def plot_growth_timeline(self, cumulative=True, plot_method="d3"):
'''
Plots a graph depicting the growth of each cluster's size as a
function of time.
'''
assert self.clusters != []
if plot_method == "matplotlib":
for cluster in self.clusters:
documents = cluster.get_documents()
if len(documents) > 0:
dates, counts = aggregate_data([doc['date'] for doc in documents.values()], cumulative=cumulative)
t = MatplotlibTimeline(dates, counts, cumulative=cumulative)
t.plot()
t.show()
elif plot_method=="d3":
data = []; meta = []
for cluster in self.clusters:
documents = cluster.get_documents()
cluster.analyse()
if len(documents) > 0:
data.append([doc.date for doc in documents.values()])
meta.append({"Terms" :cluster.get_most_frequent_terms(N=8),
"Authors": [len(cluster.get_authors())], #should be wrapped in a list
"Locations": cluster.get_locations(N=2),
"Main entities": cluster.get_persons(N=2)})
dates = []; counts = []
for d in data:
t_dates, t_counts = aggregate_data(d, cumulative)
dates.append([num2date(date).strftime('%Y-%m-%d %H:%M:%S') for date in t_dates])
counts.append(t_counts)
final_dates = dates
final_counts = [count.tolist() for count in counts]
t = D3Timeline(final_dates, final_counts, meta, cumulative=cumulative)
t.plot(url='timeline_growth.html')
top_clusters = sorted(top_clusters, key=lambda x: -x[0])[:20]
meta = []
top_clusters = sorted(top_clusters, key=lambda x: x[1])
for i, cluster in enumerate(top_clusters):
cluster_struct = cluster[2]
cluster_struct.analyse()
meta.append({"title":"event"+str(i),
"date":cluster[1].strftime('%Y-%m-%d %H:%M:%S'),
"keywords":cluster_struct.get_most_frequent_terms(N=9),
"authors": len(cluster_struct.get_authors()),
"locations": cluster_struct.get_locations(),
"namedEntities": cluster_struct.get_persons()})
data = [[doc.date for doc in items]]
dates = []
counts = []
for d in data:
t_dates, t_counts = aggregate_data(d, cumulative=False)
dates.append([num2date(date).strftime('%Y-%m-%d %H:%M:%S') for date in t_dates])
counts.append(t_counts)
final_dates = dates
final_counts = [count.tolist() for count in counts]
t = D3Timeline(final_dates, final_counts, meta=meta, cumulative=False)
t.plot(url='timeline_hackathon.html')