from typing import Any

import pandas as pd

from fs_report.models import Config


def executive_scan_frequency_transform(
    data: list[dict[str, Any]] | pd.DataFrame, config: Config | None = None
) -> pd.DataFrame:
    """
    Transform findings data for the Executive Summary scan frequency chart.
    Groups by day, week, month, or quarter based on the date range.
    Returns a DataFrame with a 'period_label' attribute for chart labeling.
    """
    if config is None:
        raise ValueError(
            "Config object is required for executive_scan_frequency_transform"
        )
    if isinstance(data, pd.DataFrame):
        if data.empty:
            df = pd.DataFrame()
            df.period_label = "Day"  # type: ignore[attr-defined]
            return df
        df = data.copy()
    elif not data:
        df = pd.DataFrame()
        df.period_label = "Day"  # type: ignore[attr-defined]
        return df
    else:
        df = pd.DataFrame(data)
    if "detected" not in df.columns:
        df = pd.DataFrame()
        df.period_label = "Day"  # type: ignore[attr-defined]
        return df
    df["detected_dt"] = pd.to_datetime(df["detected"], errors="coerce", utc=True)
    # Strip timezone so .dt.to_period() doesn't emit a UserWarning
    df["detected_dt"] = df["detected_dt"].dt.tz_localize(None)
    # Determine period length
    start = pd.to_datetime(config.start_date)
    end = pd.to_datetime(config.end_date)
    period_days = (end - start).days + 1
    # Infer grouping period
    if period_days <= 31:
        period_label = "Day"
        df["period"] = df["detected_dt"].dt.strftime("%Y-%m-%d")
    elif period_days <= 180:
        period_label = "Week"
        df["period"] = (
            df["detected_dt"]
            .dt.to_period("W")
            .apply(lambda r: r.start_time.strftime("%Y-%m-%d"))
        )
    elif period_days <= 730:
        period_label = "Month"
        df["period"] = df["detected_dt"].dt.strftime("%Y-%m")
    else:
        period_label = "Quarter"
        df["period"] = df["detected_dt"].dt.to_period("Q").astype(str)
    result = (
        df.groupby("period")
        .agg(finding_count=("id", "count"))
        .reset_index()
        .sort_values("period")
    )
    # Attach the period label for use in chart labeling
    result.period_label = period_label  # type: ignore[attr-defined]
    return result
