Merge branch 'dev' of http://git.fast-fun.cn:92/lmstack/data_analyze_platform into dev

LIB/FRONTEND/AlgoTest/Algo1/Dockerfile

@@ -22,13 +22,12 @@ RUN pip install -i https://pypi.tuna.tsinghua.edu.cn/simple \
 #RUN rm -r /usr/bin
 #RUN rm -r /bin
 
-
-
-# 复制算法文件到 容器
+# 配置算法路径
 ARG ALGO_PATH=LIB/MIDDLE/AlgoTest/Algo1
+# 配置版本路径
 ARG ALGO_VERSION=V_1_0_0
 
-# 复制公共库文件到 容器
+# 复制文件文件到 容器
 ADD LIB/BACKEND/ /LIB/BACKEND
 ADD ${ALGO_PATH}/${ALGO_VERSION}/ /${ALGO_VERSION}/
 ADD ${ALGO_PATH}/main.py /main.py

LIB/MIDDLE/AlgoTest/Algo1/main.py

@@ -1,16 +1,36 @@
 import pandas as pd
-from LIB.BACKEND import DBManager # 以相对路径的方式引入！！！！！
-import V_1_0_0.core as core # 以相对路径的方式引入！！！！！
-
+import time
+from LIB.BACKEND import DBManager,Log # 以相对路径的方式引入！！！！！
+from V_1_0_0 import core # 以相对路径的方式引入！！！！！
+import os
 if __name__ == '__main__':
     
-    sn = "PK10001A326000123"
+    # 获取环境变量
+    env_dist = os.environ
+    host = env_dist.get("HOST", '120.25.223.1')
+    port = env_dist.get("PORT", '4901')
+    db = env_dist.get("DB", 'ali')
+    user = env_dist.get("ROOT", 'root')
+    password = env_dist.get("PASSWORD", '123456')
+    
+    # 日志配置
+    now_str = time.strftime("%Y-%m-%d %H:%M:%S", time.localtime()).replace(":","_")
+    log_path = 'log/' + now_str
+    if not os.path.exists(log_path):
+        os.makedirs(log_path)
+    log = Log.Mylog(log_name='cal_sor', log_level = 'info')
+    log.set_file_hl(file_name='{}/info.log'.format(log_path), log_level='info', size=1024* 1024 * 100)
+    log.set_file_hl(file_name='{}/error.log'.format(log_path), log_level='error', size=1024* 1024 * 100)
+    logger = log.get_logger()
+    
+    # 准备算法数据
+    sn = "PK10001A326000123" 
     st = '2021-07-06 00:00:00'
     et = '2021-07-07 20:00:00'
-
     dbManager = DBManager.DBManager()
     df_data = dbManager.get_data(sn=sn, start_time=st, end_time=et, data_groups=['bms', 'gps', 'accum', 'system'])
-# 
     df_bms = df_data['bms']
+    # 调用核心算法
     calSor = core.CalSor();
+    # 接收算法结果
     print(calSor.calSor(df_bms,df_bms))

demo.ipynb

@@ -2,164 +2,183 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 2,
-   "source": [
-    "# 获取数据\r\n",
-    "import sys\r\n",
-    "from LIB.BACKEND import DBManager\r\n",
-    "\r\n",
-    "sn = \"PK10001A326000123\"\r\n",
-    "st = '2021-07-06 00:00:00'\r\n",
-    "et = '2021-07-07 20:00:00'\r\n",
-    "\r\n",
-    "dbManager = DBManager.DBManager()\r\n",
-    "df_data = dbManager.get_data(sn=sn, start_time=st, end_time=et, data_groups=['bms', 'gps', 'accum', 'system'])\r\n",
-    "# \r\n",
-    "df_bms = df_data['bms']\r\n",
-    "df_gps = df_data['gps']\r\n",
-    "df_accum = df_data['accum']\r\n",
-    "df_system = df_data['system']"
-   ],
+   "execution_count": 6,
+   "metadata": {},
    "outputs": [
     {
-     "output_type": "stream",
      "name": "stdout",
+     "output_type": "stream",
      "text": [
-      "### start to get data PK10001A326000123 from 2021-07-06 00:00:00 to 2021-07-07 20:00:00\n",
-      "# get data from 2021-07-06 00:00:00 to 2021-07-07 00:00:00......... \n",
-      "Server Error, retry 1...... \n",
-      "# get data from 2021-07-07 00:00:00 to 2021-07-07 20:00:00......... \n",
-      "all data-getting done, bms_count is 0, gps_count is 0, system_count is 0, accum_count is 0 \n",
+      "### start to get data PK50001A100000100 from 2021-02-10 22:54:36 to 2021-02-20 22:54:36\n",
+      "# get data from 2021-02-19 22:54:36 to 2021-02-20 22:54:36......... \n",
+      "all data-getting done, bms_count is 19444, gps_count is 5960, system_count is 566, accum_count is 2391 \n",
       "\n"
      ]
     }
    ],
-   "metadata": {}
+   "source": [
+    "# 获取数据\n",
+    "import sys\n",
+    "from LIB.BACKEND import DBManager\n",
+    "\n",
+    "sn = \"PK50001A100000100\"\n",
+    "st = '2021-02-10 22:54:36'\n",
+    "et = '2021-02-20 22:54:36'\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']"
+   ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "source": [
-    "# 下载数据 \r\n",
-    "import sys\r\n",
-    "from LIB.BACKEND import Tools\r\n",
-    "\r\n",
-    "tools = Tools.Tools()\r\n",
-    "write_path = r''\r\n",
-    "sn = \"PK50001A100000680\"\r\n",
-    "\r\n",
-    "st = '2021-07-06 00:00:00'\r\n",
-    "et = '2021-07-07 20:00:00'\r\n",
-    "tools.data_download(write_path=write_path, sn=sn, start_time=st, end_time=et, data_groups=['bms'])"
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "",
+     "evalue": "",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31mPython 3.8.12 64-bit ('algo_dev_env': conda) 需要安装 ipykernel。\n",
+      "Run the following command to install 'ipykernel' into the Python environment. \n",
+      "Command: 'conda install -n algo_dev_env ipykernel --update-deps --force-reinstall'"
+     ]
+    }
    ],
+   "source": [
+    "import plotly.io as pio\n",
+    "pio.renderers.default = 'iframe_connected'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
    "outputs": [],
-   "metadata": {}
+   "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\r\n",
-    "import sys\r\n",
-    "from LIB.BACKEND import DataPreProcess\r\n",
-    "\r\n",
-    "dataPrePro = DataPreProcess.DataPreProcess()\r\n",
-    "# 时间完全相同的数据仅保留一行\r\n",
-    "df_bms_pro, df_gps_pro = dataPrePro.time_filter(df_bms, df_gps)\r\n",
-    "\r\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）\r\n",
-    "df_bms_pro = dataPrePro.data_split_by_status(df_bms_pro)\r\n",
-    "df_bms_pro = dataPrePro.data_split_by_time(df_bms_pro)\r\n",
-    "\r\n",
-    "# bms数据将两次充电间的状态合并\r\n",
-    "df_bms_pro = dataPrePro.combine_drive_stand(df_bms_pro)\r\n",
-    "# bms 数据计算行车和充电开始前后的静置时间\r\n",
-    "df_bms_pro = dataPrePro.cal_stand_time(df_bms_pro)\r\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)\r\n",
-    "df_bms_pro, df_gps_pro, res_record= dataPrePro.gps_data_judge(df_bms_pro, df_gps_pro)\r\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)\r\n",
+    "# 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)"
-   ],
-   "outputs": [],
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
-    "# 单cycle指标统计\r\n",
-    "import sys\r\n",
-    "from LIB.BACKEND import IndexStaByOneCycle\r\n",
-    "\r\n",
-    "indexSta = IndexStaByOneCycle.IndexStaByOneCycle()\r\n",
-    "\r\n",
-    "data_number_list = sorted(list(set(df_bms[(df_bms['data_status'].isin(['drive']))]['data_split_by_status'])))\r\n",
-    "for data_number in data_number_list[:]:\r\n",
-    "    df_sel_bms = df_bms[df_bms['data_split_by_status'] == data_number]\r\n",
-    "    df_sel_bms = df_sel_bms.reset_index(drop=True)\r\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'])]\r\n",
-    "    df_sel_gps = df_sel_gps.reset_index(drop=True)\r\n",
-    "\r\n",
-    "    print(indexSta.odo_sta(np.array(df_sel_gps['odo'])))\r\n",
-    "    print(indexSta.capacity_sta(40, np.array(df_sel_bms['SOC[%]']), np.array(df_sel_bms['SOH[%]'])))\r\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]'])))\r\n",
-    "    print(indexSta.acc_time_sta(np.array(df_sel_bms['时间戳'])))\r\n",
-    "    print(indexSta.mean_temp_sta(np.array(df_sel_bms['单体温度1'])))\r\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'])))\r\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]'])))\r\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]'])))\r\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'])))\r\n",
+    "# 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"
-   ],
-   "outputs": [],
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "code",
    "execution_count": 2,
-   "source": [
-    "# 生成pydoc 说明文档\r\n",
-    "!python -m pydoc -w LIB\\BACKEND\\DataPreProcess.py"
-   ],
+   "metadata": {},
    "outputs": [
     {
-     "output_type": "stream",
      "name": "stdout",
+     "output_type": "stream",
      "text": [
       "problem in LIB\\BACKEND\\DataPreProcess.py - ModuleNotFoundError: No module named 'DBManager'\n"
      ]
     }
    ],
-   "metadata": {}
+   "source": [
+    "# 生成pydoc 说明文档\n",
+    "!python -m pydoc -w LIB\\BACKEND\\DataPreProcess.py"
+   ]
   },
   {
    "cell_type": "code",
    "execution_count": 1,
-   "source": [
-    "from LIB.BACKEND import DBManager, Log\r\n",
-    "log = Log.Mylog(log_name='signal_monitor', log_level = 'info')\r\n",
-    "log.set_file_hl(file_name='info.log', log_level='info')\r\n",
-    "log.set_file_hl(file_name='error.log', log_level='error')\r\n",
-    "logger = log.get_logger()\r\n"
-   ],
+   "metadata": {},
    "outputs": [],
-   "metadata": {}
+   "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": 6,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "logger.error(\"ttt5\")"
-   ],
-   "outputs": [],
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "source": [],
+   "metadata": {},
    "outputs": [],
-   "metadata": {}
+   "source": []
   }
  ],
  "metadata": {
@@ -167,23 +186,23 @@
    "hash": "b3ba2566441a7c06988d0923437866b63cedc61552a5af99d1f4fb67d367b25f"
   },
   "kernelspec": {
-   "name": "python3",
-   "display_name": "Python 3.8.8 64-bit ('base': conda)"
+   "display_name": "Python 3.8.8 64-bit ('base': conda)",
+   "name": "python3"
   },
   "language_info": {
-   "name": "python",
-   "version": "3.8.8",
-   "mimetype": "text/x-python",
    "codemirror_mode": {
     "name": "ipython",
     "version": 3
    },
-   "pygments_lexer": "ipython3",
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
    "nbconvert_exporter": "python",
-   "file_extension": ".py"
+   "pygments_lexer": "ipython3",
+   "version": "3.8.12"
   },
   "orig_nbformat": 4
  },
  "nbformat": 4,
  "nbformat_minor": 2
-}
+}