"""Boxplot chart generation."""
from __future__ import annotations

from typing import Any

import duckdb

from visual_notebook.viz._common import (
    PALETTE, axis_style, base_chart, title, tooltip_style,
)


def compute_boxplot(
    con: duckdb.DuckDBPyConnection,
    table_name: str,
    column: str,
    group_col: str | None = None,
) -> dict[str, Any]:
    """Compute boxplot data and return ECharts spec."""
    if group_col:
        # Get unique groups
        groups = con.execute(f"""
            SELECT DISTINCT "{group_col}" as grp
            FROM "{table_name}"
            WHERE "{column}" IS NOT NULL AND "{group_col}" IS NOT NULL
            ORDER BY grp
        """).fetchall()

        group_names = []
        box_data = []
        for row in groups:
            grp = row[0]
            stats = con.execute(f"""
                SELECT
                    MIN("{column}") as min_val,
                    PERCENTILE_CONT(0.25) WITHIN GROUP (ORDER BY "{column}") as q25,
                    PERCENTILE_CONT(0.50) WITHIN GROUP (ORDER BY "{column}") as q50,
                    PERCENTILE_CONT(0.75) WITHIN GROUP (ORDER BY "{column}") as q75,
                    MAX("{column}") as max_val
                FROM "{table_name}"
                WHERE "{column}" IS NOT NULL AND "{group_col}" = {grp!r}
            """).fetchone()

            group_names.append(str(grp))
            if stats and stats[1] is not None:
                min_v = float(stats[0])
                q25_v = float(stats[1])
                q50_v = float(stats[2])
                q75_v = float(stats[3])
                max_v = float(stats[4])
                # Compute whiskers (1.5 * IQR)
                iqr = q75_v - q25_v
                lower_whisker = max(min_v, q25_v - 1.5 * iqr)
                upper_whisker = min(max_v, q75_v + 1.5 * iqr)
                box_data.append([lower_whisker, q25_v, q50_v, q75_v, upper_whisker])
            else:
                box_data.append([0, 0, 0, 0, 0])

        x_data = group_names
        label = f"Distribution of {column} by {group_col}"
    else:
        # Single boxplot for the column
        stats = con.execute(f"""
            SELECT
                MIN("{column}") as min_val,
                PERCENTILE_CONT(0.25) WITHIN GROUP (ORDER BY "{column}") as q25,
                PERCENTILE_CONT(0.50) WITHIN GROUP (ORDER BY "{column}") as q50,
                PERCENTILE_CONT(0.75) WITHIN GROUP (ORDER BY "{column}") as q75,
                MAX("{column}") as max_val
            FROM "{table_name}"
            WHERE "{column}" IS NOT NULL
        """).fetchone()

        min_v = float(stats[0])
        q25_v = float(stats[1])
        q50_v = float(stats[2])
        q75_v = float(stats[3])
        max_v = float(stats[4])
        iqr = q75_v - q25_v
        lower_whisker = max(min_v, q25_v - 1.5 * iqr)
        upper_whisker = min(max_v, q75_v + 1.5 * iqr)

        x_data = [column]
        box_data = [[lower_whisker, q25_v, q50_v, q75_v, upper_whisker]]
        label = f"Distribution of {column}"

    chart = base_chart()
    chart["title"] = title(label)
    chart["tooltip"] = {**tooltip_style(), "trigger": "item"}
    chart["xAxis"] = {**axis_style(group_col or column), "type": "category", "data": x_data}
    chart["yAxis"] = {**axis_style(column), "type": "value"}
    chart["series"] = [{
        "type": "boxplot",
        "data": box_data,
        "itemStyle": {"color": PALETTE[0], "borderColor": PALETTE[4]},
    }]

    return chart