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