.__G)
for node in nx.nodes(self.__G):
cliques[self.__G.nodes[node]['clique']].append(node)
return cliques
def generate_nbad_either(num_nodes: int):
n = int(np.round((np.sqrt(4 * num_nodes + 1) - 3) / 2))
nnodes_U = n**2 - n + 3
U = generate_nbad_unioning_fast(nnodes_U)
nnodes_M = 4 * (n - 1) + 5
M = generate_nbad_neighborbl(nnodes_M)
M.remove_node('t')
U.remove_node('s')
G = nx.union(M, U, rename=('M-', 'U-'))
#merge the appropriate nodes
for i in range(n):
#connect 'M-t_{i}' with 'U-({i},0)'
G.add_edge('M-t_{}'.format(i), 'U-({}, 0)'.format(i))
G = nx.relabel_nodes(G, {'M-s': 's', 'U-t': 't'})
return G
def compute_node_equivalence(g1, g2):
helper_graph = nx.union(g1, g2, rename=(
'g1-',
'g2-')) #nodes start with `rename` prefix. Used to check for cycles
equivalence_g1 = {} # maps nodes from g1 to g2
equivalence_g2 = {} # maps nodes from g2 to g1
len_g1 = len(g1.nodes)
len_g2 = len(g2.nodes)
similarity_matrix = compute_node_similarity_matrix(g1, g2)
try:
similarity_matrix = similarity_flooding(similarity_matrix,
g1,
g2,
alpha=0.1,
n_iter=50)
except Exception:
print("Similarity flooding failed. PCG graph has no nodes or edges.")
edit_cost_matrix = compute_edit_cost_matrix(similarity_matrix, 0.4, 0.4)
rows, cols = linear_sum_assignment(edit_cost_matrix)
nodes_g1 = list(g1.nodes)
nodes_g2 = list(g2.nodes)
for pairs in zip(rows, cols):
if pairs[0] < len_g1 and pairs[1] < len_g2:
# nodes are equivalent
eq_g1 = nodes_g1[pairs[0]]
eq_g2 = nodes_g2[pairs[1]]
merged_helper = nx.algorithms.minors.contracted_nodes(
helper_graph, 'g1-' + eq_g1, 'g2-' + eq_g2, self_loops=False)
try:
if 'g1-inputs.0' in merged_helper:
cycles = nx.algorithms.cycles.find_cycle(
merged_helper, source='g1-inputs.0')
if 'g2-inputs.0' in merged_helper:
cycles = nx.algorithms.cycles.find_cycle(
merged_helper, source='g2-inputs.0')
except nx.NetworkXNoCycle as n:
equivalence_g2[eq_g2] = eq_g1
equivalence_g1[eq_g1] = eq_g2
helper_graph = merged_helper
return equivalence_g1, equivalence_g2
def kcore_decompose_graph(graph=None, max_deg=None):
g = graph.copy()
high_degree_nodes = []
low_degree_nodes = []
m = min([e[1] for e in g.degree()])
t = max(max_deg, m)
for n in g.nodes():
if len(list(g.neighbors(n))) > t:
high_degree_nodes.append(n)
else:
low_degree_nodes.append(n)
g_high_degree0 = g.subgraph(high_degree_nodes)
n_nodes = len(high_degree_nodes)
g_low_degree0 = g.subgraph(low_degree_nodes)
g_union = g_low_degree0.copy()
while n_nodes > 0:
high_degree_nodes = []
low_degree_nodes = []
m = min([e[1] for e in g_high_degree0.degree()])
t = max(max_deg, m)
for n in g_high_degree0.nodes():
if len(list(g_high_degree0.neighbors(n))) > t:
high_degree_nodes.append(n)
else:
low_degree_nodes.append(n)
g_high_degree = g_high_degree0.subgraph(high_degree_nodes)
n_nodes = len(high_degree_nodes)
g_low_degree = g_high_degree0.subgraph(low_degree_nodes)
g_union = nx.union(g_union, g_low_degree)
g_high_degree0 = g_high_degree
return g_union