def _get_lexicon_html(self, terms):
lexicon_html = ''
for i, term in enumerate(terms):
lexicon_html += '<b>' + ClickableTerms.get_clickable_term(term, self.term_plot_interface) + '</b>'
if self.include_category_labels:
category = self.category_projection.category_counts.loc[term].idxmax()
lexicon_html += (
' (<i>%s</i>)' %
ClickableTerms.get_clickable_term(category, self.category_plot_interface,
self.term_plot_interface))
if i != len(terms) - 1:
lexicon_html += ',\n'
return lexicon_html
def _get_lexicon_html(self, terms):
lexicon_html = ''
for i, term in enumerate(terms):
lexicon_html += '<b>' + ClickableTerms.get_clickable_term(term, self.term_plot_interface) + '</b>'
if self.include_category_labels:
category = self.category_projection.category_counts.loc[term].idxmax()
lexicon_html += (
' (<i>%s</i>)' %
ClickableTerms.get_clickable_term(category, self.category_plot_interface,
self.term_plot_interface))
if i != len(terms) - 1:
lexicon_html += ',\n'
return lexicon_html
def get_graph(self):
table = '<div class="timelinecontainer"><table class="timelinetable">'
table += '<tr><th>' + '</th><th>'.join(sorted(self.corpus.get_categories())) + '</th></tr>'
min_font_size = 7
max_font_size = 20
display_df = self.rank_df[lambda df: df.Rank < self.num_rows]
bin_boundaries = np.histogram_bin_edges(
np.log(display_df.Frequency), bins=max_font_size - min_font_size
)
display_df['FontSize'] = display_df.Frequency.apply(np.log).apply(
lambda x: bisect_left(bin_boundaries, x) + min_font_size
)
for rank, group_df in display_df.groupby('Rank'):
table += '<tr><td class="clickabletd">' + '</td><td class="clickabletd">'.join([
ClickableTerms.get_clickable_term(
row.Term,
style="font-size: " + str(row.FontSize)
)
for _, row in group_df.sort_values(by='Category').iterrows()
]) + '</td></tr>'
table += '</table></div>'
return table