# -*- coding:utf-8 -*-
import json
import time
from datetime import datetime, timedelta
import pendulum

from pot_libs.settings import SETTING
from unify_api.modules.electric.dao.electric_dao import load_add_to_compy_ids
from unify_api.utils.common_utils import round_2
from pot_libs.aredis_util.aredis_utils import RedisUtils
from pot_libs.logger import log
from pot_libs.mysql_util.mysql_util import MysqlUtil
from unify_api import constants
from unify_api.constants import Importance, SDU_ALARM_LIST
from unify_api.modules.alarm_manager.dao.list_static_dao import \
    alarm_aggs_importance, \
    sdu_alarm_importance_dao
from unify_api.modules.common.dao.common_dao import monitor_point_join
from unify_api.modules.common.procedures.common_utils import get_electric_index
from unify_api.modules.common.procedures.points import proxy_points, \
    get_points_num
from unify_api.modules.common.procedures.pttl_max import load_pttl_max
from unify_api.modules.home_page.components.count_info_cps import (
    MaxResidualCurrent,
    ElectricInfo,
)
from unify_api.utils.time_format import last30_day_range
from unify_api.modules.home_page.dao.count_info_dao import (
    get_inline_by_cid, get_power_factor_kpi, get_pcvf_kpi, get_economic_kpi,
    get_md_space, get_tc_runtime, compy_real_pf, compy_lst_month_pf
)
from unify_api.modules.electric_optimization.dao.power_index import (
    price_policy_by_cid
)
from unify_api.utils.taos_new import parse_td_columns, get_td_table_name, \
    td3_tbl_compate, get_td_engine_data
from unify_api.utils.time_format import CST
from unify_api.constants import PHASE_LINE_LANG
from unify_api.modules.common.dao.common_dao import (
    load_user_lang, load_monitor_names
)


async def other_info(cid):
    today_alarm_count, alarm_count, has_alarm_days = 0, 0, 0
    sql = "SELECT DATE_FORMAT(event_datetime,'%%Y-%%m-%%d') event_date, " \
          "count(id) doc_count FROM `point_1min_event` where cid=%s " \
          "and event_mode='alarm' GROUP BY event_date ORDER BY event_date desc"
    async with MysqlUtil() as conn:
        datas = await conn.fetchall(sql, args=(cid,))
    now_time = datetime.now()
    # 获取到工厂安装时间create_time
    async with MysqlUtil() as conn:
        company_sql = "select create_time from company where cid = %s"
    company = await conn.fetchone(company_sql, (cid,))
    create_time_timestamp = company["create_time"]
    create_time = datetime.fromtimestamp(create_time_timestamp)
    if not datas:
        # 1. 增加逻辑，新增工厂如果还没有事件产生
        # 系统安全运行天数： 当前时间 - 工厂安装时间 + 1
        safe_run_days = (now_time - create_time).days + 1
        return today_alarm_count, safe_run_days, alarm_count
    # 5. 构造返回
    # 如果每天都有报警, 防止安全运行天数-1天, 所以total_days +2
    total_days = (now_time - create_time).days + 2
    for data in datas:
        if data["event_date"] == str(now_time)[:10]:
            today_alarm_count = data["doc_count"]
        if data["doc_count"]:
            # 不安全运行的天数
            has_alarm_days += 1
        alarm_count += data["doc_count"]
    safe_run_days = total_days - has_alarm_days
    log.info(
        f"today_alarm_count={today_alarm_count} safe_run_days={safe_run_days}")
    return today_alarm_count, safe_run_days, alarm_count


def datetime_to_timestamp(dt):
    ans_time = time.mktime(dt.timetuple())
    return ans_time


async def electric_use_info(cid):
    """1.5用电安全指数"""
    now = str(datetime.now())
    start = str(datetime.now() - timedelta(30))
    score_events = ['overU', 'underTotalPF', 'underPhasePF', 'overTHDI',
                    'overTHDU', 'underU', 'unbalanceI', 'overPR',
                    'overResidualCurrent', 'unbalanceU', 'overI']
    score_sql = f"select importance, count(importance) doc_count from " \
                f"point_1min_event where cid=%s and event_datetime BETWEEN" \
                f"'{start}' and '{now}' and event_type in %s " \
                f"GROUP BY importance"
    alarm_sql = f"select importance, count(importance) doc_count from " \
                f"point_1min_event where cid=%s and event_datetime BETWEEN" \
                f"'{start}' and '{now}' " \
                f"GROUP BY importance"
    first_score, second_score, third_score = 0, 0, 0
    async with MysqlUtil() as conn:
        score_datas = await conn.fetchall(score_sql, args=(cid, score_events))
        alarm_datas = await conn.fetchall(alarm_sql, args=(cid,))
    for data in score_datas:
        if data["importance"] == Importance.First.value:
            first_score += data["doc_count"]
        elif data["importance"] == Importance.Second.value:
            second_score += data["doc_count"]
        elif data["importance"] == Importance.Third.value:
            third_score += data["doc_count"]
    point_len = await proxy_points([cid])
    alarm_score = (
        (first_score * 2 + second_score * 1 + third_score * 0.5) / point_len
        if point_len else 0
    )
    alarm_score = 15 if alarm_score >= 15 else alarm_score
    electric_use_score = get_electric_index(alarm_score)
    first_alarm, second_alarm, third_alarm = 0, 0, 0
    for data in alarm_datas:
        if data["importance"] == Importance.First.value:
            first_alarm += data["doc_count"]
        elif data["importance"] == Importance.Second.value:
            second_alarm += data["doc_count"]
        elif data["importance"] == Importance.Third.value:
            third_alarm += data["doc_count"]
    return ElectricInfo(
        first_alarm_cnt=first_alarm,
        second_alarm_cnt=second_alarm,
        third_alarm_cnt=third_alarm,
        alarm_score=alarm_score,
        electric_use_score=electric_use_score,
    )


async def normal_rate_of_location(cid):
    """获取温度和漏电流达标率"""
    d_stats = {"residual_current": {"total": 0, "normal": 0},
               "temperature": {"total": 0, "normal": 0}, }
    sql = "select l.lid, l.ad_type type, s.threshold from " \
          "location l left join soe_config_record s on l.lid=s.lid " \
          "where l.cid = %s and  s.enable=1 and l.ad_type in %s " \
          "and s.etype in %s;"
    async with MysqlUtil() as conn:
        locs = await conn.fetchall(sql, (cid,
                                         ("residual_current", "temperature"),
                                         ("overTemp", "overResidualCurrent")))
        loc_infos = {loc["lid"]: loc for loc in locs}
        lids = list(loc_infos.keys())
        if not lids:
            return "100%", "100%"

        prefix = f"real_time:adio:{SETTING.mysql_db}"
        keys = [f"{prefix}:{lid}" for lid in lids]
        rt_rlts = await RedisUtils().mget(keys)
        rt_adios = [json.loads(r) for r in rt_rlts if r]
        d_rt_adio = {adio["lid"]: adio for adio in rt_adios}

        now_ts = pendulum.now(tz=CST).int_timestamp
        for lid, d_loc in loc_infos.items():
            d_stats[d_loc["type"]]["total"] += 1
            if lid not in d_rt_adio:
                continue

            if not isinstance(d_loc["threshold"], (float, int)):
                continue

            try:
                d_adio = d_rt_adio[lid]
                if (now_ts - d_adio["ts"]) > constants.REAL_EXP_TIME:
                    log.warn(f"adio_current location_id={lid} has expire!")
                    continue

                if d_adio["v"] < d_loc["threshold"]:
                    d_stats[d_loc["type"]]["normal"] += 1
            except Exception as e:
                log.exception(f"parse real time adio:{d_adio} exc, e:{e}")

        if d_stats["temperature"]["total"] == 0:
            temp_qr = "100%"
        else:
            norm = d_stats["temperature"]["normal"]
            total = d_stats["temperature"]["total"]
            temp_qr = str(round((norm / total) * 100, )) + "%"

        if d_stats["residual_current"]["total"] == 0:
            rc_qr = "100%"
        else:
            norm = d_stats["residual_current"]["normal"]
            total = d_stats["residual_current"]["total"]
            rc_qr = str(round((norm / total) * 100)) + "%"
        return temp_qr, rc_qr


async def real_time_load(cid, end_dt=None):
    """实时负荷"""
    td_tbls = []
    add_to_compy_ids = await load_add_to_compy_ids(cid)
    if not add_to_compy_ids:
        return 0
    for item in add_to_compy_ids:
        mtid, sid = item["mtid"], item["sid"]
        tbl = get_td_table_name("electric", mtid)
        td_tbls.append(tbl)
        td_tbls.append(f"s_{sid.lower()}_e")

    td_mt_tables = td3_tbl_compate(td_tbls)
    if not end_dt:
        end_dt = pendulum.now(tz=CST)
    s_dt = end_dt.subtract(minutes=15)
    sql = f"SELECT last_row(mdptime, pttl) FROM electric_stb " \
          f"WHERE TBNAME IN {td_mt_tables} " \
          f"AND ts>='{str(s_dt)}' AND ts <='{str(end_dt)}' group by tbname;"
    url = f"{SETTING.stb_url}db_electric?tz=Asia/Shanghai"
    is_succ, results = await get_td_engine_data(url, sql)
    if not is_succ or not results:
        return 0

    head = parse_td_columns(results)
    datas = []
    for res in results["data"]:
        datas.append(dict(zip(head, res)))
    total = 0
    for item in datas:
        if not item:
            continue
        total += item["pttl"]
    return total


async def power_count_info(cid):
    """近30天負荷最大值"""
    now = datetime.now()
    start_time = (now - timedelta(30)).strftime("%Y-%m-%d 00:00:00")
    end_time = now.strftime("%Y-%m-%d %H:%M:%S")

    max_30d_load, _time = await load_pttl_max(cid, start_time, end_time, -1)
    cur_load = await real_time_load(cid)
    return round_2(cur_load), round_2(max_30d_load)


async def load_aiao_max(user_id, cid, s_dts, e_dts, filed):
    value_max, location_name, occur_time = None, None, None
    sql = f"SELECT a.value_max,a.value_max_time,p.name,p.mtid,b.item FROM " \
          f"`location_15min_aiao` a LEFT JOIN location b on a.lid=b.lid " \
          f"LEFT JOIN point p on p.mtid=a.mtid where " \
          f"a.create_time BETWEEN '{s_dts}' and '{e_dts}' and a.cid=%s " \
          f" and a.ad_type=%s order by a.value_max desc limit 1"
    async with MysqlUtil() as conn:
        datas = await conn.fetchone(sql, args=(cid, filed))

    if datas:
        lang = await load_user_lang(user_id)
        mtid = datas["mtid"]
        if lang != "zh_CN":
            mtr_names = await load_monitor_names([mtid], lang)
            mtr_name = mtr_names[mtid]
        else:
            mtr_name = datas["name"]

        value_max = round(datas["value_max"], 2)
        item_name = "漏电流" if datas['item'] == 'default' else datas['item']
        if lang != "zh_CN":
            item_name = PHASE_LINE_LANG.get(datas["item"], {}).get(lang, "")
        location_name = f"{mtr_name}_{item_name}"
        occur_time = datas.get("value_max_time")
        occur_time = str(occur_time) if occur_time else None
    return MaxResidualCurrent(
        max=value_max,
        location_name=location_name,
        occur_time=occur_time,
    )


async def company_power_use_info(company_id, start, end):
    async with MysqlUtil() as conn:
        sql = f"SELECT sum(kwh) kwh, sum(charge) charge FROM " \
              f"`company_15min_power` where create_time BETWEEN " \
              f"'{start}' and '{end}' AND cid=%s"
        datas = await conn.fetchone(sql, args=(company_id,))
    return datas


async def get_company_charge_price(company_id, es_time_start, es_time_end):
    power_use_info = await company_power_use_info(company_id, es_time_start,
                                                  es_time_end)
    if power_use_info["kwh"]:
        unit_price = power_use_info["charge"] / power_use_info["kwh"]
    else:
        unit_price = ""
    return unit_price


async def power_charge_price(cid):
    """ 首页获取昨日平均电价， 上月平均电价"""
    # 昨日平均电价
    now = datetime.now()
    yestday = now - timedelta(1)
    yestday_start = datetime(yestday.year, yestday.month, yestday.day, 0, 0, 0)
    yestday_end = yestday_start + timedelta(1)
    yestday_datas = await company_power_use_info(cid, str(yestday_start),
                                                 str(yestday_end))
    if yestday_datas["kwh"]:
        yestoday_price = yestday_datas["charge"] / yestday_datas["kwh"]
    else:
        yestoday_price = ""
    if now.month == 1:
        last_month = 12
        year = now.year - 1
        last_month_start = datetime(year=year, month=last_month, day=1)
    else:
        last_month_start = datetime(year=now.year, month=now.month - 1, day=1)
    last_month_end = datetime(year=now.year, month=now.month, day=1)
    last_month_datas = await company_power_use_info(cid, str(last_month_start),
                                                    str(last_month_end)
                                                    )
    if last_month_datas["kwh"]:
        last_month_price = last_month_datas["charge"] / last_month_datas["kwh"]
    else:
        last_month_price = ""
    return round_2(yestoday_price), round_2(last_month_price)


async def cal_power_factor(cid):
    """首页获取实时功率因数， 上月功率因数"""
    real_cos = await compy_real_pf(cid)
    lst_month_cos = await compy_lst_month_pf(cid)
    if lst_month_cos:
        lst_month_cos = round(lst_month_cos, 2)
    return real_cos, lst_month_cos


async def optimization_count_info(company_id: int):
    """
    首页用电经济指数和用电优化模块统计数据
    :param company_id:
    :return:
    """

    async with MysqlUtil() as conn:
        sql = "SELECT inlid, `name` FROM inline WHERE cid=%s"
        inlines = await conn.fetchall(sql, args=(company_id,))
        inline_ids = [inline["inlid"] for inline in inlines]

    now = datetime.now()
    start_time = (
        pendulum.datetime(now.year, now.month, 1)
            .subtract(months=1)
            .strftime("%Y-%m-%d %H:%M:%S")
    )
    end_time = pendulum.datetime(now.year, now.month, 1).strftime(
        "%Y-%m-%d %H:%M:%S")
    power_use_info = await company_power_use_info(company_id, start_time,
                                                  end_time)
    month_charge = power_use_info.get("charge")
    month_kwh = power_use_info.get("kwh")
    count_info_map = {
        "avg_price": month_charge / month_kwh if month_kwh else ""
    }
    if not inline_ids:
        count_info_map.update(
            {
                "power_factor": {"save_charge": "", "kpi_x": "", "desc": "", },
                "pcvf": {"save_charge": "", "kpi_x": "", "desc": "", },
                "power_save": {"save_charge": "", "kpi_x": "", "desc": "", },
                "md_space": {"save_charge": "", "kpi_x": "", "desc": "", },
                "save_percent": 0,
                "md_space_p": "",
                "mean_load_factor": "",
            }
        )
        return count_info_map

    now = datetime.now()
    if now.month == 1:
        last_month_dt = datetime(year=now.year - 1, month=12, day=1)
    else:
        last_month_dt = datetime(year=now.year, month=now.month - 1, day=1)
    last_month_str = datetime.strftime(last_month_dt, "%Y-%m-%d")
    # 功率因数
    async with MysqlUtil() as conn:
        sql = "SELECT inlid, `cos`, save_charge pf_cost, kpi_x, save_charge " \
              "FROM algo_power_factor_result WHERE inlid in %s " \
              "and month=%s"
        power_factor_results = await conn.fetchall(sql, args=(
            inline_ids, last_month_str))
        total_pf_save = round(
            sum([i["pf_cost"] for i in power_factor_results if
                 i["pf_cost"] and i["pf_cost"] >= 0]),
            2,
        )
        total_pf_save = 0 if total_pf_save <= 0 else total_pf_save

        pf_kpi_x_list = [
            i["kpi_x"] for i in power_factor_results if
            type(i["kpi_x"]) in [int, float]
        ]
        pf_kpi_x = min(pf_kpi_x_list) if len(pf_kpi_x_list) else ""
        if pf_kpi_x == "":
            pf_desc = ""
        elif pf_kpi_x >= 0.9:
            pf_desc = "无空间"
        elif 0.85 < pf_kpi_x < 0.9:
            pf_desc = "空间较小"
        elif 0.8 < pf_kpi_x <= 0.85:
            pf_desc = "空间适中"
        else:
            pf_desc = "空间较大"

        count_info_map["power_factor"] = {
            "save_charge": total_pf_save if pf_kpi_x != "" else "",
            "kpi_x": pf_kpi_x,
            "desc": pf_desc,
        }

    # 移峰填谷指数
    async with MysqlUtil() as conn:
        sql = "select `score`, `cost_save` from `algo_plsi_result` " \
              "where `inlid` in %s and `month` = %s"
        pcvfs = await conn.fetchall(sql, args=(inline_ids, last_month_str))

        pcvf_kpi_x_list = [i["score"] for i in pcvfs if
                           type(i["score"]) in [int, float]]
        pcvf_kpi_x = min(pcvf_kpi_x_list) if len(pcvf_kpi_x_list) else ""
        total_pcvf_save = round(
            sum([i["cost_save"] for i in pcvfs if
                 i["cost_save"] and i["cost_save"] >= 0]), 2
        )

        if pcvf_kpi_x == "":
            pcvf_desc = ""
        elif pcvf_kpi_x >= 90:
            pcvf_desc = "无空间"
        elif 80 < pcvf_kpi_x < 90:
            pcvf_desc = "空间较小"
        elif 70 < pcvf_kpi_x <= 80:
            pcvf_desc = "空间适中"
        else:
            pcvf_desc = "空间较大"

        total_pcvf_save = 0 if total_pcvf_save <= 0 else total_pcvf_save
        count_info_map["pcvf"] = {
            "save_charge": total_pcvf_save if pcvf_kpi_x != "" else "",
            "kpi_x": pcvf_kpi_x,
            "desc": pcvf_desc,
        }

    # 经济运行
    async with MysqlUtil() as conn:
        sql = "select `kpi_x`, `save_charge`, `mean_load_factor` " \
              "from `algo_economic_operation_result` where " \
              "`inlid` in %s and `month` = %s"
        economic_operations = await conn.fetchall(sql, args=(
            inline_ids, last_month_str))
        economic_kpi_x_list = [
            i["kpi_x"] for i in economic_operations if
            type(i["kpi_x"]) in [int, float]
        ]
        economic_kpi_x = max(
            economic_kpi_x_list) if economic_kpi_x_list else ""
        total_economic_save = round(
            sum(
                [
                    i["save_charge"]
                    for i in economic_operations
                    if i["save_charge"] and i["save_charge"] >= 0
                ]
            ),
            2,
        )
        total_economic_save = 0 if total_economic_save <= 0 else \
            total_economic_save
        if economic_kpi_x == "":
            economic_desc = ""
        elif economic_kpi_x <= 0.6:
            economic_desc = "无空间"
        elif 0.6 < economic_kpi_x <= 0.7:
            economic_desc = "空间较小"
        elif 0.7 < economic_kpi_x <= 0.8:
            economic_desc = "空间适中"
        else:
            economic_desc = "空间较大"

        count_info_map["power_save"] = {
            "save_charge": total_economic_save if economic_kpi_x != "" else "",
            "kpi_x": economic_kpi_x,
            "desc": economic_desc,
        }

    # 最大需量
    async with MysqlUtil() as conn:
        sql = (
            "select a.inline_md_charge, a.kpi_x, a.save_charge "
            "from `algo_md_space_analysis_result` a "
            "inner join`algo_md_space_analysis_unit` b "
            " on a.space_analysis_id=b.id "
            "where b.inlid in %s and a.month = %s and b.valid=1;"
        )
        md_spaces = await conn.fetchall(sql, args=(inline_ids, last_month_str))

        md_space_kpi_x_list = [i["kpi_x"] for i in md_spaces if
                               type(i["kpi_x"]) in [int, float]]
        md_space_kpi_x = max(md_space_kpi_x_list) if len(
            md_space_kpi_x_list) else ""
        total_md_space_save = round(
            sum(
                [i["save_charge"] for i in md_spaces if
                 i["save_charge"] and i["save_charge"] >= 0]
            ),
            2,
        )
        total_md_space_save = 0 if total_md_space_save <= 0 else \
            total_md_space_save
        if md_space_kpi_x == "":
            md_space_desc = ""
        elif md_space_kpi_x <= 0:
            md_space_desc = "无空间"
        elif 0 < md_space_kpi_x <= 0.1:
            md_space_desc = "空间较小"
        elif 0.1 < md_space_kpi_x <= 0.2:
            md_space_desc = "空间适中"
        else:
            md_space_desc = "空间较大"
        count_info_map["md_space"] = {
            "save_charge": total_md_space_save if md_space_kpi_x != "" else "",
            "kpi_x": md_space_kpi_x,
            "desc": md_space_desc,
        }

    total_save_cost = 0
    for _, item in count_info_map.items():
        total_save_cost += (
            item["save_charge"] if isinstance(item, dict) and item[
                "save_charge"] else 0
        )
    save_percent = total_save_cost / month_charge if month_charge else ""
    count_info_map["save_percent"] = save_percent

    # 计算最大需量
    async with MysqlUtil() as conn:
        sql = "select `price_md`,`price_tc` from `price_policy` where `cid`=%s"
        price_policy = await conn.fetchone(sql, args=(company_id,))

    total_md_space_charge = sum(
        [i["inline_md_charge"] for i in md_spaces if i["inline_md_charge"]])
    total_md_space_p = (
        total_md_space_charge / price_policy["price_md"]
        if price_policy and price_policy["price_md"]
        else ""
    )
    count_info_map["md_space_p"] = total_md_space_p

    # 经济运行最低负载率
    mean_load_factors = [
        i["mean_load_factor"] for i in economic_operations if
        i["mean_load_factor"]
    ]
    count_info_map["mean_load_factor"] = ""
    if mean_load_factors:
        count_info_map["mean_load_factor"] = min(mean_load_factors)
    return count_info_map


async def electric_use_info_sdu(cid):
    start, end = last30_day_range()
    first_alarm_cnt = 0
    second_alarm_cnt = 0
    third_alarm_cnt = 0
    sql = f"select importance,count(1) doc_count from point_1min_event " \
          f"where cid={cid} and event_datetime BETWEEN '{start}' and '{end}' " \
          f"and event_type in {tuple(SDU_ALARM_LIST)} GROUP BY importance"
    log.info(f"sql:{sql}")
    async with MysqlUtil() as conn:
        datas = await conn.fetchall(sql)
    for data in datas:
        if data["importance"] == Importance.First.value:
            first_alarm_cnt += data["doc_count"]
        elif data["importance"] == Importance.Second.value:
            second_alarm_cnt += data["doc_count"]
        elif data["importance"] == Importance.Third.value:
            third_alarm_cnt += data["doc_count"]
    point_len = await get_points_num(cid)
    alarm_score = (
        (
                first_alarm_cnt * 2 + second_alarm_cnt * 1 + third_alarm_cnt * 0.5) / point_len
        if point_len
        else 0
    )
    alarm_score = 15 if alarm_score >= 15 else alarm_score
    electric_use_score = get_electric_index(alarm_score)
    return ElectricInfo(
        first_alarm_cnt=first_alarm_cnt,
        second_alarm_cnt=second_alarm_cnt,
        third_alarm_cnt=third_alarm_cnt,
        alarm_score=alarm_score,
        electric_use_score=electric_use_score,
    )


async def electric_use_info_points_sdu(start, end, points):
    """用电安全指数, 识电u, 根据points来计算"""
    sql = f"select importance,count(*) as doc_count from  point_1min_event " \
          f"where  pid in %s and  event_datetime BETWEEN %s and %s " \
          f"and event_type in %s group by importance"
    async with MysqlUtil() as conn:
        results = await conn.fetchall(sql, args=(points, start, end,
                                                 SDU_ALARM_LIST))

    first_alarm_cnt = 0
    second_alarm_cnt = 0
    third_alarm_cnt = 0
    for result in results:
        if result["importance"] == Importance.First.value:
            first_alarm_cnt += result["doc_count"]
        elif result["importance"] == Importance.Second.value:
            second_alarm_cnt += result["doc_count"]
        elif result["importance"] == Importance.Third.value:
            third_alarm_cnt += result["doc_count"]

    alarm_score = (first_alarm_cnt * 2 + second_alarm_cnt * 1 +
                   third_alarm_cnt * 0.5) / len(points)

    if alarm_score >= 15:
        alarm_score = 15

    electric_use_score = get_electric_index(alarm_score)

    log.info(
        "point_len={} alarm_score={} electric_use_score={}".format(
            len(points), alarm_score, electric_use_score
        )
    )
    return ElectricInfo(
        first_alarm_cnt=first_alarm_cnt,
        second_alarm_cnt=second_alarm_cnt,
        third_alarm_cnt=third_alarm_cnt,
        alarm_score=alarm_score,
        electric_use_score=electric_use_score,
    )


async def optimization_count_info_new(company_id: int):
    """
    首页用电经济指数和用电优化模块统计数据
    :param company_id:
    :return:
    """

    inlines = await get_inline_by_cid(company_id)
    inline_ids = [inline["inlid"] for inline in inlines]

    # 获取公司上月用电
    now = datetime.now()
    es_start_time = (
        pendulum.datetime(now.year, now.month, 1)
            .subtract(months=1)
            .strftime("%Y-%m-%dT%H:%M:%S+08:00")
    )
    es_end_time = pendulum.datetime(now.year, now.month, 1).strftime(
        "%Y-%m-%dT%H:%M:%S+08:00")
    power_use_info = await company_power_use_info(company_id,
                                                  es_start_time,
                                                  es_end_time)
    month_charge = power_use_info["charge"]
    count_info_map = {
        "avg_price": power_use_info["charge"] / power_use_info["kwh"]
        if power_use_info["kwh"]
        else ""
    }
    if not inline_ids:
        count_info_map.update(
            {
                "power_factor": {"save_charge": "", "kpi_x": "", "desc": "", },
                "pcvf": {"save_charge": "", "kpi_x": "", "desc": "", },
                "power_save": {"save_charge": "", "kpi_x": "", "desc": "", },
                "md_space": {"save_charge": "", "kpi_x": "", "desc": "", },
                "save_percent": 0,
                "md_space_p": "",
                "mean_load_factor": "",
            }
        )
        return count_info_map
    if now.month == 1:
        last_month_dt = datetime(year=now.year - 1, month=12, day=1)
    else:
        last_month_dt = datetime(year=now.year, month=now.month - 1, day=1)
    last_month_str = datetime.strftime(last_month_dt, "%Y-%m")
    # 功率因数

    power_factor_results = await get_power_factor_kpi(inline_ids,
                                                      last_month_dt)
    total_pf_save = round(
        sum([i["pf_cost"] for i in power_factor_results if
             i["pf_cost"] and i["pf_cost"] >= 0]),
        2,
    )
    total_pf_save = 0 if total_pf_save <= 0 else total_pf_save
    pf_kpi_x_list = [
        (i["name"], i["kpi_x"]) for i in power_factor_results if
        type(i["kpi_x"]) in [int, float]
    ]

    if len(pf_kpi_x_list):
        pf_kpi_x_num = [pf_kpi[1] for pf_kpi in pf_kpi_x_list]
        pf_kpi_x = min(pf_kpi_x_num)
        if pf_kpi_x >= 0.9:
            pf_desc = "上月功率因数比较合理，请继续保持"
        else:
            pf_kpi_x_name = []
            for index, kpi_num in enumerate(pf_kpi_x_num):
                if kpi_num < 0.9:
                    pf_kpi_x_name.append(pf_kpi_x_list[index][0])
            pf_desc = f"{'、'.join(pf_kpi_x_name)}可以进行无功补偿"
    else:
        pf_kpi_x = ""
        pf_desc = ""
    if pf_kpi_x == "":
        pf_space = ""
    elif pf_kpi_x >= 0.9:
        pf_space = "无空间"
    elif 0.85 < pf_kpi_x < 0.9:
        pf_space = "空间较小"
    elif 0.8 < pf_kpi_x <= 0.85:
        pf_space = "空间适中"
    else:
        pf_space = "空间较大"
    count_info_map["power_factor"] = {
        "save_charge": total_pf_save if pf_kpi_x != "" else "",
        "kpi_x": pf_kpi_x,
        "desc": pf_desc,
        "space": pf_space
    }
    # 移峰填谷指数
    pcvfs = await get_pcvf_kpi(inline_ids, last_month_str)
    pcvf_kpi_x_list = [(i["name"], i["score"]) for i in pcvfs if
                       type(i["score"]) in [int, float]]

    if len(pcvf_kpi_x_list):
        pcvf_kpi_x_num = [pcvf_kpi[1] for pcvf_kpi in pcvf_kpi_x_list]
        pcvf_kpi_x = min(pcvf_kpi_x_num)
        pcvf_kpi_x_name = []

        if pcvf_kpi_x < 70:
            for index, kpi_num in enumerate(pcvf_kpi_x_num):
                if kpi_num < 70:
                    pcvf_kpi_x_name.append(pcvf_kpi_x_list[index][0])
            pcvf_desc = f"{'、'.join(pcvf_kpi_x_name)}可以进行无功补偿"
        elif pcvf_kpi_x < 90:
            for index, kpi_num in enumerate(pcvf_kpi_x_num):
                if kpi_num < 90:
                    pcvf_kpi_x_name.append(pcvf_kpi_x_list[index][0])
            pcvf_desc = f"请合理调整{'、'.join(pcvf_kpi_x_name)}用电时间或" \
                        f"引入新能源，转移高峰电量至低谷"
        else:
            pcvf_desc = "平均电价处于较低水平，请继续保持"

    else:
        pcvf_kpi_x = ""
        pcvf_desc = ""
    if pcvf_kpi_x == "":
        pcvf_space = ""
    elif pcvf_kpi_x >= 90:
        pcvf_space = "无空间"
    elif 80 < pcvf_kpi_x < 90:
        pcvf_space = "空间较小"
    elif 70 < pcvf_kpi_x <= 80:
        pcvf_space = "空间适中"
    else:
        pcvf_space = "空间较大"
    total_pcvf_save = round(
        sum([i["cost_save"] for i in pcvfs if
             i["cost_save"] and i["cost_save"] >= 0]), 2
    )

    total_pcvf_save = 0 if total_pcvf_save <= 0 else total_pcvf_save
    count_info_map["pcvf"] = {
        "save_charge": total_pcvf_save if pcvf_kpi_x != "" else "",
        "kpi_x": pcvf_kpi_x,
        "desc": pcvf_desc,
        "space": pcvf_space
    }

    # 经济运行
    economic_operations = await get_economic_kpi(inline_ids, last_month_str)
    economic_kpi_x_list = [
        (i["name"], i["kpi_x"]) for i in economic_operations if
        type(i["kpi_x"]) in [int, float]
    ]
    total_economic_save = round(
        sum(
            [
                i["save_charge"]
                for i in economic_operations
                if i["save_charge"] and i["save_charge"] >= 0
            ]
        ),
        2,
    )
    if len(economic_kpi_x_list):
        econ_kpi_x_num = [econ_kpi[1] for econ_kpi in economic_kpi_x_list]
        econ_kpi_x = max(econ_kpi_x_num)
        econ_kpi_x_min = min(econ_kpi_x_num)
        econ_kpi_x_name = []
        if econ_kpi_x >= 0.9:
            for index, kpi_max in enumerate(econ_kpi_x_num):
                if kpi_max >= 0.9:
                    econ_kpi_x_name.append(economic_kpi_x_list[index][0])
            economic_desc = f"{'、'.join(econ_kpi_x_name)}负载率超过" \
                            f"安全阈值，应考虑扩容"
        elif econ_kpi_x >= 0.7:
            for index, kpi_max in enumerate(econ_kpi_x_num):
                if kpi_max >= 0.7:
                    econ_kpi_x_name.append(economic_kpi_x_list[index][0])
            economic_desc = f"{'、'.join(econ_kpi_x_name)}负载率较高，" \
                            f"可考虑调整负荷或扩容"
        elif econ_kpi_x_min < 0.6 and econ_kpi_x < 0.9:
            for index, kpi_max in enumerate(econ_kpi_x_num):
                if kpi_max < 0.6:
                    econ_kpi_x_name.append(economic_kpi_x_list[index][0])
            economic_desc = f"{'、'.join(econ_kpi_x_name)}负载率较低，" \
                            f"请考虑容改需或更换小容量变压器"
        else:
            economic_desc = "进线负载率比较经济，请继续保持"
    else:
        econ_kpi_x = ""
        economic_desc = ""
    if econ_kpi_x == "":
        econ_space = ""
    elif econ_kpi_x <= 0.6:
        econ_space = "无空间"
    elif 0.6 < econ_kpi_x <= 0.7:
        econ_space = "空间较小"
    elif 0.7 < econ_kpi_x <= 0.8:
        econ_space = "空间适中"
    else:
        econ_space = "空间较大"
    count_info_map["power_save"] = {
        "save_charge": total_economic_save if econ_kpi_x != "" else "",
        "kpi_x": econ_kpi_x,
        "desc": economic_desc,
        "space": econ_space
    }

    #  容量、需量价格
    price_policy = await price_policy_by_cid(company_id)
    price_md = price_policy["price_md"] if price_policy["price_md"] else 0
    price_tc = price_policy["price_tc"] if price_policy["price_tc"] else 0
    # 最大需量
    md_spaces = await get_md_space(inline_ids, last_month_dt)

    md_space_kpi_x_list = [i["kpi_x"] for i in md_spaces if
                           type(i["kpi_x"]) in [int, float]]
    md_space_kpi_x = max(md_space_kpi_x_list) if len(
        md_space_kpi_x_list) else ""
    total_md_space_save = round(
        sum(
            [i["save_charge"] for i in md_spaces if
             i["save_charge"] and i["save_charge"] >= 0]
        ),
        2,
    )
    total_md_space_save = 0 if total_md_space_save <= 0 else total_md_space_save
    if md_space_kpi_x == "":
        md_space_space = ""
    elif md_space_kpi_x <= 0:
        md_space_space = "无空间"
    elif 0 < md_space_kpi_x <= 0.1:
        md_space_space = "空间较小"
    elif 0.1 < md_space_kpi_x <= 0.2:
        md_space_space = "空间适中"
    else:
        md_space_space = "空间较大"
    md_space_tc_runtimes = await get_tc_runtime(inline_ids)
    md_space_name = []
    for index, item in enumerate(md_spaces):
        if type(item["inline_md_predict"]) in [int, float] and \
                md_space_tc_runtimes[index]["tc_runtime"] * price_tc >= \
                price_md * item["inline_md_predict"]:
            md_space_name.append(md_space_tc_runtimes[index]["name"])

    if len(md_space_name):
        md_space_desc = f"若次月负荷无较大变动，建议{'、'.join(md_space_name)}" \
                        f"选择按最大需量计费"
    else:
        md_space_desc = "不存在容改需空间"

    count_info_map["md_space"] = {
        "save_charge": total_md_space_save if md_space_kpi_x != "" else "",
        "kpi_x": md_space_kpi_x,
        "desc": md_space_desc,
        "space": md_space_space
    }
    # 节费率
    total_save_cost = 0
    for _, item in count_info_map.items():
        total_save_cost += (
            item["save_charge"] if isinstance(item, dict) and item[
                "save_charge"] else 0
        )
    save_percent = total_save_cost / month_charge if month_charge else ""
    count_info_map["save_percent"] = save_percent
    # 计算最大需量
    total_md_space_charge = sum(
        [i["inline_md_charge"] for i in md_spaces if i["inline_md_charge"]])
    total_md_space_p = (
        total_md_space_charge / price_policy["price_md"]
        if price_policy and price_policy["price_md"]
        else ""
    )
    count_info_map["md_space_p"] = total_md_space_p
    # 经济运行最低负载率
    mean_load_factors = [
        i["mean_load_factor"] for i in economic_operations if
        i["mean_load_factor"]
    ]
    count_info_map["mean_load_factor"] = ""
    if mean_load_factors:
        count_info_map["mean_load_factor"] = min(mean_load_factors)
    return count_info_map


async def cid_alarm_importance_count(cid, start, end):
    """计算工厂报警数, 按报警等级"""
    monitor_point_list = await monitor_point_join(cid)
    point_list = [i["pid"] for i in monitor_point_list]
    es_res = await sdu_alarm_importance_dao(start, end, point_list)
    es_res_key = {i["key"]: i for i in es_res}

    res_list = []
    for info in monitor_point_list:
        name = info.get("name")
        point_id = info.get("pid")
        tmp_dic = {"name": name, "alarm_count": 0,
                   "first": 0, "second": 0, "third": 0}
        if point_id in es_res_key:
            inner_bucket = es_res_key[point_id]["importance"]["buckets"]
            for b in inner_bucket:
                if b["key"] == Importance.First.value:
                    tmp_dic["first"] += b["doc_count"]
                elif b["key"] == Importance.Second.value:
                    tmp_dic["second"] += b["doc_count"]
                elif b["key"] == Importance.Third.value:
                    tmp_dic["third"] += b["doc_count"]

        tmp_dic["alarm_count"] = tmp_dic["first"] + tmp_dic["second"] + \
                                 tmp_dic["third"]
        res_list.append(tmp_dic)
    # 按照报警数, 排top5
    res_list_sort = sorted(res_list, key=lambda i: i["alarm_count"],
                           reverse=True)[:5]
    return res_list_sort


async def alarm_importance_count_total(cid, start, end):
    """计算工厂报警数, 按报警等级"""
    es_res = await alarm_aggs_importance(cid, start, end)
    first_cnt, second_cnt, third_cnt = 0, 0, 0
    for buckets in es_res:
        if buckets["importance"] == Importance.First.value:
            first_cnt += buckets["alarm_count"]
        elif buckets["importance"] == Importance.Second.value:
            second_cnt += buckets["alarm_count"]
        elif buckets["importance"] == Importance.Third.value:
            third_cnt += buckets["alarm_count"]
    return {
        "first_cnt": first_cnt,
        "second_cnt": second_cnt,
        "third_cnt": third_cnt,
        "total_cnt": first_cnt + second_cnt + third_cnt
    }


def safety_ratio_res(score, client_name):
    """根据安全指数, 求安全风险系数"""
    # wechat和web共用
    if 90 <= score <= 100:
        return "风险较低" if client_name == "wechat" else "当前安全状况良好，请继续保持！"
    elif 80 <= score < 90:
        return "低风险" if client_name == "wechat" else "当前安全状况较好，请关注报警信息！"
    elif 60 <= score < 80:
        return "中风险" if client_name == "wechat" else "当前安全状况一般，请密切关注报警信息！"
    elif 40 <= score < 60:
        return "风险较高" if client_name == "wechat" else "当前安全状况较差，请重视报警信息！"
    elif 0 <= score < 40:
        return "高风险" if client_name == "wechat" else "当前安全状况很差，请重视报警信息并处理！"
    else:
        log.error(f"score:{score}, 安全指数不在0-100范围内")
        return ""


def health_status_res(score, client_name):
    """根据健康指数, 求健康状态"""
    if 90 <= score <= 100:
        return "状态优" if client_name == "wechat" else "当前健康状况良好，请继续保持！"
    elif 75 <= score < 90:
        return "状态较好" if client_name == "wechat" else "当前健康状况较好，请关注电能质量信息！"
    elif 60 <= score < 75:
        return "状况一般" if client_name == "wechat" else "当前健康状况一般，请密切关注电能质量信息！"
    elif 0 <= score < 60:
        return "状态差" if client_name == "wechat" else "当前健康状况差，请重视电能质量信息并改善！"
    else:
        log.error(f"score:{score}, 健康指数不在0-100范围内")
        return ""


def carbon_status_res(score):
    """根据碳排指数, 求达标情况, wechat使用"""
    if 80 <= score <= 100:
        return "达标"
    elif 0 <= score < 80:
        return "不达标"
    else:
        log.error(f"score:{score}, 碳排指数不在0-100范围内")
        return ""


def carbon_status_res_web(score):
    """根据碳排指数, 求达标情况, web使用"""
    if not isinstance(score, int):
        return ""
    if 90 <= score <= 100:
        return "当前能耗达标且水平很低，请继续保持！"
    elif 80 <= score < 90:
        return "当前能耗达标且水平较低，请继续保持！"
    elif 60 <= score < 80:
        return "当前能耗不达标，请关注能耗控制！"
    elif 0 <= score < 60:
        return "当前能耗不达标且水平较高，请重视能耗控制！"
    else:
        log.error(f"score:{score}, 碳排指数不在0-100范围内")
        return ""


def economic_index_desc(score, client_name):
    """经济指数, 返回文字说明"""
    if 90 <= score <= 100:
        return "状态良好" if client_name == "wechat" else "当前经济性良好，请继续保持"
    elif 75 <= score < 90:
        return "存在空间" if client_name == "wechat" else "存在空间，建议关注优化措施"
    elif 60 <= score < 75:
        return "空间较大" if client_name == "wechat" else "空间较大，建议尝试优化措施！"
    elif 0 <= score < 60:
        return "空间很大" if client_name == "wechat" else "空间很大，建议采取优化措施！"
    else:
        log.error(f"score:{score}, 经济指数不在0-100范围内")
        return ""
