def network_plot(ancestor_pno, adj, colormap, show=True, savefn=None, title="PCA coloring in Patent "):
# colormap maps from node name to color.
G = get_graph(adj)
G.graph["ancestor"] = ancestor_pno
ancestor_idx = G.nodes().index(G.graph["ancestor"])
# defaults to black.
node_colors = [hex2rgb(colormap.get(node, "#ffffff")) for node in G.nodes()]
default_node_size = 50
node_sizes = [default_node_size for node in G.nodes()]
node_sizes[ancestor_idx] = 6 * default_node_size
f = plt.figure()
f.set_size_inches(18.5, 10.5)
nx.draw_networkx(
G,
nx.spring_layout(G, iterations=20000),
# cmap=plt.get_cmap('jet'),
node_color=node_colors,
node_size=node_sizes,
with_labels=False,
fontsize=1,
# font_weight = 'bold',
linewidths=0.5,
width=0.5,
)
plt.title(title + str(ancestor_pno))
if savefn is not None:
print "saving plot..."
plt.savefig(savefn, dpi=100)
if show:
plt.show()
def community_colors(db, pno, threshold, show_vis = False, savefn=None):
# Get the patents in the lineage and the adjacency dictionary.
lineage = crawl_lineage(
db, pno, n_generations = 5,
enforce_func = lambda x: len(x.get('citedby', [])) >= threshold,
flatten=True
)
adj = subnet_adj_dict(lineage)
# detect communities
detector = detect.CommunityDetector(adj)
communities = detector.run()
n_communities = len(communities)
community_lookup = util.get_community_lookup(communities)
# assign each patent a color, and provide a lookup dictionary
colors = visualize.discrete_color_scheme(n_communities+1)
node_color_lookup = {node: colors[community_lookup[node]] for node in adj.keys()}
# make the visualization.
G = visualize.get_graph(adj)
G.graph['ancestor'] = pno
ancestor_idx = G.nodes().index(G.graph['ancestor'])
node_colors = [colors[community_lookup[node]] for node in G.nodes()]
node_colors[G.nodes().index(G.graph['ancestor'])] = colors[n_communities]
default_node_size = 60
node_sizes = [default_node_size for node in G.nodes()]
node_sizes[ancestor_idx] = 6*default_node_size
f = plt.figure()
f.set_size_inches(18.5, 10.5)
if savefn is not None or show_vis:
nx.draw_networkx(
G,
nx.spring_layout(G, iterations=20000),
node_color=node_colors,
node_size = node_sizes,
with_labels = False,
fontsize=1,
# font_weight = 'bold',
linewidths=.5,
width=.5
)
if savefn is not None:
plt.title('Communities in Patent {}'.format(pno))
plt.savefig(savefn, dpi=100)
if show_vis:
plt.show()
return node_color_lookup
def network_plot(ancestor_pno,
adj,
colormap,
show=True,
savefn=None,
title='PCA coloring in Patent '):
# colormap maps from node name to color.
G = get_graph(adj)
G.graph['ancestor'] = ancestor_pno
ancestor_idx = G.nodes().index(G.graph['ancestor'])
# defaults to black.
node_colors = [
hex2rgb(colormap.get(node, '#ffffff')) for node in G.nodes()
]
default_node_size = 50
node_sizes = [default_node_size for node in G.nodes()]
node_sizes[ancestor_idx] = 6 * default_node_size
f = plt.figure()
f.set_size_inches(18.5, 10.5)
nx.draw_networkx(
G,
nx.spring_layout(G, iterations=20000),
#cmap=plt.get_cmap('jet'),
node_color=node_colors,
node_size=node_sizes,
with_labels=False,
fontsize=1,
#font_weight = 'bold',
linewidths=.5,
width=.5)
plt.title(title + str(ancestor_pno))
if savefn is not None:
print "saving plot..."
plt.savefig(savefn, dpi=100)
if show:
plt.show()
patent_adj = data.get_patent_adj()
# Build a community detector object and run the algorithm
detector = detect.CommunityDetector(patent_adj)
communities = detector.run()
n_communities = len(communities)
#Build a lookup table for node to color.
community_lookup = {}
for i,c in enumerate(communities):
for pno in c:
community_lookup[pno] = i
colors = visualize.discrete_color_scheme(n_communities+1)
# Get a graph from the adjacency list.
G = visualize.get_graph(patent_adj)
G.graph['ancestor'] = 4723129
node_colors = [colors[community_lookup[node]] for node in G.nodes()]
# Set the ancestor to its own color.
node_colors[G.nodes().index(G.graph['ancestor'])] = colors[n_communities]
nx.draw(G, nx.spring_layout(G, iterations=10000), cmap=plt.get_cmap('jet'), node_color=node_colors, node_size=100)
#nx.draw_spring(G, cmap=plt.get_cmap('jet'), node_color=node_colors, node_size=100)
# what detector.A and detector.S after a phase1 and phase2
A = np.array([[ 4., 1., 1.],
[ 1., 3., 0.],
[ 1., 0., 3.]])
S = np.array([[ 1., 0., 0.],
[ 0., 1., 0.],
[ 0., 0., 1.]])
# Build a community detector object and run the algorithm
detector = detect.CommunityDetector(wiki_adj_dict)
communities = detector.run()
n_communities = len(communities)
# Build a lookup table for node to color.
community_lookup = {}
for i,c in enumerate(communities):
for pno in c:
community_lookup[pno] = i
colors = visualize.discrete_color_scheme(n_communities)
# Get a graph from the adjacency list.
G = visualize.get_graph(wiki_adj_dict)
node_colors = [colors[community_lookup[node]] for node in G.nodes()]
# Set the ancestor to its own color.
#node_colors[G.nodes().index(G.graph['ancestor'])] = colors[n_communities]
nx.draw(G, cmap=plt.get_cmap('jet'), node_color=node_colors)
#
