"""列概况和预览：上传时在 Parquet 上用 DuckDB 计算，存进文件记录，供 describe_data 和接口返回"""

import math
from pathlib import Path
from typing import Any

import duckdb

from app.datafiles.query import connect, json_value, quote_identifier
from app.schemas.data_file import DataColumn

PREVIEW_ROWS = 20
SAMPLE_ROWS = 5
EXAMPLES = 3
TOP_VALUES = 5
# 去重值不超过这么多的文本、布尔列才给出现次数最多的值
TOP_VALUES_MAX_DISTINCT = 50
# 样例值从开头这么多行里取
EXAMPLE_SCAN_ROWS = 1000
PROFILE_CELL_CHARS = 50
PREVIEW_CELL_CHARS = 200


def profile_table(
    path: Path, columns: list[DataColumn], notes: dict[str, str]
) -> tuple[dict[str, Any], list[list[Any]]]:
    """返回列概况和接口用的预览（前 PREVIEW_ROWS 行）"""
    connection = connect({"t": path})
    try:
        rows = connection.execute("SELECT count(*) FROM t").fetchone()[0]
        connection.execute("SUMMARIZE t")
        fields = [column[0] for column in connection.description]
        summary = {row[0]: dict(zip(fields, row)) for row in connection.fetchall()}
        head = connection.execute(f"FROM t LIMIT {EXAMPLE_SCAN_ROWS}").fetchall()
        profiled = [
            _profile_column(
                connection,
                column,
                summary[column.name],
                head,
                position,
                notes.get(column.name),
            )
            for position, column in enumerate(columns)
        ]
        sample = connection.execute(
            f"FROM t USING SAMPLE reservoir({SAMPLE_ROWS} ROWS) REPEATABLE (42)"
        ).fetchall()
    finally:
        connection.close()
    profile = {
        "rows": rows,
        "columns": profiled,
        "sample": [
            [json_value(value, PROFILE_CELL_CHARS) for value in row] for row in sample
        ],
    }
    preview = [
        [json_value(value, PREVIEW_CELL_CHARS) for value in row]
        for row in head[:PREVIEW_ROWS]
    ]
    return profile, preview


def _profile_column(
    connection: duckdb.DuckDBPyConnection,
    column: DataColumn,
    stats: dict[str, Any],
    head: list[tuple],
    position: int,
    note: str | None,
) -> dict[str, Any]:
    item: dict[str, Any] = {"name": column.name, "type": column.type}
    if column.format:
        item["format"] = column.format
    null_ratio = float(stats["null_percentage"]) / 100
    item["null_ratio"] = round(null_ratio, 4)
    # approx_unique 是估算值，小表上可能比行数还大
    item["distinct"] = min(
        stats["approx_unique"], round(stats["count"] * (1 - null_ratio))
    )
    if column.type == "number":
        for key in ("min", "max", "avg", "q25", "q50", "q75"):
            item[key] = _number(stats[key])
    elif column.type in ("date", "datetime"):
        # SUMMARIZE 的时间用空格分隔，和预览里的 ISO 写法统一
        item["min"], item["max"] = (
            None if stats[key] is None else stats[key].replace(" ", "T", 1)
            for key in ("min", "max")
        )
    if (
        column.type in ("text", "boolean")
        and stats["approx_unique"] <= TOP_VALUES_MAX_DISTINCT
    ):
        name = quote_identifier(column.name)
        counts = connection.execute(
            f"SELECT {name}, count(*) AS n FROM t WHERE {name} IS NOT NULL "
            f"GROUP BY 1 ORDER BY n DESC, 1 LIMIT {TOP_VALUES}"
        ).fetchall()
        item["top_values"] = [
            {"value": json_value(value, PROFILE_CELL_CHARS), "count": count}
            for value, count in counts
        ]
    examples: list[Any] = []
    for row in head:
        value = json_value(row[position], PROFILE_CELL_CHARS)
        if value is not None and value not in examples:
            examples.append(value)
            if len(examples) == EXAMPLES:
                break
    item["examples"] = examples
    if note:
        item["note"] = note
    return item


def _number(text: str | None) -> int | float | None:
    """SUMMARIZE 的统计值是字符串；整数不带小数点，其余保留 6 位小数；Parquet 里的 inf 返回空"""
    if text is None:
        return None
    value = float(text)
    if not math.isfinite(value):
        return None
    if value.is_integer() and abs(value) < 2**53:
        return int(value)
    return round(value, 6)