def scatter_authors(self,
measure="betweenness centrality",
thresh=15, **kwargs):
"""Scatter-plot with position based on interaction and cluster
measure, color based on number of comments, and size on avg comment
length"""
project, show, _ = ac.handle_kwargs(**kwargs)
x_measure, y_measure = [" ".join([netw, measure]) for netw in
["interaction", "cluster"]]
axes = self.author_frame.plot(
kind='scatter',
x=x_measure, y=y_measure,
c='total comments',
s=self.author_frame['word counts'] / self.author_frame[
'total comments'],
cmap="viridis_r",
sharex=False,
title="Author-activity and centrality in {}".format(project))
for name, data in self.author_frame.iterrows():
if data['total comments'] >= thresh:
axes.text(data[x_measure], data[y_measure], name,
fontsize=6)
ac.fake_legend([50, 100, 250], title="Average wordcount of comments")
ac.show_or_save(show)
def scatter_authors_hits(self, thresh=10, **kwargs):
"""Scatter-plot based on hits-algorithm for hubs and authorities"""
project, show, _ = ac.handle_kwargs(**kwargs)
hits = self.__hits()
axes = hits.plot(
kind='scatter',
x='hubs', y='authorities',
c='total comments',
s=hits['word counts'] / hits['total comments'],
cmap="viridis_r",
sharex=False,
title="Hubs and Authorities in {}".format(project))
for name, data in hits.iterrows():
if data['total comments'] >= thresh:
axes.text(data['hubs'], data['authorities'], name,
fontsize=6)
ac.fake_legend([50, 100, 250], title="Average wordcount of comments")
ac.show_or_save(show)
