import pandas as pd


class TemporalAnalysis:
    def avg_reply_time_per_emotion(self, df: pd.DataFrame) -> list[dict]:
        df = 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, df: pd.DataFrame) -> list[dict]:
        per_day = df.groupby("date").size().reset_index(name="count")

        return per_day.to_dict(orient="records")

    def heatmap(self, df: pd.DataFrame) -> list[dict]:
        weekday_order = [
            "Monday",
            "Tuesday",
            "Wednesday",
            "Thursday",
            "Friday",
            "Saturday",
            "Sunday",
        ]

        df = df.copy()
        df["weekday"] = pd.Categorical(
            df["weekday"], categories=weekday_order, ordered=True
        )

        heatmap = (
            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")