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     "execution_count": 5,
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   "source": [
    "from pywebio.input import *\n",
    "from pywebio.output import *\n",
    "from pywebio.session import *\n",
    "import pywebio.pin as pin\n",
    "from pywebio import start_server\n",
    "import pandas as pd\n",
    "from io import StringIO\n",
    "\n",
    "# field: v:[字段名， 单位， 数据类型， 格式, 默认空值填充规则， 默认无效值填充规则]\n",
    "fields = {\n",
    "            '设备码': ['sn', ['-'], '-', '-', '-', '-'], \n",
    "            '时间': ['time', ['-'], '-', ['2023-01-01 00:00:00', '-'],'删除整行', '删除整行'], \n",
    "            '电池包电流': ['pack_crnt', ['A', 'mA'], '-', '-', '向上临近填充', '向上临近填充'], \n",
    "            '电池包电压': ['pack_volt', ['V', 'mV'], '-', '-', '向上临近填充', '向上临近填充'], \n",
    "            '电池包soc': ['soc', ['%', '-'], '-', '-', '向上临近填充', '向上临近填充'],\n",
    "            '电池包soh': ['soh', ['%', '-'], '-', '-', '向上临近填充', '向上临近填充'], \n",
    "            '单体电压列表': ['cell_voltage', ['V', 'mV'], '-', [\"*,*,*,...\", \"[*,*,*,...]\"], '向上临近填充', '向上临近填充'], \n",
    "            '单体温度列表': ['cell_temp', ['℃'], '-', [\"*,*,*,...\", \"[*,*,*,...]\"], '向上临近填充', '向上临近填充'],\n",
    "            '绝缘电阻': ['ins_resis', ['Ω', 'kΩ'], '-', '-', '向上临近填充', '向上临近填充'], \n",
    "            # '速度': ['speed', ['m/s', 'Km/h'], '-', '-', '向上临近填充', '向上临近填充'], \n",
    "            # '海拔': ['altitude', ['Km', 'm'], '-', '-', '向上临近填充', '向上临近填充'],\n",
    "            # '经度': ['longitude', ['-'], '-', '-', '向上临近填充', '向上临近填充'], \n",
    "            # '纬度': ['latitude', ['-'] ,'-', '-', '向上临近填充', '向上临近填充'], \n",
    "            # '累积行驶里程': ['mileage', ['Km', 'm'], '-', '-', '向上临近填充', '向上临近填充']\n",
    "        }\n",
    "\n",
    "def t(a):\n",
    "    print(a)\n",
    "def main():\n",
    "        set_env(title='神目电池安全卫士', output_animation=False)\n",
    "        pin.pin_on_change('none_assign_1', onchange=t)\n",
    "    # while True:\n",
    "        # 上传文件要求: 需要有表头，单体信息格式为 “**，**，**”\n",
    "        file = file_upload(accept=['.csv', '.xlsx','.xls'], placeholder=\"请上传文件，支持 .csv, .xlsx, .xls 文件\")\n",
    "        # filename = '001PB047000001B230100075_2101TS_20230328171402_20230328193302.csv'\n",
    "        # df = pd.read_csv(filename)\n",
    "        filename = file['filename']\n",
    "        put_markdown(f\"## {filename} 文件读取成功，展示前5条数据如下\")\n",
    "        if filename.endswith(\".csv\"):\n",
    "            df = pd.read_csv(StringIO(file['content'].decode('utf-8')))\n",
    "        elif filename.endswith(\".xlsx\"):\n",
    "            df = pd.read_excel(file['content'])\n",
    "        elif filename.endswith(\".xls\"):\n",
    "            df = pd.read_excel(file['content'])\n",
    "        with use_scope('show_data'):\n",
    "            # 展示文件的数据\n",
    "            columns = list(df.columns)\n",
    "            put_table(tdata=df.to_dict(orient='records')[0:5],header=columns)\n",
    "        with use_scope('set_field'):\n",
    "            # 对文件的列进行选择\n",
    "            put_markdown(f\"## 字段自动化识别结果如下，可人工进行修改\")\n",
    "            \n",
    "            header = ['列名','字段','单位','格式']\n",
    "            values = []\n",
    "            i = 0\n",
    "            for k,v in fields.items():\n",
    "                values.append({\n",
    "                    '列名': k,\n",
    "                    '字段': pin.put_select(name=f\"field_{i}\", options=columns),\n",
    "                    '单位': pin.put_select(name=f\"unit_{i}\", options=v[1]),\n",
    "                    '格式': pin.put_select(name=f\"format_{i}\", options=v[3]),\n",
    "                })\n",
    "                i = i + 1\n",
    "            put_table(values,header=header)\n",
    "        with use_scope('data_claen'):\n",
    "            # 对文件的列进行选择\n",
    "            put_markdown(f\"## 数据清洗规则\")\n",
    "            header = ['列名', '空值处理', '空值指定值', '无效值处理', '无效值判定值', '无效值指定值']\n",
    "            clean_rule = ['-', '删除整行', '向上临近填充', '向下临近填充', '中位数填充', '平均值填充', '指定值填充']\n",
    "            values = []\n",
    "            i = 0\n",
    "            for k,v in fields.items():\n",
    "                values.append({\n",
    "                    '列名': k,\n",
    "                    '空值处理': pin.put_select(name=f\"none_{i}\", options=clean_rule, value=v[4]),\n",
    "                    '空值指定值': pin.put_input(name=f\"none_assign_{i}\", type='float'),\n",
    "                    '无效值处理': pin.put_select(name=f\"invalid_{i}\", options=clean_rule, value=v[5]),\n",
    "                    '无效值判定值': pin.put_input(name=f\"invalid_judge_{i}\", type='float'),\n",
    "                    '无效值指定值': pin.put_input(name=f\"invalie_assign_{i}\", type='float'),\n",
    "                })\n",
    "                i = i + 1\n",
    "            put_table(values,header=header)\n",
    "            \n",
    "        with use_scope('data_download'):\n",
    "            # 对文件的列进行选择\n",
    "            put_markdown(f\"## 导出清洗后的数据\")\n",
    "            df.to_csv(\"after_clean.csv\")\n",
    "            with open(\"./after_clean.csv\", 'rb') as f:\n",
    "                content = f.read()\n",
    "            put_file('data.csv', content, '下载清洗后的数据文件')\n",
    "            \n",
    "        with use_scope('algo_invoke'):\n",
    "            # 对文件的列进行选择\n",
    "            put_markdown(f\"## 算法模块\")\n",
    "            put_markdown(f\"**1. 基本信息统计模块**\")\n",
    "            header = ['数据包内电池包数', '数据起始时间', '数据结束时间', '数据时长（天）', '电池包最高活跃度', '电池包平均活跃度', \n",
    "                      '总数据量', '日最大数据量']\n",
    "            values = [None] * len(header)\n",
    "            put_table([values],header=header)\n",
    "            \n",
    "            put_markdown(f\"**2. 电池信息分析模块**\")\n",
    "            header = ['SN', '电池类型', '电池包电芯串联数', '温度采样个数']\n",
    "            values = [None] * len(header)\n",
    "            put_table([values],header=header)\n",
    "            \n",
    "            put_markdown(f\"**3. 电池安全诊断模块**\")\n",
    "            header = ['诊断指标', '数值', '诊断结果', '推荐数值']\n",
    "            values = [None] * len(header)\n",
    "            put_table([values],header=header)\n",
    "            put_text('''\n",
    "xxxxxxxx电池于xxxx年xx月xx日发生电芯过压故障 ，故障电芯为xx号电芯,故障发生时最高电芯电压为xxV\n",
    "xxxxxxxx电池于xxxx年xx月xx日发生绝缘离群故障 ，故障发生时绝缘值为xxxxx\n",
    "\n",
    "电池是否存在热失控特征\n",
    "电池是否存在析锂特征\n",
    "电池是否存在内短路特征''')\n",
    "            \n",
    "            put_markdown(f\"**4. 电池性能诊断模块**\")\n",
    "            put_markdown(f\" 4.1 SOH衰减信息\")\n",
    "            put_text('''***''')\n",
    "            put_markdown(f\" 4.2 一致性信息\")\n",
    "            header = ['SOC一致性', '内阻一致性', 'SOH一致性', '温度一致性']\n",
    "            values = [None] * len(header)\n",
    "            put_table([values],header=header)\n",
    "            put_markdown(\"**一致性绘图**\")\n",
    "            \n",
    "            put_markdown(f\"**5. 电池画像模块**\")\n",
    "            put_markdown(f\" 5.1 电池充电习惯\")\n",
    "            header = ['日均充电次数', '累计充电安时量', '累计充电次数']\n",
    "            values = [None] * len(header)\n",
    "            put_table([values],header=header)\n",
    "            put_markdown(\"**充电时间段分布图**\")\n",
    "            put_markdown(\"**充电起始soc分布图**\")\n",
    "            put_markdown(\"**充电结束soc分布图**\")\n",
    "            \n",
    "            put_markdown(f\" 5.2 电池放电习惯\")\n",
    "            header = ['日均放电次数', '累计放电安时量', '累计放电次数']\n",
    "            values = [None] * len(header)\n",
    "            put_table([values],header=header)\n",
    "            put_markdown(\"**放电时间段分布图**\")\n",
    "            put_markdown(\"**放电起始soc分布图**\")\n",
    "            put_markdown(\"**放电结束soc分布图**\")\n",
    "            \n",
    "            put_markdown(f\"5.3 电池存储习惯\")\n",
    "            header = ['存储soc均值', '存储温度均值', '最大存储时长']\n",
    "            values = [None] * len(header)\n",
    "            put_table([values],header=header)\n",
    "            put_markdown(\"**存储温度分布**\")\n",
    "            \n",
    "            \n",
    "\n",
    "    \n",
    "from utils import common\n",
    "common.State(5) == common.State.data_clean_exec\n",
    "# start_server(main, port=8080, debug=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "from pywebio import start_server\n",
    "from pywebio.output import put_table\n",
    "from pywebio.input import input\n",
    "import os\n",
    "\n",
    "\n",
    "def main():\n",
    "    while True:\n",
    "        question = input('Ask something')\n",
    "        put_table([\n",
    "            ['Q:', question],\n",
    "            ['A:', \"answer\"]\n",
    "        ])\n",
    "\n",
    "if __name__ == '__main__':\n",
    "    start_server(main, port=8080, debug=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "from utils import image\n",
    "image.plot_example().render_notebook()\n",
    "\n",
    "# 根据列名筛选\n",
    "\n",
    "        \n",
    "# 通过数据内容验证已确认的字段\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "map_res"
   ]
  }
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