{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "### start to get data MGMCLN750N215N097 from 2022-01-01 00:00:00 to 2022-03-01 00:00:00\n",
      "# get data from 2022-01-08 00:00:00 to 2022-01-09 00:00:00......... "
     ]
    }
   ],
   "source": [
    "# 获取数据\n",
    "import sys\n",
    "from LIB.BACKEND import DBManager\n",
    "from LIB.BACKEND import DataPreProcess\n",
    "\n",
    "dataPrePro = DataPreProcess.DataPreProcess()\n",
    "\n",
    "sn = \"MGMCLN750N215N097\"\n",
    "st = '2022-01-01 00:00:00'\n",
    "et = '2022-03-01 00:00:00'\n",
    "\n",
    "dbManager = DBManager.DBManager()\n",
    "df_data = dbManager.get_data(sn=sn, start_time=st, end_time=et, data_groups=['bms', 'gps', 'accum', 'system'])\n",
    "# \n",
    "df_bms = df_data['bms']\n",
    "df_gps = df_data['gps']\n",
    "df_accum = df_data['accum']\n",
    "df_system = df_data['system']\n",
    "\n",
    "df_bms = dataPrePro.data_fault_tag(sn,df_bms)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_bms[['时间戳','fault_tag']].to_csv('test.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 下载数据 \n",
    "import sys\n",
    "from LIB.BACKEND import Tools\n",
    "\n",
    "tools = Tools.Tools()\n",
    "write_path = r''\n",
    "sn = \"PK50001A100000680\"\n",
    "\n",
    "st = '2021-07-06 00:00:00'\n",
    "et = '2021-07-07 20:00:00'\n",
    "tools.data_download(write_path=write_path, sn=sn, start_time=st, end_time=et, data_groups=['bms'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 数据预处理\n",
    "import sys\n",
    "from LIB.BACKEND import DataPreProcess\n",
    "\n",
    "dataPrePro = DataPreProcess.DataPreProcess()\n",
    "# 时间完全相同的数据仅保留一行\n",
    "df_bms_pro, df_gps_pro = dataPrePro.time_filter(df_bms, df_gps)\n",
    "\n",
    "# bms数据按照电流和状态分段, 然后在状态分段内部，根据时间跳变继续分段（解决段内数据丢失）\n",
    "df_bms_pro = dataPrePro.data_split_by_status(df_bms_pro)\n",
    "df_bms_pro = dataPrePro.data_split_by_time(df_bms_pro)\n",
    "\n",
    "# bms数据将两次充电间的状态合并\n",
    "df_bms_pro = dataPrePro.combine_drive_stand(df_bms_pro)\n",
    "# bms 数据计算行车和充电开始前后的静置时间\n",
    "df_bms_pro = dataPrePro.cal_stand_time(df_bms_pro)\n",
    "# gps 数据可靠性判断, 并增加里程和速度至gps数据(根据未合并的数据段判断)\n",
    "df_bms_pro, df_gps_pro, res_record= dataPrePro.gps_data_judge(df_bms_pro, df_gps_pro)\n",
    "# gps 数据可靠性判断, 并增加里程和速度至gps数据(根据已合并的数据段判断)\n",
    "df_bms_pro, df_gps_pro, res_record= dataPrePro.data_gps_judge_after_combine(df_bms_pro, df_gps_pro)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 单cycle指标统计\n",
    "import sys\n",
    "from LIB.BACKEND import IndexStaByOneCycle\n",
    "\n",
    "indexSta = IndexStaByOneCycle.IndexStaByOneCycle()\n",
    "\n",
    "data_number_list = sorted(list(set(df_bms[(df_bms['data_status'].isin(['drive']))]['data_split_by_status'])))\n",
    "for data_number in data_number_list[:]:\n",
    "    df_sel_bms = df_bms[df_bms['data_split_by_status'] == data_number]\n",
    "    df_sel_bms = df_sel_bms.reset_index(drop=True)\n",
    "    df_sel_gps = df_gps[(df_gps['时间戳']>df_sel_bms.loc[0,'时间戳']) & (df_gps['时间戳']<df_sel_bms.loc[len(df_sel_bms)-1,'时间戳'])]\n",
    "    df_sel_gps = df_sel_gps.reset_index(drop=True)\n",
    "\n",
    "    print(indexSta.odo_sta(np.array(df_sel_gps['odo'])))\n",
    "    print(indexSta.capacity_sta(40, np.array(df_sel_bms['SOC[%]']), np.array(df_sel_bms['SOH[%]'])))\n",
    "    print(indexSta.energy_sta(40, np.array(df_sel_bms['SOC[%]']), np.array(df_sel_bms['SOH[%]']),np.array(df_sel_bms['总电压[V]'])))\n",
    "    print(indexSta.acc_time_sta(np.array(df_sel_bms['时间戳'])))\n",
    "    print(indexSta.mean_temp_sta(np.array(df_sel_bms['单体温度1'])))\n",
    "    print(indexSta.temp_change_rate_sta(np.array(df_sel_bms['时间戳']), np.array(df_sel_bms['单体温度1'])))\n",
    "    print(indexSta.dischrg_max_pwr_sta(np.array(df_sel_bms['总电压[V]']), np.array(df_sel_bms['总电流[A]'])))\n",
    "    print(indexSta.chrg_max_pwr_sta(np.array(df_sel_bms['总电压[V]']), np.array(df_sel_bms['总电流[A]'])))\n",
    "    print(indexSta.speed_sta(indexSta.odo_sta(np.array(df_sel_gps['odo'])), indexSta.acc_time_sta(np.array(df_sel_gps['时间戳'])), np.array(df_sel_gps['speed'])))\n",
    "    break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "problem in LIB\\BACKEND\\DataPreProcess.py - ModuleNotFoundError: No module named 'DBManager'\n"
     ]
    }
   ],
   "source": [
    "# 生成pydoc 说明文档\n",
    "!python -m pydoc -w LIB\\BACKEND\\DataPreProcess.py"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from LIB.BACKEND import DBManager, Log\n",
    "log = Log.Mylog(log_name='signal_monitor', log_level = 'info')\n",
    "log.set_file_hl(file_name='info.log', log_level='info')\n",
    "log.set_file_hl(file_name='error.log', log_level='error')\n",
    "logger = log.get_logger()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "### start to get data PK50001A100000608 from 2022-01-16 10:47:16 to 2022-01-18 10:47:16\n",
      "# get data from 2022-01-17 10:47:16 to 2022-01-18 10:47:16......... \n",
      "all data-getting done, bms_count is 5763, gps_count is 0, system_count is 0, accum_count is 0 \n",
      "\n"
     ]
    }
   ],
   "source": [
    "import LIB.BACKEND.OPENAPI.OpenApi as OpenApi\n",
    "from LIB.BACKEND import DBManager\n",
    "from LIB.BACKEND import DBManager\n",
    "\n",
    "\n",
    "\n",
    "dbManager = DBManager.DBManager()\n",
    "o = OpenApi.OpenApi()\n",
    "# df_fault = o.get_omp_fatult_tag(sn='PK50001A100000608')\n",
    "\n",
    "sn = \"PK50001A100000608\"\n",
    "st = '2022-01-16 10:47:16'\n",
    "et = '2022-01-18 10:47:16'\n",
    "\n",
    "dbManager = DBManager.DBManager()\n",
    "df_data = dbManager.get_data(sn=sn, start_time=st, end_time=et, data_groups=['bms'])['bms']\n",
    "df_data['fault_tag'] = None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 先按故障码分组，相同故障码的起始和终止时间 取最大区间\n",
    "import pandas as pd\n",
    "import pdb\n",
    "for index in df_fault.index:\n",
    "    startT = df_fault.loc[index, 'faultTime']\n",
    "    endT = df_fault.loc[index, 'endTime']\n",
    "    tagTypeText = df_fault.loc[index, 'tagTypeText']\n",
    "    childTagText = df_fault.loc[index, 'childTagText']\n",
    "    tagType = df_fault.loc[index, 'tagType']\n",
    "    childTag = df_fault.loc[index, 'childTag']\n",
    "    # 标签拼接\n",
    "    new_tag = []\n",
    "    if pd.isnull(tagType) or len(tagTypeText) <= 0:\n",
    "        continue\n",
    "    elif pd.isnull(childTag) or len(childTagText) <= 0:\n",
    "        new_tag.append(tagTypeText)\n",
    "    else:\n",
    "        for child in childTagText.split(\";\"):\n",
    "            new_tag.append(tagTypeText+':' + child)\n",
    "    # 原始数据对应故障时间的index\n",
    "    for index2 in df_data[(df_data['时间戳']>=startT) & (df_data['时间戳']<endT)].index:\n",
    "        old_tag = df_data.loc[index2, 'fault_tag']\n",
    "        if pd.isnull(old_tag) or len(old_tag) <= 0:\n",
    "            df_data.loc[index2,'fault_tag'] = ';'.join(new_tag)\n",
    "        else:\n",
    "            tag = old_tag.split(\";\")\n",
    "            tag.extend(new_tag)\n",
    "            df_data.loc[index2,'fault_tag'] = ';'.join(list(set(tag)))\n",
    "            "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [],
   "source": [
    "            tag = old_tag.split(\";\")\n",
    "            tag.extend(new_tag)\n",
    "            df_data.loc[index2,'fault_tag'] = ';'.join(list(set(tag)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_data.to_csv('test.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "        from LIB.BACKEND.OPENAPI import OpenApi\n",
    "        o = OpenApi.OpenApi()\n",
    "        df_fault = o.get_omp_fatult_tag()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_fault.to_csv('c.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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   "display_name": "Python 3.8.8 64-bit ('base': conda)",
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  "language_info": {
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   "file_extension": ".py",
   "mimetype": "text/x-python",
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