def translate_graph(nodes_data, edges_data):
N = len(nodes_data) # number of nodes
a = {n[0]: i for i, n in enumerate(nodes_data)}
g2 = rag.RAG()
# generating the edges with the new numeration
for e in edges_data:
g2.add_edge(a[e[0]], a[e[1]], weight=e[2]['weight'])
# reassinging the values to each node
for n, values in nodes_data:
n2 = a[n]
for k, v in values.items():
g2.node[n2][k] = v
return g2
def translate_graph(nodes_data, edges_data, threshold=threshold):
N = len(nodes_data) # number of nodes
a = {n[0]: i for i, n in enumerate(nodes_data)}
a_inv = {i: n[0] for i, n in enumerate(nodes_data)}
g2 = rag.RAG()
centroids = [np.array(n[1]['centroid']) for n in nodes_data]
A = np.array(centroids)
B = np.repeat(A, N, axis=1)
X, Y = B[:, 0:N], B[:, N:2 * N]
D = np.sqrt((X - X.T)**2 + (Y - Y.T)**2)
# checks that the nodes will be connected with at least one other node to preserve the final number of nodes
# in the resulting graph
index_unconnected_nodes = np.invert((D < threshold).sum(axis=1) > 1)
# sum the number problematic nodes indices has to be zero
if not index_unconnected_nodes.sum() == 0:
D2 = skip_diag_strided(D)
D_0 = D2.min(axis=1)[index_unconnected_nodes]
threshold = 2 * D_0.max()
print(
'warning: Max distance is less than theshold, reassining theshold to 0.7min_dist=',
threshold)
D = np.triu(D, k=1)
D_1 = D > 0
D_th = D < threshold
D_th = D_th * D_1
a1, a2 = np.nonzero(D_th)
# generating the edges with the new numeration
for i, j in zip(a1, a2):
distance = D[i, j]
g2.add_edge(i, j, weight=distance)
# reassinging the values to each node
for n, values in nodes_data:
n2 = a[n]
for k, v in values.items():
g2.node[n2][k] = v
return g2
w1 = g[n].get(src, {'weight': -np.inf})['weight']
w2 = g[n].get(dst, {'weight': -np.inf})['weight']
return {'weight': max(w1, w2)}
def display(g, title):
"""Displays a graph with the given title."""
pos = nx.circular_layout(g)
plt.figure()
plt.title(title)
nx.draw(g, pos)
nx.draw_networkx_edge_labels(g, pos, font_size=20)
g = rag.RAG()
g.add_edge(1, 2, weight=10)
g.add_edge(2, 3, weight=20)
g.add_edge(3, 4, weight=30)
g.add_edge(4, 1, weight=40)
g.add_edge(1, 3, weight=50)
# Assigning dummy labels.
for n in g.nodes():
g.node[n]['labels'] = [n]
gc = g.copy()
display(g, "Original Graph")
g.merge_nodes(1, 3)