"""Download CSV and validate the nine PRBS identification parameters."""

import csv
import io
import math


REQUIRED_FIELDS = {
    "q_in_val", "dt", "n_order", "t_c", "levels",
    "dead_area", "xa_full", "V_val", "repeat",
}


def validate_identification_config(config) -> dict:
    """Validate a parsed config mapping and normalize numeric values."""
    if not isinstance(config, dict):
        raise ValueError("辨识配置必须是参数映射")
    actual = set(config)
    if actual != REQUIRED_FIELDS:
        missing = sorted(REQUIRED_FIELDS - actual)
        extra = sorted(actual - REQUIRED_FIELDS)
        raise ValueError(f"辨识配置字段错误，缺少={missing}，多余={extra}")

    scalar_fields = {
        "q_in_val", "dt", "t_c", "dead_area", "xa_full", "V_val"
    }
    for field in scalar_fields:
        value = config[field]
        if (isinstance(value, bool) or not isinstance(value, (int, float))
                or not math.isfinite(float(value))):
            raise ValueError(f"辨识参数 {field} 必须是有限数字")

    for field in ("n_order", "repeat"):
        value = config[field]
        if isinstance(value, bool) or not isinstance(value, int):
            raise ValueError(f"辨识参数 {field} 必须是整数")

    levels = config["levels"]
    if not isinstance(levels, list) or len(levels) < 2:
        raise ValueError("levels 必须是至少包含 2 项的数组")
    if len(levels) & (len(levels) - 1):
        raise ValueError("levels 长度必须是 2 的整数次幂")
    normalized_levels = []
    for value in levels:
        if (isinstance(value, bool) or not isinstance(value, (int, float))
                or not math.isfinite(float(value)) or not 0 <= value <= 100):
            raise ValueError("levels 中的开度必须是 0 到 100 的有限数字")
        normalized_levels.append(float(value))

    if config["q_in_val"] < 0:
        raise ValueError("q_in_val 不能小于 0")
    if config["dt"] <= 0 or config["t_c"] <= 0:
        raise ValueError("dt 和 t_c 必须大于 0")
    if config["t_c"] < config["dt"]:
        raise ValueError("t_c 必须大于等于 dt，确保每个码元至少采样一次")
    if config["n_order"] < 2:
        raise ValueError("n_order 必须大于等于 2")
    if config["repeat"] <= 0:
        raise ValueError("repeat 必须是正整数")
    if config["dead_area"] < 0 or config["xa_full"] <= config["dead_area"]:
        raise ValueError("必须满足 0 <= dead_area < xa_full")
    if config["xa_full"] < 1000:
        raise ValueError("xa_full 不能小于前置行程扫描上限 1000")
    if config["V_val"] <= 0:
        raise ValueError("V_val 必须大于 0")

    return {
        "q_in_val": float(config["q_in_val"]),
        "dt": float(config["dt"]),
        "n_order": config["n_order"],
        "t_c": float(config["t_c"]),
        "levels": normalized_levels,
        "dead_area": float(config["dead_area"]),
        "xa_full": float(config["xa_full"]),
        "V_val": float(config["V_val"]),
        "repeat": config["repeat"],
    }


def parse_identification_config_csv(csv_text: str) -> dict:
    """Parse a two-column CSV into the validated identification config.

    The CSV must use ``parameter,value`` as its header. ``levels`` is one
    quoted comma-separated value, for example ``"10,20,30,40"``.
    """
    try:
        reader = csv.DictReader(io.StringIO(csv_text))
        fieldnames = [name.strip() for name in (reader.fieldnames or [])]
        if fieldnames != ["parameter", "value"]:
            raise ValueError("CSV 表头必须为 parameter,value")

        raw = {}
        for row in reader:
            if None in row:
                raise ValueError("CSV 每行只能包含 parameter 和 value 两列")
            parameter = (row.get("parameter") or "").strip()
            value = (row.get("value") or "").strip()
            if not parameter:
                raise ValueError("CSV 存在空参数名")
            if parameter in raw:
                raise ValueError(f"CSV 参数重复: {parameter}")
            raw[parameter] = value
    except csv.Error as exc:
        raise ValueError(f"辨识配置 CSV 格式错误: {exc}") from exc

    actual = set(raw)
    if actual != REQUIRED_FIELDS:
        missing = sorted(REQUIRED_FIELDS - actual)
        extra = sorted(actual - REQUIRED_FIELDS)
        raise ValueError(f"辨识配置字段错误，缺少={missing}，多余={extra}")

    try:
        levels = [float(value.strip()) for value in raw["levels"].split(",")]
        config = {
            "q_in_val": float(raw["q_in_val"]),
            "dt": float(raw["dt"]),
            "n_order": int(raw["n_order"]),
            "t_c": float(raw["t_c"]),
            "levels": levels,
            "dead_area": float(raw["dead_area"]),
            "xa_full": float(raw["xa_full"]),
            "V_val": float(raw["V_val"]),
            "repeat": int(raw["repeat"]),
        }
    except (TypeError, ValueError) as exc:
        raise ValueError(f"辨识配置 CSV 参数值无效: {exc}") from exc
    return validate_identification_config(config)


def download_identification_config(timeout=20) -> dict:
    """Download the current customer's CSV config through the cloud server."""
    import requests
    from api import data_record_url, the_folder

    try:
        response = requests.post(data_record_url, json={
            "type": "getIdentificationConfig",
            "deviceId": the_folder,
        }, timeout=timeout)
        response.raise_for_status()
        result = response.json()
    except Exception as exc:
        raise ValueError(f"连接云服务器辨识配置服务失败: {exc}") from exc

    if not result.get("success"):
        raise ValueError(result.get("errMsg", "云服务器未返回辨识配置"))
    try:
        config_response = requests.get(result["url"], timeout=timeout)
        config_response.raise_for_status()
        return parse_identification_config_csv(config_response.text)
    except (KeyError, ValueError, requests.RequestException) as exc:
        raise ValueError(f"下载或解析辨识配置 CSV 失败: {exc}") from exc