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Finish off the links between frontend and backend #10

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dylan merged 24 commits from feat/add-frontend-pages into main 2026-03-18 20:30:19 +00:00
Conversation 0 Commits 24 Files Changed 21 +22 -8
2 changed files with 22 additions and 8 deletions
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26
server/analysis/interactional.py
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@@ -63,11 +63,25 @@ class InteractionAnalysis:
pairs.sort(key=lambda x: x[1], reverse=True) pairs.sort(key=lambda x: x[1], reverse=True)
return pairs[:top_n] return pairs[:top_n]
def initiator_ratio(self, df: pd.DataFrame): def conversation_concentration(self, df: pd.DataFrame) -> dict:
starters = df["reply_to"].isna().sum() if "type" not in df.columns:
total = len(df) return {}
if total == 0: comments = df[df["type"] == "comment"]
return 0 if comments.empty:
return {}
return round(starters / total, 2) author_counts = comments["author"].value_counts()
total_comments = len(comments)
total_authors = len(author_counts)
top_10_pct_n = max(1, int(total_authors * 0.1))
top_10_pct_share = round(author_counts.head(top_10_pct_n).sum() / total_comments, 4)
return {
"total_commenting_authors": total_authors,
"top_10pct_author_count": top_10_pct_n,
"top_10pct_comment_share": float(top_10_pct_share),
"single_comment_authors": int((author_counts == 1).sum()),
"single_comment_author_ratio": float(round((author_counts == 1).sum() / total_authors, 4)),
}

4
server/analysis/stat_gen.py
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@@ -121,8 +121,8 @@ class StatGen:
return { return {
"average_thread_depth": self.interaction_analysis.average_thread_depth(filtered_df), "average_thread_depth": self.interaction_analysis.average_thread_depth(filtered_df),
"top_interaction_pairs": self.interaction_analysis.top_interaction_pairs(filtered_df, top_n=100), "top_interaction_pairs": self.interaction_analysis.top_interaction_pairs(filtered_df, top_n=100),
"initiator_ratio": self.interaction_analysis.initiator_ratio(filtered_df), "interaction_graph": self.interaction_analysis.interaction_graph(filtered_df),
"interaction_graph": self.interaction_analysis.interaction_graph(filtered_df) "conversation_concentration": self.interaction_analysis.conversation_concentration(filtered_df)
} }
def cultural(self, df: pd.DataFrame, filters: dict | None = None) -> dict: def cultural(self, df: pd.DataFrame, filters: dict | None = None) -> dict: