{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "取ERP采购价+ERP尺寸+实际尺寸，需要国家+条目+邮编+order_id\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\joker\\AppData\\Local\\Temp\\ipykernel_34576\\54471085.py:105: UserWarning: pandas only supports SQLAlchemy connectable (engine/connection) or database string URI or sqlite3 DBAPI2 connection. Other DBAPI2 objects are not tested. Please consider using SQLAlchemy.\n",
      "  df=pd.read_sql(\"SELECT * FROM `order_complet4` WHERE buy_amount is not null and `实际尺寸售价` IS NULL limit 100\",db.con)\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from utils.gtools import MySQLconnect\n",
    "\n",
    "# 读取需要计算的包裹信息\n",
    "with MySQLconnect('ads') as db:\n",
    "    sql = r\"\"\"  \n",
    "       # 限制范围是测量时间，取得SKU种类为1且数量为1的订单，且重复SKU只取最近的订单\n",
    "\n",
    "WITH\n",
    "t1 AS (\n",
    "SELECT\n",
    "order_id,\n",
    "SKU,\n",
    "order_date,\n",
    "sum(CASE WHEN opl.order_product_id LIKE '%\\_%' ESCAPE '\\\\' \n",
    "         AND opl.order_product_id NOT LIKE '%\\_%\\_%' ESCAPE '\\\\' THEN product_num END) AS product_num,\n",
    "DATE_FORMAT(order_date,\"%Y-%m-%d\") AS 订单时间,\n",
    "count(opl.SKU) AS 产品种类\n",
    "FROM\n",
    "dws.fact_order_product_list opl\n",
    "WHERE\n",
    "  NOT EXISTS (\n",
    "    SELECT 1 \n",
    "    FROM dws.log_order_reissue_detail AS r \n",
    "    WHERE r.order_product_id = opl.order_product_id\n",
    "  )\n",
    "AND order_date >= \"20250501\"\n",
    "AND order_date < \"20250612\"\n",
    "AND SKU <> \"\"\n",
    "GROUP BY order_id\n",
    ")\n",
    ",\n",
    "t2 AS (\n",
    "SELECT\t\t\t\n",
    "            a.`包裹测量时间`,\n",
    "\t\t\t\t\t\tt1.order_id,\n",
    "\t\t\t\t\t\tt1.SKU,\n",
    "\t\t\t\t\t\tt1.order_date,\n",
    "            a.包裹号,\n",
    "            a.快递公司,\n",
    "            a.运输方式,\n",
    "\t\t\t\t\t\ta.`目的国`,\n",
    "            d.postcode,\n",
    "            CONCAT(\n",
    "            '\"', b.package, '\": {',\n",
    "            '\"长\": ', length, ', ',\n",
    "            '\"宽\": ', width, ', ',\n",
    "            '\"高\": ', hight, ', ',\n",
    "            '\"重量\": ', weight, '}'\n",
    "        ) AS package_json\n",
    "        FROM\n",
    "\t\t\t\tt1\n",
    "            LEFT JOIN order_express a ON t1.order_id = a.单号\n",
    "            JOIN package_vol_info b ON a.`包裹号` = b.package\n",
    "            JOIN order_list d ON a.`单号` = d.order_id \n",
    "        WHERE\n",
    "            a.`包裹状态` IN ( '客户签收', '已经投递') \n",
    "            AND b.hight > 0 \n",
    "            AND b.length > 0 \n",
    "            AND b.width > 0 \n",
    "            AND b.hight > 0 \n",
    "            AND b.weight > 0\n",
    "--             AND a.`目的国` = \"United States\"\n",
    "\t\t\t\t\t\tAND t1.product_num = 1\n",
    "\t\t\t\t\t\tAND t1.产品种类=1\n",
    "\t\t\t\t\t\tAND a.`包裹测量时间` >= '2025-05-01'\n",
    "\t\t\t\t\t\tAND a.`包裹测量时间` < '2025-06-12'\n",
    "),\n",
    "t3 AS (\n",
    "SELECT\n",
    "t2.*,\n",
    "sku.成本价 AS ERP采购价,\n",
    "ess.erp_package_vol AS ERP包裹数据,\n",
    "CONCAT('{', GROUP_CONCAT(package_json SEPARATOR ','), '}') AS 实际包裹数据,\n",
    "ROW_NUMBER() OVER (PARTITION BY SKU ORDER BY 包裹测量时间 DESC) as rn\n",
    "FROM\n",
    "t2\n",
    "LEFT JOIN dwd.dim_erp_sku_package_vol_info ess ON t2.SKU=ess.erp_sku\n",
    "LEFT JOIN stg_bayshop_litfad_sku sku ON t2.SKU=sku.SKU\n",
    "WHERE\n",
    "ess.`erp_package_vol`<>\"{}\" AND ess.`erp_package_vol`<>\"\"\n",
    "GROUP BY order_id\n",
    ")\n",
    "SELECT\n",
    "包裹测量时间,\n",
    "order_id,\n",
    "SKU,\n",
    "DATE_FORMAT(order_date,\"%Y-%M-%D\") AS 订单时间,\n",
    "包裹号,\n",
    "`快递公司`,\n",
    "`运输方式`,\n",
    "`目的国`,\n",
    "postcode,\n",
    "ERP采购价,\n",
    "ERP包裹数据,\n",
    "实际包裹数据\n",
    "FROM\n",
    "t3\n",
    "WHERE\n",
    "rn=1\n",
    "\n",
    "\n",
    "\n",
    "    \"\"\"\n",
    "    df=pd.read_sql(\"SELECT * FROM `order_complet4` WHERE buy_amount is not null and `实际尺寸售价` IS NULL limit 100\",db.con)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "取实际采购价（当前已有ERP采购价+ERP尺寸+实际尺寸），输入：df['order_id']，输出：df['采购成本']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from utils.gtools import MySQLconnect\n",
    "\n",
    "ods = MySQLconnect(\"ods\")\n",
    "engine = ods.engine()\n",
    "cursor = ods.connect().cursor()\n",
    "\n",
    "batch_size = 50000  # 每次查询 500 个 order_id，避免 SQL 语句过长\n",
    "order_id_list = df[\"order_id\"].drop_duplicates().tolist() # 取出所有 order_id\n",
    "# df['postcode'] = \"38016\"\n",
    "# 存储分批查询的结果\n",
    "result_dfs1 = []\n",
    "result_dfs2 = []\n",
    "for i in range(0, len(order_id_list), batch_size):\n",
    "    batch_order_ids = order_id_list[i:i + batch_size]  # 取当前批次的 order_id\n",
    "    param = \",\".join(f\"'{order_id}'\" for order_id in batch_order_ids)\n",
    "\n",
    "    purchase_order_sql = f\"\"\"\n",
    "    WITH t1 AS (\n",
    "        SELECT LEFT(ol.out_detials_outlink_id, 15) AS order_id,\n",
    "               SUM(out_detials_qty * price) AS instock_cost,\n",
    "               NULL AS buy_cost\n",
    "        FROM ods.outstock_list ol\n",
    "        JOIN ods.instock_list il ON ol.store_in_id = il.id \n",
    "        WHERE LEFT(ol.out_detials_outlink_id, 15) IN ({param})\n",
    "        GROUP BY LEFT(ol.out_detials_outlink_id, 15)\n",
    "        \n",
    "        UNION ALL\n",
    "        \n",
    "        SELECT LEFT(order_product_id, 15) AS order_id, \n",
    "               NULL AS instock_cost,\n",
    "               SUM(buy_num * actual_price) AS buy_cost\n",
    "        FROM warehouse_purchasing\n",
    "        WHERE LEFT(order_product_id, 15) IN ({param}) \n",
    "              AND buy_audit = \"采购完成\"\n",
    "        GROUP BY LEFT(order_product_id, 15)\n",
    "    )\n",
    "    SELECT order_id,\n",
    "           SUM(CASE \n",
    "               WHEN instock_cost IS NULL THEN buy_cost\n",
    "               ELSE instock_cost \n",
    "           END) AS 采购成本\n",
    "    FROM t1 \n",
    "    GROUP BY order_id\n",
    "    \"\"\"\n",
    " \n",
    "\n",
    "    batch_df1 = pd.read_sql(purchase_order_sql, con=engine)  # 运行 SQL 查询\n",
    "    result_dfs1.append(batch_df1)  # 存入结果列表\n",
    "    print(f\"已完成 {i + batch_size} 个 order_id 的查询\")\n",
    "\n",
    "# 合并所有查询结果\n",
    "purchase_order_df1 = pd.concat(result_dfs1, ignore_index=True)\n",
    "purchase_order_df1[\"order_id\"] = purchase_order_df1[\"order_id\"].astype(str)\n",
    "\n",
    "\n",
    "# 转换数据类型，确保匹配\n",
    "df[\"order_id\"] = df[\"order_id\"].astype(str)\n",
    "\n",
    "# 进行合并\n",
    "df = pd.merge(df, purchase_order_df1, on='order_id', how='left')\n",
    "# 复制到剪贴板\n",
    "df.to_clipboard(index=False)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "计算标准网站售价，输入尺寸，输出售价和订单物流费"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'df' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[3], line 16\u001b[0m\n\u001b[0;32m     14\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m (sell_price, order_price, order_type)\n\u001b[0;32m     15\u001b[0m \u001b[38;5;66;03m# 计算当前售价\u001b[39;00m\n\u001b[1;32m---> 16\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m index,row \u001b[38;5;129;01min\u001b[39;00m df\u001b[38;5;241m.\u001b[39miterrows():\n\u001b[0;32m     17\u001b[0m     price \u001b[38;5;241m=\u001b[39m row[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mbuy_amount\u001b[39m\u001b[38;5;124m'\u001b[39m]\n\u001b[0;32m     18\u001b[0m     \u001b[38;5;66;03m# package_dict = json.loads(row['erp_package_vol'])\u001b[39;00m\n",
      "\u001b[1;31mNameError\u001b[0m: name 'df' is not defined"
     ]
    }
   ],
   "source": [
    "def call_sell_price(price, package_dict,head_type=\"海运\"):\n",
    "    import json\n",
    "    from sell.sell_price import call_sell_and_order_price\n",
    "    try:\n",
    "        package_dict = json.loads(package_dict)\n",
    "        all_sell_price, order_price, order_type = call_sell_and_order_price(price, package_dict,head_type)\n",
    "    except Exception as e:\n",
    "        print(f\" 报错: {e}\")\n",
    "        return  (\"\",\"\",\"\")\n",
    "    if all_sell_price == 0:\n",
    "        return  (\"\",\"\",\"\")\n",
    "    sell_price= all_sell_price\n",
    "    # logis_price = all_sell_price[1]\n",
    "    return (sell_price, order_price, order_type)\n",
    "# 计算当前售价\n",
    "for index,row in df.iterrows():\n",
    "    price = row['buy_amount']\n",
    "    # package_dict = json.loads(row['erp_package_vol'])\n",
    "    sell_price = call_sell_price(price, row['package_json'],\"海运\")\n",
    "    print(sell_price)\n",
    "    df.loc[index,'网站售价'] = sell_price[0]\n",
    "    df.loc[index,'订单物流费'] = sell_price[1]\n",
    "    df.loc[index,'尾端类型'] = sell_price[2]\n",
    "    print(f\"SKU: {row['sku']} 网站售价: {sell_price[0]}  订单物流费: {sell_price[1]} 尾端类型: {sell_price[2]}\")\n",
    "df.to_clipboard(index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_clipboard()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "计算实际渠道物流费用"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from utils.countryOperator import OperateCountry\n",
    "from utils.logisticsBill import BillFactory\n",
    "from utils.Package import Package, Package_group\n",
    "import pandas as pd\n",
    "import json\n",
    "import re\n",
    "# 美国 \n",
    "from utils.logisticsBill import Billing\n",
    "import requests\n",
    "\n",
    "for index, row in df.iterrows():\n",
    "    opCountry = OperateCountry('US')\n",
    "    postcode = row['postcode']\n",
    "    if pd.isna(postcode) or str(postcode).lower() == \"nan\":\n",
    "        continue\n",
    "    try:\n",
    "        package_dict = json.loads(row['实际包裹数据'])\n",
    "    except Exception as e:\n",
    "        print(f\"行 {index} 解析失败: {e}\")\n",
    "        print(row['实际包裹数据'])\n",
    "        continue\n",
    "    packages = Package_group()\n",
    "    def extract_number(value):\n",
    "    # 提取字符串中的第一个数字\n",
    "        match = re.search(r\"[-+]?\\d*\\.\\d+|\\d+\", str(value))\n",
    "        return float(match.group()) if match else 0.0\n",
    "    for key, package in package_dict.items():\n",
    "        package['长'] = extract_number(package['长'])\n",
    "        package['宽'] = extract_number(package['宽'])\n",
    "        package['高'] = extract_number(package['高'])\n",
    "        package['重量'] = extract_number(package['重量'])\n",
    "    \n",
    "        if package['长'] == 0 or package['宽'] == 0 or package['高'] == 0 or package['重量'] == 0:\n",
    "            continue\n",
    "        packages.add_package(Package(key,package['长'], package['宽'], package['高'], package['重量']))\n",
    "    if packages is None:\n",
    "        continue\n",
    "    if \"海运\" in row['运输方式']:\n",
    "        head_type = 1\n",
    "    else:\n",
    "        head_type = 0\n",
    "\n",
    "    # if \"FEDEX-SAIR-G\" in row['快递公司']:\n",
    "    #     company_name = \"Fedex-GROUD\"\n",
    "    # elif \"FEDEX-SAIR-H\" in row['快递公司']:\n",
    "    #     company_name = \"Fedex-HOME\"\n",
    "    # elif \"FEDEX02\" in row['快递公司']:\n",
    "    #     company_name = \"Fedex-彩虹小马\"\n",
    "    # elif \"大包\" in row['快递公司'] or row['快递公司'] == '海MS-FEDEX':\n",
    "    #     company_name = \"Fedex-金宏亚\"\n",
    "    # elif \"GIGA\" in row['快递公司']:\n",
    "    #     company_name = \"大健-GIGA\"\n",
    "    # elif \"CEVA\" in row['快递公司']:\n",
    "    #     company_name = \"大健-CEVA\"\n",
    "    # elif \"USPS\" in row['快递公司']:\n",
    "    #     company_name = \"Fedex-GROUD\"\n",
    "    # else:\n",
    "    #     company_name = \"大健-Metro\"\n",
    "    \n",
    "    bill = Billing(str(index),opCountry,packages,postcode,company_name=\"Fedex-GROUD\",head_type=head_type,beizhu='1')\n",
    "    head_price = bill.head_amount[0]\n",
    "    tail_price = bill.tail_amount[0]\n",
    "    if \"USPS\" in row['快递公司']:\n",
    "        tail_price = tail_price/2\n",
    "    # df.loc[index,'头程CNY'] = head_price\n",
    "    df.loc[index,'头程CNY'] = head_price\n",
    "    # df.loc[index,'最优渠道'] = bill.company_name\n",
    "    print(f\"行 {index} 处理完成\")\n",
    "    \n",
    "df.to_clipboard(index=False)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from utils.gtools import MySQLconnect\n",
    "import pandas as pd\n",
    "df = pd.read_clipboard()\n",
    "log = MySQLconnect('logistics')\n",
    "pd.io.sql.to_sql(df, 'table_name', con=log.engine(), if_exists='replace', index=False)"
   ]
  }
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