{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 15,
   "source": [
    "from LIB.BACKEND import DBManager\r\n",
    "from LIB.MIDDLE.CellStateEstimation.Common import log\r\n",
    "import datetime\r\n",
    "import pandas as pd\r\n",
    "import numpy as np\r\n",
    "import os   \r\n",
    "from DataPreProcessMGMC import DataPreProcess\r\n",
    "from scipy import stats\r\n",
    "\r\n",
    "#log信息配置\r\n",
    "mylog=log.Mylog('log.txt','error')\r\n",
    "mylog.logcfg()\r\n",
    "\r\n",
    "dbManager = DBManager.DBManager()\r\n",
    "dataSOH = pd.read_excel('../V1_0_0/sn-20210903.xlsx',sheet_name='MGMC')\r\n",
    "fileNames = dataSOH['sn']\r\n",
    "fileNames = list(fileNames)\r\n",
    "\r\n",
    "data_GMGC=pd.DataFrame()\r\n",
    "for k in range(len(fileNames)):\r\n",
    "    # try:    \r\n",
    "        sn = fileNames[k]\r\n",
    "        df_data = dbManager.get_data(sn=sn, start_time='2021-10-15 00:00:00', end_time='2021-11-06 00:00:00', data_groups=['bms'])\r\n",
    "        data_bms = df_data['bms']\r\n",
    "        data_bms['sn'] = sn\r\n",
    "        df_sheetCat = DataPreProcess.data_split_by_status(DataPreProcess,data_bms, drive_interval_threshold=120, charge_interval_threshold=300, drive_stand_threshold=120, charge_stand_threshold=300)\r\n",
    "        unique_status_idx = np.unique(df_sheetCat.data_split_by_status.values)\r\n",
    "        data_result = pd.DataFrame()\r\n",
    "        list_result = []\r\n",
    "        for n in unique_status_idx:\r\n",
    "            list_result.append(df_sheetCat[df_sheetCat.data_split_by_status==n]) \r\n",
    "        \r\n",
    "        maxI=[]\r\n",
    "        modI=[]\r\n",
    "        lenI=[]\r\n",
    "        temp1_start=[]\r\n",
    "        temp2_start=[]\r\n",
    "        temp1_fin=[]\r\n",
    "        temp2_fin=[]\r\n",
    "        time_start=[]\r\n",
    "        time_fin=[]\r\n",
    "        time_drivefin=[]\r\n",
    "        temp1_drivefin=[]\r\n",
    "        temp2_drivefin=[]\r\n",
    "        sn2=[]\r\n",
    "        \r\n",
    "        for i in range(1,len(list_result)):\r\n",
    "            if list_result[i]['data_status'].values[0]=='charge':\r\n",
    "            \r\n",
    "                maxI.append(max(list_result[i]['总电流[A]'].values[:10]))\r\n",
    "                modI.append(stats.mode(list_result[i]['总电流[A]'].values[:10])[0][0])\r\n",
    "                lenI.append(len(list_result[i]['总电流[A]'].values))\r\n",
    "                temp1_start.append(list_result[i]['单体温度1'].values[0])\r\n",
    "                temp2_start.append(list_result[i]['单体温度2'].values[0])\r\n",
    "                temp1_fin.append(list_result[i]['单体温度1'].values[-1])\r\n",
    "                temp2_fin.append(list_result[i]['单体温度2'].values[-1])\r\n",
    "                time_start.append(list_result[i]['时间戳'].values[0])\r\n",
    "                time_fin.append(list_result[i]['时间戳'].values[-1])\r\n",
    "                time_drivefin.append(list_result[i-1]['时间戳'].values[0])\r\n",
    "                temp1_drivefin.append(list_result[i-1]['单体温度1'].values[0])\r\n",
    "                temp2_drivefin.append(list_result[i-1]['单体温度2'].values[0])           \r\n",
    "                sn2.append(list_result[i]['sn'].values[0])\r\n",
    "        dic={\"初始充电电流max\":maxI,\"初始充电电流mod\":modI,\"电流长度\":lenI,\"初始充电温度1\":temp1_start,\"初始充电温度2\":temp2_start,\r\n",
    "            \"结束充电温度1\":temp1_fin,\"结束充电温度2\":temp2_fin,\"初始充电时间\":time_start,\r\n",
    "            \"结束充电时间\":time_fin,\"行驶结束时间\":time_drivefin,\"行驶结束温度1\":temp1_drivefin,\r\n",
    "            \"行驶结束温度2\":temp2_drivefin,\"sn\":sn2}\r\n",
    "        df_analyse=pd.DataFrame(dic)\r\n",
    "\r\n",
    "        data_analyse=df_analyse[df_analyse['电流长度'].values>5]\r\n",
    "\r\n",
    "        mean_temp1=[]\r\n",
    "        mean_temp2=[]\r\n",
    "        mean_temp3=[]\r\n",
    "        time_diff_stand=[]\r\n",
    "        time_diff_charge=[]\r\n",
    "\r\n",
    "        for i in range(len(data_analyse)):\r\n",
    "            meantemp1=(data_analyse['初始充电温度1'].values[i]+data_analyse['初始充电温度2'].values[i])/2\r\n",
    "            mean_temp1.append(meantemp1)\r\n",
    "            meantemp2=(data_analyse['结束充电温度1'].values[i]+data_analyse['结束充电温度2'].values[i])/2\r\n",
    "            mean_temp2.append(meantemp2)\r\n",
    "            meantemp3=(data_analyse['行驶结束温度1'].values[i]+data_analyse['行驶结束温度2'].values[i])/2\r\n",
    "            mean_temp3.append(meantemp3)\r\n",
    "            td_charge=float(np.diff([data_analyse['初始充电时间'].values[i],data_analyse['结束充电时间'].values[i]])/np.timedelta64(1, 'm'))\r\n",
    "            time_diff_charge.append(td_charge)\r\n",
    "            td_stand=float(np.diff([data_analyse['行驶结束时间'].values[i],data_analyse['初始充电时间'].values[i]])/np.timedelta64(1, 'm'))\r\n",
    "            time_diff_stand.append(td_stand) \r\n",
    "   \r\n",
    "        data_analyse['初始充电温度']=mean_temp1\r\n",
    "        data_analyse['结束充电温度']=mean_temp2\r\n",
    "        data_analyse['行驶结束温度']=mean_temp3\r\n",
    "        data_analyse['闲置时长']=time_diff_stand\r\n",
    "        data_analyse['充电时长']=time_diff_charge\r\n",
    "\r\n",
    "        diff_temp=[]\r\n",
    "        for i in range(len(data_analyse)):\r\n",
    "            difftemp=data_analyse['结束充电温度'].values[i]-data_analyse['初始充电温度'].values[i]\r\n",
    "            diff_temp.append(difftemp)\r\n",
    "        data_analyse['充电温差']=diff_temp\r\n",
    "\r\n",
    "        data_select=data_analyse[data_analyse['充电时长'].values>5]\r\n",
    "\r\n",
    "        data_select.to_csv('./result/data'+sn+'.csv')\r\n",
    "\r\n",
    "        data_GMGC = data_GMGC.append(data_select)\r\n",
    "        data_GMGC.to_csv('./result/dataGMGC.csv')\r\n",
    "\r\n",
    "    # except Exception as e:\r\n",
    "    #     print(repr(e))\r\n",
    "    #     mylog.logopt(sn,e)\r\n",
    "    #     pass  \r\n",
    "\r\n",
    "\r\n",
    "\r\n",
    "\r\n",
    "\r\n",
    "\r\n",
    "\r\n",
    "\r\n",
    "\r\n",
    "\r\n"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "### start to get data MGMCLN750N215H001 from 2021-10-15 00:00:00 to 2021-11-06 00:00:00\n",
      "# get data from 2021-11-05 00:00:00 to 2021-11-06 00:00:00......... \n",
      "all data-getting done, bms_count is 11389, gps_count is 0, system_count is 0, accum_count is 0 \n",
      "\n",
      "### start to get data MGMCLN750N215H002 from 2021-10-15 00:00:00 to 2021-11-06 00:00:00\n",
      "# get data from 2021-10-27 00:00:00 to 2021-10-28 00:00:00......... "
     ]
    },
    {
     "output_type": "error",
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-15-5cf632b57461>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m     21\u001b[0m     \u001b[1;31m# try:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     22\u001b[0m         \u001b[0msn\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mfileNames\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mk\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 23\u001b[1;33m         \u001b[0mdf_data\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdbManager\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_data\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msn\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0msn\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mstart_time\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'2021-10-15 00:00:00'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mend_time\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'2021-11-06 00:00:00'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdata_groups\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'bms'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     24\u001b[0m         \u001b[0mdata_bms\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdf_data\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'bms'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     25\u001b[0m         \u001b[0mdata_bms\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'sn'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0msn\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Platform\\platform_python\\data_analyze_platform\\LIB\\BACKEND\\DBManager.py\u001b[0m in \u001b[0;36mget_data\u001b[1;34m(self, url, sn, start_time, end_time, data_groups)\u001b[0m\n\u001b[0;32m    351\u001b[0m                         \u001b[1;32mif\u001b[0m \u001b[0mdata_group\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m'bms'\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    352\u001b[0m                             \u001b[0mfile_url\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0murl\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m12\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msn\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m+\u001b[0m \u001b[1;34m\"&from=\"\u001b[0m\u001b[1;33m+\u001b[0m\u001b[0mtimefrom\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstrftime\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'%Y-%m-%d %H:%M:%S'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m+\u001b[0m\u001b[1;34m\"&to=\"\u001b[0m\u001b[1;33m+\u001b[0m\u001b[0mtimeto\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstrftime\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'%Y-%m-%d %H:%M:%S'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 353\u001b[1;33m                             \u001b[0mbms_data\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mDBManager\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_data\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfile_url\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'bms'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mmode\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    354\u001b[0m                         \u001b[1;32mif\u001b[0m \u001b[0mdata_group\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m'gps'\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    355\u001b[0m                             \u001b[0mfile_url\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0murl\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m16\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msn\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m+\u001b[0m \u001b[1;34m\"&from=\"\u001b[0m\u001b[1;33m+\u001b[0m\u001b[0mtimefrom\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstrftime\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'%Y-%m-%d %H:%M:%S'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m+\u001b[0m\u001b[1;34m\"&to=\"\u001b[0m\u001b[1;33m+\u001b[0m\u001b[0mtimeto\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstrftime\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'%Y-%m-%d %H:%M:%S'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Platform\\platform_python\\data_analyze_platform\\LIB\\BACKEND\\DBManager.py\u001b[0m in \u001b[0;36m_get_data\u001b[1;34m(urls, type_name, mode)\u001b[0m\n\u001b[0;32m    236\u001b[0m                     \u001b[1;32mcontinue\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    237\u001b[0m                 \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 238\u001b[1;33m                     \u001b[0mdata_blocks\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mconcatenate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata_blocks\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mdata_block\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    239\u001b[0m                 \u001b[1;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    240\u001b[0m                     \u001b[1;32mif\u001b[0m \u001b[1;34m'all the input array dimensions for the concatenation axis must match exactly'\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mstr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0me\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mor\u001b[0m\u001b[0;31m \u001b[0m\u001b[0;31m\\\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m<__array_function__ internals>\u001b[0m in \u001b[0;36mconcatenate\u001b[1;34m(*args, **kwargs)\u001b[0m\n",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "source": [
    "data_GMGC.append(data_select)"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "   初始充电电流max  初始充电电流mod  电流长度  初始充电温度1  初始充电温度2  结束充电温度1  结束充电温度2  \\\n",
       "0        7.7        7.7  1025      6.0      7.0     11.0     11.0   \n",
       "2        7.7        7.7  1031      7.0      7.0     10.0      9.0   \n",
       "\n",
       "               初始充电时间              结束充电时间              行驶结束时间  行驶结束温度1  \\\n",
       "0 2021-10-17 12:23:57 2021-10-17 15:14:28 2021-10-15 21:38:13     18.0   \n",
       "2 2021-10-18 06:21:53 2021-10-18 09:13:32 2021-10-18 06:21:49      7.0   \n",
       "\n",
       "   行驶结束温度2                 sn  初始充电温度  结束充电温度  行驶结束温度         闲置时长  \\\n",
       "0     16.0  MGMCLN750N215H001     6.5    11.0    17.0  2325.733333   \n",
       "2      7.0  MGMCLN750N215H001     7.0     9.5     7.0     0.066667   \n",
       "\n",
       "         充电时长  充电温差  \n",
       "0  170.516667   4.5  \n",
       "2  171.650000   2.5  "
      ],
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>初始充电电流max</th>\n",
       "      <th>初始充电电流mod</th>\n",
       "      <th>电流长度</th>\n",
       "      <th>初始充电温度1</th>\n",
       "      <th>初始充电温度2</th>\n",
       "      <th>结束充电温度1</th>\n",
       "      <th>结束充电温度2</th>\n",
       "      <th>初始充电时间</th>\n",
       "      <th>结束充电时间</th>\n",
       "      <th>行驶结束时间</th>\n",
       "      <th>行驶结束温度1</th>\n",
       "      <th>行驶结束温度2</th>\n",
       "      <th>sn</th>\n",
       "      <th>初始充电温度</th>\n",
       "      <th>结束充电温度</th>\n",
       "      <th>行驶结束温度</th>\n",
       "      <th>闲置时长</th>\n",
       "      <th>充电时长</th>\n",
       "      <th>充电温差</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7.7</td>\n",
       "      <td>7.7</td>\n",
       "      <td>1025</td>\n",
       "      <td>6.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>2021-10-17 12:23:57</td>\n",
       "      <td>2021-10-17 15:14:28</td>\n",
       "      <td>2021-10-15 21:38:13</td>\n",
       "      <td>18.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>MGMCLN750N215H001</td>\n",
       "      <td>6.5</td>\n",
       "      <td>11.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>2325.733333</td>\n",
       "      <td>170.516667</td>\n",
       "      <td>4.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7.7</td>\n",
       "      <td>7.7</td>\n",
       "      <td>1031</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>2021-10-18 06:21:53</td>\n",
       "      <td>2021-10-18 09:13:32</td>\n",
       "      <td>2021-10-18 06:21:49</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>MGMCLN750N215H001</td>\n",
       "      <td>7.0</td>\n",
       "      <td>9.5</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.066667</td>\n",
       "      <td>171.650000</td>\n",
       "      <td>2.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "metadata": {},
     "execution_count": 17
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   "execution_count": null,
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   "outputs": [],
   "metadata": {}
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