refactor: extract temporal analysis into it's own class

server/analysis/temporal.py

@@ -0,0 +1,70 @@
+import pandas as pd
+
+class TemporalAnalysis:
+    def __init__(self, df: pd.DataFrame):
+        self.df = df
+  
+    def avg_reply_time_per_emotion(self) -> dict:
+        df = self.df.copy()
+
+        replies = df[
+            (df["type"] == "comment") &
+            (df["reply_to"].notna()) &
+            (df["reply_to"] != "")
+        ]
+
+        id_to_time = df.set_index("id")["dt"].to_dict()
+
+        def compute_reply_time(row):
+            reply_id = row["reply_to"]
+            parent_time = id_to_time.get(reply_id)
+
+            if parent_time is None:
+                return None
+
+            return (row["dt"] - parent_time).total_seconds()
+        
+        replies["reply_time"] = replies.apply(compute_reply_time, axis=1)
+        emotion_cols = [col for col in df.columns if col.startswith("emotion_") and col not in ("emotion_neutral", "emotion_surprise")]
+        replies["dominant_emotion"] = replies[emotion_cols].idxmax(axis=1)
+        
+        grouped = (
+            replies
+            .groupby("dominant_emotion")["reply_time"]
+            .agg(["mean", "count"])
+            .reset_index()
+        )
+
+        return grouped.to_dict(orient="records")
+    
+    def posts_per_day(self) -> dict:
+        per_day = (
+            self.df.groupby("date")
+            .size()
+            .reset_index(name="count")
+        )
+
+        return per_day.to_dict(orient="records")
+    
+    def heatmap(self) -> dict:
+        weekday_order = [
+            "Monday", "Tuesday", "Wednesday",
+            "Thursday", "Friday", "Saturday", "Sunday"
+        ]
+
+        self.df["weekday"] = pd.Categorical(
+            self.df["weekday"],
+            categories=weekday_order,
+            ordered=True
+        )
+
+        heatmap = (
+            self.df
+            .groupby(["weekday", "hour"], observed=True)
+            .size()
+            .unstack(fill_value=0)
+            .reindex(columns=range(24), fill_value=0)
+        )
+
+        heatmap.columns = heatmap.columns.map(str)
+        return heatmap.to_dict(orient="records")