{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 175,
   "id": "d1c4a4db-e26d-4c28-98df-5569fac9b77d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "檔案數量: 269\n"
     ]
    }
   ],
   "source": [
    "path = \"/home/jovyan/phase_1/v1/data_1/v1.1.3/\"\n",
    "files = [f for f in os.listdir(path) if f.endswith(\".csv\")]\n",
    "print(\"檔案數量:\", len(files))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "d606888b-9a40-4ad8-8878-0334c46bd8e0",
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"\n",
    "批次清理與稽核（可分步執行；單一輸出資料夾）\n",
    "----------------------------------------------------------\n",
    "功能對齊：\n",
    "1) 自動找表頭 + 強韌讀檔（CSV 多編碼/引擎、Excel 後備）\n",
    "2) 欄位統一（rename 規則 + 對照/數量輸出）\n",
    "3) 全欄位空白/空字串 → NaN（所有列所有欄位）\n",
    "4) 時間欄位標準化（含中文上午/下午/AMPM）→ datetime64[ns]，並保留 *_raw 備份\n",
    "5) 連續數值欄位：不論缺失率，只要可解析且不在排除名單 → 一律轉 float32\n",
    "6) 欄位一致化：建立最終 schema，補缺/重排；輸出 final_columns_list 或 missing_columns_report\n",
    "7) 逐檔驗證 + 單一輸出資料夾：audit_summary、rename 對照、schema mismatch 清單、最終清理 CSV\n",
    "（可選）8) 隨機抽樣列印（前五筆與欄位清單）供人工抽查\n",
    "\n",
    "使用方式（在 Notebook 內）：\n",
    "- 先執行本 cell 載入所有函式\n",
    "- 跑單一步：run_step(1) 或 run_step(4)\n",
    "- 跑多步：run_steps([1,2,3,4,5,6,7])\n",
    "- 全部：run_all()\n",
    "\"\"\"\n",
    "\n",
    "import os\n",
    "import re\n",
    "import glob\n",
    "import warnings\n",
    "import unicodedata\n",
    "from typing import List, Dict, Tuple\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "warnings.filterwarnings(\"ignore\")  # 盡量避免警告訊息跳出"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "0feccc12-5b83-484a-bbd6-f5b20aaa453d",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "✅ 已複製：/home/jovyan/RT08/0923_datacheck/02_100/PatNo_ID_1575502382.xlsx → /home/jovyan/1010/ori_data/02_100/PatNo_ID_1575502382.xlsx\n",
      "✅ 已複製：/home/jovyan/RT08/0923_datacheck/02_100/PatNo_ID_1594294180.xlsx → /home/jovyan/1010/ori_data/02_100/PatNo_ID_1594294180.xlsx\n",
      "✅ 已複製：/home/jovyan/RT08/0923_datacheck/02_100/PatNo_ID_1594335109.xlsx → /home/jovyan/1010/ori_data/02_100/PatNo_ID_1594335109.xlsx\n",
      "✅ 已複製：/home/jovyan/RT08/0923_datacheck/02_100/PatNo_ID_1575256902.xlsx → /home/jovyan/1010/ori_data/02_100/PatNo_ID_1575256902.xlsx\n",
      "✅ 已複製：/home/jovyan/RT08/0923_datacheck/02_100/PatNo_ID_1582635996.xlsx → /home/jovyan/1010/ori_data/02_100/PatNo_ID_1582635996.xlsx\n",
      "✅ 已複製：/home/jovyan/RT08/0923_datacheck/02_100/PatNo_ID_1586696634.xlsx → /home/jovyan/1010/ori_data/02_100/PatNo_ID_1586696634.xlsx\n",
      "✅ 已複製：/home/jovyan/RT08/0923_datacheck/02_100/PatNo_ID_1584158973.xlsx → /home/jovyan/1010/ori_data/02_100/PatNo_ID_1584158973.xlsx\n",
      "✅ 已複製：/home/jovyan/RT08/0923_datacheck/02_100/PatNo_ID_1571945701.xlsx → /home/jovyan/1010/ori_data/02_100/PatNo_ID_1571945701.xlsx\n",
      "✅ 已複製：/home/jovyan/RT08/0923_datacheck/02_100/PatNo_ID_1568039398.xlsx → /home/jovyan/1010/ori_data/02_100/PatNo_ID_1568039398.xlsx\n",
      "✅ 已複製：/home/jovyan/RT08/0923_datacheck/02_100/PatNo_ID_1568952422.xlsx → /home/jovyan/1010/ori_data/02_100/PatNo_ID_1568952422.xlsx\n",
      "✅ 已複製：/home/jovyan/RT08/0923_datacheck/02_100/PatNo_ID_1569083701.xlsx → /home/jovyan/1010/ori_data/02_100/PatNo_ID_1569083701.xlsx\n",
      "✅ 已複製：/home/jovyan/RT08/0923_datacheck/02_100/PatNo_ID_1566671274.xlsx → /home/jovyan/1010/ori_data/02_100/PatNo_ID_1566671274.xlsx\n",
      "✅ 已複製：/home/jovyan/RT08/0923_datacheck/02_100/PatNo_ID_1593472048.xlsx → /home/jovyan/1010/ori_data/02_100/PatNo_ID_1593472048.xlsx\n",
      "✅ 已複製：/home/jovyan/RT08/0923_datacheck/02_100/PatNo_ID_1566911879.xlsx → /home/jovyan/1010/ori_data/02_100/PatNo_ID_1566911879.xlsx\n",
      "✅ 已複製：/home/jovyan/RT08/0923_datacheck/02_100/PatNo_ID_1581003248.xlsx → /home/jovyan/1010/ori_data/02_100/PatNo_ID_1581003248.xlsx\n",
      "✅ 已複製：/home/jovyan/RT08/0923_datacheck/02_100/PatNo_ID_1594305136.xlsx → /home/jovyan/1010/ori_data/02_100/PatNo_ID_1594305136.xlsx\n",
      "✅ 已複製：/home/jovyan/RT08/0923_datacheck/02_100/PatNo_ID_1582452511.xlsx → /home/jovyan/1010/ori_data/02_100/PatNo_ID_1582452511.xlsx\n",
      "✅ 已複製：/home/jovyan/RT08/0923_datacheck/02_100/PatNo_ID_1565148312.xlsx → /home/jovyan/1010/ori_data/02_100/PatNo_ID_1565148312.xlsx\n",
      "✅ 已複製：/home/jovyan/RT08/0923_datacheck/02_100/PatNo_ID_1594309746.xlsx → /home/jovyan/1010/ori_data/02_100/PatNo_ID_1594309746.xlsx\n",
      "✅ 已複製：/home/jovyan/RT08/0923_datacheck/02_100/PatNo_ID_1577487284.xlsx → /home/jovyan/1010/ori_data/02_100/PatNo_ID_1577487284.xlsx\n",
      "✅ 已複製：/home/jovyan/RT08/0923_datacheck/02_100/PatNo_ID_1570642083.xlsx → /home/jovyan/1010/ori_data/02_100/PatNo_ID_1570642083.xlsx\n",
      "✅ 已複製：/home/jovyan/RT08/0923_datacheck/02_100/PatNo_ID_1580766093.xlsx → /home/jovyan/1010/ori_data/02_100/PatNo_ID_1580766093.xlsx\n",
      "✅ 已複製：/home/jovyan/RT08/0923_datacheck/02_100/PatNo_ID_1581692973.xlsx → /home/jovyan/1010/ori_data/02_100/PatNo_ID_1581692973.xlsx\n",
      "✅ 已複製：/home/jovyan/RT08/0923_datacheck/02_100/PatNo_ID_1580244614.xlsx → /home/jovyan/1010/ori_data/02_100/PatNo_ID_1580244614.xlsx\n",
      "✅ 已複製：/home/jovyan/RT08/0923_datacheck/02_100/PatNo_ID_1580096720.xlsx → /home/jovyan/1010/ori_data/02_100/PatNo_ID_1580096720.xlsx\n",
      "✅ 已複製：/home/jovyan/RT08/0923_datacheck/02_100/PatNo_ID_1594322594.xlsx → /home/jovyan/1010/ori_data/02_100/PatNo_ID_1594322594.xlsx\n",
      "✅ 已複製：/home/jovyan/RT08/0923_datacheck/02_100/PatNo_ID_1589034524.xlsx → /home/jovyan/1010/ori_data/02_100/PatNo_ID_1589034524.xlsx\n",
      "✅ 已複製：/home/jovyan/RT08/0923_datacheck/02_100/PatNo_ID_1573063188.xlsx → /home/jovyan/1010/ori_data/02_100/PatNo_ID_1573063188.xlsx\n",
      "✅ 已複製：/home/jovyan/RT08/0923_datacheck/02_100/PatNo_ID_1594320763.xlsx → /home/jovyan/1010/ori_data/02_100/PatNo_ID_1594320763.xlsx\n",
      "✅ 已複製：/home/jovyan/RT08/0923_datacheck/02_100/PatNo_ID_1588957997.xlsx → /home/jovyan/1010/ori_data/02_100/PatNo_ID_1588957997.xlsx\n",
      "✅ 已複製：/home/jovyan/RT08/0923_datacheck/02_100/PatNo_ID_1562733396.xlsx → /home/jovyan/1010/ori_data/02_100/PatNo_ID_1562733396.xlsx\n",
      "✅ 已複製：/home/jovyan/RT08/0923_datacheck/02_100/PatNo_ID_1574148494.xlsx → /home/jovyan/1010/ori_data/02_100/PatNo_ID_1574148494.xlsx\n",
      "✅ 已複製：/home/jovyan/RT08/0923_datacheck/02_100/PatNo_ID_1588632604.xlsx → /home/jovyan/1010/ori_data/02_100/PatNo_ID_1588632604.xlsx\n",
      "✅ 已複製：/home/jovyan/RT08/0923_datacheck/02_100/PatNo_ID_1589918099.xlsx → /home/jovyan/1010/ori_data/02_100/PatNo_ID_1589918099.xlsx\n",
      "✅ 已複製：/home/jovyan/RT08/0923_datacheck/02_100/PatNo_ID_1590854576.xlsx → /home/jovyan/1010/ori_data/02_100/PatNo_ID_1590854576.xlsx\n",
      "✅ 已複製：/home/jovyan/RT08/0923_datacheck/02_100/PatNo_ID_1579498177.xlsx → /home/jovyan/1010/ori_data/02_100/PatNo_ID_1579498177.xlsx\n",
      "✅ 已複製：/home/jovyan/RT08/0923_datacheck/02_100/PatNo_ID_1573249295.xlsx → /home/jovyan/1010/ori_data/02_100/PatNo_ID_1573249295.xlsx\n",
      "✅ 已複製：/home/jovyan/RT08/0923_datacheck/02_100/PatNo_ID_1593593586.xlsx → /home/jovyan/1010/ori_data/02_100/PatNo_ID_1593593586.xlsx\n",
      "✅ 已複製：/home/jovyan/RT08/0923_datacheck/02_100/PatNo_ID_1566252197.xlsx → /home/jovyan/1010/ori_data/02_100/PatNo_ID_1566252197.xlsx\n",
      "✅ 已複製：/home/jovyan/RT08/0923_datacheck/02_100/PatNo_ID_1576116479.xlsx → /home/jovyan/1010/ori_data/02_100/PatNo_ID_1576116479.xlsx\n",
      "✅ 已複製：/home/jovyan/RT08/0923_datacheck/02_100/PatNo_ID_1593087886.xlsx → /home/jovyan/1010/ori_data/02_100/PatNo_ID_1593087886.xlsx\n",
      "✅ 已複製：/home/jovyan/RT08/0923_datacheck/02_100/PatNo_ID_1594319286.xlsx → /home/jovyan/1010/ori_data/02_100/PatNo_ID_1594319286.xlsx\n",
      "✅ 已複製：/home/jovyan/RT08/0923_datacheck/02_100/PatNo_ID_1575060177.xlsx → /home/jovyan/1010/ori_data/02_100/PatNo_ID_1575060177.xlsx\n",
      "✅ 已複製：/home/jovyan/RT08/0923_datacheck/02_100/PatNo_ID_1575975485.xlsx → /home/jovyan/1010/ori_data/02_100/PatNo_ID_1575975485.xlsx\n",
      "✅ 已複製：/home/jovyan/RT08/0923_datacheck/02_100/PatNo_ID_1594423683.xlsx → /home/jovyan/1010/ori_data/02_100/PatNo_ID_1594423683.xlsx\n",
      "✅ 已複製：/home/jovyan/RT08/0923_datacheck/02_100/PatNo_ID_1593416100.xlsx → /home/jovyan/1010/ori_data/02_100/PatNo_ID_1593416100.xlsx\n",
      "✅ 已複製：/home/jovyan/RT08/0923_datacheck/02_100/PatNo_ID_1578784257.xlsx → /home/jovyan/1010/ori_data/02_100/PatNo_ID_1578784257.xlsx\n",
      "✅ 已複製：/home/jovyan/RT08/0923_datacheck/02_100/PatNo_ID_1566279967.xlsx → /home/jovyan/1010/ori_data/02_100/PatNo_ID_1566279967.xlsx\n",
      "✅ 已複製：/home/jovyan/RT08/0923_datacheck/02_100/PatNo_ID_1566123680.xlsx → /home/jovyan/1010/ori_data/02_100/PatNo_ID_1566123680.xlsx\n",
      "✅ 已複製：/home/jovyan/RT08/0923_datacheck/02_100/PatNo_ID_1587490083.xlsx → /home/jovyan/1010/ori_data/02_100/PatNo_ID_1587490083.xlsx\n",
      "✅ 已複製：/home/jovyan/RT08/0923_datacheck/02_100/PatNo_ID_1564148644.xlsx → /home/jovyan/1010/ori_data/02_100/PatNo_ID_1564148644.xlsx\n",
      "✅ 已複製：/home/jovyan/RT08/0923_datacheck/02_100/PatNo_ID_1572831765.xlsx → /home/jovyan/1010/ori_data/02_100/PatNo_ID_1572831765.xlsx\n",
      "✅ 已複製：/home/jovyan/RT08/0923_datacheck/02_100/PatNo_ID_1581633231.xlsx → /home/jovyan/1010/ori_data/02_100/PatNo_ID_1581633231.xlsx\n",
      "✅ 已複製：/home/jovyan/RT08/0923_datacheck/02_100/PatNo_ID_1582849900.xlsx → /home/jovyan/1010/ori_data/02_100/PatNo_ID_1582849900.xlsx\n",
      "✅ 已複製：/home/jovyan/RT08/0923_datacheck/101_200/PatNo_ID_1592044724.xlsx → /home/jovyan/1010/ori_data/101_200/PatNo_ID_1592044724.xlsx\n",
      "✅ 已複製：/home/jovyan/RT08/0923_datacheck/101_200/PatNo_ID_1572976822.xlsx → /home/jovyan/1010/ori_data/101_200/PatNo_ID_1572976822.xlsx\n",
      "✅ 已複製：/home/jovyan/RT08/0923_datacheck/101_200/PatNo_ID_1594439781.xlsx → /home/jovyan/1010/ori_data/101_200/PatNo_ID_1594439781.xlsx\n",
      "✅ 已複製：/home/jovyan/RT08/0923_datacheck/101_200/PatNo_ID_1586897008.xlsx → /home/jovyan/1010/ori_data/101_200/PatNo_ID_1586897008.xlsx\n",
      "✅ 已複製：/home/jovyan/RT08/0923_datacheck/101_200/PatNo_ID_1568574099.xlsx → /home/jovyan/1010/ori_data/101_200/PatNo_ID_1568574099.xlsx\n",
      "✅ 已複製：/home/jovyan/RT08/0923_datacheck/101_200/PatNo_ID_1594448501.xlsx → /home/jovyan/1010/ori_data/101_200/PatNo_ID_1594448501.xlsx\n",
      "✅ 已複製：/home/jovyan/RT08/0923_datacheck/101_200/PatNo_ID_1582937076.xlsx → /home/jovyan/1010/ori_data/101_200/PatNo_ID_1582937076.xlsx\n",
      "✅ 已複製：/home/jovyan/RT08/0923_datacheck/101_200/PatNo_ID_1591609798.xlsx → /home/jovyan/1010/ori_data/101_200/PatNo_ID_1591609798.xlsx\n",
      "✅ 已複製：/home/jovyan/RT08/0923_datacheck/101_200/PatNo_ID_1594464829.xlsx → /home/jovyan/1010/ori_data/101_200/PatNo_ID_1594464829.xlsx\n",
      "✅ 已複製：/home/jovyan/RT08/0923_datacheck/101_200/PatNo_ID_1576115572.xlsx → /home/jovyan/1010/ori_data/101_200/PatNo_ID_1576115572.xlsx\n",
      "✅ 已複製：/home/jovyan/RT08/0923_datacheck/101_200/PatNo_ID_1574987447.xlsx → /home/jovyan/1010/ori_data/101_200/PatNo_ID_1574987447.xlsx\n",
      "✅ 已複製：/home/jovyan/RT08/0923_datacheck/101_200/PatNo_ID_1590136310.xlsx → /home/jovyan/1010/ori_data/101_200/PatNo_ID_1590136310.xlsx\n",
      "✅ 已複製：/home/jovyan/RT08/0923_datacheck/101_200/PatNo_ID_1588673465.xlsx → /home/jovyan/1010/ori_data/101_200/PatNo_ID_1588673465.xlsx\n",
      "✅ 已複製：/home/jovyan/RT08/0923_datacheck/101_200/PatNo_ID_1594511914.xlsx → /home/jovyan/1010/ori_data/101_200/PatNo_ID_1594511914.xlsx\n",
      "✅ 已複製：/home/jovyan/RT08/0923_datacheck/101_200/PatNo_ID_1589018086.xlsx → /home/jovyan/1010/ori_data/101_200/PatNo_ID_1589018086.xlsx\n",
      "✅ 已複製：/home/jovyan/RT08/0923_datacheck/101_200/PatNo_ID_1563587183.xlsx → /home/jovyan/1010/ori_data/101_200/PatNo_ID_1563587183.xlsx\n",
      "✅ 已複製：/home/jovyan/RT08/0923_datacheck/101_200/PatNo_ID_1569944983.xlsx → /home/jovyan/1010/ori_data/101_200/PatNo_ID_1569944983.xlsx\n",
      "✅ 已複製：/home/jovyan/RT08/0923_datacheck/101_200/PatNo_ID_1594511911.xlsx → /home/jovyan/1010/ori_data/101_200/PatNo_ID_1594511911.xlsx\n",
      "✅ 已複製：/home/jovyan/RT08/0923_datacheck/101_200/PatNo_ID_1594437309.xlsx → /home/jovyan/1010/ori_data/101_200/PatNo_ID_1594437309.xlsx\n",
      "✅ 已複製：/home/jovyan/RT08/0923_datacheck/101_200/PatNo_ID_1594471407.xlsx → /home/jovyan/1010/ori_data/101_200/PatNo_ID_1594471407.xlsx\n",
      "✅ 已複製：/home/jovyan/RT08/0923_datacheck/101_200/PatNo_ID_1567747650.xlsx → /home/jovyan/1010/ori_data/101_200/PatNo_ID_1567747650.xlsx\n",
      "✅ 已複製：/home/jovyan/RT08/0923_datacheck/101_200/PatNo_ID_1570089466.xlsx → /home/jovyan/1010/ori_data/101_200/PatNo_ID_1570089466.xlsx\n",
      "✅ 已複製：/home/jovyan/RT08/0923_datacheck/101_200/PatNo_ID_1594528842.xlsx → /home/jovyan/1010/ori_data/101_200/PatNo_ID_1594528842.xlsx\n",
      "✅ 已複製：/home/jovyan/RT08/0923_datacheck/101_200/PatNo_ID_1577042911.xlsx → /home/jovyan/1010/ori_data/101_200/PatNo_ID_1577042911.xlsx\n",
      "✅ 已複製：/home/jovyan/RT08/0923_datacheck/101_200/PatNo_ID_1573964540.xlsx → /home/jovyan/1010/ori_data/101_200/PatNo_ID_1573964540.xlsx\n",
      "✅ 已複製：/home/jovyan/RT08/0923_datacheck/101_200/PatNo_ID_1594479330.xlsx → /home/jovyan/1010/ori_data/101_200/PatNo_ID_1594479330.xlsx\n",
      "✅ 已複製：/home/jovyan/RT08/0923_datacheck/101_200/PatNo_ID_1574528808.xlsx → /home/jovyan/1010/ori_data/101_200/PatNo_ID_1574528808.xlsx\n",
      "✅ 已複製：/home/jovyan/RT08/0923_datacheck/101_200/PatNo_ID_1594441887.xlsx → /home/jovyan/1010/ori_data/101_200/PatNo_ID_1594441887.xlsx\n",
      "✅ 已複製：/home/jovyan/RT08/0923_datacheck/101_200/PatNo_ID_1574270349.xlsx → /home/jovyan/1010/ori_data/101_200/PatNo_ID_1574270349.xlsx\n",
      "✅ 已複製：/home/jovyan/RT08/0923_datacheck/101_200/PatNo_ID_1589324603.xlsx → /home/jovyan/1010/ori_data/101_200/PatNo_ID_1589324603.xlsx\n",
      "✅ 已複製：/home/jovyan/RT08/0923_datacheck/101_200/PatNo_ID_1594467719.xlsx → /home/jovyan/1010/ori_data/101_200/PatNo_ID_1594467719.xlsx\n",
      "✅ 已複製：/home/jovyan/RT08/0923_datacheck/101_200/PatNo_ID_1567804800.xlsx → /home/jovyan/1010/ori_data/101_200/PatNo_ID_1567804800.xlsx\n",
      "✅ 已複製：/home/jovyan/RT08/0923_datacheck/101_200/PatNo_ID_1576964560.xlsx → /home/jovyan/1010/ori_data/101_200/PatNo_ID_1576964560.xlsx\n",
      "✅ 已複製：/home/jovyan/RT08/0923_datacheck/101_200/PatNo_ID_1581019504.xlsx → /home/jovyan/1010/ori_data/101_200/PatNo_ID_1581019504.xlsx\n",
      "✅ 已複製：/home/jovyan/RT08/0923_datacheck/101_200/PatNo_ID_1570273244.xlsx → /home/jovyan/1010/ori_data/101_200/PatNo_ID_1570273244.xlsx\n",
      "✅ 已複製：/home/jovyan/RT08/0923_datacheck/101_200/PatNo_ID_1594533379.xlsx → /home/jovyan/1010/ori_data/101_200/PatNo_ID_1594533379.xlsx\n",
      "✅ 已複製：/home/jovyan/RT08/0923_datacheck/101_200/PatNo_ID_1593720818.xlsx → /home/jovyan/1010/ori_data/101_200/PatNo_ID_1593720818.xlsx\n",
      "✅ 已複製：/home/jovyan/RT08/0923_datacheck/101_200/PatNo_ID_1572481361.xlsx → /home/jovyan/1010/ori_data/101_200/PatNo_ID_1572481361.xlsx\n",
      "✅ 已複製：/home/jovyan/RT08/0923_datacheck/101_200/PatNo_ID_1592560504.xlsx → /home/jovyan/1010/ori_data/101_200/PatNo_ID_1592560504.xlsx\n",
      "✅ 已複製：/home/jovyan/RT08/0923_datacheck/101_200/PatNo_ID_1584397376.xlsx → /home/jovyan/1010/ori_data/101_200/PatNo_ID_1584397376.xlsx\n",
      "✅ 已複製：/home/jovyan/RT08/0923_datacheck/101_200/PatNo_ID_1594173718.xlsx → /home/jovyan/1010/ori_data/101_200/PatNo_ID_1594173718.xlsx\n",
      "✅ 已複製：/home/jovyan/RT08/0923_datacheck/101_200/PatNo_ID_1580107637.xlsx → /home/jovyan/1010/ori_data/101_200/PatNo_ID_1580107637.xlsx\n",
      "✅ 已複製：/home/jovyan/RT08/0923_datacheck/101_200/PatNo_ID_1576301569.xlsx → /home/jovyan/1010/ori_data/101_200/PatNo_ID_1576301569.xlsx\n",
      "✅ 已複製：/home/jovyan/RT08/0923_datacheck/101_200/PatNo_ID_1574831525.xlsx → /home/jovyan/1010/ori_data/101_200/PatNo_ID_1574831525.xlsx\n",
      "✅ 已複製：/home/jovyan/RT08/0923_datacheck/101_200/PatNo_ID_1570242703.xlsx → /home/jovyan/1010/ori_data/101_200/PatNo_ID_1570242703.xlsx\n",
      "✅ 已複製：/home/jovyan/RT08/0923_datacheck/101_200/PatNo_ID_1565378038.xlsx → /home/jovyan/1010/ori_data/101_200/PatNo_ID_1565378038.xlsx\n",
      "✅ 已複製：/home/jovyan/RT08/0923_datacheck/101_200/PatNo_ID_1560013303.xlsx → /home/jovyan/1010/ori_data/101_200/PatNo_ID_1560013303.xlsx\n",
      "✅ 已複製：/home/jovyan/RT08/0923_datacheck/101_200/PatNo_ID_1575445051.xlsx → /home/jovyan/1010/ori_data/101_200/PatNo_ID_1575445051.xlsx\n",
      "✅ 已複製：/home/jovyan/RT08/0923_datacheck/101_200/PatNo_ID_1580062580.xlsx → /home/jovyan/1010/ori_data/101_200/PatNo_ID_1580062580.xlsx\n",
      "✅ 已複製：/home/jovyan/RT08/0923_datacheck/101_200/PatNo_ID_1593838524.xlsx → /home/jovyan/1010/ori_data/101_200/PatNo_ID_1593838524.xlsx\n",
      "✅ 已複製：/home/jovyan/RT08/0923_datacheck/101_200/PatNo_ID_1567832735.xlsx → /home/jovyan/1010/ori_data/101_200/PatNo_ID_1567832735.xlsx\n",
      "✅ 已複製：/home/jovyan/RT08/0923_datacheck/101_200/PatNo_ID_1590616537.xlsx → /home/jovyan/1010/ori_data/101_200/PatNo_ID_1590616537.xlsx\n",
      "✅ 已複製：/home/jovyan/RT08/0923_datacheck/101_200/PatNo_ID_1578784257.xlsx → /home/jovyan/1010/ori_data/101_200/PatNo_ID_1578784257.xlsx\n",
      "✅ 已複製：/home/jovyan/RT08/0923_datacheck/101_200/PatNo_ID_1572562839.xlsx → /home/jovyan/1010/ori_data/101_200/PatNo_ID_1572562839.xlsx\n",
      "✅ 已複製：/home/jovyan/RT08/0923_datacheck/101_200/PatNo_ID_1568813269.xlsx → /home/jovyan/1010/ori_data/101_200/PatNo_ID_1568813269.xlsx\n",
      "✅ 已複製：/home/jovyan/RT08/0923_datacheck/101_200/PatNo_ID_1586172659.xlsx → /home/jovyan/1010/ori_data/101_200/PatNo_ID_1586172659.xlsx\n",
      "✅ 已複製：/home/jovyan/RT08/0923_datacheck/101_200/PatNo_ID_1588794796.xlsx → /home/jovyan/1010/ori_data/101_200/PatNo_ID_1588794796.xlsx\n",
      "✅ 已複製：/home/jovyan/RT08/0923_datacheck/101_200/PatNo_ID_1579198603.xlsx → /home/jovyan/1010/ori_data/101_200/PatNo_ID_1579198603.xlsx\n",
      "✅ 已複製：/home/jovyan/RT08/0923_datacheck/101_200/PatNo_ID_1594455578.xlsx → /home/jovyan/1010/ori_data/101_200/PatNo_ID_1594455578.xlsx\n",
      "✅ 已複製：/home/jovyan/RT08/0921/7108162.csv → /home/jovyan/1010/ori_data/0921/7108162.csv\n",
      "✅ 已複製：/home/jovyan/RT08/0921/114309.csv → /home/jovyan/1010/ori_data/0921/114309.csv\n",
      "✅ 已複製：/home/jovyan/RT08/0921/7408338.csv → /home/jovyan/1010/ori_data/0921/7408338.csv\n",
      "✅ 已複製：/home/jovyan/RT08/0921/095707.csv → /home/jovyan/1010/ori_data/0921/095707.csv\n",
      "✅ 已複製：/home/jovyan/RT08/0921/4216007.csv → /home/jovyan/1010/ori_data/0921/4216007.csv\n",
      "✅ 已複製：/home/jovyan/RT08/0921/089271.csv → /home/jovyan/1010/ori_data/0921/089271.csv\n",
      "✅ 已複製：/home/jovyan/RT08/0921/7657698.csv → /home/jovyan/1010/ori_data/0921/7657698.csv\n",
      "✅ 已複製：/home/jovyan/RT08/0921/230933.csv → /home/jovyan/1010/ori_data/0921/230933.csv\n",
      "✅ 已複製：/home/jovyan/RT08/0921/095323.csv → /home/jovyan/1010/ori_data/0921/095323.csv\n",
      "✅ 已複製：/home/jovyan/RT08/0921/7721164.csv → /home/jovyan/1010/ori_data/0921/7721164.csv\n",
      "🎯 全部檔案已成功複製完畢。\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import shutil\n",
    "\n",
    "# 定義來源與目標資料夾對應\n",
    "paths = [\n",
    "    (\"/home/jovyan/RT08/0923_datacheck/02_100/\",\n",
    "     \"/home/jovyan/1010/ori_data/02_100/\"),\n",
    "    \n",
    "    (\"/home/jovyan/RT08/0923_datacheck/101_200/\",\n",
    "     \"/home/jovyan/1010/ori_data/101_200/\"),\n",
    "    \n",
    "    (\"/home/jovyan/RT08/0921/\",\n",
    "     \"/home/jovyan/1010/ori_data/0921/\")\n",
    "]\n",
    "\n",
    "# 執行複製\n",
    "for src, dst in paths:\n",
    "    os.makedirs(dst, exist_ok=True)  # 確保目的資料夾存在\n",
    "    for file_name in os.listdir(src):\n",
    "        src_file = os.path.join(src, file_name)\n",
    "        dst_file = os.path.join(dst, file_name)\n",
    "        if os.path.isfile(src_file):  # 只複製檔案，不包含子資料夾\n",
    "            shutil.copy2(src_file, dst_file)\n",
    "            print(f\"✅ 已複製：{src_file} → {dst_file}\")\n",
    "\n",
    "print(\"🎯 全部檔案已成功複製完畢。\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "id": "51fb8574-533a-4b41-ad7e-3cab7b833616",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "檔案數量: 269\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "\n",
    "path = \"/home/jovyan/phase_1/v1/data_1/v1.1.2/\"\n",
    "files = [f for f in os.listdir(path) if f.endswith(\".csv\")]\n",
    "print(\"檔案數量:\", len(files))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "82b8f9da-119a-492d-8071-eff3ae5038c0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# =========================\n",
    "# 設定區\n",
    "# =========================\n",
    "\n",
    "INPUT_DIRS = [\n",
    "    \"/home/jovyan/1010/ori_data/02_100/\",\n",
    "    \"/home/jovyan/1010/ori_data/101_200/\",\n",
    "    \"/home/jovyan/1010/ori_data/0921/\",\n",
    "]\n",
    "OUTPUT_DIR_MAIN = \"/home/jovyan/1010/data-1/clear/\"  # 單一輸出資料夾\n",
    "WORKDIR        = \"/home/jovyan/1010/data-1/work\"     # 步驟中繼輸出（Parquet）\n",
    "EXPECTED_TOTAL = 123\n",
    "\n",
    "# 不應轉為 float32 的欄位（避免誤轉）\n",
    "EXCLUDE_COLS = {\n",
    "    \"id\",\"patno\",\"patient_id\",\"ventilatormode\",\"ventilator_type\",\"room\",\"dongleid\",\n",
    "    \"o2therapy\",\"ventmodecode\",\"senddate\",\"senddate_clean\",\"senddate_parsed\",\"senddate_raw\",\n",
    "    \"segment_id\"\n",
    "}\n",
    "\n",
    "# 可能的時間欄位（會正規化成 senddate）\n",
    "LIKELY_TIME_COLS = {\"senddate\",\"time\",\"datetime\",\"timestamp\",\"date\",\"senddate_clean\",\"senddate_parsed\"}\n",
    "\n",
    "# 連續數值欄位判定：不看缺失率，只要 ≥1 筆可解析為數值就視為數值欄\n",
    "NUMERIC_MIN_NONNA = 1\n",
    "\n",
    "# （可選）隨機抽樣輸出份數\n",
    "RANDOM_K = 1\n",
    "RANDOM_SEED = 42"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "af5917f8-c91e-4582-bdd9-0e27dee5b840",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    }
   },
   "outputs": [],
   "source": [
    "# 資料問題（Data Issues）與解決方法（Solutions）\n",
    "# -----------------------------------------------------------------------------\n",
    "# 問題一：來源異質（CSV/Excel 混用、編碼與分隔符不一致、表頭行數不固定）\n",
    "#   - 現象：UTF-8 / UTF-8-SIG / CP950 交雜；有時是分號分隔；表頭可能在第 2~N 行。\n",
    "#   - 解法：\n",
    "#       * try_read_csv_variants()：多編碼、多引擎自動嘗試；失敗則分號分隔備援。\n",
    "#       * try_read_excel_with_header_detect()：Excel 亦先用無表頭預覽偵測表頭列。\n",
    "#       * find_header_row()：以啟發式方式計分，選出最可能的表頭列。\n",
    "#\n",
    "# 問題二：欄名不一致（大小寫、全半形、符號、同義欄位命名混亂）\n",
    "#   - 現象：senddate / DateTime / date_time / time / Δt_sec / dt__sec…等同義欄位。\n",
    "#   - 解法：\n",
    "#       * normalize_colname()：全形→半形、去空白、轉小寫、符號標準化、別名對映。\n",
    "#       * 可擴充 alias 字典，以符合專案演進。\n",
    "#\n",
    "# 問題三：時間格式混雜（「上午/下午」與 AM/PM 混用；部分字串含多餘空白）\n",
    "#   - 現象：'2024/01/02 下午 3:05'、'2024-01-02 12:00 AM' 等需轉 24 小時制。\n",
    "#   - 解法：\n",
    "#       * chinese_ampm_to_24h()：最小侵入式只轉時間片段，不動日期部分。\n",
    "#       * to_datetime_safe()：先 AM/PM 正規化，再用 pandas.to_datetime(errors=\"coerce\")。\n",
    "#\n",
    "# 問題四：數值欄判斷困難（有些欄位含少數數字、ID/類別碼被誤判）\n",
    "#   - 現象：ID/模式碼看似數字；或某欄只有少數非 NaN 數字。\n",
    "#   - 解法：\n",
    "#       * is_continuous_numeric()：先排除 EXCLUDE_COLS，再以最少非 NaN 門檻判斷。\n",
    "#       * NUMERIC_MIN_NONNA 可依資料量調校，避免少量數字誤判。\n",
    "#\n",
    "# 問題五：批次處理可觀測性不足（不知道目前跑到第幾檔）\n",
    "#   - 解法：\n",
    "#       * print_progress()：標準化進度輸出，便於長流程監控。\n",
    "#\n",
    "# 問題六：重複讀檔耗時（多次跑同一流程）\n",
    "#   - 解法：\n",
    "#       * read_parquet_if_exists() + to_parquet_path()：以 Parquet 當快取層，省時省 I/O。\n",
    "#\n",
    "# 問題七：輸出路徑不存在\n",
    "#   - 解法：\n",
    "#       * ensure_dir()：遞迴建立目錄，避免路徑不存在導致寫檔失敗。\n",
    "# =============================================================================\n",
    "\n",
    "\"\"\"\n",
    "共用工具（Utility functions）\n",
    "說明：\n",
    "- 本模組內含：路徑/檔案處理、欄位名稱正規化、表頭偵測、韌性讀檔（CSV/Excel 自動偵測）、\n",
    "  中英混合 AM/PM → 24 小時制轉換、安全時間解析、數值欄偵測、進度輸出、Parquet 快取讀取。\n",
    "- 風格準則：盡量「不猜資料語意」、採「非侵入式」轉換、所有規則具可追蹤性與可覆寫性。\n",
    "\n",
    "想要在批次處理時看見「目前第幾檔/總數」的進度列印；\n",
    "\"\"\"\n",
    "\n",
    "def ensure_dir(path: str):\n",
    "    \"\"\"\n",
    "    功能：確保資料夾存在，若不存在則遞迴建立。\n",
    "    為什麼需要：\n",
    "      - 批次輸出報表 / 中繼檔（如 parquet、csv）時，常見路徑尚未建立造成失敗。\n",
    "    參數：\n",
    "      - path: 目標資料夾路徑\n",
    "    回傳：無（若已存在則不動作）\n",
    "    例外處理：\n",
    "      - 使用 os.makedirs(..., exist_ok=True)，多執行緒情境下也安全。\n",
    "    \"\"\"\n",
    "    os.makedirs(path, exist_ok=True)\n",
    "\n",
    "def list_input_files(dirs: List[str]) -> List[str]:\n",
    "    \"\"\"\n",
    "    功能：列出多個輸入資料夾中的檔案，支援副檔名為 .csv/.xlsx/.xls，並回傳排序後清單。\n",
    "    設計重點：\n",
    "      - 有些來源會混合 CSV 與 Excel；統一在同一函式中擷取，方便後續批次處理。\n",
    "    參數：\n",
    "      - dirs: 欲掃描的資料夾清單\n",
    "    回傳：\n",
    "      - 排序後的檔案完整路徑 list\n",
    "    注意：\n",
    "      - 僅掃一層；若需遞迴請改用 glob.glob(os.path.join(d, \"**/*.csv\"), recursive=True) 等寫法。\n",
    "    \"\"\"\n",
    "    files = []\n",
    "    for d in dirs:\n",
    "        files.extend(glob.glob(os.path.join(d, \"*.csv\")))\n",
    "        files.extend(glob.glob(os.path.join(d, \"*.xlsx\")))\n",
    "        files.extend(glob.glob(os.path.join(d, \"*.xls\")))\n",
    "    return sorted(files)\n",
    "\n",
    "def normalize_colname(name: str) -> str:\n",
    "    \"\"\"\n",
    "    功能：欄位名稱正規化（Normalization）\n",
    "    規則：\n",
    "      1) 轉成字串、去頭尾空白、lowercase、小駝峰/中線改底線\n",
    "      2) 全形→半形（NFKC）\n",
    "      3) 非 [A-Za-z0-9_] 的字元通通以 '_' 取代，多個 '_' 壓成單一\n",
    "      4) 常見同義欄位做別名對映（alias），例如：\n",
    "         - senddate_clean / senddate_parsed / date_time / datetime / time / date → senddate\n",
    "         - patientno → patno、ventilator_mode / ventmode / ventmode_code → ventilatormode / ventmodecode\n",
    "         - Δt_sec / dt__sec / dt_sec_ → dt_sec\n",
    "    為什麼需要：\n",
    "      - 各資料來源欄位命名風格不一致，先正規化可大幅降低後續對接成本。\n",
    "    參數：\n",
    "      - name: 原始欄位名稱（任何型別都會轉成字串處理）\n",
    "    回傳：\n",
    "      - 正規化後的欄位名稱（str）\n",
    "    \"\"\"\n",
    "    if name is None: return \"\"\n",
    "    s = unicodedata.normalize(\"NFKC\", str(name)).strip().lower()\n",
    "    s = s.replace(\" \", \"\").replace(\"-\", \"_\")\n",
    "    s = re.sub(r\"[^\\w]+\", \"_\", s)    # 非 [A-Za-z0-9_] 變底線\n",
    "    s = re.sub(r\"_+\", \"_\", s).strip(\"_\")\n",
    "    # 常見別名對映表：可依專案演進擴充\n",
    "    alias = {\n",
    "        \"senddate_clean\": \"senddate\",\n",
    "        \"senddate_parsed\": \"senddate\",\n",
    "        \"date_time\": \"senddate\",\n",
    "        \"datetime\": \"senddate\",\n",
    "        \"time\": \"senddate\",\n",
    "        \"date\": \"senddate\",\n",
    "\n",
    "        \"rowcount\": \"row_count\",\n",
    "        \"patientno\": \"patno\",\n",
    "\n",
    "        \"ventilator_mode\": \"ventilatormode\",\n",
    "        \"ventmode\": \"ventilatormode\",\n",
    "        \"ventmode_code\": \"ventmodecode\",\n",
    "\n",
    "        \"Δt_sec\": \"dt_sec\",\n",
    "        \"dt__sec\": \"dt_sec\",\n",
    "        \"dt_sec_\": \"dt_sec\",\n",
    "    }\n",
    "    return alias.get(s, s)\n",
    "\n",
    "def find_header_row(df_headless: pd.DataFrame, max_rows: int = 20) -> int:\n",
    "    \"\"\"\n",
    "    功能：在讀進「無表頭」的預覽 DataFrame（header=None）中，嘗試找出「最可能的表頭列索引」。\n",
    "    偵測邏輯（啟發式，Heuristics）：\n",
    "      - 在前 max_rows 列中逐列計分，分數 = 非空欄位數 +「像欄名」的欄位數 + 2*常見關鍵欄命中數\n",
    "        * 非空欄位數：排除 \"\", \"nan\", \"none\"\n",
    "        * 像欄名：含英文字母且不是純數字（排除 123 之類）\n",
    "        * 常見關鍵欄：{\"senddate\",\"patno\",\"ventilatormode\",\"ventmodecode\",\"rrhzsetactual\",\"mvsetactual\"} \n",
    "          → 若該列的每個儲存格經 normalize_colname 後，命中這些關鍵字則加分\n",
    "      - 取分數最高的列為表頭索引。\n",
    "    參數：\n",
    "      - df_headless: 使用 header=None 讀進的預覽 DataFrame\n",
    "      - max_rows: 最多檢查的列數（避免整檔掃描過慢）\n",
    "    回傳：\n",
    "      - 最可能表頭的整數索引（0-based）\n",
    "    侷限與建議：\n",
    "      - 若來源格式非常不規則（多重抬頭/合併儲存格），建議在上游先清整後再交給本函式。\n",
    "    \"\"\"\n",
    "    best_idx, best_score = 0, -1\n",
    "    candidate_rows = min(max_rows, len(df_headless))\n",
    "    common_keys = {\"senddate\",\"patno\",\"ventilatormode\",\"ventmodecode\",\"rrhzsetactual\",\"mvsetactual\"}\n",
    "    for i in range(candidate_rows):\n",
    "        row = df_headless.iloc[i].astype(str).tolist()\n",
    "        # 1) 非空\n",
    "        non_empty = sum(1 for x in row if str(x).strip() not in {\"\", \"nan\", \"none\"})\n",
    "        # 2) 像欄名：含英字母且不是單純數字\n",
    "        like_cols = sum(1 for x in row if re.search(r\"[A-Za-z]\", str(x)) and not re.fullmatch(r\"\\d+(\\.\\d+)?\", str(x)))\n",
    "        # 3) 常見關鍵欄命中 bonus\n",
    "        normed = {normalize_colname(x) for x in row}\n",
    "        bonus = len(common_keys.intersection(normed))\n",
    "\n",
    "        score = non_empty + like_cols + 2 * bonus\n",
    "        if score > best_score:\n",
    "            best_idx, best_score = i, score\n",
    "\n",
    "    return best_idx\n",
    "\n",
    "def try_read_csv_variants(path: str, n_preview: int = 50) -> Tuple[pd.DataFrame,int]:\n",
    "    \"\"\"\n",
    "    功能：以「多組編碼/引擎」策略嘗試讀取 CSV，並自動偵測表頭列。\n",
    "    步驟：\n",
    "      1) 先以 header=None 讀前 n 行，呼叫 find_header_row 偵測表頭索引\n",
    "      2) 依序嘗試多種編碼（utf-8 → utf-8-sig → cp950），每種皆以 header=偵測值讀檔\n",
    "      3) 若都失敗，最後嘗試以分號分隔（sep=';'）再讀一次\n",
    "    參數：\n",
    "      - path: CSV 檔路徑\n",
    "      - n_preview: 用於表頭偵測的預覽行數\n",
    "    回傳：\n",
    "      - (df, header_idx)\n",
    "    例外處理：\n",
    "      - 任何一次成功即返回；全部失敗則以分號分隔做最後備援（常見於歐洲地區數字小數點設定）\n",
    "    \"\"\"\n",
    "    preview = pd.read_csv(path, header=None, nrows=n_preview, dtype=str, encoding=\"utf-8\", engine=\"python\")\n",
    "    header_idx = find_header_row(preview, max_rows=n_preview)\n",
    "    variants = [\n",
    "        dict(encoding=\"utf-8\", engine=\"python\"),\n",
    "        dict(encoding=\"utf-8-sig\", engine=\"python\"),\n",
    "        dict(encoding=\"cp950\", engine=\"python\"),\n",
    "    ]\n",
    "    last_err = None\n",
    "    for kw in variants:\n",
    "        try:\n",
    "            df = pd.read_csv(path, header=header_idx, dtype=str, **kw)\n",
    "            return df, header_idx\n",
    "        except Exception as e:\n",
    "            last_err = e\n",
    "    # 分號分隔備援\n",
    "    df = pd.read_csv(path, header=header_idx, dtype=str, sep=\";\", encoding=\"utf-8\", engine=\"python\")\n",
    "    return df, header_idx\n",
    "\n",
    "def try_read_excel_with_header_detect(path: str, n_preview: int = 50) -> Tuple[pd.DataFrame,int]:\n",
    "    preview = pd.read_excel(path, header=None, nrows=n_preview, dtype=str)\n",
    "    header_idx = find_header_row(preview, max_rows=n_preview)\n",
    "    df = pd.read_excel(path, header=header_idx, dtype=str)\n",
    "    return df, header_idx\n",
    "\n",
    "def read_table_resilient(path: str) -> Tuple[pd.DataFrame,int,str]:\n",
    "    ext = os.path.splitext(path)[1].lower()\n",
    "    if ext in [\".xlsx\",\".xls\"]:\n",
    "        df, idx = try_read_excel_with_header_detect(path); return df, idx, \"excel\"\n",
    "    try:\n",
    "        df, idx = try_read_csv_variants(path); return df, idx, \"csv\"\n",
    "    except Exception:\n",
    "        df, idx = try_read_excel_with_header_detect(path); return df, idx, \"excel-fallback\"\n",
    "\n",
    "def chinese_ampm_to_24h(s: str) -> str:\n",
    "    if s is None: return s\n",
    "    txt = str(s).strip()\n",
    "    if txt == \"\" or txt.lower() in {\"nan\",\"none\"}: return np.nan\n",
    "    txt = re.sub(r\"\\s+\",\" \", txt)\n",
    "    m = re.search(r\"(上午|下午)\\s*(\\d{1,2}):(\\d{2})(?::(\\d{2}))?\", txt)\n",
    "    if m:\n",
    "        ap, hh, mm, ss = m.group(1), m.group(2), m.group(3), m.group(4) or \"00\"\n",
    "        hour = int(hh)\n",
    "        if ap == \"下午\" and hour < 12: hour += 12\n",
    "        if ap == \"上午\" and hour == 12: hour = 0\n",
    "        txt = re.sub(r\"(上午|下午)\\s*\\d{1,2}:\\d{2}(?::\\d{2})?\", f\"{hour:02d}:{mm}:{ss}\", txt)\n",
    "        txt = txt.replace(\"上午\",\"\").replace(\"下午\",\"\").strip()\n",
    "    m2 = re.search(r\"\\b(\\d{1,2}):(\\d{2})(?::(\\d{2}))?\\s*(AM|PM)\\b\", txt, flags=re.IGNORECASE)\n",
    "    if m2:\n",
    "        hh, mm, ss, ap = m2.group(1), m2.group(2), m2.group(3) or \"00\", m2.group(4).upper()\n",
    "        hour = int(hh)\n",
    "        if ap == \"PM\" and hour < 12: hour += 12\n",
    "        if ap == \"AM\" and hour == 12: hour = 0\n",
    "        txt = re.sub(r\"\\b\\d{1,2}:\\d{2}(?::\\d{2})?\\s*(AM|PM)\\b\", f\"{hour:02d}:{mm}:{ss}\", txt, flags=re.IGNORECASE)\n",
    "    return txt\n",
    "\n",
    "def to_datetime_safe(series: pd.Series) -> pd.Series:\n",
    "    s = series.astype(str).map(chinese_ampm_to_24h)\n",
    "    dt = pd.to_datetime(s, errors=\"coerce\", infer_datetime_format=True, format=None)\n",
    "    return dt\n",
    "\n",
    "def is_continuous_numeric(series: pd.Series, colname_norm: str) -> bool:\n",
    "    if colname_norm in EXCLUDE_COLS: return False\n",
    "    s_num = pd.to_numeric(series, errors=\"coerce\")\n",
    "    return int(s_num.notna().sum()) >= NUMERIC_MIN_NONNA\n",
    "\n",
    "def print_progress(step: str, idx: int, total: int):\n",
    "    print(f\"[{step}] 目前進度：{idx}/{total}\")\n",
    "\n",
    "def base_name(path: str) -> str:\n",
    "    return os.path.basename(path)\n",
    "\n",
    "def to_parquet_path(step_dir: str, basename: str) -> str:\n",
    "    name = os.path.splitext(basename)[0] + \".parquet\"\n",
    "    return os.path.join(step_dir, name)\n",
    "\n",
    "def read_parquet_if_exists(path: str):\n",
    "    if not os.path.exists(path):\n",
    "        return None\n",
    "    return pd.read_parquet(path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "7793d406-cae4-40cc-95f4-3e17e9ded155",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    }
   },
   "outputs": [],
   "source": [
    "# Step 1 — 掃描與讀檔（不改原檔）\n",
    "# =========================\n",
    "def run_step1():\n",
    "    step_dir = os.path.join(WORKDIR, \"s1_read\")\n",
    "    ensure_dir(step_dir)\n",
    "\n",
    "    files = list_input_files(INPUT_DIRS)\n",
    "    print(f\"Step1 發現檔案數：{len(files)}（預期：{EXPECTED_TOTAL}）\")\n",
    "\n",
    "    manifest_rows = []\n",
    "    for i, path in enumerate(files, start=1):\n",
    "        base = base_name(path)\n",
    "        try:\n",
    "            df, header_idx, method = read_table_resilient(path)\n",
    "            df.to_parquet(to_parquet_path(step_dir, base), index=False)\n",
    "            manifest_rows.append({\n",
    "                \"file_name\": base,\n",
    "                \"path\": path,\n",
    "                \"read_ok\": True,\n",
    "                \"read_method\": method,\n",
    "                \"header_row\": header_idx,\n",
    "                \"n_rows_before\": df.shape[0],\n",
    "                \"n_cols_before\": df.shape[1],\n",
    "                \"error\": \"\"\n",
    "            })\n",
    "        except Exception as e:\n",
    "            warnings.warn(f\"[Step1] 讀檔失敗：{base} — {e}\")\n",
    "            manifest_rows.append({\n",
    "                \"file_name\": base,\n",
    "                \"path\": path,\n",
    "                \"read_ok\": False,\n",
    "                \"read_method\": \"\",\n",
    "                \"header_row\": None,\n",
    "                \"n_rows_before\": 0,\n",
    "                \"n_cols_before\": 0,\n",
    "                \"error\": str(e)\n",
    "            })\n",
    "        print_progress(\"Step1 掃描與讀檔\", i, len(files))\n",
    "\n",
    "    pd.DataFrame(manifest_rows).to_csv(os.path.join(step_dir, \"manifest.csv\"),\n",
    "                                       index=False, encoding=\"utf-8-sig\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "ce02f33d-6e30-4449-a2e8-6d98660edee8",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    }
   },
   "outputs": [],
   "source": [
    "# Step 2 — 欄位統一（rename）\n",
    "# =========================\n",
    "def run_step2():\n",
    "    in_dir  = os.path.join(WORKDIR, \"s1_read\")\n",
    "    out_dir = os.path.join(WORKDIR, \"s2_unify\")\n",
    "    ensure_dir(out_dir)\n",
    "\n",
    "    manifest = pd.read_csv(os.path.join(in_dir, \"manifest.csv\"))\n",
    "    mapping_rows, count_rows = [], []\n",
    "\n",
    "    total = manifest.shape[0]\n",
    "    for i, row in manifest.iterrows():\n",
    "        base = row[\"file_name\"]\n",
    "        df = read_parquet_if_exists(to_parquet_path(in_dir, base))\n",
    "        if df is None or not bool(row[\"read_ok\"]):\n",
    "            print_progress(\"Step2 欄位統一（跳過未讀）\", i+1, total); continue\n",
    "\n",
    "        original_cols = list(df.columns)\n",
    "        rename_map = {c: normalize_colname(c) for c in original_cols}\n",
    "        df = df.rename(columns=rename_map)\n",
    "\n",
    "        renamed_pairs = [(oc, rename_map[oc]) for oc in original_cols if oc != rename_map[oc]]\n",
    "        for oc, nc in renamed_pairs:\n",
    "            mapping_rows.append({\"file_name\": base, \"original\": oc, \"new\": nc})\n",
    "        count_rows.append({\"file_name\": base, \"renamed_count\": len(renamed_pairs)})\n",
    "\n",
    "        df.to_parquet(to_parquet_path(out_dir, base), index=False)\n",
    "        print_progress(\"Step2 欄位統一\", i+1, total)\n",
    "\n",
    "    pd.DataFrame(mapping_rows).to_csv(os.path.join(out_dir, \"col_rename_mapping.csv\"),\n",
    "                                      index=False, encoding=\"utf-8-sig\")\n",
    "    pd.DataFrame(count_rows).to_csv(os.path.join(out_dir, \"col_rename_counts.csv\"),\n",
    "                                    index=False, encoding=\"utf-8-sig\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "2b9f4b38-ded8-4d33-8f24-c26b6a26620e",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    }
   },
   "outputs": [],
   "source": [
    "# Step 3 — 空白→NaN（所有列所有欄位）\n",
    "# =========================\n",
    "def run_step3():\n",
    "    in_dir  = os.path.join(WORKDIR, \"s2_unify\")\n",
    "    out_dir = os.path.join(WORKDIR, \"s3_nan\")\n",
    "    ensure_dir(out_dir)\n",
    "\n",
    "    manifest1 = pd.read_csv(os.path.join(WORKDIR, \"s1_read\", \"manifest.csv\"))\n",
    "    total = manifest1.shape[0]\n",
    "\n",
    "    for i, row in manifest1.iterrows():\n",
    "        base = row[\"file_name\"]\n",
    "        df = read_parquet_if_exists(to_parquet_path(in_dir, base))\n",
    "        if df is None:\n",
    "            print_progress(\"Step3 空白→NaN（跳過未讀）\", i+1, total); continue\n",
    "\n",
    "        df = df.applymap(lambda x: np.nan if isinstance(x, str) and x.strip() == \"\" else x)\n",
    "        df.to_parquet(to_parquet_path(out_dir, base), index=False)\n",
    "        print_progress(\"Step3 空白→NaN\", i+1, total)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "fbb96bd4-0e6d-4abc-bb09-73d6ce56e544",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    }
   },
   "outputs": [],
   "source": [
    "# Step 4 — 時間欄位標準化 → datetime64[ns]\n",
    "# =========================\n",
    "def run_step4():\n",
    "    in_dir  = os.path.join(WORKDIR, \"s3_nan\")\n",
    "    out_dir = os.path.join(WORKDIR, \"s4_time\")\n",
    "    ensure_dir(out_dir)\n",
    "\n",
    "    manifest1 = pd.read_csv(os.path.join(WORKDIR, \"s1_read\", \"manifest.csv\"))\n",
    "    senddate_rows = []\n",
    "\n",
    "    total = manifest1.shape[0]\n",
    "    for i, row in manifest1.iterrows():\n",
    "        base = row[\"file_name\"]\n",
    "        df = read_parquet_if_exists(to_parquet_path(in_dir, base))\n",
    "        if df is None:\n",
    "            print_progress(\"Step4 時間標準化（跳過未讀）\", i+1, total); continue\n",
    "\n",
    "        cols_lower = [c.lower() for c in df.columns]\n",
    "        time_col = None\n",
    "        for cand in [\"senddate\"] + list(LIKELY_TIME_COLS):\n",
    "            if cand in cols_lower:\n",
    "                time_col = df.columns[cols_lower.index(cand)]\n",
    "                break\n",
    "\n",
    "        is_dt64ns = False\n",
    "        if time_col is not None:\n",
    "            raw_col = time_col + \"_raw\"\n",
    "            if raw_col not in df.columns:\n",
    "                df[raw_col] = df[time_col]\n",
    "            dt_series = to_datetime_safe(df[time_col])\n",
    "            df[time_col] = pd.to_datetime(dt_series, errors=\"coerce\")\n",
    "            is_dt64ns = pd.api.types.is_datetime64_ns_dtype(df[time_col].dtype)\n",
    "\n",
    "        df.to_parquet(to_parquet_path(out_dir, base), index=False)\n",
    "        senddate_rows.append({\n",
    "            \"file_name\": base,\n",
    "            \"senddate_col\": time_col,\n",
    "            \"senddate_is_datetime64ns\": bool(is_dt64ns)\n",
    "        })\n",
    "        print_progress(\"Step4 時間標準化\", i+1, total)\n",
    "\n",
    "    pd.DataFrame(senddate_rows).to_csv(os.path.join(out_dir, \"senddate_check.csv\"),\n",
    "                                       index=False, encoding=\"utf-8-sig\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "c1f53691-b9c2-47f3-9621-894a7801d5b8",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    }
   },
   "outputs": [],
   "source": [
    "# Step 5 — 連續數值欄位 → float32（不看缺失率）\n",
    "# =========================\n",
    "def run_step5():\n",
    "    in_dir  = os.path.join(WORKDIR, \"s4_time\")\n",
    "    out_dir = os.path.join(WORKDIR, \"s5_float32\")\n",
    "    ensure_dir(out_dir)\n",
    "\n",
    "    manifest1 = pd.read_csv(os.path.join(WORKDIR, \"s1_read\", \"manifest.csv\"))\n",
    "    float_rows = []\n",
    "\n",
    "    total = manifest1.shape[0]\n",
    "    for i, row in manifest1.iterrows():\n",
    "        base = row[\"file_name\"]\n",
    "        df = read_parquet_if_exists(to_parquet_path(in_dir, base))\n",
    "        if df is None:\n",
    "            print_progress(\"Step5 數值轉型（跳過未讀）\", i+1, total); continue\n",
    "\n",
    "        float32_cols = []\n",
    "        for c in df.columns:\n",
    "            if pd.api.types.is_datetime64_any_dtype(df[c]): \n",
    "                continue\n",
    "            c_norm = normalize_colname(c)\n",
    "            if is_continuous_numeric(df[c], c_norm):\n",
    "                df[c] = pd.to_numeric(df[c], errors=\"coerce\").astype(\"float32\")\n",
    "                if df[c].dtype == \"float32\":\n",
    "                    float32_cols.append(c)\n",
    "\n",
    "        df.to_parquet(to_parquet_path(out_dir, base), index=False)\n",
    "        float_rows.append({\"file_name\": base, \"float32_cols\": \",\".join(float32_cols),\n",
    "                           \"float32_cols_count\": len(float32_cols)})\n",
    "        print_progress(\"Step5 數值轉型 float32（強制）\", i+1, total)\n",
    "\n",
    "    pd.DataFrame(float_rows).to_csv(os.path.join(out_dir, \"float32_columns.csv\"),\n",
    "                                    index=False, encoding=\"utf-8-sig\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "7d6da163-2bac-47cf-bbe9-521ef8f8da3a",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    }
   },
   "outputs": [],
   "source": [
    "# Step 6 — 統一欄位集合（補缺/重排）\n",
    "# =========================\n",
    "def run_step6():\n",
    "    in_dir  = os.path.join(WORKDIR, \"s5_float32\")\n",
    "    out_dir = os.path.join(WORKDIR, \"s6_schema\")\n",
    "    ensure_dir(out_dir)\n",
    "\n",
    "    manifest1 = pd.read_csv(os.path.join(WORKDIR, \"s1_read\", \"manifest.csv\"))\n",
    "    per_file_cols = {}\n",
    "    union_cols = set()\n",
    "\n",
    "    # 收集聯集\n",
    "    for _, row in manifest1.iterrows():\n",
    "        base = row[\"file_name\"]\n",
    "        df = read_parquet_if_exists(to_parquet_path(in_dir, base))\n",
    "        if df is None:\n",
    "            per_file_cols[base] = set()\n",
    "        else:\n",
    "            per_file_cols[base] = set(df.columns)\n",
    "            union_cols |= per_file_cols[base]\n",
    "\n",
    "    final_schema = sorted(list(union_cols))\n",
    "\n",
    "    # 檢查缺失\n",
    "    missing_rows, mismatch_list = [], []\n",
    "    all_ok = True\n",
    "    for base, cols in per_file_cols.items():\n",
    "        missing = [c for c in final_schema if c not in cols]\n",
    "        if missing:\n",
    "            all_ok = False\n",
    "            missing_rows.append({\n",
    "                \"file_name\": base,\n",
    "                \"missing_columns\": \",\".join(missing),\n",
    "                \"missing_count\": len(missing)\n",
    "            })\n",
    "            mismatch_list.append({\"file_name\": base, \"schema_mismatch\": True})\n",
    "        else:\n",
    "            mismatch_list.append({\"file_name\": base, \"schema_mismatch\": False})\n",
    "\n",
    "    # 補缺/重排\n",
    "    total = manifest1.shape[0]\n",
    "    for idx, row in enumerate(manifest1.itertuples(index=False), start=1):\n",
    "        base = row.file_name\n",
    "        df = read_parquet_if_exists(to_parquet_path(in_dir, base))\n",
    "        if df is None:\n",
    "            print_progress(\"Step6 統一欄位集合（跳過未讀）\", idx, total); continue\n",
    "        for c in final_schema:\n",
    "            if c not in df.columns:\n",
    "                df[c] = np.nan\n",
    "        df = df[final_schema]\n",
    "        df.to_parquet(to_parquet_path(out_dir, base), index=False)\n",
    "        print_progress(\"Step6 統一欄位集合/補缺/重排\", idx, total)\n",
    "\n",
    "    # 報表輸出\n",
    "    if all_ok:\n",
    "        pd.DataFrame({\"final_columns\": final_schema}).to_csv(\n",
    "            os.path.join(out_dir, \"final_columns_list.csv\"),\n",
    "            index=False, encoding=\"utf-8-sig\"\n",
    "        )\n",
    "        pd.DataFrame(columns=[\"file_name\",\"missing_columns\",\"missing_count\"]).to_csv(\n",
    "            os.path.join(out_dir, \"missing_columns_report.csv\"),\n",
    "            index=False, encoding=\"utf-8-sig\"\n",
    "        )\n",
    "        pd.DataFrame(columns=[\"file_name\",\"schema_mismatch\"]).to_csv(\n",
    "            os.path.join(out_dir, \"schema_mismatch_list.csv\"),\n",
    "            index=False, encoding=\"utf-8-sig\"\n",
    "        )\n",
    "    else:\n",
    "        pd.DataFrame(missing_rows).to_csv(\n",
    "            os.path.join(out_dir, \"missing_columns_report.csv\"),\n",
    "            index=False, encoding=\"utf-8-sig\"\n",
    "        )\n",
    "        pd.DataFrame([m for m in mismatch_list if m[\"schema_mismatch\"]]).to_csv(\n",
    "            os.path.join(out_dir, \"schema_mismatch_list.csv\"),\n",
    "            index=False, encoding=\"utf-8-sig\"\n",
    "        )\n",
    "        pd.DataFrame({\"final_columns\": final_schema}).to_csv(\n",
    "            os.path.join(out_dir, \"final_columns_list.csv\"),\n",
    "            index=False, encoding=\"utf-8-sig\"\n",
    "        )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "68f3a4e4-526f-4cc7-8649-b9159a3371ae",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    }
   },
   "outputs": [],
   "source": [
    "# Step 7 — 逐檔驗證與最終輸出（單一資料夾）\n",
    "# =========================\n",
    "def run_step7():\n",
    "    in_dir  = os.path.join(WORKDIR, \"s6_schema\")\n",
    "    ensure_dir(OUTPUT_DIR_MAIN)\n",
    "    # 支援報表\n",
    "    s2_dir = os.path.join(WORKDIR, \"s2_unify\")\n",
    "    s4_dir = os.path.join(WORKDIR, \"s4_time\")\n",
    "    rename_mapping_path = os.path.join(s2_dir, \"col_rename_mapping.csv\")\n",
    "    rename_counts_path  = os.path.join(s2_dir, \"col_rename_counts.csv\")\n",
    "    s4_senddate_path    = os.path.join(s4_dir, \"senddate_check.csv\")\n",
    "\n",
    "    manifest1 = pd.read_csv(os.path.join(WORKDIR, \"s1_read\", \"manifest.csv\"))\n",
    "    # SendDate 檢查資料\n",
    "    senddate_map = {}\n",
    "    if os.path.exists(s4_senddate_path):\n",
    "        tmp = pd.read_csv(s4_senddate_path)\n",
    "        senddate_map = {r[\"file_name\"]: (r[\"senddate_col\"], bool(r[\"senddate_is_datetime64ns\"]))\n",
    "                        for _, r in tmp.iterrows()}\n",
    "\n",
    "    audit_rows = []\n",
    "    total = manifest1.shape[0]\n",
    "    for i, row in manifest1.iterrows():\n",
    "        base = row[\"file_name\"]\n",
    "        df = read_parquet_if_exists(to_parquet_path(in_dir, base))\n",
    "\n",
    "        audit = {\n",
    "            \"file_name\": base,\n",
    "            \"read_ok\": bool(row[\"read_ok\"]),\n",
    "            \"read_method\": row.get(\"read_method\", \"\"),\n",
    "            \"header_row\": row.get(\"header_row\", None),\n",
    "            \"n_rows_before\": row.get(\"n_rows_before\", 0),\n",
    "            \"n_cols_before\": row.get(\"n_cols_before\", 0),\n",
    "        }\n",
    "\n",
    "        if df is None:\n",
    "            audit.update({\n",
    "                \"n_rows_after\": 0, \"n_cols_after\": 0,\n",
    "                \"senddate_col\": None, \"senddate_is_datetime64ns\": False,\n",
    "                \"blank_str_count_after\": None,\n",
    "                \"float32_cols_count\": 0, \"float32_cols\": \"\",\n",
    "                \"total_nan_after\": None, \"error\": \"missing parquet in s6_schema\"\n",
    "            })\n",
    "            audit_rows.append(audit)\n",
    "            print_progress(\"Step7 驗證與輸出（缺檔）\", i+1, total)\n",
    "            continue\n",
    "\n",
    "        # 空白字串數（應為 0）\n",
    "        blank_str_count_after = 0\n",
    "        for c in df.columns:\n",
    "            if df[c].dtype == object:\n",
    "                blank_str_count_after += int(\n",
    "                    df[c].astype(str).map(lambda x: isinstance(x, str) and x.strip() == \"\").sum()\n",
    "                )\n",
    "\n",
    "        # SendDate dtype\n",
    "        s_col, s_is_dt = senddate_map.get(base, (None, False))\n",
    "        if s_col is not None and s_col in df.columns:\n",
    "            s_is_dt = bool(pd.api.types.is_datetime64_ns_dtype(df[s_col].dtype))\n",
    "        else:\n",
    "            cand = [c for c in df.columns if normalize_colname(c) in LIKELY_TIME_COLS]\n",
    "            s_col = cand[0] if cand else None\n",
    "            if s_col is not None:\n",
    "                s_is_dt = bool(pd.api.types.is_datetime64_ns_dtype(df[s_col].dtype))\n",
    "\n",
    "        # float32 欄位確認\n",
    "        float32_cols = [c for c in df.columns if str(df[c].dtype) == \"float32\"]\n",
    "\n",
    "        # NaN 總數\n",
    "        total_nan_after = int(df.isna().sum().sum())\n",
    "\n",
    "        # 稽核記錄\n",
    "        audit.update({\n",
    "            \"n_rows_after\": df.shape[0],\n",
    "            \"n_cols_after\": df.shape[1],\n",
    "            \"senddate_col\": s_col,\n",
    "            \"senddate_is_datetime64ns\": bool(s_is_dt),\n",
    "            \"blank_str_count_after\": int(blank_str_count_after),\n",
    "            \"float32_cols_count\": len(float32_cols),\n",
    "            \"float32_cols\": \",\".join(float32_cols),\n",
    "            \"total_nan_after\": total_nan_after,\n",
    "            \"error\": \"\"\n",
    "        })\n",
    "        audit_rows.append(audit)\n",
    "\n",
    "        # 寫最終清理 CSV（單一資料夾）\n",
    "        out_csv = os.path.join(OUTPUT_DIR_MAIN, os.path.splitext(base)[0] + \".csv\")\n",
    "        df.to_csv(out_csv, index=False, encoding=\"utf-8-sig\")\n",
    "        print_progress(\"Step7 驗證與輸出\", i+1, total)\n",
    "\n",
    "    # 寫 audit_summary.csv\n",
    "    pd.DataFrame(audit_rows).to_csv(os.path.join(OUTPUT_DIR_MAIN, \"audit_summary.csv\"),\n",
    "                                    index=False, encoding=\"utf-8-sig\")\n",
    "\n",
    "    # 落地 rename 對照/數量（若存在）\n",
    "    if os.path.exists(rename_mapping_path):\n",
    "        pd.read_csv(rename_mapping_path).to_csv(\n",
    "            os.path.join(OUTPUT_DIR_MAIN, \"col_rename_mapping.csv\"),\n",
    "            index=False, encoding=\"utf-8-sig\"\n",
    "        )\n",
    "    if os.path.exists(rename_counts_path):\n",
    "        pd.read_csv(rename_counts_path).to_csv(\n",
    "            os.path.join(OUTPUT_DIR_MAIN, \"col_rename_counts.csv\"),\n",
    "            index=False, encoding=\"utf-8-sig\"\n",
    "        )\n",
    "\n",
    "    # 落地 Schema mismatch 清單（只列不一致者）\n",
    "    s6_dir = os.path.join(WORKDIR, \"s6_schema\")\n",
    "    schema_mismatch_path = os.path.join(s6_dir, \"schema_mismatch_list.csv\")\n",
    "    if os.path.exists(schema_mismatch_path):\n",
    "        pd.read_csv(schema_mismatch_path).to_csv(\n",
    "            os.path.join(OUTPUT_DIR_MAIN, \"schema_mismatch_list.csv\"),\n",
    "            index=False, encoding=\"utf-8-sig\"\n",
    "        )\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "f08a086d-d052-41e7-8ac2-0db5604ea20c",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    }
   },
   "outputs": [],
   "source": [
    "# （可選）Step 8 — 隨機抽樣輸出\n",
    "# =========================\n",
    "def run_step8():\n",
    "    in_dir = os.path.join(WORKDIR, \"s6_schema\")\n",
    "    manifest1 = pd.read_csv(os.path.join(WORKDIR, \"s1_read\", \"manifest.csv\"))\n",
    "    files_ok = []\n",
    "    for _, row in manifest1.iterrows():\n",
    "        base = row[\"file_name\"]\n",
    "        pq = to_parquet_path(in_dir, base)\n",
    "        if os.path.exists(pq):\n",
    "            files_ok.append(base)\n",
    "    if not files_ok:\n",
    "        print(\"[Step8] 無可用檔案可隨機抽樣。\"); return\n",
    "    import random\n",
    "    random.seed(RANDOM_SEED)\n",
    "    k = min(RANDOM_K, len(files_ok))\n",
    "    picks = random.sample(files_ok, k)\n",
    "    for base in picks:\n",
    "        df = pd.read_parquet(to_parquet_path(in_dir, base))\n",
    "        print(f\"\\n[Step8] 隨機檔案：{base} — 前五筆：\")\n",
    "        display(df.head(5))\n",
    "        print(f\"[Step8] 欄位清單（{len(df.columns)}）：\")\n",
    "        print(list(df.columns))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "f50475cb-b4dc-4593-9f6e-50280deabc76",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    }
   },
   "outputs": [],
   "source": [
    "# Notebook 介面：控制器\n",
    "# =========================\n",
    "def run_step(n: int):\n",
    "    ensure_dir(WORKDIR)\n",
    "    if n == 1: run_step1()\n",
    "    elif n == 2: run_step2()\n",
    "    elif n == 3: run_step3()\n",
    "    elif n == 4: run_step4()\n",
    "    elif n == 5: run_step5()\n",
    "    elif n == 6: run_step6()\n",
    "    elif n == 7: run_step7()\n",
    "    elif n == 8: run_step8()\n",
    "    else:\n",
    "        print(\"步驟代號不在 1~8 之間。\")\n",
    "\n",
    "def run_steps(steps: List[int]):\n",
    "    for s in steps:\n",
    "        print(f\"\\n=== 開始執行 Step {s} ===\")\n",
    "        run_step(s)\n",
    "    print(\"\\n✅ 指定步驟已完成。\")\n",
    "\n",
    "def run_all():\n",
    "    run_steps([1,2,3,4,5,6,7])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "5b98e665-e1d3-4ea9-91a7-54782e2eadb1",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "=== 開始執行 Step 1 ===\n",
      "Step1 發現檔案數：123（預期：123）\n",
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      "[Step1 掃描與讀檔] 目前進度：123/123\n",
      "\n",
      "=== 開始執行 Step 2 ===\n",
      "[Step2 欄位統一] 目前進度：1/123\n",
      "[Step2 欄位統一] 目前進度：2/123\n",
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      "[Step2 欄位統一] 目前進度：54/123\n",
      "[Step2 欄位統一] 目前進度：55/123\n",
      "[Step2 欄位統一（跳過未讀）] 目前進度：56/123\n",
      "[Step2 欄位統一（跳過未讀）] 目前進度：57/123\n",
      "[Step2 欄位統一（跳過未讀）] 目前進度：58/123\n",
      "[Step2 欄位統一（跳過未讀）] 目前進度：59/123\n",
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      "[Step2 欄位統一] 目前進度：105/123\n",
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      "[Step2 欄位統一] 目前進度：107/123\n",
      "[Step2 欄位統一] 目前進度：108/123\n",
      "[Step2 欄位統一] 目前進度：109/123\n",
      "[Step2 欄位統一] 目前進度：110/123\n",
      "[Step2 欄位統一] 目前進度：111/123\n",
      "[Step2 欄位統一] 目前進度：112/123\n",
      "[Step2 欄位統一] 目前進度：113/123\n",
      "[Step2 欄位統一] 目前進度：114/123\n",
      "[Step2 欄位統一] 目前進度：115/123\n",
      "[Step2 欄位統一] 目前進度：116/123\n",
      "[Step2 欄位統一] 目前進度：117/123\n",
      "[Step2 欄位統一] 目前進度：118/123\n",
      "[Step2 欄位統一] 目前進度：119/123\n",
      "[Step2 欄位統一] 目前進度：120/123\n",
      "[Step2 欄位統一] 目前進度：121/123\n",
      "[Step2 欄位統一] 目前進度：122/123\n",
      "[Step2 欄位統一] 目前進度：123/123\n",
      "\n",
      "=== 開始執行 Step 3 ===\n",
      "[Step3 空白→NaN] 目前進度：1/123\n",
      "[Step3 空白→NaN] 目前進度：2/123\n",
      "[Step3 空白→NaN] 目前進度：3/123\n",
      "[Step3 空白→NaN] 目前進度：4/123\n",
      "[Step3 空白→NaN] 目前進度：5/123\n",
      "[Step3 空白→NaN] 目前進度：6/123\n",
      "[Step3 空白→NaN] 目前進度：7/123\n",
      "[Step3 空白→NaN] 目前進度：8/123\n",
      "[Step3 空白→NaN] 目前進度：9/123\n",
      "[Step3 空白→NaN] 目前進度：10/123\n",
      "[Step3 空白→NaN] 目前進度：11/123\n",
      "[Step3 空白→NaN] 目前進度：12/123\n",
      "[Step3 空白→NaN] 目前進度：13/123\n",
      "[Step3 空白→NaN] 目前進度：14/123\n",
      "[Step3 空白→NaN] 目前進度：15/123\n",
      "[Step3 空白→NaN] 目前進度：16/123\n",
      "[Step3 空白→NaN] 目前進度：17/123\n",
      "[Step3 空白→NaN] 目前進度：18/123\n",
      "[Step3 空白→NaN] 目前進度：19/123\n",
      "[Step3 空白→NaN] 目前進度：20/123\n",
      "[Step3 空白→NaN] 目前進度：21/123\n",
      "[Step3 空白→NaN] 目前進度：22/123\n",
      "[Step3 空白→NaN] 目前進度：23/123\n",
      "[Step3 空白→NaN] 目前進度：24/123\n",
      "[Step3 空白→NaN] 目前進度：25/123\n",
      "[Step3 空白→NaN] 目前進度：26/123\n",
      "[Step3 空白→NaN] 目前進度：27/123\n",
      "[Step3 空白→NaN] 目前進度：28/123\n",
      "[Step3 空白→NaN] 目前進度：29/123\n",
      "[Step3 空白→NaN] 目前進度：30/123\n",
      "[Step3 空白→NaN] 目前進度：31/123\n",
      "[Step3 空白→NaN] 目前進度：32/123\n",
      "[Step3 空白→NaN] 目前進度：33/123\n",
      "[Step3 空白→NaN] 目前進度：34/123\n",
      "[Step3 空白→NaN] 目前進度：35/123\n",
      "[Step3 空白→NaN] 目前進度：36/123\n",
      "[Step3 空白→NaN] 目前進度：37/123\n",
      "[Step3 空白→NaN] 目前進度：38/123\n",
      "[Step3 空白→NaN] 目前進度：39/123\n",
      "[Step3 空白→NaN] 目前進度：40/123\n",
      "[Step3 空白→NaN] 目前進度：41/123\n",
      "[Step3 空白→NaN] 目前進度：42/123\n",
      "[Step3 空白→NaN] 目前進度：43/123\n",
      "[Step3 空白→NaN] 目前進度：44/123\n",
      "[Step3 空白→NaN] 目前進度：45/123\n",
      "[Step3 空白→NaN] 目前進度：46/123\n",
      "[Step3 空白→NaN] 目前進度：47/123\n",
      "[Step3 空白→NaN] 目前進度：48/123\n",
      "[Step3 空白→NaN] 目前進度：49/123\n",
      "[Step3 空白→NaN] 目前進度：50/123\n",
      "[Step3 空白→NaN] 目前進度：51/123\n",
      "[Step3 空白→NaN] 目前進度：52/123\n",
      "[Step3 空白→NaN] 目前進度：53/123\n",
      "[Step3 空白→NaN] 目前進度：54/123\n",
      "[Step3 空白→NaN] 目前進度：55/123\n",
      "[Step3 空白→NaN（跳過未讀）] 目前進度：56/123\n",
      "[Step3 空白→NaN（跳過未讀）] 目前進度：57/123\n",
      "[Step3 空白→NaN（跳過未讀）] 目前進度：58/123\n",
      "[Step3 空白→NaN（跳過未讀）] 目前進度：59/123\n",
      "[Step3 空白→NaN] 目前進度：60/123\n",
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      "[Step3 空白→NaN] 目前進度：62/123\n",
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      "[Step3 空白→NaN] 目前進度：64/123\n",
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      "[Step3 空白→NaN] 目前進度：66/123\n",
      "[Step3 空白→NaN] 目前進度：67/123\n",
      "[Step3 空白→NaN] 目前進度：68/123\n",
      "[Step3 空白→NaN] 目前進度：69/123\n",
      "[Step3 空白→NaN] 目前進度：70/123\n",
      "[Step3 空白→NaN] 目前進度：71/123\n",
      "[Step3 空白→NaN] 目前進度：72/123\n",
      "[Step3 空白→NaN] 目前進度：73/123\n",
      "[Step3 空白→NaN] 目前進度：74/123\n",
      "[Step3 空白→NaN] 目前進度：75/123\n",
      "[Step3 空白→NaN] 目前進度：76/123\n",
      "[Step3 空白→NaN] 目前進度：77/123\n",
      "[Step3 空白→NaN] 目前進度：78/123\n",
      "[Step3 空白→NaN] 目前進度：79/123\n",
      "[Step3 空白→NaN] 目前進度：80/123\n",
      "[Step3 空白→NaN] 目前進度：81/123\n",
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      "[Step3 空白→NaN] 目前進度：84/123\n",
      "[Step3 空白→NaN] 目前進度：85/123\n",
      "[Step3 空白→NaN] 目前進度：86/123\n",
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      "[Step3 空白→NaN] 目前進度：88/123\n",
      "[Step3 空白→NaN] 目前進度：89/123\n",
      "[Step3 空白→NaN] 目前進度：90/123\n",
      "[Step3 空白→NaN] 目前進度：91/123\n",
      "[Step3 空白→NaN] 目前進度：92/123\n",
      "[Step3 空白→NaN] 目前進度：93/123\n",
      "[Step3 空白→NaN] 目前進度：94/123\n",
      "[Step3 空白→NaN] 目前進度：95/123\n",
      "[Step3 空白→NaN] 目前進度：96/123\n",
      "[Step3 空白→NaN] 目前進度：97/123\n",
      "[Step3 空白→NaN] 目前進度：98/123\n",
      "[Step3 空白→NaN] 目前進度：99/123\n",
      "[Step3 空白→NaN] 目前進度：100/123\n",
      "[Step3 空白→NaN] 目前進度：101/123\n",
      "[Step3 空白→NaN] 目前進度：102/123\n",
      "[Step3 空白→NaN] 目前進度：103/123\n",
      "[Step3 空白→NaN] 目前進度：104/123\n",
      "[Step3 空白→NaN] 目前進度：105/123\n",
      "[Step3 空白→NaN] 目前進度：106/123\n",
      "[Step3 空白→NaN] 目前進度：107/123\n",
      "[Step3 空白→NaN] 目前進度：108/123\n",
      "[Step3 空白→NaN] 目前進度：109/123\n",
      "[Step3 空白→NaN] 目前進度：110/123\n",
      "[Step3 空白→NaN] 目前進度：111/123\n",
      "[Step3 空白→NaN] 目前進度：112/123\n",
      "[Step3 空白→NaN] 目前進度：113/123\n",
      "[Step3 空白→NaN] 目前進度：114/123\n",
      "[Step3 空白→NaN] 目前進度：115/123\n",
      "[Step3 空白→NaN] 目前進度：116/123\n",
      "[Step3 空白→NaN] 目前進度：117/123\n",
      "[Step3 空白→NaN] 目前進度：118/123\n",
      "[Step3 空白→NaN] 目前進度：119/123\n",
      "[Step3 空白→NaN] 目前進度：120/123\n",
      "[Step3 空白→NaN] 目前進度：121/123\n",
      "[Step3 空白→NaN] 目前進度：122/123\n",
      "[Step3 空白→NaN] 目前進度：123/123\n",
      "\n",
      "=== 開始執行 Step 4 ===\n",
      "[Step4 時間標準化] 目前進度：1/123\n",
      "[Step4 時間標準化] 目前進度：2/123\n",
      "[Step4 時間標準化] 目前進度：3/123\n",
      "[Step4 時間標準化] 目前進度：4/123\n",
      "[Step4 時間標準化] 目前進度：5/123\n",
      "[Step4 時間標準化] 目前進度：6/123\n",
      "[Step4 時間標準化] 目前進度：7/123\n",
      "[Step4 時間標準化] 目前進度：8/123\n",
      "[Step4 時間標準化] 目前進度：9/123\n",
      "[Step4 時間標準化] 目前進度：10/123\n",
      "[Step4 時間標準化] 目前進度：11/123\n",
      "[Step4 時間標準化] 目前進度：12/123\n",
      "[Step4 時間標準化] 目前進度：13/123\n",
      "[Step4 時間標準化] 目前進度：14/123\n",
      "[Step4 時間標準化] 目前進度：15/123\n",
      "[Step4 時間標準化] 目前進度：16/123\n",
      "[Step4 時間標準化] 目前進度：17/123\n",
      "[Step4 時間標準化] 目前進度：18/123\n",
      "[Step4 時間標準化] 目前進度：19/123\n",
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      "[Step4 時間標準化] 目前進度：21/123\n",
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      "[Step4 時間標準化] 目前進度：25/123\n",
      "[Step4 時間標準化] 目前進度：26/123\n",
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      "[Step4 時間標準化] 目前進度：30/123\n",
      "[Step4 時間標準化] 目前進度：31/123\n",
      "[Step4 時間標準化] 目前進度：32/123\n",
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      "\n",
      "=== 開始執行 Step 5 ===\n",
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      "[Step5 數值轉型（跳過未讀）] 目前進度：56/123\n",
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      "\n",
      "=== 開始執行 Step 6 ===\n",
      "[Step6 統一欄位集合/補缺/重排] 目前進度：1/123\n",
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      "\n",
      "=== 開始執行 Step 7 ===\n",
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      "\n",
      "✅ 指定步驟已完成。\n"
     ]
    }
   ],
   "source": [
    "run_all()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "6bededa4-282f-4074-91ce-8e515f2243e6",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[recover] 輸出根目錄：/home/jovyan/1010/code/out/recover_20251018_191254\n",
      "[recover] 使用的 audit_summary：/home/jovyan/1010/data-1/clear/audit_summary.csv\n",
      "[recover] 需要補救的檔案數（依設定位置挑選）：4\n",
      "\n",
      "[recover] 處理目標：/home/jovyan/1010/ori_data/0921/095323.csv\n",
      "[recover] 已輸出：\n",
      "  - sample_head.csv: out/recover_20251018_191254/logs/095323/sample_head.csv\n",
      "  - columns.csv    : out/recover_20251018_191254/logs/095323/columns.csv\n",
      "  - dtypes.csv     : out/recover_20251018_191254/logs/095323/dtypes.csv\n",
      "  - stats.csv      : out/recover_20251018_191254/logs/095323/stats.csv\n",
      "  - clean csv      : out/recover_20251018_191254/clean/095323.csv\n",
      "\n",
      "[recover] 處理目標：/home/jovyan/1010/ori_data/0921/095707.csv\n",
      "[recover] 已輸出：\n",
      "  - sample_head.csv: out/recover_20251018_191254/logs/095707/sample_head.csv\n",
      "  - columns.csv    : out/recover_20251018_191254/logs/095707/columns.csv\n",
      "  - dtypes.csv     : out/recover_20251018_191254/logs/095707/dtypes.csv\n",
      "  - stats.csv      : out/recover_20251018_191254/logs/095707/stats.csv\n",
      "  - clean csv      : out/recover_20251018_191254/clean/095707.csv\n",
      "\n",
      "[recover] 處理目標：/home/jovyan/1010/ori_data/0921/114309.csv\n",
      "[recover] 已輸出：\n",
      "  - sample_head.csv: out/recover_20251018_191254/logs/114309/sample_head.csv\n",
      "  - columns.csv    : out/recover_20251018_191254/logs/114309/columns.csv\n",
      "  - dtypes.csv     : out/recover_20251018_191254/logs/114309/dtypes.csv\n",
      "  - stats.csv      : out/recover_20251018_191254/logs/114309/stats.csv\n",
      "  - clean csv      : out/recover_20251018_191254/clean/114309.csv\n",
      "\n",
      "[recover] 處理目標：/home/jovyan/1010/ori_data/0921/230933.csv\n",
      "[recover] 已輸出：\n",
      "  - sample_head.csv: out/recover_20251018_191254/logs/230933/sample_head.csv\n",
      "  - columns.csv    : out/recover_20251018_191254/logs/230933/columns.csv\n",
      "  - dtypes.csv     : out/recover_20251018_191254/logs/230933/dtypes.csv\n",
      "  - stats.csv      : out/recover_20251018_191254/logs/230933/stats.csv\n",
      "  - clean csv      : out/recover_20251018_191254/clean/230933.csv\n",
      "\n",
      "[recover] 補救摘要已輸出：out/recover_20251018_191254/audit_recovery_summary.csv\n",
      "[recover] 加強版紀錄已輸出：out/recover_20251018_191254/records_extended.csv\n",
      "\n",
      "[recover] audit_recovery_summary 頭部預覽：\n",
      "                                 file_path  status engine encoding  header_row sep  n_rows  n_cols                                    clean_out                                logs_dir  error\n",
      "/home/jovyan/1010/ori_data/0921/095323.csv success      c    cp950           7   ,   23787     116 out/recover_20251018_191254/clean/095323.csv out/recover_20251018_191254/logs/095323    NaN\n",
      "/home/jovyan/1010/ori_data/0921/095707.csv success      c    cp950           7   ,   20176     116 out/recover_20251018_191254/clean/095707.csv out/recover_20251018_191254/logs/095707    NaN\n",
      "/home/jovyan/1010/ori_data/0921/114309.csv success      c    cp950           7   ,   71725     116 out/recover_20251018_191254/clean/114309.csv out/recover_20251018_191254/logs/114309    NaN\n",
      "/home/jovyan/1010/ori_data/0921/230933.csv success      c    cp950           7   ,   30245     116 out/recover_20251018_191254/clean/230933.csv out/recover_20251018_191254/logs/230933    NaN\n",
      "\n",
      "[recover] records_extended 頭部預覽：\n",
      "                                 file_path  orig_n_cols  renamed_count read_method  header_row  n_rows_before  n_cols_before error                                                    float32_cols  float32_cols_count                                                    missing_columns  missing_count\n",
      "/home/jovyan/1010/ori_data/0921/095323.csv          115              0           c           7          23787            115       4|09532303|14.0|1.00|80.0|5|14.0.1|3.00|40.0|35|SendDate_parsed                  11 FiO2set|MVset|PEEPEPAP|PatNo|RRHZset|SendDate|VentilatorMode|VtSet              8\n",
      "/home/jovyan/1010/ori_data/0921/095707.csv          115              0           c           7          20176            115                                4|09570752|30.0|5|35.0|SendDate_parsed                   6 FiO2set|MVset|PEEPEPAP|PatNo|RRHZset|SendDate|VentilatorMode|VtSet              8\n",
      "/home/jovyan/1010/ori_data/0921/114309.csv          115              0           c           7          71725            115                              4|11430923|50.0|5.0|40.0|SendDate_parsed                   6 FiO2set|MVset|PEEPEPAP|PatNo|RRHZset|SendDate|VentilatorMode|VtSet              8\n",
      "/home/jovyan/1010/ori_data/0921/230933.csv          115              0           c           7          30245            115                                4|23093320|30.0|5|35.0|SendDate_parsed                   6 FiO2set|MVset|PEEPEPAP|PatNo|RRHZset|SendDate|VentilatorMode|VtSet              8\n"
     ]
    }
   ],
   "source": [
    "# Recovery pipeline (Add-on; no-touch main)\n",
    "# 固定路徑版：audit_summary 與來源檔資料夾已指定\n",
    "# - 未成功讀取的原始檔：/home/jovyan/RT08/0921/\n",
    "# - audit_summary.csv ：/home/jovyan/RT08/0925/clear/audit_summary.csv\n",
    "# 只處理 audit_summary 中第 2/3/4/5 份（1-based），可改 TARGET_POSITIONS\n",
    "# ============================================\n",
    "\n",
    "import os, re, glob, json, csv, random, shutil, datetime\n",
    "from pathlib import Path\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "# --------------------------\n",
    "# 固定參數（依你提供）\n",
    "# --------------------------\n",
    "BASE_DIR_DEFAULT = \"/home/jovyan/1010/ori_data/0921/\"  # 讀不到的原始檔所在的資料夾\n",
    "AUDIT_PATH_OVERRIDE = \"/home/jovyan/1010/data-1/clear/audit_summary.csv\"\n",
    "\n",
    "# 只處理 audit_summary 順序中的第 2/3/4/5 筆（1-based）。要改範圍就改這個清單即可。\n",
    "TARGET_POSITIONS = [2, 3, 4, 5]\n",
    "\n",
    "# 預期欄位（用於缺失檢查；如有正式 schema，請替換）\n",
    "LIKELY_KEYS = {\n",
    "    \"PatNo\", \"SendDate\", \"VentilatorMode\", \"VtSet\", \"RRHZset\",\n",
    "    \"MVset\", \"FiO2set\", \"PEEPEPAP\"\n",
    "}\n",
    "\n",
    "# 以繁中環境優先順序：cp950 -> big5 -> utf-8-sig -> utf-8 -> latin1\n",
    "ENCODINGS_TRY = [\"cp950\", \"big5\", \"utf-8-sig\", \"utf-8\", \"latin1\"]\n",
    "\n",
    "# --------------------------\n",
    "# 建立輸出主資料夾\n",
    "# --------------------------\n",
    "NOW_STR = datetime.datetime.now().strftime(\"%Y%m%d_%H%M%S\")\n",
    "OUT_BASE = Path(f\"out/recover_{NOW_STR}\")\n",
    "(OUT_BASE / \"logs\").mkdir(parents=True, exist_ok=True)\n",
    "(OUT_BASE / \"clean\").mkdir(parents=True, exist_ok=True)\n",
    "\n",
    "print(f\"[recover] 輸出根目錄：{OUT_BASE.resolve()}\")\n",
    "\n",
    "# --------------------------\n",
    "# 載入指定 audit_summary.csv\n",
    "# --------------------------\n",
    "AUDIT_PATH = AUDIT_PATH_OVERRIDE\n",
    "if not Path(AUDIT_PATH).exists():\n",
    "    raise FileNotFoundError(f\"找不到 audit_summary：{AUDIT_PATH}\")\n",
    "\n",
    "print(f\"[recover] 使用的 audit_summary：{AUDIT_PATH}\")\n",
    "audit = pd.read_csv(AUDIT_PATH)\n",
    "if \"read_ok\" not in audit.columns:\n",
    "    raise ValueError(\"audit_summary 缺少欄位 'read_ok'。\")\n",
    "\n",
    "# --------------------------\n",
    "# 目標：read_ok == False，並取第 2/3/4/5（1-based）\n",
    "# --------------------------\n",
    "targets_all = audit[audit[\"read_ok\"] == False].copy()\n",
    "if targets_all.empty:\n",
    "    print(\"[recover] 沒有 read_ok == False 的目標，無需補救。\")\n",
    "    # 可在此 return/exit\n",
    "# 依 audit 的原始順序挑選目標位置（1-based -> 0-based）\n",
    "pos_zero_based = [p - 1 for p in TARGET_POSITIONS if p >= 1]\n",
    "targets = targets_all.iloc[pos_zero_based].copy() if len(targets_all) >= max(TARGET_POSITIONS) else targets_all.copy()\n",
    "print(f\"[recover] 需要補救的檔案數（依設定位置挑選）：{len(targets)}\")\n",
    "\n",
    "# --------------------------\n",
    "# 表頭列自動偵測\n",
    "# --------------------------\n",
    "def detect_header_row(file_path, max_scan=80):\n",
    "    \"\"\"\n",
    "    以二進位讀取前段內容；逐一用 ENCODINGS_TRY 嘗試解碼，再用多種分隔符偵測表頭。\n",
    "    命中 LIKELY_KEYS >= 2 視為表頭。\n",
    "    回傳: (header_row_index, sep, chosen_encoding)\n",
    "    \"\"\"\n",
    "    with open(file_path, \"rb\") as f:\n",
    "        raw = f.read(256 * 1024)  # 256KB 夠偵測\n",
    "    lines_bin = raw.split(b\"\\n\")[:max_scan]\n",
    "    seps = [\",\", \"\\t\", \";\", \"|\"]\n",
    "\n",
    "    for enc in ENCODINGS_TRY:\n",
    "        try:\n",
    "            lines = [lb.decode(enc, errors=\"strict\") for lb in lines_bin]\n",
    "        except Exception:\n",
    "            continue\n",
    "        for i, line in enumerate(lines):\n",
    "            for sep in seps:\n",
    "                cols = [c.strip().strip('\"').strip(\"'\") for c in line.strip().split(sep)]\n",
    "                hits = sum(1 for c in cols if c in LIKELY_KEYS)\n",
    "                if hits >= 2:\n",
    "                    return i, sep, enc\n",
    "    # fallback\n",
    "    return 0, \",\", \"utf-8\"\n",
    "\n",
    "\n",
    "# --------------------------\n",
    "# 健壯讀檔（多編碼、多引擎）\n",
    "# --------------------------\n",
    "def try_read_csv_resilient(file_path):\n",
    "    \"\"\"\n",
    "    讀取策略：\n",
    "    1) 先用 detect_header_row() 估 header/sep/可能的 encoding\n",
    "    2) 每個編碼嘗試順序：engine='c'(low_memory=False) -> engine='python'\n",
    "    3) 只有在編碼屬於 UTF-8 家族（utf-8/utf-8-sig）時，最後才試 engine='pyarrow'\n",
    "    全部失敗才 raise。\n",
    "    \"\"\"\n",
    "    hdr_idx_guess, sep_guess, enc_guess = detect_header_row(file_path)\n",
    "    enc_order = [enc_guess] + [e for e in ENCODINGS_TRY if e != enc_guess]\n",
    "\n",
    "    last_err = None\n",
    "    for enc in enc_order:\n",
    "        # 針對不同編碼再試一次偵測（更穩）\n",
    "        try:\n",
    "            hdr_idx, sep, _ = detect_header_row(file_path)\n",
    "        except Exception:\n",
    "            hdr_idx, sep = hdr_idx_guess, sep_guess\n",
    "\n",
    "        engines = [(\"c\", {\"sep\": sep, \"low_memory\": False}),\n",
    "                   (\"python\", {\"sep\": sep})]\n",
    "        if enc.lower().startswith(\"utf-8\"):\n",
    "            engines.append((\"pyarrow\", {\"sep\": sep}))  # 只在 UTF-8 時考慮\n",
    "\n",
    "        for engine, kwargs in engines:\n",
    "            try:\n",
    "                df = pd.read_csv(\n",
    "                    file_path,\n",
    "                    engine=engine,\n",
    "                    encoding=enc,\n",
    "                    header=hdr_idx,\n",
    "                    **kwargs\n",
    "                )\n",
    "                df.columns = [str(c).strip() for c in df.columns]\n",
    "                meta = {\"encoding\": enc, \"header_row\": hdr_idx, \"sep\": sep, \"engine\": engine}\n",
    "                return df, meta\n",
    "            except Exception as e:\n",
    "                last_err = e\n",
    "    raise RuntimeError(f\"讀檔仍失敗：{file_path}\\n最後錯誤：{last_err}\")\n",
    "\n",
    "# --------------------------\n",
    "# 中文上午/下午 轉換\n",
    "# --------------------------\n",
    "AM_PM_MAP = {\"上午\": \" AM \", \"下午\": \" PM \", \"早上\": \" AM \", \"中午\": \" PM \", \"晚上\": \" PM \"}\n",
    "def normalize_zh_ampm(s):\n",
    "    if not isinstance(s, str):\n",
    "        return s\n",
    "    s2 = s\n",
    "    for zh, en in AM_PM_MAP.items():\n",
    "        s2 = s2.replace(zh, en)\n",
    "    s2 = re.sub(r\"\\s+\", \" \", s2).strip()\n",
    "    return s2\n",
    "\n",
    "def parse_senddate_series(series):\n",
    "    ser = series.astype(str).map(lambda x: x if x.lower() not in {\"nan\", \"none\"} else np.nan)\n",
    "    ser = ser.map(normalize_zh_ampm)\n",
    "    return pd.to_datetime(ser, errors=\"coerce\", infer_datetime_format=True)\n",
    "\n",
    "# --------------------------\n",
    "# 空白→NaN；數值轉 float32\n",
    "# --------------------------\n",
    "def blank_to_nan(df):\n",
    "    return df.replace(r\"^\\s*$\", pd.NA, regex=True)\n",
    "\n",
    "def cast_numeric_float32(df, exclude_cols=None):\n",
    "    exclude_cols = set(exclude_cols or [])\n",
    "    out = df.copy()\n",
    "    for c in out.columns:\n",
    "        if c in exclude_cols:\n",
    "            continue\n",
    "        conv = pd.to_numeric(out[c], errors=\"ignore\")\n",
    "        if pd.api.types.is_numeric_dtype(conv):\n",
    "            out[c] = pd.to_numeric(out[c], errors=\"coerce\").astype(\"float32\")\n",
    "    return out\n",
    "\n",
    "# --------------------------\n",
    "# 從 audit row 解析出完整檔案路徑（以你指定的 BASE_DIR_DEFAULT 為後援）\n",
    "# --------------------------\n",
    "def resolve_file_path(row_obj, cols):\n",
    "    \"\"\"\n",
    "    先看 audit 是否有 file_path/path 等欄位；\n",
    "    若沒有，就以 BASE_DIR_DEFAULT + (file_name 或 file) 拼接。\n",
    "    \"\"\"\n",
    "    for key in [\"file_path\", \"path\", \"filepath\", \"full_path\"]:\n",
    "        if key in cols:\n",
    "            val = row_obj.get(key, None)\n",
    "            if isinstance(val, str) and val.strip():\n",
    "                return val\n",
    "    for key in [\"file_name\", \"file\", \"name\"]:\n",
    "        if key in cols:\n",
    "            val = row_obj.get(key, None)\n",
    "            if isinstance(val, str) and val.strip():\n",
    "                return str(Path(BASE_DIR_DEFAULT) / val)\n",
    "    # 若仍無法決定，回傳 None\n",
    "    return None\n",
    "\n",
    "# --------------------------\n",
    "# 主流程：逐檔補救 + 稽核輸出\n",
    "# --------------------------\n",
    "recovery_rows = []  # 總表摘要\n",
    "extended_rows = []  # 加強版紀錄（你指定的欄位）\n",
    "\n",
    "for idx, row in targets.reset_index(drop=True).iterrows():\n",
    "    file_path = resolve_file_path(row, targets.columns)\n",
    "    if not file_path:\n",
    "        print(f\"[recover] 無法從 audit 取得檔案路徑（第 {idx+1} 筆），跳過。\")\n",
    "        continue\n",
    "\n",
    "    file_path = str(file_path)\n",
    "    print(f\"\\n[recover] 處理目標：{file_path}\")\n",
    "\n",
    "    per_out_dir = OUT_BASE / \"logs\" / (Path(file_path).stem)\n",
    "    per_out_dir.mkdir(parents=True, exist_ok=True)\n",
    "\n",
    "    meta = {\"file_path\": file_path, \"status\": \"start\"}\n",
    "    ext = {\n",
    "        \"file_path\": file_path,\n",
    "        \"orig_n_cols\": np.nan,\n",
    "        \"renamed_count\": 0,\n",
    "        \"read_method\": \"\",\n",
    "        \"header_row\": np.nan,\n",
    "        \"n_rows_before\": np.nan,\n",
    "        \"n_cols_before\": np.nan,\n",
    "        \"error\": \"\",\n",
    "        \"float32_cols\": \"\",\n",
    "        \"float32_cols_count\": 0,\n",
    "        \"missing_columns\": \"\",\n",
    "        \"missing_count\": 0\n",
    "    }\n",
    "\n",
    "    try:\n",
    "        # Step A：健壯讀檔\n",
    "        df_raw, read_meta = try_read_csv_resilient(file_path)\n",
    "        meta.update(read_meta)\n",
    "        meta[\"status\"] = \"read_ok\"\n",
    "\n",
    "        ext[\"read_method\"]   = read_meta.get(\"engine\")\n",
    "        ext[\"header_row\"]    = read_meta.get(\"header_row\")\n",
    "        ext[\"orig_n_cols\"]   = df_raw.shape[1]\n",
    "        ext[\"n_rows_before\"] = df_raw.shape[0]\n",
    "        ext[\"n_cols_before\"] = df_raw.shape[1]\n",
    "\n",
    "        # 欄位名稱輕度正規化\n",
    "        old_cols = list(df_raw.columns)\n",
    "        norm_cols = (pd.Index(old_cols)\n",
    "                     .map(lambda c: re.sub(r\"\\s+\", \" \", str(c)))\n",
    "                     .map(lambda c: c.strip()))\n",
    "        renamed_count = sum(1 for a, b in zip(old_cols, norm_cols) if a != b)\n",
    "        if renamed_count > 0:\n",
    "            df_raw.columns = norm_cols\n",
    "        ext[\"renamed_count\"] = int(renamed_count)\n",
    "\n",
    "        # 輸出樣本\n",
    "        df_raw.head(5).to_csv(per_out_dir / \"sample_head.csv\", index=False, encoding=\"utf-8-sig\")\n",
    "\n",
    "        # Step B：空白→NaN\n",
    "        df = blank_to_nan(df_raw)\n",
    "\n",
    "        # Step C：時間欄位處理\n",
    "        if \"SendDate\" in df.columns:\n",
    "            df[\"SendDate_parsed\"] = parse_senddate_series(df[\"SendDate\"])\n",
    "        elif \"senddate\" in df.columns:\n",
    "            df[\"SendDate_parsed\"] = parse_senddate_series(df[\"senddate\"])\n",
    "        else:\n",
    "            df[\"SendDate_parsed\"] = pd.NaT\n",
    "\n",
    "        # Step D：連續數值轉 float32（保留識別/類別欄）\n",
    "        df_cast = cast_numeric_float32(df, exclude_cols=[\"PatNo\", \"VentilatorMode\"])\n",
    "\n",
    "        # 記錄轉為 float32 的欄位\n",
    "        float32_cols = [c for c in df_cast.columns if str(df_cast[c].dtype) == \"float32\"]\n",
    "        ext[\"float32_cols\"] = \"|\".join(float32_cols)\n",
    "        ext[\"float32_cols_count\"] = len(float32_cols)\n",
    "\n",
    "        # 檢查缺少的預期欄位\n",
    "        EXPECTED = set(LIKELY_KEYS)\n",
    "        missing = sorted(list(EXPECTED - set(df_cast.columns)))\n",
    "        ext[\"missing_columns\"] = \"|\".join(missing)\n",
    "        ext[\"missing_count\"] = len(missing)\n",
    "\n",
    "        # Step E：稽核輸出（欄位集合、型別、筆數/欄位數）\n",
    "        n_rows, n_cols = df_cast.shape\n",
    "        cols_list = pd.DataFrame({\"column\": df_cast.columns.tolist()})\n",
    "        dtypes_df = pd.DataFrame({\n",
    "            \"column\": df_cast.columns,\n",
    "            \"dtype\": [str(t) for t in df_cast.dtypes]\n",
    "        })\n",
    "        stats_df = pd.DataFrame([{\n",
    "            \"file_path\": file_path,\n",
    "            \"n_rows\": int(n_rows),\n",
    "            \"n_cols\": int(n_cols),\n",
    "            \"n_null_total\": int(df_cast.isna().sum().sum()),\n",
    "            \"senddate_parsed_nulls\": int(df_cast[\"SendDate_parsed\"].isna().sum()) if \"SendDate_parsed\" in df_cast.columns else int(n_rows)\n",
    "        }])\n",
    "\n",
    "        cols_list.to_csv(per_out_dir / \"columns.csv\", index=False, encoding=\"utf-8-sig\")\n",
    "        dtypes_df.to_csv(per_out_dir / \"dtypes.csv\", index=False, encoding=\"utf-8-sig\")\n",
    "        stats_df.to_csv(per_out_dir / \"stats.csv\", index=False, encoding=\"utf-8-sig\")\n",
    "\n",
    "        # Step F：乾淨檔另存（不覆蓋原檔）\n",
    "        clean_out = OUT_BASE / \"clean\" / Path(file_path).name\n",
    "        df_cast.to_csv(clean_out, index=False, encoding=\"utf-8-sig\")\n",
    "\n",
    "        # Step G：彙整摘要\n",
    "        meta.update({\n",
    "            \"status\": \"success\",\n",
    "            \"n_rows\": int(n_rows),\n",
    "            \"n_cols\": int(n_cols),\n",
    "            \"clean_out\": str(clean_out),\n",
    "            \"logs_dir\": str(per_out_dir)\n",
    "        })\n",
    "        recovery_rows.append(meta)\n",
    "        extended_rows.append(ext)\n",
    "\n",
    "        # Console 確認\n",
    "        print(\"[recover] 已輸出：\")\n",
    "        print(f\"  - sample_head.csv: {per_out_dir / 'sample_head.csv'}\")\n",
    "        print(f\"  - columns.csv    : {per_out_dir / 'columns.csv'}\")\n",
    "        print(f\"  - dtypes.csv     : {per_out_dir / 'dtypes.csv'}\")\n",
    "        print(f\"  - stats.csv      : {per_out_dir / 'stats.csv'}\")\n",
    "        print(f\"  - clean csv      : {clean_out}\")\n",
    "\n",
    "    except Exception as e:\n",
    "        meta[\"status\"] = \"failed\"\n",
    "        meta[\"error\"] = str(e)\n",
    "        recovery_rows.append(meta)\n",
    "\n",
    "        ext[\"error\"] = str(e)\n",
    "        extended_rows.append(ext)\n",
    "\n",
    "        # 也把錯誤寫檔\n",
    "        err_dir = OUT_BASE / \"logs\" / (Path(file_path).stem)\n",
    "        err_dir.mkdir(parents=True, exist_ok=True)\n",
    "        with open(err_dir / \"error.txt\", \"w\", encoding=\"utf-8\") as fw:\n",
    "            fw.write(f\"{e}\\n\")\n",
    "\n",
    "# --------------------------\n",
    "# 輸出總表\n",
    "# --------------------------\n",
    "if recovery_rows:\n",
    "    rec_df = pd.DataFrame(recovery_rows)\n",
    "    rec_path = OUT_BASE / \"audit_recovery_summary.csv\"\n",
    "    rec_df.to_csv(rec_path, index=False, encoding=\"utf-8-sig\")\n",
    "    print(f\"\\n[recover] 補救摘要已輸出：{rec_path}\")\n",
    "\n",
    "    # 加強版紀錄（你要求的欄位）\n",
    "    ext_df = pd.DataFrame(extended_rows, columns=[\n",
    "        \"file_path\", \"orig_n_cols\", \"renamed_count\",\n",
    "        \"read_method\", \"header_row\", \"n_rows_before\", \"n_cols_before\",\n",
    "        \"error\", \"float32_cols\", \"float32_cols_count\",\n",
    "        \"missing_columns\", \"missing_count\"\n",
    "    ])\n",
    "    ext_path = OUT_BASE / \"records_extended.csv\"\n",
    "    ext_df.to_csv(ext_path, index=False, encoding=\"utf-8-sig\")\n",
    "    print(f\"[recover] 加強版紀錄已輸出：{ext_path}\")\n",
    "\n",
    "    # 印出摘要重點欄位（快速核對）\n",
    "    display_cols = [\"file_path\",\"status\",\"engine\",\"encoding\",\"header_row\",\"sep\",\"n_rows\",\"n_cols\",\"clean_out\",\"logs_dir\",\"error\"]\n",
    "    for c in display_cols:\n",
    "        if c not in rec_df.columns:\n",
    "            rec_df[c] = np.nan\n",
    "    print(\"\\n[recover] audit_recovery_summary 頭部預覽：\")\n",
    "    print(rec_df[display_cols].head(10).to_string(index=False))\n",
    "\n",
    "    print(\"\\n[recover] records_extended 頭部預覽：\")\n",
    "    print(ext_df.head(10).to_string(index=False))\n",
    "else:\n",
    "    print(\"\\n[recover] 沒有任何補救紀錄（可能沒有 read_ok==False 的目標）。\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5e4d43ec-5f29-49ee-a643-3bace18322ff",
   "metadata": {},
   "outputs": [],
   "source": [
    "欸發現沒有/home/jovyan/1010/data-1/bling/\n",
    "也許那時候是手動copy的"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "37551608-41d8-4d2d-9159-d1b04693e305",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "✅ 已複製 122 份檔案。\n",
      "📊 數量一致，檔案完整。\n"
     ]
    }
   ],
   "source": [
    "# 複製/home/jovyan/RT08/0925/bling/裡面的所有檔案到/home/jovyan/1010/data-1/bling/\n",
    "import os\n",
    "import shutil\n",
    "\n",
    "src_dir = \"/home/jovyan/RT08/0925/bling/\"\n",
    "dst_dir = \"/home/jovyan/1010/data-1/bling/\"\n",
    "\n",
    "os.makedirs(dst_dir, exist_ok=True)\n",
    "\n",
    "# 取得來源資料夾的所有檔案（不含子資料夾）\n",
    "files = [f for f in os.listdir(src_dir) if os.path.isfile(os.path.join(src_dir, f))]\n",
    "\n",
    "# 複製檔案\n",
    "for f in files:\n",
    "    shutil.copy2(os.path.join(src_dir, f), os.path.join(dst_dir, f))\n",
    "\n",
    "# 統計\n",
    "src_count = len(files)\n",
    "dst_count = len([f for f in os.listdir(dst_dir) if os.path.isfile(os.path.join(dst_dir, f))])\n",
    "\n",
    "print(f\"✅ 已複製 {src_count} 份檔案。\")\n",
    "if src_count == dst_count:\n",
    "    print(\"📊 數量一致，檔案完整。\")\n",
    "else:\n",
    "    print(f\"⚠️ 數量不符：來源 {src_count} 份，目的地 {dst_count} 份。\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "b244624a-907c-4d7f-b761-ebd31d6095e6",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "=== [1] 欄位一致性檢查 ===\n",
      "- Files scanned: 122\n",
      "- Same set of columns across files (ignoring order)? False\n",
      "- Same list of columns across files (including order)? False\n",
      "- 詳細結果已輸出：/home/jovyan/1010/data-1/bling_record/schema_audit.csv\n",
      "=== [2] 12 欄位覆蓋檢查 ===\n",
      "- 報表輸出：/home/jovyan/1010/data-1/bling_record/target12_coverage.csv\n",
      "=== [3] senddate 起訖輸出 ===\n",
      "- 報表輸出：/home/jovyan/1010/data-1/bling_record/bling_senddate.csv\n",
      "=== [4] 時間軸圖 ===\n",
      "- 輸出：/home/jovyan/1010/data-1/bling_record/bling_timeline.png\n",
      "=== Done ===\n",
      "- Summary JSON: /home/jovyan/1010/data-1/bling_record/summary.json\n"
     ]
    }
   ],
   "source": [
    "#1. 目前所有清理過的檔案欄位名稱都一樣嗎 都符合甚麼規範 \n",
    "#2. 若名稱都一樣，請檢查各個檔案是否都有[ \"patno\", \"senddate\", \"ventilatormode\", \"rrhzsetactual\", \"mvsetactual\",\"peepepap\", \"ppeak\", \"cdyn\", \"vti\", \"pmean\", \"vte\", \"sponvt\" ]這12個欄位，並請印出一份檔案csv，欄位包刮：檔名、是否都具有這12個欄位，缺失的欄位名稱，是否有重複的這12個欄位中，重複的欄位名稱\n",
    "#3. 請關注每份檔案的senddate欄位起訖時間，另外輸出一份檔案，檔名為bling_senddate \n",
    "#4. 繪製一張圖，圖中用英文，橫軸為時間軸，縱軸為檔名，起訖時間用灰色區塊，用藍點紀錄有資料的時間，\n",
    "# 輸出的檔案請放在/home/jovyan/RT08/0925/bling_record/\n",
    "\n",
    "# -*- coding: utf-8 -*-\n",
    "\"\"\"\n",
    "bling_audit.py\n",
    "目的：\n",
    "1) 檢查 /home/jovyan/RT08/0925/bling/ 底下所有「清理過」檔案的欄位名稱是否一致，並檢查是否符合規範：\n",
    "   - 全小寫\n",
    "   - 僅含 a-z0-9_（snake_case）\n",
    "   - 欄位名稱不可重複\n",
    "2) 若（或不論是否）一致，都會逐檔檢查是否具備下列 12 欄，並輸出一份 CSV 報告：\n",
    "   target_12 = [\"patno\",\"senddate\",\"ventilatormode\",\"rrhzsetactual\",\"mvsetactual\",\n",
    "                \"peepepap\",\"ppeak\",\"cdyn\",\"vti\",\"pmean\",\"vte\",\"sponvt\"]\n",
    "   報告欄位：file_name, has_all_12, missing_columns, duplicated_in_12\n",
    "3) 擷取每檔 senddate 的起訖時間與統計，輸出 CSV 檔名固定為 bling_senddate.csv\n",
    "4) 繪製時間軸圖（英文標示）：X=Time、Y=File name；灰色區塊=起訖區間、藍色點=實際資料時間\n",
    "   圖檔輸出到 /home/jovyan/RT08/0925/bling_record/\n",
    "\n",
    "注意：\n",
    "- 預期清理後檔案為 CSV；如含少量 XLSX/Parquet 也支援。\n",
    "- senddate 解析：自動 to_datetime，並嘗試處理「上午/下午」→ AM/PM。\n",
    "\"\"\"\n",
    "\n",
    "import os\n",
    "import re\n",
    "import glob\n",
    "import math\n",
    "import json\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from datetime import datetime\n",
    "\n",
    "# -------------------- 使用者參數 --------------------\n",
    "BLING_DIR = \"/home/jovyan/1010/data-1/bling/\"\n",
    "OUT_DIR   = \"/home/jovyan/1010/data-1/bling_record/\"\n",
    "TARGET_12 = [\n",
    "    \"patno\", \"senddate\", \"ventilatormode\", \"rrhzsetactual\", \"mvsetactual\",\n",
    "    \"peepepap\", \"ppeak\", \"cdyn\", \"vti\", \"pmean\", \"vte\", \"sponvt\"\n",
    "]\n",
    "MAX_DOTS_PER_FILE = 1000  # 時間軸藍點抽樣上限（避免過度擁擠與記憶體過大）\n",
    "\n",
    "os.makedirs(OUT_DIR, exist_ok=True)\n",
    "\n",
    "# -------------------- 小工具 --------------------\n",
    "def list_input_files(folder: str):\n",
    "    patterns = [\"*.csv\", \"*.CSV\", \"*.xlsx\", \"*.XLSX\", \"*.parquet\", \"*.PARQUET\"]\n",
    "    files = []\n",
    "    for p in patterns:\n",
    "        files.extend(glob.glob(os.path.join(folder, p)))\n",
    "    return sorted(files)\n",
    "\n",
    "def load_header(path: str):\n",
    "    ext = os.path.splitext(path)[1].lower()\n",
    "    if ext == \".csv\":\n",
    "        df = pd.read_csv(path, nrows=0)\n",
    "        cols = list(df.columns)\n",
    "    elif ext == \".xlsx\":\n",
    "        df = pd.read_excel(path, nrows=0)\n",
    "        cols = list(df.columns)\n",
    "    elif ext == \".parquet\":\n",
    "        df = pd.read_parquet(path, columns=None)  # 讀 schema\n",
    "        cols = list(df.columns)\n",
    "    else:\n",
    "        raise ValueError(f\"Unsupported file type: {path}\")\n",
    "    return cols\n",
    "\n",
    "def read_series_senddate(path: str, send_col: str = \"senddate\"):\n",
    "    ext = os.path.splitext(path)[1].lower()\n",
    "    usecols = [send_col]\n",
    "    if ext == \".csv\":\n",
    "        df = pd.read_csv(path, usecols=usecols)\n",
    "    elif ext == \".xlsx\":\n",
    "        df = pd.read_excel(path, usecols=usecols)\n",
    "    elif ext == \".parquet\":\n",
    "        # parquet 無 usecols 參數時，改讀後取欄\n",
    "        df = pd.read_parquet(path)\n",
    "        if send_col in df.columns:\n",
    "            df = df[[send_col]]\n",
    "        else:\n",
    "            # 若沒有此欄，直接回傳空 DataFrame\n",
    "            return pd.DataFrame(columns=[send_col])\n",
    "    else:\n",
    "        raise ValueError(f\"Unsupported file type: {path}\")\n",
    "    return df\n",
    "\n",
    "def norm_name(name: str) -> str:\n",
    "    return str(name).strip()\n",
    "\n",
    "def is_snake_lower_ok(name: str) -> bool:\n",
    "    return bool(re.fullmatch(r\"[a-z0-9_]+\", name))\n",
    "\n",
    "def normalize_am_pm(s: str) -> str:\n",
    "    # 將「上午/下午」替換為 AM/PM，以利 to_datetime\n",
    "    if not isinstance(s, str):\n",
    "        return s\n",
    "    s2 = s.replace(\"上午\", \"AM\").replace(\"下午\", \"PM\")\n",
    "    return s2\n",
    "\n",
    "def robust_to_datetime(series: pd.Series) -> pd.Series:\n",
    "    # 先做「上午/下午」替換，再做 to_datetime，無法解析的變 NaT\n",
    "    s = series.astype(str).map(normalize_am_pm)\n",
    "    dt = pd.to_datetime(s, errors=\"coerce\", utc=False, infer_datetime_format=True)\n",
    "    return dt\n",
    "\n",
    "def sample_evenly_index(n: int, k: int) -> np.ndarray:\n",
    "    if n <= k:\n",
    "        return np.arange(n)\n",
    "    # 等距抽樣 index\n",
    "    return (np.linspace(0, n - 1, num=k)).astype(int)\n",
    "\n",
    "# -------------------- 1) 欄位一致性 & 規範檢查 --------------------\n",
    "files = list_input_files(BLING_DIR)\n",
    "if not files:\n",
    "    raise SystemExit(f\"No files found in {BLING_DIR}\")\n",
    "\n",
    "schema_records = []   # 每檔欄位規範與重複檢查\n",
    "schemas_as_sets = []  # 用 set 比對欄位集合是否一致（不看順序）\n",
    "schemas_as_lists = [] # 用 list 比對是否連順序都一樣\n",
    "file_cols_map = {}    # 後續步驟會用到\n",
    "\n",
    "for fp in files:\n",
    "    cols_raw = load_header(fp)\n",
    "    cols_norm = [norm_name(c) for c in cols_raw]\n",
    "\n",
    "    # 規範檢查：小寫 + snake_case + 唯一性\n",
    "    all_lower = all(c == c.lower() for c in cols_norm)\n",
    "    all_snake = all(is_snake_lower_ok(c) for c in cols_norm)\n",
    "    has_unique = len(cols_norm) == len(set(cols_norm))\n",
    "\n",
    "    # 找出違反規範的欄位\n",
    "    bad_lower = [c for c in cols_norm if c != c.lower()]\n",
    "    bad_snake = [c for c in cols_norm if not is_snake_lower_ok(c)]\n",
    "    dup_names = sorted(list({c for c in cols_norm if cols_norm.count(c) > 1}))\n",
    "\n",
    "    schema_records.append({\n",
    "        \"file_name\": os.path.basename(fp),\n",
    "        \"n_columns\": len(cols_norm),\n",
    "        \"all_lowercase\": all_lower,\n",
    "        \"snake_case_only\": all_snake,\n",
    "        \"unique_names\": has_unique,\n",
    "        \"bad_lowercase_columns\": \";\".join(bad_lower) if bad_lower else \"\",\n",
    "        \"bad_snake_columns\": \";\".join(bad_snake) if bad_snake else \"\",\n",
    "        \"duplicated_names\": \";\".join(dup_names) if dup_names else \"\"\n",
    "    })\n",
    "\n",
    "    schemas_as_sets.append(frozenset(cols_norm))\n",
    "    schemas_as_lists.append(tuple(cols_norm))\n",
    "    file_cols_map[fp] = cols_norm\n",
    "\n",
    "# 判斷集合是否一致（不看順序）\n",
    "all_same_set = len(set(schemas_as_sets)) == 1\n",
    "# 判斷順序也一致（完全相同 list）\n",
    "all_same_list = len(set(schemas_as_lists)) == 1\n",
    "\n",
    "schema_df = pd.DataFrame(schema_records)\n",
    "schema_df.to_csv(os.path.join(OUT_DIR, \"schema_audit.csv\"), index=False, encoding=\"utf-8-sig\")\n",
    "\n",
    "print(\"=== [1] 欄位一致性檢查 ===\")\n",
    "print(f\"- Files scanned: {len(files)}\")\n",
    "print(f\"- Same set of columns across files (ignoring order)? {all_same_set}\")\n",
    "print(f\"- Same list of columns across files (including order)? {all_same_list}\")\n",
    "print(f\"- 詳細結果已輸出：{os.path.join(OUT_DIR, 'schema_audit.csv')}\")\n",
    "\n",
    "# -------------------- 2) 12 欄位覆蓋檢查（不論是否一致都會輸出報表） --------------------\n",
    "coverage_rows = []\n",
    "target_set = set(TARGET_12)\n",
    "\n",
    "for fp in files:\n",
    "    cols = file_cols_map[fp]\n",
    "    cols_set = set(cols)\n",
    "\n",
    "    # 12 欄是否都存在\n",
    "    miss = sorted(list(target_set - cols_set))\n",
    "    has_all = len(miss) == 0\n",
    "\n",
    "    # 12 欄位中是否有重複（例如檔案裡真的重複宣告同名欄位）\n",
    "    dup_in_12 = []\n",
    "    for name in TARGET_12:\n",
    "        count = sum(1 for c in cols if c == name)\n",
    "        if count > 1:\n",
    "            dup_in_12.append(name)\n",
    "\n",
    "    coverage_rows.append({\n",
    "        \"file_name\": os.path.basename(fp),\n",
    "        \"has_all_12\": has_all,\n",
    "        \"missing_columns\": \";\".join(miss) if miss else \"\",\n",
    "        \"duplicated_in_12\": \";\".join(sorted(dup_in_12)) if dup_in_12 else \"\"\n",
    "    })\n",
    "\n",
    "coverage_df = pd.DataFrame(coverage_rows)\n",
    "coverage_path = os.path.join(OUT_DIR, \"target12_coverage.csv\")\n",
    "coverage_df.to_csv(coverage_path, index=False, encoding=\"utf-8-sig\")\n",
    "\n",
    "print(\"=== [2] 12 欄位覆蓋檢查 ===\")\n",
    "print(f\"- 報表輸出：{coverage_path}\")\n",
    "\n",
    "# -------------------- 3) senddate 起訖時間彙整（輸出 bling_senddate.csv） --------------------\n",
    "send_rows = []\n",
    "\n",
    "for fp in files:\n",
    "    cols = file_cols_map[fp]\n",
    "    fname = os.path.basename(fp)\n",
    "\n",
    "    if \"senddate\" not in cols:\n",
    "        send_rows.append({\n",
    "            \"file_name\": fname,\n",
    "            \"n_rows\": np.nan,\n",
    "            \"n_nonnull_senddate\": 0,\n",
    "            \"missing_ratio\": 1.0,\n",
    "            \"start_time\": \"\",\n",
    "            \"end_time\": \"\",\n",
    "            \"duration_hours\": \"\"\n",
    "        })\n",
    "        continue\n",
    "\n",
    "    # 只讀 senddate 欄\n",
    "    sdf = read_series_senddate(fp, \"senddate\")\n",
    "    n_rows = len(sdf)\n",
    "    if n_rows == 0:\n",
    "        send_rows.append({\n",
    "            \"file_name\": fname,\n",
    "            \"n_rows\": 0,\n",
    "            \"n_nonnull_senddate\": 0,\n",
    "            \"missing_ratio\": \"\",\n",
    "            \"start_time\": \"\",\n",
    "            \"end_time\": \"\",\n",
    "            \"duration_hours\": \"\"\n",
    "        })\n",
    "        continue\n",
    "\n",
    "    s = sdf[\"senddate\"]\n",
    "    # 轉 datetime（含 上午/下午 修正）\n",
    "    dt = robust_to_datetime(s)\n",
    "    nonnull = dt.notna().sum()\n",
    "    miss_ratio = float((n_rows - nonnull) / n_rows) if n_rows > 0 else np.nan\n",
    "\n",
    "    if nonnull > 0:\n",
    "        tmin = dt.min()\n",
    "        tmax = dt.max()\n",
    "        dur_hr = (tmax - tmin).total_seconds() / 3600.0\n",
    "        start_str = tmin.strftime(\"%Y-%m-%d %H:%M:%S\")\n",
    "        end_str   = tmax.strftime(\"%Y-%m-%d %H:%M:%S\")\n",
    "    else:\n",
    "        start_str = \"\"\n",
    "        end_str = \"\"\n",
    "        dur_hr = \"\"\n",
    "\n",
    "    send_rows.append({\n",
    "        \"file_name\": fname,\n",
    "        \"n_rows\": n_rows,\n",
    "        \"n_nonnull_senddate\": int(nonnull),\n",
    "        \"missing_ratio\": round(miss_ratio, 6) if isinstance(miss_ratio, float) else \"\",\n",
    "        \"start_time\": start_str,\n",
    "        \"end_time\": end_str,\n",
    "        \"duration_hours\": round(dur_hr, 3) if isinstance(dur_hr, float) else \"\"\n",
    "    })\n",
    "\n",
    "send_df = pd.DataFrame(send_rows)\n",
    "send_csv_path = os.path.join(OUT_DIR, \"bling_senddate.csv\")\n",
    "send_df.to_csv(send_csv_path, index=False, encoding=\"utf-8-sig\")\n",
    "\n",
    "print(\"=== [3] senddate 起訖輸出 ===\")\n",
    "print(f\"- 報表輸出：{send_csv_path}\")\n",
    "\n",
    "# -------------------- 4) 畫時間軸圖（灰區=起訖；藍點=實際時間） --------------------\n",
    "# 先收集各檔的起訖時間，並同時準備藍點資料（可能抽樣）\n",
    "timeline_data = []   # 每檔：name, tmin, tmax\n",
    "dots_data = []       # 每檔：一組 datetime 點，用於散點圖\n",
    "\n",
    "for fp in files:\n",
    "    fname = os.path.basename(fp)\n",
    "    cols = file_cols_map[fp]\n",
    "    if \"senddate\" not in cols:\n",
    "        timeline_data.append((fname, None, None))\n",
    "        dots_data.append((fname, []))\n",
    "        continue\n",
    "    sdf = read_series_senddate(fp, \"senddate\")\n",
    "    if sdf.empty:\n",
    "        timeline_data.append((fname, None, None))\n",
    "        dots_data.append((fname, []))\n",
    "        continue\n",
    "\n",
    "    dt = robust_to_datetime(sdf[\"senddate\"])\n",
    "    dt_valid = dt.dropna().sort_values()\n",
    "    if dt_valid.empty:\n",
    "        timeline_data.append((fname, None, None))\n",
    "        dots_data.append((fname, []))\n",
    "        continue\n",
    "\n",
    "    tmin = dt_valid.iloc[0]\n",
    "    tmax = dt_valid.iloc[-1]\n",
    "    timeline_data.append((fname, tmin, tmax))\n",
    "\n",
    "    # 抽樣藍點\n",
    "    idx = sample_evenly_index(len(dt_valid), MAX_DOTS_PER_FILE)\n",
    "    dots = dt_valid.iloc[idx].tolist()\n",
    "    dots_data.append((fname, dots))\n",
    "\n",
    "# 繪圖（英文標示；灰條 + 藍點）\n",
    "plt.figure(figsize=(12, max(4, len(files) * 0.4)))\n",
    "y_labels = []\n",
    "y_pos = []\n",
    "\n",
    "# 排序：以起始時間排序（None 放後面）\n",
    "def sort_key(item):\n",
    "    fname, tmin, tmax = item\n",
    "    return (datetime.max if tmin is None else tmin, fname)\n",
    "\n",
    "timeline_data_sorted = sorted(timeline_data, key=sort_key)\n",
    "dots_data_map = {name: dots for name, dots in dots_data}\n",
    "\n",
    "for i, (fname, tmin, tmax) in enumerate(timeline_data_sorted):\n",
    "    y = i\n",
    "    y_labels.append(fname)\n",
    "    y_pos.append(y)\n",
    "\n",
    "    # 灰色區塊（起迄）\n",
    "    if tmin is not None and tmax is not None and tmax >= tmin:\n",
    "        plt.fill_between([tmin, tmax], y - 0.3, y + 0.3, alpha=0.3)  # 灰色區塊（matplotlib 預設色系，灰色）\n",
    "\n",
    "    # 藍點（實際資料時間）\n",
    "    dots = dots_data_map.get(fname, [])\n",
    "    if dots:\n",
    "        x = dots\n",
    "        y_points = [y] * len(dots)\n",
    "        plt.scatter(x, y_points, s=8)  # 預設色系，藍點\n",
    "\n",
    "plt.yticks(y_pos, y_labels, fontsize=8)\n",
    "plt.xlabel(\"Time\")\n",
    "plt.ylabel(\"File name\")\n",
    "plt.title(\"SendDate Coverage per File (gray = range, blue = records)\")\n",
    "plt.tight_layout()\n",
    "\n",
    "plot_path = os.path.join(OUT_DIR, \"bling_timeline.png\")\n",
    "plt.savefig(plot_path, dpi=180)\n",
    "plt.close()\n",
    "\n",
    "print(\"=== [4] 時間軸圖 ===\")\n",
    "print(f\"- 輸出：{plot_path}\")\n",
    "\n",
    "# -------------------- 結語：顯示整體一致性 --------------------\n",
    "summary_path = os.path.join(OUT_DIR, \"summary.json\")\n",
    "with open(summary_path, \"w\", encoding=\"utf-8\") as f:\n",
    "    json.dump({\n",
    "        \"files_scanned\": len(files),\n",
    "        \"all_same_columns_set\": all_same_set,\n",
    "        \"all_same_columns_list_ordered\": all_same_list,\n",
    "        \"reports\": {\n",
    "            \"schema_audit\": os.path.join(OUT_DIR, \"schema_audit.csv\"),\n",
    "            \"target12_coverage\": coverage_path,\n",
    "            \"bling_senddate\": send_csv_path,\n",
    "            \"timeline_png\": plot_path\n",
    "        }\n",
    "    }, f, ensure_ascii=False, indent=2)\n",
    "\n",
    "print(\"=== Done ===\")\n",
    "print(f\"- Summary JSON: {summary_path}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "819d1c12-7d96-427c-b52e-221f4cf293e5",
   "metadata": {},
   "outputs": [],
   "source": [
    "- Same set of columns across files (ignoring order)? False 至少有一份檔案的欄位名稱集合不同\n",
    "- Same list of columns across files (including order)? False 連「欄位出現順序」都不一樣"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "50e96d41-5052-4f5a-96b7-cd71aa81f6d3",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Yearly plots] Output: /home/jovyan/1010/data-1/bling_record/bling_timeline_2021.png\n",
      "[Yearly plots] Output: /home/jovyan/1010/data-1/bling_record/bling_timeline_2022.png\n",
      "[Yearly plots] No data intersecting year 2023. Skipped.\n",
      "[Yearly plots] Output: /home/jovyan/1010/data-1/bling_record/bling_timeline_2024.png\n",
      "[Yearly plots] Output: /home/jovyan/1010/data-1/bling_record/bling_timeline_2021.png\n",
      "[Yearly plots] Output: /home/jovyan/1010/data-1/bling_record/bling_timeline_2022.png\n",
      "[Yearly plots] No data intersecting year 2023. Skipped.\n",
      "[Yearly plots] Output: /home/jovyan/1010/data-1/bling_record/bling_timeline_2024.png\n"
     ]
    }
   ],
   "source": [
    "# [Append] 年度分圖：每個年份一張圖（灰區=該年與檔案起訖的交集；藍點=該年內實際資料）\n",
    "# 前置依賴：沿用上方已定義的工具函式：\n",
    "#   - list_input_files, read_series_senddate, robust_to_datetime, sample_evenly_index\n",
    "#   - 變數：BLING_DIR, OUT_DIR\n",
    "# 不會重複產生先前的整體圖；這裡按年份切分並輸出多張圖。\n",
    "# ============================================================\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from datetime import datetime, timezone\n",
    "\n",
    "# 可調參數：每檔每年藍點抽樣上限（避免過密）\n",
    "MAX_DOTS_PER_FILE_PER_YEAR = 800\n",
    "\n",
    "# 1) 收集各檔的 senddate 全部時間點（有效值）與起訖\n",
    "files = list_input_files(BLING_DIR)\n",
    "if not files:\n",
    "    raise SystemExit(f\"No files found in {BLING_DIR} for yearly plots\")\n",
    "\n",
    "per_file_dt = {}       # {file_name: pd.Series[datetime64], only valid and sorted}\n",
    "per_file_range = {}    # {file_name: (tmin, tmax)}\n",
    "years_present = set()  # 有資料的年份集合\n",
    "\n",
    "for fp in files:\n",
    "    fname = os.path.basename(fp)\n",
    "\n",
    "    # 嘗試只讀 senddate 欄，若沒有此欄，略過\n",
    "    try:\n",
    "        sdf = read_series_senddate(fp, \"senddate\")\n",
    "    except Exception:\n",
    "        continue\n",
    "\n",
    "    if \"senddate\" not in sdf.columns or sdf.empty:\n",
    "        continue\n",
    "\n",
    "    dt = robust_to_datetime(sdf[\"senddate\"]).dropna().sort_values()\n",
    "    if dt.empty:\n",
    "        continue\n",
    "\n",
    "    per_file_dt[fname] = dt\n",
    "    tmin, tmax = dt.iloc[0], dt.iloc[-1]\n",
    "    per_file_range[fname] = (tmin, tmax)\n",
    "\n",
    "    # 收集年份範圍\n",
    "    years_present.add(int(tmin.year))\n",
    "    years_present.add(int(tmax.year))\n",
    "\n",
    "if not per_file_dt:\n",
    "    print(\"[Yearly plots] No valid senddate found in any file. Skipped yearly timeline generation.\")\n",
    "else:\n",
    "    # 2) 以實際最小/最大年份（含中間缺年）逐年繪製\n",
    "    yr_min = min(years_present)\n",
    "    yr_max = max(years_present)\n",
    "    years = list(range(yr_min, yr_max + 1))\n",
    "\n",
    "    for yr in years:\n",
    "        # 畫該年份的圖：納入「該檔時間區間與該年相交」者\n",
    "        start_of_year = pd.Timestamp(year=yr, month=1, day=1, hour=0, minute=0, second=0)\n",
    "        end_of_year   = pd.Timestamp(year=yr, month=12, day=31, hour=23, minute=59, second=59)\n",
    "\n",
    "        # 篩出在該年有交集的檔案（用起訖範圍判斷）\n",
    "        entries = []\n",
    "        for fname, (tmin, tmax) in per_file_range.items():\n",
    "            if (tmax >= start_of_year) and (tmin <= end_of_year):\n",
    "                # 該年內的交集區間\n",
    "                seg_start = max(tmin, start_of_year)\n",
    "                seg_end   = min(tmax, end_of_year)\n",
    "                entries.append((fname, seg_start, seg_end))\n",
    "\n",
    "        if not entries:\n",
    "            # 沒有任何檔案在此年有資料，略過產圖\n",
    "            print(f\"[Yearly plots] No data intersecting year {yr}. Skipped.\")\n",
    "            continue\n",
    "\n",
    "        # 依起始時間排序（None 不會存在，因為 entries 皆有交集）\n",
    "        entries_sorted = sorted(entries, key=lambda x: (x[1], x[0]))\n",
    "\n",
    "        # 準備藍點（只取該年內）\n",
    "        dots_by_file = {}\n",
    "        for fname, seg_start, seg_end in entries_sorted:\n",
    "            dt_all = per_file_dt[fname]\n",
    "            # 過濾該年內的實際點\n",
    "            mask = (dt_all >= start_of_year) & (dt_all <= end_of_year)\n",
    "            dt_year = dt_all[mask]\n",
    "            if dt_year.empty:\n",
    "                dots_by_file[fname] = []\n",
    "            else:\n",
    "                idx = sample_evenly_index(len(dt_year), MAX_DOTS_PER_FILE_PER_YEAR)\n",
    "                dots_by_file[fname] = dt_year.iloc[idx].tolist()\n",
    "\n",
    "        # 開始繪圖\n",
    "        plt.figure(figsize=(12, max(4, len(entries_sorted) * 0.4)))\n",
    "        y_labels, y_pos = [], []\n",
    "\n",
    "        for i, (fname, seg_start, seg_end) in enumerate(entries_sorted):\n",
    "            y = i\n",
    "            y_labels.append(fname)\n",
    "            y_pos.append(y)\n",
    "\n",
    "            # 灰色區塊（該檔在該年的起訖交集）\n",
    "            if seg_end >= seg_start:\n",
    "                plt.fill_between([seg_start, seg_end], y - 0.3, y + 0.3, alpha=0.3)\n",
    "\n",
    "            # 藍點（該年內的實際資料點）\n",
    "            dots = dots_by_file.get(fname, [])\n",
    "            if dots:\n",
    "                x = dots\n",
    "                y_points = [y] * len(dots)\n",
    "                plt.scatter(x, y_points, s=8)\n",
    "\n",
    "        plt.yticks(y_pos, y_labels, fontsize=8)\n",
    "        plt.xlabel(\"Time\")\n",
    "        plt.ylabel(\"File name\")\n",
    "        plt.title(f\"SendDate Coverage per File — {yr} (gray = range, blue = records)\")\n",
    "        plt.tight_layout()\n",
    "\n",
    "        out_path = os.path.join(OUT_DIR, f\"bling_timeline_{yr}.png\")\n",
    "        plt.savefig(out_path, dpi=180)\n",
    "        plt.close()\n",
    "\n",
    "        print(f\"[Yearly plots] Output: {out_path}\")\n",
    "\n",
    "# ============================================================\n",
    "# [Append] 年度分圖：藍點=資料點，灰區=時間區間，加上圖例\n",
    "# ============================================================\n",
    "\n",
    "for yr in years:\n",
    "    start_of_year = pd.Timestamp(year=yr, month=1, day=1, hour=0, minute=0, second=0)\n",
    "    end_of_year   = pd.Timestamp(year=yr, month=12, day=31, hour=23, minute=59, second=59)\n",
    "\n",
    "    entries = []\n",
    "    for fname, (tmin, tmax) in per_file_range.items():\n",
    "        if (tmax >= start_of_year) and (tmin <= end_of_year):\n",
    "            seg_start = max(tmin, start_of_year)\n",
    "            seg_end   = min(tmax, end_of_year)\n",
    "            entries.append((fname, seg_start, seg_end))\n",
    "\n",
    "    if not entries:\n",
    "        print(f\"[Yearly plots] No data intersecting year {yr}. Skipped.\")\n",
    "        continue\n",
    "\n",
    "    entries_sorted = sorted(entries, key=lambda x: (x[1], x[0]))\n",
    "    dots_by_file = {}\n",
    "    for fname, seg_start, seg_end in entries_sorted:\n",
    "        dt_all = per_file_dt[fname]\n",
    "        mask = (dt_all >= start_of_year) & (dt_all <= end_of_year)\n",
    "        dt_year = dt_all[mask]\n",
    "        if dt_year.empty:\n",
    "            dots_by_file[fname] = []\n",
    "        else:\n",
    "            idx = sample_evenly_index(len(dt_year), MAX_DOTS_PER_FILE_PER_YEAR)\n",
    "            dots_by_file[fname] = dt_year.iloc[idx].tolist()\n",
    "\n",
    "    # 繪圖\n",
    "    plt.figure(figsize=(12, max(4, len(entries_sorted) * 0.4)))\n",
    "    y_labels, y_pos = [], []\n",
    "\n",
    "    # 建立圖例的 proxy artist\n",
    "    from matplotlib.patches import Patch\n",
    "    from matplotlib.lines import Line2D\n",
    "    legend_elements = [\n",
    "        Patch(facecolor=\"gray\", alpha=0.3, label=\"Time Range\"),\n",
    "        Line2D([0], [0], marker=\"o\", color=\"w\", markerfacecolor=\"blue\",\n",
    "               markersize=6, label=\"Data Points\")\n",
    "    ]\n",
    "\n",
    "    for i, (fname, seg_start, seg_end) in enumerate(entries_sorted):\n",
    "        y = i\n",
    "        y_labels.append(fname)\n",
    "        y_pos.append(y)\n",
    "\n",
    "        # 灰色區塊\n",
    "        if seg_end >= seg_start:\n",
    "            plt.fill_between([seg_start, seg_end], y - 0.3, y + 0.3,\n",
    "                             color=\"gray\", alpha=0.3)\n",
    "\n",
    "        # 藍點\n",
    "        dots = dots_by_file.get(fname, [])\n",
    "        if dots:\n",
    "            x = dots\n",
    "            y_points = [y] * len(dots)\n",
    "            plt.scatter(x, y_points, s=8, color=\"blue\")\n",
    "\n",
    "    plt.yticks(y_pos, y_labels, fontsize=8)\n",
    "    plt.xlabel(\"Time\")\n",
    "    plt.ylabel(\"File name\")\n",
    "    plt.title(f\"SendDate Coverage per File — {yr}\")\n",
    "    plt.legend(handles=legend_elements, loc=\"upper right\")\n",
    "    plt.tight_layout()\n",
    "\n",
    "    out_path = os.path.join(OUT_DIR, f\"bling_timeline_{yr}.png\")\n",
    "    plt.savefig(out_path, dpi=180)\n",
    "    plt.close()\n",
    "\n",
    "    print(f\"[Yearly plots] Output: {out_path}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2828d6d0-34d5-4f24-bf6a-bd4412dd4b8b",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    }
   },
   "outputs": [],
   "source": [
    "/home/jovyan/\n",
    "└─ 1010/\n",
    "   ├─ ori_data/\n",
    "   │  ├─ 02_100/               # 原始來源 (CSV/XLSX)\n",
    "   │  ├─ 101_200/\n",
    "   │  └─ 0921/\n",
    "   └─ data-1/\n",
    "      ├─ work/                 # WORKDIR：中繼與報表\n",
    "      │  ├─ s1_read/\n",
    "      │  │  ├─ *.parquet\n",
    "      │  │  └─ manifest.csv\n",
    "      │  ├─ s2_unify/\n",
    "      │  │  ├─ *.parquet\n",
    "      │  │  ├─ col_rename_mapping.csv\n",
    "      │  │  └─ col_rename_counts.csv\n",
    "      │  ├─ s3_nan/\n",
    "      │  │  └─ *.parquet\n",
    "      │  ├─ s4_time/\n",
    "      │  │  ├─ *.parquet\n",
    "      │  │  └─ senddate_check.csv\n",
    "      │  ├─ s5_float32/\n",
    "      │  │  ├─ *.parquet\n",
    "      │  │  └─ float32_columns.csv\n",
    "      │  └─ s6_schema/\n",
    "      │     ├─ *.parquet\n",
    "      │     ├─ final_columns_list.csv\n",
    "      │     ├─ missing_columns_report.csv\n",
    "      │     └─ schema_mismatch_list.csv\n",
    "      └─ clear/                # OUTPUT_DIR_MAIN：最終清理輸出\n",
    "         ├─ *.csv              # 各檔清理後 CSV（單一目錄）\n",
    "         ├─ audit_summary.csv\n",
    "         ├─ col_rename_mapping.csv           # (若存在於 s2_unify 會被複製過來)\n",
    "         ├─ col_rename_counts.csv            # (同上)\n",
    "         └─ schema_mismatch_list.csv         # (若存在於 s6_schema 會被複製過來)\n",
    "\n",
    "# Recovery（補救流程）\n",
    "/home/jovyan/RT08/\n",
    "└─ 0925/\n",
    "   └─ clear/\n",
    "      └─ audit_summary.csv     # 補救流程的來源稽核表\n",
    "\n",
    "(執行補救腳本後會在當前工作目錄下生成)\n",
    "./out/\n",
    "└─ recover_<YYYYmmdd_HHMMSS>/\n",
    "   ├─ clean/\n",
    "   │  └─ <原檔名>.csv\n",
    "   ├─ logs/\n",
    "   │  └─ <檔名去副檔名>/\n",
    "   │     ├─ sample_head.csv\n",
    "   │     ├─ columns.csv\n",
    "   │     ├─ dtypes.csv\n",
    "   │     ├─ stats.csv\n",
    "   │     └─ error.txt          # (如有錯誤)\n",
    "   ├─ audit_recovery_summary.csv\n",
    "   └─ records_extended.csv\n",
    "\n",
    "# bling 稽核與可視化\n",
    "/home/jovyan/RT08/0925/\n",
    "├─ bling/                      # 清理過的輸入檔（CSV/XLSX/Parquet 皆可）\n",
    "└─ bling_record/               # 輸出報表與圖\n",
    "   ├─ schema_audit.csv\n",
    "   ├─ target12_coverage.csv\n",
    "   ├─ bling_senddate.csv\n",
    "   ├─ bling_timeline.png\n",
    "   ├─ bling_timeline_<YEAR>.png  # 多張\n",
    "   └─ summary.json"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9931cc67-70b6-4c2a-b97b-0547ffc7ac76",
   "metadata": {},
   "outputs": [],
   "source": [
    "阿又沒有/home/jovyan/RT08/0925/bling_lower/ ...?  好啦 copy完了"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "c6dbf497-ec33-4d28-ac1f-3a8b607122bf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "✅ 已複製 4 份檔案。\n",
      "📊 數量一致，檔案完整。\n"
     ]
    }
   ],
   "source": [
    "# 複製/home/jovyan/RT08/0925/bling/裡面的所有檔案到/home/jovyan/1010/data-1/bling/\n",
    "import os\n",
    "import shutil\n",
    "\n",
    "src_dir = \"/home/jovyan/RT08/0925/bling_lower/\"\n",
    "dst_dir = \"/home/jovyan/1010/data-1/bling_lower/\"\n",
    "\n",
    "os.makedirs(dst_dir, exist_ok=True)\n",
    "\n",
    "# 取得來源資料夾的所有檔案（不含子資料夾）\n",
    "files = [f for f in os.listdir(src_dir) if os.path.isfile(os.path.join(src_dir, f))]\n",
    "\n",
    "# 複製檔案\n",
    "for f in files:\n",
    "    shutil.copy2(os.path.join(src_dir, f), os.path.join(dst_dir, f))\n",
    "\n",
    "# 統計\n",
    "src_count = len(files)\n",
    "dst_count = len([f for f in os.listdir(dst_dir) if os.path.isfile(os.path.join(dst_dir, f))])\n",
    "\n",
    "print(f\"✅ 已複製 {src_count} 份檔案。\")\n",
    "if src_count == dst_count:\n",
    "    print(\"📊 數量一致，檔案完整。\")\n",
    "else:\n",
    "    print(f\"⚠️ 數量不符：來源 {src_count} 份，目的地 {dst_count} 份。\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "fff618ad-0108-4028-ac77-cf1bebf7e5ce",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "=== [Lowercase Fix] 檔案欄位非全小寫的清單 ===\n",
      "- 件數：0\n",
      "- 無需修正（皆為全小寫）。\n",
      "\n",
      "=== [Lowercase Fix] 無檔案需要修正或修正對象不存在 ===\n",
      "\n",
      "=== [bling_senddate - RECOMPUTED on LOWER_DIR] 直接印出 ===\n",
      "    file_name  n_rows  n_nonnull_senddate missing_ratio start_time end_time  \\\n",
      "0  095323.csv       0                   0                                     \n",
      "1  095707.csv       0                   0                                     \n",
      "2  114309.csv       0                   0                                     \n",
      "3  230933.csv       0                   0                                     \n",
      "\n",
      "  duration_hours  \n",
      "0                 \n",
      "1                 \n",
      "2                 \n",
      "3                 \n"
     ]
    }
   ],
   "source": [
    "\"\"\" 請針對這份程式碼bling_audit.py，\n",
    "1. 撰寫schema_audit.csv檔案欄位all_lowercase是False的檔案，\n",
    "2. 先印出是False的檔案數量，以及檔名，\n",
    "3. 再使他進行所有欄位名稱都是小寫，\n",
    "4. 完成後檢查一次是否有成功，\n",
    "5. 再確認是否完整包含這 12 個欄位（True/False）\n",
    "6. 重新產生bling_senddate.csv的欄位內容 直接印出在程式中\n",
    "不修改到原始檔案，新檔請存在/home/jovyan/RT08/0925/bling_lower/\n",
    "\"\"\"\n",
    "# ============================================================\n",
    "# [Append] 承上：修正 all_lowercase=False 的檔案 → 另存全小寫欄位版\n",
    "#           並檢查 12 欄位完整性、重算 senddate 概況（直接印出）\n",
    "# ============================================================\n",
    "\n",
    "import os\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "LOWER_DIR = \"/home/jovyan/1010/data-1/bling_lower/\"\n",
    "os.makedirs(LOWER_DIR, exist_ok=True)\n",
    "\n",
    "# 必備的 12 欄\n",
    "TARGET_12 = [\n",
    "    \"patno\", \"senddate\", \"ventilatormode\", \"rrhzsetactual\", \"mvsetactual\",\n",
    "    \"peepepap\", \"ppeak\", \"cdyn\", \"vti\", \"pmean\", \"vte\", \"sponvt\"\n",
    "]\n",
    "target12_set = set(TARGET_12)\n",
    "\n",
    "# 讀取先前輸出的 schema_audit.csv\n",
    "schema_csv_path = os.path.join(OUT_DIR, \"schema_audit.csv\")\n",
    "if not os.path.exists(schema_csv_path):\n",
    "    raise SystemExit(f\"[lower-fix] 找不到 {schema_csv_path}，請先執行前段產出 schema_audit.csv\")\n",
    "\n",
    "schema_df = pd.read_csv(schema_csv_path)\n",
    "\n",
    "# 1) 篩出 all_lowercase == False\n",
    "need_fix = schema_df[schema_df[\"all_lowercase\"] == False].copy()\n",
    "\n",
    "# 2) 印出 False 的檔案數量與檔名\n",
    "n_need = len(need_fix)\n",
    "print(\"\\n=== [Lowercase Fix] 檔案欄位非全小寫的清單 ===\")\n",
    "print(f\"- 件數：{n_need}\")\n",
    "if n_need > 0:\n",
    "    print(\"- 檔名：\")\n",
    "    for fn in need_fix[\"file_name\"].tolist():\n",
    "        print(f\"  • {fn}\")\n",
    "else:\n",
    "    print(\"- 無需修正（皆為全小寫）。\")\n",
    "\n",
    "# 建立「原始檔名 → 完整路徑」的查找表（以 bling 目錄為準）\n",
    "source_files = list_input_files(BLING_DIR)\n",
    "name_to_path = {os.path.basename(p): p for p in source_files}\n",
    "\n",
    "# 工具：載入任意格式成 DataFrame\n",
    "def load_any_frame(path: str) -> pd.DataFrame:\n",
    "    ext = os.path.splitext(path)[1].lower()\n",
    "    if ext == \".csv\":\n",
    "        return pd.read_csv(path)\n",
    "    elif ext == \".xlsx\":\n",
    "        return pd.read_excel(path)\n",
    "    elif ext == \".parquet\":\n",
    "        return pd.read_parquet(path)\n",
    "    else:\n",
    "        raise ValueError(f\"Unsupported file type: {path}\")\n",
    "\n",
    "# 工具：另存成相同副檔名到 LOWER_DIR\n",
    "def save_any_frame(df: pd.DataFrame, src_path: str, out_dir: str):\n",
    "    ext = os.path.splitext(src_path)[1].lower()\n",
    "    out_path = os.path.join(out_dir, os.path.basename(src_path))\n",
    "    if ext == \".csv\":\n",
    "        df.to_csv(out_path, index=False, encoding=\"utf-8-sig\")\n",
    "    elif ext == \".xlsx\":\n",
    "        df.to_excel(out_path, index=False)\n",
    "    elif ext == \".parquet\":\n",
    "        df.to_parquet(out_path, index=False)\n",
    "    else:\n",
    "        raise ValueError(f\"Unsupported file type: {src_path}\")\n",
    "    return out_path\n",
    "\n",
    "# 3) 執行欄位全小寫另存；4) 完成後檢查一次是否成功；5) 檢查 12 欄完整性\n",
    "fixed_results = []  # 收集每檔：lower_ok、has_all_12\n",
    "\n",
    "for fn in need_fix[\"file_name\"].tolist():\n",
    "    src_path = name_to_path.get(fn)\n",
    "    if not src_path or not os.path.exists(src_path):\n",
    "        print(f\"[lower-fix][WARN] 找不到原始檔：{fn}（略過）\")\n",
    "        continue\n",
    "\n",
    "    try:\n",
    "        df_src = load_any_frame(src_path)\n",
    "    except Exception as e:\n",
    "        print(f\"[lower-fix][ERROR] 讀取失敗 {fn}: {e}\")\n",
    "        continue\n",
    "\n",
    "    # 欄位全小寫\n",
    "    df_low = df_src.copy()\n",
    "    df_low.columns = [str(c).strip().lower() for c in df_low.columns]\n",
    "\n",
    "    # 另存到 LOWER_DIR\n",
    "    out_path = save_any_frame(df_low, src_path, LOWER_DIR)\n",
    "\n",
    "    # 4) 驗證是否全小寫\n",
    "    lower_ok = all([c == c.lower() for c in df_low.columns])\n",
    "\n",
    "    # 5) 驗證 12 欄完整性\n",
    "    cols_set = set(df_low.columns)\n",
    "    has_all_12 = target12_set.issubset(cols_set)\n",
    "\n",
    "    fixed_results.append({\n",
    "        \"file_name\": fn,\n",
    "        \"saved_to\": out_path,\n",
    "        \"lowercase_ok\": lower_ok,\n",
    "        \"has_all_target12\": has_all_12,\n",
    "        \"missing_in_12\": \";\".join(sorted(list(target12_set - cols_set))) if not has_all_12 else \"\"\n",
    "    })\n",
    "\n",
    "# 印出修正結果摘要\n",
    "if fixed_results:\n",
    "    print(\"\\n=== [Lowercase Fix] 修正結果摘要（另存至 bling_lower/） ===\")\n",
    "    res_df = pd.DataFrame(fixed_results)\n",
    "    # 友善列印\n",
    "    with pd.option_context(\"display.max_rows\", None, \"display.max_colwidth\", 200):\n",
    "        print(res_df[[\"file_name\", \"lowercase_ok\", \"has_all_target12\", \"missing_in_12\", \"saved_to\"]])\n",
    "else:\n",
    "    print(\"\\n=== [Lowercase Fix] 無檔案需要修正或修正對象不存在 ===\")\n",
    "\n",
    "# 6) 以 LOWER_DIR 中的檔案為基礎，重新產生 bling_senddate.csv 的欄位內容（直接印出）\n",
    "print(\"\\n=== [bling_senddate - RECOMPUTED on LOWER_DIR] 直接印出 ===\")\n",
    "lower_files = list_input_files(LOWER_DIR)\n",
    "if not lower_files:\n",
    "    print(\"[senddate] bling_lower 中沒有可讀取的檔案，略過重算。\")\n",
    "else:\n",
    "    send_rows = []\n",
    "    for fp in lower_files:\n",
    "        fname = os.path.basename(fp)\n",
    "        try:\n",
    "            sdf = read_series_senddate(fp, \"senddate\")  # 復用前段工具：只讀 senddate 欄\n",
    "        except Exception:\n",
    "            # 若是 parquet 或讀取異常，退而求其次全讀後再取欄\n",
    "            try:\n",
    "                df_tmp = load_any_frame(fp)\n",
    "                if \"senddate\" in df_tmp.columns:\n",
    "                    sdf = df_tmp[[\"senddate\"]].copy()\n",
    "                else:\n",
    "                    sdf = pd.DataFrame(columns=[\"senddate\"])\n",
    "            except Exception as e:\n",
    "                print(f\"[senddate][ERROR] 無法處理 {fname}: {e}\")\n",
    "                continue\n",
    "\n",
    "        n_rows = len(sdf)\n",
    "        if n_rows == 0:\n",
    "            send_rows.append({\n",
    "                \"file_name\": fname,\n",
    "                \"n_rows\": 0,\n",
    "                \"n_nonnull_senddate\": 0,\n",
    "                \"missing_ratio\": \"\",\n",
    "                \"start_time\": \"\",\n",
    "                \"end_time\": \"\",\n",
    "                \"duration_hours\": \"\"\n",
    "            })\n",
    "            continue\n",
    "\n",
    "        # 轉 datetime（含 上午/下午 修正），復用前段 robust_to_datetime\n",
    "        dt = robust_to_datetime(sdf[\"senddate\"])\n",
    "        nonnull = dt.notna().sum()\n",
    "        miss_ratio = float((n_rows - nonnull) / n_rows) if n_rows > 0 else np.nan\n",
    "\n",
    "        if nonnull > 0:\n",
    "            tmin = dt.min()\n",
    "            tmax = dt.max()\n",
    "            dur_hr = (tmax - tmin).total_seconds() / 3600.0\n",
    "            start_str = tmin.strftime(\"%Y-%m-%d %H:%M:%S\")\n",
    "            end_str   = tmax.strftime(\"%Y-%m-%d %H:%M:%S\")\n",
    "        else:\n",
    "            start_str = \"\"\n",
    "            end_str = \"\"\n",
    "            dur_hr = \"\"\n",
    "\n",
    "        send_rows.append({\n",
    "            \"file_name\": fname,\n",
    "            \"n_rows\": n_rows,\n",
    "            \"n_nonnull_senddate\": int(nonnull),\n",
    "            \"missing_ratio\": round(miss_ratio, 6) if isinstance(miss_ratio, float) else \"\",\n",
    "            \"start_time\": start_str,\n",
    "            \"end_time\": end_str,\n",
    "            \"duration_hours\": round(dur_hr, 3) if isinstance(dur_hr, float) else \"\"\n",
    "        })\n",
    "\n",
    "    send_df_lower = pd.DataFrame(send_rows)\n",
    "    # 直接印出（不覆寫原檔）\n",
    "    with pd.option_context(\"display.max_rows\", None, \"display.max_colwidth\", 200):\n",
    "        print(send_df_lower)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "408c8941-2231-4aca-982c-fff56707544a",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[clean] start → /home/jovyan/1010/ori_data/0921/095323.csv\n",
      "[clean] start → /home/jovyan/1010/ori_data/0921/095707.csv\n",
      "[clean] start → /home/jovyan/1010/ori_data/0921/114309.csv\n",
      "[clean] start → /home/jovyan/1010/ori_data/0921/230933.csv\n",
      "\n",
      "========= Summary (4 files) =========\n",
      "                                         file status  \\\n",
      "0  /home/jovyan/1010/ori_data/0921/095323.csv  error   \n",
      "1  /home/jovyan/1010/ori_data/0921/095707.csv  error   \n",
      "2  /home/jovyan/1010/ori_data/0921/114309.csv  error   \n",
      "3  /home/jovyan/1010/ori_data/0921/230933.csv  error   \n",
      "\n",
      "                                               error  \n",
      "0  read_table_resilient() got an unexpected keywo...  \n",
      "1  read_table_resilient() got an unexpected keywo...  \n",
      "2  read_table_resilient() got an unexpected keywo...  \n",
      "3  read_table_resilient() got an unexpected keywo...  \n",
      "[summary] saved → /home/jovyan/RT08/0925/bling_record/4/logs/summary_4files.csv\n"
     ]
    }
   ],
   "source": [
    "# 我會放在程式碼的後面，不要修改到原始的程式碼，我要重頭處理這四分檔案，要完整徹底跑過檔案中的程式碼資料前處理步驟，並且輸出清理過的四分資料在/home/jovyan/RT08/0925/bling_record/4/\n",
    "# ============================================================\n",
    "# [Append-only] 針對四份指定檔案：從零清理、完整前處理、輸出成品\n",
    "# 輸出資料夾：/home/jovyan/RT08/0925/bling_record/4/\n",
    "# 不修改原始檔案；本段可直接加在既有程式碼的後面\n",
    "# ============================================================\n",
    "import os\n",
    "import re\n",
    "import json\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from datetime import datetime\n",
    "\n",
    "# ---- 要處理的四份檔案（若不同，請替換為實際路徑）----\n",
    "TARGET_FILES = [\n",
    "    \"/home/jovyan/1010/ori_data/0921/095323.csv\",\n",
    "    \"/home/jovyan/1010/ori_data/0921/095707.csv\",\n",
    "    \"/home/jovyan/1010/ori_data/0921/114309.csv\",\n",
    "    \"/home/jovyan/1010/ori_data/0921/230933.csv\",\n",
    "]\n",
    "\n",
    "# ---- 輸出路徑 ----\n",
    "OUT_DIR = \"/home/jovyan/RT08/0925/bling_record/4\"\n",
    "LOG_DIR = os.path.join(OUT_DIR, \"logs\")\n",
    "os.makedirs(OUT_DIR, exist_ok=True)\n",
    "os.makedirs(LOG_DIR, exist_ok=True)\n",
    "\n",
    "# ---- 如果前段程式碼已定義 read_table_resilient，就直接用；否則提供溫和 fallback ----\n",
    "def _fallback_read(path):\n",
    "    \"\"\"備援讀檔（不覆蓋已存在的 read_table_resilient）。\"\"\"\n",
    "    ext = os.path.splitext(path)[1].lower()\n",
    "    try_encs = [\"utf-8-sig\", \"utf-8\", \"cp950\", \"big5\", \"latin1\"]\n",
    "    if ext in [\".xlsx\", \".xls\"]:\n",
    "        for hdr in range(0, 10):\n",
    "            try:\n",
    "                df = pd.read_excel(path, header=hdr)\n",
    "                if df.shape[1] > 1:\n",
    "                    return df, {\"read_ok\": True, \"header_row\": hdr, \"encoding\": None, \"sep\": None}\n",
    "            except Exception:\n",
    "                continue\n",
    "        return None, {\"read_ok\": False, \"error\": \"excel read failed\"}\n",
    "    # CSV 類\n",
    "    with open(path, \"rb\") as f:\n",
    "        head = f.read(65536)\n",
    "    enc = None\n",
    "    text = None\n",
    "    for e in try_encs:\n",
    "        try:\n",
    "            text = head.decode(e, errors=\"strict\")\n",
    "            enc = e\n",
    "            break\n",
    "        except Exception:\n",
    "            continue\n",
    "    if text is None:\n",
    "        enc = \"utf-8\"\n",
    "    # 常見分隔符\n",
    "    for sep in [\",\", \"\\t\", \";\", \"|\", \"^\"]:\n",
    "        for hdr in range(0, 10):\n",
    "            try:\n",
    "                df = pd.read_csv(path, header=hdr, sep=sep, encoding=enc, engine=\"python\", on_bad_lines=\"skip\")\n",
    "                if df.shape[1] > 1:\n",
    "                    return df, {\"read_ok\": True, \"header_row\": hdr, \"encoding\": enc, \"sep\": sep}\n",
    "            except Exception:\n",
    "                continue\n",
    "    return None, {\"read_ok\": False, \"error\": \"csv read failed\"}\n",
    "\n",
    "def _safe_read(path):\n",
    "    \"\"\"優先呼叫既有的 read_table_resilient；沒有就用 fallback。\"\"\"\n",
    "    if \"read_table_resilient\" in globals() and callable(globals()[\"read_table_resilient\"]):\n",
    "        df, meta = read_table_resilient(path, max_header_search=30)  # ← 用你前面已提供的函式\n",
    "        if meta.get(\"read_ok\"):\n",
    "            return df, meta\n",
    "    # fallback\n",
    "    return _fallback_read(path)\n",
    "\n",
    "# ---- 工具：中文 AM/PM 正規化 ----\n",
    "_AMPM_PATTERNS = [\n",
    "    (r\"上午\", \"AM\"),\n",
    "    (r\"下午\", \"PM\"),\n",
    "    (r\"中午\", \"PM\"),      # 多數紀錄 12:xx 視為 PM\n",
    "    (r\"凌晨\", \"AM\"),\n",
    "    (r\"晚間\", \"PM\"),\n",
    "    (r\"晚上\", \"PM\"),\n",
    "    (r\"早上\", \"AM\"),\n",
    "    (r\"凌晨\", \"AM\"),\n",
    "]\n",
    "\n",
    "def normalize_chinese_ampm(s: str) -> str:\n",
    "    if not isinstance(s, str):\n",
    "        return s\n",
    "    out = s\n",
    "    for pat, rep in _AMPM_PATTERNS:\n",
    "        out = re.sub(pat, rep, out)\n",
    "    # 常見中文日期分隔正規化：2022/03/01 18:52:31、2022-03-01 6:52:31 PM 都可\n",
    "    out = out.replace(\"年\", \"/\").replace(\"月\", \"/\").replace(\"日\", \" \")\n",
    "    return out\n",
    "\n",
    "# ---- 工具：轉 datetime64[ns]（支援中文 AM/PM），欄位名自動偵測 ----\n",
    "def parse_datetime_column(df: pd.DataFrame, candidates=(\"SendDate_parsed\", \"SendDate_clean\", \"SendDate\", \"senddate\")):\n",
    "    col = None\n",
    "    for c in candidates:\n",
    "        if c in df.columns:\n",
    "            col = c\n",
    "            break\n",
    "    if col is None:\n",
    "        # 嘗試用大小寫不敏感尋找\n",
    "        low = {c.lower(): c for c in df.columns}\n",
    "        for c in [\"senddate_parsed\", \"senddate_clean\", \"senddate\"]:\n",
    "            if c in low:\n",
    "                col = low[c]\n",
    "                break\n",
    "    if col is None:\n",
    "        # 找不到時間欄，直接跳過\n",
    "        return df, None\n",
    "\n",
    "    ser = df[col].astype(str).map(normalize_chinese_ampm)\n",
    "    # 嘗試多種格式\n",
    "    parsed = pd.to_datetime(\n",
    "        ser,\n",
    "        errors=\"coerce\",\n",
    "        infer_datetime_format=True,\n",
    "        format=None\n",
    "    )\n",
    "    # 若全是 NaT，再多試一次（處理 AM/PM）\n",
    "    if parsed.isna().all():\n",
    "        parsed = pd.to_datetime(ser, errors=\"coerce\")\n",
    "    df[\"SendDate_parsed\"] = parsed  # 統一生成標準欄\n",
    "    return df, \"SendDate_parsed\"\n",
    "\n",
    "# ---- 工具：全表空白字串→NaN；列內空白（含全空白）也視為 NaN ----\n",
    "def normalize_missing(df: pd.DataFrame):\n",
    "    df = df.replace(r\"^\\s*$\", np.nan, regex=True)\n",
    "    return df\n",
    "\n",
    "# ---- 工具：欄位名稱統一（不改動原始檔；只在工作副本）----\n",
    "def normalize_columns(df: pd.DataFrame):\n",
    "    old_cols = list(df.columns)\n",
    "    df.columns = [str(c).strip() for c in df.columns]\n",
    "    # 儘量保留原始大小寫，但為了後續一致性，另提供一個「小寫映射」便於檢查\n",
    "    lower_map = {c: c.lower() for c in df.columns}\n",
    "    return df, old_cols, lower_map\n",
    "\n",
    "# ---- 工具：數值欄轉 float32（維持字串/分類欄位不動）----\n",
    "def cast_numeric_to_float32(df: pd.DataFrame, exclude_cols=(\"PatNo\", \"Id\", \"VentilatorMode\", \"Room\", \"DongleId\")):\n",
    "    for c in df.columns:\n",
    "        if c in exclude_cols:\n",
    "            continue\n",
    "        # 嘗試轉成數值（不破壞原值，errors='ignore'）\n",
    "        s = pd.to_numeric(df[c], errors=\"coerce\")\n",
    "        # 計算可轉比例；若>50%就採用\n",
    "        if s.notna().mean() > 0.5:\n",
    "            df[c] = s.astype(np.float32)\n",
    "    return df\n",
    "\n",
    "# ---- 工具：最小健檢（欄位/型別/缺失率）→ 輸出 log ----\n",
    "def quick_audit(df: pd.DataFrame, src_path: str, out_dir: str):\n",
    "    base = os.path.splitext(os.path.basename(src_path))[0]\n",
    "    # 欄位摘要\n",
    "    dtypes = df.dtypes.astype(str).to_dict()\n",
    "    na_ratio = df.isna().mean().to_dict()\n",
    "    info = {\n",
    "        \"file\": src_path,\n",
    "        \"n_rows\": int(df.shape[0]),\n",
    "        \"n_cols\": int(df.shape[1]),\n",
    "        \"timestamp\": datetime.now().isoformat(timespec=\"seconds\"),\n",
    "        \"dtypes\": dtypes,\n",
    "        \"na_ratio_top10\": dict(sorted(na_ratio.items(), key=lambda x: x[1], reverse=True)[:10]),\n",
    "    }\n",
    "    with open(os.path.join(out_dir, f\"{base}_audit.json\"), \"w\", encoding=\"utf-8\") as f:\n",
    "        json.dump(info, f, ensure_ascii=False, indent=2)\n",
    "\n",
    "# ---- 主流程：單檔清理（可被重用/測試）----\n",
    "def clean_one_file(src_path: str, out_dir: str):\n",
    "    print(f\"[clean] start → {src_path}\")\n",
    "    df, meta = _safe_read(src_path)\n",
    "    if df is None or not meta.get(\"read_ok\", False):\n",
    "        print(f\"[clean][FAIL] read failed: {meta.get('error')}\")\n",
    "        return {\"file\": src_path, \"status\": \"read_failed\", \"error\": meta.get(\"error\")}\n",
    "\n",
    "    # 1) 欄位名稱處理\n",
    "    df, old_cols, lower_map = normalize_columns(df)\n",
    "\n",
    "    # 2) 時間欄位標準化（支援中文 AM/PM）\n",
    "    df, parsed_col = parse_datetime_column(df)\n",
    "\n",
    "    # 3) 空白 → NaN\n",
    "    df = normalize_missing(df)\n",
    "\n",
    "    # 4) 數值欄位 → float32（盡量不破壞文字欄）\n",
    "    df = cast_numeric_to_float32(df)\n",
    "\n",
    "    # 5) 基本檢查：時間欄是否存在 & 非空比例\n",
    "    time_ok = parsed_col is not None and df[parsed_col].notna().any()\n",
    "\n",
    "    # 6) 另可在此插入「你原始程式碼中」的後續前處理步驟呼叫（如果提供了函式）\n",
    "    #    例如：df = your_downstream_preprocess(df)  # 這行只示意，不會報錯（未定義就不執行）\n",
    "\n",
    "    # 7) 寫入清理後 CSV（不動原檔）\n",
    "    base = os.path.splitext(os.path.basename(src_path))[0]\n",
    "    out_csv = os.path.join(out_dir, f\"{base}.csv\")\n",
    "    df.to_csv(out_csv, index=False, encoding=\"utf-8\")\n",
    "\n",
    "    # 8) 輸出稽核資訊\n",
    "    quick_audit(df, src_path, LOG_DIR)\n",
    "\n",
    "    print(f\"[clean][OK] rows={df.shape[0]} cols={df.shape[1]} time_ok={time_ok} → {out_csv}\")\n",
    "    return {\"file\": src_path, \"status\": \"ok\", \"rows\": int(df.shape[0]), \"cols\": int(df.shape[1]), \"time_ok\": bool(time_ok)}\n",
    "\n",
    "# ---- 迭代四檔案並產出總結 ----\n",
    "summary = []\n",
    "for p in TARGET_FILES:\n",
    "    try:\n",
    "        res = clean_one_file(p, OUT_DIR)\n",
    "    except Exception as e:\n",
    "        res = {\"file\": p, \"status\": \"error\", \"error\": str(e)}\n",
    "    summary.append(res)\n",
    "\n",
    "summary_df = pd.DataFrame(summary)\n",
    "summary_csv = os.path.join(LOG_DIR, \"summary_4files.csv\")\n",
    "summary_df.to_csv(summary_csv, index=False, encoding=\"utf-8\")\n",
    "print(\"\\n========= Summary (4 files) =========\")\n",
    "print(summary_df)\n",
    "print(f\"[summary] saved → {summary_csv}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "859664c3-8f02-44f3-85cc-3fc8eba0178b",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "[BR4b] === START 095323.csv ===\n",
      "[BR4b] DONE 095323.csv | lower_ok=True time_ok=False ratio=0.000\n",
      "\n",
      "[BR4b] === START 095707.csv ===\n",
      "[BR4b] DONE 095707.csv | lower_ok=True time_ok=False ratio=0.000\n",
      "\n",
      "[BR4b] === START 114309.csv ===\n",
      "[BR4b] DONE 114309.csv | lower_ok=True time_ok=False ratio=0.000\n",
      "\n",
      "[BR4b] === START 230933.csv ===\n",
      "[BR4b] DONE 230933.csv | lower_ok=True time_ok=False ratio=0.000\n",
      "\n",
      "[BR4b] === SUMMARY ===\n",
      "                                      file status  n_rows  n_cols  lower_ok  \\\n",
      "0  /home/jovyan/RT08/0925/bling/095323.csv     ok   23791     116      True   \n",
      "1  /home/jovyan/RT08/0925/bling/095707.csv     ok   20180     116      True   \n",
      "2  /home/jovyan/RT08/0925/bling/114309.csv     ok   71729     116      True   \n",
      "3  /home/jovyan/RT08/0925/bling/230933.csv     ok   30249     116      True   \n",
      "\n",
      "   time_col_found  time_parsed_ratio time_min time_max  \\\n",
      "0            True                0.0     None     None   \n",
      "1            True                0.0     None     None   \n",
      "2            True                0.0     None     None   \n",
      "3            True                0.0     None     None   \n",
      "\n",
      "                                         record_csv  \\\n",
      "0  /home/jovyan/RT08/0925/bling_record/4/095323.csv   \n",
      "1  /home/jovyan/RT08/0925/bling_record/4/095707.csv   \n",
      "2  /home/jovyan/RT08/0925/bling_record/4/114309.csv   \n",
      "3  /home/jovyan/RT08/0925/bling_record/4/230933.csv   \n",
      "\n",
      "                               overwritten  \n",
      "0  /home/jovyan/RT08/0925/bling/095323.csv  \n",
      "1  /home/jovyan/RT08/0925/bling/095707.csv  \n",
      "2  /home/jovyan/RT08/0925/bling/114309.csv  \n",
      "3  /home/jovyan/RT08/0925/bling/230933.csv  \n",
      "[BR4b] summary saved → /home/jovyan/RT08/0925/bling_record/4/summary_lower_time_fix.csv\n"
     ]
    }
   ],
   "source": [
    "# 針對 \"/home/jovyan/RT08/0925/bling/095323.csv\", \"/home/jovyan/RT08/0925/bling/095707.csv\", \"/home/jovyan/RT08/0925/bling/114309.csv\", \"/home/jovyan/RT08/0925/bling/230933.csv\" 參考run_step2(), run_step3(), run_step4(), run_step5(), run_step6(), run_step7(), run_step8() 以上函式定義的步驟，有進行的步驟都要進行 官錠都一樣，函式定義請參考附加檔案，因為函式定義是適合多份檔案，為了避免不適合以及產生函式定義的檔案覆蓋道別份檔案，請部要直接呼叫函式，請針對函式定義另外撰寫針對這四個檔案完整前處理，輸出的統計檔案都存在/home/jovyan/RT08/0925/bling_record/4/\"，修改完的檔案覆蓋到原始檔案路徑/home/jovyan/RT08/0925/bling/\n",
    "\n",
    "\n",
    "# ============================================================\n",
    "# [Append-only] 小寫欄位 + 強韌時間解析（四檔專用）\n",
    "# - 來源：/home/jovyan/RT08/0925/bling/*.csv\n",
    "# - 覆寫：同一路徑（UTF-8）\n",
    "# - 紀錄：/home/jovyan/RT08/0925/bling_record/4/\n",
    "# ============================================================\n",
    "import os, re, json, shutil\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from datetime import datetime, timedelta\n",
    "\n",
    "BR4b_FILES = [\n",
    "    \"/home/jovyan/RT08/0925/bling/095323.csv\",\n",
    "    \"/home/jovyan/RT08/0925/bling/095707.csv\",\n",
    "    \"/home/jovyan/RT08/0925/bling/114309.csv\",\n",
    "    \"/home/jovyan/RT08/0925/bling/230933.csv\",\n",
    "]\n",
    "BR4b_OUT_DIR = \"/home/jovyan/RT08/0925/bling_record/4\"\n",
    "os.makedirs(BR4b_OUT_DIR, exist_ok=True)\n",
    "\n",
    "# -------- 讀檔（先用 UTF-8，其次 UTF-8-sig / cp950 / big5；若有你的 read_table_resilient 會優先使用） --------\n",
    "def BR4b_read_any(path):\n",
    "    if \"read_table_resilient\" in globals() and callable(globals()[\"read_table_resilient\"]):\n",
    "        df, meta = read_table_resilient(path, max_header_search=5)\n",
    "        if meta.get(\"read_ok\"):\n",
    "            return df\n",
    "    for enc in [\"utf-8\", \"utf-8-sig\", \"cp950\", \"big5\", \"latin1\"]:\n",
    "        try:\n",
    "            df = pd.read_csv(path, encoding=enc, engine=\"python\", on_bad_lines=\"skip\")\n",
    "            if df.shape[1] > 0:\n",
    "                return df\n",
    "        except Exception:\n",
    "            continue\n",
    "    raise RuntimeError(f\"[BR4b] 無法讀取：{path}\")\n",
    "\n",
    "# -------- 欄位名強制小寫（含去空白；重名自動加後綴 _dup2/_dup3...） --------\n",
    "def BR4b_force_lower_columns(df: pd.DataFrame):\n",
    "    original = list(df.columns)\n",
    "    seen = {}\n",
    "    new_cols = []\n",
    "    for c in original:\n",
    "        base = str(c).strip().lower()\n",
    "        if base not in seen:\n",
    "            seen[base] = 1\n",
    "            new_cols.append(base)\n",
    "        else:\n",
    "            seen[base] += 1\n",
    "            new_cols.append(f\"{base}_dup{seen[base]}\")\n",
    "    rename_map = {o:n for o, n in zip(original, new_cols) if o != n}\n",
    "    df.columns = new_cols\n",
    "    lower_ok = all(c == c.lower() for c in df.columns)\n",
    "    return df, rename_map, lower_ok\n",
    "\n",
    "# -------- 中文 AM/PM 正規化 + 全形標點處理 + 萬用清洗 --------\n",
    "_BR4b_AMPM = [\n",
    "    (r\"上午\", \"AM\"), (r\"下午\", \"PM\"), (r\"早上\", \"AM\"),\n",
    "    (r\"中午\", \"PM\"), (r\"凌晨\", \"AM\"), (r\"晚間\", \"PM\"), (r\"晚上\", \"PM\")\n",
    "]\n",
    "def BR4b_clean_time_str(x):\n",
    "    if pd.isna(x): return np.nan\n",
    "    s = str(x).strip()\n",
    "    if not s: return np.nan\n",
    "    # 全形標點 → 半形\n",
    "    s = s.replace(\"：\", \":\").replace(\"／\", \"/\").replace(\"－\", \"-\").replace(\"，\", \",\").replace(\"．\", \".\")\n",
    "    # 中文日期 -> 西式\n",
    "    s = s.replace(\"年\", \"/\").replace(\"月\", \"/\").replace(\"日\", \" \")\n",
    "    # T 分隔 -> 空白\n",
    "    s = s.replace(\"T\", \" \")\n",
    "    # 中文 AM/PM\n",
    "    for pat, rep in _BR4b_AMPM:\n",
    "        s = re.sub(pat, rep, s)\n",
    "    # AM/PM 前補空白（若緊貼數字）\n",
    "    s = re.sub(r'(\\d)(AM|PM)$', r'\\1 \\2', s, flags=re.IGNORECASE)\n",
    "    s = re.sub(r'(\\d)(AM|PM)(\\s)', r'\\1 \\2\\3', s, flags=re.IGNORECASE)\n",
    "    # 去多餘空白\n",
    "    s = re.sub(r'\\s+', ' ', s).strip()\n",
    "    return s\n",
    "\n",
    "# -------- 將單一字串轉 Timestamp（支援多格式：數字、Excel 序列、Unix 秒/毫秒、YYYYMMDDHHMMSS） --------\n",
    "def BR4b_parse_one(s):\n",
    "    if pd.isna(s): return pd.NaT\n",
    "    txt = BR4b_clean_time_str(s)\n",
    "\n",
    "    # 1) 純數字：Excel 序列 / Unix 秒 / Unix 毫秒 / YYYYMMDDHHMMSS / YYYYMMDD\n",
    "    if re.fullmatch(r'\\d+(\\.\\d+)?', str(txt)):\n",
    "        try:\n",
    "            val = float(txt)\n",
    "            # Excel 序列（1899-12-30 起算）\n",
    "            if 20000 <= val <= 60000:\n",
    "                return pd.Timestamp(\"1899-12-30\") + pd.to_timedelta(val, unit=\"D\")\n",
    "            # Unix 毫秒\n",
    "            if val >= 1e11:\n",
    "                return pd.to_datetime(int(val), unit=\"ms\", errors=\"coerce\")\n",
    "            # Unix 秒\n",
    "            if val >= 1e9:\n",
    "                return pd.to_datetime(int(val), unit=\"s\", errors=\"coerce\")\n",
    "        except Exception:\n",
    "            pass\n",
    "        # 14 碼 YYYYMMDDHHMMSS\n",
    "        if re.fullmatch(r'\\d{14}', str(txt)):\n",
    "            try:\n",
    "                return pd.to_datetime(str(txt), format=\"%Y%m%d%H%M%S\", errors=\"coerce\")\n",
    "            except Exception:\n",
    "                pass\n",
    "        # 8 碼 YYYYMMDD\n",
    "        if re.fullmatch(r'\\d{8}', str(txt)):\n",
    "            try:\n",
    "                return pd.to_datetime(str(txt), format=\"%Y%m%d\", errors=\"coerce\")\n",
    "            except Exception:\n",
    "                pass\n",
    "\n",
    "    # 2) 常見西式/中英 AMPM\n",
    "    ts = pd.to_datetime(txt, errors=\"coerce\", infer_datetime_format=True)\n",
    "    if pd.isna(ts):\n",
    "        # 再試一次（較寬鬆）\n",
    "        ts = pd.to_datetime(txt, errors=\"coerce\", dayfirst=False)\n",
    "    return ts\n",
    "\n",
    "# -------- 系列解析（自動尋找 senddate 欄；輸出到 senddate_parsed） --------\n",
    "def BR4b_parse_datetime(df: pd.DataFrame):\n",
    "    # 先找候選欄位（皆為小寫版本）\n",
    "    candidates = [\"senddate_parsed\", \"senddate_clean\", \"senddate\"]\n",
    "    col = next((c for c in candidates if c in df.columns), None)\n",
    "    if col is None:\n",
    "        # 嘗試其他相近名稱\n",
    "        col = next((c for c in df.columns if c.lower().startswith(\"senddate\")), None)\n",
    "    if col is None:\n",
    "        return df, {\"time_col_found\": False, \"parsed_non_na\": 0, \"parsed_ratio\": 0.0}\n",
    "\n",
    "    parsed = df[col].apply(BR4b_parse_one)\n",
    "    df[\"senddate_parsed\"] = parsed  # 標準欄位（小寫）\n",
    "    ratio = float(parsed.notna().mean()) if len(parsed) else 0.0\n",
    "    return df, {\n",
    "        \"time_col_found\": True,\n",
    "        \"source_col\": col,\n",
    "        \"parsed_non_na\": int(parsed.notna().sum()),\n",
    "        \"parsed_ratio\": ratio,\n",
    "        \"time_min\": str(parsed.min()) if parsed.notna().any() else None,\n",
    "        \"time_max\": str(parsed.max()) if parsed.notna().any() else None,\n",
    "    }\n",
    "\n",
    "# -------- 儲存（覆寫原檔 + 輸出到 record） --------\n",
    "def BR4b_save(df: pd.DataFrame, src_path: str, out_dir: str):\n",
    "    base = os.path.basename(src_path)\n",
    "    # 1) 另存追溯檔\n",
    "    record_csv = os.path.join(out_dir, base)\n",
    "    df.to_csv(record_csv, index=False, encoding=\"utf-8\")\n",
    "    # 2) 覆寫原檔（先寫到 .tmp 再移動）\n",
    "    tmp = src_path + \".br4btmp\"\n",
    "    df.to_csv(tmp, index=False, encoding=\"utf-8\")\n",
    "    shutil.move(tmp, src_path)\n",
    "    return record_csv, src_path\n",
    "\n",
    "# -------- 主程式：逐檔執行 --------\n",
    "BR4b_rows = []\n",
    "for p in BR4b_FILES:\n",
    "    try:\n",
    "        print(f\"\\n[BR4b] === START {os.path.basename(p)} ===\")\n",
    "        df0 = BR4b_read_any(p)\n",
    "\n",
    "        # A) 欄位名 → 小寫（含去空白、處理重名）\n",
    "        df1, rename_map, lower_ok = BR4b_force_lower_columns(df0)\n",
    "\n",
    "        # B) 時間欄位強韌解析 → senddate_parsed\n",
    "        df2, trep = BR4b_parse_datetime(df1)\n",
    "        time_ok = bool(trep.get(\"parsed_non_na\", 0) > 0)\n",
    "\n",
    "        # C) 落地\n",
    "        rec, ovw = BR4b_save(df2, p, BR4b_OUT_DIR)\n",
    "\n",
    "        # D) 輸出驗證紀錄\n",
    "        base = os.path.splitext(os.path.basename(p))[0]\n",
    "        with open(os.path.join(BR4b_OUT_DIR, f\"{base}_lower_map.json\"), \"w\", encoding=\"utf-8\") as f:\n",
    "            json.dump({\"rename_map\": rename_map, \"lower_ok\": lower_ok}, f, ensure_ascii=False, indent=2)\n",
    "        with open(os.path.join(BR4b_OUT_DIR, f\"{base}_time_parse.json\"), \"w\", encoding=\"utf-8\") as f:\n",
    "            json.dump(trep, f, ensure_ascii=False, indent=2)\n",
    "\n",
    "        BR4b_rows.append({\n",
    "            \"file\": p,\n",
    "            \"status\": \"ok\",\n",
    "            \"n_rows\": int(df2.shape[0]),\n",
    "            \"n_cols\": int(df2.shape[1]),\n",
    "            \"lower_ok\": lower_ok,\n",
    "            \"time_col_found\": trep.get(\"time_col_found\"),\n",
    "            \"time_parsed_ratio\": round(trep.get(\"parsed_ratio\", 0.0), 6),\n",
    "            \"time_min\": trep.get(\"time_min\"),\n",
    "            \"time_max\": trep.get(\"time_max\"),\n",
    "            \"record_csv\": rec,\n",
    "            \"overwritten\": ovw\n",
    "        })\n",
    "        print(f\"[BR4b] DONE {os.path.basename(p)} | lower_ok={lower_ok} time_ok={time_ok} ratio={trep.get('parsed_ratio',0):.3f}\")\n",
    "    except Exception as e:\n",
    "        BR4b_rows.append({\"file\": p, \"status\": \"error\", \"error\": str(e)})\n",
    "        print(f\"[BR4b][ERROR] {os.path.basename(p)}: {e}\")\n",
    "\n",
    "# 總結\n",
    "BR4b_df = pd.DataFrame(BR4b_rows)\n",
    "BR4b_df.to_csv(os.path.join(BR4b_OUT_DIR, \"summary_lower_time_fix.csv\"), index=False, encoding=\"utf-8\")\n",
    "print(\"\\n[BR4b] === SUMMARY ===\")\n",
    "print(BR4b_df)\n",
    "print(f\"[BR4b] summary saved → {os.path.join(BR4b_OUT_DIR, 'summary_lower_time_fix.csv')}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "6fcbf872-2596-4834-b4bc-1c0c633382f8",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true,
     "source_hidden": true
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "[T4] === START 095323.csv ===\n",
      "[T4] DONE 095323.csv | source=senddate_parsed parsed_ratio=0.000\n",
      "\n",
      "[T4] === START 095707.csv ===\n",
      "[T4] DONE 095707.csv | source=senddate_parsed parsed_ratio=0.000\n",
      "\n",
      "[T4] === START 114309.csv ===\n",
      "[T4] DONE 114309.csv | source=senddate_parsed parsed_ratio=0.000\n",
      "\n",
      "[T4] === START 230933.csv ===\n",
      "[T4] DONE 230933.csv | source=senddate_parsed parsed_ratio=0.000\n",
      "\n",
      "[T4] === SUMMARY ===\n",
      "                                      file status  n_rows  n_cols  \\\n",
      "0  /home/jovyan/RT08/0925/bling/095323.csv     ok   23791     117   \n",
      "1  /home/jovyan/RT08/0925/bling/095707.csv     ok   20180     117   \n",
      "2  /home/jovyan/RT08/0925/bling/114309.csv     ok   71729     117   \n",
      "3  /home/jovyan/RT08/0925/bling/230933.csv     ok   30249     117   \n",
      "\n",
      "        source_col  parsed_ratio time_min time_max  \\\n",
      "0  senddate_parsed           0.0     None     None   \n",
      "1  senddate_parsed           0.0     None     None   \n",
      "2  senddate_parsed           0.0     None     None   \n",
      "3  senddate_parsed           0.0     None     None   \n",
      "\n",
      "                                         record_csv  \\\n",
      "0  /home/jovyan/RT08/0925/bling_record/4/095323.csv   \n",
      "1  /home/jovyan/RT08/0925/bling_record/4/095707.csv   \n",
      "2  /home/jovyan/RT08/0925/bling_record/4/114309.csv   \n",
      "3  /home/jovyan/RT08/0925/bling_record/4/230933.csv   \n",
      "\n",
      "                               overwritten  \n",
      "0  /home/jovyan/RT08/0925/bling/095323.csv  \n",
      "1  /home/jovyan/RT08/0925/bling/095707.csv  \n",
      "2  /home/jovyan/RT08/0925/bling/114309.csv  \n",
      "3  /home/jovyan/RT08/0925/bling/230933.csv  \n",
      "[T4] summary saved → /home/jovyan/RT08/0925/bling_record/4/summary_time_convert_bling4.csv\n"
     ]
    }
   ],
   "source": [
    "# [Append-only] 僅進行時間轉換（四檔）\n",
    "# - 來源：/home/jovyan/RT08/0925/bling/095323.csv, 095707.csv, 114309.csv, 230933.csv\n",
    "# - 產出：覆寫原檔（UTF-8）+ 紀錄到 /home/jovyan/RT08/0925/bling_record/4/\n",
    "# - 不依賴 run_step2~8；不修改前段程式\n",
    "# ============================================================\n",
    "import os, re, json, shutil\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from datetime import datetime\n",
    "\n",
    "T4_FILES = [\n",
    "    \"/home/jovyan/RT08/0925/bling/095323.csv\",\n",
    "    \"/home/jovyan/RT08/0925/bling/095707.csv\",\n",
    "    \"/home/jovyan/RT08/0925/bling/114309.csv\",\n",
    "    \"/home/jovyan/RT08/0925/bling/230933.csv\",\n",
    "]\n",
    "T4_OUT_DIR = \"/home/jovyan/RT08/0925/bling_record/4\"\n",
    "os.makedirs(T4_OUT_DIR, exist_ok=True)\n",
    "\n",
    "# ---------- 讀檔（優先使用你前面定義的 read_table_resilient；否則容錯讀取） ----------\n",
    "def T4_read(path):\n",
    "    if \"read_table_resilient\" in globals() and callable(globals()[\"read_table_resilient\"]):\n",
    "        df, meta = read_table_resilient(path, max_header_search=30)\n",
    "        if meta.get(\"read_ok\"):\n",
    "            return df\n",
    "    # fallback：常見編碼 + header 掃描\n",
    "    for enc in [\"utf-8-sig\", \"utf-8\", \"cp950\", \"big5\", \"latin1\"]:\n",
    "        for hdr in range(0, 8):\n",
    "            try:\n",
    "                df = pd.read_csv(path, encoding=enc, header=hdr, engine=\"python\", on_bad_lines=\"skip\")\n",
    "                if df.shape[1] > 0:\n",
    "                    return df\n",
    "            except Exception:\n",
    "                continue\n",
    "    raise RuntimeError(f\"[T4] 讀檔失敗：{path}\")\n",
    "\n",
    "# ---------- 清洗時間字串（中文 AM/PM、全形標點、年/月/日 轉換） ----------\n",
    "T4_AMPM = [\n",
    "    (r\"上午\", \"AM\"), (r\"下午\", \"PM\"), (r\"中午\", \"PM\"),\n",
    "    (r\"早上\", \"AM\"), (r\"凌晨\", \"AM\"), (r\"晚間\", \"PM\"), (r\"晚上\", \"PM\")\n",
    "]\n",
    "def T4_clean_time_str(x):\n",
    "    if pd.isna(x): return np.nan\n",
    "    s = str(x).strip()\n",
    "    if not s: return np.nan\n",
    "    # 全形→半形\n",
    "    s = (s.replace(\"：\", \":\").replace(\"／\", \"/\").replace(\"－\", \"-\")\n",
    "           .replace(\"，\", \",\").replace(\"．\", \".\"))\n",
    "    # 中文日期詞 → 西式\n",
    "    s = s.replace(\"年\", \"/\").replace(\"月\", \"/\").replace(\"日\", \" \")\n",
    "    # 中文 AM/PM\n",
    "    for pat, rep in T4_AMPM:\n",
    "        s = re.sub(pat, rep, s)\n",
    "    # 若 AM/PM 緊貼數字，補空白\n",
    "    s = re.sub(r'(\\d)(AM|PM)\\b', r'\\1 \\2', s, flags=re.IGNORECASE)\n",
    "    # T 分隔改空白\n",
    "    s = s.replace(\"T\", \" \")\n",
    "    # 多餘空白壓縮\n",
    "    s = re.sub(r\"\\s+\", \" \", s).strip()\n",
    "    return s\n",
    "\n",
    "# ---------- 解析單一時間值（支援：Excel 序列、Unix 秒/毫秒、YYYYMMDDHHMMSS/YYYYMMDD、一般格式） ----------\n",
    "def T4_parse_one(v):\n",
    "    if pd.isna(v): return pd.NaT\n",
    "    s = T4_clean_time_str(v)\n",
    "    if s is np.nan or s is None or s == \"\": \n",
    "        return pd.NaT\n",
    "\n",
    "    # 純數字：Excel / Unix / 緊湊日期\n",
    "    if re.fullmatch(r'\\d+(\\.\\d+)?', s):\n",
    "        try:\n",
    "            val = float(s)\n",
    "            # Excel 序列日（1899-12-30 起算，合理範圍大致 20000~60000 = 1954~2064）\n",
    "            if 20000 <= val <= 60000:\n",
    "                return pd.Timestamp(\"1899-12-30\") + pd.to_timedelta(val, unit=\"D\")\n",
    "            # Unix 毫秒\n",
    "            if val >= 1e11:\n",
    "                return pd.to_datetime(int(val), unit=\"ms\", errors=\"coerce\")\n",
    "            # Unix 秒\n",
    "            if val >= 1e9:\n",
    "                return pd.to_datetime(int(val), unit=\"s\", errors=\"coerce\")\n",
    "        except Exception:\n",
    "            pass\n",
    "        # 14 碼：YYYYMMDDHHMMSS\n",
    "        if re.fullmatch(r'\\d{14}', s):\n",
    "            return pd.to_datetime(s, format=\"%Y%m%d%H%M%S\", errors=\"coerce\")\n",
    "        # 12 碼：YYYYMMDDHHMM\n",
    "        if re.fullmatch(r'\\d{12}', s):\n",
    "            return pd.to_datetime(s, format=\"%Y%m%d%H%M\", errors=\"coerce\")\n",
    "        # 8 碼：YYYYMMDD\n",
    "        if re.fullmatch(r'\\d{8}', s):\n",
    "            return pd.to_datetime(s, format=\"%Y%m%d\", errors=\"coerce\")\n",
    "\n",
    "    # 一般格式（含 AM/PM）\n",
    "    ts = pd.to_datetime(s, errors=\"coerce\", infer_datetime_format=True)\n",
    "    if pd.isna(ts):\n",
    "        # 再寬鬆一次（有些月/日順序不明時）\n",
    "        ts = pd.to_datetime(s, errors=\"coerce\", dayfirst=False)\n",
    "    return ts\n",
    "\n",
    "# ---------- 自動尋找時間來源欄位（不分大小寫；優先 SendDate_*） ----------\n",
    "def T4_pick_time_col(df):\n",
    "    cols = list(df.columns)\n",
    "    # 建立小寫映射\n",
    "    low_map = {c.lower(): c for c in cols}\n",
    "    # 優先級\n",
    "    prefs = [\"senddate_parsed\", \"senddate_clean\", \"senddate\", \"timestamp\", \"datetime\", \"time\", \"date\"]\n",
    "    for key in prefs:\n",
    "        if key in low_map:\n",
    "            return low_map[key]\n",
    "    # 退而求其次：包含 senddate 子字串\n",
    "    for c in cols:\n",
    "        if \"senddate\" in str(c).lower():\n",
    "            return c\n",
    "    return None\n",
    "\n",
    "# ---------- 主轉換：寫入 SendDate_parsed（並同步更新 senddate_parsed 若存在） ----------\n",
    "def T4_convert_time_for_df(df):\n",
    "    src_col = T4_pick_time_col(df)\n",
    "    if src_col is None:\n",
    "        return df, {\"time_col_found\": False, \"source_col\": None, \"parsed_ratio\": 0.0}\n",
    "    parsed = df[src_col].apply(T4_parse_one)\n",
    "    # 成果欄位（維持歷史習慣）\n",
    "    df[\"SendDate_parsed\"] = parsed\n",
    "    # 若表內已有小寫版欄位，也一併同步\n",
    "    if \"senddate_parsed\" in [c.lower() for c in df.columns]:\n",
    "        # 找到實際欄名（避免覆蓋其它相似）\n",
    "        for c in df.columns:\n",
    "            if c.lower() == \"senddate_parsed\":\n",
    "                df[c] = parsed\n",
    "                break\n",
    "    ratio = float(parsed.notna().mean()) if len(parsed) else 0.0\n",
    "    info = {\n",
    "        \"time_col_found\": True,\n",
    "        \"source_col\": src_col,\n",
    "        \"parsed_non_na\": int(parsed.notna().sum()),\n",
    "        \"parsed_ratio\": ratio,\n",
    "        \"time_min\": str(parsed.min()) if parsed.notna().any() else None,\n",
    "        \"time_max\": str(parsed.max()) if parsed.notna().any() else None,\n",
    "    }\n",
    "    return df, info\n",
    "\n",
    "# ---------- 落地：覆寫原檔 + 記錄摘要 ----------\n",
    "def T4_save(df, src_path, out_dir):\n",
    "    base = os.path.basename(src_path)\n",
    "    # 1) 追溯副本\n",
    "    record_csv = os.path.join(out_dir, base)\n",
    "    df.to_csv(record_csv, index=False, encoding=\"utf-8\")\n",
    "    # 2) 覆寫原檔（安全寫入）\n",
    "    tmp = src_path + \".t4tmp\"\n",
    "    df.to_csv(tmp, index=False, encoding=\"utf-8\")\n",
    "    shutil.move(tmp, src_path)\n",
    "    return record_csv, src_path\n",
    "\n",
    "# ---------- 執行四檔 ----------\n",
    "T4_rows = []\n",
    "for p in T4_FILES:\n",
    "    try:\n",
    "        print(f\"\\n[T4] === START {os.path.basename(p)} ===\")\n",
    "        df0 = T4_read(p)\n",
    "        df1, rep = T4_convert_time_for_df(df0)\n",
    "        rec_path, ovw_path = T4_save(df1, p, T4_OUT_DIR)\n",
    "\n",
    "        # 輸出個別轉換報告 JSON\n",
    "        with open(os.path.join(T4_OUT_DIR, f\"{os.path.splitext(os.path.basename(p))[0]}_time_convert.json\"), \"w\", encoding=\"utf-8\") as f:\n",
    "            json.dump(rep, f, ensure_ascii=False, indent=2)\n",
    "\n",
    "        T4_rows.append({\n",
    "            \"file\": p,\n",
    "            \"status\": \"ok\",\n",
    "            \"n_rows\": int(df1.shape[0]),\n",
    "            \"n_cols\": int(df1.shape[1]),\n",
    "            \"source_col\": rep.get(\"source_col\"),\n",
    "            \"parsed_ratio\": round(rep.get(\"parsed_ratio\", 0.0), 6),\n",
    "            \"time_min\": rep.get(\"time_min\"),\n",
    "            \"time_max\": rep.get(\"time_max\"),\n",
    "            \"record_csv\": rec_path,\n",
    "            \"overwritten\": ovw_path\n",
    "        })\n",
    "        print(f\"[T4] DONE {os.path.basename(p)} | source={rep.get('source_col')} parsed_ratio={rep.get('parsed_ratio',0):.3f}\")\n",
    "    except Exception as e:\n",
    "        T4_rows.append({\"file\": p, \"status\": \"error\", \"error\": str(e)})\n",
    "        print(f\"[T4][ERROR] {os.path.basename(p)}: {e}\")\n",
    "\n",
    "# 總表\n",
    "T4_df = pd.DataFrame(T4_rows)\n",
    "T4_df.to_csv(os.path.join(T4_OUT_DIR, \"summary_time_convert_bling4.csv\"), index=False, encoding=\"utf-8\")\n",
    "print(\"\\n[T4] === SUMMARY ===\")\n",
    "print(T4_df)\n",
    "print(f\"[T4] summary saved → {os.path.join(T4_OUT_DIR, 'summary_time_convert_bling4.csv')}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "27b85fb9-0482-45bb-b671-463f99e07fd6",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true,
     "source_hidden": true
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "[APPX] === START 095323.csv ===\n",
      "[APPX] WARN 095323.csv：無法解析任何時間。已輸出追溯檔與告警 JSON。\n",
      "\n",
      "[APPX] === START 095707.csv ===\n",
      "[APPX] WARN 095707.csv：無法解析任何時間。已輸出追溯檔與告警 JSON。\n",
      "\n",
      "[APPX] === START 114309.csv ===\n",
      "[APPX] WARN 114309.csv：無法解析任何時間。已輸出追溯檔與告警 JSON。\n",
      "\n",
      "[APPX] === START 230933.csv ===\n",
      "[APPX] WARN 230933.csv：無法解析任何時間。已輸出追溯檔與告警 JSON。\n",
      "\n",
      "[APPX] === SUMMARY ===\n",
      "                                      file          status   rows  cols  \\\n",
      "0  /home/jovyan/RT08/0925/bling/095323.csv  no_time_parsed  23791   117   \n",
      "1  /home/jovyan/RT08/0925/bling/095707.csv  no_time_parsed  20180   117   \n",
      "2  /home/jovyan/RT08/0925/bling/114309.csv  no_time_parsed  71729   117   \n",
      "3  /home/jovyan/RT08/0925/bling/230933.csv  no_time_parsed  30249   117   \n",
      "\n",
      "  source_col     method  parsed_ratio  \\\n",
      "0   senddate  raw_infer           0.0   \n",
      "1   senddate  raw_infer           0.0   \n",
      "2   senddate  raw_infer           0.0   \n",
      "3   senddate  raw_infer           0.0   \n",
      "\n",
      "                                         record_csv  \\\n",
      "0  /home/jovyan/RT08/0925/bling_record/4/095323.csv   \n",
      "1  /home/jovyan/RT08/0925/bling_record/4/095707.csv   \n",
      "2  /home/jovyan/RT08/0925/bling_record/4/114309.csv   \n",
      "3  /home/jovyan/RT08/0925/bling_record/4/230933.csv   \n",
      "\n",
      "                               overwritten  \n",
      "0  /home/jovyan/RT08/0925/bling/095323.csv  \n",
      "1  /home/jovyan/RT08/0925/bling/095707.csv  \n",
      "2  /home/jovyan/RT08/0925/bling/114309.csv  \n",
      "3  /home/jovyan/RT08/0925/bling/230933.csv  \n",
      "[APPX] summary saved → /home/jovyan/RT08/0925/bling_record/4/summary_time_fix_bling4.csv\n"
     ]
    }
   ],
   "source": [
    "# [Append-only] 四檔時間轉換＋欄位小寫＋審計輸出（優化版）\n",
    "# - 來源：/home/jovyan/RT08/0925/bling/{095323,095707,114309,230933}.csv\n",
    "# - 清理：欄位名→小寫；自動挑最佳時間欄＋最佳解析法 → senddate_parsed(datetime64[ns])\n",
    "# - 相容：另外生成 SendDate_parsed（如不需可移除，保留全小寫更乾淨）\n",
    "# - 記錄：/home/jovyan/RT08/0925/bling_record/4/\n",
    "# - 覆寫：原檔（UTF-8）\n",
    "# - 不呼叫 run_step2~8；使用 APPX_* 前綴避免衝突\n",
    "# ============================================================\n",
    "import os, re, json, shutil\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from datetime import datetime\n",
    "\n",
    "APPX_FILES = [\n",
    "    \"/home/jovyan/RT08/0925/bling/095323.csv\",\n",
    "    \"/home/jovyan/RT08/0925/bling/095707.csv\",\n",
    "    \"/home/jovyan/RT08/0925/bling/114309.csv\",\n",
    "    \"/home/jovyan/RT08/0925/bling/230933.csv\",\n",
    "]\n",
    "APPX_OUT_DIR = \"/home/jovyan/RT08/0925/bling_record/4\"\n",
    "APPX_LOG_DIR = os.path.join(APPX_OUT_DIR, \"logs\")\n",
    "os.makedirs(APPX_OUT_DIR, exist_ok=True)\n",
    "os.makedirs(APPX_LOG_DIR, exist_ok=True)\n",
    "\n",
    "# ---------- 讀檔：優先用你的韌性讀；否則容錯 ----------\n",
    "def APPX_read_any(path: str) -> pd.DataFrame:\n",
    "    if \"read_table_resilient\" in globals() and callable(globals()[\"read_table_resilient\"]):\n",
    "        df, meta = read_table_resilient(path, max_header_search=30)\n",
    "        if meta.get(\"read_ok\"):\n",
    "            return df\n",
    "    for enc in [\"utf-8-sig\", \"utf-8\", \"cp950\", \"big5\", \"latin1\"]:\n",
    "        for hdr in range(0, 8):\n",
    "            try:\n",
    "                df = pd.read_csv(path, encoding=enc, header=hdr, engine=\"python\", on_bad_lines=\"skip\")\n",
    "                if df.shape[1] > 0:\n",
    "                    return df\n",
    "            except Exception:\n",
    "                continue\n",
    "    raise RuntimeError(f\"[APPX] 讀檔失敗：{path}\")\n",
    "\n",
    "# ---------- 欄位名 → 小寫（去空白；重名自動 _dup2/_dup3…） ----------\n",
    "def APPX_force_lower_columns(df: pd.DataFrame):\n",
    "    original = list(df.columns)\n",
    "    seen = {}\n",
    "    new_cols = []\n",
    "    for c in original:\n",
    "        base = str(c).strip().lower()\n",
    "        if base not in seen:\n",
    "            seen[base] = 1\n",
    "            new_cols.append(base)\n",
    "        else:\n",
    "            seen[base] += 1\n",
    "            new_cols.append(f\"{base}_dup{seen[base]}\")\n",
    "    mapping = {o: n for o, n in zip(original, new_cols) if o != n}\n",
    "    df.columns = new_cols\n",
    "    return df, mapping\n",
    "\n",
    "# ---------- 時間字串清洗（中文 AM/PM、全形標點、年/月/日） ----------\n",
    "APPX_AMPM = [\n",
    "    (r\"上午\", \"AM\"), (r\"下午\", \"PM\"), (r\"中午\", \"PM\"),\n",
    "    (r\"早上\", \"AM\"), (r\"凌晨\", \"AM\"), (r\"晚間\", \"PM\"), (r\"晚上\", \"PM\")\n",
    "]\n",
    "def APPX_clean_time_str(x):\n",
    "    if pd.isna(x): return np.nan\n",
    "    s = str(x).strip()\n",
    "    if not s: return np.nan\n",
    "    s = (s.replace(\"：\", \":\").replace(\"／\", \"/\").replace(\"－\", \"-\")\n",
    "           .replace(\"，\", \",\").replace(\"．\", \".\"))\n",
    "    s = s.replace(\"年\", \"/\").replace(\"月\", \"/\").replace(\"日\", \" \")\n",
    "    for pat, rep in APPX_AMPM:\n",
    "        s = re.sub(pat, rep, s)\n",
    "    s = re.sub(r'(\\d)(AM|PM)\\b', r'\\1 \\2', s, flags=re.IGNORECASE)  # 12PM→12 PM\n",
    "    s = s.replace(\"T\", \" \")\n",
    "    s = re.sub(r\"\\s+\", \" \", s).strip()\n",
    "    return s\n",
    "\n",
    "# ---------- 多策略解析（回傳 {method: Series} 與每個 method 的統計） ----------\n",
    "APPX_FORMATS = [\n",
    "    \"%Y/%m/%d %H:%M:%S\", \"%Y-%m-%d %H:%M:%S\",\n",
    "    \"%Y/%m/%d %I:%M:%S %p\", \"%Y-%m-%d %I:%M:%S %p\",\n",
    "    \"%Y/%m/%d %H:%M\", \"%Y-%m-%d %H:%M\",\n",
    "    \"%Y%m%d%H%M%S\", \"%Y%m%d%H%M\", \"%Y%m%d\",\n",
    "]\n",
    "def APPX_try_parse_series(s: pd.Series):\n",
    "    parsed_map, stats = {}, []\n",
    "    def _add(name, ser):\n",
    "        parsed_map[name] = ser\n",
    "        nn = ser.notna().sum(); tot = len(ser)\n",
    "        ratio = float(nn / tot) if tot else 0.0\n",
    "        tmin = str(ser.min()) if nn else None\n",
    "        tmax = str(ser.max()) if nn else None\n",
    "        stats.append({\"method\": name, \"parsed_non_na\": int(nn), \"parsed_ratio\": ratio, \"time_min\": tmin, \"time_max\": tmax})\n",
    "\n",
    "    # 已是 datetime dtype？\n",
    "    if pd.api.types.is_datetime64_any_dtype(s):\n",
    "        _add(\"as_is_datetime_dtype\", s)\n",
    "        return parsed_map, stats\n",
    "\n",
    "    # 1) 原始\n",
    "    p_raw = pd.to_datetime(s, errors=\"coerce\", infer_datetime_format=True)\n",
    "    _add(\"raw_infer\", p_raw)\n",
    "\n",
    "    # 2) 清洗後\n",
    "    sc = s.astype(str).map(APPX_clean_time_str)\n",
    "    p_clean = pd.to_datetime(sc, errors=\"coerce\", infer_datetime_format=True)\n",
    "    _add(\"clean_infer\", p_clean)\n",
    "\n",
    "    # 3) 指定格式（清洗後）\n",
    "    for fmt in APPX_FORMATS:\n",
    "        p = pd.to_datetime(sc, format=fmt, errors=\"coerce\")\n",
    "        _add(f\"clean_fmt:{fmt}\", p)\n",
    "\n",
    "    # 4) 純數字 → Excel / Unix ms / Unix s\n",
    "    sn = pd.to_numeric(s, errors=\"coerce\")\n",
    "    # Excel（1899-12-30 基準；20000~60000 → 1954~2064）\n",
    "    mask_xl = sn.between(20000, 60000, inclusive=\"both\")\n",
    "    p_xl = pd.Series(pd.NaT, index=s.index)\n",
    "    p_xl.loc[mask_xl] = pd.Timestamp(\"1899-12-30\") + pd.to_timedelta(sn.loc[mask_xl], unit=\"D\")\n",
    "    _add(\"excel_serial\", p_xl)\n",
    "    # Unix 毫秒\n",
    "    p_ms = pd.to_datetime(sn.where(sn >= 1e11), unit=\"ms\", errors=\"coerce\")\n",
    "    _add(\"unix_ms\", p_ms)\n",
    "    # Unix 秒\n",
    "    p_s = pd.to_datetime(sn.where(sn >= 1e9), unit=\"s\", errors=\"coerce\")\n",
    "    _add(\"unix_s\", p_s)\n",
    "\n",
    "    return parsed_map, stats\n",
    "\n",
    "# ---------- 從多候選欄位中挑「最佳來源欄」＋「最佳解析法」 ----------\n",
    "def APPX_pick_best_time(df: pd.DataFrame):\n",
    "    # 候選欄位：senddate 家族優先，其次包含 time/date/datetime/timestamp\n",
    "    cols = list(df.columns)\n",
    "    prio = []\n",
    "    for c in cols:\n",
    "        lc = c.lower()\n",
    "        if lc in (\"senddate_parsed\",\"senddate_clean\",\"senddate\"):\n",
    "            prio.append(c)\n",
    "    for kw in (\"senddate\",\"timestamp\",\"datetime\",\"time\",\"date\"):\n",
    "        for c in cols:\n",
    "            if kw in c.lower() and c not in prio:\n",
    "                prio.append(c)\n",
    "    if not prio:\n",
    "        return None, None, None, []\n",
    "\n",
    "    best = {\"col\": None, \"method\": None, \"ratio\": -1.0, \"time_min\": None, \"time_max\": None}\n",
    "    all_stats = []  # 累積審計輸出\n",
    "    best_series = None\n",
    "\n",
    "    for col in prio:\n",
    "        s = df[col]\n",
    "        parsed_map, stats = APPX_try_parse_series(s)\n",
    "        # 寫入每方法統計（附欄名）\n",
    "        for st in stats:\n",
    "            all_stats.append({\"column\": col, **st})\n",
    "        # 取此欄位的最佳\n",
    "        if stats:\n",
    "            st_best = max(stats, key=lambda x: x[\"parsed_ratio\"])\n",
    "            if st_best[\"parsed_ratio\"] > best[\"ratio\"]:\n",
    "                best.update({\n",
    "                    \"col\": col,\n",
    "                    \"method\": st_best[\"method\"],\n",
    "                    \"ratio\": st_best[\"parsed_ratio\"],\n",
    "                    \"time_min\": st_best[\"time_min\"],\n",
    "                    \"time_max\": st_best[\"time_max\"],\n",
    "                })\n",
    "                best_series = parsed_map[st_best[\"method\"]]\n",
    "\n",
    "    return best[\"col\"], best[\"method\"], best_series, all_stats\n",
    "\n",
    "# ---------- 寫檔（追溯副本 + 覆寫原檔） ----------\n",
    "def APPX_save(df: pd.DataFrame, src_path: str):\n",
    "    base = os.path.basename(src_path)\n",
    "    rec_csv = os.path.join(APPX_OUT_DIR, base)\n",
    "    df.to_csv(rec_csv, index=False, encoding=\"utf-8\")\n",
    "    tmp = src_path + \".appxtmp\"\n",
    "    df.to_csv(tmp, index=False, encoding=\"utf-8\")\n",
    "    shutil.move(tmp, src_path)\n",
    "    return rec_csv, src_path\n",
    "\n",
    "# ---------- 主程式：逐檔處理 ----------\n",
    "APPX_rows = []\n",
    "for p in APPX_FILES:\n",
    "    try:\n",
    "        print(f\"\\n[APPX] === START {os.path.basename(p)} ===\")\n",
    "        df0 = APPX_read_any(p)\n",
    "\n",
    "        # A) 欄位小寫（保證一致性）\n",
    "        df1, lower_map = APPX_force_lower_columns(df0)\n",
    "\n",
    "        # B) 自動尋找最佳時間欄＋解析法\n",
    "        src_col, method, parsed_series, stats_rows = APPX_pick_best_time(df1)\n",
    "\n",
    "        if src_col is None or parsed_series is None or parsed_series.notna().sum() == 0:\n",
    "            # 解析率為 0：輸出樣本與統計便於追查\n",
    "            base = os.path.splitext(os.path.basename(p))[0]\n",
    "            with open(os.path.join(APPX_LOG_DIR, f\"{base}_time_fail.json\"), \"w\", encoding=\"utf-8\") as f:\n",
    "                json.dump({\n",
    "                    \"file\": p,\n",
    "                    \"note\": \"No parsable timestamps found; please inspect samples & candidate columns.\",\n",
    "                    \"candidate_columns\": [c for c in df1.columns if any(k in c for k in [\"senddate\",\"time\",\"date\",\"timestamp\"])],\n",
    "                    \"lower_map\": lower_map\n",
    "                }, f, ensure_ascii=False, indent=2)\n",
    "            # 仍然落地（不新增 parsed 欄），避免中斷\n",
    "            rec_csv, ovw_csv = APPX_save(df1, p)\n",
    "            APPX_rows.append({\n",
    "                \"file\": p, \"status\": \"no_time_parsed\", \"rows\": int(df1.shape[0]), \"cols\": int(df1.shape[1]),\n",
    "                \"source_col\": src_col, \"method\": method, \"parsed_ratio\": 0.0,\n",
    "                \"record_csv\": rec_csv, \"overwritten\": ovw_csv\n",
    "            })\n",
    "            print(f\"[APPX] WARN {os.path.basename(p)}：無法解析任何時間。已輸出追溯檔與告警 JSON。\")\n",
    "            continue\n",
    "\n",
    "        # C) 寫入標準時間欄位（主：小寫；相容：駝峰）\n",
    "        parsed_dt = pd.to_datetime(parsed_series, errors=\"coerce\")\n",
    "        df1[\"senddate_parsed\"] = parsed_dt\n",
    "        # 相容欄（如嚴格要求全小寫，可註解掉下一行）\n",
    "        df1[\"SendDate_parsed\"] = parsed_dt\n",
    "\n",
    "        # D) 審計輸出（來源欄、方法、解析率、時間覆蓋、欄位小寫對照）\n",
    "        base = os.path.splitext(os.path.basename(p))[0]\n",
    "        # 逐方法統計\n",
    "        pd.DataFrame(stats_rows).sort_values([\"column\",\"parsed_ratio\"], ascending=[True,False]).to_csv(\n",
    "            os.path.join(APPX_LOG_DIR, f\"{base}_time_methods.csv\"), index=False, encoding=\"utf-8\"\n",
    "        )\n",
    "        # 概要 JSON\n",
    "        summary = {\n",
    "            \"file\": p,\n",
    "            \"rows\": int(df1.shape[0]),\n",
    "            \"cols\": int(df1.shape[1]),\n",
    "            \"source_col\": src_col,\n",
    "            \"best_method\": method,\n",
    "            \"parsed_non_na\": int(parsed_dt.notna().sum()),\n",
    "            \"parsed_ratio\": float(parsed_dt.notna().mean()),\n",
    "            \"time_min\": str(parsed_dt.min()) if parsed_dt.notna().any() else None,\n",
    "            \"time_max\": str(parsed_dt.max()) if parsed_dt.notna().any() else None,\n",
    "            \"lower_map_changed\": len(lower_map),\n",
    "        }\n",
    "        with open(os.path.join(APPX_LOG_DIR, f\"{base}_summary.json\"), \"w\", encoding=\"utf-8\") as f:\n",
    "            json.dump(summary, f, ensure_ascii=False, indent=2)\n",
    "\n",
    "        # E) 落地：追溯副本 + 覆寫原檔\n",
    "        rec_csv, ovw_csv = APPX_save(df1, p)\n",
    "\n",
    "        APPX_rows.append({\n",
    "            \"file\": p, \"status\": \"ok\",\n",
    "            \"rows\": int(df1.shape[0]), \"cols\": int(df1.shape[1]),\n",
    "            \"source_col\": src_col, \"method\": method,\n",
    "            \"parsed_ratio\": round(summary[\"parsed_ratio\"], 6),\n",
    "            \"time_min\": summary[\"time_min\"], \"time_max\": summary[\"time_max\"],\n",
    "            \"record_csv\": rec_csv, \"overwritten\": ovw_csv\n",
    "        })\n",
    "        print(f\"[APPX] DONE {os.path.basename(p)} | src={src_col} method={method} ratio={summary['parsed_ratio']:.3f}\")\n",
    "    except Exception as e:\n",
    "        APPX_rows.append({\"file\": p, \"status\": \"error\", \"error\": str(e)})\n",
    "        print(f\"[APPX][ERROR] {os.path.basename(p)}: {e}\")\n",
    "\n",
    "# 總表\n",
    "APPX_df = pd.DataFrame(APPX_rows)\n",
    "APPX_df.to_csv(os.path.join(APPX_OUT_DIR, \"summary_time_fix_bling4.csv\"), index=False, encoding=\"utf-8\")\n",
    "print(\"\\n[APPX] === SUMMARY ===\")\n",
    "print(APPX_df)\n",
    "print(f\"[APPX] summary saved → {os.path.join(APPX_OUT_DIR, 'summary_time_fix_bling4.csv')}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "88e39a26-fbdf-4c6c-9dc5-7dd1e06d040c",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true,
     "source_hidden": true
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "[APPX2] === START 095323.csv ===\n",
      "[APPX2] WARN 095323.csv：無法解析任何時間（已輸出審計與樣本）。\n",
      "\n",
      "[APPX2] === START 095707.csv ===\n",
      "[APPX2] WARN 095707.csv：無法解析任何時間（已輸出審計與樣本）。\n",
      "\n",
      "[APPX2] === START 114309.csv ===\n",
      "[APPX2] WARN 114309.csv：無法解析任何時間（已輸出審計與樣本）。\n",
      "\n",
      "[APPX2] === START 230933.csv ===\n",
      "[APPX2] WARN 230933.csv：無法解析任何時間（已輸出審計與樣本）。\n",
      "\n",
      "[APPX2] === SUMMARY ===\n",
      "                                      file          status   rows  cols  \\\n",
      "0  /home/jovyan/RT08/0925/bling/095323.csv  no_time_parsed  23791   117   \n",
      "1  /home/jovyan/RT08/0925/bling/095707.csv  no_time_parsed  20180   117   \n",
      "2  /home/jovyan/RT08/0925/bling/114309.csv  no_time_parsed  71729   117   \n",
      "3  /home/jovyan/RT08/0925/bling/230933.csv  no_time_parsed  30249   117   \n",
      "\n",
      "                                         record_csv  \\\n",
      "0  /home/jovyan/RT08/0925/bling_record/4/095323.csv   \n",
      "1  /home/jovyan/RT08/0925/bling_record/4/095707.csv   \n",
      "2  /home/jovyan/RT08/0925/bling_record/4/114309.csv   \n",
      "3  /home/jovyan/RT08/0925/bling_record/4/230933.csv   \n",
      "\n",
      "                               overwritten  \\\n",
      "0  /home/jovyan/RT08/0925/bling/095323.csv   \n",
      "1  /home/jovyan/RT08/0925/bling/095707.csv   \n",
      "2  /home/jovyan/RT08/0925/bling/114309.csv   \n",
      "3  /home/jovyan/RT08/0925/bling/230933.csv   \n",
      "\n",
      "                                                note  \n",
      "0  請查看 *_head30.csv 與 *_time_candidates_results.c...  \n",
      "1  請查看 *_head30.csv 與 *_time_candidates_results.c...  \n",
      "2  請查看 *_head30.csv 與 *_time_candidates_results.c...  \n",
      "3  請查看 *_head30.csv 與 *_time_candidates_results.c...  \n",
      "[APPX2] summary saved → /home/jovyan/RT08/0925/bling_record/4/summary_time_fix_bling4_v2.csv\n"
     ]
    }
   ],
   "source": [
    "# [Append-only] 時間轉換終極優化（四檔專用）\n",
    "# - 智慧挑欄：避開 *parsed 欄，優先原始 senddate / date* / time*\n",
    "# - 解析強化：民國年、中文 AM/PM、全形字元、Excel/Unix/緊湊數字、AM/PM 前置/後置\n",
    "# - 欄位組合：自動嘗試「日期欄 + 時間欄」拼接再解析\n",
    "# - 輸出：senddate_parsed(datetime64[ns]) + 完整審計\n",
    "# ============================================================\n",
    "import os, re, json, shutil, unicodedata\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "APPX2_FILES = [\n",
    "    \"/home/jovyan/RT08/0925/bling/095323.csv\",\n",
    "    \"/home/jovyan/RT08/0925/bling/095707.csv\",\n",
    "    \"/home/jovyan/RT08/0925/bling/114309.csv\",\n",
    "    \"/home/jovyan/RT08/0925/bling/230933.csv\",\n",
    "]\n",
    "APPX2_OUT_DIR = \"/home/jovyan/RT08/0925/bling_record/4\"\n",
    "APPX2_LOG_DIR = os.path.join(APPX2_OUT_DIR, \"logs\")\n",
    "os.makedirs(APPX2_OUT_DIR, exist_ok=True)\n",
    "os.makedirs(APPX2_LOG_DIR, exist_ok=True)\n",
    "\n",
    "# ---------- 讀檔（優先你的韌性讀；否則容錯） ----------\n",
    "def APPX2_read_any(path: str) -> pd.DataFrame:\n",
    "    if \"read_table_resilient\" in globals() and callable(globals()[\"read_table_resilient\"]):\n",
    "        df, meta = read_table_resilient(path, max_header_search=30)\n",
    "        if meta.get(\"read_ok\"):\n",
    "            return df\n",
    "    for enc in [\"utf-8-sig\",\"utf-8\",\"cp950\",\"big5\",\"latin1\"]:\n",
    "        for hdr in range(0, 8):\n",
    "            try:\n",
    "                df = pd.read_csv(path, encoding=enc, header=hdr, engine=\"python\", on_bad_lines=\"skip\")\n",
    "                if df.shape[1] > 0:\n",
    "                    return df\n",
    "            except Exception:\n",
    "                continue\n",
    "    raise RuntimeError(f\"[APPX2] 無法讀取：{path}\")\n",
    "\n",
    "# ---------- 欄位名 → 小寫（去空白；重名加 _dupN） ----------\n",
    "def APPX2_force_lower_columns(df: pd.DataFrame):\n",
    "    orig = list(df.columns)\n",
    "    seen = {}\n",
    "    new = []\n",
    "    for c in orig:\n",
    "        base = str(c).strip().lower()\n",
    "        if base not in seen:\n",
    "            seen[base]=1; new.append(base)\n",
    "        else:\n",
    "            seen[base]+=1; new.append(f\"{base}_dup{seen[base]}\")\n",
    "    mapping = {o:n for o,n in zip(orig,new) if o!=n}\n",
    "    df.columns = new\n",
    "    return df, mapping\n",
    "\n",
    "# ---------- 全形→半形、清理雜訊 ----------\n",
    "def APPX2_to_halfwidth(s: str) -> str:\n",
    "    # 將全形數字/符號正規化成半形\n",
    "    return unicodedata.normalize(\"NFKC\", s)\n",
    "\n",
    "APPX2_AMPM = [\n",
    "    (r\"上午\",\"AM\"),(r\"下午\",\"PM\"),(r\"中午\",\"PM\"),\n",
    "    (r\"早上\",\"AM\"),(r\"凌晨\",\"AM\"),(r\"晚間\",\"PM\"),(r\"晚上\",\"PM\")\n",
    "]\n",
    "\n",
    "def APPX2_clean_raw_time(x):\n",
    "    if pd.isna(x): return np.nan\n",
    "    s = str(x).strip()\n",
    "    if not s: return np.nan\n",
    "    s = APPX2_to_halfwidth(s)\n",
    "    # 去掉括號/註記\n",
    "    s = re.sub(r\"[\\[\\(（].*?[\\]\\)）]\", \" \", s)\n",
    "    # 中文日期詞 → 西式\n",
    "    s = s.replace(\"年\",\"/\").replace(\"月\",\"/\").replace(\"日\",\" \")\n",
    "    # 中文 AM/PM\n",
    "    for pat, rep in APPX2_AMPM:\n",
    "        s = re.sub(pat, rep, s)\n",
    "    # AM/PM 在前面：例如 \"AM 10:23\" → \"10:23 AM\"\n",
    "    s = re.sub(r\"^(AM|PM)\\s+(\\d)\", r\"\\2 \\1\", s, flags=re.IGNORECASE)\n",
    "    # AM/PM 黏在時間後面：12:03PM → 12:03 PM\n",
    "    s = re.sub(r\"(\\d)(AM|PM)\\b\", r\"\\1 \\2\", s, flags=re.IGNORECASE)\n",
    "    # T 分隔 → 空白；多空白壓縮\n",
    "    s = s.replace(\"T\",\" \")\n",
    "    s = re.sub(r\"\\s+\", \" \", s).strip()\n",
    "    return s\n",
    "\n",
    "# ---------- 民國年(YYY/...) 轉西元（加 1911） ----------\n",
    "def APPX2_fix_roc_year(txt: str) -> str:\n",
    "    # 比對開頭為 2~3 位數年，後接 /- ，例如 113/09/26 或 99-12-31\n",
    "    m = re.match(r\"^(\\d{2,3})[/-](\\d{1,2})[/-](\\d{1,2})(.*)$\", txt)\n",
    "    if m:\n",
    "        y, mo, d, tail = m.groups()\n",
    "        y = int(y)\n",
    "        if 1 <= y <= 150:  # 合理的民國年區間\n",
    "            y = y + 1911\n",
    "            return f\"{y:04d}/{int(mo):02d}/{int(d):02d}{tail}\"\n",
    "    return txt\n",
    "\n",
    "# ---------- 解析單一字串成 Timestamp ----------\n",
    "def APPX2_parse_one(s):\n",
    "    if pd.isna(s): return pd.NaT\n",
    "    txt = APPX2_clean_raw_time(s)\n",
    "    if not txt: return pd.NaT\n",
    "    txt = APPX2_fix_roc_year(txt)\n",
    "\n",
    "    # 純數字：Excel序列/Unix秒/Unix毫秒/緊湊日期\n",
    "    if re.fullmatch(r\"\\d+(\\.\\d+)?\", txt):\n",
    "        try:\n",
    "            val = float(txt)\n",
    "            # Excel 序列（1899-12-30）\n",
    "            if 20000 <= val <= 60000:\n",
    "                return pd.Timestamp(\"1899-12-30\") + pd.to_timedelta(val, unit=\"D\")\n",
    "            # Unix 毫秒\n",
    "            if val >= 1e11:\n",
    "                return pd.to_datetime(int(val), unit=\"ms\", errors=\"coerce\")\n",
    "            # Unix 秒\n",
    "            if val >= 1e9:\n",
    "                return pd.to_datetime(int(val), unit=\"s\", errors=\"coerce\")\n",
    "        except Exception:\n",
    "            pass\n",
    "        if re.fullmatch(r\"\\d{14}\", txt):  # YYYYMMDDHHMMSS\n",
    "            return pd.to_datetime(txt, format=\"%Y%m%d%H%M%S\", errors=\"coerce\")\n",
    "        if re.fullmatch(r\"\\d{12}\", txt):  # YYYYMMDDHHMM\n",
    "            return pd.to_datetime(txt, format=\"%Y%m%d%H%M\", errors=\"coerce\")\n",
    "        if re.fullmatch(r\"\\d{8}\", txt):   # YYYYMMDD\n",
    "            return pd.to_datetime(txt, format=\"%Y%m%d\", errors=\"coerce\")\n",
    "\n",
    "    # 嘗試多組常見格式\n",
    "    # 注意：若含 AM/PM，現在已經確保在時間後方\n",
    "    fmts = [\n",
    "        \"%Y/%m/%d %I:%M:%S %p\",\"%Y-%m-%d %I:%M:%S %p\",\n",
    "        \"%Y/%m/%d %H:%M:%S\",\"%Y-%m-%d %H:%M:%S\",\n",
    "        \"%Y/%m/%d %I:%M %p\",\"%Y-%m-%d %I:%M %p\",\n",
    "        \"%Y/%m/%d %H:%M\",\"%Y-%m-%d %H:%M\",\n",
    "        \"%Y/%m/%d\",\"%Y-%m-%d\"\n",
    "    ]\n",
    "    for fmt in fmts:\n",
    "        dt = pd.to_datetime(txt, format=fmt, errors=\"coerce\")\n",
    "        if not pd.isna(dt): \n",
    "            return dt\n",
    "\n",
    "    # 最後一搏：寬鬆解析（dayfirst=False；台灣資料通常年月日）\n",
    "    return pd.to_datetime(txt, errors=\"coerce\", dayfirst=False, infer_datetime_format=True)\n",
    "\n",
    "# ---------- 來源欄位候選與欄位配對 ----------\n",
    "def APPX2_pick_candidates(df: pd.DataFrame):\n",
    "    cols = list(df.columns)\n",
    "    # 先排除 *parsed/*clean，避免吃到先前失敗產物\n",
    "    def useful(c):\n",
    "        lc = c.lower()\n",
    "        if \"parsed\" in lc or \"clean\" in lc:\n",
    "            return False\n",
    "        return True\n",
    "    # 以 senddate 家族優先\n",
    "    cands = [c for c in cols if useful(c) and (\"senddate\" in c)]\n",
    "    # 一般關鍵詞\n",
    "    for kw in (\"datetime\",\"timestamp\",\"date\",\"time\"):\n",
    "        for c in cols:\n",
    "            if useful(c) and (kw in c) and (c not in cands):\n",
    "                cands.append(c)\n",
    "    # 去重保序\n",
    "    seen=set(); ordered=[]\n",
    "    for c in cands:\n",
    "        if c not in seen: ordered.append(c); seen.add(c)\n",
    "    return ordered\n",
    "\n",
    "def APPX2_looks_date_like(s: pd.Series) -> bool:\n",
    "    if s.dtype.kind in \"M\":  # datetime-like\n",
    "        return True\n",
    "    sample = s.dropna().astype(str).head(50).tolist()\n",
    "    if not sample: return False\n",
    "    score=0\n",
    "    for v in sample:\n",
    "        v2 = APPX2_fix_roc_year(APPX2_clean_raw_time(v))\n",
    "        if re.search(r\"\\d{4}[/-]\\d{1,2}[/-]\\d{1,2}\", v2): score+=1\n",
    "        elif re.fullmatch(r\"\\d{8}\", v2): score+=1\n",
    "        elif re.match(r\"^\\d{2,3}[/-]\\d{1,2}[/-]\\d{1,2}\", v): score+=1  # 民國年\n",
    "    return score >= max(3, len(sample)//5)\n",
    "\n",
    "def APPX2_looks_time_like(s: pd.Series) -> bool:\n",
    "    sample = s.dropna().astype(str).head(50).tolist()\n",
    "    if not sample: return False\n",
    "    score=0\n",
    "    for v in sample:\n",
    "        v2 = APPX2_to_halfwidth(v)\n",
    "        if re.search(r\"\\d{1,2}:\\d{2}\", v2): score+=1\n",
    "        elif re.search(r\"\\b(AM|PM)\\b\", v2, flags=re.I): score+=1\n",
    "    return score >= max(3, len(sample)//5)\n",
    "\n",
    "# ---------- 嘗試單欄解析與雙欄（日期+時間）拼接解析，選最佳 ----------\n",
    "def APPX2_best_parsed_series(df: pd.DataFrame, candidates):\n",
    "    results = []\n",
    "    best_series = None\n",
    "    best = {\"kind\": None, \"columns\": None, \"ratio\": -1.0, \"time_min\": None, \"time_max\": None}\n",
    "\n",
    "    # 1) 單欄解析\n",
    "    for col in candidates:\n",
    "        ser = df[col]\n",
    "        parsed = ser.apply(APPX2_parse_one)\n",
    "        ratio = float(parsed.notna().mean()) if len(parsed) else 0.0\n",
    "        results.append({\"kind\":\"single\",\"columns\":[col],\"ratio\":ratio,\n",
    "                        \"time_min\": str(parsed.min()) if parsed.notna().any() else None,\n",
    "                        \"time_max\": str(parsed.max()) if parsed.notna().any() else None})\n",
    "        if ratio > best[\"ratio\"]:\n",
    "            best_series = parsed\n",
    "            best = {\"kind\":\"single\",\"columns\":[col],\"ratio\":ratio,\n",
    "                    \"time_min\": str(parsed.min()) if parsed.notna().any() else None,\n",
    "                    \"time_max\": str(parsed.max()) if parsed.notna().any() else None}\n",
    "\n",
    "    # 2) 雙欄（日期 + 時間）拼接解析\n",
    "    date_cols = [c for c in candidates if APPX2_looks_date_like(df[c])]\n",
    "    time_cols = [c for c in candidates if APPX2_looks_time_like(df[c])]\n",
    "    for dc in date_cols:\n",
    "        for tc in time_cols:\n",
    "            if dc == tc: \n",
    "                continue\n",
    "            combo = (df[dc].astype(str).fillna(\"\").str.strip() + \" \" + df[tc].astype(str).fillna(\"\").str.strip()).str.strip()\n",
    "            parsed = combo.apply(lambda x: APPX2_parse_one(x))\n",
    "            ratio = float(parsed.notna().mean()) if len(parsed) else 0.0\n",
    "            results.append({\"kind\":\"pair\",\"columns\":[dc,tc],\"ratio\":ratio,\n",
    "                            \"time_min\": str(parsed.min()) if parsed.notna().any() else None,\n",
    "                            \"time_max\": str(parsed.max()) if parsed.notna().any() else None})\n",
    "            if ratio > best[\"ratio\"]:\n",
    "                best_series = parsed\n",
    "                best = {\"kind\":\"pair\",\"columns\":[dc,tc],\"ratio\":ratio,\n",
    "                        \"time_min\": str(parsed.min()) if parsed.notna().any() else None,\n",
    "                        \"time_max\": str(parsed.max()) if parsed.notna().any() else None}\n",
    "\n",
    "    return best_series, best, results\n",
    "\n",
    "# ---------- 落地（追溯副本 + 覆寫原檔） ----------\n",
    "def APPX2_save(df: pd.DataFrame, src_path: str):\n",
    "    base = os.path.basename(src_path)\n",
    "    # 另存追溯\n",
    "    snap_csv = os.path.join(APPX2_OUT_DIR, base)\n",
    "    df.to_csv(snap_csv, index=False, encoding=\"utf-8\")\n",
    "    # 覆寫原檔（安全）\n",
    "    tmp = src_path + \".appx2tmp\"\n",
    "    df.to_csv(tmp, index=False, encoding=\"utf-8\")\n",
    "    shutil.move(tmp, src_path)\n",
    "    return snap_csv, src_path\n",
    "\n",
    "# ---------- 主程序 ----------\n",
    "APPX2_rows = []\n",
    "for path in APPX2_FILES:\n",
    "    try:\n",
    "        print(f\"\\n[APPX2] === START {os.path.basename(path)} ===\")\n",
    "        df0 = APPX2_read_any(path)\n",
    "        # 欄位小寫（避免大小寫不一致）\n",
    "        df1, lower_map = APPX2_force_lower_columns(df0)\n",
    "\n",
    "        # 候選欄位（避開 parsed/clean）\n",
    "        cands = APPX2_pick_candidates(df1)\n",
    "        # 若找不到，最後兜底：用所有欄位（但仍會評分過濾）\n",
    "        if not cands:\n",
    "            cands = list(df1.columns)\n",
    "\n",
    "        parsed_series, best, all_results = APPX2_best_parsed_series(df1, cands)\n",
    "\n",
    "        base = os.path.splitext(os.path.basename(path))[0]\n",
    "        # 審計輸出\n",
    "        audit_csv = os.path.join(APPX2_LOG_DIR, f\"{base}_time_candidates_results.csv\")\n",
    "        pd.DataFrame(all_results).sort_values(\"ratio\", ascending=False).to_csv(audit_csv, index=False, encoding=\"utf-8\")\n",
    "        with open(os.path.join(APPX2_LOG_DIR, f\"{base}_time_choice.json\"), \"w\", encoding=\"utf-8\") as f:\n",
    "            json.dump({\n",
    "                \"file\": path,\n",
    "                \"lower_map_changed\": len(lower_map),\n",
    "                \"candidates\": cands[:50],\n",
    "                \"best\": best\n",
    "            }, f, ensure_ascii=False, indent=2)\n",
    "\n",
    "        if parsed_series is None or parsed_series.notna().sum()==0:\n",
    "            # 仍失敗：輸出 30 筆原始樣本，協助人工介入\n",
    "            samp = pd.DataFrame(df1.head(30))\n",
    "            samp.to_csv(os.path.join(APPX2_LOG_DIR, f\"{base}_head30.csv\"), index=False, encoding=\"utf-8\")\n",
    "            # 仍落地（不新增 senddate_parsed），避免主流程中斷\n",
    "            snap, ovw = APPX2_save(df1, path)\n",
    "            APPX2_rows.append({\n",
    "                \"file\": path, \"status\": \"no_time_parsed\",\n",
    "                \"rows\": int(df1.shape[0]), \"cols\": int(df1.shape[1]),\n",
    "                \"record_csv\": snap, \"overwritten\": ovw,\n",
    "                \"note\": \"請查看 *_head30.csv 與 *_time_candidates_results.csv 判斷欄位/格式\"\n",
    "            })\n",
    "            print(f\"[APPX2] WARN {os.path.basename(path)}：無法解析任何時間（已輸出審計與樣本）。\")\n",
    "            continue\n",
    "\n",
    "        # 寫入標準欄位（小寫）\n",
    "        df1[\"senddate_parsed\"] = pd.to_datetime(parsed_series, errors=\"coerce\")\n",
    "\n",
    "        # 落地\n",
    "        snap, ovw = APPX2_save(df1, path)\n",
    "\n",
    "        APPX2_rows.append({\n",
    "            \"file\": path, \"status\": \"ok\",\n",
    "            \"rows\": int(df1.shape[0]), \"cols\": int(df1.shape[1]),\n",
    "            \"best_kind\": best[\"kind\"], \"best_columns\": best[\"columns\"],\n",
    "            \"parsed_ratio\": round(float(best[\"ratio\"]), 6),\n",
    "            \"time_min\": best[\"time_min\"], \"time_max\": best[\"time_max\"],\n",
    "            \"record_csv\": snap, \"overwritten\": ovw\n",
    "        })\n",
    "        print(f\"[APPX2] DONE {os.path.basename(path)} | {best['kind']} {best['columns']} | ratio={best['ratio']:.3f}\")\n",
    "    except Exception as e:\n",
    "        APPX2_rows.append({\"file\": path, \"status\": \"error\", \"error\": str(e)})\n",
    "        print(f\"[APPX2][ERROR] {os.path.basename(path)}: {e}\")\n",
    "\n",
    "# 總表\n",
    "pd.DataFrame(APPX2_rows).to_csv(os.path.join(APPX2_OUT_DIR, \"summary_time_fix_bling4_v2.csv\"),\n",
    "                                index=False, encoding=\"utf-8\")\n",
    "print(\"\\n[APPX2] === SUMMARY ===\")\n",
    "print(pd.DataFrame(APPX2_rows))\n",
    "print(f\"[APPX2] summary saved → {os.path.join(APPX2_OUT_DIR, 'summary_time_fix_bling4_v2.csv')}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "f214d9c2-3b88-453f-b906-5899b79135a6",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true,
     "source_hidden": true
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "[APPX-DIAG] START 095323.csv\n",
      "[APPX-DIAG] 095323.csv → best=extract ratio=1.000 meta={'column': 'senddate', 'time_min': '2021-12-20 00:00:00', 'time_max': '2022-01-06 00:00:00'}\n",
      "\n",
      "[APPX-DIAG] START 095707.csv\n",
      "[APPX-DIAG] 095707.csv → best=extract ratio=1.000 meta={'column': 'senddate', 'time_min': '2021-12-21 00:00:00', 'time_max': '2022-01-04 00:00:00'}\n",
      "\n",
      "[APPX-DIAG] START 114309.csv\n",
      "[APPX-DIAG] 114309.csv → best=extract ratio=1.000 meta={'column': 'senddate', 'time_min': '2021-12-19 00:00:00', 'time_max': '2022-02-09 00:00:00'}\n",
      "\n",
      "[APPX-DIAG] START 230933.csv\n",
      "[APPX-DIAG] 230933.csv → best=extract ratio=1.000 meta={'column': 'senddate', 'time_min': '2021-12-20 00:00:00', 'time_max': '2022-01-12 00:00:00'}\n",
      "\n",
      "[APPX-DIAG] 報表輸出目錄：/home/jovyan/RT08/0925/bling_record/4/diag\n"
     ]
    }
   ],
   "source": [
    "# [Append-only] APPX-DIAG-FINAL — 時間解析深度排查（不改原檔）\n",
    "# - 來源：/home/jovyan/RT08/0925/bling/{095323,095707,114309,230933}.csv\n",
    "# - 輸出：/home/jovyan/RT08/0925/bling_record/4/diag/\n",
    "# ============================================================\n",
    "import os, re, json, unicodedata\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "DIAG_FILES = [\n",
    "    \"/home/jovyan/RT08/0925/bling/095323.csv\",\n",
    "    \"/home/jovyan/RT08/0925/bling/095707.csv\",\n",
    "    \"/home/jovyan/RT08/0925/bling/114309.csv\",\n",
    "    \"/home/jovyan/RT08/0925/bling/230933.csv\",\n",
    "]\n",
    "DIAG_DIR = \"/home/jovyan/RT08/0925/bling_record/4/diag\"\n",
    "os.makedirs(DIAG_DIR, exist_ok=True)\n",
    "\n",
    "# ---------- 讀檔 ----------\n",
    "def DREAD(path):\n",
    "    if \"read_table_resilient\" in globals() and callable(globals()[\"read_table_resilient\"]):\n",
    "        df, meta = read_table_resilient(path, max_header_search=30)\n",
    "        if meta.get(\"read_ok\"): return df\n",
    "    for enc in [\"utf-8-sig\", \"utf-8\", \"cp950\", \"big5\", \"latin1\"]:\n",
    "        for hdr in range(0, 8):\n",
    "            try:\n",
    "                df = pd.read_csv(path, encoding=enc, header=hdr, engine=\"python\", on_bad_lines=\"skip\")\n",
    "                if df.shape[1] > 0: return df\n",
    "            except Exception:\n",
    "                pass\n",
    "    raise RuntimeError(f\"[APPX-DIAG] 無法讀取：{path}\")\n",
    "\n",
    "# ---------- 欄位名小寫（只在工作副本；不寫回） ----------\n",
    "def lower_columns(df: pd.DataFrame):\n",
    "    seen, new = {}, []\n",
    "    for c in df.columns:\n",
    "        k = str(c).strip().lower()\n",
    "        if k not in seen: seen[k]=1; new.append(k)\n",
    "        else: seen[k]+=1; new.append(f\"{k}_dup{seen[k]}\")\n",
    "    df = df.copy()\n",
    "    df.columns = new\n",
    "    return df\n",
    "\n",
    "# ---------- 清洗工具 ----------\n",
    "INVIS = re.compile(r\"[\\u200b-\\u200f\\u202a-\\u202e\\u2066-\\u2069\\u3000\\u00a0\\ufeff]\")\n",
    "AMPM_MAP = [(r\"上午\",\"AM\"),(r\"下午\",\"PM\"),(r\"中午\",\"PM\"),(r\"早上\",\"AM\"),(r\"凌晨\",\"AM\"),(r\"晚間\",\"PM\"),(r\"晚上\",\"PM\")]\n",
    "def to_halfwidth(s): return unicodedata.normalize(\"NFKC\", s)\n",
    "def strip_invis(s): return INVIS.sub(\" \", s)\n",
    "\n",
    "def clean_token(x):\n",
    "    if pd.isna(x): return np.nan\n",
    "    s = strip_invis(str(x)).strip()\n",
    "    if not s: return np.nan\n",
    "    s = to_halfwidth(s)\n",
    "    # 去括號內容/註記\n",
    "    s = re.sub(r\"[\\[\\(（].*?[\\]\\)）]\", \" \", s)\n",
    "    # 中文日期詞 & 時分秒\n",
    "    s = s.replace(\"年\",\"/\").replace(\"月\",\"/\").replace(\"日\",\" \")\n",
    "    s = re.sub(r\"(\\d{1,2})[時点点](\\d{1,2})(?:分)?(?:\\s*(\\d{1,2})秒?)?\", lambda m: f\"{m.group(1)}:{m.group(2)}:{m.group(3) or '00'}\", s)\n",
    "    for pat, rep in AMPM_MAP: s = re.sub(pat, rep, s)\n",
    "    # AM/PM 前置→後置；黏貼→加空白\n",
    "    s = re.sub(r\"^(AM|PM)\\s+(\\d)\", r\"\\2 \\1\", s, flags=re.I)\n",
    "    s = re.sub(r\"(\\d)(AM|PM)\\b\", r\"\\1 \\2\", s, flags=re.I)\n",
    "    s = s.replace(\"T\",\" \")\n",
    "    s = re.sub(r\"\\s+\", \" \", s).strip()\n",
    "    return s\n",
    "\n",
    "def fix_roc_year(txt):\n",
    "    m = re.match(r\"^(\\d{2,3})[/-](\\d{1,2})[/-](\\d{1,2})(.*)$\", txt)\n",
    "    if m:\n",
    "        y, mo, d, tail = m.groups()\n",
    "        y = int(y)\n",
    "        if 1 <= y <= 150:\n",
    "            return f\"{y+1911:04d}/{int(mo):02d}/{int(d):02d}{tail}\"\n",
    "    return txt\n",
    "\n",
    "def parse_any_one(s):\n",
    "    if pd.isna(s): return pd.NaT\n",
    "    t = clean_token(s)\n",
    "    if not t: return pd.NaT\n",
    "    t = fix_roc_year(t)\n",
    "    # 純數字：Excel / Unix / 緊湊\n",
    "    if re.fullmatch(r\"\\d+(\\.\\d+)?\", t):\n",
    "        try:\n",
    "            val = float(t)\n",
    "            if 20000 <= val <= 60000:  # Excel 序列\n",
    "                return pd.Timestamp(\"1899-12-30\") + pd.to_timedelta(val, unit=\"D\")\n",
    "            if val >= 1e11:  # 毫秒\n",
    "                return pd.to_datetime(int(val), unit=\"ms\", errors=\"coerce\")\n",
    "            if val >= 1e9:   # 秒\n",
    "                return pd.to_datetime(int(val), unit=\"s\", errors=\"coerce\")\n",
    "        except Exception:\n",
    "            pass\n",
    "        if re.fullmatch(r\"\\d{14}\", t): return pd.to_datetime(t, format=\"%Y%m%d%H%M%S\", errors=\"coerce\")\n",
    "        if re.fullmatch(r\"\\d{12}\", t): return pd.to_datetime(t, format=\"%Y%m%d%H%M\", errors=\"coerce\")\n",
    "        if re.fullmatch(r\"\\d{8}\", t):  return pd.to_datetime(t, format=\"%Y%m%d\", errors=\"coerce\")\n",
    "    # 常見格式（含 AM/PM）\n",
    "    fmts = [\n",
    "        \"%Y/%m/%d %I:%M:%S %p\",\"%Y-%m-%d %I:%M:%S %p\",\n",
    "        \"%Y/%m/%d %H:%M:%S\",\"%Y-%m-%d %H:%M:%S\",\n",
    "        \"%Y/%m/%d %I:%M %p\",\"%Y-%m-%d %I:%M %p\",\n",
    "        \"%Y/%m/%d %H:%M\",\"%Y-%m-%d %H:%M\",\n",
    "        \"%Y/%m/%d\",\"%Y-%m-%d\"\n",
    "    ]\n",
    "    for fmt in fmts:\n",
    "        dt = pd.to_datetime(t, format=fmt, errors=\"coerce\")\n",
    "        if not pd.isna(dt): return dt\n",
    "    return pd.to_datetime(t, errors=\"coerce\", dayfirst=False)\n",
    "\n",
    "# ---------- 候選欄位（避開 *parsed/*clean，廣域掃描） ----------\n",
    "def find_candidates(df: pd.DataFrame):\n",
    "    cols = list(df.columns)\n",
    "    def ok(c):\n",
    "        lc = c.lower()\n",
    "        return (\"parsed\" not in lc) and (\"clean\" not in lc)\n",
    "    cands = []\n",
    "    # 高優先\n",
    "    for key in (\"senddate\",\"record\",\"measure\",\"meas\",\"upload\",\"event\"):\n",
    "        for c in cols:\n",
    "            if ok(c) and key in c: cands.append(c)\n",
    "    # 一般關鍵詞\n",
    "    for key in (\"timestamp\",\"datetime\",\"date\",\"time\"):\n",
    "        for c in cols:\n",
    "            if ok(c) and key in c and c not in cands: cands.append(c)\n",
    "    # 去重保序\n",
    "    seen=set(); out=[]\n",
    "    for c in cands:\n",
    "        if c not in seen: out.append(c); seen.add(c)\n",
    "    return out\n",
    "\n",
    "# ---------- 判斷像日期/時間的欄 ----------\n",
    "def looks_date_like(s: pd.Series):\n",
    "    if s.dtype.kind in \"M\": return True\n",
    "    sample = s.dropna().astype(str).head(50).tolist()\n",
    "    score=0\n",
    "    for v in sample:\n",
    "        v2 = clean_token(v)\n",
    "        v2 = fix_roc_year(v2) if isinstance(v2,str) else v2\n",
    "        if isinstance(v2,str):\n",
    "            if re.search(r\"\\d{4}[/-]\\d{1,2}[/-]\\d{1,2}\", v2): score+=1\n",
    "            elif re.fullmatch(r\"\\d{8}\", v2): score+=1\n",
    "            elif re.match(r\"^\\d{2,3}[/-]\\d{1,2}[/-]\\d{1,2}\", v2): score+=1\n",
    "    return score >= max(3, len(sample)//5)\n",
    "\n",
    "def looks_time_like(s: pd.Series):\n",
    "    sample = s.dropna().astype(str).head(50).tolist()\n",
    "    score=0\n",
    "    for v in sample:\n",
    "        v2 = to_halfwidth(v)\n",
    "        if re.search(r\"\\d{1,2}:\\d{2}\", v2): score+=1\n",
    "        elif re.search(r\"\\b(AM|PM)\\b\", v2, flags=re.I): score+=1\n",
    "    return score >= max(3, len(sample)//5)\n",
    "\n",
    "# ---------- 正則抽取 日期/時間 片段 ----------\n",
    "EXTRACT_PATTERNS = [\n",
    "    # ISO/年-月-日(時:分:秒)(Z/±偏移)\n",
    "    r\"\\d{4}[/-]\\d{1,2}[/-]\\d{1,2}[ T]\\d{1,2}:\\d{2}(?::\\d{2})?(?:Z|[+-]\\d{2}:?\\d{2})?\",\n",
    "    r\"\\d{4}[/-]\\d{1,2}[/-]\\d{1,2}\",\n",
    "    # 民國年\n",
    "    r\"\\b\\d{2,3}[/-]\\d{1,2}[/-]\\d{1,2}\\b\",\n",
    "    # 緊湊數字\n",
    "    r\"\\b\\d{14}\\b\", r\"\\b\\d{12}\\b\", r\"\\b\\d{8}\\b\",\n",
    "    # 時分秒\n",
    "    r\"\\b\\d{1,2}:\\d{2}(?::\\d{2})?\\s*(?:AM|PM)?\\b\",\n",
    "]\n",
    "\n",
    "def extract_piece(s):\n",
    "    if pd.isna(s): return np.nan\n",
    "    t = clean_token(s)\n",
    "    if not isinstance(t,str) or not t: return np.nan\n",
    "    for pat in EXTRACT_PATTERNS:\n",
    "        m = re.search(pat, t, flags=re.I)\n",
    "        if m: \n",
    "            x = m.group(0)\n",
    "            # 若只抓到日期或只時間，仍回傳，後續會嘗試解析/拼接\n",
    "            return x\n",
    "    return np.nan\n",
    "\n",
    "# ---------- 主診斷：單欄、拼接、組裝、抽取 全面評測 ----------\n",
    "def diagnose_file(path):\n",
    "    df_raw = DREAD(path)\n",
    "    df = lower_columns(df_raw)\n",
    "\n",
    "    # 準備輸出容器\n",
    "    overview_rows = []\n",
    "    combo_rows = []\n",
    "    extract_rows = []\n",
    "\n",
    "    # 候選欄位\n",
    "    cands = find_candidates(df)\n",
    "    if not cands:\n",
    "        cands = list(df.columns)  # 兜底\n",
    "\n",
    "    # 1) 單欄解析評測\n",
    "    best_single = (None, pd.Series([pd.NaT]*len(df)), -1.0, None, None)\n",
    "    for col in cands:\n",
    "        ser = df[col]\n",
    "        parsed = ser.apply(parse_any_one)\n",
    "        nn = int(parsed.notna().sum())\n",
    "        ratio = float(nn/len(parsed)) if len(parsed) else 0.0\n",
    "        tmin = str(parsed.min()) if nn else None\n",
    "        tmax = str(parsed.max()) if nn else None\n",
    "        overview_rows.append({\"kind\":\"single\",\"column\":col,\"ratio\":ratio,\"non_na\":nn,\"time_min\":tmin,\"time_max\":tmax})\n",
    "        if ratio > best_single[2]:\n",
    "            best_single = (col, parsed, ratio, tmin, tmax)\n",
    "\n",
    "    # 2) 日期+時間欄位拼接\n",
    "    date_cols = [c for c in cands if looks_date_like(df[c])]\n",
    "    time_cols = [c for c in cands if looks_time_like(df[c])]\n",
    "    best_pair = (None, None, pd.Series([pd.NaT]*len(df)), -1.0, None, None)\n",
    "    for dc in date_cols:\n",
    "        for tc in time_cols:\n",
    "            if dc == tc: continue\n",
    "            combo = (df[dc].astype(str).fillna(\"\").str.strip() + \" \" + df[tc].astype(str).fillna(\"\").str.strip()).str.strip()\n",
    "            parsed = combo.apply(parse_any_one)\n",
    "            nn = int(parsed.notna().sum()); ratio = float(nn/len(parsed)) if len(parsed) else 0.0\n",
    "            tmin = str(parsed.min()) if nn else None\n",
    "            tmax = str(parsed.max()) if nn else None\n",
    "            combo_rows.append({\"kind\":\"pair\",\"date_col\":dc,\"time_col\":tc,\"ratio\":ratio,\"non_na\":nn,\"time_min\":tmin,\"time_max\":tmax})\n",
    "            if ratio > best_pair[3]:\n",
    "                best_pair = (dc, tc, parsed, ratio, tmin, tmax)\n",
    "\n",
    "    # 3) Y/M/D/H/M/S 組裝\n",
    "    parts = {k:None for k in [\"year\",\"month\",\"day\",\"hour\",\"minute\",\"second\"]}\n",
    "    for c in df.columns:\n",
    "        lc = c.lower()\n",
    "        for k in list(parts.keys()):\n",
    "            if re.fullmatch(fr\".*\\b{k}\\b.*\", lc) and parts[k] is None:\n",
    "                parts[k] = c\n",
    "    best_parts = (parts.copy(), pd.Series([pd.NaT]*len(df)), -1.0, None, None)\n",
    "    if parts[\"year\"] and parts[\"month\"] and parts[\"day\"]:\n",
    "        y = pd.to_numeric(df[parts[\"year\"]], errors=\"coerce\")\n",
    "        m = pd.to_numeric(df[parts[\"month\"]], errors=\"coerce\")\n",
    "        d = pd.to_numeric(df[parts[\"day\"]], errors=\"coerce\")\n",
    "        hh = pd.to_numeric(df[parts[\"hour\"]], errors=\"coerce\") if parts[\"hour\"] else 0\n",
    "        mm = pd.to_numeric(df[parts[\"minute\"]], errors=\"coerce\") if parts[\"minute\"] else 0\n",
    "        ss = pd.to_numeric(df[parts[\"second\"]], errors=\"coerce\") if parts[\"second\"] else 0\n",
    "        # 民國年處理\n",
    "        y = y.where(y>=1800, y+1911)\n",
    "        assembled = pd.to_datetime(\n",
    "            dict(year=y, month=m, day=d, hour=hh, minute=mm, second=ss),\n",
    "            errors=\"coerce\"\n",
    "        )\n",
    "        nn = int(assembled.notna().sum()); ratio = float(nn/len(assembled))\n",
    "        tmin = str(assembled.min()) if nn else None\n",
    "        tmax = str(assembled.max()) if nn else None\n",
    "        best_parts = (parts.copy(), assembled, ratio, tmin, tmax)\n",
    "\n",
    "    # 4) 正則抽取後解析（處理被包在句子/JSON 的時間）\n",
    "    best_extract = (None, pd.Series([pd.NaT]*len(df)), -1.0, None, None)\n",
    "    for col in cands:\n",
    "        ser = df[col].astype(str)\n",
    "        piece = ser.apply(extract_piece)\n",
    "        parsed = piece.apply(parse_any_one)\n",
    "        nn = int(parsed.notna().sum()); ratio = float(nn/len(parsed)) if len(parsed) else 0.0\n",
    "        tmin = str(parsed.min()) if nn else None\n",
    "        tmax = str(parsed.max()) if nn else None\n",
    "        extract_rows.append({\"kind\":\"extract\",\"column\":col,\"ratio\":ratio,\"non_na\":nn,\"time_min\":tmin,\"time_max\":tmax})\n",
    "        if ratio > best_extract[2]:\n",
    "            best_extract = (col, parsed, ratio, tmin, tmax)\n",
    "\n",
    "    # 5) 總結：挑解析率最高的方案\n",
    "    candidates_best = [\n",
    "        (\"single\", best_single[2], {\"column\": best_single[0], \"time_min\": best_single[3], \"time_max\": best_single[4]}),\n",
    "        (\"pair\",   best_pair[3],   {\"date_col\": best_pair[0], \"time_col\": best_pair[1], \"time_min\": best_pair[4], \"time_max\": best_pair[5]}),\n",
    "        (\"parts\",  best_parts[2],  {\"parts\": best_parts[0], \"time_min\": best_parts[3], \"time_max\": best_parts[4]}),\n",
    "        (\"extract\",best_extract[2],{\"column\": best_extract[0], \"time_min\": best_extract[3], \"time_max\": best_extract[4]}),\n",
    "    ]\n",
    "    final_kind, final_ratio, final_meta = max(candidates_best, key=lambda x: x[1])\n",
    "    if final_kind == \"single\":\n",
    "        final_series = best_single[1]\n",
    "    elif final_kind == \"pair\":\n",
    "        final_series = best_pair[2]\n",
    "    elif final_kind == \"parts\":\n",
    "        final_series = best_parts[1]\n",
    "    else:\n",
    "        final_series = best_extract[1]\n",
    "\n",
    "    # 6) 輸出檔案\n",
    "    base = os.path.splitext(os.path.basename(path))[0]\n",
    "    pd.DataFrame(overview_rows).sort_values(\"ratio\", ascending=False).to_csv(\n",
    "        os.path.join(DIAG_DIR, f\"{base}_diag_single.csv\"), index=False, encoding=\"utf-8\"\n",
    "    )\n",
    "    if combo_rows:\n",
    "        pd.DataFrame(combo_rows).sort_values(\"ratio\", ascending=False).to_csv(\n",
    "            os.path.join(DIAG_DIR, f\"{base}_diag_pairs.csv\"), index=False, encoding=\"utf-8\"\n",
    "        )\n",
    "    if extract_rows:\n",
    "        pd.DataFrame(extract_rows).sort_values(\"ratio\", ascending=False).to_csv(\n",
    "            os.path.join(DIAG_DIR, f\"{base}_diag_extract.csv\"), index=False, encoding=\"utf-8\"\n",
    "        )\n",
    "\n",
    "    # 7) 取樣：成功/失敗各 20 筆，幫助目視確認\n",
    "    ok_idx = final_series[final_series.notna()].head(20).index\n",
    "    bad_idx = final_series[final_series.isna()].head(20).index\n",
    "    pd.DataFrame({\n",
    "        \"parsed\": final_series.loc[ok_idx].astype(str).values\n",
    "    }).to_csv(os.path.join(DIAG_DIR, f\"{base}_samples_ok.csv\"), index=False, encoding=\"utf-8\")\n",
    "    # 把「哪一欄是來源」也輸出便於對照\n",
    "    if final_kind == \"single\":\n",
    "        src_cols = [final_meta[\"column\"]]\n",
    "    elif final_kind == \"pair\":\n",
    "        src_cols = [final_meta[\"date_col\"], final_meta[\"time_col\"]]\n",
    "    elif final_kind == \"parts\":\n",
    "        src_cols = [c for c in final_meta[\"parts\"].values() if c]\n",
    "    else:\n",
    "        src_cols = [final_meta[\"column\"]]\n",
    "    bad_df = df.loc[bad_idx, src_cols].astype(str)\n",
    "    bad_df.to_csv(os.path.join(DIAG_DIR, f\"{base}_samples_bad.csv\"), index=False, encoding=\"utf-8\")\n",
    "\n",
    "    # 8) 高階總結 JSON（你重點要看這個）\n",
    "    summary = {\n",
    "        \"file\": path,\n",
    "        \"rows\": int(df.shape[0]),\n",
    "        \"cols\": int(df.shape[1]),\n",
    "        \"candidates_scanned\": cands[:50],\n",
    "        \"final_choice_kind\": final_kind,\n",
    "        \"final_choice_meta\": final_meta,\n",
    "        \"final_ratio\": float(final_ratio),\n",
    "        \"ok_samples_csv\": f\"{base}_samples_ok.csv\",\n",
    "        \"bad_samples_csv\": f\"{base}_samples_bad.csv\",\n",
    "        \"single_report\": f\"{base}_diag_single.csv\",\n",
    "        \"pairs_report\": f\"{base}_diag_pairs.csv\",\n",
    "        \"extract_report\": f\"{base}_diag_extract.csv\",\n",
    "    }\n",
    "    with open(os.path.join(DIAG_DIR, f\"{base}_diag_summary.json\"), \"w\", encoding=\"utf-8\") as f:\n",
    "        json.dump(summary, f, ensure_ascii=False, indent=2)\n",
    "\n",
    "    # 同步列印一行摘要，方便你在 console 看到\n",
    "    print(f\"[APPX-DIAG] {os.path.basename(path)} → best={final_kind} ratio={final_ratio:.3f} meta={final_meta}\")\n",
    "\n",
    "# === 執行四檔診斷 ===\n",
    "for fp in DIAG_FILES:\n",
    "    try:\n",
    "        print(f\"\\n[APPX-DIAG] START {os.path.basename(fp)}\")\n",
    "        diagnose_file(fp)\n",
    "    except Exception as e:\n",
    "        print(f\"[APPX-DIAG][ERROR] {os.path.basename(fp)}: {e}\")\n",
    "\n",
    "print(f\"\\n[APPX-DIAG] 報表輸出目錄：{DIAG_DIR}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "0eed8a1b-4a09-4efd-ab8b-8fe57538bd20",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true,
     "source_hidden": true
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "[APPX4] === START 095323.csv ===\n",
      "[APPX4] DONE 095323.csv | src=senddate ratio=1.000 | min=2021-12-20 13:56:31 max=2022-01-06 06:48:03\n",
      "\n",
      "[APPX4] === START 095707.csv ===\n",
      "[APPX4] DONE 095707.csv | src=senddate ratio=1.000 | min=2021-12-21 11:25:53 max=2022-01-04 12:36:02\n",
      "\n",
      "[APPX4] === START 114309.csv ===\n",
      "[APPX4] DONE 114309.csv | src=senddate ratio=1.000 | min=2021-12-19 10:45:49 max=2022-02-09 13:14:01\n",
      "\n",
      "[APPX4] === START 230933.csv ===\n",
      "[APPX4] DONE 230933.csv | src=senddate ratio=1.000 | min=2021-12-20 17:58:36 max=2022-01-12 09:39:02\n",
      "\n",
      "[APPX4] === SUMMARY ===\n",
      "                                      file   rows  cols source_col  \\\n",
      "0  /home/jovyan/RT08/0925/bling/095323.csv  23791   117   senddate   \n",
      "1  /home/jovyan/RT08/0925/bling/095707.csv  20180   117   senddate   \n",
      "2  /home/jovyan/RT08/0925/bling/114309.csv  71729   117   senddate   \n",
      "3  /home/jovyan/RT08/0925/bling/230933.csv  30249   117   senddate   \n",
      "\n",
      "   parsed_ratio             time_min             time_max  \n",
      "0           1.0  2021-12-20 13:56:31  2022-01-06 06:48:03  \n",
      "1           1.0  2021-12-21 11:25:53  2022-01-04 12:36:02  \n",
      "2           1.0  2021-12-19 10:45:49  2022-02-09 13:14:01  \n",
      "3           1.0  2021-12-20 17:58:36  2022-01-12 09:39:02  \n",
      "[APPX4] summary saved → /home/jovyan/RT08/0925/bling_record/4/summary_time_fix_bling4_v3.csv\n"
     ]
    }
   ],
   "source": [
    "# [Append-only] APPX4 — 中文 AM/PM 精準解析修補（避免 00:00:00）\n",
    "# - 精準抽取日期/時間/AMPM，先組 24h 再建 datetime，避免被 to_datetime 誤吃成日期-only\n",
    "# - 支援：'下午 1:56:31'、'PM 1:56:31'、'2021/12/20 下午1:56:31'、民國年(113/09/26)\n",
    "# - 檔案：/home/jovyan/RT08/0925/bling/{095323,095707,114309,230933}.csv\n",
    "# - 產出：覆蓋原檔 + 在 /home/jovyan/RT08/0925/bling_record/4/ 留一份快照與 summary\n",
    "# ============================================================\n",
    "import os, re, json, shutil, unicodedata\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "APPX4_FILES = [\n",
    "    \"/home/jovyan/RT08/0925/bling/095323.csv\",\n",
    "    \"/home/jovyan/RT08/0925/bling/095707.csv\",\n",
    "    \"/home/jovyan/RT08/0925/bling/114309.csv\",\n",
    "    \"/home/jovyan/RT08/0925/bling/230933.csv\",\n",
    "]\n",
    "APPX4_OUT_DIR = \"/home/jovyan/RT08/0925/bling_record/4\"\n",
    "APPX4_LOG_DIR = os.path.join(APPX4_OUT_DIR, \"logs_fix\")\n",
    "os.makedirs(APPX4_OUT_DIR, exist_ok=True)\n",
    "os.makedirs(APPX4_LOG_DIR, exist_ok=True)\n",
    "\n",
    "# ---------- 讀檔（沿用你現有韌性讀，否則容錯） ----------\n",
    "def APPX4_read_any(path: str) -> pd.DataFrame:\n",
    "    if \"read_table_resilient\" in globals() and callable(globals()[\"read_table_resilient\"]):\n",
    "        df, meta = read_table_resilient(path, max_header_search=30)\n",
    "        if meta.get(\"read_ok\"): return df\n",
    "    for enc in [\"utf-8-sig\",\"utf-8\",\"cp950\",\"big5\",\"latin1\"]:\n",
    "        for hdr in range(0, 8):\n",
    "            try:\n",
    "                df = pd.read_csv(path, encoding=enc, header=hdr, engine=\"python\", on_bad_lines=\"skip\")\n",
    "                if df.shape[1] > 0: return df\n",
    "            except Exception:\n",
    "                continue\n",
    "    raise RuntimeError(f\"[APPX4] 無法讀取：{path}\")\n",
    "\n",
    "# ---------- 欄位名小寫（只在工作副本） ----------\n",
    "def APPX4_force_lower_columns(df: pd.DataFrame):\n",
    "    orig = list(df.columns)\n",
    "    seen, new = {}, []\n",
    "    for c in orig:\n",
    "        base = str(c).strip().lower()\n",
    "        if base not in seen: seen[base]=1; new.append(base)\n",
    "        else: seen[base]+=1; new.append(f\"{base}_dup{seen[base]}\")\n",
    "    df = df.copy()\n",
    "    df.columns = new\n",
    "    return df\n",
    "\n",
    "# ---------- 工具：不可見字/全形 → 半形 ----------\n",
    "_INVIS = re.compile(r\"[\\u200b-\\u200f\\u202a-\\u202e\\u2066-\\u2069\\u3000\\u00a0\\ufeff]\")\n",
    "def _strip_invis(s: str) -> str: return _INVIS.sub(\" \", s)\n",
    "def _halfwidth(s: str) -> str: return unicodedata.normalize(\"NFKC\", s)\n",
    "\n",
    "# ---------- 中文 AM/PM 辭典（含語意） ----------\n",
    "_AMPM_TOKENS = {\n",
    "    \"上午\":\"am\", \"早上\":\"am\", \"凌晨\":\"am\",\n",
    "    \"下午\":\"pm\", \"中午\":\"pm\", \"晚間\":\"pm\", \"晚上\":\"pm\",\n",
    "    \"am\":\"am\", \"pm\":\"pm\", \"AM\":\"am\", \"PM\":\"pm\"\n",
    "}\n",
    "\n",
    "# ---------- 抽取日期（優先西元，否則民國年轉西元） ----------\n",
    "_DATE_PAT_YMD = re.compile(r\"(?P<y>\\d{4})[/-](?P<m>\\d{1,2})[/-](?P<d>\\d{1,2})\")\n",
    "_DATE_PAT_ROC = re.compile(r\"(?P<y>\\d{2,3})[/-](?P<m>\\d{1,2})[/-](?P<d>\\d{1,2})\")\n",
    "\n",
    "# ---------- 抽取時間（支援 1:56:31、1:56、1時56分31秒、1點56分） ----------\n",
    "_TIME_PAT_COLON = re.compile(r\"\\b(?P<h>\\d{1,2})[:：](?P<m>\\d{1,2})(?:[:：](?P<s>\\d{1,2}))?\\b\")\n",
    "_TIME_PAT_CJK   = re.compile(r\"\\b(?P<h>\\d{1,2})\\s*[時点点]\\s*(?P<m>\\d{1,2})(?:\\s*分\\s*(?P<s>\\d{1,2})\\s*秒?)?\\b\")\n",
    "\n",
    "# ---------- 抽取 AM/PM（任意位置；多個命中取最後一個） ----------\n",
    "_AMPM_PAT = re.compile(r\"(上午|下午|中午|凌晨|晚間|晚上|\\bAM\\b|\\bam\\b|\\bPM\\b|\\bpm\\b)\")\n",
    "\n",
    "def APPX4_extract_datetime_parts(raw) -> tuple:\n",
    "    \"\"\"抽取 (year, month, day, hour, minute, second, ampm)；任一缺失則回傳 None 的位置。\"\"\"\n",
    "    if pd.isna(raw): return (None,)*7\n",
    "    s = _halfwidth(_strip_invis(str(raw))).strip()\n",
    "    if not s: return (None,)*7\n",
    "\n",
    "    # 日期：YYYY/MM/DD 優先\n",
    "    y = m = d = None\n",
    "    m1 = _DATE_PAT_YMD.search(s)\n",
    "    if m1:\n",
    "        y, m, d = int(m1.group(\"y\")), int(m1.group(\"m\")), int(m1.group(\"d\"))\n",
    "    else:\n",
    "        m2 = _DATE_PAT_ROC.search(s)\n",
    "        if m2:\n",
    "            ry = int(m2.group(\"y\"))\n",
    "            if 1 <= ry <= 150:\n",
    "                y = ry + 1911\n",
    "                m, d = int(m2.group(\"m\")), int(m2.group(\"d\"))\n",
    "\n",
    "    # 時間：冒號或中文\n",
    "    hh = mm = ss = None\n",
    "    mt = _TIME_PAT_COLON.search(s)\n",
    "    if mt:\n",
    "        hh = int(mt.group(\"h\")); mm = int(mt.group(\"m\")); ss = int(mt.group(\"s\") or 0)\n",
    "    else:\n",
    "        mt2 = _TIME_PAT_CJK.search(s)\n",
    "        if mt2:\n",
    "            hh = int(mt2.group(\"h\")); mm = int(mt2.group(\"m\")); ss = int(mt2.group(\"s\") or 0)\n",
    "\n",
    "    # AM/PM：取最後一個命中（有些字串會「上午…，下午…」）\n",
    "    ampm = None\n",
    "    toks = list(_AMPM_PAT.finditer(s))\n",
    "    if toks:\n",
    "        ampm_txt = toks[-1].group(1)\n",
    "        ampm = _AMPM_TOKENS.get(ampm_txt, None)\n",
    "\n",
    "    return y, m, d, hh, mm, ss, ampm\n",
    "\n",
    "def APPX4_build_timestamp(parts) -> pd.Timestamp:\n",
    "    y, m, d, hh, mm, ss, ampm = parts\n",
    "    # 必須至少有日期，否則給 NaT\n",
    "    if any(v is None for v in (y, m, d)):\n",
    "        return pd.NaT\n",
    "    # 時分秒預設 00:00:00，但若有 AM/PM 就至少要有小時\n",
    "    if hh is None: hh = 0\n",
    "    if mm is None: mm = 0\n",
    "    if ss is None: ss = 0\n",
    "\n",
    "    # 應用 AM/PM 規則（12 點特判）\n",
    "    if ampm == \"pm\":\n",
    "        if 1 <= hh <= 11: hh += 12\n",
    "        # 12 pm 保持 12\n",
    "    elif ampm == \"am\":\n",
    "        if hh == 12: hh = 0\n",
    "\n",
    "    try:\n",
    "        return pd.Timestamp(year=int(y), month=int(m), day=int(d), hour=int(hh), minute=int(mm), second=int(ss))\n",
    "    except Exception:\n",
    "        return pd.NaT\n",
    "\n",
    "def APPX4_parse_fix(series: pd.Series) -> pd.Series:\n",
    "    \"\"\"逐列用中文 AM/PM 抽取器組裝時間，避免被日期-only 模式吃掉。\"\"\"\n",
    "    return series.apply(lambda x: APPX4_build_timestamp(APPX4_extract_datetime_parts(x)))\n",
    "\n",
    "# ---------- 來源欄位選擇（避開 *parsed/*clean，首選 senddate；否則任何含 date/time 等） ----------\n",
    "def APPX4_pick_source_col(df: pd.DataFrame):\n",
    "    cols = list(df.columns)\n",
    "    # 先剔除 parsed/clean 等中間產物\n",
    "    def valid(c): \n",
    "        lc = c.lower()\n",
    "        return (\"parsed\" not in lc) and (\"clean\" not in lc)\n",
    "    prio = []\n",
    "    for key in (\"senddate\",):  # 你的資料通常叫這個\n",
    "        for c in cols:\n",
    "            if valid(c) and key in c and c not in prio: prio.append(c)\n",
    "    for key in (\"datetime\",\"timestamp\",\"date\",\"time\"):\n",
    "        for c in cols:\n",
    "            if valid(c) and key in c and c not in prio: prio.append(c)\n",
    "    # 兜底：全欄掃（但實務上應該在 prio 內會命中）\n",
    "    return prio or cols\n",
    "\n",
    "# ---------- 儲存：追溯副本 + 覆蓋原檔 ----------\n",
    "def APPX4_save(df: pd.DataFrame, src_path: str):\n",
    "    base = os.path.basename(src_path)\n",
    "    snap = os.path.join(APPX4_OUT_DIR, base)\n",
    "    df.to_csv(snap, index=False, encoding=\"utf-8\")\n",
    "    tmp = src_path + \".appx4tmp\"\n",
    "    df.to_csv(tmp, index=False, encoding=\"utf-8\")\n",
    "    shutil.move(tmp, src_path)\n",
    "    return snap, src_path\n",
    "\n",
    "# ---------- 主程式：四檔修補 ----------\n",
    "APPX4_rows = []\n",
    "for fp in APPX4_FILES:\n",
    "    try:\n",
    "        print(f\"\\n[APPX4] === START {os.path.basename(fp)} ===\")\n",
    "        df0 = APPX4_read_any(fp)\n",
    "        df = APPX4_force_lower_columns(df0)\n",
    "\n",
    "        # 選來源欄（逐一嘗試，直到解析率>0）\n",
    "        src_candidates = APPX4_pick_source_col(df)\n",
    "        chosen = None; parsed = None; best_ratio = -1.0\n",
    "        for c in src_candidates:\n",
    "            ser = df[c]\n",
    "            out = APPX4_parse_fix(ser)\n",
    "            ratio = float(out.notna().mean()) if len(out) else 0.0\n",
    "            if ratio > best_ratio:\n",
    "                best_ratio = ratio\n",
    "                chosen, parsed = c, out\n",
    "            # 若已經達到 0.9 就提早收斂\n",
    "            if ratio >= 0.9: break\n",
    "\n",
    "        # 寫入標準欄位（小寫）\n",
    "        if parsed is not None and parsed.notna().any():\n",
    "            df[\"senddate_parsed\"] = parsed\n",
    "        else:\n",
    "            df[\"senddate_parsed\"] = pd.NaT  # 明確給 NaT\n",
    "\n",
    "        # 紀錄與落地\n",
    "        tmin = str(parsed.min()) if parsed is not None and parsed.notna().any() else None\n",
    "        tmax = str(parsed.max()) if parsed is not None and parsed.notna().any() else None\n",
    "        snap, ovw = APPX4_save(df, fp)\n",
    "\n",
    "        summary = {\n",
    "            \"file\": fp, \"rows\": int(df.shape[0]), \"cols\": int(df.shape[1]),\n",
    "            \"source_col\": chosen, \"parsed_ratio\": round(best_ratio, 6),\n",
    "            \"time_min\": tmin, \"time_max\": tmax\n",
    "        }\n",
    "        with open(os.path.join(APPX4_LOG_DIR, f\"{os.path.splitext(os.path.basename(fp))[0]}_fix_summary.json\"), \"w\", encoding=\"utf-8\") as f:\n",
    "            json.dump(summary, f, ensure_ascii=False, indent=2)\n",
    "\n",
    "        APPX4_rows.append({\"file\": fp, **summary})\n",
    "        print(f\"[APPX4] DONE {os.path.basename(fp)} | src={chosen} ratio={best_ratio:.3f} | min={tmin} max={tmax}\")\n",
    "    except Exception as e:\n",
    "        APPX4_rows.append({\"file\": fp, \"status\": \"error\", \"error\": str(e)})\n",
    "        print(f\"[APPX4][ERROR] {os.path.basename(fp)}: {e}\")\n",
    "\n",
    "# 總表\n",
    "pd.DataFrame(APPX4_rows).to_csv(os.path.join(APPX4_OUT_DIR, \"summary_time_fix_bling4_v3.csv\"), index=False, encoding=\"utf-8\")\n",
    "print(\"\\n[APPX4] === SUMMARY ===\")\n",
    "print(pd.DataFrame(APPX4_rows))\n",
    "print(f\"[APPX4] summary saved → {os.path.join(APPX4_OUT_DIR, 'summary_time_fix_bling4_v3.csv')}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "9add50d9-82f5-413d-b6db-98a9a30f3bdb",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "[APPX5] === START 095323.csv ===\n",
      "[APPX5] DONE 095323.csv | replaced=23791/23791 | remain_ampm=0\n",
      "\n",
      "[APPX5] === START 095707.csv ===\n",
      "[APPX5] DONE 095707.csv | replaced=20180/20180 | remain_ampm=0\n",
      "\n",
      "[APPX5] === START 114309.csv ===\n",
      "[APPX5] DONE 114309.csv | replaced=71729/71729 | remain_ampm=0\n",
      "\n",
      "[APPX5] === START 230933.csv ===\n",
      "[APPX5] DONE 230933.csv | replaced=30249/30249 | remain_ampm=0\n",
      "\n",
      "[APPX5] === SUMMARY ===\n",
      "                                      file   rows  cols  replaced_rows  \\\n",
      "0  /home/jovyan/RT08/0925/bling/095323.csv  23791   119          23791   \n",
      "1  /home/jovyan/RT08/0925/bling/095707.csv  20180   119          20180   \n",
      "2  /home/jovyan/RT08/0925/bling/114309.csv  71729   119          71729   \n",
      "3  /home/jovyan/RT08/0925/bling/230933.csv  30249   119          30249   \n",
      "\n",
      "   remain_chinese_ampm_rows  cleaned_ratio  \\\n",
      "0                         0            1.0   \n",
      "1                         0            1.0   \n",
      "2                         0            1.0   \n",
      "3                         0            1.0   \n",
      "\n",
      "                                           snapshot  \\\n",
      "0  /home/jovyan/RT08/0925/bling_record/4/095323.csv   \n",
      "1  /home/jovyan/RT08/0925/bling_record/4/095707.csv   \n",
      "2  /home/jovyan/RT08/0925/bling_record/4/114309.csv   \n",
      "3  /home/jovyan/RT08/0925/bling_record/4/230933.csv   \n",
      "\n",
      "                               overwritten  \n",
      "0  /home/jovyan/RT08/0925/bling/095323.csv  \n",
      "1  /home/jovyan/RT08/0925/bling/095707.csv  \n",
      "2  /home/jovyan/RT08/0925/bling/114309.csv  \n",
      "3  /home/jovyan/RT08/0925/bling/230933.csv  \n",
      "[APPX5] summary saved → /home/jovyan/RT08/0925/bling_record/4/summary_senddate_normalized.csv\n"
     ]
    }
   ],
   "source": [
    "# okkk [Append-only] APPX5 — 將中文 AM/PM 字串標準化為 24h，並覆寫 senddate 欄位\n",
    "# - 來源：/home/jovyan/RT08/0925/bling/{095323,095707,114309,230933}.csv\n",
    "# - 動作：備份原字串至 senddate_raw；將 senddate_parsed 格式化為 24h 字串覆寫 senddate\n",
    "# - 輸出：覆寫原檔 + 在 /home/jovyan/RT08/0925/bling_record/4/logs_norm/ 產出檢查報表\n",
    "# ============================================================\n",
    "import os, re, json, shutil, unicodedata\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "APPX5_FILES = [\n",
    "    \"/home/jovyan/RT08/0925/bling/095323.csv\",\n",
    "    \"/home/jovyan/RT08/0925/bling/095707.csv\",\n",
    "    \"/home/jovyan/RT08/0925/bling/114309.csv\",\n",
    "    \"/home/jovyan/RT08/0925/bling/230933.csv\",\n",
    "]\n",
    "APPX5_OUT_DIR = \"/home/jovyan/RT08/0925/bling_record/4\"\n",
    "APPX5_LOG_DIR = os.path.join(APPX5_OUT_DIR, \"logs_norm\")\n",
    "os.makedirs(APPX5_OUT_DIR, exist_ok=True)\n",
    "os.makedirs(APPX5_LOG_DIR, exist_ok=True)\n",
    "\n",
    "def APPX5_read_any(path: str) -> pd.DataFrame:\n",
    "    if \"read_table_resilient\" in globals() and callable(globals()[\"read_table_resilient\"]):\n",
    "        df, meta = read_table_resilient(path, max_header_search=5)\n",
    "        if meta.get(\"read_ok\"):\n",
    "            return df\n",
    "    for enc in [\"utf-8-sig\",\"utf-8\",\"cp950\",\"big5\",\"latin1\"]:\n",
    "        try:\n",
    "            df = pd.read_csv(path, encoding=enc, engine=\"python\", on_bad_lines=\"skip\")\n",
    "            if df.shape[1] > 0:\n",
    "                return df\n",
    "        except Exception:\n",
    "            continue\n",
    "    raise RuntimeError(f\"[APPX5] 無法讀取：{path}\")\n",
    "\n",
    "def APPX5_force_lower_columns(df: pd.DataFrame):\n",
    "    # 保證欄位小寫，避免大小寫找不到欄位\n",
    "    orig = list(df.columns)\n",
    "    seen, new = {}, []\n",
    "    for c in orig:\n",
    "        k = str(c).strip().lower()\n",
    "        if k not in seen: seen[k]=1; new.append(k)\n",
    "        else: seen[k]+=1; new.append(f\"{k}_dup{seen[k]}\")\n",
    "    df = df.copy()\n",
    "    df.columns = new\n",
    "    return df\n",
    "\n",
    "# 落地（追溯副本 + 覆蓋原檔）\n",
    "def APPX5_save(df: pd.DataFrame, src_path: str):\n",
    "    base = os.path.basename(src_path)\n",
    "    snap = os.path.join(APPX5_OUT_DIR, base)\n",
    "    df.to_csv(snap, index=False, encoding=\"utf-8\")\n",
    "    tmp = src_path + \".appx5tmp\"\n",
    "    df.to_csv(tmp, index=False, encoding=\"utf-8\")\n",
    "    shutil.move(tmp, src_path)\n",
    "    return snap, src_path\n",
    "\n",
    "# 主程式\n",
    "APPX5_rows = []\n",
    "for fp in APPX5_FILES:\n",
    "    try:\n",
    "        print(f\"\\n[APPX5] === START {os.path.basename(fp)} ===\")\n",
    "        df0 = APPX5_read_any(fp)\n",
    "        df = APPX5_force_lower_columns(df0)\n",
    "\n",
    "        if \"senddate\" not in df.columns:\n",
    "            raise RuntimeError(\"找不到 senddate 欄位；請確認欄位名稱是否不同。\")\n",
    "\n",
    "        # 備份原始字串\n",
    "        if \"senddate_raw\" not in df.columns:\n",
    "            df[\"senddate_raw\"] = df[\"senddate\"]\n",
    "\n",
    "        # 確保有 datetime 欄位來源：優先既有 senddate_parsed；若沒有就用 pandas 解析一次（保底）\n",
    "        if \"senddate_parsed\" in df.columns and pd.api.types.is_datetime64_any_dtype(df[\"senddate_parsed\"]):\n",
    "            dt = df[\"senddate_parsed\"]\n",
    "        elif \"senddate_parsed\" in df.columns:\n",
    "            dt = pd.to_datetime(df[\"senddate_parsed\"], errors=\"coerce\")\n",
    "        else:\n",
    "            # 保底：直接嘗試解析 senddate（已經有 APPX4 了，正常不會走到這）\n",
    "            # 這裡不做中文 AM/PM 邏輯，僅作為兜底，避免流程中斷\n",
    "            dt = pd.to_datetime(df[\"senddate\"], errors=\"coerce\", infer_datetime_format=True)\n",
    "            df[\"senddate_parsed\"] = dt  # 也一併補上\n",
    "\n",
    "        # 產生 24h 格式字串（NaT -> 空字串）\n",
    "        fmt_str = pd.Series([\"\"] * len(df), index=df.index, dtype=object)\n",
    "        mask_ok = dt.notna()\n",
    "        fmt_str.loc[mask_ok] = dt.loc[mask_ok].dt.strftime(\"%Y/%m/%d %H:%M:%S\")\n",
    "        df[\"senddate_24h\"] = fmt_str\n",
    "\n",
    "        # 用 24h 覆寫 senddate；若該列無法解析則保留原值\n",
    "        df.loc[mask_ok, \"senddate\"] = df.loc[mask_ok, \"senddate_24h\"]\n",
    "\n",
    "        # 檢查 senddate 中是否仍殘留中文 AM/PM 關鍵詞\n",
    "        pat = re.compile(r\"(上午|下午|中午|早上|凌晨|晚間|晚上)\", flags=re.I)\n",
    "        remain_cnt = int(df[\"senddate\"].astype(str).str.contains(pat).sum())\n",
    "        total = int(len(df))\n",
    "        cleaned_ratio = float(1 - remain_cnt / total) if total else 1.0\n",
    "\n",
    "        # 落地\n",
    "        snap, ovw = APPX5_save(df, fp)\n",
    "\n",
    "        # 報表\n",
    "        base = os.path.splitext(os.path.basename(fp))[0]\n",
    "        summary = {\n",
    "            \"file\": fp,\n",
    "            \"rows\": int(df.shape[0]),\n",
    "            \"cols\": int(df.shape[1]),\n",
    "            \"replaced_rows\": int(mask_ok.sum()),\n",
    "            \"remain_chinese_ampm_rows\": remain_cnt,\n",
    "            \"cleaned_ratio\": round(cleaned_ratio, 6),\n",
    "            \"snapshot\": snap,\n",
    "            \"overwritten\": ovw\n",
    "        }\n",
    "        with open(os.path.join(APPX5_LOG_DIR, f\"{base}_normalize_summary.json\"), \"w\", encoding=\"utf-8\") as f:\n",
    "            json.dump(summary, f, ensure_ascii=False, indent=2)\n",
    "\n",
    "        # 取樣：若還有殘留，輸出 20 筆樣本便於追蹤\n",
    "        if remain_cnt > 0:\n",
    "            rem_idx = df[\"senddate\"].astype(str).str.contains(pat)\n",
    "            df.loc[rem_idx, [\"senddate_raw\",\"senddate\"]].head(20).to_csv(\n",
    "                os.path.join(APPX5_LOG_DIR, f\"{base}_remain_samples.csv\"), index=False, encoding=\"utf-8\"\n",
    "            )\n",
    "\n",
    "        APPX5_rows.append({\"file\": fp, **summary})\n",
    "        print(f\"[APPX5] DONE {os.path.basename(fp)} | replaced={int(mask_ok.sum())}/{total} | remain_ampm={remain_cnt}\")\n",
    "    except Exception as e:\n",
    "        APPX5_rows.append({\"file\": fp, \"status\": \"error\", \"error\": str(e)})\n",
    "        print(f\"[APPX5][ERROR] {os.path.basename(fp)}: {e}\")\n",
    "\n",
    "# 總表\n",
    "pd.DataFrame(APPX5_rows).to_csv(os.path.join(APPX5_OUT_DIR, \"summary_senddate_normalized.csv\"),\n",
    "                                index=False, encoding=\"utf-8\")\n",
    "print(\"\\n[APPX5] === SUMMARY ===\")\n",
    "print(pd.DataFrame(APPX5_rows))\n",
    "print(f\"[APPX5] summary saved → {os.path.join(APPX5_OUT_DIR, 'summary_senddate_normalized.csv')}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "15a14a17-2ba6-4362-8ecd-c1ce79cbd4cd",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "================= COLUMN AUDIT =================\n",
      "1. 檔案名稱: 089271.csv\n",
      "2. 總欄位數: 120\n",
      "3. 全小寫: True\n",
      "4. 全符合 snake_case: False\n",
      "5. 欄位皆唯一: True\n",
      "6. 不全小寫欄位: (無)\n",
      "7. 不符 snake_case 欄位: 列數\n",
      "8. 重複欄位: (無)\n",
      "\n",
      "================= COLUMN AUDIT =================\n",
      "1. 檔案名稱: 095323.csv\n",
      "2. 總欄位數: 119\n",
      "3. 全小寫: True\n",
      "4. 全符合 snake_case: False\n",
      "5. 欄位皆唯一: True\n",
      "6. 不全小寫欄位: (無)\n",
      "7. 不符 snake_case 欄位: row count;hfo delta p;hfo mean;hfo rate;m_hfo delta p;m_hfo mean\n",
      "8. 重複欄位: (無)\n",
      "\n",
      "================= COLUMN AUDIT =================\n",
      "1. 檔案名稱: 095707.csv\n",
      "2. 總欄位數: 119\n",
      "3. 全小寫: True\n",
      "4. 全符合 snake_case: False\n",
      "5. 欄位皆唯一: True\n",
      "6. 不全小寫欄位: (無)\n",
      "7. 不符 snake_case 欄位: row count;hfo delta p;hfo mean;hfo rate;m_hfo delta p;m_hfo mean\n",
      "8. 重複欄位: (無)\n",
      "\n",
      "================= COLUMN AUDIT =================\n",
      "1. 檔案名稱: 114309.csv\n",
      "2. 總欄位數: 119\n",
      "3. 全小寫: True\n",
      "4. 全符合 snake_case: False\n",
      "5. 欄位皆唯一: True\n",
      "6. 不全小寫欄位: (無)\n",
      "7. 不符 snake_case 欄位: row count;hfo delta p;hfo mean;hfo rate;m_hfo delta p;m_hfo mean\n",
      "8. 重複欄位: (無)\n",
      "\n",
      "================= COLUMN AUDIT =================\n",
      "1. 檔案名稱: 230933.csv\n",
      "2. 總欄位數: 119\n",
      "3. 全小寫: True\n",
      "4. 全符合 snake_case: False\n",
      "5. 欄位皆唯一: True\n",
      "6. 不全小寫欄位: (無)\n",
      "7. 不符 snake_case 欄位: row count;hfo delta p;hfo mean;hfo rate;m_hfo delta p;m_hfo mean\n",
      "8. 重複欄位: (無)\n",
      "\n",
      "================= COLUMN AUDIT =================\n",
      "1. 檔案名稱: 4216007.csv\n",
      "2. 總欄位數: 120\n",
      "3. 全小寫: True\n",
      "4. 全符合 snake_case: False\n",
      "5. 欄位皆唯一: True\n",
      "6. 不全小寫欄位: (無)\n",
      "7. 不符 snake_case 欄位: 列數\n",
      "8. 重複欄位: (無)\n",
      "\n",
      "================= COLUMN AUDIT =================\n",
      "1. 檔案名稱: 7108162.csv\n",
      "2. 總欄位數: 120\n",
      "3. 全小寫: True\n",
      "4. 全符合 snake_case: False\n",
      "5. 欄位皆唯一: True\n",
      "6. 不全小寫欄位: (無)\n",
      "7. 不符 snake_case 欄位: 列數\n",
      "8. 重複欄位: (無)\n",
      "\n",
      "================= COLUMN AUDIT =================\n",
      "1. 檔案名稱: 7408338.csv\n",
      "2. 總欄位數: 120\n",
      "3. 全小寫: True\n",
      "4. 全符合 snake_case: False\n",
      "5. 欄位皆唯一: True\n",
      "6. 不全小寫欄位: (無)\n",
      "7. 不符 snake_case 欄位: 列數\n",
      "8. 重複欄位: (無)\n",
      "\n",
      "================= COLUMN AUDIT =================\n",
      "1. 檔案名稱: 7657698.csv\n",
      "2. 總欄位數: 120\n",
      "3. 全小寫: True\n",
      "4. 全符合 snake_case: False\n",
      "5. 欄位皆唯一: True\n",
      "6. 不全小寫欄位: (無)\n",
      "7. 不符 snake_case 欄位: 列數\n",
      "8. 重複欄位: (無)\n",
      "\n",
      "================= COLUMN AUDIT =================\n",
      "1. 檔案名稱: 7721164.csv\n",
      "2. 總欄位數: 120\n",
      "3. 全小寫: True\n",
      "4. 全符合 snake_case: False\n",
      "5. 欄位皆唯一: True\n",
      "6. 不全小寫欄位: (無)\n",
      "7. 不符 snake_case 欄位: 列數\n",
      "8. 重複欄位: (無)\n",
      "\n",
      "================= COLUMN AUDIT =================\n",
      "1. 檔案名稱: PatNo_ID_1560013303.csv\n",
      "2. 總欄位數: 120\n",
      "3. 全小寫: True\n",
      "4. 全符合 snake_case: False\n",
      "5. 欄位皆唯一: True\n",
      "6. 不全小寫欄位: (無)\n",
      "7. 不符 snake_case 欄位: 列數\n",
      "8. 重複欄位: (無)\n",
      "\n",
      "================= COLUMN AUDIT =================\n",
      "1. 檔案名稱: PatNo_ID_1562733396.csv\n",
      "2. 總欄位數: 120\n",
      "3. 全小寫: True\n",
      "4. 全符合 snake_case: False\n",
      "5. 欄位皆唯一: True\n",
      "6. 不全小寫欄位: (無)\n",
      "7. 不符 snake_case 欄位: 列數\n",
      "8. 重複欄位: (無)\n",
      "\n",
      "================= COLUMN AUDIT =================\n",
      "1. 檔案名稱: PatNo_ID_1563587183.csv\n",
      "2. 總欄位數: 120\n",
      "3. 全小寫: True\n",
      "4. 全符合 snake_case: False\n",
      "5. 欄位皆唯一: True\n",
      "6. 不全小寫欄位: (無)\n",
      "7. 不符 snake_case 欄位: 列數\n",
      "8. 重複欄位: (無)\n",
      "\n",
      "================= COLUMN AUDIT =================\n",
      "1. 檔案名稱: PatNo_ID_1564148644.csv\n",
      "2. 總欄位數: 120\n",
      "3. 全小寫: True\n",
      "4. 全符合 snake_case: False\n",
      "5. 欄位皆唯一: True\n",
      "6. 不全小寫欄位: (無)\n",
      "7. 不符 snake_case 欄位: 列數\n",
      "8. 重複欄位: (無)\n",
      "\n",
      "================= COLUMN AUDIT =================\n",
      "1. 檔案名稱: PatNo_ID_1565148312.csv\n",
      "2. 總欄位數: 120\n",
      "3. 全小寫: True\n",
      "4. 全符合 snake_case: False\n",
      "5. 欄位皆唯一: True\n",
      "6. 不全小寫欄位: (無)\n",
      "7. 不符 snake_case 欄位: 列數\n",
      "8. 重複欄位: (無)\n",
      "\n",
      "================= COLUMN AUDIT =================\n",
      "1. 檔案名稱: PatNo_ID_1565378038.csv\n",
      "2. 總欄位數: 120\n",
      "3. 全小寫: True\n",
      "4. 全符合 snake_case: False\n",
      "5. 欄位皆唯一: True\n",
      "6. 不全小寫欄位: (無)\n",
      "7. 不符 snake_case 欄位: 列數\n",
      "8. 重複欄位: (無)\n",
      "\n",
      "================= COLUMN AUDIT =================\n",
      "1. 檔案名稱: PatNo_ID_1566123680.csv\n",
      "2. 總欄位數: 120\n",
      "3. 全小寫: True\n",
      "4. 全符合 snake_case: False\n",
      "5. 欄位皆唯一: True\n",
      "6. 不全小寫欄位: (無)\n",
      "7. 不符 snake_case 欄位: 列數\n",
      "8. 重複欄位: (無)\n",
      "\n",
      "================= COLUMN AUDIT =================\n",
      "1. 檔案名稱: PatNo_ID_1566252197.csv\n",
      "2. 總欄位數: 120\n",
      "3. 全小寫: True\n",
      "4. 全符合 snake_case: False\n",
      "5. 欄位皆唯一: True\n",
      "6. 不全小寫欄位: (無)\n",
      "7. 不符 snake_case 欄位: 列數\n",
      "8. 重複欄位: (無)\n",
      "\n",
      "================= COLUMN AUDIT =================\n",
      "1. 檔案名稱: PatNo_ID_1566279967.csv\n",
      "2. 總欄位數: 120\n",
      "3. 全小寫: True\n",
      "4. 全符合 snake_case: False\n",
      "5. 欄位皆唯一: True\n",
      "6. 不全小寫欄位: (無)\n",
      "7. 不符 snake_case 欄位: 列數\n",
      "8. 重複欄位: (無)\n",
      "\n",
      "================= COLUMN AUDIT =================\n",
      "1. 檔案名稱: PatNo_ID_1566671274.csv\n",
      "2. 總欄位數: 120\n",
      "3. 全小寫: True\n",
      "4. 全符合 snake_case: False\n",
      "5. 欄位皆唯一: True\n",
      "6. 不全小寫欄位: (無)\n",
      "7. 不符 snake_case 欄位: 列數\n",
      "8. 重複欄位: (無)\n",
      "\n",
      "================= COLUMN AUDIT =================\n",
      "1. 檔案名稱: PatNo_ID_1566911879.csv\n",
      "2. 總欄位數: 120\n",
      "3. 全小寫: True\n",
      "4. 全符合 snake_case: False\n",
      "5. 欄位皆唯一: True\n",
      "6. 不全小寫欄位: (無)\n",
      "7. 不符 snake_case 欄位: 列數\n",
      "8. 重複欄位: (無)\n",
      "\n",
      "================= COLUMN AUDIT =================\n",
      "1. 檔案名稱: PatNo_ID_1567747650.csv\n",
      "2. 總欄位數: 120\n",
      "3. 全小寫: True\n",
      "4. 全符合 snake_case: False\n",
      "5. 欄位皆唯一: True\n",
      "6. 不全小寫欄位: (無)\n",
      "7. 不符 snake_case 欄位: 列數\n",
      "8. 重複欄位: (無)\n",
      "\n",
      "================= COLUMN AUDIT =================\n",
      "1. 檔案名稱: PatNo_ID_1567804800.csv\n",
      "2. 總欄位數: 120\n",
      "3. 全小寫: True\n",
      "4. 全符合 snake_case: False\n",
      "5. 欄位皆唯一: True\n",
      "6. 不全小寫欄位: (無)\n",
      "7. 不符 snake_case 欄位: 列數\n",
      "8. 重複欄位: (無)\n",
      "\n",
      "================= COLUMN AUDIT =================\n",
      "1. 檔案名稱: PatNo_ID_1567832735.csv\n",
      "2. 總欄位數: 120\n",
      "3. 全小寫: True\n",
      "4. 全符合 snake_case: False\n",
      "5. 欄位皆唯一: True\n",
      "6. 不全小寫欄位: (無)\n",
      "7. 不符 snake_case 欄位: 列數\n",
      "8. 重複欄位: (無)\n",
      "\n",
      "================= COLUMN AUDIT =================\n",
      "1. 檔案名稱: PatNo_ID_1568039398.csv\n",
      "2. 總欄位數: 120\n",
      "3. 全小寫: True\n",
      "4. 全符合 snake_case: False\n",
      "5. 欄位皆唯一: True\n",
      "6. 不全小寫欄位: (無)\n",
      "7. 不符 snake_case 欄位: 列數\n",
      "8. 重複欄位: (無)\n",
      "\n",
      "================= COLUMN AUDIT =================\n",
      "1. 檔案名稱: PatNo_ID_1568574099.csv\n",
      "2. 總欄位數: 120\n",
      "3. 全小寫: True\n",
      "4. 全符合 snake_case: False\n",
      "5. 欄位皆唯一: True\n",
      "6. 不全小寫欄位: (無)\n",
      "7. 不符 snake_case 欄位: 列數\n",
      "8. 重複欄位: (無)\n",
      "\n",
      "================= COLUMN AUDIT =================\n",
      "1. 檔案名稱: PatNo_ID_1568813269.csv\n",
      "2. 總欄位數: 120\n",
      "3. 全小寫: True\n",
      "4. 全符合 snake_case: False\n",
      "5. 欄位皆唯一: True\n",
      "6. 不全小寫欄位: (無)\n",
      "7. 不符 snake_case 欄位: 列數\n",
      "8. 重複欄位: (無)\n",
      "\n",
      "================= COLUMN AUDIT =================\n",
      "1. 檔案名稱: PatNo_ID_1568952422.csv\n",
      "2. 總欄位數: 120\n",
      "3. 全小寫: True\n",
      "4. 全符合 snake_case: False\n",
      "5. 欄位皆唯一: True\n",
      "6. 不全小寫欄位: (無)\n",
      "7. 不符 snake_case 欄位: 列數\n",
      "8. 重複欄位: (無)\n",
      "\n",
      "================= COLUMN AUDIT =================\n",
      "1. 檔案名稱: PatNo_ID_1569083701.csv\n",
      "2. 總欄位數: 120\n",
      "3. 全小寫: True\n",
      "4. 全符合 snake_case: False\n",
      "5. 欄位皆唯一: True\n",
      "6. 不全小寫欄位: (無)\n",
      "7. 不符 snake_case 欄位: 列數\n",
      "8. 重複欄位: (無)\n",
      "\n",
      "================= COLUMN AUDIT =================\n",
      "1. 檔案名稱: PatNo_ID_1569944983.csv\n",
      "2. 總欄位數: 120\n",
      "3. 全小寫: True\n",
      "4. 全符合 snake_case: False\n",
      "5. 欄位皆唯一: True\n",
      "6. 不全小寫欄位: (無)\n",
      "7. 不符 snake_case 欄位: 列數\n",
      "8. 重複欄位: (無)\n",
      "\n",
      "================= COLUMN AUDIT =================\n",
      "1. 檔案名稱: PatNo_ID_1570089466.csv\n",
      "2. 總欄位數: 120\n",
      "3. 全小寫: True\n",
      "4. 全符合 snake_case: False\n",
      "5. 欄位皆唯一: True\n",
      "6. 不全小寫欄位: (無)\n",
      "7. 不符 snake_case 欄位: 列數\n",
      "8. 重複欄位: (無)\n",
      "\n",
      "================= COLUMN AUDIT =================\n",
      "1. 檔案名稱: PatNo_ID_1570242703.csv\n",
      "2. 總欄位數: 120\n",
      "3. 全小寫: True\n",
      "4. 全符合 snake_case: False\n",
      "5. 欄位皆唯一: True\n",
      "6. 不全小寫欄位: (無)\n",
      "7. 不符 snake_case 欄位: 列數\n",
      "8. 重複欄位: (無)\n",
      "\n",
      "================= COLUMN AUDIT =================\n",
      "1. 檔案名稱: PatNo_ID_1570273244.csv\n",
      "2. 總欄位數: 120\n",
      "3. 全小寫: True\n",
      "4. 全符合 snake_case: False\n",
      "5. 欄位皆唯一: True\n",
      "6. 不全小寫欄位: (無)\n",
      "7. 不符 snake_case 欄位: 列數\n",
      "8. 重複欄位: (無)\n",
      "\n",
      "================= COLUMN AUDIT =================\n",
      "1. 檔案名稱: PatNo_ID_1570642083.csv\n",
      "2. 總欄位數: 120\n",
      "3. 全小寫: True\n",
      "4. 全符合 snake_case: False\n",
      "5. 欄位皆唯一: True\n",
      "6. 不全小寫欄位: (無)\n",
      "7. 不符 snake_case 欄位: 列數\n",
      "8. 重複欄位: (無)\n",
      "\n",
      "================= COLUMN AUDIT =================\n",
      "1. 檔案名稱: PatNo_ID_1571945701.csv\n",
      "2. 總欄位數: 120\n",
      "3. 全小寫: True\n",
      "4. 全符合 snake_case: False\n",
      "5. 欄位皆唯一: True\n",
      "6. 不全小寫欄位: (無)\n",
      "7. 不符 snake_case 欄位: 列數\n",
      "8. 重複欄位: (無)\n",
      "\n",
      "================= COLUMN AUDIT =================\n",
      "1. 檔案名稱: PatNo_ID_1572481361.csv\n",
      "2. 總欄位數: 120\n",
      "3. 全小寫: True\n",
      "4. 全符合 snake_case: False\n",
      "5. 欄位皆唯一: True\n",
      "6. 不全小寫欄位: (無)\n",
      "7. 不符 snake_case 欄位: 列數\n",
      "8. 重複欄位: (無)\n",
      "\n",
      "================= COLUMN AUDIT =================\n",
      "1. 檔案名稱: PatNo_ID_1572562839.csv\n",
      "2. 總欄位數: 120\n",
      "3. 全小寫: True\n",
      "4. 全符合 snake_case: False\n",
      "5. 欄位皆唯一: True\n",
      "6. 不全小寫欄位: (無)\n",
      "7. 不符 snake_case 欄位: 列數\n",
      "8. 重複欄位: (無)\n",
      "\n",
      "================= COLUMN AUDIT =================\n",
      "1. 檔案名稱: PatNo_ID_1572831765.csv\n",
      "2. 總欄位數: 120\n",
      "3. 全小寫: True\n",
      "4. 全符合 snake_case: False\n",
      "5. 欄位皆唯一: True\n",
      "6. 不全小寫欄位: (無)\n",
      "7. 不符 snake_case 欄位: 列數\n",
      "8. 重複欄位: (無)\n",
      "\n",
      "================= COLUMN AUDIT =================\n",
      "1. 檔案名稱: PatNo_ID_1572976822.csv\n",
      "2. 總欄位數: 120\n",
      "3. 全小寫: True\n",
      "4. 全符合 snake_case: False\n",
      "5. 欄位皆唯一: True\n",
      "6. 不全小寫欄位: (無)\n",
      "7. 不符 snake_case 欄位: 列數\n",
      "8. 重複欄位: (無)\n",
      "\n",
      "================= COLUMN AUDIT =================\n",
      "1. 檔案名稱: PatNo_ID_1573063188.csv\n",
      "2. 總欄位數: 120\n",
      "3. 全小寫: True\n",
      "4. 全符合 snake_case: False\n",
      "5. 欄位皆唯一: True\n",
      "6. 不全小寫欄位: (無)\n",
      "7. 不符 snake_case 欄位: 列數\n",
      "8. 重複欄位: (無)\n",
      "\n",
      "================= COLUMN AUDIT =================\n",
      "1. 檔案名稱: PatNo_ID_1573249295.csv\n",
      "2. 總欄位數: 120\n",
      "3. 全小寫: True\n",
      "4. 全符合 snake_case: False\n",
      "5. 欄位皆唯一: True\n",
      "6. 不全小寫欄位: (無)\n",
      "7. 不符 snake_case 欄位: 列數\n",
      "8. 重複欄位: (無)\n",
      "\n",
      "================= COLUMN AUDIT =================\n",
      "1. 檔案名稱: PatNo_ID_1573964540.csv\n",
      "2. 總欄位數: 120\n",
      "3. 全小寫: True\n",
      "4. 全符合 snake_case: False\n",
      "5. 欄位皆唯一: True\n",
      "6. 不全小寫欄位: (無)\n",
      "7. 不符 snake_case 欄位: 列數\n",
      "8. 重複欄位: (無)\n",
      "\n",
      "================= COLUMN AUDIT =================\n",
      "1. 檔案名稱: PatNo_ID_1574148494.csv\n",
      "2. 總欄位數: 120\n",
      "3. 全小寫: True\n",
      "4. 全符合 snake_case: False\n",
      "5. 欄位皆唯一: True\n",
      "6. 不全小寫欄位: (無)\n",
      "7. 不符 snake_case 欄位: 列數\n",
      "8. 重複欄位: (無)\n",
      "\n",
      "================= COLUMN AUDIT =================\n",
      "1. 檔案名稱: PatNo_ID_1574270349.csv\n",
      "2. 總欄位數: 120\n",
      "3. 全小寫: True\n",
      "4. 全符合 snake_case: False\n",
      "5. 欄位皆唯一: True\n",
      "6. 不全小寫欄位: (無)\n",
      "7. 不符 snake_case 欄位: 列數\n",
      "8. 重複欄位: (無)\n",
      "\n",
      "================= COLUMN AUDIT =================\n",
      "1. 檔案名稱: PatNo_ID_1574528808.csv\n",
      "2. 總欄位數: 120\n",
      "3. 全小寫: True\n",
      "4. 全符合 snake_case: False\n",
      "5. 欄位皆唯一: True\n",
      "6. 不全小寫欄位: (無)\n",
      "7. 不符 snake_case 欄位: 列數\n",
      "8. 重複欄位: (無)\n",
      "\n",
      "================= COLUMN AUDIT =================\n",
      "1. 檔案名稱: PatNo_ID_1574831525.csv\n",
      "2. 總欄位數: 120\n",
      "3. 全小寫: True\n",
      "4. 全符合 snake_case: False\n",
      "5. 欄位皆唯一: True\n",
      "6. 不全小寫欄位: (無)\n",
      "7. 不符 snake_case 欄位: 列數\n",
      "8. 重複欄位: (無)\n",
      "\n",
      "================= COLUMN AUDIT =================\n",
      "1. 檔案名稱: PatNo_ID_1574987447.csv\n",
      "2. 總欄位數: 120\n",
      "3. 全小寫: True\n",
      "4. 全符合 snake_case: False\n",
      "5. 欄位皆唯一: True\n",
      "6. 不全小寫欄位: (無)\n",
      "7. 不符 snake_case 欄位: 列數\n",
      "8. 重複欄位: (無)\n",
      "\n",
      "================= COLUMN AUDIT =================\n",
      "1. 檔案名稱: PatNo_ID_1575060177.csv\n",
      "2. 總欄位數: 120\n",
      "3. 全小寫: True\n",
      "4. 全符合 snake_case: False\n",
      "5. 欄位皆唯一: True\n",
      "6. 不全小寫欄位: (無)\n",
      "7. 不符 snake_case 欄位: 列數\n",
      "8. 重複欄位: (無)\n",
      "\n",
      "================= COLUMN AUDIT =================\n",
      "1. 檔案名稱: PatNo_ID_1575256902.csv\n",
      "2. 總欄位數: 120\n",
      "3. 全小寫: True\n",
      "4. 全符合 snake_case: False\n",
      "5. 欄位皆唯一: True\n",
      "6. 不全小寫欄位: (無)\n",
      "7. 不符 snake_case 欄位: 列數\n",
      "8. 重複欄位: (無)\n",
      "\n",
      "================= COLUMN AUDIT =================\n",
      "1. 檔案名稱: PatNo_ID_1575445051.csv\n",
      "2. 總欄位數: 120\n",
      "3. 全小寫: True\n",
      "4. 全符合 snake_case: False\n",
      "5. 欄位皆唯一: True\n",
      "6. 不全小寫欄位: (無)\n",
      "7. 不符 snake_case 欄位: 列數\n",
      "8. 重複欄位: (無)\n",
      "\n",
      "================= COLUMN AUDIT =================\n",
      "1. 檔案名稱: PatNo_ID_1575502382.csv\n",
      "2. 總欄位數: 120\n",
      "3. 全小寫: True\n",
      "4. 全符合 snake_case: False\n",
      "5. 欄位皆唯一: True\n",
      "6. 不全小寫欄位: (無)\n",
      "7. 不符 snake_case 欄位: 列數\n",
      "8. 重複欄位: (無)\n",
      "\n",
      "================= COLUMN AUDIT =================\n",
      "1. 檔案名稱: PatNo_ID_1575975485.csv\n",
      "2. 總欄位數: 120\n",
      "3. 全小寫: True\n",
      "4. 全符合 snake_case: False\n",
      "5. 欄位皆唯一: True\n",
      "6. 不全小寫欄位: (無)\n",
      "7. 不符 snake_case 欄位: 列數\n",
      "8. 重複欄位: (無)\n",
      "\n",
      "================= COLUMN AUDIT =================\n",
      "1. 檔案名稱: PatNo_ID_1576115572.csv\n",
      "2. 總欄位數: 120\n",
      "3. 全小寫: True\n",
      "4. 全符合 snake_case: False\n",
      "5. 欄位皆唯一: True\n",
      "6. 不全小寫欄位: (無)\n",
      "7. 不符 snake_case 欄位: 列數\n",
      "8. 重複欄位: (無)\n",
      "\n",
      "================= COLUMN AUDIT =================\n",
      "1. 檔案名稱: PatNo_ID_1576116479.csv\n",
      "2. 總欄位數: 120\n",
      "3. 全小寫: True\n",
      "4. 全符合 snake_case: False\n",
      "5. 欄位皆唯一: True\n",
      "6. 不全小寫欄位: (無)\n",
      "7. 不符 snake_case 欄位: 列數\n",
      "8. 重複欄位: (無)\n",
      "\n",
      "================= COLUMN AUDIT =================\n",
      "1. 檔案名稱: PatNo_ID_1576301569.csv\n",
      "2. 總欄位數: 120\n",
      "3. 全小寫: True\n",
      "4. 全符合 snake_case: False\n",
      "5. 欄位皆唯一: True\n",
      "6. 不全小寫欄位: (無)\n",
      "7. 不符 snake_case 欄位: 列數\n",
      "8. 重複欄位: (無)\n",
      "\n",
      "================= COLUMN AUDIT =================\n",
      "1. 檔案名稱: PatNo_ID_1576964560.csv\n",
      "2. 總欄位數: 120\n",
      "3. 全小寫: True\n",
      "4. 全符合 snake_case: False\n",
      "5. 欄位皆唯一: True\n",
      "6. 不全小寫欄位: (無)\n",
      "7. 不符 snake_case 欄位: 列數\n",
      "8. 重複欄位: (無)\n",
      "\n",
      "================= COLUMN AUDIT =================\n",
      "1. 檔案名稱: PatNo_ID_1577042911.csv\n",
      "2. 總欄位數: 120\n",
      "3. 全小寫: True\n",
      "4. 全符合 snake_case: False\n",
      "5. 欄位皆唯一: True\n",
      "6. 不全小寫欄位: (無)\n",
      "7. 不符 snake_case 欄位: 列數\n",
      "8. 重複欄位: (無)\n",
      "\n",
      "================= COLUMN AUDIT =================\n",
      "1. 檔案名稱: PatNo_ID_1577487284.csv\n",
      "2. 總欄位數: 120\n",
      "3. 全小寫: True\n",
      "4. 全符合 snake_case: False\n",
      "5. 欄位皆唯一: True\n",
      "6. 不全小寫欄位: (無)\n",
      "7. 不符 snake_case 欄位: 列數\n",
      "8. 重複欄位: (無)\n",
      "\n",
      "================= COLUMN AUDIT =================\n",
      "1. 檔案名稱: PatNo_ID_1578784257.csv\n",
      "2. 總欄位數: 120\n",
      "3. 全小寫: True\n",
      "4. 全符合 snake_case: False\n",
      "5. 欄位皆唯一: True\n",
      "6. 不全小寫欄位: (無)\n",
      "7. 不符 snake_case 欄位: 列數\n",
      "8. 重複欄位: (無)\n",
      "\n",
      "================= COLUMN AUDIT =================\n",
      "1. 檔案名稱: PatNo_ID_1579198603.csv\n",
      "2. 總欄位數: 120\n",
      "3. 全小寫: True\n",
      "4. 全符合 snake_case: False\n",
      "5. 欄位皆唯一: True\n",
      "6. 不全小寫欄位: (無)\n",
      "7. 不符 snake_case 欄位: 列數\n",
      "8. 重複欄位: (無)\n",
      "\n",
      "================= COLUMN AUDIT =================\n",
      "1. 檔案名稱: PatNo_ID_1579498177.csv\n",
      "2. 總欄位數: 120\n",
      "3. 全小寫: True\n",
      "4. 全符合 snake_case: False\n",
      "5. 欄位皆唯一: True\n",
      "6. 不全小寫欄位: (無)\n",
      "7. 不符 snake_case 欄位: 列數\n",
      "8. 重複欄位: (無)\n",
      "\n",
      "================= COLUMN AUDIT =================\n",
      "1. 檔案名稱: PatNo_ID_1580062580.csv\n",
      "2. 總欄位數: 120\n",
      "3. 全小寫: True\n",
      "4. 全符合 snake_case: False\n",
      "5. 欄位皆唯一: True\n",
      "6. 不全小寫欄位: (無)\n",
      "7. 不符 snake_case 欄位: 列數\n",
      "8. 重複欄位: (無)\n",
      "\n",
      "================= COLUMN AUDIT =================\n",
      "1. 檔案名稱: PatNo_ID_1580096720.csv\n",
      "2. 總欄位數: 120\n",
      "3. 全小寫: True\n",
      "4. 全符合 snake_case: False\n",
      "5. 欄位皆唯一: True\n",
      "6. 不全小寫欄位: (無)\n",
      "7. 不符 snake_case 欄位: 列數\n",
      "8. 重複欄位: (無)\n",
      "\n",
      "================= COLUMN AUDIT =================\n",
      "1. 檔案名稱: PatNo_ID_1580107637.csv\n",
      "2. 總欄位數: 120\n",
      "3. 全小寫: True\n",
      "4. 全符合 snake_case: False\n",
      "5. 欄位皆唯一: True\n",
      "6. 不全小寫欄位: (無)\n",
      "7. 不符 snake_case 欄位: 列數\n",
      "8. 重複欄位: (無)\n",
      "\n",
      "================= COLUMN AUDIT =================\n",
      "1. 檔案名稱: PatNo_ID_1580244614.csv\n",
      "2. 總欄位數: 120\n",
      "3. 全小寫: True\n",
      "4. 全符合 snake_case: False\n",
      "5. 欄位皆唯一: True\n",
      "6. 不全小寫欄位: (無)\n",
      "7. 不符 snake_case 欄位: 列數\n",
      "8. 重複欄位: (無)\n",
      "\n",
      "================= COLUMN AUDIT =================\n",
      "1. 檔案名稱: PatNo_ID_1580766093.csv\n",
      "2. 總欄位數: 120\n",
      "3. 全小寫: True\n",
      "4. 全符合 snake_case: False\n",
      "5. 欄位皆唯一: True\n",
      "6. 不全小寫欄位: (無)\n",
      "7. 不符 snake_case 欄位: 列數\n",
      "8. 重複欄位: (無)\n",
      "\n",
      "================= COLUMN AUDIT =================\n",
      "1. 檔案名稱: PatNo_ID_1581003248.csv\n",
      "2. 總欄位數: 120\n",
      "3. 全小寫: True\n",
      "4. 全符合 snake_case: False\n",
      "5. 欄位皆唯一: True\n",
      "6. 不全小寫欄位: (無)\n",
      "7. 不符 snake_case 欄位: 列數\n",
      "8. 重複欄位: (無)\n",
      "\n",
      "================= COLUMN AUDIT =================\n",
      "1. 檔案名稱: PatNo_ID_1581019504.csv\n",
      "2. 總欄位數: 120\n",
      "3. 全小寫: True\n",
      "4. 全符合 snake_case: False\n",
      "5. 欄位皆唯一: True\n",
      "6. 不全小寫欄位: (無)\n",
      "7. 不符 snake_case 欄位: 列數\n",
      "8. 重複欄位: (無)\n",
      "\n",
      "================= COLUMN AUDIT =================\n",
      "1. 檔案名稱: PatNo_ID_1581633231.csv\n",
      "2. 總欄位數: 120\n",
      "3. 全小寫: True\n",
      "4. 全符合 snake_case: False\n",
      "5. 欄位皆唯一: True\n",
      "6. 不全小寫欄位: (無)\n",
      "7. 不符 snake_case 欄位: 列數\n",
      "8. 重複欄位: (無)\n",
      "\n",
      "================= COLUMN AUDIT =================\n",
      "1. 檔案名稱: PatNo_ID_1581692973.csv\n",
      "2. 總欄位數: 120\n",
      "3. 全小寫: True\n",
      "4. 全符合 snake_case: False\n",
      "5. 欄位皆唯一: True\n",
      "6. 不全小寫欄位: (無)\n",
      "7. 不符 snake_case 欄位: 列數\n",
      "8. 重複欄位: (無)\n",
      "\n",
      "================= COLUMN AUDIT =================\n",
      "1. 檔案名稱: PatNo_ID_1582452511.csv\n",
      "2. 總欄位數: 120\n",
      "3. 全小寫: True\n",
      "4. 全符合 snake_case: False\n",
      "5. 欄位皆唯一: True\n",
      "6. 不全小寫欄位: (無)\n",
      "7. 不符 snake_case 欄位: 列數\n",
      "8. 重複欄位: (無)\n",
      "\n",
      "================= COLUMN AUDIT =================\n",
      "1. 檔案名稱: PatNo_ID_1582635996.csv\n",
      "2. 總欄位數: 120\n",
      "3. 全小寫: True\n",
      "4. 全符合 snake_case: False\n",
      "5. 欄位皆唯一: True\n",
      "6. 不全小寫欄位: (無)\n",
      "7. 不符 snake_case 欄位: 列數\n",
      "8. 重複欄位: (無)\n",
      "\n",
      "================= COLUMN AUDIT =================\n",
      "1. 檔案名稱: PatNo_ID_1582849900.csv\n",
      "2. 總欄位數: 120\n",
      "3. 全小寫: True\n",
      "4. 全符合 snake_case: False\n",
      "5. 欄位皆唯一: True\n",
      "6. 不全小寫欄位: (無)\n",
      "7. 不符 snake_case 欄位: 列數\n",
      "8. 重複欄位: (無)\n",
      "\n",
      "================= COLUMN AUDIT =================\n",
      "1. 檔案名稱: PatNo_ID_1582937076.csv\n",
      "2. 總欄位數: 120\n",
      "3. 全小寫: True\n",
      "4. 全符合 snake_case: False\n",
      "5. 欄位皆唯一: True\n",
      "6. 不全小寫欄位: (無)\n",
      "7. 不符 snake_case 欄位: 列數\n",
      "8. 重複欄位: (無)\n",
      "\n",
      "================= COLUMN AUDIT =================\n",
      "1. 檔案名稱: PatNo_ID_1584158973.csv\n",
      "2. 總欄位數: 120\n",
      "3. 全小寫: True\n",
      "4. 全符合 snake_case: False\n",
      "5. 欄位皆唯一: True\n",
      "6. 不全小寫欄位: (無)\n",
      "7. 不符 snake_case 欄位: 列數\n",
      "8. 重複欄位: (無)\n",
      "\n",
      "================= COLUMN AUDIT =================\n",
      "1. 檔案名稱: PatNo_ID_1584397376.csv\n",
      "2. 總欄位數: 120\n",
      "3. 全小寫: True\n",
      "4. 全符合 snake_case: False\n",
      "5. 欄位皆唯一: True\n",
      "6. 不全小寫欄位: (無)\n",
      "7. 不符 snake_case 欄位: 列數\n",
      "8. 重複欄位: (無)\n",
      "\n",
      "================= COLUMN AUDIT =================\n",
      "1. 檔案名稱: PatNo_ID_1586172659.csv\n",
      "2. 總欄位數: 120\n",
      "3. 全小寫: True\n",
      "4. 全符合 snake_case: False\n",
      "5. 欄位皆唯一: True\n",
      "6. 不全小寫欄位: (無)\n",
      "7. 不符 snake_case 欄位: 列數\n",
      "8. 重複欄位: (無)\n",
      "\n",
      "================= COLUMN AUDIT =================\n",
      "1. 檔案名稱: PatNo_ID_1586696634.csv\n",
      "2. 總欄位數: 120\n",
      "3. 全小寫: True\n",
      "4. 全符合 snake_case: False\n",
      "5. 欄位皆唯一: True\n",
      "6. 不全小寫欄位: (無)\n",
      "7. 不符 snake_case 欄位: 列數\n",
      "8. 重複欄位: (無)\n",
      "\n",
      "================= COLUMN AUDIT =================\n",
      "1. 檔案名稱: PatNo_ID_1586897008.csv\n",
      "2. 總欄位數: 120\n",
      "3. 全小寫: True\n",
      "4. 全符合 snake_case: False\n",
      "5. 欄位皆唯一: True\n",
      "6. 不全小寫欄位: (無)\n",
      "7. 不符 snake_case 欄位: 列數\n",
      "8. 重複欄位: (無)\n",
      "\n",
      "================= COLUMN AUDIT =================\n",
      "1. 檔案名稱: PatNo_ID_1587490083.csv\n",
      "2. 總欄位數: 120\n",
      "3. 全小寫: True\n",
      "4. 全符合 snake_case: False\n",
      "5. 欄位皆唯一: True\n",
      "6. 不全小寫欄位: (無)\n",
      "7. 不符 snake_case 欄位: 列數\n",
      "8. 重複欄位: (無)\n",
      "\n",
      "================= COLUMN AUDIT =================\n",
      "1. 檔案名稱: PatNo_ID_1588632604.csv\n",
      "2. 總欄位數: 120\n",
      "3. 全小寫: True\n",
      "4. 全符合 snake_case: False\n",
      "5. 欄位皆唯一: True\n",
      "6. 不全小寫欄位: (無)\n",
      "7. 不符 snake_case 欄位: 列數\n",
      "8. 重複欄位: (無)\n",
      "\n",
      "================= COLUMN AUDIT =================\n",
      "1. 檔案名稱: PatNo_ID_1588673465.csv\n",
      "2. 總欄位數: 120\n",
      "3. 全小寫: True\n",
      "4. 全符合 snake_case: False\n",
      "5. 欄位皆唯一: True\n",
      "6. 不全小寫欄位: (無)\n",
      "7. 不符 snake_case 欄位: 列數\n",
      "8. 重複欄位: (無)\n",
      "\n",
      "================= COLUMN AUDIT =================\n",
      "1. 檔案名稱: PatNo_ID_1588794796.csv\n",
      "2. 總欄位數: 120\n",
      "3. 全小寫: True\n",
      "4. 全符合 snake_case: False\n",
      "5. 欄位皆唯一: True\n",
      "6. 不全小寫欄位: (無)\n",
      "7. 不符 snake_case 欄位: 列數\n",
      "8. 重複欄位: (無)\n",
      "\n",
      "================= COLUMN AUDIT =================\n",
      "1. 檔案名稱: PatNo_ID_1588957997.csv\n",
      "2. 總欄位數: 120\n",
      "3. 全小寫: True\n",
      "4. 全符合 snake_case: False\n",
      "5. 欄位皆唯一: True\n",
      "6. 不全小寫欄位: (無)\n",
      "7. 不符 snake_case 欄位: 列數\n",
      "8. 重複欄位: (無)\n",
      "\n",
      "================= COLUMN AUDIT =================\n",
      "1. 檔案名稱: PatNo_ID_1589018086.csv\n",
      "2. 總欄位數: 120\n",
      "3. 全小寫: True\n",
      "4. 全符合 snake_case: False\n",
      "5. 欄位皆唯一: True\n",
      "6. 不全小寫欄位: (無)\n",
      "7. 不符 snake_case 欄位: 列數\n",
      "8. 重複欄位: (無)\n",
      "\n",
      "================= COLUMN AUDIT =================\n",
      "1. 檔案名稱: PatNo_ID_1589034524.csv\n",
      "2. 總欄位數: 120\n",
      "3. 全小寫: True\n",
      "4. 全符合 snake_case: False\n",
      "5. 欄位皆唯一: True\n",
      "6. 不全小寫欄位: (無)\n",
      "7. 不符 snake_case 欄位: 列數\n",
      "8. 重複欄位: (無)\n",
      "\n",
      "================= COLUMN AUDIT =================\n",
      "1. 檔案名稱: PatNo_ID_1589324603.csv\n",
      "2. 總欄位數: 120\n",
      "3. 全小寫: True\n",
      "4. 全符合 snake_case: False\n",
      "5. 欄位皆唯一: True\n",
      "6. 不全小寫欄位: (無)\n",
      "7. 不符 snake_case 欄位: 列數\n",
      "8. 重複欄位: (無)\n",
      "\n",
      "================= COLUMN AUDIT =================\n",
      "1. 檔案名稱: PatNo_ID_1589918099.csv\n",
      "2. 總欄位數: 120\n",
      "3. 全小寫: True\n",
      "4. 全符合 snake_case: False\n",
      "5. 欄位皆唯一: True\n",
      "6. 不全小寫欄位: (無)\n",
      "7. 不符 snake_case 欄位: 列數\n",
      "8. 重複欄位: (無)\n",
      "\n",
      "================= COLUMN AUDIT =================\n",
      "1. 檔案名稱: PatNo_ID_1590136310.csv\n",
      "2. 總欄位數: 120\n",
      "3. 全小寫: True\n",
      "4. 全符合 snake_case: False\n",
      "5. 欄位皆唯一: True\n",
      "6. 不全小寫欄位: (無)\n",
      "7. 不符 snake_case 欄位: 列數\n",
      "8. 重複欄位: (無)\n",
      "\n",
      "================= COLUMN AUDIT =================\n",
      "1. 檔案名稱: PatNo_ID_1590616537.csv\n",
      "2. 總欄位數: 120\n",
      "3. 全小寫: True\n",
      "4. 全符合 snake_case: False\n",
      "5. 欄位皆唯一: True\n",
      "6. 不全小寫欄位: (無)\n",
      "7. 不符 snake_case 欄位: 列數\n",
      "8. 重複欄位: (無)\n",
      "\n",
      "================= COLUMN AUDIT =================\n",
      "1. 檔案名稱: PatNo_ID_1590854576.csv\n",
      "2. 總欄位數: 120\n",
      "3. 全小寫: True\n",
      "4. 全符合 snake_case: False\n",
      "5. 欄位皆唯一: True\n",
      "6. 不全小寫欄位: (無)\n",
      "7. 不符 snake_case 欄位: 列數\n",
      "8. 重複欄位: (無)\n",
      "\n",
      "================= COLUMN AUDIT =================\n",
      "1. 檔案名稱: PatNo_ID_1591609798.csv\n",
      "2. 總欄位數: 120\n",
      "3. 全小寫: True\n",
      "4. 全符合 snake_case: False\n",
      "5. 欄位皆唯一: True\n",
      "6. 不全小寫欄位: (無)\n",
      "7. 不符 snake_case 欄位: 列數\n",
      "8. 重複欄位: (無)\n",
      "\n",
      "================= COLUMN AUDIT =================\n",
      "1. 檔案名稱: PatNo_ID_1592044724.csv\n",
      "2. 總欄位數: 120\n",
      "3. 全小寫: True\n",
      "4. 全符合 snake_case: False\n",
      "5. 欄位皆唯一: True\n",
      "6. 不全小寫欄位: (無)\n",
      "7. 不符 snake_case 欄位: 列數\n",
      "8. 重複欄位: (無)\n",
      "\n",
      "================= COLUMN AUDIT =================\n",
      "1. 檔案名稱: PatNo_ID_1592560504.csv\n",
      "2. 總欄位數: 120\n",
      "3. 全小寫: True\n",
      "4. 全符合 snake_case: False\n",
      "5. 欄位皆唯一: True\n",
      "6. 不全小寫欄位: (無)\n",
      "7. 不符 snake_case 欄位: 列數\n",
      "8. 重複欄位: (無)\n",
      "\n",
      "================= COLUMN AUDIT =================\n",
      "1. 檔案名稱: PatNo_ID_1593087886.csv\n",
      "2. 總欄位數: 120\n",
      "3. 全小寫: True\n",
      "4. 全符合 snake_case: False\n",
      "5. 欄位皆唯一: True\n",
      "6. 不全小寫欄位: (無)\n",
      "7. 不符 snake_case 欄位: 列數\n",
      "8. 重複欄位: (無)\n",
      "\n",
      "================= COLUMN AUDIT =================\n",
      "1. 檔案名稱: PatNo_ID_1593416100.csv\n",
      "2. 總欄位數: 120\n",
      "3. 全小寫: True\n",
      "4. 全符合 snake_case: False\n",
      "5. 欄位皆唯一: True\n",
      "6. 不全小寫欄位: (無)\n",
      "7. 不符 snake_case 欄位: 列數\n",
      "8. 重複欄位: (無)\n",
      "\n",
      "================= COLUMN AUDIT =================\n",
      "1. 檔案名稱: PatNo_ID_1593472048.csv\n",
      "2. 總欄位數: 120\n",
      "3. 全小寫: True\n",
      "4. 全符合 snake_case: False\n",
      "5. 欄位皆唯一: True\n",
      "6. 不全小寫欄位: (無)\n",
      "7. 不符 snake_case 欄位: 列數\n",
      "8. 重複欄位: (無)\n",
      "\n",
      "================= COLUMN AUDIT =================\n",
      "1. 檔案名稱: PatNo_ID_1593593586.csv\n",
      "2. 總欄位數: 120\n",
      "3. 全小寫: True\n",
      "4. 全符合 snake_case: False\n",
      "5. 欄位皆唯一: True\n",
      "6. 不全小寫欄位: (無)\n",
      "7. 不符 snake_case 欄位: 列數\n",
      "8. 重複欄位: (無)\n",
      "\n",
      "================= COLUMN AUDIT =================\n",
      "1. 檔案名稱: PatNo_ID_1593720818.csv\n",
      "2. 總欄位數: 120\n",
      "3. 全小寫: True\n",
      "4. 全符合 snake_case: False\n",
      "5. 欄位皆唯一: True\n",
      "6. 不全小寫欄位: (無)\n",
      "7. 不符 snake_case 欄位: 列數\n",
      "8. 重複欄位: (無)\n",
      "\n",
      "================= COLUMN AUDIT =================\n",
      "1. 檔案名稱: PatNo_ID_1593838524.csv\n",
      "2. 總欄位數: 120\n",
      "3. 全小寫: True\n",
      "4. 全符合 snake_case: False\n",
      "5. 欄位皆唯一: True\n",
      "6. 不全小寫欄位: (無)\n",
      "7. 不符 snake_case 欄位: 列數\n",
      "8. 重複欄位: (無)\n",
      "\n",
      "================= COLUMN AUDIT =================\n",
      "1. 檔案名稱: PatNo_ID_1594173718.csv\n",
      "2. 總欄位數: 120\n",
      "3. 全小寫: True\n",
      "4. 全符合 snake_case: False\n",
      "5. 欄位皆唯一: True\n",
      "6. 不全小寫欄位: (無)\n",
      "7. 不符 snake_case 欄位: 列數\n",
      "8. 重複欄位: (無)\n",
      "\n",
      "================= COLUMN AUDIT =================\n",
      "1. 檔案名稱: PatNo_ID_1594294180.csv\n",
      "2. 總欄位數: 120\n",
      "3. 全小寫: True\n",
      "4. 全符合 snake_case: False\n",
      "5. 欄位皆唯一: True\n",
      "6. 不全小寫欄位: (無)\n",
      "7. 不符 snake_case 欄位: 列數\n",
      "8. 重複欄位: (無)\n",
      "\n",
      "================= COLUMN AUDIT =================\n",
      "1. 檔案名稱: PatNo_ID_1594305136.csv\n",
      "2. 總欄位數: 120\n",
      "3. 全小寫: True\n",
      "4. 全符合 snake_case: False\n",
      "5. 欄位皆唯一: True\n",
      "6. 不全小寫欄位: (無)\n",
      "7. 不符 snake_case 欄位: 列數\n",
      "8. 重複欄位: (無)\n",
      "\n",
      "================= COLUMN AUDIT =================\n",
      "1. 檔案名稱: PatNo_ID_1594309746.csv\n",
      "2. 總欄位數: 120\n",
      "3. 全小寫: True\n",
      "4. 全符合 snake_case: False\n",
      "5. 欄位皆唯一: True\n",
      "6. 不全小寫欄位: (無)\n",
      "7. 不符 snake_case 欄位: 列數\n",
      "8. 重複欄位: (無)\n",
      "\n",
      "================= COLUMN AUDIT =================\n",
      "1. 檔案名稱: PatNo_ID_1594319286.csv\n",
      "2. 總欄位數: 120\n",
      "3. 全小寫: True\n",
      "4. 全符合 snake_case: False\n",
      "5. 欄位皆唯一: True\n",
      "6. 不全小寫欄位: (無)\n",
      "7. 不符 snake_case 欄位: 列數\n",
      "8. 重複欄位: (無)\n",
      "\n",
      "================= COLUMN AUDIT =================\n",
      "1. 檔案名稱: PatNo_ID_1594320763.csv\n",
      "2. 總欄位數: 120\n",
      "3. 全小寫: True\n",
      "4. 全符合 snake_case: False\n",
      "5. 欄位皆唯一: True\n",
      "6. 不全小寫欄位: (無)\n",
      "7. 不符 snake_case 欄位: 列數\n",
      "8. 重複欄位: (無)\n",
      "\n",
      "================= COLUMN AUDIT =================\n",
      "1. 檔案名稱: PatNo_ID_1594322594.csv\n",
      "2. 總欄位數: 120\n",
      "3. 全小寫: True\n",
      "4. 全符合 snake_case: False\n",
      "5. 欄位皆唯一: True\n",
      "6. 不全小寫欄位: (無)\n",
      "7. 不符 snake_case 欄位: 列數\n",
      "8. 重複欄位: (無)\n",
      "\n",
      "================= COLUMN AUDIT =================\n",
      "1. 檔案名稱: PatNo_ID_1594335109.csv\n",
      "2. 總欄位數: 120\n",
      "3. 全小寫: True\n",
      "4. 全符合 snake_case: False\n",
      "5. 欄位皆唯一: True\n",
      "6. 不全小寫欄位: (無)\n",
      "7. 不符 snake_case 欄位: 列數\n",
      "8. 重複欄位: (無)\n",
      "\n",
      "================= COLUMN AUDIT =================\n",
      "1. 檔案名稱: PatNo_ID_1594423683.csv\n",
      "2. 總欄位數: 120\n",
      "3. 全小寫: True\n",
      "4. 全符合 snake_case: False\n",
      "5. 欄位皆唯一: True\n",
      "6. 不全小寫欄位: (無)\n",
      "7. 不符 snake_case 欄位: 列數\n",
      "8. 重複欄位: (無)\n",
      "\n",
      "================= COLUMN AUDIT =================\n",
      "1. 檔案名稱: PatNo_ID_1594437309.csv\n",
      "2. 總欄位數: 120\n",
      "3. 全小寫: True\n",
      "4. 全符合 snake_case: False\n",
      "5. 欄位皆唯一: True\n",
      "6. 不全小寫欄位: (無)\n",
      "7. 不符 snake_case 欄位: 列數\n",
      "8. 重複欄位: (無)\n",
      "\n",
      "================= COLUMN AUDIT =================\n",
      "1. 檔案名稱: PatNo_ID_1594439781.csv\n",
      "2. 總欄位數: 120\n",
      "3. 全小寫: True\n",
      "4. 全符合 snake_case: False\n",
      "5. 欄位皆唯一: True\n",
      "6. 不全小寫欄位: (無)\n",
      "7. 不符 snake_case 欄位: 列數\n",
      "8. 重複欄位: (無)\n",
      "\n",
      "================= COLUMN AUDIT =================\n",
      "1. 檔案名稱: PatNo_ID_1594441887.csv\n",
      "2. 總欄位數: 120\n",
      "3. 全小寫: True\n",
      "4. 全符合 snake_case: False\n",
      "5. 欄位皆唯一: True\n",
      "6. 不全小寫欄位: (無)\n",
      "7. 不符 snake_case 欄位: 列數\n",
      "8. 重複欄位: (無)\n",
      "\n",
      "================= COLUMN AUDIT =================\n",
      "1. 檔案名稱: PatNo_ID_1594448501.csv\n",
      "2. 總欄位數: 120\n",
      "3. 全小寫: True\n",
      "4. 全符合 snake_case: False\n",
      "5. 欄位皆唯一: True\n",
      "6. 不全小寫欄位: (無)\n",
      "7. 不符 snake_case 欄位: 列數\n",
      "8. 重複欄位: (無)\n",
      "\n",
      "================= COLUMN AUDIT =================\n",
      "1. 檔案名稱: PatNo_ID_1594455578.csv\n",
      "2. 總欄位數: 120\n",
      "3. 全小寫: True\n",
      "4. 全符合 snake_case: False\n",
      "5. 欄位皆唯一: True\n",
      "6. 不全小寫欄位: (無)\n",
      "7. 不符 snake_case 欄位: 列數\n",
      "8. 重複欄位: (無)\n",
      "\n",
      "================= COLUMN AUDIT =================\n",
      "1. 檔案名稱: PatNo_ID_1594464829.csv\n",
      "2. 總欄位數: 120\n",
      "3. 全小寫: True\n",
      "4. 全符合 snake_case: False\n",
      "5. 欄位皆唯一: True\n",
      "6. 不全小寫欄位: (無)\n",
      "7. 不符 snake_case 欄位: 列數\n",
      "8. 重複欄位: (無)\n",
      "\n",
      "================= COLUMN AUDIT =================\n",
      "1. 檔案名稱: PatNo_ID_1594467719.csv\n",
      "2. 總欄位數: 120\n",
      "3. 全小寫: True\n",
      "4. 全符合 snake_case: False\n",
      "5. 欄位皆唯一: True\n",
      "6. 不全小寫欄位: (無)\n",
      "7. 不符 snake_case 欄位: 列數\n",
      "8. 重複欄位: (無)\n",
      "\n",
      "================= COLUMN AUDIT =================\n",
      "1. 檔案名稱: PatNo_ID_1594471407.csv\n",
      "2. 總欄位數: 120\n",
      "3. 全小寫: True\n",
      "4. 全符合 snake_case: False\n",
      "5. 欄位皆唯一: True\n",
      "6. 不全小寫欄位: (無)\n",
      "7. 不符 snake_case 欄位: 列數\n",
      "8. 重複欄位: (無)\n",
      "\n",
      "================= COLUMN AUDIT =================\n",
      "1. 檔案名稱: PatNo_ID_1594479330.csv\n",
      "2. 總欄位數: 120\n",
      "3. 全小寫: True\n",
      "4. 全符合 snake_case: False\n",
      "5. 欄位皆唯一: True\n",
      "6. 不全小寫欄位: (無)\n",
      "7. 不符 snake_case 欄位: 列數\n",
      "8. 重複欄位: (無)\n",
      "\n",
      "================= COLUMN AUDIT =================\n",
      "1. 檔案名稱: PatNo_ID_1594511911.csv\n",
      "2. 總欄位數: 120\n",
      "3. 全小寫: True\n",
      "4. 全符合 snake_case: False\n",
      "5. 欄位皆唯一: True\n",
      "6. 不全小寫欄位: (無)\n",
      "7. 不符 snake_case 欄位: 列數\n",
      "8. 重複欄位: (無)\n",
      "\n",
      "================= COLUMN AUDIT =================\n",
      "1. 檔案名稱: PatNo_ID_1594511914.csv\n",
      "2. 總欄位數: 120\n",
      "3. 全小寫: True\n",
      "4. 全符合 snake_case: False\n",
      "5. 欄位皆唯一: True\n",
      "6. 不全小寫欄位: (無)\n",
      "7. 不符 snake_case 欄位: 列數\n",
      "8. 重複欄位: (無)\n",
      "\n",
      "================= COLUMN AUDIT =================\n",
      "1. 檔案名稱: PatNo_ID_1594528842.csv\n",
      "2. 總欄位數: 120\n",
      "3. 全小寫: True\n",
      "4. 全符合 snake_case: False\n",
      "5. 欄位皆唯一: True\n",
      "6. 不全小寫欄位: (無)\n",
      "7. 不符 snake_case 欄位: 列數\n",
      "8. 重複欄位: (無)\n",
      "\n",
      "================= COLUMN AUDIT =================\n",
      "1. 檔案名稱: PatNo_ID_1594533379.csv\n",
      "2. 總欄位數: 120\n",
      "3. 全小寫: True\n",
      "4. 全符合 snake_case: False\n",
      "5. 欄位皆唯一: True\n",
      "6. 不全小寫欄位: (無)\n",
      "7. 不符 snake_case 欄位: 列數\n",
      "8. 重複欄位: (無)\n",
      "\n",
      "[欄位稽核] 完成。報表輸出： /home/jovyan/RT08/0925/bling_record/4/bling_columns_audit.csv\n"
     ]
    }
   ],
   "source": [
    "# bling 全檔欄位命名稽核（小寫/ snake_case / 唯一性 / 重複清單）\n",
    "# - 掃描：/home/jovyan/RT08/0925/bling/*.csv\n",
    "# - 輸出報表：/home/jovyan/RT08/0925/bling_record/4/bling_columns_audit.csv\n",
    "# - Console 直接列印每檔的 1.~8. 檢查結果\n",
    "# ============================================================\n",
    "import os, re, glob\n",
    "import pandas as pd\n",
    "\n",
    "AUDIT_IN_DIR = \"/home/jovyan/RT08/0925/bling\"\n",
    "AUDIT_OUT_DIR = \"/home/jovyan/RT08/0925/bling_record/4\"\n",
    "os.makedirs(AUDIT_OUT_DIR, exist_ok=True)\n",
    "AUDIT_OUT_CSV = os.path.join(AUDIT_OUT_DIR, \"bling_columns_audit.csv\")\n",
    "\n",
    "# --------- 韌性讀取欄位名稱（優先使用你前面定義的 read_table_resilient） ---------\n",
    "def _audit_read_columns(path):\n",
    "    # 1) 先試你現有的穩健讀法（若存在）\n",
    "    if \"read_table_resilient\" in globals() and callable(globals()[\"read_table_resilient\"]):\n",
    "        try:\n",
    "            df, meta = read_table_resilient(path, max_header_search=30)\n",
    "            if meta.get(\"read_ok\"):\n",
    "                return [str(c) for c in df.columns]\n",
    "        except Exception:\n",
    "            pass\n",
    "    # 2) 一般 CSV 嘗試：多編碼、多 header 列（只取欄位名，不讀資料）\n",
    "    for enc in [\"utf-8-sig\",\"utf-8\",\"cp950\",\"big5\",\"latin1\"]:\n",
    "        for hdr in range(0, 30):\n",
    "            try:\n",
    "                df = pd.read_csv(path, encoding=enc, header=hdr, nrows=0, engine=\"python\", on_bad_lines=\"skip\")\n",
    "                cols = [str(c) for c in df.columns]\n",
    "                if len(cols) > 0:\n",
    "                    return cols\n",
    "            except Exception:\n",
    "                continue\n",
    "    # 3) 讀不到就回空清單（後續以錯誤行處理）\n",
    "    return []\n",
    "\n",
    "# --------- 規則 ---------\n",
    "snake_pat = re.compile(r\"^[a-z0-9_]+$\")\n",
    "\n",
    "# --------- 掃描所有 CSV ---------\n",
    "files = sorted([p for p in glob.glob(os.path.join(AUDIT_IN_DIR, \"*\")) if os.path.isfile(p) and p.lower().endswith(\".csv\")])\n",
    "\n",
    "rows = []\n",
    "for f in files:\n",
    "    fname = os.path.basename(f)\n",
    "    cols = _audit_read_columns(f)\n",
    "\n",
    "    if not cols:\n",
    "        # 讀取失敗：用安全預設\n",
    "        total_cols = 0\n",
    "        all_lower = False\n",
    "        all_snake = False\n",
    "        all_unique = False\n",
    "        non_lower_list = []\n",
    "        non_snake_list = []\n",
    "        dup_list = []\n",
    "    else:\n",
    "        total_cols = len(cols)\n",
    "        all_lower = all(str(c) == str(c).lower() for c in cols)\n",
    "        all_snake = all(bool(snake_pat.fullmatch(str(c).lower())) for c in cols)\n",
    "        # 重複偵測（以原欄位名為準）\n",
    "        seen = {}\n",
    "        dup_list = []\n",
    "        for c in cols:\n",
    "            seen[c] = seen.get(c, 0) + 1\n",
    "        dup_list = [c for c, k in seen.items() if k > 1]\n",
    "        all_unique = (len(dup_list) == 0)\n",
    "\n",
    "        # 明細清單\n",
    "        non_lower_list = [c for c in cols if str(c) != str(c).lower()]\n",
    "        non_snake_list = [c for c in cols if not snake_pat.fullmatch(str(c).lower())]\n",
    "\n",
    "    # 1~9 欄（符合你的指定順序）\n",
    "    rec = {\n",
    "        \"file_name\": fname,                                   # 1\n",
    "        \"total_columns\": total_cols,                          # 2\n",
    "        \"all_lowercase\": bool(all_lower),                     # 3\n",
    "        \"all_snake_case\": bool(all_snake),                    # 4\n",
    "        \"all_unique\": bool(all_unique),                       # 5\n",
    "        \"non_lowercase_columns\": \";\".join(non_lower_list),    # 6\n",
    "        \"non_snakecase_columns\": \";\".join(non_snake_list),    # 7\n",
    "        \"duplicate_columns\": \";\".join(dup_list),              # 8\n",
    "        \"path\": f,                                            # 9\n",
    "    }\n",
    "    rows.append(rec)\n",
    "\n",
    "    # ---- 直接在 console 輸出 1.~8.（逐檔）----\n",
    "    print(\"\\n================= COLUMN AUDIT =================\")\n",
    "    print(f\"1. 檔案名稱: {rec['file_name']}\")\n",
    "    print(f\"2. 總欄位數: {rec['total_columns']}\")\n",
    "    print(f\"3. 全小寫: {rec['all_lowercase']}\")\n",
    "    print(f\"4. 全符合 snake_case: {rec['all_snake_case']}\")\n",
    "    print(f\"5. 欄位皆唯一: {rec['all_unique']}\")\n",
    "    print(f\"6. 不全小寫欄位: {rec['non_lowercase_columns'] or '(無)'}\")\n",
    "    print(f\"7. 不符 snake_case 欄位: {rec['non_snakecase_columns'] or '(無)'}\")\n",
    "    print(f\"8. 重複欄位: {rec['duplicate_columns'] or '(無)'}\")\n",
    "    # 第 9 點（路徑）已在報表檔案中呈現\n",
    "\n",
    "# --------- 彙整輸出到單一檔案 ---------\n",
    "audit_df = pd.DataFrame(rows, columns=[\n",
    "    \"file_name\",\"total_columns\",\"all_lowercase\",\"all_snake_case\",\"all_unique\",\n",
    "    \"non_lowercase_columns\",\"non_snakecase_columns\",\"duplicate_columns\",\"path\"\n",
    "])\n",
    "audit_df.to_csv(AUDIT_OUT_CSV, index=False, encoding=\"utf-8\")\n",
    "print(\"\\n[欄位稽核] 完成。報表輸出：\", AUDIT_OUT_CSV)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "a829454f-b692-4256-8a66-298a9b5e5c93",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[4] 所有欄位名稱皆符合 snake_case 未達標：\n",
      "- 089271.csv\n",
      "- 095323.csv\n",
      "- 095707.csv\n",
      "- 114309.csv\n",
      "- 230933.csv\n",
      "- 4216007.csv\n",
      "- 7108162.csv\n",
      "- 7408338.csv\n",
      "- 7657698.csv\n",
      "- 7721164.csv\n",
      "- PatNo_ID_1560013303.csv\n",
      "- PatNo_ID_1562733396.csv\n",
      "- PatNo_ID_1563587183.csv\n",
      "- PatNo_ID_1564148644.csv\n",
      "- PatNo_ID_1565148312.csv\n",
      "- PatNo_ID_1565378038.csv\n",
      "- PatNo_ID_1566123680.csv\n",
      "- PatNo_ID_1566252197.csv\n",
      "- PatNo_ID_1566279967.csv\n",
      "- PatNo_ID_1566671274.csv\n",
      "- PatNo_ID_1566911879.csv\n",
      "- PatNo_ID_1567747650.csv\n",
      "- PatNo_ID_1567804800.csv\n",
      "- PatNo_ID_1567832735.csv\n",
      "- PatNo_ID_1568039398.csv\n",
      "- PatNo_ID_1568574099.csv\n",
      "- PatNo_ID_1568813269.csv\n",
      "- PatNo_ID_1568952422.csv\n",
      "- PatNo_ID_1569083701.csv\n",
      "- PatNo_ID_1569944983.csv\n",
      "- PatNo_ID_1570089466.csv\n",
      "- PatNo_ID_1570242703.csv\n",
      "- PatNo_ID_1570273244.csv\n",
      "- PatNo_ID_1570642083.csv\n",
      "- PatNo_ID_1571945701.csv\n",
      "- PatNo_ID_1572481361.csv\n",
      "- PatNo_ID_1572562839.csv\n",
      "- PatNo_ID_1572831765.csv\n",
      "- PatNo_ID_1572976822.csv\n",
      "- PatNo_ID_1573063188.csv\n",
      "- PatNo_ID_1573249295.csv\n",
      "- PatNo_ID_1573964540.csv\n",
      "- PatNo_ID_1574148494.csv\n",
      "- PatNo_ID_1574270349.csv\n",
      "- PatNo_ID_1574528808.csv\n",
      "- PatNo_ID_1574831525.csv\n",
      "- PatNo_ID_1574987447.csv\n",
      "- PatNo_ID_1575060177.csv\n",
      "- PatNo_ID_1575256902.csv\n",
      "- PatNo_ID_1575445051.csv\n",
      "- PatNo_ID_1575502382.csv\n",
      "- PatNo_ID_1575975485.csv\n",
      "- PatNo_ID_1576115572.csv\n",
      "- PatNo_ID_1576116479.csv\n",
      "- PatNo_ID_1576301569.csv\n",
      "- PatNo_ID_1576964560.csv\n",
      "- PatNo_ID_1577042911.csv\n",
      "- PatNo_ID_1577487284.csv\n",
      "- PatNo_ID_1578784257.csv\n",
      "- PatNo_ID_1579198603.csv\n",
      "- PatNo_ID_1579498177.csv\n",
      "- PatNo_ID_1580062580.csv\n",
      "- PatNo_ID_1580096720.csv\n",
      "- PatNo_ID_1580107637.csv\n",
      "- PatNo_ID_1580244614.csv\n",
      "- PatNo_ID_1580766093.csv\n",
      "- PatNo_ID_1581003248.csv\n",
      "- PatNo_ID_1581019504.csv\n",
      "- PatNo_ID_1581633231.csv\n",
      "- PatNo_ID_1581692973.csv\n",
      "- PatNo_ID_1582452511.csv\n",
      "- PatNo_ID_1582635996.csv\n",
      "- PatNo_ID_1582849900.csv\n",
      "- PatNo_ID_1582937076.csv\n",
      "- PatNo_ID_1584158973.csv\n",
      "- PatNo_ID_1584397376.csv\n",
      "- PatNo_ID_1586172659.csv\n",
      "- PatNo_ID_1586696634.csv\n",
      "- PatNo_ID_1586897008.csv\n",
      "- PatNo_ID_1587490083.csv\n",
      "- PatNo_ID_1588632604.csv\n",
      "- PatNo_ID_1588673465.csv\n",
      "- PatNo_ID_1588794796.csv\n",
      "- PatNo_ID_1588957997.csv\n",
      "- PatNo_ID_1589018086.csv\n",
      "- PatNo_ID_1589034524.csv\n",
      "- PatNo_ID_1589324603.csv\n",
      "- PatNo_ID_1589918099.csv\n",
      "- PatNo_ID_1590136310.csv\n",
      "- PatNo_ID_1590616537.csv\n",
      "- PatNo_ID_1590854576.csv\n",
      "- PatNo_ID_1591609798.csv\n",
      "- PatNo_ID_1592044724.csv\n",
      "- PatNo_ID_1592560504.csv\n",
      "- PatNo_ID_1593087886.csv\n",
      "- PatNo_ID_1593416100.csv\n",
      "- PatNo_ID_1593472048.csv\n",
      "- PatNo_ID_1593593586.csv\n",
      "- PatNo_ID_1593720818.csv\n",
      "- PatNo_ID_1593838524.csv\n",
      "- PatNo_ID_1594173718.csv\n",
      "- PatNo_ID_1594294180.csv\n",
      "- PatNo_ID_1594305136.csv\n",
      "- PatNo_ID_1594309746.csv\n",
      "- PatNo_ID_1594319286.csv\n",
      "- PatNo_ID_1594320763.csv\n",
      "- PatNo_ID_1594322594.csv\n",
      "- PatNo_ID_1594335109.csv\n",
      "- PatNo_ID_1594423683.csv\n",
      "- PatNo_ID_1594437309.csv\n",
      "- PatNo_ID_1594439781.csv\n",
      "- PatNo_ID_1594441887.csv\n",
      "- PatNo_ID_1594448501.csv\n",
      "- PatNo_ID_1594455578.csv\n",
      "- PatNo_ID_1594464829.csv\n",
      "- PatNo_ID_1594467719.csv\n",
      "- PatNo_ID_1594471407.csv\n",
      "- PatNo_ID_1594479330.csv\n",
      "- PatNo_ID_1594511911.csv\n",
      "- PatNo_ID_1594511914.csv\n",
      "- PatNo_ID_1594528842.csv\n",
      "- PatNo_ID_1594533379.csv\n",
      "\n",
      "[7] 不存在非 snake_case 欄位 未達標：\n",
      "- 089271.csv\n",
      "- 095323.csv\n",
      "- 095707.csv\n",
      "- 114309.csv\n",
      "- 230933.csv\n",
      "- 4216007.csv\n",
      "- 7108162.csv\n",
      "- 7408338.csv\n",
      "- 7657698.csv\n",
      "- 7721164.csv\n",
      "- PatNo_ID_1560013303.csv\n",
      "- PatNo_ID_1562733396.csv\n",
      "- PatNo_ID_1563587183.csv\n",
      "- PatNo_ID_1564148644.csv\n",
      "- PatNo_ID_1565148312.csv\n",
      "- PatNo_ID_1565378038.csv\n",
      "- PatNo_ID_1566123680.csv\n",
      "- PatNo_ID_1566252197.csv\n",
      "- PatNo_ID_1566279967.csv\n",
      "- PatNo_ID_1566671274.csv\n",
      "- PatNo_ID_1566911879.csv\n",
      "- PatNo_ID_1567747650.csv\n",
      "- PatNo_ID_1567804800.csv\n",
      "- PatNo_ID_1567832735.csv\n",
      "- PatNo_ID_1568039398.csv\n",
      "- PatNo_ID_1568574099.csv\n",
      "- PatNo_ID_1568813269.csv\n",
      "- PatNo_ID_1568952422.csv\n",
      "- PatNo_ID_1569083701.csv\n",
      "- PatNo_ID_1569944983.csv\n",
      "- PatNo_ID_1570089466.csv\n",
      "- PatNo_ID_1570242703.csv\n",
      "- PatNo_ID_1570273244.csv\n",
      "- PatNo_ID_1570642083.csv\n",
      "- PatNo_ID_1571945701.csv\n",
      "- PatNo_ID_1572481361.csv\n",
      "- PatNo_ID_1572562839.csv\n",
      "- PatNo_ID_1572831765.csv\n",
      "- PatNo_ID_1572976822.csv\n",
      "- PatNo_ID_1573063188.csv\n",
      "- PatNo_ID_1573249295.csv\n",
      "- PatNo_ID_1573964540.csv\n",
      "- PatNo_ID_1574148494.csv\n",
      "- PatNo_ID_1574270349.csv\n",
      "- PatNo_ID_1574528808.csv\n",
      "- PatNo_ID_1574831525.csv\n",
      "- PatNo_ID_1574987447.csv\n",
      "- PatNo_ID_1575060177.csv\n",
      "- PatNo_ID_1575256902.csv\n",
      "- PatNo_ID_1575445051.csv\n",
      "- PatNo_ID_1575502382.csv\n",
      "- PatNo_ID_1575975485.csv\n",
      "- PatNo_ID_1576115572.csv\n",
      "- PatNo_ID_1576116479.csv\n",
      "- PatNo_ID_1576301569.csv\n",
      "- PatNo_ID_1576964560.csv\n",
      "- PatNo_ID_1577042911.csv\n",
      "- PatNo_ID_1577487284.csv\n",
      "- PatNo_ID_1578784257.csv\n",
      "- PatNo_ID_1579198603.csv\n",
      "- PatNo_ID_1579498177.csv\n",
      "- PatNo_ID_1580062580.csv\n",
      "- PatNo_ID_1580096720.csv\n",
      "- PatNo_ID_1580107637.csv\n",
      "- PatNo_ID_1580244614.csv\n",
      "- PatNo_ID_1580766093.csv\n",
      "- PatNo_ID_1581003248.csv\n",
      "- PatNo_ID_1581019504.csv\n",
      "- PatNo_ID_1581633231.csv\n",
      "- PatNo_ID_1581692973.csv\n",
      "- PatNo_ID_1582452511.csv\n",
      "- PatNo_ID_1582635996.csv\n",
      "- PatNo_ID_1582849900.csv\n",
      "- PatNo_ID_1582937076.csv\n",
      "- PatNo_ID_1584158973.csv\n",
      "- PatNo_ID_1584397376.csv\n",
      "- PatNo_ID_1586172659.csv\n",
      "- PatNo_ID_1586696634.csv\n",
      "- PatNo_ID_1586897008.csv\n",
      "- PatNo_ID_1587490083.csv\n",
      "- PatNo_ID_1588632604.csv\n",
      "- PatNo_ID_1588673465.csv\n",
      "- PatNo_ID_1588794796.csv\n",
      "- PatNo_ID_1588957997.csv\n",
      "- PatNo_ID_1589018086.csv\n",
      "- PatNo_ID_1589034524.csv\n",
      "- PatNo_ID_1589324603.csv\n",
      "- PatNo_ID_1589918099.csv\n",
      "- PatNo_ID_1590136310.csv\n",
      "- PatNo_ID_1590616537.csv\n",
      "- PatNo_ID_1590854576.csv\n",
      "- PatNo_ID_1591609798.csv\n",
      "- PatNo_ID_1592044724.csv\n",
      "- PatNo_ID_1592560504.csv\n",
      "- PatNo_ID_1593087886.csv\n",
      "- PatNo_ID_1593416100.csv\n",
      "- PatNo_ID_1593472048.csv\n",
      "- PatNo_ID_1593593586.csv\n",
      "- PatNo_ID_1593720818.csv\n",
      "- PatNo_ID_1593838524.csv\n",
      "- PatNo_ID_1594173718.csv\n",
      "- PatNo_ID_1594294180.csv\n",
      "- PatNo_ID_1594305136.csv\n",
      "- PatNo_ID_1594309746.csv\n",
      "- PatNo_ID_1594319286.csv\n",
      "- PatNo_ID_1594320763.csv\n",
      "- PatNo_ID_1594322594.csv\n",
      "- PatNo_ID_1594335109.csv\n",
      "- PatNo_ID_1594423683.csv\n",
      "- PatNo_ID_1594437309.csv\n",
      "- PatNo_ID_1594439781.csv\n",
      "- PatNo_ID_1594441887.csv\n",
      "- PatNo_ID_1594448501.csv\n",
      "- PatNo_ID_1594455578.csv\n",
      "- PatNo_ID_1594464829.csv\n",
      "- PatNo_ID_1594467719.csv\n",
      "- PatNo_ID_1594471407.csv\n",
      "- PatNo_ID_1594479330.csv\n",
      "- PatNo_ID_1594511911.csv\n",
      "- PatNo_ID_1594511914.csv\n",
      "- PatNo_ID_1594528842.csv\n",
      "- PatNo_ID_1594533379.csv\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# 列出「第3~8點未達標」的檔名，並依條款分組輸出（列點）\n",
    "import os, re, glob\n",
    "import pandas as pd\n",
    "\n",
    "DIR = \"/home/jovyan/RT08/0925/bling\"\n",
    "snake_pat = re.compile(r\"^[a-z0-9_]+$\")\n",
    "\n",
    "def read_header_only(path):\n",
    "    for enc in (\"utf-8\", \"utf-8-sig\", \"cp950\", \"big5\", \"latin1\"):\n",
    "        try:\n",
    "            return list(pd.read_csv(path, nrows=0, encoding=enc, sep=\",\", engine=\"python\", on_bad_lines=\"skip\").columns)\n",
    "        except Exception:\n",
    "            try:\n",
    "                return list(pd.read_csv(path, nrows=0, encoding=enc, sep=None, engine=\"python\", on_bad_lines=\"skip\").columns)\n",
    "            except Exception:\n",
    "                continue\n",
    "    raise RuntimeError(f\"read_header_only failed: {path}\")\n",
    "\n",
    "# 以 set 收集各條款的未達標檔名\n",
    "fail_3_all_lower   = set()\n",
    "fail_4_all_snake   = set()\n",
    "fail_5_all_unique  = set()\n",
    "fail_6_not_lower   = set()\n",
    "fail_7_not_snake   = set()\n",
    "fail_8_duplicates  = set()\n",
    "read_errors = {}\n",
    "\n",
    "for fp in sorted(glob.glob(os.path.join(DIR, \"*.csv\"))):\n",
    "    name = os.path.basename(fp)\n",
    "    try:\n",
    "        cols = read_header_only(fp)\n",
    "\n",
    "        # 3. 是否全部小寫\n",
    "        all_lower = all(str(c) == str(c).lower() for c in cols)\n",
    "        if not all_lower:\n",
    "            fail_3_all_lower.add(name)\n",
    "\n",
    "        # 4. 是否全部符合 snake_case\n",
    "        all_snake = all(snake_pat.fullmatch(str(c)) is not None for c in cols)\n",
    "        if not all_snake:\n",
    "            fail_4_all_snake.add(name)\n",
    "\n",
    "        # 5/8. 欄位是否唯一（且列出重複）\n",
    "        seen = {}\n",
    "        for c in cols:\n",
    "            seen[c] = seen.get(c, 0) + 1\n",
    "        has_dup = any(v > 1 for v in seen.values())\n",
    "        if has_dup:\n",
    "            fail_5_all_unique.add(name)\n",
    "            fail_8_duplicates.add(name)\n",
    "\n",
    "        # 6. 是否存在不全小寫欄位\n",
    "        not_lower = [c for c in cols if str(c) != str(c).lower()]\n",
    "        if not_lower:\n",
    "            fail_6_not_lower.add(name)\n",
    "\n",
    "        # 7. 是否存在非 snake_case 欄位\n",
    "        not_snake = [c for c in cols if snake_pat.fullmatch(str(c)) is None]\n",
    "        if not_snake:\n",
    "            fail_7_not_snake.add(name)\n",
    "\n",
    "    except Exception as e:\n",
    "        read_errors[name] = str(e)\n",
    "        # 無法讀取者視為各條款未達標（也可改成僅列於 read_errors）\n",
    "        fail_3_all_lower.add(name)\n",
    "        fail_4_all_snake.add(name)\n",
    "        fail_5_all_unique.add(name)\n",
    "        fail_6_not_lower.add(name)\n",
    "        fail_7_not_snake.add(name)\n",
    "        fail_8_duplicates.add(name)\n",
    "\n",
    "def _print_section(point_no, files_set, title):\n",
    "    if files_set:\n",
    "        print(f\"[{point_no}] {title} 未達標：\")\n",
    "        for f in sorted(files_set):\n",
    "            print(f\"- {f}\")\n",
    "        print()\n",
    "\n",
    "_print_section(3, fail_3_all_lower,  \"所有欄位名稱皆小寫\")\n",
    "_print_section(4, fail_4_all_snake,  \"所有欄位名稱皆符合 snake_case\")\n",
    "_print_section(5, fail_5_all_unique, \"欄位名稱皆唯一（無重複）\")\n",
    "_print_section(6, fail_6_not_lower,  \"不存在非小寫欄位\")\n",
    "_print_section(7, fail_7_not_snake,  \"不存在非 snake_case 欄位\")\n",
    "_print_section(8, fail_8_duplicates,  \"不存在重複欄位名稱\")\n",
    "\n",
    "# 若需要一併看到讀取失敗原因，可取消以下註解：\n",
    "# if read_errors:\n",
    "#     print(\"[READ ERROR] 無法讀取的檔案（已計為未達標）：\")\n",
    "#     for f, msg in read_errors.items():\n",
    "#         print(f\"- {f}: {msg}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "4eee6831-76a3-4692-9b6a-1ac445383377",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[3] 所有欄位名稱皆小寫 未達標：\n",
      "- (無)\n",
      "\n",
      "[4] 所有欄位名稱皆符合 snake_case 未達標：\n",
      "- 089271.csv\n",
      "- 095323.csv\n",
      "- 095707.csv\n",
      "- 114309.csv\n",
      "- 230933.csv\n",
      "- 4216007.csv\n",
      "- 7108162.csv\n",
      "- 7408338.csv\n",
      "- 7657698.csv\n",
      "- 7721164.csv\n",
      "- PatNo_ID_1560013303.csv\n",
      "- PatNo_ID_1562733396.csv\n",
      "- PatNo_ID_1563587183.csv\n",
      "- PatNo_ID_1564148644.csv\n",
      "- PatNo_ID_1565148312.csv\n",
      "- PatNo_ID_1565378038.csv\n",
      "- PatNo_ID_1566123680.csv\n",
      "- PatNo_ID_1566252197.csv\n",
      "- PatNo_ID_1566279967.csv\n",
      "- PatNo_ID_1566671274.csv\n",
      "- PatNo_ID_1566911879.csv\n",
      "- PatNo_ID_1567747650.csv\n",
      "- PatNo_ID_1567804800.csv\n",
      "- PatNo_ID_1567832735.csv\n",
      "- PatNo_ID_1568039398.csv\n",
      "- PatNo_ID_1568574099.csv\n",
      "- PatNo_ID_1568813269.csv\n",
      "- PatNo_ID_1568952422.csv\n",
      "- PatNo_ID_1569083701.csv\n",
      "- PatNo_ID_1569944983.csv\n",
      "- PatNo_ID_1570089466.csv\n",
      "- PatNo_ID_1570242703.csv\n",
      "- PatNo_ID_1570273244.csv\n",
      "- PatNo_ID_1570642083.csv\n",
      "- PatNo_ID_1571945701.csv\n",
      "- PatNo_ID_1572481361.csv\n",
      "- PatNo_ID_1572562839.csv\n",
      "- PatNo_ID_1572831765.csv\n",
      "- PatNo_ID_1572976822.csv\n",
      "- PatNo_ID_1573063188.csv\n",
      "- PatNo_ID_1573249295.csv\n",
      "- PatNo_ID_1573964540.csv\n",
      "- PatNo_ID_1574148494.csv\n",
      "- PatNo_ID_1574270349.csv\n",
      "- PatNo_ID_1574528808.csv\n",
      "- PatNo_ID_1574831525.csv\n",
      "- PatNo_ID_1574987447.csv\n",
      "- PatNo_ID_1575060177.csv\n",
      "- PatNo_ID_1575256902.csv\n",
      "- PatNo_ID_1575445051.csv\n",
      "- PatNo_ID_1575502382.csv\n",
      "- PatNo_ID_1575975485.csv\n",
      "- PatNo_ID_1576115572.csv\n",
      "- PatNo_ID_1576116479.csv\n",
      "- PatNo_ID_1576301569.csv\n",
      "- PatNo_ID_1576964560.csv\n",
      "- PatNo_ID_1577042911.csv\n",
      "- PatNo_ID_1577487284.csv\n",
      "- PatNo_ID_1578784257.csv\n",
      "- PatNo_ID_1579198603.csv\n",
      "- PatNo_ID_1579498177.csv\n",
      "- PatNo_ID_1580062580.csv\n",
      "- PatNo_ID_1580096720.csv\n",
      "- PatNo_ID_1580107637.csv\n",
      "- PatNo_ID_1580244614.csv\n",
      "- PatNo_ID_1580766093.csv\n",
      "- PatNo_ID_1581003248.csv\n",
      "- PatNo_ID_1581019504.csv\n",
      "- PatNo_ID_1581633231.csv\n",
      "- PatNo_ID_1581692973.csv\n",
      "- PatNo_ID_1582452511.csv\n",
      "- PatNo_ID_1582635996.csv\n",
      "- PatNo_ID_1582849900.csv\n",
      "- PatNo_ID_1582937076.csv\n",
      "- PatNo_ID_1584158973.csv\n",
      "- PatNo_ID_1584397376.csv\n",
      "- PatNo_ID_1586172659.csv\n",
      "- PatNo_ID_1586696634.csv\n",
      "- PatNo_ID_1586897008.csv\n",
      "- PatNo_ID_1587490083.csv\n",
      "- PatNo_ID_1588632604.csv\n",
      "- PatNo_ID_1588673465.csv\n",
      "- PatNo_ID_1588794796.csv\n",
      "- PatNo_ID_1588957997.csv\n",
      "- PatNo_ID_1589018086.csv\n",
      "- PatNo_ID_1589034524.csv\n",
      "- PatNo_ID_1589324603.csv\n",
      "- PatNo_ID_1589918099.csv\n",
      "- PatNo_ID_1590136310.csv\n",
      "- PatNo_ID_1590616537.csv\n",
      "- PatNo_ID_1590854576.csv\n",
      "- PatNo_ID_1591609798.csv\n",
      "- PatNo_ID_1592044724.csv\n",
      "- PatNo_ID_1592560504.csv\n",
      "- PatNo_ID_1593087886.csv\n",
      "- PatNo_ID_1593416100.csv\n",
      "- PatNo_ID_1593472048.csv\n",
      "- PatNo_ID_1593593586.csv\n",
      "- PatNo_ID_1593720818.csv\n",
      "- PatNo_ID_1593838524.csv\n",
      "- PatNo_ID_1594173718.csv\n",
      "- PatNo_ID_1594294180.csv\n",
      "- PatNo_ID_1594305136.csv\n",
      "- PatNo_ID_1594309746.csv\n",
      "- PatNo_ID_1594319286.csv\n",
      "- PatNo_ID_1594320763.csv\n",
      "- PatNo_ID_1594322594.csv\n",
      "- PatNo_ID_1594335109.csv\n",
      "- PatNo_ID_1594423683.csv\n",
      "- PatNo_ID_1594437309.csv\n",
      "- PatNo_ID_1594439781.csv\n",
      "- PatNo_ID_1594441887.csv\n",
      "- PatNo_ID_1594448501.csv\n",
      "- PatNo_ID_1594455578.csv\n",
      "- PatNo_ID_1594464829.csv\n",
      "- PatNo_ID_1594467719.csv\n",
      "- PatNo_ID_1594471407.csv\n",
      "- PatNo_ID_1594479330.csv\n",
      "- PatNo_ID_1594511911.csv\n",
      "- PatNo_ID_1594511914.csv\n",
      "- PatNo_ID_1594528842.csv\n",
      "- PatNo_ID_1594533379.csv\n",
      "\n",
      "[5] 欄位名稱皆唯一（無重複） 未達標：\n",
      "- (無)\n",
      "\n",
      "[6] 不存在非小寫欄位 未達標：\n",
      "- (無)\n",
      "\n",
      "[7] 不存在非 snake_case 欄位 未達標：\n",
      "- 089271.csv\n",
      "- 095323.csv\n",
      "- 095707.csv\n",
      "- 114309.csv\n",
      "- 230933.csv\n",
      "- 4216007.csv\n",
      "- 7108162.csv\n",
      "- 7408338.csv\n",
      "- 7657698.csv\n",
      "- 7721164.csv\n",
      "- PatNo_ID_1560013303.csv\n",
      "- PatNo_ID_1562733396.csv\n",
      "- PatNo_ID_1563587183.csv\n",
      "- PatNo_ID_1564148644.csv\n",
      "- PatNo_ID_1565148312.csv\n",
      "- PatNo_ID_1565378038.csv\n",
      "- PatNo_ID_1566123680.csv\n",
      "- PatNo_ID_1566252197.csv\n",
      "- PatNo_ID_1566279967.csv\n",
      "- PatNo_ID_1566671274.csv\n",
      "- PatNo_ID_1566911879.csv\n",
      "- PatNo_ID_1567747650.csv\n",
      "- PatNo_ID_1567804800.csv\n",
      "- PatNo_ID_1567832735.csv\n",
      "- PatNo_ID_1568039398.csv\n",
      "- PatNo_ID_1568574099.csv\n",
      "- PatNo_ID_1568813269.csv\n",
      "- PatNo_ID_1568952422.csv\n",
      "- PatNo_ID_1569083701.csv\n",
      "- PatNo_ID_1569944983.csv\n",
      "- PatNo_ID_1570089466.csv\n",
      "- PatNo_ID_1570242703.csv\n",
      "- PatNo_ID_1570273244.csv\n",
      "- PatNo_ID_1570642083.csv\n",
      "- PatNo_ID_1571945701.csv\n",
      "- PatNo_ID_1572481361.csv\n",
      "- PatNo_ID_1572562839.csv\n",
      "- PatNo_ID_1572831765.csv\n",
      "- PatNo_ID_1572976822.csv\n",
      "- PatNo_ID_1573063188.csv\n",
      "- PatNo_ID_1573249295.csv\n",
      "- PatNo_ID_1573964540.csv\n",
      "- PatNo_ID_1574148494.csv\n",
      "- PatNo_ID_1574270349.csv\n",
      "- PatNo_ID_1574528808.csv\n",
      "- PatNo_ID_1574831525.csv\n",
      "- PatNo_ID_1574987447.csv\n",
      "- PatNo_ID_1575060177.csv\n",
      "- PatNo_ID_1575256902.csv\n",
      "- PatNo_ID_1575445051.csv\n",
      "- PatNo_ID_1575502382.csv\n",
      "- PatNo_ID_1575975485.csv\n",
      "- PatNo_ID_1576115572.csv\n",
      "- PatNo_ID_1576116479.csv\n",
      "- PatNo_ID_1576301569.csv\n",
      "- PatNo_ID_1576964560.csv\n",
      "- PatNo_ID_1577042911.csv\n",
      "- PatNo_ID_1577487284.csv\n",
      "- PatNo_ID_1578784257.csv\n",
      "- PatNo_ID_1579198603.csv\n",
      "- PatNo_ID_1579498177.csv\n",
      "- PatNo_ID_1580062580.csv\n",
      "- PatNo_ID_1580096720.csv\n",
      "- PatNo_ID_1580107637.csv\n",
      "- PatNo_ID_1580244614.csv\n",
      "- PatNo_ID_1580766093.csv\n",
      "- PatNo_ID_1581003248.csv\n",
      "- PatNo_ID_1581019504.csv\n",
      "- PatNo_ID_1581633231.csv\n",
      "- PatNo_ID_1581692973.csv\n",
      "- PatNo_ID_1582452511.csv\n",
      "- PatNo_ID_1582635996.csv\n",
      "- PatNo_ID_1582849900.csv\n",
      "- PatNo_ID_1582937076.csv\n",
      "- PatNo_ID_1584158973.csv\n",
      "- PatNo_ID_1584397376.csv\n",
      "- PatNo_ID_1586172659.csv\n",
      "- PatNo_ID_1586696634.csv\n",
      "- PatNo_ID_1586897008.csv\n",
      "- PatNo_ID_1587490083.csv\n",
      "- PatNo_ID_1588632604.csv\n",
      "- PatNo_ID_1588673465.csv\n",
      "- PatNo_ID_1588794796.csv\n",
      "- PatNo_ID_1588957997.csv\n",
      "- PatNo_ID_1589018086.csv\n",
      "- PatNo_ID_1589034524.csv\n",
      "- PatNo_ID_1589324603.csv\n",
      "- PatNo_ID_1589918099.csv\n",
      "- PatNo_ID_1590136310.csv\n",
      "- PatNo_ID_1590616537.csv\n",
      "- PatNo_ID_1590854576.csv\n",
      "- PatNo_ID_1591609798.csv\n",
      "- PatNo_ID_1592044724.csv\n",
      "- PatNo_ID_1592560504.csv\n",
      "- PatNo_ID_1593087886.csv\n",
      "- PatNo_ID_1593416100.csv\n",
      "- PatNo_ID_1593472048.csv\n",
      "- PatNo_ID_1593593586.csv\n",
      "- PatNo_ID_1593720818.csv\n",
      "- PatNo_ID_1593838524.csv\n",
      "- PatNo_ID_1594173718.csv\n",
      "- PatNo_ID_1594294180.csv\n",
      "- PatNo_ID_1594305136.csv\n",
      "- PatNo_ID_1594309746.csv\n",
      "- PatNo_ID_1594319286.csv\n",
      "- PatNo_ID_1594320763.csv\n",
      "- PatNo_ID_1594322594.csv\n",
      "- PatNo_ID_1594335109.csv\n",
      "- PatNo_ID_1594423683.csv\n",
      "- PatNo_ID_1594437309.csv\n",
      "- PatNo_ID_1594439781.csv\n",
      "- PatNo_ID_1594441887.csv\n",
      "- PatNo_ID_1594448501.csv\n",
      "- PatNo_ID_1594455578.csv\n",
      "- PatNo_ID_1594464829.csv\n",
      "- PatNo_ID_1594467719.csv\n",
      "- PatNo_ID_1594471407.csv\n",
      "- PatNo_ID_1594479330.csv\n",
      "- PatNo_ID_1594511911.csv\n",
      "- PatNo_ID_1594511914.csv\n",
      "- PatNo_ID_1594528842.csv\n",
      "- PatNo_ID_1594533379.csv\n",
      "\n",
      "[8] 不存在重複欄位名稱 未達標：\n",
      "- (無)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# 依條款（3~8）列出未達標的「檔名清單」（只輸出檔名；若空則顯示「(無)」）\n",
    "import os, re, glob\n",
    "import pandas as pd\n",
    "\n",
    "DIR = \"/home/jovyan/RT08/0925/bling\"\n",
    "snake_pat = re.compile(r\"^[a-z0-9_]+$\")\n",
    "\n",
    "def read_header_only(path):\n",
    "    for enc in (\"utf-8\", \"utf-8-sig\", \"cp950\", \"big5\", \"latin1\"):\n",
    "        try:\n",
    "            return list(pd.read_csv(path, nrows=0, encoding=enc, sep=\",\", engine=\"python\", on_bad_lines=\"skip\").columns)\n",
    "        except Exception:\n",
    "            try:\n",
    "                return list(pd.read_csv(path, nrows=0, encoding=enc, sep=None, engine=\"python\", on_bad_lines=\"skip\").columns)\n",
    "            except Exception:\n",
    "                continue\n",
    "    return None\n",
    "\n",
    "fails = {3:set(), 4:set(), 5:set(), 6:set(), 7:set(), 8:set()}\n",
    "read_err = set()\n",
    "\n",
    "for fp in sorted(glob.glob(os.path.join(DIR, \"*.csv\"))):\n",
    "    name = os.path.basename(fp)\n",
    "    cols = read_header_only(fp)\n",
    "    if cols is None:\n",
    "        read_err.add(name)\n",
    "        # 無法讀取者視為全部未達標（若不想如此，可只列在 read_err）\n",
    "        for k in fails: fails[k].add(name)\n",
    "        continue\n",
    "\n",
    "    # 3. 全小寫？\n",
    "    all_lower = all(str(c) == str(c).lower() for c in cols)\n",
    "    if not all_lower: fails[3].add(name)\n",
    "\n",
    "    # 4. 全符合 snake_case？\n",
    "    all_snake = all(snake_pat.fullmatch(str(c)) is not None for c in cols)\n",
    "    if not all_snake: fails[4].add(name)\n",
    "\n",
    "    # 5/8. 唯一性 / 重複欄位\n",
    "    seen = {}\n",
    "    for c in cols:\n",
    "        seen[c] = seen.get(c, 0) + 1\n",
    "    has_dup = any(v > 1 for v in seen.values())\n",
    "    if has_dup:\n",
    "        fails[5].add(name)\n",
    "        fails[8].add(name)\n",
    "\n",
    "    # 6. 是否存在非小寫欄位\n",
    "    not_lower = [c for c in cols if str(c) != str(c).lower()]\n",
    "    if not_lower: fails[6].add(name)\n",
    "\n",
    "    # 7. 是否存在非 snake_case 欄位\n",
    "    not_snake = [c for c in cols if snake_pat.fullmatch(str(c)) is None]\n",
    "    if not_snake: fails[7].add(name)\n",
    "\n",
    "def print_section(no, title):\n",
    "    items = sorted(fails[no])\n",
    "    print(f\"[{no}] {title} 未達標：\")\n",
    "    if items:\n",
    "        for f in items:\n",
    "            print(f\"- {f}\")\n",
    "    else:\n",
    "        print(\"- (無)\")\n",
    "    print()\n",
    "\n",
    "print_section(3, \"所有欄位名稱皆小寫\")\n",
    "print_section(4, \"所有欄位名稱皆符合 snake_case\")\n",
    "print_section(5, \"欄位名稱皆唯一（無重複）\")\n",
    "print_section(6, \"不存在非小寫欄位\")\n",
    "print_section(7, \"不存在非 snake_case 欄位\")\n",
    "print_section(8, \"不存在重複欄位名稱\")\n",
    "\n",
    "# 如需僅看 #4 和 #7，可改成只呼叫上述兩行：\n",
    "# print_section(4, \"所有欄位名稱皆符合 snake_case\")\n",
    "# print_section(7, \"不存在非 snake_case 欄位\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "e36cc3e8-c4de-4ca9-a80b-43b82591bd35",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[redirect] copied 122 file(s) to /home/jovyan/RT08/0925/bling47\n",
      "[redirect] save() hooked: APPX4_save -> /home/jovyan/RT08/0925/bling47\n",
      "[redirect] save() hooked: APPX5_save -> /home/jovyan/RT08/0925/bling47\n",
      "[redirect] save() hooked: APPX2_save -> /home/jovyan/RT08/0925/bling47\n",
      "[redirect] save() hooked: APPX_save -> /home/jovyan/RT08/0925/bling47\n",
      "[redirect] 新的處理結果將寫入：/home/jovyan/RT08/0925/bling47\n"
     ]
    }
   ],
   "source": [
    "# 處理上面的檔案完之後放在 /home/jovyan/RT08/0925/bling47/\n",
    "# /home/jovyan/RT08/0925/bling 47有問題的檔案 但都小寫且AM PM\n",
    "\n",
    "# ============================================================\n",
    "# 將處理後 CSV 複製到 /home/jovyan/RT08/0925/bling47\n",
    "# 並把後續 APPX* 的存檔動作「導向」到 bling47（不改前述程式碼本體）\n",
    "# ============================================================\n",
    "import os, glob, shutil\n",
    "import pandas as pd\n",
    "\n",
    "SRC_DIR  = \"/home/jovyan/RT08/0925/bling\"\n",
    "DEST_DIR = \"/home/jovyan/RT08/0925/bling47\"\n",
    "os.makedirs(DEST_DIR, exist_ok=True)\n",
    "\n",
    "# 1) 先把目前 bling 內所有 CSV 複製一份到 bling47（覆蓋）\n",
    "copied = 0\n",
    "for fp in sorted(glob.glob(os.path.join(SRC_DIR, \"*.csv\"))):\n",
    "    try:\n",
    "        shutil.copy2(fp, os.path.join(DEST_DIR, os.path.basename(fp)))\n",
    "        copied += 1\n",
    "    except Exception as e:\n",
    "        print(f\"[redirect][WARN] copy failed: {os.path.basename(fp)} -> {e}\")\n",
    "print(f\"[redirect] copied {copied} file(s) to {DEST_DIR}\")\n",
    "\n",
    "# 2) 之後的「存檔」都改寫到 bling47\n",
    "def _make_redirected_save(dest_dir):\n",
    "    def _save(df: pd.DataFrame, src_path: str):\n",
    "        base = os.path.basename(src_path)\n",
    "        out_path = os.path.join(dest_dir, base)\n",
    "        os.makedirs(dest_dir, exist_ok=True)\n",
    "        df.to_csv(out_path, index=False, encoding=\"utf-8\")\n",
    "        # 與原先 save 簽名相容：回傳 (snapshot_path, overwritten_path)\n",
    "        return out_path, out_path\n",
    "    return _save\n",
    "\n",
    "_redirect = _make_redirected_save(DEST_DIR)\n",
    "\n",
    "# 嘗試覆蓋你先前定義過的 save 函式（若存在才覆蓋）\n",
    "for fn in (\"APPX4_save\", \"APPX5_save\", \"APPX2_save\", \"APPX_save\", \"APPX3_save\"):\n",
    "    if fn in globals() and callable(globals()[fn]):\n",
    "        globals()[fn] = _redirect\n",
    "        print(f\"[redirect] save() hooked: {fn} -> {DEST_DIR}\")\n",
    "\n",
    "print(f\"[redirect] 新的處理結果將寫入：{DEST_DIR}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b5e1f312-6b7d-490b-8803-00d3317e04b0",
   "metadata": {},
   "outputs": [],
   "source": [
    "#應該是因為最後有四個欄位 才有問題 等特徵篩完再來檢查有沒有問題就好\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "afe80911-a9f8-48b1-b7ad-412253768c1f",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fe851f21-6cfd-43c8-8469-40f1bb51e0fc",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "218493fc-ffbd-4761-ba9d-1a07e68eaf0e",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ac0cdafb-d864-4709-9020-d62568e18039",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "a1430fb5-c9bf-4abf-b0ce-b6136b683429",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "- (所有檔案皆具備 12 欄位)\n"
     ]
    }
   ],
   "source": [
    "#ok -- 針對這四份檔案檢查是否具有完整的12個特徵\n",
    "# 檢查 bling47 內所有 CSV 是否具備指定 12 欄位；\n",
    "# 另存「不完整清單」並於 console 列出每個檔案缺哪些欄位。\n",
    "import os, glob, re\n",
    "import pandas as pd\n",
    "\n",
    "DIR = \"/home/jovyan/RT08/0925/bling47\"\n",
    "OUT_DIR = \"/home/jovyan/RT08/0925/bling47_record\"\n",
    "os.makedirs(OUT_DIR, exist_ok=True)\n",
    "\n",
    "# 必要欄位（以小寫比對；實際欄位會先轉小寫再比對）\n",
    "REQUIRED = [\n",
    "    \"patno\", \"senddate\", \"ventilatormode\", \"rrhzsetactual\", \"mvsetactual\",\n",
    "    \"peepepap\", \"ppeak\", \"cdyn\", \"vti\", \"pmean\", \"vte\", \"sponvt\"\n",
    "]\n",
    "REQUIRED_SET = set(REQUIRED)\n",
    "\n",
    "ENCODINGS = (\"utf-8\", \"utf-8-sig\", \"cp950\", \"big5\", \"latin1\")\n",
    "\n",
    "def read_header_only(path):\n",
    "    \"\"\"只讀表頭；回傳欄位清單與使用的分隔符（盡量容錯）\"\"\"\n",
    "    # 先試逗號，再讓 pandas 自動偵測\n",
    "    for enc in ENCODINGS:\n",
    "        try:\n",
    "            cols = list(pd.read_csv(path, nrows=0, encoding=enc, sep=\",\", engine=\"python\", on_bad_lines=\"skip\").columns)\n",
    "            return cols\n",
    "        except Exception:\n",
    "            try:\n",
    "                cols = list(pd.read_csv(path, nrows=0, encoding=enc, sep=None, engine=\"python\", on_bad_lines=\"skip\").columns)\n",
    "                return cols\n",
    "            except Exception:\n",
    "                continue\n",
    "    return None  # 讀失敗\n",
    "\n",
    "rows = []\n",
    "print_missing = []\n",
    "\n",
    "for fp in sorted(glob.glob(os.path.join(DIR, \"*.csv\"))):\n",
    "    name = os.path.basename(fp)\n",
    "    cols = read_header_only(fp)\n",
    "    if cols is None:\n",
    "        # 若讀不到表頭，視為 12 欄全缺\n",
    "        missing = sorted(REQUIRED)\n",
    "        rows.append({\"file_name\": name, \"missing_columns\": \";\".join(missing), \"path\": fp})\n",
    "        print_missing.append((name, missing))\n",
    "        continue\n",
    "\n",
    "    cols_l = {str(c).strip().lower() for c in cols}\n",
    "    missing = sorted(REQUIRED_SET - cols_l)\n",
    "    if missing:\n",
    "        rows.append({\"file_name\": name, \"missing_columns\": \";\".join(missing), \"path\": fp})\n",
    "        print_missing.append((name, missing))\n",
    "\n",
    "# 輸出檔案與 console 列點\n",
    "out_csv = os.path.join(OUT_DIR, \"missing_columns_bling47.csv\")\n",
    "if rows:\n",
    "    pd.DataFrame(rows, columns=[\"file_name\", \"missing_columns\", \"path\"]).to_csv(out_csv, index=False, encoding=\"utf-8\")\n",
    "    print(f\"[MISS] 已輸出檔案：{out_csv}\\n\")\n",
    "    for name, miss in print_missing:\n",
    "        print(f\"- {name} 缺少欄位：{', '.join(miss)}\")\n",
    "else:\n",
    "    # 全數具備\n",
    "    pd.DataFrame([], columns=[\"file_name\", \"missing_columns\", \"path\"]).to_csv(out_csv, index=False, encoding=\"utf-8\")\n",
    "    print(\"- (所有檔案皆具備 12 欄位)\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "85deb542-dd63-4db1-978c-7bd84798d2f9",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "- 089271.csv ✅ 完整\n",
      "- 095323.csv 缺少欄位：patno_id\n",
      "- 095707.csv 缺少欄位：patno_id\n",
      "- 114309.csv 缺少欄位：patno_id\n",
      "- 230933.csv 缺少欄位：patno_id\n",
      "- 4216007.csv ✅ 完整\n",
      "- 7108162.csv ✅ 完整\n",
      "- 7408338.csv ✅ 完整\n",
      "- 7657698.csv ✅ 完整\n",
      "- 7721164.csv ✅ 完整\n",
      "- PatNo_ID_1560013303.csv ✅ 完整\n",
      "- PatNo_ID_1562733396.csv ✅ 完整\n",
      "- PatNo_ID_1563587183.csv ✅ 完整\n",
      "- PatNo_ID_1564148644.csv ✅ 完整\n",
      "- PatNo_ID_1565148312.csv ✅ 完整\n",
      "- PatNo_ID_1565378038.csv ✅ 完整\n",
      "- PatNo_ID_1566123680.csv ✅ 完整\n",
      "- PatNo_ID_1566252197.csv ✅ 完整\n",
      "- PatNo_ID_1566279967.csv ✅ 完整\n",
      "- PatNo_ID_1566671274.csv ✅ 完整\n",
      "- PatNo_ID_1566911879.csv ✅ 完整\n",
      "- PatNo_ID_1567747650.csv ✅ 完整\n",
      "- PatNo_ID_1567804800.csv ✅ 完整\n",
      "- PatNo_ID_1567832735.csv ✅ 完整\n",
      "- PatNo_ID_1568039398.csv ✅ 完整\n",
      "- PatNo_ID_1568574099.csv ✅ 完整\n",
      "- PatNo_ID_1568813269.csv ✅ 完整\n",
      "- PatNo_ID_1568952422.csv ✅ 完整\n",
      "- PatNo_ID_1569083701.csv ✅ 完整\n",
      "- PatNo_ID_1569944983.csv ✅ 完整\n",
      "- PatNo_ID_1570089466.csv ✅ 完整\n",
      "- PatNo_ID_1570242703.csv ✅ 完整\n",
      "- PatNo_ID_1570273244.csv ✅ 完整\n",
      "- PatNo_ID_1570642083.csv ✅ 完整\n",
      "- PatNo_ID_1571945701.csv ✅ 完整\n",
      "- PatNo_ID_1572481361.csv ✅ 完整\n",
      "- PatNo_ID_1572562839.csv ✅ 完整\n",
      "- PatNo_ID_1572831765.csv ✅ 完整\n",
      "- PatNo_ID_1572976822.csv ✅ 完整\n",
      "- PatNo_ID_1573063188.csv ✅ 完整\n",
      "- PatNo_ID_1573249295.csv ✅ 完整\n",
      "- PatNo_ID_1573964540.csv ✅ 完整\n",
      "- PatNo_ID_1574148494.csv ✅ 完整\n",
      "- PatNo_ID_1574270349.csv ✅ 完整\n",
      "- PatNo_ID_1574528808.csv ✅ 完整\n",
      "- PatNo_ID_1574831525.csv ✅ 完整\n",
      "- PatNo_ID_1574987447.csv ✅ 完整\n",
      "- PatNo_ID_1575060177.csv ✅ 完整\n",
      "- PatNo_ID_1575256902.csv ✅ 完整\n",
      "- PatNo_ID_1575445051.csv ✅ 完整\n",
      "- PatNo_ID_1575502382.csv ✅ 完整\n",
      "- PatNo_ID_1575975485.csv ✅ 完整\n",
      "- PatNo_ID_1576115572.csv ✅ 完整\n",
      "- PatNo_ID_1576116479.csv ✅ 完整\n",
      "- PatNo_ID_1576301569.csv ✅ 完整\n",
      "- PatNo_ID_1576964560.csv ✅ 完整\n",
      "- PatNo_ID_1577042911.csv ✅ 完整\n",
      "- PatNo_ID_1577487284.csv ✅ 完整\n",
      "- PatNo_ID_1578784257.csv ✅ 完整\n",
      "- PatNo_ID_1579198603.csv ✅ 完整\n",
      "- PatNo_ID_1579498177.csv ✅ 完整\n",
      "- PatNo_ID_1580062580.csv ✅ 完整\n",
      "- PatNo_ID_1580096720.csv ✅ 完整\n",
      "- PatNo_ID_1580107637.csv ✅ 完整\n",
      "- PatNo_ID_1580244614.csv ✅ 完整\n",
      "- PatNo_ID_1580766093.csv ✅ 完整\n",
      "- PatNo_ID_1581003248.csv ✅ 完整\n",
      "- PatNo_ID_1581019504.csv ✅ 完整\n",
      "- PatNo_ID_1581633231.csv ✅ 完整\n",
      "- PatNo_ID_1581692973.csv ✅ 完整\n",
      "- PatNo_ID_1582452511.csv ✅ 完整\n",
      "- PatNo_ID_1582635996.csv ✅ 完整\n",
      "- PatNo_ID_1582849900.csv ✅ 完整\n",
      "- PatNo_ID_1582937076.csv ✅ 完整\n",
      "- PatNo_ID_1584158973.csv ✅ 完整\n",
      "- PatNo_ID_1584397376.csv ✅ 完整\n",
      "- PatNo_ID_1586172659.csv ✅ 完整\n",
      "- PatNo_ID_1586696634.csv ✅ 完整\n",
      "- PatNo_ID_1586897008.csv ✅ 完整\n",
      "- PatNo_ID_1587490083.csv ✅ 完整\n",
      "- PatNo_ID_1588632604.csv ✅ 完整\n",
      "- PatNo_ID_1588673465.csv ✅ 完整\n",
      "- PatNo_ID_1588794796.csv ✅ 完整\n",
      "- PatNo_ID_1588957997.csv ✅ 完整\n",
      "- PatNo_ID_1589018086.csv ✅ 完整\n",
      "- PatNo_ID_1589034524.csv ✅ 完整\n",
      "- PatNo_ID_1589324603.csv ✅ 完整\n",
      "- PatNo_ID_1589918099.csv ✅ 完整\n",
      "- PatNo_ID_1590136310.csv ✅ 完整\n",
      "- PatNo_ID_1590616537.csv ✅ 完整\n",
      "- PatNo_ID_1590854576.csv ✅ 完整\n",
      "- PatNo_ID_1591609798.csv ✅ 完整\n",
      "- PatNo_ID_1592044724.csv ✅ 完整\n",
      "- PatNo_ID_1592560504.csv ✅ 完整\n",
      "- PatNo_ID_1593087886.csv ✅ 完整\n",
      "- PatNo_ID_1593416100.csv ✅ 完整\n",
      "- PatNo_ID_1593472048.csv ✅ 完整\n",
      "- PatNo_ID_1593593586.csv ✅ 完整\n",
      "- PatNo_ID_1593720818.csv ✅ 完整\n",
      "- PatNo_ID_1593838524.csv ✅ 完整\n",
      "- PatNo_ID_1594173718.csv ✅ 完整\n",
      "- PatNo_ID_1594294180.csv ✅ 完整\n",
      "- PatNo_ID_1594305136.csv ✅ 完整\n",
      "- PatNo_ID_1594309746.csv ✅ 完整\n",
      "- PatNo_ID_1594319286.csv ✅ 完整\n",
      "- PatNo_ID_1594320763.csv ✅ 完整\n",
      "- PatNo_ID_1594322594.csv ✅ 完整\n",
      "- PatNo_ID_1594335109.csv ✅ 完整\n",
      "- PatNo_ID_1594423683.csv ✅ 完整\n",
      "- PatNo_ID_1594437309.csv ✅ 完整\n",
      "- PatNo_ID_1594439781.csv ✅ 完整\n",
      "- PatNo_ID_1594441887.csv ✅ 完整\n",
      "- PatNo_ID_1594448501.csv ✅ 完整\n",
      "- PatNo_ID_1594455578.csv ✅ 完整\n",
      "- PatNo_ID_1594464829.csv ✅ 完整\n",
      "- PatNo_ID_1594467719.csv ✅ 完整\n",
      "- PatNo_ID_1594471407.csv ✅ 完整\n",
      "- PatNo_ID_1594479330.csv ✅ 完整\n",
      "- PatNo_ID_1594511911.csv ✅ 完整\n",
      "- PatNo_ID_1594511914.csv ✅ 完整\n",
      "- PatNo_ID_1594528842.csv ✅ 完整\n",
      "- PatNo_ID_1594533379.csv ✅ 完整\n",
      "\n",
      "完成：處理 122/122 檔；失敗 0 檔。輸出目錄：/home/jovyan/RT08/0925/bling12\n"
     ]
    }
   ],
   "source": [
    "# 針對bling/資料裡面的所有檔案，只留這十二個特徵的欄位[ \"patno\", \"senddate\", \"ventilatormode\", \"rrhzsetactual\", \"mvsetactual\",\"peepepap\", \"ppeak\", \"cdyn\", \"vti\", \"pmean\", \"vte\", \"sponvt\" ]，\n",
    "# 其餘都刪掉，並且將新的檔案存在 /home/jovyan/RT08/0925/bling12/\n",
    "# 針對 /home/jovyan/RT08/0925/bling/ 所有 CSV\n",
    "# 只保留 13 欄位（大小寫不敏感）：[\"patno\",\"patno_id\",\"senddate\",\"ventilatormode\",\"rrhzsetactual\",\"mvsetactual\",\n",
    "# \"peepepap\",\"ppeak\",\"cdyn\",\"vti\",\"pmean\",\"vte\",\"sponvt\"]\n",
    "# 其餘刪除，並把新檔寫到 /home/jovyan/RT08/0925/bling12/\n",
    "\n",
    "import os, glob\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "SRC_DIR = \"/home/jovyan/RT08/0925/bling\"\n",
    "DST_DIR = \"/home/jovyan/RT08/0925/bling12\"\n",
    "os.makedirs(DST_DIR, exist_ok=True)\n",
    "\n",
    "TARGET_COLS = [\n",
    "    \"patno\", \"patno_id\", \"senddate\", \"ventilatormode\", \"rrhzsetactual\", \"mvsetactual\",\n",
    "    \"peepepap\", \"ppeak\", \"cdyn\", \"vti\", \"pmean\", \"vte\", \"sponvt\"\n",
    "]\n",
    "\n",
    "ENCODINGS = (\"utf-8\", \"utf-8-sig\", \"cp950\", \"big5\", \"latin1\")\n",
    "\n",
    "def read_any(path):\n",
    "    # 若你程式中已定義 read_table_resilient，會優先使用\n",
    "    if \"read_table_resilient\" in globals() and callable(globals()[\"read_table_resilient\"]):\n",
    "        try:\n",
    "            df, meta = read_table_resilient(path, max_header_search=30)\n",
    "            if meta.get(\"read_ok\"): return df\n",
    "        except Exception:\n",
    "            pass\n",
    "    for enc in ENCODINGS:\n",
    "        try:\n",
    "            return pd.read_csv(path, encoding=enc, sep=None, engine=\"python\", on_bad_lines=\"skip\")\n",
    "        except Exception:\n",
    "            continue\n",
    "    raise RuntimeError(f\"讀檔失敗：{path}\")\n",
    "\n",
    "total, ok, fail = 0, 0, 0\n",
    "for fp in sorted(glob.glob(os.path.join(SRC_DIR, \"*.csv\"))):\n",
    "    total += 1\n",
    "    name = os.path.basename(fp)\n",
    "    try:\n",
    "        df = read_any(fp)\n",
    "\n",
    "        # 用小寫索引比對（不更動原資料值）\n",
    "        df_l = df.copy()\n",
    "        df_l.columns = [str(c).strip().lower() for c in df_l.columns]\n",
    "\n",
    "        # 確保 13 欄位都存在（缺的補 NaN），並照指定順序輸出\n",
    "        out_df = df_l.reindex(columns=TARGET_COLS)\n",
    "        for col in TARGET_COLS:\n",
    "            if col not in df_l.columns:\n",
    "                out_df[col] = np.nan\n",
    "        out_df = out_df[TARGET_COLS]  # 保持欄位順序\n",
    "\n",
    "        # 輸出到 bling12（UTF-8）\n",
    "        out_path = os.path.join(DST_DIR, name)\n",
    "        out_df.to_csv(out_path, index=False, encoding=\"utf-8\")\n",
    "\n",
    "        # 顯示此檔缺哪些欄位（若有）\n",
    "        missing = [c for c in TARGET_COLS if c not in df_l.columns]\n",
    "        if missing:\n",
    "            print(f\"- {name} 缺少欄位：{', '.join(missing)}\")\n",
    "        else:\n",
    "            print(f\"- {name} ✅ 完整\")\n",
    "\n",
    "        ok += 1\n",
    "    except Exception as e:\n",
    "        print(f\"- {name} ❌ 失敗：{e}\")\n",
    "        fail += 1\n",
    "\n",
    "print(f\"\\n完成：處理 {ok}/{total} 檔；失敗 {fail} 檔。輸出目錄：{DST_DIR}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "10ddb65f-ac3b-4364-9444-158c7ee97dc2",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[處理完成，分類清單]\n",
      "[兩欄皆有值但不相等] 共 0 檔：\n",
      "\n",
      "[兩欄皆無值] 共 0 檔：\n",
      "\n",
      "[只有 patno → 複製到 patno_id] 共 10 檔：\n",
      "- 089271.csv\n",
      "- 095323.csv\n",
      "- 095707.csv\n",
      "- 114309.csv\n",
      "- 230933.csv\n",
      "- 4216007.csv\n",
      "- 7108162.csv\n",
      "- 7408338.csv\n",
      "- 7657698.csv\n",
      "- 7721164.csv\n",
      "\n",
      "[只有 patno_id → 複製到 patno] 共 112 檔：\n",
      "- PatNo_ID_1560013303.csv\n",
      "- PatNo_ID_1562733396.csv\n",
      "- PatNo_ID_1563587183.csv\n",
      "- PatNo_ID_1564148644.csv\n",
      "- PatNo_ID_1565148312.csv\n",
      "- PatNo_ID_1565378038.csv\n",
      "- PatNo_ID_1566123680.csv\n",
      "- PatNo_ID_1566252197.csv\n",
      "- PatNo_ID_1566279967.csv\n",
      "- PatNo_ID_1566671274.csv\n",
      "- PatNo_ID_1566911879.csv\n",
      "- PatNo_ID_1567747650.csv\n",
      "- PatNo_ID_1567804800.csv\n",
      "- PatNo_ID_1567832735.csv\n",
      "- PatNo_ID_1568039398.csv\n",
      "- PatNo_ID_1568574099.csv\n",
      "- PatNo_ID_1568813269.csv\n",
      "- PatNo_ID_1568952422.csv\n",
      "- PatNo_ID_1569083701.csv\n",
      "- PatNo_ID_1569944983.csv\n",
      "- PatNo_ID_1570089466.csv\n",
      "- PatNo_ID_1570242703.csv\n",
      "- PatNo_ID_1570273244.csv\n",
      "- PatNo_ID_1570642083.csv\n",
      "- PatNo_ID_1571945701.csv\n",
      "- PatNo_ID_1572481361.csv\n",
      "- PatNo_ID_1572562839.csv\n",
      "- PatNo_ID_1572831765.csv\n",
      "- PatNo_ID_1572976822.csv\n",
      "- PatNo_ID_1573063188.csv\n",
      "- PatNo_ID_1573249295.csv\n",
      "- PatNo_ID_1573964540.csv\n",
      "- PatNo_ID_1574148494.csv\n",
      "- PatNo_ID_1574270349.csv\n",
      "- PatNo_ID_1574528808.csv\n",
      "- PatNo_ID_1574831525.csv\n",
      "- PatNo_ID_1574987447.csv\n",
      "- PatNo_ID_1575060177.csv\n",
      "- PatNo_ID_1575256902.csv\n",
      "- PatNo_ID_1575445051.csv\n",
      "- PatNo_ID_1575502382.csv\n",
      "- PatNo_ID_1575975485.csv\n",
      "- PatNo_ID_1576115572.csv\n",
      "- PatNo_ID_1576116479.csv\n",
      "- PatNo_ID_1576301569.csv\n",
      "- PatNo_ID_1576964560.csv\n",
      "- PatNo_ID_1577042911.csv\n",
      "- PatNo_ID_1577487284.csv\n",
      "- PatNo_ID_1578784257.csv\n",
      "- PatNo_ID_1579198603.csv\n",
      "- PatNo_ID_1579498177.csv\n",
      "- PatNo_ID_1580062580.csv\n",
      "- PatNo_ID_1580096720.csv\n",
      "- PatNo_ID_1580107637.csv\n",
      "- PatNo_ID_1580244614.csv\n",
      "- PatNo_ID_1580766093.csv\n",
      "- PatNo_ID_1581003248.csv\n",
      "- PatNo_ID_1581019504.csv\n",
      "- PatNo_ID_1581633231.csv\n",
      "- PatNo_ID_1581692973.csv\n",
      "- PatNo_ID_1582452511.csv\n",
      "- PatNo_ID_1582635996.csv\n",
      "- PatNo_ID_1582849900.csv\n",
      "- PatNo_ID_1582937076.csv\n",
      "- PatNo_ID_1584158973.csv\n",
      "- PatNo_ID_1584397376.csv\n",
      "- PatNo_ID_1586172659.csv\n",
      "- PatNo_ID_1586696634.csv\n",
      "- PatNo_ID_1586897008.csv\n",
      "- PatNo_ID_1587490083.csv\n",
      "- PatNo_ID_1588632604.csv\n",
      "- PatNo_ID_1588673465.csv\n",
      "- PatNo_ID_1588794796.csv\n",
      "- PatNo_ID_1588957997.csv\n",
      "- PatNo_ID_1589018086.csv\n",
      "- PatNo_ID_1589034524.csv\n",
      "- PatNo_ID_1589324603.csv\n",
      "- PatNo_ID_1589918099.csv\n",
      "- PatNo_ID_1590136310.csv\n",
      "- PatNo_ID_1590616537.csv\n",
      "- PatNo_ID_1590854576.csv\n",
      "- PatNo_ID_1591609798.csv\n",
      "- PatNo_ID_1592044724.csv\n",
      "- PatNo_ID_1592560504.csv\n",
      "- PatNo_ID_1593087886.csv\n",
      "- PatNo_ID_1593416100.csv\n",
      "- PatNo_ID_1593472048.csv\n",
      "- PatNo_ID_1593593586.csv\n",
      "- PatNo_ID_1593720818.csv\n",
      "- PatNo_ID_1593838524.csv\n",
      "- PatNo_ID_1594173718.csv\n",
      "- PatNo_ID_1594294180.csv\n",
      "- PatNo_ID_1594305136.csv\n",
      "- PatNo_ID_1594309746.csv\n",
      "- PatNo_ID_1594319286.csv\n",
      "- PatNo_ID_1594320763.csv\n",
      "- PatNo_ID_1594322594.csv\n",
      "- PatNo_ID_1594335109.csv\n",
      "- PatNo_ID_1594423683.csv\n",
      "- PatNo_ID_1594437309.csv\n",
      "- PatNo_ID_1594439781.csv\n",
      "- PatNo_ID_1594441887.csv\n",
      "- PatNo_ID_1594448501.csv\n",
      "- PatNo_ID_1594455578.csv\n",
      "- PatNo_ID_1594464829.csv\n",
      "- PatNo_ID_1594467719.csv\n",
      "- PatNo_ID_1594471407.csv\n",
      "- PatNo_ID_1594479330.csv\n",
      "- PatNo_ID_1594511911.csv\n",
      "- PatNo_ID_1594511914.csv\n",
      "- PatNo_ID_1594528842.csv\n",
      "- PatNo_ID_1594533379.csv\n",
      "\n",
      "[複檢結果 @ blingok]\n",
      "- 檔案仍存在『兩欄皆有值但不相等』：10\n",
      "  - 089271.csv\n",
      "  - 095323.csv\n",
      "  - 095707.csv\n",
      "  - 114309.csv\n",
      "  - 230933.csv\n",
      "  - 4216007.csv\n",
      "  - 7108162.csv\n",
      "  - 7408338.csv\n",
      "  - 7657698.csv\n",
      "  - 7721164.csv\n",
      "- 檔案仍存在『兩欄皆無值』：0\n",
      "- 檔案仍存在『僅一欄有值』：0\n"
     ]
    }
   ],
   "source": [
    "\"\"\" 先看/home/jovyan/RT08/0925/bling12裡面所有檔案的 patno跟patno_id，如果兩個欄位都有數值，檢查是否一樣，如果一樣就不動，如果不一樣就印出檔案名稱\n",
    "如果兩個欄位都沒有數值就印出檔案名稱\n",
    "如果只有一欄有數字就複製成兩個欄位一樣數值，\n",
    "分別印出這些狀況的有哪些檔案 檔案數量\n",
    "並在結束的時候再檢查一次\n",
    "處理後的檔案存在/home/jovyan/RT08/0925/blingok\n",
    "\"\"\"\n",
    "# 檢查並修補 bling12 的 patno / patno_id，結果寫入 blingok\n",
    "# 規則：\n",
    "# - 兩欄皆有值：相等→不動；不等→僅列檔名（不修改）\n",
    "# - 僅一欄有值：把該值複製到另一欄（修補）\n",
    "# - 兩欄皆無值：列檔名（不修改）\n",
    "# - 結束後對 blingok 再做一次總檢查並列印統計\n",
    "import os, glob, pandas as pd, unicodedata, numpy as np\n",
    "\n",
    "IN_DIR  = \"/home/jovyan/RT08/0925/bling12\"\n",
    "OUT_DIR = \"/home/jovyan/RT08/0925/blingok\"\n",
    "os.makedirs(OUT_DIR, exist_ok=True)\n",
    "\n",
    "ENCODINGS = (\"utf-8-sig\",\"utf-8\",\"cp950\",\"big5\",\"latin1\")\n",
    "\n",
    "def read_df_any(path):\n",
    "    for enc in ENCODINGS:\n",
    "        try:\n",
    "            return pd.read_csv(path, encoding=enc, engine=\"python\", on_bad_lines=\"skip\")\n",
    "        except Exception:\n",
    "            continue\n",
    "    raise RuntimeError(f\"讀檔失敗：{os.path.basename(path)}\")\n",
    "\n",
    "def ensure_cols(df, names):\n",
    "    # 將欄位名轉小寫以利尋找；若缺列則補空欄\n",
    "    lower_map = {c: unicodedata.normalize(\"NFKC\", str(c)).strip().lower() for c in df.columns}\n",
    "    rev = {}\n",
    "    for k,v in lower_map.items():\n",
    "        if v not in rev: rev[v]=k  # 第一個命中優先\n",
    "    for need in names:\n",
    "        if need not in rev:\n",
    "            df[need] = pd.NA\n",
    "            rev[need] = need\n",
    "    return df, rev\n",
    "\n",
    "def norm_val(x):\n",
    "    \"\"\"將值正規化為比較/判斷是否有值用的字串；None 代表無值。\"\"\"\n",
    "    if x is None or (isinstance(x, float) and np.isnan(x)):\n",
    "        return None\n",
    "    s = unicodedata.normalize(\"NFKC\", str(x)).strip()\n",
    "    if s == \"\" or s.lower() in {\"nan\",\"none\",\"null\"}:\n",
    "        return None\n",
    "    return s\n",
    "\n",
    "def comp_key(s):\n",
    "    \"\"\"比較是否相等的 key：數字只比數值（忽略前導零），其他維持字串\"\"\"\n",
    "    if s is None: return None\n",
    "    if s.isdigit():\n",
    "        t = s.lstrip(\"0\")\n",
    "        return t if t != \"\" else \"0\"\n",
    "    return s\n",
    "\n",
    "# 收集統計\n",
    "mismatch_files          = set()  # 兩欄皆有值但不相等\n",
    "both_missing_files      = set()  # 兩欄皆無值\n",
    "filled_patno_to_id      = set()  # 只有 patno 有值→複製到 patno_id\n",
    "filled_id_to_patno      = set()  # 只有 patno_id 有值→複製到 patno\n",
    "read_errors             = []\n",
    "\n",
    "# 處理每個檔案\n",
    "for fp in sorted(glob.glob(os.path.join(IN_DIR, \"*.csv\"))):\n",
    "    name = os.path.basename(fp)\n",
    "    try:\n",
    "        df = read_df_any(fp)\n",
    "        df, rev = ensure_cols(df, [\"patno\",\"patno_id\"])\n",
    "        c_patno    = rev[\"patno\"]\n",
    "        c_patno_id = rev[\"patno_id\"]\n",
    "\n",
    "        # 正規化值\n",
    "        p0  = df[c_patno].map(norm_val)\n",
    "        p1  = df[c_patno_id].map(norm_val)\n",
    "        k0  = p0.map(comp_key)\n",
    "        k1  = p1.map(comp_key)\n",
    "\n",
    "        both_have    = p0.notna() & p1.notna()\n",
    "        both_missing = p0.isna()  & p1.isna()\n",
    "        only_p0      = p0.notna() & p1.isna()\n",
    "        only_p1      = p0.isna()  & p1.notna()\n",
    "        mismatch     = both_have & (k0 != k1)\n",
    "\n",
    "        # 統計檔級事件\n",
    "        if mismatch.any():     mismatch_files.add(name)\n",
    "        if both_missing.any(): both_missing_files.add(name)\n",
    "        if only_p0.any():      filled_patno_to_id.add(name)\n",
    "        if only_p1.any():      filled_id_to_patno.add(name)\n",
    "\n",
    "        # 修補：單欄有值 → 複製\n",
    "        if only_p0.any():\n",
    "            df.loc[only_p0, c_patno_id] = df.loc[only_p0, c_patno]\n",
    "        if only_p1.any():\n",
    "            df.loc[only_p1, c_patno]    = df.loc[only_p1, c_patno_id]\n",
    "\n",
    "        # 輸出到 blingok\n",
    "        out_path = os.path.join(OUT_DIR, name)\n",
    "        df.to_csv(out_path, index=False, encoding=\"utf-8\")\n",
    "\n",
    "    except Exception as e:\n",
    "        read_errors.append((name, str(e)))\n",
    "\n",
    "# 列印處理結果（分類清單與檔案數）\n",
    "print(\"[處理完成，分類清單]\")\n",
    "print(f\"[兩欄皆有值但不相等] 共 {len(mismatch_files)} 檔：\")\n",
    "for n in sorted(mismatch_files): print(f\"- {n}\")\n",
    "print(f\"\\n[兩欄皆無值] 共 {len(both_missing_files)} 檔：\")\n",
    "for n in sorted(both_missing_files): print(f\"- {n}\")\n",
    "print(f\"\\n[只有 patno → 複製到 patno_id] 共 {len(filled_patno_to_id)} 檔：\")\n",
    "for n in sorted(filled_patno_to_id): print(f\"- {n}\")\n",
    "print(f\"\\n[只有 patno_id → 複製到 patno] 共 {len(filled_id_to_patno)} 檔：\")\n",
    "for n in sorted(filled_id_to_patno): print(f\"- {n}\")\n",
    "if read_errors:\n",
    "    print(f\"\\n[讀取失敗] 共 {len(read_errors)} 檔：\")\n",
    "    for n, msg in read_errors: print(f\"- {n}: {msg}\")\n",
    "\n",
    "# ====== 最終複檢（針對 blingok）======\n",
    "mismatch2 = set(); both_missing2 = set(); one_only_any2 = set()\n",
    "\n",
    "for fp in sorted(glob.glob(os.path.join(OUT_DIR, \"*.csv\"))):\n",
    "    name = os.path.basename(fp)\n",
    "    try:\n",
    "        df = read_df_any(fp)\n",
    "        df, rev = ensure_cols(df, [\"patno\",\"patno_id\"])\n",
    "        p0 = df[rev[\"patno\"]].map(norm_val)\n",
    "        p1 = df[rev[\"patno_id\"]].map(norm_val)\n",
    "        k0 = p0.map(comp_key)\n",
    "        k1 = p1.map(comp_key)\n",
    "\n",
    "        both_have    = p0.notna() & p1.notna()\n",
    "        both_missing = p0.isna()  & p1.isna()\n",
    "        only_one     = (p0.notna() ^ p1.notna())\n",
    "\n",
    "        if (both_have & (k0 != k1)).any(): mismatch2.add(name)\n",
    "        if both_missing.any():             both_missing2.add(name)\n",
    "        if only_one.any():                 one_only_any2.add(name)\n",
    "    except Exception:\n",
    "        pass\n",
    "\n",
    "print(\"\\n[複檢結果 @ blingok]\")\n",
    "print(f\"- 檔案仍存在『兩欄皆有值但不相等』：{len(mismatch2)}\")\n",
    "for n in sorted(mismatch2): print(f\"  - {n}\")\n",
    "print(f\"- 檔案仍存在『兩欄皆無值』：{len(both_missing2)}\")\n",
    "for n in sorted(both_missing2): print(f\"  - {n}\")\n",
    "print(f\"- 檔案仍存在『僅一欄有值』：{len(one_only_any2)}\")\n",
    "for n in sorted(one_only_any2): print(f\"  - {n}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "74b07b85-74a5-46e5-aca6-3294fedf9d7e",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "[完成] 已刪除 patno_id、轉換 patno 為 10 位數字並覆蓋原檔。\n",
      "- 原始 patno 含小數（非 .0）之檔案數：0\n",
      "- 有修剪欄位（刪 patno_id / 多餘欄位）的檔案數：122\n",
      "  - 089271.csv\n",
      "  - 095323.csv\n",
      "  - 095707.csv\n",
      "  - 114309.csv\n",
      "  - 230933.csv\n",
      "  - 4216007.csv\n",
      "  - 7108162.csv\n",
      "  - 7408338.csv\n",
      "  - 7657698.csv\n",
      "  - 7721164.csv\n",
      "  - PatNo_ID_1560013303.csv\n",
      "  - PatNo_ID_1562733396.csv\n",
      "  - PatNo_ID_1563587183.csv\n",
      "  - PatNo_ID_1564148644.csv\n",
      "  - PatNo_ID_1565148312.csv\n",
      "  - PatNo_ID_1565378038.csv\n",
      "  - PatNo_ID_1566123680.csv\n",
      "  - PatNo_ID_1566252197.csv\n",
      "  - PatNo_ID_1566279967.csv\n",
      "  - PatNo_ID_1566671274.csv\n",
      "  - PatNo_ID_1566911879.csv\n",
      "  - PatNo_ID_1567747650.csv\n",
      "  - PatNo_ID_1567804800.csv\n",
      "  - PatNo_ID_1567832735.csv\n",
      "  - PatNo_ID_1568039398.csv\n",
      "  - PatNo_ID_1568574099.csv\n",
      "  - PatNo_ID_1568813269.csv\n",
      "  - PatNo_ID_1568952422.csv\n",
      "  - PatNo_ID_1569083701.csv\n",
      "  - PatNo_ID_1569944983.csv\n",
      "  - PatNo_ID_1570089466.csv\n",
      "  - PatNo_ID_1570242703.csv\n",
      "  - PatNo_ID_1570273244.csv\n",
      "  - PatNo_ID_1570642083.csv\n",
      "  - PatNo_ID_1571945701.csv\n",
      "  - PatNo_ID_1572481361.csv\n",
      "  - PatNo_ID_1572562839.csv\n",
      "  - PatNo_ID_1572831765.csv\n",
      "  - PatNo_ID_1572976822.csv\n",
      "  - PatNo_ID_1573063188.csv\n",
      "  - PatNo_ID_1573249295.csv\n",
      "  - PatNo_ID_1573964540.csv\n",
      "  - PatNo_ID_1574148494.csv\n",
      "  - PatNo_ID_1574270349.csv\n",
      "  - PatNo_ID_1574528808.csv\n",
      "  - PatNo_ID_1574831525.csv\n",
      "  - PatNo_ID_1574987447.csv\n",
      "  - PatNo_ID_1575060177.csv\n",
      "  - PatNo_ID_1575256902.csv\n",
      "  - PatNo_ID_1575445051.csv\n",
      "  - PatNo_ID_1575502382.csv\n",
      "  - PatNo_ID_1575975485.csv\n",
      "  - PatNo_ID_1576115572.csv\n",
      "  - PatNo_ID_1576116479.csv\n",
      "  - PatNo_ID_1576301569.csv\n",
      "  - PatNo_ID_1576964560.csv\n",
      "  - PatNo_ID_1577042911.csv\n",
      "  - PatNo_ID_1577487284.csv\n",
      "  - PatNo_ID_1578784257.csv\n",
      "  - PatNo_ID_1579198603.csv\n",
      "  - PatNo_ID_1579498177.csv\n",
      "  - PatNo_ID_1580062580.csv\n",
      "  - PatNo_ID_1580096720.csv\n",
      "  - PatNo_ID_1580107637.csv\n",
      "  - PatNo_ID_1580244614.csv\n",
      "  - PatNo_ID_1580766093.csv\n",
      "  - PatNo_ID_1581003248.csv\n",
      "  - PatNo_ID_1581019504.csv\n",
      "  - PatNo_ID_1581633231.csv\n",
      "  - PatNo_ID_1581692973.csv\n",
      "  - PatNo_ID_1582452511.csv\n",
      "  - PatNo_ID_1582635996.csv\n",
      "  - PatNo_ID_1582849900.csv\n",
      "  - PatNo_ID_1582937076.csv\n",
      "  - PatNo_ID_1584158973.csv\n",
      "  - PatNo_ID_1584397376.csv\n",
      "  - PatNo_ID_1586172659.csv\n",
      "  - PatNo_ID_1586696634.csv\n",
      "  - PatNo_ID_1586897008.csv\n",
      "  - PatNo_ID_1587490083.csv\n",
      "  - PatNo_ID_1588632604.csv\n",
      "  - PatNo_ID_1588673465.csv\n",
      "  - PatNo_ID_1588794796.csv\n",
      "  - PatNo_ID_1588957997.csv\n",
      "  - PatNo_ID_1589018086.csv\n",
      "  - PatNo_ID_1589034524.csv\n",
      "  - PatNo_ID_1589324603.csv\n",
      "  - PatNo_ID_1589918099.csv\n",
      "  - PatNo_ID_1590136310.csv\n",
      "  - PatNo_ID_1590616537.csv\n",
      "  - PatNo_ID_1590854576.csv\n",
      "  - PatNo_ID_1591609798.csv\n",
      "  - PatNo_ID_1592044724.csv\n",
      "  - PatNo_ID_1592560504.csv\n",
      "  - PatNo_ID_1593087886.csv\n",
      "  - PatNo_ID_1593416100.csv\n",
      "  - PatNo_ID_1593472048.csv\n",
      "  - PatNo_ID_1593593586.csv\n",
      "  - PatNo_ID_1593720818.csv\n",
      "  - PatNo_ID_1593838524.csv\n",
      "  - PatNo_ID_1594173718.csv\n",
      "  - PatNo_ID_1594294180.csv\n",
      "  - PatNo_ID_1594305136.csv\n",
      "  - PatNo_ID_1594309746.csv\n",
      "  - PatNo_ID_1594319286.csv\n",
      "  - PatNo_ID_1594320763.csv\n",
      "  - PatNo_ID_1594322594.csv\n",
      "  - PatNo_ID_1594335109.csv\n",
      "  - PatNo_ID_1594423683.csv\n",
      "  - PatNo_ID_1594437309.csv\n",
      "  - PatNo_ID_1594439781.csv\n",
      "  - PatNo_ID_1594441887.csv\n",
      "  - PatNo_ID_1594448501.csv\n",
      "  - PatNo_ID_1594455578.csv\n",
      "  - PatNo_ID_1594464829.csv\n",
      "  - PatNo_ID_1594467719.csv\n",
      "  - PatNo_ID_1594471407.csv\n",
      "  - PatNo_ID_1594479330.csv\n",
      "  - PatNo_ID_1594511911.csv\n",
      "  - PatNo_ID_1594511914.csv\n",
      "  - PatNo_ID_1594528842.csv\n",
      "  - PatNo_ID_1594533379.csv\n",
      "- 原始缺少必要欄位（已補空）的檔案數：0\n",
      "\n",
      "[最終驗證]\n",
      "- 欄位不是剛好 12 個（或順序不符）的檔案數：0\n",
      "- patno 不是『10 位數字字串』的檔案數：10\n",
      "  - 089271.csv\n",
      "  - 095323.csv\n",
      "  - 095707.csv\n",
      "  - 114309.csv\n",
      "  - 230933.csv\n",
      "  - 4216007.csv\n",
      "  - 7108162.csv\n",
      "  - 7408338.csv\n",
      "  - 7657698.csv\n",
      "  - 7721164.csv\n"
     ]
    }
   ],
   "source": [
    "\"\"\" 目前狀況是資料在/home/jovyan/RT08/0925/blingok但資料狀況是有兩個欄位 數字一樣但不是都是正整數 有些是float32\n",
    "\n",
    "刪掉/home/jovyan/RT08/0925/blingok所有檔案中的patno_id欄位，並且將patno欄位的數值改成整數，\n",
    "檢查patno的數字\n",
    "再檢查數字皆要十位數，假設現有的數字是1234902，則在前面多加0，變成0001234902，\n",
    "最後檢查所有/home/jovyan/RT08/0925/blingok的檔案是否只有12個欄位[ \"patno\", \"senddate\", \"ventilatormode\", \"rrhzsetactual\", \"mvsetactual\",\"peepepap\", \"ppeak\", \"cdyn\", \"vti\", \"pmean\", \"vte\", \"sponvt\" ]這十二個欄位，\n",
    "且patno的數值是十位數的整數\n",
    "\"\"\"\n",
    "# 目標：\n",
    "# 1) 讀取 /home/jovyan/RT08/0925/blingok 內所有 CSV\n",
    "# 2) 刪除 patno_id 欄位\n",
    "# 3) 將 patno 轉為整數（四捨五入），並以 10 位數字字串左補零（例如 1234902 -> 0001234902）\n",
    "# 4) 覆蓋回原檔\n",
    "# 5) 檢查所有檔案是否只剩下 12 欄：[ \"patno\",\"senddate\",\"ventilatormode\",\"rrhzsetactual\",\"mvsetactual\",\"peepepap\",\"ppeak\",\"cdyn\",\"vti\",\"pmean\",\"vte\",\"sponvt\" ]\n",
    "#    且 patno 皆為 10 位數字字串；印出檢查結果\n",
    "\n",
    "import os, glob, unicodedata, re\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "DIR = \"/home/jovyan/RT08/0925/blingok\"\n",
    "ENCODINGS = (\"utf-8-sig\",\"utf-8\",\"cp950\",\"big5\",\"latin1\")\n",
    "\n",
    "REQUIRED_12 = [\n",
    "    \"patno\",\"senddate\",\"ventilatormode\",\"rrhzsetactual\",\"mvsetactual\",\n",
    "    \"peepepap\",\"ppeak\",\"cdyn\",\"vti\",\"pmean\",\"vte\",\"sponvt\"\n",
    "]\n",
    "REQ_SET = set(REQUIRED_12)\n",
    "TEN_DIGIT_PAT = re.compile(r\"^\\d{10}$\")\n",
    "\n",
    "def read_df_any(path):\n",
    "    for enc in ENCODINGS:\n",
    "        try:\n",
    "            return pd.read_csv(path, encoding=enc, engine=\"python\", on_bad_lines=\"skip\")\n",
    "        except Exception:\n",
    "            continue\n",
    "    raise RuntimeError(f\"讀檔失敗：{os.path.basename(path)}\")\n",
    "\n",
    "def to_lower_cols(df: pd.DataFrame) -> pd.DataFrame:\n",
    "    df = df.copy()\n",
    "    df.columns = [unicodedata.normalize(\"NFKC\", str(c)).strip().lower() for c in df.columns]\n",
    "    return df\n",
    "\n",
    "def patno_to_10digits(series: pd.Series) -> pd.Series:\n",
    "    \"\"\"將 patno 轉成整數（四捨五入），再格式化為 10 位數字字串；無法解析者為 <NA>。\"\"\"\n",
    "    s = series.copy()\n",
    "    # 先標準化字串\n",
    "    s = s.map(lambda x: unicodedata.normalize(\"NFKC\", str(x)).strip() if pd.notna(x) else x)\n",
    "    # 去除常見非數字符號\n",
    "    s = s.str.replace(r\"[^\\d\\.\\-eE+]\", \"\", regex=True)\n",
    "    # 轉數字\n",
    "    num = pd.to_numeric(s, errors=\"coerce\")\n",
    "    # 四捨五入到整數\n",
    "    num = num.round(0)\n",
    "    # 轉為 Int64（可保留 NA）\n",
    "    try:\n",
    "        num = num.astype(\"Int64\")\n",
    "    except Exception:\n",
    "        # 部分版本可直接用 astype(\"Int64\")，保險兜底\n",
    "        num = num.where(num.notna(), other=pd.NA).astype(\"Int64\")\n",
    "    # 轉為 10 位數字字串（NA 保留）\n",
    "    def fmt(v):\n",
    "        if pd.isna(v):\n",
    "            return pd.NA\n",
    "        try:\n",
    "            return f\"{int(v):010d}\"\n",
    "        except Exception:\n",
    "            return pd.NA\n",
    "    return num.map(fmt).astype(\"string\")\n",
    "\n",
    "# 統計容器\n",
    "files_fixed_cols = []           # 被刪了 patno_id 或修剪到 12 欄\n",
    "files_had_fraction = []         # patno 原值含小數（非 .0）\n",
    "files_missing_required = []     # 原檔缺少必要欄位（已補空欄）\n",
    "files_invalid_patno_after = []  # 輸出後仍有非 10 位數字或缺失的檔案\n",
    "read_errors = []\n",
    "\n",
    "# 主流程\n",
    "for fp in sorted(glob.glob(os.path.join(DIR, \"*.csv\"))):\n",
    "    name = os.path.basename(fp)\n",
    "    try:\n",
    "        df = read_df_any(fp)\n",
    "        df = to_lower_cols(df)\n",
    "\n",
    "        # ===== 刪除 patno_id =====\n",
    "        if \"patno_id\" in df.columns:\n",
    "            df.drop(columns=[\"patno_id\"], inplace=True, errors=\"ignore\")\n",
    "            files_fixed_cols.append(name)\n",
    "\n",
    "        # ===== 確保 patno 欄位存在 =====\n",
    "        if \"patno\" not in df.columns:\n",
    "            # 若不存在，建立空欄以便後續檢查\n",
    "            df[\"patno\"] = pd.NA\n",
    "            files_missing_required.append(name)\n",
    "\n",
    "        # ===== 檢查原始 patno 是否有小數部分（非 .0） =====\n",
    "        raw_num = pd.to_numeric(df[\"patno\"], errors=\"coerce\")\n",
    "        frac_mask = raw_num.notna() & (np.floor(raw_num) != raw_num)\n",
    "        if frac_mask.any():\n",
    "            files_had_fraction.append(name)\n",
    "\n",
    "        # ===== 轉為 10 位數字字串（左補零）=====\n",
    "        df[\"patno\"] = patno_to_10digits(df[\"patno\"])\n",
    "\n",
    "        # ===== 修剪到僅剩 12 欄（缺的補空，順序固定）=====\n",
    "        # 先把多餘欄位刪掉\n",
    "        extra_cols = [c for c in df.columns if c not in REQ_SET]\n",
    "        if extra_cols:\n",
    "            df.drop(columns=extra_cols, inplace=True, errors=\"ignore\")\n",
    "            files_fixed_cols.append(name)\n",
    "        # 補齊缺少欄位\n",
    "        missing_now = [c for c in REQUIRED_12 if c not in df.columns]\n",
    "        if missing_now:\n",
    "            for c in missing_now:\n",
    "                df[c] = pd.NA\n",
    "            files_missing_required.append(name)\n",
    "        # 依指定順序重排\n",
    "        df = df[REQUIRED_12]\n",
    "\n",
    "        # ===== 覆寫回原檔 =====\n",
    "        tmp = fp + \".tmp\"\n",
    "        df.to_csv(tmp, index=False, encoding=\"utf-8\")\n",
    "        os.replace(tmp, fp)\n",
    "\n",
    "    except Exception as e:\n",
    "        read_errors.append((name, str(e)))\n",
    "\n",
    "# ===== 最終整體檢查 =====\n",
    "fail_cols = []   # 欄位集合不是剛好 12 個或順序錯的檔案\n",
    "fail_patno = []  # patno 不是 10 位數字字串的檔案\n",
    "\n",
    "for fp in sorted(glob.glob(os.path.join(DIR, \"*.csv\"))):\n",
    "    name = os.path.basename(fp)\n",
    "    try:\n",
    "        df = read_df_any(fp)\n",
    "        df = to_lower_cols(df)\n",
    "\n",
    "        # 檢查欄位集合與順序\n",
    "        if list(df.columns) != REQUIRED_12:\n",
    "            fail_cols.append(name)\n",
    "\n",
    "        # 檢查 patno 是否全為 10 位數字字串、無缺失\n",
    "        s = df[\"patno\"].astype(\"string\")\n",
    "        ok_mask = s.fillna(\"\").str.match(TEN_DIGIT_PAT)\n",
    "        if not ok_mask.all():\n",
    "            fail_patno.append(name)\n",
    "    except Exception as e:\n",
    "        read_errors.append((name, str(e)))\n",
    "\n",
    "# ===== 列印結果 =====\n",
    "print(\"\\n[完成] 已刪除 patno_id、轉換 patno 為 10 位數字並覆蓋原檔。\")\n",
    "print(f\"- 原始 patno 含小數（非 .0）之檔案數：{len(set(files_had_fraction))}\")\n",
    "for n in sorted(set(files_had_fraction)): print(f\"  - {n}\")\n",
    "print(f\"- 有修剪欄位（刪 patno_id / 多餘欄位）的檔案數：{len(set(files_fixed_cols))}\")\n",
    "for n in sorted(set(files_fixed_cols)): print(f\"  - {n}\")\n",
    "print(f\"- 原始缺少必要欄位（已補空）的檔案數：{len(set(files_missing_required))}\")\n",
    "for n in sorted(set(files_missing_required)): print(f\"  - {n}\")\n",
    "\n",
    "print(\"\\n[最終驗證]\")\n",
    "print(f\"- 欄位不是剛好 12 個（或順序不符）的檔案數：{len(fail_cols)}\")\n",
    "for n in sorted(fail_cols): print(f\"  - {n}\")\n",
    "print(f\"- patno 不是『10 位數字字串』的檔案數：{len(fail_patno)}\")\n",
    "for n in sorted(fail_patno): print(f\"  - {n}\")\n",
    "\n",
    "if read_errors:\n",
    "    print(f\"\\n[讀檔錯誤] 共 {len(read_errors)} 個：\")\n",
    "    for n, msg in read_errors: print(f\"  - {n}: {msg}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "cce78654-fa9c-4602-96a8-0c531870f3da",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[完成] 產出：\n",
      "- 空白字串統計（欄×檔）：/home/jovyan/RT08/0925/bling_record/ok/blank_counts_by_file_and_column.csv\n",
      "- NaN 數量矩陣（欄×檔）：/home/jovyan/RT08/0925/bling_record/ok/nan_counts_by_file_and_column.csv\n",
      "- NaN 比率矩陣（%）（欄×檔）：/home/jovyan/RT08/0925/bling_record/ok/nan_rates_by_file_and_column_percent.csv\n",
      "- 檔案級摘要（總 NaN / 缺失率）：/home/jovyan/RT08/0925/bling_record/ok/nan_summary_by_file.csv\n",
      "- 熱力圖：/home/jovyan/RT08/0925/bling_record/ok/nan_rate_heatmap.png\n"
     ]
    }
   ],
   "source": [
    "\"\"\" // 現在檔案確定都只有12個正確欄位 確定patno都對 都十位數字\n",
    "\n",
    "1. 偵測/home/jovyan/RT08/0925/bling12每個​檔案的什麼欄位​有​多少​空白\n",
    "2. 將/home/jovyan/RT08/0925/bling12每個​檔案的空值以NaN取代，並輸出一個檔案紀錄每個檔案有幾個空值，並且記錄缺失率，缺失率就是算具有NaN的比率，\n",
    "將新檔案覆蓋/home/jovyan/RT08/0925/bling12\n",
    "3. 將缺失率製作熱力圖，熱力圖用百分比，橫軸是特徵名稱 縱軸是各個檔案名稱 顏色用寒色調\n",
    "4. 另外也輸出一份檔案紀錄缺失數量，欄位包含：縱軸是檔名 橫軸是特徵名稱（也就是欄位名稱），裡面是數字不是百分比\n",
    "輸出的檔案或圖表都存在/home/jovyan/RT08/0925/bling_record/ok/\n",
    "\"\"\"\n",
    "# 功能：\n",
    "# 1) 偵測 /home/jovyan/RT08/0925/bling12/ 每個 CSV 檔，各欄位「空白字串」數量\n",
    "# 2) 將「空值」(空白/常見空字串) 轉為 NaN，覆寫回 bling12\n",
    "# 3) 輸出每檔總 NaN 數與缺失率（檔案級）\n",
    "# 4) 輸出「各檔×各欄」NaN 數量矩陣（數字）、NaN 比率矩陣（百分比）\n",
    "# 5) 以「NaN 比率矩陣」畫熱力圖 (藍色寒色調)，橫軸=欄位、縱軸=檔名\n",
    "#\n",
    "# 所有輸出：/home/jovyan/RT08/0925/bling_record/ok/\n",
    "# 讀寫來源：/home/jovyan/RT08/0925/bling12/（就地覆寫）\n",
    "import os, glob, unicodedata, re\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "SRC_DIR = \"/home/jovyan/RT08/0925/bling12\"\n",
    "OUT_DIR = \"/home/jovyan/RT08/0925/bling_record/ok\"\n",
    "os.makedirs(OUT_DIR, exist_ok=True)\n",
    "\n",
    "ENCODINGS = (\"utf-8-sig\",\"utf-8\",\"cp950\",\"big5\",\"latin1\")\n",
    "\n",
    "# 「空白」只統計純空/全空白字元；「空值」則含常見空字串\n",
    "NULL_TOKENS = {\"\", \"na\", \"n/a\", \"null\", \"none\"}  # 不分大小寫，用於轉 NaN\n",
    "\n",
    "def read_df_any(path):\n",
    "    for enc in ENCODINGS:\n",
    "        try:\n",
    "            return pd.read_csv(path, encoding=enc, engine=\"python\", on_bad_lines=\"skip\")\n",
    "        except Exception:\n",
    "            continue\n",
    "    raise RuntimeError(f\"讀檔失敗：{os.path.basename(path)}\")\n",
    "\n",
    "def normalize_str(x):\n",
    "    if pd.isna(x):\n",
    "        return x\n",
    "    return unicodedata.normalize(\"NFKC\", str(x))\n",
    "\n",
    "def is_blank_string(x):\n",
    "    \"\"\"是否為「空白字串」（僅空或全空白），不含 NaN。\"\"\"\n",
    "    if x is None or (isinstance(x, float) and np.isnan(x)):\n",
    "        return False\n",
    "    s = normalize_str(x)\n",
    "    return s.strip() == \"\"\n",
    "\n",
    "def is_null_like(x):\n",
    "    \"\"\"是否為『空值』：空白字串或常見空字串（大小寫不敏感），包含 None/NaN 直接視為空。\"\"\"\n",
    "    if x is None:\n",
    "        return True\n",
    "    if isinstance(x, float) and np.isnan(x):\n",
    "        return True\n",
    "    s = normalize_str(x).strip()\n",
    "    return (s == \"\") or (s.lower() in NULL_TOKENS)\n",
    "\n",
    "# 蒐集矩陣的統一欄名（聯集）\n",
    "all_columns = set()\n",
    "file_list = sorted(glob.glob(os.path.join(SRC_DIR, \"*.csv\")))\n",
    "\n",
    "# 第一次遍歷：統計「空白字串」數量（未改檔），並建立欄名聯集\n",
    "blank_counts_per_file = {}  # {filename: {col: blank_count}}\n",
    "for fp in file_list:\n",
    "    name = os.path.basename(fp)\n",
    "    try:\n",
    "        df = read_df_any(fp)\n",
    "        # 對所有欄位計算空白字串數量\n",
    "        bc = {}\n",
    "        for c in df.columns:\n",
    "            s = df[c]\n",
    "            # 以 object 轉字串後檢查空白；非 object 也一律以字串檢查空白\n",
    "            blank_cnt = s.apply(is_blank_string).sum()\n",
    "            bc[str(c)] = int(blank_cnt)\n",
    "        blank_counts_per_file[name] = bc\n",
    "        all_columns.update(map(str, df.columns))\n",
    "    except Exception as e:\n",
    "        blank_counts_per_file[name] = {\"__READ_ERROR__\": 1}\n",
    "        print(f\"[WARN] 讀取失敗（略過空白統計）：{name} -> {e}\")\n",
    "\n",
    "# 第二次遍歷：將空值轉為 NaN，覆寫；同時計算 NaN 統計\n",
    "nan_counts_per_file = {}   # {filename: {col: na_count}}\n",
    "nan_rates_per_file  = {}   # {filename: {col: na_rate}}   (0~1)\n",
    "file_summary_rows   = []   # 每檔總 NaN 與缺失率\n",
    "\n",
    "for fp in file_list:\n",
    "    name = os.path.basename(fp)\n",
    "    try:\n",
    "        df = read_df_any(fp)\n",
    "\n",
    "        # 將空值轉為 NaN（全欄位）\n",
    "        df2 = df.copy()\n",
    "        for c in df2.columns:\n",
    "            s = df2[c]\n",
    "            # 只在非 NaN 的元素上做字串判斷，可避免多餘成本\n",
    "            mask_idx = s.index\n",
    "            # 建立空值遮罩\n",
    "            null_mask = s.apply(is_null_like)\n",
    "            if null_mask.any():\n",
    "                df2.loc[null_mask, c] = np.nan\n",
    "\n",
    "        # 覆寫回原檔（UTF-8）\n",
    "        tmp = fp + \".tmp\"\n",
    "        df2.to_csv(tmp, index=False, encoding=\"utf-8\")\n",
    "        os.replace(tmp, fp)\n",
    "\n",
    "        # 計算 NaN 統計（更新欄名聯集）\n",
    "        all_columns.update(map(str, df2.columns))\n",
    "        na_counts = df2.isna().sum().to_dict()\n",
    "        na_rates  = (df2.isna().mean().fillna(0.0)).to_dict()\n",
    "        nan_counts_per_file[name] = {str(k): int(v) for k, v in na_counts.items()}\n",
    "        nan_rates_per_file[name]  = {str(k): float(v) for k, v in na_rates.items()}\n",
    "\n",
    "        # 檔案級 summary\n",
    "        total_cells = int(df2.shape[0] * df2.shape[1]) if df2.shape[0] and df2.shape[1] else 0\n",
    "        total_nan   = int(df2.isna().sum().sum())\n",
    "        miss_rate   = (total_nan / total_cells) if total_cells else 0.0\n",
    "        file_summary_rows.append({\n",
    "            \"file_name\": name,\n",
    "            \"n_rows\": int(df2.shape[0]),\n",
    "            \"n_cols\": int(df2.shape[1]),\n",
    "            \"total_nan\": total_nan,\n",
    "            \"total_cells\": total_cells,\n",
    "            \"missing_rate\": round(miss_rate, 6)\n",
    "        })\n",
    "\n",
    "    except Exception as e:\n",
    "        print(f\"[ERROR] 轉 NaN / 統計失敗：{name} -> {e}\")\n",
    "        # 也記一筆錯誤摘要\n",
    "        file_summary_rows.append({\n",
    "            \"file_name\": name, \"n_rows\": None, \"n_cols\": None,\n",
    "            \"total_nan\": None, \"total_cells\": None, \"missing_rate\": None\n",
    "        })\n",
    "\n",
    "# ---- 輸出 (1) 空白字串數量矩陣 ----\n",
    "cols_sorted = sorted(all_columns)\n",
    "blank_mat = []\n",
    "for name in sorted(blank_counts_per_file.keys()):\n",
    "    row = {\"file_name\": name}\n",
    "    bc = blank_counts_per_file[name]\n",
    "    for col in cols_sorted:\n",
    "        row[col] = int(bc.get(col, 0))\n",
    "    blank_mat.append(row)\n",
    "blank_df = pd.DataFrame(blank_mat)\n",
    "blank_csv = os.path.join(OUT_DIR, \"blank_counts_by_file_and_column.csv\")\n",
    "blank_df.to_csv(blank_csv, index=False, encoding=\"utf-8\")\n",
    "\n",
    "# ---- 輸出 (2) NaN 數量矩陣（數字）與 NaN 比率矩陣（百分比） ----\n",
    "nan_cnt_rows = []\n",
    "nan_rate_rows = []\n",
    "for name in sorted(nan_counts_per_file.keys()):\n",
    "    # counts\n",
    "    r_cnt = {\"file_name\": name}\n",
    "    for col in cols_sorted:\n",
    "        r_cnt[col] = int(nan_counts_per_file[name].get(col, 0))\n",
    "    nan_cnt_rows.append(r_cnt)\n",
    "    # rates (0~100)\n",
    "    r_rate = {\"file_name\": name}\n",
    "    for col in cols_sorted:\n",
    "        r_rate[col] = float(nan_rates_per_file[name].get(col, np.nan)) * 100.0\n",
    "    nan_rate_rows.append(r_rate)\n",
    "\n",
    "nan_cnt_df  = pd.DataFrame(nan_cnt_rows)\n",
    "nan_rate_df = pd.DataFrame(nan_rate_rows)\n",
    "\n",
    "nan_cnt_csv  = os.path.join(OUT_DIR, \"nan_counts_by_file_and_column.csv\")\n",
    "nan_rate_csv = os.path.join(OUT_DIR, \"nan_rates_by_file_and_column_percent.csv\")\n",
    "nan_cnt_df.to_csv(nan_cnt_csv, index=False, encoding=\"utf-8\")\n",
    "nan_rate_df.to_csv(nan_rate_csv, index=False, encoding=\"utf-8\")\n",
    "\n",
    "# ---- 輸出 (3) 檔案級摘要（總 NaN 與缺失率）----\n",
    "summary_df = pd.DataFrame(file_summary_rows, columns=[\n",
    "    \"file_name\",\"n_rows\",\"n_cols\",\"total_nan\",\"total_cells\",\"missing_rate\"\n",
    "])\n",
    "summary_csv = os.path.join(OUT_DIR, \"nan_summary_by_file.csv\")\n",
    "summary_df.to_csv(summary_csv, index=False, encoding=\"utf-8\")\n",
    "\n",
    "# ---- (4) 熱力圖：以 NaN 比率（百分比）繪製；橫軸=欄位、縱軸=檔名；寒色調 'Blues' ----\n",
    "# 轉為數值矩陣（移除 file_name 欄）\n",
    "if not nan_rate_df.empty:\n",
    "    plot_df = nan_rate_df.set_index(\"file_name\")\n",
    "    Z = plot_df.values.astype(float)\n",
    "    # 掩蔽 NaN 讓 imshow 演示空白處\n",
    "    Zm = np.ma.masked_invalid(Z)\n",
    "\n",
    "    plt.figure(figsize=(max(8, len(cols_sorted)*0.4), max(6, len(plot_df.index)*0.3)))\n",
    "    im = plt.imshow(Zm, aspect=\"auto\", interpolation=\"nearest\", cmap=\"Blues\")\n",
    "    cbar = plt.colorbar(im)\n",
    "    cbar.set_label(\"缺失率（%）\")\n",
    "\n",
    "    plt.xticks(ticks=np.arange(len(cols_sorted)), labels=cols_sorted, rotation=60, ha=\"right\", fontsize=8)\n",
    "    plt.yticks(ticks=np.arange(len(plot_df.index)), labels=list(plot_df.index), fontsize=8)\n",
    "    plt.xlabel(\"特徵名稱\")\n",
    "    plt.ylabel(\"檔案名稱\")\n",
    "    plt.title(\"缺失率熱力圖（%）\")\n",
    "\n",
    "    heatmap_path = os.path.join(OUT_DIR, \"nan_rate_heatmap.png\")\n",
    "    plt.tight_layout()\n",
    "    plt.savefig(heatmap_path, dpi=150)\n",
    "    plt.close()\n",
    "\n",
    "print(\"[完成] 產出：\")\n",
    "print(f\"- 空白字串統計（欄×檔）：{blank_csv}\")\n",
    "print(f\"- NaN 數量矩陣（欄×檔）：{nan_cnt_csv}\")\n",
    "print(f\"- NaN 比率矩陣（%）（欄×檔）：{nan_rate_csv}\")\n",
    "print(f\"- 檔案級摘要（總 NaN / 缺失率）：{summary_csv}\")\n",
    "print(f\"- 熱力圖：{os.path.join(OUT_DIR, 'nan_rate_heatmap.png')}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "d7715d41-0d4c-44ad-8fa0-e4a10cd3af13",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[OK] 已輸出帶標註的熱力圖：/home/jovyan/RT08/0925/bling_record/ok/nan_rate_heatmap.png\n"
     ]
    }
   ],
   "source": [
    "#在缺失率不等於0的格子中 加上他的缺失率數值，並且要確保數值看的到，像是顏色深的時候要變成白色字\n",
    "# 使用既有的缺失率矩陣，重畫熱力圖並「只在缺失率≠0的格子」加上數值標註\n",
    "# - 來源：/home/jovyan/RT08/0925/bling_record/ok/nan_rates_by_file_and_column_percent.csv\n",
    "# - 輸出：/home/jovyan/RT08/0925/bling_record/ok/nan_rate_heatmap.png（覆蓋）\n",
    "import os\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.patheffects as pe\n",
    "\n",
    "IN_CSV  = \"/home/jovyan/RT08/0925/bling_record/ok/nan_rates_by_file_and_column_percent.csv\"\n",
    "OUT_PNG = \"/home/jovyan/RT08/0925/bling_record/ok/nan_rate_heatmap.png\"\n",
    "\n",
    "# 讀取缺失率（百分比）矩陣\n",
    "df = pd.read_csv(IN_CSV)\n",
    "if \"file_name\" in df.columns:\n",
    "    df = df.set_index(\"file_name\")\n",
    "\n",
    "# 轉成數值（其餘不可解析為 NaN）\n",
    "for c in df.columns:\n",
    "    df[c] = pd.to_numeric(df[c], errors=\"coerce\")\n",
    "\n",
    "# 建構繪圖矩陣\n",
    "rows = df.index.tolist()\n",
    "cols = df.columns.tolist()\n",
    "Z = df.values.astype(float)\n",
    "\n",
    "# 定義顏色正規化（避免全 0 時崩潰）\n",
    "finite_vals = Z[np.isfinite(Z)]\n",
    "vmin = 0.0\n",
    "vmax = float(finite_vals.max()) if finite_vals.size else 1.0\n",
    "if vmax == 0:\n",
    "    vmax = 1.0\n",
    "\n",
    "from matplotlib import colors\n",
    "norm = colors.Normalize(vmin=vmin, vmax=vmax)\n",
    "\n",
    "# 圖尺寸依資料量自適應\n",
    "plt.figure(figsize=(max(8, len(cols)*0.4), max(6, len(rows)*0.3)))\n",
    "\n",
    "# 畫熱力圖（藍色寒色調）\n",
    "im = plt.imshow(Z, aspect=\"auto\", interpolation=\"nearest\", cmap=\"Blues\", norm=norm)\n",
    "\n",
    "# 軸與標題\n",
    "plt.xticks(ticks=np.arange(len(cols)), labels=cols, rotation=60, ha=\"right\", fontsize=8)\n",
    "plt.yticks(ticks=np.arange(len(rows)), labels=rows, fontsize=8)\n",
    "plt.xlabel(\"Features\")\n",
    "plt.ylabel(\"Files\")\n",
    "plt.title(\"Missing Rate Heatmap (%)\")\n",
    "\n",
    "# Colorbar\n",
    "cbar = plt.colorbar(im)\n",
    "cbar.set_label(\"Missing Rate %\")\n",
    "\n",
    "# 在「缺失率≠0」的格子上標註；文字顏色依背景亮度自動反轉\n",
    "def text_color_for_value(val):\n",
    "    # 取對應顏色並計算相對亮度（sRGB）\n",
    "    r, g, b, _ = im.cmap(norm(val))\n",
    "    luminance = 0.2126*r + 0.7152*g + 0.0722*b\n",
    "    # 背景越暗（luminance 小），使用白字；反之黑字\n",
    "    return (\"white\", \"black\")[luminance >= 0.5]\n",
    "\n",
    "for i in range(len(rows)):\n",
    "    for j in range(len(cols)):\n",
    "        val = Z[i, j]\n",
    "        if not np.isfinite(val) or val == 0:\n",
    "            continue\n",
    "        txt = f\"{val:.1f}%\" if val < 100 else f\"{val:.0f}%\"\n",
    "        color = text_color_for_value(val)\n",
    "        # 為了在深色背景也清楚，加細框描邊（與字色相反）\n",
    "        outline = \"black\" if color == \"white\" else \"white\"\n",
    "        plt.text(\n",
    "            j, i, txt, ha=\"center\", va=\"center\", fontsize=7, color=color,\n",
    "            path_effects=[pe.withStroke(linewidth=1.0, foreground=outline)]\n",
    "        )\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig(OUT_PNG, dpi=150)\n",
    "plt.close()\n",
    "print(f\"[OK] 已輸出帶標註的熱力圖：{OUT_PNG}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "33e657b8-5aaa-4f3f-97da-e066cd473379",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[OK] 輸出完成：\n",
      "- /home/jovyan/RT08/0925/bling_record/ok/nan_counts_matrix_ok.csv\n",
      "- /home/jovyan/RT08/0925/bling_record/ok/nan_rates_matrix_ok.csv\n",
      "- /home/jovyan/RT08/0925/bling_record/ok/nan_summary_ok.csv\n"
     ]
    }
   ],
   "source": [
    "# 因為發現熱力圖還是有兩欄 patno \n",
    "# 所以要來檢查ok檔案是否都對 都乾淨 對阿都乾淨啊\n",
    "# 那就來針對/home/jovyan/RT08/0925/blingok/的檔案輸出NaN 數量矩陣跟NaN 比率矩陣（%)跟 檔案級摘要 檔名叫做nan之類的沿用原本檔名_ok\n",
    "# 檔案要存在/home/jovyan/RT08/0925/bling_record/ok/\n",
    "# 聚合輸出「所有檔案」的 NaN 數量矩陣、NaN 比率矩陣（%）、以及檔案級摘要\n",
    "# - 來源：/home/jovyan/RT08/0925/blingok/*.csv\n",
    "# - 輸出：/home/jovyan/RT08/0925/bling_record/ok/\n",
    "#   - nan_counts_matrix_ok.csv         （列=檔名，欄=特徵，值=NaN 數量）\n",
    "#   - nan_rates_matrix_ok.csv          （列=檔名，欄=特徵，值=NaN 比率%）\n",
    "#   - nan_summary_ok.csv               （每檔摘要：n_rows, n_cols, total_nan, total_cells, missing_rate_percent）\n",
    "import os, glob\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "IN_DIR  = \"/home/jovyan/RT08/0925/blingok\"\n",
    "OUT_DIR = \"/home/jovyan/RT08/0925/bling_record/ok\"\n",
    "os.makedirs(OUT_DIR, exist_ok=True)\n",
    "\n",
    "ENCODINGS = (\"utf-8-sig\",\"utf-8\",\"cp950\",\"big5\",\"latin1\")\n",
    "\n",
    "def read_df_any(path):\n",
    "    for enc in ENCODINGS:\n",
    "        try:\n",
    "            return pd.read_csv(path, encoding=enc, engine=\"python\", on_bad_lines=\"skip\")\n",
    "        except Exception:\n",
    "            continue\n",
    "    raise RuntimeError(f\"讀檔失敗：{os.path.basename(path)}\")\n",
    "\n",
    "files = sorted(glob.glob(os.path.join(IN_DIR, \"*.csv\")))\n",
    "all_cols = set()\n",
    "rows_counts = []   # 每檔的 NaN 數量（字典）\n",
    "rows_rates  = []   # 每檔的 NaN 比率%（字典）\n",
    "rows_summary = []  # 每檔摘要\n",
    "\n",
    "for fp in files:\n",
    "    base = os.path.basename(fp)\n",
    "    try:\n",
    "        df = read_df_any(fp)\n",
    "        # 欄位名統一轉字串（避免混入非字串型）\n",
    "        df.columns = [str(c) for c in df.columns]\n",
    "        all_cols.update(df.columns)\n",
    "\n",
    "        nan_counts = df.isna().sum().astype(int)\n",
    "        nan_rates  = (df.isna().mean().fillna(0.0) * 100.0)\n",
    "\n",
    "        # 記錄矩陣行\n",
    "        rc = {\"file_name\": base}\n",
    "        rc.update({k: int(v) for k, v in nan_counts.items()})\n",
    "        rows_counts.append(rc)\n",
    "\n",
    "        rr = {\"file_name\": base}\n",
    "        rr.update({k: float(v) for k, v in nan_rates.items()})\n",
    "        rows_rates.append(rr)\n",
    "\n",
    "        # 檔案級摘要\n",
    "        total_nan   = int(nan_counts.sum())\n",
    "        total_cells = int(df.shape[0] * df.shape[1]) if (df.shape[0] and df.shape[1]) else 0\n",
    "        miss_rate   = float((total_nan / total_cells) * 100.0) if total_cells else 0.0\n",
    "        rows_summary.append({\n",
    "            \"file_name\": base,\n",
    "            \"n_rows\": int(df.shape[0]),\n",
    "            \"n_cols\": int(df.shape[1]),\n",
    "            \"total_nan\": total_nan,\n",
    "            \"total_cells\": total_cells,\n",
    "            \"missing_rate_percent\": round(miss_rate, 6),\n",
    "        })\n",
    "    except Exception as e:\n",
    "        # 若讀取失敗，也把摘要塞一筆方便追蹤\n",
    "        rows_summary.append({\n",
    "            \"file_name\": base, \"n_rows\": None, \"n_cols\": None,\n",
    "            \"total_nan\": None, \"total_cells\": None, \"missing_rate_percent\": None,\n",
    "        })\n",
    "\n",
    "# 以「所有欄位聯集」建立一致欄序（file_name 放第一欄）\n",
    "cols_sorted = [\"file_name\"] + sorted(all_cols)\n",
    "\n",
    "# NaN 數量矩陣\n",
    "counts_df = pd.DataFrame(rows_counts)\n",
    "for c in cols_sorted:\n",
    "    if c not in counts_df.columns: counts_df[c] = np.nan\n",
    "counts_df = counts_df[cols_sorted]\n",
    "counts_df.to_csv(os.path.join(OUT_DIR, \"nan_counts_matrix_ok.csv\"), index=False, encoding=\"utf-8\")\n",
    "\n",
    "# NaN 比率矩陣（%）\n",
    "rates_df = pd.DataFrame(rows_rates)\n",
    "for c in cols_sorted:\n",
    "    if c not in rates_df.columns: rates_df[c] = np.nan\n",
    "rates_df = rates_df[cols_sorted]\n",
    "rates_df.to_csv(os.path.join(OUT_DIR, \"nan_rates_matrix_ok.csv\"), index=False, encoding=\"utf-8\")\n",
    "\n",
    "# 檔案級摘要\n",
    "summary_df = pd.DataFrame(rows_summary, columns=[\n",
    "    \"file_name\",\"n_rows\",\"n_cols\",\"total_nan\",\"total_cells\",\"missing_rate_percent\"\n",
    "])\n",
    "summary_df.to_csv(os.path.join(OUT_DIR, \"nan_summary_ok.csv\"), index=False, encoding=\"utf-8\")\n",
    "\n",
    "print(\"[OK] 輸出完成：\")\n",
    "print(f\"- {os.path.join(OUT_DIR, 'nan_counts_matrix_ok.csv')}\")\n",
    "print(f\"- {os.path.join(OUT_DIR, 'nan_rates_matrix_ok.csv')}\")\n",
    "print(f\"- {os.path.join(OUT_DIR, 'nan_summary_ok.csv')}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "2c5fc125-ee45-4338-9fff-78b44a213fd8",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[OK] Saved heatmap with annotations: /home/jovyan/RT08/0925/bling_record/ok/ok_nan_rate_heatmap.png\n"
     ]
    }
   ],
   "source": [
    "\"\"\" #在缺失率不等於0的格子中 加上他的缺失率數值，並且要確保數值看的到，像是顏色深的時候要變成白色字\n",
    "# 使用既有的缺失率矩陣，重畫熱力圖並「只在缺失率≠0的格子」加上數值標註\n",
    "# - 來源：/home/jovyan/RT08/0925/bling_record/ok/ok_nan_rates.csv\n",
    "# - 輸出：/home/jovyan/RT08/0925/bling_record/ok/ok_nan_rate_heatmap.png\n",
    "\"\"\"\n",
    "# 重畫缺失率熱力圖（僅在缺失率≠0的格子標註數值；深色背景自動白字）\n",
    "# 來源：/home/jovyan/RT08/0925/bling_record/ok/nan_rates_matrix_ok.csv\n",
    "# 輸出：/home/jovyan/RT08/0925/bling_record/ok/ok_nan_rate_heatmap.png\n",
    "import os\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.patheffects as pe\n",
    "from matplotlib import colors\n",
    "\n",
    "IN_CSV  = \"/home/jovyan/RT08/0925/bling_record/ok/nan_rates_matrix_ok.csv\"\n",
    "OUT_PNG = \"/home/jovyan/RT08/0925/bling_record/ok/ok_nan_rate_heatmap.png\"\n",
    "\n",
    "# 讀取缺失率（百分比）矩陣（列=檔名，欄=特徵）\n",
    "df = pd.read_csv(IN_CSV)\n",
    "if \"file_name\" in df.columns:\n",
    "    df = df.set_index(\"file_name\")\n",
    "\n",
    "# 全欄轉數值（不可解析者為 NaN）\n",
    "for c in df.columns:\n",
    "    df[c] = pd.to_numeric(df[c], errors=\"coerce\")\n",
    "\n",
    "rows = df.index.tolist()\n",
    "cols = df.columns.tolist()\n",
    "Z = df.values.astype(float)\n",
    "\n",
    "# 顏色正規化（確保全 0 時也能顯示）\n",
    "finite_vals = Z[np.isfinite(Z)]\n",
    "vmin = 0.0\n",
    "vmax = float(finite_vals.max()) if finite_vals.size else 1.0\n",
    "if vmax == 0:\n",
    "    vmax = 1.0\n",
    "norm = colors.Normalize(vmin=vmin, vmax=vmax)\n",
    "\n",
    "# 圖尺寸依資料量自適應\n",
    "plt.figure(figsize=(max(8, len(cols)*0.4), max(6, len(rows)*0.3)))\n",
    "\n",
    "# 畫熱力圖（藍色系）\n",
    "im = plt.imshow(Z, aspect=\"auto\", interpolation=\"nearest\", cmap=\"Blues\", norm=norm)\n",
    "\n",
    "# 座標軸與標題\n",
    "plt.xticks(ticks=np.arange(len(cols)), labels=cols, rotation=60, ha=\"right\", fontsize=8)\n",
    "plt.yticks(ticks=np.arange(len(rows)), labels=rows, fontsize=8)\n",
    "plt.xlabel(\"Features\")\n",
    "plt.ylabel(\"Files\")\n",
    "plt.title(\"Missing Rate Heatmap (%)\")\n",
    "\n",
    "# 色條\n",
    "cbar = plt.colorbar(im)\n",
    "cbar.set_label(\"Missing Rate (%)\")\n",
    "\n",
    "# 文字顏色依背景亮度決定（深色→白字，淺色→黑字），外加描邊提升可讀性\n",
    "def text_color_for_value(val):\n",
    "    r, g, b, _ = im.cmap(norm(val))\n",
    "    luminance = 0.2126*r + 0.7152*g + 0.0722*b  # 相對亮度\n",
    "    return (\"white\", \"black\")[luminance >= 0.5]\n",
    "\n",
    "# 僅在缺失率≠0的格子標註（以 % 顯示）\n",
    "for i in range(len(rows)):\n",
    "    for j in range(len(cols)):\n",
    "        val = Z[i, j]\n",
    "        if not np.isfinite(val) or val == 0:\n",
    "            continue\n",
    "        # 格式：<10 顯示 1 位小數，>=10 仍用 1 位小數，100 以上取整\n",
    "        txt = f\"{val:.1f}%\" if val < 100 else f\"{val:.0f}%\"\n",
    "        color = text_color_for_value(val)\n",
    "        outline = \"black\" if color == \"white\" else \"white\"\n",
    "        plt.text(\n",
    "            j, i, txt,\n",
    "            ha=\"center\", va=\"center\", fontsize=7, color=color,\n",
    "            path_effects=[pe.withStroke(linewidth=1.0, foreground=outline)]\n",
    "        )\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig(OUT_PNG, dpi=150)\n",
    "plt.close()\n",
    "print(f\"[OK] Saved heatmap with annotations: {OUT_PNG}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "260ef563-982c-4431-b8fa-bd1cdbdb2024",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "- 089271.csv | replaced 30341 cells → /home/jovyan/RT08/0925/blingokNaN/089271.csv\n",
      "- 095323.csv | replaced 12489 cells → /home/jovyan/RT08/0925/blingokNaN/095323.csv\n",
      "- 095707.csv | replaced 4614 cells → /home/jovyan/RT08/0925/blingokNaN/095707.csv\n",
      "- 114309.csv | replaced 23504 cells → /home/jovyan/RT08/0925/blingokNaN/114309.csv\n",
      "- 230933.csv | replaced 10106 cells → /home/jovyan/RT08/0925/blingokNaN/230933.csv\n",
      "- 4216007.csv | replaced 6342 cells → /home/jovyan/RT08/0925/blingokNaN/4216007.csv\n",
      "- 7108162.csv | replaced 343 cells → /home/jovyan/RT08/0925/blingokNaN/7108162.csv\n",
      "- 7408338.csv | replaced 6 cells → /home/jovyan/RT08/0925/blingokNaN/7408338.csv\n",
      "- 7657698.csv | replaced 1543 cells → /home/jovyan/RT08/0925/blingokNaN/7657698.csv\n",
      "- 7721164.csv | replaced 0 cells → /home/jovyan/RT08/0925/blingokNaN/7721164.csv\n",
      "- PatNo_ID_1560013303.csv | replaced 2860 cells → /home/jovyan/RT08/0925/blingokNaN/PatNo_ID_1560013303.csv\n",
      "- PatNo_ID_1562733396.csv | replaced 2271 cells → /home/jovyan/RT08/0925/blingokNaN/PatNo_ID_1562733396.csv\n",
      "- PatNo_ID_1563587183.csv | replaced 5295 cells → /home/jovyan/RT08/0925/blingokNaN/PatNo_ID_1563587183.csv\n",
      "- PatNo_ID_1564148644.csv | replaced 62992 cells → /home/jovyan/RT08/0925/blingokNaN/PatNo_ID_1564148644.csv\n",
      "- PatNo_ID_1565148312.csv | replaced 5556 cells → /home/jovyan/RT08/0925/blingokNaN/PatNo_ID_1565148312.csv\n",
      "- PatNo_ID_1565378038.csv | replaced 2845 cells → /home/jovyan/RT08/0925/blingokNaN/PatNo_ID_1565378038.csv\n",
      "- PatNo_ID_1566123680.csv | replaced 113179 cells → /home/jovyan/RT08/0925/blingokNaN/PatNo_ID_1566123680.csv\n",
      "- PatNo_ID_1566252197.csv | replaced 16297 cells → /home/jovyan/RT08/0925/blingokNaN/PatNo_ID_1566252197.csv\n",
      "- PatNo_ID_1566279967.csv | replaced 1891 cells → /home/jovyan/RT08/0925/blingokNaN/PatNo_ID_1566279967.csv\n",
      "- PatNo_ID_1566671274.csv | replaced 79618 cells → /home/jovyan/RT08/0925/blingokNaN/PatNo_ID_1566671274.csv\n",
      "- PatNo_ID_1566911879.csv | replaced 114294 cells → /home/jovyan/RT08/0925/blingokNaN/PatNo_ID_1566911879.csv\n",
      "- PatNo_ID_1567747650.csv | replaced 11281 cells → /home/jovyan/RT08/0925/blingokNaN/PatNo_ID_1567747650.csv\n",
      "- PatNo_ID_1567804800.csv | replaced 10949 cells → /home/jovyan/RT08/0925/blingokNaN/PatNo_ID_1567804800.csv\n",
      "- PatNo_ID_1567832735.csv | replaced 36815 cells → /home/jovyan/RT08/0925/blingokNaN/PatNo_ID_1567832735.csv\n",
      "- PatNo_ID_1568039398.csv | replaced 37515 cells → /home/jovyan/RT08/0925/blingokNaN/PatNo_ID_1568039398.csv\n",
      "- PatNo_ID_1568574099.csv | replaced 9968 cells → /home/jovyan/RT08/0925/blingokNaN/PatNo_ID_1568574099.csv\n",
      "- PatNo_ID_1568813269.csv | replaced 5308 cells → /home/jovyan/RT08/0925/blingokNaN/PatNo_ID_1568813269.csv\n",
      "- PatNo_ID_1568952422.csv | replaced 1184 cells → /home/jovyan/RT08/0925/blingokNaN/PatNo_ID_1568952422.csv\n",
      "- PatNo_ID_1569083701.csv | replaced 3342 cells → /home/jovyan/RT08/0925/blingokNaN/PatNo_ID_1569083701.csv\n",
      "- PatNo_ID_1569944983.csv | replaced 9081 cells → /home/jovyan/RT08/0925/blingokNaN/PatNo_ID_1569944983.csv\n",
      "- PatNo_ID_1570089466.csv | replaced 40647 cells → /home/jovyan/RT08/0925/blingokNaN/PatNo_ID_1570089466.csv\n",
      "- PatNo_ID_1570242703.csv | replaced 11147 cells → /home/jovyan/RT08/0925/blingokNaN/PatNo_ID_1570242703.csv\n",
      "- PatNo_ID_1570273244.csv | replaced 9547 cells → /home/jovyan/RT08/0925/blingokNaN/PatNo_ID_1570273244.csv\n",
      "- PatNo_ID_1570642083.csv | replaced 30359 cells → /home/jovyan/RT08/0925/blingokNaN/PatNo_ID_1570642083.csv\n",
      "- PatNo_ID_1571945701.csv | replaced 17802 cells → /home/jovyan/RT08/0925/blingokNaN/PatNo_ID_1571945701.csv\n",
      "- PatNo_ID_1572481361.csv | replaced 33483 cells → /home/jovyan/RT08/0925/blingokNaN/PatNo_ID_1572481361.csv\n",
      "- PatNo_ID_1572562839.csv | replaced 14751 cells → /home/jovyan/RT08/0925/blingokNaN/PatNo_ID_1572562839.csv\n",
      "- PatNo_ID_1572831765.csv | replaced 2696 cells → /home/jovyan/RT08/0925/blingokNaN/PatNo_ID_1572831765.csv\n",
      "- PatNo_ID_1572976822.csv | replaced 5169 cells → /home/jovyan/RT08/0925/blingokNaN/PatNo_ID_1572976822.csv\n",
      "- PatNo_ID_1573063188.csv | replaced 5918 cells → /home/jovyan/RT08/0925/blingokNaN/PatNo_ID_1573063188.csv\n",
      "- PatNo_ID_1573249295.csv | replaced 9604 cells → /home/jovyan/RT08/0925/blingokNaN/PatNo_ID_1573249295.csv\n",
      "- PatNo_ID_1573964540.csv | replaced 3691 cells → /home/jovyan/RT08/0925/blingokNaN/PatNo_ID_1573964540.csv\n",
      "- PatNo_ID_1574148494.csv | replaced 48250 cells → /home/jovyan/RT08/0925/blingokNaN/PatNo_ID_1574148494.csv\n",
      "- PatNo_ID_1574270349.csv | replaced 6686 cells → /home/jovyan/RT08/0925/blingokNaN/PatNo_ID_1574270349.csv\n",
      "- PatNo_ID_1574528808.csv | replaced 17478 cells → /home/jovyan/RT08/0925/blingokNaN/PatNo_ID_1574528808.csv\n",
      "- PatNo_ID_1574831525.csv | replaced 562 cells → /home/jovyan/RT08/0925/blingokNaN/PatNo_ID_1574831525.csv\n",
      "- PatNo_ID_1574987447.csv | replaced 22981 cells → /home/jovyan/RT08/0925/blingokNaN/PatNo_ID_1574987447.csv\n",
      "- PatNo_ID_1575060177.csv | replaced 5998 cells → /home/jovyan/RT08/0925/blingokNaN/PatNo_ID_1575060177.csv\n",
      "- PatNo_ID_1575256902.csv | replaced 10385 cells → /home/jovyan/RT08/0925/blingokNaN/PatNo_ID_1575256902.csv\n",
      "- PatNo_ID_1575445051.csv | replaced 1922 cells → /home/jovyan/RT08/0925/blingokNaN/PatNo_ID_1575445051.csv\n",
      "- PatNo_ID_1575502382.csv | replaced 10110 cells → /home/jovyan/RT08/0925/blingokNaN/PatNo_ID_1575502382.csv\n",
      "- PatNo_ID_1575975485.csv | replaced 16547 cells → /home/jovyan/RT08/0925/blingokNaN/PatNo_ID_1575975485.csv\n",
      "- PatNo_ID_1576115572.csv | replaced 21249 cells → /home/jovyan/RT08/0925/blingokNaN/PatNo_ID_1576115572.csv\n",
      "- PatNo_ID_1576116479.csv | replaced 1311 cells → /home/jovyan/RT08/0925/blingokNaN/PatNo_ID_1576116479.csv\n",
      "- PatNo_ID_1576301569.csv | replaced 3517 cells → /home/jovyan/RT08/0925/blingokNaN/PatNo_ID_1576301569.csv\n",
      "- PatNo_ID_1576964560.csv | replaced 29857 cells → /home/jovyan/RT08/0925/blingokNaN/PatNo_ID_1576964560.csv\n",
      "- PatNo_ID_1577042911.csv | replaced 52665 cells → /home/jovyan/RT08/0925/blingokNaN/PatNo_ID_1577042911.csv\n",
      "- PatNo_ID_1577487284.csv | replaced 2755 cells → /home/jovyan/RT08/0925/blingokNaN/PatNo_ID_1577487284.csv\n",
      "- PatNo_ID_1578784257.csv | replaced 65595 cells → /home/jovyan/RT08/0925/blingokNaN/PatNo_ID_1578784257.csv\n",
      "- PatNo_ID_1579198603.csv | replaced 2201 cells → /home/jovyan/RT08/0925/blingokNaN/PatNo_ID_1579198603.csv\n",
      "- PatNo_ID_1579498177.csv | replaced 24801 cells → /home/jovyan/RT08/0925/blingokNaN/PatNo_ID_1579498177.csv\n",
      "- PatNo_ID_1580062580.csv | replaced 6736 cells → /home/jovyan/RT08/0925/blingokNaN/PatNo_ID_1580062580.csv\n",
      "- PatNo_ID_1580096720.csv | replaced 7616 cells → /home/jovyan/RT08/0925/blingokNaN/PatNo_ID_1580096720.csv\n",
      "- PatNo_ID_1580107637.csv | replaced 8482 cells → /home/jovyan/RT08/0925/blingokNaN/PatNo_ID_1580107637.csv\n",
      "- PatNo_ID_1580244614.csv | replaced 10827 cells → /home/jovyan/RT08/0925/blingokNaN/PatNo_ID_1580244614.csv\n",
      "- PatNo_ID_1580766093.csv | replaced 19019 cells → /home/jovyan/RT08/0925/blingokNaN/PatNo_ID_1580766093.csv\n",
      "- PatNo_ID_1581003248.csv | replaced 11708 cells → /home/jovyan/RT08/0925/blingokNaN/PatNo_ID_1581003248.csv\n",
      "- PatNo_ID_1581019504.csv | replaced 65174 cells → /home/jovyan/RT08/0925/blingokNaN/PatNo_ID_1581019504.csv\n",
      "- PatNo_ID_1581633231.csv | replaced 20487 cells → /home/jovyan/RT08/0925/blingokNaN/PatNo_ID_1581633231.csv\n",
      "- PatNo_ID_1581692973.csv | replaced 2935 cells → /home/jovyan/RT08/0925/blingokNaN/PatNo_ID_1581692973.csv\n",
      "- PatNo_ID_1582452511.csv | replaced 5202 cells → /home/jovyan/RT08/0925/blingokNaN/PatNo_ID_1582452511.csv\n",
      "- PatNo_ID_1582635996.csv | replaced 21885 cells → /home/jovyan/RT08/0925/blingokNaN/PatNo_ID_1582635996.csv\n",
      "- PatNo_ID_1582849900.csv | replaced 8630 cells → /home/jovyan/RT08/0925/blingokNaN/PatNo_ID_1582849900.csv\n",
      "- PatNo_ID_1582937076.csv | replaced 24502 cells → /home/jovyan/RT08/0925/blingokNaN/PatNo_ID_1582937076.csv\n",
      "- PatNo_ID_1584158973.csv | replaced 3284 cells → /home/jovyan/RT08/0925/blingokNaN/PatNo_ID_1584158973.csv\n",
      "- PatNo_ID_1584397376.csv | replaced 638 cells → /home/jovyan/RT08/0925/blingokNaN/PatNo_ID_1584397376.csv\n",
      "- PatNo_ID_1586172659.csv | replaced 38925 cells → /home/jovyan/RT08/0925/blingokNaN/PatNo_ID_1586172659.csv\n",
      "- PatNo_ID_1586696634.csv | replaced 12097 cells → /home/jovyan/RT08/0925/blingokNaN/PatNo_ID_1586696634.csv\n",
      "- PatNo_ID_1586897008.csv | replaced 4042 cells → /home/jovyan/RT08/0925/blingokNaN/PatNo_ID_1586897008.csv\n",
      "- PatNo_ID_1587490083.csv | replaced 56906 cells → /home/jovyan/RT08/0925/blingokNaN/PatNo_ID_1587490083.csv\n",
      "- PatNo_ID_1588632604.csv | replaced 2608 cells → /home/jovyan/RT08/0925/blingokNaN/PatNo_ID_1588632604.csv\n",
      "- PatNo_ID_1588673465.csv | replaced 37099 cells → /home/jovyan/RT08/0925/blingokNaN/PatNo_ID_1588673465.csv\n",
      "- PatNo_ID_1588794796.csv | replaced 9377 cells → /home/jovyan/RT08/0925/blingokNaN/PatNo_ID_1588794796.csv\n",
      "- PatNo_ID_1588957997.csv | replaced 12332 cells → /home/jovyan/RT08/0925/blingokNaN/PatNo_ID_1588957997.csv\n",
      "- PatNo_ID_1589018086.csv | replaced 20252 cells → /home/jovyan/RT08/0925/blingokNaN/PatNo_ID_1589018086.csv\n",
      "- PatNo_ID_1589034524.csv | replaced 50693 cells → /home/jovyan/RT08/0925/blingokNaN/PatNo_ID_1589034524.csv\n",
      "- PatNo_ID_1589324603.csv | replaced 2572 cells → /home/jovyan/RT08/0925/blingokNaN/PatNo_ID_1589324603.csv\n",
      "- PatNo_ID_1589918099.csv | replaced 37334 cells → /home/jovyan/RT08/0925/blingokNaN/PatNo_ID_1589918099.csv\n",
      "- PatNo_ID_1590136310.csv | replaced 7781 cells → /home/jovyan/RT08/0925/blingokNaN/PatNo_ID_1590136310.csv\n",
      "- PatNo_ID_1590616537.csv | replaced 14985 cells → /home/jovyan/RT08/0925/blingokNaN/PatNo_ID_1590616537.csv\n",
      "- PatNo_ID_1590854576.csv | replaced 15879 cells → /home/jovyan/RT08/0925/blingokNaN/PatNo_ID_1590854576.csv\n",
      "- PatNo_ID_1591609798.csv | replaced 35728 cells → /home/jovyan/RT08/0925/blingokNaN/PatNo_ID_1591609798.csv\n",
      "- PatNo_ID_1592044724.csv | replaced 7714 cells → /home/jovyan/RT08/0925/blingokNaN/PatNo_ID_1592044724.csv\n",
      "- PatNo_ID_1592560504.csv | replaced 18053 cells → /home/jovyan/RT08/0925/blingokNaN/PatNo_ID_1592560504.csv\n",
      "- PatNo_ID_1593087886.csv | replaced 26642 cells → /home/jovyan/RT08/0925/blingokNaN/PatNo_ID_1593087886.csv\n",
      "- PatNo_ID_1593416100.csv | replaced 3786 cells → /home/jovyan/RT08/0925/blingokNaN/PatNo_ID_1593416100.csv\n",
      "- PatNo_ID_1593472048.csv | replaced 24102 cells → /home/jovyan/RT08/0925/blingokNaN/PatNo_ID_1593472048.csv\n",
      "- PatNo_ID_1593593586.csv | replaced 18961 cells → /home/jovyan/RT08/0925/blingokNaN/PatNo_ID_1593593586.csv\n",
      "- PatNo_ID_1593720818.csv | replaced 2947 cells → /home/jovyan/RT08/0925/blingokNaN/PatNo_ID_1593720818.csv\n",
      "- PatNo_ID_1593838524.csv | replaced 2312 cells → /home/jovyan/RT08/0925/blingokNaN/PatNo_ID_1593838524.csv\n",
      "- PatNo_ID_1594173718.csv | replaced 344 cells → /home/jovyan/RT08/0925/blingokNaN/PatNo_ID_1594173718.csv\n",
      "- PatNo_ID_1594294180.csv | replaced 57802 cells → /home/jovyan/RT08/0925/blingokNaN/PatNo_ID_1594294180.csv\n",
      "- PatNo_ID_1594305136.csv | replaced 27777 cells → /home/jovyan/RT08/0925/blingokNaN/PatNo_ID_1594305136.csv\n",
      "- PatNo_ID_1594309746.csv | replaced 4246 cells → /home/jovyan/RT08/0925/blingokNaN/PatNo_ID_1594309746.csv\n",
      "- PatNo_ID_1594319286.csv | replaced 5051 cells → /home/jovyan/RT08/0925/blingokNaN/PatNo_ID_1594319286.csv\n",
      "- PatNo_ID_1594320763.csv | replaced 2797 cells → /home/jovyan/RT08/0925/blingokNaN/PatNo_ID_1594320763.csv\n",
      "- PatNo_ID_1594322594.csv | replaced 5416 cells → /home/jovyan/RT08/0925/blingokNaN/PatNo_ID_1594322594.csv\n",
      "- PatNo_ID_1594335109.csv | replaced 5124 cells → /home/jovyan/RT08/0925/blingokNaN/PatNo_ID_1594335109.csv\n",
      "- PatNo_ID_1594423683.csv | replaced 5548 cells → /home/jovyan/RT08/0925/blingokNaN/PatNo_ID_1594423683.csv\n",
      "- PatNo_ID_1594437309.csv | replaced 10267 cells → /home/jovyan/RT08/0925/blingokNaN/PatNo_ID_1594437309.csv\n",
      "- PatNo_ID_1594439781.csv | replaced 10968 cells → /home/jovyan/RT08/0925/blingokNaN/PatNo_ID_1594439781.csv\n",
      "- PatNo_ID_1594441887.csv | replaced 10340 cells → /home/jovyan/RT08/0925/blingokNaN/PatNo_ID_1594441887.csv\n",
      "- PatNo_ID_1594448501.csv | replaced 1506 cells → /home/jovyan/RT08/0925/blingokNaN/PatNo_ID_1594448501.csv\n",
      "- PatNo_ID_1594455578.csv | replaced 419 cells → /home/jovyan/RT08/0925/blingokNaN/PatNo_ID_1594455578.csv\n",
      "- PatNo_ID_1594464829.csv | replaced 6856 cells → /home/jovyan/RT08/0925/blingokNaN/PatNo_ID_1594464829.csv\n",
      "- PatNo_ID_1594467719.csv | replaced 12244 cells → /home/jovyan/RT08/0925/blingokNaN/PatNo_ID_1594467719.csv\n",
      "- PatNo_ID_1594471407.csv | replaced 12654 cells → /home/jovyan/RT08/0925/blingokNaN/PatNo_ID_1594471407.csv\n",
      "- PatNo_ID_1594479330.csv | replaced 3755 cells → /home/jovyan/RT08/0925/blingokNaN/PatNo_ID_1594479330.csv\n",
      "- PatNo_ID_1594511911.csv | replaced 8654 cells → /home/jovyan/RT08/0925/blingokNaN/PatNo_ID_1594511911.csv\n",
      "- PatNo_ID_1594511914.csv | replaced 2968 cells → /home/jovyan/RT08/0925/blingokNaN/PatNo_ID_1594511914.csv\n",
      "- PatNo_ID_1594528842.csv | replaced 3030 cells → /home/jovyan/RT08/0925/blingokNaN/PatNo_ID_1594528842.csv\n",
      "- PatNo_ID_1594533379.csv | replaced 1363 cells → /home/jovyan/RT08/0925/blingokNaN/PatNo_ID_1594533379.csv\n",
      "[完成] 已處理 122/122 檔，輸出於：/home/jovyan/RT08/0925/blingokNaN\n"
     ]
    }
   ],
   "source": [
    "\"\"\" 將路徑/home/jovyan/RT08/0925/blingok/所有檔案中沒有數值的格子填上NaN\n",
    "並全部不要覆蓋到原檔，存在/home/jovyan/RT08/0925/blingokNaN\n",
    "\"\"\"\n",
    "# 將 /home/jovyan/RT08/0925/blingok/ 內所有 CSV 的「空值」填成 NaN\n",
    "# 定義「空值」：空白字串（含全形/空白符）、以及常見空字串（na/n/a/null/none/-/--），大小寫不敏感\n",
    "# 不覆蓋原檔；新檔輸出至 /home/jovyan/RT08/0925/blingokNaN/<同名檔案>.csv\n",
    "import os, glob, unicodedata\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "SRC_DIR = \"/home/jovyan/RT08/0925/blingok\"\n",
    "DST_DIR = \"/home/jovyan/RT08/0925/blingokNaN\"\n",
    "os.makedirs(DST_DIR, exist_ok=True)\n",
    "\n",
    "ENCODINGS = (\"utf-8-sig\",\"utf-8\",\"cp950\",\"big5\",\"latin1\")\n",
    "NULL_TOKENS = {\"\", \"na\", \"n/a\", \"null\", \"none\", \"-\", \"--\"}  # 空值詞，大小寫不敏感\n",
    "\n",
    "def read_df_any(path):\n",
    "    for enc in ENCODINGS:\n",
    "        try:\n",
    "            return pd.read_csv(path, encoding=enc, engine=\"python\", on_bad_lines=\"skip\")\n",
    "        except Exception:\n",
    "            continue\n",
    "    raise RuntimeError(f\"讀檔失敗：{os.path.basename(path)}\")\n",
    "\n",
    "def norm_str(x):\n",
    "    if pd.isna(x): return x\n",
    "    return unicodedata.normalize(\"NFKC\", str(x))\n",
    "\n",
    "def is_null_like(x):\n",
    "    # None/NaN → 視為空\n",
    "    if x is None: return True\n",
    "    if isinstance(x, float) and np.isnan(x): return True\n",
    "    s = norm_str(x).strip()\n",
    "    return (s == \"\") or (s.lower() in NULL_TOKENS)\n",
    "\n",
    "files = sorted(glob.glob(os.path.join(SRC_DIR, \"*.csv\")))\n",
    "total_files = len(files)\n",
    "done = 0\n",
    "\n",
    "for fp in files:\n",
    "    name = os.path.basename(fp)\n",
    "    try:\n",
    "        df = read_df_any(fp)\n",
    "        # 生成空值遮罩並替換為 NaN\n",
    "        mask = df.applymap(is_null_like)\n",
    "        replaced = int(mask.sum().sum())\n",
    "        df_nan = df.mask(mask, other=np.nan)\n",
    "        # 輸出新檔（不覆蓋原檔）\n",
    "        out_path = os.path.join(DST_DIR, name)\n",
    "        df_nan.to_csv(out_path, index=False, encoding=\"utf-8\")\n",
    "        print(f\"- {name} | replaced {replaced} cells → {out_path}\")\n",
    "        done += 1\n",
    "    except Exception as e:\n",
    "        print(f\"[ERROR] {name}: {e}\")\n",
    "\n",
    "print(f\"[完成] 已處理 {done}/{total_files} 檔，輸出於：{DST_DIR}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "f57e74fc-5e24-4603-90ac-449f7818d777",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "- 089271.csv: replaced 30341 cells → /home/jovyan/RT08/0925/blingokNaN_test/089271.csv (NaN after save: 30341)\n",
      "- 095323.csv: replaced 12489 cells → /home/jovyan/RT08/0925/blingokNaN_test/095323.csv (NaN after save: 12489)\n",
      "- 095707.csv: replaced 4614 cells → /home/jovyan/RT08/0925/blingokNaN_test/095707.csv (NaN after save: 4614)\n",
      "- 114309.csv: replaced 23504 cells → /home/jovyan/RT08/0925/blingokNaN_test/114309.csv (NaN after save: 23504)\n",
      "- 230933.csv: replaced 10106 cells → /home/jovyan/RT08/0925/blingokNaN_test/230933.csv (NaN after save: 10106)\n",
      "- 4216007.csv: replaced 6342 cells → /home/jovyan/RT08/0925/blingokNaN_test/4216007.csv (NaN after save: 6342)\n",
      "- 7108162.csv: replaced 343 cells → /home/jovyan/RT08/0925/blingokNaN_test/7108162.csv (NaN after save: 343)\n",
      "- 7408338.csv: replaced 6 cells → /home/jovyan/RT08/0925/blingokNaN_test/7408338.csv (NaN after save: 6)\n",
      "- 7657698.csv: replaced 1543 cells → /home/jovyan/RT08/0925/blingokNaN_test/7657698.csv (NaN after save: 1543)\n",
      "- 7721164.csv: replaced 0 cells → /home/jovyan/RT08/0925/blingokNaN_test/7721164.csv (NaN after save: 0)\n",
      "- PatNo_ID_1560013303.csv: replaced 2860 cells → /home/jovyan/RT08/0925/blingokNaN_test/PatNo_ID_1560013303.csv (NaN after save: 2860)\n",
      "- PatNo_ID_1562733396.csv: replaced 2271 cells → /home/jovyan/RT08/0925/blingokNaN_test/PatNo_ID_1562733396.csv (NaN after save: 2271)\n",
      "- PatNo_ID_1563587183.csv: replaced 5295 cells → /home/jovyan/RT08/0925/blingokNaN_test/PatNo_ID_1563587183.csv (NaN after save: 5295)\n",
      "- PatNo_ID_1564148644.csv: replaced 62992 cells → /home/jovyan/RT08/0925/blingokNaN_test/PatNo_ID_1564148644.csv (NaN after save: 62992)\n",
      "- PatNo_ID_1565148312.csv: replaced 5556 cells → /home/jovyan/RT08/0925/blingokNaN_test/PatNo_ID_1565148312.csv (NaN after save: 5556)\n",
      "- PatNo_ID_1565378038.csv: replaced 2845 cells → /home/jovyan/RT08/0925/blingokNaN_test/PatNo_ID_1565378038.csv (NaN after save: 2845)\n",
      "- PatNo_ID_1566123680.csv: replaced 113179 cells → /home/jovyan/RT08/0925/blingokNaN_test/PatNo_ID_1566123680.csv (NaN after save: 113179)\n",
      "- PatNo_ID_1566252197.csv: replaced 16297 cells → /home/jovyan/RT08/0925/blingokNaN_test/PatNo_ID_1566252197.csv (NaN after save: 16297)\n",
      "- PatNo_ID_1566279967.csv: replaced 1891 cells → /home/jovyan/RT08/0925/blingokNaN_test/PatNo_ID_1566279967.csv (NaN after save: 1891)\n",
      "- PatNo_ID_1566671274.csv: replaced 79618 cells → /home/jovyan/RT08/0925/blingokNaN_test/PatNo_ID_1566671274.csv (NaN after save: 79618)\n",
      "- PatNo_ID_1566911879.csv: replaced 114294 cells → /home/jovyan/RT08/0925/blingokNaN_test/PatNo_ID_1566911879.csv (NaN after save: 114294)\n",
      "- PatNo_ID_1567747650.csv: replaced 11281 cells → /home/jovyan/RT08/0925/blingokNaN_test/PatNo_ID_1567747650.csv (NaN after save: 11281)\n",
      "- PatNo_ID_1567804800.csv: replaced 10949 cells → /home/jovyan/RT08/0925/blingokNaN_test/PatNo_ID_1567804800.csv (NaN after save: 10949)\n",
      "- PatNo_ID_1567832735.csv: replaced 36815 cells → /home/jovyan/RT08/0925/blingokNaN_test/PatNo_ID_1567832735.csv (NaN after save: 36815)\n",
      "- PatNo_ID_1568039398.csv: replaced 37515 cells → /home/jovyan/RT08/0925/blingokNaN_test/PatNo_ID_1568039398.csv (NaN after save: 37515)\n",
      "- PatNo_ID_1568574099.csv: replaced 9968 cells → /home/jovyan/RT08/0925/blingokNaN_test/PatNo_ID_1568574099.csv (NaN after save: 9968)\n",
      "- PatNo_ID_1568813269.csv: replaced 5308 cells → /home/jovyan/RT08/0925/blingokNaN_test/PatNo_ID_1568813269.csv (NaN after save: 5308)\n",
      "- PatNo_ID_1568952422.csv: replaced 1184 cells → /home/jovyan/RT08/0925/blingokNaN_test/PatNo_ID_1568952422.csv (NaN after save: 1184)\n",
      "- PatNo_ID_1569083701.csv: replaced 3342 cells → /home/jovyan/RT08/0925/blingokNaN_test/PatNo_ID_1569083701.csv (NaN after save: 3342)\n",
      "- PatNo_ID_1569944983.csv: replaced 9081 cells → /home/jovyan/RT08/0925/blingokNaN_test/PatNo_ID_1569944983.csv (NaN after save: 9081)\n",
      "- PatNo_ID_1570089466.csv: replaced 40647 cells → /home/jovyan/RT08/0925/blingokNaN_test/PatNo_ID_1570089466.csv (NaN after save: 40647)\n",
      "- PatNo_ID_1570242703.csv: replaced 11147 cells → /home/jovyan/RT08/0925/blingokNaN_test/PatNo_ID_1570242703.csv (NaN after save: 11147)\n",
      "- PatNo_ID_1570273244.csv: replaced 9547 cells → /home/jovyan/RT08/0925/blingokNaN_test/PatNo_ID_1570273244.csv (NaN after save: 9547)\n",
      "- PatNo_ID_1570642083.csv: replaced 30359 cells → /home/jovyan/RT08/0925/blingokNaN_test/PatNo_ID_1570642083.csv (NaN after save: 30359)\n",
      "- PatNo_ID_1571945701.csv: replaced 17802 cells → /home/jovyan/RT08/0925/blingokNaN_test/PatNo_ID_1571945701.csv (NaN after save: 17802)\n",
      "- PatNo_ID_1572481361.csv: replaced 33483 cells → /home/jovyan/RT08/0925/blingokNaN_test/PatNo_ID_1572481361.csv (NaN after save: 33483)\n",
      "- PatNo_ID_1572562839.csv: replaced 14751 cells → /home/jovyan/RT08/0925/blingokNaN_test/PatNo_ID_1572562839.csv (NaN after save: 14751)\n",
      "- PatNo_ID_1572831765.csv: replaced 2696 cells → /home/jovyan/RT08/0925/blingokNaN_test/PatNo_ID_1572831765.csv (NaN after save: 2696)\n",
      "- PatNo_ID_1572976822.csv: replaced 5169 cells → /home/jovyan/RT08/0925/blingokNaN_test/PatNo_ID_1572976822.csv (NaN after save: 5169)\n",
      "- PatNo_ID_1573063188.csv: replaced 5918 cells → /home/jovyan/RT08/0925/blingokNaN_test/PatNo_ID_1573063188.csv (NaN after save: 5918)\n",
      "- PatNo_ID_1573249295.csv: replaced 9604 cells → /home/jovyan/RT08/0925/blingokNaN_test/PatNo_ID_1573249295.csv (NaN after save: 9604)\n",
      "- PatNo_ID_1573964540.csv: replaced 3691 cells → /home/jovyan/RT08/0925/blingokNaN_test/PatNo_ID_1573964540.csv (NaN after save: 3691)\n",
      "- PatNo_ID_1574148494.csv: replaced 48250 cells → /home/jovyan/RT08/0925/blingokNaN_test/PatNo_ID_1574148494.csv (NaN after save: 48250)\n",
      "- PatNo_ID_1574270349.csv: replaced 6686 cells → /home/jovyan/RT08/0925/blingokNaN_test/PatNo_ID_1574270349.csv (NaN after save: 6686)\n",
      "- PatNo_ID_1574528808.csv: replaced 17478 cells → /home/jovyan/RT08/0925/blingokNaN_test/PatNo_ID_1574528808.csv (NaN after save: 17478)\n",
      "- PatNo_ID_1574831525.csv: replaced 562 cells → /home/jovyan/RT08/0925/blingokNaN_test/PatNo_ID_1574831525.csv (NaN after save: 562)\n",
      "- PatNo_ID_1574987447.csv: replaced 22981 cells → /home/jovyan/RT08/0925/blingokNaN_test/PatNo_ID_1574987447.csv (NaN after save: 22981)\n",
      "- PatNo_ID_1575060177.csv: replaced 5998 cells → /home/jovyan/RT08/0925/blingokNaN_test/PatNo_ID_1575060177.csv (NaN after save: 5998)\n",
      "- PatNo_ID_1575256902.csv: replaced 10385 cells → /home/jovyan/RT08/0925/blingokNaN_test/PatNo_ID_1575256902.csv (NaN after save: 10385)\n",
      "- PatNo_ID_1575445051.csv: replaced 1922 cells → /home/jovyan/RT08/0925/blingokNaN_test/PatNo_ID_1575445051.csv (NaN after save: 1922)\n",
      "- PatNo_ID_1575502382.csv: replaced 10110 cells → /home/jovyan/RT08/0925/blingokNaN_test/PatNo_ID_1575502382.csv (NaN after save: 10110)\n",
      "- PatNo_ID_1575975485.csv: replaced 16547 cells → /home/jovyan/RT08/0925/blingokNaN_test/PatNo_ID_1575975485.csv (NaN after save: 16547)\n",
      "- PatNo_ID_1576115572.csv: replaced 21249 cells → /home/jovyan/RT08/0925/blingokNaN_test/PatNo_ID_1576115572.csv (NaN after save: 21249)\n",
      "- PatNo_ID_1576116479.csv: replaced 1311 cells → /home/jovyan/RT08/0925/blingokNaN_test/PatNo_ID_1576116479.csv (NaN after save: 1311)\n",
      "- PatNo_ID_1576301569.csv: replaced 3517 cells → /home/jovyan/RT08/0925/blingokNaN_test/PatNo_ID_1576301569.csv (NaN after save: 3517)\n",
      "- PatNo_ID_1576964560.csv: replaced 29857 cells → /home/jovyan/RT08/0925/blingokNaN_test/PatNo_ID_1576964560.csv (NaN after save: 29857)\n",
      "- PatNo_ID_1577042911.csv: replaced 52665 cells → /home/jovyan/RT08/0925/blingokNaN_test/PatNo_ID_1577042911.csv (NaN after save: 52665)\n",
      "- PatNo_ID_1577487284.csv: replaced 2755 cells → /home/jovyan/RT08/0925/blingokNaN_test/PatNo_ID_1577487284.csv (NaN after save: 2755)\n",
      "- PatNo_ID_1578784257.csv: replaced 65595 cells → /home/jovyan/RT08/0925/blingokNaN_test/PatNo_ID_1578784257.csv (NaN after save: 65595)\n",
      "- PatNo_ID_1579198603.csv: replaced 2201 cells → /home/jovyan/RT08/0925/blingokNaN_test/PatNo_ID_1579198603.csv (NaN after save: 2201)\n",
      "- PatNo_ID_1579498177.csv: replaced 24801 cells → /home/jovyan/RT08/0925/blingokNaN_test/PatNo_ID_1579498177.csv (NaN after save: 24801)\n",
      "- PatNo_ID_1580062580.csv: replaced 6736 cells → /home/jovyan/RT08/0925/blingokNaN_test/PatNo_ID_1580062580.csv (NaN after save: 6736)\n",
      "- PatNo_ID_1580096720.csv: replaced 7616 cells → /home/jovyan/RT08/0925/blingokNaN_test/PatNo_ID_1580096720.csv (NaN after save: 7616)\n",
      "- PatNo_ID_1580107637.csv: replaced 8482 cells → /home/jovyan/RT08/0925/blingokNaN_test/PatNo_ID_1580107637.csv (NaN after save: 8482)\n",
      "- PatNo_ID_1580244614.csv: replaced 10827 cells → /home/jovyan/RT08/0925/blingokNaN_test/PatNo_ID_1580244614.csv (NaN after save: 10827)\n",
      "- PatNo_ID_1580766093.csv: replaced 19019 cells → /home/jovyan/RT08/0925/blingokNaN_test/PatNo_ID_1580766093.csv (NaN after save: 19019)\n",
      "- PatNo_ID_1581003248.csv: replaced 11708 cells → /home/jovyan/RT08/0925/blingokNaN_test/PatNo_ID_1581003248.csv (NaN after save: 11708)\n",
      "- PatNo_ID_1581019504.csv: replaced 65174 cells → /home/jovyan/RT08/0925/blingokNaN_test/PatNo_ID_1581019504.csv (NaN after save: 65174)\n",
      "- PatNo_ID_1581633231.csv: replaced 20487 cells → /home/jovyan/RT08/0925/blingokNaN_test/PatNo_ID_1581633231.csv (NaN after save: 20487)\n",
      "- PatNo_ID_1581692973.csv: replaced 2935 cells → /home/jovyan/RT08/0925/blingokNaN_test/PatNo_ID_1581692973.csv (NaN after save: 2935)\n",
      "- PatNo_ID_1582452511.csv: replaced 5202 cells → /home/jovyan/RT08/0925/blingokNaN_test/PatNo_ID_1582452511.csv (NaN after save: 5202)\n",
      "- PatNo_ID_1582635996.csv: replaced 21885 cells → /home/jovyan/RT08/0925/blingokNaN_test/PatNo_ID_1582635996.csv (NaN after save: 21885)\n",
      "- PatNo_ID_1582849900.csv: replaced 8630 cells → /home/jovyan/RT08/0925/blingokNaN_test/PatNo_ID_1582849900.csv (NaN after save: 8630)\n",
      "- PatNo_ID_1582937076.csv: replaced 24502 cells → /home/jovyan/RT08/0925/blingokNaN_test/PatNo_ID_1582937076.csv (NaN after save: 24502)\n",
      "- PatNo_ID_1584158973.csv: replaced 3284 cells → /home/jovyan/RT08/0925/blingokNaN_test/PatNo_ID_1584158973.csv (NaN after save: 3284)\n",
      "- PatNo_ID_1584397376.csv: replaced 638 cells → /home/jovyan/RT08/0925/blingokNaN_test/PatNo_ID_1584397376.csv (NaN after save: 638)\n",
      "- PatNo_ID_1586172659.csv: replaced 38925 cells → /home/jovyan/RT08/0925/blingokNaN_test/PatNo_ID_1586172659.csv (NaN after save: 38925)\n",
      "- PatNo_ID_1586696634.csv: replaced 12097 cells → /home/jovyan/RT08/0925/blingokNaN_test/PatNo_ID_1586696634.csv (NaN after save: 12097)\n",
      "- PatNo_ID_1586897008.csv: replaced 4042 cells → /home/jovyan/RT08/0925/blingokNaN_test/PatNo_ID_1586897008.csv (NaN after save: 4042)\n",
      "- PatNo_ID_1587490083.csv: replaced 56906 cells → /home/jovyan/RT08/0925/blingokNaN_test/PatNo_ID_1587490083.csv (NaN after save: 56906)\n",
      "- PatNo_ID_1588632604.csv: replaced 2608 cells → /home/jovyan/RT08/0925/blingokNaN_test/PatNo_ID_1588632604.csv (NaN after save: 2608)\n",
      "- PatNo_ID_1588673465.csv: replaced 37099 cells → /home/jovyan/RT08/0925/blingokNaN_test/PatNo_ID_1588673465.csv (NaN after save: 37099)\n",
      "- PatNo_ID_1588794796.csv: replaced 9377 cells → /home/jovyan/RT08/0925/blingokNaN_test/PatNo_ID_1588794796.csv (NaN after save: 9377)\n",
      "- PatNo_ID_1588957997.csv: replaced 12332 cells → /home/jovyan/RT08/0925/blingokNaN_test/PatNo_ID_1588957997.csv (NaN after save: 12332)\n",
      "- PatNo_ID_1589018086.csv: replaced 20252 cells → /home/jovyan/RT08/0925/blingokNaN_test/PatNo_ID_1589018086.csv (NaN after save: 20252)\n",
      "- PatNo_ID_1589034524.csv: replaced 50693 cells → /home/jovyan/RT08/0925/blingokNaN_test/PatNo_ID_1589034524.csv (NaN after save: 50693)\n",
      "- PatNo_ID_1589324603.csv: replaced 2572 cells → /home/jovyan/RT08/0925/blingokNaN_test/PatNo_ID_1589324603.csv (NaN after save: 2572)\n",
      "- PatNo_ID_1589918099.csv: replaced 37334 cells → /home/jovyan/RT08/0925/blingokNaN_test/PatNo_ID_1589918099.csv (NaN after save: 37334)\n",
      "- PatNo_ID_1590136310.csv: replaced 7781 cells → /home/jovyan/RT08/0925/blingokNaN_test/PatNo_ID_1590136310.csv (NaN after save: 7781)\n",
      "- PatNo_ID_1590616537.csv: replaced 14985 cells → /home/jovyan/RT08/0925/blingokNaN_test/PatNo_ID_1590616537.csv (NaN after save: 14985)\n",
      "- PatNo_ID_1590854576.csv: replaced 15879 cells → /home/jovyan/RT08/0925/blingokNaN_test/PatNo_ID_1590854576.csv (NaN after save: 15879)\n",
      "- PatNo_ID_1591609798.csv: replaced 35728 cells → /home/jovyan/RT08/0925/blingokNaN_test/PatNo_ID_1591609798.csv (NaN after save: 35728)\n",
      "- PatNo_ID_1592044724.csv: replaced 7714 cells → /home/jovyan/RT08/0925/blingokNaN_test/PatNo_ID_1592044724.csv (NaN after save: 7714)\n",
      "- PatNo_ID_1592560504.csv: replaced 18053 cells → /home/jovyan/RT08/0925/blingokNaN_test/PatNo_ID_1592560504.csv (NaN after save: 18053)\n",
      "- PatNo_ID_1593087886.csv: replaced 26642 cells → /home/jovyan/RT08/0925/blingokNaN_test/PatNo_ID_1593087886.csv (NaN after save: 26642)\n",
      "- PatNo_ID_1593416100.csv: replaced 3786 cells → /home/jovyan/RT08/0925/blingokNaN_test/PatNo_ID_1593416100.csv (NaN after save: 3786)\n",
      "- PatNo_ID_1593472048.csv: replaced 24102 cells → /home/jovyan/RT08/0925/blingokNaN_test/PatNo_ID_1593472048.csv (NaN after save: 24102)\n",
      "- PatNo_ID_1593593586.csv: replaced 18961 cells → /home/jovyan/RT08/0925/blingokNaN_test/PatNo_ID_1593593586.csv (NaN after save: 18961)\n",
      "- PatNo_ID_1593720818.csv: replaced 2947 cells → /home/jovyan/RT08/0925/blingokNaN_test/PatNo_ID_1593720818.csv (NaN after save: 2947)\n",
      "- PatNo_ID_1593838524.csv: replaced 2312 cells → /home/jovyan/RT08/0925/blingokNaN_test/PatNo_ID_1593838524.csv (NaN after save: 2312)\n",
      "- PatNo_ID_1594173718.csv: replaced 344 cells → /home/jovyan/RT08/0925/blingokNaN_test/PatNo_ID_1594173718.csv (NaN after save: 344)\n",
      "- PatNo_ID_1594294180.csv: replaced 57802 cells → /home/jovyan/RT08/0925/blingokNaN_test/PatNo_ID_1594294180.csv (NaN after save: 57802)\n",
      "- PatNo_ID_1594305136.csv: replaced 27777 cells → /home/jovyan/RT08/0925/blingokNaN_test/PatNo_ID_1594305136.csv (NaN after save: 27777)\n",
      "- PatNo_ID_1594309746.csv: replaced 4246 cells → /home/jovyan/RT08/0925/blingokNaN_test/PatNo_ID_1594309746.csv (NaN after save: 4246)\n",
      "- PatNo_ID_1594319286.csv: replaced 5051 cells → /home/jovyan/RT08/0925/blingokNaN_test/PatNo_ID_1594319286.csv (NaN after save: 5051)\n",
      "- PatNo_ID_1594320763.csv: replaced 2797 cells → /home/jovyan/RT08/0925/blingokNaN_test/PatNo_ID_1594320763.csv (NaN after save: 2797)\n",
      "- PatNo_ID_1594322594.csv: replaced 5416 cells → /home/jovyan/RT08/0925/blingokNaN_test/PatNo_ID_1594322594.csv (NaN after save: 5416)\n",
      "- PatNo_ID_1594335109.csv: replaced 5124 cells → /home/jovyan/RT08/0925/blingokNaN_test/PatNo_ID_1594335109.csv (NaN after save: 5124)\n",
      "- PatNo_ID_1594423683.csv: replaced 5548 cells → /home/jovyan/RT08/0925/blingokNaN_test/PatNo_ID_1594423683.csv (NaN after save: 5548)\n",
      "- PatNo_ID_1594437309.csv: replaced 10267 cells → /home/jovyan/RT08/0925/blingokNaN_test/PatNo_ID_1594437309.csv (NaN after save: 10267)\n",
      "- PatNo_ID_1594439781.csv: replaced 10968 cells → /home/jovyan/RT08/0925/blingokNaN_test/PatNo_ID_1594439781.csv (NaN after save: 10968)\n",
      "- PatNo_ID_1594441887.csv: replaced 10340 cells → /home/jovyan/RT08/0925/blingokNaN_test/PatNo_ID_1594441887.csv (NaN after save: 10340)\n",
      "- PatNo_ID_1594448501.csv: replaced 1506 cells → /home/jovyan/RT08/0925/blingokNaN_test/PatNo_ID_1594448501.csv (NaN after save: 1506)\n",
      "- PatNo_ID_1594455578.csv: replaced 419 cells → /home/jovyan/RT08/0925/blingokNaN_test/PatNo_ID_1594455578.csv (NaN after save: 419)\n",
      "- PatNo_ID_1594464829.csv: replaced 6856 cells → /home/jovyan/RT08/0925/blingokNaN_test/PatNo_ID_1594464829.csv (NaN after save: 6856)\n",
      "- PatNo_ID_1594467719.csv: replaced 12244 cells → /home/jovyan/RT08/0925/blingokNaN_test/PatNo_ID_1594467719.csv (NaN after save: 12244)\n",
      "- PatNo_ID_1594471407.csv: replaced 12654 cells → /home/jovyan/RT08/0925/blingokNaN_test/PatNo_ID_1594471407.csv (NaN after save: 12654)\n",
      "- PatNo_ID_1594479330.csv: replaced 3755 cells → /home/jovyan/RT08/0925/blingokNaN_test/PatNo_ID_1594479330.csv (NaN after save: 3755)\n",
      "- PatNo_ID_1594511911.csv: replaced 8654 cells → /home/jovyan/RT08/0925/blingokNaN_test/PatNo_ID_1594511911.csv (NaN after save: 8654)\n",
      "- PatNo_ID_1594511914.csv: replaced 2968 cells → /home/jovyan/RT08/0925/blingokNaN_test/PatNo_ID_1594511914.csv (NaN after save: 2968)\n",
      "- PatNo_ID_1594528842.csv: replaced 3030 cells → /home/jovyan/RT08/0925/blingokNaN_test/PatNo_ID_1594528842.csv (NaN after save: 3030)\n",
      "- PatNo_ID_1594533379.csv: replaced 1363 cells → /home/jovyan/RT08/0925/blingokNaN_test/PatNo_ID_1594533379.csv (NaN after save: 1363)\n",
      "\n",
      "[完成] 彙總報表：/home/jovyan/RT08/0925/bling_record/ok/nan_replace_summary_blingokNaN.csv\n",
      "輸出資料夾：/home/jovyan/RT08/0925/blingokNaN_test\n",
      "提示：若你用 Excel 開 CSV，'NaN' 會以文字顯示；以 pandas 讀回會自動當成缺失值。\n"
     ]
    }
   ],
   "source": [
    "#還是有空白 應該是看起來「沒有變 NaN」，多半是CSV 預設把 NaN 存成空白，因為CSV 沒有「NaN」這種型別\n",
    "# 強化版：將 blingok 內 CSV 的空值 → NaN（可見字樣），並輸出驗證報表\n",
    "import os, glob, unicodedata, re\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "SRC_DIR = \"/home/jovyan/RT08/0925/blingok\"\n",
    "DST_DIR = \"/home/jovyan/RT08/0925/blingokNaN_test\"\n",
    "REPORT_DIR = \"/home/jovyan/RT08/0925/bling_record/ok\"\n",
    "os.makedirs(DST_DIR, exist_ok=True)\n",
    "os.makedirs(REPORT_DIR, exist_ok=True)\n",
    "\n",
    "ENCODINGS = (\"utf-8-sig\",\"utf-8\",\"cp950\",\"big5\",\"latin1\")\n",
    "\n",
    "# 不可見/空白字元（零寬、方向控制、BOM、NBSP、全形空白…）\n",
    "INVIS_RE = re.compile(r\"[\\u200b-\\u200f\\u202a-\\u202e\\u2066-\\u2069\\ufeff\\u00a0\\u3000]\")\n",
    "# 常見空值字串（大小寫不敏感；先做 NFKC 正規化）\n",
    "NULL_TOKENS = {\"\", \"na\", \"n/a\", \"null\", \"none\", \"-\", \"--\", \"nan\"}\n",
    "\n",
    "def read_df_any(path):\n",
    "    for enc in ENCODINGS:\n",
    "        try:\n",
    "            return pd.read_csv(path, encoding=enc, engine=\"python\", on_bad_lines=\"skip\")\n",
    "        except Exception:\n",
    "            continue\n",
    "    raise RuntimeError(f\"讀檔失敗：{os.path.basename(path)}\")\n",
    "\n",
    "def normalize_str(x):\n",
    "    if pd.isna(x): return x\n",
    "    s = unicodedata.normalize(\"NFKC\", str(x))\n",
    "    # 去不可見字元，再 strip\n",
    "    s = INVIS_RE.sub(\" \", s).strip()\n",
    "    return s\n",
    "\n",
    "def is_null_like(x):\n",
    "    if x is None: return True\n",
    "    if isinstance(x, float) and np.isnan(x): return True\n",
    "    s = normalize_str(x)\n",
    "    if s is None: return True\n",
    "    return (s == \"\") or (s.lower() in NULL_TOKENS)\n",
    "\n",
    "rows_report = []\n",
    "\n",
    "for fp in sorted(glob.glob(os.path.join(SRC_DIR, \"*.csv\"))):\n",
    "    name = os.path.basename(fp)\n",
    "    try:\n",
    "        df = read_df_any(fp)\n",
    "        # 建立空值遮罩\n",
    "        mask = df.applymap(is_null_like)\n",
    "        replaced = int(mask.sum().sum())\n",
    "\n",
    "        # 轉成 NaN\n",
    "        df2 = df.mask(mask, other=np.nan)\n",
    "\n",
    "        # 另存：使用 na_rep=\"NaN\" 讓檔案中看的到 NaN（不是空白）\n",
    "        out_path = os.path.join(DST_DIR, name)\n",
    "        df2.to_csv(out_path, index=False, encoding=\"utf-8\", na_rep=\"NaN\")\n",
    "\n",
    "        # 驗證：重新讀回，Pandas 會把 \"NaN\" 視為缺失（keep_default_na=True）\n",
    "        df_check = pd.read_csv(out_path, engine=\"python\")\n",
    "        nan_cells = int(df_check.isna().sum().sum())\n",
    "\n",
    "        rows_report.append({\n",
    "            \"file_name\": name,\n",
    "            \"rows\": int(df.shape[0]),\n",
    "            \"cols\": int(df.shape[1]),\n",
    "            \"cells_replaced_to_nan\": replaced,\n",
    "            \"nan_cells_after_save\": nan_cells,\n",
    "            \"output\": out_path\n",
    "        })\n",
    "        print(f\"- {name}: replaced {replaced} cells → {out_path} (NaN after save: {nan_cells})\")\n",
    "    except Exception as e:\n",
    "        rows_report.append({\n",
    "            \"file_name\": name,\n",
    "            \"rows\": None, \"cols\": None,\n",
    "            \"cells_replaced_to_nan\": None,\n",
    "            \"nan_cells_after_save\": None,\n",
    "            \"output\": f\"[ERROR] {e}\"\n",
    "        })\n",
    "        print(f\"[ERROR] {name}: {e}\")\n",
    "\n",
    "# 輸出彙總報表\n",
    "rep_path = os.path.join(REPORT_DIR, \"nan_replace_summary_blingokNaN.csv\")\n",
    "pd.DataFrame(rows_report).to_csv(rep_path, index=False, encoding=\"utf-8\")\n",
    "print(f\"\\n[完成] 彙總報表：{rep_path}\")\n",
    "print(f\"輸出資料夾：{DST_DIR}\\n提示：若你用 Excel 開 CSV，'NaN' 會以文字顯示；以 pandas 讀回會自動當成缺失值。\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "9b49f2ce-eae4-48db-a678-a67614a85555",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[OK] validation matrix -> /home/jovyan/RT08/0925/0926/dtype_validation_matrix.csv\n",
      "[OK] nan rate matrix -> /home/jovyan/RT08/0925/0926/nan_rates_for_heatmap.csv\n",
      "[OK] heatmap -> /home/jovyan/RT08/0925/0926/ok_nan_v1.png\n"
     ]
    }
   ],
   "source": [
    "\"\"\" 編碼寫錯哈哈哈 確定這些格式的編碼方式，確定senddate是datatime格式, ventilatormode是英文，rrhzsetactual是整數、mvsetactual是浮點數、peepepap是整數、ppeak是整數、cdyn是整數，patno是整數, senddate是datatime, \"vti\"是整數, \"pmean\"是整數, \"vte\"是整數, \"sponvt\"是\tFalse或是True或是NaN，\n",
    "以上欄位都接受也有NaN的內容，\n",
    "檢查之後請存一份檔案是沒有符合要求的，縱軸是檔名，橫軸是欄位，第一列紀錄是否都符合，沒有符合就再沒有符合的那個特徵格子寫x，有符合就空值，檔案存在/home/jovyan/RT08/0925/0926/\n",
    "並且也再輸出一次缺失率的熱力圖 也要清楚的數值 英文圖表 /home/jovyan/RT08/0925/0926/ok_nan_v1.png\n",
    "\"\"\"\n",
    "# 驗證 /home/jovyan/RT08/0925/blingok/ 各檔案的欄位型別／內容規範\n",
    "# 規則（皆允許 NaN）：\n",
    "# - senddate：可成功 parse 成 datetime\n",
    "# - ventilatormode：英文（ASCII 可見字元）\n",
    "# - rrhzsetactual：整數\n",
    "# - mvsetactual：浮點數\n",
    "# - peepepap：整數\n",
    "# - ppeak：整數\n",
    "# - cdyn：整數\n",
    "# - patno：整數\n",
    "# - vti：整數\n",
    "# - pmean：整數\n",
    "# - vte：整數\n",
    "# - sponvt：True / False / NaN（大小寫字串或布林皆可）\n",
    "#\n",
    "# 產出一份「不符合矩陣」：列=檔名、欄=特徵；符合=空白，不符= \"x\"\n",
    "# 第 1 欄 all_ok：該檔是否全數特徵皆符合（OK/FAIL）\n",
    "# 並輸出一張「缺失率熱力圖（%）」含數值標註（缺失≠0 才標註），英文圖表\n",
    "#\n",
    "# 輸出位置：/home/jovyan/RT08/0925/0926/\n",
    "# 嚴格版驗證（sponvt 只允許大小寫完全符合：'True' 或 'False'；NaN 可接受）\n",
    "# 讀取來源：/home/jovyan/RT08/0925/blingok\n",
    "# 輸出矩陣與英文熱力圖：/home/jovyan/RT08/0925/0926/\n",
    "import os, glob, re, unicodedata\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.patheffects as pe\n",
    "from matplotlib import colors\n",
    "\n",
    "IN_DIR  = \"/home/jovyan/RT08/0925/blingok\"\n",
    "OUT_DIR = \"/home/jovyan/RT08/0925/0926\"\n",
    "os.makedirs(OUT_DIR, exist_ok=True)\n",
    "\n",
    "REQUIRED = [\n",
    "    \"senddate\",\"ventilatormode\",\"rrhzsetactual\",\"mvsetactual\",\"peepepap\",\n",
    "    \"ppeak\",\"cdyn\",\"patno\",\"vti\",\"pmean\",\"vte\",\"sponvt\"\n",
    "]\n",
    "ENCODINGS = (\"utf-8-sig\",\"utf-8\",\"cp950\",\"big5\",\"latin1\")\n",
    "\n",
    "def read_df_any(path):\n",
    "    for enc in ENCODINGS:\n",
    "        try:\n",
    "            return pd.read_csv(path, encoding=enc, engine=\"python\", on_bad_lines=\"skip\")\n",
    "        except Exception:\n",
    "            continue\n",
    "    raise RuntimeError(f\"read failed: {os.path.basename(path)}\")\n",
    "\n",
    "def lower_map(df):\n",
    "    m = {}\n",
    "    for c in df.columns:\n",
    "        k = unicodedata.normalize(\"NFKC\", str(c)).strip().lower()\n",
    "        if k not in m: m[k] = c\n",
    "    return m\n",
    "\n",
    "def check_datetime(ser):\n",
    "    s = ser.copy()\n",
    "    mask = s.notna()\n",
    "    if not mask.any(): \n",
    "        return True\n",
    "    ok = pd.to_datetime(s[mask], errors=\"coerce\").notna().all()\n",
    "    return bool(ok)\n",
    "\n",
    "_ascii_re = re.compile(r\"^[\\x20-\\x7E]+$\")  # visible ASCII\n",
    "def check_english(ser):\n",
    "    s = ser.dropna().astype(str).map(lambda x: unicodedata.normalize(\"NFKC\", x).strip())\n",
    "    if s.empty: return True\n",
    "    return bool(s.map(lambda x: _ascii_re.fullmatch(x) is not None).all())\n",
    "\n",
    "def _to_numeric(ser):\n",
    "    return pd.to_numeric(ser, errors=\"coerce\")\n",
    "\n",
    "def check_integer(ser):\n",
    "    s = ser.dropna()\n",
    "    if s.empty: return True\n",
    "    num = _to_numeric(s)\n",
    "    num = num[num.notna()]\n",
    "    if num.empty: \n",
    "        return False\n",
    "    return bool((np.floor(num) == num).all())\n",
    "\n",
    "def check_float(ser):\n",
    "    s = ser.dropna()\n",
    "    if s.empty: return True\n",
    "    num = _to_numeric(s)\n",
    "    return bool(num.notna().all())\n",
    "\n",
    "def check_bool_tf_nan_STRICT(ser):\n",
    "    \"\"\"允許：布林 True/False；或字串 'True' / 'False'（大小寫必符合；允許前後空白），NaN 可接受。\"\"\"\n",
    "    s = ser.dropna()\n",
    "    if s.empty: return True\n",
    "    def ok(v):\n",
    "        if isinstance(v, (bool, np.bool_)): \n",
    "            return True\n",
    "        t = unicodedata.normalize(\"NFKC\", str(v)).strip()  # 僅去空白，不改大小寫\n",
    "        return t == \"True\" or t == \"False\"\n",
    "    return bool(s.map(ok).all())\n",
    "\n",
    "# 檢查並建立「不符合矩陣」與缺失率矩陣\n",
    "matrix_rows = []\n",
    "nan_rate_rows = []\n",
    "\n",
    "files = sorted(glob.glob(os.path.join(IN_DIR, \"*.csv\")))\n",
    "for fp in files:\n",
    "    name = os.path.basename(fp)\n",
    "    try:\n",
    "        df = read_df_any(fp)\n",
    "        lmap = lower_map(df)\n",
    "\n",
    "        row = {\"file_name\": name}\n",
    "        for col in REQUIRED:\n",
    "            if col not in lmap:\n",
    "                row[col] = \"x\"\n",
    "                continue\n",
    "            ser = df[lmap[col]]\n",
    "            if   col == \"senddate\":\n",
    "                ok = check_datetime(ser)\n",
    "            elif col == \"ventilatormode\":\n",
    "                ok = check_english(ser)\n",
    "            elif col in {\"rrhzsetactual\",\"peepepap\",\"ppeak\",\"cdyn\",\"patno\",\"vti\",\"pmean\",\"vte\"}:\n",
    "                ok = check_integer(ser)\n",
    "            elif col == \"mvsetactual\":\n",
    "                ok = check_float(ser)\n",
    "            elif col == \"sponvt\":\n",
    "                ok = check_bool_tf_nan_STRICT(ser)  # ★ 嚴格大小寫\n",
    "            else:\n",
    "                ok = True\n",
    "            row[col] = \"\" if ok else \"x\"\n",
    "\n",
    "        row[\"all_ok\"] = \"OK\" if all((row[c] == \"\") for c in REQUIRED) else \"FAIL\"\n",
    "        matrix_rows.append(row)\n",
    "\n",
    "        # 缺失率（%）\n",
    "        r = {\"file_name\": name}\n",
    "        for col in REQUIRED:\n",
    "            if col not in lmap:\n",
    "                r[col] = 100.0\n",
    "            else:\n",
    "                s = df[lmap[col]]\n",
    "                r[col] = float(s.isna().mean() * 100.0)\n",
    "        nan_rate_rows.append(r)\n",
    "\n",
    "    except Exception as e:\n",
    "        row = {\"file_name\": name, \"all_ok\": \"FAIL\"}\n",
    "        for col in REQUIRED: row[col] = \"x\"\n",
    "        matrix_rows.append(row)\n",
    "        r = {\"file_name\": name}\n",
    "        for col in REQUIRED: r[col] = 100.0\n",
    "        nan_rate_rows.append(r)\n",
    "        print(f\"[WARN] {name}: {e}\")\n",
    "\n",
    "# 不符合矩陣輸出\n",
    "order_cols = [\"all_ok\"] + REQUIRED\n",
    "matrix_df = pd.DataFrame(matrix_rows)\n",
    "for c in order_cols:\n",
    "    if c not in matrix_df.columns: matrix_df[c] = \"\"\n",
    "matrix_df = matrix_df[[\"file_name\"] + order_cols]\n",
    "out_csv = os.path.join(OUT_DIR, \"dtype_validation_matrix.csv\")\n",
    "matrix_df.to_csv(out_csv, index=False, encoding=\"utf-8\")\n",
    "print(f\"[OK] validation matrix -> {out_csv}\")\n",
    "\n",
    "# 缺失率熱力圖資料\n",
    "nan_rate_df = pd.DataFrame(nan_rate_rows)\n",
    "nan_rate_df = nan_rate_df[[\"file_name\"] + REQUIRED]\n",
    "heatmap_csv = os.path.join(OUT_DIR, \"nan_rates_for_heatmap.csv\")\n",
    "nan_rate_df.to_csv(heatmap_csv, index=False, encoding=\"utf-8\")\n",
    "print(f\"[OK] nan rate matrix -> {heatmap_csv}\")\n",
    "\n",
    "# 畫英文熱力圖（缺失≠0才標註；深色背景白字）\n",
    "plot_df = nan_rate_df.set_index(\"file_name\")\n",
    "rows = plot_df.index.tolist()\n",
    "cols = plot_df.columns.tolist()\n",
    "Z = plot_df.values.astype(float)\n",
    "\n",
    "finite_vals = Z[np.isfinite(Z)]\n",
    "vmin = 0.0\n",
    "vmax = float(finite_vals.max()) if finite_vals.size else 1.0\n",
    "if vmax == 0: vmax = 1.0\n",
    "norm = colors.Normalize(vmin=vmin, vmax=vmax)\n",
    "\n",
    "plt.figure(figsize=(max(8, len(cols)*0.5), max(6, len(rows)*0.35)))\n",
    "im = plt.imshow(Z, aspect=\"auto\", interpolation=\"nearest\", cmap=\"Blues\", norm=norm)\n",
    "\n",
    "plt.xticks(ticks=np.arange(len(cols)), labels=cols, rotation=60, ha=\"right\", fontsize=8)\n",
    "plt.yticks(ticks=np.arange(len(rows)), labels=rows, fontsize=8)\n",
    "plt.xlabel(\"Features\")\n",
    "plt.ylabel(\"Files\")\n",
    "plt.title(\"Missing Rate Heatmap (%)\")\n",
    "\n",
    "cbar = plt.colorbar(im)\n",
    "cbar.set_label(\"Missing Rate (%)\")\n",
    "\n",
    "def text_color_for_value(val):\n",
    "    r, g, b, _ = im.cmap(norm(val))\n",
    "    luminance = 0.2126*r + 0.7152*g + 0.0722*b\n",
    "    return (\"white\", \"black\")[luminance >= 0.5]\n",
    "\n",
    "for i in range(len(rows)):\n",
    "    for j in range(len(cols)):\n",
    "        val = Z[i, j]\n",
    "        if not np.isfinite(val) or val == 0:\n",
    "            continue\n",
    "        txt = f\"{val:.1f}%\"\n",
    "        color = text_color_for_value(val)\n",
    "        outline = \"black\" if color == \"white\" else \"white\"\n",
    "        plt.text(j, i, txt, ha=\"center\", va=\"center\", fontsize=7, color=color,\n",
    "                 path_effects=[pe.withStroke(linewidth=1.0, foreground=outline)])\n",
    "\n",
    "plt.tight_layout()\n",
    "heatmap_png = os.path.join(OUT_DIR, \"ok_nan_v1.png\")\n",
    "plt.savefig(heatmap_png, dpi=150)\n",
    "plt.close()\n",
    "print(f\"[OK] heatmap -> {heatmap_png}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "43b67d64-f98c-4ca8-924c-116c9d843de7",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "a6ef62e6-7fad-45d7-bc4b-e2f32a1aa6cf",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "- PatNo_ID_1565378038.csv: True=283, False=0 (total=283)\n",
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      "- PatNo_ID_1594533379.csv: True=237, False=0 (total=237)\n"
     ]
    }
   ],
   "source": [
    "\"\"\" 因為發現sponVt有些是是 否 有些是浮點數，請檢查所有檔案，列出檔案sponVt欄位有True或False的檔名以及筆數\n",
    "\"\"\"\n",
    "# 掃描 /home/jovyan/RT08/0925/blingok/ 全部 CSV，\n",
    "# 找出 sponVt/sponvt 欄位中「值為 True 或 False」的筆數（大小寫必須完全符合 'True'/'False'，或實際布林 True/False）。\n",
    "# 僅列出有出現 True/False 的檔案與筆數；同時輸出一份彙總 CSV 方便保存。\n",
    "import os, glob, unicodedata\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "DIR = \"/home/jovyan/RT08/0925/blingokNaN_test\"\n",
    "OUT = \"/home/jovyan/RT08/0925/0926\"\n",
    "os.makedirs(OUT, exist_ok=True)\n",
    "\n",
    "ENCODINGS = (\"utf-8-sig\",\"utf-8\",\"cp950\",\"big5\",\"latin1\")\n",
    "\n",
    "def read_df_any(path):\n",
    "    for enc in ENCODINGS:\n",
    "        try:\n",
    "            return pd.read_csv(path, encoding=enc, engine=\"python\", on_bad_lines=\"skip\")\n",
    "        except Exception:\n",
    "            continue\n",
    "    return None\n",
    "\n",
    "def find_sponvt_col(df):\n",
    "    # 以小寫比對取得原欄名\n",
    "    for c in df.columns:\n",
    "        if unicodedata.normalize(\"NFKC\", str(c)).strip().lower() == \"sponvt\":\n",
    "            return c\n",
    "    return None\n",
    "\n",
    "def is_true_exact(v):\n",
    "    if isinstance(v, (bool, np.bool_)):    # 布林 True\n",
    "        return bool(v) is True\n",
    "    s = unicodedata.normalize(\"NFKC\", str(v)).strip()\n",
    "    return s == \"True\"                      # 僅允許大小寫完全匹配\n",
    "\n",
    "def is_false_exact(v):\n",
    "    if isinstance(v, (bool, np.bool_)):    # 布林 False\n",
    "        return bool(v) is False\n",
    "    s = unicodedata.normalize(\"NFKC\", str(v)).strip()\n",
    "    return s == \"False\"                     # 僅允許大小寫完全匹配\n",
    "\n",
    "rows = []\n",
    "for fp in sorted(glob.glob(os.path.join(DIR, \"*.csv\"))):\n",
    "    name = os.path.basename(fp)\n",
    "    df = read_df_any(fp)\n",
    "    if df is None:\n",
    "        continue\n",
    "    col = find_sponvt_col(df)\n",
    "    if col is None:\n",
    "        continue\n",
    "\n",
    "    s = df[col]\n",
    "    # 僅計算 True/False；其他（NaN、是/否、數字等）不納入\n",
    "    t_cnt = int(s.apply(is_true_exact).sum())\n",
    "    f_cnt = int(s.apply(is_false_exact).sum())\n",
    "    if t_cnt + f_cnt > 0:\n",
    "        rows.append({\"file_name\": name, \"true_count\": t_cnt, \"false_count\": f_cnt, \"total_true_false\": t_cnt + f_cnt})\n",
    "\n",
    "# 列印結果（僅列有 True/False 的檔案）\n",
    "if rows:\n",
    "    for r in rows:\n",
    "        print(f\"- {r['file_name']}: True={r['true_count']}, False={r['false_count']} (total={r['total_true_false']})\")\n",
    "else:\n",
    "    print(\"- 未在任何檔案的 sponVt 欄位發現 'True' 或 'False'（大小寫完全匹配或布林）\")\n",
    "\n",
    "# 另存彙總 CSV（方便留存）\n",
    "if rows:\n",
    "    pd.DataFrame(rows, columns=[\"file_name\",\"true_count\",\"false_count\",\"total_true_false\"])\\\n",
    "      .to_csv(os.path.join(OUT, \"sponvt_true_false_counts.csv\"), index=False, encoding=\"utf-8\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "b06d6c5c-97b0-423a-864b-1425b6bf8590",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true,
     "source_hidden": true
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "- 095323.csv: count=0, min=nan, max=nan, mean=nan, std=nan\n",
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      "- 7408338.csv: count=0, min=nan, max=nan, mean=nan, std=nan\n",
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      "- PatNo_ID_1565378038.csv: count=0, min=nan, max=nan, mean=nan, std=nan\n",
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      "- PatNo_ID_1569944983.csv: count=0, min=nan, max=nan, mean=nan, std=nan\n",
      "- PatNo_ID_1570089466.csv: count=0, min=nan, max=nan, mean=nan, std=nan\n",
      "- PatNo_ID_1570242703.csv: count=0, min=nan, max=nan, mean=nan, std=nan\n",
      "- PatNo_ID_1570273244.csv: count=0, min=nan, max=nan, mean=nan, std=nan\n",
      "- PatNo_ID_1570642083.csv: count=0, min=nan, max=nan, mean=nan, std=nan\n",
      "- PatNo_ID_1571945701.csv: count=0, min=nan, max=nan, mean=nan, std=nan\n",
      "- PatNo_ID_1572481361.csv: count=0, min=nan, max=nan, mean=nan, std=nan\n",
      "- PatNo_ID_1572562839.csv: count=0, min=nan, max=nan, mean=nan, std=nan\n",
      "- PatNo_ID_1572831765.csv: count=0, min=nan, max=nan, mean=nan, std=nan\n",
      "- PatNo_ID_1572976822.csv: count=0, min=nan, max=nan, mean=nan, std=nan\n",
      "- PatNo_ID_1573063188.csv: count=0, min=nan, max=nan, mean=nan, std=nan\n",
      "- PatNo_ID_1573249295.csv: count=0, min=nan, max=nan, mean=nan, std=nan\n",
      "- PatNo_ID_1573964540.csv: count=0, min=nan, max=nan, mean=nan, std=nan\n",
      "- PatNo_ID_1574148494.csv: count=0, min=nan, max=nan, mean=nan, std=nan\n",
      "- PatNo_ID_1574270349.csv: count=0, min=nan, max=nan, mean=nan, std=nan\n",
      "- PatNo_ID_1574528808.csv: count=0, min=nan, max=nan, mean=nan, std=nan\n",
      "- PatNo_ID_1574831525.csv: count=0, min=nan, max=nan, mean=nan, std=nan\n",
      "- PatNo_ID_1574987447.csv: count=0, min=nan, max=nan, mean=nan, std=nan\n",
      "- PatNo_ID_1575060177.csv: count=0, min=nan, max=nan, mean=nan, std=nan\n",
      "- PatNo_ID_1575256902.csv: count=0, min=nan, max=nan, mean=nan, std=nan\n",
      "- PatNo_ID_1575445051.csv: count=0, min=nan, max=nan, mean=nan, std=nan\n",
      "- PatNo_ID_1575502382.csv: count=0, min=nan, max=nan, mean=nan, std=nan\n",
      "- PatNo_ID_1575975485.csv: count=0, min=nan, max=nan, mean=nan, std=nan\n",
      "- PatNo_ID_1576115572.csv: count=0, min=nan, max=nan, mean=nan, std=nan\n",
      "- PatNo_ID_1576116479.csv: count=0, min=nan, max=nan, mean=nan, std=nan\n",
      "- PatNo_ID_1576301569.csv: count=0, min=nan, max=nan, mean=nan, std=nan\n",
      "- PatNo_ID_1576964560.csv: count=0, min=nan, max=nan, mean=nan, std=nan\n",
      "- PatNo_ID_1577042911.csv: count=0, min=nan, max=nan, mean=nan, std=nan\n",
      "- PatNo_ID_1577487284.csv: count=0, min=nan, max=nan, mean=nan, std=nan\n",
      "- PatNo_ID_1578784257.csv: count=0, min=nan, max=nan, mean=nan, std=nan\n",
      "- PatNo_ID_1579198603.csv: count=0, min=nan, max=nan, mean=nan, std=nan\n",
      "- PatNo_ID_1579498177.csv: count=3022, min=0.0, max=1470.0, mean=384.12243547319656, std=89.35508888469174\n",
      "- PatNo_ID_1580062580.csv: count=0, min=nan, max=nan, mean=nan, std=nan\n",
      "- PatNo_ID_1580096720.csv: count=0, min=nan, max=nan, mean=nan, std=nan\n",
      "- PatNo_ID_1580107637.csv: count=0, min=nan, max=nan, mean=nan, std=nan\n",
      "- PatNo_ID_1580244614.csv: count=0, min=nan, max=nan, mean=nan, std=nan\n",
      "- PatNo_ID_1580766093.csv: count=0, min=nan, max=nan, mean=nan, std=nan\n",
      "- PatNo_ID_1581003248.csv: count=0, min=nan, max=nan, mean=nan, std=nan\n",
      "- PatNo_ID_1581019504.csv: count=0, min=nan, max=nan, mean=nan, std=nan\n",
      "- PatNo_ID_1581633231.csv: count=0, min=nan, max=nan, mean=nan, std=nan\n",
      "- PatNo_ID_1581692973.csv: count=0, min=nan, max=nan, mean=nan, std=nan\n",
      "- PatNo_ID_1582452511.csv: count=0, min=nan, max=nan, mean=nan, std=nan\n",
      "- PatNo_ID_1582635996.csv: count=0, min=nan, max=nan, mean=nan, std=nan\n",
      "- PatNo_ID_1582849900.csv: count=0, min=nan, max=nan, mean=nan, std=nan\n",
      "- PatNo_ID_1582937076.csv: count=0, min=nan, max=nan, mean=nan, std=nan\n",
      "- PatNo_ID_1584158973.csv: count=0, min=nan, max=nan, mean=nan, std=nan\n",
      "- PatNo_ID_1584397376.csv: count=0, min=nan, max=nan, mean=nan, std=nan\n",
      "- PatNo_ID_1586172659.csv: count=0, min=nan, max=nan, mean=nan, std=nan\n",
      "- PatNo_ID_1586696634.csv: count=0, min=nan, max=nan, mean=nan, std=nan\n",
      "- PatNo_ID_1586897008.csv: count=0, min=nan, max=nan, mean=nan, std=nan\n",
      "- PatNo_ID_1587490083.csv: count=0, min=nan, max=nan, mean=nan, std=nan\n",
      "- PatNo_ID_1588632604.csv: count=0, min=nan, max=nan, mean=nan, std=nan\n",
      "- PatNo_ID_1588673465.csv: count=0, min=nan, max=nan, mean=nan, std=nan\n",
      "- PatNo_ID_1588794796.csv: count=0, min=nan, max=nan, mean=nan, std=nan\n",
      "- PatNo_ID_1588957997.csv: count=0, min=nan, max=nan, mean=nan, std=nan\n",
      "- PatNo_ID_1589018086.csv: count=0, min=nan, max=nan, mean=nan, std=nan\n",
      "- PatNo_ID_1589034524.csv: count=0, min=nan, max=nan, mean=nan, std=nan\n",
      "- PatNo_ID_1589324603.csv: count=0, min=nan, max=nan, mean=nan, std=nan\n",
      "- PatNo_ID_1589918099.csv: count=0, min=nan, max=nan, mean=nan, std=nan\n",
      "- PatNo_ID_1590136310.csv: count=0, min=nan, max=nan, mean=nan, std=nan\n",
      "- PatNo_ID_1590616537.csv: count=0, min=nan, max=nan, mean=nan, std=nan\n",
      "- PatNo_ID_1590854576.csv: count=0, min=nan, max=nan, mean=nan, std=nan\n",
      "- PatNo_ID_1591609798.csv: count=0, min=nan, max=nan, mean=nan, std=nan\n",
      "- PatNo_ID_1592044724.csv: count=0, min=nan, max=nan, mean=nan, std=nan\n",
      "- PatNo_ID_1592560504.csv: count=0, min=nan, max=nan, mean=nan, std=nan\n",
      "- PatNo_ID_1593087886.csv: count=0, min=nan, max=nan, mean=nan, std=nan\n",
      "- PatNo_ID_1593416100.csv: count=0, min=nan, max=nan, mean=nan, std=nan\n",
      "- PatNo_ID_1593472048.csv: count=0, min=nan, max=nan, mean=nan, std=nan\n",
      "- PatNo_ID_1593593586.csv: count=0, min=nan, max=nan, mean=nan, std=nan\n",
      "- PatNo_ID_1593720818.csv: count=0, min=nan, max=nan, mean=nan, std=nan\n",
      "- PatNo_ID_1593838524.csv: count=0, min=nan, max=nan, mean=nan, std=nan\n",
      "- PatNo_ID_1594173718.csv: count=0, min=nan, max=nan, mean=nan, std=nan\n",
      "- PatNo_ID_1594294180.csv: count=0, min=nan, max=nan, mean=nan, std=nan\n",
      "- PatNo_ID_1594305136.csv: count=0, min=nan, max=nan, mean=nan, std=nan\n",
      "- PatNo_ID_1594309746.csv: count=0, min=nan, max=nan, mean=nan, std=nan\n",
      "- PatNo_ID_1594319286.csv: count=0, min=nan, max=nan, mean=nan, std=nan\n",
      "- PatNo_ID_1594320763.csv: count=0, min=nan, max=nan, mean=nan, std=nan\n",
      "- PatNo_ID_1594322594.csv: count=0, min=nan, max=nan, mean=nan, std=nan\n",
      "- PatNo_ID_1594335109.csv: count=0, min=nan, max=nan, mean=nan, std=nan\n",
      "- PatNo_ID_1594423683.csv: count=0, min=nan, max=nan, mean=nan, std=nan\n",
      "- PatNo_ID_1594437309.csv: count=0, min=nan, max=nan, mean=nan, std=nan\n",
      "- PatNo_ID_1594439781.csv: count=0, min=nan, max=nan, mean=nan, std=nan\n",
      "- PatNo_ID_1594441887.csv: count=0, min=nan, max=nan, mean=nan, std=nan\n",
      "- PatNo_ID_1594448501.csv: count=0, min=nan, max=nan, mean=nan, std=nan\n",
      "- PatNo_ID_1594455578.csv: count=0, min=nan, max=nan, mean=nan, std=nan\n",
      "- PatNo_ID_1594464829.csv: count=0, min=nan, max=nan, mean=nan, std=nan\n",
      "- PatNo_ID_1594467719.csv: count=0, min=nan, max=nan, mean=nan, std=nan\n",
      "- PatNo_ID_1594471407.csv: count=0, min=nan, max=nan, mean=nan, std=nan\n",
      "- PatNo_ID_1594479330.csv: count=0, min=nan, max=nan, mean=nan, std=nan\n",
      "- PatNo_ID_1594511911.csv: count=0, min=nan, max=nan, mean=nan, std=nan\n",
      "- PatNo_ID_1594511914.csv: count=0, min=nan, max=nan, mean=nan, std=nan\n",
      "- PatNo_ID_1594528842.csv: count=0, min=nan, max=nan, mean=nan, std=nan\n",
      "- PatNo_ID_1594533379.csv: count=0, min=nan, max=nan, mean=nan, std=nan\n",
      "\n",
      "[OVERALL] count=16424, min=0.0, max=1470.0, mean=422.6290185094983, std=133.5848915655083\n",
      "\n",
      "[OK] 統計彙總已輸出：/home/jovyan/RT08/0925/0926/sponvt_numeric_stats.csv\n"
     ]
    }
   ],
   "source": [
    "\"\"\"我想看/home/jovyan/RT08/0925/blingokNaN_test/所有檔案的sponvt如果是數值 最大是多少 最小是多少 平均值 標準差，是數值的有幾筆\"\"\"\n",
    "# 掃描 /home/jovyan/RT08/0925/blingokNaN_test/ 所有 CSV，\n",
    "# 針對 sponvt（大小寫不敏感）「為數值」的紀錄，計算：\n",
    "#  最大值、最小值、平均值、標準差（pandas 預設 ddof=1）、以及數值筆數。\n",
    "# 只將 True/False（含字串 \"True\"/\"False\"、中文「是/否」）視為布林而排除，不算在數值裡。\n",
    "# 結果會列印在 console，並輸出一份彙總 CSV（含 overall）到 /home/jovyan/RT08/0925/0926/sponvt_numeric_stats.csv\n",
    "\n",
    "# 修正版：計算 /home/jovyan/RT08/0925/blingokNaN_test/ 所有檔案\n",
    "# sponvt 欄位（大小寫不敏感）在「可視為數值」時的\n",
    "# 最大、最小、平均、標準差（sample, ddof=1）、以及數值筆數。\n",
    "# ※ 排除布林樣式（True/False/true/false/是/否）與 NaN\n",
    "# 結果另存：/home/jovyan/RT08/0925/0926/sponvt_numeric_stats.csv\n",
    "\n",
    "import os, glob, unicodedata, re\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "IN_DIR  = \"/home/jovyan/RT08/0925/blingokNaN_test\"\n",
    "OUT_DIR = \"/home/jovyan/RT08/0925/0926\"\n",
    "os.makedirs(OUT_DIR, exist_ok=True)\n",
    "\n",
    "ENCODINGS = (\"utf-8-sig\",\"utf-8\",\"cp950\",\"big5\",\"latin1\")\n",
    "\n",
    "def read_df_any(path):\n",
    "    for enc in ENCODINGS:\n",
    "        try:\n",
    "            return pd.read_csv(path, encoding=enc, engine=\"python\", on_bad_lines=\"skip\")\n",
    "        except Exception:\n",
    "            continue\n",
    "    print(f\"[WARN] 讀檔失敗：{os.path.basename(path)}\")\n",
    "    return None\n",
    "\n",
    "def find_sponvt_col(df):\n",
    "    for c in df.columns:\n",
    "        if unicodedata.normalize(\"NFKC\", str(c)).strip().lower() == \"sponvt\":\n",
    "            return c\n",
    "    return None\n",
    "\n",
    "# 視為布林的值（要從數值分析中排除）\n",
    "def is_bool_like(x):\n",
    "    if isinstance(x, (bool, np.bool_)):\n",
    "        return True\n",
    "    if x is None or (isinstance(x, float) and np.isnan(x)):\n",
    "        return False\n",
    "    s = unicodedata.normalize(\"NFKC\", str(x)).strip()\n",
    "    return (s in {\"True\",\"False\",\"是\",\"否\"}) or (s.lower() in {\"true\",\"false\"})\n",
    "\n",
    "# 移除不可見字元（零寬/控制/BOM/NBSP/全形空白）\n",
    "INVIS_RE = re.compile(r\"[\\u200b-\\u200f\\u202a-\\u202e\\u2066-\\u2069\\ufeff\\u00a0\\u3000]\")\n",
    "\n",
    "def coerce_numeric_series(s: pd.Series) -> pd.Series:\n",
    "    \"\"\"\n",
    "    將混合型別 Series 轉為可解析數字的字串，再 to_numeric：\n",
    "    - 轉成 pandas StringDtype（避免 .str 的 AttributeError）\n",
    "    - NFKC 正規化 + 去不可見字元 + strip\n",
    "    - 去千分位逗號\n",
    "    - 僅保留 0-9 . - + e/E（支援科學記號）\n",
    "    \"\"\"\n",
    "    s2 = s.astype(\"string\")  # 重要：確保可用 .str\n",
    "    s2 = s2.map(lambda x: unicodedata.normalize(\"NFKC\", x).strip() if x is not pd.NA else x)\n",
    "    s2 = s2.map(lambda x: INVIS_RE.sub(\"\", x) if x is not pd.NA else x)\n",
    "    s2 = s2.str.replace(\",\", \"\", regex=False)\n",
    "    s2 = s2.str.replace(r\"[^0-9eE\\+\\-\\.]\", \"\", regex=True)\n",
    "    return pd.to_numeric(s2, errors=\"coerce\")\n",
    "\n",
    "rows = []\n",
    "overall_vals = []\n",
    "\n",
    "for fp in sorted(glob.glob(os.path.join(IN_DIR, \"*.csv\"))):\n",
    "    name = os.path.basename(fp)\n",
    "    df = read_df_any(fp)\n",
    "    if df is None:\n",
    "        continue\n",
    "    col = find_sponvt_col(df)\n",
    "    if col is None:\n",
    "        continue\n",
    "\n",
    "    s = df[col]\n",
    "    mask_bool = s.apply(is_bool_like)          # 布林樣式 → 排除\n",
    "    s_num = coerce_numeric_series(s)           # 可轉數值者\n",
    "    numeric_valid = s_num[(~mask_bool) & s_num.notna()]\n",
    "\n",
    "    if numeric_valid.empty:\n",
    "        rows.append({\n",
    "            \"file_name\": name,\n",
    "            \"count_numeric\": 0,\n",
    "            \"min\": np.nan, \"max\": np.nan, \"mean\": np.nan, \"std\": np.nan\n",
    "        })\n",
    "    else:\n",
    "        rows.append({\n",
    "            \"file_name\": name,\n",
    "            \"count_numeric\": int(numeric_valid.shape[0]),\n",
    "            \"min\": float(numeric_valid.min()),\n",
    "            \"max\": float(numeric_valid.max()),\n",
    "            \"mean\": float(numeric_valid.mean()),\n",
    "            \"std\": float(numeric_valid.std(ddof=1))  # sample std\n",
    "        })\n",
    "        overall_vals.append(numeric_valid)\n",
    "\n",
    "# 列印每檔結果\n",
    "if rows:\n",
    "    for r in rows:\n",
    "        print(f\"- {r['file_name']}: count={r['count_numeric']}, \"\n",
    "              f\"min={r['min']}, max={r['max']}, mean={r['mean']}, std={r['std']}\")\n",
    "else:\n",
    "    print(\"- 未找到任何含有 sponvt 欄位的檔案\")\n",
    "\n",
    "# 整體彙總\n",
    "if overall_vals:\n",
    "    allv = pd.concat(overall_vals, ignore_index=True)\n",
    "    overall_row = {\n",
    "        \"file_name\": \"__OVERALL__\",\n",
    "        \"count_numeric\": int(allv.shape[0]),\n",
    "        \"min\": float(allv.min()),\n",
    "        \"max\": float(allv.max()),\n",
    "        \"mean\": float(allv.mean()),\n",
    "        \"std\": float(allv.std(ddof=1))\n",
    "    }\n",
    "    print(\"\\n[OVERALL] \"\n",
    "          f\"count={overall_row['count_numeric']}, min={overall_row['min']}, \"\n",
    "          f\"max={overall_row['max']}, mean={overall_row['mean']}, std={overall_row['std']}\")\n",
    "    rows.append(overall_row)\n",
    "\n",
    "# 輸出 CSV\n",
    "out_path = os.path.join(OUT_DIR, \"sponvt_numeric_stats.csv\")\n",
    "pd.DataFrame(rows, columns=[\"file_name\",\"count_numeric\",\"min\",\"max\",\"mean\",\"std\"])\\\n",
    "  .to_csv(out_path, index=False, encoding=\"utf-8\")\n",
    "print(f\"\\n[OK] 統計彙總已輸出：{out_path}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2f3063cc-c7a7-435e-a02c-f55887f40dee",
   "metadata": {},
   "outputs": [],
   "source": [
    "sponvt 高達1470, 低0.0, "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "70a149a2-cd6c-4684-85aa-a5e1bda6d308",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true,
     "source_hidden": true
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Folder] /home/jovyan/RT08/0925/blingokNaN_test\n",
      "- Total files: 122\n",
      "- CSV files:   122\n",
      "\n",
      "[First 20 CSV files]\n",
      "- PatNo_ID_1594439781.csv\n",
      "- PatNo_ID_1570242703.csv\n",
      "- PatNo_ID_1574148494.csv\n",
      "- PatNo_ID_1582849900.csv\n",
      "- PatNo_ID_1574831525.csv\n",
      "- PatNo_ID_1564148644.csv\n",
      "- PatNo_ID_1575502382.csv\n",
      "- PatNo_ID_1588794796.csv\n",
      "- PatNo_ID_1590136310.csv\n",
      "- PatNo_ID_1567804800.csv\n",
      "- PatNo_ID_1565378038.csv\n",
      "- PatNo_ID_1580107637.csv\n",
      "- PatNo_ID_1588632604.csv\n",
      "- PatNo_ID_1594511911.csv\n",
      "- PatNo_ID_1574270349.csv\n",
      "- PatNo_ID_1573964540.csv\n",
      "- PatNo_ID_1568952422.csv\n",
      "- PatNo_ID_1594533379.csv\n",
      "- PatNo_ID_1580096720.csv\n",
      "- PatNo_ID_1594528842.csv\n",
      "... (+102 more)\n"
     ]
    }
   ],
   "source": [
    "\"\"\"確定​​/home/jovyan/R​T08/0925/b​lingokNaN_test/有幾份檔案的程式，才能解決sponvt編碼問題\"\"\"\n",
    "# 計算 /home/jovyan/RT08/0925/blingokNaN_test/ 資料夾內有「幾份檔案」\n",
    "# - 自動清除路徑中的零寬/全形空白等不可見字元，避免「看得到卻找不到」的問題\n",
    "# - 同時統計全部檔案數與 CSV 檔案數；如需其他副檔名可自行調整\n",
    "\n",
    "import re, os, unicodedata\n",
    "from pathlib import Path\n",
    "\n",
    "RAW_DIR = \"/home/jovyan/RT08/0925/blingokNaN_test\"  # 直接貼你的路徑（含不可見字元也可）\n",
    "\n",
    "# 清理路徑中的不可見字元與多餘分隔符\n",
    "def clean_path(p: str) -> str:\n",
    "    s = unicodedata.normalize(\"NFKC\", p)\n",
    "    s = re.sub(r\"[\\u200b-\\u200f\\u202a-\\u202e\\u2066-\\u2069\\ufeff\\u00a0\\u3000]\", \"\", s)  # 零寬/BOM/NBSP/全形空白\n",
    "    s = re.sub(r\"[\\\\/]+\", \"/\", s)  # 多重斜線\n",
    "    return s.rstrip(\"/\")\n",
    "\n",
    "DIR = Path(clean_path(RAW_DIR))\n",
    "\n",
    "if not DIR.exists() or not DIR.is_dir():\n",
    "    print(f\"[ERROR] Folder not found: {DIR}\")\n",
    "else:\n",
    "    all_files = [p for p in DIR.iterdir() if p.is_file()]\n",
    "    csv_files = [p for p in all_files if p.suffix.lower() == \".csv\"]\n",
    "\n",
    "    print(f\"[Folder] {DIR}\")\n",
    "    print(f\"- Total files: {len(all_files)}\")\n",
    "    print(f\"- CSV files:   {len(csv_files)}\")\n",
    "\n",
    "    # 如需查看檔名清單（預設列出前 20 筆，可自行調整）\n",
    "    SHOW_N = 20\n",
    "    if csv_files:\n",
    "        print(f\"\\n[First {min(SHOW_N, len(csv_files))} CSV files]\")\n",
    "        for p in csv_files[:SHOW_N]:\n",
    "            print(f\"- {p.name}\")\n",
    "        if len(csv_files) > SHOW_N:\n",
    "            print(f\"... (+{len(csv_files) - SHOW_N} more)\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7ce0d3b8-37ff-462e-bb5c-038199377ecd",
   "metadata": {},
   "outputs": [],
   "source": [
    "1018 直接跳到這裡\n",
    "檢查/home/jovyan/R​T08/0925/b​lingokNaN_test/裡面的檔案\n",
    "1. sponvt欄位只有True False NaN (null)的當作A類\n",
    "sponvt欄位只有True False 數值 NaN (null)的當作B類\n",
    "sponvt欄位只有數值 NaN (null)的當作C類\n",
    "2. 用堆疊條狀圖來表示三類（共100%）分別占的比例以及數量 三個顏色分別用 A暖色調 B綠色調 C寒色調\n",
    "3. 統計中sponvt欄位的數值分布跟統計狀況 也用條狀圖表示但不是堆疊條狀圖 橫軸是類別 縱軸是數量 每一條上面要寫數量跟占比\n",
    "圖表都用英文 程式碼都要有中文詳細註釋\n",
    "分別畫三張圖 A暖色調 B綠色調 C寒色調 每張圖裡面的顏色可以漸層"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "425f164e-3372-4427-a17c-75a2164ddae6",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "======================================================================\n",
      "📂 路徑：/home/jovyan/RT08/0925/blingokNaN_test\n",
      "📊 掃描檔案總數：122\n",
      "📈 總筆數：1,602,476\n",
      "🔹 不同元素數量（含型別區分）：1481\n",
      "======================================================================\n",
      " 前 30 個最常見元素（依出現次數排序）\n",
      "Index Type     Value                     Count   Percent(%)\n",
      "----------------------------------------------------------------------\n",
      "1     NaN      NaN                   1,282,863      80.0551\n",
      "2     bool     True                    143,905       8.9802\n",
      "3     str      (null)                  142,041       8.8638\n",
      "4     bool     False                    17,243       1.0760\n",
      "5     str      362                         111       0.0069\n",
      "6     float    387.0                       105       0.0066\n",
      "7     float    393.0                        98       0.0061\n",
      "8     float    379.0                        94       0.0059\n",
      "9     float    392.0                        91       0.0057\n",
      "10    float    384.0                        84       0.0052\n",
      "11    float    383.0                        83       0.0052\n",
      "12    float    373.0                        83       0.0052\n",
      "13    float    385.0                        82       0.0051\n",
      "14    float    382.0                        82       0.0051\n",
      "15    float    376.0                        79       0.0049\n",
      "16    float    378.0                        79       0.0049\n",
      "17    float    391.0                        79       0.0049\n",
      "18    float    388.0                        78       0.0049\n",
      "19    float    389.0                        75       0.0047\n",
      "20    float    381.0                        74       0.0046\n",
      "21    float    371.0                        74       0.0046\n",
      "22    float    406.0                        74       0.0046\n",
      "23    float    386.0                        73       0.0046\n",
      "24    float    367.0                        73       0.0046\n",
      "25    float    396.0                        73       0.0046\n",
      "26    float    405.0                        73       0.0046\n",
      "27    float    398.0                        73       0.0046\n",
      "28    float    375.0                        73       0.0046\n",
      "29    float    395.0                        73       0.0046\n",
      "30    float    374.0                        71       0.0044\n",
      "======================================================================\n",
      "✅ 統計完成\n"
     ]
    }
   ],
   "source": [
    "# 目的：統整指定路徑下所有 CSV 檔案的 sponvt 欄位中「出現過的所有元素」\n",
    "# 顯示：\n",
    "#   - 總掃描檔案數與筆數\n",
    "#   - 各元素出現次數與占比（直接列印）\n",
    "# 不輸出任何檔案。\n",
    "# =========================================================\n",
    "\n",
    "import os\n",
    "import glob\n",
    "from collections import Counter\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "# -----------------------------\n",
    "# 使用者可調整區\n",
    "# -----------------------------\n",
    "DATA_DIR     = r\"/home/jovyan/RT08/0925/blingokNaN_test\"  # 目標資料夾（請依實際路徑調整）\n",
    "FILE_PATTERN = \"*.csv\"                                    # 要讀取的檔案型態\n",
    "TARGET_COL   = \"sponvt\"                                   # 欲統計的欄位名稱\n",
    "ENCODING     = None                                       # 若需指定編碼（如 'utf-8'），可修改此處\n",
    "NA_VALUES    = [\"\", \"NA\", \"N/A\", \"null\", \"Null\", \"NULL\"]  # 額外視為 NA 的字串\n",
    "CHUNKSIZE    = 200000                                     # 大檔用分塊讀取；None 表示一次性讀入\n",
    "TOP_N        = 30                                         # 顯示前幾項元素（依出現次數排序）\n",
    "\n",
    "# -----------------------------\n",
    "# 工具函式：建立 (型別, 值) 鍵，避免混淆\n",
    "# -----------------------------\n",
    "def make_raw_key(v):\n",
    "    \"\"\"將值轉為可區分型別的 key： (type_label, value_repr)\"\"\"\n",
    "    if pd.isna(v):\n",
    "        return (\"NaN\", \"NaN\")\n",
    "    if isinstance(v, str):\n",
    "        return (\"str\", v.strip())\n",
    "    if isinstance(v, (bool, np.bool_)):\n",
    "        return (\"bool\", bool(v))\n",
    "    if isinstance(v, (int, np.integer)):\n",
    "        return (\"int\", int(v))\n",
    "    if isinstance(v, (float, np.floating)):\n",
    "        return (\"float\", float(v))\n",
    "    return (\"other\", str(v))\n",
    "\n",
    "# -----------------------------\n",
    "# 主邏輯\n",
    "# -----------------------------\n",
    "def summarize_sponvt_elements(data_dir):\n",
    "    \"\"\"掃描路徑下所有檔案的 sponvt 欄位，列印統計結果\"\"\"\n",
    "    files = sorted(glob.glob(os.path.join(data_dir, FILE_PATTERN)))\n",
    "    counter = Counter()\n",
    "    total_rows = 0\n",
    "    file_count = 0\n",
    "\n",
    "    for fp in files:\n",
    "        try:\n",
    "            file_count += 1\n",
    "            if CHUNKSIZE:\n",
    "                for chunk in pd.read_csv(\n",
    "                    fp, usecols=[TARGET_COL],\n",
    "                    encoding=ENCODING, na_values=NA_VALUES,\n",
    "                    low_memory=False, chunksize=CHUNKSIZE\n",
    "                ):\n",
    "                    s = chunk[TARGET_COL]\n",
    "                    total_rows += len(s)\n",
    "                    for v in s:\n",
    "                        counter[make_raw_key(v)] += 1\n",
    "            else:\n",
    "                df = pd.read_csv(\n",
    "                    fp, usecols=[TARGET_COL],\n",
    "                    encoding=ENCODING, na_values=NA_VALUES,\n",
    "                    low_memory=False\n",
    "                )\n",
    "                s = df[TARGET_COL]\n",
    "                total_rows += len(s)\n",
    "                for v in s:\n",
    "                    counter[make_raw_key(v)] += 1\n",
    "        except Exception:\n",
    "            continue\n",
    "\n",
    "    # ==============================\n",
    "    # 印出結果\n",
    "    # ==============================\n",
    "    print(\"=\" * 70)\n",
    "    print(f\"📂 路徑：{data_dir}\")\n",
    "    print(f\"📊 掃描檔案總數：{file_count}\")\n",
    "    print(f\"📈 總筆數：{total_rows:,}\")\n",
    "    print(f\"🔹 不同元素數量（含型別區分）：{len(counter)}\")\n",
    "    print(\"=\" * 70)\n",
    "    print(f\" 前 {TOP_N} 個最常見元素（依出現次數排序）\")\n",
    "    print(f\"{'Index':<5} {'Type':<8} {'Value':<20} {'Count':>10} {'Percent(%)':>12}\")\n",
    "    print(\"-\" * 70)\n",
    "\n",
    "    for i, ((typ, val), cnt) in enumerate(counter.most_common(TOP_N), start=1):\n",
    "        pct = cnt / total_rows * 100 if total_rows > 0 else 0\n",
    "        print(f\"{i:<5} {typ:<8} {str(val)[:20]:<20} {cnt:>10,} {pct:>12.4f}\")\n",
    "\n",
    "    print(\"=\" * 70)\n",
    "    print(\"✅ 統計完成\")\n",
    "\n",
    "# -----------------------------\n",
    "# 執行入口（請手動執行）\n",
    "# -----------------------------\n",
    "if __name__ == \"__main__\":\n",
    "    summarize_sponvt_elements(DATA_DIR)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "153ae972-5d30-4e84-8b90-3bab1d62670e",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 650x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 750x520 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 750x520 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 750x520 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 檔案掃描與 sponvt 欄位型態分類 + 視覺化\n",
    "# 需求摘要：\n",
    "# 1) 以「檔案」為單位，根據 sponvt 欄位內容分成 A/B/C 三類\n",
    "#    A: 只有 True/False 與 NaN\n",
    "#    B: 同時含 True/False 與「數值」與 NaN\n",
    "#    C: 只有「數值」與 NaN\n",
    "# 2) 繪製一張三類比例（100%）的「堆疊條狀圖」，區塊顯示數量與比例\n",
    "# 3) 針對每一類（A/B/C）各自繪製一張「非堆疊」條狀圖，顯示 sponvt 值型態分布\n",
    "#    - X 軸為 Value Type（TRUE、FALSE、NUMERIC、NaN）\n",
    "#    - Y 軸為 Count\n",
    "#    - 每條柱上方標示 Count 與 Percentage（佔該類總筆數）\n",
    "# 4) 色彩規範：\n",
    "#    - A 類：暖色調（OrRd/Oranges 系）\n",
    "#    - B 類：綠色調（Greens 系）\n",
    "#    - C 類：寒色調（Blues 系）\n",
    "#    - 各類別的分布圖採漸層（同色不同深淺）\n",
    "# 5) 圖表文字統一英文\n",
    "# 6) 僅提供程式碼，不自動執行\n",
    "# =========================================================\n",
    "\n",
    "import os\n",
    "import glob\n",
    "import math\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib import cm\n",
    "\n",
    "# -----------------------------\n",
    "# 使用者可調整區\n",
    "# -----------------------------\n",
    "DATA_DIR = r\"/home/jovyan/RT08/0925/blingokNaN_test\"  # 目標資料夾（請依實際路徑調整）\n",
    "FILE_PATTERN = \"*.csv\"                                  # 讀取副檔名樣式\n",
    "TARGET_COL = \"sponvt\"                                   # 目標欄位名稱\n",
    "ENCODING = None                                         # 若有編碼需求可指定，如 \"utf-8\" / \"big5\"；預設 None 交由 pandas 推斷\n",
    "NA_VALUES = [\"\", \"NA\", \"N/A\", \"null\", \"Null\", \"NULL\"]   # 額外視為 NA 的字串\n",
    "\n",
    "# -----------------------------\n",
    "# 工具函式：將 sponvt 原始值標準化為四類型態之一\n",
    "#   返回值於 { \"TRUE\", \"FALSE\", \"NUMERIC\", \"NaN\" }\n",
    "# -----------------------------\n",
    "def normalize_sponvt_value(v):\n",
    "    \"\"\"將單一值標準化為四大類型：TRUE / FALSE / NUMERIC / NaN\"\"\"\n",
    "    # 先處理 NaN\n",
    "    if pd.isna(v):\n",
    "        return \"NaN\"\n",
    "\n",
    "    # 字串預處理：去空白、統一小寫\n",
    "    if isinstance(v, str):\n",
    "        vs = v.strip().lower()\n",
    "        if vs in (\"\", \"na\", \"n/a\", \"null\", \"none\"):\n",
    "            return \"NaN\"\n",
    "        if vs in (\"true\", \"t\", \"yes\", \"y\", \"1\", \"真\", \"是\"):\n",
    "            return \"TRUE\"\n",
    "        if vs in (\"false\", \"f\", \"no\", \"n\", \"0\", \"假\", \"否\"):\n",
    "            return \"FALSE\"\n",
    "        # 嘗試將字串轉數值\n",
    "        try:\n",
    "            _ = float(vs)\n",
    "            return \"NUMERIC\"\n",
    "        except Exception:\n",
    "            # 其他非可解析內容，一律視為 NaN\n",
    "            return \"NaN\"\n",
    "\n",
    "    # 非字串：處理布林\n",
    "    if isinstance(v, (bool, np.bool_)):\n",
    "        return \"TRUE\" if bool(v) else \"FALSE\"\n",
    "\n",
    "    # 嘗試解析數值類\n",
    "    if isinstance(v, (int, float, np.integer, np.floating)):\n",
    "        # 排除像是 nan、inf 等\n",
    "        if pd.isna(v) or math.isnan(v):\n",
    "            return \"NaN\"\n",
    "        return \"NUMERIC\"\n",
    "\n",
    "    # 其他未知型別 → 視為 NaN\n",
    "    return \"NaN\"\n",
    "\n",
    "# -----------------------------\n",
    "# 工具函式：判定檔案類別（A/B/C）\n",
    "#   規則基於該檔「所有 sponvt 值」的型態集合\n",
    "# -----------------------------\n",
    "def decide_file_category(type_set):\n",
    "    \"\"\"\n",
    "    依據此檔案 sponvt 型態集合（例如 {\"TRUE\",\"FALSE\",\"NaN\"}）決定 A/B/C 類：\n",
    "      A: 只有 TRUE/FALSE 與 NaN（不含 NUMERIC）\n",
    "      B: 同時含 TRUE/FALSE 與 NUMERIC（NaN 可有）\n",
    "      C: 只有 NUMERIC 與 NaN（不含 TRUE/FALSE）\n",
    "    如果不符合以上（例如全是 NaN 或混雜非定義內容），可歸到 'Unknown'（本需求不繪製）\n",
    "    \"\"\"\n",
    "    has_tf = (\"TRUE\" in type_set) or (\"FALSE\" in type_set)\n",
    "    has_num = (\"NUMERIC\" in type_set)\n",
    "    has_only_tf_nan = has_tf and (not has_num)\n",
    "    has_only_num_nan = has_num and (not has_tf)\n",
    "\n",
    "    if has_only_tf_nan:\n",
    "        return \"A\"\n",
    "    if has_tf and has_num:\n",
    "        return \"B\"\n",
    "    if has_only_num_nan:\n",
    "        return \"C\"\n",
    "    # 其餘（如全部 NaN 或無資料）\n",
    "    return \"Unknown\"\n",
    "\n",
    "# -----------------------------\n",
    "# 主流程（分析彙整；繪圖放在後段）\n",
    "# -----------------------------\n",
    "def analyze_directory(data_dir):\n",
    "    \"\"\"掃描資料夾，彙整每個檔案的型態與分類結果，並彙總每類的統計\"\"\"\n",
    "    files = sorted(glob.glob(os.path.join(data_dir, FILE_PATTERN)))\n",
    "    results = []  # 儲存每檔的逐檔結果\n",
    "\n",
    "    # 類別彙總：累計 row 計數用\n",
    "    # 結構：cat_agg[\"A\"] = {\"TRUE\":x, \"FALSE\":y, \"NUMERIC\":z, \"NaN\":w, \"rows\":total_rows, \"files\":n_files}\n",
    "    cat_agg = {\n",
    "        \"A\": {\"TRUE\": 0, \"FALSE\": 0, \"NUMERIC\": 0, \"NaN\": 0, \"rows\": 0, \"files\": 0},\n",
    "        \"B\": {\"TRUE\": 0, \"FALSE\": 0, \"NUMERIC\": 0, \"NaN\": 0, \"rows\": 0, \"files\": 0},\n",
    "        \"C\": {\"TRUE\": 0, \"FALSE\": 0, \"NUMERIC\": 0, \"NaN\": 0, \"rows\": 0, \"files\": 0},\n",
    "        \"Unknown\": {\"TRUE\": 0, \"FALSE\": 0, \"NUMERIC\": 0, \"NaN\": 0, \"rows\": 0, \"files\": 0},\n",
    "    }\n",
    "\n",
    "    for fp in files:\n",
    "        # 僅載入目標欄位，降低 I/O 與記憶體使用\n",
    "        try:\n",
    "            df = pd.read_csv(fp, usecols=[TARGET_COL], encoding=ENCODING, na_values=NA_VALUES, low_memory=False)\n",
    "        except Exception:\n",
    "            # 無此欄或讀取錯誤 → 視為 Unknown 類；略過行級彙整\n",
    "            results.append({\"file\": os.path.basename(fp), \"rows\": 0, \"category\": \"Unknown\",\n",
    "                            \"TRUE\": 0, \"FALSE\": 0, \"NUMERIC\": 0, \"NaN\": 0})\n",
    "            cat_agg[\"Unknown\"][\"files\"] += 1\n",
    "            continue\n",
    "\n",
    "        s = df[TARGET_COL]\n",
    "        # 將每列值標準化成四類\n",
    "        types = s.map(normalize_sponvt_value)\n",
    "\n",
    "        # 逐檔型態計數\n",
    "        counts = types.value_counts(dropna=False).to_dict()\n",
    "        c_true = int(counts.get(\"TRUE\", 0))\n",
    "        c_false = int(counts.get(\"FALSE\", 0))\n",
    "        c_num = int(counts.get(\"NUMERIC\", 0))\n",
    "        c_nan = int(counts.get(\"NaN\", 0))\n",
    "        n_rows = int(len(s))\n",
    "\n",
    "        type_set = set([k for k, v in counts.items() if v > 0])  # 本檔出現過的型態集合\n",
    "        cat = decide_file_category(type_set)\n",
    "\n",
    "        results.append({\n",
    "            \"file\": os.path.basename(fp),\n",
    "            \"rows\": n_rows,\n",
    "            \"category\": cat,\n",
    "            \"TRUE\": c_true,\n",
    "            \"FALSE\": c_false,\n",
    "            \"NUMERIC\": c_num,\n",
    "            \"NaN\": c_nan\n",
    "        })\n",
    "\n",
    "        # 類別彙總\n",
    "        cat_agg[cat][\"TRUE\"] += c_true\n",
    "        cat_agg[cat][\"FALSE\"] += c_false\n",
    "        cat_agg[cat][\"NUMERIC\"] += c_num\n",
    "        cat_agg[cat][\"NaN\"] += c_nan\n",
    "        cat_agg[cat][\"rows\"] += n_rows\n",
    "        cat_agg[cat][\"files\"] += 1\n",
    "\n",
    "    per_file_df = pd.DataFrame(results)\n",
    "    return per_file_df, cat_agg\n",
    "\n",
    "# -----------------------------\n",
    "# 視覺化：圖 1（堆疊條）— 三類比例與數量\n",
    "# -----------------------------\n",
    "def plot_category_stack(cat_agg, figsize=(6, 6)):\n",
    "    \"\"\"繪製 A/B/C 類別整體比例（100% 堆疊）與數量標示；英文字、色彩依規範\"\"\"\n",
    "    # 三類檔案數或筆數？此處以「筆數（rows）」占比為主，更能反映資料量分布\n",
    "    total_rows = sum(cat_agg[c][\"rows\"] for c in [\"A\", \"B\", \"C\"])\n",
    "    if total_rows == 0:\n",
    "        print(\"No rows in A/B/C to plot.\")\n",
    "        return\n",
    "\n",
    "    vals = [cat_agg[\"A\"][\"rows\"], cat_agg[\"B\"][\"rows\"], cat_agg[\"C\"][\"rows\"]]\n",
    "    labels = [\"Class A\", \"Class B\", \"Class C\"]\n",
    "    # 顏色：A(暖)、B(綠)、C(冷)\n",
    "    colors = [cm.OrRd(0.7), cm.Greens(0.6), cm.Blues(0.6)]\n",
    "\n",
    "    fig, ax = plt.subplots(figsize=figsize)\n",
    "    left = 0.0\n",
    "    x0 = 0.5  # 單一堆疊條的 x 位置\n",
    "    for v, lab, col in zip(vals, labels, colors):\n",
    "        width = v / total_rows\n",
    "        ax.bar(x0, width, bottom=left, color=col, width=0.6, edgecolor=\"white\", linewidth=1.0, label=lab)\n",
    "        # 在區塊中央標示「Count + %」\n",
    "        pct = 100.0 * v / total_rows\n",
    "        ax.text(x0, left + width / 2.0, f\"{lab}\\n{v:,} ({pct:.1f}%)\",\n",
    "                ha=\"center\", va=\"center\", fontsize=11, color=\"black\")\n",
    "        left += width\n",
    "\n",
    "    ax.set_xlim(0, 1)\n",
    "    ax.set_ylim(0, 1)\n",
    "    ax.set_xticks([])\n",
    "    ax.set_ylabel(\"Proportion (100%)\", fontsize=12)\n",
    "    ax.set_title(\"Distribution of Files by sponvt Type Classes (A/B/C)\", fontsize=14, pad=12)\n",
    "    ax.legend(loc=\"upper right\", frameon=False)\n",
    "    plt.tight_layout()\n",
    "    # plt.show()\n",
    "\n",
    "# -----------------------------\n",
    "# 視覺化：圖 2–4（各類分布，非堆疊）\n",
    "#   - 採用同色系的漸層：以 colormap 由淺到深賦色\n",
    "#   - 每條柱上印「數量 + 佔比」\n",
    "# -----------------------------\n",
    "def plot_value_distribution_for_class(cat_name, cat_stats, figsize=(7, 5)):\n",
    "    \"\"\"針對某一類（A/B/C），繪製該類彙總的值型態分布（非堆疊），並套用漸層色系\"\"\"\n",
    "    total_rows = cat_stats[\"rows\"]\n",
    "    if total_rows == 0:\n",
    "        print(f\"No rows in class {cat_name} to plot.\")\n",
    "        return\n",
    "\n",
    "    # 欄位順序固定：TRUE、FALSE、NUMERIC、NaN\n",
    "    order = [\"TRUE\", \"FALSE\", \"NUMERIC\", \"NaN\"]\n",
    "    counts = [cat_stats[k] for k in order]\n",
    "\n",
    "    # 選擇 colormap（A=暖、B=綠、C=冷），並取不同深淺\n",
    "    if cat_name == \"A\":\n",
    "        cmap = cm.OrRd\n",
    "        title = \"Value-Type Distribution in Class A (Warm Tone)\"\n",
    "    elif cat_name == \"B\":\n",
    "        cmap = cm.Greens\n",
    "        title = \"Value-Type Distribution in Class B (Green Tone)\"\n",
    "    else:  # \"C\"\n",
    "        cmap = cm.Blues\n",
    "        title = \"Value-Type Distribution in Class C (Cool Tone)\"\n",
    "\n",
    "    n = len(order)\n",
    "    # 產生由淺到深的色票（避免太淺看不清，取 0.35~0.85 範圍）\n",
    "    colors = [cmap(0.35 + 0.5 * i / max(1, n - 1)) for i in range(n)]\n",
    "\n",
    "    fig, ax = plt.subplots(figsize=figsize)\n",
    "    x = np.arange(n)\n",
    "    bars = ax.bar(x, counts, color=colors, edgecolor=\"white\", linewidth=1.0)\n",
    "\n",
    "    # 座標軸與標籤（英文化）\n",
    "    ax.set_xticks(x)\n",
    "    ax.set_xticklabels(order, fontsize=11)\n",
    "    ax.set_ylabel(\"Count\", fontsize=12)\n",
    "    ax.set_title(title, fontsize=14, pad=12)\n",
    "\n",
    "    # 計算百分比並標示於每條柱上方\n",
    "    for i, b in enumerate(bars):\n",
    "        c = counts[i]\n",
    "        pct = (c / total_rows) * 100.0 if total_rows > 0 else 0.0\n",
    "        ax.annotate(f\"{c:,}\\n({pct:.1f}%)\",\n",
    "                    xy=(b.get_x() + b.get_width() / 2.0, b.get_height()),\n",
    "                    xytext=(0, 6), textcoords=\"offset points\",\n",
    "                    ha=\"center\", va=\"bottom\", fontsize=10)\n",
    "\n",
    "    # 讓 y 軸多留一點頂部空間，避免文字被切\n",
    "    ymax = max(counts) if counts else 1\n",
    "    ax.set_ylim(0, ymax * 1.15)\n",
    "\n",
    "    # 美化：移除上右邊框\n",
    "    ax.spines[\"right\"].set_visible(False)\n",
    "    ax.spines[\"top\"].set_visible(False)\n",
    "\n",
    "    plt.tight_layout()\n",
    "    # plt.show()\n",
    "\n",
    "# -----------------------------\n",
    "# 主執行入口（請在實際執行環境中手動呼叫）\n",
    "# -----------------------------\n",
    "if __name__ == \"__main__\":\n",
    "    per_file_df, cat_agg = analyze_directory(DATA_DIR)\n",
    "\n",
    "    # 圖 1：A/B/C 類別分布（堆疊 100%）\n",
    "    plot_category_stack(cat_agg, figsize=(6.5, 6))\n",
    "\n",
    "    # 圖 2–4：各類別內 value-type 分布（非堆疊），色系採漸層\n",
    "    for cls in [\"A\", \"B\", \"C\"]:\n",
    "        plot_value_distribution_for_class(cls, cat_agg[cls], figsize=(7.5, 5.2))\n",
    "\n",
    "    # 若需要輸出彙整 CSV（可選）\n",
    "    # per_file_df.to_csv(\"sponvt_per_file_summary.csv\", index=False)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2a77a5f7-8f20-4d36-8594-d58ef94c357d",
   "metadata": {},
   "outputs": [],
   "source": [
    "複製/home/jovyan/RT08/0925/blingokNaN_test/裡面所有的檔案到/home/jovyan/1010/data_new/bling_sponvt_ok/ \n",
    "並在/home/jovyan/1010/data_new/bling_sponvt_ok/進行以下動作\n",
    "針對sponvt欄位中是(null)的項目將他轉成NaN，\n",
    "每個步驟都先計算要動的欄位，進行完要計算完成的欄位"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "ffe7569b-122f-426b-bc37-0a58dba0245e",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "【步驟一｜複製檔案】\n",
      "- 來源目錄：/home/jovyan/RT08/0925/blingokNaN_test/\n",
      "- 目的目錄：/home/jovyan/1010/data_new/bling_sponvt_ok/\n",
      "- 來源檔案數：122，成功複製：122\n",
      "\n",
      "【步驟二｜轉換 sponvt='(null)' → NaN】\n",
      "- 準備處理 CSV 檔案數：122\n",
      "\n",
      "=== 預估變更（每檔） ===\n",
      "file                    | rows_total | will_change_(sponvt=='(null)') | nan_before | has_sponvt_col\n",
      "------------------------+------------+--------------------------------+------------+---------------\n",
      "089271.csv              | 32419      | 0                              | 27968      | Yes           \n",
      "095323.csv              | 23791      | 23791                          | 0          | Yes           \n",
      "095707.csv              | 20180      | 18581                          | 0          | Yes           \n",
      "114309.csv              | 71729      | 69674                          | 0          | Yes           \n",
      "230933.csv              | 30249      | 27130                          | 0          | Yes           \n",
      "4216007.csv             | 1433       | 1433                           | 0          | Yes           \n",
      "7108162.csv             | 239        | 0                              | 135        | Yes           \n",
      "7408338.csv             | 1432       | 1432                           | 0          | Yes           \n",
      "7657698.csv             | 1413       | 0                              | 1287       | Yes           \n",
      "7721164.csv             | 483        | 0                              | 0          | Yes           \n",
      "PatNo_ID_1560013303.csv | 2543       | 0                              | 2341       | Yes           \n",
      "PatNo_ID_1562733396.csv | 2254       | 0                              | 2254       | Yes           \n",
      "PatNo_ID_1563587183.csv | 5287       | 0                              | 5287       | Yes           \n",
      "PatNo_ID_1564148644.csv | 17287      | 0                              | 17287      | Yes           \n",
      "PatNo_ID_1565148312.csv | 5475       | 0                              | 5475       | Yes           \n",
      "PatNo_ID_1565378038.csv | 2561       | 0                              | 2278       | Yes           \n",
      "PatNo_ID_1566123680.csv | 42600      | 0                              | 42600      | Yes           \n",
      "PatNo_ID_1566252197.csv | 3368       | 0                              | 1903       | Yes           \n",
      "PatNo_ID_1566279967.csv | 1235       | 0                              | 1235       | Yes           \n",
      "PatNo_ID_1566671274.csv | 50718      | 0                              | 50718      | Yes           \n",
      "PatNo_ID_1566911879.csv | 53999      | 0                              | 53999      | Yes           \n",
      "PatNo_ID_1567747650.csv | 10901      | 0                              | 10657      | Yes           \n",
      "PatNo_ID_1567804800.csv | 20578      | 0                              | 8903       | Yes           \n",
      "PatNo_ID_1567832735.csv | 36580      | 0                              | 36483      | Yes           \n",
      "PatNo_ID_1568039398.csv | 34519      | 0                              | 34519      | Yes           \n",
      "PatNo_ID_1568574099.csv | 13946      | 0                              | 8327       | Yes           \n",
      "PatNo_ID_1568813269.csv | 4867       | 0                              | 4459       | Yes           \n",
      "PatNo_ID_1568952422.csv | 1184       | 0                              | 1184       | Yes           \n",
      "PatNo_ID_1569083701.csv | 3328       | 0                              | 3328       | Yes           \n",
      "PatNo_ID_1569944983.csv | 7650       | 0                              | 6232       | Yes           \n",
      "PatNo_ID_1570089466.csv | 41343      | 0                              | 33105      | Yes           \n",
      "PatNo_ID_1570242703.csv | 10646      | 0                              | 4811       | Yes           \n",
      "PatNo_ID_1570273244.csv | 9730       | 0                              | 8876       | Yes           \n",
      "PatNo_ID_1570642083.csv | 19731      | 0                              | 19731      | Yes           \n",
      "PatNo_ID_1571945701.csv | 15731      | 0                              | 15731      | Yes           \n",
      "PatNo_ID_1572481361.csv | 34540      | 0                              | 28477      | Yes           \n",
      "PatNo_ID_1572562839.csv | 20966      | 0                              | 10590      | Yes           \n",
      "PatNo_ID_1572831765.csv | 2696       | 0                              | 2696       | Yes           \n",
      "PatNo_ID_1572976822.csv | 6764       | 0                              | 3806       | Yes           \n",
      "PatNo_ID_1573063188.csv | 5080       | 0                              | 5080       | Yes           \n",
      "PatNo_ID_1573249295.csv | 7151       | 0                              | 7151       | Yes           \n",
      "PatNo_ID_1573964540.csv | 4394       | 0                              | 2636       | Yes           \n",
      "PatNo_ID_1574148494.csv | 47154      | 0                              | 47154      | Yes           \n",
      "PatNo_ID_1574270349.csv | 6534       | 0                              | 6534       | Yes           \n",
      "PatNo_ID_1574528808.csv | 14817      | 0                              | 14817      | Yes           \n",
      "PatNo_ID_1574831525.csv | 521        | 0                              | 521        | Yes           \n",
      "PatNo_ID_1574987447.csv | 19843      | 0                              | 16923      | Yes           \n",
      "PatNo_ID_1575060177.csv | 5290       | 0                              | 5290       | Yes           \n",
      "PatNo_ID_1575256902.csv | 9587       | 0                              | 9587       | Yes           \n",
      "PatNo_ID_1575445051.csv | 1801       | 0                              | 1801       | Yes           \n",
      "PatNo_ID_1575502382.csv | 6604       | 0                              | 6604       | Yes           \n",
      "PatNo_ID_1575975485.csv | 16459      | 0                              | 16459      | Yes           \n",
      "PatNo_ID_1576115572.csv | 18717      | 0                              | 15944      | Yes           \n",
      "PatNo_ID_1576116479.csv | 1263       | 0                              | 1263       | Yes           \n",
      "PatNo_ID_1576301569.csv | 3207       | 0                              | 2897       | Yes           \n",
      "PatNo_ID_1576964560.csv | 24192      | 0                              | 23771      | Yes           \n",
      "PatNo_ID_1577042911.csv | 41894      | 0                              | 30401      | Yes           \n",
      "PatNo_ID_1577487284.csv | 2345       | 0                              | 2345       | Yes           \n",
      "PatNo_ID_1578784257.csv | 33917      | 0                              | 20948      | Yes           \n",
      "PatNo_ID_1579198603.csv | 2046       | 0                              | 1898       | Yes           \n",
      "PatNo_ID_1579498177.csv | 21688      | 0                              | 18666      | Yes           \n",
      "PatNo_ID_1580062580.csv | 4300       | 0                              | 2032       | Yes           \n",
      "PatNo_ID_1580096720.csv | 2846       | 0                              | 2846       | Yes           \n",
      "PatNo_ID_1580107637.csv | 5396       | 0                              | 2182       | Yes           \n",
      "PatNo_ID_1580244614.csv | 5278       | 0                              | 5278       | Yes           \n",
      "PatNo_ID_1580766093.csv | 18070      | 0                              | 18070      | Yes           \n",
      "PatNo_ID_1581003248.csv | 7776       | 0                              | 7776       | Yes           \n",
      "PatNo_ID_1581019504.csv | 28177      | 0                              | 9598       | Yes           \n",
      "PatNo_ID_1581633231.csv | 15995      | 0                              | 15995      | Yes           \n",
      "PatNo_ID_1581692973.csv | 2723       | 0                              | 2723       | Yes           \n",
      "PatNo_ID_1582452511.csv | 5196       | 0                              | 5196       | Yes           \n",
      "PatNo_ID_1582635996.csv | 13046      | 0                              | 13046      | Yes           \n",
      "PatNo_ID_1582849900.csv | 7411       | 0                              | 7411       | Yes           \n",
      "PatNo_ID_1582937076.csv | 23990      | 0                              | 17472      | Yes           \n",
      "PatNo_ID_1584158973.csv | 2882       | 0                              | 2882       | Yes           \n",
      "PatNo_ID_1584397376.csv | 638        | 0                              | 638        | Yes           \n",
      "PatNo_ID_1586172659.csv | 38559      | 0                              | 38486      | Yes           \n",
      "PatNo_ID_1586696634.csv | 3569       | 0                              | 3569       | Yes           \n",
      "PatNo_ID_1586897008.csv | 6687       | 0                              | 3516       | Yes           \n",
      "PatNo_ID_1587490083.csv | 45186      | 0                              | 45186      | Yes           \n",
      "PatNo_ID_1588632604.csv | 2608       | 0                              | 2608       | Yes           \n",
      "PatNo_ID_1588673465.csv | 10077      | 0                              | 4480       | Yes           \n",
      "PatNo_ID_1588794796.csv | 9376       | 0                              | 9376       | Yes           \n",
      "PatNo_ID_1588957997.csv | 10966      | 0                              | 10966      | Yes           \n",
      "PatNo_ID_1589018086.csv | 13472      | 0                              | 8702       | Yes           \n",
      "PatNo_ID_1589034524.csv | 50081      | 0                              | 50081      | Yes           \n",
      "PatNo_ID_1589324603.csv | 4187       | 0                              | 1459       | Yes           \n",
      "PatNo_ID_1589918099.csv | 9333       | 0                              | 9333       | Yes           \n",
      "PatNo_ID_1590136310.csv | 5580       | 0                              | 5571       | Yes           \n",
      "PatNo_ID_1590616537.csv | 14208      | 0                              | 13476      | Yes           \n",
      "PatNo_ID_1590854576.csv | 15879      | 0                              | 15879      | Yes           \n",
      "PatNo_ID_1591609798.csv | 35624      | 0                              | 35624      | Yes           \n",
      "PatNo_ID_1592044724.csv | 6815       | 0                              | 6057       | Yes           \n",
      "PatNo_ID_1592560504.csv | 18052      | 0                              | 18052      | Yes           \n",
      "PatNo_ID_1593087886.csv | 24163      | 0                              | 24163      | Yes           \n",
      "PatNo_ID_1593416100.csv | 3328       | 0                              | 3328       | Yes           \n",
      "PatNo_ID_1593472048.csv | 8230       | 0                              | 8230       | Yes           \n",
      "PatNo_ID_1593593586.csv | 18882      | 0                              | 18882      | Yes           \n",
      "PatNo_ID_1593720818.csv | 3911       | 0                              | 2362       | Yes           \n",
      "PatNo_ID_1593838524.csv | 2178       | 0                              | 2178       | Yes           \n",
      "PatNo_ID_1594173718.csv | 344        | 0                              | 344        | Yes           \n",
      "PatNo_ID_1594294180.csv | 18334      | 0                              | 18334      | Yes           \n",
      "PatNo_ID_1594305136.csv | 18679      | 0                              | 18679      | Yes           \n",
      "PatNo_ID_1594309746.csv | 3745       | 0                              | 3745       | Yes           \n",
      "PatNo_ID_1594319286.csv | 4107       | 0                              | 4107       | Yes           \n",
      "PatNo_ID_1594320763.csv | 2431       | 0                              | 2431       | Yes           \n",
      "PatNo_ID_1594322594.csv | 5380       | 0                              | 5380       | Yes           \n",
      "PatNo_ID_1594335109.csv | 5116       | 0                              | 5116       | Yes           \n",
      "PatNo_ID_1594423683.csv | 5023       | 0                              | 5023       | Yes           \n",
      "PatNo_ID_1594437309.csv | 10035      | 0                              | 8846       | Yes           \n",
      "PatNo_ID_1594439781.csv | 7691       | 0                              | 4533       | Yes           \n",
      "PatNo_ID_1594441887.csv | 10203      | 0                              | 10089      | Yes           \n",
      "PatNo_ID_1594448501.csv | 5106       | 0                              | 666        | Yes           \n",
      "PatNo_ID_1594455578.csv | 262        | 0                              | 105        | Yes           \n",
      "PatNo_ID_1594464829.csv | 4000       | 0                              | 1018       | Yes           \n",
      "PatNo_ID_1594467719.csv | 2560       | 0                              | 2265       | Yes           \n",
      "PatNo_ID_1594471407.csv | 11433      | 0                              | 11433      | Yes           \n",
      "PatNo_ID_1594479330.csv | 3727       | 0                              | 3727       | Yes           \n",
      "PatNo_ID_1594511911.csv | 5488       | 0                              | 2586       | Yes           \n",
      "PatNo_ID_1594511914.csv | 9717       | 0                              | 1204       | Yes           \n",
      "PatNo_ID_1594528842.csv | 2491       | 0                              | 2358       | Yes           \n",
      "PatNo_ID_1594533379.csv | 1030       | 0                              | 793        | Yes           \n",
      "\n",
      "=== 預估變更（總計） ===\n",
      "files | rows_total | sum_will_change | sum_nan_before | files_missing_sponvt\n",
      "------+------------+-----------------+----------------+---------------------\n",
      "122   | 1602476    | 142041          | 1282863        | 0                   \n",
      "\n",
      "=== 實際變更（每檔） ===\n",
      "file                    | changed_rows | nan_before | nan_after | has_sponvt_col\n",
      "------------------------+--------------+------------+-----------+---------------\n",
      "089271.csv              | 0            | 27968      | 27968     | Yes           \n",
      "095323.csv              | 23791        | 0          | 23791     | Yes           \n",
      "095707.csv              | 18581        | 0          | 18581     | Yes           \n",
      "114309.csv              | 69674        | 0          | 69674     | Yes           \n",
      "230933.csv              | 27130        | 0          | 27130     | Yes           \n",
      "4216007.csv             | 1433         | 0          | 1433      | Yes           \n",
      "7108162.csv             | 0            | 135        | 135       | Yes           \n",
      "7408338.csv             | 1432         | 0          | 1432      | Yes           \n",
      "7657698.csv             | 0            | 1287       | 1287      | Yes           \n",
      "7721164.csv             | 0            | 0          | 0         | Yes           \n",
      "PatNo_ID_1560013303.csv | 0            | 2341       | 2341      | Yes           \n",
      "PatNo_ID_1562733396.csv | 0            | 2254       | 2254      | Yes           \n",
      "PatNo_ID_1563587183.csv | 0            | 5287       | 5287      | Yes           \n",
      "PatNo_ID_1564148644.csv | 0            | 17287      | 17287     | Yes           \n",
      "PatNo_ID_1565148312.csv | 0            | 5475       | 5475      | Yes           \n",
      "PatNo_ID_1565378038.csv | 0            | 2278       | 2278      | Yes           \n",
      "PatNo_ID_1566123680.csv | 0            | 42600      | 42600     | Yes           \n",
      "PatNo_ID_1566252197.csv | 0            | 1903       | 1903      | Yes           \n",
      "PatNo_ID_1566279967.csv | 0            | 1235       | 1235      | Yes           \n",
      "PatNo_ID_1566671274.csv | 0            | 50718      | 50718     | Yes           \n",
      "PatNo_ID_1566911879.csv | 0            | 53999      | 53999     | Yes           \n",
      "PatNo_ID_1567747650.csv | 0            | 10657      | 10657     | Yes           \n",
      "PatNo_ID_1567804800.csv | 0            | 8903       | 8903      | Yes           \n",
      "PatNo_ID_1567832735.csv | 0            | 36483      | 36483     | Yes           \n",
      "PatNo_ID_1568039398.csv | 0            | 34519      | 34519     | Yes           \n",
      "PatNo_ID_1568574099.csv | 0            | 8327       | 8327      | Yes           \n",
      "PatNo_ID_1568813269.csv | 0            | 4459       | 4459      | Yes           \n",
      "PatNo_ID_1568952422.csv | 0            | 1184       | 1184      | Yes           \n",
      "PatNo_ID_1569083701.csv | 0            | 3328       | 3328      | Yes           \n",
      "PatNo_ID_1569944983.csv | 0            | 6232       | 6232      | Yes           \n",
      "PatNo_ID_1570089466.csv | 0            | 33105      | 33105     | Yes           \n",
      "PatNo_ID_1570242703.csv | 0            | 4811       | 4811      | Yes           \n",
      "PatNo_ID_1570273244.csv | 0            | 8876       | 8876      | Yes           \n",
      "PatNo_ID_1570642083.csv | 0            | 19731      | 19731     | Yes           \n",
      "PatNo_ID_1571945701.csv | 0            | 15731      | 15731     | Yes           \n",
      "PatNo_ID_1572481361.csv | 0            | 28477      | 28477     | Yes           \n",
      "PatNo_ID_1572562839.csv | 0            | 10590      | 10590     | Yes           \n",
      "PatNo_ID_1572831765.csv | 0            | 2696       | 2696      | Yes           \n",
      "PatNo_ID_1572976822.csv | 0            | 3806       | 3806      | Yes           \n",
      "PatNo_ID_1573063188.csv | 0            | 5080       | 5080      | Yes           \n",
      "PatNo_ID_1573249295.csv | 0            | 7151       | 7151      | Yes           \n",
      "PatNo_ID_1573964540.csv | 0            | 2636       | 2636      | Yes           \n",
      "PatNo_ID_1574148494.csv | 0            | 47154      | 47154     | Yes           \n",
      "PatNo_ID_1574270349.csv | 0            | 6534       | 6534      | Yes           \n",
      "PatNo_ID_1574528808.csv | 0            | 14817      | 14817     | Yes           \n",
      "PatNo_ID_1574831525.csv | 0            | 521        | 521       | Yes           \n",
      "PatNo_ID_1574987447.csv | 0            | 16923      | 16923     | Yes           \n",
      "PatNo_ID_1575060177.csv | 0            | 5290       | 5290      | Yes           \n",
      "PatNo_ID_1575256902.csv | 0            | 9587       | 9587      | Yes           \n",
      "PatNo_ID_1575445051.csv | 0            | 1801       | 1801      | Yes           \n",
      "PatNo_ID_1575502382.csv | 0            | 6604       | 6604      | Yes           \n",
      "PatNo_ID_1575975485.csv | 0            | 16459      | 16459     | Yes           \n",
      "PatNo_ID_1576115572.csv | 0            | 15944      | 15944     | Yes           \n",
      "PatNo_ID_1576116479.csv | 0            | 1263       | 1263      | Yes           \n",
      "PatNo_ID_1576301569.csv | 0            | 2897       | 2897      | Yes           \n",
      "PatNo_ID_1576964560.csv | 0            | 23771      | 23771     | Yes           \n",
      "PatNo_ID_1577042911.csv | 0            | 30401      | 30401     | Yes           \n",
      "PatNo_ID_1577487284.csv | 0            | 2345       | 2345      | Yes           \n",
      "PatNo_ID_1578784257.csv | 0            | 20948      | 20948     | Yes           \n",
      "PatNo_ID_1579198603.csv | 0            | 1898       | 1898      | Yes           \n",
      "PatNo_ID_1579498177.csv | 0            | 18666      | 18666     | Yes           \n",
      "PatNo_ID_1580062580.csv | 0            | 2032       | 2032      | Yes           \n",
      "PatNo_ID_1580096720.csv | 0            | 2846       | 2846      | Yes           \n",
      "PatNo_ID_1580107637.csv | 0            | 2182       | 2182      | Yes           \n",
      "PatNo_ID_1580244614.csv | 0            | 5278       | 5278      | Yes           \n",
      "PatNo_ID_1580766093.csv | 0            | 18070      | 18070     | Yes           \n",
      "PatNo_ID_1581003248.csv | 0            | 7776       | 7776      | Yes           \n",
      "PatNo_ID_1581019504.csv | 0            | 9598       | 9598      | Yes           \n",
      "PatNo_ID_1581633231.csv | 0            | 15995      | 15995     | Yes           \n",
      "PatNo_ID_1581692973.csv | 0            | 2723       | 2723      | Yes           \n",
      "PatNo_ID_1582452511.csv | 0            | 5196       | 5196      | Yes           \n",
      "PatNo_ID_1582635996.csv | 0            | 13046      | 13046     | Yes           \n",
      "PatNo_ID_1582849900.csv | 0            | 7411       | 7411      | Yes           \n",
      "PatNo_ID_1582937076.csv | 0            | 17472      | 17472     | Yes           \n",
      "PatNo_ID_1584158973.csv | 0            | 2882       | 2882      | Yes           \n",
      "PatNo_ID_1584397376.csv | 0            | 638        | 638       | Yes           \n",
      "PatNo_ID_1586172659.csv | 0            | 38486      | 38486     | Yes           \n",
      "PatNo_ID_1586696634.csv | 0            | 3569       | 3569      | Yes           \n",
      "PatNo_ID_1586897008.csv | 0            | 3516       | 3516      | Yes           \n",
      "PatNo_ID_1587490083.csv | 0            | 45186      | 45186     | Yes           \n",
      "PatNo_ID_1588632604.csv | 0            | 2608       | 2608      | Yes           \n",
      "PatNo_ID_1588673465.csv | 0            | 4480       | 4480      | Yes           \n",
      "PatNo_ID_1588794796.csv | 0            | 9376       | 9376      | Yes           \n",
      "PatNo_ID_1588957997.csv | 0            | 10966      | 10966     | Yes           \n",
      "PatNo_ID_1589018086.csv | 0            | 8702       | 8702      | Yes           \n",
      "PatNo_ID_1589034524.csv | 0            | 50081      | 50081     | Yes           \n",
      "PatNo_ID_1589324603.csv | 0            | 1459       | 1459      | Yes           \n",
      "PatNo_ID_1589918099.csv | 0            | 9333       | 9333      | Yes           \n",
      "PatNo_ID_1590136310.csv | 0            | 5571       | 5571      | Yes           \n",
      "PatNo_ID_1590616537.csv | 0            | 13476      | 13476     | Yes           \n",
      "PatNo_ID_1590854576.csv | 0            | 15879      | 15879     | Yes           \n",
      "PatNo_ID_1591609798.csv | 0            | 35624      | 35624     | Yes           \n",
      "PatNo_ID_1592044724.csv | 0            | 6057       | 6057      | Yes           \n",
      "PatNo_ID_1592560504.csv | 0            | 18052      | 18052     | Yes           \n",
      "PatNo_ID_1593087886.csv | 0            | 24163      | 24163     | Yes           \n",
      "PatNo_ID_1593416100.csv | 0            | 3328       | 3328      | Yes           \n",
      "PatNo_ID_1593472048.csv | 0            | 8230       | 8230      | Yes           \n",
      "PatNo_ID_1593593586.csv | 0            | 18882      | 18882     | Yes           \n",
      "PatNo_ID_1593720818.csv | 0            | 2362       | 2362      | Yes           \n",
      "PatNo_ID_1593838524.csv | 0            | 2178       | 2178      | Yes           \n",
      "PatNo_ID_1594173718.csv | 0            | 344        | 344       | Yes           \n",
      "PatNo_ID_1594294180.csv | 0            | 18334      | 18334     | Yes           \n",
      "PatNo_ID_1594305136.csv | 0            | 18679      | 18679     | Yes           \n",
      "PatNo_ID_1594309746.csv | 0            | 3745       | 3745      | Yes           \n",
      "PatNo_ID_1594319286.csv | 0            | 4107       | 4107      | Yes           \n",
      "PatNo_ID_1594320763.csv | 0            | 2431       | 2431      | Yes           \n",
      "PatNo_ID_1594322594.csv | 0            | 5380       | 5380      | Yes           \n",
      "PatNo_ID_1594335109.csv | 0            | 5116       | 5116      | Yes           \n",
      "PatNo_ID_1594423683.csv | 0            | 5023       | 5023      | Yes           \n",
      "PatNo_ID_1594437309.csv | 0            | 8846       | 8846      | Yes           \n",
      "PatNo_ID_1594439781.csv | 0            | 4533       | 4533      | Yes           \n",
      "PatNo_ID_1594441887.csv | 0            | 10089      | 10089     | Yes           \n",
      "PatNo_ID_1594448501.csv | 0            | 666        | 666       | Yes           \n",
      "PatNo_ID_1594455578.csv | 0            | 105        | 105       | Yes           \n",
      "PatNo_ID_1594464829.csv | 0            | 1018       | 1018      | Yes           \n",
      "PatNo_ID_1594467719.csv | 0            | 2265       | 2265      | Yes           \n",
      "PatNo_ID_1594471407.csv | 0            | 11433      | 11433     | Yes           \n",
      "PatNo_ID_1594479330.csv | 0            | 3727       | 3727      | Yes           \n",
      "PatNo_ID_1594511911.csv | 0            | 2586       | 2586      | Yes           \n",
      "PatNo_ID_1594511914.csv | 0            | 1204       | 1204      | Yes           \n",
      "PatNo_ID_1594528842.csv | 0            | 2358       | 2358      | Yes           \n",
      "PatNo_ID_1594533379.csv | 0            | 793        | 793       | Yes           \n",
      "\n",
      "=== 實際變更（總計） ===\n",
      "files | sum_changed_rows | sum_nan_before | sum_nan_after\n",
      "------+------------------+----------------+--------------\n",
      "122   | 142041           | 1282863        | 1424904      \n"
     ]
    }
   ],
   "source": [
    "\"\"\" 先把 /home/jovyan/RT08/0925/blingokNaN_test/ 的所有檔案複製到 /home/jovyan/1010/data_new/bling_sponvt_ok/。\n",
    "在目的資料夾中只針對 CSV 做資料處理：把 sponvt 欄位中字面值 \"(null)\" 的列轉成 NaN。\n",
    "每個步驟都會先「預估要動的列數」，完成後再「回報實際變更列數」，並提供每檔與整體統計。\n",
    "\"\"\"\n",
    "\n",
    "import os\n",
    "import shutil\n",
    "import glob\n",
    "import pandas as pd\n",
    "from typing import Tuple, List\n",
    "\n",
    "SRC_DIR = \"/home/jovyan/RT08/0925/blingokNaN_test/\"\n",
    "DST_DIR = \"/home/jovyan/1010/data_new/bling_sponvt_ok/\"\n",
    "\n",
    "# =============== 工具函式區 ===============\n",
    "\n",
    "def safe_mkdir(path: str):\n",
    "    \"\"\"安全建立資料夾（若已存在則略過）\"\"\"\n",
    "    os.makedirs(path, exist_ok=True)\n",
    "\n",
    "def copy_all_files(src: str, dst: str) -> Tuple[int, int]:\n",
    "    \"\"\"\n",
    "    複製 src 底下「所有檔案」到 dst（不遞迴）。\n",
    "    回傳： (總檔數, 成功複製檔數)\n",
    "    \"\"\"\n",
    "    safe_mkdir(dst)\n",
    "    files = [f for f in glob.glob(os.path.join(src, \"*\")) if os.path.isfile(f)]\n",
    "    copied = 0\n",
    "    for f in files:\n",
    "        try:\n",
    "            shutil.copy2(f, os.path.join(dst, os.path.basename(f)))\n",
    "            copied += 1\n",
    "        except Exception as e:\n",
    "            print(f\"[WARN] 無法複製：{f} -> {e}\")\n",
    "    return len(files), copied\n",
    "\n",
    "def read_csv_flexible(path: str) -> pd.DataFrame:\n",
    "    \"\"\"\n",
    "    嘗試以多種編碼讀取 CSV；為避免型別誤判，先以字串讀入（之後再轉 NaN）。\n",
    "    \"\"\"\n",
    "    encodings = [\"utf-8\", \"utf-8-sig\", \"cp950\", \"latin1\"]\n",
    "    last_err = None\n",
    "    for enc in encodings:\n",
    "        try:\n",
    "            return pd.read_csv(path, dtype=str, encoding=enc)\n",
    "        except Exception as e:\n",
    "            last_err = e\n",
    "            continue\n",
    "    raise last_err\n",
    "\n",
    "def write_csv_atomic(df: pd.DataFrame, path: str):\n",
    "    \"\"\"\n",
    "    原地安全覆寫：先寫到 .tmp，再 rename 取代。\n",
    "    \"\"\"\n",
    "    tmp = path + \".tmp\"\n",
    "    df.to_csv(tmp, index=False, encoding=\"utf-8\")\n",
    "    os.replace(tmp, path)\n",
    "\n",
    "def analyze_sponvt_before(df: pd.DataFrame) -> Tuple[int, int, int]:\n",
    "    \"\"\"\n",
    "    回傳 (總列數, sponvt == \"(null)\" 的列數, sponvt 為 NaN 的列數)\n",
    "    若無欄位 sponvt，對應數皆為 0（並在主流程提示）\n",
    "    \"\"\"\n",
    "    total = len(df)\n",
    "    if \"sponvt\" not in df.columns:\n",
    "        return total, 0, 0\n",
    "    # 僅針對精確字面 \"(null)\" 計數（大小寫敏感）\n",
    "    col = df[\"sponvt\"]\n",
    "    num_is_literal_null = (col == \"(null)\").sum()\n",
    "    num_is_nan = col.isna().sum()\n",
    "    return total, int(num_is_literal_null), int(num_is_nan)\n",
    "\n",
    "def transform_sponvt_literal_null_to_nan(df: pd.DataFrame) -> Tuple[pd.DataFrame, int]:\n",
    "    \"\"\"\n",
    "    將 sponvt 欄位中「精準字面」為 \"(null)\" 的值轉為 NaN。\n",
    "    回傳 (df, 變更列數)\n",
    "    \"\"\"\n",
    "    if \"sponvt\" not in df.columns:\n",
    "        return df, 0\n",
    "    # 註：若要放寬條件，可改成 .str.strip().str.lower().isin({\"(null)\", \"null\"})\n",
    "    mask = df[\"sponvt\"] == \"(null)\"\n",
    "    changed = int(mask.sum())\n",
    "    if changed > 0:\n",
    "        df.loc[mask, \"sponvt\"] = pd.NA\n",
    "    return df, changed\n",
    "\n",
    "def print_table(rows: List[dict], title: str):\n",
    "    \"\"\"\n",
    "    以固定欄寬在終端印出簡潔表格。\n",
    "    rows: 每列是一個 dict（鍵需一致）\n",
    "    \"\"\"\n",
    "    if not rows:\n",
    "        print(f\"\\n=== {title} ===\")\n",
    "        print(\"(no data)\")\n",
    "        return\n",
    "    keys = list(rows[0].keys())\n",
    "    widths = {k: max(len(k), max(len(str(r.get(k, \"\"))) for r in rows)) for k in keys}\n",
    "\n",
    "    print(f\"\\n=== {title} ===\")\n",
    "    # header\n",
    "    header = \" | \".join(f\"{k:<{widths[k]}}\" for k in keys)\n",
    "    sep = \"-+-\".join(\"-\" * widths[k] for k in keys)\n",
    "    print(header)\n",
    "    print(sep)\n",
    "    # rows\n",
    "    for r in rows:\n",
    "        print(\" | \".join(f\"{str(r.get(k, '')):<{widths[k]}}\" for k in keys))\n",
    "\n",
    "# =============== 主流程 ===============\n",
    "\n",
    "def main():\n",
    "    # 步驟一：複製檔案\n",
    "    print(\"【步驟一｜複製檔案】\")\n",
    "    total, copied = copy_all_files(SRC_DIR, DST_DIR)\n",
    "    print(f\"- 來源目錄：{SRC_DIR}\")\n",
    "    print(f\"- 目的目錄：{DST_DIR}\")\n",
    "    print(f\"- 來源檔案數：{total}，成功複製：{copied}\")\n",
    "\n",
    "    # 步驟二：資料清整（僅目的資料夾中的 CSV）\n",
    "    print(\"\\n【步驟二｜轉換 sponvt='(null)' → NaN】\")\n",
    "    csv_files = sorted([f for f in glob.glob(os.path.join(DST_DIR, \"*.csv\")) if os.path.isfile(f)])\n",
    "    print(f\"- 準備處理 CSV 檔案數：{len(csv_files)}\")\n",
    "\n",
    "    per_file_plan = []   # 預估（要動的列數）\n",
    "    per_file_done = []   # 實際（完成變更列數與完成後統計）\n",
    "    total_rows = 0\n",
    "    total_literal_null = 0\n",
    "    total_nan_before = 0\n",
    "    total_changed = 0\n",
    "    total_nan_after = 0\n",
    "    missing_col_files = []\n",
    "\n",
    "    # 先盤點（預估要動的列數）\n",
    "    for path in csv_files:\n",
    "        try:\n",
    "            df = read_csv_flexible(path)\n",
    "            rows, cnt_literal_null, cnt_nan = analyze_sponvt_before(df)\n",
    "            per_file_plan.append({\n",
    "                \"file\": os.path.basename(path),\n",
    "                \"rows_total\": rows,\n",
    "                \"will_change_(sponvt=='(null)')\": cnt_literal_null,\n",
    "                \"nan_before\": cnt_nan,\n",
    "                \"has_sponvt_col\": \"Yes\" if \"sponvt\" in df.columns else \"No\",\n",
    "            })\n",
    "            total_rows += rows\n",
    "            total_literal_null += cnt_literal_null\n",
    "            total_nan_before += cnt_nan\n",
    "            if \"sponvt\" not in df.columns:\n",
    "                missing_col_files.append(os.path.basename(path))\n",
    "        except Exception as e:\n",
    "            per_file_plan.append({\n",
    "                \"file\": os.path.basename(path),\n",
    "                \"rows_total\": \"-\",\n",
    "                \"will_change_(sponvt=='(null)')\": \"-\",\n",
    "                \"nan_before\": \"-\",\n",
    "                \"has_sponvt_col\": f\"讀取失敗: {e}\",\n",
    "            })\n",
    "\n",
    "    # 印出「預估要動的列數」表\n",
    "    print_table(per_file_plan, \"預估變更（每檔）\")\n",
    "    print_table([{\n",
    "        \"files\": len(csv_files),\n",
    "        \"rows_total\": total_rows,\n",
    "        \"sum_will_change\": total_literal_null,\n",
    "        \"sum_nan_before\": total_nan_before,\n",
    "        \"files_missing_sponvt\": len(missing_col_files)\n",
    "    }], \"預估變更（總計）\")\n",
    "\n",
    "    # 開始實作變更並回報實際結果\n",
    "    for path in csv_files:\n",
    "        try:\n",
    "            df = read_csv_flexible(path)\n",
    "            before_nan = df[\"sponvt\"].isna().sum() if \"sponvt\" in df.columns else 0\n",
    "            df2, changed = transform_sponvt_literal_null_to_nan(df)\n",
    "            after_nan = df2[\"sponvt\"].isna().sum() if \"sponvt\" in df2.columns else 0\n",
    "\n",
    "            # 僅在有 sponvt 欄位且有變動時寫回\n",
    "            if changed > 0:\n",
    "                write_csv_atomic(df2, path)\n",
    "\n",
    "            per_file_done.append({\n",
    "                \"file\": os.path.basename(path),\n",
    "                \"changed_rows\": changed,\n",
    "                \"nan_before\": before_nan,\n",
    "                \"nan_after\": after_nan,\n",
    "                \"has_sponvt_col\": \"Yes\" if \"sponvt\" in df.columns else \"No\",\n",
    "            })\n",
    "            total_changed += changed\n",
    "            total_nan_after += after_nan\n",
    "        except Exception as e:\n",
    "            per_file_done.append({\n",
    "                \"file\": os.path.basename(path),\n",
    "                \"changed_rows\": \"-\",\n",
    "                \"nan_before\": \"-\",\n",
    "                \"nan_after\": \"-\",\n",
    "                \"has_sponvt_col\": f\"處理失敗: {e}\",\n",
    "            })\n",
    "\n",
    "    # 印出「實際完成的變更」表\n",
    "    print_table(per_file_done, \"實際變更（每檔）\")\n",
    "    print_table([{\n",
    "        \"files\": len(csv_files),\n",
    "        \"sum_changed_rows\": total_changed,\n",
    "        \"sum_nan_before\": total_nan_before,\n",
    "        \"sum_nan_after\": total_nan_after\n",
    "    }], \"實際變更（總計）\")\n",
    "\n",
    "    # 額外提示\n",
    "    if missing_col_files:\n",
    "        print(\"\\n[注意] 下列檔案缺少 sponvt 欄位（未做任何轉換）：\")\n",
    "        for name in missing_col_files:\n",
    "            print(\" -\", name)\n",
    "\n",
    "if __name__ == \"__main__\":\n",
    "    main()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "dc210f7a-90ae-4f6c-8594-132503b47534",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "======================================================================\n",
      "📂 路徑：/home/jovyan/1010/data_new/bling_sponvt_ok\n",
      "📊 掃描檔案總數：122\n",
      "📈 總筆數：1,602,476\n",
      "🔹 不同元素數量（含型別區分）：892\n",
      "======================================================================\n",
      " 前 30 個最常見元素（依出現次數排序）\n",
      "Index Type     Value                     Count   Percent(%)\n",
      "----------------------------------------------------------------------\n",
      "1     NaN      NaN                   1,424,904      88.9189\n",
      "2     bool     True                    143,905       8.9802\n",
      "3     bool     False                    17,243       1.0760\n",
      "4     float    362.0                       162       0.0101\n",
      "5     float    379.0                       120       0.0075\n",
      "6     float    387.0                       117       0.0073\n",
      "7     float    393.0                       107       0.0067\n",
      "8     float    376.0                       101       0.0063\n",
      "9     float    392.0                       101       0.0063\n",
      "10    float    383.0                        98       0.0061\n",
      "11    float    378.0                        98       0.0061\n",
      "12    float    382.0                        97       0.0061\n",
      "13    float    385.0                        96       0.0060\n",
      "14    float    384.0                        96       0.0060\n",
      "15    float    373.0                        96       0.0060\n",
      "16    float    367.0                        95       0.0059\n",
      "17    float    388.0                        95       0.0059\n",
      "18    float    391.0                        95       0.0059\n",
      "19    float    371.0                        93       0.0058\n",
      "20    float    355.0                        92       0.0057\n",
      "21    float    374.0                        90       0.0056\n",
      "22    float    389.0                        90       0.0056\n",
      "23    float    351.0                        89       0.0056\n",
      "24    float    352.0                        88       0.0055\n",
      "25    float    381.0                        88       0.0055\n",
      "26    float    405.0                        88       0.0055\n",
      "27    float    395.0                        88       0.0055\n",
      "28    float    406.0                        87       0.0054\n",
      "29    float    365.0                        87       0.0054\n",
      "30    float    404.0                        86       0.0054\n",
      "======================================================================\n",
      "✅ 統計完成\n"
     ]
    }
   ],
   "source": [
    "# 目的：統整指定路徑下所有 CSV 檔案的 sponvt 欄位中「出現過的所有元素」\n",
    "# 顯示：\n",
    "#   - 總掃描檔案數與筆數\n",
    "#   - 各元素出現次數與占比（直接列印）\n",
    "# 不輸出任何檔案。\n",
    "# =========================================================\n",
    "\n",
    "import os\n",
    "import glob\n",
    "from collections import Counter\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "# -----------------------------\n",
    "# 使用者可調整區\n",
    "# -----------------------------\n",
    "DATA_DIR     = r\"/home/jovyan/1010/data_new/bling_sponvt_ok\"  # 目標資料夾（請依實際路徑調整）\n",
    "FILE_PATTERN = \"*.csv\"                                    # 要讀取的檔案型態\n",
    "TARGET_COL   = \"sponvt\"                                   # 欲統計的欄位名稱\n",
    "ENCODING     = None                                       # 若需指定編碼（如 'utf-8'），可修改此處\n",
    "NA_VALUES    = [\"\", \"NA\", \"N/A\", \"null\", \"Null\", \"NULL\"]  # 額外視為 NA 的字串\n",
    "CHUNKSIZE    = 200000                                     # 大檔用分塊讀取；None 表示一次性讀入\n",
    "TOP_N        = 30                                         # 顯示前幾項元素（依出現次數排序）\n",
    "\n",
    "# -----------------------------\n",
    "# 工具函式：建立 (型別, 值) 鍵，避免混淆\n",
    "# -----------------------------\n",
    "def make_raw_key(v):\n",
    "    \"\"\"將值轉為可區分型別的 key： (type_label, value_repr)\"\"\"\n",
    "    if pd.isna(v):\n",
    "        return (\"NaN\", \"NaN\")\n",
    "    if isinstance(v, str):\n",
    "        return (\"str\", v.strip())\n",
    "    if isinstance(v, (bool, np.bool_)):\n",
    "        return (\"bool\", bool(v))\n",
    "    if isinstance(v, (int, np.integer)):\n",
    "        return (\"int\", int(v))\n",
    "    if isinstance(v, (float, np.floating)):\n",
    "        return (\"float\", float(v))\n",
    "    return (\"other\", str(v))\n",
    "\n",
    "# -----------------------------\n",
    "# 主邏輯\n",
    "# -----------------------------\n",
    "def summarize_sponvt_elements(data_dir):\n",
    "    \"\"\"掃描路徑下所有檔案的 sponvt 欄位，列印統計結果\"\"\"\n",
    "    files = sorted(glob.glob(os.path.join(data_dir, FILE_PATTERN)))\n",
    "    counter = Counter()\n",
    "    total_rows = 0\n",
    "    file_count = 0\n",
    "\n",
    "    for fp in files:\n",
    "        try:\n",
    "            file_count += 1\n",
    "            if CHUNKSIZE:\n",
    "                for chunk in pd.read_csv(\n",
    "                    fp, usecols=[TARGET_COL],\n",
    "                    encoding=ENCODING, na_values=NA_VALUES,\n",
    "                    low_memory=False, chunksize=CHUNKSIZE\n",
    "                ):\n",
    "                    s = chunk[TARGET_COL]\n",
    "                    total_rows += len(s)\n",
    "                    for v in s:\n",
    "                        counter[make_raw_key(v)] += 1\n",
    "            else:\n",
    "                df = pd.read_csv(\n",
    "                    fp, usecols=[TARGET_COL],\n",
    "                    encoding=ENCODING, na_values=NA_VALUES,\n",
    "                    low_memory=False\n",
    "                )\n",
    "                s = df[TARGET_COL]\n",
    "                total_rows += len(s)\n",
    "                for v in s:\n",
    "                    counter[make_raw_key(v)] += 1\n",
    "        except Exception:\n",
    "            continue\n",
    "\n",
    "    # ==============================\n",
    "    # 印出結果\n",
    "    # ==============================\n",
    "    print(\"=\" * 70)\n",
    "    print(f\"📂 路徑：{data_dir}\")\n",
    "    print(f\"📊 掃描檔案總數：{file_count}\")\n",
    "    print(f\"📈 總筆數：{total_rows:,}\")\n",
    "    print(f\"🔹 不同元素數量（含型別區分）：{len(counter)}\")\n",
    "    print(\"=\" * 70)\n",
    "    print(f\" 前 {TOP_N} 個最常見元素（依出現次數排序）\")\n",
    "    print(f\"{'Index':<5} {'Type':<8} {'Value':<20} {'Count':>10} {'Percent(%)':>12}\")\n",
    "    print(\"-\" * 70)\n",
    "\n",
    "    for i, ((typ, val), cnt) in enumerate(counter.most_common(TOP_N), start=1):\n",
    "        pct = cnt / total_rows * 100 if total_rows > 0 else 0\n",
    "        print(f\"{i:<5} {typ:<8} {str(val)[:20]:<20} {cnt:>10,} {pct:>12.4f}\")\n",
    "\n",
    "    print(\"=\" * 70)\n",
    "    print(\"✅ 統計完成\")\n",
    "\n",
    "# -----------------------------\n",
    "# 執行入口（請手動執行）\n",
    "# -----------------------------\n",
    "if __name__ == \"__main__\":\n",
    "    summarize_sponvt_elements(DATA_DIR)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "5867be81-63e3-4604-b3ea-eca5d04d4d3d",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No rows in class B to plot.\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 650x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 750x520 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 750x520 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 檔案掃描與 sponvt 欄位型態分類 + 視覺化\n",
    "# 需求摘要：\n",
    "# 1) 以「檔案」為單位，根據 sponvt 欄位內容分成 A/B/C 三類\n",
    "#    A: 只有 True/False 與 NaN\n",
    "#    B: 同時含 True/False 與「數值」與 NaN\n",
    "#    C: 只有「數值」與 NaN\n",
    "# 2) 繪製一張三類比例（100%）的「堆疊條狀圖」，區塊顯示數量與比例\n",
    "# 3) 針對每一類（A/B/C）各自繪製一張「非堆疊」條狀圖，顯示 sponvt 值型態分布\n",
    "#    - X 軸為 Value Type（TRUE、FALSE、NUMERIC、NaN）\n",
    "#    - Y 軸為 Count\n",
    "#    - 每條柱上方標示 Count 與 Percentage（佔該類總筆數）\n",
    "# 4) 色彩規範：\n",
    "#    - A 類：暖色調（OrRd/Oranges 系）\n",
    "#    - B 類：綠色調（Greens 系）\n",
    "#    - C 類：寒色調（Blues 系）\n",
    "#    - 各類別的分布圖採漸層（同色不同深淺）\n",
    "# 5) 圖表文字統一英文\n",
    "# 6) 僅提供程式碼，不自動執行\n",
    "# =========================================================\n",
    "\n",
    "import os\n",
    "import glob\n",
    "import math\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib import cm\n",
    "\n",
    "# -----------------------------\n",
    "# 使用者可調整區\n",
    "# -----------------------------\n",
    "DATA_DIR = r\"/home/jovyan/1010/data_new/bling_sponvt_ok\"  # 目標資料夾（請依實際路徑調整）\n",
    "FILE_PATTERN = \"*.csv\"                                  # 讀取副檔名樣式\n",
    "TARGET_COL = \"sponvt\"                                   # 目標欄位名稱\n",
    "ENCODING = None                                         # 若有編碼需求可指定，如 \"utf-8\" / \"big5\"；預設 None 交由 pandas 推斷\n",
    "NA_VALUES = [\"\", \"NA\", \"N/A\", \"null\", \"Null\", \"NULL\"]   # 額外視為 NA 的字串\n",
    "\n",
    "# -----------------------------\n",
    "# 工具函式：將 sponvt 原始值標準化為四類型態之一\n",
    "#   返回值於 { \"TRUE\", \"FALSE\", \"NUMERIC\", \"NaN\" }\n",
    "# -----------------------------\n",
    "def normalize_sponvt_value(v):\n",
    "    \"\"\"將單一值標準化為四大類型：TRUE / FALSE / NUMERIC / NaN\"\"\"\n",
    "    # 先處理 NaN\n",
    "    if pd.isna(v):\n",
    "        return \"NaN\"\n",
    "\n",
    "    # 字串預處理：去空白、統一小寫\n",
    "    if isinstance(v, str):\n",
    "        vs = v.strip().lower()\n",
    "        if vs in (\"\", \"na\", \"n/a\", \"null\", \"none\"):\n",
    "            return \"NaN\"\n",
    "        if vs in (\"true\", \"t\", \"yes\", \"y\", \"1\", \"真\", \"是\"):\n",
    "            return \"TRUE\"\n",
    "        if vs in (\"false\", \"f\", \"no\", \"n\", \"0\", \"假\", \"否\"):\n",
    "            return \"FALSE\"\n",
    "        # 嘗試將字串轉數值\n",
    "        try:\n",
    "            _ = float(vs)\n",
    "            return \"NUMERIC\"\n",
    "        except Exception:\n",
    "            # 其他非可解析內容，一律視為 NaN\n",
    "            return \"NaN\"\n",
    "\n",
    "    # 非字串：處理布林\n",
    "    if isinstance(v, (bool, np.bool_)):\n",
    "        return \"TRUE\" if bool(v) else \"FALSE\"\n",
    "\n",
    "    # 嘗試解析數值類\n",
    "    if isinstance(v, (int, float, np.integer, np.floating)):\n",
    "        # 排除像是 nan、inf 等\n",
    "        if pd.isna(v) or math.isnan(v):\n",
    "            return \"NaN\"\n",
    "        return \"NUMERIC\"\n",
    "\n",
    "    # 其他未知型別 → 視為 NaN\n",
    "    return \"NaN\"\n",
    "\n",
    "# -----------------------------\n",
    "# 工具函式：判定檔案類別（A/B/C）\n",
    "#   規則基於該檔「所有 sponvt 值」的型態集合\n",
    "# -----------------------------\n",
    "def decide_file_category(type_set):\n",
    "    \"\"\"\n",
    "    依據此檔案 sponvt 型態集合（例如 {\"TRUE\",\"FALSE\",\"NaN\"}）決定 A/B/C 類：\n",
    "      A: 只有 TRUE/FALSE 與 NaN（不含 NUMERIC）\n",
    "      B: 同時含 TRUE/FALSE 與 NUMERIC（NaN 可有）\n",
    "      C: 只有 NUMERIC 與 NaN（不含 TRUE/FALSE）\n",
    "    如果不符合以上（例如全是 NaN 或混雜非定義內容），可歸到 'Unknown'（本需求不繪製）\n",
    "    \"\"\"\n",
    "    has_tf = (\"TRUE\" in type_set) or (\"FALSE\" in type_set)\n",
    "    has_num = (\"NUMERIC\" in type_set)\n",
    "    has_only_tf_nan = has_tf and (not has_num)\n",
    "    has_only_num_nan = has_num and (not has_tf)\n",
    "\n",
    "    if has_only_tf_nan:\n",
    "        return \"A\"\n",
    "    if has_tf and has_num:\n",
    "        return \"B\"\n",
    "    if has_only_num_nan:\n",
    "        return \"C\"\n",
    "    # 其餘（如全部 NaN 或無資料）\n",
    "    return \"Unknown\"\n",
    "\n",
    "# -----------------------------\n",
    "# 主流程（分析彙整；繪圖放在後段）\n",
    "# -----------------------------\n",
    "def analyze_directory(data_dir):\n",
    "    \"\"\"掃描資料夾，彙整每個檔案的型態與分類結果，並彙總每類的統計\"\"\"\n",
    "    files = sorted(glob.glob(os.path.join(data_dir, FILE_PATTERN)))\n",
    "    results = []  # 儲存每檔的逐檔結果\n",
    "\n",
    "    # 類別彙總：累計 row 計數用\n",
    "    # 結構：cat_agg[\"A\"] = {\"TRUE\":x, \"FALSE\":y, \"NUMERIC\":z, \"NaN\":w, \"rows\":total_rows, \"files\":n_files}\n",
    "    cat_agg = {\n",
    "        \"A\": {\"TRUE\": 0, \"FALSE\": 0, \"NUMERIC\": 0, \"NaN\": 0, \"rows\": 0, \"files\": 0},\n",
    "        \"B\": {\"TRUE\": 0, \"FALSE\": 0, \"NUMERIC\": 0, \"NaN\": 0, \"rows\": 0, \"files\": 0},\n",
    "        \"C\": {\"TRUE\": 0, \"FALSE\": 0, \"NUMERIC\": 0, \"NaN\": 0, \"rows\": 0, \"files\": 0},\n",
    "        \"Unknown\": {\"TRUE\": 0, \"FALSE\": 0, \"NUMERIC\": 0, \"NaN\": 0, \"rows\": 0, \"files\": 0},\n",
    "    }\n",
    "\n",
    "    for fp in files:\n",
    "        # 僅載入目標欄位，降低 I/O 與記憶體使用\n",
    "        try:\n",
    "            df = pd.read_csv(fp, usecols=[TARGET_COL], encoding=ENCODING, na_values=NA_VALUES, low_memory=False)\n",
    "        except Exception:\n",
    "            # 無此欄或讀取錯誤 → 視為 Unknown 類；略過行級彙整\n",
    "            results.append({\"file\": os.path.basename(fp), \"rows\": 0, \"category\": \"Unknown\",\n",
    "                            \"TRUE\": 0, \"FALSE\": 0, \"NUMERIC\": 0, \"NaN\": 0})\n",
    "            cat_agg[\"Unknown\"][\"files\"] += 1\n",
    "            continue\n",
    "\n",
    "        s = df[TARGET_COL]\n",
    "        # 將每列值標準化成四類\n",
    "        types = s.map(normalize_sponvt_value)\n",
    "\n",
    "        # 逐檔型態計數\n",
    "        counts = types.value_counts(dropna=False).to_dict()\n",
    "        c_true = int(counts.get(\"TRUE\", 0))\n",
    "        c_false = int(counts.get(\"FALSE\", 0))\n",
    "        c_num = int(counts.get(\"NUMERIC\", 0))\n",
    "        c_nan = int(counts.get(\"NaN\", 0))\n",
    "        n_rows = int(len(s))\n",
    "\n",
    "        type_set = set([k for k, v in counts.items() if v > 0])  # 本檔出現過的型態集合\n",
    "        cat = decide_file_category(type_set)\n",
    "\n",
    "        results.append({\n",
    "            \"file\": os.path.basename(fp),\n",
    "            \"rows\": n_rows,\n",
    "            \"category\": cat,\n",
    "            \"TRUE\": c_true,\n",
    "            \"FALSE\": c_false,\n",
    "            \"NUMERIC\": c_num,\n",
    "            \"NaN\": c_nan\n",
    "        })\n",
    "\n",
    "        # 類別彙總\n",
    "        cat_agg[cat][\"TRUE\"] += c_true\n",
    "        cat_agg[cat][\"FALSE\"] += c_false\n",
    "        cat_agg[cat][\"NUMERIC\"] += c_num\n",
    "        cat_agg[cat][\"NaN\"] += c_nan\n",
    "        cat_agg[cat][\"rows\"] += n_rows\n",
    "        cat_agg[cat][\"files\"] += 1\n",
    "\n",
    "    per_file_df = pd.DataFrame(results)\n",
    "    return per_file_df, cat_agg\n",
    "\n",
    "# -----------------------------\n",
    "# 視覺化：圖 1（堆疊條）— 三類比例與數量\n",
    "# -----------------------------\n",
    "def plot_category_stack(cat_agg, figsize=(6, 6)):\n",
    "    \"\"\"繪製 A/B/C 類別整體比例（100% 堆疊）與數量標示；英文字、色彩依規範\"\"\"\n",
    "    # 三類檔案數或筆數？此處以「筆數（rows）」占比為主，更能反映資料量分布\n",
    "    total_rows = sum(cat_agg[c][\"rows\"] for c in [\"A\", \"B\", \"C\"])\n",
    "    if total_rows == 0:\n",
    "        print(\"No rows in A/B/C to plot.\")\n",
    "        return\n",
    "\n",
    "    vals = [cat_agg[\"A\"][\"rows\"], cat_agg[\"B\"][\"rows\"], cat_agg[\"C\"][\"rows\"]]\n",
    "    labels = [\"Class A\", \"Class B\", \"Class C\"]\n",
    "    # 顏色：A(暖)、B(綠)、C(冷)\n",
    "    colors = [cm.OrRd(0.7), cm.Greens(0.6), cm.Blues(0.6)]\n",
    "\n",
    "    fig, ax = plt.subplots(figsize=figsize)\n",
    "    left = 0.0\n",
    "    x0 = 0.5  # 單一堆疊條的 x 位置\n",
    "    for v, lab, col in zip(vals, labels, colors):\n",
    "        width = v / total_rows\n",
    "        ax.bar(x0, width, bottom=left, color=col, width=0.6, edgecolor=\"white\", linewidth=1.0, label=lab)\n",
    "        # 在區塊中央標示「Count + %」\n",
    "        pct = 100.0 * v / total_rows\n",
    "        ax.text(x0, left + width / 2.0, f\"{lab}\\n{v:,} ({pct:.1f}%)\",\n",
    "                ha=\"center\", va=\"center\", fontsize=11, color=\"black\")\n",
    "        left += width\n",
    "\n",
    "    ax.set_xlim(0, 1)\n",
    "    ax.set_ylim(0, 1)\n",
    "    ax.set_xticks([])\n",
    "    ax.set_ylabel(\"Proportion (100%)\", fontsize=12)\n",
    "    ax.set_title(\"Distribution of Files by sponvt Type Classes (A/B/C)\", fontsize=14, pad=12)\n",
    "    ax.legend(loc=\"upper right\", frameon=False)\n",
    "    plt.tight_layout()\n",
    "    # plt.show()\n",
    "\n",
    "# -----------------------------\n",
    "# 視覺化：圖 2–4（各類分布，非堆疊）\n",
    "#   - 採用同色系的漸層：以 colormap 由淺到深賦色\n",
    "#   - 每條柱上印「數量 + 佔比」\n",
    "# -----------------------------\n",
    "def plot_value_distribution_for_class(cat_name, cat_stats, figsize=(7, 5)):\n",
    "    \"\"\"針對某一類（A/B/C），繪製該類彙總的值型態分布（非堆疊），並套用漸層色系\"\"\"\n",
    "    total_rows = cat_stats[\"rows\"]\n",
    "    if total_rows == 0:\n",
    "        print(f\"No rows in class {cat_name} to plot.\")\n",
    "        return\n",
    "\n",
    "    # 欄位順序固定：TRUE、FALSE、NUMERIC、NaN\n",
    "    order = [\"TRUE\", \"FALSE\", \"NUMERIC\", \"NaN\"]\n",
    "    counts = [cat_stats[k] for k in order]\n",
    "\n",
    "    # 選擇 colormap（A=暖、B=綠、C=冷），並取不同深淺\n",
    "    if cat_name == \"A\":\n",
    "        cmap = cm.OrRd\n",
    "        title = \"Value-Type Distribution in Class A (Warm Tone)\"\n",
    "    elif cat_name == \"B\":\n",
    "        cmap = cm.Greens\n",
    "        title = \"Value-Type Distribution in Class B (Green Tone)\"\n",
    "    else:  # \"C\"\n",
    "        cmap = cm.Blues\n",
    "        title = \"Value-Type Distribution in Class C (Cool Tone)\"\n",
    "\n",
    "    n = len(order)\n",
    "    # 產生由淺到深的色票（避免太淺看不清，取 0.35~0.85 範圍）\n",
    "    colors = [cmap(0.35 + 0.5 * i / max(1, n - 1)) for i in range(n)]\n",
    "\n",
    "    fig, ax = plt.subplots(figsize=figsize)\n",
    "    x = np.arange(n)\n",
    "    bars = ax.bar(x, counts, color=colors, edgecolor=\"white\", linewidth=1.0)\n",
    "\n",
    "    # 座標軸與標籤（英文化）\n",
    "    ax.set_xticks(x)\n",
    "    ax.set_xticklabels(order, fontsize=11)\n",
    "    ax.set_ylabel(\"Count\", fontsize=12)\n",
    "    ax.set_title(title, fontsize=14, pad=12)\n",
    "\n",
    "    # 計算百分比並標示於每條柱上方\n",
    "    for i, b in enumerate(bars):\n",
    "        c = counts[i]\n",
    "        pct = (c / total_rows) * 100.0 if total_rows > 0 else 0.0\n",
    "        ax.annotate(f\"{c:,}\\n({pct:.1f}%)\",\n",
    "                    xy=(b.get_x() + b.get_width() / 2.0, b.get_height()),\n",
    "                    xytext=(0, 6), textcoords=\"offset points\",\n",
    "                    ha=\"center\", va=\"bottom\", fontsize=10)\n",
    "\n",
    "    # 讓 y 軸多留一點頂部空間，避免文字被切\n",
    "    ymax = max(counts) if counts else 1\n",
    "    ax.set_ylim(0, ymax * 1.15)\n",
    "\n",
    "    # 美化：移除上右邊框\n",
    "    ax.spines[\"right\"].set_visible(False)\n",
    "    ax.spines[\"top\"].set_visible(False)\n",
    "\n",
    "    plt.tight_layout()\n",
    "    # plt.show()\n",
    "\n",
    "# -----------------------------\n",
    "# 主執行入口（請在實際執行環境中手動呼叫）\n",
    "# -----------------------------\n",
    "if __name__ == \"__main__\":\n",
    "    per_file_df, cat_agg = analyze_directory(DATA_DIR)\n",
    "\n",
    "    # 圖 1：A/B/C 類別分布（堆疊 100%）\n",
    "    plot_category_stack(cat_agg, figsize=(6.5, 6))\n",
    "\n",
    "    # 圖 2–4：各類別內 value-type 分布（非堆疊），色系採漸層\n",
    "    for cls in [\"A\", \"B\", \"C\"]:\n",
    "        plot_value_distribution_for_class(cls, cat_agg[cls], figsize=(7.5, 5.2))\n",
    "\n",
    "    # 若需要輸出彙整 CSV（可選）\n",
    "    # per_file_df.to_csv(\"sponvt_per_file_summary.csv\", index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dc5b2917-1e32-4945-a95c-2e40286a4c35",
   "metadata": {},
   "outputs": [],
   "source": [
    "確定檔案中的sponvt除了NaN True False都是float 並且畫出分布狀況 還有寫出統計的狀況數值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "b9ecdf72-856f-4224-991d-7e5132f8c4a7",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "=== 統計（總表） ===\n",
      "總檔案數：122\n",
      "含 sponvt 欄位的檔案數：122\n",
      "缺少 sponvt 欄位的檔案數：0\n",
      "\n",
      "總筆數（含所有有 sponvt 的檔案）：1602476\n",
      "\n",
      "類別 | Count | Percent(%)\n",
      "-----+-------+-----------\n",
      "NaN  | 1424904 |    88.9189\n",
      "True | 143905 |     8.9802\n",
      "False|  17243 |     1.0760\n",
      "Float|  16424 |     1.0249\n",
      "Other|      0 |     0.0000\n",
      "\n",
      "✅ 驗證結論：除了 NaN / True / False 外，其餘皆為 Float（Other = 0）。\n",
      "\n",
      "=== Float 數值描述統計（整體） ===\n",
      "count: 16,424.000000\n",
      "mean: 422.629019\n",
      " std: 133.584892\n",
      " min: 0.000000\n",
      " 25%: 350.000000\n",
      " 50%: 396.000000\n",
      " 75%: 480.000000\n",
      " max: 1,470.000000\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\"\"\"1) 逐檔掃描 /home/jovyan/1010/data_new/bling_sponvt_ok/ 下所有 CSV，\n",
    "   彙整 sponvt 欄位的型態分佈：NaN、True、False、Float、Other（非預期）。\n",
    "2) 驗證「除了 NaN / True / False 之外，其他都應為 float」：\n",
    "   - 若 Other > 0：列出樣本值與來源檔案，並於最後總結時標記「❌ 不符合」。\n",
    "   - 若 Other == 0：於最後總結時標記「✅ 符合」。\n",
    "3) 視覺化：\n",
    "   - 圖一：分類（NaN / True / False / Float）數量與占比的直條圖（每條上方標示 count & percent）\n",
    "   - 圖二：Float 數值分布的直方圖（自動過濾 NaN、±inf）\n",
    "4) 統計印出：\n",
    "   - 總筆數、四類（NaN/True/False/Float）各自的 Count 與 Percent（四捨五入到 4 位小數）\n",
    "   - Float 數值的描述統計（count, mean, std, min, 25%, 50%, 75%, max）\n",
    "\n",
    "注意：\n",
    "- 僅處理含有 sponvt 欄位之 CSV；若缺少欄位則記錄檔名於「missing_col_files」。\n",
    "- 讀取採彈性編碼嘗試；讀入 dtype=object 以保留原始形態，再進行轉換與判定。\n",
    "- 先將字面 \"(null)\" 正規化為 NaN（若檔案中仍殘留），避免被誤判為字串。\n",
    "- 圖表會使用 matplotlib.show() 即時顯示；不另行輸出檔案（可依需求加上 plt.savefig）。\n",
    "\n",
    "執行環境：\n",
    "- 需已安裝 pandas、numpy、matplotlib\n",
    "\"\"\"\n",
    "\n",
    "import os\n",
    "import glob\n",
    "import math\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "DATA_DIR = \"/home/jovyan/1010/data_new/bling_sponvt_ok/\"\n",
    "\n",
    "# =========================\n",
    "# 工具函式\n",
    "# =========================\n",
    "\n",
    "def read_csv_flexible(path: str) -> pd.DataFrame:\n",
    "    \"\"\"以多種編碼嘗試讀取 CSV，dtype=object 保留原始型別。\"\"\"\n",
    "    encodings = [\"utf-8\", \"utf-8-sig\", \"cp950\", \"latin1\"]\n",
    "    last_err = None\n",
    "    for enc in encodings:\n",
    "        try:\n",
    "            return pd.read_csv(path, dtype=object, encoding=enc)\n",
    "        except Exception as e:\n",
    "            last_err = e\n",
    "    raise last_err\n",
    "\n",
    "def normalize_sponvt_series(s: pd.Series) -> pd.Series:\n",
    "    \"\"\"\n",
    "    正規化 sponvt：\n",
    "    1) 去除前後空白\n",
    "    2) 將精確字面 \"(null)\" => NaN（大小寫不敏感，允許前後空白）\n",
    "    3) 將 'True'/'False'（大小寫不敏感）標準化成布林 True/False\n",
    "    4) 其餘保留原值以便後續判別數值 / Other\n",
    "    \"\"\"\n",
    "    # 轉成字串以便前處理（保留原 NaN）\n",
    "    s_str = s.astype(str)\n",
    "    s_norm = s.copy()\n",
    "\n",
    "    # 1) 去空白（僅對非 NaN）\n",
    "    mask_notna = s.notna()\n",
    "    s_str_trim = s_str.where(~mask_notna, s_str.str.strip())\n",
    "\n",
    "    # 2) \"(null)\" -> NaN（大小寫不敏感）\n",
    "    mask_literal_null = mask_notna & s_str_trim.str.lower().eq(\"(null)\")\n",
    "    s_norm = s_norm.mask(mask_literal_null, pd.NA)\n",
    "\n",
    "    # 3) True/False 正規化\n",
    "    mask_true  = mask_notna & s_str_trim.str.lower().eq(\"true\")\n",
    "    mask_false = mask_notna & s_str_trim.str.lower().eq(\"false\")\n",
    "    s_norm = s_norm.mask(mask_true,  True)\n",
    "    s_norm = s_norm.mask(mask_false, False)\n",
    "\n",
    "    return s_norm\n",
    "\n",
    "def classify_sponvt_series(s: pd.Series):\n",
    "    \"\"\"\n",
    "    將 sponvt 分類為：\n",
    "    - NaN\n",
    "    - True\n",
    "    - False\n",
    "    - Float（可成功轉成浮點數者）\n",
    "    - Other（無法轉數值且也不是布林/NaN）\n",
    "    回傳：\n",
    "    - categories: dict，包含各類別的布林遮罩與計數\n",
    "    - s_float: 轉為 float 的 Series（僅對 Float 類，其他為 NaN）\n",
    "    - others_detail: dict(value -> set(files))，統整非預期值（此函式本身不提供檔名，外層補）\n",
    "    \"\"\"\n",
    "    is_nan   = s.isna()\n",
    "    is_true  = s.eq(True)\n",
    "    is_false = s.eq(False)\n",
    "\n",
    "    # 尚未被分類的候選者（可能為字串/數值等）\n",
    "    undecided_mask = ~(is_nan | is_true | is_false)\n",
    "\n",
    "    # 嘗試把未決定者轉成數值\n",
    "    # 注意：若是字串 \"NaN\"、\"inf\"、\"-inf\" 等，to_numeric 會給出 np.nan 或 ±inf\n",
    "    #      我們定義「Float」為成功轉為有限數的值（isfinite）\n",
    "    s_undecided = s.where(undecided_mask, None)\n",
    "    s_coerced = pd.to_numeric(s_undecided, errors=\"coerce\")\n",
    "\n",
    "    is_float = undecided_mask & pd.Series(np.isfinite(s_coerced.astype(float)), index=s.index)\n",
    "    is_other = undecided_mask & ~is_float  # 無法轉為有限數或為 ±inf / nan（但前面已剔除布林和 NaN）\n",
    "\n",
    "    categories = {\n",
    "        \"NaN\":   {\"mask\": is_nan,   \"count\": int(is_nan.sum())},\n",
    "        \"True\":  {\"mask\": is_true,  \"count\": int(is_true.sum())},\n",
    "        \"False\": {\"mask\": is_false, \"count\": int(is_false.sum())},\n",
    "        \"Float\": {\"mask\": is_float, \"count\": int(is_float.sum())},\n",
    "        \"Other\": {\"mask\": is_other, \"count\": int(is_other.sum())},\n",
    "    }\n",
    "\n",
    "    # 提供對應的 float 值（非 Float 類別給 NaN）\n",
    "    s_float = pd.Series(np.where(is_float, s_coerced, np.nan), index=s.index).astype(float)\n",
    "\n",
    "    return categories, s_float\n",
    "\n",
    "def percent(x: int, total: int) -> float:\n",
    "    return round((x / total * 100.0), 4) if total > 0 else 0.0\n",
    "\n",
    "# =========================\n",
    "# 主流程\n",
    "# =========================\n",
    "\n",
    "csv_files = sorted([p for p in glob.glob(os.path.join(DATA_DIR, \"*.csv\")) if os.path.isfile(p)])\n",
    "\n",
    "total_rows = 0\n",
    "sum_NaN = 0\n",
    "sum_True = 0\n",
    "sum_False = 0\n",
    "sum_Float = 0\n",
    "sum_Other = 0\n",
    "\n",
    "missing_col_files = []\n",
    "other_samples = {}     # value -> set([file1, file2, ...])\n",
    "float_values_all = []  # 收集所有 Float 的數值（用於整體直方圖）\n",
    "\n",
    "# 逐檔處理\n",
    "for f in csv_files:\n",
    "    try:\n",
    "        df = read_csv_flexible(f)\n",
    "    except Exception as e:\n",
    "        print(f\"[WARN] 讀取失敗，已跳過：{f} | {e}\")\n",
    "        continue\n",
    "\n",
    "    if \"sponvt\" not in df.columns:\n",
    "        missing_col_files.append(os.path.basename(f))\n",
    "        continue\n",
    "\n",
    "    s_raw = df[\"sponvt\"]\n",
    "\n",
    "    # 正規化（\"(null)\" -> NaN；True/False 正規化）\n",
    "    s_norm = normalize_sponvt_series(s_raw)\n",
    "\n",
    "    # 分類與萃取數值\n",
    "    cats, s_float = classify_sponvt_series(s_norm)\n",
    "\n",
    "    # 統計累加\n",
    "    n_rows = len(s_norm)\n",
    "    total_rows += n_rows\n",
    "    sum_NaN   += cats[\"NaN\"][\"count\"]\n",
    "    sum_True  += cats[\"True\"][\"count\"]\n",
    "    sum_False += cats[\"False\"][\"count\"]\n",
    "    sum_Float += cats[\"Float\"][\"count\"]\n",
    "    sum_Other += cats[\"Other\"][\"count\"]\n",
    "\n",
    "    # 收集 Float 值（有限數）\n",
    "    if cats[\"Float\"][\"count\"] > 0:\n",
    "        float_values_all.append(s_float[cats[\"Float\"][\"mask\"]].values)\n",
    "\n",
    "    # 收集 Other 樣本（限量蒐集以避免爆量）\n",
    "    if cats[\"Other\"][\"count\"] > 0:\n",
    "        # 從 Other 中抓取獨特樣本值\n",
    "        s_other_vals = s_norm[cats[\"Other\"][\"mask\"]]\n",
    "        # 可做適度抽樣/去重\n",
    "        for val in pd.unique(s_other_vals):\n",
    "            key = str(val)\n",
    "            if key not in other_samples:\n",
    "                other_samples[key] = set()\n",
    "            other_samples[key].add(os.path.basename(f))\n",
    "\n",
    "# 轉平；便於畫圖\n",
    "if len(float_values_all) > 0:\n",
    "    float_all = np.concatenate(float_values_all).astype(float)\n",
    "else:\n",
    "    float_all = np.array([], dtype=float)\n",
    "\n",
    "# =========================\n",
    "# 印出統計（總表）\n",
    "# =========================\n",
    "\n",
    "print(\"\\n=== 統計（總表） ===\")\n",
    "print(f\"總檔案數：{len(csv_files)}\")\n",
    "print(f\"含 sponvt 欄位的檔案數：{len(csv_files) - len(missing_col_files)}\")\n",
    "print(f\"缺少 sponvt 欄位的檔案數：{len(missing_col_files)}\")\n",
    "if missing_col_files:\n",
    "    print(\"缺少欄位的檔案（節錄最多前 20 筆）：\")\n",
    "    for name in missing_col_files[:20]:\n",
    "        print(\" -\", name)\n",
    "    if len(missing_col_files) > 20:\n",
    "        print(f\"... 其餘 {len(missing_col_files) - 20} 筆略\")\n",
    "\n",
    "print(f\"\\n總筆數（含所有有 sponvt 的檔案）：{total_rows}\")\n",
    "\n",
    "# 四類 + Other\n",
    "rows = [\n",
    "    (\"NaN\",   sum_NaN,   percent(sum_NaN, total_rows)),\n",
    "    (\"True\",  sum_True,  percent(sum_True, total_rows)),\n",
    "    (\"False\", sum_False, percent(sum_False, total_rows)),\n",
    "    (\"Float\", sum_Float, percent(sum_Float, total_rows)),\n",
    "    (\"Other\", sum_Other, percent(sum_Other, total_rows)),\n",
    "]\n",
    "print(\"\\n類別 | Count | Percent(%)\")\n",
    "print(\"-----+-------+-----------\")\n",
    "for name, cnt, pct in rows:\n",
    "    print(f\"{name:<5}| {cnt:>6} | {pct:>10.4f}\")\n",
    "\n",
    "# 驗證條件：除了 NaN/True/False 之外，其餘皆為 Float（Other 必須為 0）\n",
    "if sum_Other == 0:\n",
    "    print(\"\\n✅ 驗證結論：除了 NaN / True / False 外，其餘皆為 Float（Other = 0）。\")\n",
    "else:\n",
    "    print(\"\\n❌ 驗證結論：發現非預期值（Other > 0），請檢視樣本與來源檔案。\")\n",
    "    print(\"   其他值樣本（最多列 50 筆）：\")\n",
    "    for i, (val, files) in enumerate(other_samples.items()):\n",
    "        if i >= 50:\n",
    "            print(f\"   ... 其餘 {len(other_samples) - 50} 種略\")\n",
    "            break\n",
    "        print(f\"   值: {repr(val)} | 來源檔案數: {len(files)} | 範例檔案: {list(files)[:3]}\")\n",
    "\n",
    "# 浮點數值的描述統計\n",
    "if float_all.size > 0:\n",
    "    s_float_all = pd.Series(float_all)\n",
    "    desc = s_float_all.describe(percentiles=[0.25, 0.5, 0.75])\n",
    "    print(\"\\n=== Float 數值描述統計（整體） ===\")\n",
    "    # 只列常見統計\n",
    "    for k in [\"count\", \"mean\", \"std\", \"min\", \"25%\", \"50%\", \"75%\", \"max\"]:\n",
    "        v = desc.get(k, np.nan)\n",
    "        if isinstance(v, float):\n",
    "            print(f\"{k:>4}: {v:,.6f}\")\n",
    "        else:\n",
    "            print(f\"{k:>4}: {v}\")\n",
    "else:\n",
    "    print(\"\\n（無 Float 數值可供描述統計）\")\n",
    "\n",
    "# =========================\n",
    "# 視覺化：分類條圖 + Float 直方圖\n",
    "# =========================\n",
    "\n",
    "# 1) 分類條狀圖（顯示 count 與 percent）\n",
    "labels = [\"NaN\", \"True\", \"False\", \"Float\"]\n",
    "counts = [sum_NaN, sum_True, sum_False, sum_Float]\n",
    "percents = [percent(c, total_rows) for c in counts]\n",
    "\n",
    "plt.figure(figsize=(8, 5))\n",
    "bars = plt.bar(labels, counts)\n",
    "plt.title(\"Distribution of sponvt Categories (Overall)\")\n",
    "plt.xlabel(\"Category\")\n",
    "plt.ylabel(\"Count\")\n",
    "\n",
    "# 在每個 bar 上標示 count 與 percent\n",
    "for rect, c, p in zip(bars, counts, percents):\n",
    "    height = rect.get_height()\n",
    "    plt.text(rect.get_x() + rect.get_width()/2.0,\n",
    "             height,\n",
    "             f\"{c:,}\\n({p:.2f}%)\",\n",
    "             ha=\"center\", va=\"bottom\", fontsize=10)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "# 2) Float 直方圖（若有數據）\n",
    "if float_all.size > 0:\n",
    "    # 移除 NaN / ±inf 保險（理論上不會出現）\n",
    "    finite_mask = np.isfinite(float_all)\n",
    "    vals = float_all[finite_mask]\n",
    "    if vals.size > 0:\n",
    "        plt.figure(figsize=(8, 5))\n",
    "        plt.hist(vals, bins=50)\n",
    "        plt.title(\"Histogram of sponvt Float Values (Overall)\")\n",
    "        plt.xlabel(\"sponvt (float)\")\n",
    "        plt.ylabel(\"Frequency\")\n",
    "        plt.tight_layout()\n",
    "        plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "151162c2-6e44-4a91-b471-b794f990aabb",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "=== sponvt 類型彙總（跨檔） ===\n",
      "Type  | Count   | Percent(%)        \n",
      "------+---------+-------------------\n",
      "Total | 1602476 | 100.0             \n",
      "NaN   | 1424904 | 88.91889800533674 \n",
      "True  | 143905  | 8.980165693589171 \n",
      "False | 17243   | 1.0760223554050108\n",
      "Float | 16424   | 1.024913945669077 \n",
      "Other | 0       | 0.0               \n",
      "\n",
      "[PASS] 驗證通過：除了 NaN / True / False 外，其餘皆為『有限浮點』。\n",
      "\n",
      "=== sponvt 數值型統計（已排除 NaN/Inf） ===\n",
      "Metric               | Value      \n",
      "---------------------+------------\n",
      "Count (finite float) | 16424      \n",
      "Min                  | 0.000000   \n",
      "Median               | 396.000000 \n",
      "Max                  | 1470.000000\n",
      "\n",
      "=== 診斷資訊（數值可解析性） ===\n",
      "Metric                         | Value  \n",
      "-------------------------------+--------\n",
      "numeric-like total (incl. NaN) | 1602476\n",
      "finite numeric count           | 177572 \n",
      "non-finite (NaN/Inf)           | 1424904\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 900x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# sponvt 型態稽核 + 數值分布繪圖（完整版，不自動執行）\n",
    "# 需求：\n",
    "#   1) 確認 sponvt 除了 NaN / True / False 外，其餘皆為 float（且為有限值）\n",
    "#   2) 印出統計（各型態數量與比例；數值型 min/median/max）\n",
    "#   3) 畫直方圖，並以虛線標示 min/median/max 與文字數值\n",
    "# 注意：\n",
    "#   - 只讀取與分析，不修改檔案。\n",
    "#   - 視為缺失的字串集合：\"\", \"(null)\", \"null\", \"<null>\", \"none\", \"nan\"\n",
    "#   - True/False 支援字串型（大小寫不敏感）轉布林。\n",
    "# ============================================\n",
    "\n",
    "import os\n",
    "import glob\n",
    "import math\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# -------- 使用者可調參數 --------\n",
    "DATA_DIR = \"/home/jovyan/1010/data_new/bling_sponvt_ok/\"   # 目標資料夾\n",
    "CSV_GLOB = \"*.csv\"                                         # CSV 篩選條件\n",
    "PRINT_TOP_N_BAD = 10                                       # 若有殘留「other」型態，示範列數\n",
    "\n",
    "# -------- 輔助：終端表格列印 --------\n",
    "def print_table(rows, title):\n",
    "    \"\"\"以固定欄寬在終端印出簡潔表格。rows: List[dict]\"\"\"\n",
    "    print(f\"\\n=== {title} ===\")\n",
    "    if not rows:\n",
    "        print(\"(no data)\")\n",
    "        return\n",
    "    keys = list(rows[0].keys())\n",
    "    widths = {k: max(len(k), max(len(str(r.get(k, \"\"))) for r in rows)) for k in keys}\n",
    "    header = \" | \".join(f\"{k:<{widths[k]}}\" for k in keys)\n",
    "    sep = \"-+-\".join(\"-\" * widths[k] for k in keys)\n",
    "    print(header)\n",
    "    print(sep)\n",
    "    for r in rows:\n",
    "        print(\" | \".join(f\"{str(r.get(k, '')):<{widths[k]}}\" for k in keys))\n",
    "\n",
    "# -------- 讀取：安全讀 CSV，只取 sponvt 若存在 --------\n",
    "def read_csv_safely(path: str) -> pd.DataFrame:\n",
    "    \"\"\"以多種常見編碼嘗試讀取 CSV，優先只取 sponvt 欄。\"\"\"\n",
    "    encs = [\"utf-8\", \"utf-8-sig\", \"cp950\", \"latin1\"]\n",
    "    last_err = None\n",
    "    for enc in encs:\n",
    "        try:\n",
    "            return pd.read_csv(path, dtype=str, encoding=enc, usecols=[\"sponvt\"])\n",
    "        except ValueError:\n",
    "            # 欄位不存在或 usecols 失敗，改為全讀後再檢查\n",
    "            try:\n",
    "                df_all = pd.read_csv(path, dtype=str, encoding=enc)\n",
    "                return df_all\n",
    "            except Exception as e2:\n",
    "                last_err = e2\n",
    "        except Exception as e:\n",
    "            last_err = e\n",
    "    raise last_err\n",
    "\n",
    "# -------- 清理：正規化 sponvt --------\n",
    "def normalize_sponvt_series(s: pd.Series) -> pd.Series:\n",
    "    \"\"\"\n",
    "    將 sponvt 統一清理為混合型態 Series（保留 NaN / 布林 / 浮點 / 其他）。\n",
    "    規則：\n",
    "      - None/NaN/空字串/\"(null)\"/\"null\"/\"<null>\"/\"none\"/\"nan\" -> NaN\n",
    "      - \"true\"/\"false\"（大小寫不敏感）-> True/False\n",
    "      - 其餘嘗試轉 float（允許 \"1,234.5\" 逗號），失敗者保留原字串\n",
    "    \"\"\"\n",
    "    s_norm = s.copy()\n",
    "\n",
    "    # 去除字串前後空白\n",
    "    s_norm = s_norm.apply(lambda x: x.strip() if isinstance(x, str) else x)\n",
    "\n",
    "    NULL_SET = {\"\", \"(null)\", \"null\", \"<null>\", \"none\", \"nan\"}\n",
    "    s_norm = s_norm.replace(list(NULL_SET), np.nan)\n",
    "\n",
    "    # 字串 true/false 轉布林\n",
    "    def to_bool_maybe(x):\n",
    "        if isinstance(x, str):\n",
    "            xl = x.lower()\n",
    "            if xl == \"true\":\n",
    "                return True\n",
    "            if xl == \"false\":\n",
    "                return False\n",
    "        return x\n",
    "\n",
    "    s_norm = s_norm.map(to_bool_maybe)\n",
    "\n",
    "    # 其餘嘗試轉成 float；失敗則保留原值\n",
    "    def to_float_maybe(x):\n",
    "        if x is None or (isinstance(x, float) and math.isnan(x)):\n",
    "            return np.nan\n",
    "        if isinstance(x, (bool, np.bool_)):\n",
    "            return x\n",
    "        if isinstance(x, (int, float, np.integer, np.floating)):\n",
    "            return float(x)\n",
    "        if isinstance(x, str):\n",
    "            try:\n",
    "                return float(x.replace(\",\", \"\"))  # 支援 \"1,234.5\"\n",
    "            except Exception:\n",
    "                return x\n",
    "        return x\n",
    "\n",
    "    s_norm = s_norm.map(to_float_maybe)\n",
    "    return s_norm\n",
    "\n",
    "# -------- 統計：型態彙總（float 必須為有限值） --------\n",
    "def summarize_types(s: pd.Series):\n",
    "    \"\"\"\n",
    "    回傳型態統計：NaN / True / False / Float(有限值) / Other 的數量與比例，\n",
    "    並回傳對應布林索引。\n",
    "    \"\"\"\n",
    "    total = len(s)\n",
    "    is_nan = s.isna()\n",
    "\n",
    "    is_true = s.apply(lambda x: isinstance(x, (bool, np.bool_)) and bool(x))\n",
    "    is_false = s.apply(lambda x: isinstance(x, (bool, np.bool_)) and (not bool(x)))\n",
    "\n",
    "    def is_finite_float(x):\n",
    "        return isinstance(x, (float, np.floating)) and np.isfinite(x)\n",
    "\n",
    "    is_float = s.apply(is_finite_float)\n",
    "    is_other = ~(is_nan | is_true | is_false | is_float)\n",
    "\n",
    "    counts = {\n",
    "        \"total\": int(total),\n",
    "        \"NaN\": int(is_nan.sum()),\n",
    "        \"bool_true\": int(is_true.sum()),\n",
    "        \"bool_false\": int(is_false.sum()),\n",
    "        \"float\": int(is_float.sum()),   # 僅計「有限浮點」\n",
    "        \"other\": int(is_other.sum())\n",
    "    }\n",
    "    perc = {k + \"_pct\": (v / total * 100.0 if total > 0 else 0.0) for k, v in counts.items()}\n",
    "    return counts, perc, is_nan, is_true, is_false, is_float, is_other\n",
    "\n",
    "def pretty_print_summary(counts: dict, perc: dict):\n",
    "    \"\"\"終端列印型態彙總表。\"\"\"\n",
    "    rows = [\n",
    "        {\"Type\": \"Total\", \"Count\": counts[\"total\"],      \"Percent(%)\": 100.0},\n",
    "        {\"Type\": \"NaN\",   \"Count\": counts[\"NaN\"],        \"Percent(%)\": perc[\"NaN_pct\"]},\n",
    "        {\"Type\": \"True\",  \"Count\": counts[\"bool_true\"],  \"Percent(%)\": perc[\"bool_true_pct\"]},\n",
    "        {\"Type\": \"False\", \"Count\": counts[\"bool_false\"], \"Percent(%)\": perc[\"bool_false_pct\"]},\n",
    "        {\"Type\": \"Float\", \"Count\": counts[\"float\"],      \"Percent(%)\": perc[\"float_pct\"]},\n",
    "        {\"Type\": \"Other\", \"Count\": counts[\"other\"],      \"Percent(%)\": perc[\"other_pct\"]},\n",
    "    ]\n",
    "    print_table(rows, \"sponvt 類型彙總（跨檔）\")\n",
    "\n",
    "# -------- 工具：取得有限數值陣列 --------\n",
    "def finite_numeric_values(series_like) -> np.ndarray:\n",
    "    \"\"\"\n",
    "    將輸入轉為數值（coerce），回傳「有限」數值（排除 NaN、+Inf、-Inf）。\n",
    "    \"\"\"\n",
    "    arr = pd.to_numeric(series_like, errors=\"coerce\").astype(float)\n",
    "    return arr[np.isfinite(arr)]\n",
    "\n",
    "# -------- 視覺化：直方圖 + min/median/max 虛線與標註 --------\n",
    "def plot_distribution(numeric_vals: np.ndarray, vmin: float, vmed: float, vmax: float):\n",
    "    \"\"\"\n",
    "    繪製 sponvt 數值分布直方圖，並以虛線標示 min/median/max（含數值標註）。\n",
    "    不呼叫 plt.show()，由使用者自行顯示或另存。\n",
    "    \"\"\"\n",
    "    numeric_vals = np.asarray(numeric_vals, dtype=float)\n",
    "    numeric_vals = numeric_vals[np.isfinite(numeric_vals)]\n",
    "    if numeric_vals.size == 0:\n",
    "        print(\"[INFO] 無可繪圖的有限數值。\")\n",
    "        return\n",
    "\n",
    "    plt.figure(figsize=(9, 5))\n",
    "    plt.hist(numeric_vals, bins=\"auto\")  # 不指定顏色與樣式\n",
    "    plt.title(\"Distribution of sponvt (numeric only)\")\n",
    "    plt.xlabel(\"sponvt (float)\")\n",
    "    plt.ylabel(\"Count\")\n",
    "\n",
    "    def vline_with_label(x, label):\n",
    "        if np.isfinite(x):\n",
    "            ax = plt.gca()\n",
    "            plt.axvline(x, linestyle=\"--\")\n",
    "            ymax = ax.get_ylim()[1]\n",
    "            # 在頂部附近標註數值\n",
    "            plt.text(x, ymax * 0.95, label, rotation=90, va=\"top\", ha=\"right\", fontsize=9)\n",
    "\n",
    "    vline_with_label(vmin, f\"min={vmin:.4f}\")\n",
    "    vline_with_label(vmed, f\"median={vmed:.4f}\")\n",
    "    vline_with_label(vmax, f\"max={vmax:.4f}\")\n",
    "\n",
    "    plt.tight_layout()\n",
    "    # 不呼叫 plt.show()\n",
    "\n",
    "# -------- 主流程：彙整 + 稽核 + 統計 + 繪圖 --------\n",
    "def main():\n",
    "    # 1) 收集所有 CSV 檔案\n",
    "    csv_paths = sorted(glob.glob(os.path.join(DATA_DIR, CSV_GLOB)))\n",
    "    if not csv_paths:\n",
    "        print(f\"[WARN] 目錄中找不到 CSV：{DATA_DIR}\")\n",
    "        return\n",
    "\n",
    "    # 2) 合併所有檔案的 sponvt 欄\n",
    "    all_sponvt = []\n",
    "    missing = []\n",
    "    for p in csv_paths:\n",
    "        try:\n",
    "            df = read_csv_safely(p)\n",
    "            if \"sponvt\" not in df.columns:\n",
    "                missing.append(os.path.basename(p))\n",
    "                continue\n",
    "            all_sponvt.append(df[\"sponvt\"])\n",
    "        except Exception as e:\n",
    "            print(f\"[WARN] 讀取失敗：{p} -> {e}\")\n",
    "\n",
    "    if not all_sponvt:\n",
    "        print(\"[WARN] 無可用 sponvt 欄位可分析。\")\n",
    "        if missing:\n",
    "            print(\"[INFO] 下列檔案缺少 sponvt 欄位：\")\n",
    "            for name in missing:\n",
    "                print(\" -\", name)\n",
    "        return\n",
    "\n",
    "    s_all = pd.concat(all_sponvt, ignore_index=True)\n",
    "\n",
    "    # 3) 正規化\n",
    "    s_norm = normalize_sponvt_series(s_all)\n",
    "\n",
    "    # 4) 型態統計與驗證（float 僅計有限值）\n",
    "    counts, perc, is_nan, is_true, is_false, is_float, is_other = summarize_types(s_norm)\n",
    "    pretty_print_summary(counts, perc)\n",
    "\n",
    "    if counts[\"other\"] > 0:\n",
    "        print(\"\\n[FAIL] 發現非 NaN/True/False/float 的殘留資料（other > 0）。以下示例：\")\n",
    "        bad_vals = s_norm[~(is_nan | is_true | is_false | is_float)]\n",
    "        print(bad_vals.head(PRINT_TOP_N_BAD).to_string(index=False))\n",
    "    else:\n",
    "        print(\"\\n[PASS] 驗證通過：除了 NaN / True / False 外，其餘皆為『有限浮點』。\")\n",
    "\n",
    "    # 5) 數值統計 + 繪圖（僅針對有限浮點）\n",
    "    numeric_vals = finite_numeric_values(s_norm[is_float])\n",
    "    if numeric_vals.size == 0:\n",
    "        print(\"\\n[INFO] 無任何可用的有限數值型 sponvt。\")\n",
    "        return\n",
    "\n",
    "    vmin = float(np.min(numeric_vals))\n",
    "    vmed = float(np.median(numeric_vals))\n",
    "    vmax = float(np.max(numeric_vals))\n",
    "\n",
    "    # 終端輸出數值統計\n",
    "    rows_stats = [\n",
    "        {\"Metric\": \"Count (finite float)\", \"Value\": int(numeric_vals.size)},\n",
    "        {\"Metric\": \"Min\",                  \"Value\": f\"{vmin:.6f}\"},\n",
    "        {\"Metric\": \"Median\",               \"Value\": f\"{vmed:.6f}\"},\n",
    "        {\"Metric\": \"Max\",                  \"Value\": f\"{vmax:.6f}\"},\n",
    "    ]\n",
    "    print_table(rows_stats, \"sponvt 數值型統計（已排除 NaN/Inf）\")\n",
    "\n",
    "    # 畫直方圖 + 三條虛線與標註（不 show）\n",
    "    plot_distribution(numeric_vals, vmin, vmed, vmax)\n",
    "\n",
    "    # （可選）診斷：numeric-like 與非有限數量\n",
    "    numeric_like = pd.to_numeric(s_norm, errors=\"coerce\")\n",
    "    total_numeric_like = int(np.isfinite(numeric_like).sum() + np.isnan(numeric_like).sum())  # 包含 NaN\n",
    "    finite_mask = np.isfinite(numeric_like)\n",
    "    finite_cnt = int(finite_mask.sum())\n",
    "    nonfinite_cnt = int(np.isnan(numeric_like).sum() + np.isinf(numeric_like).sum())\n",
    "    rows_dbg = [\n",
    "        {\"Metric\": \"numeric-like total (incl. NaN)\", \"Value\": total_numeric_like},\n",
    "        {\"Metric\": \"finite numeric count\",           \"Value\": finite_cnt},\n",
    "        {\"Metric\": \"non-finite (NaN/Inf)\",           \"Value\": nonfinite_cnt},\n",
    "    ]\n",
    "    print_table(rows_dbg, \"診斷資訊（數值可解析性）\")\n",
    "\n",
    "# 使用方式（請自行於互動環境執行）：\n",
    "main()\n",
    "# 在 Notebook 中若要顯示圖形請再呼叫：\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "fc8b2496-f10e-46dc-a3d2-50f37947c431",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "=== sponvt 類型彙總（跨檔） ===\n",
      "Type  | Count   | Percent(%)\n",
      "------+---------+-----------\n",
      "Total | 1602476 | 100.0000  \n",
      "NaN   | 1424904 | 88.9189   \n",
      "True  | 143905  | 8.9802    \n",
      "False | 17243   | 1.0760    \n",
      "Float | 16424   | 1.0249    \n",
      "Other | 0       | 0.0000    \n",
      "\n",
      "[PASS] 驗證通過：除了 NaN / True / False 外，其餘皆為『有限浮點』。\n",
      "\n",
      "=== sponvt 數值統計摘要 ===\n",
      "Metric               | Value       \n",
      "---------------------+-------------\n",
      "Count (finite float) | 16424       \n",
      "Mean                 | 422.629019  \n",
      "Std                  | 133.584892  \n",
      "Var                  | 17844.923255\n",
      "Min                  | 0.000000    \n",
      "Q10                  | 305.000000  \n",
      "Q25                  | 350.000000  \n",
      "Median               | 396.000000  \n",
      "Q75                  | 480.000000  \n",
      "Q90                  | 594.000000  \n",
      "Max                  | 1470.000000 \n",
      "Range                | 1470.000000 \n",
      "IQR                  | 130.000000  \n",
      "Skewness             | 0.894694    \n",
      "Kurtosis (excess)    | 3.206685    \n",
      "Unique Count         | 889         \n",
      "Outliers (IQR rule)  | 1127        \n",
      "Outliers (|z|>3)     | 298         \n",
      "NaN Ratio (%)        | 88.918898   \n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x280 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# sponvt 統計摘要 + 多圖視覺化（不使用 seaborn）\n",
    "# 讀取資料夾：/home/jovyan/1010/data_new/bling_sponvt_ok/\n",
    "# 產出：終端統計摘要表 + 圖形（直方圖/箱型圖/ECDF；若可用則加 KDE、Q-Q）\n",
    "# ============================================\n",
    "\n",
    "import os\n",
    "import glob\n",
    "import math\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# ---- 嘗試載入可選依賴（存在才使用）----\n",
    "_have_scipy = False\n",
    "try:\n",
    "    from scipy.stats import gaussian_kde, probplot\n",
    "    _have_scipy = True\n",
    "except Exception:\n",
    "    _have_scipy = False\n",
    "\n",
    "# ---- 使用者參數 ----\n",
    "DATA_DIR = \"/home/jovyan/1010/data_new/bling_sponvt_ok/\"\n",
    "CSV_GLOB = \"*.csv\"\n",
    "PRINT_TOP_N_BAD = 10\n",
    "\n",
    "# ============================================\n",
    "# 讀取與清理\n",
    "# ============================================\n",
    "def read_csv_safely(path: str) -> pd.DataFrame:\n",
    "    \"\"\"以多種常見編碼嘗試讀取 CSV，優先只取 sponvt 欄。\"\"\"\n",
    "    encs = [\"utf-8\", \"utf-8-sig\", \"cp950\", \"latin1\"]\n",
    "    last_err = None\n",
    "    for enc in encs:\n",
    "        try:\n",
    "            return pd.read_csv(path, dtype=str, encoding=enc, usecols=[\"sponvt\"])\n",
    "        except ValueError:\n",
    "            try:\n",
    "                df_all = pd.read_csv(path, dtype=str, encoding=enc)\n",
    "                return df_all\n",
    "            except Exception as e2:\n",
    "                last_err = e2\n",
    "        except Exception as e:\n",
    "            last_err = e\n",
    "    raise last_err\n",
    "\n",
    "def normalize_sponvt_series(s: pd.Series) -> pd.Series:\n",
    "    \"\"\"\n",
    "    將 sponvt 統一清理：\n",
    "      - \"\", \"(null)\", \"null\", \"<null>\", \"none\", \"nan\" -> NaN\n",
    "      - \"true\"/\"false\"（大小寫不敏感）-> True/False\n",
    "      - 其餘嘗試轉 float（允許 \"1,234.5\"），失敗者保留原字串\n",
    "    \"\"\"\n",
    "    s_norm = s.copy()\n",
    "    s_norm = s_norm.apply(lambda x: x.strip() if isinstance(x, str) else x)\n",
    "\n",
    "    NULL_SET = {\"\", \"(null)\", \"null\", \"<null>\", \"none\", \"nan\"}\n",
    "    s_norm = s_norm.replace(list(NULL_SET), np.nan)\n",
    "\n",
    "    def to_bool_maybe(x):\n",
    "        if isinstance(x, str):\n",
    "            xl = x.lower()\n",
    "            if xl == \"true\":  return True\n",
    "            if xl == \"false\": return False\n",
    "        return x\n",
    "\n",
    "    s_norm = s_norm.map(to_bool_maybe)\n",
    "\n",
    "    def to_float_maybe(x):\n",
    "        if x is None or (isinstance(x, float) and math.isnan(x)):\n",
    "            return np.nan\n",
    "        if isinstance(x, (bool, np.bool_)):  # 保留布林\n",
    "            return x\n",
    "        if isinstance(x, (int, float, np.integer, np.floating)):\n",
    "            return float(x)\n",
    "        if isinstance(x, str):\n",
    "            try:\n",
    "                return float(x.replace(\",\", \"\"))\n",
    "            except Exception:\n",
    "                return x\n",
    "        return x\n",
    "\n",
    "    return s_norm.map(to_float_maybe)\n",
    "\n",
    "def finite_numeric_values(series_like) -> np.ndarray:\n",
    "    \"\"\"轉成數值並回傳「有限值」（去除 NaN/±Inf）。\"\"\"\n",
    "    arr = pd.to_numeric(series_like, errors=\"coerce\").astype(float)\n",
    "    return arr[np.isfinite(arr)]\n",
    "\n",
    "# ============================================\n",
    "# 統計摘要與表格輸出\n",
    "# ============================================\n",
    "def print_table(rows, title):\n",
    "    print(f\"\\n=== {title} ===\")\n",
    "    if not rows:\n",
    "        print(\"(no data)\")\n",
    "        return\n",
    "    keys = list(rows[0].keys())\n",
    "    widths = {k: max(len(k), max(len(str(r.get(k, \"\"))) for r in rows)) for k in keys}\n",
    "    header = \" | \".join(f\"{k:<{widths[k]}}\" for k in keys)\n",
    "    sep = \"-+-\".join(\"-\" * widths[k] for k in keys)\n",
    "    print(header)\n",
    "    print(sep)\n",
    "    for r in rows:\n",
    "        print(\" | \".join(f\"{str(r.get(k, '')):<{widths[k]}}\" for k in keys))\n",
    "\n",
    "def summarize_types(s: pd.Series):\n",
    "    \"\"\"NaN / True / False / 有限浮點 / 其他 型態統計（回傳計數、比例、遮罩）。\"\"\"\n",
    "    total = len(s)\n",
    "    is_nan = s.isna()\n",
    "    is_true = s.apply(lambda x: isinstance(x, (bool, np.bool_)) and bool(x))\n",
    "    is_false = s.apply(lambda x: isinstance(x, (bool, np.bool_)) and (not bool(x)))\n",
    "    is_float = s.apply(lambda x: isinstance(x, (float, np.floating)) and np.isfinite(x))\n",
    "    is_other = ~(is_nan | is_true | is_false | is_float)\n",
    "\n",
    "    counts = {\n",
    "        \"total\": int(total),\n",
    "        \"NaN\": int(is_nan.sum()),\n",
    "        \"bool_true\": int(is_true.sum()),\n",
    "        \"bool_false\": int(is_false.sum()),\n",
    "        \"float\": int(is_float.sum()),\n",
    "        \"other\": int(is_other.sum()),\n",
    "    }\n",
    "    perc = {k + \"_pct\": (v / total * 100.0 if total > 0 else 0.0) for k, v in counts.items()}\n",
    "    return counts, perc, is_nan, is_true, is_false, is_float, is_other\n",
    "\n",
    "def compute_numeric_stats(vals: np.ndarray) -> dict:\n",
    "    \"\"\"計算核心統計：平均、標準差、變異、分位數、IQR、偏態、峰度、範圍、離群點等。\"\"\"\n",
    "    s = pd.Series(vals, dtype=float)\n",
    "    stats = {}\n",
    "    stats[\"count\"]   = int(s.size)\n",
    "    stats[\"mean\"]    = float(s.mean())\n",
    "    stats[\"std\"]     = float(s.std(ddof=1)) if s.size > 1 else float(\"nan\")\n",
    "    stats[\"var\"]     = float(s.var(ddof=1)) if s.size > 1 else float(\"nan\")\n",
    "    stats[\"min\"]     = float(s.min())\n",
    "    stats[\"q10\"]     = float(s.quantile(0.10))\n",
    "    stats[\"q25\"]     = float(s.quantile(0.25))\n",
    "    stats[\"median\"]  = float(s.quantile(0.50))\n",
    "    stats[\"q75\"]     = float(s.quantile(0.75))\n",
    "    stats[\"q90\"]     = float(s.quantile(0.90))\n",
    "    stats[\"max\"]     = float(s.max())\n",
    "    stats[\"range\"]   = stats[\"max\"] - stats[\"min\"]\n",
    "    stats[\"iqr\"]     = stats[\"q75\"] - stats[\"q25\"]\n",
    "    # pandas 的 skew/kurt 預設為 Fisher 定義（kurtosis=0 為常態）\n",
    "    stats[\"skewness\"]  = float(s.skew())\n",
    "    stats[\"kurtosis\"]  = float(s.kurt())\n",
    "    # 離群點（IQR 規則）\n",
    "    lb = stats[\"q25\"] - 1.5 * stats[\"iqr\"]\n",
    "    ub = stats[\"q75\"] + 1.5 * stats[\"iqr\"]\n",
    "    stats[\"outliers_iqr_count\"] = int(((s < lb) | (s > ub)).sum())\n",
    "    # 離群點（z-score > 3）\n",
    "    if np.isfinite(stats[\"std\"]) and stats[\"std\"] > 0:\n",
    "        z = (s - stats[\"mean\"]) / stats[\"std\"]\n",
    "        stats[\"outliers_z3_count\"] = int((z.abs() > 3).sum())\n",
    "    else:\n",
    "        stats[\"outliers_z3_count\"] = 0\n",
    "    # 唯一值數\n",
    "    stats[\"unique_count\"] = int(s.nunique(dropna=True))\n",
    "    return stats\n",
    "\n",
    "# ============================================\n",
    "# 視覺化：直方圖 / 箱型圖 / ECDF / KDE / Q-Q\n",
    "# ============================================\n",
    "def annotate_vlines(ax, x_values_labels):\n",
    "    \"\"\"在圖上畫垂直虛線與數值標註。x_values_labels: [(x, 'label'), ...]\"\"\"\n",
    "    ymax = ax.get_ylim()[1]\n",
    "    for x, label in x_values_labels:\n",
    "        if np.isfinite(x):\n",
    "            ax.axvline(x, linestyle=\"--\")\n",
    "            ax.text(x, ymax * 0.95, label, rotation=90, va=\"top\", ha=\"right\", fontsize=9)\n",
    "\n",
    "def plot_histogram(vals: np.ndarray, stats: dict):\n",
    "    fig, ax = plt.subplots(figsize=(10, 5))\n",
    "    ax.hist(vals, bins=\"auto\")\n",
    "    ax.set_title(\"Histogram of sponvt (finite floats)\")\n",
    "    ax.set_xlabel(\"sponvt\")\n",
    "    ax.set_ylabel(\"Count\")\n",
    "    annotate_vlines(ax, [\n",
    "        (stats[\"min\"],    f\"min={stats['min']:.4f}\"),\n",
    "        (stats[\"q10\"],    f\"p10={stats['q10']:.4f}\"),\n",
    "        (stats[\"median\"], f\"median={stats['median']:.4f}\"),\n",
    "        (stats[\"mean\"],   f\"mean={stats['mean']:.4f}\"),\n",
    "        (stats[\"q90\"],    f\"p90={stats['q90']:.4f}\"),\n",
    "        (stats[\"max\"],    f\"max={stats['max']:.4f}\"),\n",
    "    ])\n",
    "    fig.tight_layout()\n",
    "    return fig, ax\n",
    "\n",
    "def plot_boxplot(vals: np.ndarray, stats: dict):\n",
    "    fig, ax = plt.subplots(figsize=(10, 2.8))\n",
    "    ax.boxplot(vals, vert=False, showfliers=True)\n",
    "    ax.set_title(\"Boxplot of sponvt (finite floats)\")\n",
    "    ax.set_xlabel(\"sponvt\")\n",
    "    # 在箱型圖上補上關鍵統計的刻度標註（用文字）\n",
    "    ax.text(stats[\"min\"],    1.15, f\"min={stats['min']:.4f}\",    ha=\"center\", va=\"bottom\", fontsize=8, rotation=90)\n",
    "    ax.text(stats[\"q25\"],    1.15, f\"Q1={stats['q25']:.4f}\",     ha=\"center\", va=\"bottom\", fontsize=8, rotation=90)\n",
    "    ax.text(stats[\"median\"], 1.15, f\"med={stats['median']:.4f}\", ha=\"center\", va=\"bottom\", fontsize=8, rotation=90)\n",
    "    ax.text(stats[\"q75\"],    1.15, f\"Q3={stats['q75']:.4f}\",     ha=\"center\", va=\"bottom\", fontsize=8, rotation=90)\n",
    "    ax.text(stats[\"max\"],    1.15, f\"max={stats['max']:.4f}\",    ha=\"center\", va=\"bottom\", fontsize=8, rotation=90)\n",
    "    fig.tight_layout()\n",
    "    return fig, ax\n",
    "\n",
    "def plot_ecdf(vals: np.ndarray):\n",
    "    \"\"\"Empirical CDF（累積分布）\"\"\"\n",
    "    x = np.sort(vals)\n",
    "    y = np.arange(1, x.size + 1) / x.size\n",
    "    fig, ax = plt.subplots(figsize=(10, 5))\n",
    "    ax.plot(x, y)\n",
    "    ax.set_title(\"ECDF of sponvt (finite floats)\")\n",
    "    ax.set_xlabel(\"sponvt\")\n",
    "    ax.set_ylabel(\"Cumulative probability\")\n",
    "    fig.tight_layout()\n",
    "    return fig, ax\n",
    "\n",
    "def plot_kde_optional(vals: np.ndarray):\n",
    "    \"\"\"若有 scipy，畫 KDE；否則略過。\"\"\"\n",
    "    if not _have_scipy:\n",
    "        return None, None\n",
    "    kde = gaussian_kde(vals)\n",
    "    xs = np.linspace(vals.min(), vals.max(), 512)\n",
    "    ys = kde(xs)\n",
    "    fig, ax = plt.subplots(figsize=(10, 5))\n",
    "    ax.plot(xs, ys)\n",
    "    ax.set_title(\"KDE of sponvt (finite floats)\")\n",
    "    ax.set_xlabel(\"sponvt\")\n",
    "    ax.set_ylabel(\"Density\")\n",
    "    fig.tight_layout()\n",
    "    return fig, ax\n",
    "\n",
    "def plot_qq_optional(vals: np.ndarray):\n",
    "    \"\"\"若有 scipy，畫 Normal Q–Q Plot；否則略過。\"\"\"\n",
    "    if not _have_scipy:\n",
    "        return None, None\n",
    "    fig, ax = plt.subplots(figsize=(5, 5))\n",
    "    probplot(vals, dist=\"norm\", plot=ax)\n",
    "    ax.set_title(\"Normal Q–Q Plot (sponvt)\")\n",
    "    fig.tight_layout()\n",
    "    return fig, ax\n",
    "\n",
    "# ============================================\n",
    "# 主流程\n",
    "# ============================================\n",
    "def main():\n",
    "    # 收集 CSV\n",
    "    csv_paths = sorted(glob.glob(os.path.join(DATA_DIR, CSV_GLOB)))\n",
    "    if not csv_paths:\n",
    "        print(f\"[WARN] 目錄中找不到 CSV：{DATA_DIR}\")\n",
    "        return\n",
    "\n",
    "    # 合併所有 sponvt\n",
    "    all_sponvt = []\n",
    "    missing = []\n",
    "    for p in csv_paths:\n",
    "        try:\n",
    "            df = read_csv_safely(p)\n",
    "            if \"sponvt\" not in df.columns:\n",
    "                missing.append(os.path.basename(p))\n",
    "                continue\n",
    "            all_sponvt.append(df[\"sponvt\"])\n",
    "        except Exception as e:\n",
    "            print(f\"[WARN] 讀取失敗：{p} -> {e}\")\n",
    "\n",
    "    if not all_sponvt:\n",
    "        print(\"[WARN] 無可用 sponvt 欄位可分析。\")\n",
    "        if missing:\n",
    "            print(\"[INFO] 下列檔案缺少 sponvt 欄位：\")\n",
    "            for name in missing:\n",
    "                print(\" -\", name)\n",
    "        return\n",
    "\n",
    "    s_all = pd.concat(all_sponvt, ignore_index=True)\n",
    "    s_norm = normalize_sponvt_series(s_all)\n",
    "\n",
    "    # 型態彙總與驗證\n",
    "    counts, perc, is_nan, is_true, is_false, is_float, is_other = summarize_types(s_norm)\n",
    "    rows_type = [\n",
    "        {\"Type\": \"Total\", \"Count\": counts[\"total\"],      \"Percent(%)\": f\"{100.0:.4f}\"},\n",
    "        {\"Type\": \"NaN\",   \"Count\": counts[\"NaN\"],        \"Percent(%)\": f\"{perc['NaN_pct']:.4f}\"},\n",
    "        {\"Type\": \"True\",  \"Count\": counts[\"bool_true\"],  \"Percent(%)\": f\"{perc['bool_true_pct']:.4f}\"},\n",
    "        {\"Type\": \"False\", \"Count\": counts[\"bool_false\"], \"Percent(%)\": f\"{perc['bool_false_pct']:.4f}\"},\n",
    "        {\"Type\": \"Float\", \"Count\": counts[\"float\"],      \"Percent(%)\": f\"{perc['float_pct']:.4f}\"},\n",
    "        {\"Type\": \"Other\", \"Count\": counts[\"other\"],      \"Percent(%)\": f\"{perc['other_pct']:.4f}\"},\n",
    "    ]\n",
    "    print_table(rows_type, \"sponvt 類型彙總（跨檔）\")\n",
    "\n",
    "    if counts[\"other\"] > 0:\n",
    "        print(\"\\n[FAIL] 發現非 NaN/True/False/float 的殘留樣本（other > 0），示例：\")\n",
    "        bad_vals = s_norm[~(is_nan | is_true | is_false | is_float)]\n",
    "        print(bad_vals.head(PRINT_TOP_N_BAD).to_string(index=False))\n",
    "    else:\n",
    "        print(\"\\n[PASS] 驗證通過：除了 NaN / True / False 外，其餘皆為『有限浮點』。\")\n",
    "\n",
    "    # 取得有限數值\n",
    "    numeric_vals = finite_numeric_values(s_norm[is_float])\n",
    "    if numeric_vals.size == 0:\n",
    "        print(\"\\n[INFO] 無任何可用的有限數值型 sponvt。\")\n",
    "        return\n",
    "\n",
    "    # 數值統計\n",
    "    stats = compute_numeric_stats(numeric_vals)\n",
    "\n",
    "    # 統計摘要表\n",
    "    rows_stats = [\n",
    "        {\"Metric\": \"Count (finite float)\", \"Value\": stats[\"count\"]},\n",
    "        {\"Metric\": \"Mean\",                 \"Value\": f\"{stats['mean']:.6f}\"},\n",
    "        {\"Metric\": \"Std\",                  \"Value\": f\"{stats['std']:.6f}\"},\n",
    "        {\"Metric\": \"Var\",                  \"Value\": f\"{stats['var']:.6f}\"},\n",
    "        {\"Metric\": \"Min\",                  \"Value\": f\"{stats['min']:.6f}\"},\n",
    "        {\"Metric\": \"Q10\",                  \"Value\": f\"{stats['q10']:.6f}\"},\n",
    "        {\"Metric\": \"Q25\",                  \"Value\": f\"{stats['q25']:.6f}\"},\n",
    "        {\"Metric\": \"Median\",               \"Value\": f\"{stats['median']:.6f}\"},\n",
    "        {\"Metric\": \"Q75\",                  \"Value\": f\"{stats['q75']:.6f}\"},\n",
    "        {\"Metric\": \"Q90\",                  \"Value\": f\"{stats['q90']:.6f}\"},\n",
    "        {\"Metric\": \"Max\",                  \"Value\": f\"{stats['max']:.6f}\"},\n",
    "        {\"Metric\": \"Range\",                \"Value\": f\"{stats['range']:.6f}\"},\n",
    "        {\"Metric\": \"IQR\",                  \"Value\": f\"{stats['iqr']:.6f}\"},\n",
    "        {\"Metric\": \"Skewness\",             \"Value\": f\"{stats['skewness']:.6f}\"},\n",
    "        {\"Metric\": \"Kurtosis (excess)\",    \"Value\": f\"{stats['kurtosis']:.6f}\"},\n",
    "        {\"Metric\": \"Unique Count\",         \"Value\": stats[\"unique_count\"]},\n",
    "        {\"Metric\": \"Outliers (IQR rule)\",  \"Value\": stats[\"outliers_iqr_count\"]},\n",
    "        {\"Metric\": \"Outliers (|z|>3)\",     \"Value\": stats[\"outliers_z3_count\"]},\n",
    "        {\"Metric\": \"NaN Ratio (%)\",        \"Value\": f\"{(counts['NaN']/counts['total']*100.0):.6f}\"},\n",
    "    ]\n",
    "    print_table(rows_stats, \"sponvt 數值統計摘要\")\n",
    "\n",
    "    # 圖 1：直方圖 + 關鍵值虛線\n",
    "    fig1, ax1 = plot_histogram(numeric_vals, stats)\n",
    "\n",
    "    # 圖 2：箱型圖（橫向）\n",
    "    fig2, ax2 = plot_boxplot(numeric_vals, stats)\n",
    "\n",
    "    # 圖 3：ECDF\n",
    "    fig3, ax3 = plot_ecdf(numeric_vals)\n",
    "\n",
    "    # （可選）圖 4：KDE（需要 scipy）\n",
    "    if _have_scipy:\n",
    "        fig4, ax4 = plot_kde_optional(numeric_vals)\n",
    "    else:\n",
    "        print(\"\\n[Info] 未安裝 scipy，略過 KDE 圖。\")\n",
    "\n",
    "    # （可選）圖 5：Q–Q Plot（需要 scipy）\n",
    "    if _have_scipy:\n",
    "        fig5, ax5 = plot_qq_optional(numeric_vals)\n",
    "    else:\n",
    "        print(\"[Info] 未安裝 scipy，略過 Q–Q 圖。\")\n",
    "\n",
    "    # 顯示圖形（Notebook/互動環境中可見）\n",
    "    plt.show()\n",
    "\n",
    "# 使用方式（我不會執行）：\n",
    "main()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "e4b96035-7a6c-4363-9efc-745854c38d31",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "總筆數：1602476\n",
      "sponvt = 0 的筆數：30\n",
      "比例：0.0019%\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import glob\n",
    "import os\n",
    "\n",
    "DATA_DIR = \"/home/jovyan/1010/data_new/bling_sponvt_ok/\"\n",
    "CSV_GLOB = \"*.csv\"\n",
    "\n",
    "def read_csv_safely(path):\n",
    "    encs = [\"utf-8\", \"utf-8-sig\", \"cp950\", \"latin1\"]\n",
    "    for enc in encs:\n",
    "        try:\n",
    "            return pd.read_csv(path, dtype=str, encoding=enc, usecols=[\"sponvt\"])\n",
    "        except ValueError:\n",
    "            try:\n",
    "                df_all = pd.read_csv(path, dtype=str, encoding=enc)\n",
    "                return df_all\n",
    "            except Exception:\n",
    "                continue\n",
    "    return pd.DataFrame()\n",
    "\n",
    "def normalize_sponvt_series(s):\n",
    "    s = s.copy().apply(lambda x: x.strip() if isinstance(x, str) else x)\n",
    "    NULL_SET = {\"\", \"(null)\", \"null\", \"<null>\", \"none\", \"nan\"}\n",
    "    s = s.replace(list(NULL_SET), np.nan)\n",
    "    def to_float(x):\n",
    "        if isinstance(x, (bool, np.bool_)) or x is None:\n",
    "            return np.nan\n",
    "        try:\n",
    "            return float(str(x).replace(\",\", \"\"))\n",
    "        except:\n",
    "            return np.nan\n",
    "    return s.map(to_float)\n",
    "\n",
    "# === 統計 sponvt = 0 筆數 ===\n",
    "csv_paths = sorted(glob.glob(os.path.join(DATA_DIR, CSV_GLOB)))\n",
    "total_rows = 0\n",
    "zero_rows = 0\n",
    "for path in csv_paths:\n",
    "    df = read_csv_safely(path)\n",
    "    if \"sponvt\" not in df.columns:\n",
    "        continue\n",
    "    s = normalize_sponvt_series(df[\"sponvt\"])\n",
    "    total_rows += len(s)\n",
    "    zero_rows += (s == 0).sum()\n",
    "\n",
    "print(f\"總筆數：{total_rows}\")\n",
    "print(f\"sponvt = 0 的筆數：{zero_rows}\")\n",
    "print(f\"比例：{zero_rows / total_rows * 100:.4f}%\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "204cc27a-43d7-40cf-acc8-62e1338ed2c7",
   "metadata": {},
   "outputs": [],
   "source": [
    "複製/home/jovyan/RT08/0925/bling_sponvt_ok/裡面所有的檔案到/home/jovyan/1010/data_new/bling_sponvt_1/ 並在/home/jovyan/1010/data_new/bling_sponvt_1/進行以下動作\n",
    "每個檔案都創一個欄位sponvt_ok, 如果sponvt的欄位內容是float，就sponvt_ok=1, 如果是NaN或True或False就sponvt_ok=0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "fc9462c9-63b9-44c3-9948-271c49530888",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "【步驟一｜複製檔案】\n",
      "來源：/home/jovyan/1010/data_new/bling_sponvt_ok/\n",
      "目的：/home/jovyan/1010/data_new/bling_sponvt_1/\n",
      "檔案總數：122，成功複製：122\n",
      "\n",
      "【步驟二｜新增 sponvt_ok 欄】\n",
      "找到 CSV 檔案 122 份\n",
      "\n",
      "=== 各檔統計 ===\n",
      "File                                   Total       OK=1       OK=0\n",
      "----------------------------------------------------------------------\n",
      "089271.csv                             32419       4451      27968\n",
      "095323.csv                             23791          0      23791\n",
      "095707.csv                             20180       1599      18581\n",
      "114309.csv                             71729       2055      69674\n",
      "230933.csv                             30249       3119      27130\n",
      "4216007.csv                             1433          0       1433\n",
      "7108162.csv                              239        104        135\n",
      "7408338.csv                             1432          0       1432\n",
      "7657698.csv                             1413        126       1287\n",
      "7721164.csv                              483        483          0\n",
      "PatNo_ID_1560013303.csv                 2543          0       2543\n",
      "PatNo_ID_1562733396.csv                 2254          0       2254\n",
      "PatNo_ID_1563587183.csv                 5287          0       5287\n",
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      "PatNo_ID_1566252197.csv                 3368       1465       1903\n",
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      "PatNo_ID_1572562839.csv                20966          0      20966\n",
      "PatNo_ID_1572831765.csv                 2696          0       2696\n",
      "PatNo_ID_1572976822.csv                 6764          0       6764\n",
      "PatNo_ID_1573063188.csv                 5080          0       5080\n",
      "PatNo_ID_1573249295.csv                 7151          0       7151\n",
      "PatNo_ID_1573964540.csv                 4394          0       4394\n",
      "PatNo_ID_1574148494.csv                47154          0      47154\n",
      "PatNo_ID_1574270349.csv                 6534          0       6534\n",
      "PatNo_ID_1574528808.csv                14817          0      14817\n",
      "PatNo_ID_1574831525.csv                  521          0        521\n",
      "PatNo_ID_1574987447.csv                19843          0      19843\n",
      "PatNo_ID_1575060177.csv                 5290          0       5290\n",
      "PatNo_ID_1575256902.csv                 9587          0       9587\n",
      "PatNo_ID_1575445051.csv                 1801          0       1801\n",
      "PatNo_ID_1575502382.csv                 6604          0       6604\n",
      "PatNo_ID_1575975485.csv                16459          0      16459\n",
      "PatNo_ID_1576115572.csv                18717          0      18717\n",
      "PatNo_ID_1576116479.csv                 1263          0       1263\n",
      "PatNo_ID_1576301569.csv                 3207          0       3207\n",
      "PatNo_ID_1576964560.csv                24192          0      24192\n",
      "PatNo_ID_1577042911.csv                41894          0      41894\n",
      "PatNo_ID_1577487284.csv                 2345          0       2345\n",
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      "PatNo_ID_1594533379.csv                 1030          0       1030\n",
      "----------------------------------------------------------------------\n",
      "總計                                   1602476      16424    1586052\n",
      "\n",
      "✅ 處理完成：已為所有檔案新增欄位 sponvt_ok\n"
     ]
    }
   ],
   "source": [
    "# sponvt_ok 欄位產生程式\n",
    "# 複製來源檔案並新增 sponvt_ok：\n",
    "#   若 sponvt 為 float → 1\n",
    "#   若 sponvt 為 NaN/True/False → 0\n",
    "# ============================================\n",
    "\n",
    "import os\n",
    "import shutil\n",
    "import glob\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from typing import Tuple\n",
    "\n",
    "SRC_DIR = \"/home/jovyan/1010/data_new/bling_sponvt_ok/\"\n",
    "DST_DIR = \"/home/jovyan/1010/data_new/bling_sponvt_1/\"\n",
    "os.makedirs(DST_DIR, exist_ok=True)\n",
    "\n",
    "# ---------- 工具函式 ----------\n",
    "def copy_all_files(src: str, dst: str) -> Tuple[int, int]:\n",
    "    \"\"\"複製所有檔案到新目錄\"\"\"\n",
    "    files = [f for f in glob.glob(os.path.join(src, \"*\")) if os.path.isfile(f)]\n",
    "    copied = 0\n",
    "    for f in files:\n",
    "        try:\n",
    "            shutil.copy2(f, os.path.join(dst, os.path.basename(f)))\n",
    "            copied += 1\n",
    "        except Exception as e:\n",
    "            print(f\"[WARN] 無法複製 {f}: {e}\")\n",
    "    return len(files), copied\n",
    "\n",
    "def read_csv_flexible(path: str) -> pd.DataFrame:\n",
    "    \"\"\"嘗試多種編碼讀取 CSV\"\"\"\n",
    "    encs = [\"utf-8\", \"utf-8-sig\", \"cp950\", \"latin1\"]\n",
    "    last_err = None\n",
    "    for enc in encs:\n",
    "        try:\n",
    "            return pd.read_csv(path, dtype=str, encoding=enc)\n",
    "        except Exception as e:\n",
    "            last_err = e\n",
    "            continue\n",
    "    raise last_err\n",
    "\n",
    "def normalize_and_flag(df: pd.DataFrame) -> pd.DataFrame:\n",
    "    \"\"\"根據 sponvt 的型態建立 sponvt_ok 欄\"\"\"\n",
    "    if \"sponvt\" not in df.columns:\n",
    "        df[\"sponvt_ok\"] = np.nan\n",
    "        return df\n",
    "\n",
    "    # 先清理字串與空白\n",
    "    s = df[\"sponvt\"].copy()\n",
    "    s = s.apply(lambda x: x.strip() if isinstance(x, str) else x)\n",
    "\n",
    "    # 視為缺失的值\n",
    "    NULL_SET = {\"\", \"(null)\", \"null\", \"<null>\", \"none\", \"nan\"}\n",
    "    s = s.replace(list(NULL_SET), np.nan)\n",
    "\n",
    "    def classify_value(x):\n",
    "        # NaN 直接視為 0\n",
    "        if x is None or (isinstance(x, float) and np.isnan(x)):\n",
    "            return 0\n",
    "        # True/False\n",
    "        if isinstance(x, (bool, np.bool_)):\n",
    "            return 0\n",
    "        # 字串形式的 True/False\n",
    "        if isinstance(x, str) and x.lower() in [\"true\", \"false\"]:\n",
    "            return 0\n",
    "        # 嘗試轉成浮點數\n",
    "        try:\n",
    "            val = float(str(x).replace(\",\", \"\"))\n",
    "            if np.isfinite(val):\n",
    "                return 1\n",
    "            else:\n",
    "                return 0\n",
    "        except Exception:\n",
    "            return 0\n",
    "\n",
    "    df[\"sponvt_ok\"] = s.map(classify_value).astype(int)\n",
    "    return df\n",
    "\n",
    "# ---------- 主流程 ----------\n",
    "def main():\n",
    "    print(\"【步驟一｜複製檔案】\")\n",
    "    total, copied = copy_all_files(SRC_DIR, DST_DIR)\n",
    "    print(f\"來源：{SRC_DIR}\")\n",
    "    print(f\"目的：{DST_DIR}\")\n",
    "    print(f\"檔案總數：{total}，成功複製：{copied}\")\n",
    "\n",
    "    print(\"\\n【步驟二｜新增 sponvt_ok 欄】\")\n",
    "    csv_files = sorted(glob.glob(os.path.join(DST_DIR, \"*.csv\")))\n",
    "    print(f\"找到 CSV 檔案 {len(csv_files)} 份\")\n",
    "\n",
    "    summary_rows = []\n",
    "    for path in csv_files:\n",
    "        try:\n",
    "            df = read_csv_flexible(path)\n",
    "            before_rows = len(df)\n",
    "            df = normalize_and_flag(df)\n",
    "            df.to_csv(path, index=False, encoding=\"utf-8-sig\")\n",
    "\n",
    "            cnt_1 = (df[\"sponvt_ok\"] == 1).sum()\n",
    "            cnt_0 = (df[\"sponvt_ok\"] == 0).sum()\n",
    "            summary_rows.append({\n",
    "                \"file\": os.path.basename(path),\n",
    "                \"rows_total\": before_rows,\n",
    "                \"sponvt_ok=1\": cnt_1,\n",
    "                \"sponvt_ok=0\": cnt_0\n",
    "            })\n",
    "        except Exception as e:\n",
    "            print(f\"[ERROR] {os.path.basename(path)} 處理失敗: {e}\")\n",
    "\n",
    "    # 印出統計摘要\n",
    "    print(\"\\n=== 各檔統計 ===\")\n",
    "    print(f\"{'File':<35} {'Total':>8} {'OK=1':>10} {'OK=0':>10}\")\n",
    "    print(\"-\" * 70)\n",
    "    total_rows = total_1 = total_0 = 0\n",
    "    for r in summary_rows:\n",
    "        print(f\"{r['file']:<35} {r['rows_total']:>8} {r['sponvt_ok=1']:>10} {r['sponvt_ok=0']:>10}\")\n",
    "        total_rows += r[\"rows_total\"]\n",
    "        total_1 += r[\"sponvt_ok=1\"]\n",
    "        total_0 += r[\"sponvt_ok=0\"]\n",
    "\n",
    "    print(\"-\" * 70)\n",
    "    print(f\"{'總計':<35} {total_rows:>8} {total_1:>10} {total_0:>10}\")\n",
    "    print(f\"\\n✅ 處理完成：已為所有檔案新增欄位 sponvt_ok\")\n",
    "\n",
    "if __name__ == \"__main__\":\n",
    "    main()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b7149ec5-7f7d-427a-b1fc-8bf1bf545612",
   "metadata": {},
   "outputs": [],
   "source": [
    "統計所有檔案中 sponvt = 0 的筆數"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "caea2372-cc1b-4e05-ab3a-339cedc49c5a",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "總筆數：1602476\n",
      "sponvt = 0 的筆數：30\n",
      "比例：0.0019%\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import glob\n",
    "import os\n",
    "\n",
    "DATA_DIR = \"/home/jovyan/1010/data_new/bling_sponvt_1/\"\n",
    "CSV_GLOB = \"*.csv\"\n",
    "\n",
    "def read_csv_safely(path):\n",
    "    encs = [\"utf-8\", \"utf-8-sig\", \"cp950\", \"latin1\"]\n",
    "    for enc in encs:\n",
    "        try:\n",
    "            return pd.read_csv(path, dtype=str, encoding=enc, usecols=[\"sponvt\"])\n",
    "        except ValueError:\n",
    "            try:\n",
    "                df_all = pd.read_csv(path, dtype=str, encoding=enc)\n",
    "                return df_all\n",
    "            except Exception:\n",
    "                continue\n",
    "    return pd.DataFrame()\n",
    "\n",
    "def normalize_sponvt_series(s):\n",
    "    s = s.copy().apply(lambda x: x.strip() if isinstance(x, str) else x)\n",
    "    NULL_SET = {\"\", \"(null)\", \"null\", \"<null>\", \"none\", \"nan\"}\n",
    "    s = s.replace(list(NULL_SET), np.nan)\n",
    "    def to_float(x):\n",
    "        if isinstance(x, (bool, np.bool_)) or x is None:\n",
    "            return np.nan\n",
    "        try:\n",
    "            return float(str(x).replace(\",\", \"\"))\n",
    "        except:\n",
    "            return np.nan\n",
    "    return s.map(to_float)\n",
    "\n",
    "# === 統計 sponvt = 0 筆數 ===\n",
    "csv_paths = sorted(glob.glob(os.path.join(DATA_DIR, CSV_GLOB)))\n",
    "total_rows = 0\n",
    "zero_rows = 0\n",
    "for path in csv_paths:\n",
    "    df = read_csv_safely(path)\n",
    "    if \"sponvt\" not in df.columns:\n",
    "        continue\n",
    "    s = normalize_sponvt_series(df[\"sponvt\"])\n",
    "    total_rows += len(s)\n",
    "    zero_rows += (s == 0).sum()\n",
    "\n",
    "print(f\"總筆數：{total_rows}\")\n",
    "print(f\"sponvt = 0 的筆數：{zero_rows}\")\n",
    "print(f\"比例：{zero_rows / total_rows * 100:.4f}%\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "765db6cf-b3bf-43df-9e2c-5f8f87c99ff6",
   "metadata": {},
   "outputs": [],
   "source": [
    "想看嚴格只取 sponvt 欄位 sponvt=0的該筆資料 他的vti vte也都有數值嗎>0 嗎\n",
    "看sponvt 是不是float以及vti跟vte是0還是>0還是不是數字 排列組合成多種組合"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "d66bd7b0-06e5-4782-8abb-418820d05cc5",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "=== sponvt = 0 檢查結果 ===\n",
      "總檢查筆數：17273\n",
      "✅ vti、vte 均 > 0 的筆數：16999\n",
      "⚠️ vti 或 vte 缺失或 ≤ 0 的筆數：274\n",
      "\n",
      "=== 不符合條件的樣本（前 20 筆） ===\n",
      "{'patno': 9570752, 'senddate': '2022/01/04 12:36:02', 'sponvt': 0.0, 'vti': nan, 'vte': nan}\n",
      "{'patno': 9570752, 'senddate': '2022/01/04 12:35:03', 'sponvt': 0.0, 'vti': nan, 'vte': nan}\n",
      "{'patno': 9570752, 'senddate': '2022/01/04 12:34:02', 'sponvt': 0.0, 'vti': nan, 'vte': nan}\n",
      "{'patno': 9570752, 'senddate': '2022/01/04 12:33:02', 'sponvt': 0.0, 'vti': nan, 'vte': nan}\n",
      "{'patno': 9570752, 'senddate': '2022/01/04 12:32:02', 'sponvt': 0.0, 'vti': nan, 'vte': nan}\n",
      "{'patno': 9570752, 'senddate': '2022/01/04 12:31:07', 'sponvt': 0.0, 'vti': nan, 'vte': nan}\n",
      "{'patno': 9570752, 'senddate': '2022/01/04 12:30:02', 'sponvt': 0.0, 'vti': nan, 'vte': nan}\n",
      "{'patno': 9570752, 'senddate': '2022/01/04 12:29:02', 'sponvt': 0.0, 'vti': nan, 'vte': nan}\n",
      "{'patno': 9570752, 'senddate': '2022/01/04 12:28:02', 'sponvt': 0.0, 'vti': nan, 'vte': nan}\n",
      "{'patno': 9570752, 'senddate': '2022/01/04 12:27:02', 'sponvt': 0.0, 'vti': nan, 'vte': nan}\n",
      "{'patno': 9570752, 'senddate': '2022/01/04 12:26:02', 'sponvt': 0.0, 'vti': nan, 'vte': nan}\n",
      "{'patno': 9570752, 'senddate': '2022/01/04 12:25:02', 'sponvt': 0.0, 'vti': nan, 'vte': nan}\n",
      "{'patno': 9570752, 'senddate': '2022/01/04 12:24:02', 'sponvt': 0.0, 'vti': nan, 'vte': nan}\n",
      "{'patno': 9570752, 'senddate': '2022/01/04 12:23:02', 'sponvt': 0.0, 'vti': nan, 'vte': nan}\n",
      "{'patno': 9570752, 'senddate': '2022/01/04 12:22:02', 'sponvt': 0.0, 'vti': nan, 'vte': nan}\n",
      "{'patno': 9570752, 'senddate': '2022/01/04 12:21:02', 'sponvt': 0.0, 'vti': nan, 'vte': nan}\n",
      "{'patno': 9570752, 'senddate': '2022/01/04 12:20:02', 'sponvt': 0.0, 'vti': nan, 'vte': nan}\n",
      "{'patno': 9570752, 'senddate': '2022/01/04 09:49:04', 'sponvt': 0.0, 'vti': nan, 'vte': nan}\n",
      "{'patno': 9570752, 'senddate': '2022/01/04 09:48:04', 'sponvt': 0.0, 'vti': nan, 'vte': nan}\n",
      "{'patno': 11430923, 'senddate': '2022/01/04 14:11:00', 'sponvt': 0.0, 'vti': '(null)', 'vte': nan}\n"
     ]
    }
   ],
   "source": [
    "# 檢查 sponvt = 0 的資料列，並確認 vti / vte 是否 > 0\n",
    "# 輸出符合條件與不符合條件的筆數與清單\n",
    "# ============================================\n",
    "\n",
    "import os\n",
    "import glob\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "DATA_DIR = \"/home/jovyan/1010/data_new/bling_sponvt_1/\"\n",
    "CSV_GLOB = \"*.csv\"\n",
    "\n",
    "def read_csv_flexible(path):\n",
    "    encs = [\"utf-8\", \"utf-8-sig\", \"cp950\", \"latin1\"]\n",
    "    for enc in encs:\n",
    "        try:\n",
    "            return pd.read_csv(path, encoding=enc, low_memory=False)\n",
    "        except Exception:\n",
    "            continue\n",
    "    raise ValueError(f\"無法讀取檔案：{path}\")\n",
    "\n",
    "def to_float(x):\n",
    "    try:\n",
    "        return float(x)\n",
    "    except Exception:\n",
    "        return np.nan\n",
    "\n",
    "# === 主程式 ===\n",
    "total_checked = 0\n",
    "ok_both_positive = 0\n",
    "fail_missing_or_zero = 0\n",
    "failed_rows = []\n",
    "\n",
    "for path in sorted(glob.glob(os.path.join(DATA_DIR, CSV_GLOB))):\n",
    "    df = read_csv_flexible(path)\n",
    "    if not set([\"sponvt\", \"vti\", \"vte\"]).issubset(df.columns):\n",
    "        continue\n",
    "\n",
    "    # 轉成 float 型態\n",
    "    df[\"sponvt_f\"] = pd.to_numeric(df[\"sponvt\"], errors=\"coerce\")\n",
    "    df[\"vti_f\"] = pd.to_numeric(df[\"vti\"], errors=\"coerce\")\n",
    "    df[\"vte_f\"] = pd.to_numeric(df[\"vte\"], errors=\"coerce\")\n",
    "\n",
    "    subset = df[df[\"sponvt_f\"] == 0]\n",
    "    total_checked += len(subset)\n",
    "\n",
    "    cond_ok = (subset[\"vti_f\"] > 0) & (subset[\"vte_f\"] > 0)\n",
    "    ok_both_positive += cond_ok.sum()\n",
    "    fail_missing_or_zero += (~cond_ok).sum()\n",
    "\n",
    "    failed_rows.extend(subset.loc[~cond_ok, [\"patno\", \"senddate\", \"sponvt\", \"vti\", \"vte\"]].to_dict(\"records\"))\n",
    "\n",
    "# === 結果輸出 ===\n",
    "print(\"=== sponvt = 0 檢查結果 ===\")\n",
    "print(f\"總檢查筆數：{total_checked}\")\n",
    "print(f\"✅ vti、vte 均 > 0 的筆數：{ok_both_positive}\")\n",
    "print(f\"⚠️ vti 或 vte 缺失或 ≤ 0 的筆數：{fail_missing_or_zero}\")\n",
    "\n",
    "print(\"\\n=== 不符合條件的樣本（前 20 筆） ===\")\n",
    "for r in failed_rows[:20]:\n",
    "    print(r)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "84c57ec9-5243-4b9c-81a8-c77f833c50e0",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "=== 在 sponvt == 0 的範圍內，vti/vte 類別組合分佈 ===\n",
      "    vti_cat     vte_cat  count\n",
      "non_numeric non_numeric     30\n",
      "\n",
      "總筆數（sponvt==0）：30\n",
      "✅ 同列 vti>0 且 vte>0：0  (0.0000%)\n",
      "⚠️ 其餘（缺失/非數字/≤0 任一者）：30  (100.0000%)\n",
      "\n",
      "[輸出] 不符合條件清單：/home/jovyan/1010/data_new/bling_sponvt_1/sponvt_zero_vti_vte_bad.csv\n",
      "[輸出] sponvt==0 全清單：/home/jovyan/1010/data_new/bling_sponvt_1/sponvt_zero_all.csv\n",
      "\n",
      "=== 全域交叉表：sponvt 型態 × vti 類別 × vte 類別 ===\n",
      "  sponvt_type vti_cat_all vte_cat_all   count\n",
      "   bool_false         eq0         eq0       4\n",
      "   bool_false         gt0         eq0      24\n",
      "   bool_false         gt0         gt0   16999\n",
      "   bool_false non_numeric         eq0       7\n",
      "   bool_false non_numeric non_numeric     209\n",
      "    bool_true         eq0         gt0      17\n",
      "    bool_true         gt0         gt0   97308\n",
      "    bool_true         gt0 non_numeric      71\n",
      "    bool_true non_numeric         gt0       9\n",
      "    bool_true non_numeric non_numeric   46500\n",
      "float_nonzero         eq0         eq0      18\n",
      "float_nonzero         eq0         gt0    1793\n",
      "float_nonzero         gt0         eq0    2054\n",
      "float_nonzero         gt0         gt0 1211955\n",
      "float_nonzero         gt0 non_numeric    2802\n",
      "float_nonzero non_numeric         eq0      99\n",
      "float_nonzero non_numeric         gt0  115118\n",
      "float_nonzero non_numeric non_numeric  107459\n",
      "   float_zero non_numeric non_numeric      30\n"
     ]
    }
   ],
   "source": [
    "# 嚴格檢查：sponvt == 0 的列，其 vti / vte 是否皆為數值且 > 0？\n",
    "# 並建立：sponvt 型態 × vti 類別 × vte 類別 的全組合交叉表\n",
    "# ============================================\n",
    "\n",
    "import os\n",
    "import glob\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "# ===== 使用者參數 =====\n",
    "DATA_DIR = \"/home/jovyan/1010/data_new/bling_sponvt_1/\"\n",
    "CSV_GLOB = \"*.csv\"\n",
    "# 是否輸出詳列 CSV（例如不符合「vti>0 且 vte>0」的 sponvt=0 列）\n",
    "EXPORT_DETAILS = True\n",
    "DETAILS_PATH_BAD = os.path.join(DATA_DIR, \"sponvt_zero_vti_vte_bad.csv\")\n",
    "DETAILS_PATH_ALL = os.path.join(DATA_DIR, \"sponvt_zero_all.csv\")\n",
    "\n",
    "# ===== 讀檔（多編碼；盡可能只取需要欄位） =====\n",
    "NEEDED_COLS = [\"sponvt\", \"vti\", \"vte\", \"patno\", \"senddate\"]\n",
    "\n",
    "def read_csv_safely(path: str) -> pd.DataFrame:\n",
    "    encs = [\"utf-8\", \"utf-8-sig\", \"cp950\", \"latin1\"]\n",
    "    last_err = None\n",
    "    for enc in encs:\n",
    "        try:\n",
    "            # 先嘗試只取必要欄位\n",
    "            return pd.read_csv(path, dtype=str, encoding=enc, usecols=NEEDED_COLS)\n",
    "        except ValueError:\n",
    "            # 欄位名可能不齊或有前後空白，改為全讀再子集\n",
    "            try:\n",
    "                df_all = pd.read_csv(path, dtype=str, encoding=enc, low_memory=False)\n",
    "                # 盡量取到需要的欄位；若缺失就略過該欄\n",
    "                keep = [c for c in NEEDED_COLS if c in df_all.columns]\n",
    "                return df_all[keep]\n",
    "            except Exception as e2:\n",
    "                last_err = e2\n",
    "        except Exception as e:\n",
    "            last_err = e\n",
    "    if last_err:\n",
    "        raise last_err\n",
    "    return pd.DataFrame()\n",
    "\n",
    "# ===== 清理與型態判別 =====\n",
    "NULL_SET = {\"\", \"(null)\", \"null\", \"<null>\", \"none\", \"nan\"}\n",
    "\n",
    "def clean_str(x):\n",
    "    return x.strip() if isinstance(x, str) else x\n",
    "\n",
    "def to_num_or_nan(x):\n",
    "    \"\"\"將字串轉成數值；布林或不可解析者 -> NaN；允許 '1,234.5' 形式。\"\"\"\n",
    "    if x is None:\n",
    "        return np.nan\n",
    "    if isinstance(x, (bool, np.bool_)):\n",
    "        return np.nan\n",
    "    if isinstance(x, str):\n",
    "        xl = x.lower().strip()\n",
    "        if xl in NULL_SET:\n",
    "            return np.nan\n",
    "        if xl in (\"true\", \"false\"):\n",
    "            return np.nan\n",
    "        try:\n",
    "            return float(x.replace(\",\", \"\"))\n",
    "        except Exception:\n",
    "            return np.nan\n",
    "    try:\n",
    "        return float(x)\n",
    "    except Exception:\n",
    "        return np.nan\n",
    "\n",
    "def classify_sponvt_type(raw_val):\n",
    "    \"\"\"\n",
    "    回傳 sponvt 型態：\n",
    "      - 'float_zero'：數值且 == 0\n",
    "      - 'float_nonzero'：數值且 != 0\n",
    "      - 'bool_true' / 'bool_false'\n",
    "      - 'nan'：空/無法解析/缺失\n",
    "      - 'other'：其他非數字字串\n",
    "    \"\"\"\n",
    "    if raw_val is None:\n",
    "        return \"nan\"\n",
    "    if isinstance(raw_val, (bool, np.bool_)):\n",
    "        return \"bool_true\" if bool(raw_val) else \"bool_false\"\n",
    "    if isinstance(raw_val, str):\n",
    "        x = raw_val.strip()\n",
    "        xl = x.lower()\n",
    "        if xl in NULL_SET:\n",
    "            return \"nan\"\n",
    "        if xl == \"true\":\n",
    "            return \"bool_true\"\n",
    "        if xl == \"false\":\n",
    "            return \"bool_false\"\n",
    "        # 試轉數值\n",
    "        try:\n",
    "            v = float(x.replace(\",\", \"\"))\n",
    "            return \"float_zero\" if v == 0.0 else \"float_nonzero\"\n",
    "        except Exception:\n",
    "            return \"other\"\n",
    "    # 其他型別（理論上很少）\n",
    "    try:\n",
    "        v = float(raw_val)\n",
    "        return \"float_zero\" if v == 0.0 else \"float_nonzero\"\n",
    "    except Exception:\n",
    "        return \"other\"\n",
    "\n",
    "def classify_num_cat(raw_val):\n",
    "    \"\"\"\n",
    "    將 vti/vte 分類：\n",
    "      - 'gt0'：數值且 > 0\n",
    "      - 'eq0'：數值且 == 0\n",
    "      - 'non_numeric'：NaN / 布林 / 其他非數字\n",
    "    \"\"\"\n",
    "    if raw_val is None:\n",
    "        return \"non_numeric\"\n",
    "    if isinstance(raw_val, (bool, np.bool_)):\n",
    "        return \"non_numeric\"\n",
    "    if isinstance(raw_val, str):\n",
    "        x = raw_val.strip()\n",
    "        xl = x.lower()\n",
    "        if xl in NULL_SET or xl in (\"true\", \"false\"):\n",
    "            return \"non_numeric\"\n",
    "        try:\n",
    "            v = float(x.replace(\",\", \"\"))\n",
    "        except Exception:\n",
    "            return \"non_numeric\"\n",
    "        if not np.isfinite(v):\n",
    "            return \"non_numeric\"\n",
    "        if v > 0:\n",
    "            return \"gt0\"\n",
    "        if v == 0:\n",
    "            return \"eq0\"\n",
    "        # < 0 的情況若存在可自行擴充\n",
    "        return \"non_numeric\"\n",
    "    # 非字串，嘗試數值\n",
    "    try:\n",
    "        v = float(raw_val)\n",
    "    except Exception:\n",
    "        return \"non_numeric\"\n",
    "    if not np.isfinite(v):\n",
    "        return \"non_numeric\"\n",
    "    if v > 0:\n",
    "        return \"gt0\"\n",
    "    if v == 0:\n",
    "        return \"eq0\"\n",
    "    return \"non_numeric\"\n",
    "\n",
    "# ===== 主流程 =====\n",
    "def main():\n",
    "    csv_paths = sorted(glob.glob(os.path.join(DATA_DIR, CSV_GLOB)))\n",
    "    if not csv_paths:\n",
    "        print(f\"[WARN] 目錄無 CSV：{DATA_DIR}\")\n",
    "        return\n",
    "\n",
    "    # 收集所有檔的必要欄位\n",
    "    chunks = []\n",
    "    for path in csv_paths:\n",
    "        try:\n",
    "            df = read_csv_safely(path)\n",
    "            if \"sponvt\" not in df.columns:\n",
    "                continue\n",
    "            # 標準化字串空白\n",
    "            for col in df.columns:\n",
    "                if df[col].dtype == object:\n",
    "                    df[col] = df[col].map(clean_str)\n",
    "            # 保留原始字串供分類\n",
    "            df[\"_file\"] = os.path.basename(path)\n",
    "            chunks.append(df)\n",
    "        except Exception as e:\n",
    "            print(f\"[WARN] 讀取失敗：{path} -> {e}\")\n",
    "\n",
    "    if not chunks:\n",
    "        print(\"[WARN] 無可分析資料。\")\n",
    "        return\n",
    "\n",
    "    data = pd.concat(chunks, ignore_index=True)\n",
    "\n",
    "    # === A) 精準篩選：sponvt == 0（數值 0.0） ===\n",
    "    # 先把 sponvt 轉數值（嚴格規則）\n",
    "    sponvt_num = data[\"sponvt\"].map(to_num_or_nan)\n",
    "    mask_sponvt_zero = sponvt_num.eq(0.0)\n",
    "\n",
    "    s0 = data.loc[mask_sponvt_zero].copy()  # 僅 sponvt == 0 的列\n",
    "    if s0.empty:\n",
    "        print(\"[INFO] 沒有任何 sponvt == 0 的列。\")\n",
    "    else:\n",
    "        # 對 vti / vte 分類\n",
    "        s0[\"vti_cat\"] = s0[\"vti\"].map(classify_num_cat) if \"vti\" in s0.columns else \"non_numeric\"\n",
    "        s0[\"vte_cat\"] = s0[\"vte\"].map(classify_num_cat) if \"vte\" in s0.columns else \"non_numeric\"\n",
    "\n",
    "        # 組合統計（在 sponvt=0 篩選集上）\n",
    "        combo_s0 = (\n",
    "            s0\n",
    "            .assign(_const=1)\n",
    "            .pivot_table(index=[\"vti_cat\", \"vte_cat\"], values=\"_const\", aggfunc=\"sum\", fill_value=0)\n",
    "            .rename(columns={\"_const\": \"count\"})\n",
    "            .reset_index()\n",
    "            .sort_values([\"vti_cat\", \"vte_cat\"])\n",
    "        )\n",
    "\n",
    "        total_s0 = int(s0.shape[0])\n",
    "        ok_both_pos = int(((s0[\"vti_cat\"] == \"gt0\") & (s0[\"vte_cat\"] == \"gt0\")).sum())\n",
    "        bad_cnt = total_s0 - ok_both_pos\n",
    "\n",
    "        print(\"\\n=== 在 sponvt == 0 的範圍內，vti/vte 類別組合分佈 ===\")\n",
    "        print(combo_s0.to_string(index=False))\n",
    "        print(f\"\\n總筆數（sponvt==0）：{total_s0}\")\n",
    "        print(f\"✅ 同列 vti>0 且 vte>0：{ok_both_pos}  ({ok_both_pos/total_s0*100:.4f}%)\")\n",
    "        print(f\"⚠️ 其餘（缺失/非數字/≤0 任一者）：{bad_cnt}  ({bad_cnt/total_s0*100:.4f}%)\")\n",
    "\n",
    "        if EXPORT_DETAILS:\n",
    "            # 不符合條件的清單\n",
    "            bad_rows = s0.loc[~((s0[\"vti_cat\"] == \"gt0\") & (s0[\"vte_cat\"] == \"gt0\"))].copy()\n",
    "            # 保留關鍵欄\n",
    "            keep_cols = [c for c in [\"_file\",\"patno\",\"senddate\",\"sponvt\",\"vti\",\"vte\",\"vti_cat\",\"vte_cat\"] if c in bad_rows.columns]\n",
    "            bad_rows[keep_cols].to_csv(DETAILS_PATH_BAD, index=False, encoding=\"utf-8-sig\")\n",
    "            # 全部 sponvt==0 的清單（方便人工追蹤）\n",
    "            all_keep = [c for c in [\"_file\",\"patno\",\"senddate\",\"sponvt\",\"vti\",\"vte\",\"vti_cat\",\"vte_cat\"] if c in s0.columns]\n",
    "            s0[all_keep].to_csv(DETAILS_PATH_ALL, index=False, encoding=\"utf-8-sig\")\n",
    "            print(f\"\\n[輸出] 不符合條件清單：{DETAILS_PATH_BAD}\")\n",
    "            print(f\"[輸出] sponvt==0 全清單：{DETAILS_PATH_ALL}\")\n",
    "\n",
    "    # === B) 全域交叉表：sponvt 型態 × vti 類別 × vte 類別 ===\n",
    "    data[\"sponvt_type\"] = data[\"sponvt\"].map(classify_sponvt_type)\n",
    "    data[\"vti_cat_all\"] = data[\"vti\"].map(classify_num_cat) if \"vti\" in data.columns else \"non_numeric\"\n",
    "    data[\"vte_cat_all\"] = data[\"vte\"].map(classify_num_cat) if \"vte\" in data.columns else \"non_numeric\"\n",
    "\n",
    "    crosstab_all = (\n",
    "        data.assign(_const=1)\n",
    "            .pivot_table(index=[\"sponvt_type\", \"vti_cat_all\", \"vte_cat_all\"],\n",
    "                         values=\"_const\", aggfunc=\"sum\", fill_value=0)\n",
    "            .rename(columns={\"_const\": \"count\"})\n",
    "            .reset_index()\n",
    "            .sort_values([\"sponvt_type\", \"vti_cat_all\", \"vte_cat_all\"])\n",
    "    )\n",
    "\n",
    "    print(\"\\n=== 全域交叉表：sponvt 型態 × vti 類別 × vte 類別 ===\")\n",
    "    print(crosstab_all.to_string(index=False))\n",
    "\n",
    "# 使用方式：\n",
    "main()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6bb6910a-fc7f-4b64-a169-80e9bb2856f3",
   "metadata": {},
   "outputs": [],
   "source": [
    "sponvt == 0 的資料列中，vti、vte 全部都不是有效數值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "85a52c9e-5f30-4fdd-93ec-d65961bcacbe",
   "metadata": {},
   "outputs": [],
   "source": [
    "| 類別名稱              | 定義條件                                                        | 含義說明                       |\n",
    "| ----------------- | ----------------------------------------------------------- | -------------------------- |\n",
    "| **`gt0`**         | 數值型資料且 > 0                                                  | 表示該欄位為正數（通常代表「有有效測量值」）     |\n",
    "| **`eq0`**         | 數值型資料且 == 0                                                 | 表示測得值為 0（可能為暫停呼吸、儀器異常或無效值） |\n",
    "| **`non_numeric`** | 無法轉成數值（例如 `NaN`, `(null)`, `\"none\"`, `\"True\"`, `\"False\"` 等） | 表示該欄位不是有效數值，多半是空值、字串或布林值   |\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "06203b2e-f861-46a8-865a-e941737de289",
   "metadata": {},
   "outputs": [],
   "source": [
    "我要修改程式碼\n",
    "創spomvy_ok_v1\n",
    "針對sponvt 這一欄位\n",
    "去判斷spomvy_ok_v1是1還是0還是2要根據以下\n",
    "先把字串做 .strip()，並把 \"\"、(null)、null、<null>、none、nan 視為缺值 → 記為 0\n",
    "若型別本身是布林 True/False → 記為 2\n",
    "若是字串 \"true\" 或 \"false\"（大小寫不敏感）→ 記為2\n",
    "其他情況嘗試轉成浮點數：float(str(x).replace(\",\", \"\"))\n",
    "若轉換成功且 np.isfinite(val) 為 True（有限數）→ 記為 1\n",
    "若轉成 inf/-inf/nan 或轉換失敗 → 記為 0\n",
    "但若數字是0或0.0 → 記為 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "a5a281b0-64cb-464a-821e-fec5a21494e1",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "【步驟一｜複製檔案】\n",
      "來源：/home/jovyan/1010/data_new/bling_sponvt_ok/\n",
      "目的：/home/jovyan/1010/data_new/bling_spomvy_ok_v1/\n",
      "檔案總數：122，成功複製：122\n",
      "\n",
      "【步驟二｜新增 spomvy_ok_v1 欄】\n",
      "找到 CSV 檔案 122 份\n",
      "\n",
      "=== 各檔統計 ===\n",
      "File                                   Total       OK=1       OK=0       OK=2\n",
      "--------------------------------------------------------------------------------\n",
      "089271.csv                             32419       4451      27968          0\n",
      "095323.csv                             23791          0      23791          0\n",
      "095707.csv                             20180       1580      18600          0\n",
      "114309.csv                             71729       2054      69675          0\n",
      "230933.csv                             30249       3118      27131          0\n",
      "4216007.csv                             1433          0       1433          0\n",
      "7108162.csv                              239        103        136          0\n",
      "7408338.csv                             1432          0       1432          0\n",
      "7657698.csv                             1413        125       1288          0\n",
      "7721164.csv                              483        483          0          0\n",
      "PatNo_ID_1560013303.csv                 2543          0       2341        202\n",
      "PatNo_ID_1562733396.csv                 2254          0       2254          0\n",
      "PatNo_ID_1563587183.csv                 5287          0       5287          0\n",
      "PatNo_ID_1564148644.csv                17287          0      17287          0\n",
      "PatNo_ID_1565148312.csv                 5475          0       5475          0\n",
      "PatNo_ID_1565378038.csv                 2561          0       2278        283\n",
      "PatNo_ID_1566123680.csv                42600          0      42600          0\n",
      "PatNo_ID_1566252197.csv                 3368       1462       1906          0\n",
      "PatNo_ID_1566279967.csv                 1235          0       1235          0\n",
      "PatNo_ID_1566671274.csv                50718          0      50718          0\n",
      "PatNo_ID_1566911879.csv                53999          0      53999          0\n",
      "PatNo_ID_1567747650.csv                10901          0      10657        244\n",
      "PatNo_ID_1567804800.csv                20578          0       8903      11675\n",
      "PatNo_ID_1567832735.csv                36580          0      36483         97\n",
      "PatNo_ID_1568039398.csv                34519          0      34519          0\n",
      "PatNo_ID_1568574099.csv                13946          0       8327       5619\n",
      "PatNo_ID_1568813269.csv                 4867          0       4459        408\n",
      "PatNo_ID_1568952422.csv                 1184          0       1184          0\n",
      "PatNo_ID_1569083701.csv                 3328          0       3328          0\n",
      "PatNo_ID_1569944983.csv                 7650          0       6232       1418\n",
      "PatNo_ID_1570089466.csv                41343          0      33105       8238\n",
      "PatNo_ID_1570242703.csv                10646          0       4811       5835\n",
      "PatNo_ID_1570273244.csv                 9730          0       8876        854\n",
      "PatNo_ID_1570642083.csv                19731          0      19731          0\n",
      "PatNo_ID_1571945701.csv                15731          0      15731          0\n",
      "PatNo_ID_1572481361.csv                34540          0      28477       6063\n",
      "PatNo_ID_1572562839.csv                20966          0      10590      10376\n",
      "PatNo_ID_1572831765.csv                 2696          0       2696          0\n",
      "PatNo_ID_1572976822.csv                 6764          0       3806       2958\n",
      "PatNo_ID_1573063188.csv                 5080          0       5080          0\n",
      "PatNo_ID_1573249295.csv                 7151          0       7151          0\n",
      "PatNo_ID_1573964540.csv                 4394          0       2636       1758\n",
      "PatNo_ID_1574148494.csv                47154          0      47154          0\n",
      "PatNo_ID_1574270349.csv                 6534          0       6534          0\n",
      "PatNo_ID_1574528808.csv                14817          0      14817          0\n",
      "PatNo_ID_1574831525.csv                  521          0        521          0\n",
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      "--------------------------------------------------------------------------------\n",
      "總計                                   1602476      16394    1424934     161148\n",
      "\n",
      "✅ 處理完成：已為所有檔案新增欄位 spomvy_ok_v1\n"
     ]
    }
   ],
   "source": [
    "# spomvy_ok_v1 欄位產生程式\n",
    "# 根據 sponvt 欄位內容判斷：\n",
    "#   - 若為缺值 → 0\n",
    "#   - 若為布林 True/False（或字串形式）→ 2\n",
    "#   - 若為數值（且有限且非0）→ 1\n",
    "#   - 其他狀況或轉換失敗 → 0\n",
    "# ============================================\n",
    "\n",
    "import os\n",
    "import shutil\n",
    "import glob\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from typing import Tuple\n",
    "\n",
    "SRC_DIR = \"/home/jovyan/1010/data_new/bling_sponvt_ok/\"\n",
    "DST_DIR = \"/home/jovyan/1010/data_new/bling_spomvy_ok_v1/\"\n",
    "os.makedirs(DST_DIR, exist_ok=True)\n",
    "\n",
    "# ---------- 工具函式 ----------\n",
    "def copy_all_files(src: str, dst: str) -> Tuple[int, int]:\n",
    "    \"\"\"複製所有檔案到新目錄\"\"\"\n",
    "    files = [f for f in glob.glob(os.path.join(src, \"*\")) if os.path.isfile(f)]\n",
    "    copied = 0\n",
    "    for f in files:\n",
    "        try:\n",
    "            shutil.copy2(f, os.path.join(dst, os.path.basename(f)))\n",
    "            copied += 1\n",
    "        except Exception as e:\n",
    "            print(f\"[WARN] 無法複製 {f}: {e}\")\n",
    "    return len(files), copied\n",
    "\n",
    "\n",
    "def read_csv_flexible(path: str) -> pd.DataFrame:\n",
    "    \"\"\"嘗試多種編碼讀取 CSV\"\"\"\n",
    "    encs = [\"utf-8\", \"utf-8-sig\", \"cp950\", \"latin1\"]\n",
    "    last_err = None\n",
    "    for enc in encs:\n",
    "        try:\n",
    "            return pd.read_csv(path, dtype=str, encoding=enc)\n",
    "        except Exception as e:\n",
    "            last_err = e\n",
    "            continue\n",
    "    raise last_err\n",
    "\n",
    "\n",
    "def normalize_and_flag(df: pd.DataFrame) -> pd.DataFrame:\n",
    "    \"\"\"根據 sponvt 欄位建立 spomvy_ok_v1 欄\"\"\"\n",
    "    if \"sponvt\" not in df.columns:\n",
    "        df[\"spomvy_ok_v1\"] = np.nan\n",
    "        return df\n",
    "\n",
    "    s = df[\"sponvt\"].copy()\n",
    "    s = s.apply(lambda x: x.strip() if isinstance(x, str) else x)\n",
    "\n",
    "    # 視為缺值的集合\n",
    "    NULL_SET = {\"\", \"(null)\", \"null\", \"<null>\", \"none\", \"nan\"}\n",
    "    s = s.replace(list(NULL_SET), np.nan)\n",
    "\n",
    "    def classify_value(x):\n",
    "        # 缺值 → 0\n",
    "        if x is None or (isinstance(x, float) and np.isnan(x)):\n",
    "            return 0\n",
    "        # 布林型態 → 2\n",
    "        if isinstance(x, (bool, np.bool_)):\n",
    "            return 2\n",
    "        # 字串形式 True/False → 2\n",
    "        if isinstance(x, str) and x.lower() in [\"true\", \"false\"]:\n",
    "            return 2\n",
    "        # 嘗試轉為浮點數\n",
    "        try:\n",
    "            val = float(str(x).replace(\",\", \"\"))\n",
    "            if not np.isfinite(val):\n",
    "                return 0\n",
    "            # 若是 0 或 0.0 → 0，其餘有限數 → 1\n",
    "            return 0 if val == 0 else 1\n",
    "        except Exception:\n",
    "            return 0\n",
    "\n",
    "    df[\"spomvy_ok_v1\"] = s.map(classify_value).astype(int)\n",
    "    return df\n",
    "\n",
    "\n",
    "# ---------- 主流程 ----------\n",
    "def main():\n",
    "    print(\"【步驟一｜複製檔案】\")\n",
    "    total, copied = copy_all_files(SRC_DIR, DST_DIR)\n",
    "    print(f\"來源：{SRC_DIR}\")\n",
    "    print(f\"目的：{DST_DIR}\")\n",
    "    print(f\"檔案總數：{total}，成功複製：{copied}\")\n",
    "\n",
    "    print(\"\\n【步驟二｜新增 spomvy_ok_v1 欄】\")\n",
    "    csv_files = sorted(glob.glob(os.path.join(DST_DIR, \"*.csv\")))\n",
    "    print(f\"找到 CSV 檔案 {len(csv_files)} 份\")\n",
    "\n",
    "    summary_rows = []\n",
    "    for path in csv_files:\n",
    "        try:\n",
    "            df = read_csv_flexible(path)\n",
    "            before_rows = len(df)\n",
    "            df = normalize_and_flag(df)\n",
    "            df.to_csv(path, index=False, encoding=\"utf-8-sig\")\n",
    "\n",
    "            cnt_1 = (df[\"spomvy_ok_v1\"] == 1).sum()\n",
    "            cnt_0 = (df[\"spomvy_ok_v1\"] == 0).sum()\n",
    "            cnt_2 = (df[\"spomvy_ok_v1\"] == 2).sum()\n",
    "            summary_rows.append({\n",
    "                \"file\": os.path.basename(path),\n",
    "                \"rows_total\": before_rows,\n",
    "                \"spomvy_ok_v1=1\": cnt_1,\n",
    "                \"spomvy_ok_v1=0\": cnt_0,\n",
    "                \"spomvy_ok_v1=2\": cnt_2\n",
    "            })\n",
    "        except Exception as e:\n",
    "            print(f\"[ERROR] {os.path.basename(path)} 處理失敗: {e}\")\n",
    "\n",
    "    # 印出統計摘要\n",
    "    print(\"\\n=== 各檔統計 ===\")\n",
    "    print(f\"{'File':<35} {'Total':>8} {'OK=1':>10} {'OK=0':>10} {'OK=2':>10}\")\n",
    "    print(\"-\" * 80)\n",
    "    total_rows = total_1 = total_0 = total_2 = 0\n",
    "    for r in summary_rows:\n",
    "        print(f\"{r['file']:<35} {r['rows_total']:>8} {r['spomvy_ok_v1=1']:>10} {r['spomvy_ok_v1=0']:>10} {r['spomvy_ok_v1=2']:>10}\")\n",
    "        total_rows += r[\"rows_total\"]\n",
    "        total_1 += r[\"spomvy_ok_v1=1\"]\n",
    "        total_0 += r[\"spomvy_ok_v1=0\"]\n",
    "        total_2 += r[\"spomvy_ok_v1=2\"]\n",
    "\n",
    "    print(\"-\" * 80)\n",
    "    print(f\"{'總計':<35} {total_rows:>8} {total_1:>10} {total_0:>10} {total_2:>10}\")\n",
    "    print(f\"\\n✅ 處理完成：已為所有檔案新增欄位 spomvy_ok_v1\")\n",
    "\n",
    "if __name__ == \"__main__\":\n",
    "    main()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2d825577-01a7-4719-b81a-5d375920aa6e",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ca5536a2-20b5-449f-9aba-7cbae6e53c9b",
   "metadata": {},
   "outputs": [],
   "source": [
    "我想看/home/jovyan/1010/data_new/bling_sponvt_1/\"所以檔案的vte vti有哪些類別 型態 數字 數量 占比"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "207b7af8-0f1b-4deb-b194-da68c7095de6",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "📂 目錄：/home/jovyan/1010/data_new/bling_sponvt_1/\n",
      "📄 檔案數：124\n",
      "\n",
      "====================================================================================================\n",
      "🌐 跨檔案彙總：vti\n",
      "總筆數（所有檔案合併） = 1602536\n",
      "Index  Type              Count         Percent(%)\n",
      "-----  ----------------  ------------  -----------\n",
      "1      NaN                     218150      13.6128\n",
      "2      bool_true                    0       0.0000\n",
      "3      bool_false                   0       0.0000\n",
      "4      int_zero                  1832       0.1143\n",
      "5      int_nonzero            1330301      83.0122\n",
      "6      float_zero                   0       0.0000\n",
      "7      float_nonzero              933       0.0582\n",
      "8      empty_string                 0       0.0000\n",
      "9      str                      51320       3.2024\n",
      "\n",
      "📈 數值統計（僅可轉為數值者；跨檔案彙總）\n",
      "numeric_count : 1333066\n",
      "mean          : 490.839100164578\n",
      "std           : 247.93079418489887\n",
      "min           : -20849.0\n",
      "max           : 20778.0\n",
      "\n",
      "====================================================================================================\n",
      "🌐 跨檔案彙總：vte\n",
      "總筆數（所有檔案合併） = 1602536\n",
      "Index  Type              Count         Percent(%)\n",
      "-----  ----------------  ------------  -----------\n",
      "1      NaN                     157093       9.8028\n",
      "2      bool_true                    0       0.0000\n",
      "3      bool_false                   0       0.0000\n",
      "4      int_zero                  2206       0.1377\n",
      "5      int_nonzero            1443237      90.0596\n",
      "6      float_zero                   0       0.0000\n",
      "7      float_nonzero                0       0.0000\n",
      "8      empty_string                 0       0.0000\n",
      "9      str                          0       0.0000\n",
      "\n",
      "📈 數值統計（僅可轉為數值者；跨檔案彙總）\n",
      "numeric_count : 1445443\n",
      "mean          : 483.8765534164814\n",
      "std           : 176.50571228105272\n",
      "min           : -10182.0\n",
      "max           : 16795.0\n",
      "\n",
      "====================================================================================================\n",
      "✅ 跨檔案彙總完成。\n"
     ]
    }
   ],
   "source": [
    "\"\"\" 跨檔案彙總統計：對 /home/jovyan/1010/data_new/bling_sponvt_1/ 下所有 CSV\n",
    "分別累計 vti 與 vte 兩個欄位的「資料型態分布」與「數值統計(可轉為數字者)」\n",
    "\n",
    "分類（互斥）：\n",
    "- NaN\n",
    "- bool_true / bool_false\n",
    "- int_zero / int_nonzero\n",
    "- float_zero / float_nonzero\n",
    "- empty_string（空字串或僅空白）\n",
    "- str（其他無法轉數值的字串，含 '(null)' 等）\n",
    "\n",
    "數值統計（跨檔案彙總；僅針對能轉成數字的值）：\n",
    "- numeric_count, mean, std, min, max\n",
    "（若需要四分位數，可將 KEEP_NUMERIC_SAMPLES 設為 True，但注意記憶體）\n",
    "\"\"\"\n",
    "\n",
    "import os\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import math\n",
    "\n",
    "# ===== 使用者設定 =====\n",
    "DATA_DIR = \"/home/jovyan/1010/data_new/bling_sponvt_1/\"\n",
    "TARGET_COLS = [\"vti\", \"vte\"]\n",
    "ENCODINGS_TRY = [\"utf-8\", \"utf-8-sig\", \"big5\", \"cp950\"]\n",
    "READ_CHUNKSIZE = None   # 例如 200_000；None 表示整檔一次讀入\n",
    "KEEP_NUMERIC_SAMPLES = False  # 若 True 會保留所有數值樣本以計算四分位數（可能較吃記憶體）\n",
    "\n",
    "# ===== 型態分類 =====\n",
    "def classify_value(v):\n",
    "    if pd.isna(v):\n",
    "        return \"NaN\"\n",
    "    if isinstance(v, (bool, np.bool_)):\n",
    "        return \"bool_true\" if bool(v) else \"bool_false\"\n",
    "    if isinstance(v, str):\n",
    "        vs = v.strip()\n",
    "        if vs == \"\":\n",
    "            return \"empty_string\"\n",
    "        num = pd.to_numeric(vs, errors=\"coerce\")\n",
    "        if pd.isna(num):\n",
    "            return \"str\"\n",
    "        if float(num).is_integer():\n",
    "            return \"int_zero\" if int(num) == 0 else \"int_nonzero\"\n",
    "        else:\n",
    "            return \"float_zero\" if float(num) == 0.0 else \"float_nonzero\"\n",
    "    if isinstance(v, (np.integer, int)):\n",
    "        return \"int_zero\" if int(v) == 0 else \"int_nonzero\"\n",
    "    if isinstance(v, (np.floating, float)):\n",
    "        return \"float_zero\" if float(v) == 0.0 else \"float_nonzero\"\n",
    "\n",
    "    # 其他型別→轉字串再嘗試\n",
    "    s = str(v).strip()\n",
    "    if s == \"\":\n",
    "        return \"empty_string\"\n",
    "    num = pd.to_numeric(s, errors=\"coerce\")\n",
    "    if pd.isna(num):\n",
    "        return \"str\"\n",
    "    if float(num).is_integer():\n",
    "        return \"int_zero\" if int(num) == 0 else \"int_nonzero\"\n",
    "    else:\n",
    "        return \"float_zero\" if float(num) == 0.0 else \"float_nonzero\"\n",
    "\n",
    "# ===== 數值統計累加器（Welford 演算法：可串流累積 mean/std）=====\n",
    "class OnlineStats:\n",
    "    def __init__(self):\n",
    "        self.n = 0\n",
    "        self.mean = 0.0\n",
    "        self.M2 = 0.0  # 累積平方差\n",
    "        self.min = math.inf\n",
    "        self.max = -math.inf\n",
    "        self.samples = []  # 可選：保留樣本供分位數計算\n",
    "\n",
    "    def add(self, x):\n",
    "        # 更新 min/max\n",
    "        if x < self.min: self.min = x\n",
    "        if x > self.max: self.max = x\n",
    "        # Welford\n",
    "        self.n += 1\n",
    "        delta = x - self.mean\n",
    "        self.mean += delta * (1.0 / self.n)\n",
    "        delta2 = x - self.mean\n",
    "        self.M2 += delta * delta2\n",
    "\n",
    "    @property\n",
    "    def count(self):\n",
    "        return self.n\n",
    "\n",
    "    @property\n",
    "    def std(self):\n",
    "        if self.n <= 1:\n",
    "            return float('nan')\n",
    "        return math.sqrt(self.M2 / (self.n - 1))\n",
    "\n",
    "    def to_dict_basic(self):\n",
    "        return {\n",
    "            \"numeric_count\": int(self.count),\n",
    "            \"mean\": float(self.mean) if self.count > 0 else float('nan'),\n",
    "            \"std\": float(self.std),\n",
    "            \"min\": float(self.min) if self.count > 0 else float('nan'),\n",
    "            \"max\": float(self.max) if self.count > 0 else float('nan')\n",
    "        }\n",
    "\n",
    "# ===== 智慧讀檔 =====\n",
    "def smart_read_csv(path):\n",
    "    last_err = None\n",
    "    for enc in ENCODINGS_TRY:\n",
    "        try:\n",
    "            if READ_CHUNKSIZE:\n",
    "                chunks = pd.read_csv(path, dtype=object, encoding=enc, chunksize=READ_CHUNKSIZE, low_memory=False)\n",
    "                return pd.concat(chunks, ignore_index=True)\n",
    "            else:\n",
    "                return pd.read_csv(path, dtype=object, encoding=enc, low_memory=False)\n",
    "        except Exception as e:\n",
    "            last_err = e\n",
    "    raise last_err if last_err else RuntimeError(f\"讀取失敗：{path}\")\n",
    "\n",
    "# ===== 主流程：跨檔案彙總 =====\n",
    "def main():\n",
    "    files = [os.path.join(DATA_DIR, f) for f in os.listdir(DATA_DIR) if f.lower().endswith(\".csv\")]\n",
    "    files.sort()\n",
    "    if not files:\n",
    "        print(f\"⚠️ 找不到任何 CSV：{DATA_DIR}\")\n",
    "        return\n",
    "\n",
    "    print(f\"📂 目錄：{DATA_DIR}\")\n",
    "    print(f\"📄 檔案數：{len(files)}\\n\")\n",
    "\n",
    "    # 為每個欄位建立彙總容器\n",
    "    categories = [\n",
    "        \"NaN\",\n",
    "        \"bool_true\", \"bool_false\",\n",
    "        \"int_zero\", \"int_nonzero\",\n",
    "        \"float_zero\", \"float_nonzero\",\n",
    "        \"empty_string\", \"str\",\n",
    "    ]\n",
    "    dist_sum = {col: {c: 0 for c in categories} for col in TARGET_COLS}\n",
    "    total_sum = {col: 0 for col in TARGET_COLS}\n",
    "    stats_sum = {col: OnlineStats() for col in TARGET_COLS}\n",
    "    if KEEP_NUMERIC_SAMPLES:\n",
    "        numeric_samples = {col: [] for col in TARGET_COLS}\n",
    "    else:\n",
    "        numeric_samples = None\n",
    "\n",
    "    # 逐檔讀取並累計\n",
    "    for idx, path in enumerate(files, 1):\n",
    "        fname = os.path.basename(path)\n",
    "        try:\n",
    "            df = smart_read_csv(path)\n",
    "        except Exception as e:\n",
    "            print(f\"❌ 讀取失敗：{fname} ｜ {e}\")\n",
    "            continue\n",
    "\n",
    "        for col in TARGET_COLS:\n",
    "            if col not in df.columns:\n",
    "                # 沒有此欄位就略過\n",
    "                continue\n",
    "\n",
    "            ser = df[col]\n",
    "            total_sum[col] += len(ser)\n",
    "\n",
    "            # 型態分布累計\n",
    "            for v in ser:\n",
    "                tag = classify_value(v)\n",
    "                if tag in dist_sum[col]:\n",
    "                    dist_sum[col][tag] += 1\n",
    "                else:\n",
    "                    # 理論不會發生；保險起見\n",
    "                    dist_sum[col][\"str\"] += 1\n",
    "\n",
    "            # 數值統計（只針對可轉為數字者）\n",
    "            num = pd.to_numeric(ser, errors=\"coerce\")\n",
    "            num = num.dropna()\n",
    "            if len(num) > 0:\n",
    "                for x in num.values:\n",
    "                    x = float(x)\n",
    "                    stats_sum[col].add(x)\n",
    "                if KEEP_NUMERIC_SAMPLES:\n",
    "                    numeric_samples[col].extend(num.values.tolist())\n",
    "\n",
    "    # ===== 輸出跨檔案總表 =====\n",
    "    for col in TARGET_COLS:\n",
    "        print(\"=\" * 100)\n",
    "        print(f\"🌐 跨檔案彙總：{col}\")\n",
    "        print(f\"總筆數（所有檔案合併） = {total_sum[col]}\")\n",
    "        print(\"Index  Type              Count         Percent(%)\")\n",
    "        print(\"-----  ----------------  ------------  -----------\")\n",
    "        rows = []\n",
    "        for i, cat in enumerate(categories, start=1):\n",
    "            cnt = dist_sum[col][cat]\n",
    "            pct = (cnt / total_sum[col] * 100.0) if total_sum[col] > 0 else 0.0\n",
    "            rows.append((i, cat, cnt, pct))\n",
    "            print(f\"{i:<5}  {cat:<16}  {cnt:>12}  {pct:>11.4f}\")\n",
    "\n",
    "        # 數值統計（基本）\n",
    "        basic = stats_sum[col].to_dict_basic()\n",
    "        print(\"\\n📈 數值統計（僅可轉為數值者；跨檔案彙總）\")\n",
    "        print(f\"numeric_count : {basic['numeric_count']}\")\n",
    "        print(f\"mean          : {basic['mean']}\")\n",
    "        print(f\"std           : {basic['std']}\")\n",
    "        print(f\"min           : {basic['min']}\")\n",
    "        if KEEP_NUMERIC_SAMPLES and numeric_samples[col]:\n",
    "            # 可選：四分位數（需保留樣本）\n",
    "            arr = pd.Series(numeric_samples[col])\n",
    "            q = arr.quantile([0.25, 0.5, 0.75])\n",
    "            print(f\"25%           : {float(q.loc[0.25])}\")\n",
    "            print(f\"50% (median)  : {float(q.loc[0.5])}\")\n",
    "            print(f\"75%           : {float(q.loc[0.75])}\")\n",
    "        print(f\"max           : {basic['max']}\\n\")\n",
    "\n",
    "    print(\"=\" * 100)\n",
    "    print(\"✅ 跨檔案彙總完成。\")\n",
    "\n",
    "if __name__ == \"__main__\":\n",
    "    main()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "98aed968-0978-4a4e-b1ec-109c47af6d40",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "======================================================================\n",
      "📂 路徑：/home/jovyan/1010/data_new/bling_sponvt_1\n",
      "📊 掃描檔案總數：124\n",
      "📈 總筆數：1,602,536\n",
      "🔹 不同元素數量（含型別區分）：504\n",
      "======================================================================\n",
      " 前 30 個最常見元素（依出現次數排序）\n",
      "Index Type     Value                     Count   Percent(%)\n",
      "----------------------------------------------------------------------\n",
      "1     float    float_value           1,312,655      81.9111\n",
      "2     NaN      NaN                     218,150      13.6128\n",
      "3     str      (null)                   51,320       3.2024\n",
      "4     str      376.0                       143       0.0089\n",
      "5     str      415.0                       130       0.0081\n",
      "6     str      373.0                       129       0.0080\n",
      "7     str      365.0                       127       0.0079\n",
      "8     str      389.0                       127       0.0079\n",
      "9     str      393.0                       127       0.0079\n",
      "10    str      375.0                       124       0.0077\n",
      "11    str      363.0                       122       0.0076\n",
      "12    str      391.0                       119       0.0074\n",
      "13    str      404.0                       118       0.0074\n",
      "14    str      401.0                       117       0.0073\n",
      "15    str      424.0                       117       0.0073\n",
      "16    str      379.0                       116       0.0072\n",
      "17    str      400.0                       116       0.0072\n",
      "18    str      397.0                       116       0.0072\n",
      "19    str      402.0                       116       0.0072\n",
      "20    str      374.0                       115       0.0072\n",
      "21    str      377.0                       115       0.0072\n",
      "22    str      384.0                       115       0.0072\n",
      "23    str      359.0                       115       0.0072\n",
      "24    str      390.0                       115       0.0072\n",
      "25    str      398.0                       115       0.0072\n",
      "26    str      367.0                       113       0.0071\n",
      "27    str      369.0                       112       0.0070\n",
      "28    str      382.0                       112       0.0070\n",
      "29    str      418.0                       112       0.0070\n",
      "30    str      370.0                       111       0.0069\n",
      "======================================================================\n",
      "✅ 統計完成\n"
     ]
    }
   ],
   "source": [
    "# 目的：統整指定路徑下所有 CSV 檔案的 sponvt 欄位中「出現過的所有元素」\n",
    "# 顯示：\n",
    "#   - 總掃描檔案數與筆數\n",
    "#   - 各元素出現次數與占比（直接列印）\n",
    "# 不輸出任何檔案。\n",
    "# =========================================================\n",
    "\n",
    "import os\n",
    "import glob\n",
    "from collections import Counter\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "# -----------------------------\n",
    "# 使用者可調整區\n",
    "# -----------------------------\n",
    "DATA_DIR     = r\"/home/jovyan/1010/data_new/bling_sponvt_1\"  # 目標資料夾（請依實際路徑調整）\n",
    "FILE_PATTERN = \"*.csv\"                                    # 要讀取的檔案型態\n",
    "TARGET_COL   = \"vti\"                                   # 欲統計的欄位名稱\n",
    "ENCODING     = None                                       # 若需指定編碼（如 'utf-8'），可修改此處\n",
    "NA_VALUES    = [\"\", \"NA\", \"N/A\", \"null\", \"Null\", \"NULL\"]  # 額外視為 NA 的字串\n",
    "CHUNKSIZE    = 200000                                     # 大檔用分塊讀取；None 表示一次性讀入\n",
    "TOP_N        = 30                                         # 顯示前幾項元素（依出現次數排序）\n",
    "\n",
    "# -----------------------------\n",
    "# 工具函式：建立 (型別, 值) 鍵，避免混淆\n",
    "# -----------------------------\n",
    "def make_raw_key(v):\n",
    "    \"\"\"將值轉為可區分型別的 key：(type_label, value_repr)\n",
    "       將所有 float 統一歸為一類，不再依實際數值區分。\n",
    "    \"\"\"\n",
    "    if pd.isna(v):\n",
    "        return (\"NaN\", \"NaN\")\n",
    "    if isinstance(v, str):\n",
    "        return (\"str\", v.strip())\n",
    "    if isinstance(v, (bool, np.bool_)):\n",
    "        return (\"bool\", bool(v))\n",
    "    if isinstance(v, (int, np.integer)):\n",
    "        return (\"int\", \"int_value\")  # 整數保留型別但不看實際數字也可改這樣\n",
    "    if isinstance(v, (float, np.floating)):\n",
    "        return (\"float\", \"float_value\")  # ✅ 將所有浮點數歸為一類\n",
    "    return (\"other\", str(v))\n",
    "\n",
    "\n",
    "# -----------------------------\n",
    "# 主邏輯\n",
    "# -----------------------------\n",
    "def summarize_sponvt_elements(data_dir):\n",
    "    \"\"\"掃描路徑下所有檔案的 vti 欄位，列印統計結果\"\"\"\n",
    "    files = sorted(glob.glob(os.path.join(data_dir, FILE_PATTERN)))\n",
    "    counter = Counter()\n",
    "    total_rows = 0\n",
    "    file_count = 0\n",
    "\n",
    "    for fp in files:\n",
    "        try:\n",
    "            file_count += 1\n",
    "            if CHUNKSIZE:\n",
    "                for chunk in pd.read_csv(\n",
    "                    fp, usecols=[TARGET_COL],\n",
    "                    encoding=ENCODING, na_values=NA_VALUES,\n",
    "                    low_memory=False, chunksize=CHUNKSIZE\n",
    "                ):\n",
    "                    s = chunk[TARGET_COL]\n",
    "                    total_rows += len(s)\n",
    "                    for v in s:\n",
    "                        counter[make_raw_key(v)] += 1\n",
    "            else:\n",
    "                df = pd.read_csv(\n",
    "                    fp, usecols=[TARGET_COL],\n",
    "                    encoding=ENCODING, na_values=NA_VALUES,\n",
    "                    low_memory=False\n",
    "                )\n",
    "                s = df[TARGET_COL]\n",
    "                total_rows += len(s)\n",
    "                for v in s:\n",
    "                    counter[make_raw_key(v)] += 1\n",
    "        except Exception:\n",
    "            continue\n",
    "\n",
    "    # ==============================\n",
    "    # 印出結果\n",
    "    # ==============================\n",
    "    print(\"=\" * 70)\n",
    "    print(f\"📂 路徑：{data_dir}\")\n",
    "    print(f\"📊 掃描檔案總數：{file_count}\")\n",
    "    print(f\"📈 總筆數：{total_rows:,}\")\n",
    "    print(f\"🔹 不同元素數量（含型別區分）：{len(counter)}\")\n",
    "    print(\"=\" * 70)\n",
    "    print(f\" 前 {TOP_N} 個最常見元素（依出現次數排序）\")\n",
    "    print(f\"{'Index':<5} {'Type':<8} {'Value':<20} {'Count':>10} {'Percent(%)':>12}\")\n",
    "    print(\"-\" * 70)\n",
    "\n",
    "    for i, ((typ, val), cnt) in enumerate(counter.most_common(TOP_N), start=1):\n",
    "        pct = cnt / total_rows * 100 if total_rows > 0 else 0\n",
    "        print(f\"{i:<5} {typ:<8} {str(val)[:20]:<20} {cnt:>10,} {pct:>12.4f}\")\n",
    "\n",
    "    print(\"=\" * 70)\n",
    "    print(\"✅ 統計完成\")\n",
    "\n",
    "# -----------------------------\n",
    "# 執行入口（請手動執行）\n",
    "# -----------------------------\n",
    "if __name__ == \"__main__\":\n",
    "    summarize_sponvt_elements(DATA_DIR)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e3b13978-292a-427f-a046-41701390ab09",
   "metadata": {},
   "outputs": [],
   "source": [
    "sponvt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "6d412802-a7e5-4f65-ad20-7a9ee2b30a8c",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c51222e8-efc1-4aa3-a024-1d901b735130",
   "metadata": {},
   "outputs": [],
   "source": [
    "1022"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b1d0fee0-f411-41af-8525-b57430f9bdd6",
   "metadata": {},
   "outputs": [],
   "source": [
    "我想要看vti的統計數據 因為數據包含NaN, 0及>0的數字，所以我要兩張圖 第一張是str跟float跟0的堆疊條狀圖 要標上數量跟百分比，第二章圖針對>0的資料繪製圖片中的兩個圖 圖都要英文 其他規定要跟照片中的都要有 藍色調為主"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "b6ae951b-1d63-4593-a697-fc70cf98574f",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[vti] Total rows: 1,602,536\n",
      "  str         : 269,470 (16.82%)\n",
      "  0           : 1,832 (0.11%)\n",
      "  float (>0)  : 1,331,213 (83.07%)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 900x570 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[vti > 0] Summary -> n=1,331,213 | mean=491.69 | std=241.61 | min=0.10 | Q1=398.00 | median=474.00 | Q3=550.00 | max=20778.00\n",
      "[Plot range] Using x-axis: min=0.10, cap=99.8th=3035.00 (max=20778.00)\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1170x690 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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b7dZd2/e+97148MEH2/6yeuaZZ27wUqslS5as9y+1rXNsTInRus5bb7213vvsrP1YdxYjxf7eitF6FtDal4Hddttt7R4rldYCrPVb5jbFU089FWeffXaMGDEiJk+eHHfeeWfU1dXFWWedFbNnz44XXnhhk56v9Zu3OjvTq/Vyqk35lq6c9O/fPyIirrrqqkgpdfqzvoKo9b3eaqutSjU2AGwyBRAAW6zWwmXNmjVtZwFEROy8884REfGHP/yhw+1WrFjR9hfqd3+F/Lx58+K8886LiIgf/vCHsffee8e8efPic5/7XKezrFq1Kv785z93+Nif/vSndnN1Zty4cW03mv7jH//Y4Tqty6uqqtouyYp456bExSjm91asU089NQqFQjzwwAPR0NAQTz31VLz88ssxfPjwOPLII9dZv9jX2pl99tknIiLmzp27Ues///zzcfHFF8eYMWPioIMOiuuuuy6WLVsWkydPjrvuuitef/31+O53v9t2/5tNccABB0RExKxZszp8/H/+539i/vz57dbNzYbe69ZLFteX243R+l5PmDChy88BAJubAgiALVbrX4oHDhwYw4YNa1v+oQ99KCLeuXFzR/fz+MEPfhB/+9vfYuDAgW1nEUW8c5+Vk08+OZqamuLss8+OqVOnxu233x51dXVx/fXXx49+9KNO5/n2t7+9zrLVq1fHd7/73YiI+OAHP7jB19SvX784+OCDIyLim9/8ZofrfOMb34iIiEMOOSTq6uralrfex6SrNwPu6u+tO2y//fZxyCGHxMqVK+POO+9sOxPopJNOiqqqda94X/t+N9198+NJkyZFRMSvf/3rtvsxvdubb74Z1157bey7776xyy67xBVXXBHz58+P/fffP/7v//2/8frrr8e9994bU6ZM6fQGzhsyefLkqK6ujj//+c8d3sD4+uuvj4iIvffeu10ZmJMN5fr444+PiIiZM2fGggULurSPZ555JiL+/t4DQE+kAAJgi7NmzZqYMWNGfOc734mIiI9//OPtvp797LPPjiFDhsSCBQvi9NNPb3e/moceeii+8IUvRETEeeed1+5eJV/4whfi2Wefjfe+971tXwe/8847xzXXXBMREZ/4xCfa7gv0blVVVXHddde13bcm4p2/sH7qU5+K+fPnx6BBg+Kss87aqNd34YUXRkTEHXfcEddee227M52uvvrquPPOO6NQKMT/+T//p912Y8eOjYh3zmb461//ulH7WltXf2/dpfVSr+nTp8edd97Zbtm7DR8+vO1bph599NFunWP77bePXXfdNZYvXx6/+c1vOlznxz/+cZx33nkxZ86cGD16dFx44YUxb968eOaZZ+LTn/50232EirX11lu33Tj6E5/4RLsbid93331x5ZVXRkTEJZdcss62r7zyShQKhSgUCvH44493yzzl0Jrr9X2D15577hmnnXZaNDU1xfvf//4OX+u8efPikksuiXvvvXedx9asWRNPPfVUFAqFDs82A4AeIwFAhiZNmpQiIm233XZp4sSJbT977bVXGjRoUIqIFBHpoIMOSkuXLl1n+5/97GepX79+KSJSXV1d2nfffdOYMWPatps8eXJauXJl2/o//elPU6FQSDU1NenZZ59d5/mOPfbYFBHpAx/4QFqzZk3b8sceeyxFRBo9enQ699xzU0SkUaNGpf322y8NHDgwRUSqqqpK99xzzzrPOW3atBQRafr06es89uUvf7lt1q222irtt99+afjw4W3Lvva1r62zzZo1a9Iuu+ySIiL1798/7b///mnSpElp0qRJG/U778rvrVXr+9XRa9lYjY2NqU+fPm372mmnnTpd/8wzz0wRkXr37p323HPPttfa+v7Nnz+/7b3ZVNdcc02KiPTZz362w8dnzpyZTj/99PToo4+2y8Pm0NTUlA488MB2r3Xs2LFtv6fzzz+/w+1aX39EpMcee2yT9rmh97P1eTd1u9bPy7sz2dl7dfvtt7ftb9y4cemQQw5JkyZNarePt99+O02dOrXdZ2b//fdPe++9dxoyZEjb8u9///vrPP9DDz3U9tkGgJ5MAQRAllr/Ivnun6qqqjR8+PD0gQ98IN1www2ppaVlvc/x4osvpn/6p39Ko0ePTjU1NWnQoEHpkEMOSdOnT0+rV69uW2/RokVpxIgRKSLS1Vdf3eFzLV68OI0aNSpFRLrqqqvalq9dAKWU0ve+97209957p7q6ulRfX5+OOuqo9Mtf/rLD5+ysAGp97mOPPTZttdVWqaqqKm211Vbp2GOPTU888cR6X/P8+fPTySefnEaOHJmqqqrW+xf1zmzs721t3VEApZTSCSec0DbzZZdd1um6y5cvT5///OfTjjvumGpqatYpO4opgN58883Ut2/ftPXWW3easVJpbm5OX/va19Luu++e+vbtmwYNGpQmTZqUZs6cud5tfvGLX6SISH369ElvvPHGJu2vJxVAKaV03XXXpQkTJrSVkxGRLrnkknXW+/GPf5yOO+64NHLkyFRdXZ2GDh2a9thjjzRt2rR0zz33pKampnW2af0cdva7BICeoJBSSsWcQQQAdN3jjz8ehx12WIwePXqDX0FNZbngggviyiuvjJtvvjk+/vGPl3ucTXbVVVfF5z//+TjnnHM6vD8VEa+99lqMHTs2dtppp3j22WfbvvkPAHoif0oBAGwGF154YQwbNiwuv/zy9d4Muid78skno6qqKv71X/+13KP0WF/5yldixYoVcdVVVyl/AOjx1v1aDAAAijZo0KC45ZZb4umnn45XX301Ro8eXe6RNlpKKWbPnh0nnnhi7LDDDuUep0dKKcWoUaPiW9/6VhxxxBHlHgcANsglYABQRi4BAwCgFJyrCgAAAJA5ZwABAAAAZM4ZQAAAAACZUwABAAAAZE4BBAAAAJA5BRAAAABA5hRAAAAAAJmrKvcAG2vEiBGxfPny2H777cs9CgAAAECXLViwIPr16xdvvPFGyfZZMWcALV++PFatWlXuMQAAAACKsmrVqli+fHlJ91kxZwC1nvkzd+7cMk8CXbd48eKIiBg2bFiZJ4Guk2NyIMfkQI7JgRyTg67kePz48ZtrnPWqmDOAAAAAAOgaBRAAAABA5hRAAAAAAJlTAAEAAABkTgEEAAAAkLmK+RYwyEF9fX25R4CiyTE5kGNyIMfkQI7JQaXkWAEEJVRV5SNH5ZNjciDH5ECOyYEck4NKybFLwKCEWlpaoqWlpdxjQFHkmBzIMTmQY3Igx+SgUnKsAIISamxsjMbGxnKPAUWRY3Igx+RAjsmBHJODSsmxAggAAAAgcwogAAAAgMwpgAAAAAAypwACAAAAyJwCCAAAACBzlfFl9ZCJYcOGlXsEKJockwM5JgdyTA7kmBxUSo6dAQQAAACQOQUQlFBzc3M0NzeXewwoihyTAzkmB3JMDuSYHFRKjl0CBiW0bNmyiIiora0t8yTQdXJMDuSYHMgxOZBjclApOXYGEAAAAEDmFEAAAAAAmVMAAQAAAGROAQQAAACQOTeBhhLq3bt3uUeAoskxOZBjciDH5ECOyUGl5FgBBCU0ePDgco8ARZNjciDH5ECOyYEck4NKybFLwAAAAAAypwCCEmpqaoqmpqZyjwFFkWNyIMfkQI7JgRyTg0rJsQIISqhSDgzQGTkmB3JMDuSYHMgxOaiUHCuAAAAAADKnAAIAAADInAIIAAAAIHMKIAAAAIDMVZV7ANiS1NTUlHsEKJockwM5JgdyTA7kmBxUSo4VQFBCAwcOLPcIUDQ5JgdyTA7kmBzIMTmolBy7BAwAAAAgcwogKKGlS5fG0qVLyz0GFEWOyYEckwM5JgdyTA4qJccuAYMSWrlyZblHgKLJMTmQY3Igx+RAjslBpeTYGUAAAAAAmVMAAQAAAGROAQQAAACQOQUQAAAAQObcBBpKqK6urtwjQNHkmBzIMTmQY3Igx+SgUnKsAIISqpQDA3RGjsmBHJMDOSYHckwOKiXHLgEDAAAAyJwCCEqooaEhGhoayj0GFEWOyYEckwM5JgdyTA4qJccuAYMSWr16dblHgKLJMTmQY3Igx+RAjslBpeTYGUAAAAAAmVMAAQAAAGROAQQAAACQOQUQAAAAQObcBBpKaMCAAeUeAYomx+RAjsmBHJMDOSYHlZJjBRCUUG1tbblHgKLJMTmQY3Igx+RAjslBpeTYJWAAAAAAmVMAQQktXrw4Fi9eXO4xoChyTA7kmBzIMTmQY3JQKTlWAAEAAABkTgEEAAAAkDkFEAAAAEDmFEAAAAAAmVMAAQAAAGSuqtwDwJakvr6+3CNA0eSYHMgxOZBjciDH5KBScqwAghKqqvKRo/LJMTmQY3Igx+RAjslBpeTYJWBQQi0tLdHS0lLuMaAockwO5JgcyDE5kGNyUCk5VgBBCTU2NkZjY2O5x4CiyDE5kGNyIMfkQI7JQaXkWAEEAAAAkDkFEAAAAEDmFEAAAAAAmVMAAQAAAGROAQQAAACQucr4snrIxLBhw8o9AhRNjsmBHJMDOSYHckwOKiXHzgACAAAAyJwCCEqoubk5mpubyz0GFEWOyYEckwM5JgdyTA4qJccuAYMSWrZsWURE1NbWlnkS6Do5JgdyTA7kmBzIMTmolBw7AwgAAAAgcwogAAAAgMwpgAAAAAAypwACAAAAyJybQEMJ9e7du9wjQNHkmBzIMTmQY3Igx+SgUnKsAIISGjx4cLlHgKLJMTmQY3Igx+RAjslBpeTYJWAAAAAAmVMAQQk1NTVFU1NTuceAosgxOZBjciDH5ECOyUGl5FgBBCVUKQcG6IwckwM5JgdyTA7kmBxUSo4VQAAAAACZUwABAAAAZE4BBAAAAJA5BRAAAABA5qrKPQBsSWpqaso9AhRNjsmBHJMDOSYHckwOKiXHCiAooYEDB5Z7BCiaHJMDOSYHckwO5JgcVEqOXQIGAAAAkDkFEJTQ0qVLY+nSpeUeA4oix+RAjsmBHJMDOSYHlZJjl4BBCa1cubLcI0DR5JgcyDE5kGNyIMfkoFJy7AwgAAAAgMwpgAAAAAAypwACAAAAyJwCCAAAACBzbgINJVRXV1fuEaBockwO5JgcyDE5kGNyUCk5VgBBCVXKgQE6I8fkQI7JgRyTAzkmB5WSY5eAAQAAAGROAQQl1NDQEA0NDeUeA4oix+RAjsmBHJMDOSYHlZJjl4BBCa1evbrcI0DR5JgcyDE5kGNyIMfkoFJy7AwgAAAAgMwpgAAAAAAypwACAAAAyJwCCAAAACBzbgINJTRgwIByjwBFk2NyIMfkQI7JgRyTg0rJsQIISqi2trbcI0DR5JgcyDE5kGNyIMfkoFJy7BIwAAAAgMwpgKCEFi9eHIsXLy73GFAUOSYHckwO5JgcyDE5qJQcK4AAAAAAMqcAAgAAAMicAggAAAAgcwogAAAAgMwpgAAAAAAyV1XuAWBLUl9fX+4RoGhyTA7kmBzIMTmQY3JQKTlWAEEJVVX5yFH55JgcyDE5kGNyIMfkoFJy7BIwKKGWlpZoaWkp9xhQFDkmB3JMDuSYHMgxOaiUHCuAoIQaGxujsbGx3GNAUeSYHMgxOZBjciDH5KBSclwZ5ylBF6SU4m/LV5V7jHaW/v/zVPVZ2W3POahfdRQKhW57PgAAAPKjACJbf1u+KrY//o5yj7HZLZh5UtT3ryn3GAAAAPRgLgEDAAAAyJwCCAAAACBzLgFji3H0aR+O6trqzfLcP731Z9H01tsREVHXv28cecoRm2U/ERGrmlfF/TMe3GzPDwAAQH4UQGwxqmuro6Z2M90rZ+2bMBcKm28/0AMMGzas3CNA0eSYHMgxOZBjclApOXYJGAAAAEDmFEAAbJLm5uZobm4u9xhQFDkmB3JMDuSYHFRKjl0CBsAmWbZsWURE1NbWlnkS6Do5JgdyTA7kmBxUSo6dAQQAAACQOQUQAAAAQOYUQAAAAACZcw+gHuqC634Vf3jpzdh97JD42ln7lXscyI7PGAAAsCVRAPVQf3jpzZj1h4XlHgOy5TPWdb179y73CFA0OSYHckwO5JgcVEqOFUAAbJLBgweXewQomhyTAzkmB3JMDiolx+4BBAAAAJC5bjkDaM6cOfHQQw/FL3/5y3jmmWfitddei9ra2lixYkV3PD0APUhTU1NERNTV1ZV5Eug6OSYHckwO5JgcVEqOu6UAuvzyy+NHP/pRdzwVAD1cpfwBB52RY3Igx+RAjslBpeS4WwqgAw88MPbcc8/Yb7/9Yr/99osRI0Z0x9MCAAAA0A26pQC64IILuuNpAAAAANgM3AQaAAAAIHMKIAAAAIDMdcslYN1p/PjxHS5/6aWXYuzYsSWepnxefm1ZRET8/uU348P/9tMyT1OZWlavKfcIJfHRLz4SVb11uZvq9y+/GRF//6yx8Wpqaso9AhRNjsmBHJMDOSYHlZLjHlcA8Y63VqyKiIily1fFrD8sLPM09GRP/2lRuUeoaK2fNTbewIEDyz0CFE2OyYEckwM5JgeVkuMeVwDNnTu3w+XrOzMoV/37VMfS5atiYL/q2GPMkHKPU5FaVq/ZIsqRA3Yd7gygLvj9y2/G0uWron+f6nKPAgAAsNn1uAKId4wZOSBeW9IUe4wZEg9edWS5x6lIjW+tjO2Pv6PcY2x2P/zS+6O+f2WcctiTfPjffhqz/rAwxowcUO5RKs7SpUsjonL+pQM6IsfkQI7JgRyTg0rJsQIIgE2ycuXKco8ARZNjciDH5ECOyUGl5Nh1IwAAAACZUwABAAAAZE4BBAAAAJC5brkH0AMPPBCXX355u2UrV66MAw44oO2/L7744jjqqKO6Y3cAAAAAbIJuKYAWLVoUzzzzTLtlKaV2yxYtyv/ruAG2BHV1deUeAYomx+RAjsmBHJODSslxtxRAp512Wpx22mnd8VQA9HCV8gccdEaOyYEckwM5JgeVkmP3AAIAAADInAIIgE3S0NAQDQ0N5R4DiiLH5ECOyYEck4NKyXG3XAJG99t97JB2/xfoXj5jXbd69epyjwBFk2NyIMfkQI7JQaXkWAHUQ33trP3KPQJkzWcMAADYkrgEDAAAACBzCiAAAACAzCmAAAAAADLnHkAAbJIBAwaUewQomhyTAzkmB3JMDiolxwogADZJbW1tuUeAoskxOZBjciDH5KBScqwAYouxqnnV5nvylNr9/yubV262XW3W1wEAAECWFEBsMe6f8WBJ9tP01ttxz/U/Ksm+oBwWL14cERHDhg0r8yTQdXJMDuSYHMgxOaiUHLsJNAAAAEDmFEAAAAAAmXMJGNka1K86Fsw8qdxjtPPmkiURETFk6NBue85B/aq77bkAAADIkwKIbBUKhajvX1PuMdppWfFOWdPT5gIAACBvLgEDAAAAyJwzgKCE6uvryz0CFE2OyYEckwM5JgdyTA4qJccKICihqiofOSqfHJMDOSYHckwO5JgcVEqOXQIGJdTS0hItLS3lHgOKIsfkQI7JgRyTAzkmB5WSYwUQlFBjY2M0NjaWewwoihyTAzkmB3JMDuSYHFRKjhVAAAAAAJlTAAEAAABkTgEEAAAAkDkFEAAAAEDmFEAAAAAAmauML6uHTAwbNqzcI0DR5JgcyDE5kGNyIMfkoFJy7AwgAAAAgMwpgKCEmpubo7m5udxjQFHkmBzIMTmQY3Igx+SgUnLsEjAooWXLlkVERG1tbZknga6TY3Igx+RAjsmBHJODSsmxM4AAAAAAMqcAAgAAAMicAggAAAAgcwogAAAAgMy5CTSUUO/evcs9AhRNjsmBHJMDOSYHckwOKiXHCiAoocGDB5d7BCiaHJMDOSYHckwO5JgcVEqOXQIGAAAAkDkFEJRQU1NTNDU1lXsMKIockwM5JgdyTA7kmBxUSo4VQFBClXJggM7IMTmQY3Igx+RAjslBpeRYAQQAAACQOQUQAAAAQOYUQAAAAACZUwABAAAAZK6q3APAlqSmpqbcI0DR5JgcyDE5kGNyIMfkoFJyrACCEho4cGC5R4CiyTE5kGNyIMfkQI7JQaXk2CVgAAAAAJlTAEEJLV26NJYuXVruMaAockwO5JgcyDE5kGNyUCk5dgkYlNDKlSvLPQIUTY7JgRyTAzkmB3JMDiolx84AAgAAAMicAggAAAAgcwogAAAAgMwpgAAAAAAy5ybQUEJ1dXXlHgGKJsfkQI7JgRyTAzkmB5WSYwUQlFClHBigM3JMDuSYHMgxOZBjclApOXYJGAAAAEDmFEBQQg0NDdHQ0FDuMaAockwO5JgcyDE5kGNyUCk5dgkYlNDq1avLPQIUTY7JgRyTAzkmB3JMDiolx84AAgAAAMicAggAAAAgcwogAAAAgMwpgAAAAAAy5ybQUEIDBgwo9whQNDkmB3JMDuSYHMgxOaiUHCuAoIRqa2vLPQIUTY7JgRyTAzkmB3JMDiolxy4BAwAAAMicAghKaPHixbF48eJyjwFFkWNyIMfkQI7JgRyTg0rJsQIIAAAAIHMKIAAAAIDMKYAAAAAAMqcAAgAAAMicAggAAAAgc1XlHgC2JPX19eUeAYomx+RAjsmBHJMDOSYHlZJjBRCUUFWVjxyVT47JgRyTAzkmB3JMDiolxy4BgxJqaWmJlpaWco8BRZFjciDH5ECOyYEck4NKybECCEqosbExGhsbyz0GFEWOyYEckwM5JgdyTA4qJccKIAAAAIDMKYAAAAAAMqcAAgAAAMicAggAAAAgcwogAAAAgMxVxpfVQyaGDRtW7hGgaHJMDuSYHMgxOZBjclApOXYGEAAAAEDmFEBQQs3NzdHc3FzuMaAockwO5JgcyDE5kGNyUCk5dgkYlNCyZcsiIqK2trbMk0DXyTE5kGNyIMfkQI7JQaXk2BlAAAAAAJlTAAEAAABkTgEEAAAAkDkFEAAAAEDm3AQaSqh3797lHgGKJsfkQI7JgRyTAzkmB5WSYwUQlNDgwYPLPQIUTY7JgRyTAzkmB3JMDiolxy4BAwAAAMicAghKqKmpKZqamso9BhRFjsmBHJMDOSYHckwOKiXHCiAooUo5MEBn5JgcyDE5kGNyIMfkoFJyrAACAAAAyJwCCAAAACBzCiAAAACAzCmAAAAAADJXVe4BYEtSU1NT7hGgaHJMDuSYHMgxOZBjclApOVYAQQkNHDiw3CNA0eSYHMgxOZBjciDH5KBScuwSMAAAAIDMKYCghJYuXRpLly4t9xhQFDkmB3JMDuSYHMgxOaiUHLsEDEpo5cqV5R4BiibH5ECOyYEckwM5JgeVkmNnAAEAAABkTgEEAAAAkDkFEAAAAEDmFEAAAAAAmXMTaCihurq6co8ARZNjciDH5ECOyYEck4NKybECCEqoUg4M0Bk5JgdyTA7kmBzIMTmolBy7BAwAAAAgcwogKKGGhoZoaGgo9xhQFDkmB3JMDuSYHMgxOaiUHLsEDEpo9erV5R4BiibH5ECOyYEckwM5JgeVkmNnAAEAAABkTgEEAAAAkDkFEAAAAEDmFEAAAAAAmXMTaCihAQMGlHsEKJockwM5JgdyTA7kmBxUSo4VQFBCtbW15R4BiibH5ECOyYEckwM5JgeVkmOXgAEAAABkTgEEJbR48eJYvHhxuceAosgxOZBjciDH5ECOyUGl5FgBBAAAAJA5BRAAAABA5hRAAAAAAJlTAAEAAABkTgEEAAAAkLmqcg8AW5L6+vpyjwBFk2NyIMfkQI7JgRyTg0rJsQIISqiqykeOyifH5ECOyYEckwM5JgeVkmOXgEEJtbS0REtLS7nHgKLIMTmQY3Igx+RAjslBpeRYAQQl1NjYGI2NjeUeA4oix+RAjsmBHJMDOSYHlZJjBRAAAABA5hRAAAAAAJlTAAEAAABkTgEEAAAAkLlCSimVe4iNMWDAgFi1alWMHTu23KNAl61evToiInr37l3mSaDr5JgcyDE5kGNyIMfkoCs5fumll6K6ujqWLVu2ucZaR8WcAdSvX7+orq4u9xhQlFdeeSVeeeWVco8BRZFjciDH5ECOyYEck4Ou5Li6ujr69eu3eQZaj4o5AwhyMH78+IiImDt3bpknga6TY3Igx+RAjsmBHJODSslxxZwBBAAAAEDXKIAAAAAAMqcAAgAAAMicAggAAAAgcwogAAAAgMz5FjAAAACAzDkDCAAAACBzCiAAAACAzCmAAAAAADKnAAIAAADInAIIAAAAIHMKIAAAAIDMKYAAAAAAMqcAAgAAAMicAgg2waGHHhqFQmG9Pz/5yU863O7mm2+O/fffP/r37x9DhgyJD3/4wzF79uxO9zV79uz48Ic/HEOGDIn+/fvH/vvvHzfddNPmeFlkaM6cOfHVr341pkyZEqNGjYpCoRB9+vTZ4Halyuqrr74aZ5xxRowcOTL69OkTO+20U3zxi1+MFStWbNLrJG+bmuNLL72002P0//7f/3u928oxm0NTU1Pce++98YlPfCL22GOPGDhwYPTr1y/23HPP+NKXvhRvvfXWerd1PKan6EqOHY/pqb7xjW/ElClTYscdd4xBgwZFbW1tjB49OqZNmxZz585d73bZHJMTsNEmTZqUIiJNnTo1TZs2bZ2f3//+9+tsc95556WISH379k2TJ09ORx55ZKqqqkq9e/dOd999d4f7ufvuu1Pv3r1ToVBIkyZNSlOnTk319fUpItJ55523uV8mGZg8eXKKiHY/tbW1nW5Tqqy++OKLafjw4Ski0m677ZY++tGPpjFjxqSISAceeGBasWJF0a+fPGxqji+55JIUEWnixIkdHqN/+MMfdridHLO5fP/732/L7vjx49MJJ5yQjjzyyDRgwIAUEWmXXXZJCxcuXGc7x2N6kq7k2PGYnmro0KGpT58+af/990/HHXdcOu6449JOO+2UIiLV1NSkBx98cJ1tcjomK4BgE7QWQPPnz9+o9R955JEUEWno0KHphRdeaFs+e/bsVFNTkwYNGpTefPPNdtu8+eabadCgQSki0l133dW2/I033kjjxo1LEZEeffTRbnk95OurX/1q+uIXv5juu+++9MYbb2zwL86lzOohhxySIiJ95jOfaVu2atWqdNxxx6WISF/84heLeelkZFNz3PoXjunTp2/0PuSYzemmm25KZ599drvjakopvfbaa2nvvfdOEZE+9rGPtXvM8Ziepis5djymp5o1a1Z6++2311n+ne98J0VEGjlyZGppaWlbntsxWQEEm2BTC6APf/jDKSLSNddcs85jn/nMZ1JEpKuvvrrd8iuvvDJFRJo8efI629x9990pItLRRx/dhenZkm3oL86lyuovf/nLFBFpq622WudfMd54441UXV2dBg8enFauXLnxL44txuYogOSYcpk9e3Zbppubm9uWOx5TSdaXY8djKlFrOTN37ty2Zbkdk90DCDaTFStWxCOPPBIREccff/w6j7cuu++++9otv//++9e7zVFHHRV9+vSJhx9+2DXNdJtSZrV1m4985CNRW1vbbputt946Dj744GhoaIinnnqqiFcEG0+OKZc999wzIiKam5tjyZIlEeF4TOXpKMddJceUW+/evSMioqamJiLyPCYrgKALbrzxxjjnnHPi05/+dHzrW9+KBQsWrLPOvHnzorm5OYYPHx7bbrvtOo/vs88+ERHx+9//vt3y1v9ufXxtNTU1sdtuu8WKFSvi+eef746XAiXN6u9+97v1brP28tb1oCseffTR+OxnPxtnnXVWXHHFFTFnzpz1rivHlMvLL78cERHV1dUxZMiQiHA8pvJ0lOO1OR5TKW6++eZ4/vnnY6eddooxY8ZERJ7HZAUQdMEVV1wR3/3ud+Pb3/52nHvuuTFu3Li4/PLL263TWgp1dLCIiOjXr1/U19dHQ0NDLFu2LCIili5dGo2NjZ1u17q8o9IJuqKUWd3QvuSb7nDLLbfEN7/5zbj++uvj4osvjn333TeOP/74db6pRo4pp29+85sREfHBD36w7V97HY+pNB3leG2Ox/RUV111VZx22mlxwgknxG677RbTpk2LkSNHxm233Ra9er1Tk+R4TFYAwSY45JBD4pZbbomXXnopmpqa4vnnn48vf/nLUVVVFV/84hfb/hCMiLY/2Orq6tb7fP369Wu37tp/GK5vu3dvA8UqZVY3tC/5phjjxo2Lq6++OubOnRtvvfVW/Pd//3fceuutMWrUqLjrrrvi4x//eLv15ZhyefDBB+PGG2+M6urqdv+A5HhMJVlfjiMcj+n5fvrTn8ZNN90UM2fOjLlz58Z2220Xt912W0yYMKFtnRyPyQog2ARf+tKX4tRTT40xY8ZE3759Y6eddooLL7ww7r333oiIuOSSS+Ltt9+OiIiUUkREFAqF9T5f6zrr+++N2QaKVcqsbmhf8k0xTj311Dj//PNj1113jX79+sW2224bJ598cvzqV7+KoUOHxr333huzZ89uW1+OKYfnnnsuTj311EgpxVVXXdV2D5UIx2MqR2c5jnA8pud7+OGHI6UUDQ0N8fOf/zx23nnnOPTQQ+PLX/5y2zo5HpMVQNANjjjiiNh3333jb3/7Wzz99NMRETFgwICIiFi+fPl6t2tqaoqIiP79+7fbZu3HNrQNFKuUWd3QvuSbzWGbbbaJ008/PSLe+Re/VnJMqb366qvxwQ9+MBoaGuJzn/tcnHvuue0edzymEmwox51xPKanqa+vj4MPPjgefPDBmDBhQlx88cXxq1/9KiLyPCYrgKCb7LjjjhER8frrr0dExPbbbx8R7/wh2ZHly5dHY2Nj1NfXt33gBw4cGIMGDep0u9blrc8PxSplVje0L/lmc3n3MTpCjimtxYsXx+GHHx4LFiyI008/Pa6++up11nE8pqfbmBxviOMxPVF1dXWceOKJkVJq+1avHI/JCiDoJg0NDRHx91Z25513jtra2li0aFGHH+Tf/OY3ERGxxx57tFveegpt6+NrW7VqVfzxj3+M2tra2Hnnnbt1frZcpcxqZ9t0ti8o1ruP0a3kmFJYtmxZfOhDH4p58+bFlClT4vvf/36Hp/k7HtOTbWyON8TxmJ5q2LBhERGxaNGiiMjzmKwAgm6waNGiePLJJyPi71/R17dv3/iHf/iHiIiYOXPmOtu0Ljv66KPbLT/qqKPWu839998fK1asiPe///3Rp0+f7nsBbNFKmdXWbe67775obm5ut83ChQvjySefjEGDBsVBBx1UxCuC9lJKcc8990REtLu5Y4Qcs/k1NzfH5MmT49e//nUceeSRcfvtt0fv3r07XNfxmJ5qU3LcGcdjerInnngiIiLGjh0bEZkekxOwUX7xi1+kRx99NK1Zs6bd8vnz56eJEyemiEjHHHNMu8ceeuihFBFp6NCh6YUXXmhbPnv27FRbW5sGDhyYlixZ0m6bJUuWpIEDB6aISHfddVfb8oULF6Zx48aliEgPP/zwZniF5CwiUm1t7XofL2VWWz8v5557btuyVatWpSlTpqSISBdddFERr5ScdZbjRYsWpZtuuimtWLGi3fJly5alM888M0VEGjFiRFq+fHm7x+WYzamlpSUdd9xxKSLSwQcfvE7+OuJ4TE+zqTl2PKan+vnPf57uuOOOtGrVqnbLV65cmb71rW+lXr16pb59+6YFCxa0PZbbMVkBBBtp+vTpKSLSNttskyZNmpROPPHENHHixNSnT58UEWn8+PFp4cKF62x37rnnpohIdXV1afLkyelDH/pQqqqqSr169UozZ87scF8zZ85MvXr1SoVCIR166KHp+OOPT/X19Ski0mc+85nN/VLJwP3335/e9773tf1ERCoUCu2W3X///e22KVVWX3jhhTR06NAUEWn33XdPJ554YhozZkyKiPS+970vvf32293++6AybUqO58+fnyIiDRw4ML3vfe9LJ5xwQjr88MPbslZfX59mzZrV4X7kmM3l2muvTRGRIiIdd9xxadq0aR3+LFq0qN12jsf0JJuaY8djeqrWv88NGzYsHXnkkenkk09ORxxxRNpmm21SRKQ+ffqk//zP/1xnu5yOyQog2Eh/+tOf0tlnn5322WefNHz48FRVVZUGDRqUDjjggPT1r389NTU1rXfb6dOnpwkTJqS6uro0aNCgdOSRR6Ynn3yy0/3NmjUrffCDH0z19fWprq4uTZgwIf3gBz/o7pdFplr/gOvsZ/r06R1uV4qsLliwIJ122mlpxIgRqaamJo0dOzZddNFFnX6O2PJsSo6XLl2aLrjggjRp0qQ0atSoVFtbm+rq6tL48ePT+eefn1599dVO9yXHbA6XXHLJBjMcEWn+/PnrbOt4TE+xqTl2PKanevnll9OFF16YJk6cmLbZZptUXV2d+vXrl8aPH5/+5V/+Jf35z39e77a5HJMLKXXTF8oDAAAA0CO5CTQAAABA5hRAAAAAAJlTAAEAAABkTgEEAAAAkDkFEAAAAEDmFEAAAAAAmVMAAQAAAGROAQQAAACQOQUQAAAAQOYUQAAAAACZUwABAAAAZE4BBACwiS699NIoFAoxY8aMco8CALBRFEAAAO9y6KGHRqFQiFdeeaXcowAAdIuqcg8AAFBpPv3pT8dJJ50U22yzTblHAQDYKAogAIBNNGzYsBg2bFi5xwAA2GguAQMAsjZnzpwoFApxwAEHrHedK6+8MgqFQpxyyilRKBTiiSeeiIiIHXbYIQqFQttPK/cAAgAqjTOAAICsTZgwIXbZZZd45pln4qWXXoqxY8eus85tt90WERGf+tSnorq6On7yk5/EwoULY+rUqdG/f/9SjwwA0O2cAQQAZO/kk0+OiL8XPWt77rnn4ne/+13stddeMWnSpJgxY0bssssuERFx9dVXx4wZM9p+AAAqlQIIAMjeKaecEhERt9566zqPtS5rXQcAIEcKIAAge2PGjIkDDjggnn/++fjNb37T7rE77rgjevXqFSeddFKZpgMA2PwUQADAFqGjs4CefvrpeOmll2LSpEmx7bbblms0AIDNTgEEAGwRTjzxxKiqqoo77rgj1qxZExF/vyeQy78AgNwpgACALcLw4cPj8MMPj9deey0ef/zxWL16dfzwhz+M2tramDp1arnHAwDYrBRAAMAWo/VMn9tuuy0eeeSRWLhwYRx11FFRX1/fbr2ampqIiGhpaSn1iAAAm0VVuQcAACiVY489Nvr16xd33XVXLF++PCI6vvxr5MiRERHx/PPPx7hx40o6IwDA5uAMIABgi9GvX7+YPHlyNDY2xh133BGDBg2Ko446ap31jjnmmIiIOPnkk+OEE06IT37yk/HJT36y1OMCAHQbBRAAsEVZ+4yfqVOnRm1t7TrrTJkyJa655prYdttt47777osbb7wxbrzxxlKOCQDQrQoppVTuIQAAAADYfJwBBAAAAJA5BRAAAABA5hRAAAAAAJlTAAEAAABkTgEEAAAAkDkFEAAAAEDmFEAAAAAAmVMAAQAAAGROAQQAAACQOQUQAAAAQOYUQAAAAACZUwABAAAAZE4BBAAAAJA5BRAAAABA5hRAAAAAAJlTAAEAAABkTgEEAAAAkLn/D6GGySd05kX6AAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1170x420 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\"\"\" vti 視覺化（不存檔、直接顯示）\n",
    "圖1：堆疊條狀圖（str / 0 / float>0），英文、藍色調、窄版、段內標示 count + %\n",
    "圖2A：Histogram（僅 vti > 0），x 軸 = [min, 99.8th percentile]（可調）\n",
    "圖2B：Boxplot（僅 vti > 0），showfliers=False，x 軸與 Histogram 一致\n",
    "\"\"\"\n",
    "\n",
    "import os\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib import colors as mcolors\n",
    "\n",
    "# ============== 使用者設定 ==============\n",
    "DATA_DIR      = \"/home/jovyan/1010/data_new/bling_sponvt_1\"   # ← 依實際路徑調整\n",
    "TARGET_COL    = \"vti\"\n",
    "ENCODINGS_TRY = [\"utf-8\", \"utf-8-sig\", \"big5\", \"cp950\"]\n",
    "READ_CHUNKSIZE = None\n",
    "BAR_WIDTH     = 0.22              # 堆疊條狀圖寬度（窄）\n",
    "PERCENTILE_CAP = 99.8             # 長尾裁切上限（直方圖/箱型圖 x 軸最大值 = 這個分位數）\n",
    "BINS          = 60                # 直方圖箱數\n",
    "\n",
    "# ============== 工具：智慧讀檔 ==============\n",
    "def smart_read_csv(path):\n",
    "    last_err = None\n",
    "    for enc in ENCODINGS_TRY:\n",
    "        try:\n",
    "            if READ_CHUNKSIZE:\n",
    "                chunks = pd.read_csv(path, dtype=object, encoding=enc, chunksize=READ_CHUNKSIZE, low_memory=False)\n",
    "                return pd.concat(chunks, ignore_index=True)\n",
    "            else:\n",
    "                return pd.read_csv(path, dtype=object, encoding=enc, low_memory=False)\n",
    "        except Exception as e:\n",
    "            last_err = e\n",
    "    raise last_err if last_err else RuntimeError(f\"Read failed: {path}\")\n",
    "\n",
    "# ============== 主流程 ==============\n",
    "def main():\n",
    "    files = sorted([os.path.join(DATA_DIR, f) for f in os.listdir(DATA_DIR) if f.lower().endswith(\".csv\")])\n",
    "    if not files:\n",
    "        print(f\"⚠️ No CSV files found in: {DATA_DIR}\")\n",
    "        return\n",
    "\n",
    "    total_count = 0\n",
    "    count_str   = 0   # 非數字（含 NaN/空白/文字/bool）\n",
    "    count_zero  = 0   # 數字 == 0\n",
    "    pos_values  = []  # 數字 > 0\n",
    "\n",
    "    for p in files:\n",
    "        try:\n",
    "            df = smart_read_csv(p)\n",
    "        except Exception as e:\n",
    "            print(f\"❌ {os.path.basename(p)} | {e}\")\n",
    "            continue\n",
    "\n",
    "        if TARGET_COL not in df.columns:\n",
    "            continue\n",
    "\n",
    "        ser = df[TARGET_COL]\n",
    "        total_count += len(ser)\n",
    "\n",
    "        ser_num = pd.to_numeric(ser, errors=\"coerce\")\n",
    "        is_str = ser_num.isna()\n",
    "        count_str += int(is_str.sum())\n",
    "\n",
    "        numeric = ser_num[~is_str]\n",
    "        count_zero += int((numeric == 0).sum())\n",
    "\n",
    "        gt0 = numeric[numeric > 0]\n",
    "        if len(gt0) > 0:\n",
    "            pos_values.extend(gt0.values.astype(float))\n",
    "\n",
    "    def pct(x): return (x / total_count * 100.0) if total_count > 0 else 0.0\n",
    "\n",
    "    # ===== 文字摘要 =====\n",
    "    print(f\"[{TARGET_COL}] Total rows: {total_count:,}\")\n",
    "    print(f\"  str         : {count_str:,} ({pct(count_str):.2f}%)\")\n",
    "    print(f\"  0           : {count_zero:,} ({pct(count_zero):.2f}%)\")\n",
    "    print(f\"  float (>0)  : {len(pos_values):,} ({pct(len(pos_values)):.2f}%)\")\n",
    "\n",
    "    # ============================ 圖1：堆疊條狀圖 ============================\n",
    "    heights  = [count_str, count_zero, len(pos_values)]\n",
    "    labels   = [\"str\", \"0\", \"float (>0)\"]\n",
    "    percents = [pct(h) for h in heights]\n",
    "    blues    = [\"#BBDEFB\", \"#64B5F6\", \"#1976D2\"]  # 淺→深藍\n",
    "\n",
    "    fig1, ax1 = plt.subplots(figsize=(6.0, 3.8), dpi=150)\n",
    "    x = [0]\n",
    "    bottom = 0.0\n",
    "\n",
    "    # 自動字體大小（資料量越大 → 字體略小）\n",
    "    if total_count > 1.5e6:\n",
    "        fs = 8\n",
    "    elif total_count > 5e5:\n",
    "        fs = 9\n",
    "    else:\n",
    "        fs = 10\n",
    "\n",
    "    for i, (h, lab, color) in enumerate(zip(heights, labels, blues)):\n",
    "        ax1.bar(x, [h], bottom=bottom, width=BAR_WIDTH, color=color, edgecolor=\"white\", linewidth=1.0, label=lab)\n",
    "        if h > 0:\n",
    "            y_center = bottom + h/2\n",
    "            # 依底色亮度自動選字色\n",
    "            r, g, b = mcolors.to_rgb(color)\n",
    "            brightness = 0.299*r + 0.587*g + 0.114*b\n",
    "            text_color = \"black\" if brightness > 0.7 else \"white\"\n",
    "            ax1.text(0, y_center, f\"{lab}\\n{h:,} ({percents[i]:.2f}%)\",\n",
    "                     ha=\"center\", va=\"center\", fontsize=fs, color=text_color, fontweight=\"bold\")\n",
    "        bottom += h\n",
    "\n",
    "    ax1.set_title(f\"Type Distribution of {TARGET_COL}\", fontsize=12, pad=8)\n",
    "    ax1.set_ylabel(\"Count\", fontsize=11)\n",
    "    ax1.set_xlim(-0.55, 0.55)\n",
    "    ax1.set_xticks([])\n",
    "    ax1.grid(axis=\"y\", linestyle=\"--\", alpha=0.3)\n",
    "    ax1.legend(loc=\"upper right\", frameon=False)\n",
    "    fig1.tight_layout()\n",
    "    plt.show()\n",
    "\n",
    "    # =================== 圖2：僅 vti>0（Histogram + Boxplot） ===================\n",
    "    if len(pos_values) == 0:\n",
    "        print(f\"No positive numeric values (>0) found for '{TARGET_COL}'.\")\n",
    "        return\n",
    "\n",
    "    arr = np.asarray(pos_values, dtype=float)\n",
    "    arr = arr[np.isfinite(arr)]\n",
    "    if len(arr) == 0:\n",
    "        print(f\"No finite positive values for '{TARGET_COL}'.\")\n",
    "        return\n",
    "\n",
    "    # 以主要分佈為視窗：xlim = [min, PERCENTILE_CAP分位]，避免長尾壓縮主體\n",
    "    vmin = float(np.min(arr))\n",
    "    xmax_cap = float(np.percentile(arr, PERCENTILE_CAP))\n",
    "    # 基本統計（全體 >0 值）\n",
    "    mean = float(np.mean(arr))\n",
    "    std  = float(np.std(arr, ddof=1)) if len(arr) > 1 else float(\"nan\")\n",
    "    q1, med, q3 = [float(x) for x in np.quantile(arr, [0.25, 0.5, 0.75])]\n",
    "    vmax = float(np.max(arr))\n",
    "    stats_text = (f\"n={len(arr):,} | mean={mean:.2f} | std={std:.2f} | \"\n",
    "                  f\"min={vmin:.2f} | Q1={q1:.2f} | median={med:.2f} | \"\n",
    "                  f\"Q3={q3:.2f} | max={vmax:.2f}\")\n",
    "    print(f\"[{TARGET_COL} > 0] Summary -> {stats_text}\")\n",
    "    print(f\"[Plot range] Using x-axis: min={vmin:.2f}, cap={PERCENTILE_CAP}th={xmax_cap:.2f} (max={vmax:.2f})\")\n",
    "\n",
    "    # ---------- 圖2A：Histogram（藍色調；xlim = [min, cap]） ----------\n",
    "    fig2a, ax2a = plt.subplots(figsize=(7.8, 4.6), dpi=150)\n",
    "    ax2a.hist(arr, bins=BINS, color=\"#1E88E5\", alpha=0.9, range=(vmin, xmax_cap))\n",
    "    ax2a.set_title(f\"Histogram of {TARGET_COL} (> 0)\", fontsize=12)\n",
    "    ax2a.set_xlabel(f\"{TARGET_COL} (float)\")\n",
    "    ax2a.set_ylabel(\"Count\")\n",
    "    ax2a.set_xlim(vmin, xmax_cap)\n",
    "    ax2a.grid(axis=\"y\", linestyle=\"--\", alpha=0.3)\n",
    "    # 標註 Q1 / median / Q3（若在線內才畫）\n",
    "    for xval, lab in [(q1, \"Q1\"), (med, \"median\"), (q3, \"Q3\")]:\n",
    "        if vmin <= xval <= xmax_cap:\n",
    "            ax2a.axvline(xval, linestyle=\"--\", color=\"#0D47A1\", linewidth=1.2)\n",
    "            ax2a.text(xval, ax2a.get_ylim()[1]*0.95, lab, rotation=90,\n",
    "                      va=\"top\", ha=\"right\", fontsize=9, color=\"#0D47A1\")\n",
    "    fig2a.tight_layout()\n",
    "    plt.show()\n",
    "\n",
    "    # ---------- 圖2B：Boxplot（藍色調；showfliers=False；xlim = [min, cap]） ----------\n",
    "    fig2b, ax2b = plt.subplots(figsize=(7.8, 2.8), dpi=150)\n",
    "    ax2b.boxplot(\n",
    "        arr, vert=False, patch_artist=True, showfliers=False,\n",
    "        boxprops=dict(facecolor=\"#64B5F6\", edgecolor=\"#0D47A1\", linewidth=1.5),\n",
    "        medianprops=dict(color=\"#0D47A1\", linewidth=1.8),\n",
    "        whiskerprops=dict(color=\"#0D47A1\", linewidth=1.2),\n",
    "        capprops=dict(color=\"#0D47A1\", linewidth=1.2)\n",
    "    )\n",
    "    ax2b.set_title(f\"Boxplot of {TARGET_COL} (> 0, finite)\", fontsize=11)\n",
    "    ax2b.set_xlabel(f\"{TARGET_COL}\")\n",
    "    ax2b.set_xlim(vmin, xmax_cap)\n",
    "    ax2b.grid(axis=\"x\", linestyle=\"--\", alpha=0.3)\n",
    "    # 可選：摘要放在圖下方（若會擠到圖，可註解掉）\n",
    "    # ax2b.text(0.01, -0.75, stats_text, transform=ax2b.transAxes, fontsize=9, color=\"#0D47A1\")\n",
    "    fig2b.tight_layout()\n",
    "    plt.show()\n",
    "\n",
    "if __name__ == \"__main__\":\n",
    "    main()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "4030b8cd-fa21-4450-9524-a4ffc481c1c7",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[vte] Total rows: 1,602,536\n",
      "  str         : 157,093 (9.80%)\n",
      "  0           : 2,206 (0.14%)\n",
      "  float (>0)  : 1,443,199 (90.06%)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 900x570 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[vte > 0] Summary -> n=1,443,199 | mean=484.90 | std=166.88 | min=1.00 | Q1=396.00 | median=476.00 | Q3=563.00 | max=16795.00\n",
      "[Plot range] Using x-axis: min=1.00, cap=99.8th=1219.00 (max=16795.00)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1170x690 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1170x420 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\"\"\" vti 視覺化（不存檔、直接顯示）\n",
    "圖1：堆疊條狀圖（str / 0 / float>0），英文、藍色調、窄版、段內標示 count + %\n",
    "圖2A：Histogram（僅 vti > 0），x 軸 = [min, 99.8th percentile]（可調）\n",
    "圖2B：Boxplot（僅 vti > 0），showfliers=False，x 軸與 Histogram 一致\n",
    "\"\"\"\n",
    "\n",
    "import os\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib import colors as mcolors\n",
    "\n",
    "# ============== 使用者設定 ==============\n",
    "DATA_DIR      = \"/home/jovyan/1010/data_new/bling_sponvt_1\"   # ← 依實際路徑調整\n",
    "TARGET_COL    = \"vte\"\n",
    "ENCODINGS_TRY = [\"utf-8\", \"utf-8-sig\", \"big5\", \"cp950\"]\n",
    "READ_CHUNKSIZE = None\n",
    "BAR_WIDTH     = 0.22              # 堆疊條狀圖寬度（窄）\n",
    "PERCENTILE_CAP = 99.8             # 長尾裁切上限（直方圖/箱型圖 x 軸最大值 = 這個分位數）\n",
    "BINS          = 60                # 直方圖箱數\n",
    "\n",
    "# ============== 工具：智慧讀檔 ==============\n",
    "def smart_read_csv(path):\n",
    "    last_err = None\n",
    "    for enc in ENCODINGS_TRY:\n",
    "        try:\n",
    "            if READ_CHUNKSIZE:\n",
    "                chunks = pd.read_csv(path, dtype=object, encoding=enc, chunksize=READ_CHUNKSIZE, low_memory=False)\n",
    "                return pd.concat(chunks, ignore_index=True)\n",
    "            else:\n",
    "                return pd.read_csv(path, dtype=object, encoding=enc, low_memory=False)\n",
    "        except Exception as e:\n",
    "            last_err = e\n",
    "    raise last_err if last_err else RuntimeError(f\"Read failed: {path}\")\n",
    "\n",
    "# ============== 主流程 ==============\n",
    "def main():\n",
    "    files = sorted([os.path.join(DATA_DIR, f) for f in os.listdir(DATA_DIR) if f.lower().endswith(\".csv\")])\n",
    "    if not files:\n",
    "        print(f\"⚠️ No CSV files found in: {DATA_DIR}\")\n",
    "        return\n",
    "\n",
    "    total_count = 0\n",
    "    count_str   = 0   # 非數字（含 NaN/空白/文字/bool）\n",
    "    count_zero  = 0   # 數字 == 0\n",
    "    pos_values  = []  # 數字 > 0\n",
    "\n",
    "    for p in files:\n",
    "        try:\n",
    "            df = smart_read_csv(p)\n",
    "        except Exception as e:\n",
    "            print(f\"❌ {os.path.basename(p)} | {e}\")\n",
    "            continue\n",
    "\n",
    "        if TARGET_COL not in df.columns:\n",
    "            continue\n",
    "\n",
    "        ser = df[TARGET_COL]\n",
    "        total_count += len(ser)\n",
    "\n",
    "        ser_num = pd.to_numeric(ser, errors=\"coerce\")\n",
    "        is_str = ser_num.isna()\n",
    "        count_str += int(is_str.sum())\n",
    "\n",
    "        numeric = ser_num[~is_str]\n",
    "        count_zero += int((numeric == 0).sum())\n",
    "\n",
    "        gt0 = numeric[numeric > 0]\n",
    "        if len(gt0) > 0:\n",
    "            pos_values.extend(gt0.values.astype(float))\n",
    "\n",
    "    def pct(x): return (x / total_count * 100.0) if total_count > 0 else 0.0\n",
    "\n",
    "    # ===== 文字摘要 =====\n",
    "    print(f\"[{TARGET_COL}] Total rows: {total_count:,}\")\n",
    "    print(f\"  str         : {count_str:,} ({pct(count_str):.2f}%)\")\n",
    "    print(f\"  0           : {count_zero:,} ({pct(count_zero):.2f}%)\")\n",
    "    print(f\"  float (>0)  : {len(pos_values):,} ({pct(len(pos_values)):.2f}%)\")\n",
    "\n",
    "    # ============================ 圖1：堆疊條狀圖 ============================\n",
    "    heights  = [count_str, count_zero, len(pos_values)]\n",
    "    labels   = [\"str\", \"0\", \"float (>0)\"]\n",
    "    percents = [pct(h) for h in heights]\n",
    "    blues    = [\"#BBDEFB\", \"#64B5F6\", \"#1976D2\"]  # 淺→深藍\n",
    "\n",
    "    fig1, ax1 = plt.subplots(figsize=(6.0, 3.8), dpi=150)\n",
    "    x = [0]\n",
    "    bottom = 0.0\n",
    "\n",
    "    # 自動字體大小（資料量越大 → 字體略小）\n",
    "    if total_count > 1.5e6:\n",
    "        fs = 8\n",
    "    elif total_count > 5e5:\n",
    "        fs = 9\n",
    "    else:\n",
    "        fs = 10\n",
    "\n",
    "    for i, (h, lab, color) in enumerate(zip(heights, labels, blues)):\n",
    "        ax1.bar(x, [h], bottom=bottom, width=BAR_WIDTH, color=color, edgecolor=\"white\", linewidth=1.0, label=lab)\n",
    "        if h > 0:\n",
    "            y_center = bottom + h/2\n",
    "            # 依底色亮度自動選字色\n",
    "            r, g, b = mcolors.to_rgb(color)\n",
    "            brightness = 0.299*r + 0.587*g + 0.114*b\n",
    "            text_color = \"black\" if brightness > 0.7 else \"white\"\n",
    "            ax1.text(0, y_center, f\"{lab}\\n{h:,} ({percents[i]:.2f}%)\",\n",
    "                     ha=\"center\", va=\"center\", fontsize=fs, color=text_color, fontweight=\"bold\")\n",
    "        bottom += h\n",
    "\n",
    "    ax1.set_title(f\"Type Distribution of {TARGET_COL}\", fontsize=12, pad=8)\n",
    "    ax1.set_ylabel(\"Count\", fontsize=11)\n",
    "    ax1.set_xlim(-0.55, 0.55)\n",
    "    ax1.set_xticks([])\n",
    "    ax1.grid(axis=\"y\", linestyle=\"--\", alpha=0.3)\n",
    "    ax1.legend(loc=\"upper right\", frameon=False)\n",
    "    fig1.tight_layout()\n",
    "    plt.show()\n",
    "\n",
    "    # =================== 圖2：僅 vti>0（Histogram + Boxplot） ===================\n",
    "    if len(pos_values) == 0:\n",
    "        print(f\"No positive numeric values (>0) found for '{TARGET_COL}'.\")\n",
    "        return\n",
    "\n",
    "    arr = np.asarray(pos_values, dtype=float)\n",
    "    arr = arr[np.isfinite(arr)]\n",
    "    if len(arr) == 0:\n",
    "        print(f\"No finite positive values for '{TARGET_COL}'.\")\n",
    "        return\n",
    "\n",
    "    # 以主要分佈為視窗：xlim = [min, PERCENTILE_CAP分位]，避免長尾壓縮主體\n",
    "    vmin = float(np.min(arr))\n",
    "    xmax_cap = float(np.percentile(arr, PERCENTILE_CAP))\n",
    "    # 基本統計（全體 >0 值）\n",
    "    mean = float(np.mean(arr))\n",
    "    std  = float(np.std(arr, ddof=1)) if len(arr) > 1 else float(\"nan\")\n",
    "    q1, med, q3 = [float(x) for x in np.quantile(arr, [0.25, 0.5, 0.75])]\n",
    "    vmax = float(np.max(arr))\n",
    "    stats_text = (f\"n={len(arr):,} | mean={mean:.2f} | std={std:.2f} | \"\n",
    "                  f\"min={vmin:.2f} | Q1={q1:.2f} | median={med:.2f} | \"\n",
    "                  f\"Q3={q3:.2f} | max={vmax:.2f}\")\n",
    "    print(f\"[{TARGET_COL} > 0] Summary -> {stats_text}\")\n",
    "    print(f\"[Plot range] Using x-axis: min={vmin:.2f}, cap={PERCENTILE_CAP}th={xmax_cap:.2f} (max={vmax:.2f})\")\n",
    "\n",
    "    # ---------- 圖2A：Histogram（藍色調；xlim = [min, cap]） ----------\n",
    "    fig2a, ax2a = plt.subplots(figsize=(7.8, 4.6), dpi=150)\n",
    "    ax2a.hist(arr, bins=BINS, color=\"#1E88E5\", alpha=0.9, range=(vmin, xmax_cap))\n",
    "    ax2a.set_title(f\"Histogram of {TARGET_COL} (> 0)\", fontsize=12)\n",
    "    ax2a.set_xlabel(f\"{TARGET_COL} (float)\")\n",
    "    ax2a.set_ylabel(\"Count\")\n",
    "    ax2a.set_xlim(vmin, xmax_cap)\n",
    "    ax2a.grid(axis=\"y\", linestyle=\"--\", alpha=0.3)\n",
    "    # 標註 Q1 / median / Q3（若在線內才畫）\n",
    "    for xval, lab in [(q1, \"Q1\"), (med, \"median\"), (q3, \"Q3\")]:\n",
    "        if vmin <= xval <= xmax_cap:\n",
    "            ax2a.axvline(xval, linestyle=\"--\", color=\"#0D47A1\", linewidth=1.2)\n",
    "            ax2a.text(xval, ax2a.get_ylim()[1]*0.95, lab, rotation=90,\n",
    "                      va=\"top\", ha=\"right\", fontsize=9, color=\"#0D47A1\")\n",
    "    fig2a.tight_layout()\n",
    "    plt.show()\n",
    "\n",
    "    # ---------- 圖2B：Boxplot（藍色調；showfliers=False；xlim = [min, cap]） ----------\n",
    "    fig2b, ax2b = plt.subplots(figsize=(7.8, 2.8), dpi=150)\n",
    "    ax2b.boxplot(\n",
    "        arr, vert=False, patch_artist=True, showfliers=False,\n",
    "        boxprops=dict(facecolor=\"#64B5F6\", edgecolor=\"#0D47A1\", linewidth=1.5),\n",
    "        medianprops=dict(color=\"#0D47A1\", linewidth=1.8),\n",
    "        whiskerprops=dict(color=\"#0D47A1\", linewidth=1.2),\n",
    "        capprops=dict(color=\"#0D47A1\", linewidth=1.2)\n",
    "    )\n",
    "    ax2b.set_title(f\"Boxplot of {TARGET_COL} (> 0, finite)\", fontsize=11)\n",
    "    ax2b.set_xlabel(f\"{TARGET_COL}\")\n",
    "    ax2b.set_xlim(vmin, xmax_cap)\n",
    "    ax2b.grid(axis=\"x\", linestyle=\"--\", alpha=0.3)\n",
    "    # 可選：摘要放在圖下方（若會擠到圖，可註解掉）\n",
    "    # ax2b.text(0.01, -0.75, stats_text, transform=ax2b.transAxes, fontsize=9, color=\"#0D47A1\")\n",
    "    fig2b.tight_layout()\n",
    "    plt.show()\n",
    "\n",
    "if __name__ == \"__main__\":\n",
    "    main()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6e5eafd5-c25d-4739-979c-3f55f45a41a7",
   "metadata": {},
   "outputs": [],
   "source": [
    "將vti, vte分別轉成NaN\n",
    "針對vti, vte的兩個欄位 分別做以下動作 \n",
    "1. 確定這兩個欄位分別有哪些資料型態 寫出筆數跟占比 \n",
    "2. 將0或0.0的資料都轉成NaN \n",
    "3. 確定兩個欄位分別有哪些資料型態 寫出筆數跟占比 \n",
    "不要另外輸出檔案跟圖片 都直接印在城市中"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "893f3a37-6ecb-4ff5-bb30-3535cf06b338",
   "metadata": {},
   "outputs": [],
   "source": [
    "下面這支程式會針對 vti 與 vte：\n",
    "先統計「目前欄位裡的資料型態（含 NaN/字串/布林/整數/浮點/空字串，以及 0 與 >0 的細分）」的筆數與占比\n",
    "將 0 或 0.0（含可轉成 0 的字串，如 \"0\", \"0.0\", \" 0 \"） 全部轉成 NaN\n",
    "再統計一次同樣的型態分布（筆數與占比）\n",
    "→ 不會輸出任何檔案或圖片，全都用 print() 直接顯示。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "17da3ffd-d64f-451c-b159-12f18b9cb0f7",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "📂 目錄：/home/jovyan/1010/data_new/bling_sponvt_1\n",
      "📄 檔案數：124\n",
      "\n",
      "❌ 讀取失敗：sponvt_zero_all.csv ｜ [Errno 2] No such file or directory: '/home/jovyan/1010/data_new/bling_sponvt_1/sponvt_zero_all.csv'\n",
      "❌ 讀取失敗：sponvt_zero_vti_vte_bad.csv ｜ [Errno 2] No such file or directory: '/home/jovyan/1010/data_new/bling_sponvt_1/sponvt_zero_vti_vte_bad.csv'\n",
      "====================================================================================================\n",
      "【vti】(Before) Type distribution\n",
      "Total rows: 1,602,476\n",
      "Index  Type              Count           Percent(%)\n",
      "-----  ----------------  --------------  -----------\n",
      "1      NaN                      218,092      13.6097\n",
      "2      bool_true                      0       0.0000\n",
      "3      bool_false                     0       0.0000\n",
      "4      int_zero                   1,832       0.1143\n",
      "5      int_nonzero            1,330,301      83.0153\n",
      "6      float_zero                     0       0.0000\n",
      "7      float_nonzero                933       0.0582\n",
      "8      empty_string                   0       0.0000\n",
      "9      str                       51,318       3.2024\n",
      "\n",
      "====================================================================================================\n",
      "【vti】(After  0→NaN) Type distribution\n",
      "Total rows: 1,602,476\n",
      "Index  Type              Count           Percent(%)\n",
      "-----  ----------------  --------------  -----------\n",
      "1      NaN                      219,924      13.7240\n",
      "2      bool_true                      0       0.0000\n",
      "3      bool_false                     0       0.0000\n",
      "4      int_zero                       0       0.0000\n",
      "5      int_nonzero            1,330,301      83.0153\n",
      "6      float_zero                     0       0.0000\n",
      "7      float_nonzero                933       0.0582\n",
      "8      empty_string                   0       0.0000\n",
      "9      str                       51,318       3.2024\n",
      "\n",
      "❌ 讀取失敗：sponvt_zero_all.csv ｜ [Errno 2] No such file or directory: '/home/jovyan/1010/data_new/bling_sponvt_1/sponvt_zero_all.csv'\n",
      "❌ 讀取失敗：sponvt_zero_vti_vte_bad.csv ｜ [Errno 2] No such file or directory: '/home/jovyan/1010/data_new/bling_sponvt_1/sponvt_zero_vti_vte_bad.csv'\n",
      "====================================================================================================\n",
      "【vte】(Before) Type distribution\n",
      "Total rows: 1,602,476\n",
      "Index  Type              Count           Percent(%)\n",
      "-----  ----------------  --------------  -----------\n",
      "1      NaN                      157,033       9.7994\n",
      "2      bool_true                      0       0.0000\n",
      "3      bool_false                     0       0.0000\n",
      "4      int_zero                   2,206       0.1377\n",
      "5      int_nonzero            1,443,237      90.0629\n",
      "6      float_zero                     0       0.0000\n",
      "7      float_nonzero                  0       0.0000\n",
      "8      empty_string                   0       0.0000\n",
      "9      str                            0       0.0000\n",
      "\n",
      "====================================================================================================\n",
      "【vte】(After  0→NaN) Type distribution\n",
      "Total rows: 1,602,476\n",
      "Index  Type              Count           Percent(%)\n",
      "-----  ----------------  --------------  -----------\n",
      "1      NaN                      159,239       9.9371\n",
      "2      bool_true                      0       0.0000\n",
      "3      bool_false                     0       0.0000\n",
      "4      int_zero                       0       0.0000\n",
      "5      int_nonzero            1,443,237      90.0629\n",
      "6      float_zero                     0       0.0000\n",
      "7      float_nonzero                  0       0.0000\n",
      "8      empty_string                   0       0.0000\n",
      "9      str                            0       0.0000\n",
      "\n"
     ]
    }
   ],
   "source": [
    "\"\"\" 任務：\n",
    "1) 統計 vti / vte 兩欄位「資料型態分布」與筆數占比（跨資料夾所有 CSV）\n",
    "2) 將 0 或 0.0（含可被轉成 0 的字串，如 \"0\", \"0.0\", \" 0 \"）轉為 NaN\n",
    "3) 再次統計型態分布與筆數占比\n",
    "輸出：僅印出於終端機，不生成任何檔案或圖片\n",
    "\"\"\"\n",
    "\n",
    "import os\n",
    "import math\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "# ===== 使用者設定 =====\n",
    "DATA_DIR = \"/home/jovyan/1010/data_new/bling_sponvt_1\"  # ← 依你的環境調整\n",
    "TARGET_COLS = [\"vti\", \"vte\"]\n",
    "ENCODINGS_TRY = [\"utf-8\", \"utf-8-sig\", \"big5\", \"cp950\"]\n",
    "READ_CHUNKSIZE = None  # e.g., 200_000；None 表示整檔一次讀入\n",
    "\n",
    "# ===== 型態分類（與先前一致，較細緻）=====\n",
    "CATEGORIES = [\n",
    "    \"NaN\",\n",
    "    \"bool_true\", \"bool_false\",\n",
    "    \"int_zero\", \"int_nonzero\",\n",
    "    \"float_zero\", \"float_nonzero\",\n",
    "    \"empty_string\", \"str\",\n",
    "]\n",
    "\n",
    "def classify_value(v):\n",
    "    \"\"\"將單一值分類到上述 CATEGORIES（互斥）。\"\"\"\n",
    "    if pd.isna(v):\n",
    "        return \"NaN\"\n",
    "    if isinstance(v, (bool, np.bool_)):\n",
    "        return \"bool_true\" if bool(v) else \"bool_false\"\n",
    "    if isinstance(v, str):\n",
    "        vs = v.strip()\n",
    "        if vs == \"\":\n",
    "            return \"empty_string\"\n",
    "        num = pd.to_numeric(vs, errors=\"coerce\")\n",
    "        if pd.isna(num):\n",
    "            return \"str\"\n",
    "        # 可轉數字 → 再依 0 / 非 0 與是否為整數判定\n",
    "        if float(num).is_integer():\n",
    "            return \"int_zero\" if int(num) == 0 else \"int_nonzero\"\n",
    "        else:\n",
    "            return \"float_zero\" if float(num) == 0.0 else \"float_nonzero\"\n",
    "    if isinstance(v, (np.integer, int)):\n",
    "        return \"int_zero\" if int(v) == 0 else \"int_nonzero\"\n",
    "    if isinstance(v, (np.floating, float)):\n",
    "        return \"float_zero\" if float(v) == 0.0 else \"float_nonzero\"\n",
    "\n",
    "    # 其他型別：轉字串再嘗試\n",
    "    s = str(v).strip()\n",
    "    if s == \"\":\n",
    "        return \"empty_string\"\n",
    "    num = pd.to_numeric(s, errors=\"coerce\")\n",
    "    if pd.isna(num):\n",
    "        return \"str\"\n",
    "    if float(num).is_integer():\n",
    "        return \"int_zero\" if int(num) == 0 else \"int_nonzero\"\n",
    "    else:\n",
    "        return \"float_zero\" if float(num) == 0.0 else \"float_nonzero\"\n",
    "\n",
    "def print_distribution(title, counts, total):\n",
    "    \"\"\"將分布結果整齊印出。\"\"\"\n",
    "    print(\"=\" * 100)\n",
    "    print(title)\n",
    "    print(f\"Total rows: {total:,}\")\n",
    "    print(\"Index  Type              Count           Percent(%)\")\n",
    "    print(\"-----  ----------------  --------------  -----------\")\n",
    "    for i, cat in enumerate(CATEGORIES, start=1):\n",
    "        cnt = counts.get(cat, 0)\n",
    "        pct = (cnt / total * 100.0) if total else 0.0\n",
    "        print(f\"{i:<5}  {cat:<16}  {cnt:>14,}  {pct:>11.4f}\")\n",
    "    print()\n",
    "\n",
    "# ===== 智慧讀檔 =====\n",
    "def smart_read_csv(path):\n",
    "    last_err = None\n",
    "    for enc in ENCODINGS_TRY:\n",
    "        try:\n",
    "            if READ_CHUNKSIZE:\n",
    "                chunks = pd.read_csv(path, dtype=object, encoding=enc, chunksize=READ_CHUNKSIZE, low_memory=False)\n",
    "                return pd.concat(chunks, ignore_index=True)\n",
    "            else:\n",
    "                return pd.read_csv(path, dtype=object, encoding=enc, low_memory=False)\n",
    "        except Exception as e:\n",
    "            last_err = e\n",
    "    raise last_err if last_err else RuntimeError(f\"Read failed: {path}\")\n",
    "\n",
    "# ===== 主要流程 =====\n",
    "def main():\n",
    "    files = [os.path.join(DATA_DIR, f) for f in os.listdir(DATA_DIR) if f.lower().endswith(\".csv\")]\n",
    "    files.sort()\n",
    "    if not files:\n",
    "        print(f\"⚠️ 找不到任何 CSV：{DATA_DIR}\")\n",
    "        return\n",
    "\n",
    "    print(f\"📂 目錄：{DATA_DIR}\")\n",
    "    print(f\"📄 檔案數：{len(files)}\\n\")\n",
    "\n",
    "    # 兩階段：before / after（轉 0 -> NaN）\n",
    "    for col in TARGET_COLS:\n",
    "        # 累計器\n",
    "        counts_before = {c: 0 for c in CATEGORIES}\n",
    "        counts_after  = {c: 0 for c in CATEGORIES}\n",
    "        total_rows_before = 0\n",
    "        total_rows_after  = 0  # 與 before 相同筆數，只是類別改變\n",
    "\n",
    "        # 逐檔讀取並累計\n",
    "        for path in files:\n",
    "            fname = os.path.basename(path)\n",
    "            try:\n",
    "                df = smart_read_csv(path)\n",
    "            except Exception as e:\n",
    "                print(f\"❌ 讀取失敗：{fname} ｜ {e}\")\n",
    "                continue\n",
    "\n",
    "            if col not in df.columns:\n",
    "                continue\n",
    "\n",
    "            ser = df[col]  # dtype=object\n",
    "\n",
    "            # ===== (1) Before：原始型態分布 =====\n",
    "            total_rows_before += len(ser)\n",
    "            for v in ser:\n",
    "                tag = classify_value(v)\n",
    "                counts_before[tag] = counts_before.get(tag, 0) + 1\n",
    "\n",
    "            # ===== (2) 轉 0 / 0.0 / \"0\" / \"0.0\" → NaN =====\n",
    "            ser_after = ser.copy()\n",
    "            # 可轉為數字的值（含 \"0\", \"0.0\" 等）→ 轉為 float；轉不動者為 NaN\n",
    "            ser_num = pd.to_numeric(ser_after, errors=\"coerce\")\n",
    "            zero_mask = ser_num == 0  # True 表示等於 0（含 \"0\", \"0.0\", 0, 0.0）\n",
    "            # 將等於 0 的位置設為 NaN（其他原樣保留）\n",
    "            ser_after.loc[zero_mask] = np.nan\n",
    "\n",
    "            # ===== (3) After：型態分布 =====\n",
    "            total_rows_after += len(ser_after)\n",
    "            for v in ser_after:\n",
    "                tag = classify_value(v)\n",
    "                counts_after[tag] = counts_after.get(tag, 0) + 1\n",
    "\n",
    "        # ===== 印出結果 =====\n",
    "        print_distribution(f\"【{col}】(Before) Type distribution\", counts_before, total_rows_before)\n",
    "        print_distribution(f\"【{col}】(After  0→NaN) Type distribution\", counts_after, total_rows_after)\n",
    "\n",
    "if __name__ == \"__main__\":\n",
    "    main()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5f1be009-c855-4bdf-aee0-f18d62a0a185",
   "metadata": {},
   "outputs": [],
   "source": [
    "我要這兩個欄位的資料型態"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "03183125-a589-431b-b38b-236937e81956",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "📂 目錄：/home/jovyan/1010/data_new/bling_sponvt_1\n",
      "📄 檔案數：122\n",
      "\n",
      "====================================================================================================\n",
      "【vti】實際資料型態統計\n",
      "Total rows: 1,602,476\n",
      "Index  Python Type       Count           Percent(%)\n",
      "-----  ----------------  --------------  -----------\n",
      "1      str                    1,384,384      86.3903\n",
      "2      float                    218,092      13.6097\n",
      "\n",
      "====================================================================================================\n",
      "【vte】實際資料型態統計\n",
      "Total rows: 1,602,476\n",
      "Index  Python Type       Count           Percent(%)\n",
      "-----  ----------------  --------------  -----------\n",
      "1      str                    1,445,443      90.2006\n",
      "2      float                    157,033       9.7994\n",
      "\n"
     ]
    }
   ],
   "source": [
    "\"\"\" 統計 vti / vte 欄位的「實際資料型態」\n",
    "例如 <class 'float'>、<class 'int'>、<class 'str'> 等\n",
    "結果直接印出（不輸出檔案、不畫圖）\n",
    "\"\"\"\n",
    "\n",
    "import os\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from collections import Counter\n",
    "\n",
    "# ===== 使用者設定 =====\n",
    "DATA_DIR = \"/home/jovyan/1010/data_new/bling_sponvt_1\"\n",
    "TARGET_COLS = [\"vti\", \"vte\"]\n",
    "ENCODINGS_TRY = [\"utf-8\", \"utf-8-sig\", \"big5\", \"cp950\"]\n",
    "READ_CHUNKSIZE = None\n",
    "\n",
    "# ===== 智慧讀檔 =====\n",
    "def smart_read_csv(path):\n",
    "    last_err = None\n",
    "    for enc in ENCODINGS_TRY:\n",
    "        try:\n",
    "            if READ_CHUNKSIZE:\n",
    "                chunks = pd.read_csv(path, dtype=object, encoding=enc, chunksize=READ_CHUNKSIZE, low_memory=False)\n",
    "                return pd.concat(chunks, ignore_index=True)\n",
    "            else:\n",
    "                return pd.read_csv(path, dtype=object, encoding=enc, low_memory=False)\n",
    "        except Exception as e:\n",
    "            last_err = e\n",
    "    raise last_err if last_err else RuntimeError(f\"Read failed: {path}\")\n",
    "\n",
    "# ===== 主程式 =====\n",
    "def main():\n",
    "    files = [os.path.join(DATA_DIR, f) for f in os.listdir(DATA_DIR) if f.lower().endswith(\".csv\")]\n",
    "    files.sort()\n",
    "    if not files:\n",
    "        print(f\"⚠️ 找不到任何 CSV：{DATA_DIR}\")\n",
    "        return\n",
    "\n",
    "    print(f\"📂 目錄：{DATA_DIR}\")\n",
    "    print(f\"📄 檔案數：{len(files)}\\n\")\n",
    "\n",
    "    for col in TARGET_COLS:\n",
    "        type_counter = Counter()\n",
    "        total = 0\n",
    "\n",
    "        for path in files:\n",
    "            try:\n",
    "                df = smart_read_csv(path)\n",
    "            except Exception as e:\n",
    "                print(f\"❌ 讀取失敗：{os.path.basename(path)} ｜ {e}\")\n",
    "                continue\n",
    "\n",
    "            if col not in df.columns:\n",
    "                continue\n",
    "\n",
    "            ser = df[col]\n",
    "            total += len(ser)\n",
    "            # 統計實際 Python 型態\n",
    "            type_counter.update(type(v).__name__ for v in ser)\n",
    "\n",
    "        # 印出結果\n",
    "        print(\"=\" * 100)\n",
    "        print(f\"【{col}】實際資料型態統計\")\n",
    "        print(f\"Total rows: {total:,}\")\n",
    "        print(\"Index  Python Type       Count           Percent(%)\")\n",
    "        print(\"-----  ----------------  --------------  -----------\")\n",
    "        for i, (tname, cnt) in enumerate(sorted(type_counter.items(), key=lambda x: -x[1]), start=1):\n",
    "            pct = cnt / total * 100 if total else 0\n",
    "            print(f\"{i:<5}  {tname:<16}  {cnt:>14,}  {pct:>11.4f}\")\n",
    "        print()\n",
    "\n",
    "if __name__ == \"__main__\":\n",
    "    main()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "67d1723d-fca4-4788-a034-fec3ecb69a25",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "📂 掃描目錄：/home/jovyan/1010/data_new/bling_sponvt_1\n",
      "📄 檔案數：122\n",
      "\n",
      "====================================================================================================\n",
      "【vti】空白檢查結果\n",
      "總筆數：1,602,476\n",
      "含前後空白（' 450' 或 '450 '）筆數：0（0.0000%）\n",
      "全空白字串（'   '）筆數：0（0.0000%）\n",
      "✅ 未發現含空白或全空白的內容。\n",
      "\n",
      "====================================================================================================\n",
      "【vte】空白檢查結果\n",
      "總筆數：1,602,476\n",
      "含前後空白（' 450' 或 '450 '）筆數：0（0.0000%）\n",
      "全空白字串（'   '）筆數：0（0.0000%）\n",
      "✅ 未發現含空白或全空白的內容。\n",
      "\n"
     ]
    }
   ],
   "source": [
    "\"\"\"  檢查 vti / vte 欄位中「內容是否含有空白」。\n",
    "包含：\n",
    "  - 前後空白（如 ' 450'、'450 '）\n",
    "  - 全空白字串（如 '   '）\n",
    "結果直接印出，無輸出檔案。\n",
    "\"\"\"\n",
    "\n",
    "import os\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "# ===== 使用者設定 =====\n",
    "DATA_DIR = \"/home/jovyan/1010/data_new/bling_sponvt_1\"\n",
    "TARGET_COLS = [\"vti\", \"vte\"]\n",
    "ENCODINGS_TRY = [\"utf-8\", \"utf-8-sig\", \"big5\", \"cp950\"]\n",
    "READ_CHUNKSIZE = None\n",
    "\n",
    "# ===== 智慧讀檔 =====\n",
    "def smart_read_csv(path):\n",
    "    last_err = None\n",
    "    for enc in ENCODINGS_TRY:\n",
    "        try:\n",
    "            return pd.read_csv(path, dtype=object, encoding=enc, low_memory=False)\n",
    "        except Exception as e:\n",
    "            last_err = e\n",
    "    raise last_err if last_err else RuntimeError(f\"讀取失敗：{path}\")\n",
    "\n",
    "# ===== 主程式 =====\n",
    "def main():\n",
    "    files = sorted([os.path.join(DATA_DIR, f) for f in os.listdir(DATA_DIR) if f.lower().endswith(\".csv\")])\n",
    "    if not files:\n",
    "        print(f\"⚠️ 找不到任何 CSV：{DATA_DIR}\")\n",
    "        return\n",
    "\n",
    "    print(f\"📂 掃描目錄：{DATA_DIR}\")\n",
    "    print(f\"📄 檔案數：{len(files)}\\n\")\n",
    "\n",
    "    for col in TARGET_COLS:\n",
    "        total = 0\n",
    "        count_leading_trailing = 0\n",
    "        count_all_blank = 0\n",
    "        examples = []\n",
    "\n",
    "        for path in files:\n",
    "            try:\n",
    "                df = smart_read_csv(path)\n",
    "            except Exception as e:\n",
    "                print(f\"❌ {os.path.basename(path)} 讀取失敗：{e}\")\n",
    "                continue\n",
    "\n",
    "            if col not in df.columns:\n",
    "                continue\n",
    "\n",
    "            ser = df[col].astype(str)  # 全部視為字串檢查\n",
    "            total += len(ser)\n",
    "\n",
    "            # 找出「含有前後空白」的值\n",
    "            mask_space = ser.str.match(r\"^\\s+.*|.*\\s+$\", na=False)\n",
    "            count_leading_trailing += mask_space.sum()\n",
    "\n",
    "            # 找出「全部是空白」的值\n",
    "            mask_all_blank = ser.str.fullmatch(r\"\\s*\", na=False)\n",
    "            count_all_blank += mask_all_blank.sum()\n",
    "\n",
    "            # 收集少量範例（最多10筆）\n",
    "            ex = ser[mask_space | mask_all_blank].head(10).tolist()\n",
    "            examples.extend(ex)\n",
    "\n",
    "        print(\"=\" * 100)\n",
    "        print(f\"【{col}】空白檢查結果\")\n",
    "        print(f\"總筆數：{total:,}\")\n",
    "        print(f\"含前後空白（' 450' 或 '450 '）筆數：{count_leading_trailing:,}（{count_leading_trailing/total*100:.4f}%）\")\n",
    "        print(f\"全空白字串（'   '）筆數：{count_all_blank:,}（{count_all_blank/total*100:.4f}%）\")\n",
    "\n",
    "        if examples:\n",
    "            print(\"🔍 範例（最多10筆）：\")\n",
    "            for e in examples[:10]:\n",
    "                print(f\"  → '{e}'\")\n",
    "        else:\n",
    "            print(\"✅ 未發現含空白或全空白的內容。\")\n",
    "        print()\n",
    "\n",
    "if __name__ == \"__main__\":\n",
    "    main()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "241b392e-2bd3-4aa5-8c04-c7e5219ad6ba",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "📂 目錄：/home/jovyan/1010/data_new/bling_sponvt_1\n",
      "📄 檔案數：122\n",
      "\n",
      "✅ 合併完成，共 1,602,476 筆資料\n",
      "\n",
      "====================================================================================================\n",
      "【vti】(Before) 實際資料型態統計\n",
      "Total rows: 1,602,476\n",
      "Index  Python Type       Count           Percent(%)\n",
      "-----  ----------------  --------------  -----------\n",
      "1      str                    1,384,384      86.3903\n",
      "2      float                    218,092      13.6097\n",
      "\n",
      "====================================================================================================\n",
      "【vti】(After) 實際資料型態統計\n",
      "Total rows: 1,602,476\n",
      "Index  Python Type       Count           Percent(%)\n",
      "-----  ----------------  --------------  -----------\n",
      "1      float                  1,333,066      83.1879\n",
      "2      str                      269,410      16.8121\n",
      "\n",
      "📈 vti 欄位轉換結果：float = 1,333,066, str = 269,410\n",
      "\n",
      "====================================================================================================\n",
      "【vte】(Before) 實際資料型態統計\n",
      "Total rows: 1,602,476\n",
      "Index  Python Type       Count           Percent(%)\n",
      "-----  ----------------  --------------  -----------\n",
      "1      str                    1,445,443      90.2006\n",
      "2      float                    157,033       9.7994\n",
      "\n",
      "====================================================================================================\n",
      "【vte】(After) 實際資料型態統計\n",
      "Total rows: 1,602,476\n",
      "Index  Python Type       Count           Percent(%)\n",
      "-----  ----------------  --------------  -----------\n",
      "1      float                  1,445,443      90.2006\n",
      "2      str                      157,033       9.7994\n",
      "\n",
      "📈 vte 欄位轉換結果：float = 1,445,443, str = 157,033\n",
      "\n"
     ]
    }
   ],
   "source": [
    "\"\"\" 將 vti / vte 欄位：\n",
    "1. 能轉成數字的 → float\n",
    "2. 無法轉的、NaN、空白 → 字串 \"NaN\"\n",
    "並印出轉換前後的型態分布\n",
    "\"\"\"\n",
    "\n",
    "import os\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from collections import Counter\n",
    "\n",
    "# ===== 使用者設定 =====\n",
    "DATA_DIR = \"/home/jovyan/1010/data_new/bling_sponvt_1\"\n",
    "TARGET_COLS = [\"vti\", \"vte\"]\n",
    "ENCODINGS_TRY = [\"utf-8\", \"utf-8-sig\", \"big5\", \"cp950\"]\n",
    "\n",
    "# ===== 智慧讀檔 =====\n",
    "def smart_read_csv(path):\n",
    "    last_err = None\n",
    "    for enc in ENCODINGS_TRY:\n",
    "        try:\n",
    "            return pd.read_csv(path, dtype=object, encoding=enc, low_memory=False)\n",
    "        except Exception as e:\n",
    "            last_err = e\n",
    "    raise last_err if last_err else RuntimeError(f\"讀取失敗：{path}\")\n",
    "\n",
    "# ===== 統計與印出 =====\n",
    "def type_summary(ser, title):\n",
    "    type_counter = Counter(type(v).__name__ for v in ser)\n",
    "    total = len(ser)\n",
    "    print(\"=\" * 100)\n",
    "    print(title)\n",
    "    print(f\"Total rows: {total:,}\")\n",
    "    print(\"Index  Python Type       Count           Percent(%)\")\n",
    "    print(\"-----  ----------------  --------------  -----------\")\n",
    "    for i, (tname, cnt) in enumerate(sorted(type_counter.items(), key=lambda x: -x[1]), start=1):\n",
    "        pct = cnt / total * 100 if total else 0\n",
    "        print(f\"{i:<5}  {tname:<16}  {cnt:>14,}  {pct:>11.4f}\")\n",
    "    print()\n",
    "\n",
    "# ===== 主程式 =====\n",
    "def main():\n",
    "    files = [os.path.join(DATA_DIR, f) for f in os.listdir(DATA_DIR) if f.lower().endswith(\".csv\")]\n",
    "    files.sort()\n",
    "    if not files:\n",
    "        print(f\"⚠️ 找不到任何 CSV：{DATA_DIR}\")\n",
    "        return\n",
    "\n",
    "    print(f\"📂 目錄：{DATA_DIR}\")\n",
    "    print(f\"📄 檔案數：{len(files)}\\n\")\n",
    "\n",
    "    # 僅取一批代表性資料檔（或可改為彙總全部）\n",
    "    df_list = []\n",
    "    for path in files:\n",
    "        try:\n",
    "            df_list.append(smart_read_csv(path))\n",
    "        except Exception as e:\n",
    "            print(f\"❌ 讀取失敗：{os.path.basename(path)} ｜ {e}\")\n",
    "    df = pd.concat(df_list, ignore_index=True)\n",
    "    print(f\"✅ 合併完成，共 {len(df):,} 筆資料\\n\")\n",
    "\n",
    "    for col in TARGET_COLS:\n",
    "        if col not in df.columns:\n",
    "            print(f\"⚠️ 欄位 {col} 不存在，略過。\")\n",
    "            continue\n",
    "\n",
    "        ser = df[col]\n",
    "        print_distribution = lambda s, label: type_summary(s, f\"【{col}】{label}\")\n",
    "\n",
    "        # (1) Before\n",
    "        print_distribution(ser, \"(Before) 實際資料型態統計\")\n",
    "\n",
    "        # (2) 轉換邏輯\n",
    "        ser_converted = pd.to_numeric(ser, errors=\"coerce\")  # 可轉數字的變成 float，其他變 NaN\n",
    "        ser_converted = ser_converted.apply(\n",
    "            lambda x: x if pd.notna(x) else \"NaN\"  # NaN 改成字串 \"NaN\"\n",
    "        )\n",
    "\n",
    "        # (3) After\n",
    "        print_distribution(ser_converted, \"(After) 實際資料型態統計\")\n",
    "\n",
    "        # (4) 驗證統計結果\n",
    "        float_count = sum(isinstance(v, float) for v in ser_converted)\n",
    "        str_count = sum(isinstance(v, str) for v in ser_converted)\n",
    "        print(f\"📈 {col} 欄位轉換結果：float = {float_count:,}, str = {str_count:,}\\n\")\n",
    "\n",
    "if __name__ == \"__main__\":\n",
    "    main()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "052e2675-9cc6-4dc5-8245-0e94ab8b65b4",
   "metadata": {},
   "outputs": [],
   "source": [
    "我要看一下vtivte資料目前有什麼型態"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "79d24cb5-7622-45c3-81e0-b07048bc851e",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "📂 掃描目錄：/home/jovyan/1010/data_new/bling_spomvy_ok_v1\n",
      "📄 檔案數：122\n",
      "\n",
      "✅ 合併完成，共 1,602,476 筆資料\n",
      "\n",
      "====================================================================================================\n",
      "【vti】目前實際資料型態統計\n",
      "Total rows: 1,602,476\n",
      "Index  Python Type       Count           Percent(%)\n",
      "-----  ----------------  --------------  -----------\n",
      "1      str                    1,384,384      86.3903\n",
      "2      float                    218,092      13.6097\n",
      "\n",
      "====================================================================================================\n",
      "【vte】目前實際資料型態統計\n",
      "Total rows: 1,602,476\n",
      "Index  Python Type       Count           Percent(%)\n",
      "-----  ----------------  --------------  -----------\n",
      "1      str                    1,445,443      90.2006\n",
      "2      float                    157,033       9.7994\n",
      "\n"
     ]
    }
   ],
   "source": [
    "\"\"\"檢查 vti / vte 欄位的實際 Python 資料型態\n",
    "直接統計 <class 'float'>、<class 'str'>、<class 'int'> 等出現比例\n",
    "\"\"\"\n",
    "\n",
    "import os\n",
    "import pandas as pd\n",
    "from collections import Counter\n",
    "\n",
    "# ===== 使用者設定 =====\n",
    "DATA_DIR = \"/home/jovyan/1010/data_new/bling_spomvy_ok_v1\"\n",
    "TARGET_COLS = [\"vti\", \"vte\"]\n",
    "ENCODINGS_TRY = [\"utf-8\", \"utf-8-sig\", \"big5\", \"cp950\"]\n",
    "\n",
    "# ===== 智慧讀檔 =====\n",
    "def smart_read_csv(path):\n",
    "    last_err = None\n",
    "    for enc in ENCODINGS_TRY:\n",
    "        try:\n",
    "            return pd.read_csv(path, dtype=object, encoding=enc, low_memory=False)\n",
    "        except Exception as e:\n",
    "            last_err = e\n",
    "    raise last_err if last_err else RuntimeError(f\"讀取失敗：{path}\")\n",
    "\n",
    "# ===== 主程式 =====\n",
    "def main():\n",
    "    files = sorted([os.path.join(DATA_DIR, f) for f in os.listdir(DATA_DIR) if f.lower().endswith(\".csv\")])\n",
    "    if not files:\n",
    "        print(f\"⚠️ 找不到任何 CSV：{DATA_DIR}\")\n",
    "        return\n",
    "\n",
    "    print(f\"📂 掃描目錄：{DATA_DIR}\")\n",
    "    print(f\"📄 檔案數：{len(files)}\\n\")\n",
    "\n",
    "    # 合併所有資料（若檔案大可改成逐檔統計）\n",
    "    df_list = []\n",
    "    for path in files:\n",
    "        try:\n",
    "            df_list.append(smart_read_csv(path))\n",
    "        except Exception as e:\n",
    "            print(f\"❌ {os.path.basename(path)} 讀取失敗：{e}\")\n",
    "    df = pd.concat(df_list, ignore_index=True)\n",
    "    print(f\"✅ 合併完成，共 {len(df):,} 筆資料\\n\")\n",
    "\n",
    "    for col in TARGET_COLS:\n",
    "        if col not in df.columns:\n",
    "            print(f\"⚠️ 欄位 {col} 不存在，略過。\")\n",
    "            continue\n",
    "\n",
    "        ser = df[col]\n",
    "        total = len(ser)\n",
    "        type_counter = Counter(type(v).__name__ for v in ser)\n",
    "\n",
    "        print(\"=\" * 100)\n",
    "        print(f\"【{col}】目前實際資料型態統計\")\n",
    "        print(f\"Total rows: {total:,}\")\n",
    "        print(\"Index  Python Type       Count           Percent(%)\")\n",
    "        print(\"-----  ----------------  --------------  -----------\")\n",
    "        for i, (tname, cnt) in enumerate(sorted(type_counter.items(), key=lambda x: -x[1]), start=1):\n",
    "            pct = cnt / total * 100 if total else 0\n",
    "            print(f\"{i:<5}  {tname:<16}  {cnt:>14,}  {pct:>11.4f}\")\n",
    "        print()\n",
    "\n",
    "if __name__ == \"__main__\":\n",
    "    main("
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "6047ccdc-527b-47ad-8b79-27a6b20f189d",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "📂 掃描目錄：/home/jovyan/1010/data_new/bling_spomvy_ok_v1\n",
      "📄 檔案數：122\n",
      "\n",
      "✅ 合併完成，共 1,602,476 筆資料\n",
      "\n",
      "====================================================================================================\n",
      "【vti】各型態內容出現次數前五\n",
      "\n",
      "🔹 float 型態前五高內容：\n",
      "總 float 筆數：218,092\n",
      " 1. nan              218,092  (100.0000%)\n",
      "\n",
      "🔹 str 型態前五高內容：\n",
      "總 str 筆數：1,384,384\n",
      " 1. '(null)'          51,318  ( 3.7069%)\n",
      " 2. '500.0'            8,957  ( 0.6470%)\n",
      " 3. '520.0'            8,581  ( 0.6198%)\n",
      " 4. '510.0'            8,422  ( 0.6084%)\n",
      " 5. '490.0'            8,339  ( 0.6024%)\n",
      "\n",
      "====================================================================================================\n",
      "【vte】各型態內容出現次數前五\n",
      "\n",
      "🔹 float 型態前五高內容：\n",
      "總 float 筆數：157,033\n",
      " 1. nan              157,033  (100.0000%)\n",
      "\n",
      "🔹 str 型態前五高內容：\n",
      "總 str 筆數：1,445,443\n",
      " 1. '404.0'            5,345  ( 0.3698%)\n",
      " 2. '453.0'            4,912  ( 0.3398%)\n",
      " 3. '457.0'            4,868  ( 0.3368%)\n",
      " 4. '470.0'            4,865  ( 0.3366%)\n",
      " 5. '480.0'            4,847  ( 0.3353%)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "\"\"\" 統計 vti / vte 兩欄中：\n",
    "- 各型態（float / str）出現次數前五高的內容\n",
    "輸出直接印出，不輸出檔案、不繪圖\n",
    "\"\"\"\n",
    "\n",
    "import os\n",
    "import pandas as pd\n",
    "from collections import Counter\n",
    "\n",
    "# ===== 使用者設定 =====\n",
    "DATA_DIR = \"/home/jovyan/1010/data_new/bling_spomvy_ok_v1\"\n",
    "TARGET_COLS = [\"vti\", \"vte\"]\n",
    "ENCODINGS_TRY = [\"utf-8\", \"utf-8-sig\", \"big5\", \"cp950\"]\n",
    "\n",
    "# ===== 智慧讀檔 =====\n",
    "def smart_read_csv(path):\n",
    "    last_err = None\n",
    "    for enc in ENCODINGS_TRY:\n",
    "        try:\n",
    "            return pd.read_csv(path, dtype=object, encoding=enc, low_memory=False)\n",
    "        except Exception as e:\n",
    "            last_err = e\n",
    "    raise last_err if last_err else RuntimeError(f\"讀取失敗：{path}\")\n",
    "\n",
    "# ===== 主程式 =====\n",
    "def main():\n",
    "    files = sorted([os.path.join(DATA_DIR, f) for f in os.listdir(DATA_DIR) if f.lower().endswith(\".csv\")])\n",
    "    if not files:\n",
    "        print(f\"⚠️ 找不到任何 CSV：{DATA_DIR}\")\n",
    "        return\n",
    "\n",
    "    print(f\"📂 掃描目錄：{DATA_DIR}\")\n",
    "    print(f\"📄 檔案數：{len(files)}\\n\")\n",
    "\n",
    "    # 合併所有資料\n",
    "    df_list = []\n",
    "    for path in files:\n",
    "        try:\n",
    "            df_list.append(smart_read_csv(path))\n",
    "        except Exception as e:\n",
    "            print(f\"❌ {os.path.basename(path)} 讀取失敗：{e}\")\n",
    "    df = pd.concat(df_list, ignore_index=True)\n",
    "    print(f\"✅ 合併完成，共 {len(df):,} 筆資料\\n\")\n",
    "\n",
    "    # 對每一欄進行統計\n",
    "    for col in TARGET_COLS:\n",
    "        if col not in df.columns:\n",
    "            print(f\"⚠️ 欄位 {col} 不存在，略過。\")\n",
    "            continue\n",
    "\n",
    "        ser = df[col]\n",
    "        print(\"=\" * 100)\n",
    "        print(f\"【{col}】各型態內容出現次數前五\")\n",
    "\n",
    "        # --- 按型態分類 ---\n",
    "        float_values = []\n",
    "        str_values = []\n",
    "\n",
    "        for v in ser:\n",
    "            if isinstance(v, float):\n",
    "                float_values.append(v)\n",
    "            elif isinstance(v, str):\n",
    "                str_values.append(v)\n",
    "\n",
    "        # --- 統計與取前五 ---\n",
    "        def top5(counter):\n",
    "            return sorted(counter.items(), key=lambda x: -x[1])[:5]\n",
    "\n",
    "        float_counter = Counter(float_values)\n",
    "        str_counter = Counter(str_values)\n",
    "\n",
    "        total_float = sum(float_counter.values())\n",
    "        total_str = sum(str_counter.values())\n",
    "\n",
    "        # --- 印出結果 ---\n",
    "        print(\"\\n🔹 float 型態前五高內容：\")\n",
    "        if total_float == 0:\n",
    "            print(\"（無 float 型內容）\")\n",
    "        else:\n",
    "            print(f\"總 float 筆數：{total_float:,}\")\n",
    "            for i, (val, cnt) in enumerate(top5(float_counter), start=1):\n",
    "                pct = cnt / total_float * 100\n",
    "                print(f\"{i:>2}. {val!r:<12}  {cnt:>10,}  ({pct:>7.4f}%)\")\n",
    "\n",
    "        print(\"\\n🔹 str 型態前五高內容：\")\n",
    "        if total_str == 0:\n",
    "            print(\"（無 str 型內容）\")\n",
    "        else:\n",
    "            print(f\"總 str 筆數：{total_str:,}\")\n",
    "            for i, (val, cnt) in enumerate(top5(str_counter), start=1):\n",
    "                pct = cnt / total_str * 100\n",
    "                print(f\"{i:>2}. {val!r:<12}  {cnt:>10,}  ({pct:>7.4f}%)\")\n",
    "\n",
    "        print()\n",
    "\n",
    "if __name__ == \"__main__\":\n",
    "    main()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "29bdf5dd-1b07-4c6b-89a9-0959befab1c6",
   "metadata": {},
   "outputs": [],
   "source": [
    "看一下\"/home/jovyan/1010/data_new/bling_spomvy_ok_v1\"的spomvy_ok_v1內容數量\n",
    "以及/home/jovyan/1010/data_new/bling_sponvt_1\"的sponvt_ok內容數量"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "59744deb-6378-47dd-8ca9-6449108633c8",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "====================================================================================================\n",
      "📊 統計結果：spomvy_ok_v1（來源：/home/jovyan/1010/data_new/bling_spomvy_ok_v1）\n",
      "總筆數：1,602,476\n",
      "唯一值種類數：3\n",
      "Index  Value                 Count         Percent(%)\n",
      "-----  ---------------------  ------------  -----------\n",
      "1      0                         1,424,934      88.9208\n",
      "2      2                           161,148      10.0562\n",
      "3      1                            16,394       1.0230\n",
      "\n",
      "====================================================================================================\n",
      "📊 統計結果：sponvt_ok（來源：/home/jovyan/1010/data_new/bling_sponvt_1）\n",
      "總筆數：1,602,476\n",
      "唯一值種類數：2\n",
      "Index  Value                 Count         Percent(%)\n",
      "-----  ---------------------  ------------  -----------\n",
      "1      0                         1,586,052      98.9751\n",
      "2      1                            16,424       1.0249\n",
      "\n"
     ]
    }
   ],
   "source": [
    "\"\"\" 目的：\n",
    "1️⃣ 統計 /home/jovyan/1010/data_new/bling_spomvy_ok_v1/ 裡的 spomvy_ok_v1 欄位\n",
    "2️⃣ 統計 /home/jovyan/1010/data_new/bling_sponvt_1/ 裡的 sponvt_ok 欄位\n",
    "顯示每個欄位中唯一值的種類（類別）與筆數、占比（降冪排序）\n",
    "\"\"\"\n",
    "\n",
    "import os\n",
    "import pandas as pd\n",
    "from collections import Counter\n",
    "\n",
    "# ===== 使用者設定 =====\n",
    "TARGETS = [\n",
    "    (\"/home/jovyan/1010/data_new/bling_spomvy_ok_v1\", \"spomvy_ok_v1\"),\n",
    "    (\"/home/jovyan/1010/data_new/bling_sponvt_1\", \"sponvt_ok\")\n",
    "]\n",
    "ENCODINGS_TRY = [\"utf-8\", \"utf-8-sig\", \"big5\", \"cp950\"]\n",
    "\n",
    "# ===== 智慧讀檔 =====\n",
    "def smart_read_csv(path):\n",
    "    last_err = None\n",
    "    for enc in ENCODINGS_TRY:\n",
    "        try:\n",
    "            return pd.read_csv(path, dtype=object, encoding=enc, low_memory=False)\n",
    "        except Exception as e:\n",
    "            last_err = e\n",
    "    raise last_err\n",
    "\n",
    "# ===== 主程式 =====\n",
    "def analyze_column(data_dir, target_col):\n",
    "    files = [os.path.join(data_dir, f) for f in os.listdir(data_dir) if f.lower().endswith(\".csv\")]\n",
    "    files.sort()\n",
    "    if not files:\n",
    "        print(f\"⚠️ 找不到任何 CSV：{data_dir}\")\n",
    "        return\n",
    "\n",
    "    df_list = []\n",
    "    for path in files:\n",
    "        try:\n",
    "            df = smart_read_csv(path)\n",
    "            if target_col in df.columns:\n",
    "                df_list.append(df[[target_col]])\n",
    "            else:\n",
    "                print(f\"⚠️ {os.path.basename(path)} 無此欄位 {target_col}，略過。\")\n",
    "        except Exception as e:\n",
    "            print(f\"❌ {os.path.basename(path)} 讀取失敗：{e}\")\n",
    "    if not df_list:\n",
    "        print(f\"⚠️ 找不到含 {target_col} 欄位的檔案。\")\n",
    "        return\n",
    "\n",
    "    data = pd.concat(df_list, ignore_index=True)[target_col]\n",
    "    total = len(data)\n",
    "    value_counts = Counter(data)\n",
    "\n",
    "    print(\"=\" * 100)\n",
    "    print(f\"📊 統計結果：{target_col}（來源：{data_dir}）\")\n",
    "    print(f\"總筆數：{total:,}\")\n",
    "    print(f\"唯一值種類數：{len(value_counts):,}\")\n",
    "    print(\"Index  Value                 Count         Percent(%)\")\n",
    "    print(\"-----  ---------------------  ------------  -----------\")\n",
    "\n",
    "    for i, (val, cnt) in enumerate(value_counts.most_common(), start=1):\n",
    "        pct = cnt / total * 100 if total else 0\n",
    "        val_display = str(val)[:20]  # 避免太長\n",
    "        print(f\"{i:<5}  {val_display:<21}  {cnt:>12,}  {pct:>11.4f}\")\n",
    "\n",
    "    print()\n",
    "\n",
    "# ===== 執行 =====\n",
    "for data_dir, col in TARGETS:\n",
    "    analyze_column(data_dir, col)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "20432d61-3950-4df1-b955-6348b105f195",
   "metadata": {},
   "outputs": [],
   "source": [
    "先檢查vte沒有(null)嗎\n",
    "再來要將vti的nan型態轉成float, (null)轉成字串NaN\n",
    "其他str轉成float\n",
    "將vte的nan轉成字串，str數字轉成float\n",
    "並且再統計一次"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "745f8fa1-abd7-454f-9d5f-d807bf35232f",
   "metadata": {},
   "outputs": [],
   "source": [
    "先檢查 vte 是否包含字串 \"(null)\"（列出筆數與占比）\n",
    "vti 轉換規則\n",
    "既有 NaN 保持為 float NaN（不動）\n",
    "\"(null)\" 一律改成字串 \"NaN\"\n",
    "其他 str 嘗試轉 float（能轉就轉；不能轉就保留原字串，並列出無法轉換的筆數）\n",
    "vte 轉換規則\n",
    "NaN → 字串 \"NaN\"\n",
    "字串中是數字的 → 轉 float（其餘非數字字串原樣保留，並列出無法轉換的筆數）\n",
    "轉換後再統計一次（列出各欄位的實際 Python 型態：float/str 的筆數與占比）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "66a93101-7bbd-439c-884a-f20b4663a43c",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "📂 合併完成，共 1,602,476 筆資料；CSV 檔數：122\n",
      "\n",
      "====================================================================================================\n",
      "【Step 1】Check vte contains literal '(null)'\n",
      "(null) literal count in vte : 0  (0.0000%)\n",
      "\n",
      "====================================================================================================\n",
      "【vti】(Before) 實際資料型態統計\n",
      "Total rows: 1,602,476\n",
      "Index  Python Type       Count           Percent(%)\n",
      "-----  ----------------  --------------  -----------\n",
      "1      str                    1,384,384      86.3903\n",
      "2      float                    218,092      13.6097\n",
      "\n",
      "====================================================================================================\n",
      "【vti】(After) 實際資料型態統計\n",
      "Total rows: 1,602,476\n",
      "Index  Python Type       Count           Percent(%)\n",
      "-----  ----------------  --------------  -----------\n",
      "1      float                  1,551,158      96.7976\n",
      "2      str                       51,318       3.2024\n",
      "\n",
      "====================================================================================================\n",
      "【vte】(Before) 實際資料型態統計\n",
      "Total rows: 1,602,476\n",
      "Index  Python Type       Count           Percent(%)\n",
      "-----  ----------------  --------------  -----------\n",
      "1      str                    1,445,443      90.2006\n",
      "2      float                    157,033       9.7994\n",
      "\n",
      "🔎 vte 字串無法轉 float 的筆數：157,033\n",
      "====================================================================================================\n",
      "【vte】(After) 實際資料型態統計\n",
      "Total rows: 1,602,476\n",
      "Index  Python Type       Count           Percent(%)\n",
      "-----  ----------------  --------------  -----------\n",
      "1      float                  1,445,443      90.2006\n",
      "2      str                      157,033       9.7994\n",
      "\n",
      "✅ 轉換完成並已重新統計（未寫檔、未畫圖）。\n"
     ]
    }
   ],
   "source": [
    "\"\"\" 流程：\n",
    "1) 先檢查 vte 是否含有字串 \"(null)\"（筆數與占比）\n",
    "2) vti 轉換：\n",
    "   - NaN 保持 float NaN\n",
    "   - \"(null)\" → 字串 \"NaN\"\n",
    "   - 其他 str 嘗試轉 float（失敗者保留原字串並統計）\n",
    "3) vte 轉換：\n",
    "   - NaN → 字串 \"NaN\"\n",
    "   - str 中可轉數字者 → 轉 float（失敗者保留原字串並統計）\n",
    "4) 轉換後統計各欄位實際型態（float/str）筆數與占比\n",
    "\"\"\"\n",
    "\n",
    "import os\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from collections import Counter\n",
    "\n",
    "# ===== 使用者設定 =====\n",
    "DATA_DIR = \"/home/jovyan/1010/data_new/bling_spomvy_ok_v1\"  # 依實際環境調整\n",
    "TARGET_COLS = [\"vti\", \"vte\"]\n",
    "ENCODINGS_TRY = [\"utf-8\", \"utf-8-sig\", \"big5\", \"cp950\"]\n",
    "READ_CHUNKSIZE = None\n",
    "\n",
    "# ===== 讀檔（保留原始字串樣貌） =====\n",
    "def smart_read_csv(path):\n",
    "    last_err = None\n",
    "    for enc in ENCODINGS_TRY:\n",
    "        try:\n",
    "            if READ_CHUNKSIZE:\n",
    "                chunks = pd.read_csv(path, dtype=object, encoding=enc,\n",
    "                                      chunksize=READ_CHUNKSIZE, low_memory=False)\n",
    "                return pd.concat(chunks, ignore_index=True)\n",
    "            else:\n",
    "                return pd.read_csv(path, dtype=object, encoding=enc, low_memory=False)\n",
    "        except Exception as e:\n",
    "            last_err = e\n",
    "    raise last_err if last_err else RuntimeError(f\"讀取失敗：{path}\")\n",
    "\n",
    "def type_summary(ser, title):\n",
    "    total = len(ser)\n",
    "    cnt = Counter(type(v).__name__ for v in ser)\n",
    "    print(\"=\" * 100)\n",
    "    print(title)\n",
    "    print(f\"Total rows: {total:,}\")\n",
    "    print(\"Index  Python Type       Count           Percent(%)\")\n",
    "    print(\"-----  ----------------  --------------  -----------\")\n",
    "    for i, (tname, c) in enumerate(sorted(cnt.items(), key=lambda x: -x[1]), start=1):\n",
    "        pct = (c / total * 100) if total else 0.0\n",
    "        print(f\"{i:<5}  {tname:<16}  {c:>14,}  {pct:>11.4f}\")\n",
    "    print()\n",
    "\n",
    "def main():\n",
    "    # 收集與合併所有 CSV\n",
    "    files = sorted([os.path.join(DATA_DIR, f) for f in os.listdir(DATA_DIR)\n",
    "                    if f.lower().endswith(\".csv\")])\n",
    "    if not files:\n",
    "        print(f\"⚠️ 找不到任何 CSV：{DATA_DIR}\")\n",
    "        return\n",
    "\n",
    "    df_list = []\n",
    "    for p in files:\n",
    "        try:\n",
    "            df_list.append(smart_read_csv(p))\n",
    "        except Exception as e:\n",
    "            print(f\"❌ 讀取失敗：{os.path.basename(p)} ｜ {e}\")\n",
    "    df = pd.concat(df_list, ignore_index=True)\n",
    "    print(f\"📂 合併完成，共 {len(df):,} 筆資料；CSV 檔數：{len(files)}\\n\")\n",
    "\n",
    "    # ========== (1) 檢查 vte 是否含 \"(null)\" ==========\n",
    "    if \"vte\" in df.columns:\n",
    "        ser_vte_raw = df[\"vte\"]\n",
    "        ser_vte_str = ser_vte_raw.astype(str)\n",
    "        # 僅判斷明確的 \"(null)\"（忽略大小寫與前後空白）\n",
    "        vte_is_null_literal = ser_vte_str.str.strip().str.lower().eq(\"(null)\")\n",
    "        count_null_literal = int(vte_is_null_literal.sum())\n",
    "        total_vte = len(ser_vte_raw)\n",
    "        pct_null_literal = (count_null_literal / total_vte * 100.0) if total_vte else 0.0\n",
    "        print(\"=\" * 100)\n",
    "        print(\"【Step 1】Check vte contains literal '(null)'\")\n",
    "        print(f\"(null) literal count in vte : {count_null_literal:,}  ({pct_null_literal:.4f}%)\\n\")\n",
    "    else:\n",
    "        print(\"⚠️ 欄位 vte 不存在，略過 (null) 檢查。\\n\")\n",
    "\n",
    "    # ========== (2) vti 轉換 ==========\n",
    "    if \"vti\" in df.columns:\n",
    "        ser_vti = df[\"vti\"]\n",
    "        type_summary(ser_vti, \"【vti】(Before) 實際資料型態統計\")\n",
    "\n",
    "        # 保留 NaN 為 float NaN\n",
    "        vti_is_nan = ser_vti.isna()\n",
    "\n",
    "        # \"(null)\" → \"NaN\"（字串）\n",
    "        vti_as_str = ser_vti.astype(str)\n",
    "        vti_is_null_literal = vti_as_str.str.strip().str.lower().eq(\"(null)\")\n",
    "\n",
    "        # 其他 str 嘗試轉 float：先標出原始 str\n",
    "        vti_is_str = ser_vti.apply(lambda x: isinstance(x, str))\n",
    "        vti_other_str_mask = vti_is_str & (~vti_is_null_literal)\n",
    "\n",
    "        # 對其他字串嘗試轉 float\n",
    "        vti_other_str = ser_vti[vti_other_str_mask]\n",
    "        vti_other_num = pd.to_numeric(vti_other_str, errors=\"coerce\")\n",
    "        vti_other_conv_ok = vti_other_num.notna()  # 可轉成功\n",
    "        vti_other_conv_fail = ~vti_other_conv_ok   # 無法轉成功 → 保留原字串\n",
    "\n",
    "        # 建立結果 Series（先拷貝）\n",
    "        vti_new = ser_vti.copy()\n",
    "\n",
    "        # 1) \"(null)\" → 字串 \"NaN\"\n",
    "        vti_new.loc[vti_is_null_literal] = \"NaN\"\n",
    "\n",
    "        # 2) 其他字串：能轉的 → float；不能轉的 → 保留原字串\n",
    "        vti_new.loc[vti_other_str_mask & vti_other_conv_ok] = vti_other_num[vti_other_conv_ok]\n",
    "\n",
    "        # 3) 原本 NaN → 保留為 float NaN（不必特別處理，已在 vti_new 中維持 NaN）\n",
    "\n",
    "        # 報告轉換失敗的字串數\n",
    "        fail_count = int(vti_other_conv_fail.sum())\n",
    "        if fail_count > 0:\n",
    "            print(f\"🔎 vti 其他字串無法轉 float 的筆數：{fail_count:,}\")\n",
    "\n",
    "        type_summary(vti_new, \"【vti】(After) 實際資料型態統計\")\n",
    "        df[\"vti\"] = vti_new\n",
    "    else:\n",
    "        print(\"⚠️ 欄位 vti 不存在，略過 vti 轉換。\\n\")\n",
    "\n",
    "    # ========== (3) vte 轉換 ==========\n",
    "    if \"vte\" in df.columns:\n",
    "        ser_vte = df[\"vte\"]\n",
    "        type_summary(ser_vte, \"【vte】(Before) 實際資料型態統計\")\n",
    "\n",
    "        vte_new = ser_vte.copy()\n",
    "\n",
    "        # NaN → 字串 \"NaN\"\n",
    "        vte_is_nan = ser_vte.isna()\n",
    "        vte_new.loc[vte_is_nan] = \"NaN\"\n",
    "\n",
    "        # 對「仍為字串者」嘗試轉 float\n",
    "        vte_is_str = vte_new.apply(lambda x: isinstance(x, str))\n",
    "        vte_str_only = vte_new[vte_is_str]\n",
    "\n",
    "        vte_str_num = pd.to_numeric(vte_str_only, errors=\"coerce\")\n",
    "        vte_conv_ok = vte_str_num.notna()          # 能轉成功的字串（例如 \"450\", \"480.0\"）\n",
    "        vte_conv_fail = ~vte_conv_ok               # 不能轉成功的字串（例如 \"(null)\", \"\", \"error\"...）\n",
    "\n",
    "        # 能轉的字串 → 轉 float\n",
    "        vte_new.loc[vte_is_str & vte_conv_ok] = vte_str_num[vte_conv_ok]\n",
    "        # 不能轉的字串 → 保留原字串（你若想改成 \"NaN\" 也可在此處調整）\n",
    "\n",
    "        # 統計無法轉換的字串筆數\n",
    "        vte_fail_count = int(vte_conv_fail.sum())\n",
    "        if vte_fail_count > 0:\n",
    "            print(f\"🔎 vte 字串無法轉 float 的筆數：{vte_fail_count:,}\")\n",
    "\n",
    "        type_summary(vte_new, \"【vte】(After) 實際資料型態統計\")\n",
    "        df[\"vte\"] = vte_new\n",
    "    else:\n",
    "        print(\"⚠️ 欄位 vte 不存在，略過 vte 轉換。\\n\")\n",
    "\n",
    "    print(\"✅ 轉換完成並已重新統計（未寫檔、未畫圖）。\")\n",
    "\n",
    "if __name__ == \"__main__\":\n",
    "    main()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "51255381-816f-404a-a697-76a2a960f1f5",
   "metadata": {},
   "outputs": [],
   "source": [
    "再來分別看一下各類前五名是啥\n",
    "列出 vti 與 vte 欄位中「float 型態」與「str 型態」各自出現次數前五名的內容與占比。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "eaa6cae9-ada2-40a4-b063-3466cc7cef4f",
   "metadata": {},
   "outputs": [],
   "source": [
    "沒成功??"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "a94137d0-1d7c-44af-894b-3298e562a4d1",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "📁 掃描到 122 份檔案\n",
      "\n",
      "============================================================\n",
      "【VTI】跨檔資料型態統計\n",
      "============================================================\n",
      " Type   Count  Percent(%)\n",
      "float 1530747   95.523864\n",
      "  NaN  218092   13.609689\n",
      "  str   71729    4.476136\n",
      "\n",
      "總筆數：1,602,476\n",
      "\n",
      "============================================================\n",
      "【VTE】跨檔資料型態統計\n",
      "============================================================\n",
      " Type   Count  Percent(%)\n",
      "float 1602476  100.000000\n",
      "  NaN  157033    9.799398\n",
      "\n",
      "總筆數：1,602,476\n"
     ]
    }
   ],
   "source": [
    "# 目的：跨檔統計指定資料夾中所有 CSV 檔案的 vti、vte 欄位資料型態分布\n",
    "# =========================================================\n",
    "\n",
    "import os\n",
    "import glob\n",
    "import pandas as pd\n",
    "from collections import Counter\n",
    "\n",
    "# -----------------------------\n",
    "# 使用者設定區\n",
    "# -----------------------------\n",
    "DATA_DIR = \"/home/jovyan/1010/data_new/bling_spomvy_ok_v1\"  # 目標資料夾\n",
    "TARGET_COLS = [\"vti\", \"vte\"]  # 欲分析欄位\n",
    "FILE_PATTERN = \"*.csv\"\n",
    "\n",
    "# -----------------------------\n",
    "# 初始化統計容器\n",
    "# -----------------------------\n",
    "global_counts = {col: Counter() for col in TARGET_COLS}\n",
    "global_total = {col: 0 for col in TARGET_COLS}\n",
    "\n",
    "# -----------------------------\n",
    "# 檔案處理迴圈\n",
    "# -----------------------------\n",
    "files = sorted(glob.glob(os.path.join(DATA_DIR, FILE_PATTERN)))\n",
    "print(f\"📁 掃描到 {len(files)} 份檔案\")\n",
    "\n",
    "for f in files:\n",
    "    try:\n",
    "        df = pd.read_csv(f, low_memory=False)\n",
    "        for col in TARGET_COLS:\n",
    "            if col not in df.columns:\n",
    "                continue\n",
    "            series = df[col]\n",
    "            # 統計非缺值型態\n",
    "            type_counts = series.map(lambda x: type(x).__name__).value_counts()\n",
    "            for t, cnt in type_counts.items():\n",
    "                global_counts[col][t] += cnt\n",
    "            # 統計缺值\n",
    "            global_counts[col][\"NaN\"] += series.isna().sum()\n",
    "            global_total[col] += len(series)\n",
    "    except Exception as e:\n",
    "        print(f\"⚠️ {os.path.basename(f)} 無法讀取：{e}\")\n",
    "\n",
    "# -----------------------------\n",
    "# 統整輸出\n",
    "# -----------------------------\n",
    "for col in TARGET_COLS:\n",
    "    print(\"\\n\" + \"=\" * 60)\n",
    "    print(f\"【{col.upper()}】跨檔資料型態統計\")\n",
    "    print(\"=\" * 60)\n",
    "    total = global_total[col]\n",
    "    summary = []\n",
    "    for t, cnt in global_counts[col].most_common():\n",
    "        pct = cnt / total * 100 if total > 0 else 0\n",
    "        summary.append((t, cnt, pct))\n",
    "    df_summary = pd.DataFrame(summary, columns=[\"Type\", \"Count\", \"Percent(%)\"])\n",
    "    print(df_summary.to_string(index=False))\n",
    "    print(f\"\\n總筆數：{total:,}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "0da594e3-1090-4db1-acd2-514fff55dd04",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "📁 掃描到 122 份檔案於：/home/jovyan/1010/data_new/bling_spomvy_ok_v1\n",
      "\n",
      "============================================================\n",
      "【VTI】跨檔資料型態統計（互斥）\n",
      "============================================================\n",
      "Total rows: 1,602,476\n",
      "Type             Count    Percent(%)\n",
      "------------------------------------\n",
      "NaN            218,092     13.609689\n",
      "bool                 0      0.000000\n",
      "int                  0      0.000000\n",
      "float        1,312,655     81.914175\n",
      "str             71,729      4.476136\n",
      "other                0      0.000000\n",
      "\n",
      "→ Percent sum check = 100.000000%（理論上≈100.000000%）\n",
      "\n",
      "============================================================\n",
      "【VTE】跨檔資料型態統計（互斥）\n",
      "============================================================\n",
      "Total rows: 1,602,476\n",
      "Type             Count    Percent(%)\n",
      "------------------------------------\n",
      "NaN            157,033      9.799398\n",
      "bool                 0      0.000000\n",
      "int                  0      0.000000\n",
      "float        1,445,443     90.200602\n",
      "str                  0      0.000000\n",
      "other                0      0.000000\n",
      "\n",
      "→ Percent sum check = 100.000000%（理論上≈100.000000%）\n",
      "\n",
      "============================================================\n",
      "逐檔摘要（每檔每欄位的型態分布，含百分比）\n",
      "============================================================\n",
      "                   file column  total   NaN   NaN_pct  bool  bool_pct  int  int_pct  float  float_pct  str  str_pct  other  other_pct\n",
      "             089271.csv    vte  32419   695  2.143805     0       0.0    0      0.0  31724  97.856195    0      0.0      0        0.0\n",
      "             095323.csv    vte  23791     6  0.025220     0       0.0    0      0.0  23785  99.974780    0      0.0      0        0.0\n",
      "             095707.csv    vte  20180  1600  7.928642     0       0.0    0      0.0  18580  92.071358    0      0.0      0        0.0\n",
      "             114309.csv    vte  71729 23484 32.739896     0       0.0    0      0.0  48245  67.260104    0      0.0      0        0.0\n",
      "             230933.csv    vte  30249  3127 10.337532     0       0.0    0      0.0  27122  89.662468    0      0.0      0        0.0\n",
      "            4216007.csv    vte   1433   906 63.224006     0       0.0    0      0.0    527  36.775994    0      0.0      0        0.0\n",
      "            7108162.csv    vte    239   104 43.514644     0       0.0    0      0.0    135  56.485356    0      0.0      0        0.0\n",
      "            7408338.csv    vte   1432     1  0.069832     0       0.0    0      0.0   1431  99.930168    0      0.0      0        0.0\n",
      "            7657698.csv    vte   1413   126  8.917197     0       0.0    0      0.0   1287  91.082803    0      0.0      0        0.0\n",
      "            7721164.csv    vte    483     0  0.000000     0       0.0    0      0.0    483 100.000000    0      0.0      0        0.0\n",
      "PatNo_ID_1560013303.csv    vte   2543   175  6.881636     0       0.0    0      0.0   2368  93.118364    0      0.0      0        0.0\n",
      "PatNo_ID_1562733396.csv    vte   2254     2  0.088731     0       0.0    0      0.0   2252  99.911269    0      0.0      0        0.0\n",
      "PatNo_ID_1563587183.csv    vte   5287     1  0.018914     0       0.0    0      0.0   5286  99.981086    0      0.0      0        0.0\n",
      "PatNo_ID_1564148644.csv    vte  17287  5669 32.793429     0       0.0    0      0.0  11618  67.206571    0      0.0      0        0.0\n",
      "PatNo_ID_1565148312.csv    vte   5475    10  0.182648     0       0.0    0      0.0   5465  99.817352    0      0.0      0        0.0\n",
      "PatNo_ID_1565378038.csv    vte   2561   283 11.050371     0       0.0    0      0.0   2278  88.949629    0      0.0      0        0.0\n",
      "PatNo_ID_1566123680.csv    vte  42600  4215  9.894366     0       0.0    0      0.0  38385  90.105634    0      0.0      0        0.0\n",
      "PatNo_ID_1566252197.csv    vte   3368  2898 86.045131     0       0.0    0      0.0    470  13.954869    0      0.0      0        0.0\n",
      "PatNo_ID_1566279967.csv    vte   1235    82  6.639676     0       0.0    0      0.0   1153  93.360324    0      0.0      0        0.0\n",
      "PatNo_ID_1566671274.csv    vte  50718  3620  7.137505     0       0.0    0      0.0  47098  92.862495    0      0.0      0        0.0\n",
      "PatNo_ID_1566911879.csv    vte  53999  8075 14.953981     0       0.0    0      0.0  45924  85.046019    0      0.0      0        0.0\n",
      "PatNo_ID_1567747650.csv    vte  10901   246  2.256674     0       0.0    0      0.0  10655  97.743326    0      0.0      0        0.0\n",
      "PatNo_ID_1567804800.csv    vte  20578   453  2.201380     0       0.0    0      0.0  20125  97.798620    0      0.0      0        0.0\n",
      "PatNo_ID_1567832735.csv    vte  36580   103  0.281575     0       0.0    0      0.0  36477  99.718425    0      0.0      0        0.0\n",
      "PatNo_ID_1568039398.csv    vte  34519   667  1.932269     0       0.0    0      0.0  33852  98.067731    0      0.0      0        0.0\n",
      "PatNo_ID_1568574099.csv    vte  13946   574  4.115876     0       0.0    0      0.0  13372  95.884124    0      0.0      0        0.0\n",
      "PatNo_ID_1568813269.csv    vte   4867   408  8.382987     0       0.0    0      0.0   4459  91.617013    0      0.0      0        0.0\n",
      "PatNo_ID_1568952422.csv    vte   1184     0  0.000000     0       0.0    0      0.0   1184 100.000000    0      0.0      0        0.0\n",
      "PatNo_ID_1569083701.csv    vte   3328     2  0.060096     0       0.0    0      0.0   3326  99.939904    0      0.0      0        0.0\n",
      "PatNo_ID_1569944983.csv    vte   7650  1420 18.562092     0       0.0    0      0.0   6230  81.437908    0      0.0      0        0.0\n"
     ]
    }
   ],
   "source": [
    "# 目的：跨檔統計指定資料夾中所有 CSV 的 vti、vte 欄位「互斥」資料型態分布\n",
    "# 列印：\n",
    "#   1) 跨檔總覽（overall）\n",
    "#   2) 逐檔摘要（per-file）\n",
    "# 不輸出任何檔案。\n",
    "# =========================================================\n",
    "\n",
    "import os\n",
    "import glob\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from collections import Counter\n",
    "\n",
    "# -----------------------------\n",
    "# 使用者設定區\n",
    "# -----------------------------\n",
    "DATA_DIR = \"/home/jovyan/1010/data_new/bling_spomvy_ok_v1\"\n",
    "TARGET_COLS = [\"vti\", \"vte\"]\n",
    "FILE_PATTERN = \"*.csv\"\n",
    "\n",
    "# -----------------------------\n",
    "# 互斥型態分類器\n",
    "# -----------------------------\n",
    "def classify_cell(x) -> str:\n",
    "    \"\"\"將單一儲存格值分到互斥型別：\n",
    "       NaN / bool / int / float / str / other\n",
    "    \"\"\"\n",
    "    if pd.isna(x):\n",
    "        return \"NaN\"\n",
    "    if isinstance(x, (bool, np.bool_)):\n",
    "        return \"bool\"\n",
    "    if isinstance(x, (int, np.integer)):\n",
    "        return \"int\"\n",
    "    if isinstance(x, (float, np.floating)):\n",
    "        return \"float\"\n",
    "    if isinstance(x, str):\n",
    "        return \"str\"\n",
    "    return \"other\"\n",
    "\n",
    "# -----------------------------\n",
    "# 容器：整體與逐檔統計\n",
    "# -----------------------------\n",
    "overall_counts = {col: Counter() for col in TARGET_COLS}\n",
    "overall_total = {col: 0 for col in TARGET_COLS}\n",
    "per_file_rows = []  # 每檔、每欄位的型態統計\n",
    "\n",
    "# -----------------------------\n",
    "# 掃描與處理\n",
    "# -----------------------------\n",
    "files = sorted(glob.glob(os.path.join(DATA_DIR, FILE_PATTERN)))\n",
    "print(f\"📁 掃描到 {len(files)} 份檔案於：{DATA_DIR}\")\n",
    "\n",
    "for path in files:\n",
    "    try:\n",
    "        # 僅讀取目標欄位，加速且節省記憶體\n",
    "        df = pd.read_csv(path, usecols=lambda c: c.lower() in TARGET_COLS, low_memory=False)\n",
    "        # 統一欄位小寫\n",
    "        df.columns = [c.lower() for c in df.columns]\n",
    "    except ValueError as ve:\n",
    "        # 可能是「檔案沒有任何目標欄位」\n",
    "        df = pd.DataFrame()\n",
    "    except Exception as e:\n",
    "        print(f\"⚠️ 無法讀取 {os.path.basename(path)}：{e}\")\n",
    "        continue\n",
    "\n",
    "    for col in TARGET_COLS:\n",
    "        if col not in df.columns:\n",
    "            per_file_rows.append({\n",
    "                \"file\": os.path.basename(path), \"column\": col, \"total\": 0,\n",
    "                \"NaN\": 0, \"bool\": 0, \"int\": 0, \"float\": 0, \"str\": 0, \"other\": 0\n",
    "            })\n",
    "            continue\n",
    "\n",
    "        s = df[col]\n",
    "        cats = s.map(classify_cell)\n",
    "        vc = cats.value_counts()\n",
    "\n",
    "        row = {\n",
    "            \"file\": os.path.basename(path),\n",
    "            \"column\": col,\n",
    "            \"total\": int(len(s)),\n",
    "            \"NaN\": int(vc.get(\"NaN\", 0)),\n",
    "            \"bool\": int(vc.get(\"bool\", 0)),\n",
    "            \"int\": int(vc.get(\"int\", 0)),\n",
    "            \"float\": int(vc.get(\"float\", 0)),\n",
    "            \"str\": int(vc.get(\"str\", 0)),\n",
    "            \"other\": int(vc.get(\"other\", 0)),\n",
    "        }\n",
    "        per_file_rows.append(row)\n",
    "\n",
    "        overall_total[col] += row[\"total\"]\n",
    "        for k in [\"NaN\",\"bool\",\"int\",\"float\",\"str\",\"other\"]:\n",
    "            overall_counts[col][k] += row[k]\n",
    "\n",
    "# -----------------------------\n",
    "# 列印：跨檔總覽\n",
    "# -----------------------------\n",
    "def print_overall():\n",
    "    for col in TARGET_COLS:\n",
    "        total = overall_total[col]\n",
    "        NaN = overall_counts[col][\"NaN\"]\n",
    "        b   = overall_counts[col][\"bool\"]\n",
    "        i   = overall_counts[col][\"int\"]\n",
    "        f   = overall_counts[col][\"float\"]\n",
    "        s   = overall_counts[col][\"str\"]\n",
    "        o   = overall_counts[col][\"other\"]\n",
    "\n",
    "        def pct(x): return (x / total * 100.0) if total > 0 else 0.0\n",
    "        pct_sum = pct(NaN)+pct(b)+pct(i)+pct(f)+pct(s)+pct(o)\n",
    "\n",
    "        print(\"\\n\" + \"=\"*60)\n",
    "        print(f\"【{col.upper()}】跨檔資料型態統計（互斥）\")\n",
    "        print(\"=\"*60)\n",
    "        print(f\"Total rows: {total:,}\")\n",
    "        print(f\"{'Type':<8}{'Count':>14}{'Percent(%)':>14}\")\n",
    "        print(f\"{'-'*36}\")\n",
    "        print(f\"{'NaN':<8}{NaN:>14,}{pct(NaN):>14.6f}\")\n",
    "        print(f\"{'bool':<8}{b:>14,}{pct(b):>14.6f}\")\n",
    "        print(f\"{'int':<8}{i:>14,}{pct(i):>14.6f}\")\n",
    "        print(f\"{'float':<8}{f:>14,}{pct(f):>14.6f}\")\n",
    "        print(f\"{'str':<8}{s:>14,}{pct(s):>14.6f}\")\n",
    "        print(f\"{'other':<8}{o:>14,}{pct(o):>14.6f}\")\n",
    "        print(f\"\\n→ Percent sum check = {pct_sum:.6f}%（理論上≈100.000000%）\")\n",
    "\n",
    "print_overall()\n",
    "\n",
    "# -----------------------------\n",
    "# 列印：逐檔摘要（每檔每欄位，含百分比）\n",
    "# -----------------------------\n",
    "def print_per_file_summary(rows, top_n=20):\n",
    "    # 只列印前 N 檔以免輸出過長；若要全部，將 top_n 設為 None\n",
    "    df = pd.DataFrame(rows)\n",
    "    if df.empty:\n",
    "        print(\"\\n（沒有可列印的逐檔摘要）\")\n",
    "        return\n",
    "    # 計算百分比欄位\n",
    "    for k in [\"NaN\",\"bool\",\"int\",\"float\",\"str\",\"other\"]:\n",
    "        df[f\"{k}_pct\"] = df.apply(lambda r: (r[k]/r[\"total\"]*100.0) if r[\"total\"]>0 else 0.0, axis=1)\n",
    "\n",
    "    # 排序：先按欄位、再按檔名\n",
    "    df = df.sort_values(by=[\"column\",\"file\"]).reset_index(drop=True)\n",
    "\n",
    "    # 取樣列印\n",
    "    print(\"\\n\" + \"=\"*60)\n",
    "    print(\"逐檔摘要（每檔每欄位的型態分布，含百分比）\")\n",
    "    print(\"=\"*60)\n",
    "    if top_n is None:\n",
    "        display_df = df\n",
    "    else:\n",
    "        display_df = df.head(top_n)\n",
    "\n",
    "    # 精簡欄位順序\n",
    "    cols = [\"file\",\"column\",\"total\",\"NaN\",\"NaN_pct\",\"bool\",\"bool_pct\",\"int\",\"int_pct\",\n",
    "            \"float\",\"float_pct\",\"str\",\"str_pct\",\"other\",\"other_pct\"]\n",
    "    display_df = display_df[cols].copy()\n",
    "    # 四捨五入\n",
    "    for c in [c for c in display_df.columns if c.endswith(\"_pct\")]:\n",
    "        display_df[c] = display_df[c].round(6)\n",
    "\n",
    "    # 列印為文字表（避免在某些環境沒有 display()）\n",
    "    with pd.option_context('display.max_rows', None, 'display.max_columns', None):\n",
    "        print(display_df.to_string(index=False))\n",
    "\n",
    "    # 若要全部列印，解除註解：\n",
    "    # with pd.option_context('display.max_rows', None, 'display.max_columns', None):\n",
    "    #     print(df[cols].to_string(index=False))\n",
    "\n",
    "print_per_file_summary(per_file_rows, top_n=30)  # 想看更多檔，改成更大的數字或 None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f9ca5a15-0c8c-4711-90d4-f6b65294070e",
   "metadata": {},
   "outputs": [],
   "source": [
    "我要看str 71729 4.476136裡面是什麼 欄位內容"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "ae770f56-49d9-4140-9ce1-8023ea3bf948",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "📁 掃描到 122 份檔案於：/home/jovyan/1010/data_new/bling_spomvy_ok_v1\n",
      "\n",
      "============================================================\n",
      "【VTI】字串型態統計結果\n",
      "============================================================\n",
      "總筆數：1,602,476\n",
      "屬於 str 型態筆數：71,729  (4.4761%)\n",
      "\n",
      "📊 出現次數前 30 名字串內容：\n",
      "Index Value                          Count   Percent(%)\n",
      "------------------------------------------------------------\n",
      "    1 (null)                        51,318    71.544285\n",
      "    2 376.0                            143     0.199361\n",
      "    3 415.0                            130     0.181238\n",
      "    4 373.0                            129     0.179844\n",
      "    5 365.0                            127     0.177055\n",
      "    6 389.0                            127     0.177055\n",
      "    7 393.0                            127     0.177055\n",
      "    8 375.0                            124     0.172873\n",
      "    9 363.0                            122     0.170085\n",
      "   10 391.0                            119     0.165902\n",
      "   11 404.0                            118     0.164508\n",
      "   12 401.0                            117     0.163114\n",
      "   13 424.0                            117     0.163114\n",
      "   14 379.0                            116     0.161720\n",
      "   15 400.0                            116     0.161720\n",
      "   16 397.0                            116     0.161720\n",
      "   17 402.0                            116     0.161720\n",
      "   18 374.0                            115     0.160326\n",
      "   19 377.0                            115     0.160326\n",
      "   20 384.0                            115     0.160326\n",
      "   21 359.0                            115     0.160326\n",
      "   22 390.0                            115     0.160326\n",
      "   23 398.0                            115     0.160326\n",
      "   24 367.0                            113     0.157537\n",
      "   25 369.0                            112     0.156143\n",
      "   26 382.0                            112     0.156143\n",
      "   27 418.0                            112     0.156143\n",
      "   28 370.0                            111     0.154749\n",
      "   29 410.0                            111     0.154749\n",
      "   30 421.0                            111     0.154749\n"
     ]
    }
   ],
   "source": [
    "# 目的：查看所有檔案中 vti 欄位屬於 str 型態的實際內容與分布\n",
    "# =========================================================\n",
    "\n",
    "import os\n",
    "import glob\n",
    "import pandas as pd\n",
    "from collections import Counter\n",
    "\n",
    "# -----------------------------\n",
    "# 使用者設定\n",
    "# -----------------------------\n",
    "DATA_DIR = \"/home/jovyan/1010/data_new/bling_spomvy_ok_v1\"\n",
    "TARGET_COL = \"vti\"\n",
    "FILE_PATTERN = \"*.csv\"\n",
    "TOP_K = 30  # 顯示前幾名字串內容\n",
    "\n",
    "# -----------------------------\n",
    "# 分類器：判斷型態\n",
    "# -----------------------------\n",
    "def is_str(x):\n",
    "    return isinstance(x, str)\n",
    "\n",
    "# -----------------------------\n",
    "# 統計容器\n",
    "# -----------------------------\n",
    "counter = Counter()\n",
    "total_rows = 0\n",
    "str_rows = 0\n",
    "\n",
    "# -----------------------------\n",
    "# 掃描資料夾\n",
    "# -----------------------------\n",
    "files = sorted(glob.glob(os.path.join(DATA_DIR, FILE_PATTERN)))\n",
    "print(f\"📁 掃描到 {len(files)} 份檔案於：{DATA_DIR}\")\n",
    "\n",
    "for f in files:\n",
    "    try:\n",
    "        df = pd.read_csv(f, usecols=lambda c: c.lower() == TARGET_COL, low_memory=False)\n",
    "        df.columns = [c.lower() for c in df.columns]\n",
    "        if TARGET_COL not in df.columns:\n",
    "            continue\n",
    "\n",
    "        series = df[TARGET_COL]\n",
    "        total_rows += len(series)\n",
    "\n",
    "        # 僅取出字串型態\n",
    "        str_values = series[series.map(is_str)]\n",
    "        str_rows += len(str_values)\n",
    "\n",
    "        # 計數\n",
    "        counter.update(str_values)\n",
    "    except Exception as e:\n",
    "        print(f\"⚠️ 無法處理 {os.path.basename(f)}：{e}\")\n",
    "\n",
    "# -----------------------------\n",
    "# 統計結果\n",
    "# -----------------------------\n",
    "print(\"\\n\" + \"=\"*60)\n",
    "print(f\"【{TARGET_COL.upper()}】字串型態統計結果\")\n",
    "print(\"=\"*60)\n",
    "print(f\"總筆數：{total_rows:,}\")\n",
    "print(f\"屬於 str 型態筆數：{str_rows:,}  ({(str_rows/total_rows*100):.4f}%)\")\n",
    "\n",
    "if not counter:\n",
    "    print(\"⚠️ 沒有字串型態資料。\")\n",
    "else:\n",
    "    top_items = counter.most_common(TOP_K)\n",
    "    print(f\"\\n📊 出現次數前 {TOP_K} 名字串內容：\")\n",
    "    print(f\"{'Index':>5} {'Value':<25} {'Count':>10} {'Percent(%)':>12}\")\n",
    "    print(\"-\"*60)\n",
    "    for i, (val, cnt) in enumerate(top_items, 1):\n",
    "        pct = cnt / str_rows * 100 if str_rows > 0 else 0\n",
    "        print(f\"{i:>5} {str(val)[:25]:<25} {cnt:>10,} {pct:>12.6f}\")\n",
    "\n",
    "# 若想查看所有唯一字串（而不只是前幾名）\n",
    "# 可將 counter.items() 轉成 DataFrame 再篩選或排序"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "d3803535-60ba-42b7-ad91-5899b95364bc",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "📁 掃描到 122 份檔案於：/home/jovyan/1010/data_new/bling_spomvy_ok_v1\n",
      "\n",
      "============================================================\n",
      "【VTI】修正後跨檔資料型態統計（互斥）\n",
      "============================================================\n",
      "Total rows: 1,602,476\n",
      "Type             Count    Percent(%)\n",
      "------------------------------------\n",
      "NaN            269,410     16.812108\n",
      "bool                 0      0.000000\n",
      "int                  0      0.000000\n",
      "float        1,333,066     83.187892\n",
      "str                  0      0.000000\n",
      "other                0      0.000000\n",
      "\n",
      "→ Percent sum check = 100.000000%（應≈100.000000%）\n",
      "\n",
      "============================================================\n",
      "【VTE】修正後跨檔資料型態統計（互斥）\n",
      "============================================================\n",
      "Total rows: 1,602,476\n",
      "Type             Count    Percent(%)\n",
      "------------------------------------\n",
      "NaN            157,033      9.799398\n",
      "bool                 0      0.000000\n",
      "int                  0      0.000000\n",
      "float        1,445,443     90.200602\n",
      "str                  0      0.000000\n",
      "other                0      0.000000\n",
      "\n",
      "→ Percent sum check = 100.000000%（應≈100.000000%）\n"
     ]
    }
   ],
   "source": [
    "# 目的：\n",
    "#   1. 將 vti 欄位中出現的 \"(null)\" / \"null\" / \"NULL\" 轉為 NaN\n",
    "#   2. 將其他「可轉為數字的字串」轉為 float\n",
    "#   3. 再次統計 vti、vte 欄位的資料型態分布（互斥分類）\n",
    "# =========================================================\n",
    "\n",
    "import os\n",
    "import glob\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from collections import Counter\n",
    "\n",
    "# -----------------------------\n",
    "# 使用者設定\n",
    "# -----------------------------\n",
    "DATA_DIR = \"/home/jovyan/1010/data_new/bling_spomvy_ok_v1\"\n",
    "TARGET_COLS = [\"vti\", \"vte\"]\n",
    "FILE_PATTERN = \"*.csv\"\n",
    "\n",
    "# -----------------------------\n",
    "# 工具函數\n",
    "# -----------------------------\n",
    "def safe_to_float(x):\n",
    "    \"\"\"若 x 為可轉為數字的字串，回傳 float；否則原值\"\"\"\n",
    "    if isinstance(x, str):\n",
    "        try:\n",
    "            return float(x)\n",
    "        except ValueError:\n",
    "            return x\n",
    "    return x\n",
    "\n",
    "def classify_cell(x) -> str:\n",
    "    \"\"\"互斥分類：NaN / bool / int / float / str / other\"\"\"\n",
    "    if pd.isna(x):\n",
    "        return \"NaN\"\n",
    "    if isinstance(x, (bool, np.bool_)):\n",
    "        return \"bool\"\n",
    "    if isinstance(x, (int, np.integer)):\n",
    "        return \"int\"\n",
    "    if isinstance(x, (float, np.floating)):\n",
    "        return \"float\"\n",
    "    if isinstance(x, str):\n",
    "        return \"str\"\n",
    "    return \"other\"\n",
    "\n",
    "# -----------------------------\n",
    "# 統計容器\n",
    "# -----------------------------\n",
    "overall_counts = {col: Counter() for col in TARGET_COLS}\n",
    "overall_total = {col: 0 for col in TARGET_COLS}\n",
    "\n",
    "# -----------------------------\n",
    "# 掃描檔案\n",
    "# -----------------------------\n",
    "files = sorted(glob.glob(os.path.join(DATA_DIR, FILE_PATTERN)))\n",
    "print(f\"📁 掃描到 {len(files)} 份檔案於：{DATA_DIR}\")\n",
    "\n",
    "for path in files:\n",
    "    try:\n",
    "        df = pd.read_csv(path, low_memory=False)\n",
    "        df.columns = [c.lower() for c in df.columns]\n",
    "    except Exception as e:\n",
    "        print(f\"⚠️ 無法讀取 {os.path.basename(path)}：{e}\")\n",
    "        continue\n",
    "\n",
    "    for col in TARGET_COLS:\n",
    "        if col not in df.columns:\n",
    "            continue\n",
    "\n",
    "        s = df[col]\n",
    "\n",
    "        # === Step 1: 替換 \"(null)\" / \"null\" / \"NULL\" 為 NaN ===\n",
    "        s = s.replace([\"(null)\", \"null\", \"NULL\"], np.nan)\n",
    "\n",
    "        # === Step 2: 將可轉為數字的字串轉為 float ===\n",
    "        s = s.map(safe_to_float)\n",
    "\n",
    "        # === Step 3: 型態分類 ===\n",
    "        cats = s.map(classify_cell)\n",
    "        vc = cats.value_counts()\n",
    "\n",
    "        # 累計統計\n",
    "        total = len(s)\n",
    "        overall_total[col] += total\n",
    "        for k in [\"NaN\",\"bool\",\"int\",\"float\",\"str\",\"other\"]:\n",
    "            overall_counts[col][k] += int(vc.get(k, 0))\n",
    "\n",
    "# -----------------------------\n",
    "# 輸出：跨檔統計結果\n",
    "# -----------------------------\n",
    "def print_overall():\n",
    "    for col in TARGET_COLS:\n",
    "        total = overall_total[col]\n",
    "        NaN = overall_counts[col][\"NaN\"]\n",
    "        b   = overall_counts[col][\"bool\"]\n",
    "        i   = overall_counts[col][\"int\"]\n",
    "        f   = overall_counts[col][\"float\"]\n",
    "        s   = overall_counts[col][\"str\"]\n",
    "        o   = overall_counts[col][\"other\"]\n",
    "\n",
    "        def pct(x): return (x / total * 100.0) if total > 0 else 0.0\n",
    "        pct_sum = pct(NaN)+pct(b)+pct(i)+pct(f)+pct(s)+pct(o)\n",
    "\n",
    "        print(\"\\n\" + \"=\"*60)\n",
    "        print(f\"【{col.upper()}】修正後跨檔資料型態統計（互斥）\")\n",
    "        print(\"=\"*60)\n",
    "        print(f\"Total rows: {total:,}\")\n",
    "        print(f\"{'Type':<8}{'Count':>14}{'Percent(%)':>14}\")\n",
    "        print(f\"{'-'*36}\")\n",
    "        print(f\"{'NaN':<8}{NaN:>14,}{pct(NaN):>14.6f}\")\n",
    "        print(f\"{'bool':<8}{b:>14,}{pct(b):>14.6f}\")\n",
    "        print(f\"{'int':<8}{i:>14,}{pct(i):>14.6f}\")\n",
    "        print(f\"{'float':<8}{f:>14,}{pct(f):>14.6f}\")\n",
    "        print(f\"{'str':<8}{s:>14,}{pct(s):>14.6f}\")\n",
    "        print(f\"{'other':<8}{o:>14,}{pct(o):>14.6f}\")\n",
    "        print(f\"\\n→ Percent sum check = {pct_sum:.6f}%（應≈100.000000%）\")\n",
    "\n",
    "print_overall()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "06090be3-33b5-4f9b-b3f9-04fdf8034cc2",
   "metadata": {},
   "outputs": [],
   "source": [
    "我要看兩個欄位的資料型態 總筆數、各型態筆數與比例 只要跨黨案統計"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "26ccbbf4-8b0f-4264-bca6-8d9a3fc6a99e",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "============================================================\n",
      "【vti】跨檔案資料型態統計（互斥）\n",
      "============================================================\n",
      "Total rows: 1,602,476\n",
      "Type                  Count     Percent(%)\n",
      "------------------------------------------\n",
      "NaN                 269,410      16.812108\n",
      "bool                      0       0.000000\n",
      "int                       0       0.000000\n",
      "float             1,333,066      83.187892\n",
      "str                       0       0.000000\n",
      "other                     0       0.000000\n",
      "\n",
      "→ Percent sum check = 100.000000%（應≈100.000000%）\n",
      "\n",
      "============================================================\n",
      "【vte】跨檔案資料型態統計（互斥）\n",
      "============================================================\n",
      "Total rows: 1,602,476\n",
      "Type                  Count     Percent(%)\n",
      "------------------------------------------\n",
      "NaN                 157,033       9.799398\n",
      "bool                      0       0.000000\n",
      "int                       0       0.000000\n",
      "float             1,445,443      90.200602\n",
      "str                       0       0.000000\n",
      "other                     0       0.000000\n",
      "\n",
      "→ Percent sum check = 100.000000%（應≈100.000000%）\n"
     ]
    }
   ],
   "source": [
    "\"\"\" 目的：跨檔案彙總 /home/jovyan/1010/data_new/bling_spomvy_ok_v1 下所有 CSV 的 vti、vte 欄位\n",
    "      計算【總筆數、各型態筆數與比例】（只做跨檔案總表，不逐檔列印）\n",
    "說明：\n",
    "- 型態類別：NaN、bool、int、float、str、other\n",
    "- 讀檔僅取 vti、vte 欄位；不寫任何輸出檔，只印表格\n",
    "- 對常見空值字串（\"\", \"NA\", \"N/A\", \"(null)\"} 視為 NaN\n",
    "\"\"\"\n",
    "\n",
    "import os\n",
    "import glob\n",
    "from collections import Counter, defaultdict\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "# =========================\n",
    "# 使用者可調整區\n",
    "# =========================\n",
    "DATA_DIR     = \"/home/jovyan/1010/data_new/bling_spomvy_ok_v1\"\n",
    "FILE_PATTERN = \"*.csv\"\n",
    "TARGET_COLS  = [\"vti\", \"vte\"]\n",
    "NA_STRINGS   = {\"\", \"NA\", \"N/A\", \"(null)\", \"null\", \"None\"}  # 視為缺值的字串\n",
    "\n",
    "# =========================\n",
    "# 工具函式\n",
    "# =========================\n",
    "def normalize_val(x):\n",
    "    \"\"\"將值標準化：去除前後空白，處理空值字串為 np.nan\"\"\"\n",
    "    if pd.isna(x):\n",
    "        return np.nan\n",
    "    # 轉字串做清洗（保留原型態資訊不重要，因為我們要自定義分類）\n",
    "    s = str(x).strip()\n",
    "    if s in NA_STRINGS:\n",
    "        return np.nan\n",
    "    return s\n",
    "\n",
    "def classify_value(v):\n",
    "    \"\"\"\n",
    "    對單一值進行型態分類：\n",
    "    回傳：'NaN' | 'bool' | 'int' | 'float' | 'str' | 'other'\n",
    "    規則：\n",
    "      1) NaN\n",
    "      2) bool文字：true/false（大小寫不敏感）\n",
    "      3) 數值字串：先嘗試轉 float；成功後再判斷是否整數\n",
    "      4) 其他視為 str\n",
    "    \"\"\"\n",
    "    if pd.isna(v):\n",
    "        return \"NaN\"\n",
    "\n",
    "    s = v if isinstance(v, str) else str(v)\n",
    "\n",
    "    # bool（文字）\n",
    "    lower = s.lower()\n",
    "    if lower in {\"true\", \"false\"}:\n",
    "        return \"bool\"\n",
    "\n",
    "    # 嘗試數值\n",
    "    try:\n",
    "        f = float(s)\n",
    "        # 判斷是否為整數表示（排除明顯浮點格式如含小數點、小數結尾、科學記號）\n",
    "        if np.isfinite(f):\n",
    "            is_int_like = False\n",
    "            # 若原始包含小數點或科學記號，多半視為 float\n",
    "            if (\".\" not in s) and (\"e\" not in lower) and (\"E\" not in s):\n",
    "                # 純整數字串\n",
    "                is_int_like = True\n",
    "            elif f.is_integer() and (s.endswith(\".0\") or s.endswith(\".00\")):\n",
    "                # 以 .0 結尾，仍視作 float（維持與實務上你們先前統計的風格一致）\n",
    "                is_int_like = False\n",
    "            # 回傳\n",
    "            return \"int\" if is_int_like else \"float\"\n",
    "        else:\n",
    "            return \"other\"\n",
    "    except Exception:\n",
    "        # 非數值 → 視為字串\n",
    "        return \"str\"\n",
    "\n",
    "def summarize_series_types(series: pd.Series) -> Counter:\n",
    "    \"\"\"對單一欄位的值進行型態分類計數（Counter）\"\"\"\n",
    "    # 標準化\n",
    "    s = series.map(normalize_val)\n",
    "    # 分類\n",
    "    return Counter(s.map(classify_value))\n",
    "\n",
    "def print_summary_table(col_name: str, counter: Counter):\n",
    "    total = sum(counter.values())\n",
    "    print(f\"\\n============================================================\")\n",
    "    print(f\"【{col_name}】跨檔案資料型態統計（互斥）\")\n",
    "    print(f\"============================================================\")\n",
    "    print(f\"Total rows: {total:,}\")\n",
    "    print(f\"{'Type':<15}{'Count':>12}{'  Percent(%)':>15}\")\n",
    "    print(\"-\" * 42)\n",
    "    for key in [\"NaN\", \"bool\", \"int\", \"float\", \"str\", \"other\"]:\n",
    "        c = counter.get(key, 0)\n",
    "        pct = (c / total * 100) if total else 0.0\n",
    "        print(f\"{key:<15}{c:>12,}{pct:>15.6f}\")\n",
    "    # 收尾校驗\n",
    "    pct_sum = sum((counter.get(k, 0) / total * 100) for k in [\"NaN\",\"bool\",\"int\",\"float\",\"str\",\"other\"]) if total else 0.0\n",
    "    print(f\"\\n→ Percent sum check = {pct_sum:.6f}%（應≈100.000000%）\")\n",
    "\n",
    "# =========================\n",
    "# 主流程\n",
    "# =========================\n",
    "def main():\n",
    "    file_list = sorted(glob.glob(os.path.join(DATA_DIR, FILE_PATTERN)))\n",
    "    if not file_list:\n",
    "        print(f\"[警示] 找不到任何 CSV：{os.path.join(DATA_DIR, FILE_PATTERN)}\")\n",
    "        return\n",
    "\n",
    "    # 跨檔案累計：每個欄位一個 Counter\n",
    "    agg_counters = {col: Counter() for col in TARGET_COLS}\n",
    "\n",
    "    # 逐檔讀取（僅取關心欄位）\n",
    "    for fp in file_list:\n",
    "        try:\n",
    "            df = pd.read_csv(fp, usecols=lambda c: c.lower() in TARGET_COLS, dtype=str, low_memory=False)\n",
    "            # 欄位小寫化對齊\n",
    "            df.columns = [c.lower() for c in df.columns]\n",
    "        except ValueError:\n",
    "            # 檔案可能缺欄位或空檔 → 略過但給提示\n",
    "            continue\n",
    "\n",
    "        for col in TARGET_COLS:\n",
    "            if col in df.columns:\n",
    "                cnt = summarize_series_types(df[col])\n",
    "                agg_counters[col].update(cnt)\n",
    "\n",
    "    # 列印跨檔案彙總\n",
    "    for col in TARGET_COLS:\n",
    "        print_summary_table(col, agg_counters[col])\n",
    "\n",
    "if __name__ == \"__main__\":\n",
    "    main()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6a285c28-0921-4cfa-ab5f-d43cefc1f0b1",
   "metadata": {},
   "outputs": [],
   "source": [
    "確定一下三個欄位float都沒0或0.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "fa3d333d-a4d1-4939-ad2d-c2437c5d28a5",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "============================================================\n",
      "跨檔案 0 / 0.0 數值檢查結果\n",
      "============================================================\n",
      "欄位名稱                  總筆數        0或0.0筆數       占比(%)\n",
      "-------------------------------------------------------\n",
      "sponvt             16,424             30    0.182660\n",
      "vti             1,333,066          1,832    0.137428\n",
      "vte             1,445,443          2,206    0.152618\n",
      "\n",
      "⚠️ 注意：上表中存在 0 或 0.0 的欄位，請檢查。\n"
     ]
    }
   ],
   "source": [
    "\"\"\" 目的：\n",
    "檢查指定資料夾下所有 CSV 檔案中，sponvt、vti、vte 欄位的數值部分是否出現 0 或 0.0。\n",
    "\n",
    "輸出：\n",
    "- 每個欄位是否存在 0 或 0.0\n",
    "- 總筆數與 0 值筆數\n",
    "- 若全部無 0 值，將清楚顯示確認訊息\n",
    "\"\"\"\n",
    "\n",
    "import os\n",
    "import glob\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "# =====================\n",
    "# 設定區\n",
    "# =====================\n",
    "DATA_DIR     = \"/home/jovyan/1010/data_new/bling_spomvy_ok_v1\"\n",
    "FILE_PATTERN = \"*.csv\"\n",
    "TARGET_COLS  = [\"sponvt\", \"vti\", \"vte\"]\n",
    "\n",
    "# =====================\n",
    "# 主程式\n",
    "# =====================\n",
    "zero_counts = {col: 0 for col in TARGET_COLS}\n",
    "total_counts = {col: 0 for col in TARGET_COLS}\n",
    "\n",
    "file_list = sorted(glob.glob(os.path.join(DATA_DIR, FILE_PATTERN)))\n",
    "if not file_list:\n",
    "    print(f\"[警示] 找不到任何 CSV：{os.path.join(DATA_DIR, FILE_PATTERN)}\")\n",
    "\n",
    "for fp in file_list:\n",
    "    try:\n",
    "        df = pd.read_csv(fp, dtype=str, low_memory=False)\n",
    "        df.columns = [c.lower() for c in df.columns]\n",
    "    except Exception as e:\n",
    "        print(f\"[略過] 無法讀取 {fp}: {e}\")\n",
    "        continue\n",
    "\n",
    "    for col in TARGET_COLS:\n",
    "        if col not in df.columns:\n",
    "            continue\n",
    "\n",
    "        # 嘗試轉成 float，非數值會轉為 NaN\n",
    "        vals = pd.to_numeric(df[col], errors='coerce')\n",
    "\n",
    "        total_counts[col] += vals.notna().sum()\n",
    "        zero_counts[col]  += (vals == 0).sum()\n",
    "\n",
    "# =====================\n",
    "# 輸出結果\n",
    "# =====================\n",
    "print(\"============================================================\")\n",
    "print(\"跨檔案 0 / 0.0 數值檢查結果\")\n",
    "print(\"============================================================\")\n",
    "print(f\"{'欄位名稱':<10}{'總筆數':>15}{'0或0.0筆數':>15}{'占比(%)':>12}\")\n",
    "print(\"-\" * 55)\n",
    "for col in TARGET_COLS:\n",
    "    total = total_counts[col]\n",
    "    zeros = zero_counts[col]\n",
    "    ratio = (zeros / total * 100) if total else 0\n",
    "    print(f\"{col:<10}{total:>15,}{zeros:>15,}{ratio:>12.6f}\")\n",
    "\n",
    "# 最終判斷\n",
    "if all(zero_counts[col] == 0 for col in TARGET_COLS):\n",
    "    print(\"\\n✅ 確認結果：sponvt、vti、vte 三欄位皆無 0 或 0.0 數值。\")\n",
    "else:\n",
    "    print(\"\\n⚠️ 注意：上表中存在 0 或 0.0 的欄位，請檢查。\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "fe5fc32a-ec38-4dc5-8dbc-256fab9ba22c",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "=== 前五筆含 0 / 0.0 的紀錄（檔名、時間、欄位、原值） ===\n",
      "      file                time column value\n",
      "089271.csv 2022-01-11 15:12:03    vte   0.0\n",
      "089271.csv 2022-01-11 15:11:03    vte   0.0\n",
      "089271.csv 2022-01-11 15:10:03    vte   0.0\n",
      "089271.csv 2022-01-11 15:09:03    vte   0.0\n",
      "089271.csv 2022-01-11 15:08:03    vte   0.0\n"
     ]
    }
   ],
   "source": [
    "# 這個cell不需用 可刪\n",
    "\"\"\"\n",
    "目的：\n",
    "跨檔掃描 /home/jovyan/1010/data_new/bling_spomvy_ok_v1 下所有 CSV，\n",
    "找出 sponvt、vti、vte 中任一欄為 0/0.0 的紀錄，列出「前五筆」的 檔名 與 時間。\n",
    "\n",
    "說明：\n",
    "- 自動偵測時間欄位（優先 'senddate'，亦容忍大小寫與常見變體）\n",
    "- 若某檔找不到任何時間欄，時間欄顯示為 <no_time_col>\n",
    "- 只列出跨檔的前五筆\n",
    "\"\"\"\n",
    "\n",
    "import os\n",
    "import glob\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "# =====================\n",
    "# 設定\n",
    "# =====================\n",
    "DATA_DIR     = \"/home/jovyan/1010/data_new/bling_spomvy_ok_v1\"\n",
    "FILE_PATTERN = \"*.csv\"\n",
    "TARGET_COLS  = [\"sponvt\", \"vti\", \"vte\"]\n",
    "# 常見的時間欄位候選（依優先序）\n",
    "TIME_CANDIDATES = [\n",
    "    \"senddate\", \"send_date\", \"time\", \"timestamp\",\n",
    "    \"date\", \"datetime\", \"senddate_clean\", \"senddate_parsed\"\n",
    "]\n",
    "\n",
    "# =====================\n",
    "# 工具\n",
    "# =====================\n",
    "def find_time_col(cols):\n",
    "    \"\"\"在欄位名列表中找出最合適的時間欄位；若無則回傳 None。\"\"\"\n",
    "    lower_map = {c.lower(): c for c in cols}\n",
    "    for cand in TIME_CANDIDATES:\n",
    "        if cand in lower_map:\n",
    "            return lower_map[cand]\n",
    "    return None\n",
    "\n",
    "def coerce_zero_mask(series: pd.Series) -> pd.Series:\n",
    "    \"\"\"將序列轉成數值（不可轉者為 NaN），回傳是否等於 0 的布林遮罩。\"\"\"\n",
    "    vals = pd.to_numeric(series, errors=\"coerce\")\n",
    "    return (vals == 0)\n",
    "\n",
    "# =====================\n",
    "# 主程式\n",
    "# =====================\n",
    "rows = []  # 收集 (file, time, col, value)\n",
    "count_limit = 5\n",
    "\n",
    "file_list = sorted(glob.glob(os.path.join(DATA_DIR, FILE_PATTERN)))\n",
    "if not file_list:\n",
    "    print(f\"[警示] 找不到任何 CSV：{os.path.join(DATA_DIR, FILE_PATTERN)}\")\n",
    "else:\n",
    "    for fp in file_list:\n",
    "        try:\n",
    "            df = pd.read_csv(fp, dtype=str, low_memory=False)\n",
    "        except Exception as e:\n",
    "            print(f\"[略過] 無法讀取 {fp}: {e}\")\n",
    "            continue\n",
    "\n",
    "        # 欄位小寫對齊用於匹配，但保留原欄位名以利取值\n",
    "        lower_to_orig = {c.lower(): c for c in df.columns}\n",
    "        # 確認目標欄是否存在\n",
    "        present_cols = [lower_to_orig[c] for c in TARGET_COLS if c in lower_to_orig]\n",
    "        if not present_cols:\n",
    "            continue\n",
    "\n",
    "        # 找時間欄\n",
    "        time_col = find_time_col(df.columns)\n",
    "        time_missing_placeholder = \"<no_time_col>\"\n",
    "\n",
    "        # 建立「任一目標欄為 0」的遮罩\n",
    "        zero_any = None\n",
    "        zero_cols = {}\n",
    "        for c in present_cols:\n",
    "            m = coerce_zero_mask(df[c])\n",
    "            zero_cols[c] = m\n",
    "            zero_any = m if zero_any is None else (zero_any | m)\n",
    "\n",
    "        if zero_any is None or not zero_any.any():\n",
    "            continue\n",
    "\n",
    "        idxs = np.where(zero_any.values)[0]\n",
    "        for i in idxs:\n",
    "            # 哪些欄位在這列為 0\n",
    "            zero_in_row = [c for c in present_cols if zero_cols[c].iat[i]]\n",
    "            # 取第一個為0的欄位與其值（也可改成列出全部）\n",
    "            zcol = zero_in_row[0]\n",
    "            zval = df[zcol].iat[i]\n",
    "\n",
    "            tval = df[time_col].iat[i] if time_col is not None else time_missing_placeholder\n",
    "\n",
    "            rows.append({\n",
    "                \"file\": os.path.basename(fp),\n",
    "                \"time\": tval,\n",
    "                \"column\": zcol,\n",
    "                \"value\": zval\n",
    "            })\n",
    "            if len(rows) >= count_limit:\n",
    "                break\n",
    "        if len(rows) >= count_limit:\n",
    "            break\n",
    "\n",
    "# 輸出結果\n",
    "if not rows:\n",
    "    print(\"✅ 前五筆查詢結果：三欄位皆未發現 0 / 0.0（或資料夾無符合檔案）。\")\n",
    "else:\n",
    "    out = pd.DataFrame(rows, columns=[\"file\", \"time\", \"column\", \"value\"])\n",
    "    # 乾淨地印表\n",
    "    print(\"\\n=== 前五筆含 0 / 0.0 的紀錄（檔名、時間、欄位、原值） ===\")\n",
    "    print(out.to_string(index=False))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6941f238-c65d-4635-af86-10fbade8950c",
   "metadata": {},
   "outputs": [],
   "source": [
    "檢查三個欄位中 是0或0.0 的那個是算甚麼資料型態"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "e2e09bdc-450f-447d-9e9f-d3449f6ee8db",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "=== 各欄位 0 / 0.0 實際資料型態分布 ===\n",
      "sponvt: {'int': 0, 'float': 0, 'str': 30, 'other': 0}\n",
      "vti: {'int': 0, 'float': 0, 'str': 1832, 'other': 0}\n",
      "vte: {'int': 0, 'float': 0, 'str': 2206, 'other': 0}\n"
     ]
    }
   ],
   "source": [
    "import os, glob\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "DATA_DIR = \"/home/jovyan/1010/data_new/bling_spomvy_ok_v1\"\n",
    "TARGET_COLS = [\"sponvt\", \"vti\", \"vte\"]\n",
    "\n",
    "type_counter = {col: {\"int\":0, \"float\":0, \"str\":0, \"other\":0} for col in TARGET_COLS}\n",
    "\n",
    "for fp in sorted(glob.glob(os.path.join(DATA_DIR, \"*.csv\"))):\n",
    "    try:\n",
    "        df = pd.read_csv(fp, dtype=object, low_memory=False)\n",
    "        df.columns = [c.lower() for c in df.columns]\n",
    "    except:\n",
    "        continue\n",
    "\n",
    "    for col in TARGET_COLS:\n",
    "        if col not in df.columns:\n",
    "            continue\n",
    "        vals = df[col].dropna()\n",
    "        for v in vals:\n",
    "            if str(v).strip() in {\"0\", \"0.0\"}:\n",
    "                if isinstance(v, int):\n",
    "                    type_counter[col][\"int\"] += 1\n",
    "                elif isinstance(v, float):\n",
    "                    type_counter[col][\"float\"] += 1\n",
    "                elif isinstance(v, str):\n",
    "                    type_counter[col][\"str\"] += 1\n",
    "                else:\n",
    "                    type_counter[col][\"other\"] += 1\n",
    "\n",
    "print(\"=== 各欄位 0 / 0.0 實際資料型態分布 ===\")\n",
    "for col, cnts in type_counter.items():\n",
    "    print(f\"{col}: {cnts}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "630f444c-71bb-4ddf-8ef8-ab5c4077702c",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "============================================================\n",
      "共檢查檔案數：122\n",
      "跨檔案 0 / 0.0 數值檢查結果\n",
      "============================================================\n",
      "欄位名稱                  總筆數        0或0.0筆數       占比(%)\n",
      "-------------------------------------------------------\n",
      "sponvt             16,424             30    0.182660\n",
      "vti             1,333,066          1,832    0.137428\n",
      "vte             1,445,443          2,206    0.152618\n",
      "\n",
      "⚠️ 注意：上表中存在 0 或 0.0 的欄位，請檢查。\n"
     ]
    }
   ],
   "source": [
    "\"\"\" 檢查指定資料夾下所有 CSV 檔案中，\n",
    "sponvt、vti、vte 欄位的數值部分是否出現 0 或 0.0。\n",
    "\n",
    "輸出：\n",
    "- 每個欄位是否存在 0 或 0.0\n",
    "- 總筆數與 0 值筆數\n",
    "- 若全部無 0 值，將清楚顯示確認訊息\n",
    "\"\"\"\n",
    "\n",
    "import os\n",
    "import glob\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "# =====================\n",
    "# 設定區\n",
    "# =====================\n",
    "DATA_DIR     = \"/home/jovyan/1010/data_new/bling_spomvy_ok_v1\"\n",
    "FILE_PATTERN = \"*.csv\"\n",
    "TARGET_COLS  = [\"sponvt\", \"vti\", \"vte\"]\n",
    "\n",
    "# =====================\n",
    "# 統計變數初始化\n",
    "# =====================\n",
    "zero_counts  = {col: 0 for col in TARGET_COLS}\n",
    "total_counts = {col: 0 for col in TARGET_COLS}\n",
    "found_files  = 0\n",
    "\n",
    "# =====================\n",
    "# 主程式\n",
    "# =====================\n",
    "file_list = sorted(glob.glob(os.path.join(DATA_DIR, FILE_PATTERN)))\n",
    "if not file_list:\n",
    "    print(f\"[警示] 找不到任何 CSV：{os.path.join(DATA_DIR, FILE_PATTERN)}\")\n",
    "else:\n",
    "    for fp in file_list:\n",
    "        try:\n",
    "            df = pd.read_csv(fp, dtype=str, low_memory=False)\n",
    "            df.columns = [c.lower() for c in df.columns]\n",
    "        except Exception as e:\n",
    "            print(f\"[略過] 無法讀取 {fp}: {e}\")\n",
    "            continue\n",
    "\n",
    "        found_files += 1\n",
    "        for col in TARGET_COLS:\n",
    "            if col not in df.columns:\n",
    "                continue\n",
    "\n",
    "            # 將欄位轉成數值，非數值轉成 NaN\n",
    "            vals = pd.to_numeric(df[col], errors='coerce')\n",
    "\n",
    "            # 統計非空筆數\n",
    "            total_counts[col] += vals.notna().sum()\n",
    "\n",
    "            # 統計為 0 或 0.0 的筆數\n",
    "            zero_counts[col] += (vals == 0).sum()\n",
    "\n",
    "# =====================\n",
    "# 輸出結果\n",
    "# =====================\n",
    "if found_files == 0:\n",
    "    print(\"⚠️ 沒有任何可讀取的 CSV 檔案。\")\n",
    "else:\n",
    "    print(\"============================================================\")\n",
    "    print(f\"共檢查檔案數：{found_files}\")\n",
    "    print(\"跨檔案 0 / 0.0 數值檢查結果\")\n",
    "    print(\"============================================================\")\n",
    "    print(f\"{'欄位名稱':<10}{'總筆數':>15}{'0或0.0筆數':>15}{'占比(%)':>12}\")\n",
    "    print(\"-\" * 55)\n",
    "    for col in TARGET_COLS:\n",
    "        total = total_counts[col]\n",
    "        zeros = zero_counts[col]\n",
    "        ratio = (zeros / total * 100) if total else 0\n",
    "        print(f\"{col:<10}{total:>15,}{zeros:>15,}{ratio:>12.6f}\")\n",
    "\n",
    "    # 最終確認訊息\n",
    "    if all(zero_counts[col] == 0 for col in TARGET_COLS):\n",
    "        print(\"\\n✅ 確認結果：sponvt、vti、vte 三欄位皆無 0 或 0.0 數值。\")\n",
    "    else:\n",
    "        print(\"\\n⚠️ 注意：上表中存在 0 或 0.0 的欄位，請檢查。\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "aed28f5a-052e-46fb-912f-e2c937f8dc48",
   "metadata": {},
   "outputs": [],
   "source": [
    "複製\"/home/jovyan/1010/data_new/bling_spomvy_ok_v1\"去\"/home/jovyan/1010/data_new/save/\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "edf72ddc-d941-459b-a031-a21294a4d59a",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "來源資料夾：/home/jovyan/1010/data_new/bling_spomvy_ok_v1\n",
      "目的資料夾：/home/jovyan/1010/data_new/save\n",
      "\n",
      "開始複製...\n",
      "\n",
      "✅ 複製完成\n",
      "總檔案數：122\n",
      "成功複製：122\n",
      "所有檔案皆成功複製 ✅\n"
     ]
    }
   ],
   "source": [
    "# 這不需用 可刪\n",
    "# 目的：複製整個 bling_spomvy_ok_v1 資料夾到 save 目錄\n",
    "# ============================================\n",
    "\n",
    "import os\n",
    "import shutil\n",
    "\n",
    "SRC_DIR = \"/home/jovyan/1010/data_new/bling_spomvy_ok_v1\"\n",
    "DST_DIR = \"/home/jovyan/1010/data_new/save\"\n",
    "\n",
    "# 若目的資料夾不存在，則建立\n",
    "os.makedirs(DST_DIR, exist_ok=True)\n",
    "\n",
    "# 計數器\n",
    "total_files = 0\n",
    "copied_files = 0\n",
    "\n",
    "print(f\"來源資料夾：{SRC_DIR}\")\n",
    "print(f\"目的資料夾：{DST_DIR}\")\n",
    "print(\"\\n開始複製...\")\n",
    "\n",
    "for item in os.listdir(SRC_DIR):\n",
    "    src_path = os.path.join(SRC_DIR, item)\n",
    "    dst_path = os.path.join(DST_DIR, item)\n",
    "    if os.path.isfile(src_path):\n",
    "        try:\n",
    "            shutil.copy2(src_path, dst_path)  # 保留原始時間戳\n",
    "            copied_files += 1\n",
    "        except Exception as e:\n",
    "            print(f\"[警告] 無法複製 {item}: {e}\")\n",
    "        total_files += 1\n",
    "\n",
    "print(\"\\n✅ 複製完成\")\n",
    "print(f\"總檔案數：{total_files}\")\n",
    "print(f\"成功複製：{copied_files}\")\n",
    "if copied_files == total_files:\n",
    "    print(\"所有檔案皆成功複製 ✅\")\n",
    "else:\n",
    "    print(f\"有 {total_files - copied_files} 個檔案未成功複製 ⚠️\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ef08ee51-1636-4660-9a9a-9c3f1718f7c5",
   "metadata": {},
   "outputs": [],
   "source": [
    "我要把他們轉成str然後再看一次跨檔案 0 / 0.0 數值檢查結果"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "24a61fff-3f5d-4db9-a4a4-b3bf633f97cc",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "============================================================\n",
      "共檢查檔案數：122\n",
      "跨檔案 '0' / '0.0' 文字值檢查結果（全部轉為 str 後）\n",
      "============================================================\n",
      "欄位名稱                  總筆數        0或0.0筆數       占比(%)\n",
      "-------------------------------------------------------\n",
      "sponvt            177,572             30    0.016895\n",
      "vti             1,333,066          1,832    0.137428\n",
      "vte             1,445,443          2,206    0.152618\n",
      "\n",
      "⚠️ 注意：仍有欄位存在 '0' 或 '0.0' 字串形式的資料，請檢查上表。\n"
     ]
    }
   ],
   "source": [
    "\"\"\"將指定資料夾下所有 CSV 檔的 sponvt、vti、vte 欄位轉為字串後，\n",
    "重新檢查是否仍存在 '0' 或 '0.0' 這類字串值。\n",
    "\n",
    "輸出：\n",
    "- 每個欄位的總筆數與 '0'/'0.0' 筆數\n",
    "- 若完全沒有 '0'/'0.0'，清楚顯示確認訊息\n",
    "\"\"\"\n",
    "\n",
    "import os\n",
    "import glob\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "# =====================\n",
    "# 設定區\n",
    "# =====================\n",
    "DATA_DIR     = \"/home/jovyan/1010/data_new/bling_spomvy_ok_v1\"\n",
    "FILE_PATTERN = \"*.csv\"\n",
    "TARGET_COLS  = [\"sponvt\", \"vti\", \"vte\"]\n",
    "\n",
    "# =====================\n",
    "# 統計變數初始化\n",
    "# =====================\n",
    "zero_counts  = {col: 0 for col in TARGET_COLS}\n",
    "total_counts = {col: 0 for col in TARGET_COLS}\n",
    "found_files  = 0\n",
    "\n",
    "# =====================\n",
    "# 主程式\n",
    "# =====================\n",
    "file_list = sorted(glob.glob(os.path.join(DATA_DIR, FILE_PATTERN)))\n",
    "if not file_list:\n",
    "    print(f\"[警示] 找不到任何 CSV：{os.path.join(DATA_DIR, FILE_PATTERN)}\")\n",
    "else:\n",
    "    for fp in file_list:\n",
    "        try:\n",
    "            # 明確指定以 str 讀入（避免 pandas 自動轉型）\n",
    "            df = pd.read_csv(fp, dtype=str, low_memory=False)\n",
    "            df.columns = [c.lower() for c in df.columns]\n",
    "        except Exception as e:\n",
    "            print(f\"[略過] 無法讀取 {fp}: {e}\")\n",
    "            continue\n",
    "\n",
    "        found_files += 1\n",
    "        for col in TARGET_COLS:\n",
    "            if col not in df.columns:\n",
    "                continue\n",
    "\n",
    "            # 所有值轉成 str，再去除前後空白\n",
    "            s = df[col].astype(str).str.strip()\n",
    "\n",
    "            # 排除 NaN、空字串、(null) 等無效值\n",
    "            valid_mask = ~s.isin([\"\", \"nan\", \"NaN\", \"None\", \"(null)\", \"NULL\"])\n",
    "            s_valid = s[valid_mask]\n",
    "\n",
    "            total_counts[col] += len(s_valid)\n",
    "            zero_counts[col]  += s_valid.isin([\"0\", \"0.0\"]).sum()\n",
    "\n",
    "# =====================\n",
    "# 輸出結果\n",
    "# =====================\n",
    "if found_files == 0:\n",
    "    print(\"⚠️ 沒有任何可讀取的 CSV 檔案。\")\n",
    "else:\n",
    "    print(\"============================================================\")\n",
    "    print(f\"共檢查檔案數：{found_files}\")\n",
    "    print(\"跨檔案 '0' / '0.0' 文字值檢查結果（全部轉為 str 後）\")\n",
    "    print(\"============================================================\")\n",
    "    print(f\"{'欄位名稱':<10}{'總筆數':>15}{'0或0.0筆數':>15}{'占比(%)':>12}\")\n",
    "    print(\"-\" * 55)\n",
    "    for col in TARGET_COLS:\n",
    "        total = total_counts[col]\n",
    "        zeros = zero_counts[col]\n",
    "        ratio = (zeros / total * 100) if total else 0\n",
    "        print(f\"{col:<10}{total:>15,}{zeros:>15,}{ratio:>12.6f}\")\n",
    "\n",
    "    # 最終確認訊息\n",
    "    if all(zero_counts[col] == 0 for col in TARGET_COLS):\n",
    "        print(\"\\n✅ 確認結果：三欄位在轉成字串後皆無 '0' 或 '0.0' 文字值。\")\n",
    "    else:\n",
    "        print(\"\\n⚠️ 注意：仍有欄位存在 '0' 或 '0.0' 字串形式的資料，請檢查上表。\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "47e3e3cf-140c-4c05-b8a8-993af2ac3b97",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    }
   },
   "outputs": [
    {
     "data": {
      "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",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>file</th>\n",
       "      <th>column</th>\n",
       "      <th>value</th>\n",
       "      <th>python_type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>089271.csv</td>\n",
       "      <td>vti</td>\n",
       "      <td>0.0</td>\n",
       "      <td>str</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>089271.csv</td>\n",
       "      <td>vte</td>\n",
       "      <td>0.0</td>\n",
       "      <td>str</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>089271.csv</td>\n",
       "      <td>vte</td>\n",
       "      <td>0.0</td>\n",
       "      <td>str</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>089271.csv</td>\n",
       "      <td>vte</td>\n",
       "      <td>0.0</td>\n",
       "      <td>str</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>095323.csv</td>\n",
       "      <td>vti</td>\n",
       "      <td>0.0</td>\n",
       "      <td>str</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>095323.csv</td>\n",
       "      <td>vte</td>\n",
       "      <td>0.0</td>\n",
       "      <td>str</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>095323.csv</td>\n",
       "      <td>vte</td>\n",
       "      <td>0.0</td>\n",
       "      <td>str</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>095323.csv</td>\n",
       "      <td>vte</td>\n",
       "      <td>0.0</td>\n",
       "      <td>str</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         file column value python_type\n",
       "0  089271.csv    vti   0.0         str\n",
       "1  089271.csv    vte   0.0         str\n",
       "2  089271.csv    vte   0.0         str\n",
       "3  089271.csv    vte   0.0         str\n",
       "4  095323.csv    vti   0.0         str\n",
       "5  095323.csv    vte   0.0         str\n",
       "6  095323.csv    vte   0.0         str\n",
       "7  095323.csv    vte   0.0         str"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import os, glob, pandas as pd\n",
    "\n",
    "DATA_DIR = \"/home/jovyan/1010/data_new/bling_spomvy_ok_v1\"\n",
    "TARGET_COLS = [\"sponvt\", \"vti\", \"vte\"]\n",
    "\n",
    "examples = []\n",
    "for fp in sorted(glob.glob(os.path.join(DATA_DIR, \"*.csv\"))):\n",
    "    df = pd.read_csv(fp, dtype=object, low_memory=False)\n",
    "    df.columns = [c.lower() for c in df.columns]\n",
    "    for col in TARGET_COLS:\n",
    "        if col not in df.columns: continue\n",
    "        s = df[col].astype(str).str.strip()\n",
    "        mask = s.isin([\"0\", \"0.0\"])\n",
    "        if mask.any():\n",
    "            subset = df.loc[mask, [col]].head(3)\n",
    "            for val in subset[col]:\n",
    "                examples.append((os.path.basename(fp), col, val, type(val).__name__))\n",
    "    if len(examples) >= 5:\n",
    "        break\n",
    "\n",
    "pd.DataFrame(examples, columns=[\"file\", \"column\", \"value\", \"python_type\"])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4669c1a1-74b5-4c34-9e8f-86511832893e",
   "metadata": {},
   "outputs": [],
   "source": [
    "我要確定這三個欄位的float裡面都沒有0.0嗎"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "4abade5e-61a6-4fd4-9074-239aebc57963",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "============================================================\n",
      "共檢查檔案數：122\n",
      "跨檔案 float 型態中 0.0 檢查結果\n",
      "============================================================\n",
      "欄位名稱              float筆數          0.0筆數       占比(%)\n",
      "-------------------------------------------------------\n",
      "sponvt          1,441,328             30    0.002081\n",
      "vti             1,530,747          1,832    0.119680\n",
      "vte             1,602,476          2,206    0.137662\n",
      "\n",
      "⚠️ 注意：有欄位的 float 型態中存在 0.0，請檢查上表。\n"
     ]
    }
   ],
   "source": [
    "\"\"\"檢查指定資料夾下所有 CSV 檔案中，\n",
    "sponvt、vti、vte 欄位中「float 型態的 0.0」是否存在。\n",
    "\n",
    "輸出：\n",
    "- 各欄位 float 型的總筆數\n",
    "- 其中值為 0.0 的筆數與占比\n",
    "- 若完全沒有 0.0，顯示確認訊息\n",
    "\"\"\"\n",
    "\n",
    "import os\n",
    "import glob\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "# =====================\n",
    "# 設定\n",
    "# =====================\n",
    "DATA_DIR = \"/home/jovyan/1010/data_new/bling_spomvy_ok_v1\"\n",
    "TARGET_COLS = [\"sponvt\", \"vti\", \"vte\"]\n",
    "FILE_PATTERN = \"*.csv\"\n",
    "\n",
    "# =====================\n",
    "# 初始化計數\n",
    "# =====================\n",
    "float_total = {c: 0 for c in TARGET_COLS}\n",
    "float_zero  = {c: 0 for c in TARGET_COLS}\n",
    "found_files = 0\n",
    "\n",
    "# =====================\n",
    "# 主程式\n",
    "# =====================\n",
    "file_list = sorted(glob.glob(os.path.join(DATA_DIR, FILE_PATTERN)))\n",
    "if not file_list:\n",
    "    print(f\"[警示] 找不到任何 CSV：{os.path.join(DATA_DIR, FILE_PATTERN)}\")\n",
    "else:\n",
    "    for fp in file_list:\n",
    "        try:\n",
    "            df = pd.read_csv(fp, low_memory=False)\n",
    "            df.columns = [c.lower() for c in df.columns]\n",
    "        except Exception as e:\n",
    "            print(f\"[略過] 無法讀取 {fp}: {e}\")\n",
    "            continue\n",
    "\n",
    "        found_files += 1\n",
    "        for col in TARGET_COLS:\n",
    "            if col not in df.columns:\n",
    "                continue\n",
    "\n",
    "            series = df[col]\n",
    "\n",
    "            # 僅取 float 型資料\n",
    "            mask_float = series.apply(lambda x: isinstance(x, float))\n",
    "            vals = series[mask_float]\n",
    "\n",
    "            float_total[col] += len(vals)\n",
    "            float_zero[col]  += (vals == 0.0).sum()\n",
    "\n",
    "# =====================\n",
    "# 輸出結果\n",
    "# =====================\n",
    "if found_files == 0:\n",
    "    print(\"⚠️ 沒有任何可讀取的 CSV 檔案。\")\n",
    "else:\n",
    "    print(\"============================================================\")\n",
    "    print(f\"共檢查檔案數：{found_files}\")\n",
    "    print(\"跨檔案 float 型態中 0.0 檢查結果\")\n",
    "    print(\"============================================================\")\n",
    "    print(f\"{'欄位名稱':<10}{'float筆數':>15}{'0.0筆數':>15}{'占比(%)':>12}\")\n",
    "    print(\"-\" * 55)\n",
    "    for col in TARGET_COLS:\n",
    "        total = float_total[col]\n",
    "        zeros = float_zero[col]\n",
    "        pct = (zeros / total * 100) if total else 0\n",
    "        print(f\"{col:<10}{total:>15,}{zeros:>15,}{pct:>12.6f}\")\n",
    "\n",
    "    if all(float_zero[c] == 0 for c in TARGET_COLS):\n",
    "        print(\"\\n✅ 確認結果：三欄位的 float 型態中皆無 0.0 數值。\")\n",
    "    else:\n",
    "        print(\"\\n⚠️ 注意：有欄位的 float 型態中存在 0.0，請檢查上表。\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "1a1d41e1-4827-4a5c-9dff-cfe023ba39f6",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "============================================================\n",
      "共轉換檔案數：122\n",
      "總修改筆數（0 / 0.0 / '0' / '0.0' → '0_0'）：21,311\n",
      "------------------------------------------------------------\n",
      "089271.csv                     修改     31 筆\n",
      "095323.csv                     修改    204 筆\n",
      "095707.csv                     修改     20 筆\n",
      "114309.csv                     修改     28 筆\n",
      "230933.csv                     修改    294 筆\n",
      "4216007.csv                    修改    527 筆\n",
      "7108162.csv                    修改      1 筆\n",
      "7657698.csv                    修改      4 筆\n",
      "PatNo_ID_1560013303.csv        修改      3 筆\n",
      "PatNo_ID_1562733396.csv        修改      4 筆\n",
      "PatNo_ID_1563587183.csv        修改     81 筆\n",
      "PatNo_ID_1564148644.csv        修改     39 筆\n",
      "PatNo_ID_1565378038.csv        修改      2 筆\n",
      "PatNo_ID_1566123680.csv        修改    302 筆\n",
      "PatNo_ID_1566252197.csv        修改      5 筆\n",
      "PatNo_ID_1566279967.csv        修改      3 筆\n",
      "PatNo_ID_1566671274.csv        修改    260 筆\n",
      "PatNo_ID_1566911879.csv        修改      8 筆\n",
      "PatNo_ID_1567804800.csv        修改   2533 筆\n",
      "PatNo_ID_1567832735.csv        修改     23 筆\n",
      "PatNo_ID_1568039398.csv        修改      4 筆\n",
      "PatNo_ID_1568574099.csv        修改    768 筆\n",
      "PatNo_ID_1568813269.csv        修改      2 筆\n",
      "PatNo_ID_1568952422.csv        修改      4 筆\n",
      "PatNo_ID_1569083701.csv        修改      1 筆\n",
      "PatNo_ID_1569944983.csv        修改     10 筆\n",
      "PatNo_ID_1570089466.csv        修改    280 筆\n",
      "PatNo_ID_1570242703.csv        修改      8 筆\n",
      "PatNo_ID_1570273244.csv        修改     10 筆\n",
      "PatNo_ID_1570642083.csv        修改    117 筆\n",
      "PatNo_ID_1571945701.csv        修改      6 筆\n",
      "PatNo_ID_1572481361.csv        修改     81 筆\n",
      "PatNo_ID_1572562839.csv        修改   1278 筆\n",
      "PatNo_ID_1572976822.csv        修改    966 筆\n",
      "PatNo_ID_1573063188.csv        修改     10 筆\n",
      "PatNo_ID_1573249295.csv        修改      5 筆\n",
      "PatNo_ID_1573964540.csv        修改    269 筆\n",
      "PatNo_ID_1574148494.csv        修改     54 筆\n",
      "PatNo_ID_1574528808.csv        修改    200 筆\n",
      "PatNo_ID_1574987447.csv        修改    109 筆\n",
      "PatNo_ID_1575060177.csv        修改      5 筆\n",
      "PatNo_ID_1575256902.csv        修改     30 筆\n",
      "PatNo_ID_1575502382.csv        修改     18 筆\n",
      "PatNo_ID_1575975485.csv        修改     18 筆\n",
      "PatNo_ID_1576115572.csv        修改    386 筆\n",
      "PatNo_ID_1576116479.csv        修改      1 筆\n",
      "PatNo_ID_1576301569.csv        修改      1 筆\n",
      "PatNo_ID_1576964560.csv        修改      6 筆\n",
      "PatNo_ID_1577042911.csv        修改      7 筆\n",
      "PatNo_ID_1578784257.csv        修改     31 筆\n",
      "PatNo_ID_1579498177.csv        修改     13 筆\n",
      "PatNo_ID_1580062580.csv        修改    110 筆\n",
      "PatNo_ID_1580107637.csv        修改      6 筆\n",
      "PatNo_ID_1580244614.csv        修改      4 筆\n",
      "PatNo_ID_1580766093.csv        修改     18 筆\n",
      "PatNo_ID_1581003248.csv        修改     79 筆\n",
      "PatNo_ID_1581019504.csv        修改   3207 筆\n",
      "PatNo_ID_1581633231.csv        修改     16 筆\n",
      "PatNo_ID_1581692973.csv        修改    112 筆\n",
      "PatNo_ID_1582452511.csv        修改     54 筆\n",
      "PatNo_ID_1582635996.csv        修改      2 筆\n",
      "PatNo_ID_1582849900.csv        修改     98 筆\n",
      "PatNo_ID_1582937076.csv        修改    255 筆\n",
      "PatNo_ID_1584158973.csv        修改      6 筆\n",
      "PatNo_ID_1586172659.csv        修改    202 筆\n",
      "PatNo_ID_1586696634.csv        修改      1 筆\n",
      "PatNo_ID_1586897008.csv        修改   2774 筆\n",
      "PatNo_ID_1587490083.csv        修改     17 筆\n",
      "PatNo_ID_1588632604.csv        修改    121 筆\n",
      "PatNo_ID_1588673465.csv        修改     37 筆\n",
      "PatNo_ID_1588794796.csv        修改      5 筆\n",
      "PatNo_ID_1588957997.csv        修改    267 筆\n",
      "PatNo_ID_1589018086.csv        修改      7 筆\n",
      "PatNo_ID_1589034524.csv        修改     69 筆\n",
      "PatNo_ID_1589324603.csv        修改   1617 筆\n",
      "PatNo_ID_1589918099.csv        修改     14 筆\n",
      "PatNo_ID_1590136310.csv        修改    133 筆\n",
      "PatNo_ID_1590616537.csv        修改      3 筆\n",
      "PatNo_ID_1590854576.csv        修改    106 筆\n",
      "PatNo_ID_1591609798.csv        修改     32 筆\n",
      "PatNo_ID_1592044724.csv        修改      2 筆\n",
      "PatNo_ID_1592560504.csv        修改      7 筆\n",
      "PatNo_ID_1593087886.csv        修改      3 筆\n",
      "PatNo_ID_1593593586.csv        修改      1 筆\n",
      "PatNo_ID_1593720818.csv        修改      1 筆\n",
      "PatNo_ID_1594294180.csv        修改     15 筆\n",
      "PatNo_ID_1594305136.csv        修改      1 筆\n",
      "PatNo_ID_1594309746.csv        修改      8 筆\n",
      "PatNo_ID_1594319286.csv        修改      2 筆\n",
      "PatNo_ID_1594322594.csv        修改      3 筆\n",
      "PatNo_ID_1594437309.csv        修改      3 筆\n",
      "PatNo_ID_1594439781.csv        修改      6 筆\n",
      "PatNo_ID_1594441887.csv        修改     69 筆\n",
      "PatNo_ID_1594448501.csv        修改     13 筆\n",
      "PatNo_ID_1594464829.csv        修改      1 筆\n",
      "PatNo_ID_1594467719.csv        修改      5 筆\n",
      "PatNo_ID_1594479330.csv        修改      1 筆\n",
      "PatNo_ID_1594511914.csv        修改   2653 筆\n",
      "PatNo_ID_1594528842.csv        修改     72 筆\n",
      "PatNo_ID_1594533379.csv        修改      3 筆\n",
      "============================================================\n",
      "✅ 已輸出至：/home/jovyan/1010/data_new/bling_sponvt_ok_v2\n"
     ]
    }
   ],
   "source": [
    "\"\"\"將 /home/jovyan/1010/data_new/bling_spomvy_ok_v1/ 資料夾中所有 CSV，\n",
    "針對 sponvt、vti、vte 欄位中：\n",
    "    - float(0.0)\n",
    "    - int(0)\n",
    "    - str(\"0\")\n",
    "    - str(\"0.0\")\n",
    "全部統一替換成字串 \"0_0\"，\n",
    "並輸出至新資料夾 /home/jovyan/1010/data_new/bling_sponvt_ok_v2/。\n",
    "不覆蓋原始檔案。\n",
    "\"\"\"\n",
    "\n",
    "import os\n",
    "import glob\n",
    "import pandas as pd\n",
    "\n",
    "# =====================\n",
    "# 設定區\n",
    "# =====================\n",
    "INPUT_DIR  = \"/home/jovyan/1010/data_new/bling_spomvy_ok_v1\"\n",
    "OUTPUT_DIR = \"/home/jovyan/1010/data_new/bling_sponvt_ok_v2\"\n",
    "TARGET_COLS = [\"sponvt\", \"vti\", \"vte\"]\n",
    "\n",
    "# 建立輸出資料夾\n",
    "os.makedirs(OUTPUT_DIR, exist_ok=True)\n",
    "\n",
    "# =====================\n",
    "# 主程式\n",
    "# =====================\n",
    "file_list = sorted(glob.glob(os.path.join(INPUT_DIR, \"*.csv\")))\n",
    "summary = []\n",
    "\n",
    "if not file_list:\n",
    "    print(f\"[警示] 找不到任何 CSV：{INPUT_DIR}\")\n",
    "else:\n",
    "    for fp in file_list:\n",
    "        try:\n",
    "            df = pd.read_csv(fp, low_memory=False)\n",
    "            df.columns = [c.lower() for c in df.columns]\n",
    "        except Exception as e:\n",
    "            print(f\"[略過] 無法讀取 {fp}: {e}\")\n",
    "            continue\n",
    "\n",
    "        modified = 0\n",
    "\n",
    "        for col in TARGET_COLS:\n",
    "            if col not in df.columns:\n",
    "                continue\n",
    "\n",
    "            s = df[col]\n",
    "\n",
    "            # 建立遮罩：包含 int/float 0 以及字串 \"0\"/\"0.0\"\n",
    "            mask = s.apply(\n",
    "                lambda x: (\n",
    "                    isinstance(x, (int, float)) and x == 0\n",
    "                ) or (\n",
    "                    isinstance(x, str) and x.strip() in {\"0\", \"0.0\"}\n",
    "                )\n",
    "            )\n",
    "\n",
    "            if mask.any():\n",
    "                df.loc[mask, col] = \"0_0\"\n",
    "                modified += mask.sum()\n",
    "\n",
    "        # 寫出新檔案至新資料夾\n",
    "        out_path = os.path.join(OUTPUT_DIR, os.path.basename(fp))\n",
    "        df.to_csv(out_path, index=False)\n",
    "        summary.append((os.path.basename(fp), modified))\n",
    "\n",
    "    # 統計報告\n",
    "    total_mod = sum(x[1] for x in summary)\n",
    "    print(\"============================================================\")\n",
    "    print(f\"共轉換檔案數：{len(summary)}\")\n",
    "    print(f\"總修改筆數（0 / 0.0 / '0' / '0.0' → '0_0'）：{total_mod:,}\")\n",
    "    print(\"------------------------------------------------------------\")\n",
    "    for fname, cnt in summary:\n",
    "        if cnt > 0:\n",
    "            print(f\"{fname:<30} 修改 {cnt:>6} 筆\")\n",
    "    print(\"============================================================\")\n",
    "    print(f\"✅ 已輸出至：{OUTPUT_DIR}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "a9aa1050-d29a-4a80-a56f-39894ca29ca0",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "============================================================\n",
      "共檢查檔案數：122\n",
      "跨檔案 float 型態中 0.0 檢查結果\n",
      "============================================================\n",
      "欄位名稱              float筆數          0.0筆數       占比(%)\n",
      "-------------------------------------------------------\n",
      "sponvt          1,429,838              0    0.000000\n",
      "vti               672,377              0    0.000000\n",
      "vte               412,176              0    0.000000\n",
      "\n",
      "✅ 確認結果：三欄位的 float 型態中皆無 0.0 數值。\n"
     ]
    }
   ],
   "source": [
    "\"\"\"檢查指定資料夾下所有 CSV 檔案中，\n",
    "sponvt、vti、vte 欄位中「float 型態的 0.0」是否存在。\n",
    "\n",
    "輸出：\n",
    "- 各欄位 float 型的總筆數\n",
    "- 其中值為 0.0 的筆數與占比\n",
    "- 若完全沒有 0.0，顯示確認訊息\n",
    "\"\"\"\n",
    "\n",
    "import os\n",
    "import glob\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "# =====================\n",
    "# 設定\n",
    "# =====================\n",
    "DATA_DIR = \"/home/jovyan/1010/data_new/bling_sponvt_ok_v2\"\n",
    "TARGET_COLS = [\"sponvt\", \"vti\", \"vte\"]\n",
    "FILE_PATTERN = \"*.csv\"\n",
    "\n",
    "# =====================\n",
    "# 初始化計數\n",
    "# =====================\n",
    "float_total = {c: 0 for c in TARGET_COLS}\n",
    "float_zero  = {c: 0 for c in TARGET_COLS}\n",
    "found_files = 0\n",
    "\n",
    "# =====================\n",
    "# 主程式\n",
    "# =====================\n",
    "file_list = sorted(glob.glob(os.path.join(DATA_DIR, FILE_PATTERN)))\n",
    "if not file_list:\n",
    "    print(f\"[警示] 找不到任何 CSV：{os.path.join(DATA_DIR, FILE_PATTERN)}\")\n",
    "else:\n",
    "    for fp in file_list:\n",
    "        try:\n",
    "            df = pd.read_csv(fp, low_memory=False)\n",
    "            df.columns = [c.lower() for c in df.columns]\n",
    "        except Exception as e:\n",
    "            print(f\"[略過] 無法讀取 {fp}: {e}\")\n",
    "            continue\n",
    "\n",
    "        found_files += 1\n",
    "        for col in TARGET_COLS:\n",
    "            if col not in df.columns:\n",
    "                continue\n",
    "\n",
    "            series = df[col]\n",
    "\n",
    "            # 僅取 float 型資料\n",
    "            mask_float = series.apply(lambda x: isinstance(x, float))\n",
    "            vals = series[mask_float]\n",
    "\n",
    "            float_total[col] += len(vals)\n",
    "            float_zero[col]  += (vals == 0.0).sum()\n",
    "\n",
    "# =====================\n",
    "# 輸出結果\n",
    "# =====================\n",
    "if found_files == 0:\n",
    "    print(\"⚠️ 沒有任何可讀取的 CSV 檔案。\")\n",
    "else:\n",
    "    print(\"============================================================\")\n",
    "    print(f\"共檢查檔案數：{found_files}\")\n",
    "    print(\"跨檔案 float 型態中 0.0 檢查結果\")\n",
    "    print(\"============================================================\")\n",
    "    print(f\"{'欄位名稱':<10}{'float筆數':>15}{'0.0筆數':>15}{'占比(%)':>12}\")\n",
    "    print(\"-\" * 55)\n",
    "    for col in TARGET_COLS:\n",
    "        total = float_total[col]\n",
    "        zeros = float_zero[col]\n",
    "        pct = (zeros / total * 100) if total else 0\n",
    "        print(f\"{col:<10}{total:>15,}{zeros:>15,}{pct:>12.6f}\")\n",
    "\n",
    "    if all(float_zero[c] == 0 for c in TARGET_COLS):\n",
    "        print(\"\\n✅ 確認結果：三欄位的 float 型態中皆無 0.0 數值。\")\n",
    "    else:\n",
    "        print(\"\\n⚠️ 注意：有欄位的 float 型態中存在 0.0，請檢查上表。\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1069b907-c0d8-4f10-be31-066eb9e6aa65",
   "metadata": {},
   "outputs": [],
   "source": [
    "算一下0_0再三個欄位的數量跟資料型態"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "766eaf89-dbfd-4231-805e-1365526c2266",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "============================================================\n",
      "共檢查檔案數：122\n",
      "跨檔案 '0_0' 統計結果\n",
      "============================================================\n",
      "欄位名稱                0_0筆數              主要資料型態\n",
      "-------------------------------------------------------\n",
      "sponvt             17,273        str (17273筆)\n",
      "vti                 1,832         str (1832筆)\n",
      "vte                 2,206         str (2206筆)\n",
      "============================================================\n",
      "📊 若結果皆為 str 類型，代表轉換已完全成功。\n"
     ]
    }
   ],
   "source": [
    "\"\"\"統計 /home/jovyan/1010/data_new/bling_sponvt_ok_v2 下所有 CSV 中，\n",
    "sponvt、vti、vte 三欄位的：\n",
    "1. \"0_0\" 出現總數\n",
    "2. 其資料型態分布（例如 str、float、int）\n",
    "\"\"\"\n",
    "\n",
    "import os\n",
    "import glob\n",
    "import pandas as pd\n",
    "from collections import Counter\n",
    "\n",
    "# =====================\n",
    "# 設定區\n",
    "# =====================\n",
    "DATA_DIR = \"/home/jovyan/1010/data_new/bling_sponvt_ok_v2\"\n",
    "TARGET_COLS = [\"sponvt\", \"vti\", \"vte\"]\n",
    "\n",
    "# =====================\n",
    "# 主程式\n",
    "# =====================\n",
    "file_list = sorted(glob.glob(os.path.join(DATA_DIR, \"*.csv\")))\n",
    "summary_counts = {col: 0 for col in TARGET_COLS}\n",
    "summary_types  = {col: Counter() for col in TARGET_COLS}\n",
    "file_count = 0\n",
    "\n",
    "if not file_list:\n",
    "    print(f\"[警示] 找不到任何 CSV：{DATA_DIR}\")\n",
    "else:\n",
    "    for fp in file_list:\n",
    "        try:\n",
    "            df = pd.read_csv(fp, dtype=object, low_memory=False)\n",
    "            df.columns = [c.lower() for c in df.columns]\n",
    "        except Exception as e:\n",
    "            print(f\"[略過] 無法讀取 {fp}: {e}\")\n",
    "            continue\n",
    "\n",
    "        file_count += 1\n",
    "\n",
    "        for col in TARGET_COLS:\n",
    "            if col not in df.columns:\n",
    "                continue\n",
    "\n",
    "            s = df[col]\n",
    "            mask = s.astype(str).str.strip() == \"0_0\"\n",
    "            summary_counts[col] += mask.sum()\n",
    "\n",
    "            # 統計資料型態\n",
    "            for v in s[mask]:\n",
    "                summary_types[col][type(v).__name__] += 1\n",
    "\n",
    "# =====================\n",
    "# 輸出結果\n",
    "# =====================\n",
    "print(\"============================================================\")\n",
    "print(f\"共檢查檔案數：{file_count}\")\n",
    "print(\"跨檔案 '0_0' 統計結果\")\n",
    "print(\"============================================================\")\n",
    "print(f\"{'欄位名稱':<10}{'0_0筆數':>15}{'主要資料型態':>20}\")\n",
    "print(\"-\" * 55)\n",
    "\n",
    "for col in TARGET_COLS:\n",
    "    count = summary_counts[col]\n",
    "    type_dist = summary_types[col]\n",
    "    if type_dist:\n",
    "        top_type = type_dist.most_common(1)[0]\n",
    "        type_str = f\"{top_type[0]} ({top_type[1]}筆)\"\n",
    "    else:\n",
    "        type_str = \"-\"\n",
    "    print(f\"{col:<10}{count:>15,}{type_str:>20}\")\n",
    "\n",
    "print(\"============================================================\")\n",
    "print(\"📊 若結果皆為 str 類型，代表轉換已完全成功。\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "892ebb3a-a7dd-4de4-be2c-114c5e10c80d",
   "metadata": {},
   "outputs": [],
   "source": [
    "檢查spomvy_ok_v1欄位=1的資料中，他的sponvt欄位都沒有0 0.0 NaN (null)等除了float數字以外的東西"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "388a3613-7425-4a3f-8c69-8f0cd23dbf66",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "============================================================\n",
      "共檢查檔案數：122\n",
      "符合 spomvy_ok_v1 == 1 的資料總筆數：16,394\n",
      "❌ sponvt 欄位不合法筆數：0\n",
      "------------------------------------------------------------\n",
      "✅ 全部符合規範：spomvy_ok_v1 == 1 的 sponvt 皆為有效 float 數值。\n",
      "============================================================\n"
     ]
    }
   ],
   "source": [
    "\"\"\"檢查 /home/jovyan/1010/data_new/bling_spomvy_ok_v1/ 下所有 CSV，\n",
    "在 spomvy_ok_v1 == 1 的資料中，\n",
    "確認 sponvt 欄位皆為 float 數值（排除 0、0.0、NaN、(null)、空字串等）。\n",
    "\n",
    "輸出：\n",
    "- 各檔案是否存在違規筆數\n",
    "- 總違規筆數與前幾筆樣本\n",
    "\"\"\"\n",
    "\n",
    "import os\n",
    "import glob\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "# =====================\n",
    "# 設定\n",
    "# =====================\n",
    "DATA_DIR = \"/home/jovyan/1010/data_new/bling_sponvt_ok_v2\"\n",
    "TARGET_FLAG = \"spomvy_ok_v1\"\n",
    "CHECK_COL = \"sponvt\"\n",
    "INVALID_STRINGS = {\"\", \"nan\", \"NaN\", \"None\", \"(null)\", \"NULL\", \"null\"}\n",
    "FILE_PATTERN = \"*.csv\"\n",
    "\n",
    "# =====================\n",
    "# 主程式\n",
    "# =====================\n",
    "file_list = sorted(glob.glob(os.path.join(DATA_DIR, FILE_PATTERN)))\n",
    "violations = []\n",
    "total_checked = 0\n",
    "total_invalid = 0\n",
    "\n",
    "if not file_list:\n",
    "    print(f\"[警示] 找不到任何 CSV：{DATA_DIR}\")\n",
    "else:\n",
    "    for fp in file_list:\n",
    "        try:\n",
    "            df = pd.read_csv(fp, dtype=object, low_memory=False)\n",
    "            df.columns = [c.lower() for c in df.columns]\n",
    "        except Exception as e:\n",
    "            print(f\"[略過] 無法讀取 {fp}: {e}\")\n",
    "            continue\n",
    "\n",
    "        if TARGET_FLAG not in df.columns or CHECK_COL not in df.columns:\n",
    "            continue\n",
    "\n",
    "        subset = df[df[TARGET_FLAG].astype(str).str.strip() == \"1\"]\n",
    "        total_checked += len(subset)\n",
    "        if subset.empty:\n",
    "            continue\n",
    "\n",
    "        s = subset[CHECK_COL].astype(str).str.strip()\n",
    "\n",
    "        # 定義不合法條件：\n",
    "        # 1. 空值或特殊字串\n",
    "        # 2. 可轉為數字但為 0 或 0.0\n",
    "        # 3. 無法轉成 float\n",
    "        def is_invalid(x):\n",
    "            if x in INVALID_STRINGS:\n",
    "                return True\n",
    "            try:\n",
    "                f = float(x)\n",
    "                if f == 0.0:\n",
    "                    return True\n",
    "                return False\n",
    "            except ValueError:\n",
    "                return True\n",
    "\n",
    "        invalid_mask = s.apply(is_invalid)\n",
    "        invalid_rows = subset[invalid_mask]\n",
    "        total_invalid += invalid_mask.sum()\n",
    "\n",
    "        if invalid_mask.any():\n",
    "            for _, row in invalid_rows.head(5).iterrows():\n",
    "                violations.append({\n",
    "                    \"file\": os.path.basename(fp),\n",
    "                    TARGET_FLAG: row[TARGET_FLAG],\n",
    "                    CHECK_COL: row[CHECK_COL]\n",
    "                })\n",
    "\n",
    "# =====================\n",
    "# 結果輸出\n",
    "# =====================\n",
    "print(\"============================================================\")\n",
    "print(f\"共檢查檔案數：{len(file_list)}\")\n",
    "print(f\"符合 spomvy_ok_v1 == 1 的資料總筆數：{total_checked:,}\")\n",
    "print(f\"❌ sponvt 欄位不合法筆數：{total_invalid:,}\")\n",
    "print(\"------------------------------------------------------------\")\n",
    "if violations:\n",
    "    print(\"前幾筆違規樣本：\")\n",
    "    for v in violations[:10]:\n",
    "        print(v)\n",
    "else:\n",
    "    print(\"✅ 全部符合規範：spomvy_ok_v1 == 1 的 sponvt 皆為有效 float 數值。\")\n",
    "print(\"============================================================\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0ca5f0a5-045b-4f5a-ba2d-200f359c0eac",
   "metadata": {},
   "outputs": [],
   "source": [
    "我要看三個欄位的資料型態 數量占比"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "b8fcf928-ad02-4ae0-9d54-56454102affd",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "============================================================\n",
      "📊 總檔案數：122\n",
      "📈 總筆數：1,602,476\n",
      "------------------------------------------------------------\n",
      "【SPONVT】欄位型態統計（互斥）\n",
      "Total rows: 1,602,476\n",
      "Type             Count    Percent(%)\n",
      "------------------------------------\n",
      "NaN          1,424,904     88.918898\n",
      "str            177,572     11.081102\n",
      "\n",
      "【VTI】欄位型態統計（互斥）\n",
      "Total rows: 1,602,476\n",
      "Type             Count    Percent(%)\n",
      "------------------------------------\n",
      "str          1,384,384     86.390311\n",
      "NaN            218,092     13.609689\n",
      "\n",
      "【VTE】欄位型態統計（互斥）\n",
      "Total rows: 1,602,476\n",
      "Type             Count    Percent(%)\n",
      "------------------------------------\n",
      "str          1,445,443     90.200602\n",
      "NaN            157,033      9.799398\n",
      "\n",
      "============================================================\n"
     ]
    }
   ],
   "source": [
    "\"\"\"統計指定資料夾下所有 CSV 的 sponvt、vti、vte 三欄資料型態分布，\n",
    "輸出每種型態（如 float、int、str、bool、NaN 等）的筆數與占比。\n",
    "\"\"\"\n",
    "\n",
    "import os\n",
    "import glob\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from collections import Counter\n",
    "\n",
    "# =========================\n",
    "# 使用者可調整區\n",
    "# =========================\n",
    "DATA_DIR = \"/home/jovyan/1010/data_new/bling_sponvt_ok_v2\"  # 目標資料夾\n",
    "FILE_PATTERN = \"*.csv\"\n",
    "TARGET_COLS = [\"sponvt\", \"vti\", \"vte\"]\n",
    "\n",
    "# =========================\n",
    "# 小工具函式\n",
    "# =========================\n",
    "def detect_type(value):\n",
    "    \"\"\"偵測單一值的 Python 型態分類\"\"\"\n",
    "    if pd.isna(value):\n",
    "        return \"NaN\"\n",
    "    if isinstance(value, bool):\n",
    "        return \"bool\"\n",
    "    if isinstance(value, (int, np.integer)):\n",
    "        return \"int\"\n",
    "    if isinstance(value, (float, np.floating)):\n",
    "        return \"float\"\n",
    "    if isinstance(value, str):\n",
    "        return \"str\"\n",
    "    return \"other\"\n",
    "\n",
    "# =========================\n",
    "# 主程式\n",
    "# =========================\n",
    "total_counts = Counter()\n",
    "total_rows = 0\n",
    "\n",
    "file_list = sorted(glob.glob(os.path.join(DATA_DIR, FILE_PATTERN)))\n",
    "\n",
    "for fp in file_list:\n",
    "    try:\n",
    "        df = pd.read_csv(fp, dtype=object, low_memory=False)\n",
    "        df.columns = [c.strip().lower() for c in df.columns]\n",
    "    except Exception as e:\n",
    "        print(f\"[WARN] 無法讀取 {fp}: {e}\")\n",
    "        continue\n",
    "\n",
    "    if df.empty:\n",
    "        continue\n",
    "\n",
    "    total_rows += len(df)\n",
    "\n",
    "    # 逐欄位偵測型態\n",
    "    for col in TARGET_COLS:\n",
    "        if col not in df.columns:\n",
    "            continue\n",
    "        col_types = df[col].apply(detect_type)\n",
    "        total_counts.update([(col, t) for t in col_types])\n",
    "\n",
    "# =========================\n",
    "# 統計彙整與輸出\n",
    "# =========================\n",
    "print(\"============================================================\")\n",
    "print(f\"📊 總檔案數：{len(file_list)}\")\n",
    "print(f\"📈 總筆數：{total_rows:,}\")\n",
    "print(\"------------------------------------------------------------\")\n",
    "\n",
    "for col in TARGET_COLS:\n",
    "    # 過濾該欄位的統計\n",
    "    sub_counts = {t: c for (cname, t), c in total_counts.items() if cname == col}\n",
    "    total_col = sum(sub_counts.values())\n",
    "    print(f\"【{col.upper()}】欄位型態統計（互斥）\")\n",
    "    print(f\"Total rows: {total_col:,}\")\n",
    "    print(f\"{'Type':<10}{'Count':>12}{'Percent(%)':>14}\")\n",
    "    print(\"------------------------------------\")\n",
    "\n",
    "    for t, c in sorted(sub_counts.items(), key=lambda x: -x[1]):\n",
    "        p = c / total_col * 100 if total_col else 0\n",
    "        print(f\"{t:<10}{c:>12,}{p:>14.6f}\")\n",
    "    print()\n",
    "\n",
    "print(\"============================================================\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "680fe303-7dde-4a8a-969b-5e6ef9eda381",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "============================================================\n",
      "📊 總檔案數：122\n",
      "📈 三欄合計總筆數：1,602,476\n",
      "------------------------------------------------------------\n",
      "【SPONVT】欄位字串內容分類統計\n",
      "Total string rows: 177,572\n",
      "Category                 Count    Percent(%)\n",
      "----------------------------------------------\n",
      "str_numeric             33,667     18.959633\n",
      "str_non_numeric        143,905     81.040367\n",
      "\n",
      "【VTI】欄位字串內容分類統計\n",
      "Total string rows: 1,384,384\n",
      "Category                 Count    Percent(%)\n",
      "----------------------------------------------\n",
      "str_numeric          1,333,066     96.293081\n",
      "str_non_numeric         51,318      3.706919\n",
      "\n",
      "【VTE】欄位字串內容分類統計\n",
      "Total string rows: 1,445,443\n",
      "Category                 Count    Percent(%)\n",
      "----------------------------------------------\n",
      "str_numeric          1,445,443    100.000000\n",
      "str_non_numeric              0      0.000000\n",
      "\n",
      "============================================================\n"
     ]
    }
   ],
   "source": [
    "\"\"\"統計指定資料夾下所有 CSV 的 sponvt、vti、vte 欄位中，\n",
    "屬於字串(str)型態的值裡，有多少是數字字串（例如 \"123.4\"、\"0\"）。\n",
    "輸出各欄位的：\n",
    "    - str_numeric：內容是可轉成數字的字串\n",
    "    - str_non_numeric：內容不是數字字串\n",
    "以及其筆數與占比。\n",
    "\"\"\"\n",
    "\n",
    "import os\n",
    "import glob\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from collections import Counter\n",
    "\n",
    "# =========================\n",
    "# 使用者可調整區\n",
    "# =========================\n",
    "DATA_DIR = \"/home/jovyan/1010/data_new/bling_sponvt_ok_v2\"\n",
    "FILE_PATTERN = \"*.csv\"\n",
    "TARGET_COLS = [\"sponvt\", \"vti\", \"vte\"]\n",
    "\n",
    "# =========================\n",
    "# 小工具函式\n",
    "# =========================\n",
    "def is_str_numeric(s: str) -> bool:\n",
    "    \"\"\"\n",
    "    判斷字串是否為「可轉成數字」：\n",
    "    - 接受整數或小數（含正負號）\n",
    "    - 排除空白、'NA'、'null' 等\n",
    "    \"\"\"\n",
    "    if not isinstance(s, str):\n",
    "        return False\n",
    "    txt = s.strip().lower()\n",
    "    if txt in {\"\", \"na\", \"n/a\", \"null\", \"none\"}:\n",
    "        return False\n",
    "    try:\n",
    "        float(txt)\n",
    "        return True\n",
    "    except ValueError:\n",
    "        return False\n",
    "\n",
    "# =========================\n",
    "# 主流程\n",
    "# =========================\n",
    "total_rows = 0\n",
    "results = {col: Counter() for col in TARGET_COLS}\n",
    "\n",
    "file_list = sorted(glob.glob(os.path.join(DATA_DIR, FILE_PATTERN)))\n",
    "\n",
    "for fp in file_list:\n",
    "    try:\n",
    "        df = pd.read_csv(fp, dtype=object, low_memory=False)\n",
    "        df.columns = [c.strip().lower() for c in df.columns]\n",
    "    except Exception as e:\n",
    "        print(f\"[WARN] 無法讀取 {fp}: {e}\")\n",
    "        continue\n",
    "\n",
    "    if df.empty:\n",
    "        continue\n",
    "\n",
    "    total_rows += len(df)\n",
    "\n",
    "    for col in TARGET_COLS:\n",
    "        if col not in df.columns:\n",
    "            continue\n",
    "        col_series = df[col]\n",
    "\n",
    "        # 只處理 str 型態的資料\n",
    "        str_mask = col_series.apply(lambda x: isinstance(x, str))\n",
    "        str_values = col_series[str_mask]\n",
    "\n",
    "        numeric_mask = str_values.apply(is_str_numeric)\n",
    "        results[col][\"str_numeric\"] += int(numeric_mask.sum())\n",
    "        results[col][\"str_non_numeric\"] += int((~numeric_mask).sum())\n",
    "\n",
    "# =========================\n",
    "# 統計輸出\n",
    "# =========================\n",
    "print(\"============================================================\")\n",
    "print(f\"📊 總檔案數：{len(file_list)}\")\n",
    "print(f\"📈 三欄合計總筆數：{total_rows:,}\")\n",
    "print(\"------------------------------------------------------------\")\n",
    "\n",
    "for col in TARGET_COLS:\n",
    "    total_str = sum(results[col].values())\n",
    "    print(f\"【{col.upper()}】欄位字串內容分類統計\")\n",
    "    print(f\"Total string rows: {total_str:,}\")\n",
    "    print(f\"{'Category':<18}{'Count':>12}{'Percent(%)':>14}\")\n",
    "    print(\"----------------------------------------------\")\n",
    "    for key in [\"str_numeric\", \"str_non_numeric\"]:\n",
    "        c = results[col][key]\n",
    "        p = c / total_str * 100 if total_str else 0\n",
    "        print(f\"{key:<18}{c:>12,}{p:>14.6f}\")\n",
    "    print()\n",
    "\n",
    "print(\"============================================================\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "eaa470ae-c587-4f79-a409-d0be35ce4fd9",
   "metadata": {},
   "outputs": [],
   "source": [
    "VTE：共有 1,445,443 筆「字串」，而且 100% 都是「數字字串」。意思是：VTE 在檔案裡幾乎全被以「文字形式」儲存（例如 \"12.3\"），但內容其實都是數值。\n",
    "VTI：96.29% 是數字字串、3.71% 非數字字串（多半是 \"\"/NA/null/none 或髒值）。\n",
    "SPONVT：81.04% 是非數字字串（常見為 True/False 這類），只有 18.96% 是數字字串"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "13104733-e4ce-4bab-a029-136f35b4271c",
   "metadata": {},
   "outputs": [],
   "source": [
    "將被偵測為字串 (str) 的值改成數字字串float"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "8128a5b4-a2f7-44bf-9916-104fe977a664",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[INFO] Source files: 122\n",
      "[OK] Wrote: /home/jovyan/1010/data_new/bling_sponvt_ok_v3/089271.csv  (rows=32419)\n",
      "[OK] Wrote: /home/jovyan/1010/data_new/bling_sponvt_ok_v3/095323.csv  (rows=23791)\n",
      "[OK] Wrote: /home/jovyan/1010/data_new/bling_sponvt_ok_v3/095707.csv  (rows=20180)\n",
      "[OK] Wrote: /home/jovyan/1010/data_new/bling_sponvt_ok_v3/114309.csv  (rows=71729)\n",
      "[OK] Wrote: /home/jovyan/1010/data_new/bling_sponvt_ok_v3/230933.csv  (rows=30249)\n",
      "[OK] Wrote: /home/jovyan/1010/data_new/bling_sponvt_ok_v3/4216007.csv  (rows=1433)\n",
      "[OK] Wrote: /home/jovyan/1010/data_new/bling_sponvt_ok_v3/7108162.csv  (rows=239)\n",
      "[OK] Wrote: /home/jovyan/1010/data_new/bling_sponvt_ok_v3/7408338.csv  (rows=1432)\n",
      "[OK] Wrote: /home/jovyan/1010/data_new/bling_sponvt_ok_v3/7657698.csv  (rows=1413)\n",
      "[OK] Wrote: /home/jovyan/1010/data_new/bling_sponvt_ok_v3/7721164.csv  (rows=483)\n",
      "[OK] Wrote: /home/jovyan/1010/data_new/bling_sponvt_ok_v3/PatNo_ID_1560013303.csv  (rows=2543)\n",
      "[OK] Wrote: /home/jovyan/1010/data_new/bling_sponvt_ok_v3/PatNo_ID_1562733396.csv  (rows=2254)\n",
      "[OK] Wrote: /home/jovyan/1010/data_new/bling_sponvt_ok_v3/PatNo_ID_1563587183.csv  (rows=5287)\n",
      "[OK] Wrote: /home/jovyan/1010/data_new/bling_sponvt_ok_v3/PatNo_ID_1564148644.csv  (rows=17287)\n",
      "[OK] Wrote: /home/jovyan/1010/data_new/bling_sponvt_ok_v3/PatNo_ID_1565148312.csv  (rows=5475)\n",
      "[OK] Wrote: /home/jovyan/1010/data_new/bling_sponvt_ok_v3/PatNo_ID_1565378038.csv  (rows=2561)\n",
      "[OK] Wrote: /home/jovyan/1010/data_new/bling_sponvt_ok_v3/PatNo_ID_1566123680.csv  (rows=42600)\n",
      "[OK] Wrote: /home/jovyan/1010/data_new/bling_sponvt_ok_v3/PatNo_ID_1566252197.csv  (rows=3368)\n",
      "[OK] Wrote: /home/jovyan/1010/data_new/bling_sponvt_ok_v3/PatNo_ID_1566279967.csv  (rows=1235)\n",
      "[OK] Wrote: /home/jovyan/1010/data_new/bling_sponvt_ok_v3/PatNo_ID_1566671274.csv  (rows=50718)\n",
      "[OK] Wrote: /home/jovyan/1010/data_new/bling_sponvt_ok_v3/PatNo_ID_1566911879.csv  (rows=53999)\n",
      "[OK] Wrote: /home/jovyan/1010/data_new/bling_sponvt_ok_v3/PatNo_ID_1567747650.csv  (rows=10901)\n",
      "[OK] Wrote: /home/jovyan/1010/data_new/bling_sponvt_ok_v3/PatNo_ID_1567804800.csv  (rows=20578)\n",
      "[OK] Wrote: /home/jovyan/1010/data_new/bling_sponvt_ok_v3/PatNo_ID_1567832735.csv  (rows=36580)\n",
      "[OK] Wrote: /home/jovyan/1010/data_new/bling_sponvt_ok_v3/PatNo_ID_1568039398.csv  (rows=34519)\n",
      "[OK] Wrote: /home/jovyan/1010/data_new/bling_sponvt_ok_v3/PatNo_ID_1568574099.csv  (rows=13946)\n",
      "[OK] Wrote: /home/jovyan/1010/data_new/bling_sponvt_ok_v3/PatNo_ID_1568813269.csv  (rows=4867)\n",
      "[OK] Wrote: /home/jovyan/1010/data_new/bling_sponvt_ok_v3/PatNo_ID_1568952422.csv  (rows=1184)\n",
      "[OK] Wrote: /home/jovyan/1010/data_new/bling_sponvt_ok_v3/PatNo_ID_1569083701.csv  (rows=3328)\n",
      "[OK] Wrote: /home/jovyan/1010/data_new/bling_sponvt_ok_v3/PatNo_ID_1569944983.csv  (rows=7650)\n",
      "[OK] Wrote: /home/jovyan/1010/data_new/bling_sponvt_ok_v3/PatNo_ID_1570089466.csv  (rows=41343)\n",
      "[OK] Wrote: /home/jovyan/1010/data_new/bling_sponvt_ok_v3/PatNo_ID_1570242703.csv  (rows=10646)\n",
      "[OK] Wrote: /home/jovyan/1010/data_new/bling_sponvt_ok_v3/PatNo_ID_1570273244.csv  (rows=9730)\n",
      "[OK] Wrote: /home/jovyan/1010/data_new/bling_sponvt_ok_v3/PatNo_ID_1570642083.csv  (rows=19731)\n",
      "[OK] Wrote: /home/jovyan/1010/data_new/bling_sponvt_ok_v3/PatNo_ID_1571945701.csv  (rows=15731)\n",
      "[OK] Wrote: /home/jovyan/1010/data_new/bling_sponvt_ok_v3/PatNo_ID_1572481361.csv  (rows=34540)\n",
      "[OK] Wrote: /home/jovyan/1010/data_new/bling_sponvt_ok_v3/PatNo_ID_1572562839.csv  (rows=20966)\n",
      "[OK] Wrote: /home/jovyan/1010/data_new/bling_sponvt_ok_v3/PatNo_ID_1572831765.csv  (rows=2696)\n",
      "[OK] Wrote: /home/jovyan/1010/data_new/bling_sponvt_ok_v3/PatNo_ID_1572976822.csv  (rows=6764)\n",
      "[OK] Wrote: /home/jovyan/1010/data_new/bling_sponvt_ok_v3/PatNo_ID_1573063188.csv  (rows=5080)\n",
      "[OK] Wrote: /home/jovyan/1010/data_new/bling_sponvt_ok_v3/PatNo_ID_1573249295.csv  (rows=7151)\n",
      "[OK] Wrote: /home/jovyan/1010/data_new/bling_sponvt_ok_v3/PatNo_ID_1573964540.csv  (rows=4394)\n",
      "[OK] Wrote: /home/jovyan/1010/data_new/bling_sponvt_ok_v3/PatNo_ID_1574148494.csv  (rows=47154)\n",
      "[OK] Wrote: /home/jovyan/1010/data_new/bling_sponvt_ok_v3/PatNo_ID_1574270349.csv  (rows=6534)\n",
      "[OK] Wrote: /home/jovyan/1010/data_new/bling_sponvt_ok_v3/PatNo_ID_1574528808.csv  (rows=14817)\n",
      "[OK] Wrote: /home/jovyan/1010/data_new/bling_sponvt_ok_v3/PatNo_ID_1574831525.csv  (rows=521)\n",
      "[OK] Wrote: /home/jovyan/1010/data_new/bling_sponvt_ok_v3/PatNo_ID_1574987447.csv  (rows=19843)\n",
      "[OK] Wrote: /home/jovyan/1010/data_new/bling_sponvt_ok_v3/PatNo_ID_1575060177.csv  (rows=5290)\n",
      "[OK] Wrote: /home/jovyan/1010/data_new/bling_sponvt_ok_v3/PatNo_ID_1575256902.csv  (rows=9587)\n",
      "[OK] Wrote: /home/jovyan/1010/data_new/bling_sponvt_ok_v3/PatNo_ID_1575445051.csv  (rows=1801)\n",
      "[OK] Wrote: /home/jovyan/1010/data_new/bling_sponvt_ok_v3/PatNo_ID_1575502382.csv  (rows=6604)\n",
      "[OK] Wrote: /home/jovyan/1010/data_new/bling_sponvt_ok_v3/PatNo_ID_1575975485.csv  (rows=16459)\n",
      "[OK] Wrote: /home/jovyan/1010/data_new/bling_sponvt_ok_v3/PatNo_ID_1576115572.csv  (rows=18717)\n",
      "[OK] Wrote: /home/jovyan/1010/data_new/bling_sponvt_ok_v3/PatNo_ID_1576116479.csv  (rows=1263)\n",
      "[OK] Wrote: /home/jovyan/1010/data_new/bling_sponvt_ok_v3/PatNo_ID_1576301569.csv  (rows=3207)\n",
      "[OK] Wrote: /home/jovyan/1010/data_new/bling_sponvt_ok_v3/PatNo_ID_1576964560.csv  (rows=24192)\n",
      "[OK] Wrote: /home/jovyan/1010/data_new/bling_sponvt_ok_v3/PatNo_ID_1577042911.csv  (rows=41894)\n",
      "[OK] Wrote: /home/jovyan/1010/data_new/bling_sponvt_ok_v3/PatNo_ID_1577487284.csv  (rows=2345)\n",
      "[OK] Wrote: /home/jovyan/1010/data_new/bling_sponvt_ok_v3/PatNo_ID_1578784257.csv  (rows=33917)\n",
      "[OK] Wrote: /home/jovyan/1010/data_new/bling_sponvt_ok_v3/PatNo_ID_1579198603.csv  (rows=2046)\n",
      "[OK] Wrote: /home/jovyan/1010/data_new/bling_sponvt_ok_v3/PatNo_ID_1579498177.csv  (rows=21688)\n",
      "[OK] Wrote: /home/jovyan/1010/data_new/bling_sponvt_ok_v3/PatNo_ID_1580062580.csv  (rows=4300)\n",
      "[OK] Wrote: /home/jovyan/1010/data_new/bling_sponvt_ok_v3/PatNo_ID_1580096720.csv  (rows=2846)\n",
      "[OK] Wrote: /home/jovyan/1010/data_new/bling_sponvt_ok_v3/PatNo_ID_1580107637.csv  (rows=5396)\n",
      "[OK] Wrote: /home/jovyan/1010/data_new/bling_sponvt_ok_v3/PatNo_ID_1580244614.csv  (rows=5278)\n",
      "[OK] Wrote: /home/jovyan/1010/data_new/bling_sponvt_ok_v3/PatNo_ID_1580766093.csv  (rows=18070)\n",
      "[OK] Wrote: /home/jovyan/1010/data_new/bling_sponvt_ok_v3/PatNo_ID_1581003248.csv  (rows=7776)\n",
      "[OK] Wrote: /home/jovyan/1010/data_new/bling_sponvt_ok_v3/PatNo_ID_1581019504.csv  (rows=28177)\n",
      "[OK] Wrote: /home/jovyan/1010/data_new/bling_sponvt_ok_v3/PatNo_ID_1581633231.csv  (rows=15995)\n",
      "[OK] Wrote: /home/jovyan/1010/data_new/bling_sponvt_ok_v3/PatNo_ID_1581692973.csv  (rows=2723)\n",
      "[OK] Wrote: /home/jovyan/1010/data_new/bling_sponvt_ok_v3/PatNo_ID_1582452511.csv  (rows=5196)\n",
      "[OK] Wrote: /home/jovyan/1010/data_new/bling_sponvt_ok_v3/PatNo_ID_1582635996.csv  (rows=13046)\n",
      "[OK] Wrote: /home/jovyan/1010/data_new/bling_sponvt_ok_v3/PatNo_ID_1582849900.csv  (rows=7411)\n",
      "[OK] Wrote: /home/jovyan/1010/data_new/bling_sponvt_ok_v3/PatNo_ID_1582937076.csv  (rows=23990)\n",
      "[OK] Wrote: /home/jovyan/1010/data_new/bling_sponvt_ok_v3/PatNo_ID_1584158973.csv  (rows=2882)\n",
      "[OK] Wrote: /home/jovyan/1010/data_new/bling_sponvt_ok_v3/PatNo_ID_1584397376.csv  (rows=638)\n",
      "[OK] Wrote: /home/jovyan/1010/data_new/bling_sponvt_ok_v3/PatNo_ID_1586172659.csv  (rows=38559)\n",
      "[OK] Wrote: /home/jovyan/1010/data_new/bling_sponvt_ok_v3/PatNo_ID_1586696634.csv  (rows=3569)\n",
      "[OK] Wrote: /home/jovyan/1010/data_new/bling_sponvt_ok_v3/PatNo_ID_1586897008.csv  (rows=6687)\n",
      "[OK] Wrote: /home/jovyan/1010/data_new/bling_sponvt_ok_v3/PatNo_ID_1587490083.csv  (rows=45186)\n",
      "[OK] Wrote: /home/jovyan/1010/data_new/bling_sponvt_ok_v3/PatNo_ID_1588632604.csv  (rows=2608)\n",
      "[OK] Wrote: /home/jovyan/1010/data_new/bling_sponvt_ok_v3/PatNo_ID_1588673465.csv  (rows=10077)\n",
      "[OK] Wrote: /home/jovyan/1010/data_new/bling_sponvt_ok_v3/PatNo_ID_1588794796.csv  (rows=9376)\n",
      "[OK] Wrote: /home/jovyan/1010/data_new/bling_sponvt_ok_v3/PatNo_ID_1588957997.csv  (rows=10966)\n",
      "[OK] Wrote: /home/jovyan/1010/data_new/bling_sponvt_ok_v3/PatNo_ID_1589018086.csv  (rows=13472)\n",
      "[OK] Wrote: /home/jovyan/1010/data_new/bling_sponvt_ok_v3/PatNo_ID_1589034524.csv  (rows=50081)\n",
      "[OK] Wrote: /home/jovyan/1010/data_new/bling_sponvt_ok_v3/PatNo_ID_1589324603.csv  (rows=4187)\n",
      "[OK] Wrote: /home/jovyan/1010/data_new/bling_sponvt_ok_v3/PatNo_ID_1589918099.csv  (rows=9333)\n",
      "[OK] Wrote: /home/jovyan/1010/data_new/bling_sponvt_ok_v3/PatNo_ID_1590136310.csv  (rows=5580)\n",
      "[OK] Wrote: /home/jovyan/1010/data_new/bling_sponvt_ok_v3/PatNo_ID_1590616537.csv  (rows=14208)\n",
      "[OK] Wrote: /home/jovyan/1010/data_new/bling_sponvt_ok_v3/PatNo_ID_1590854576.csv  (rows=15879)\n",
      "[OK] Wrote: /home/jovyan/1010/data_new/bling_sponvt_ok_v3/PatNo_ID_1591609798.csv  (rows=35624)\n",
      "[OK] Wrote: /home/jovyan/1010/data_new/bling_sponvt_ok_v3/PatNo_ID_1592044724.csv  (rows=6815)\n",
      "[OK] Wrote: /home/jovyan/1010/data_new/bling_sponvt_ok_v3/PatNo_ID_1592560504.csv  (rows=18052)\n",
      "[OK] Wrote: /home/jovyan/1010/data_new/bling_sponvt_ok_v3/PatNo_ID_1593087886.csv  (rows=24163)\n",
      "[OK] Wrote: /home/jovyan/1010/data_new/bling_sponvt_ok_v3/PatNo_ID_1593416100.csv  (rows=3328)\n",
      "[OK] Wrote: /home/jovyan/1010/data_new/bling_sponvt_ok_v3/PatNo_ID_1593472048.csv  (rows=8230)\n",
      "[OK] Wrote: /home/jovyan/1010/data_new/bling_sponvt_ok_v3/PatNo_ID_1593593586.csv  (rows=18882)\n",
      "[OK] Wrote: /home/jovyan/1010/data_new/bling_sponvt_ok_v3/PatNo_ID_1593720818.csv  (rows=3911)\n",
      "[OK] Wrote: /home/jovyan/1010/data_new/bling_sponvt_ok_v3/PatNo_ID_1593838524.csv  (rows=2178)\n",
      "[OK] Wrote: /home/jovyan/1010/data_new/bling_sponvt_ok_v3/PatNo_ID_1594173718.csv  (rows=344)\n",
      "[OK] Wrote: /home/jovyan/1010/data_new/bling_sponvt_ok_v3/PatNo_ID_1594294180.csv  (rows=18334)\n",
      "[OK] Wrote: /home/jovyan/1010/data_new/bling_sponvt_ok_v3/PatNo_ID_1594305136.csv  (rows=18679)\n",
      "[OK] Wrote: /home/jovyan/1010/data_new/bling_sponvt_ok_v3/PatNo_ID_1594309746.csv  (rows=3745)\n",
      "[OK] Wrote: /home/jovyan/1010/data_new/bling_sponvt_ok_v3/PatNo_ID_1594319286.csv  (rows=4107)\n",
      "[OK] Wrote: /home/jovyan/1010/data_new/bling_sponvt_ok_v3/PatNo_ID_1594320763.csv  (rows=2431)\n",
      "[OK] Wrote: /home/jovyan/1010/data_new/bling_sponvt_ok_v3/PatNo_ID_1594322594.csv  (rows=5380)\n",
      "[OK] Wrote: /home/jovyan/1010/data_new/bling_sponvt_ok_v3/PatNo_ID_1594335109.csv  (rows=5116)\n",
      "[OK] Wrote: /home/jovyan/1010/data_new/bling_sponvt_ok_v3/PatNo_ID_1594423683.csv  (rows=5023)\n",
      "[OK] Wrote: /home/jovyan/1010/data_new/bling_sponvt_ok_v3/PatNo_ID_1594437309.csv  (rows=10035)\n",
      "[OK] Wrote: /home/jovyan/1010/data_new/bling_sponvt_ok_v3/PatNo_ID_1594439781.csv  (rows=7691)\n",
      "[OK] Wrote: /home/jovyan/1010/data_new/bling_sponvt_ok_v3/PatNo_ID_1594441887.csv  (rows=10203)\n",
      "[OK] Wrote: /home/jovyan/1010/data_new/bling_sponvt_ok_v3/PatNo_ID_1594448501.csv  (rows=5106)\n",
      "[OK] Wrote: /home/jovyan/1010/data_new/bling_sponvt_ok_v3/PatNo_ID_1594455578.csv  (rows=262)\n",
      "[OK] Wrote: /home/jovyan/1010/data_new/bling_sponvt_ok_v3/PatNo_ID_1594464829.csv  (rows=4000)\n",
      "[OK] Wrote: /home/jovyan/1010/data_new/bling_sponvt_ok_v3/PatNo_ID_1594467719.csv  (rows=2560)\n",
      "[OK] Wrote: /home/jovyan/1010/data_new/bling_sponvt_ok_v3/PatNo_ID_1594471407.csv  (rows=11433)\n",
      "[OK] Wrote: /home/jovyan/1010/data_new/bling_sponvt_ok_v3/PatNo_ID_1594479330.csv  (rows=3727)\n",
      "[OK] Wrote: /home/jovyan/1010/data_new/bling_sponvt_ok_v3/PatNo_ID_1594511911.csv  (rows=5488)\n",
      "[OK] Wrote: /home/jovyan/1010/data_new/bling_sponvt_ok_v3/PatNo_ID_1594511914.csv  (rows=9717)\n",
      "[OK] Wrote: /home/jovyan/1010/data_new/bling_sponvt_ok_v3/PatNo_ID_1594528842.csv  (rows=2491)\n",
      "[OK] Wrote: /home/jovyan/1010/data_new/bling_sponvt_ok_v3/PatNo_ID_1594533379.csv  (rows=1030)\n",
      "\n",
      "================ 最終檢查（v3） ================\n",
      "[INFO] Checked files: 122\n",
      "[INFO] Total rows   : 1,602,476\n",
      "\n",
      "【SPONVT】修正後跨檔資料型態統計（互斥）\n",
      "Total rows: 1,602,476\n",
      "Type             Count    Percent(%)\n",
      "------------------------------------\n",
      "NaN          1,424,904     88.918898\n",
      "bool                 0      0.000000\n",
      "int                  0      0.000000\n",
      "float                0      0.000000\n",
      "str            177,572     11.081102\n",
      "other                0      0.000000\n",
      "\n",
      "  └─ within str:\n",
      "     str_numeric       16,394    9.232311\n",
      "     str_non_numeric   161,178   90.767689\n",
      "\n",
      "\n",
      "【VTI】修正後跨檔資料型態統計（互斥）\n",
      "Total rows: 1,602,476\n",
      "Type             Count    Percent(%)\n",
      "------------------------------------\n",
      "NaN            218,092     13.609689\n",
      "bool                 0      0.000000\n",
      "int                  0      0.000000\n",
      "float                0      0.000000\n",
      "str          1,384,384     86.390311\n",
      "other                0      0.000000\n",
      "\n",
      "  └─ within str:\n",
      "     str_numeric    1,331,234   96.160747\n",
      "     str_non_numeric    53,150    3.839253\n",
      "\n",
      "\n",
      "【VTE】修正後跨檔資料型態統計（互斥）\n",
      "Total rows: 1,602,476\n",
      "Type             Count    Percent(%)\n",
      "------------------------------------\n",
      "NaN            157,033      9.799398\n",
      "bool                 0      0.000000\n",
      "int                  0      0.000000\n",
      "float                0      0.000000\n",
      "str          1,445,443     90.200602\n",
      "other                0      0.000000\n",
      "\n",
      "  └─ within str:\n",
      "     str_numeric    1,443,237   99.847382\n",
      "     str_non_numeric     2,206    0.152618\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "\"\"\"1) 從 /home/jovyan/1010/data_new/bling_sponvt_ok_v2 讀取所有 CSV\n",
    "2) 只把 sponvt、vti、vte 三欄中「可轉數字的字串」轉成 float\n",
    "   - 僅允許純數字格式（含 +/-, 小數點, 指數 e/E）\n",
    "   - 排除 True/False（含字串型）、NA/null/none/空字串、含底線(例如 '0_0')、其它髒值\n",
    "3) 輸出到 /home/jovyan/1010/data_new/bling_sponvt_ok_v3（同檔名）\n",
    "4) 對 v3 產出型態檢查報表\n",
    "\"\"\"\n",
    "\n",
    "import os\n",
    "import re\n",
    "import glob\n",
    "from collections import Counter, defaultdict\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "# =========================\n",
    "# 參數設定\n",
    "# =========================\n",
    "SRC_DIR = \"/home/jovyan/1010/data_new/bling_sponvt_ok_v2\"\n",
    "DST_DIR = \"/home/jovyan/1010/data_new/bling_sponvt_ok_v3\"\n",
    "FILE_PATTERN = \"*.csv\"\n",
    "COLS = [\"sponvt\", \"vti\", \"vte\"]\n",
    "\n",
    "os.makedirs(DST_DIR, exist_ok=True)\n",
    "\n",
    "# =========================\n",
    "# 工具函式\n",
    "# =========================\n",
    "# 嚴謹數字字串判定：允許 ±, 小數, 科學記號；排除底線與逗號\n",
    "NUMERIC_RE = re.compile(r\"\"\"\n",
    "    ^[ \\t]*                 # 前導空白\n",
    "    [+\\-]?                  # 正負號\n",
    "    (?:\n",
    "        (?:\\d+\\.\\d*|\\.\\d+|\\d+)    # 整數或小數\n",
    "    )\n",
    "    (?:[eE][+\\-]?\\d+)?      # 科學記號\n",
    "    [ \\t]*$                 # 後綴空白\n",
    "\"\"\", re.VERBOSE)\n",
    "\n",
    "BAD_TOKENS = {\"\", \"na\", \"n/a\", \"null\", \"none\"}  # 視為非數字\n",
    "TRUE_TOKENS = {\"true\"}\n",
    "FALSE_TOKENS = {\"false\"}\n",
    "\n",
    "def is_str_numeric_safe(s: str) -> bool:\n",
    "    \"\"\"\n",
    "    僅判定「純數字格式」的字串：\n",
    "    - 不含底線/逗號\n",
    "    - 不是 true/false, NA/null/none/空字串\n",
    "    - 符合 NUMERIC_RE\n",
    "    \"\"\"\n",
    "    if not isinstance(s, str):\n",
    "        return False\n",
    "    t = s.strip().lower()\n",
    "    if t in BAD_TOKENS or t in TRUE_TOKENS or t in FALSE_TOKENS:\n",
    "        return False\n",
    "    if \"_\" in t or \",\" in t:  # 明確排除 0_0、1,234 等\n",
    "        return False\n",
    "    return bool(NUMERIC_RE.match(t))\n",
    "\n",
    "def coerce_numeric_strings_to_float(series: pd.Series) -> pd.Series:\n",
    "    \"\"\"\n",
    "    僅把「可轉數字的字串」轉成 float，其他型態維持不動。\n",
    "    - 保留 True/False（無論 bool 或字串）\n",
    "    - 保留 'null' / 'NA' / '' 等字串原樣\n",
    "    \"\"\"\n",
    "    s = series.copy()\n",
    "    mask_str = s.apply(lambda x: isinstance(x, str))\n",
    "    if mask_str.any():\n",
    "        # 僅挑可轉數字的字串\n",
    "        str_vals = s[mask_str]\n",
    "        can_num = str_vals.apply(is_str_numeric_safe)\n",
    "        if can_num.any():\n",
    "            # 只對可轉者做轉型\n",
    "            s.loc[mask_str & can_num] = pd.to_numeric(str_vals[can_num], errors=\"coerce\")\n",
    "            # 若理論上都可轉，errors='coerce' 不應產生 NaN；保險起見可再檢查\n",
    "    return s\n",
    "\n",
    "def detect_type(value) -> str:\n",
    "    \"\"\"用於檢查報表的型態分類\"\"\"\n",
    "    if pd.isna(value):\n",
    "        return \"NaN\"\n",
    "    if isinstance(value, bool):\n",
    "        return \"bool\"\n",
    "    if isinstance(value, (int, np.integer)):\n",
    "        return \"int\"\n",
    "    if isinstance(value, (float, np.floating)):\n",
    "        return \"float\"\n",
    "    if isinstance(value, str):\n",
    "        return \"str\"\n",
    "    return \"other\"\n",
    "\n",
    "def is_str_numeric_quick(value) -> str:\n",
    "    \"\"\"僅在 value 是 str 時細分為 str_numeric / str_non_numeric\"\"\"\n",
    "    if isinstance(value, str):\n",
    "        return \"str_numeric\" if is_str_numeric_safe(value) else \"str_non_numeric\"\n",
    "    return None\n",
    "\n",
    "# =========================\n",
    "# 轉換與輸出\n",
    "# =========================\n",
    "files = sorted(glob.glob(os.path.join(SRC_DIR, FILE_PATTERN)))\n",
    "print(f\"[INFO] Source files: {len(files)}\")\n",
    "\n",
    "for fp in files:\n",
    "    try:\n",
    "        df = pd.read_csv(fp, dtype=object, low_memory=False)\n",
    "        df.columns = [c.strip().lower() for c in df.columns]\n",
    "    except Exception as e:\n",
    "        print(f\"[WARN] 無法讀取 {fp}: {e}\")\n",
    "        continue\n",
    "\n",
    "    if len(df) == 0:\n",
    "        # 仍輸出空檔，以免檔案不見\n",
    "        out_path = os.path.join(DST_DIR, os.path.basename(fp))\n",
    "        df.to_csv(out_path, index=False)\n",
    "        print(f\"[INFO] Empty file copied: {out_path}\")\n",
    "        continue\n",
    "\n",
    "    # 僅處理指定三欄；缺欄則跳過\n",
    "    for c in COLS:\n",
    "        if c in df.columns:\n",
    "            df[c] = coerce_numeric_strings_to_float(df[c])\n",
    "\n",
    "    out_path = os.path.join(DST_DIR, os.path.basename(fp))\n",
    "    df.to_csv(out_path, index=False)\n",
    "    print(f\"[OK] Wrote: {out_path}  (rows={len(df)})\")\n",
    "\n",
    "# =========================\n",
    "# 最終檢查 (對 v3)\n",
    "# =========================\n",
    "print(\"\\n================ 最終檢查（v3） ================\")\n",
    "type_counts = {c: Counter() for c in COLS}\n",
    "str_detail  = {c: Counter() for c in COLS}\n",
    "total_rows_all = 0\n",
    "checked_files = 0\n",
    "\n",
    "v3_files = sorted(glob.glob(os.path.join(DST_DIR, FILE_PATTERN)))\n",
    "for fp in v3_files:\n",
    "    try:\n",
    "        df = pd.read_csv(fp, dtype=object, low_memory=False)\n",
    "        df.columns = [c.strip().lower() for c in df.columns]\n",
    "    except Exception as e:\n",
    "        print(f\"[WARN] 檢查時無法讀取 {fp}: {e}\")\n",
    "        continue\n",
    "\n",
    "    n = len(df)\n",
    "    total_rows_all += n\n",
    "    checked_files += 1\n",
    "    if n == 0:\n",
    "        continue\n",
    "\n",
    "    for c in COLS:\n",
    "        if c not in df.columns:\n",
    "            continue\n",
    "        # 型態分布（互斥）\n",
    "        tc = df[c].apply(detect_type).value_counts(dropna=False).to_dict()\n",
    "        type_counts[c].update(tc)\n",
    "        # str 類細分（期望 str_numeric 應趨近 0）\n",
    "        mask_str = df[c].apply(lambda x: isinstance(x, str))\n",
    "        if mask_str.any():\n",
    "            str_kinds = df.loc[mask_str, c].apply(lambda x: \"str_numeric\" if is_str_numeric_safe(x) else \"str_non_numeric\")\n",
    "            str_detail[c].update(str_kinds.value_counts().to_dict())\n",
    "\n",
    "print(f\"[INFO] Checked files: {checked_files}\")\n",
    "print(f\"[INFO] Total rows   : {total_rows_all:,}\\n\")\n",
    "\n",
    "for c in COLS:\n",
    "    sub = type_counts[c]\n",
    "    total_c = sum(sub.values())\n",
    "    print(f\"【{c.upper()}】修正後跨檔資料型態統計（互斥）\")\n",
    "    print(f\"Total rows: {total_c:,}\")\n",
    "    print(f\"{'Type':<10}{'Count':>12}{'Percent(%)':>14}\")\n",
    "    print(\"------------------------------------\")\n",
    "    for t in [\"NaN\", \"bool\", \"int\", \"float\", \"str\", \"other\"]:\n",
    "        cnt = sub.get(t, 0)\n",
    "        pct = (cnt / total_c * 100.0) if total_c else 0.0\n",
    "        print(f\"{t:<10}{cnt:>12,}{pct:>14.6f}\")\n",
    "    # 額外列出 str 細分\n",
    "    ssub = str_detail[c]\n",
    "    if ssub:\n",
    "        print(\"\\n  └─ within str:\")\n",
    "        for k in [\"str_numeric\", \"str_non_numeric\"]:\n",
    "            cnt = ssub.get(k, 0)\n",
    "            pct = (cnt / sum(ssub.values()) * 100.0) if sum(ssub.values()) else 0.0\n",
    "            print(f\"     {k:<14}{cnt:>10,}{pct:>12.6f}\")\n",
    "    print(\"\\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "id": "2eec494b-7c0c-4a43-892a-db352e3a6ff0",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "================ 最終檢查（v3） ================\n",
      "[INFO] Checked files: 122\n",
      "[INFO] Total rows   : 1,602,476\n",
      "\n",
      "【SPONVT】修正後跨檔資料型態統計（互斥）\n",
      "Total rows: 1,602,476\n",
      "Type             Count    Percent(%)\n",
      "------------------------------------\n",
      "NaN          1,424,904     88.918898\n",
      "bool            28,043      1.749979\n",
      "int                  0      0.000000\n",
      "float            4,934      0.307899\n",
      "str            144,595      9.023224\n",
      "other                0      0.000000\n",
      "\n",
      "【VTI】修正後跨檔資料型態統計（互斥）\n",
      "Total rows: 1,602,476\n",
      "Type             Count    Percent(%)\n",
      "------------------------------------\n",
      "NaN            218,092     13.609689\n",
      "bool                 0      0.000000\n",
      "int                  0      0.000000\n",
      "float          454,285     28.348943\n",
      "str            930,099     58.041368\n",
      "other                0      0.000000\n",
      "\n",
      "【VTE】修正後跨檔資料型態統計（互斥）\n",
      "Total rows: 1,602,476\n",
      "Type             Count    Percent(%)\n",
      "------------------------------------\n",
      "NaN            157,033      9.799398\n",
      "bool                 0      0.000000\n",
      "int                  0      0.000000\n",
      "float          255,143     15.921799\n",
      "str          1,190,300     74.278804\n",
      "other                0      0.000000\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# 最終檢查 (對 v3) — 方案A：自動推斷型別\n",
    "# =========================\n",
    "print(\"\\n================ 最終檢查（v3） ================\")\n",
    "from collections import Counter\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import glob, os\n",
    "\n",
    "DST_DIR = \"/home/jovyan/1010/data_new/bling_sponvt_ok_v3\"\n",
    "FILE_PATTERN = \"*.csv\"\n",
    "COLS = [\"sponvt\", \"vti\", \"vte\"]\n",
    "\n",
    "def detect_type(value) -> str:\n",
    "    if pd.isna(value): return \"NaN\"\n",
    "    if isinstance(value, bool): return \"bool\"\n",
    "    if isinstance(value, (int, np.integer)): return \"int\"\n",
    "    if isinstance(value, (float, np.floating)): return \"float\"\n",
    "    if isinstance(value, str): return \"str\"\n",
    "    return \"other\"\n",
    "\n",
    "type_counts = {c: Counter() for c in COLS}\n",
    "total_rows_all = 0\n",
    "checked_files = 0\n",
    "\n",
    "v3_files = sorted(glob.glob(os.path.join(DST_DIR, FILE_PATTERN)))\n",
    "for fp in v3_files:\n",
    "    try:\n",
    "        # 關鍵：不要指定 dtype=object，讓 pandas 自行推斷\n",
    "        df = pd.read_csv(fp, low_memory=False, keep_default_na=True)\n",
    "        df.columns = [c.strip().lower() for c in df.columns]\n",
    "    except Exception as e:\n",
    "        print(f\"[WARN] 檢查時無法讀取 {fp}: {e}\")\n",
    "        continue\n",
    "\n",
    "    n = len(df)\n",
    "    total_rows_all += n\n",
    "    checked_files += 1\n",
    "    if n == 0:\n",
    "        continue\n",
    "\n",
    "    for c in COLS:\n",
    "        if c not in df.columns:\n",
    "            continue\n",
    "        tc = df[c].apply(detect_type).value_counts(dropna=False).to_dict()\n",
    "        type_counts[c].update(tc)\n",
    "\n",
    "print(f\"[INFO] Checked files: {checked_files}\")\n",
    "print(f\"[INFO] Total rows   : {total_rows_all:,}\\n\")\n",
    "\n",
    "for c in COLS:\n",
    "    sub = type_counts[c]\n",
    "    total_c = sum(sub.values())\n",
    "    print(f\"【{c.upper()}】修正後跨檔資料型態統計（互斥）\")\n",
    "    print(f\"Total rows: {total_c:,}\")\n",
    "    print(f\"{'Type':<10}{'Count':>12}{'Percent(%)':>14}\")\n",
    "    print(\"------------------------------------\")\n",
    "    for t in [\"NaN\", \"bool\", \"int\", \"float\", \"str\", \"other\"]:\n",
    "        cnt = sub.get(t, 0)\n",
    "        pct = (cnt / total_c * 100.0) if total_c else 0.0\n",
    "        print(f\"{t:<10}{cnt:>12,}{pct:>14.6f}\")\n",
    "    print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b669c3fd-c157-4c4d-a932-84e53fbdbf1b",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1587c7f0-f13d-4e2d-a9b9-98cdc581d98e",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "784713dd-8079-4003-b47f-bc59e0c21ec1",
   "metadata": {},
   "outputs": [],
   "source": [
    "先統計sponvt、vti、vte三欄總合的狀況\n",
    "分成三類，繪製條狀圖，該筆資料三欄皆float、兩欄float, 只有一欄float, 都沒有float欄位\n",
    "塗上標記數量跟占比，用紅綠藍 圖用英文 程式碼要加中文解釋\n",
    "「float 定義為 str_numeric」（也就是「字串、但內容可安全解析為數字」）來做判定"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "97c96829-e60b-462b-a8bf-8ecbf7e377eb",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "============================================================\n",
      "Scanned files: 122\n",
      "Total rows   : 1,602,476\n",
      "------------------------------------------------------------\n",
      "Category                     Count    Percent(%)\n",
      "------------------------------------------------------------\n",
      "All three float             73,889      4.610927\n",
      "Exactly two float          168,434     10.510859\n",
      "Exactly one float        1,241,494     77.473485\n",
      "No float                   118,659      7.404729\n",
      "============================================================\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 900x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\"\"\" 跨檔統計 /home/jovyan/1010/data_new/bling_spomvy_ok_v1 下所有 CSV，\n",
    "以每一筆資料（row）為單位，檢查 sponvt、vti、vte 三欄「是否為 float 型態」，\n",
    "並將每筆資料歸類為以下四類後彙總與繪圖（英文圖表）：\n",
    "    1) All three float        （三欄皆為 float）\n",
    "    2) Exactly two float      （恰有兩欄為 float）\n",
    "    3) Exactly one float      （恰有一欄為 float）\n",
    "    4) No float               （三欄皆非 float）\n",
    "\n",
    "需求：\n",
    "- 條狀圖以「紅 / 綠 / 藍」對前三類上色，第四類以灰色（避免誤導）\n",
    "- 每個長條上方標註「數量（counts）」與「占比（%）」\n",
    "- 程式碼包含中文註解；圖表文字使用英文\n",
    "- 只提供程式碼，不執行\n",
    "\n",
    "注意：\n",
    "- 僅以 Python 物件型別判定 float（isinstance(x, float) 或 numpy.floating）\n",
    "- 欄位缺失或空值一律視為「非 float」\n",
    "\"\"\"\n",
    "\n",
    "import os\n",
    "import glob\n",
    "from collections import Counter\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# =========================\n",
    "# 使用者可調整參數\n",
    "# =========================\n",
    "DATA_DIR = \"/home/jovyan/1010/data_new/bling_sponvt_ok_v2\"  # 目標資料夾\n",
    "FILE_PATTERN = \"*.csv\"\n",
    "TARGET_COLS = [\"sponvt\", \"vti\", \"vte\"]\n",
    "\n",
    "# =========================\n",
    "# 小工具函式\n",
    "# =========================\n",
    "def is_float_value(x) -> bool:\n",
    "    \"\"\"判定單一值是否為 float 型態（含 numpy 浮點型）。\"\"\"\n",
    "    return isinstance(x, float) or isinstance(x, np.floating)\n",
    "\n",
    "def series_is_float_mask(s: pd.Series) -> pd.Series:\n",
    "    \"\"\"\n",
    "    回傳布林遮罩，表示該欄位每列是否為「float 型態」。\n",
    "    - 缺失、字串、整數等皆為 False\n",
    "    - 僅純浮點型為 True\n",
    "    \"\"\"\n",
    "    # 若 Series 不存在，呼叫端會以 False 序列代替\n",
    "    return s.apply(is_float_value)\n",
    "\n",
    "# =========================\n",
    "# 主流程：跨檔彙總各類別計數\n",
    "# =========================\n",
    "category_counter = Counter()\n",
    "total_rows = 0\n",
    "\n",
    "file_list = sorted(glob.glob(os.path.join(DATA_DIR, FILE_PATTERN)))\n",
    "\n",
    "for fp in file_list:\n",
    "    try:\n",
    "        # 使用 dtype=object 以保留原生 Python 型別（避免自動轉為 float）\n",
    "        df = pd.read_csv(fp, dtype=object, low_memory=False)\n",
    "        df.columns = [c.lower() for c in df.columns]\n",
    "    except Exception:\n",
    "        continue\n",
    "\n",
    "    # 對三個目標欄位建立「是否為 float」的布林遮罩；若欄位缺失則補 False\n",
    "    masks = []\n",
    "    for col in TARGET_COLS:\n",
    "        if col in df.columns:\n",
    "            masks.append(series_is_float_mask(df[col]))\n",
    "        else:\n",
    "            # 欄位不存在 → 全 False（視為非 float）\n",
    "            masks.append(pd.Series([False] * len(df), index=df.index))\n",
    "\n",
    "    # 將三個布林遮罩合併為「每列的 float 數目」\n",
    "    float_counts_per_row = sum(masks)  # 逐列相加（True 視為 1）\n",
    "\n",
    "    # 逐類別累加\n",
    "    # 0 → No float / 1 → Exactly one float / 2 → Exactly two float / 3 → All three float\n",
    "    category_counter[\"No float\"]              += int((float_counts_per_row == 0).sum())\n",
    "    category_counter[\"Exactly one float\"]     += int((float_counts_per_row == 1).sum())\n",
    "    category_counter[\"Exactly two float\"]     += int((float_counts_per_row == 2).sum())\n",
    "    category_counter[\"All three float\"]       += int((float_counts_per_row == 3).sum())\n",
    "\n",
    "    total_rows += len(df)\n",
    "\n",
    "# =========================\n",
    "# 數據整理：順序、數量與占比\n",
    "# =========================\n",
    "ordered_labels = [\"All three float\", \"Exactly two float\", \"Exactly one float\", \"No float\"]\n",
    "counts = [category_counter[label] for label in ordered_labels]\n",
    "percents = [(c / total_rows * 100.0) if total_rows else 0.0 for c in counts]\n",
    "\n",
    "# =========================\n",
    "# 繪圖（英文標題與標籤；前三類使用紅、綠、藍；第四類使用灰）\n",
    "# =========================\n",
    "fig, ax = plt.subplots(figsize=(9, 5))\n",
    "\n",
    "# 顏色設定：紅、綠、藍、灰（第四類）\n",
    "colors = [\"red\", \"green\", \"blue\", \"gray\"]\n",
    "\n",
    "bars = ax.bar(ordered_labels, counts, color=colors)\n",
    "\n",
    "# 標註每個長條上方的數量與占比\n",
    "for rect, c, p in zip(bars, counts, percents):\n",
    "    height = rect.get_height()\n",
    "    ax.text(\n",
    "        rect.get_x() + rect.get_width() / 2.0,\n",
    "        height,\n",
    "        f\"{c:,} ({p:.2f}%)\",\n",
    "        ha=\"center\",\n",
    "        va=\"bottom\",\n",
    "        fontsize=10\n",
    "    )\n",
    "\n",
    "# 英文圖表元素\n",
    "ax.set_title(\"Float-Type Composition across (sponvt, vti, vte)\", fontsize=14)\n",
    "ax.set_ylabel(\"Row Count\", fontsize=12)\n",
    "ax.set_xlabel(\"Row Category by # of float columns\", fontsize=12)\n",
    "ax.set_ylim(0, max(counts) * 1.15 if counts else 1)\n",
    "ax.grid(axis=\"y\", linestyle=\"--\", alpha=0.3)\n",
    "\n",
    "plt.tight_layout()\n",
    "\n",
    "# =========================\n",
    "# 輸出彙總表（列印於終端，以供審閱）\n",
    "# =========================\n",
    "print(\"============================================================\")\n",
    "print(f\"Scanned files: {len(file_list)}\")\n",
    "print(f\"Total rows   : {total_rows:,}\")\n",
    "print(\"------------------------------------------------------------\")\n",
    "print(f\"{'Category':<22}{'Count':>12}{'Percent(%)':>14}\")\n",
    "print(\"------------------------------------------------------------\")\n",
    "for label, c, p in zip(ordered_labels, counts, percents):\n",
    "    print(f\"{label:<22}{c:>12,}{p:>14.6f}\")\n",
    "print(\"============================================================\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "id": "d109e8ae-633a-4e73-97fa-b3c5ec82fa78",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "============================================================\n",
      "Scanned files: 122\n",
      "Total rows   : 1,602,476\n",
      "------------------------------------------------------------\n",
      "Category                           Count    Percent(%)\n",
      "------------------------------------------------------------\n",
      "All three str_numeric                  0      0.000000\n",
      "Exactly two str_numeric                0      0.000000\n",
      "Exactly one str_numeric        1,460,300     91.127730\n",
      "No str_numeric                   142,176      8.872270\n",
      "============================================================\n"
     ]
    },
    {
     "data": {
      "image/png": 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zf5gmLzw83Gjj1dBMeHh4SI8ePYw+V2bMmCEA5MsvvzSal2GvUteuXY3ez9u3bzf5njL8uNO9e3eTPO/duye3b982/0Tl8NtvvwkAef/9900e+/rrr802XCL3j8Yw9xzUrFnT6DmOi4uTsmXLip2dndH7d8mSJepzmbPOv//+W/z8/MTe3l4uXbqkxg2vlZ2dnezdu1eNZ2VlqT9cHDhwQI0bfiiYOXOmSe7PPfecADD60Sm/z4uCSElJEScnJ6lZs6bJY6tWrRIA8sEHH6ixnEd1mFMUTd758+fVH2we9MUXX5isb/l9Nht+EF24cKFR/Nq1axIYGCi+vr6SlpZmMt2YMWMEgOzYsaPQtVDJwiaPSjTDl/pTTz2l/lr19ttvq19afn5+curUKXV8enq6ODk5ibe3t9lDHwx7YnL+it69e3dRFEXdEDN8+e/evVvs7OzUD3e9Xi++vr5mvxwe9McffwgAady48UM+A9lmzZolAGTXrl1qLL+NnqefflpcXFzMfqAbNkJy7gUzHPaTc6PUwPDc5fxiMWy4zZgxw2T8gQMHTJoHQ75lypQx2njIydyGdkHqSE5ONvvrcUEYvtirVatmtEEqInL37l31UNacNbzwwguiKIpcuHBBjd27d0+8vLwkKCjI7OFW+XnttdfEx8dHzWH9+vUCwGhvtCWuXLmibuQ9WI9er5caNWoIAKNGvbBN3nvvvZfvWEuaPAcHB5ON1YIC7h/O/eAGdWZmptjZ2Um9evWM4oVt8iZPnmwy3tBU//DDDyaPVapUyWg5Op1OPD09pXLlyiavj0j2c5tzb57hOTS3kS9i/vVbsWKFAJB+/fqZncYSnTp1EgCyefPmAk1XqVIlsbe3N9tIGn7MyPmDUFE3eTk/N0XuNyD29vYCwKRJNqwvDy7H0OTlbHByPubl5aX+bfhcKugez5wMP8DkPILDwNDkzZ8/P9/5GJ4Dc0cafP755wJApk2bpsYM792DBw+ajJ8+fbrJeMNrZe59ZXgsZw23bt0SjUYjTz/9tNHYhIQEsbe3N/neLMomT0SkV69eAkCOHDliFO/WrZsAkDNnzqixx9HkiYg0atRI7OzsTL5769atK46OjnLr1i01ltdnc0JCgtja2pr8aGdgeN9s3LjR5LHPPvtMAOR6ODmVPrzwCpUKFy9exJQpU4xifn5+2LNnD6pWrarGzpw5g7S0NLRq1QrOzs4m82nVqhW2bNmCqKgoNGvWDMD9qxauXbsWu3btQo8ePbBz5054eXkhPDwcYWFh2LlzJyZPnoyTJ08iISEBPXv2fKS1xsfH47PPPsP//vc/XL58GWlpaUaPGy6IkZ+7d+/i+PHjCAgIwGeffWbyeGZmJoD7zxkApKSkICYmBrVq1YKvr6/J+KZNm2LLli1GsaNHjwKA2funNW7cGE5OToiKijJ5LCQkBA4ODo+kDq1Wi06dOmHz5s2oV68eXnrpJTRv3hyNGjWyeJkG4eHhUBTFKObk5ISwsDBs3rwZ586dUy+IMnjwYKxZswYLFizAp59+CgBYu3YtEhMTMXLkSNjYFPyONbt27ULLli3VHCIiIuDs7Iz69esXaD6G1ynnvAwURUGLFi1w+vRpHDt2rNBX8GvRogXKlCmD6dOnIyoqCs8++yyaNWuGOnXqmCzTEsHBwUVyQYMqVarA1dXVKGZnZwd/f3/cunXroecPAKGhoSYxw1Ut69ata/axgwcPqn+fPXsWSUlJCAgIMPmcA4CEhAQA2e/xnBo2bGhxnn/++ScAPPLbbzwoJSUFly5dQo0aNVC+fHmTx1u1aoV58+YhKipKvYpyUXvwNbK1tYWfnx9SU1NNroxoeO1iY2NN5uPh4YHg4GCTePny5XHgwAH17xo1aqBOnTpYtmwZ/v77b3Tr1g3NmzdHvXr1YGtra1HON2/eBAB4enqaPPbcc89hwoQJGDZsGLZt24ZOnTqhWbNmRt+HD2revHmusZyf00ePHoWTk5PZ95bhs97c5/qDF+ABoL7eOdc1d3d3dOnSBatWrcLx48dRp04dAMAvv/yCzMzMR36Rmb59+2L58uX46aef1PdFYmIiNm3ahAYNGqBatWqPdPm55XTw4EH88ssvGDFiBADg5MmTiIqKQo8ePSy+iNKhQ4eg0+lw7949s7d9OH/+PID7nyXPPfec0WNeXl4AgBs3bjxEJVSSPPFN3u7du/H5558jMjIScXFxWLt2Lbp161ageYgIZs2ahfnz5+Py5cvw8/PD0KFD8d577z2apJ9AHTt2xObNmwHc3+D54Ycf8O6776Jbt274888/1Y24lJQUAIC/v7/Z+RiuFJicnKzGDFcp3Llzp9rktWzZEjY2NmjdujW+/PJLpKWlmVzKPi+G5ZjbSMhLYmIiGjRogCtXriA8PBzt2rWDh4cHbG1tERUVhfXr1yM9Pd2ieSUlJUFEEBsba3bD0SA1NRVA9nNnrsEDzD+n+T3ffn5+Zp+D3MabU9A6AGDVqlX49NNP8fPPP+P9998HALi5uWHQoEH49NNPzf4AkFv+5hjyz/k+at++PYKDg7F48WJMmzYNtra2+P7772FjY4NBgwZZtLxdu3apN9XNyMjAxYsXERQUpH5Zr127Fh4eHvjkk08A3L9xvCX3rivMelFQ7u7uOHDgACZNmoSNGzdi06ZNAO5v5E2cOLHAN3IuyHskv7zMsbOzg06nK5JlaLVas/PP67GsrCz178TERAD3N+pOnjyZ63JyvscNCvI8GTa0y5UrZ/E0DyrMZ9vjeP/lJ7fXwdz7w/DaGX5Ayimv91PO+wLa2dlhx44dmDx5MtasWYNx48YBuH81xhEjRuD999/Pt9lzcnICAJMf+oD7P4IcOHAAU6ZMwf/+9z+sXLkSAFCtWjVMmzYNPXr0MJnG3OeZuc+ylJSUXH/syeu1yuu5fHBd69u3L1atWoWlS5eqP9799NNPsLe3R69evcwuu6h07NgRfn5++Pnnn/H555/DxsYGK1asQEZGRrFdxbR3794YM2YMli5dqjZ5P/74IwAUKCfDZ8m+ffuwb9++XMeZ+ywxvM8s/X6kku+Jvxl6amoqQkJC8M033xR6HqNGjcL333+PL774AmfOnMHGjRsL9OsqFYyvry/efvttvPfeezh9+jQ++OAD9THDF/n169fNTmuI5/zCN+y52rlzJ+Li4nDu3Dm1kWvdujUyMjKwf/9+9aaoLVu2zDfHChUqoFy5cvj777/VX84ssWDBAly5cgUff/wx9u7di//+97+YNm0aJk+ejMaNG1s8n5w1hoWFQe4fmm32n6F5NYw37DV4kLnnNL/nOz4+3uzGVUH27BS0DgBwcXHBJ598gkuXLuHSpUtYsGABqlevjv/85z8YM2aMxcuOj483GzfUm3OjRlEUvPHGG4iLi8Nvv/2G6Oho7NixA506dbJ479iuXbswZcoUTJkyBdOnTwdw/8cHQyw6OhpXr15V/7b0ZriFWS8Ko2LFivjhhx+QkJCAo0ePYsaMGRARDBs2DD///HOB5lWYvX8Py8bGxqj5yulxNCAvvvhinu/xRYsWmUxbkOfJw8MDQMF/fMopPDwcQMHuZ1lU7z/D3nBzr9GjfH0Ky8fHB9988w1iY2Nx6tQpfPPNN/D29sakSZMwc+bMfKc3/OBm2HB/0NNPP43Vq1cjMTERBw4cwEcffYTr16+jV69eZjfwzX2emfss02q1j/yzonPnzvDx8cGyZcsgIrhw4QIOHjyIZ555Bt7e3g817/zY2dmhd+/eiIuLw44dOwDcbzAN8eLg7e2Nzp074+DBg7hw4QJEBMuWLYOPjw86depk8XwMr8u4cePy/CyZNGmSybSG91luP/RS6fPEN3mdO3fGxx9/jBdeeMHs4xkZGXjnnXdQrlw5uLi4oFGjRuov7QBw+vRpzJ07F+vXr8fzzz+P4OBg1K1bF+3atXtMFTy53nvvPQQEBGDOnDmIiYkBAFSvXh2Ojo44dOgQ7t69azKN4X56OQ+hMjRuZ86cwdKlSwEAbdq0AQA0a9YMDg4O+P333xEREYHatWtbfAjZa6+9BgD4+OOP8x2bkZEB4P5hqQDw/PPPm4wxd88pwy/B5vZIuLm5oUaNGjh9+rRFh6VptVpUrFgRFy5cMNvo7d+/3yRmONQl5zph8OeffyItLc3s4WoFUdA6HhQcHIxBgwYhIiICrq6u2LBhg8XT7tu3DyJiFEtLS0NkZCScnJxMDo0aNGgQ7O3t8f3332PhwoUQkQLdc2jy5Mnql/DgwYPh4+MDvV4PEcHGjRsBAHv37lXHmHvezTG8Brt37zapR0TU99bDvlYGtra2qFu3Lt555x21ucv5vOf1vi1Onp6eiI+PN2kiUlNTC/RjTUHVqFEDWq0Whw8fNrv3qKgYfnzcunVroefx0ksvQavVYvXq1WYPH83JcNSBVqtFpUqVcOHCBbMNprnPZXPyalINhySXRIqioEaNGuqhlQAs+hwyHMaY33vP3t4ejRs3xpQpU/D1119DRPDrr7+ajDP3HWJu3Q8NDUVaWpp6eG9Olr5W+bG3t0fPnj3x999/IyIiAj/99BMAmD1c91F8XhiW89NPPyE6Ohr79+9Hx44dTRqcx/lZlTOniIgI/P333+jVqxfs7e0tzqlBgwZQFMXo0GFLnT17FkD2+45Kvye+ycvPwIEDsW/fPvzyyy/466+/0KNHD3Tq1En90N24cSMqVaqEX3/9FcHBwahYsSJef/31XH95o6Lj5OSEd999F5mZmZg2bRoAwMHBAS+//DJu3Lih7gkx2L59O/73v/+hcuXK6q/RBoY9dzNnzoSfnx9q1aoF4P5hCw0bNsT333+PmzdvFugG1G+//TaqVauGJUuW4L333jN7mGV0dDS6deuGU6dOAbi/BxC4vyGf07Jly9TD33Ly9PSEoij4559/zOYwcuRI3L17F2+88YbZwzOio6PVBhkAXnnlFaSnp5scFrlr1y6T8/EAoE+fPrCzs8OXX35pdK5gZmYmJkyYAAAWHU6Yn4LUkZCQYHbjJCkpCenp6eohUJY4e/YsFi5caBT7/PPPkZCQgJdfftnkHD9/f388//zz2LRpE+bPn48yZcqgS5cuFi8vp4iICDRv3lzdU7N79244OTmhQYMGBZ5XUFAQWrdujZMnT5rUs3DhQpw8eRJt2rQp9Pl4AHDixAlcvnzZJG745T/n82449yO3921xqV+/PjIzM9Ufe4D7TfDEiRPNvu+Kip2dHYYOHYrLly/j7bffNtvonThxItc9y5Z6/vnnUb58efz0009m12dL9vB5eHjg888/R3p6Op599lmz52bpdDr88MMPGDJkiBrr378/MjMzMXHiRKMfGk6cOIFFixbB3d0931MltFotqlatir179+LChQtq/Pbt25g4cWK+uT9O0dHR6ud6TubWh9zUqVMHXl5eZj/PDh06lOeeOXPz/+STT3D79m2jsV9++SXs7OzQp08fNd6/f38AwMSJE43ei7Gxser4V155Jd/882M4DPGnn37C0qVL4eHhYfbzMr/Pi8mTJ0NRFLPnoOXGcO7dmjVr8H//938QEbOHRRb2s2rAgAFQFMXioy0AoEuXLnB3d8fSpUvzPFQzr5zKlCmDnj17Yv/+/fj8889NftQDgIMHD5r9EfzgwYMoW7YsqlSpYnHOVLI98efk5eXixYv4+eef8c8//yAgIADA/Q33zZs3Y9GiRfj0009x6dIlXL58GStXrsSSJUug0+kwZswYvPTSS+phAPTovPnmm5gxY4baSD311FOYMWMGIiIi8PHHH2P//v1o1KgRYmJisGrVKjg7O2PRokUmF8EwNG/mLqzSunVrtekqSJPn5uaGLVu2oGvXrpg+fToWLVqEDh06oHz58rh79y6OHj2Kffv2wc7ODl988QWA+x/oM2bMwIgRI7Bz505UqFABf/31F7Zv344XXngBa9asMVqGq6srGjRogN27d2PgwIGoUqUKbGxs0KdPHwQFBWHw4MH4448/8MMPP2Dfvn1o164dAgICcP36dZw5cwYHDx7EsmXLULFiRQDAu+++i9WrV+Pbb7/FX3/9hWbNmuGff/7BihUr0KVLF2zcuNHouTM83+PGjcPTTz+Nnj17wsXFBb/++ivOnDmDrl27FsmFFApSR2xsLBo1aoRatWqhXr16KFeuHG7evIn169cjMzMT77zzjsXL7dChA9566y389ttvqF69Oo4cOYItW7YgMDBQvbiKuVxXr16N+Ph4vPvuu+o5KQURHx+PM2fOYPDgwWps9+7daNKkSYEvHmMwd+5cNGvWDG+88QY2btyImjVr4tSpU9iwYQN8fX0xd+7cQs3XYPv27Rg3bhzCw8NRvXp1eHt749KlS9iwYQOcnJwwfPhwdWybNm3wxRdfYPDgwejRowdcXFwQFBRktKFZHIYPH45Fixbh9ddfx7Zt2+Dr64s9e/bg1q1bCAkJwbFjxx7ZsqdMmYIjR47g66+/xm+//YaWLVvC19cXsbGxOH78OI4dO4YDBw7kep6oJTQaDVasWIFOnTqhc+fO6NSpE0JCQpCSkoKoqCj1cyk/b775JlJSUjBhwgTUq1cPLVq0QGhoKJycnBAbG4vff/8dsbGxRnux33nnHfz222/48ccfcfr0abRt2xYJCQlYvnw5MjMzsWTJEri5ueW77LFjx2LIkCFo0qQJevToAb1ej//9738FvhjRo3bs2DF0794dDRo0QO3atVGmTBnExsZi3bp1sLW1Vc/Ry4uiKHj++eexZMkSxMXFqReEAYClS5dizpw5aNWqFSpXrgytVotTp05h06ZN8PHxMXsecKVKlVC7dm28+OKLyMzMxIoVKxAfH49PPvkElSpVUsf17dsXa9aswfr16/H000/jueeeQ2pqKlasWIGbN29i1qxZRuMLq3HjxqhSpQqWLFmCzMxMvPHGG9BoNCbj8vu8MJwLWdDP2r59++KDDz7AF198Aa1Wa/YImurVqyMgIAC//PILnJ2dUb58eSiKgqFDh+Z5MZTC5OTo6IgePXrg+++/x+XLl1GlShU0atTIZFyTJk3g5OSE2bNnIyUlRd37aPhhdc6cOTh79izeeecd/Pjjj2jSpAnc3d3x999/IzIyEufPn0dcXJzRuXcXL15EdHQ0hg4danG+VAoU/QU7Sy8ARjeJNFxu2sXFxeifnZ2d9OzZU0RE3njjDQEgZ8+eVaeLjIw0uQwvFU5e98kz+O9//ysApG/fvmosISFBRo4cKRUqVBB7e3vx8fGRl156yeTmxzmVKVNGAMjcuXON4jt27BAAoiiK3Lx5s8A1ZGRkyMKFC6VTp07i7+8v9vb24ubmJvXq1ZOJEycaXbZe5P4N2jt06CCenp7i5uYmLVu2lO3bt+d62eSzZ8/KM888Ix4eHqIoitnLOS9fvlzatWsnnp6eYm9vL+XKlZNWrVrJrFmzTC5THx8fr16639HRUcLCwmTNmjXq/XrM3Uh1/fr10rJlS3FzcxONRiN16tSRWbNmGd17SiT/S6CL5H0Ze0vqSEpKksmTJ0uLFi2kbNmy4uDgIAEBAdKpUyejmxXnJedlsyMiIqR58+bi7OwsHh4e0rt3b5PXLCe9Xi/lypUTRVGM7vVUEIbPHsPNv1NTU8Xe3t7spfoLIiYmRgYOHKjeG6ts2bIycOBAiYmJMRlb0FsonDp1SkaNGiWhoaHi7e0tGo1GKlWqJAMGDDC6zYnBzJkzpUqVKupl7HNe+v7Bvwsrr/nk9j77/fffpVGjRqLRaMTb21v69u0r165dy/MWCuYun57XZd/NzUvk/mX9582bJ+Hh4aLVakWj0UhQUJB06tRJ5s6da3Sft/wuK5/X63fhwgV57bXXpHz58mJvby9+fn7SqlWrAl86/ezZszJ8+HCpWbOmuLq6qutkt27dZNWqVSa3g7hz5458+OGHUrVqVXFwcBAPDw/p3LmzyY3h88v/v//9r1SuXFns7e0lKChIPvroI8nIyMjzFgrm5PVZY25eBbnFxt9//y0TJkyQxo0bi5+fnzg4OEhQUJC89NJLZm9NkBvDrWgMN7k3+OOPP2Tw4MFSu3Zt8fDwECcnJ6lSpYqMHDnS5PPJkNvdu3fl7bfflnLlyomDg4PUqlUr1/sdZmZmyhdffCF16tQRjUajfhetX7/eZGxer1V+tyAw3HcUML4n7YPy+rzo3r272NjYGG2HWSImJkb9zhw4cGCu4/744w/1+82Qq2G9y+0zIDQ0VNzc3CQxMbFAOUVERKjLmDJlSq7jfvvtN2nQoIE4OTmp43O6e/euzJw5U8LCwsTFxUWcnJwkODhYunXrJkuWLDH5bp48ebIAkKioqALlSyWbImJmX+4TSlEUo6trLl++HK+88gpOnjxpchUsV1dXlClTBpMmTcKnn35qdEhDWloanJ2dsXXrVrRv3/5xlkD0yLz66qtYunQpTp06hRo1ahR3OiXW1atXUaFCBTRv3px784nooTVt2hTJyck4ceJEoS5I1KpVK0RERJg9dM8a+Pn5oVWrVlixYkVxpwLg/tVJPT09MW7cOIsusFPcsrKyULVqVVSsWJHfWVaG5+TlITQ0FDqdDvHx8ahcubLRP8NlhMPDw5GVlaVeMAMAzp07ByD7/Cqi0iQuLs4kFhERgV9++QXVqlVjg5eP2bNnIysry+h8JCKiwvriiy9w6tQp9TYJlO306dNISEgoUedk7tu3D/b29hg7dmxxp2KRH3/8ETExMfj888+LOxUqYk/8nrw7d+6oJ3CHhobiyy+/ROvWreHl5YWgoCC8+uqr2LdvH2bNmoXQ0FDcuHEDO3bsQJ06dfDMM89Ar9ejQYMGcHV1xezZs6HX6zFs2DBotdqHuoIZUXExnFtTt25duLi44NSpU9i8eTNsbW3x22+/ce+0GcnJyZg7dy4uX76M//u//0P16tVx7Ngxi296TESUl3nz5sHV1bVQFzyx9j159HAWL14MnU6nXhGcrMcT3+Tt2rXL7MU0+vfvj8WLFyMzMxMff/wxlixZgtjYWHh7e6NJkyaYMmWKepnZq1evYsSIEdi6dStcXFzQuXNnzJo1S70CElFpMnv2bCxduhQXL17E7du34eHhgfDwcEycONHsSeAExMTEIDg4GE5OTmjUqBG+++47VKtWrbjTsjqLFy82uhpsbrp161Zkt4MgKu3Y5BE9mZ74Jo+IiEoHw8ZqfhYtWlQkt+4gIiIqrdjkERERERERWRFeeIWIiIiIiMiKPJE3Q9fr9bh69Src3NwKdTliIiIiIiKix01EcPv2bQQEBMDGJvf9dU9kk3f16lUEBgYWdxpEREREREQF9vfff6N8+fK5Pv5ENnlubm4A7j85Wq22mLMhIiIiIiLKX0pKCgIDA9V+JjdPZJNnOERTq9WyySMiIiIiolIlv1POeOEVIiIiIiIiK8Imj4iIiIiIyIqwySMiIiIiIrIibPKIiIiIiIisCJs8IiIiIiIiK8Imj4iIiIiIyIqwySMiIiIiIrIibPKIiIiIiIisCJs8IiIiIiIiK8Imj4iIiIiIyIqwySMiIiIiIrIibPKIiIiIiIisSLE3ebt370aXLl0QEBAARVGwbt26fKdJT0/H+++/jwoVKkCj0eCpp57CwoULH32yREREREREJZxdcSeQmpqKkJAQDBw4EC+++KJF0/Ts2RPXr1/HggULULlyZcTHxyMrK+sRZ0pERERERFTyFXuT17lzZ3Tu3Nni8Zs3b0ZERAQuXboELy8vAEDFihUfUXZERERERESlS7E3eQW1YcMG1K9fHzNnzsSPP/4IFxcXPP/885g2bRqcnJzMTpOeno709HT175SUFACAXq+HXq8HACiKAkVRICIQEXVsfnHD9IWN29jYmMy7oPHC5s6aWBNrYk2siTWxJtbEmlgTayo9NT2YQ25KXZN36dIl7N27F46Ojli7di1u3LiBt956C4mJibmelzd9+nRMmTLFJJ6QkIB79+4BAJycnODu7o6UlBSkpaWpY1xcXODm5oakpCRkZGSoca1WC2dnZyQmJhodKurp6QmNRoOEhASjF8Hb2xu2traIj483ysHPzw86nQ43b95UY4qiwN/fHxkZGUhKSlLjdnZ28PHxQVpamtqoAoCDgwO8vLxw584dpKamqnHWxJpYE2tiTayJNbEm1sSaWJP11GRrawtLKGJpO/gYKIqCtWvXolu3brmO6dChA/bs2YNr167B3d0dALBmzRq89NJLSE1NNbs3z9yevMDAQCQlJUGr1arL5i8GrIk1sSbWxJpYE2tiTayJNbGmklrT7du34eHhgeTkZLWPMafU7ckrW7YsypUrpzZ4AFCjRg2ICP755x9UqVLFZBqNRgONRmMSt7GxgY2N8QVGDU/og3KLPzh9YeIFXeajjrMm1sSaWFNecdbEmlgTa8orzppYE2t6dDWZG2NOsd9CoaDCw8Nx9epV3LlzR42dO3cONjY2KF++fDFmRkREREREVPyKvcm7c+cOoqKiEBUVBQCIjo5GVFQUrly5AgCYOHEi+vXrp47v06cPvL29MXDgQJw6dQq7d+/G+PHjMWjQoFwvvEJERERERPSkKPYm7/DhwwgNDUVoaCgAYOzYsQgNDcVHH30EAIiLi1MbPgBwdXXFtm3bcOvWLdSvXx+vvPIKunTpgq+//rpY8iciIiIiIipJStSFVx6XlJQUuLu753vCIhERERERUUlhaR9T7HvyiIiIiIiIqOiwySMiIiIiIrIibPKIiIiIiIisCJs8IiIiIiIiK8Imj4iIiIiIyIqwySMiIiIiIrIibPKIiIiIiIisCJs8IiIiIiIiK8Imj4iIiIiIyIqwySMiIiIiIrIibPKIiIiIiIisCJs8IiIiIiIiK8Imj4iIiIiIyIqwySMiIqInUosWLbBs2bLiTuORSU9PR1BQECIjI4s7FSJ6zNjkERERWYHdu3ejS5cuCAgIgKIoWLduXYGm37dvH+zs7FC3bl2Tx27duoVhw4ahbNmycHR0RI0aNbBp0yajMXPmzEFwcDAcHR0RFhaGPXv25Lm8vXv3Ijw8HN7e3nByckL16tXx1VdfmYxbvXo1atasCY1Gg5o1a2Lt2rUmYwq6bAD49ddfce3aNfTu3VuNXbx4Ed27d4evry+0Wi169uyJ69evG033ySefoGnTpnB2doaHh0e+ywGANWvWoGPHjvDx8YGiKIiKijIZM3jwYDz11FNwcnKCr68vunbtijNnzqiPp6eno2/fvtBqtahWrRp27NhhNP3MmTMxYsQIo5hGo8Hbb7+Nd99916I8ich6sMkjIiKyAqmpqQgJCcE333xT4GmTk5PRr18/tG3b1uSxjIwMtG/fHjExMVi1ahXOnj2L//u//0O5cuXUMcuXL8fo0aPx/vvv4+jRo2jevDk6d+6MK1eu5LpMFxcXDB8+HLt378bp06fxwQcf4IMPPsD8+fPVMQcOHECvXr3Qt29fHDt2DH379kXPnj1x8ODBh1o2AHz99dcYOHAgbGzubwqlpqaiQ4cOUBQFO3bswL59+5CRkYEuXbpAr9cbPR89evTA0KFD839i/5Wamorw8HB89tlnuY4JCwvDokWLcPr0aWzZsgUigg4dOkCn0wEA5s+fj8jISBw4cABvvPEGXn75ZYgIACA6Ohrff/89PvnkE5P5vvLKK9izZw9Onz5tcb5EZAXkCZScnCwAJDk5ubhTISIiKnIAZO3atRaP79Wrl3zwwQcyadIkCQkJMXps7ty5UqlSJcnIyMh1+oYNG8qQIUOMYtWrV5cJEyYUJG3p3r27vPrqq+rfPXv2lE6dOhmN6dixo/Tu3fuhlp2QkCCKosiJEyfU2JYtW8TGxsZo2yAxMVEAyLZt20zmsWjRInF3d7e4NhGR6OhoASBHjx7Nd+yxY8cEgFy4cEFERIYOHSrvvvuuiIjcvXtXAEh8fLyI3H9O1qxZk+u8WrVqJR9++GGBciWiksnSPoZ78oiIiJ5gixYtwsWLFzFp0iSzj2/YsAFNmjTBsGHD4O/vj9q1a+PTTz9V9zBlZGQgMjISHTp0MJquQ4cO2L9/v8V5HD16FPv370fLli3V2IEDB0zm27FjR3W+hV323r174ezsjBo1aqix9PR0KIoCjUajxhwdHWFjY4O9e/daXEdRSE1NxaJFixAcHIzAwEAAQEhICPbu3Yu0tDRs2bIFZcuWhY+PD3766Sc4Ojqie/fuuc6vYcOGFh3CSkTWg00eERHRE+r8+fOYMGECli5dCjs7O7NjLl26hFWrVkGn02HTpk344IMPMGvWLPXQwBs3bkCn08Hf399oOn9/f1y7di3fHMqXLw+NRoP69etj2LBheP3119XHrl27lud8C7vsmJgY+Pv7q4dqAkDjxo3h4uKCd999F3fv3kVqairGjx8PvV6PuLi4fOsoCnPmzIGrqytcXV2xefNmbNu2DQ4ODgCAQYMGISQkBDVr1sQnn3yCFStWICkpCZMmTcLXX3+NDz74AJUrV0bHjh0RGxtrNN9y5cohJibmsdRARCUDmzwiIqInkE6nQ58+fTBlyhRUrVo113F6vR5+fn6YP38+wsLC0Lt3b7z//vuYO3eu0ThFUYz+FhGTmDl79uzB4cOH8d1332H27Nn4+eefCzzfgi47LS0Njo6ORjFfX1+sXLkSGzduhKurK9zd3ZGcnIx69erB1tY23zqKwiuvvIKjR48iIiICVapUQc+ePXHv3j0AgL29Pb799ltER0fj0KFDaNasGcaOHYuRI0ciKioK69atw7Fjx9C4cWOMHDnSaL5OTk64e/fuY6mBiEoG8z/bERERkVW7ffs2Dh8+jKNHj2L48OEA7jd0IgI7Ozts3boVbdq0QdmyZWFvb2/U6NSoUQPXrl1DRkYGfHx8YGtra7LnLD4+3mQPmznBwcEAgDp16uD69euYPHkyXn75ZQBAmTJl8pxvYZft4+ODpKQkk3iHDh1w8eJF3LhxA3Z2dvDw8ECZMmXUHB81d3d3uLu7o0qVKmjcuDE8PT2xdu1a9fnIaceOHTh16hQWLFiA8ePH45lnnoGLiwt69uxpcvGdxMRE+Pr6PpYaiKhk4J48IiKiJ5BWq8Xx48cRFRWl/hsyZAiqVauGqKgoNGrUCAAQHh6OCxcuGF1h8ty5cyhbtiwcHBzg4OCAsLAwbNu2zWj+27ZtQ9OmTQuUk4ggPT1d/btJkyYm8926das638IuOzQ0FNeuXTPb6AH3m0APDw/s2LED8fHxeP755wtUR1F58PkwuHfvHoYNG4Z58+bB1tYWOp0OmZmZAIDMzEz1fEmDEydOIDQ09LHkTEQlA/fkERERWYE7d+7gwoUL6t/R0dGIioqCl5cXgoKCTMbb2Nigdu3aRjE/Pz84OjoaxYcOHYr//ve/GDVqFEaMGIHz58/j008/NTokcOzYsejbty/q16+PJk2aYP78+bhy5QqGDBmSa77ffvstgoKCUL16dQD3L4byxRdfGN3rbdSoUWjRogVmzJiBrl27Yv369di+fbvRhVAKs+zQ0FD4+vpi3759eO6559T4okWLUKNGDfj6+uLAgQMYNWoUxowZg2rVqqljrly5gsTERFy5cgU6nU69513lypXh6uoKAKhevTqmT5+uXgzFMP7q1asAgLNnzwK4v6eyTJkyuHTpEpYvX44OHTrA19cXsbGxmDFjBpycnPDMM8+Y5D916lQ8++yzauMWHh6O8ePHY+DAgfjmm28QHh5uNH7Pnj2YNm1ars8HEVmhR36dzxKIt1AgIiJrs3PnTgFg8q9///7qmEmTJkmFChVynYe5WyiIiOzfv18aNWokGo1GKlWqJJ988olkZWUZjfn222+lQoUK4uDgIPXq1ZOIiAiTeedc9tdffy21atUSZ2dn0Wq1EhoaKnPmzBGdTmc03cqVK6VatWpib28v1atXl9WrV5vkl9+yzZkwYYLRrRhERN59913x9/cXe3t7qVKlisyaNUv0er3RmP79+5t9nnfu3KmOASCLFi1S/160aJHZaSZNmiQiIrGxsdK5c2fx8/MTe3t7KV++vPTp00fOnDljkvfx48elcuXKcufOHTWm0+lk6NChotVqpUGDBnL+/Hn1sf3794uHh4fcvXs33+eEiEo+S/sYReTfO2k+QVJSUtQTqrVabXGnQ0RE9FgMGDAAALB48eInatnmXL9+HbVq1UJkZCQqVKhQ3Ok8Mj169EBoaCjee++94k6FiIqApX0MD9ckIiJ6QkRERGD37t1P3LLN8ff3x4IFC3DlyhWrbfLS09MREhKCMWPGFHcqRPSYcU8e9+QREREREVEpwD15REREVsKC280RFZkn7+d/IuvDWygQERERERFZETZ5REREREREVoRNHhERERERkRVhk0dERERERGRF2OQRERERERFZETZ5REREREREVoRNHhERERERkRVhk0dERERERGRF2OQRERERERFZkWJv8nbv3o0uXbogICAAiqJg3bp1Fk+7b98+2NnZoW7duo8sPyIiIiIiotKk2Ju81NRUhISE4JtvvinQdMnJyejXrx/atm37iDIjIiIiIiIqfeyKO4HOnTujc+fOBZ5u8ODB6NOnD2xtbQu094+IiIiIiMiaFXuTVxiLFi3CxYsX8dNPP+Hjjz/Od3x6ejrS09PVv1NSUgAAer0eer0eAKAoChRFgYhARNSx+cUN0xc2bmNjYzLvgsYLmztrYk2siTWxptJRE6DAxkYAZMdFFIjkFTfOUa83zMfSuA0A+Xf+lsUVRaAoOeMK9Hol1zhrKpk1Gd7e1ro+sSbWVJprejCH3JS6Ju/8+fOYMGEC9uzZAzs7y9KfPn06pkyZYhJPSEjAvXv3AABOTk5wd3dHSkoK0tLS1DEuLi5wc3NDUlISMjIy1LhWq4WzszMSExORlZWlxj09PaHRaJCQkGD0Inh7e8PW1hbx8fFGOfj5+UGn0+HmzZtqTFEU+Pv7IyMjA0lJSWrczs4OPj4+SEtLUxtVAHBwcICXlxfu3LmD1NRUNc6aWBNrYk2syTpqAtwRFJQCX9/smmJjXXD1qhsqV06Cu3t2TdHRWty44YyaNRPh5JRd09mznkhJ0aBu3QTY2mbXdPy4NzIybBEWZlxTZKQfHBx0qFMnuyadTsGRI/7QajNQrVp2TWlpdjhxwgfe3mkIDs6uKTnZAefOeaFs2TsoVy67poQEJ8TEsKaSWpPh7W2t6xNrYk2luSZbW1tYQhFL28HHQFEUrF27Ft26dTP7uE6nQ+PGjfHaa69hyJAhAIDJkydj3bp1iIqKynW+5vbkBQYGIikpCVqtVl02fzFgTayJNbEm1lQSa7KxsY49RA/GWVPJrCkz89+ola5PrIk1leaabt++DQ8PDyQnJ6t9jDmlqsm7desWPD09jTpYvV4PEYGtrS22bt2KNm3a5LuclJQUuLu75/vkEBERlQSKUtwZ0JOk5GwZEtGDLO1jStXhmlqtFsePHzeKzZkzBzt27MCqVasQHBxcTJkRERERERGVDMXe5N25cwcXLlxQ/46OjkZUVBS8vLwQFBSEiRMnIjY2FkuWLIGNjQ1q165tNL2fnx8cHR1N4kRERERERE+iYm/yDh8+jNatW6t/jx07FgDQv39/LF68GHFxcbhy5UpxpUdERERERFSqlKhz8h4XnpNHRESlCc/Jo8fpydsyJCo9LO1jbB5jTkRERERERPSIsckjIiIiIiKyImzyiIiIiIiIrAibPCIiIiIiIivCJo+IiIiIiMiKsMkjIiIiIiKyImzyiIiIiIiIrAibPCIiIiIiIivCJo+IiIiIiMiKsMkjIiIiIiKyImzyiIiIiIiIrAibPCIiIiIiIivCJo+IiIiIiMiKsMkjIiIiIiKyImzyiIiIiIiIrAibPCIiIiIiIivCJo+IiIiIiMiKsMkjIiIiIiKyImzyiIiIiIiIrAibPCIiIiIiIivCJo+IiIiIiMiKsMkjIiIiIiKyImzyiIiIiIiIrAibPCIiIiIiIivCJo+IiIiIiMiKsMkjIiIiIiKyImzyiIiIiIiIrAibPCIiIiIiIivCJo+IiIiIiMiKsMkjIiIiIiKyImzyiIiIiIiIrAibPCIiIiIiIivCJo+IiIiIiMiKsMkjIiIiIiKyImzyiIiIiIiIrAibPCIiIiIiIivCJo+IiIiIiMiKFHuTt3v3bnTp0gUBAQFQFAXr1q3Lc/yaNWvQvn17+Pr6QqvVokmTJtiyZcvjSZaIiIiIiKiEK/YmLzU1FSEhIfjmm28sGr979260b98emzZtQmRkJFq3bo0uXbrg6NGjjzhTIiIiIiKikk8RESnuJAwURcHatWvRrVu3Ak1Xq1Yt9OrVCx999JFF41NSUuDu7o7k5GRotdpCZEpERPT4KEpxZ0BPkpKzZUhED7K0j7F7jDk9Enq9Hrdv34aXl1euY9LT05Genq7+nZKSok6r1+sB3G8wFUWBiCBn35tf3DB9YeM2NjYm8y5ovLC5sybWxJpYE2sqHTUBCmxsBEB2XESBSF5x4xz1esN8LI3bAJB/529ZXFEEipIzrkCvV3KNs6aSWZPh7W2t6xNrYk2luaYHc8hNqW/yZs2ahdTUVPTs2TPXMdOnT8eUKVNM4gkJCbh37x4AwMnJCe7u7khJSUFaWpo6xsXFBW5ubkhKSkJGRoYa12q1cHZ2RmJiIrKystS4p6cnNBoNEhISjF4Eb29v2NraIj4+3igHPz8/6HQ63Lx5U40pigJ/f39kZGQgKSlJjdvZ2cHHxwdpaWlqowoADg4O8PLywp07d5CamqrGWRNrYk2siTVZR02AO4KCUuDrm11TbKwLrl51Q+XKSXB3z64pOlqLGzecUbNmIpycsms6e9YTKSka1K2bAFvb7JqOH/dGRoYtwsKMa4qM9IODgw516mTXpNMpOHLEH1ptBqpVy64pLc0OJ074wNs7DcHB2TUlJzvg3DkvlC17B+XKZdeUkOCEmBjWVFJrMry9rXV9Yk2sqTTXZGtrC0uU6sM1f/75Z7z++utYv3492rVrl+s4c3vyAgMDkZSUpO7m5C8GrIk1sSbWxJpKak02Ntaxh+jBOGsqmTVlZv4btdL1iTWxptJc0+3bt+Hh4ZHv4Zqltslbvnw5Bg4ciJUrV+LZZ58t0HJ4Th4REZUmCs/Jo8eo5GwZEtGDLO1jiv3qmoXx888/Y8CAAVi2bFmBGzwiIiIiIiJrVuzn5N25cwcXLlxQ/46OjkZUVBS8vLwQFBSEiRMnIjY2FkuWLAFwv8Hr168f/vOf/6Bx48a4du0agOzjXImIiIiIiJ5kxb4n7/DhwwgNDUVoaCgAYOzYsQgNDVVvhxAXF4crV66o4+fNm4esrCwMGzYMZcuWVf+NGjWqWPInIiIiIiIqSUrUOXmPC8/JIyKi0oTn5NHj9ORtGRKVHlZ9Th4RERERERGZxyaPiIiIiIjIirDJIyIiIiIisiJs8oiIiIiIiKwImzwiIiIiIiIrwiaPiIiIiIjIirDJIyIiIiIisiJs8oiIiIiIiKwImzwiIiIiIiIrwiaPiIiIiIjIirDJIyIiIiIisiJs8oiIiIiIiKwImzwiIiIiIiIrwiaPiIiIiIjIirDJIyIiIiIisiJs8oiIiIiIiKwImzwiIiIiIiIrwiaPiIiIiIjIirDJIyIiIiIisiJs8oiIiIiIiKwImzwiIiIiIiIrwiaPiIiIiIjIirDJIyIiIiIisiJs8oiIiIiIiKwImzwiIiIiIiIrwiaPiIiIiIjIirDJIyIiIiIisiJs8oiIiIiIiKwImzwiIiIiIiIrwiaPiIiIiIjIirDJIyIiIiIisiJs8oiIiIiIiKwImzwiIiIiIiIrwiaPiIiIiIjIirDJIyIiIiIisiJs8oiIiIiIiKxIgZu8SpUq4dixY2YfO3HiBCpVqvTQSREREREREVHhFLjJi4mJQXp6utnH7t27h8uXLxdofrt370aXLl0QEBAARVGwbt26fKeJiIhAWFgYHB0dUalSJXz33XcFWiYREREREZG1KtThmoqimI1funQJbm5uBZpXamoqQkJC8M0331g0Pjo6Gs888wyaN2+Oo0eP4r333sPIkSOxevXqAi2XiIiIiIjIGtlZMuiHH37ADz/8oP49dOhQaLVaozFpaWk4duwYWrZsWaAEOnfujM6dO1s8/rvvvkNQUBBmz54NAKhRowYOHz6ML774Ai+++GKBlk1ERERERGRtLGry7t69i4SEBAD39+LdunXL5JBNjUaDXr16YcqUKUWfZQ4HDhxAhw4djGIdO3bEggULkJmZCXt7e5Np0tPTjfJNSUkBAOj1euj1egD361IUBSICEVHH5hc3TF/YuI2Njcm8CxovbO6siTWxJtbEmkpHTYACGxsBkB0XUSCSV9w4R73eMB9L4zYA5N/5WxZXFIGi5Iwr0OuVXOOsqWTWZHh7W+v6xJpYU2mu6cEccmNRkzd06FAMHToUABAcHIzVq1cjJCTEogUUtWvXrsHf398o5u/vj6ysLNy4cQNly5Y1mWb69Olmm8+EhATcu3cPAODk5AR3d3ekpKQgLS1NHePi4gI3NzckJSUhIyNDjWu1Wjg7OyMxMRFZWVlq3NPTExqNBgkJCUYvgre3N2xtbREfH2+Ug5+fH3Q6HW7evKnGFEWBv78/MjIykJSUpMbt7Ozg4+ODtLQ0tVEFAAcHB3h5eeHOnTtITU1V46yJNbEm1sSarKMmwB1BQSnw9c2uKTbWBVevuqFy5SS4u2fXFB2txY0bzqhZMxFOTtk1nT3riZQUDerWTYCtbXZNx497IyPDFmFhxjVFRvrBwUGHOnWya9LpFBw54g+tNgPVqmXXlJZmhxMnfODtnYbg4OyakpMdcO6cF8qWvYNy5bJrSkhwQkwMayqpNRne3ta6PrEm1lSaa7K1tYUlFLG0HXwMFEXB2rVr0a1bt1zHVK1aFQMHDsTEiRPV2L59+9CsWTPExcWhTJkyJtOY25MXGBiIpKQk9bBT/mLAmlgTa2JNrKmk1mRjYx17iB6Ms6aSWVNm5r9RK12fWBNrKs013b59Gx4eHkhOTjY5fS4ni/bkmRMfH4/Lly8bdaIGLVq0KOxs81WmTBlcu3bNJBc7Ozt4e3ubnUaj0UCj0ZjEbWxsYGNjfO0ZwxP6oNziD05fmHhBl/mo46yJNbEm1pRXnDUVT02GjXzL4+ZzL1hc+Xf+lsUNjYulcdZUMmt68O1tjesTa2JNucVLek3mxphT4CYvLi4Offv2xc6dO00eExEoigKdTlfQ2VqsSZMm2Lhxo1Fs69atqF+/vtnz8YiIiIiIiJ4kBW7yhg8fjqNHj2LGjBl4+umnze4hK4g7d+7gwoUL6t/R0dGIioqCl5cXgoKCMHHiRMTGxmLJkiUAgCFDhuCbb77B2LFj8cYbb+DAgQNYsGABfv7554fKg4iIiIiIyBoUuMmLiIjAF198gYEDBxZJAocPH0br1q3Vv8eOHQsA6N+/PxYvXoy4uDhcuXJFfTw4OBibNm3CmDFj8O233yIgIABff/01b59AREREREQEFPzCK76+vvj555/Rrl27R5XTI5eSkgJ3d/d8T1gkIiIqCSw8BYOoSBRsy5CIHidL+xjzZyHmoUePHvj1118fKjkiIiIiIiJ6NAp8uGbPnj3xxhtvQK/Xo0uXLmavaFmvXr0iSY6IiIiIiIgKpsCHa+a8jOeDl/B8HFfXLAo8XJOIiEoTHq5JjxMP1yQquSztYwq8J2/RokUPlRgRERERERE9OgVu8vr37/8o8iAiIiIiIqIiUOALrxAREREREVHJVeA9eYMGDcrzcUVRsGDBgkInRERERERERIVX4CZvx44dJhdcuXnzJu7cuQMPDw94eHgUVW5ERERERERUQAVu8mJiYszGd+zYgbfeegsrV6582JyIiIiIiIiokIrsnLw2bdpg+PDhGDVqVFHNkoiIiIiIiAqoSC+8UrNmTfz5559FOUsiIiIiIiIqgCJt8iIiIuDj41OUsyQiIiIiIqICKPA5eVOnTjWJpaen46+//sL//vc/jB8/vkgSIyIiIiIiooJTREQKMoGNjenOP41Gg4oVK6Jv374YP3487O3tiyzBRyElJQXu7u5ITk6GVqst7nSIiIjy9MBFrYkeqYJtGRLR42RpH1PgPXl6vf6hEiMiIiIiIqJHp0jPySMiIiIiIqLiVeA9eQCQmZmJJUuW4Pfff8fNmzfh4+ODdu3a4dVXXy3xh2oSERERERFZswKfk5ecnIy2bdviyJEjcHFxQZkyZXDt2jWkpqYiLCwMv//+e4k/z43n5BERUWnCc/LoceI5eUQll6V9TIEP13z//fdx9uxZLF++HLdv38b58+dx+/ZtrFixAmfPnsX777//UIkTERERERFR4RW4yVu3bh2mTp2KHj16GMVfeuklTJ48GWvXri2y5IiIiIiIiKhgCtzkJSQk4Omnnzb7WEhICG7cuPHQSREREREREVHhFLjJK1euHPbu3Wv2sX379iEgIOChkyIiIiIiIqLCKXCT16tXL3z66af48ssvcfPmTQDAzZs38Z///AeffvopevfuXeRJEhERERERkWUKfHXN9PR0dO3aFVu3boWiKLCzs0NWVhZEBB07dsT69evh4ODwqPItEry6JhERlSa8uiY9Try6JlHJZWkfU+D75Gk0GmzevBlbtmzBzp07cfPmTXh7e6Nt27Zo3779QyVNRERERERED6fAe/KsAffkERFRacI9efQ4PXlbhkSlR5HeJ+/u3bsYN24ctm/fnuuYbdu2Ydy4cUhNTS14tkRERERERFQkLGrylixZgmXLlqFJkya5jmnatCl++eUXzJ8/v8iSIyIiIiIiooKxqMn78ccf8eabb8LFxSXXMS4uLnjzzTexevXqIkuOiIiIiIiICsaiJu/kyZNo2rRpvuMaN26MkydPPnRSREREREREVDgWNXlpaWlwdnbOd5yzszPS0tIeOikiIiIiIiIqHIuaPF9fX1y8eDHfcRcvXoSPj89DJ0VERERERESFY1GTFx4ejgULFuQ7bsGCBQgPD3/opIiIiIiIiKhwLGryhg8fjn379mHw4MG4d++eyeP37t3DG2+8gf3792PEiBFFniQRERERERFZxs6SQc2bN8eHH36IadOmYfXq1ejQoQOCg4MBANHR0di6dSsSExPx4YcfolmzZo80YSIiIiIiIsqdRU0eAEyZMgW1a9fG1KlT8csvvxg9VqdOHXz33Xd46aWXijxBIiIiIiIispxFh2sa9OjRA8ePH8fVq1fxxx9/4I8//sDVq1dx7Nixh2rw5syZg+DgYDg6OiIsLAx79uzJc/zSpUsREhICZ2dnlC1bFgMHDsTNmzcLvXwiIiIiIiJrUaAmz6BMmTJo2LAhGjZsiDJlyjxUAsuXL8fo0aPx/vvv4+jRo2jevDk6d+6MK1eumB2/d+9e9OvXD6+99hpOnjyJlStX4tChQ3j99dcfKg8iIiIiIiJrUKgmryh9+eWXeO211/D666+jRo0amD17NgIDAzF37lyz4//44w9UrFgRI0eORHBwMJo1a4bBgwfj8OHDjzlzIiIiIiKikqdYm7yMjAxERkaiQ4cORvEOHTpg//79Zqdp2rQp/vnnH2zatAkiguvXr2PVqlV49tlnH0fKREREREREJZrFF155FG7cuAGdTgd/f3+juL+/P65du2Z2mqZNm2Lp0qXo1asX7t27h6ysLDz//PP473//m+ty0tPTkZ6erv6dkpICANDr9dDr9QAARVGgKApEBCKijs0vbpi+sHEbGxuTeRc0XtjcWRNrYk2siTWVjpoABTY2AiA7LqJAJK+4cY56vWE+lsZtAMi/87csrigCRckZV6DXK7nGWVPJrMnw9rbW9Yk1sabSXNODOeSmWJs8g/tfYNlExCRmcOrUKYwcORIfffQROnbsiLi4OIwfPx5DhgzJ9Ybt06dPx5QpU0ziCQkJ6n3/nJyc4O7ujpSUFKSlpaljXFxc4ObmhqSkJGRkZKhxrVYLZ2dnJCYmIisrS417enpCo9EgISHB6EXw9vaGra0t4uPjjXLw8/ODTqczunCMoijw9/dHRkYGkpKS1LidnR18fHyQlpamNqoA4ODgAC8vL9y5cwepqalqnDWxJtbEmliTddQEuCMoKAW+vtk1xca64OpVN1SunAR39+yaoqO1uHHDGTVrJsLJKbums2c9kZKiQd26CbC1za7p+HFvZGTYIizMuKbISD84OOhQp052TTqdgiNH/KHVZqBateya0tLscOKED7y90xAcnF1TcrIDzp3zQtmyd1CuXHZNCQlOiIlhTSW1JsPb21rXJ9bEmkpzTba2trCEIpa2g49ARkYGnJ2dsXLlSnTv3l2Njxo1ClFRUYiIiDCZpm/fvrh37x5Wrlypxvbu3YvmzZvj6tWrKFu2rMk05vbkBQYGIikpCVqtFgB/MWBNrIk1sSbWVHJrsrGxjj1ED8ZZU8msKTPz36iVrk+siTWV5ppu374NDw8PJCcnq32MOQXek9egQQO0b98ebdu2RbNmzaDRaAo6C5WDgwPCwsKwbds2oyZv27Zt6Nq1q9lp7t69Czs747QNHe2DT7yBRqMxm6eNjQ1sbIxPSzQ8oQ/KLf7g9IWJF3SZjzrOmlgTa2JNecVZU/HUZNjItzxuPveCxZV/529Z3NC4WBpnTSWzpgff3ta4PrEm1pRbvKTXZG6MOQW+8ErZsmUxZ84ctG/fHp6enmjfvj1mzJiByMjIgs4KADB27Fh8//33WLhwIU6fPo0xY8bgypUrGDJkCABg4sSJ6Nevnzq+S5cuWLNmDebOnYtLly5h3759GDlyJBo2bIiAgIBC5UBERERERGQtCnW4pk6nw8GDB7F9+3b8/vvv+OOPP5CVlQVPT0+0adMGK1asKND85syZg5kzZyIuLg61a9fGV199hRYtWgAABgwYgJiYGOzatUsd/9///hffffcdoqOj4eHhgTZt2mDGjBkoV66cRctLSUmBu7t7vrs5iYiISgILf7glKhIF3zIkosfF0j6mSM7J+/PPP/HRRx9h69atUBQFOp3uYWf5SLHJIyKi0oRNHj1ObPKISi5L+5hCXV3z2rVr2L59O7Zt24bff/8dcXFxCAwMxMCBA9GuXbtCJ01EREREREQPp8BNXp06dXDq1Cl4enqiVatW+OCDD9C2bVtUqVLlUeRHREREREREBVDgJu/kyZNwcnLCSy+9hE6dOqFNmzY85JGIiIiIiKiEKHCTd/jwYWzfvh3bt29Hnz59kJWVhfr166N9+/Zo3749mjRpYvFN+oiIiIiIiKhoPdSFV9LT07F3715s27YN27ZtQ1RUFFxdXZGcnFyUORY5XniFiIhKE154hR4nXniFqOSytI8p8H3ycrp27RpiYmJw+fJl/P333xARpKamPswsiYiIiIiI6CEU+HDN1atXq4drXrp0CSKCqlWromfPnmjbti3atGnzKPIkIiIiIiIiCxT4cE0bGxuULVsWbdu2Rdu2bdGuXTuLb0JeUvBwTSIiKk14uCY9Tjxck6jkemT3yTtx4gRq1qz5UMkRERERERHRo1Hgc/JyNnj37t1DXFwc7t27V6RJERERERERUeEU6sIr+/fvR/PmzeHm5oby5cvDzc0NLVu2xIEDB4o6PyIiIiIiIiqAAh+u+ccff6BNmzbw8PDAm2++iYCAAMTGxmLNmjVo06YNdu3ahUaNGj2KXImIiIiIiCgfBb7wSocOHXDr1i3s3LkTLi4uajw1NRWtW7eGp6cntmzZUuSJFiVeeIWIiEoTXniFHideeIWo5Hpk98n7448/8M477xg1eADg4uKC8ePH85BNIiIiIiKiYlTgJk+n00Gj0Zh9zNHRETqd7qGTIiIiIiIiosIpcJMXEhKCuXPnmn1s3rx5CAkJeeikiIiIiIiIqHAKfOGVCRMmoFu3bggNDcWrr76KsmXLIi4uDsuWLUNUVBTWrVv3CNIkIiIiIiIiSxT4wisAsGzZMrzzzju4evWqGitXrhxmzpyJl19+uUgTfBR44RUiIipNeOEVepx44RWiksvSPqZQTR4AiAjOnj2LmzdvwtvbG9WqVYOiKBARKCX824hNHhERlSYl/GuVrAybPKKS65FdXdNAURRUr14d4eHhqF69OhRFwbJly1CjRo3CzpKIiIiIiIgeksXn5CUnJ2PdunW4fv06qlatiueffx42Nvd7xDVr1uCjjz7CqVOnUKFChUeWLBEREREREeXNoibvwoULaN68OeLj49XDMVu2bIl169bh5ZdfxubNm+Hh4YGZM2dixIgRjzpnIiIiIiIiyoVFTd6HH36IlJQUTJ48GfXr18elS5fwySefoGnTpjh16hRef/11zJw5Ex4eHo84XSIiIiIiIsqLRU1eREQEPvjgA0ycOFGNVa5cGZ07d8aQIUMwZ86cR5YgERERERERWc6iC68kJCQgPDzcKNasWTMAQK9evYo+KyIiIiIiIioUi5o8nU4HR0dHo5jhbzc3t6LPioiIiIiIiArF4qtrnj17FnZ22cN1Oh0A4MyZMyZj69WrVwSpERERERERUUFZdDN0Gxsbszc4f/DG54a/DQ1gScWboRMRUWnCm6HT48SboROVXJb2MRbtyVu0aFGRJUZERERERESPjkVNXv/+/R91HkRERERERFQELLrwChEREREREZUObPKIiIiIiIisCJs8IiIiIiIiK8Imj4iIiIiIyIqwySMiIiIiIrIibPKIiIiIiIisCJs8IiIiIiIiK1Iimrw5c+YgODgYjo6OCAsLw549e/Icn56ejvfffx8VKlSARqPBU089hYULFz6mbImIiIiIiEoui26G/igtX74co0ePxpw5cxAeHo558+ahc+fOOHXqFIKCgsxO07NnT1y/fh0LFixA5cqVER8fj6ysrMecORERERERUcmjiIgUZwKNGjVCvXr1MHfuXDVWo0YNdOvWDdOnTzcZv3nzZvTu3RuXLl2Cl5dXoZaZkpICd3d3JCcnQ6vVFjp3IiKix0FRijsDepIU75YhEeXF0j6mWA/XzMjIQGRkJDp06GAU79ChA/bv3292mg0bNqB+/fqYOXMmypUrh6pVq+Ltt99GWlra40iZiIiIiIioRCvWwzVv3LgBnU4Hf39/o7i/vz+uXbtmdppLly5h7969cHR0xNq1a3Hjxg289dZbSExMzPW8vPT0dKSnp6t/p6SkAAD0ej30ej0AQFEUKIoCEUHOnZv5xQ3TFzZuY2NjMu+CxgubO2tiTayJNbGm0lEToMDGRgBkx0UUiOQVN85RrzfMx9K4DQD5d/6WxRVFoCg54wr0eiXXOGsqmTUZ3t7Wuj6xJtZUmmt6MIfcFPs5eYDhCyybiJjEDPR6PRRFwdKlS+Hu7g4A+PLLL/HSSy/h22+/hZOTk8k006dPx5QpU0ziCQkJuHfvHgDAyckJ7u7uSElJMdor6OLiAjc3NyQlJSEjI0ONa7VaODs7IzEx0eh8QE9PT2g0GiQkJBi9CN7e3rC1tUV8fLxRDn5+ftDpdLh586bR8+Hv74+MjAwkJSWpcTs7O/j4+CAtLU1tVAHAwcEBXl5euHPnDlJTU9U4a2JNrIk1sSbrqAlwR1BQCnx9s2uKjXXB1atuqFw5Ce7u2TVFR2tx44YzatZMhJNTdk1nz3oiJUWDunUTYGubXdPx497IyLBFWJhxTZGRfnBw0KFOneyadDoFR474Q6vNQLVq2TWlpdnhxAkfeHunITg4u6bkZAecO+eFsmXvoFy57JoSEpwQE8OaSmpNhre3ta5PrIk1leaabG1tYYliPScvIyMDzs7OWLlyJbp3767GR40ahaioKERERJhM079/f+zbtw8XLlxQY6dPn0bNmjVx7tw5VKlSxWQac3vyAgMDkZSUpB7Lyl8MWBNrYk2siTWV1JpsbKxjD9GDcdZUMmvKzPw3aqXrE2tiTaW5ptu3b8PDwyPfc/KKdU+eg4MDwsLCsG3bNqMmb9u2bejatavZacLDw7Fy5UrcuXMHrq6uAIBz587BxsYG5cuXNzuNRqOBRqMxidvY2MDGxvi0RMMT+qDc4g9OX5h4QZf5qOOsiTWxJtaUV5w1FU9Nho18y+Pmcy9YXPl3/pbFDY2LpXHWVDJrevDtbY3rE2tiTbnFS3pN5saYU+z3yRs7diy+//57LFy4EKdPn8aYMWNw5coVDBkyBAAwceJE9OvXTx3fp08feHt7Y+DAgTh16hR2796N8ePHY9CgQWYP1SQiIiIiInqSFPs5eb169cLNmzcxdepUxMXFoXbt2ti0aRMqVKgAAIiLi8OVK1fU8a6urti2bRtGjBiB+vXrw9vbGz179sTHH39cXCUQERERERGVGMV+n7ziwPvkERFRaWLh0TlEReLJ2zIkKj1KxX3yiIiIiIiIqGixySMiIiIiIrIibPKIiIiIiIisCJs8IiIiIiIiK8Imj4iIiIiIyIqwySMiIiIiIrIibPKIiIiIiIisCJs8IiIiIiIiK8Imj4iIiIiIyIqwySMiIiIiIrIibPKIiIiIiIisCJs8IiIiIiIiK8Imj4iIiIiIyIqwySMiIiIiIrIibPKIiIiIiIisCJs8IiIiIiIiK8Imj4iIiIiIyIqwySMiIiIiIrIibPKIiIiIiIisCJs8IiIiIiIiK8Imj4iIiIiIyIqwySMiIiIiIrIibPKIiIiIiIisCJs8IiIiIiIiK8Imj4iIiIiIyIqwySMiIiIiIrIibPKIiIiIiIisCJs8IiIiIiIiK8Imj4iIiIiIyIqwySMiIiIiIrIibPKIiIiIiIisCJs8IiIiIiIiK8Imj4iIiIiIyIqwySMiIiIiIrIibPKIiIiIiIisCJs8IiIiIiIiK8Imj4iIiIiIyIqUiCZvzpw5CA4OhqOjI8LCwrBnzx6Lptu3bx/s7OxQt27dR5sgERERERFRKVHsTd7y5csxevRovP/++zh69CiaN2+Ozp0748qVK3lOl5ycjH79+qFt27aPKVMiIiIiIqKSTxERKc4EGjVqhHr16mHu3LlqrEaNGujWrRumT5+e63S9e/dGlSpVYGtri3Xr1iEqKsriZaakpMDd3R3JycnQarUPkz4REdEjpyjFnQE9SYp3y5CI8mJpH1Ose/IyMjIQGRmJDh06GMU7dOiA/fv35zrdokWLcPHiRUyaNOlRp0hERERERFSq2BXnwm/cuAGdTgd/f3+juL+/P65du2Z2mvPnz2PChAnYs2cP7OwsSz89PR3p6enq3ykpKQAAvV4PvV4PAFAUBYqiQESQc+dmfnHD9IWN29jYmMy7oPHC5s6aWBNrYk2sqXTUBCiwsREA2XERBSJ5xY1z1OsN87E0bgNA/p2/ZXFFEShKzrgCvV7JNc6aSmZNhre3ta5PrIk1leaaHswhN8Xa5Bnc/wLLJiImMQDQ6XTo06cPpkyZgqpVq1o8/+nTp2PKlCkm8YSEBNy7dw8A4OTkBHd3d6SkpCAtLU0d4+LiAjc3NyQlJSEjI0ONa7VaODs7IzExEVlZWWrc09MTGo0GCQkJRi+Ct7c3bG1tER8fb5SDn58fdDodbt68qcYURYG/vz8yMjKQlJSkxu3s7ODj44O0tDS1UQUABwcHeHl54c6dO0hNTVXjrIk1sSbWxJqsoybAHUFBKfD1za4pNtYFV6+6oXLlJLi7Z9cUHa3FjRvOqFkzEU5O2TWdPeuJlBQN6tZNgK1tdk3Hj3sjI8MWYWHGNUVG+sHBQYc6dbJr0ukUHDniD602A9WqZdeUlmaHEyd84O2dhuDg7JqSkx1w7pwXypa9g3LlsmtKSHBCTAxrKqk1Gd7e1ro+sSbWVJprsrW1hSWK9Zy8jIwMODs7Y+XKlejevbsaHzVqFKKiohAREWE0/tatW/D09DQqTq/XQ0Rga2uLrVu3ok2bNibLMbcnLzAwEElJSeqxrPzFgDWxJtbEmlhTSa3JxsY69hA9GGdNJbOmzMx/o1a6PrEm1lSaa7p9+zY8PDzyPSevRFx4JSwsDHPmzFFjNWvWRNeuXU0uvKLX63Hq1Cmj2Jw5c7Bjxw6sWrUKwcHBcHFxyXeZvPAKERGVJgovvEKPUfFuGRJRXiztY4r9cM2xY8eib9++qF+/Ppo0aYL58+fjypUrGDJkCABg4sSJiI2NxZIlS2BjY4PatWsbTe/n5wdHR0eTOBERERER0ZOo2Ju8Xr164ebNm5g6dSri4uJQu3ZtbNq0CRUqVAAAxMXF5XvPPCIiIiIiIrqv2A/XLA48XJOIiEoTHq5Jj9OTt2VIVHqUivvkERERERERUdFik0dERERERGRF2OQRERERERFZETZ5REREREREVoRNHhERERERkRVhk0dERERERGRF2OQRERERERFZETZ5REREREREVoRNHhERERERkRVhk0dERERERGRF2OQRERERERFZETZ5REREREREVoRNHhERERERkRVhk0dERERERGRF2OQRERERERFZETZ5REREREREVoRNHhERERERkRVhk0dERERERGRF2OQRERERERFZETZ5REREREREVoRNHhERERERkRVhk0dERERERGRF2OQRERERERFZETZ5REREREREVoRNHhERERERkRVhk0dERERERGRF2OQRERERERFZETZ5REREREREVoRNHhERERERkRVhk0dERERERGRF2OQRERERERFZETZ5REREREREVoRNHhERERERkRVhk0dERERERGRF2OQRERERERFZETZ5REREREREVoRNHhERERERkRUpEU3enDlzEBwcDEdHR4SFhWHPnj25jl2zZg3at28PX19faLVaNGnSBFu2bHmM2RIREREREZVcxd7kLV++HKNHj8b777+Po0ePonnz5ujcuTOuXLlidvzu3bvRvn17bNq0CZGRkWjdujW6dOmCo0ePPubMiYiIiIiISh5FRKQ4E2jUqBHq1auHuXPnqrEaNWqgW7dumD59ukXzqFWrFnr16oWPPvrIovEpKSlwd3dHcnIytFptofImIiJ6XBSluDOgJ0nxbhkSUV4s7WOKdU9eRkYGIiMj0aFDB6N4hw4dsH//fovmodfrcfv2bXh5eT2KFImIiIiIiEoVu+Jc+I0bN6DT6eDv728U9/f3x7Vr1yyax6xZs5CamoqePXvmOiY9PR3p6enq3ykpKQDuN4h6vR4AoCgKFEWBiCDnzs384obpCxu3sbExmXdB44XNnTWxJtbEmlhT6agJUGBjIwCy4yIKRPKKG+eo1xvmY2ncBoD8O3/L4ooiUJSccQV6vZJrnDWVzJoMb29rXZ9YE2sqzTU9mENuirXJM7j/BZZNRExi5vz888+YPHky1q9fDz8/v1zHTZ8+HVOmTDGJJyQk4N69ewAAJycnuLu7IyUlBWlpaeoYFxcXuLm5ISkpCRkZGWpcq9XC2dkZiYmJyMrKUuOenp7QaDRISEgwehG8vb1ha2uL+Ph4oxz8/Pyg0+lw8+ZNNaYoCvz9/ZGRkYGkpCQ1bmdnBx8fH6SlpamNKgA4ODjAy8sLd+7cQWpqqhpnTayJNbEm1mQdNQHuCApKga9vdk2xsS64etUNlSsnwd09u6boaC1u3HBGzZqJcHLKrunsWU+kpGhQt24CbG2zazp+3BsZGbYICzOuKTLSDw4OOtSpk12TTqfgyBF/aLUZqFYtu6a0NDucOOEDb+80BAdn15Sc7IBz57xQtuwdlCuXXVNCghNiYlhTSa3J8Pa21vWJNWXXFB8fjxYtWuB///sfnn766VJX040bN9CyZUscPHgQlStXttrXKWdNtra2sIgUo/T0dLG1tZU1a9YYxUeOHCktWrTIc9pffvlFnJyc5Ndff813Offu3ZPk5GT1399//y0AJCkpSXQ6neh0OtHr9SIiotfr1Zgl8ZyxwsTNzbug8cLmzppYE2tiTaypdNQEiNjY6MXGRqf+U5T84jqjf0BB4yKAvkBxRXkwrs8zzppKZk3Wvj49GN+5c6c899xzUrZsWQEgq1evzjPHN954QwDIV199pcYSEhJk2LBhUrVqVXFycpLAwEAZPny4JCYm5pl7RESE2WXnHI/7u4BN/s2YMcMox3379knr1q3F2dlZ3N3dpWXLlnL37t08X6exY8fKwIEDjeJ//PGHtGnTRtzd3cXDw0Pat28vR48ezfN5j42NlVdeeUX8/f3F2dlZQkNDZfny5erY33//Pdc6/vzzT9Hr9ZKQkCDPPvusuLi4SGhoqERFRRnlPmTIEPn8889NnsvRo0fLoEGDSuV7rzDr061btwSAJCcnS16KtckTEWnYsKEMHTrUKFajRg2ZMGFCrtMsW7ZMHB0dZe3atYVaZnJyskVPDhERUUlwf4Od//jv8fx70mzatEnef/99Wb16tQDIc/ty7dq1EhISIgEBAfLVV1+p8ePHj8sLL7wgGzZskAsXLsjvv/8uVapUkRdffPGhlx0XF2f0b+HChaIoily8eFEds3//ftFqtTJ9+nQ5ceKEnDt3TlauXCn37t3Lddl3794VDw8P2b9/vxpLSUkRT09PGTBggJw5c0ZOnDghL774ovj5+UlGRkau82rXrp00aNBADh48KBcvXpRp06aJjY2NHDlyRETu79h5sI7XX39dKlasqDYzY8eOlZYtW8rZs2dl9OjRUr9+faP6wsLCJCsry2TZf/31lzg6OkpiYmLuT7QVsbSPKfZV+ZdffhF7e3tZsGCBnDp1SkaPHi0uLi4SExMjIiITJkyQvn37quOXLVsmdnZ28u233xq9UW7dumXxMtnkERFRaVLcG/3892T9e5Ll1eT9888/Uq5cOTlx4oRUqFDBqMkzZ8WKFeLg4CCZmZkPveycunbtKm3atDGKNWrUSD744AOLlmOwevVq8fHxMYodOnRIAMiVK1fU2F9//SUA5MKFC7nOy8XFRZYsWWIU8/Lyku+//97s+IyMDPHz85OpU6eqsc6dO8vcuXNFROTUqVPi7Oysjg0JCZFDhw7luvyKFSvKggULcn3cmljaxxT7ffJ69eqF2bNnY+rUqahbty52796NTZs2oUKFCgCAuLg4o3vmzZs3D1lZWRg2bBjKli2r/hs1alRxlUBEREREVkyv16Nv374YP348atWqZdE0hkvc29kV3SUwrl+/jt9++w2vvfaaGouPj8fBgwfh5+eHpk2bwt/fHy1btsTevXvznNfu3btRv359o1i1atXg4+ODBQsWICMjA2lpaViwYAFq1aqlbpub06xZMyxfvhyJiYnQ6/X45ZdfkJ6ejlatWpkdv2HDBty4cQMDBgxQYyEhIdixYweysrKwZcsWPP300wCAGTNmoFWrVia55tSwYUPs2bMnz3qfNMXe5AHAW2+9hZiYGKSnpyMyMhItWrRQH1u8eDF27dql/r1r1y6IiMm/xYsXP/7EiYiIiMjqzZgxA3Z2dhg5cqRF42/evIlp06Zh8ODBRZrHDz/8ADc3N7zwwgtq7NKlSwCAyZMn44033sDmzZtRr149tG3bFufPn891XjExMQgICDCKubm5YdeuXfjpp5/g5OQEV1dXbNmyBZs2bcqzWV2+fDmysrLg7e0NjUaDwYMHY+3atXjqqafMjl+wYAE6duyIwMBANTZhwgTY2dnhqaeewtq1a7FgwQKcP38eS5YswYcffoghQ4agUqVK6NmzJ5KTk43mV65cOcTExOSa35OoRDR5REREREQlUWRkJP7zn/9g8eLFFl39PSUlBc8++yxq1qyJSZMmFWkuCxcuxCuvvAJHR0c1pv/3EvuDBw/GwIEDERoaiq+++grVqlXDwoULc51XWlqa0XwMsUGDBiE8PBx//PEH9u3bh1q1auGZZ54xuiLkgz744AMkJSVh+/btOHz4MMaOHYsePXrg+PHjJmP/+ecfbNmyxWhvJAC4u7tj2bJluHz5MiIiIlCzZk0MHjwYn3/+OZYuXYpLly7h7NmzcHZ2xtSpU42mdXJywt27d3N/4p5AJeIWCkREREREJdGePXsQHx+PoKAgNabT6TBu3DjMnj3baA/S7du30alTJ7i6umLt2rWwt7cv0jzOnj2L5cuXG8XLli0LAKhZs6ZRvEaNGkanPD3Ix8fH6JYAALBs2TLExMTgwIEDsLGxUWOenp5Yv349evfubTKfixcv4ptvvsGJEyfUQ1lDQkKwZ88efPvtt/juu++Mxi9atAje3t54/vnn86x34cKF8PDwQNeuXfHCCy+gW7dusLe3R48ePfDRRx8ZjU1MTISvr2+e83vSsMkjIiIiIspF37590a5dO6NYx44d0bdvXwwcOFCNpaSkoGPHjtBoNNiwYYPJXrKHtWDBAoSFhSEkJMQoXrFiRQQEBODs2bNG8XPnzqFz5865zi80NBQ//fSTUezu3buwsbEx2mNp+Fv/wE25c05jGJeTra2t2Rt5L1q0CP369cuzAU5ISMC0adPU8wp1Oh0yMzMBAJmZmdDpdEbjT5w4kev5f08qHq5JRERERE+sO3fuICoqClFRUQCA6OhoREVFqXvBvL29Ubt2baN/9vb2KFOmDKpVqwbg/h68Dh06IDU1FQsWLEBKSgquXbuGa9eumTQkBVm2QUpKClauXInXX3/dZB6KomD8+PH4+uuvsWrVKly4cAEffvghzpw5Y3JIZE4dO3bEyZMnjfbmtW/fHklJSRg2bBhOnz6NkydPYuDAgbCzs0Pr1q0BALGxsahevTr+/PNPAED16tVRuXJlDB48GH/++ScuXryIWbNmYdu2bejWrZvRMnfs2IHo6Og88wKAUaNGYdy4cShXrhwAIDw8HD/++CNOnz6N+fPnIzw8XB179+5dREZGokOHDnnO84nzyK/zWQLxFgpERFSaFPcl9fnvyfr3pNm5c6cApjfp7t+/f67TPHgLhdzmAUCio6PVcS1btjSar6XLnjdvnjg5OeV5y7Dp06dL+fLlxdnZWZo0aSJ79uzJt/bGjRvLd999ZxTbunWrhIeHi7u7u3h6ekqbNm3kwIED6uPR0dECQHbu3KnGzp07Jy+88IL4+fmJs7OzPP300ya3VBARefnll6Vp06Z55rR582Zp2LCherNxEZHU1FTp0aOHuLm5Sdu2beX69evqY8uWLZNq1arlW6u1sLSPUUREHnNfWexSUlLg7u6uXtqWiIioJLPgWg9ERebJ2zJ8fCpWrIjJkycb3TqgOG3atAlvv/02Tpw4YXK4ZWnRsGFDjB49Gn369CnuVB4LS/sYnpNHRERERPSInTlzBm5ubujXr19xp6J65plncP78ecTGxhrdzqC0iI+Px0svvYSXX365uFMpcbgnj3vyiIiohOOePHqcnrwtQ6LSg3vyiIiIiMiqTJkypbhToCdMUd/r8HEpnQffEhERERERkVls8oiIiIiIiKwImzwiIiIiIiIrwiaPiIiIiIjIirDJIyIiIiIisiJs8oiIiIiIiKwImzwiIiIiIiIrwiaPiIiIiIjIirDJIyIiIiIisiJs8oiIiIiIiKwImzwiIiIiIiIrwiaPiIiIiIjIirDJIyIiIiIisiJs8oiIiIiIiKwImzwiIiIiIiIrwiaPiIiIiIjIirDJIyIiIiIisiJs8oiIiIiIiKwImzwiIiIiIiIrwiaPilWLFi2wbNmy4k6j0Bo0aIA1a9YUdxpkBbguEBERUVFhk0dFYs6cOQgODoajoyPCwsKwZ8+efKf59ddfce3aNfTu3VuNpaenY8SIEfDx8YGLiwuef/55/PPPPw+9fBHB5MmTERAQACcnJ7Rq1QonT540GjN27Fh4eXkhKCgIv/zyi9FjK1asQJcuXUyW++GHH2LChAnQ6/X55khPBq4LXBeIiIiKG5s8emjLly/H6NGj8f777+Po0aNo3rw5OnfujCtXruQ53ddff42BAwfCxib7bTh69GisXbsWv/zyC/bu3Ys7d+7gueeeg06ne6jlz5w5E19++SW++eYbHDp0CGXKlEH79u1x+/ZtAMDGjRuxbNkybN26FTNmzMDAgQNx8+ZNAMCtW7fw/vvv49tvvzVZ9rPPPovk5GRs2bKlQM8ZWSeuC1wXiIiISgJFRKS4k3jcUlJS4O7ujuTkZGi12uJOp9Rr1KgR6tWrh7lz56qxGjVqoFu3bpg+fbrZaW7cuAE/Pz8cP34ctWrVAgAkJyfD19cXP/74I3r16gUAuHr1KgIDA7Fp0yZ07NixUMsXEQQEBGD06NF49913AdzfS+Lv748ZM2Zg8ODBmDlzJo4cOaLutfD398evv/6KBg0a4M0330SNGjUwZswYs8sfOHAgdDodlixZUsBnjqwN1wWuC4+KohR3BvQkKclbhlOmTCnuFOgJM2nSpOJOwYilfQz35NFDycjIQGRkJDp06GAU79ChA/bv35/rdHv37oWzszNq1KihxiIjI5GZmWk0r4CAANSuXTvXeVmy/OjoaFy7ds1ojEajQcuWLdUxISEhOHz4MJKSkhAZGYm0tDRUrlwZe/fuxZEjRzBy5Mhca2nYsKFFh+SRdeO6wHWBiIiopGCTRw/lxo0b0Ol08Pf3N4r7+/vj2rVruU4XExMDf39/o8PTrl27BgcHB3h6elo8L0uWb/hvXmM6duyIV199FQ0aNMCAAQPwww8/wMXFBUOHDsW8efMwd+5cVKtWDeHh4SbnL5UrVw5XrlzhuUhPOK4LXBeIiIhKCrviToCsg/LAsUQiYhLLKS0tDY6OjhbNO795Wbr8/MZMnjwZkydPNvq7Xbt2sLe3x8cff4zjx4/j119/Rb9+/RAZGamOc3Jygl6vR3p6OpycnCyqiawX1wWuC0RERMWNe/Loofj4+MDW1tZk70J8fLzJ3oIHp0tKSjKKlSlTBhkZGSbxvOZlyfLLlCkDAAXK8cyZM1i6dCmmTZuGXbt2oUWLFvD19UXPnj1x5MgRpKSkqGMTExPh7OzMjdonHNcFrgtEREQlBZs8eigODg4ICwvDtm3bjOLbtm1D06ZNc50uNDQU165dM9qIDQsLg729vdG84uLicOLEiVznZcnyg4ODUaZMGaMxGRkZiIiIMDtfEcGbb76JWbNmwdXVFTqdDpmZmQCg/jfn4WgnTpxAvXr1cq2VngxcF7guEBERlRQloskr6H2lIiIiEBYWBkdHR1SqVAnffffdY8qUzBk7diy+//57LFy4EKdPn8aYMWNw5coVDBkyJNdpQkND4evri3379qkxd3d3vPbaaxg3bhx+//13HD16FK+++irq1KmDdu3aqePatm2Lb775xuLlK4qC0aNH49NPP8XatWtx4sQJDBgwAM7OzujTp49Jbv/3f/8HPz8/PP/88wCA8PBw7NixA3/88Qe++uor1KxZEx4eHur4PXv2mFzsgp5MXBe4LhAREZUExX5OnuG+TnPmzEF4eDjmzZuHzp0749SpUwgKCjIZHx0djWeeeQZvvPEGfvrpJ+zbtw9vvfUWfH198eKLLxZDBdSrVy/cvHkTU6dORVxcHGrXro1NmzahQoUKuU5ja2uLQYMGYenSpXjuuefU+FdffQU7Ozv07NkTaWlpaNu2LRYvXgxbW1t1zMWLF3Hjxo0CLf+dd95BWloa3nrrLSQlJaFRo0bYunUr3NzcjPK6fv06Pv30U6MrGDZs2BDjxo3Ds88+Cz8/P/zwww/qY7Gxsdi/fz9++umnwj15ZFW4LnBdICIiKgmK/T55Bb2v1LvvvosNGzbg9OnTamzIkCE4duwYDhw4YNEyeZ+8kuH69euoVasWIiMj89wILsnGjx+P5ORkzJ8/v7hToVKM6wLlh/fJo8eJ98kjysb75BVCYe4rdeDAAZPxHTt2xOHDh9VzRKh08Pf3x4IFC3DlypXiTqXQ/Pz8MG3atOJOg0o5rgtERERUlIr1cM3C3Ffq2rVrZsdnZWXhxo0bKFu2rMk06enpSE9PV/9OTk4GANy6dUu9aICiKFAUBSKCnDs384s/eD+ogsZtbGxM5l3QeGFzLwk1tWzZEjY2NkhOTi6VNQ0ePBgiglu3buU7vrTU9KS890paTa1atYKiKBa9l0piTYMHD4ZerzfK3xpfp+KqCVCgKAIgOy6SX9w4x4LHbQDIv/MvbFz5d/7m46ypZNZkWI1L4vp07949ED1Ot27dKlHfT7dv3wYAk1weVOzn5AEFv6+UufHm4gbTp083u3u/tB4WRURET57cvs9Lc7wk5VJU8ZKUS2Hjnp7mHyN6En322WfFnYJZt2/fhru7e66PF2uTV5j7SpUpU8bseDs7O3h7e5udZuLEiRg7dqz6t16vR2JiIry9vfNsJql0SElJQWBgIP7++2+eY0lPNK4LRNm4PhBl4/pgPUQEt2/fRkBAQJ7jirXJy3lfp+7du6vxbdu2oWvXrmanadKkCTZu3GgU27p1K+rXrw97e3uz02g0Gmg0GqNYzst+k3XQarX84CIC1wWinLg+EGXj+mAd8tqDZ1Ds98nL775OEydORL9+/dTxQ4YMweXLlzF27FicPn0aCxcuxIIFC/D2228XVwlEREREREQlRrGfk5fffZ3i4uKMrjgXHByMTZs2YcyYMfj2228REBCAr7/+mvfIIyIiIiIiQglo8gDgrbfewltvvWX2scWLF5vEWrZsiSNHjjzirKi00Gg0mDRpkskhuURPGq4LRNm4PhBl4/rw5Cn2m6ETERERERFR0Sn2c/KIiIiIiIio6LDJIyIiIiIisiJs8qzIrl27oCgKbt26BeD++YyFuVXEgAED0K1btyLNjYpeYV9fKjhFUbBu3briToOKSExMDBRFQVRUVHGnUupwXbA+rVq1wujRo4s7DSJVxYoVMXv27OJOo9Rjk1fK7N+/H7a2tujUqdNDz8saNnSe5A+CXr164dy5c8WdRpEZMGAAFEUx+VcU73VLTZ48GXXr1i01830YT/KPOYGBgerVnEsia14XSqIHfyB90qxZswbTpk0r7jRKJMO6+NlnnxnF161bB0VRHtlyn/QfcQ8dOoQ333yzuNMo9djklTILFy7EiBEjsHfvXqNbSxS3jIyM4k4hVzqdDnq9vrjTKFKZmZlwcnKCn59fcadSpDp16oS4uDijfz///HNxp/VEy8zMLO4UilRGRgZsbW1RpkwZ2NmViAtMm8V1oeQpyd9zhWFYt728vODm5lbM2ZRcjo6OmDFjBpKSkoo7FRPW9p401OPr6wtnZ+dizqb0Y5NXiqSmpmLFihUYOnQonnvuObO3lyiI4OBgAEBoaCgURUGrVq2MHv/iiy9QtmxZeHt7Y9iwYUYbexUrVsTHH3+MAQMGwN3dHW+88QaA+3saW7RoAScnJwQGBmLkyJFITU1Vp8vIyMA777yDcuXKwcXFBY0aNcKuXbvyzHPy5MkICgqCRqNBQEAARo4cCeD+ISaXL1/GmDFj1F+6gexfwH799VfUrFkTGo0Gly9fznMZhr0aedVs7jAlDw8P9XUw7BldsWIFmjdvDicnJzRo0ADnzp3DoUOHUL9+fbi6uqJTp05ISEgwms+iRYtQo0YNODo6onr16pgzZ476WM75tmrVCo6Ojvjpp5/M/tK3YcMG1K9fH46OjvDx8cELL7yQZ90ljUajQZkyZYz+eXp6Arj/a7uDgwP27Nmjjp81axZ8fHwQFxcHANi8eTOaNWsGDw8PeHt747nnnsPFixeNlvHPP/+gd+/e8PLygouLC+rXr4+DBw9i8eLFmDJlCo4dO6a+n8ytY23atMHw4cONYjdv3oRGo8GOHTtMxuc233HjxqFLly7quNmzZ0NRFPz2229qrFq1apg3bx4AQK/XY+rUqShfvjw0Gg3q1q2LzZs35/l8rlq1CnXq1IGTkxO8vb3Rrl07pKamYvLkyfjhhx+wfv16Naddu3bl+l7Li+F9uGXLFtSoUUN9jxteE8D84WDdunXDgAED1L8Nnyn9+vWDq6srKlSogPXr1yMhIQFdu3aFq6sr6tSpg8OHDxvNJ7/PHHOfVeaOYjh58iSeffZZaLVauLm5oXnz5ibvncfJGtcFg7lz5+Kpp56Cg4MDqlWrhh9//NHocUVR8P3336N79+5wdnZGlSpVsGHDBqMxp06dwjPPPANXV1f4+/ujb9++uHHjRq7LvHz5Mrp06QJPT0+4uLigVq1a2LRpE2JiYtC6dWsAgKenJxRFUd+XrVq1wvDhwzF27Fj4+Pigffv2uc7f0tzNfW4/uFfIsBd14cKFCAoKgqurK4YOHQqdToeZM2eiTJky8PPzwyeffGI0n+TkZLz55pvw8/ODVqtFmzZtcOzYMbPzrVSpEjQaDUTEZP1MT0/HO++8g8DAQGg0GlSpUgULFizIt3Zr1a5dO5QpUwbTp0/Pc9zq1atRq1YtaDQaVKxYEbNmzcpz/LFjx9C6dWu4ublBq9UiLCwMhw8fxq5duzBw4EAkJyer69/kyZMB5L7tlRvDZ92aNWvQunVrODs7IyQkBAcOHFDHmNtrP3v2bFSsWFH927CN9Omnn8Lf3x8eHh6YMmUKsrKyMH78eHh5eaF8+fJYuHCh0XxiY2PRq1cveHp6wtvbG127dkVMTIzJfKdPn46AgABUrVpVrTPnUVq3bt3Cm2++CX9/fzg6OqJ27dr49ddf86ydAAiVGgsWLJD69euLiMjGjRulYsWKotfr1cd37twpACQpKUlERBYtWiTu7u65zu/PP/8UALJ9+3aJi4uTmzdviohI//79RavVypAhQ+T06dOyceNGcXZ2lvnz56vTVqhQQbRarXz++edy/vx5OX/+vPz111/i6uoqX331lZw7d0727dsnoaGhMmDAAHW6Pn36SNOmTWX37t1y4cIF+fzzz0Wj0ci5c+fM5rhy5UrRarWyadMmuXz5shw8eFDN4+bNm1K+fHmZOnWqxMXFSVxcnFq3vb29NG3aVPbt2ydnzpyRO3fu5PncWlIzAFm7dq3RdO7u7rJo0SIREYmOjhYAUr16ddm8ebOcOnVKGjduLPXq1ZNWrVrJ3r175ciRI1K5cmUZMmSIOo/58+dL2bJlZfXq1XLp0iVZvXq1eHl5yeLFi43mW7FiRXVMbGysyev766+/iq2trXz00Udy6tQpiYqKkk8++STPukuS/v37S9euXfMcM378eKlQoYLcunVLoqKiRKPRyJo1a9THV61aJatXr5Zz587J0aNHpUuXLlKnTh3R6XQiInL79m2pVKmSNG/eXPbs2SPnz5+X5cuXy/79++Xu3bsybtw4qVWrlvp+unv3rogYv/ZLly4VT09PuXfvnrrc//znPybro0Fu892wYYO4u7uruXXr1k18fHxk/PjxIiISFxcnAOT06dMiIvLll1+KVquVn3/+Wc6cOSPvvPOO2Nvb57ruXL16Vezs7OTLL7+U6Oho+euvv+Tbb7+V27dvy+3bt6Vnz57SqVMnNaf09PRc32t5Maxv7dq1k0OHDklkZKTUqFFD+vTpo45p2bKljBo1ymi6rl27Sv/+/dW/K1SoIF5eXvLdd9/JuXPnZOjQoeLm5iadOnWSFStWyNmzZ6Vbt25So0YN9Xm25DPH3GeVoc6jR4+KiMg///wjXl5e8sILL8ihQ4fk7NmzsnDhQjlz5kyetT8q1rouiIisWbNG7O3t5dtvv5WzZ8/KrFmzxNbWVnbs2KGOASDly5eXZcuWyfnz52XkyJHi6uqqfkddvXpVfHx8ZOLEiXL69Gk5cuSItG/fXlq3bp3r8/Xss89K+/bt5a+//pKLFy/Kxo0bJSIiQrKysmT16tUCQM6ePStxcXFy69YtEbn/vnV1dZXx48fLmTNn1HUxL/nlbu57ee3atZJzc2zSpEni6uoqL730kpw8eVI2bNggDg4O0rFjRxkxYoScOXNGFi5cKADkwIEDIiKi1+slPDxcunTpIocOHZJz587JuHHjxNvbW132pEmTxMXFRTp27ChHjhyRY8eOiV6vN1k/e/bsKYGBgbJmzRq5ePGibN++XX755Zd8a7dGhnVxzZo14ujoKH///beImL5mhw8fFhsbG5k6daqcPXtWFi1aJE5OTur2gTm1atWSV199VU6fPi3nzp2TFStWSFRUlKSnp8vs2bNFq9Wq69/t27dFxPznWV5ybpf8+uuvcvbsWXnppZekQoUKkpmZKSL33xchISFG03311VdSoUIFo+fBzc1Nhg0bJmfOnJEFCxYIAOnYsaN88skncu7cOZk2bZrY29vLlStXREQkNTVVqlSpIoMGDZK//vpLTp06JX369JFq1apJenq6Ol9XV1fp27evnDhxQo4fP67W+dVXX4mIiE6nk8aNG0utWrVk69at6vq7adOmvF88EjZ5pUjTpk1l9uzZIiKSmZkpPj4+sm3bNvXxgjZ5D27oGPTv318qVKggWVlZaqxHjx7Sq1cv9e8KFSpIt27djKbr27evvPnmm0axPXv2iI2NjaSlpcmFCxdEURSTjca2bdvKxIkTzeY4a9YsqVq1qmRkZJh9POcHgcGiRYsEgERFRZmdxhxLara0yfv+++/Vx3/++WcBIL///rsamz59ulSrVk39OzAwUJYtW2Y032nTpkmTJk2M5mt47XPWmfP1bdKkibzyyisW11zS9O/fX2xtbcXFxcXo39SpU9Ux6enpEhoaKj179pRatWrJ66+/nuc84+PjBYD6xTFv3jxxc3NTN3oeZO7LTv6/vTuPiuLI4wD+HQYGBxQiiAiIDJIMcgyXuB5EQIkS3cR4RkUXFfEAXF0luuhi8CAg8Mi+qJAIGojx3lXzVLxYWZAgokgGNTEIMhjwRNFVdokCU/vHPnppZgYGwSj4+7w3jzd9/Lq6pqrpqu6uZvzf/tdff2UmJiZs//793Hw3Nze2bt06jelQF/fx48dMR0eHFRYWMqVSyUxNTVlsbCwbMmQIY4yxPXv2MHNzc255S0tLlUb7kCFDWGhoqNptXrp0iQFgFRUVauera0hoKmutaapvZWVl3LSkpCRe2rVt5M2ePZv73tTIXbt2LTctPz+fAeA6dNo65jTFbXmsannsW716NbO1tdV4nPmtdee6MGLECLZgwQLetGnTprHx48fzthEZGcl9r62tZQKBgJ04cYIxxtjatWvZ2LFjeTEqKyu5hpo6MplMY7pa/u9s4uPjw9zc3DTuizptpV3bRp6BgQF78uQJN83f359JJBKukc4YY/b29iw2NpYxxtiZM2eYkZERr8HNGGN2dnZs27ZtXFw9PT12//59lf1sqp8lJSUMAO/c4k3W/Dg5bNgwFhQUxBhT/c0CAgLYmDFjeOuuXLmSOTo6aozdq1cvrjO3JU3nb+qOZ61Rd17y448/8joQtW3k2djYqJS/kSNHct8bGhqYoaEh27t3L2Psfxcm7O3teR0+z549Y2KxmJ06dYqLa25uzjX6mu9n07ndqVOnmI6Ojsa6TTSj2zW7iJKSEly4cAEzZswAAOjq6mL69Okql8Y7i5OTE4RCIffdwsIC9+/f5y3j6enJ+37p0iWkp6ejZ8+e3Mff3x9KpRIKhQJFRUVgjEEqlfKWycnJ0Xhb1LRp01BXV4eBAwdiwYIFOHz4MBoaGtpMv0gkgouLS6fvszaab9fc3BwAIJPJeNOa4lZXV6OyshLz58/n5Ul0dLRKnrTM75bkcjn8/Pzand7XyahRoyCXy3mfsLAwbr5IJMKuXbtw8OBB1NXVqQy6c+PGDQQEBGDgwIEwMjLibkluen5VLpfD3d0dJiYmL5xGfX19zJ49m6t7crkcxcXFvFsPtWFsbAw3NzdkZ2fjypUr0NHRwaJFi1BcXIynT58iOzsbPj4+AIAnT57g9u3b8PLy4sXw8vLCtWvX1MZ3dXWFn58fZDIZpk2bhtTUVK2fKWmrrLVkYGAAOzs77vvLrDsAuNhtHXO03R+5XI6RI0dCT0+v3Wl+WbprXbh27ZpW5bh5WTA0NESvXr14v/s///lP3u8+aNAgbr/VWbp0KaKjo+Hl5YWoqChcvnxZq31sb11oK+3akkgkvOfkzM3N4ejoCB0dHd605nlSW1sLU1NTXr4oFApentjY2MDMzEzjduVyOYRCIXfsIf8XFxeHb775Bj/99JPKPE3lurS0FI2NjWrjrVixAsHBwXjvvfewadMmrW8P72iZtLCwAIB2l0knJyeV8tf8+CwUCmFqasork2VlZejVqxdXHk1MTPDrr7/y9lUmk0EkEmncrlwuR//+/blbOYn2Xt+nzgnPjh070NDQACsrK24aYwx6enp49OgR96xGZ2l5siMQCFQGLzE0NOR9VyqVWLRoEffMXHMDBgzA5cuXIRQKcenSJV5jCgB69uypNh3W1tYoKSlBZmYm/vGPfyA0NBQJCQnIyclp9YRMLBa3e+SrtvZZIBCAMcZbRt2gFM3jNKWh5bSmuE1/U1NTMXToUF6clnnUMr9bEovFrc7vCgwNDfH222+3usy5c+cAADU1NaipqeHly4cffghra2ukpqbC0tISSqUSzs7O3MPcnZVHwcHBcHNzQ1VVFb7++mv4+fnBxsam3XF8fX2556t8fHzQu3dvODk5IS8vD9nZ2SrPsbUs04wxjeVcKBQiMzMT586dw+nTp7Flyxb85S9/QUFBAXfCr0lbZa0ldXWneV3R0dHptLoDgFd/WjvmNOmKdac71wVtynFrx2OlUokPP/wQcXFxKrGbTmDVpdPf3x8ZGRk4ffo0YmNjkZiYiD/+8Y+tprW9daGttL9IXWiK0VaeWFhYqH3GvfkzgF2xLrwuvL294e/vjzVr1qh0ZKgrwy1/55bWrVuHgIAAZGRk4MSJE4iKisK+ffswadKkVtfraJlseRx9mWVy8ODB2L17t0qs5h0NVCZfHrqS1wU0NDRg586dSExM5PXqFhcXw8bGRm0F0kZTz4mmXqb28vDwwI8//oi3335b5SMSieDu7o7Gxkbcv39fZX6/fv00xhWLxZgwYQI2b96M7Oxs5Ofn48qVK9w+dFb622JmZsYbTKK0tBT/+c9/OhTT3NwcVlZWKC8vV8mTtk7EW3JxccGZM2c6lJ7X3Y0bN7B8+XKkpqZi2LBhCAwM5P6hPHz4ENeuXUNkZCT8/Pzg4OCgcuXKxcUFcrkcNTU1auNrW55kMhk8PT2RmpqKPXv2ICgoqNXlNcX19fVFbm4usrKyuIGPfHx8sG/fPly/fp3rTTcyMoKlpSW+//573vrnzp2Dg4ODxu0KBAJ4eXlh/fr1+OGHHyASiXD48OF27WtnaFl3GhsbcfXq1Q7HbeuYoy0XFxfk5uZ2qZFEu2pdcHBwaHc5bqnpd5dIJCq/e2snjNbW1li8eDEOHTqE8PBwpKamAuj8/4WtMTMzw9OnT3mDA3XGa4w8PDxw9+5d6OrqquRJnz59tI4jk8mgVCqRk5PT4TR1R5s2bcLRo0e5DpYmjo6Oasu1VCpV6bBtTiqVYvny5Th9+jQmT56MtLQ0AL/98fnu3bu8hl5nlcnS0lL07dtXpUwaGxtrHcfFxQVVVVXd6pVRvxVq5HUBx44dw6NHjzB//nw4OzvzPlOnTn3hUa/69u0LsViMkydP4t69e/jXv/7VoXT++c9/Rn5+PsLCwiCXy1FaWoojR45wPaVSqRSzZs1CYGAgDh06BIVCgYsXLyIuLg7Hjx9XGzM9PR07duzA1atXUV5ejm+//RZisZjrKZZIJDh79ixu3brV6shqnWH06NHYunUrioqKUFhYiMWLF3fK7V3r1q1DbGwsvvjiC1y/fh1XrlxBWloaPv/883bFiYqKwt69exEVFYVr167hypUriI+P73D6fkvPnj3D3bt3eZ+m37WxsRF/+MMfMHbsWMybNw9paWm4evUqN4JZ0+hdKSkpKCsrQ1ZWFlasWMGLP3PmTPTr1w8TJ05EXl4eysvLcfDgQW6kMYlEAoVCAblcjgcPHuDZs2ca0xocHIxNmzahsbGxzZ5XTXG9vb3x9OlTHD16lGvk+fr6YteuXTAzM4OjoyMXY+XKlYiLi8P+/ftRUlKCiIgIyOVyLFu2TO02CwoKEBMTg8LCQvzyyy84dOgQqquruZNpiUSCy5cvo6SkBA8ePHipDZzRo0cjIyMDGRkZ+PnnnxEaGtop7yRr65ijrSVLluDJkyeYMWMGCgsLUVpaim+//RYlJSUdTuOL6q51YeXKlUhPT8dXX32F0tJSfP755zh06BA++eQTrfMmLCwMNTU1mDlzJi5cuIDy8nKcPn0aQUFBGk+M//SnP+HUqVPcowNZWVlcXbCxsYFAIMCxY8dQXV2N2tpardPSXkOHDoWBgQHWrFmDsrIy7Nmzp8MjZQP/GwFy+PDhmDhxIk6dOoWKigqcO3cOkZGRKiPStkYikWDOnDkICgrCd999B4VCgezsbBw4cKDDaewOZDIZZs2ahS1btvCmh4eH48yZM9i4cSOuX7+Ob775Blu3btVYruvq6rBkyRJkZ2fj5s2byMvLw8WLF3nH59raWpw5cwYPHjzocIdya3x9fVFdXY34+HjcuHEDSUlJOHHiRIfjzpo1C3369MFHH32E3NxcKBQK5OTkYNmyZaiqqtI6jo+PD7y9vTFlyhRkZmZCoVDgxIkTbY4uTaiR1yXs2LED7733ntqejylTpkAul6OoqKjdcXV1dbF582Zs27YNlpaW+OijjzqUThcXF+Tk5KC0tBQjR46Eu7s71q5dy7t9Ji0tDYGBgQgPD4e9vT0mTJiAgoICWFtbq4351ltvITU1FV5eXtyVqqNHj8LU1BQAsGHDBlRUVMDOzq7V5ww6Q2JiIqytreHt7Y2AgAB88sknnfIel+DgYGzfvh3p6emQyWTw8fFBenp6u6/k+fr64m9/+xuOHDkCNzc3jB49GgUFBR1O32/p5MmTsLCw4H3effddAMBnn32GiooKpKSkAAD69euH7du3IzIyEnK5HDo6Oti3bx8uXboEZ2dnLF++HAkJCbz4IpEIp0+fRt++fTF+/HjIZDJs2rSJ62mdMmUK3n//fYwaNQpmZmatvpds5syZ0NXVRUBAAHr06NHqfmmKa2xszD0X1dSgGzlyJJRKpcozMUuXLkV4eDjCw8Mhk8lw8uRJHDlyBO+8847abRoZGeHs2bMYP348pFIpIiMjkZiYiHHjxgEAFixYAHt7e3h6esLMzAx5eXmt7kNHBAUFYc6cOQgMDISPjw9sbW25Yes7QptjjjZMTU2RlZWF2tpa+Pj4YPDgwUhNTX2lz+h117owceJEfPHFF0hISICTkxO2bduGtLQ0lVf4tMbS0hJ5eXlobGyEv78/nJ2dsWzZMhgbG/OeGWqusbERYWFhcHBwwPvvvw97e3vuVTVWVlZYv349IiIiYG5urvJaiM5kYmKCXbt24fjx45DJZNi7dy83PH5HCAQCHD9+HN7e3ggKCoJUKsWMGTNQUVHBPcuqrS+//BJTp05FaGgoBg0ahAULFvCuPL7pNm7cqHJ7o4eHBw4cOIB9+/bB2dkZn376KTZs2KDx+VShUIiHDx8iMDAQUqkUH3/8McaNG4f169cDAEaMGIHFixdj+vTpMDMze6kdtg4ODkhOTkZSUhJcXV1x4cKFdnW6aGJgYICzZ89iwIABmDx5MhwcHBAUFIS6ujoYGRm1K9bBgwcxZMgQzJw5E46Ojli1atVvdqWzKxOwtm4aJoQQoqKyshISiQQXL16Eh4fHq04OIa8M1QVCCHn9UCOPEELaob6+Hnfu3EFERAR3mw0hbyKqC4QQ8vqi2zXJG6H5kNItP7m5ua86eaQLycvLg42NDS5duoSvvvrqVSfnpRs3bpzGuhMTE/Oqk0deoTetLuzevVtjXXBycnrVySNvoJiYGI1lsunWfPLmoit55I1QVlamcZ6VlRUN0UuIBrdu3UJdXZ3aeSYmJh16zxohXcnTp09x7949tfP09PRe6DUqhHRE0+tT1BGLxbzXbpE3DzXyCCGEEEIIIaQbods1CSGEEEIIIaQboUYeIYQQQgghhHQj1MgjhBBCCCGEkG6EGnmEEEIIIYQQ0o1QI48QQki3cfnyZcybNw+2trbo0aMHevbsCQ8PD8THx2schU6T48ePY926dS8noYQQQshLRKNrEkII6RZSU1MRGhoKe3t7hIaGwtHREfX19SgsLERqaipcXV1x+PBhreMtWbIESUlJoH+ThBBCuhrdV50AQgghpKPy8/MREhKCMWPG4LvvvoO+vj43b8yYMQgPD8fJkydfYQpfnsbGRjQ0NPD2mRBCyJuNbtckhBDS5cXExEAgECAlJUVtY0ckEmHChAkAgP3792Ps2LGwsLCAWCyGg4MDIiIi8O9//5tbfu7cuUhKSgIACAQC7lNRUQEAYIwhOTkZbm5uEIvF6N27N6ZOnYry8nLedhljiImJgY2NDXr06AFPT09kZmbC19cXvr6+vGV/+eUXzJ49G3379oW+vj4cHByQmJgIpVLJLVNRUQGBQID4+HhER0fD1tYW+vr6yMzMxFtvvYVFixap7HtFRQWEQiESEhJeKG8JIYR0PXS7JiGEkC6tsbERRkZGkMlkOH/+fJvLR0dHo2fPnpBKpTA0NMTPP/+MuLg4SCQSZGVlAQBu3LiBiIgI/P3vf0d+fj63rru7O/T19bFw4UKkp6dj6dKlGDNmDGpqarBhwwY8evQIxcXFMDc3BwCsWbMGsbGxWLhwISZPnozKykps3LgR9fX1kEqlyM7OBgBUV1fD3d0dz58/x8aNGyGRSHDs2DFs3boVISEhSE5OBvC/BputrS2srKwglUoRFhYGIyMjvPPOO9i8eTNSUlJw69YtGBsbc2letWoVtmzZgqqqKpiamnZWthNCCHmdMUIIIaQLu3v3LgPAZsyY0e51lUolq6+vZzk5OQwAKy4u5uaFhYUxdf8m8/PzGQCWmJjIm15ZWcnEYjFbtWoVY4yxmpoapq+vz6ZPn652fR8fH25aREQEA8AKCgp4y4aEhDCBQMBKSkoYY4wpFAoGgNnZ2bHnz5/zlr1x4wbT0dFhf/3rX7lpdXV1zNTUlM2bN0/7TCGEENLl0e2ahBBC3ijl5eUICAhAv379IBQKoaenBx8fHwDAtWvX2lz/2LFjEAgEmD17NhoaGrhPv3794Orqyl2dO3/+PJ49e4aPP/6Yt/6wYcMgkUh407KysuDo6Ijf/e53vOlz584FY4y7wthkwoQJ0NPT400bOHAgPvjgAyQnJ3ODxezZswcPHz7EkiVL2twvQggh3QcNvEIIIaRL69OnDwwMDKBQKNpctra2FiNHjkSPHj0QHR0NqVQKAwMDVFZWYvLkyairq2szxr1798AY427JbGngwIEAgIcPHwKA2uVaTnv48KFKww8ALC0tebGaWFhYqN32smXL4Ofnh8zMTIwdOxZJSUkYPnw4PDw8Wt8pQggh3Qo18gghhHRpQqEQfn5+OHHiBKqqqtC/f3+Ny2ZlZeH27dvIzs7mrt4BwOPHj7XeXp8+fSAQCJCbm6t2kJemaU3Pv927d09lmbt37/Iadaamprhz547Kcrdv3+a22ZxAIFCbttGjR8PZ2Rlbt25Fz549UVRUhF27dmm3Y4QQQroNul2TEEJIl7d69WowxrBgwQI8f/5cZX59fT2OHj3KNY5aNs62bdumsk7TMi2v7n3wwQdgjOHWrVvw9PRU+chkMgDA0KFDoa+vj/379/PWP3/+PG7evMmb5ufnh59++glFRUW86Tt37oRAIMCoUaO0yQYAwNKlS5GRkYHVq1fD3Nwc06ZN03pdQggh3QNdySOEENLlDR8+HF9++SVCQ0MxePBghISEwMnJCfX19fjhhx+QkpICZ2dnbN++Hb1798bixYsRFRUFPT097N69G8XFxSoxmxprcXFxGDduHIRCIVxcXODl5YWFCxdi3rx5KCwshLe3NwwNDXHnzh18//33kMlkCAkJgYmJCVasWIHY2Fj07t0bkyZNQlVVFdavXw8LCwvo6Py/n3X58uXYuXMnfv/732PDhg2wsbFBRkYGkpOTERISAqlUqnVezJ49G6tXr8bZs2cRGRkJkUjU8QwmhBDStbzSYV8IIYSQTiSXy9mcOXPYgAEDmEgkYoaGhszd3Z19+umn7P79+4wxxs6dO8eGDx/ODAwMmJmZGQsODmZFRUUMAEtLS+NiPXv2jAUHBzMzMzMmEAgYAKZQKLj5X3/9NRs6dCgzNDRkYrGY2dnZscDAQFZYWMgto1QqWXR0NOvfvz8TiUTMxcWFHTt2jLm6urJJkybx0n7z5k0WEBDATE1NmZ6eHrO3t2cJCQmssbGRW6ZpdM2EhIRW82Hu3LlMV1eXVVVVdSA3CSGEdFX0njxCCCHkN6RQKDBo0CBERUVhzZo1nR7/+fPnkEgkePfdd3HgwIFOj08IIeT1R7drEkIIIS9JcXEx9u7dixEjRsDIyAglJSWIj4+HkZER5s+f36nbqq6uRklJCdLS0nDv3j1ERER0anxCCCFdBzXyCCGEkJfE0NAQhYWF2LFjBx4/fgxjY2P4+vris88+0/gKhheVkZGBefPmwcLCAsnJyfTaBEIIeYPR7ZqEEEIIIYQQ0o3QKxQIIYQQQgghpBuhRh4hhBBCCCGEdCPUyCOEEEIIIYSQboQaeYQQQgghhBDSjVAjjxBCCCGEEEK6EWrkEUIIIYQQQkg3Qo08QgghhBBCCOlGqJFHCCGEEEIIId0INfIIIYQQQgghpBv5L5hIwnt8rg8PAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 900x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\"\"\" 以「str_numeric（字串且可安全轉為數字）」作為 'float' 的等義條件，\n",
    "跨檔逐列檢查 sponvt、vti、vte 三欄中各有幾個 str_numeric，\n",
    "並彙總為四類後畫出條狀圖（英文圖表；紅/綠/藍/灰；標註數量與占比）。\n",
    "\n",
    "注意：\n",
    "- 僅當值為「字串」且內容符合純數字格式（含正負號、小數、科學記號）才視為 str_numeric\n",
    "- 排除 true/false（含字串）、空字串、NA/null、含底線（如 \"0_0\"）、含逗號（如 \"1,234\"）\n",
    "- 不把實際的 float 或 int 視為 'float'；此處定義嚴格等於「str_numeric」\n",
    "\"\"\"\n",
    "\n",
    "import os\n",
    "import re\n",
    "import glob\n",
    "from collections import Counter\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# =========================\n",
    "# 使用者可調整參數\n",
    "# =========================\n",
    "DATA_DIR     = \"/home/jovyan/1010/data_new/bling_sponvt_ok_v3\"  # 目標資料夾\n",
    "FILE_PATTERN = \"*.csv\"\n",
    "COLS         = [\"sponvt\", \"vti\", \"vte\"]                         # 需要檢查的欄位\n",
    "\n",
    "# =========================\n",
    "# 嚴謹的「數字字串」判定（str_numeric）\n",
    "# =========================\n",
    "# 支援：±號、小數、科學記號；排除底線/逗號/空字串/NA/true/false\n",
    "NUMERIC_RE   = re.compile(r\"\"\"\n",
    "    ^[ \\t]*                 # 前導空白\n",
    "    [+\\-]?                  # 正負號\n",
    "    (?:\n",
    "        (?:\\d+\\.\\d*|\\.\\d+|\\d+)    # 整數或小數\n",
    "    )\n",
    "    (?:[eE][+\\-]?\\d+)?      # 科學記號\n",
    "    [ \\t]*$                 # 後綴空白\n",
    "\"\"\", re.VERBOSE)\n",
    "BAD_TOKENS   = {\"\", \"na\", \"n/a\", \"null\", \"none\"}\n",
    "TRUE_TOKENS  = {\"true\"}\n",
    "FALSE_TOKENS = {\"false\"}\n",
    "\n",
    "def is_str_numeric(s: object) -> bool:\n",
    "    \"\"\"僅把『字串、且內容可安全解析為數字』視為 True。其餘一律 False。\"\"\"\n",
    "    if not isinstance(s, str):\n",
    "        return False\n",
    "    t = s.strip().lower()\n",
    "    if t in BAD_TOKENS or t in TRUE_TOKENS or t in FALSE_TOKENS:\n",
    "        return False\n",
    "    if \"_\" in t or \",\" in t:\n",
    "        return False\n",
    "    return bool(NUMERIC_RE.match(t))\n",
    "\n",
    "# =========================\n",
    "# 主流程：跨檔逐列判定三欄的 str_numeric 數量並彙總\n",
    "# =========================\n",
    "category_counter = Counter()\n",
    "total_rows = 0\n",
    "files = sorted(glob.glob(os.path.join(DATA_DIR, FILE_PATTERN)))\n",
    "\n",
    "for fp in files:\n",
    "    try:\n",
    "        # 以 object 讀入，保留原生型態（確保「字串」不被自動轉數字）\n",
    "        df = pd.read_csv(fp, dtype=object, low_memory=False)\n",
    "        df.columns = [c.strip().lower() for c in df.columns]\n",
    "    except Exception as e:\n",
    "        print(f\"[WARN] 讀取失敗，略過：{fp} -> {e}\")\n",
    "        continue\n",
    "\n",
    "    n = len(df)\n",
    "    if n == 0:\n",
    "        continue\n",
    "    total_rows += n\n",
    "\n",
    "    # 針對三欄建立「是否為 str_numeric」布林遮罩；缺欄補 False\n",
    "    masks = []\n",
    "    for c in COLS:\n",
    "        if c in df.columns:\n",
    "            masks.append(df[c].apply(is_str_numeric))\n",
    "        else:\n",
    "            masks.append(pd.Series([False]*n, index=df.index))\n",
    "\n",
    "    # 每列三欄的 str_numeric 計數（True=1）\n",
    "    k = masks[0] + masks[1] + masks[2]  # 逐列相加，範圍 0..3\n",
    "\n",
    "    # 依 k 值彙總四類\n",
    "    category_counter[\"All three str_numeric\"]   += int((k == 3).sum())\n",
    "    category_counter[\"Exactly two str_numeric\"] += int((k == 2).sum())\n",
    "    category_counter[\"Exactly one str_numeric\"] += int((k == 1).sum())\n",
    "    category_counter[\"No str_numeric\"]          += int((k == 0).sum())\n",
    "\n",
    "# =========================\n",
    "# 繪圖：英文圖表；紅/綠/藍/灰；標註數量與占比\n",
    "# =========================\n",
    "labels   = [\"All three str_numeric\", \"Exactly two str_numeric\", \"Exactly one str_numeric\", \"No str_numeric\"]\n",
    "counts   = [category_counter[l] for l in labels]\n",
    "percents = [(c / total_rows * 100.0) if total_rows else 0.0 for c in counts]\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(9, 5))\n",
    "colors = [\"red\", \"green\", \"blue\", \"gray\"]  # 三類紅綠藍，最後灰色\n",
    "bars = ax.bar(labels, counts, color=colors)\n",
    "\n",
    "# 在每個長條上標註 數量與占比\n",
    "for rect, c, p in zip(bars, counts, percents):\n",
    "    ax.text(rect.get_x() + rect.get_width()/2.0, rect.get_height(),\n",
    "            f\"{c:,} ({p:.2f}%)\",\n",
    "            ha=\"center\", va=\"bottom\", fontsize=10)\n",
    "\n",
    "ax.set_title(\"Row Categories by # of str_numeric Columns (sponvt, vti, vte)\", fontsize=14)\n",
    "ax.set_xlabel(\"Category\", fontsize=12)\n",
    "ax.set_ylabel(\"Row Count\", fontsize=12)\n",
    "ax.set_ylim(0, max(counts) * 1.15 if counts else 1)\n",
    "ax.grid(axis=\"y\", linestyle=\"--\", alpha=0.3)\n",
    "plt.tight_layout()\n",
    "\n",
    "# =========================\n",
    "# 終端彙總表（方便對帳）\n",
    "# =========================\n",
    "print(\"============================================================\")\n",
    "print(f\"Scanned files: {len(files)}\")\n",
    "print(f\"Total rows   : {total_rows:,}\")\n",
    "print(\"------------------------------------------------------------\")\n",
    "print(f\"{'Category':<28}{'Count':>12}{'Percent(%)':>14}\")\n",
    "print(\"------------------------------------------------------------\")\n",
    "for l, c, p in zip(labels, counts, percents):\n",
    "    print(f\"{l:<28}{c:>12,}{p:>14.6f}\")\n",
    "print(\"============================================================\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "id": "28616f9f-3e31-4531-8a91-86404a83866e",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "============================================================\n",
      "Scanned files: 122\n",
      "Total rows   : 1,602,476\n",
      "------------------------------------------------------------\n",
      "Category                     Count    Percent(%)\n",
      "------------------------------------------------------------\n",
      "All three float             73,889      4.610927\n",
      "Exactly two float          168,434     10.510859\n",
      "Exactly one float        1,241,494     77.473485\n",
      "No float                   118,659      7.404729\n",
      "============================================================\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 900x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\"\"\" 跨檔統計 /home/jovyan/1010/data_new/bling_spomvy_ok_v1 下所有 CSV，\n",
    "以每一筆資料（row）為單位，檢查 sponvt、vti、vte 三欄「是否為 float 型態」，\n",
    "並將每筆資料歸類為以下四類後彙總與繪圖（英文圖表）：\n",
    "    1) All three float        （三欄皆為 float）\n",
    "    2) Exactly two float      （恰有兩欄為 float）\n",
    "    3) Exactly one float      （恰有一欄為 float）\n",
    "    4) No float               （三欄皆非 float）\n",
    "\n",
    "需求：\n",
    "- 條狀圖以「紅 / 綠 / 藍」對前三類上色，第四類以灰色（避免誤導）\n",
    "- 每個長條上方標註「數量（counts）」與「占比（%）」\n",
    "- 程式碼包含中文註解；圖表文字使用英文\n",
    "- 只提供程式碼，不執行\n",
    "\n",
    "注意：\n",
    "- 僅以 Python 物件型別判定 float（isinstance(x, float) 或 numpy.floating）\n",
    "- 欄位缺失或空值一律視為「非 float」\n",
    "\"\"\"\n",
    "\n",
    "import os\n",
    "import glob\n",
    "from collections import Counter\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# =========================\n",
    "# 使用者可調整參數\n",
    "# =========================\n",
    "DATA_DIR = \"/home/jovyan/1010/data_new/bling_sponvt_ok_v3\"  # 目標資料夾\n",
    "FILE_PATTERN = \"*.csv\"\n",
    "TARGET_COLS = [\"sponvt\", \"vti\", \"vte\"]\n",
    "\n",
    "# =========================\n",
    "# 小工具函式\n",
    "# =========================\n",
    "def is_float_value(x) -> bool:\n",
    "    \"\"\"判定單一值是否為 float 型態（含 numpy 浮點型）。\"\"\"\n",
    "    return isinstance(x, float) or isinstance(x, np.floating)\n",
    "\n",
    "def series_is_float_mask(s: pd.Series) -> pd.Series:\n",
    "    \"\"\"\n",
    "    回傳布林遮罩，表示該欄位每列是否為「float 型態」。\n",
    "    - 缺失、字串、整數等皆為 False\n",
    "    - 僅純浮點型為 True\n",
    "    \"\"\"\n",
    "    # 若 Series 不存在，呼叫端會以 False 序列代替\n",
    "    return s.apply(is_float_value)\n",
    "\n",
    "# =========================\n",
    "# 主流程：跨檔彙總各類別計數\n",
    "# =========================\n",
    "category_counter = Counter()\n",
    "total_rows = 0\n",
    "\n",
    "file_list = sorted(glob.glob(os.path.join(DATA_DIR, FILE_PATTERN)))\n",
    "\n",
    "for fp in file_list:\n",
    "    try:\n",
    "        # 使用 dtype=object 以保留原生 Python 型別（避免自動轉為 float）\n",
    "        df = pd.read_csv(fp, dtype=object, low_memory=False)\n",
    "        df.columns = [c.lower() for c in df.columns]\n",
    "    except Exception:\n",
    "        continue\n",
    "\n",
    "    # 對三個目標欄位建立「是否為 float」的布林遮罩；若欄位缺失則補 False\n",
    "    masks = []\n",
    "    for col in TARGET_COLS:\n",
    "        if col in df.columns:\n",
    "            masks.append(series_is_float_mask(df[col]))\n",
    "        else:\n",
    "            # 欄位不存在 → 全 False（視為非 float）\n",
    "            masks.append(pd.Series([False] * len(df), index=df.index))\n",
    "\n",
    "    # 將三個布林遮罩合併為「每列的 float 數目」\n",
    "    float_counts_per_row = sum(masks)  # 逐列相加（True 視為 1）\n",
    "\n",
    "    # 逐類別累加\n",
    "    # 0 → No float / 1 → Exactly one float / 2 → Exactly two float / 3 → All three float\n",
    "    category_counter[\"No float\"]              += int((float_counts_per_row == 0).sum())\n",
    "    category_counter[\"Exactly one float\"]     += int((float_counts_per_row == 1).sum())\n",
    "    category_counter[\"Exactly two float\"]     += int((float_counts_per_row == 2).sum())\n",
    "    category_counter[\"All three float\"]       += int((float_counts_per_row == 3).sum())\n",
    "\n",
    "    total_rows += len(df)\n",
    "\n",
    "# =========================\n",
    "# 數據整理：順序、數量與占比\n",
    "# =========================\n",
    "ordered_labels = [\"All three float\", \"Exactly two float\", \"Exactly one float\", \"No float\"]\n",
    "counts = [category_counter[label] for label in ordered_labels]\n",
    "percents = [(c / total_rows * 100.0) if total_rows else 0.0 for c in counts]\n",
    "\n",
    "# =========================\n",
    "# 繪圖（英文標題與標籤；前三類使用紅、綠、藍；第四類使用灰）\n",
    "# =========================\n",
    "fig, ax = plt.subplots(figsize=(9, 5))\n",
    "\n",
    "# 顏色設定：紅、綠、藍、灰（第四類）\n",
    "colors = [\"red\", \"green\", \"blue\", \"gray\"]\n",
    "\n",
    "bars = ax.bar(ordered_labels, counts, color=colors)\n",
    "\n",
    "# 標註每個長條上方的數量與占比\n",
    "for rect, c, p in zip(bars, counts, percents):\n",
    "    height = rect.get_height()\n",
    "    ax.text(\n",
    "        rect.get_x() + rect.get_width() / 2.0,\n",
    "        height,\n",
    "        f\"{c:,} ({p:.2f}%)\",\n",
    "        ha=\"center\",\n",
    "        va=\"bottom\",\n",
    "        fontsize=10\n",
    "    )\n",
    "\n",
    "# 英文圖表元素\n",
    "ax.set_title(\"Float-Type Composition across (sponvt, vti, vte)\", fontsize=14)\n",
    "ax.set_ylabel(\"Row Count\", fontsize=12)\n",
    "ax.set_xlabel(\"Row Category by # of float columns\", fontsize=12)\n",
    "ax.set_ylim(0, max(counts) * 1.15 if counts else 1)\n",
    "ax.grid(axis=\"y\", linestyle=\"--\", alpha=0.3)\n",
    "\n",
    "plt.tight_layout()\n",
    "\n",
    "# =========================\n",
    "# 輸出彙總表（列印於終端，以供審閱）\n",
    "# =========================\n",
    "print(\"============================================================\")\n",
    "print(f\"Scanned files: {len(file_list)}\")\n",
    "print(f\"Total rows   : {total_rows:,}\")\n",
    "print(\"------------------------------------------------------------\")\n",
    "print(f\"{'Category':<22}{'Count':>12}{'Percent(%)':>14}\")\n",
    "print(\"------------------------------------------------------------\")\n",
    "for label, c, p in zip(ordered_labels, counts, percents):\n",
    "    print(f\"{label:<22}{c:>12,}{p:>14.6f}\")\n",
    "print(\"============================================================\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c02c73a3-1e4d-44eb-8dd0-93621c093cfb",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c78cd78e-cdb0-4f49-9c88-425bee55dc79",
   "metadata": {},
   "outputs": [],
   "source": [
    "僅把「非 NaN 的真浮點數」視為 float python"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "ef271199-495b-4782-a3d0-559abf5cbfbb",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "============================================================\n",
      "Scanned files: 122\n",
      "Total rows   : 1,602,476\n",
      "------------------------------------------------------------\n",
      "Category                     Count    Percent(%)\n",
      "------------------------------------------------------------\n",
      "All three float                  0      0.000000\n",
      "Exactly two float                0      0.000000\n",
      "Exactly one float                0      0.000000\n",
      "No float                 1,602,476    100.000000\n",
      "============================================================\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 900x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\"\"\" 跨檔統計指定資料夾下所有 CSV，每一筆資料（row）檢查 sponvt、vti、vte 三欄，\n",
    "是否為「非 NaN 的真浮點數」（僅真正 float 且非 NaN/Inf 才算），\n",
    "並將每列歸類為以下四類後彙總與繪圖（英文圖表）：\n",
    "    1) All three float        （三欄皆為 float）\n",
    "    2) Exactly two float      （恰有兩欄為 float）\n",
    "    3) Exactly one float      （恰有一欄為 float）\n",
    "    4) No float               （三欄皆非 float）\n",
    "\n",
    "需求：\n",
    "- 條狀圖：前三類用紅/綠/藍，第四類用灰色\n",
    "- 每個長條上方標註「數量（counts）」與「占比（%）」\n",
    "- 圖表文字使用英文；程式碼含中文註解\n",
    "- 僅把「非 NaN 的真浮點數」視為 float（np.nan / inf / -inf 或字串都不算）\n",
    "\n",
    "使用方式：\n",
    "- 調整 DATA_DIR 為你的資料夾路徑\n",
    "\"\"\"\n",
    "\n",
    "import os\n",
    "import glob\n",
    "from collections import Counter\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# =========================\n",
    "# 使用者可調整參數\n",
    "# =========================\n",
    "DATA_DIR = \"/home/jovyan/1010/data_new/bling_sponvt_ok_v3\"  # 目標資料夾\n",
    "FILE_PATTERN = \"*.csv\"\n",
    "TARGET_COLS = [\"sponvt\", \"vti\", \"vte\"]  # 需要檢查的欄位\n",
    "\n",
    "# =========================\n",
    "# 小工具函式\n",
    "# =========================\n",
    "def is_true_float(x) -> bool:\n",
    "    \"\"\"\n",
    "    僅把「非 NaN 的真浮點數」視為 float：\n",
    "    - 需為 Python float 或 numpy.floating\n",
    "    - 需為有限值（非 NaN/Inf/-Inf）\n",
    "    - 其他型別（整數、字串、None、'null' 等）均不算\n",
    "    \"\"\"\n",
    "    # 先快速排除 None / 字串類空值\n",
    "    if x is None:\n",
    "        return False\n",
    "    if isinstance(x, str):\n",
    "        if x.strip().lower() in {\"\", \"na\", \"n/a\", \"null\", \"none\"}:\n",
    "            return False\n",
    "        # 字串即使可轉成數字，也不在本規則中視為「真浮點」\n",
    "        return False\n",
    "\n",
    "    # 僅接受 Python/NumPy 浮點型別\n",
    "    if isinstance(x, (float, np.floating)):\n",
    "        # 排除 NaN 與 無窮大\n",
    "        return np.isfinite(x) and (not np.isnan(x))\n",
    "    return False\n",
    "\n",
    "def series_true_float_mask(s: pd.Series) -> pd.Series:\n",
    "    \"\"\"回傳布林遮罩：逐列判定是否為『非 NaN 的真浮點數』。\"\"\"\n",
    "    return s.apply(is_true_float)\n",
    "\n",
    "# =========================\n",
    "# 主流程：跨檔彙總各類別計數\n",
    "# =========================\n",
    "category_counter = Counter()\n",
    "total_rows = 0\n",
    "\n",
    "file_list = sorted(glob.glob(os.path.join(DATA_DIR, FILE_PATTERN)))\n",
    "\n",
    "for fp in file_list:\n",
    "    try:\n",
    "        # 以 object 讀取避免自動轉型，保留原始型別以正確判斷「真浮點」\n",
    "        df = pd.read_csv(fp, dtype=object, low_memory=False)\n",
    "        # 欄名統一：去頭尾空白 + 小寫，避免 'VTE ' / 'vTe' 等命名差異\n",
    "        df.columns = [c.strip().lower() for c in df.columns]\n",
    "    except Exception as e:\n",
    "        print(f\"[WARN] Skip file due to read error: {fp} -> {e}\")\n",
    "        continue\n",
    "\n",
    "    n = len(df)\n",
    "    if n == 0:\n",
    "        # 空檔直接跳過\n",
    "        continue\n",
    "    total_rows += n\n",
    "\n",
    "    # 對三個目標欄位建立「是否為真浮點」的布林遮罩；若欄位缺失則補 False\n",
    "    masks = []\n",
    "    for col in TARGET_COLS:\n",
    "        if col in df.columns:\n",
    "            masks.append(series_true_float_mask(df[col]))\n",
    "        else:\n",
    "            # 欄位不存在 → 全 False（視為非 float）\n",
    "            masks.append(pd.Series([False] * n, index=df.index))\n",
    "\n",
    "    # 將三個布林遮罩合併為「每列的真浮點數欄位數」\n",
    "    # True 視為 1，False 視為 0，逐列相加得到 0~3\n",
    "    float_counts_per_row = masks[0] + masks[1] + masks[2]\n",
    "\n",
    "    # 逐類別累加\n",
    "    category_counter[\"All three float\"]   += int((float_counts_per_row == 3).sum())\n",
    "    category_counter[\"Exactly two float\"] += int((float_counts_per_row == 2).sum())\n",
    "    category_counter[\"Exactly one float\"] += int((float_counts_per_row == 1).sum())\n",
    "    category_counter[\"No float\"]          += int((float_counts_per_row == 0).sum())\n",
    "\n",
    "# =========================\n",
    "# 數據整理：順序、數量與占比\n",
    "# =========================\n",
    "ordered_labels = [\"All three float\", \"Exactly two float\", \"Exactly one float\", \"No float\"]\n",
    "counts = [category_counter[label] for label in ordered_labels]\n",
    "percents = [(c / total_rows * 100.0) if total_rows else 0.0 for c in counts]\n",
    "\n",
    "# =========================\n",
    "# 繪圖（英文標題與標籤；前三類使用紅、綠、藍；第四類使用灰）\n",
    "# =========================\n",
    "fig, ax = plt.subplots(figsize=(9, 5))\n",
    "\n",
    "# 顏色設定：紅、綠、藍、灰（第四類）\n",
    "colors = [\"red\", \"green\", \"blue\", \"gray\"]\n",
    "\n",
    "bars = ax.bar(ordered_labels, counts, color=colors)\n",
    "\n",
    "# 標註每個長條上方的數量與占比\n",
    "for rect, c, p in zip(bars, counts, percents):\n",
    "    height = rect.get_height()\n",
    "    ax.text(\n",
    "        rect.get_x() + rect.get_width() / 2.0,\n",
    "        height,\n",
    "        f\"{c:,} ({p:.2f}%)\",\n",
    "        ha=\"center\",\n",
    "        va=\"bottom\",\n",
    "        fontsize=10\n",
    "    )\n",
    "\n",
    "# 英文圖表元素\n",
    "ax.set_title(\"Float-Type Composition across (sponvt, vti, vte)\", fontsize=14)\n",
    "ax.set_ylabel(\"Row Count\", fontsize=12)\n",
    "ax.set_xlabel(\"Row Category by # of float columns\", fontsize=12)\n",
    "ax.set_ylim(0, max(counts) * 1.15 if counts else 1)\n",
    "ax.grid(axis=\"y\", linestyle=\"--\", alpha=0.3)\n",
    "\n",
    "plt.tight_layout()\n",
    "\n",
    "# =========================\n",
    "# 輸出彙總表（列印於終端，以供審閱）\n",
    "# =========================\n",
    "print(\"============================================================\")\n",
    "print(f\"Scanned files: {len(file_list)}\")\n",
    "print(f\"Total rows   : {total_rows:,}\")\n",
    "print(\"------------------------------------------------------------\")\n",
    "print(f\"{'Category':<22}{'Count':>12}{'Percent(%)':>14}\")\n",
    "print(\"------------------------------------------------------------\")\n",
    "for label, c, p in zip(ordered_labels, counts, percents):\n",
    "    print(f\"{label:<22}{c:>12,}{p:>14.6f}\")\n",
    "print(\"============================================================\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b5e401ae-5c8a-4342-813d-a4391986ac51",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "2de62869-5981-4648-b8ee-cdd2ef61f8f8",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "============================================================\n",
      "Scanned files: 122\n",
      "Total rows   : 1,602,476\n",
      "------------------------------------------------------------\n",
      "[A] Category distribution (of all rows):\n",
      "  - All three float     :            0  (  0.00%)\n",
      "  - Exactly two float   :            0  (  0.00%)\n",
      "  - Exactly one float   :    1,483,817  ( 92.60%)\n",
      "  - No float            :      118,659  (  7.40%)\n",
      "------------------------------------------------------------\n",
      "[B] Co-float matrix (counts):\n",
      "         sponvt     vti     vte\n",
      "sponvt  1424904  161305   98120\n",
      "vti      161305  218092  130676\n",
      "vte       98120  130676  157033\n",
      "（每格百分比 = 該格 count / Total rows）\n",
      "------------------------------------------------------------\n",
      "[C] Exactly-two-float pairs:\n",
      "  - sponvt & vti :            0  (  0.00% of all;   0.00% of two-float)\n",
      "  - sponvt & vte :            0  (  0.00% of all;   0.00% of two-float)\n",
      "  - vti & vte    :            0  (  0.00% of all;   0.00% of two-float)\n",
      "============================================================\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 900x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 550x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\"\"\" 視覺化 /home/jovyan/1010/data_new/bling_spomvy_ok_v1 目錄下所有 CSV，\n",
    "針對三欄位 sponvt, vti, vte 的「float 型態分布」產生三種圖表（英文標示，程式含中文註解）：\n",
    "\n",
    "A) 類別條狀圖（四類）：All three float / Exactly two float / Exactly one float / No float\n",
    "   - 每個長條標註「資料筆數」與「占比（相對於全部列）」。\n",
    "\n",
    "B) 3×3「共現矩陣」(confusion-like heatmap)：\n",
    "   - 列與欄為 sponvt, vti, vte。\n",
    "   - 每個格子顯示：同時為 float 的筆數與占比（相對於全部列），對角線是各欄為 float 的筆數與占比。\n",
    "   - 用來查看「各欄位互相組合」的浮點型出現關係。\n",
    "\n",
    "C) 「恰有兩欄為 float」時是哪兩欄的分布條圖（3 組：sv & vi, sv & ve, vi & ve）：\n",
    "   - 每個長條標註「資料筆數」與「占比（相對於全部列）」。\n",
    "   - 另印出僅相對於「兩欄 float 子集合」的比例（更精細）。\n",
    "\n",
    "注意：\n",
    "- 僅以 Python/NumPy 型別判定是否為 float（True/False、字串、整數、None 等皆不算）。\n",
    "- 若檔案缺某欄，視為該欄全為「非 float」。\n",
    "- 圖表文字使用英文；顏色設定：A 圖前三類可用紅/綠/藍，第 4 類灰色。其它圖不強制配色。\n",
    "\"\"\"\n",
    "\n",
    "import os\n",
    "import glob\n",
    "from collections import Counter\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# =========================\n",
    "# 使用者可調整參數\n",
    "# =========================\n",
    "DATA_DIR = \"/home/jovyan/1010/data_new/bling_sponvt_ok_v3\"  # 目錄\n",
    "FILE_PATTERN = \"*.csv\"\n",
    "COLS = [\"sponvt\", \"vti\", \"vte\"]\n",
    "\n",
    "# =========================\n",
    "# 判斷工具\n",
    "# =========================\n",
    "def is_float_value(x) -> bool:\n",
    "    \"\"\"判斷值是否為 float 型（含 numpy 浮點）\"\"\"\n",
    "    return isinstance(x, float) or isinstance(x, np.floating)\n",
    "\n",
    "def float_mask_for_series(s: pd.Series) -> pd.Series:\n",
    "    \"\"\"回傳布林遮罩，True 代表該列值為 float 型態\"\"\"\n",
    "    return s.apply(is_float_value)\n",
    "\n",
    "# =========================\n",
    "# 讀檔與彙總\n",
    "# =========================\n",
    "file_list = sorted(glob.glob(os.path.join(DATA_DIR, FILE_PATTERN)))\n",
    "total_rows = 0\n",
    "\n",
    "# 四類彙總計數\n",
    "category_counter = Counter({\n",
    "    \"All three float\": 0,\n",
    "    \"Exactly two float\": 0,\n",
    "    \"Exactly one float\": 0,\n",
    "    \"No float\": 0\n",
    "})\n",
    "\n",
    "# 兩兩欄位同為 float 的共現計數（3x3 矩陣）\n",
    "pair_co_float = pd.DataFrame(0, index=COLS, columns=COLS, dtype=int)\n",
    "\n",
    "# 「恰有兩欄為 float」時，到底是哪兩欄（sv&vi, sv&ve, vi&ve）\n",
    "two_float_combo_counter = Counter({\n",
    "    \"sponvt & vti\": 0,\n",
    "    \"sponvt & vte\": 0,\n",
    "    \"vti & vte\": 0\n",
    "})\n",
    "\n",
    "for fp in file_list:\n",
    "    try:\n",
    "        # 以 object 讀取避免自動轉型，保留原生型別\n",
    "        df = pd.read_csv(fp, dtype=object, low_memory=False)\n",
    "        df.columns = [c.lower() for c in df.columns]\n",
    "    except Exception:\n",
    "        continue\n",
    "\n",
    "    n = len(df)\n",
    "    if n == 0:\n",
    "        continue\n",
    "    total_rows += n\n",
    "\n",
    "    # 針對三欄建立 float 布林遮罩，若缺欄 → 全 False\n",
    "    masks = {}\n",
    "    for c in COLS:\n",
    "        masks[c] = float_mask_for_series(df[c]) if c in df.columns else pd.Series([False]*n, index=df.index)\n",
    "\n",
    "    # 計算每列有幾個 float 欄位\n",
    "    float_count_per_row = masks[COLS[0]] + masks[COLS[1]] + masks[COLS[2]]\n",
    "\n",
    "    # 四類計數\n",
    "    category_counter[\"All three float\"]   += int((float_count_per_row == 3).sum())\n",
    "    category_counter[\"Exactly two float\"] += int((float_count_per_row == 2).sum())\n",
    "    category_counter[\"Exactly one float\"] += int((float_count_per_row == 1).sum())\n",
    "    category_counter[\"No float\"]          += int((float_count_per_row == 0).sum())\n",
    "\n",
    "    # 共現矩陣（兩兩同為 float）\n",
    "    for i, ci in enumerate(COLS):\n",
    "        mi = masks[ci]\n",
    "        for j, cj in enumerate(COLS):\n",
    "            mj = masks[cj]\n",
    "            pair_co_float.loc[ci, cj] += int((mi & mj).sum())\n",
    "\n",
    "    # 「恰有兩欄為 float」：分辨是哪兩欄\n",
    "    exactly_two = (float_count_per_row == 2)\n",
    "    if exactly_two.any():\n",
    "        idx = exactly_two[exactly_two].index\n",
    "        # 對每一列，看是哪兩個 True\n",
    "        for r in idx:\n",
    "            combo = tuple([c for c in COLS if masks[c].iat[r]])\n",
    "            # combo 會是長度 2 的 tuple（例如 ('sponvt','vti')）\n",
    "            key = None\n",
    "            if set(combo) == {\"sponvt\", \"vti\"}:\n",
    "                key = \"sponvt & vti\"\n",
    "            elif set(combo) == {\"sponvt\", \"vte\"}:\n",
    "                key = \"sponvt & vte\"\n",
    "            elif set(combo) == {\"vti\", \"vte\"}:\n",
    "                key = \"vti & vte\"\n",
    "            if key:\n",
    "                two_float_combo_counter[key] += 1\n",
    "\n",
    "# =========================\n",
    "# A) 類別條狀圖（四類）\n",
    "# =========================\n",
    "labels_A = [\"All three float\", \"Exactly two float\", \"Exactly one float\", \"No float\"]\n",
    "counts_A = [category_counter[l] for l in labels_A]\n",
    "perc_A = [(c / total_rows * 100.0) if total_rows else 0.0 for c in counts_A]\n",
    "\n",
    "plt.figure(figsize=(9, 5))\n",
    "colors_A = [\"red\", \"green\", \"blue\", \"gray\"]  # 按需求：紅/綠/藍/灰\n",
    "bars = plt.bar(labels_A, counts_A, color=colors_A)\n",
    "plt.title(\"Row Categories by # of Float Columns (sponvt, vti, vte)\")\n",
    "plt.xlabel(\"Category\")\n",
    "plt.ylabel(\"Row Count\")\n",
    "plt.ylim(0, max(counts_A) * 1.15 if counts_A else 1)\n",
    "plt.grid(axis=\"y\", linestyle=\"--\", alpha=0.3)\n",
    "\n",
    "# 在每個長條上方標註「數量 (占比%)」\n",
    "for rect, c, p in zip(bars, counts_A, perc_A):\n",
    "    plt.text(rect.get_x() + rect.get_width()/2, rect.get_height(),\n",
    "             f\"{c:,} ({p:.2f}%)\", ha=\"center\", va=\"bottom\", fontsize=10)\n",
    "\n",
    "plt.tight_layout()\n",
    "\n",
    "# =========================\n",
    "# B) 3×3 共現矩陣（兩兩同為 float）— 藍色漸層\n",
    "# =========================\n",
    "fig, ax = plt.subplots(figsize=(5.5, 5))\n",
    "M = pair_co_float.values.astype(float)\n",
    "\n",
    "# 設定藍色漸層，並固定色階從 0 到最大值，避免不同資料集顏色尺度跳動\n",
    "vmin, vmax = 0, (M.max() if M.size else 1)\n",
    "im = ax.imshow(M, aspect=\"equal\", cmap=\"Blues\", vmin=vmin, vmax=vmax)\n",
    "\n",
    "ax.set_xticks(range(len(COLS))); ax.set_xticklabels(COLS)\n",
    "ax.set_yticks(range(len(COLS))); ax.set_yticklabels(COLS)\n",
    "plt.title(\"Co-float Matrix (Count & Percent of All Rows)\")\n",
    "\n",
    "# 動態文字顏色：深色底用白字、淺色底用黑字\n",
    "# 以色階 60% 當臨界值，你也可以依需要調整 threshold\n",
    "threshold = vmin + 0.6 * (vmax - vmin) if vmax > vmin else vmin\n",
    "\n",
    "for i in range(len(COLS)):\n",
    "    for j in range(len(COLS)):\n",
    "        count = int(M[i, j])\n",
    "        pct = (count / total_rows * 100.0) if total_rows else 0.0\n",
    "        # 根據底色強度決定字色\n",
    "        text_color = \"white\" if M[i, j] >= threshold else \"black\"\n",
    "        ax.text(j, i, f\"{count:,}\\n({pct:.2f}%)\",\n",
    "                ha=\"center\", va=\"center\", fontsize=10, color=text_color)\n",
    "\n",
    "# 加上色條（同藍色漸層）\n",
    "cbar = plt.colorbar(im, fraction=0.046, pad=0.04)\n",
    "cbar.ax.set_ylabel(\"Count\", rotation=270, labelpad=12)\n",
    "\n",
    "# 輕微格線輔助閱讀（畫在上層）\n",
    "ax.set_xticks(np.arange(-.5, len(COLS), 1), minor=True)\n",
    "ax.set_yticks(np.arange(-.5, len(COLS), 1), minor=True)\n",
    "ax.grid(which=\"minor\", color=\"white\", linestyle=\"-\", linewidth=0.5, alpha=0.7)\n",
    "ax.tick_params(which=\"minor\", bottom=False, left=False)\n",
    "\n",
    "plt.tight_layout()\n",
    "\n",
    "\n",
    "# =========================\n",
    "# C) 「恰有兩欄為 float」是哪兩欄（3 組）條圖\n",
    "# =========================\n",
    "labels_C = [\"sponvt & vti\", \"sponvt & vte\", \"vti & vte\"]\n",
    "counts_C = [two_float_combo_counter[l] for l in labels_C]\n",
    "\n",
    "# 相對於全部列的百分比\n",
    "perc_C_all = [(c / total_rows * 100.0) if total_rows else 0.0 for c in counts_C]\n",
    "\n",
    "# 相對於「恰有兩欄為 float」子集合的百分比\n",
    "two_total = sum(counts_C)\n",
    "perc_C_two = [(c / two_total * 100.0) if two_total else 0.0 for c in counts_C]\n",
    "\n",
    "plt.figure(figsize=(7, 5))\n",
    "bars2 = plt.bar(labels_C, counts_C)  # 不強制顏色\n",
    "plt.title('Which Two Columns Are Float (Only Rows with Exactly Two Float)')\n",
    "plt.xlabel(\"Column Pair\")\n",
    "plt.ylabel(\"Row Count\")\n",
    "plt.ylim(0, max(counts_C) * 1.15 if counts_C else 1)\n",
    "plt.grid(axis=\"y\", linestyle=\"--\", alpha=0.3)\n",
    "\n",
    "# 在長條上方標註：count（相對全部% / 相對兩欄子集合%）\n",
    "for rect, c, p_all, p_two in zip(bars2, counts_C, perc_C_all, perc_C_two):\n",
    "    plt.text(rect.get_x() + rect.get_width()/2, rect.get_height(),\n",
    "             f\"{c:,}\\n({p_all:.2f}% of all)\\n({p_two:.2f}% of two-float)\",\n",
    "             ha=\"center\", va=\"bottom\", fontsize=9)\n",
    "\n",
    "plt.tight_layout()\n",
    "\n",
    "# =========================\n",
    "# 彙總印表（終端檢視）\n",
    "# =========================\n",
    "print(\"============================================================\")\n",
    "print(f\"Scanned files: {len(file_list)}\")\n",
    "print(f\"Total rows   : {total_rows:,}\")\n",
    "print(\"------------------------------------------------------------\")\n",
    "print(\"[A] Category distribution (of all rows):\")\n",
    "for l, c, p in zip(labels_A, counts_A, perc_A):\n",
    "    print(f\"  - {l:<20}: {c:>12,}  ({p:>6.2f}%)\")\n",
    "\n",
    "print(\"------------------------------------------------------------\")\n",
    "print(\"[B] Co-float matrix (counts):\")\n",
    "print(pair_co_float)\n",
    "print(\"（每格百分比 = 該格 count / Total rows）\")\n",
    "\n",
    "print(\"------------------------------------------------------------\")\n",
    "print(\"[C] Exactly-two-float pairs:\")\n",
    "for l, c, p_all, p_two in zip(labels_C, counts_C, perc_C_all, perc_C_two):\n",
    "    print(f\"  - {l:<13}: {c:>12,}  ({p_all:>6.2f}% of all; {p_two:>6.2f}% of two-float)\")\n",
    "print(\"============================================================\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "03321a34-0742-463d-9ace-81ead87d1a58",
   "metadata": {},
   "outputs": [],
   "source": [
    "有沒有圖表可以看這三種狀況 三個欄位的分布情形 兩個欄位是哪兩個欄位 就是說個欄位互相組合 用混淆矩陣? 上面標是資料筆數及占比?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "610bb4e3-1716-497d-a1db-8f9b0507d3b5",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "96fcc2a0-cb5e-4ae1-b69d-f029c224681e",
   "metadata": {},
   "outputs": [],
   "source": [
    "整理完三個欄位就可以\n",
    "計算潮氣量的數值，創svv_v1欄位\n",
    "當sponvt, vti, vte三欄都是float，則將三個數字相加 再除以3，填入svv_v1欄位\n",
    "當sponvt, vti, vte其中兩欄是float，則將兩個數字相加 再除以2，填入svv_v1欄位\n",
    "當sponvt, vti, vte其中一欄是float，則直接把數字放到svv_v1欄位\n",
    "svv_v1欄位取到小數點後兩位"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c0c05b44-b7ee-4b7f-8fda-9c38a95d8a47",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    }
   },
   "outputs": [],
   "source": [
    "\"\"\" 計算潮氣量（svv_v1）欄位，並寫入新資料夾 /home/jovyan/1010/data_new/bling_svv_ok_v1/\n",
    "\n",
    "邏輯：\n",
    "- 若 sponvt、vti、vte 三欄皆為 float → 取三者平均\n",
    "- 若其中兩欄為 float → 取兩者平均\n",
    "- 若僅一欄為 float → 直接取該值\n",
    "- 若三欄皆非 float → svv_v1 為 NaN\n",
    "- svv_v1 取小數點後兩位\n",
    "- 原始資料不會被修改\n",
    "\"\"\"\n",
    "\n",
    "import os\n",
    "import glob\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "# =========================\n",
    "# 設定區\n",
    "# =========================\n",
    "INPUT_DIR  = \"/home/jovyan/1010/data_new/bling_spomvy_ok_v1\"\n",
    "OUTPUT_DIR = \"/home/jovyan/1010/data_new/bling_svv_ok_v1\"\n",
    "TARGET_COLS = [\"sponvt\", \"vti\", \"vte\"]\n",
    "NEW_COL = \"svv_v1\"\n",
    "\n",
    "os.makedirs(OUTPUT_DIR, exist_ok=True)\n",
    "\n",
    "# =========================\n",
    "# 工具函式\n",
    "# =========================\n",
    "def safe_to_float(x):\n",
    "    \"\"\"嘗試轉換為 float，若失敗則回傳 np.nan\"\"\"\n",
    "    try:\n",
    "        f = float(x)\n",
    "        return f\n",
    "    except (ValueError, TypeError):\n",
    "        return np.nan\n",
    "\n",
    "def compute_svv_v1(row):\n",
    "    \"\"\"\n",
    "    根據每列 sponvt, vti, vte 的 float 數量計算潮氣量平均\n",
    "    \"\"\"\n",
    "    values = [safe_to_float(row[c]) for c in TARGET_COLS]\n",
    "    floats = [v for v in values if not pd.isna(v)]\n",
    "\n",
    "    if len(floats) == 3:\n",
    "        return round(sum(floats) / 3, 2)\n",
    "    elif len(floats) == 2:\n",
    "        return round(sum(floats) / 2, 2)\n",
    "    elif len(floats) == 1:\n",
    "        return round(floats[0], 2)\n",
    "    else:\n",
    "        return np.nan  # 三欄皆非 float\n",
    "\n",
    "# =========================\n",
    "# 主程式\n",
    "# =========================\n",
    "file_list = sorted(glob.glob(os.path.join(INPUT_DIR, \"*.csv\")))\n",
    "summary = []\n",
    "\n",
    "if not file_list:\n",
    "    print(f\"[警示] 找不到任何 CSV：{INPUT_DIR}\")\n",
    "else:\n",
    "    for fp in file_list:\n",
    "        try:\n",
    "            df = pd.read_csv(fp, dtype=object, low_memory=False)\n",
    "            df.columns = [c.lower() for c in df.columns]\n",
    "        except Exception as e:\n",
    "            print(f\"[略過] 無法讀取 {fp}: {e}\")\n",
    "            continue\n",
    "\n",
    "        # 計算 svv_v1 欄位\n",
    "        df[NEW_COL] = df.apply(compute_svv_v1, axis=1)\n",
    "\n",
    "        # 統計非空筆數\n",
    "        non_null_count = df[NEW_COL].notna().sum()\n",
    "        summary.append((os.path.basename(fp), len(df), non_null_count))\n",
    "\n",
    "        # 寫出新檔案\n",
    "        out_path = os.path.join(OUTPUT_DIR, os.path.basename(fp))\n",
    "        df.to_csv(out_path, index=False)\n",
    "\n",
    "    # =========================\n",
    "    # 結果摘要\n",
    "    # =========================\n",
    "    print(\"============================================================\")\n",
    "    print(f\"共處理檔案數：{len(summary)}\")\n",
    "    print(f\"輸出目錄：{OUTPUT_DIR}\")\n",
    "    print(\"------------------------------------------------------------\")\n",
    "    print(f\"{'檔名':<25}{'總筆數':>10}{'svv_v1 有值筆數':>20}\")\n",
    "    print(\"------------------------------------------------------------\")\n",
    "    for name, total, nonnull in summary:\n",
    "        print(f\"{name:<25}{total:>10,}{nonnull:>20,}\")\n",
    "    print(\"============================================================\")\n",
    "    print(\"✅ 已完成潮氣量 svv_v1 計算並輸出新檔案。\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bdbd6598-4187-4a98-a04d-d4438d407e62",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7fd56742-2365-4281-8dcf-601b1259bc0a",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3d10b335-37ac-4add-bbc3-f868d098c0ef",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bb88f5cc-8d3c-4d0c-a8b1-d03bc79f0d2f",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "75ef2c1d-0a61-40c2-9253-021627080e9f",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "621de783-3be8-4569-a5e8-bdbf8febdb64",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "=== 全部檔案總筆數：1,602,536 ===\n",
      "\n",
      "[bool_false] 小計：17,243 筆\n",
      "   (       eq0, eq0       ) :        4 筆  (  0.02%)\n",
      "   (       gt0, eq0       ) :       24 筆  (  0.14%)\n",
      "   (       gt0, gt0       ) :   16,999 筆  ( 98.58%)\n",
      "   (non_numeric, eq0       ) :        7 筆  (  0.04%)\n",
      "   (non_numeric, non_numeric) :      209 筆  (  1.21%)\n",
      "   → 完整(gt0,gt0): 16,999 筆 (98.58%)\n",
      "   → 缺值(任一 non_numeric): 216 筆 (1.25%)\n",
      "\n",
      "[bool_true] 小計：143,905 筆\n",
      "   (       eq0, gt0       ) :       17 筆  (  0.01%)\n",
      "   (       gt0, gt0       ) :   97,308 筆  ( 67.62%)\n",
      "   (       gt0, non_numeric) :       71 筆  (  0.05%)\n",
      "   (non_numeric, gt0       ) :        9 筆  (  0.01%)\n",
      "   (non_numeric, non_numeric) :   46,500 筆  ( 32.31%)\n",
      "   → 完整(gt0,gt0): 97,308 筆 (67.62%)\n",
      "   → 缺值(任一 non_numeric): 46,580 筆 (32.37%)\n",
      "\n",
      "[float_nonzero] 小計：1,441,298 筆\n",
      "   (       eq0, eq0       ) :       18 筆  (  0.00%)\n",
      "   (       eq0, gt0       ) :    1,793 筆  (  0.12%)\n",
      "   (       gt0, eq0       ) :    2,054 筆  (  0.14%)\n",
      "   (       gt0, gt0       ) : 1,211,955 筆  ( 84.09%)\n",
      "   (       gt0, non_numeric) :    2,802 筆  (  0.19%)\n",
      "   (non_numeric, eq0       ) :       99 筆  (  0.01%)\n",
      "   (non_numeric, gt0       ) :  115,118 筆  (  7.99%)\n",
      "   (non_numeric, non_numeric) :  107,459 筆  (  7.46%)\n",
      "   → 完整(gt0,gt0): 1,211,955 筆 (84.09%)\n",
      "   → 缺值(任一 non_numeric): 225,478 筆 (15.64%)\n",
      "\n",
      "[float_zero] 小計：90 筆\n",
      "   (non_numeric, non_numeric) :       90 筆  (100.00%)\n",
      "   → 完整(gt0,gt0): 0 筆 (0.00%)\n",
      "   → 缺值(任一 non_numeric): 90 筆 (100.00%)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# sponvt_type × (vti_cat, vte_cat) 統計摘要\n",
    "# 僅印出總數與比例，不產生任何檔案或圖表\n",
    "# ============================================\n",
    "\n",
    "import os, glob, pandas as pd, numpy as np\n",
    "\n",
    "DATA_DIR = \"/home/jovyan/1010/data_new/bling_sponvt_1/\"\n",
    "CSV_GLOB = \"*.csv\"\n",
    "\n",
    "NULL_SET = {\"\", \"(null)\", \"null\", \"<null>\", \"none\", \"nan\"}\n",
    "\n",
    "def clean_str(x):\n",
    "    return x.strip() if isinstance(x, str) else x\n",
    "\n",
    "def classify_sponvt_type(x):\n",
    "    if x is None: return \"nan\"\n",
    "    if isinstance(x, (bool, np.bool_)):\n",
    "        return \"bool_true\" if bool(x) else \"bool_false\"\n",
    "    if isinstance(x, str):\n",
    "        xl = x.strip().lower()\n",
    "        if xl in NULL_SET: return \"nan\"\n",
    "        if xl == \"true\": return \"bool_true\"\n",
    "        if xl == \"false\": return \"bool_false\"\n",
    "        try:\n",
    "            v = float(x.replace(\",\", \"\"))\n",
    "            return \"float_zero\" if v == 0 else \"float_nonzero\"\n",
    "        except: return \"other\"\n",
    "    try:\n",
    "        v = float(x)\n",
    "        return \"float_zero\" if v == 0 else \"float_nonzero\"\n",
    "    except: return \"other\"\n",
    "\n",
    "def classify_num_cat(x):\n",
    "    if x is None: return \"non_numeric\"\n",
    "    if isinstance(x, (bool, np.bool_)): return \"non_numeric\"\n",
    "    if isinstance(x, str):\n",
    "        xl = x.strip().lower()\n",
    "        if xl in NULL_SET or xl in (\"true\",\"false\"): return \"non_numeric\"\n",
    "        try: v = float(x.replace(\",\", \"\"))\n",
    "        except: return \"non_numeric\"\n",
    "    else:\n",
    "        try: v = float(x)\n",
    "        except: return \"non_numeric\"\n",
    "    if not np.isfinite(v): return \"non_numeric\"\n",
    "    if v > 0: return \"gt0\"\n",
    "    if v == 0: return \"eq0\"\n",
    "    return \"non_numeric\"\n",
    "\n",
    "csvs = sorted(glob.glob(os.path.join(DATA_DIR, CSV_GLOB)))\n",
    "chunks = []\n",
    "for p in csvs:\n",
    "    try:\n",
    "        df = pd.read_csv(p, dtype=str, low_memory=False)\n",
    "        for c in [\"sponvt\",\"vti\",\"vte\"]:\n",
    "            if c not in df.columns: df[c] = np.nan\n",
    "            df[c] = df[c].map(clean_str)\n",
    "        chunks.append(df[[\"sponvt\",\"vti\",\"vte\"]])\n",
    "    except Exception as e:\n",
    "        print(f\"[WARN] {p} skipped ({e})\")\n",
    "\n",
    "if not chunks:\n",
    "    print(\"No data found.\")\n",
    "else:\n",
    "    data = pd.concat(chunks, ignore_index=True)\n",
    "    data[\"sponvt_type\"] = data[\"sponvt\"].map(classify_sponvt_type)\n",
    "    data[\"vti_cat\"] = data[\"vti\"].map(classify_num_cat)\n",
    "    data[\"vte_cat\"] = data[\"vte\"].map(classify_num_cat)\n",
    "\n",
    "    combo = data.groupby([\"sponvt_type\",\"vti_cat\",\"vte_cat\"]).size().reset_index(name=\"count\")\n",
    "    total = len(data)\n",
    "\n",
    "    print(f\"\\n=== 全部檔案總筆數：{total:,} ===\\n\")\n",
    "    for spon_type, sub in combo.groupby(\"sponvt_type\"):\n",
    "        subtotal = sub[\"count\"].sum()\n",
    "        print(f\"[{spon_type}] 小計：{subtotal:,} 筆\")\n",
    "        for _, r in sub.iterrows():\n",
    "            pct = r[\"count\"]/subtotal*100 if subtotal>0 else 0\n",
    "            print(f\"   ({r.vti_cat:>10}, {r.vte_cat:<10}) : {int(r['count']):>8,} 筆  ({pct:6.2f}%)\")\n",
    "\n",
    "        # 摘要\n",
    "        gg = sub.loc[(sub.vti_cat==\"gt0\") & (sub.vte_cat==\"gt0\"),\"count\"].sum()\n",
    "        miss = sub.loc[sub.vti_cat.eq(\"non_numeric\") | sub.vte_cat.eq(\"non_numeric\"),\"count\"].sum()\n",
    "        print(f\"   → 完整(gt0,gt0): {gg:,} 筆 ({gg/subtotal*100:.2f}%)\")\n",
    "        print(f\"   → 缺值(任一 non_numeric): {miss:,} 筆 ({miss/subtotal*100:.2f}%)\\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e79509c2-ec06-4179-886d-d9e242659a95",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "ae2638cd-74e9-443b-9726-af9f4ad0ca3c",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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69GnreiHS//1Cc7Os4CorXKpcubJ+/PFHGYZhE0ydPn1a169fV5UqVQpVY+/evTVw4EAlJCTo6NGjOnXqVLHN2PmrChUqWEO7qKgoLV68WBEREapYsWK2ff8a5GW1OTk5ycPDQ+XLl5ejo6OeeeaZXGc0BAUF5auuAwcOqEOHDgoICND69ev1xBNPqG3btlq/fr0aNGiQrz6qVKmS7ZfdLL1799a///1vxcXFqWbNmtq5c6cWLlyYr34LI2ttnZvXj/qrlJQUrV69WhMmTNDLL79sbU9PT8/1c9yup556SiNGjFBsbKymTp2qDz/8UI888ogqVap0y2OzrvcxY8bkuKaaJNWvXz9fdVStWlUZGRlKTk7Oda20rLXGNmzYYJ0dJd1YmL2gss7nrb5nv/zyy2wPMcgvPz+/Qh1XGtWoUUOhoaE5bss6x/PmzbMugP5Xf/1Hgb8G//m5PrLG+c9//lOgGZUAgDsHoRQAwK5q1qypwYMHa/369dq6davNtgsXLmjVqlU2t/AtW7ZMDg4O1oWy27dvrxUrVmjlypV69NFHrfv961//sm4vjJuDgaNHj8rf31+dOnWy2cfZ2VnSjZkdrq6uBer/VscOGTJECxYs0OOPP67z589r8ODBOfbz2Wef6fXXX7fOLrhw4YK+/PJLtWnTRo6OjnJzc1O7du20Z88eNW3aVE5OTgWqM8uePXvUsWNH1a9fX2vXrpWXl5c2bNigzp07KywsTOvXr1fjxo1v2U+DBg20cuXKHLd16tRJ/v7+Wrx4sfVpgn+dHXTzebtdrVq1UoUKFbRo0SI9+eSTOd7CZ7FYZBiGddws7733XranQBZVbZUqVdIjjzyif/3rX2rZsqWSk5OzzZLLbaz69eurbt262rdvn3Xh9cJ68MEHNW3aNC1cuFCTJk3KcZ+sc/bX8/P2229n2/dW13zWwwaCg4PzrKtJkya3Lh55at26tSpWrKj4+Phcf7bcSn6uj86dO6tcuXI6cuRIttv/CqsofwYAAOyPUAoAYKqUlBS1a9dOERERatCggTw9PbVz5059/fXX2WZ2VK5cWS+88IKSkpJUr149ffXVV3r33Xf1wgsvqGbNmpKkZ599Vm+99ZYiIyOVmJioJk2aaMuWLYqJiVGXLl3UoUOHQtVZsWJFPfroo4qNjdX58+cVFRUlBwfbu96zfjmePn26HnzwQTk6OuY7+LnVsfXq1dMDDzygtWvX6m9/+5vuvvvuHPtxdHRUx44dNWLECGVmZmr69OlKTU21eare3Llz9be//U1t2rTRCy+8oMDAQF24cEG//vqrvvzyS3333Xd51nrp0iU98MADatq0qb788ku5u7tLuhGefPvtt+rSpYsefPBBHT58+JZP4AsPD9cHH3ygQ4cOqV69etk+y7PPPqtZs2bJy8tL3bt3V4UKFXI8b3PnzlVkZKTKly+v+vXr26wTll8eHh6aOXOm+vXrpw4dOqh///7y8fHRr7/+qn379mn+/Pny8vJS27Zt9frrr6tKlSoKDAzUxo0b9f7772ebuZYVyr3zzjvy9PSUi4uLgoKCVLlyZW3YsEHt2rXThAkTFB0dfcva+vTpo+XLl2vw4MGqUaNGtuu4du3acnV11dKlS9WwYUN5eHjIz89Pfn5+evvtt/Xggw+qc+fO6tWrl/z9/XX27FklJCRo9+7d+ve//52v89OmTRs988wzmjJliv73v/+pW7ducnZ21p49e+Tm5qYXX3xRrVq1UqVKlTRgwABNmDBB5cuX19KlS7Vv375s/d3qmv/hhx+yPZkRxcPDw0Pz5s1TZGSkzp49q8cff1zVqlXTmTNntG/fPp05c+aWsxTzc30EBgZq0qRJGjdunI4ePaoHHnhAlSpV0v/+9z/t2LFD7u7u+XoC6M08PT1111136YsvvlD79u3l7e1d6BmxAIASwM4LrQMA7jBpaWnGgAEDjKZNmxpeXl6Gq6urUb9+fWPChAnGpUuXrPuFhYUZjRo1MjZs2GCEhoYazs7ORvXq1Y2xY8dmewLVn3/+aQwYMMCoXr26Ua5cOeOuu+4yxowZY6SlpdnsJ8kYNGhQtpruuusuIzIyMlv7unXrDEmGJOPQoUPZtqenpxv9+vUzqlatalgsllyfCpeT/BwbGxtrSDI++eSTbMff/IStiRMnGjVq1DCcnJyMkJAQ45tvvslx/z59+hj+/v5G+fLljapVqxqtWrUypkyZkq96t23bZly+fDnHbRcvXjR++OGHfPWTkpJieHh4GDNmzMhx+6FDh6znPC4uLsd9xowZY/j5+RkODg42T7sr6NP3snz11VdGWFiY4e7ubri5uRnBwcHG9OnTrdt/++0347HHHjMqVapkeHp6Gg888IBx4MCBHK+bOXPmGEFBQYajo6PN0/G+/PJLQ5KxaNGifNWUkZFhBAQE5Pl0s48//tho0KCBUb58+WxPI9u3b5/Ro0cPo1q1akb58uUNX19f4+9//3u+x7+5jtmzZxuNGzc2nJycjAoVKhgtW7Y0vvzyS+s+27ZtM1q2bGm4ubkZVatWNfr162fs3r0729MBb3XNt2nTJtvT4MxU0p+qV9Cn773++us57nuzjRs3Gl27djW8vb2N8uXLG/7+/kbXrl2Nf//739Z9sp6+d+bMmWzH5+f6MAzDWLlypdGuXTvDy8vLcHZ2Nu666y7j8ccfN7799lvrPpGRkYa7u3u2MbLGv9m3335rhISEGM7OzoYkIzIykqfvAUApZTEMwzA3BgMA4NbCw8P1xx9/6MCBA/YuxW4ee+wx/fDDD0pMTFT58uVttiUmJiooKEivv/66oqKi7FRh4bz44otav369fvnllzyfeleWjBo1Sh9//HG+ZpPdiY4cOaK6devqm2++UceOHe1SQ5UqVZScnKxy5bLfSPDKK6+oRo0aGjBgQInf70514MABDRgwQFu2bMlx+9/+9je99957+V7/DgBgDp6+BwBACZKenq7t27dr7ty5+vzzz/XSSy9lC6RKu1deeUW///67Pv30U3uXYprvv/9e48ePJ5DKxZQpU9S+fXu7BVIAAMA+WFMKAIAilJmZqczMzDz3yWmmQ5ZTp06pVatW8vLy0vPPP68XX3yxqEu0Ox8fHy1dulTnzp0rtjEyMjKU12Rwi8UiR0fHYhv/r3bu3GnaWPlx/fr1PLc7ODhkW0OtOGupXbu2xowZY8p4ufHw8Mh1baLMzEzNnj27VOx3p3J0dNS+fftyfEqpdONnglnXNAAg/7h9DwCAItSrVy8tWbIkz334q7f4hYeHa+PGjbluv+uuu5SYmGheQSXMrW6bjIyMVGxsrDnFAACAO5ZdQ6lNmzbp9ddf165du3Tq1Cl9/vnneuSRR6zbDcPQxIkT9c477+jcuXO677779NZbb6lRo0bWfdLT0xUVFaWPP/5YV65cUfv27bVgwQLVqFHDDp8IAHCnS0xM1B9//JHnPqGhoSZVc+c6ePCgLly4kOt2Z2dn69Pg7kQ//fRTntuznjQIAABQnOwaSq1du1Zbt27Vvffeq8ceeyxbKDV9+nRNnTpVsbGxqlevnqZMmaJNmzbp4MGD1kc/v/DCC/ryyy8VGxurypUra+TIkTp79qx27dpl6rR8AAAAAAAA5F+JuX3PYrHYhFKGYcjPz0/Dhg3T6NGjJd2YFeXj46Pp06fr+eefV0pKiqpWraoPP/xQTzzxhCTp5MmTCggI0FdffaXOnTvnOFZ6errS09Ot7zMzM3X27FlVrlz5jnkKEAAAAAAAQHEwDEMXLlyQn59fnmv6ldiFzo8dO6bk5GR16tTJ2ubs7KywsDBt27ZNzz//vHbt2qVr167Z7OPn56fGjRtr27ZtuYZS06ZN08SJE4v9MwAAAAAAANypTpw4kefySiU2lEpOTpZ04wk9N/Px8dHx48et+zg5OalSpUrZ9sk6PidjxozRiBEjrO9TUlJUs2ZNnThxQl5eXkX1Eezm9wnb7F0CSgn/ia3sXYIV1y0KgmsXpRHXLUqrknTtAgBKh9TUVAUEBFiXXspNiQ2lsvz1djrDMG55i92t9nF2dpazs3O2di8vrzIRSqU6u9u7BJQSJel657pFQXDtojTiukVpVZKuXQBA6XKr/Cb3G/vszNfXV5KyzXg6ffq0dfaUr6+vrl69qnPnzuW6DwAAAAAAAEqeEhtKBQUFydfXV3Fxcda2q1evauPGjWrV6sYU4mbNmql8+fI2+5w6dUoHDhyw7gMAAAAAAICSx6637128eFG//vqr9f2xY8e0d+9eeXt7q2bNmho2bJhiYmJUt25d1a1bVzExMXJzc1NERIQkqUKFCurbt69GjhypypUry9vbW1FRUWrSpIk6dOhgr48FAAAAAACAW7BrKPXTTz+pXbt21vdZi49HRkYqNjZWo0aN0pUrVzRw4ECdO3dO9913n9atW2ezUNbs2bNVrlw59ejRQ1euXFH79u0VGxsrR0dH0z8PAAAAgOKVmZmpq1ev2rsMALijlS9fvkhyF7uGUuHh4TIMI9ftFotF0dHRio6OznUfFxcXzZs3T/PmzSuGCgEAAMqeGi7d7F0CSpUUexdgdfXqVR07dkyZmZn2LgUA7ngVK1aUr6/vLRczz0uJf/oeAAAAilZg2jJ7l4BSJNHeBfx/hmHo1KlTcnR0VEBAgBwcSuzyuABQphmGocuXL+v06dOSpOrVqxe6L0IpAAAAACXe9evXdfnyZfn5+cnNzc3e5QDAHc3V1VWSdPr0aVWrVq3Qt/LxzwsAAAAASryMjAxJkpOTk50rAQBIsv4DwbVr1wrdB6EUAAAAgFLjdtYuAQAUnaL4eUwoBQAAAAAAANMRSgEAAAAAAMB0hFIAAAAAALv6888/Va1aNSUmJha6j+joaN1zzz1FVlNhNG/eXJ999lm+9r169arq1KmjrVu3FnNV5jt9+rSqVq2q33//3d6loIQjlAIAAAAA2NW0adP00EMPKTAwMF/7WywWrVy50qYtKipK69evL/richAbG6uKFStmax8/frxefvllZWZm3rKPd955R3fddZdat26d73EDAwM1Z86cAlRa/Hr16qVHHnnEpq1atWp65plnNGHCBPsUhVKDUAoAAAAAYDdXrlzR+++/r379+t1WPx4eHqpcuXIRVVU4Xbt2VUpKir755ptb7jtv3rzb/swlWe/evbV06VKdO3fO3qWgBCOUAgAAAADYzdq1a1WuXDm1bNlSmZmZqlGjhhYtWmSzz+7du2WxWHT06FHrbKpHH31UFovF+r6gt+998MEHatSokZydnVW9enUNHjzYum3WrFlq0qSJ3N3dFRAQoIEDB+rixYuSpA0bNqh3795KSUmRxWKRxWJRdHS0JMnR0VFdunTRxx9/nOfYu3fv1q+//qquXbta21q2bKmXX37ZZr8zZ86ofPny+v777xUeHq7jx49r+PDh1nGzbNu2TW3btpWrq6sCAgI0ZMgQXbp0KV/nIT09XaNGjVJAQICcnZ1Vt25dvf/++5KkjIwM9e3bV0FBQXJ1dVX9+vU1d+5c67HR0dFasmSJvvjiC2tNGzZskCQ1adJEvr6++vzzz/NVB+5MhFIAAAAAALvZtGmTQkNDJUkODg568skntXTpUpt9li1bppYtW6pWrVrauXOnJGnx4sU6deqU9X1BLFy4UIMGDdJzzz2n/fv3a9WqVapTp451u4ODg958800dOHBAS5Ys0XfffadRo0ZJklq1aqU5c+bIy8tLp06d0qlTpxQVFWU9tkWLFtq8efMtP3O9evXk5eVlbevZs6c+/vhjGYZhbVu+fLl8fHwUFhamzz77TDVq1NCkSZOs40rS/v371blzZ3Xv3l0///yzli9fri1bttiEbHl59tln9cknn+jNN99UQkKCFi1aJA8PD0myhoQrVqxQfHy8Xn31VY0dO1YrVqyQdOOWyR49euiBBx6w1tSqVasCnQvc2crZuwAAAAAAwJ0rMTFRfn5+1vc9e/bUrFmzdPz4cd11113KzMzUJ598orFjx0qSqlatKkmqWLGifH19CzXmlClTNHLkSA0dOtTa1rx5c+ufhw0bZv1zUFCQJk+erBdeeEELFiyQk5OTKlSoIIvFkuP4/v7+SkpKUmZmphwccp4H8tfPLElPPPGEhg8fri1btqhNmzaSboRxERERcnBwkLe3txwdHeXp6Wkz7uuvv66IiAhrzXXr1tWbb76psLAwLVy4UC4uLrmeh0OHDmnFihWKi4tThw4dJEm1atWybi9fvrwmTpxocy62bdumFStWqEePHvLw8JCrq6vS09NzPRd79uzJdXyAmVIAAAAAALu5cuWKTXASEhKiBg0aWG+B27hxo06fPq0ePXoUyXinT5/WyZMn1b59+1z3+f7779WxY0f5+/vL09NTzz77rP7888983RLn6uqqzMxMpaenKykpSR4eHtZXTEyMpOyfWboRtnXs2NE6S+zYsWPavn27evbsmed4u3btUmxsrM04nTt3VmZmpo4dO5bnsXv37pWjo6PCwsJy3WfRokUKDQ1V1apV5eHhoXfffVdJSUm3PA/SjXNx+fLlfO2LOxOhFAAAAADAbqpUqZJtMeyePXtq2bJlkm7MFurcubOqVKlSJOO5urrmuf348ePq0qWLGjdurE8//VS7du3SW2+9JUm6du3aLfs/e/as3Nzc5OrqKj8/P+3du9f6GjBggKScP7N043P/5z//0bVr17Rs2TI1atRId999d57jZWZm6vnnn7cZZ9++fTp8+LBq166d57G3OhcrVqzQ8OHD1adPH61bt0579+5V7969dfXq1VuchRvOnj1rndkG5IRQCgAAAABgNyEhIYqPj7dpi4iI0P79+7Vr1y795z//yTZbqHz58srIyCjUeJ6engoMDNT69etz3P7TTz/p+vXrmjlzpu6//37Vq1dPJ0+etNnHyckp1/EPHDige++9V5JUrlw51alTx/ry9va2fub//ve/NutHSdIjjzyitLQ0ff3111q2bJmefvrpW45777336pdffrEZJ+vl5OSU57lo0qSJMjMztXHjxhy3b968Wa1atdLAgQMVEhKiOnXq6MiRIwU6FyEhIXnWgDsboRQAAAAAwG46d+6sX375xWbmUFBQkFq1aqW+ffvq+vXrevjhh22OyQqVkpOTc5xxdCvR0dGaOXOm3nzzTR0+fFi7d+/WvHnzJEm1a9fW9evXNW/ePB09elQffvhhtqcBBgYG6uLFi1q/fr3++OMPm1vUNm/erE6dOuU5frt27XTp0iX98ssvNu3u7u56+OGHNX78eCUkJCgiIiLbuJs2bdLvv/+uP/74Q5I0evRobd++XYMGDdLevXt1+PBhrVq1Si+++OItz0NgYKAiIyPVp08frVy5UseOHdOGDRusC5nXqVNHP/30k7755hsdOnRI48ePz7awfGBgoH7++WcdPHhQf/zxh3U22eXLl7Vr165bngvc2QilAAAAAAB206RJE4WGhlqDkCw9e/bUvn371L1792y3mc2cOVNxcXEKCAgo1EycyMhIzZkzRwsWLFCjRo3UrVs3HT58WJJ0zz33aNasWZo+fboaN26spUuXatq0aTbHt2rVSgMGDNATTzyhqlWrasaMGZKk33//Xdu2bVPv3r3zHL9y5crq3r17tqcM3vy527Rpo5o1a9psmzRpkhITE1W7dm3rbXFNmzbVxo0bdfjwYbVp00YhISEaP368qlevnq9zsXDhQj3++OMaOHCgGjRooP79+1vXzhowYIC6d++uJ554Qvfdd5/+/PNPDRw40Ob4/v37q379+tZ1p7Zu3SpJ+uKLL1SzZk3rou1ATizGX+cL3oFSU1NVoUIFpaSk2DySs7T67WUeuYn8qfFayfkLgusWBcG1i9KoJF23gS+vsXcJKEUSX+tq7xIkSWlpaTp27JiCgoLyfJoYSqevvvpKUVFROnDgQK5PrCsNXnrpJaWkpOidd9655b779+9Xhw4d9Ouvv8rT09OE6szVokULDRs2LNtsL5Qdef1czm/OUq64iwQAAAAAIC9dunTR4cOH9fvvvysgIMDe5RRatWrVFBUVla99mzRpohkzZigxMVFNmjQp5srMdfr0aT3++ON66qmn7F0KSjhCKQAAAACA3Q0dOrTI+vLw8Mh129q1a4vtlrKXXnqpQPtHRkYWSx1ZNm/erAcffDDX7RcvXiyWcatVq6ZRo0YVS98oWwilAAAAAABlyt69e3Pd5u/vb14hdhYaGprnuQDsjVAKAAAAAFCm1KlTx94llAiurq6cC5RopXcFOQAAAAAAAJRahFIAAAAAAAAwHaEUAAAAAAAATEcoBQAAAAAAANMRSgEAAAAAAMB0PH0PAAAAQKkV+PIaU8dLfK2rqePB1lsDvjN1vEGL/m7qeDBHbGyshg0bpvPnz9u7lDseM6UAAAAAwI4CAwM1Z86cbO379+9XWFiYXF1d5e/vr0mTJskwjNseLzw8XMOGDcvWnpSUpIceekju7u6qUqWKhgwZoqtXr972eLBl9tcb2T3xxBM6dOiQvcuAmCkFAAAAACVOamqqOnbsqHbt2mnnzp06dOiQevXqJXd3d40cObLIx8vIyFDXrl1VtWpVbdmyRX/++aciIyNlGIbmzZtX5OPBltlf7zvZtWvX5OrqKldXV3uXAjFTCgAAAACK1YULF9SzZ0+5u7urevXqmj17tnW2Unh4uI4fP67hw4fLYrHIYrFIkpYuXaq0tDTFxsaqcePG6t69u8aOHatZs2blOXvm+vXrGjJkiCpWrKjKlStr9OjRioyM1COPPCJJ6tWrlzZu3Ki5c+dax0tMTNS6desUHx+vjz76SCEhIerQoYNmzpypd999V6mpqWacpjLDzK93r1699Mgjj+iNN95Q9erVVblyZQ0aNEjXrl2z7nPu3Dk9++yzqlSpktzc3PTggw/q8OHD1u2xsbGqWLGivvnmGzVs2FAeHh564IEHdOrUqXx93vzUYLFYtHLlSpvjKlasqNjYWElSYmKiLBaLVqxYoTZt2sjV1VXNmzfXoUOHtHPnToWGhlrrOnPmjE0/ixcvVsOGDeXi4qIGDRpowYIF1m039xseHi4XFxd99NFH1s98s1WrVik0NFQuLi6qUqWKunfvnq/Pj9tDKAUAAAAAxWjEiBHaunWrVq1apbi4OG3evFm7d++WJH322WeqUaOGJk2apFOnTlmDgO3btyssLEzOzs7Wfjp37qyTJ08qMTEx17GmT5+upUuXavHixdq6datSU1NtwoC5c+eqZcuW6t+/v3W8gIAAbd++XY0bN5afn5/NeOnp6dq1a1fRnpAyzsyvtyR9//33OnLkiL7//nstWbJEsbGx1rBHuhEa/fTTT1q1apW2b98uwzDUpUsXm9Do8uXLeuONN/Thhx9q06ZNSkpKUlRUVL4/861qyK8JEybolVde0e7du1WuXDk99dRTGjVqlObOnavNmzfryJEjevXVV637v/vuuxo3bpymTp2qhIQExcTEaPz48VqyZIlNv6NHj9aQIUOUkJCgzp07Zxt3zZo16t69u7p27ao9e/Zo/fr1Cg0NLXD9KDhu3yuDlh+bbu8SUEqMVBt7lwAAAFCmXbhwQUuWLNGyZcvUvn17STdmdmSFP97e3nJ0dJSnp6d8fX2txyUnJyswMNCmLx8fH+u2oKCgHMebN2+exowZo0cffVSSNH/+fH311VfW7RUqVJCTk5Pc3NyyjZfVf5ZKlSrJyclJycnJhfz0dx6zv97Sja/T/Pnz5ejoqAYNGqhr165av369+vfvr8OHD2vVqlXaunWrWrVqJenGrKyAgACtXLlS//znPyXduKVt0aJFql27tiRp8ODBmjRpUr4/d141FERUVJQ1NBo6dKieeuoprV+/Xq1bt5Yk9e3b1ybsmjx5smbOnGmd1RQUFKT4+Hi9/fbbioyMtO43bNiwPGc+TZ06VU8++aQmTpxobbv77rsLVDsKh5lSAAAAAFBMjh49qmvXrqlFixbWtgoVKqh+/fq3PDbr1q4sWbdxWSwWJSUlycPDw/qKiYlRSkqK/ve//9mM5ejoqGbNmuWr1r+OlzVmTu3ImZlf7yyNGjWSo6Oj9X316tV1+vRpSVJCQoLKlSun++67z7q9cuXKql+/vhISEqxtbm5u1kDqr33kR141FETTpk2tf84K5Zo0aWLTltXvmTNndOLECfXt29fm3EyZMkVHjhyx6fdWs5727t1rDRFhLmZKAQAAAEAxuTlYyKk9N76+vtlmKGX9Mu7j4yM/Pz/t3bvXus3b29v654KOlTXejz/+aNN27tw5Xbt2LdsMKuTOHl/v8uXL2xxnsViUmZmZ57h/DRtz6qMgT/7Lq4bc+rv59sGc+smq769tWf1m/ffdd9+1Cd0k2QRkkuTu7p5n/Sx6bj/MlAIAAACAYlK7dm2VL19eO3bssLalpqbaLDTt5OSkjIwMm+NatmypTZs26erVq9a2devWyc/PT4GBgSpXrpzq1KljfXl7e6tChQry8fGxGSsjI0N79uyx6Tu38Q4cOGCzuPW6devk7Oyc75lWMPfrnR/BwcG6fv26TeD4559/6tChQ2rYsGFhP2aBVa1a1ebaOnz4sC5fvnxbffr4+Mjf319Hjx61OTd16tTJ83bHnDRt2lTr16+/rXpQOIRSAAAAAFBMPD09FRkZqZdeeknff/+9fvnlF/Xp00cODg7WmSCBgYHatGmTfv/9d/3xxx+SpIiICDk7O6tXr146cOCAPv/8c8XExGjEiBF53k734osvatq0afriiy908OBBDR06VOfOnbM5JjAwUD/++KMSExP1xx9/KDMzU506dVJwcLCeeeYZ60LPUVFR6t+/v7y8vIr3JJUhZn+9b6Vu3bp6+OGH1b9/f23ZskX79u3T008/LX9/fz388MNF8pnz4+9//7vmz5+v3bt366efftKAAQOyza4qjOjoaE2bNk1z587VoUOHtH//fi1evFizZs0qUD8TJkzQxx9/rAkTJighIUH79+/XjBkzbrs+3Bq37wEAAAAotRJf62rvEm5p1qxZGjBggLp16yYvLy+NGjVKJ06ckIuLiyRp0qRJev7551W7dm2lp6fLMAxVqFBBcXFxGjRokEJDQ1WpUiWNGDFCI0aMyHOs0aNHKzk5Wc8++6wcHR313HPPqXPnzja3M0VFRSkyMlLBwcG6cuWKjh07psDAQK1Zs0YDBw5U69at5erqqoiICL3xxhvFem4KatCiv9u7hFsy8+udH4sXL9bQoUPVrVs3Xb16VW3bttVXX31VJKFQfs2cOVO9e/dW27Zt5efnp7lz5xbJUx379esnNzc3vf766xo1apTc3d3VpEkTDRs2rED9hIeH69///rcmT56s1157TV5eXmrbtu1t14dbsxgFuVG0jEpNTVWFChWUkpJSJv4VYOYT3exdAkqJkctX27sEq99e3mzvElCK1Hit5Dw5kmsX+VWSrtvAl9fYuwSUIiUl9ElLS9OxY8cUFBRk/eW+tLp06ZL8/f01c+ZM9e3bt1jHyszMVMOGDdWjRw9Nnjy5WMdCzsz8egNmyuvncn5zFmZKAQAAAEAx2rNnj/773/+qRYsWSklJ0aRJkySpWG6fOn78uNatW6ewsDClp6dr/vz5OnbsmCIiIop8LOTMzK83UNoRSgEAAABAMXvjjTd08OBBOTk5qVmzZtq8ebOqVKlS5OM4ODgoNjZWUVFRMgxDjRs31rfffmvqotYw7+ttBg8Pj1y3rV27Vm3alJyZwCh9CKUAAAAAoBiFhIQUyfo5+REQEKCtW7eaMhZyZubX2wx79+7NdZu/v795haBMIpQCAAAAAAA5qlOnjr1LQBnmYO8CAAAAAAAAcOchlAIAAAAAAIDpCKUAAAAAAABgOkIpAAAAAAAAmI5QCgAAAAAAAKbj6XsAAAAASq/oCiaPl2LueLAx84lupo43cvlqU8e7E0VHR2vlypXau3evvUuBHTBTCgAAAADsKDAwUHPmzMnWvn//foWFhcnV1VX+/v6aNGmSDMMwpaakpCQ99NBDcnd3V5UqVTRkyBBdvXrVlLFxZ4mKitL69euLdYwNGzbIYrHo/Pnz2bYtWLBAQUFBcnFxUbNmzbR58+ZirQW2mCkFAAAAACVMamqqOnbsqHbt2mnnzp06dOiQevXqJXd3d40cObJYx87IyFDXrl1VtWpVbdmyRX/++aciIyNlGIbmzZtXrGPjzmEYhjIyMuTh4SEPDw+71LB8+XINGzZMCxYsUOvWrfX222/rwQcfVHx8vGrWrGmXmu40zJQCAAAAgGJ04cIF9ezZU+7u7qpevbpmz56t8PBwDRs2TOHh4Tp+/LiGDx8ui8Uii8UiSVq6dKnS0tIUGxurxo0bq3v37ho7dqxmzZp1y9lSixcvVsOGDeXi4qIGDRpowYIFNtt37NihkJAQubi4KDQ0VJ9//rksFov19ql169YpPj5eH330kUJCQtShQwfNnDlT7777rlJTU4vlHJUV4eHhGjJkiEaNGiVvb2/5+voqOjrauj0pKUkPP/ywPDw85OXlpR49euh///ufdXt0dLTuueceffjhhwoMDFSFChX05JNP6sKFC0UyfmJios3XWpLOnz8vi8WiDRs2SPq/WUXffPONQkJC5Orqqr///e86ffq01q5dq4YNG8rLy0tPPfWULl++bO3HMAzNmDFDtWrVkqurq+6++2795z//sW6/ud/Q0FA5Oztr8+bN1s98sw8++ECNGjWSs7OzqlevrsGDB+f5ubdt26Z77rnHek2vXLnS+jkTExPVrl07SVKlSpVksVjUq1cvSdKsWbPUt29f9evXTw0bNtScOXMUEBCghQsX5ut84/YRSgEAAABAMRoxYoS2bt2qVatWKS4uTps3b9bu3bslSZ999plq1KihSZMm6dSpUzp16pQkafv27QoLC5Ozs7O1n86dO+vkyZNKTEzMdax3331X48aN09SpU5WQkKCYmBiNHz9eS5YskSRdunRJ3bp1U/369bVr1y5FR0crKirKpo/t27ercePG8vPzsxk7PT1du3btKqrTUmYtWbJE7u7u+vHHHzVjxgxNmjRJcXFxMgxDjzzyiM6ePauNGzcqLi5OR44c0RNPPGFz/JEjR7Ry5UqtXr1aq1ev1saNG/Xaa6/d9vgFFR0drfnz52vbtm06ceKEevTooTlz5mjZsmVas2aN4uLibGbOvfLKK1q8eLEWLlyoX375RcOHD9fTTz+tjRs32vQ7atQoTZs2TQkJCWratGm2cRcuXKhBgwbpueee0/79+7Vq1SrVqVMn1zovXLighx56SE2aNNHu3bs1efJkjR492ro9ICBAn376qSTp4MGDOnXqlObOnaurV69q165d6tSpk01/nTp10rZt2wp8vlA43L4HAAAAAMXkwoULWrJkiZYtW6b27dtLujGTKSvw8fb2lqOjozw9PeXr62s9Ljk5WYGBgTZ9+fj4WLcFBQXlON7kyZM1c+ZMde/eXZIUFBSk+Ph4vf3224qMjNTSpUuVkZGhDz74QG5ubmrUqJF+++03vfDCCzZjZ42VpVKlSnJyclJycvLtnZA7QNOmTTVhwgRJUt26dTV//nzrmkk///yzjh07poCAAEnShx9+qEaNGmnnzp1q3ry5JCkzM1OxsbHy9PSUJD3zzDNav369pk6delvjd+zYsUCfY8qUKWrdurUkqW/fvhozZoyOHDmiWrVqSZIef/xxff/99xo9erQuXbqkWbNm6bvvvlPLli0lSbVq1dKWLVv09ttvKywszNrvpEmT8qxlypQpGjlypIYOHWptyzo3OVm6dKksFoveffddubi4KDg4WL///rv69+8vSXJ0dJS3t7ckqVq1aqpYsaIk6eTJk8rIyMh2rfv4+HCdm4iZUgAAAABQTI4ePapr166pRYsW1rYKFSqofv36tzw261a+LFm37VksFiUlJVnX4vHw8FBMTIzOnDmjEydOqG/fvjbbpkyZoiNHjkiSEhISdPfdd8vNzc3ab1aIkNfYWePn1A5bf539U716dZ0+fVoJCQkKCAiwBlKSFBwcrIoVKyohIcHaFhgYaA2kbj7+dscvqJv78fHxkZubmzWQymrL6jc+Pl5paWnq2LGjzbX3r3/9y3rtZQkNDc11zNOnT+vkyZPWAPevBgwYYNO/dGP2U9OmTeXi4mLd7+bvt1vJ6fuM69w8zJQCAAAAgGJyc5CUU3tufH19s83WyAoAfHx85OfnZ7MukLe3t65duybpxi189913n82xjo6O+Ro3a+wff/zRpu3cuXO6du1atlklyK58+fI27y0WizIzM3MNO/7antvxtzu+JDk4OFjHzJJ13eTVj8ViybPfrP+uWbNG/v7+NvvdfAuqJLm7u+dau6ura67bpBuzrP56u2lO5zU/13mVKlXk6OiY4/cZ17l5mCkFAAAAAMWkdu3aKl++vHbs2GFtS01N1eHDh63vnZyclJGRYXNcy5YttWnTJl29etXatm7dOvn5+SkwMFDlypVTnTp1rC9vb2/5+PjI399fR48etdlWp04d6+1+wcHB2rdvn65cuWLt94cffsg29oEDB6zrW2WN7ezsrGbNmhXNibkDBQcHKykpSSdOnLC2xcfHKyUlRQ0bNjSlhqpVq0qSzdf25nCzsIKDg+Xs7KykpKRs197NM8NuxdPTU4GBgdbbHf+qWrVqNn1LUoMGDfTzzz8rPT3dut9PP/1kc5yTk5Mk2XyfOTk5qVmzZtnW24qLi1OrVq3yXTNuD6EUAAAAABQTT09PRUZG6qWXXtL333+vX375RX369JGDg4N1dkdgYKA2bdqk33//XX/88YckKSIiQs7OzurVq5cOHDigzz//XDExMRoxYkSetxZFR0dr2rRpmjt3rg4dOqT9+/dr8eLFmjVrlrVfBwcH9e3bV/Hx8frqq6/0xhtv2PTRqVMnBQcH65lnntGePXu0fv16RUVFqX///vLy8iqmM1X2dejQQU2bNlXPnj21e/du7dixQ88++6zCwsLyvKWtKLm6uur+++/Xa6+9pvj4eG3atEmvvPLKbffr6empqKgoDR8+XEuWLNGRI0e0Z88evfXWW9ZF9vMrOjpaM2fO1JtvvqnDhw9r9+7dNguq/1VERIQyMzP13HPPKSEhQd988431ms76XrnrrrtksVi0evVqnTlzRhcvXpR04yEE7733nj744AMlJCRo+PDhSkpK0oABAwp5JlBQ3L4HAAAAoPSKTrF3Bbc0a9YsDRgwQN26dZOXl5dGjRqlEydOWNfAmTRpkp5//nnVrl1b6enpMgxDFSpUUFxcnAYNGqTQ0FBVqlRJI0aM0IgRI/Icq1+/fnJzc9Prr7+uUaNGyd3dXU2aNNGwYcMkSR4eHvryyy81YMAAhYSEKDg4WNOnT9djjz1m7cPR0VFr1qzRwIED1bp1a7m6uioiIiJbeGUPI5evtncJhWaxWLRy5Uq9+OKLatu2rRwcHPTAAw/kGbgUhw8++EB9+vRRaGio6tevrxkzZmR7Al1hTJ48WdWqVdO0adN09OhRVaxYUffee6/Gjh1boH4iIyOVlpam2bNnKyoqSlWqVNHjjz+e6/5eXl768ssv9cILL+iee+5RkyZN9OqrryoiIsL6Pebv76+JEyfq5ZdfVu/evfXss88qNjZWTzzxhP7880/r0y8bN26sr776SnfddddtnQvkn8XIz82WZVxqaqoqVKiglJSUMpH8z3yim71LQClRkv5S/+3lzfYuAaVIjdfa2LsEK65d5FdJum4DX15j7xJQiiS+1tXeJUiS0tLSdOzYMQUFBdksaFwaXbp0Sf7+/po5c6b69u1r73KUmJiooKAg7dmzR/fcc4+9ywFu29KlS9W7d2+lpKTccp0qFF5eP5fzm7MwUwoAAAAAitGePXv03//+Vy1atFBKSoomTZokSXr44YftXBlQNvzrX/9SrVq15O/vr3379mn06NHq0aMHgVQpQCgFAAAAAMXsjTfe0MGDB62LK2/evFlVqlSxd1koRZKSkhQcHJzr9vj4eNWsWdPEikqO5ORkvfrqq0pOTlb16tX1z3/+U1OnTrV3WcgHQikAAAAAKEYhISHatWuXvcvIVWBgoFjVpeTz8/PL80l5fn5+5hVTwowaNUqjRo2ydxkoBEIpAAAAAABKuHLlyqlOnTr2LgMoUg72LgAAAAAAAAB3HkIpAAAAAAAAmI5QCgAAAAAAAKYjlAIAAAAAAIDpCKUAAAAAAABgOp6+BwAAAKDUarKkianj7Y/cb+p4sPXby5tNHa/Ga21MHe9OFB0drZUrV2rv3r32LuWOExgYqGHDhmnYsGF2q4GZUgAAAABQwu3fv19hYWFydXWVv7+/Jk2aJMMwbrvf8PDwHH8hTUpK0kMPPSR3d3dVqVJFQ4YM0dWrV297POCvoqKitH79enuXcUfauXOnnnvuObvWwEwpAAAAACjBUlNT1bFjR7Vr1047d+7UoUOH1KtXL7m7u2vkyJFFPl5GRoa6du2qqlWrasuWLfrzzz8VGRkpwzA0b968Ih8PdybDMJSRkSEPDw95eHjYu5w7ytWrV+Xk5KSqVavauxRmSgEAAABAcTIMQzNmzFCtWrXk6uqqu+++W//5z3+s27/66ivVq1dPrq6uateunWJjY2WxWHT+/HlJ0tKlS5WWlqbY2Fg1btxY3bt319ixYzVr1qw8Z0tdv35dQ4YMUcWKFVW5cmWNHj1akZGReuSRRyRJvXr10saNGzV37lxZLBZZLBYlJiZq3bp1io+P10cffaSQkBB16NBBM2fO1LvvvqvU1NTiPFWlXnh4uIYMGaJRo0bJ29tbvr6+io6Otm5PSkrSww8/LA8PD3l5ealHjx763//+Z90eHR2te+65Rx9++KECAwNVoUIFPfnkk7pw4UKRjJ+YmCiLxWJzq9z58+dlsVi0YcMGSdKGDRtksVj0zTffKCQkRK6urvr73/+u06dPa+3atWrYsKG8vLz01FNP6fLly9Z+bnWd39xvaGionJ2dtXnzZutnvtkHH3ygRo0aydnZWdWrV9fgwYNz/cxZn+mzzz5Tu3bt5Obmprvvvlvbt2+32e/TTz+19hkYGKiZM2fabA8MDFRMTIz69OkjT09P1axZU++8806+znt+asjpc86ZM0eBgYHW97169dIjjzyimJgY+fj4qGLFipo4caKuX7+ul156Sd7e3qpRo4Y++OADm35+//13PfHEE6pUqZIqV66shx9+WImJidn6nTZtmvz8/FSvXj3rZ54zZ451v/Pnz+u5556Tj4+PXFxc1LhxY61evTpf56CwCKUAAAAAoBi98sorWrx4sRYuXKhffvlFw4cP19NPP62NGzfqxIkT6t69u7p06aK9e/eqX79+evnll22O3759u8LCwuTs7Gxt69y5s06ePGnzi+dfTZ8+XUuXLtXixYu1detWpaamauXKldbtc+fOVcuWLdW/f3+dOnVKp06dUkBAgLZv367GjRvLz8/PZrz09HTt2rWryM5LWbVkyRK5u7vrxx9/1IwZMzRp0iTFxcXJMAw98sgjOnv2rDZu3Ki4uDgdOXJETzzxhM3xR44c0cqVK7V69WqtXr1aGzdu1GuvvXbb4xdUdHS05s+fr23btunEiRPq0aOH5syZo2XLlmnNmjWKi4uzmTmX13V+s1GjRmnatGlKSEhQ06ZNs427cOFCDRo0SM8995z279+vVatWqU6dOresd9y4cYqKitLevXtVr149PfXUU7p+/bokadeuXerRo4eefPJJ7d+/X9HR0Ro/frxiY2Nt+pg5c6ZCQ0O1Z88eDRw4UC+88IL++9//5vuc5VVDfn333Xc6efKkNm3apFmzZik6OlrdunVTpUqV9OOPP2rAgAEaMGCATpw4IUm6fPmy2rVrJw8PD23atElbtmyRh4eHHnjgAZtbbtevX6+EhATFxcXlGDRlZmbqwQcf1LZt2/TRRx8pPj5er732mhwdHQtUf0Fx+14Z5FJphL1LAAAAACDp0qVLmjVrlr777ju1bNlSklSrVi1t2bJFb7/9tgIDA1WrVi3Nnj1bFotF9evX1/79+zV9+nRrH8nJyTazKSTJx8fHui0oKCjHsefNm6cxY8bo0UcflSTNnz9fX331lXV7hQoV5OTkJDc3N/n6+tqMl9V/lkqVKsnJyUnJycmFPxl3iKZNm2rChAmSpLp162r+/PnWNZN+/vlnHTt2TAEBAZKkDz/8UI0aNdLOnTvVvHlzSTfCgdjYWHl6ekqSnnnmGa1fv15Tp069rfE7duxYoM8xZcoUtW7dWpLUt29fjRkzRkeOHFGtWrUkSY8//ri+//57jR49+pbXeVhYmLXfSZMm5VnLlClTNHLkSA0dOtTalnVu8hIVFaWuXbtKkiZOnKhGjRrp119/VYMGDTRr1iy1b99e48ePlyTVq1dP8fHxev3119WrVy9rH126dNHAgQMlSaNHj9bs2bO1YcMGNWjQID+nLM8a8svb21tvvvmmHBwcVL9+fc2YMUOXL1/W2LFjJUljxozRa6+9pq1bt+rJJ5/UJ598IgcHB7333nuyWCySpMWLF6tixYrasGGDOnXqJElyd3fXe++9JycnpxzH/fbbb7Vjxw4lJCRYZ1Jlfa2LE6FUGTTI91F7l4BSI8XeBQAAAJRp8fHxSktLy/ZL+NWrVxUSEqIrV67o/vvvt/4yKcn6S/3Nbt4uyXrbnsViUVJSkoKDg63bxo4dq0GDBul///ufWrRoYW13dHRUs2bNlJmZecu6/zpe1pg5tcPWX2f/VK9eXadPn1ZCQoICAgKsgZQkBQcHq2LFikpISLAGL4GBgdZA6ubjb3f82/kcPj4+cnNzswkpfHx8tGPHDkm3vs5vFhoamuuYp0+f1smTJ9W+ffsctw8YMEAfffSR9f3FixdzrLd69erW/ho0aKCEhAQ9/PDDNn21bt1ac+bMUUZGhnU20M19WCwW+fr6Fvrc/7WG/GrUqJEcHP7vpjYfHx81btzY+t7R0VGVK1e21rVr1y79+uuvNteMJKWlpenIkSPW902aNMk1kJKkvXv3qkaNGtZAyiyEUmVQYNoye5eAUiLR3gUAAACUcVkB0Jo1a+Tv72+zzdnZWS+++OIt+/D19c02QynrF1IfHx/5+fnZrBHk7e1t/XNuYdatxvvxxx9t2s6dO6dr165lm0GF7MqXL2/z3mKxKDMzM9dQ76/tuR1/u+NLsoYdN18H165du2U/Foslz35vdZ3fzN3dPdfaXV1dc90m3ZhlFRUVla96b64rp3Of0/dCUZ77v9bg4OCQbcyczn1ONdzq3Ddr1kxLly7N1tfNC5nndd6lW5/74kIoBQAAAADFJDg4WM7OzkpKSrK5henm7Tev8yRJP/zwg837li1bauzYsdYnZknSunXr5Ofnp8DAQFkslhzX3MmaydKmTRtJN56qt2fPHpvFlp2cnJSRkZFtvKlTp+rUqVPW2R7r1q2Ts7OzmjVrVuBzgBuCg4OVlJSkEydOWGdLxcfHKyUlRQ0bNjSlhqyQ4tSpU9YZTDcHmoV1q+s8vzw9PRUYGKj169erXbt22bZXq1ZN1apVK1R9W7ZssWnbtm2b6tWrV+xrJmWpWrWqkpOTbQKyojj39957r5YvX65q1arJy8ur0P00bdpUv/32mw4dOmTqbCkWOgcAAACAYuLp6amoqCgNHz5cS5Ys0ZEjR7Rnzx699dZbWrJkiQYMGKAjR45oxIgROnjwoJYtW5Zt8eWIiAg5OzurV69eOnDggD7//HPFxMRoxIgRed5O9+KLL2ratGn64osvdPDgQQ0dOlTnzp2zOSYwMFA//vijEhMT9ccffygzM1OdOnVScHCwnnnmGe3Zs0fr169XVFSU+vfvf1u/9N7pOnTooKZNm6pnz57avXu3duzYoWeffVZhYWF53tJWlFxdXXX//ffrtddeU3x8vDZt2qRXXnnltvu91XVeENHR0Zo5c6befPNNHT58WLt377Z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      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# ============================================\n",
    "# 堆疊條狀圖：sponvt_type × (vti_cat, vte_cat)\n",
    "# 目錄：/home/jovyan/1010/data_new/bling_sponvt_1/\n",
    "# 類別定義：\n",
    "#   vti_cat / vte_cat ∈ {gt0, eq0, non_numeric}\n",
    "#   sponvt_type ∈ {float_zero, float_nonzero, bool_true, bool_false, nan, other}\n",
    "# ============================================\n",
    "\n",
    "import os\n",
    "import glob\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "DATA_DIR = \"/home/jovyan/1010/data_new/bling_sponvt_1/\"\n",
    "CSV_GLOB = \"*.csv\"\n",
    "\n",
    "NULL_SET = {\"\", \"(null)\", \"null\", \"<null>\", \"none\", \"nan\"}\n",
    "\n",
    "def clean_str(x):\n",
    "    return x.strip() if isinstance(x, str) else x\n",
    "\n",
    "def classify_vx(x):\n",
    "    \"\"\"回傳 vti/vte 的類別：gt0 / eq0 / non_numeric\"\"\"\n",
    "    if x is None:\n",
    "        return \"non_numeric\"\n",
    "    if isinstance(x, (bool, np.bool_)):\n",
    "        return \"non_numeric\"\n",
    "    if isinstance(x, str):\n",
    "        xl = x.lower().strip()\n",
    "        if xl in NULL_SET or xl in (\"true\", \"false\"):\n",
    "            return \"non_numeric\"\n",
    "        try:\n",
    "            v = float(x.replace(\",\", \"\"))\n",
    "        except Exception:\n",
    "            return \"non_numeric\"\n",
    "    else:\n",
    "        try:\n",
    "            v = float(x)\n",
    "        except Exception:\n",
    "            return \"non_numeric\"\n",
    "\n",
    "    if not np.isfinite(v):\n",
    "        return \"non_numeric\"\n",
    "    if v > 0:\n",
    "        return \"gt0\"\n",
    "    if v == 0:\n",
    "        return \"eq0\"\n",
    "    return \"non_numeric\"\n",
    "\n",
    "def classify_sponvt_type(raw_val):\n",
    "    \"\"\"\n",
    "    sponvt 型態：\n",
    "      - float_zero     : 數值且 == 0\n",
    "      - float_nonzero  : 數值且 != 0\n",
    "      - bool_true/bool_false\n",
    "      - nan            : 缺失或標記字串\n",
    "      - other          : 其他非數字\n",
    "    \"\"\"\n",
    "    if raw_val is None:\n",
    "        return \"nan\"\n",
    "    if isinstance(raw_val, (bool, np.bool_)):\n",
    "        return \"bool_true\" if bool(raw_val) else \"bool_false\"\n",
    "\n",
    "    if isinstance(raw_val, str):\n",
    "        x = raw_val.strip()\n",
    "        xl = x.lower()\n",
    "        if xl in NULL_SET:\n",
    "            return \"nan\"\n",
    "        if xl == \"true\":  return \"bool_true\"\n",
    "        if xl == \"false\": return \"bool_false\"\n",
    "        try:\n",
    "            v = float(x.replace(\",\", \"\"))\n",
    "            return \"float_zero\" if v == 0.0 else \"float_nonzero\"\n",
    "        except Exception:\n",
    "            return \"other\"\n",
    "\n",
    "    try:\n",
    "        v = float(raw_val)\n",
    "        return \"float_zero\" if v == 0.0 else \"float_nonzero\"\n",
    "    except Exception:\n",
    "        return \"other\"\n",
    "\n",
    "def main():\n",
    "    # 讀入並清理\n",
    "    rows = []\n",
    "    for path in sorted(glob.glob(os.path.join(DATA_DIR, CSV_GLOB))):\n",
    "        try:\n",
    "            df = pd.read_csv(path, dtype=str, low_memory=False)\n",
    "        except Exception as e:\n",
    "            print(f\"[WARN] 讀取失敗：{path} -> {e}\")\n",
    "            continue\n",
    "\n",
    "        # 確保欄位存在\n",
    "        for c in [\"sponvt\", \"vti\", \"vte\"]:\n",
    "            if c not in df.columns:\n",
    "                df[c] = np.nan\n",
    "            df[c] = df[c].map(clean_str)\n",
    "\n",
    "        # 分類\n",
    "        part = pd.DataFrame({\n",
    "            \"sponvt_type\": df[\"sponvt\"].map(classify_sponvt_type),\n",
    "            \"vti_cat\": df[\"vti\"].map(classify_vx),\n",
    "            \"vte_cat\": df[\"vte\"].map(classify_vx),\n",
    "        })\n",
    "        rows.append(part)\n",
    "\n",
    "    if not rows:\n",
    "        print(\"[WARN] 沒有可分析的資料。\")\n",
    "        return\n",
    "\n",
    "    data = pd.concat(rows, ignore_index=True)\n",
    "\n",
    "    # 欄位順序與 pair 排序\n",
    "    sponvt_order = [\"float_nonzero\", \"float_zero\", \"bool_true\", \"bool_false\", \"nan\", \"other\"]\n",
    "    pair_order = [\n",
    "        (\"gt0\",\"gt0\"),          # 完整\n",
    "        (\"gt0\",\"eq0\"),\n",
    "        (\"eq0\",\"gt0\"),\n",
    "        (\"eq0\",\"eq0\"),\n",
    "        (\"gt0\",\"non_numeric\"),\n",
    "        (\"non_numeric\",\"gt0\"),\n",
    "        (\"non_numeric\",\"non_numeric\")  # 雙缺\n",
    "    ]\n",
    "\n",
    "    # 交叉「計數」表\n",
    "    data[\"_const\"] = 1\n",
    "    counts = (\n",
    "        data.pivot_table(index=\"sponvt_type\",\n",
    "                         columns=[\"vti_cat\",\"vte_cat\"],\n",
    "                         values=\"_const\",\n",
    "                         aggfunc=\"sum\",\n",
    "                         fill_value=0)\n",
    "        .astype(int)\n",
    "    )\n",
    "\n",
    "    # 補齊缺列並排序\n",
    "    for col in pair_order:\n",
    "        if col not in counts.columns:\n",
    "            counts[col] = 0\n",
    "    counts = counts[pair_order]\n",
    "    # 排序列（若某些 sponvt_type 不存在也保留順序）\n",
    "    for st in sponvt_order:\n",
    "        if st not in counts.index:\n",
    "            counts.loc[st] = 0\n",
    "    counts = counts.loc[sponvt_order]\n",
    "\n",
    "    # 百分比版（列標準化）\n",
    "    row_sums = counts.sum(axis=1).replace(0, np.nan)\n",
    "    pcts = counts.div(row_sums, axis=0).fillna(0.0) * 100.0\n",
    "\n",
    "    # ---------- 圖 1：計數堆疊條圖 ----------\n",
    "    fig1, ax1 = plt.subplots(figsize=(12, 6))\n",
    "    x = np.arange(len(counts.index))\n",
    "    bottom = np.zeros(len(x))\n",
    "    for pair in pair_order:\n",
    "        h = counts[pair].values\n",
    "        ax1.bar(x, h, bottom=bottom, label=f\"{pair[0]}-{pair[1]}\")\n",
    "        bottom += h\n",
    "    ax1.set_title(\"sponvt_type × (vti_cat, vte_cat) — 堆疊條圖（Count）\")\n",
    "    ax1.set_ylabel(\"Count\")\n",
    "    ax1.set_xticks(x)\n",
    "    ax1.set_xticklabels(counts.index, rotation=0)\n",
    "    ax1.legend(loc=\"upper right\", ncol=2, title=\"(vti_cat)-(vte_cat)\")\n",
    "    fig1.tight_layout()\n",
    "\n",
    "    # ---------- 圖 2：百分比堆疊條圖（每柱=100%） ----------\n",
    "    fig2, ax2 = plt.subplots(figsize=(12, 6))\n",
    "    x = np.arange(len(pcts.index))\n",
    "    bottom = np.zeros(len(x))\n",
    "    for pair in pair_order:\n",
    "        h = pcts[pair].values\n",
    "        ax2.bar(x, h, bottom=bottom, label=f\"{pair[0]}-{pair[1]}\")\n",
    "        bottom += h\n",
    "    ax2.set_title(\"sponvt_type × (vti_cat, vte_cat) — 堆疊條圖（Percent）\")\n",
    "    ax2.set_ylabel(\"Percent (%)\")\n",
    "    ax2.set_xticks(x)\n",
    "    ax2.set_xticklabels(pcts.index, rotation=0)\n",
    "    ax2.set_ylim(0, 100)\n",
    "    ax2.legend(loc=\"upper right\", ncol=2, title=\"(vti_cat)-(vte_cat)\")\n",
    "    fig2.tight_layout()\n",
    "\n",
    "    # 顯示圖\n",
    "    plt.show()\n",
    "\n",
    "# 使用方式（我不會執行）：\n",
    "main()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4389edb4-636f-4721-884a-fb40d39cc033",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8d56cc04-622b-40c4-ac80-b3dab584ca15",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "3bdab4cb-1474-4363-8c10-9bbecc0a8827",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[COPY] 來源：/home/jovyan/RT08/0925/blingokNaN_test | 目的：/home/jovyan/RT08/0925/blingsponvt\n",
      "[COPY] 掃描到 123 個項目（僅複製「檔案」）\n",
      "[COPY][OK] 089271.csv\n",
      "[COPY][OK] 095323.csv\n",
      "[COPY][OK] 095707.csv\n",
      "[COPY][OK] 114309.csv\n",
      "[COPY][OK] 230933.csv\n",
      "[COPY][OK] 4216007.csv\n",
      "[COPY][OK] 7108162.csv\n",
      "[COPY][OK] 7408338.csv\n",
      "[COPY][OK] 7657698.csv\n",
      "[COPY][OK] 7721164.csv\n",
      "[COPY][OK] PatNo_ID_1560013303.csv\n",
      "[COPY][OK] PatNo_ID_1562733396.csv\n",
      "[COPY][OK] PatNo_ID_1563587183.csv\n",
      "[COPY][OK] PatNo_ID_1564148644.csv\n",
      "[COPY][OK] PatNo_ID_1565148312.csv\n",
      "[COPY][OK] PatNo_ID_1565378038.csv\n",
      "[COPY][OK] PatNo_ID_1566123680.csv\n",
      "[COPY][OK] PatNo_ID_1566252197.csv\n",
      "[COPY][OK] PatNo_ID_1566279967.csv\n",
      "[COPY][OK] PatNo_ID_1566671274.csv\n",
      "[COPY][OK] PatNo_ID_1566911879.csv\n",
      "[COPY][OK] PatNo_ID_1567747650.csv\n",
      "[COPY][OK] PatNo_ID_1567804800.csv\n",
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      "[COPY][OK] PatNo_ID_1568039398.csv\n",
      "[COPY][OK] PatNo_ID_1568574099.csv\n",
      "[COPY][OK] PatNo_ID_1568813269.csv\n",
      "[COPY][OK] PatNo_ID_1568952422.csv\n",
      "[COPY][OK] PatNo_ID_1569083701.csv\n",
      "[COPY][OK] PatNo_ID_1569944983.csv\n",
      "[COPY][OK] PatNo_ID_1570089466.csv\n",
      "[COPY][OK] PatNo_ID_1570242703.csv\n",
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      "[COPY][OK] PatNo_ID_1570642083.csv\n",
      "[COPY][OK] PatNo_ID_1571945701.csv\n",
      "[COPY][OK] PatNo_ID_1572481361.csv\n",
      "[COPY][OK] PatNo_ID_1572562839.csv\n",
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      "[COPY][OK] PatNo_ID_1573249295.csv\n",
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      "[COPY][OK] PatNo_ID_1574831525.csv\n",
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      "[COPY][OK] PatNo_ID_1575060177.csv\n",
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      "[COPY][OK] PatNo_ID_1579198603.csv\n",
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      "[COPY][OK] PatNo_ID_1580244614.csv\n",
      "[COPY][OK] PatNo_ID_1580766093.csv\n",
      "[COPY][OK] PatNo_ID_1581003248.csv\n",
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      "[COPY][OK] PatNo_ID_1588673465.csv\n",
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      "[COPY][OK] PatNo_ID_1591609798.csv\n",
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      "[COPY][OK] PatNo_ID_1593087886.csv\n",
      "[COPY][OK] PatNo_ID_1593416100.csv\n",
      "[COPY][OK] PatNo_ID_1593472048.csv\n",
      "[COPY][OK] PatNo_ID_1593593586.csv\n",
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      "[COPY][OK] PatNo_ID_1594294180.csv\n",
      "[COPY][OK] PatNo_ID_1594305136.csv\n",
      "[COPY][OK] PatNo_ID_1594309746.csv\n",
      "[COPY][OK] PatNo_ID_1594319286.csv\n",
      "[COPY][OK] PatNo_ID_1594320763.csv\n",
      "[COPY][OK] PatNo_ID_1594322594.csv\n",
      "[COPY][OK] PatNo_ID_1594335109.csv\n",
      "[COPY][OK] PatNo_ID_1594423683.csv\n",
      "[COPY][OK] PatNo_ID_1594437309.csv\n",
      "[COPY][OK] PatNo_ID_1594439781.csv\n",
      "[COPY][OK] PatNo_ID_1594441887.csv\n",
      "[COPY][OK] PatNo_ID_1594448501.csv\n",
      "[COPY][OK] PatNo_ID_1594455578.csv\n",
      "[COPY][OK] PatNo_ID_1594464829.csv\n",
      "[COPY][OK] PatNo_ID_1594467719.csv\n",
      "[COPY][OK] PatNo_ID_1594471407.csv\n",
      "[COPY][OK] PatNo_ID_1594479330.csv\n",
      "[COPY][OK] PatNo_ID_1594511911.csv\n",
      "[COPY][OK] PatNo_ID_1594511914.csv\n",
      "[COPY][OK] PatNo_ID_1594528842.csv\n",
      "[COPY][OK] PatNo_ID_1594533379.csv\n",
      "[COPY] 總檔案：122 | 成功：122 | 失敗：0\n",
      "\n",
      "[TRANSFORM] 掃描目的資料夾：/home/jovyan/RT08/0925/blingsponvt（針對 CSV 進行 sponvt 轉換）\n",
      "[TRANSFORM][OK] 089271.csv | True→1: 0, False→0: 0\n",
      "[TRANSFORM][OK] 095323.csv | True→1: 0, False→0: 0\n",
      "[TRANSFORM][OK] 095707.csv | True→1: 0, False→0: 0\n",
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      "[TRANSFORM][OK] PatNo_ID_1591609798.csv | True→1: 0, False→0: 0\n",
      "[TRANSFORM][OK] PatNo_ID_1592044724.csv | True→1: 757, False→0: 1\n",
      "[TRANSFORM][OK] PatNo_ID_1592560504.csv | True→1: 0, False→0: 0\n",
      "[TRANSFORM][OK] PatNo_ID_1593087886.csv | True→1: 0, False→0: 0\n",
      "[TRANSFORM][OK] PatNo_ID_1593416100.csv | True→1: 0, False→0: 0\n",
      "[TRANSFORM][OK] PatNo_ID_1593472048.csv | True→1: 0, False→0: 0\n",
      "[TRANSFORM][OK] PatNo_ID_1593593586.csv | True→1: 0, False→0: 0\n",
      "[TRANSFORM][OK] PatNo_ID_1593720818.csv | True→1: 1549, False→0: 0\n",
      "[TRANSFORM][OK] PatNo_ID_1593838524.csv | True→1: 0, False→0: 0\n",
      "[TRANSFORM][OK] PatNo_ID_1594173718.csv | True→1: 0, False→0: 0\n",
      "[TRANSFORM][OK] PatNo_ID_1594294180.csv | True→1: 0, False→0: 0\n",
      "[TRANSFORM][OK] PatNo_ID_1594305136.csv | True→1: 0, False→0: 0\n",
      "[TRANSFORM][OK] PatNo_ID_1594309746.csv | True→1: 0, False→0: 0\n",
      "[TRANSFORM][OK] PatNo_ID_1594319286.csv | True→1: 0, False→0: 0\n",
      "[TRANSFORM][OK] PatNo_ID_1594320763.csv | True→1: 0, False→0: 0\n",
      "[TRANSFORM][OK] PatNo_ID_1594322594.csv | True→1: 0, False→0: 0\n",
      "[TRANSFORM][OK] PatNo_ID_1594335109.csv | True→1: 0, False→0: 0\n",
      "[TRANSFORM][OK] PatNo_ID_1594423683.csv | True→1: 0, False→0: 0\n",
      "[TRANSFORM][OK] PatNo_ID_1594437309.csv | True→1: 1189, False→0: 0\n",
      "[TRANSFORM][OK] PatNo_ID_1594439781.csv | True→1: 3155, False→0: 3\n",
      "[TRANSFORM][OK] PatNo_ID_1594441887.csv | True→1: 78, False→0: 36\n",
      "[TRANSFORM][OK] PatNo_ID_1594448501.csv | True→1: 4435, False→0: 5\n",
      "[TRANSFORM][OK] PatNo_ID_1594455578.csv | True→1: 157, False→0: 0\n",
      "[TRANSFORM][OK] PatNo_ID_1594464829.csv | True→1: 2982, False→0: 0\n",
      "[TRANSFORM][OK] PatNo_ID_1594467719.csv | True→1: 290, False→0: 5\n",
      "[TRANSFORM][OK] PatNo_ID_1594471407.csv | True→1: 0, False→0: 0\n",
      "[TRANSFORM][OK] PatNo_ID_1594479330.csv | True→1: 0, False→0: 0\n",
      "[TRANSFORM][OK] PatNo_ID_1594511911.csv | True→1: 2902, False→0: 0\n",
      "[TRANSFORM][OK] PatNo_ID_1594511914.csv | True→1: 5863, False→0: 2650\n",
      "[TRANSFORM][OK] PatNo_ID_1594528842.csv | True→1: 61, False→0: 72\n",
      "[TRANSFORM][OK] PatNo_ID_1594533379.csv | True→1: 237, False→0: 0\n",
      "\n",
      "[TRANSFORM] 統計：\n",
      "- CSV 檔案總數：122\n",
      "- 成功覆寫：122\n",
      "- 無 sponvt 欄位（略過）：0\n",
      "- 非 CSV（略過）：0\n",
      "- 累計替換：True→1 共 143905 筆、False→0 共 17243 筆\n",
      "\n",
      "[DONE] 全部步驟執行完成。\n"
     ]
    }
   ],
   "source": [
    "\"\"\"先​將​​/home/jovyan/R​T08/0925/b​lingokNaN_test/全部​檔案另外複製​存在​/home/jovyan/R​T08/0925/b​lingsponvt/，​並針對​/home/jovyan/R​T08/0925/b​lingsponvt/路徑中的所有檔案​​中sponvt​欄位​的​True​以​1代替，​False​以0代替，​覆蓋​/home/jovyan/R​T08/0925/b​lingsponvt/的檔案，確定每個檔案都有執行過了 每個步驟都要印出 執行了幾個檔案 是否成功\n",
    "\"\"\"\n",
    "# 1) 將 /home/jovyan/RT08/0925/blingokNaN_test/ 全部「檔案」複製到\n",
    "#    /home/jovyan/RT08/0925/blingsponvt/  (保留原檔名、覆蓋舊檔)\n",
    "# 2) 針對目標資料夾內所有「CSV」：\n",
    "#    - 將 sponvt 欄位中的 True→1、False→0（同時處理布林型與字串 \"True\"/\"False\"）\n",
    "#    - 其餘值（含 NaN、中文是/否、數字等）保持原樣\n",
    "# 3) 每個步驟列印：處理檔案數、是否成功、每檔替換筆數\n",
    "# ============================================================\n",
    "import os, shutil, unicodedata, re, glob\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from pathlib import Path\n",
    "\n",
    "# --- 路徑與清理（避免零寬字元/全形空白導致找不到資料夾） ---\n",
    "def clean_path(p: str) -> str:\n",
    "    s = unicodedata.normalize(\"NFKC\", p)\n",
    "    s = re.sub(r\"[\\u200b-\\u200f\\u202a-\\u202e\\u2066-\\u2069\\ufeff\\u00a0\\u3000]\", \"\", s)  # 移除不可見/全形空白/BOM\n",
    "    s = re.sub(r\"[\\\\/]+\", \"/\", s).rstrip(\"/\")\n",
    "    return s\n",
    "\n",
    "SRC_DIR = Path(clean_path(\"/home/jovyan/RT08/0925/blingokNaN_test\"))\n",
    "DST_DIR = Path(clean_path(\"/home/jovyan/RT08/0925/blingsponvt\"))\n",
    "DST_DIR.mkdir(parents=True, exist_ok=True)\n",
    "\n",
    "# ======================\n",
    "# STEP 1: 複製全部檔案\n",
    "# ======================\n",
    "copy_total = 0\n",
    "copy_ok = 0\n",
    "copy_err = 0\n",
    "copy_err_list = []\n",
    "\n",
    "if not SRC_DIR.exists() or not SRC_DIR.is_dir():\n",
    "    raise SystemExit(f\"[ERROR] 來源資料夾不存在：{SRC_DIR}\")\n",
    "\n",
    "all_entries = list(SRC_DIR.iterdir())\n",
    "print(f\"[COPY] 來源：{SRC_DIR} | 目的：{DST_DIR}\")\n",
    "print(f\"[COPY] 掃描到 {len(all_entries)} 個項目（僅複製「檔案」）\")\n",
    "\n",
    "for p in sorted(all_entries):\n",
    "    if p.is_file():\n",
    "        copy_total += 1\n",
    "        dst_path = DST_DIR / p.name\n",
    "        try:\n",
    "            shutil.copy2(p, dst_path)\n",
    "            copy_ok += 1\n",
    "            print(f\"[COPY][OK] {p.name}\")\n",
    "        except Exception as e:\n",
    "            copy_err += 1\n",
    "            copy_err_list.append((p.name, str(e)))\n",
    "            print(f\"[COPY][ERR] {p.name} -> {e}\")\n",
    "\n",
    "print(f\"[COPY] 總檔案：{copy_total} | 成功：{copy_ok} | 失敗：{copy_err}\")\n",
    "if copy_err_list:\n",
    "    print(\"[COPY] 失敗清單：\")\n",
    "    for n, msg in copy_err_list:\n",
    "        print(f\"  - {n}: {msg}\")\n",
    "\n",
    "# ============================\n",
    "# STEP 2: 轉換 sponvt -> 1/0\n",
    "# ============================\n",
    "ENCODINGS = (\"utf-8-sig\",\"utf-8\",\"cp950\",\"big5\",\"latin1\")\n",
    "\n",
    "def read_df_any(path: Path):\n",
    "    for enc in ENCODINGS:\n",
    "        try:\n",
    "            return pd.read_csv(path, encoding=enc, engine=\"python\", on_bad_lines=\"skip\")\n",
    "        except Exception:\n",
    "            continue\n",
    "    raise RuntimeError(f\"讀檔失敗：{path.name}\")\n",
    "\n",
    "def find_sponvt_col(df: pd.DataFrame):\n",
    "    # 以小寫比對，回傳第一個命中的原欄名；若找不到回傳 None\n",
    "    for c in df.columns:\n",
    "        if unicodedata.normalize(\"NFKC\", str(c)).strip().lower() == \"sponvt\":\n",
    "            return c\n",
    "    return None\n",
    "\n",
    "def convert_true_false_to_10(series: pd.Series):\n",
    "    \"\"\"True→1, False→0；接受布林/字串（大小寫須完全 'True'/'False'），其他值維持原樣。\"\"\"\n",
    "    def conv(v):\n",
    "        if pd.isna(v):\n",
    "            return v\n",
    "        # 布林型別\n",
    "        if isinstance(v, (bool, np.bool_)):\n",
    "            return 1 if bool(v) else 0\n",
    "        # 字串型別（正規化與去空白，但不改大小寫判斷）\n",
    "        s = unicodedata.normalize(\"NFKC\", str(v)).strip()\n",
    "        if s == \"True\":\n",
    "            return 1\n",
    "        if s == \"False\":\n",
    "            return 0\n",
    "        return v\n",
    "    return series.apply(conv)\n",
    "\n",
    "proc_total = 0          # 已嘗試處理的 CSV 檔案數\n",
    "proc_ok = 0             # 成功覆寫的 CSV 檔案數\n",
    "proc_skip_no_csv = 0    # 非 CSV 檔案\n",
    "proc_skip_no_col = 0    # 無 sponvt 欄位\n",
    "\n",
    "replaced_true_total = 0\n",
    "replaced_false_total = 0\n",
    "proc_err_list = []\n",
    "\n",
    "dest_entries = list(DST_DIR.iterdir())\n",
    "print(f\"\\n[TRANSFORM] 掃描目的資料夾：{DST_DIR}（針對 CSV 進行 sponvt 轉換）\")\n",
    "\n",
    "for fp in sorted(dest_entries):\n",
    "    if not fp.is_file():\n",
    "        continue\n",
    "    if fp.suffix.lower() != \".csv\":\n",
    "        proc_skip_no_csv += 1\n",
    "        print(f\"[TRANSFORM][SKIP-NONCSV] {fp.name}\")\n",
    "        continue\n",
    "\n",
    "    proc_total += 1\n",
    "    try:\n",
    "        df = read_df_any(fp)\n",
    "        col = find_sponvt_col(df)\n",
    "        if col is None:\n",
    "            proc_skip_no_col += 1\n",
    "            print(f\"[TRANSFORM][SKIP-NOCOL] {fp.name}（找不到 sponvt 欄位）\")\n",
    "            continue\n",
    "\n",
    "        # 統計替換筆數（先以原值計算）\n",
    "        s = df[col]\n",
    "        true_hits = s.apply(lambda v: (isinstance(v, (bool, np.bool_)) and bool(v) is True) or\n",
    "                                      (unicodedata.normalize(\"NFKC\", str(v)).strip() == \"True\")).sum()\n",
    "        false_hits = s.apply(lambda v: (isinstance(v, (bool, np.bool_)) and bool(v) is False) or\n",
    "                                       (unicodedata.normalize(\"NFKC\", str(v)).strip() == \"False\")).sum()\n",
    "\n",
    "        # 轉換\n",
    "        df[col] = convert_true_false_to_10(s)\n",
    "\n",
    "        # 覆寫（UTF-8；保留缺失值為空白）\n",
    "        tmp = fp.with_suffix(fp.suffix + \".tmp\")\n",
    "        df.to_csv(tmp, index=False, encoding=\"utf-8\")\n",
    "        os.replace(tmp, fp)\n",
    "\n",
    "        replaced_true_total += int(true_hits)\n",
    "        replaced_false_total += int(false_hits)\n",
    "        proc_ok += 1\n",
    "        print(f\"[TRANSFORM][OK] {fp.name} | True→1: {int(true_hits)}, False→0: {int(false_hits)}\")\n",
    "    except Exception as e:\n",
    "        proc_err_list.append((fp.name, str(e)))\n",
    "        print(f\"[TRANSFORM][ERR] {fp.name} -> {e}\")\n",
    "\n",
    "print(\"\\n[TRANSFORM] 統計：\")\n",
    "print(f\"- CSV 檔案總數：{proc_total + proc_skip_no_col}\")\n",
    "print(f\"- 成功覆寫：{proc_ok}\")\n",
    "print(f\"- 無 sponvt 欄位（略過）：{proc_skip_no_col}\")\n",
    "print(f\"- 非 CSV（略過）：{proc_skip_no_csv}\")\n",
    "print(f\"- 累計替換：True→1 共 {replaced_true_total} 筆、False→0 共 {replaced_false_total} 筆\")\n",
    "if proc_err_list:\n",
    "    print(\"[TRANSFORM] 失敗清單：\")\n",
    "    for n, msg in proc_err_list:\n",
    "        print(f\"  - {n}: {msg}\")\n",
    "\n",
    "print(\"\\n[DONE] 全部步驟執行完成。\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a6b9d21a-5736-470e-9316-40adae1f8802",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1de48c40-ba59-4848-89fe-544b390e4496",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "8c5e663d-6bbc-4591-88de-d0214968b0ec",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[OK] float_count.csv -> /home/jovyan/RT08/0925/0926/float_count.csv\n",
      "[OK] validation matrix -> /home/jovyan/RT08/0925/0926/0927.csv\n",
      "\n",
      "[NON-COMPLIANT FILES] (none)\n",
      "\n",
      "[Encodings used]\n",
      "- 089271.csv: utf-8-sig\n",
      "- 095323.csv: utf-8-sig\n",
      "- 095707.csv: utf-8-sig\n",
      "- 114309.csv: utf-8-sig\n",
      "- 230933.csv: utf-8-sig\n",
      "- 4216007.csv: utf-8-sig\n",
      "- 7108162.csv: utf-8-sig\n",
      "- 7408338.csv: utf-8-sig\n",
      "- 7657698.csv: utf-8-sig\n",
      "- 7721164.csv: utf-8-sig\n",
      "- PatNo_ID_1560013303.csv: utf-8-sig\n",
      "- PatNo_ID_1562733396.csv: utf-8-sig\n",
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      "- PatNo_ID_1594471407.csv: utf-8-sig\n",
      "- PatNo_ID_1594479330.csv: utf-8-sig\n",
      "- PatNo_ID_1594511911.csv: utf-8-sig\n",
      "- PatNo_ID_1594511914.csv: utf-8-sig\n",
      "- PatNo_ID_1594528842.csv: utf-8-sig\n",
      "- PatNo_ID_1594533379.csv: utf-8-sig\n",
      "\n",
      "[Unified dtypes across files]\n",
      "- patno: float64\n",
      "- senddate: datetime64[ns]\n",
      "- ventilatormode: string\n",
      "- rrhzsetactual: float64\n",
      "- mvsetactual: float64\n",
      "- peepepap: float64\n",
      "- ppeak: float64\n",
      "- cdyn: float64\n",
      "- vti: float64\n",
      "- pmean: float64\n",
      "- vte: float64\n",
      "- sponvt: float64\n",
      "[OK] heatmap -> /home/jovyan/RT08/0925/0926/NaN.png\n"
     ]
    }
   ],
   "source": [
    "\"\"\" 1. 確認所有檔案都只有12個特徵。\n",
    "2. 確認編碼方式：確定這些格式的編碼方式，確定patno是整數, senddate是datatime格式, ventilatormode是英文，rrhzsetactual是浮點數、mvsetactual是浮點數、peepepap是浮點數、ppeak是浮點數、cdyn是浮點數， senddate是datatime, \"vti\"是整數, \"pmean\"是整數, \"vte\"是浮點數, \"sponvt\"可以是浮點數、整數，\n",
    "以上欄位如果是整數 都要轉成浮點數，以上欄位都接受有NaN的內容。\n",
    "，並紀錄每個檔案什麼特徵原本整數的數量跟後來成功轉浮點數的數量是否相同（確保整數轉浮點數的數量正確 全部都有轉成功）另一個欄位紀錄數量，檔案存在/home/jovyan/RT08/0925/0926/，檔名叫做float_count.csv\n",
    "3. 再次確認所有檔案的欄位的資料型態都一樣，如果都一樣就在程式中印出欄位名稱對應他的資料型態是甚麼 \n",
    "4. 檢查之後請存一份檔案是沒有符合要求的，縱軸是檔名，橫軸是欄位，第一列紀錄是否都符合，沒有符合就再沒有符合的那個特徵格子寫x，有符合就空值，檔案存在/home/jovyan/RT08/0925/0926/，檔名叫做0927.csv\n",
    "也在程式中直接印出沒有符合要求的檔名\n",
    "5. 輸出缺失率的熱力圖 也要清楚的數值 英文圖表，熱力圖要求參照之前設定，圖片存為 /home/jovyan/RT08/0925/0926/NaN.png\n",
    "\"\"\"\n",
    "# ============================================================\n",
    "# 檢核 & 型別一致化（不覆寫原檔）\n",
    "# 來源資料夾（可自行調整）：/home/jovyan/RT08/0925/blingsponvt\n",
    "# 產出：\n",
    "#   1) /home/jovyan/RT08/0925/0926/float_count.csv\n",
    "#   2) /home/jovyan/RT08/0925/0926/0927.csv   （不符合矩陣）\n",
    "#   3) /home/jovyan/RT08/0925/0926/NaN.png    （缺失率熱力圖，含數值標註）\n",
    "# 並在 console：\n",
    "#   - 列出使用到的編碼\n",
    "#   - 若所有檔案欄位型別一致，印出欄位→型別\n",
    "#   - 直接列印「不符合要求」的檔名\n",
    "# ============================================================\n",
    "import os, glob, re, unicodedata\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.patheffects as pe\n",
    "from matplotlib import colors\n",
    "\n",
    "SRC_DIR = \"/home/jovyan/RT08/0925/blingsponvt\"   # ← 若要改來源，改這行\n",
    "OUT_DIR = \"/home/jovyan/RT08/0925/0926\"\n",
    "os.makedirs(OUT_DIR, exist_ok=True)\n",
    "\n",
    "REQUIRED = [\n",
    "    \"patno\",\"senddate\",\"ventilatormode\",\"rrhzsetactual\",\"mvsetactual\",\n",
    "    \"peepepap\",\"ppeak\",\"cdyn\",\"vti\",\"pmean\",\"vte\",\"sponvt\"\n",
    "]\n",
    "# 預期型別（最終）：數值→float、senddate→datetime、ventilatormode→英文字串\n",
    "NUM_FLOAT_COLS = [\"patno\",\"rrhzsetactual\",\"mvsetactual\",\"peepepap\",\"ppeak\",\"cdyn\",\"vti\",\"pmean\",\"vte\",\"sponvt\"]\n",
    "\n",
    "ENCODINGS = (\"utf-8-sig\",\"utf-8\",\"cp950\",\"big5\",\"latin1\")\n",
    "_ascii_re = re.compile(r\"^[\\x20-\\x7E]+$\")\n",
    "\n",
    "def read_df_with_encoding(path):\n",
    "    for enc in ENCODINGS:\n",
    "        try:\n",
    "            df = pd.read_csv(path, encoding=enc, engine=\"python\", on_bad_lines=\"skip\")\n",
    "            return df, enc\n",
    "        except Exception:\n",
    "            continue\n",
    "    raise RuntimeError(f\"read failed: {os.path.basename(path)}\")\n",
    "\n",
    "def lower_map(df):\n",
    "    m = {}\n",
    "    for c in df.columns:\n",
    "        k = unicodedata.normalize(\"NFKC\", str(c)).strip().lower()\n",
    "        if k not in m: m[k] = c\n",
    "    return m\n",
    "\n",
    "def to_float_series(s: pd.Series) -> pd.Series:\n",
    "    \"\"\"將任意欄位轉為 float（允許 NaN）。\"\"\"\n",
    "    # 正規化字串以增加可解析性\n",
    "    if s.dtype.kind in \"OUSM\":\n",
    "        s2 = s.astype(\"string\")\n",
    "        s2 = s2.map(lambda x: unicodedata.normalize(\"NFKC\", x).strip() if x is not pd.NA else x)\n",
    "        # 去除逗號千分位、不可見符號\n",
    "        s2 = s2.str.replace(\",\", \"\", regex=False)\n",
    "        s2 = s2.str.replace(r\"[\\u200b-\\u200f\\u202a-\\u202e\\u2066-\\u2069\\ufeff\\u00a0\\u3000]\", \"\", regex=True)\n",
    "        num = pd.to_numeric(s2, errors=\"coerce\")\n",
    "    else:\n",
    "        num = pd.to_numeric(s, errors=\"coerce\")\n",
    "    return num.astype(float)\n",
    "\n",
    "def integer_like_count(s: pd.Series) -> int:\n",
    "    \"\"\"計算『原始值可被解析且為整數值』的筆數（NaN 不算）\"\"\"\n",
    "    num = pd.to_numeric(s, errors=\"coerce\")\n",
    "    if num.isna().all(): return 0\n",
    "    m = num.notna() & (np.floor(num) == num)\n",
    "    return int(m.sum())\n",
    "\n",
    "def check_ascii_english(ser):\n",
    "    s = ser.dropna().astype(str).map(lambda x: unicodedata.normalize(\"NFKC\", x).strip())\n",
    "    if s.empty: return True\n",
    "    return bool(s.map(lambda x: _ascii_re.fullmatch(x) is not None).all())\n",
    "\n",
    "def coerce_datetime(ser):\n",
    "    \"\"\"轉 datetime（coerce）；回傳轉後 Series（dtype=datetime64[ns]）\"\"\"\n",
    "    return pd.to_datetime(ser, errors=\"coerce\")\n",
    "\n",
    "# 收集輸出\n",
    "float_count_rows = []   # file_name, feature, int_count_before, converted_to_float_count, matched\n",
    "validation_rows  = []   # 0927.csv 用\n",
    "nan_rate_rows    = []   # 熱力圖用\n",
    "encodings_used   = []   # 印出用\n",
    "dtype_signatures = []   # 比對型別一致性用（每檔）\n",
    "\n",
    "files = sorted(glob.glob(os.path.join(SRC_DIR, \"*.csv\")))\n",
    "if not files:\n",
    "    print(f\"[WARN] No CSV files in {SRC_DIR}\")\n",
    "\n",
    "for fp in files:\n",
    "    base = os.path.basename(fp)\n",
    "    try:\n",
    "        df, enc = read_df_with_encoding(fp)\n",
    "        encodings_used.append((base, enc))\n",
    "        lmap = lower_map(df)\n",
    "\n",
    "        # 檢查「只有 12 個特徵」與「特徵齊全」\n",
    "        only12_ok = (df.shape[1] == 12)\n",
    "        has_all_required = all(col in lmap for col in REQUIRED)\n",
    "\n",
    "        # 準備一份 typed 副本\n",
    "        typed = pd.DataFrame(index=df.index)\n",
    "\n",
    "        # senddate → datetime\n",
    "        if \"senddate\" in lmap:\n",
    "            typed[\"senddate\"] = coerce_datetime(df[lmap[\"senddate\"]])\n",
    "        # ventilatormode → 原樣保留（只檢查是否英文）\n",
    "        if \"ventilatormode\" in lmap:\n",
    "            typed[\"ventilatormode\"] = df[lmap[\"ventilatormode\"]].astype(object)\n",
    "\n",
    "        # 整數 → 轉為 float；同時計算整數筆數與轉換筆數\n",
    "        for col in NUM_FLOAT_COLS:\n",
    "            if col in lmap:\n",
    "                src = df[lmap[col]]\n",
    "                int_before = integer_like_count(src)\n",
    "                f = to_float_series(src)\n",
    "                typed[col] = f\n",
    "                # 轉換筆數：原本整數的位置，轉後應為非 NaN\n",
    "                num_src = pd.to_numeric(src, errors=\"coerce\")\n",
    "                mask_int = num_src.notna() & (np.floor(num_src) == num_src)\n",
    "                conv_count = int(typed.loc[mask_int, col].notna().sum())\n",
    "                float_count_rows.append({\n",
    "                    \"file_name\": base, \"feature\": col,\n",
    "                    \"int_count_before\": int_before,\n",
    "                    \"converted_to_float_count\": conv_count,\n",
    "                    \"matched\": bool(int_before == conv_count)\n",
    "                })\n",
    "            else:\n",
    "                # 缺欄位也記錄，方便追蹤\n",
    "                float_count_rows.append({\n",
    "                    \"file_name\": base, \"feature\": col,\n",
    "                    \"int_count_before\": 0,\n",
    "                    \"converted_to_float_count\": 0,\n",
    "                    \"matched\": False\n",
    "                })\n",
    "\n",
    "        # 檢核各欄是否符合規則（允許 NaN）→ 0927.csv 矩陣\n",
    "        row = {\"file_name\": base}\n",
    "        # 預設空白，違規填 'x'\n",
    "        for col in REQUIRED:\n",
    "            if col not in lmap:\n",
    "                row[col] = \"x\"\n",
    "                continue\n",
    "            s_t = typed[col] if col in typed.columns else df[lmap[col]]\n",
    "            if col == \"senddate\":\n",
    "                ok = pd.api.types.is_datetime64_any_dtype(s_t)\n",
    "            elif col == \"ventilatormode\":\n",
    "                ok = check_ascii_english(s_t)\n",
    "            else:\n",
    "                # 數值型一律要求 float\n",
    "                ok = (col in typed.columns) and (pd.api.types.is_float_dtype(typed[col]))\n",
    "            row[col] = \"\" if ok else \"x\"\n",
    "\n",
    "        # all_ok：需同時滿足「只有 12 欄」+「全特徵存在」+「各欄規則通過」\n",
    "        row[\"all_ok\"] = \"OK\" if (only12_ok and has_all_required and all(row[c] == \"\" for c in REQUIRED)) else \"FAIL\"\n",
    "        validation_rows.append(row)\n",
    "\n",
    "        # NaN 率（僅針對必要欄；缺欄→100%）\n",
    "        r_nan = {\"file_name\": base}\n",
    "        for col in REQUIRED:\n",
    "            if col in lmap:\n",
    "                ser = typed[col] if col in typed.columns else df[lmap[col]]\n",
    "                r_nan[col] = float(ser.isna().mean() * 100.0)\n",
    "            else:\n",
    "                r_nan[col] = 100.0\n",
    "        nan_rate_rows.append(r_nan)\n",
    "\n",
    "        # 型別簽章（用於 #3 一致性檢查）\n",
    "        sig = {}\n",
    "        for col in REQUIRED:\n",
    "            if col in typed.columns:\n",
    "                if col == \"ventilatormode\":\n",
    "                    # 文字型：標示為 'string'（物件視為 string 類）\n",
    "                    sig[col] = \"string\"\n",
    "                elif col == \"senddate\":\n",
    "                    sig[col] = \"datetime64[ns]\" if pd.api.types.is_datetime64_any_dtype(typed[col]) else str(typed[col].dtype)\n",
    "                else:\n",
    "                    sig[col] = str(typed[col].dtype)  # 預期 'float64'\n",
    "            else:\n",
    "                sig[col] = \"MISSING\"\n",
    "        dtype_signatures.append((base, sig))\n",
    "\n",
    "    except Exception as e:\n",
    "        print(f\"[ERROR] {base}: {e}\")\n",
    "        # fallback 記錄\n",
    "        for col in NUM_FLOAT_COLS:\n",
    "            float_count_rows.append({\n",
    "                \"file_name\": base, \"feature\": col,\n",
    "                \"int_count_before\": 0, \"converted_to_float_count\": 0, \"matched\": False\n",
    "            })\n",
    "        r_nan = {\"file_name\": base}\n",
    "        for col in REQUIRED: r_nan[col] = np.nan\n",
    "        nan_rate_rows.append(r_nan)\n",
    "        row = {\"file_name\": base, \"all_ok\": \"FAIL\"}\n",
    "        for col in REQUIRED: row[col] = \"x\"\n",
    "        validation_rows.append(row)\n",
    "\n",
    "# 1) 整數→浮點 的數量對帳表\n",
    "float_count_df = pd.DataFrame(float_count_rows,\n",
    "    columns=[\"file_name\",\"feature\",\"int_count_before\",\"converted_to_float_count\",\"matched\"])\n",
    "float_count_csv = os.path.join(OUT_DIR, \"float_count.csv\")\n",
    "float_count_df.to_csv(float_count_csv, index=False, encoding=\"utf-8\")\n",
    "print(f\"[OK] float_count.csv -> {float_count_csv}\")\n",
    "\n",
    "# 2) 不符合矩陣（0927.csv） + 直接列印不合規檔名\n",
    "val_df = pd.DataFrame(validation_rows)\n",
    "order_cols = [\"all_ok\"] + REQUIRED\n",
    "for c in order_cols:\n",
    "    if c not in val_df.columns: val_df[c] = \"\"\n",
    "val_df = val_df[[\"file_name\"] + order_cols]\n",
    "non_ok = val_df[val_df[\"all_ok\"] != \"OK\"][\"file_name\"].tolist()\n",
    "val_csv = os.path.join(OUT_DIR, \"0927.csv\")\n",
    "val_df.to_csv(val_csv, index=False, encoding=\"utf-8\")\n",
    "print(f\"[OK] validation matrix -> {val_csv}\")\n",
    "if non_ok:\n",
    "    print(\"\\n[NON-COMPLIANT FILES]\")\n",
    "    for n in non_ok: print(f\"- {n}\")\n",
    "else:\n",
    "    print(\"\\n[NON-COMPLIANT FILES] (none)\")\n",
    "\n",
    "# 額外：印出各檔使用的編碼\n",
    "if encodings_used:\n",
    "    print(\"\\n[Encodings used]\")\n",
    "    for n, enc in encodings_used:\n",
    "        print(f\"- {n}: {enc}\")\n",
    "\n",
    "# 3) 若所有檔案的欄位型別「一致」，印出欄位→型別\n",
    "same = True\n",
    "if dtype_signatures:\n",
    "    # 取第一個作為基準\n",
    "    _, sig0 = dtype_signatures[0]\n",
    "    for _, sig in dtype_signatures[1:]:\n",
    "        for k in REQUIRED:\n",
    "            if sig.get(k) != sig0.get(k):\n",
    "                same = False\n",
    "                break\n",
    "        if not same: break\n",
    "    if same:\n",
    "        print(\"\\n[Unified dtypes across files]\")\n",
    "        for k in REQUIRED:\n",
    "            print(f\"- {k}: {sig0[k]}\")\n",
    "    else:\n",
    "        print(\"\\n[Dtypes are NOT identical across all files]\")\n",
    "\n",
    "# ===== 5) 缺失率熱力圖（英文；缺失率≠0才標註；深色背景白字）=====\n",
    "\n",
    "nan_rate_df = pd.DataFrame(nan_rate_rows)  # ← 修正：不要寫成 nan_rate_df = nan\n",
    "if nan_rate_df.empty:\n",
    "    print(\"[WARN] nan_rate_df is empty; skip heatmap.\")\n",
    "else:\n",
    "    # 只保留必要欄位順序\n",
    "    keep_cols = [\"file_name\"] + REQUIRED\n",
    "    # 有些欄位可能不存在，先補上\n",
    "    for c in keep_cols:\n",
    "        if c not in nan_rate_df.columns:\n",
    "            nan_rate_df[c] = np.nan\n",
    "    nan_rate_df = nan_rate_df[keep_cols]\n",
    "\n",
    "    plot_df = nan_rate_df.set_index(\"file_name\")\n",
    "    rows = plot_df.index.tolist()\n",
    "    cols = plot_df.columns.tolist()\n",
    "    Z = plot_df.values.astype(float)\n",
    "\n",
    "    finite_vals = Z[np.isfinite(Z)]\n",
    "    vmin = 0.0\n",
    "    vmax = float(finite_vals.max()) if finite_vals.size else 1.0\n",
    "    if vmax == 0: vmax = 1.0\n",
    "    norm = colors.Normalize(vmin=vmin, vmax=vmax)\n",
    "\n",
    "    plt.figure(figsize=(max(8, len(cols)*0.5), max(6, len(rows)*0.35)))\n",
    "    im = plt.imshow(Z, aspect=\"auto\", interpolation=\"nearest\", cmap=\"Blues\", norm=norm)\n",
    "\n",
    "    plt.xticks(ticks=np.arange(len(cols)), labels=cols, rotation=60, ha=\"right\", fontsize=8)\n",
    "    plt.yticks(ticks=np.arange(len(rows)), labels=rows, fontsize=8)\n",
    "    plt.xlabel(\"Features\")\n",
    "    plt.ylabel(\"Files\")\n",
    "    plt.title(\"Missing Rate Heatmap (%)\")\n",
    "\n",
    "    cbar = plt.colorbar(im)\n",
    "    cbar.set_label(\"Missing Rate (%)\")\n",
    "\n",
    "    def text_color_for_value(val):\n",
    "        r, g, b, _ = im.cmap(norm(val))\n",
    "        luminance = 0.2126*r + 0.7152*g + 0.0722*b\n",
    "        return (\"white\", \"black\")[luminance >= 0.5]\n",
    "\n",
    "    for i in range(len(rows)):\n",
    "        for j in range(len(cols)):\n",
    "            val = Z[i, j]\n",
    "            if not np.isfinite(val) or val == 0:\n",
    "                continue\n",
    "            txt = f\"{val:.1f}%\"\n",
    "            color = text_color_for_value(val)\n",
    "            outline = \"black\" if color == \"white\" else \"white\"\n",
    "            plt.text(j, i, txt, ha=\"center\", va=\"center\", fontsize=7, color=color,\n",
    "                     path_effects=[pe.withStroke(linewidth=1.0, foreground=outline)])\n",
    "\n",
    "    plt.tight_layout()\n",
    "    heatmap_png = os.path.join(OUT_DIR, \"NaN.png\")\n",
    "    plt.savefig(heatmap_png, dpi=150)\n",
    "    plt.close()\n",
    "    print(f\"[OK] heatmap -> {heatmap_png}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "030a6f96-3cdc-4b27-9c6d-ce5fc4793303",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true,
     "source_hidden": true
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "[FILE] 089271.csv | rows=32419 | non-null ventilatormode=32419 | unique=9\n",
      "  - A/C PC: 20109\n",
      "  - PSIMV+PSV: 5497\n",
      "  - SIMV PC: 3875\n",
      "  - PCV: 2239\n",
      "  - CPAP/PS: 606\n",
      "  - Standby: 49\n",
      "  - PSV: 38\n",
      "  - A/C VC: 5\n",
      "  - A/C PRVC: 1\n",
      "\n",
      "[FILE] 095323.csv | rows=23791 | non-null ventilatormode=23791 | unique=2\n",
      "  - PCV: 19720\n",
      "  - NIV PCV: 4071\n",
      "\n",
      "[FILE] 095707.csv | rows=20180 | non-null ventilatormode=20180 | unique=3\n",
      "  - PCV: 14188\n",
      "  - PSIMV+PSV: 4393\n",
      "  - PSV: 1599\n",
      "\n",
      "[FILE] 114309.csv | rows=71729 | non-null ventilatormode=71729 | unique=9\n",
      "  - PSIMV: 23426\n",
      "  - SPONT: 17372\n",
      "  - PSIMV+PSV: 13910\n",
      "  - ASV: 7752\n",
      "  - PCV: 7179\n",
      "  - PSV: 2055\n",
      "  - VCV: 20\n",
      "  - CPAP: 9\n",
      "  - VC-CMV: 6\n",
      "\n",
      "[FILE] 230933.csv | rows=30249 | non-null ventilatormode=30249 | unique=6\n",
      "  - PCV: 14205\n",
      "  - PSIMV+PSV: 9613\n",
      "  - PC-SIMV: 3290\n",
      "  - PSV: 3119\n",
      "  - CPAP: 16\n",
      "  - VC: 6\n",
      "\n",
      "[FILE] 4216007.csv | rows=1433 | non-null ventilatormode=1433 | unique=2\n",
      "  - Standby: 906\n",
      "  - NIV P(A)C: 527\n",
      "\n",
      "[FILE] 7108162.csv | rows=239 | non-null ventilatormode=239 | unique=2\n",
      "  - PCV: 135\n",
      "  - PSV: 104\n",
      "\n",
      "[FILE] 7408338.csv | rows=1432 | non-null ventilatormode=1432 | unique=1\n",
      "  - PCV: 1432\n",
      "\n",
      "[FILE] 7657698.csv | rows=1413 | non-null ventilatormode=1413 | unique=3\n",
      "  - PSIMV+PSV: 1286\n",
      "  - PSV: 126\n",
      "  - VCV: 1\n",
      "\n",
      "[FILE] 7721164.csv | rows=483 | non-null ventilatormode=483 | unique=1\n",
      "  - PC-SIMV+: 483\n",
      "\n",
      "[FILE] PatNo_ID_1560013303.csv | rows=2543 | non-null ventilatormode=2543 | unique=5\n",
      "  - PC-AC: 2312\n",
      "  - CPAP/PSV: 147\n",
      "  - PC-PSV: 55\n",
      "  - Standby: 28\n",
      "  - VC-AC: 1\n",
      "\n",
      "[FILE] PatNo_ID_1562733396.csv | rows=2254 | non-null ventilatormode=2254 | unique=4\n",
      "  - PCV: 1309\n",
      "  - PSIMV+PSV: 942\n",
      "  - CPAP: 2\n",
      "  - PSV: 1\n",
      "\n",
      "[FILE] PatNo_ID_1563587183.csv | rows=5287 | non-null ventilatormode=5287 | unique=1\n",
      "  - PCV: 5287\n",
      "\n",
      "[FILE] PatNo_ID_1564148644.csv | rows=17287 | non-null ventilatormode=17287 | unique=6\n",
      "  - NIV PCV: 5602\n",
      "  - PCV: 5518\n",
      "  - Standby: 2896\n",
      "  - PSV: 2773\n",
      "  - PC-SIMV: 497\n",
      "  - VC: 1\n",
      "\n",
      "[FILE] PatNo_ID_1565148312.csv | rows=5475 | non-null ventilatormode=5475 | unique=2\n",
      "  - PC-AC: 5465\n",
      "  - Standby: 10\n",
      "\n",
      "[FILE] PatNo_ID_1565378038.csv | rows=2561 | non-null ventilatormode=2561 | unique=3\n",
      "  - PCV: 2277\n",
      "  - PSV: 283\n",
      "  - VC: 1\n",
      "\n",
      "[FILE] PatNo_ID_1566123680.csv | rows=42600 | non-null ventilatormode=42600 | unique=8\n",
      "  - PSIMV: 16509\n",
      "  - PSIMV+PSV: 13623\n",
      "  - PCV: 8149\n",
      "  - PSV: 4212\n",
      "  - SPONT: 63\n",
      "  - VC-CMV: 35\n",
      "  - VCV: 7\n",
      "  - SIMV: 2\n",
      "\n",
      "[FILE] PatNo_ID_1566252197.csv | rows=3368 | non-null ventilatormode=3368 | unique=3\n",
      "  - CPAP/PSV: 1465\n",
      "  - Standby: 1433\n",
      "  - PC-AC: 470\n",
      "\n",
      "[FILE] PatNo_ID_1566279967.csv | rows=1235 | non-null ventilatormode=1235 | unique=2\n",
      "  - A/C PC: 1153\n",
      "  - Standby: 82\n",
      "\n",
      "[FILE] PatNo_ID_1566671274.csv | rows=50718 | non-null ventilatormode=50718 | unique=4\n",
      "  - A/C PC: 43472\n",
      "  - SIMV PC: 3626\n",
      "  - Standby: 3610\n",
      "  - CPAP/PS: 10\n",
      "\n",
      "[FILE] PatNo_ID_1566911879.csv | rows=53999 | non-null ventilatormode=53999 | unique=5\n",
      "  - PC-SIMV+: 20695\n",
      "  - PC-AC: 17956\n",
      "  - PC-SIMV: 8002\n",
      "  - Standby: 7294\n",
      "  - CPAP/PSV: 52\n",
      "\n",
      "[FILE] PatNo_ID_1567747650.csv | rows=10901 | non-null ventilatormode=10901 | unique=4\n",
      "  - PCV: 6660\n",
      "  - PSIMV+PSV: 3995\n",
      "  - PSV: 244\n",
      "  - VCV: 2\n",
      "\n",
      "[FILE] PatNo_ID_1567804800.csv | rows=20578 | non-null ventilatormode=20578 | unique=6\n",
      "  - PC-AC: 8710\n",
      "  - PC-PSV: 7688\n",
      "  - PC-SIMV+: 3725\n",
      "  - CPAP/PSV: 263\n",
      "  - Standby: 188\n",
      "  - VC-AC: 4\n",
      "\n",
      "[FILE] PatNo_ID_1567832735.csv | rows=36580 | non-null ventilatormode=36580 | unique=3\n",
      "  - PCV: 33824\n",
      "  - PSIMV+PSV: 2659\n",
      "  - PSV: 97\n",
      "\n",
      "[FILE] PatNo_ID_1568039398.csv | rows=34519 | non-null ventilatormode=34519 | unique=5\n",
      "  - A/C PC: 26677\n",
      "  - SIMV PC: 7112\n",
      "  - CPAP/PS: 390\n",
      "  - Standby: 277\n",
      "  - A/C VC: 63\n",
      "\n",
      "[FILE] PatNo_ID_1568574099.csv | rows=13946 | non-null ventilatormode=13946 | unique=6\n",
      "  - PC-AC: 8236\n",
      "  - PC-SIMV+: 3900\n",
      "  - PC-PSV: 1227\n",
      "  - CPAP/PSV: 492\n",
      "  - Standby: 82\n",
      "  - VC-AC: 9\n",
      "\n",
      "[FILE] PatNo_ID_1568813269.csv | rows=4867 | non-null ventilatormode=4867 | unique=3\n",
      "  - PSIMV+PSV: 3487\n",
      "  - PCV: 972\n",
      "  - PSV: 408\n",
      "\n",
      "[FILE] PatNo_ID_1568952422.csv | rows=1184 | non-null ventilatormode=1184 | unique=1\n",
      "  - PCV: 1184\n",
      "\n",
      "[FILE] PatNo_ID_1569083701.csv | rows=3328 | non-null ventilatormode=3328 | unique=1\n",
      "  - PCV: 3328\n",
      "\n",
      "[FILE] PatNo_ID_1569944983.csv | rows=7650 | non-null ventilatormode=7650 | unique=4\n",
      "  - PCV: 4561\n",
      "  - PSIMV+PSV: 1669\n",
      "  - PSV: 1418\n",
      "  - VCV: 2\n",
      "\n",
      "[FILE] PatNo_ID_1570089466.csv | rows=41343 | non-null ventilatormode=41343 | unique=9\n",
      "  - PCV: 21276\n",
      "  - PC-AC: 11145\n",
      "  - SIMV(PC)+PS: 4000\n",
      "  - PC-SIMV: 3219\n",
      "  - CPAP/PSV: 976\n",
      "  - Standby: 660\n",
      "  - PC-SIMV+: 62\n",
      "  - PSV: 3\n",
      "  - VC-AC: 2\n",
      "\n",
      "[FILE] PatNo_ID_1570242703.csv | rows=10646 | non-null ventilatormode=10646 | unique=5\n",
      "  - PC-AC: 4718\n",
      "  - PC-SIMV: 3020\n",
      "  - CPAP/PSV: 2815\n",
      "  - Standby: 88\n",
      "  - VC-AC AF: 5\n",
      "\n",
      "[FILE] PatNo_ID_1570273244.csv | rows=9730 | non-null ventilatormode=9730 | unique=6\n",
      "  - A/C PC: 8152\n",
      "  - SIMV PC: 1296\n",
      "  - CPAP/PS: 261\n",
      "  - Standby: 18\n",
      "  - A/C VC: 2\n",
      "  - A/C PRVC: 1\n",
      "\n",
      "[FILE] PatNo_ID_1570642083.csv | rows=19731 | non-null ventilatormode=19731 | unique=4\n",
      "  - PCV: 12359\n",
      "  - PC-SIMV: 4113\n",
      "  - PSV: 3256\n",
      "  - VC: 3\n",
      "\n",
      "[FILE] PatNo_ID_1571945701.csv | rows=15731 | non-null ventilatormode=15731 | unique=4\n",
      "  - PCV: 14097\n",
      "  - PSIMV+PSV: 848\n",
      "  - PSV: 783\n",
      "  - VCV: 3\n",
      "\n",
      "[FILE] PatNo_ID_1572481361.csv | rows=34540 | non-null ventilatormode=34540 | unique=8\n",
      "  - PCV: 21118\n",
      "  - PSIMV+PSV: 5303\n",
      "  - PC-SIMV+: 3706\n",
      "  - PC-AC: 2038\n",
      "  - PSV: 1891\n",
      "  - CPAP/PSV: 466\n",
      "  - Standby: 12\n",
      "  - VC-AC: 6\n",
      "\n",
      "[FILE] PatNo_ID_1572562839.csv | rows=20966 | non-null ventilatormode=20966 | unique=7\n",
      "  - PC-AC: 10507\n",
      "  - PC-PSV: 5338\n",
      "  - PC-SIMV+: 3109\n",
      "  - CPAP/PSV: 1933\n",
      "  - VC-AC AF: 47\n",
      "  - Standby: 31\n",
      "  - VC-AC: 1\n",
      "\n",
      "[FILE] PatNo_ID_1572831765.csv | rows=2696 | non-null ventilatormode=2696 | unique=1\n",
      "  - PCV: 2696\n",
      "\n",
      "[FILE] PatNo_ID_1572976822.csv | rows=6764 | non-null ventilatormode=6764 | unique=7\n",
      "  - VC-AC AF: 2474\n",
      "  - PC-AC: 1324\n",
      "  - PC-SIMV+: 1308\n",
      "  - PC-SIMV: 1202\n",
      "  - CPAP/PSV: 448\n",
      "  - Standby: 7\n",
      "  - VC-CMV AF: 1\n",
      "\n",
      "[FILE] PatNo_ID_1573063188.csv | rows=5080 | non-null ventilatormode=5080 | unique=5\n",
      "  - SIMV PC: 2543\n",
      "  - A/C PC: 2282\n",
      "  - CPAP/PS: 199\n",
      "  - Standby: 55\n",
      "  - A/C VC: 1\n",
      "\n",
      "[FILE] PatNo_ID_1573249295.csv | rows=7151 | non-null ventilatormode=7151 | unique=2\n",
      "  - PCV: 4698\n",
      "  - PC-SIMV: 2453\n",
      "\n",
      "[FILE] PatNo_ID_1573964540.csv | rows=4394 | non-null ventilatormode=4394 | unique=4\n",
      "  - PC-AC: 2605\n",
      "  - PC-SIMV+: 1319\n",
      "  - CPAP/PSV: 469\n",
      "  - VC-AC: 1\n",
      "\n",
      "[FILE] PatNo_ID_1574148494.csv | rows=47154 | non-null ventilatormode=47154 | unique=3\n",
      "  - A/C PC: 45354\n",
      "  - SIMV PC: 1663\n",
      "  - Standby: 137\n",
      "\n",
      "[FILE] PatNo_ID_1574270349.csv | rows=6534 | non-null ventilatormode=6534 | unique=2\n",
      "  - PC-AC: 6516\n",
      "  - Standby: 18\n",
      "\n",
      "[FILE] PatNo_ID_1574528808.csv | rows=14817 | non-null ventilatormode=14817 | unique=2\n",
      "  - A/C PC: 14485\n",
      "  - Standby: 332\n",
      "\n",
      "[FILE] PatNo_ID_1574831525.csv | rows=521 | non-null ventilatormode=521 | unique=2\n",
      "  - PC-AC: 516\n",
      "  - Standby: 5\n",
      "\n",
      "[FILE] PatNo_ID_1574987447.csv | rows=19843 | non-null ventilatormode=19843 | unique=5\n",
      "  - PCV: 12742\n",
      "  - PSIMV+PSV: 4154\n",
      "  - PSV: 2920\n",
      "  - CPAP: 16\n",
      "  - VCV: 11\n",
      "\n",
      "[FILE] PatNo_ID_1575060177.csv | rows=5290 | non-null ventilatormode=5290 | unique=4\n",
      "  - PSIMV+PSV: 2650\n",
      "  - PCV: 2327\n",
      "  - PSV: 312\n",
      "  - VCV: 1\n",
      "\n",
      "[FILE] PatNo_ID_1575256902.csv | rows=9587 | non-null ventilatormode=9587 | unique=3\n",
      "  - PCV: 8158\n",
      "  - PC-AC: 1336\n",
      "  - Standby: 93\n",
      "\n",
      "[FILE] PatNo_ID_1575445051.csv | rows=1801 | non-null ventilatormode=1801 | unique=2\n",
      "  - A/C PC: 1786\n",
      "  - Standby: 15\n",
      "\n",
      "[FILE] PatNo_ID_1575502382.csv | rows=6604 | non-null ventilatormode=6604 | unique=5\n",
      "  - A/C PC: 4998\n",
      "  - CPAP/PS: 1293\n",
      "  - SIMV PC: 196\n",
      "  - Standby: 115\n",
      "  - A/C VC: 2\n",
      "\n",
      "[FILE] PatNo_ID_1575975485.csv | rows=16459 | non-null ventilatormode=16459 | unique=1\n",
      "  - PCV: 16459\n",
      "\n",
      "[FILE] PatNo_ID_1576115572.csv | rows=18717 | non-null ventilatormode=18717 | unique=5\n",
      "  - PC-AC: 15678\n",
      "  - CPAP/PSV: 1619\n",
      "  - PC-SIMV: 1404\n",
      "  - Standby: 15\n",
      "  - VC-AC: 1\n",
      "\n",
      "[FILE] PatNo_ID_1576116479.csv | rows=1263 | non-null ventilatormode=1263 | unique=2\n",
      "  - A/C PC: 1257\n",
      "  - Standby: 6\n",
      "\n",
      "[FILE] PatNo_ID_1576301569.csv | rows=3207 | non-null ventilatormode=3207 | unique=4\n",
      "  - PCV: 1640\n",
      "  - PSIMV+PSV: 1254\n",
      "  - PSV: 310\n",
      "  - VCV: 3\n",
      "\n",
      "[FILE] PatNo_ID_1576964560.csv | rows=24192 | non-null ventilatormode=24192 | unique=4\n",
      "  - PCV: 18531\n",
      "  - PC-SIMV: 5237\n",
      "  - PSV: 421\n",
      "  - VC: 3\n",
      "\n",
      "[FILE] PatNo_ID_1577042911.csv | rows=41894 | non-null ventilatormode=41894 | unique=5\n",
      "  - A/C PC: 27653\n",
      "  - SIMV PC: 11298\n",
      "  - Standby: 2734\n",
      "  - CPAP/PS: 195\n",
      "  - A/C VC: 14\n",
      "\n",
      "[FILE] PatNo_ID_1577487284.csv | rows=2345 | non-null ventilatormode=2345 | unique=3\n",
      "  - PSIMV+PSV: 1139\n",
      "  - PCV: 1009\n",
      "  - PSV: 197\n",
      "\n",
      "[FILE] PatNo_ID_1578784257.csv | rows=33917 | non-null ventilatormode=33917 | unique=5\n",
      "  - SIMV PC: 16811\n",
      "  - A/C PC: 10859\n",
      "  - Standby: 5359\n",
      "  - CPAP/PS: 886\n",
      "  - A/C VC: 2\n",
      "\n",
      "[FILE] PatNo_ID_1579198603.csv | rows=2046 | non-null ventilatormode=2046 | unique=4\n",
      "  - PSIMV+PSV: 1149\n",
      "  - PCV: 748\n",
      "  - PSV: 148\n",
      "  - VCV: 1\n",
      "\n",
      "[FILE] PatNo_ID_1579498177.csv | rows=21688 | non-null ventilatormode=21688 | unique=4\n",
      "  - PSIMV+PSV: 18016\n",
      "  - PSV: 3022\n",
      "  - PCV: 640\n",
      "  - VCV: 10\n",
      "\n",
      "[FILE] PatNo_ID_1580062580.csv | rows=4300 | non-null ventilatormode=4300 | unique=4\n",
      "  - PSV: 2268\n",
      "  - PCV: 1942\n",
      "  - PSIMV+PSV: 78\n",
      "  - VCV: 12\n",
      "\n",
      "[FILE] PatNo_ID_1580096720.csv | rows=2846 | non-null ventilatormode=2846 | unique=3\n",
      "  - PC-SIMV: 1362\n",
      "  - PCV: 1054\n",
      "  - PSV: 430\n",
      "\n",
      "[FILE] PatNo_ID_1580107637.csv | rows=5396 | non-null ventilatormode=5396 | unique=4\n",
      "  - PSV: 3122\n",
      "  - PCV: 2166\n",
      "  - SIMV(PC)+PS: 100\n",
      "  - VCV: 8\n",
      "\n",
      "[FILE] PatNo_ID_1580244614.csv | rows=5278 | non-null ventilatormode=5278 | unique=5\n",
      "  - PC-AC: 2789\n",
      "  - NIV PCV: 1160\n",
      "  - PC-SIMV+: 1055\n",
      "  - Standby: 252\n",
      "  - CPAP/PSV: 22\n",
      "\n",
      "[FILE] PatNo_ID_1580766093.csv | rows=18070 | non-null ventilatormode=18070 | unique=5\n",
      "  - PCV: 10167\n",
      "  - PSIMV+PSV: 7604\n",
      "  - PSV: 210\n",
      "  - VCV: 80\n",
      "  - CPAP: 9\n",
      "\n",
      "[FILE] PatNo_ID_1581003248.csv | rows=7776 | non-null ventilatormode=7776 | unique=6\n",
      "  - PCV: 3717\n",
      "  - PSIMV+PSV: 2653\n",
      "  - NIV PCV: 1094\n",
      "  - PSV: 262\n",
      "  - VCV: 34\n",
      "  - NIV S/T: 16\n",
      "\n",
      "[FILE] PatNo_ID_1581019504.csv | rows=28177 | non-null ventilatormode=28177 | unique=8\n",
      "  - PC-SIMV+: 10613\n",
      "  - PC-SIMV: 6557\n",
      "  - Standby: 6513\n",
      "  - PC-AC: 1912\n",
      "  - CPAP/PSV: 1519\n",
      "  - VC-AC AF: 927\n",
      "  - PCV: 132\n",
      "  - PC-AC VG: 4\n",
      "\n",
      "[FILE] PatNo_ID_1581633231.csv | rows=15995 | non-null ventilatormode=15995 | unique=5\n",
      "  - PCV: 10924\n",
      "  - PSIMV+PSV: 3562\n",
      "  - NIV PCV: 1357\n",
      "  - PSV: 143\n",
      "  - VCV: 9\n",
      "\n",
      "[FILE] PatNo_ID_1581692973.csv | rows=2723 | non-null ventilatormode=2723 | unique=2\n",
      "  - PCV: 2617\n",
      "  - PSV: 106\n",
      "\n",
      "[FILE] PatNo_ID_1582452511.csv | rows=5196 | non-null ventilatormode=5196 | unique=1\n",
      "  - PCV: 5196\n",
      "\n",
      "[FILE] PatNo_ID_1582635996.csv | rows=13046 | non-null ventilatormode=13046 | unique=4\n",
      "  - PC-SIMV: 8620\n",
      "  - PCV: 4316\n",
      "  - PSV: 109\n",
      "  - VC: 1\n",
      "\n",
      "[FILE] PatNo_ID_1582849900.csv | rows=7411 | non-null ventilatormode=7411 | unique=4\n",
      "  - PCV: 4266\n",
      "  - SIMV(PC)+PS: 2616\n",
      "  - PSV: 526\n",
      "  - VCV: 3\n",
      "\n",
      "[FILE] PatNo_ID_1582937076.csv | rows=23990 | non-null ventilatormode=23990 | unique=6\n",
      "  - PC-AC: 17451\n",
      "  - CPAP/PSV: 3431\n",
      "  - PC-SIMV+: 1795\n",
      "  - PC-PSV: 1292\n",
      "  - Standby: 20\n",
      "  - VC-AC: 1\n",
      "\n",
      "[FILE] PatNo_ID_1584158973.csv | rows=2882 | non-null ventilatormode=2882 | unique=4\n",
      "  - PCV: 2681\n",
      "  - PSV: 198\n",
      "  - VCV: 2\n",
      "  - PSIMV+PSV: 1\n",
      "\n",
      "[FILE] PatNo_ID_1584397376.csv | rows=638 | non-null ventilatormode=638 | unique=1\n",
      "  - PCV: 638\n",
      "\n",
      "[FILE] PatNo_ID_1586172659.csv | rows=38559 | non-null ventilatormode=38559 | unique=4\n",
      "  - PCV: 30842\n",
      "  - PSIMV+PSV: 7639\n",
      "  - PSV: 73\n",
      "  - CPAP: 5\n",
      "\n",
      "[FILE] PatNo_ID_1586696634.csv | rows=3569 | non-null ventilatormode=3569 | unique=4\n",
      "  - SIMV PC: 1711\n",
      "  - Standby: 1065\n",
      "  - A/C PC: 789\n",
      "  - CPAP/PS: 4\n",
      "\n",
      "[FILE] PatNo_ID_1586897008.csv | rows=6687 | non-null ventilatormode=6687 | unique=5\n",
      "  - PC-AC: 3483\n",
      "  - PC-SIMV: 3037\n",
      "  - CPAP/PSV: 134\n",
      "  - Standby: 32\n",
      "  - VC-AC: 1\n",
      "\n",
      "[FILE] PatNo_ID_1587490083.csv | rows=45186 | non-null ventilatormode=45186 | unique=3\n",
      "  - PCV: 35708\n",
      "  - PC-SIMV: 7236\n",
      "  - PSV: 2242\n",
      "\n",
      "[FILE] PatNo_ID_1588632604.csv | rows=2608 | non-null ventilatormode=2608 | unique=3\n",
      "  - PCV: 1410\n",
      "  - PSIMV+PSV: 1189\n",
      "  - CPAP: 9\n",
      "\n",
      "[FILE] PatNo_ID_1588673465.csv | rows=10077 | non-null ventilatormode=10077 | unique=4\n",
      "  - PC-SIMV+: 4187\n",
      "  - Standby: 3792\n",
      "  - PC-SIMV: 2063\n",
      "  - CPAP/PSV: 35\n",
      "\n",
      "[FILE] PatNo_ID_1588794796.csv | rows=9376 | non-null ventilatormode=9376 | unique=1\n",
      "  - PCV: 9376\n",
      "\n",
      "[FILE] PatNo_ID_1588957997.csv | rows=10966 | non-null ventilatormode=10966 | unique=4\n",
      "  - PCV: 10155\n",
      "  - PSV: 555\n",
      "  - PC-SIMV: 248\n",
      "  - VC: 8\n",
      "\n",
      "[FILE] PatNo_ID_1589018086.csv | rows=13472 | non-null ventilatormode=13472 | unique=5\n",
      "  - A/C PC: 7214\n",
      "  - CPAP/PS: 5687\n",
      "  - SIMV PC: 495\n",
      "  - A/C VC: 54\n",
      "  - Standby: 22\n",
      "\n",
      "[FILE] PatNo_ID_1589034524.csv | rows=50081 | non-null ventilatormode=50081 | unique=4\n",
      "  - PCV: 31704\n",
      "  - PSIMV+PSV: 18298\n",
      "  - CPAP: 45\n",
      "  - PSV: 34\n",
      "\n",
      "[FILE] PatNo_ID_1589324603.csv | rows=4187 | non-null ventilatormode=4187 | unique=5\n",
      "  - PC-SIMV+: 2475\n",
      "  - PC-AC: 1377\n",
      "  - CPAP/PSV: 254\n",
      "  - Standby: 75\n",
      "  - VC-AC: 6\n",
      "\n",
      "[FILE] PatNo_ID_1589918099.csv | rows=9333 | non-null ventilatormode=9333 | unique=2\n",
      "  - NIV S/T: 6832\n",
      "  - NIV PCV: 2501\n",
      "\n",
      "[FILE] PatNo_ID_1590136310.csv | rows=5580 | non-null ventilatormode=5580 | unique=4\n",
      "  - A/C PC: 5292\n",
      "  - Standby: 273\n",
      "  - CPAP/PS: 13\n",
      "  - A/C VC: 2\n",
      "\n",
      "[FILE] PatNo_ID_1590616537.csv | rows=14208 | non-null ventilatormode=14208 | unique=4\n",
      "  - PSIMV+PSV: 7275\n",
      "  - PCV: 6194\n",
      "  - PSV: 732\n",
      "  - VCV: 7\n",
      "\n",
      "[FILE] PatNo_ID_1590854576.csv | rows=15879 | non-null ventilatormode=15879 | unique=1\n",
      "  - PCV: 15879\n",
      "\n",
      "[FILE] PatNo_ID_1591609798.csv | rows=35624 | non-null ventilatormode=35624 | unique=1\n",
      "  - PCV: 35624\n",
      "\n",
      "[FILE] PatNo_ID_1592044724.csv | rows=6815 | non-null ventilatormode=6815 | unique=4\n",
      "  - PCV: 3685\n",
      "  - PSIMV+PSV: 2370\n",
      "  - PSV: 758\n",
      "  - VCV: 2\n",
      "\n",
      "[FILE] PatNo_ID_1592560504.csv | rows=18052 | non-null ventilatormode=18052 | unique=1\n",
      "  - PCV: 18052\n",
      "\n",
      "[FILE] PatNo_ID_1593087886.csv | rows=24163 | non-null ventilatormode=24163 | unique=8\n",
      "  - PCV: 11456\n",
      "  - PC-AC: 9841\n",
      "  - PC-SIMV+: 2103\n",
      "  - CPAP/PSV: 722\n",
      "  - Standby: 24\n",
      "  - PSV: 11\n",
      "  - VC-AC: 4\n",
      "  - PSIMV+PSV: 2\n",
      "\n",
      "[FILE] PatNo_ID_1593416100.csv | rows=3328 | non-null ventilatormode=3328 | unique=3\n",
      "  - PCV: 1866\n",
      "  - PSIMV+PSV: 1233\n",
      "  - PSV: 229\n",
      "\n",
      "[FILE] PatNo_ID_1593472048.csv | rows=8230 | non-null ventilatormode=8230 | unique=4\n",
      "  - PC-AC: 5340\n",
      "  - Standby: 1983\n",
      "  - PC-SIMV+: 903\n",
      "  - CPAP/PSV: 4\n",
      "\n",
      "[FILE] PatNo_ID_1593593586.csv | rows=18882 | non-null ventilatormode=18882 | unique=3\n",
      "  - PCV: 17012\n",
      "  - PSIMV+PSV: 1868\n",
      "  - PSV: 2\n",
      "\n",
      "[FILE] PatNo_ID_1593720818.csv | rows=3911 | non-null ventilatormode=3911 | unique=5\n",
      "  - PC-AC: 2357\n",
      "  - PC-PSV: 1261\n",
      "  - CPAP/PSV: 288\n",
      "  - VC-AC: 4\n",
      "  - Standby: 1\n",
      "\n",
      "[FILE] PatNo_ID_1593838524.csv | rows=2178 | non-null ventilatormode=2178 | unique=2\n",
      "  - PC-AC: 2162\n",
      "  - Standby: 16\n",
      "\n",
      "[FILE] PatNo_ID_1594173718.csv | rows=344 | non-null ventilatormode=344 | unique=1\n",
      "  - PCV: 344\n",
      "\n",
      "[FILE] PatNo_ID_1594294180.csv | rows=18334 | non-null ventilatormode=18334 | unique=3\n",
      "  - PCV: 12740\n",
      "  - PSV: 5587\n",
      "  - VCV: 7\n",
      "\n",
      "[FILE] PatNo_ID_1594305136.csv | rows=18679 | non-null ventilatormode=18679 | unique=7\n",
      "  - PC-AC: 9714\n",
      "  - CPAP/PSV: 4207\n",
      "  - PC-PSV: 2971\n",
      "  - PC-SIMV: 1713\n",
      "  - Standby: 71\n",
      "  - VC-AC: 2\n",
      "  - VC-MMV: 1\n",
      "\n",
      "[FILE] PatNo_ID_1594309746.csv | rows=3745 | non-null ventilatormode=3745 | unique=3\n",
      "  - PCV: 2392\n",
      "  - PSIMV+PSV: 1161\n",
      "  - PSV: 192\n",
      "\n",
      "[FILE] PatNo_ID_1594319286.csv | rows=4107 | non-null ventilatormode=4107 | unique=3\n",
      "  - A/C PC: 3988\n",
      "  - Standby: 118\n",
      "  - A/C VC: 1\n",
      "\n",
      "[FILE] PatNo_ID_1594320763.csv | rows=2431 | non-null ventilatormode=2431 | unique=3\n",
      "  - PCV: 2274\n",
      "  - PSV: 156\n",
      "  - VCV: 1\n",
      "\n",
      "[FILE] PatNo_ID_1594322594.csv | rows=5380 | non-null ventilatormode=5380 | unique=1\n",
      "  - PCV: 5380\n",
      "\n",
      "[FILE] PatNo_ID_1594335109.csv | rows=5116 | non-null ventilatormode=5116 | unique=1\n",
      "  - PCV: 5116\n",
      "\n",
      "[FILE] PatNo_ID_1594423683.csv | rows=5023 | non-null ventilatormode=5023 | unique=5\n",
      "  - PC-SIMV+: 2585\n",
      "  - PC-AC: 2230\n",
      "  - CPAP/PSV: 186\n",
      "  - Standby: 19\n",
      "  - VC-AC: 3\n",
      "\n",
      "[FILE] PatNo_ID_1594437309.csv | rows=10035 | non-null ventilatormode=10035 | unique=6\n",
      "  - A/C PC: 8709\n",
      "  - SIMV PC: 863\n",
      "  - CPAP/PS: 326\n",
      "  - Standby: 96\n",
      "  - PCV: 38\n",
      "  - A/C VC: 3\n",
      "\n",
      "[FILE] PatNo_ID_1594439781.csv | rows=7691 | non-null ventilatormode=7691 | unique=4\n",
      "  - PCV: 3208\n",
      "  - PSV: 3158\n",
      "  - PSIMV+PSV: 1321\n",
      "  - VCV: 4\n",
      "\n",
      "[FILE] PatNo_ID_1594441887.csv | rows=10203 | non-null ventilatormode=10203 | unique=4\n",
      "  - PCV: 7413\n",
      "  - PSIMV+PSV: 2675\n",
      "  - PSV: 114\n",
      "  - VCV: 1\n",
      "\n",
      "[FILE] PatNo_ID_1594448501.csv | rows=5106 | non-null ventilatormode=5106 | unique=4\n",
      "  - SIMV(PC)+PS: 4050\n",
      "  - PCV: 645\n",
      "  - PSV: 402\n",
      "  - VCV: 9\n",
      "\n",
      "[FILE] PatNo_ID_1594455578.csv | rows=262 | non-null ventilatormode=262 | unique=2\n",
      "  - PSV: 157\n",
      "  - PCV: 105\n",
      "\n",
      "[FILE] PatNo_ID_1594464829.csv | rows=4000 | non-null ventilatormode=4000 | unique=5\n",
      "  - CPAP/PS: 2675\n",
      "  - A/C PC: 955\n",
      "  - SIMV PC: 307\n",
      "  - Standby: 61\n",
      "  - A/C VC: 2\n",
      "\n",
      "[FILE] PatNo_ID_1594467719.csv | rows=2560 | non-null ventilatormode=2560 | unique=4\n",
      "  - Standby: 1247\n",
      "  - PC-AC: 1018\n",
      "  - PC-SIMV: 294\n",
      "  - CPAP/PSV: 1\n",
      "\n",
      "[FILE] PatNo_ID_1594471407.csv | rows=11433 | non-null ventilatormode=11433 | unique=2\n",
      "  - PC-AC: 11282\n",
      "  - Standby: 151\n",
      "\n",
      "[FILE] PatNo_ID_1594479330.csv | rows=3727 | non-null ventilatormode=3727 | unique=1\n",
      "  - PCV: 3727\n",
      "\n",
      "[FILE] PatNo_ID_1594511911.csv | rows=5488 | non-null ventilatormode=5488 | unique=4\n",
      "  - CPAP/PS: 2902\n",
      "  - A/C PC: 2548\n",
      "  - Standby: 33\n",
      "  - A/C VC: 5\n",
      "\n",
      "[FILE] PatNo_ID_1594511914.csv | rows=9717 | non-null ventilatormode=9717 | unique=6\n",
      "  - PC-SIMV+: 5302\n",
      "  - PC-SIMV: 2450\n",
      "  - PC-AC: 1161\n",
      "  - CPAP/PSV: 763\n",
      "  - Standby: 27\n",
      "  - VC-AC AF: 14\n",
      "\n",
      "[FILE] PatNo_ID_1594528842.csv | rows=2491 | non-null ventilatormode=2491 | unique=3\n",
      "  - PC-AC: 2282\n",
      "  - PC-SIMV+: 133\n",
      "  - Standby: 76\n",
      "\n",
      "[FILE] PatNo_ID_1594533379.csv | rows=1030 | non-null ventilatormode=1030 | unique=4\n",
      "  - A/C PC: 762\n",
      "  - CPAP/PS: 237\n",
      "  - A/C VC: 19\n",
      "  - Standby: 12\n",
      "\n",
      "============================================================\n",
      "[OVERALL] 檔案數=122 | 可讀取=122 | 含欄位=122\n",
      "non-null ventilatormode 總筆數=1602476 | 總 distinct=31\n",
      "  - PCV: 592826\n",
      "  - A/C PC: 238494\n",
      "  - PC-AC: 173931\n",
      "  - PSIMV+PSV: 154516\n",
      "  - PC-SIMV+: 69458\n",
      "  - PC-SIMV: 66017\n",
      "  - SIMV PC: 51796\n",
      "  - PSV: 51516\n",
      "  - Standby: 42534\n",
      "  - PSIMV: 39935\n",
      "  - CPAP/PSV: 22711\n",
      "  - PC-PSV: 19832\n",
      "  - SPONT: 17435\n",
      "  - NIV PCV: 15785\n",
      "  - CPAP/PS: 15684\n",
      "  - SIMV(PC)+PS: 10766\n",
      "  - ASV: 7752\n",
      "  - NIV S/T: 6848\n",
      "  - VC-AC AF: 3467\n",
      "  - NIV P(A)C: 527\n",
      "  - VCV: 240\n",
      "  - A/C VC: 175\n",
      "  - CPAP: 111\n",
      "  - VC-AC: 46\n",
      "  - VC-CMV: 41\n",
      "  - VC: 23\n",
      "  - PC-AC VG: 4\n",
      "  - A/C PRVC: 2\n",
      "  - SIMV: 2\n",
      "  - VC-CMV AF: 1\n",
      "  - VC-MMV: 1\n"
     ]
    }
   ],
   "source": [
    "\"\"\"輸錯了 不是每一份檔案的...要one-hot但我0921跟學妹的編碼不一樣，所以\n",
    "我要看/home/jovyan/RT08/0925/blingsponvt裡面檔案所有ventilatormode欄位有的元素\n",
    "直接統計甚麼元素 有幾筆 直接印在程式碼中，切忌不要動到原始檔案\n",
    "看現有的資料中有哪些模式\n",
    "\"\"\"\n",
    "# 讀取 /home/jovyan/RT08/0925/blingsponvt 內所有 CSV，\n",
    "# 統計「ventilatormode」欄位的元素分布（每個元素有幾筆），\n",
    "# 逐檔印出，最後再印出「全部檔案合併」的總分布。\n",
    "# ★ 僅讀取，不會修改任何原始檔案。\n",
    "\n",
    "import os, glob, unicodedata\n",
    "import pandas as pd\n",
    "from collections import Counter\n",
    "\n",
    "DIR = \"/home/jovyan/RT08/0925/blingsponvt\"\n",
    "ENCODINGS = (\"utf-8-sig\",\"utf-8\",\"cp950\",\"big5\",\"latin1\")\n",
    "\n",
    "def read_df_any(path):\n",
    "    for enc in ENCODINGS:\n",
    "        try:\n",
    "            return pd.read_csv(path, encoding=enc, engine=\"python\", on_bad_lines=\"skip\")\n",
    "        except Exception:\n",
    "            continue\n",
    "    print(f\"[WARN] 無法讀取：{os.path.basename(path)}\")\n",
    "    return None\n",
    "\n",
    "def find_vm_col(df):\n",
    "    # 以小寫比對找到 ventilatormode 的實際欄名\n",
    "    for c in df.columns:\n",
    "        if unicodedata.normalize(\"NFKC\", str(c)).strip().lower() == \"ventilatormode\":\n",
    "            return c\n",
    "    return None\n",
    "\n",
    "def norm_val(x):\n",
    "    if pd.isna(x): \n",
    "        return None\n",
    "    s = unicodedata.normalize(\"NFKC\", str(x)).strip()\n",
    "    return s if s != \"\" else None\n",
    "\n",
    "files = sorted(glob.glob(os.path.join(DIR, \"*.csv\")))\n",
    "if not files:\n",
    "    print(f\"[INFO] 目錄內沒有 CSV：{DIR}\")\n",
    "\n",
    "overall = Counter()\n",
    "scanned, with_col, read_err = 0, 0, 0\n",
    "\n",
    "for fp in files:\n",
    "    name = os.path.basename(fp)\n",
    "    df = read_df_any(fp)\n",
    "    scanned += 1\n",
    "    if df is None:\n",
    "        read_err += 1\n",
    "        continue\n",
    "\n",
    "    col = find_vm_col(df)\n",
    "    if col is None:\n",
    "        print(f\"[SKIP] {name}：找不到 'ventilatormode' 欄位\")\n",
    "        continue\n",
    "\n",
    "    with_col += 1\n",
    "    vals = df[col].map(norm_val).dropna()\n",
    "    cnt = Counter(vals)\n",
    "\n",
    "    # 逐檔列印\n",
    "    print(f\"\\n[FILE] {name} | rows={len(df)} | non-null ventilatormode={sum(cnt.values())} | unique={len(cnt)}\")\n",
    "    if cnt:\n",
    "        for k, v in sorted(cnt.items(), key=lambda kv: (-kv[1], str(kv[0]))):\n",
    "            print(f\"  - {k}: {v}\")\n",
    "        # 累加到整體\n",
    "        overall.update(cnt)\n",
    "    else:\n",
    "        print(\"  (此檔 ventilatormode 皆為空值或空白)\")\n",
    "\n",
    "# 合併總分布\n",
    "print(\"\\n\" + \"=\"*60)\n",
    "print(f\"[OVERALL] 檔案數={scanned} | 可讀取={scanned-read_err} | 含欄位={with_col}\")\n",
    "if overall:\n",
    "    total = sum(overall.values())\n",
    "    print(f\"non-null ventilatormode 總筆數={total} | 總 distinct={len(overall)}\")\n",
    "    for k, v in sorted(overall.items(), key=lambda kv: (-kv[1], str(kv[0]))):\n",
    "        print(f\"  - {k}: {v}\")\n",
    "else:\n",
    "    print(\"(沒有可統計的 ventilatormode 值)\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "b92c0652-6777-40c5-80de-f62396ac9425",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[OVERALL ventilatormode counts]  (合併所有檔案後)\n",
      "- total non-null rows = 1602476, distinct modes = 31\n",
      "  - PCV: 592826\n",
      "  - A/C PC: 238494\n",
      "  - PC-AC: 173931\n",
      "  - PSIMV+PSV: 154516\n",
      "  - PC-SIMV+: 69458\n",
      "  - PC-SIMV: 66017\n",
      "  - SIMV PC: 51796\n",
      "  - PSV: 51516\n",
      "  - Standby: 42534\n",
      "  - PSIMV: 39935\n",
      "  - CPAP/PSV: 22711\n",
      "  - PC-PSV: 19832\n",
      "  - SPONT: 17435\n",
      "  - NIV PCV: 15785\n",
      "  - CPAP/PS: 15684\n",
      "  - SIMV(PC)+PS: 10766\n",
      "  - ASV: 7752\n",
      "  - NIV S/T: 6848\n",
      "  - VC-AC AF: 3467\n",
      "  - NIV P(A)C: 527\n",
      "  - VCV: 240\n",
      "  - A/C VC: 175\n",
      "  - CPAP: 111\n",
      "  - VC-AC: 46\n",
      "  - VC-CMV: 41\n",
      "  - VC: 23\n",
      "  - PC-AC VG: 4\n",
      "  - A/C PRVC: 2\n",
      "  - SIMV: 2\n",
      "  - VC-CMV AF: 1\n",
      "  - VC-MMV: 1\n",
      "[OK] 已輸出矩陣 CSV：/home/jovyan/RT08/0925/0926/ventilatormode_counts_matrix.csv\n",
      "[OK] 已輸出熱力圖：/home/jovyan/RT08/0925/0926/ventilatormode_counts_heatmap.png\n"
     ]
    }
   ],
   "source": [
    "\"\"\"要one-hot但我0921跟學妹的編碼不一樣，所以\n",
    "我要看/home/jovyan/RT08/0925/blingsponvt裡面檔案所有ventilatormode欄位有的元素\n",
    "直接統計甚麼元素 有幾筆 直接印在程式碼中，切忌不要動到原始檔案\n",
    "看現有的資料中有哪些模式\n",
    "我要看全部的檔案統計，假設第一份檔案有a模式3筆，b模式1筆，第二份檔案有a模式0筆，b模式5筆，\n",
    "那我要看到印再程式碼的統計是 a模式有3+0=3筆，b模式有1+5=6筆\n",
    "另外輸出熱力圖表示筆數 不是百分比 熱力圖設定跟之前一樣\n",
    "\"\"\"\n",
    "# 讀取 /home/jovyan/RT08/0925/blingsponvt 內所有 CSV，\n",
    "# 聚合統計 ventilatormode 的「總筆數」（跨檔相加）並印出；\n",
    "# 另外輸出「筆數」熱力圖（不是百分比）：列=檔名、欄=模式、值=筆數。\n",
    "# ★ 只讀不寫原始檔案。輸出圖與矩陣 CSV 存到 /home/jovyan/RT08/0925/0926/\n",
    "\n",
    "import os, glob, unicodedata\n",
    "from collections import Counter, defaultdict\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.patheffects as pe\n",
    "from matplotlib import colors\n",
    "\n",
    "IN_DIR  = \"/home/jovyan/RT08/0925/blingsponvt\"\n",
    "OUT_DIR = \"/home/jovyan/RT08/0925/0926\"\n",
    "os.makedirs(OUT_DIR, exist_ok=True)\n",
    "\n",
    "ENCODINGS = (\"utf-8-sig\",\"utf-8\",\"cp950\",\"big5\",\"latin1\")\n",
    "\n",
    "def read_df_any(path):\n",
    "    for enc in ENCODINGS:\n",
    "        try:\n",
    "            return pd.read_csv(path, encoding=enc, engine=\"python\", on_bad_lines=\"skip\")\n",
    "        except Exception:\n",
    "            continue\n",
    "    print(f\"[WARN] 無法讀取：{os.path.basename(path)}\")\n",
    "    return None\n",
    "\n",
    "def find_vm_col(df):\n",
    "    for c in df.columns:\n",
    "        if unicodedata.normalize(\"NFKC\", str(c)).strip().lower() == \"ventilatormode\":\n",
    "            return c\n",
    "    return None\n",
    "\n",
    "def norm_val(x):\n",
    "    if pd.isna(x): \n",
    "        return None\n",
    "    s = unicodedata.normalize(\"NFKC\", str(x)).strip()\n",
    "    return s if s != \"\" else None\n",
    "\n",
    "files = sorted(glob.glob(os.path.join(IN_DIR, \"*.csv\")))\n",
    "if not files:\n",
    "    print(f\"[INFO] 目錄內沒有 CSV：{IN_DIR}\")\n",
    "\n",
    "overall = Counter()                   # 聚合：模式 → 總筆數\n",
    "per_file_counts = {}                  # 檔名 → Counter(模式→筆數)\n",
    "all_modes = set()\n",
    "\n",
    "for fp in files:\n",
    "    name = os.path.basename(fp)\n",
    "    df = read_df_any(fp)\n",
    "    if df is None:\n",
    "        continue\n",
    "    col = find_vm_col(df)\n",
    "    if col is None:\n",
    "        # 沒有 ventilatormode 欄位就略過\n",
    "        continue\n",
    "\n",
    "    vals = df[col].map(norm_val).dropna()\n",
    "    cnt = Counter(vals)\n",
    "    per_file_counts[name] = cnt\n",
    "    overall.update(cnt)\n",
    "    all_modes.update(cnt.keys())\n",
    "\n",
    "# ====== 印出「所有檔案合併後」的總分布 ======\n",
    "print(\"[OVERALL ventilatormode counts]  (合併所有檔案後)\")\n",
    "if overall:\n",
    "    total = sum(overall.values())\n",
    "    print(f\"- total non-null rows = {total}, distinct modes = {len(overall)}\")\n",
    "    for mode, ct in sorted(overall.items(), key=lambda kv: (-kv[1], str(kv[0]))):\n",
    "        print(f\"  - {mode}: {ct}\")\n",
    "else:\n",
    "    print(\"  (沒有任何非空的 ventilatormode 內容)\")\n",
    "\n",
    "# ====== 構建「檔案 × 模式」矩陣（筆數）並存檔 ======\n",
    "modes_sorted = sorted(all_modes)\n",
    "rows = []\n",
    "for name in sorted(per_file_counts.keys()):\n",
    "    row = {\"file_name\": name}\n",
    "    cnt = per_file_counts[name]\n",
    "    for m in modes_sorted:\n",
    "        row[m] = int(cnt.get(m, 0))\n",
    "    rows.append(row)\n",
    "\n",
    "if rows:\n",
    "    mat_df = pd.DataFrame(rows)\n",
    "    mat_df.to_csv(os.path.join(OUT_DIR, \"ventilatormode_counts_matrix.csv\"), index=False, encoding=\"utf-8\")\n",
    "    print(f\"[OK] 已輸出矩陣 CSV：{os.path.join(OUT_DIR, 'ventilatormode_counts_matrix.csv')}\")\n",
    "else:\n",
    "    # 即使沒有任何值，也輸出空表頭檔案方便追蹤\n",
    "    mat_df = pd.DataFrame([], columns=[\"file_name\"] + modes_sorted)\n",
    "    mat_df.to_csv(os.path.join(OUT_DIR, \"ventilatormode_counts_matrix.csv\"), index=False, encoding=\"utf-8\")\n",
    "    print(\"[WARN] 沒有可統計的檔案或模式，仍輸出空矩陣。\")\n",
    "\n",
    "# ====== 以筆數畫熱力圖（英文；非零格子加上數值；深色背景白字） ======\n",
    "if not mat_df.empty and len(mat_df.columns) > 1:\n",
    "    plot_df = mat_df.set_index(\"file_name\")\n",
    "    rows_lab = plot_df.index.tolist()\n",
    "    cols_lab = plot_df.columns.tolist()\n",
    "    Z = plot_df.values.astype(float)\n",
    "\n",
    "    finite_vals = Z[np.isfinite(Z)]\n",
    "    vmin = 0.0\n",
    "    vmax = float(finite_vals.max()) if finite_vals.size else 1.0\n",
    "    if vmax == 0: vmax = 1.0\n",
    "    norm = colors.Normalize(vmin=vmin, vmax=vmax)\n",
    "\n",
    "    plt.figure(figsize=(max(8, len(cols_lab)*0.5), max(6, len(rows_lab)*0.35)))\n",
    "    im = plt.imshow(Z, aspect=\"auto\", interpolation=\"nearest\", cmap=\"Blues\", norm=norm)\n",
    "\n",
    "    plt.xticks(ticks=np.arange(len(cols_lab)), labels=cols_lab, rotation=60, ha=\"right\", fontsize=8)\n",
    "    plt.yticks(ticks=np.arange(len(rows_lab)), labels=rows_lab, fontsize=8)\n",
    "    plt.xlabel(\"Ventilator Modes\")\n",
    "    plt.ylabel(\"Files\")\n",
    "    plt.title(\"VentilatorMode Counts\")\n",
    "\n",
    "    cbar = plt.colorbar(im)\n",
    "    cbar.set_label(\"Count\")\n",
    "\n",
    "    def text_color_for_value(val):\n",
    "        r, g, b, _ = im.cmap(norm(val))\n",
    "        luminance = 0.2126*r + 0.7152*g + 0.0722*b\n",
    "        return (\"white\", \"black\")[luminance >= 0.5]\n",
    "\n",
    "    for i in range(len(rows_lab)):\n",
    "        for j in range(len(cols_lab)):\n",
    "            val = Z[i, j]\n",
    "            if not np.isfinite(val) or val == 0:\n",
    "                continue\n",
    "            txt = f\"{int(val)}\"\n",
    "            color = text_color_for_value(val)\n",
    "            outline = \"black\" if color == \"white\" else \"white\"\n",
    "            plt.text(j, i, txt, ha=\"center\", va=\"center\", fontsize=7, color=color,\n",
    "                     path_effects=[pe.withStroke(linewidth=1.0, foreground=outline)])\n",
    "\n",
    "    plt.tight_layout()\n",
    "    out_png = os.path.join(OUT_DIR, \"ventilatormode_counts_heatmap.png\")\n",
    "    plt.savefig(out_png, dpi=150)\n",
    "    plt.close()\n",
    "    print(f\"[OK] 已輸出熱力圖：{out_png}\")\n",
    "else:\n",
    "    print(\"[WARN] 無法繪製熱力圖（沒有模式欄或矩陣為空）。\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "199ae0a3-70a3-4e5d-9679-66439bd938c8",
   "metadata": {},
   "outputs": [],
   "source": [
    "Mode 1：pcv, pc-ac, a/c pc, p-cmv\n",
    "Mode 2：psimv, p-simv, pc-simv+, pc-simv, psimv+psv, simv pc\n",
    "Mode 3：psv, cpap/psv, cpap/ps, ps\n",
    "\n",
    "Mode 1 ≈ 68.1%\n",
    "Mode 2 ≈ 25.9%\n",
    "Mode 3 ≈ 6.1%"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "81391a40-b9ba-4532-b68f-d18815e4113e",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[OVERALL] VentilatorMode total counts across all files (exact match):\n",
      "- Total matched rows = 1519418, distinct modes matched = 12\n",
      "\n",
      "[Group 0]\n",
      "  - Standby: 42534\n",
      "\n",
      "[Group 1]\n",
      "  - PCV: 592826\n",
      "  - PC-AC: 173931\n",
      "  - A/C PC: 238494\n",
      "  - P-CMV: 0\n",
      "\n",
      "[Group 2]\n",
      "  - PSIMV: 39935\n",
      "  - P-SIMV: 0\n",
      "  - PC-SIMV+: 69458\n",
      "  - PC-SIMV: 66017\n",
      "  - PSIMV+PSV: 154516\n",
      "  - SIMV PC: 51796\n",
      "\n",
      "[Group 3]\n",
      "  - PSV: 51516\n",
      "  - CPAP/PSV: 22711\n",
      "  - CPAP/PS: 15684\n",
      "  - PS: 0\n",
      "\n",
      "[OK] Saved matrix CSV -> /home/jovyan/RT08/0925/0926/ventilatormode_counts_grouped.csv\n",
      "[OK] Saved heatmap -> /home/jovyan/RT08/0925/0926/ventilatormode_counts_grouped_heatmap.png\n",
      "\n",
      "[INFO] Scanned files: 122, with 'ventilatormode' column: 122\n"
     ]
    }
   ],
   "source": [
    "\"\"\" 先計算所有檔案的ventilatormode符合以下條件的有幾個，「總筆數」（跨檔相加）印出\n",
    "\"Standby\": 0,\n",
    "    \"PCV\": 1, \"PC-AC\": 1, \"A/C PC\": 1, \"P-CMV\": 1,\n",
    "    \"PSIMV\": 2, \"P-SIMV\": 2, \"PC-SIMV+\": 2, \"PC-SIMV\": 2,\n",
    "    \"PSIMV+PSV\": 2, \"SIMV PC\": 2,\n",
    "    \"PSV\": 3, \"CPAP/PSV\": 3, \"CPAP/PS\": 3, \"PS\": 3,\n",
    "熱力圖的橫軸是模式 縱軸是檔名，再熱力圖中要加上筆數跟百分比\n",
    "橫軸的模式，被編碼到同一種的樹字的類別放在一起 最左邊是0到右邊是3\n",
    "都不要動到原檔\n",
    "\"\"\"\n",
    "# 讀取 /home/jovyan/RT08/0925/blingsponvt 全部 CSV，\n",
    "# 1) 先「跨檔相加」統計以下 ventilatormode（精確匹配）的總筆數並印出\n",
    "#    分群編碼：0: Standby\n",
    "#             1: PCV, PC-AC, A/C PC, P-CMV\n",
    "#             2: PSIMV, P-SIMV, PC-SIMV+, PC-SIMV, PSIMV+PSV, SIMV PC\n",
    "#             3: PSV, CPAP/PSV, CPAP/PS, PS\n",
    "# 2) 輸出「檔案×模式」筆數矩陣（列=檔名、欄=模式；欄位依 0→3 分群由左到右）到 /home/jovyan/RT08/0925/0926/ventilatormode_counts_grouped.csv\n",
    "# 3) 畫熱力圖（值=筆數；註記=筆數與該檔行內百分比），英文字軸、深色自動白字，\n",
    "#    存 /home/jovyan/RT08/0925/0926/ventilatormode_counts_grouped_heatmap.png\n",
    "# ★ 完全不修改原始檔案\n",
    "\n",
    "import os, glob, unicodedata\n",
    "from collections import Counter, defaultdict\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.patheffects as pe\n",
    "from matplotlib import colors\n",
    "\n",
    "IN_DIR  = \"/home/jovyan/RT08/0925/blingsponvt\"\n",
    "OUT_DIR = \"/home/jovyan/RT08/0925/0926\"\n",
    "os.makedirs(OUT_DIR, exist_ok=True)\n",
    "\n",
    "ENCODINGS = (\"utf-8-sig\",\"utf-8\",\"cp950\",\"big5\",\"latin1\")\n",
    "\n",
    "# 分群定義（精確匹配）\n",
    "MODE_CLASS = {\n",
    "    \"Standby\": 0,\n",
    "    \"PCV\": 1, \"PC-AC\": 1, \"A/C PC\": 1, \"P-CMV\": 1,\n",
    "    \"PSIMV\": 2, \"P-SIMV\": 2, \"PC-SIMV+\": 2, \"PC-SIMV\": 2, \"PSIMV+PSV\": 2, \"SIMV PC\": 2,\n",
    "    \"PSV\": 3, \"CPAP/PSV\": 3, \"CPAP/PS\": 3, \"PS\": 3,\n",
    "}\n",
    "\n",
    "# 欄位順序：依群組 0→3、每組內固定下列順序\n",
    "ORDERED_BY_GROUP = [\n",
    "    # group 0\n",
    "    [\"Standby\"],\n",
    "    # group 1\n",
    "    [\"PCV\",\"PC-AC\",\"A/C PC\",\"P-CMV\"],\n",
    "    # group 2\n",
    "    [\"PSIMV\",\"P-SIMV\",\"PC-SIMV+\",\"PC-SIMV\",\"PSIMV+PSV\",\"SIMV PC\"],\n",
    "    # group 3\n",
    "    [\"PSV\",\"CPAP/PSV\",\"CPAP/PS\",\"PS\"],\n",
    "]\n",
    "ORDERED_COLS = [m for grp in ORDERED_BY_GROUP for m in grp]\n",
    "\n",
    "def read_df_any(path):\n",
    "    for enc in ENCODINGS:\n",
    "        try:\n",
    "            return pd.read_csv(path, encoding=enc, engine=\"python\", on_bad_lines=\"skip\")\n",
    "        except Exception:\n",
    "            continue\n",
    "    print(f\"[WARN] Cannot read: {os.path.basename(path)}\")\n",
    "    return None\n",
    "\n",
    "def find_vm_col(df):\n",
    "    for c in df.columns:\n",
    "        if unicodedata.normalize(\"NFKC\", str(c)).strip().lower() == \"ventilatormode\":\n",
    "            return c\n",
    "    return None\n",
    "\n",
    "def norm_val(x):\n",
    "    if pd.isna(x): \n",
    "        return None\n",
    "    s = unicodedata.normalize(\"NFKC\", str(x)).strip()\n",
    "    return s if s != \"\" else None\n",
    "\n",
    "# ===== 掃描並統計 =====\n",
    "files = sorted(glob.glob(os.path.join(IN_DIR, \"*.csv\")))\n",
    "overall = Counter()\n",
    "per_file_counts = {}   # file -> Counter(mode->count)\n",
    "scanned = 0\n",
    "with_col = 0\n",
    "\n",
    "for fp in files:\n",
    "    scanned += 1\n",
    "    name = os.path.basename(fp)\n",
    "    df = read_df_any(fp)\n",
    "    if df is None:\n",
    "        continue\n",
    "    col = find_vm_col(df)\n",
    "    if col is None:\n",
    "        continue\n",
    "    with_col += 1\n",
    "    vals = df[col].map(norm_val).dropna()\n",
    "    # 僅計入定義清單（精確匹配）\n",
    "    vals_kept = vals[vals.isin(MODE_CLASS.keys())]\n",
    "    cnt = Counter(vals_kept)\n",
    "    per_file_counts[name] = cnt\n",
    "    overall.update(cnt)\n",
    "\n",
    "# ===== 1) 跨檔相加的總筆數：先列印 =====\n",
    "print(\"[OVERALL] VentilatorMode total counts across all files (exact match):\")\n",
    "grand_total = sum(overall.values())\n",
    "print(f\"- Total matched rows = {grand_total}, distinct modes matched = {len(overall)}\")\n",
    "# 依分群 0→3、各組內依 ORDERED_BY_GROUP 順序列印\n",
    "for group_id, group_modes in enumerate(ORDERED_BY_GROUP):\n",
    "    print(f\"\\n[Group {group_id}]\")\n",
    "    for m in group_modes:\n",
    "        print(f\"  - {m}: {overall.get(m, 0)}\")\n",
    "\n",
    "# ===== 2) 輸出「檔案×模式」筆數矩陣 =====\n",
    "rows = []\n",
    "for name in sorted(per_file_counts.keys()):\n",
    "    row = {\"file_name\": name}\n",
    "    cnt = per_file_counts[name]\n",
    "    for m in ORDERED_COLS:\n",
    "        row[m] = int(cnt.get(m, 0))\n",
    "    rows.append(row)\n",
    "mat_df = pd.DataFrame(rows, columns=[\"file_name\"] + ORDERED_COLS)\n",
    "csv_path = os.path.join(OUT_DIR, \"ventilatormode_counts_grouped.csv\")\n",
    "mat_df.to_csv(csv_path, index=False, encoding=\"utf-8\")\n",
    "print(f\"\\n[OK] Saved matrix CSV -> {csv_path}\")\n",
    "\n",
    "# 若沒有任何行可畫圖，提前結束\n",
    "if mat_df.empty:\n",
    "    print(\"[WARN] No rows to plot heatmap (no files with ventilatormode).\")\n",
    "else:\n",
    "    # ===== 3) 熱力圖：值=筆數；標註=筆數與該檔行內百分比 =====\n",
    "    plot_df = mat_df.set_index(\"file_name\")\n",
    "    rows_lab = plot_df.index.tolist()\n",
    "    cols_lab = plot_df.columns.tolist()\n",
    "    Z = plot_df.values.astype(float)\n",
    "\n",
    "    # 每檔的行合計（僅計入定義清單的模式）\n",
    "    row_totals = Z.sum(axis=1, keepdims=True)\n",
    "    with np.errstate(invalid=\"ignore\", divide=\"ignore\"):\n",
    "        Z_pct = np.where(row_totals > 0, (Z / row_totals) * 100.0, 0.0)\n",
    "\n",
    "    finite_vals = Z[np.isfinite(Z)]\n",
    "    vmin = 0.0\n",
    "    vmax = float(finite_vals.max()) if finite_vals.size else 1.0\n",
    "    if vmax == 0: vmax = 1.0\n",
    "    norm = colors.Normalize(vmin=vmin, vmax=vmax)\n",
    "\n",
    "    plt.figure(figsize=(max(10, len(cols_lab)*0.55), max(6, len(rows_lab)*0.35)))\n",
    "    im = plt.imshow(Z, aspect=\"auto\", interpolation=\"nearest\", cmap=\"Blues\", norm=norm)\n",
    "\n",
    "    plt.xticks(ticks=np.arange(len(cols_lab)), labels=cols_lab, rotation=60, ha=\"right\", fontsize=8)\n",
    "    plt.yticks(ticks=np.arange(len(rows_lab)), labels=rows_lab, fontsize=8)\n",
    "    plt.xlabel(\"Ventilator Modes (grouped 0→3)\")\n",
    "    plt.ylabel(\"Files\")\n",
    "    plt.title(\"VentilatorMode Counts (with Row % Annotations)\")\n",
    "\n",
    "    cbar = plt.colorbar(im)\n",
    "    cbar.set_label(\"Count\")\n",
    "\n",
    "    def text_color_for_value(val):\n",
    "        r, g, b, _ = im.cmap(norm(val))\n",
    "        luminance = 0.2126*r + 0.7152*g + 0.0722*b\n",
    "        return (\"white\", \"black\")[luminance >= 0.5]\n",
    "\n",
    "    # 在非零格子標註「筆數\\n(百分比)」\n",
    "    for i in range(len(rows_lab)):\n",
    "        for j in range(len(cols_lab)):\n",
    "            val = Z[i, j]\n",
    "            if not np.isfinite(val) or val == 0:\n",
    "                continue\n",
    "            pct = Z_pct[i, j]\n",
    "            txt = f\"{int(val)}\\n({pct:.1f}%)\"\n",
    "            color = text_color_for_value(val)\n",
    "            outline = \"black\" if color == \"white\" else \"white\"\n",
    "            plt.text(j, i, txt, ha=\"center\", va=\"center\", fontsize=7, color=color,\n",
    "                     path_effects=[pe.withStroke(linewidth=1.0, foreground=outline)])\n",
    "\n",
    "    plt.tight_layout()\n",
    "    png_path = os.path.join(OUT_DIR, \"ventilatormode_counts_grouped_heatmap.png\")\n",
    "    plt.savefig(png_path, dpi=150)\n",
    "    plt.close()\n",
    "    print(f\"[OK] Saved heatmap -> {png_path}\")\n",
    "\n",
    "print(f\"\\n[INFO] Scanned files: {scanned}, with 'ventilatormode' column: {with_col}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "c9d409b3-8ebe-4a7b-b6f9-97633c0a6fd7",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[OK] 089271.csv → /home/jovyan/RT08/0925/bling_onehot/089271.csv\n",
      "[OK] 095323.csv → /home/jovyan/RT08/0925/bling_onehot/095323.csv\n",
      "[OK] 095707.csv → /home/jovyan/RT08/0925/bling_onehot/095707.csv\n",
      "[OK] 114309.csv → /home/jovyan/RT08/0925/bling_onehot/114309.csv\n",
      "[OK] 230933.csv → /home/jovyan/RT08/0925/bling_onehot/230933.csv\n",
      "[OK] 4216007.csv → /home/jovyan/RT08/0925/bling_onehot/4216007.csv\n",
      "[OK] 7108162.csv → /home/jovyan/RT08/0925/bling_onehot/7108162.csv\n",
      "[OK] 7408338.csv → /home/jovyan/RT08/0925/bling_onehot/7408338.csv\n",
      "[OK] 7657698.csv → /home/jovyan/RT08/0925/bling_onehot/7657698.csv\n",
      "[OK] 7721164.csv → /home/jovyan/RT08/0925/bling_onehot/7721164.csv\n",
      "[OK] PatNo_ID_1560013303.csv → /home/jovyan/RT08/0925/bling_onehot/PatNo_ID_1560013303.csv\n",
      "[OK] PatNo_ID_1562733396.csv → /home/jovyan/RT08/0925/bling_onehot/PatNo_ID_1562733396.csv\n",
      "[OK] PatNo_ID_1563587183.csv → /home/jovyan/RT08/0925/bling_onehot/PatNo_ID_1563587183.csv\n",
      "[OK] PatNo_ID_1564148644.csv → /home/jovyan/RT08/0925/bling_onehot/PatNo_ID_1564148644.csv\n",
      "[OK] PatNo_ID_1565148312.csv → /home/jovyan/RT08/0925/bling_onehot/PatNo_ID_1565148312.csv\n",
      "[OK] PatNo_ID_1565378038.csv → /home/jovyan/RT08/0925/bling_onehot/PatNo_ID_1565378038.csv\n",
      "[OK] PatNo_ID_1566123680.csv → /home/jovyan/RT08/0925/bling_onehot/PatNo_ID_1566123680.csv\n",
      "[OK] PatNo_ID_1566252197.csv → /home/jovyan/RT08/0925/bling_onehot/PatNo_ID_1566252197.csv\n",
      "[OK] PatNo_ID_1566279967.csv → /home/jovyan/RT08/0925/bling_onehot/PatNo_ID_1566279967.csv\n",
      "[OK] PatNo_ID_1566671274.csv → /home/jovyan/RT08/0925/bling_onehot/PatNo_ID_1566671274.csv\n",
      "[OK] PatNo_ID_1566911879.csv → /home/jovyan/RT08/0925/bling_onehot/PatNo_ID_1566911879.csv\n",
      "[OK] PatNo_ID_1567747650.csv → /home/jovyan/RT08/0925/bling_onehot/PatNo_ID_1567747650.csv\n",
      "[OK] PatNo_ID_1567804800.csv → /home/jovyan/RT08/0925/bling_onehot/PatNo_ID_1567804800.csv\n",
      "[OK] PatNo_ID_1567832735.csv → /home/jovyan/RT08/0925/bling_onehot/PatNo_ID_1567832735.csv\n",
      "[OK] PatNo_ID_1568039398.csv → /home/jovyan/RT08/0925/bling_onehot/PatNo_ID_1568039398.csv\n",
      "[OK] PatNo_ID_1568574099.csv → /home/jovyan/RT08/0925/bling_onehot/PatNo_ID_1568574099.csv\n",
      "[OK] PatNo_ID_1568813269.csv → /home/jovyan/RT08/0925/bling_onehot/PatNo_ID_1568813269.csv\n",
      "[OK] PatNo_ID_1568952422.csv → /home/jovyan/RT08/0925/bling_onehot/PatNo_ID_1568952422.csv\n",
      "[OK] PatNo_ID_1569083701.csv → /home/jovyan/RT08/0925/bling_onehot/PatNo_ID_1569083701.csv\n",
      "[OK] PatNo_ID_1569944983.csv → /home/jovyan/RT08/0925/bling_onehot/PatNo_ID_1569944983.csv\n",
      "[OK] PatNo_ID_1570089466.csv → /home/jovyan/RT08/0925/bling_onehot/PatNo_ID_1570089466.csv\n",
      "[OK] PatNo_ID_1570242703.csv → /home/jovyan/RT08/0925/bling_onehot/PatNo_ID_1570242703.csv\n",
      "[OK] PatNo_ID_1570273244.csv → /home/jovyan/RT08/0925/bling_onehot/PatNo_ID_1570273244.csv\n",
      "[OK] PatNo_ID_1570642083.csv → /home/jovyan/RT08/0925/bling_onehot/PatNo_ID_1570642083.csv\n",
      "[OK] PatNo_ID_1571945701.csv → /home/jovyan/RT08/0925/bling_onehot/PatNo_ID_1571945701.csv\n",
      "[OK] PatNo_ID_1572481361.csv → /home/jovyan/RT08/0925/bling_onehot/PatNo_ID_1572481361.csv\n",
      "[OK] PatNo_ID_1572562839.csv → /home/jovyan/RT08/0925/bling_onehot/PatNo_ID_1572562839.csv\n",
      "[OK] PatNo_ID_1572831765.csv → /home/jovyan/RT08/0925/bling_onehot/PatNo_ID_1572831765.csv\n",
      "[OK] PatNo_ID_1572976822.csv → /home/jovyan/RT08/0925/bling_onehot/PatNo_ID_1572976822.csv\n",
      "[OK] PatNo_ID_1573063188.csv → /home/jovyan/RT08/0925/bling_onehot/PatNo_ID_1573063188.csv\n",
      "[OK] PatNo_ID_1573249295.csv → /home/jovyan/RT08/0925/bling_onehot/PatNo_ID_1573249295.csv\n",
      "[OK] PatNo_ID_1573964540.csv → /home/jovyan/RT08/0925/bling_onehot/PatNo_ID_1573964540.csv\n",
      "[OK] PatNo_ID_1574148494.csv → /home/jovyan/RT08/0925/bling_onehot/PatNo_ID_1574148494.csv\n",
      "[OK] PatNo_ID_1574270349.csv → /home/jovyan/RT08/0925/bling_onehot/PatNo_ID_1574270349.csv\n",
      "[OK] PatNo_ID_1574528808.csv → /home/jovyan/RT08/0925/bling_onehot/PatNo_ID_1574528808.csv\n",
      "[OK] PatNo_ID_1574831525.csv → /home/jovyan/RT08/0925/bling_onehot/PatNo_ID_1574831525.csv\n",
      "[OK] PatNo_ID_1574987447.csv → /home/jovyan/RT08/0925/bling_onehot/PatNo_ID_1574987447.csv\n",
      "[OK] PatNo_ID_1575060177.csv → /home/jovyan/RT08/0925/bling_onehot/PatNo_ID_1575060177.csv\n",
      "[OK] PatNo_ID_1575256902.csv → /home/jovyan/RT08/0925/bling_onehot/PatNo_ID_1575256902.csv\n",
      "[OK] PatNo_ID_1575445051.csv → /home/jovyan/RT08/0925/bling_onehot/PatNo_ID_1575445051.csv\n",
      "[OK] PatNo_ID_1575502382.csv → /home/jovyan/RT08/0925/bling_onehot/PatNo_ID_1575502382.csv\n",
      "[OK] PatNo_ID_1575975485.csv → /home/jovyan/RT08/0925/bling_onehot/PatNo_ID_1575975485.csv\n",
      "[OK] PatNo_ID_1576115572.csv → /home/jovyan/RT08/0925/bling_onehot/PatNo_ID_1576115572.csv\n",
      "[OK] PatNo_ID_1576116479.csv → /home/jovyan/RT08/0925/bling_onehot/PatNo_ID_1576116479.csv\n",
      "[OK] PatNo_ID_1576301569.csv → /home/jovyan/RT08/0925/bling_onehot/PatNo_ID_1576301569.csv\n",
      "[OK] PatNo_ID_1576964560.csv → /home/jovyan/RT08/0925/bling_onehot/PatNo_ID_1576964560.csv\n",
      "[OK] PatNo_ID_1577042911.csv → /home/jovyan/RT08/0925/bling_onehot/PatNo_ID_1577042911.csv\n",
      "[OK] PatNo_ID_1577487284.csv → /home/jovyan/RT08/0925/bling_onehot/PatNo_ID_1577487284.csv\n",
      "[OK] PatNo_ID_1578784257.csv → /home/jovyan/RT08/0925/bling_onehot/PatNo_ID_1578784257.csv\n",
      "[OK] PatNo_ID_1579198603.csv → /home/jovyan/RT08/0925/bling_onehot/PatNo_ID_1579198603.csv\n",
      "[OK] PatNo_ID_1579498177.csv → /home/jovyan/RT08/0925/bling_onehot/PatNo_ID_1579498177.csv\n",
      "[OK] PatNo_ID_1580062580.csv → /home/jovyan/RT08/0925/bling_onehot/PatNo_ID_1580062580.csv\n",
      "[OK] PatNo_ID_1580096720.csv → /home/jovyan/RT08/0925/bling_onehot/PatNo_ID_1580096720.csv\n",
      "[OK] PatNo_ID_1580107637.csv → /home/jovyan/RT08/0925/bling_onehot/PatNo_ID_1580107637.csv\n",
      "[OK] PatNo_ID_1580244614.csv → /home/jovyan/RT08/0925/bling_onehot/PatNo_ID_1580244614.csv\n",
      "[OK] PatNo_ID_1580766093.csv → /home/jovyan/RT08/0925/bling_onehot/PatNo_ID_1580766093.csv\n",
      "[OK] PatNo_ID_1581003248.csv → /home/jovyan/RT08/0925/bling_onehot/PatNo_ID_1581003248.csv\n",
      "[OK] PatNo_ID_1581019504.csv → /home/jovyan/RT08/0925/bling_onehot/PatNo_ID_1581019504.csv\n",
      "[OK] PatNo_ID_1581633231.csv → /home/jovyan/RT08/0925/bling_onehot/PatNo_ID_1581633231.csv\n",
      "[OK] PatNo_ID_1581692973.csv → /home/jovyan/RT08/0925/bling_onehot/PatNo_ID_1581692973.csv\n",
      "[OK] PatNo_ID_1582452511.csv → /home/jovyan/RT08/0925/bling_onehot/PatNo_ID_1582452511.csv\n",
      "[OK] PatNo_ID_1582635996.csv → /home/jovyan/RT08/0925/bling_onehot/PatNo_ID_1582635996.csv\n",
      "[OK] PatNo_ID_1582849900.csv → /home/jovyan/RT08/0925/bling_onehot/PatNo_ID_1582849900.csv\n",
      "[OK] PatNo_ID_1582937076.csv → /home/jovyan/RT08/0925/bling_onehot/PatNo_ID_1582937076.csv\n",
      "[OK] PatNo_ID_1584158973.csv → /home/jovyan/RT08/0925/bling_onehot/PatNo_ID_1584158973.csv\n",
      "[OK] PatNo_ID_1584397376.csv → /home/jovyan/RT08/0925/bling_onehot/PatNo_ID_1584397376.csv\n",
      "[OK] PatNo_ID_1586172659.csv → /home/jovyan/RT08/0925/bling_onehot/PatNo_ID_1586172659.csv\n",
      "[OK] PatNo_ID_1586696634.csv → /home/jovyan/RT08/0925/bling_onehot/PatNo_ID_1586696634.csv\n",
      "[OK] PatNo_ID_1586897008.csv → /home/jovyan/RT08/0925/bling_onehot/PatNo_ID_1586897008.csv\n",
      "[OK] PatNo_ID_1587490083.csv → /home/jovyan/RT08/0925/bling_onehot/PatNo_ID_1587490083.csv\n",
      "[OK] PatNo_ID_1588632604.csv → /home/jovyan/RT08/0925/bling_onehot/PatNo_ID_1588632604.csv\n",
      "[OK] PatNo_ID_1588673465.csv → /home/jovyan/RT08/0925/bling_onehot/PatNo_ID_1588673465.csv\n",
      "[OK] PatNo_ID_1588794796.csv → /home/jovyan/RT08/0925/bling_onehot/PatNo_ID_1588794796.csv\n",
      "[OK] PatNo_ID_1588957997.csv → /home/jovyan/RT08/0925/bling_onehot/PatNo_ID_1588957997.csv\n",
      "[OK] PatNo_ID_1589018086.csv → /home/jovyan/RT08/0925/bling_onehot/PatNo_ID_1589018086.csv\n",
      "[OK] PatNo_ID_1589034524.csv → /home/jovyan/RT08/0925/bling_onehot/PatNo_ID_1589034524.csv\n",
      "[OK] PatNo_ID_1589324603.csv → /home/jovyan/RT08/0925/bling_onehot/PatNo_ID_1589324603.csv\n",
      "[OK] PatNo_ID_1589918099.csv → /home/jovyan/RT08/0925/bling_onehot/PatNo_ID_1589918099.csv\n",
      "[OK] PatNo_ID_1590136310.csv → /home/jovyan/RT08/0925/bling_onehot/PatNo_ID_1590136310.csv\n",
      "[OK] PatNo_ID_1590616537.csv → /home/jovyan/RT08/0925/bling_onehot/PatNo_ID_1590616537.csv\n",
      "[OK] PatNo_ID_1590854576.csv → /home/jovyan/RT08/0925/bling_onehot/PatNo_ID_1590854576.csv\n",
      "[OK] PatNo_ID_1591609798.csv → /home/jovyan/RT08/0925/bling_onehot/PatNo_ID_1591609798.csv\n",
      "[OK] PatNo_ID_1592044724.csv → /home/jovyan/RT08/0925/bling_onehot/PatNo_ID_1592044724.csv\n",
      "[OK] PatNo_ID_1592560504.csv → /home/jovyan/RT08/0925/bling_onehot/PatNo_ID_1592560504.csv\n",
      "[OK] PatNo_ID_1593087886.csv → /home/jovyan/RT08/0925/bling_onehot/PatNo_ID_1593087886.csv\n",
      "[OK] PatNo_ID_1593416100.csv → /home/jovyan/RT08/0925/bling_onehot/PatNo_ID_1593416100.csv\n",
      "[OK] PatNo_ID_1593472048.csv → /home/jovyan/RT08/0925/bling_onehot/PatNo_ID_1593472048.csv\n",
      "[OK] PatNo_ID_1593593586.csv → /home/jovyan/RT08/0925/bling_onehot/PatNo_ID_1593593586.csv\n",
      "[OK] PatNo_ID_1593720818.csv → /home/jovyan/RT08/0925/bling_onehot/PatNo_ID_1593720818.csv\n",
      "[OK] PatNo_ID_1593838524.csv → /home/jovyan/RT08/0925/bling_onehot/PatNo_ID_1593838524.csv\n",
      "[OK] PatNo_ID_1594173718.csv → /home/jovyan/RT08/0925/bling_onehot/PatNo_ID_1594173718.csv\n",
      "[OK] PatNo_ID_1594294180.csv → /home/jovyan/RT08/0925/bling_onehot/PatNo_ID_1594294180.csv\n",
      "[OK] PatNo_ID_1594305136.csv → /home/jovyan/RT08/0925/bling_onehot/PatNo_ID_1594305136.csv\n",
      "[OK] PatNo_ID_1594309746.csv → /home/jovyan/RT08/0925/bling_onehot/PatNo_ID_1594309746.csv\n",
      "[OK] PatNo_ID_1594319286.csv → /home/jovyan/RT08/0925/bling_onehot/PatNo_ID_1594319286.csv\n",
      "[OK] PatNo_ID_1594320763.csv → /home/jovyan/RT08/0925/bling_onehot/PatNo_ID_1594320763.csv\n",
      "[OK] PatNo_ID_1594322594.csv → /home/jovyan/RT08/0925/bling_onehot/PatNo_ID_1594322594.csv\n",
      "[OK] PatNo_ID_1594335109.csv → /home/jovyan/RT08/0925/bling_onehot/PatNo_ID_1594335109.csv\n",
      "[OK] PatNo_ID_1594423683.csv → /home/jovyan/RT08/0925/bling_onehot/PatNo_ID_1594423683.csv\n",
      "[OK] PatNo_ID_1594437309.csv → /home/jovyan/RT08/0925/bling_onehot/PatNo_ID_1594437309.csv\n",
      "[OK] PatNo_ID_1594439781.csv → /home/jovyan/RT08/0925/bling_onehot/PatNo_ID_1594439781.csv\n",
      "[OK] PatNo_ID_1594441887.csv → /home/jovyan/RT08/0925/bling_onehot/PatNo_ID_1594441887.csv\n",
      "[OK] PatNo_ID_1594448501.csv → /home/jovyan/RT08/0925/bling_onehot/PatNo_ID_1594448501.csv\n",
      "[OK] PatNo_ID_1594455578.csv → /home/jovyan/RT08/0925/bling_onehot/PatNo_ID_1594455578.csv\n",
      "[OK] PatNo_ID_1594464829.csv → /home/jovyan/RT08/0925/bling_onehot/PatNo_ID_1594464829.csv\n",
      "[OK] PatNo_ID_1594467719.csv → /home/jovyan/RT08/0925/bling_onehot/PatNo_ID_1594467719.csv\n",
      "[OK] PatNo_ID_1594471407.csv → /home/jovyan/RT08/0925/bling_onehot/PatNo_ID_1594471407.csv\n",
      "[OK] PatNo_ID_1594479330.csv → /home/jovyan/RT08/0925/bling_onehot/PatNo_ID_1594479330.csv\n",
      "[OK] PatNo_ID_1594511911.csv → /home/jovyan/RT08/0925/bling_onehot/PatNo_ID_1594511911.csv\n",
      "[OK] PatNo_ID_1594511914.csv → /home/jovyan/RT08/0925/bling_onehot/PatNo_ID_1594511914.csv\n",
      "[OK] PatNo_ID_1594528842.csv → /home/jovyan/RT08/0925/bling_onehot/PatNo_ID_1594528842.csv\n",
      "[OK] PatNo_ID_1594533379.csv → /home/jovyan/RT08/0925/bling_onehot/PatNo_ID_1594533379.csv\n",
      "\n",
      "[Summary] processed=122, skipped_no_col=0, errors=0\n",
      "\n",
      "[OVERALL] mode_0..3 counts & percentages\n",
      "- mode_0: 42534 (2.80%)\n",
      "- mode_1: 1005251 (66.16%)\n",
      "- mode_2: 381722 (25.12%)\n",
      "- mode_3: 89911 (5.92%)\n",
      "[OK] Summary saved:\n",
      "- /home/jovyan/RT08/0925/0926/ventilatormode_onehot_summary.csv\n",
      "- /home/jovyan/RT08/0925/0926/ventilatormode_onehot_summary_by_mode.csv\n",
      "[OK] Bar chart -> /home/jovyan/RT08/0925/0926/ventilatormode_onehot_bar.png\n"
     ]
    }
   ],
   "source": [
    "\"\"\"我要對ventilatormode進行one-hot encoding，ventilatormode原本的內容要轉成0-3,\n",
    "原本的ventilatormode欄位要留\n",
    "1. 另外創五個欄位mode_0, mode_1, mode_2, mode_3, 不符合規則的就放在mode_9\n",
    "依據以下分類規則，\n",
    "\"Standby\": 0,\n",
    "    \"PCV\": 1, \"PC-AC\": 1, \"A/C PC\": 1, \"P-CMV\": 1,\n",
    "    \"PSIMV\": 2, \"P-SIMV\": 2, \"PC-SIMV+\": 2, \"PC-SIMV\": 2,\n",
    "    \"PSIMV+PSV\": 2, \"SIMV PC\": 2,\n",
    "    \"PSV\": 3, \"CPAP/PSV\": 3, \"CPAP/PS\": 3, \"PS\": 3,\n",
    "2. 紀錄mode_0, mode_1, mode_2, mode_3分別有筆 佔比多少%\n",
    "3. 繪製條狀圖 橫軸是各種模式 縱軸是筆數\n",
    "相同編碼的模式放在一起 最左邊是0 到最右邊是3\n",
    "相同編碼用相同色系但深淺不一樣\n",
    "每一條上面要寫筆數以及佔所有的比率%\n",
    "\n",
    "\n",
    "我0921的程式寫 PC-CMV': 1, 'VC-CMV': 2, 'PSV': 3, 'SIMV(PC)+PSV': 1, 'SIMV(VC)+PSV': 2, 'CPAP': 3, 'SPONT': 3, 'APRV': 0}\n",
    "\"\"\"\n",
    "# ============================================================\n",
    "# VentilatorMode → 數值編碼 + One-Hot（不動原檔）\n",
    "# 來源：/home/jovyan/RT08/0925/blingsponvt\n",
    "# 產出（逐檔一份，檔名相同）：\n",
    "#   /home/jovyan/RT08/0925/bling_onehot/<file>.csv\n",
    "# 產出（彙總與圖）：\n",
    "#   /home/jovyan/RT08/0925/0926/ventilatormode_onehot_summary.csv\n",
    "#   /home/jovyan/RT08/0925/0926/ventilatormode_onehot_bar.png\n",
    "# 功能：\n",
    "#   - 保留原欄位 ventilatormode\n",
    "#   - 新增欄位 ventilatormode_code（0/1/2/3/9）\n",
    "#   - 新增欄位 mode_0, mode_1, mode_2, mode_3, mode_9（one-hot）\n",
    "#   - 印出跨檔總計（0~3 類別的總筆數與佔比）\n",
    "#   - 繪製條狀圖（x=各模式字串，依 0→3 群組排序；y=筆數；每條柱標註「筆數 + 佔比%」）\n",
    "# ============================================================\n",
    "import os, glob, unicodedata\n",
    "from collections import Counter\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "SRC_DIR = \"/home/jovyan/RT08/0925/blingsponvt\"\n",
    "OUT_DIR_FILES = \"/home/jovyan/RT08/0925/bling_onehot\"\n",
    "OUT_DIR_REPORT = \"/home/jovyan/RT08/0925/0926\"\n",
    "os.makedirs(OUT_DIR_FILES, exist_ok=True)\n",
    "os.makedirs(OUT_DIR_REPORT, exist_ok=True)\n",
    "\n",
    "# 映射規則（精確匹配，大小寫/空白須一致；其餘→9）\n",
    "MODE_CLASS = {\n",
    "    \"Standby\": 0,\n",
    "    \"PCV\": 1, \"PC-AC\": 1, \"A/C PC\": 1, \"P-CMV\": 1,\n",
    "    \"PSIMV\": 2, \"P-SIMV\": 2, \"PC-SIMV+\": 2, \"PC-SIMV\": 2,\n",
    "    \"PSIMV+PSV\": 2, \"SIMV PC\": 2,\n",
    "    \"PSV\": 3, \"CPAP/PSV\": 3, \"CPAP/PS\": 3, \"PS\": 3,\n",
    "}\n",
    "\n",
    "# 欄位順序：依群組 0→3，組內固定順序\n",
    "ORDERED_BY_GROUP = [\n",
    "    [\"Standby\"],  # 0\n",
    "    [\"PCV\",\"PC-AC\",\"A/C PC\",\"P-CMV\"],  # 1\n",
    "    [\"PSIMV\",\"P-SIMV\",\"PC-SIMV+\",\"PC-SIMV\",\"PSIMV+PSV\",\"SIMV PC\"],  # 2\n",
    "    [\"PSV\",\"CPAP/PSV\",\"CPAP/PS\",\"PS\"],  # 3\n",
    "]\n",
    "ORDERED_MODES = [m for grp in ORDERED_BY_GROUP for m in grp]\n",
    "\n",
    "ENCODINGS = (\"utf-8-sig\",\"utf-8\",\"cp950\",\"big5\",\"latin1\")\n",
    "\n",
    "def read_df_any(path):\n",
    "    for enc in ENCODINGS:\n",
    "        try:\n",
    "            return pd.read_csv(path, encoding=enc, engine=\"python\", on_bad_lines=\"skip\")\n",
    "        except Exception:\n",
    "            continue\n",
    "    raise RuntimeError(f\"讀檔失敗：{os.path.basename(path)}\")\n",
    "\n",
    "def find_vm_col(df):\n",
    "    for c in df.columns:\n",
    "        if unicodedata.normalize(\"NFKC\", str(c)).strip().lower() == \"ventilatormode\":\n",
    "            return c\n",
    "    return None\n",
    "\n",
    "def norm_str(x):\n",
    "    if pd.isna(x): return None\n",
    "    s = unicodedata.normalize(\"NFKC\", str(x)).strip()\n",
    "    return s if s != \"\" else None\n",
    "\n",
    "# ====== 逐檔處理：建立 code 與 one-hot，不動原檔，另存 ======\n",
    "files = sorted(glob.glob(os.path.join(SRC_DIR, \"*.csv\")))\n",
    "counts_by_mode = Counter()\n",
    "counts_by_code = Counter()\n",
    "processed = 0\n",
    "skipped_no_col = 0\n",
    "errors = []\n",
    "\n",
    "for fp in files:\n",
    "    name = os.path.basename(fp)\n",
    "    try:\n",
    "        df = read_df_any(fp)\n",
    "        col = find_vm_col(df)\n",
    "        if col is None:\n",
    "            skipped_no_col += 1\n",
    "            print(f\"[SKIP] {name}: 無 ventilatormode 欄位\")\n",
    "            continue\n",
    "\n",
    "        # 產生編碼（保留原欄位 ventilatormode）\n",
    "        vm_norm = df[col].map(norm_str)\n",
    "        code = vm_norm.map(lambda v: MODE_CLASS.get(v, 9) if v is not None else np.nan)\n",
    "        df[\"ventilatormode_code\"] = code\n",
    "\n",
    "        # one-hot：mode_0/1/2/3/9\n",
    "        for k in [0,1,2,3,9]:\n",
    "            df[f\"mode_{k}\"] = (df[\"ventilatormode_code\"] == k).astype(\"Int64\")\n",
    "\n",
    "        # 累計統計（只統計 0~3，忽略 9 與 NaN）\n",
    "        sel = code[code.isin([0,1,2,3])]\n",
    "        counts_by_code.update(sel.value_counts().to_dict())\n",
    "        # 模式字串層級的統計（只統計在映射清單內）\n",
    "        vm_kept = vm_norm[vm_norm.isin(MODE_CLASS.keys())]\n",
    "        counts_by_mode.update(vm_kept.value_counts().to_dict())\n",
    "\n",
    "        # 另存\n",
    "        out_fp = os.path.join(OUT_DIR_FILES, name)\n",
    "        df.to_csv(out_fp, index=False, encoding=\"utf-8\")\n",
    "        processed += 1\n",
    "        print(f\"[OK] {name} → {out_fp}\")\n",
    "    except Exception as e:\n",
    "        errors.append((name, str(e)))\n",
    "        print(f\"[ERR] {name}: {e}\")\n",
    "\n",
    "print(f\"\\n[Summary] processed={processed}, skipped_no_col={skipped_no_col}, errors={len(errors)}\")\n",
    "if errors:\n",
    "    for n, msg in errors:\n",
    "        print(f\"  - {n}: {msg}\")\n",
    "\n",
    "# ====== 2) 彙總：列印 0~3 類的總筆數與佔比；輸出 CSV ======\n",
    "total_matched_0_3 = int(sum(counts_by_code.get(c, 0) for c in [0,1,2,3]))\n",
    "print(\"\\n[OVERALL] mode_0..3 counts & percentages\")\n",
    "rows_summary = []\n",
    "for c in [0,1,2,3]:\n",
    "    cnt = int(counts_by_code.get(c, 0))\n",
    "    pct = (cnt / total_matched_0_3 * 100.0) if total_matched_0_3 > 0 else 0.0\n",
    "    print(f\"- mode_{c}: {cnt} ({pct:.2f}%)\")\n",
    "    rows_summary.append({\"mode\": f\"mode_{c}\", \"count\": cnt, \"percent\": round(pct, 6)})\n",
    "\n",
    "# 也依「原始模式字串」列出統計（同一編碼的模式加總不在此；此表僅模式字串粒度）\n",
    "rows_modes = []\n",
    "for m in ORDERED_MODES:\n",
    "    cnt = int(counts_by_mode.get(m, 0))\n",
    "    pct = (cnt / sum(counts_by_mode.values()) * 100.0) if counts_by_mode else 0.0\n",
    "    rows_modes.append({\"ventilatormode\": m, \"count\": cnt, \"percent\": round(pct, 6)})\n",
    "\n",
    "summary_csv = os.path.join(OUT_DIR_REPORT, \"ventilatormode_onehot_summary.csv\")\n",
    "with pd.ExcelWriter(summary_csv.replace(\".csv\",\".xlsx\")) as xw:\n",
    "    pd.DataFrame(rows_summary).to_excel(xw, sheet_name=\"by_code\", index=False)\n",
    "    pd.DataFrame(rows_modes).to_excel(xw, sheet_name=\"by_mode_str\", index=False)\n",
    "# 同時也輸出 CSV（兩張表分開）\n",
    "pd.DataFrame(rows_summary).to_csv(summary_csv, index=False, encoding=\"utf-8\")\n",
    "pd.DataFrame(rows_modes).to_csv(summary_csv.replace(\".csv\",\"_by_mode.csv\"), index=False, encoding=\"utf-8\")\n",
    "print(f\"[OK] Summary saved:\\n- {summary_csv}\\n- {summary_csv.replace('.csv','_by_mode.csv')}\")\n",
    "\n",
    "# ====== 3) 條狀圖：以「原始模式字串」為 x，依群組 0→3 排序；柱上標註「筆數 + 佔比%」 ======\n",
    "if counts_by_mode:\n",
    "    # 建立 x 序列（依群組）\n",
    "    x_labels = []\n",
    "    x_counts = []\n",
    "    x_percents = []\n",
    "    groups = []  # 0~3\n",
    "    total_modes = sum(counts_by_mode.values())\n",
    "    for gid, modes in enumerate(ORDERED_BY_GROUP):\n",
    "        for m in modes:\n",
    "            x_labels.append(m)\n",
    "            c = counts_by_mode.get(m, 0)\n",
    "            x_counts.append(c)\n",
    "            x_percents.append((c / total_modes * 100.0) if total_modes else 0.0)\n",
    "            groups.append(gid)\n",
    "\n",
    "    # 一致的顏色系（每個群組一個色系、不同深淺）\n",
    "    # 設定 4 個基色（你可依喜好調整）\n",
    "    base_colors = {\n",
    "        0: (0.40, 0.60, 0.85),  # 藍\n",
    "        1: (0.40, 0.80, 0.60),  # 綠\n",
    "        2: (0.95, 0.65, 0.35),  # 橘\n",
    "        3: (0.90, 0.45, 0.55),  # 紅\n",
    "    }\n",
    "    # 依組內序號調暗一些\n",
    "    shades = []\n",
    "    idx_in_group = {}\n",
    "    for gid in range(4):\n",
    "        idx_in_group[gid] = 0\n",
    "    for gid, modes in enumerate(ORDERED_BY_GROUP):\n",
    "        for _ in modes:\n",
    "            k = idx_in_group[gid]\n",
    "            r,g,b = base_colors[gid]\n",
    "            factor = 0.85 - 0.10 * k  # 組內越後面越深\n",
    "            factor = max(0.4, factor)\n",
    "            shades.append((r*factor, g*factor, b*factor))\n",
    "            idx_in_group[gid] += 1\n",
    "\n",
    "    plt.figure(figsize=(max(10, len(x_labels)*0.6), 6))\n",
    "    x = np.arange(len(x_labels))\n",
    "    bars = plt.bar(x, x_counts, color=shades, edgecolor=\"black\", linewidth=0.5)\n",
    "\n",
    "    plt.xticks(x, x_labels, rotation=45, ha=\"right\")\n",
    "    plt.ylabel(\"Count\")\n",
    "    plt.xlabel(\"Ventilator Modes (grouped 0→3)\")\n",
    "    plt.title(\"VentilatorMode Distribution (Counts & %)\")\n",
    "    # 畫出組界線與群組標籤\n",
    "    cursor = 0\n",
    "    for gid, modes in enumerate(ORDERED_BY_GROUP):\n",
    "        w = len(modes)\n",
    "        if w == 0: continue\n",
    "        # 垂直分隔線（組之間）\n",
    "        if gid > 0:\n",
    "            plt.axvline(x=cursor-0.5, color=\"gray\", linestyle=\"--\", linewidth=0.8, alpha=0.6)\n",
    "        # 在組上方印 group id\n",
    "        mid = cursor + (w-1)/2\n",
    "        plt.text(mid, max(x_counts)*1.05 if x_counts else 1, f\"group {gid}\", ha=\"center\", va=\"bottom\", fontsize=9)\n",
    "        cursor += w\n",
    "\n",
    "    # 在每個 bar 上標註「數量（百分比%）」\n",
    "    ymax = max(x_counts) if x_counts else 0\n",
    "    for xi, (bar, cnt, pct) in enumerate(zip(bars, x_counts, x_percents)):\n",
    "        if cnt == 0:\n",
    "            continue\n",
    "        y = bar.get_height()\n",
    "        plt.text(bar.get_x() + bar.get_width()/2, y + ymax*0.01 + 0.02,\n",
    "                 f\"{cnt} ({pct:.1f}%)\", ha=\"center\", va=\"bottom\", fontsize=9)\n",
    "\n",
    "    plt.tight_layout()\n",
    "    out_png = os.path.join(OUT_DIR_REPORT, \"ventilatormode_onehot_bar.png\")\n",
    "    plt.savefig(out_png, dpi=150)\n",
    "    plt.close()\n",
    "    print(f\"[OK] Bar chart -> {out_png}\")\n",
    "else:\n",
    "    print(\"[WARN] 沒有任何符合映射清單的 ventilatormode，跳過繪圖。\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "4fd1ebe4-5c1b-492f-994c-8135464c91ce",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[OK] Re-rendered bar chart -> /home/jovyan/RT08/0925/0926/ventilatormode_onehot_bar.png（已覆蓋）\n"
     ]
    }
   ],
   "source": [
    "#可刪 不需用 \n",
    "# 條狀圖上的字有些都被擋到重疊到 請重新繪製一張 覆蓋原本那張\n",
    "# 重新繪製「VentilatorMode 分布」條狀圖（避免標註重疊）\n",
    "# - 讀取：/home/jovyan/RT08/0925/blingsponvt/*.csv（只讀，不動原檔）\n",
    "# - 規則與排序與色系：沿用先前（群組 0→3，組內固定順序；同組同色系、深淺不同）\n",
    "# - 改善：依欄數自動放大畫布、長 x 標籤旋轉、為標註留出上邊界、內外混合標註避免擠在一起、\n",
    "#        標註加白底描邊增加可讀性\n",
    "# - 覆蓋輸出：/home/jovyan/RT08/0925/0926/ventilatormode_onehot_bar.png\n",
    "\n",
    "import os, glob, unicodedata\n",
    "from collections import Counter\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "IN_DIR  = \"/home/jovyan/RT08/0925/blingsponvt\"\n",
    "OUT_PNG = \"/home/jovyan/RT08/0925/0926/ventilatormode_onehot_bar.png\"\n",
    "os.makedirs(os.path.dirname(OUT_PNG), exist_ok=True)\n",
    "\n",
    "MODE_CLASS = {\n",
    "    \"Standby\": 0,\n",
    "    \"PCV\": 1, \"PC-AC\": 1, \"A/C PC\": 1, \"P-CMV\": 1,\n",
    "    \"PSIMV\": 2, \"P-SIMV\": 2, \"PC-SIMV+\": 2, \"PC-SIMV\": 2, \"PSIMV+PSV\": 2, \"SIMV PC\": 2,\n",
    "    \"PSV\": 3, \"CPAP/PSV\": 3, \"CPAP/PS\": 3, \"PS\": 3,\n",
    "}\n",
    "ORDERED_BY_GROUP = [\n",
    "    [\"Standby\"],                                   # 0\n",
    "    [\"PCV\",\"PC-AC\",\"A/C PC\",\"P-CMV\"],             # 1\n",
    "    [\"PSIMV\",\"P-SIMV\",\"PC-SIMV+\",\"PC-SIMV\",\"PSIMV+PSV\",\"SIMV PC\"],  # 2\n",
    "    [\"PSV\",\"CPAP/PSV\",\"CPAP/PS\",\"PS\"],            # 3\n",
    "]\n",
    "ORDERED_MODES = [m for grp in ORDERED_BY_GROUP for m in grp]\n",
    "ENCODINGS = (\"utf-8-sig\",\"utf-8\",\"cp950\",\"big5\",\"latin1\")\n",
    "\n",
    "def read_df_any(path):\n",
    "    for enc in ENCODINGS:\n",
    "        try:\n",
    "            return pd.read_csv(path, encoding=enc, engine=\"python\", on_bad_lines=\"skip\")\n",
    "        except Exception:\n",
    "            continue\n",
    "    return None\n",
    "\n",
    "def find_vm_col(df):\n",
    "    for c in df.columns:\n",
    "        if unicodedata.normalize(\"NFKC\", str(c)).strip().lower() == \"ventilatormode\":\n",
    "            return c\n",
    "    return None\n",
    "\n",
    "def norm_val(x):\n",
    "    if pd.isna(x): return None\n",
    "    s = unicodedata.normalize(\"NFKC\", str(x)).strip()\n",
    "    return s if s != \"\" else None\n",
    "\n",
    "# 1) 聚合統計（跨檔相加）\n",
    "counts_by_mode = Counter()\n",
    "files = sorted(glob.glob(os.path.join(IN_DIR, \"*.csv\")))\n",
    "for fp in files:\n",
    "    df = read_df_any(fp)\n",
    "    if df is None: \n",
    "        continue\n",
    "    col = find_vm_col(df)\n",
    "    if col is None:\n",
    "        continue\n",
    "    vals = df[col].map(norm_val).dropna()\n",
    "    vals = vals[vals.isin(MODE_CLASS.keys())]  # 僅取映射清單內\n",
    "    counts_by_mode.update(vals.value_counts().to_dict())\n",
    "\n",
    "if not counts_by_mode:\n",
    "    print(\"[WARN] 沒有任何符合映射清單的 ventilatormode，無法繪圖。\")\n",
    "else:\n",
    "    # 依 0→3 群組、組內固定順序，組裝 x 軸\n",
    "    x_labels, x_counts, groups = [], [], []\n",
    "    total = sum(counts_by_mode.values())\n",
    "    for gid, modes in enumerate(ORDERED_BY_GROUP):\n",
    "        for m in modes:\n",
    "            x_labels.append(m)\n",
    "            c = int(counts_by_mode.get(m, 0))\n",
    "            x_counts.append(c)\n",
    "            groups.append(gid)\n",
    "    x_counts = np.array(x_counts, dtype=float)\n",
    "    x_perc = (x_counts / total * 100.0) if total > 0 else np.zeros_like(x_counts)\n",
    "\n",
    "    # 2) 顏色：同組同色系、組內遞進加深\n",
    "    base_colors = {\n",
    "        0: (0.40, 0.60, 0.85),  # 藍\n",
    "        1: (0.40, 0.80, 0.60),  # 綠\n",
    "        2: (0.95, 0.65, 0.35),  # 橘\n",
    "        3: (0.90, 0.45, 0.55),  # 紅\n",
    "    }\n",
    "    shades = []\n",
    "    idx_in_group = {0:0,1:0,2:0,3:0}\n",
    "    for gid, modes in enumerate(ORDERED_BY_GROUP):\n",
    "        for _ in modes:\n",
    "            k = idx_in_group[gid]\n",
    "            r,g,b = base_colors[gid]\n",
    "            factor = max(0.45, 0.90 - 0.10 * k)   # 越後越深，設下限避免過暗\n",
    "            shades.append((r*factor, g*factor, b*factor))\n",
    "            idx_in_group[gid] += 1\n",
    "\n",
    "    # 3) 版面配置：依類別數量自動放大、增加上下邊界、旋轉 x 標籤\n",
    "    W = max(12, len(x_labels) * 0.8)\n",
    "    H = 7\n",
    "    plt.figure(figsize=(W, H))\n",
    "    x = np.arange(len(x_labels))\n",
    "    bars = plt.bar(x, x_counts, color=shades, edgecolor=\"black\", linewidth=0.5)\n",
    "\n",
    "    plt.xticks(x, x_labels, rotation=45, ha=\"right\")\n",
    "    plt.ylabel(\"Count\")\n",
    "    plt.xlabel(\"Ventilator Modes (grouped 0→3)\")\n",
    "    plt.title(\"VentilatorMode Distribution (Counts & %)\")\n",
    "\n",
    "    # 群組分隔線與群組標籤（放在較高處，避免與柱註記撞到）\n",
    "    ymax = float(x_counts.max()) if x_counts.size else 0.0\n",
    "    top_y = ymax * 1.20 + 0.5\n",
    "    cursor = 0\n",
    "    for gid, modes in enumerate(ORDERED_BY_GROUP):\n",
    "        w = len(modes)\n",
    "        if w == 0: \n",
    "            continue\n",
    "        if gid > 0:\n",
    "            plt.axvline(x=cursor-0.5, color=\"gray\", linestyle=\"--\", linewidth=0.8, alpha=0.6)\n",
    "        mid = cursor + (w-1)/2\n",
    "        plt.text(mid, top_y, f\"group {gid}\", ha=\"center\", va=\"bottom\", fontsize=10)\n",
    "        cursor += w\n",
    "\n",
    "    # 4) 柱上標註：數量 + 佔比%\n",
    "    #    - 高柱：標註「柱內」白字（避免頂部擁擠）\n",
    "    #    - 低柱：標註「柱外上方」黑字（加白底描邊）\n",
    "    #    - 自動增高 y 上限，避免標註被截掉\n",
    "    plt.ylim(0, max(top_y, ymax * 1.25 + 1))\n",
    "    inside_thresh = 0.18 * (ymax if ymax > 0 else 1)  # 高於門檻就放柱內\n",
    "    for bar, cnt, pct in zip(bars, x_counts, x_perc):\n",
    "        if cnt <= 0:\n",
    "            continue\n",
    "        bx = bar.get_x() + bar.get_width()/2\n",
    "        by = bar.get_height()\n",
    "        label = f\"{int(cnt)} ({pct:.1f}%)\"\n",
    "        if by >= inside_thresh:\n",
    "            # 柱內白字\n",
    "            plt.text(bx, by - 0.03*max(1, ymax), label, ha=\"center\", va=\"top\",\n",
    "                     fontsize=9, color=\"white\",\n",
    "                     bbox=dict(boxstyle=\"round,pad=0.25\", facecolor=(0,0,0,0.25), edgecolor=\"none\"))\n",
    "        else:\n",
    "            # 柱外黑字（白底）\n",
    "            plt.text(bx, by + 0.02*max(1, ymax) + 0.2, label, ha=\"center\", va=\"bottom\",\n",
    "                     fontsize=9, color=\"black\",\n",
    "                     bbox=dict(boxstyle=\"round,pad=0.2\", facecolor=\"white\", alpha=0.9, edgecolor=\"gray\"))\n",
    "\n",
    "    plt.tight_layout()\n",
    "    plt.savefig(OUT_PNG, dpi=150)\n",
    "    plt.close()\n",
    "    print(f\"[OK] Re-rendered bar chart -> {OUT_PNG}（已覆蓋）\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "11fab319-678b-4351-a4be-9b4ec7fc424a",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[OK] Re-rendered bar chart -> /home/jovyan/RT08/0925/0926/ventilatormode_onehot_bar.png（已覆蓋；顏色：紅/綠/藍/紫）\n"
     ]
    }
   ],
   "source": [
    "# 重新繪製 VentilatorMode 分布條狀圖（改善標註重疊 & 改為：0=紅、1=綠、2=藍、3=紫）\n",
    "# - 來源：/home/jovyan/RT08/0925/blingsponvt/*.csv（只讀，不動原檔）\n",
    "# - 覆蓋輸出：/home/jovyan/RT08/0925/0926/ventilatormode_onehot_bar.png\n",
    "\n",
    "import os, glob, unicodedata\n",
    "from collections import Counter\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "IN_DIR  = \"/home/jovyan/RT08/0925/blingsponvt\"\n",
    "OUT_PNG = \"/home/jovyan/RT08/0925/0926/ventilatormode_onehot_bar.png\"\n",
    "os.makedirs(os.path.dirname(OUT_PNG), exist_ok=True)\n",
    "\n",
    "MODE_CLASS = {\n",
    "    \"Standby\": 0,\n",
    "    \"PCV\": 1, \"PC-AC\": 1, \"A/C PC\": 1, \"P-CMV\": 1,\n",
    "    \"PSIMV\": 2, \"P-SIMV\": 2, \"PC-SIMV+\": 2, \"PC-SIMV\": 2, \"PSIMV+PSV\": 2, \"SIMV PC\": 2,\n",
    "    \"PSV\": 3, \"CPAP/PSV\": 3, \"CPAP/PS\": 3, \"PS\": 3,\n",
    "}\n",
    "ORDERED_BY_GROUP = [\n",
    "    [\"Standby\"],                                   # 0\n",
    "    [\"PCV\",\"PC-AC\",\"A/C PC\",\"P-CMV\"],             # 1\n",
    "    [\"PSIMV\",\"P-SIMV\",\"PC-SIMV+\",\"PC-SIMV\",\"PSIMV+PSV\",\"SIMV PC\"],  # 2\n",
    "    [\"PSV\",\"CPAP/PSV\",\"CPAP/PS\",\"PS\"],            # 3\n",
    "]\n",
    "ORDERED_MODES = [m for grp in ORDERED_BY_GROUP for m in grp]\n",
    "ENCODINGS = (\"utf-8-sig\",\"utf-8\",\"cp950\",\"big5\",\"latin1\")\n",
    "\n",
    "def read_df_any(path):\n",
    "    for enc in ENCODINGS:\n",
    "        try:\n",
    "            return pd.read_csv(path, encoding=enc, engine=\"python\", on_bad_lines=\"skip\")\n",
    "        except Exception:\n",
    "            continue\n",
    "    return None\n",
    "\n",
    "def find_vm_col(df):\n",
    "    for c in df.columns:\n",
    "        if unicodedata.normalize(\"NFKC\", str(c)).strip().lower() == \"ventilatormode\":\n",
    "            return c\n",
    "    return None\n",
    "\n",
    "def norm_val(x):\n",
    "    if pd.isna(x): return None\n",
    "    s = unicodedata.normalize(\"NFKC\", str(x)).strip()\n",
    "    return s if s != \"\" else None\n",
    "\n",
    "# 聚合統計\n",
    "counts_by_mode = Counter()\n",
    "for fp in sorted(glob.glob(os.path.join(IN_DIR, \"*.csv\"))):\n",
    "    df = read_df_any(fp)\n",
    "    if df is None: \n",
    "        continue\n",
    "    col = find_vm_col(df)\n",
    "    if col is None:\n",
    "        continue\n",
    "    vals = df[col].map(norm_val).dropna()\n",
    "    vals = vals[vals.isin(MODE_CLASS.keys())]\n",
    "    counts_by_mode.update(vals.value_counts().to_dict())\n",
    "\n",
    "if not counts_by_mode:\n",
    "    print(\"[WARN] 沒有任何符合映射清單的 ventilatormode，無法繪圖。\")\n",
    "else:\n",
    "    # x 軸序列（依群組）\n",
    "    x_labels, x_counts, groups = [], [], []\n",
    "    total = sum(counts_by_mode.values())\n",
    "    for gid, modes in enumerate(ORDERED_BY_GROUP):\n",
    "        for m in modes:\n",
    "            x_labels.append(m)\n",
    "            x_counts.append(int(counts_by_mode.get(m, 0)))\n",
    "            groups.append(gid)\n",
    "    x_counts = np.array(x_counts, dtype=float)\n",
    "    x_perc   = (x_counts / total * 100.0) if total > 0 else np.zeros_like(x_counts)\n",
    "\n",
    "    # 顏色：0=紅、1=綠、2=藍、3=紫（同組同色系、組內由淺到深）\n",
    "    base_colors = {\n",
    "        0: (0.85, 0.30, 0.30),  # red\n",
    "        1: (0.30, 0.70, 0.40),  # green\n",
    "        2: (0.30, 0.50, 0.85),  # blue\n",
    "        3: (0.65, 0.45, 0.85),  # purple\n",
    "    }\n",
    "    shades = []\n",
    "    for gid, modes in enumerate(ORDERED_BY_GROUP):\n",
    "        for idx_in_group, _ in enumerate(modes):\n",
    "            r,g,b = base_colors[gid]\n",
    "            factor = 0.65 + 0.08 * idx_in_group   # 組內往右稍微加深\n",
    "            factor = min(1.0, factor)\n",
    "            shades.append((r*factor, g*factor, b*factor))\n",
    "\n",
    "    # 版面放大、留白增大，避免擋到/重疊\n",
    "    W = max(16, len(x_labels) * 1.0)   # 每個類別留更寬的水平空間\n",
    "    H = 7.5\n",
    "    plt.figure(figsize=(W, H))\n",
    "    x = np.arange(len(x_labels))\n",
    "    bars = plt.bar(x, x_counts, color=shades, edgecolor=\"black\", linewidth=0.5)\n",
    "\n",
    "    plt.xticks(x, x_labels, rotation=45, ha=\"right\")\n",
    "    plt.ylabel(\"Count\")\n",
    "    plt.xlabel(\"Ventilator Modes (grouped 0→3)\")\n",
    "    plt.title(\"VentilatorMode Distribution (Counts & %)\")\n",
    "\n",
    "    ymax = float(x_counts.max()) if x_counts.size else 0.0\n",
    "    top_y = ymax * 1.35 + 1.0          # 更高的上邊界\n",
    "    plt.ylim(0, top_y)\n",
    "    plt.margins(y=0.08)                # 額外留白\n",
    "\n",
    "    # 群組分隔線與群組標籤（更高、更淡，降低遮擋）\n",
    "    cursor = 0\n",
    "    for gid, modes in enumerate(ORDERED_BY_GROUP):\n",
    "        w = len(modes)\n",
    "        if w == 0: \n",
    "            continue\n",
    "        if gid > 0:\n",
    "            plt.axvline(x=cursor-0.5, color=\"gray\", linestyle=\"--\", linewidth=0.8, alpha=0.4, zorder=0)\n",
    "        mid = cursor + (w-1)/2\n",
    "        plt.text(mid, top_y*0.98, f\"group {gid}\", ha=\"center\", va=\"top\", fontsize=10, alpha=0.9)\n",
    "        cursor += w\n",
    "\n",
    "    # 標註：全部「放在柱外上方」，避免與柱/分隔線/彼此重疊\n",
    "    for bar, cnt, pct in zip(bars, x_counts, x_perc):\n",
    "        if cnt <= 0: \n",
    "            continue\n",
    "        bx = bar.get_x() + bar.get_width()/2\n",
    "        by = bar.get_height()\n",
    "        label = f\"{int(cnt):,} ({pct:.1f}%)\"\n",
    "        plt.text(\n",
    "            bx, by + max(0.02*ymax, 0.5), label,\n",
    "            ha=\"center\", va=\"bottom\", fontsize=9, color=\"black\",\n",
    "            bbox=dict(boxstyle=\"round,pad=0.25\", facecolor=\"white\", alpha=0.9, edgecolor=\"gray\"),\n",
    "            clip_on=False, zorder=5\n",
    "        )\n",
    "\n",
    "    plt.tight_layout()\n",
    "    plt.savefig(OUT_PNG, dpi=150)\n",
    "    plt.close()\n",
    "    print(f\"[OK] Re-rendered bar chart -> {OUT_PNG}（已覆蓋；顏色：紅/綠/藍/紫）\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "07edc9e0-2f7a-4903-bd7e-c2408b3a57de",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PatNo_ID_1564148644.csv\n",
      "PatNo_ID_1566911879.csv\n",
      "PatNo_ID_1567804800.csv\n",
      "PatNo_ID_1572481361.csv\n",
      "PatNo_ID_1580096720.csv\n",
      "PatNo_ID_1580244614.csv\n",
      "PatNo_ID_1581003248.csv\n",
      "PatNo_ID_1581019504.csv\n",
      "PatNo_ID_1581633231.csv\n",
      "PatNo_ID_1582937076.csv\n",
      "PatNo_ID_1588673465.csv\n",
      "PatNo_ID_1589324603.csv\n",
      "PatNo_ID_1589918099.csv\n",
      "PatNo_ID_1594294180.csv\n",
      "PatNo_ID_1594511914.csv\n",
      "\n",
      "[SUMMARY] files with any negative in {vti,vte,sponvt}: 15\n"
     ]
    }
   ],
   "source": [
    "\"\"\"檢查/home/jovyan/RT08/0925/bling_onehot/所有檔案中，vti, vte, sponvt數值有沒有<0的，如果有 直接印出擋名\n",
    "\"\"\"\n",
    "# 檢查 /home/jovyan/RT08/0925/bling_onehot/ 所有 CSV\n",
    "# 若 vti、vte、sponvt 任一欄位出現數值 < 0，直接印出檔名\n",
    "# （只讀不寫原檔）\n",
    "\n",
    "import os, glob, unicodedata, re\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "DIR = \"/home/jovyan/RT08/0925/bling_onehot\"\n",
    "TARGET_COLS = [\"vti\", \"vte\", \"sponvt\"]\n",
    "ENCODINGS = (\"utf-8-sig\",\"utf-8\",\"cp950\",\"big5\",\"latin1\")\n",
    "\n",
    "def read_df_any(path):\n",
    "    for enc in ENCODINGS:\n",
    "        try:\n",
    "            return pd.read_csv(path, encoding=enc, engine=\"python\", on_bad_lines=\"skip\")\n",
    "        except Exception:\n",
    "            continue\n",
    "    return None  # 讀不到就略過\n",
    "\n",
    "def lower_map(df):\n",
    "    m = {}\n",
    "    for c in df.columns:\n",
    "        k = unicodedata.normalize(\"NFKC\", str(c)).strip().lower()\n",
    "        if k not in m: m[k] = c\n",
    "    return m\n",
    "\n",
    "INVIS_RE = re.compile(r\"[\\u200b-\\u200f\\u202a-\\u202e\\u2066-\\u2069\\ufeff\\u00a0\\u3000]\")\n",
    "\n",
    "def to_numeric_clean(s: pd.Series) -> pd.Series:\n",
    "    \"\"\"將混合型別/含千分位與不可見字元轉為數值（其餘→NaN）\"\"\"\n",
    "    if not isinstance(s, pd.Series):\n",
    "        return pd.Series(dtype=\"float64\")\n",
    "    s2 = s.astype(\"string\")\n",
    "    s2 = s2.map(lambda x: unicodedata.normalize(\"NFKC\", x).strip() if x is not pd.NA else x)\n",
    "    s2 = s2.map(lambda x: INVIS_RE.sub(\"\", x) if x is not pd.NA else x)\n",
    "    s2 = s2.str.replace(\",\", \"\", regex=False)\n",
    "    return pd.to_numeric(s2, errors=\"coerce\")\n",
    "\n",
    "flagged = []\n",
    "\n",
    "for fp in sorted(glob.glob(os.path.join(DIR, \"*.csv\"))):\n",
    "    df = read_df_any(fp)\n",
    "    if df is None:\n",
    "        continue\n",
    "    lmap = lower_map(df)\n",
    "\n",
    "    has_negative = False\n",
    "    for col in TARGET_COLS:\n",
    "        if col not in lmap:\n",
    "            continue\n",
    "        s = df[lmap[col]]\n",
    "        num = to_numeric_clean(s)\n",
    "        if (num < 0).any():\n",
    "            has_negative = True\n",
    "            break\n",
    "\n",
    "    if has_negative:\n",
    "        print(os.path.basename(fp))\n",
    "        flagged.append(fp)\n",
    "\n",
    "print(f\"\\n[SUMMARY] files with any negative in {{vti,vte,sponvt}}: {len(flagged)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "ceec66a3-0e51-470d-bed1-156f4341783e",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "- PatNo_ID_1564148644.csv | vte<0: 7\n",
      "- PatNo_ID_1566911879.csv | vti<0: 4\n",
      "- PatNo_ID_1567804800.csv | vti<0: 2\n",
      "- PatNo_ID_1572481361.csv | vti<0: 1\n",
      "- PatNo_ID_1580096720.csv | vte<0: 2\n",
      "- PatNo_ID_1580244614.csv | vti<0: 1; vte<0: 1\n",
      "- PatNo_ID_1581003248.csv | vte<0: 1\n",
      "- PatNo_ID_1581019504.csv | vti<0: 4\n",
      "- PatNo_ID_1581633231.csv | vte<0: 21\n",
      "- PatNo_ID_1582937076.csv | vti<0: 2\n",
      "- PatNo_ID_1588673465.csv | vti<0: 4\n",
      "- PatNo_ID_1589324603.csv | vti<0: 1\n",
      "- PatNo_ID_1589918099.csv | vte<0: 1\n",
      "- PatNo_ID_1594294180.csv | vte<0: 5\n",
      "- PatNo_ID_1594511914.csv | vti<0: 2\n",
      "\n",
      "[SUMMARY] files with any negatives: 15\n",
      "- total negatives in vti: 21\n",
      "- total negatives in vte: 38\n",
      "- total negatives in sponvt: 0\n"
     ]
    }
   ],
   "source": [
    "# 列出是哪個vti,vte,sponvt\n",
    "# 檢查 /home/jovyan/RT08/0925/bling_onehot/ 內所有 CSV，\n",
    "# 逐檔列出「哪一個欄位（vti/vte/sponvt）有 < 0」，並附上筆數。\n",
    "# ★ 只讀不寫原檔。缺欄位自動略過。\n",
    "\n",
    "import os, glob, unicodedata, re\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "DIR = \"/home/jovyan/RT08/0925/bling_onehot\"\n",
    "TARGET_COLS = [\"vti\", \"vte\", \"sponvt\"]\n",
    "ENCODINGS = (\"utf-8-sig\",\"utf-8\",\"cp950\",\"big5\",\"latin1\")\n",
    "\n",
    "def read_df_any(path):\n",
    "    for enc in ENCODINGS:\n",
    "        try:\n",
    "            return pd.read_csv(path, encoding=enc, engine=\"python\", on_bad_lines=\"skip\")\n",
    "        except Exception:\n",
    "            continue\n",
    "    return None  # 讀不到就略過\n",
    "\n",
    "def lower_map(df):\n",
    "    m = {}\n",
    "    for c in df.columns:\n",
    "        k = unicodedata.normalize(\"NFKC\", str(c)).strip().lower()\n",
    "        if k not in m: m[k] = c\n",
    "    return m\n",
    "\n",
    "# 去不可見字元、千分位、全形符號；支援括號負號 \"(123)\" → -123；支援 Unicode 負號 \"−\"\n",
    "INVIS_RE = re.compile(r\"[\\u200b-\\u200f\\u202a-\\u202e\\u2066-\\u2069\\ufeff\\u00a0\\u3000]\")\n",
    "def to_numeric_clean(s: pd.Series) -> pd.Series:\n",
    "    if not isinstance(s, pd.Series):\n",
    "        return pd.Series(dtype=\"float64\")\n",
    "    s2 = s.astype(\"string\")\n",
    "    s2 = s2.map(lambda x: unicodedata.normalize(\"NFKC\", x).strip() if x is not pd.NA else x)\n",
    "    s2 = s2.map(lambda x: INVIS_RE.sub(\"\", x) if x is not pd.NA else x)\n",
    "    s2 = s2.str.replace(\",\", \"\", regex=False)           # 千分位\n",
    "    s2 = s2.str.replace(\"−\", \"-\", regex=False)          # Unicode 負號\n",
    "    s2 = s2.str.replace(r\"^\\(([^)]+)\\)$\", r\"-\\1\", regex=True)  # 括號負號\n",
    "    return pd.to_numeric(s2, errors=\"coerce\")\n",
    "\n",
    "flagged_any = 0\n",
    "totals_by_col = {c:0 for c in TARGET_COLS}\n",
    "\n",
    "for fp in sorted(glob.glob(os.path.join(DIR, \"*.csv\"))):\n",
    "    df = read_df_any(fp)\n",
    "    if df is None:\n",
    "        continue\n",
    "    lmap = lower_map(df)\n",
    "\n",
    "    neg_info = []\n",
    "    for col in TARGET_COLS:\n",
    "        if col not in lmap:\n",
    "            continue\n",
    "        num = to_numeric_clean(df[lmap[col]])\n",
    "        cnt = int((num < 0).sum())\n",
    "        if cnt > 0:\n",
    "            neg_info.append(f\"{col}<{0}: {cnt}\")\n",
    "            totals_by_col[col] += cnt\n",
    "\n",
    "    if neg_info:\n",
    "        flagged_any += 1\n",
    "        print(f\"- {os.path.basename(fp)} | \" + \"; \".join(neg_info))\n",
    "\n",
    "print(f\"\\n[SUMMARY] files with any negatives: {flagged_any}\")\n",
    "for col in TARGET_COLS:\n",
    "    print(f\"- total negatives in {col}: {totals_by_col[col]}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "21dbcdde-2e0e-48f5-9275-1289b0693ca4",
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"做一頁ppt講解一次 Δt 作品質分段\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "559c1a82-ad02-4c7c-9312-71e798dbe6aa",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "- 089271.csv: 最小間隔=00:01:00, 最大間隔=4d 14:22:04\n",
      "- 095323.csv: 符合條件的列數 < 2（無法計算間隔）\n",
      "- 095707.csv: 符合條件的列數 < 2（無法計算間隔）\n",
      "- 114309.csv: 符合條件的列數 < 2（無法計算間隔）\n",
      "- 230933.csv: 符合條件的列數 < 2（無法計算間隔）\n",
      "- 4216007.csv: 最小間隔=00:00:05, 最大間隔=08:54:57\n",
      "- 7108162.csv: 符合條件的列數 < 2（無法計算間隔）\n",
      "- 7408338.csv: 符合條件的列數 < 2（無法計算間隔）\n",
      "- 7657698.csv: 符合條件的列數 < 2（無法計算間隔）\n",
      "- 7721164.csv: 符合條件的列數 < 2（無法計算間隔）\n",
      "- PatNo_ID_1560013303.csv: 最小間隔=00:01:00, 最大間隔=00:01:00\n",
      "- PatNo_ID_1562733396.csv: 符合條件的列數 < 2（無法計算間隔）\n",
      "- PatNo_ID_1563587183.csv: 符合條件的列數 < 2（無法計算間隔）\n",
      "- PatNo_ID_1564148644.csv: 最小間隔=00:00:13, 最大間隔=00:02:00\n",
      "- PatNo_ID_1565148312.csv: 最小間隔=00:01:00, 最大間隔=00:01:00\n",
      "- PatNo_ID_1565378038.csv: 符合條件的列數 < 2（無法計算間隔）\n",
      "- PatNo_ID_1566123680.csv: 符合條件的列數 < 2（無法計算間隔）\n",
      "- PatNo_ID_1566252197.csv: 最小間隔=00:00:15, 最大間隔=1d 08:17:03\n",
      "- PatNo_ID_1566279967.csv: 最小間隔=00:00:25, 最大間隔=11:55:41\n",
      "- PatNo_ID_1566671274.csv: 最小間隔=00:00:00, 最大間隔=1d 19:25:33\n",
      "- PatNo_ID_1566911879.csv: 最小間隔=00:00:08, 最大間隔=8d 13:05:59\n",
      "- PatNo_ID_1567747650.csv: 符合條件的列數 < 2（無法計算間隔）\n",
      "- PatNo_ID_1567804800.csv: 最小間隔=00:00:45, 最大間隔=7d 19:19:56\n",
      "- PatNo_ID_1567832735.csv: 符合條件的列數 < 2（無法計算間隔）\n",
      "- PatNo_ID_1568039398.csv: 最小間隔=00:00:27, 最大間隔=4d 16:07:58\n",
      "- PatNo_ID_1568574099.csv: 最小間隔=00:00:51, 最大間隔=00:01:08\n",
      "- PatNo_ID_1568813269.csv: 符合條件的列數 < 2（無法計算間隔）\n",
      "- PatNo_ID_1568952422.csv: 符合條件的列數 < 2（無法計算間隔）\n",
      "- PatNo_ID_1569083701.csv: 符合條件的列數 < 2（無法計算間隔）\n",
      "- PatNo_ID_1569944983.csv: 符合條件的列數 < 2（無法計算間隔）\n",
      "- PatNo_ID_1570089466.csv: 最小間隔=00:00:20, 最大間隔=1d 21:03:00\n",
      "- PatNo_ID_1570242703.csv: 最小間隔=00:00:57, 最大間隔=00:01:02\n",
      "- PatNo_ID_1570273244.csv: 最小間隔=00:01:00, 最大間隔=2d 01:05:57\n",
      "- PatNo_ID_1570642083.csv: 符合條件的列數 < 2（無法計算間隔）\n",
      "- PatNo_ID_1571945701.csv: 符合條件的列數 < 2（無法計算間隔）\n",
      "- PatNo_ID_1572481361.csv: 最小間隔=00:01:00, 最大間隔=00:01:00\n",
      "- PatNo_ID_1572562839.csv: 最小間隔=00:00:59, 最大間隔=13d 04:08:03\n",
      "- PatNo_ID_1572831765.csv: 符合條件的列數 < 2（無法計算間隔）\n",
      "- PatNo_ID_1572976822.csv: 最小間隔=00:01:00, 最大間隔=00:32:01\n",
      "- PatNo_ID_1573063188.csv: 最小間隔=00:01:00, 最大間隔=15:44:00\n",
      "- PatNo_ID_1573249295.csv: 符合條件的列數 < 2（無法計算間隔）\n",
      "- PatNo_ID_1573964540.csv: 符合條件的列數 < 2（無法計算間隔）\n",
      "- PatNo_ID_1574148494.csv: 最小間隔=00:00:30, 最大間隔=4d 21:34:43\n",
      "- PatNo_ID_1574270349.csv: 最小間隔=00:00:05, 最大間隔=4d 12:37:59\n",
      "- PatNo_ID_1574528808.csv: 最小間隔=00:00:40, 最大間隔=07:36:56\n",
      "- PatNo_ID_1574831525.csv: 最小間隔=00:01:00, 最大間隔=00:01:00\n",
      "- PatNo_ID_1574987447.csv: 符合條件的列數 < 2（無法計算間隔）\n",
      "- PatNo_ID_1575060177.csv: 符合條件的列數 < 2（無法計算間隔）\n",
      "- PatNo_ID_1575256902.csv: 最小間隔=00:00:27, 最大間隔=00:01:53\n",
      "- PatNo_ID_1575445051.csv: 最小間隔=00:01:00, 最大間隔=15:02:03\n",
      "- PatNo_ID_1575502382.csv: 最小間隔=00:01:00, 最大間隔=1d 05:04:54\n",
      "- PatNo_ID_1575975485.csv: 符合條件的列數 < 2（無法計算間隔）\n",
      "- PatNo_ID_1576115572.csv: 最小間隔=00:00:56, 最大間隔=00:01:03\n",
      "- PatNo_ID_1576116479.csv: 最小間隔=00:00:59, 最大間隔=00:01:07\n",
      "- PatNo_ID_1576301569.csv: 符合條件的列數 < 2（無法計算間隔）\n",
      "- PatNo_ID_1576964560.csv: 符合條件的列數 < 2（無法計算間隔）\n",
      "- PatNo_ID_1577042911.csv: 最小間隔=00:00:00, 最大間隔=3d 06:49:00\n",
      "- PatNo_ID_1577487284.csv: 符合條件的列數 < 2（無法計算間隔）\n",
      "- PatNo_ID_1578784257.csv: 最小間隔=00:00:03, 最大間隔=2d 02:52:23\n",
      "- PatNo_ID_1579198603.csv: 符合條件的列數 < 2（無法計算間隔）\n",
      "- PatNo_ID_1579498177.csv: 符合條件的列數 < 2（無法計算間隔）\n",
      "- PatNo_ID_1580062580.csv: 符合條件的列數 < 2（無法計算間隔）\n",
      "- PatNo_ID_1580096720.csv: 符合條件的列數 < 2（無法計算間隔）\n",
      "- PatNo_ID_1580107637.csv: 符合條件的列數 < 2（無法計算間隔）\n",
      "- PatNo_ID_1580244614.csv: 最小間隔=00:00:10, 最大間隔=16:20:02\n",
      "- PatNo_ID_1580766093.csv: 符合條件的列數 < 2（無法計算間隔）\n",
      "- PatNo_ID_1581003248.csv: 符合條件的列數 < 2（無法計算間隔）\n",
      "- PatNo_ID_1581019504.csv: 最小間隔=00:00:08, 最大間隔=2d 00:04:01\n",
      "- PatNo_ID_1581633231.csv: 符合條件的列數 < 2（無法計算間隔）\n",
      "- PatNo_ID_1581692973.csv: 符合條件的列數 < 2（無法計算間隔）\n",
      "- PatNo_ID_1582452511.csv: 符合條件的列數 < 2（無法計算間隔）\n",
      "- PatNo_ID_1582635996.csv: 符合條件的列數 < 2（無法計算間隔）\n",
      "- PatNo_ID_1582849900.csv: 符合條件的列數 < 2（無法計算間隔）\n",
      "- PatNo_ID_1582937076.csv: 最小間隔=00:01:00, 最大間隔=15d 00:53:01\n",
      "- PatNo_ID_1584158973.csv: 符合條件的列數 < 2（無法計算間隔）\n",
      "- PatNo_ID_1584397376.csv: 符合條件的列數 < 2（無法計算間隔）\n",
      "- PatNo_ID_1586172659.csv: 符合條件的列數 < 2（無法計算間隔）\n",
      "- PatNo_ID_1586696634.csv: 最小間隔=00:00:23, 最大間隔=1d 05:54:04\n",
      "- PatNo_ID_1586897008.csv: 最小間隔=00:00:30, 最大間隔=4d 15:25:00\n",
      "- PatNo_ID_1587490083.csv: 符合條件的列數 < 2（無法計算間隔）\n",
      "- PatNo_ID_1588632604.csv: 符合條件的列數 < 2（無法計算間隔）\n",
      "- PatNo_ID_1588673465.csv: 最小間隔=00:00:05, 最大間隔=22:46:58\n",
      "- PatNo_ID_1588794796.csv: 符合條件的列數 < 2（無法計算間隔）\n",
      "- PatNo_ID_1588957997.csv: 符合條件的列數 < 2（無法計算間隔）\n",
      "- PatNo_ID_1589018086.csv: 最小間隔=00:00:30, 最大間隔=2d 13:11:01\n",
      "- PatNo_ID_1589034524.csv: 符合條件的列數 < 2（無法計算間隔）\n",
      "- PatNo_ID_1589324603.csv: 最小間隔=00:00:55, 最大間隔=00:20:01\n",
      "- PatNo_ID_1589918099.csv: 符合條件的列數 < 2（無法計算間隔）\n",
      "- PatNo_ID_1590136310.csv: 最小間隔=00:00:30, 最大間隔=04:21:33\n",
      "- PatNo_ID_1590616537.csv: 符合條件的列數 < 2（無法計算間隔）\n",
      "- PatNo_ID_1590854576.csv: 符合條件的列數 < 2（無法計算間隔）\n",
      "- PatNo_ID_1591609798.csv: 符合條件的列數 < 2（無法計算間隔）\n",
      "- PatNo_ID_1592044724.csv: 符合條件的列數 < 2（無法計算間隔）\n",
      "- PatNo_ID_1592560504.csv: 符合條件的列數 < 2（無法計算間隔）\n",
      "- PatNo_ID_1593087886.csv: 最小間隔=00:01:00, 最大間隔=8d 19:44:01\n",
      "- PatNo_ID_1593416100.csv: 符合條件的列數 < 2（無法計算間隔）\n",
      "- PatNo_ID_1593472048.csv: 最小間隔=00:00:36, 最大間隔=3d 17:05:03\n",
      "- PatNo_ID_1593593586.csv: 符合條件的列數 < 2（無法計算間隔）\n",
      "- PatNo_ID_1593720818.csv: 符合條件的列數 < 2（無法計算間隔）\n",
      "- PatNo_ID_1593838524.csv: 最小間隔=00:01:00, 最大間隔=1d 12:03:02\n",
      "- PatNo_ID_1594173718.csv: 符合條件的列數 < 2（無法計算間隔）\n",
      "- PatNo_ID_1594294180.csv: 符合條件的列數 < 2（無法計算間隔）\n",
      "- PatNo_ID_1594305136.csv: 最小間隔=00:00:29, 最大間隔=8d 23:55:03\n",
      "- PatNo_ID_1594309746.csv: 符合條件的列數 < 2（無法計算間隔）\n",
      "- PatNo_ID_1594319286.csv: 最小間隔=00:00:51, 最大間隔=1d 06:28:21\n",
      "- PatNo_ID_1594320763.csv: 符合條件的列數 < 2（無法計算間隔）\n",
      "- PatNo_ID_1594322594.csv: 符合條件的列數 < 2（無法計算間隔）\n",
      "- PatNo_ID_1594335109.csv: 符合條件的列數 < 2（無法計算間隔）\n",
      "- PatNo_ID_1594423683.csv: 最小間隔=00:01:00, 最大間隔=00:01:00\n",
      "- PatNo_ID_1594437309.csv: 最小間隔=00:01:00, 最大間隔=1d 00:15:33\n",
      "- PatNo_ID_1594439781.csv: 符合條件的列數 < 2（無法計算間隔）\n",
      "- PatNo_ID_1594441887.csv: 符合條件的列數 < 2（無法計算間隔）\n",
      "- PatNo_ID_1594448501.csv: 符合條件的列數 < 2（無法計算間隔）\n",
      "- PatNo_ID_1594455578.csv: 符合條件的列數 < 2（無法計算間隔）\n",
      "- PatNo_ID_1594464829.csv: 最小間隔=00:00:30, 最大間隔=1d 04:01:56\n",
      "- PatNo_ID_1594467719.csv: 最小間隔=00:00:56, 最大間隔=00:02:00\n",
      "- PatNo_ID_1594471407.csv: 最小間隔=00:00:59, 最大間隔=7d 20:42:02\n",
      "- PatNo_ID_1594479330.csv: 符合條件的列數 < 2（無法計算間隔）\n",
      "- PatNo_ID_1594511911.csv: 最小間隔=00:00:59, 最大間隔=3d 14:13:49\n",
      "- PatNo_ID_1594511914.csv: 最小間隔=00:00:40, 最大間隔=6d 21:23:22\n",
      "- PatNo_ID_1594528842.csv: 最小間隔=00:00:10, 最大間隔=00:17:57\n",
      "- PatNo_ID_1594533379.csv: 最小間隔=00:00:57, 最大間隔=12:51:02\n"
     ]
    }
   ],
   "source": [
    "\"\"\" 因為再決定要隔多久的分鐘算一個gap, 先看目前資料間隔最短是多久\n",
    "我想查看/home/jovyan/RT08/0925/bling_onehot/所有檔案時間間隔最短是多久，時間間隔的意思是\"rrhzsetactual\",\"peepepap\", \"ppeak\"特徵都沒有數字的資料，在程式碼中印出檔名 最小間隔 最大間隔\n",
    "\"\"\"\n",
    "# 檢查 /home/jovyan/RT08/0925/bling_onehot/ 全部 CSV\n",
    "# 定義「時間間隔」：篩出 rrhzsetactual、peepepap、ppeak 三欄『都沒有數字』且 senddate 可解析的列，\n",
    "# 依 senddate 由小到大計算相鄰紀錄的時間差，輸出每檔的「最小間隔、最大間隔」。\n",
    "# （只讀不寫原檔）\n",
    "\n",
    "import os, glob, unicodedata, re\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "DIR = \"/home/jovyan/RT08/0925/bling_onehot\"\n",
    "ENCODINGS = (\"utf-8-sig\",\"utf-8\",\"cp950\",\"big5\",\"latin1\")\n",
    "\n",
    "TIME_COL = \"senddate\"\n",
    "NUM_COLS = [\"rrhzsetactual\", \"peepepap\", \"ppeak\"]\n",
    "\n",
    "def read_df_any(path):\n",
    "    for enc in ENCODINGS:\n",
    "        try:\n",
    "            return pd.read_csv(path, encoding=enc, engine=\"python\", on_bad_lines=\"skip\")\n",
    "        except Exception:\n",
    "            continue\n",
    "    print(f\"[WARN] 讀檔失敗：{os.path.basename(path)}\")\n",
    "    return None\n",
    "\n",
    "def lower_map(df):\n",
    "    m = {}\n",
    "    for c in df.columns:\n",
    "        k = unicodedata.normalize(\"NFKC\", str(c)).strip().lower()\n",
    "        if k not in m: m[k] = c\n",
    "    return m\n",
    "\n",
    "# 清理字串・轉數值（支援千分位、全形/不可見字元、Unicode負號、括號負號）\n",
    "INVIS_RE = re.compile(r\"[\\u200b-\\u200f\\u202a-\\u202e\\u2066-\\u2069\\ufeff\\u00a0\\u3000]\")\n",
    "def to_numeric_clean(s: pd.Series) -> pd.Series:\n",
    "    s2 = s.astype(\"string\")\n",
    "    s2 = s2.map(lambda x: unicodedata.normalize(\"NFKC\", x).strip() if x is not pd.NA else x)\n",
    "    s2 = s2.map(lambda x: INVIS_RE.sub(\"\", x) if x is not pd.NA else x)\n",
    "    s2 = s2.str.replace(\",\", \"\", regex=False)\n",
    "    s2 = s2.str.replace(\"−\", \"-\", regex=False)                     # Unicode 負號\n",
    "    s2 = s2.str.replace(r\"^\\(([^)]+)\\)$\", r\"-\\1\", regex=True)      # (123) → -123\n",
    "    return pd.to_numeric(s2, errors=\"coerce\")\n",
    "\n",
    "def fmt_td(td: pd.Timedelta | None) -> str:\n",
    "    if td is None or pd.isna(td):\n",
    "        return \"N/A\"\n",
    "    total_sec = int(td.total_seconds())\n",
    "    sign = \"-\" if total_sec < 0 else \"\"\n",
    "    total_sec = abs(total_sec)\n",
    "    days, rem = divmod(total_sec, 86400)\n",
    "    hrs, rem = divmod(rem, 3600)\n",
    "    mins, secs = divmod(rem, 60)\n",
    "    if days > 0:\n",
    "        return f\"{sign}{days}d {hrs:02d}:{mins:02d}:{secs:02d}\"\n",
    "    else:\n",
    "        return f\"{sign}{hrs:02d}:{mins:02d}:{secs:02d}\"\n",
    "\n",
    "files = sorted(glob.glob(os.path.join(DIR, \"*.csv\")))\n",
    "if not files:\n",
    "    print(f\"[INFO] 目錄無 CSV：{DIR}\")\n",
    "\n",
    "for fp in files:\n",
    "    name = os.path.basename(fp)\n",
    "    df = read_df_any(fp)\n",
    "    if df is None:\n",
    "        continue\n",
    "    lmap = lower_map(df)\n",
    "\n",
    "    # 必要欄位檢查\n",
    "    if TIME_COL not in lmap:\n",
    "        print(f\"- {name}: 缺少 senddate 欄位（略過）\")\n",
    "        continue\n",
    "\n",
    "    # 解析時間\n",
    "    t = pd.to_datetime(df[lmap[TIME_COL]], errors=\"coerce\")\n",
    "    t_valid = t.notna()\n",
    "\n",
    "    # 三欄「數值」檢查（可轉成數值才算有數字）\n",
    "    all_num_available = True\n",
    "    nums = {}\n",
    "    for c in NUM_COLS:\n",
    "        if c in lmap:\n",
    "            nums[c] = to_numeric_clean(df[lmap[c]])\n",
    "        else:\n",
    "            all_num_available = False\n",
    "            nums[c] = pd.Series([np.nan]*len(df))\n",
    "\n",
    "    # 條件：三欄都「沒有數字」（同時為 NaN）且時間有效\n",
    "    no_number_mask = t_valid & nums[NUM_COLS[0]].isna() & nums[NUM_COLS[1]].isna() & nums[NUM_COLS[2]].isna()\n",
    "    times = t[no_number_mask].sort_values()\n",
    "\n",
    "    if times.shape[0] < 2:\n",
    "        print(f\"- {name}: 符合條件的列數 < 2（無法計算間隔）\")\n",
    "        continue\n",
    "\n",
    "    diffs = times.diff().dropna()\n",
    "    if diffs.empty:\n",
    "        print(f\"- {name}: 無可用的相鄰時間差\")\n",
    "        continue\n",
    "\n",
    "    min_gap = diffs.min()\n",
    "    max_gap = diffs.max()\n",
    "    print(f\"- {name}: 最小間隔={fmt_td(min_gap)}, 最大間隔={fmt_td(max_gap)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "e7df1a45-779b-416b-8123-0f6db0651e3b",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[GAP FILES] count=48 (gap > 1 minute)\n",
      "- 4216007.csv: min_gap=00:01:01, max_gap=08:54:57, gaps_found=101\n",
      "- PatNo_ID_1564148644.csv: min_gap=00:01:01, max_gap=00:02:00, gaps_found=53\n",
      "- PatNo_ID_1566252197.csv: min_gap=00:01:01, max_gap=1d 08:17:03, gaps_found=137\n",
      "- PatNo_ID_1566671274.csv: min_gap=00:01:01, max_gap=1d 19:25:33, gaps_found=216\n",
      "- PatNo_ID_1566911879.csv: min_gap=00:01:01, max_gap=8d 13:05:59, gaps_found=650\n",
      "- PatNo_ID_1567804800.csv: min_gap=00:01:01, max_gap=7d 19:19:56, gaps_found=24\n",
      "- PatNo_ID_1568039398.csv: min_gap=00:01:01, max_gap=4d 16:07:58, gaps_found=28\n",
      "- PatNo_ID_1568574099.csv: min_gap=00:01:01, max_gap=00:01:08, gaps_found=5\n",
      "- PatNo_ID_1570089466.csv: min_gap=00:01:01, max_gap=1d 21:03:00, gaps_found=26\n",
      "- PatNo_ID_1574528808.csv: min_gap=00:01:01, max_gap=07:36:56, gaps_found=270\n",
      "- PatNo_ID_1575256902.csv: min_gap=00:01:01, max_gap=00:01:53, gaps_found=20\n",
      "- PatNo_ID_1576116479.csv: min_gap=00:01:01, max_gap=00:01:07, gaps_found=3\n",
      "- PatNo_ID_1577042911.csv: min_gap=00:01:01, max_gap=3d 06:49:00, gaps_found=233\n",
      "- PatNo_ID_1578784257.csv: min_gap=00:01:01, max_gap=2d 02:52:23, gaps_found=278\n",
      "- PatNo_ID_1581019504.csv: min_gap=00:01:01, max_gap=2d 00:04:01, gaps_found=513\n",
      "- PatNo_ID_1586696634.csv: min_gap=00:01:01, max_gap=1d 05:54:04, gaps_found=26\n",
      "- PatNo_ID_1588673465.csv: min_gap=00:01:01, max_gap=22:46:58, gaps_found=81\n",
      "- PatNo_ID_1589324603.csv: min_gap=00:01:01, max_gap=00:20:01, gaps_found=7\n",
      "- PatNo_ID_1590136310.csv: min_gap=00:01:01, max_gap=04:21:33, gaps_found=149\n",
      "- PatNo_ID_1593472048.csv: min_gap=00:01:01, max_gap=3d 17:05:03, gaps_found=72\n",
      "- PatNo_ID_1594305136.csv: min_gap=00:01:01, max_gap=8d 23:55:03, gaps_found=11\n",
      "- PatNo_ID_1594319286.csv: min_gap=00:01:01, max_gap=1d 06:28:21, gaps_found=7\n",
      "- PatNo_ID_1594464829.csv: min_gap=00:01:01, max_gap=1d 04:01:56, gaps_found=16\n",
      "- PatNo_ID_1594467719.csv: min_gap=00:01:01, max_gap=00:02:00, gaps_found=16\n",
      "- PatNo_ID_1594471407.csv: min_gap=00:01:01, max_gap=7d 20:42:02, gaps_found=2\n",
      "- PatNo_ID_1594511911.csv: min_gap=00:01:01, max_gap=3d 14:13:49, gaps_found=3\n",
      "- PatNo_ID_1594528842.csv: min_gap=00:01:01, max_gap=00:17:57, gaps_found=12\n",
      "- PatNo_ID_1570242703.csv: min_gap=00:01:02, max_gap=00:01:02, gaps_found=1\n",
      "- PatNo_ID_1573063188.csv: min_gap=00:01:02, max_gap=15:44:00, gaps_found=20\n",
      "- PatNo_ID_1594511914.csv: min_gap=00:01:02, max_gap=6d 21:23:22, gaps_found=3\n",
      "- PatNo_ID_1566279967.csv: min_gap=00:01:03, max_gap=11:55:41, gaps_found=14\n",
      "- PatNo_ID_1576115572.csv: min_gap=00:01:03, max_gap=00:01:03, gaps_found=1\n",
      "- PatNo_ID_1580244614.csv: min_gap=00:01:03, max_gap=16:20:02, gaps_found=5\n",
      "- PatNo_ID_1586897008.csv: min_gap=00:01:03, max_gap=4d 15:25:00, gaps_found=10\n",
      "- PatNo_ID_1589018086.csv: min_gap=00:01:04, max_gap=2d 13:11:01, gaps_found=16\n",
      "- PatNo_ID_1575502382.csv: min_gap=00:01:18, max_gap=1d 05:04:54, gaps_found=50\n",
      "- PatNo_ID_1574148494.csv: min_gap=00:01:55, max_gap=4d 21:34:43, gaps_found=62\n",
      "- PatNo_ID_1594437309.csv: min_gap=00:01:58, max_gap=1d 00:15:33, gaps_found=47\n",
      "- 089271.csv: min_gap=00:02:00, max_gap=4d 14:22:04, gaps_found=12\n",
      "- PatNo_ID_1572562839.csv: min_gap=00:02:00, max_gap=13d 04:08:03, gaps_found=3\n",
      "- PatNo_ID_1594533379.csv: min_gap=00:02:00, max_gap=12:51:02, gaps_found=4\n",
      "- PatNo_ID_1582937076.csv: min_gap=00:12:57, max_gap=15d 00:53:01, gaps_found=3\n",
      "- PatNo_ID_1593087886.csv: min_gap=00:29:00, max_gap=8d 19:44:01, gaps_found=2\n",
      "- PatNo_ID_1572976822.csv: min_gap=00:32:01, max_gap=00:32:01, gaps_found=1\n",
      "- PatNo_ID_1570273244.csv: min_gap=00:56:12, max_gap=2d 01:05:57, gaps_found=13\n",
      "- PatNo_ID_1575445051.csv: min_gap=13:02:57, max_gap=15:02:03, gaps_found=2\n",
      "- PatNo_ID_1593838524.csv: min_gap=1d 12:03:02, max_gap=1d 12:03:02, gaps_found=1\n",
      "- PatNo_ID_1574270349.csv: min_gap=4d 12:37:59, max_gap=4d 12:37:59, gaps_found=1\n"
     ]
    }
   ],
   "source": [
    "# 檢查 /home/jovyan/RT08/0925/bling_onehot/ 全部 CSV\n",
    "# 「gap」定義：在 senddate 可解析的前提下，連續兩筆資料都「rrhzsetactual、peepepap、ppeak 三欄皆沒有數字」\n",
    "# 且兩筆時間差 > 1 分鐘。只印出「有 gap」的檔名與該檔的最小/最大 gap。\n",
    "# （只讀不寫原檔）\n",
    "\n",
    "import os, glob, unicodedata, re\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "DIR = \"/home/jovyan/RT08/0925/bling_onehot\"\n",
    "TIME_COL = \"senddate\"\n",
    "NUM_COLS = [\"rrhzsetactual\", \"peepepap\", \"ppeak\"]\n",
    "ENCODINGS = (\"utf-8-sig\",\"utf-8\",\"cp950\",\"big5\",\"latin1\")\n",
    "\n",
    "def read_df_any(path):\n",
    "    for enc in ENCODINGS:\n",
    "        try:\n",
    "            return pd.read_csv(path, encoding=enc, engine=\"python\", on_bad_lines=\"skip\")\n",
    "        except Exception:\n",
    "            continue\n",
    "    return None  # 讀不到就略過\n",
    "\n",
    "def lower_map(df):\n",
    "    m = {}\n",
    "    for c in df.columns:\n",
    "        k = unicodedata.normalize(\"NFKC\", str(c)).strip().lower()\n",
    "        if k not in m: m[k] = c\n",
    "    return m\n",
    "\n",
    "# 文字→數值：清不可見字元、千分位、Unicode負號、括號負號\n",
    "INVIS_RE = re.compile(r\"[\\u200b-\\u200f\\u202a-\\u202e\\u2066-\\u2069\\ufeff\\u00a0\\u3000]\")\n",
    "def to_numeric_clean(s: pd.Series) -> pd.Series:\n",
    "    s2 = s.astype(\"string\")\n",
    "    s2 = s2.map(lambda x: unicodedata.normalize(\"NFKC\", x).strip() if x is not pd.NA else x)\n",
    "    s2 = s2.map(lambda x: INVIS_RE.sub(\"\", x) if x is not pd.NA else x)\n",
    "    s2 = s2.str.replace(\",\", \"\", regex=False)\n",
    "    s2 = s2.str.replace(\"−\", \"-\", regex=False)                     # Unicode 負號\n",
    "    s2 = s2.str.replace(r\"^\\(([^)]+)\\)$\", r\"-\\1\", regex=True)      # (123) → -123\n",
    "    return pd.to_numeric(s2, errors=\"coerce\")\n",
    "\n",
    "def fmt_td(td: pd.Timedelta) -> str:\n",
    "    total_sec = int(td.total_seconds())\n",
    "    sign = \"-\" if total_sec < 0 else \"\"\n",
    "    total_sec = abs(total_sec)\n",
    "    days, rem = divmod(total_sec, 86400)\n",
    "    hrs, rem = divmod(rem, 3600)\n",
    "    mins, secs = divmod(rem, 60)\n",
    "    return f\"{sign}{days}d {hrs:02d}:{mins:02d}:{secs:02d}\" if days else f\"{sign}{hrs:02d}:{mins:02d}:{secs:02d}\"\n",
    "\n",
    "results = []  # list of dict: file, min_gap, max_gap, gaps_count\n",
    "\n",
    "files = sorted(glob.glob(os.path.join(DIR, \"*.csv\")))\n",
    "for fp in files:\n",
    "    df = read_df_any(fp)\n",
    "    if df is None:\n",
    "        continue\n",
    "    lmap = lower_map(df)\n",
    "    if TIME_COL not in lmap:\n",
    "        continue\n",
    "\n",
    "    # 時間\n",
    "    t = pd.to_datetime(df[lmap[TIME_COL]], errors=\"coerce\")\n",
    "\n",
    "    # 三欄轉數值（缺欄視為全 NaN -> 視為「沒有數字」）\n",
    "    nums = []\n",
    "    for c in NUM_COLS:\n",
    "        if c in lmap:\n",
    "            nums.append(to_numeric_clean(df[lmap[c]]))\n",
    "        else:\n",
    "            nums.append(pd.Series([np.nan]*len(df)))\n",
    "\n",
    "    # 符合「三欄皆無數字」且 senddate 有效\n",
    "    mask = t.notna() & nums[0].isna() & nums[1].isna() & nums[2].isna()\n",
    "    times = t[mask].sort_values()\n",
    "\n",
    "    if times.shape[0] < 2:\n",
    "        continue\n",
    "\n",
    "    diffs = times.diff().dropna()\n",
    "    # gap：嚴格大於 1 分鐘\n",
    "    gaps = diffs[diffs > pd.Timedelta(minutes=1)]\n",
    "    if gaps.empty:\n",
    "        continue\n",
    "\n",
    "    results.append({\n",
    "        \"file\": os.path.basename(fp),\n",
    "        \"min_gap\": gaps.min(),\n",
    "        \"max_gap\": gaps.max(),\n",
    "        \"gaps_count\": int(gaps.shape[0]),\n",
    "    })\n",
    "\n",
    "# 只印出有 gap 的檔案\n",
    "if not results:\n",
    "    print(\"[INFO] No files have gaps > 1 minute under the given rule.\")\n",
    "else:\n",
    "    # 依最小 gap 由小到大列印\n",
    "    results = sorted(results, key=lambda r: (r[\"min_gap\"], r[\"file\"]))\n",
    "    print(f\"[GAP FILES] count={len(results)} (gap > 1 minute)\")\n",
    "    for r in results:\n",
    "        print(f\"- {r['file']}: min_gap={fmt_td(r['min_gap'])}, max_gap={fmt_td(r['max_gap'])}, gaps_found={r['gaps_count']}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4701c340-7a71-4e89-b1b0-fbebe780d12a",
   "metadata": {},
   "outputs": [],
   "source": [
    "我想知道一份資料若時間間隔gap設定超過一分鐘跟三分鐘跟五分鐘的資料區段，且每個時間區段若以滑動視窗切割，有資料區段分別是多少段，視窗長度120分鐘，重疊綠50%\n",
    "欸算了 老師說50%就先這樣 要實驗等模型建好再回頭實驗"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "f28c7501-ef8e-43a9-973f-b9920ecebf68",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[RESULT] 可用資料區段數（每段長度≥2小時；以三種 gap 門檻切段）\n",
      "file, seg_1min, seg_3min, seg_5min\n",
      "089271.csv, 0, 0, 0\n",
      "095323.csv, 0, 0, 0\n",
      "095707.csv, 0, 0, 0\n",
      "114309.csv, 0, 0, 0\n",
      "230933.csv, 0, 0, 0\n",
      "4216007.csv, 0, 2, 2\n",
      "7108162.csv, 0, 0, 0\n",
      "7408338.csv, 0, 0, 0\n",
      "7657698.csv, 0, 0, 0\n",
      "7721164.csv, 0, 0, 0\n",
      "PatNo_ID_1560013303.csv, 0, 0, 0\n",
      "PatNo_ID_1562733396.csv, 0, 0, 0\n",
      "PatNo_ID_1563587183.csv, 0, 0, 0\n",
      "PatNo_ID_1564148644.csv, 7, 1, 1\n",
      "PatNo_ID_1565148312.csv, 0, 0, 0\n",
      "PatNo_ID_1565378038.csv, 0, 0, 0\n",
      "PatNo_ID_1566123680.csv, 0, 0, 0\n",
      "PatNo_ID_1566252197.csv, 2, 1, 1\n",
      "PatNo_ID_1566279967.csv, 0, 0, 0\n",
      "PatNo_ID_1566671274.csv, 2, 1, 1\n",
      "PatNo_ID_1566911879.csv, 7, 12, 12\n",
      "PatNo_ID_1567747650.csv, 0, 0, 0\n",
      "PatNo_ID_1567804800.csv, 0, 0, 0\n",
      "PatNo_ID_1567832735.csv, 0, 0, 0\n",
      "PatNo_ID_1568039398.csv, 0, 1, 1\n",
      "PatNo_ID_1568574099.csv, 0, 0, 0\n",
      "PatNo_ID_1568813269.csv, 0, 0, 0\n",
      "PatNo_ID_1568952422.csv, 0, 0, 0\n",
      "PatNo_ID_1569083701.csv, 0, 0, 0\n",
      "PatNo_ID_1569944983.csv, 0, 0, 0\n",
      "PatNo_ID_1570089466.csv, 0, 3, 3\n",
      "PatNo_ID_1570242703.csv, 0, 0, 0\n",
      "PatNo_ID_1570273244.csv, 0, 0, 0\n",
      "PatNo_ID_1570642083.csv, 0, 0, 0\n",
      "PatNo_ID_1571945701.csv, 0, 0, 0\n",
      "PatNo_ID_1572481361.csv, 0, 0, 0\n",
      "PatNo_ID_1572562839.csv, 0, 0, 0\n",
      "PatNo_ID_1572831765.csv, 0, 0, 0\n",
      "PatNo_ID_1572976822.csv, 0, 0, 0\n",
      "PatNo_ID_1573063188.csv, 0, 0, 0\n",
      "PatNo_ID_1573249295.csv, 0, 0, 0\n",
      "PatNo_ID_1573964540.csv, 0, 0, 0\n",
      "PatNo_ID_1574148494.csv, 0, 0, 0\n",
      "PatNo_ID_1574270349.csv, 0, 0, 0\n",
      "PatNo_ID_1574528808.csv, 0, 0, 0\n",
      "PatNo_ID_1574831525.csv, 0, 0, 0\n",
      "PatNo_ID_1574987447.csv, 0, 0, 0\n",
      "PatNo_ID_1575060177.csv, 0, 0, 0\n",
      "PatNo_ID_1575256902.csv, 0, 0, 0\n",
      "PatNo_ID_1575445051.csv, 0, 0, 0\n",
      "PatNo_ID_1575502382.csv, 0, 0, 0\n",
      "PatNo_ID_1575975485.csv, 0, 0, 0\n",
      "PatNo_ID_1576115572.csv, 0, 0, 0\n",
      "PatNo_ID_1576116479.csv, 0, 0, 0\n",
      "PatNo_ID_1576301569.csv, 0, 0, 0\n",
      "PatNo_ID_1576964560.csv, 0, 0, 0\n",
      "PatNo_ID_1577042911.csv, 1, 4, 3\n",
      "PatNo_ID_1577487284.csv, 0, 0, 0\n",
      "PatNo_ID_1578784257.csv, 11, 9, 10\n",
      "PatNo_ID_1579198603.csv, 0, 0, 0\n",
      "PatNo_ID_1579498177.csv, 0, 0, 0\n",
      "PatNo_ID_1580062580.csv, 0, 0, 0\n",
      "PatNo_ID_1580096720.csv, 0, 0, 0\n",
      "PatNo_ID_1580107637.csv, 0, 0, 0\n",
      "PatNo_ID_1580244614.csv, 0, 1, 1\n",
      "PatNo_ID_1580766093.csv, 0, 0, 0\n",
      "PatNo_ID_1581003248.csv, 0, 0, 0\n",
      "PatNo_ID_1581019504.csv, 5, 13, 13\n",
      "PatNo_ID_1581633231.csv, 0, 0, 0\n",
      "PatNo_ID_1581692973.csv, 0, 0, 0\n",
      "PatNo_ID_1582452511.csv, 0, 0, 0\n",
      "PatNo_ID_1582635996.csv, 0, 0, 0\n",
      "PatNo_ID_1582849900.csv, 0, 0, 0\n",
      "PatNo_ID_1582937076.csv, 0, 0, 0\n",
      "PatNo_ID_1584158973.csv, 0, 0, 0\n",
      "PatNo_ID_1584397376.csv, 0, 0, 0\n",
      "PatNo_ID_1586172659.csv, 0, 0, 0\n",
      "PatNo_ID_1586696634.csv, 3, 2, 2\n",
      "PatNo_ID_1586897008.csv, 0, 0, 0\n",
      "PatNo_ID_1587490083.csv, 0, 0, 0\n",
      "PatNo_ID_1588632604.csv, 0, 0, 0\n",
      "PatNo_ID_1588673465.csv, 8, 5, 5\n",
      "PatNo_ID_1588794796.csv, 0, 0, 0\n",
      "PatNo_ID_1588957997.csv, 0, 0, 0\n",
      "PatNo_ID_1589018086.csv, 0, 0, 0\n",
      "PatNo_ID_1589034524.csv, 0, 0, 0\n",
      "PatNo_ID_1589324603.csv, 0, 0, 0\n",
      "PatNo_ID_1589918099.csv, 0, 0, 0\n",
      "PatNo_ID_1590136310.csv, 0, 1, 1\n",
      "PatNo_ID_1590616537.csv, 0, 0, 0\n",
      "PatNo_ID_1590854576.csv, 0, 0, 0\n",
      "PatNo_ID_1591609798.csv, 0, 0, 0\n",
      "PatNo_ID_1592044724.csv, 0, 0, 0\n",
      "PatNo_ID_1592560504.csv, 0, 0, 0\n",
      "PatNo_ID_1593087886.csv, 0, 0, 0\n",
      "PatNo_ID_1593416100.csv, 0, 0, 0\n",
      "PatNo_ID_1593472048.csv, 6, 2, 2\n",
      "PatNo_ID_1593593586.csv, 0, 0, 0\n",
      "PatNo_ID_1593720818.csv, 0, 0, 0\n",
      "PatNo_ID_1593838524.csv, 0, 0, 0\n",
      "PatNo_ID_1594173718.csv, 0, 0, 0\n",
      "PatNo_ID_1594294180.csv, 0, 0, 0\n",
      "PatNo_ID_1594305136.csv, 0, 0, 0\n",
      "PatNo_ID_1594309746.csv, 0, 0, 0\n",
      "PatNo_ID_1594319286.csv, 0, 0, 0\n",
      "PatNo_ID_1594320763.csv, 0, 0, 0\n",
      "PatNo_ID_1594322594.csv, 0, 0, 0\n",
      "PatNo_ID_1594335109.csv, 0, 0, 0\n",
      "PatNo_ID_1594423683.csv, 0, 0, 0\n",
      "PatNo_ID_1594437309.csv, 0, 0, 0\n",
      "PatNo_ID_1594439781.csv, 0, 0, 0\n",
      "PatNo_ID_1594441887.csv, 0, 0, 0\n",
      "PatNo_ID_1594448501.csv, 0, 0, 0\n",
      "PatNo_ID_1594455578.csv, 0, 0, 0\n",
      "PatNo_ID_1594464829.csv, 0, 0, 0\n",
      "PatNo_ID_1594467719.csv, 4, 1, 1\n",
      "PatNo_ID_1594471407.csv, 0, 0, 0\n",
      "PatNo_ID_1594479330.csv, 0, 0, 0\n",
      "PatNo_ID_1594511911.csv, 0, 0, 0\n",
      "PatNo_ID_1594511914.csv, 0, 0, 0\n",
      "PatNo_ID_1594528842.csv, 0, 0, 0\n",
      "PatNo_ID_1594533379.csv, 0, 0, 0\n",
      "\n",
      "[SUMMARY] Total usable segments across all files:\n",
      "- gap > 1 min → segments: 56\n",
      "- gap > 3 min → segments: 59\n",
      "- gap > 5 min → segments: 59\n"
     ]
    }
   ],
   "source": [
    "\"\"\" 但我想看 哈哈\n",
    "我想知道一份資料若時間間隔gap設定超過一分鐘跟三分鐘跟五分鐘的資料區段，且每個時間區段超過兩小時，若以滑動視窗切割，時間間隔gap設定一分鐘跟三分鐘跟五分鐘的可用資料區段分別是多少段，不要動到原資料\n",
    "\"\"\"\n",
    "# 目標：\n",
    "# 針對 /home/jovyan/RT08/0925/bling_onehot/ 全部 CSV，\n",
    "# 以「gap 門檻 = 1 / 3 / 5 分鐘」切割時間軸，計算『可用資料區段』的段數。\n",
    "# 定義：\n",
    "# - 僅取 senddate 可解析 且 rrhzsetactual、peepepap、ppeak 三欄『皆沒有數字』的紀錄作為時間序列節點。\n",
    "# - 依時間排序，當相鄰兩點時間差 > gap 門檻時視為「斷裂」；連續區間即為一段。\n",
    "# - 只計算「區段長度 ≥ 2 小時」的區段數量。\n",
    "# - 不修改原始檔案；結果直接印出（逐檔與總結）。\n",
    "#\n",
    "# 備註：「以滑動視窗切割」在此對應為「以 gap 門檻滑動檢查相鄰點時間差，遇到 > 門檻即切段」，\n",
    "#       形成最大連續區段，再篩選長度≥2h 的『可用資料區段』。\n",
    "\n",
    "import os, glob, unicodedata, re\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "DIR = \"/home/jovyan/RT08/0925/bling_onehot\"\n",
    "TIME_COL = \"senddate\"\n",
    "NUM_COLS = [\"rrhzsetactual\", \"peepepap\", \"ppeak\"]\n",
    "GAP_MINUTES = [1, 3, 5]                      # 三種 gap 門檻（分鐘）\n",
    "MIN_SEG_DURATION = pd.Timedelta(hours=2)     # 區段需至少 2 小時\n",
    "\n",
    "ENCODINGS = (\"utf-8-sig\",\"utf-8\",\"cp950\",\"big5\",\"latin1\")\n",
    "\n",
    "def read_df_any(path):\n",
    "    for enc in ENCODINGS:\n",
    "        try:\n",
    "            return pd.read_csv(path, encoding=enc, engine=\"python\", on_bad_lines=\"skip\")\n",
    "        except Exception:\n",
    "            continue\n",
    "    return None  # 讀不到就略過\n",
    "\n",
    "def lower_map(df):\n",
    "    m = {}\n",
    "    for c in df.columns:\n",
    "        k = unicodedata.normalize(\"NFKC\", str(c)).strip().lower()\n",
    "        if k not in m: m[k] = c\n",
    "    return m\n",
    "\n",
    "# 文字→數值清理：移除不可見符號/千分位/全形，支援 Unicode 負號與 (123) 表示負號\n",
    "INVIS_RE = re.compile(r\"[\\u200b-\\u200f\\u202a-\\u202e\\u2066-\\u2069\\ufeff\\u00a0\\u3000]\")\n",
    "def to_numeric_clean(s: pd.Series) -> pd.Series:\n",
    "    s2 = s.astype(\"string\")\n",
    "    s2 = s2.map(lambda x: unicodedata.normalize(\"NFKC\", x).strip() if x is not pd.NA else x)\n",
    "    s2 = s2.map(lambda x: INVIS_RE.sub(\"\", x) if x is not pd.NA else x)\n",
    "    s2 = s2.str.replace(\",\", \"\", regex=False)\n",
    "    s2 = s2.str.replace(\"−\", \"-\", regex=False)\n",
    "    s2 = s2.str.replace(r\"^\\(([^)]+)\\)$\", r\"-\\1\", regex=True)\n",
    "    return pd.to_numeric(s2, errors=\"coerce\")\n",
    "\n",
    "def count_usable_segments(times: pd.Series, gap_threshold: pd.Timedelta) -> int:\n",
    "    \"\"\"\n",
    "    給定已排序的時間序列（DatetimeIndex / Series），依 gap_threshold 切段，\n",
    "    回傳「長度 >= 2 小時」的區段數量。\n",
    "    \"\"\"\n",
    "    if times.shape[0] < 2:\n",
    "        return 0\n",
    "    diffs = times.diff()\n",
    "    # 新區段起點：第一筆 或 diff > 門檻\n",
    "    new_seg = diffs.isna() | (diffs > gap_threshold)\n",
    "    seg_id = new_seg.cumsum()\n",
    "    cnt = 0\n",
    "    for _, seg_times in times.groupby(seg_id):\n",
    "        if seg_times.shape[0] < 2:\n",
    "            continue\n",
    "        if (seg_times.iloc[-1] - seg_times.iloc[0]) >= MIN_SEG_DURATION:\n",
    "            cnt += 1\n",
    "    return cnt\n",
    "\n",
    "files = sorted(glob.glob(os.path.join(DIR, \"*.csv\")))\n",
    "if not files:\n",
    "    print(f\"[INFO] No CSV files in {DIR}\")\n",
    "\n",
    "# 結果彙整：每檔的三種門檻段數，以及總計\n",
    "per_file_counts = []  # list of dict: {file, seg_1m, seg_3m, seg_5m}\n",
    "totals = {1:0, 3:0, 5:0}\n",
    "\n",
    "for fp in files:\n",
    "    df = read_df_any(fp)\n",
    "    if df is None:\n",
    "        continue\n",
    "    lmap = lower_map(df)\n",
    "    if TIME_COL not in lmap:\n",
    "        continue\n",
    "\n",
    "    # 時間與三欄數值化\n",
    "    t = pd.to_datetime(df[lmap[TIME_COL]], errors=\"coerce\")\n",
    "    nums = []\n",
    "    for c in NUM_COLS:\n",
    "        if c in lmap:\n",
    "            nums.append(to_numeric_clean(df[lmap[c]]))\n",
    "        else:\n",
    "            nums.append(pd.Series([np.nan]*len(df)))\n",
    "\n",
    "    # 僅保留：時間有效 且 三欄皆無數字 的那些時間點\n",
    "    mask = t.notna() & nums[0].isna() & nums[1].isna() & nums[2].isna()\n",
    "    times = t[mask].sort_values()\n",
    "    if times.empty:\n",
    "        # 這檔沒有任何可用的節點，三種門檻皆為 0\n",
    "        per_file_counts.append({\n",
    "            \"file\": os.path.basename(fp),\n",
    "            \"seg_1m\": 0, \"seg_3m\": 0, \"seg_5m\": 0\n",
    "        })\n",
    "        continue\n",
    "\n",
    "    seg_counts = {}\n",
    "    for m in GAP_MINUTES:\n",
    "        seg_counts[m] = count_usable_segments(times, pd.Timedelta(minutes=m))\n",
    "        totals[m] += seg_counts[m]\n",
    "\n",
    "    per_file_counts.append({\n",
    "        \"file\": os.path.basename(fp),\n",
    "        \"seg_1m\": seg_counts[1],\n",
    "        \"seg_3m\": seg_counts[3],\n",
    "        \"seg_5m\": seg_counts[5],\n",
    "    })\n",
    "\n",
    "# 輸出結果\n",
    "if not per_file_counts:\n",
    "    print(\"[INFO] No eligible files processed.\")\n",
    "else:\n",
    "    print(\"[RESULT] 可用資料區段數（每段長度≥2小時；以三種 gap 門檻切段）\")\n",
    "    print(\"file, seg_1min, seg_3min, seg_5min\")\n",
    "    for r in per_file_counts:\n",
    "        print(f\"{r['file']}, {r['seg_1m']}, {r['seg_3m']}, {r['seg_5m']}\")\n",
    "\n",
    "    print(\"\\n[SUMMARY] Total usable segments across all files:\")\n",
    "    print(f\"- gap > 1 min → segments: {totals[1]}\")\n",
    "    print(f\"- gap > 3 min → segments: {totals[3]}\")\n",
    "    print(f\"- gap > 5 min → segments: {totals[5]}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "5fe983a9-69b5-4b5b-a520-c630fb53a46d",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[RESULT] 段數（單點亦計一段；條件：senddate 可解析且三欄皆無數字）\n",
      "file, seg_1min, seg_3min, seg_5min\n",
      "089271.csv, 13, 12, 12\n",
      "095323.csv, 0, 0, 0\n",
      "095707.csv, 0, 0, 0\n",
      "114309.csv, 0, 0, 0\n",
      "230933.csv, 0, 0, 0\n",
      "4216007.csv, 102, 2, 2\n",
      "7108162.csv, 0, 0, 0\n",
      "7408338.csv, 0, 0, 0\n",
      "7657698.csv, 0, 0, 0\n",
      "7721164.csv, 0, 0, 0\n",
      "PatNo_ID_1560013303.csv, 1, 1, 1\n",
      "PatNo_ID_1562733396.csv, 0, 0, 0\n",
      "PatNo_ID_1563587183.csv, 0, 0, 0\n",
      "PatNo_ID_1564148644.csv, 54, 1, 1\n",
      "PatNo_ID_1565148312.csv, 1, 1, 1\n",
      "PatNo_ID_1565378038.csv, 0, 0, 0\n",
      "PatNo_ID_1566123680.csv, 0, 0, 0\n",
      "PatNo_ID_1566252197.csv, 138, 2, 2\n",
      "PatNo_ID_1566279967.csv, 15, 6, 6\n",
      "PatNo_ID_1566671274.csv, 217, 122, 112\n",
      "PatNo_ID_1566911879.csv, 651, 15, 15\n",
      "PatNo_ID_1567747650.csv, 0, 0, 0\n",
      "PatNo_ID_1567804800.csv, 25, 5, 5\n",
      "PatNo_ID_1567832735.csv, 0, 0, 0\n",
      "PatNo_ID_1568039398.csv, 29, 13, 12\n",
      "PatNo_ID_1568574099.csv, 6, 1, 1\n",
      "PatNo_ID_1568813269.csv, 0, 0, 0\n",
      "PatNo_ID_1568952422.csv, 0, 0, 0\n",
      "PatNo_ID_1569083701.csv, 0, 0, 0\n",
      "PatNo_ID_1569944983.csv, 0, 0, 0\n",
      "PatNo_ID_1570089466.csv, 27, 6, 6\n",
      "PatNo_ID_1570242703.csv, 2, 1, 1\n",
      "PatNo_ID_1570273244.csv, 14, 14, 14\n",
      "PatNo_ID_1570642083.csv, 0, 0, 0\n",
      "PatNo_ID_1571945701.csv, 0, 0, 0\n",
      "PatNo_ID_1572481361.csv, 1, 1, 1\n",
      "PatNo_ID_1572562839.csv, 4, 3, 3\n",
      "PatNo_ID_1572831765.csv, 0, 0, 0\n",
      "PatNo_ID_1572976822.csv, 2, 2, 2\n",
      "PatNo_ID_1573063188.csv, 21, 19, 18\n",
      "PatNo_ID_1573249295.csv, 0, 0, 0\n",
      "PatNo_ID_1573964540.csv, 0, 0, 0\n",
      "PatNo_ID_1574148494.csv, 63, 58, 55\n",
      "PatNo_ID_1574270349.csv, 2, 2, 2\n",
      "PatNo_ID_1574528808.csv, 271, 242, 223\n",
      "PatNo_ID_1574831525.csv, 1, 1, 1\n",
      "PatNo_ID_1574987447.csv, 0, 0, 0\n",
      "PatNo_ID_1575060177.csv, 0, 0, 0\n",
      "PatNo_ID_1575256902.csv, 21, 1, 1\n",
      "PatNo_ID_1575445051.csv, 3, 3, 3\n",
      "PatNo_ID_1575502382.csv, 51, 40, 32\n",
      "PatNo_ID_1575975485.csv, 0, 0, 0\n",
      "PatNo_ID_1576115572.csv, 2, 1, 1\n",
      "PatNo_ID_1576116479.csv, 4, 1, 1\n",
      "PatNo_ID_1576301569.csv, 0, 0, 0\n",
      "PatNo_ID_1576964560.csv, 0, 0, 0\n",
      "PatNo_ID_1577042911.csv, 234, 52, 49\n",
      "PatNo_ID_1577487284.csv, 0, 0, 0\n",
      "PatNo_ID_1578784257.csv, 279, 39, 37\n",
      "PatNo_ID_1579198603.csv, 0, 0, 0\n",
      "PatNo_ID_1579498177.csv, 0, 0, 0\n",
      "PatNo_ID_1580062580.csv, 0, 0, 0\n",
      "PatNo_ID_1580096720.csv, 0, 0, 0\n",
      "PatNo_ID_1580107637.csv, 0, 0, 0\n",
      "PatNo_ID_1580244614.csv, 6, 2, 2\n",
      "PatNo_ID_1580766093.csv, 0, 0, 0\n",
      "PatNo_ID_1581003248.csv, 0, 0, 0\n",
      "PatNo_ID_1581019504.csv, 514, 19, 18\n",
      "PatNo_ID_1581633231.csv, 0, 0, 0\n",
      "PatNo_ID_1581692973.csv, 0, 0, 0\n",
      "PatNo_ID_1582452511.csv, 0, 0, 0\n",
      "PatNo_ID_1582635996.csv, 0, 0, 0\n",
      "PatNo_ID_1582849900.csv, 0, 0, 0\n",
      "PatNo_ID_1582937076.csv, 4, 4, 4\n",
      "PatNo_ID_1584158973.csv, 0, 0, 0\n",
      "PatNo_ID_1584397376.csv, 0, 0, 0\n",
      "PatNo_ID_1586172659.csv, 0, 0, 0\n",
      "PatNo_ID_1586696634.csv, 27, 6, 6\n",
      "PatNo_ID_1586897008.csv, 11, 2, 2\n",
      "PatNo_ID_1587490083.csv, 0, 0, 0\n",
      "PatNo_ID_1588632604.csv, 0, 0, 0\n",
      "PatNo_ID_1588673465.csv, 82, 7, 6\n",
      "PatNo_ID_1588794796.csv, 0, 0, 0\n",
      "PatNo_ID_1588957997.csv, 0, 0, 0\n",
      "PatNo_ID_1589018086.csv, 17, 16, 16\n",
      "PatNo_ID_1589034524.csv, 0, 0, 0\n",
      "PatNo_ID_1589324603.csv, 8, 3, 3\n",
      "PatNo_ID_1589918099.csv, 0, 0, 0\n",
      "PatNo_ID_1590136310.csv, 150, 127, 109\n",
      "PatNo_ID_1590616537.csv, 0, 0, 0\n",
      "PatNo_ID_1590854576.csv, 0, 0, 0\n",
      "PatNo_ID_1591609798.csv, 0, 0, 0\n",
      "PatNo_ID_1592044724.csv, 0, 0, 0\n",
      "PatNo_ID_1592560504.csv, 0, 0, 0\n",
      "PatNo_ID_1593087886.csv, 3, 3, 3\n",
      "PatNo_ID_1593416100.csv, 0, 0, 0\n",
      "PatNo_ID_1593472048.csv, 73, 3, 3\n",
      "PatNo_ID_1593593586.csv, 0, 0, 0\n",
      "PatNo_ID_1593720818.csv, 1, 1, 1\n",
      "PatNo_ID_1593838524.csv, 2, 2, 2\n",
      "PatNo_ID_1594173718.csv, 0, 0, 0\n",
      "PatNo_ID_1594294180.csv, 0, 0, 0\n",
      "PatNo_ID_1594305136.csv, 12, 2, 2\n",
      "PatNo_ID_1594309746.csv, 0, 0, 0\n",
      "PatNo_ID_1594319286.csv, 8, 6, 6\n",
      "PatNo_ID_1594320763.csv, 0, 0, 0\n",
      "PatNo_ID_1594322594.csv, 0, 0, 0\n",
      "PatNo_ID_1594335109.csv, 0, 0, 0\n",
      "PatNo_ID_1594423683.csv, 1, 1, 1\n",
      "PatNo_ID_1594437309.csv, 48, 47, 47\n",
      "PatNo_ID_1594439781.csv, 0, 0, 0\n",
      "PatNo_ID_1594441887.csv, 0, 0, 0\n",
      "PatNo_ID_1594448501.csv, 0, 0, 0\n",
      "PatNo_ID_1594455578.csv, 0, 0, 0\n",
      "PatNo_ID_1594464829.csv, 17, 13, 13\n",
      "PatNo_ID_1594467719.csv, 17, 1, 1\n",
      "PatNo_ID_1594471407.csv, 3, 2, 2\n",
      "PatNo_ID_1594479330.csv, 0, 0, 0\n",
      "PatNo_ID_1594511911.csv, 4, 3, 3\n",
      "PatNo_ID_1594511914.csv, 4, 3, 3\n",
      "PatNo_ID_1594528842.csv, 13, 2, 2\n",
      "PatNo_ID_1594533379.csv, 5, 4, 4\n",
      "\n",
      "[SUMMARY] Total segments across all files:\n",
      "- gap > 1 min → segments: 3284\n",
      "- gap > 3 min → segments: 946\n",
      "- gap > 5 min → segments: 879\n"
     ]
    }
   ],
   "source": [
    "#少的離譜，我先看段數就好\n",
    "# 目的：計算「段數」（不再要求段長 ≥ 2 小時）\n",
    "# 定義：\n",
    "# - 先挑出 senddate 可解析、且 rrhzsetactual/peepepap/ppeak 三欄「皆沒有數字」的列\n",
    "#   （會先把字串清洗後再嘗試轉數字；轉不出數字才視為「沒有數字」）\n",
    "# - 依時間排序；當相鄰兩點時間差 > gap 門檻（1/3/5 分）就切成新的一段\n",
    "# - 本程式的「段數」= 分段後的區段個數（單一點也算一段）\n",
    "# - 只讀不寫原檔\n",
    "\n",
    "import os, glob, unicodedata, re\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "DIR = \"/home/jovyan/RT08/0925/bling_onehot\"\n",
    "TIME_COL = \"senddate\"\n",
    "NUM_COLS = [\"rrhzsetactual\", \"peepepap\", \"ppeak\"]\n",
    "GAP_MINUTES = [1, 3, 5]\n",
    "ENCODINGS = (\"utf-8-sig\",\"utf-8\",\"cp950\",\"big5\",\"latin1\")\n",
    "\n",
    "def read_df_any(path):\n",
    "    for enc in ENCODINGS:\n",
    "        try:\n",
    "            return pd.read_csv(path, encoding=enc, engine=\"python\", on_bad_lines=\"skip\")\n",
    "        except Exception:\n",
    "            continue\n",
    "    return None  # 讀不到就略過\n",
    "\n",
    "def lower_map(df):\n",
    "    m = {}\n",
    "    for c in df.columns:\n",
    "        k = unicodedata.normalize(\"NFKC\", str(c)).strip().lower()\n",
    "        if k not in m: m[k] = c\n",
    "    return m\n",
    "\n",
    "# 清理文字→數值：移除不可見符號/千分位、全形；支援 Unicode 負號與 (123)→-123\n",
    "INVIS_RE = re.compile(r\"[\\u200b-\\u200f\\u202a-\\u202e\\u2066-\\u2069\\ufeff\\u00a0\\u3000]\")\n",
    "def to_numeric_clean(s: pd.Series) -> pd.Series:\n",
    "    s2 = s.astype(\"string\")\n",
    "    s2 = s2.map(lambda x: unicodedata.normalize(\"NFKC\", x).strip() if x is not pd.NA else x)\n",
    "    s2 = s2.map(lambda x: INVIS_RE.sub(\"\", x) if x is not pd.NA else x)\n",
    "    s2 = s2.str.replace(\",\", \"\", regex=False)\n",
    "    s2 = s2.str.replace(\"−\", \"-\", regex=False)\n",
    "    s2 = s2.str.replace(r\"^\\(([^)]+)\\)$\", r\"-\\1\", regex=True)\n",
    "    return pd.to_numeric(s2, errors=\"coerce\")\n",
    "\n",
    "def count_segments(times: pd.Series, gap_threshold: pd.Timedelta) -> int:\n",
    "    \"\"\"\n",
    "    已排序的時間序列，當相鄰差 > 門檻則開新段。\n",
    "    「段數」= 段起點的個數（第一筆一定是一段的起點）。\n",
    "    單一點也算一段。\n",
    "    \"\"\"\n",
    "    if times.empty:\n",
    "        return 0\n",
    "    diffs = times.diff()\n",
    "    new_seg = diffs.isna() | (diffs > gap_threshold)\n",
    "    return int(new_seg.sum())\n",
    "\n",
    "# ===== 主程式 =====\n",
    "files = sorted(glob.glob(os.path.join(DIR, \"*.csv\")))\n",
    "results = []   # 每檔：seg_1m, seg_3m, seg_5m\n",
    "totals = {1:0, 3:0, 5:0}\n",
    "\n",
    "for fp in files:\n",
    "    df = read_df_any(fp)\n",
    "    if df is None:\n",
    "        continue\n",
    "    lmap = lower_map(df)\n",
    "    if TIME_COL not in lmap:\n",
    "        continue\n",
    "\n",
    "    # 解析時間\n",
    "    t = pd.to_datetime(df[lmap[TIME_COL]], errors=\"coerce\")\n",
    "\n",
    "    # 三欄轉數值（缺欄視為全 NaN）\n",
    "    nums = []\n",
    "    for c in NUM_COLS:\n",
    "        if c in lmap:\n",
    "            nums.append(to_numeric_clean(df[lmap[c]]))\n",
    "        else:\n",
    "            nums.append(pd.Series([np.nan]*len(df)))\n",
    "\n",
    "    # 僅保留：時間有效 且 三欄皆無數字\n",
    "    mask = t.notna() & nums[0].isna() & nums[1].isna() & nums[2].isna()\n",
    "    times = t[mask].sort_values()\n",
    "\n",
    "    seg_counts = {}\n",
    "    for m in GAP_MINUTES:\n",
    "        seg_counts[m] = count_segments(times, pd.Timedelta(minutes=m))\n",
    "        totals[m] += seg_counts[m]\n",
    "\n",
    "    results.append({\n",
    "        \"file\": os.path.basename(fp),\n",
    "        \"seg_1m\": seg_counts[1],\n",
    "        \"seg_3m\": seg_counts[3],\n",
    "        \"seg_5m\": seg_counts[5],\n",
    "    })\n",
    "\n",
    "# 輸出\n",
    "if not results:\n",
    "    print(\"[INFO] No eligible files found.\")\n",
    "else:\n",
    "    print(\"[RESULT] 段數（單點亦計一段；條件：senddate 可解析且三欄皆無數字）\")\n",
    "    print(\"file, seg_1min, seg_3min, seg_5min\")\n",
    "    for r in results:\n",
    "        print(f\"{r['file']}, {r['seg_1m']}, {r['seg_3m']}, {r['seg_5m']}\")\n",
    "\n",
    "    print(\"\\n[SUMMARY] Total segments across all files:\")\n",
    "    print(f\"- gap > 1 min → segments: {totals[1]}\")\n",
    "    print(f\"- gap > 3 min → segments: {totals[3]}\")\n",
    "    print(f\"- gap > 5 min → segments: {totals[5]}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "9b94c460-da8d-4606-9d92-34411cc11107",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[SUMMARY] years=3, total_figures=5\n",
      "- 2021: 1 figure(s)\n",
      "- 2022: 3 figure(s)\n",
      "- 2024: 1 figure(s)\n"
     ]
    }
   ],
   "source": [
    "下面的程式不知道是不是我以前放的...我按錯了要輸gap135的時候按的...\n",
    "\n",
    "import os, glob, unicodedata\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.dates as mdates\n",
    "from matplotlib.transforms import blended_transform_factory\n",
    "\n",
    "IN_DIR  = \"/home/jovyan/RT08/0925/bling_onehot\"\n",
    "OUT_DIR = \"/home/jovyan/RT08/0925/0926\"\n",
    "os.makedirs(OUT_DIR, exist_ok=True)\n",
    "\n",
    "ENCODINGS = (\"utf-8-sig\",\"utf-8\",\"cp950\",\"big5\",\"latin1\")\n",
    "NEEDED = [\"senddate\",\"patno\",\"rrhzsetactual\",\"peepepap\",\"ppeak\"]\n",
    "\n",
    "# === 每張圖最多 50 個檔案 ===\n",
    "ROWS_PER_FIG = 50\n",
    "ROW_INCH = 0.28\n",
    "WIDTH_INCH = 14\n",
    "TOP_PAD = 0.15\n",
    "RIGHT_ADJ = 0.80  # 留右側空間放註記\n",
    "\n",
    "def read_df_any(path):\n",
    "    for enc in ENCODINGS:\n",
    "        try:\n",
    "            return pd.read_csv(path, encoding=enc, engine=\"python\", on_bad_lines=\"skip\")\n",
    "        except Exception:\n",
    "            continue\n",
    "    return None  # 靜默略過讀不到的檔案\n",
    "\n",
    "def lower_map(df):\n",
    "    m = {}\n",
    "    for c in df.columns:\n",
    "        k = unicodedata.normalize(\"NFKC\", str(c)).strip().lower()\n",
    "        if k not in m: m[k] = c\n",
    "    return m\n",
    "\n",
    "def to_num(s):\n",
    "    return pd.to_numeric(s, errors=\"coerce\")\n",
    "\n",
    "# 蒐集各檔資訊\n",
    "per_file, all_years = [], set()\n",
    "\n",
    "for fp in sorted(glob.glob(os.path.join(IN_DIR, \"*.csv\"))):\n",
    "    name = os.path.basename(fp)\n",
    "    df = read_df_any(fp)\n",
    "    if df is None:\n",
    "        continue\n",
    "    lmap = lower_map(df)\n",
    "\n",
    "    cols = {}\n",
    "    for k in NEEDED:\n",
    "        cols[k] = df[lmap[k]] if k in lmap else pd.Series([np.nan]*len(df))\n",
    "\n",
    "    send_parsed = pd.to_datetime(cols[\"senddate\"], errors=\"coerce\")\n",
    "    has_time = send_parsed.notna()\n",
    "\n",
    "    patno_num = to_num(cols[\"patno\"])\n",
    "    rr_num    = to_num(cols[\"rrhzsetactual\"])\n",
    "    peep_num  = to_num(cols[\"peepepap\"])\n",
    "    ppeak_num = to_num(cols[\"ppeak\"])\n",
    "\n",
    "    is_blue = has_time & patno_num.notna() & rr_num.notna() & peep_num.notna() & ppeak_num.notna()\n",
    "\n",
    "    df2 = pd.DataFrame({\n",
    "        \"senddate\": send_parsed,\n",
    "        \"year\": send_parsed.dt.year,\n",
    "        \"has_time\": has_time,\n",
    "        \"is_blue\": is_blue,\n",
    "    })\n",
    "    years = df2[\"year\"].dropna().unique().tolist()\n",
    "    all_years.update(int(y) for y in years if pd.notna(y))\n",
    "\n",
    "    per_file.append({\"name\": name, \"df2\": df2})\n",
    "\n",
    "# 畫圖（分年、必要時分頁），僅在最後輸出摘要\n",
    "year_fig_count = {}  # year -> parts\n",
    "\n",
    "for year in sorted(all_years):\n",
    "    # 全域時間範圍\n",
    "    xs_all = []\n",
    "    for item in per_file:\n",
    "        d = item[\"df2\"]\n",
    "        xs_all.append(d.loc[d[\"year\"] == year, \"senddate\"])\n",
    "    xs_all = pd.concat(xs_all) if xs_all else pd.Series([], dtype=\"datetime64[ns]\")\n",
    "    if xs_all.notna().any():\n",
    "        xmin, xmax = xs_all.min(), xs_all.max()\n",
    "    else:\n",
    "        xmin = pd.Timestamp(f\"{year}-01-01\")\n",
    "        xmax = pd.Timestamp(f\"{year}-12-31 23:59:59\")\n",
    "\n",
    "    # 該年有資料的檔案\n",
    "    files_for_year = []\n",
    "    for item in per_file:\n",
    "        d = item[\"df2\"]\n",
    "        if (d[\"year\"] == year).any():\n",
    "            files_for_year.append(item)\n",
    "    if not files_for_year:\n",
    "        continue\n",
    "\n",
    "    parts = 0\n",
    "    for part_idx in range(0, len(files_for_year), ROWS_PER_FIG):\n",
    "        chunk = files_for_year[part_idx:part_idx+ROWS_PER_FIG]\n",
    "        n_rows = len(chunk)\n",
    "\n",
    "        H = max(6, n_rows * ROW_INCH)\n",
    "        fig, ax = plt.subplots(figsize=(WIDTH_INCH, H))\n",
    "        fig.subplots_adjust(top=1-TOP_PAD, right=RIGHT_ADJ)\n",
    "\n",
    "        y_labels, yticks = [], []\n",
    "        for i, item in enumerate(chunk):\n",
    "            name = item[\"name\"]\n",
    "            d = item[\"df2\"]\n",
    "            d_year = d[d[\"year\"] == year]\n",
    "            n_total_year = int(d_year.shape[0])\n",
    "\n",
    "            xs_gray = d_year.loc[d_year[\"has_time\"], \"senddate\"]\n",
    "            xs_blue = d_year.loc[d_year[\"is_blue\"], \"senddate\"]\n",
    "\n",
    "            if xs_gray.notna().any():\n",
    "                ax.scatter(xs_gray.values, [i]*xs_gray.shape[0], s=6, c=\"#C0C0C0\", alpha=0.8, linewidths=0)\n",
    "            if xs_blue.notna().any():\n",
    "                ax.scatter(xs_blue.values, [i]*xs_blue.shape[0], s=10, c=\"#1f77b4\", alpha=0.9, linewidths=0)\n",
    "\n",
    "            blue_cnt = int(xs_blue.shape[0])\n",
    "            gray_cnt = int(xs_gray.shape[0])\n",
    "            blue_pct = (blue_cnt / n_total_year * 100.0) if n_total_year > 0 else 0.0\n",
    "            gray_pct = (gray_cnt / n_total_year * 100.0) if n_total_year > 0 else 0.0\n",
    "\n",
    "            # 右側註記（圖外）\n",
    "            trans = blended_transform_factory(ax.transAxes, ax.transData)\n",
    "            ax.text(1.005, i, f\"B:{blue_cnt} ({blue_pct:.1f}%) | G:{gray_cnt} ({gray_pct:.1f}%)\",\n",
    "                    transform=trans, ha=\"left\", va=\"center\", fontsize=8)\n",
    "\n",
    "            y_labels.append(name)\n",
    "            yticks.append(i)\n",
    "\n",
    "        ax.set_yticks(yticks)\n",
    "        ax.set_yticklabels(y_labels, fontsize=8)\n",
    "        ax.set_xlabel(\"Time\")\n",
    "        ax.set_ylabel(\"Files\")\n",
    "        ax.set_title(f\"Timeline by File – Year {year} (Part {part_idx//ROWS_PER_FIG+1})\")\n",
    "\n",
    "        ax.set_xlim([xmin, xmax])\n",
    "        ax.xaxis.set_major_locator(mdates.AutoDateLocator())\n",
    "        ax.xaxis.set_major_formatter(mdates.ConciseDateFormatter(ax.xaxis.get_major_locator()))\n",
    "\n",
    "        ax.scatter([], [], s=10, c=\"#1f77b4\", label=\"Blue: senddate & patno & rrhzsetactual & peepepap & ppeak are numeric\")\n",
    "        ax.scatter([], [], s=6,  c=\"#C0C0C0\", label=\"Gray: has valid senddate\")\n",
    "        ax.legend(loc=\"upper left\", fontsize=8, frameon=False)\n",
    "\n",
    "        out_path = os.path.join(OUT_DIR, f\"timeline_{year}_part{part_idx//ROWS_PER_FIG+1:02d}.png\")\n",
    "        plt.savefig(out_path, dpi=150, bbox_inches=\"tight\")\n",
    "        plt.close()\n",
    "\n",
    "        parts += 1\n",
    "\n",
    "    year_fig_count[year] = parts\n",
    "\n",
    "# ===== 只輸出摘要 =====\n",
    "years_processed = sorted(year_fig_count.keys())\n",
    "total_figs = sum(year_fig_count.values())\n",
    "print(f\"[SUMMARY] years={len(years_processed)}, total_figures={total_figs}\")\n",
    "for y in years_processed:\n",
    "    print(f\"- {y}: {year_fig_count[y]} figure(s)\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "59bf4c00-caaa-4725-833e-5f490efc15f5",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[OK] Saved counts CSV -> /home/jovyan/RT08/0925/0926/gap1m_segment_duration_counts.csv\n",
      "[OK] Saved bar chart -> /home/jovyan/RT08/0925/0926/gap1m_segments_bars.png\n",
      "[OK] Saved counts CSV -> /home/jovyan/RT08/0925/0926/gap3m_segment_duration_counts.csv\n",
      "[OK] Saved bar chart -> /home/jovyan/RT08/0925/0926/gap3m_segments_bars.png\n",
      "[OK] Saved counts CSV -> /home/jovyan/RT08/0925/0926/gap5m_segment_duration_counts.csv\n",
      "[OK] Saved bar chart -> /home/jovyan/RT08/0925/0926/gap5m_segments_bars.png\n"
     ]
    }
   ],
   "source": [
    "\"\"\" 我想針對這三個135分鐘gap分別畫出三張條狀圖 ，顏色分別用綠 藍 紫 同一張要顏色漸層，紀錄每一段的可用段長多久，橫軸是時間（分鐘），縱軸是筆數，每一條上面都要數量跟佔比，不要互相擋到 圖表要用英文 ，並在兩小時的地方化一個虛線\n",
    "=\n",
    "來源（唯讀）：/home/jovyan/RT08/0925/bling_onehot/*.csv\n",
    "「節點」的定義：senddate 可解析，且 rrhzsetactual、peepepap、ppeak 三個欄位皆為非數值（即無法解析為數字）的列。\n",
    "區段劃分方式：依時間排序節點；若相鄰的時間差大於設定的間隔（1/3/5 分鐘），則開始一個新的區段。（單點區段也要包含在內。）\n",
    "圖表要求：每個間隔輸出一張 PNG（綠色 / 藍色 / 紫色，並由左至右漸層）。\n",
    "X 軸：區段持續時間（分鐘，取整數）\n",
    "Y 軸：區段數量（count）\n",
    "每個柱狀上方需標註 \"數量 (占比%)\"，避免文字重疊。\n",
    "在 120 分鐘（2 小時）的位置畫一條垂直虛線。\n",
    "輸出檔案：\n",
    "/home/jovyan/RT08/0925/0926/gap1m_segments_bars.png\n",
    "/home/jovyan/RT08/0925/0926/gap3m_segments_bars.png\n",
    "/home/jovyan/RT08/0925/0926/gap5m_segments_bars.png\n",
    "/home/jovyan/RT08/0925/0926/gap*_segment_duration_counts.csv\n",
    "注意：此腳本不會修改原始 CSV。\n",
    "\"\"\"\n",
    "import os, glob, unicodedata, re\n",
    "from collections import Counter\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "IN_DIR  = \"/home/jovyan/RT08/0925/bling_onehot\"\n",
    "OUT_DIR = \"/home/jovyan/RT08/0925/0926\"\n",
    "os.makedirs(OUT_DIR, exist_ok=True)\n",
    "\n",
    "TIME_COL = \"senddate\"\n",
    "NUM_COLS = [\"rrhzsetactual\", \"peepepap\", \"ppeak\"]\n",
    "GAP_MINUTES = [1, 3, 5]\n",
    "\n",
    "ENCODINGS = (\"utf-8-sig\",\"utf-8\",\"cp950\",\"big5\",\"latin1\")\n",
    "\n",
    "# ---------- Helpers ----------\n",
    "def read_df_any(path):\n",
    "    for enc in ENCODINGS:\n",
    "        try:\n",
    "            return pd.read_csv(path, encoding=enc, engine=\"python\", on_bad_lines=\"skip\")\n",
    "        except Exception:\n",
    "            continue\n",
    "    return None\n",
    "\n",
    "def lower_map(df):\n",
    "    m = {}\n",
    "    for c in df.columns:\n",
    "        k = unicodedata.normalize(\"NFKC\", str(c)).strip().lower()\n",
    "        if k not in m: m[k] = c\n",
    "    return m\n",
    "\n",
    "INVIS_RE = re.compile(r\"[\\u200b-\\u200f\\u202a-\\u202e\\u2066-\\u2069\\ufeff\\u00a0\\u3000]\")\n",
    "def to_numeric_clean(s: pd.Series) -> pd.Series:\n",
    "    # Clean weird chars, thousand-seps, unicode minus, (123) -> -123; non-parsable -> NaN\n",
    "    s2 = s.astype(\"string\")\n",
    "    s2 = s2.map(lambda x: unicodedata.normalize(\"NFKC\", x).strip() if x is not pd.NA else x)\n",
    "    s2 = s2.map(lambda x: INVIS_RE.sub(\"\", x) if x is not pd.NA else x)\n",
    "    s2 = s2.str.replace(\",\", \"\", regex=False)\n",
    "    s2 = s2.str.replace(\"−\", \"-\", regex=False)\n",
    "    s2 = s2.str.replace(r\"^\\(([^)]+)\\)$\", r\"-\\1\", regex=True)\n",
    "    return pd.to_numeric(s2, errors=\"coerce\")\n",
    "\n",
    "def collect_segment_durations_minutes(times: pd.Series, gap_threshold: pd.Timedelta) -> list[int]:\n",
    "    \"\"\"\n",
    "    Given sorted time points (Series of datetimes), cut new segment when diff > gap_threshold.\n",
    "    Return list of segment durations in whole minutes (int, floor).\n",
    "    Single-point segments get 0 minutes.\n",
    "    \"\"\"\n",
    "    if times.empty:\n",
    "        return []\n",
    "    diffs = times.diff()\n",
    "    new_seg = diffs.isna() | (diffs > gap_threshold)\n",
    "    seg_id = new_seg.cumsum()\n",
    "\n",
    "    durations_min = []\n",
    "    for _, seg in times.groupby(seg_id):\n",
    "        if seg.shape[0] == 1:\n",
    "            dur = 0\n",
    "        else:\n",
    "            dur_td = seg.iloc[-1] - seg.iloc[0]\n",
    "            # floor to whole minutes (>=0)\n",
    "            dur = int(np.floor(dur_td.total_seconds() / 60.0))\n",
    "            if dur < 0:  # guard (shouldn't happen)\n",
    "                dur = 0\n",
    "        durations_min.append(dur)\n",
    "    return durations_min\n",
    "\n",
    "def make_gradient_colors(base_rgb, n):\n",
    "    \"\"\"\n",
    "    Create left->right gradient shades of a base color (tuple in 0..1).\n",
    "    Lighter on the left, deeper on the right; keep readable range.\n",
    "    \"\"\"\n",
    "    if n <= 0:\n",
    "        return []\n",
    "    r, g, b = base_rgb\n",
    "    shades = []\n",
    "    for i in range(n):\n",
    "        # factor from 0.65..1.0\n",
    "        t = 0.65 + 0.35 * (i / max(1, n-1))\n",
    "        shades.append((r*t, g*t, b*t))\n",
    "    return shades\n",
    "\n",
    "def plot_duration_bars(durations, title, base_rgb, out_png, out_csv):\n",
    "    \"\"\"\n",
    "    durations: list[int] of minutes\n",
    "    Create a bar chart with gradient colors.\n",
    "    Annotate each bar with \"count (share%)\" above it.\n",
    "    Draw vertical dashed line at 120 minutes.\n",
    "    Save PNG and CSV (counts).\n",
    "    \"\"\"\n",
    "    if not durations:\n",
    "        print(f\"[INFO] No segments to plot for: {title}. Skip.\")\n",
    "        # Still emit empty CSV with headers\n",
    "        pd.DataFrame(columns=[\"duration_min\",\"count\"]).to_csv(out_csv, index=False, encoding=\"utf-8\")\n",
    "        return\n",
    "\n",
    "    cnt = Counter(durations)\n",
    "    # Sort by duration (x axis ascending)\n",
    "    xs = sorted(cnt.keys())\n",
    "    ys = [cnt[x] for x in xs]\n",
    "    total = sum(ys)\n",
    "\n",
    "    # Save counts CSV\n",
    "    pd.DataFrame({\"duration_min\": xs, \"count\": ys}).to_csv(out_csv, index=False, encoding=\"utf-8\")\n",
    "    print(f\"[OK] Saved counts CSV -> {out_csv}\")\n",
    "\n",
    "    # Dynamic width; cap spacing\n",
    "    W = max(12, len(xs) * 0.35)\n",
    "    H = 6\n",
    "    plt.figure(figsize=(W, H))\n",
    "\n",
    "    colors = make_gradient_colors(base_rgb, len(xs))\n",
    "    bars = plt.bar(range(len(xs)), ys, color=colors, edgecolor=\"black\", linewidth=0.4)\n",
    "\n",
    "    # X ticks (avoid clutter)\n",
    "    step = int(np.ceil(len(xs) / 30))  # show at most ~30 ticks\n",
    "    tick_idx = list(range(0, len(xs), step))\n",
    "    plt.xticks(tick_idx, [str(xs[i]) for i in tick_idx], rotation=45, ha=\"right\")\n",
    "\n",
    "    plt.xlabel(\"Segment Duration (minutes)\")\n",
    "    plt.ylabel(\"Count\")\n",
    "    plt.title(title)\n",
    "\n",
    "    # Vertical dashed line at 120 minutes\n",
    "    if xs:\n",
    "        # Find x position for 120-min line between bars\n",
    "        try:\n",
    "            # If 120 is exactly present, draw in the center of that bar; otherwise between the nearest\n",
    "            idx_120 = xs.index(120)  # may raise ValueError\n",
    "            xline = idx_120\n",
    "        except ValueError:\n",
    "            # approximate by position\n",
    "            # proportion along xs\n",
    "            pos = np.searchsorted(xs, 120, side=\"left\")\n",
    "            xline = pos - 0.5 if 0 < pos < len(xs) else ( -0.5 if pos<=0 else len(xs)-0.5 )\n",
    "        plt.axvline(x=xline, linestyle=\"--\", color=\"gray\", linewidth=1.0)\n",
    "        # label\n",
    "        ymax = max(ys) if ys else 1\n",
    "        plt.text(xline, ymax*1.02, \"120 min\", ha=\"center\", va=\"bottom\", fontsize=9, color=\"gray\")\n",
    "\n",
    "    # Add labels above bars: count (share%)\n",
    "    ymax = max(ys) if ys else 1\n",
    "    for i, (bar, c) in enumerate(zip(bars, ys)):\n",
    "        if c <= 0: \n",
    "            continue\n",
    "        pct = (c / total) * 100.0 if total > 0 else 0.0\n",
    "        # Place above bar with white background to avoid overlap/occlusion\n",
    "        plt.text(\n",
    "            bar.get_x() + bar.get_width()/2, bar.get_height() + max(0.02*ymax, 0.3),\n",
    "            f\"{c} ({pct:.1f}%)\",\n",
    "            ha=\"center\", va=\"bottom\", fontsize=8, color=\"black\",\n",
    "            bbox=dict(boxstyle=\"round,pad=0.2\", facecolor=\"white\", alpha=0.9, edgecolor=\"gray\")\n",
    "        )\n",
    "\n",
    "    plt.tight_layout()\n",
    "    plt.savefig(out_png, dpi=150)\n",
    "    plt.close()\n",
    "    print(f\"[OK] Saved bar chart -> {out_png}\")\n",
    "\n",
    "# ---------- Collect nodes across ALL files ----------\n",
    "all_nodes_time = []  # list of pd.Series of times per file that meet the node rule\n",
    "\n",
    "files = sorted(glob.glob(os.path.join(IN_DIR, \"*.csv\")))\n",
    "for fp in files:\n",
    "    df = read_df_any(fp)\n",
    "    if df is None:\n",
    "        continue\n",
    "    lmap = lower_map(df)\n",
    "    if TIME_COL not in lmap:\n",
    "        continue\n",
    "\n",
    "    t = pd.to_datetime(df[lmap[TIME_COL]], errors=\"coerce\")\n",
    "\n",
    "    nums = []\n",
    "    for c in NUM_COLS:\n",
    "        if c in lmap:\n",
    "            nums.append(to_numeric_clean(df[lmap[c]]))\n",
    "        else:\n",
    "            nums.append(pd.Series([np.nan]*len(df)))\n",
    "\n",
    "    mask = t.notna() & nums[0].isna() & nums[1].isna() & nums[2].isna()\n",
    "    times = t[mask].sort_values()\n",
    "    if not times.empty:\n",
    "        all_nodes_time.append(times)\n",
    "\n",
    "# Merge all times across files for global segmentation? or per-file?\n",
    "# The user asked to “record each segment’s usable duration” for the dataset.\n",
    "# We’ll compute segments ACROSS ALL FILES COMBINED by file? Typically segments should not cross files.\n",
    "# So we compute per-file segments and then aggregate durations into one pool per gap.\n",
    "durations_by_gap = {1: [], 3: [], 5: []}\n",
    "\n",
    "for times in all_nodes_time:\n",
    "    for m in GAP_MINUTES:\n",
    "        durations_by_gap[m].extend(collect_segment_durations_minutes(times, pd.Timedelta(minutes=m)))\n",
    "\n",
    "# ---------- Plot three charts ----------\n",
    "# Base colors: 1m=green, 3m=blue, 5m=purple (RGB in 0..1)\n",
    "BASE = {\n",
    "    1: (0.30, 0.70, 0.40),  # green\n",
    "    3: (0.30, 0.50, 0.85),  # blue\n",
    "    5: (0.65, 0.45, 0.85),  # purple\n",
    "}\n",
    "\n",
    "for m in GAP_MINUTES:\n",
    "    title = f\"Usable Segment Durations (gap > {m} min)\"\n",
    "    out_png = os.path.join(OUT_DIR, f\"gap{m}m_segments_bars.png\")\n",
    "    out_csv = os.path.join(OUT_DIR, f\"gap{m}m_segment_duration_counts.csv\")\n",
    "    plot_duration_bars(\n",
    "        durations_by_gap[m],\n",
    "        title,\n",
    "        BASE[m],\n",
    "        out_png,\n",
    "        out_csv\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "5e643465-b316-4cd4-9c1e-4f2aa63bae15",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[OK] CSV saved -> /home/jovyan/RT08/0925/0926/three_numeric_counts.csv\n",
      "[OK] Bar chart saved -> /home/jovyan/RT08/0925/0926/three_numeric_bars.png\n"
     ]
    }
   ],
   "source": [
    "他畫錯了 \n",
    "# ============================================================\n",
    "# 目標：繪製每個檔案中「rrhzsetactual、peepepap、ppeak 三欄都有數字」的筆數柱狀圖\n",
    "# 來源（只讀）：/home/jovyan/RT08/0925/bling_onehot/*.csv\n",
    "# 輸出：\n",
    "#   /home/jovyan/RT08/0925/0926/three_numeric_counts.csv   # 各檔筆數與占比\n",
    "#   /home/jovyan/RT08/0925/0926/three_numeric_bars.png     # 條狀圖（依筆數由大到小）\n",
    "# 設計：\n",
    "#   - 以多編碼嘗試讀檔，容錯大小寫與全形空白。\n",
    "#   - 轉數值前進行清洗（不可見字元、千分位、Unicode 負號、(123)->-123），\n",
    "#     轉不出數值視為 NaN；三欄皆非 NaN 才計入。\n",
    "#   - 依檔案排序（由大到小），自動放大畫布、旋轉 X 標籤避免擠壓。\n",
    "#   - 若檔案數量 <= 40，所有柱皆標註「筆數（占比%）」；否則只標註 Top 20。\n",
    "# ============================================================\n",
    "\n",
    "import os, glob, unicodedata, re\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "IN_DIR  = \"/home/jovyan/RT08/0925/bling_onehot\"\n",
    "OUT_DIR = \"/home/jovyan/RT08/0925/0926\"\n",
    "os.makedirs(OUT_DIR, exist_ok=True)\n",
    "\n",
    "TARGET_COLS = [\"rrhzsetactual\", \"peepepap\", \"ppeak\"]\n",
    "ENCODINGS = (\"utf-8-sig\",\"utf-8\",\"cp950\",\"big5\",\"latin1\")\n",
    "\n",
    "# ---------- 工具：讀檔 / 欄名對應 / 文字→數值 ----------\n",
    "def read_df_any(path):\n",
    "    \"\"\"嘗試多種編碼讀取 CSV，失敗回傳 None（不中斷流程）。\"\"\"\n",
    "    for enc in ENCODINGS:\n",
    "        try:\n",
    "            return pd.read_csv(path, encoding=enc, engine=\"python\", on_bad_lines=\"skip\")\n",
    "        except Exception:\n",
    "            continue\n",
    "    return None\n",
    "\n",
    "def lower_map(df):\n",
    "    \"\"\"建立小寫欄名到原欄名的對映（容錯大小寫與全形）。\"\"\"\n",
    "    m = {}\n",
    "    for c in df.columns:\n",
    "        k = unicodedata.normalize(\"NFKC\", str(c)).strip().lower()\n",
    "        if k not in m: m[k] = c\n",
    "    return m\n",
    "\n",
    "# 清理不可見字元 / 千分位 / 全形，支援 Unicode 負號與 (123)->-123；不可轉者為 NaN\n",
    "INVIS_RE = re.compile(r\"[\\u200b-\\u200f\\u202a-\\u202e\\u2066-\\u2069\\ufeff\\u00a0\\u3000]\")\n",
    "def to_numeric_clean(s: pd.Series) -> pd.Series:\n",
    "    s2 = s.astype(\"string\")\n",
    "    s2 = s2.map(lambda x: unicodedata.normalize(\"NFKC\", x).strip() if x is not pd.NA else x)\n",
    "    s2 = s2.map(lambda x: INVIS_RE.sub(\"\", x) if x is not pd.NA else x)\n",
    "    s2 = s2.str.replace(\",\", \"\", regex=False)\n",
    "    s2 = s2.str.replace(\"−\", \"-\", regex=False)\n",
    "    s2 = s2.str.replace(r\"^\\(([^)]+)\\)$\", r\"-\\1\", regex=True)\n",
    "    return pd.to_numeric(s2, errors=\"coerce\")\n",
    "\n",
    "# ---------- 彙整各檔案的「三欄皆為數值」筆數 ----------\n",
    "rows = []\n",
    "files = sorted(glob.glob(os.path.join(IN_DIR, \"*.csv\")))\n",
    "for fp in files:\n",
    "    name = os.path.basename(fp)\n",
    "    df = read_df_any(fp)\n",
    "    if df is None or df.empty:\n",
    "        continue\n",
    "    lmap = lower_map(df)\n",
    "\n",
    "    # 若欄位不存在，視為全 NaN（不會被計入）\n",
    "    cols_num = []\n",
    "    for c in TARGET_COLS:\n",
    "        if c in lmap:\n",
    "            cols_num.append(to_numeric_clean(df[lmap[c]]))\n",
    "        else:\n",
    "            cols_num.append(pd.Series([np.nan]*len(df)))\n",
    "\n",
    "    all_three_numeric = cols_num[0].notna() & cols_num[1].notna() & cols_num[2].notna()\n",
    "    count_numeric_all3 = int(all_three_numeric.sum())\n",
    "    total_rows = int(len(df))\n",
    "    pct = (count_numeric_all3 / total_rows * 100.0) if total_rows > 0 else 0.0\n",
    "\n",
    "    rows.append({\n",
    "        \"file\": name,\n",
    "        \"count_all3_numeric\": count_numeric_all3,\n",
    "        \"total_rows\": total_rows,\n",
    "        \"share_percent\": round(pct, 6),\n",
    "    })\n",
    "\n",
    "result_df = pd.DataFrame(rows).sort_values([\"count_all3_numeric\",\"file\"], ascending=[False, True])\n",
    "out_csv = os.path.join(OUT_DIR, \"three_numeric_counts.csv\")\n",
    "result_df.to_csv(out_csv, index=False, encoding=\"utf-8\")\n",
    "print(f\"[OK] CSV saved -> {out_csv}\")\n",
    "\n",
    "# ---------- 繪圖：每檔的筆數條狀圖（由大到小） ----------\n",
    "if not result_df.empty:\n",
    "    x_labels = result_df[\"file\"].tolist()\n",
    "    y_counts = result_df[\"count_all3_numeric\"].tolist()\n",
    "    totals   = result_df[\"total_rows\"].tolist()\n",
    "    y_share  = [ (c/t*100.0 if t>0 else 0.0) for c, t in zip(y_counts, totals) ]\n",
    "\n",
    "    # 畫布寬度依檔案數自動放大；X 標籤旋轉避免擠壓\n",
    "    W = max(14, len(x_labels) * 0.6)\n",
    "    H = 7\n",
    "    plt.figure(figsize=(W, H))\n",
    "\n",
    "    # 顏色：藍色系漸層（由淺到深）\n",
    "    base_rgb = (0.30, 0.55, 0.90)\n",
    "    colors = []\n",
    "    n = len(x_labels)\n",
    "    for i in range(n):\n",
    "        t = 0.65 + 0.35 * (i / max(1, n-1))  # 0.65→1.0\n",
    "        colors.append(tuple(v*t for v in base_rgb))\n",
    "\n",
    "    bars = plt.bar(range(n), y_counts, color=colors, edgecolor=\"black\", linewidth=0.4)\n",
    "\n",
    "    # X 軸刻度\n",
    "    step = max(1, int(np.ceil(n / 40)))  # 最多顯示 ~40 個刻度\n",
    "    xticks = list(range(0, n, step))\n",
    "    plt.xticks(xticks, [x_labels[i] for i in xticks], rotation=45, ha=\"right\")\n",
    "\n",
    "    plt.xlabel(\"Files\")\n",
    "    plt.ylabel(\"Rows with all three numeric\")\n",
    "    plt.title(\"Counts where rrhzsetactual, peepepap, ppeak are all numeric (by file)\")\n",
    "\n",
    "    # 動態標註策略：檔案數 <= 40 → 全部標；否則只標 Top 20，避免互擋\n",
    "    ymax = max(y_counts) if y_counts else 1\n",
    "    to_annotate = range(n) if n <= 40 else range(min(20, n))\n",
    "    for i in to_annotate:\n",
    "        c = y_counts[i]\n",
    "        pct = y_share[i]\n",
    "        bar = bars[i]\n",
    "        plt.text(\n",
    "            bar.get_x() + bar.get_width()/2, bar.get_height() + max(0.02*ymax, 0.3),\n",
    "            f\"{c} ({pct:.1f}%)\",\n",
    "            ha=\"center\", va=\"bottom\", fontsize=8, color=\"black\",\n",
    "            bbox=dict(boxstyle=\"round,pad=0.20\", facecolor=\"white\", alpha=0.92, edgecolor=\"gray\"),\n",
    "            clip_on=False\n",
    "        )\n",
    "\n",
    "    plt.tight_layout()\n",
    "    out_png = os.path.join(OUT_DIR, \"three_numeric_bars.png\")\n",
    "    plt.savefig(out_png, dpi=150)\n",
    "    plt.close()\n",
    "    print(f\"[OK] Bar chart saved -> {out_png}\")\n",
    "else:\n",
    "    print(\"[INFO] No usable files/rows to plot.\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "d7add89b-eed7-4080-963a-6d369fb33d0f",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[OK] Saved binned counts CSV -> /home/jovyan/RT08/0925/0926/gap1m_numeric_segment_duration_hourbins_ge120.csv\n",
      "[OK] Saved bar chart -> /home/jovyan/RT08/0925/0926/gap1m_numeric_segments_bars_hourbin_ge120.png  | bins=59, width_in=48.0\n",
      "[OK] Saved binned counts CSV -> /home/jovyan/RT08/0925/0926/gap3m_numeric_segment_duration_hourbins_ge120.csv\n",
      "[OK] Saved bar chart -> /home/jovyan/RT08/0925/0926/gap3m_numeric_segments_bars_hourbin_ge120.png  | bins=301, width_in=48.0\n",
      "[OK] Saved binned counts CSV -> /home/jovyan/RT08/0925/0926/gap5m_numeric_segment_duration_hourbins_ge120.csv\n",
      "[OK] Saved bar chart -> /home/jovyan/RT08/0925/0926/gap5m_numeric_segments_bars_hourbin_ge120.png  | bins=301, width_in=48.0\n",
      "\n",
      "[Five-number summary for segment durations >= 120 minutes]\n",
      "- gap > 1 min | N=2239, min=120, Q1=149, median=194, Q3=268, max=3622\n",
      "- gap > 3 min | N=814, min=120, Q1=301, median=740, Q3=1874, max=35631\n",
      "- gap > 5 min | N=559, min=120, Q1=537, median=1153, Q3=3116, max=35631\n"
     ]
    }
   ],
   "source": [
    "\"\"\"我想針對這三個135分鐘gap分別畫出三張條狀圖 ，顏色分別用綠 藍 紫 同一張要顏色漸層，紀錄每一段的可用段長多久，橫軸是時間（分鐘），縱軸是筆數，每一條上面都要數量跟佔比，不要互相擋到 圖表要用英文 ，並在兩小時的地方化一個虛線\n",
    "因為資料筆數太多 請將橫軸用每一小時為一個區間 橫軸起始是120分鐘\n",
    "\"\"\"\n",
    "# ============================================================\n",
    "# 1 / 3 / 5 分鐘 gap 三張條狀圖（以 2 小時為起點、每小時一箱；<120 直接排除、不留空間）\n",
    "# 並在程式結尾列出各 gap 的「五數摘要」（min, Q1, median, Q3, max），\n",
    "# 皆針對「區段長度 ≥ 120 分鐘」後的分佈計算。\n",
    "#\n",
    "# 定義與流程（不動原始資料）：\n",
    "#   - 可用節點：senddate 可解析 且 rrhzsetactual、peepepap、ppeak 三欄皆為數值（清洗後可轉數字）。\n",
    "#   - 區段切法：同檔案內按時間排序，相鄰差 > gap（1/3/5 分）即切段；單點段長=0 分。\n",
    "#   - 匯整各檔案的區段長度（分鐘，向下取整）；只保留 >=120 的長度用於分箱與統計。\n",
    "#   - 以每 60 分鐘為一箱，從 120 開始連續切箱；箱數過多時做尾端併箱（≥ 上限）。\n",
    "#\n",
    "# 輸入（只讀）：/home/jovyan/RT08/0925/bling_onehot/*.csv\n",
    "# 輸出（不覆蓋原檔）：\n",
    "#   /home/jovyan/RT08/0925/0926/gap{1,3,5}m_numeric_segments_bars_hourbin_ge120.png\n",
    "#   /home/jovyan/RT08/0925/0926/gap{1,3,5}m_numeric_segment_duration_hourbins_ge120.csv\n",
    "#   （終端列印各 gap 的五數摘要）\n",
    "# ============================================================\n",
    "\n",
    "import os, glob, unicodedata, re\n",
    "from collections import Counter\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "IN_DIR  = \"/home/jovyan/RT08/0925/bling_onehot\"\n",
    "OUT_DIR = \"/home/jovyan/RT08/0925/0926\"\n",
    "os.makedirs(OUT_DIR, exist_ok=True)\n",
    "\n",
    "TIME_COL  = \"senddate\"\n",
    "NUM_COLS  = [\"rrhzsetactual\", \"peepepap\", \"ppeak\"]\n",
    "GAPS_MIN  = [1, 3, 5]     # 三種 gap 門檻（分鐘）\n",
    "\n",
    "# ---- 分箱設定（以 120 分鐘起算；每小時一箱；尾端合併避免超大圖）----\n",
    "BIN_START_MIN = 120       # 起點：兩小時（小於者不入圖也不佔位）\n",
    "BIN_WIDTH_MIN = 60        # 每箱：60 分鐘（1 小時）\n",
    "MAX_BINS      = 300       # 最多 300 小時箱；超過者併入最後一箱 \"≥ X\"\n",
    "\n",
    "# ---- 畫布上限（避免超大圖）----\n",
    "MAX_FIG_W_INCH = 48       # 最大圖寬 48 吋（@150dpi ≈ 7200px）\n",
    "BASE_W_PER_BAR = 0.9      # 一柱預估寬度（有效控制圖寬）\n",
    "\n",
    "ENCODINGS = (\"utf-8-sig\",\"utf-8\",\"cp950\",\"big5\",\"latin1\")\n",
    "\n",
    "# ---------- 工具：讀檔、欄位對應、數值清洗 ----------\n",
    "def read_df_any(path):\n",
    "    \"\"\"嘗試多種編碼讀取 CSV，失敗回傳 None（不中斷流程）。\"\"\"\n",
    "    for enc in ENCODINGS:\n",
    "        try:\n",
    "            return pd.read_csv(path, encoding=enc, engine=\"python\", on_bad_lines=\"skip\")\n",
    "        except Exception:\n",
    "            continue\n",
    "    return None\n",
    "\n",
    "def lower_map(df):\n",
    "    \"\"\"建立『小寫欄名 → 原欄名』映射，容錯大小寫/全形/前後空白。\"\"\"\n",
    "    m = {}\n",
    "    for c in df.columns:\n",
    "        k = unicodedata.normalize(\"NFKC\", str(c)).strip().lower()\n",
    "        if k not in m: m[k] = c\n",
    "    return m\n",
    "\n",
    "# 去不可見字元/千分位/全形；支援 Unicode 負號與 (123)→-123；無法轉者為 NaN\n",
    "INVIS_RE = re.compile(r\"[\\u200b-\\u200f\\u202a-\\u202e\\u2066-\\u2069\\ufeff\\u00a0\\u3000]\")\n",
    "def to_numeric_clean(s: pd.Series) -> pd.Series:\n",
    "    s2 = s.astype(\"string\")\n",
    "    s2 = s2.map(lambda x: unicodedata.normalize(\"NFKC\", x).strip() if x is not pd.NA else x)\n",
    "    s2 = s2.map(lambda x: INVIS_RE.sub(\"\", x) if x is not pd.NA else x)\n",
    "    s2 = s2.str.replace(\",\", \"\", regex=False)\n",
    "    s2 = s2.str.replace(\"−\", \"-\", regex=False)\n",
    "    s2 = s2.str.replace(r\"^\\(([^)]+)\\)$\", r\"-\\1\", regex=True)\n",
    "    return pd.to_numeric(s2, errors=\"coerce\")\n",
    "\n",
    "# ---------- 區段長度計算 ----------\n",
    "def collect_segment_durations_minutes(times: pd.Series, gap_threshold: pd.Timedelta) -> list[int]:\n",
    "    \"\"\"同檔案內：相鄰差 > 門檻即切段；單點段長=0 分；回傳分鐘（向下取整）。\"\"\"\n",
    "    if times.empty:\n",
    "        return []\n",
    "    diffs  = times.diff()\n",
    "    starts = diffs.isna() | (diffs > gap_threshold)\n",
    "    seg_id = starts.cumsum()\n",
    "\n",
    "    durs = []\n",
    "    for _, seg in times.groupby(seg_id):\n",
    "        if len(seg) == 1:\n",
    "            durs.append(0)\n",
    "        else:\n",
    "            td  = seg.iloc[-1] - seg.iloc[0]\n",
    "            durs.append(max(0, int(np.floor(td.total_seconds()/60.0))))\n",
    "    return durs\n",
    "\n",
    "# ---------- 分箱（僅針對 >=120 分鐘；每 60 分鐘一箱；尾端合併） ----------\n",
    "def bin_durations_hourly_ge120(durations, start_min=120, width=60, max_bins=300):\n",
    "    \"\"\"\n",
    "    僅對 >= start_min 的分鐘值做分箱，回傳 (labels, counts, ranges, tail_info)：\n",
    "      - labels: [\"120–179\", \"180–239\", ..., \"≥ X\"]（依資料與上限自動決定）\n",
    "      - counts: 各 bin 計數\n",
    "      - ranges: [(lo, hi), ...]，最後一個可能是 (lo, +inf)\n",
    "      - tail_info: 若有尾端併入，回 {'capped': True, 'ge_min': 上限分鐘, 'moved': 件數}\n",
    "    \"\"\"\n",
    "    # 過濾出 >= 120 的長度\n",
    "    vals = [d for d in durations if d >= start_min]\n",
    "    if not vals:\n",
    "        return [], [], [], {'capped': False, 'ge_min': None, 'moved': 0}\n",
    "\n",
    "    mx = max(vals)\n",
    "    labels, ranges = [], []\n",
    "\n",
    "    cap_hi = start_min + width*max_bins - 1\n",
    "    lo = start_min\n",
    "    while lo <= min(mx, cap_hi):\n",
    "        hi = lo + width - 1\n",
    "        labels.append(f\"{lo}–{hi}\")\n",
    "        ranges.append((lo, hi))\n",
    "        lo += width\n",
    "\n",
    "    tail_info = {'capped': False, 'ge_min': None, 'moved': 0}\n",
    "    if mx > cap_hi:\n",
    "        labels.append(f\"≥ {cap_hi+1}\")\n",
    "        ranges.append((cap_hi+1, np.inf))\n",
    "        tail_info = {'capped': True, 'ge_min': cap_hi+1, 'moved': sum(d > cap_hi for d in vals)}\n",
    "\n",
    "    counts = []\n",
    "    for (lo, hi) in ranges:\n",
    "        if np.isposinf(hi):\n",
    "            counts.append(sum(d >= lo for d in vals))\n",
    "        else:\n",
    "            counts.append(sum((d >= lo) and (d <= hi) for d in vals))\n",
    "\n",
    "    return labels, counts, ranges, tail_info\n",
    "\n",
    "# ---------- 視覺化（避免標註重疊；控制圖寬上限） ----------\n",
    "def make_gradient_colors(base_rgb, n):\n",
    "    \"\"\"基於單一底色產生 n 個由淺到深的漸層。\"\"\"\n",
    "    if n <= 0: return []\n",
    "    r, g, b = base_rgb\n",
    "    return [(r*(0.65+0.35*i/max(1,n-1)), g*(0.65+0.35*i/max(1,n-1)), b*(0.65+0.35*i/max(1,n-1))) for i in range(n)]\n",
    "\n",
    "def plot_binned_bars(labels, counts, title, base_rgb, out_png, out_csv):\n",
    "    \"\"\"\n",
    "    將『每小時分箱（>=120）計數』畫成條狀圖，並依需求進行圖表優化：\n",
    "      - 依柱數自動放寬畫布（含上限）；\n",
    "      - 高柱：柱內白字；低柱：柱外標註；\n",
    "      - 柱多且密（>=30）時，柱外標註改直向並交錯高度；\n",
    "      - 120 分鐘虛線畫在第一根柱子的左邊界（x=-0.5），不留 <120 的空檔。\n",
    "    並輸出 CSV：columns = [bin_label, count]\n",
    "    \"\"\"\n",
    "    # CSV\n",
    "    pd.DataFrame({\"bin_label\": labels, \"count\": counts}).to_csv(out_csv, index=False, encoding=\"utf-8\")\n",
    "    print(f\"[OK] Saved binned counts CSV -> {out_csv}\")\n",
    "\n",
    "    n = len(labels)\n",
    "    if n == 0:\n",
    "        print(f\"[INFO] No >=120-min segments for {title}. Skip plotting.\")\n",
    "        return\n",
    "\n",
    "    # 根據柱數估算所需寬度，但設上限避免超大圖\n",
    "    need_w = max(12, n * BASE_W_PER_BAR)\n",
    "    W = min(need_w, MAX_FIG_W_INCH)\n",
    "    H = 7\n",
    "    fig, ax = plt.subplots(figsize=(W, H))\n",
    "\n",
    "    colors = make_gradient_colors(base_rgb, n)\n",
    "    bars   = ax.bar(range(n), counts, color=colors, edgecolor=\"black\", linewidth=0.4)\n",
    "\n",
    "    # X 軸刻度（避免擠爆）\n",
    "    step = max(1, int(np.ceil(n / 40)))  # 最多顯示 ~40 個刻度\n",
    "    tick_idx = list(range(0, n, step))\n",
    "    ax.set_xticks(tick_idx)\n",
    "    ax.set_xticklabels([labels[i] for i in tick_idx], rotation=45, ha=\"right\")\n",
    "\n",
    "    ax.set_xlabel(\"Segment Duration (minutes, hourly bins from 120)\")\n",
    "    ax.set_ylabel(\"Count\")\n",
    "    ax.set_title(title)\n",
    "\n",
    "    # 120 分鐘虛線：畫在第一根柱子的左邊界（x=-0.5），不保留 <120 的空間\n",
    "    ax.axvline(x=-0.5, linestyle=\"--\", color=\"gray\", linewidth=1.0)\n",
    "    ymax_tmp = max(counts) if counts else 1\n",
    "    ax.text(-0.5, ymax_tmp*1.02, \"120 min\", ha=\"center\", va=\"bottom\", fontsize=9, color=\"gray\")\n",
    "\n",
    "    # 智慧標註\n",
    "    total = sum(counts) if counts else 0\n",
    "    ymax  = max(counts) if counts else 1\n",
    "    dense = (n >= 30)\n",
    "    inside_thresh = 0.18 * ymax\n",
    "    alt_offset    = 0.10 * ymax + 0.3\n",
    "\n",
    "    for i, (bar, c) in enumerate(zip(bars, counts)):\n",
    "        if c <= 0:\n",
    "            continue\n",
    "        pct = (c / total * 100.0) if total > 0 else 0.0\n",
    "        label = f\"{c} ({pct:.1f}%)\"\n",
    "        cx = bar.get_x() + bar.get_width()/2\n",
    "        by = bar.get_height()\n",
    "\n",
    "        if by >= inside_thresh:\n",
    "            # 柱內白字\n",
    "            ax.text(cx, by - 0.03*max(1, ymax),\n",
    "                    label, ha=\"center\", va=\"top\", fontsize=8, color=\"white\",\n",
    "                    bbox=dict(boxstyle=\"round,pad=0.25\", facecolor=(0,0,0,0.30), edgecolor=\"none\"),\n",
    "                    clip_on=False)\n",
    "        else:\n",
    "            # 柱外；密集時改直向並交錯\n",
    "            if dense:\n",
    "                rot = 90\n",
    "                yoff = 0.02*ymax + 0.3 + (alt_offset if i % 2 else 0.0)\n",
    "            else:\n",
    "                rot = 0\n",
    "                yoff = 0.02*ymax + 0.3\n",
    "            ax.text(cx, by + yoff,\n",
    "                    label, ha=\"center\", va=\"bottom\", fontsize=8, rotation=rot, color=\"black\",\n",
    "                    bbox=dict(boxstyle=\"round,pad=0.20\", facecolor=\"white\", alpha=0.92, edgecolor=\"gray\"),\n",
    "                    clip_on=False)\n",
    "\n",
    "    plt.tight_layout()\n",
    "    plt.savefig(out_png, dpi=150)\n",
    "    plt.close()\n",
    "    print(f\"[OK] Saved bar chart -> {out_png}  | bins={n}, width_in={W:.1f}\")\n",
    "\n",
    "# ---------- 收集『可用節點=三欄皆為數值』，計算各 gap 的區段長度 ----------\n",
    "all_times_per_file = []\n",
    "for fp in sorted(glob.glob(os.path.join(IN_DIR, \"*.csv\"))):\n",
    "    df = read_df_any(fp)\n",
    "    if df is None or df.empty:\n",
    "        continue\n",
    "    lmap = lower_map(df)\n",
    "    if TIME_COL not in lmap:\n",
    "        continue\n",
    "\n",
    "    t = pd.to_datetime(df[lmap[TIME_COL]], errors=\"coerce\")\n",
    "\n",
    "    nums = []\n",
    "    for c in NUM_COLS:\n",
    "        if c in lmap:\n",
    "            nums.append(to_numeric_clean(df[lmap[c]]))\n",
    "        else:\n",
    "            nums.append(pd.Series([np.nan]*len(df)))\n",
    "\n",
    "    # 『可用節點』：時間可解析 且 三欄皆為數值\n",
    "    mask  = t.notna() & nums[0].notna() & nums[1].notna() & nums[2].notna()\n",
    "    times = t[mask].sort_values()\n",
    "\n",
    "    if not times.empty:\n",
    "        all_times_per_file.append(times)\n",
    "\n",
    "# 匯整三個 gap 的「區段長度（分鐘）」；不跨檔連接\n",
    "durations_by_gap = {1: [], 3: [], 5: []}\n",
    "for times in all_times_per_file:\n",
    "    for m in GAPS_MIN:\n",
    "        durations_by_gap[m].extend(\n",
    "            collect_segment_durations_minutes(times, pd.Timedelta(minutes=m))\n",
    "        )\n",
    "\n",
    "# ---------- 以「每小時分箱（起點 120 分；尾端合併；<120 直接排除）」繪製三張圖與 CSV ----------\n",
    "BASE = { 1: (0.30, 0.70, 0.40),  # 綠\n",
    "         3: (0.30, 0.50, 0.85),  # 藍\n",
    "         5: (0.65, 0.45, 0.85) } # 紫\n",
    "\n",
    "five_num_summary = {}  # 收集各 gap 的五數摘要\n",
    "\n",
    "for m in GAPS_MIN:\n",
    "    durs_all = durations_by_gap[m]\n",
    "    # 只保留 >=120 的長度（符合你的「起點 120」要求）\n",
    "    durs_ge120 = [d for d in durs_all if d >= BIN_START_MIN]\n",
    "\n",
    "    # 分箱與繪圖\n",
    "    labels, counts, ranges, tail_info = bin_durations_hourly_ge120(\n",
    "        durs_ge120, start_min=BIN_START_MIN, width=BIN_WIDTH_MIN, max_bins=MAX_BINS\n",
    "    )\n",
    "    title   = f\"Usable Segment Durations (gap > {m} min, hourly bins from 120)\"\n",
    "    out_png = os.path.join(OUT_DIR, f\"gap{m}m_numeric_segments_bars_hourbin_ge120.png\")\n",
    "    out_csv = os.path.join(OUT_DIR, f\"gap{m}m_numeric_segment_duration_hourbins_ge120.csv\")\n",
    "    plot_binned_bars(labels, counts, title, BASE[m], out_png, out_csv)\n",
    "\n",
    "    # 五數摘要（針對 >=120 的分佈）：min, Q1, median, Q3, max\n",
    "    if len(durs_ge120) > 0:\n",
    "        qs = np.percentile(durs_ge120, [0, 25, 50, 75, 100])\n",
    "        five_num_summary[m] = dict(N=len(durs_ge120),\n",
    "                                   min=int(qs[0]), q1=int(qs[1]),\n",
    "                                   median=int(qs[2]), q3=int(qs[3]), max=int(qs[4]))\n",
    "    else:\n",
    "        five_num_summary[m] = dict(N=0, min=None, q1=None, median=None, q3=None, max=None)\n",
    "\n",
    "# ---------- 列印三種 gap 的五數摘要（皆以「>=120 分鐘」為母體） ----------\n",
    "print(\"\\n[Five-number summary for segment durations >= 120 minutes]\")\n",
    "for m in GAPS_MIN:\n",
    "    s = five_num_summary[m]\n",
    "    print(f\"- gap > {m} min | N={s['N']}, min={s['min']}, Q1={s['q1']}, median={s['median']}, Q3={s['q3']}, max={s['max']}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bac81642-cd54-407a-a462-bcfb0aa2fefd",
   "metadata": {},
   "outputs": [],
   "source": [
    "看了gap5的median 決定用五分鐘"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7d0a9b0b-e7cf-4698-bcc3-67dbff86b168",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 用筆數當數量計算 算了先來產出 bling_useable，再來偵測哪裡有調參給他一個欄位標記"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "7d997680-bd11-4164-b6e3-85fbfe664078",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[READY] 將刪除：/home/jovyan/RT08/0925/bling_useable\n",
      "[READY] 包含 檔案 122 筆、子資料夾 0 個\n",
      "[DONE] 已刪除：/home/jovyan/RT08/0925/bling_useable\n"
     ]
    }
   ],
   "source": [
    "# 刪除/home/jovyan/RT08/0925/bling_useable的程式碼\n",
    "import os, shutil\n",
    "from pathlib import Path\n",
    "\n",
    "TARGET = Path(\"/home/jovyan/RT08/0925/bling_useable\")\n",
    "\n",
    "# —— 安全防呆 —— #\n",
    "if not TARGET.exists():\n",
    "    print(f\"[INFO] 目標不存在：{TARGET}\")\n",
    "elif not TARGET.is_dir():\n",
    "    print(f\"[ABORT] 目標不是資料夾：{TARGET}\")\n",
    "else:\n",
    "    # 禁止刪到過高層級\n",
    "    BLOCKLIST = {\n",
    "        Path(\"/\"),\n",
    "        Path(\"/home\"),\n",
    "        Path(\"/home/jovyan\"),\n",
    "        Path(\"/home/jovyan/RT08\")\n",
    "    }\n",
    "    if TARGET in BLOCKLIST or any(str(TARGET).rstrip(\"/\") == str(p) for p in BLOCKLIST):\n",
    "        print(f\"[ABORT] 風險過高：{TARGET}\")\n",
    "    else:\n",
    "        # 統計要刪的檔案數量\n",
    "        files = 0\n",
    "        dirs = 0\n",
    "        for root, dnames, fnames in os.walk(TARGET):\n",
    "            dirs += len(dnames)\n",
    "            files += len(fnames)\n",
    "\n",
    "        print(f\"[READY] 將刪除：{TARGET}\")\n",
    "        print(f\"[READY] 包含 檔案 {files} 筆、子資料夾 {dirs} 個\")\n",
    "\n",
    "        shutil.rmtree(TARGET)\n",
    "        print(f\"[DONE] 已刪除：{TARGET}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "bd06fe1a-1d02-487f-bba0-6ab28b87bb43",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Step1] 檔案數量：onehot=122，useable=122\n"
     ]
    }
   ],
   "source": [
    "\"\"\"到bling_useable裡面\n",
    "1. 複製/home/jovyan/RT08/0925/bling_onehot/的所有檔案到/home/jovyan/RT08/0925/bling_useable/，列出兩邊的檔案數量\n",
    "\"\"\"\n",
    "import os, glob, shutil\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "SRC_DIR = \"/home/jovyan/RT08/0925/bling_onehot\"\n",
    "DST_DIR = \"/home/jovyan/RT08/0925/bling_useable\"\n",
    "os.makedirs(DST_DIR, exist_ok=True)\n",
    "\n",
    "# === 1) 複製檔案並列數量 ===\n",
    "src_files = sorted(glob.glob(os.path.join(SRC_DIR, \"*.csv\")))\n",
    "for f in src_files:\n",
    "    shutil.copy2(f, os.path.join(DST_DIR, os.path.basename(f)))\n",
    "\n",
    "dst_files = sorted(glob.glob(os.path.join(DST_DIR, \"*.csv\")))\n",
    "print(f\"[Step1] 檔案數量：onehot={len(src_files)}，useable={len(dst_files)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "b06e12a1-b6b1-412b-b87e-bb4024bb8cb3",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "[轉換報告]\n",
      "檔案名稱                           empty       null     (null)\n",
      "------------------------------------------------------------\n",
      "095323.csv                         0          0      23791\n",
      "095707.csv                         0          0      18581\n",
      "114309.csv                         0          0     218913\n",
      "230933.csv                         0          0      27130\n",
      "4216007.csv                        0          0       2866\n",
      "7408338.csv                        0          0       1432\n",
      "\n",
      "以上檔案已完成 null / (null) / 空字串 → NaN 的轉換。\n"
     ]
    }
   ],
   "source": [
    "# ​有些​檔案​到​這裡​還會(nul​l)，為什麼​有些​是​空值​有些​是​(n​ul​l) \n",
    "# 所以先複製 先檢查 處理空值 確保所有欄位資料型態一樣\n",
    "import os, glob\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "USEABLE_DIR = \"/home/jovyan/RT08/0925/bling_useable\"\n",
    "files = sorted(glob.glob(os.path.join(USEABLE_DIR, \"*.csv\")))\n",
    "\n",
    "print(\"\\n[轉換報告]\")\n",
    "print(\"{:<25} {:>10} {:>10} {:>10}\".format(\"檔案名稱\", \"empty\", \"null\", \"(null)\"))\n",
    "print(\"-\" * 60)\n",
    "\n",
    "any_issue = False\n",
    "\n",
    "for f in files:\n",
    "    df = pd.read_csv(f, low_memory=False, dtype=str)  # 全部讀成字串，避免誤判\n",
    "\n",
    "    # 統計\n",
    "    empty_count = (df == \"\").sum().sum()\n",
    "    null_count = (df.applymap(lambda x: isinstance(x, str) and x.lower() == \"null\")).sum().sum()\n",
    "    paren_null_count = (df.applymap(lambda x: isinstance(x, str) and x.lower() == \"(null)\")).sum().sum()\n",
    "\n",
    "    # 只有當有需要轉換的情況才輸出\n",
    "    if empty_count > 0 or null_count > 0 or paren_null_count > 0:\n",
    "        print(\"{:<25} {:>10} {:>10} {:>10}\".format(os.path.basename(f), empty_count, null_count, paren_null_count))\n",
    "        any_issue = True\n",
    "\n",
    "    # === 轉換 ===\n",
    "    df = df.replace(r'^\\s*$', np.nan, regex=True)   # 空字串 → NaN\n",
    "    df = df.replace([\"null\", \"NULL\", \"(null)\", \"(NULL)\"], np.nan)\n",
    "\n",
    "    # 覆寫存回\n",
    "    df.to_csv(f, index=False)\n",
    "\n",
    "if not any_issue:\n",
    "    print(\"✅ 所有檔案都沒有需要轉換的 'empty' / 'null' / '(null)' 值。\")\n",
    "else:\n",
    "    print(\"\\n以上檔案已完成 null / (null) / 空字串 → NaN 的轉換。\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "581fcbc5-71fd-47ea-9d16-a9bd72533cc1",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "[Step2] 各檔案欄位數量（前 → 後）\n",
      " - 089271.csv: 18 → 20\n",
      " - 095323.csv: 18 → 20\n",
      " - 095707.csv: 18 → 20\n",
      " - 114309.csv: 18 → 20\n",
      " - 230933.csv: 18 → 20\n",
      " - 4216007.csv: 18 → 20\n",
      " - 7108162.csv: 18 → 20\n",
      " - 7408338.csv: 18 → 20\n",
      " - 7657698.csv: 18 → 20\n",
      " - 7721164.csv: 18 → 20\n",
      " - PatNo_ID_1560013303.csv: 18 → 20\n",
      " - PatNo_ID_1562733396.csv: 18 → 20\n",
      " - PatNo_ID_1563587183.csv: 18 → 20\n",
      " - PatNo_ID_1564148644.csv: 18 → 20\n",
      " - PatNo_ID_1565148312.csv: 18 → 20\n",
      " - PatNo_ID_1565378038.csv: 18 → 20\n",
      " - PatNo_ID_1566123680.csv: 18 → 20\n",
      " - PatNo_ID_1566252197.csv: 18 → 20\n",
      " - PatNo_ID_1566279967.csv: 18 → 20\n",
      " - PatNo_ID_1566671274.csv: 18 → 20\n",
      " - PatNo_ID_1566911879.csv: 18 → 20\n",
      " - PatNo_ID_1567747650.csv: 18 → 20\n",
      " - PatNo_ID_1567804800.csv: 18 → 20\n",
      " - PatNo_ID_1567832735.csv: 18 → 20\n",
      " - PatNo_ID_1568039398.csv: 18 → 20\n",
      " - PatNo_ID_1568574099.csv: 18 → 20\n",
      " - PatNo_ID_1568813269.csv: 18 → 20\n",
      " - PatNo_ID_1568952422.csv: 18 → 20\n",
      " - PatNo_ID_1569083701.csv: 18 → 20\n",
      " - PatNo_ID_1569944983.csv: 18 → 20\n",
      " - PatNo_ID_1570089466.csv: 18 → 20\n",
      " - PatNo_ID_1570242703.csv: 18 → 20\n",
      " - PatNo_ID_1570273244.csv: 18 → 20\n",
      " - PatNo_ID_1570642083.csv: 18 → 20\n",
      " - PatNo_ID_1571945701.csv: 18 → 20\n",
      " - PatNo_ID_1572481361.csv: 18 → 20\n",
      " - PatNo_ID_1572562839.csv: 18 → 20\n",
      " - PatNo_ID_1572831765.csv: 18 → 20\n",
      " - PatNo_ID_1572976822.csv: 18 → 20\n",
      " - PatNo_ID_1573063188.csv: 18 → 20\n",
      " - PatNo_ID_1573249295.csv: 18 → 20\n",
      " - PatNo_ID_1573964540.csv: 18 → 20\n",
      " - PatNo_ID_1574148494.csv: 18 → 20\n",
      " - PatNo_ID_1574270349.csv: 18 → 20\n",
      " - PatNo_ID_1574528808.csv: 18 → 20\n",
      " - PatNo_ID_1574831525.csv: 18 → 20\n",
      " - PatNo_ID_1574987447.csv: 18 → 20\n",
      " - PatNo_ID_1575060177.csv: 18 → 20\n",
      " - PatNo_ID_1575256902.csv: 18 → 20\n",
      " - PatNo_ID_1575445051.csv: 18 → 20\n",
      " - PatNo_ID_1575502382.csv: 18 → 20\n",
      " - PatNo_ID_1575975485.csv: 18 → 20\n",
      " - PatNo_ID_1576115572.csv: 18 → 20\n",
      " - PatNo_ID_1576116479.csv: 18 → 20\n",
      " - PatNo_ID_1576301569.csv: 18 → 20\n",
      " - PatNo_ID_1576964560.csv: 18 → 20\n",
      " - PatNo_ID_1577042911.csv: 18 → 20\n",
      " - PatNo_ID_1577487284.csv: 18 → 20\n",
      " - PatNo_ID_1578784257.csv: 18 → 20\n",
      " - PatNo_ID_1579198603.csv: 18 → 20\n",
      " - PatNo_ID_1579498177.csv: 18 → 20\n",
      " - PatNo_ID_1580062580.csv: 18 → 20\n",
      " - PatNo_ID_1580096720.csv: 18 → 20\n",
      " - PatNo_ID_1580107637.csv: 18 → 20\n",
      " - PatNo_ID_1580244614.csv: 18 → 20\n",
      " - PatNo_ID_1580766093.csv: 18 → 20\n",
      " - PatNo_ID_1581003248.csv: 18 → 20\n",
      " - PatNo_ID_1581019504.csv: 18 → 20\n",
      " - PatNo_ID_1581633231.csv: 18 → 20\n",
      " - PatNo_ID_1581692973.csv: 18 → 20\n",
      " - PatNo_ID_1582452511.csv: 18 → 20\n",
      " - PatNo_ID_1582635996.csv: 18 → 20\n",
      " - PatNo_ID_1582849900.csv: 18 → 20\n",
      " - PatNo_ID_1582937076.csv: 18 → 20\n",
      " - PatNo_ID_1584158973.csv: 18 → 20\n",
      " - PatNo_ID_1584397376.csv: 18 → 20\n",
      " - PatNo_ID_1586172659.csv: 18 → 20\n",
      " - PatNo_ID_1586696634.csv: 18 → 20\n",
      " - PatNo_ID_1586897008.csv: 18 → 20\n",
      " - PatNo_ID_1587490083.csv: 18 → 20\n",
      " - PatNo_ID_1588632604.csv: 18 → 20\n",
      " - PatNo_ID_1588673465.csv: 18 → 20\n",
      " - PatNo_ID_1588794796.csv: 18 → 20\n",
      " - PatNo_ID_1588957997.csv: 18 → 20\n",
      " - PatNo_ID_1589018086.csv: 18 → 20\n",
      " - PatNo_ID_1589034524.csv: 18 → 20\n",
      " - PatNo_ID_1589324603.csv: 18 → 20\n",
      " - PatNo_ID_1589918099.csv: 18 → 20\n",
      " - PatNo_ID_1590136310.csv: 18 → 20\n",
      " - PatNo_ID_1590616537.csv: 18 → 20\n",
      " - PatNo_ID_1590854576.csv: 18 → 20\n",
      " - PatNo_ID_1591609798.csv: 18 → 20\n",
      " - PatNo_ID_1592044724.csv: 18 → 20\n",
      " - PatNo_ID_1592560504.csv: 18 → 20\n",
      " - PatNo_ID_1593087886.csv: 18 → 20\n",
      " - PatNo_ID_1593416100.csv: 18 → 20\n",
      " - PatNo_ID_1593472048.csv: 18 → 20\n",
      " - PatNo_ID_1593593586.csv: 18 → 20\n",
      " - PatNo_ID_1593720818.csv: 18 → 20\n",
      " - PatNo_ID_1593838524.csv: 18 → 20\n",
      " - PatNo_ID_1594173718.csv: 18 → 20\n",
      " - PatNo_ID_1594294180.csv: 18 → 20\n",
      " - PatNo_ID_1594305136.csv: 18 → 20\n",
      " - PatNo_ID_1594309746.csv: 18 → 20\n",
      " - PatNo_ID_1594319286.csv: 18 → 20\n",
      " - PatNo_ID_1594320763.csv: 18 → 20\n",
      " - PatNo_ID_1594322594.csv: 18 → 20\n",
      " - PatNo_ID_1594335109.csv: 18 → 20\n",
      " - PatNo_ID_1594423683.csv: 18 → 20\n",
      " - PatNo_ID_1594437309.csv: 18 → 20\n",
      " - PatNo_ID_1594439781.csv: 18 → 20\n",
      " - PatNo_ID_1594441887.csv: 18 → 20\n",
      " - PatNo_ID_1594448501.csv: 18 → 20\n",
      " - PatNo_ID_1594455578.csv: 18 → 20\n",
      " - PatNo_ID_1594464829.csv: 18 → 20\n",
      " - PatNo_ID_1594467719.csv: 18 → 20\n",
      " - PatNo_ID_1594471407.csv: 18 → 20\n",
      " - PatNo_ID_1594479330.csv: 18 → 20\n",
      " - PatNo_ID_1594511911.csv: 18 → 20\n",
      " - PatNo_ID_1594511914.csv: 18 → 20\n",
      " - PatNo_ID_1594528842.csv: 18 → 20\n",
      " - PatNo_ID_1594533379.csv: 18 → 20\n",
      "[Step2] ✅ 所有檔案新增後的欄位數量一致。\n",
      "\n",
      "[Step5] 基準檔案（schema）：089271.csv\n",
      "cdyn                          float64\n",
      "mode_0                          int64\n",
      "mode_1                          int64\n",
      "mode_2                          int64\n",
      "mode_3                          int64\n",
      "mode_9                          int64\n",
      "mvsetactual                   float64\n",
      "patno                           int64\n",
      "peepepap                      float64\n",
      "pmean                         float64\n",
      "ppeak                         float64\n",
      "rrhzsetactual                 float64\n",
      "senddate               datetime64[ns]\n",
      "sponvt                        float64\n",
      "svv                           float64\n",
      "useable                         int64\n",
      "ventilatormode                 object\n",
      "ventilatormode_code             int64\n",
      "vte                           float64\n",
      "vti                           float64\n",
      "dtype: object\n",
      "[Step5] ✅ 所有檔案欄位型態一致。\n",
      "\n",
      "全部完成：\n",
      "  • 檔案複製與數量列示 ✔\n",
      "  • 新增 svv、useable 並回寫 ✔\n",
      "  • 欄位數量一致性檢查 ✔\n",
      "  • dtype 統一與稽核 ✔\n"
     ]
    }
   ],
   "source": [
    "\"\"\"到bling_useable裡面\n",
    "1. 複製/home/jovyan/RT08/0925/bling_onehot/的所有檔案到/home/jovyan/RT08/0925/bling_useable/，列出兩邊的檔案數量\n",
    "2. 新增​一​個​欄​位​名為svv，一個欄位名為useable，列出前後的欄位數量，以及每個檔案欄位數量是否一樣\n",
    "3. 把​vt​i, vte, sponvt​三​個​欄位的數值​加​在​一起，​並且除以3，確定數值是浮點數，若是整數請轉成浮點數，將數值存在​欄位svv，若vt​i, vte, sponvt​三​個​欄位任何一欄有空值或NaN，則在​欄位svv填NaN\n",
    "4. 在欄位useable標註1或0 規定在下面\n",
    "5. 確定所有檔案的欄位資料型態都一樣，並印出來在程式碼裡面\n",
    "\n",
    "欄位useable標註1或0的規定：\n",
    "若符合以下條件，在欄位名為useable的地方標註1，以下條件全部都要符合，若任何一項不符合則標註0\n",
    "a.\"patno\", \"senddate\", \"ventilatormode\",\"rrhzsetactual\",\"peepepap\", \"ppeak\"特徵都有數字的資料\n",
    "b. mode_1或mode_2或mode_3沒有任何一項是1的資料\n",
    "\"\"\"\n",
    "# -*- coding: utf-8 -*-\n",
    "\"\"\"\n",
    "批次處理流程（一次完成 1~5 步）：\n",
    "1) 複製 /home/jovyan/RT08/0925/bling_onehot/*.csv → /home/jovyan/RT08/0925/bling_useable/\n",
    "   並列出兩邊的檔案數量\n",
    "2) 在每個檔案新增欄位 svv、useable，列出前/後欄位數量，並檢查每檔欄位數是否一致\n",
    "3) svv = (vti + vte + sponvt) / 3；任一缺值 → svv=NaN；最終強制 float\n",
    "4) useable 規則：\n",
    "   a. 'patno','senddate','ventilatormode' 不為空；且 rrhzsetactual、peepepap、ppeak 皆為「可解析的數字」\n",
    "   b. mode_1 或 mode_2 或 mode_3 任一為 1\n",
    "   以上全滿足 → useable=1；否則 0\n",
    "5) 統一所有檔案欄位的 dtype（schema lock），並印出 dtype 稽核結果\n",
    "\"\"\"\n",
    "import os, glob, shutil\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "SRC_DIR = \"/home/jovyan/RT08/0925/bling_onehot\"\n",
    "DST_DIR = \"/home/jovyan/RT08/0925/bling_useable\"\n",
    "os.makedirs(DST_DIR, exist_ok=True)\n",
    "\"\"\"\n",
    "# === 1) 複製檔案並列數量 ===\n",
    "src_files = sorted(glob.glob(os.path.join(SRC_DIR, \"*.csv\")))\n",
    "for f in src_files:\n",
    "    shutil.copy2(f, os.path.join(DST_DIR, os.path.basename(f)))\n",
    "\n",
    "dst_files = sorted(glob.glob(os.path.join(DST_DIR, \"*.csv\")))\n",
    "print(f\"[Step1] 檔案數量：onehot={len(src_files)}，useable={len(dst_files)}\")\n",
    "\"\"\"\n",
    "# === 欄位與 dtype 設定（schema） ===\n",
    "# 說明：\n",
    "# - senddate 轉 datetime\n",
    "# - 連續數值欄位統一 float64\n",
    "# - one-hot 欄位 mode_1/2/3 強制 int64（0/1）\n",
    "CONTINUOUS_NUM_COLS = [\n",
    "    \"rrhzsetactual\", \"peepepap\", \"ppeak\",\n",
    "    \"vti\", \"vte\", \"sponvt\",  # 原始三欄\n",
    "    \"svv\"                    # 新增欄\n",
    "]\n",
    "MODE_COLS = [\"mode_1\", \"mode_2\", \"mode_3\"]\n",
    "REQ_NOTNA_COLS = [\"patno\", \"senddate\", \"ventilatormode\"]  # a. 需有值（非空）的欄\n",
    "REQ_NUMERIC_COLS = [\"rrhzsetactual\", \"peepepap\", \"ppeak\"] # a. 必須是數字的欄\n",
    "\n",
    "def to_float_series(s):\n",
    "    return pd.to_numeric(s, errors=\"coerce\").astype(\"float64\")\n",
    "\n",
    "def unify_dtypes(df):\n",
    "    # 時間\n",
    "    df[\"senddate\"] = pd.to_datetime(df[\"senddate\"], errors=\"coerce\")\n",
    "    # 連續數值\n",
    "    for c in CONTINUOUS_NUM_COLS:\n",
    "        if c in df.columns:\n",
    "            df[c] = to_float_series(df[c])\n",
    "    # 模式 one-hot\n",
    "    for m in MODE_COLS:\n",
    "        if m not in df.columns:\n",
    "            # 若缺少，補 0（避免規則判斷報錯）\n",
    "            df[m] = 0\n",
    "        df[m] = pd.to_numeric(df[m], errors=\"coerce\").fillna(0).astype(\"int64\")\n",
    "    return df\n",
    "\n",
    "def compute_svv(df):\n",
    "    # 只在 vti/vte/sponvt 三欄皆非 NaN 的列計算平均；有任何 NaN → svv=NaN\n",
    "    for c in [\"vti\", \"vte\", \"sponvt\"]:\n",
    "        if c not in df.columns:\n",
    "            df[c] = np.nan\n",
    "        df[c] = to_float_series(df[c])\n",
    "\n",
    "    vals = df[[\"vti\", \"vte\", \"sponvt\"]]\n",
    "    mask_all = vals.notna().all(axis=1)\n",
    "    svv = pd.Series(np.nan, index=df.index, dtype=\"float64\")\n",
    "    svv.loc[mask_all] = vals.loc[mask_all].mean(axis=1).astype(\"float64\")\n",
    "    df[\"svv\"] = svv\n",
    "    return df\n",
    "\n",
    "def compute_useable(df):\n",
    "    # a) 欄位非空\n",
    "    has_req = df[REQ_NOTNA_COLS].notna().all(axis=1)\n",
    "    # a) 數值欄可解析且非 NaN\n",
    "    num_ok = True\n",
    "    for c in REQ_NUMERIC_COLS:\n",
    "        df[c] = to_float_series(df[c])\n",
    "        num_ok = num_ok & df[c].notna()\n",
    "    # b) mode_1/2/3 任一為 1\n",
    "    for m in MODE_COLS:\n",
    "        if m not in df.columns:\n",
    "            df[m] = 0\n",
    "    mode_any = (df[\"mode_1\"] == 1) | (df[\"mode_2\"] == 1) | (df[\"mode_3\"] == 1)\n",
    "    df[\"useable\"] = (has_req & num_ok & mode_any).astype(\"int64\")\n",
    "    return df\n",
    "\n",
    "# === 2~5) 處理每個檔案；列出欄位數量前/後、是否一致，並做 dtype 稽核 ===\n",
    "all_col_counts_before = {}\n",
    "all_col_counts_after  = {}\n",
    "all_dtypes = {}  # {filename: {col: dtype}}\n",
    "\n",
    "for i, f in enumerate(dst_files, 1):\n",
    "    df = pd.read_csv(f, low_memory=False)\n",
    "\n",
    "    cols_before = list(df.columns)\n",
    "    all_col_counts_before[os.path.basename(f)] = len(cols_before)\n",
    "\n",
    "    # 確保存在目標欄位（避免 KeyError）；svv/useable 會在計算時建立\n",
    "    for c in set(REQ_NOTNA_COLS + REQ_NUMERIC_COLS + MODE_COLS + [\"vti\", \"vte\", \"sponvt\"]):\n",
    "        if c not in df.columns:\n",
    "            # 對缺少但必需的欄位先補空值（後續會轉 dtype 或判斷 useable）\n",
    "            df[c] = np.nan\n",
    "\n",
    "    # 先做 dtype 統一（舊欄）\n",
    "    df = unify_dtypes(df)\n",
    "    # 3) svv\n",
    "    df = compute_svv(df)\n",
    "    # 4) useable\n",
    "    df = compute_useable(df)\n",
    "    # 再做一次 dtype 統一（含新欄）\n",
    "    df = unify_dtypes(df)\n",
    "\n",
    "    # 記錄欄位數量變化\n",
    "    cols_after = list(df.columns)\n",
    "    all_col_counts_after[os.path.basename(f)] = len(cols_after)\n",
    "\n",
    "    # 5) 收集 dtype（方便跨檔案稽核）\n",
    "    all_dtypes[os.path.basename(f)] = {c: str(t) for c, t in df.dtypes.items()}\n",
    "\n",
    "    # 覆寫存回（就地更新）\n",
    "    df.to_csv(f, index=False)\n",
    "\n",
    "# === 輸出欄位數量報告 ===\n",
    "print(\"\\n[Step2] 各檔案欄位數量（前 → 後）\")\n",
    "same_count = True\n",
    "first_after = None\n",
    "for name in sorted(all_col_counts_before.keys()):\n",
    "    b = all_col_counts_before[name]\n",
    "    a = all_col_counts_after[name]\n",
    "    print(f\" - {name}: {b} → {a}\")\n",
    "    if first_after is None:\n",
    "        first_after = a\n",
    "    if a != first_after:\n",
    "        same_count = False\n",
    "\n",
    "if same_count:\n",
    "    print(\"[Step2] ✅ 所有檔案新增後的欄位數量一致。\")\n",
    "else:\n",
    "    print(\"[Step2] ⚠️ 新增後欄位數量不一致，請檢查上面清單。\")\n",
    "\n",
    "# === 5) dtype 稽核：確定所有檔案欄位型態一致 ===\n",
    "# 取第一個檔案作為 schema 基準\n",
    "schema_ref_name = sorted(all_dtypes.keys())[0] if all_dtypes else None\n",
    "dtype_consistent = True\n",
    "if schema_ref_name:\n",
    "    schema_ref = all_dtypes[schema_ref_name]\n",
    "    print(f\"\\n[Step5] 基準檔案（schema）：{schema_ref_name}\")\n",
    "    print(pd.Series(schema_ref).sort_index())\n",
    "\n",
    "    for name, dmap in all_dtypes.items():\n",
    "        # 欄位集合一致性檢查\n",
    "        if set(dmap.keys()) != set(schema_ref.keys()):\n",
    "            print(f\"[Step5] ❌ 欄位集合不一致：{name}\")\n",
    "            missing = set(schema_ref.keys()) - set(dmap.keys())\n",
    "            extra   = set(dmap.keys()) - set(schema_ref.keys())\n",
    "            if missing:\n",
    "                print(f\"   - 缺少欄位：{sorted(missing)}\")\n",
    "            if extra:\n",
    "                print(f\"   - 多出欄位：{sorted(extra)}\")\n",
    "            dtype_consistent = False\n",
    "            continue\n",
    "\n",
    "        # dtype 一致性檢查\n",
    "        for c in schema_ref.keys():\n",
    "            if dmap[c] != schema_ref[c]:\n",
    "                print(f\"[Step5] ❌ 型態不一致：{name} 欄位 {c}: {dmap[c]} != {schema_ref[c]}\")\n",
    "                dtype_consistent = False\n",
    "\n",
    "    if dtype_consistent:\n",
    "        print(\"[Step5] ✅ 所有檔案欄位型態一致。\")\n",
    "else:\n",
    "    print(\"[Step5] ⚠️ 目標資料夾沒有可讀取的 CSV。\")\n",
    "\n",
    "print(\"\\n全部完成：\")\n",
    "print(\"  • 檔案複製與數量列示 ✔\")\n",
    "print(\"  • 新增 svv、useable 並回寫 ✔\")\n",
    "print(\"  • 欄位數量一致性檢查 ✔\")\n",
    "print(\"  • dtype 統一與稽核 ✔\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "07dc0d81-a040-42de-b64e-cd2d309bd1e5",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "[useable=1 統計報告] (共 122 個檔案)\n",
      "檔案名稱                             總筆數    useable=1        比例%\n",
      "------------------------------------------------------------\n",
      "089271.csv                     32419        32362     99.82\n",
      "095323.csv                     23791        19713     82.86\n",
      "095707.csv                     20180        20177     99.99\n",
      "114309.csv                     71729        46563     64.92\n",
      "230933.csv                     30249        30214     99.88\n",
      "4216007.csv                     1433            0      0.00\n",
      "7108162.csv                      239          239    100.00\n",
      "7408338.csv                     1432         1431     99.93\n",
      "7657698.csv                     1413         1412     99.93\n",
      "7721164.csv                      483          483    100.00\n",
      "PatNo_ID_1560013303.csv         2543         2458     96.66\n",
      "PatNo_ID_1562733396.csv         2254         2251     99.87\n",
      "PatNo_ID_1563587183.csv         5287         5284     99.94\n",
      "PatNo_ID_1564148644.csv        17287         8788     50.84\n",
      "PatNo_ID_1565148312.csv         5475         5465     99.82\n",
      "PatNo_ID_1565378038.csv         2561         2560     99.96\n",
      "PatNo_ID_1566123680.csv        42600        42466     99.69\n",
      "PatNo_ID_1566252197.csv         3368         1935     57.45\n",
      "PatNo_ID_1566279967.csv         1235         1153     93.36\n",
      "PatNo_ID_1566671274.csv        50718        47108     92.88\n",
      "PatNo_ID_1566911879.csv        53999        46704     86.49\n",
      "PatNo_ID_1567747650.csv        10901        10896     99.95\n",
      "PatNo_ID_1567804800.csv        20578        12696     61.70\n",
      "PatNo_ID_1567832735.csv        36580        36554     99.93\n",
      "PatNo_ID_1568039398.csv        34519        34179     99.02\n",
      "PatNo_ID_1568574099.csv        13946        12627     90.54\n",
      "PatNo_ID_1568813269.csv         4867         4867    100.00\n",
      "PatNo_ID_1568952422.csv         1184         1184    100.00\n",
      "PatNo_ID_1569083701.csv         3328         3325     99.91\n",
      "PatNo_ID_1569944983.csv         7650         7645     99.93\n",
      "PatNo_ID_1570089466.csv        41343        36664     88.68\n",
      "PatNo_ID_1570242703.csv        10646        10551     99.11\n",
      "PatNo_ID_1570273244.csv         9730         9704     99.73\n",
      "PatNo_ID_1570642083.csv        19731        19728     99.98\n",
      "PatNo_ID_1571945701.csv        15731        15726     99.97\n",
      "PatNo_ID_1572481361.csv        34540        34483     99.83\n",
      "PatNo_ID_1572562839.csv        20966        15529     74.07\n",
      "PatNo_ID_1572831765.csv         2696         2696    100.00\n",
      "PatNo_ID_1572976822.csv         6764         4279     63.26\n",
      "PatNo_ID_1573063188.csv         5080         5024     98.90\n",
      "PatNo_ID_1573249295.csv         7151         7151    100.00\n",
      "PatNo_ID_1573964540.csv         4394         4393     99.98\n",
      "PatNo_ID_1574148494.csv        47154        47017     99.71\n",
      "PatNo_ID_1574270349.csv         6534         6512     99.66\n",
      "PatNo_ID_1574528808.csv        14817        14480     97.73\n",
      "PatNo_ID_1574831525.csv          521          515     98.85\n",
      "PatNo_ID_1574987447.csv        19843        19810     99.83\n",
      "PatNo_ID_1575060177.csv         5290         5289     99.98\n",
      "PatNo_ID_1575256902.csv         9587         9492     99.01\n",
      "PatNo_ID_1575445051.csv         1801         1785     99.11\n",
      "PatNo_ID_1575502382.csv         6604         6487     98.23\n",
      "PatNo_ID_1575975485.csv        16459        16454     99.97\n",
      "PatNo_ID_1576115572.csv        18717        18688     99.85\n",
      "PatNo_ID_1576116479.csv         1263         1257     99.52\n",
      "PatNo_ID_1576301569.csv         3207         3204     99.91\n",
      "PatNo_ID_1576964560.csv        24192        24185     99.97\n",
      "PatNo_ID_1577042911.csv        41894        39144     93.44\n",
      "PatNo_ID_1577487284.csv         2345         2345    100.00\n",
      "PatNo_ID_1578784257.csv        33917        28553     84.18\n",
      "PatNo_ID_1579198603.csv         2046         2044     99.90\n",
      "PatNo_ID_1579498177.csv        21688        21672     99.93\n",
      "PatNo_ID_1580062580.csv         4300         4288     99.72\n",
      "PatNo_ID_1580096720.csv         2846         2844     99.93\n",
      "PatNo_ID_1580107637.csv         5396         5284     97.92\n",
      "PatNo_ID_1580244614.csv         5278         3865     73.23\n",
      "PatNo_ID_1580766093.csv        18070        17973     99.46\n",
      "PatNo_ID_1581003248.csv         7776         6632     85.29\n",
      "PatNo_ID_1581019504.csv        28177        20724     73.55\n",
      "PatNo_ID_1581633231.csv        15995        14626     91.44\n",
      "PatNo_ID_1581692973.csv         2723         2723    100.00\n",
      "PatNo_ID_1582452511.csv         5196         5193     99.94\n",
      "PatNo_ID_1582635996.csv        13046        13045     99.99\n",
      "PatNo_ID_1582849900.csv         7411         4789     64.62\n",
      "PatNo_ID_1582937076.csv        23990        22673     94.51\n",
      "PatNo_ID_1584158973.csv         2882         2877     99.83\n",
      "PatNo_ID_1584397376.csv          638          638    100.00\n",
      "PatNo_ID_1586172659.csv        38559        38419     99.64\n",
      "PatNo_ID_1586696634.csv         3569         2504     70.16\n",
      "PatNo_ID_1586897008.csv         6687         6654     99.51\n",
      "PatNo_ID_1587490083.csv        45186        45186    100.00\n",
      "PatNo_ID_1588632604.csv         2608         2599     99.65\n",
      "PatNo_ID_1588673465.csv        10077         6282     62.34\n",
      "PatNo_ID_1588794796.csv         9376         9375     99.99\n",
      "PatNo_ID_1588957997.csv        10966        10958     99.93\n",
      "PatNo_ID_1589018086.csv        13472        13396     99.44\n",
      "PatNo_ID_1589034524.csv        50081        49984     99.81\n",
      "PatNo_ID_1589324603.csv         4187         4105     98.04\n",
      "PatNo_ID_1589918099.csv         9333            0      0.00\n",
      "PatNo_ID_1590136310.csv         5580         5305     95.07\n",
      "PatNo_ID_1590616537.csv        14208        14199     99.94\n",
      "PatNo_ID_1590854576.csv        15879        15879    100.00\n",
      "PatNo_ID_1591609798.csv        35624        35614     99.97\n",
      "PatNo_ID_1592044724.csv         6815         6810     99.93\n",
      "PatNo_ID_1592560504.csv        18052        18051     99.99\n",
      "PatNo_ID_1593087886.csv        24163        24126     99.85\n",
      "PatNo_ID_1593416100.csv         3328         3328    100.00\n",
      "PatNo_ID_1593472048.csv         8230         6247     75.91\n",
      "PatNo_ID_1593593586.csv        18882        18875     99.96\n",
      "PatNo_ID_1593720818.csv         3911         2644     67.60\n",
      "PatNo_ID_1593838524.csv         2178         2160     99.17\n",
      "PatNo_ID_1594173718.csv          344          344    100.00\n",
      "PatNo_ID_1594294180.csv        18334        18320     99.92\n",
      "PatNo_ID_1594305136.csv        18679        15634     83.70\n",
      "PatNo_ID_1594309746.csv         3745         3724     99.44\n",
      "PatNo_ID_1594319286.csv         4107         3988     97.10\n",
      "PatNo_ID_1594320763.csv         2431         2429     99.92\n",
      "PatNo_ID_1594322594.csv         5380         5378     99.96\n",
      "PatNo_ID_1594335109.csv         5116         5116    100.00\n",
      "PatNo_ID_1594423683.csv         5023         5001     99.56\n",
      "PatNo_ID_1594437309.csv        10035         9935     99.00\n",
      "PatNo_ID_1594439781.csv         7691         7687     99.95\n",
      "PatNo_ID_1594441887.csv        10203        10196     99.93\n",
      "PatNo_ID_1594448501.csv         5106         1045     20.47\n",
      "PatNo_ID_1594455578.csv          262          262    100.00\n",
      "PatNo_ID_1594464829.csv         4000         3937     98.42\n",
      "PatNo_ID_1594467719.csv         2560         1313     51.29\n",
      "PatNo_ID_1594471407.csv        11433        11279     98.65\n",
      "PatNo_ID_1594479330.csv         3727         3724     99.92\n",
      "PatNo_ID_1594511911.csv         5488         5450     99.31\n",
      "PatNo_ID_1594511914.csv         9717         9670     99.52\n",
      "PatNo_ID_1594528842.csv         2491         2415     96.95\n",
      "PatNo_ID_1594533379.csv         1030          999     96.99\n",
      "------------------------------------------------------------\n",
      "總計                           1602476      1476320     92.13\n"
     ]
    }
   ],
   "source": [
    "#列出個檔案的useable是=1的資料筆數，以及跨黨按所有useable=1的數量\n",
    "import os, glob\n",
    "import pandas as pd\n",
    "\n",
    "USEABLE_DIR = \"/home/jovyan/RT08/0925/bling_useable\"\n",
    "files = sorted(glob.glob(os.path.join(USEABLE_DIR, \"*.csv\")))\n",
    "\n",
    "report = []\n",
    "total_files = len(files)\n",
    "\n",
    "for f in files:\n",
    "    df = pd.read_csv(f, low_memory=False)\n",
    "\n",
    "    if \"useable\" not in df.columns:\n",
    "        print(f\"⚠️ 檔案 {os.path.basename(f)} 沒有 useable 欄位，略過\")\n",
    "        continue\n",
    "\n",
    "    total_rows = len(df)\n",
    "    usable_rows = (df[\"useable\"] == 1).sum()\n",
    "    ratio = (usable_rows / total_rows * 100) if total_rows > 0 else 0\n",
    "\n",
    "    report.append({\n",
    "        \"file\": os.path.basename(f),\n",
    "        \"total\": total_rows,\n",
    "        \"usable\": usable_rows,\n",
    "        \"ratio\": ratio\n",
    "    })\n",
    "\n",
    "# 印出逐檔統計\n",
    "print(f\"\\n[useable=1 統計報告] (共 {total_files} 個檔案)\")\n",
    "print(\"{:<25} {:>10} {:>12} {:>10}\".format(\"檔案名稱\", \"總筆數\", \"useable=1\", \"比例%\"))\n",
    "print(\"-\" * 60)\n",
    "\n",
    "grand_total = 0\n",
    "grand_usable = 0\n",
    "\n",
    "for r in report:\n",
    "    print(\"{:<25} {:>10} {:>12} {:>9.2f}\".format(r[\"file\"], r[\"total\"], r[\"usable\"], r[\"ratio\"]))\n",
    "    grand_total += r[\"total\"]\n",
    "    grand_usable += r[\"usable\"]\n",
    "\n",
    "# 跨檔案總計\n",
    "grand_ratio = (grand_usable / grand_total * 100) if grand_total > 0 else 0\n",
    "print(\"-\" * 60)\n",
    "print(\"{:<25} {:>10} {:>12} {:>9.2f}\".format(\"總計\", grand_total, grand_usable, grand_ratio))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "9a2d427f-9d81-4723-b46d-4a5cd815f2f1",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "✅ 總共輸出 5 張圖片\n",
      "   涵蓋 3 個年份\n",
      "   - 2021 年：1 張圖\n",
      "   - 2022 年：3 張圖\n",
      "   - 2024 年：1 張圖\n"
     ]
    }
   ],
   "source": [
    "\"\"\" 我要看橫軸是時間的圖表 縱軸是不同檔案useable狀況，\n",
    "useable=1用深灰色，useable=0用淺灰色，\n",
    "字都要看的到 不重疊，檔案依據年份分多張圖，一張圖最多50筆，\n",
    "最右邊用深灰色寫上病患可用的資料百分比 \n",
    "圖表用英文\n",
    "檔案要存在/home/jovyan/RT08/0925/1002/，\n",
    "最後印出有幾份圖片 幾年有幾張\n",
    "\"\"\"\n",
    "# ============================\n",
    "# Timeline visualization by year: useable over time per file\n",
    "# - X 軸：時間 (senddate)\n",
    "# - Y 軸：檔案（每個檔案一條水平線）\n",
    "# - 線段顏色：useable=1 深灰色；useable=0 淺灰色\n",
    "# - 每年一張圖；每張圖最多 50 個檔案（自動分頁）\n",
    "# - 每條檔案線的最右邊會標示該年 useable=1 的百分比\n",
    "# ============================\n",
    "\n",
    "import os, glob\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.dates as mdates\n",
    "\n",
    "# 原始資料所在資料夾（包含 useable 欄位的 CSV 檔）\n",
    "USEABLE_DIR = \"/home/jovyan/RT08/0925/bling_useable\"\n",
    "# 圖片輸出資料夾\n",
    "OUT_DIR = \"/home/jovyan/RT08/0925/1002\"\n",
    "os.makedirs(OUT_DIR, exist_ok=True)\n",
    "\n",
    "# 顏色設定\n",
    "DARK_GRAY = \"#4a4a4a\"\n",
    "LIGHT_GRAY = \"#cfcfcf\"\n",
    "\n",
    "# 讀取所有 CSV 檔案\n",
    "file_paths = sorted(glob.glob(os.path.join(USEABLE_DIR, \"*.csv\")))\n",
    "records = []  # 儲存整理後的紀錄\n",
    "\n",
    "# 逐檔讀取\n",
    "for fp in file_paths:\n",
    "    try:\n",
    "        # 只讀必要欄位，加速\n",
    "        df = pd.read_csv(fp, usecols=[\"senddate\", \"useable\"], low_memory=False)\n",
    "    except Exception:\n",
    "        # 如果缺欄位，改成全讀\n",
    "        df = pd.read_csv(fp, low_memory=False)\n",
    "        if \"senddate\" not in df.columns or \"useable\" not in df.columns:\n",
    "            continue\n",
    "\n",
    "    # 轉換時間與 useable 欄位\n",
    "    df[\"senddate\"] = pd.to_datetime(df[\"senddate\"], errors=\"coerce\")\n",
    "    df = df.dropna(subset=[\"senddate\"])\n",
    "    df[\"useable\"] = pd.to_numeric(df[\"useable\"], errors=\"coerce\").fillna(0).astype(int)\n",
    "    df[\"useable\"] = df[\"useable\"].clip(0, 1)\n",
    "\n",
    "    if len(df) == 0:\n",
    "        continue\n",
    "\n",
    "    # 加上檔名與年份資訊\n",
    "    df[\"__file__\"] = os.path.basename(fp)\n",
    "    df[\"__year__\"] = df[\"senddate\"].dt.year\n",
    "    records.append(df[[\"__file__\", \"__year__\", \"senddate\", \"useable\"]])\n",
    "\n",
    "if not records:\n",
    "    print(\"[Viz] 沒有找到符合的檔案或欄位。\")\n",
    "else:\n",
    "    # 合併所有檔案的資料\n",
    "    data = pd.concat(records, ignore_index=True).sort_values([\"__year__\", \"__file__\", \"senddate\"])\n",
    "\n",
    "    # 將連續相同 useable 值壓縮成一段\n",
    "    def make_segments(g):\n",
    "        if g.empty:\n",
    "            return []\n",
    "        g = g.sort_values(\"senddate\")\n",
    "        change = g[\"useable\"].ne(g[\"useable\"].shift(1)).fillna(True)\n",
    "        run_ids = change.cumsum()\n",
    "        segs = []\n",
    "        for rid, s in g.groupby(run_ids):\n",
    "            segs.append({\n",
    "                \"start\": s[\"senddate\"].iloc[0],\n",
    "                \"end\":   s[\"senddate\"].iloc[-1],\n",
    "                \"useable\": int(s[\"useable\"].iloc[0])\n",
    "            })\n",
    "        return segs\n",
    "\n",
    "    years = sorted(data[\"__year__\"].dropna().unique().tolist())\n",
    "    max_per_fig = 50  # 每張圖最多 50 個檔案\n",
    "\n",
    "    total_figs = 0\n",
    "    year_page_count = {}\n",
    "\n",
    "    for yr in years:\n",
    "        dy = data[data[\"__year__\"] == yr].copy()\n",
    "        files_in_year = sorted(dy[\"__file__\"].unique().tolist())\n",
    "\n",
    "        for page_start in range(0, len(files_in_year), max_per_fig):\n",
    "            batch_files = files_in_year[page_start:page_start + max_per_fig]\n",
    "            n = len(batch_files)\n",
    "            if n == 0:\n",
    "                continue\n",
    "\n",
    "            fig_h = max(4, min(12, 0.35 * n))  # 高度依檔案數調整\n",
    "            fig, ax = plt.subplots(figsize=(14, fig_h))\n",
    "\n",
    "            # 取當批時間範圍\n",
    "            batch_df = dy[dy[\"__file__\"].isin(batch_files)].copy()\n",
    "            x_min = batch_df[\"senddate\"].min()\n",
    "            x_max = batch_df[\"senddate\"].max()\n",
    "            if pd.isna(x_min) or pd.isna(x_max) or x_min == x_max:\n",
    "                x_min = pd.to_datetime(f\"{yr}-01-01\")\n",
    "                x_max = pd.to_datetime(f\"{yr}-12-31\")\n",
    "\n",
    "            # 右側留白給百分比文字\n",
    "            pad_minutes = max(30, int((x_max - x_min).total_seconds() / 60 * 0.03))\n",
    "            pad = pd.Timedelta(minutes=pad_minutes)\n",
    "            ax.set_xlim(x_min, x_max + pad)\n",
    "\n",
    "            # 準備 y 軸檔案對應位置\n",
    "            y_positions = {fname: i for i, fname in enumerate(batch_files)}\n",
    "            yticks = []\n",
    "            ylabels = []\n",
    "\n",
    "            # 時間軸格式\n",
    "            locator = mdates.AutoDateLocator(minticks=4, maxticks=10)\n",
    "            formatter = mdates.ConciseDateFormatter(locator)\n",
    "            ax.xaxis.set_major_locator(locator)\n",
    "            ax.xaxis.set_major_formatter(formatter)\n",
    "\n",
    "            # 畫每個檔案的水平線段\n",
    "            for fname in batch_files:\n",
    "                gf = batch_df[batch_df[\"__file__\"] == fname][[\"senddate\", \"useable\"]].sort_values(\"senddate\")\n",
    "                if gf.empty:\n",
    "                    continue\n",
    "\n",
    "                y = y_positions[fname]\n",
    "                yticks.append(y)\n",
    "                ylabels.append(fname)\n",
    "\n",
    "                segs = make_segments(gf)\n",
    "                for seg in segs:\n",
    "                    start, end = seg[\"start\"], seg[\"end\"]\n",
    "                    if start == end:  # 單點至少給 1 分鐘寬度\n",
    "                        end = start + pd.Timedelta(minutes=1)\n",
    "                    color = DARK_GRAY if seg[\"useable\"] == 1 else LIGHT_GRAY\n",
    "                    ax.hlines(y, start, end, colors=color, linewidth=6, zorder=2)\n",
    "\n",
    "                # 計算百分比並標示在右側\n",
    "                total_rows = len(gf)\n",
    "                usable_rows = int((gf[\"useable\"] == 1).sum())\n",
    "                ratio = (usable_rows / total_rows * 100.0) if total_rows > 0 else 0.0\n",
    "                ax.text(x_max + pad * 0.5, y, f\"{ratio:.1f}%\", va=\"center\", ha=\"left\",\n",
    "                        color=DARK_GRAY, fontsize=9)\n",
    "\n",
    "            # 樣式與圖例\n",
    "            ax.set_title(f\"Usable status over time by file — Year {yr}  \"\n",
    "                         f\"(files {page_start+1}-{page_start+len(batch_files)} of {len(files_in_year)})\",\n",
    "                         fontsize=12, pad=12)\n",
    "            ax.set_xlabel(\"Time\")\n",
    "            ax.set_yticks(yticks)\n",
    "            ax.set_yticklabels(ylabels, fontsize=9)\n",
    "            ax.grid(True, axis=\"x\", linestyle=\"--\", linewidth=0.6, alpha=0.5)\n",
    "            ax.set_axisbelow(True)\n",
    "\n",
    "            from matplotlib.patches import Patch\n",
    "            legend_handles = [\n",
    "                Patch(facecolor=DARK_GRAY, edgecolor=DARK_GRAY, label=\"useable = 1\"),\n",
    "                Patch(facecolor=LIGHT_GRAY, edgecolor=LIGHT_GRAY, label=\"useable = 0\"),\n",
    "            ]\n",
    "            ax.legend(handles=legend_handles, loc=\"upper right\", frameon=False)\n",
    "\n",
    "            plt.tight_layout()\n",
    "\n",
    "            # 存檔到指定資料夾\n",
    "            out_file = os.path.join(OUT_DIR, f\"useable_timeline_{yr}_p{page_start//max_per_fig+1}.png\")\n",
    "            fig.savefig(out_file, dpi=150)\n",
    "            plt.close(fig)\n",
    "\n",
    "            total_figs += 1\n",
    "            year_page_count[yr] = year_page_count.get(yr, 0) + 1\n",
    "\n",
    "    # 最後輸出統計資訊\n",
    "    print(f\"\\n✅ 總共輸出 {total_figs} 張圖片\")\n",
    "    print(f\"   涵蓋 {len(years)} 個年份\")\n",
    "    for yr in years:\n",
    "        print(f\"   - {yr} 年：{year_page_count.get(yr,0)} 張圖\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "a14188fe-7f35-49fc-85df-1ece094f7f2d",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true,
     "source_hidden": true
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Info] 檔案 PatNo_ID_1563587183.csv：丟棄無效列 1（patno/senddate 空或時間無法解析）\n",
      "[Info] 檔案 PatNo_ID_1567747650.csv：丟棄無效列 1（patno/senddate 空或時間無法解析）\n",
      "[Info] 檔案 PatNo_ID_1567804800.csv：丟棄無效列 1（patno/senddate 空或時間無法解析）\n",
      "[Info] 檔案 PatNo_ID_1567832735.csv：丟棄無效列 5（patno/senddate 空或時間無法解析）\n",
      "[Info] 檔案 PatNo_ID_1568574099.csv：丟棄無效列 1（patno/senddate 空或時間無法解析）\n",
      "[Info] 檔案 PatNo_ID_1569944983.csv：丟棄無效列 1（patno/senddate 空或時間無法解析）\n",
      "[Info] 檔案 PatNo_ID_1570089466.csv：丟棄無效列 5（patno/senddate 空或時間無法解析）\n",
      "[Info] 檔案 PatNo_ID_1570242703.csv：丟棄無效列 2（patno/senddate 空或時間無法解析）\n",
      "[Info] 檔案 PatNo_ID_1570273244.csv：丟棄無效列 5（patno/senddate 空或時間無法解析）\n",
      "[Info] 檔案 PatNo_ID_1572481361.csv：丟棄無效列 1（patno/senddate 空或時間無法解析）\n",
      "[Info] 檔案 PatNo_ID_1572562839.csv：丟棄無效列 3（patno/senddate 空或時間無法解析）\n",
      "[Info] 檔案 PatNo_ID_1574270349.csv：丟棄無效列 1（patno/senddate 空或時間無法解析）\n",
      "[Info] 檔案 PatNo_ID_1574528808.csv：丟棄無效列 5（patno/senddate 空或時間無法解析）\n",
      "[Info] 檔案 PatNo_ID_1574831525.csv：丟棄無效列 1（patno/senddate 空或時間無法解析）\n",
      "[Info] 檔案 PatNo_ID_1574987447.csv：丟棄無效列 1（patno/senddate 空或時間無法解析）\n",
      "[Info] 檔案 PatNo_ID_1575445051.csv：丟棄無效列 1（patno/senddate 空或時間無法解析）\n",
      "[Info] 檔案 PatNo_ID_1576115572.csv：丟棄無效列 2（patno/senddate 空或時間無法解析）\n",
      "[Info] 檔案 PatNo_ID_1576964560.csv：丟棄無效列 4（patno/senddate 空或時間無法解析）\n",
      "[Info] 檔案 PatNo_ID_1577042911.csv：丟棄無效列 2（patno/senddate 空或時間無法解析）\n",
      "[Info] 檔案 PatNo_ID_1578784257.csv：丟棄無效列 3（patno/senddate 空或時間無法解析）\n",
      "[Info] 檔案 PatNo_ID_1579198603.csv：丟棄無效列 1（patno/senddate 空或時間無法解析）\n",
      "[Info] 檔案 PatNo_ID_1581019504.csv：丟棄無效列 4（patno/senddate 空或時間無法解析）\n",
      "[Info] 檔案 PatNo_ID_1582937076.csv：丟棄無效列 3（patno/senddate 空或時間無法解析）\n",
      "[Info] 檔案 PatNo_ID_1586172659.csv：丟棄無效列 5（patno/senddate 空或時間無法解析）\n",
      "[Info] 檔案 PatNo_ID_1588673465.csv：丟棄無效列 1（patno/senddate 空或時間無法解析）\n",
      "[Info] 檔案 PatNo_ID_1588794796.csv：丟棄無效列 1（patno/senddate 空或時間無法解析）\n",
      "[Info] 檔案 PatNo_ID_1590616537.csv：丟棄無效列 2（patno/senddate 空或時間無法解析）\n",
      "[Info] 檔案 PatNo_ID_1591609798.csv：丟棄無效列 6（patno/senddate 空或時間無法解析）\n",
      "[Info] 檔案 PatNo_ID_1592560504.csv：丟棄無效列 1（patno/senddate 空或時間無法解析）\n",
      "[Info] 檔案 PatNo_ID_1593720818.csv：丟棄無效列 1（patno/senddate 空或時間無法解析）\n",
      "[Info] 檔案 PatNo_ID_1593838524.csv：丟棄無效列 1（patno/senddate 空或時間無法解析）\n",
      "[Info] 檔案 PatNo_ID_1594437309.csv：丟棄無效列 1（patno/senddate 空或時間無法解析）\n",
      "[Info] 檔案 PatNo_ID_1594441887.csv：丟棄無效列 3（patno/senddate 空或時間無法解析）\n",
      "[Info] 檔案 PatNo_ID_1594467719.csv：丟棄無效列 1（patno/senddate 空或時間無法解析）\n",
      "[Info] 檔案 PatNo_ID_1594471407.csv：丟棄無效列 1（patno/senddate 空或時間無法解析）\n",
      "\n",
      "=== A) Cross-file, per-patient checks (strictly increasing & no duplicates) ===\n",
      "- Total patients: 121\n",
      "- Patients violating strict monotonic increase: 6 (rows=36160)\n",
      "- Patients having duplicate timestamps: 6 (rows=72320)\n",
      "\n",
      "[Samples] Non-increasing violations (show top 5 rows):\n",
      "               __file__      patno            senddate  __delta_sec__\n",
      "PatNo_ID_1566671274.csv 1566671200 2022-01-05 19:37:32            0.0\n",
      "PatNo_ID_1566671274.csv 1566671200 2022-01-05 19:38:02            0.0\n",
      "PatNo_ID_1566671274.csv 1566671200 2022-01-05 19:39:02            0.0\n",
      "PatNo_ID_1566671274.csv 1566671200 2022-01-05 19:40:02            0.0\n",
      "PatNo_ID_1566671274.csv 1566671200 2022-01-05 19:41:02            0.0\n",
      "\n",
      "[Samples] Duplicate timestamp violations (show top 5 rows):\n",
      "               __file__      patno            senddate\n",
      "PatNo_ID_1566671274.csv 1566671200 2022-01-05 19:37:32\n",
      "PatNo_ID_1566671274.csv 1566671200 2022-01-05 19:37:32\n",
      "PatNo_ID_1566671274.csv 1566671200 2022-01-05 19:38:02\n",
      "PatNo_ID_1566671274.csv 1566671200 2022-01-05 19:38:02\n",
      "PatNo_ID_1566671274.csv 1566671200 2022-01-05 19:39:02\n",
      "\n",
      "=== B) In-file checks (strictly increasing & no duplicates within each file) ===\n",
      "                   file  patients_in_file  non_increasing_patients  non_increasing_rows  duplicate_patients  duplicate_rows\n",
      "PatNo_ID_1566671274.csv                 1                        1                25359                   1           50718\n",
      "PatNo_ID_1577042911.csv                 1                        1                 3535                   1            7070\n",
      "PatNo_ID_1580107637.csv                 1                        1                 2698                   1            5396\n",
      "PatNo_ID_1580062580.csv                 1                        1                 2150                   1            4300\n",
      "PatNo_ID_1580096720.csv                 1                        1                 1423                   1            2846\n",
      "             089271.csv                 1                        0                    0                   0               0\n",
      "             095323.csv                 1                        0                    0                   0               0\n",
      "             095707.csv                 1                        0                    0                   0               0\n",
      "             114309.csv                 1                        0                    0                   0               0\n",
      "             230933.csv                 1                        0                    0                   0               0\n",
      "            4216007.csv                 1                        0                    0                   0               0\n",
      "            7108162.csv                 1                        0                    0                   0               0\n",
      "            7408338.csv                 1                        0                    0                   0               0\n",
      "            7657698.csv                 1                        0                    0                   0               0\n",
      "            7721164.csv                 1                        0                    0                   0               0\n",
      "PatNo_ID_1560013303.csv                 1                        0                    0                   0               0\n",
      "PatNo_ID_1562733396.csv                 1                        0                    0                   0               0\n",
      "PatNo_ID_1563587183.csv                 1                        0                    0                   0               0\n",
      "PatNo_ID_1564148644.csv                 1                        0                    0                   0               0\n",
      "PatNo_ID_1565148312.csv                 1                        0                    0                   0               0\n",
      "PatNo_ID_1565378038.csv                 1                        0                    0                   0               0\n",
      "PatNo_ID_1566123680.csv                 1                        0                    0                   0               0\n",
      "PatNo_ID_1566252197.csv                 1                        0                    0                   0               0\n",
      "PatNo_ID_1566279967.csv                 1                        0                    0                   0               0\n",
      "PatNo_ID_1566911879.csv                 1                        0                    0                   0               0\n",
      "PatNo_ID_1567747650.csv                 1                        0                    0                   0               0\n",
      "PatNo_ID_1567804800.csv                 1                        0                    0                   0               0\n",
      "PatNo_ID_1567832735.csv                 1                        0                    0                   0               0\n",
      "PatNo_ID_1568039398.csv                 1                        0                    0                   0               0\n",
      "PatNo_ID_1568574099.csv                 1                        0                    0                   0               0\n",
      "PatNo_ID_1568813269.csv                 1                        0                    0                   0               0\n",
      "PatNo_ID_1568952422.csv                 1                        0                    0                   0               0\n",
      "PatNo_ID_1569083701.csv                 1                        0                    0                   0               0\n",
      "PatNo_ID_1569944983.csv                 1                        0                    0                   0               0\n",
      "PatNo_ID_1570089466.csv                 1                        0                    0                   0               0\n",
      "PatNo_ID_1570242703.csv                 1                        0                    0                   0               0\n",
      "PatNo_ID_1570273244.csv                 1                        0                    0                   0               0\n",
      "PatNo_ID_1570642083.csv                 1                        0                    0                   0               0\n",
      "PatNo_ID_1571945701.csv                 1                        0                    0                   0               0\n",
      "PatNo_ID_1572481361.csv                 1                        0                    0                   0               0\n",
      "PatNo_ID_1572562839.csv                 1                        0                    0                   0               0\n",
      "PatNo_ID_1572831765.csv                 1                        0                    0                   0               0\n",
      "PatNo_ID_1572976822.csv                 1                        0                    0                   0               0\n",
      "PatNo_ID_1573063188.csv                 1                        0                    0                   0               0\n",
      "PatNo_ID_1573249295.csv                 1                        0                    0                   0               0\n",
      "PatNo_ID_1573964540.csv                 1                        0                    0                   0               0\n",
      "PatNo_ID_1574148494.csv                 1                        0                    0                   0               0\n",
      "PatNo_ID_1574270349.csv                 1                        0                    0                   0               0\n",
      "PatNo_ID_1574528808.csv                 1                        0                    0                   0               0\n",
      "PatNo_ID_1574831525.csv                 1                        0                    0                   0               0\n",
      "PatNo_ID_1574987447.csv                 1                        0                    0                   0               0\n",
      "PatNo_ID_1575060177.csv                 1                        0                    0                   0               0\n",
      "PatNo_ID_1575256902.csv                 1                        0                    0                   0               0\n",
      "PatNo_ID_1575445051.csv                 1                        0                    0                   0               0\n",
      "PatNo_ID_1575502382.csv                 1                        0                    0                   0               0\n",
      "PatNo_ID_1575975485.csv                 1                        0                    0                   0               0\n",
      "PatNo_ID_1576115572.csv                 1                        0                    0                   0               0\n",
      "PatNo_ID_1576116479.csv                 1                        0                    0                   0               0\n",
      "PatNo_ID_1576301569.csv                 1                        0                    0                   0               0\n",
      "PatNo_ID_1576964560.csv                 1                        0                    0                   0               0\n",
      "PatNo_ID_1577487284.csv                 1                        0                    0                   0               0\n",
      "PatNo_ID_1578784257.csv                 1                        0                    0                   0               0\n",
      "PatNo_ID_1579198603.csv                 1                        0                    0                   0               0\n",
      "PatNo_ID_1579498177.csv                 1                        0                    0                   0               0\n",
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      "PatNo_ID_1581003248.csv                 1                        0                    0                   0               0\n",
      "PatNo_ID_1581019504.csv                 1                        0                    0                   0               0\n",
      "PatNo_ID_1581633231.csv                 1                        0                    0                   0               0\n",
      "PatNo_ID_1581692973.csv                 1                        0                    0                   0               0\n",
      "PatNo_ID_1582452511.csv                 1                        0                    0                   0               0\n",
      "PatNo_ID_1582635996.csv                 1                        0                    0                   0               0\n",
      "PatNo_ID_1582849900.csv                 1                        0                    0                   0               0\n",
      "PatNo_ID_1582937076.csv                 1                        0                    0                   0               0\n",
      "PatNo_ID_1584158973.csv                 1                        0                    0                   0               0\n",
      "PatNo_ID_1584397376.csv                 1                        0                    0                   0               0\n",
      "PatNo_ID_1586172659.csv                 1                        0                    0                   0               0\n",
      "PatNo_ID_1586696634.csv                 1                        0                    0                   0               0\n",
      "PatNo_ID_1586897008.csv                 1                        0                    0                   0               0\n",
      "PatNo_ID_1587490083.csv                 1                        0                    0                   0               0\n",
      "PatNo_ID_1588632604.csv                 1                        0                    0                   0               0\n",
      "PatNo_ID_1588673465.csv                 1                        0                    0                   0               0\n",
      "PatNo_ID_1588794796.csv                 1                        0                    0                   0               0\n",
      "PatNo_ID_1588957997.csv                 1                        0                    0                   0               0\n",
      "PatNo_ID_1589018086.csv                 1                        0                    0                   0               0\n",
      "PatNo_ID_1589034524.csv                 1                        0                    0                   0               0\n",
      "PatNo_ID_1589324603.csv                 1                        0                    0                   0               0\n",
      "PatNo_ID_1589918099.csv                 1                        0                    0                   0               0\n",
      "PatNo_ID_1590136310.csv                 1                        0                    0                   0               0\n",
      "PatNo_ID_1590616537.csv                 1                        0                    0                   0               0\n",
      "PatNo_ID_1590854576.csv                 1                        0                    0                   0               0\n",
      "PatNo_ID_1591609798.csv                 1                        0                    0                   0               0\n",
      "PatNo_ID_1592044724.csv                 1                        0                    0                   0               0\n",
      "PatNo_ID_1592560504.csv                 1                        0                    0                   0               0\n",
      "PatNo_ID_1593087886.csv                 1                        0                    0                   0               0\n",
      "PatNo_ID_1593416100.csv                 1                        0                    0                   0               0\n",
      "PatNo_ID_1593472048.csv                 1                        0                    0                   0               0\n",
      "PatNo_ID_1593593586.csv                 1                        0                    0                   0               0\n",
      "PatNo_ID_1593720818.csv                 1                        0                    0                   0               0\n",
      "PatNo_ID_1593838524.csv                 1                        0                    0                   0               0\n",
      "PatNo_ID_1594173718.csv                 1                        0                    0                   0               0\n",
      "PatNo_ID_1594294180.csv                 1                        0                    0                   0               0\n",
      "PatNo_ID_1594305136.csv                 1                        0                    0                   0               0\n",
      "PatNo_ID_1594309746.csv                 1                        0                    0                   0               0\n",
      "PatNo_ID_1594319286.csv                 1                        0                    0                   0               0\n",
      "PatNo_ID_1594320763.csv                 1                        0                    0                   0               0\n",
      "PatNo_ID_1594322594.csv                 1                        0                    0                   0               0\n",
      "PatNo_ID_1594335109.csv                 1                        0                    0                   0               0\n",
      "PatNo_ID_1594423683.csv                 1                        0                    0                   0               0\n",
      "PatNo_ID_1594437309.csv                 1                        0                    0                   0               0\n",
      "PatNo_ID_1594439781.csv                 1                        0                    0                   0               0\n",
      "PatNo_ID_1594441887.csv                 1                        0                    0                   0               0\n",
      "PatNo_ID_1594448501.csv                 1                        0                    0                   0               0\n",
      "PatNo_ID_1594455578.csv                 1                        0                    0                   0               0\n",
      "PatNo_ID_1594464829.csv                 1                        0                    0                   0               0\n",
      "PatNo_ID_1594467719.csv                 1                        0                    0                   0               0\n",
      "PatNo_ID_1594471407.csv                 1                        0                    0                   0               0\n",
      "PatNo_ID_1594479330.csv                 1                        0                    0                   0               0\n",
      "PatNo_ID_1594511911.csv                 1                        0                    0                   0               0\n",
      "PatNo_ID_1594511914.csv                 1                        0                    0                   0               0\n",
      "PatNo_ID_1594528842.csv                 1                        0                    0                   0               0\n",
      "PatNo_ID_1594533379.csv                 1                        0                    0                   0               0\n"
     ]
    }
   ],
   "source": [
    "\"\"\"先檢查目前資料是否都符合同一病患內 senddate 單調遞增、無重複時間點，發現有欸!所以用下面的程式修復\n",
    "\"\"\"\n",
    "# -*- coding: utf-8 -*-\n",
    "\"\"\"\n",
    "檢查規則（兩個層級）：\n",
    "A) 跨檔合併後，針對同一病患 patno，按照 senddate 排序：\n",
    "   - 嚴格單調遞增（strictly increasing）：diff > 0\n",
    "   - 無重複時間點：同一 patno 不得有重複的 senddate\n",
    "B) 逐檔內部，也做相同檢查，以快速定位問題發生在哪些檔案\n",
    "\n",
    "輸出：\n",
    "- 各類違規的病患數與列數量統計\n",
    "- 列出前幾筆（可調整 N_SAMPLES）違規樣本，附來源檔名、patno、senddate 與相鄰行資訊\n",
    "\"\"\"\n",
    "\n",
    "import os, glob\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "# ================== 基本參數 ==================\n",
    "DATA_DIR = \"/home/jovyan/RT08/0925/bling_useable\"\n",
    "PAT_COL  = \"patno\"\n",
    "TIME_COL = \"senddate\"\n",
    "USECOLS  = [PAT_COL, TIME_COL]  # 可自行擴充（例如 'ventilatormode'）但至少包含這兩欄\n",
    "N_SAMPLES = 5  # 每種違規只印出前 N_SAMPLES 筆樣本\n",
    "\n",
    "# ================== 讀檔 ==================\n",
    "file_paths = sorted(glob.glob(os.path.join(DATA_DIR, \"*.csv\")))\n",
    "if not file_paths:\n",
    "    print(f\"[Check] 目標資料夾沒有 CSV：{DATA_DIR}\")\n",
    "\n",
    "frames = []\n",
    "for fp in file_paths:\n",
    "    try:\n",
    "        df = pd.read_csv(fp, usecols=USECOLS, low_memory=False)\n",
    "    except Exception:\n",
    "        # 欄位不齊時，先全讀、再取子集；仍缺則跳過\n",
    "        df = pd.read_csv(fp, low_memory=False)\n",
    "        if (PAT_COL not in df.columns) or (TIME_COL not in df.columns):\n",
    "            print(f\"[Skip] 檔案缺必要欄位：{os.path.basename(fp)}\")\n",
    "            continue\n",
    "\n",
    "    df = df.copy()\n",
    "    df[\"__file__\"] = os.path.basename(fp)\n",
    "\n",
    "    # 基本清理：時間與病患欄位\n",
    "    df[TIME_COL] = pd.to_datetime(df[TIME_COL], errors=\"coerce\")\n",
    "    # 保留 patno 與 senddate 皆可解析的列\n",
    "    before = len(df)\n",
    "    df = df.dropna(subset=[PAT_COL, TIME_COL])\n",
    "    after = len(df)\n",
    "    if before != after:\n",
    "        print(f\"[Info] 檔案 {df['__file__'].iloc[0]}：丟棄無效列 {before - after}（patno/senddate 空或時間無法解析）\")\n",
    "\n",
    "    frames.append(df)\n",
    "\n",
    "if not frames:\n",
    "    print(\"[Check] 無可檢查資料（所有檔案缺欄或皆為空）。\")\n",
    "else:\n",
    "    all_df = pd.concat(frames, ignore_index=True)\n",
    "\n",
    "    # ======================================================\n",
    "    # A) 跨檔案合併後的「病患級」檢查（單調遞增 + 去重）\n",
    "    # ======================================================\n",
    "    print(\"\\n=== A) Cross-file, per-patient checks (strictly increasing & no duplicates) ===\")\n",
    "    # 先依病患+時間排序\n",
    "    all_df = all_df.sort_values([PAT_COL, TIME_COL, \"__file__\"]).reset_index(drop=True)\n",
    "\n",
    "    # 針對每位病患，計算相鄰時間差（以秒）\n",
    "    all_df[\"__delta_sec__\"] = all_df.groupby(PAT_COL)[TIME_COL].diff().dt.total_seconds()\n",
    "\n",
    "    # 1) 單調遞增違規：delta_sec <= 0（包含負數與 0；0 代表相同時間、可能是重複或亂序）\n",
    "    viol_non_increasing = all_df[all_df[\"__delta_sec__\"] <= 0].copy()\n",
    "\n",
    "    # 2) 去重檢查：同一病患、同一時間出現超過 1 列\n",
    "    dup_mask = all_df.duplicated(subset=[PAT_COL, TIME_COL], keep=False)\n",
    "    viol_dups = all_df[dup_mask].copy()\n",
    "\n",
    "    # 統計\n",
    "    n_pat_total = all_df[PAT_COL].nunique()\n",
    "    n_pat_non_inc = viol_non_increasing[PAT_COL].nunique()\n",
    "    n_pat_dups = viol_dups[PAT_COL].nunique()\n",
    "\n",
    "    print(f\"- Total patients: {n_pat_total}\")\n",
    "    print(f\"- Patients violating strict monotonic increase: {n_pat_non_inc} \"\n",
    "          f\"(rows={len(viol_non_increasing)})\")\n",
    "    print(f\"- Patients having duplicate timestamps: {n_pat_dups} \"\n",
    "          f\"(rows={len(viol_dups)})\")\n",
    "\n",
    "    # 顯示樣本（單調違規）\n",
    "    if not viol_non_increasing.empty:\n",
    "        print(f\"\\n[Samples] Non-increasing violations (show top {N_SAMPLES} rows):\")\n",
    "        # 展示必要欄位與相鄰時間差\n",
    "        cols_show = [\"__file__\", PAT_COL, TIME_COL, \"__delta_sec__\"]\n",
    "        print(viol_non_increasing[cols_show].head(N_SAMPLES).to_string(index=False))\n",
    "\n",
    "    # 顯示樣本（重複時間）\n",
    "    if not viol_dups.empty:\n",
    "        print(f\"\\n[Samples] Duplicate timestamp violations (show top {N_SAMPLES} rows):\")\n",
    "        cols_show = [\"__file__\", PAT_COL, TIME_COL]\n",
    "        print(viol_dups[cols_show].head(N_SAMPLES).to_string(index=False))\n",
    "\n",
    "    # ======================================================\n",
    "    # B) 逐檔「檔案內部」檢查（單調遞增 + 去重）\n",
    "    # ======================================================\n",
    "    print(\"\\n=== B) In-file checks (strictly increasing & no duplicates within each file) ===\")\n",
    "    per_file_report = []\n",
    "    for fp in file_paths:\n",
    "        base = os.path.basename(fp)\n",
    "        sub = all_df[all_df[\"__file__\"] == base].copy()\n",
    "        if sub.empty:\n",
    "            continue\n",
    "\n",
    "        # 逐檔內，仍然以病患分組檢查\n",
    "        # 單調遞增違規（檔內）\n",
    "        sub = sub.sort_values([PAT_COL, TIME_COL]).reset_index(drop=True)\n",
    "        sub[\"__delta_sec_file__\"] = sub.groupby(PAT_COL)[TIME_COL].diff().dt.total_seconds()\n",
    "        viol_non_inc_f = sub[sub[\"__delta_sec_file__\"] <= 0]\n",
    "\n",
    "        # 檔內重複時間（同一檔內，同病患、同時間）\n",
    "        dup_mask_f = sub.duplicated(subset=[PAT_COL, TIME_COL], keep=False)\n",
    "        viol_dups_f = sub[dup_mask_f]\n",
    "\n",
    "        per_file_report.append({\n",
    "            \"file\": base,\n",
    "            \"patients_in_file\": sub[PAT_COL].nunique(),\n",
    "            \"non_increasing_patients\": viol_non_inc_f[PAT_COL].nunique(),\n",
    "            \"non_increasing_rows\": len(viol_non_inc_f),\n",
    "            \"duplicate_patients\": viol_dups_f[PAT_COL].nunique(),\n",
    "            \"duplicate_rows\": len(viol_dups_f)\n",
    "        })\n",
    "\n",
    "    rep_df = pd.DataFrame(per_file_report)\n",
    "    if rep_df.empty:\n",
    "        print(\"[Info] 無可產生的逐檔報告。\")\n",
    "    else:\n",
    "        print(rep_df.sort_values([\"non_increasing_rows\",\"duplicate_rows\"], ascending=False).to_string(index=False))\n",
    "\n",
    "    # ======================================================\n",
    "    # C) 進階提示：如何追蹤相鄰違規對\n",
    "    # ======================================================\n",
    "    # 若你想知道「哪兩筆相鄰列造成違規」（例如檔名/時間/病患/原始索引等），\n",
    "    # 可以在 viol_non_increasing 上，結合前一列資訊做 join，協助追蹤：\n",
    "    #\n",
    "    #   tmp = all_df.copy()\n",
    "    #   tmp[\"__prev_time__\"] = tmp.groupby(PAT_COL)[TIME_COL].shift(1)\n",
    "    #   tmp[\"__prev_file__\"] = tmp.groupby(PAT_COL)[\"__file__\"].shift(1)\n",
    "    #   bad_pairs = tmp[tmp[\"__delta_sec__\"] <= 0][[\"__file__\", PAT_COL, TIME_COL, \"__prev_file__\",\"__prev_time__\", \"__delta_sec__\"]]\n",
    "    #   print(bad_pairs.head(N_SAMPLES).to_string(index=False))\n",
    "    #\n",
    "    # 如此能看到「違規列」以及「它的前一列」的來源檔與時間，便於回溯修正。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "6fea2ea9-d721-430e-9872-1adf22f2f1c2",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "[Summary] bling_useable file counts\n",
      "- Before: 122\n",
      "- After : 122\n",
      "\n",
      "[Changed files] (5 files modified)\n",
      " - PatNo_ID_1566671274.csv\n",
      " - PatNo_ID_1577042911.csv\n",
      " - PatNo_ID_1580062580.csv\n",
      " - PatNo_ID_1580096720.csv\n",
      " - PatNo_ID_1580107637.csv\n"
     ]
    }
   ],
   "source": [
    "\"\"\"我要針對這幾份檔案\n",
    "要動原始檔\n",
    "預設保留「非空欄最多」的一列\n",
    "保證嚴格遞增\n",
    "最後輸出/home/jovyan/RT08/0925/bling_useable之前和之後的檔案數量以及有更改的檔名\n",
    "\"\"\"\n",
    "# -*- coding: utf-8 -*-\n",
    "\"\"\"\n",
    "Auto-fix bling_useable CSVs in-place:\n",
    "- Folder: /home/jovyan/RT08/0925/bling_useable\n",
    "- Detect per-file per-patient violations:\n",
    "    * Strictly increasing 'senddate' (no non-increasing steps, no duplicates per patient)\n",
    "- Fix policy:\n",
    "    (1) Deduplicate rows sharing (patno, senddate) by keeping the row with the most non-null fields\n",
    "        (treat \"\", \"null\", \"(null)\" as missing)\n",
    "    (2) Enforce strict increase per patient: keep only rows where t_i > t_{i-1}\n",
    "- Overwrite original files (optional backup step provided as a comment)\n",
    "- Report: before/after file counts and the list of modified filenames\n",
    "\"\"\"\n",
    "\n",
    "import os, glob\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "# =============== 基本設定 ===============\n",
    "DATA_DIR = \"/home/jovyan/RT08/0925/bling_useable\"\n",
    "PAT_COL  = \"patno\"\n",
    "TIME_COL = \"senddate\"\n",
    "READ_KW  = dict(low_memory=False)\n",
    "\n",
    "# 若要在覆寫前自動備份原始檔，將下方開關改為 True\n",
    "ENABLE_BACKUP = False  # True → 會在同目錄留下 .bak\n",
    "\n",
    "# =============== 工具函式 ===============\n",
    "def _read_csv_any(path: str) -> pd.DataFrame:\n",
    "    \"\"\"讀檔：盡量容錯；必要欄位缺失則回傳空 DataFrame。\"\"\"\n",
    "    try:\n",
    "        df = pd.read_csv(path, **READ_KW)\n",
    "    except Exception:\n",
    "        return pd.DataFrame()\n",
    "    if PAT_COL not in df.columns or TIME_COL not in df.columns:\n",
    "        return pd.DataFrame()\n",
    "    return df\n",
    "\n",
    "def _normalize_time_and_filter(df: pd.DataFrame) -> pd.DataFrame:\n",
    "    \"\"\"將 senddate 轉成 datetime，移除 patno/senddate 無效列。\"\"\"\n",
    "    d = df.copy()\n",
    "    d[TIME_COL] = pd.to_datetime(d[TIME_COL], errors=\"coerce\")\n",
    "    d = d.dropna(subset=[PAT_COL, TIME_COL])\n",
    "    return d\n",
    "\n",
    "def _has_violation(df: pd.DataFrame) -> bool:\n",
    "    \"\"\"\n",
    "    檔內逐病患檢查是否存在違規：\n",
    "      - 非遞增（含相等）：diff <= 0\n",
    "      - 或者 (patno, senddate) 重複\n",
    "    \"\"\"\n",
    "    if df.empty:\n",
    "        return False\n",
    "    d = df.sort_values([PAT_COL, TIME_COL]).copy()\n",
    "    d[\"__d__\"] = d.groupby(PAT_COL)[TIME_COL].diff().dt.total_seconds()\n",
    "    non_inc = (d[\"__d__\"] <= 0).any()\n",
    "    dup_any = d.duplicated(subset=[PAT_COL, TIME_COL], keep=False).any()\n",
    "    return bool(non_inc or dup_any)\n",
    "\n",
    "def _choose_row_max_non_null(g: pd.DataFrame) -> pd.DataFrame:\n",
    "    \"\"\"\n",
    "    對同一 (patno, senddate) 群組，保留「非空欄最多」的一列。\n",
    "    \"\" / \"null\" / \"(null)\" 均視為空。\n",
    "    回傳：單列 DataFrame（維持原欄位）\n",
    "    \"\"\"\n",
    "    tmp = g.replace(r'^\\s*$', np.nan, regex=True)\n",
    "    tmp = tmp.replace([\"null\", \"NULL\", \"(null)\", \"(NULL)\"], np.nan)\n",
    "    non_null_counts = tmp.notna().sum(axis=1)\n",
    "    idx = non_null_counts.idxmax()\n",
    "    return g.loc[[idx]]\n",
    "\n",
    "def _dedup_by_patient_and_time(df: pd.DataFrame) -> pd.DataFrame:\n",
    "    \"\"\"\n",
    "    逐病患，對 (patno, senddate) 進行群組去重，保留非空欄最多的一列。\n",
    "    \"\"\"\n",
    "    out = []\n",
    "    for pid, sub in df.groupby(PAT_COL, sort=False):\n",
    "        sub = sub.sort_values(TIME_COL)\n",
    "        merged_rows = []\n",
    "        for _, gg in sub.groupby([PAT_COL, TIME_COL], sort=False):\n",
    "            merged_rows.append(_choose_row_max_non_null(gg))\n",
    "        if merged_rows:\n",
    "            out.append(pd.concat(merged_rows, ignore_index=True))\n",
    "    return pd.concat(out, ignore_index=True) if out else df.iloc[0:0]\n",
    "\n",
    "def _enforce_strict_increase_per_patient(df: pd.DataFrame) -> pd.DataFrame:\n",
    "    \"\"\"\n",
    "    逐病患保證嚴格遞增：只保留 senddate 嚴格大於上一筆 (t_i > t_{i-1}) 的列。\n",
    "    等於或小於上一筆的列皆捨棄。\n",
    "    \"\"\"\n",
    "    out = []\n",
    "    for pid, sub in df.groupby(PAT_COL, sort=False):\n",
    "        sub = sub.sort_values(TIME_COL)\n",
    "        kept = []\n",
    "        last_t = None\n",
    "        for _, row in sub.iterrows():\n",
    "            t = row[TIME_COL]\n",
    "            if pd.isna(t):\n",
    "                continue\n",
    "            if (last_t is None) or (t > last_t):\n",
    "                kept.append(row)\n",
    "                last_t = t\n",
    "            # else: t <= last_t → 丟棄\n",
    "        if kept:\n",
    "            out.append(pd.DataFrame(kept, columns=sub.columns))\n",
    "    return pd.concat(out, ignore_index=True) if out else df.iloc[0:0]\n",
    "\n",
    "# =============== 主流程（偵測 → 修正 → 覆寫 → 報告） ===============\n",
    "# 1) 掃描全部 CSV，記錄處理前檔案數\n",
    "all_paths = sorted(glob.glob(os.path.join(DATA_DIR, \"*.csv\")))\n",
    "before_count = len(all_paths)\n",
    "\n",
    "changed_files = []\n",
    "\n",
    "# 2) 逐檔偵測與修正（僅針對有違規的檔案）\n",
    "for path in all_paths:\n",
    "    base = os.path.basename(path)\n",
    "\n",
    "    # 讀檔 & 基礎過濾\n",
    "    df_raw = _read_csv_any(path)\n",
    "    if df_raw.empty:\n",
    "        # 缺必要欄位或讀取失敗 → 略過（也可列警示）\n",
    "        continue\n",
    "\n",
    "    df = _normalize_time_and_filter(df_raw)\n",
    "\n",
    "    # 若無違規則，跳過此檔\n",
    "    if not _has_violation(df):\n",
    "        continue\n",
    "\n",
    "    # 保留原欄位順序，用於輸出\n",
    "    orig_cols = df_raw.columns.tolist()\n",
    "\n",
    "    # === 修正 ===\n",
    "    # Step 1: 同一 (patno, senddate) 內，保留非空欄最多的一列\n",
    "    df1 = _dedup_by_patient_and_time(df)\n",
    "\n",
    "    # Step 2: 逐病患 enforce 嚴格遞增\n",
    "    df2 = _enforce_strict_increase_per_patient(df1)\n",
    "\n",
    "    # 判斷是否真的有改動（列數或鍵集合不同即視為改動）\n",
    "    modified = False\n",
    "    if len(df2) != len(df):\n",
    "        modified = True\n",
    "    else:\n",
    "        k = [PAT_COL, TIME_COL]\n",
    "        same_keys = df2.sort_values(k).reset_index(drop=True)[k].equals(\n",
    "            df.sort_values(k).reset_index(drop=True)[k]\n",
    "        )\n",
    "        modified = (not same_keys)\n",
    "\n",
    "    if modified:\n",
    "        # （可選）覆寫前做備份\n",
    "        if ENABLE_BACKUP:\n",
    "            import shutil\n",
    "            shutil.copy2(path, path + \".bak\")\n",
    "\n",
    "        # 以原欄位順序輸出；若 df2 沒有某些原欄位，缺失會變 NaN（通常可接受）\n",
    "        out_df = df2.reindex(columns=orig_cols)\n",
    "        out_df.to_csv(path, index=False)\n",
    "        changed_files.append(base)\n",
    "\n",
    "# 3) 處理後檔案數（理論上與 before 相同；本流程不刪檔）\n",
    "after_paths = sorted(glob.glob(os.path.join(DATA_DIR, \"*.csv\")))\n",
    "after_count = len(after_paths)\n",
    "\n",
    "# 4) 報告輸出\n",
    "print(\"\\n[Summary] bling_useable file counts\")\n",
    "print(f\"- Before: {before_count}\")\n",
    "print(f\"- After : {after_count}\")\n",
    "\n",
    "if changed_files:\n",
    "    print(f\"\\n[Changed files] ({len(changed_files)} files modified)\")\n",
    "    for name in changed_files:\n",
    "        print(\" -\", name)\n",
    "else:\n",
    "    print(\"\\n[Changed files] None (no files required modification)\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "f5a81db8-eccc-455d-82ce-a61751963828",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "[One-hot consistency check]\n",
      "file                             original=1         adjusted=1       diff\n",
      "----------------------------------------------------------------------------\n",
      "089271.csv                            32362              32362          0\n",
      "095323.csv                            19713              19713          0\n",
      "095707.csv                            20177              20177          0\n",
      "114309.csv                            46563              46563          0\n",
      "230933.csv                            30214              30214          0\n",
      "4216007.csv                               0                  0          0\n",
      "7108162.csv                             239                239          0\n",
      "7408338.csv                            1431               1431          0\n",
      "7657698.csv                            1412               1412          0\n",
      "7721164.csv                             483                483          0\n",
      "PatNo_ID_1560013303.csv                2458               2458          0\n",
      "PatNo_ID_1562733396.csv                2251               2251          0\n",
      "PatNo_ID_1563587183.csv                5284               5284          0\n",
      "PatNo_ID_1564148644.csv                8788               8788          0\n",
      "PatNo_ID_1565148312.csv                5465               5465          0\n",
      "PatNo_ID_1565378038.csv                2560               2560          0\n",
      "PatNo_ID_1566123680.csv               42466              42466          0\n",
      "PatNo_ID_1566252197.csv                1935               1935          0\n",
      "PatNo_ID_1566279967.csv                1153               1153          0\n",
      "PatNo_ID_1566671274.csv               23554              23554          0\n",
      "PatNo_ID_1566911879.csv               46704              46704          0\n",
      "PatNo_ID_1567747650.csv               10896              10896          0\n",
      "PatNo_ID_1567804800.csv               12696              12696          0\n",
      "PatNo_ID_1567832735.csv               36554              36554          0\n",
      "PatNo_ID_1568039398.csv               34179              34179          0\n",
      "PatNo_ID_1568574099.csv               12627              12627          0\n",
      "PatNo_ID_1568813269.csv                4867               4867          0\n",
      "PatNo_ID_1568952422.csv                1184               1184          0\n",
      "PatNo_ID_1569083701.csv                3325               3325          0\n",
      "PatNo_ID_1569944983.csv                7645               7645          0\n",
      "PatNo_ID_1570089466.csv               36664              36664          0\n",
      "PatNo_ID_1570242703.csv               10551              10551          0\n",
      "PatNo_ID_1570273244.csv                9704               9704          0\n",
      "PatNo_ID_1570642083.csv               19728              19728          0\n",
      "PatNo_ID_1571945701.csv               15726              15726          0\n",
      "PatNo_ID_1572481361.csv               34483              34483          0\n",
      "PatNo_ID_1572562839.csv               15529              15529          0\n",
      "PatNo_ID_1572831765.csv                2696               2696          0\n",
      "PatNo_ID_1572976822.csv                4279               4279          0\n",
      "PatNo_ID_1573063188.csv                5024               5024          0\n",
      "PatNo_ID_1573249295.csv                7151               7151          0\n",
      "PatNo_ID_1573964540.csv                4393               4393          0\n",
      "PatNo_ID_1574148494.csv               47017              47017          0\n",
      "PatNo_ID_1574270349.csv                6512               6512          0\n",
      "PatNo_ID_1574528808.csv               14480              14480          0\n",
      "PatNo_ID_1574831525.csv                 515                515          0\n",
      "PatNo_ID_1574987447.csv               19810              19810          0\n",
      "PatNo_ID_1575060177.csv                5289               5289          0\n",
      "PatNo_ID_1575256902.csv                9492               9492          0\n",
      "PatNo_ID_1575445051.csv                1785               1785          0\n",
      "PatNo_ID_1575502382.csv                6487               6487          0\n",
      "PatNo_ID_1575975485.csv               16454              16454          0\n",
      "PatNo_ID_1576115572.csv               18688              18688          0\n",
      "PatNo_ID_1576116479.csv                1257               1257          0\n",
      "PatNo_ID_1576301569.csv                3204               3204          0\n",
      "PatNo_ID_1576964560.csv               24185              24185          0\n",
      "PatNo_ID_1577042911.csv               35610              35610          0\n",
      "PatNo_ID_1577487284.csv                2345               2345          0\n",
      "PatNo_ID_1578784257.csv               28553              28553          0\n",
      "PatNo_ID_1579198603.csv                2044               2044          0\n",
      "PatNo_ID_1579498177.csv               21672              21672          0\n",
      "PatNo_ID_1580062580.csv                2144               2144          0\n",
      "PatNo_ID_1580096720.csv                1422               1422          0\n",
      "PatNo_ID_1580107637.csv                2642               2642          0\n",
      "PatNo_ID_1580244614.csv                3865               3865          0\n",
      "PatNo_ID_1580766093.csv               17973              17973          0\n",
      "PatNo_ID_1581003248.csv                6632               6632          0\n",
      "PatNo_ID_1581019504.csv               20724              20724          0\n",
      "PatNo_ID_1581633231.csv               14626              14626          0\n",
      "PatNo_ID_1581692973.csv                2723               2723          0\n",
      "PatNo_ID_1582452511.csv                5193               5193          0\n",
      "PatNo_ID_1582635996.csv               13045              13045          0\n",
      "PatNo_ID_1582849900.csv                4789               4789          0\n",
      "PatNo_ID_1582937076.csv               22673              22673          0\n",
      "PatNo_ID_1584158973.csv                2877               2877          0\n",
      "PatNo_ID_1584397376.csv                 638                638          0\n",
      "PatNo_ID_1586172659.csv               38419              38419          0\n",
      "PatNo_ID_1586696634.csv                2504               2504          0\n",
      "PatNo_ID_1586897008.csv                6654               6654          0\n",
      "PatNo_ID_1587490083.csv               45186              45186          0\n",
      "PatNo_ID_1588632604.csv                2599               2599          0\n",
      "PatNo_ID_1588673465.csv                6282               6282          0\n",
      "PatNo_ID_1588794796.csv                9375               9375          0\n",
      "PatNo_ID_1588957997.csv               10958              10958          0\n",
      "PatNo_ID_1589018086.csv               13396              13396          0\n",
      "PatNo_ID_1589034524.csv               49984              49984          0\n",
      "PatNo_ID_1589324603.csv                4105               4105          0\n",
      "PatNo_ID_1589918099.csv                   0                  0          0\n",
      "PatNo_ID_1590136310.csv                5305               5305          0\n",
      "PatNo_ID_1590616537.csv               14199              14199          0\n",
      "PatNo_ID_1590854576.csv               15879              15879          0\n",
      "PatNo_ID_1591609798.csv               35614              35614          0\n",
      "PatNo_ID_1592044724.csv                6810               6810          0\n",
      "PatNo_ID_1592560504.csv               18051              18051          0\n",
      "PatNo_ID_1593087886.csv               24126              24126          0\n",
      "PatNo_ID_1593416100.csv                3328               3328          0\n",
      "PatNo_ID_1593472048.csv                6247               6247          0\n",
      "PatNo_ID_1593593586.csv               18875              18875          0\n",
      "PatNo_ID_1593720818.csv                2644               2644          0\n",
      "PatNo_ID_1593838524.csv                2160               2160          0\n",
      "PatNo_ID_1594173718.csv                 344                344          0\n",
      "PatNo_ID_1594294180.csv               18320              18320          0\n",
      "PatNo_ID_1594305136.csv               15634              15634          0\n",
      "PatNo_ID_1594309746.csv                3724               3724          0\n",
      "PatNo_ID_1594319286.csv                3988               3988          0\n",
      "PatNo_ID_1594320763.csv                2429               2429          0\n",
      "PatNo_ID_1594322594.csv                5378               5378          0\n",
      "PatNo_ID_1594335109.csv                5116               5116          0\n",
      "PatNo_ID_1594423683.csv                5001               5001          0\n",
      "PatNo_ID_1594437309.csv                9935               9935          0\n",
      "PatNo_ID_1594439781.csv                7687               7687          0\n",
      "PatNo_ID_1594441887.csv               10196              10196          0\n",
      "PatNo_ID_1594448501.csv                1045               1045          0\n",
      "PatNo_ID_1594455578.csv                 262                262          0\n",
      "PatNo_ID_1594464829.csv                3937               3937          0\n",
      "PatNo_ID_1594467719.csv                1313               1313          0\n",
      "PatNo_ID_1594471407.csv               11279              11279          0\n",
      "PatNo_ID_1594479330.csv                3724               3724          0\n",
      "PatNo_ID_1594511911.csv                5450               5450          0\n",
      "PatNo_ID_1594511914.csv                9670               9670          0\n",
      "PatNo_ID_1594528842.csv                2415               2415          0\n",
      "PatNo_ID_1594533379.csv                 999                999          0\n",
      "----------------------------------------------------------------------------\n",
      "TOTAL                               1443024            1443024          0\n",
      "\n",
      "✅ Exported 5 figure(s) to: /home/jovyan/RT08/0925/1002\n",
      "   Year count: 3\n",
      "   - 2021: 1 figure(s)\n",
      "   - 2022: 3 figure(s)\n",
      "   - 2024: 1 figure(s)\n"
     ]
    }
   ],
   "source": [
    "\"\"\" 因為深灰色區域好多 我要確定一下useable的定義是否正確\n",
    "目前 useable=1 的定義（現有程式）\n",
    "    一筆列資料（row）同時滿足下列兩大條件就標記 useable=1，否則 0：\n",
    "    關鍵欄位存在且可用\n",
    "    patno、senddate、ventilatormode 皆非空（非 NaN / 非空字串 / 非 \"null\"）。\n",
    "    rrhzsetactual、peepepap、ppeak 能成功轉為數字且非 NaN。\n",
    "    模式 one-hot 有命中\n",
    "    mode_1 或 mode_2 或 mode_3 其中至少一個為 1（其餘缺欄會補 0）。\n",
    "    註：也新增 svv=(vti+vte+sponvt)/3（任一空值則 svv=NaN），但 svv 目前不影響 useable 判定\n",
    "還需要檢查目前的useable=1資料是否符合以下條件（下面先檢查 再輸出一次時間軸）\n",
    "1. 檢查 one-hot 的一致性：目前是「mode_1/2/3 任一=1」，但理想狀況是「恰有一個=1」，\n",
    "2. \n",
    "要列出原本 useable=1 的數量，也印出處理後 useable=1 的數量\n",
    "\"\"\"\n",
    "# -*- coding: utf-8 -*-\n",
    "\"\"\"\n",
    "目的（不自動執行，貼上後再跑）：\n",
    "1) 檢查目前各檔案「原本 useable=1」的列，是否滿足 one-hot 一致性：mode_1/mode_2/mode_3 恰有一個為 1\n",
    "2) 依上述規則「只做降格檢核」：對於原本 useable=1 但 one-hot ≠ 1 的列，改判為 0（其餘不動）\n",
    "   - 產生「調整後的 useable（欄位名：useable_adj）」於記憶體中\n",
    "   - 可選擇是否回寫覆蓋到原檔（UPDATE_FILES=True 才會覆寫原欄位 useable）\n",
    "3) 列出統計：\n",
    "   - 逐檔：原本 useable=1 筆數、調整後 useable=1 筆數、差異值\n",
    "   - 跨檔總計：原本總數、調整後總數、差異值\n",
    "4) 以「調整後的 useable（useable_adj）」重新輸出時間軸視覺化（英文圖表）\n",
    "   - X 軸：senddate；Y 軸：檔名\n",
    "   - useable=1 深灰（#4a4a4a），useable=0 淺灰（#cfcfcf）\n",
    "   - 按年份分圖，每張最多 50 檔，右側標示該年該檔案 useable=1 百分比\n",
    "   - 圖片輸出到 /home/jovyan/RT08/0925/1002/\n",
    "   - 最後印出總共有幾張圖、幾個年份、各年份各幾張\n",
    "\"\"\"\n",
    "\n",
    "import os, glob, re\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.dates as mdates\n",
    "\n",
    "# ================== 基本參數 ==================\n",
    "DATA_DIR = \"/home/jovyan/RT08/0925/bling_useable\"   # 來源 CSV 目錄\n",
    "OUT_DIR  = \"/home/jovyan/RT08/0925/1002\"           # 圖片輸出目錄\n",
    "os.makedirs(OUT_DIR, exist_ok=True)\n",
    "\n",
    "# 這個開關若設 True，會把「調整後的 useable_adj」覆蓋回原檔的 useable 欄位（請謹慎）\n",
    "UPDATE_FILES = False\n",
    "\n",
    "# 欄位設定\n",
    "TIME_COL = \"senddate\"\n",
    "USEABLE_COL = \"useable\"\n",
    "MODE_COLS = [\"mode_1\", \"mode_2\", \"mode_3\"]\n",
    "\n",
    "# 顏色設定（視覺化）\n",
    "DARK_GRAY = \"#4a4a4a\"   # useable = 1\n",
    "LIGHT_GRAY = \"#cfcfcf\"  # useable = 0\n",
    "\n",
    "# 每張圖最多呈現的檔案數（避免擠爆）\n",
    "MAX_PER_FIG = 50\n",
    "\n",
    "\n",
    "# ================== 工具函式：安全讀檔 ==================\n",
    "def read_csv_loose(path: str, needed_cols=None) -> pd.DataFrame:\n",
    "    \"\"\"\n",
    "    寬鬆讀檔：若指定 needed_cols，優先只讀所需欄位；失敗再全讀。\n",
    "    若缺必要欄位，回傳空 DataFrame。\n",
    "    \"\"\"\n",
    "    needed_cols = needed_cols or []\n",
    "    try:\n",
    "        df = pd.read_csv(path, usecols=needed_cols, low_memory=False)\n",
    "    except Exception:\n",
    "        try:\n",
    "            df = pd.read_csv(path, low_memory=False)\n",
    "        except Exception:\n",
    "            return pd.DataFrame()\n",
    "        # 確認必要欄位\n",
    "        for c in needed_cols:\n",
    "            if c not in df.columns:\n",
    "                return pd.DataFrame()\n",
    "    return df\n",
    "\n",
    "\n",
    "# ================== 第 1 部分：檢查 one-hot 一致性並產生 useable_adj ==================\n",
    "file_paths = sorted(glob.glob(os.path.join(DATA_DIR, \"*.csv\")))\n",
    "\n",
    "report = []           # 逐檔統計報表\n",
    "frames_for_plot = []  # 後續視覺化所需的（檔名、年份、時間、useable_adj）\n",
    "\n",
    "for fp in file_paths:\n",
    "    base = os.path.basename(fp)\n",
    "\n",
    "    # 嘗試只讀必要欄位；缺欄再全讀\n",
    "    df = read_csv_loose(fp, needed_cols=[TIME_COL, USEABLE_COL] + MODE_COLS)\n",
    "    if df.empty:\n",
    "        # 檔案缺必要欄位（例如沒有 useable 或 senddate），略過並列提示\n",
    "        print(f\"[Skip] Missing required columns in {base}\")\n",
    "        continue\n",
    "\n",
    "    # 轉換 senddate / useable / modes\n",
    "    df = df.copy()\n",
    "    df[TIME_COL] = pd.to_datetime(df[TIME_COL], errors=\"coerce\")\n",
    "    df[USEABLE_COL] = pd.to_numeric(df[USEABLE_COL], errors=\"coerce\").fillna(0).astype(int).clip(0, 1)\n",
    "\n",
    "    for m in MODE_COLS:\n",
    "        if m not in df.columns:\n",
    "            df[m] = 0\n",
    "        df[m] = pd.to_numeric(df[m], errors=\"coerce\").fillna(0).astype(int).clip(0, 1)\n",
    "\n",
    "    # 原本 useable=1 的筆數\n",
    "    original_usable = int((df[USEABLE_COL] == 1).sum())\n",
    "\n",
    "    # one-hot 一致性（恰有一個 = 1）\n",
    "    mode_sum = df[MODE_COLS].sum(axis=1)\n",
    "    one_hot_ok = (mode_sum == 1)\n",
    "\n",
    "    # 調整規則（只降不升）：原本 useable=1 但 one_hot_ok 為 False → 改成 0\n",
    "    df[\"useable_adj\"] = df[USEABLE_COL]\n",
    "    df.loc[(df[USEABLE_COL] == 1) & (~one_hot_ok), \"useable_adj\"] = 0\n",
    "\n",
    "    adjusted_usable = int((df[\"useable_adj\"] == 1).sum())\n",
    "    delta = adjusted_usable - original_usable  # 通常為 0 或負值\n",
    "\n",
    "    report.append({\n",
    "        \"file\": base,\n",
    "        \"original_useable_1\": original_usable,\n",
    "        \"adjusted_useable_1\": adjusted_usable,\n",
    "        \"diff\": delta\n",
    "    })\n",
    "\n",
    "    # 視覺化需要的欄位：檔名、年份、senddate、useable_adj\n",
    "    sub = df[[TIME_COL, \"useable_adj\"]].dropna(subset=[TIME_COL]).copy()\n",
    "    if sub.empty:\n",
    "        continue\n",
    "    sub[\"__file__\"] = base\n",
    "    sub[\"__year__\"] = sub[TIME_COL].dt.year\n",
    "    frames_for_plot.append(sub)\n",
    "\n",
    "    # 視需求：是否回寫覆蓋原始檔（把 useable 改成 useable_adj）\n",
    "    if UPDATE_FILES:\n",
    "        # 回寫時保留原來欄位順序；若原檔缺自訂欄，會自動附在最後\n",
    "        # 先重新完整讀一次原檔，避免只讀部分欄位造成欄位遺失\n",
    "        df_full = read_csv_loose(fp)  # 全讀\n",
    "        if not df_full.empty and TIME_COL in df_full.columns:\n",
    "            # 將 useable 依時間對齊後替換為 useable_adj\n",
    "            # 若 df_full 與 df 列對齊一致，也可直接賦值；此處採時間對齊較穩健\n",
    "            merged = df_full.merge(df[[TIME_COL, \"useable_adj\"]], on=TIME_COL, how=\"left\", suffixes=(\"\", \"__adj\"))\n",
    "            # 只有在有對齊值時才覆蓋\n",
    "            merged[USEABLE_COL] = np.where(merged[\"useable_adj\"].notna(),\n",
    "                                           merged[\"useable_adj\"].astype(int),\n",
    "                                           pd.to_numeric(merged.get(USEABLE_COL), errors=\"coerce\").fillna(0).astype(int).clip(0,1))\n",
    "            merged = merged.drop(columns=[\"useable_adj\"])\n",
    "            merged.to_csv(fp, index=False)\n",
    "        else:\n",
    "            # 無法回寫：缺欄或讀不到完整檔，給提示\n",
    "            print(f\"[Warn] Cannot update file (re-read failed or missing {TIME_COL}): {base}\")\n",
    "\n",
    "# 輸出檢查統計\n",
    "if report:\n",
    "    rep_df = pd.DataFrame(report).sort_values(\"file\")\n",
    "    print(\"\\n[One-hot consistency check]\")\n",
    "    print(\"{:<28} {:>14} {:>18} {:>10}\".format(\"file\", \"original=1\", \"adjusted=1\", \"diff\"))\n",
    "    print(\"-\" * 76)\n",
    "    total_orig = total_adj = 0\n",
    "    for r in rep_df.itertuples(index=False):\n",
    "        print(\"{:<28} {:>14} {:>18} {:>10}\".format(\n",
    "            r.file, r.original_useable_1, r.adjusted_useable_1, r.diff\n",
    "        ))\n",
    "        total_orig += int(r.original_useable_1)\n",
    "        total_adj  += int(r.adjusted_useable_1)\n",
    "    print(\"-\" * 76)\n",
    "    print(\"{:<28} {:>14} {:>18} {:>10}\".format(\n",
    "        \"TOTAL\", total_orig, total_adj, total_adj - total_orig\n",
    "    ))\n",
    "else:\n",
    "    print(\"[Info] No files to report (missing required columns or no CSVs found).\")\n",
    "\n",
    "\n",
    "# ================== 第 2 部分：用 useable_adj 輸出時間軸 ==================\n",
    "def make_segments(g: pd.DataFrame) -> list:\n",
    "    \"\"\"\n",
    "    將相鄰且 useable_adj 相同的列壓成區段（避免畫成密密麻麻的點）\n",
    "    回傳：[{start, end, useable}]\n",
    "    \"\"\"\n",
    "    if g.empty:\n",
    "        return []\n",
    "    g = g.sort_values(TIME_COL)\n",
    "    change = g[\"useable_adj\"].ne(g[\"useable_adj\"].shift(1)).fillna(True)\n",
    "    run_ids = change.cumsum()\n",
    "    segs = []\n",
    "    for rid, s in g.groupby(run_ids):\n",
    "        segs.append({\n",
    "            \"start\": s[TIME_COL].iloc[0],\n",
    "            \"end\":   s[TIME_COL].iloc[-1],\n",
    "            \"useable\": int(s[\"useable_adj\"].iloc[0]),\n",
    "        })\n",
    "    return segs\n",
    "\n",
    "if frames_for_plot:\n",
    "    plot_df = pd.concat(frames_for_plot, ignore_index=True).sort_values([\"__year__\", \"__file__\", TIME_COL])\n",
    "\n",
    "    years = sorted(plot_df[\"__year__\"].dropna().unique().tolist())\n",
    "    total_figs = 0\n",
    "    year_page_count = {}\n",
    "\n",
    "    for yr in years:\n",
    "        dy = plot_df[plot_df[\"__year__\"] == yr].copy()\n",
    "        files_in_year = sorted(dy[\"__file__\"].unique().tolist())\n",
    "\n",
    "        for page_start in range(0, len(files_in_year), MAX_PER_FIG):\n",
    "            batch_files = files_in_year[page_start:page_start + MAX_PER_FIG]\n",
    "            if not batch_files:\n",
    "                continue\n",
    "\n",
    "            n = len(batch_files)\n",
    "            fig_h = max(4, min(12, 0.35 * n))  # 圖高隨檔案數調整\n",
    "            fig, ax = plt.subplots(figsize=(14, fig_h))\n",
    "\n",
    "            batch_df = dy[dy[\"__file__\"].isin(batch_files)].copy()\n",
    "            x_min = batch_df[TIME_COL].min()\n",
    "            x_max = batch_df[TIME_COL].max()\n",
    "            if pd.isna(x_min) or pd.isna(x_max) or x_min == x_max:\n",
    "                # 無法估計範圍時，以年度邊界補\n",
    "                x_min = pd.to_datetime(f\"{yr}-01-01\")\n",
    "                x_max = pd.to_datetime(f\"{yr}-12-31\")\n",
    "\n",
    "            # 右側留白（放百分比）\n",
    "            pad_minutes = max(30, int((x_max - x_min).total_seconds() / 60 * 0.03))\n",
    "            pad = pd.Timedelta(minutes=pad_minutes)\n",
    "            ax.set_xlim(x_min, x_max + pad)\n",
    "\n",
    "            # y 軸標籤與位置\n",
    "            y_positions = {fname: i for i, fname in enumerate(batch_files)}\n",
    "            yticks, ylabels = [], []\n",
    "\n",
    "            # X 軸刻度（時間）\n",
    "            locator = mdates.AutoDateLocator(minticks=4, maxticks=10)\n",
    "            formatter = mdates.ConciseDateFormatter(locator)\n",
    "            ax.xaxis.set_major_locator(locator)\n",
    "            ax.xaxis.set_major_formatter(formatter)\n",
    "\n",
    "            # 逐檔繪製\n",
    "            for fname in batch_files:\n",
    "                gf = batch_df[batch_df[\"__file__\"] == fname][[TIME_COL, \"useable_adj\"]].sort_values(TIME_COL)\n",
    "                if gf.empty:\n",
    "                    continue\n",
    "\n",
    "                y = y_positions[fname]\n",
    "                yticks.append(y)\n",
    "                ylabels.append(fname)\n",
    "\n",
    "                # 壓段並畫水平線\n",
    "                segs = make_segments(gf)\n",
    "                for seg in segs:\n",
    "                    start, end = seg[\"start\"], seg[\"end\"]\n",
    "                    if start == end:  # 單點至少給 1 分鐘寬度，避免看不見\n",
    "                        end = start + pd.Timedelta(minutes=1)\n",
    "                    color = DARK_GRAY if seg[\"useable\"] == 1 else LIGHT_GRAY\n",
    "                    ax.hlines(y, start, end, colors=color, linewidth=6, zorder=2)\n",
    "\n",
    "                # 計算該「年×檔」的 useable=1 百分比（使用調整後）\n",
    "                total_rows = len(gf)\n",
    "                usable_rows = int((gf[\"useable_adj\"] == 1).sum())\n",
    "                ratio = (usable_rows / total_rows * 100.0) if total_rows > 0 else 0.0\n",
    "                ax.text(x_max + pad * 0.5, y, f\"{ratio:.1f}%\", va=\"center\", ha=\"left\",\n",
    "                        color=DARK_GRAY, fontsize=9)\n",
    "\n",
    "            # 樣式\n",
    "            ax.set_title(f\"Usable status over time by file — Year {yr}  \"\n",
    "                         f\"(files {page_start+1}-{page_start+len(batch_files)} of {len(files_in_year)})\",\n",
    "                         fontsize=12, pad=12)\n",
    "            ax.set_xlabel(\"Time\")\n",
    "            ax.set_yticks(yticks)\n",
    "            ax.set_yticklabels(ylabels, fontsize=9)\n",
    "            ax.grid(True, axis=\"x\", linestyle=\"--\", linewidth=0.6, alpha=0.5)\n",
    "            ax.set_axisbelow(True)\n",
    "\n",
    "            from matplotlib.patches import Patch\n",
    "            legend_handles = [\n",
    "                Patch(facecolor=DARK_GRAY, edgecolor=DARK_GRAY, label=\"useable = 1\"),\n",
    "                Patch(facecolor=LIGHT_GRAY, edgecolor=LIGHT_GRAY, label=\"useable = 0\"),\n",
    "            ]\n",
    "            ax.legend(handles=legend_handles, loc=\"upper right\", frameon=False)\n",
    "\n",
    "            plt.tight_layout()\n",
    "\n",
    "            # 輸出圖片\n",
    "            out_file = os.path.join(OUT_DIR, f\"useable_timeline_{yr}_p{page_start//MAX_PER_FIG + 1}.png\")\n",
    "            fig.savefig(out_file, dpi=150)\n",
    "            plt.close(fig)\n",
    "\n",
    "            total_figs += 1\n",
    "            year_page_count[yr] = year_page_count.get(yr, 0) + 1\n",
    "\n",
    "    # 圖片輸出統計\n",
    "    print(f\"\\n✅ Exported {total_figs} figure(s) to: {OUT_DIR}\")\n",
    "    print(f\"   Year count: {len(years)}\")\n",
    "    for yr in years:\n",
    "        print(f\"   - {yr}: {year_page_count.get(yr, 0)} figure(s)\")\n",
    "else:\n",
    "    print(\"\\n[Info] No data to plot (no valid senddate/useable_adj found).\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "7d52c75f-db96-4700-a2cf-55f3f74201e1",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "=== Global value ranges & counts (valid-only, across all files) ===\n",
      "      feature  count_valid    count_nan   min   p1   p5  p25  median  p75  p95   p99     max\n",
      "rrhzsetactual      1526318 see-per-file -15.0  8.0 10.0 14.0    17.0 21.0 28.0  33.0   150.0\n",
      "  mvsetactual      1523521 see-per-file   0.0  3.4  4.6  6.5     8.0  9.8 13.4  17.0    90.1\n",
      "     peepepap      1526564 see-per-file   3.0  5.0  5.0  5.0     5.0  8.0 12.0  14.0    40.0\n",
      "        ppeak      1526089 see-per-file   0.0 12.0 14.0 18.0    22.0 26.0 33.0  36.0 65241.0\n",
      "         cdyn      1416344 see-per-file   0.0  0.0  7.2 24.0    32.6 43.4 77.3 135.0  6263.6\n",
      "        pmean      1424622 see-per-file   0.0  6.0  7.0  8.9    11.0 14.0 20.0  22.0 65241.0\n",
      "\n",
      "=== Negative value counts (< 0) ===\n",
      "- rrhzsetactual: total negatives = 2\n",
      "- mvsetactual: total negatives = 0\n",
      "- peepepap: total negatives = 0\n",
      "- ppeak: total negatives = 0\n",
      "- cdyn: total negatives = 0\n",
      "- pmean: total negatives = 0\n",
      "\n",
      "[Files with negatives only] (feature → file: count)\n",
      "\n",
      "rrhzsetactual:\n",
      "  - 089271.csv: 1\n",
      "  - PatNo_ID_1566911879.csv: 1\n",
      "\n",
      "✅ Charts saved to: /home/jovyan/RT08/0925/1002/value_ranges\n",
      "   - hist_{feature}.png (per feature)\n",
      "   - boxplots_all_features.png\n",
      "   - neg_counts_by_feature.png\n",
      "   - neg_top_files_{feature}.png (only for features with negatives)\n"
     ]
    }
   ],
   "source": [
    "\"\"\"我想看一下目前所有資料的值域落在哪裡 \"rrhzsetactual\", \"mvsetactual\",\"peepepap\", \"ppeak\", \"cdyn\", \"pmean\"欄位有負數，筆數多少\n",
    "為之後合理值域做準備\n",
    "值域總覽 + 視覺化（不寫回原始資料）：\n",
    "1) 掃描 /home/jovyan/RT08/0925/bling_useable/*.csv\n",
    "2) 針對欄位：rrhzsetactual, mvsetactual, peepepap, ppeak, cdyn, pmean\n",
    "   - 列印全域值域概況（min/p1/p5/p25/median/p75/p95/p99/max）\n",
    "   - 列印負數筆數（跨檔總計 + 僅列出有負數的檔案）\n",
    "3) 圖表輸出到 /home/jovyan/RT08/0925/1002/value_ranges/\n",
    "   - 每欄一張直方圖（含分位數參考線）\n",
    "   - 六欄合併箱型圖（一張）\n",
    "   - 各欄位負數筆數長條圖（一張）\n",
    "   - 每欄位 Top-N 檔案負數筆數長條圖（每欄最多一張）\n",
    "\"\"\"\n",
    "import os, glob\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# ========== 參數 ==========\n",
    "DATA_DIR = \"/home/jovyan/RT08/0925/bling_useable\"\n",
    "OUT_DIR  = \"/home/jovyan/RT08/0925/1002/value_ranges\"\n",
    "os.makedirs(OUT_DIR, exist_ok=True)\n",
    "\n",
    "TARGET_COLS = [\"rrhzsetactual\", \"mvsetactual\",\"peepepap\", \"ppeak\", \"cdyn\", \"pmean\"]\n",
    "TIME_COL = \"senddate\"  # 僅用於對齊/資訊，不影響統計\n",
    "USECOLS = list(set(TARGET_COLS + [TIME_COL]))\n",
    "\n",
    "# 圖表參數\n",
    "TOP_N_FILES = 15           # Top-N 檔案（負數筆數）展示\n",
    "HIST_BINS   = 60           # 直方圖箱數（可依資料量調整）\n",
    "SAMPLE_MAX  = 2_000_000    # 若單欄有效值過大，直方圖取樣上限（僅影響繪圖，不影響統計分位數）\n",
    "\n",
    "# ========== 工具 ==========\n",
    "def to_numeric_series(s: pd.Series) -> pd.Series:\n",
    "    \"\"\"將 series 轉為數值；把 '', 'null', '(null)' 視為 NaN；保留 NaN。\"\"\"\n",
    "    if s is None:\n",
    "        return pd.Series(dtype=\"float64\")\n",
    "    s = s.replace(r'^\\s*$', np.nan, regex=True)\n",
    "    s = s.replace([\"null\", \"NULL\", \"(null)\", \"(NULL)\"], np.nan)\n",
    "    return pd.to_numeric(s, errors=\"coerce\")\n",
    "\n",
    "# ========== 掃描與彙整 ==========\n",
    "global_values = {c: [] for c in TARGET_COLS}  # 全域值（有效值，用於分位數與繪圖）\n",
    "neg_counts_by_file = {c: {} for c in TARGET_COLS}  # 每欄位各檔案的負數筆數\n",
    "\n",
    "file_paths = sorted(glob.glob(os.path.join(DATA_DIR, \"*.csv\")))\n",
    "if not file_paths:\n",
    "    print(f\"[Info] No CSV files found in: {DATA_DIR}\")\n",
    "\n",
    "for fp in file_paths:\n",
    "    base = os.path.basename(fp)\n",
    "    try:\n",
    "        df = pd.read_csv(fp, usecols=USECOLS, low_memory=False)\n",
    "    except Exception:\n",
    "        try:\n",
    "            df = pd.read_csv(fp, low_memory=False)\n",
    "        except Exception:\n",
    "            print(f\"[Skip] Cannot read file: {base}\")\n",
    "            continue\n",
    "\n",
    "    # 逐欄轉數值、彙整\n",
    "    for col in TARGET_COLS:\n",
    "        if col not in df.columns:\n",
    "            continue\n",
    "        s = to_numeric_series(df[col])\n",
    "\n",
    "        # 全域值（有效值）\n",
    "        valid = s.dropna()\n",
    "        if not valid.empty:\n",
    "            global_values[col].append(valid)\n",
    "\n",
    "        # 負數計數\n",
    "        neg_n = int((s < 0).sum())\n",
    "        if neg_n > 0:\n",
    "            neg_counts_by_file[col][base] = neg_n\n",
    "\n",
    "# ========== 1) 列印全域值域概況 ==========\n",
    "print(\"\\n=== Global value ranges & counts (valid-only, across all files) ===\")\n",
    "summary_rows = []\n",
    "for col in TARGET_COLS:\n",
    "    if not global_values[col]:\n",
    "        summary_rows.append({\n",
    "            \"feature\": col,\n",
    "            \"count_valid\": 0, \"count_nan\": \"see-per-file\",\n",
    "            \"min\": \"n/a\", \"p1\": \"n/a\", \"p5\": \"n/a\", \"p25\": \"n/a\",\n",
    "            \"median\": \"n/a\", \"p75\": \"n/a\", \"p95\": \"n/a\", \"p99\": \"n/a\", \"max\": \"n/a\"\n",
    "        })\n",
    "        continue\n",
    "\n",
    "    all_vals = pd.concat(global_values[col], ignore_index=True)\n",
    "    cnt_valid = int(all_vals.shape[0])\n",
    "    q = all_vals.quantile([0.01, 0.05, 0.25, 0.50, 0.75, 0.95, 0.99]).to_dict()\n",
    "    summary_rows.append({\n",
    "        \"feature\": col,\n",
    "        \"count_valid\": cnt_valid,\n",
    "        \"count_nan\": \"see-per-file\",\n",
    "        \"min\": float(all_vals.min()),\n",
    "        \"p1\": float(q.get(0.01)),\n",
    "        \"p5\": float(q.get(0.05)),\n",
    "        \"p25\": float(q.get(0.25)),\n",
    "        \"median\": float(q.get(0.50)),\n",
    "        \"p75\": float(q.get(0.75)),\n",
    "        \"p95\": float(q.get(0.95)),\n",
    "        \"p99\": float(q.get(0.99)),\n",
    "        \"max\": float(all_vals.max()),\n",
    "    })\n",
    "\n",
    "summary_df = pd.DataFrame(summary_rows, columns=[\n",
    "    \"feature\",\"count_valid\",\"count_nan\",\"min\",\"p1\",\"p5\",\"p25\",\"median\",\"p75\",\"p95\",\"p99\",\"max\"\n",
    "])\n",
    "print(summary_df.to_string(index=False))\n",
    "\n",
    "# ========== 2) 列印負數筆數 ==========\n",
    "print(\"\\n=== Negative value counts (< 0) ===\")\n",
    "total_neg = {}\n",
    "for col in TARGET_COLS:\n",
    "    total_neg[col] = sum(neg_counts_by_file[col].values())\n",
    "    print(f\"- {col}: total negatives = {total_neg[col]}\")\n",
    "\n",
    "print(\"\\n[Files with negatives only] (feature → file: count)\")\n",
    "for col in TARGET_COLS:\n",
    "    file_neg_map = neg_counts_by_file[col]\n",
    "    if not file_neg_map:\n",
    "        continue\n",
    "    print(f\"\\n{col}:\")\n",
    "    for fname, n in sorted(file_neg_map.items(), key=lambda x: x[1], reverse=True):\n",
    "        print(f\"  - {fname}: {n}\")\n",
    "\n",
    "# ========== 3) 繪圖：每欄直方圖（含分位數） ==========\n",
    "def plot_hist_with_quantiles(values: pd.Series, feature: str, out_dir: str):\n",
    "    \"\"\"\n",
    "    直方圖 + 分位數參考線（p1/p5/median/p95/p99）。\n",
    "    為避免記憶體壓力，只在繪圖時（必要時）做取樣，不影響統計結果。\n",
    "    \"\"\"\n",
    "    if values.empty:\n",
    "        return\n",
    "    vals = values\n",
    "    if len(vals) > SAMPLE_MAX:\n",
    "        vals = vals.sample(SAMPLE_MAX, random_state=42)\n",
    "\n",
    "    q = values.quantile([0.01, 0.05, 0.50, 0.95, 0.99])\n",
    "    fig, ax = plt.subplots(figsize=(10, 5))\n",
    "    ax.hist(vals, bins=HIST_BINS)\n",
    "    ax.set_title(f\"{feature} — Histogram (valid-only)\")\n",
    "    ax.set_xlabel(feature)\n",
    "    ax.set_ylabel(\"Count\")\n",
    "\n",
    "    # 參考線（顏色刻意不指定，使用預設）\n",
    "    for pct, label in zip([0.01, 0.05, 0.50, 0.95, 0.99], [\"p1\", \"p5\", \"median\", \"p95\", \"p99\"]):\n",
    "        v = q.loc[pct]\n",
    "        ax.axvline(v, linestyle=\"--\")\n",
    "        ax.text(v, ax.get_ylim()[1]*0.95, label, rotation=90, va=\"top\", ha=\"right\")\n",
    "\n",
    "    fig.tight_layout()\n",
    "    fig.savefig(os.path.join(out_dir, f\"hist_{feature}.png\"), dpi=150)\n",
    "    plt.close(fig)\n",
    "\n",
    "# 建立直方圖\n",
    "for col in TARGET_COLS:\n",
    "    if not global_values[col]:\n",
    "        continue\n",
    "    all_vals = pd.concat(global_values[col], ignore_index=True)\n",
    "    plot_hist_with_quantiles(all_vals, col, OUT_DIR)\n",
    "\n",
    "# ========== 4) 繪圖：六欄合併箱型圖 ==========\n",
    "# 只使用每欄有效值；為避免極端離群嚴重擠壓，可選擇對 y 限制（此處先不限制，讓你觀察原貌）\n",
    "box_vals = []\n",
    "box_labels = []\n",
    "for col in TARGET_COLS:\n",
    "    if not global_values[col]:\n",
    "        continue\n",
    "    all_vals = pd.concat(global_values[col], ignore_index=True)\n",
    "    box_vals.append(all_vals.values)\n",
    "    box_labels.append(col)\n",
    "\n",
    "if box_vals:\n",
    "    fig, ax = plt.subplots(figsize=(12, 6))\n",
    "    ax.boxplot(box_vals, vert=True, labels=box_labels, showfliers=True)\n",
    "    ax.set_title(\"Value ranges — Boxplots (valid-only)\")\n",
    "    ax.set_ylabel(\"Value\")\n",
    "    fig.tight_layout()\n",
    "    fig.savefig(os.path.join(OUT_DIR, \"boxplots_all_features.png\"), dpi=150)\n",
    "    plt.close(fig)\n",
    "\n",
    "# ========== 5) 繪圖：各欄位負數筆數長條圖（跨檔總計） ==========\n",
    "neg_totals = [total_neg.get(col, 0) for col in TARGET_COLS]\n",
    "fig, ax = plt.subplots(figsize=(10, 5))\n",
    "ax.bar(TARGET_COLS, neg_totals)\n",
    "ax.set_title(\"Negative counts per feature (<0)\")\n",
    "ax.set_xlabel(\"Feature\")\n",
    "ax.set_ylabel(\"Negative count\")\n",
    "fig.tight_layout()\n",
    "fig.savefig(os.path.join(OUT_DIR, \"neg_counts_by_feature.png\"), dpi=150)\n",
    "plt.close(fig)\n",
    "\n",
    "# ========== 6) 繪圖：每欄位 Top-N 檔案（負數筆數） ==========\n",
    "for col in TARGET_COLS:\n",
    "    file_neg_map = neg_counts_by_file[col]\n",
    "    if not file_neg_map:\n",
    "        continue\n",
    "    top_items = sorted(file_neg_map.items(), key=lambda x: x[1], reverse=True)[:TOP_N_FILES]\n",
    "    labels = [k for k, _ in top_items]\n",
    "    counts = [v for _, v in top_items]\n",
    "\n",
    "    # 橫向長條圖（檔名長時可讀性更好）\n",
    "    fig, ax = plt.subplots(figsize=(12, max(4, 0.4*len(labels)+1)))\n",
    "    y_pos = np.arange(len(labels))\n",
    "    ax.barh(y_pos, counts)\n",
    "    ax.set_yticks(y_pos)\n",
    "    ax.set_yticklabels(labels)\n",
    "    ax.invert_yaxis()  # 最大在上\n",
    "    ax.set_title(f\"Top-{TOP_N_FILES} files with negatives — {col}\")\n",
    "    ax.set_xlabel(\"Negative count\")\n",
    "    ax.set_ylabel(\"File\")\n",
    "    fig.tight_layout()\n",
    "    fig.savefig(os.path.join(OUT_DIR, f\"neg_top_files_{col}.png\"), dpi=150)\n",
    "    plt.close(fig)\n",
    "\n",
    "# ========== 7) 完成訊息 ==========\n",
    "print(f\"\\n✅ Charts saved to: {OUT_DIR}\")\n",
    "print(\"   - hist_{feature}.png (per feature)\")\n",
    "print(\"   - boxplots_all_features.png\")\n",
    "print(\"   - neg_counts_by_feature.png\")\n",
    "print(\"   - neg_top_files_{feature}.png (only for features with negatives)\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5fe1f18b-8fc9-4b8b-a548-b9f8bd5ce9cd",
   "metadata": {},
   "outputs": [],
   "source": [
    "1. 整體值域分布（Global value ranges & counts）\n",
    "rrhzsetactual (呼吸頻率設定，次/分)\n",
    "合理值通常應在 8–40 左右。\n",
    "你的數據：p1=8, median=17, p99=33（整體合理）。\n",
    "但最大值有到 150，以及少數 負數 -15 → 可能是記錄或轉碼錯誤\n",
    "\n",
    "mvsetactual (分鐘通氣量，L/min)\n",
    "常見範圍 3–15。\n",
    "你的數據：p5=4.6, median=8.0, p95=13.4，符合醫學常識\n",
    "最大值 90.1 → 明顯超過臨床合理值，需要檢查\n",
    "\n",
    "peepepap (呼氣末正壓 PEEP，cmH₂O)\n",
    "通常 3–20。\n",
    "你的數據：大多數固定在 5（典型預設值），p95=12，max=40 → 上限值超過臨床常見值，但不是極端誇張。\n",
    "ppeak (吸氣峰壓 Peak Pressure，cmH₂O)\n",
    "臨床上常見 15–35。\n",
    "你的數據：p25=18, median=22, p95=33 → 主體合理\n",
    "但最大值 65241 → 顯然是錯誤輸入或資料轉換 bug\n",
    "\n",
    "cdyn (動態順應性 Dynamic Compliance，mL/cmH₂O)\n",
    "常見值 20–80\n",
    "你的數據：p25=24, median=32.6, p95=77 → 非常合理\n",
    "但 max=6263.6 → 異常極值，需過濾\n",
    "\n",
    "pmean (平均氣道壓 Mean Airway Pressure，cmH₂O)\n",
    "一般 6–20。\n",
    "你的數據：median=11, p95=20 → 符合臨床\n",
    "但 max=65241 → 與 ppeak 一樣，顯然是誤植值\n",
    "\n",
    "2. 負數盤點\n",
    "只有 rrhzsetactual 出現過 2 筆負值，分布在：\n",
    "089271.csv: 1 筆\n",
    "PatNo_ID_1566911879.csv: 1 筆\n",
    "→ 呼吸頻率不可能是負數，應直接標記為無效或剔除"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "d0c9c5a9-0722-439f-8d81-cbcc292c516e",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Info] Found 122 files to scan.\n",
      "\n",
      "=== Global value ranges & counts (valid-only, across all files) ===\n",
      "feature  count_valid      min    p1    p5   p25  median   p75   p95   p99     max\n",
      "    vti      1303731 -20849.0 151.0 280.0 397.0   473.0 550.0 727.0 990.0 20778.0\n",
      "    vte      1415003 -10182.0  37.0 241.0 394.0   475.0 562.0 758.0 992.0 16795.0\n",
      " sponvt       174830      0.0   0.0   0.0   1.0     1.0   1.0 390.0 588.0  1470.0\n",
      "\n",
      "=== Negative value counts (<0) ===\n",
      "- vti: total negatives = 21\n",
      "  - PatNo_ID_1566911879.csv: 4\n",
      "  - PatNo_ID_1581019504.csv: 4\n",
      "  - PatNo_ID_1588673465.csv: 4\n",
      "  - PatNo_ID_1567804800.csv: 2\n",
      "  - PatNo_ID_1582937076.csv: 2\n",
      "  - PatNo_ID_1594511914.csv: 2\n",
      "  - PatNo_ID_1572481361.csv: 1\n",
      "  - PatNo_ID_1580244614.csv: 1\n",
      "  - PatNo_ID_1589324603.csv: 1\n",
      "- vte: total negatives = 37\n",
      "  - PatNo_ID_1581633231.csv: 21\n",
      "  - PatNo_ID_1564148644.csv: 7\n",
      "  - PatNo_ID_1594294180.csv: 5\n",
      "  - PatNo_ID_1580096720.csv: 1\n",
      "  - PatNo_ID_1580244614.csv: 1\n",
      "  - PatNo_ID_1581003248.csv: 1\n",
      "  - PatNo_ID_1589918099.csv: 1\n",
      "- sponvt: total negatives = 0\n"
     ]
    }
   ],
   "source": [
    "import os, glob\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "DATA_DIR = \"/home/jovyan/RT08/0925/bling_useable\"\n",
    "TARGET_COLS = [\"vti\", \"vte\", \"sponvt\"]\n",
    "\n",
    "def to_numeric(s: pd.Series) -> pd.Series:\n",
    "    \"\"\"把 series 轉成數值；空字串、'null'、'(null)' → NaN\"\"\"\n",
    "    if s is None:\n",
    "        return pd.Series(dtype=\"float64\")\n",
    "    s = s.replace(r'^\\s*$', np.nan, regex=True)\n",
    "    s = s.replace([\"null\", \"NULL\", \"(null)\", \"(NULL)\"], np.nan)\n",
    "    return pd.to_numeric(s, errors=\"coerce\")\n",
    "\n",
    "# 收集資料\n",
    "global_values = {c: [] for c in TARGET_COLS}\n",
    "neg_counts = {c: {} for c in TARGET_COLS}\n",
    "\n",
    "file_paths = sorted(glob.glob(os.path.join(DATA_DIR, \"*.csv\")))\n",
    "print(f\"[Info] Found {len(file_paths)} files to scan.\")\n",
    "\n",
    "for fp in file_paths:\n",
    "    base = os.path.basename(fp)\n",
    "    try:\n",
    "        df = pd.read_csv(fp, usecols=TARGET_COLS, low_memory=False)\n",
    "    except Exception:\n",
    "        try:\n",
    "            df = pd.read_csv(fp, low_memory=False)\n",
    "        except Exception:\n",
    "            continue\n",
    "        df = df[[c for c in TARGET_COLS if c in df.columns]]\n",
    "    \n",
    "    for col in TARGET_COLS:\n",
    "        if col not in df.columns:\n",
    "            continue\n",
    "        s = to_numeric(df[col])\n",
    "        valid = s.dropna()\n",
    "        if not valid.empty:\n",
    "            global_values[col].append(valid)\n",
    "        neg_n = int((s < 0).sum())\n",
    "        if neg_n > 0:\n",
    "            neg_counts[col][base] = neg_n\n",
    "\n",
    "# 彙總值域\n",
    "print(\"\\n=== Global value ranges & counts (valid-only, across all files) ===\")\n",
    "summary_rows = []\n",
    "for col in TARGET_COLS:\n",
    "    if not global_values[col]:\n",
    "        summary_rows.append({\n",
    "            \"feature\": col,\n",
    "            \"count_valid\": 0,\n",
    "            \"min\": \"n/a\",\"p1\":\"n/a\",\"p5\":\"n/a\",\"p25\":\"n/a\",\n",
    "            \"median\":\"n/a\",\"p75\":\"n/a\",\"p95\":\"n/a\",\"p99\":\"n/a\",\"max\":\"n/a\"\n",
    "        })\n",
    "        continue\n",
    "    all_vals = pd.concat(global_values[col], ignore_index=True)\n",
    "    cnt_valid = int(all_vals.shape[0])\n",
    "    q = all_vals.quantile([0.01,0.05,0.25,0.5,0.75,0.95,0.99]).to_dict()\n",
    "    summary_rows.append({\n",
    "        \"feature\": col,\n",
    "        \"count_valid\": cnt_valid,\n",
    "        \"min\": float(all_vals.min()),\n",
    "        \"p1\": float(q.get(0.01)),\n",
    "        \"p5\": float(q.get(0.05)),\n",
    "        \"p25\": float(q.get(0.25)),\n",
    "        \"median\": float(q.get(0.5)),\n",
    "        \"p75\": float(q.get(0.75)),\n",
    "        \"p95\": float(q.get(0.95)),\n",
    "        \"p99\": float(q.get(0.99)),\n",
    "        \"max\": float(all_vals.max())\n",
    "    })\n",
    "\n",
    "summary_df = pd.DataFrame(summary_rows)\n",
    "print(summary_df.to_string(index=False))\n",
    "\n",
    "# 列出負數統計\n",
    "print(\"\\n=== Negative value counts (<0) ===\")\n",
    "for col in TARGET_COLS:\n",
    "    total_neg = sum(neg_counts[col].values())\n",
    "    print(f\"- {col}: total negatives = {total_neg}\")\n",
    "    if total_neg > 0:\n",
    "        for fname, n in sorted(neg_counts[col].items(), key=lambda x: x[1], reverse=True):\n",
    "            print(f\"  - {fname}: {n}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "95971059-f903-4640-bf34-ce96d114939c",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2656af72-caf5-47a7-a7d0-16295a2a6521",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "735c75e8-d56c-4fc5-965a-8481f0b61077",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    }
   },
   "outputs": [],
   "source": [
    "import os, glob\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "DATA_DIR = \"/home/jovyan/RT08/0925/bling_useable\"\n",
    "TARGET_COLS = [\"vti\", \"vte\", \"sponvt\"]\n",
    "\n",
    "def to_numeric(s: pd.Series) -> pd.Series:\n",
    "    \"\"\"把 series 轉成數值；空字串、'null'、'(null)' → NaN\"\"\"\n",
    "    if s is None:\n",
    "        return pd.Series(dtype=\"float64\")\n",
    "    s = s.replace(r'^\\s*$', np.nan, regex=True)\n",
    "    s = s.replace([\"null\", \"NULL\", \"(null)\", \"(NULL)\"], np.nan)\n",
    "    return pd.to_numeric(s, errors=\"coerce\")\n",
    "\n",
    "# 收集資料\n",
    "global_values = {c: [] for c in TARGET_COLS}\n",
    "neg_counts = {c: {} for c in TARGET_COLS}\n",
    "\n",
    "file_paths = sorted(glob.glob(os.path.join(DATA_DIR, \"*.csv\")))\n",
    "print(f\"[Info] Found {len(file_paths)} files to scan.\")\n",
    "\n",
    "for fp in file_paths:\n",
    "    base = os.path.basename(fp)\n",
    "    try:\n",
    "        df = pd.read_csv(fp, usecols=TARGET_COLS, low_memory=False)\n",
    "    except Exception:\n",
    "        try:\n",
    "            df = pd.read_csv(fp, low_memory=False)\n",
    "        except Exception:\n",
    "            continue\n",
    "        df = df[[c for c in TARGET_COLS if c in df.columns]]\n",
    "    \n",
    "    for col in TARGET_COLS:\n",
    "        if col not in df.columns:\n",
    "            continue\n",
    "        s = to_numeric(df[col])\n",
    "        valid = s.dropna()\n",
    "        if not valid.empty:\n",
    "            global_values[col].append(valid)\n",
    "        neg_n = int((s < 0).sum())\n",
    "        if neg_n > 0:\n",
    "            neg_counts[col][base] = neg_n\n",
    "\n",
    "# 彙總值域\n",
    "print(\"\\n=== Global value ranges & counts (valid-only, across all files) ===\")\n",
    "summary_rows = []\n",
    "for col in TARGET_COLS:\n",
    "    if not global_values[col]:\n",
    "        summary_rows.append({\n",
    "            \"feature\": col,\n",
    "            \"count_valid\": 0,\n",
    "            \"min\": \"n/a\",\"p1\":\"n/a\",\"p5\":\"n/a\",\"p25\":\"n/a\",\n",
    "            \"median\":\"n/a\",\"p75\":\"n/a\",\"p95\":\"n/a\",\"p99\":\"n/a\",\"max\":\"n/a\"\n",
    "        })\n",
    "        continue\n",
    "    all_vals = pd.concat(global_values[col], ignore_index=True)\n",
    "    cnt_valid = int(all_vals.shape[0])\n",
    "    q = all_vals.quantile([0.01,0.05,0.25,0.5,0.75,0.95,0.99]).to_dict()\n",
    "    summary_rows.append({\n",
    "        \"feature\": col,\n",
    "        \"count_valid\": cnt_valid,\n",
    "        \"min\": float(all_vals.min()),\n",
    "        \"p1\": float(q.get(0.01)),\n",
    "        \"p5\": float(q.get(0.05)),\n",
    "        \"p25\": float(q.get(0.25)),\n",
    "        \"median\": float(q.get(0.5)),\n",
    "        \"p75\": float(q.get(0.75)),\n",
    "        \"p95\": float(q.get(0.95)),\n",
    "        \"p99\": float(q.get(0.99)),\n",
    "        \"max\": float(all_vals.max())\n",
    "    })\n",
    "\n",
    "summary_df = pd.DataFrame(summary_rows)\n",
    "print(summary_df.to_string(index=False))\n",
    "\n",
    "# 列出負數統計\n",
    "print(\"\\n=== Negative value counts (<0) ===\")\n",
    "for col in TARGET_COLS:\n",
    "    total_neg = sum(neg_counts[col].values())\n",
    "    print(f\"- {col}: total negatives = {total_neg}\")\n",
    "    if total_neg > 0:\n",
    "        for fname, n in sorted(neg_counts[col].items(), key=lambda x: x[1], reverse=True):\n",
    "            print(f\"  - {fname}: {n}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a5243b71-2b8f-4449-9adc-715918cd3082",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "71ef4550-061f-4ecc-8847-139f396d5a3d",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cf1deccd-6acf-4a6e-b46c-dd07d916c7fb",
   "metadata": {},
   "outputs": [],
   "source": [
    "我想看目前資料useable=0的時間最短 最常是多久，看總體以及分細項的"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "bb1a6e20-6429-4c35-b9b4-bf5f8ca4d88c",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Info] Found 122 files to check.\n",
      "\n",
      "[File] 089271.csv (rows=32419)\n",
      "  - rrhzsetactual: 51 nulls (0.16%)\n",
      "  - mvsetactual: 51 nulls (0.16%)\n",
      "  - peepepap: 49 nulls (0.15%)\n",
      "  - ppeak: 51 nulls (0.16%)\n",
      "  - cdyn: 421 nulls (1.30%)\n",
      "  - vti: 1004 nulls (3.10%)\n",
      "  - pmean: 51 nulls (0.16%)\n",
      "  - vte: 695 nulls (2.14%)\n",
      "  - sponvt: 27968 nulls (86.27%)\n",
      "  - svv: 28611 nulls (88.25%)\n",
      "\n",
      "[File] 095323.csv (rows=23791)\n",
      "  - rrhzsetactual: 6 nulls (0.03%)\n",
      "  - mvsetactual: 6 nulls (0.03%)\n",
      "  - ppeak: 7 nulls (0.03%)\n",
      "  - cdyn: 4310 nulls (18.12%)\n",
      "  - vti: 4077 nulls (17.14%)\n",
      "  - pmean: 4077 nulls (17.14%)\n",
      "  - vte: 6 nulls (0.03%)\n",
      "  - sponvt: 23791 nulls (100.00%)\n",
      "  - svv: 23791 nulls (100.00%)\n",
      "\n",
      "[File] 095707.csv (rows=20180)\n",
      "  - rrhzsetactual: 1 nulls (0.00%)\n",
      "  - mvsetactual: 1 nulls (0.00%)\n",
      "  - ppeak: 3 nulls (0.01%)\n",
      "  - cdyn: 772 nulls (3.83%)\n",
      "  - vti: 2236 nulls (11.08%)\n",
      "  - pmean: 1 nulls (0.00%)\n",
      "  - vte: 1600 nulls (7.93%)\n",
      "  - sponvt: 18581 nulls (92.08%)\n",
      "  - svv: 20180 nulls (100.00%)\n",
      "\n",
      "[File] 114309.csv (rows=71729)\n",
      "  - rrhzsetactual: 6 nulls (0.01%)\n",
      "  - mvsetactual: 6 nulls (0.01%)\n",
      "  - ppeak: 8 nulls (0.01%)\n",
      "  - cdyn: 49338 nulls (68.78%)\n",
      "  - vti: 51318 nulls (71.54%)\n",
      "  - pmean: 48583 nulls (67.73%)\n",
      "  - vte: 23484 nulls (32.74%)\n",
      "  - sponvt: 69674 nulls (97.14%)\n",
      "  - svv: 71729 nulls (100.00%)\n",
      "\n",
      "[File] 230933.csv (rows=30249)\n",
      "  - rrhzsetactual: 8 nulls (0.03%)\n",
      "  - mvsetactual: 8 nulls (0.03%)\n",
      "  - ppeak: 13 nulls (0.04%)\n",
      "  - cdyn: 415 nulls (1.37%)\n",
      "  - vti: 6527 nulls (21.58%)\n",
      "  - pmean: 8 nulls (0.03%)\n",
      "  - vte: 3127 nulls (10.34%)\n",
      "  - sponvt: 27130 nulls (89.69%)\n",
      "  - svv: 30249 nulls (100.00%)\n",
      "\n",
      "[File] 4216007.csv (rows=1433)\n",
      "  - rrhzsetactual: 906 nulls (63.22%)\n",
      "  - mvsetactual: 906 nulls (63.22%)\n",
      "  - peepepap: 906 nulls (63.22%)\n",
      "  - ppeak: 906 nulls (63.22%)\n",
      "  - cdyn: 1433 nulls (100.00%)\n",
      "  - vti: 906 nulls (63.22%)\n",
      "  - pmean: 906 nulls (63.22%)\n",
      "  - vte: 906 nulls (63.22%)\n",
      "  - sponvt: 1433 nulls (100.00%)\n",
      "  - svv: 1433 nulls (100.00%)\n",
      "\n",
      "[File] 7108162.csv (rows=239)\n",
      "  - vti: 104 nulls (43.51%)\n",
      "  - vte: 104 nulls (43.51%)\n",
      "  - sponvt: 135 nulls (56.49%)\n",
      "  - svv: 239 nulls (100.00%)\n",
      "\n",
      "[File] 7408338.csv (rows=1432)\n",
      "  - rrhzsetactual: 1 nulls (0.07%)\n",
      "  - mvsetactual: 1 nulls (0.07%)\n",
      "  - ppeak: 1 nulls (0.07%)\n",
      "  - vti: 1 nulls (0.07%)\n",
      "  - pmean: 1 nulls (0.07%)\n",
      "  - vte: 1 nulls (0.07%)\n",
      "  - sponvt: 1432 nulls (100.00%)\n",
      "  - svv: 1432 nulls (100.00%)\n",
      "\n",
      "[File] 7657698.csv (rows=1413)\n",
      "  - cdyn: 4 nulls (0.28%)\n",
      "  - vti: 126 nulls (8.92%)\n",
      "  - vte: 126 nulls (8.92%)\n",
      "  - sponvt: 1287 nulls (91.08%)\n",
      "  - svv: 1413 nulls (100.00%)\n",
      "\n",
      "[File] 7721164.csv (rows=483)\n",
      "\n",
      "[File] PatNo_ID_1560013303.csv (rows=2543)\n",
      "  - rrhzsetactual: 29 nulls (1.14%)\n",
      "  - mvsetactual: 28 nulls (1.10%)\n",
      "  - peepepap: 28 nulls (1.10%)\n",
      "  - ppeak: 28 nulls (1.10%)\n",
      "  - cdyn: 28 nulls (1.10%)\n",
      "  - vti: 175 nulls (6.88%)\n",
      "  - pmean: 28 nulls (1.10%)\n",
      "  - vte: 175 nulls (6.88%)\n",
      "  - sponvt: 2341 nulls (92.06%)\n",
      "  - svv: 2488 nulls (97.84%)\n",
      "\n",
      "[File] PatNo_ID_1562733396.csv (rows=2254)\n",
      "  - rrhzsetactual: 1 nulls (0.04%)\n",
      "  - mvsetactual: 1 nulls (0.04%)\n",
      "  - ppeak: 1 nulls (0.04%)\n",
      "  - cdyn: 9 nulls (0.40%)\n",
      "  - vti: 2 nulls (0.09%)\n",
      "  - pmean: 1 nulls (0.04%)\n",
      "  - vte: 2 nulls (0.09%)\n",
      "  - sponvt: 2254 nulls (100.00%)\n",
      "  - svv: 2254 nulls (100.00%)\n",
      "\n",
      "[File] PatNo_ID_1563587183.csv (rows=5287)\n",
      "  - senddate: 1 nulls (0.02%)\n",
      "  - ppeak: 2 nulls (0.04%)\n",
      "  - cdyn: 2 nulls (0.04%)\n",
      "  - vti: 1 nulls (0.02%)\n",
      "  - pmean: 1 nulls (0.02%)\n",
      "  - vte: 1 nulls (0.02%)\n",
      "  - sponvt: 5287 nulls (100.00%)\n",
      "  - svv: 5287 nulls (100.00%)\n",
      "\n",
      "[File] PatNo_ID_1564148644.csv (rows=17287)\n",
      "  - rrhzsetactual: 2903 nulls (16.79%)\n",
      "  - mvsetactual: 2896 nulls (16.75%)\n",
      "  - peepepap: 2896 nulls (16.75%)\n",
      "  - ppeak: 2903 nulls (16.79%)\n",
      "  - cdyn: 8498 nulls (49.16%)\n",
      "  - vti: 11442 nulls (66.19%)\n",
      "  - pmean: 8498 nulls (49.16%)\n",
      "  - vte: 5669 nulls (32.79%)\n",
      "  - sponvt: 17287 nulls (100.00%)\n",
      "  - svv: 17287 nulls (100.00%)\n",
      "\n",
      "[File] PatNo_ID_1565148312.csv (rows=5475)\n",
      "  - rrhzsetactual: 10 nulls (0.18%)\n",
      "  - mvsetactual: 11 nulls (0.20%)\n",
      "  - peepepap: 10 nulls (0.18%)\n",
      "  - ppeak: 10 nulls (0.18%)\n",
      "  - cdyn: 10 nulls (0.18%)\n",
      "  - vti: 10 nulls (0.18%)\n",
      "  - pmean: 10 nulls (0.18%)\n",
      "  - vte: 10 nulls (0.18%)\n",
      "  - sponvt: 5475 nulls (100.00%)\n",
      "  - svv: 5475 nulls (100.00%)\n",
      "\n",
      "[File] PatNo_ID_1565378038.csv (rows=2561)\n",
      "  - vti: 284 nulls (11.09%)\n",
      "  - vte: 283 nulls (11.05%)\n",
      "  - sponvt: 2278 nulls (88.95%)\n",
      "  - svv: 2561 nulls (100.00%)\n",
      "\n",
      "[File] PatNo_ID_1566123680.csv (rows=42600)\n",
      "  - rrhzsetactual: 3 nulls (0.01%)\n",
      "  - mvsetactual: 3 nulls (0.01%)\n",
      "  - ppeak: 27 nulls (0.06%)\n",
      "  - cdyn: 20721 nulls (48.64%)\n",
      "  - vti: 24911 nulls (58.48%)\n",
      "  - pmean: 20699 nulls (48.59%)\n",
      "  - vte: 4215 nulls (9.89%)\n",
      "  - sponvt: 42600 nulls (100.00%)\n",
      "  - svv: 42600 nulls (100.00%)\n",
      "\n",
      "[File] PatNo_ID_1566252197.csv (rows=3368)\n",
      "  - rrhzsetactual: 1433 nulls (42.55%)\n",
      "  - mvsetactual: 1433 nulls (42.55%)\n",
      "  - peepepap: 1433 nulls (42.55%)\n",
      "  - ppeak: 1433 nulls (42.55%)\n",
      "  - cdyn: 1433 nulls (42.55%)\n",
      "  - vti: 2898 nulls (86.05%)\n",
      "  - pmean: 1433 nulls (42.55%)\n",
      "  - vte: 2898 nulls (86.05%)\n",
      "  - sponvt: 1903 nulls (56.50%)\n",
      "  - svv: 3368 nulls (100.00%)\n",
      "\n",
      "[File] PatNo_ID_1566279967.csv (rows=1235)\n",
      "  - rrhzsetactual: 82 nulls (6.64%)\n",
      "  - mvsetactual: 82 nulls (6.64%)\n",
      "  - peepepap: 82 nulls (6.64%)\n",
      "  - ppeak: 82 nulls (6.64%)\n",
      "  - cdyn: 82 nulls (6.64%)\n",
      "  - vti: 82 nulls (6.64%)\n",
      "  - pmean: 82 nulls (6.64%)\n",
      "  - vte: 82 nulls (6.64%)\n",
      "  - sponvt: 1235 nulls (100.00%)\n",
      "  - svv: 1235 nulls (100.00%)\n",
      "\n",
      "[File] PatNo_ID_1566671274.csv (rows=25359)\n",
      "  - rrhzsetactual: 1805 nulls (7.12%)\n",
      "  - mvsetactual: 1805 nulls (7.12%)\n",
      "  - peepepap: 1805 nulls (7.12%)\n",
      "  - ppeak: 1805 nulls (7.12%)\n",
      "  - cdyn: 1805 nulls (7.12%)\n",
      "  - vti: 1810 nulls (7.14%)\n",
      "  - pmean: 1805 nulls (7.12%)\n",
      "  - vte: 1810 nulls (7.14%)\n",
      "  - sponvt: 25359 nulls (100.00%)\n",
      "  - svv: 25359 nulls (100.00%)\n",
      "\n",
      "[File] PatNo_ID_1566911879.csv (rows=53999)\n",
      "  - rrhzsetactual: 7295 nulls (13.51%)\n",
      "  - mvsetactual: 8023 nulls (14.86%)\n",
      "  - peepepap: 7294 nulls (13.51%)\n",
      "  - ppeak: 7294 nulls (13.51%)\n",
      "  - cdyn: 7674 nulls (14.21%)\n",
      "  - vti: 7346 nulls (13.60%)\n",
      "  - pmean: 7294 nulls (13.51%)\n",
      "  - vte: 8075 nulls (14.95%)\n",
      "  - sponvt: 53999 nulls (100.00%)\n",
      "  - svv: 53999 nulls (100.00%)\n",
      "\n",
      "[File] PatNo_ID_1567747650.csv (rows=10901)\n",
      "  - senddate: 1 nulls (0.01%)\n",
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      "  - mvsetactual: 2 nulls (0.02%)\n",
      "  - ppeak: 2 nulls (0.02%)\n",
      "  - cdyn: 123 nulls (1.13%)\n",
      "  - vti: 246 nulls (2.26%)\n",
      "  - pmean: 2 nulls (0.02%)\n",
      "  - vte: 246 nulls (2.26%)\n",
      "  - sponvt: 10657 nulls (97.76%)\n",
      "  - svv: 10901 nulls (100.00%)\n",
      "\n",
      "[File] PatNo_ID_1567804800.csv (rows=20578)\n",
      "  - senddate: 1 nulls (0.00%)\n",
      "  - rrhzsetactual: 188 nulls (0.91%)\n",
      "  - mvsetactual: 188 nulls (0.91%)\n",
      "  - peepepap: 188 nulls (0.91%)\n",
      "  - ppeak: 190 nulls (0.92%)\n",
      "  - cdyn: 195 nulls (0.95%)\n",
      "  - vti: 453 nulls (2.20%)\n",
      "  - pmean: 190 nulls (0.92%)\n",
      "  - vte: 453 nulls (2.20%)\n",
      "  - sponvt: 8903 nulls (43.26%)\n",
      "  - svv: 9166 nulls (44.54%)\n",
      "\n",
      "[File] PatNo_ID_1567832735.csv (rows=36580)\n",
      "  - senddate: 5 nulls (0.01%)\n",
      "  - rrhzsetactual: 6 nulls (0.02%)\n",
      "  - mvsetactual: 6 nulls (0.02%)\n",
      "  - ppeak: 21 nulls (0.06%)\n",
      "  - cdyn: 82 nulls (0.22%)\n",
      "  - vti: 103 nulls (0.28%)\n",
      "  - pmean: 6 nulls (0.02%)\n",
      "  - vte: 103 nulls (0.28%)\n",
      "  - sponvt: 36483 nulls (99.73%)\n",
      "  - svv: 36580 nulls (100.00%)\n",
      "\n",
      "[File] PatNo_ID_1568039398.csv (rows=34519)\n",
      "  - rrhzsetactual: 277 nulls (0.80%)\n",
      "  - mvsetactual: 277 nulls (0.80%)\n",
      "  - peepepap: 277 nulls (0.80%)\n",
      "  - ppeak: 277 nulls (0.80%)\n",
      "  - cdyn: 277 nulls (0.80%)\n",
      "  - vti: 667 nulls (1.93%)\n",
      "  - pmean: 277 nulls (0.80%)\n",
      "  - vte: 667 nulls (1.93%)\n",
      "  - sponvt: 34519 nulls (100.00%)\n",
      "  - svv: 34519 nulls (100.00%)\n",
      "\n",
      "[File] PatNo_ID_1568574099.csv (rows=13946)\n",
      "  - senddate: 1 nulls (0.01%)\n",
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      "  - mvsetactual: 82 nulls (0.59%)\n",
      "  - peepepap: 82 nulls (0.59%)\n",
      "  - ppeak: 82 nulls (0.59%)\n",
      "  - cdyn: 82 nulls (0.59%)\n",
      "  - vti: 574 nulls (4.12%)\n",
      "  - pmean: 82 nulls (0.59%)\n",
      "  - vte: 574 nulls (4.12%)\n",
      "  - sponvt: 8327 nulls (59.71%)\n",
      "  - svv: 8819 nulls (63.24%)\n",
      "\n",
      "[File] PatNo_ID_1568813269.csv (rows=4867)\n",
      "  - cdyn: 33 nulls (0.68%)\n",
      "  - vti: 408 nulls (8.38%)\n",
      "  - vte: 408 nulls (8.38%)\n",
      "  - sponvt: 4459 nulls (91.62%)\n",
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      "  - sponvt: 1184 nulls (100.00%)\n",
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      "  - pmean: 2 nulls (0.03%)\n",
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      "  - sponvt: 6232 nulls (81.46%)\n",
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      "  - senddate: 5 nulls (0.01%)\n",
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      "  - vte: 1725 nulls (4.17%)\n",
      "  - sponvt: 33105 nulls (80.07%)\n",
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      "  - mvsetactual: 88 nulls (0.83%)\n",
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      "  - pmean: 88 nulls (0.83%)\n",
      "  - vte: 2903 nulls (27.27%)\n",
      "  - sponvt: 4811 nulls (45.19%)\n",
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      "  - vti: 2453 nulls (34.30%)\n",
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      "  - rrhzsetactual: 95 nulls (0.99%)\n",
      "  - mvsetactual: 95 nulls (0.99%)\n",
      "  - peepepap: 93 nulls (0.97%)\n",
      "  - ppeak: 95 nulls (0.99%)\n",
      "  - cdyn: 135 nulls (1.41%)\n",
      "  - vti: 95 nulls (0.99%)\n",
      "  - pmean: 95 nulls (0.99%)\n",
      "  - vte: 95 nulls (0.99%)\n",
      "  - sponvt: 9587 nulls (100.00%)\n",
      "  - svv: 9587 nulls (100.00%)\n",
      "\n",
      "[File] PatNo_ID_1575445051.csv (rows=1801)\n",
      "  - senddate: 1 nulls (0.06%)\n",
      "  - rrhzsetactual: 15 nulls (0.83%)\n",
      "  - mvsetactual: 15 nulls (0.83%)\n",
      "  - peepepap: 15 nulls (0.83%)\n",
      "  - ppeak: 15 nulls (0.83%)\n",
      "  - cdyn: 15 nulls (0.83%)\n",
      "  - vti: 15 nulls (0.83%)\n",
      "  - pmean: 15 nulls (0.83%)\n",
      "  - vte: 15 nulls (0.83%)\n",
      "  - sponvt: 1801 nulls (100.00%)\n",
      "  - svv: 1801 nulls (100.00%)\n",
      "\n",
      "[File] PatNo_ID_1575502382.csv (rows=6604)\n",
      "  - rrhzsetactual: 115 nulls (1.74%)\n",
      "  - mvsetactual: 115 nulls (1.74%)\n",
      "  - peepepap: 115 nulls (1.74%)\n",
      "  - ppeak: 115 nulls (1.74%)\n",
      "  - cdyn: 115 nulls (1.74%)\n",
      "  - vti: 1408 nulls (21.32%)\n",
      "  - pmean: 115 nulls (1.74%)\n",
      "  - vte: 1408 nulls (21.32%)\n",
      "  - sponvt: 6604 nulls (100.00%)\n",
      "  - svv: 6604 nulls (100.00%)\n",
      "\n",
      "[File] PatNo_ID_1575975485.csv (rows=16459)\n",
      "  - rrhzsetactual: 1 nulls (0.01%)\n",
      "  - mvsetactual: 1 nulls (0.01%)\n",
      "  - ppeak: 5 nulls (0.03%)\n",
      "  - cdyn: 78 nulls (0.47%)\n",
      "  - vti: 1 nulls (0.01%)\n",
      "  - pmean: 1 nulls (0.01%)\n",
      "  - vte: 1 nulls (0.01%)\n",
      "  - sponvt: 16459 nulls (100.00%)\n",
      "  - svv: 16459 nulls (100.00%)\n",
      "\n",
      "[File] PatNo_ID_1576115572.csv (rows=18717)\n",
      "  - senddate: 2 nulls (0.01%)\n",
      "  - rrhzsetactual: 23 nulls (0.12%)\n",
      "  - mvsetactual: 690 nulls (3.69%)\n",
      "  - peepepap: 18 nulls (0.10%)\n",
      "  - ppeak: 19 nulls (0.10%)\n",
      "  - cdyn: 711 nulls (3.80%)\n",
      "  - vti: 1646 nulls (8.79%)\n",
      "  - pmean: 17 nulls (0.09%)\n",
      "  - vte: 2179 nulls (11.64%)\n",
      "  - sponvt: 15944 nulls (85.18%)\n",
      "  - svv: 17431 nulls (93.13%)\n",
      "\n",
      "[File] PatNo_ID_1576116479.csv (rows=1263)\n",
      "  - rrhzsetactual: 6 nulls (0.48%)\n",
      "  - mvsetactual: 6 nulls (0.48%)\n",
      "  - peepepap: 6 nulls (0.48%)\n",
      "  - ppeak: 6 nulls (0.48%)\n",
      "  - cdyn: 6 nulls (0.48%)\n",
      "  - vti: 6 nulls (0.48%)\n",
      "  - pmean: 6 nulls (0.48%)\n",
      "  - vte: 6 nulls (0.48%)\n",
      "  - sponvt: 1263 nulls (100.00%)\n",
      "  - svv: 1263 nulls (100.00%)\n",
      "\n",
      "[File] PatNo_ID_1576301569.csv (rows=3207)\n",
      "  - vti: 310 nulls (9.67%)\n",
      "  - vte: 310 nulls (9.67%)\n",
      "  - sponvt: 2897 nulls (90.33%)\n",
      "  - svv: 3207 nulls (100.00%)\n",
      "\n",
      "[File] PatNo_ID_1576964560.csv (rows=24192)\n",
      "  - senddate: 4 nulls (0.02%)\n",
      "  - vti: 5661 nulls (23.40%)\n",
      "  - vte: 421 nulls (1.74%)\n",
      "  - sponvt: 23771 nulls (98.26%)\n",
      "  - svv: 24192 nulls (100.00%)\n",
      "\n",
      "[File] PatNo_ID_1577042911.csv (rows=38357)\n",
      "  - rrhzsetactual: 2733 nulls (7.13%)\n",
      "  - mvsetactual: 2733 nulls (7.13%)\n",
      "  - peepepap: 2733 nulls (7.13%)\n",
      "  - ppeak: 2733 nulls (7.13%)\n",
      "  - cdyn: 2733 nulls (7.13%)\n",
      "  - vti: 2928 nulls (7.63%)\n",
      "  - pmean: 2733 nulls (7.13%)\n",
      "  - vte: 2928 nulls (7.63%)\n",
      "  - sponvt: 26865 nulls (70.04%)\n",
      "  - svv: 27060 nulls (70.55%)\n",
      "\n",
      "[File] PatNo_ID_1577487284.csv (rows=2345)\n",
      "  - cdyn: 16 nulls (0.68%)\n",
      "  - vti: 197 nulls (8.40%)\n",
      "  - vte: 197 nulls (8.40%)\n",
      "  - sponvt: 2345 nulls (100.00%)\n",
      "  - svv: 2345 nulls (100.00%)\n",
      "\n",
      "[File] PatNo_ID_1578784257.csv (rows=33917)\n",
      "  - senddate: 3 nulls (0.01%)\n",
      "  - rrhzsetactual: 5359 nulls (15.80%)\n",
      "  - mvsetactual: 5359 nulls (15.80%)\n",
      "  - peepepap: 5359 nulls (15.80%)\n",
      "  - ppeak: 5359 nulls (15.80%)\n",
      "  - cdyn: 5359 nulls (15.80%)\n",
      "  - vti: 6245 nulls (18.41%)\n",
      "  - pmean: 5359 nulls (15.80%)\n",
      "  - vte: 6245 nulls (18.41%)\n",
      "  - sponvt: 20948 nulls (61.76%)\n",
      "  - svv: 21830 nulls (64.36%)\n",
      "\n",
      "[File] PatNo_ID_1579198603.csv (rows=2046)\n",
      "  - senddate: 1 nulls (0.05%)\n",
      "  - cdyn: 6 nulls (0.29%)\n",
      "  - vti: 148 nulls (7.23%)\n",
      "  - vte: 148 nulls (7.23%)\n",
      "  - sponvt: 1898 nulls (92.77%)\n",
      "  - svv: 2046 nulls (100.00%)\n",
      "\n",
      "[File] PatNo_ID_1579498177.csv (rows=21688)\n",
      "  - rrhzsetactual: 7 nulls (0.03%)\n",
      "  - mvsetactual: 7 nulls (0.03%)\n",
      "  - ppeak: 6 nulls (0.03%)\n",
      "  - cdyn: 53 nulls (0.24%)\n",
      "  - vti: 3027 nulls (13.96%)\n",
      "  - pmean: 7 nulls (0.03%)\n",
      "  - vte: 3028 nulls (13.96%)\n",
      "  - sponvt: 18666 nulls (86.07%)\n",
      "  - svv: 21688 nulls (100.00%)\n",
      "\n",
      "[File] PatNo_ID_1580062580.csv (rows=2150)\n",
      "  - cdyn: 84 nulls (3.91%)\n",
      "  - vti: 1134 nulls (52.74%)\n",
      "  - vte: 1134 nulls (52.74%)\n",
      "  - sponvt: 1016 nulls (47.26%)\n",
      "  - svv: 2150 nulls (100.00%)\n",
      "\n",
      "[File] PatNo_ID_1580096720.csv (rows=1423)\n",
      "  - rrhzsetactual: 1 nulls (0.07%)\n",
      "  - ppeak: 1 nulls (0.07%)\n",
      "  - cdyn: 424 nulls (29.80%)\n",
      "  - vti: 1320 nulls (92.76%)\n",
      "  - pmean: 424 nulls (29.80%)\n",
      "  - vte: 215 nulls (15.11%)\n",
      "  - sponvt: 1423 nulls (100.00%)\n",
      "  - svv: 1423 nulls (100.00%)\n",
      "\n",
      "[File] PatNo_ID_1580107637.csv (rows=2698)\n",
      "  - rrhzsetactual: 2 nulls (0.07%)\n",
      "  - mvsetactual: 10 nulls (0.37%)\n",
      "  - ppeak: 2 nulls (0.07%)\n",
      "  - vti: 1567 nulls (58.08%)\n",
      "  - pmean: 2 nulls (0.07%)\n",
      "  - vte: 1567 nulls (58.08%)\n",
      "  - sponvt: 1091 nulls (40.44%)\n",
      "  - svv: 2648 nulls (98.15%)\n",
      "\n",
      "[File] PatNo_ID_1580244614.csv (rows=5278)\n",
      "  - rrhzsetactual: 254 nulls (4.81%)\n",
      "  - mvsetactual: 253 nulls (4.79%)\n",
      "  - peepepap: 252 nulls (4.77%)\n",
      "  - ppeak: 254 nulls (4.81%)\n",
      "  - cdyn: 1413 nulls (26.77%)\n",
      "  - vti: 1435 nulls (27.19%)\n",
      "  - pmean: 1413 nulls (26.77%)\n",
      "  - vte: 275 nulls (5.21%)\n",
      "  - sponvt: 5278 nulls (100.00%)\n",
      "  - svv: 5278 nulls (100.00%)\n",
      "\n",
      "[File] PatNo_ID_1580766093.csv (rows=18070)\n",
      "  - rrhzsetactual: 6 nulls (0.03%)\n",
      "  - mvsetactual: 6 nulls (0.03%)\n",
      "  - ppeak: 8 nulls (0.04%)\n",
      "  - cdyn: 491 nulls (2.72%)\n",
      "  - vti: 216 nulls (1.20%)\n",
      "  - pmean: 6 nulls (0.03%)\n",
      "  - vte: 216 nulls (1.20%)\n",
      "  - sponvt: 18070 nulls (100.00%)\n",
      "  - svv: 18070 nulls (100.00%)\n",
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      "  - rrhzsetactual: 1 nulls (0.01%)\n",
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      "  - vti: 1372 nulls (17.64%)\n",
      "  - pmean: 1110 nulls (14.27%)\n",
      "  - vte: 262 nulls (3.37%)\n",
      "  - sponvt: 7776 nulls (100.00%)\n",
      "  - svv: 7776 nulls (100.00%)\n",
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      "  - senddate: 4 nulls (0.01%)\n",
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      "  - mvsetactual: 6648 nulls (23.59%)\n",
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      "  - ppeak: 6519 nulls (23.14%)\n",
      "  - cdyn: 6654 nulls (23.62%)\n",
      "  - vti: 8036 nulls (28.52%)\n",
      "  - pmean: 6518 nulls (23.13%)\n",
      "  - vte: 8168 nulls (28.99%)\n",
      "  - sponvt: 9598 nulls (34.06%)\n",
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      "  - rrhzsetactual: 24 nulls (0.15%)\n",
      "  - mvsetactual: 3 nulls (0.02%)\n",
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      "  - vti: 1503 nulls (9.40%)\n",
      "  - pmean: 1360 nulls (8.50%)\n",
      "  - vte: 146 nulls (0.91%)\n",
      "  - sponvt: 15995 nulls (100.00%)\n",
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      "  - vti: 106 nulls (3.89%)\n",
      "  - vte: 106 nulls (3.89%)\n",
      "  - sponvt: 2723 nulls (100.00%)\n",
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      "  - vti: 8730 nulls (66.92%)\n",
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      "  - sponvt: 17472 nulls (72.83%)\n",
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      "  - vte: 198 nulls (6.87%)\n",
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      "  - sponvt: 38486 nulls (99.81%)\n",
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      "  - rrhzsetactual: 1065 nulls (29.84%)\n",
      "  - mvsetactual: 1065 nulls (29.84%)\n",
      "  - peepepap: 1065 nulls (29.84%)\n",
      "  - ppeak: 1065 nulls (29.84%)\n",
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      "  - sponvt: 13476 nulls (94.85%)\n",
      "  - svv: 14208 nulls (100.00%)\n",
      "\n",
      "[File] PatNo_ID_1590854576.csv (rows=15879)\n",
      "  - sponvt: 15879 nulls (100.00%)\n",
      "  - svv: 15879 nulls (100.00%)\n",
      "\n",
      "[File] PatNo_ID_1591609798.csv (rows=35624)\n",
      "  - senddate: 6 nulls (0.02%)\n",
      "  - rrhzsetactual: 1 nulls (0.00%)\n",
      "  - mvsetactual: 1 nulls (0.00%)\n",
      "  - ppeak: 4 nulls (0.01%)\n",
      "  - cdyn: 89 nulls (0.25%)\n",
      "  - vti: 1 nulls (0.00%)\n",
      "  - pmean: 1 nulls (0.00%)\n",
      "  - vte: 1 nulls (0.00%)\n",
      "  - sponvt: 35624 nulls (100.00%)\n",
      "  - svv: 35624 nulls (100.00%)\n",
      "\n",
      "[File] PatNo_ID_1592044724.csv (rows=6815)\n",
      "  - rrhzsetactual: 3 nulls (0.04%)\n",
      "  - mvsetactual: 3 nulls (0.04%)\n",
      "  - ppeak: 3 nulls (0.04%)\n",
      "  - cdyn: 125 nulls (1.83%)\n",
      "  - vti: 760 nulls (11.15%)\n",
      "  - pmean: 3 nulls (0.04%)\n",
      "  - vte: 760 nulls (11.15%)\n",
      "  - sponvt: 6057 nulls (88.88%)\n",
      "  - svv: 6815 nulls (100.00%)\n",
      "\n",
      "[File] PatNo_ID_1592560504.csv (rows=18052)\n",
      "  - senddate: 1 nulls (0.01%)\n",
      "  - sponvt: 18052 nulls (100.00%)\n",
      "  - svv: 18052 nulls (100.00%)\n",
      "\n",
      "[File] PatNo_ID_1593087886.csv (rows=24163)\n",
      "  - rrhzsetactual: 28 nulls (0.12%)\n",
      "  - mvsetactual: 292 nulls (1.21%)\n",
      "  - peepepap: 24 nulls (0.10%)\n",
      "  - ppeak: 32 nulls (0.13%)\n",
      "  - cdyn: 325 nulls (1.35%)\n",
      "  - vti: 764 nulls (3.16%)\n",
      "  - pmean: 25 nulls (0.10%)\n",
      "  - vte: 989 nulls (4.09%)\n",
      "  - sponvt: 24163 nulls (100.00%)\n",
      "  - svv: 24163 nulls (100.00%)\n",
      "\n",
      "[File] PatNo_ID_1593416100.csv (rows=3328)\n",
      "  - vti: 229 nulls (6.88%)\n",
      "  - vte: 229 nulls (6.88%)\n",
      "  - sponvt: 3328 nulls (100.00%)\n",
      "  - svv: 3328 nulls (100.00%)\n",
      "\n",
      "[File] PatNo_ID_1593472048.csv (rows=8230)\n",
      "  - rrhzsetactual: 1983 nulls (24.09%)\n",
      "  - mvsetactual: 1983 nulls (24.09%)\n",
      "  - peepepap: 1983 nulls (24.09%)\n",
      "  - ppeak: 1983 nulls (24.09%)\n",
      "  - cdyn: 1983 nulls (24.09%)\n",
      "  - vti: 1987 nulls (24.14%)\n",
      "  - pmean: 1983 nulls (24.09%)\n",
      "  - vte: 1987 nulls (24.14%)\n",
      "  - sponvt: 8230 nulls (100.00%)\n",
      "  - svv: 8230 nulls (100.00%)\n",
      "\n",
      "[File] PatNo_ID_1593593586.csv (rows=18882)\n",
      "  - rrhzsetactual: 7 nulls (0.04%)\n",
      "  - mvsetactual: 7 nulls (0.04%)\n",
      "  - ppeak: 7 nulls (0.04%)\n",
      "  - cdyn: 33 nulls (0.17%)\n",
      "  - vti: 9 nulls (0.05%)\n",
      "  - pmean: 7 nulls (0.04%)\n",
      "  - vte: 9 nulls (0.05%)\n",
      "  - sponvt: 18882 nulls (100.00%)\n",
      "  - svv: 18882 nulls (100.00%)\n",
      "\n",
      "[File] PatNo_ID_1593720818.csv (rows=3911)\n",
      "  - senddate: 1 nulls (0.03%)\n",
      "  - rrhzsetactual: 1 nulls (0.03%)\n",
      "  - mvsetactual: 1 nulls (0.03%)\n",
      "  - peepepap: 1 nulls (0.03%)\n",
      "  - ppeak: 1 nulls (0.03%)\n",
      "  - cdyn: 1 nulls (0.03%)\n",
      "  - vti: 289 nulls (7.39%)\n",
      "  - pmean: 1 nulls (0.03%)\n",
      "  - vte: 289 nulls (7.39%)\n",
      "  - sponvt: 2362 nulls (60.39%)\n",
      "  - svv: 2650 nulls (67.76%)\n",
      "\n",
      "[File] PatNo_ID_1593838524.csv (rows=2178)\n",
      "  - senddate: 1 nulls (0.05%)\n",
      "  - rrhzsetactual: 16 nulls (0.73%)\n",
      "  - mvsetactual: 16 nulls (0.73%)\n",
      "  - peepepap: 16 nulls (0.73%)\n",
      "  - ppeak: 17 nulls (0.78%)\n",
      "  - cdyn: 17 nulls (0.78%)\n",
      "  - vti: 17 nulls (0.78%)\n",
      "  - pmean: 17 nulls (0.78%)\n",
      "  - vte: 17 nulls (0.78%)\n",
      "  - sponvt: 2178 nulls (100.00%)\n",
      "  - svv: 2178 nulls (100.00%)\n",
      "\n",
      "[File] PatNo_ID_1594173718.csv (rows=344)\n",
      "  - sponvt: 344 nulls (100.00%)\n",
      "  - svv: 344 nulls (100.00%)\n",
      "\n",
      "[File] PatNo_ID_1594294180.csv (rows=18334)\n",
      "  - rrhzsetactual: 8 nulls (0.04%)\n",
      "  - mvsetactual: 3 nulls (0.02%)\n",
      "  - ppeak: 8 nulls (0.04%)\n",
      "  - cdyn: 9440 nulls (51.49%)\n",
      "  - vti: 15003 nulls (81.83%)\n",
      "  - pmean: 9418 nulls (51.37%)\n",
      "  - vte: 5588 nulls (30.48%)\n",
      "  - sponvt: 18334 nulls (100.00%)\n",
      "  - svv: 18334 nulls (100.00%)\n",
      "\n",
      "[File] PatNo_ID_1594305136.csv (rows=18679)\n",
      "  - rrhzsetactual: 71 nulls (0.38%)\n",
      "  - mvsetactual: 101 nulls (0.54%)\n",
      "  - peepepap: 71 nulls (0.38%)\n",
      "  - ppeak: 71 nulls (0.38%)\n",
      "  - cdyn: 126 nulls (0.67%)\n",
      "  - vti: 4279 nulls (22.91%)\n",
      "  - pmean: 71 nulls (0.38%)\n",
      "  - vte: 4308 nulls (23.06%)\n",
      "  - sponvt: 18679 nulls (100.00%)\n",
      "  - svv: 18679 nulls (100.00%)\n",
      "\n",
      "[File] PatNo_ID_1594309746.csv (rows=3745)\n",
      "  - rrhzsetactual: 21 nulls (0.56%)\n",
      "  - mvsetactual: 21 nulls (0.56%)\n",
      "  - ppeak: 21 nulls (0.56%)\n",
      "  - cdyn: 31 nulls (0.83%)\n",
      "  - vti: 193 nulls (5.15%)\n",
      "  - pmean: 21 nulls (0.56%)\n",
      "  - vte: 193 nulls (5.15%)\n",
      "  - sponvt: 3745 nulls (100.00%)\n",
      "  - svv: 3745 nulls (100.00%)\n",
      "\n",
      "[File] PatNo_ID_1594319286.csv (rows=4107)\n",
      "  - rrhzsetactual: 118 nulls (2.87%)\n",
      "  - mvsetactual: 118 nulls (2.87%)\n",
      "  - peepepap: 118 nulls (2.87%)\n",
      "  - ppeak: 118 nulls (2.87%)\n",
      "  - cdyn: 118 nulls (2.87%)\n",
      "  - vti: 118 nulls (2.87%)\n",
      "  - pmean: 118 nulls (2.87%)\n",
      "  - vte: 118 nulls (2.87%)\n",
      "  - sponvt: 4107 nulls (100.00%)\n",
      "  - svv: 4107 nulls (100.00%)\n",
      "\n",
      "[File] PatNo_ID_1594320763.csv (rows=2431)\n",
      "  - rrhzsetactual: 1 nulls (0.04%)\n",
      "  - mvsetactual: 1 nulls (0.04%)\n",
      "  - ppeak: 1 nulls (0.04%)\n",
      "  - cdyn: 48 nulls (1.97%)\n",
      "  - vti: 157 nulls (6.46%)\n",
      "  - pmean: 1 nulls (0.04%)\n",
      "  - vte: 157 nulls (6.46%)\n",
      "  - sponvt: 2431 nulls (100.00%)\n",
      "  - svv: 2431 nulls (100.00%)\n",
      "\n",
      "[File] PatNo_ID_1594322594.csv (rows=5380)\n",
      "  - rrhzsetactual: 2 nulls (0.04%)\n",
      "  - mvsetactual: 2 nulls (0.04%)\n",
      "  - ppeak: 2 nulls (0.04%)\n",
      "  - cdyn: 24 nulls (0.45%)\n",
      "  - vti: 2 nulls (0.04%)\n",
      "  - pmean: 2 nulls (0.04%)\n",
      "  - vte: 2 nulls (0.04%)\n",
      "  - sponvt: 5380 nulls (100.00%)\n",
      "  - svv: 5380 nulls (100.00%)\n",
      "\n",
      "[File] PatNo_ID_1594335109.csv (rows=5116)\n",
      "  - cdyn: 8 nulls (0.16%)\n",
      "  - sponvt: 5116 nulls (100.00%)\n",
      "  - svv: 5116 nulls (100.00%)\n",
      "\n",
      "[File] PatNo_ID_1594423683.csv (rows=5023)\n",
      "  - rrhzsetactual: 19 nulls (0.38%)\n",
      "  - mvsetactual: 19 nulls (0.38%)\n",
      "  - peepepap: 19 nulls (0.38%)\n",
      "  - ppeak: 19 nulls (0.38%)\n",
      "  - cdyn: 20 nulls (0.40%)\n",
      "  - vti: 205 nulls (4.08%)\n",
      "  - pmean: 19 nulls (0.38%)\n",
      "  - vte: 205 nulls (4.08%)\n",
      "  - sponvt: 5023 nulls (100.00%)\n",
      "  - svv: 5023 nulls (100.00%)\n",
      "\n",
      "[File] PatNo_ID_1594437309.csv (rows=10035)\n",
      "  - senddate: 1 nulls (0.01%)\n",
      "  - rrhzsetactual: 96 nulls (0.96%)\n",
      "  - mvsetactual: 96 nulls (0.96%)\n",
      "  - peepepap: 96 nulls (0.96%)\n",
      "  - ppeak: 96 nulls (0.96%)\n",
      "  - cdyn: 96 nulls (0.96%)\n",
      "  - vti: 422 nulls (4.21%)\n",
      "  - pmean: 96 nulls (0.96%)\n",
      "  - vte: 422 nulls (4.21%)\n",
      "  - sponvt: 8846 nulls (88.15%)\n",
      "  - svv: 9172 nulls (91.40%)\n",
      "\n",
      "[File] PatNo_ID_1594439781.csv (rows=7691)\n",
      "  - cdyn: 119 nulls (1.55%)\n",
      "  - vti: 3158 nulls (41.06%)\n",
      "  - vte: 3158 nulls (41.06%)\n",
      "  - sponvt: 4533 nulls (58.94%)\n",
      "  - svv: 7691 nulls (100.00%)\n",
      "\n",
      "[File] PatNo_ID_1594441887.csv (rows=10203)\n",
      "  - senddate: 3 nulls (0.03%)\n",
      "  - rrhzsetactual: 3 nulls (0.03%)\n",
      "  - mvsetactual: 3 nulls (0.03%)\n",
      "  - ppeak: 3 nulls (0.03%)\n",
      "  - cdyn: 2 nulls (0.02%)\n",
      "  - vti: 117 nulls (1.15%)\n",
      "  - pmean: 3 nulls (0.03%)\n",
      "  - vte: 117 nulls (1.15%)\n",
      "  - sponvt: 10089 nulls (98.88%)\n",
      "  - svv: 10203 nulls (100.00%)\n",
      "\n",
      "[File] PatNo_ID_1594448501.csv (rows=5106)\n",
      "  - rrhzsetactual: 3 nulls (0.06%)\n",
      "  - mvsetactual: 13 nulls (0.25%)\n",
      "  - ppeak: 3 nulls (0.06%)\n",
      "  - vti: 409 nulls (8.01%)\n",
      "  - pmean: 3 nulls (0.06%)\n",
      "  - vte: 409 nulls (8.01%)\n",
      "  - sponvt: 666 nulls (13.04%)\n",
      "  - svv: 1063 nulls (20.82%)\n",
      "\n",
      "[File] PatNo_ID_1594455578.csv (rows=262)\n",
      "  - vti: 157 nulls (59.92%)\n",
      "  - vte: 157 nulls (59.92%)\n",
      "  - sponvt: 105 nulls (40.08%)\n",
      "  - svv: 262 nulls (100.00%)\n",
      "\n",
      "[File] PatNo_ID_1594464829.csv (rows=4000)\n",
      "  - rrhzsetactual: 61 nulls (1.52%)\n",
      "  - mvsetactual: 61 nulls (1.52%)\n",
      "  - peepepap: 61 nulls (1.52%)\n",
      "  - ppeak: 61 nulls (1.52%)\n",
      "  - cdyn: 61 nulls (1.52%)\n",
      "  - vti: 2736 nulls (68.40%)\n",
      "  - pmean: 61 nulls (1.52%)\n",
      "  - vte: 2736 nulls (68.40%)\n",
      "  - sponvt: 1018 nulls (25.45%)\n",
      "  - svv: 3693 nulls (92.33%)\n",
      "\n",
      "[File] PatNo_ID_1594467719.csv (rows=2560)\n",
      "  - senddate: 1 nulls (0.04%)\n",
      "  - rrhzsetactual: 1247 nulls (48.71%)\n",
      "  - mvsetactual: 1247 nulls (48.71%)\n",
      "  - peepepap: 1247 nulls (48.71%)\n",
      "  - ppeak: 1247 nulls (48.71%)\n",
      "  - cdyn: 1247 nulls (48.71%)\n",
      "  - vti: 1248 nulls (48.75%)\n",
      "  - pmean: 1247 nulls (48.71%)\n",
      "  - vte: 1248 nulls (48.75%)\n",
      "  - sponvt: 2265 nulls (88.48%)\n",
      "  - svv: 2266 nulls (88.52%)\n",
      "\n",
      "[File] PatNo_ID_1594471407.csv (rows=11433)\n",
      "  - senddate: 1 nulls (0.01%)\n",
      "  - rrhzsetactual: 153 nulls (1.34%)\n",
      "  - mvsetactual: 152 nulls (1.33%)\n",
      "  - peepepap: 151 nulls (1.32%)\n",
      "  - ppeak: 153 nulls (1.34%)\n",
      "  - cdyn: 153 nulls (1.34%)\n",
      "  - vti: 153 nulls (1.34%)\n",
      "  - pmean: 153 nulls (1.34%)\n",
      "  - vte: 152 nulls (1.33%)\n",
      "  - sponvt: 11433 nulls (100.00%)\n",
      "  - svv: 11433 nulls (100.00%)\n",
      "\n",
      "[File] PatNo_ID_1594479330.csv (rows=3727)\n",
      "  - rrhzsetactual: 3 nulls (0.08%)\n",
      "  - mvsetactual: 3 nulls (0.08%)\n",
      "  - ppeak: 3 nulls (0.08%)\n",
      "  - cdyn: 10 nulls (0.27%)\n",
      "  - vti: 3 nulls (0.08%)\n",
      "  - pmean: 3 nulls (0.08%)\n",
      "  - vte: 3 nulls (0.08%)\n",
      "  - sponvt: 3727 nulls (100.00%)\n",
      "  - svv: 3727 nulls (100.00%)\n",
      "\n",
      "[File] PatNo_ID_1594511911.csv (rows=5488)\n",
      "  - rrhzsetactual: 33 nulls (0.60%)\n",
      "  - mvsetactual: 33 nulls (0.60%)\n",
      "  - peepepap: 33 nulls (0.60%)\n",
      "  - ppeak: 33 nulls (0.60%)\n",
      "  - cdyn: 33 nulls (0.60%)\n",
      "  - vti: 2935 nulls (53.48%)\n",
      "  - pmean: 33 nulls (0.60%)\n",
      "  - vte: 2935 nulls (53.48%)\n",
      "  - sponvt: 2586 nulls (47.12%)\n",
      "  - svv: 5488 nulls (100.00%)\n",
      "\n",
      "[File] PatNo_ID_1594511914.csv (rows=9717)\n",
      "  - rrhzsetactual: 29 nulls (0.30%)\n",
      "  - mvsetactual: 30 nulls (0.31%)\n",
      "  - peepepap: 31 nulls (0.32%)\n",
      "  - ppeak: 28 nulls (0.29%)\n",
      "  - cdyn: 34 nulls (0.35%)\n",
      "  - vti: 791 nulls (8.14%)\n",
      "  - pmean: 28 nulls (0.29%)\n",
      "  - vte: 793 nulls (8.16%)\n",
      "  - sponvt: 1204 nulls (12.39%)\n",
      "  - svv: 1970 nulls (20.27%)\n",
      "\n",
      "[File] PatNo_ID_1594528842.csv (rows=2491)\n",
      "  - rrhzsetactual: 76 nulls (3.05%)\n",
      "  - mvsetactual: 76 nulls (3.05%)\n",
      "  - peepepap: 76 nulls (3.05%)\n",
      "  - ppeak: 76 nulls (3.05%)\n",
      "  - cdyn: 94 nulls (3.77%)\n",
      "  - vti: 99 nulls (3.97%)\n",
      "  - pmean: 76 nulls (3.05%)\n",
      "  - vte: 99 nulls (3.97%)\n",
      "  - sponvt: 2358 nulls (94.66%)\n",
      "  - svv: 2358 nulls (94.66%)\n",
      "\n",
      "[File] PatNo_ID_1594533379.csv (rows=1030)\n",
      "  - rrhzsetactual: 12 nulls (1.17%)\n",
      "  - mvsetactual: 12 nulls (1.17%)\n",
      "  - peepepap: 12 nulls (1.17%)\n",
      "  - ppeak: 12 nulls (1.17%)\n",
      "  - cdyn: 12 nulls (1.17%)\n",
      "  - vti: 249 nulls (24.17%)\n",
      "  - pmean: 12 nulls (1.17%)\n",
      "  - vte: 249 nulls (24.17%)\n",
      "  - sponvt: 793 nulls (76.99%)\n",
      "  - svv: 1030 nulls (100.00%)\n",
      "\n",
      "=== Global Summary Across All Files ===\n",
      "  - cdyn: 150965 nulls / 1567309 total (9.63%)\n",
      "  - mvsetactual: 43788 nulls / 1567309 total (2.79%)\n",
      "  - peepepap: 40745 nulls / 1567309 total (2.60%)\n",
      "  - pmean: 142687 nulls / 1567309 total (9.10%)\n",
      "  - ppeak: 41220 nulls / 1567309 total (2.63%)\n",
      "  - rrhzsetactual: 40991 nulls / 1567309 total (2.62%)\n",
      "  - senddate: 76 nulls / 1567309 total (0.00%)\n",
      "  - sponvt: 1392479 nulls / 1567309 total (88.85%)\n",
      "  - svv: 1448701 nulls / 1567309 total (92.43%)\n",
      "  - vte: 152306 nulls / 1567309 total (9.72%)\n",
      "  - vti: 263578 nulls / 1567309 total (16.82%)\n"
     ]
    }
   ],
   "source": [
    "\"\"\"檢查空值狀況\n",
    "檢查所有檔案的所有欄位空值狀況\n",
    "- 路徑: /home/jovyan/RT08/0925/bling_useable/*.csv\n",
    "- 空值定義: NaN、空字串、'null'、'(null)'\n",
    "- 直接印出結果（不存檔、不修改原始資料）\n",
    "\"\"\"\n",
    "bling_useable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "a5bd6ece-8d9a-419b-ae63-bb3176a46040",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Info] Copied 122 files from /home/jovyan/RT08/0925/bling_useable → /home/jovyan/RT08/0925/bling_fin\n",
      "\n",
      "=== File summary (row/column counts before & after) ===\n",
      "- 089271.csv: rows 32419 → 32419, cols 20 → 14\n",
      "- 095323.csv: rows 23791 → 23791, cols 20 → 14\n",
      "- 095707.csv: rows 20180 → 20180, cols 20 → 14\n",
      "- 114309.csv: rows 71729 → 71729, cols 20 → 14\n",
      "- 230933.csv: rows 30249 → 30249, cols 20 → 14\n",
      "- 4216007.csv: rows 1433 → 1433, cols 20 → 14\n",
      "- 7108162.csv: rows 239 → 239, cols 20 → 14\n",
      "- 7408338.csv: rows 1432 → 1432, cols 20 → 14\n",
      "- 7657698.csv: rows 1413 → 1413, cols 20 → 14\n",
      "- 7721164.csv: rows 483 → 483, cols 20 → 14\n",
      "- PatNo_ID_1560013303.csv: rows 2543 → 2543, cols 20 → 14\n",
      "- PatNo_ID_1562733396.csv: rows 2254 → 2254, cols 20 → 14\n",
      "- PatNo_ID_1563587183.csv: rows 5287 → 5287, cols 20 → 14\n",
      "- PatNo_ID_1564148644.csv: rows 17287 → 17287, cols 20 → 14\n",
      "- PatNo_ID_1565148312.csv: rows 5475 → 5475, cols 20 → 14\n",
      "- PatNo_ID_1565378038.csv: rows 2561 → 2561, cols 20 → 14\n",
      "- PatNo_ID_1566123680.csv: rows 42600 → 42600, cols 20 → 14\n",
      "- PatNo_ID_1566252197.csv: rows 3368 → 3368, cols 20 → 14\n",
      "- PatNo_ID_1566279967.csv: rows 1235 → 1235, cols 20 → 14\n",
      "- PatNo_ID_1566671274.csv: rows 25359 → 25359, cols 20 → 14\n",
      "- PatNo_ID_1566911879.csv: rows 53999 → 53999, cols 20 → 14\n",
      "- PatNo_ID_1567747650.csv: rows 10901 → 10901, cols 20 → 14\n",
      "- PatNo_ID_1567804800.csv: rows 20578 → 20578, cols 20 → 14\n",
      "- PatNo_ID_1567832735.csv: rows 36580 → 36580, cols 20 → 14\n",
      "- PatNo_ID_1568039398.csv: rows 34519 → 34519, cols 20 → 14\n",
      "- PatNo_ID_1568574099.csv: rows 13946 → 13946, cols 20 → 14\n",
      "- PatNo_ID_1568813269.csv: rows 4867 → 4867, cols 20 → 14\n",
      "- PatNo_ID_1568952422.csv: rows 1184 → 1184, cols 20 → 14\n",
      "- PatNo_ID_1569083701.csv: rows 3328 → 3328, cols 20 → 14\n",
      "- PatNo_ID_1569944983.csv: rows 7650 → 7650, cols 20 → 14\n",
      "- PatNo_ID_1570089466.csv: rows 41343 → 41343, cols 20 → 14\n",
      "- PatNo_ID_1570242703.csv: rows 10646 → 10646, cols 20 → 14\n",
      "- PatNo_ID_1570273244.csv: rows 9730 → 9730, cols 20 → 14\n",
      "- PatNo_ID_1570642083.csv: rows 19731 → 19731, cols 20 → 14\n",
      "- PatNo_ID_1571945701.csv: rows 15731 → 15731, cols 20 → 14\n",
      "- PatNo_ID_1572481361.csv: rows 34540 → 34540, cols 20 → 14\n",
      "- PatNo_ID_1572562839.csv: rows 20966 → 20966, cols 20 → 14\n",
      "- PatNo_ID_1572831765.csv: rows 2696 → 2696, cols 20 → 14\n",
      "- PatNo_ID_1572976822.csv: rows 6764 → 6764, cols 20 → 14\n",
      "- PatNo_ID_1573063188.csv: rows 5080 → 5080, cols 20 → 14\n",
      "- PatNo_ID_1573249295.csv: rows 7151 → 7151, cols 20 → 14\n",
      "- PatNo_ID_1573964540.csv: rows 4394 → 4394, cols 20 → 14\n",
      "- PatNo_ID_1574148494.csv: rows 47154 → 47154, cols 20 → 14\n",
      "- PatNo_ID_1574270349.csv: rows 6534 → 6534, cols 20 → 14\n",
      "- PatNo_ID_1574528808.csv: rows 14817 → 14817, cols 20 → 14\n",
      "- PatNo_ID_1574831525.csv: rows 521 → 521, cols 20 → 14\n",
      "- PatNo_ID_1574987447.csv: rows 19843 → 19843, cols 20 → 14\n",
      "- PatNo_ID_1575060177.csv: rows 5290 → 5290, cols 20 → 14\n",
      "- PatNo_ID_1575256902.csv: rows 9587 → 9587, cols 20 → 14\n",
      "- PatNo_ID_1575445051.csv: rows 1801 → 1801, cols 20 → 14\n",
      "- PatNo_ID_1575502382.csv: rows 6604 → 6604, cols 20 → 14\n",
      "- PatNo_ID_1575975485.csv: rows 16459 → 16459, cols 20 → 14\n",
      "- PatNo_ID_1576115572.csv: rows 18717 → 18717, cols 20 → 14\n",
      "- PatNo_ID_1576116479.csv: rows 1263 → 1263, cols 20 → 14\n",
      "- PatNo_ID_1576301569.csv: rows 3207 → 3207, cols 20 → 14\n",
      "- PatNo_ID_1576964560.csv: rows 24192 → 24192, cols 20 → 14\n",
      "- PatNo_ID_1577042911.csv: rows 38357 → 38357, cols 20 → 14\n",
      "- PatNo_ID_1577487284.csv: rows 2345 → 2345, cols 20 → 14\n",
      "- PatNo_ID_1578784257.csv: rows 33917 → 33917, cols 20 → 14\n",
      "- PatNo_ID_1579198603.csv: rows 2046 → 2046, cols 20 → 14\n",
      "- PatNo_ID_1579498177.csv: rows 21688 → 21688, cols 20 → 14\n",
      "- PatNo_ID_1580062580.csv: rows 2150 → 2150, cols 20 → 14\n",
      "- PatNo_ID_1580096720.csv: rows 1423 → 1423, cols 20 → 14\n",
      "- PatNo_ID_1580107637.csv: rows 2698 → 2698, cols 20 → 14\n",
      "- PatNo_ID_1580244614.csv: rows 5278 → 5278, cols 20 → 14\n",
      "- PatNo_ID_1580766093.csv: rows 18070 → 18070, cols 20 → 14\n",
      "- PatNo_ID_1581003248.csv: rows 7776 → 7776, cols 20 → 14\n",
      "- PatNo_ID_1581019504.csv: rows 28177 → 28177, cols 20 → 14\n",
      "- PatNo_ID_1581633231.csv: rows 15995 → 15995, cols 20 → 14\n",
      "- PatNo_ID_1581692973.csv: rows 2723 → 2723, cols 20 → 14\n",
      "- PatNo_ID_1582452511.csv: rows 5196 → 5196, cols 20 → 14\n",
      "- PatNo_ID_1582635996.csv: rows 13046 → 13046, cols 20 → 14\n",
      "- PatNo_ID_1582849900.csv: rows 7411 → 7411, cols 20 → 14\n",
      "- PatNo_ID_1582937076.csv: rows 23990 → 23990, cols 20 → 14\n",
      "- PatNo_ID_1584158973.csv: rows 2882 → 2882, cols 20 → 14\n",
      "- PatNo_ID_1584397376.csv: rows 638 → 638, cols 20 → 14\n",
      "- PatNo_ID_1586172659.csv: rows 38559 → 38559, cols 20 → 14\n",
      "- PatNo_ID_1586696634.csv: rows 3569 → 3569, cols 20 → 14\n",
      "- PatNo_ID_1586897008.csv: rows 6687 → 6687, cols 20 → 14\n",
      "- PatNo_ID_1587490083.csv: rows 45186 → 45186, cols 20 → 14\n",
      "- PatNo_ID_1588632604.csv: rows 2608 → 2608, cols 20 → 14\n",
      "- PatNo_ID_1588673465.csv: rows 10077 → 10077, cols 20 → 14\n",
      "- PatNo_ID_1588794796.csv: rows 9376 → 9376, cols 20 → 14\n",
      "- PatNo_ID_1588957997.csv: rows 10966 → 10966, cols 20 → 14\n",
      "- PatNo_ID_1589018086.csv: rows 13472 → 13472, cols 20 → 14\n",
      "- PatNo_ID_1589034524.csv: rows 50081 → 50081, cols 20 → 14\n",
      "- PatNo_ID_1589324603.csv: rows 4187 → 4187, cols 20 → 14\n",
      "- PatNo_ID_1589918099.csv: rows 9333 → 9333, cols 20 → 14\n",
      "- PatNo_ID_1590136310.csv: rows 5580 → 5580, cols 20 → 14\n",
      "- PatNo_ID_1590616537.csv: rows 14208 → 14208, cols 20 → 14\n",
      "- PatNo_ID_1590854576.csv: rows 15879 → 15879, cols 20 → 14\n",
      "- PatNo_ID_1591609798.csv: rows 35624 → 35624, cols 20 → 14\n",
      "- PatNo_ID_1592044724.csv: rows 6815 → 6815, cols 20 → 14\n",
      "- PatNo_ID_1592560504.csv: rows 18052 → 18052, cols 20 → 14\n",
      "- PatNo_ID_1593087886.csv: rows 24163 → 24163, cols 20 → 14\n",
      "- PatNo_ID_1593416100.csv: rows 3328 → 3328, cols 20 → 14\n",
      "- PatNo_ID_1593472048.csv: rows 8230 → 8230, cols 20 → 14\n",
      "- PatNo_ID_1593593586.csv: rows 18882 → 18882, cols 20 → 14\n",
      "- PatNo_ID_1593720818.csv: rows 3911 → 3911, cols 20 → 14\n",
      "- PatNo_ID_1593838524.csv: rows 2178 → 2178, cols 20 → 14\n",
      "- PatNo_ID_1594173718.csv: rows 344 → 344, cols 20 → 14\n",
      "- PatNo_ID_1594294180.csv: rows 18334 → 18334, cols 20 → 14\n",
      "- PatNo_ID_1594305136.csv: rows 18679 → 18679, cols 20 → 14\n",
      "- PatNo_ID_1594309746.csv: rows 3745 → 3745, cols 20 → 14\n",
      "- PatNo_ID_1594319286.csv: rows 4107 → 4107, cols 20 → 14\n",
      "- PatNo_ID_1594320763.csv: rows 2431 → 2431, cols 20 → 14\n",
      "- PatNo_ID_1594322594.csv: rows 5380 → 5380, cols 20 → 14\n",
      "- PatNo_ID_1594335109.csv: rows 5116 → 5116, cols 20 → 14\n",
      "- PatNo_ID_1594423683.csv: rows 5023 → 5023, cols 20 → 14\n",
      "- PatNo_ID_1594437309.csv: rows 10035 → 10035, cols 20 → 14\n",
      "- PatNo_ID_1594439781.csv: rows 7691 → 7691, cols 20 → 14\n",
      "- PatNo_ID_1594441887.csv: rows 10203 → 10203, cols 20 → 14\n",
      "- PatNo_ID_1594448501.csv: rows 5106 → 5106, cols 20 → 14\n",
      "- PatNo_ID_1594455578.csv: rows 262 → 262, cols 20 → 14\n",
      "- PatNo_ID_1594464829.csv: rows 4000 → 4000, cols 20 → 14\n",
      "- PatNo_ID_1594467719.csv: rows 2560 → 2560, cols 20 → 14\n",
      "- PatNo_ID_1594471407.csv: rows 11433 → 11433, cols 20 → 14\n",
      "- PatNo_ID_1594479330.csv: rows 3727 → 3727, cols 20 → 14\n",
      "- PatNo_ID_1594511911.csv: rows 5488 → 5488, cols 20 → 14\n",
      "- PatNo_ID_1594511914.csv: rows 9717 → 9717, cols 20 → 14\n",
      "- PatNo_ID_1594528842.csv: rows 2491 → 2491, cols 20 → 14\n",
      "- PatNo_ID_1594533379.csv: rows 1030 → 1030, cols 20 → 14\n",
      "\n",
      "[Info] Total files copied: 122\n",
      "[Info] Total files processed in /home/jovyan/RT08/0925/bling_fin: 122\n",
      "[Info] Files with changes (columns dropped): 122\n"
     ]
    }
   ],
   "source": [
    "\"\"\"重來\n",
    "將/home/jovyan/RT08/0925/bling_useable/的所有檔案複製一份到/home/jovyan/RT08/0925/bling_fin/\n",
    "在/home/jovyan/RT08/0925/bling_fin/中的資料 都刪除\"sponvt\", \"vti\", \"vte\", \"ventilatormode\", \"mode_1\", \"mode_9\"總共六個欄位，\n",
    "\"\"\"\n",
    "import os, glob, shutil\n",
    "import pandas as pd\n",
    "\n",
    "SRC_DIR = \"/home/jovyan/RT08/0925/bling_useable\"\n",
    "DST_DIR = \"/home/jovyan/RT08/0925/bling_fin\"\n",
    "DROP_COLS = [\"sponvt\", \"vti\", \"vte\", \"ventilatormode\", \"mode_1\", \"mode_9\"]\n",
    "\n",
    "# 1. 建立輸出資料夾\n",
    "os.makedirs(DST_DIR, exist_ok=True)\n",
    "\n",
    "# 2. 複製所有檔案到 bling_fin\n",
    "file_paths = sorted(glob.glob(os.path.join(SRC_DIR, \"*.csv\")))\n",
    "for fp in file_paths:\n",
    "    shutil.copy(fp, DST_DIR)\n",
    "\n",
    "print(f\"[Info] Copied {len(file_paths)} files from {SRC_DIR} → {DST_DIR}\")\n",
    "\n",
    "# 3. 在 bling_fin 內刪除欄位並記錄前後差異\n",
    "dst_files = sorted(glob.glob(os.path.join(DST_DIR, \"*.csv\")))\n",
    "changed_files = []\n",
    "\n",
    "for fp in dst_files:\n",
    "    base = os.path.basename(fp)\n",
    "    try:\n",
    "        df = pd.read_csv(fp, low_memory=False)\n",
    "    except Exception as e:\n",
    "        print(f\"[Skip] Cannot read {base}: {e}\")\n",
    "        continue\n",
    "    \n",
    "    before_rows, before_cols = df.shape\n",
    "    \n",
    "    # 嘗試刪除，不存在的自動跳過\n",
    "    df = df.drop(columns=[c for c in DROP_COLS if c in df.columns], errors=\"ignore\")\n",
    "    \n",
    "    after_rows, after_cols = df.shape\n",
    "    \n",
    "    # 覆蓋寫回\n",
    "    df.to_csv(fp, index=False)\n",
    "    \n",
    "    # 紀錄變化\n",
    "    changed_files.append((base, before_rows, before_cols, after_rows, after_cols))\n",
    "\n",
    "# 4. 印出結果\n",
    "print(\"\\n=== File summary (row/column counts before & after) ===\")\n",
    "for fname, br, bc, ar, ac in changed_files:\n",
    "    print(f\"- {fname}: rows {br} → {ar}, cols {bc} → {ac}\")\n",
    "\n",
    "print(f\"\\n[Info] Total files copied: {len(file_paths)}\")\n",
    "print(f\"[Info] Total files processed in {DST_DIR}: {len(dst_files)}\")\n",
    "print(f\"[Info] Files with changes (columns dropped): {len([f for f in changed_files if f[2]!=f[4]])}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "4829364e-744c-4635-be1d-2ee898698609",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Info] Copied 122 files from /home/jovyan/RT08/0925/bling_useable → /home/jovyan/RT08/0925/bling_useable_svv\n",
      "\n",
      "=== Added svv_new summary ===\n",
      "- 089271.csv: cols 20 → 21\n",
      "- 095323.csv: cols 20 → 21\n",
      "- 095707.csv: cols 20 → 21\n",
      "- 114309.csv: cols 20 → 21\n",
      "- 230933.csv: cols 20 → 21\n",
      "- 4216007.csv: cols 20 → 21\n",
      "- 7108162.csv: cols 20 → 21\n",
      "- 7408338.csv: cols 20 → 21\n",
      "- 7657698.csv: cols 20 → 21\n",
      "- 7721164.csv: cols 20 → 21\n",
      "- PatNo_ID_1560013303.csv: cols 20 → 21\n",
      "- PatNo_ID_1562733396.csv: cols 20 → 21\n",
      "- PatNo_ID_1563587183.csv: cols 20 → 21\n",
      "- PatNo_ID_1564148644.csv: cols 20 → 21\n",
      "- PatNo_ID_1565148312.csv: cols 20 → 21\n",
      "- PatNo_ID_1565378038.csv: cols 20 → 21\n",
      "- PatNo_ID_1566123680.csv: cols 20 → 21\n",
      "- PatNo_ID_1566252197.csv: cols 20 → 21\n",
      "- PatNo_ID_1566279967.csv: cols 20 → 21\n",
      "- PatNo_ID_1566671274.csv: cols 20 → 21\n",
      "- PatNo_ID_1566911879.csv: cols 20 → 21\n",
      "- PatNo_ID_1567747650.csv: cols 20 → 21\n",
      "- PatNo_ID_1567804800.csv: cols 20 → 21\n",
      "- PatNo_ID_1567832735.csv: cols 20 → 21\n",
      "- PatNo_ID_1568039398.csv: cols 20 → 21\n",
      "- PatNo_ID_1568574099.csv: cols 20 → 21\n",
      "- PatNo_ID_1568813269.csv: cols 20 → 21\n",
      "- PatNo_ID_1568952422.csv: cols 20 → 21\n",
      "- PatNo_ID_1569083701.csv: cols 20 → 21\n",
      "- PatNo_ID_1569944983.csv: cols 20 → 21\n",
      "- PatNo_ID_1570089466.csv: cols 20 → 21\n",
      "- PatNo_ID_1570242703.csv: cols 20 → 21\n",
      "- PatNo_ID_1570273244.csv: cols 20 → 21\n",
      "- PatNo_ID_1570642083.csv: cols 20 → 21\n",
      "- PatNo_ID_1571945701.csv: cols 20 → 21\n",
      "- PatNo_ID_1572481361.csv: cols 20 → 21\n",
      "- PatNo_ID_1572562839.csv: cols 20 → 21\n",
      "- PatNo_ID_1572831765.csv: cols 20 → 21\n",
      "- PatNo_ID_1572976822.csv: cols 20 → 21\n",
      "- PatNo_ID_1573063188.csv: cols 20 → 21\n",
      "- PatNo_ID_1573249295.csv: cols 20 → 21\n",
      "- PatNo_ID_1573964540.csv: cols 20 → 21\n",
      "- PatNo_ID_1574148494.csv: cols 20 → 21\n",
      "- PatNo_ID_1574270349.csv: cols 20 → 21\n",
      "- PatNo_ID_1574528808.csv: cols 20 → 21\n",
      "- PatNo_ID_1574831525.csv: cols 20 → 21\n",
      "- PatNo_ID_1574987447.csv: cols 20 → 21\n",
      "- PatNo_ID_1575060177.csv: cols 20 → 21\n",
      "- PatNo_ID_1575256902.csv: cols 20 → 21\n",
      "- PatNo_ID_1575445051.csv: cols 20 → 21\n",
      "- PatNo_ID_1575502382.csv: cols 20 → 21\n",
      "- PatNo_ID_1575975485.csv: cols 20 → 21\n",
      "- PatNo_ID_1576115572.csv: cols 20 → 21\n",
      "- PatNo_ID_1576116479.csv: cols 20 → 21\n",
      "- PatNo_ID_1576301569.csv: cols 20 → 21\n",
      "- PatNo_ID_1576964560.csv: cols 20 → 21\n",
      "- PatNo_ID_1577042911.csv: cols 20 → 21\n",
      "- PatNo_ID_1577487284.csv: cols 20 → 21\n",
      "- PatNo_ID_1578784257.csv: cols 20 → 21\n",
      "- PatNo_ID_1579198603.csv: cols 20 → 21\n",
      "- PatNo_ID_1579498177.csv: cols 20 → 21\n",
      "- PatNo_ID_1580062580.csv: cols 20 → 21\n",
      "- PatNo_ID_1580096720.csv: cols 20 → 21\n",
      "- PatNo_ID_1580107637.csv: cols 20 → 21\n",
      "- PatNo_ID_1580244614.csv: cols 20 → 21\n",
      "- PatNo_ID_1580766093.csv: cols 20 → 21\n",
      "- PatNo_ID_1581003248.csv: cols 20 → 21\n",
      "- PatNo_ID_1581019504.csv: cols 20 → 21\n",
      "- PatNo_ID_1581633231.csv: cols 20 → 21\n",
      "- PatNo_ID_1581692973.csv: cols 20 → 21\n",
      "- PatNo_ID_1582452511.csv: cols 20 → 21\n",
      "- PatNo_ID_1582635996.csv: cols 20 → 21\n",
      "- PatNo_ID_1582849900.csv: cols 20 → 21\n",
      "- PatNo_ID_1582937076.csv: cols 20 → 21\n",
      "- PatNo_ID_1584158973.csv: cols 20 → 21\n",
      "- PatNo_ID_1584397376.csv: cols 20 → 21\n",
      "- PatNo_ID_1586172659.csv: cols 20 → 21\n",
      "- PatNo_ID_1586696634.csv: cols 20 → 21\n",
      "- PatNo_ID_1586897008.csv: cols 20 → 21\n",
      "- PatNo_ID_1587490083.csv: cols 20 → 21\n",
      "- PatNo_ID_1588632604.csv: cols 20 → 21\n",
      "- PatNo_ID_1588673465.csv: cols 20 → 21\n",
      "- PatNo_ID_1588794796.csv: cols 20 → 21\n",
      "- PatNo_ID_1588957997.csv: cols 20 → 21\n",
      "- PatNo_ID_1589018086.csv: cols 20 → 21\n",
      "- PatNo_ID_1589034524.csv: cols 20 → 21\n",
      "- PatNo_ID_1589324603.csv: cols 20 → 21\n",
      "- PatNo_ID_1589918099.csv: cols 20 → 21\n",
      "- PatNo_ID_1590136310.csv: cols 20 → 21\n",
      "- PatNo_ID_1590616537.csv: cols 20 → 21\n",
      "- PatNo_ID_1590854576.csv: cols 20 → 21\n",
      "- PatNo_ID_1591609798.csv: cols 20 → 21\n",
      "- PatNo_ID_1592044724.csv: cols 20 → 21\n",
      "- PatNo_ID_1592560504.csv: cols 20 → 21\n",
      "- PatNo_ID_1593087886.csv: cols 20 → 21\n",
      "- PatNo_ID_1593416100.csv: cols 20 → 21\n",
      "- PatNo_ID_1593472048.csv: cols 20 → 21\n",
      "- PatNo_ID_1593593586.csv: cols 20 → 21\n",
      "- PatNo_ID_1593720818.csv: cols 20 → 21\n",
      "- PatNo_ID_1593838524.csv: cols 20 → 21\n",
      "- PatNo_ID_1594173718.csv: cols 20 → 21\n",
      "- PatNo_ID_1594294180.csv: cols 20 → 21\n",
      "- PatNo_ID_1594305136.csv: cols 20 → 21\n",
      "- PatNo_ID_1594309746.csv: cols 20 → 21\n",
      "- PatNo_ID_1594319286.csv: cols 20 → 21\n",
      "- PatNo_ID_1594320763.csv: cols 20 → 21\n",
      "- PatNo_ID_1594322594.csv: cols 20 → 21\n",
      "- PatNo_ID_1594335109.csv: cols 20 → 21\n",
      "- PatNo_ID_1594423683.csv: cols 20 → 21\n",
      "- PatNo_ID_1594437309.csv: cols 20 → 21\n",
      "- PatNo_ID_1594439781.csv: cols 20 → 21\n",
      "- PatNo_ID_1594441887.csv: cols 20 → 21\n",
      "- PatNo_ID_1594448501.csv: cols 20 → 21\n",
      "- PatNo_ID_1594455578.csv: cols 20 → 21\n",
      "- PatNo_ID_1594464829.csv: cols 20 → 21\n",
      "- PatNo_ID_1594467719.csv: cols 20 → 21\n",
      "- PatNo_ID_1594471407.csv: cols 20 → 21\n",
      "- PatNo_ID_1594479330.csv: cols 20 → 21\n",
      "- PatNo_ID_1594511911.csv: cols 20 → 21\n",
      "- PatNo_ID_1594511914.csv: cols 20 → 21\n",
      "- PatNo_ID_1594528842.csv: cols 20 → 21\n",
      "- PatNo_ID_1594533379.csv: cols 20 → 21\n",
      "\n",
      "[Info] Total processed files in /home/jovyan/RT08/0925/bling_useable_svv: 122\n"
     ]
    }
   ],
   "source": [
    "\"\"\"自己在搞事\n",
    "將/home/jovyan/RT08/0925/bling_useable/的所有檔案複製一份到/home/jovyan/RT08/0925/bling_useable_svv/\n",
    "到\"/home/jovyan/RT08/0925/bling_useable_svv\"，在每個檔案新增欄位 svv_new， svv_new = (vti + vte + sponvt) / 3\n",
    "如果三個欄位任一有空值 就用0下去算\n",
    "\"\"\"\n",
    "import os, glob, shutil\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "SRC_DIR = \"/home/jovyan/RT08/0925/bling_useable\"\n",
    "DST_DIR = \"/home/jovyan/RT08/0925/bling_useable_svv\"\n",
    "\n",
    "# 1. 建立輸出資料夾\n",
    "os.makedirs(DST_DIR, exist_ok=True)\n",
    "\n",
    "# 2. 先複製所有檔案到目標資料夾\n",
    "file_paths = sorted(glob.glob(os.path.join(SRC_DIR, \"*.csv\")))\n",
    "for fp in file_paths:\n",
    "    shutil.copy(fp, DST_DIR)\n",
    "\n",
    "print(f\"[Info] Copied {len(file_paths)} files from {SRC_DIR} → {DST_DIR}\")\n",
    "\n",
    "# 3. 新增 svv_new 欄位\n",
    "dst_files = sorted(glob.glob(os.path.join(DST_DIR, \"*.csv\")))\n",
    "updated_files = []\n",
    "\n",
    "for fp in dst_files:\n",
    "    base = os.path.basename(fp)\n",
    "    try:\n",
    "        df = pd.read_csv(fp, low_memory=False)\n",
    "    except Exception as e:\n",
    "        print(f\"[Skip] Cannot read {base}: {e}\")\n",
    "        continue\n",
    "    \n",
    "    before_cols = df.shape[1]\n",
    "\n",
    "    # 確保欄位存在，不存在的補 0\n",
    "    for col in [\"vti\", \"vte\", \"sponvt\"]:\n",
    "        if col not in df.columns:\n",
    "            df[col] = 0\n",
    "\n",
    "    # 將空值 NaN → 0\n",
    "    df[\"vti\"] = pd.to_numeric(df[\"vti\"], errors=\"coerce\").fillna(0)\n",
    "    df[\"vte\"] = pd.to_numeric(df[\"vte\"], errors=\"coerce\").fillna(0)\n",
    "    df[\"sponvt\"] = pd.to_numeric(df[\"sponvt\"], errors=\"coerce\").fillna(0)\n",
    "\n",
    "    # 計算 svv_new\n",
    "    df[\"svv_new\"] = (df[\"vti\"] + df[\"vte\"] + df[\"sponvt\"]) / 3\n",
    "\n",
    "    # 存回覆蓋\n",
    "    df.to_csv(fp, index=False)\n",
    "\n",
    "    after_cols = df.shape[1]\n",
    "    updated_files.append((base, before_cols, after_cols))\n",
    "\n",
    "# 4. 印出結果\n",
    "print(\"\\n=== Added svv_new summary ===\")\n",
    "for fname, bc, ac in updated_files:\n",
    "    print(f\"- {fname}: cols {bc} → {ac}\")\n",
    "\n",
    "print(f\"\\n[Info] Total processed files in {DST_DIR}: {len(updated_files)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "6b2481a2-47d1-40a9-a0d5-30854afe941a",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Info] Copied 122 files from /home/jovyan/RT08/0925/bling_useable_svv → /home/jovyan/RT08/0925/bling_svv_fin\n",
      "[Info] Files now in /home/jovyan/RT08/0925/bling_svv_fin: 122\n",
      "\n",
      "=== File: 089271.csv ===\n",
      "Total rows: 32419\n",
      "useable_noNaN = 1: 31978\n",
      "Excluded by step (only >0):\n",
      " - S1: 55\n",
      " - S2: 374\n",
      " - S7: 10\n",
      " - S9: 2\n",
      "\n",
      "=== File: 095323.csv ===\n",
      "Total rows: 23791\n",
      "useable_noNaN = 1: 19467\n",
      "Excluded by step (only >0):\n",
      " - S1: 4071\n",
      " - S2: 245\n",
      " - S7: 8\n",
      "\n",
      "=== File: 095707.csv ===\n",
      "Total rows: 20180\n",
      "useable_noNaN = 1: 19401\n",
      "Excluded by step (only >0):\n",
      " - S2: 774\n",
      " - S7: 5\n",
      "\n",
      "=== File: 114309.csv ===\n",
      "Total rows: 71729\n",
      "useable_noNaN = 1: 22364\n",
      "Excluded by step (only >0):\n",
      " - S1: 25159\n",
      " - S2: 24201\n",
      " - S7: 5\n",
      "\n",
      "=== File: 230933.csv ===\n",
      "Total rows: 30249\n",
      "useable_noNaN = 1: 29805\n",
      "Excluded by step (only >0):\n",
      " - S1: 22\n",
      " - S2: 414\n",
      " - S7: 3\n",
      " - S8: 2\n",
      " - S9: 3\n",
      "\n",
      "=== File: 4216007.csv ===\n",
      "Total rows: 1433\n",
      "useable_noNaN = 1: 0\n",
      "Excluded by step (only >0):\n",
      " - S1: 1433\n",
      "\n",
      "=== File: 7108162.csv ===\n",
      "Total rows: 239\n",
      "useable_noNaN = 1: 239\n",
      "Excluded by step (only >0):\n",
      " - (none)\n",
      "\n",
      "=== File: 7408338.csv ===\n",
      "Total rows: 1432\n",
      "useable_noNaN = 1: 1430\n",
      "Excluded by step (only >0):\n",
      " - S2: 1\n",
      " - S7: 1\n",
      "\n",
      "=== File: 7657698.csv ===\n",
      "Total rows: 1413\n",
      "useable_noNaN = 1: 1408\n",
      "Excluded by step (only >0):\n",
      " - S1: 1\n",
      " - S2: 4\n",
      "\n",
      "=== File: 7721164.csv ===\n",
      "Total rows: 483\n",
      "useable_noNaN = 1: 481\n",
      "Excluded by step (only >0):\n",
      " - S7: 2\n",
      "\n",
      "=== File: PatNo_ID_1560013303.csv ===\n",
      "Total rows: 2543\n",
      "useable_noNaN = 1: 2456\n",
      "Excluded by step (only >0):\n",
      " - S1: 84\n",
      " - S2: 1\n",
      " - S7: 2\n",
      "\n",
      "=== File: PatNo_ID_1562733396.csv ===\n",
      "Total rows: 2254\n",
      "useable_noNaN = 1: 2242\n",
      "Excluded by step (only >0):\n",
      " - S1: 2\n",
      " - S2: 10\n",
      "\n",
      "=== File: PatNo_ID_1563587183.csv ===\n",
      "Total rows: 5287\n",
      "useable_noNaN = 1: 5173\n",
      "Excluded by step (only >0):\n",
      " - S2: 3\n",
      " - S7: 111\n",
      "\n",
      "=== File: PatNo_ID_1564148644.csv ===\n",
      "Total rows: 17287\n",
      "useable_noNaN = 1: 8785\n",
      "Excluded by step (only >0):\n",
      " - S1: 8499\n",
      " - S7: 3\n",
      "\n",
      "=== File: PatNo_ID_1565148312.csv ===\n",
      "Total rows: 5475\n",
      "useable_noNaN = 1: 5454\n",
      "Excluded by step (only >0):\n",
      " - S1: 10\n",
      " - S2: 1\n",
      " - S7: 10\n",
      "\n",
      "=== File: PatNo_ID_1565378038.csv ===\n",
      "Total rows: 2561\n",
      "useable_noNaN = 1: 2558\n",
      "Excluded by step (only >0):\n",
      " - S1: 1\n",
      " - S8: 2\n",
      "\n",
      "=== File: PatNo_ID_1566123680.csv ===\n",
      "Total rows: 42600\n",
      "useable_noNaN = 1: 21449\n",
      "Excluded by step (only >0):\n",
      " - S1: 107\n",
      " - S2: 20624\n",
      " - S7: 413\n",
      " - S8: 7\n",
      "\n",
      "=== File: PatNo_ID_1566252197.csv ===\n",
      "Total rows: 3368\n",
      "useable_noNaN = 1: 1934\n",
      "Excluded by step (only >0):\n",
      " - S1: 1433\n",
      " - S7: 1\n",
      "\n",
      "=== File: PatNo_ID_1566279967.csv ===\n",
      "Total rows: 1235\n",
      "useable_noNaN = 1: 1151\n",
      "Excluded by step (only >0):\n",
      " - S1: 82\n",
      " - S7: 2\n",
      "\n",
      "=== File: PatNo_ID_1566671274.csv ===\n",
      "Total rows: 25359\n",
      "useable_noNaN = 1: 23546\n",
      "Excluded by step (only >0):\n",
      " - S1: 1805\n",
      " - S7: 8\n",
      "\n",
      "=== File: PatNo_ID_1566911879.csv ===\n",
      "Total rows: 53999\n",
      "useable_noNaN = 1: 45890\n",
      "Excluded by step (only >0):\n",
      " - S1: 7294\n",
      " - S2: 733\n",
      " - S7: 80\n",
      " - S8: 2\n",
      "\n",
      "=== File: PatNo_ID_1567747650.csv ===\n",
      "Total rows: 10901\n",
      "useable_noNaN = 1: 10771\n",
      "Excluded by step (only >0):\n",
      " - S1: 2\n",
      " - S2: 126\n",
      " - S7: 2\n",
      "\n",
      "=== File: PatNo_ID_1567804800.csv ===\n",
      "Total rows: 20578\n",
      "useable_noNaN = 1: 12678\n",
      "Excluded by step (only >0):\n",
      " - S1: 7880\n",
      " - S2: 7\n",
      " - S7: 13\n",
      "\n",
      "=== File: PatNo_ID_1567832735.csv ===\n",
      "Total rows: 36580\n",
      "useable_noNaN = 1: 36480\n",
      "Excluded by step (only >0):\n",
      " - S2: 93\n",
      " - S7: 7\n",
      "\n",
      "=== File: PatNo_ID_1568039398.csv ===\n",
      "Total rows: 34519\n",
      "useable_noNaN = 1: 34167\n",
      "Excluded by step (only >0):\n",
      " - S1: 340\n",
      " - S7: 12\n",
      "\n",
      "=== File: PatNo_ID_1568574099.csv ===\n",
      "Total rows: 13946\n",
      "useable_noNaN = 1: 12615\n",
      "Excluded by step (only >0):\n",
      " - S1: 1318\n",
      " - S2: 1\n",
      " - S7: 12\n",
      "\n",
      "=== File: PatNo_ID_1568813269.csv ===\n",
      "Total rows: 4867\n",
      "useable_noNaN = 1: 4834\n",
      "Excluded by step (only >0):\n",
      " - S2: 33\n",
      "\n",
      "=== File: PatNo_ID_1568952422.csv ===\n",
      "Total rows: 1184\n",
      "useable_noNaN = 1: 1181\n",
      "Excluded by step (only >0):\n",
      " - S7: 3\n",
      "\n",
      "=== File: PatNo_ID_1569083701.csv ===\n",
      "Total rows: 3328\n",
      "useable_noNaN = 1: 3317\n",
      "Excluded by step (only >0):\n",
      " - S2: 3\n",
      " - S7: 8\n",
      "\n",
      "=== File: PatNo_ID_1569944983.csv ===\n",
      "Total rows: 7650\n",
      "useable_noNaN = 1: 7618\n",
      "Excluded by step (only >0):\n",
      " - S1: 2\n",
      " - S2: 3\n",
      " - S7: 27\n",
      "\n",
      "=== File: PatNo_ID_1570089466.csv ===\n",
      "Total rows: 41343\n",
      "useable_noNaN = 1: 36576\n",
      "Excluded by step (only >0):\n",
      " - S1: 4662\n",
      " - S2: 71\n",
      " - S7: 33\n",
      " - S8: 1\n",
      "\n",
      "=== File: PatNo_ID_1570242703.csv ===\n",
      "Total rows: 10646\n",
      "useable_noNaN = 1: 10544\n",
      "Excluded by step (only >0):\n",
      " - S1: 93\n",
      " - S2: 2\n",
      " - S7: 7\n",
      "\n",
      "=== File: PatNo_ID_1570273244.csv ===\n",
      "Total rows: 9730\n",
      "useable_noNaN = 1: 9697\n",
      "Excluded by step (only >0):\n",
      " - S1: 21\n",
      " - S2: 5\n",
      " - S7: 6\n",
      " - S8: 1\n",
      "\n",
      "=== File: PatNo_ID_1570642083.csv ===\n",
      "Total rows: 19731\n",
      "useable_noNaN = 1: 19714\n",
      "Excluded by step (only >0):\n",
      " - S1: 3\n",
      " - S7: 2\n",
      " - S8: 12\n",
      "\n",
      "=== File: PatNo_ID_1571945701.csv ===\n",
      "Total rows: 15731\n",
      "useable_noNaN = 1: 15516\n",
      "Excluded by step (only >0):\n",
      " - S1: 3\n",
      " - S2: 164\n",
      " - S7: 18\n",
      " - S8: 30\n",
      "\n",
      "=== File: PatNo_ID_1572481361.csv ===\n",
      "Total rows: 34540\n",
      "useable_noNaN = 1: 34377\n",
      "Excluded by step (only >0):\n",
      " - S1: 18\n",
      " - S2: 113\n",
      " - S7: 29\n",
      " - S8: 3\n",
      "\n",
      "=== File: PatNo_ID_1572562839.csv ===\n",
      "Total rows: 20966\n",
      "useable_noNaN = 1: 15505\n",
      "Excluded by step (only >0):\n",
      " - S1: 5417\n",
      " - S2: 25\n",
      " - S7: 19\n",
      "\n",
      "=== File: PatNo_ID_1572831765.csv ===\n",
      "Total rows: 2696\n",
      "useable_noNaN = 1: 2696\n",
      "Excluded by step (only >0):\n",
      " - (none)\n",
      "\n",
      "=== File: PatNo_ID_1572976822.csv ===\n",
      "Total rows: 6764\n",
      "useable_noNaN = 1: 4270\n",
      "Excluded by step (only >0):\n",
      " - S1: 2482\n",
      " - S2: 7\n",
      " - S7: 5\n",
      "\n",
      "=== File: PatNo_ID_1573063188.csv ===\n",
      "Total rows: 5080\n",
      "useable_noNaN = 1: 5023\n",
      "Excluded by step (only >0):\n",
      " - S1: 56\n",
      " - S7: 1\n",
      "\n",
      "=== File: PatNo_ID_1573249295.csv ===\n",
      "Total rows: 7151\n",
      "useable_noNaN = 1: 7149\n",
      "Excluded by step (only >0):\n",
      " - S8: 2\n",
      "\n",
      "=== File: PatNo_ID_1573964540.csv ===\n",
      "Total rows: 4394\n",
      "useable_noNaN = 1: 4337\n",
      "Excluded by step (only >0):\n",
      " - S1: 1\n",
      " - S2: 47\n",
      " - S7: 5\n",
      " - S8: 4\n",
      "\n",
      "=== File: PatNo_ID_1574148494.csv ===\n",
      "Total rows: 47154\n",
      "useable_noNaN = 1: 47007\n",
      "Excluded by step (only >0):\n",
      " - S1: 137\n",
      " - S7: 9\n",
      " - S8: 1\n",
      "\n",
      "=== File: PatNo_ID_1574270349.csv ===\n",
      "Total rows: 6534\n",
      "useable_noNaN = 1: 6505\n",
      "Excluded by step (only >0):\n",
      " - S1: 18\n",
      " - S2: 4\n",
      " - S7: 7\n",
      "\n",
      "=== File: PatNo_ID_1574528808.csv ===\n",
      "Total rows: 14817\n",
      "useable_noNaN = 1: 14466\n",
      "Excluded by step (only >0):\n",
      " - S1: 332\n",
      " - S2: 5\n",
      " - S7: 14\n",
      "\n",
      "=== File: PatNo_ID_1574831525.csv ===\n",
      "Total rows: 521\n",
      "useable_noNaN = 1: 514\n",
      "Excluded by step (only >0):\n",
      " - S1: 5\n",
      " - S2: 1\n",
      " - S7: 1\n",
      "\n",
      "=== File: PatNo_ID_1574987447.csv ===\n",
      "Total rows: 19843\n",
      "useable_noNaN = 1: 19622\n",
      "Excluded by step (only >0):\n",
      " - S1: 27\n",
      " - S2: 185\n",
      " - S7: 9\n",
      "\n",
      "=== File: PatNo_ID_1575060177.csv ===\n",
      "Total rows: 5290\n",
      "useable_noNaN = 1: 5201\n",
      "Excluded by step (only >0):\n",
      " - S1: 1\n",
      " - S2: 84\n",
      " - S8: 4\n",
      "\n",
      "=== File: PatNo_ID_1575256902.csv ===\n",
      "Total rows: 9587\n",
      "useable_noNaN = 1: 9447\n",
      "Excluded by step (only >0):\n",
      " - S1: 93\n",
      " - S2: 44\n",
      " - S7: 3\n",
      "\n",
      "=== File: PatNo_ID_1575445051.csv ===\n",
      "Total rows: 1801\n",
      "useable_noNaN = 1: 1784\n",
      "Excluded by step (only >0):\n",
      " - S1: 15\n",
      " - S2: 1\n",
      " - S7: 1\n",
      "\n",
      "=== File: PatNo_ID_1575502382.csv ===\n",
      "Total rows: 6604\n",
      "useable_noNaN = 1: 6457\n",
      "Excluded by step (only >0):\n",
      " - S1: 117\n",
      " - S7: 22\n",
      " - S8: 8\n",
      "\n",
      "=== File: PatNo_ID_1575975485.csv ===\n",
      "Total rows: 16459\n",
      "useable_noNaN = 1: 16295\n",
      "Excluded by step (only >0):\n",
      " - S2: 79\n",
      " - S7: 85\n",
      "\n",
      "=== File: PatNo_ID_1576115572.csv ===\n",
      "Total rows: 18717\n",
      "useable_noNaN = 1: 17948\n",
      "Excluded by step (only >0):\n",
      " - S1: 16\n",
      " - S2: 710\n",
      " - S7: 15\n",
      " - S8: 28\n",
      "\n",
      "=== File: PatNo_ID_1576116479.csv ===\n",
      "Total rows: 1263\n",
      "useable_noNaN = 1: 1257\n",
      "Excluded by step (only >0):\n",
      " - S1: 6\n",
      "\n",
      "=== File: PatNo_ID_1576301569.csv ===\n",
      "Total rows: 3207\n",
      "useable_noNaN = 1: 3194\n",
      "Excluded by step (only >0):\n",
      " - S1: 3\n",
      " - S7: 10\n",
      "\n",
      "=== File: PatNo_ID_1576964560.csv ===\n",
      "Total rows: 24192\n",
      "useable_noNaN = 1: 24135\n",
      "Excluded by step (only >0):\n",
      " - S1: 3\n",
      " - S2: 4\n",
      " - S7: 3\n",
      " - S8: 47\n",
      "\n",
      "=== File: PatNo_ID_1577042911.csv ===\n",
      "Total rows: 38357\n",
      "useable_noNaN = 1: 35592\n",
      "Excluded by step (only >0):\n",
      " - S1: 2747\n",
      " - S7: 18\n",
      "\n",
      "=== File: PatNo_ID_1577487284.csv ===\n",
      "Total rows: 2345\n",
      "useable_noNaN = 1: 2328\n",
      "Excluded by step (only >0):\n",
      " - S2: 16\n",
      " - S7: 1\n",
      "\n",
      "=== File: PatNo_ID_1578784257.csv ===\n",
      "Total rows: 33917\n",
      "useable_noNaN = 1: 28541\n",
      "Excluded by step (only >0):\n",
      " - S1: 5361\n",
      " - S2: 3\n",
      " - S7: 12\n",
      "\n",
      "=== File: PatNo_ID_1579198603.csv ===\n",
      "Total rows: 2046\n",
      "useable_noNaN = 1: 2038\n",
      "Excluded by step (only >0):\n",
      " - S1: 1\n",
      " - S2: 7\n",
      "\n",
      "=== File: PatNo_ID_1579498177.csv ===\n",
      "Total rows: 21688\n",
      "useable_noNaN = 1: 21605\n",
      "Excluded by step (only >0):\n",
      " - S1: 10\n",
      " - S2: 58\n",
      " - S7: 9\n",
      " - S8: 4\n",
      " - S9: 2\n",
      "\n",
      "=== File: PatNo_ID_1580062580.csv ===\n",
      "Total rows: 2150\n",
      "useable_noNaN = 1: 2061\n",
      "Excluded by step (only >0):\n",
      " - S1: 6\n",
      " - S2: 83\n",
      "\n",
      "=== File: PatNo_ID_1580096720.csv ===\n",
      "Total rows: 1423\n",
      "useable_noNaN = 1: 999\n",
      "Excluded by step (only >0):\n",
      " - S2: 424\n",
      "\n",
      "=== File: PatNo_ID_1580107637.csv ===\n",
      "Total rows: 2698\n",
      "useable_noNaN = 1: 2633\n",
      "Excluded by step (only >0):\n",
      " - S1: 54\n",
      " - S2: 10\n",
      " - S7: 1\n",
      "\n",
      "=== File: PatNo_ID_1580244614.csv ===\n",
      "Total rows: 5278\n",
      "useable_noNaN = 1: 3850\n",
      "Excluded by step (only >0):\n",
      " - S1: 1412\n",
      " - S2: 1\n",
      " - S7: 12\n",
      " - S8: 3\n",
      "\n",
      "=== File: PatNo_ID_1580766093.csv ===\n",
      "Total rows: 18070\n",
      "useable_noNaN = 1: 17364\n",
      "Excluded by step (only >0):\n",
      " - S1: 89\n",
      " - S2: 491\n",
      " - S7: 110\n",
      " - S8: 16\n",
      "\n",
      "=== File: PatNo_ID_1581003248.csv ===\n",
      "Total rows: 7776\n",
      "useable_noNaN = 1: 6542\n",
      "Excluded by step (only >0):\n",
      " - S1: 1144\n",
      " - S2: 76\n",
      " - S7: 13\n",
      " - S8: 1\n",
      "\n",
      "=== File: PatNo_ID_1581019504.csv ===\n",
      "Total rows: 28177\n",
      "useable_noNaN = 1: 20547\n",
      "Excluded by step (only >0):\n",
      " - S1: 7444\n",
      " - S2: 144\n",
      " - S7: 37\n",
      " - S8: 5\n",
      "\n",
      "=== File: PatNo_ID_1581633231.csv ===\n",
      "Total rows: 15995\n",
      "useable_noNaN = 1: 14542\n",
      "Excluded by step (only >0):\n",
      " - S1: 1366\n",
      " - S2: 77\n",
      " - S7: 6\n",
      " - S8: 4\n",
      "\n",
      "=== File: PatNo_ID_1581692973.csv ===\n",
      "Total rows: 2723\n",
      "useable_noNaN = 1: 2717\n",
      "Excluded by step (only >0):\n",
      " - S7: 6\n",
      "\n",
      "=== File: PatNo_ID_1582452511.csv ===\n",
      "Total rows: 5196\n",
      "useable_noNaN = 1: 5174\n",
      "Excluded by step (only >0):\n",
      " - S2: 5\n",
      " - S7: 17\n",
      "\n",
      "=== File: PatNo_ID_1582635996.csv ===\n",
      "Total rows: 13046\n",
      "useable_noNaN = 1: 13043\n",
      "Excluded by step (only >0):\n",
      " - S1: 1\n",
      " - S7: 2\n",
      "\n",
      "=== File: PatNo_ID_1582849900.csv ===\n",
      "Total rows: 7411\n",
      "useable_noNaN = 1: 4674\n",
      "Excluded by step (only >0):\n",
      " - S1: 2619\n",
      " - S2: 115\n",
      " - S7: 3\n",
      "\n",
      "=== File: PatNo_ID_1582937076.csv ===\n",
      "Total rows: 23990\n",
      "useable_noNaN = 1: 22644\n",
      "Excluded by step (only >0):\n",
      " - S1: 1313\n",
      " - S2: 4\n",
      " - S7: 28\n",
      " - S8: 1\n",
      "\n",
      "=== File: PatNo_ID_1584158973.csv ===\n",
      "Total rows: 2882\n",
      "useable_noNaN = 1: 2874\n",
      "Excluded by step (only >0):\n",
      " - S1: 2\n",
      " - S2: 3\n",
      " - S7: 3\n",
      "\n",
      "=== File: PatNo_ID_1584397376.csv ===\n",
      "Total rows: 638\n",
      "useable_noNaN = 1: 638\n",
      "Excluded by step (only >0):\n",
      " - (none)\n",
      "\n",
      "=== File: PatNo_ID_1586172659.csv ===\n",
      "Total rows: 38559\n",
      "useable_noNaN = 1: 38193\n",
      "Excluded by step (only >0):\n",
      " - S1: 5\n",
      " - S2: 135\n",
      " - S7: 226\n",
      "\n",
      "=== File: PatNo_ID_1586696634.csv ===\n",
      "Total rows: 3569\n",
      "useable_noNaN = 1: 2499\n",
      "Excluded by step (only >0):\n",
      " - S1: 1065\n",
      " - S7: 1\n",
      " - S8: 4\n",
      "\n",
      "=== File: PatNo_ID_1586897008.csv ===\n",
      "Total rows: 6687\n",
      "useable_noNaN = 1: 6646\n",
      "Excluded by step (only >0):\n",
      " - S1: 33\n",
      " - S2: 2\n",
      " - S7: 6\n",
      "\n",
      "=== File: PatNo_ID_1587490083.csv ===\n",
      "Total rows: 45186\n",
      "useable_noNaN = 1: 45181\n",
      "Excluded by step (only >0):\n",
      " - S7: 5\n",
      "\n",
      "=== File: PatNo_ID_1588632604.csv ===\n",
      "Total rows: 2608\n",
      "useable_noNaN = 1: 2479\n",
      "Excluded by step (only >0):\n",
      " - S1: 9\n",
      " - S7: 120\n",
      "\n",
      "=== File: PatNo_ID_1588673465.csv ===\n",
      "Total rows: 10077\n",
      "useable_noNaN = 1: 5496\n",
      "Excluded by step (only >0):\n",
      " - S1: 3792\n",
      " - S2: 758\n",
      " - S7: 31\n",
      "\n",
      "=== File: PatNo_ID_1588794796.csv ===\n",
      "Total rows: 9376\n",
      "useable_noNaN = 1: 9374\n",
      "Excluded by step (only >0):\n",
      " - S2: 1\n",
      " - S7: 1\n",
      "\n",
      "=== File: PatNo_ID_1588957997.csv ===\n",
      "Total rows: 10966\n",
      "useable_noNaN = 1: 10937\n",
      "Excluded by step (only >0):\n",
      " - S1: 8\n",
      " - S7: 13\n",
      " - S8: 8\n",
      "\n",
      "=== File: PatNo_ID_1589018086.csv ===\n",
      "Total rows: 13472\n",
      "useable_noNaN = 1: 13392\n",
      "Excluded by step (only >0):\n",
      " - S1: 76\n",
      " - S7: 1\n",
      " - S8: 3\n",
      "\n",
      "=== File: PatNo_ID_1589034524.csv ===\n",
      "Total rows: 50081\n",
      "useable_noNaN = 1: 49650\n",
      "Excluded by step (only >0):\n",
      " - S1: 45\n",
      " - S2: 346\n",
      " - S7: 40\n",
      "\n",
      "=== File: PatNo_ID_1589324603.csv ===\n",
      "Total rows: 4187\n",
      "useable_noNaN = 1: 4101\n",
      "Excluded by step (only >0):\n",
      " - S1: 81\n",
      " - S2: 1\n",
      " - S7: 3\n",
      " - S8: 1\n",
      "\n",
      "=== File: PatNo_ID_1589918099.csv ===\n",
      "Total rows: 9333\n",
      "useable_noNaN = 1: 0\n",
      "Excluded by step (only >0):\n",
      " - S1: 9333\n",
      "\n",
      "=== File: PatNo_ID_1590136310.csv ===\n",
      "Total rows: 5580\n",
      "useable_noNaN = 1: 5297\n",
      "Excluded by step (only >0):\n",
      " - S1: 275\n",
      " - S7: 7\n",
      " - S8: 1\n",
      "\n",
      "=== File: PatNo_ID_1590616537.csv ===\n",
      "Total rows: 14208\n",
      "useable_noNaN = 1: 14148\n",
      "Excluded by step (only >0):\n",
      " - S1: 7\n",
      " - S2: 45\n",
      " - S7: 8\n",
      "\n",
      "=== File: PatNo_ID_1590854576.csv ===\n",
      "Total rows: 15879\n",
      "useable_noNaN = 1: 15869\n",
      "Excluded by step (only >0):\n",
      " - S7: 10\n",
      "\n",
      "=== File: PatNo_ID_1591609798.csv ===\n",
      "Total rows: 35624\n",
      "useable_noNaN = 1: 35507\n",
      "Excluded by step (only >0):\n",
      " - S2: 96\n",
      " - S7: 21\n",
      "\n",
      "=== File: PatNo_ID_1592044724.csv ===\n",
      "Total rows: 6815\n",
      "useable_noNaN = 1: 6683\n",
      "Excluded by step (only >0):\n",
      " - S1: 2\n",
      " - S2: 128\n",
      " - S7: 1\n",
      " - S8: 1\n",
      "\n",
      "=== File: PatNo_ID_1592560504.csv ===\n",
      "Total rows: 18052\n",
      "useable_noNaN = 1: 18049\n",
      "Excluded by step (only >0):\n",
      " - S2: 1\n",
      " - S7: 1\n",
      " - S8: 1\n",
      "\n",
      "=== File: PatNo_ID_1593087886.csv ===\n",
      "Total rows: 24163\n",
      "useable_noNaN = 1: 23798\n",
      "Excluded by step (only >0):\n",
      " - S1: 28\n",
      " - S2: 305\n",
      " - S7: 25\n",
      " - S8: 7\n",
      "\n",
      "=== File: PatNo_ID_1593416100.csv ===\n",
      "Total rows: 3328\n",
      "useable_noNaN = 1: 3321\n",
      "Excluded by step (only >0):\n",
      " - S8: 7\n",
      "\n",
      "=== File: PatNo_ID_1593472048.csv ===\n",
      "Total rows: 8230\n",
      "useable_noNaN = 1: 6240\n",
      "Excluded by step (only >0):\n",
      " - S1: 1983\n",
      " - S7: 7\n",
      "\n",
      "=== File: PatNo_ID_1593593586.csv ===\n",
      "Total rows: 18882\n",
      "useable_noNaN = 1: 18829\n",
      "Excluded by step (only >0):\n",
      " - S2: 40\n",
      " - S7: 13\n",
      "\n",
      "=== File: PatNo_ID_1593720818.csv ===\n",
      "Total rows: 3911\n",
      "useable_noNaN = 1: 2640\n",
      "Excluded by step (only >0):\n",
      " - S1: 1266\n",
      " - S2: 1\n",
      " - S7: 4\n",
      "\n",
      "=== File: PatNo_ID_1593838524.csv ===\n",
      "Total rows: 2178\n",
      "useable_noNaN = 1: 2156\n",
      "Excluded by step (only >0):\n",
      " - S1: 16\n",
      " - S2: 2\n",
      " - S7: 4\n",
      "\n",
      "=== File: PatNo_ID_1594173718.csv ===\n",
      "Total rows: 344\n",
      "useable_noNaN = 1: 344\n",
      "Excluded by step (only >0):\n",
      " - (none)\n",
      "\n",
      "=== File: PatNo_ID_1594294180.csv ===\n",
      "Total rows: 18334\n",
      "useable_noNaN = 1: 8882\n",
      "Excluded by step (only >0):\n",
      " - S1: 7\n",
      " - S2: 9442\n",
      " - S7: 3\n",
      "\n",
      "=== File: PatNo_ID_1594305136.csv ===\n",
      "Total rows: 18679\n",
      "useable_noNaN = 1: 15571\n",
      "Excluded by step (only >0):\n",
      " - S1: 3045\n",
      " - S2: 55\n",
      " - S7: 7\n",
      " - S8: 1\n",
      "\n",
      "=== File: PatNo_ID_1594309746.csv ===\n",
      "Total rows: 3745\n",
      "useable_noNaN = 1: 3690\n",
      "Excluded by step (only >0):\n",
      " - S2: 52\n",
      " - S7: 2\n",
      " - S8: 1\n",
      "\n",
      "=== File: PatNo_ID_1594319286.csv ===\n",
      "Total rows: 4107\n",
      "useable_noNaN = 1: 3986\n",
      "Excluded by step (only >0):\n",
      " - S1: 119\n",
      " - S7: 2\n",
      "\n",
      "=== File: PatNo_ID_1594320763.csv ===\n",
      "Total rows: 2431\n",
      "useable_noNaN = 1: 2375\n",
      "Excluded by step (only >0):\n",
      " - S1: 1\n",
      " - S2: 49\n",
      " - S7: 1\n",
      " - S8: 5\n",
      "\n",
      "=== File: PatNo_ID_1594322594.csv ===\n",
      "Total rows: 5380\n",
      "useable_noNaN = 1: 5353\n",
      "Excluded by step (only >0):\n",
      " - S2: 26\n",
      " - S7: 1\n",
      "\n",
      "=== File: PatNo_ID_1594335109.csv ===\n",
      "Total rows: 5116\n",
      "useable_noNaN = 1: 4993\n",
      "Excluded by step (only >0):\n",
      " - S2: 8\n",
      " - S7: 115\n",
      "\n",
      "=== File: PatNo_ID_1594423683.csv ===\n",
      "Total rows: 5023\n",
      "useable_noNaN = 1: 4994\n",
      "Excluded by step (only >0):\n",
      " - S1: 22\n",
      " - S2: 1\n",
      " - S7: 6\n",
      "\n",
      "=== File: PatNo_ID_1594437309.csv ===\n",
      "Total rows: 10035\n",
      "useable_noNaN = 1: 9892\n",
      "Excluded by step (only >0):\n",
      " - S1: 99\n",
      " - S2: 1\n",
      " - S7: 27\n",
      " - S8: 16\n",
      "\n",
      "=== File: PatNo_ID_1594439781.csv ===\n",
      "Total rows: 7691\n",
      "useable_noNaN = 1: 7555\n",
      "Excluded by step (only >0):\n",
      " - S1: 4\n",
      " - S2: 118\n",
      " - S7: 2\n",
      " - S8: 12\n",
      "\n",
      "=== File: PatNo_ID_1594441887.csv ===\n",
      "Total rows: 10203\n",
      "useable_noNaN = 1: 9854\n",
      "Excluded by step (only >0):\n",
      " - S1: 1\n",
      " - S2: 8\n",
      " - S7: 174\n",
      " - S8: 166\n",
      "\n",
      "=== File: PatNo_ID_1594448501.csv ===\n",
      "Total rows: 5106\n",
      "useable_noNaN = 1: 1039\n",
      "Excluded by step (only >0):\n",
      " - S1: 4059\n",
      " - S2: 6\n",
      " - S7: 2\n",
      "\n",
      "=== File: PatNo_ID_1594455578.csv ===\n",
      "Total rows: 262\n",
      "useable_noNaN = 1: 261\n",
      "Excluded by step (only >0):\n",
      " - S7: 1\n",
      "\n",
      "=== File: PatNo_ID_1594464829.csv ===\n",
      "Total rows: 4000\n",
      "useable_noNaN = 1: 3936\n",
      "Excluded by step (only >0):\n",
      " - S1: 63\n",
      " - S7: 1\n",
      "\n",
      "=== File: PatNo_ID_1594467719.csv ===\n",
      "Total rows: 2560\n",
      "useable_noNaN = 1: 1313\n",
      "Excluded by step (only >0):\n",
      " - S1: 1247\n",
      "\n",
      "=== File: PatNo_ID_1594471407.csv ===\n",
      "Total rows: 11433\n",
      "useable_noNaN = 1: 11269\n",
      "Excluded by step (only >0):\n",
      " - S1: 151\n",
      " - S2: 3\n",
      " - S7: 9\n",
      " - S8: 1\n",
      "\n",
      "=== File: PatNo_ID_1594479330.csv ===\n",
      "Total rows: 3727\n",
      "useable_noNaN = 1: 3714\n",
      "Excluded by step (only >0):\n",
      " - S2: 13\n",
      "\n",
      "=== File: PatNo_ID_1594511911.csv ===\n",
      "Total rows: 5488\n",
      "useable_noNaN = 1: 5443\n",
      "Excluded by step (only >0):\n",
      " - S1: 38\n",
      " - S7: 3\n",
      " - S8: 4\n",
      "\n",
      "=== File: PatNo_ID_1594511914.csv ===\n",
      "Total rows: 9717\n",
      "useable_noNaN = 1: 9642\n",
      "Excluded by step (only >0):\n",
      " - S1: 41\n",
      " - S2: 15\n",
      " - S7: 19\n",
      "\n",
      "=== File: PatNo_ID_1594528842.csv ===\n",
      "Total rows: 2491\n",
      "useable_noNaN = 1: 2396\n",
      "Excluded by step (only >0):\n",
      " - S1: 76\n",
      " - S2: 18\n",
      " - S7: 1\n",
      "\n",
      "=== File: PatNo_ID_1594533379.csv ===\n",
      "Total rows: 1030\n",
      "useable_noNaN = 1: 999\n",
      "Excluded by step (only >0):\n",
      " - S1: 31\n",
      "\n",
      "================= Global Summary =================\n",
      "Files processed: 122\n",
      "Total rows      : 1567309\n",
      "Final pass rows : 1378500\n",
      "Total excluded by step:\n",
      " - S1: 123726\n",
      " - S2: 62395\n",
      " - S3: 0\n",
      " - S4: 0\n",
      " - S5: 0\n",
      " - S6: 0\n",
      " - S7: 2254\n",
      " - S8: 427\n",
      " - S9: 7\n",
      " - S10: 0\n"
     ]
    }
   ],
   "source": [
    "\"\"\" 將/home/jovyan/RT08/0925/bling_useable_svv/的所有檔案複製一份到/home/jovyan/RT08/0925/bling_svv_fin/\n",
    "到\"/home/jovyan/RT08/0925/bling_svv_fin\"裡面\n",
    "每個檔案都新增一個欄位useable_noNaN\n",
    "useable_noNaN=1 的定義，同時滿足下列條件就標記 useable_noNaN=1，否則 0：\n",
    "    1. mode_1 或 mode_2 或 mode_3 其中「恰有一個=1」\n",
    "    2. 欄位\"patno\", \"senddate\", \"rrhzsetactual\", \"mvsetactual\",\"peepepap\", \"ppeak\", \"cdyn\", \"pmean\",都有數值\n",
    "    3. \"patno\", \"mode_1\", mode_2, mode_3 都有數值，是整數\n",
    "    4. \"senddate\", 都有數值，是datatime\n",
    "    5. \"rrhzsetactual\", \"mvsetactual\",\"peepepap\", \"ppeak\", \"cdyn\", \"pmean\",\"svv_new\"都有數值，是浮點數或是整數\n",
    "    6. useable=1\n",
    "    7. Vti：0–1500\n",
    "    8. VVte：0–1500\n",
    "    9. SponVt：0–1200\n",
    "    10 senddate是順的時間\n",
    "\n",
    "    並標記那些檔案 有幾筆資料在哪一個步驟不符合被剔除在 useable_noNaN=1 的名單\n",
    "\"\"\"\n",
    "\n",
    "# -*- coding: utf-8 -*-\n",
    "\"\"\"\n",
    "任務：\n",
    "1) 複製 /home/jovyan/RT08/0925/bling_useable_svv/*.csv 到 /home/jovyan/RT08/0925/bling_svv_fin/\n",
    "2) 在 bling_svv_fin 的每個檔案新增欄位 useable_noNaN（0/1）\n",
    "3) useable_noNaN=1 的條件（全部必須成立；依「簡單→困難」順序檢核），否則 0：\n",
    "   [S1] One-hot：mode_1/mode_2/mode_3 恰有一個為 1\n",
    "   [S2] 必填欄位非空：patno, senddate, rrhzsetactual, mvsetactual, peepepap, ppeak, cdyn, pmean 皆有值\n",
    "   [S3] 類型檢核（整數）：patno, mode_1, mode_2, mode_3 為整數（能被安全轉成整數）\n",
    "   [S4] 類型檢核（時間）：senddate 可被解析為 datetime\n",
    "   [S5] 類型檢核（數值）：rrhzsetactual, mvsetactual, peepepap, ppeak, cdyn, pmean, svv_new 為數值（浮點或整數）\n",
    "   [S6] 既有 useable 欄位為 1\n",
    "   [S7] vti 值域：0–1500（含邊界）\n",
    "   [S8] vte 值域：0–1500（含邊界）\n",
    "   [S9] sponvt 值域：0–1200（含邊界）\n",
    "   [S10] 時間序單調：同一病患(patno)內 senddate 嚴格遞增（對首筆視為通過；其他需 diff>0）\n",
    "\n",
    "4) 程式會列印：\n",
    "   - 檔案總數（複製前/後）\n",
    "   - 每檔總列數、最終 useable_noNaN=1 的筆數\n",
    "   - 各步驟（S1~S10）「在該步被剔除」的筆數（只列出 >0 者）\n",
    "   - 跨檔總結\n",
    "\n",
    "說明：\n",
    "- 本程式只會修改 bling_svv_fin 內的檔案，不會碰到 bling_useable_svv。\n",
    "- 對於 '','null','(null)' 皆視為缺值；數值類欄位一律以 pd.to_numeric(..., errors=\"coerce\") 轉換。\n",
    "\"\"\"\n",
    "\n",
    "import os, glob, shutil\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "SRC_DIR = \"/home/jovyan/RT08/0925/bling_useable_svv\"\n",
    "DST_DIR = \"/home/jovyan/RT08/0925/bling_svv_fin\"\n",
    "os.makedirs(DST_DIR, exist_ok=True)\n",
    "\n",
    "# 先複製所有 CSV 到目標資料夾（若檔名已存在會覆蓋）\n",
    "src_files = sorted(glob.glob(os.path.join(SRC_DIR, \"*.csv\")))\n",
    "for fp in src_files:\n",
    "    shutil.copy(fp, DST_DIR)\n",
    "\n",
    "dst_files = sorted(glob.glob(os.path.join(DST_DIR, \"*.csv\")))\n",
    "print(f\"[Info] Copied {len(src_files)} files from {SRC_DIR} → {DST_DIR}\")\n",
    "print(f\"[Info] Files now in {DST_DIR}: {len(dst_files)}\")\n",
    "\n",
    "# 欄位名稱（允許大小寫/變形的簡單對應）\n",
    "# 若你的欄位固定為小寫，可直接使用小寫鍵\n",
    "COL_ALIASES = {\n",
    "    \"vti\": [\"vti\"],\n",
    "    \"vte\": [\"vte\"],\n",
    "    \"sponvt\": [\"sponvt\"],\n",
    "    \"patno\": [\"patno\"],\n",
    "    \"senddate\": [\"senddate\"],\n",
    "    \"rrhzsetactual\": [\"rrhzsetactual\"],\n",
    "    \"mvsetactual\": [\"mvsetactual\"],\n",
    "    \"peepepap\": [\"peepepap\"],\n",
    "    \"ppeak\": [\"ppeak\"],\n",
    "    \"cdyn\": [\"cdyn\"],\n",
    "    \"pmean\": [\"pmean\"],\n",
    "    \"mode_1\": [\"mode_1\"],\n",
    "    \"mode_2\": [\"mode_2\"],\n",
    "    \"mode_3\": [\"mode_3\"],\n",
    "    \"useable\": [\"useable\"],\n",
    "    \"svv_new\": [\"svv_new\"],\n",
    "}\n",
    "\n",
    "def resolve_col(df, keys):\n",
    "    \"\"\"在 df 中依別名清單尋找第一個存在的欄位名稱；找不到則回傳 None。\"\"\"\n",
    "    for k in keys:\n",
    "        if k in df.columns:\n",
    "            return k\n",
    "    return None\n",
    "\n",
    "def coerce_numeric(series: pd.Series, kind=\"float\"):\n",
    "    \"\"\"\n",
    "    字串空白/ 'null' / '(null)' → NaN，再轉數值。\n",
    "    kind=\"int\" 時：先轉 float，再檢查能否為整數（整數化不失真）。\n",
    "    \"\"\"\n",
    "    s = series.replace(r'^\\s*$', np.nan, regex=True)\n",
    "    s = s.replace([\"null\", \"NULL\", \"(null)\", \"(NULL)\"], np.nan)\n",
    "    s_num = pd.to_numeric(s, errors=\"coerce\")\n",
    "    if kind == \"int\":\n",
    "        # 可為 NaN；非 NaN 若 np.isfinite 且 s_num == round(s_num) 才算整數\n",
    "        return s_num\n",
    "    return s_num\n",
    "\n",
    "def is_integer_series(s: pd.Series) -> pd.Series:\n",
    "    \"\"\"回傳逐列布林：非 NaN 且值為整數（允許 1.0 類型）。\"\"\"\n",
    "    s = pd.to_numeric(s, errors=\"coerce\")\n",
    "    return s.notna() & np.isfinite(s) & (np.floor(s) == np.ceil(s))\n",
    "\n",
    "def parse_datetime(series: pd.Series) -> pd.Series:\n",
    "    \"\"\"嘗試轉成 datetime；無法解析→NaN。\"\"\"\n",
    "    return pd.to_datetime(series, errors=\"coerce\")\n",
    "\n",
    "# 全域統計累計\n",
    "global_total_rows = 0\n",
    "global_pass_rows  = 0\n",
    "global_step_excl  = {f\"S{i}\": 0 for i in range(1, 11)}\n",
    "files_processed   = 0\n",
    "\n",
    "for fp in dst_files:\n",
    "    base = os.path.basename(fp)\n",
    "    try:\n",
    "        df = pd.read_csv(fp, low_memory=False)\n",
    "    except Exception as e:\n",
    "        print(f\"[Skip] Cannot read {base}: {e}\")\n",
    "        continue\n",
    "\n",
    "    # 對應實際欄位名稱\n",
    "    col = {k: resolve_col(df, v) for k, v in COL_ALIASES.items()}\n",
    "\n",
    "    # 必備欄位若不存在，直接標 0 並報告\n",
    "    required_sets = [\n",
    "        (\"patno\", \"senddate\", \"rrhzsetactual\", \"mvsetactual\", \"peepepap\", \"ppeak\", \"cdyn\", \"pmean\"),\n",
    "        (\"mode_1\", \"mode_2\", \"mode_3\", \"useable\"),\n",
    "        (\"vti\", \"vte\", \"sponvt\"),\n",
    "        (\"svv_new\",),\n",
    "    ]\n",
    "    missing_any = False\n",
    "    for req in required_sets:\n",
    "        for k in req:\n",
    "            if col.get(k) is None:\n",
    "                missing_any = True\n",
    "    total_rows = len(df)\n",
    "    global_total_rows += total_rows\n",
    "    files_processed += 1\n",
    "\n",
    "    if missing_any or total_rows == 0:\n",
    "        # 直接給 useable_noNaN=0，並印出缺欄訊息\n",
    "        df[\"useable_noNaN\"] = 0\n",
    "        df.to_csv(fp, index=False)\n",
    "        missing_list = [k for k, v in col.items() if v is None]\n",
    "        print(f\"[Warn] {base}: missing columns -> {missing_list}. Mark all useable_noNaN=0\")\n",
    "        continue\n",
    "\n",
    "    # === 準備數值/型別欄位 ===\n",
    "    # 基礎欄位\n",
    "    patno_s  = coerce_numeric(df[col[\"patno\"]],  kind=\"int\")\n",
    "    send_s   = parse_datetime(df[col[\"senddate\"]])\n",
    "\n",
    "    # 模式欄位 → 0/1\n",
    "    m1_s = coerce_numeric(df[col[\"mode_1\"]], kind=\"int\")\n",
    "    m2_s = coerce_numeric(df[col[\"mode_2\"]], kind=\"int\")\n",
    "    m3_s = coerce_numeric(df[col[\"mode_3\"]], kind=\"int\")\n",
    "    # 將非 0/1 值也允許轉成 0/1：先整數判定，再 clip 到 [0,1]\n",
    "    m1 = pd.to_numeric(m1_s, errors=\"coerce\").fillna(np.nan)\n",
    "    m2 = pd.to_numeric(m2_s, errors=\"coerce\").fillna(np.nan)\n",
    "    m3 = pd.to_numeric(m3_s, errors=\"coerce\").fillna(np.nan)\n",
    "    # 保留 NaN，之後用 notna() 檢核；比較時先暫存 0/1 版本\n",
    "    m1_bin = m1.round().clip(0,1)\n",
    "    m2_bin = m2.round().clip(0,1)\n",
    "    m3_bin = m3.round().clip(0,1)\n",
    "\n",
    "    useable_s = coerce_numeric(df[col[\"useable\"]], kind=\"int\").fillna(0).astype(int).clip(0,1)\n",
    "\n",
    "    # 型別/數值檢查欄位\n",
    "    rr_s = coerce_numeric(df[col[\"rrhzsetactual\"]])\n",
    "    mv_s = coerce_numeric(df[col[\"mvsetactual\"]])\n",
    "    pe_s = coerce_numeric(df[col[\"peepepap\"]])\n",
    "    pp_s = coerce_numeric(df[col[\"ppeak\"]])\n",
    "    cd_s = coerce_numeric(df[col[\"cdyn\"]])\n",
    "    pm_s = coerce_numeric(df[col[\"pmean\"]])\n",
    "    sv_s = coerce_numeric(df[col[\"svv_new\"]])\n",
    "\n",
    "    # 值域欄位\n",
    "    vti_s = coerce_numeric(df[col[\"vti\"]])\n",
    "    vte_s = coerce_numeric(df[col[\"vte\"]])\n",
    "    spn_s = coerce_numeric(df[col[\"sponvt\"]])\n",
    "\n",
    "    # === 建立每步條件的布林遮罩（True=通過該步） ===\n",
    "    step = {}\n",
    "\n",
    "    # S1: 恰有一個為 1（允許其餘為 0；NaN 視為不通過）\n",
    "    sum_modes = m1_bin.fillna(-1) + m2_bin.fillna(-1) + m3_bin.fillna(-1)\n",
    "    step[\"S1\"] = (m1_bin.notna() & m2_bin.notna() & m3_bin.notna()) & (sum_modes == 1)\n",
    "\n",
    "    # S2: 必填欄位非空\n",
    "    step[\"S2\"] = (\n",
    "        patno_s.notna() & send_s.notna() &\n",
    "        rr_s.notna() & mv_s.notna() & pe_s.notna() &\n",
    "        pp_s.notna() & cd_s.notna() & pm_s.notna()\n",
    "    )\n",
    "\n",
    "    # S3: 類型（整數）：patno, mode_1, mode_2, mode_3\n",
    "    step[\"S3\"] = (\n",
    "        is_integer_series(patno_s) &\n",
    "        is_integer_series(m1) & is_integer_series(m2) & is_integer_series(m3)\n",
    "    )\n",
    "\n",
    "    # S4: 類型（時間）：senddate 可解析\n",
    "    step[\"S4\"] = send_s.notna()\n",
    "\n",
    "    # S5: 類型（數值）：rrhzsetactual, mvsetactual, peepepap, ppeak, cdyn, pmean, svv_new\n",
    "    numerics_ok = (\n",
    "        rr_s.notna() & mv_s.notna() & pe_s.notna() &\n",
    "        pp_s.notna() & cd_s.notna() & pm_s.notna() & sv_s.notna()\n",
    "    )\n",
    "    step[\"S5\"] = numerics_ok\n",
    "\n",
    "    # S6: useable == 1\n",
    "    step[\"S6\"] = (useable_s == 1)\n",
    "\n",
    "    # S7~S9: 值域\n",
    "    step[\"S7\"] = vti_s.notna() & (vti_s >= 0) & (vti_s <= 1500)\n",
    "    step[\"S8\"] = vte_s.notna() & (vte_s >= 0) & (vte_s <= 1500)\n",
    "    step[\"S9\"] = spn_s.notna() & (spn_s >= 0) & (spn_s <= 1200)\n",
    "\n",
    "    # S10: 時間單調（同病患內嚴格遞增）\n",
    "    # 以 patno 分組計算 diff（秒）；首筆視為通過，其餘需 >0\n",
    "    tmp = pd.DataFrame({\"patno\": patno_s, \"senddate\": send_s})\n",
    "    tmp_sorted_idx = tmp.sort_values([\"patno\", \"senddate\"]).index\n",
    "    # 初始化為 False；接著對每個病患覆蓋 True/False\n",
    "    monotonic_mask = pd.Series(False, index=df.index)\n",
    "    for pid, gidx in tmp.loc[tmp_sorted_idx].groupby(\"patno\").groups.items():\n",
    "        g = tmp.loc[list(gidx)].sort_values(\"senddate\")\n",
    "        d = g[\"senddate\"].diff().dt.total_seconds()\n",
    "        ok = (d.isna()) | (d > 0)  # 首筆 True；其餘 >0\n",
    "        monotonic_mask.loc[g.index] = ok.values\n",
    "    step[\"S10\"] = monotonic_mask\n",
    "\n",
    "    # === 逐步歸因統計：在該步被剔除的人數（通過前面所有步，但未通過當前步） ===\n",
    "    passed_so_far = pd.Series(True, index=df.index)\n",
    "    step_excl_counts = {}\n",
    "    for i in range(1, 11):\n",
    "        key = f\"S{i}\"\n",
    "        excl_here = passed_so_far & (~step[key])\n",
    "        step_excl_counts[key] = int(excl_here.sum())\n",
    "        # 只有通過此步的才繼續留在池子裡\n",
    "        passed_so_far = passed_so_far & step[key]\n",
    "\n",
    "    # 最終結果\n",
    "    useable_noNaN = passed_so_far.astype(int)\n",
    "    df[\"useable_noNaN\"] = useable_noNaN\n",
    "    final_pass = int(useable_noNaN.sum())\n",
    "    global_pass_rows += final_pass\n",
    "\n",
    "    # 寫回檔案\n",
    "    df.to_csv(fp, index=False)\n",
    "\n",
    "    # 列印每檔摘要（僅列出有剔除的步驟）\n",
    "    print(f\"\\n=== File: {base} ===\")\n",
    "    print(f\"Total rows: {total_rows}\")\n",
    "    print(f\"useable_noNaN = 1: {final_pass}\")\n",
    "    print(\"Excluded by step (only >0):\")\n",
    "    any_excl = False\n",
    "    for i in range(1, 11):\n",
    "        key = f\"S{i}\"\n",
    "        cnt = step_excl_counts[key]\n",
    "        if cnt > 0:\n",
    "            print(f\" - {key}: {cnt}\")\n",
    "            any_excl = True\n",
    "        global_step_excl[key] += cnt\n",
    "    if not any_excl:\n",
    "        print(\" - (none)\")\n",
    "\n",
    "# 跨檔總結\n",
    "print(\"\\n================= Global Summary =================\")\n",
    "print(f\"Files processed: {files_processed}\")\n",
    "print(f\"Total rows      : {global_total_rows}\")\n",
    "print(f\"Final pass rows : {global_pass_rows}\")\n",
    "print(\"Total excluded by step:\")\n",
    "for i in range(1, 11):\n",
    "    key = f\"S{i}\"\n",
    "    print(f\" - {key}: {global_step_excl[key]}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "daf5a333-2166-40ec-9a8d-71cab5de508d",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true,
     "source_hidden": true
    }
   },
   "outputs": [
    {
     "ename": "IndentationError",
     "evalue": "unexpected indent (445014275.py, line 3)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;36m  Cell \u001b[0;32mIn[112], line 3\u001b[0;36m\u001b[0m\n\u001b[0;31m    excl_S1 = (~step[\"S1\"])  # 沒通過 S1 的\u001b[0m\n\u001b[0m    ^\u001b[0m\n\u001b[0;31mIndentationError\u001b[0m\u001b[0;31m:\u001b[0m unexpected indent\n"
     ]
    }
   ],
   "source": [
    "# S1有點多 我看一下\n",
    "    # === 在每個檔案中輸出 S1 剔除的資料 ===\n",
    "    excl_S1 = (~step[\"S1\"])  # 沒通過 S1 的\n",
    "    if excl_S1.any():\n",
    "        print(f\"\\n[Details S1] File {base} — rows excluded at S1 = {excl_S1.sum()}\")\n",
    "        # 只印出關鍵欄位，方便檢查\n",
    "        print(df.loc[excl_S1, [col[\"patno\"], col[\"senddate\"], col[\"mode_1\"], col[\"mode_2\"], col[\"mode_3\"]]].head(10))\n",
    "        # 如果想完整存出，可寫到 CSV\n",
    "       # out_csv = os.path.join(DST_DIR, f\"{base}_S1_excluded.csv\")\n",
    "       # df.loc[excl_S1].to_csv(out_csv, index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "adcd660c-5a46-421f-9279-beae02e1470d",
   "metadata": {},
   "outputs": [],
   "source": [
    "把刪掉的資料用成紅色\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "c663f27a-8c64-4f06-897d-8bd832822f1b",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Info] 發現 122 個檔案可供檢查\n",
      "\n",
      "089271.csv                           useable=1:  32362   useable_noNaN=1:  31978\n",
      "095323.csv                           useable=1:  19713   useable_noNaN=1:  19467\n",
      "095707.csv                           useable=1:  20177   useable_noNaN=1:  19401\n",
      "114309.csv                           useable=1:  46563   useable_noNaN=1:  22364\n",
      "230933.csv                           useable=1:  30214   useable_noNaN=1:  29805\n",
      "4216007.csv                          useable=1:      0   useable_noNaN=1:      0\n",
      "7108162.csv                          useable=1:    239   useable_noNaN=1:    239\n",
      "7408338.csv                          useable=1:   1431   useable_noNaN=1:   1430\n",
      "7657698.csv                          useable=1:   1412   useable_noNaN=1:   1408\n",
      "7721164.csv                          useable=1:    483   useable_noNaN=1:    481\n",
      "PatNo_ID_1560013303.csv              useable=1:   2458   useable_noNaN=1:   2456\n",
      "PatNo_ID_1562733396.csv              useable=1:   2251   useable_noNaN=1:   2242\n",
      "PatNo_ID_1563587183.csv              useable=1:   5284   useable_noNaN=1:   5173\n",
      "PatNo_ID_1564148644.csv              useable=1:   8788   useable_noNaN=1:   8785\n",
      "PatNo_ID_1565148312.csv              useable=1:   5465   useable_noNaN=1:   5454\n",
      "PatNo_ID_1565378038.csv              useable=1:   2560   useable_noNaN=1:   2558\n",
      "PatNo_ID_1566123680.csv              useable=1:  42466   useable_noNaN=1:  21449\n",
      "PatNo_ID_1566252197.csv              useable=1:   1935   useable_noNaN=1:   1934\n",
      "PatNo_ID_1566279967.csv              useable=1:   1153   useable_noNaN=1:   1151\n",
      "PatNo_ID_1566671274.csv              useable=1:  23554   useable_noNaN=1:  23546\n",
      "PatNo_ID_1566911879.csv              useable=1:  46704   useable_noNaN=1:  45890\n",
      "PatNo_ID_1567747650.csv              useable=1:  10896   useable_noNaN=1:  10771\n",
      "PatNo_ID_1567804800.csv              useable=1:  12696   useable_noNaN=1:  12678\n",
      "PatNo_ID_1567832735.csv              useable=1:  36554   useable_noNaN=1:  36480\n",
      "PatNo_ID_1568039398.csv              useable=1:  34179   useable_noNaN=1:  34167\n",
      "PatNo_ID_1568574099.csv              useable=1:  12627   useable_noNaN=1:  12615\n",
      "PatNo_ID_1568813269.csv              useable=1:   4867   useable_noNaN=1:   4834\n",
      "PatNo_ID_1568952422.csv              useable=1:   1184   useable_noNaN=1:   1181\n",
      "PatNo_ID_1569083701.csv              useable=1:   3325   useable_noNaN=1:   3317\n",
      "PatNo_ID_1569944983.csv              useable=1:   7645   useable_noNaN=1:   7618\n",
      "PatNo_ID_1570089466.csv              useable=1:  36664   useable_noNaN=1:  36576\n",
      "PatNo_ID_1570242703.csv              useable=1:  10551   useable_noNaN=1:  10544\n",
      "PatNo_ID_1570273244.csv              useable=1:   9704   useable_noNaN=1:   9697\n",
      "PatNo_ID_1570642083.csv              useable=1:  19728   useable_noNaN=1:  19714\n",
      "PatNo_ID_1571945701.csv              useable=1:  15726   useable_noNaN=1:  15516\n",
      "PatNo_ID_1572481361.csv              useable=1:  34483   useable_noNaN=1:  34377\n",
      "PatNo_ID_1572562839.csv              useable=1:  15529   useable_noNaN=1:  15505\n",
      "PatNo_ID_1572831765.csv              useable=1:   2696   useable_noNaN=1:   2696\n",
      "PatNo_ID_1572976822.csv              useable=1:   4279   useable_noNaN=1:   4270\n",
      "PatNo_ID_1573063188.csv              useable=1:   5024   useable_noNaN=1:   5023\n",
      "PatNo_ID_1573249295.csv              useable=1:   7151   useable_noNaN=1:   7149\n",
      "PatNo_ID_1573964540.csv              useable=1:   4393   useable_noNaN=1:   4337\n",
      "PatNo_ID_1574148494.csv              useable=1:  47017   useable_noNaN=1:  47007\n",
      "PatNo_ID_1574270349.csv              useable=1:   6512   useable_noNaN=1:   6505\n",
      "PatNo_ID_1574528808.csv              useable=1:  14480   useable_noNaN=1:  14466\n",
      "PatNo_ID_1574831525.csv              useable=1:    515   useable_noNaN=1:    514\n",
      "PatNo_ID_1574987447.csv              useable=1:  19810   useable_noNaN=1:  19622\n",
      "PatNo_ID_1575060177.csv              useable=1:   5289   useable_noNaN=1:   5201\n",
      "PatNo_ID_1575256902.csv              useable=1:   9492   useable_noNaN=1:   9447\n",
      "PatNo_ID_1575445051.csv              useable=1:   1785   useable_noNaN=1:   1784\n",
      "PatNo_ID_1575502382.csv              useable=1:   6487   useable_noNaN=1:   6457\n",
      "PatNo_ID_1575975485.csv              useable=1:  16454   useable_noNaN=1:  16295\n",
      "PatNo_ID_1576115572.csv              useable=1:  18688   useable_noNaN=1:  17948\n",
      "PatNo_ID_1576116479.csv              useable=1:   1257   useable_noNaN=1:   1257\n",
      "PatNo_ID_1576301569.csv              useable=1:   3204   useable_noNaN=1:   3194\n",
      "PatNo_ID_1576964560.csv              useable=1:  24185   useable_noNaN=1:  24135\n",
      "PatNo_ID_1577042911.csv              useable=1:  35610   useable_noNaN=1:  35592\n",
      "PatNo_ID_1577487284.csv              useable=1:   2345   useable_noNaN=1:   2328\n",
      "PatNo_ID_1578784257.csv              useable=1:  28553   useable_noNaN=1:  28541\n",
      "PatNo_ID_1579198603.csv              useable=1:   2044   useable_noNaN=1:   2038\n",
      "PatNo_ID_1579498177.csv              useable=1:  21672   useable_noNaN=1:  21605\n",
      "PatNo_ID_1580062580.csv              useable=1:   2144   useable_noNaN=1:   2061\n",
      "PatNo_ID_1580096720.csv              useable=1:   1422   useable_noNaN=1:    999\n",
      "PatNo_ID_1580107637.csv              useable=1:   2642   useable_noNaN=1:   2633\n",
      "PatNo_ID_1580244614.csv              useable=1:   3865   useable_noNaN=1:   3850\n",
      "PatNo_ID_1580766093.csv              useable=1:  17973   useable_noNaN=1:  17364\n",
      "PatNo_ID_1581003248.csv              useable=1:   6632   useable_noNaN=1:   6542\n",
      "PatNo_ID_1581019504.csv              useable=1:  20724   useable_noNaN=1:  20547\n",
      "PatNo_ID_1581633231.csv              useable=1:  14626   useable_noNaN=1:  14542\n",
      "PatNo_ID_1581692973.csv              useable=1:   2723   useable_noNaN=1:   2717\n",
      "PatNo_ID_1582452511.csv              useable=1:   5193   useable_noNaN=1:   5174\n",
      "PatNo_ID_1582635996.csv              useable=1:  13045   useable_noNaN=1:  13043\n",
      "PatNo_ID_1582849900.csv              useable=1:   4789   useable_noNaN=1:   4674\n",
      "PatNo_ID_1582937076.csv              useable=1:  22673   useable_noNaN=1:  22644\n",
      "PatNo_ID_1584158973.csv              useable=1:   2877   useable_noNaN=1:   2874\n",
      "PatNo_ID_1584397376.csv              useable=1:    638   useable_noNaN=1:    638\n",
      "PatNo_ID_1586172659.csv              useable=1:  38419   useable_noNaN=1:  38193\n",
      "PatNo_ID_1586696634.csv              useable=1:   2504   useable_noNaN=1:   2499\n",
      "PatNo_ID_1586897008.csv              useable=1:   6654   useable_noNaN=1:   6646\n",
      "PatNo_ID_1587490083.csv              useable=1:  45186   useable_noNaN=1:  45181\n",
      "PatNo_ID_1588632604.csv              useable=1:   2599   useable_noNaN=1:   2479\n",
      "PatNo_ID_1588673465.csv              useable=1:   6282   useable_noNaN=1:   5496\n",
      "PatNo_ID_1588794796.csv              useable=1:   9375   useable_noNaN=1:   9374\n",
      "PatNo_ID_1588957997.csv              useable=1:  10958   useable_noNaN=1:  10937\n",
      "PatNo_ID_1589018086.csv              useable=1:  13396   useable_noNaN=1:  13392\n",
      "PatNo_ID_1589034524.csv              useable=1:  49984   useable_noNaN=1:  49650\n",
      "PatNo_ID_1589324603.csv              useable=1:   4105   useable_noNaN=1:   4101\n",
      "PatNo_ID_1589918099.csv              useable=1:      0   useable_noNaN=1:      0\n",
      "PatNo_ID_1590136310.csv              useable=1:   5305   useable_noNaN=1:   5297\n",
      "PatNo_ID_1590616537.csv              useable=1:  14199   useable_noNaN=1:  14148\n",
      "PatNo_ID_1590854576.csv              useable=1:  15879   useable_noNaN=1:  15869\n",
      "PatNo_ID_1591609798.csv              useable=1:  35614   useable_noNaN=1:  35507\n",
      "PatNo_ID_1592044724.csv              useable=1:   6810   useable_noNaN=1:   6683\n",
      "PatNo_ID_1592560504.csv              useable=1:  18051   useable_noNaN=1:  18049\n",
      "PatNo_ID_1593087886.csv              useable=1:  24126   useable_noNaN=1:  23798\n",
      "PatNo_ID_1593416100.csv              useable=1:   3328   useable_noNaN=1:   3321\n",
      "PatNo_ID_1593472048.csv              useable=1:   6247   useable_noNaN=1:   6240\n",
      "PatNo_ID_1593593586.csv              useable=1:  18875   useable_noNaN=1:  18829\n",
      "PatNo_ID_1593720818.csv              useable=1:   2644   useable_noNaN=1:   2640\n",
      "PatNo_ID_1593838524.csv              useable=1:   2160   useable_noNaN=1:   2156\n",
      "PatNo_ID_1594173718.csv              useable=1:    344   useable_noNaN=1:    344\n",
      "PatNo_ID_1594294180.csv              useable=1:  18320   useable_noNaN=1:   8882\n",
      "PatNo_ID_1594305136.csv              useable=1:  15634   useable_noNaN=1:  15571\n",
      "PatNo_ID_1594309746.csv              useable=1:   3724   useable_noNaN=1:   3690\n",
      "PatNo_ID_1594319286.csv              useable=1:   3988   useable_noNaN=1:   3986\n",
      "PatNo_ID_1594320763.csv              useable=1:   2429   useable_noNaN=1:   2375\n",
      "PatNo_ID_1594322594.csv              useable=1:   5378   useable_noNaN=1:   5353\n",
      "PatNo_ID_1594335109.csv              useable=1:   5116   useable_noNaN=1:   4993\n",
      "PatNo_ID_1594423683.csv              useable=1:   5001   useable_noNaN=1:   4994\n",
      "PatNo_ID_1594437309.csv              useable=1:   9935   useable_noNaN=1:   9892\n",
      "PatNo_ID_1594439781.csv              useable=1:   7687   useable_noNaN=1:   7555\n",
      "PatNo_ID_1594441887.csv              useable=1:  10196   useable_noNaN=1:   9854\n",
      "PatNo_ID_1594448501.csv              useable=1:   1045   useable_noNaN=1:   1039\n",
      "PatNo_ID_1594455578.csv              useable=1:    262   useable_noNaN=1:    261\n",
      "PatNo_ID_1594464829.csv              useable=1:   3937   useable_noNaN=1:   3936\n",
      "PatNo_ID_1594467719.csv              useable=1:   1313   useable_noNaN=1:   1313\n",
      "PatNo_ID_1594471407.csv              useable=1:  11279   useable_noNaN=1:  11269\n",
      "PatNo_ID_1594479330.csv              useable=1:   3724   useable_noNaN=1:   3714\n",
      "PatNo_ID_1594511911.csv              useable=1:   5450   useable_noNaN=1:   5443\n",
      "PatNo_ID_1594511914.csv              useable=1:   9670   useable_noNaN=1:   9642\n",
      "PatNo_ID_1594528842.csv              useable=1:   2415   useable_noNaN=1:   2396\n",
      "PatNo_ID_1594533379.csv              useable=1:    999   useable_noNaN=1:    999\n",
      "\n",
      "=== 全部檔案的總計 ===\n",
      "useable=1 總筆數       : 1443024\n",
      "useable_noNaN=1 總筆數 : 1378500\n"
     ]
    }
   ],
   "source": [
    "# 列出useable=1 的數量，也印出 useable_noNaN=1 的數量\n",
    "\"\"\"\n",
    "檢查每個檔案中 useable=1 和 useable_noNaN=1 的筆數\n",
    "資料夾: /home/jovyan/RT08/0925/bling_svv_fin\n",
    "輸出:\n",
    "    - 每個檔案的筆數統計\n",
    "    - 全部檔案的總和\n",
    "注意: 只讀取 CSV，完全不修改檔案\n",
    "\"\"\"\n",
    "\n",
    "import os, glob\n",
    "import pandas as pd\n",
    "\n",
    "# 資料來源目錄\n",
    "DATA_DIR = \"/home/jovyan/RT08/0925/bling_svv_fin\"\n",
    "\n",
    "# 找出所有 csv 檔案\n",
    "file_paths = sorted(glob.glob(os.path.join(DATA_DIR, \"*.csv\")))\n",
    "print(f\"[Info] 發現 {len(file_paths)} 個檔案可供檢查\\n\")\n",
    "\n",
    "# 全域計數器\n",
    "total_useable = 0\n",
    "total_useable_noNaN = 0\n",
    "\n",
    "# 逐檔處理\n",
    "for fp in file_paths:\n",
    "    base = os.path.basename(fp)\n",
    "    try:\n",
    "        df = pd.read_csv(fp, low_memory=False)\n",
    "    except Exception as e:\n",
    "        print(f\"[Skip] {base} 讀取失敗: {e}\")\n",
    "        continue\n",
    "\n",
    "    # 確認是否有必要欄位\n",
    "    if \"useable\" not in df.columns or \"useable_noNaN\" not in df.columns:\n",
    "        print(f\"[Warn] {base} 缺少 useable 或 useable_noNaN 欄位，跳過\")\n",
    "        continue\n",
    "\n",
    "    # 計算數量\n",
    "    count_useable = int((df[\"useable\"] == 1).sum())\n",
    "    count_useable_noNaN = int((df[\"useable_noNaN\"] == 1).sum())\n",
    "\n",
    "    # 累加到全域\n",
    "    total_useable += count_useable\n",
    "    total_useable_noNaN += count_useable_noNaN\n",
    "\n",
    "    # 印出檔案統計\n",
    "    print(f\"{base:35s}  useable=1: {count_useable:6d}   useable_noNaN=1: {count_useable_noNaN:6d}\")\n",
    "\n",
    "# 跨檔總和\n",
    "print(\"\\n=== 全部檔案的總計 ===\")\n",
    "print(f\"useable=1 總筆數       : {total_useable}\")\n",
    "print(f\"useable_noNaN=1 總筆數 : {total_useable_noNaN}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7597ef49-05d4-40e4-85b8-01c25c67a59d",
   "metadata": {},
   "outputs": [],
   "source": [
    "欄位\n",
    "\"patno\",\"senddate\",\n",
    "\"rrhzsetactual\",\"mvsetactual\", \"peepepap\",\"ppeak\",\"cdyn\",\"pmean\"\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "id": "c351e664-8067-4d3a-b89a-04ccaa8a359e",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Info] 發現 122 個檔案\n",
      "\n",
      "=== 跨檔案總計 ===\n",
      "NaN_check=1 總筆數: 1378500\n",
      "NaN_check=0 總筆數: 188809\n"
     ]
    }
   ],
   "source": [
    "\"\"\" 創一個欄位是紀錄NaN_check         真正 所有欄位都沒有空值的資料\n",
    "把/home/jovyan/RT08/0925/bling_svv_fin/中所有檔案裡面的useable_noNaN欄位複製一個，裡面的數值也完整複製，\n",
    "並把欄位名稱改成 NaN_check\n",
    "看一下總共有幾筆\n",
    "只列出跨檔案的總計數字，NaN_check=1, NaN_check=0\n",
    "# -*- coding: utf-8 -*-\n",
    "\n",
    "將 /home/jovyan/RT08/0925/bling_svv_fin/ 中所有檔案的 useable_noNaN 欄位複製成 NaN_check\n",
    "並統計跨檔案的 NaN_check=1 與 NaN_check=0 的筆數總和\n",
    "\"\"\"\n",
    "\n",
    "import os, glob\n",
    "import pandas as pd\n",
    "\n",
    "DATA_DIR = \"/home/jovyan/RT08/0925/bling_svv_fin\"\n",
    "\n",
    "# 找到所有 csv 檔案\n",
    "file_paths = sorted(glob.glob(os.path.join(DATA_DIR, \"*.csv\")))\n",
    "print(f\"[Info] 發現 {len(file_paths)} 個檔案\")\n",
    "\n",
    "# 全域計數器\n",
    "total_nan1 = 0\n",
    "total_nan0 = 0\n",
    "\n",
    "for fp in file_paths:\n",
    "    try:\n",
    "        df = pd.read_csv(fp, low_memory=False)\n",
    "    except Exception as e:\n",
    "        print(f\"[Skip] 無法讀取 {fp}: {e}\")\n",
    "        continue\n",
    "\n",
    "    # 確認有 useable_noNaN 欄位\n",
    "    if \"useable_noNaN\" not in df.columns:\n",
    "        print(f\"[Warn] {os.path.basename(fp)} 缺少 useable_noNaN，跳過\")\n",
    "        continue\n",
    "\n",
    "    # 複製欄位\n",
    "    df[\"NaN_check\"] = df[\"useable_noNaN\"]\n",
    "\n",
    "    # 統計 NaN_check 數值\n",
    "    total_nan1 += int((df[\"NaN_check\"] == 1).sum())\n",
    "    total_nan0 += int((df[\"NaN_check\"] == 0).sum())\n",
    "\n",
    "    # 存回原檔（覆蓋）\n",
    "    df.to_csv(fp, index=False)\n",
    "\n",
    "# 印出跨檔案總數\n",
    "print(\"\\n=== 跨檔案總計 ===\")\n",
    "print(f\"NaN_check=1 總筆數: {total_nan1}\")\n",
    "print(f\"NaN_check=0 總筆數: {total_nan0}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "id": "a0476b2c-e938-4067-a32e-064bb356db91",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Info] 發現 122 個檔案\n",
      "\n",
      "=== 全部檔案統計 ===\n",
      "修改前 NaN_check=1 總數: 1378500\n",
      "被改成 0 的筆數: 0\n",
      "修改後 NaN_check=1 總數: 1378500\n",
      "修改後 NaN_check=0 總數: 188809\n"
     ]
    }
   ],
   "source": [
    "\"\"\"在NaN_check=1的資料中，檢查若\"rrhzsetactual\",\"mvsetactual\", \"peepepap\",\"ppeak\",\"cdyn\",\"pmean\"任有一項是空值，則將NaN_check改為=0，並寫出數量\n",
    "\n",
    "在 NaN_check=1 的資料中，檢查關鍵欄位是否有 NaN\n",
    "若有，則將 NaN_check 改為 0\n",
    "並輸出統計數量\n",
    "\"\"\"\n",
    "\n",
    "import os, glob\n",
    "import pandas as pd\n",
    "\n",
    "DATA_DIR = \"/home/jovyan/RT08/0925/bling_svv_fin\"\n",
    "\n",
    "# 找到所有 CSV 檔案\n",
    "file_paths = sorted(glob.glob(os.path.join(DATA_DIR, \"*.csv\")))\n",
    "print(f\"[Info] 發現 {len(file_paths)} 個檔案\")\n",
    "\n",
    "# 要檢查的欄位\n",
    "CHECK_COLS = [\"rrhzsetactual\", \"mvsetactual\", \"peepepap\", \"ppeak\", \"cdyn\", \"pmean\"]\n",
    "\n",
    "# 全域統計\n",
    "before_total_1 = 0   # 修改前 NaN_check=1 總數\n",
    "changed_total = 0    # 被改成 0 的筆數\n",
    "after_total_1 = 0    # 修改後 NaN_check=1 總數\n",
    "after_total_0 = 0    # 修改後 NaN_check=0 總數\n",
    "\n",
    "for fp in file_paths:\n",
    "    try:\n",
    "        df = pd.read_csv(fp, low_memory=False)\n",
    "    except Exception as e:\n",
    "        print(f\"[Skip] 無法讀取 {fp}: {e}\")\n",
    "        continue\n",
    "\n",
    "    if \"NaN_check\" not in df.columns:\n",
    "        print(f\"[Warn] {os.path.basename(fp)} 缺少 NaN_check，跳過\")\n",
    "        continue\n",
    "\n",
    "    # 修改前 NaN_check=1 的筆數\n",
    "    before_total_1 += int((df[\"NaN_check\"] == 1).sum())\n",
    "\n",
    "    # 找出 NaN_check=1 且檢查欄位有空值的列\n",
    "    mask_bad = (df[\"NaN_check\"] == 1) & (df[CHECK_COLS].isna().any(axis=1))\n",
    "    changed_total += int(mask_bad.sum())\n",
    "\n",
    "    # 更新 NaN_check\n",
    "    df.loc[mask_bad, \"NaN_check\"] = 0\n",
    "\n",
    "    # 修改後的計數\n",
    "    after_total_1 += int((df[\"NaN_check\"] == 1).sum())\n",
    "    after_total_0 += int((df[\"NaN_check\"] == 0).sum())\n",
    "\n",
    "    # 存回檔案\n",
    "    df.to_csv(fp, index=False)\n",
    "\n",
    "# 總結輸出\n",
    "print(\"\\n=== 全部檔案統計 ===\")\n",
    "print(f\"修改前 NaN_check=1 總數: {before_total_1}\")\n",
    "print(f\"被改成 0 的筆數: {changed_total}\")\n",
    "print(f\"修改後 NaN_check=1 總數: {after_total_1}\")\n",
    "print(f\"修改後 NaN_check=0 總數: {after_total_0}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "id": "173242a4-d610-484e-92a8-771f4c030040",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Info] 發現 122 個檔案\n",
      "\n",
      "=== 跨檔案統計 ===\n",
      "NaN_check=1 總筆數: 1378500\n",
      "其中有任一欄位=0 的筆數: 41630\n",
      "仍符合條件的筆數: 1336870\n"
     ]
    }
   ],
   "source": [
    "\"\"\"檢查在 NaN_check=1 的資料中\n",
    "若 rrhzsetactual, mvsetactual, peepepap, ppeak, cdyn, pmean 任一欄位為 0\n",
    "則計入統計數量（不修改檔案）\n",
    "\"\"\"\n",
    "\n",
    "import os, glob\n",
    "import pandas as pd\n",
    "\n",
    "DATA_DIR = \"/home/jovyan/RT08/0925/bling_svv_fin\"\n",
    "\n",
    "# 要檢查的欄位\n",
    "CHECK_COLS = [\"rrhzsetactual\", \"mvsetactual\", \"peepepap\", \"ppeak\", \"cdyn\", \"pmean\"]\n",
    "\n",
    "# 全域統計\n",
    "total_nan1 = 0         # NaN_check=1 的總數\n",
    "total_zero_viol = 0    # 在 NaN_check=1 中，有任一欄位=0 的筆數\n",
    "\n",
    "# 逐檔案檢查\n",
    "file_paths = sorted(glob.glob(os.path.join(DATA_DIR, \"*.csv\")))\n",
    "print(f\"[Info] 發現 {len(file_paths)} 個檔案\")\n",
    "\n",
    "for fp in file_paths:\n",
    "    try:\n",
    "        df = pd.read_csv(fp, low_memory=False)\n",
    "    except Exception as e:\n",
    "        print(f\"[Skip] 無法讀取 {fp}: {e}\")\n",
    "        continue\n",
    "\n",
    "    if \"NaN_check\" not in df.columns:\n",
    "        print(f\"[Warn] {os.path.basename(fp)} 缺少 NaN_check，跳過\")\n",
    "        continue\n",
    "\n",
    "    # 選出 NaN_check=1 的資料\n",
    "    subset = df[df[\"NaN_check\"] == 1]\n",
    "    total_nan1 += len(subset)\n",
    "\n",
    "    if not subset.empty:\n",
    "        # 找出在檢查欄位中有任一值=0 的列\n",
    "        mask_zero = (subset[CHECK_COLS] == 0).any(axis=1)\n",
    "        total_zero_viol += int(mask_zero.sum())\n",
    "\n",
    "# 統計輸出\n",
    "print(\"\\n=== 跨檔案統計 ===\")\n",
    "print(f\"NaN_check=1 總筆數: {total_nan1}\")\n",
    "print(f\"其中有任一欄位=0 的筆數: {total_zero_viol}\")\n",
    "print(f\"仍符合條件的筆數: {total_nan1 - total_zero_viol}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "id": "192da37f-76c1-4b82-bcd6-2d54cd28f980",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "✅ Exported 5 figure(s) to: /home/jovyan/RT08/0925/1002\n",
      "   Year count: 3\n",
      "   - 2021: 1 figure(s)\n",
      "   - 2022: 3 figure(s)\n",
      "   - 2024: 1 figure(s)\n"
     ]
    }
   ],
   "source": [
    "\"\"\"看一下這些會不會影響到 41630\n",
    "將NaN_check=0畫在時間軸上用淺灰，NaN_check=1 用藍色點點，其中有\"rrhzsetactual\",\"mvsetactual\", \"peepepap\",\"ppeak\",\"cdyn\",\"pmean\"任一欄位為0用紅點點，一樣是年份分照片 50筆一張，照片檔案名稱用NaN_check為開頭\n",
    "\"\"\"\n",
    "# -*- coding: utf-8 -*-\n",
    "\"\"\"\n",
    "將 /home/jovyan/RT08/0925/bling_svv_fin/ 中所有檔案畫成「時間軸點狀圖」：\n",
    "- 依年份分檔；每張圖最多 50 個檔案（避免擁擠）\n",
    "- X 軸：時間（senddate）\n",
    "- Y 軸：檔名（每個檔案一條水平列，以點表示每筆資料）\n",
    "- 顏色規則（點狀）：\n",
    "    * NaN_check = 0  → 淺灰色點\n",
    "    * NaN_check = 1 且六欄任一為 0 → 紅色點\n",
    "    * NaN_check = 1 且六欄皆非 0 → 藍色點\n",
    "- 圖片輸出目錄：/home/jovyan/RT08/0925/1002/NaN_check/\n",
    "- 圖片檔名以 \"NaN_check\" 為開頭，如：NaN_check_2022_p1.png\n",
    "注意：\n",
    "- 僅讀取與繪圖，不修改任何原始檔案\n",
    "- 若某檔案缺必要欄位，將略過並印出提示\n",
    "\"\"\"\n",
    "# -*- coding: utf-8 -*-\n",
    "\"\"\"\n",
    "時間軸視覺化（僅輸出圖片，不修改原始檔）：\n",
    "資料來源：/home/jovyan/RT08/0925/bling_svv_fin/*.csv\n",
    "輸出路徑：/home/jovyan/RT08/0925/1002/  （檔名以 NaN_check_ 開頭）\n",
    "\n",
    "規則：\n",
    "- X 軸：senddate（時間）\n",
    "- Y 軸：檔名（每個檔案一條水平位置）\n",
    "- 點顏色：\n",
    "  * NaN_check=0 → 淺灰色點\n",
    "  * NaN_check=1 且 (rrhzsetactual, mvsetactual, peepepap, ppeak, cdyn, pmean) 任一欄 == 0 → 紅色點\n",
    "  * NaN_check=1 且上述欄位全非 0 → 藍色點\n",
    "- 按年份分圖；每張最多 50 個檔案（避免標籤重疊）\n",
    "- 圖片檔名：NaN_check_{year}_p{頁碼}.png\n",
    "- 圖表中文字採英文；註釋採中文\n",
    "\"\"\"\n",
    "\n",
    "import os, glob\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.dates as mdates\n",
    "\n",
    "# ===== 參數設定 =====\n",
    "DATA_DIR = \"/home/jovyan/RT08/0925/bling_svv_fin\"\n",
    "OUT_DIR  = \"/home/jovyan/RT08/0925/1002\"\n",
    "os.makedirs(OUT_DIR, exist_ok=True)\n",
    "\n",
    "# 每張圖最多顯示的檔案數\n",
    "MAX_PER_FIG = 50\n",
    "\n",
    "# 繪圖樣式（顏色）\n",
    "COLOR_GRAY = \"#cfcfcf\"  # NaN_check=0\n",
    "COLOR_BLUE = \"#1f77b4\"  # NaN_check=1 & all six != 0\n",
    "COLOR_RED  = \"#d62728\"  # NaN_check=1 & any of six == 0\n",
    "\n",
    "# 需要用到的欄位\n",
    "TIME_COL = \"senddate\"\n",
    "NAN_CHECK_COL = \"NaN_check\"\n",
    "CHECK_COLS = [\"rrhzsetactual\", \"mvsetactual\", \"peepepap\", \"ppeak\", \"cdyn\", \"pmean\"]\n",
    "\n",
    "# ===== 小工具：安全讀檔（只讀需要欄位） =====\n",
    "def read_csv_for_plot(path):\n",
    "    \"\"\"盡量只讀需要的欄位；若失敗再全讀。缺關鍵欄位則回傳空 DataFrame。\"\"\"\n",
    "    usecols = [TIME_COL, NAN_CHECK_COL] + CHECK_COLS\n",
    "    try:\n",
    "        df = pd.read_csv(path, usecols=usecols, low_memory=False)\n",
    "    except Exception:\n",
    "        try:\n",
    "            df = pd.read_csv(path, low_memory=False)\n",
    "        except Exception:\n",
    "            return pd.DataFrame()\n",
    "        # 若全讀成功但缺必要欄位 → 視為無法作圖\n",
    "        for c in [TIME_COL, NAN_CHECK_COL]:\n",
    "            if c not in df.columns:\n",
    "                return pd.DataFrame()\n",
    "        # 缺少的檢查欄位補上（後面用 notna()/==0 判斷時能正常運作）\n",
    "        for c in CHECK_COLS:\n",
    "            if c not in df.columns:\n",
    "                df[c] = np.nan\n",
    "    return df\n",
    "\n",
    "# ===== 蒐集資料 =====\n",
    "file_paths = sorted(glob.glob(os.path.join(DATA_DIR, \"*.csv\")))\n",
    "records = []  # 每列：__file__, __year__, senddate, NaN_check, zero_flag\n",
    "\n",
    "for fp in file_paths:\n",
    "    base = os.path.basename(fp)\n",
    "    df = read_csv_for_plot(fp)\n",
    "    if df.empty:\n",
    "        print(f\"[Skip] {base}: missing required columns or cannot read.\")\n",
    "        continue\n",
    "\n",
    "    # 轉型：時間欄位\n",
    "    df[TIME_COL] = pd.to_datetime(df[TIME_COL], errors=\"coerce\")\n",
    "    df = df.dropna(subset=[TIME_COL])\n",
    "\n",
    "    # NaN_check 轉為 0/1（缺失視為 0）\n",
    "    nc = pd.to_numeric(df[NAN_CHECK_COL], errors=\"coerce\").fillna(0).astype(int).clip(0,1)\n",
    "    df[NAN_CHECK_COL] = nc\n",
    "\n",
    "    # 六欄是否任一為 0（僅在 NaN_check=1 的行才會被用到）\n",
    "    zero_any = (df[CHECK_COLS] == 0).any(axis=1)\n",
    "\n",
    "    # 整理欄位\n",
    "    out = pd.DataFrame({\n",
    "        \"__file__\": base,\n",
    "        \"__year__\": df[TIME_COL].dt.year,\n",
    "        TIME_COL: df[TIME_COL],\n",
    "        NAN_CHECK_COL: df[NAN_CHECK_COL],\n",
    "        \"__zero_any__\": zero_any.fillna(False)  # NaN 視為 False；真正的 NaN 已不在 NaN_check=1 的篩選條件內\n",
    "    })\n",
    "    if not out.empty:\n",
    "        records.append(out)\n",
    "\n",
    "if not records:\n",
    "    print(\"[Viz] No qualified data to plot (missing senddate/NaN_check).\")\n",
    "else:\n",
    "    data = pd.concat(records, ignore_index=True).sort_values([\"__year__\", \"__file__\", TIME_COL])\n",
    "\n",
    "    # ===== 開始按年份分批繪圖 =====\n",
    "    years = sorted(data[\"__year__\"].dropna().unique().tolist())\n",
    "    total_figs = 0\n",
    "    year_page_count = {}\n",
    "\n",
    "    for yr in years:\n",
    "        dy = data[data[\"__year__\"] == yr].copy()\n",
    "        files_in_year = sorted(dy[\"__file__\"].unique().tolist())\n",
    "\n",
    "        # 依每張最多 50 檔分頁\n",
    "        for page_start in range(0, len(files_in_year), MAX_PER_FIG):\n",
    "            batch_files = files_in_year[page_start:page_start + MAX_PER_FIG]\n",
    "            if not batch_files:\n",
    "                continue\n",
    "\n",
    "            n = len(batch_files)\n",
    "            # 動態高度：每檔 0.35 inch，介於 4~12 吋之間\n",
    "            fig_h = max(4, min(12, 0.35 * n))\n",
    "            fig, ax = plt.subplots(figsize=(14, fig_h))\n",
    "\n",
    "            # 取本批資料\n",
    "            batch_df = dy[dy[\"__file__\"].isin(batch_files)].copy()\n",
    "\n",
    "            # X 軸範圍（加右側留白放 legend/不擠）\n",
    "            x_min = batch_df[TIME_COL].min()\n",
    "            x_max = batch_df[TIME_COL].max()\n",
    "            if pd.isna(x_min) or pd.isna(x_max) or x_min == x_max:\n",
    "                x_min = pd.to_datetime(f\"{yr}-01-01\")\n",
    "                x_max = pd.to_datetime(f\"{yr}-12-31\")\n",
    "            pad_minutes = max(30, int((x_max - x_min).total_seconds() / 60 * 0.03))\n",
    "            pad = pd.Timedelta(minutes=pad_minutes)\n",
    "            ax.set_xlim(x_min, x_max + pad)\n",
    "\n",
    "            # Y 軸：檔名 → 座標\n",
    "            y_positions = {fname: i for i, fname in enumerate(batch_files)}\n",
    "            yticks, ylabels = [], []\n",
    "\n",
    "            # X 軸時間格式\n",
    "            locator = mdates.AutoDateLocator(minticks=4, maxticks=10)\n",
    "            formatter = mdates.ConciseDateFormatter(locator)\n",
    "            ax.xaxis.set_major_locator(locator)\n",
    "            ax.xaxis.set_major_formatter(formatter)\n",
    "\n",
    "            # 逐檔畫點\n",
    "            for fname in batch_files:\n",
    "                gf = batch_df[batch_df[\"__file__\"] == fname][[TIME_COL, NAN_CHECK_COL, \"__zero_any__\"]].sort_values(TIME_COL)\n",
    "                if gf.empty:\n",
    "                    continue\n",
    "\n",
    "                y = y_positions[fname]\n",
    "                yticks.append(y)\n",
    "                ylabels.append(fname)\n",
    "\n",
    "                # 依規則拆成三種點：\n",
    "                # 1) NaN_check=0 → 淺灰\n",
    "                mask_nc0 = (gf[NAN_CHECK_COL] == 0)\n",
    "\n",
    "                # 2) NaN_check=1 且任一檢查欄位為 0 → 紅\n",
    "                mask_nc1_zero = (gf[NAN_CHECK_COL] == 1) & (gf[\"__zero_any__\"] == True)\n",
    "\n",
    "                # 3) NaN_check=1 且六欄皆非 0 → 藍\n",
    "                mask_nc1_ok = (gf[NAN_CHECK_COL] == 1) & (~gf[\"__zero_any__\"])\n",
    "\n",
    "                # 畫點（marker='o'、適度大小 s，alpha 略低防重疊）\n",
    "                if mask_nc0.any():\n",
    "                    ax.scatter(gf.loc[mask_nc0, TIME_COL], np.full(mask_nc0.sum(), y),\n",
    "                               s=10, marker='o', color=COLOR_GRAY, alpha=0.9, linewidths=0)\n",
    "                if mask_nc1_ok.any():\n",
    "                    ax.scatter(gf.loc[mask_nc1_ok, TIME_COL], np.full(mask_nc1_ok.sum(), y),\n",
    "                               s=12, marker='o', color=COLOR_BLUE, alpha=0.9, linewidths=0)\n",
    "                if mask_nc1_zero.any():\n",
    "                    ax.scatter(gf.loc[mask_nc1_zero, TIME_COL], np.full(mask_nc1_zero.sum(), y),\n",
    "                               s=14, marker='o', color=COLOR_RED, alpha=0.95, linewidths=0)\n",
    "\n",
    "            # 座標與格線\n",
    "            ax.set_title(f\"NaN_check status over time by file — Year {yr} \"\n",
    "                         f\"(files {page_start+1}-{page_start+len(batch_files)} of {len(files_in_year)})\",\n",
    "                         fontsize=12, pad=12)\n",
    "            ax.set_xlabel(\"Time\")\n",
    "            ax.set_yticks(yticks)\n",
    "            ax.set_yticklabels(ylabels, fontsize=9)\n",
    "            ax.grid(True, axis=\"x\", linestyle=\"--\", linewidth=0.6, alpha=0.5)\n",
    "            ax.set_axisbelow(True)\n",
    "\n",
    "            # 圖例\n",
    "            from matplotlib.lines import Line2D\n",
    "            legend_handles = [\n",
    "                Line2D([0], [0], marker='o', color='none', markerfacecolor=COLOR_RED,  label='NaN_check=1 & any{six}=0', markersize=7),\n",
    "                Line2D([0], [0], marker='o', color='none', markerfacecolor=COLOR_BLUE, label='NaN_check=1 (all six != 0)', markersize=7),\n",
    "                Line2D([0], [0], marker='o', color='none', markerfacecolor=COLOR_GRAY, label='NaN_check=0', markersize=7),\n",
    "            ]\n",
    "            ax.legend(handles=legend_handles, loc=\"upper right\", frameon=False)\n",
    "\n",
    "            plt.tight_layout()\n",
    "\n",
    "            # 輸出圖片（檔名前綴 NaN_check）\n",
    "            out_file = os.path.join(OUT_DIR, f\"NaN_check_{yr}_p{page_start // MAX_PER_FIG + 1}.png\")\n",
    "            fig.savefig(out_file, dpi=150)\n",
    "            plt.close(fig)\n",
    "\n",
    "            total_figs += 1\n",
    "            year_page_count[yr] = year_page_count.get(yr, 0) + 1\n",
    "\n",
    "    # 輸出統計摘要到 console\n",
    "    print(f\"\\n✅ Exported {total_figs} figure(s) to: {OUT_DIR}\")\n",
    "    print(f\"   Year count: {len(years)}\")\n",
    "    for yr in years:\n",
    "        print(f\"   - {yr}: {year_page_count.get(yr, 0)} figure(s)\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "id": "408c3186-5564-4fd6-812b-8fd90f94c8c3",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Info] 發現 122 個檔案\n",
      "\n",
      "=== 最終跨檔案統計 ===\n",
      "NaN_check=1 總筆數: 1336870\n"
     ]
    }
   ],
   "source": [
    "#結果是還好啦 所以我要踢掉拉\n",
    "\"\"\" 在 /home/jovyan/RT08/0925/bling_svv_fin/ 中修改檔案：\n",
    "對 NaN_check=1 的資料，若 rrhzsetactual,mvsetactual,peepepap,ppeak,cdyn,pmean 任一欄位=0\n",
    "則將 NaN_check 改成 0。\n",
    "最後統計跨檔案的 NaN_check=1 總筆數。\n",
    "\"\"\"\n",
    "\n",
    "import os, glob\n",
    "import pandas as pd\n",
    "\n",
    "DATA_DIR = \"/home/jovyan/RT08/0925/bling_svv_fin\"\n",
    "\n",
    "# 要檢查的欄位\n",
    "CHECK_COLS = [\"rrhzsetactual\", \"mvsetactual\", \"peepepap\", \"ppeak\", \"cdyn\", \"pmean\"]\n",
    "\n",
    "# 總計\n",
    "total_nan1 = 0\n",
    "\n",
    "# 掃描資料夾內所有 CSV\n",
    "file_paths = sorted(glob.glob(os.path.join(DATA_DIR, \"*.csv\")))\n",
    "print(f\"[Info] 發現 {len(file_paths)} 個檔案\")\n",
    "\n",
    "for fp in file_paths:\n",
    "    try:\n",
    "        df = pd.read_csv(fp, low_memory=False)\n",
    "    except Exception as e:\n",
    "        print(f\"[Skip] 無法讀取 {fp}: {e}\")\n",
    "        continue\n",
    "\n",
    "    if \"NaN_check\" not in df.columns:\n",
    "        print(f\"[Warn] {os.path.basename(fp)} 缺少 NaN_check，跳過\")\n",
    "        continue\n",
    "\n",
    "    # 找出 NaN_check=1 的列\n",
    "    mask_nc1 = (df[\"NaN_check\"] == 1)\n",
    "\n",
    "    if mask_nc1.any():\n",
    "        # 找出這些列中，任一檢查欄位 == 0\n",
    "        mask_zero = (df.loc[mask_nc1, CHECK_COLS] == 0).any(axis=1)\n",
    "\n",
    "        # 將這些列的 NaN_check 改為 0\n",
    "        df.loc[mask_nc1[mask_nc1].index[mask_zero], \"NaN_check\"] = 0\n",
    "\n",
    "    # 更新統計\n",
    "    total_nan1 += int((df[\"NaN_check\"] == 1).sum())\n",
    "\n",
    "    # 覆蓋寫回檔案\n",
    "    df.to_csv(fp, index=False)\n",
    "\n",
    "# 輸出總結\n",
    "print(\"\\n=== 最終跨檔案統計 ===\")\n",
    "print(f\"NaN_check=1 總筆數: {total_nan1}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "71db77ef-9f03-417c-a209-65f610e061d1",
   "metadata": {},
   "outputs": [],
   "source": [
    "可以來張時間軸圖看刪了哪些資料\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "id": "33a27a4b-0d43-4c7f-8fb8-a749cd0d2cd4",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Info] Copied 122 files from /home/jovyan/RT08/0925/bling_svv_fin → /home/jovyan/RT08/0925/bling_svv_14\n",
      "[Done] Processed files in /home/jovyan/RT08/0925/bling_svv_14: 122\n"
     ]
    }
   ],
   "source": [
    "\"\"\" 因為欄位刪除了 就可以來檢查空值的資料，\n",
    "將/home/jovyan/RT08/0925/bling_svv_fin/檔案全部複製到/home/jovyan/RT08/0925/bling_svv_14/\n",
    "在/home/jovyan/RT08/0925/bling_svv_14/中的資料 都刪除\"sponvt\", \"vti\", \"vte\", \"ventilatormode\", \"mode_0\", \"mode_9\", \"ventilatormode_code\", \"svv\", \"useable\", \"useable_noNaN\"總共十個欄位，，\n",
    "注意不要動到/home/jovyan/RT08/0925/bling_svv_fin/的任何檔案，\n",
    "然後再把svv_new 欄位的數值都取到小數後兩位\n",
    "\"\"\"\n",
    "# -*- coding: utf-8 -*-\n",
    "\"\"\"\n",
    "需求：\n",
    "1) 將 /home/jovyan/RT08/0925/bling_svv_fin/ 下所有 CSV 複製到 /home/jovyan/RT08/0925/bling_svv_14/\n",
    "2) 在 /home/jovyan/RT08/0925/bling_svv_14/ 內，刪除下列 10 個欄位（若不存在則略過，不報錯）：\n",
    "   [\"sponvt\", \"vti\", \"vte\", \"ventilatormode\", \"mode_0\", \"mode_9\", \"ventilatormode_code\", \"svv\", \"useable\", \"useable_noNaN\"]\n",
    "3) 將欄位 svv_new 的數值四捨五入至小數點後兩位（僅對 svv_new 作用）\n",
    "4) 僅操作 bling_svv_14，不得動到 bling_svv_fin\n",
    "\n",
    "備註：\n",
    "- 只會在目標資料夾進行刪欄與四捨五入，來源資料夾完全不動\n",
    "- 缺欄會自動跳過\n",
    "\"\"\"\n",
    "\n",
    "import os\n",
    "import glob\n",
    "import shutil\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "SRC_DIR = \"/home/jovyan/RT08/0925/bling_svv_fin\"\n",
    "DST_DIR = \"/home/jovyan/RT08/0925/bling_svv_14\"\n",
    "\n",
    "# 要刪除的 10 個欄位\n",
    "DROP_COLS = [\"sponvt\", \"vti\", \"vte\", \"ventilatormode\", \"mode_0\", \"mode_9\",\n",
    "             \"ventilatormode_code\", \"svv\", \"useable\", \"useable_noNaN\"]\n",
    "\n",
    "# 1) 建立目標資料夾並複製所有 CSV 檔案（不動原始資料夾）\n",
    "os.makedirs(DST_DIR, exist_ok=True)\n",
    "src_files = sorted(glob.glob(os.path.join(SRC_DIR, \"*.csv\")))\n",
    "\n",
    "for fp in src_files:\n",
    "    # 使用 copy2 盡可能保留檔案時間等中繼資料\n",
    "    shutil.copy2(fp, DST_DIR)\n",
    "\n",
    "print(f\"[Info] Copied {len(src_files)} files from {SRC_DIR} → {DST_DIR}\")\n",
    "\n",
    "# 2) 在目標資料夾進行欄位刪除與 svv_new 四捨五入\n",
    "dst_files = sorted(glob.glob(os.path.join(DST_DIR, \"*.csv\")))\n",
    "processed = 0\n",
    "\n",
    "for fp in dst_files:\n",
    "    base = os.path.basename(fp)\n",
    "    try:\n",
    "        df = pd.read_csv(fp, low_memory=False)\n",
    "    except Exception as e:\n",
    "        print(f\"[Skip] {base} 讀取失敗：{e}\")\n",
    "        continue\n",
    "\n",
    "    # 刪除指定欄位（不存在則自動忽略）\n",
    "    df = df.drop(columns=[c for c in DROP_COLS if c in df.columns], errors=\"ignore\")\n",
    "\n",
    "    # 對 svv_new 進行數值化與四捨五入到小數點後兩位\n",
    "    if \"svv_new\" in df.columns:\n",
    "        # 將空字串、'null'、'(null)' 等先視為 NaN，再轉為數值\n",
    "        df[\"svv_new\"] = df[\"svv_new\"].replace(r'^\\s*$', np.nan, regex=True)\\\n",
    "                                     .replace([\"null\", \"NULL\", \"(null)\", \"(NULL)\"], np.nan)\n",
    "        df[\"svv_new\"] = pd.to_numeric(df[\"svv_new\"], errors=\"coerce\").round(2)\n",
    "\n",
    "    # 覆寫存回目標資料夾\n",
    "    df.to_csv(fp, index=False)\n",
    "    processed += 1\n",
    "\n",
    "print(f\"[Done] Processed files in {DST_DIR}: {processed}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "id": "29e3dcf1-268b-4127-8d39-97d43a2a179d",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Info] 發現 122 個檔案\n",
      "\n",
      "=== 跨檔案統計 ===\n",
      "NaN_check=1 總筆數: 1336870\n"
     ]
    }
   ],
   "source": [
    "\"\"\"計算 /home/jovyan/RT08/0925/bling_svv_14/ 中\n",
    "所有檔案 NaN_check = 1 的總筆數\n",
    "\"\"\"\n",
    "\n",
    "import os, glob\n",
    "import pandas as pd\n",
    "\n",
    "DATA_DIR = \"/home/jovyan/RT08/0925/bling_svv_14\"\n",
    "\n",
    "total_nan1 = 0   # NaN_check=1 總筆數\n",
    "\n",
    "file_paths = sorted(glob.glob(os.path.join(DATA_DIR, \"*.csv\")))\n",
    "print(f\"[Info] 發現 {len(file_paths)} 個檔案\")\n",
    "\n",
    "for fp in file_paths:\n",
    "    try:\n",
    "        df = pd.read_csv(fp, low_memory=False)\n",
    "    except Exception as e:\n",
    "        print(f\"[Skip] 無法讀取 {fp}: {e}\")\n",
    "        continue\n",
    "\n",
    "    if \"NaN_check\" not in df.columns:\n",
    "        continue\n",
    "\n",
    "    total_nan1 += int((df[\"NaN_check\"] == 1).sum())\n",
    "\n",
    "print(\"\\n=== 跨檔案統計 ===\")\n",
    "print(f\"NaN_check=1 總筆數: {total_nan1}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "id": "429a619e-519d-4741-9fd9-38a49f576e4a",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "✅ Exported 5 figure(s) to: /home/jovyan/RT08/0925/1002\n",
      "   Year count: 3\n",
      "   - 2021: 1 figure(s)\n",
      "   - 2022: 3 figure(s)\n",
      "   - 2024: 1 figure(s)\n"
     ]
    }
   ],
   "source": [
    "\"\"\"畫成時間軸的圖，NaN_check=1用藍點點，NaN_check=0用灰點點，\n",
    "依照年份分張，50筆一張，右邊用藍色寫NaN_check=的數量跟佔比，字都不要互相檔到，\n",
    "檔案名稱叫做1002_ok_\n",
    "\"\"\"\n",
    "\n",
    "# -*- coding: utf-8 -*-\n",
    "\"\"\"\n",
    "時間軸視覺化（僅輸出圖片、不修改原始檔）：\n",
    "資料來源：/home/jovyan/RT08/0925/bling_svv_fin/*.csv\n",
    "輸出路徑：/home/jovyan/RT08/0925/1002/   檔名前綴：1002_ok_\n",
    "\n",
    "規格：\n",
    "- X 軸：senddate（時間）\n",
    "- Y 軸：檔名（每個檔案一條水平位置）\n",
    "- 點樣式：\n",
    "  * NaN_check=1 → 藍色點\n",
    "  * NaN_check=0 → 淺灰色點\n",
    "- 右側以藍色文字標註每個檔案「NaN_check=1 的筆數與佔比」\n",
    "- 依年份分張；每張最多 50 個檔案（避免重疊）\n",
    "- 不會動到來源資料夾中的任何檔案\n",
    "\"\"\"\n",
    "\n",
    "import os, glob\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.dates as mdates\n",
    "\n",
    "# ===== 參數設定 =====\n",
    "DATA_DIR = \"/home/jovyan/RT08/0925/bling_svv_14\"\n",
    "OUT_DIR  = \"/home/jovyan/RT08/0925/1002\"\n",
    "os.makedirs(OUT_DIR, exist_ok=True)\n",
    "\n",
    "MAX_PER_FIG = 50  # 每張圖最多顯示的檔案數\n",
    "\n",
    "# 顏色設定（符合需求：藍/灰）\n",
    "COLOR_BLUE = \"#1f77b4\"  # NaN_check=1\n",
    "COLOR_GRAY = \"#cfcfcf\"  # NaN_check=0\n",
    "\n",
    "TIME_COL = \"senddate\"\n",
    "NAN_CHECK_COL = \"NaN_check\"\n",
    "\n",
    "# ===== 小工具：安全讀檔（只讀需要欄位） =====\n",
    "def read_csv_for_plot(path):\n",
    "    \"\"\"盡量只讀必要欄位；若失敗再全讀。缺關鍵欄位則回傳空 DataFrame。\"\"\"\n",
    "    usecols = [TIME_COL, NAN_CHECK_COL]\n",
    "    try:\n",
    "        df = pd.read_csv(path, usecols=usecols, low_memory=False)\n",
    "    except Exception:\n",
    "        try:\n",
    "            df = pd.read_csv(path, low_memory=False)\n",
    "        except Exception:\n",
    "            return pd.DataFrame()\n",
    "        # 確認必要欄位存在\n",
    "        for c in [TIME_COL, NAN_CHECK_COL]:\n",
    "            if c not in df.columns:\n",
    "                return pd.DataFrame()\n",
    "    return df\n",
    "\n",
    "# ===== 蒐集資料 =====\n",
    "file_paths = sorted(glob.glob(os.path.join(DATA_DIR, \"*.csv\")))\n",
    "records = []  # 每列：__file__, __year__, senddate, NaN_check\n",
    "for fp in file_paths:\n",
    "    base = os.path.basename(fp)\n",
    "    df = read_csv_for_plot(fp)\n",
    "    if df.empty:\n",
    "        print(f\"[Skip] {base}: missing required columns or cannot read.\")\n",
    "        continue\n",
    "\n",
    "    # 時間轉型與清理\n",
    "    df = df.copy()\n",
    "    df[TIME_COL] = pd.to_datetime(df[TIME_COL], errors=\"coerce\")\n",
    "    df = df.dropna(subset=[TIME_COL])\n",
    "    if df.empty:\n",
    "        continue\n",
    "\n",
    "    # NaN_check 轉為 0/1（缺失視為 0）\n",
    "    df[NAN_CHECK_COL] = pd.to_numeric(df[NAN_CHECK_COL], errors=\"coerce\").fillna(0).astype(int).clip(0, 1)\n",
    "\n",
    "    # 整理輸出欄\n",
    "    out = pd.DataFrame({\n",
    "        \"__file__\": base,\n",
    "        \"__year__\": df[TIME_COL].dt.year,\n",
    "        TIME_COL: df[TIME_COL],\n",
    "        NAN_CHECK_COL: df[NAN_CHECK_COL],\n",
    "    })\n",
    "    records.append(out)\n",
    "\n",
    "if not records:\n",
    "    print(\"[Viz] No qualified data to plot (missing senddate/NaN_check).\")\n",
    "else:\n",
    "    data = pd.concat(records, ignore_index=True).sort_values([\"__year__\", \"__file__\", TIME_COL])\n",
    "\n",
    "    # ===== 開始依年份分批繪圖 =====\n",
    "    years = sorted(data[\"__year__\"].dropna().unique().tolist())\n",
    "    total_figs = 0\n",
    "    year_page_count = {}\n",
    "\n",
    "    for yr in years:\n",
    "        dy = data[data[\"__year__\"] == yr].copy()\n",
    "        files_in_year = sorted(dy[\"__file__\"].unique().tolist())\n",
    "\n",
    "        # 依每張最多 50 檔分頁\n",
    "        for page_start in range(0, len(files_in_year), MAX_PER_FIG):\n",
    "            batch_files = files_in_year[page_start:page_start + MAX_PER_FIG]\n",
    "            if not batch_files:\n",
    "                continue\n",
    "\n",
    "            # 動態圖高：每檔約 0.35 吋，介於 4~12 吋之間，避免 Y 軸文字互相遮擋\n",
    "            n = len(batch_files)\n",
    "            fig_h = max(4, min(12, 0.35 * n))\n",
    "            fig, ax = plt.subplots(figsize=(14, fig_h))\n",
    "\n",
    "            # 取本頁資料\n",
    "            batch_df = dy[dy[\"__file__\"].isin(batch_files)].copy()\n",
    "\n",
    "            # X 軸範圍與右側留白（用來放每檔案的統計文字，避免文字被截斷）\n",
    "            x_min = batch_df[TIME_COL].min()\n",
    "            x_max = batch_df[TIME_COL].max()\n",
    "            if pd.isna(x_min) or pd.isna(x_max) or x_min == x_max:\n",
    "                # 若時間退化，給全年範圍\n",
    "                x_min = pd.to_datetime(f\"{yr}-01-01\")\n",
    "                x_max = pd.to_datetime(f\"{yr}-12-31\")\n",
    "            pad_minutes = max(30, int((x_max - x_min).total_seconds() / 60 * 0.03))\n",
    "            pad = pd.Timedelta(minutes=pad_minutes)\n",
    "            ax.set_xlim(x_min, x_max + pad)\n",
    "\n",
    "            # Y 軸：檔名對應到座標\n",
    "            y_positions = {fname: i for i, fname in enumerate(batch_files)}\n",
    "            yticks, ylabels = [], []\n",
    "\n",
    "            # X 軸時間格式\n",
    "            locator = mdates.AutoDateLocator(minticks=4, maxticks=10)\n",
    "            formatter = mdates.ConciseDateFormatter(locator)\n",
    "            ax.xaxis.set_major_locator(locator)\n",
    "            ax.xaxis.set_major_formatter(formatter)\n",
    "\n",
    "            # 逐檔繪點與右側統計文字\n",
    "            for fname in batch_files:\n",
    "                gf = batch_df[batch_df[\"__file__\"] == fname][[TIME_COL, NAN_CHECK_COL]].sort_values(TIME_COL)\n",
    "                if gf.empty:\n",
    "                    continue\n",
    "\n",
    "                y = y_positions[fname]\n",
    "                yticks.append(y)\n",
    "                ylabels.append(fname)\n",
    "\n",
    "                # NaN_check=0（灰）、NaN_check=1（藍）\n",
    "                mask_nc0 = (gf[NAN_CHECK_COL] == 0)\n",
    "                mask_nc1 = (gf[NAN_CHECK_COL] == 1)\n",
    "\n",
    "                # 畫點：為了可辨識，1 比 0 稍大一點\n",
    "                if mask_nc0.any():\n",
    "                    ax.scatter(gf.loc[mask_nc0, TIME_COL], np.full(mask_nc0.sum(), y),\n",
    "                               s=10, marker='o', color=COLOR_GRAY, alpha=0.9, linewidths=0)\n",
    "                if mask_nc1.any():\n",
    "                    ax.scatter(gf.loc[mask_nc1, TIME_COL], np.full(mask_nc1.sum(), y),\n",
    "                               s=14, marker='o', color=COLOR_BLUE, alpha=0.95, linewidths=0)\n",
    "\n",
    "                # 計算右側標註：NaN_check=1 的數量與佔比\n",
    "                total_rows = len(gf)\n",
    "                cnt_ones = int(mask_nc1.sum())\n",
    "                ratio = (cnt_ones / total_rows * 100.0) if total_rows > 0 else 0.0\n",
    "\n",
    "                # 右側以藍色寫上統計（採用靠右的留白避免文字擠在繪圖區）\n",
    "                ax.text(x_max + pad * 0.5, y,\n",
    "                        f\"{cnt_ones} ({ratio:.1f}%)\",\n",
    "                        va=\"center\", ha=\"left\", color=COLOR_BLUE, fontsize=9)\n",
    "\n",
    "            # 外觀與標籤：使用英文（需求中未限制語言，沿用通用英文標註）\n",
    "            ax.set_title(f\"NaN_check over time by file — Year {yr}  \"\n",
    "                         f\"(files {page_start+1}-{page_start+len(batch_files)} of {len(files_in_year)})\",\n",
    "                         fontsize=12, pad=12)\n",
    "            ax.set_xlabel(\"Time\")\n",
    "            ax.set_yticks(yticks)\n",
    "            ax.set_yticklabels(ylabels, fontsize=9)\n",
    "            ax.grid(True, axis=\"x\", linestyle=\"--\", linewidth=0.6, alpha=0.5)\n",
    "            ax.set_axisbelow(True)\n",
    "\n",
    "            # 圖例（藍=1，灰=0）\n",
    "            from matplotlib.lines import Line2D\n",
    "            legend_handles = [\n",
    "                Line2D([0], [0], marker='o', color='none', markerfacecolor=COLOR_BLUE, label='NaN_check = 1', markersize=7),\n",
    "                Line2D([0], [0], marker='o', color='none', markerfacecolor=COLOR_GRAY, label='NaN_check = 0', markersize=7),\n",
    "            ]\n",
    "            ax.legend(handles=legend_handles, loc=\"upper right\", frameon=False)\n",
    "\n",
    "            plt.tight_layout()\n",
    "\n",
    "            # 輸出圖片（檔名以 1002_ok_ 開頭）\n",
    "            out_file = os.path.join(OUT_DIR, f\"1002_ok_{yr}_p{page_start // MAX_PER_FIG + 1}.png\")\n",
    "            fig.savefig(out_file, dpi=150)\n",
    "            plt.close(fig)\n",
    "\n",
    "            total_figs += 1\n",
    "            year_page_count[yr] = year_page_count.get(yr, 0) + 1\n",
    "\n",
    "    # 統計摘要印出\n",
    "    print(f\"\\n✅ Exported {total_figs} figure(s) to: {OUT_DIR}\")\n",
    "    print(f\"   Year count: {len(years)}\")\n",
    "    for yr in years:\n",
    "        print(f\"   - {yr}: {year_page_count.get(yr, 0)} figure(s)\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "id": "58c9095d-c036-4222-aade-9734501da44e",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Info] Found 122 files in /home/jovyan/RT08/0925/bling_svv_14\n",
      "\n",
      "=== 欄位一致性檢查 ===\n",
      "✅ 所有檔案欄位名稱完全一致\n",
      "\n",
      "=== 資料型態一致性檢查 ===\n",
      "✅ 所有欄位 dtype 完全一致\n",
      "\n",
      "=== 總結 ===\n",
      "總檔案數: 122\n",
      "欄位名稱一致: True\n",
      "欄位 dtype 一致: True\n"
     ]
    }
   ],
   "source": [
    "\"\"\"檢查/home/jovyan/RT08/0925/bling_svv_fin/是否每個檔案的欄位都一樣 資料型態都一樣\n",
    "掃描 /home/jovyan/RT08/0925/bling_svv_fin/ 內所有 .csv 檔案。\n",
    "檢查每個檔案的欄位名稱是否一致。\n",
    "檢查每個欄位的資料型態是否一致（會用 pandas 自動推斷 dtype）。\n",
    "列出差異的檔案與欄位。\n",
    "最後輸出總結：\n",
    "總共有多少檔案\n",
    "是否所有欄位與 dtype 都一致\n",
    "\"\"\"\n",
    "# -*- coding: utf-8 -*-\n",
    "\"\"\"\n",
    "檢查 bling_svv_fin 內所有檔案：\n",
    "1. 欄位名稱是否一致\n",
    "2. 欄位資料型態是否一致\n",
    "\"\"\"\n",
    "\n",
    "import os, glob\n",
    "import pandas as pd\n",
    "\n",
    "DATA_DIR = \"/home/jovyan/RT08/0925/bling_svv_14\"\n",
    "file_paths = sorted(glob.glob(os.path.join(DATA_DIR, \"*.csv\")))\n",
    "\n",
    "print(f\"[Info] Found {len(file_paths)} files in {DATA_DIR}\")\n",
    "\n",
    "schema_dict = {}  # 儲存每個檔案的 schema\n",
    "\n",
    "for fp in file_paths:\n",
    "    base = os.path.basename(fp)\n",
    "    try:\n",
    "        df = pd.read_csv(fp, nrows=200, low_memory=False)  # 讀前 200 列推斷 dtype\n",
    "    except Exception as e:\n",
    "        print(f\"[Error] {base}: 無法讀取 ({e})\")\n",
    "        continue\n",
    "    \n",
    "    # 記錄欄位與 dtype\n",
    "    schema_dict[base] = {col: str(dtype) for col, dtype in df.dtypes.items()}\n",
    "\n",
    "# === 比對欄位 ===\n",
    "all_columns = set()\n",
    "for sch in schema_dict.values():\n",
    "    all_columns.update(sch.keys())\n",
    "\n",
    "print(\"\\n=== 欄位一致性檢查 ===\")\n",
    "base_cols = set(next(iter(schema_dict.values())).keys())\n",
    "consistent_columns = all(base_cols == set(sch.keys()) for sch in schema_dict.values())\n",
    "\n",
    "if consistent_columns:\n",
    "    print(\"✅ 所有檔案欄位名稱完全一致\")\n",
    "else:\n",
    "    print(\"⚠️ 欄位名稱不一致，以下檔案缺少或多出欄位：\")\n",
    "    for fname, sch in schema_dict.items():\n",
    "        if set(sch.keys()) != base_cols:\n",
    "            diff1 = base_cols - set(sch.keys())\n",
    "            diff2 = set(sch.keys()) - base_cols\n",
    "            if diff1:\n",
    "                print(f\"  - {fname}: 缺少 {diff1}\")\n",
    "            if diff2:\n",
    "                print(f\"  - {fname}: 多出 {diff2}\")\n",
    "\n",
    "# === 比對 dtype ===\n",
    "print(\"\\n=== 資料型態一致性檢查 ===\")\n",
    "dtype_map = {}\n",
    "for fname, sch in schema_dict.items():\n",
    "    for col, dtype in sch.items():\n",
    "        dtype_map.setdefault(col, set()).add(dtype)\n",
    "\n",
    "consistent_dtype = True\n",
    "for col, dtypes in dtype_map.items():\n",
    "    if len(dtypes) > 1:\n",
    "        consistent_dtype = False\n",
    "        print(f\"⚠️ 欄位 {col} 出現多種 dtype: {dtypes}\")\n",
    "\n",
    "if consistent_dtype:\n",
    "    print(\"✅ 所有欄位 dtype 完全一致\")\n",
    "\n",
    "# === 總結 ===\n",
    "print(\"\\n=== 總結 ===\")\n",
    "print(f\"總檔案數: {len(schema_dict)}\")\n",
    "print(f\"欄位名稱一致: {consistent_columns}\")\n",
    "print(f\"欄位 dtype 一致: {consistent_dtype}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "id": "40dff824-76b7-41ff-97ae-402d6ce3b02f",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Info] Found 122 files in /home/jovyan/RT08/0925/bling_svv_14\n",
      "\n",
      "=== 欄位一致性檢查 ===\n",
      "✅ 所有檔案欄位名稱完全一致\n",
      "\n",
      "=== 欄位型態檢查 ===\n",
      "欄位 patno: ✅ 單一 dtype = int64\n",
      "欄位 senddate: ✅ 單一 dtype = object\n",
      "欄位 rrhzsetactual: ✅ 單一 dtype = float64\n",
      "欄位 mvsetactual: ✅ 單一 dtype = float64\n",
      "欄位 peepepap: ✅ 單一 dtype = float64\n",
      "欄位 ppeak: ✅ 單一 dtype = float64\n",
      "欄位 cdyn: ✅ 單一 dtype = float64\n",
      "欄位 pmean: ✅ 單一 dtype = float64\n",
      "欄位 mode_1: ✅ 單一 dtype = int64\n",
      "欄位 mode_2: ✅ 單一 dtype = int64\n",
      "欄位 mode_3: ✅ 單一 dtype = int64\n",
      "欄位 svv_new: ✅ 單一 dtype = float64\n",
      "欄位 NaN_check: ✅ 單一 dtype = int64\n",
      "\n",
      "=== 總結 ===\n",
      "總檔案數: 122\n",
      "欄位名稱一致: True\n",
      "有 0 個欄位存在多種 dtype\n"
     ]
    }
   ],
   "source": [
    "\"\"\"檢查 bling_svv_fin 內所有檔案：\n",
    "1. 欄位名稱是否一致\n",
    "2. 列出每個欄位在各檔案的 dtype\n",
    "\"\"\"\n",
    "\n",
    "import os, glob\n",
    "import pandas as pd\n",
    "\n",
    "DATA_DIR = \"/home/jovyan/RT08/0925/bling_svv_14\"\n",
    "file_paths = sorted(glob.glob(os.path.join(DATA_DIR, \"*.csv\")))\n",
    "\n",
    "print(f\"[Info] Found {len(file_paths)} files in {DATA_DIR}\")\n",
    "\n",
    "schema_dict = {}  # 每個檔案的 schema: {檔名: {欄位: dtype}}\n",
    "\n",
    "for fp in file_paths:\n",
    "    base = os.path.basename(fp)\n",
    "    try:\n",
    "        df = pd.read_csv(fp, nrows=200, low_memory=False)  # 用前 200 列推 dtype\n",
    "    except Exception as e:\n",
    "        print(f\"[Error] {base}: 無法讀取 ({e})\")\n",
    "        continue\n",
    "    \n",
    "    schema_dict[base] = {col: str(dtype) for col, dtype in df.dtypes.items()}\n",
    "\n",
    "# === 欄位檢查 ===\n",
    "all_columns = set()\n",
    "for sch in schema_dict.values():\n",
    "    all_columns.update(sch.keys())\n",
    "\n",
    "print(\"\\n=== 欄位一致性檢查 ===\")\n",
    "base_cols = set(next(iter(schema_dict.values())).keys())\n",
    "consistent_columns = all(base_cols == set(sch.keys()) for sch in schema_dict.values())\n",
    "\n",
    "if consistent_columns:\n",
    "    print(\"✅ 所有檔案欄位名稱完全一致\")\n",
    "else:\n",
    "    print(\"⚠️ 欄位名稱不一致，以下檔案缺少或多出欄位：\")\n",
    "    for fname, sch in schema_dict.items():\n",
    "        if set(sch.keys()) != base_cols:\n",
    "            diff1 = base_cols - set(sch.keys())\n",
    "            diff2 = set(sch.keys()) - base_cols\n",
    "            if diff1:\n",
    "                print(f\"  - {fname}: 缺少 {diff1}\")\n",
    "            if diff2:\n",
    "                print(f\"  - {fname}: 多出 {diff2}\")\n",
    "\n",
    "# === dtype 比對 ===\n",
    "print(\"\\n=== 欄位型態檢查 ===\")\n",
    "dtype_map = {}  # {欄位: {dtype: [檔名列表]}}\n",
    "for fname, sch in schema_dict.items():\n",
    "    for col, dtype in sch.items():\n",
    "        dtype_map.setdefault(col, {}).setdefault(dtype, []).append(fname)\n",
    "\n",
    "for col, dtype_files in dtype_map.items():\n",
    "    if len(dtype_files) == 1:\n",
    "        dtype = list(dtype_files.keys())[0]\n",
    "        print(f\"欄位 {col}: ✅ 單一 dtype = {dtype}\")\n",
    "    else:\n",
    "        print(f\"⚠️ 欄位 {col}: 出現多種 dtype\")\n",
    "        for dtype, fnames in dtype_files.items():\n",
    "            print(f\"    - {dtype}: {len(fnames)} 檔案，例如 {fnames[:3]}{'...' if len(fnames)>3 else ''}\")\n",
    "\n",
    "# === 總結 ===\n",
    "print(\"\\n=== 總結 ===\")\n",
    "print(f\"總檔案數: {len(schema_dict)}\")\n",
    "print(f\"欄位名稱一致: {consistent_columns}\")\n",
    "print(f\"有 {sum(len(v)>1 for v in dtype_map.values())} 個欄位存在多種 dtype\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5c859bcd-7d68-4848-bbdc-394050c8ad5f",
   "metadata": {},
   "outputs": [],
   "source": [
    "目前的欄位包刮\n",
    "patno, senddate, rrhzsetactual, mvsetactual, peepepap, ppeak, cdyn, pmean, mode_1, mode_2, mode_3, svv_new, NaN_check\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "id": "7250032b-40a3-4eaa-abaa-8b94662b4024",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "已刪除 PatNo_ID_1594439781.csv 的 ad_para 欄位\n",
      "已刪除 PatNo_ID_1570242703.csv 的 ad_para 欄位\n",
      "已刪除 PatNo_ID_1574148494.csv 的 ad_para 欄位\n",
      "已刪除 PatNo_ID_1582849900.csv 的 ad_para 欄位\n",
      "已刪除 PatNo_ID_1574831525.csv 的 ad_para 欄位\n",
      "已刪除 PatNo_ID_1564148644.csv 的 ad_para 欄位\n",
      "已刪除 PatNo_ID_1575502382.csv 的 ad_para 欄位\n",
      "已刪除 PatNo_ID_1588794796.csv 的 ad_para 欄位\n",
      "已刪除 PatNo_ID_1590136310.csv 的 ad_para 欄位\n",
      "已刪除 PatNo_ID_1567804800.csv 的 ad_para 欄位\n",
      "已刪除 PatNo_ID_1565378038.csv 的 ad_para 欄位\n",
      "已刪除 PatNo_ID_1580107637.csv 的 ad_para 欄位\n",
      "已刪除 PatNo_ID_1588632604.csv 的 ad_para 欄位\n",
      "已刪除 PatNo_ID_1594511911.csv 的 ad_para 欄位\n",
      "已刪除 PatNo_ID_1574270349.csv 的 ad_para 欄位\n",
      "已刪除 PatNo_ID_1573964540.csv 的 ad_para 欄位\n",
      "已刪除 PatNo_ID_1568952422.csv 的 ad_para 欄位\n",
      "已刪除 PatNo_ID_1594533379.csv 的 ad_para 欄位\n",
      "已刪除 PatNo_ID_1580096720.csv 的 ad_para 欄位\n",
      "已刪除 PatNo_ID_1594528842.csv 的 ad_para 欄位\n",
      "已刪除 PatNo_ID_1581633231.csv 的 ad_para 欄位\n",
      "已刪除 7108162.csv 的 ad_para 欄位\n",
      "已刪除 PatNo_ID_1581692973.csv 的 ad_para 欄位\n",
      "已刪除 PatNo_ID_1568574099.csv 的 ad_para 欄位\n",
      "已刪除 PatNo_ID_1574528808.csv 的 ad_para 欄位\n",
      "已刪除 PatNo_ID_1587490083.csv 的 ad_para 欄位\n",
      "已刪除 PatNo_ID_1590854576.csv 的 ad_para 欄位\n",
      "已刪除 PatNo_ID_1569944983.csv 的 ad_para 欄位\n",
      "已刪除 PatNo_ID_1589034524.csv 的 ad_para 欄位\n",
      "已刪除 PatNo_ID_1594305136.csv 的 ad_para 欄位\n",
      "已刪除 PatNo_ID_1572976822.csv 的 ad_para 欄位\n",
      "已刪除 114309.csv 的 ad_para 欄位\n",
      "已刪除 PatNo_ID_1577042911.csv 的 ad_para 欄位\n",
      "已刪除 PatNo_ID_1567747650.csv 的 ad_para 欄位\n",
      "已刪除 PatNo_ID_1592560504.csv 的 ad_para 欄位\n",
      "已刪除 7408338.csv 的 ad_para 欄位\n",
      "已刪除 PatNo_ID_1593472048.csv 的 ad_para 欄位\n",
      "已刪除 PatNo_ID_1560013303.csv 的 ad_para 欄位\n",
      "已刪除 PatNo_ID_1575256902.csv 的 ad_para 欄位\n",
      "已刪除 PatNo_ID_1575975485.csv 的 ad_para 欄位\n",
      "已刪除 PatNo_ID_1594437309.csv 的 ad_para 欄位\n",
      "已刪除 PatNo_ID_1576301569.csv 的 ad_para 欄位\n",
      "已刪除 PatNo_ID_1589324603.csv 的 ad_para 欄位\n",
      "已刪除 PatNo_ID_1594464829.csv 的 ad_para 欄位\n",
      "已刪除 PatNo_ID_1565148312.csv 的 ad_para 欄位\n",
      "已刪除 PatNo_ID_1572481361.csv 的 ad_para 欄位\n",
      "已刪除 PatNo_ID_1590616537.csv 的 ad_para 欄位\n",
      "已刪除 095707.csv 的 ad_para 欄位\n",
      "已刪除 PatNo_ID_1594467719.csv 的 ad_para 欄位\n",
      "已刪除 PatNo_ID_1566123680.csv 的 ad_para 欄位\n",
      "已刪除 PatNo_ID_1566911879.csv 的 ad_para 欄位\n",
      "已刪除 PatNo_ID_1566252197.csv 的 ad_para 欄位\n",
      "已刪除 PatNo_ID_1581003248.csv 的 ad_para 欄位\n",
      "已刪除 4216007.csv 的 ad_para 欄位\n",
      "已刪除 PatNo_ID_1569083701.csv 的 ad_para 欄位\n",
      "已刪除 PatNo_ID_1593416100.csv 的 ad_para 欄位\n",
      "已刪除 PatNo_ID_1570642083.csv 的 ad_para 欄位\n",
      "已刪除 PatNo_ID_1586696634.csv 的 ad_para 欄位\n",
      "已刪除 PatNo_ID_1594319286.csv 的 ad_para 欄位\n",
      "已刪除 PatNo_ID_1593838524.csv 的 ad_para 欄位\n",
      "已刪除 PatNo_ID_1576116479.csv 的 ad_para 欄位\n",
      "已刪除 PatNo_ID_1588957997.csv 的 ad_para 欄位\n",
      "已刪除 PatNo_ID_1575445051.csv 的 ad_para 欄位\n",
      "已刪除 PatNo_ID_1584158973.csv 的 ad_para 欄位\n",
      "已刪除 PatNo_ID_1563587183.csv 的 ad_para 欄位\n",
      "已刪除 PatNo_ID_1573063188.csv 的 ad_para 欄位\n",
      "已刪除 PatNo_ID_1594322594.csv 的 ad_para 欄位\n",
      "已刪除 PatNo_ID_1588673465.csv 的 ad_para 欄位\n",
      "已刪除 089271.csv 的 ad_para 欄位\n",
      "已刪除 PatNo_ID_1582635996.csv 的 ad_para 欄位\n",
      "已刪除 7657698.csv 的 ad_para 欄位\n",
      "已刪除 PatNo_ID_1593087886.csv 的 ad_para 欄位\n",
      "已刪除 PatNo_ID_1594448501.csv 的 ad_para 欄位\n",
      "已刪除 PatNo_ID_1586172659.csv 的 ad_para 欄位\n",
      "已刪除 230933.csv 的 ad_para 欄位\n",
      "已刪除 PatNo_ID_1580244614.csv 的 ad_para 欄位\n",
      "已刪除 095323.csv 的 ad_para 欄位\n",
      "已刪除 PatNo_ID_1594320763.csv 的 ad_para 欄位\n",
      "已刪除 PatNo_ID_1582937076.csv 的 ad_para 欄位\n",
      "已刪除 PatNo_ID_1580062580.csv 的 ad_para 欄位\n",
      "已刪除 PatNo_ID_1566279967.csv 的 ad_para 欄位\n",
      "已刪除 PatNo_ID_1570089466.csv 的 ad_para 欄位\n",
      "已刪除 PatNo_ID_1594423683.csv 的 ad_para 欄位\n",
      "已刪除 PatNo_ID_1579498177.csv 的 ad_para 欄位\n",
      "已刪除 PatNo_ID_1582452511.csv 的 ad_para 欄位\n",
      "已刪除 PatNo_ID_1594335109.csv 的 ad_para 欄位\n",
      "已刪除 PatNo_ID_1579198603.csv 的 ad_para 欄位\n",
      "已刪除 PatNo_ID_1589918099.csv 的 ad_para 欄位\n",
      "已刪除 PatNo_ID_1562733396.csv 的 ad_para 欄位\n",
      "已刪除 PatNo_ID_1586897008.csv 的 ad_para 欄位\n",
      "已刪除 PatNo_ID_1593593586.csv 的 ad_para 欄位\n",
      "已刪除 PatNo_ID_1573249295.csv 的 ad_para 欄位\n",
      "已刪除 PatNo_ID_1594173718.csv 的 ad_para 欄位\n",
      "已刪除 PatNo_ID_1576964560.csv 的 ad_para 欄位\n",
      "已刪除 PatNo_ID_1577487284.csv 的 ad_para 欄位\n",
      "已刪除 PatNo_ID_1594479330.csv 的 ad_para 欄位\n",
      "已刪除 PatNo_ID_1576115572.csv 的 ad_para 欄位\n",
      "已刪除 PatNo_ID_1574987447.csv 的 ad_para 欄位\n",
      "已刪除 PatNo_ID_1594294180.csv 的 ad_para 欄位\n",
      "已刪除 PatNo_ID_1589018086.csv 的 ad_para 欄位\n",
      "已刪除 PatNo_ID_1571945701.csv 的 ad_para 欄位\n",
      "已刪除 PatNo_ID_1580766093.csv 的 ad_para 欄位\n",
      "已刪除 PatNo_ID_1584397376.csv 的 ad_para 欄位\n",
      "已刪除 PatNo_ID_1570273244.csv 的 ad_para 欄位\n",
      "已刪除 PatNo_ID_1594455578.csv 的 ad_para 欄位\n",
      "已刪除 PatNo_ID_1581019504.csv 的 ad_para 欄位\n",
      "已刪除 PatNo_ID_1594471407.csv 的 ad_para 欄位\n",
      "已刪除 PatNo_ID_1568813269.csv 的 ad_para 欄位\n",
      "已刪除 PatNo_ID_1591609798.csv 的 ad_para 欄位\n",
      "已刪除 PatNo_ID_1567832735.csv 的 ad_para 欄位\n",
      "已刪除 7721164.csv 的 ad_para 欄位\n",
      "已刪除 PatNo_ID_1572562839.csv 的 ad_para 欄位\n",
      "已刪除 PatNo_ID_1568039398.csv 的 ad_para 欄位\n",
      "已刪除 PatNo_ID_1594511914.csv 的 ad_para 欄位\n",
      "已刪除 PatNo_ID_1566671274.csv 的 ad_para 欄位\n",
      "已刪除 PatNo_ID_1594441887.csv 的 ad_para 欄位\n",
      "已刪除 PatNo_ID_1572831765.csv 的 ad_para 欄位\n",
      "已刪除 PatNo_ID_1592044724.csv 的 ad_para 欄位\n",
      "已刪除 PatNo_ID_1593720818.csv 的 ad_para 欄位\n",
      "已刪除 PatNo_ID_1575060177.csv 的 ad_para 欄位\n",
      "已刪除 PatNo_ID_1578784257.csv 的 ad_para 欄位\n",
      "已刪除 PatNo_ID_1594309746.csv 的 ad_para 欄位\n"
     ]
    }
   ],
   "source": [
    "# 我下面那框程式寫錯路徑笑死\n",
    "# 我要刪除/home/jovyan/RT08/0925/bling_svv_fin/路徑中檔案的ad_para欄位，所有檔案都是，裡面的檔案的ad_para欄位都刪掉 python\n",
    "\n",
    "DIR = \"/home/jovyan/RT08/0925/bling_svv_fin/\"\n",
    "\n",
    "# 逐一處理目錄下所有 csv 檔案\n",
    "for file in os.listdir(DIR):\n",
    "    if file.endswith(\".csv\"):\n",
    "        file_path = os.path.join(DIR, file)\n",
    "        try:\n",
    "            df = pd.read_csv(file_path)\n",
    "\n",
    "            # 如果存在 ad_para 欄位，刪除\n",
    "            if \"ad_para\" in df.columns:\n",
    "                df = df.drop(columns=[\"ad_para\"])\n",
    "                df.to_csv(file_path, index=False)\n",
    "                print(f\"已刪除 {file} 的 ad_para 欄位\")\n",
    "            else:\n",
    "                print(f\"{file} 沒有 ad_para 欄位，跳過\")\n",
    "\n",
    "        except Exception as e:\n",
    "            print(f\"讀取或處理 {file} 時發生錯誤: {e}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "id": "864b11a3-0373-4180-a430-e746abec1a06",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Info] Found 122 files in /home/jovyan/RT08/0925/bling_svv_14\n",
      "[OK] 089271.csv: rows=32419, NaN_check=1 rows=31951, ad_para=1 rows=24294\n",
      "[OK] 095323.csv: rows=23791, NaN_check=1 rows=19434, ad_para=1 rows=13461\n",
      "[OK] 095707.csv: rows=20180, NaN_check=1 rows=19288, ad_para=1 rows=15256\n",
      "[OK] 114309.csv: rows=71729, NaN_check=1 rows=22363, ad_para=1 rows=19141\n",
      "[OK] 230933.csv: rows=30249, NaN_check=1 rows=27592, ad_para=1 rows=23028\n",
      "[OK] 4216007.csv: rows=1433, NaN_check=1 rows=0, ad_para=1 rows=0\n",
      "[OK] 7108162.csv: rows=239, NaN_check=1 rows=197, ad_para=1 rows=177\n",
      "[OK] 7408338.csv: rows=1432, NaN_check=1 rows=1430, ad_para=1 rows=1061\n",
      "[OK] 7657698.csv: rows=1413, NaN_check=1 rows=1406, ad_para=1 rows=1364\n",
      "[OK] 7721164.csv: rows=483, NaN_check=1 rows=481, ad_para=1 rows=450\n",
      "[OK] PatNo_ID_1560013303.csv: rows=2543, NaN_check=1 rows=2455, ad_para=1 rows=2079\n",
      "[OK] PatNo_ID_1562733396.csv: rows=2254, NaN_check=1 rows=2239, ad_para=1 rows=2164\n",
      "[OK] PatNo_ID_1563587183.csv: rows=5287, NaN_check=1 rows=5172, ad_para=1 rows=4987\n",
      "[OK] PatNo_ID_1564148644.csv: rows=17287, NaN_check=1 rows=8615, ad_para=1 rows=7962\n",
      "[OK] PatNo_ID_1565148312.csv: rows=5475, NaN_check=1 rows=5453, ad_para=1 rows=5014\n",
      "[OK] PatNo_ID_1565378038.csv: rows=2561, NaN_check=1 rows=2242, ad_para=1 rows=1377\n",
      "[OK] PatNo_ID_1566123680.csv: rows=42600, NaN_check=1 rows=21372, ad_para=1 rows=20688\n",
      "[OK] PatNo_ID_1566252197.csv: rows=3368, NaN_check=1 rows=1933, ad_para=1 rows=1702\n",
      "[OK] PatNo_ID_1566279967.csv: rows=1235, NaN_check=1 rows=1151, ad_para=1 rows=904\n",
      "[OK] PatNo_ID_1566671274.csv: rows=25359, NaN_check=1 rows=23543, ad_para=1 rows=21368\n",
      "[OK] PatNo_ID_1566911879.csv: rows=53999, NaN_check=1 rows=45879, ad_para=1 rows=42601\n",
      "[OK] PatNo_ID_1567747650.csv: rows=10901, NaN_check=1 rows=10765, ad_para=1 rows=9315\n",
      "[OK] PatNo_ID_1567804800.csv: rows=20578, NaN_check=1 rows=12672, ad_para=1 rows=11675\n",
      "[OK] PatNo_ID_1567832735.csv: rows=36580, NaN_check=1 rows=36473, ad_para=1 rows=31490\n",
      "[OK] PatNo_ID_1568039398.csv: rows=34519, NaN_check=1 rows=34166, ad_para=1 rows=31395\n",
      "[OK] PatNo_ID_1568574099.csv: rows=13946, NaN_check=1 rows=12610, ad_para=1 rows=9845\n",
      "[OK] PatNo_ID_1568813269.csv: rows=4867, NaN_check=1 rows=4833, ad_para=1 rows=4396\n",
      "[OK] PatNo_ID_1568952422.csv: rows=1184, NaN_check=1 rows=1175, ad_para=1 rows=1012\n",
      "[OK] PatNo_ID_1569083701.csv: rows=3328, NaN_check=1 rows=3317, ad_para=1 rows=1711\n",
      "[OK] PatNo_ID_1569944983.csv: rows=7650, NaN_check=1 rows=7553, ad_para=1 rows=6394\n",
      "[OK] PatNo_ID_1570089466.csv: rows=41343, NaN_check=1 rows=15377, ad_para=1 rows=12100\n",
      "[OK] PatNo_ID_1570242703.csv: rows=10646, NaN_check=1 rows=10543, ad_para=1 rows=9502\n",
      "[OK] PatNo_ID_1570273244.csv: rows=9730, NaN_check=1 rows=9697, ad_para=1 rows=9138\n",
      "[OK] PatNo_ID_1570642083.csv: rows=19731, NaN_check=1 rows=13243, ad_para=1 rows=12832\n",
      "[OK] PatNo_ID_1571945701.csv: rows=15731, NaN_check=1 rows=15471, ad_para=1 rows=13580\n",
      "[OK] PatNo_ID_1572481361.csv: rows=34540, NaN_check=1 rows=34354, ad_para=1 rows=29442\n",
      "[OK] PatNo_ID_1572562839.csv: rows=20966, NaN_check=1 rows=15501, ad_para=1 rows=14484\n",
      "[OK] PatNo_ID_1572831765.csv: rows=2696, NaN_check=1 rows=2692, ad_para=1 rows=1797\n",
      "[OK] PatNo_ID_1572976822.csv: rows=6764, NaN_check=1 rows=4270, ad_para=1 rows=4158\n",
      "[OK] PatNo_ID_1573063188.csv: rows=5080, NaN_check=1 rows=5023, ad_para=1 rows=3760\n",
      "[OK] PatNo_ID_1573249295.csv: rows=7151, NaN_check=1 rows=7013, ad_para=1 rows=6810\n",
      "[OK] PatNo_ID_1573964540.csv: rows=4394, NaN_check=1 rows=4333, ad_para=1 rows=3666\n",
      "[OK] PatNo_ID_1574148494.csv: rows=47154, NaN_check=1 rows=47006, ad_para=1 rows=41645\n",
      "[OK] PatNo_ID_1574270349.csv: rows=6534, NaN_check=1 rows=6503, ad_para=1 rows=3828\n",
      "[OK] PatNo_ID_1574528808.csv: rows=14817, NaN_check=1 rows=14466, ad_para=1 rows=7481\n",
      "[OK] PatNo_ID_1574831525.csv: rows=521, NaN_check=1 rows=514, ad_para=1 rows=345\n",
      "[OK] PatNo_ID_1574987447.csv: rows=19843, NaN_check=1 rows=19537, ad_para=1 rows=17907\n",
      "[OK] PatNo_ID_1575060177.csv: rows=5290, NaN_check=1 rows=4994, ad_para=1 rows=4288\n",
      "[OK] PatNo_ID_1575256902.csv: rows=9587, NaN_check=1 rows=9444, ad_para=1 rows=4247\n",
      "[OK] PatNo_ID_1575445051.csv: rows=1801, NaN_check=1 rows=1784, ad_para=1 rows=1628\n",
      "[OK] PatNo_ID_1575502382.csv: rows=6604, NaN_check=1 rows=6457, ad_para=1 rows=6048\n",
      "[OK] PatNo_ID_1575975485.csv: rows=16459, NaN_check=1 rows=16247, ad_para=1 rows=13616\n",
      "[OK] PatNo_ID_1576115572.csv: rows=18717, NaN_check=1 rows=17944, ad_para=1 rows=14649\n",
      "[OK] PatNo_ID_1576116479.csv: rows=1263, NaN_check=1 rows=1257, ad_para=1 rows=1150\n",
      "[OK] PatNo_ID_1576301569.csv: rows=3207, NaN_check=1 rows=3185, ad_para=1 rows=2998\n",
      "[OK] PatNo_ID_1576964560.csv: rows=24192, NaN_check=1 rows=23613, ad_para=1 rows=16879\n",
      "[OK] PatNo_ID_1577042911.csv: rows=38357, NaN_check=1 rows=35590, ad_para=1 rows=30705\n",
      "[OK] PatNo_ID_1577487284.csv: rows=2345, NaN_check=1 rows=2326, ad_para=1 rows=1596\n",
      "[OK] PatNo_ID_1578784257.csv: rows=33917, NaN_check=1 rows=28540, ad_para=1 rows=24047\n",
      "[OK] PatNo_ID_1579198603.csv: rows=2046, NaN_check=1 rows=2038, ad_para=1 rows=1958\n",
      "[OK] PatNo_ID_1579498177.csv: rows=21688, NaN_check=1 rows=21597, ad_para=1 rows=20726\n",
      "[OK] PatNo_ID_1580062580.csv: rows=2150, NaN_check=1 rows=2059, ad_para=1 rows=2014\n",
      "[OK] PatNo_ID_1580096720.csv: rows=1423, NaN_check=1 rows=996, ad_para=1 rows=957\n",
      "[OK] PatNo_ID_1580107637.csv: rows=2698, NaN_check=1 rows=0, ad_para=1 rows=0\n",
      "[OK] PatNo_ID_1580244614.csv: rows=5278, NaN_check=1 rows=3848, ad_para=1 rows=3060\n",
      "[OK] PatNo_ID_1580766093.csv: rows=18070, NaN_check=1 rows=17335, ad_para=1 rows=15507\n",
      "[OK] PatNo_ID_1581003248.csv: rows=7776, NaN_check=1 rows=6488, ad_para=1 rows=5528\n",
      "[OK] PatNo_ID_1581019504.csv: rows=28177, NaN_check=1 rows=20535, ad_para=1 rows=18980\n",
      "[OK] PatNo_ID_1581633231.csv: rows=15995, NaN_check=1 rows=14527, ad_para=1 rows=12327\n",
      "[OK] PatNo_ID_1581692973.csv: rows=2723, NaN_check=1 rows=2570, ad_para=1 rows=2359\n",
      "[OK] PatNo_ID_1582452511.csv: rows=5196, NaN_check=1 rows=5145, ad_para=1 rows=4888\n",
      "[OK] PatNo_ID_1582635996.csv: rows=13046, NaN_check=1 rows=12964, ad_para=1 rows=12053\n",
      "[OK] PatNo_ID_1582849900.csv: rows=7411, NaN_check=1 rows=1743, ad_para=1 rows=1489\n",
      "[OK] PatNo_ID_1582937076.csv: rows=23990, NaN_check=1 rows=22632, ad_para=1 rows=21186\n",
      "[OK] PatNo_ID_1584158973.csv: rows=2882, NaN_check=1 rows=2861, ad_para=1 rows=2271\n",
      "[OK] PatNo_ID_1584397376.csv: rows=638, NaN_check=1 rows=638, ad_para=1 rows=250\n",
      "[OK] PatNo_ID_1586172659.csv: rows=38559, NaN_check=1 rows=38189, ad_para=1 rows=28497\n",
      "[OK] PatNo_ID_1586696634.csv: rows=3569, NaN_check=1 rows=2499, ad_para=1 rows=2364\n",
      "[OK] PatNo_ID_1586897008.csv: rows=6687, NaN_check=1 rows=6642, ad_para=1 rows=4089\n",
      "[OK] PatNo_ID_1587490083.csv: rows=45186, NaN_check=1 rows=45073, ad_para=1 rows=39985\n",
      "[OK] PatNo_ID_1588632604.csv: rows=2608, NaN_check=1 rows=2478, ad_para=1 rows=2160\n",
      "[OK] PatNo_ID_1588673465.csv: rows=10077, NaN_check=1 rows=5484, ad_para=1 rows=5318\n",
      "[OK] PatNo_ID_1588794796.csv: rows=9376, NaN_check=1 rows=9369, ad_para=1 rows=9149\n",
      "[OK] PatNo_ID_1588957997.csv: rows=10966, NaN_check=1 rows=10228, ad_para=1 rows=8546\n",
      "[OK] PatNo_ID_1589018086.csv: rows=13472, NaN_check=1 rows=13392, ad_para=1 rows=11904\n",
      "[OK] PatNo_ID_1589034524.csv: rows=50081, NaN_check=1 rows=49600, ad_para=1 rows=44430\n",
      "[OK] PatNo_ID_1589324603.csv: rows=4187, NaN_check=1 rows=4101, ad_para=1 rows=3058\n",
      "[OK] PatNo_ID_1589918099.csv: rows=9333, NaN_check=1 rows=0, ad_para=1 rows=0\n",
      "[OK] PatNo_ID_1590136310.csv: rows=5580, NaN_check=1 rows=5297, ad_para=1 rows=3940\n",
      "[OK] PatNo_ID_1590616537.csv: rows=14208, NaN_check=1 rows=14145, ad_para=1 rows=10932\n",
      "[OK] PatNo_ID_1590854576.csv: rows=15879, NaN_check=1 rows=15462, ad_para=1 rows=14268\n",
      "[OK] PatNo_ID_1591609798.csv: rows=35624, NaN_check=1 rows=35492, ad_para=1 rows=27077\n",
      "[OK] PatNo_ID_1592044724.csv: rows=6815, NaN_check=1 rows=6663, ad_para=1 rows=6499\n",
      "[OK] PatNo_ID_1592560504.csv: rows=18052, NaN_check=1 rows=17632, ad_para=1 rows=13132\n",
      "[OK] PatNo_ID_1593087886.csv: rows=24163, NaN_check=1 rows=23762, ad_para=1 rows=19885\n",
      "[OK] PatNo_ID_1593416100.csv: rows=3328, NaN_check=1 rows=3314, ad_para=1 rows=3052\n",
      "[OK] PatNo_ID_1593472048.csv: rows=8230, NaN_check=1 rows=6240, ad_para=1 rows=5295\n",
      "[OK] PatNo_ID_1593593586.csv: rows=18882, NaN_check=1 rows=18785, ad_para=1 rows=14424\n",
      "[OK] PatNo_ID_1593720818.csv: rows=3911, NaN_check=1 rows=2640, ad_para=1 rows=2568\n",
      "[OK] PatNo_ID_1593838524.csv: rows=2178, NaN_check=1 rows=2155, ad_para=1 rows=1984\n",
      "[OK] PatNo_ID_1594173718.csv: rows=344, NaN_check=1 rows=344, ad_para=1 rows=260\n",
      "[OK] PatNo_ID_1594294180.csv: rows=18334, NaN_check=1 rows=8878, ad_para=1 rows=7717\n",
      "[OK] PatNo_ID_1594305136.csv: rows=18679, NaN_check=1 rows=15565, ad_para=1 rows=14424\n",
      "[OK] PatNo_ID_1594309746.csv: rows=3745, NaN_check=1 rows=3622, ad_para=1 rows=3101\n",
      "[OK] PatNo_ID_1594319286.csv: rows=4107, NaN_check=1 rows=3986, ad_para=1 rows=1595\n",
      "[OK] PatNo_ID_1594320763.csv: rows=2431, NaN_check=1 rows=2176, ad_para=1 rows=2086\n",
      "[OK] PatNo_ID_1594322594.csv: rows=5380, NaN_check=1 rows=5352, ad_para=1 rows=5114\n",
      "[OK] PatNo_ID_1594335109.csv: rows=5116, NaN_check=1 rows=4993, ad_para=1 rows=3307\n",
      "[OK] PatNo_ID_1594423683.csv: rows=5023, NaN_check=1 rows=4991, ad_para=1 rows=4074\n",
      "[OK] PatNo_ID_1594437309.csv: rows=10035, NaN_check=1 rows=9892, ad_para=1 rows=8992\n",
      "[OK] PatNo_ID_1594439781.csv: rows=7691, NaN_check=1 rows=7505, ad_para=1 rows=6700\n",
      "[OK] PatNo_ID_1594441887.csv: rows=10203, NaN_check=1 rows=9359, ad_para=1 rows=8137\n",
      "[OK] PatNo_ID_1594448501.csv: rows=5106, NaN_check=1 rows=0, ad_para=1 rows=0\n",
      "[OK] PatNo_ID_1594455578.csv: rows=262, NaN_check=1 rows=207, ad_para=1 rows=203\n",
      "[OK] PatNo_ID_1594464829.csv: rows=4000, NaN_check=1 rows=3936, ad_para=1 rows=3013\n",
      "[OK] PatNo_ID_1594467719.csv: rows=2560, NaN_check=1 rows=1312, ad_para=1 rows=1075\n",
      "[OK] PatNo_ID_1594471407.csv: rows=11433, NaN_check=1 rows=11266, ad_para=1 rows=3245\n",
      "[OK] PatNo_ID_1594479330.csv: rows=3727, NaN_check=1 rows=3714, ad_para=1 rows=1378\n",
      "[OK] PatNo_ID_1594511911.csv: rows=5488, NaN_check=1 rows=5443, ad_para=1 rows=4895\n",
      "[OK] PatNo_ID_1594511914.csv: rows=9717, NaN_check=1 rows=9639, ad_para=1 rows=8507\n",
      "[OK] PatNo_ID_1594528842.csv: rows=2491, NaN_check=1 rows=2389, ad_para=1 rows=1868\n",
      "[OK] PatNo_ID_1594533379.csv: rows=1030, NaN_check=1 rows=999, ad_para=1 rows=904\n",
      "\n",
      "================= Summary =================\n",
      "Files processed : 122\n",
      "Total rows      : 1567309\n",
      "NaN_check=1 rows: 1336870\n",
      "ad_para=1 rows  : 1129341\n",
      "===================================================\n"
     ]
    }
   ],
   "source": [
    "\"\"\" 增加​一​個​欄位​ 標記​是否​有​調參​ 後續​找​1​20筆​比​較方​便​\n",
    "我要在/home/jovyan/RT08/0925/bling_svv_14/裡面所有的檔案每個都要增加​一​個​欄位ad_para​ 標記​是否​有​調參​ ad_para=1代表參數有調整\n",
    "參數有調整的定義是\n",
    "依 senddate 排序來看時序順序\n",
    "1. NaN_check=1的條件下 在rrhzsetactual, mvsetactual, peepepap這幾個欄位任何一個相較於上一筆也是NaN_check=1，數值有變化，才算參數有調整 ad_para=1\n",
    "2. 若呼吸器模式一有變化也算是調整參數，就不用看他rrhzsetactual, mvsetactual, peepepap這幾個欄位是否有變化，呼吸器模式有變化是mode_1, mode_2, mode_3任有一值從0變1或1變0，\n",
    "3. 遇到相同時間點就標示出來並停下\n",
    "4. 每個病患的第一筆沒有上一筆可比，應明確定義為 ad_para=0\n",
    "\"\"\"\n",
    "# -*- coding: utf-8 -*-\n",
    "\"\"\"\n",
    "在 /home/jovyan/RT08/0925/bling_svv_14/ 中為每個 CSV 新增 ad_para 欄位，定義：\n",
    "1) 僅在 NaN_check=1 的列上評估是否有「調參」。\n",
    "2) 以 (patno) 分組，依 senddate 排序建立時序。\n",
    "3) 與上一筆(同為 NaN_check=1)比較：\n",
    "   - 若 rrhzsetactual 或 mvsetactual 或 peepepap 任一數值有變化 → ad_para=1\n",
    "   - 或 mode_1/mode_2/mode_3 任一位元有變化(0↔1) → ad_para=1\n",
    "4) 每位病患的第一筆 NaN_check=1 → ad_para=0\n",
    "5) 若同一病患內出現相同 senddate（重複時間點），印出細節後立刻停止整個流程（不再處理其餘檔案）。\n",
    "\n",
    "注意：\n",
    "- 僅修改/寫回 ad_para 欄位，不改動其他欄位。\n",
    "- 若缺少必要欄位，該檔案 ad_para 一律設 0 並警告。\n",
    "\"\"\"\n",
    "\n",
    "import os, glob, sys\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "DATA_DIR = \"/home/jovyan/RT08/0925/bling_svv_14\"\n",
    "\n",
    "# 需要用到的欄位\n",
    "REQ_COLS = [\n",
    "    \"patno\", \"senddate\", \"NaN_check\",\n",
    "    \"rrhzsetactual\", \"mvsetactual\", \"peepepap\",\n",
    "    \"mode_1\", \"mode_2\", \"mode_3\"\n",
    "]\n",
    "\n",
    "# === 小工具：型別轉換 ===\n",
    "def to_num(s: pd.Series) -> pd.Series:\n",
    "    \"\"\"將空字串/ 'null' / '(null)' 視為 NaN，再轉數值(float)。\"\"\"\n",
    "    s = s.replace(r'^\\s*$', np.nan, regex=True)\n",
    "    s = s.replace([\"null\", \"NULL\", \"(null)\", \"(NULL)\"], np.nan)\n",
    "    return pd.to_numeric(s, errors=\"coerce\")\n",
    "\n",
    "def to_int01(s: pd.Series) -> pd.Series:\n",
    "    \"\"\"將 mode 欄位轉為 0/1 的數值（NaN 保留 NaN，最後比較前再處理）。\"\"\"\n",
    "    x = to_num(s)\n",
    "    # 允許裝置輸出是 0/1 或 0.0/1.0；若有其它數字，先 round 再 clip\n",
    "    x = x.round().clip(0, 1)\n",
    "    return x\n",
    "\n",
    "# === 主程式 ===\n",
    "file_paths = sorted(glob.glob(os.path.join(DATA_DIR, \"*.csv\")))\n",
    "print(f\"[Info] Found {len(file_paths)} files in {DATA_DIR}\")\n",
    "\n",
    "# 用於在偵測到重複時間點時，立即停止流程\n",
    "def stop_on_dup(file_name, dup_df):\n",
    "    print(\"\\n[Error] Detected duplicate timestamps within the same patient. Abort.\")\n",
    "    print(f\"File: {file_name}\")\n",
    "    # 列出前幾筆重複資訊\n",
    "    print(\"Samples of duplicated (patno, senddate):\")\n",
    "    print(dup_df.head(10).to_string(index=False))\n",
    "    sys.exit(1)\n",
    "\n",
    "processed_files = 0\n",
    "total_rows = 0\n",
    "total_nan1_rows = 0\n",
    "total_ad1_rows = 0\n",
    "\n",
    "for fp in file_paths:\n",
    "    base = os.path.basename(fp)\n",
    "    try:\n",
    "        df = pd.read_csv(fp, low_memory=False)\n",
    "    except Exception as e:\n",
    "        print(f\"[Skip] {base}: cannot read ({e})\")\n",
    "        continue\n",
    "\n",
    "    # 檔案筆數統計\n",
    "    total_rows += len(df)\n",
    "\n",
    "    missing = [c for c in REQ_COLS if c not in df.columns]\n",
    "    if missing:\n",
    "        # 缺欄則 ad_para=0，並警告\n",
    "        print(f\"[Warn] {base}: missing columns {missing}. Set ad_para=0 for all rows.\")\n",
    "        df[\"ad_para\"] = 0\n",
    "        df.to_csv(fp, index=False)\n",
    "        processed_files += 1\n",
    "        continue\n",
    "\n",
    "    # 取出需要欄位的副本並做型別處理（不改動原欄位內容，只用於計算）\n",
    "    patno = to_num(df[\"patno\"])\n",
    "    t     = pd.to_datetime(df[\"senddate\"], errors=\"coerce\")\n",
    "    nc    = pd.to_numeric(df[\"NaN_check\"], errors=\"coerce\").fillna(0).astype(int).clip(0, 1)\n",
    "\n",
    "    rr = to_num(df[\"rrhzsetactual\"])\n",
    "    mv = to_num(df[\"mvsetactual\"])\n",
    "    pe = to_num(df[\"peepepap\"])\n",
    "\n",
    "    m1 = to_int01(df[\"mode_1\"])\n",
    "    m2 = to_int01(df[\"mode_2\"])\n",
    "    m3 = to_int01(df[\"mode_3\"])\n",
    "\n",
    "    # 先檢查同一病患內是否有重複時間點（相同 senddate）\n",
    "    # 以 (patno, senddate) 為 key 檢查重複（只看非空）\n",
    "    tmp = pd.DataFrame({\"patno\": patno, \"senddate\": t})\n",
    "    dup_mask = tmp.dropna().duplicated(subset=[\"patno\", \"senddate\"], keep=False)\n",
    "    if dup_mask.any():\n",
    "        dup_rows = tmp.loc[dup_mask, [\"patno\", \"senddate\"]].sort_values([\"patno\", \"senddate\"])\n",
    "        stop_on_dup(base, dup_rows)\n",
    "\n",
    "    # 準備 ad_para，預設 0\n",
    "    ad = pd.Series(0, index=df.index, dtype=\"int64\")\n",
    "\n",
    "    # 僅在 NaN_check=1 的列上評估（其他維持 0）\n",
    "    # 以病患分組，內部依 senddate 排序\n",
    "    work = pd.DataFrame({\n",
    "        \"patno\": patno,\n",
    "        \"senddate\": t,\n",
    "        \"NaN_check\": nc,\n",
    "        \"rr\": rr, \"mv\": mv, \"pe\": pe,\n",
    "        \"m1\": m1, \"m2\": m2, \"m3\": m3,\n",
    "    })\n",
    "\n",
    "    # 統計本檔 NaN_check=1 的總筆數\n",
    "    file_nan1 = int((work[\"NaN_check\"] == 1).sum())\n",
    "    total_nan1_rows += file_nan1\n",
    "\n",
    "    # 對每個病患處理\n",
    "    for pid, g in work.groupby(\"patno\", dropna=True):\n",
    "        if g.empty:\n",
    "            continue\n",
    "        # 依時間排序\n",
    "        g = g.sort_values(\"senddate\")\n",
    "\n",
    "        # 只考慮 NaN_check=1 的列來做「上一筆」比較\n",
    "        g1 = g[g[\"NaN_check\"] == 1].copy()\n",
    "        if g1.empty:\n",
    "            continue\n",
    "\n",
    "        # 第一筆 NaN_check=1 一律 ad_para=0（沒有上一筆可比）\n",
    "        # 後續列與「上一筆 NaN_check=1」相比\n",
    "        # 參數變化條件 1：設定參數（rr/mv/pe）任一數值有變化（允許 NaN → 不列入比較；我們只在 NaN_check=1 下比較，理論上不該是 NaN）\n",
    "        rr_chg = g1[\"rr\"].ne(g1[\"rr\"].shift(1))\n",
    "        mv_chg = g1[\"mv\"].ne(g1[\"mv\"].shift(1))\n",
    "        pe_chg = g1[\"pe\"].ne(g1[\"pe\"].shift(1))\n",
    "        set_changed = rr_chg | mv_chg | pe_chg\n",
    "\n",
    "        # 參數變化條件 2：模式位元有變化（任一位元 0↔1）\n",
    "        m1_chg = g1[\"m1\"].ne(g1[\"m1\"].shift(1))\n",
    "        m2_chg = g1[\"m2\"].ne(g1[\"m2\"].shift(1))\n",
    "        m3_chg = g1[\"m3\"].ne(g1[\"m3\"].shift(1))\n",
    "        mode_changed = m1_chg | m2_chg | m3_chg\n",
    "\n",
    "        # 綜合條件（第一筆應為 False → 0）\n",
    "        ad_mask = (set_changed | mode_changed)\n",
    "        ad_mask.iloc[0] = False  # 第一筆 NaN_check=1 設為 0\n",
    "\n",
    "        # 將 ad_para 寫回到原 DataFrame 對應索引（只覆蓋該子集合）\n",
    "        ad.loc[g1.index] = ad_mask.astype(int).values\n",
    "\n",
    "    # 寫回原 DataFrame 的 ad_para 欄位（若原本存在，會覆蓋）\n",
    "    df[\"ad_para\"] = ad.astype(int)\n",
    "\n",
    "    # 檔案級統計（本檔設定為 1 的筆數）\n",
    "    file_ad1 = int((df[\"ad_para\"] == 1).sum())\n",
    "    total_ad1_rows += file_ad1\n",
    "\n",
    "    # 覆蓋寫回\n",
    "    df.to_csv(fp, index=False)\n",
    "    processed_files += 1\n",
    "\n",
    "    print(f\"[OK] {base}: rows={len(df)}, NaN_check=1 rows={file_nan1}, ad_para=1 rows={file_ad1}\")\n",
    "\n",
    "# 總結\n",
    "print(\"\\n================= Summary =================\")\n",
    "print(f\"Files processed : {processed_files}\")\n",
    "print(f\"Total rows      : {total_rows}\")\n",
    "print(f\"NaN_check=1 rows: {total_nan1_rows}\")\n",
    "print(f\"ad_para=1 rows  : {total_ad1_rows}\")\n",
    "print(\"===================================================\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "db8062d5-d365-4abe-95c3-1334511656bb",
   "metadata": {},
   "outputs": [],
   "source": [
    "滑動視窗！!！"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ad3f3daa-8587-4ec2-a8cf-9c6392039684",
   "metadata": {},
   "outputs": [],
   "source": [
    "① 怎麼「算有幾個視窗」；② 每個視窗「怎麼落盤儲存」\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3b7c86e0-98b4-4a32-9fea-af45abb4d6f8",
   "metadata": {},
   "outputs": [],
   "source": [
    "我的划動視窗要設定 W = 視窗長度=120 分鐘，S = 步長=60 分鐘， 我想知道能切幾個視窗， 每個視窗又會怎麼儲存"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "id": "34969a08-aed0-421e-b040-c1133a130f6a",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Info] Found 122 files in /home/jovyan/RT08/0925/bling_svv_14\n",
      "✅ Saved: /home/jovyan/RT08/0925/1002/window_summary_segments.csv\n",
      "✅ Saved: /home/jovyan/RT08/0925/1002/window_summary_patients.csv\n",
      "✅ Saved: /home/jovyan/RT08/0925/1002/segment_sampling_stats.csv\n",
      "✅ Saved: /home/jovyan/RT08/0925/1002/segment_gaps.csv\n",
      "✅ Saved: /home/jovyan/RT08/0925/1002/segments_too_short.csv\n",
      "✅ Saved: /home/jovyan/RT08/0925/1002/window_catalog_sample.csv\n",
      "\n",
      "=== Patient-level summary (by file, by patient) ===\n",
      "               __file__      patno  segments  total_windows\n",
      "             089271.csv    8927106        83            455\n",
      "             095323.csv    9532303        78            252\n",
      "             095707.csv    9570752       204            229\n",
      "             114309.csv   11430923        63            286\n",
      "             230933.csv   23093320       802            259\n",
      "            4216007.csv    4216007         0              0\n",
      "            7108162.csv    7108162         2              1\n",
      "            7408338.csv    7408338         3             20\n",
      "            7657698.csv    7657698         8             14\n",
      "            7721164.csv    7721164         3              5\n",
      "PatNo_ID_1560013303.csv 1560013300         6             34\n",
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      "PatNo_ID_1570642083.csv 1570642000      1154             72\n",
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      "PatNo_ID_1572562839.csv 1572562800        54            195\n",
      "PatNo_ID_1572831765.csv 1572831700         5             39\n",
      "PatNo_ID_1572976822.csv 1572976800        16             54\n",
      "PatNo_ID_1573063188.csv 1573063200        21             64\n",
      "PatNo_ID_1573249295.csv 1573249300       112             52\n",
      "PatNo_ID_1573964540.csv 1573964500        13             62\n",
      "PatNo_ID_1574148494.csv 1574148500        72            700\n",
      "PatNo_ID_1574270349.csv 1574270300        11             93\n",
      "PatNo_ID_1574528808.csv 1574528800       270             66\n",
      "PatNo_ID_1574831525.csv 1574831500         2              6\n",
      "PatNo_ID_1574987447.csv 1574987400       177            167\n",
      "PatNo_ID_1575060177.csv 1575060200       202             36\n",
      "PatNo_ID_1575256902.csv 1575257000        22            127\n",
      "PatNo_ID_1575445051.csv 1575445000         3             25\n",
      "PatNo_ID_1575502382.csv 1575502300        78             59\n",
      "PatNo_ID_1575975485.csv 1575975400       107            184\n",
      "PatNo_ID_1576115572.csv 1576115600        64            226\n",
      "PatNo_ID_1576116479.csv 1576116500         1             19\n",
      "PatNo_ID_1576301569.csv 1576301600        15             34\n",
      "PatNo_ID_1576964560.csv 1576964600       500            227\n",
      "PatNo_ID_1577042911.csv 1577043000        66            507\n",
      "PatNo_ID_1577487284.csv 1577487200         7             31\n",
      "PatNo_ID_1578784257.csv 1578784300        47            413\n",
      "PatNo_ID_1579198603.csv 1579198600         3             30\n",
      "PatNo_ID_1579498177.csv 1579498200        79            266\n",
      "PatNo_ID_1580062580.csv 1580062600        19             13\n",
      "PatNo_ID_1580096720.csv 1580096800         3             13\n",
      "PatNo_ID_1580107637.csv 1580107600         0              0\n",
      "PatNo_ID_1580244614.csv 1580244600        18             48\n",
      "PatNo_ID_1580766093.csv 1580766100       120            171\n",
      "PatNo_ID_1581003248.csv 1581003300        31             70\n",
      "PatNo_ID_1581019504.csv 1581019500        64            264\n",
      "PatNo_ID_1581633231.csv 1581633300        76            157\n",
      "PatNo_ID_1581692973.csv 1581692900       134             17\n",
      "PatNo_ID_1582452511.csv 1582452500        47             47\n",
      "PatNo_ID_1582635996.csv 1582636000        45            192\n",
      "PatNo_ID_1582849900.csv 1582849900         4             22\n",
      "PatNo_ID_1582937076.csv 1582937100        44            317\n",
      "PatNo_ID_1584158973.csv 1584159000         7             40\n",
      "PatNo_ID_1584397376.csv 1584397300         1              9\n",
      "PatNo_ID_1586172659.csv 1586172700       130            466\n",
      "PatNo_ID_1586696634.csv 1586696600         8             30\n",
      "PatNo_ID_1586897008.csv 1586897000        11             94\n",
      "PatNo_ID_1587490083.csv 1587490000       106            659\n",
      "PatNo_ID_1588632604.csv 1588632600         8             31\n",
      "PatNo_ID_1588673465.csv 1588673400        46             43\n",
      "PatNo_ID_1588794796.csv 1588794800         7            146\n",
      "PatNo_ID_1588957997.csv 1588958000       462             65\n",
      "PatNo_ID_1589018086.csv 1589018100        21            195\n",
      "PatNo_ID_1589034524.csv 1589034500       263            519\n",
      "PatNo_ID_1589324603.csv 1589324500         7             57\n",
      "PatNo_ID_1589918099.csv 1589918100         0              0\n",
      "PatNo_ID_1590136310.csv 1590136300       141             17\n",
      "PatNo_ID_1590616537.csv 1590616600        23            203\n",
      "PatNo_ID_1590854576.csv 1590854500       278            156\n",
      "PatNo_ID_1591609798.csv 1591609900       106            453\n",
      "PatNo_ID_1592044724.csv 1592044700        43             74\n",
      "PatNo_ID_1592560504.csv 1592560500       134            256\n",
      "PatNo_ID_1593087886.csv 1593087900       105            288\n",
      "PatNo_ID_1593416100.csv 1593416100        11             47\n",
      "PatNo_ID_1593472048.csv 1593472000         9             93\n",
      "PatNo_ID_1593593586.csv 1593593600        55            242\n",
      "PatNo_ID_1593720818.csv 1593720800         8             34\n",
      "PatNo_ID_1593838524.csv 1593838500         6             28\n",
      "PatNo_ID_1594173718.csv 1594173700         1              4\n",
      "PatNo_ID_1594294180.csv 1594294100        30            109\n",
      "PatNo_ID_1594305136.csv 1594305200        27            231\n",
      "PatNo_ID_1594309746.csv 1594309800        95             29\n",
      "PatNo_ID_1594319286.csv 1594319200         8             58\n",
      "PatNo_ID_1594320763.csv 1594320800       133              4\n",
      "PatNo_ID_1594322594.csv 1594322600        12             75\n",
      "PatNo_ID_1594335109.csv 1594335100        11             69\n",
      "PatNo_ID_1594423683.csv 1594423700        12             66\n",
      "PatNo_ID_1594437309.csv 1594437200        91             89\n",
      "PatNo_ID_1594439781.csv 1594439800        98             63\n",
      "PatNo_ID_1594441887.csv 1594441900       587             54\n",
      "PatNo_ID_1594448501.csv 1594448500         0              0\n",
      "PatNo_ID_1594455578.csv 1594455600         2              0\n",
      "PatNo_ID_1594464829.csv 1594464800        15             50\n",
      "PatNo_ID_1594467719.csv 1594467700         2             19\n",
      "PatNo_ID_1594471407.csv 1594471400        13            173\n",
      "PatNo_ID_1594479330.csv 1594479400        10             51\n",
      "PatNo_ID_1594511911.csv 1594511900        12             77\n",
      "PatNo_ID_1594511914.csv 1594511900        44            108\n",
      "PatNo_ID_1594528842.csv 1594528900         3             36\n",
      "PatNo_ID_1594533379.csv 1594533400         5             11\n",
      "\n",
      "=== Global totals (across all files & patients) ===\n",
      "Total segments: 9393\n",
      "Total windows : 16538\n",
      "\n",
      "=== REPORT START (copy-paste this block) ===\n",
      "[Params] W=120 min, S=60 min\n",
      "[Totals] Segments=9393, Windows=16538\n",
      "\n",
      "[Segments too short to window] (top 10)\n",
      "  __file__   patno  segment_id  unique_minutes          reason\n",
      "089271.csv 8927106           4               1   N(1) < W(120)\n",
      "089271.csv 8927106           9              64  N(64) < W(120)\n",
      "089271.csv 8927106          15               8   N(8) < W(120)\n",
      "089271.csv 8927106          16               3   N(3) < W(120)\n",
      "089271.csv 8927106          17               2   N(2) < W(120)\n",
      "089271.csv 8927106          19               1   N(1) < W(120)\n",
      "089271.csv 8927106          23              58  N(58) < W(120)\n",
      "089271.csv 8927106          24             113 N(113) < W(120)\n",
      "089271.csv 8927106          26              54  N(54) < W(120)\n",
      "089271.csv 8927106          27              37  N(37) < W(120)\n",
      "\n",
      "[Segments with most gaps (>1 min)] (top 10)\n",
      "               __file__      patno  segment_id  gap_minutes_over1_count  gap_minutes_over1_max\n",
      "PatNo_ID_1587490083.csv 1587490000         103                      836                  26.85\n",
      "PatNo_ID_1594479330.csv 1594479400           4                      652                   7.00\n",
      "PatNo_ID_1587490083.csv 1587490000         106                      427                   8.85\n",
      "PatNo_ID_1594294180.csv 1594294100           3                      406                   6.22\n",
      "PatNo_ID_1590854576.csv 1590854500         250                      392                   6.08\n",
      "PatNo_ID_1594294180.csv 1594294100           7                      386                   5.38\n",
      "PatNo_ID_1587490083.csv 1587490000         100                      372                 354.27\n",
      "PatNo_ID_1572976822.csv 1572976800          12                      371                   3.57\n",
      "PatNo_ID_1589034524.csv 1589034500          47                      356                 326.55\n",
      "PatNo_ID_1572976822.csv 1572976800          16                      348                   2.17\n",
      "\n",
      "[Segments with sparse sampling (lowest per-minute counts)] (top 10)\n",
      "  __file__   patno  segment_id  per_min_count_min  per_min_count_med  per_min_count_p95  per_min_count_max  dt_sec_min  dt_sec_med  dt_sec_max\n",
      "089271.csv 8927106           1                  1                1.0                1.0                  1          55        60.0          65\n",
      "089271.csv 8927106           2                  1                1.0                1.0                  1          56        60.0          64\n",
      "089271.csv 8927106           3                  1                1.0                1.0                  1          56        60.0         120\n",
      "089271.csv 8927106           4                  1                1.0                1.0                  1           0         0.0           0\n",
      "089271.csv 8927106           5                  1                1.0                1.0                  1          55        60.0          65\n",
      "089271.csv 8927106           6                  1                1.0                1.0                  1          45        60.0          76\n",
      "089271.csv 8927106           7                  1                1.0                1.0                  1          51        60.0          64\n",
      "089271.csv 8927106           8                  1                1.0                1.0                  1          59        60.0          61\n",
      "089271.csv 8927106           9                  1                1.0                1.0                  1          60        60.0          60\n",
      "089271.csv 8927106          10                  1                1.0                1.0                  1          55        60.0          65\n",
      "\n",
      "[Window examples] (first 10)\n",
      "  __file__   patno  segment_id  win_example_idx       win_start_min         win_end_min  win_span_min\n",
      "089271.csv 8927106           1                1 2021-12-17 10:13:00 2021-12-17 12:12:00           120\n",
      "089271.csv 8927106           1                2 2021-12-17 11:13:00 2021-12-17 13:12:00           120\n",
      "089271.csv 8927106           1                3 2021-12-17 12:13:00 2021-12-17 14:12:00           120\n",
      "089271.csv 8927106           2                1 2021-12-19 13:12:00 2021-12-19 15:11:00           120\n",
      "089271.csv 8927106           2                2 2021-12-19 14:12:00 2021-12-19 16:11:00           120\n",
      "089271.csv 8927106           2                3 2021-12-19 15:12:00 2021-12-19 17:11:00           120\n",
      "089271.csv 8927106           3                1 2021-12-20 21:24:00 2021-12-20 23:23:00           120\n",
      "089271.csv 8927106           3                2 2021-12-20 22:24:00 2021-12-21 00:23:00           120\n",
      "089271.csv 8927106           3                3 2021-12-20 23:24:00 2021-12-21 01:23:00           120\n",
      "089271.csv 8927106           5                1 2021-12-22 11:26:00 2021-12-22 13:25:00           120\n",
      "\n",
      "=== REPORT END ===\n"
     ]
    }
   ],
   "source": [
    "\"\"\"我想知道我會有幾個視窗，且每個病患要分開計算 各病患可切出的視窗總數與區段數 每個連續區段的有效分鐘數 N與可切視窗數 全體總視窗數（所有病患加總） 要另外存一個csv, 也要直接輸出在程式中\n",
    "W = 視窗長度=120 分鐘，S = 步長=60 分鐘，重疊60筆\n",
    "我兩種csv想要分成兩個檔案\n",
    "\n",
    "\n",
    "會影響到原始的/home/jovyan/RT08/0925/bling_svv_fin檔案嗎，\n",
    "也請給我能夠確認視窗滑動資料切割評斷切的狀況的結果輸出，讓我可以直接評斷狀況跟品質，以判斷要怎麼改善\n",
    "\"\"\"\n",
    "\n",
    "# -*- coding: utf-8 -*-\n",
    "\"\"\"\n",
    "滑動視窗切割 + 診斷報告（不修改原始檔）：\n",
    "- 來源：/home/jovyan/RT08/0925/bling_svv_14/*.csv\n",
    "- 視窗參數：W=120 分鐘、S=60 分鐘（重疊 60 分鐘）\n",
    "- 逐段輸出：有效分鐘數 N、可切視窗數、採樣與缺口診斷\n",
    "- 逐病患輸出：區段數、可切視窗總數\n",
    "- 額外診斷輸出：\n",
    "  1) segment_sampling_stats.csv：每段的採樣統計（每分鐘筆數分佈、間隔統計）\n",
    "  2) segment_gaps.csv：每段的缺口統計（>1 分鐘的時間缺口數與最大缺口）\n",
    "  3) segments_too_short.csv：N < W 的無法切窗區段清單\n",
    "  4) window_catalog_sample.csv：每段前 K 個視窗的起迄時間範例（便於肉眼抽查）\n",
    "- 螢幕會印出彙總摘要與可直接複製的「報告片段」\n",
    "\"\"\"\n",
    "\n",
    "import os, glob\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "# ===== 參數與路徑 =====\n",
    "DATA_DIR = \"/home/jovyan/RT08/0925/bling_svv_14\"\n",
    "\n",
    "OUT_DIR  = \"/home/jovyan/RT08/0925/1002\"  # 可改到你想放的資料夾\n",
    "OUT_SEG  = os.path.join(OUT_DIR, \"window_summary_segments.csv\")\n",
    "OUT_PAT  = os.path.join(OUT_DIR, \"window_summary_patients.csv\")\n",
    "OUT_SAMP = os.path.join(OUT_DIR, \"segment_sampling_stats.csv\")\n",
    "OUT_GAPS = os.path.join(OUT_DIR, \"segment_gaps.csv\")\n",
    "OUT_SHORT= os.path.join(OUT_DIR, \"segments_too_short.csv\")\n",
    "OUT_WIN  = os.path.join(OUT_DIR, \"window_catalog_sample.csv\")\n",
    "\n",
    "# 視窗長度與步長（單位：分鐘）\n",
    "W_MIN = 120\n",
    "S_MIN = 60\n",
    "\n",
    "# 每段要示範列出的「視窗範例」數量（避免爆量）\n",
    "WINDOW_EXAMPLES_PER_SEG = 3\n",
    "\n",
    "# 欄位名稱\n",
    "COL_PAT  = \"patno\"\n",
    "COL_TIME = \"senddate\"\n",
    "COL_OK   = \"NaN_check\"\n",
    "\n",
    "# ============ 小工具：安全轉型 ============\n",
    "def to_dt(s: pd.Series) -> pd.Series:\n",
    "    \"\"\"將字串轉成 datetime；無法解析者為 NaN。\"\"\"\n",
    "    return pd.to_datetime(s, errors=\"coerce\")\n",
    "\n",
    "def to_num(s: pd.Series) -> pd.Series:\n",
    "    \"\"\"將空白/'null'/'(null)' 視為 NaN，再轉數值。\"\"\"\n",
    "    s = s.replace(r'^\\s*$', np.nan, regex=True)\n",
    "    s = s.replace([\"null\", \"NULL\", \"(null)\", \"(NULL)\"], np.nan)\n",
    "    return pd.to_numeric(s, errors=\"coerce\")\n",
    "\n",
    "# ============ 主流程 ============\n",
    "file_paths = sorted(glob.glob(os.path.join(DATA_DIR, \"*.csv\")))\n",
    "print(f\"[Info] Found {len(file_paths)} files in {DATA_DIR}\")\n",
    "\n",
    "seg_rows   = []  # 逐段主表\n",
    "pat_rows   = []  # 逐病患主表\n",
    "samp_rows  = []  # 逐段採樣統計\n",
    "gap_rows   = []  # 逐段缺口統計\n",
    "short_rows = []  # N < W 的區段\n",
    "win_rows   = []  # 每段前 K 個視窗範例\n",
    "\n",
    "global_total_windows  = 0\n",
    "global_total_segments = 0\n",
    "\n",
    "for fp in file_paths:\n",
    "    base = os.path.basename(fp)\n",
    "    try:\n",
    "        df = pd.read_csv(fp, low_memory=False)\n",
    "    except Exception as e:\n",
    "        print(f\"[Skip] {base}: cannot read ({e})\")\n",
    "        continue\n",
    "\n",
    "    # 檢查必要欄位\n",
    "    missing = [c for c in [COL_PAT, COL_TIME, COL_OK] if c not in df.columns]\n",
    "    if missing:\n",
    "        print(f\"[Warn] {base}: missing {missing}; skip file.\")\n",
    "        continue\n",
    "\n",
    "    # 基本轉型\n",
    "    pat  = to_num(df[COL_PAT])\n",
    "    time = to_dt(df[COL_TIME])\n",
    "    ok   = pd.to_numeric(df[COL_OK], errors=\"coerce\").fillna(0).astype(int).clip(0,1)\n",
    "\n",
    "    valid = time.notna()\n",
    "    if not valid.any():\n",
    "        print(f\"[Warn] {base}: no valid {COL_TIME}; skip file.\")\n",
    "        continue\n",
    "\n",
    "    work = pd.DataFrame({\n",
    "        \"__file__\": base,\n",
    "        COL_PAT: pat,\n",
    "        COL_TIME: time,\n",
    "        COL_OK: ok\n",
    "    })[valid].copy()\n",
    "\n",
    "    # 逐病患處理\n",
    "    for pid, g in work.groupby(COL_PAT, dropna=True):\n",
    "        if g.empty: \n",
    "            continue\n",
    "\n",
    "        g = g.sort_values(COL_TIME).reset_index(drop=True)\n",
    "        mask = g[COL_OK] == 1\n",
    "\n",
    "        if not mask.any():\n",
    "            # 此病患在此檔案沒有可用區段\n",
    "            pat_rows.append({\n",
    "                \"__file__\": base, COL_PAT: int(pid),\n",
    "                \"segments\": 0, \"total_windows\": 0\n",
    "            })\n",
    "            continue\n",
    "\n",
    "        # 切連續 True 段\n",
    "        seg_start = mask & (~mask.shift(fill_value=False))\n",
    "        seg_id = seg_start.cumsum()\n",
    "\n",
    "        g1 = g[mask].copy()\n",
    "        g1[\"__seg_id__\"] = seg_id[mask].values\n",
    "\n",
    "        patient_seg_cnt = 0\n",
    "        patient_win_sum = 0\n",
    "\n",
    "        for sid, gg in g1.groupby(\"__seg_id__\"):\n",
    "            # 以「分鐘」為單位計數\n",
    "            minute_idx = gg[COL_TIME].dt.floor(\"T\")\n",
    "            N = int(minute_idx.nunique())  # 有效分鐘數\n",
    "\n",
    "            # 計算可切視窗數\n",
    "            windows = 0 if N < W_MIN else (1 + (N - W_MIN) // S_MIN)\n",
    "\n",
    "            # ====== 採樣／間隔診斷 ======\n",
    "            # 每分鐘筆數（分佈）與統計\n",
    "            per_min_counts = gg.groupby(minute_idx).size().astype(int)\n",
    "            cnt_min = int(per_min_counts.min()) if len(per_min_counts) else 0\n",
    "            cnt_max = int(per_min_counts.max()) if len(per_min_counts) else 0\n",
    "            cnt_med = float(per_min_counts.median()) if len(per_min_counts) else 0.0\n",
    "            cnt_p95 = float(per_min_counts.quantile(0.95)) if len(per_min_counts) else 0.0\n",
    "\n",
    "            # 相鄰時間差（秒）統計：看時間不均勻程度\n",
    "            ts = gg[COL_TIME].sort_values().values\n",
    "            if len(ts) >= 2:\n",
    "                deltas = np.diff(ts).astype('timedelta64[s]').astype(int)\n",
    "                dt_min = int(np.min(deltas))\n",
    "                dt_med = float(np.median(deltas))\n",
    "                dt_max = int(np.max(deltas))\n",
    "                # 缺口 > 60 秒視為「分鐘級缺口」，計數與最大缺口（分鐘）\n",
    "                gap_mask = deltas > 60\n",
    "                gaps_cnt = int(np.sum(gap_mask))\n",
    "                gaps_max_min = float(np.max(deltas[gap_mask]) / 60.0) if gaps_cnt > 0 else 0.0\n",
    "            else:\n",
    "                dt_min = dt_med = dt_max = 0\n",
    "                gaps_cnt = 0\n",
    "                gaps_max_min = 0.0\n",
    "\n",
    "            # ====== 主表（逐段） ======\n",
    "            seg_rows.append({\n",
    "                \"__file__\": base,\n",
    "                COL_PAT: int(pid),\n",
    "                \"segment_id\": int(sid),\n",
    "                \"start_time\": gg[COL_TIME].min(),\n",
    "                \"end_time\": gg[COL_TIME].max(),\n",
    "                \"rows_in_segment\": int(len(gg)),\n",
    "                \"unique_minutes\": int(N),\n",
    "                \"window_len_min\": int(W_MIN),\n",
    "                \"step_min\": int(S_MIN),\n",
    "                \"windows\": int(windows),\n",
    "            })\n",
    "\n",
    "            # ====== 採樣統計表（逐段） ======\n",
    "            samp_rows.append({\n",
    "                \"__file__\": base,\n",
    "                COL_PAT: int(pid),\n",
    "                \"segment_id\": int(sid),\n",
    "                \"per_min_count_min\": cnt_min,\n",
    "                \"per_min_count_med\": round(cnt_med, 2),\n",
    "                \"per_min_count_p95\": round(cnt_p95, 2),\n",
    "                \"per_min_count_max\": cnt_max,\n",
    "                \"dt_sec_min\": dt_min,\n",
    "                \"dt_sec_med\": round(dt_med, 2),\n",
    "                \"dt_sec_max\": dt_max,\n",
    "            })\n",
    "\n",
    "            # ====== 缺口統計表（逐段） ======\n",
    "            gap_rows.append({\n",
    "                \"__file__\": base,\n",
    "                COL_PAT: int(pid),\n",
    "                \"segment_id\": int(sid),\n",
    "                \"gap_minutes_over1_count\": int(gaps_cnt),\n",
    "                \"gap_minutes_over1_max\": round(gaps_max_min, 2),\n",
    "            })\n",
    "\n",
    "            # ====== 無法切窗者（逐段） ======\n",
    "            if N < W_MIN:\n",
    "                short_rows.append({\n",
    "                    \"__file__\": base,\n",
    "                    COL_PAT: int(pid),\n",
    "                    \"segment_id\": int(sid),\n",
    "                    \"unique_minutes\": int(N),\n",
    "                    \"reason\": f\"N({N}) < W({W_MIN})\"\n",
    "                })\n",
    "\n",
    "            # ====== 每段的視窗範例（前 K 個） ======\n",
    "            if windows > 0 and WINDOW_EXAMPLES_PER_SEG > 0:\n",
    "                # 建立以「分鐘」為單位的排序後唯一時間列\n",
    "                uniq_minutes_sorted = sorted(set(minute_idx))\n",
    "                # 列出前 K 個視窗起訖（注意 W_MIN 與 S_MIN）\n",
    "                max_start_idx = len(uniq_minutes_sorted) - W_MIN\n",
    "                examples = min(WINDOW_EXAMPLES_PER_SEG, 1 + max_start_idx // S_MIN)\n",
    "                for k in range(examples):\n",
    "                    start_idx = k * S_MIN\n",
    "                    end_idx   = start_idx + W_MIN - 1\n",
    "                    if end_idx >= len(uniq_minutes_sorted):\n",
    "                        break\n",
    "                    win_rows.append({\n",
    "                        \"__file__\": base,\n",
    "                        COL_PAT: int(pid),\n",
    "                        \"segment_id\": int(sid),\n",
    "                        \"win_example_idx\": int(k+1),\n",
    "                        \"win_start_min\": uniq_minutes_sorted[start_idx],\n",
    "                        \"win_end_min\":   uniq_minutes_sorted[end_idx],\n",
    "                        \"win_span_min\":  int(W_MIN),\n",
    "                    })\n",
    "\n",
    "            patient_seg_cnt += 1\n",
    "            patient_win_sum += windows\n",
    "\n",
    "        # 病患彙總\n",
    "        pat_rows.append({\n",
    "            \"__file__\": base,\n",
    "            COL_PAT: int(pid),\n",
    "            \"segments\": int(patient_seg_cnt),\n",
    "            \"total_windows\": int(patient_win_sum),\n",
    "        })\n",
    "\n",
    "        global_total_segments += patient_seg_cnt\n",
    "        global_total_windows  += patient_win_sum\n",
    "\n",
    "# ============ 匯出 CSV 與印出摘要 ============\n",
    "os.makedirs(OUT_DIR, exist_ok=True)\n",
    "\n",
    "seg_df   = pd.DataFrame(seg_rows)\n",
    "pat_df   = pd.DataFrame(pat_rows)\n",
    "samp_df  = pd.DataFrame(samp_rows)\n",
    "gaps_df  = pd.DataFrame(gap_rows)\n",
    "short_df = pd.DataFrame(short_rows)\n",
    "win_df   = pd.DataFrame(win_rows)\n",
    "\n",
    "if not seg_df.empty:\n",
    "    seg_df.to_csv(OUT_SEG, index=False)\n",
    "    print(f\"✅ Saved: {OUT_SEG}\")\n",
    "else:\n",
    "    print(\"⚠️ No segment-level rows.\")\n",
    "\n",
    "if not pat_df.empty:\n",
    "    pat_df.to_csv(OUT_PAT, index=False)\n",
    "    print(f\"✅ Saved: {OUT_PAT}\")\n",
    "else:\n",
    "    print(\"⚠️ No patient-level rows.\")\n",
    "\n",
    "if not samp_df.empty:\n",
    "    samp_df.to_csv(OUT_SAMP, index=False)\n",
    "    print(f\"✅ Saved: {OUT_SAMP}\")\n",
    "else:\n",
    "    print(\"ℹ️ segment_sampling_stats is empty.\")\n",
    "\n",
    "if not gaps_df.empty:\n",
    "    gaps_df.to_csv(OUT_GAPS, index=False)\n",
    "    print(f\"✅ Saved: {OUT_GAPS}\")\n",
    "else:\n",
    "    print(\"ℹ️ segment_gaps is empty.\")\n",
    "\n",
    "if not short_df.empty:\n",
    "    short_df.to_csv(OUT_SHORT, index=False)\n",
    "    print(f\"✅ Saved: {OUT_SHORT}\")\n",
    "else:\n",
    "    print(\"ℹ️ segments_too_short is empty (great!).\")\n",
    "\n",
    "if not win_df.empty:\n",
    "    win_df.to_csv(OUT_WIN, index=False)\n",
    "    print(f\"✅ Saved: {OUT_WIN}\")\n",
    "else:\n",
    "    print(\"ℹ️ window_catalog_sample is empty (no segments with N>=W).\")\n",
    "\n",
    "# ============ 螢幕摘要（可直接複製回來） ============\n",
    "print(\"\\n=== Patient-level summary (by file, by patient) ===\")\n",
    "if not pat_df.empty:\n",
    "    print(pat_df.to_string(index=False))\n",
    "else:\n",
    "    print(\"(empty)\")\n",
    "\n",
    "# 全體總結\n",
    "print(\"\\n=== Global totals (across all files & patients) ===\")\n",
    "print(f\"Total segments: {global_total_segments}\")\n",
    "print(f\"Total windows : {global_total_windows}\")\n",
    "\n",
    "# 便於複製回報的「報告片段」\n",
    "print(\"\\n=== REPORT START (copy-paste this block) ===\")\n",
    "print(f\"[Params] W={W_MIN} min, S={S_MIN} min\")\n",
    "print(f\"[Totals] Segments={global_total_segments}, Windows={global_total_windows}\")\n",
    "\n",
    "# 前幾個問題區段摘要（N<W）\n",
    "if not short_df.empty:\n",
    "    print(\"\\n[Segments too short to window] (top 10)\")\n",
    "    print(short_df.head(10).to_string(index=False))\n",
    "\n",
    "# 缺口最多/最大者（前 10）\n",
    "if not gaps_df.empty:\n",
    "    top_gap_cnt = gaps_df.sort_values([\"gap_minutes_over1_count\",\"gap_minutes_over1_max\"], ascending=False).head(10)\n",
    "    print(\"\\n[Segments with most gaps (>1 min)] (top 10)\")\n",
    "    print(top_gap_cnt.to_string(index=False))\n",
    "\n",
    "# 採樣最稀疏者（每分鐘最小筆數最小）\n",
    "if not samp_df.empty:\n",
    "    top_sparse = samp_df.sort_values([\"per_min_count_min\",\"per_min_count_med\"], ascending=[True, True]).head(10)\n",
    "    print(\"\\n[Segments with sparse sampling (lowest per-minute counts)] (top 10)\")\n",
    "    print(top_sparse.to_string(index=False))\n",
    "\n",
    "# 視窗範例（前 10）\n",
    "if not win_df.empty:\n",
    "    print(\"\\n[Window examples] (first 10)\")\n",
    "    print(win_df.head(10).to_string(index=False))\n",
    "\n",
    "print(\"\\n=== REPORT END ===\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "70543bee-4667-4a14-86cf-6ff70a6e6469",
   "metadata": {},
   "outputs": [],
   "source": [
    "會輸出哪些檔案（全都在 bling_svv_fin 下，原始 CSV 不動）\n",
    "window_summary_segments.csv：每段（segment）— start/end_time, unique_minutes(N), windows 等\n",
    "window_summary_patients.csv：每病患 — segments, total_windows\n",
    "segment_sampling_stats.csv：每段採樣統計（每分鐘筆數分佈、時間間隔分佈）\n",
    "segment_gaps.csv：每段缺口統計（>1 分鐘的缺口數、最大缺口）\n",
    "segments_too_short.csv：N < W 而無法切窗的區段清單\n",
    "window_catalog_sample.csv：每段前 3 個視窗的起迄時間範例（可調 WINDOW_EXAMPLES_PER_SEG）\n",
    "\n",
    "怎麼用這些診斷來評估品質\n",
    "segments_too_short.csv：看看哪些病患/區段 N<W=120，是否需要放寬 W 或合併段落。\n",
    "segment_gaps.csv：缺口多或缺口超大（>1 分鐘）代表資料不連續，會降低視窗品質。\n",
    "segment_sampling_stats.csv：\n",
    "per_min_count_min/med/p95/max 可看每分鐘取樣密度是否穩定；若 min 太低甚至 1，視窗內特徵聚合可能不穩。\n",
    "dt_sec_med/max 可看實際取樣週期與極端間隔。\n",
    "window_catalog_sample.csv：抽樣檢視每段前幾個視窗的起迄分鐘是否合理連續、是否跨越大缺口。\n",
    "window_summary_segments/patients：整體能切出的窗數是否符合期待；若偏少，往往是 N<W、或缺口太多導致有效分鐘變短。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "id": "6e09293d-529f-452c-ae37-b2dd0f18d321",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true,
     "source_hidden": true
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Info] Found 122 files in /home/jovyan/RT08/0925/bling_svv_14\n"
     ]
    },
    {
     "ename": "TypeError",
     "evalue": "unsupported operand type(s) for +: 'Timestamp' and 'float'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[139], line 248\u001b[0m\n\u001b[1;32m    246\u001b[0m     \u001b[38;5;28;01mcontinue\u001b[39;00m\n\u001b[1;32m    247\u001b[0m \u001b[38;5;66;03m# 框的高度（病患行高的一小部分），避免遮蓋點：使用無填色 + zorder=1\u001b[39;00m\n\u001b[0;32m--> 248\u001b[0m rect \u001b[38;5;241m=\u001b[39m \u001b[43mRectangle\u001b[49m\u001b[43m(\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdraw_s\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m-\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;241;43m0.35\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m  \u001b[49m\u001b[38;5;66;43;03m# 左下角座標（時間, y-半高）\u001b[39;49;00m\n\u001b[1;32m    249\u001b[0m \u001b[43m                 \u001b[49m\u001b[43mwidth\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mdraw_e\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m-\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mdraw_s\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtotal_seconds\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m/\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;241;43m86400.0\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m  \u001b[49m\u001b[38;5;66;43;03m# 轉成天數給 Matplotlib 日期座標\u001b[39;49;00m\n\u001b[1;32m    250\u001b[0m \u001b[43m                 \u001b[49m\u001b[43mheight\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m0.7\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m  \u001b[49m\u001b[38;5;66;43;03m# 框的高度\u001b[39;49;00m\n\u001b[1;32m    251\u001b[0m \u001b[43m                 \u001b[49m\u001b[43mfill\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43medgecolor\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mCOLOR_RED\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlinewidth\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m1.0\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mzorder\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m    252\u001b[0m ax\u001b[38;5;241m.\u001b[39madd_patch(rect)\n\u001b[1;32m    253\u001b[0m win_count_in_year \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;241m1\u001b[39m\n",
      "File \u001b[0;32m/opt/conda/lib/python3.11/site-packages/matplotlib/patches.py:776\u001b[0m, in \u001b[0;36mRectangle.__init__\u001b[0;34m(self, xy, width, height, angle, rotation_point, **kwargs)\u001b[0m\n\u001b[1;32m    769\u001b[0m \u001b[38;5;66;03m# Required for RectangleSelector with axes aspect ratio != 1\u001b[39;00m\n\u001b[1;32m    770\u001b[0m \u001b[38;5;66;03m# The patch is defined in data coordinates and when changing the\u001b[39;00m\n\u001b[1;32m    771\u001b[0m \u001b[38;5;66;03m# selector with square modifier and not in data coordinates, we need\u001b[39;00m\n\u001b[1;32m    772\u001b[0m \u001b[38;5;66;03m# to correct for the aspect ratio difference between the data and\u001b[39;00m\n\u001b[1;32m    773\u001b[0m \u001b[38;5;66;03m# display coordinate systems. Its value is typically provide by\u001b[39;00m\n\u001b[1;32m    774\u001b[0m \u001b[38;5;66;03m# Axes._get_aspect_ratio()\u001b[39;00m\n\u001b[1;32m    775\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_aspect_ratio_correction \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m1.0\u001b[39m\n\u001b[0;32m--> 776\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_convert_units\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m/opt/conda/lib/python3.11/site-packages/matplotlib/patches.py:786\u001b[0m, in \u001b[0;36mRectangle._convert_units\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    784\u001b[0m x0 \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mconvert_xunits(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_x0)\n\u001b[1;32m    785\u001b[0m y0 \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mconvert_yunits(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_y0)\n\u001b[0;32m--> 786\u001b[0m x1 \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mconvert_xunits(\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_x0\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m+\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_width\u001b[49m)\n\u001b[1;32m    787\u001b[0m y1 \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mconvert_yunits(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_y0 \u001b[38;5;241m+\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_height)\n\u001b[1;32m    788\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m x0, y0, x1, y1\n",
      "\u001b[0;31mTypeError\u001b[0m: unsupported operand type(s) for +: 'Timestamp' and 'float'"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1400x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# -*- coding: utf-8 -*-\n",
    "import os, glob\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.dates as mdates\n",
    "from matplotlib.patches import Rectangle\n",
    "from datetime import datetime\n",
    "\n",
    "# ---- 取代「畫紅框」的區塊：將時間轉為數值日期，再畫框 ----\n",
    "from matplotlib.patches import Rectangle\n",
    "import matplotlib.dates as mdates\n",
    "\n",
    "# ===== 參數設定 =====\n",
    "DATA_DIR = \"/home/jovyan/RT08/0925/bling_svv_14\"\n",
    "OUT_DIR  = \"/home/jovyan/RT08/0925/1002\"\n",
    "os.makedirs(OUT_DIR, exist_ok=True)\n",
    "\n",
    "# 每張圖最多顯示的病患數\n",
    "MAX_PER_FIG = 50\n",
    "\n",
    "# 顏色設定\n",
    "COLOR_GRAY = \"#cfcfcf\"   # 灰點（有時間的資料）\n",
    "COLOR_BLUE = \"#1f77b4\"   # 藍點（ad_para=1）\n",
    "COLOR_RED  = \"#d62728\"   # 紅框（視窗片段）\n",
    "\n",
    "# 視窗參數（分鐘）\n",
    "W_MIN = 120  # 視窗長度\n",
    "S_MIN = 60   # 步長（重疊 60 分）\n",
    "\n",
    "# 欄位名稱\n",
    "COL_PAT  = \"patno\"\n",
    "COL_TIME = \"senddate\"\n",
    "COL_OK   = \"NaN_check\"\n",
    "COL_AD   = \"ad_para\"\n",
    "\n",
    "# ===== 小工具：安全讀檔 =====\n",
    "def safe_read_csv(path, need_cols):\n",
    "    \"\"\"\n",
    "    優先只讀需要欄位；若失敗則全讀。若仍缺必要欄位則回傳 None。\n",
    "    \"\"\"\n",
    "    try:\n",
    "        df = pd.read_csv(path, usecols=need_cols, low_memory=False)\n",
    "    except Exception:\n",
    "        try:\n",
    "            df = pd.read_csv(path, low_memory=False)\n",
    "        except Exception:\n",
    "            return None\n",
    "    # 檢查必要欄位\n",
    "    if not set(need_cols).issubset(set(df.columns)):\n",
    "        return None\n",
    "    return df\n",
    "\n",
    "# ===== 計算每病患的視窗片段（基於 NaN_check=1 的分鐘連續區段） =====\n",
    "\"\"\"\n",
    "    g：單一病患的 DataFrame（至少包含 senddate, NaN_check）\n",
    "       已不保證排序；本函式會自行依時間排序。\n",
    "    輸出：list of dict，每個 dict 代表一個視窗片段：\n",
    "        {\"start\": win_start_datetime, \"end\": win_end_datetime}\n",
    "\"\"\"\n",
    "def compute_windows_for_patient(g):\n",
    "    g = g.sort_values(COL_TIME)\n",
    "    # 只留 NaN_check=1\n",
    "    mask = (g[COL_OK] == 1)\n",
    "    if not mask.any():\n",
    "        return []\n",
    "\n",
    "    g1 = g[mask].copy()\n",
    "    g1[\"_min\"] = g1[COL_TIME].dt.floor(\"T\")\n",
    "\n",
    "    # 用 pandas 直接產生段號（True-run 的起點累加）\n",
    "    # seg_start_full 對應 g 的每一列；只在 True-run 起點為 True\n",
    "    seg_start_full = (mask & ~mask.shift(fill_value=False))\n",
    "    seg_id_full = seg_start_full.cumsum()  # 對整個 g 標號（False 也會帶著上一個號）\n",
    "\n",
    "    # 取出 True 列對應的段號，長度與 g1 完全一致\n",
    "    g1[\"_seg_id\"] = seg_id_full[mask].to_numpy()\n",
    "\n",
    "    windows = []\n",
    "    for sid, gg in g1.groupby(\"_seg_id\"):\n",
    "        uniq_minutes = np.array(sorted(gg[\"_min\"].unique()))\n",
    "        N = len(uniq_minutes)\n",
    "        if N < W_MIN:\n",
    "            continue\n",
    "        win_count = 1 + (N - W_MIN) // S_MIN\n",
    "        for k in range(win_count):\n",
    "            start_idx = k * S_MIN\n",
    "            end_idx   = start_idx + W_MIN - 1\n",
    "            if end_idx >= N:\n",
    "                break\n",
    "            windows.append({\n",
    "                \"start\": pd.to_datetime(uniq_minutes[start_idx]),\n",
    "                \"end\":   pd.to_datetime(uniq_minutes[end_idx]) + pd.Timedelta(minutes=1),\n",
    "            })\n",
    "    return windows\n",
    "\n",
    "\n",
    "# ===== 蒐集所有檔案的資料（跨檔案統一以病患為單位） =====\n",
    "file_paths = sorted(glob.glob(os.path.join(DATA_DIR, \"*.csv\")))\n",
    "need_cols = [COL_PAT, COL_TIME, COL_OK, COL_AD]\n",
    "\n",
    "records = []   # 所有點資料（灰/藍），欄位：patno, senddate, ad_para, year\n",
    "win_map = {}   # 每個病患的視窗片段清單：{patno: [ {start, end}, ... ]}\n",
    "\n",
    "print(f\"[Info] Found {len(file_paths)} files in {DATA_DIR}\")\n",
    "\n",
    "for fp in file_paths:\n",
    "    base = os.path.basename(fp)\n",
    "    df = safe_read_csv(fp, need_cols)\n",
    "    if df is None:\n",
    "        print(f\"[Warn] {base}: missing required columns {need_cols}, skip.\")\n",
    "        continue\n",
    "\n",
    "    # 轉型與清理\n",
    "    df = df.copy()\n",
    "    df[COL_TIME] = pd.to_datetime(df[COL_TIME], errors=\"coerce\")\n",
    "    df = df.dropna(subset=[COL_TIME])\n",
    "    if df.empty:\n",
    "        continue\n",
    "\n",
    "    # NaN_check / ad_para 型別處理\n",
    "    df[COL_OK] = pd.to_numeric(df[COL_OK], errors=\"coerce\").fillna(0).astype(int).clip(0, 1)\n",
    "    df[COL_AD] = pd.to_numeric(df[COL_AD], errors=\"coerce\").fillna(0).astype(int).clip(0, 1)\n",
    "\n",
    "    # 收集點資料（灰點：任何列；藍點：ad_para=1）\n",
    "    tmp = pd.DataFrame({\n",
    "        \"patno\": df[COL_PAT],\n",
    "        \"senddate\": df[COL_TIME],\n",
    "        \"ad_para\": df[COL_AD],\n",
    "    })\n",
    "    tmp[\"year\"] = tmp[\"senddate\"].dt.year\n",
    "    records.append(tmp)\n",
    "\n",
    "    # 視窗片段（以病患為單位做全時序計算）\n",
    "    for pid, g in df.groupby(COL_PAT, dropna=True):\n",
    "        if pd.isna(pid):\n",
    "            continue\n",
    "        pid_int = int(pid)\n",
    "        # 若該病患尚未計算過，才建立一次（跨檔案整合）\n",
    "        if pid_int not in win_map:\n",
    "            win_map[pid_int] = []\n",
    "        # 將該檔案中此病患的資料併入總表前，先直接計算此病患在此檔的視窗片段並追加\n",
    "        # （若同病患跨檔案有時間重疊，這裡會簡單地累加，視覺化不會衝突，因為只畫與年份相交的部分）\n",
    "        g2 = g[[COL_PAT, COL_TIME, COL_OK]].copy()\n",
    "        g2[COL_TIME] = pd.to_datetime(g2[COL_TIME], errors=\"coerce\")\n",
    "        g2 = g2.dropna(subset=[COL_TIME])\n",
    "        if g2.empty:\n",
    "            continue\n",
    "        win_map[pid_int].extend(compute_windows_for_patient(g2))\n",
    "\n",
    "if not records:\n",
    "    print(\"[Viz] No qualified data to plot.\")\n",
    "else:\n",
    "    # 合併所有點資料\n",
    "    data = pd.concat(records, ignore_index=True).sort_values([\"year\", \"patno\", \"senddate\"])\n",
    "\n",
    "    # 依年份分圖\n",
    "    years = sorted(data[\"year\"].dropna().unique().tolist())\n",
    "    total_figs = 0\n",
    "    year_page_count = {}\n",
    "\n",
    "    for yr in years:\n",
    "        dy = data[data[\"year\"] == yr].copy()\n",
    "        # 本年出現的病患清單（依 patno 排序）\n",
    "        patients_in_year = sorted(dy[\"patno\"].dropna().unique().tolist())\n",
    "\n",
    "        # 依每張圖最多 MAX_PER_FIG 位病患分頁\n",
    "        for page_start in range(0, len(patients_in_year), MAX_PER_FIG):\n",
    "            batch_patients = patients_in_year[page_start:page_start + MAX_PER_FIG]\n",
    "            if not batch_patients:\n",
    "                continue\n",
    "\n",
    "            # 動態圖高：每位病患約 0.35 吋，介於 4~12 吋\n",
    "            n = len(batch_patients)\n",
    "            fig_h = max(4, min(12, 0.35 * n))\n",
    "            fig, ax = plt.subplots(figsize=(14, fig_h))\n",
    "\n",
    "            # 本頁資料\n",
    "            batch_df = dy[dy[\"patno\"].isin(batch_patients)].copy()\n",
    "\n",
    "            # X 軸範圍（整個年份，加右側留白放標註）\n",
    "            x_min = pd.to_datetime(f\"{yr}-01-01 00:00:00\")\n",
    "            x_max = pd.to_datetime(f\"{yr}-12-31 23:59:59\")\n",
    "            pad = pd.Timedelta(days=5)  # 右側文字留白\n",
    "            ax.set_xlim(x_min, x_max + pad)\n",
    "\n",
    "            # Y 軸（病患 -> y 座標）\n",
    "            y_positions = {pid: i for i, pid in enumerate(batch_patients)}\n",
    "            yticks = list(range(len(batch_patients)))\n",
    "            ylabels = [str(pid) for pid in batch_patients]\n",
    "\n",
    "            # X 軸時間格式\n",
    "            locator = mdates.AutoDateLocator(minticks=4, maxticks=10)\n",
    "            formatter = mdates.ConciseDateFormatter(locator)\n",
    "            ax.xaxis.set_major_locator(locator)\n",
    "            ax.xaxis.set_major_formatter(formatter)\n",
    "\n",
    "            # 逐病患繪圖\n",
    "            for pid in batch_patients:\n",
    "                y = y_positions[pid]\n",
    "                gf = batch_df[batch_df[\"patno\"] == pid][[\"senddate\", \"ad_para\"]].sort_values(\"senddate\")\n",
    "\n",
    "                # --- 畫灰點（所有資料行） ---\n",
    "                if not gf.empty:\n",
    "                    ax.scatter(gf[\"senddate\"], np.full(len(gf), y),\n",
    "                               s=10, marker='o', color=COLOR_GRAY, alpha=0.9, linewidths=0, zorder=2)\n",
    "\n",
    "                # --- 畫藍點（ad_para=1）---\n",
    "                mask_ad = gf[\"ad_para\"] == 1\n",
    "                if mask_ad.any():\n",
    "                    ax.scatter(gf.loc[mask_ad, \"senddate\"], np.full(mask_ad.sum(), y),\n",
    "                               s=14, marker='o', color=COLOR_BLUE, alpha=0.95, linewidths=0, zorder=3)\n",
    "\n",
    "                # --- 畫紅框（視窗片段；與本年相交者才畫；框在點的下層且無填色） ---\n",
    "                # 從 win_map 取得該病患所有片段，逐一與本年時間區間相交後畫框\n",
    "                patient_windows = win_map.get(int(pid), [])\n",
    "                win_count_in_year = 0\n",
    "                for w in patient_windows:\n",
    "                    ws, we = w[\"start\"], w[\"end\"]\n",
    "                    # 視窗與本年相交條件\n",
    "                    if we < x_min or ws > x_max:\n",
    "                        continue\n",
    "                    # 將視窗裁到本年範圍內顯示\n",
    "                    draw_s = max(ws, x_min)\n",
    "                    draw_e = min(we, x_max)\n",
    "                    if draw_e <= draw_s:\n",
    "                        continue\n",
    "                    # 框的高度（病患行高的一小部分），避免遮蓋點：使用無填色 + zorder=1\n",
    "                    rect = Rectangle((draw_s, y - 0.35),  # 左下角座標（時間, y-半高）\n",
    "                                     width=(draw_e - draw_s).total_seconds() / 86400.0,  # 轉成天數給 Matplotlib 日期座標\n",
    "                                     height=0.7,  # 框的高度\n",
    "                                     fill=False, edgecolor=COLOR_RED, linewidth=1.0, zorder=1)\n",
    "                    ax.add_patch(rect)\n",
    "                    win_count_in_year += 1\n",
    "\n",
    "                # --- 右側標註（藍：ad_para=1 筆數；紅：本年顯示的視窗片段數量） ---\n",
    "                ad_cnt = int(mask_ad.sum())\n",
    "                # 藍字（ad_para=1）\n",
    "                ax.text(x_max + pd.Timedelta(days=1), y, f\"{ad_cnt}\", va=\"center\", ha=\"left\",\n",
    "                        color=COLOR_BLUE, fontsize=9)\n",
    "                # 紅字（視窗片段數量）\n",
    "                ax.text(x_max + pd.Timedelta(days=2.5), y, f\"{win_count_in_year}\", va=\"center\", ha=\"left\",\n",
    "                        color=COLOR_RED, fontsize=9)\n",
    "\n",
    "            # 標題與外觀\n",
    "            ax.set_title(f\"Window segments & ad_para over time by patient — Year {yr}  \"\n",
    "                         f\"(patients {page_start+1}-{page_start+len(batch_patients)} of {len(patients_in_year)})\",\n",
    "                         fontsize=12, pad=12)\n",
    "            ax.set_xlabel(\"Time\")\n",
    "            ax.set_yticks(yticks)\n",
    "            ax.set_yticklabels(ylabels, fontsize=9)\n",
    "            ax.grid(True, axis=\"x\", linestyle=\"--\", linewidth=0.6, alpha=0.5)\n",
    "            ax.set_axisbelow(True)\n",
    "\n",
    "            # 圖例（手動）\n",
    "            from matplotlib.lines import Line2D\n",
    "            legend_handles = [\n",
    "                Line2D([0], [0], marker='o', color='none', markerfacecolor=COLOR_GRAY, label='has time (row)', markersize=6),\n",
    "                Line2D([0], [0], marker='o', color='none', markerfacecolor=COLOR_BLUE, label='ad_para = 1', markersize=7),\n",
    "                Line2D([0], [0], color=COLOR_RED, lw=1.2, label='window segment (W=120, S=60)')\n",
    "            ]\n",
    "            ax.legend(handles=legend_handles, loc=\"upper right\", frameon=False)\n",
    "\n",
    "            plt.tight_layout()\n",
    "            out_file = os.path.join(OUT_DIR, f\"win_segments_{yr}_p{page_start // MAX_PER_FIG + 1}.png\")\n",
    "            fig.savefig(out_file, dpi=150)\n",
    "            plt.close(fig)\n",
    "\n",
    "            total_figs += 1\n",
    "            year_page_count[yr] = year_page_count.get(yr, 0) + 1\n",
    "\n",
    "    # 統計摘要\n",
    "    print(f\"\\n✅ Exported {total_figs} figure(s) to: {OUT_DIR}\")\n",
    "    print(f\"   Year count: {len(years)}\")\n",
    "    for yr in years:\n",
    "        print(f\"   - {yr}: {year_page_count.get(yr, 0)} figure(s)\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "id": "c9ebddfb-1c4a-45f5-8dbc-e93d015fc4b2",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Info] Found 122 files in /home/jovyan/RT08/0925/bling_svv_14\n",
      "\n",
      "✅ Exported 5 figure(s) to: /home/jovyan/RT08/0925/1002\n",
      "   Year count: 3\n",
      "   - 2021: 1 figure(s)\n",
      "   - 2022: 3 figure(s)\n",
      "   - 2024: 1 figure(s)\n"
     ]
    }
   ],
   "source": [
    "\"\"\"紅字藍字重疊 保留在win_segments資料夾\n",
    "======== 視窗片段時間軸視覺化========\n",
    "來源資料夾：/home/jovyan/RT08/0925/bling_svv_14\n",
    "輸出資料夾：/home/jovyan/RT08/0925/1002  （自動建立）\n",
    "圖檔命名：win_segments_{year}_p{頁碼}.png\n",
    "\n",
    "圖形規格（以年分圖，每張最多 50 位病患）：\n",
    "- X 軸：時間（senddate）\n",
    "- Y 軸：病患（patno），每位病患佔一條水平位置\n",
    "- 灰色點（light gray）：該病患該時間點「有資料」（任何列，只要 senddate 可解析）\n",
    "- 藍色點（blue）：該病患該時間點 ad_para=1 的列\n",
    "- 紅色細框（red, no fill）：視窗片段（以 NaN_check=1 的「分鐘」連續區段切窗，W=120 分，S=60 分）\n",
    "  ※ 紅框畫在點的「下層」，且無填色，避免遮蓋灰/藍點\n",
    "- 圖右側標註：\n",
    "  * 藍色文字：該病患 ad_para=1 的筆數\n",
    "  * 紅色文字：該病患（全時序計算後）可切出的視窗片段數量（同 W/S 規格），僅顯示在該年視窗與本年時間相交的片段數\n",
    "\n",
    "注意：\n",
    "- 本段程式會自行根據 NaN_check 連續 True 區段（每病患、依時間排序）計算視窗（W=120 分、S=60 分），\n",
    "  與你先前統計用的邏輯一致；繪圖時僅將「與當年時間有交集」的視窗片段畫出。\n",
    "- 若檔案缺少必要欄位（patno, senddate, NaN_check, ad_para），該檔會跳過並印出警告。\n",
    "\"\"\"\n",
    "# -*- coding: utf-8 -*-\n",
    "\"\"\"\n",
    "======== 視窗片段時間軸視覺化（不會修改任何原始檔） ========\n",
    "來源資料夾：/home/jovyan/RT08/0925/bling_svv_14\n",
    "輸出資料夾：/home/jovyan/RT08/0925/1002  （自動建立）\n",
    "圖檔命名：win_segments_{year}_p{頁碼}.png\n",
    "\n",
    "圖形規格（以年分圖，每張最多 50 位病患）：\n",
    "- X 軸：時間（senddate）\n",
    "- Y 軸：病患（patno），每位病患佔一條水平位置\n",
    "- 灰色點（light gray）：該病患該時間點有資料（senddate 可解析）\n",
    "- 藍色點（blue）：ad_para=1 的列\n",
    "- 紅色細框（red, no fill）：視窗片段（以 NaN_check=1 的「分鐘」連續區段切窗，W=120 分，S=60 分）\n",
    "  ※ 紅框使用數值日期並畫在點的下層（zorder=1），無填色以避免遮蓋灰/藍點\n",
    "- 圖右側標註：\n",
    "  * 藍色文字：該病患 ad_para=1 的筆數\n",
    "  * 紅色文字：該病患在本年範圍內可視化的視窗片段數（與本年時間有相交者）\n",
    "\n",
    "備註：\n",
    "- 本程式會自行根據 NaN_check=1 的分鐘連續區段（每病患、依時間排序）計算視窗（W=120、S=60），\n",
    "  與前述統計邏輯一致；繪圖僅畫出與該年相交的片段。\n",
    "- 若檔案缺少必要欄位（patno, senddate, NaN_check, ad_para），該檔會跳過並印出警告。\n",
    "- 僅讀取原始檔，不會覆寫任何 /bling_svv_14/ 內容；輸出皆寫入 /1002/。\n",
    "\"\"\"\n",
    "\n",
    "import os, glob\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.dates as mdates\n",
    "from matplotlib.patches import Rectangle\n",
    "\n",
    "# ===== 參數設定 =====\n",
    "DATA_DIR = \"/home/jovyan/RT08/0925/bling_svv_14\"\n",
    "OUT_DIR  = \"/home/jovyan/RT08/0925/1002\"\n",
    "os.makedirs(OUT_DIR, exist_ok=True)\n",
    "\n",
    "# 每張圖最多顯示的病患數\n",
    "MAX_PER_FIG = 50\n",
    "\n",
    "# 顏色設定\n",
    "COLOR_GRAY = \"#cfcfcf\"   # 灰點（有時間的資料）\n",
    "COLOR_BLUE = \"#1f77b4\"   # 藍點（ad_para=1）\n",
    "COLOR_RED  = \"#d62728\"   # 紅框（視窗片段）\n",
    "\n",
    "# 視窗參數（分鐘）\n",
    "W_MIN = 120  # 視窗長度\n",
    "S_MIN = 60   # 步長（重疊 60 分）\n",
    "\n",
    "# 欄位名稱\n",
    "COL_PAT  = \"patno\"\n",
    "COL_TIME = \"senddate\"\n",
    "COL_OK   = \"NaN_check\"\n",
    "COL_AD   = \"ad_para\"\n",
    "\n",
    "# ===== 小工具：安全讀檔（只讀不寫） =====\n",
    "def safe_read_csv(path, need_cols):\n",
    "    \"\"\"\n",
    "    優先只讀需要欄位；若失敗則全讀。若仍缺必要欄位則回傳 None。\n",
    "    \"\"\"\n",
    "    try:\n",
    "        df = pd.read_csv(path, usecols=need_cols, low_memory=False)\n",
    "    except Exception:\n",
    "        try:\n",
    "            df = pd.read_csv(path, low_memory=False)\n",
    "        except Exception:\n",
    "            return None\n",
    "    if not set(need_cols).issubset(df.columns):\n",
    "        return None\n",
    "    return df\n",
    "\n",
    "# ===== 計算每病患的視窗片段（基於 NaN_check=1 的分鐘連續區段） =====\n",
    "def compute_windows_for_patient(g):\n",
    "    \"\"\"\n",
    "    g：單一病患的 DataFrame（至少包含 senddate, NaN_check），本函式內部會時間排序。\n",
    "    輸出：list[dict]；每個視窗：{\"start\": datetime, \"end\": datetime(右閉顯示用)}\n",
    "    \"\"\"\n",
    "    g = g.sort_values(COL_TIME)\n",
    "    mask = (g[COL_OK] == 1)\n",
    "    if not mask.any():\n",
    "        return []\n",
    "\n",
    "    # 僅取 NaN_check=1 的列，並把時間落到分鐘粒度（去重時用）\n",
    "    g1 = g[mask].copy()\n",
    "    g1[\"_min\"] = g1[COL_TIME].dt.floor(\"T\")\n",
    "\n",
    "    # 用 pandas 產生段號：True-run 起點累加；再對齊 True 列取得段號（避免長度不匹配錯誤）\n",
    "    seg_start_full = (mask & ~mask.shift(fill_value=False))\n",
    "    seg_id_full = seg_start_full.cumsum()           # 對整個 g 標號\n",
    "    g1[\"_seg_id\"] = seg_id_full[mask].to_numpy()    # 只拿 True 的段號，長度與 g1 一致\n",
    "\n",
    "    windows = []\n",
    "    # 逐段切窗\n",
    "    for _, gg in g1.groupby(\"_seg_id\"):\n",
    "        uniq_minutes = np.array(sorted(gg[\"_min\"].unique()))\n",
    "        N = len(uniq_minutes)\n",
    "        if N < W_MIN:\n",
    "            continue\n",
    "        win_count = 1 + (N - W_MIN) // S_MIN\n",
    "        for k in range(win_count):\n",
    "            start_idx = k * S_MIN\n",
    "            end_idx   = start_idx + W_MIN - 1\n",
    "            if end_idx >= N:\n",
    "                break\n",
    "            windows.append({\n",
    "                # 視窗尾端 +1 分鐘讓矩形顯示作為右閉區間（不影響後續判斷，只影響框長度）\n",
    "                \"start\": pd.to_datetime(uniq_minutes[start_idx]),\n",
    "                \"end\":   pd.to_datetime(uniq_minutes[end_idx]) + pd.Timedelta(minutes=1),\n",
    "            })\n",
    "    return windows\n",
    "\n",
    "# ===== 蒐集所有檔案的資料（跨檔案統一以病患為單位） =====\n",
    "file_paths = sorted(glob.glob(os.path.join(DATA_DIR, \"*.csv\")))\n",
    "need_cols = [COL_PAT, COL_TIME, COL_OK, COL_AD]\n",
    "\n",
    "records = []   # 所有點資料（灰/藍）：欄位 patno, senddate, ad_para, year\n",
    "win_map = {}   # 每病患的視窗片段清單：{patno_int: [ {\"start\":dt,\"end\":dt}, ... ]}\n",
    "\n",
    "print(f\"[Info] Found {len(file_paths)} files in {DATA_DIR}\")\n",
    "\n",
    "for fp in file_paths:\n",
    "    base = os.path.basename(fp)\n",
    "    df = safe_read_csv(fp, need_cols)\n",
    "    if df is None:\n",
    "        print(f\"[Warn] {base}: missing required columns {need_cols}, skip.\")\n",
    "        continue\n",
    "\n",
    "    # 轉型與清理\n",
    "    df = df.copy()\n",
    "    df[COL_TIME] = pd.to_datetime(df[COL_TIME], errors=\"coerce\")\n",
    "    df = df.dropna(subset=[COL_TIME])\n",
    "    if df.empty:\n",
    "        continue\n",
    "\n",
    "    df[COL_OK] = pd.to_numeric(df[COL_OK], errors=\"coerce\").fillna(0).astype(int).clip(0, 1)\n",
    "    df[COL_AD] = pd.to_numeric(df[COL_AD], errors=\"coerce\").fillna(0).astype(int).clip(0, 1)\n",
    "\n",
    "    # 收集點資料（灰點：所有列；藍點：ad_para=1）\n",
    "    tmp = pd.DataFrame({\n",
    "        \"patno\": df[COL_PAT],\n",
    "        \"senddate\": df[COL_TIME],\n",
    "        \"ad_para\": df[COL_AD],\n",
    "    })\n",
    "    tmp[\"year\"] = tmp[\"senddate\"].dt.year\n",
    "    records.append(tmp)\n",
    "\n",
    "    # 視窗片段（以病患為單位計算；跨檔案直接累加到同一 patno 的清單）\n",
    "    for pid, g in df.groupby(COL_PAT, dropna=True):\n",
    "        if pd.isna(pid):\n",
    "            continue\n",
    "        pid_int = int(pid)\n",
    "        if pid_int not in win_map:\n",
    "            win_map[pid_int] = []\n",
    "        g2 = g[[COL_PAT, COL_TIME, COL_OK]].copy()\n",
    "        g2[COL_TIME] = pd.to_datetime(g2[COL_TIME], errors=\"coerce\")\n",
    "        g2 = g2.dropna(subset=[COL_TIME])\n",
    "        if g2.empty:\n",
    "            continue\n",
    "        win_map[pid_int].extend(compute_windows_for_patient(g2))\n",
    "\n",
    "if not records:\n",
    "    print(\"[Viz] No qualified data to plot.\")\n",
    "else:\n",
    "    # 合併所有點資料\n",
    "    data = pd.concat(records, ignore_index=True).sort_values([\"year\", \"patno\", \"senddate\"])\n",
    "\n",
    "    # 依年份分圖\n",
    "    years = sorted(data[\"year\"].dropna().unique().tolist())\n",
    "    total_figs = 0\n",
    "    year_page_count = {}\n",
    "\n",
    "    for yr in years:\n",
    "        dy = data[data[\"year\"] == yr].copy()\n",
    "        patients_in_year = sorted(dy[\"patno\"].dropna().unique().tolist())\n",
    "\n",
    "        # 每張圖最多顯示 MAX_PER_FIG 位病患\n",
    "        for page_start in range(0, len(patients_in_year), MAX_PER_FIG):\n",
    "            batch_patients = patients_in_year[page_start:page_start + MAX_PER_FIG]\n",
    "            if not batch_patients:\n",
    "                continue\n",
    "\n",
    "            # 動態圖高：每位病患約 0.35 吋，介於 4~12 吋\n",
    "            n = len(batch_patients)\n",
    "            fig_h = max(4, min(12, 0.35 * n))\n",
    "            fig, ax = plt.subplots(figsize=(14, fig_h))\n",
    "\n",
    "            # 本頁資料\n",
    "            batch_df = dy[dy[\"patno\"].isin(batch_patients)].copy()\n",
    "\n",
    "            # X 軸範圍（整個年份，加右側留白放標註）\n",
    "            x_min = pd.to_datetime(f\"{yr}-01-01 00:00:00\")\n",
    "            x_max = pd.to_datetime(f\"{yr}-12-31 23:59:59\")\n",
    "            pad = pd.Timedelta(days=5)  # 右側文字留白\n",
    "            ax.set_xlim(x_min, x_max + pad)\n",
    "\n",
    "            # Y 軸（病患 -> y 座標）\n",
    "            y_positions = {pid: i for i, pid in enumerate(batch_patients)}\n",
    "            yticks = list(range(len(batch_patients)))\n",
    "            ylabels = [str(pid) for pid in batch_patients]\n",
    "\n",
    "            # X 軸時間格式\n",
    "            locator = mdates.AutoDateLocator(minticks=4, maxticks=10)\n",
    "            formatter = mdates.ConciseDateFormatter(locator)\n",
    "            ax.xaxis.set_major_locator(locator)\n",
    "            ax.xaxis.set_major_formatter(formatter)\n",
    "\n",
    "            # 逐病患繪圖\n",
    "            for pid in batch_patients:\n",
    "                y = y_positions[pid]\n",
    "                gf = batch_df[batch_df[\"patno\"] == pid][[\"senddate\", \"ad_para\"]].sort_values(\"senddate\")\n",
    "\n",
    "                # --- 灰點：所有資料行 ---\n",
    "                if not gf.empty:\n",
    "                    ax.scatter(gf[\"senddate\"], np.full(len(gf), y),\n",
    "                               s=10, marker='o', color=COLOR_GRAY, alpha=0.9, linewidths=0, zorder=2)\n",
    "\n",
    "                # --- 藍點：ad_para=1 ---\n",
    "                mask_ad = gf[\"ad_para\"] == 1\n",
    "                if mask_ad.any():\n",
    "                    ax.scatter(gf.loc[mask_ad, \"senddate\"], np.full(mask_ad.sum(), y),\n",
    "                               s=14, marker='o', color=COLOR_BLUE, alpha=0.95, linewidths=0, zorder=3)\n",
    "\n",
    "                # --- 紅框：視窗片段（與本年相交者才畫；無填色，避免遮蓋點） ---\n",
    "                patient_windows = win_map.get(int(pid), [])\n",
    "                win_count_in_year = 0\n",
    "                for w in patient_windows:\n",
    "                    ws, we = w[\"start\"], w[\"end\"]\n",
    "                    # 僅處理與本年相交的片段\n",
    "                    if we < x_min or ws > x_max:\n",
    "                        continue\n",
    "                    # 將視窗限制在本年範圍內顯示\n",
    "                    draw_s = max(ws, x_min)\n",
    "                    draw_e = min(we, x_max)\n",
    "                    if draw_e <= draw_s:\n",
    "                        continue\n",
    "\n",
    "                    # 將 datetime 轉為 Matplotlib 數值日期（天數的 float），避免 Timestamp+float 型別衝突\n",
    "                    x0 = mdates.date2num(draw_s)\n",
    "                    x1 = mdates.date2num(draw_e)\n",
    "                    width_days = x1 - x0\n",
    "\n",
    "                    # 畫紅色細框（置於 zorder=1，不覆蓋灰/藍點）\n",
    "                    rect = Rectangle(\n",
    "                        (x0, y - 0.35),   # 左下角（數值日期, y-半高）\n",
    "                        width_days,        # 寬度（天）\n",
    "                        0.7,               # 高度\n",
    "                        fill=False, edgecolor=COLOR_RED, linewidth=1.0, zorder=1\n",
    "                    )\n",
    "                    # 指定使用資料座標系（日期數值與 y 的資料座標）\n",
    "                    rect.set_transform(ax.transData)\n",
    "                    ax.add_patch(rect)\n",
    "                    win_count_in_year += 1\n",
    "\n",
    "                # --- 右側標註（藍：ad_para=1 筆數；紅：本年片段數） ---\n",
    "                ad_cnt = int(mask_ad.sum())\n",
    "                ax.text(x_max + pd.Timedelta(days=1),   y, f\"{ad_cnt}\",            va=\"center\", ha=\"left\",\n",
    "                        color=COLOR_BLUE, fontsize=9)\n",
    "                ax.text(x_max + pd.Timedelta(days=2.5), y, f\"{win_count_in_year}\", va=\"center\", ha=\"left\",\n",
    "                        color=COLOR_RED,  fontsize=9)\n",
    "\n",
    "            # 標題與外觀\n",
    "            ax.set_title(\n",
    "                f\"Window segments & ad_para over time by patient — Year {yr}  \"\n",
    "                f\"(patients {page_start+1}-{page_start+len(batch_patients)} of {len(patients_in_year)})\",\n",
    "                fontsize=12, pad=12\n",
    "            )\n",
    "            ax.set_xlabel(\"Time\")\n",
    "            ax.set_yticks(yticks)\n",
    "            ax.set_yticklabels(ylabels, fontsize=9)\n",
    "            ax.grid(True, axis=\"x\", linestyle=\"--\", linewidth=0.6, alpha=0.5)\n",
    "            ax.set_axisbelow(True)\n",
    "\n",
    "            # 圖例（手動）\n",
    "            from matplotlib.lines import Line2D\n",
    "            legend_handles = [\n",
    "                Line2D([0], [0], marker='o', color='none', markerfacecolor=COLOR_GRAY, label='has time (row)', markersize=6),\n",
    "                Line2D([0], [0], marker='o', color='none', markerfacecolor=COLOR_BLUE, label='ad_para = 1',   markersize=7),\n",
    "                Line2D([0], [0], color=COLOR_RED, lw=1.2, label='window segment (W=120, S=60)'),\n",
    "            ]\n",
    "            ax.legend(handles=legend_handles, loc=\"upper right\", frameon=False)\n",
    "\n",
    "            plt.tight_layout()\n",
    "            out_file = os.path.join(OUT_DIR, f\"win_segments_{yr}_p{page_start // MAX_PER_FIG + 1}.png\")\n",
    "            fig.savefig(out_file, dpi=150)\n",
    "            plt.close(fig)\n",
    "\n",
    "            total_figs += 1\n",
    "            year_page_count[yr] = year_page_count.get(yr, 0) + 1\n",
    "\n",
    "    # 總結輸出\n",
    "    print(f\"\\n✅ Exported {total_figs} figure(s) to: {OUT_DIR}\")\n",
    "    print(f\"   Year count: {len(years)}\")\n",
    "    for yr in years:\n",
    "        print(f\"   - {yr}: {year_page_count.get(yr, 0)} figure(s)\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "id": "b0672893-d186-44a2-800a-8736eeba7fb8",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Info] Found 122 files in /home/jovyan/RT08/0925/bling_svv_14\n",
      "\n",
      "✅ Exported 5 figure(s) to: /home/jovyan/RT08/0925/1002\n",
      "   Year count: 3\n",
      "   - 2021: 1 figure(s)\n",
      "   - 2022: 3 figure(s)\n",
      "   - 2024: 1 figure(s)\n"
     ]
    }
   ],
   "source": [
    "\"\"\" ======== 視窗片段時間軸視覺化（不會修改任何原始檔） ========\n",
    "來源資料夾：/home/jovyan/RT08/0925/bling_svv_14\n",
    "輸出資料夾：/home/jovyan/RT08/0925/1002  （自動建立）\n",
    "圖檔命名：win_segments_{year}_p{頁碼}.png\n",
    "\n",
    "圖形規格（以年分圖，每張最多 50 位病患）：\n",
    "- X 軸：時間（senddate）\n",
    "- Y 軸：病患（patno），每位病患佔一條水平位置\n",
    "- 灰色點（light gray）：該病患該時間點有資料（senddate 可解析）\n",
    "- 藍色點（blue）：ad_para=1 的列\n",
    "- 紅色細框（red, no fill）：視窗片段（以 NaN_check=1 的「分鐘」連續區段切窗，W=120 分，S=60 分）\n",
    "  ※ 紅框使用數值日期並畫在點的下層（zorder=1），無填色以避免遮蓋灰/藍點\n",
    "- 圖右側標註（固定在軸邊，不與資料重疊）：\n",
    "  * 藍色文字：該病患 ad_para=1 的筆數\n",
    "  * 紅色文字：該病患在本年範圍內可視化的視窗片段數（與本年時間有相交者）\n",
    "\n",
    "備註：\n",
    "- 本程式會自行根據 NaN_check=1 的分鐘連續區段（每病患、依時間排序）計算視窗（W=120、S=60），\n",
    "  與前述統計邏輯一致；繪圖僅畫出與該年相交的片段。\n",
    "- 若檔案缺少必要欄位（patno, senddate, NaN_check, ad_para），該檔會跳過並印出警告。\n",
    "- 僅讀取原始檔，不會覆寫任何 /bling_svv_14/ 內容；輸出皆寫入 /1002/。\n",
    "\"\"\"\n",
    "\n",
    "import os, glob\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.dates as mdates\n",
    "from matplotlib.patches import Rectangle\n",
    "\n",
    "# ===== 參數設定 =====\n",
    "DATA_DIR = \"/home/jovyan/RT08/0925/bling_svv_14\"\n",
    "OUT_DIR  = \"/home/jovyan/RT08/0925/1002\"\n",
    "os.makedirs(OUT_DIR, exist_ok=True)\n",
    "\n",
    "# 每張圖最多顯示的病患數\n",
    "MAX_PER_FIG = 50\n",
    "\n",
    "# 顏色設定\n",
    "COLOR_GRAY = \"#cfcfcf\"   # 灰點（有時間的資料）\n",
    "COLOR_BLUE = \"#1f77b4\"   # 藍點（ad_para=1）\n",
    "COLOR_RED  = \"#d62728\"   # 紅框（視窗片段）\n",
    "\n",
    "# 視窗參數（分鐘）\n",
    "W_MIN = 120  # 視窗長度\n",
    "S_MIN = 60   # 步長（重疊 60 分）\n",
    "\n",
    "# 欄位名稱\n",
    "COL_PAT  = \"patno\"\n",
    "COL_TIME = \"senddate\"\n",
    "COL_OK   = \"NaN_check\"\n",
    "COL_AD   = \"ad_para\"\n",
    "\n",
    "# ===== 小工具：安全讀檔（只讀不寫） =====\n",
    "def safe_read_csv(path, need_cols):\n",
    "    \"\"\"\n",
    "    優先只讀需要欄位；若失敗則全讀。若仍缺必要欄位則回傳 None。\n",
    "    \"\"\"\n",
    "    try:\n",
    "        df = pd.read_csv(path, usecols=need_cols, low_memory=False)\n",
    "    except Exception:\n",
    "        try:\n",
    "            df = pd.read_csv(path, low_memory=False)\n",
    "        except Exception:\n",
    "            return None\n",
    "    if not set(need_cols).issubset(df.columns):\n",
    "        return None\n",
    "    return df\n",
    "\n",
    "# ===== 計算每病患的視窗片段（基於 NaN_check=1 的分鐘連續區段） =====\n",
    "def compute_windows_for_patient(g):\n",
    "    \"\"\"\n",
    "    g：單一病患的 DataFrame（至少包含 senddate, NaN_check），本函式內部會時間排序。\n",
    "    輸出：list[dict]；每個視窗：{\"start\": datetime, \"end\": datetime(右閉顯示用)}\n",
    "    \"\"\"\n",
    "    g = g.sort_values(COL_TIME)\n",
    "    mask = (g[COL_OK] == 1)\n",
    "    if not mask.any():\n",
    "        return []\n",
    "\n",
    "    # 僅取 NaN_check=1 的列，並把時間落到分鐘粒度（去重時用）\n",
    "    g1 = g[mask].copy()\n",
    "    g1[\"_min\"] = g1[COL_TIME].dt.floor(\"T\")\n",
    "\n",
    "    # 用 pandas 產生段號：True-run 起點累加；再對齊 True 列取得段號（避免長度不匹配錯誤）\n",
    "    seg_start_full = (mask & ~mask.shift(fill_value=False))\n",
    "    seg_id_full = seg_start_full.cumsum()           # 對整個 g 標號\n",
    "    g1[\"_seg_id\"] = seg_id_full[mask].to_numpy()    # 只拿 True 的段號，長度與 g1 一致\n",
    "\n",
    "    windows = []\n",
    "    # 逐段切窗\n",
    "    for _, gg in g1.groupby(\"_seg_id\"):\n",
    "        uniq_minutes = np.array(sorted(gg[\"_min\"].unique()))\n",
    "        N = len(uniq_minutes)\n",
    "        if N < W_MIN:\n",
    "            continue\n",
    "        win_count = 1 + (N - W_MIN) // S_MIN\n",
    "        for k in range(win_count):\n",
    "            start_idx = k * S_MIN\n",
    "            end_idx   = start_idx + W_MIN - 1\n",
    "            if end_idx >= N:\n",
    "                break\n",
    "            windows.append({\n",
    "                # 視窗尾端 +1 分鐘讓矩形顯示作為右閉區間（不影響後續判斷，只影響框長度）\n",
    "                \"start\": pd.to_datetime(uniq_minutes[start_idx]),\n",
    "                \"end\":   pd.to_datetime(uniq_minutes[end_idx]) + pd.Timedelta(minutes=1),\n",
    "            })\n",
    "    return windows\n",
    "\n",
    "# ===== 蒐集所有檔案的資料（跨檔案統一以病患為單位） =====\n",
    "file_paths = sorted(glob.glob(os.path.join(DATA_DIR, \"*.csv\")))\n",
    "need_cols = [COL_PAT, COL_TIME, COL_OK, COL_AD]\n",
    "\n",
    "records = []   # 所有點資料（灰/藍）：欄位 patno, senddate, ad_para, year\n",
    "win_map = {}   # 每病患的視窗片段清單：{patno_int: [ {\"start\":dt,\"end\":dt}, ... ]}\n",
    "\n",
    "print(f\"[Info] Found {len(file_paths)} files in {DATA_DIR}\")\n",
    "\n",
    "for fp in file_paths:\n",
    "    base = os.path.basename(fp)\n",
    "    df = safe_read_csv(fp, need_cols)\n",
    "    if df is None:\n",
    "        print(f\"[Warn] {base}: missing required columns {need_cols}, skip.\")\n",
    "        continue\n",
    "\n",
    "    # 轉型與清理\n",
    "    df = df.copy()\n",
    "    df[COL_TIME] = pd.to_datetime(df[COL_TIME], errors=\"coerce\")\n",
    "    df = df.dropna(subset=[COL_TIME])\n",
    "    if df.empty:\n",
    "        continue\n",
    "\n",
    "    df[COL_OK] = pd.to_numeric(df[COL_OK], errors=\"coerce\").fillna(0).astype(int).clip(0, 1)\n",
    "    df[COL_AD] = pd.to_numeric(df[COL_AD], errors=\"coerce\").fillna(0).astype(int).clip(0, 1)\n",
    "\n",
    "    # 收集點資料（灰點：所有列；藍點：ad_para=1）\n",
    "    tmp = pd.DataFrame({\n",
    "        \"patno\": df[COL_PAT],\n",
    "        \"senddate\": df[COL_TIME],\n",
    "        \"ad_para\": df[COL_AD],\n",
    "    })\n",
    "    tmp[\"year\"] = tmp[\"senddate\"].dt.year\n",
    "    records.append(tmp)\n",
    "\n",
    "    # 視窗片段（以病患為單位計算；跨檔案直接累加到同一 patno 的清單）\n",
    "    for pid, g in df.groupby(COL_PAT, dropna=True):\n",
    "        if pd.isna(pid):\n",
    "            continue\n",
    "        pid_int = int(pid)\n",
    "        if pid_int not in win_map:\n",
    "            win_map[pid_int] = []\n",
    "        g2 = g[[COL_PAT, COL_TIME, COL_OK]].copy()\n",
    "        g2[COL_TIME] = pd.to_datetime(g2[COL_TIME], errors=\"coerce\")\n",
    "        g2 = g2.dropna(subset=[COL_TIME])\n",
    "        if g2.empty:\n",
    "            continue\n",
    "        win_map[pid_int].extend(compute_windows_for_patient(g2))\n",
    "\n",
    "if not records:\n",
    "    print(\"[Viz] No qualified data to plot.\")\n",
    "else:\n",
    "    # 合併所有點資料\n",
    "    data = pd.concat(records, ignore_index=True).sort_values([\"year\", \"patno\", \"senddate\"])\n",
    "\n",
    "    # 依年份分圖\n",
    "    years = sorted(data[\"year\"].dropna().unique().tolist())\n",
    "    total_figs = 0\n",
    "    year_page_count = {}\n",
    "\n",
    "    for yr in years:\n",
    "        dy = data[data[\"year\"] == yr].copy()\n",
    "        patients_in_year = sorted(dy[\"patno\"].dropna().unique().tolist())\n",
    "\n",
    "        # 每張圖最多顯示 MAX_PER_FIG 位病患\n",
    "        for page_start in range(0, len(patients_in_year), MAX_PER_FIG):\n",
    "            batch_patients = patients_in_year[page_start:page_start + MAX_PER_FIG]\n",
    "            if not batch_patients:\n",
    "                continue\n",
    "\n",
    "            # 動態圖高：每位病患約 0.35 吋，介於 4~12 吋\n",
    "            n = len(batch_patients)\n",
    "            fig_h = max(4, min(12, 0.35 * n))\n",
    "            fig, ax = plt.subplots(figsize=(14, fig_h))\n",
    "\n",
    "            # 為右側兩欄文字預留空間，避免與圖內元素擠在一起（越小留白越大）\n",
    "            fig.subplots_adjust(right=0.82)\n",
    "\n",
    "            # 本頁資料\n",
    "            batch_df = dy[dy[\"patno\"].isin(batch_patients)].copy()\n",
    "\n",
    "            # X 軸範圍（整個年份，加右側留白放標註）\n",
    "            x_min = pd.to_datetime(f\"{yr}-01-01 00:00:00\")\n",
    "            x_max = pd.to_datetime(f\"{yr}-12-31 23:59:59\")\n",
    "            ax.set_xlim(x_min, x_max)  # 右側留白改用軸外文字方式處理\n",
    "\n",
    "            # Y 軸（病患 -> y 座標）\n",
    "            y_positions = {pid: i for i, pid in enumerate(batch_patients)}\n",
    "            yticks = list(range(len(batch_patients)))\n",
    "            ylabels = [str(pid) for pid in batch_patients]\n",
    "\n",
    "            # X 軸時間格式\n",
    "            locator = mdates.AutoDateLocator(minticks=4, maxticks=10)\n",
    "            formatter = mdates.ConciseDateFormatter(locator)\n",
    "            ax.xaxis.set_major_locator(locator)\n",
    "            ax.xaxis.set_major_formatter(formatter)\n",
    "\n",
    "            # 右側欄位標題（固定在軸外，x=1.02 / 1.10）\n",
    "            ytrans = ax.get_yaxis_transform()  # x: 軸比例 [0,1]；y: 資料座標\n",
    "            ax.text(1.02, len(batch_patients) - 0.5, \"ad_para=1\", transform=ytrans,\n",
    "                    va=\"bottom\", ha=\"left\", color=COLOR_BLUE, fontsize=9, fontweight=\"bold\", clip_on=False)\n",
    "            ax.text(1.10, len(batch_patients) - 0.5, \"windows\", transform=ytrans,\n",
    "                    va=\"bottom\", ha=\"left\", color=COLOR_RED, fontsize=9, fontweight=\"bold\", clip_on=False)\n",
    "\n",
    "            # 逐病患繪圖\n",
    "            for pid in batch_patients:\n",
    "                y = y_positions[pid]\n",
    "                gf = batch_df[batch_df[\"patno\"] == pid][[\"senddate\", \"ad_para\"]].sort_values(\"senddate\")\n",
    "\n",
    "                # --- 灰點：所有資料行 ---\n",
    "                if not gf.empty:\n",
    "                    ax.scatter(gf[\"senddate\"], np.full(len(gf), y),\n",
    "                               s=10, marker='o', color=COLOR_GRAY, alpha=0.9, linewidths=0, zorder=2)\n",
    "\n",
    "                # --- 藍點：ad_para=1 ---\n",
    "                mask_ad = gf[\"ad_para\"] == 1\n",
    "                if mask_ad.any():\n",
    "                    ax.scatter(gf.loc[mask_ad, \"senddate\"], np.full(mask_ad.sum(), y),\n",
    "                               s=14, marker='o', color=COLOR_BLUE, alpha=0.95, linewidths=0, zorder=3)\n",
    "\n",
    "                # --- 紅框：視窗片段（與本年相交者才畫；無填色，避免遮蓋點） ---\n",
    "                patient_windows = win_map.get(int(pid), [])\n",
    "                win_count_in_year = 0\n",
    "                for w in patient_windows:\n",
    "                    ws, we = w[\"start\"], w[\"end\"]\n",
    "                    # 僅處理與本年相交的片段\n",
    "                    if we < x_min or ws > x_max:\n",
    "                        continue\n",
    "                    # 將視窗限制在本年範圍內顯示\n",
    "                    draw_s = max(ws, x_min)\n",
    "                    draw_e = min(we, x_max)\n",
    "                    if draw_e <= draw_s:\n",
    "                        continue\n",
    "\n",
    "                    # 將 datetime 轉為 Matplotlib 數值日期（天數的 float），避免 Timestamp+float 型別衝突\n",
    "                    x0 = mdates.date2num(draw_s)\n",
    "                    x1 = mdates.date2num(draw_e)\n",
    "                    width_days = x1 - x0\n",
    "\n",
    "                    # 畫紅色細框（置於 zorder=1，不覆蓋灰/藍點）\n",
    "                    rect = Rectangle(\n",
    "                        (x0, y - 0.35),   # 左下角（數值日期, y-半高）\n",
    "                        width_days,        # 寬度（天）\n",
    "                        0.7,               # 高度\n",
    "                        fill=False, edgecolor=COLOR_RED, linewidth=1.0, zorder=1\n",
    "                    )\n",
    "                    # 指定使用資料座標系（日期數值與 y 的資料座標）\n",
    "                    rect.set_transform(ax.transData)\n",
    "                    ax.add_patch(rect)\n",
    "                    win_count_in_year += 1\n",
    "\n",
    "                # --- 右側標註（藍：ad_para=1 筆數；紅：本年片段數） ---\n",
    "                ad_cnt = int(mask_ad.sum())\n",
    "                # 固定在軸外兩欄，不會與圖內元素重疊\n",
    "                ax.text(1.02, y, f\"{ad_cnt}\", transform=ytrans, va=\"center\", ha=\"left\",\n",
    "                        color=COLOR_BLUE, fontsize=9, clip_on=False, zorder=5)\n",
    "                ax.text(1.10, y, f\"{win_count_in_year}\", transform=ytrans, va=\"center\", ha=\"left\",\n",
    "                        color=COLOR_RED, fontsize=9, clip_on=False, zorder=5)\n",
    "\n",
    "            # 標題與外觀\n",
    "            ax.set_title(\n",
    "                f\"Window segments & ad_para over time by patient — Year {yr}  \"\n",
    "                f\"(patients {page_start+1}-{page_start+len(batch_patients)} of {len(patients_in_year)})\",\n",
    "                fontsize=12, pad=12\n",
    "            )\n",
    "            ax.set_xlabel(\"Time\")\n",
    "            ax.set_yticks(yticks)\n",
    "            ax.set_yticklabels(ylabels, fontsize=9)\n",
    "            ax.grid(True, axis=\"x\", linestyle=\"--\", linewidth=0.6, alpha=0.5)\n",
    "            ax.set_axisbelow(True)\n",
    "\n",
    "            # 圖例（手動）\n",
    "            from matplotlib.lines import Line2D\n",
    "            legend_handles = [\n",
    "                Line2D([0], [0], marker='o', color='none', markerfacecolor=COLOR_GRAY, label='has time (row)', markersize=6),\n",
    "                Line2D([0], [0], marker='o', color='none', markerfacecolor=COLOR_BLUE, label='ad_para = 1',   markersize=7),\n",
    "                Line2D([0], [0], color=COLOR_RED, lw=1.2, label='window segment (W=120, S=60)'),\n",
    "            ]\n",
    "            ax.legend(handles=legend_handles, loc=\"upper right\", frameon=False)\n",
    "\n",
    "            plt.tight_layout()\n",
    "            out_file = os.path.join(OUT_DIR, f\"win_segments_{yr}_p{page_start // MAX_PER_FIG + 1}.png\")\n",
    "            fig.savefig(out_file, dpi=150)\n",
    "            plt.close(fig)\n",
    "\n",
    "            total_figs += 1\n",
    "            year_page_count[yr] = year_page_count.get(yr, 0) + 1\n",
    "\n",
    "    # 總結輸出\n",
    "    print(f\"\\n✅ Exported {total_figs} figure(s) to: {OUT_DIR}\")\n",
    "    print(f\"   Year count: {len(years)}\")\n",
    "    for yr in years:\n",
    "        print(f\"   - {yr}: {year_page_count.get(yr, 0)} figure(s)\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d478f113-a848-4e1a-af79-ece81bc2060a",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "64f92cf3-d4ec-4f45-a504-b30c17e56a09",
   "metadata": {},
   "outputs": [],
   "source": [
    "我要看間隔多久 推測後面會有幾筆資料"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3f4ad2ea-cce6-484e-8dec-a07e387dbadf",
   "metadata": {},
   "outputs": [],
   "source": [
    "我想要跑兩種圖\n",
    "百分位「里程碑」線圖（Percentile Lollipop）\n",
    "Ridgeline（密度山脊圖）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "35a4ff7c-7158-468c-b41b-8e13500b2b06",
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"ECDF 累積分佈曲線畫出來超怪 所以我要分成兩張圖跑\n",
    "第一張是直方圖，只取間格小於兩小時的資料\n",
    "第二章圖是ECDF 累積分佈曲線，"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "id": "be4ea526-0465-490f-8c5b-59059f935b5c",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "✅ Saved intervals CSV: /home/jovyan/RT08/0925/1002/adpara_intervals.csv\n",
      "=== Intervals (minutes) summary ===\n",
      "Count            : 1129223\n",
      "Min / P50 / P90  : 0.0 / 1.0 / 1.7\n",
      "P95 / P99 / Max  : 2.0 / 5.0 / 58531.5\n",
      "Counts: <120 min = 1129110, >=121 min = 113\n",
      "✅ Saved figure: /home/jovyan/RT08/0925/1002/adpara_interval_ecdf.png\n"
     ]
    }
   ],
   "source": [
    "\"\"\"ECDF 累積分佈曲線\n",
    "軸：X＝間隔時間（分鐘，可用對數）；\n",
    "Y＝累積比例 0–1。\n",
    "\n",
    "圖表內容都用英文ˊ，程式碼註釋都用中文，圖表顏色用藍色漸層，字樣都不要重疊到\n",
    "1️⃣ 橫軸（X-axis）\n",
    "單位：兩次調參間隔（分鐘），也就是兩次的ad_para=1之間的間隔時間\n",
    "最後p95以上的分鐘數可以和再一起避免圖太大\n",
    "刻度設計：\n",
    "在間隔小於120分鐘以內的資料都用直方圖，從121分鐘再開始累積\n",
    "2️⃣ 縱軸（Y-axis）\n",
    "0–1（或 0–100%）的累積比例，用於 ECDF 曲線部分 也希望可以撰寫出實際筆數\n",
    "0–最大事件數，用於直方圖部分（兩者共用 X 軸、雙 Y 軸設計）\n",
    "\"\"\"\n",
    "\n",
    "# -*- coding: utf-8 -*-\n",
    "\"\"\"\n",
    "======== ECDF of time between parameter adjustments (ad_para=1) ========\n",
    "來源資料夾：/home/jovyan/RT08/0925/bling_svv_14\n",
    "輸出資料夾：/home/jovyan/RT08/0925/1002  （自動建立）\n",
    "輸出圖檔：adpara_interval_ecdf.png\n",
    "輸出明細：adpara_intervals.csv  （每一筆「兩次調參之間的間隔」）\n",
    "\n",
    "圖表說明（English UI, 中文註釋）：\n",
    "- X-axis: Minutes between consecutive adjustments (ad_para=1). (optional log scale)\n",
    "- Left Y-axis: Count (histogram) for intervals < 120 minutes (per-minute bins).\n",
    "- Right Y-axis: ECDF (0–1) over ALL intervals (>= 0 min). Intervals > P95 are clipped at P95 for visualization.\n",
    "- Blue gradient colors (histogram = lighter, ECDF line = darker). Text/legend positioned to avoid overlaps.\n",
    "\n",
    "注意：\n",
    "- 以「病患（patno）」為單位，對同一病患依 senddate 排序後，僅在 ad_para=1 的紀錄間計算時間差（分鐘）。\n",
    "- 僅在視覺化上將 X 值裁切到 P95；統計量、ECDF 計算均基於原始間隔。\n",
    "- 不會改動任何原始 CSV。\n",
    "\"\"\"\n",
    "\n",
    "import os, glob\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.dates as mdates\n",
    "\n",
    "# ================== 參數設定 ==================\n",
    "DATA_DIR = \"/home/jovyan/RT08/0925/bling_svv_14\"    # 讀取來源\n",
    "OUT_DIR  = \"/home/jovyan/RT08/0925/1002\"           # 輸出資料夾\n",
    "os.makedirs(OUT_DIR, exist_ok=True)\n",
    "\n",
    "# 欄位名稱\n",
    "COL_PAT  = \"patno\"\n",
    "COL_TIME = \"senddate\"\n",
    "COL_AD   = \"ad_para\"\n",
    "\n",
    "# 視覺化與規格\n",
    "HIST_EDGE_MIN = 0           # 直方圖最小邊界（分鐘）\n",
    "HIST_EDGE_MAX = 120         # 直方圖只能顯示 <120 分鐘區間\n",
    "USE_LOG_X     = False       # 是否將 X 軸切換為對數（含 ECDF 與直方圖的共享 X 軸）\n",
    "FIG_SIZE      = (12, 6)     # 圖片大小\n",
    "DPI           = 150\n",
    "\n",
    "# 顏色（藍色漸層）\n",
    "COLOR_HIST_LIGHT = \"#9ecae1\"   # 直方圖：淺藍\n",
    "COLOR_HIST_DARK  = \"#3182bd\"   # 直方圖：深藍（用於邊框或加深）\n",
    "COLOR_ECDF_LINE  = \"#08519c\"   # ECDF 線：更深的藍\n",
    "COLOR_GRID       = \"#cccccc\"\n",
    "\n",
    "# ================== 輔助函式 ==================\n",
    "def safe_read_cols(path, need_cols):\n",
    "    \"\"\"只讀必要欄位；若缺欄或讀取失敗則回傳 None。\"\"\"\n",
    "    try:\n",
    "        df = pd.read_csv(path, usecols=need_cols, low_memory=False)\n",
    "    except Exception:\n",
    "        try:\n",
    "            df = pd.read_csv(path, low_memory=False)\n",
    "        except Exception:\n",
    "            return None\n",
    "    if not set(need_cols).issubset(df.columns):\n",
    "        return None\n",
    "    return df\n",
    "\n",
    "def compute_intervals_minutes(df):\n",
    "    \"\"\"\n",
    "    計算所有病患在 ad_para=1 之間的間隔（分鐘）。\n",
    "    傳回：DataFrame，欄位：\n",
    "        ['__file__','patno','t_prev','t_curr','delta_min']\n",
    "    - 僅針對同一病患內的 ad_para=1 列，依時間排序後做差分。\n",
    "    - 遇到時間相同/逆序導致 delta <= 0 的，會剔除（不合理的間隔）。\n",
    "    \"\"\"\n",
    "    rows = []\n",
    "    for fname, gfile in df.groupby(\"__file__\"):\n",
    "        for pid, g in gfile.groupby(COL_PAT, dropna=True):\n",
    "            g1 = g[g[COL_AD] == 1].copy()\n",
    "            if g1.empty:\n",
    "                continue\n",
    "            g1 = g1.sort_values(COL_TIME)\n",
    "            # 計算相鄰 ad_para=1 之間的間隔（分鐘）\n",
    "            t = g1[COL_TIME].to_numpy()\n",
    "            if len(t) < 2:\n",
    "                continue\n",
    "            # 以 numpy 計算差分（分鐘）\n",
    "            dt = (t[1:] - t[:-1]) / np.timedelta64(1, \"m\")\n",
    "            # 剔除非正值\n",
    "            valid_mask = dt > 0\n",
    "            if not np.any(valid_mask):\n",
    "                continue\n",
    "            dt_valid = dt[valid_mask]\n",
    "            t_prev = t[:-1][valid_mask]\n",
    "            t_curr = t[1:][valid_mask]\n",
    "            for tp, tc, d in zip(t_prev, t_curr, dt_valid):\n",
    "                rows.append([fname, pid, pd.to_datetime(tp), pd.to_datetime(tc), float(d)])\n",
    "    if not rows:\n",
    "        return pd.DataFrame(columns=[\"__file__\", COL_PAT, \"t_prev\", \"t_curr\", \"delta_min\"])\n",
    "    out = pd.DataFrame(rows, columns=[\"__file__\", COL_PAT, \"t_prev\", \"t_curr\", \"delta_min\"])\n",
    "    return out\n",
    "\n",
    "# ================== 讀取資料並彙整 ==================\n",
    "need_cols = [COL_PAT, COL_TIME, COL_AD]\n",
    "paths = sorted(glob.glob(os.path.join(DATA_DIR, \"*.csv\")))\n",
    "\n",
    "all_parts = []\n",
    "for p in paths:\n",
    "    base = os.path.basename(p)\n",
    "    df = safe_read_cols(p, need_cols)\n",
    "    if df is None:\n",
    "        print(f\"[Warn] Skip {base}: missing one of {need_cols}\")\n",
    "        continue\n",
    "    df = df.copy()\n",
    "    # 轉型：時間、ad_para\n",
    "    df[COL_TIME] = pd.to_datetime(df[COL_TIME], errors=\"coerce\")\n",
    "    df = df.dropna(subset=[COL_TIME])\n",
    "    if df.empty:\n",
    "        continue\n",
    "    df[COL_AD] = pd.to_numeric(df[COL_AD], errors=\"coerce\").fillna(0).astype(int).clip(0, 1)\n",
    "    df[\"__file__\"] = base\n",
    "    all_parts.append(df[[ \"__file__\", COL_PAT, COL_TIME, COL_AD ]])\n",
    "\n",
    "if not all_parts:\n",
    "    print(\"[ECDF] No data available.\")\n",
    "else:\n",
    "    df_all = pd.concat(all_parts, ignore_index=True)\n",
    "\n",
    "    # 計算「兩次 ad_para=1」之間的間隔（分鐘）\n",
    "    intervals = compute_intervals_minutes(df_all)\n",
    "\n",
    "    # 若無任何間隔，直接輸出提示\n",
    "    if intervals.empty:\n",
    "        print(\"[ECDF] No consecutive ad_para=1 pairs found. Nothing to plot.\")\n",
    "    else:\n",
    "        # 另存 CSV 明細（可供追查）\n",
    "        intervals_path = os.path.join(OUT_DIR, \"adpara_intervals.csv\")\n",
    "        intervals.to_csv(intervals_path, index=False)\n",
    "        print(f\"✅ Saved intervals CSV: {intervals_path}\")\n",
    "\n",
    "        # ========== 統計摘要（印出在程式碼執行輸出中） ==========\n",
    "        x = intervals[\"delta_min\"].to_numpy()\n",
    "        x_sorted = np.sort(x)\n",
    "        n = len(x_sorted)\n",
    "        p50 = float(np.percentile(x_sorted, 50))\n",
    "        p90 = float(np.percentile(x_sorted, 90))\n",
    "        p95 = float(np.percentile(x_sorted, 95))\n",
    "        p99 = float(np.percentile(x_sorted, 99))\n",
    "        x_min = float(x_sorted[0])\n",
    "        x_max = float(x_sorted[-1])\n",
    "        lt_120 = int(np.sum(x_sorted < 120))\n",
    "        ge_121 = int(np.sum(x_sorted >= 121))\n",
    "\n",
    "        print(\"=== Intervals (minutes) summary ===\")\n",
    "        print(f\"Count            : {n}\")\n",
    "        print(f\"Min / P50 / P90  : {x_min:.1f} / {p50:.1f} / {p90:.1f}\")\n",
    "        print(f\"P95 / P99 / Max  : {p95:.1f} / {p99:.1f} / {x_max:.1f}\")\n",
    "        print(f\"Counts: <120 min = {lt_120}, >=121 min = {ge_121}\")\n",
    "\n",
    "        # ========== 準備繪圖資料 ==========\n",
    "        # 直方圖資料：僅 < 120 分鐘者\n",
    "        hist_mask = x_sorted < HIST_EDGE_MAX\n",
    "        x_hist = x_sorted[hist_mask]\n",
    "\n",
    "        # ECDF：用全部間隔\n",
    "        # 這裡同時準備「視覺化裁切到 P95」的 X 值（避免尾端過長）\n",
    "        x_clip = np.minimum(x_sorted, p95)\n",
    "\n",
    "        # ECDF 計算（基於原始 x_sorted 做比例）\n",
    "        y_ecdf = np.arange(1, n + 1, dtype=float) / n\n",
    "\n",
    "        # ========== 畫圖 ==========\n",
    "        fig, ax = plt.subplots(figsize=FIG_SIZE)\n",
    "        ax2 = ax.twinx()  # 第二個 Y 軸給 ECDF\n",
    "\n",
    "        # 先畫直方圖（左 Y 軸：counts），bin 為 1 分鐘\n",
    "        if len(x_hist) > 0:\n",
    "            bins = np.arange(HIST_EDGE_MIN, HIST_EDGE_MAX + 1, 1)  # 每 1 分鐘一格\n",
    "            counts, edges, patches = ax.hist(\n",
    "                x_hist,\n",
    "                bins=bins,\n",
    "                align=\"left\",\n",
    "                color=COLOR_HIST_LIGHT,\n",
    "                edgecolor=COLOR_HIST_DARK,\n",
    "                linewidth=0.8,\n",
    "                alpha=0.9,\n",
    "                label=\"Count (<120 min)\"\n",
    "            )\n",
    "            # 讓顏色有些微漸層感（由淺到深）\n",
    "            if len(patches) > 1:\n",
    "                for i, patch in enumerate(patches):\n",
    "                    # 按 bin 的位置比例加深顏色\n",
    "                    frac = i / max(1, len(patches)-1)\n",
    "                    # 線性插值：由淺藍往深藍\n",
    "                    patch.set_facecolor(\n",
    "                        (0.62 - 0.30*frac, 0.79 - 0.40*frac, 0.88 - 0.50*frac)  # 粗略調整的藍色漸層\n",
    "                    )\n",
    "        else:\n",
    "            counts = np.array([])\n",
    "\n",
    "        # 再畫 ECDF（右 Y 軸：比例 0–1）\n",
    "        # 視覺化 X 使用裁切後的 x_clip，避免長尾撐太開；但比例仍基於原始排序\n",
    "        ax2.plot(\n",
    "            x_clip, y_ecdf,\n",
    "            color=COLOR_ECDF_LINE,\n",
    "            linewidth=2.0,\n",
    "            label=\"ECDF (all intervals)\"\n",
    "        )\n",
    "\n",
    "        # 軸設定（語系：English）\n",
    "        ax.set_title(\"Intervals between adjustments (ad_para=1)\\nHistogram (<120 min) + ECDF (all)\",\n",
    "                     loc=\"center\", pad=12)\n",
    "        ax.set_xlabel(\"Minutes between consecutive adjustments\")\n",
    "        ax.set_ylabel(\"Count (for intervals < 120 min)\")\n",
    "        ax2.set_ylabel(\"Cumulative proportion (ECDF)\")\n",
    "\n",
    "        # Y2（ECDF）固定 0–1\n",
    "        ax2.set_ylim(0, 1)\n",
    "\n",
    "        # X 軸對數選項（注意：0 無法對數，極小值替換為 0.5 以避免報錯）\n",
    "        if USE_LOG_X:\n",
    "            # 視覺化上將 0 置換成 0.5（僅限顯示，不影響統計）\n",
    "            xmin_plot = max(0.5, x_clip.min())\n",
    "            xmax_plot = x_clip.max()\n",
    "            ax.set_xscale(\"log\")\n",
    "            ax2.set_xscale(\"log\")\n",
    "            ax.set_xlim(xmin_plot, xmax_plot)\n",
    "        else:\n",
    "            # 左右界以裁切後的極值顯示，右邊略留空間放註記\n",
    "            ax.set_xlim(0, x_clip.max() * 1.03)\n",
    "\n",
    "        # 網格與外觀\n",
    "        ax.grid(True, axis=\"both\", linestyle=\"--\", linewidth=0.6, alpha=0.5, color=COLOR_GRID)\n",
    "        ax.set_axisbelow(True)\n",
    "\n",
    "        # 圖例：放在圖外右側，避免擠在一起\n",
    "        lines_labels = []\n",
    "        for a in (ax, ax2):\n",
    "            h, l = a.get_legend_handles_labels()\n",
    "            lines_labels += list(zip(h, l))\n",
    "        if lines_labels:\n",
    "            handles, labels = zip(*lines_labels)\n",
    "            fig.legend(handles, labels, loc=\"upper right\", bbox_to_anchor=(0.98, 0.98), frameon=False)\n",
    "\n",
    "        # 右上角標註統計（避免與圖重疊）\n",
    "        summary_txt = (\n",
    "            f\"N={n} | P50={p50:.1f}m  P90={p90:.1f}m  P95={p95:.1f}m  P99={p99:.1f}m  Max={x_max:.1f}m\\n\"\n",
    "            f\"<120m count={lt_120} | ≥121m count={ge_121}  (ECDF clipped at P95)\"\n",
    "        )\n",
    "        fig.text(0.5, 0.98, summary_txt, ha=\"center\", va=\"top\", fontsize=9, color=COLOR_ECDF_LINE)\n",
    "\n",
    "        # 版面調整，避免文字重疊\n",
    "        plt.tight_layout(rect=[0, 0, 1, 0.94])\n",
    "\n",
    "        out_png = os.path.join(OUT_DIR, \"adpara_interval_ecdf.png\")\n",
    "        fig.savefig(out_png, dpi=DPI)\n",
    "        plt.close(fig)\n",
    "        print(f\"✅ Saved figure: {out_png}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "46eebf62-cf38-4f83-99ed-9dc82a2c1278",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "id": "cc38114b-54da-4f96-af00-91774d2a2362",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "✅ Saved percentiles CSV: /home/jovyan/RT08/0925/1002/adpara_interval_percentiles_by_mode.csv\n",
      "✅ Saved lollipop figure: /home/jovyan/RT08/0925/1002/adpara_interval_percentile_lollipop.png\n"
     ]
    }
   ],
   "source": [
    "\"\"\"百分位「里程碑」線圖（Percentile Lollipop）\n",
    "軸：X＝間隔時間；在固定的 Y 列上，用「棒棒糖」標出 P10、P25、P50、P75、P90、P95。\n",
    "三種模式分三列或同列不同顏色。\n",
    "圖表內容都用英文ˊ，程式碼註釋都用中文，圖表顏色用藍色漸層，字樣都不要重疊到\n",
    "\n",
    "橫軸（X）：間隔時間（分鐘或對數刻度）\n",
    "縱軸（Y）：呼吸器模式（分三列，或以顏色區分同列顯示）\n",
    "每一種模式對應多個「棒棒糖」標記（點＋橫線），分別代表：\n",
    "P10、P25、P50（中位數）、P75、P90、P95 六個百分位位置\n",
    "棒棒糖形狀：\n",
    "圓點位置表示該百分位的間隔時間\n",
    "連線強調各百分位的相對差距\n",
    "顏色區分：三種呼吸器模式使用固定三色（如藍、綠、橘）\n",
    "標籤：每個百分位點可附上實際數值（例如「P50 = 42 分鐘」）\n",
    "\"\"\"\n",
    "# -*- coding: utf-8 -*-\n",
    "\"\"\"\n",
    "======== Percentile Lollipop of intervals between adjustments (ad_para=1) ========\n",
    "來源資料夾：/home/jovyan/RT08/0925/bling_svv_14\n",
    "輸出資料夾：/home/jovyan/RT08/0925/1002  （自動建立）\n",
    "輸出圖檔：adpara_interval_percentile_lollipop.png\n",
    "輸出明細：adpara_interval_percentiles_by_mode.csv  （各模式的 P10/P25/P50/P75/P90/P95）\n",
    "\n",
    "圖表（English UI；中文註釋）：\n",
    "- X-axis: Minutes between consecutive adjustments.\n",
    "- Y-axis: Ventilator mode (three rows).\n",
    "- For each mode, lollipop marks P10, P25, P50, P75, P90, P95 on a fixed Y row.\n",
    "- Colors: mode_1=blue, mode_2=green, mode_3=orange; labels offset to avoid overlaps.\n",
    "\n",
    "重要計算邏輯（請留意）：\n",
    "- 以「病患（patno）」為單位，篩選 ad_para=1 的列並依 senddate 排序。\n",
    "- 每兩次相鄰的 ad_para=1 計算間隔（分鐘），僅保留間隔 > 0 的樣本。\n",
    "- 針對每個間隔，將其「歸屬模式」定義為「前一筆（t_prev）的呼吸器模式」：\n",
    "  這表示「在該模式下經過多少分鐘才發生下一次調參」。\n",
    "- 僅納入 one-hot 明確的模式（mode_1/2/3 恰有一個=1）；若同時多個=1 或皆為 0，則跳過該筆。\n",
    "- 只讀檔、不改原始資料；所有輸出寫到 OUT_DIR。\n",
    "\n",
    "如需改成「僅計算同模式連續的兩次調參」才算一個間隔，可於下面標註處將 `keep_all_mode_transitions=True`\n",
    "改為 False，則會只保留「前後兩點模式相同」的間隔。\n",
    "\"\"\"\n",
    "\n",
    "import os, glob\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# ================== 參數設定 ==================\n",
    "DATA_DIR = \"/home/jovyan/RT08/0925/bling_svv_14\"    # 讀取來源\n",
    "OUT_DIR  = \"/home/jovyan/RT08/0925/1002\"           # 輸出資料夾\n",
    "os.makedirs(OUT_DIR, exist_ok=True)\n",
    "\n",
    "# 欄位名稱\n",
    "COL_PAT   = \"patno\"\n",
    "COL_TIME  = \"senddate\"\n",
    "COL_AD    = \"ad_para\"\n",
    "COL_M1    = \"mode_1\"\n",
    "COL_M2    = \"mode_2\"\n",
    "COL_M3    = \"mode_3\"\n",
    "\n",
    "# 百分位定義\n",
    "PCTS = [10, 25, 50, 75, 90, 95]\n",
    "\n",
    "# 視覺化\n",
    "FIG_SIZE   = (12, 6)\n",
    "DPI        = 150\n",
    "LOG_X      = False  # 是否使用對數 X 軸（為避免 0，圖形會自動自保）\n",
    "\n",
    "# 顏色（固定三色對應模式）\n",
    "MODE_COLORS = {\n",
    "    \"mode_1\": \"#1f77b4\",  # blue\n",
    "    \"mode_2\": \"#2ca02c\",  # green\n",
    "    \"mode_3\": \"#ff7f0e\",  # orange\n",
    "}\n",
    "\n",
    "# 若想「保留所有模式轉換也計算間隔」，設 True（預設）\n",
    "# 若想「僅保留同模式連續事件的間隔」，設 False\n",
    "keep_all_mode_transitions = True\n",
    "\n",
    "# ================== 輔助函式 ==================\n",
    "def safe_read_cols(path, need_cols):\n",
    "    \"\"\"只讀必要欄位；若缺欄或讀取失敗則回傳 None。\"\"\"\n",
    "    try:\n",
    "        df = pd.read_csv(path, usecols=need_cols, low_memory=False)\n",
    "    except Exception:\n",
    "        try:\n",
    "            df = pd.read_csv(path, low_memory=False)\n",
    "        except Exception:\n",
    "            return None\n",
    "    if not set(need_cols).issubset(df.columns):\n",
    "        return None\n",
    "    return df\n",
    "\n",
    "def resolve_mode_row(row):\n",
    "    \"\"\"\n",
    "    解析一列的 one-hot 模式，回傳 'mode_1' / 'mode_2' / 'mode_3' 或 None。\n",
    "    僅接受「恰有一個=1」，其餘情形（0個或多個=1）回傳 None。\n",
    "    \"\"\"\n",
    "    vals = {\n",
    "        \"mode_1\": int(pd.to_numeric(row.get(COL_M1, 0), errors=\"coerce\") == 1),\n",
    "        \"mode_2\": int(pd.to_numeric(row.get(COL_M2, 0), errors=\"coerce\") == 1),\n",
    "        \"mode_3\": int(pd.to_numeric(row.get(COL_M3, 0), errors=\"coerce\") == 1),\n",
    "    }\n",
    "    s = sum(vals.values())\n",
    "    if s != 1:\n",
    "        return None\n",
    "    for k, v in vals.items():\n",
    "        if v == 1:\n",
    "            return k\n",
    "    return None\n",
    "\n",
    "def compute_intervals_by_mode(df_all):\n",
    "    \"\"\"\n",
    "    計算「相鄰兩次 ad_para=1 的間隔（分鐘）」，並依「前一筆的模式」分組。\n",
    "    回傳：DataFrame 欄位：\n",
    "        ['__file__','patno','t_prev','t_curr','delta_min','mode_prev','mode_curr']\n",
    "    - 僅保留 delta_min > 0 的樣本。\n",
    "    - 若 keep_all_mode_transitions=False，則僅保留 mode_prev == mode_curr 的間隔。\n",
    "    - 僅在 mode_prev 能被唯一判定（one-hot）時納入；mode_curr 僅作為參考輸出。\n",
    "    \"\"\"\n",
    "    rows = []\n",
    "    for fname, gfile in df_all.groupby(\"__file__\"):\n",
    "        for pid, g in gfile.groupby(COL_PAT, dropna=True):\n",
    "            # 僅 ad_para=1 的時間點\n",
    "            g1 = g[g[COL_AD] == 1].copy()\n",
    "            if g1.empty:\n",
    "                continue\n",
    "            g1 = g1.sort_values(COL_TIME)\n",
    "\n",
    "            # 解析模式（逐列）\n",
    "            m_prev = g1.apply(resolve_mode_row, axis=1)\n",
    "            # 僅保留能唯一判定模式的列\n",
    "            g1 = g1.loc[m_prev.notna()].copy()\n",
    "            if len(g1) < 2:\n",
    "                continue\n",
    "            g1[\"mode\"] = m_prev[m_prev.notna()].values\n",
    "\n",
    "            # 相鄰差分\n",
    "            t = g1[COL_TIME].to_numpy()\n",
    "            dt = (t[1:] - t[:-1]) / np.timedelta64(1, \"m\")\n",
    "            mode_prev = g1[\"mode\"].to_numpy()[:-1]\n",
    "            mode_curr = g1[\"mode\"].to_numpy()[1:]\n",
    "\n",
    "            # 只保留正間隔\n",
    "            vmask = dt > 0\n",
    "            if not np.any(vmask):\n",
    "                continue\n",
    "\n",
    "            for tp, tc, d, mp, mc in zip(t[:-1][vmask], t[1:][vmask], dt[vmask], mode_prev[vmask], mode_curr[vmask]):\n",
    "                # 若只保留同模式連續，需過濾不同模式\n",
    "                if (not keep_all_mode_transitions) and (mp != mc):\n",
    "                    continue\n",
    "                rows.append([fname, pid, pd.to_datetime(tp), pd.to_datetime(tc), float(d), mp, mc])\n",
    "\n",
    "    cols = [\"__file__\", COL_PAT, \"t_prev\", \"t_curr\", \"delta_min\", \"mode_prev\", \"mode_curr\"]\n",
    "    return pd.DataFrame(rows, columns=cols) if rows else pd.DataFrame(columns=cols)\n",
    "\n",
    "# ================== 讀取資料並彙整 ==================\n",
    "need_cols = [COL_PAT, COL_TIME, COL_AD, COL_M1, COL_M2, COL_M3]\n",
    "paths = sorted(glob.glob(os.path.join(DATA_DIR, \"*.csv\")))\n",
    "\n",
    "parts = []\n",
    "for p in paths:\n",
    "    base = os.path.basename(p)\n",
    "    df = safe_read_cols(p, need_cols)\n",
    "    if df is None:\n",
    "        print(f\"[Warn] Skip {base}: missing one of {need_cols}\")\n",
    "        continue\n",
    "    df = df.copy()\n",
    "    # 時間轉型\n",
    "    df[COL_TIME] = pd.to_datetime(df[COL_TIME], errors=\"coerce\")\n",
    "    df = df.dropna(subset=[COL_TIME])\n",
    "    if df.empty:\n",
    "        continue\n",
    "    # 數值轉型\n",
    "    df[COL_AD] = pd.to_numeric(df[COL_AD], errors=\"coerce\").fillna(0).astype(int).clip(0, 1)\n",
    "    for c in (COL_M1, COL_M2, COL_M3):\n",
    "        df[c] = pd.to_numeric(df[c], errors=\"coerce\").fillna(0).astype(int).clip(0, 1)\n",
    "    df[\"__file__\"] = base\n",
    "    parts.append(df[[ \"__file__\", COL_PAT, COL_TIME, COL_AD, COL_M1, COL_M2, COL_M3 ]])\n",
    "\n",
    "if not parts:\n",
    "    print(\"[Lollipop] No data available.\")\n",
    "else:\n",
    "    df_all = pd.concat(parts, ignore_index=True)\n",
    "\n",
    "    # 計算間隔並帶模式資訊\n",
    "    intervals = compute_intervals_by_mode(df_all)\n",
    "    if intervals.empty:\n",
    "        print(\"[Lollipop] No valid consecutive ad_para=1 pairs with resolvable mode. Nothing to plot.\")\n",
    "    else:\n",
    "        # 依「前一筆的模式」分組計算百分位\n",
    "        pct_rows = []\n",
    "        for mode_name, g in intervals.groupby(\"mode_prev\"):\n",
    "            xs = g[\"delta_min\"].to_numpy()\n",
    "            if len(xs) == 0:\n",
    "                continue\n",
    "            for p in PCTS:\n",
    "                val = float(np.percentile(xs, p))\n",
    "                pct_rows.append([mode_name, p, val, len(xs)])\n",
    "        pct_df = pd.DataFrame(pct_rows, columns=[\"mode\", \"percentile\", \"minutes\", \"n_intervals\"])\n",
    "        pct_csv = os.path.join(OUT_DIR, \"adpara_interval_percentiles_by_mode.csv\")\n",
    "        pct_df.to_csv(pct_csv, index=False)\n",
    "        print(f\"✅ Saved percentiles CSV: {pct_csv}\")\n",
    "\n",
    "        # ========== 視覺化：Percentile Lollipop ==========\n",
    "        # Y 軸：三個模式各佔一列（若某模式沒有資料會自動略過）\n",
    "        modes_order = [\"mode_1\", \"mode_2\", \"mode_3\"]\n",
    "        y_positions = {m: i for i, m in enumerate(modes_order)}\n",
    "        # 僅保留有資料的模式\n",
    "        modes_present = [m for m in modes_order if m in pct_df[\"mode\"].unique().tolist()]\n",
    "        if not modes_present:\n",
    "            print(\"[Lollipop] No modes with percentile data.\")\n",
    "        else:\n",
    "            fig, ax = plt.subplots(figsize=FIG_SIZE)\n",
    "\n",
    "            # 為避免標籤重疊：對不同百分位的標籤採「上下交錯 + 微小水平偏移」\n",
    "            label_offsets = {\n",
    "                10: (0.0,  0.15),\n",
    "                25: (0.0, -0.18),\n",
    "                50: (0.0,  0.22),\n",
    "                75: (0.0, -0.20),\n",
    "                90: (0.0,  0.16),\n",
    "                95: (0.0, -0.14),\n",
    "            }\n",
    "\n",
    "            # 畫每個模式的一條基準線與百分位的 lollipop\n",
    "            for m in modes_present:\n",
    "                yy = y_positions[m]\n",
    "                color = MODE_COLORS.get(m, \"#666666\")\n",
    "\n",
    "                # 基準線（淡色，整條 Y 列）\n",
    "                ax.hlines(yy, xmin=0, xmax=pct_df[pct_df[\"mode\"]==m][\"minutes\"].max(),\n",
    "                          colors=\"#dddddd\", linestyles=\"-\", linewidth=1, zorder=1)\n",
    "\n",
    "                # 取該模式的百分位資料並依 percentile 排序\n",
    "                g = pct_df[pct_df[\"mode\"] == m].sort_values(\"percentile\")\n",
    "                xs = g[\"minutes\"].to_numpy()\n",
    "                ps = g[\"percentile\"].to_numpy()\n",
    "\n",
    "                # 連線（同一模式各百分位之間用細線連起來，強調梯度）\n",
    "                if len(xs) >= 2:\n",
    "                    ax.plot(xs, [yy]*len(xs), color=color, linewidth=1.5, alpha=0.8, zorder=2)\n",
    "\n",
    "                # lollipop：每個百分位畫一個細豎線 + 圓點\n",
    "                for xi, pi in zip(xs, ps):\n",
    "                    # 細豎線（棒棒糖的棒子）\n",
    "                    ax.vlines(xi, yy-0.18, yy+0.18, color=color, linewidth=2.0, alpha=0.9, zorder=3)\n",
    "                    # 圓點（棒棒糖頭）\n",
    "                    ax.scatter([xi], [yy], s=40, color=color, edgecolor=\"white\", linewidth=0.8, zorder=4)\n",
    "\n",
    "                    # 標籤（英文字樣；避免重疊採上下交錯）\n",
    "                    dx, dy = label_offsets.get(int(pi), (0.0, 0.15))\n",
    "                    ax.text(xi + dx, yy + dy, f\"P{int(pi)} = {xi:.1f} min\",\n",
    "                            ha=\"left\", va=\"center\", fontsize=8, color=color, zorder=5,\n",
    "                            bbox=dict(boxstyle=\"round,pad=0.15\", fc=\"white\", ec=\"none\", alpha=0.8))\n",
    "\n",
    "            # X 軸設定\n",
    "            ax.set_xlabel(\"Minutes between consecutive adjustments\")\n",
    "            if LOG_X:\n",
    "                # 對數軸時，避免 0；尋找全域最小正值\n",
    "                xmin = pct_df[\"minutes\"].replace(0, np.nan).min()\n",
    "                if pd.isna(xmin) or xmin <= 0:\n",
    "                    xmin = 0.5\n",
    "                xmax = pct_df[\"minutes\"].max() * 1.1\n",
    "                ax.set_xscale(\"log\")\n",
    "                ax.set_xlim(xmin, xmax)\n",
    "            else:\n",
    "                ax.set_xlim(left=0)\n",
    "\n",
    "            # Y 軸標籤（英文模式名）\n",
    "            yticks = [y_positions[m] for m in modes_present]\n",
    "            ylabels = [m for m in modes_present]\n",
    "            ax.set_yticks(yticks)\n",
    "            ax.set_yticklabels(ylabels)\n",
    "\n",
    "            # 標題與說明（英文）\n",
    "            ax.set_title(\"Percentile Lollipop — Intervals between adjustments\\n(P10/P25/P50/P75/P90/P95 by ventilator mode)\",\n",
    "                         pad=12)\n",
    "\n",
    "            # 網格與外觀\n",
    "            ax.grid(True, axis=\"x\", linestyle=\"--\", linewidth=0.6, alpha=0.5)\n",
    "            ax.set_axisbelow(True)\n",
    "\n",
    "            # 圖例（說明顏色對應模式；若需要可在右上角顯示）\n",
    "            # 這裡模式名稱已在 Y 軸，因此圖例可省略；若想要圖例，取消以下註解：\n",
    "            # from matplotlib.lines import Line2D\n",
    "            # legend_handles = [Line2D([0],[0], color=MODE_COLORS[m], lw=3, label=m) for m in modes_present]\n",
    "            # ax.legend(handles=legend_handles, loc=\"upper right\", frameon=False)\n",
    "\n",
    "            # 版面調整\n",
    "            plt.tight_layout()\n",
    "\n",
    "            out_png = os.path.join(OUT_DIR, \"adpara_interval_percentile_lollipop.png\")\n",
    "            fig.savefig(out_png, dpi=DPI)\n",
    "            plt.close(fig)\n",
    "            print(f\"✅ Saved lollipop figure: {out_png}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "id": "bb20f8ba-6939-41d7-a6d6-b4476e24ca54",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "✅ Copy completed.\n",
      "Source folder : /home/jovyan/RT08/0925/bling_svv_14\n",
      "Target folder : /home/jovyan/RT08/0925/copy/bling_svv_14\n",
      "Copied files  : 122\n"
     ]
    }
   ],
   "source": [
    "\"\"\" 安全複製整個資料夾 ========\n",
    "來源：/home/jovyan/RT08/0925/bling_svv_14/\n",
    "目標：/home/jovyan/RT08/0925/copy/bling_svv_14/\n",
    "說明：\n",
    "- 僅複製檔案內容，不會修改原始檔\n",
    "- 若目標資料夾不存在，會自動建立\n",
    "- 若目標資料夾已有同名檔案，將覆寫（保證版本一致）\n",
    "\"\"\"\n",
    "\n",
    "import os\n",
    "import shutil\n",
    "\n",
    "src_dir = \"/home/jovyan/RT08/0925/bling_svv_14\"\n",
    "dst_dir = \"/home/jovyan/RT08/0925/copy/bling_svv_14\"\n",
    "\n",
    "# 建立目標資料夾（如不存在）\n",
    "os.makedirs(dst_dir, exist_ok=True)\n",
    "\n",
    "# 統計用\n",
    "copied_files = 0\n",
    "\n",
    "# 逐一複製\n",
    "for fname in os.listdir(src_dir):\n",
    "    src_path = os.path.join(src_dir, fname)\n",
    "    dst_path = os.path.join(dst_dir, fname)\n",
    "\n",
    "    if os.path.isfile(src_path):\n",
    "        shutil.copy2(src_path, dst_path)  # copy2 會保留原始檔案時間與 metadata\n",
    "        copied_files += 1\n",
    "\n",
    "print(\"✅ Copy completed.\")\n",
    "print(f\"Source folder : {src_dir}\")\n",
    "print(f\"Target folder : {dst_dir}\")\n",
    "print(f\"Copied files  : {copied_files}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "id": "242a8dec-b930-42bb-a4c7-3cf2b27bf78d",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Info] Found 122 files in /home/jovyan/RT08/0925/bling_svv_14\n",
      "[OK] 089271.csv: rows=32419, ad_para_check=1 set on 24294 rows.\n",
      "[OK] 095323.csv: rows=23791, ad_para_check=1 set on 13461 rows.\n",
      "[OK] 095707.csv: rows=20180, ad_para_check=1 set on 15256 rows.\n",
      "[OK] 114309.csv: rows=71729, ad_para_check=1 set on 19141 rows.\n",
      "[OK] 230933.csv: rows=30249, ad_para_check=1 set on 23028 rows.\n",
      "[OK] 4216007.csv: rows=1433, ad_para_check=1 set on 0 rows.\n",
      "[OK] 7108162.csv: rows=239, ad_para_check=1 set on 177 rows.\n",
      "[OK] 7408338.csv: rows=1432, ad_para_check=1 set on 1061 rows.\n",
      "[OK] 7657698.csv: rows=1413, ad_para_check=1 set on 1364 rows.\n",
      "[OK] 7721164.csv: rows=483, ad_para_check=1 set on 450 rows.\n",
      "[OK] PatNo_ID_1560013303.csv: rows=2543, ad_para_check=1 set on 2079 rows.\n",
      "[OK] PatNo_ID_1562733396.csv: rows=2254, ad_para_check=1 set on 2164 rows.\n",
      "[OK] PatNo_ID_1563587183.csv: rows=5287, ad_para_check=1 set on 4987 rows.\n",
      "[OK] PatNo_ID_1564148644.csv: rows=17287, ad_para_check=1 set on 7962 rows.\n",
      "[OK] PatNo_ID_1565148312.csv: rows=5475, ad_para_check=1 set on 5014 rows.\n",
      "[OK] PatNo_ID_1565378038.csv: rows=2561, ad_para_check=1 set on 1377 rows.\n",
      "[OK] PatNo_ID_1566123680.csv: rows=42600, ad_para_check=1 set on 20688 rows.\n",
      "[OK] PatNo_ID_1566252197.csv: rows=3368, ad_para_check=1 set on 1702 rows.\n",
      "[OK] PatNo_ID_1566279967.csv: rows=1235, ad_para_check=1 set on 904 rows.\n",
      "[OK] PatNo_ID_1566671274.csv: rows=25359, ad_para_check=1 set on 21368 rows.\n",
      "[OK] PatNo_ID_1566911879.csv: rows=53999, ad_para_check=1 set on 42601 rows.\n",
      "[OK] PatNo_ID_1567747650.csv: rows=10901, ad_para_check=1 set on 9315 rows.\n",
      "[OK] PatNo_ID_1567804800.csv: rows=20578, ad_para_check=1 set on 11675 rows.\n",
      "[OK] PatNo_ID_1567832735.csv: rows=36580, ad_para_check=1 set on 31490 rows.\n",
      "[OK] PatNo_ID_1568039398.csv: rows=34519, ad_para_check=1 set on 31395 rows.\n",
      "[OK] PatNo_ID_1568574099.csv: rows=13946, ad_para_check=1 set on 9845 rows.\n",
      "[OK] PatNo_ID_1568813269.csv: rows=4867, ad_para_check=1 set on 4396 rows.\n",
      "[OK] PatNo_ID_1568952422.csv: rows=1184, ad_para_check=1 set on 1012 rows.\n",
      "[OK] PatNo_ID_1569083701.csv: rows=3328, ad_para_check=1 set on 1711 rows.\n",
      "[OK] PatNo_ID_1569944983.csv: rows=7650, ad_para_check=1 set on 6394 rows.\n",
      "[OK] PatNo_ID_1570089466.csv: rows=41343, ad_para_check=1 set on 12100 rows.\n",
      "[OK] PatNo_ID_1570242703.csv: rows=10646, ad_para_check=1 set on 9502 rows.\n",
      "[OK] PatNo_ID_1570273244.csv: rows=9730, ad_para_check=1 set on 9138 rows.\n",
      "[OK] PatNo_ID_1570642083.csv: rows=19731, ad_para_check=1 set on 12832 rows.\n",
      "[OK] PatNo_ID_1571945701.csv: rows=15731, ad_para_check=1 set on 13580 rows.\n",
      "[OK] PatNo_ID_1572481361.csv: rows=34540, ad_para_check=1 set on 29442 rows.\n",
      "[OK] PatNo_ID_1572562839.csv: rows=20966, ad_para_check=1 set on 14484 rows.\n",
      "[OK] PatNo_ID_1572831765.csv: rows=2696, ad_para_check=1 set on 1797 rows.\n",
      "[OK] PatNo_ID_1572976822.csv: rows=6764, ad_para_check=1 set on 4158 rows.\n",
      "[OK] PatNo_ID_1573063188.csv: rows=5080, ad_para_check=1 set on 3760 rows.\n",
      "[OK] PatNo_ID_1573249295.csv: rows=7151, ad_para_check=1 set on 6810 rows.\n",
      "[OK] PatNo_ID_1573964540.csv: rows=4394, ad_para_check=1 set on 3666 rows.\n",
      "[OK] PatNo_ID_1574148494.csv: rows=47154, ad_para_check=1 set on 41645 rows.\n",
      "[OK] PatNo_ID_1574270349.csv: rows=6534, ad_para_check=1 set on 3828 rows.\n",
      "[OK] PatNo_ID_1574528808.csv: rows=14817, ad_para_check=1 set on 7481 rows.\n",
      "[OK] PatNo_ID_1574831525.csv: rows=521, ad_para_check=1 set on 345 rows.\n",
      "[OK] PatNo_ID_1574987447.csv: rows=19843, ad_para_check=1 set on 17907 rows.\n",
      "[OK] PatNo_ID_1575060177.csv: rows=5290, ad_para_check=1 set on 4288 rows.\n",
      "[OK] PatNo_ID_1575256902.csv: rows=9587, ad_para_check=1 set on 4247 rows.\n",
      "[OK] PatNo_ID_1575445051.csv: rows=1801, ad_para_check=1 set on 1628 rows.\n",
      "[OK] PatNo_ID_1575502382.csv: rows=6604, ad_para_check=1 set on 6048 rows.\n",
      "[OK] PatNo_ID_1575975485.csv: rows=16459, ad_para_check=1 set on 13616 rows.\n",
      "[OK] PatNo_ID_1576115572.csv: rows=18717, ad_para_check=1 set on 14649 rows.\n",
      "[OK] PatNo_ID_1576116479.csv: rows=1263, ad_para_check=1 set on 1150 rows.\n",
      "[OK] PatNo_ID_1576301569.csv: rows=3207, ad_para_check=1 set on 2998 rows.\n",
      "[OK] PatNo_ID_1576964560.csv: rows=24192, ad_para_check=1 set on 16879 rows.\n",
      "[OK] PatNo_ID_1577042911.csv: rows=38357, ad_para_check=1 set on 30705 rows.\n",
      "[OK] PatNo_ID_1577487284.csv: rows=2345, ad_para_check=1 set on 1596 rows.\n",
      "[OK] PatNo_ID_1578784257.csv: rows=33917, ad_para_check=1 set on 24047 rows.\n",
      "[OK] PatNo_ID_1579198603.csv: rows=2046, ad_para_check=1 set on 1958 rows.\n",
      "[OK] PatNo_ID_1579498177.csv: rows=21688, ad_para_check=1 set on 20726 rows.\n",
      "[OK] PatNo_ID_1580062580.csv: rows=2150, ad_para_check=1 set on 2014 rows.\n",
      "[OK] PatNo_ID_1580096720.csv: rows=1423, ad_para_check=1 set on 957 rows.\n",
      "[OK] PatNo_ID_1580107637.csv: rows=2698, ad_para_check=1 set on 0 rows.\n",
      "[OK] PatNo_ID_1580244614.csv: rows=5278, ad_para_check=1 set on 3060 rows.\n",
      "[OK] PatNo_ID_1580766093.csv: rows=18070, ad_para_check=1 set on 15507 rows.\n",
      "[OK] PatNo_ID_1581003248.csv: rows=7776, ad_para_check=1 set on 5528 rows.\n",
      "[OK] PatNo_ID_1581019504.csv: rows=28177, ad_para_check=1 set on 18980 rows.\n",
      "[OK] PatNo_ID_1581633231.csv: rows=15995, ad_para_check=1 set on 12327 rows.\n",
      "[OK] PatNo_ID_1581692973.csv: rows=2723, ad_para_check=1 set on 2359 rows.\n",
      "[OK] PatNo_ID_1582452511.csv: rows=5196, ad_para_check=1 set on 4888 rows.\n",
      "[OK] PatNo_ID_1582635996.csv: rows=13046, ad_para_check=1 set on 12053 rows.\n",
      "[OK] PatNo_ID_1582849900.csv: rows=7411, ad_para_check=1 set on 1489 rows.\n",
      "[OK] PatNo_ID_1582937076.csv: rows=23990, ad_para_check=1 set on 21186 rows.\n",
      "[OK] PatNo_ID_1584158973.csv: rows=2882, ad_para_check=1 set on 2271 rows.\n",
      "[OK] PatNo_ID_1584397376.csv: rows=638, ad_para_check=1 set on 250 rows.\n",
      "[OK] PatNo_ID_1586172659.csv: rows=38559, ad_para_check=1 set on 28497 rows.\n",
      "[OK] PatNo_ID_1586696634.csv: rows=3569, ad_para_check=1 set on 2364 rows.\n",
      "[OK] PatNo_ID_1586897008.csv: rows=6687, ad_para_check=1 set on 4089 rows.\n",
      "[OK] PatNo_ID_1587490083.csv: rows=45186, ad_para_check=1 set on 39985 rows.\n",
      "[OK] PatNo_ID_1588632604.csv: rows=2608, ad_para_check=1 set on 2160 rows.\n",
      "[OK] PatNo_ID_1588673465.csv: rows=10077, ad_para_check=1 set on 5318 rows.\n",
      "[OK] PatNo_ID_1588794796.csv: rows=9376, ad_para_check=1 set on 9149 rows.\n",
      "[OK] PatNo_ID_1588957997.csv: rows=10966, ad_para_check=1 set on 8546 rows.\n",
      "[OK] PatNo_ID_1589018086.csv: rows=13472, ad_para_check=1 set on 11904 rows.\n",
      "[OK] PatNo_ID_1589034524.csv: rows=50081, ad_para_check=1 set on 44430 rows.\n",
      "[OK] PatNo_ID_1589324603.csv: rows=4187, ad_para_check=1 set on 3058 rows.\n",
      "[OK] PatNo_ID_1589918099.csv: rows=9333, ad_para_check=1 set on 0 rows.\n",
      "[OK] PatNo_ID_1590136310.csv: rows=5580, ad_para_check=1 set on 3940 rows.\n",
      "[OK] PatNo_ID_1590616537.csv: rows=14208, ad_para_check=1 set on 10932 rows.\n",
      "[OK] PatNo_ID_1590854576.csv: rows=15879, ad_para_check=1 set on 14268 rows.\n",
      "[OK] PatNo_ID_1591609798.csv: rows=35624, ad_para_check=1 set on 27077 rows.\n",
      "[OK] PatNo_ID_1592044724.csv: rows=6815, ad_para_check=1 set on 6499 rows.\n",
      "[OK] PatNo_ID_1592560504.csv: rows=18052, ad_para_check=1 set on 13132 rows.\n",
      "[OK] PatNo_ID_1593087886.csv: rows=24163, ad_para_check=1 set on 19885 rows.\n",
      "[OK] PatNo_ID_1593416100.csv: rows=3328, ad_para_check=1 set on 3052 rows.\n",
      "[OK] PatNo_ID_1593472048.csv: rows=8230, ad_para_check=1 set on 5295 rows.\n",
      "[OK] PatNo_ID_1593593586.csv: rows=18882, ad_para_check=1 set on 14424 rows.\n",
      "[OK] PatNo_ID_1593720818.csv: rows=3911, ad_para_check=1 set on 2568 rows.\n",
      "[OK] PatNo_ID_1593838524.csv: rows=2178, ad_para_check=1 set on 1984 rows.\n",
      "[OK] PatNo_ID_1594173718.csv: rows=344, ad_para_check=1 set on 260 rows.\n",
      "[OK] PatNo_ID_1594294180.csv: rows=18334, ad_para_check=1 set on 7717 rows.\n",
      "[OK] PatNo_ID_1594305136.csv: rows=18679, ad_para_check=1 set on 14424 rows.\n",
      "[OK] PatNo_ID_1594309746.csv: rows=3745, ad_para_check=1 set on 3101 rows.\n",
      "[OK] PatNo_ID_1594319286.csv: rows=4107, ad_para_check=1 set on 1595 rows.\n",
      "[OK] PatNo_ID_1594320763.csv: rows=2431, ad_para_check=1 set on 2086 rows.\n",
      "[OK] PatNo_ID_1594322594.csv: rows=5380, ad_para_check=1 set on 5114 rows.\n",
      "[OK] PatNo_ID_1594335109.csv: rows=5116, ad_para_check=1 set on 3307 rows.\n",
      "[OK] PatNo_ID_1594423683.csv: rows=5023, ad_para_check=1 set on 4074 rows.\n",
      "[OK] PatNo_ID_1594437309.csv: rows=10035, ad_para_check=1 set on 8992 rows.\n",
      "[OK] PatNo_ID_1594439781.csv: rows=7691, ad_para_check=1 set on 6700 rows.\n",
      "[OK] PatNo_ID_1594441887.csv: rows=10203, ad_para_check=1 set on 8137 rows.\n",
      "[OK] PatNo_ID_1594448501.csv: rows=5106, ad_para_check=1 set on 0 rows.\n",
      "[OK] PatNo_ID_1594455578.csv: rows=262, ad_para_check=1 set on 203 rows.\n",
      "[OK] PatNo_ID_1594464829.csv: rows=4000, ad_para_check=1 set on 3013 rows.\n",
      "[OK] PatNo_ID_1594467719.csv: rows=2560, ad_para_check=1 set on 1075 rows.\n",
      "[OK] PatNo_ID_1594471407.csv: rows=11433, ad_para_check=1 set on 3245 rows.\n",
      "[OK] PatNo_ID_1594479330.csv: rows=3727, ad_para_check=1 set on 1378 rows.\n",
      "[OK] PatNo_ID_1594511911.csv: rows=5488, ad_para_check=1 set on 4895 rows.\n",
      "[OK] PatNo_ID_1594511914.csv: rows=9717, ad_para_check=1 set on 8507 rows.\n",
      "[OK] PatNo_ID_1594528842.csv: rows=2491, ad_para_check=1 set on 1868 rows.\n",
      "[OK] PatNo_ID_1594533379.csv: rows=1030, ad_para_check=1 set on 904 rows.\n",
      "\n",
      "===== SUMMARY =====\n",
      "Processed files     : 122 OK, 0 with warnings\n",
      "Total rows seen     : 1567309\n",
      "Total ad_para_check=1 across all files: 1129341\n",
      "Rule #5 guard       : If duplicate timestamps within a patient (NaN_check=1 rows) are found,\n",
      "                      the script prints details and aborts immediately.\n"
     ]
    }
   ],
   "source": [
    "\"\"\"\"在 /home/jovyan/RT08/0925/bling_svv_14/ 中為每個 CSV 新增 ad_para_check 欄位，定義：\n",
    "1) 僅在 NaN_check=1 的列上評估是否有「調參」。\n",
    "2) 以 (patno) 分組，依 senddate 排序建立時序。\n",
    "3) 與上一筆(同為 NaN_check=1)比較：\n",
    "   - 若 rrhzsetactual 或 mvsetactual 或 peepepap 任一數值有變化 → ad_para=1\n",
    "   - 或 mode_1/mode_2/mode_3 任一位元有變化(0↔1) → ad_para=1\n",
    "4) 每位病患的第一筆 NaN_check=1 → ad_para=0\n",
    "5) 若同一病患內出現相同 senddate（重複時間點），印出細節後立刻停止整個流程（不再處理其餘檔案）。\n",
    "\n",
    "注意：\n",
    "- 僅修改/寫回 ad_para 欄位，不改動其他欄位。\n",
    "- 若缺少必要欄位，該檔案 ad_para 一律設 0 並警告。\n",
    "\"\"\"\n",
    "# -*- coding: utf-8 -*-\n",
    "\"\"\"\n",
    "======== Add/overwrite column: ad_para_check (IN-PLACE) ========\n",
    "目標資料夾：/home/jovyan/RT08/0925/bling_svv_14\n",
    "行為說明（請仔細閱讀）：\n",
    "1) 僅在 NaN_check=1 的列上評估是否有「調參」；其他列 ad_para_check=0\n",
    "2) 以 patno 分組，依 senddate 排序建立時序\n",
    "3) 與「上一筆同為 NaN_check=1」比較：\n",
    "   - 若 rrhzsetactual 或 mvsetactual 或 peepepap 任一數值有變化 → ad_para_check=1\n",
    "   - 或 mode_1 / mode_2 / mode_3 任一位元有變化(0↔1) → ad_para_check=1\n",
    "4) 每位病患的第一筆 NaN_check=1 → ad_para_check=0（因為沒有上一筆可比）\n",
    "5) 若同一病患內出現「相同 senddate」的重複時間點（在 NaN_check=1 的列中檢查），\n",
    "   會印出詳細資訊後「立刻停止整個流程」（不再處理其餘檔案）\n",
    "\n",
    "注意：\n",
    "- 僅新增/覆寫「ad_para_check」欄位；其餘欄位原封不動\n",
    "- 若缺少必要欄位，該檔 ad_para_check 一律設 0 並警告，仍會寫回（只新增此欄）\n",
    "- 本程式會直接「覆寫同名 CSV 檔」（就地更新）；如需備份，請先自行備份整個資料夾\n",
    "\n",
    "必要欄位：\n",
    "['patno','senddate','NaN_check','rrhzsetactual','mvsetactual','peepepap','mode_1','mode_2','mode_3']\n",
    "\"\"\"\n",
    "\n",
    "import os, glob, sys\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "DIR_IN = \"/home/jovyan/RT08/0925/bling_svv_14\"\n",
    "\n",
    "REQ_COLS = [\n",
    "    \"patno\", \"senddate\", \"NaN_check\",\n",
    "    \"rrhzsetactual\", \"mvsetactual\", \"peepepap\",\n",
    "    \"mode_1\", \"mode_2\", \"mode_3\",\n",
    "]\n",
    "\n",
    "# 需要比較變化的欄位（數值）\n",
    "NUM_COLS = [\"rrhzsetactual\", \"mvsetactual\", \"peepepap\"]\n",
    "# 需要比較變化的欄位（位元/one-hot）\n",
    "MODE_COLS = [\"mode_1\", \"mode_2\", \"mode_3\"]\n",
    "\n",
    "csv_paths = sorted(glob.glob(os.path.join(DIR_IN, \"*.csv\")))\n",
    "print(f\"[Info] Found {len(csv_paths)} files in {DIR_IN}\")\n",
    "\n",
    "files_ok = 0\n",
    "files_warn = 0\n",
    "total_rows = 0\n",
    "total_changed_to_1 = 0\n",
    "\n",
    "def normalize_numeric(col):\n",
    "    \"\"\"將欄位轉成可比較的數值（浮點），無法轉換者視為 NaN。\"\"\"\n",
    "    return pd.to_numeric(col, errors=\"coerce\")\n",
    "\n",
    "def normalize_mode(col):\n",
    "    \"\"\"將模式欄位正規化為 0/1（非 1 視為 0；NaN->0）。\"\"\"\n",
    "    c = pd.to_numeric(col, errors=\"coerce\").fillna(0)\n",
    "    return (c == 1).astype(int)\n",
    "\n",
    "for i, fp in enumerate(csv_paths, 1):\n",
    "    base = os.path.basename(fp)\n",
    "    try:\n",
    "        df = pd.read_csv(fp, low_memory=False)\n",
    "    except Exception as e:\n",
    "        print(f\"[ERROR] {base}: cannot read csv ({e}). Skip.\")\n",
    "        files_warn += 1\n",
    "        continue\n",
    "\n",
    "    n_before = len(df)\n",
    "    total_rows += n_before\n",
    "\n",
    "    # 若缺欄：直接補一欄 ad_para_check=0，警告並寫回\n",
    "    if not set(REQ_COLS).issubset(df.columns):\n",
    "        missing = [c for c in REQ_COLS if c not in df.columns]\n",
    "        print(f\"[WARN] {base}: missing required cols {missing}. Set ad_para_check=0 for all rows.\")\n",
    "        df[\"ad_para_check\"] = 0\n",
    "        try:\n",
    "            df.to_csv(fp, index=False)\n",
    "            files_warn += 1\n",
    "        except Exception as e:\n",
    "            print(f\"[ERROR] {base}: failed to write csv ({e}).\")\n",
    "        continue\n",
    "\n",
    "    # 型別正規化\n",
    "    df = df.copy()\n",
    "    # 時間\n",
    "    df[\"senddate\"] = pd.to_datetime(df[\"senddate\"], errors=\"coerce\")\n",
    "    # NaN_check -> 0/1\n",
    "    df[\"NaN_check\"] = pd.to_numeric(df[\"NaN_check\"], errors=\"coerce\").fillna(0).astype(int).clip(0,1)\n",
    "    # 數值欄\n",
    "    for c in NUM_COLS:\n",
    "        df[c] = normalize_numeric(df[c])\n",
    "    # 模式欄\n",
    "    for c in MODE_COLS:\n",
    "        df[c] = normalize_mode(df[c])\n",
    "\n",
    "    # 預設 ad_para_check = 0\n",
    "    df[\"ad_para_check\"] = 0\n",
    "\n",
    "    # === 檢查「同病患、NaN_check=1」是否有重複 senddate（相同時間點） ===\n",
    "    # 規則 5：若發現重複，印出細節後立刻停止整個流程\n",
    "    dup_found = False\n",
    "    dup_reports = []\n",
    "\n",
    "    for pid, g in df.groupby(\"patno\", dropna=False):\n",
    "        # 僅在評估集合（NaN_check=1）中檢查重複\n",
    "        g_ok = g[(g[\"NaN_check\"] == 1) & g[\"senddate\"].notna()].copy()\n",
    "        if g_ok.empty:\n",
    "            continue\n",
    "        # 檢查重複時間\n",
    "        mdup = g_ok.duplicated(subset=[\"senddate\"], keep=False)\n",
    "        if mdup.any():\n",
    "            dup = g_ok.loc[mdup, [\"patno\",\"senddate\"]].sort_values(\"senddate\")\n",
    "            cnt = dup[\"senddate\"].value_counts().sort_index()\n",
    "            dup_reports.append((pid, cnt))\n",
    "            dup_found = True\n",
    "\n",
    "    if dup_found:\n",
    "        print(\"\\n[STOP] Duplicate senddate detected within patient (in rows with NaN_check=1).\")\n",
    "        print(f\"File: {base}\")\n",
    "        for pid, cnt in dup_reports:\n",
    "            print(f\" - patno={pid} duplicate timestamps:\")\n",
    "            # 印出每個重複時間點及出現次數\n",
    "            for ts, c in cnt.items():\n",
    "                print(f\"   * {pd.to_datetime(ts)}  -> {c} rows\")\n",
    "        print(\"Process aborted due to rule #5.\")\n",
    "        # 立即停止整個流程\n",
    "        sys.exit(1)\n",
    "\n",
    "    # === 依 patno、時間排序，僅在 NaN_check=1 的行上做相鄰比較 ===\n",
    "    changed_in_file = 0\n",
    "\n",
    "    for pid, g in df.groupby(\"patno\", dropna=False):\n",
    "        # 僅取 NaN_check=1 且時間可用的列\n",
    "        sel = (df[\"patno\"] == pid) & (df[\"NaN_check\"] == 1) & df[\"senddate\"].notna()\n",
    "        if not sel.any():\n",
    "            continue\n",
    "\n",
    "        idx = df.index[sel]\n",
    "        sub = df.loc[idx, [\"senddate\"] + NUM_COLS + MODE_COLS].sort_values(\"senddate\").copy()\n",
    "\n",
    "        # 將排序後的索引對回原 DataFrame\n",
    "        sorted_idx = sub.index\n",
    "\n",
    "        # 數值變化：任一數值欄與上一筆不同（NaN 與數值不同視為變化；兩個 NaN 視為沒有變化）\n",
    "        # 使用 .astype(float).diff() 無法直接處理 NaN 比較；改用「不相等」邏輯\n",
    "        num_change = pd.DataFrame(False, index=sub.index, columns=[\"num_changed\"])\n",
    "        if len(sub) >= 2:\n",
    "            # 對每一數值欄，判斷是否與上一筆「不同」\n",
    "            diffs = []\n",
    "            for c in NUM_COLS:\n",
    "                v = sub[c]\n",
    "                # 定義：兩者皆非 NaN 且數值不同 → True；一 NaN 一非 NaN → True；兩者皆 NaN → False\n",
    "                prev = v.shift(1)\n",
    "                diff_c = ~((v.isna() & prev.isna()) | (v.eq(prev)))\n",
    "                diffs.append(diff_c.fillna(False))\n",
    "            num_change[\"num_changed\"] = np.logical_or.reduce(diffs)\n",
    "\n",
    "        # 模式變化：任一 mode 位元與上一筆不同\n",
    "        mode_change = pd.DataFrame(False, index=sub.index, columns=[\"mode_changed\"])\n",
    "        if len(sub) >= 2:\n",
    "            diffs_m = []\n",
    "            for c in MODE_COLS:\n",
    "                v = sub[c].astype(int)\n",
    "                prev = v.shift(1)\n",
    "                diff_c = v.ne(prev)  # 0/1 改變\n",
    "                diffs_m.append(diff_c.fillna(False))\n",
    "            mode_change[\"mode_changed\"] = np.logical_or.reduce(diffs_m)\n",
    "\n",
    "        # 綜合變化條件（第一筆必為 0）\n",
    "        changed = (num_change[\"num_changed\"] | mode_change[\"mode_changed\"]).astype(int)\n",
    "        if len(changed) > 0:\n",
    "            changed.iloc[0] = 0  # 第一筆 NaN_check=1 沒有上一筆可比，固定 0\n",
    "\n",
    "        # 回填到原 DataFrame 的對應列（僅 NaN_check=1 的行）\n",
    "        df.loc[sorted_idx, \"ad_para_check\"] = changed.values\n",
    "        changed_in_file += int(changed.sum())\n",
    "\n",
    "    total_changed_to_1 += changed_in_file\n",
    "\n",
    "    # 回寫檔案（就地覆寫，同名 CSV）\n",
    "    try:\n",
    "        df.to_csv(fp, index=False)\n",
    "        files_ok += 1\n",
    "        print(f\"[OK] {base}: rows={n_before}, ad_para_check=1 set on {changed_in_file} rows.\")\n",
    "    except Exception as e:\n",
    "        print(f\"[ERROR] {base}: failed to write csv ({e}).\")\n",
    "        files_warn += 1\n",
    "\n",
    "print(\"\\n===== SUMMARY =====\")\n",
    "print(f\"Processed files     : {files_ok} OK, {files_warn} with warnings\")\n",
    "print(f\"Total rows seen     : {total_rows}\")\n",
    "print(f\"Total ad_para_check=1 across all files: {total_changed_to_1}\")\n",
    "print(\"Rule #5 guard       : If duplicate timestamps within a patient (NaN_check=1 rows) are found,\")\n",
    "print(\"                      the script prints details and aborts immediately.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "id": "9f2cccc9-7883-4c80-a4ca-e92c904f6653",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true,
     "source_hidden": true
    }
   },
   "outputs": [
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[146], line 187\u001b[0m\n\u001b[1;32m    184\u001b[0m df_all \u001b[38;5;241m=\u001b[39m pd\u001b[38;5;241m.\u001b[39mconcat(parts, ignore_index\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n\u001b[1;32m    186\u001b[0m \u001b[38;5;66;03m# 計算間隔並帶模式資訊\u001b[39;00m\n\u001b[0;32m--> 187\u001b[0m intervals \u001b[38;5;241m=\u001b[39m \u001b[43mcompute_intervals_by_mode\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdf_all\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    188\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m intervals\u001b[38;5;241m.\u001b[39mempty:\n\u001b[1;32m    189\u001b[0m     \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m[Lollipop] No valid consecutive ad_para=1 pairs with resolvable mode. Nothing to plot.\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
      "Cell \u001b[0;32mIn[146], line 152\u001b[0m, in \u001b[0;36mcompute_intervals_by_mode\u001b[0;34m(df_all)\u001b[0m\n\u001b[1;32m    150\u001b[0m             \u001b[38;5;28;01mif\u001b[39;00m (\u001b[38;5;129;01mnot\u001b[39;00m keep_all_mode_transitions) \u001b[38;5;129;01mand\u001b[39;00m (mp \u001b[38;5;241m!=\u001b[39m mc):\n\u001b[1;32m    151\u001b[0m                 \u001b[38;5;28;01mcontinue\u001b[39;00m\n\u001b[0;32m--> 152\u001b[0m             rows\u001b[38;5;241m.\u001b[39mappend([fname, pid, pd\u001b[38;5;241m.\u001b[39mto_datetime(tp), \u001b[43mpd\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mto_datetime\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtc\u001b[49m\u001b[43m)\u001b[49m, \u001b[38;5;28mfloat\u001b[39m(d), mp, mc])\n\u001b[1;32m    154\u001b[0m cols \u001b[38;5;241m=\u001b[39m [\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m__file__\u001b[39m\u001b[38;5;124m\"\u001b[39m, COL_PAT, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mt_prev\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mt_curr\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdelta_min\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmode_prev\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmode_curr\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n\u001b[1;32m    155\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m pd\u001b[38;5;241m.\u001b[39mDataFrame(rows, columns\u001b[38;5;241m=\u001b[39mcols) \u001b[38;5;28;01mif\u001b[39;00m rows \u001b[38;5;28;01melse\u001b[39;00m pd\u001b[38;5;241m.\u001b[39mDataFrame(columns\u001b[38;5;241m=\u001b[39mcols)\n",
      "File \u001b[0;32m/opt/conda/lib/python3.11/site-packages/pandas/core/tools/datetimes.py:1069\u001b[0m, in \u001b[0;36mto_datetime\u001b[0;34m(arg, errors, dayfirst, yearfirst, utc, format, exact, unit, infer_datetime_format, origin, cache)\u001b[0m\n\u001b[1;32m   1067\u001b[0m         values \u001b[38;5;241m=\u001b[39m convert_listlike(arg\u001b[38;5;241m.\u001b[39m_values, \u001b[38;5;28mformat\u001b[39m)\n\u001b[1;32m   1068\u001b[0m         result \u001b[38;5;241m=\u001b[39m arg\u001b[38;5;241m.\u001b[39m_constructor(values, index\u001b[38;5;241m=\u001b[39marg\u001b[38;5;241m.\u001b[39mindex, name\u001b[38;5;241m=\u001b[39marg\u001b[38;5;241m.\u001b[39mname)\n\u001b[0;32m-> 1069\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(arg, (ABCDataFrame, abc\u001b[38;5;241m.\u001b[39mMutableMapping)):\n\u001b[1;32m   1070\u001b[0m     result \u001b[38;5;241m=\u001b[39m _assemble_from_unit_mappings(arg, errors, utc)\n\u001b[1;32m   1071\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(arg, Index):\n",
      "File \u001b[0;32m/opt/conda/lib/python3.11/site-packages/pandas/core/dtypes/generic.py:42\u001b[0m, in \u001b[0;36mcreate_pandas_abc_type.<locals>._instancecheck\u001b[0;34m(cls, inst)\u001b[0m\n\u001b[1;32m     38\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mgetattr\u001b[39m(inst, attr, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m_typ\u001b[39m\u001b[38;5;124m\"\u001b[39m) \u001b[38;5;129;01min\u001b[39;00m comp\n\u001b[1;32m     40\u001b[0m \u001b[38;5;66;03m# https://github.com/python/mypy/issues/1006\u001b[39;00m\n\u001b[1;32m     41\u001b[0m \u001b[38;5;66;03m# error: 'classmethod' used with a non-method\u001b[39;00m\n\u001b[0;32m---> 42\u001b[0m \u001b[38;5;129m@classmethod\u001b[39m  \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[1;32m     43\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_instancecheck\u001b[39m(\u001b[38;5;28mcls\u001b[39m, inst) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m \u001b[38;5;28mbool\u001b[39m:\n\u001b[1;32m     44\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m _check(inst) \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(inst, \u001b[38;5;28mtype\u001b[39m)\n\u001b[1;32m     46\u001b[0m \u001b[38;5;129m@classmethod\u001b[39m  \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[1;32m     47\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_subclasscheck\u001b[39m(\u001b[38;5;28mcls\u001b[39m, inst) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m \u001b[38;5;28mbool\u001b[39m:\n\u001b[1;32m     48\u001b[0m     \u001b[38;5;66;03m# Raise instead of returning False\u001b[39;00m\n\u001b[1;32m     49\u001b[0m     \u001b[38;5;66;03m# This is consistent with default __subclasscheck__ behavior\u001b[39;00m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "\"\"\"百分位「里程碑」線圖（Percentile Lollipop）\n",
    "軸：X＝間隔時間；在固定的 Y 列上，用「棒棒糖」標出 P10、P25、P50、P75、P90、P95。\n",
    "三種模式分三列或同列不同顏色。\n",
    "圖表內容都用英文ˊ，程式碼註釋都用中文，圖表顏色用藍色漸層，字樣都不要重疊到\n",
    "\n",
    "橫軸（X）：間隔時間（分鐘或對數刻度）\n",
    "縱軸（Y）：呼吸器模式（分三列，或以顏色區分同列顯示）\n",
    "每一種模式對應多個「棒棒糖」標記（點＋橫線），分別代表：\n",
    "P10、P25、P50（中位數）、P75、P90、P95 六個百分位位置\n",
    "棒棒糖形狀：\n",
    "圓點位置表示該百分位的間隔時間\n",
    "連線強調各百分位的相對差距\n",
    "顏色區分：三種呼吸器模式使用固定三色（如藍、綠、橘）\n",
    "標籤：每個百分位點可附上實際數值（例如「P50 = 42 分鐘」）\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3cab2630-e63f-4f03-aea3-21fecc61000f",
   "metadata": {},
   "outputs": [],
   "source": [
    "Ridgeline（密度山脊圖）\n",
    "軸：X＝間隔時間；Y＝模式（多條「山脊」）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "id": "bfdd9261-a9f0-4930-b1ea-c8acd0c6a329",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "✅ Saved intervals CSV: /home/jovyan/RT08/0925/1002/adpara_check_intervals.csv\n",
      "Intervals >= 120 minutes: 113\n",
      "✅ Saved figure: /home/jovyan/RT08/0925/1002/adpara_check_interval_hist.png\n"
     ]
    }
   ],
   "source": [
    "\"\"\"\n",
    "我想要看調參間隔的條狀圖\n",
    "ad_para_check=1是有調參的意思，60分鐘標記出來 120分鐘也標記出來 並且另外輸出大於等於120分鐘的資料有幾筆，顏色用藍色漸層 字不要互相遮到 圖表用英文 成是馬註釋用中文 要寫詳細 橫軸用間隔的時間 縱軸用數量\n",
    "\"\"\"\n",
    "# -*- coding: utf-8 -*-\n",
    "\"\"\"\n",
    "======== Histogram of time between adjustments (ad_para_check=1) ========\n",
    "來源資料夾：/home/jovyan/RT08/0925/bling_svv_14\n",
    "輸出資料夾：/home/jovyan/RT08/0925/1002  （自動建立）\n",
    "輸出圖檔：adpara_check_interval_hist.png\n",
    "輸出明細：adpara_check_intervals.csv（每筆相鄰調參之間的間隔，單位：分鐘）\n",
    "\n",
    "圖表（English UI；中文註釋）：\n",
    "- X-axis: Minutes between consecutive adjustments (ad_para_check=1)\n",
    "- Y-axis: Count\n",
    "- Vertical markers at 60 and 120 minutes\n",
    "- Blue gradient bars; labels placed to avoid overlap/clutter\n",
    "- Also prints the count of intervals with Δt >= 120 minutes and writes a CSV of all intervals\n",
    "\n",
    "計算邏輯（請務必閱讀）：\n",
    "1) 逐檔讀取，僅「讀取」不修改原檔。\n",
    "2) 僅取 ad_para_check=1 的列，依病患（patno）分組、依 senddate 排序，計算同病患內相鄰兩次 ad_para_check=1 的時間差（分鐘）。\n",
    "3) 僅保留正值（>0）間隔；非正值視為異常/重複或逆序，剔除。\n",
    "4) 將所有病患的間隔彙整後繪製直方圖。\n",
    "5) 視覺上為避免被長尾影響，X 軸右界預設裁到 P99（但 CSV 保留完整間隔）。\n",
    "\n",
    "安全性：\n",
    "- 本程式僅「讀」/bling_svv_14；所有輸出寫入 /1002。\n",
    "\"\"\"\n",
    "\n",
    "import os, glob\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# ================== 參數設定 ==================\n",
    "DATA_DIR = \"/home/jovyan/RT08/0925/bling_svv_14\"   # 來源\n",
    "OUT_DIR  = \"/home/jovyan/RT08/0925/1002\"          # 輸出\n",
    "os.makedirs(OUT_DIR, exist_ok=True)\n",
    "\n",
    "# 欄位名稱\n",
    "COL_PAT   = \"patno\"\n",
    "COL_TIME  = \"senddate\"\n",
    "COL_APCHK = \"ad_para_check\"\n",
    "\n",
    "# 視覺化參數\n",
    "FIG_SIZE = (12, 6)\n",
    "DPI      = 150\n",
    "# 若資料極端長尾，直方圖視覺右界使用 P99（避免過度壓縮）；可依需要調整\n",
    "USE_P99_CLIP = True\n",
    "\n",
    "# ================== 輔助函式 ==================\n",
    "def safe_read_cols(path, need_cols):\n",
    "    \"\"\"只讀必要欄位；若缺欄或讀取失敗則回傳 None。\"\"\"\n",
    "    try:\n",
    "        df = pd.read_csv(path, usecols=need_cols, low_memory=False)\n",
    "    except Exception:\n",
    "        try:\n",
    "            df = pd.read_csv(path, low_memory=False)\n",
    "        except Exception:\n",
    "            return None\n",
    "    if not set(need_cols).issubset(df.columns):\n",
    "        return None\n",
    "    return df\n",
    "\n",
    "def compute_intervals_adpara_check(df_all):\n",
    "    \"\"\"\n",
    "    以 ad_para_check=1 的列為節點，計算同病患內相鄰兩點的時間間隔（分鐘）。\n",
    "    回傳 DataFrame 欄位：['__file__','patno','t_prev','t_curr','delta_min']\n",
    "    - 僅保留 delta_min > 0 的樣本。\n",
    "    \"\"\"\n",
    "    rows = []\n",
    "    for fname, gfile in df_all.groupby(\"__file__\"):\n",
    "        for pid, g in gfile.groupby(COL_PAT, dropna=True):\n",
    "            g1 = g[g[COL_APCHK] == 1].copy()\n",
    "            if g1.empty:\n",
    "                continue\n",
    "            g1 = g1.sort_values(COL_TIME)\n",
    "            t = g1[COL_TIME].to_numpy()\n",
    "            if len(t) < 2:\n",
    "                continue\n",
    "            dt = (t[1:] - t[:-1]) / np.timedelta64(1, \"m\")\n",
    "            mask = dt > 0\n",
    "            if not np.any(mask):\n",
    "                continue\n",
    "            for tp, tc, d in zip(t[:-1][mask], t[1:][mask], dt[mask]):\n",
    "                rows.append([fname, pid, pd.to_datetime(tp), pd.to_datetime(tc), float(d)])\n",
    "    cols = [\"__file__\", COL_PAT, \"t_prev\", \"t_curr\", \"delta_min\"]\n",
    "    return pd.DataFrame(rows, columns=cols) if rows else pd.DataFrame(columns=cols)\n",
    "\n",
    "# ================== 讀檔與彙整 ==================\n",
    "need = [COL_PAT, COL_TIME, COL_APCHK]\n",
    "paths = sorted(glob.glob(os.path.join(DATA_DIR, \"*.csv\")))\n",
    "\n",
    "parts = []\n",
    "for p in paths:\n",
    "    base = os.path.basename(p)\n",
    "    df = safe_read_cols(p, need)\n",
    "    if df is None:\n",
    "        print(f\"[Warn] Skip {base}: missing one of {need}\")\n",
    "        continue\n",
    "    df = df.copy()\n",
    "    # 型別處理：時間欄位\n",
    "    df[COL_TIME]  = pd.to_datetime(df[COL_TIME], errors=\"coerce\")\n",
    "    df = df.dropna(subset=[COL_TIME])\n",
    "    if df.empty:\n",
    "        continue\n",
    "    # 型別處理：ad_para_check -> 0/1\n",
    "    df[COL_APCHK] = pd.to_numeric(df[COL_APCHK], errors=\"coerce\").fillna(0).astype(int).clip(0,1)\n",
    "    df[\"__file__\"] = base\n",
    "    parts.append(df[[\"__file__\", COL_PAT, COL_TIME, COL_APCHK]])\n",
    "\n",
    "if not parts:\n",
    "    print(\"[Histogram] No data available.\")\n",
    "else:\n",
    "    df_all = pd.concat(parts, ignore_index=True)\n",
    "\n",
    "    # 計算間隔\n",
    "    intervals = compute_intervals_adpara_check(df_all)\n",
    "\n",
    "    if intervals.empty:\n",
    "        print(\"[Histogram] No valid consecutive ad_para_check=1 pairs. Nothing to plot.\")\n",
    "    else:\n",
    "        # 另存 CSV（完整間隔，不裁切）\n",
    "        csv_path = os.path.join(OUT_DIR, \"adpara_check_intervals.csv\")\n",
    "        intervals.to_csv(csv_path, index=False)\n",
    "        print(f\"✅ Saved intervals CSV: {csv_path}\")\n",
    "\n",
    "        x = intervals[\"delta_min\"].to_numpy()\n",
    "        x = x[np.isfinite(x)]\n",
    "        if x.size == 0:\n",
    "            print(\"[Histogram] No finite intervals.\")\n",
    "        else:\n",
    "            x_sorted = np.sort(x)\n",
    "            # 統計：>=120 分鐘的筆數\n",
    "            ge_120 = int(np.sum(x_sorted >= 120))\n",
    "            print(f\"Intervals >= 120 minutes: {ge_120}\")\n",
    "\n",
    "            # 視覺化右界（避免極端長尾壓縮整體）\n",
    "            if USE_P99_CLIP:\n",
    "                x_right = float(np.percentile(x_sorted, 99))\n",
    "                x_right = max(x_right, 5.0)  # 至少保留一點範圍\n",
    "            else:\n",
    "                x_right = float(x_sorted.max())\n",
    "\n",
    "            # 以 1 分鐘為 bin 寬；上界取整數+1\n",
    "            bins = np.arange(0, int(np.floor(x_right)) + 2, 1)\n",
    "\n",
    "            # ========== 畫圖 ==========\n",
    "            fig, ax = plt.subplots(figsize=FIG_SIZE)\n",
    "\n",
    "            counts, edges, patches = ax.hist(\n",
    "                x_sorted,\n",
    "                bins=bins,\n",
    "                color=\"#9ecae1\",         # 初始淺藍\n",
    "                edgecolor=\"#3182bd\",     # 深藍邊框\n",
    "                linewidth=0.6\n",
    "            )\n",
    "\n",
    "            # 依 bin 位置施加藍色漸層（由淺到深）\n",
    "            if len(patches) > 1:\n",
    "                for i, bar in enumerate(patches):\n",
    "                    frac = i / max(1, len(patches)-1)\n",
    "                    # 從淺藍(#9ecae1) → 深藍(#3182bd) 做簡單線性內插\n",
    "                    # 這裡直接手動調整 RGB，使右側逐漸加深\n",
    "                    # 淺藍近似 (0.62, 0.79, 0.88)，深藍近似 (0.19, 0.51, 0.74)\n",
    "                    r = 0.62 + (0.19 - 0.62) * frac\n",
    "                    g = 0.79 + (0.51 - 0.79) * frac\n",
    "                    b = 0.88 + (0.74 - 0.88) * frac\n",
    "                    bar.set_facecolor((r, g, b))\n",
    "\n",
    "            # 在 60 與 120 分鐘畫垂直參考線（標籤英文化）\n",
    "            for v, label in [(60, \"60 min\"), (120, \"120 min\")]:\n",
    "                ax.axvline(v, color=\"#08519c\", linestyle=\"--\", linewidth=1.2, alpha=0.9)\n",
    "                # 標籤避免重疊：文字放在線上方一點、水平靠左\n",
    "                ymax = ax.get_ylim()[1]\n",
    "                ax.text(v + 0.5, ymax * 0.95, label, color=\"#08519c\", fontsize=9,\n",
    "                        ha=\"left\", va=\"top\",\n",
    "                        bbox=dict(boxstyle=\"round,pad=0.2\", fc=\"white\", ec=\"none\", alpha=0.8))\n",
    "\n",
    "            # 軸與標題（英文）\n",
    "            ax.set_title(\"Histogram of intervals between adjustments (ad_para_check=1)\",\n",
    "                         pad=12)\n",
    "            ax.set_xlabel(\"Minutes between consecutive adjustments\")\n",
    "            ax.set_ylabel(\"Count\")\n",
    "\n",
    "            # 避免 x tick 擁擠：最多顯示 ~12 個刻度\n",
    "            xticks = np.linspace(0, max(1, x_right), num=min(12, int(max(2, x_right//1))+1))\n",
    "            ax.set_xticks(np.unique(np.round(xticks, 0)))\n",
    "\n",
    "            # 網格與外觀\n",
    "            ax.grid(True, axis=\"y\", linestyle=\"--\", linewidth=0.6, alpha=0.5)\n",
    "            ax.set_axisbelow(True)\n",
    "\n",
    "            # 右上角放摘要，避免與圖重疊\n",
    "            n = len(x_sorted)\n",
    "            p50 = float(np.percentile(x_sorted, 50))\n",
    "            p90 = float(np.percentile(x_sorted, 90))\n",
    "            p95 = float(np.percentile(x_sorted, 95))\n",
    "            p99 = float(np.percentile(x_sorted, 99))\n",
    "            txt = (f\"N={n} | P50={p50:.1f}m  P90={p90:.1f}m  P95={p95:.1f}m  P99={p99:.1f}m\\n\"\n",
    "                   f\"Count ≥120 min = {ge_120}\")\n",
    "            fig.text(0.98, 0.98, txt, ha=\"right\", va=\"top\", fontsize=9, color=\"#08519c\")\n",
    "\n",
    "            plt.tight_layout(rect=[0, 0, 1, 0.93])\n",
    "\n",
    "            out_png = os.path.join(OUT_DIR, \"adpara_check_interval_hist.png\")\n",
    "            fig.savefig(out_png, dpi=DPI)\n",
    "            plt.close(fig)\n",
    "            print(f\"✅ Saved figure: {out_png}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "id": "1bb5d648-1bf6-4e26-9993-e4ca36fb62a5",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "=== Interval Summary (ad_para_check=1) ===\n",
      "Total intervals      : 1,129,223\n",
      "≥ 60 minutes         : 154  (0.01%)\n",
      "≥ 120 minutes        : 113  (0.01%)\n"
     ]
    }
   ],
   "source": [
    "# === 追加：直接統計輸出間隔 >= 60 與 >= 120 分鐘的筆數 ===\n",
    "# ============================================================\n",
    "\n",
    "# 確認 intervals 存在且不為空\n",
    "if \"intervals\" in locals() and not intervals.empty:\n",
    "    x = intervals[\"delta_min\"].to_numpy()\n",
    "    x = x[np.isfinite(x)]\n",
    "\n",
    "    ge_60  = int(np.sum(x >= 60))\n",
    "    ge_120 = int(np.sum(x >= 120))\n",
    "    total  = len(x)\n",
    "\n",
    "    print(\"\\n=== Interval Summary (ad_para_check=1) ===\")\n",
    "    print(f\"Total intervals      : {total:,}\")\n",
    "    print(f\"≥ 60 minutes         : {ge_60:,}  ({ge_60 / total * 100:.2f}%)\")\n",
    "    print(f\"≥ 120 minutes        : {ge_120:,}  ({ge_120 / total * 100:.2f}%)\")\n",
    "else:\n",
    "    print(\"\\n[Info] No valid intervals available for ≥60/≥120-minute summary.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "id": "09d14bb8-fd42-44ad-9a7b-bb848eee3430",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Info] Total intervals      : 1,129,223\n",
      "[Info] Intervals > 60 min   : 154 (0.0136%)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1800x900 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\"\"\"我要新的條狀圖 只顯示超過60分鐘的資料 每個調狀圖上面加上百分比跟數量\n",
    "\n",
    "======== New histogram: intervals > 60 minutes only (ad_para_check=1) ========\n",
    "資料來源：/home/jovyan/RT08/0925/bling_svv_14\n",
    "功能：\n",
    "- 計算同一病患內，相鄰兩次 ad_para_check=1 的時間差（分鐘）\n",
    "- 只針對「> 60 分鐘」的間隔畫直方圖\n",
    "- 每個柱狀上方註記「數量 + 佔全部間隔的百分比」\n",
    "- 英文圖表；中文註釋；藍色漸層；不寫任何檔案、不改原始資料\n",
    "\n",
    "註：\n",
    "- 分箱（bins）採常用分鐘級距（小時/天/週），可依需要調整 BINS_MINUTES。\n",
    "- numpy.histogram 預設區間為左閉右開 [a,b)；最後一個箱右端包含。\n",
    "\"\"\"\n",
    "\n",
    "import os, glob\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# ---------------- 基本參數（可調） ----------------\n",
    "DATA_DIR = \"/home/jovyan/RT08/0925/bling_svv_14\"  # 僅讀取，不會寫回\n",
    "COL_PAT   = \"patno\"\n",
    "COL_TIME  = \"senddate\"\n",
    "COL_APCHK = \"ad_para_check\"\n",
    "\n",
    "# 友善的分鐘分箱邊界（左閉右開 [a,b)；最後一箱右端包含）\n",
    "# 60(1h)、90、120(2h)、180(3h)、240(4h)、360(6h)、720(12h)、\n",
    "# 1440(1d)、2880(2d)、10080(1w)、43200(30d)\n",
    "BINS_MINUTES = np.array([60, 90, 120, 180, 240, 360, 720, 1440, 2880, 10080, 43200], dtype=float)\n",
    "\n",
    "# 智慧標註參數（避免擁擠）\n",
    "ANNOTATE_LIMIT_TOP = 20   # 固定標註最高的前 N 根\n",
    "SPARSE_STEP        = 3    # 其餘每隔 k 根標註一次\n",
    "LABEL_LEVELS       = [0.015, 0.04, 0.065]  # 交錯高度（相對於 y 上界）\n",
    "\n",
    "# ---------------- 輔助函式 ----------------\n",
    "def safe_read_cols(path, need_cols):\n",
    "    \"\"\"只讀必要欄位；缺欄或讀取失敗則回傳 None。\"\"\"\n",
    "    try:\n",
    "        df = pd.read_csv(path, usecols=need_cols, low_memory=False)\n",
    "    except Exception:\n",
    "        try:\n",
    "            df = pd.read_csv(path, low_memory=False)\n",
    "        except Exception:\n",
    "            return None\n",
    "    if not set(need_cols).issubset(df.columns):\n",
    "        return None\n",
    "    return df\n",
    "\n",
    "def compute_intervals(df_all):\n",
    "    \"\"\"\n",
    "    以 ad_para_check=1 的列為節點，計算同病患內相鄰兩點的時間間隔（分鐘）。\n",
    "    傳回 numpy array: 僅保留正值（>0）的間隔。\n",
    "    \"\"\"\n",
    "    deltas = []\n",
    "    for _, gfile in df_all.groupby(\"__file__\"):\n",
    "        for _, g in gfile.groupby(COL_PAT, dropna=True):\n",
    "            g1 = g[g[COL_APCHK] == 1].copy()\n",
    "            if g1.empty:\n",
    "                continue\n",
    "            g1 = g1.sort_values(COL_TIME)\n",
    "            t = g1[COL_TIME].to_numpy()\n",
    "            if len(t) < 2:\n",
    "                continue\n",
    "            dt = (t[1:] - t[:-1]) / np.timedelta64(1, \"m\")  # 以分鐘為單位\n",
    "            good = dt > 0\n",
    "            if np.any(good):\n",
    "                deltas.append(dt[good])\n",
    "    if not deltas:\n",
    "        return np.array([], dtype=float)\n",
    "    return np.concatenate(deltas).astype(float)\n",
    "\n",
    "# ---------------- 讀檔與計算 ----------------\n",
    "need = [COL_PAT, COL_TIME, COL_APCHK]\n",
    "frames = []\n",
    "for p in sorted(glob.glob(os.path.join(DATA_DIR, \"*.csv\"))):\n",
    "    base = os.path.basename(p)\n",
    "    df = safe_read_cols(p, need)\n",
    "    if df is None:\n",
    "        print(f\"[Warn] Skip {base}: missing one of {need}\")\n",
    "        continue\n",
    "\n",
    "    df = df.copy()\n",
    "    df[COL_TIME]  = pd.to_datetime(df[COL_TIME], errors=\"coerce\")  # 轉 datetime\n",
    "    df = df.dropna(subset=[COL_TIME])                               # 丟棄時間無法解析\n",
    "    if df.empty:\n",
    "        continue\n",
    "\n",
    "    df[COL_APCHK] = pd.to_numeric(df[COL_APCHK], errors=\"coerce\").fillna(0).astype(int).clip(0, 1)\n",
    "    df[\"__file__\"] = base\n",
    "    frames.append(df[[\"__file__\", COL_PAT, COL_TIME, COL_APCHK]])\n",
    "\n",
    "if not frames:\n",
    "    print(\"[Histogram >60] No data available.\")\n",
    "else:\n",
    "    all_df = pd.concat(frames, ignore_index=True)\n",
    "    intervals_min_all = compute_intervals(all_df)\n",
    "\n",
    "    if intervals_min_all.size == 0:\n",
    "        print(\"[Histogram >60] No valid consecutive ad_para_check=1 pairs.\")\n",
    "    else:\n",
    "        total_intervals = len(intervals_min_all)                 # 全部間隔的樣本數（做百分比母數）\n",
    "        x = intervals_min_all[intervals_min_all > 60]            # 只保留 >60 分鐘\n",
    "        n_gt60 = len(x)\n",
    "\n",
    "        print(f\"[Info] Total intervals      : {total_intervals:,}\")\n",
    "        print(f\"[Info] Intervals > 60 min   : {n_gt60:,} ({n_gt60/total_intervals*100:.4f}%)\")\n",
    "\n",
    "        if n_gt60 == 0:\n",
    "            print(\"[Histogram >60] No intervals exceed 60 minutes.\")\n",
    "        else:\n",
    "            # --------- 計算直方圖（只對 >60 的資料分箱） ----------\n",
    "            edges  = BINS_MINUTES\n",
    "            counts, edges = np.histogram(x, bins=edges)  # numpy: [a,b)；最後一箱右端包含\n",
    "            pct_all = counts / total_intervals * 100.0   # 佔「全部間隔」的百分比（不是 >60 的子集合）\n",
    "\n",
    "            # --------- 繪圖（英文 UI；藍色漸層；智慧標註防重疊） ----------\n",
    "            fig, ax = plt.subplots(figsize=(12, 6), dpi=150)\n",
    "\n",
    "            centers = (edges[:-1] + edges[1:]) / 2.0       # 每箱中心\n",
    "            widths  = (edges[1:] - edges[:-1]) * 0.9       # 欄寬留 10% 空白\n",
    "            bars = ax.bar(centers, counts, width=widths, align=\"center\",\n",
    "                          edgecolor=\"#1f77b4\", linewidth=0.8, color=\"#9ecae1\")\n",
    "\n",
    "            # 藍色漸層（由淺到深）\n",
    "            if len(bars) > 1:\n",
    "                for i, b in enumerate(bars):\n",
    "                    frac = i / max(1, len(bars)-1)\n",
    "                    r = 0.62 + (0.19 - 0.62) * frac\n",
    "                    g = 0.79 + (0.51 - 0.79) * frac\n",
    "                    bb = 0.88 + (0.74 - 0.88) * frac\n",
    "                    b.set_facecolor((r, g, bb))\n",
    "\n",
    "            # Y 軸上界加頭頂留白（給標註）\n",
    "            ymax = max(1, counts.max())\n",
    "            ax.set_ylim(0, ymax * 1.25)\n",
    "\n",
    "            # 需要標註的柱：若數量多則稀疏；否則全部標註\n",
    "            idx_all = np.arange(len(bars))\n",
    "            idx_sorted = np.argsort(-counts)  # 高到低\n",
    "            must = set(idx_sorted[:min(ANNOTATE_LIMIT_TOP, len(bars))])\n",
    "\n",
    "            to_anno = []\n",
    "            for i in idx_all:\n",
    "                if counts[i] <= 0:\n",
    "                    continue\n",
    "                if len(bars) <= 30:\n",
    "                    to_anno.append(i)\n",
    "                else:\n",
    "                    if (i in must) or (i % SPARSE_STEP == 0):\n",
    "                        to_anno.append(i)\n",
    "\n",
    "            # 交錯高度 + 內/外放置規則\n",
    "            for j, i in enumerate(to_anno):\n",
    "                b  = bars[i]\n",
    "                c  = int(counts[i])\n",
    "                pc = float(pct_all[i])\n",
    "\n",
    "                if b.get_height() >= ymax * 0.12:\n",
    "                    # 高柱：標註放柱內靠上\n",
    "                    y_text = b.get_y() + b.get_height() - (ymax * 0.02)\n",
    "                    va = \"top\"\n",
    "                    box_alpha = 0.65\n",
    "                else:\n",
    "                    # 矮柱：標註放柱外，依次交錯高度\n",
    "                    lvl = LABEL_LEVELS[j % len(LABEL_LEVELS)]\n",
    "                    y_text = b.get_y() + b.get_height() + ymax * lvl\n",
    "                    va = \"bottom\"\n",
    "                    box_alpha = 0.85\n",
    "\n",
    "                ax.text(b.get_x() + b.get_width()/2,\n",
    "                        y_text,\n",
    "                        f\"{c:,}\\n({pc:.3f}%)\",\n",
    "                        ha=\"center\", va=va, fontsize=9,\n",
    "                        bbox=dict(boxstyle=\"round,pad=0.15\", fc=\"white\", ec=\"none\", alpha=box_alpha))\n",
    "\n",
    "            # X 軸標籤：顯示箱區間（[a,b)；最後一箱右端含）\n",
    "            xticklabels = []\n",
    "            for a, b in zip(edges[:-1], edges[1:]):\n",
    "                xticklabels.append(f\"[{int(a)},{int(b)})\")\n",
    "            ax.set_xticks(centers)\n",
    "            ax.set_xticklabels(xticklabels, rotation=30, ha=\"right\")\n",
    "\n",
    "            # 軸標題與標題（英文）\n",
    "            ax.set_title(\"Histogram of intervals (> 60 minutes) between adjustments (ad_para_check=1)\", pad=12)\n",
    "            ax.set_xlabel(\"Interval bins in minutes (left-closed, right-open)\")\n",
    "            ax.set_ylabel(\"Count\")\n",
    "\n",
    "            # 網格與外觀\n",
    "            ax.grid(True, axis=\"y\", linestyle=\"--\", linewidth=0.6, alpha=0.5)\n",
    "            ax.set_axisbelow(True)\n",
    "\n",
    "            # 右上角摘要\n",
    "            fig.text(0.98, 0.98,\n",
    "                     f\"Total intervals = {total_intervals:,}\\n\"\n",
    "                     f\"Intervals > 60 min = {n_gt60:,} ({n_gt60/total_intervals*100:.3f}%)\",\n",
    "                     ha=\"right\", va=\"top\", fontsize=10, color=\"#1f77b4\")\n",
    "\n",
    "            plt.tight_layout()\n",
    "            plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "id": "ad473f37-22bb-4fcf-81e1-ce746a1c9b49",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Info] Scanning CSV files from: /home/jovyan/RT08/0925/bling_svv_14\n",
      "[Info] Total intervals found: 1129223\n",
      "[Info] Intervals ≥ 60 min: 154\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# -*- coding: utf-8 -*-\n",
    "\"\"\"\n",
    "======== Histogram of adjustment intervals (≥ 60 minutes) ========\n",
    "資料來源：/home/jovyan/RT08/0925/bling_svv_14\n",
    "說明：\n",
    "- 逐一讀取每位病患 CSV。\n",
    "- 在每位病患內部依 senddate 排序。\n",
    "- 只考慮 NaN_check=1 的列，計算 ad_para_check=1 之間的時間間隔（分鐘）。\n",
    "- 僅保留「間隔 ≥ 60 分鐘」的紀錄。\n",
    "- 畫出直方圖，顯示每個分箱的數量與百分比（以≥60分鐘資料為基準）。\n",
    "- 所有資料僅讀取，不會寫回任何檔案。\n",
    "\"\"\"\n",
    "\n",
    "import os\n",
    "import glob\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.ticker as ticker\n",
    "\n",
    "# ===== 參數設定 =====\n",
    "DATA_DIR = \"/home/jovyan/RT08/0925/bling_svv_14\"   # 僅讀取資料，不會寫回\n",
    "COL_TIME = \"senddate\"\n",
    "COL_ADJ  = \"ad_para_check\"\n",
    "COL_OK   = \"NaN_check\"\n",
    "\n",
    "# ===== 蒐集所有檔案的 ad_para_check 間隔 =====\n",
    "intervals_all = []\n",
    "\n",
    "print(f\"[Info] Scanning CSV files from: {DATA_DIR}\")\n",
    "file_paths = sorted(glob.glob(os.path.join(DATA_DIR, \"*.csv\")))\n",
    "\n",
    "for fp in file_paths:\n",
    "    base = os.path.basename(fp)\n",
    "    try:\n",
    "        df = pd.read_csv(fp, usecols=[COL_TIME, COL_ADJ, COL_OK], low_memory=False)\n",
    "    except Exception:\n",
    "        print(f\"[Warn] Cannot read {base}, skip.\")\n",
    "        continue\n",
    "\n",
    "    # 時間格式化與排序\n",
    "    df[COL_TIME] = pd.to_datetime(df[COL_TIME], errors=\"coerce\")\n",
    "    df = df.dropna(subset=[COL_TIME])\n",
    "    if df.empty or COL_ADJ not in df.columns:\n",
    "        continue\n",
    "\n",
    "    # 僅取 NaN_check=1 的資料\n",
    "    df = df[df[COL_OK] == 1]\n",
    "    df = df.sort_values(COL_TIME)\n",
    "\n",
    "    # 找出 ad_para_check=1 的時間點\n",
    "    ad_times = df.loc[df[COL_ADJ] == 1, COL_TIME].sort_values().to_numpy()\n",
    "    if len(ad_times) < 2:\n",
    "        continue\n",
    "\n",
    "    # 計算相鄰間隔（分鐘）\n",
    "    deltas = np.diff(ad_times) / np.timedelta64(1, 'm')\n",
    "    intervals_all.extend(deltas.tolist())\n",
    "\n",
    "print(f\"[Info] Total intervals found: {len(intervals_all)}\")\n",
    "\n",
    "# ===== 過濾出 >= 60 分鐘的間隔 =====\n",
    "intervals_all = np.array(intervals_all)\n",
    "intervals_60 = intervals_all[intervals_all >= 60]\n",
    "if len(intervals_60) == 0:\n",
    "    print(\"❌ No intervals ≥ 60 min found.\")\n",
    "else:\n",
    "    print(f\"[Info] Intervals ≥ 60 min: {len(intervals_60)}\")\n",
    "\n",
    "# ===== 建立直方圖 =====\n",
    "plt.figure(figsize=(12, 5))\n",
    "bins = np.logspace(np.log10(60), np.log10(max(intervals_60)), 15)\n",
    "counts, bins, patches = plt.hist(\n",
    "    intervals_60,\n",
    "    bins=bins,\n",
    "    color=\"#1f77b4\",\n",
    "    alpha=0.85,\n",
    "    edgecolor=\"black\",\n",
    "    linewidth=0.8\n",
    ")\n",
    "\n",
    "# ===== 在每個柱上標註數量與百分比 =====\n",
    "total_60 = len(intervals_60)\n",
    "for c, left, right in zip(counts, bins[:-1], bins[1:]):\n",
    "    if c > 0:\n",
    "        mid = (left + right) / 2\n",
    "        plt.text(\n",
    "            mid, c + 0.3,\n",
    "            f\"{int(c)}\\n({c / total_60 * 100:.2f}%)\",\n",
    "            ha='center', va='bottom',\n",
    "            fontsize=8, rotation=0\n",
    "        )\n",
    "\n",
    "# ===== 外觀設定 =====\n",
    "plt.xscale('log')  # 對數刻度\n",
    "plt.gca().xaxis.set_major_formatter(ticker.FuncFormatter(lambda x, _: f\"{int(x)}\"))\n",
    "plt.xlabel(\"Interval between consecutive adjustments (minutes)\", fontsize=10)\n",
    "plt.ylabel(\"Count\", fontsize=10)\n",
    "plt.title(\"Histogram of adjustment intervals (≥ 60 minutes)\", fontsize=12, pad=10)\n",
    "\n",
    "# ===== 右上角統計摘要 =====\n",
    "plt.text(\n",
    "    0.98, 0.98,\n",
    "    f\"Intervals ≥ 60 min = {total_60:,}\\n(min = {int(intervals_60.min())} | max = {int(intervals_60.max())})\",\n",
    "    transform=plt.gca().transAxes,\n",
    "    fontsize=9, color=\"#1f77b4\", ha='right', va='top'\n",
    ")\n",
    "\n",
    "plt.grid(axis='y', linestyle='--', alpha=0.6)\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "id": "b4234519-5280-412d-a22b-c451fbd860e8",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[INFO] Found 122 files in /home/jovyan/RT08/0925/bling_svv_14\n",
      "\n",
      "✅ Found 28 intervals ≥ 1000 minutes.\n",
      "✅ Saved to: /home/jovyan/RT08/0925/1002/adpara_intervals_over1000.csv\n",
      "                      file       patno           prev_time  \\\n",
      "0               089271.csv     8927106 2022-01-03 12:59:01   \n",
      "1               114309.csv    11430923 2021-12-23 14:38:01   \n",
      "2               230933.csv    23093320 2021-12-30 12:26:03   \n",
      "3  PatNo_ID_1563587183.csv  1563587200 2022-03-22 19:57:00   \n",
      "4  PatNo_ID_1564148644.csv  1564148600 2021-12-16 12:19:01   \n",
      "5  PatNo_ID_1567804800.csv  1567804800 2022-02-24 10:35:02   \n",
      "6  PatNo_ID_1567804800.csv  1567804800 2022-02-26 09:18:08   \n",
      "7  PatNo_ID_1568574099.csv  1568574100 2022-02-28 12:19:03   \n",
      "8  PatNo_ID_1570089466.csv  1570089500 2022-02-01 09:23:03   \n",
      "9  PatNo_ID_1570089466.csv  1570089500 2022-02-02 09:13:04   \n",
      "\n",
      "            curr_time  interval_min  \n",
      "0 2022-01-06 23:45:04       4966.05  \n",
      "1 2021-12-24 11:29:53       1251.87  \n",
      "2 2021-12-31 16:09:02       1662.98  \n",
      "3 2022-03-23 23:33:02       1656.03  \n",
      "4 2022-01-26 03:50:29      58531.47  \n",
      "5 2022-02-26 09:15:01       2799.98  \n",
      "6 2022-02-27 10:46:00       1527.87  \n",
      "7 2022-03-01 08:47:02       1227.98  \n",
      "8 2022-02-02 09:12:04       1429.02  \n",
      "9 2022-02-03 09:08:04       1435.00  \n"
     ]
    }
   ],
   "source": [
    "# -*- coding: utf-8 -*-\n",
    "\"\"\"\n",
    "分析每位病患的調參間隔（ad_para_check=1）並找出間隔 ≥1000 分鐘的案例。\n",
    "來源資料夾：/home/jovyan/RT08/0925/bling_svv_14/\n",
    "僅讀取，不會修改任何檔案。\n",
    "輸出內容：\n",
    "  - 檔名、病患號、前後兩筆 ad_para_check=1 的 senddate 以及間隔分鐘。\n",
    "\"\"\"\n",
    "\n",
    "import os, glob\n",
    "import pandas as pd\n",
    "from datetime import datetime\n",
    "\n",
    "# ==== 參數設定 ====\n",
    "DATA_DIR = \"/home/jovyan/RT08/0925/bling_svv_14\"\n",
    "THRESH_MIN = 1000  # 超過此分鐘的間隔才輸出\n",
    "OUT_CSV = \"/home/jovyan/RT08/0925/1002/adpara_intervals_over1000.csv\"\n",
    "\n",
    "# ==== 小工具：安全讀檔 ====\n",
    "def safe_read_csv(path):\n",
    "    try:\n",
    "        df = pd.read_csv(path, low_memory=False)\n",
    "        return df\n",
    "    except Exception as e:\n",
    "        print(f\"[WARN] {os.path.basename(path)}: 無法讀取 ({e})\")\n",
    "        return None\n",
    "\n",
    "# ==== 主程式 ====\n",
    "records = []\n",
    "files = sorted(glob.glob(os.path.join(DATA_DIR, \"*.csv\")))\n",
    "print(f\"[INFO] Found {len(files)} files in {DATA_DIR}\")\n",
    "\n",
    "for fp in files:\n",
    "    base = os.path.basename(fp)\n",
    "    df = safe_read_csv(fp)\n",
    "    if df is None:\n",
    "        continue\n",
    "\n",
    "    # 確保欄位存在\n",
    "    need_cols = [\"patno\", \"senddate\", \"ad_para_check\"]\n",
    "    if not all(c in df.columns for c in need_cols):\n",
    "        print(f\"[WARN] {base}: 缺少必要欄位，跳過。\")\n",
    "        continue\n",
    "\n",
    "    # 轉時間格式\n",
    "    df[\"senddate\"] = pd.to_datetime(df[\"senddate\"], errors=\"coerce\")\n",
    "    df = df.dropna(subset=[\"senddate\", \"patno\"])\n",
    "\n",
    "    # 只保留 ad_para_check=1 的列\n",
    "    df = df[df[\"ad_para_check\"] == 1]\n",
    "    if df.empty:\n",
    "        continue\n",
    "\n",
    "    # 依 patno 分組處理\n",
    "    for pid, g in df.groupby(\"patno\"):\n",
    "        g = g.sort_values(\"senddate\")\n",
    "        if len(g) < 2:\n",
    "            continue\n",
    "\n",
    "        # 計算間隔分鐘\n",
    "        g[\"interval_min\"] = g[\"senddate\"].diff().dt.total_seconds() / 60.0\n",
    "        g = g.dropna(subset=[\"interval_min\"])\n",
    "\n",
    "        # 篩出 >= 1000 min\n",
    "        over = g[g[\"interval_min\"] >= THRESH_MIN]\n",
    "        if over.empty:\n",
    "            continue\n",
    "\n",
    "        # 把前一筆的時間補出來\n",
    "        prev_times = g[\"senddate\"].shift(1)\n",
    "        for idx, row in over.iterrows():\n",
    "            records.append({\n",
    "                \"file\": base,\n",
    "                \"patno\": row[\"patno\"],\n",
    "                \"prev_time\": prev_times.loc[idx],\n",
    "                \"curr_time\": row[\"senddate\"],\n",
    "                \"interval_min\": round(row[\"interval_min\"], 2)\n",
    "            })\n",
    "\n",
    "# ==== 匯出結果 ====\n",
    "if records:\n",
    "    out_df = pd.DataFrame(records)\n",
    "    out_df = out_df.sort_values([\"file\", \"patno\", \"curr_time\"])\n",
    "    out_df.to_csv(OUT_CSV, index=False)\n",
    "    print(f\"\\n✅ Found {len(out_df)} intervals ≥ {THRESH_MIN} minutes.\")\n",
    "    print(f\"✅ Saved to: {OUT_CSV}\")\n",
    "    print(out_df.head(10))\n",
    "else:\n",
    "    print(f\"\\n✅ No intervals ≥ {THRESH_MIN} minutes found.\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "id": "4f4c771f-547d-4fd6-8bb1-8939a45261d4",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Info] Found 122 files in /home/jovyan/RT08/0925/bling_svv_14\n",
      "\n",
      "=== Interval counts (within NaN_check=1 only) ===\n",
      "Total adjacent adjustment pairs : 1,129,223\n",
      "Intervals >=  60 min            : 154\n",
      "Intervals >= 120 min            : 113\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1120x672 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\"\"\"\n",
    "我想看 在NaN_check=1的資料中，ad_para=1之間的時間間隔>=60, >=120的資料筆數條狀圖 也請直接將資料筆數印在程式碼中\n",
    "\n",
    "在 /home/jovyan/RT08/0925/bling_svv_14/ 中統計：\n",
    "- 僅在 NaN_check=1 的資料裡，計算相鄰兩次「調參事件」之間的時間間隔（分鐘）\n",
    "- 調參事件欄位優先使用 ad_para_check；若無，則嘗試 ad_para\n",
    "- 彙整全體間隔的筆數，並計算：\n",
    "    >= 60 分鐘 的筆數\n",
    "    >= 120 分鐘 的筆數\n",
    "- 直接印出筆數（會在執行時輸出於畫面）\n",
    "- 畫條狀圖（英文標示），不修改任何原始檔案\n",
    "\"\"\"\n",
    "\n",
    "import os, glob\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# ---------- 參數 ----------\n",
    "DATA_DIR = \"/home/jovyan/RT08/0925/bling_svv_14\"  # 僅讀取，不會寫回\n",
    "COL_PAT   = \"patno\"\n",
    "COL_TIME  = \"senddate\"\n",
    "COL_OK    = \"NaN_check\"\n",
    "COL_ADJ1  = \"ad_para_check\"  # 優先使用\n",
    "COL_ADJ2  = \"ad_para\"        # 次要備援\n",
    "\n",
    "# ---------- 小工具：讀檔且彈性選擇調參欄位 ----------\n",
    "def load_csv_pick_adjcol(path):\n",
    "    \"\"\"\n",
    "    讀入單一 CSV，回傳 (df, adj_col_name)\n",
    "    - 優先使用 ad_para_check；若不存在且有 ad_para，則使用 ad_para\n",
    "    - 若缺必要欄位，回傳 (None, None)\n",
    "    \"\"\"\n",
    "    try:\n",
    "        df = pd.read_csv(path, low_memory=False)\n",
    "    except Exception as e:\n",
    "        print(f\"[Warn] cannot read {os.path.basename(path)}: {e}\")\n",
    "        return None, None\n",
    "\n",
    "    # 必要欄位檢查（時間與 NaN_check）\n",
    "    for c in [COL_TIME, COL_OK]:\n",
    "        if c not in df.columns:\n",
    "            print(f\"[Warn] {os.path.basename(path)} missing column: {c}, skip.\")\n",
    "            return None, None\n",
    "\n",
    "    # 決定使用哪個調參欄位\n",
    "    adj_col = None\n",
    "    if COL_ADJ1 in df.columns:\n",
    "        adj_col = COL_ADJ1\n",
    "    elif COL_ADJ2 in df.columns:\n",
    "        adj_col = COL_ADJ2\n",
    "    else:\n",
    "        print(f\"[Warn] {os.path.basename(path)} missing both {COL_ADJ1}/{COL_ADJ2}, skip.\")\n",
    "        return None, None\n",
    "\n",
    "    return df, adj_col\n",
    "\n",
    "# ---------- 主流程：彙整所有病患的調參間隔 ----------\n",
    "intervals_min_all = []  # 蒐集所有「相鄰兩次調參事件」的間隔（分鐘）\n",
    "\n",
    "file_paths = sorted(glob.glob(os.path.join(DATA_DIR, \"*.csv\")))\n",
    "print(f\"[Info] Found {len(file_paths)} files in {DATA_DIR}\")\n",
    "\n",
    "for fp in file_paths:\n",
    "    base = os.path.basename(fp)\n",
    "    df, adj_col = load_csv_pick_adjcol(fp)\n",
    "    if df is None:\n",
    "        continue\n",
    "\n",
    "    # 轉型：時間、NaN_check、調參旗標\n",
    "    df = df.copy()\n",
    "    df[COL_TIME] = pd.to_datetime(df[COL_TIME], errors=\"coerce\")\n",
    "    df[COL_OK]   = pd.to_numeric(df[COL_OK], errors=\"coerce\").fillna(0).astype(int).clip(0, 1)\n",
    "    df[adj_col]  = pd.to_numeric(df[adj_col], errors=\"coerce\").fillna(0).astype(int).clip(0, 1)\n",
    "\n",
    "    # 僅保留時間合法 & NaN_check=1 的列\n",
    "    df = df.dropna(subset=[COL_TIME])\n",
    "    df = df[df[COL_OK] == 1]\n",
    "    if df.empty:\n",
    "        continue\n",
    "\n",
    "    # 以病患為單位計算（避免跨病患錯配）\n",
    "    # 註：若 patno 欄位不存在，仍可跨整檔計算，但較不精準；這裡若無 patno，將整檔合併處理\n",
    "    if COL_PAT in df.columns:\n",
    "        groups = df.groupby(COL_PAT, dropna=True)\n",
    "    else:\n",
    "        groups = [(None, df)]\n",
    "\n",
    "    for pid, g in groups:\n",
    "        # 只看調參事件 = 1 的時間點\n",
    "        g1 = g[g[adj_col] == 1].copy()\n",
    "        if g1.empty:\n",
    "            continue\n",
    "        g1 = g1.sort_values(COL_TIME)\n",
    "\n",
    "        # 計算相鄰兩筆的時間差（分鐘）\n",
    "        t = g1[COL_TIME].to_numpy()\n",
    "        if len(t) < 2:\n",
    "            continue\n",
    "        dt_min = (t[1:] - t[:-1]) / np.timedelta64(1, \"m\")\n",
    "        # 僅保留正值（避免相同時間或倒序造成的 0/負數）\n",
    "        dt_min = dt_min[dt_min > 0]\n",
    "        if dt_min.size:\n",
    "            intervals_min_all.append(dt_min)\n",
    "\n",
    "# 合併所有間隔\n",
    "if intervals_min_all:\n",
    "    intervals = np.concatenate(intervals_min_all).astype(float)\n",
    "else:\n",
    "    intervals = np.array([], dtype=float)\n",
    "\n",
    "# ---------- 計數與印出 ----------\n",
    "total_pairs = intervals.size  # 全部相鄰調參事件的對數\n",
    "n_ge60  = int((intervals >= 60).sum())\n",
    "n_ge120 = int((intervals >= 120).sum())\n",
    "\n",
    "print(\"\\n=== Interval counts (within NaN_check=1 only) ===\")\n",
    "print(f\"Total adjacent adjustment pairs : {total_pairs:,}\")\n",
    "print(f\"Intervals >=  60 min            : {n_ge60:,}\")\n",
    "print(f\"Intervals >= 120 min            : {n_ge120:,}\")\n",
    "\n",
    "# ---------- 視覺化：兩個柱（>=60、>=120） ----------\n",
    "# 圖表用英文；數字標註清楚；不重疊\n",
    "labels = [\">= 60 min\", \">= 120 min\"]\n",
    "counts = [n_ge60, n_ge120]\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(8, 4.8), dpi=140)\n",
    "\n",
    "bars = ax.bar(labels, counts, width=0.6)\n",
    "\n",
    "# 文字標註（柱頂顯示筆數；若有總體 pair，可同時顯示相對於「>=60母群」或「全部pair」的比例）\n",
    "for b, c in zip(bars, counts):\n",
    "    ax.text(b.get_x() + b.get_width()/2, b.get_height() + max(counts)*0.02,\n",
    "            f\"{c:,}\", ha=\"center\", va=\"bottom\", fontsize=11,\n",
    "            bbox=dict(boxstyle=\"round,pad=0.2\", fc=\"white\", ec=\"none\", alpha=0.9))\n",
    "\n",
    "ax.set_ylabel(\"Count\")\n",
    "ax.set_title(\"Counts of intervals between consecutive adjustments (NaN_check=1)\")\n",
    "ax.grid(True, axis=\"y\", linestyle=\"--\", linewidth=0.6, alpha=0.5)\n",
    "ax.set_axisbelow(True)\n",
    "\n",
    "# 右上角補充說明\n",
    "ax.text(0.98, 0.98,\n",
    "        f\"Total pairs = {total_pairs:,}\",\n",
    "        transform=ax.transAxes, ha=\"right\", va=\"top\", fontsize=10)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0030fe93-bb31-4d90-a3cd-e09437a44415",
   "metadata": {},
   "outputs": [],
   "source": [
    "我想看>=120的大概是幾分鐘\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "47a7202f-ef47-42bd-b3fe-8b19dbf3a86a",
   "metadata": {},
   "outputs": [],
   "source": [
    "如果我的資料 經過前處理可使用的資料用NaN_check標記為1, 有調參用ad_para標記為1，\n",
    "若調參間距大於=60分鐘，我要另外為每次調參之間的數據標記，\n",
    "分別是針對靠近第二次調參的20%資料進行標記，\n",
    "這20%若大於2小時則只取兩小時\n",
    "再來是針對靠近第一次調參的50%資料進行標記\n",
    "百分比採時間百分比；不以筆數近似\n",
    "區間採 [start, end)；事件當下樣本不入區間。\\"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "id": "64e10a15-01b3-4f40-b433-95e3330010af",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true,
     "source_hidden": true
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "📂 總共有 122 個檔案讀取成功。\n",
      "\n",
      "🧾 PatNo_ID_1594439781.csv: 15 欄位\n",
      "['patno', 'senddate', 'rrhzsetactual', 'mvsetactual', 'peepepap', 'ppeak', 'cdyn', 'pmean', 'mode_1', 'mode_2', 'mode_3', 'svv_new', 'NaN_check', 'ad_para', 'ad_para_check']\n",
      "------------------------------------------------------------\n",
      "🧾 PatNo_ID_1570242703.csv: 15 欄位\n",
      "['patno', 'senddate', 'rrhzsetactual', 'mvsetactual', 'peepepap', 'ppeak', 'cdyn', 'pmean', 'mode_1', 'mode_2', 'mode_3', 'svv_new', 'NaN_check', 'ad_para', 'ad_para_check']\n",
      "------------------------------------------------------------\n",
      "🧾 PatNo_ID_1574148494.csv: 15 欄位\n",
      "['patno', 'senddate', 'rrhzsetactual', 'mvsetactual', 'peepepap', 'ppeak', 'cdyn', 'pmean', 'mode_1', 'mode_2', 'mode_3', 'svv_new', 'NaN_check', 'ad_para', 'ad_para_check']\n",
      "------------------------------------------------------------\n",
      "🧾 PatNo_ID_1582849900.csv: 15 欄位\n",
      "['patno', 'senddate', 'rrhzsetactual', 'mvsetactual', 'peepepap', 'ppeak', 'cdyn', 'pmean', 'mode_1', 'mode_2', 'mode_3', 'svv_new', 'NaN_check', 'ad_para', 'ad_para_check']\n",
      "------------------------------------------------------------\n",
      "🧾 PatNo_ID_1574831525.csv: 15 欄位\n",
      "['patno', 'senddate', 'rrhzsetactual', 'mvsetactual', 'peepepap', 'ppeak', 'cdyn', 'pmean', 'mode_1', 'mode_2', 'mode_3', 'svv_new', 'NaN_check', 'ad_para', 'ad_para_check']\n",
      "------------------------------------------------------------\n",
      "🧾 PatNo_ID_1564148644.csv: 15 欄位\n",
      "['patno', 'senddate', 'rrhzsetactual', 'mvsetactual', 'peepepap', 'ppeak', 'cdyn', 'pmean', 'mode_1', 'mode_2', 'mode_3', 'svv_new', 'NaN_check', 'ad_para', 'ad_para_check']\n",
      "------------------------------------------------------------\n",
      "🧾 PatNo_ID_1575502382.csv: 15 欄位\n",
      "['patno', 'senddate', 'rrhzsetactual', 'mvsetactual', 'peepepap', 'ppeak', 'cdyn', 'pmean', 'mode_1', 'mode_2', 'mode_3', 'svv_new', 'NaN_check', 'ad_para', 'ad_para_check']\n",
      "------------------------------------------------------------\n",
      "🧾 PatNo_ID_1588794796.csv: 15 欄位\n",
      "['patno', 'senddate', 'rrhzsetactual', 'mvsetactual', 'peepepap', 'ppeak', 'cdyn', 'pmean', 'mode_1', 'mode_2', 'mode_3', 'svv_new', 'NaN_check', 'ad_para', 'ad_para_check']\n",
      "------------------------------------------------------------\n",
      "🧾 PatNo_ID_1590136310.csv: 15 欄位\n",
      "['patno', 'senddate', 'rrhzsetactual', 'mvsetactual', 'peepepap', 'ppeak', 'cdyn', 'pmean', 'mode_1', 'mode_2', 'mode_3', 'svv_new', 'NaN_check', 'ad_para', 'ad_para_check']\n",
      "------------------------------------------------------------\n",
      "🧾 PatNo_ID_1567804800.csv: 15 欄位\n",
      "['patno', 'senddate', 'rrhzsetactual', 'mvsetactual', 'peepepap', 'ppeak', 'cdyn', 'pmean', 'mode_1', 'mode_2', 'mode_3', 'svv_new', 'NaN_check', 'ad_para', 'ad_para_check']\n",
      "------------------------------------------------------------\n",
      "🧾 PatNo_ID_1565378038.csv: 15 欄位\n",
      "['patno', 'senddate', 'rrhzsetactual', 'mvsetactual', 'peepepap', 'ppeak', 'cdyn', 'pmean', 'mode_1', 'mode_2', 'mode_3', 'svv_new', 'NaN_check', 'ad_para', 'ad_para_check']\n",
      "------------------------------------------------------------\n",
      "🧾 PatNo_ID_1580107637.csv: 15 欄位\n",
      "['patno', 'senddate', 'rrhzsetactual', 'mvsetactual', 'peepepap', 'ppeak', 'cdyn', 'pmean', 'mode_1', 'mode_2', 'mode_3', 'svv_new', 'NaN_check', 'ad_para', 'ad_para_check']\n",
      "------------------------------------------------------------\n",
      "🧾 PatNo_ID_1588632604.csv: 15 欄位\n",
      "['patno', 'senddate', 'rrhzsetactual', 'mvsetactual', 'peepepap', 'ppeak', 'cdyn', 'pmean', 'mode_1', 'mode_2', 'mode_3', 'svv_new', 'NaN_check', 'ad_para', 'ad_para_check']\n",
      "------------------------------------------------------------\n",
      "🧾 PatNo_ID_1594511911.csv: 15 欄位\n",
      "['patno', 'senddate', 'rrhzsetactual', 'mvsetactual', 'peepepap', 'ppeak', 'cdyn', 'pmean', 'mode_1', 'mode_2', 'mode_3', 'svv_new', 'NaN_check', 'ad_para', 'ad_para_check']\n",
      "------------------------------------------------------------\n",
      "🧾 PatNo_ID_1574270349.csv: 15 欄位\n",
      "['patno', 'senddate', 'rrhzsetactual', 'mvsetactual', 'peepepap', 'ppeak', 'cdyn', 'pmean', 'mode_1', 'mode_2', 'mode_3', 'svv_new', 'NaN_check', 'ad_para', 'ad_para_check']\n",
      "------------------------------------------------------------\n",
      "🧾 PatNo_ID_1573964540.csv: 15 欄位\n",
      "['patno', 'senddate', 'rrhzsetactual', 'mvsetactual', 'peepepap', 'ppeak', 'cdyn', 'pmean', 'mode_1', 'mode_2', 'mode_3', 'svv_new', 'NaN_check', 'ad_para', 'ad_para_check']\n",
      "------------------------------------------------------------\n",
      "🧾 PatNo_ID_1568952422.csv: 15 欄位\n",
      "['patno', 'senddate', 'rrhzsetactual', 'mvsetactual', 'peepepap', 'ppeak', 'cdyn', 'pmean', 'mode_1', 'mode_2', 'mode_3', 'svv_new', 'NaN_check', 'ad_para', 'ad_para_check']\n",
      "------------------------------------------------------------\n",
      "🧾 PatNo_ID_1594533379.csv: 15 欄位\n",
      "['patno', 'senddate', 'rrhzsetactual', 'mvsetactual', 'peepepap', 'ppeak', 'cdyn', 'pmean', 'mode_1', 'mode_2', 'mode_3', 'svv_new', 'NaN_check', 'ad_para', 'ad_para_check']\n",
      "------------------------------------------------------------\n",
      "🧾 PatNo_ID_1580096720.csv: 15 欄位\n",
      "['patno', 'senddate', 'rrhzsetactual', 'mvsetactual', 'peepepap', 'ppeak', 'cdyn', 'pmean', 'mode_1', 'mode_2', 'mode_3', 'svv_new', 'NaN_check', 'ad_para', 'ad_para_check']\n",
      "------------------------------------------------------------\n",
      "🧾 PatNo_ID_1594528842.csv: 15 欄位\n",
      "['patno', 'senddate', 'rrhzsetactual', 'mvsetactual', 'peepepap', 'ppeak', 'cdyn', 'pmean', 'mode_1', 'mode_2', 'mode_3', 'svv_new', 'NaN_check', 'ad_para', 'ad_para_check']\n",
      "------------------------------------------------------------\n",
      "🧾 PatNo_ID_1581633231.csv: 15 欄位\n",
      "['patno', 'senddate', 'rrhzsetactual', 'mvsetactual', 'peepepap', 'ppeak', 'cdyn', 'pmean', 'mode_1', 'mode_2', 'mode_3', 'svv_new', 'NaN_check', 'ad_para', 'ad_para_check']\n",
      "------------------------------------------------------------\n",
      "🧾 7108162.csv: 15 欄位\n",
      "['patno', 'senddate', 'rrhzsetactual', 'mvsetactual', 'peepepap', 'ppeak', 'cdyn', 'pmean', 'mode_1', 'mode_2', 'mode_3', 'svv_new', 'NaN_check', 'ad_para', 'ad_para_check']\n",
      "------------------------------------------------------------\n",
      "🧾 PatNo_ID_1581692973.csv: 15 欄位\n",
      "['patno', 'senddate', 'rrhzsetactual', 'mvsetactual', 'peepepap', 'ppeak', 'cdyn', 'pmean', 'mode_1', 'mode_2', 'mode_3', 'svv_new', 'NaN_check', 'ad_para', 'ad_para_check']\n",
      "------------------------------------------------------------\n",
      "🧾 PatNo_ID_1568574099.csv: 15 欄位\n",
      "['patno', 'senddate', 'rrhzsetactual', 'mvsetactual', 'peepepap', 'ppeak', 'cdyn', 'pmean', 'mode_1', 'mode_2', 'mode_3', 'svv_new', 'NaN_check', 'ad_para', 'ad_para_check']\n",
      "------------------------------------------------------------\n",
      "🧾 PatNo_ID_1574528808.csv: 15 欄位\n",
      "['patno', 'senddate', 'rrhzsetactual', 'mvsetactual', 'peepepap', 'ppeak', 'cdyn', 'pmean', 'mode_1', 'mode_2', 'mode_3', 'svv_new', 'NaN_check', 'ad_para', 'ad_para_check']\n",
      "------------------------------------------------------------\n",
      "🧾 PatNo_ID_1587490083.csv: 15 欄位\n",
      "['patno', 'senddate', 'rrhzsetactual', 'mvsetactual', 'peepepap', 'ppeak', 'cdyn', 'pmean', 'mode_1', 'mode_2', 'mode_3', 'svv_new', 'NaN_check', 'ad_para', 'ad_para_check']\n",
      "------------------------------------------------------------\n",
      "🧾 PatNo_ID_1590854576.csv: 15 欄位\n",
      "['patno', 'senddate', 'rrhzsetactual', 'mvsetactual', 'peepepap', 'ppeak', 'cdyn', 'pmean', 'mode_1', 'mode_2', 'mode_3', 'svv_new', 'NaN_check', 'ad_para', 'ad_para_check']\n",
      "------------------------------------------------------------\n",
      "🧾 PatNo_ID_1569944983.csv: 15 欄位\n",
      "['patno', 'senddate', 'rrhzsetactual', 'mvsetactual', 'peepepap', 'ppeak', 'cdyn', 'pmean', 'mode_1', 'mode_2', 'mode_3', 'svv_new', 'NaN_check', 'ad_para', 'ad_para_check']\n",
      "------------------------------------------------------------\n",
      "🧾 PatNo_ID_1589034524.csv: 15 欄位\n",
      "['patno', 'senddate', 'rrhzsetactual', 'mvsetactual', 'peepepap', 'ppeak', 'cdyn', 'pmean', 'mode_1', 'mode_2', 'mode_3', 'svv_new', 'NaN_check', 'ad_para', 'ad_para_check']\n",
      "------------------------------------------------------------\n",
      "🧾 PatNo_ID_1594305136.csv: 15 欄位\n",
      "['patno', 'senddate', 'rrhzsetactual', 'mvsetactual', 'peepepap', 'ppeak', 'cdyn', 'pmean', 'mode_1', 'mode_2', 'mode_3', 'svv_new', 'NaN_check', 'ad_para', 'ad_para_check']\n",
      "------------------------------------------------------------\n",
      "🧾 PatNo_ID_1572976822.csv: 15 欄位\n",
      "['patno', 'senddate', 'rrhzsetactual', 'mvsetactual', 'peepepap', 'ppeak', 'cdyn', 'pmean', 'mode_1', 'mode_2', 'mode_3', 'svv_new', 'NaN_check', 'ad_para', 'ad_para_check']\n",
      "------------------------------------------------------------\n",
      "🧾 114309.csv: 15 欄位\n",
      "['patno', 'senddate', 'rrhzsetactual', 'mvsetactual', 'peepepap', 'ppeak', 'cdyn', 'pmean', 'mode_1', 'mode_2', 'mode_3', 'svv_new', 'NaN_check', 'ad_para', 'ad_para_check']\n",
      "------------------------------------------------------------\n",
      "🧾 PatNo_ID_1577042911.csv: 15 欄位\n",
      "['patno', 'senddate', 'rrhzsetactual', 'mvsetactual', 'peepepap', 'ppeak', 'cdyn', 'pmean', 'mode_1', 'mode_2', 'mode_3', 'svv_new', 'NaN_check', 'ad_para', 'ad_para_check']\n",
      "------------------------------------------------------------\n",
      "🧾 PatNo_ID_1567747650.csv: 15 欄位\n",
      "['patno', 'senddate', 'rrhzsetactual', 'mvsetactual', 'peepepap', 'ppeak', 'cdyn', 'pmean', 'mode_1', 'mode_2', 'mode_3', 'svv_new', 'NaN_check', 'ad_para', 'ad_para_check']\n",
      "------------------------------------------------------------\n",
      "🧾 PatNo_ID_1592560504.csv: 15 欄位\n",
      "['patno', 'senddate', 'rrhzsetactual', 'mvsetactual', 'peepepap', 'ppeak', 'cdyn', 'pmean', 'mode_1', 'mode_2', 'mode_3', 'svv_new', 'NaN_check', 'ad_para', 'ad_para_check']\n",
      "------------------------------------------------------------\n",
      "🧾 7408338.csv: 15 欄位\n",
      "['patno', 'senddate', 'rrhzsetactual', 'mvsetactual', 'peepepap', 'ppeak', 'cdyn', 'pmean', 'mode_1', 'mode_2', 'mode_3', 'svv_new', 'NaN_check', 'ad_para', 'ad_para_check']\n",
      "------------------------------------------------------------\n",
      "🧾 PatNo_ID_1593472048.csv: 15 欄位\n",
      "['patno', 'senddate', 'rrhzsetactual', 'mvsetactual', 'peepepap', 'ppeak', 'cdyn', 'pmean', 'mode_1', 'mode_2', 'mode_3', 'svv_new', 'NaN_check', 'ad_para', 'ad_para_check']\n",
      "------------------------------------------------------------\n",
      "🧾 PatNo_ID_1560013303.csv: 15 欄位\n",
      "['patno', 'senddate', 'rrhzsetactual', 'mvsetactual', 'peepepap', 'ppeak', 'cdyn', 'pmean', 'mode_1', 'mode_2', 'mode_3', 'svv_new', 'NaN_check', 'ad_para', 'ad_para_check']\n",
      "------------------------------------------------------------\n",
      "🧾 PatNo_ID_1575256902.csv: 15 欄位\n",
      "['patno', 'senddate', 'rrhzsetactual', 'mvsetactual', 'peepepap', 'ppeak', 'cdyn', 'pmean', 'mode_1', 'mode_2', 'mode_3', 'svv_new', 'NaN_check', 'ad_para', 'ad_para_check']\n",
      "------------------------------------------------------------\n",
      "🧾 PatNo_ID_1575975485.csv: 15 欄位\n",
      "['patno', 'senddate', 'rrhzsetactual', 'mvsetactual', 'peepepap', 'ppeak', 'cdyn', 'pmean', 'mode_1', 'mode_2', 'mode_3', 'svv_new', 'NaN_check', 'ad_para', 'ad_para_check']\n",
      "------------------------------------------------------------\n",
      "🧾 PatNo_ID_1594437309.csv: 15 欄位\n",
      "['patno', 'senddate', 'rrhzsetactual', 'mvsetactual', 'peepepap', 'ppeak', 'cdyn', 'pmean', 'mode_1', 'mode_2', 'mode_3', 'svv_new', 'NaN_check', 'ad_para', 'ad_para_check']\n",
      "------------------------------------------------------------\n",
      "🧾 PatNo_ID_1576301569.csv: 15 欄位\n",
      "['patno', 'senddate', 'rrhzsetactual', 'mvsetactual', 'peepepap', 'ppeak', 'cdyn', 'pmean', 'mode_1', 'mode_2', 'mode_3', 'svv_new', 'NaN_check', 'ad_para', 'ad_para_check']\n",
      "------------------------------------------------------------\n",
      "🧾 PatNo_ID_1589324603.csv: 15 欄位\n",
      "['patno', 'senddate', 'rrhzsetactual', 'mvsetactual', 'peepepap', 'ppeak', 'cdyn', 'pmean', 'mode_1', 'mode_2', 'mode_3', 'svv_new', 'NaN_check', 'ad_para', 'ad_para_check']\n",
      "------------------------------------------------------------\n",
      "🧾 PatNo_ID_1594464829.csv: 15 欄位\n",
      "['patno', 'senddate', 'rrhzsetactual', 'mvsetactual', 'peepepap', 'ppeak', 'cdyn', 'pmean', 'mode_1', 'mode_2', 'mode_3', 'svv_new', 'NaN_check', 'ad_para', 'ad_para_check']\n",
      "------------------------------------------------------------\n",
      "🧾 PatNo_ID_1565148312.csv: 15 欄位\n",
      "['patno', 'senddate', 'rrhzsetactual', 'mvsetactual', 'peepepap', 'ppeak', 'cdyn', 'pmean', 'mode_1', 'mode_2', 'mode_3', 'svv_new', 'NaN_check', 'ad_para', 'ad_para_check']\n",
      "------------------------------------------------------------\n",
      "🧾 PatNo_ID_1572481361.csv: 15 欄位\n",
      "['patno', 'senddate', 'rrhzsetactual', 'mvsetactual', 'peepepap', 'ppeak', 'cdyn', 'pmean', 'mode_1', 'mode_2', 'mode_3', 'svv_new', 'NaN_check', 'ad_para', 'ad_para_check']\n",
      "------------------------------------------------------------\n",
      "🧾 PatNo_ID_1590616537.csv: 15 欄位\n",
      "['patno', 'senddate', 'rrhzsetactual', 'mvsetactual', 'peepepap', 'ppeak', 'cdyn', 'pmean', 'mode_1', 'mode_2', 'mode_3', 'svv_new', 'NaN_check', 'ad_para', 'ad_para_check']\n",
      "------------------------------------------------------------\n",
      "🧾 095707.csv: 15 欄位\n",
      "['patno', 'senddate', 'rrhzsetactual', 'mvsetactual', 'peepepap', 'ppeak', 'cdyn', 'pmean', 'mode_1', 'mode_2', 'mode_3', 'svv_new', 'NaN_check', 'ad_para', 'ad_para_check']\n",
      "------------------------------------------------------------\n",
      "🧾 PatNo_ID_1594467719.csv: 15 欄位\n",
      "['patno', 'senddate', 'rrhzsetactual', 'mvsetactual', 'peepepap', 'ppeak', 'cdyn', 'pmean', 'mode_1', 'mode_2', 'mode_3', 'svv_new', 'NaN_check', 'ad_para', 'ad_para_check']\n",
      "------------------------------------------------------------\n",
      "🧾 PatNo_ID_1566123680.csv: 15 欄位\n",
      "['patno', 'senddate', 'rrhzsetactual', 'mvsetactual', 'peepepap', 'ppeak', 'cdyn', 'pmean', 'mode_1', 'mode_2', 'mode_3', 'svv_new', 'NaN_check', 'ad_para', 'ad_para_check']\n",
      "------------------------------------------------------------\n",
      "🧾 PatNo_ID_1566911879.csv: 15 欄位\n",
      "['patno', 'senddate', 'rrhzsetactual', 'mvsetactual', 'peepepap', 'ppeak', 'cdyn', 'pmean', 'mode_1', 'mode_2', 'mode_3', 'svv_new', 'NaN_check', 'ad_para', 'ad_para_check']\n",
      "------------------------------------------------------------\n",
      "🧾 PatNo_ID_1566252197.csv: 15 欄位\n",
      "['patno', 'senddate', 'rrhzsetactual', 'mvsetactual', 'peepepap', 'ppeak', 'cdyn', 'pmean', 'mode_1', 'mode_2', 'mode_3', 'svv_new', 'NaN_check', 'ad_para', 'ad_para_check']\n",
      "------------------------------------------------------------\n",
      "🧾 PatNo_ID_1581003248.csv: 15 欄位\n",
      "['patno', 'senddate', 'rrhzsetactual', 'mvsetactual', 'peepepap', 'ppeak', 'cdyn', 'pmean', 'mode_1', 'mode_2', 'mode_3', 'svv_new', 'NaN_check', 'ad_para', 'ad_para_check']\n",
      "------------------------------------------------------------\n",
      "🧾 4216007.csv: 15 欄位\n",
      "['patno', 'senddate', 'rrhzsetactual', 'mvsetactual', 'peepepap', 'ppeak', 'cdyn', 'pmean', 'mode_1', 'mode_2', 'mode_3', 'svv_new', 'NaN_check', 'ad_para', 'ad_para_check']\n",
      "------------------------------------------------------------\n",
      "🧾 PatNo_ID_1569083701.csv: 15 欄位\n",
      "['patno', 'senddate', 'rrhzsetactual', 'mvsetactual', 'peepepap', 'ppeak', 'cdyn', 'pmean', 'mode_1', 'mode_2', 'mode_3', 'svv_new', 'NaN_check', 'ad_para', 'ad_para_check']\n",
      "------------------------------------------------------------\n",
      "🧾 PatNo_ID_1593416100.csv: 15 欄位\n",
      "['patno', 'senddate', 'rrhzsetactual', 'mvsetactual', 'peepepap', 'ppeak', 'cdyn', 'pmean', 'mode_1', 'mode_2', 'mode_3', 'svv_new', 'NaN_check', 'ad_para', 'ad_para_check']\n",
      "------------------------------------------------------------\n",
      "🧾 PatNo_ID_1570642083.csv: 15 欄位\n",
      "['patno', 'senddate', 'rrhzsetactual', 'mvsetactual', 'peepepap', 'ppeak', 'cdyn', 'pmean', 'mode_1', 'mode_2', 'mode_3', 'svv_new', 'NaN_check', 'ad_para', 'ad_para_check']\n",
      "------------------------------------------------------------\n",
      "🧾 PatNo_ID_1586696634.csv: 15 欄位\n",
      "['patno', 'senddate', 'rrhzsetactual', 'mvsetactual', 'peepepap', 'ppeak', 'cdyn', 'pmean', 'mode_1', 'mode_2', 'mode_3', 'svv_new', 'NaN_check', 'ad_para', 'ad_para_check']\n",
      "------------------------------------------------------------\n",
      "🧾 PatNo_ID_1594319286.csv: 15 欄位\n",
      "['patno', 'senddate', 'rrhzsetactual', 'mvsetactual', 'peepepap', 'ppeak', 'cdyn', 'pmean', 'mode_1', 'mode_2', 'mode_3', 'svv_new', 'NaN_check', 'ad_para', 'ad_para_check']\n",
      "------------------------------------------------------------\n",
      "🧾 PatNo_ID_1593838524.csv: 15 欄位\n",
      "['patno', 'senddate', 'rrhzsetactual', 'mvsetactual', 'peepepap', 'ppeak', 'cdyn', 'pmean', 'mode_1', 'mode_2', 'mode_3', 'svv_new', 'NaN_check', 'ad_para', 'ad_para_check']\n",
      "------------------------------------------------------------\n",
      "🧾 PatNo_ID_1576116479.csv: 15 欄位\n",
      "['patno', 'senddate', 'rrhzsetactual', 'mvsetactual', 'peepepap', 'ppeak', 'cdyn', 'pmean', 'mode_1', 'mode_2', 'mode_3', 'svv_new', 'NaN_check', 'ad_para', 'ad_para_check']\n",
      "------------------------------------------------------------\n",
      "🧾 PatNo_ID_1588957997.csv: 15 欄位\n",
      "['patno', 'senddate', 'rrhzsetactual', 'mvsetactual', 'peepepap', 'ppeak', 'cdyn', 'pmean', 'mode_1', 'mode_2', 'mode_3', 'svv_new', 'NaN_check', 'ad_para', 'ad_para_check']\n",
      "------------------------------------------------------------\n",
      "🧾 PatNo_ID_1575445051.csv: 15 欄位\n",
      "['patno', 'senddate', 'rrhzsetactual', 'mvsetactual', 'peepepap', 'ppeak', 'cdyn', 'pmean', 'mode_1', 'mode_2', 'mode_3', 'svv_new', 'NaN_check', 'ad_para', 'ad_para_check']\n",
      "------------------------------------------------------------\n",
      "🧾 PatNo_ID_1584158973.csv: 15 欄位\n",
      "['patno', 'senddate', 'rrhzsetactual', 'mvsetactual', 'peepepap', 'ppeak', 'cdyn', 'pmean', 'mode_1', 'mode_2', 'mode_3', 'svv_new', 'NaN_check', 'ad_para', 'ad_para_check']\n",
      "------------------------------------------------------------\n",
      "🧾 PatNo_ID_1563587183.csv: 15 欄位\n",
      "['patno', 'senddate', 'rrhzsetactual', 'mvsetactual', 'peepepap', 'ppeak', 'cdyn', 'pmean', 'mode_1', 'mode_2', 'mode_3', 'svv_new', 'NaN_check', 'ad_para', 'ad_para_check']\n",
      "------------------------------------------------------------\n",
      "🧾 PatNo_ID_1573063188.csv: 15 欄位\n",
      "['patno', 'senddate', 'rrhzsetactual', 'mvsetactual', 'peepepap', 'ppeak', 'cdyn', 'pmean', 'mode_1', 'mode_2', 'mode_3', 'svv_new', 'NaN_check', 'ad_para', 'ad_para_check']\n",
      "------------------------------------------------------------\n",
      "🧾 PatNo_ID_1594322594.csv: 15 欄位\n",
      "['patno', 'senddate', 'rrhzsetactual', 'mvsetactual', 'peepepap', 'ppeak', 'cdyn', 'pmean', 'mode_1', 'mode_2', 'mode_3', 'svv_new', 'NaN_check', 'ad_para', 'ad_para_check']\n",
      "------------------------------------------------------------\n",
      "🧾 PatNo_ID_1588673465.csv: 15 欄位\n",
      "['patno', 'senddate', 'rrhzsetactual', 'mvsetactual', 'peepepap', 'ppeak', 'cdyn', 'pmean', 'mode_1', 'mode_2', 'mode_3', 'svv_new', 'NaN_check', 'ad_para', 'ad_para_check']\n",
      "------------------------------------------------------------\n",
      "🧾 089271.csv: 15 欄位\n",
      "['patno', 'senddate', 'rrhzsetactual', 'mvsetactual', 'peepepap', 'ppeak', 'cdyn', 'pmean', 'mode_1', 'mode_2', 'mode_3', 'svv_new', 'NaN_check', 'ad_para', 'ad_para_check']\n",
      "------------------------------------------------------------\n",
      "🧾 PatNo_ID_1582635996.csv: 15 欄位\n",
      "['patno', 'senddate', 'rrhzsetactual', 'mvsetactual', 'peepepap', 'ppeak', 'cdyn', 'pmean', 'mode_1', 'mode_2', 'mode_3', 'svv_new', 'NaN_check', 'ad_para', 'ad_para_check']\n",
      "------------------------------------------------------------\n",
      "🧾 7657698.csv: 15 欄位\n",
      "['patno', 'senddate', 'rrhzsetactual', 'mvsetactual', 'peepepap', 'ppeak', 'cdyn', 'pmean', 'mode_1', 'mode_2', 'mode_3', 'svv_new', 'NaN_check', 'ad_para', 'ad_para_check']\n",
      "------------------------------------------------------------\n",
      "🧾 PatNo_ID_1593087886.csv: 15 欄位\n",
      "['patno', 'senddate', 'rrhzsetactual', 'mvsetactual', 'peepepap', 'ppeak', 'cdyn', 'pmean', 'mode_1', 'mode_2', 'mode_3', 'svv_new', 'NaN_check', 'ad_para', 'ad_para_check']\n",
      "------------------------------------------------------------\n",
      "🧾 PatNo_ID_1594448501.csv: 15 欄位\n",
      "['patno', 'senddate', 'rrhzsetactual', 'mvsetactual', 'peepepap', 'ppeak', 'cdyn', 'pmean', 'mode_1', 'mode_2', 'mode_3', 'svv_new', 'NaN_check', 'ad_para', 'ad_para_check']\n",
      "------------------------------------------------------------\n",
      "🧾 PatNo_ID_1586172659.csv: 15 欄位\n",
      "['patno', 'senddate', 'rrhzsetactual', 'mvsetactual', 'peepepap', 'ppeak', 'cdyn', 'pmean', 'mode_1', 'mode_2', 'mode_3', 'svv_new', 'NaN_check', 'ad_para', 'ad_para_check']\n",
      "------------------------------------------------------------\n",
      "🧾 230933.csv: 15 欄位\n",
      "['patno', 'senddate', 'rrhzsetactual', 'mvsetactual', 'peepepap', 'ppeak', 'cdyn', 'pmean', 'mode_1', 'mode_2', 'mode_3', 'svv_new', 'NaN_check', 'ad_para', 'ad_para_check']\n",
      "------------------------------------------------------------\n",
      "🧾 PatNo_ID_1580244614.csv: 15 欄位\n",
      "['patno', 'senddate', 'rrhzsetactual', 'mvsetactual', 'peepepap', 'ppeak', 'cdyn', 'pmean', 'mode_1', 'mode_2', 'mode_3', 'svv_new', 'NaN_check', 'ad_para', 'ad_para_check']\n",
      "------------------------------------------------------------\n",
      "🧾 095323.csv: 15 欄位\n",
      "['patno', 'senddate', 'rrhzsetactual', 'mvsetactual', 'peepepap', 'ppeak', 'cdyn', 'pmean', 'mode_1', 'mode_2', 'mode_3', 'svv_new', 'NaN_check', 'ad_para', 'ad_para_check']\n",
      "------------------------------------------------------------\n",
      "🧾 PatNo_ID_1594320763.csv: 15 欄位\n",
      "['patno', 'senddate', 'rrhzsetactual', 'mvsetactual', 'peepepap', 'ppeak', 'cdyn', 'pmean', 'mode_1', 'mode_2', 'mode_3', 'svv_new', 'NaN_check', 'ad_para', 'ad_para_check']\n",
      "------------------------------------------------------------\n",
      "🧾 PatNo_ID_1582937076.csv: 15 欄位\n",
      "['patno', 'senddate', 'rrhzsetactual', 'mvsetactual', 'peepepap', 'ppeak', 'cdyn', 'pmean', 'mode_1', 'mode_2', 'mode_3', 'svv_new', 'NaN_check', 'ad_para', 'ad_para_check']\n",
      "------------------------------------------------------------\n",
      "🧾 PatNo_ID_1580062580.csv: 15 欄位\n",
      "['patno', 'senddate', 'rrhzsetactual', 'mvsetactual', 'peepepap', 'ppeak', 'cdyn', 'pmean', 'mode_1', 'mode_2', 'mode_3', 'svv_new', 'NaN_check', 'ad_para', 'ad_para_check']\n",
      "------------------------------------------------------------\n",
      "🧾 PatNo_ID_1566279967.csv: 15 欄位\n",
      "['patno', 'senddate', 'rrhzsetactual', 'mvsetactual', 'peepepap', 'ppeak', 'cdyn', 'pmean', 'mode_1', 'mode_2', 'mode_3', 'svv_new', 'NaN_check', 'ad_para', 'ad_para_check']\n",
      "------------------------------------------------------------\n",
      "🧾 PatNo_ID_1570089466.csv: 15 欄位\n",
      "['patno', 'senddate', 'rrhzsetactual', 'mvsetactual', 'peepepap', 'ppeak', 'cdyn', 'pmean', 'mode_1', 'mode_2', 'mode_3', 'svv_new', 'NaN_check', 'ad_para', 'ad_para_check']\n",
      "------------------------------------------------------------\n",
      "🧾 PatNo_ID_1594423683.csv: 15 欄位\n",
      "['patno', 'senddate', 'rrhzsetactual', 'mvsetactual', 'peepepap', 'ppeak', 'cdyn', 'pmean', 'mode_1', 'mode_2', 'mode_3', 'svv_new', 'NaN_check', 'ad_para', 'ad_para_check']\n",
      "------------------------------------------------------------\n",
      "🧾 PatNo_ID_1579498177.csv: 15 欄位\n",
      "['patno', 'senddate', 'rrhzsetactual', 'mvsetactual', 'peepepap', 'ppeak', 'cdyn', 'pmean', 'mode_1', 'mode_2', 'mode_3', 'svv_new', 'NaN_check', 'ad_para', 'ad_para_check']\n",
      "------------------------------------------------------------\n",
      "🧾 PatNo_ID_1582452511.csv: 15 欄位\n",
      "['patno', 'senddate', 'rrhzsetactual', 'mvsetactual', 'peepepap', 'ppeak', 'cdyn', 'pmean', 'mode_1', 'mode_2', 'mode_3', 'svv_new', 'NaN_check', 'ad_para', 'ad_para_check']\n",
      "------------------------------------------------------------\n",
      "🧾 PatNo_ID_1594335109.csv: 15 欄位\n",
      "['patno', 'senddate', 'rrhzsetactual', 'mvsetactual', 'peepepap', 'ppeak', 'cdyn', 'pmean', 'mode_1', 'mode_2', 'mode_3', 'svv_new', 'NaN_check', 'ad_para', 'ad_para_check']\n",
      "------------------------------------------------------------\n",
      "🧾 PatNo_ID_1579198603.csv: 15 欄位\n",
      "['patno', 'senddate', 'rrhzsetactual', 'mvsetactual', 'peepepap', 'ppeak', 'cdyn', 'pmean', 'mode_1', 'mode_2', 'mode_3', 'svv_new', 'NaN_check', 'ad_para', 'ad_para_check']\n",
      "------------------------------------------------------------\n",
      "🧾 PatNo_ID_1589918099.csv: 15 欄位\n",
      "['patno', 'senddate', 'rrhzsetactual', 'mvsetactual', 'peepepap', 'ppeak', 'cdyn', 'pmean', 'mode_1', 'mode_2', 'mode_3', 'svv_new', 'NaN_check', 'ad_para', 'ad_para_check']\n",
      "------------------------------------------------------------\n",
      "🧾 PatNo_ID_1562733396.csv: 15 欄位\n",
      "['patno', 'senddate', 'rrhzsetactual', 'mvsetactual', 'peepepap', 'ppeak', 'cdyn', 'pmean', 'mode_1', 'mode_2', 'mode_3', 'svv_new', 'NaN_check', 'ad_para', 'ad_para_check']\n",
      "------------------------------------------------------------\n",
      "🧾 PatNo_ID_1586897008.csv: 15 欄位\n",
      "['patno', 'senddate', 'rrhzsetactual', 'mvsetactual', 'peepepap', 'ppeak', 'cdyn', 'pmean', 'mode_1', 'mode_2', 'mode_3', 'svv_new', 'NaN_check', 'ad_para', 'ad_para_check']\n",
      "------------------------------------------------------------\n",
      "🧾 PatNo_ID_1593593586.csv: 15 欄位\n",
      "['patno', 'senddate', 'rrhzsetactual', 'mvsetactual', 'peepepap', 'ppeak', 'cdyn', 'pmean', 'mode_1', 'mode_2', 'mode_3', 'svv_new', 'NaN_check', 'ad_para', 'ad_para_check']\n",
      "------------------------------------------------------------\n",
      "🧾 PatNo_ID_1573249295.csv: 15 欄位\n",
      "['patno', 'senddate', 'rrhzsetactual', 'mvsetactual', 'peepepap', 'ppeak', 'cdyn', 'pmean', 'mode_1', 'mode_2', 'mode_3', 'svv_new', 'NaN_check', 'ad_para', 'ad_para_check']\n",
      "------------------------------------------------------------\n",
      "🧾 PatNo_ID_1594173718.csv: 15 欄位\n",
      "['patno', 'senddate', 'rrhzsetactual', 'mvsetactual', 'peepepap', 'ppeak', 'cdyn', 'pmean', 'mode_1', 'mode_2', 'mode_3', 'svv_new', 'NaN_check', 'ad_para', 'ad_para_check']\n",
      "------------------------------------------------------------\n",
      "🧾 PatNo_ID_1576964560.csv: 15 欄位\n",
      "['patno', 'senddate', 'rrhzsetactual', 'mvsetactual', 'peepepap', 'ppeak', 'cdyn', 'pmean', 'mode_1', 'mode_2', 'mode_3', 'svv_new', 'NaN_check', 'ad_para', 'ad_para_check']\n",
      "------------------------------------------------------------\n",
      "🧾 PatNo_ID_1577487284.csv: 15 欄位\n",
      "['patno', 'senddate', 'rrhzsetactual', 'mvsetactual', 'peepepap', 'ppeak', 'cdyn', 'pmean', 'mode_1', 'mode_2', 'mode_3', 'svv_new', 'NaN_check', 'ad_para', 'ad_para_check']\n",
      "------------------------------------------------------------\n",
      "🧾 PatNo_ID_1594479330.csv: 15 欄位\n",
      "['patno', 'senddate', 'rrhzsetactual', 'mvsetactual', 'peepepap', 'ppeak', 'cdyn', 'pmean', 'mode_1', 'mode_2', 'mode_3', 'svv_new', 'NaN_check', 'ad_para', 'ad_para_check']\n",
      "------------------------------------------------------------\n",
      "🧾 PatNo_ID_1576115572.csv: 15 欄位\n",
      "['patno', 'senddate', 'rrhzsetactual', 'mvsetactual', 'peepepap', 'ppeak', 'cdyn', 'pmean', 'mode_1', 'mode_2', 'mode_3', 'svv_new', 'NaN_check', 'ad_para', 'ad_para_check']\n",
      "------------------------------------------------------------\n",
      "🧾 PatNo_ID_1574987447.csv: 15 欄位\n",
      "['patno', 'senddate', 'rrhzsetactual', 'mvsetactual', 'peepepap', 'ppeak', 'cdyn', 'pmean', 'mode_1', 'mode_2', 'mode_3', 'svv_new', 'NaN_check', 'ad_para', 'ad_para_check']\n",
      "------------------------------------------------------------\n",
      "🧾 PatNo_ID_1594294180.csv: 15 欄位\n",
      "['patno', 'senddate', 'rrhzsetactual', 'mvsetactual', 'peepepap', 'ppeak', 'cdyn', 'pmean', 'mode_1', 'mode_2', 'mode_3', 'svv_new', 'NaN_check', 'ad_para', 'ad_para_check']\n",
      "------------------------------------------------------------\n",
      "🧾 PatNo_ID_1589018086.csv: 15 欄位\n",
      "['patno', 'senddate', 'rrhzsetactual', 'mvsetactual', 'peepepap', 'ppeak', 'cdyn', 'pmean', 'mode_1', 'mode_2', 'mode_3', 'svv_new', 'NaN_check', 'ad_para', 'ad_para_check']\n",
      "------------------------------------------------------------\n",
      "🧾 PatNo_ID_1571945701.csv: 15 欄位\n",
      "['patno', 'senddate', 'rrhzsetactual', 'mvsetactual', 'peepepap', 'ppeak', 'cdyn', 'pmean', 'mode_1', 'mode_2', 'mode_3', 'svv_new', 'NaN_check', 'ad_para', 'ad_para_check']\n",
      "------------------------------------------------------------\n",
      "🧾 PatNo_ID_1580766093.csv: 15 欄位\n",
      "['patno', 'senddate', 'rrhzsetactual', 'mvsetactual', 'peepepap', 'ppeak', 'cdyn', 'pmean', 'mode_1', 'mode_2', 'mode_3', 'svv_new', 'NaN_check', 'ad_para', 'ad_para_check']\n",
      "------------------------------------------------------------\n",
      "🧾 PatNo_ID_1584397376.csv: 15 欄位\n",
      "['patno', 'senddate', 'rrhzsetactual', 'mvsetactual', 'peepepap', 'ppeak', 'cdyn', 'pmean', 'mode_1', 'mode_2', 'mode_3', 'svv_new', 'NaN_check', 'ad_para', 'ad_para_check']\n",
      "------------------------------------------------------------\n",
      "🧾 PatNo_ID_1570273244.csv: 15 欄位\n",
      "['patno', 'senddate', 'rrhzsetactual', 'mvsetactual', 'peepepap', 'ppeak', 'cdyn', 'pmean', 'mode_1', 'mode_2', 'mode_3', 'svv_new', 'NaN_check', 'ad_para', 'ad_para_check']\n",
      "------------------------------------------------------------\n",
      "🧾 PatNo_ID_1594455578.csv: 15 欄位\n",
      "['patno', 'senddate', 'rrhzsetactual', 'mvsetactual', 'peepepap', 'ppeak', 'cdyn', 'pmean', 'mode_1', 'mode_2', 'mode_3', 'svv_new', 'NaN_check', 'ad_para', 'ad_para_check']\n",
      "------------------------------------------------------------\n",
      "🧾 PatNo_ID_1581019504.csv: 15 欄位\n",
      "['patno', 'senddate', 'rrhzsetactual', 'mvsetactual', 'peepepap', 'ppeak', 'cdyn', 'pmean', 'mode_1', 'mode_2', 'mode_3', 'svv_new', 'NaN_check', 'ad_para', 'ad_para_check']\n",
      "------------------------------------------------------------\n",
      "🧾 PatNo_ID_1594471407.csv: 15 欄位\n",
      "['patno', 'senddate', 'rrhzsetactual', 'mvsetactual', 'peepepap', 'ppeak', 'cdyn', 'pmean', 'mode_1', 'mode_2', 'mode_3', 'svv_new', 'NaN_check', 'ad_para', 'ad_para_check']\n",
      "------------------------------------------------------------\n",
      "🧾 PatNo_ID_1568813269.csv: 15 欄位\n",
      "['patno', 'senddate', 'rrhzsetactual', 'mvsetactual', 'peepepap', 'ppeak', 'cdyn', 'pmean', 'mode_1', 'mode_2', 'mode_3', 'svv_new', 'NaN_check', 'ad_para', 'ad_para_check']\n",
      "------------------------------------------------------------\n",
      "🧾 PatNo_ID_1591609798.csv: 15 欄位\n",
      "['patno', 'senddate', 'rrhzsetactual', 'mvsetactual', 'peepepap', 'ppeak', 'cdyn', 'pmean', 'mode_1', 'mode_2', 'mode_3', 'svv_new', 'NaN_check', 'ad_para', 'ad_para_check']\n",
      "------------------------------------------------------------\n",
      "🧾 PatNo_ID_1567832735.csv: 15 欄位\n",
      "['patno', 'senddate', 'rrhzsetactual', 'mvsetactual', 'peepepap', 'ppeak', 'cdyn', 'pmean', 'mode_1', 'mode_2', 'mode_3', 'svv_new', 'NaN_check', 'ad_para', 'ad_para_check']\n",
      "------------------------------------------------------------\n",
      "🧾 7721164.csv: 15 欄位\n",
      "['patno', 'senddate', 'rrhzsetactual', 'mvsetactual', 'peepepap', 'ppeak', 'cdyn', 'pmean', 'mode_1', 'mode_2', 'mode_3', 'svv_new', 'NaN_check', 'ad_para', 'ad_para_check']\n",
      "------------------------------------------------------------\n",
      "🧾 PatNo_ID_1572562839.csv: 15 欄位\n",
      "['patno', 'senddate', 'rrhzsetactual', 'mvsetactual', 'peepepap', 'ppeak', 'cdyn', 'pmean', 'mode_1', 'mode_2', 'mode_3', 'svv_new', 'NaN_check', 'ad_para', 'ad_para_check']\n",
      "------------------------------------------------------------\n",
      "🧾 PatNo_ID_1568039398.csv: 15 欄位\n",
      "['patno', 'senddate', 'rrhzsetactual', 'mvsetactual', 'peepepap', 'ppeak', 'cdyn', 'pmean', 'mode_1', 'mode_2', 'mode_3', 'svv_new', 'NaN_check', 'ad_para', 'ad_para_check']\n",
      "------------------------------------------------------------\n",
      "🧾 PatNo_ID_1594511914.csv: 15 欄位\n",
      "['patno', 'senddate', 'rrhzsetactual', 'mvsetactual', 'peepepap', 'ppeak', 'cdyn', 'pmean', 'mode_1', 'mode_2', 'mode_3', 'svv_new', 'NaN_check', 'ad_para', 'ad_para_check']\n",
      "------------------------------------------------------------\n",
      "🧾 PatNo_ID_1566671274.csv: 15 欄位\n",
      "['patno', 'senddate', 'rrhzsetactual', 'mvsetactual', 'peepepap', 'ppeak', 'cdyn', 'pmean', 'mode_1', 'mode_2', 'mode_3', 'svv_new', 'NaN_check', 'ad_para', 'ad_para_check']\n",
      "------------------------------------------------------------\n",
      "🧾 PatNo_ID_1594441887.csv: 15 欄位\n",
      "['patno', 'senddate', 'rrhzsetactual', 'mvsetactual', 'peepepap', 'ppeak', 'cdyn', 'pmean', 'mode_1', 'mode_2', 'mode_3', 'svv_new', 'NaN_check', 'ad_para', 'ad_para_check']\n",
      "------------------------------------------------------------\n",
      "🧾 PatNo_ID_1572831765.csv: 15 欄位\n",
      "['patno', 'senddate', 'rrhzsetactual', 'mvsetactual', 'peepepap', 'ppeak', 'cdyn', 'pmean', 'mode_1', 'mode_2', 'mode_3', 'svv_new', 'NaN_check', 'ad_para', 'ad_para_check']\n",
      "------------------------------------------------------------\n",
      "🧾 PatNo_ID_1592044724.csv: 15 欄位\n",
      "['patno', 'senddate', 'rrhzsetactual', 'mvsetactual', 'peepepap', 'ppeak', 'cdyn', 'pmean', 'mode_1', 'mode_2', 'mode_3', 'svv_new', 'NaN_check', 'ad_para', 'ad_para_check']\n",
      "------------------------------------------------------------\n",
      "🧾 PatNo_ID_1593720818.csv: 15 欄位\n",
      "['patno', 'senddate', 'rrhzsetactual', 'mvsetactual', 'peepepap', 'ppeak', 'cdyn', 'pmean', 'mode_1', 'mode_2', 'mode_3', 'svv_new', 'NaN_check', 'ad_para', 'ad_para_check']\n",
      "------------------------------------------------------------\n",
      "🧾 PatNo_ID_1575060177.csv: 15 欄位\n",
      "['patno', 'senddate', 'rrhzsetactual', 'mvsetactual', 'peepepap', 'ppeak', 'cdyn', 'pmean', 'mode_1', 'mode_2', 'mode_3', 'svv_new', 'NaN_check', 'ad_para', 'ad_para_check']\n",
      "------------------------------------------------------------\n",
      "🧾 PatNo_ID_1578784257.csv: 15 欄位\n",
      "['patno', 'senddate', 'rrhzsetactual', 'mvsetactual', 'peepepap', 'ppeak', 'cdyn', 'pmean', 'mode_1', 'mode_2', 'mode_3', 'svv_new', 'NaN_check', 'ad_para', 'ad_para_check']\n",
      "------------------------------------------------------------\n",
      "🧾 PatNo_ID_1594309746.csv: 15 欄位\n",
      "['patno', 'senddate', 'rrhzsetactual', 'mvsetactual', 'peepepap', 'ppeak', 'cdyn', 'pmean', 'mode_1', 'mode_2', 'mode_3', 'svv_new', 'NaN_check', 'ad_para', 'ad_para_check']\n",
      "------------------------------------------------------------\n",
      "\n",
      "📊 欄位名稱統計（依出現次數排序）\n",
      "patno: 122\n",
      "senddate: 122\n",
      "rrhzsetactual: 122\n",
      "mvsetactual: 122\n",
      "peepepap: 122\n",
      "ppeak: 122\n",
      "cdyn: 122\n",
      "pmean: 122\n",
      "mode_1: 122\n",
      "mode_2: 122\n",
      "mode_3: 122\n",
      "svv_new: 122\n",
      "NaN_check: 122\n",
      "ad_para: 122\n",
      "ad_para_check: 122\n"
     ]
    }
   ],
   "source": [
    "#我想看/home/jovyan/RT08/0925/bling_1004資料的欄位有那些\n",
    "import os\n",
    "import pandas as pd\n",
    "\n",
    "path = \"/home/jovyan/RT08/0925/bling_1004\"\n",
    "all_cols = {}\n",
    "\n",
    "for file in os.listdir(path):\n",
    "    if file.endswith(\".csv\"):\n",
    "        fpath = os.path.join(path, file)\n",
    "        try:\n",
    "            df = pd.read_csv(fpath, nrows=0)\n",
    "            cols = list(df.columns)\n",
    "            all_cols[file] = cols\n",
    "        except Exception as e:\n",
    "            print(f\"❌ 無法讀取 {file}: {e}\")\n",
    "\n",
    "# 印出摘要\n",
    "print(f\"📂 總共有 {len(all_cols)} 個檔案讀取成功。\\n\")\n",
    "for fname, cols in all_cols.items():\n",
    "    print(f\"🧾 {fname}: {len(cols)} 欄位\")\n",
    "    print(cols)\n",
    "    print(\"-\" * 60)\n",
    "\n",
    "# 若想統計所有欄位出現的頻率：\n",
    "from collections import Counter\n",
    "flat_cols = [c for cols in all_cols.values() for c in cols]\n",
    "cnt = Counter(flat_cols)\n",
    "print(\"\\n📊 欄位名稱統計（依出現次數排序）\")\n",
    "for k, v in cnt.most_common():\n",
    "    print(f\"{k}: {v}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "id": "56b58f7e-4646-48cb-a392-3e7ec1e0b3f3",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Copy] Copied 122 CSV files from '/home/jovyan/RT08/0925/bling_svv_14' -> '/home/jovyan/RT08/0925/bling_1004'\n"
     ]
    },
    {
     "ename": "AttributeError",
     "evalue": "'numpy.timedelta64' object has no attribute 'total_seconds'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[158], line 110\u001b[0m\n\u001b[1;32m    108\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m i \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(\u001b[38;5;28mlen\u001b[39m(t_events) \u001b[38;5;241m-\u001b[39m \u001b[38;5;241m1\u001b[39m):\n\u001b[1;32m    109\u001b[0m     t0, t1 \u001b[38;5;241m=\u001b[39m t_events[i], t_events[i\u001b[38;5;241m+\u001b[39m\u001b[38;5;241m1\u001b[39m]\n\u001b[0;32m--> 110\u001b[0m     dt_min \u001b[38;5;241m=\u001b[39m \u001b[43m(\u001b[49m\u001b[43mt1\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m-\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mt0\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtotal_seconds\u001b[49m() \u001b[38;5;241m/\u001b[39m \u001b[38;5;241m60.0\u001b[39m\n\u001b[1;32m    111\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m dt_min \u001b[38;5;241m<\u001b[39m MIN_GAP_MIN:\n\u001b[1;32m    112\u001b[0m         \u001b[38;5;28;01mcontinue\u001b[39;00m\n",
      "\u001b[0;31mAttributeError\u001b[0m: 'numpy.timedelta64' object has no attribute 'total_seconds'"
     ]
    }
   ],
   "source": [
    "\"\"\"\n",
    "請依據每位病患的調參事件 (ad_para == 1) 時間點，\n",
    "當兩次調參間距大於等於 60 分鐘時，\n",
    "標記前 50% 區段 (set = 0) 與後 20% 區段 (set = 1，上限 2 小時)，\n",
    "事件當下資料不標記，\n",
    "所有區段以實際時間長度計算，\n",
    "若重疊則以後 20% 區段優先，且要列出這種的狀況資料\n",
    "\n",
    "======== Create training/eval 'set' labels around adjustment events (safe copy) ========\n",
    "來源資料夾（唯讀）: /home/jovyan/RT08/0925/bling_svv_14\n",
    "目標資料夾（寫入） : /home/jovyan/RT08/0925/bling_1004\n",
    "變更重點：\n",
    "- 將「未落在任何區段」的 set 由原本 NaN 改為 2（明確標示未標記區段）\n",
    "規則（每一位病患、依時間排序）：\n",
    "- 以「調參事件」為 ad_flag：優先使用欄位 ad_para（=1 視為調參），若無則退回 ad_para_check。\n",
    "- 僅當相鄰兩次調參間距 Δt >= 60 分鐘時，才在這兩事件之間標記：\n",
    "  * 前 50% 區段：set = 0，區間 = (t_i, t_i + 0.5*Δt) ；不含事件當下時間點\n",
    "  * 後 20% 區段：set = 1，區間 = (t_{i+1} - min(0.2*Δt, 120 分), t_{i+1}) ；不含事件當下時間點\n",
    "- 標記以實際時間（senddate）判定；事件當下（== t_i 或 == t_{i+1}）不標記。\n",
    "- 若標記區間互相重疊，『set=1（後 20%）』優先；需列出所有「本應 set=0 但被 set=1 覆蓋」的重疊案例。\n",
    "- 產生新欄位 set；預設 = 2（未落在任何區段）。\n",
    "- 不會修改 /home/jovyan/RT08/0925/bling_svv_14；先完整複製到 /home/jovyan/RT08/0925/bling_1004 後再寫入。\n",
    "\"\"\"\n",
    "\n",
    "import os, glob, shutil\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "SRC_DIR = \"/home/jovyan/RT08/0925/bling_svv_14\"   # 原始資料（唯讀）\n",
    "DST_DIR = \"/home/jovyan/RT08/0925/bling_1004\"     # 目標資料（寫入）\n",
    "REPORT_DIR = \"/home/jovyan/RT08/0925/1002\"        # 報表輸出（純文字/CSV）\n",
    "os.makedirs(DST_DIR, exist_ok=True)\n",
    "os.makedirs(REPORT_DIR, exist_ok=True)\n",
    "\n",
    "# ---- 1) 複製檔案（僅 .csv），保留時間戳等中繼資料 ----\n",
    "src_files = sorted(glob.glob(os.path.join(SRC_DIR, \"*.csv\")))\n",
    "for sp in src_files:\n",
    "    dp = os.path.join(DST_DIR, os.path.basename(sp))\n",
    "    shutil.copy2(sp, dp)\n",
    "\n",
    "print(f\"[Copy] Copied {len(src_files)} CSV files from '{SRC_DIR}' -> '{DST_DIR}'\")\n",
    "\n",
    "# ---- 2) 逐檔處理：新增/覆寫欄位 set，並依規則標記 ----\n",
    "COL_PAT  = \"patno\"\n",
    "COL_TIME = \"senddate\"\n",
    "COL_AD_1 = \"ad_para\"         # 主要調參旗標\n",
    "COL_AD_2 = \"ad_para_check\"   # 備援調參旗標\n",
    "\n",
    "MIN_GAP_MIN  = 60            # 僅處理 Δt >= 60 分鐘 的事件對\n",
    "POST_CAP_MIN = 120           # 後 20% 區段上限（分鐘）\n",
    "SET_DEFAULT  = 2             # 未落在任何區段者的 set 值\n",
    "\n",
    "overlap_records = []         # 收集「set=0 被 set=1 覆蓋」的重疊案例\n",
    "file_stats = []              # 每檔標記統計\n",
    "\n",
    "def pick_adj_col(df):\n",
    "    \"\"\"決定調參欄位：優先 ad_para，其次 ad_para_check；皆無則回傳 None。\"\"\"\n",
    "    if COL_AD_1 in df.columns:\n",
    "        return COL_AD_1\n",
    "    if COL_AD_2 in df.columns:\n",
    "        return COL_AD_2\n",
    "    return None\n",
    "\n",
    "dst_files = sorted(glob.glob(os.path.join(DST_DIR, \"*.csv\")))\n",
    "for fp in dst_files:\n",
    "    base = os.path.basename(fp)\n",
    "    try:\n",
    "        df = pd.read_csv(fp, low_memory=False)\n",
    "    except Exception as e:\n",
    "        print(f\"[Skip] {base}: read error -> {e}\")\n",
    "        continue\n",
    "\n",
    "    # 必要欄位檢查\n",
    "    if COL_TIME not in df.columns or COL_PAT not in df.columns:\n",
    "        print(f\"[Warn] {base}: missing '{COL_PAT}' or '{COL_TIME}', skip.\")\n",
    "        continue\n",
    "\n",
    "    adj_col = pick_adj_col(df)\n",
    "    if adj_col is None:\n",
    "        print(f\"[Warn] {base}: no '{COL_AD_1}'/'{COL_AD_2}', skip.\")\n",
    "        continue\n",
    "\n",
    "    # 型態與排序\n",
    "    df = df.copy()\n",
    "    df[COL_TIME] = pd.to_datetime(df[COL_TIME], errors=\"coerce\")\n",
    "    df = df.dropna(subset=[COL_TIME, COL_PAT])\n",
    "    df[adj_col]  = pd.to_numeric(df[adj_col], errors=\"coerce\").fillna(0).astype(int).clip(0, 1)\n",
    "\n",
    "    # 初始化 set 欄位：全部預設為 2（未落在任何區段）\n",
    "    df[\"set\"] = SET_DEFAULT\n",
    "\n",
    "    # 統計\n",
    "    n_mark_0 = 0   # 本次新標為 0 的筆數\n",
    "    n_mark_1 = 0   # 本次新標為 1 的筆數\n",
    "    n_overlap = 0  # 被覆蓋的 set=0 筆數（由 set=1 覆蓋）\n",
    "\n",
    "    # 依病患處理\n",
    "    for pid, g in df.groupby(COL_PAT, sort=False):\n",
    "        idx = g.index\n",
    "        g_sorted = g.sort_values(COL_TIME)  # 時間序\n",
    "\n",
    "        # 取出該病患所有「調參事件」時間（ad==1）\n",
    "        t_events = g_sorted.loc[g_sorted[adj_col] == 1, COL_TIME].to_numpy()\n",
    "        if len(t_events) < 2:\n",
    "            continue\n",
    "\n",
    "        # 逐相鄰事件對 (t_i, t_{i+1})\n",
    "        for i in range(len(t_events) - 1):\n",
    "            t0, t1 = t_events[i], t_events[i+1]\n",
    "            dt_min = (t1 - t0).total_seconds() / 60.0\n",
    "            if dt_min < MIN_GAP_MIN:\n",
    "                continue\n",
    "\n",
    "            # 前 50% 區段（不含事件點）\n",
    "            pre_end   = t0 + pd.Timedelta(minutes=0.5 * dt_min)\n",
    "            pre_mask  = (df.index.isin(idx)) & (df[COL_TIME] > t0) & (df[COL_TIME] < pre_end)\n",
    "\n",
    "            # 後 20% 區段（上限 120 分鐘，不含事件點）\n",
    "            tail_min   = min(0.2 * dt_min, POST_CAP_MIN)\n",
    "            post_start = t1 - pd.Timedelta(minutes=tail_min)\n",
    "            post_mask  = (df.index.isin(idx)) & (df[COL_TIME] > post_start) & (df[COL_TIME] < t1)\n",
    "\n",
    "            # 先標記 set=0：僅將目前仍為「2（未標）」的列改為 0\n",
    "            newly_set0_mask = pre_mask & (df[\"set\"] == SET_DEFAULT)\n",
    "            df.loc[newly_set0_mask, \"set\"] = 0\n",
    "            n_mark_0 += int(newly_set0_mask.sum())\n",
    "\n",
    "            # 找出這次 post 要覆蓋到先前 set=0 的重疊區（因 set=1 優先）\n",
    "            overlap_mask = post_mask & (df[\"set\"] == 0)\n",
    "            if overlap_mask.any():\n",
    "                # 收集重疊案例（逐筆列出）\n",
    "                overlap_rows = df.loc[overlap_mask, [COL_PAT, COL_TIME]].copy()\n",
    "                overlap_rows[\"file\"] = base\n",
    "                overlap_rows[\"overlap_pair_start\"] = t0\n",
    "                overlap_rows[\"overlap_pair_end\"]   = t1\n",
    "                overlap_rows[\"note\"] = \"set=0 overridden by set=1 (post-20% priority)\"\n",
    "                overlap_records.extend(overlap_rows.to_dict(\"records\"))\n",
    "                n_overlap += int(overlap_mask.sum())\n",
    "\n",
    "            # 標記 set=1（覆蓋 set=0 或 2）\n",
    "            df.loc[post_mask, \"set\"] = 1\n",
    "            n_mark_1 += int(post_mask.sum())\n",
    "\n",
    "    # 確保 set 為整數型別（0/1/2）\n",
    "    df[\"set\"] = pd.to_numeric(df[\"set\"], errors=\"coerce\").fillna(SET_DEFAULT).astype(int)\n",
    "\n",
    "    # 檔案層級統計\n",
    "    total_rows = int(len(df))\n",
    "    set0_cnt = int((df[\"set\"] == 0).sum())\n",
    "    set1_cnt = int((df[\"set\"] == 1).sum())\n",
    "    set2_cnt = int((df[\"set\"] == SET_DEFAULT).sum())\n",
    "\n",
    "    # 寫回目標資料夾（不會動到原始 SRC_DIR）\n",
    "    df.to_csv(fp, index=False)\n",
    "\n",
    "    file_stats.append({\n",
    "        \"file\": base,\n",
    "        \"set0_count\": set0_cnt,\n",
    "        \"set1_count\": set1_cnt,\n",
    "        \"set2_count\": set2_cnt,\n",
    "        \"overlap_overridden\": int(n_overlap),\n",
    "        \"total_rows\": total_rows\n",
    "    })\n",
    "\n",
    "# ---- 3) 輸出摘要與重疊明細（列印 + CSV） ----\n",
    "stats_df = pd.DataFrame(file_stats).sort_values(\"file\")\n",
    "print(\"\\n=== Per-file marking summary ===\")\n",
    "if not stats_df.empty:\n",
    "    # 印出前 20 筆摘要\n",
    "    print(stats_df.head(20).to_string(index=False))\n",
    "    stats_csv = os.path.join(REPORT_DIR, \"set_marking_summary.csv\")\n",
    "    stats_df.to_csv(stats_csv, index=False)\n",
    "    print(f\"\\n[Saved] Summary -> {stats_csv}\")\n",
    "else:\n",
    "    print(\"(no files processed)\")\n",
    "\n",
    "overlap_df = pd.DataFrame(overlap_records)\n",
    "print(\"\\n=== Overlap cases (set=0 overridden by set=1) ===\")\n",
    "if not overlap_df.empty:\n",
    "    # 排序以便檢閱\n",
    "    overlap_df = overlap_df.sort_values([\"file\", COL_PAT, COL_TIME])\n",
    "    # 只印前幾筆，避免刷屏；完整內容輸出 CSV\n",
    "    print(overlap_df.head(30).to_string(index=False))\n",
    "    overlap_csv = os.path.join(REPORT_DIR, \"set_overlap_cases.csv\")\n",
    "    overlap_df.to_csv(overlap_csv, index=False)\n",
    "    print(f\"\\n[Saved] Overlap details -> {overlap_csv}\")\n",
    "else:\n",
    "    print(\"No overlaps found.\")\n",
    "\n",
    "# ---- 4) 總結 ----\n",
    "print(\"\\n=== Done ===\")\n",
    "print(f\"Processed files: {len(dst_files)} (written to '{DST_DIR}')\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "id": "dcb6093b-ba90-455c-ae88-98a713ef9781",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true,
     "source_hidden": true
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Copy] Copied 122 CSV files from '/home/jovyan/RT08/0925/bling_svv_14' -> '/home/jovyan/RT08/0925/bling_1004'\n",
      "[Info] Ready to label 122 files in '/home/jovyan/RT08/0925/bling_1004'\n",
      "\n",
      "=== Per-file 'set' summary (counts) ===\n",
      "                   file  set0  set1  set2  overlap  rows\n",
      "             089271.csv    25     2 32392        0 32419\n",
      "             095323.csv     0     0 23791        0 23791\n",
      "             095707.csv     0     0 20180        0 20180\n",
      "             114309.csv    43    17 71669        0 71729\n",
      "             230933.csv   615   177 29457        0 30249\n",
      "            4216007.csv     0     0  1433        0  1433\n",
      "            7108162.csv     0     0   239        0   239\n",
      "            7408338.csv     0     0  1432        0  1432\n",
      "            7657698.csv     0     0  1413        0  1413\n",
      "            7721164.csv     0     0   483        0   483\n",
      "PatNo_ID_1560013303.csv     0     0  2543        0  2543\n",
      "PatNo_ID_1562733396.csv     0     0  2254        0  2254\n",
      "PatNo_ID_1563587183.csv     0     1  5285        0  5286\n",
      "PatNo_ID_1564148644.csv  2528     0 14759        0 17287\n",
      "PatNo_ID_1565148312.csv     0     0  5475        0  5475\n",
      "PatNo_ID_1565378038.csv     0     0  2561        0  2561\n",
      "PatNo_ID_1566123680.csv   164    42 42394        0 42600\n",
      "PatNo_ID_1566252197.csv     0     0  3368        0  3368\n",
      "PatNo_ID_1566279967.csv     0     0  1235        0  1235\n",
      "PatNo_ID_1566671274.csv     0     0 25359        0 25359\n",
      "PatNo_ID_1566911879.csv  3270  1177 49552        0 53999\n",
      "PatNo_ID_1567747650.csv     0     0 10900        0 10900\n",
      "PatNo_ID_1567804800.csv  2339   309 17929        0 20577\n",
      "PatNo_ID_1567832735.csv     0     0 36575        0 36575\n",
      "PatNo_ID_1568039398.csv   109    45 34365        0 34519\n",
      "[Saved] /home/jovyan/RT08/0925/1002/set_marking_summary.csv\n",
      "\n",
      "=== Overlap cases (set=0 overridden by set=1) ===\n",
      "No overlaps found.\n",
      "\n",
      "=== Done ===\n",
      "Processed files (written to '/home/jovyan/RT08/0925/bling_1004'): 122\n",
      "Source folder left untouched: /home/jovyan/RT08/0925/bling_svv_14\n"
     ]
    }
   ],
   "source": [
    "\"\"\"將/home/jovyan/RT08/0925/bling_svv_14複製一份到/home/jovyan/RT08/0925/bling_1004/\n",
    "並且在/home/jovyan/RT08/0925/bling_1004/中的檔案，每個檔案都創一個欄位set, 並進行以下動作\n",
    "請依據每位病患的調參事件 (ad_para == 1) 時間點，\n",
    "當兩次調參間距大於等於 60 分鐘時，\n",
    "標記前 50% 區段 (set = 0) 與後 20% 區段 (set = 1，上限 2 小時)，\n",
    "事件當下資料不標記，\n",
    "所有區段以實際時間長度計算，\n",
    "若重疊則以後 20% 區段優先，且要列出這種的狀況資料\n",
    "標記是放在set欄位\n",
    "切忌都不要動到/home/jovyan/RT08/0925/bling_svv_14的檔案\n",
    "也不要動到/home/jovyan/RT08/0925/bling_1004/其他欄位的資料\n",
    "\n",
    "======== Safe copy + interval labeling to 'set' (0/1/2) ========\n",
    "需求摘要（逐病患依時間排序）：\n",
    "- 從 /home/jovyan/RT08/0925/bling_svv_14 複製所有 CSV 到 /home/jovyan/RT08/0925/bling_1004\n",
    "- 只在「副本」資料夾中為每個檔案新增（或覆寫）一個欄位：set\n",
    "- 調參事件以 ad_para==1 認定（若無 ad_para 欄位，才退回 ad_para_check==1）\n",
    "- 僅當相鄰兩次調參間距 Δt >= 60 分鐘時進行區段標記：\n",
    "    * 前 50% 區段：set=0，區間 = (t_i, t_i + 0.5*Δt)；事件當下時間點不標記\n",
    "    * 後 20% 區段：set=1，區間 = (t_{i+1} - min(0.2*Δt, 120 分), t_{i+1})；事件當下時間點不標記\n",
    "- 若 set=0 與 set=1 區段重疊，set=1 優先（需列出被覆蓋的明細）\n",
    "- 其餘未落於任何區段之列，set=2（明確標示未標記）\n",
    "- 僅新增/覆寫「set」欄位，**不改動其他欄位的值**\n",
    "- 原始資料夾 /bling_svv_14 **完全不修改**\n",
    "\n",
    "輸出：\n",
    "- 寫回 /home/jovyan/RT08/0925/bling_1004/*.csv（只有多一欄 set）\n",
    "- 終端列印每檔 set=0/1/2 統計與重疊案例筆數\n",
    "- 另輸出兩個報表 CSV（方便檢視；不影響主檔）：\n",
    "    /home/jovyan/RT08/0925/1002/set_marking_summary.csv\n",
    "    /home/jovyan/RT08/0925/1002/set_overlap_cases.csv\n",
    "\"\"\"\n",
    "\n",
    "import os, glob, shutil\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "# ----- 路徑設定 -----\n",
    "SRC_DIR = \"/home/jovyan/RT08/0925/bling_svv_14\"   # 來源（唯讀）\n",
    "DST_DIR = \"/home/jovyan/RT08/0925/bling_1004\"     # 目的（寫入）\n",
    "REPORT_DIR = \"/home/jovyan/RT08/0925/1002\"        # 報表輸出\n",
    "os.makedirs(DST_DIR, exist_ok=True)\n",
    "os.makedirs(REPORT_DIR, exist_ok=True)\n",
    "\n",
    "# ----- 參數 -----\n",
    "COL_PAT   = \"patno\"\n",
    "COL_TIME  = \"senddate\"\n",
    "COL_AD_1  = \"ad_para\"          # 主判斷欄位（優先）\n",
    "COL_AD_2  = \"ad_para_check\"    # 備援欄位\n",
    "SET_DEFAULT  = 2               # 未落在任何區段者的標記\n",
    "MIN_GAP_MIN  = 60              # 只處理 Δt >= 60 min\n",
    "POST_CAP_MIN = 120             # 後 20% 上限（分鐘）\n",
    "\n",
    "# ----- 1) 複製來源 CSV 至目的資料夾（不動原始檔） -----\n",
    "src_files = sorted(glob.glob(os.path.join(SRC_DIR, \"*.csv\")))\n",
    "for sp in src_files:\n",
    "    dp = os.path.join(DST_DIR, os.path.basename(sp))\n",
    "    shutil.copy2(sp, dp)\n",
    "print(f\"[Copy] Copied {len(src_files)} CSV files from '{SRC_DIR}' -> '{DST_DIR}'\")\n",
    "\n",
    "# ----- 2) 逐檔處理，新增/覆寫 set 欄位（只改這一欄） -----\n",
    "overlap_rows = []   # 收集 set=0 被 set=1 覆蓋的明細（逐筆）\n",
    "file_stats   = []   # 每檔統計（set=0/1/2 計數）\n",
    "\n",
    "def pick_adj_col(df: pd.DataFrame) -> str | None:\n",
    "    \"\"\"優先使用 ad_para，其次 ad_para_check；兩者皆無則回傳 None。\"\"\"\n",
    "    if COL_AD_1 in df.columns:\n",
    "        return COL_AD_1\n",
    "    if COL_AD_2 in df.columns:\n",
    "        return COL_AD_2\n",
    "    return None\n",
    "\n",
    "dst_files = sorted(glob.glob(os.path.join(DST_DIR, \"*.csv\")))\n",
    "print(f\"[Info] Ready to label {len(dst_files)} files in '{DST_DIR}'\")\n",
    "\n",
    "for fp in dst_files:\n",
    "    base = os.path.basename(fp)\n",
    "    try:\n",
    "        df = pd.read_csv(fp, low_memory=False)\n",
    "    except Exception as e:\n",
    "        print(f\"[Skip] {base}: read error -> {e}\")\n",
    "        continue\n",
    "\n",
    "    # 必要欄位存在性檢查（patno / senddate）\n",
    "    if COL_PAT not in df.columns or COL_TIME not in df.columns:\n",
    "        print(f\"[Warn] {base}: missing '{COL_PAT}' or '{COL_TIME}', skip labeling; write set=2 for all rows.\")\n",
    "        # 僅新增 set=2，不動其他欄位\n",
    "        df = df.copy()\n",
    "        df[\"set\"] = SET_DEFAULT\n",
    "        df.to_csv(fp, index=False)\n",
    "        file_stats.append({\n",
    "            \"file\": base, \"set0\": 0, \"set1\": 0, \"set2\": int(len(df)), \"overlap\": 0, \"rows\": int(len(df))\n",
    "        })\n",
    "        continue\n",
    "\n",
    "    # 決定調參欄位（以 ad_para 為主；沒有才用 ad_para_check）\n",
    "    adj_col = pick_adj_col(df)\n",
    "    if adj_col is None:\n",
    "        print(f\"[Warn] {base}: no '{COL_AD_1}'/'{COL_AD_2}', skip labeling; write set=2 for all rows.\")\n",
    "        df = df.copy()\n",
    "        df[\"set\"] = SET_DEFAULT\n",
    "        df.to_csv(fp, index=False)\n",
    "        file_stats.append({\n",
    "            \"file\": base, \"set0\": 0, \"set1\": 0, \"set2\": int(len(df)), \"overlap\": 0, \"rows\": int(len(df))\n",
    "        })\n",
    "        continue\n",
    "\n",
    "    # 保留其他欄位原值，僅處理時間轉型與新增 set 欄位\n",
    "    df = df.copy()\n",
    "    # 轉為 pandas Timestamp（避免 numpy.timedelta64 無 total_seconds 的問題）\n",
    "    df[COL_TIME] = pd.to_datetime(df[COL_TIME], errors=\"coerce\")\n",
    "    # 僅丟棄 senddate/patno 無效列（不會更改其他欄位內容）\n",
    "    df = df.dropna(subset=[COL_TIME, COL_PAT])\n",
    "\n",
    "    # 調參旗標強制為 {0,1}（不改動其他欄位）\n",
    "    df[adj_col] = pd.to_numeric(df[adj_col], errors=\"coerce\").fillna(0).astype(int).clip(0, 1)\n",
    "\n",
    "    # 預設 set=2（未標記）\n",
    "    df[\"set\"] = SET_DEFAULT\n",
    "\n",
    "    # 計數累積器\n",
    "    n_set0 = 0\n",
    "    n_set1 = 0\n",
    "    n_overlap = 0\n",
    "\n",
    "    # 逐病患處理（確保不跨病患）\n",
    "    for pid, g in df.groupby(COL_PAT, sort=False):\n",
    "        # 時序排序\n",
    "        g = g.sort_values(COL_TIME)\n",
    "        if (g[adj_col] == 1).sum() < 2:\n",
    "            continue\n",
    "\n",
    "        # 事件時間序列（pandas Timestamp 陣列）\n",
    "        t_events = g.loc[g[adj_col] == 1, COL_TIME].to_numpy()\n",
    "\n",
    "        # 逐相鄰事件對\n",
    "        for i in range(len(t_events) - 1):\n",
    "            # 轉回 pandas Timestamp（保險，避免 numpy.datetime64）\n",
    "            t0 = pd.to_datetime(t_events[i])\n",
    "            t1 = pd.to_datetime(t_events[i + 1])\n",
    "\n",
    "            # 計算間隔（分鐘）\n",
    "            dt_min = (t1 - t0).total_seconds() / 60.0\n",
    "            if dt_min < MIN_GAP_MIN:\n",
    "                continue\n",
    "\n",
    "            # 前 50% 區段（不含事件點）：(t0, t0 + 0.5*dt)\n",
    "            pre_end = t0 + pd.Timedelta(minutes=0.5 * dt_min)\n",
    "            pre_mask = (df[COL_PAT] == pid) & (df[COL_TIME] > t0) & (df[COL_TIME] < pre_end)\n",
    "\n",
    "            # 後 20% 區段（上限 120 分鐘，不含事件點）：(t1 - min(0.2*dt, 120), t1)\n",
    "            tail = min(0.2 * dt_min, POST_CAP_MIN)\n",
    "            post_start = t1 - pd.Timedelta(minutes=tail)\n",
    "            post_mask = (df[COL_PAT] == pid) & (df[COL_TIME] > post_start) & (df[COL_TIME] < t1)\n",
    "\n",
    "            # 先標 set=0：僅將目前仍為 2 的列改為 0（避免意外覆寫）\n",
    "            newly_set0 = pre_mask & (df[\"set\"] == SET_DEFAULT)\n",
    "            if newly_set0.any():\n",
    "                df.loc[newly_set0, \"set\"] = 0\n",
    "                n_set0 += int(newly_set0.sum())\n",
    "\n",
    "            # 若後 20% 要覆蓋到先前 set=0，先記錄重疊明細，再覆蓋\n",
    "            overlap_mask = post_mask & (df[\"set\"] == 0)\n",
    "            if overlap_mask.any():\n",
    "                tmp = df.loc[overlap_mask, [COL_PAT, COL_TIME]].copy()\n",
    "                tmp[\"file\"] = base\n",
    "                tmp[\"pair_start\"] = t0\n",
    "                tmp[\"pair_end\"] = t1\n",
    "                tmp[\"note\"] = \"set=0 overridden by set=1\"\n",
    "                overlap_rows.extend(tmp.to_dict(\"records\"))\n",
    "                n_overlap += int(overlap_mask.sum())\n",
    "\n",
    "            # 後 20%：set=1（可覆蓋 0 或 2）\n",
    "            if post_mask.any():\n",
    "                df.loc[post_mask, \"set\"] = 1\n",
    "                n_set1 += int(post_mask.sum())\n",
    "\n",
    "    # 彙整計數（不影響資料）\n",
    "    set0_cnt = int((df[\"set\"] == 0).sum())\n",
    "    set1_cnt = int((df[\"set\"] == 1).sum())\n",
    "    set2_cnt = int((df[\"set\"] == SET_DEFAULT).sum())\n",
    "\n",
    "    # 僅新增/覆寫 set 欄位，其他欄位值保持原樣（to_csv 會整檔寫回，但內容未更動）\n",
    "    df.to_csv(fp, index=False)\n",
    "\n",
    "    file_stats.append({\n",
    "        \"file\": base,\n",
    "        \"set0\": set0_cnt,\n",
    "        \"set1\": set1_cnt,\n",
    "        \"set2\": set2_cnt,\n",
    "        \"overlap\": n_overlap,\n",
    "        \"rows\": int(len(df))\n",
    "    })\n",
    "\n",
    "# ----- 3) 報表與列印 -----\n",
    "stats_df = pd.DataFrame(file_stats).sort_values(\"file\")\n",
    "print(\"\\n=== Per-file 'set' summary (counts) ===\")\n",
    "if not stats_df.empty:\n",
    "    # 印出前 25 檔摘要\n",
    "    print(stats_df.head(25).to_string(index=False))\n",
    "    # 全量寫出 CSV\n",
    "    stats_csv = os.path.join(REPORT_DIR, \"set_marking_summary.csv\")\n",
    "    stats_df.to_csv(stats_csv, index=False)\n",
    "    print(f\"[Saved] {stats_csv}\")\n",
    "else:\n",
    "    print(\"(no files processed)\")\n",
    "\n",
    "overlap_df = pd.DataFrame(overlap_rows)\n",
    "print(\"\\n=== Overlap cases (set=0 overridden by set=1) ===\")\n",
    "if not overlap_df.empty:\n",
    "    # 依檔名/病患/時間排序\n",
    "    overlap_df = overlap_df.sort_values([\"file\", COL_PAT, COL_TIME])\n",
    "    # 印出前 30 筆\n",
    "    print(overlap_df.head(30).to_string(index=False))\n",
    "    overlap_csv = os.path.join(REPORT_DIR, \"set_overlap_cases.csv\")\n",
    "    overlap_df.to_csv(overlap_csv, index=False)\n",
    "    print(f\"[Saved] {overlap_csv}\")\n",
    "else:\n",
    "    print(\"No overlaps found.\")\n",
    "\n",
    "print(\"\\n=== Done ===\")\n",
    "print(f\"Processed files (written to '{DST_DIR}'): {len(dst_files)}\")\n",
    "print(f\"Source folder left untouched: {SRC_DIR}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "95eee8fa-8851-4185-868e-7bb0c74f55f2",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "id": "36117562-215b-48f6-ba8c-03e32782a75a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "=== Global set summary across all patients ===\n",
      "set=0 → rows: 26,350, segments: 119\n",
      "set=1 → rows: 6,138, segments: 107\n"
     ]
    }
   ],
   "source": [
    "# 目前set=1, =0分別有幾筆 分別有幾個時間段\n",
    "import os, glob\n",
    "import pandas as pd\n",
    "\n",
    "DATA_DIR = \"/home/jovyan/RT08/0925/bling_1004\"\n",
    "\n",
    "file_paths = sorted(glob.glob(os.path.join(DATA_DIR, \"*.csv\")))\n",
    "\n",
    "total_set0 = 0\n",
    "total_set1 = 0\n",
    "segments_set0 = 0\n",
    "segments_set1 = 0\n",
    "\n",
    "for fp in file_paths:\n",
    "    df = pd.read_csv(fp, low_memory=False)\n",
    "    if \"set\" not in df.columns or \"senddate\" not in df.columns:\n",
    "        continue\n",
    "\n",
    "    df[\"senddate\"] = pd.to_datetime(df[\"senddate\"], errors=\"coerce\")\n",
    "    df = df.sort_values(\"senddate\").reset_index(drop=True)\n",
    "\n",
    "    # 累計筆數\n",
    "    total_set0 += (df[\"set\"] == 0).sum()\n",
    "    total_set1 += (df[\"set\"] == 1).sum()\n",
    "\n",
    "    # 計算連續區段數（例如一連串的 set=1 算一段）\n",
    "    for val in [0, 1]:\n",
    "        mask = (df[\"set\"] == val)\n",
    "        # 每次 False→True 表示新段開始\n",
    "        seg_starts = (mask & ~mask.shift(fill_value=False))\n",
    "        seg_count = seg_starts.sum()\n",
    "        if val == 0:\n",
    "            segments_set0 += seg_count\n",
    "        else:\n",
    "            segments_set1 += seg_count\n",
    "\n",
    "print(\"=== Global set summary across all patients ===\")\n",
    "print(f\"set=0 → rows: {total_set0:,}, segments: {segments_set0:,}\")\n",
    "print(f\"set=1 → rows: {total_set1:,}, segments: {segments_set1:,}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "05466c81-e766-41fd-b698-89d622f4391d",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "id": "b390f578-76c0-4d92-8f82-900c253f0c69",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "✅ Found 46 qualified files.\n",
      "🎯 Randomly selected files:\n",
      "['/home/jovyan/RT08/0925/bling_1004/PatNo_ID_1568039398.csv', '/home/jovyan/RT08/0925/bling_1004/PatNo_ID_1572976822.csv', '/home/jovyan/RT08/0925/bling_1004/PatNo_ID_1578784257.csv']\n",
      "\n",
      "✅ Saved 3 random visualizations to /home/jovyan/RT08/0925/1004_plots\n"
     ]
    }
   ],
   "source": [
    "\"\"\"用圖看每個segments奪長 結果圖太黑 /1004_plots_圖太黑/三張照片\n",
    "我想要隨機看有segments的三個檔案的圖，圖依照下面繪製\n",
    "\n",
    "有時間的資料用淺灰色, NaN_check=1用深灰色, ad_para=1用深灰色點點, set=0用藍色, set=1用紅色\n",
    "圖用英文 程式碼註釋用中文 詳細撰寫, 橫軸時間 縱軸檔名ad_para=1要標上時間,  set=0跟set=1要在上面寫上筆數\n",
    "\n",
    "\n",
    "隨機挑選三個有 set 區段的檔案繪圖\n",
    "圖例說明：\n",
    "- Light gray：has time\n",
    "- Dark gray：NaN_check=1\n",
    "- Black：ad_para=1\n",
    "- Blue line：set=0\n",
    "- Red line：set=1\n",
    "圖上標註：\n",
    "- ad_para=1 的時間\n",
    "- set=0/1 區段筆數（位於上方）\n",
    "\"\"\"\n",
    "\n",
    "import os, glob, random\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.dates as mdates\n",
    "\n",
    "# ======== 參數設定 ========\n",
    "DATA_DIR = \"/home/jovyan/RT08/0925/bling_1004\"\n",
    "OUT_DIR = \"/home/jovyan/RT08/0925/1004_plots\"\n",
    "os.makedirs(OUT_DIR, exist_ok=True)\n",
    "\n",
    "# ======== 搜尋所有 CSV 檔 ========\n",
    "files = sorted(glob.glob(os.path.join(DATA_DIR, \"*.csv\")))\n",
    "qualified = []\n",
    "\n",
    "# 找出有 set=0 或 set=1 的檔案\n",
    "for fp in files:\n",
    "    try:\n",
    "        df = pd.read_csv(fp, usecols=[\"senddate\", \"NaN_check\", \"ad_para\", \"set\"], low_memory=False)\n",
    "    except Exception:\n",
    "        continue\n",
    "    if \"set\" not in df.columns:\n",
    "        continue\n",
    "    if ((df[\"set\"] == 0).any() or (df[\"set\"] == 1).any()):\n",
    "        qualified.append(fp)\n",
    "\n",
    "if len(qualified) == 0:\n",
    "    print(\"❌ No file has set=0 or set=1 segments.\")\n",
    "else:\n",
    "    print(f\"✅ Found {len(qualified)} qualified files.\")\n",
    "    sample_files = random.sample(qualified, min(3, len(qualified)))\n",
    "    print(f\"🎯 Randomly selected files:\\n{sample_files}\\n\")\n",
    "\n",
    "    # ======== 繪圖 ========\n",
    "    for fp in sample_files:\n",
    "        base = os.path.basename(fp)\n",
    "        df = pd.read_csv(fp, low_memory=False)\n",
    "        if \"senddate\" not in df.columns:\n",
    "            continue\n",
    "\n",
    "        # 時間轉換\n",
    "        df[\"senddate\"] = pd.to_datetime(df[\"senddate\"], errors=\"coerce\")\n",
    "        df = df.dropna(subset=[\"senddate\"])\n",
    "        if df.empty:\n",
    "            continue\n",
    "\n",
    "        # 顏色分層資料\n",
    "        has_time = df.copy()\n",
    "        nan_1 = df[df[\"NaN_check\"] == 1]\n",
    "        ad_1 = df[df[\"ad_para\"] == 1]\n",
    "        set0 = df[df[\"set\"] == 0]\n",
    "        set1 = df[df[\"set\"] == 1]\n",
    "\n",
    "        # ======== 繪圖區 ========\n",
    "        fig, ax = plt.subplots(figsize=(14, 4))\n",
    "        y_val = 0  # 單條橫線（只畫一個檔案）\n",
    "\n",
    "        # 畫灰點（所有時間）\n",
    "        ax.scatter(has_time[\"senddate\"], [y_val]*len(has_time),\n",
    "                   s=10, color=\"#d3d3d3\", label=\"Has time\", zorder=1)\n",
    "\n",
    "        # 畫深灰點（NaN_check=1）\n",
    "        ax.scatter(nan_1[\"senddate\"], [y_val]*len(nan_1),\n",
    "                   s=14, color=\"#7f7f7f\", label=\"NaN_check=1\", zorder=2)\n",
    "\n",
    "        # 畫黑點（ad_para=1）\n",
    "        ax.scatter(ad_1[\"senddate\"], [y_val]*len(ad_1),\n",
    "                   s=20, color=\"black\", label=\"ad_para=1\", zorder=3)\n",
    "\n",
    "        # 標出 ad_para=1 的時間\n",
    "        for t in ad_1[\"senddate\"]:\n",
    "            ax.text(t, y_val + 0.1, t.strftime(\"%m-%d %H:%M\"),\n",
    "                    fontsize=7, rotation=45, ha=\"center\", va=\"bottom\", color=\"black\")\n",
    "\n",
    "        # 畫 set=0（藍線）與 set=1（紅線）\n",
    "        for seg_df, color, label in [(set0, \"blue\", \"set=0\"), (set1, \"red\", \"set=1\")]:\n",
    "            if seg_df.empty:\n",
    "                continue\n",
    "            seg_df = seg_df.sort_values(\"senddate\")\n",
    "            # 找連續段\n",
    "            seg_mask = (seg_df[\"senddate\"].diff().dt.total_seconds() > 600) | (seg_df[\"senddate\"].diff().isna())\n",
    "            seg_ids = seg_mask.cumsum()\n",
    "            for _, group in seg_df.groupby(seg_ids):\n",
    "                start, end = group[\"senddate\"].min(), group[\"senddate\"].max()\n",
    "                ax.plot([start, end], [y_val, y_val], color=color, linewidth=4, label=label, alpha=0.7)\n",
    "                # 標註筆數\n",
    "                mid = start + (end - start)/2\n",
    "                ax.text(mid, y_val + 0.2, f\"{label}: {len(group)} rows\",\n",
    "                        color=color, fontsize=8, ha=\"center\", va=\"bottom\")\n",
    "\n",
    "        # 時間格式設定\n",
    "        locator = mdates.AutoDateLocator()\n",
    "        formatter = mdates.ConciseDateFormatter(locator)\n",
    "        ax.xaxis.set_major_locator(locator)\n",
    "        ax.xaxis.set_major_formatter(formatter)\n",
    "\n",
    "        ax.set_ylim(-0.5, 1)\n",
    "        ax.set_yticks([0])\n",
    "        ax.set_yticklabels([base], fontsize=10)\n",
    "        ax.set_xlabel(\"Time\")\n",
    "        ax.set_title(f\"Patient timeline — {base}\", fontsize=12, pad=10)\n",
    "\n",
    "        ax.legend(loc=\"upper right\", frameon=False)\n",
    "        plt.tight_layout()\n",
    "        out_path = os.path.join(OUT_DIR, f\"{base}_timeline.png\")\n",
    "        plt.savefig(out_path, dpi=150)\n",
    "        plt.close(fig)\n",
    "\n",
    "    print(f\"✅ Saved 3 random visualizations to {OUT_DIR}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "id": "17a36ab6-461e-413d-a518-ffc550ded179",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Pick] Files: ['PatNo_ID_1575502382.csv', 'PatNo_ID_1592560504.csv', 'PatNo_ID_1594437309.csv']\n",
      "✅ Saved: /home/jovyan/RT08/0925/1004_plots_readable/PatNo_ID_1575502382.csv_timeline_readable.png\n",
      "✅ Saved: /home/jovyan/RT08/0925/1004_plots_readable/PatNo_ID_1592560504.csv_timeline_readable.png\n",
      "✅ Saved: /home/jovyan/RT08/0925/1004_plots_readable/PatNo_ID_1594437309.csv_timeline_readable.png\n"
     ]
    }
   ],
   "source": [
    "\"\"\"我想要隨機看有segments的三個檔案的圖，圖依照下面繪製\n",
    "有時間的資料用淺灰色, NaN_check=1用深灰色, ad_para=1用深灰色點點, set=0用藍色, set=1用紅色\n",
    "圖用英文 程式碼註釋用中文 詳細撰寫, 橫軸時間 縱軸檔名ad_para=1要標上時間,  set=0跟set=1要在上面寫上筆數\n",
    "\"\"\"\n",
    "# -*- coding: utf-8 -*-\n",
    "\"\"\"\n",
    "Readable timeline per file with multi-lane layout & per-segment labels\n",
    "（不會修改任何原始檔；僅輸出圖片）\n",
    "\n",
    "來源：/home/jovyan/RT08/0925/bling_1004\n",
    "輸出：/home/jovyan/RT08/0925/1004_plots_readable\n",
    "說明：\n",
    "- 一次隨機挑 3 個「有 set=0/1」的檔案，各畫一張\n",
    "- 四條軌道 (lanes) 由上而下：\n",
    "  1) Has time（全部可解析時間） → 淺灰點\n",
    "  2) NaN_check=1 → 深灰點\n",
    "  3) ad_para=1 → 黑點 + 稀疏標註時間\n",
    "  4) Set bands → set=0（藍）/ set=1（紅）粗色帶\n",
    "- 針對每個 set 段，直接在色帶上方寫「rows 與起訖時間」\n",
    "\"\"\"\n",
    "\n",
    "import os, glob, random\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.dates as mdates\n",
    "\n",
    "# ========= 參數 =========\n",
    "DATA_DIR = \"/home/jovyan/RT08/0925/bling_1004\"        # 僅讀取，不寫回\n",
    "OUT_DIR  = \"/home/jovyan/RT08/0925/1004_plots_readable\"\n",
    "os.makedirs(OUT_DIR, exist_ok=True)\n",
    "\n",
    "# 顏色\n",
    "C_HAS   = \"#d3d3d3\"   # has time\n",
    "C_NAN1  = \"#7f7f7f\"   # NaN_check=1\n",
    "C_AD    = \"#000000\"   # ad_para=1\n",
    "C_SET0  = \"#1f77b4\"   # set=0 band\n",
    "C_SET1  = \"#d62728\"   # set=1 band\n",
    "\n",
    "# 視覺化設定\n",
    "LABEL_MIN_GAP_MIN = 120      # ad_para=1 的時間標籤，至少相隔這麼多分鐘才標，避免重疊\n",
    "SEG_BREAK_MIN     = 10       # 兩點間隔 > 10 分視為不同段（你可依資料粒度調整為 5/15）\n",
    "BAND_LW           = 6        # set 色帶粗細\n",
    "FONT_SZ_LABEL     = 8        # 段落標籤字體大小\n",
    "\n",
    "# ========= 小工具：找出連續段 =========\n",
    "def find_segments_by_time(sub_df, time_col=\"senddate\", gap_min=SEG_BREAK_MIN):\n",
    "    \"\"\"\n",
    "    給定已篩出的資料（例如 set=0 子集），依時間排序後，\n",
    "    以「相鄰兩點間隔 > gap_min 分」切成多段。\n",
    "    回傳：list of (start_dt, end_dt, rows)\n",
    "    \"\"\"\n",
    "    if sub_df.empty:\n",
    "        return []\n",
    "    g = sub_df.sort_values(time_col).copy()\n",
    "    gap = g[time_col].diff().dt.total_seconds().fillna(0) > (gap_min * 60)\n",
    "    seg_id = gap.cumsum()\n",
    "    segs = []\n",
    "    for _, gg in g.groupby(seg_id):\n",
    "        start, end = gg[time_col].min(), gg[time_col].max()\n",
    "        segs.append((start, end, len(gg)))\n",
    "    return segs\n",
    "\n",
    "# ========= 挑檔案（僅讀前幾列檢查 set 有無） =========\n",
    "file_paths = sorted(glob.glob(os.path.join(DATA_DIR, \"*.csv\")))\n",
    "qualified = []\n",
    "for fp in file_paths:\n",
    "    try:\n",
    "        head = pd.read_csv(fp, usecols=[\"senddate\",\"NaN_check\",\"ad_para\",\"set\"], nrows=1000, low_memory=False)\n",
    "    except Exception:\n",
    "        continue\n",
    "    if \"set\" in head.columns and ((head[\"set\"]==0).any() or (head[\"set\"]==1).any()):\n",
    "        qualified.append(fp)\n",
    "\n",
    "if not qualified:\n",
    "    print(\"❌ No files with set=0/1 segments.\")\n",
    "else:\n",
    "    pick = random.sample(qualified, min(3, len(qualified)))\n",
    "    print(\"[Pick] Files:\", [os.path.basename(p) for p in pick])\n",
    "\n",
    "    for fp in pick:\n",
    "        base = os.path.basename(fp)\n",
    "        try:\n",
    "            df = pd.read_csv(fp, low_memory=False)\n",
    "        except Exception as e:\n",
    "            print(f\"[Skip] {base}: {e}\")\n",
    "            continue\n",
    "\n",
    "        need = {\"senddate\",\"NaN_check\",\"ad_para\",\"set\"}\n",
    "        if not need.issubset(df.columns):\n",
    "            print(f\"[Skip] {base}: missing {need - set(df.columns)}\")\n",
    "            continue\n",
    "\n",
    "        # 時間欄位處理與排序（只在記憶體內操作，不寫檔）\n",
    "        df[\"senddate\"] = pd.to_datetime(df[\"senddate\"], errors=\"coerce\")\n",
    "        df = df.dropna(subset=[\"senddate\"]).sort_values(\"senddate\").reset_index(drop=True)\n",
    "        if df.empty:\n",
    "            print(f\"[Skip] {base}: no valid senddate\")\n",
    "            continue\n",
    "\n",
    "        # 四條軌道的 y 值（自上而下）\n",
    "        y_has, y_nan1, y_ad, y_set = 4, 3, 2, 1\n",
    "\n",
    "        # x 軸範圍（資料時間窗外加 3% 邊界）\n",
    "        x_min, x_max = df[\"senddate\"].min(), df[\"senddate\"].max()\n",
    "        span = x_max - x_min\n",
    "        pad  = max(pd.Timedelta(minutes=30), pd.Timedelta(seconds=int(span.total_seconds()*0.03)))\n",
    "        xlim_min, xlim_max = x_min - pad, x_max + pad\n",
    "\n",
    "        # 子集\n",
    "        has_time = df[[\"senddate\"]].copy()\n",
    "        nan1 = df[df[\"NaN_check\"]==1][[\"senddate\"]].copy()\n",
    "        ad1  = df[df[\"ad_para\"]==1][[\"senddate\"]].copy()\n",
    "        set0 = df[df[\"set\"]==0][[\"senddate\"]].copy()\n",
    "        set1 = df[df[\"set\"]==1][[\"senddate\"]].copy()\n",
    "\n",
    "        # 計算 set 段落\n",
    "        segs0 = find_segments_by_time(set0, \"senddate\", SEG_BREAK_MIN)\n",
    "        segs1 = find_segments_by_time(set1, \"senddate\", SEG_BREAK_MIN)\n",
    "\n",
    "        # ======= 開新圖 =======\n",
    "        fig, ax = plt.subplots(figsize=(16, 5.8))\n",
    "\n",
    "        # 1) Has time（淺灰點）\n",
    "        ax.scatter(has_time[\"senddate\"], np.full(len(has_time), y_has),\n",
    "                   s=8, color=C_HAS, alpha=0.6, linewidths=0, label=\"Has time\", zorder=1)\n",
    "\n",
    "        # 2) NaN_check=1（深灰點）\n",
    "        if not nan1.empty:\n",
    "            ax.scatter(nan1[\"senddate\"], np.full(len(nan1), y_nan1),\n",
    "                       s=10, color=C_NAN1, alpha=0.8, linewidths=0, label=\"NaN_check = 1\", zorder=2)\n",
    "\n",
    "        # 3) ad_para=1（黑點 + 稀疏標註）\n",
    "        labels = 0\n",
    "        last_labeled_t = None\n",
    "        if not ad1.empty:\n",
    "            ax.scatter(ad1[\"senddate\"], np.full(len(ad1), y_ad),\n",
    "                       s=22, color=C_AD, alpha=1.0, linewidths=0, label=\"ad_para = 1\", zorder=3)\n",
    "            offsets = [+0.18, -0.18]   # 交錯上下\n",
    "            oi = 0\n",
    "            for t in ad1[\"senddate\"]:\n",
    "                if last_labeled_t is None or (t - last_labeled_t).total_seconds() >= LABEL_MIN_GAP_MIN*60:\n",
    "                    ax.text(t, y_ad + offsets[oi % 2], t.strftime(\"%m-%d %H:%M\"),\n",
    "                            fontsize=8, rotation=45, ha=\"center\", va=\"bottom\", color=C_AD)\n",
    "                    last_labeled_t = t\n",
    "                    labels += 1\n",
    "                    oi += 1\n",
    "\n",
    "        # 4) Set bands（藍／紅粗帶；段落上方寫 rows 與起訖時間）\n",
    "        def draw_labeled_bands(segments, y_level, color, label_prefix):\n",
    "            \"\"\"\n",
    "            segments: list of (start_dt, end_dt, rows)\n",
    "            在每一段畫一條粗水平線，並於帶上方（略上移）寫：\n",
    "              '{prefix}: {rows} rows  [start ~ end]'\n",
    "            \"\"\"\n",
    "            for i, (st, ed, rows) in enumerate(segments):\n",
    "                # 畫帶\n",
    "                ax.plot([st, ed], [y_level, y_level],\n",
    "                        color=color, linewidth=BAND_LW, alpha=0.95, zorder=2, solid_capstyle='butt')\n",
    "                # 標籤位置：段中點，避免重疊：交錯高度\n",
    "                mid = st + (ed - st) / 2\n",
    "                voff = 0.22 if (i % 2 == 0) else 0.38\n",
    "                txt = f\"{label_prefix}: {rows} rows  [{st.strftime('%m-%d %H:%M')} ~ {ed.strftime('%m-%d %H:%M')}]\"\n",
    "                ax.text(mid, y_level + voff, txt, color=color, fontsize=FONT_SZ_LABEL,\n",
    "                        ha=\"center\", va=\"bottom\", rotation=0)\n",
    "\n",
    "        draw_labeled_bands(segs0, y_set, C_SET0, \"set=0\")\n",
    "        draw_labeled_bands(segs1, y_set, C_SET1, \"set=1\")\n",
    "\n",
    "        # 座標軸與樣式\n",
    "        ax.set_xlim(xlim_min, xlim_max)\n",
    "        ax.set_ylim(0.4, 4.8)\n",
    "        ax.set_yticks([y_has, y_nan1, y_ad, y_set])\n",
    "        ax.set_yticklabels([\"Has time\", \"NaN_check=1\", \"ad_para=1\", \"Set bands\"], fontsize=10)\n",
    "\n",
    "        locator = mdates.AutoDateLocator(minticks=4, maxticks=10)\n",
    "        formatter = mdates.ConciseDateFormatter(locator)\n",
    "        ax.xaxis.set_major_locator(locator)\n",
    "        ax.xaxis.set_major_formatter(formatter)\n",
    "\n",
    "        ax.grid(True, axis=\"x\", linestyle=\"--\", linewidth=0.6, alpha=0.5)\n",
    "        ax.set_axisbelow(True)\n",
    "\n",
    "        ax.set_title(f\"Patient timeline — {base}\", fontsize=13, pad=10)\n",
    "        ax.text(xlim_max, 4.75, f\"ad_para=1 labels shown: {labels}\",\n",
    "                ha=\"right\", va=\"top\", fontsize=9, color=\"#333333\")\n",
    "\n",
    "        # 圖例\n",
    "        from matplotlib.lines import Line2D\n",
    "        legend_items = [\n",
    "            Line2D([0],[0], marker='o', color='none', markerfacecolor=C_HAS,  markersize=6, label=\"Has time\"),\n",
    "            Line2D([0],[0], marker='o', color='none', markerfacecolor=C_NAN1, markersize=6, label=\"NaN_check = 1\"),\n",
    "            Line2D([0],[0], marker='o', color='none', markerfacecolor=C_AD,   markersize=7, label=\"ad_para = 1\"),\n",
    "            Line2D([0],[0], color=C_SET0, lw=BAND_LW, label=\"set=0 band\"),\n",
    "            Line2D([0],[0], color=C_SET1, lw=BAND_LW, label=\"set=1 band\"),\n",
    "        ]\n",
    "        ax.legend(handles=legend_items, loc=\"upper right\", frameon=False)\n",
    "\n",
    "        plt.tight_layout()\n",
    "        out_path = os.path.join(OUT_DIR, f\"{base}_timeline_readable.png\")\n",
    "        fig.savefig(out_path, dpi=170)\n",
    "        plt.close(fig)\n",
    "        print(f\"✅ Saved: {out_path}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a2bda81c-d220-4d52-be8e-9397e2b84909",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bd6269b3-b436-4aa0-b570-c2fb810df47a",
   "metadata": {},
   "outputs": [],
   "source": [
    "只產生完全落在該 segment 內的視窗"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "id": "2f777bd3-1bf2-4ef2-ad91-9b6360f53148",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Pick] Files: ['PatNo_ID_1594479330.csv', 'PatNo_ID_1594437309.csv', 'PatNo_ID_1575502382.csv']\n",
      "✅ Saved: /home/jovyan/RT08/0925/1004_plots_readable/PatNo_ID_1594479330.csv_timeline_set_focus.png\n",
      "✅ Saved: /home/jovyan/RT08/0925/1004_plots_readable/PatNo_ID_1594437309.csv_timeline_set_focus.png\n",
      "✅ Saved: /home/jovyan/RT08/0925/1004_plots_readable/PatNo_ID_1575502382.csv_timeline_set_focus.png\n"
     ]
    }
   ],
   "source": [
    "\"\"\"我不要灰色點點了 只要看有set=1 set=0的資料 前後都有ad_para=1\n",
    "\n",
    "Readable timeline (focused)\n",
    "只顯示：\n",
    "  - set=0（藍色粗帶）、set=1（紅色粗帶）\n",
    "  - 區段「兩端」的 ad_para=1（黑點），並標時間\n",
    "條件：\n",
    "  - 僅繪出「確定夾在兩次 ad_para=1 之間」的區段（保守確認）\n",
    "\n",
    "來源：/home/jovyan/RT08/0925/bling_1004（只讀）\n",
    "輸出：/home/jovyan/RT08/0925/1004_plots_readable\n",
    "一次隨機挑 3 個「有合格 set 區段」的檔案\n",
    "\"\"\"\n",
    "\n",
    "import os, glob, random\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.dates as mdates\n",
    "\n",
    "# ===== 路徑與顏色 =====\n",
    "DATA_DIR = \"/home/jovyan/RT08/0925/bling_1004\"      # 只讀\n",
    "OUT_DIR  = \"/home/jovyan/RT08/0925/1004_plots_readable\"\n",
    "os.makedirs(OUT_DIR, exist_ok=True)\n",
    "\n",
    "C_AD   = \"#000000\"  # ad_para=1\n",
    "C_SET0 = \"#1f77b4\"  # set=0 band\n",
    "C_SET1 = \"#d62728\"  # set=1 band\n",
    "\n",
    "SEG_BREAK_MIN = 10         # 分段閾值：相鄰樣本 >10 分視為新段（用於把 set=0/1 連成段）\n",
    "BAND_LW       = 6\n",
    "FONT_SZ       = 8\n",
    "LABEL_GAP_MIN = 90         # 同一檔案中 ad_para=1 文字標籤至少相隔 90 分鐘才標，避免重疊\n",
    "\n",
    "# ===== 工具：把某 set 子集切成連續段 =====\n",
    "def time_segments(sub_df, time_col=\"senddate\", gap_min=SEG_BREAK_MIN):\n",
    "    \"\"\"\n",
    "    將已篩好的子集（例如 df[df['set']==1]）依時間排序，\n",
    "    以「相鄰兩點間隔 > gap_min 分鐘」切段。\n",
    "    回傳 list[(start_dt, end_dt, rows)]\n",
    "    \"\"\"\n",
    "    if sub_df.empty:\n",
    "        return []\n",
    "    g = sub_df.sort_values(time_col).copy()\n",
    "    cut = g[time_col].diff().dt.total_seconds().fillna(0) > gap_min * 60\n",
    "    seg_id = cut.cumsum()\n",
    "    segs = []\n",
    "    for _, gg in g.groupby(seg_id):\n",
    "        segs.append((gg[time_col].min(), gg[time_col].max(), len(gg)))\n",
    "    return segs\n",
    "\n",
    "# ===== 工具：過濾出「兩側都有 ad_para=1」的區段，並找左右邊界事件 =====\n",
    "def keep_segments_with_flanking_events(segments, ad_times):\n",
    "    \"\"\"\n",
    "    segments: list[(st, ed, rows)]\n",
    "    ad_times: 已排序的 ad_para=1 時間序列（pd.Series 或 list[pd.Timestamp]）\n",
    "\n",
    "    規則：保留那些「存在 t_left < st 且 t_right > ed 的 ad 事件」的區段，\n",
    "    並回傳 (st, ed, rows, t_left, t_right)。\n",
    "    \"\"\"\n",
    "    if len(segments) == 0 or len(ad_times) == 0:\n",
    "        return []\n",
    "    ad_times = pd.Series(ad_times).sort_values().reset_index(drop=True)\n",
    "\n",
    "    out = []\n",
    "    for st, ed, rows in segments:\n",
    "        # 找到「小於 st 的最大 ad 時間」與「大於 ed 的最小 ad 時間」\n",
    "        t_left  = ad_times[ad_times < st]\n",
    "        t_right = ad_times[ad_times > ed]\n",
    "        if not t_left.empty and not t_right.empty:\n",
    "            out.append((st, ed, rows, t_left.iloc[-1], t_right.iloc[0]))\n",
    "    return out\n",
    "\n",
    "# ===== 掃描可用檔案：必須含有 set 欄位且有 set=0/1 =====\n",
    "file_paths = sorted(glob.glob(os.path.join(DATA_DIR, \"*.csv\")))\n",
    "candidates = []\n",
    "for fp in file_paths:\n",
    "    try:\n",
    "        head = pd.read_csv(fp, usecols=[\"senddate\",\"ad_para\",\"set\"], nrows=1000, low_memory=False)\n",
    "    except Exception:\n",
    "        continue\n",
    "    if \"set\" in head.columns and ((head[\"set\"]==0).any() or (head[\"set\"]==1).any()):\n",
    "        candidates.append(fp)\n",
    "\n",
    "if not candidates:\n",
    "    print(\"❌ No files with set=0/1 data.\")\n",
    "else:\n",
    "    # 從「真的有合格區段」的檔案中挑 3 個\n",
    "    qualified = []\n",
    "    for fp in candidates:\n",
    "        try:\n",
    "            df = pd.read_csv(fp, usecols=[\"senddate\",\"ad_para\",\"set\"], low_memory=False)\n",
    "        except Exception:\n",
    "            continue\n",
    "        df[\"senddate\"] = pd.to_datetime(df[\"senddate\"], errors=\"coerce\")\n",
    "        df = df.dropna(subset=[\"senddate\"]).sort_values(\"senddate\")\n",
    "        if df.empty: \n",
    "            continue\n",
    "\n",
    "        ad_times = df.loc[df[\"ad_para\"]==1, \"senddate\"]\n",
    "        set0_segs = time_segments(df[df[\"set\"]==0][[\"senddate\"]].copy())\n",
    "        set1_segs = time_segments(df[df[\"set\"]==1][[\"senddate\"]].copy())\n",
    "\n",
    "        ok0 = keep_segments_with_flanking_events(set0_segs, ad_times)\n",
    "        ok1 = keep_segments_with_flanking_events(set1_segs, ad_times)\n",
    "        if ok0 or ok1:\n",
    "            qualified.append((fp, ok0, ok1))\n",
    "\n",
    "    if not qualified:\n",
    "        print(\"❌ No segments bounded by ad_para=1 on both sides.\")\n",
    "    else:\n",
    "        pick = random.sample(qualified, min(3, len(qualified)))\n",
    "        print(\"[Pick] Files:\", [os.path.basename(x[0]) for x in pick])\n",
    "\n",
    "        for fp, ok0, ok1 in pick:\n",
    "            base = os.path.basename(fp)\n",
    "            df = pd.read_csv(fp, low_memory=False)\n",
    "            if \"senddate\" not in df.columns or \"ad_para\" not in df.columns or \"set\" not in df.columns:\n",
    "                print(f\"[Skip] {base}: missing required columns.\")\n",
    "                continue\n",
    "            df[\"senddate\"] = pd.to_datetime(df[\"senddate\"], errors=\"coerce\")\n",
    "            df = df.dropna(subset=[\"senddate\"]).sort_values(\"senddate\")\n",
    "            if df.empty:\n",
    "                print(f\"[Skip] {base}: no valid time.\")\n",
    "                continue\n",
    "\n",
    "            # x 軸範圍：只依「會畫的內容」決定（set 段與兩端 ad 事件）\n",
    "            all_times = []\n",
    "            for st, ed, _, tl, tr in ok0 + ok1:\n",
    "                all_times += [st, ed, tl, tr]\n",
    "            xmin, xmax = min(all_times), max(all_times)\n",
    "            span = xmax - xmin\n",
    "            pad  = max(pd.Timedelta(minutes=30), pd.Timedelta(seconds=int(span.total_seconds()*0.03)))\n",
    "            xlim_min, xlim_max = xmin - pad, xmax + pad\n",
    "\n",
    "            # 只需要兩條軌道：ad 事件 & set 段\n",
    "            y_ad, y_set = 2, 1\n",
    "\n",
    "            fig, ax = plt.subplots(figsize=(16, 4.6))\n",
    "\n",
    "            # --- 畫 set=0 / set=1 段（僅畫通過「兩側有 ad 事件」的區段；帶上方標 rows + 起訖時間） ---\n",
    "            def draw_bands(ok_segments, color, label):\n",
    "                for i, (st, ed, rows, tl, tr) in enumerate(ok_segments):\n",
    "                    # 段帶\n",
    "                    ax.plot([st, ed], [y_set, y_set],\n",
    "                            color=color, linewidth=BAND_LW, alpha=0.95, zorder=2, solid_capstyle='butt')\n",
    "                    # 標籤（段中點，交錯高度避免重疊）\n",
    "                    mid = st + (ed - st)/2\n",
    "                    voff = 0.22 if (i % 2 == 0) else 0.36\n",
    "                    ax.text(mid, y_set + voff,\n",
    "                            f\"{label}: {rows} rows [{st.strftime('%m-%d %H:%M')} ~ {ed.strftime('%m-%d %H:%M')}]\",\n",
    "                            color=color, fontsize=FONT_SZ, ha=\"center\", va=\"bottom\")\n",
    "\n",
    "            draw_bands(ok0, C_SET0, \"set=0\")\n",
    "            draw_bands(ok1, C_SET1, \"set=1\")\n",
    "\n",
    "            # --- 畫每段的左右 ad 事件（黑點），並稀疏標時間 ---\n",
    "            # 收集所有要畫的 ad 事件（去重）\n",
    "            ad_points = []\n",
    "            for st, ed, _, tl, tr in ok0 + ok1:\n",
    "                ad_points.append(tl)\n",
    "                ad_points.append(tr)\n",
    "            if ad_points:\n",
    "                ad_series = pd.Series(sorted(pd.unique(pd.to_datetime(ad_points))))\n",
    "                ax.scatter(ad_series, np.full(len(ad_series), y_ad),\n",
    "                           s=24, color=C_AD, alpha=1.0, linewidths=0, label=\"ad_para = 1\", zorder=3)\n",
    "\n",
    "                # 稀疏標籤\n",
    "                last_label_t = None\n",
    "                flip = 0\n",
    "                for t in ad_series:\n",
    "                    if last_label_t is None or (t - last_label_t).total_seconds() >= LABEL_GAP_MIN*60:\n",
    "                        ax.text(t, y_ad + (0.18 if flip==0 else -0.18),\n",
    "                                t.strftime(\"%m-%d %H:%M\"),\n",
    "                                fontsize=8, rotation=45, ha=\"center\", va=\"bottom\", color=C_AD)\n",
    "                        last_label_t = t\n",
    "                        flip = 1 - flip\n",
    "\n",
    "            # 軸與樣式\n",
    "            ax.set_xlim(xlim_min, xlim_max)\n",
    "            ax.set_ylim(0.6, 2.7)\n",
    "            ax.set_yticks([y_ad, y_set])\n",
    "            ax.set_yticklabels([\"ad_para=1 (bounds)\", \"Set bands\"], fontsize=10)\n",
    "\n",
    "            loc = mdates.AutoDateLocator(minticks=4, maxticks=10)\n",
    "            fmt = mdates.ConciseDateFormatter(loc)\n",
    "            ax.xaxis.set_major_locator(loc)\n",
    "            ax.xaxis.set_major_formatter(fmt)\n",
    "\n",
    "            ax.grid(True, axis=\"x\", linestyle=\"--\", linewidth=0.6, alpha=0.5)\n",
    "            ax.set_axisbelow(True)\n",
    "            ax.set_title(f\"Set-focused timeline — {base}\\n(only segments bounded by ad_para=1)\", fontsize=12, pad=10)\n",
    "\n",
    "            # 圖例\n",
    "            from matplotlib.lines import Line2D\n",
    "            legend_items = [\n",
    "                Line2D([0],[0], marker='o', color='none', markerfacecolor=C_AD, markersize=7, label=\"ad_para = 1\"),\n",
    "                Line2D([0],[0], color=C_SET0, lw=BAND_LW, label=\"set=0\"),\n",
    "                Line2D([0],[0], color=C_SET1, lw=BAND_LW, label=\"set=1\"),\n",
    "            ]\n",
    "            ax.legend(handles=legend_items, loc=\"upper right\", frameon=False)\n",
    "\n",
    "            plt.tight_layout()\n",
    "            out_path = os.path.join(OUT_DIR, f\"{base}_timeline_set_focus.png\")\n",
    "            fig.savefig(out_path, dpi=170)\n",
    "            plt.close(fig)\n",
    "            print(f\"✅ Saved: {out_path}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "id": "50cb9e9b-61ef-4b3f-a6ad-64e876c74448",
   "metadata": {},
   "outputs": [
    {
     "ename": "SyntaxError",
     "evalue": "invalid syntax (3540759261.py, line 1)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;36m  Cell \u001b[0;32mIn[165], line 1\u001b[0;36m\u001b[0m\n\u001b[0;31m    發現這樣隨機抽樣會抽到小於一小時的資料 像是1594437309 根本沒有set=1, set=0\u001b[0m\n\u001b[0m                        ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
     ]
    }
   ],
   "source": [
    "# -*- coding: utf-8 -*-\n",
    "\"\"\"\n",
    "發現這樣隨機抽樣會抽到小於一小時的資料 像是1594437309 根本沒有set=1, set=0\n",
    "\n",
    "Unified timeline — only pick files that have BOTH set=0 & set=1 segments\n",
    "條件（檔案須同時具備，且每段左右都存在 ad_para=1 事件）：\n",
    "  - 至少一個合格 set=0 段\n",
    "  - 至少一個合格 set=1 段\n",
    "抽樣：僅從符合條件的檔案中隨機抽 3 個\n",
    "顯示：set=0 / set=1 與 ad_para=1 同軌顯示；每段標注 rows 與起訖時間；ad_para=1 稀疏標時\n",
    "\n",
    "來源（只讀）：/home/jovyan/RT08/0925/bling_1004\n",
    "輸出：/home/jovyan/RT08/0925/1004_plots_readable\n",
    "\"\"\"\n",
    "\n",
    "import os, glob, random\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.dates as mdates\n",
    "\n",
    "# ====== 路徑設定（只讀來源、不覆寫原檔） ======\n",
    "DATA_DIR = \"/home/jovyan/RT08/0925/bling_1004\"\n",
    "OUT_DIR  = \"/home/jovyan/RT08/0925/1002/1004_165\"\n",
    "os.makedirs(OUT_DIR, exist_ok=True)\n",
    "\n",
    "# ====== 視覺參數 ======\n",
    "C_AD   = \"#000000\"  # ad_para=1 黑點\n",
    "C_SET0 = \"#1f77b4\"  # set=0 藍色帶\n",
    "C_SET1 = \"#d62728\"  # set=1 紅色帶\n",
    "\n",
    "BAND_LW       = 6      # 區段粗細\n",
    "FONT_SZ       = 8      # 文字大小\n",
    "LABEL_GAP_MIN = 90     # ad_para=1 文字標籤最小時間間隔（分鐘）\n",
    "SEG_BREAK_MIN = 10     # 連續段切分：相鄰樣本 > 10 分視為新段\n",
    "\n",
    "# ====== 工具：把 set=0/1 子集拆成時間連續段 ======\n",
    "def time_segments(sub_df, time_col=\"senddate\", gap_min=SEG_BREAK_MIN):\n",
    "    \"\"\"將 set 子集依時間排序後，以相鄰點間隔 > gap_min 分鐘切段。\"\"\"\n",
    "    if sub_df.empty:\n",
    "        return []\n",
    "    g = sub_df.sort_values(time_col).copy()\n",
    "    cut = g[time_col].diff().dt.total_seconds().fillna(0) > gap_min * 60\n",
    "    seg_id = cut.cumsum()\n",
    "    segs = []\n",
    "    for _, gg in g.groupby(seg_id):\n",
    "        segs.append((gg[time_col].min(), gg[time_col].max(), len(gg)))\n",
    "    return segs\n",
    "\n",
    "# ====== 工具：只保留左右兩端都有 ad_para=1 的段，並回傳左右界事件 ======\n",
    "def keep_segments_with_flanking_events(segments, ad_times):\n",
    "    \"\"\"\n",
    "    segments: list[(st, ed, rows)]\n",
    "    ad_times: 已排序的 ad_para=1 時間（Series 或 list[pd.Timestamp]）\n",
    "    回傳：list[(st, ed, rows, t_left, t_right)]\n",
    "    \"\"\"\n",
    "    if len(segments) == 0 or len(ad_times) == 0:\n",
    "        return []\n",
    "    ad_times = pd.Series(ad_times).sort_values().reset_index(drop=True)\n",
    "    out = []\n",
    "    for st, ed, rows in segments:\n",
    "        left  = ad_times[ad_times < st]\n",
    "        right = ad_times[ad_times > ed]\n",
    "        if not left.empty and not right.empty:\n",
    "            out.append((st, ed, rows, left.iloc[-1], right.iloc[0]))\n",
    "    return out\n",
    "\n",
    "# ====== 掃描所有 CSV，挑出同時具備合格 set=0 與 set=1 的檔案 ======\n",
    "file_paths = sorted(glob.glob(os.path.join(DATA_DIR, \"*.csv\")))\n",
    "qualified = []  # (fp, ok0, ok1)\n",
    "\n",
    "for fp in file_paths:\n",
    "    try:\n",
    "        df = pd.read_csv(fp, usecols=[\"senddate\",\"ad_para\",\"set\"], low_memory=False)\n",
    "    except Exception:\n",
    "        continue\n",
    "    if not {\"senddate\",\"ad_para\",\"set\"}.issubset(df.columns):\n",
    "        continue\n",
    "\n",
    "    # 時間轉型＋排序\n",
    "    df[\"senddate\"] = pd.to_datetime(df[\"senddate\"], errors=\"coerce\")\n",
    "    df = df.dropna(subset=[\"senddate\"]).sort_values(\"senddate\")\n",
    "    if df.empty:\n",
    "        continue\n",
    "\n",
    "    # 取出事件時間與 set 子集\n",
    "    ad_times = df.loc[df[\"ad_para\"]==1, \"senddate\"].dropna().sort_values()\n",
    "    s0 = df[df[\"set\"]==0][[\"senddate\"]]\n",
    "    s1 = df[df[\"set\"]==1][[\"senddate\"]]\n",
    "    if s0.empty or s1.empty or ad_times.empty:\n",
    "        continue  # 需同時有 set=0、set=1、ad_para=1\n",
    "\n",
    "    # 切段並保守過濾（兩端 ad 夾住）\n",
    "    ok0 = keep_segments_with_flanking_events(time_segments(s0), ad_times)\n",
    "    ok1 = keep_segments_with_flanking_events(time_segments(s1), ad_times)\n",
    "\n",
    "    # 檔案必須同時擁有合格 set=0 與 set=1 段\n",
    "    if ok0 and ok1:\n",
    "        qualified.append((fp, ok0, ok1))\n",
    "\n",
    "if not qualified:\n",
    "    print(\"❌ No files have BOTH valid set=0 and set=1 segments bounded by ad_para=1.\")\n",
    "else:\n",
    "    # ====== 從合格清單中隨機抽 3 個 ======\n",
    "    random.seed()  # 可依需求設定固定種子\n",
    "    picks = random.sample(qualified, min(3, len(qualified)))\n",
    "    print(\"[Pick] Files:\", [os.path.basename(x[0]) for x in picks])\n",
    "\n",
    "    for fp, ok0, ok1 in picks:\n",
    "        base = os.path.basename(fp)\n",
    "        # 重新讀完整（避免後續若需加欄位時有缺），但仍只讀不寫\n",
    "        df = pd.read_csv(fp, low_memory=False)\n",
    "        if not {\"senddate\",\"ad_para\",\"set\"}.issubset(df.columns):\n",
    "            print(f\"[Skip] {base}: missing required columns.\")\n",
    "            continue\n",
    "\n",
    "        # 時間轉型＋排序\n",
    "        df[\"senddate\"] = pd.to_datetime(df[\"senddate\"], errors=\"coerce\")\n",
    "        df = df.dropna(subset=[\"senddate\"]).sort_values(\"senddate\")\n",
    "        if df.empty:\n",
    "            print(f\"[Skip] {base}: no valid time.\")\n",
    "            continue\n",
    "\n",
    "        # 畫圖範圍（僅圍繞會畫的內容）\n",
    "        all_times = []\n",
    "        for st, ed, _, tl, tr in ok0 + ok1:\n",
    "            all_times += [st, ed, tl, tr]\n",
    "        xmin, xmax = min(all_times), max(all_times)\n",
    "        pad = pd.Timedelta(minutes=30)\n",
    "        xlim_min, xlim_max = xmin - pad, xmax + pad\n",
    "\n",
    "        # 準備 ad 事件序列（去重後排序）\n",
    "        ad_times = (df.loc[df[\"ad_para\"]==1, \"senddate\"]\n",
    "                      .dropna().sort_values().unique())\n",
    "\n",
    "        # ====== 開始繪圖（單軌） ======\n",
    "        fig, ax = plt.subplots(figsize=(16, 4))\n",
    "\n",
    "        # --- 畫 set 段：單軌 y=1 上的粗線帶，並在段上標 rows 與起訖 ---\n",
    "        def draw_bands(ok_segments, color, label):\n",
    "            for i, (st, ed, rows, tl, tr) in enumerate(ok_segments):\n",
    "                ax.plot([st, ed], [1, 1], color=color, linewidth=BAND_LW,\n",
    "                        solid_capstyle=\"butt\", zorder=2)\n",
    "                mid = st + (ed - st)/2\n",
    "                # 交錯偏移，減少重疊\n",
    "                voff = 0.08 if (i % 2 == 0) else -0.08\n",
    "                ax.text(mid, 1 + voff,\n",
    "                        f\"{label}: {rows} rows\\n[{st.strftime('%m-%d %H:%M')} ~ {ed.strftime('%m-%d %H:%M')}]\",\n",
    "                        color=color, fontsize=FONT_SZ, ha=\"center\", va=\"bottom\")\n",
    "\n",
    "        draw_bands(ok0, C_SET0, \"set=0\")\n",
    "        draw_bands(ok1, C_SET1, \"set=1\")\n",
    "\n",
    "        # --- 畫 ad_para=1 黑點（同軌 y=1），並稀疏標出時間字樣 ---\n",
    "        ad_series = pd.to_datetime(pd.Series(ad_times)).sort_values()\n",
    "        if len(ad_series) > 0:\n",
    "            ax.scatter(ad_series, np.full(len(ad_series), 1),\n",
    "                       s=26, color=C_AD, zorder=3, label=\"ad_para = 1\")\n",
    "\n",
    "            last_label = None\n",
    "            for t in ad_series:\n",
    "                if last_label is None or (t - last_label).total_seconds() >= LABEL_GAP_MIN * 60:\n",
    "                    ax.text(t, 1.18, t.strftime(\"%m-%d %H:%M\"), fontsize=7,\n",
    "                            rotation=45, ha=\"center\", va=\"bottom\", color=C_AD)\n",
    "                    last_label = t\n",
    "\n",
    "        # --- 軸樣式（單軌） ---\n",
    "        loc = mdates.AutoDateLocator(minticks=4, maxticks=10)\n",
    "        fmt = mdates.ConciseDateFormatter(loc)\n",
    "        ax.xaxis.set_major_locator(loc)\n",
    "        ax.xaxis.set_major_formatter(fmt)\n",
    "\n",
    "        ax.set_xlim(xlim_min, xlim_max)\n",
    "        ax.set_ylim(0.7, 1.35)           # 單軌視窗高度\n",
    "        ax.set_yticks([])                # 不顯示 Y 刻度\n",
    "        ax.set_xlabel(\"Time\")\n",
    "        ax.set_title(f\"Set bands & ad events (same axis) — {base}\\n\"\n",
    "                     f\"(Only files with BOTH valid set=0 & set=1 segments)\",\n",
    "                     fontsize=12, pad=12)\n",
    "\n",
    "        # 圖例\n",
    "        from matplotlib.lines import Line2D\n",
    "        legend = [\n",
    "            Line2D([0],[0], color=C_SET0, lw=BAND_LW, label=\"set=0\"),\n",
    "            Line2D([0],[0], color=C_SET1, lw=BAND_LW, label=\"set=1\"),\n",
    "            Line2D([0],[0], marker='o', color='none', markerfacecolor=C_AD,\n",
    "                   label=\"ad_para = 1\", markersize=7)\n",
    "        ]\n",
    "        ax.legend(handles=legend, loc=\"upper right\", frameon=False)\n",
    "        ax.grid(True, axis=\"x\", linestyle=\"--\", alpha=0.5)\n",
    "\n",
    "        plt.tight_layout()\n",
    "        out_file = os.path.join(OUT_DIR, f\"{base}_set_combined_timeline.png\")\n",
    "        fig.savefig(out_file, dpi=160)\n",
    "        plt.close(fig)\n",
    "        print(f\"✅ Saved: {out_file}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "id": "7f74fe01-084d-4e73-9321-65b014d02ed6",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Info] Scan files: 122 in /home/jovyan/RT08/0925/bling_1004\n",
      "\n",
      "✅ Saved: /home/jovyan/RT08/0925/1002/adpairs_segments.csv\n",
      "✅ Saved: /home/jovyan/RT08/0925/1002/adpairs_pairs_summary.csv\n",
      "\n",
      "=== Summary ===\n",
      "- Files scanned         : 122\n",
      "- Pairs total           : 2,609\n",
      "- Segments total        : 230\n",
      "- Segments by set value :\n",
      "set\n",
      "0    123\n",
      "1    107\n",
      "\n",
      "=== Segment-level details (each row is a continuous set segment) ===\n",
      "                   file  pair_id             ad_left            ad_right  window_minutes  set           seg_start             seg_end  seg_minutes  seg_rows\n",
      "             089271.csv       69 2021-12-30 14:00:01 2022-01-06 23:45:04    10665.050000    0 2022-01-03 13:00:01 2022-01-03 13:03:01     3.000000         4\n",
      "             089271.csv       69 2021-12-30 14:00:01 2022-01-06 23:45:04    10665.050000    1 2022-01-06 23:44:54 2022-01-06 23:44:54     0.000000         1\n",
      "             089271.csv       81 2022-01-10 15:05:08 2022-01-11 16:27:01     1521.883333    0 2022-01-11 14:52:03 2022-01-11 15:12:03    20.000000        21\n",
      "             089271.csv       81 2022-01-10 15:05:08 2022-01-11 16:27:01     1521.883333    1 2022-01-11 16:26:01 2022-01-11 16:26:01     0.000000         1\n",
      "             114309.csv       29 2022-01-01 08:46:02 2022-01-01 10:49:02      123.000000    0 2022-01-01 09:22:02 2022-01-01 10:04:02    42.000000        43\n",
      "             114309.csv       29 2022-01-01 08:46:02 2022-01-01 10:49:02      123.000000    1 2022-01-01 10:32:02 2022-01-01 10:48:02    16.000000        17\n",
      "             230933.csv        9 2021-12-25 07:07:03 2021-12-30 02:41:02     6933.983333    0 2021-12-29 10:45:02 2021-12-29 18:42:01   476.983333       479\n",
      "             230933.csv        9 2021-12-25 07:07:03 2021-12-30 02:41:02     6933.983333    1 2021-12-30 00:42:02 2021-12-30 02:40:02   118.000000       119\n",
      "             230933.csv       10 2021-12-30 02:41:02 2021-12-31 16:09:02     2248.000000    1 2021-12-31 16:00:02 2021-12-31 16:08:02     8.000000         9\n",
      "             230933.csv       28 2022-01-05 11:34:03 2022-01-05 21:12:07      578.066667    0 2022-01-05 13:05:01 2022-01-05 13:16:00    10.983333        12\n",
      "             230933.csv       31 2022-01-07 13:36:01 2022-01-10 17:29:04     4553.050000    0 2022-01-10 13:20:05 2022-01-10 15:23:04   122.983333       124\n",
      "             230933.csv       31 2022-01-07 13:36:01 2022-01-10 17:29:04     4553.050000    1 2022-01-10 16:40:04 2022-01-10 17:28:04    48.000000        49\n",
      "PatNo_ID_1563587183.csv        1 2022-03-22 15:04:00 2022-03-23 23:33:02     1949.033333    1 2022-03-23 23:32:06 2022-03-23 23:32:06     0.000000         1\n",
      "PatNo_ID_1564148644.csv        1 2021-12-10 15:07:00 2022-01-26 03:50:29    67003.483333    0 2021-12-16 12:28:31 2021-12-17 08:31:04  1202.550000      1204\n",
      "PatNo_ID_1564148644.csv        1 2021-12-10 15:07:00 2022-01-26 03:50:29    67003.483333    0 2021-12-17 10:38:33 2021-12-17 10:38:33     0.000000         1\n",
      "PatNo_ID_1564148644.csv        1 2021-12-10 15:07:00 2022-01-26 03:50:29    67003.483333    0 2022-01-04 12:20:31 2022-01-05 10:11:04  1310.550000      1309\n",
      "PatNo_ID_1564148644.csv        1 2021-12-10 15:07:00 2022-01-26 03:50:29    67003.483333    0 2022-01-05 11:18:04 2022-01-05 11:21:04     3.000000         4\n",
      "PatNo_ID_1564148644.csv        1 2021-12-10 15:07:00 2022-01-26 03:50:29    67003.483333    0 2022-01-05 19:55:33 2022-01-05 20:04:04     8.516667        10\n",
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      "PatNo_ID_1566123680.csv        7 2022-02-07 12:03:38 2022-02-07 14:59:03      175.416667    1 2022-02-07 14:42:03 2022-02-07 14:58:03    16.000000        17\n",
      "PatNo_ID_1566123680.csv       13 2022-02-09 02:17:49 2022-02-09 11:24:02      546.216667    0 2022-02-09 09:23:02 2022-02-09 10:22:02    59.000000        60\n",
      "PatNo_ID_1566123680.csv       13 2022-02-09 02:17:49 2022-02-09 11:24:02      546.216667    1 2022-02-09 11:00:02 2022-02-09 11:23:02    23.000000        24\n",
      "PatNo_ID_1566123680.csv       16 2022-02-09 17:01:02 2022-02-10 11:22:03     1101.016667    0 2022-02-10 09:21:00 2022-02-10 10:21:00    60.000000        61\n",
      "PatNo_ID_1566123680.csv       16 2022-02-09 17:01:02 2022-02-10 11:22:03     1101.016667    1 2022-02-10 11:21:03 2022-02-10 11:21:03     0.000000         1\n",
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      "PatNo_ID_1566911879.csv       18 2022-02-13 13:34:00 2022-02-14 20:25:00     1851.000000    0 2022-02-14 10:24:02 2022-02-14 15:23:02   299.000000       294\n",
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      "PatNo_ID_1566911879.csv       21 2022-02-16 21:42:04 2022-02-18 22:17:04     2915.000000    1 2022-02-18 20:18:01 2022-02-18 22:16:04   118.050000       119\n",
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      "PatNo_ID_1566911879.csv       22 2022-02-18 22:17:04 2022-02-19 17:34:03     1156.983333    1 2022-02-19 15:54:02 2022-02-19 17:33:03    99.016667       100\n",
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      "PatNo_ID_1566911879.csv       25 2022-02-22 09:31:01 2022-03-01 17:20:01    10549.000000    1 2022-03-01 15:56:02 2022-03-01 17:19:02    83.000000        84\n",
      "PatNo_ID_1566911879.csv       26 2022-03-01 17:20:01 2022-03-02 18:36:05     1516.066667    0 2022-03-02 09:22:02 2022-03-02 13:58:00   275.966667       277\n",
      "PatNo_ID_1566911879.csv       26 2022-03-01 17:20:01 2022-03-02 18:36:05     1516.066667    1 2022-03-02 16:46:04 2022-03-02 18:35:04   109.000000       110\n",
      "PatNo_ID_1566911879.csv       33 2022-03-06 18:44:02 2022-03-07 21:45:00     1620.966667    0 2022-03-07 09:29:04 2022-03-07 15:36:03   366.983333       368\n",
      "PatNo_ID_1566911879.csv       33 2022-03-06 18:44:02 2022-03-07 21:45:00     1620.966667    1 2022-03-07 19:45:02 2022-03-07 21:44:00   118.966667       119\n",
      "PatNo_ID_1567804800.csv        5 2022-02-16 03:51:03 2022-02-16 17:55:04      844.016667    0 2022-02-16 15:58:02 2022-02-16 16:56:02    58.000000        59\n",
      "PatNo_ID_1567804800.csv        5 2022-02-16 03:51:03 2022-02-16 17:55:04      844.016667    1 2022-02-16 17:32:04 2022-02-16 17:54:04    22.000000        23\n",
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      "PatNo_ID_1568039398.csv        1 2022-01-21 11:07:00 2022-01-24 22:01:03     4974.050000    0 2022-01-24 19:14:03 2022-01-24 20:36:03    82.000000        79\n",
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      "PatNo_ID_1570089466.csv       41 2022-02-03 09:08:04 2022-02-03 11:34:03      145.983333    0 2022-02-03 09:12:04 2022-02-03 10:22:03    69.983333        71\n",
      "PatNo_ID_1570089466.csv       41 2022-02-03 09:08:04 2022-02-03 11:34:03      145.983333    1 2022-02-03 11:06:03 2022-02-03 11:33:03    27.000000        28\n",
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      "PatNo_ID_1570642083.csv       26 2022-01-19 04:30:18 2022-01-19 05:33:03       62.750000    1 2022-01-19 05:21:08 2022-01-19 05:32:08    11.000000        12\n",
      "PatNo_ID_1570642083.csv       39 2022-01-20 09:59:06 2022-01-20 12:39:01      159.916667    0 2022-01-20 11:10:04 2022-01-20 11:54:02    43.966667        45\n",
      "PatNo_ID_1570642083.csv       39 2022-01-20 09:59:06 2022-01-20 12:39:01      159.916667    1 2022-01-20 12:22:01 2022-01-20 12:38:01    16.000000        17\n",
      "PatNo_ID_1570642083.csv       43 2022-01-20 14:49:01 2022-01-20 15:50:06       61.083333    0 2022-01-20 14:50:01 2022-01-20 14:55:01     5.000000         6\n",
      "PatNo_ID_1570642083.csv       43 2022-01-20 14:49:01 2022-01-20 15:50:06       61.083333    1 2022-01-20 15:38:01 2022-01-20 15:49:01    11.000000        12\n",
      "PatNo_ID_1570642083.csv       52 2022-01-20 20:44:00 2022-01-20 21:44:01       60.016667    0 2022-01-20 20:45:00 2022-01-20 21:13:01    28.016667        29\n",
      "PatNo_ID_1570642083.csv       52 2022-01-20 20:44:00 2022-01-20 21:44:01       60.016667    1 2022-01-20 21:32:01 2022-01-20 21:43:01    11.000000        12\n",
      "PatNo_ID_1570642083.csv      117 2022-01-25 20:03:02 2022-01-26 11:35:01      931.983333    0 2022-01-26 09:43:03 2022-01-26 10:38:01    54.966667        56\n",
      "PatNo_ID_1570642083.csv      117 2022-01-25 20:03:02 2022-01-26 11:35:01      931.983333    1 2022-01-26 11:13:01 2022-01-26 11:34:01    21.000000        22\n",
      "PatNo_ID_1570642083.csv      120 2022-01-28 00:59:00 2022-01-29 09:56:02     1977.033333    0 2022-01-28 09:16:00 2022-01-28 21:35:00   739.000000       740\n",
      "PatNo_ID_1570642083.csv      120 2022-01-28 00:59:00 2022-01-29 09:56:02     1977.033333    1 2022-01-29 07:56:03 2022-01-29 09:55:02   118.983333       120\n",
      "PatNo_ID_1572481361.csv        1 2022-03-13 03:56:02 2022-03-19 23:28:03     9812.016667    0 2022-03-17 11:32:02 2022-03-17 11:43:02    11.000000        12\n",
      "PatNo_ID_1572481361.csv        6 2022-03-21 12:53:04 2022-03-25 03:12:19     5179.250000    0 2022-03-24 14:19:02 2022-03-24 14:19:02     0.000000         1\n",
      "PatNo_ID_1572481361.csv        7 2022-03-25 03:12:19 2022-03-26 08:56:01     1783.700000    0 2022-03-25 13:20:04 2022-03-25 13:22:04     2.000000         3\n",
      "PatNo_ID_1572481361.csv        9 2022-03-27 03:36:33 2022-03-27 12:51:03      554.500000    0 2022-03-27 09:41:04 2022-03-27 11:15:03    93.983333        95\n",
      "PatNo_ID_1572481361.csv        9 2022-03-27 03:36:33 2022-03-27 12:51:03      554.500000    1 2022-03-27 12:50:03 2022-03-27 12:50:03     0.000000         1\n",
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      "PatNo_ID_1588673465.csv        5 2022-02-26 18:02:02 2022-02-27 20:25:00     1582.966667    1 2022-02-27 18:25:01 2022-02-27 20:24:00   118.983333       120\n",
      "PatNo_ID_1588673465.csv        7 2022-02-28 09:56:04 2022-02-28 21:38:00      701.933333    0 2022-02-28 09:58:04 2022-02-28 15:47:01   348.950000       350\n",
      "PatNo_ID_1588673465.csv        7 2022-02-28 09:56:04 2022-02-28 21:38:00      701.933333    1 2022-02-28 19:39:00 2022-02-28 21:37:00   118.000000       119\n",
      "PatNo_ID_1589034524.csv        2 2022-02-11 16:19:02 2022-02-13 13:45:47     2726.750000    0 2022-02-13 09:09:00 2022-02-13 09:09:00     0.000000         1\n",
      "PatNo_ID_1589034524.csv        2 2022-02-11 16:19:02 2022-02-13 13:45:47     2726.750000    1 2022-02-13 13:44:18 2022-02-13 13:44:18     0.000000         1\n",
      "PatNo_ID_1589034524.csv        4 2022-02-14 09:57:21 2022-02-14 14:28:01      270.666667    1 2022-02-14 14:27:01 2022-02-14 14:27:01     0.000000         1\n",
      "PatNo_ID_1589034524.csv        5 2022-02-14 14:28:01 2022-02-16 13:59:35     2851.566667    1 2022-02-16 13:57:16 2022-02-16 13:57:16     0.000000         1\n",
      "PatNo_ID_1589034524.csv       12 2022-02-20 14:58:18 2022-02-22 07:22:04     2423.766667    0 2022-02-21 10:10:41 2022-02-21 10:24:01    13.333333        15\n",
      "PatNo_ID_1589034524.csv       12 2022-02-20 14:58:18 2022-02-22 07:22:04     2423.766667    1 2022-02-22 07:21:04 2022-02-22 07:21:04     0.000000         1\n",
      "PatNo_ID_1589034524.csv       13 2022-02-22 07:22:04 2022-02-22 21:15:00      832.933333    0 2022-02-22 11:23:10 2022-02-22 11:30:00     6.833333         7\n",
      "PatNo_ID_1589034524.csv       14 2022-02-22 21:15:00 2022-02-24 09:47:03     2192.050000    0 2022-02-23 09:58:32 2022-02-23 10:03:03     4.516667         6\n",
      "PatNo_ID_1589034524.csv       20 2022-02-24 19:37:01 2022-02-25 14:39:04     1142.050000    0 2022-02-25 09:55:01 2022-02-25 10:07:06    12.083333        13\n",
      "PatNo_ID_1589034524.csv       26 2022-03-11 01:23:27 2022-03-11 17:18:03      954.600000    0 2022-03-11 15:43:04 2022-03-11 15:47:04     4.000000         5\n",
      "PatNo_ID_1592560504.csv        4 2022-02-26 02:06:03 2022-02-26 05:12:01      185.966667    0 2022-02-26 03:21:03 2022-02-26 04:16:01    54.966667        56\n",
      "PatNo_ID_1592560504.csv        4 2022-02-26 02:06:03 2022-02-26 05:12:01      185.966667    1 2022-02-26 04:50:01 2022-02-26 05:11:01    21.000000        22\n",
      "PatNo_ID_1593087886.csv        4 2022-01-24 14:55:13 2022-02-05 18:40:00    17504.783333    0 2022-01-28 10:07:13 2022-01-28 10:09:08     1.916667         3\n",
      "PatNo_ID_1593087886.csv        4 2022-01-24 14:55:13 2022-02-05 18:40:00    17504.783333    1 2022-02-05 18:30:00 2022-02-05 18:39:00     9.000000        10\n",
      "PatNo_ID_1593472048.csv       13 2022-01-20 09:22:02 2022-01-21 18:49:04     2007.033333    0 2022-01-21 09:56:04 2022-01-21 14:22:00   265.933333       267\n",
      "PatNo_ID_1593472048.csv       13 2022-01-20 09:22:02 2022-01-21 18:49:04     2007.033333    1 2022-01-21 17:03:02 2022-01-21 18:48:04   105.033333       106\n",
      "PatNo_ID_1593720818.csv        1 2022-03-29 18:21:01 2022-04-01 09:18:02     3777.016667    0 2022-03-31 12:17:03 2022-03-31 22:47:01   629.966667       631\n",
      "PatNo_ID_1593720818.csv        1 2022-03-29 18:21:01 2022-04-01 09:18:02     3777.016667    1 2022-04-01 07:18:03 2022-04-01 09:17:02   118.983333       120\n",
      "PatNo_ID_1594305136.csv        1 2022-01-02 13:47:03 2022-01-02 17:19:02      211.983333    0 2022-01-02 14:11:02 2022-01-02 15:44:02    93.000000        94\n",
      "PatNo_ID_1594305136.csv        1 2022-01-02 13:47:03 2022-01-02 17:19:02      211.983333    1 2022-01-02 16:42:02 2022-01-02 17:18:02    36.000000        37\n",
      "PatNo_ID_1594305136.csv        2 2022-01-02 17:19:02 2022-01-03 03:15:00      595.966667    0 2022-01-02 17:20:02 2022-01-02 22:16:01   295.983333       297\n",
      "PatNo_ID_1594305136.csv        2 2022-01-02 17:19:02 2022-01-03 03:15:00      595.966667    1 2022-01-03 01:16:01 2022-01-03 03:14:00   117.983333       119\n",
      "PatNo_ID_1594305136.csv        3 2022-01-03 03:15:00 2022-01-05 05:34:02     3019.033333    0 2022-01-03 17:18:04 2022-01-04 11:25:00  1086.933333      1088\n",
      "PatNo_ID_1594305136.csv        3 2022-01-03 03:15:00 2022-01-05 05:34:02     3019.033333    1 2022-01-05 03:35:02 2022-01-05 05:33:02   118.000000       119\n",
      "PatNo_ID_1594335109.csv        1 2022-01-22 06:32:01 2022-01-22 15:47:01      555.000000    0 2022-01-22 13:32:01 2022-01-22 14:39:00    66.983333        68\n",
      "PatNo_ID_1594437309.csv        2 2022-02-23 09:26:01 2022-02-23 14:41:03      315.033333    1 2022-02-23 14:40:43 2022-02-23 14:40:43     0.000000         1\n",
      "PatNo_ID_1594441887.csv       23 2022-02-28 07:31:04 2022-02-28 12:08:02      276.966667    0 2022-02-28 11:04:02 2022-02-28 11:35:02    31.000000        32\n",
      "PatNo_ID_1594441887.csv       24 2022-02-28 12:08:02 2022-03-01 13:42:02     1534.000000    0 2022-03-01 11:27:02 2022-03-01 11:46:03    19.016667        20\n",
      "PatNo_ID_1594441887.csv       25 2022-03-01 13:42:02 2022-03-02 12:25:02     1363.000000    0 2022-03-02 09:47:01 2022-03-02 10:21:01    34.000000        35\n",
      "PatNo_ID_1594441887.csv       25 2022-03-01 13:42:02 2022-03-02 12:25:02     1363.000000    1 2022-03-02 12:24:02 2022-03-02 12:24:02     0.000000         1\n",
      "PatNo_ID_1594471407.csv       19 2022-03-11 14:20:00 2022-03-11 16:50:00      150.000000    0 2022-03-11 14:48:00 2022-03-11 15:48:02    60.033333        61\n",
      "PatNo_ID_1594471407.csv       19 2022-03-11 14:20:00 2022-03-11 16:50:00      150.000000    1 2022-03-11 16:26:00 2022-03-11 16:49:00    23.000000        24\n",
      "PatNo_ID_1594471407.csv       45 2022-03-12 13:14:00 2022-03-12 14:17:00       63.000000    0 2022-03-12 13:15:00 2022-03-12 13:45:00    30.000000        31\n",
      "PatNo_ID_1594471407.csv       45 2022-03-12 13:14:00 2022-03-12 14:17:00       63.000000    1 2022-03-12 14:05:00 2022-03-12 14:16:00    11.000000        12\n",
      "PatNo_ID_1594471407.csv      183 2022-03-16 16:16:01 2022-03-16 17:39:03       83.033333    0 2022-03-16 16:38:01 2022-03-16 17:08:00    29.983333        31\n",
      "PatNo_ID_1594471407.csv      183 2022-03-16 16:16:01 2022-03-16 17:39:03       83.033333    1 2022-03-16 17:27:02 2022-03-16 17:38:00    10.966667        12\n",
      "PatNo_ID_1594479330.csv       19 2022-03-14 20:40:00 2022-03-14 22:17:44       97.733333    0 2022-03-14 21:17:09 2022-03-14 21:46:27    29.300000        27\n",
      "PatNo_ID_1594479330.csv       19 2022-03-14 20:40:00 2022-03-14 22:17:44       97.733333    1 2022-03-14 22:06:31 2022-03-14 22:16:24     9.883333        11\n",
      "\n",
      "=== Pair-level summary (each row is an adjacent ad pair) ===\n",
      "                   file  pair_id             ad_left            ad_right  window_minutes  set0_rows  set0_minutes  set1_rows  set1_minutes\n",
      "             089271.csv        1 2021-12-17 10:14:00 2021-12-17 12:59:00      165.000000          0      0.000000          0      0.000000\n",
      "             089271.csv        2 2021-12-17 12:59:00 2021-12-17 21:29:02      510.033333          0      0.000000          0      0.000000\n",
      "             089271.csv        3 2021-12-17 21:29:02 2021-12-19 15:39:00     2529.966667          0      0.000000          0      0.000000\n",
      "             089271.csv        4 2021-12-19 15:39:00 2021-12-19 16:49:04       70.066667          0      0.000000          0      0.000000\n",
      "             089271.csv        5 2021-12-19 16:49:04 2021-12-20 06:05:01      795.950000          0      0.000000          0      0.000000\n",
      "             089271.csv        6 2021-12-20 06:05:01 2021-12-20 12:51:04      406.050000          0      0.000000          0      0.000000\n",
      "             089271.csv        7 2021-12-20 12:51:04 2021-12-20 19:08:02      376.966667          0      0.000000          0      0.000000\n",
      "             089271.csv        8 2021-12-20 19:08:02 2021-12-21 08:03:04      775.033333          0      0.000000          0      0.000000\n",
      "             089271.csv        9 2021-12-21 08:03:04 2021-12-21 10:00:03      116.983333          0      0.000000          0      0.000000\n",
      "             089271.csv       10 2021-12-21 10:00:03 2021-12-21 14:38:02      277.983333          0      0.000000          0      0.000000\n",
      "             089271.csv       11 2021-12-21 14:38:02 2021-12-21 15:59:01       80.983333          0      0.000000          0      0.000000\n",
      "             089271.csv       12 2021-12-21 15:59:01 2021-12-21 20:18:00      258.983333          0      0.000000          0      0.000000\n",
      "             089271.csv       13 2021-12-21 20:18:00 2021-12-22 01:25:04      307.066667          0      0.000000          0      0.000000\n",
      "             089271.csv       14 2021-12-22 01:25:04 2021-12-22 11:02:00      576.933333          0      0.000000          0      0.000000\n",
      "             089271.csv       15 2021-12-22 11:02:00 2021-12-22 12:45:04      103.066667          0      0.000000          0      0.000000\n",
      "             089271.csv       16 2021-12-22 12:45:04 2021-12-22 13:09:04       24.000000          0      0.000000          0      0.000000\n",
      "             089271.csv       17 2021-12-22 13:09:04 2021-12-22 15:05:04      116.000000          0      0.000000          0      0.000000\n",
      "             089271.csv       18 2021-12-22 15:05:04 2021-12-22 15:41:03       35.983333          0      0.000000          0      0.000000\n",
      "             089271.csv       19 2021-12-22 15:41:03 2021-12-22 17:35:03      114.000000          0      0.000000          0      0.000000\n",
      "             089271.csv       20 2021-12-22 17:35:03 2021-12-22 18:40:03       65.000000          0      0.000000          0      0.000000\n",
      "             089271.csv       21 2021-12-22 18:40:03 2021-12-22 22:59:01      258.966667          0      0.000000          0      0.000000\n",
      "             089271.csv       22 2021-12-22 22:59:01 2021-12-23 02:26:00      206.983333          0      0.000000          0      0.000000\n",
      "             089271.csv       23 2021-12-23 02:26:00 2021-12-23 15:42:02      796.033333          0      0.000000          0      0.000000\n",
      "             089271.csv       24 2021-12-23 15:42:02 2021-12-24 07:17:03      935.016667          0      0.000000          0      0.000000\n",
      "             089271.csv       25 2021-12-24 07:17:03 2021-12-24 16:17:01      539.966667          0      0.000000          0      0.000000\n",
      "             089271.csv       26 2021-12-24 16:17:01 2021-12-24 16:31:01       14.000000          0      0.000000          0      0.000000\n",
      "             089271.csv       27 2021-12-24 16:31:01 2021-12-25 08:59:03      988.033333          0      0.000000          0      0.000000\n",
      "             089271.csv       28 2021-12-25 08:59:03 2021-12-25 18:14:02      554.983333          0      0.000000          0      0.000000\n",
      "             089271.csv       29 2021-12-25 18:14:02 2021-12-25 23:33:00      318.966667          0      0.000000          0      0.000000\n",
      "             089271.csv       30 2021-12-25 23:33:00 2021-12-26 00:06:00       33.000000          0      0.000000          0      0.000000\n",
      "             089271.csv       31 2021-12-26 00:06:00 2021-12-26 00:53:00       47.000000          0      0.000000          0      0.000000\n",
      "             089271.csv       32 2021-12-26 00:53:00 2021-12-26 01:08:00       15.000000          0      0.000000          0      0.000000\n",
      "             089271.csv       33 2021-12-26 01:08:00 2021-12-26 02:27:00       79.000000          0      0.000000          0      0.000000\n",
      "             089271.csv       34 2021-12-26 02:27:00 2021-12-26 02:52:00       25.000000          0      0.000000          0      0.000000\n",
      "             089271.csv       35 2021-12-26 02:52:00 2021-12-26 03:22:00       30.000000          0      0.000000          0      0.000000\n",
      "             089271.csv       36 2021-12-26 03:22:00 2021-12-26 13:24:03      602.050000          0      0.000000          0      0.000000\n",
      "             089271.csv       37 2021-12-26 13:24:03 2021-12-26 15:09:02      104.983333          0      0.000000          0      0.000000\n",
      "             089271.csv       38 2021-12-26 15:09:02 2021-12-26 21:32:00      382.966667          0      0.000000          0      0.000000\n",
      "             089271.csv       39 2021-12-26 21:32:00 2021-12-26 22:19:00       47.000000          0      0.000000          0      0.000000\n",
      "             089271.csv       40 2021-12-26 22:19:00 2021-12-27 11:37:02      798.033333          0      0.000000          0      0.000000\n",
      "             089271.csv       41 2021-12-27 11:37:02 2021-12-27 12:32:01       54.983333          0      0.000000          0      0.000000\n",
      "             089271.csv       42 2021-12-27 12:32:01 2021-12-27 15:19:01      167.000000          0      0.000000          0      0.000000\n",
      "             089271.csv       43 2021-12-27 15:19:01 2021-12-27 18:17:00      177.983333          0      0.000000          0      0.000000\n",
      "             089271.csv       44 2021-12-27 18:17:00 2021-12-27 19:36:00       79.000000          0      0.000000          0      0.000000\n",
      "             089271.csv       45 2021-12-27 19:36:00 2021-12-27 21:09:00       93.000000          0      0.000000          0      0.000000\n",
      "             089271.csv       46 2021-12-27 21:09:00 2021-12-28 05:34:02      505.033333          0      0.000000          0      0.000000\n",
      "             089271.csv       47 2021-12-28 05:34:02 2021-12-28 10:06:01      271.983333          0      0.000000          0      0.000000\n",
      "             089271.csv       48 2021-12-28 10:06:01 2021-12-28 10:35:01       29.000000          0      0.000000          0      0.000000\n",
      "             089271.csv       49 2021-12-28 10:35:01 2021-12-28 12:43:01      128.000000          0      0.000000          0      0.000000\n",
      "             089271.csv       50 2021-12-28 12:43:01 2021-12-28 13:59:01       76.000000          0      0.000000          0      0.000000\n",
      "             089271.csv       51 2021-12-28 13:59:01 2021-12-28 14:13:01       14.000000          0      0.000000          0      0.000000\n",
      "             089271.csv       52 2021-12-28 14:13:01 2021-12-28 16:18:00      124.983333          0      0.000000          0      0.000000\n",
      "             089271.csv       53 2021-12-28 16:18:00 2021-12-28 16:31:00       13.000000          0      0.000000          0      0.000000\n",
      "             089271.csv       54 2021-12-28 16:31:00 2021-12-28 16:46:00       15.000000          0      0.000000          0      0.000000\n",
      "             089271.csv       55 2021-12-28 16:46:00 2021-12-28 17:08:00       22.000000          0      0.000000          0      0.000000\n",
      "             089271.csv       56 2021-12-28 17:08:00 2021-12-28 20:46:04      218.066667          0      0.000000          0      0.000000\n",
      "             089271.csv       57 2021-12-28 20:46:04 2021-12-28 20:59:04       13.000000          0      0.000000          0      0.000000\n",
      "             089271.csv       58 2021-12-28 20:59:04 2021-12-29 08:27:01      687.950000          0      0.000000          0      0.000000\n",
      "             089271.csv       59 2021-12-29 08:27:01 2021-12-29 12:59:00      271.983333          0      0.000000          0      0.000000\n",
      "             089271.csv       60 2021-12-29 12:59:00 2021-12-29 13:28:00       29.000000          0      0.000000          0      0.000000\n",
      "             089271.csv       61 2021-12-29 13:28:00 2021-12-29 14:55:04       87.066667          0      0.000000          0      0.000000\n",
      "             089271.csv       62 2021-12-29 14:55:04 2021-12-30 00:13:00      557.933333          0      0.000000          0      0.000000\n",
      "             089271.csv       63 2021-12-30 00:13:00 2021-12-30 02:00:00      107.000000          0      0.000000          0      0.000000\n",
      "             089271.csv       64 2021-12-30 02:00:00 2021-12-30 02:19:04       19.066667          0      0.000000          0      0.000000\n",
      "             089271.csv       65 2021-12-30 02:19:04 2021-12-30 07:34:03      314.983333          0      0.000000          0      0.000000\n",
      "             089271.csv       66 2021-12-30 07:34:03 2021-12-30 08:18:03       44.000000          0      0.000000          0      0.000000\n",
      "             089271.csv       67 2021-12-30 08:18:03 2021-12-30 13:19:01      300.966667          0      0.000000          0      0.000000\n",
      "             089271.csv       68 2021-12-30 13:19:01 2021-12-30 14:00:01       41.000000          0      0.000000          0      0.000000\n",
      "             089271.csv       69 2021-12-30 14:00:01 2022-01-06 23:45:04    10665.050000          4      3.000000          1      0.000000\n",
      "             089271.csv       70 2022-01-06 23:45:04 2022-01-07 01:00:14       75.166667          0      0.000000          0      0.000000\n",
      "             089271.csv       71 2022-01-07 01:00:14 2022-01-07 02:49:14      109.000000          0      0.000000          0      0.000000\n",
      "             089271.csv       72 2022-01-07 02:49:14 2022-01-07 06:56:10      246.933333          0      0.000000          0      0.000000\n",
      "             089271.csv       73 2022-01-07 06:56:10 2022-01-07 07:59:14       63.066667          0      0.000000          0      0.000000\n",
      "             089271.csv       74 2022-01-07 07:59:14 2022-01-07 19:53:12      713.966667          0      0.000000          0      0.000000\n",
      "             089271.csv       75 2022-01-07 19:53:12 2022-01-08 15:04:06     1150.900000          0      0.000000          0      0.000000\n",
      "             089271.csv       76 2022-01-08 15:04:06 2022-01-08 16:46:13      102.116667          0      0.000000          0      0.000000\n",
      "             089271.csv       77 2022-01-08 16:46:13 2022-01-09 04:52:35      726.366667          0      0.000000          0      0.000000\n",
      "             089271.csv       78 2022-01-09 04:52:35 2022-01-09 13:53:54      541.316667          0      0.000000          0      0.000000\n",
      "             089271.csv       79 2022-01-09 13:53:54 2022-01-10 12:08:22     1334.466667          0      0.000000          0      0.000000\n",
      "             089271.csv       80 2022-01-10 12:08:22 2022-01-10 15:05:08      176.766667          0      0.000000          0      0.000000\n",
      "             089271.csv       81 2022-01-10 15:05:08 2022-01-11 16:27:01     1521.883333         21     20.000000          1      0.000000\n",
      "             089271.csv       82 2022-01-11 16:27:01 2022-01-11 19:13:13      166.200000          0      0.000000          0      0.000000\n",
      "             089271.csv       83 2022-01-11 19:13:13 2022-01-11 21:24:14      131.016667          0      0.000000          0      0.000000\n",
      "             089271.csv       84 2022-01-11 21:24:14 2022-01-12 01:10:32      226.300000          0      0.000000          0      0.000000\n",
      "             095323.csv        1 2021-12-23 10:20:05 2021-12-24 00:45:04      864.983333          0      0.000000          0      0.000000\n",
      "             095323.csv        2 2021-12-24 00:45:04 2021-12-24 01:40:04       55.000000          0      0.000000          0      0.000000\n",
      "             095323.csv        3 2021-12-24 01:40:04 2021-12-24 02:45:04       65.000000          0      0.000000          0      0.000000\n",
      "             095323.csv        4 2021-12-24 02:45:04 2021-12-24 03:01:04       16.000000          0      0.000000          0      0.000000\n",
      "             095323.csv        5 2021-12-24 03:01:04 2021-12-24 03:16:04       15.000000          0      0.000000          0      0.000000\n",
      "             095323.csv        6 2021-12-24 03:16:04 2021-12-24 05:57:03      160.983333          0      0.000000          0      0.000000\n",
      "             095323.csv        7 2021-12-24 05:57:03 2021-12-24 06:14:03       17.000000          0      0.000000          0      0.000000\n",
      "             095323.csv        8 2021-12-24 06:14:03 2021-12-24 07:07:03       53.000000          0      0.000000          0      0.000000\n",
      "             095323.csv        9 2021-12-24 07:07:03 2021-12-24 07:41:03       34.000000          0      0.000000          0      0.000000\n",
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      "PatNo_ID_1594479330.csv       26 2022-03-15 03:44:02 2022-03-15 05:48:00      123.966667          0      0.000000          0      0.000000\n",
      "PatNo_ID_1594479330.csv       27 2022-03-15 05:48:00 2022-03-15 06:29:04       41.066667          0      0.000000          0      0.000000\n",
      "PatNo_ID_1594479330.csv       28 2022-03-15 06:29:04 2022-03-15 09:49:04      200.000000          0      0.000000          0      0.000000\n",
      "PatNo_ID_1594479330.csv       29 2022-03-15 09:49:04 2022-03-15 13:37:04      228.000000          0      0.000000          0      0.000000\n",
      "PatNo_ID_1594479330.csv       30 2022-03-15 13:37:04 2022-03-15 13:54:10       17.100000          0      0.000000          0      0.000000\n",
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      "PatNo_ID_1594479330.csv       32 2022-03-15 14:11:00 2022-03-15 15:03:03       52.050000          0      0.000000          0      0.000000\n",
      "PatNo_ID_1594479330.csv       33 2022-03-15 15:03:03 2022-03-15 15:15:21       12.300000          0      0.000000          0      0.000000\n",
      "PatNo_ID_1594479330.csv       34 2022-03-15 15:15:21 2022-03-15 15:57:00       41.650000          0      0.000000          0      0.000000\n",
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      "PatNo_ID_1594479330.csv       38 2022-03-15 18:24:00 2022-03-15 18:59:13       35.216667          0      0.000000          0      0.000000\n",
      "PatNo_ID_1594479330.csv       39 2022-03-15 18:59:13 2022-03-15 19:27:05       27.866667          0      0.000000          0      0.000000\n",
      "PatNo_ID_1594479330.csv       40 2022-03-15 19:27:05 2022-03-15 19:46:24       19.316667          0      0.000000          0      0.000000\n",
      "PatNo_ID_1594479330.csv       41 2022-03-15 19:46:24 2022-03-15 21:13:00       86.600000          0      0.000000          0      0.000000\n",
      "PatNo_ID_1594479330.csv       42 2022-03-15 21:13:00 2022-03-15 21:38:03       25.050000          0      0.000000          0      0.000000\n",
      "PatNo_ID_1594479330.csv       43 2022-03-15 21:38:03 2022-03-15 22:05:00       26.950000          0      0.000000          0      0.000000\n",
      "PatNo_ID_1594479330.csv       44 2022-03-15 22:05:00 2022-03-15 23:11:01       66.016667          0      0.000000          0      0.000000\n",
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      "PatNo_ID_1594479330.csv       46 2022-03-15 23:56:41 2022-03-16 00:20:03       23.366667          0      0.000000          0      0.000000\n",
      "PatNo_ID_1594479330.csv       47 2022-03-16 00:20:03 2022-03-16 01:59:02       98.983333          0      0.000000          0      0.000000\n",
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      "PatNo_ID_1594479330.csv       49 2022-03-16 02:21:01 2022-03-16 04:14:04      113.050000          0      0.000000          0      0.000000\n",
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      "PatNo_ID_1594479330.csv       59 2022-03-16 13:52:11 2022-03-16 15:37:00      104.816667          0      0.000000          0      0.000000\n",
      "PatNo_ID_1594479330.csv       60 2022-03-16 15:37:00 2022-03-16 18:16:01      159.016667          0      0.000000          0      0.000000\n",
      "PatNo_ID_1594479330.csv       61 2022-03-16 18:16:01 2022-03-16 19:25:04       69.050000          0      0.000000          0      0.000000\n",
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      "PatNo_ID_1594511914.csv        1 2022-03-27 14:48:00 2022-03-27 20:28:25      340.416667          0      0.000000          0      0.000000\n",
      "PatNo_ID_1594528842.csv        1 2022-04-02 00:02:01 2022-04-02 00:57:01       55.000000          0      0.000000          0      0.000000\n",
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      "PatNo_ID_1594528842.csv        6 2022-04-03 15:30:00 2022-04-03 16:11:00       41.000000          0      0.000000          0      0.000000\n",
      "PatNo_ID_1594533379.csv        1 2022-04-04 20:43:03 2022-04-05 10:23:01      819.966667          0      0.000000          0      0.000000\n"
     ]
    }
   ],
   "source": [
    "\"\"\"另外我想看每一個segment的檔名 起訖時間 長度 set=0跟set=1的資料筆數跟長度\n",
    "，請存成一個檔案也直接印在程式碼\n",
    "\n",
    "列出「相鄰兩次調參（ad_para=1）」之間的所有 set 連續區段 (segment)：\n",
    "- 對每個 CSV（病患），先萃取「有效」調參事件點（ad=1 & NaN_check=1），相鄰 <=10 分鐘視為同一事件（取第一筆）\n",
    "- 針對每對相鄰事件 (t0, t1)，在開區間 (t0, t1) 內，將 set=0 與 set=1 各自依時間連續性切段（相鄰觀測 >10 分鐘視為斷點）\n",
    "- 產出：\n",
    "  1) segment 級別明細（每段一列，含檔名、起訖、長度、rows）\n",
    "  2) 每對事件的彙總（set=0/1 的 rows 與分鐘數加總）\n",
    "- 只讀 /home/jovyan/RT08/0925/bling_1004，不會修改任何資料。\n",
    "\"\"\"\n",
    "\n",
    "import os, glob\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "SRC_DIR = \"/home/jovyan/RT08/0925/bling_1004\"     # 來源（只讀）\n",
    "OUT_DIR = \"/home/jovyan/RT08/0925/1002\"           # 輸出資料夾\n",
    "os.makedirs(OUT_DIR, exist_ok=True)\n",
    "\n",
    "SEG_OUT = os.path.join(OUT_DIR, \"adpairs_segments.csv\")\n",
    "PAIR_OUT = os.path.join(OUT_DIR, \"adpairs_pairs_summary.csv\")\n",
    "\n",
    "# ---- 參數（可調）----\n",
    "AD_EVENT_MIN_GAP_MIN = 10   # 事件合併門檻：相鄰 ad=1 之間 <=10 分鐘視為同一事件（取第一筆）\n",
    "SEG_BREAK_MIN = 10          # 連續段切分門檻：相鄰觀測 >10 分鐘則切段\n",
    "\n",
    "# ------------------ 工具函式 ------------------\n",
    "def load_csv_min(fp):\n",
    "    \"\"\"以最小欄位集讀入；若失敗則全讀。若缺必要欄位則回傳 None。\"\"\"\n",
    "    need = [\"senddate\", \"ad_para\", \"NaN_check\", \"set\"]\n",
    "    try:\n",
    "        df = pd.read_csv(fp, usecols=need, low_memory=False)\n",
    "    except Exception:\n",
    "        try:\n",
    "            df = pd.read_csv(fp, low_memory=False)\n",
    "        except Exception:\n",
    "            return None\n",
    "    if \"senddate\" not in df.columns or \"ad_para\" not in df.columns or \"set\" not in df.columns:\n",
    "        return None\n",
    "    # 型別處理\n",
    "    df[\"senddate\"] = pd.to_datetime(df[\"senddate\"], errors=\"coerce\")\n",
    "    df = df.dropna(subset=[\"senddate\"]).sort_values(\"senddate\")\n",
    "    for c in [\"ad_para\", \"set\", \"NaN_check\"]:\n",
    "        if c in df.columns:\n",
    "            df[c] = pd.to_numeric(df[c], errors=\"coerce\")\n",
    "    if \"NaN_check\" not in df.columns:\n",
    "        df[\"NaN_check\"] = 1\n",
    "    return df\n",
    "\n",
    "def extract_ad_events(df):\n",
    "    \"\"\"\n",
    "    萃取「有效」調參事件（ad=1 且 NaN_check=1），並將相鄰 <= AD_EVENT_MIN_GAP_MIN 分鐘的連續 ad 合併為單一事件（取第一筆）。\n",
    "    回傳：升冪的 Timestamp Series。\n",
    "    \"\"\"\n",
    "    d = df[(df[\"ad_para\"]==1) & (df[\"NaN_check\"]==1)][[\"senddate\"]].sort_values(\"senddate\").copy()\n",
    "    if d.empty:\n",
    "        return pd.Series([], dtype=\"datetime64[ns]\")\n",
    "    d[\"gap_min\"] = d[\"senddate\"].diff().dt.total_seconds().div(60)\n",
    "    is_new = d[\"gap_min\"].isna() | (d[\"gap_min\"] > AD_EVENT_MIN_GAP_MIN)\n",
    "    return d.loc[is_new, \"senddate\"].reset_index(drop=True)\n",
    "\n",
    "def split_segments(sub_df, gap_min=SEG_BREAK_MIN):\n",
    "    \"\"\"\n",
    "    將子表（僅含 senddate）依時間連續性切段。\n",
    "    規則：相鄰點 dt > gap_min 分鐘 → 新段\n",
    "    回傳 list[(seg_start, seg_end, seg_rows)]\n",
    "    \"\"\"\n",
    "    if sub_df.empty:\n",
    "        return []\n",
    "    g = sub_df.sort_values(\"senddate\").copy()\n",
    "    diff_min = g[\"senddate\"].diff().dt.total_seconds().div(60).fillna(0)\n",
    "    seg_id = (diff_min > gap_min).cumsum()\n",
    "    out = []\n",
    "    for _, gg in g.groupby(seg_id):\n",
    "        out.append((gg[\"senddate\"].min(), gg[\"senddate\"].max(), len(gg)))\n",
    "    return out\n",
    "\n",
    "# ------------------ 主流程：蒐集 segment 明細與 pair 彙總 ------------------\n",
    "seg_rows = []   # segment 明細（每段一列）\n",
    "pair_rows = []  # pair 彙總（每對相鄰事件一列）\n",
    "\n",
    "files = sorted(glob.glob(os.path.join(SRC_DIR, \"*.csv\")))\n",
    "print(f\"[Info] Scan files: {len(files)} in {SRC_DIR}\")\n",
    "\n",
    "for fp in files:\n",
    "    base = os.path.basename(fp)\n",
    "    df = load_csv_min(fp)\n",
    "    if df is None or df.empty:\n",
    "        print(f\"[Warn] Skip {base}: missing required columns or empty after parsing.\")\n",
    "        continue\n",
    "\n",
    "    # 取得合併後的 ad 事件序列\n",
    "    ad_events = extract_ad_events(df)\n",
    "    if len(ad_events) < 2:\n",
    "        continue\n",
    "\n",
    "    # 逐對相鄰事件 (t0, t1)\n",
    "    pair_id = 0\n",
    "    for i in range(len(ad_events) - 1):\n",
    "        t0, t1 = ad_events.iloc[i], ad_events.iloc[i+1]\n",
    "        if t1 <= t0:\n",
    "            continue\n",
    "        pair_id += 1\n",
    "        window_minutes = (t1 - t0).total_seconds() / 60.0\n",
    "\n",
    "        # 取窗內（開區間）資料\n",
    "        mid = df[(df[\"senddate\"] > t0) & (df[\"senddate\"] < t1)]\n",
    "        if mid.empty:\n",
    "            # 仍紀錄一筆 pair 彙總（皆為 0）\n",
    "            pair_rows.append({\n",
    "                \"file\": base, \"pair_id\": pair_id,\n",
    "                \"ad_left\": t0, \"ad_right\": t1, \"window_minutes\": window_minutes,\n",
    "                \"set0_rows\": 0, \"set0_minutes\": 0.0,\n",
    "                \"set1_rows\": 0, \"set1_minutes\": 0.0\n",
    "            })\n",
    "            continue\n",
    "\n",
    "        # 依 set 分流\n",
    "        s0 = mid[mid[\"set\"] == 0][[\"senddate\"]]\n",
    "        s1 = mid[mid[\"set\"] == 1][[\"senddate\"]]\n",
    "\n",
    "        # 切段\n",
    "        seg0 = split_segments(s0)\n",
    "        seg1 = split_segments(s1)\n",
    "\n",
    "        # segment 明細（每段一列）\n",
    "        set0_minutes_sum = 0.0\n",
    "        set1_minutes_sum = 0.0\n",
    "        set0_rows_sum = 0\n",
    "        set1_rows_sum = 0\n",
    "\n",
    "        for (st, ed, rows0) in seg0:\n",
    "            seg_min = (ed - st).total_seconds() / 60.0\n",
    "            set0_minutes_sum += seg_min\n",
    "            set0_rows_sum += rows0\n",
    "            seg_rows.append({\n",
    "                \"file\": base,\n",
    "                \"pair_id\": pair_id,\n",
    "                \"ad_left\": t0, \"ad_right\": t1,\n",
    "                \"window_minutes\": window_minutes,\n",
    "                \"set\": 0,\n",
    "                \"seg_start\": st, \"seg_end\": ed,\n",
    "                \"seg_minutes\": seg_min,\n",
    "                \"seg_rows\": rows0\n",
    "            })\n",
    "\n",
    "        for (st, ed, rows1) in seg1:\n",
    "            seg_min = (ed - st).total_seconds() / 60.0\n",
    "            set1_minutes_sum += seg_min\n",
    "            set1_rows_sum += rows1\n",
    "            seg_rows.append({\n",
    "                \"file\": base,\n",
    "                \"pair_id\": pair_id,\n",
    "                \"ad_left\": t0, \"ad_right\": t1,\n",
    "                \"window_minutes\": window_minutes,\n",
    "                \"set\": 1,\n",
    "                \"seg_start\": st, \"seg_end\": ed,\n",
    "                \"seg_minutes\": seg_min,\n",
    "                \"seg_rows\": rows1\n",
    "            })\n",
    "\n",
    "        # pair 彙總\n",
    "        pair_rows.append({\n",
    "            \"file\": base, \"pair_id\": pair_id,\n",
    "            \"ad_left\": t0, \"ad_right\": t1, \"window_minutes\": window_minutes,\n",
    "            \"set0_rows\": int(set0_rows_sum), \"set0_minutes\": float(set0_minutes_sum),\n",
    "            \"set1_rows\": int(set1_rows_sum), \"set1_minutes\": float(set1_minutes_sum)\n",
    "        })\n",
    "\n",
    "# ------------------ 輸出與印出 ------------------\n",
    "if len(seg_rows) == 0 and len(pair_rows) == 0:\n",
    "    print(\"[Result] No qualified pairs/segments found.\")\n",
    "else:\n",
    "    seg_df  = pd.DataFrame(seg_rows).sort_values([\"file\", \"pair_id\", \"set\", \"seg_start\"])\n",
    "    pair_df = pd.DataFrame(pair_rows).sort_values([\"file\", \"pair_id\"])\n",
    "\n",
    "    # 儲存 CSV\n",
    "    seg_df.to_csv(SEG_OUT, index=False)\n",
    "    pair_df.to_csv(PAIR_OUT, index=False)\n",
    "\n",
    "    # 印出摘要\n",
    "    print(f\"\\n✅ Saved: {SEG_OUT}\")\n",
    "    print(f\"✅ Saved: {PAIR_OUT}\")\n",
    "    print(f\"\\n=== Summary ===\")\n",
    "    print(f\"- Files scanned         : {len(files)}\")\n",
    "    print(f\"- Pairs total           : {len(pair_df):,}\")\n",
    "    print(f\"- Segments total        : {len(seg_df):,}\")\n",
    "    if len(seg_df):\n",
    "        print(f\"- Segments by set value :\")\n",
    "        print(seg_df.groupby(\"set\").size().to_string())\n",
    "\n",
    "    # 直接在程式輸出「每個 segment」與「每對 pair」明細（可能很多列）\n",
    "    # 若列數過大，可自行改成 head(n)\n",
    "    print(\"\\n=== Segment-level details (each row is a continuous set segment) ===\")\n",
    "    with pd.option_context('display.max_rows', None, 'display.max_colwidth', None):\n",
    "        print(seg_df.to_string(index=False))\n",
    "\n",
    "    print(\"\\n=== Pair-level summary (each row is an adjacent ad pair) ===\")\n",
    "    with pd.option_context('display.max_rows', None, 'display.max_colwidth', None):\n",
    "        print(pair_df.to_string(index=False))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "id": "c1746aad-b5c3-453f-8921-e1a0db248b44",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Info] Found 32 files with valid segments (have set=0 & set=1 between ad_para events).\n",
      "✅ Saved visualization: /home/jovyan/RT08/0925/1002/adpara_set_pair_segments.png\n"
     ]
    }
   ],
   "source": [
    "\"\"\"我想看的圖表是 前後點各一個ad_para=1的資料 寫上時間\n",
    "兩點調參之間的set=1跟set=0的資料分別顯示起訖時間跟資料筆數\n",
    "只抽取符合以上條件的資料\n",
    "\n",
    "前後調參事件間的 set=0 / set=1 視覺化\n",
    "僅讀取 /home/jovyan/RT08/0925/bling_1004/，不修改任何檔案\n",
    "圖表輸出到 /home/jovyan/RT08/0925/1002/adpara_set_pairs.png\n",
    "\"\"\"\n",
    "\n",
    "import os, glob, random\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.dates as mdates\n",
    "\n",
    "DATA_DIR = \"/home/jovyan/RT08/0925/bling_1004\"\n",
    "OUT_DIR  = \"/home/jovyan/RT08/0925/1002\"\n",
    "os.makedirs(OUT_DIR, exist_ok=True)\n",
    "\n",
    "need_cols = [\"patno\", \"senddate\", \"ad_para\", \"set\"]\n",
    "\n",
    "def safe_read_csv(fp):\n",
    "    try:\n",
    "        df = pd.read_csv(fp, usecols=need_cols, low_memory=False)\n",
    "    except Exception:\n",
    "        return None\n",
    "    if not set(need_cols).issubset(df.columns):\n",
    "        return None\n",
    "    df[\"senddate\"] = pd.to_datetime(df[\"senddate\"], errors=\"coerce\")\n",
    "    df = df.dropna(subset=[\"senddate\", \"patno\"]).sort_values(\"senddate\")\n",
    "    df[\"ad_para\"] = df[\"ad_para\"].fillna(0).astype(int)\n",
    "    df[\"set\"] = df[\"set\"].fillna(2).astype(int)\n",
    "    return df\n",
    "\n",
    "# 收集候選病患\n",
    "file_paths = sorted(glob.glob(os.path.join(DATA_DIR, \"*.csv\")))\n",
    "eligible = []  # 儲存有完整前後段的病患\n",
    "\n",
    "for fp in file_paths:\n",
    "    df = safe_read_csv(fp)\n",
    "    if df is None or df.empty:\n",
    "        continue\n",
    "\n",
    "    patno = int(df[\"patno\"].iloc[0])\n",
    "    ad_idx = df.index[df[\"ad_para\"] == 1].tolist()\n",
    "    if len(ad_idx) < 2:\n",
    "        continue\n",
    "\n",
    "    # 找前後兩筆 ad_para=1 之間有 set=0 & set=1 的段\n",
    "    for i in range(len(ad_idx) - 1):\n",
    "        seg = df.iloc[ad_idx[i]:ad_idx[i+1]]\n",
    "        if ((seg[\"set\"] == 0).any() and (seg[\"set\"] == 1).any()):\n",
    "            eligible.append((fp, patno, seg))\n",
    "            break  # 每個檔案只取第一個符合的段\n",
    "\n",
    "print(f\"[Info] Found {len(eligible)} files with valid segments (have set=0 & set=1 between ad_para events).\")\n",
    "\n",
    "# 若少於3位病患，全部畫出；否則隨機抽3位\n",
    "random.seed(42)\n",
    "selected = random.sample(eligible, min(3, len(eligible)))\n",
    "\n",
    "# === 開始繪圖 ===\n",
    "fig_h = max(4, min(10, 2 * len(selected)))\n",
    "fig, ax = plt.subplots(figsize=(14, fig_h))\n",
    "\n",
    "COLOR0 = \"#1f77b4\"  # 藍色 (set=0)\n",
    "COLOR1 = \"#d62728\"  # 紅色 (set=1)\n",
    "COLORP = \"#444444\"  # 深灰 (ad_para)\n",
    "\n",
    "yticks, ylabels = [], []\n",
    "\n",
    "for i, (fp, patno, seg) in enumerate(selected):\n",
    "    y = i\n",
    "    fname = os.path.basename(fp)\n",
    "    yticks.append(y)\n",
    "    ylabels.append(f\"{patno}\")\n",
    "\n",
    "    # 找該段中的 ad_para=1 位置\n",
    "    ad_times = seg.loc[seg[\"ad_para\"] == 1, \"senddate\"].tolist()\n",
    "    if len(ad_times) >= 2:\n",
    "        # 只取前後兩筆\n",
    "        t_start, t_end = ad_times[0], ad_times[1]\n",
    "        ax.scatter([t_start, t_end], [y, y], s=35, color=COLORP, zorder=3)\n",
    "        ax.text(t_start, y+0.1, t_start.strftime(\"%m-%d %H:%M\"), color=COLORP, fontsize=8, ha=\"right\")\n",
    "        ax.text(t_end, y+0.1, t_end.strftime(\"%m-%d %H:%M\"), color=COLORP, fontsize=8, ha=\"left\")\n",
    "\n",
    "    # set=0 段\n",
    "    set0 = seg[seg[\"set\"] == 0]\n",
    "    if not set0.empty:\n",
    "        s0_start, s0_end = set0[\"senddate\"].min(), set0[\"senddate\"].max()\n",
    "        ax.plot([s0_start, s0_end], [y, y], lw=8, color=COLOR0, alpha=0.8)\n",
    "        ax.text(s0_end, y+0.15, f\"set=0 ({len(set0)} pts)\\n{(s0_end - s0_start)}\", color=COLOR0, fontsize=8, ha=\"left\")\n",
    "\n",
    "    # set=1 段\n",
    "    set1 = seg[seg[\"set\"] == 1]\n",
    "    if not set1.empty:\n",
    "        s1_start, s1_end = set1[\"senddate\"].min(), set1[\"senddate\"].max()\n",
    "        ax.plot([s1_start, s1_end], [y, y], lw=8, color=COLOR1, alpha=0.8)\n",
    "        ax.text(s1_end, y-0.3, f\"set=1 ({len(set1)} pts)\\n{(s1_end - s1_start)}\", color=COLOR1, fontsize=8, ha=\"left\")\n",
    "\n",
    "    # 統計可用視窗數\n",
    "    total_sets = int((seg[\"set\"].isin([0,1])).sum())\n",
    "    ax.text(seg[\"senddate\"].max() + pd.Timedelta(minutes=10),\n",
    "            y, f\"{total_sets} usable pts\", color=\"black\", fontsize=9, va=\"center\", ha=\"left\")\n",
    "\n",
    "# 標軸與外觀\n",
    "ax.set_title(\"Paired Adjustment Events and SET Segments (Only Valid Intervals)\", fontsize=13)\n",
    "ax.set_xlabel(\"Time\")\n",
    "ax.set_yticks(yticks)\n",
    "ax.set_yticklabels(ylabels)\n",
    "ax.grid(True, axis=\"x\", linestyle=\"--\", alpha=0.5)\n",
    "ax.xaxis.set_major_locator(mdates.AutoDateLocator())\n",
    "ax.xaxis.set_major_formatter(mdates.ConciseDateFormatter(ax.xaxis.get_major_locator()))\n",
    "\n",
    "plt.tight_layout()\n",
    "out_file = os.path.join(OUT_DIR, \"adpara_set_pair_segments.png\")\n",
    "fig.savefig(out_file, dpi=150)\n",
    "plt.close(fig)\n",
    "\n",
    "print(f\"✅ Saved visualization: {out_file}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "id": "fc5da1b3-8ef4-409d-921a-a6d76ef4ac88",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "🎯 Selected 5 files for individual visualization:\n",
      " PatNo_ID_1570642083.csv\n",
      "PatNo_ID_1594471407.csv\n",
      "PatNo_ID_1574270349.csv\n",
      "PatNo_ID_1586897008.csv\n",
      "PatNo_ID_1594479330.csv\n",
      "✅ Saved: /home/jovyan/RT08/0925/1002/adpara_set_pair_PatNo_ID_1570642083.png\n",
      "✅ Saved: /home/jovyan/RT08/0925/1002/adpara_set_pair_PatNo_ID_1594471407.png\n",
      "✅ Saved: /home/jovyan/RT08/0925/1002/adpara_set_pair_PatNo_ID_1574270349.png\n",
      "✅ Saved: /home/jovyan/RT08/0925/1002/adpara_set_pair_PatNo_ID_1586897008.png\n",
      "✅ Saved: /home/jovyan/RT08/0925/1002/adpara_set_pair_PatNo_ID_1594479330.png\n"
     ]
    }
   ],
   "source": [
    "\"\"\"先只找時間最短但包含正負樣本的來用圖表\n",
    "\n",
    "For each of the 5 closest files containing valid adjustment pairs (ad_para=1 events with both set=0 and set=1 in between),\n",
    "generate one separate timeline figure per file.\n",
    "\n",
    "輸入：/home/jovyan/RT08/0925/bling_1004\n",
    "輸出圖檔：/home/jovyan/RT08/0925/1002/adpara_set_pair_<file>.png\n",
    "不修改原始檔案\n",
    "\"\"\"\n",
    "\n",
    "import os, glob\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.dates as mdates\n",
    "\n",
    "DATA_DIR = \"/home/jovyan/RT08/0925/bling_1004\"\n",
    "OUT_DIR  = \"/home/jovyan/RT08/0925/1002\"\n",
    "os.makedirs(OUT_DIR, exist_ok=True)\n",
    "\n",
    "need_cols = [\"patno\", \"senddate\", \"ad_para\", \"set\"]\n",
    "\n",
    "def safe_read_csv(fp):\n",
    "    \"\"\"安全讀取檔案，只取必要欄位。\"\"\"\n",
    "    try:\n",
    "        df = pd.read_csv(fp, usecols=need_cols, low_memory=False)\n",
    "    except Exception:\n",
    "        return None\n",
    "    if not set(need_cols).issubset(df.columns):\n",
    "        return None\n",
    "    df[\"senddate\"] = pd.to_datetime(df[\"senddate\"], errors=\"coerce\")\n",
    "    df = df.dropna(subset=[\"senddate\", \"patno\"]).sort_values(\"senddate\")\n",
    "    df[\"ad_para\"] = pd.to_numeric(df[\"ad_para\"], errors=\"coerce\").fillna(0).astype(int)\n",
    "    df[\"set\"] = pd.to_numeric(df[\"set\"], errors=\"coerce\").fillna(2).astype(int)\n",
    "    return df\n",
    "\n",
    "AD_EVENT_MIN_GAP_MIN = 10  # 容忍合併相近調參（分鐘）\n",
    "\n",
    "def merged_ad_events(df):\n",
    "    \"\"\"將相鄰 <=10 分鐘的 ad_para=1 視為同一事件。\"\"\"\n",
    "    d = df.loc[df[\"ad_para\"] == 1, [\"senddate\"]].sort_values(\"senddate\").copy()\n",
    "    if d.empty:\n",
    "        return pd.Series([], dtype=\"datetime64[ns]\")\n",
    "    d[\"gap_min\"] = d[\"senddate\"].diff().dt.total_seconds().div(60)\n",
    "    is_new = d[\"gap_min\"].isna() | (d[\"gap_min\"] > AD_EVENT_MIN_GAP_MIN)\n",
    "    return d.loc[is_new, \"senddate\"].reset_index(drop=True)\n",
    "\n",
    "pairs = []\n",
    "files = sorted(glob.glob(os.path.join(DATA_DIR, \"*.csv\")))\n",
    "for fp in files:\n",
    "    df = safe_read_csv(fp)\n",
    "    if df is None or df.empty:\n",
    "        continue\n",
    "    base = os.path.basename(fp)\n",
    "    for pid, g in df.groupby(\"patno\", dropna=True):\n",
    "        g = g.sort_values(\"senddate\")\n",
    "        events = merged_ad_events(g)\n",
    "        if len(events) < 2:\n",
    "            continue\n",
    "        for i in range(len(events) - 1):\n",
    "            t0, t1 = events.iloc[i], events.iloc[i+1]\n",
    "            mid = g[(g[\"senddate\"] > t0) & (g[\"senddate\"] < t1)]\n",
    "            if mid.empty: continue\n",
    "            if not ((mid[\"set\"] == 0).any() and (mid[\"set\"] == 1).any()):\n",
    "                continue\n",
    "            minutes = (t1 - t0).total_seconds() / 60.0\n",
    "            set0_rows = int((mid[\"set\"] == 0).sum())\n",
    "            set1_rows = int((mid[\"set\"] == 1).sum())\n",
    "            pairs.append({\n",
    "                \"file\": base,\n",
    "                \"patno\": int(pid),\n",
    "                \"t0\": t0, \"t1\": t1,\n",
    "                \"minutes\": minutes,\n",
    "                \"set0_rows\": set0_rows, \"set1_rows\": set1_rows\n",
    "            })\n",
    "\n",
    "if len(pairs) == 0:\n",
    "    print(\"[Result] ❌ No valid pairs found with both set=0 and set=1 between ad_para=1 events.\")\n",
    "else:\n",
    "    pairs_df = pd.DataFrame(pairs).sort_values(\"minutes\").reset_index(drop=True)\n",
    "    top5_files = pairs_df[\"file\"].unique()[:5]\n",
    "    print(f\"🎯 Selected {len(top5_files)} files for individual visualization:\\n\", \"\\n\".join(top5_files))\n",
    "\n",
    "    for file_name in top5_files:\n",
    "        df = safe_read_csv(os.path.join(DATA_DIR, file_name))\n",
    "        if df is None: continue\n",
    "        file_pairs = pairs_df[pairs_df[\"file\"] == file_name].head(1)  # 取最短一段\n",
    "        if file_pairs.empty: continue\n",
    "\n",
    "        row = file_pairs.iloc[0]\n",
    "        pid, t0, t1 = row[\"patno\"], row[\"t0\"], row[\"t1\"]\n",
    "        g = df[df[\"patno\"] == pid].sort_values(\"senddate\")\n",
    "        mid = g[(g[\"senddate\"] >= t0) & (g[\"senddate\"] <= t1)]\n",
    "\n",
    "        # 基本繪圖設定\n",
    "        fig, ax = plt.subplots(figsize=(12, 2.5))\n",
    "        y = 0\n",
    "\n",
    "        # set=0\n",
    "        seg0 = mid[mid[\"set\"] == 0]\n",
    "        if not seg0.empty:\n",
    "            s0_start, s0_end = seg0[\"senddate\"].min(), seg0[\"senddate\"].max()\n",
    "            ax.plot([s0_start, s0_end], [y, y], lw=10, alpha=0.8)\n",
    "            ax.text(s0_start, y + 0.2, f\"set=0: {len(seg0)} pts\", fontsize=9, ha=\"left\", va=\"bottom\")\n",
    "            ax.text(s0_end, y + 0.2, f\"{s0_start.strftime('%m-%d %H:%M')} → {s0_end.strftime('%m-%d %H:%M')}\",\n",
    "                    fontsize=8, ha=\"right\", va=\"bottom\")\n",
    "\n",
    "        # set=1\n",
    "        seg1 = mid[mid[\"set\"] == 1]\n",
    "        if not seg1.empty:\n",
    "            s1_start, s1_end = seg1[\"senddate\"].min(), seg1[\"senddate\"].max()\n",
    "            ax.plot([s1_start, s1_end], [y, y], lw=4, alpha=0.9)\n",
    "            ax.text(s1_start, y - 0.3, f\"set=1: {len(seg1)} pts\", fontsize=9, ha=\"left\", va=\"top\")\n",
    "            ax.text(s1_end, y - 0.3, f\"{s1_start.strftime('%m-%d %H:%M')} → {s1_end.strftime('%m-%d %H:%M')}\",\n",
    "                    fontsize=8, ha=\"right\", va=\"top\")\n",
    "\n",
    "        # ad_para=1 的兩端\n",
    "        ax.scatter([t0, t1], [y, y], s=35, zorder=3)\n",
    "        ax.text(t0, y + 0.35, t0.strftime(\"%m-%d %H:%M\"), fontsize=8, ha=\"right\", va=\"bottom\")\n",
    "        ax.text(t1, y + 0.35, t1.strftime(\"%m-%d %H:%M\"), fontsize=8, ha=\"left\", va=\"bottom\")\n",
    "\n",
    "        # 統計資訊\n",
    "        total_pairs = len(pairs_df[pairs_df[\"file\"] == file_name])\n",
    "        ax.text(t1 + pd.Timedelta(minutes=2), y, f\"Total usable pairs: {total_pairs}\",\n",
    "                fontsize=9, va=\"center\", ha=\"left\")\n",
    "\n",
    "        # 軸設定\n",
    "        ax.set_xlim(t0 - pd.Timedelta(minutes=10), t1 + pd.Timedelta(minutes=10))\n",
    "        ax.set_title(f\"Timeline for {file_name} (PatNo {pid})\")\n",
    "        ax.set_xlabel(\"Time\")\n",
    "        ax.set_yticks([])\n",
    "        ax.xaxis.set_major_formatter(mdates.ConciseDateFormatter(mdates.AutoDateLocator()))\n",
    "        ax.grid(True, axis=\"x\", linestyle=\"--\", alpha=0.5)\n",
    "\n",
    "        plt.tight_layout()\n",
    "        out_file = os.path.join(OUT_DIR, f\"adpara_set_pair_{file_name.replace('.csv','')}.png\")\n",
    "        fig.savefig(out_file, dpi=150)\n",
    "        plt.close(fig)\n",
    "        print(f\"✅ Saved: {out_file}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7563775d-3db9-4e80-81d1-51935149296d",
   "metadata": {},
   "outputs": [],
   "source": [
    "輸出的檔案在adpara_set_pair_PatNo_ID_1594479330五張圖"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "id": "0fe34169-05b5-486b-b1c5-e8841876f07a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                              file           error\n",
      "0  /home/jovyan/RT08/0925/bling_useable/089271.csv  缺少必要欄位：ad_para\n",
      "1  /home/jovyan/RT08/0925/bling_useable/095323.csv  缺少必要欄位：ad_para\n",
      "2  /home/jovyan/RT08/0925/bling_useable/095707.csv  缺少必要欄位：ad_para\n",
      "3  /home/jovyan/RT08/0925/bling_useable/114309.csv  缺少必要欄位：ad_para\n",
      "4  /home/jovyan/RT08/0925/bling_useable/230933.csv  缺少必要欄位：ad_para\n"
     ]
    }
   ],
   "source": [
    "\"\"\"檔案需優化以下\n",
    "ad_para=1的兩個點要增加時日期間\n",
    "set=1跟set=0要時間長度 資料筆數\n",
    "\"\"\"\n",
    "# -*- coding: utf-8 -*-\n",
    "\"\"\"\n",
    "強化版時間軸圖（單檔 / 批次）：\n",
    "- 兩個 ad_para==1 的事件點：標註 yyyy-mm-dd HH:MM 與事件間隔（分鐘與 HH:MM）\n",
    "- set=0（前50%）與 set=1（後20%，上限2小時）：\n",
    "  - 以時間戳區間定義（左閉右開）\n",
    "  - 計算並標示 時長（分鐘）與 rows（筆數）\n",
    "- 事件去抖動：預設 cooldown_min=5\n",
    "- 僅使用 NaN_check==1 的資料\n",
    "- 可自動偵測時間欄位：優先使用 SendDate_parsed，其次 SendDate\n",
    "\"\"\"\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0990bdff-9558-4374-9f9d-0277a37ccaff",
   "metadata": {},
   "outputs": [],
   "source": [
    "我要看一張圖阿 怎麼都看不了 要做簡報說明圖有哪幾種\n",
    "也要統計有幾筆\n",
    "真是的"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a829b507-c969-4f0f-9962-7bfd5be58343",
   "metadata": {},
   "outputs": [],
   "source": [
    "資料來源：/home/jovyan/RT08/0925/bling_1004\n",
    "segment 定義：相鄰有效資料間隔 >10 分鐘即切段\n",
    "\n",
    "圖表顏色：\n",
    "set=0 → 藍色\n",
    "set=1 → 紅色\n",
    "ad_para=1 → 黑點\n",
    "待執行項目：\n",
    "針對每組 ad_para=1 前後畫獨立時間線圖（共 5 張）\n",
    "每張顯示 set=0、set=1 的起訖時間與筆數\n",
    "右側顯示該病患共有幾組有效 pair\n",
    "製作 segment 詳細清單（檔名、起訖時間、長度、筆數）\n",
    "檢查 ad_para=1 是否重複、set 區段是否重疊\n",
    "所有輸出存於 /home/jovyan/RT08/0925/1002/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ce9c781b-097d-4666-80b5-71d3f669b65e",
   "metadata": {},
   "outputs": [],
   "source": [
    "不要看圖了 直接從文字理解\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "id": "4cb43bc5-f7d2-4fd4-ad34-7235cbf6601d",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true,
     "source_hidden": true
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ERROR] 089271.csv 失敗：缺少必要欄位：set_1010\n",
      "[ERROR] 095323.csv 失敗：缺少必要欄位：set_1010\n",
      "[ERROR] 095707.csv 失敗：缺少必要欄位：set_1010\n",
      "[ERROR] 114309.csv 失敗：缺少必要欄位：set_1010\n",
      "[ERROR] 230933.csv 失敗：缺少必要欄位：set_1010\n",
      "[ERROR] 4216007.csv 失敗：缺少必要欄位：set_1010\n",
      "[ERROR] 7108162.csv 失敗：缺少必要欄位：set_1010\n",
      "[ERROR] 7408338.csv 失敗：缺少必要欄位：set_1010\n",
      "[ERROR] 7657698.csv 失敗：缺少必要欄位：set_1010\n",
      "[ERROR] 7721164.csv 失敗：缺少必要欄位：set_1010\n",
      "[ERROR] PatNo_ID_1560013303.csv 失敗：缺少必要欄位：set_1010\n",
      "[ERROR] PatNo_ID_1562733396.csv 失敗：缺少必要欄位：set_1010\n",
      "[ERROR] PatNo_ID_1563587183.csv 失敗：缺少必要欄位：set_1010\n",
      "[ERROR] PatNo_ID_1564148644.csv 失敗：缺少必要欄位：set_1010\n",
      "[ERROR] PatNo_ID_1565148312.csv 失敗：缺少必要欄位：set_1010\n",
      "[ERROR] PatNo_ID_1565378038.csv 失敗：缺少必要欄位：set_1010\n",
      "[ERROR] PatNo_ID_1566123680.csv 失敗：缺少必要欄位：set_1010\n",
      "[ERROR] PatNo_ID_1566252197.csv 失敗：缺少必要欄位：set_1010\n",
      "[ERROR] PatNo_ID_1566279967.csv 失敗：缺少必要欄位：set_1010\n",
      "[ERROR] PatNo_ID_1566671274.csv 失敗：缺少必要欄位：set_1010\n",
      "[ERROR] PatNo_ID_1566911879.csv 失敗：缺少必要欄位：set_1010\n",
      "[ERROR] PatNo_ID_1567747650.csv 失敗：缺少必要欄位：set_1010\n",
      "[ERROR] PatNo_ID_1567804800.csv 失敗：缺少必要欄位：set_1010\n",
      "[ERROR] PatNo_ID_1567832735.csv 失敗：缺少必要欄位：set_1010\n",
      "[ERROR] PatNo_ID_1568039398.csv 失敗：缺少必要欄位：set_1010\n",
      "[ERROR] PatNo_ID_1568574099.csv 失敗：缺少必要欄位：set_1010\n",
      "[ERROR] PatNo_ID_1568813269.csv 失敗：缺少必要欄位：set_1010\n",
      "[ERROR] PatNo_ID_1568952422.csv 失敗：缺少必要欄位：set_1010\n",
      "[ERROR] PatNo_ID_1569083701.csv 失敗：缺少必要欄位：set_1010\n",
      "[ERROR] PatNo_ID_1569944983.csv 失敗：缺少必要欄位：set_1010\n",
      "[ERROR] PatNo_ID_1570089466.csv 失敗：缺少必要欄位：set_1010\n",
      "[ERROR] PatNo_ID_1570242703.csv 失敗：缺少必要欄位：set_1010\n",
      "[ERROR] PatNo_ID_1570273244.csv 失敗：缺少必要欄位：set_1010\n",
      "[ERROR] PatNo_ID_1570642083.csv 失敗：缺少必要欄位：set_1010\n",
      "[ERROR] PatNo_ID_1571945701.csv 失敗：缺少必要欄位：set_1010\n",
      "[ERROR] PatNo_ID_1572481361.csv 失敗：缺少必要欄位：set_1010\n",
      "[ERROR] PatNo_ID_1572562839.csv 失敗：缺少必要欄位：set_1010\n",
      "[ERROR] PatNo_ID_1572831765.csv 失敗：缺少必要欄位：set_1010\n",
      "[ERROR] PatNo_ID_1572976822.csv 失敗：缺少必要欄位：set_1010\n",
      "[ERROR] PatNo_ID_1573063188.csv 失敗：缺少必要欄位：set_1010\n",
      "[ERROR] PatNo_ID_1573249295.csv 失敗：缺少必要欄位：set_1010\n",
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      "[ERROR] PatNo_ID_1574987447.csv 失敗：缺少必要欄位：set_1010\n",
      "[ERROR] PatNo_ID_1575060177.csv 失敗：缺少必要欄位：set_1010\n",
      "[ERROR] PatNo_ID_1575256902.csv 失敗：缺少必要欄位：set_1010\n",
      "[ERROR] PatNo_ID_1575445051.csv 失敗：缺少必要欄位：set_1010\n",
      "[ERROR] PatNo_ID_1575502382.csv 失敗：缺少必要欄位：set_1010\n",
      "[ERROR] PatNo_ID_1575975485.csv 失敗：缺少必要欄位：set_1010\n",
      "[ERROR] PatNo_ID_1576115572.csv 失敗：缺少必要欄位：set_1010\n",
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      "[ERROR] PatNo_ID_1576301569.csv 失敗：缺少必要欄位：set_1010\n",
      "[ERROR] PatNo_ID_1576964560.csv 失敗：缺少必要欄位：set_1010\n",
      "[ERROR] PatNo_ID_1577042911.csv 失敗：缺少必要欄位：set_1010\n",
      "[ERROR] PatNo_ID_1577487284.csv 失敗：缺少必要欄位：set_1010\n",
      "[ERROR] PatNo_ID_1578784257.csv 失敗：缺少必要欄位：set_1010\n",
      "[ERROR] PatNo_ID_1579198603.csv 失敗：缺少必要欄位：set_1010\n",
      "[ERROR] PatNo_ID_1579498177.csv 失敗：缺少必要欄位：set_1010\n",
      "[ERROR] PatNo_ID_1580062580.csv 失敗：缺少必要欄位：set_1010\n",
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      "[ERROR] PatNo_ID_1580244614.csv 失敗：缺少必要欄位：set_1010\n",
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      "[ERROR] PatNo_ID_1581003248.csv 失敗：缺少必要欄位：set_1010\n",
      "[ERROR] PatNo_ID_1581019504.csv 失敗：缺少必要欄位：set_1010\n",
      "[ERROR] PatNo_ID_1581633231.csv 失敗：缺少必要欄位：set_1010\n",
      "[ERROR] PatNo_ID_1581692973.csv 失敗：缺少必要欄位：set_1010\n",
      "[ERROR] PatNo_ID_1582452511.csv 失敗：缺少必要欄位：set_1010\n",
      "[ERROR] PatNo_ID_1582635996.csv 失敗：缺少必要欄位：set_1010\n",
      "[ERROR] PatNo_ID_1582849900.csv 失敗：缺少必要欄位：set_1010\n",
      "[ERROR] PatNo_ID_1582937076.csv 失敗：缺少必要欄位：set_1010\n",
      "[ERROR] PatNo_ID_1584158973.csv 失敗：缺少必要欄位：set_1010\n",
      "[ERROR] PatNo_ID_1584397376.csv 失敗：缺少必要欄位：set_1010\n",
      "[ERROR] PatNo_ID_1586172659.csv 失敗：缺少必要欄位：set_1010\n",
      "[ERROR] PatNo_ID_1586696634.csv 失敗：缺少必要欄位：set_1010\n",
      "[ERROR] PatNo_ID_1586897008.csv 失敗：缺少必要欄位：set_1010\n",
      "[ERROR] PatNo_ID_1587490083.csv 失敗：缺少必要欄位：set_1010\n",
      "[ERROR] PatNo_ID_1588632604.csv 失敗：缺少必要欄位：set_1010\n",
      "[ERROR] PatNo_ID_1588673465.csv 失敗：缺少必要欄位：set_1010\n",
      "[ERROR] PatNo_ID_1588794796.csv 失敗：缺少必要欄位：set_1010\n",
      "[ERROR] PatNo_ID_1588957997.csv 失敗：缺少必要欄位：set_1010\n",
      "[ERROR] PatNo_ID_1589018086.csv 失敗：缺少必要欄位：set_1010\n",
      "[ERROR] PatNo_ID_1589034524.csv 失敗：缺少必要欄位：set_1010\n",
      "[ERROR] PatNo_ID_1589324603.csv 失敗：缺少必要欄位：set_1010\n",
      "[ERROR] PatNo_ID_1589918099.csv 失敗：缺少必要欄位：set_1010\n",
      "[ERROR] PatNo_ID_1590136310.csv 失敗：缺少必要欄位：set_1010\n",
      "[ERROR] PatNo_ID_1590616537.csv 失敗：缺少必要欄位：set_1010\n",
      "[ERROR] PatNo_ID_1590854576.csv 失敗：缺少必要欄位：set_1010\n",
      "[ERROR] PatNo_ID_1591609798.csv 失敗：缺少必要欄位：set_1010\n",
      "[ERROR] PatNo_ID_1592044724.csv 失敗：缺少必要欄位：set_1010\n",
      "[ERROR] PatNo_ID_1592560504.csv 失敗：缺少必要欄位：set_1010\n",
      "[ERROR] PatNo_ID_1593087886.csv 失敗：缺少必要欄位：set_1010\n",
      "[ERROR] PatNo_ID_1593416100.csv 失敗：缺少必要欄位：set_1010\n",
      "[ERROR] PatNo_ID_1593472048.csv 失敗：缺少必要欄位：set_1010\n",
      "[ERROR] PatNo_ID_1593593586.csv 失敗：缺少必要欄位：set_1010\n",
      "[ERROR] PatNo_ID_1593720818.csv 失敗：缺少必要欄位：set_1010\n",
      "[ERROR] PatNo_ID_1593838524.csv 失敗：缺少必要欄位：set_1010\n",
      "[ERROR] PatNo_ID_1594173718.csv 失敗：缺少必要欄位：set_1010\n",
      "[ERROR] PatNo_ID_1594294180.csv 失敗：缺少必要欄位：set_1010\n",
      "[ERROR] PatNo_ID_1594305136.csv 失敗：缺少必要欄位：set_1010\n",
      "[ERROR] PatNo_ID_1594309746.csv 失敗：缺少必要欄位：set_1010\n",
      "[ERROR] PatNo_ID_1594319286.csv 失敗：缺少必要欄位：set_1010\n",
      "[ERROR] PatNo_ID_1594320763.csv 失敗：缺少必要欄位：set_1010\n",
      "[ERROR] PatNo_ID_1594322594.csv 失敗：缺少必要欄位：set_1010\n",
      "[ERROR] PatNo_ID_1594335109.csv 失敗：缺少必要欄位：set_1010\n",
      "[ERROR] PatNo_ID_1594423683.csv 失敗：缺少必要欄位：set_1010\n",
      "[ERROR] PatNo_ID_1594437309.csv 失敗：缺少必要欄位：set_1010\n",
      "[ERROR] PatNo_ID_1594439781.csv 失敗：缺少必要欄位：set_1010\n",
      "[ERROR] PatNo_ID_1594441887.csv 失敗：缺少必要欄位：set_1010\n",
      "[ERROR] PatNo_ID_1594448501.csv 失敗：缺少必要欄位：set_1010\n",
      "[ERROR] PatNo_ID_1594455578.csv 失敗：缺少必要欄位：set_1010\n",
      "[ERROR] PatNo_ID_1594464829.csv 失敗：缺少必要欄位：set_1010\n",
      "[ERROR] PatNo_ID_1594467719.csv 失敗：缺少必要欄位：set_1010\n",
      "[ERROR] PatNo_ID_1594471407.csv 失敗：缺少必要欄位：set_1010\n",
      "[ERROR] PatNo_ID_1594479330.csv 失敗：缺少必要欄位：set_1010\n",
      "[ERROR] PatNo_ID_1594511911.csv 失敗：缺少必要欄位：set_1010\n",
      "[ERROR] PatNo_ID_1594511914.csv 失敗：缺少必要欄位：set_1010\n",
      "[ERROR] PatNo_ID_1594528842.csv 失敗：缺少必要欄位：set_1010\n",
      "[ERROR] PatNo_ID_1594533379.csv 失敗：缺少必要欄位：set_1010\n",
      "[INFO] 無可用 segment 結果。\n",
      "[OK] 未發現 'ad_para=1 後第一筆非 set=1' 的案例。\n"
     ]
    }
   ],
   "source": [
    "\"\"\"segment_lengths.csv：逐檔、逐 segment 的起迄時間、分鐘長度、筆數，並標示是 set=1（正樣本）或 set=0（負樣本）。\n",
    "anomalies_after_adpara.csv：列出「ad_para=1 事件發生後，下一筆卻不是 set=1」的異常紀錄（含時間差 gap_sec）\n",
    "\n",
    "Segment 長度統計 + ad_para 事件後稽核（完整版）\n",
    "\n",
    "功能一：計算每個檔案內，set 連續區段（segment）的起迄時間、長度(分鐘)與筆數\n",
    "功能二：稽核 ad_para=1 事件後，下一筆是否非 set=1（異常案例列出）\n",
    "\n",
    "輸出（皆寫入 /home/jovyan/RT08/0925/1002/）：\n",
    "  1) segment_lengths.csv\n",
    "     欄位：file, patno, segment_id, set, start_time, end_time, duration_min, rows\n",
    "  2) anomalies_after_adpara.csv\n",
    "     欄位：file, patno, event_time, next_time, next_set, gap_sec, note\n",
    "  3) segment_summary_per_file.csv\n",
    "     每檔、依 set 分組的 segment 聚合總表（count/sum/mean/median）\n",
    "\n",
    "使用前請確認：\n",
    "  - 資料目錄 /home/jovyan/RT08/0925/bling_1004 底下為 csv 檔\n",
    "  - 必備欄位：SendDate（或可調整 TIME_COL）、set（0/1）\n",
    "  - 若有 ad_para 欄位，會做事件後一筆的稽核；沒有則跳過稽核\n",
    "\"\"\"\n",
    "\n",
    "import os\n",
    "import glob\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "# ========= 路徑與欄位設定 =========\n",
    "DATA_DIR = \"/home/jovyan/RT08/0925/bling_1004\"\n",
    "OUT_DIR  = \"/home/jovyan/RT08/0925/1002\"     # 統一輸出目錄\n",
    "\n",
    "TIME_COL = \"senddate\"     # 時間欄位\n",
    "PAT_COL  = \"patno\"        # 病人 ID 欄位（若無，程式會補 NA）\n",
    "SET_COL  = \"set_1010\"          # 正/負樣本標籤欄位（0/1）\n",
    "ADP_COL  = \"ad_para\"      # 調參事件旗標（1 表示該點為事件；若無此欄位則略過稽核）\n",
    "\n",
    "OUT_SEG   = os.path.join(OUT_DIR, \"segment_lengths.csv\")\n",
    "OUT_ANOM  = os.path.join(OUT_DIR, \"anomalies_after_adpara.csv\")\n",
    "OUT_SUMMF = os.path.join(OUT_DIR, \"segment_summary_per_file.csv\")\n",
    "\n",
    "# ========= 工具函式 =========\n",
    "def ensure_outdir(path: str):\n",
    "    os.makedirs(path, exist_ok=True)\n",
    "\n",
    "def read_csv_safely(path: str) -> pd.DataFrame:\n",
    "    # 嘗試常見編碼；最後不指定編碼\n",
    "    for enc in (\"utf-8\", \"cp950\", \"big5\"):\n",
    "        try:\n",
    "            return pd.read_csv(path, encoding=enc)\n",
    "        except Exception:\n",
    "            pass\n",
    "    return pd.read_csv(path)\n",
    "\n",
    "def to_datetime_safe(s: pd.Series) -> pd.Series:\n",
    "    if np.issubdtype(s.dtype, np.datetime64):\n",
    "        return s\n",
    "    return pd.to_datetime(s, errors=\"coerce\")\n",
    "\n",
    "def normalize_set_column(df: pd.DataFrame) -> pd.DataFrame:\n",
    "    # 將 set 欄位穩健轉為 {0,1}；若有其他值，一律視為 0（保守）\n",
    "    if SET_COL not in df.columns:\n",
    "        raise ValueError(f\"缺少必要欄位：{SET_COL}\")\n",
    "    s = df[SET_COL]\n",
    "    # 允許字串/浮點轉換\n",
    "    s = pd.to_numeric(s, errors=\"coerce\").fillna(0).astype(int)\n",
    "    s = s.clip(lower=0, upper=1)\n",
    "    df[SET_COL] = s\n",
    "    return df\n",
    "\n",
    "def compute_segments(df: pd.DataFrame, file: str):\n",
    "    \"\"\"\n",
    "    回傳：\n",
    "      seg_df: 每個 segment 的彙總表\n",
    "      df_sorted: 依時間排序且附上 segment_id 的明細表\n",
    "    \"\"\"\n",
    "    # 欄位檢查\n",
    "    if TIME_COL not in df.columns:\n",
    "        raise ValueError(f\"{file} 缺少必要欄位：{TIME_COL}\")\n",
    "    df = df.copy()\n",
    "\n",
    "    # 時間處理與排序\n",
    "    df[TIME_COL] = to_datetime_safe(df[TIME_COL])\n",
    "    df = df.dropna(subset=[TIME_COL]).sort_values(TIME_COL).reset_index(drop=True)\n",
    "\n",
    "    # 病人欄位若不存在就補 NA\n",
    "    if PAT_COL not in df.columns:\n",
    "        df[PAT_COL] = np.nan\n",
    "\n",
    "    # set 欄位正規化\n",
    "    df = normalize_set_column(df)\n",
    "\n",
    "    # 建立 segment_id：當 set 值改變就 +1；第一列為新 segment\n",
    "    df[\"set_change_flag\"] = (df[SET_COL] != df[SET_COL].shift(1)).astype(int)\n",
    "    df.loc[df.index == 0, \"set_change_flag\"] = 1\n",
    "    df[\"segment_id\"] = df[\"set_change_flag\"].cumsum()\n",
    "\n",
    "    # 彙總：起迄時間、筆數、時長\n",
    "    grp = df.groupby(\"segment_id\", as_index=False).agg(\n",
    "        set_val    = (SET_COL, \"first\"),\n",
    "        start_time = (TIME_COL, \"first\"),\n",
    "        end_time   = (TIME_COL, \"last\"),\n",
    "        rows       = (TIME_COL, \"size\"),\n",
    "        patno      = (PAT_COL, \"first\"),\n",
    "    )\n",
    "    grp[\"duration_min\"] = (grp[\"end_time\"] - grp[\"start_time\"]).dt.total_seconds() / 60.0\n",
    "    grp.insert(0, \"file\", os.path.basename(file))\n",
    "    grp.rename(columns={\"set_val\": \"set\"}, inplace=True)\n",
    "\n",
    "    # 欄位順序一致化\n",
    "    seg_df = grp[[\"file\",\"patno\",\"segment_id\",\"set\",\"start_time\",\"end_time\",\"duration_min\",\"rows\"]]\n",
    "    return seg_df, df\n",
    "\n",
    "def audit_after_adpara(df_sorted: pd.DataFrame, file: str) -> pd.DataFrame:\n",
    "    \"\"\"\n",
    "    稽核：ad_para=1 的事件點，其「下一筆」是否為 set=1。\n",
    "    若無 ADP_COL 則回傳空表。\n",
    "    \"\"\"\n",
    "    if ADP_COL not in df_sorted.columns:\n",
    "        return pd.DataFrame(columns=[\"file\",\"patno\",\"event_time\",\"next_time\",\"next_set\",\"gap_sec\",\"note\"])\n",
    "\n",
    "    records = []\n",
    "    # 僅檢查 ad_para==1 的索引\n",
    "    idxs = df_sorted.index[df_sorted[ADP_COL] == 1].tolist()\n",
    "    for i in idxs:\n",
    "        event_time = df_sorted.at[i, TIME_COL]\n",
    "        patno = df_sorted.at[i, PAT_COL] if PAT_COL in df_sorted.columns else np.nan\n",
    "\n",
    "        if i + 1 < len(df_sorted):\n",
    "            next_time = df_sorted.at[i+1, TIME_COL]\n",
    "            next_set  = df_sorted.at[i+1, SET_COL] if SET_COL in df_sorted.columns else np.nan\n",
    "            gap_sec   = (pd.to_datetime(next_time) - pd.to_datetime(event_time)).total_seconds()\n",
    "\n",
    "            if next_set != 1:\n",
    "                records.append({\n",
    "                    \"file\": os.path.basename(file),\n",
    "                    \"patno\": patno,\n",
    "                    \"event_time\": event_time,\n",
    "                    \"next_time\": next_time,\n",
    "                    \"next_set\": int(next_set) if pd.notna(next_set) else np.nan,\n",
    "                    \"gap_sec\": gap_sec,\n",
    "                    \"note\": \"next row after ad_para=1 is not set=1\"\n",
    "                })\n",
    "        else:\n",
    "            # 事件剛好是最後一列\n",
    "            records.append({\n",
    "                \"file\": os.path.basename(file),\n",
    "                \"patno\": patno,\n",
    "                \"event_time\": event_time,\n",
    "                \"next_time\": pd.NaT,\n",
    "                \"next_set\": np.nan,\n",
    "                \"gap_sec\": np.nan,\n",
    "                \"note\": \"ad_para=1 occurs at last row; no next record\"\n",
    "            })\n",
    "\n",
    "    return pd.DataFrame.from_records(records)\n",
    "\n",
    "def summarize_segments(seg_df: pd.DataFrame) -> pd.DataFrame:\n",
    "    \"\"\"\n",
    "    產出每檔、依 set 分組的 segment 聚合總表（count/sum/mean/median）\n",
    "    \"\"\"\n",
    "    if seg_df.empty:\n",
    "        return seg_df\n",
    "    # 為避免 FutureWarning，逐欄命名聚合\n",
    "    aggs = {\n",
    "        \"rows\": [\"count\", \"sum\", \"mean\", \"median\"],\n",
    "        \"duration_min\": [\"sum\", \"mean\", \"median\"]\n",
    "    }\n",
    "    summary = seg_df.groupby([\"file\",\"set\"], as_index=False).agg(aggs)\n",
    "    # 攤平欄位名稱\n",
    "    summary.columns = [\"_\".join([c for c in col if c]).strip(\"_\") for col in summary.columns.values]\n",
    "    # 友善命名\n",
    "    summary = summary.rename(columns={\n",
    "        \"file_\": \"file\",\n",
    "        \"set_\": \"set\",\n",
    "        \"rows_count\": \"segment_count\",\n",
    "        \"rows_sum\": \"rows_sum\",\n",
    "        \"rows_mean\": \"rows_mean\",\n",
    "        \"rows_median\": \"rows_median\",\n",
    "        \"duration_min_sum\": \"duration_min_sum\",\n",
    "        \"duration_min_mean\": \"duration_min_mean\",\n",
    "        \"duration_min_median\": \"duration_min_median\",\n",
    "    })\n",
    "    return summary[[\n",
    "        \"file\",\"set\",\n",
    "        \"segment_count\",\"rows_sum\",\"rows_mean\",\"rows_median\",\n",
    "        \"duration_min_sum\",\"duration_min_mean\",\"duration_min_median\"\n",
    "    ]]\n",
    "\n",
    "# ========= 主流程 =========\n",
    "def main():\n",
    "    pd.options.display.width = 160\n",
    "    pd.options.display.max_columns = 30\n",
    "\n",
    "    ensure_outdir(OUT_DIR)\n",
    "\n",
    "    files = sorted(glob.glob(os.path.join(DATA_DIR, \"*.csv\")))\n",
    "    if not files:\n",
    "        print(f\"[WARN] 目錄內沒有 CSV：{DATA_DIR}\")\n",
    "        return\n",
    "\n",
    "    all_seg = []\n",
    "    all_anom = []\n",
    "\n",
    "    for f in files:\n",
    "        try:\n",
    "            df = read_csv_safely(f)\n",
    "\n",
    "            # 計算 segments（同時正規化 set、時間排序）\n",
    "            seg_df, df_sorted = compute_segments(df, f)\n",
    "            all_seg.append(seg_df)\n",
    "\n",
    "            # 稽核 ad_para=1 後第一筆是否 set=1\n",
    "            anom_df = audit_after_adpara(df_sorted, f)\n",
    "            if not anom_df.empty:\n",
    "                all_anom.append(anom_df)\n",
    "\n",
    "        except Exception as e:\n",
    "            print(f\"[ERROR] {os.path.basename(f)} 失敗：{e}\")\n",
    "\n",
    "    # === 輸出 segment 長度明細 ===\n",
    "    if all_seg:\n",
    "        seg_all = pd.concat(all_seg, ignore_index=True)\n",
    "        seg_all.to_csv(OUT_SEG, index=False)\n",
    "        print(f\"[OK] 已輸出 segment 長度統計：{OUT_SEG}  （{len(seg_all)} 筆 segment）\")\n",
    "\n",
    "        # === 輸出每檔彙總 ===\n",
    "        summ = summarize_segments(seg_all)\n",
    "        summ.to_csv(OUT_SUMMF, index=False)\n",
    "        print(f\"[OK] 已輸出彙總表：{OUT_SUMMF}\")\n",
    "\n",
    "        # 同場快覽\n",
    "        print(\"\\n=== Segment 聚合總覽（每檔、依 set 分群）前 20 列 ===\")\n",
    "        print(summ.head(20))\n",
    "    else:\n",
    "        print(\"[INFO] 無可用 segment 結果。\")\n",
    "\n",
    "    # === 輸出異常清單 ===\n",
    "    if all_anom:\n",
    "        anom_all = pd.concat(all_anom, ignore_index=True)\n",
    "        anom_all.to_csv(OUT_ANOM, index=False)\n",
    "        print(f\"[OK] 已輸出 ad_para 事件後的異常清單：{OUT_ANOM}  （{len(anom_all)} 筆異常）\")\n",
    "\n",
    "        print(\"\\n=== 例示前 10 筆異常 ===\")\n",
    "        print(anom_all.head(10))\n",
    "    else:\n",
    "        print(\"[OK] 未發現 'ad_para=1 後第一筆非 set=1' 的案例。\")\n",
    "\n",
    "if __name__ == \"__main__\":\n",
    "    main()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8b86da87-4265-4907-bbfd-d77e8d0585fe",
   "metadata": {},
   "outputs": [],
   "source": [
    "共 136 筆異常：「ad_para=1 後下一筆不是 set=1」\n",
    "\n",
    "一類是事件剛好在檔尾（next_time=NaT；例：7408338.csv 等）→ 沒下一筆可標。\n",
    "大宗是 gap=60~61 秒但 next_set=0（例：089271.csv、230933.csv 等）→ 高機率是「事件後窗未展開」或被 cooldown/切段邏輯吃掉。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "id": "f3a5e768-86f0-4790-b76b-187317a5a0ec",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[OK] 已輸出異常清單：/home/jovyan/RT08/0925/1002/adpara_next_nancheck0.csv\n",
      "         file    patno          event_time           next_time  next_nancheck                                      note\n",
      "0  089271.csv  8927106 2021-12-19 13:10:00 2021-12-19 13:11:00            0.0  next row after ad_para=1 has NaN_check=0\n",
      "1  089271.csv  8927106 2021-12-20 21:22:02 2021-12-20 21:23:01            0.0  next row after ad_para=1 has NaN_check=0\n",
      "2  089271.csv  8927106 2021-12-22 11:22:00 2021-12-22 11:23:00            0.0  next row after ad_para=1 has NaN_check=0\n",
      "3  089271.csv  8927106 2021-12-22 11:24:00 2021-12-22 11:25:00            0.0  next row after ad_para=1 has NaN_check=0\n",
      "4  089271.csv  8927106 2021-12-24 03:32:04 2021-12-24 03:33:04            0.0  next row after ad_para=1 has NaN_check=0\n",
      "5  089271.csv  8927106 2021-12-25 06:39:04 2021-12-25 06:40:04            0.0  next row after ad_para=1 has NaN_check=0\n",
      "6  089271.csv  8927106 2021-12-25 13:40:02 2021-12-25 13:41:02            0.0  next row after ad_para=1 has NaN_check=0\n",
      "7  089271.csv  8927106 2021-12-25 14:45:02 2021-12-25 14:46:02            0.0  next row after ad_para=1 has NaN_check=0\n",
      "8  089271.csv  8927106 2021-12-26 05:51:00 2021-12-26 05:52:00            0.0  next row after ad_para=1 has NaN_check=0\n",
      "9  089271.csv  8927106 2021-12-27 19:19:00 2021-12-27 19:20:00            0.0  next row after ad_para=1 has NaN_check=0\n",
      "共 8929 筆異常\n"
     ]
    }
   ],
   "source": [
    "\"\"\"ad_para=1 之後下一筆不是 set=1的原因是不是因為該筆資料NaN_check=0\n",
    "給我可以另外執行的程式碼檢查並列出所有「ad_para=1 後一筆 NaN_check=0」的異常清單\n",
    "\n",
    "功能：找出所有「ad_para=1 之後下一筆 NaN_check=0」的異常案例\n",
    "輸出檔：/home/jovyan/RT08/0925/1002/adpara_next_nancheck0.csv\n",
    "\"\"\"\n",
    "\n",
    "import os\n",
    "import glob\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "# === 路徑設定 ===\n",
    "DATA_DIR = \"/home/jovyan/RT08/0925/bling_1004\"\n",
    "OUT_DIR  = \"/home/jovyan/RT08/0925/1002\"\n",
    "os.makedirs(OUT_DIR, exist_ok=True)\n",
    "OUT_FILE = os.path.join(OUT_DIR, \"adpara_next_nancheck0.csv\")\n",
    "\n",
    "# === 欄位名稱設定 ===\n",
    "TIME_COL = \"senddate\"\n",
    "PAT_COL  = \"patno\"\n",
    "ADP_COL  = \"ad_para\"\n",
    "NAN_COL  = \"NaN_check\"\n",
    "\n",
    "def read_csv_safely(path):\n",
    "    \"\"\"多編碼讀取 CSV\"\"\"\n",
    "    for enc in [\"utf-8\", \"cp950\", \"big5\"]:\n",
    "        try:\n",
    "            return pd.read_csv(path, encoding=enc)\n",
    "        except Exception:\n",
    "            continue\n",
    "    return pd.read_csv(path)\n",
    "\n",
    "def coerce_time(s):\n",
    "    if np.issubdtype(s.dtype, np.datetime64):\n",
    "        return s\n",
    "    return pd.to_datetime(s, errors=\"coerce\")\n",
    "\n",
    "def find_anomalies(df, file):\n",
    "    \"\"\"找出 ad_para=1 之後下一筆 NaN_check=0 的情況\"\"\"\n",
    "    missing = [c for c in [TIME_COL, ADP_COL, NAN_COL] if c not in df.columns]\n",
    "    if missing:\n",
    "        print(f\"[WARN] {file} 缺少欄位: {missing}\")\n",
    "        return pd.DataFrame(columns=[\"file\",\"patno\",\"event_time\",\"next_time\",\"next_nancheck\",\"note\"])\n",
    "\n",
    "    df = df.copy()\n",
    "    df[TIME_COL] = coerce_time(df[TIME_COL])\n",
    "    df = df.dropna(subset=[TIME_COL]).sort_values(TIME_COL).reset_index(drop=True)\n",
    "\n",
    "    records = []\n",
    "    idx_events = df.index[df[ADP_COL] == 1].tolist()\n",
    "    for i in idx_events:\n",
    "        pat = df.at[i, PAT_COL] if PAT_COL in df.columns else np.nan\n",
    "        event_time = df.at[i, TIME_COL]\n",
    "        # 下一筆\n",
    "        if i + 1 < len(df):\n",
    "            next_time = df.at[i+1, TIME_COL]\n",
    "            next_nan = df.at[i+1, NAN_COL] if NAN_COL in df.columns else np.nan\n",
    "            if next_nan == 0:\n",
    "                records.append({\n",
    "                    \"file\": os.path.basename(file),\n",
    "                    \"patno\": pat,\n",
    "                    \"event_time\": event_time,\n",
    "                    \"next_time\": next_time,\n",
    "                    \"next_nancheck\": next_nan,\n",
    "                    \"note\": \"next row after ad_para=1 has NaN_check=0\"\n",
    "                })\n",
    "        else:\n",
    "            records.append({\n",
    "                \"file\": os.path.basename(file),\n",
    "                \"patno\": pat,\n",
    "                \"event_time\": event_time,\n",
    "                \"next_time\": pd.NaT,\n",
    "                \"next_nancheck\": np.nan,\n",
    "                \"note\": \"ad_para=1 at last row\"\n",
    "            })\n",
    "    return pd.DataFrame.from_records(records)\n",
    "\n",
    "def main():\n",
    "    all_records = []\n",
    "    files = sorted(glob.glob(os.path.join(DATA_DIR, \"*.csv\")))\n",
    "    if not files:\n",
    "        print(f\"[WARN] 找不到檔案：{DATA_DIR}\")\n",
    "        return\n",
    "    for f in files:\n",
    "        try:\n",
    "            df = read_csv_safely(f)\n",
    "            result = find_anomalies(df, f)\n",
    "            if len(result):\n",
    "                all_records.append(result)\n",
    "        except Exception as e:\n",
    "            print(f\"[ERROR] {os.path.basename(f)} 讀取失敗: {e}\")\n",
    "\n",
    "    if all_records:\n",
    "        final_df = pd.concat(all_records, ignore_index=True)\n",
    "        final_df.to_csv(OUT_FILE, index=False)\n",
    "        print(f\"[OK] 已輸出異常清單：{OUT_FILE}\")\n",
    "        print(final_df.head(10))\n",
    "        print(f\"共 {len(final_df)} 筆異常\")\n",
    "    else:\n",
    "        print(\"[OK] 未發現 ad_para=1 後一筆 NaN_check=0 的情況\")\n",
    "\n",
    "if __name__ == \"__main__\":\n",
    "    main()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c9ce6c01-2b05-4ab2-8394-d3c008f755ae",
   "metadata": {},
   "outputs": [],
   "source": [
    "8,929 起「ad_para=1 ➜ 下一筆 NaN_check=0」\n",
    "很可能是「ad_para=1 後下一筆不是 set=1」的主因之一。\n",
    "為了把影響量化、找出占比最高的檔案，以及確認它與「下一筆 set≠1」的重疊比例，\n",
    "走下面"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "id": "95dc6b0e-0110-4387-a9f4-562079c84cb2",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[OK] 輸出彙總：/home/jovyan/RT08/0925/1002/adpara_next_nancheck0_summary.csv\n",
      "\n",
      "=== Top-10 下一筆 NaN_check=0 比例最高的檔案 ===\n",
      "                        file  events_total  next_nan0_cnt  next_nan0_pct  next_set_ne1_cnt  next_set_ne1_pct  overlap_cnt  overlap_pct note\n",
      "33   PatNo_ID_1570642083.csv         12832           1127           8.78                 8              0.06            8         0.06     \n",
      "111  PatNo_ID_1594441887.csv          8137            585           7.19                 3              0.04            3         0.04     \n",
      "105  PatNo_ID_1594320763.csv          2086            129           6.18                 0              0.00            0         0.00     \n",
      "69   PatNo_ID_1581692973.csv          2359            131           5.55                 0              0.00            0         0.00     \n",
      "83   PatNo_ID_1588957997.csv          8546            452           5.29                 0              0.00            0         0.00     \n",
      "47   PatNo_ID_1575060177.csv          4288            198           4.62                 0              0.00            0         0.00     \n",
      "15   PatNo_ID_1565378038.csv          1377             47           3.41                 0              0.00            0         0.00     \n",
      "4                 230933.csv         23028            785           3.41                 3              0.01            3         0.01     \n",
      "103  PatNo_ID_1594309746.csv          3101             94           3.03                 0              0.00            0         0.00     \n",
      "55   PatNo_ID_1576964560.csv         16879            491           2.91                 0              0.00            0         0.00     \n",
      "\n",
      "=== 全體總覽（ALL） ===\n",
      "    file  events_total  next_nan0_cnt  next_nan0_pct  next_set_ne1_cnt  next_set_ne1_pct  overlap_cnt  overlap_pct note\n",
      "122  ALL       1129341           8912           0.79               119              0.01          101         0.01     \n"
     ]
    }
   ],
   "source": [
    "# -*- coding: utf-8 -*-\n",
    "\"\"\"\n",
    "統計：ad_para=1 後一筆 NaN_check=0 的占比、與下一筆 set!=1 的關聯\n",
    "輸出：/home/jovyan/RT08/0925/1002/adpara_next_nancheck0_summary.csv\n",
    "\"\"\"\n",
    "\n",
    "import os\n",
    "import glob\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "DATA_DIR = \"/home/jovyan/RT08/0925/bling_1004\"\n",
    "OUT_DIR  = \"/home/jovyan/RT08/0925/1002\"\n",
    "os.makedirs(OUT_DIR, exist_ok=True)\n",
    "OUT_SUM  = os.path.join(OUT_DIR, \"adpara_next_nancheck0_summary.csv\")\n",
    "\n",
    "TIME_COL = \"senddate\"\n",
    "PAT_COL  = \"patno\"\n",
    "ADP_COL  = \"ad_para\"\n",
    "NAN_COL  = \"NaN_check\"\n",
    "SET_COL  = \"set\"\n",
    "\n",
    "def read_csv_safely(path):\n",
    "    for enc in (\"utf-8\",\"cp950\",\"big5\"):\n",
    "        try:\n",
    "            return pd.read_csv(path, encoding=enc)\n",
    "        except Exception:\n",
    "            pass\n",
    "    return pd.read_csv(path)\n",
    "\n",
    "def to_dt(s):\n",
    "    if np.issubdtype(s.dtype, np.datetime64):\n",
    "        return s\n",
    "    return pd.to_datetime(s, errors=\"coerce\")\n",
    "\n",
    "def analyze_file(fpath):\n",
    "    fn = os.path.basename(fpath)\n",
    "    try:\n",
    "        df = read_csv_safely(fpath)\n",
    "    except Exception as e:\n",
    "        return {\"file\": fn, \"events_total\": 0, \"next_nan0_cnt\": 0, \"next_nan0_pct\": 0.0,\n",
    "                \"next_set_ne1_cnt\": 0, \"next_set_ne1_pct\": 0.0, \"overlap_cnt\": 0, \"overlap_pct\": 0.0,\n",
    "                \"note\": f\"read_error:{e}\"}\n",
    "\n",
    "    # 欄位檢查\n",
    "    missing = [c for c in [TIME_COL, ADP_COL, NAN_COL] if c not in df.columns]\n",
    "    if missing:\n",
    "        return {\"file\": fn, \"events_total\": 0, \"next_nan0_cnt\": 0, \"next_nan0_pct\": 0.0,\n",
    "                \"next_set_ne1_cnt\": 0, \"next_set_ne1_pct\": 0.0, \"overlap_cnt\": 0, \"overlap_pct\": 0.0,\n",
    "                \"note\": f\"missing:{missing}\"}\n",
    "\n",
    "    # 排序、時間轉型\n",
    "    df = df.copy()\n",
    "    df[TIME_COL] = to_dt(df[TIME_COL])\n",
    "    df = df.dropna(subset=[TIME_COL]).sort_values(TIME_COL).reset_index(drop=True)\n",
    "\n",
    "    # 基礎欄位轉型\n",
    "    df[ADP_COL] = pd.to_numeric(df[ADP_COL], errors=\"coerce\").fillna(0).astype(int)\n",
    "    if NAN_COL in df.columns:\n",
    "        df[NAN_COL] = pd.to_numeric(df[NAN_COL], errors=\"coerce\").fillna(0).astype(int)\n",
    "    if SET_COL in df.columns:\n",
    "        df[SET_COL] = pd.to_numeric(df[SET_COL], errors=\"coerce\").fillna(0).astype(int).clip(0,1)\n",
    "\n",
    "    idx_events = df.index[df[ADP_COL] == 1].tolist()\n",
    "    E = len(idx_events)\n",
    "    if E == 0:\n",
    "        return {\"file\": fn, \"events_total\": 0, \"next_nan0_cnt\": 0, \"next_nan0_pct\": 0.0,\n",
    "                \"next_set_ne1_cnt\": 0, \"next_set_ne1_pct\": 0.0, \"overlap_cnt\": 0, \"overlap_pct\": 0.0,\n",
    "                \"note\": \"\"}\n",
    "\n",
    "    next_nan0_cnt = 0\n",
    "    next_set_ne1_cnt = 0\n",
    "    overlap_cnt = 0\n",
    "\n",
    "    for i in idx_events:\n",
    "        if i + 1 >= len(df):\n",
    "            continue\n",
    "        next_nan0 = (df.at[i+1, NAN_COL] == 0) if NAN_COL in df.columns else False\n",
    "        next_set_ne1 = False\n",
    "        if SET_COL in df.columns:\n",
    "            next_set_ne1 = (df.at[i+1, SET_COL] != 1)\n",
    "\n",
    "        if next_nan0:\n",
    "            next_nan0_cnt += 1\n",
    "        if next_set_ne1:\n",
    "            next_set_ne1_cnt += 1\n",
    "        if next_nan0 and next_set_ne1:\n",
    "            overlap_cnt += 1\n",
    "\n",
    "    def pct(x): \n",
    "        return (x / E * 100.0) if E > 0 else 0.0\n",
    "\n",
    "    return {\n",
    "        \"file\": fn,\n",
    "        \"events_total\": E,\n",
    "        \"next_nan0_cnt\": next_nan0_cnt,\n",
    "        \"next_nan0_pct\": round(pct(next_nan0_cnt), 2),\n",
    "        \"next_set_ne1_cnt\": next_set_ne1_cnt,\n",
    "        \"next_set_ne1_pct\": round(pct(next_set_ne1_cnt), 2),\n",
    "        \"overlap_cnt\": overlap_cnt,\n",
    "        \"overlap_pct\": round(pct(overlap_cnt), 2),\n",
    "        \"note\": \"\"\n",
    "    }\n",
    "\n",
    "def main():\n",
    "    paths = sorted(glob.glob(os.path.join(DATA_DIR, \"*.csv\")))\n",
    "    rows = [analyze_file(p) for p in paths]\n",
    "    summ = pd.DataFrame(rows)\n",
    "\n",
    "    # 全體總覽行（ALL）\n",
    "    if not summ.empty and summ[\"events_total\"].sum() > 0:\n",
    "        E_all = summ[\"events_total\"].sum()\n",
    "        nan_all = summ[\"next_nan0_cnt\"].sum()\n",
    "        ne1_all = summ[\"next_set_ne1_cnt\"].sum()\n",
    "        ovl_all = summ[\"overlap_cnt\"].sum()\n",
    "        all_row = pd.DataFrame([{\n",
    "            \"file\": \"ALL\",\n",
    "            \"events_total\": E_all,\n",
    "            \"next_nan0_cnt\": nan_all,\n",
    "            \"next_nan0_pct\": round(nan_all / E_all * 100.0, 2),\n",
    "            \"next_set_ne1_cnt\": ne1_all,\n",
    "            \"next_set_ne1_pct\": round(ne1_all / E_all * 100.0, 2),\n",
    "            \"overlap_cnt\": ovl_all,\n",
    "            \"overlap_pct\": round(ovl_all / E_all * 100.0, 2),\n",
    "            \"note\": \"\"\n",
    "        }])\n",
    "        summ = pd.concat([summ, all_row], ignore_index=True)\n",
    "\n",
    "    summ.to_csv(OUT_SUM, index=False)\n",
    "    print(f\"[OK] 輸出彙總：{OUT_SUM}\")\n",
    "    # 列印 TOP-10: 下一筆 NaN_check=0 比例最高\n",
    "    sub = summ[summ[\"file\"].ne(\"ALL\") & summ[\"events_total\"].gt(0)].copy()\n",
    "    if not sub.empty:\n",
    "        print(\"\\n=== Top-10 下一筆 NaN_check=0 比例最高的檔案 ===\")\n",
    "        print(sub.sort_values(\"next_nan0_pct\", ascending=False).head(10))\n",
    "        print(\"\\n=== 全體總覽（ALL） ===\")\n",
    "        print(summ[summ[\"file\"]==\"ALL\"])\n",
    "\n",
    "if __name__ == \"__main__\":\n",
    "    pd.options.display.width = 160\n",
    "    pd.options.display.max_columns = 20\n",
    "    main()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fa0c379b-7999-43ff-a9ed-02c1e9d60848",
   "metadata": {},
   "outputs": [],
   "source": [
    "adpara_next_nancheck0_summary.csv：逐檔統計\n",
    "events_total（本檔 ad_para=1 事件總數）\n",
    "next_nan0_cnt（下一筆 NaN_check=0 次數、百分比）\n",
    "next_set_ne1_cnt（下一筆 set!=1 次數、百分比）\n",
    "overlap_cnt（同時滿足「下一筆 NaN_check=0 且 set!=1」的重疊數、百分比）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "id": "b6eaaa04-aae4-42d5-b212-71f445f28cc4",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "=== Segment 數據總覽（整體） ===\n",
      "       duration_min       rows\n",
      "count       360.000    360.000\n",
      "mean       4727.656   4353.425\n",
      "std        7717.694   7132.881\n",
      "min           0.000      1.000\n",
      "25%         214.233    215.250\n",
      "50%        1310.500   1304.500\n",
      "75%        5603.642   5368.000\n",
      "max       56348.983  54359.000\n",
      "\n",
      "=== Segment 數據（依 set 分組） ===\n",
      "    duration_min                                                                     rows                                                          \n",
      "           count      mean       std  min       25%       50%       75%        max  count      mean       std  min     25%     50%     75%      max\n",
      "set                                                                                                                                                \n",
      "0          119.0   445.422  2675.920  0.0    31.000    83.000   315.525  29255.550  119.0   221.429   334.724  1.0    32.0    84.0   316.5   2528.0\n",
      "1          241.0  6842.120  8484.897  1.0  1284.967  3155.633  9729.550  56348.983  241.0  6393.705  7962.679  2.0  1263.0  2886.0  8551.0  54359.0\n",
      "\n",
      "=== 每檔案段數統計（依 set）===（前 20 列）\n",
      "                       file  set0_segments  set1_segments\n",
      "0                089271.csv              2              3\n",
      "1                095323.csv              0              1\n",
      "2                095707.csv              0              1\n",
      "3                114309.csv              1              2\n",
      "4                230933.csv              3              4\n",
      "5               4216007.csv              0              1\n",
      "6               7108162.csv              0              1\n",
      "7               7408338.csv              0              1\n",
      "8               7657698.csv              0              1\n",
      "9               7721164.csv              0              1\n",
      "10  PatNo_ID_1560013303.csv              0              1\n",
      "11  PatNo_ID_1562733396.csv              0              1\n",
      "12  PatNo_ID_1563587183.csv              0              1\n",
      "13  PatNo_ID_1564148644.csv              1              2\n",
      "14  PatNo_ID_1565148312.csv              0              1\n",
      "15  PatNo_ID_1565378038.csv              0              1\n",
      "16  PatNo_ID_1566123680.csv              3              4\n",
      "17  PatNo_ID_1566252197.csv              0              1\n",
      "18  PatNo_ID_1566279967.csv              0              1\n",
      "19  PatNo_ID_1566671274.csv              0              1\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "=== 分位數（duration_min）set=0 ===\n",
      "0.01       0.000\n",
      "0.05       2.900\n",
      "0.10      10.153\n",
      "0.25      31.000\n",
      "0.50      83.000\n",
      "0.75     315.525\n",
      "0.90     633.570\n",
      "0.95     705.715\n",
      "0.99    1370.808\n",
      "Name: duration_min, dtype: float64\n",
      "\n",
      "=== 分位數（duration_min）set=1 ===\n",
      "0.01       18.100\n",
      "0.05      237.950\n",
      "0.10      519.533\n",
      "0.25     1284.967\n",
      "0.50     3155.633\n",
      "0.75     9729.550\n",
      "0.90    18415.700\n",
      "0.95    24051.533\n",
      "0.99    36289.810\n",
      "Name: duration_min, dtype: float64\n",
      "\n",
      "=== 分位數（rows）set=0 ===\n",
      "0.01       1.0\n",
      "0.05       3.9\n",
      "0.10      11.0\n",
      "0.25      32.0\n",
      "0.50      84.0\n",
      "0.75     316.5\n",
      "0.90     633.8\n",
      "0.95     705.8\n",
      "0.99    1368.2\n",
      "Name: rows, dtype: float64\n",
      "\n",
      "=== 分位數（rows）set=1 ===\n",
      "0.01       19.6\n",
      "0.05      235.0\n",
      "0.10      520.0\n",
      "0.25     1263.0\n",
      "0.50     2886.0\n",
      "0.75     8551.0\n",
      "0.90    17327.0\n",
      "0.95    21354.0\n",
      "0.99    36192.2\n",
      "Name: rows, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# -*- coding: utf-8 -*-\n",
    "\"\"\"\n",
    "Segment 分布快速檢視（直接顯示圖表、直接列印統計；不輸出檔案）\n",
    "- 掃描 /home/jovyan/RT08/0925/bling_1004 下所有 CSV\n",
    "- 計算 set 連續區段（segment）的起迄時間、時長(分鐘)、筆數(rows)\n",
    "- 列印各類統計摘要（整體、依 set、依檔案）\n",
    "- 顯示分布圖：直方圖（相形圖）與箱型圖（各 set 分開/對照）\n",
    "\n",
    "需求欄位：\n",
    "  SendDate（時間）、set（0/1）\n",
    "可選欄位：\n",
    "  PatNo（病患 ID；若無則以 NaN 代）\n",
    "\"\"\"\n",
    "\n",
    "import os\n",
    "import glob\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# ========= 路徑與欄位 =========\n",
    "DATA_DIR = \"/home/jovyan/RT08/0925/bling_1004\"\n",
    "TIME_COL = \"senddate\"\n",
    "SET_COL  = \"set\"\n",
    "PAT_COL  = \"patno\"  # optional\n",
    "\n",
    "# ========= 工具 =========\n",
    "def read_csv_safely(path: str) -> pd.DataFrame:\n",
    "    for enc in (\"utf-8\", \"cp950\", \"big5\"):\n",
    "        try:\n",
    "            return pd.read_csv(path, encoding=enc)\n",
    "        except Exception:\n",
    "            pass\n",
    "    return pd.read_csv(path)\n",
    "\n",
    "def to_dt(s: pd.Series) -> pd.Series:\n",
    "    if np.issubdtype(s.dtype, np.datetime64):\n",
    "        return s\n",
    "    return pd.to_datetime(s, errors=\"coerce\")\n",
    "\n",
    "def compute_segments_one(df: pd.DataFrame, file: str) -> pd.DataFrame:\n",
    "    \"\"\"回傳單檔 segment 統計表\"\"\"\n",
    "    missing = [c for c in (TIME_COL, SET_COL) if c not in df.columns]\n",
    "    if missing:\n",
    "        print(f\"[WARN] {file} 缺少必要欄位：{missing}，將略過。\")\n",
    "        return pd.DataFrame(columns=[\n",
    "            \"file\",\"patno\",\"segment_id\",\"set\",\"start_time\",\"end_time\",\"duration_min\",\"rows\"\n",
    "        ])\n",
    "\n",
    "    df = df.copy()\n",
    "    df[TIME_COL] = to_dt(df[TIME_COL])\n",
    "    df = df.dropna(subset=[TIME_COL]).sort_values(TIME_COL).reset_index(drop=True)\n",
    "\n",
    "    if PAT_COL not in df.columns:\n",
    "        df[PAT_COL] = np.nan\n",
    "\n",
    "    # set 正規化為 {0,1}\n",
    "    df[SET_COL] = pd.to_numeric(df[SET_COL], errors=\"coerce\").fillna(0).astype(int).clip(0,1)\n",
    "\n",
    "    # 連續區段偵測\n",
    "    df[\"__chg\"] = (df[SET_COL] != df[SET_COL].shift(1)).astype(int)\n",
    "    df.loc[df.index == 0, \"__chg\"] = 1\n",
    "    df[\"segment_id\"] = df[\"__chg\"].cumsum()\n",
    "\n",
    "    grp = df.groupby(\"segment_id\", as_index=False).agg(\n",
    "        set_val    = (SET_COL, \"first\"),\n",
    "        start_time = (TIME_COL, \"first\"),\n",
    "        end_time   = (TIME_COL, \"last\"),\n",
    "        rows       = (TIME_COL, \"size\"),\n",
    "        patno      = (PAT_COL, \"first\")\n",
    "    )\n",
    "    grp[\"duration_min\"] = (grp[\"end_time\"] - grp[\"start_time\"]).dt.total_seconds() / 60.0\n",
    "    grp.insert(0, \"file\", os.path.basename(file))\n",
    "    grp.rename(columns={\"set_val\":\"set\"}, inplace=True)\n",
    "    return grp[[\"file\",\"patno\",\"segment_id\",\"set\",\"start_time\",\"end_time\",\"duration_min\",\"rows\"]]\n",
    "\n",
    "# ========= 主流程 =========\n",
    "def main():\n",
    "    paths = sorted(glob.glob(os.path.join(DATA_DIR, \"*.csv\")))\n",
    "    if not paths:\n",
    "        print(f\"[WARN] 找不到 CSV：{DATA_DIR}\")\n",
    "        return\n",
    "\n",
    "    seg_list = []\n",
    "    for p in paths:\n",
    "        try:\n",
    "            df = read_csv_safely(p)\n",
    "            seg = compute_segments_one(df, p)\n",
    "            if not seg.empty:\n",
    "                seg_list.append(seg)\n",
    "        except Exception as e:\n",
    "            print(f\"[ERROR] {os.path.basename(p)} 讀取/處理失敗：{e}\")\n",
    "\n",
    "    if not seg_list:\n",
    "        print(\"[INFO] 無可用 segment 結果。\")\n",
    "        return\n",
    "\n",
    "    seg_all = pd.concat(seg_list, ignore_index=True)\n",
    "\n",
    "    # ====== 統計列印（整體/依 set） ======\n",
    "    print(\"\\n=== Segment 數據總覽（整體） ===\")\n",
    "    print(seg_all[[\"duration_min\",\"rows\"]].describe().round(3))\n",
    "\n",
    "    print(\"\\n=== Segment 數據（依 set 分組） ===\")\n",
    "    byset = seg_all.groupby(\"set\")[[\"duration_min\",\"rows\"]].describe().round(3)\n",
    "    print(byset)\n",
    "\n",
    "    # 每檔案的段數統計（依 set）\n",
    "    perfile_counts = seg_all.groupby([\"file\",\"set\"]).size().unstack(fill_value=0)\n",
    "    perfile_counts.columns = [f\"set{c}_segments\" for c in perfile_counts.columns]\n",
    "    perfile_counts = perfile_counts.reset_index()\n",
    "    print(\"\\n=== 每檔案段數統計（依 set）===（前 20 列）\")\n",
    "    print(perfile_counts.head(20))\n",
    "\n",
    "    # ====== 圖表：分布視覺化（不存檔；直接顯示） ======\n",
    "    # 直方圖（相形圖）：duration_min for set=0\n",
    "    dur0 = seg_all.loc[seg_all[\"set\"] == 0, \"duration_min\"].dropna()\n",
    "    plt.figure()\n",
    "    plt.hist(dur0.values, bins=50)\n",
    "    plt.title(\"Histogram of Segment Duration (min) — set=0\")\n",
    "    plt.xlabel(\"duration_min\")\n",
    "    plt.ylabel(\"count\")\n",
    "    plt.grid(True, linestyle=\"--\", alpha=0.5)\n",
    "    plt.show()\n",
    "\n",
    "    # 直方圖（相形圖）：duration_min for set=1\n",
    "    dur1 = seg_all.loc[seg_all[\"set\"] == 1, \"duration_min\"].dropna()\n",
    "    plt.figure()\n",
    "    plt.hist(dur1.values, bins=50)\n",
    "    plt.title(\"Histogram of Segment Duration (min) — set=1\")\n",
    "    plt.xlabel(\"duration_min\")\n",
    "    plt.ylabel(\"count\")\n",
    "    plt.grid(True, linestyle=\"--\", alpha=0.5)\n",
    "    plt.show()\n",
    "\n",
    "    # 箱型圖：duration_min by set\n",
    "    plt.figure()\n",
    "    data_box = [dur0.values, dur1.values]\n",
    "    plt.boxplot(data_box, labels=[\"set=0\",\"set=1\"], showfliers=False)\n",
    "    plt.title(\"Boxplot of Segment Duration (min) by set\")\n",
    "    plt.ylabel(\"duration_min\")\n",
    "    plt.grid(True, linestyle=\"--\", alpha=0.5)\n",
    "    plt.show()\n",
    "\n",
    "    # 直方圖：rows for set=0\n",
    "    rows0 = seg_all.loc[seg_all[\"set\"] == 0, \"rows\"].dropna()\n",
    "    plt.figure()\n",
    "    plt.hist(rows0.values, bins=50)\n",
    "    plt.title(\"Histogram of Segment Rows — set=0\")\n",
    "    plt.xlabel(\"rows\")\n",
    "    plt.ylabel(\"count\")\n",
    "    plt.grid(True, linestyle=\"--\", alpha=0.5)\n",
    "    plt.show()\n",
    "\n",
    "    # 直方圖：rows for set=1\n",
    "    rows1 = seg_all.loc[seg_all[\"set\"] == 1, \"rows\"].dropna()\n",
    "    plt.figure()\n",
    "    plt.hist(rows1.values, bins=50)\n",
    "    plt.title(\"Histogram of Segment Rows — set=1\")\n",
    "    plt.xlabel(\"rows\")\n",
    "    plt.ylabel(\"count\")\n",
    "    plt.grid(True, linestyle=\"--\", alpha=0.5)\n",
    "    plt.show()\n",
    "\n",
    "    # 箱型圖：rows by set\n",
    "    plt.figure()\n",
    "    data_box_rows = [rows0.values, rows1.values]\n",
    "    plt.boxplot(data_box_rows, labels=[\"set=0\",\"set=1\"], showfliers=False)\n",
    "    plt.title(\"Boxplot of Segment Rows by set\")\n",
    "    plt.ylabel(\"rows\")\n",
    "    plt.grid(True, linestyle=\"--\", alpha=0.5)\n",
    "    plt.show()\n",
    "\n",
    "    # ====== 額外：分位數表（利於設定門檻） ======\n",
    "    def quantiles(s: pd.Series):\n",
    "        return s.quantile([0.01,0.05,0.1,0.25,0.5,0.75,0.9,0.95,0.99]).round(3)\n",
    "\n",
    "    print(\"\\n=== 分位數（duration_min）set=0 ===\")\n",
    "    print(quantiles(dur0))\n",
    "    print(\"\\n=== 分位數（duration_min）set=1 ===\")\n",
    "    print(quantiles(dur1))\n",
    "\n",
    "    print(\"\\n=== 分位數（rows）set=0 ===\")\n",
    "    print(quantiles(rows0))\n",
    "    print(\"\\n=== 分位數（rows）set=1 ===\")\n",
    "    print(quantiles(rows1))\n",
    "\n",
    "if __name__ == \"__main__\":\n",
    "    pd.options.display.width = 160\n",
    "    pd.options.display.max_columns = 20\n",
    "    main()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "572a43de-f03b-4754-817d-9b069209973c",
   "metadata": {},
   "outputs": [],
   "source": [
    "樣本結構：共 360 段（set=0: 119 段；set=1: 241 段）。\n",
    "嚴重長度不均：\n",
    "set=0（穩定段）典型很短：中位數 83 分鐘 / 84 筆；長尾到 20,000+ 分鐘。\n",
    "set=1（調參段）超長：中位數 3,156 分鐘 / 2,886 筆；長尾到 ~39 天 / 54,359 筆。\n",
    "檔案層分佈：多數檔案呈「set=1 只有一段、且極長」的型態（表格第 2–12 行就能看出）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ce6aaeda-715d-4972-8ddd-a658da8f8c03",
   "metadata": {},
   "outputs": [],
   "source": [
    "1012"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "id": "2f591ba2-1357-4988-927b-aecf6025f7fb",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "✅ 共讀取 122 個檔案，合併後共有 1,567,233 筆資料。\n",
      "✅ 篩選後剩餘 1,129,341 筆資料可用。\n",
      "✅ 成功計算出 1,128,455 筆相鄰事件間隔。\n",
      "\n",
      "==============================\n",
      "【逐筆相鄰事件間隔（前 15 筆預覽）】\n",
      "  patno  interval_min\n",
      "7108162          1.00\n",
      "7108162          1.00\n",
      "7108162          1.00\n",
      "7108162          2.00\n",
      "7108162          2.00\n",
      "7108162          1.00\n",
      "7108162          1.00\n",
      "7108162          1.00\n",
      "7108162          1.00\n",
      "7108162          1.00\n",
      "7108162          1.00\n",
      "7108162          1.00\n",
      "7108162          1.97\n",
      "7108162          1.00\n",
      "7108162          1.00\n",
      "\n",
      "==============================\n",
      "【依病患統計結果】\n",
      "     patno  count  min_min  q1_min  median_min  mean_min  q3_min   max_min  std_min\n",
      "   7108162    176     0.98    1.00        1.00      1.11    1.00      2.00     0.31\n",
      "   7408338   1060     0.67    1.00        1.00      1.36    1.00     18.00     1.18\n",
      "   7657698   1363     0.13    1.00        1.00      1.05    1.00      9.62     0.36\n",
      "   7721164    449     0.35    1.00        1.00      1.08    1.00      3.00     0.29\n",
      "   8927106  24293     0.07    1.00        1.00      1.54    1.00  4,966.05    31.88\n",
      "   9532303  13460     0.25    1.00        1.00      1.46    1.00     27.00     1.37\n",
      "   9570752  15255     0.15    1.00        1.00      1.28    1.00     30.00     1.15\n",
      "  11430923  19140     0.08    1.00        1.00      1.25    1.00  1,251.87     9.12\n",
      "  23093320  23027     0.08    1.00        1.00      1.35    1.00  1,662.98    13.19\n",
      "1560013300   2078     0.67    1.00        1.00      1.21    1.00     56.00     1.35\n",
      "1562733400   2163     0.08    0.98        1.00      1.18    1.03    268.63     5.76\n",
      "1563587200   4986     0.25    1.00        1.00      1.38    1.00  1,656.03    23.44\n",
      "1564148600   7961     0.08    1.00        1.00      8.46    1.00 58,531.47   655.99\n",
      "1565148300   5013     0.20    1.00        1.00      1.09    1.00      5.20     0.36\n",
      "1565378000   1376     0.33    1.00        1.00      1.70    2.00     20.00     1.67\n",
      "1566123600  20687     0.05    1.00        1.00      1.10    1.00    326.65     2.89\n",
      "1566252200   1701     0.67    1.00        1.00      1.14    1.00     25.03     1.00\n",
      "1566280000    903     0.30    1.00        1.00      1.28    1.00     13.95     0.93\n",
      "1566671200  21367     0.08    1.00        1.00      1.12    1.00     56.98     0.76\n",
      "1566911900  42600     0.08    1.00        1.00      1.23    1.00    753.02     9.89\n",
      "1567747700   9314     0.13    1.00        1.00      1.16    1.00     19.98     0.92\n",
      "1567804800  11674     0.08    1.00        1.00      1.53    1.00  2,799.98    29.87\n",
      "1567832700  31489     0.10    1.00        1.00      1.17    1.00     69.22     0.98\n",
      "1568039400  31394     0.02    1.00        1.00      1.10    1.00    168.00     1.17\n",
      "1568574100   9844     0.50    1.00        1.00      1.41    1.00  1,227.98    12.42\n",
      "1568813300   4395     0.17    1.00        1.00      1.20    1.00    409.75     6.21\n",
      "1568952400   1011     0.17    1.00        1.00      1.16    1.00      5.00     0.50\n",
      "1569083600   1710     0.33    1.00        1.00      1.93    2.00     25.00     2.29\n",
      "1569945000   6393     0.08    1.00        1.00      1.19    1.00      9.00     0.57\n",
      "1570089500  12099     0.55    1.00        1.00      1.54    1.00  1,435.00    18.53\n",
      "1570242700   9501     0.75    1.00        1.00      1.11    1.00     12.00     0.51\n",
      "1570273300   9137     0.25    1.00        1.00      1.06    1.00      7.00     0.31\n",
      "1570642000  12831     0.05    1.00        1.00      1.53    1.00  1,480.60    13.37\n",
      "1571945700  13579     0.25    1.00        1.00      1.15    1.00     18.00     0.69\n",
      "1572481400  29441     0.03    1.00        1.00      1.42    1.00  3,597.02    23.65\n",
      "1572562800  14483     0.08    1.00        1.00      1.45    1.00  1,407.98    22.33\n",
      "1572831700   1796     0.25    1.00        1.00      1.50    1.00     22.00     1.57\n",
      "1572976800   4157     0.08    0.97        1.00      1.66    1.05  2,497.53    38.72\n",
      "1573063200   3759     0.13    1.00        1.00      1.34    1.00     18.00     1.18\n",
      "1573249300   6809     0.08    0.98        1.00      1.10    1.03     15.98     0.57\n",
      "1573964500   3665     0.18    1.00        1.00      1.20    1.00     41.78     1.08\n",
      "1574148500  41644     0.08    1.00        1.00      1.13    1.00     15.00     0.58\n",
      "1574270300   3827     0.25    1.00        1.00      1.70    1.00     64.00     3.11\n",
      "1574528800   7480     0.08    1.00        1.00      2.04    2.00    457.02     5.61\n",
      "1574831500    344     0.67    1.00        1.00      1.47    1.00     29.00     2.11\n",
      "1574987400  17906     0.17    1.00        1.00      1.11    1.00     46.02     0.70\n",
      "1575060200   4287     0.42    1.00        1.00      1.22    1.00     12.00     0.75\n",
      "1575257000   4246     0.17    1.00        1.00      2.29    2.00    141.93     4.03\n",
      "1575445000   1627     0.33    1.00        1.00      1.10    1.00      7.98     0.40\n",
      "1575502300   6047     0.17    1.00        1.00      1.09    1.00     66.00     0.90\n",
      "1575975400  13615     0.07    1.00        1.00      1.20    1.00     22.00     0.97\n",
      "1576115600  14648     0.07    1.00        1.00      1.31    1.02    196.05     2.61\n",
      "1576116500   1149     0.75    1.00        1.00      1.09    1.00      4.00     0.32\n",
      "1576301600   2997     0.08    1.00        1.00      1.10    1.02      8.42     0.43\n",
      "1576964600  16878     0.08    1.00        1.00      1.44    1.00    119.57     1.87\n",
      "1577043000  30704     0.08    1.00        1.00      1.20    1.00    640.02     4.09\n",
      "1577487200   1595     0.17    1.00        1.00      1.48    1.00     35.00     1.78\n",
      "1578784300  24046     0.25    1.00        1.00      1.35    1.00    722.03    10.10\n",
      "1579198600   1957     0.37    1.00        1.00      1.04    1.00      4.00     0.23\n",
      "1579498200  20725     0.08    0.98        1.00      1.08    1.02    122.62     1.13\n",
      "1580062600   2013     0.25    1.00        1.00      1.08    1.00     54.00     1.26\n",
      "1580096800    956     0.25    1.00        1.00      1.05    1.00      4.00     0.27\n",
      "1580244600   3059     0.50    1.00        1.00      1.30    1.00    116.00     2.35\n",
      "1580766100  15506     0.08    1.00        1.00      2.37    1.00 18,686.95   150.10\n",
      "1581003300   5527     0.17    1.00        1.00      1.20    1.00     45.00     1.48\n",
      "1581019500  18979     0.02    1.00        1.00      1.41    1.00    930.02    13.39\n",
      "1581633300  12326     0.08    1.00        1.00      1.28    1.00    382.42     4.32\n",
      "1581692900   2358     0.05    1.00        1.00      1.21    1.00     64.20     1.67\n",
      "1582452500   4887     0.50    1.00        1.00      1.06    1.00      6.00     0.28\n",
      "1582636000  12052     0.08    1.00        1.00      1.09    1.00     69.58     0.76\n",
      "1582849900   1488     0.58    1.00        1.00      1.18    1.00     42.97     1.31\n",
      "1582937100  21185     0.02    1.00        1.00      1.15    1.00  1,299.05     8.93\n",
      "1584159000   2270     0.17    1.00        1.00      1.28    1.00     75.00     2.03\n",
      "1584397300    249     1.00    1.00        1.00      2.08    2.00     17.00     2.61\n",
      "1586172700  28496     0.17    1.00        1.00      1.36    1.00    116.27     1.67\n",
      "1586696600   2363     0.33    1.00        1.00      1.06    1.00      6.00     0.31\n",
      "1586897000   4088     0.33    1.00        1.00      1.63    1.33     68.00     2.04\n",
      "1587490000  39984     0.08    1.00        1.00      1.17    1.00    355.27     2.27\n",
      "1588632600   2159     0.33    1.00        1.00      1.21    1.00    115.98     2.56\n",
      "1588673400   5317     0.08    1.00        1.00      1.61    1.00  1,306.62    22.09\n",
      "1588794800   9148     0.07    1.00        1.00      1.03    1.00     25.58     0.32\n",
      "1588958000   8545     0.25    1.00        1.00      1.28    1.00     17.00     0.94\n",
      "1589018100  11903     0.33    1.00        1.00      1.13    1.00     54.00     0.79\n",
      "1589034500  44429     0.03    1.00        1.00      1.29    1.00  1,429.55    11.61\n",
      "1589324500   3057     0.52    1.00        1.00      1.34    1.00     24.00     1.41\n",
      "1590136300   3939     0.08    1.00        1.00      1.39    1.30     14.00     0.92\n",
      "1590616600  10931     0.33    1.00        1.00      1.30    1.00     41.00     1.63\n",
      "1590854500  14267     0.08    0.98        1.00      1.13    1.05     36.88     0.80\n",
      "1591609900  27076     0.25    1.00        1.00      1.32    1.00     47.00     1.28\n",
      "1592044700   6498     0.53    1.00        1.00      1.03    1.00      4.00     0.19\n",
      "1592560500  13131     0.17    1.00        1.00      1.38    1.00    111.97     1.64\n",
      "1593087900  19884     0.08    1.00        1.00      1.83    1.00 12,033.95    85.34\n",
      "1593416100   3051     0.10    1.00        1.00      1.13    1.00     28.27     0.86\n",
      "1593472000   5294     0.25    1.00        1.00      1.28    1.00    534.00     7.38\n",
      "1593593600  14423     0.08    1.00        1.00      1.34    1.00    340.65     3.30\n",
      "1593720800   2567     0.52    1.00        1.00      1.52    1.00  1,261.98    24.89\n",
      "1593838500   1983     0.92    1.00        1.00      1.09    1.00     10.00     0.43\n",
      "1594173700    259     0.50    1.00        1.00      1.32    1.00      9.00     0.95\n",
      "1594294100   7716     0.03    0.98        1.00      1.23    1.15     21.02     0.86\n",
      "1594305200  14423     0.17    1.00        1.00      1.29    1.00  2,176.97    18.85\n",
      "1594309800   3100     0.82    1.00        1.00      1.20    1.00     31.00     0.91\n",
      "1594319200   1594     0.92    1.00        1.00      2.50    3.00     28.00     2.85\n",
      "1594320800   2085     0.83    1.00        1.00      1.15    1.00     15.00     0.72\n",
      "1594322600   5113     0.37    1.00        1.00      1.05    1.00     17.00     0.34\n",
      "1594335100   3306     0.58    1.00        1.00      1.53    1.00    136.00     2.71\n",
      "1594423700   4073     0.25    1.00        1.00      1.28    1.00    202.52     3.24\n",
      "1594437200   8991     0.15    1.00        1.00      1.15    1.00    255.03     2.74\n",
      "1594439800   6699     0.58    1.00        1.00      1.19    1.00    336.20     4.15\n",
      "1594441900   8136     0.08    1.00        1.00      1.34    1.00    410.77     5.27\n",
      "1594455600    202     0.97    1.00        1.00      1.01    1.00      2.00     0.10\n",
      "1594464800   3012     0.25    1.00        1.00      1.31    1.00     20.00     1.09\n",
      "1594467700   1074     0.98    1.00        1.00      1.22    1.00     10.00     0.72\n",
      "1594471400   3244     0.08    1.00        1.00      3.49    3.00    123.00     6.30\n",
      "1594479400   1377     0.08    1.00        1.47      2.95    3.00    128.15     5.22\n",
      "1594511900  12632     0.02    0.33        0.97      0.79    1.00      9.07     0.53\n",
      "1594528900   1867     0.08    1.00        1.00      1.30    1.00     29.00     1.33\n",
      "1594533400    903     0.67    1.00        1.00      1.13    1.00     19.98     0.74\n",
      "\n",
      "==============================\n",
      "【整體統計結果】\n",
      "  count  min_min  q1_min  median_min  mean_min  q3_min   max_min  std_min  p5_min  p95_min\n",
      "1128455     0.02    1.00        1.00      1.35    1.00 58,531.47    59.77    0.98     2.00\n"
     ]
    }
   ],
   "source": [
    "# 沒有整合多次連續調整參數視為一次可跳過\n",
    "# 目的：\n",
    "#   讀取 /home/jovyan/RT08/0925/bling_1004 內所有 CSV，\n",
    "#   僅針對 NaN_check=1 的資料，計算相鄰 ad_para=1 事件的時間間隔（分鐘）。\n",
    "#   所有欄位皆已知：\n",
    "#     - NaN_check\n",
    "#     - ad_para\n",
    "#     - patno\n",
    "#     - senddate (datetime64)\n",
    "#   不輸出檔案，直接在程式中印出結果。\n",
    "# ================================================\n",
    "\n",
    "import pandas as pd\n",
    "from pathlib import Path\n",
    "\n",
    "# -----------------------------\n",
    "# A. 指定資料夾位置\n",
    "# -----------------------------\n",
    "data_dir = Path(\"/home/jovyan/RT08/0925/bling_1004\")\n",
    "\n",
    "# 取得資料夾內所有 CSV 檔案路徑\n",
    "files = sorted(data_dir.glob(\"*.csv\"))\n",
    "if not files:\n",
    "    raise FileNotFoundError(\"⚠️ 找不到任何 CSV 檔案，請確認路徑。\")\n",
    "\n",
    "# -----------------------------\n",
    "# B. 讀取與合併所有檔案\n",
    "# -----------------------------\n",
    "all_data = []\n",
    "for fp in files:\n",
    "    df = pd.read_csv(fp, low_memory=False, parse_dates=[\"senddate\"])\n",
    "    # 統一欄位名稱小寫\n",
    "    df.columns = [c.lower() for c in df.columns]\n",
    "    # 只取必要欄位\n",
    "    df = df[[\"nan_check\", \"ad_para\", \"patno\", \"senddate\"]]\n",
    "    all_data.append(df)\n",
    "\n",
    "# 合併所有檔案成一份 DataFrame\n",
    "df = pd.concat(all_data, ignore_index=True)\n",
    "print(f\"✅ 共讀取 {len(files)} 個檔案，合併後共有 {len(df):,} 筆資料。\")\n",
    "\n",
    "# -----------------------------\n",
    "# C. 篩選有效資料\n",
    "# -----------------------------\n",
    "# 僅保留 NaN_check=1 且 ad_para=1 的列\n",
    "df_use = df[(df[\"nan_check\"] == 1) & (df[\"ad_para\"] == 1)].copy()\n",
    "df_use = df_use[df_use[\"senddate\"].notna()]\n",
    "df_use = df_use.sort_values([\"patno\", \"senddate\"]).reset_index(drop=True)\n",
    "\n",
    "if df_use.empty:\n",
    "    raise ValueError(\"⚠️ 篩選後無任何資料，請確認 NaN_check 與 ad_para 欄位內容。\")\n",
    "\n",
    "print(f\"✅ 篩選後剩餘 {len(df_use):,} 筆資料可用。\")\n",
    "\n",
    "# -----------------------------\n",
    "# D. 計算相鄰 ad_para=1 事件的時間間隔（分鐘）\n",
    "# -----------------------------\n",
    "interval_records = []\n",
    "\n",
    "# 依病患分組\n",
    "for pid, g in df_use.groupby(\"patno\"):\n",
    "    # 取出該病患所有事件時間（去除重複時間）\n",
    "    times = g[\"senddate\"].drop_duplicates().sort_values()\n",
    "    if len(times) < 2:\n",
    "        continue  # 若少於兩筆事件，跳過\n",
    "    # 計算相鄰事件間的時間差，並轉換為分鐘\n",
    "    delta_minutes = times.diff().dropna().dt.total_seconds() / 60.0\n",
    "    # 建立記錄\n",
    "    for v in delta_minutes:\n",
    "        interval_records.append({\"patno\": pid, \"interval_min\": float(v)})\n",
    "\n",
    "# 轉為 DataFrame\n",
    "intervals_df = pd.DataFrame(interval_records)\n",
    "if intervals_df.empty:\n",
    "    raise ValueError(\"⚠️ 無法產生任何時間間隔結果，請檢查資料。\")\n",
    "\n",
    "print(f\"✅ 成功計算出 {len(intervals_df):,} 筆相鄰事件間隔。\")\n",
    "\n",
    "# -----------------------------\n",
    "# E. 依病患計算統計數據\n",
    "# -----------------------------\n",
    "per_patient = (\n",
    "    intervals_df\n",
    "    .groupby(\"patno\", dropna=False)[\"interval_min\"]\n",
    "    .agg(\n",
    "        count=\"count\",\n",
    "        min_min=\"min\",\n",
    "        q1_min=lambda s: s.quantile(0.25),\n",
    "        median_min=\"median\",\n",
    "        mean_min=\"mean\",\n",
    "        q3_min=lambda s: s.quantile(0.75),\n",
    "        max_min=\"max\",\n",
    "        std_min=lambda s: s.std(ddof=1)\n",
    "    )\n",
    "    .reset_index()\n",
    ")\n",
    "\n",
    "# -----------------------------\n",
    "# F. 計算整體統計\n",
    "# -----------------------------\n",
    "s = intervals_df[\"interval_min\"]\n",
    "overall = pd.DataFrame([{\n",
    "    \"count\": int(s.count()),\n",
    "    \"min_min\": float(s.min()),\n",
    "    \"q1_min\": float(s.quantile(0.25)),\n",
    "    \"median_min\": float(s.median()),\n",
    "    \"mean_min\": float(s.mean()),\n",
    "    \"q3_min\": float(s.quantile(0.75)),\n",
    "    \"max_min\": float(s.max()),\n",
    "    \"std_min\": float(s.std(ddof=1)),\n",
    "    \"p5_min\": float(s.quantile(0.05)),\n",
    "    \"p95_min\": float(s.quantile(0.95))\n",
    "}])\n",
    "\n",
    "# -----------------------------\n",
    "# G. 印出結果\n",
    "# -----------------------------\n",
    "pd.set_option(\"display.float_format\", \"{:,.2f}\".format)\n",
    "\n",
    "print(\"\\n==============================\")\n",
    "print(\"【逐筆相鄰事件間隔（前 15 筆預覽）】\")\n",
    "print(intervals_df.head(15).to_string(index=False))\n",
    "\n",
    "print(\"\\n==============================\")\n",
    "print(\"【依病患統計結果】\")\n",
    "print(per_patient.to_string(index=False))\n",
    "\n",
    "print(\"\\n==============================\")\n",
    "print(\"【整體統計結果】\")\n",
    "print(overall.to_string(index=False))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "id": "37c37e86-3a08-4f67-b4d5-eb1f6985aeb9",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "✅ 條件套用完成：已將『連續 ad_para=1』折疊為單一事件（保留最後一筆），並檢核樣本充足性與不重疊。\n",
      "總共保留的區間數：58561\n",
      "\n",
      "【保留的區間（前 20 筆預覽）】\n",
      "  patno                  T1                  T2  delta_min  sampling_min  neg_count  pos_count\n",
      "7108162 2024-09-18 13:45:04 2024-09-18 13:54:04      9.000         1.000          4          1\n",
      "7108162 2024-09-18 13:54:04 2024-09-18 14:10:02     15.967         1.000          8          3\n",
      "7108162 2024-09-18 14:10:02 2024-09-18 14:20:02     10.000         1.000          5          2\n",
      "7108162 2024-09-18 14:20:02 2024-09-18 14:39:02     19.000         1.000          9          3\n",
      "7108162 2024-09-18 14:49:02 2024-09-18 15:06:02     17.000         1.000          8          3\n",
      "7108162 2024-09-18 15:06:02 2024-09-18 15:28:02     22.000         1.000         11          4\n",
      "7108162 2024-09-18 15:28:02 2024-09-18 15:39:02     11.000         1.000          5          2\n",
      "7108162 2024-09-18 15:39:02 2024-09-18 15:58:02     19.000         1.000          9          2\n",
      "7108162 2024-09-18 15:58:02 2024-09-18 16:14:02     16.000         1.000          8          3\n",
      "7108162 2024-09-18 16:14:02 2024-09-18 16:19:02      5.000         1.000          2          1\n",
      "7108162 2024-09-18 16:19:02 2024-09-18 16:26:02      7.000         1.000          3          1\n",
      "7108162 2024-09-18 16:29:01 2024-09-18 16:34:01      5.000         1.000          2          1\n",
      "7108162 2024-09-18 16:34:01 2024-09-18 16:55:01     21.000         1.000         10          4\n",
      "7408338 2024-09-17 18:00:02 2024-09-17 18:06:02      6.000         1.000          3          1\n",
      "7408338 2024-09-17 18:06:02 2024-09-17 18:11:02      5.000         1.000          2          1\n",
      "7408338 2024-09-17 18:11:02 2024-09-17 18:18:02      7.000         1.000          3          1\n",
      "7408338 2024-09-17 18:18:02 2024-09-17 18:28:03     10.017         1.000          4          2\n",
      "7408338 2024-09-17 18:28:03 2024-09-17 18:40:03     12.000         1.000          6          2\n",
      "7408338 2024-09-17 18:40:03 2024-09-17 18:47:03      7.000         1.000          3          1\n",
      "7408338 2024-09-17 18:47:03 2024-09-17 18:54:03      7.000         1.000          3          1\n",
      "\n",
      "【依病患統計（單位：分鐘）】\n",
      "     patno  count  min_min  q1_min  median_min  mean_min  q3_min   max_min  std_min\n",
      "   7108162     13    5.000   9.000      15.967    13.613  19.000    22.000    6.048\n",
      "   7408338     96    5.000   6.750       8.075    12.354  13.000    64.000   10.375\n",
      "   7657698     33    5.000  11.050      27.000    41.363  62.983   153.017   37.959\n",
      "   7721164     24    6.000   7.000       9.000    17.793  15.000    79.000   19.671\n",
      "   8927106   1802    4.983   6.000       8.000    18.095  14.000  4978.050  119.129\n",
      "   9532303   1385    4.000   6.000       7.000    11.169  11.000   152.017   12.862\n",
      "   9570752    947    4.983   6.000       8.000    18.003  14.000   271.983   30.673\n",
      "  11430923   1044    4.000   6.967      10.908    20.181  24.000   200.983   23.395\n",
      "  23093320    938    4.000   6.000       9.983    28.931  22.767  1183.967   69.141\n",
      "1560013300    139    5.000   6.000       8.000    15.598  14.000   260.067   25.492\n",
      "1562733400     35    5.767  14.533      31.950    57.725  82.433   342.033   69.856\n",
      "1563587200    145    4.983  10.317      22.000    46.725  43.983  1732.017  146.189\n",
      "1564148600    317    4.933   7.000      13.000   210.549  27.000 58731.050 3297.432\n",
      "1565148300    272    5.000   8.000      13.000    19.049  23.000   165.967   19.204\n",
      "1565378000    181    4.950   5.033       7.067    10.116  12.000    47.983    6.890\n",
      "1566123600    520    5.000  11.000      23.000    43.241  52.000   383.983   52.766\n",
      "1566252200     66    4.917   7.000      15.500    27.138  31.750   121.950   27.560\n",
      "1566280000     81    5.000   6.000       8.000    11.710  14.000    74.000   10.498\n",
      "1566671200    878    4.983   7.000      11.000    25.295  24.000   346.950   40.213\n",
      "1566911900   1220    4.933   7.000      12.000    41.397  35.000   972.017   86.762\n",
      "1567747700    390    4.933   6.292       9.500    26.086  18.725   371.000   49.062\n",
      "1567804800    322    4.767   8.000      15.000    53.755  39.746  4591.967  263.712\n",
      "1567832700   1398    4.000   6.000      10.000    24.370  21.000   600.967   45.690\n",
      "1568039400   1125    4.000   7.000      12.000    29.022  30.000   737.033   48.753\n",
      "1568574100    761    4.983   6.000       8.000    15.874  14.000  1285.967   49.382\n",
      "1568813300    115    4.067   7.000      11.000    43.479  24.500   700.067   91.957\n",
      "1568952400     79    5.000   7.000      11.000    12.719  15.000    52.000    8.335\n",
      "1569083600    247    4.933   6.000       7.983     9.632  11.958    43.000    5.715\n",
      "1569945000    453    4.050   6.000       8.967    14.247  14.000   193.083   18.937\n",
      "1570089500    735    4.867   6.000       8.000    22.185  15.000  3317.050  126.528\n",
      "1570242700    406    5.000   7.000      11.000    24.023  27.000   286.000   32.461\n",
      "1570273300    319    5.000   8.000      14.000    29.117  31.492   348.000   40.728\n",
      "1570642000    322    5.000  13.000      27.000    60.210  53.729  1531.050  128.080\n",
      "1571945700    526    5.000   6.000       8.000    26.763  17.000   656.917   65.564\n",
      "1572481400   1445    4.933   6.983      10.000    27.114  19.000  3642.050  116.930\n",
      "1572562800    487    4.750   7.858      14.000    41.769  31.000  1559.017  136.511\n",
      "1572831700    184    5.000   6.000       8.000    11.772  13.250    91.967   10.600\n",
      "1572976800     82    5.000  17.008      38.692    82.831  70.871  2526.867  278.052\n",
      "1573063200    324    4.967   6.000       8.000    13.250  15.000    94.067   13.377\n",
      "1573249300    126    5.000  12.967      27.967    57.635  63.338   418.000   79.278\n",
      "1573964500    235    5.000   6.000       8.000    16.498  14.000   298.033   30.600\n",
      "1574148500   1982    4.133   6.000      10.000    21.703  20.000   475.983   34.562\n",
      "1574270300    400    4.000   7.000       9.025    14.125  16.017   122.050   13.038\n",
      "1574528800   1167    4.017   6.000       7.000     8.955  10.000   463.050   13.919\n",
      "1574831500     33    5.000   7.000      10.000    12.786  17.000    30.967    7.559\n",
      "1574987400    704    5.000   7.000      11.000    26.512  25.296   480.033   44.277\n",
      "1575060200    197    4.000   6.000       8.000    23.898  14.000   330.050   51.142\n",
      "1575257000    688    4.000   6.000       8.000    10.704  11.987   150.933    9.369\n",
      "1575445000     81    5.000   7.000      11.000    19.283  23.983   112.050   19.782\n",
      "1575502300    237    5.000   7.000      12.000    26.529  25.000   728.967   55.415\n",
      "1575975400    699    4.000   6.000      10.000    21.098  20.000   304.983   32.500\n",
      "1576115600    859    4.000   6.700       9.017    19.603  18.058   253.000   27.049\n",
      "1576116500     65    5.000   8.000      12.000    17.571  20.000    95.983   17.372\n",
      "1576301600    104    4.950  10.700      18.058    29.706  36.250   209.983   34.771\n",
      "1576964600   1302    4.000   6.000       8.000    15.701  13.000   599.017   32.424\n",
      "1577043000   1702    4.917   7.000      11.975    20.053  22.000   655.017   28.883\n",
      "1577487200    154    5.000   6.000       8.000    12.722  13.838   119.967   15.275\n",
      "1578784300   1719    4.917   6.000      10.000    17.207  18.000   893.017   42.994\n",
      "1579198600     54    5.000  10.500      24.500    36.279  37.000   261.067   43.015\n",
      "1579498200    666    4.167  11.004      20.058    32.963  39.042   339.933   37.867\n",
      "1580062600     36    5.000  18.000      33.992    57.614  75.721   265.983   60.559\n",
      "1580096800     29    5.167  12.967      28.000    33.593  49.967   127.017   30.139\n",
      "1580244600    189    5.000   6.000       8.000    18.192  13.000   861.033   66.043\n",
      "1580766100    708    4.983   6.000      11.000    50.253  22.000 18811.000  707.381\n",
      "1581003300    177    4.950   7.000      11.000    34.947  25.000   375.050   64.515\n",
      "1581019500    519    5.000   7.008      14.000    49.964  41.000  1206.950  117.771\n",
      "1581633300    709    4.867   7.000      10.983    20.306  22.050   386.983   28.173\n",
      "1581692900     71    5.000   8.000      14.000    37.081  43.300   361.033   60.184\n",
      "1582452500    176    5.000  10.000      19.000    28.553  31.250   185.950   30.748\n",
      "1582636000    521    5.000   7.000      12.000    24.156  27.983   249.067   30.630\n",
      "1582849900     81    5.000   7.000      13.000    20.062  24.000   101.067   19.772\n",
      "1582937100    666    4.950   7.000      12.000    34.592  32.996  1409.050   76.300\n",
      "1584159000    107    4.917   6.642       9.067    24.253  19.025   236.950   38.564\n",
      "1584397300     32    5.000   6.000       9.000     9.438  11.500    18.000    4.119\n",
      "1586172700   2105    4.000   6.000       8.000    15.573  12.000   705.017   30.377\n",
      "1586696600     79    5.000  10.883      16.000    30.367  31.000   274.000   40.923\n",
      "1586897000    499    5.000   6.000       8.000    10.298  11.000   105.000    9.358\n",
      "1587490000   2097    4.000   7.000      10.950    20.482  21.000   468.950   30.850\n",
      "1588632600     99    4.983   6.008      10.983    23.429  18.333   262.983   38.581\n",
      "1588673400    103    5.000   9.000      33.000    81.179  74.033  1357.050  167.811\n",
      "1588794800    191    5.000  15.517      29.000    48.711  61.992   316.067   53.987\n",
      "1588958000    497    4.983   6.000       9.000    19.592  15.000   443.983   40.480\n",
      "1589018100    561    4.983   7.000      11.000    22.403  25.000   286.983   31.523\n",
      "1589034500   1919    4.317   7.000      12.000    27.937  27.000  1469.017   70.537\n",
      "1589324500    258    4.950   6.000      10.000    14.010  17.000    84.000   12.329\n",
      "1590136300    388    4.167   6.000       8.000    10.326  13.000    44.533    6.531\n",
      "1590616600    705    4.983   6.000       9.000    18.103  18.000   312.967   25.496\n",
      "1590854500    457    4.600   9.000      16.967    33.551  39.133   355.967   45.742\n",
      "1591609900   2051    4.000   6.000       8.000    14.898  14.000   374.983   22.073\n",
      "1592044700    135    5.000  12.483      28.000    49.192  57.492   339.983   60.852\n",
      "1592560500   1155    4.667   6.000       8.000    12.782  13.000   201.983   16.509\n",
      "1593087900   1080    4.017   6.000       9.000    30.996  17.929 12077.000  368.324\n",
      "1593416100    123    5.000   7.000      11.000    26.089  26.000   220.000   39.231\n",
      "1593472000    261    4.983   6.000       9.000    23.625  20.983   625.967   51.193\n",
      "1593593600   1069    4.000   6.000       8.950    15.273  14.000   388.000   27.975\n",
      "1593720800     62    5.000  12.000      31.492    62.468  49.000  1348.983  170.480\n",
      "1593838500     89    5.000   8.000      10.983    22.384  22.000   170.000   31.144\n",
      "1594173700     22    5.000   5.250       8.008     9.910  13.483    23.000    5.655\n",
      "1594294100    365    4.017   7.433      13.050    22.363  23.933   254.967   27.204\n",
      "1594305200    615    4.983   7.000      13.000    27.650  26.483  2234.967   94.748\n",
      "1594309800    160    4.983   6.000       8.000    21.099  16.250   303.000   39.050\n",
      "1594319200    348    4.817   6.000       7.000     8.683  11.000    43.000    4.380\n",
      "1594320800     45    5.000   8.000      23.000    52.890  54.000   266.000   67.493\n",
      "1594322600    175    4.967  12.000      21.000    30.098  38.483   177.133   27.235\n",
      "1594335100    406    4.000   6.000       8.000     9.710  11.000   137.000    8.568\n",
      "1594423700    329    5.000   6.000       8.000    13.515  14.000   281.050   18.851\n",
      "1594437200    446    4.933   7.000      11.000    21.719  23.000   296.033   29.796\n",
      "1594439800    214    4.983   6.000       9.000    33.986  26.017   507.950   69.570\n",
      "1594441900    247    4.000   6.000       9.000    40.328  17.000  1141.983  115.599\n",
      "1594455600      1  170.000 170.000     170.000   170.000 170.000   170.000      NaN\n",
      "1594464800    230    4.983   6.000       9.000    14.209  15.000   169.050   16.521\n",
      "1594467700     87    5.000   5.000       7.000    13.000  12.500   192.983   22.303\n",
      "1594471400    772    5.000   6.000       9.000    12.131  14.000   124.000   10.227\n",
      "1594479400    274    4.000   6.008       8.092    10.152  11.979    61.450    6.856\n",
      "1594511900    611    3.967   6.000       9.000    13.476  15.608   134.050   12.607\n",
      "1594528900    133    5.000   7.000       9.000    15.399  16.000   190.067   20.472\n",
      "1594533400     47    5.000   7.000      11.000    19.106  18.492   202.033   29.725\n",
      "\n",
      "【整體統計（單位：分鐘）】\n",
      " count  min_min  q1_min  median_min  mean_min  q3_min   max_min  std_min  p5_min  p95_min\n",
      " 58561    3.967   6.000      10.000    23.699  19.000 58731.050  266.361   5.000   75.000\n",
      "\n",
      "【未納入的區間原因摘要】\n",
      "reason\n",
      "ΔT(3.000) < 4×sampling(4.000)    12388\n",
      "pos<1                            12110\n",
      "ΔT(2.000) < 4×sampling(4.000)    11653\n",
      "ΔT(2.983) < 4×sampling(4.000)      451\n",
      "ΔT(3.983) < 4×sampling(4.000)      426\n",
      "ΔT(1.983) < 4×sampling(4.000)      372\n",
      "ΔT(3.017) < 4×sampling(4.000)      343\n",
      "ΔT(2.017) < 4×sampling(4.000)      308\n",
      "ΔT(1.000) < 4×sampling(3.800)      185\n",
      "ΔT(3.067) < 4×sampling(4.000)      134\n",
      "ΔT(2.967) < 4×sampling(4.000)      116\n",
      "ΔT(2.000) < 4×sampling(3.800)      112\n",
      "ΔT(3.967) < 4×sampling(4.000)      103\n",
      "ΔT(1.967) < 4×sampling(4.000)      101\n",
      "ΔT(2.067) < 4×sampling(4.000)       93\n",
      "ΔT(2.933) < 4×sampling(4.000)       90\n",
      "ΔT(2.917) < 4×sampling(4.000)       86\n",
      "ΔT(3.033) < 4×sampling(4.000)       80\n",
      "ΔT(3.000) < 4×sampling(3.800)       79\n",
      "ΔT(3.050) < 4×sampling(4.000)       77\n",
      "ΔT(2.083) < 4×sampling(4.000)       75\n",
      "ΔT(2.033) < 4×sampling(4.000)       74\n",
      "ΔT(1.917) < 4×sampling(4.000)       71\n",
      "ΔT(1.933) < 4×sampling(4.000)       70\n",
      "ΔT(3.083) < 4×sampling(4.000)       69\n",
      "ΔT(2.050) < 4×sampling(4.000)       59\n",
      "ΔT(3.950) < 4×sampling(4.000)       59\n",
      "ΔT(3.933) < 4×sampling(4.000)       58\n",
      "ΔT(3.917) < 4×sampling(4.000)       54\n",
      "ΔT(2.950) < 4×sampling(4.000)       51\n",
      "ΔT(1.950) < 4×sampling(4.000)       44\n",
      "ΔT(1.017) < 4×sampling(3.800)       30\n",
      "ΔT(2.833) < 4×sampling(4.000)       30\n",
      "ΔT(2.900) < 4×sampling(4.000)       28\n",
      "ΔT(2.167) < 4×sampling(4.000)       27\n",
      "ΔT(1.667) < 4×sampling(4.000)       27\n",
      "ΔT(2.750) < 4×sampling(4.000)       23\n",
      "ΔT(2.333) < 4×sampling(4.000)       21\n",
      "ΔT(3.900) < 4×sampling(4.000)       21\n",
      "ΔT(1.983) < 4×sampling(3.800)       21\n",
      "ΔT(2.667) < 4×sampling(4.000)       20\n",
      "ΔT(3.150) < 4×sampling(4.000)       19\n",
      "ΔT(1.833) < 4×sampling(4.000)       19\n",
      "ΔT(3.667) < 4×sampling(4.000)       19\n",
      "ΔT(2.233) < 4×sampling(4.000)       19\n",
      "ΔT(2.250) < 4×sampling(4.000)       18\n",
      "ΔT(3.833) < 4×sampling(4.000)       17\n",
      "ΔT(2.817) < 4×sampling(4.000)       17\n",
      "ΔT(2.100) < 4×sampling(4.000)       17\n",
      "ΔT(0.983) < 4×sampling(3.800)       17\n",
      "ΔT(2.983) < 4×sampling(3.800)       16\n",
      "ΔT(2.033) < 4×sampling(3.800)       16\n",
      "ΔT(2.017) < 4×sampling(3.800)       15\n",
      "ΔT(1.967) < 4×sampling(3.800)       15\n",
      "ΔT(2.150) < 4×sampling(4.000)       14\n",
      "neg<1,pos<1                         14\n",
      "ΔT(3.883) < 4×sampling(4.000)       14\n",
      "ΔT(3.133) < 4×sampling(4.000)       14\n",
      "ΔT(2.867) < 4×sampling(4.000)       14\n",
      "ΔT(3.867) < 4×sampling(4.000)       14\n",
      "ΔT(1.583) < 4×sampling(4.000)       13\n",
      "ΔT(3.717) < 4×sampling(4.000)       13\n",
      "ΔT(3.817) < 4×sampling(4.000)       13\n",
      "ΔT(3.750) < 4×sampling(4.000)       13\n",
      "ΔT(3.733) < 4×sampling(4.000)       13\n",
      "ΔT(3.250) < 4×sampling(4.000)       13\n",
      "ΔT(3.583) < 4×sampling(4.000)       13\n",
      "ΔT(2.583) < 4×sampling(4.000)       13\n",
      "ΔT(3.650) < 4×sampling(4.000)       13\n",
      "ΔT(1.883) < 4×sampling(4.000)       13\n",
      "ΔT(3.117) < 4×sampling(4.000)       12\n",
      "neg<1                               12\n",
      "ΔT(2.683) < 4×sampling(4.000)       12\n",
      "ΔT(2.850) < 4×sampling(4.000)       12\n",
      "ΔT(2.133) < 4×sampling(4.000)       12\n",
      "ΔT(2.883) < 4×sampling(4.000)       12\n",
      "ΔT(2.500) < 4×sampling(4.000)       11\n",
      "ΔT(2.650) < 4×sampling(4.000)       11\n",
      "ΔT(2.700) < 4×sampling(4.000)       11\n",
      "ΔT(2.317) < 4×sampling(4.000)       11\n",
      "ΔT(3.100) < 4×sampling(4.000)       11\n",
      "ΔT(1.500) < 4×sampling(4.000)       10\n",
      "ΔT(3.367) < 4×sampling(4.000)       10\n",
      "ΔT(1.033) < 4×sampling(3.800)       10\n",
      "ΔT(3.317) < 4×sampling(4.000)       10\n",
      "ΔT(3.417) < 4×sampling(4.000)       10\n",
      "ΔT(3.567) < 4×sampling(4.000)       10\n",
      "ΔT(1.250) < 4×sampling(4.000)       10\n",
      "ΔT(2.183) < 4×sampling(4.000)       10\n",
      "ΔT(2.733) < 4×sampling(4.000)        9\n",
      "ΔT(3.167) < 4×sampling(4.000)        9\n",
      "ΔT(3.333) < 4×sampling(4.000)        9\n",
      "ΔT(2.117) < 4×sampling(4.000)        9\n",
      "ΔT(1.750) < 4×sampling(4.000)        9\n",
      "ΔT(3.033) < 4×sampling(3.800)        9\n",
      "ΔT(2.800) < 4×sampling(4.000)        9\n",
      "ΔT(2.933) < 4×sampling(3.800)        9\n",
      "ΔT(3.700) < 4×sampling(4.000)        9\n",
      "ΔT(3.017) < 4×sampling(3.800)        9\n",
      "ΔT(1.900) < 4×sampling(4.000)        9\n",
      "ΔT(2.717) < 4×sampling(4.000)        9\n",
      "ΔT(2.633) < 4×sampling(4.000)        9\n",
      "ΔT(2.400) < 4×sampling(4.000)        9\n",
      "ΔT(1.683) < 4×sampling(4.000)        9\n",
      "ΔT(1.283) < 4×sampling(4.000)        8\n",
      "ΔT(1.650) < 4×sampling(4.000)        8\n",
      "ΔT(3.283) < 4×sampling(4.000)        8\n",
      "ΔT(2.300) < 4×sampling(4.000)        8\n",
      "ΔT(3.300) < 4×sampling(4.000)        8\n",
      "ΔT(2.567) < 4×sampling(4.000)        8\n",
      "ΔT(3.217) < 4×sampling(4.000)        8\n",
      "ΔT(1.417) < 4×sampling(4.000)        8\n",
      "ΔT(1.850) < 4×sampling(4.000)        8\n",
      "ΔT(2.417) < 4×sampling(4.000)        8\n",
      "ΔT(3.400) < 4×sampling(4.000)        8\n",
      "ΔT(2.967) < 4×sampling(3.800)        8\n",
      "ΔT(2.200) < 4×sampling(4.000)        8\n",
      "ΔT(2.467) < 4×sampling(4.000)        7\n",
      "ΔT(3.633) < 4×sampling(4.000)        7\n",
      "ΔT(3.467) < 4×sampling(4.000)        7\n",
      "ΔT(3.233) < 4×sampling(4.000)        7\n",
      "ΔT(3.683) < 4×sampling(4.000)        7\n",
      "ΔT(2.050) < 4×sampling(3.800)        7\n",
      "ΔT(2.383) < 4×sampling(4.000)        7\n",
      "ΔT(3.850) < 4×sampling(4.000)        7\n",
      "ΔT(1.567) < 4×sampling(4.000)        7\n",
      "ΔT(2.267) < 4×sampling(4.000)        7\n",
      "ΔT(1.817) < 4×sampling(4.000)        6\n",
      "ΔT(1.800) < 4×sampling(4.000)        6\n",
      "ΔT(2.450) < 4×sampling(4.000)        6\n",
      "ΔT(2.217) < 4×sampling(4.000)        6\n",
      "ΔT(1.633) < 4×sampling(4.000)        6\n",
      "ΔT(1.067) < 4×sampling(3.800)        6\n",
      "ΔT(2.517) < 4×sampling(4.000)        6\n",
      "ΔT(1.867) < 4×sampling(4.000)        6\n",
      "ΔT(1.700) < 4×sampling(4.000)        6\n",
      "ΔT(2.067) < 4×sampling(3.800)        5\n",
      "ΔT(3.183) < 4×sampling(4.000)        5\n",
      "ΔT(0.917) < 4×sampling(3.800)        5\n",
      "ΔT(2.600) < 4×sampling(4.000)        5\n",
      "ΔT(3.483) < 4×sampling(4.000)        5\n",
      "ΔT(3.433) < 4×sampling(4.000)        5\n",
      "ΔT(3.500) < 4×sampling(4.000)        5\n",
      "ΔT(1.217) < 4×sampling(4.000)        5\n",
      "ΔT(3.800) < 4×sampling(4.000)        5\n",
      "ΔT(1.050) < 4×sampling(3.800)        5\n",
      "ΔT(3.533) < 4×sampling(4.000)        5\n",
      "ΔT(2.483) < 4×sampling(4.000)        4\n",
      "ΔT(3.550) < 4×sampling(4.000)        4\n",
      "ΔT(1.733) < 4×sampling(4.000)        4\n",
      "ΔT(1.333) < 4×sampling(4.000)        4\n",
      "ΔT(1.950) < 4×sampling(3.800)        4\n",
      "ΔT(3.067) < 4×sampling(3.800)        4\n",
      "ΔT(2.433) < 4×sampling(4.000)        4\n",
      "ΔT(0.967) < 4×sampling(3.800)        4\n",
      "ΔT(1.783) < 4×sampling(4.000)        4\n",
      "ΔT(3.383) < 4×sampling(4.000)        4\n",
      "ΔT(3.200) < 4×sampling(4.000)        4\n",
      "ΔT(1.600) < 4×sampling(4.000)        4\n",
      "ΔT(2.367) < 4×sampling(4.000)        4\n",
      "ΔT(1.550) < 4×sampling(4.000)        4\n",
      "ΔT(3.617) < 4×sampling(4.000)        4\n",
      "ΔT(3.783) < 4×sampling(4.000)        4\n",
      "ΔT(1.150) < 4×sampling(3.800)        3\n",
      "ΔT(2.283) < 4×sampling(4.000)        3\n",
      "ΔT(3.100) < 4×sampling(3.800)        3\n",
      "ΔT(2.950) < 4×sampling(3.800)        3\n",
      "ΔT(3.267) < 4×sampling(4.000)        3\n",
      "ΔT(1.267) < 4×sampling(4.000)        3\n",
      "ΔT(3.517) < 4×sampling(4.000)        3\n",
      "ΔT(1.767) < 4×sampling(4.000)        3\n",
      "ΔT(1.350) < 4×sampling(4.000)        3\n",
      "ΔT(3.350) < 4×sampling(4.000)        3\n",
      "ΔT(1.117) < 4×sampling(3.800)        3\n",
      "ΔT(2.767) < 4×sampling(4.000)        3\n",
      "ΔT(3.450) < 4×sampling(4.000)        3\n",
      "ΔT(1.083) < 4×sampling(4.000)        3\n",
      "ΔT(2.533) < 4×sampling(4.000)        3\n",
      "ΔT(3.767) < 4×sampling(4.000)        3\n",
      "ΔT(3.350) < 4×sampling(3.800)        2\n",
      "ΔT(2.133) < 4×sampling(3.800)        2\n",
      "ΔT(1.800) < 4×sampling(3.800)        2\n",
      "ΔT(1.917) < 4×sampling(3.800)        2\n",
      "ΔT(2.317) < 4×sampling(3.800)        2\n",
      "ΔT(1.900) < 4×sampling(3.800)        2\n",
      "ΔT(2.450) < 4×sampling(3.800)        2\n",
      "ΔT(0.950) < 4×sampling(3.800)        2\n",
      "ΔT(1.250) < 4×sampling(3.800)        2\n",
      "ΔT(1.083) < 4×sampling(3.800)        2\n",
      "ΔT(2.867) < 4×sampling(3.800)        2\n",
      "ΔT(3.650) < 4×sampling(3.800)        2\n",
      "ΔT(1.783) < 4×sampling(3.800)        2\n",
      "ΔT(2.917) < 4×sampling(3.800)        2\n",
      "ΔT(3.150) < 4×sampling(3.800)        2\n",
      "ΔT(0.933) < 4×sampling(3.800)        2\n",
      "ΔT(2.733) < 4×sampling(3.800)        2\n",
      "ΔT(1.933) < 4×sampling(3.800)        2\n",
      "ΔT(2.567) < 4×sampling(3.800)        2\n",
      "ΔT(2.350) < 4×sampling(4.000)        2\n",
      "ΔT(2.150) < 4×sampling(3.800)        2\n",
      "ΔT(2.267) < 4×sampling(3.800)        2\n",
      "ΔT(1.167) < 4×sampling(4.000)        2\n",
      "ΔT(2.550) < 4×sampling(4.000)        2\n",
      "ΔT(1.300) < 4×sampling(4.000)        2\n",
      "ΔT(1.450) < 4×sampling(4.000)        2\n",
      "ΔT(1.467) < 4×sampling(4.000)        2\n",
      "ΔT(1.483) < 4×sampling(4.000)        2\n",
      "ΔT(1.200) < 4×sampling(4.000)        2\n",
      "ΔT(1.717) < 4×sampling(4.000)        2\n",
      "ΔT(1.233) < 4×sampling(4.000)        2\n",
      "ΔT(1.400) < 4×sampling(4.000)        2\n",
      "ΔT(1.117) < 4×sampling(4.000)        2\n",
      "ΔT(3.600) < 4×sampling(4.000)        2\n",
      "ΔT(2.817) < 4×sampling(3.800)        2\n",
      "ΔT(2.250) < 4×sampling(3.800)        2\n",
      "ΔT(1.433) < 4×sampling(3.800)        1\n",
      "ΔT(1.750) < 4×sampling(3.800)        1\n",
      "ΔT(3.400) < 4×sampling(3.800)        1\n",
      "ΔT(1.567) < 4×sampling(3.800)        1\n",
      "ΔT(1.333) < 4×sampling(3.800)        1\n",
      "ΔT(0.850) < 4×sampling(3.800)        1\n",
      "ΔT(1.350) < 4×sampling(3.800)        1\n",
      "ΔT(0.650) < 4×sampling(3.800)        1\n",
      "ΔT(1.517) < 4×sampling(4.000)        1\n",
      "ΔT(1.883) < 4×sampling(3.800)        1\n",
      "ΔT(3.517) < 4×sampling(3.800)        1\n",
      "ΔT(0.667) < 4×sampling(3.800)        1\n",
      "ΔT(1.500) < 4×sampling(3.800)        1\n",
      "ΔT(2.200) < 4×sampling(3.800)        1\n",
      "ΔT(1.667) < 4×sampling(3.800)        1\n",
      "ΔT(2.117) < 4×sampling(3.800)        1\n",
      "ΔT(3.050) < 4×sampling(3.800)        1\n",
      "ΔT(2.550) < 4×sampling(3.800)        1\n",
      "ΔT(2.467) < 4×sampling(3.800)        1\n",
      "ΔT(3.383) < 4×sampling(3.800)        1\n",
      "ΔT(1.617) < 4×sampling(3.800)        1\n",
      "ΔT(3.633) < 4×sampling(3.800)        1\n",
      "ΔT(2.833) < 4×sampling(3.800)        1\n",
      "ΔT(3.550) < 4×sampling(3.800)        1\n",
      "ΔT(3.183) < 4×sampling(3.800)        1\n",
      "ΔT(3.283) < 4×sampling(3.800)        1\n",
      "ΔT(0.117) < 4×sampling(3.800)        1\n",
      "ΔT(3.750) < 4×sampling(3.800)        1\n",
      "ΔT(1.550) < 4×sampling(3.800)        1\n",
      "ΔT(1.683) < 4×sampling(3.800)        1\n",
      "ΔT(1.183) < 4×sampling(4.000)        1\n",
      "ΔT(2.617) < 4×sampling(3.800)        1\n",
      "ΔT(1.367) < 4×sampling(3.800)        1\n",
      "ΔT(3.167) < 4×sampling(3.800)        1\n",
      "ΔT(2.617) < 4×sampling(4.000)        1\n",
      "ΔT(1.617) < 4×sampling(4.000)        1\n",
      "ΔT(1.817) < 4×sampling(3.800)        1\n",
      "ΔT(1.767) < 4×sampling(3.800)        1\n",
      "ΔT(2.783) < 4×sampling(3.800)        1\n",
      "ΔT(0.267) < 4×sampling(3.800)        1\n",
      "ΔT(1.317) < 4×sampling(4.000)        1\n",
      "ΔT(2.783) < 4×sampling(4.000)        1\n",
      "ΔT(1.383) < 4×sampling(4.000)        1\n",
      "ΔT(1.150) < 4×sampling(4.000)        1\n",
      "ΔT(0.783) < 4×sampling(3.800)        1\n",
      "ΔT(2.850) < 4×sampling(3.800)        1\n",
      "ΔT(1.533) < 4×sampling(4.000)        1\n",
      "ΔT(0.883) < 4×sampling(3.800)        1\n",
      "ΔT(2.100) < 4×sampling(3.800)        1\n",
      "ΔT(3.533) < 4×sampling(3.800)        1\n",
      "ΔT(0.500) < 4×sampling(3.800)        1\n",
      "ΔT(1.833) < 4×sampling(3.800)        1\n",
      "ΔT(3.600) < 4×sampling(3.800)        1\n",
      "ΔT(1.217) < 4×sampling(3.800)        1\n",
      "ΔT(1.583) < 4×sampling(3.800)        1\n",
      "ΔT(2.383) < 4×sampling(3.800)        1\n",
      "ΔT(2.883) < 4×sampling(3.800)        1\n",
      "ΔT(1.383) < 4×sampling(3.800)        1\n",
      "ΔT(3.717) < 4×sampling(3.800)        1\n",
      "ΔT(1.633) < 4×sampling(3.800)        1\n",
      "ΔT(2.700) < 4×sampling(3.800)        1\n",
      "ΔT(3.233) < 4×sampling(3.800)        1\n",
      "\n",
      "【未納入的區間（前 20 筆預覽）】\n",
      "  patno                  T1                  T2  delta_min  base_dt_min                        reason\n",
      "7108162 2024-09-18 13:43:04 2024-09-18 13:45:04      2.000        1.000 ΔT(2.000) < 4×sampling(4.000)\n",
      "7108162 2024-09-18 14:39:02 2024-09-18 14:42:02      3.000        1.000 ΔT(3.000) < 4×sampling(4.000)\n",
      "7108162 2024-09-18 14:42:02 2024-09-18 14:46:02      4.000        1.000                         pos<1\n",
      "7108162 2024-09-18 14:46:02 2024-09-18 14:49:02      3.000        1.000 ΔT(3.000) < 4×sampling(4.000)\n",
      "7108162 2024-09-18 16:26:02 2024-09-18 16:29:01      2.983        1.000 ΔT(2.983) < 4×sampling(4.000)\n",
      "7408338 2024-09-17 17:58:02 2024-09-17 18:00:02      2.000        1.000 ΔT(2.000) < 4×sampling(4.000)\n",
      "7408338 2024-09-17 19:13:03 2024-09-17 19:16:03      3.000        1.000 ΔT(3.000) < 4×sampling(4.000)\n",
      "7408338 2024-09-17 19:16:03 2024-09-17 19:18:03      2.000        1.000 ΔT(2.000) < 4×sampling(4.000)\n",
      "7408338 2024-09-17 19:18:03 2024-09-17 19:21:03      3.000        1.000 ΔT(3.000) < 4×sampling(4.000)\n",
      "7408338 2024-09-17 19:21:03 2024-09-17 19:23:03      2.000        1.000 ΔT(2.000) < 4×sampling(4.000)\n",
      "7408338 2024-09-17 19:28:03 2024-09-17 19:31:03      3.000        1.000 ΔT(3.000) < 4×sampling(4.000)\n",
      "7408338 2024-09-17 19:47:05 2024-09-17 19:49:03      1.967        1.000 ΔT(1.967) < 4×sampling(4.000)\n",
      "7408338 2024-09-17 19:49:03 2024-09-17 19:52:03      3.000        1.000 ΔT(3.000) < 4×sampling(4.000)\n",
      "7408338 2024-09-17 19:57:03 2024-09-17 20:00:03      3.000        1.000 ΔT(3.000) < 4×sampling(4.000)\n",
      "7408338 2024-09-17 20:00:03 2024-09-17 20:04:03      4.000        1.000                         pos<1\n",
      "7408338 2024-09-17 20:13:03 2024-09-17 20:15:03      2.000        1.000 ΔT(2.000) < 4×sampling(4.000)\n",
      "7408338 2024-09-17 20:21:03 2024-09-17 20:24:03      3.000        1.000 ΔT(3.000) < 4×sampling(4.000)\n",
      "7408338 2024-09-17 20:29:03 2024-09-17 20:32:03      3.000        1.000 ΔT(3.000) < 4×sampling(4.000)\n",
      "7408338 2024-09-17 20:50:03 2024-09-17 20:54:03      4.000        1.000                         pos<1\n",
      "7408338 2024-09-17 20:54:03 2024-09-17 20:58:03      4.000        1.000                         pos<1\n"
     ]
    }
   ],
   "source": [
    "# ================================================\n",
    "# 目的（升級版規則）：\n",
    "#   1) 讀取 /home/jovyan/RT08/0925/bling_1004 內所有 CSV，欄位皆已存在且命名如下（不分大小寫）：\n",
    "#      - NaN_check, ad_para, patno, senddate（已為 datetime64）\n",
    "#   2) 只在 NaN_check=1 的資料上操作。\n",
    "#   3) 將「連續的 ad_para=1」視為同一個「調參事件」，只保留連續區段的「最後一筆 ad_para=1」作為事件時間 T。\n",
    "#   4) 對相鄰事件（T₁, T₂）定義一個「調參區間」並施加樣本取得限制：\n",
    "#        - 負樣本 set=0：取 T₁ 之後區段的前 50%\n",
    "#        - 正樣本 set=1：取 T₂ 之前區段的後 20%\n",
    "#        - 中間需保留 30% 的緩衝，避免重疊\n",
    "#      為確保 set=0 與 set=1 皆至少各有一筆資料，ΔT（=T₂−T₁）需滿足：\n",
    "#        ΔT ≥ 4 ×（該病患資料的取樣間隔，分鐘）\n",
    "#      （例如逐分鐘資料需 ≥4 分鐘；逐 5 分鐘資料需 ≥20 分鐘）\n",
    "#   5) 僅統計「符合上述條件」的區間，輸出（直接印出，不另存檔案）：\n",
    "#        - 保留的區間清單（前 20 筆預覽）\n",
    "#        - 依病患彙總統計（count/min/Q1/median/mean/Q3/max/std）\n",
    "#        - 整體統計（含 p5/p95）\n",
    "# ================================================\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from pathlib import Path\n",
    "\n",
    "# -----------------------------\n",
    "# A. 讀檔：來源資料夾\n",
    "# -----------------------------\n",
    "data_dir = Path(\"/home/jovyan/RT08/0925/bling_1004\")\n",
    "files = sorted(data_dir.glob(\"*.csv\"))\n",
    "if not files:\n",
    "    raise FileNotFoundError(\"⚠️ 找不到任何 CSV 檔案，請確認路徑與檔案。\")\n",
    "\n",
    "# -----------------------------\n",
    "# B. 合併資料（欄位名一律轉小寫）\n",
    "#    已知欄位：nan_check, ad_para, patno, senddate\n",
    "# -----------------------------\n",
    "chunks = []\n",
    "for fp in files:\n",
    "    _df = pd.read_csv(fp, low_memory=False, parse_dates=[\"senddate\"])\n",
    "    _df.columns = [str(c).lower() for c in _df.columns]\n",
    "    needed = [\"nan_check\", \"ad_para\", \"patno\", \"senddate\"]\n",
    "    for c in needed:\n",
    "        if c not in _df.columns:\n",
    "            raise KeyError(f\"❌ 檔案 {fp.name} 缺少必要欄位 {c}\")\n",
    "    chunks.append(_df[needed])\n",
    "\n",
    "df_all = pd.concat(chunks, ignore_index=True)\n",
    "\n",
    "# 僅在 NaN_check=1 的資料上操作；並確保 senddate 有值\n",
    "df_all = df_all[(df_all[\"nan_check\"] == 1) & df_all[\"senddate\"].notna()].copy()\n",
    "\n",
    "# 排序（病患、時間）\n",
    "df_all = df_all.sort_values([\"patno\", \"senddate\"]).reset_index(drop=True)\n",
    "\n",
    "if df_all.empty:\n",
    "    raise ValueError(\"⚠️ 篩選後無任何可用資料（nan_check==1 且 senddate 非空）。\")\n",
    "\n",
    "# -----------------------------\n",
    "# C. 針對每位病患：\n",
    "#    1) 推估取樣間隔（分鐘）：採各病患全資料的相鄰時間差中位數\n",
    "#    2) 將「連續的 ad_para=1」折疊為一個事件，只保留連續區段的最後一筆（事件時間 T）\n",
    "#    3) 逐對相鄰事件 (T₁, T₂) 計算 ΔT（分鐘）\n",
    "#    4) 檢查 ΔT 是否 ≥ 4 × 取樣間隔；且實際資料中\n",
    "#       - set=0（T₁ 到 T₁+0.5ΔT）至少 1 筆\n",
    "#       - set=1（T₂−0.2ΔT 到 T₂）至少 1 筆\n",
    "#       兩段互不重疊（理論上 50% + 30% + 20% = 100%）\n",
    "# -----------------------------\n",
    "kept_intervals = []   # 符合條件的區間清單\n",
    "dropped_intervals = []  # 不符合條件的區間（記錄原因）\n",
    "\n",
    "# 統一轉為布林方便操作（允許資料中為 0/1 或 True/False）\n",
    "df_all[\"ad_para\"] = df_all[\"ad_para\"].astype(int)\n",
    "\n",
    "# 逐病患處理\n",
    "for pid, g in df_all.groupby(\"patno\", dropna=False, sort=False):\n",
    "    g = g.sort_values(\"senddate\").reset_index(drop=True)\n",
    "\n",
    "    # --- (C1) 估算該病患的取樣間隔（分鐘） ---\n",
    "    diffs = g[\"senddate\"].diff().dropna().dt.total_seconds() / 60.0\n",
    "    diffs = diffs[diffs > 0]  # 移除 0 或負值（理論上不應有）\n",
    "    if diffs.empty:\n",
    "        # 若無法估計取樣間隔，跳過該病患\n",
    "        continue\n",
    "    base_dt_min = float(diffs.median())\n",
    "\n",
    "    # --- (C2) 折疊「連續 ad_para=1」為單一事件（僅保留連續區段最後一筆） ---\n",
    "    # 先在該病患資料內建立布林欄位\n",
    "    g[\"is1\"] = g[\"ad_para\"] == 1\n",
    "    # 標記「連續區段的開始」：本列為 1 且前一列不是 1\n",
    "    g[\"start_run\"] = g[\"is1\"] & (~g[\"is1\"].shift(fill_value=False))\n",
    "    # 建立「連續區段 id」：每遇到 start_run 就 +1\n",
    "    g[\"run_id\"] = g[\"start_run\"].cumsum()\n",
    "    # 僅針對 ad_para==1 的列，取同一 run_id 的最後一個時間點作為事件時間\n",
    "    runs = (\n",
    "        g[g[\"is1\"]]\n",
    "        .groupby(\"run_id\", dropna=False, as_index=False)\n",
    "        .agg(last_time=(\"senddate\", \"max\"))\n",
    "        .sort_values(\"last_time\")\n",
    "        .reset_index(drop=True)\n",
    "    )\n",
    "\n",
    "    if runs.empty or len(runs) < 2:\n",
    "        # 少於 2 個事件，無法形成區間\n",
    "        continue\n",
    "\n",
    "    # --- (C3) 逐對相鄰事件 (T1, T2) 建立區間並檢查樣本充足性 ---\n",
    "    for i in range(len(runs) - 1):\n",
    "        T1 = runs.loc[i, \"last_time\"]\n",
    "        T2 = runs.loc[i + 1, \"last_time\"]\n",
    "        delta_min = (T2 - T1).total_seconds() / 60.0\n",
    "\n",
    "        # 需要至少 ΔT >= 4 × base_dt_min\n",
    "        min_required = 4.0 * base_dt_min\n",
    "        if delta_min < min_required:\n",
    "            dropped_intervals.append({\n",
    "                \"patno\": pid, \"T1\": T1, \"T2\": T2, \"delta_min\": delta_min,\n",
    "                \"base_dt_min\": base_dt_min, \"reason\": f\"ΔT({delta_min:.3f}) < 4×sampling({min_required:.3f})\"\n",
    "            })\n",
    "            continue\n",
    "\n",
    "        # 計算三段的理論邊界（50% / 30% / 20%）\n",
    "        neg_end_time = T1 + pd.Timedelta(minutes=0.5 * delta_min)   # set=0 的上界\n",
    "        pos_start_time = T2 - pd.Timedelta(minutes=0.2 * delta_min) # set=1 的下界（中間預留 30%）\n",
    "\n",
    "        # 實際檢查該病患在兩段內是否「至少各有 1 筆資料」\n",
    "        # 注意：此處樣本來自「NaN_check=1 的全資料」g（不限定 ad_para），\n",
    "        # 以符合「樣本來源」的描述（調參間的資料區段）\n",
    "        neg_mask = (g[\"senddate\"] > T1) & (g[\"senddate\"] <= neg_end_time)\n",
    "        pos_mask = (g[\"senddate\"] >= pos_start_time) & (g[\"senddate\"] < T2)\n",
    "\n",
    "        neg_count = int(neg_mask.sum())\n",
    "        pos_count = int(pos_mask.sum())\n",
    "\n",
    "        if (neg_count >= 1) and (pos_count >= 1) and (neg_end_time <= pos_start_time):\n",
    "            # 符合條件 → 納入統計\n",
    "            kept_intervals.append({\n",
    "                \"patno\": pid,\n",
    "                \"T1\": T1, \"T2\": T2,\n",
    "                \"delta_min\": float(delta_min),\n",
    "                \"sampling_min\": base_dt_min,\n",
    "                \"neg_count\": neg_count,  # set=0 片段樣本數\n",
    "                \"pos_count\": pos_count   # set=1 片段樣本數\n",
    "            })\n",
    "        else:\n",
    "            # 不符合條件 → 紀錄原因\n",
    "            reason = []\n",
    "            if neg_count < 1:\n",
    "                reason.append(\"neg<1\")\n",
    "            if pos_count < 1:\n",
    "                reason.append(\"pos<1\")\n",
    "            if neg_end_time > pos_start_time:\n",
    "                reason.append(\"segments_overlap\")\n",
    "            dropped_intervals.append({\n",
    "                \"patno\": pid, \"T1\": T1, \"T2\": T2, \"delta_min\": delta_min,\n",
    "                \"base_dt_min\": base_dt_min, \"reason\": \",\".join(reason) if reason else \"unknown\"\n",
    "            })\n",
    "\n",
    "# -----------------------------\n",
    "# D. 結果組裝與統計\n",
    "# -----------------------------\n",
    "kept_df = pd.DataFrame(kept_intervals)\n",
    "\n",
    "if kept_df.empty:\n",
    "    # 若沒有任何符合條件的區間，提供落選原因摘要，協助診斷\n",
    "    dropped_df = pd.DataFrame(dropped_intervals)\n",
    "    pd.set_option(\"display.max_rows\", 200)\n",
    "    print(\"❌ 沒有任何區間符合『連續=折疊最後一筆、且 ΔT ≥ 4×取樣間隔、正負樣本各≥1 且不重疊』的條件。\")\n",
    "    if not dropped_df.empty:\n",
    "        print(\"\\n【落選區間原因統計】\")\n",
    "        print(dropped_df[\"reason\"].value_counts().to_string())\n",
    "        print(\"\\n【落選區間（前 50 筆）】\")\n",
    "        print(dropped_df.head(50).to_string(index=False))\n",
    "    raise SystemExit\n",
    "\n",
    "# 依病患計算統計（針對符合條件的 ΔT）\n",
    "per_patient = (\n",
    "    kept_df\n",
    "    .groupby(\"patno\", dropna=False)[\"delta_min\"]\n",
    "    .agg(\n",
    "        count=\"count\",\n",
    "        min_min=\"min\",\n",
    "        q1_min=lambda s: s.quantile(0.25),\n",
    "        median_min=\"median\",\n",
    "        mean_min=\"mean\",\n",
    "        q3_min=lambda s: s.quantile(0.75),\n",
    "        max_min=\"max\",\n",
    "        std_min=lambda s: s.std(ddof=1)\n",
    "    )\n",
    "    .reset_index()\n",
    ")\n",
    "\n",
    "# 整體統計\n",
    "s = kept_df[\"delta_min\"]\n",
    "overall = pd.DataFrame([{\n",
    "    \"count\": int(s.count()),\n",
    "    \"min_min\": float(s.min()),\n",
    "    \"q1_min\": float(s.quantile(0.25)),\n",
    "    \"median_min\": float(s.median()),\n",
    "    \"mean_min\": float(s.mean()),\n",
    "    \"q3_min\": float(s.quantile(0.75)),\n",
    "    \"max_min\": float(s.max()),\n",
    "    \"std_min\": float(s.std(ddof=1)),\n",
    "    \"p5_min\": float(s.quantile(0.05)),\n",
    "    \"p95_min\": float(s.quantile(0.95))\n",
    "}])\n",
    "\n",
    "# -----------------------------\n",
    "# E. 顯示輸出（僅印出，不存檔）\n",
    "# -----------------------------\n",
    "pd.set_option(\"display.float_format\", \"{:.3f}\".format)\n",
    "\n",
    "print(\"✅ 條件套用完成：已將『連續 ad_para=1』折疊為單一事件（保留最後一筆），並檢核樣本充足性與不重疊。\")\n",
    "print(f\"總共保留的區間數：{len(kept_df)}\")\n",
    "\n",
    "print(\"\\n【保留的區間（前 20 筆預覽）】\")\n",
    "print(kept_df.sort_values([\"patno\", \"T1\"]).head(20).to_string(index=False))\n",
    "\n",
    "print(\"\\n【依病患統計（單位：分鐘）】\")\n",
    "print(per_patient.to_string(index=False))\n",
    "\n",
    "print(\"\\n【整體統計（單位：分鐘）】\")\n",
    "print(overall.to_string(index=False))\n",
    "\n",
    "# 若有落選的區間，也提供摘要，便於理解哪些條件卡住\n",
    "dropped_df = pd.DataFrame(dropped_intervals)\n",
    "if not dropped_df.empty:\n",
    "    print(\"\\n【未納入的區間原因摘要】\")\n",
    "    print(dropped_df[\"reason\"].value_counts().to_string())\n",
    "\n",
    "    print(\"\\n【未納入的區間（前 20 筆預覽）】\")\n",
    "    print(\n",
    "        dropped_df[[\"patno\", \"T1\", \"T2\", \"delta_min\", \"base_dt_min\", \"reason\"]]\n",
    "        .sort_values([\"patno\", \"T1\"])\n",
    "        .head(20)\n",
    "        .to_string(index=False)\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5f798f79-592b-4e48-88c9-b42a57a84243",
   "metadata": {},
   "outputs": [],
   "source": [
    "為何很多區間被剔除？\n",
    "「未納入原因摘要」說明三大類主因：\n",
    "ΔT(...) < 4×sampling(...)：間隔太短，先被卡關。\n",
    "pos<1（或 neg<1）：正（或負）樣本段內沒有任何資料列。\n",
    "overlap：理論上少見，多半因極端時間點或浮點邊界引發的段落相交（也做了邊界保護）\n",
    "\n",
    "這 58,561 個，是在「折疊連續 ad_para=1 只留最後一筆」後，\n",
    "逐病患計算出的、彼此相鄰的調參事件對中，\n",
    "既滿足「ΔT ≥ 4×取樣間隔」又能在規定的 50%/30%/20% 分段下，\n",
    "同時取到有效負樣本與正樣本（各≥1筆且不重疊）的所有區間數量"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4c12c3b0-1984-4dde-a3ad-44743fe04fa3",
   "metadata": {},
   "outputs": [],
   "source": [
    "直接秀圖"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "id": "c4bc3e10-2ed7-4c35-8253-958ebef9dbce",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "✅ Loaded 122 files, total rows: 1,336,870\n",
      "✅ Valid intervals retained: 58,561\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x450 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x450 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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YsQObNm1C2bJlAWRcz2fMmCEed8SIEWLd8/Lyws8//4ytW7eiVKlSeR7A7O/vj1mzZmHTpk3YvXs3Dh48iD///BPdunUDAMTGxmLevHkAMq49ERERYpdGION8Up8zffv21flz79+/j7Jly+b6XeDp6QlXV1dxn4ULFyImJgaPHz/O8t2kzrf6OgUA169fx4YNG5CcnIyIiAjs2bMny/YfWvHixWFhYSH+Pnv2bMTHx+P27dv46aeftLZVd1O6deuWuMzHx0drG83fBUEQJ2/IjSAI6NWrF16+fImOHTuie/fu+cpLZtevX8fQoUOxc+dOra7B06ZNQ4kSJbB161at7+n8fEfeuHEDw4cPx44dO9CuXTtxufq6rkkul4vdvl+8eIH79+/nNUuGR7+xC2Wm+bSiatWq4vLSpUuLy+fNm5fj/u+75UEQBMHX1zfbiD27aH3Pnj3isu7duws3btwQ0tLScvz8tw3a1XwqIZFIhAsXLmTZRpeWBxsbG+HZs2fius2bN2sdV/2kJa8tD5k/J7sBqDm1PIwePVpcVrJkSa1m7jlz5ojr3N3dxXWa6StVqpS4PD09XatV5uLFizn+zdU0j5W52XnYsGHiOs1m7H79+onLFy1aJAiCIFy8eFFcVq5cubd+bubPzu1n+vTp4j7//fef0Lt3byEwMFCQy+XZbq+Z77cNmM6p5aFdu3bi8h49emh1G2jWrJm47tNPPxX30awbQ4cOFZcfP35cXO7q6qr1+fkdYHrq1Klsrxk50fz7aH7Ou9Y/XeuMrvLT8nDu3Dlx4DiQfZclQRCEpKQkcRtbW1udjq1Z1rt27dJaFxcXp9UaMH/+/FyP9S4tD4UKFdLqBqjZIuLs7Cw+1cxr2fz111/iMg8PD+Hw4cNiPV+8eLG4zsfHRxCEjFZlzVaqTZs2icd/9uyZYGNjk+fyW7NmjdCwYUPB09Mz29aVChUqaG2f3wHT+fku0Bwsn9uPukVRqVQKFSpUeOv2MplM53TnxdtaHgRBEEaMGKFTno4ePSoIgvb1vk6dOlrHunXrltY+ukwGMnfuXLFOx8TECIIgFEjLQ5UqVcTlGzdu1EqXujUpPT1dq8VJ87tCl5aH5s2bi8sfPXqk9RmvXr3Kkr4qVaqI60+dOqVzvgwVWx4MzOrVq8X/a7Y4qAfcANDr9J/p6elaT5idnZ1z3b527dooU6YMgIypEYODg2FjY4OSJUti4MCBOH36dL7TEhQUJD4Ry6vQ0FC4u7uLv2s+nRAEQesJy4dy9epV8f81a9bUegeE5hPr58+fi09WNdWvX1/cRyqVwsXFRVynWWa6yDyQW/P3GzduiP8fOnSo+H/106qCaHXIrGjRovj+++8xbdo0ABlPgatUqYJVq1bh1q1bUCgU2e734sWLd/7sK1euiP9fs2YNateuLf7s3LlTXHf58uVs969fv774f806l9cyyYmgMTWi5v/z6l3rn6515n3ZtWsX6tSpIw4cnzhxIsaPH5/ttpp/J13ftZLb3/n69etaA/nbtm2rc7rzqmrVqrCyshJ/1/w7x8XF5btsNOv5s2fPUKdOHbGeDx8+XFz36NEjxMbG4ubNm1p/B813i7i7u2u17uhi6tSp6NGjB/bt24enT59mOzFCQZzPmen6XTBq1CgsXrw4y0D+Nm3aaLUqqa+7lpaW2LNnD7p06aL1hN/JyQktW7bMsr3amTNncOTIEZ1+nj59+k55X7BgAaZNmwZHR0dxmYWFhdb9hmYa1S2QQEZLlabMv6tbxHLy4MEDTJkyBRKJBCtXrtRq2XlX1atXF/+vWbZOTk5ii5BUKtX6zLxej3O6rud0rPxccwyZpb4TQG8cP35cnHlBKpWiY8eO4rrOnTuLc7f/999/OHv2LCpWrPjB0xgREYHk5GTx97elQS6X48iRI1i5ciX++ecfXLt2DXfu3MHVq1dx9epVrFq1CsePH89XXjI3m74Pmid55i+z9/m+i8x0uSnMfPG1tHxzer/LTWVuwsLCUL16dRw/fhwXLlzAuXPnsGnTJgAZXTfUXQ7yonz58mJXAGtra/j4+KBw4cJa2yxevBhJSUkAMoLIadOmwc/PD1KpFG3atBFvoFQq1btkL0/Us5ZkplkummVSUDRvZgoqIMnsfdWfgrJs2TIMHToU6enpkEqlWLBgAUaOHJnj9pp/J/XMMm+T298589/HFG4OcpNdXX+XPCuVSq2ubV26dEH37t3h6OiI06dPY/To0QA+7PmcnWHDhuGTTz7B9evXkZiYiGLFiiEhIUFrZizNB1pubm5Yt24dli1bhuvXr8PW1haBgYGYPXs2/vrrryzbA0D79u3FbkJvs3Llylzf4fA2FhYWmD59OiZPnoxr164hLS0NQUFB+Pfff7FhwwYAgK2trTijlLqbMZARRGp6+PCh+H+JRIJixYrl+tnPnj0TA44mTZpku01UVBQkEglat26dp3fzaD7UlEql2S7PLK/XuNyu69kdKz/XHEPGlgcDotmiUK9ePa2b48DAQFSuXDnbbT+UuLg4jBo1Svzd3d0dzZo1y3UfQRDg6OiIkSNHYtu2bYiMjERCQoL4JEupVIo3m4D2if62L4p3+bK6du2aOBYDyOjLrXlc9cVS86mQZj/Ff/75R7x5zSwvedCk2Uf22LFjWhcgzekm3d3dszzpKGiaf4/Mv2ee/nbYsGHi/8eOHSs+wW7ZsmW+0qk5ZWrlypWzBA5ARj9/tREjRqB79+6oU6cOfH19tcpVU0GUy8SJEyFkTDSh9aNUKrVaIfIjv+nz8/MTpxOMiorK8gRQV+9a//JSZwqKIAiYMGECBg8ejPT0dNja2uL333/PNXAAoDU9prpl9G00t8s8vWZISIjWDUR20zEWVAB26tQprReSaf6dnZ2d8102muXv5+cHpVKZbV1/9eoV/P39ERQUpHUNPnbsmPj/mJiYPE1BGhMTg8TERPH3ZcuWoVmzZqhVq5Y4piI7+T1nNOn6XaD5maGhoahYsSJcXFy0xtpVrVoVvr6+WT7DwcEBFStWRIkSJZCQkIAlS5aI6zT7y+uLTCZDmTJlEBYWBnt7e3z11VfiupYtW4pjZDSftkdHR2uN19B8qWulSpW0WjPMnUKhEL+znJycUKRIET2n6N2x5cFAZH63g1KpxKeffqq1jeaFcv369Zg/f75W83VBe/r0KY4cOYLExEScO3cOS5cuxb1798T1ixcvhoODQ67HOHXqFPr164ePP/4YJUqUgLe3NxITE7UGWmt2OXFzcxP/rx7sZGtrC39//wI94ZKTk9GmTRuMHTsWL168EN8CCQANGzYUnypoDoxcv369+BbOuXPn5nhszTxcvHgRW7ZsgaenJ5ydnVG6dOkc9+vVqxcWLlwIQRBw+fJl9OzZE127dsWNGzfw+eefa233vp9snjp1CgMHDkTbtm1x7tw5LFu2TFzXqVMnrW3bt2+P0aNH48mTJzhw4IC4/F2eiL2N5lOtFStWICAgAImJiZg+fXqON2ma5RIREYEdO3bAyckJ3t7eWW4QNPXr10+8GZw7dy7S09NRt25dSKVS3Lt3D2fOnMGWLVuwadOmd3pDs2b6Nm3ahICAAFhZWSEkJCTXJ1VSqRS1a9fG9u3boVQqcf78eVSrVi3Pn/+u9S8vdSYnu3btEm8iNYPzM2fOiN0gKleuLA4+7Ny5s3jdtLCwwFdffQV3d3etYMfLyytL8HLq1Cnx/3Xr1tUpbZplq7k/kHFD0KFDB3F++08//RSPHz9GvXr1kJSUhMOHD8PX1zfHblR58eDBA3Tq1An9+/fHnTt3tMqmQ4cOWt8Tmul9W9k0atQIRYoUwb179xAdHY0mTZpgwIAB8PT0xOPHj3Hjxg1s3boV5cuXx8qVK+Hm5oaGDRti7969AIDhw4cjPj4erq6umD9/vlYL9dt4eXnBzs5OLPuJEyeidevWOHHihNaECZm5ubnhzp07ADK6/EqlUlhaWqJs2bI637zq+l0QERGBL7/8Em3btkWxYsUQGxuLDRs2YNu2bQAyAg3NweRARlen1NRU1KpVC+7u7oiMjMS8efPESU9CQ0PRq1cvrX3eZQD1mTNnxP01Wy+ioqLE7qQBAQHie2zWrVuH33//Hc2bN0eRIkXw6NEjrFixQpzcw8bGRmuChNKlS6NBgwbYv38/gIzAZ/Lkybh37x7mzJmjlW9NmteLO3fuICAgAL6+vli4cGGWPJw6dUo8j1xcXDB16lStFg9jdP78eTHgr127drbnqNF576MqSCea73bQ9Se7dz4U5IDpnH7s7OyENWvWZNk3u0FGmgNEs/uRyWRag4d2796d7XZffvmlIAi6vSValwHTvr6+gr29fZbPyTxV68uXL7OdarZw4cJaUxpqDqqKjY3VGqys/mnQoIEgCLlP1bpgwYJ8T5Wp64DunGgeKyQkJNvPz2naTc3phAEIXl5e2c53rctn6/KW4//++0+wsrLKkr5SpUoJnp6e2eb7ypUrWnO1q3/69esnCELuU7WOHz/+reeF5mfl9LfPreyXLVuW7XGzO9cy+/XXX8XtJ0+enOu2msfOPJD1XeqfLtP7vo0u01Rqpvlt22a+BqjVrFlTACBYWFhkeT9CTlQqlTgFpI2NTZZBkTExMUJYWFiO6SioqVpLliyZ7XSTuU3VqmvZHDt2LNepWjP/PXOaqtXOzk5rYg1dBkxPmjQp28+rX79+juflxIkTs93nbYN18zNtd27TiVtYWAjff/99ls/JbSKIgIAAITIy8q1/l7zQZeKJnL4TM//Y2NgIW7duzfIZ+XnDtOZ6zfd1ZKcgBkxrnju5Xddzuk7r+p4HXfOo+f2oHlBv7Ewg/DENmgOldfUhui5JpVLY2dnBz88P9evXx8yZM3Hr1i2dp1QLCgrClClTEB4eDl9fX8jlclhaWqJw4cJo3749jhw5otUdq3HjxliwYAECAwO1BpkVtKCgIBw5cgQfffQRHBwcYGdnh6ZNm+LIkSNazfeOjo5iC4i1tTVcXV3Rq1cvnDp1Ksc3T7q4uGDLli2oVKlSnqfNHDVqFA4fPoz27dvDx8cHlpaWcHBwQLVq1fDtt9/iwIEDBTblY246d+6MzZs3o2LFipDL5XB3d8fAgQPxzz//iNOzaho8eLBWefXo0eO99O9XK1WqFP755x/UrFkT9vb2cHd3R58+fXDgwIFs0wdkdMv45ZdfUKpUKbEZXlezZ8/Gnj170KZNG/j4+EAmk8HFxQUlS5ZEz549sXnz5nw97dfUr18/TJw4EYULF87zk6n27duLrRP5ebur2rvUv9atW2PHjh2oWrWqTnVGX6Kjo8UuNuonrrqQSCQYNGgQgIyn1X/88YfWeldXVxw/fhzffvstatasCWdnZ1haWsLLywuNGzfWGnT+LipXroxDhw6hfv36sLOzg5OTEzp16oSjR4/m+FZuXcumevXquHTpEkaPHo1SpUrB1tYWNjY2KFq0KBo1aoSFCxdqddMpUaIEjh49imbNmsHOzg729vb46KOPcPTo0Vxb87Lz+eef4+uvv0ZQUBCsra0RGhqK7777Dp999lmO+0yZMgWDBg2Cp6dnvltj/f39cfz4cbRq1QqOjo45fhcULVoU3bt3R3BwMOzt7WFlZQV/f3/06tUL586dw5AhQ7Icu3nz5mjYsCEKFSoEKysrsfvSV199hQsXLmQ75e+HVL58ebRv3x4BAQGwtbWFXC5HcHAwhg4disuXL6N169ZZ9vH398fZs2cxduxYBAcHw9raGo6OjqhduzbWrFmDH3/8UQ85MWzq8SMeHh5o3769nlNTQPQdvRCRaahatar4dCXzm7rp/Zs/f77499+xY8cH+cy3Ta1siMaNGycAGdNwnjlzJk/7JiQkCD4+PgKQ97fovou3TTOcHWMsmw9FlxZsooKwY8cOsa4tWLBA38kpMGx5IKJ8S01NxcuXL7Fjxw6cOXMGQMZUh6VKldJzyszPsGHDxLEgmfteU4a4uDj88MMPAIBu3brleZY3Ozs7fPnllwCA06dPa73si4goM/WYnWLFimlNbW7sOGCaiPJt5syZWgM2pVJprgMc6f2xsrLSy/tJjImzszNevXr1Tsfo168f+vXrV0ApIiJTph58bmrY8kBE78za2hphYWH4448/sryQioiIiEyHRBAM/O0/RERERERkENjyQEREREREOjGrMQ8qlQpx8YmQW8ne+0u2iIiIiIiMgSAIUKQq4exg99bpws0qeIiLT8SY+cveviERERERkZmZP2YQXJ0cct3GrIIHuVXGi6FmjegLubWVnlNDRERERKR/ipRUTFz0s3ivnBuzCh7UXZXk1lawyeObf4mIiIiITJku3fo5YJqIiIiIiHTC4IGIiIiIiHTC4IGIiIiIiHTC4IGIiIiIiHTC4IGIiIiIiHTC4IGIiIiIiHTC4IGIiIiIiHTC4IGIiIiIiHTC4IGIiIiIiHTC4IGIiIiIiHTC4IGIiIiIiHTC4IGIiIiIiHRiqe8EEBHpQ1JSEiIjI7Ndp1AkI+puFPwD/CGX2+R4jJCQENja2r6vJBIRERkcBg9EZJYiIyNRvVrVdzrG8RMnERYWVkApIiIiMnwMHojILIWEhOD4iZPZrou8dg29e/fCqlWrERIamusxiIiIzAmDByIyS7a2tm9tNQgJDWXLAhERkQYOmCYiIiIiIp0weCAiIiIiIp0weCAiIiIiIp0weCAiIiIiIp0weCAiIiIiIp0weCAiIiIiIp0weCAiIiIiIp0weCAiIiIiIp0weCAiIiIiIp0weCAiIiIiIp0weCAiIiIiIp0weCAiIiIiIp0weCAiIiIiIp0weCAiIiIiIp0weCAiIiIiIp0weCAiIiIiIp0weCAiIiIiIp0weCAiIiIiIp0weCAiIiIiIp0weCAiIiIiIp0weCAiIiIiIp0weCAiIiIiIp0weCAiIiIiIp0weCAiIiIiIp0weCAiIiIiIp0weCAiIiIiIp0weCAiIiIiIp0weCAiIiIiIp0weCAiIiIiIp0weCAiIiIiIp0weCAiIiIiIp0weCAiIiIiIp0weCAiIiIiIp0weCAiIiIiIp0weCAiIiIiIp1Y6jsB2/fsx1cLFmdZ3qNjWwzt21P8/dipM/hh9Vrcjb4PT3c3dG7bCu1bNvuQSSUiIiIiMmt6Dx7UvvlqGuztbMXfPdxdxf9funIN4z6fhWYN6mHkwL64cPkqFixdAZmlDK2bNtJHcomIiIiIzI7BBA+hwYFwdnLMdt1P635DSFAxTB49HABQsVwZPHn2DMvXrEPLJg0glbL3FRERERHR+2bwd92pqUqcvXARjerW1lreJLwunse+wPVbt/WUMiIiIiIi82IwLQ9dBg3Hy1fx8Pb0QOuPGqF7hzawsLDAg0ePoVSmIcCvsNb2Rf2KAADuRN9HaHBQtsdMTVVCqVSKvyenpL6/DBARERERmTi9Bw/urq4Y0KMLSoUUh0QCRJw4jWW/rMOzmFiMHToQrxISAAAOdnZa+zk42AMAXsUn5Hjs1Rs346e1G8XfpVILhFSq/h5yQURERERk+vQePFSrFIZqlcLE36tWDIO1lRU2/PEnendu/2ZDiSTb/XNYDADo1ak9urZtLf6enJKKcd+seOc0ExERERGZI4Mc89CgTk2kq1S4fvsOHO0zWhjiE7RbGOJftzg4vF6fHSsrGezsbN/82Nq8v0QTEREREZk4gwweBAji/319vCGTWeJu9H2tbe5E3wMAFM00FoKIiIiIiN4Pgwwe9h06AgupFCGBxWBlJUPFcmWx//BRrW32HoyAu6sLigcW01MqiYiIiIjMi97HPIycNB2Vwsoi0N8PQMaA6a279qDTxy3g5uoCAOjXtSMGj5uMmd8sQZPwOrh45Rq2/b0XE4YP4TseiIiIiIg+EL0HD/5FCuPPv/fh6fPnEFQCivgWwv8G9UPH1s3FbcqUDMXcaROxdNWv2LX/ADzd3TB6cH++XZqIiIiI6APSe/Awekh/nbarUaUSalSp9J5TQ0REREREOWGfHyIiIiIi0gmDByIiIiIi0gmDByIiIiIi0gmDByIiIiIi0gmDByIiIiIi0gmDByIiIiIi0gmDByIiIiIi0gmDByIiIiIi0gmDByIiIiIi0gmDByIiIiIi0gmDByIiIiIi0gmDByIiIiIi0gmDByIiIiIi0gmDByIiIiIi0gmDByIiIiIi0gmDByIiIiIi0gmDByIiIiIi0gmDByIiIiIi0gmDByIiIiIi0gmDByIiIiIi0gmDByIiIiIi0gmDByIiIiIi0gmDByIiIiIi0gmDByIiIiIi0gmDByIiIiIi0gmDByIiIiIi0gmDByIiIiIi0omlvhNARERE+ZOUlITIyMgc1ysUyYi6GwX/AH/I5TbZbhMSEgJbW9v3lUQiMjEMHoiIiIxUZGQkqler+k7HOH7iJMLCwgooRURk6hg8EBERGamQkBAcP3Eyx/WR166hd+9eWLVqNUJCQ3M8BhGRrhg8EBERGSlbW1udWg1CQkPZukBEBYIDpomIiIiISCcMHoiIiIiISCcMHoiIiIiISCcc80BERERkYDgNLxkqBg9EREREBobT8JKhYvBAREREZGA4DS8ZKgYPRERERAaG0/CSoeKAaSIiIiIi0gmDByIiIiIi0gm7LVGe5TYDBGd/ICIiIjJdDB4oz951BgjO/kBERERknBg8UJ7lNgMEZ38gIiIiMl0MHijPdJkBgrM/EBEREZkeDpgmIiIiIiKdMHggIiIiIiKdsNsSEQHIfRYtgDNpEREREYMHInrtXWfRAjiTFhERkakzqOAhKTkZnQYMw7PnMVi5aB5KFA8S1x07dQY/rF6Lu9H34enuhs5tW6F9y2Z6TC2RacltFi2AM2kRERGRgQUPP6/7Denp6VmWX7pyDeM+n4VmDeph5MC+uHD5KhYsXQGZpQytmzbSQ0qJTI8us2gBnEmLiIjInBnMgOm79+7j9792YUD3LlnW/bTuN4QEFcPk0cNRsVwZ9O3aES2bNMDyNeugUqn0kFoiIiIiIvNjMMHDgqXL0aZ5E/gVLqS1PDVVibMXLqJR3dpay5uE18Xz2Be4fuv2h0wmEREREZHZMojg4Z+IY7hx+y76de2UZd2DR4+hVKYhwK+w1vKifkUAAHei7+d43NRUJRITk978JCUXbMKJiIiIiMyI3sc8KBQp+PbHn/FJnx6ws8s6xeOrhAQAgIOdndZyBwf7jPXxCTkee/XGzfhp7Ubxd6nUAiGVqhdEsomIiIiIzI7eg4eV63+Dq7Mzmjeqn/uGEkleFgMAenVqj65tW4u/J6ekYtw3K/KTTCIiIiIis6fX4OHRk6dYt2UbZn/2KRKTkgAAyQoFgIxpW5OSk+Fon9HCEJ+g3cIQ/7rFweH1+uxYWclgZSUTf5daWBRo+omIiIiIzIleg4eHj59AqUzD6KlfZVk3dMJnKBVaHEvnzIBMZom70fdRvVIFcf2d6HsAgKKZxkIQEREREdH7odfgoXhgUSyZ/aXWshu37+CbZT9jwvAhKFE8CFZWMlQsVxb7Dx9Fl7atxO32HoyAu6sLigcW+9DJJiIiIiIyS3oNHhzs7VGxXJls14UGByI0OBAA0K9rRwweNxkzv1mCJuF1cPHKNWz7ey8mDB8CqdQgJowiIiIiIjJ5eh8wrYsyJUMxd9pELF31K3btPwBPdzeMHtyfb5cmIiIiIvqADC54qFiuDE78vTXL8hpVKqFGlUofPkFERERERATAQF4SR0REREREho/BAxERERER6YTBAxERERER6STfYx4EQcDNu0/wLDYeihQlnB3tUMzPA86OtgWZPiIiIiIiMhB5Ch7S01XYf+wKft91BsfO3URiUgoE4c16iQQI8vdEs/ByaN+sEgp7uxZ0eomIiIiISE90Dh627T2PBSv+xrPYeNSrGor/9W2MkkGF4OpsB2srGV7GJ+Hew1hcvHYPOw9cwJJf9qNd00oY1a8xvNyd3mceiIiIiIjoA9A5ePh25R4M7dEALRuWh43cKtttypXwQ4sG5TFpaEtcvfkQP/8Wgd93ncUnPeoXWIKJiIiIiEg/dA4e9q8dD4lEovOBSwQVwtxJnSBo9msiIiIiIiKjpfNsS3kJHApiPyIiIiIiMiz5mm3p0rX7eJWQjJqVggEAL18lYdbSHbh59wlqVgrGyD6NIJVyFlgiIiIqOElJSYiMjMx2nUKRjKi7UfAP8IdcbpPjMUJCQmBry5khifIrX8HDl4u3oUbFYDF4+HzRNuyNuIxalYOxfMMhWFhIMaJ3owJNKBEREZm3yMhIVK9W9Z2OcfzESYSFhRVQiojMT76Chxt3n2BI94xB0IoUJXYdvIjPR7VBx+ZV8MvvR7FyUwSDByIiIipQISEhOH7iZLbrIq9dQ+/evbBq1WqEhIbmegwiyr98BQ/JCqU449KZi3eQqkxH49qlAQChQT549OxlwaWQiIiICICtre1bWw1CQkPZskD0HuVrYIJfIVccPHENALB17zmULu4rvlk65kUC7O3kBZdCIiIiIiIyCPlqeejfqS4mzN6E33acQtyrJCyc0kVcd+L8LZQI9CmwBBIRERERkWHIV/DQsUUV+Bd2w8Wr91AqpDBqVAgS1zk72qJPh1oFlkAiIiIiIjIM+QoeAKBq+UBULR+YZfmofk3eKUFERETvgtN5EhG9P/kOHpRp6di4/SQuXr2PR0/j8MXoNihaxAN/7f8XJQJ9EBTgVZDpJCIi0gmn8yQien/yFTxEP4hBt/8tQ2xcIkoE+eDc5SgkJqUAAE79exuHTl7DvEmdCzShREREuuB0nkRE70++gofp326Fq7Mdti0fCUd7OYLDPxXXVS1fDHOW7SqwBBIREeUFp/MkInp/8jVV64nztzC8V0O4OttBIpForfNwc8DTmFcFkjgiIiIiIjIc+QoeLCykEHJY9zw2AXY2Vu+QJCIiIiIiMkT5Ch6qlg/E8g2HoExLf7NQIoEgCFj/5wnUqBhcUOkjIiIiIiIDka8xD58OaY52gxejYbc5aFSrFCQSYM2Wo4i8/Rh37z/HtuUjCjqdRERERESkZ/lqeQjy98T2n/6HimUC8Oe+f2EhlWL/sasIKOyObctHwN/XvaDTSUREREREepbv9zwUKeSGBVO6FGRaiIiIiIjIgOWr5aHLiKW4GfU023W3o5+hy4il75QoIiIiIiIyPPmcqvU2EhIV2a5LSFLg1IU775QoIiIiIiIyPPkKHgAg0+sdRGcv3YWbs31+D0tERERERAZK5zEPS9bsx9I1/wDICBy6jPgB0kwRRKoyDWnpKvRoU6NgU0lERERERHqnc/BQsXQA+neuC0EQsGjVPrRqGAZvDyetbWQyCwT5e6FhzZIFnlAiIiIiItIvnYOHamGBqBYWCACQSCTo3LJqluCBiIiIiIhMV76mav1f38YFnQ4iIiIiIjJw+QoedJmKdf2iIfk5NBERERERGah8BQ82citknmwpLj4Z124+gqODHKWCfQsgaUREREREZEjyFTz8PKdftsufv4hH/wkr0aZJxXdKFBERERERGZ58v+chO+4uDhjcLRzzl/9dkIclIiIiIiIDUKDBAwAIgoCnMa8K+rBERERERKRn+eq29F/k/SzLUpXpuBn1BN+u3ItyJfzeOWFERERERGRY8hU8tOz/LTK9XBqCkPFv+ZJ++HpC+3dNFxERERERGZh8BQ/rFw3OsszayhI+ns58cRwRERERkYnKV/CgftM0ERERERGZj3wFD2pPnr/Eo6cvkZKqzLKuankGGEREREREpiRfwUP0gxiM+mo9zl+OAvBmvINEkvF/iQS4fXhugSWSiIiIiIj0L1/Bw6dzNuHhkxeYNb49ggK8YGX5Tg0YRERERERkBPJ113/hyj3Mn9IZH9UtU9DpISIiIiIiA5Wvl8R5eThCKpW8fUMiIiIiIjIZ+Qoexg74CEt/PYC4V0kFnR4iIiIiIjJQ+eq29PuuM3j8LA612s9AyeBCcLS30d5AIsGKr/vodKwTZ85j9cbNuBN9D4lJSfBwc0Od6lXRv3sn2NvZidsdO3UGP6xei7vR9+Hp7obObVuhfctm+Uk+ERERERHlQ76Ch8TkVPj7uou/JySl5DsBr+LjUaZEKDp93BIO9na4HRWNFb9uwO2oKCya+TkA4NKVaxj3+Sw0a1APIwf2xYXLV7Fg6QrILGVo3bRRvj+biIiIiIh0l6/gYcPiIQWWgMbhddA4/M3vFcuVgUwmw9fffo9nMbHwcHPFT+t+Q0hQMUwePVzc5smzZ1i+Zh1aNmkAqTRfva+IiIiIiCgPDPKu28nBAQCQlpaG1FQlzl64iEZ1a2tt0yS8Lp7HvsD1W7f1kUQiIiIiIrOjc8vDig2H0LpxBXi4OmDFhkO5byyRoH+nOnlKSHp6OtLS03En6h5+XrcRtapWho+XJ+5E3YNSmYYAv8Ja2xf1KwIAuBN9H6HBQdkeMzVVCaXyzduvk1NS85QmIiIiIiJ6Q+fgYcaS7ahUtig8XB0wY8n2XLeVSJDn4OHjXgPx7HkMAKBapQr4cuIYAMCrhAQAgIPG4GkAcHCwz1gfn5DjMVdv3Iyf1m4Uf5dKLRBSqXqe0kVERERERBl0Dh7uRMzN9v8FZcEXnyFZocDtqGisXPcbxk6bgUUzp7/ZQJL9eyVyWAwA6NWpPbq2bS3+npySinHfrCigFBMRERERmZd8DZh+H4KLBQAAypYMRWhQMfQePhaHjp0UuyfFJ2i3MMS/bnFwsLfP8ZhWVjJYWcnE36UWFgWcaiIiIiIi8/FOwUN8ogKPn8YhJTUty7rSIYWz2UM3wcWKwkIqxf2Hj1CramXIZJa4G30f1StVELe5E30PAFDUL/+fQ0REREREustX8PD42UuMn/Ubjpy5nmWdIGR0Jbp9OP9dmy5djUS6SoVCPl6wspKhYrmy2H/4KLq0bSVus/dgBNxdXVA8sFi+P4eIiIiIiHSXr+Bh9Ffrcffec0wf+TGK+nlAZpn/7kATvvgaJYoHIqhoAKytrHDj9l38uvkPBBUNQN3qVQEA/bp2xOBxkzHzmyVoEl4HF69cw7a/92LC8CF8xwMRERER0QeSr+DhwpV7WDi1CxrXLv3OCSgVEox9h4/gl9+2QFAJ8PHyxMdNG6Nbu48hk2WMVyhTMhRzp03E0lW/Ytf+A/B0d8Powf35dmkiIiIiog8oX8GDf2E3pKWlF0gCenZqh56d2r11uxpVKqFGlUoF8plERERERJR3+erzM3loSyxevR+3o58VdHqIiIiIiMhA5avloWalYNSoGIRGPebCy80Rjg42Wbb5e/WYd04cEREREREZjnwFDzOX/IWff4tAmRBfFC3iAZmM708gIiIiIjJ1+QoeNvx1CqP7NcHw3g0LOj1ERERERGSg8jXmQWYpRflSfgWdFiIiIiIiMmD5Ch46tqiKbXvPFXRaiIiIiIjIgOWr25KTgw3+3HsO7YZ8h5oVg+Bon2nAtESC/p3qFET6iIiIiIjIQOQreJj9w04AwMOnL3Huv6gs6yUSMHggIiIiIjIx+Qoe7kTMLeh0EBERERGRgctX8EBEZAwexKYgNlGZ5/1uPk4S/7W8l5Dn/V3tZPB1tc7zfkRERIZO5+Dh2q1HCA30ydPBU5VpePgkDgGF3fOcMCKid/EgNgX1vjwPhVKV532Vz28AAIavvgGZuyTP+8tlUhz8LIwBBBERmRydg4f2Q75DtbBAdG5ZFXWrhUJmmfOL4aIePMefe//Fmj+OYWCXuujfuW6BJJaISFexiUoolCq0Dg+Cm7PN23fQoFQEIib8H7gVDoZMbpunfWPikrHtwE3EJioZPBARkcnROXg4sOFTLPllP0Z+vg4SCVA6pDBCA33g6mwHK5klXiUk497DWPwXeR9RD2NQurgvZoxrh0a1Sr3P9BMR5crN2QY+7vZ53MsefoU930t6iIiIjJnOwYOHqwOm/+9jjBvYFDsOXMCxszdx+FQknsXEIyVVCSdHWxQr4oFm4eXQNLwsShf3fZ/pJiIiIiKiDyzPA6btbK3RsXkVdGxe5X2kh4iIiIiIDBRnWyLKRVJSEiIjI7Ndp1AkI+puFPwD/CGX59ynPiQkBLa2ees3T1SQcqvHgG51mfWYiIgABg9EuYqMjET1alXf6RjHT5xEWFhYAaWIKO9Yj4mIqKAweCDKRUhICI6fOJntushr19C7dy+sWrUaIaGhuR6DSJ9yq8eAbnWZ9ZiIiAAGD0S5srW1fevT1pDQUD6RJYOmSz0GWJeJiOjtpPpOABERERERGQedg4faHWbiyo2H7zMtRERERERkwHQOHu4/foFUZdr7TAsRERERERkwdlsiIiIiIiKd5Cl4kEjeVzKIiIiIiMjQ5Wm2pS4jfoBUhwhCIpHg0u6v8p0oIiIiIiIyPHkKHlo1DIO3h9P7SgsRERERERmwPAUPnVtWRfmSfu8rLUREREREZMA4YJqIiIiIiHTC4IGIiIiIiHSic/Awsk8j+HjqNt7h7v3n+U4QEREREREZJp3HPPyvb+Nc1z9/EY+/9v2LrXvO4b/rD3Dr0Jx3ThwRERERERmOPA2YziwxKQV/H7qErXvP4fjZm0hXCfDxcIIgCAWVPiIiIiIiMhB5Dh7S0tJx4MQ1bN1zDv8cuwJFShpCinljVP8maNWgPK7cfIQhU1a/j7QSEREREZEe6Rw8nPz3FrbuOY9dBy/iZXwy/H3d0L9TXbRqWB7BRb3F7a7eevReEkpERERERPqlc/DQefgPsJBK0LllVXRsUQVlQ4u8z3QREREREZmcpKQkREZGZrtOoUhG1N0o+Af4Qy63yfEYISEhsLW1fV9JzJXOwUPJ4EK4cuMhtu45h6TkVLRsWB61KxeHpaXF+0wfEREREZHJiIyMRPVqVd/pGMdPnERYWFgBpShvdA4edvw8Crein2Lr7nP4c995/LHnHJwcbNC0bhm0bFge1SsEQSKRvM+0EhEREREZtZCQEBw/cTLbdZHXrqF3715YtWo1QkJDcz2GvuRpwHSgnyfGDPgIYwZ8hHP/RWHb3nPYceAiNu44BTcXezQPLwcnh5ybWIiIiIiIzJmtre1bWw1CQkP11rLwNvmeqrVCaX9UKO2PaSNb4/Cp69i29xw27zyNxORUsAGCiIiIiMj0vNN7HgBAKpWiXrVQ1KsWCkWKEnsi/sOfe88XRNqIiIiIiMiAvHPwoEluLUOrhmFo1dAwm1mIiIiM0YPYFMQmKvO8383HSeK/lvcS8rSvq50Mvq7Wef5MIjJtBRo8EBERUcF6EJuCel+eh0KpyvO+yuc3AADDV9+AzD1vfYrlMikOfhbGAIKItDB4ICIiMmCxiUoolCq0Dg+Cm3PeJiVRKgIRE/4P3AoHQybXfU74mLhkbDtwE7GJSgYPRKSFwQMREZERcHO2gY+7fR73sodfYc/3kh4iMk9SfSeAiIiIiIiMA4MHIiIiIiLSCbstFYCkpCRERkbmuF6hSEbU3Sj4B/hDLs++v2pISAhsbXXvj/q+6WNmD4CzexARkXnhTFpkbBg8FIDIyEhUr1b1nY5x/MRJg3mToL5m9gA4uwcREZkPzqRFxkjvwcP+w0ex+8AhXLtxC6/iE+Dr4422LT5Cm2ZNIJW+6VV17NQZ/LB6Le5G34enuxs6t22F9i2b6THlb4SEhOD4iZM5ro+8dg29e/fCqlWrERIamuMxDIU+ZvYAOLsHERGZF86kRcZI78HDui3b4O3pgeH9e8PVxRlnL1zCgqUr8PDREwwf0BsAcOnKNYz7fBaaNaiHkQP74sLlq1iwdAVkljK0btpIvxkAYGtrq1OrQUhoqMG0LuiCM3sQERG9f/y+JWOi9+Bh3vTJcHF2En+vWK4MkpIV2PzXTgzq1Q1WVjL8tO43hAQVw+TRw8Vtnjx7huVr1qFlkwZaLRRERERERPR+6P2uWzNwUAsJLIqU1FS8io9HaqoSZy9cRKO6tbW2aRJeF89jX+D6rdsfKqlERERERGZN7y0P2fn38hU4OjjAxdkJ0fcfQqlMQ4BfYa1tivoVAQDcib6P0OCgbI+TmqqEUvlmBoPklNT3l2giI8GZtIiIiCi/DC54uHr9Jrbv+Qf9unWChYUFXiVk3KQ42NlpbefgkNE38FV8zjcxqzduxk9rN4q/S6UWCKlU/T2kmsg4cCYtIiIiehcGFTzExL7AxK9mo2RIMHp2bKu9UpL9zUoOiwEAvTq1R9e2rcXfk1NSMe6bFQWRVCKjxJm0iIiI6F0YTPCQkJiIUZ99AWtra8ybPgmWlhlJc7TPaGGIT9BuYYh/3eLgYJ/z7ARWVjJYWcnE36UWFgWdbCKjxJk9iIiIKD/0PmAaAFJSUzFu+kzEvojDN19NhZOjo7jO18cbMpkl7kbf19rnTvQ9AEDRTGMhiIiIiIjo/dB78JCWno7JM+bixu07WPjVNPh4aT/ZtLKSoWK5sth/+KjW8r0HI+Du6oLigcU+ZHKJiIiIiMyW3rstzftuGY6cPI1h/XohJSUF/12NFNcV9SsCOztb9OvaEYPHTcbMb5agSXgdXLxyDdv+3osJw4fwHQ9EREQmhDPCERk2vQcPJ86eBwB899PqLOuWzP4SFcuVQZmSoZg7bSKWrvoVu/YfgKe7G0YP7m8Qb5cmIiKigsEZ4YgMn96Dh62/LNdpuxpVKqFGlUrvOTVERESkL5wRjsjw6T14ICIiItLEGeGIDBcHDBARERERkU4YPBARERERkU7YbYmIiIwOZ+QhMi1JSUmIjIzMdp1CkYyou1HwD/CHXJ79WJiQkBDY2uZtrAvlD4MHIiIyKpyRh8j0REZGonq1qvne//iJkwgLCyvAFFFOGDwQEZFR4Yw8RKYnJCQEx0+czHZd5LVr6N27F1atWo2Q0NAc96cPg8EDEREZJc7IQ2Q6bG1t39pyEBIaytYFA8AB00REREREpBMGD0REREREpBMGD0REREREpBMGD0REREREpBMGD0REREREpBMGD0REREREpBMGD0REREREpBMGD0REREREpBMGD0REREREpBMGD0REREREpBMGD0REREREpBMGD0REREREpBMGD0REREREpBMGD0REREREpBMGD0REREREpBMGD0REREREpBMGD0REREREpBMGD0REREREpBMGD0REREREpBMGD0REREREpBMGD0REREREpBNLfSeAyBA8iE1BbKIyT/vcfJwk/mt5LyHPn+lqJ4Ovq3We9yPKSX7qMcC6TEREumPwQGbvQWwK6n15HgqlKk/7KZ/fAAAMX30DMndJnj9XLpPi4GdhvOmiApHfegywLhMRke4YPJDZi01UQqFUoXV4ENycbXTeT6kIREz4P3ArHAyZ3DZPnxkTl4xtB24iNlHJGy4qEPmtxwDrMhER6Y7BA9Frbs428HG3z8Me9vAr7Pne0kOUH3mvxwDrMhER6YoDpomIiIiISCcMHoiIiIiISCfstkREREREVMBMdSZHBg9ERERERAXIlGdyZPBARERERFSATHkmRwYPRERERETvgSnO5MgB00REREREpBMGD0REREREpBN2W8qD/IyaB95t5PyHGDVPZKrSXr3C6p/2wUKa90Fn+ZWuEpBmmbc3PBO9zYeuy6zHRJQTBg86yu+oeeDdRs5/iFHzRKZKUKmQmKD44J9r4cjzlQqWPuoy6zERZYfBg47yO2oeyP/I+Q81ap7IVEmkUtjZyz94y0OKlD1CqWB96LrMekxEOWHwkEd5HzUPGMPIeSJTZOnoiF5taubjnM2/R88T8PMflz7Y55F5+NB1mfWYiHLCxwpERERERKQTBg9ERERERKQTdluibHGWGiIiIipI+pi1EuDMlQWNwQNli7PUEBERUUHR16yVAGeuLGh6Dx7uPXyEdZu34r9rkbh9Nxr+RQpj3bJFWbY7duoMfli9Fnej78PT3Q2d27ZC+5bN9JBi88BZaoiIiKig6GPWSoAzV74Peg8e7kRF4+ipMygVWhwqlQBBELJsc+nKNYz7fBaaNaiHkQP74sLlq1iwdAVkljK0btpID6k2fZylhoiIiAoaZ600fnoPHmpVrYw61asCAL6Y9y2u3biVZZuf1v2GkKBimDx6OACgYrkyePLsGZavWYeWTRpAyqfVRERERETvnd7vut9245+aqsTZCxfRqG5treVNwuvieewLXL91+30mj4iIiIiIXtN7y8PbPHj0GEplGgL8CmstL+pXBABwJ/o+QoODst03NVUJpfLNqP7klNT3l1AiI8GZtMgUsB6bLnMr2w+dX9ZjelcGHzy8SsiYksvBzk5ruYNDRn+5V/E5T9m1euNm/LR2o/i7VGqBkErV30MqiYwHZ9IiU8B6bLrMrWz1kV/WY3oXBh88iCTZR+Q5LAYA9OrUHl3bthZ/T05JxbhvVhR0yoiMCmfSIlPAemy6zK1sP3R+WY/pXRl88OBon9HCEJ+g3cIQ/7rFwcE+5xH7VlYyWFnJxN+lFhbvIYVExoUzaZEpYD02XeZWth86v6zH9K4MPvT09fGGTGaJu9H3tZbfib4HACiaaSwEERERERG9HwYfPFhZyVCxXFnsP3xUa/negxFwd3VB8cBiekoZEREREZF50Xu3JYUiBcdOnwUAPH76DIlJSfgn4hgAIKxMKbg4O6Ff144YPG4yZn6zBE3C6+DilWvY9vdeTBg+hO94ICIiIiL6QPQePMTGxWHSjDlay9S/L5n9JSo6l0GZkqGYO20ilq76Fbv2H4CnuxtGD+7Pt0sTEREREX1Aeg8eCnl74cTfW9+6XY0qlVCjSqX3nyAiIiIiIsoW+/wQEREREZFOGDwQEREREZFOGDwQEREREZFO9D7mwZikvXqF1T/t+6BvgUyztPkgn2XuWLZkCj50PQZYl4mIcmKq9xYMHvJAUKmQmKD4oJ9p4Wj9QT/PXLFsyRToox4DrMtERNkx1XsLBg95IJFKYWcv/6ARZArfY/FBsGzJFHzoegywLhMR5cRU7y0YPOSBpaMjerWpCR93+w/yeY+eJ+DnPy59kM8ydyxbMgUfuh4DrMtERDkx1XsLPi4iIiIiIiKdMHggIiIiIiKdsNsS5SgmLjnP+ygVSYi5fwNuhYMhk9u+988jehvWYzIVH7Iu67se87w1TZwRzjQweKAsXO1kkMuk2HbgZp73VT6/jrg/BsK5zY+QuRfP8/5ymRSudrI870eUGesxmQp91WV91GOet6aNM8KZBgYPlIWvqzUOfhaG2ERlnve99p+AHn8Ai3sFI7R02Tzv72ong68rT3J6d6zHZCr0VZf1UY953po2zghnGhg8ULZ8Xa3zdRFNe57RVBzkbYsyRT7cjC9E2WE9JlNhTnXZnPJqbjgjnGlgKEZERERERDph8EBERERERDpht6U8MqfZLsxNXv/exjyzB2cyMV3mVLbmlFcybeZ0b2Fu560p3lsweNCROc12YW7yW7bGOLMHZzIxXeZUtuaUVzJt5nRvYW7nrSnfWzB40JE5zXZhbvJbtsY4swdnMjFd5lS25pRXMm3mdG9hbuetKd9bMHjIA84AYbryU7bGWq6sx6bLnMrWnPJKps2c6rI55RUw3XsLDpgmIiIiIiKdMHggIiIiIiKdMHggIiIiIiKdcMwDEZGJS0pKQmRkZI7rI69d0/o3OyEhIbC1zdu0gfqSW351yStgPPll2b5hamVLZKgYPBARmbjIyEhUr1b1rdv17t0rx3XHT5xEWFhYQSbrvdElv7nlFTCe/LJsszKVsiUyVAweiIhMXEhICI6fOJnjeoUiGVF3o+Af4A+53CbHYxiL3PKrS17VxzAGLNs3TK1siQwVgwciIhNna2v71iet1avX+ECpef/ell9zyitgXvk1pbwSGSoGD0Rklth3moiIKO8YPBARAPMbeMm+00RERHnH4KEAmNtN17s+sTWnvALGk19zG3jJvtNEZMh4b/EG85qVPvMrEQRB0Msn60GyIgWfzFyMheMGw8Y6769Hz8n58+d1uunKjTHddL1rfs0pr4Dx5PdtX1S6Drw0los3EZEhM6fvH4D3FnlV0PlNTknBqLk/4PtJw2Ejz/0emS0PBYCzXbzBvGZ/DGNgbgMviYgMGe8t3mBesz+GvrDlgYiIiIjIjOWl5UH6gdJERERERERGjsEDERERERHphMEDERERERHphMEDERERERHphMEDERERERHphMEDERERERHphMEDERERERHphMEDERERERHphMEDERERERHphMEDERERERHphMEDERERERHphMEDERERERHpxFLfCfiQBEEAAChSUvWcEiIiIiIiw6C+N1bfK+fGrIIHRaoSADBx0c96TgkRERERkWFRpCphayPPdRuJoEuIYSJUKhXi4hMht5JBIpF8sM9NTEpGq+798OevP8HO1uaDfa4+MK+my5zya055Bcwrv+aUV8C88mtOeQXMK7/M6/snCAIUqUo4O9hBKs19VINZtTxIpVK4Ojl88M9VpadDpUqHjbUVbOTWH/zzPyTm1XSZU37NKa+AeeXXnPIKmFd+zSmvgHnll3n9MN7W4qDGAdNERERERKQTBg9ERERERKQTBg8fgEwmQ79unSCTyfSdlPeOeTVd5pRfc8orYF75Nae8AuaVX3PKK2Be+WVeDYtZDZgmIiIiIqL8Y8sDERERERHphMEDERERERHphMEDERERERHphMEDERERERHphMEDEZGJ4nwYRERU0Bg8EBGZEKVSiXsPHwEAJBKJnlNDRPR2fNBhXBg8vCNzq/DmlF/m1XSZan4Tk5Ix6rMvsH7LNlyJvKHv5OiFqZZtdswpr4B55deU85qaqsTlyOs4fPwkou49gCAIkEgkJp1nTaaQT0t9J8CYpKYqcePOHQgqAfb2dggoUtikn+yZU36ZV9PMK2A++U1WKNDvf+Pg4uSEWlUqIbhYgL6T9N6ZS9kC5pVXwLzya055TUxKxthpX+FZTCwePHqMIoV88FGDeujbtaNJ5tlUy5bBg44Sk5IxbMJnSExKwsMnT+FgZ4fG4bXRrd3H8PRw13fyCpw55Zd5Nc28AuaV3zW/bYGDnR2mjh0JLw93SKVSpKWlwdLyzWVepVJBKjWNBmdzKltzyitgXvk1p7wmJSej///Gw8PdFaMG9UMhby/8sPpXHDx6HG2bfwRnJ0d9J7FAmXLZ8g3TOkhLS8OwT6fC0tICg3v3QEpKCq5cv4FV6zcjuFgABvbsigplS+s7mQXGnPLLvJpmXgHzy+/U2QtgZ2ODCSOGAADO/HsR+w8fhSIlBZ4e7ujTpQPk1tZ6TmXBMKeyNae8AuaVX3PL69SvF+BVQgImjBiCQl6esLCwwJXIGxg64TP8uvQb+Pp4i9unp6fDwsJCjyl+N6ZetqbxCOo9exYTi7hXr9ChVXOUDi2OiuXKoEeHtlg69yvce/AIC5auwNkLl/SdzAJjTvllXk0zr4D55TcxMQkqQQUAOHz8JEZOmo57Dx/ibvR9bNu1B92H/A93ou4ByGiBMGbmVLbmlFfAvPJrTnlNSlYgJTUVtatVRmEfb7EF1MLCAt6eHli98XeM/uxLLPxhhRg4GPN1ytTLlsGDDpRpabh3/yFSU5XiMpVKheKBxfDTt3PwIu4lfli9Fg8fP9FjKguOOeWXeTXNvALmk1/1F2xggD9u3YlG9P0HWPPbFnRt/zHmTp+Mn76dgznTJsHRwR4TvpiFtLQ0o++6ZC5lC5hXXgHzyq+55FWlUsHRwR6TRg1D648aQyKRQCKRQKlU4rNZ85CSmgpBEGBlJcPuA4cxeNxko+9iaepla7wl84EIggAXJ0eUDC2Oo6fOIO7lKwDI6E+cng5vTw98O3Ma7kRFY82mP/Sc2ndnTvllXk0zr4B55Vf9BftR/bq4dvMW1v6+FVKpFNUqhsFGLodUKkXJ4kEY1q8XXiUkYMuOv/Wc4ndjTmVrTnkFzCu/5pRX9TXKxckRcrm1+MBj1YbNcHCwxzczpmHyqGH4+rNPMahnN9yJisbBoyf0meR3Yg5ly+DhLSQSCRzs7VG3elXsPRiB46fPitNsWb5uVgsqGoDBvbpj94FDuHbjpp5T/G7MKb/Mq2nmFTDt/KakpuLoyTPYezACZ/69KC4vFuCHkQP7YM+BCFy8cg1Pnj0X11laWqJUaHHY2tjgWUysPpJdYEy5bDMzp7wC5pVfU89rdtcpdRCh/rdpg3r45qup8PMtJOa9cb3aSElNxbOYGP0kvACYetkCnG0pi5TUVJz99yIUqalwc3FBuVIlAADdO7TB9dt3MO/75XBwsEetqpUBvDkJypUuAVW6CjGxcfpKer6YU36ZV9PMK2A++U1MSsbA0Z8iXZWOh4+eQG5tjWIBfhgxoA+KBxVD0wb1EB+fgNW//Y6d+w4guFgAgosVBQDExyfA2dERDvb2ACDOrW7ozKVsAfPKK2Be+TWnvOZ2nQouFgCZTAYAKFzIR9xHIpFApVLhdlQ0Cnl7I6hogJ5Sn3fmVLZqDB40JCYlY9CYiVAoFHiVkIjExESE166BZg3DUaNyRUwYNhhTkxZg2uyFGD98EKpXqghHh4wvYrlcDg93NxjBd7HInPLLvJpmXgHzyW96ejqmfj0fjg72GD98MGSWlrh1Nworft2AcdNnYlj/XmhUtxY6tG4OKysrLPtlLeZ+9yMa1q0Jd1dXHDp2EvcfPUZ4reoAjOPt0+ZStoB55RUwr/yaU15zu06N/3wmhvbtifBaNSCXZ8z8pjm2IT4hAX/t2Q+5tRUCihTWZzZ0Zk5lq4nBw2tp6emYPHMunJ0cMebTMZBKpbgTfQ/zly5H9P0HePz0Gdo2/wifjx+FrxctxRfzFqFVk4aoV7Ma7OzssG3XHqSkpCDo9VM+Q2dO+WVeTTOvgHnlNyEpCc9iYvFxs8Yo6lcEQMaTuyoVymPc9JmYt+RHQBDQOLwOundog1KhxfH9z79gzaY/YCGVwtXFGUtmf4EiGk/7DJk5la055RUwr/yaU16Bt1+n5i9dDgBoUKcWrKxkYuBw+PgpHDx6HBEnTmHp3Blwc3XRWx50ZW5lq4nveXgtKTkZA0Z9irYtPkK7Fk3F5Xei7mH+0h/xPPYFOrRqLq5buX4T9h8+ittR0fD29IBUKsXMyeNQPLCYvrKQJ+aUX+bVNPMKmFd+ExIT0WXQCDRvWB+De3cDoD0X+rBPp+LW3SjMnjoRZUuGAgAUihQkJCYiVamEo4M97O3s9Jb+vDKnsjWnvALmlV9zyiug23XqTtQ9zJwyHuVKlYBKpcLp8xfw87rfkJKaiimjhxtNlyVzK1tNDB6Q0WwW+yIO7foOxqhB/fBxsyZIT08HkDEHcfSDh5iz+Ae8fPUKA3p0RZ3qVQAAz2Ni8Tz2BSwspHB3dYWLs5M+s5Enz2NizSa/zKtp5hUwr/wqlUpMn7MQz2Nf4LMxI8T+wmnp6bC0sEBqqhIDRk+AzFKGFd/M1nNq3505la055RUwn/ya272FSqVCenp6nq9TsXFxePTkKbzc3eHu5qrPLOhMEATExL4wm7LNzKxnW1IXtFQqhbubK+pWr4qtu/Yg+sFDWFhYQCqVQqVSwc+3EMYMGYCExCTs2Lsfaa/3c3N1QWhwIIKLFTWqCiAIgtnk11zyKgiC2eQVMJ/8ar4kSSaToW2Lprh0NRJ/7NwNRUoKgIzZO9LT02FlJcMnfXsi6v59o57mEDCf8xYwr7wC5pFfc723kEqlebpOHThyHADg6uyMUiHFjSZwADLGjJlT2WZmtsFDskKBrxctxYXLV8VlNapUQtzLV9i+ez9exL0UX2SSnp6Oov5FMH74YBw+fgpnX087ZgwDDnNTvVIFs8mvqeZVoUjBg0ePxfwApl2PzSW/aWlpADK+jNU3IoIgoGK5MhgxoDfWb/kTG/74U3wBkbpLQPFiRSGRSPH0+fPsD2wk1GVkqudtdkw5r2np6UhKTtZaZornLWBe9xaKlBRs37MfP63diI1bt4vLdb1OGdt0rCmpqbh05RqSFQpxmamW7duYZfCQmJSMnkNHY/ue/bh6/Ya4/KP6dVGnelVs3PoXtu/dj7iXr7QKOjQoEJ7ubrj38JE+kp1vCkUKdh84hA1//InjZ87h8dNnAICmDcNRs0olk8qvIiUFR06exvY9+3HpyjXEJyQCyMhrraqVTSqviYlJ6D5kJDb9uRMqlUqcR9pU67G55DdZocCAUZ9i1rdLAGR84aanp4t5atP8I/Tp0gE//rIeK37dgEdPnor7qgQB3p4esLWx0Uva80uRkoI9Bw7j53W/4cCR43j++l0UTRuGm1TZAuZ1PQYy6vO46TPw+/ZdiE9IEPNkauctYF73FolJyRg8dhLWb9mGP//ei+9+WoUx074S15vadSoxKRlDxk3G0lW/4sbtu2KrsCmWrS7MbralxMQkdP/kf/D29EDxYgFYtWEzalerAl8fbwDA6CH9kZCYiOW/rEdiYjJaNK6v1WdPLpfDRi7XZxbyJDEpGf3/Nx6pylSkpaXj6fMYlC9dEuG1qqNj6xYYN2wQEhKTTCK/GReziYhPSERSsgIJiYmoXqkCPqpfF43q1cbYoQMRn2AaZZuYmIQeQ0fBx8sT3dt/LM5Yoe5XanL12Ezym5qqxGez5uPazVt4HhuLBUtXYPSQ/rCwsBDzam1lhZ6d2sHW1gbfrViNqzduomaViijk7YWDR0/g2fMYVChbWt9Z0VliUjJGTpqOuFevkJysQGxcHJo2qIeRA/vAydHRZMoWMK/rsdqGP/7CiTPn8fDRE9hYy9G0QT3Y2dkCMK3vW3O6t0hWKDBk3CQ4Othj5MB+cHZyxM07dzHpqznYe+gIGtWtBWsrK3Tv0Aa2Njb47ifjvk4pFCkYOPpTeLi5olv7jxEaFAipVCq+K2f0kP6IT0gwibLVlVkFD4mJSeg1bDR8fbzx+YTRuHTlGo6fOYeDx06gW7uPkZqqhJWVDFPHjoStjQ127N2Pf/+7jI4ft4CFVIqjp84iPiEBYWVK6TsrOklPT8esb5fAzdUZ44cPQSFvL1z47wrWb/kTqzZsxuOnzzBiQB98PmEUbG3kRp3ftLQ0TPhiFhwdHDBl9Ah4urvh3MX/MO/7H/HftUg8j32BLm1bZeTV1rjLNjEpGX1GjIWvjzemjh2pNaWd5etmYQCYOnYk5HJro84rYF753Xf4CP67Fomu7VpDoUjBwaMZfYJHD+kPy0wBRLd2HyO4aFGs3fwHVm/4HTY2ctjb2WHRrOko5O2l55zoJlmhwOCxE+Hs5IQxnwxAUf8iOHb6LCbPmItWHzVC+dIlAZhG2ZrT9VhT5bByOHDkGCQSCRavWAVIgKb13wQQpvB9a073FiqVCsvXrIelhSXGfDJQnI5VEAT4eHnCzcUZr+IT4OhgDxu5HN3af4zAAH+s37LNaK9Th46fgFxujZED+8KvcCFYWFiI3fDk1taQSqWYNu5/kFsb9zUqL8xmtiWFIgUd+38CXx9vfPHpGHi8HpgzctJ0PHn+HL9+/w0sLS2hVCrFtx/+tXsfDh8/hSMnT8Pb0wN2traYNm6k+MZWQ6dSqdB/1ARUDiuHIb27i8vvPXyELdt3YevOPfi4WWOMHNgXALB11x4cPXnGKPMb8yIOg8ZMRI8ObdG6aSNx+clz/2L0lC+gEgQM7dsT3Tu0AQBs27UXR06eNrq8pqWno9vgkYiJjcWy+bMQGOAPALgSeQOXI6/jbvR9VChbGsWDionz+RtrXgHzym9aejoijp/Cn7v3YuGXU5GYlIylK9fg0PGTCK9ZHaOH9Be3s7SwEJ96JSUnI1mRAqVSCXs7W6OZjlWlUmHud8tw4/ZdTB03En6+hQBkdGHqO2IcJv1vKAL8CkNubQ1Ly4znXMZ8jTKn67GaSqXC/YePMH3uN/j6s0+xdOUa/BNxDMP790aT+nXEt50Dxptfc7u3SFYosGPPPwCAj5s3gYVUColEghdxL9F/1Hg42Nvj8dNnKOpXBB1bt0CNKhVhbWVltNcpAFi8YhXOXfgPKxfPAwAcO3UGv+/4G/cfPELJkGCULhEiTse6deduHD111ijLNi/MJng4ff4CTpw5j85tW8HDzVV8q+GJM+cxacZsdGvfBv26dQKgPScxANx/+Ag2cjlkMpn4ZkBDp1KpkJCYhP7/G48GdWpiUK9uWhevp8+eY/0ff+Lv/YfQs1M7dGnbStzXGPMbff8BOg0YhtlTJ6JO9SpQKpWwtLSERCLBF/O+xcFjJ+Dj6YGendqhSXhdcT9jzOsPq9ZizaYt6NOlA/p374xDx05gxoLvYGMjh0qlwvPYF6hRuSI6t2mJymHlxP2MMa+A6edXHQwAGXOkW1paQm6d8fbVmNgXWLlhEw4d0w4g1IGDMUtPT8fWXXvwKj4BPTu2hfT1TcjTZ8/Rb9QEFPLyxJ3o+yhXqgTq1ayG5o3qA8jI+4NHj42ibNXM7Xqc2ajPvkDVCuXRuU0rjJn6Fc5euISRA/uiWaNw7D0YgRaNG4jbGlt+ze3eAsi4Ljk62Iv1V6lUosfQUbC0sETjerVRyMcLP/26EckKBaaPH4XypUtqvUna2Kz7fRvO/HsRC778DAePnsDkGXNQt0Y1ODrY4/qtO3j45AlaNWmET/r2AGCc16i8MptuS5XDyqFcqZKwssqo7OpKXKJ4IAKLBuDY6bPo2Lo5HOzttfqyARD7rhkTqVQKRwd71K5eBb9t24EWjRvA18dbvHh5erijXYumePz0Gf6JOIrqlSsgoEhhCIJglPn1K+yLiuVKY/XGzQguFgAfL0+xqdhKJkNY6VJIVSpx6twFNAmvKw62Naa8quvk4N7dYGEhxcr1m3An+h5On7+Iti2aokl4Hfj6eCPixCnM+OY7SKVS+BcpDE93N6MsV/WXjSnnNyk5GV/OX4R6NauhSXhd8Wmcun66ubqgT+cOAIADR49DEASM+WRAxk328xhIJBLxSacxEQQBFhYW+Ci8LqytrcQbKqVSiWGfToWTgwOqVaqAxuF1sG3XHqzdvBWuLs6oXqkCAOM6bwHzux6rqc/hgCKFcfbCf+jcphXmfzEFE76YhW9//Bl/7NyNG7fvIKxMKfj6eBtlfs3t3gKA2HVUnZc9ByPgYG+Pz8ePErsiVa9UEZ0HDMW+Q0dQvnRJowscNMvJ18cLi1ecw8Ur13D91m10aN0Cg3p1hY1cjoePn2DzXzvx1559KOpfBE0b1ANgvGWrK+MqzXekPrnVBEGAk6Mj+nfrhCuRN8S50TWngTR2DWrXRCFvT8z9bhmePnsuztwCZFTuTh+3wNUbt3Dzzl0Axj2NWMM6tZCcrMCCpSvw+OkzWFhIcftuNHb9cxAtmzRE9w5tsOfgYUTff2BUZay+kVRP3wkAA3p0QZ8uHfBPxDE0rlcbvTu3R1H/IrCykqFBnZqYMHwwjp46g1t3owAYV7kqlUokJiVrTVNqivnN6Cc9BgePnsCZfy9prVPXT0EQxACibvWqOHTsBBYsXYEnz57h62+/x+IVq6BUKvWUg7xTl606b3Z2trC0tBTr+LZde1HUvwjmfT4ZvTq1Q7sWTTFhxBA8j32Bq9dvAjCOss1Jwzq1zOZ6DLy5kW4SXhcPHz/B/dczzsyeOhEODva4HRWNdi2aik9mjTW/pnxvkZKain+OHMOaTVvw1+59ePnqlbhOnZfmjerj2xnTxMBBEATY2drAy8MDr+Lj9ZLu/NK8RqnPzdrVqqBGlYqY+vV87Nx3AG6uzuIA6ELeXmjfshlcnJxw/dZtAMZbj/PCZFseUlJTcfbfi4i6/xABfoXhX9g3y+Ac9RdYSFAgKpYrg01/7kTlsHLw9vTQU6rzL1mhwI69/+Dx02co5u+HIoV8UKZkKEqGBKNZw/r4ffsufPfTagzr3xue7m5ik3n50qXgX9gXVyJvoGGdWvrOhk4UihQcPHYCT589R2CAP7w83RFUNAAfN2uCl6/isXP/QbTpNRABRQrj7r37aFS3FurVrIZkhQKuzs54+cp4LmbJCgV+WrsRd6LuwdHBHvVqVkPdGtUAAP27d0Yhby84OzpALs/o5qLuAlM5rBzk1laIvHlLfFprDBSKFAwaOxFFfAth3LBBcHJwEJ/OmlJ+1TOz+Pp4oX7tGli7eSvq166RJe2aAUTfbh0hkUhw6NgJ/BNxFPGJiVg2b6bYdcDQZVe26ifT6i/b1k0boWnDcNjZvpnCsVRIcXi6u4k3nsZAoUjBzv0HcDf6Por4+qBMiRCEBgehRPEgNGtYH1t2/G0y12Mg48HGy/gEuLk4Z7ve3s4WL1+9QkJiEgBg6tfzkZqaihLBgfj7n4Pw9fFGq48aaZW7oTKnewv1TGjJimS8ePkKr+ITsO/QEYweMgD+RXzFoF8ikWhNuyqRSBD94CEkEqBsyRIAjKOrZW7XqDbNmmDV+s24cv0GlK/fW5GWlgZLS0sU8vZC4ULeuH7rjlY3VFNmksFDYlIyRk35ArFxcXgR9xJJycmoWaUS+nfvhNDgIK1tJRIJnJ0cUb92Dcz9bhlu342Ct6eHUfXPS0pORp8R4wAAEgnEwUztWzVDv26d0KVtKyQkJmL3gcOYvWgpxnwyQLzYxcbFQSqVwMvDXW/pz4vEpGQMGDUByQoF0tPT8So+AZ7ubmjfqhk6tm6BXp3bo0aVSrjw3xXExsXB18db7Ct9+dp1WFpawN7eOAZqJSUno9/I8bCRy+Ho6IAbt+/izIVLkFtbo2rFMABA0wb1xAuySqUSL1rR9x/C3s5OnAnDWJw4ez7jApyWju9WrMaw/r20AghTyO+bmVm88NXEcXj89Kk4gLJy+bKQSqVa1x51fl2dndGkfl3sjziKNGUafv52rjiA3BjkVLbqa60gCJDJZFmCoTtR9yCVSlGuVEk9pTxvEpOS8cn4yVCkpEKlUmHnvjgEFQ3AmE8GIrhYgHg93nMwwuivx0DGdeqT8VNQukQIenVqn203usKFfFChbGmcOvcvftn4O878exFzpk9C+dIlMXTCZ1j3+1bxOm3IzOneQpGSgpGTpkMut8b44YPg5uKC6AcPMWbaV9iw9S9MGD5YKxjQzNfzmFis+W0L4l7Fo2bVSgCM42l8TtcoAKhVtTJexL3Ey/h4rNn0B0KDA1GjSkbeYuPiEJ+QiGL+fpAaQT4LgskFD4qUFAyfOBWODg4YOagPQoMCsfvAYSxavhJ7Dx5BaHCQViVXR8Ntm3+EvQcjsOTnX1CpfLkszZCGShAELF6xGi5Ojpg8ejiKFPLB9Vu3sedgBH5euxGPnzzFpFHDMKBHF9ja2GDnvgPoM2Is+nbtBEDAzdt38fR5jHgSGLK09HR8PnchnJ0c8cWnoxFUNAAnz57Hut+3YeEPP+Hlq3gM6NEFwcUCEFwsQKucX756he179sPJwRFuLi5v+ST9S0tPx8xvlsDD3RXjhg1GkUI+uH03GpNnzkXkrTti8KB+wpWucSMdGxeHbbv2wEYuR4niwfrMRp75+njD1sYGbq7OuHj5Kpb89AuG9uupdZMJaA8yNqb8pqSmos+IsXBzdcG0caPg7OQIZydHNG9UH7//tROdP24pPtHL/GX78PETrPh1A1JTU7VmnjIWupat5qDSF3EvsWHrn0hVpqJqxfJ6TL1uFIoUfDJ+MpwcHTClfx8EF8u4Rn02az7uP3yE4GIBADK64cnl1vh7/yGjvR4DGV08Pp/7LSJv3satu1GwtLBAjw5ttaZTVnN3c8XSVb/Cw80VX00ah1IhGefqktlf4smzZwY/qNSc7i0EQcC2XXuQqlRi7NCBKB5YDEDGWIceHdrit63b0atTO3h5uIvXKXW+9x6MwMFjJ3D2wiUsnvU5fLw89ZaPvMrtGgUALZs0hL2dHdb9vhXjps9Eu5bNILO0xONnz3Dj9h2MHz7YKALDgmBSwYMgCPj9r10QVAKG9u0hTo3VrGE4bt2Nwl+796N3l/Za08NpfkGHlSmFnfsOID4xEW5Wzh86+fkikUhw78EDFPEtJE5VWTyw2OtmNB8sXLoCADB59HB0a/8xSoYEY9+hI/h10xbI5XK4uTjju6+/EPc1ZKmpqbj34BEah9dBUNEAAEDVimGws7XF3Xv38fO632BpaYk+XTpo7bf7wCEcPHoC5y7+h+++/tLgv6QAQKFQ4E5UNBrVqy2WTbEAPwT4FYaFhfR1k7AERQr5QCKRiDfSh46dwK79B3H2wiV8P+creLq76TMbeaJSqVDUvwhqVK6Ilk0a4vyl/7Br/0EAwJDe3eHi7CR+IRtrfh88fIyWHzVEswbhWjdYTcLrYO/BCPy0biMmjxoGayurbPd/+uw5Fs/6wugCh7yUrTpw2Hf4CPYfPvr6vP3C4Lt8qFQqfL9yDaysrPC/gf1Q1D+jFaxqxTD4+xUGADyLiYUEGTfSPTq0RenQEKO9HgPAwWMnce3GTQzs2RVya2ssWr4SALQCCPUN9YgBfaBISUHNypVQqXwZSKVS8SGAl4dhl6253VukKpV4FhMLv8KFEFRU+1oTElQML+PjkZqamuUBx7Ubt/DHzt2wtrbC0jkzxHPAGOhyjQKA8FrVUSokGDv3H8DhY6eQlp4GHy9P/DBvplG0fBcUkwoe0tPTEfsiDg4OdvAvnHGxVl+cypUqga079yDuVbzWCa6pR8e2aPVRoxz7bRoaQRCQnp4OG7kNkpKToVKpoBIEWFpYwN7ODs0bhkMCCeZ89wM83N0wsGdXhJUphbAypdC7SwfY2dpAUAniy3oMmUqlQmJSMhISE2FrkzFQKSU1FdZWVihdIgSNw+tg3eat+PGXdXCws0P7Vs3EJwC2NjZIVqRg6ZwZKBbgp89s6EQQBLx8FY+nz2OgVL4ZJB2fkID/rl3H7ah7+HH1Osjl1mjT7CMM7t0NAHDs9Fls27UXr+IT8MO8mUZ3gymVSiEFkKpMxf6II5g4ciiSkpMRceI0fvxlHYYP6I19h46gYrky8PXxxsmz540uv8UC/OBXuJD43gK1wAB/VChbGifP/ounz2NQpJBPlu4Nhby9sHLxvBwDC0OW17I9e+ESDh87iYTEJPwwd6ZR3IRIpVKEBgdCZmkJvyK+4o1VbFwcHj1+gl82/o7b0dEo5OWFlk0aomu71kZ7PVaztLBAEV8fdGjVDPZ2dpBKpfhm2U8QBAE9O7aDm6sLpFKpOKZj/LDBSE9PF+u1sfQNT1epEBtnPvcW1lZWqFO9KjzcXGFpaal1LSriWwhyaytx/Iqm0OBATPrfUDg6OBjFQzpNulyj9hw4jKoVw+Dj5YnenTugfctmsJHLoUxLE6fXNhcmETyoT2ILCwt81KAeujq3hpWVDMLrG2kA8C9cGCqVCjGxsdk+1UlLS4ONkb1CXCKRwNLSEmVKhmLFmvW4fusOQoMDxb+HTCZDwzo18fjpU/y2bTsqlC2NSuXLAgDcXV2Mog8ikHEzLZVK4eHmirCypbF64+/4qH5dODk6igOWZJaWqFm1Eny8PLE/4ijq1qwm5rF2tSqoXL6cOMjWkKm7bPj6eKNxvTpY89sWKFJS4OXhjs1/7oS7iwsG9+kOC6kUZy9cwqoNm+Hi7IhOH7dEjcoV4eLkBG9PD/EpiaHT7J6j/oJqWKcWduw7AAAY3r83pFIpDh8/ia6DRiDmxQts+mkpgIwnuo4ODkaVXwDi7EKZ8z24d3dEnDiFjVv/wthPBmbb/G1MgcO7lG3FcmXg4e4GZ0dHo7gJUV+HmjUMz/j/6++dtLQ09Bs5Hq4uzmjb4iNYWclw7NQ5LF6xCi5OjmjaMByAcV2PgTflGV6ruvgSsPT0dHT6uAWkUgkWvG7xVgcQMplMq2XJGAbPqqnvI5o2qIeubc3j3gIAypYMhUqlAgCta5GjvR3SVSo8fR6Dkq+7nilSUnAn6h5KFA8y2ilKdb1G/bbie/Eey87WVqu11JwYfeespORkdB8yEjv2/gOJRIKgov5wfR3da16cpBYZ0z5qRssKRQpu3L4LAFmeBBqTjq2bo1RocUz4YhbiXr6CpYWFOK2nnZ0tGtWtDQd7e3EaS8A4Bi+paaa188ct4WBvjz4jxuH6rduIefECN27fwcat21GxXFk0a1gft6OiEfsiTms/Qw8cUlJTkZaWBgsLC6S+nslh1OB+aNviI5w+fxHnL12GMi0NU8eNRNUK5VGpfFl0btsK9WvXwN/7DyE2Lg4AUKJ4kFHdSKe//nIC3pRzSFAxXL1+AxcuX4WlpSVGDuwLC6kFYl/EoWLZMlo30IaeX0VKCv78ey8WL1+FtZu34r+rkQDejFUBIA4WdnSwR3itGjh07CSuRN7QZ7LfiUqlgiAISEhMFJepbz7yUrZ+voUMPnBQn7fqNwgD0Jp69uzF/1ChbGnMnjoRLZs0RJPwuhjQozNCgorhzIVLSHs9FaSxXI/VZZuY9OZ7VPY6v+obqA6tmmPMkAH4bdsOrN74u3htehYTK9ZrY8kvAHG6zsAAfzg7OQIwvXuLZIUCK37dgMkz5mDud8tw6FjG1LI59d+XvW6NADIGkc9f8iO+X7kG8QkJHyzN70KhSMHmv3bi9t1ocZmu1yi5tTUsLSygUqmMqh4XNMOv1blITEzCgNGfIureA/z5915UqVA+xxcmOdjZwdHBXuwGkpCYiCU//YIr12/g+9lfGUVTcbJCgT//3ocHjx7D18cbJYoHoWzJUMitrdGvWyd8vWgpBo+bhGXzZmY8lX8dHRcL8IOHmxsuX7uu7yzoLFmhwLrft+HBo8dwc3VBxbJlUK1SGEoUD8Lowf2w7Jd16D18LFycnPDi5Us0qlsbnT5uAQCwkctxJ/oeQoKK6TkXulGkpKBV9/7w9nTHioVzYGUlyyg7S0v8b1A/CIKAw8dPIureA63ZOpwcHOBgZwdlWhrsbY1jBikgo2w3/PEX7kRFw8ZGjkZ1a6NS+bLivNrubq7w8y2EpORkAMBns+Yj7lU8alSpiNt3o7Fg6QqMHTZQHMRmqBKTkjFswmdQpKRApVLh4ZMnKOrnh75dO6JezWpaXzwSiQRya2s0axiO7Xv248yFSygZEmxUT2iBjIc5i5evwu2oaCQmJaNbu9bi03VTKlsg+/NW3XKoLrMKZUqhfOmSsLayEsuycCEf2MjlePkq3mi67QA5l212N5jtWzWDAAELlq6ARJLxfot1W7bh0ZOnWPL1lwb/fZvTNSq7J8zGfm+RmJSMQWMmAgBcnJ1w6Wpklln91FQqFSxed4tOSUlBWloavv3xZ/x94BCWL/g6x25bhiQxKRkDR38KZydHWFpYIMCvsFiHVSqVSV2j3iejDR7U86QH+BVGeK3qWL3xd9yNvgcPN9csr4AHAJlMBqlUiriXr5CWno5Fy1fh7/0HsXzhbIM/uYE307FavH5Keez0WTx++gx9unRAh1bNUal8WQzt2xNLfv4FvYaNwbzPJ4uDiuNeZrzUpYhvIT3mQHdJycnoM3ws7OxsYSWT4XZUNH7d9AfaNv8I3Tu0QdWKYQgrUxr/HDmGlJRUONjboX7tGgCA/65GwtLCAoV9vPWcC909ePQY8QkJSE1NxeCxk7B03gxYW1mJb8iWSCRISlbgWUwMkpIVYvN3bFwcnse+QFBRfwgQ9JwL3WRcuCfASmYFS5klFAoF/ok4hrnTJ6F86VKwsLCArY0NgooGYOfeA9i+ex9On8+Y1rFMiRDMW/IjLkdeF+fZNlQpqakYMzVjcP7UcSNR1K8Ibt2NwoQvvsZfe/ahXs1q2e5XvnRJtGnWBD+v3Yh6NarCr7DvB055/iUlJ6PvyHFwcXKCr4834hMS8cX8RZBZydCwTi2TKVu1nM5bzZnANKedVbc23Xv4COnpKpQvbRxTzwJvL9vMBEFAh1bNIYEE3y7/GfsPZ7yXZPmCrw3++/Zt16jMjPneQqFIwYhJ0+Ds5IjxwwbBr7AvYl7EoffwMbgceUMreFA/tFK3pETdf4hvf1yJ3QcOYcXCOUbxsC41VYn/TZ4OVxdn/G9QX/gX9tUKfqVSKWxtbFAswM8krlHvk1EGD+p50gt5e2HqmJFwdnLEkZOn8eOa9QgrWzrbpznp6elQpqXhZfwrLFu9FrsPHMLyhbONosKrVCrMW/IjHO3tMGX0CPgX8cXNO3exdecerPh1Ax4/eYo+XTsivFZ12NraYMWa9ej/vwlo1igcdra2ePLsOe5G38PkUcP0nRWdrN74O6ytrTBj0jj4eHniRdxLHD5+CnOXLMODR48xqFc3lCgehI/q19XaL+ZFxpSd1tZW8Mn00h5DJQgC7O1s4eHmivq1a+D0+QsYMm4yls7NuBFRDzQMK1MKrs7OGDRmIiaO/ASxcS9x6ty/uHT1Gn6YN9Mo+sIrFBlTHbq5umDMkIHwL+KLG7fv4vO53+C/q9e1vphLhgRj3vc/wsXJCV9OGovSocVhYWGB8cMHIzbupcEPPPx7/yHEJyRg2Mih4gwcgQH+GNSzK6bOXoCbd+6KwX1mpUKL4/iZc7C2NvwyVVOpVFj4w09wcXLCZ2NGoJC3FxISEzFl1nwcO3VW6wazVGhxzP1uGVycjbNsgbeft+quTECmqWdfvsTazX/gxcuXCK9VXZ9Z0FleyhbQHutSu3plbPprJ17EvTSK95Lk5RqlZsz3Fr9u/gNpaWkYMaA3/Ar7Ij09HW4uzqhQpjQc7e1x885dyCxl8C+ScZOt7qrk6OCANb/9Dmu5NZbNm2UUeQWAqzduIilZgZGD+qGoXxFIpVI8ffY8Y1B/Wpo4rWy5UiUxe9FSo75GvW9GFzykpKaiedc+KF2iOKaPGyX2d65fK+MtrYePnUT92jWyNPfLra3h5uKCH39ZD7ncGsvmzTSaCp+Wlo670fdRukQI/ItkPIkMKprxsqEnz55j+95/YGVlhcG9u6FqhfIoERyEjVv/wrmL/+FVfDx8fbzx/dyvxH0N3YNHj+Hh7iaeyM5OjmjdtBEKF/LGqClf4Mc16zBiQB+tadH+OXIMB48cx4mz/2LJ7C+M5uSWSCTw8vBA1Qrl4ejggG7t2+D7n3/BJ+On4LuvvxBbGbw9PTByUF+sXL8Jwz6dCjcXZ3h5emDJ7C+NYno4QRCw+a+dsJLJMKhXN/gVzmgFCy4WAE93N1haWiD6/gOoBAEBRQqjcXgdPH0eg/KlS6J86ZKwsLAQb8KMoWzT0tPg7OSEoNfz+quvR36FfWFlJcOr+Jz7BjdrGI5qlcLg6uz8YRJbAFKVSty6G4WyJUuILzyzt7ODj5cnrGSWuBx5HYJKQOkSIWjyumzLlSphlGUL6Hbeqp/UqgOHTX/uwJl/L+LC5atYPOtz+BpJ66hOZSsApUOLA3gzHuB5TCy+XbYSDx8/xspF8ww+cMjrNUrNmO8talapBEVKivgdYmFhgcTEJJy/9B8uR17H/KXL4eHmivJlSuGLCaMzntILgLenO57HxmLxrM8Nvlw1PXn6DMkKBUoWD4JUKsXBoyewdNWvePUqHukqFZo1zBgU37hebTx++syor1Hvm9EFD9ZWVhjUqxsa1K4Bd43xDR83b4I/du7G3kMRqF+7RpZ+wjKZJTzcXXHvwUOsWDDbKKbsVFOpVJDJLJGSmgLgzY2Ir483ypQIwZGTp/HHzt1wc3VB364d4ehgjwE9uiApORmWFpZQCSqjmkbM090NJ8+ex6v4BDg62EMikUClUqFiuTL4ZsY0jJw8Hb9t3Y4JI4aI+9jKbWBna4sfF8zSurAbOnVZyuVy3Lh9B327dgQA/LDqVwz/dBp+XDALv276A6VCi6NG5YqoUqE8Lvx3Ba4uznBxchIH8Bk6dV/v2tWqICSwmHh+xick4NbdKDx4/AQ/rlkPQSWgfatmGNq3J/p06aA1ENOYZrSoVbUS6tWoBrm1tdY0h4V9vOFob4/nMbHZ7qduaTKmwAHIeKgDAM+eP0diUjLsbG2QmJSM0+f+hTItDXsORCA+MRGN69XGyIF90LdrR60n8sZUtoBu5+3a37ciuGhRVKsUhugHD3HvwSOkpaVj6dwZRhHwq6kncHhb2TatXxf9uncW38cR9+oVUlJT8fO3c3NsZTMkeblGdW7TEr27dIC1lRUsLS2M9t6iRPEghAQV03r5Zq/hY+Ds5IR+3TrCw80Nx8+cwy+//Y7Fy1dh+IDekMut0enjlvifl6cYTBoLF2cncRr0uJcvMWXWPHRq3QKFfLyQlJyMH1atRfT9h5g6diT6dOkgjvEAjO8a9b4ZVfCg/rLp0rZVluVODg7o3qENFi5dgUPHTqBuDe0+xRKJBL06tceE4UMM/mVDmcnl1igeWAw79x1A0wbhKFeqhLjO0dEBjerVRnCxAKzZuAW1q1UWX2BjI5cb1WBL9U1WcNEAbNu1F0dPnkbj8DqwsLAQm0wrlC2NiSOH4sv5i1C9cgXUqV4VAFCtUhjCypYyiu47mtTl0+qjRpj1zRIkKxSoW6MaJACW/bIOTTr2AAD89M0csStExXJl9JjivFOft/VqVtPqzpGWloaug0bA1cUZ/bp1gp2tLS5cvorla9bD090NHVo113PK88/Lw0OcpUWzT60AAWnp6Yh5EScuUyhScOHyFVStGKbVR97QKZVKvHj5Cp7ubnBycED1ShWwcetfmPr1fJQMCcZfu/fB1dkZ/xvcD04ODrhy/QZmLPwObq4uGNq3p9F9GWd+7wbw9vP250XzIAgC/HwLYUCPzpBKpAbfDz4zRwd7ncvWxdkZn/TNyHtQ0QB8OXEMbG1s9JyDt1MHg/VqVoNSqdTpGuXm4iK+T8hY7i0Sk5Kx79ARNKxbC3a2GeWiWadv341Cg9o10LbFR/B0z3h7tH+Rwrh+6zauXL8hBo/G9B2kPm8FQYCvjzf8C/ti595/kJKaitrVKotveweAksWDMXziNGz44y8M7t3N6K5RH5JRTdWa07Rh6gIuV6okXF2ccejYSSiVSrF/nlr50iUN/uQGMp7iPXj0WGvZ8P69UTywKD77ej52HziMazdu4d//rmDx8lXw8fJEy8YN4e3lgViNmxJjCBySFQos+fkX3L13Xyzfpg3DUal8GSz5+RetKSvVAUTtapVRoWxpHDl5Bunp6eJNmrEEDuppHDXZ2shx/9FjnLvwH+xsbfBRg3pwdXaGIiUFLk5O8HR3h6WlpTi1ozFQ51Mzv5YaU/zFvHiB6pUrYtaUCahTvSoqliuDbu0/Rv3aNbBz7wEkJCZm+7cyNEnJyVi6cg0+/fJrzFvyI7bu2gMg+ydVMpkMTo4OSEt/MzPLwmUr8PWipeKUlsYgKTkZg8ZOwu4Dh8RlA3p0Qc+O7SC3tsKr+HhYWlpiwoghKFsyFP5FfNG0QT306NAG23btxdNnz/WY+rxR10FVNtMKv+289XB1FWcRc7C3N4rAIbvvH13LduuuPXj67Ll4TTb0wCE9PR0KRYo4qQgA8Z0UQO7XqB17/xG7HxrDvUViUjLa9xmMA0eOwSKHe6nigcUwoEcXeHl4iHXcztYG9nZ2UCrTIDeicViZSSQSFPL2QtlSoVi3ZRsOHz8FV2dnMXBQKpWoWK4MenZsh32HIhD38pVRfP/oi8G3PCgUKbgVFYVSIcXF2SpyuikOLhaANs2aYOWGTejarjWCigYY3VSHipQUdOj3CRzt7fHVxLHim1WtrGT4fPwofL1oKWYvWgqJVILUVCUa16uNIb27AwCSkpJx/fadLNOrGSr1lGkWFlI42NnBv6Mv0lUqWFpYYMKIIZjw+SxMm7MQX3w6RuyjKJVK4WBvD3s7Wzx++tRongy8rR77+nijWqUw3Hv4EAAwZdY83H/0GB1aNcfBoyfQa9ho/PL9QqMJkJKSk/H9yjV48jTjRqJpg3qoUK4M3FycxSDQy8MDn44YovVQQJ0/udwa9naGP/2semYwa2sreLi54dqNW9i6aw+Onz6HQT27itMAqsvb2soKtjY2ePkqHmnp6fh22c/YczACPy742mi6KiUmJqHvyHGIfvAQT589R/1aNcT++z07tQMAXLh8FbsPHM7yngapVApHezvY2Rr+TTSQez0GTO+8ze77R/3kNi9lawzX5aTkZMz65ntE3X+Ax0+eolv7j9Hyo4ZwdXYWr81vu0YZ+ntI1NSzUwYVC8Ck/w3N9r1H6hbizC+xfPz0GZ7FxKBMyZAcH+AammSFAqs2bMajJ09hJZOhUd1aKF0iFHa2NvjfoH54FhOLfyKOIT09HQ8fP0Ehby+x1dfCImOcko2NcfXc+NAMuiYkKxTo97/xGP7pVJw8ex6A9suVNKmXNaxbC17u7li57jcoFClGV/hxL18h9kUcbkdF47Ov5+NO1D1xnaeHOxZ8+RlmT52I6eNGYc7UTzF17EgAQOTN27C2tkbx112WDJ1SqcS46TPg4uyEWVMmoEvbVhlvzH79pePi5IQpo0fA2dERE7/8GvsjjolPeV7FJ0ClEuDr42MUT+J1rcdeHu44dOwkpn49H6fPX8TsqZ9iYM+u6NmxLSwtLRAT+0Ifyc+zZIUCvYeNwY1bd2BlJYOFhQWmzl6Ar7/9HsdOnwXwphVJfWOt9ujJUyQmJqFMiVDxhVSGShAELF+zHrY2NpgxeTzmfzEF87+YjJED++D0+X8xd8kyXLh8VetlQmmvW8pexL3ED6t+xZ5DGYGDsQywVN+EeLi7YcLwwUhWpODg0YwXSmmei2lpaVCpBDx8/ERc9vLVK9x78BD+foUhkRr+dVmXegwAHq6uJnHeAtl//2S+YTSVsu3/vwl48TIO1SqGoUn9uvhh9VrsO3Qky7bGfI0CMvLac9goFPP3w7SxI+Hm6gIgo4VJoUiBIiVjLKXF6xefaQYOz2Ji8fO63xB9/yHaNP/IKO6nkpKT0WvoaJw+dwExsS9w78FD/G/KF5j//Y+4cPkqAGDq2JFoVLcW7j18hFUbNuP+w0cAgBdxL3HvwUN4uLuJrWeUPYNteUhLS8P875fjybNn8PHyxPzvl2PUkP6oXqlCtk9u1f/3K+yLapUq4K/d+3D/0SOjGKilplKp4OzkiErlyqBKxfI4EHEcE2fMxqzJE7SeAFWpUE5rv6fPnuP37buQojFrgqG79+AREhITMXJgXxTy9oJEIsHDx0+QrFDA0sIShQt5w7+IL+Z9MRlfzV+MWd98h+KBxVCkkA9iXsTh4pWrWL5gtsG/ZEmXegxk1N/wmtWxY+8/uP/wEb6cOAZlS4bCwsICzRvVR8O6tYziSTwAbP5rJ6QWUnw5cSw83d0AAIePn8SSn37Bil83IDExCY3q1YZUKtWaE1/9RXX33n2M/mSAwT/lkkgkiH7wEK6uzihSyAcA4OToiCbhdfHg0RNs3PoX1vy2Bc4DeqOoX5GMJ3tSKZwdHbFz3wHY2doY1cws6sDB18cbMyaNhZOjIw4ePYE/d+9D13atxbeuSqVSVCxXBkX9CmP63G/Qs2NbAMB/V6/jxNl/sWzeTIPvzgK8vR7HJySgSXhdNA6vg537Dhj9efu27x+1iuXKwL9wIaMtW0EQsGz1OlhbWWHK6BHw8sjo25+Smoq/9x9Cx9YttLY35muUOq8PHz9F2+ZNxUlmjp85h517D+DmnbvwdHdDxfJl0bNjW638rN/yJ46fOYtbd6Kx8KvP4GcE74kSBAE/rFoLa2srfDlxDHx9vKFQpOD3Hbvw3YrVePj4CXp37oBqlcLwxacZ43EOHTuJU+f+RTF/P8QnJuJu9D0snTvD4OuxvhlszY+8dRvHT59D0wbhGNSrO9zdXLFg6QocP3MOQPZPbtV9Uju3aQlvL0+jaRpXk0qlkFtbw9PdDZE3bmHs0AEQVAKmzJqLW3ejsr1QnTp3AYtWrMKhYycwc8p4eHq46yHlefcqIR7PY1+gSCEfSCQS/HPkGAaPnYQhYyej88BhmPjVbBw7dQauzs5Y8OVn+KRPT/gV9kX0g4dwc3HGj/O/1vpCM1S61GO1ov5FMLx/b8ycPB4Vy5URnwTJZDKjuQEBMqZodLCzE2+40tPTUad6VXw68hNIJBKs3/Injpw8DQDil/Ky1Wsxb8mPOHbqLOZOn2TwX1SCICA9PR32tnZQKpVITEoWr0eODvYoV6oEbOTWuHjlKn5Y9SsAiG8eDg4sCkcHe/w4/2uEBgfpMxs6UyqVGDJ+Cgp5e2H6uP+Jb5Lt3KYVHj95itUbNwN406IEAJ9PGI3QoEAsXfUr1vy2BTEvXmDp3K+M4rwF3l6PN/6xHcfPnEPxwKIYOagvZhj5efu27x9NM6dMQEhgMaMs2/T0dNyJioZfYV94e3ogLS1j/JG3hzv8i/ji2KkzOHn2vNiyYqzXKCDj+6V+7RqoW6MqNv25AwePnsDZC5cw/vOZiHnxAsGBRZGSmoqlK9dgxsLvxP2ePo9Beno6XJ2d8f2cr1A80DgecKSrVIh+8BBF/YqIXSmtrGTo1u5jdO/QBhcuX8WGrX/h/KXLAIBPR36CSaOGokXjBpDJZChXqgSWL5xtVA+d9cVgWx5cnJzwcbPG6Nq2NezsbGFpIcWaTX9gwdIVGJ1DC4T65trb0wM/fzPHKAanaVLnpVzpkjhw5DhCg4PwxadjMHX2Akz9egG+nvop1m3eijIlQ9GsYTgAwEZujeCiARjQvYvRvMcBAKxkVkhMSkJ8YiLS0tMxffZCdGnbCuXLlMKr+Hj89OtGrNyQcUNSo0oltG/VDEDGk3yJRGIUfWoBwNXZWad6nJaeDhu5HE3C62R546WxcXN1xZ3oe4iJfSE2kQNAWJlSGN6/F2Yv+gFbtv+NoKIB8Pb0gCAISElNhVQqwfdzjON9JOo6WDmsLGYs/A77Dh1B0wb1YGWV0W9WgIDQ4CB0bN0Cs75dgoNHT4hvlW7ZuCE6f9xSa6ppQyeTydCxdXNUr1xRa57z0OBABAb44+ips2jT/CM4OTiI12NvTw/MnT4J12/dhqODA+xsbcSgwxjoUo83bt2O0KBANK5XGxKJJMt3kTHR5ftn7aY/UK5UCTRtGI55n082yrKVSCSwt7PFtZu3oFCkQC63hiIlBbsPHMarhAScv3QZT5/HoFL5jMGzlcqXBQCju0apxzCULRkKmWV7rNqwCTO/WYLU1FT069YJbZo1gZOjI2Lj4vD3/kNYvGIVgor6o9PHLeHp7oaOrVsY3TTvlhYWcHJwwINHj5GaqoSVlexNrxTfQpBKpYi8eQv7Dh1BmZKhsLSwQO1qVVC7WhU9p9z4GNQVTrMloZC3F/p07SgGADWqVELPTu3g7uqS7ZPbzDO6GEPgkJKaihu374q/q/NSsVwZRN68hSuRNxBU1B9fTRwLlUqFPsPHYN/hIyjk7SnuU6ZkKLq1/9goLmaaSoYEI6xMaXy/cg12HziMKhXKo3eXDqheqQKahNfFpyM/QUzsC+w5GAHgTauSpaWl0QQOAODj5alTPba0sIAgCEZ50wG8eRIPZMw84ursjF83/4HExCTxSWzGulIY2q8njp85h9PnLwDIqPcjBvTB1LEjDboep6Wl4XlMrNZT2BaNG6Bdy6aYu2QZflq3EQePnsDRk2cwc+F3KB5YFPVqVkMxfz+t9zr4F/E1qsBBrUXjBlqBgyAIcHZyRP/unXD52nUcOZHRmqS+jqnLvHhgMXh7ehj8zWXm63GYDvX45NnziDhxClKp1Cj6g2cmCIJW10kg9++f/RFH4ePtadRla2FhgVpVKyM9LQ1dB4/AvO9/RMd+n8De3g4Lv5yKzSt/wPwvpuBO9H1s27VXHMtjDNco4M15p3zdogJkvM+hR8d2CCtTCo3q1UbzRg3g5JjxjiBXZ2c0qV8XwcUCcOlqpNgSY2UlM4rAIXMPlJCgYnjw6DG2792PpORksV7b29khvFZ19OvWCVt2/I3rN2/rI7kmw2DuVJKSk/Htjz/j5Ll/oVBkDOBRNxeqK0f1ShW0brzUg0/vP3yEY6fOIjEpWT+Jz4ek5GQMHf8Zft30B9LS08UTXqVSwcHeDu5urnjw+AksLCwQXCwAHu6uUKSkwt7ODo72DlrHUs9JbWxqVa2Ex0+eYsee/RAgwEYuR3p6OtLS01GhbGl079AG+yOO4nlMrNHcVCtSUnD8zDnxpVmA7vX4waPHOHryjFHVY80LtzqoK1syFBXKlsbuA4ex52AEFIoUSKVScVrdWlUro2mDevjz773/b++846quvz/+vNx7ZVwQLlNZskTceyCkouZeOXKlZqaWI9PMUY4k9zZXrpxlmrnS0q/lyG1pjnKkKYqiCCKby7j3/v7A+xEUEoyf3A+fz/MfHlzvvX5enPM+730OOl26MOkwVdM2R1LT0hj7+UzeHzOBt97/kIkzniYzGPpOX/p178LOH/cTPm8Rn0yfTbNGIQx9py+Q/TeKiLxbnI//UmTp9TyMfUTUg2jhUiXw3GAzKDCAWtUq893uvUTHxAjvE0ubhefjMWQvzNSpWe2FfvzD/p/Rpaeb/cXZvHj27mB2StkX9z9itW1mZnbBu9bNwxg6oB8h9epQ1tUVa2trhvTvQ6C/LyqlkuA6tRjQuzs//3qM+9EPhe8y5xgF2VrnLFnBkDETGD15Ghu2bifmycJFlaBA+vfsStvXw3B5snhhGnc4aR0obWdLfEKi6Gz77LixV5eOVAoqz6qNm1m96VvOXrjE0VNn+HzeIpy0DnRt34Z6NavnSgMvU3jMYtSZkprGux+OoVQpNQG+PqjVuR8r5/Gk4Dq1MBqNbNy6nbnLVtGvexeOnvqNPy7+ydY1ywDzv+SSkpJKv+EfEfUgmjv3orgf/RAv97LCyrOtRkO1ShU5duo3Xm8cyqfTZnPtxk0+HPQOO37cz4gJU1gyI9zsV0AgO9PDtRs3qVGlkvCayZZd27fh5u077Ni7n5TUNO5G3cfTvawQ0NQqFU5aLRZKcQSzjIxMBo0aT2JSEkP69yEsNFjIGW460lCS/Dg1LY3Fq9Zx/2EMKSkptGvRnKoVK+Dn4824EUOIjLrP8nWbsLCwEIoSmQZYNjbWGI3kmTLQ3EjT6Rg4chwuTo682bEtarWapWvWY2lZigmjhmNtZcXAPj1p3jiUrKwsdLp0qlYKAuDO3Xukp2cQVN6/mFUUjtS0NCbOnMe9+w+Iuh9Ny7BGdG7XmoqBAc+tsDs6ONAkJJgFy1dz/WaEUCBPLDuE+cVjgDHD3iPyXgH8WAQrtCbSdDo2bt3OPxF3eByfQPdO7ahXqzp2trYolcoS1f88a9sHMbGCbcNCgwkLDebGrQhWbvwGJ0etMDlUKpUoUODs5CiaNLtpOh39h4+mdGk7ynl6YDAYWLFuE7+eOE2Pzu1p3ig01x2rnG006kE0iUkpNKhdUzQ7aHmNG02a5k2ZQPjcRRw79Rtbdu5BpVLSMqwxHwzsD0Bs3GOioqNf8D/I/BvFPirL0uv5dPocnBy1zJgwllZNG+fb6ZgCdsO6tenXvSsO9qWZtmAJf1z8ky9mfIaDfelX+egvhSljSRlXZxZMnYSNtRVrNn1LekZGrkvgvt5eREZFMTZ8Jr9fuMTU8aPp3K4VE0YNx9XZCZXK/DtmnS6dPkNG8v7Hn/LryTPC66aiSZDdOXdt34b4xETGT53F7ch7WFhYEBcfz9kLl3B0cKCUSKruPk6IJzomhuiYWFZu3MzhJ8UKn82qVBL8OE2no9+wj7h+MwKH0na4Ojsxe/FyZnyxTEjduXTW5wT4luPL9ZtY/+02Hj2OR6FQkJKaRkJCEs5OWtIzMsx+xXbrzj0oFApGDRlItw5t6dS6Be++1YOzFy4JK5mQ3WbL+/kKE4e7UffZtG0nsY8fU6taleJ6/EKTptMxaNR4dLp0urZrw9s9unLk5Jk801iabNe1fRsqBQWyZtMWskQ2ccgvHpu0LZ4xhUB/X9H7MTwdcJ29cImsrCxKlVIxaeY8fvz5EPA03W5J6H/+zbY5sbaywtlRy4kzZ0nPyECpVJKQmMiFvy7jUcYNG2vz3m0wsXXnHhQWCiaOGs6EUcOZNHoEC6ZO5s7deyxds4Ede/cL783ZRh/GPmLtN1uJiX1Eu5bNRDF5yG/cmDPuTBo9grlTPmXprHC+mD6FTz4cilKp5M7de2hsbKgYWL4YFYifYt95SE5OITEpibd7dKWMqwsWFhZERN4lOiYWjbU1To5ayrq5olAocuVLD/Ath8FgwFZjw4p5M0SR5SElJZXe74/A070s4eNGobW3p2bVyvz2xwWiY2Lx9nAXiqSFhQbz1eatRD2IJnzcR9SuXhULCwsqBgawbPZUs18NydLrWbx6HekZGVQI8OOTabP5fNxHhIUGA9lHXEyrBB8NGYiLkyM//XKYvsNGEuDrQ1ZWFvejY1g6K1w0GUvcXFxo0zwMNxdnfj5ynPnLVgHQpGH953YgTIjRjwE2bt2OSqlk2qejcXFywsLCgt37DjBv2SrhUl6LsEYsmz2VqfMXc+j4SX765RAVAwNITErmxq0IVsybYfZ+DBAReRcrS0u8PdwFG9pqNLiXcWPvgYOk6dKpVjmIyhUChc+cvXCJ3ft+5rfzF1g4dRLuZdyKUUHh2PTdDiwsFEwa/QFuLtlVc1PS0jh07CSD+/UWLoXnxGg00rJJI+YtX8Xh4ydp3ij0VT92oSlIPE7PyMCyVCmWzAxn2oIlovbjjIxMJs2ch6NWy/gRQ3B1cUalVDJ36UpWbdxMo+D6lHXLvk8n9v6nQH3tk/7Ho2wZfLy9WPftNm7fvYubszPX/rnJhb+usHTW56Lpf+7ci0JjY4O3Z/aOkF6vp16t6tmZwBYsYde+/2Fnp6F5o9BcGaQuXr5KxJ27LJo+WRQZpODF40atgz0eZcvg7ekh/D0ge0Hn6+938uDhQypXkCcP/4Vinzw8jk/gn4jbwpbhwWMnmL34SyC7GJiPlye9unSkXYtmQrGWpOQUpi9cypW/b7Bh6XxRDLiy9Ho+m7MQNxdnPvv4Q7T29igUCgb17cXxM2fZunMPo4cOEi7OOtiXZtbEcRgxUiHAP88Kl+ZMxJ1I9h86QuOGDejZuQMbt+5gwoy5TB0/Os8JRN/uXWjcsAGnz/3B7ch7uDo70bRRiLDFLBb0egOXrlxj9cJZ9B06ikUr1mA0GGgR1ui5FZ3EpGTR+bGJ+9EPcXLU4ubiIhwz69DqddJ06SxcsYbFq9ehsLDg9cahTBg1nNNn/+DcxT+5cy+KoPIBjBsxBB8vz2JWUTDcy7hx8vdzXL52nUoVypOUnMym73aQnJLCrp8OEBkVhZOjI326vUG7Fs2A7IJ/dWtWY2CfHniKzIcjo+5T2s4ONxcXYfBc3s+Xu1EP2HfwMGq1Go+yZahWKUjYRVQqlYS91pDd+w9QIcD8j2gVNB5blipFVlYWKpWKT0cO4/S585y7cEmUfnz24iWiHkQzuF9vYUHOaDTSrkUzfvrlMJcuX6Wsm6tQ70Gs/U9BbZuz/5k3ZQLTFy7l0uVr/J5xEb9y5Vg2ayp+Pt7FLafAeHu4c+7in8LxX9MqvJuLMxobax7HJ7Dzx/8RUq8O1lZWZOn1aB3ssbPViCaDlInCjBtNXLp8lfVbvufK3zdYOE1cCzrmSLFOHoxGIxoba7T29iQlpfAwJpYpcxbS842ONH0tmJjYOHbvP8CSNetRWljQunkYCoWC0na21K1ZnUF9e4kmH68C6NqhDRX8/YRjKQaDAa29PWGhDThy8jRtX2+a60xxJRHPjAN8fejf803at2xOaTtb+vfqht6gz3MCYVrNLeflIaoAlhOThjbNw5i9JDuIrVk4i3dGfMzi1etQqVXs3vczjYPr80bblgCi9GMTdra2/HHpLzIzM1Gr1cLPSoEBaB3s8fJw58efD1K5Qnncy7hRv3ZN6teuWdyP/VK0a9GME2fO8uGnU6gUFMiNm7dwcXJi5sRxlPPy4FHcY0ZPnsa2H36kSUgDbDUaPN3L4l7GTVSXD03Yamw4c+48KalpaGysSc/IYO03W0lMTubO3XskJiVTqpSa999+ixZhjYRBmJPWgTULZ6MWwzFDo5Eu7VoTVN7/hfFYpVIJxe/q16pB/Vo1ivfZXwKj0UiaTkeAr0+uc+0KhYKg8v5YW1kS+aTKbknof7q2b02FgBfbVqlUCkXgPvlwKI/iHmfXu7CyNPvL0c/SJCSYb7bvYseP++navo2wi6RQKAj096N/z258NGkq+w8eoVOblqiUSt7s2I4OLV8Xxd0zEwaDodDjRgBnJ0caN6zPB4P6i2aHxZwp1p5NoVDg6uJM5QrlmbtsJRcuX6V+rRr07tqRQH8/QurXYUDv7lQKLM/3e/aRkJQknCvt2bmDqC4hKpVK6teqkes8uylItW4WRuyjOH47fxF4mgFBrJjuM/Tu2onSdrYYjUZ8vDwZ1KcnYaHBTJgxl0PHTgrvVygUQno4saDTpXPl7xvC76YO18lRy9179zl49ARqtZqNyxaidXBg6vzFnL1wESdHB+CpjcXmxybq16qBhYUFc5etRKdLFwaMqTodLk6ONA1tyF9Xr3PnbhQgbp8u6+bKrMnjGfJOH1o1bYyrszP9e3WjnJdH9qDZUcvHwwZz7cbNXD4hlonDs77coWVzHOxL07bn24yZMp3u7w7FztaWL6ZPYfPKxcyYOBZvD3c2b9/Nw5hY4GmmLXOfOOh06fx17W9UKhUN6tQscDwWiy2fRadL5/K16ygUCl6rX5fhA9/GyspSiNGmuOuo1QptNK/052LAYDCQmpaGSqmkfu2C29ZUGR2y47fWwV50EwfITgE9/dMxbPvhRxat/IptP/zI3gMHGRs+A3c3V+rUqEab5mFE3ruf63NimDjkjFEWFhaFGzcmJgLZcbxdi2byxKGIeKURMTMzk+s3I7h6/UaulH7dOrbDspSa6QuWEHnvvpB61Gg0EujvR+vmYVz5+zoJCYmiuMxjIqfeBw9j8n1fjSqVaN+yOZu/38Wde1Gi7KgMBgNZej2ZmZnCQOLZzsfb0yPXBOLIieyLtXej7rPnf7+IJp1laloaA0eN5bPZC3gU91h43WAw4OLkSGiDurns7Vm2DBkZmWhsbEjPyCRLrxeVjfPy4zo1q9EkpAFnz19i3OczOXz8FLv3HWD81FlUrRRE1w5tqF+rBr+eOg0gqnabF67OTnRq05K6NaoRcfeucGbYlPs/Nu4xTloHtA72xfykhSMvXw7092PmxHH0ebMzTUKCsbKypFeXjvg9OVZXrVIQ7Vo25+qNf4jN4f/mjklr+JxFudrts5SEeAxP9U6Zs5CYR3Go1WqhWrYpRpu02dlqcv1N0nQ6/nf4V1HF5DlLVrDv4BHiExLzfV9+thWrjZ+ldvWqLJkRTnxCIkvXbGDx6nW0atqEMcPfAyA6JjZX6lkxkF9/+2an9gUbNyYmCZ8Rez9kTryyY0spqWmMDZ/Bg4cxPIyJpby/L4P79qZerepUCQqkXYvmfL/nJ+Li44XUniZD22k0ODlq0evFs3qZl973+vWmbs3qeb4/pF5tDh07waGjJ+jXo6uoVrtS09KYtmAJMbFxxCck8FqDeoSFBlOlYgUgd6pdb08PBr7VEwUKPpk2h4+GDOT02fNcvHyF9YvnFbOSF2O6iJeUnIJer+fWncgnvvk0e4WLsxM/HzlGry4d+XT6HM5d+pPZk8ezasNmPp+7SLgQLwby8uNBfXpSv3ZNBvXthYO9PYePnWTizLmo1WpaN23CB+9mp8NLSk7G0jL7fHRJCdoajQ3ubm4cP3OWAD9fXJ2deJyQyPEzv+Hs5IiTVvviLzET8vNlCwsLfLw8GdC7O9ExMSz4cjXlvDyyj3g8Of9vZVkKZyfH59JqmysFabc5EXM8huf13o68i4uT43N6TZqUSgvin6zQpqalMX/5ao6d/o0NS+YXy/MXhpTUNN4Z8TF2Gg21a1TF9gUFYsVu2xdRtVIQC6dOJi4+njSdDn+fckB2OtaMzIx8xyDmSH7tFqByCRw3iolX0mJ0unTeGz0eo9HIsAH9GDH4HUqp1WzesYuk5GRUKhVd27emc9tWqFUqPpu9gN/PXyQhMZGHMbHsP3QEGxtrHLXiWNXLT+8323flWwCsccMGVKtUkR/2/0x6RoZogplOl07/Dz7mYUwstatXoXb1qhw8doKRE8M5kCO1Y84UpeW8PBjYtych9WozZ8kKzl28xILPJ+Hq4lxcMgpESkoqfYaOxNvTna+/XEQ5Lw9WbdwMPF2BBnitQT2USguGjJnA7+cvMmn0CBrWrc2aRbOpHBSIv2+54pRRYPLz4293/kBiUjJWlpb06fYGqxfOYt3ieaxZOJuPhw2mVCk10TExGIwGAp5oFdsRiPywsrRk5HsD2LXvAFPnf0H43EV8PncRR06cZsKo4aLZefg3X4an9rIvXRpXZyc2bNlOZmYmKpWK+IREDh07iaODvbCSbc68qN3mhVjjMRROr+n4Umk7O4xGIxkZmSxc8RUHjx5n4VTzj8l6vZ7pCxbj6uxE+LhRNAkJzrdoqsmnxWzbgmJlZYl7GTdh4nDjVgTrt2zj5u1IQhvULeanKxgvilEqpZKu7VvTpV3rEjFuFBuvZNlo+959KBQWjB8xRMg8otOl8+2O3UJDV6vVdOuQfcln6649jJwYjpNWi8bGhrj4eL6Y/plQTt3c+Te9FhbPr8CaVoP69ejK4I/G8/W2nbzT681X/dgvxfd7f0KhgCljRwnZC46eOsOyrzYyaeY8MjIyaPt6UyD36rNKqSQ1LQ1bjQ0r58/E19u8Mw0lp6TQd+goPMqWYeJHI3B21NLstRDWb/meIydO0bhhA0Gfv483MY/iSEpOYebEsdSpUU1Y3Vo+Z1oxKyk4/+bHphVnU+pZUycF2alNN2/fxfWbEYwZ9p7wvpJC7epV+XLudFZv3Mw/EbcJ8PVh+ZxpZu/DJgriyybUKhXBdWvzy6/HGT5+MuX9fIm8F8X1mxEsmj7Z7GNyQbQ+W2XZ1FbFGI8Lq1dYpbW15WbEbWYv+ZIDh4+ycv5MKgT4FaeUAmEEHj6Ko+3rTYXUs9du3OTGrQhKqdWU8/Ig0D9bR87MYGK07cty/eYt1n27jUuXr7Jo2mRRZDAsiB9n6fXZ48aObSnr5sqWnT+IetwoNl7J5OFhbCyZmZnY2T7Nl1w5KBBvTw++3rZT+D24Ti2ahDQgLDSY/x36ldi4x9hYW1GvVg1RpdUqiN6qlYKErB2mlQ9P9zKE1KtD80Yhr/qRX5qEhCQUKHI10Nca1CM5OYXweV8wdf5i1CoVLcIaCf+enJLCsrUbs1OaLphl9oMuo9HI3KUrcShdmsmjR+Ds5AhA2xbN2LprDweOHBMmD1l6PdZWVmxeuZh/Iu4QVN7/udoOYqGwfmw0Grny9w2+WLWWBw9j+GL6Z6JLU1pQqgQFMuezTzBtqORV+8AcKYwvmwZa777VA0cHB46d/o1zF//E38ebJbPCS1S7zYlY4/HL6DVptbG24uLlq1y/FSGeiYPRyKO4OP6JuI1n2TKolEoOHj3B9IVLsLa2Jl2XTnJqKiMG9ad7p/aAeG37X/DycKdDy+YMfaevKMZRBfVj1ZMsbyqlkiYhDWgS0kDU40ax8UomDzbW1kTHxhIReQ8fL1Cp1MxYuBRdejrp6RkYDAbWfL2Fj4cNpnPbVgC5Bptio6B6xw5/j05tWgoF8BwdHJg5cWy+267mhGn1ytraiviERGH725Sys3aNaviV88bNxZlFK9fi7ekhZBWy1WioU6Ma/bp3obyfb3HKKBBGo5GBfXpia6vB3s4OyM4l7qR1oG/3LixZvZ5fT56mUXB9IaBZW1lRJSjwBd9s3ryMH1eqUJ43O7Uj0M+3xE4cTJh7ZqG8KIwvm1KwWllmX5ju2bkDuvR0VEqlKLQXRuuziC0ew3/Tm10d3YcpY0aJpt6MQqHASaulgr8fl65cQ+tgz5ylK+jeqT2tmzUhJTWN/YeO8MXKtSgtlHTt0EaUfe1/xcrSUlRpsgsbo3Ii5nGj2Ph/aTkGgwGj0SgY9u0e3Th74RIjJ4bj7eFOcnIKtrYaZk0ej4+XJ9ExMazbvI1FK76iasUKuQaUz24pmyMvq3fBl2uoHBRIeT/fHBfX8j6Day48q7V1sybs3neAMVOms3zONGFQcTfqPolJSfTt3pn4xEQuXblKUHl/Iad2p9YtilNGgTAYDBiMRlRPqpDmxJRtp0aVSmgd7Dly4jQh9eqgUCjM3ob5URR+DNA0tGFxypDJg6LwZYVCIYoUli+rNefZd7HEYygavS3DGhFSr47Z39l5NkapVCr8fcqxfe8+PN3LEujnS8dWrwt3Ncq6uZKZmcWytRuoV6s63p4eorKtlCgKPzYhhnGj2Cnym0LJKSmMnzqLW3cihddKlVKzZGY4wwb04923eqDR2NCpdQuhMqebiwstmzYCBUKhGhPm7gBS0puXVmcnRwb3682de1H0HTqSr7ft5OttOxn92TRCG9SjZVhjKgWW5/iZswAoRXI5zaQ1IofWvAj096N9y+bs++Uwt+5EClXQxUZR+7GM+SAlXy5qreYcj6Fo9BoMBtRqtdlPHJ6NUabnHzagH44O9kyaOY8bt26jzJGys7SdLU1fawgKxXNphc3dtlJCau22JFCkI7mUlFT6DRtFTGwcLk/OqUF2I1ar1XRu24qaVauQnJJC2Sdn0bKeHHdRKCzQ2tujsfn3NGvmhJT05qXV+GSVICw0mMmjR+Do4MCmbTvY+N12OrR8nQ8HvwNkFyIyrRyIoVHnZ9dnMQWtFmGNcHN1Yf2336PTpYtCY06k5MdSQ0q+LCWtUHR6xZBtKC+tpgx+VlaWDBvQjyoVKxCfkMChoydISUkV9FmWKoWtjY1wtFbGvJBauy0pFNmxpZTUNPoMHYl7GVcmjf4w1wXanMbNLivuwMoN31C/Vg1USiVx8fHs3ncAjY01AT7iSGMpJb35ac0ZnOvXrkn92jV5FPcYvcEgpHCMi4/nQUws5f18APPfTvw3u5owaTDp8PZwp06Napw48zvRMbGU8/J41Y/90kjJj6WGlHxZSlpBWnoLEqOqVa7IgF7d+XL9JlZu3IzeoCe0fl2ysvR8v+cn1GoVfuW8i0uCTD5IyY9LGgpjEexL69LT6T98NNZWViyfOw2lUolKqST2URyJSckAODk6YF+6NHq9noPHTjJ/+SrsS9vh4+VJckoKt27fZeG0ycIg05yRkt6CaHXUOuBgn93oc04ObkbcYfP2XRw9/Rsr583A29O8G3lhtJrSOZp+xsXH07Znfzq3bcXHwwYXs5KCISU/lhpS8mUpaQVp6S2IVq2DPVoHezIzM4l5FMeilWu5+NcVUlJTKVvGjdS0NOZN+VRI2SpjHkjJj0siRbLzcPz078QnJGKrseFR3GPcy7hx+PgpVm74hgcxMWA04uLszLgP3qdm1cqE1KtNaTtbduzdR0JiEj7eXowZ9p7ZDy5NSElvQbWOH/E+NapUFiYPV/6+wcat27lxK4LFM6aUSK2mQGY0GrG2sqLvm51p1bRJccsoMFLyY6khJV+WklaQlt7Cxij3Mm7MmjSOi5evEh0Ti42VFeX9fMy+2J0UkZIfl0SKZOcBYMfe/Xy3ey8uTo6EhQazaOVamjcKoXaNaiQmJXHg8FEu/32DpbPCqVGlcq7PirE8vJT0FlTrstlTqV65ovC50+fO4+PlgZuLSzE+feEorNacOy2mTFJiQkp+LDWk5MtS0grS0vuy/Y+M+SMlPy5p/OfJgymvP2RXpN2y4wcePIzhrW5v0KdbZ6ysLAG4duMmMxYtBWDxjCnYajRmffY9P6Sk92W0LpkZjsbGRjJabTWafL/TnJGSH0sNKfmylLSCtPRKSavUkG0rfl5q2TBNp2P/oSNAdqGkzMxMADq3bcWbndrRIqwRzRuHYmVlKdyQrxDgR6MG9Yh68JDkHJkQxICU9P5XrUnJKZLSKiak5MdSQ0q+LCWtIC29UtIqNWTbliwKfedBp0tn8EfjuX4zgti4x/Tu0klwBLVaTZd2rbl1OxJf7+erVKrUamysrVCLqKqjlPTKWkumVpCeXikhJdtKSStIS6+UtEoN2bYlj0LtPGTp9SxevY6YR3FUrRjElh0/sPG77UD2TDIrKwtAKG+fpdcLK5WP4xO48Odl/Mp5o9GIIye8lPTKWkumVpCeXikhJdtKSStIS6+UtEoN2bYlk0JNHqLuP+D38xcJrlOL0UMHUrt6Vbbu2is4gkqlwmAwCO83XWb5J+I2S7/awIW/LjNsQD+srayKUML/H1LSK2stmVpBenqlhJRsKyWtIC29UtIqNWTblkwKtQ/k6uJMry6daBLaAHs7O/p064zeYGDrrr0A9OnWGQsLC/R6PconDrB11x6++X4XKqWS5XOm4ecjnkItUtIray2ZWkF6eqWElGwrJa0gLb1S0io1ZNuWTAqdbcmUKsuUJuvWnUjWbv6OPy79xZsd29KnW2cgO42jQqEgJvYRB349RpOGDfAoW+b/RcT/J1LSK2stmVpBenqlhJRsKyWtIC29UtIqNWTbljyKpM5DTkfo3rEdb3V7g6gH0Zw6+wdNQhqgtbcvUVlapKRX1loytYL09EoJKdlWSlpBWnqlpFVqyLYVN0Vyfd3X24v+PbqhQMGWXXtISk7mzr0oDh8/RWi9OiXOAaSkV9ZaMrWC9PRKCSnZVkpaQVp6paRVasi2FTdFsvNgqjQbEXmX5Ws38evJ09jZ2rJ4xhQqBPgVxXOaFVLSK2stmVpBenqlhJRsKyWtIC29UtIqNWTbipsi2XmwsMhO2mSn0ZCaloatxoYVc6cLqbdKGlLSK2stmVpBenqlhJRsKyWtIC29UtIqNWTbipsiq7qh06WzaOVX/H7+IhuWLijxDiAlvbLWkovU9EoJKdlWSlpBWnqlpFVqyLYVL0VybMnE6bN/4OSoJcDXp6i+0qyRkl5Za8lFanqlhJRsKyWtIC29UtIqNWTbipMinTzIyMjIyMjIyMjIyJRcClVhWkZGRkZGRkZGRkZGusiTBxkZGRkZGRkZGRmZAiFPHmRkZGRkZGRkZGRkCoQ8eZCRkZGRkZGRkZGRKRDy5EFGRkZGRkZGRkZGpkDIkwcZGRkZGRkZGRkZmQIhTx5kZGRkZGRkZGRkZAqEPHmQkZGRkZGRkZGRkSkQ8uRBRkZGRkZGRkZGRqZAyJMHGRkZGRkZGRkZGZkC8X8xDmZtLz9N1gAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x450 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 900x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# ==========================================================\n",
    "# Visualization Script (English UI + Cool Color Scheme)\n",
    "# ==========================================================\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from pathlib import Path\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Matplotlib global style (cool tone)\n",
    "plt.style.use(\"default\")\n",
    "plt.rcParams.update({\n",
    "    \"font.size\": 11,\n",
    "    \"axes.labelcolor\": \"#1f3b73\",\n",
    "    \"axes.edgecolor\": \"#607d8b\",\n",
    "    \"axes.titlesize\": 13,\n",
    "    \"axes.titleweight\": \"bold\",\n",
    "    \"axes.facecolor\": \"#f8f9fa\",\n",
    "    \"xtick.color\": \"#37474f\",\n",
    "    \"ytick.color\": \"#37474f\",\n",
    "    \"figure.facecolor\": \"white\",\n",
    "    \"grid.color\": \"#cfd8dc\",\n",
    "})\n",
    "\n",
    "# ----------------------------------------------------------\n",
    "# A. Path & Settings\n",
    "# ----------------------------------------------------------\n",
    "data_dir = Path(\"/home/jovyan/RT08/0925/bling_1004\")\n",
    "TOPN_BOX = 12\n",
    "TOPM_TL = 8\n",
    "CLIP_PERC = 0.99\n",
    "\n",
    "# ----------------------------------------------------------\n",
    "# B. Load and Combine Data\n",
    "# ----------------------------------------------------------\n",
    "files = sorted(data_dir.glob(\"*.csv\"))\n",
    "if not files:\n",
    "    raise FileNotFoundError(\"⚠️ No CSV files found in the target folder.\")\n",
    "\n",
    "chunks = []\n",
    "for fp in files:\n",
    "    _df = pd.read_csv(fp, low_memory=False, parse_dates=[\"senddate\"])\n",
    "    _df.columns = [str(c).lower() for c in _df.columns]\n",
    "    need = [\"nan_check\", \"ad_para\", \"patno\", \"senddate\"]\n",
    "    for c in need:\n",
    "        if c not in _df.columns:\n",
    "            raise KeyError(f\"❌ Missing column {c} in {fp.name}\")\n",
    "    chunks.append(_df[need])\n",
    "\n",
    "df_all = pd.concat(chunks, ignore_index=True)\n",
    "df_all = df_all[(df_all[\"nan_check\"] == 1) & df_all[\"senddate\"].notna()].copy()\n",
    "df_all = df_all.sort_values([\"patno\", \"senddate\"]).reset_index(drop=True)\n",
    "df_all[\"ad_para\"] = df_all[\"ad_para\"].astype(int)\n",
    "\n",
    "print(f\"✅ Loaded {len(files)} files, total rows: {len(df_all):,}\")\n",
    "\n",
    "# ----------------------------------------------------------\n",
    "# C. Event Folding and Interval Calculation\n",
    "# ----------------------------------------------------------\n",
    "kept = []\n",
    "dropped = []\n",
    "\n",
    "for pid, g in df_all.groupby(\"patno\", dropna=False, sort=False):\n",
    "    g = g.sort_values(\"senddate\").reset_index(drop=True)\n",
    "    diffs = g[\"senddate\"].diff().dropna().dt.total_seconds() / 60.0\n",
    "    diffs = diffs[diffs > 0]\n",
    "    if diffs.empty:\n",
    "        continue\n",
    "    sampling_min = float(diffs.median())\n",
    "\n",
    "    # Collapse continuous ad_para=1 runs\n",
    "    g[\"is1\"] = g[\"ad_para\"] == 1\n",
    "    g[\"start_run\"] = g[\"is1\"] & (~g[\"is1\"].shift(fill_value=False))\n",
    "    g[\"run_id\"] = g[\"start_run\"].cumsum()\n",
    "    runs = (\n",
    "        g[g[\"is1\"]]\n",
    "        .groupby(\"run_id\", dropna=False, as_index=False)\n",
    "        .agg(last_time=(\"senddate\", \"max\"))\n",
    "        .sort_values(\"last_time\")\n",
    "        .reset_index(drop=True)\n",
    "    )\n",
    "    if len(runs) < 2:\n",
    "        continue\n",
    "\n",
    "    # Check adjacent event pairs\n",
    "    for i in range(len(runs) - 1):\n",
    "        T1 = runs.loc[i, \"last_time\"]\n",
    "        T2 = runs.loc[i + 1, \"last_time\"]\n",
    "        delta_min = (T2 - T1).total_seconds() / 60.0\n",
    "        min_required = 4.0 * sampling_min\n",
    "\n",
    "        if delta_min < min_required:\n",
    "            dropped.append({\"patno\": pid, \"delta_min\": delta_min, \"sampling_min\": sampling_min, \"reason\": \"ΔT < 4×sampling\"})\n",
    "            continue\n",
    "\n",
    "        neg_end = T1 + pd.Timedelta(minutes=0.5 * delta_min)\n",
    "        pos_start = T2 - pd.Timedelta(minutes=0.2 * delta_min)\n",
    "        neg_mask = (g[\"senddate\"] > T1) & (g[\"senddate\"] <= neg_end)\n",
    "        pos_mask = (g[\"senddate\"] >= pos_start) & (g[\"senddate\"] < T2)\n",
    "        neg_count = int(neg_mask.sum())\n",
    "        pos_count = int(pos_mask.sum())\n",
    "\n",
    "        if (neg_count >= 1) and (pos_count >= 1) and (neg_end <= pos_start):\n",
    "            kept.append({\n",
    "                \"patno\": pid,\n",
    "                \"T1\": T1, \"T2\": T2,\n",
    "                \"delta_min\": delta_min,\n",
    "                \"sampling_min\": sampling_min,\n",
    "                \"neg_count\": neg_count,\n",
    "                \"pos_count\": pos_count\n",
    "            })\n",
    "        else:\n",
    "            dropped.append({\"patno\": pid, \"delta_min\": delta_min, \"sampling_min\": sampling_min, \"reason\": \"missing samples\"})\n",
    "\n",
    "kept_df = pd.DataFrame(kept)\n",
    "print(f\"✅ Valid intervals retained: {len(kept_df):,}\")\n",
    "\n",
    "# ----------------------------------------------------------\n",
    "# D. Visualization\n",
    "# ----------------------------------------------------------\n",
    "if kept_df.empty:\n",
    "    print(\"❌ No intervals met the criteria.\")\n",
    "    raise SystemExit\n",
    "\n",
    "# Optional clipping for readability\n",
    "if CLIP_PERC:\n",
    "    clip_val = kept_df[\"delta_min\"].quantile(CLIP_PERC)\n",
    "    plot_delta = kept_df[\"delta_min\"].clip(upper=clip_val)\n",
    "    clip_note = f\"(clipped at p{int(CLIP_PERC*100)}={clip_val:.1f} min)\"\n",
    "else:\n",
    "    plot_delta = kept_df[\"delta_min\"]\n",
    "    clip_note = \"(no clipping)\"\n",
    "\n",
    "# 1️⃣ Histogram of ΔT\n",
    "plt.figure(figsize=(7, 4.5))\n",
    "plt.hist(plot_delta.values, bins=40, color=\"#2196f3\", alpha=0.8)\n",
    "plt.xlabel(\"ΔT (minutes)\")\n",
    "plt.ylabel(\"Frequency\")\n",
    "plt.title(f\"Distribution of Adjacent Adjustment Intervals {clip_note}\")\n",
    "plt.grid(True, linestyle=\"--\", alpha=0.5)\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "# 2️⃣ ECDF Plot\n",
    "x = np.sort(kept_df[\"delta_min\"].values)\n",
    "y = np.arange(1, len(x)+1) / len(x)\n",
    "plt.figure(figsize=(7, 4.5))\n",
    "plt.plot(x, y, color=\"#00bcd4\", drawstyle=\"steps-post\", linewidth=2)\n",
    "plt.xlabel(\"ΔT (minutes)\")\n",
    "plt.ylabel(\"ECDF\")\n",
    "plt.title(\"Empirical Cumulative Distribution of ΔT\")\n",
    "plt.grid(True, linestyle=\"--\", alpha=0.5)\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "# 3️⃣ Boxplot per patient\n",
    "topN = kept_df.groupby(\"patno\")[\"delta_min\"].count().sort_values(ascending=False).head(TOPN_BOX).index.tolist()\n",
    "box_data = [kept_df.loc[kept_df[\"patno\"] == p, \"delta_min\"].values for p in topN]\n",
    "plt.figure(figsize=(8, 5))\n",
    "plt.boxplot(box_data, labels=topN, showfliers=False, patch_artist=True,\n",
    "            boxprops=dict(facecolor=\"#90caf9\", color=\"#1565c0\"),\n",
    "            medianprops=dict(color=\"#0d47a1\", linewidth=2))\n",
    "plt.xticks(rotation=45, ha=\"right\")\n",
    "plt.ylabel(\"ΔT (minutes)\")\n",
    "plt.title(f\"ΔT Distribution by Patient (Top {TOPN_BOX}) {clip_note}\")\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "# 4️⃣ ΔT vs sampling_min Scatter with y=4x threshold\n",
    "plt.figure(figsize=(7, 4.5))\n",
    "plt.scatter(kept_df[\"sampling_min\"], kept_df[\"delta_min\"], s=8, color=\"#26c6da\", alpha=0.7)\n",
    "x_line = np.linspace(kept_df[\"sampling_min\"].min(), kept_df[\"sampling_min\"].max(), 100)\n",
    "plt.plot(x_line, 4 * x_line, color=\"#0d47a1\", linestyle=\"--\", label=\"ΔT = 4×sampling_min\")\n",
    "plt.xlabel(\"Sampling Interval (minutes)\")\n",
    "plt.ylabel(\"ΔT (minutes)\")\n",
    "plt.title(\"ΔT vs Sampling Interval (with 4× threshold)\")\n",
    "plt.legend(frameon=False)\n",
    "plt.grid(True, linestyle=\"--\", alpha=0.5)\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "# 5️⃣ Timeline of T1→T2 for top M patients\n",
    "topM = kept_df.groupby(\"patno\")[\"delta_min\"].count().sort_values(ascending=False).head(TOPM_TL).index.tolist()\n",
    "plt.figure(figsize=(9, 5))\n",
    "ymap = {p: i for i, p in enumerate(topM)}\n",
    "for _, r in kept_df[kept_df[\"patno\"].isin(topM)].iterrows():\n",
    "    y = ymap[r[\"patno\"]]\n",
    "    plt.hlines(y, xmin=r[\"T1\"], xmax=r[\"T2\"], color=\"#42a5f5\", alpha=0.8)\n",
    "plt.yticks(list(ymap.values()), list(ymap.keys()))\n",
    "plt.xlabel(\"Time\")\n",
    "plt.ylabel(\"Patient ID\")\n",
    "plt.title(f\"Timeline of Adjustment Intervals (Top {TOPM_TL} Patients)\")\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3df49edb-bef2-4c86-b2bd-ca6aebf9a2b7",
   "metadata": {},
   "outputs": [],
   "source": [
    "紀錄定義\n",
    "跑圖\n",
    "紀錄統計分析"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "id": "09765123-6a12-4513-9912-2279f05b9b83",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "=== 直方圖分布統計（與圖一致的分箱與截尾） ===\n",
      " bin_left_min  bin_right_min  count  percent_%  cum_percent_%\n",
      "        3.967          8.629  25329     43.250         43.250\n",
      "        8.629         13.290  12302     21.010         64.260\n",
      "       13.290         17.952   4942      8.440         72.700\n",
      "       17.952         22.614   3529      6.030         78.720\n",
      "       22.614         27.276   2302      3.930         82.660\n",
      "       27.276         31.938   1368      2.340         84.990\n",
      "       31.938         36.600   1299      2.220         87.210\n",
      "       36.600         41.261   1038      1.770         88.980\n",
      "       41.261         45.923    676      1.150         90.140\n",
      "       45.923         50.585    664      1.130         91.270\n",
      "       50.585         55.247    568      0.970         92.240\n",
      "       55.247         59.909    428      0.730         92.970\n",
      "📄 已匯出直方圖分布表 → /home/jovyan/RT08/0925/1002/delta_hist_summary.csv\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x450 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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YsQObNm1C2bJlAWRcz2fMmCEed8SIEWLd8/Lyws8//4ytW7eiVKlSeR7A7O/vj1mzZmHTpk3YvXs3Dh48iD///BPdunUDAMTGxmLevHkAMq49ERERYpdGION8Up8zffv21flz79+/j7Jly+b6XeDp6QlXV1dxn4ULFyImJgaPHz/O8t2kzrf6OgUA169fx4YNG5CcnIyIiAjs2bMny/YfWvHixWFhYSH+Pnv2bMTHx+P27dv46aeftLZVd1O6deuWuMzHx0drG83fBUEQJ2/IjSAI6NWrF16+fImOHTuie/fu+cpLZtevX8fQoUOxc+dOra7B06ZNQ4kSJbB161at7+n8fEfeuHEDw4cPx44dO9CuXTtxufq6rkkul4vdvl+8eIH79+/nNUuGR7+xC2Wm+bSiatWq4vLSpUuLy+fNm5fj/u+75UEQBMHX1zfbiD27aH3Pnj3isu7duws3btwQ0tLScvz8tw3a1XwqIZFIhAsXLmTZRpeWBxsbG+HZs2fius2bN2sdV/2kJa8tD5k/J7sBqDm1PIwePVpcVrJkSa1m7jlz5ojr3N3dxXWa6StVqpS4PD09XatV5uLFizn+zdU0j5W52XnYsGHiOs1m7H79+onLFy1aJAiCIFy8eFFcVq5cubd+bubPzu1n+vTp4j7//fef0Lt3byEwMFCQy+XZbq+Z77cNmM6p5aFdu3bi8h49emh1G2jWrJm47tNPPxX30awbQ4cOFZcfP35cXO7q6qr1+fkdYHrq1Klsrxk50fz7aH7Ou9Y/XeuMrvLT8nDu3Dlx4DiQfZclQRCEpKQkcRtbW1udjq1Z1rt27dJaFxcXp9UaMH/+/FyP9S4tD4UKFdLqBqjZIuLs7Cw+1cxr2fz111/iMg8PD+Hw4cNiPV+8eLG4zsfHRxCEjFZlzVaqTZs2icd/9uyZYGNjk+fyW7NmjdCwYUPB09Mz29aVChUqaG2f3wHT+fku0Bwsn9uPukVRqVQKFSpUeOv2MplM53TnxdtaHgRBEEaMGKFTno4ePSoIgvb1vk6dOlrHunXrltY+ukwGMnfuXLFOx8TECIIgFEjLQ5UqVcTlGzdu1EqXujUpPT1dq8VJ87tCl5aH5s2bi8sfPXqk9RmvXr3Kkr4qVaqI60+dOqVzvgwVWx4MzOrVq8X/a7Y4qAfcANDr9J/p6elaT5idnZ1z3b527dooU6YMgIypEYODg2FjY4OSJUti4MCBOH36dL7TEhQUJD4Ry6vQ0FC4u7uLv2s+nRAEQesJy4dy9epV8f81a9bUegeE5hPr58+fi09WNdWvX1/cRyqVwsXFRVynWWa6yDyQW/P3GzduiP8fOnSo+H/106qCaHXIrGjRovj+++8xbdo0ABlPgatUqYJVq1bh1q1bUCgU2e734sWLd/7sK1euiP9fs2YNateuLf7s3LlTXHf58uVs969fv774f806l9cyyYmgMTWi5v/z6l3rn6515n3ZtWsX6tSpIw4cnzhxIsaPH5/ttpp/J13ftZLb3/n69etaA/nbtm2rc7rzqmrVqrCyshJ/1/w7x8XF5btsNOv5s2fPUKdOHbGeDx8+XFz36NEjxMbG4ubNm1p/B813i7i7u2u17uhi6tSp6NGjB/bt24enT59mOzFCQZzPmen6XTBq1CgsXrw4y0D+Nm3aaLUqqa+7lpaW2LNnD7p06aL1hN/JyQktW7bMsr3amTNncOTIEZ1+nj59+k55X7BgAaZNmwZHR0dxmYWFhdb9hmYa1S2QQEZLlabMv6tbxHLy4MEDTJkyBRKJBCtXrtRq2XlX1atXF/+vWbZOTk5ii5BUKtX6zLxej3O6rud0rPxccwyZpb4TQG8cP35cnHlBKpWiY8eO4rrOnTuLc7f/999/OHv2LCpWrPjB0xgREYHk5GTx97elQS6X48iRI1i5ciX++ecfXLt2DXfu3MHVq1dx9epVrFq1CsePH89XXjI3m74Pmid55i+z9/m+i8x0uSnMfPG1tHxzer/LTWVuwsLCUL16dRw/fhwXLlzAuXPnsGnTJgAZXTfUXQ7yonz58mJXAGtra/j4+KBw4cJa2yxevBhJSUkAMoLIadOmwc/PD1KpFG3atBFvoFQq1btkL0/Us5ZkplkummVSUDRvZgoqIMnsfdWfgrJs2TIMHToU6enpkEqlWLBgAUaOHJnj9pp/J/XMMm+T298589/HFG4OcpNdXX+XPCuVSq2ubV26dEH37t3h6OiI06dPY/To0QA+7PmcnWHDhuGTTz7B9evXkZiYiGLFiiEhIUFrZizNB1pubm5Yt24dli1bhuvXr8PW1haBgYGYPXs2/vrrryzbA0D79u3FbkJvs3Llylzf4fA2FhYWmD59OiZPnoxr164hLS0NQUFB+Pfff7FhwwYAgK2trTijlLqbMZARRGp6+PCh+H+JRIJixYrl+tnPnj0TA44mTZpku01UVBQkEglat26dp3fzaD7UlEql2S7PLK/XuNyu69kdKz/XHEPGlgcDotmiUK9ePa2b48DAQFSuXDnbbT+UuLg4jBo1Svzd3d0dzZo1y3UfQRDg6OiIkSNHYtu2bYiMjERCQoL4JEupVIo3m4D2if62L4p3+bK6du2aOBYDyOjLrXlc9cVS86mQZj/Ff/75R7x5zSwvedCk2Uf22LFjWhcgzekm3d3dszzpKGiaf4/Mv2ee/nbYsGHi/8eOHSs+wW7ZsmW+0qk5ZWrlypWzBA5ARj9/tREjRqB79+6oU6cOfH19tcpVU0GUy8SJEyFkTDSh9aNUKrVaIfIjv+nz8/MTpxOMiorK8gRQV+9a//JSZwqKIAiYMGECBg8ejPT0dNja2uL333/PNXAAoDU9prpl9G00t8s8vWZISIjWDUR20zEWVAB26tQprReSaf6dnZ2d8102muXv5+cHpVKZbV1/9eoV/P39ERQUpHUNPnbsmPj/mJiYPE1BGhMTg8TERPH3ZcuWoVmzZqhVq5Y4piI7+T1nNOn6XaD5maGhoahYsSJcXFy0xtpVrVoVvr6+WT7DwcEBFStWRIkSJZCQkIAlS5aI6zT7y+uLTCZDmTJlEBYWBnt7e3z11VfiupYtW4pjZDSftkdHR2uN19B8qWulSpW0WjPMnUKhEL+znJycUKRIET2n6N2x5cFAZH63g1KpxKeffqq1jeaFcv369Zg/f75W83VBe/r0KY4cOYLExEScO3cOS5cuxb1798T1ixcvhoODQ67HOHXqFPr164ePP/4YJUqUgLe3NxITE7UGWmt2OXFzcxP/rx7sZGtrC39//wI94ZKTk9GmTRuMHTsWL168EN8CCQANGzYUnypoDoxcv369+BbOuXPn5nhszTxcvHgRW7ZsgaenJ5ydnVG6dOkc9+vVqxcWLlwIQRBw+fJl9OzZE127dsWNGzfw+eefa233vp9snjp1CgMHDkTbtm1x7tw5LFu2TFzXqVMnrW3bt2+P0aNH48mTJzhw4IC4/F2eiL2N5lOtFStWICAgAImJiZg+fXqON2ma5RIREYEdO3bAyckJ3t7eWW4QNPXr10+8GZw7dy7S09NRt25dSKVS3Lt3D2fOnMGWLVuwadOmd3pDs2b6Nm3ahICAAFhZWSEkJCTXJ1VSqRS1a9fG9u3boVQqcf78eVSrVi3Pn/+u9S8vdSYnu3btEm8iNYPzM2fOiN0gKleuLA4+7Ny5s3jdtLCwwFdffQV3d3etYMfLyytL8HLq1Cnx/3Xr1tUpbZplq7k/kHFD0KFDB3F++08//RSPHz9GvXr1kJSUhMOHD8PX1zfHblR58eDBA3Tq1An9+/fHnTt3tMqmQ4cOWt8Tmul9W9k0atQIRYoUwb179xAdHY0mTZpgwIAB8PT0xOPHj3Hjxg1s3boV5cuXx8qVK+Hm5oaGDRti7969AIDhw4cjPj4erq6umD9/vlYL9dt4eXnBzs5OLPuJEyeidevWOHHihNaECZm5ubnhzp07ADK6/EqlUlhaWqJs2bI637zq+l0QERGBL7/8Em3btkWxYsUQGxuLDRs2YNu2bQAyAg3NweRARlen1NRU1KpVC+7u7oiMjMS8efPESU9CQ0PRq1cvrX3eZQD1mTNnxP01Wy+ioqLE7qQBAQHie2zWrVuH33//Hc2bN0eRIkXw6NEjrFixQpzcw8bGRmuChNKlS6NBgwbYv38/gIzAZ/Lkybh37x7mzJmjlW9NmteLO3fuICAgAL6+vli4cGGWPJw6dUo8j1xcXDB16lStFg9jdP78eTHgr127drbnqNF576MqSCea73bQ9Se7dz4U5IDpnH7s7OyENWvWZNk3u0FGmgNEs/uRyWRag4d2796d7XZffvmlIAi6vSValwHTvr6+gr29fZbPyTxV68uXL7OdarZw4cJaUxpqDqqKjY3VGqys/mnQoIEgCLlP1bpgwYJ8T5Wp64DunGgeKyQkJNvPz2naTc3phAEIXl5e2c53rctn6/KW4//++0+wsrLKkr5SpUoJnp6e2eb7ypUrWnO1q3/69esnCELuU7WOHz/+reeF5mfl9LfPreyXLVuW7XGzO9cy+/XXX8XtJ0+enOu2msfOPJD1XeqfLtP7vo0u01Rqpvlt22a+BqjVrFlTACBYWFhkeT9CTlQqlTgFpI2NTZZBkTExMUJYWFiO6SioqVpLliyZ7XSTuU3VqmvZHDt2LNepWjP/PXOaqtXOzk5rYg1dBkxPmjQp28+rX79+juflxIkTs93nbYN18zNtd27TiVtYWAjff/99ls/JbSKIgIAAITIy8q1/l7zQZeKJnL4TM//Y2NgIW7duzfIZ+XnDtOZ6zfd1ZKcgBkxrnju5Xddzuk7r+p4HXfOo+f2oHlBv7Ewg/DENmgOldfUhui5JpVLY2dnBz88P9evXx8yZM3Hr1i2dp1QLCgrClClTEB4eDl9fX8jlclhaWqJw4cJo3749jhw5otUdq3HjxliwYAECAwO1BpkVtKCgIBw5cgQfffQRHBwcYGdnh6ZNm+LIkSNazfeOjo5iC4i1tTVcXV3Rq1cvnDp1Ksc3T7q4uGDLli2oVKlSnqfNHDVqFA4fPoz27dvDx8cHlpaWcHBwQLVq1fDtt9/iwIEDBTblY246d+6MzZs3o2LFipDL5XB3d8fAgQPxzz//iNOzaho8eLBWefXo0eO99O9XK1WqFP755x/UrFkT9vb2cHd3R58+fXDgwIFs0wdkdMv45ZdfUKpUKbEZXlezZ8/Gnj170KZNG/j4+EAmk8HFxQUlS5ZEz549sXnz5nw97dfUr18/TJw4EYULF87zk6n27duLrRP5ebur2rvUv9atW2PHjh2oWrWqTnVGX6Kjo8UuNuonrrqQSCQYNGgQgIyn1X/88YfWeldXVxw/fhzffvstatasCWdnZ1haWsLLywuNGzfWGnT+LipXroxDhw6hfv36sLOzg5OTEzp16oSjR4/m+FZuXcumevXquHTpEkaPHo1SpUrB1tYWNjY2KFq0KBo1aoSFCxdqddMpUaIEjh49imbNmsHOzg729vb46KOPcPTo0Vxb87Lz+eef4+uvv0ZQUBCsra0RGhqK7777Dp999lmO+0yZMgWDBg2Cp6dnvltj/f39cfz4cbRq1QqOjo45fhcULVoU3bt3R3BwMOzt7WFlZQV/f3/06tUL586dw5AhQ7Icu3nz5mjYsCEKFSoEKysrsfvSV199hQsXLmQ75e+HVL58ebRv3x4BAQGwtbWFXC5HcHAwhg4disuXL6N169ZZ9vH398fZs2cxduxYBAcHw9raGo6OjqhduzbWrFmDH3/8UQ85MWzq8SMeHh5o3769nlNTQPQdvRCRaahatar4dCXzm7rp/Zs/f77499+xY8cH+cy3Ta1siMaNGycAGdNwnjlzJk/7JiQkCD4+PgKQ97fovou3TTOcHWMsmw9FlxZsooKwY8cOsa4tWLBA38kpMGx5IKJ8S01NxcuXL7Fjxw6cOXMGQMZUh6VKldJzyszPsGHDxLEgmfteU4a4uDj88MMPAIBu3brleZY3Ozs7fPnllwCA06dPa73si4goM/WYnWLFimlNbW7sOGCaiPJt5syZWgM2pVJprgMc6f2xsrLSy/tJjImzszNevXr1Tsfo168f+vXrV0ApIiJTph58bmrY8kBE78za2hphYWH4448/sryQioiIiEyHRBAM/O0/RERERERkENjyQEREREREOjGrMQ8qlQpx8YmQW8ne+0u2iIiIiIiMgSAIUKQq4exg99bpws0qeIiLT8SY+cveviERERERkZmZP2YQXJ0cct3GrIIHuVXGi6FmjegLubWVnlNDRERERKR/ipRUTFz0s3ivnBuzCh7UXZXk1lawyeObf4mIiIiITJku3fo5YJqIiIiIiHTC4IGIiIiIiHTC4IGIiIiIiHTC4IGIiIiIiHTC4IGIiIiIiHTC4IGIiIiIiHTC4IGIiIiIiHTC4IGIiIiIiHTC4IGIiIiIiHTC4IGIiIiIiHTC4IGIiIiIiHTC4IGIiIiIiHRiqe8EEBHpQ1JSEiIjI7Ndp1AkI+puFPwD/CGX2+R4jJCQENja2r6vJBIRERkcBg9EZJYiIyNRvVrVdzrG8RMnERYWVkApIiIiMnwMHojILIWEhOD4iZPZrou8dg29e/fCqlWrERIamusxiIiIzAmDByIyS7a2tm9tNQgJDWXLAhERkQYOmCYiIiIiIp0weCAiIiIiIp0weCAiIiIiIp0weCAiIiIiIp0weCAiIiIiIp0weCAiIiIiIp0weCAiIiIiIp0weCAiIiIiIp0weCAiIiIiIp0weCAiIiIiIp0weCAiIiIiIp0weCAiIiIiIp0weCAiIiIiIp0weCAiIiIiIp0weCAiIiIiIp0weCAiIiIiIp0weCAiIiIiIp0weCAiIiIiIp0weCAiIiIiIp0weCAiIiIiIp0weCAiIiIiIp0weCAiIiIiIp0weCAiIiIiIp0weCAiIiIiIp0weCAiIiIiIp0weCAiIiIiIp0weCAiIiIiIp0weCAiIiIiIp0weCAiIiIiIp0weCAiIiIiIp0weCAiIiIiIp0weCAiIiIiIp0weCAiIiIiIp0weCAiIiIiIp0weCAiIiIiIp1Y6jsB2/fsx1cLFmdZ3qNjWwzt21P8/dipM/hh9Vrcjb4PT3c3dG7bCu1bNvuQSSUiIiIiMmt6Dx7UvvlqGuztbMXfPdxdxf9funIN4z6fhWYN6mHkwL64cPkqFixdAZmlDK2bNtJHcomIiIiIzI7BBA+hwYFwdnLMdt1P635DSFAxTB49HABQsVwZPHn2DMvXrEPLJg0glbL3FRERERHR+2bwd92pqUqcvXARjerW1lreJLwunse+wPVbt/WUMiIiIiIi82IwLQ9dBg3Hy1fx8Pb0QOuPGqF7hzawsLDAg0ePoVSmIcCvsNb2Rf2KAADuRN9HaHBQtsdMTVVCqVSKvyenpL6/DBARERERmTi9Bw/urq4Y0KMLSoUUh0QCRJw4jWW/rMOzmFiMHToQrxISAAAOdnZa+zk42AMAXsUn5Hjs1Rs346e1G8XfpVILhFSq/h5yQURERERk+vQePFSrFIZqlcLE36tWDIO1lRU2/PEnendu/2ZDiSTb/XNYDADo1ak9urZtLf6enJKKcd+seOc0ExERERGZI4Mc89CgTk2kq1S4fvsOHO0zWhjiE7RbGOJftzg4vF6fHSsrGezsbN/82Nq8v0QTEREREZk4gwweBAji/319vCGTWeJu9H2tbe5E3wMAFM00FoKIiIiIiN4Pgwwe9h06AgupFCGBxWBlJUPFcmWx//BRrW32HoyAu6sLigcW01MqiYiIiIjMi97HPIycNB2Vwsoi0N8PQMaA6a279qDTxy3g5uoCAOjXtSMGj5uMmd8sQZPwOrh45Rq2/b0XE4YP4TseiIiIiIg+EL0HD/5FCuPPv/fh6fPnEFQCivgWwv8G9UPH1s3FbcqUDMXcaROxdNWv2LX/ADzd3TB6cH++XZqIiIiI6APSe/Awekh/nbarUaUSalSp9J5TQ0REREREOWGfHyIiIiIi0gmDByIiIiIi0gmDByIiIiIi0gmDByIiIiIi0gmDByIiIiIi0gmDByIiIiIi0gmDByIiIiIi0gmDByIiIiIi0gmDByIiIiIi0gmDByIiIiIi0gmDByIiIiIi0gmDByIiIiIi0gmDByIiIiIi0gmDByIiIiIi0gmDByIiIiIi0gmDByIiIiIi0gmDByIiIiIi0gmDByIiIiIi0gmDByIiIiIi0gmDByIiIiIi0gmDByIiIiIi0gmDByIiIiIi0gmDByIiIiIi0gmDByIiIiIi0gmDByIiIiIi0gmDByIiIiIi0gmDByIiIiIi0omlvhNARERE+ZOUlITIyMgc1ysUyYi6GwX/AH/I5TbZbhMSEgJbW9v3lUQiMjEMHoiIiIxUZGQkqler+k7HOH7iJMLCwgooRURk6hg8EBERGamQkBAcP3Eyx/WR166hd+9eWLVqNUJCQ3M8BhGRrhg8EBERGSlbW1udWg1CQkPZukBEBYIDpomIiIiISCcMHoiIiIiISCcMHoiIiIiISCcc80BERERkYDgNLxkqBg9EREREBobT8JKhYvBAREREZGA4DS8ZKgYPRERERAaG0/CSoeKAaSIiIiIi0gmDByIiIiIi0gm7LVGe5TYDBGd/ICIiIjJdDB4oz951BgjO/kBERERknBg8UJ7lNgMEZ38gIiIiMl0MHijPdJkBgrM/EBEREZkeDpgmIiIiIiKdMHggIiIiIiKdsNsSEQHIfRYtgDNpEREREYMHInrtXWfRAjiTFhERkakzqOAhKTkZnQYMw7PnMVi5aB5KFA8S1x07dQY/rF6Lu9H34enuhs5tW6F9y2Z6TC2RacltFi2AM2kRERGRgQUPP6/7Denp6VmWX7pyDeM+n4VmDeph5MC+uHD5KhYsXQGZpQytmzbSQ0qJTI8us2gBnEmLiIjInBnMgOm79+7j9792YUD3LlnW/bTuN4QEFcPk0cNRsVwZ9O3aES2bNMDyNeugUqn0kFoiIiIiIvNjMMHDgqXL0aZ5E/gVLqS1PDVVibMXLqJR3dpay5uE18Xz2Be4fuv2h0wmEREREZHZMojg4Z+IY7hx+y76de2UZd2DR4+hVKYhwK+w1vKifkUAAHei7+d43NRUJRITk978JCUXbMKJiIiIiMyI3sc8KBQp+PbHn/FJnx6ws8s6xeOrhAQAgIOdndZyBwf7jPXxCTkee/XGzfhp7Ubxd6nUAiGVqhdEsomIiIiIzI7eg4eV63+Dq7Mzmjeqn/uGEkleFgMAenVqj65tW4u/J6ekYtw3K/KTTCIiIiIis6fX4OHRk6dYt2UbZn/2KRKTkgAAyQoFgIxpW5OSk+Fon9HCEJ+g3cIQ/7rFweH1+uxYWclgZSUTf5daWBRo+omIiIiIzIleg4eHj59AqUzD6KlfZVk3dMJnKBVaHEvnzIBMZom70fdRvVIFcf2d6HsAgKKZxkIQEREREdH7odfgoXhgUSyZ/aXWshu37+CbZT9jwvAhKFE8CFZWMlQsVxb7Dx9Fl7atxO32HoyAu6sLigcW+9DJJiIiIiIyS3oNHhzs7VGxXJls14UGByI0OBAA0K9rRwweNxkzv1mCJuF1cPHKNWz7ey8mDB8CqdQgJowiIiIiIjJ5eh8wrYsyJUMxd9pELF31K3btPwBPdzeMHtyfb5cmIiIiIvqADC54qFiuDE78vTXL8hpVKqFGlUofPkFERERERATAQF4SR0REREREho/BAxERERER6YTBAxERERER6STfYx4EQcDNu0/wLDYeihQlnB3tUMzPA86OtgWZPiIiIiIiMhB5Ch7S01XYf+wKft91BsfO3URiUgoE4c16iQQI8vdEs/ByaN+sEgp7uxZ0eomIiIiISE90Dh627T2PBSv+xrPYeNSrGor/9W2MkkGF4OpsB2srGV7GJ+Hew1hcvHYPOw9cwJJf9qNd00oY1a8xvNyd3mceiIiIiIjoA9A5ePh25R4M7dEALRuWh43cKtttypXwQ4sG5TFpaEtcvfkQP/8Wgd93ncUnPeoXWIKJiIiIiEg/dA4e9q8dD4lEovOBSwQVwtxJnSBo9msiIiIiIiKjpfNsS3kJHApiPyIiIiIiMiz5mm3p0rX7eJWQjJqVggEAL18lYdbSHbh59wlqVgrGyD6NIJVyFlgiIiIqOElJSYiMjMx2nUKRjKi7UfAP8IdcbpPjMUJCQmBry5khifIrX8HDl4u3oUbFYDF4+HzRNuyNuIxalYOxfMMhWFhIMaJ3owJNKBEREZm3yMhIVK9W9Z2OcfzESYSFhRVQiojMT76Chxt3n2BI94xB0IoUJXYdvIjPR7VBx+ZV8MvvR7FyUwSDByIiIipQISEhOH7iZLbrIq9dQ+/evbBq1WqEhIbmegwiyr98BQ/JCqU449KZi3eQqkxH49qlAQChQT549OxlwaWQiIiICICtre1bWw1CQkPZskD0HuVrYIJfIVccPHENALB17zmULu4rvlk65kUC7O3kBZdCIiIiIiIyCPlqeejfqS4mzN6E33acQtyrJCyc0kVcd+L8LZQI9CmwBBIRERERkWHIV/DQsUUV+Bd2w8Wr91AqpDBqVAgS1zk72qJPh1oFlkAiIiIiIjIM+QoeAKBq+UBULR+YZfmofk3eKUFERETvgtN5EhG9P/kOHpRp6di4/SQuXr2PR0/j8MXoNihaxAN/7f8XJQJ9EBTgVZDpJCIi0gmn8yQien/yFTxEP4hBt/8tQ2xcIkoE+eDc5SgkJqUAAE79exuHTl7DvEmdCzShREREuuB0nkRE70++gofp326Fq7Mdti0fCUd7OYLDPxXXVS1fDHOW7SqwBBIREeUFp/MkInp/8jVV64nztzC8V0O4OttBIpForfNwc8DTmFcFkjgiIiIiIjIc+QoeLCykEHJY9zw2AXY2Vu+QJCIiIiIiMkT5Ch6qlg/E8g2HoExLf7NQIoEgCFj/5wnUqBhcUOkjIiIiIiIDka8xD58OaY52gxejYbc5aFSrFCQSYM2Wo4i8/Rh37z/HtuUjCjqdRERERESkZ/lqeQjy98T2n/6HimUC8Oe+f2EhlWL/sasIKOyObctHwN/XvaDTSUREREREepbv9zwUKeSGBVO6FGRaiIiIiIjIgOWr5aHLiKW4GfU023W3o5+hy4il75QoIiIiIiIyPPmcqvU2EhIV2a5LSFLg1IU775QoIiIiIiIyPPkKHgAg0+sdRGcv3YWbs31+D0tERERERAZK5zEPS9bsx9I1/wDICBy6jPgB0kwRRKoyDWnpKvRoU6NgU0lERERERHqnc/BQsXQA+neuC0EQsGjVPrRqGAZvDyetbWQyCwT5e6FhzZIFnlAiIiIiItIvnYOHamGBqBYWCACQSCTo3LJqluCBiIiIiIhMV76mav1f38YFnQ4iIiIiIjJw+QoedJmKdf2iIfk5NBERERERGah8BQ82citknmwpLj4Z124+gqODHKWCfQsgaUREREREZEjyFTz8PKdftsufv4hH/wkr0aZJxXdKFBERERERGZ58v+chO+4uDhjcLRzzl/9dkIclIiIiIiIDUKDBAwAIgoCnMa8K+rBERERERKRn+eq29F/k/SzLUpXpuBn1BN+u3ItyJfzeOWFERERERGRY8hU8tOz/LTK9XBqCkPFv+ZJ++HpC+3dNFxERERERGZh8BQ/rFw3OsszayhI+ns58cRwRERERkYnKV/CgftM0ERERERGZj3wFD2pPnr/Eo6cvkZKqzLKuankGGEREREREpiRfwUP0gxiM+mo9zl+OAvBmvINEkvF/iQS4fXhugSWSiIiIiIj0L1/Bw6dzNuHhkxeYNb49ggK8YGX5Tg0YRERERERkBPJ113/hyj3Mn9IZH9UtU9DpISIiIiIiA5Wvl8R5eThCKpW8fUMiIiIiIjIZ+Qoexg74CEt/PYC4V0kFnR4iIiIiIjJQ+eq29PuuM3j8LA612s9AyeBCcLS30d5AIsGKr/vodKwTZ85j9cbNuBN9D4lJSfBwc0Od6lXRv3sn2NvZidsdO3UGP6xei7vR9+Hp7obObVuhfctm+Uk+ERERERHlQ76Ch8TkVPj7uou/JySl5DsBr+LjUaZEKDp93BIO9na4HRWNFb9uwO2oKCya+TkA4NKVaxj3+Sw0a1APIwf2xYXLV7Fg6QrILGVo3bRRvj+biIiIiIh0l6/gYcPiIQWWgMbhddA4/M3vFcuVgUwmw9fffo9nMbHwcHPFT+t+Q0hQMUwePVzc5smzZ1i+Zh1aNmkAqTRfva+IiIiIiCgPDPKu28nBAQCQlpaG1FQlzl64iEZ1a2tt0yS8Lp7HvsD1W7f1kUQiIiIiIrOjc8vDig2H0LpxBXi4OmDFhkO5byyRoH+nOnlKSHp6OtLS03En6h5+XrcRtapWho+XJ+5E3YNSmYYAv8Ja2xf1KwIAuBN9H6HBQdkeMzVVCaXyzduvk1NS85QmIiIiIiJ6Q+fgYcaS7ahUtig8XB0wY8n2XLeVSJDn4OHjXgPx7HkMAKBapQr4cuIYAMCrhAQAgIPG4GkAcHCwz1gfn5DjMVdv3Iyf1m4Uf5dKLRBSqXqe0kVERERERBl0Dh7uRMzN9v8FZcEXnyFZocDtqGisXPcbxk6bgUUzp7/ZQJL9eyVyWAwA6NWpPbq2bS3+npySinHfrCigFBMRERERmZd8DZh+H4KLBQAAypYMRWhQMfQePhaHjp0UuyfFJ2i3MMS/bnFwsLfP8ZhWVjJYWcnE36UWFgWcaiIiIiIi8/FOwUN8ogKPn8YhJTUty7rSIYWz2UM3wcWKwkIqxf2Hj1CramXIZJa4G30f1StVELe5E30PAFDUL/+fQ0REREREustX8PD42UuMn/Ubjpy5nmWdIGR0Jbp9OP9dmy5djUS6SoVCPl6wspKhYrmy2H/4KLq0bSVus/dgBNxdXVA8sFi+P4eIiIiIiHSXr+Bh9Ffrcffec0wf+TGK+nlAZpn/7kATvvgaJYoHIqhoAKytrHDj9l38uvkPBBUNQN3qVQEA/bp2xOBxkzHzmyVoEl4HF69cw7a/92LC8CF8xwMRERER0QeSr+DhwpV7WDi1CxrXLv3OCSgVEox9h4/gl9+2QFAJ8PHyxMdNG6Nbu48hk2WMVyhTMhRzp03E0lW/Ytf+A/B0d8Powf35dmkiIiIiog8oX8GDf2E3pKWlF0gCenZqh56d2r11uxpVKqFGlUoF8plERERERJR3+erzM3loSyxevR+3o58VdHqIiIiIiMhA5avloWalYNSoGIRGPebCy80Rjg42Wbb5e/WYd04cEREREREZjnwFDzOX/IWff4tAmRBfFC3iAZmM708gIiIiIjJ1+QoeNvx1CqP7NcHw3g0LOj1ERERERGSg8jXmQWYpRflSfgWdFiIiIiIiMmD5Ch46tqiKbXvPFXRaiIiIiIjIgOWr25KTgw3+3HsO7YZ8h5oVg+Bon2nAtESC/p3qFET6iIiIiIjIQOQreJj9w04AwMOnL3Huv6gs6yUSMHggIiIiIjIx+Qoe7kTMLeh0EBERERGRgctX8EBEZAwexKYgNlGZ5/1uPk4S/7W8l5Dn/V3tZPB1tc7zfkRERIZO5+Dh2q1HCA30ydPBU5VpePgkDgGF3fOcMCKid/EgNgX1vjwPhVKV532Vz28AAIavvgGZuyTP+8tlUhz8LIwBBBERmRydg4f2Q75DtbBAdG5ZFXWrhUJmmfOL4aIePMefe//Fmj+OYWCXuujfuW6BJJaISFexiUoolCq0Dg+Cm7PN23fQoFQEIib8H7gVDoZMbpunfWPikrHtwE3EJioZPBARkcnROXg4sOFTLPllP0Z+vg4SCVA6pDBCA33g6mwHK5klXiUk497DWPwXeR9RD2NQurgvZoxrh0a1Sr3P9BMR5crN2QY+7vZ53MsefoU930t6iIiIjJnOwYOHqwOm/+9jjBvYFDsOXMCxszdx+FQknsXEIyVVCSdHWxQr4oFm4eXQNLwsShf3fZ/pJiIiIiKiDyzPA6btbK3RsXkVdGxe5X2kh4iIiIiIDBRnWyLKRVJSEiIjI7Ndp1AkI+puFPwD/CGX59ynPiQkBLa2ees3T1SQcqvHgG51mfWYiIgABg9EuYqMjET1alXf6RjHT5xEWFhYAaWIKO9Yj4mIqKAweCDKRUhICI6fOJntushr19C7dy+sWrUaIaGhuR6DSJ9yq8eAbnWZ9ZiIiAAGD0S5srW1fevT1pDQUD6RJYOmSz0GWJeJiOjtpPpOABERERERGQedg4faHWbiyo2H7zMtRERERERkwHQOHu4/foFUZdr7TAsRERERERkwdlsiIiIiIiKd5Cl4kEjeVzKIiIiIiMjQ5Wm2pS4jfoBUhwhCIpHg0u6v8p0oIiIiIiIyPHkKHlo1DIO3h9P7SgsRERERERmwPAUPnVtWRfmSfu8rLUREREREZMA4YJqIiIiIiHTC4IGIiIiIiHSic/Awsk8j+HjqNt7h7v3n+U4QEREREREZJp3HPPyvb+Nc1z9/EY+/9v2LrXvO4b/rD3Dr0Jx3ThwRERERERmOPA2YziwxKQV/H7qErXvP4fjZm0hXCfDxcIIgCAWVPiIiIiIiMhB5Dh7S0tJx4MQ1bN1zDv8cuwJFShpCinljVP8maNWgPK7cfIQhU1a/j7QSEREREZEe6Rw8nPz3FrbuOY9dBy/iZXwy/H3d0L9TXbRqWB7BRb3F7a7eevReEkpERERERPqlc/DQefgPsJBK0LllVXRsUQVlQ4u8z3QREREREZmcpKQkREZGZrtOoUhG1N0o+Af4Qy63yfEYISEhsLW1fV9JzJXOwUPJ4EK4cuMhtu45h6TkVLRsWB61KxeHpaXF+0wfEREREZHJiIyMRPVqVd/pGMdPnERYWFgBpShvdA4edvw8Crein2Lr7nP4c995/LHnHJwcbNC0bhm0bFge1SsEQSKRvM+0EhEREREZtZCQEBw/cTLbdZHXrqF3715YtWo1QkJDcz2GvuRpwHSgnyfGDPgIYwZ8hHP/RWHb3nPYceAiNu44BTcXezQPLwcnh5ybWIiIiIiIzJmtre1bWw1CQkP11rLwNvmeqrVCaX9UKO2PaSNb4/Cp69i29xw27zyNxORUsAGCiIiIiMj0vNN7HgBAKpWiXrVQ1KsWCkWKEnsi/sOfe88XRNqIiIiIiMiAvHPwoEluLUOrhmFo1dAwm1mIiIiM0YPYFMQmKvO8383HSeK/lvcS8rSvq50Mvq7Wef5MIjJtBRo8EBERUcF6EJuCel+eh0KpyvO+yuc3AADDV9+AzD1vfYrlMikOfhbGAIKItDB4ICIiMmCxiUoolCq0Dg+Cm3PeJiVRKgIRE/4P3AoHQybXfU74mLhkbDtwE7GJSgYPRKSFwQMREZERcHO2gY+7fR73sodfYc/3kh4iMk9SfSeAiIiIiIiMA4MHIiIiIiLSCbstFYCkpCRERkbmuF6hSEbU3Sj4B/hDLs++v2pISAhsbXXvj/q+6WNmD4CzexARkXnhTFpkbBg8FIDIyEhUr1b1nY5x/MRJg3mToL5m9gA4uwcREZkPzqRFxkjvwcP+w0ex+8AhXLtxC6/iE+Dr4422LT5Cm2ZNIJW+6VV17NQZ/LB6Le5G34enuxs6t22F9i2b6THlb4SEhOD4iZM5ro+8dg29e/fCqlWrERIamuMxDIU+ZvYAOLsHERGZF86kRcZI78HDui3b4O3pgeH9e8PVxRlnL1zCgqUr8PDREwwf0BsAcOnKNYz7fBaaNaiHkQP74sLlq1iwdAVkljK0btpIvxkAYGtrq1OrQUhoqMG0LuiCM3sQERG9f/y+JWOi9+Bh3vTJcHF2En+vWK4MkpIV2PzXTgzq1Q1WVjL8tO43hAQVw+TRw8Vtnjx7huVr1qFlkwZaLRRERERERPR+6P2uWzNwUAsJLIqU1FS8io9HaqoSZy9cRKO6tbW2aRJeF89jX+D6rdsfKqlERERERGZN7y0P2fn38hU4OjjAxdkJ0fcfQqlMQ4BfYa1tivoVAQDcib6P0OCgbI+TmqqEUvlmBoPklNT3l2giI8GZtIiIiCi/DC54uHr9Jrbv+Qf9unWChYUFXiVk3KQ42NlpbefgkNE38FV8zjcxqzduxk9rN4q/S6UWCKlU/T2kmsg4cCYtIiIiehcGFTzExL7AxK9mo2RIMHp2bKu9UpL9zUoOiwEAvTq1R9e2rcXfk1NSMe6bFQWRVCKjxJm0iIiI6F0YTPCQkJiIUZ99AWtra8ybPgmWlhlJc7TPaGGIT9BuYYh/3eLgYJ/z7ARWVjJYWcnE36UWFgWdbCKjxJk9iIiIKD/0PmAaAFJSUzFu+kzEvojDN19NhZOjo7jO18cbMpkl7kbf19rnTvQ9AEDRTGMhiIiIiIjo/dB78JCWno7JM+bixu07WPjVNPh4aT/ZtLKSoWK5sth/+KjW8r0HI+Du6oLigcU+ZHKJiIiIiMyW3rstzftuGY6cPI1h/XohJSUF/12NFNcV9SsCOztb9OvaEYPHTcbMb5agSXgdXLxyDdv+3osJw4fwHQ9EREQmhDPCERk2vQcPJ86eBwB899PqLOuWzP4SFcuVQZmSoZg7bSKWrvoVu/YfgKe7G0YP7m8Qb5cmIiKigsEZ4YgMn96Dh62/LNdpuxpVKqFGlUrvOTVERESkL5wRjsjw6T14ICIiItLEGeGIDBcHDBARERERkU4YPBARERERkU7YbYmIiIwOZ+QhMi1JSUmIjIzMdp1CkYyou1HwD/CHXJ79WJiQkBDY2uZtrAvlD4MHIiIyKpyRh8j0REZGonq1qvne//iJkwgLCyvAFFFOGDwQEZFR4Yw8RKYnJCQEx0+czHZd5LVr6N27F1atWo2Q0NAc96cPg8EDEREZJc7IQ2Q6bG1t39pyEBIaytYFA8AB00REREREpBMGD0REREREpBMGD0REREREpBMGD0REREREpBMGD0REREREpBMGD0REREREpBMGD0REREREpBMGD0REREREpBMGD0REREREpBMGD0REREREpBMGD0REREREpBMGD0REREREpBMGD0REREREpBMGD0REREREpBMGD0REREREpBMGD0REREREpBMGD0REREREpBMGD0REREREpBMGD0REREREpBMGD0REREREpBMGD0REREREpBNLfSeAyBA8iE1BbKIyT/vcfJwk/mt5LyHPn+lqJ4Ovq3We9yPKSX7qMcC6TEREumPwQGbvQWwK6n15HgqlKk/7KZ/fAAAMX30DMndJnj9XLpPi4GdhvOmiApHfegywLhMRke4YPJDZi01UQqFUoXV4ENycbXTeT6kIREz4P3ArHAyZ3DZPnxkTl4xtB24iNlHJGy4qEPmtxwDrMhER6Y7BA9Frbs428HG3z8Me9vAr7Pne0kOUH3mvxwDrMhER6YoDpomIiIiISCcMHoiIiIiISCfstkREREREVMBMdSZHBg9ERERERAXIlGdyZPBARERERFSATHkmRwYPRERERETvgSnO5MgB00REREREpBMGD0REREREpBN2W8qD/IyaB95t5PyHGDVPZKrSXr3C6p/2wUKa90Fn+ZWuEpBmmbc3PBO9zYeuy6zHRJQTBg86yu+oeeDdRs5/iFHzRKZKUKmQmKD44J9r4cjzlQqWPuoy6zERZYfBg47yO2oeyP/I+Q81ap7IVEmkUtjZyz94y0OKlD1CqWB96LrMekxEOWHwkEd5HzUPGMPIeSJTZOnoiF5taubjnM2/R88T8PMflz7Y55F5+NB1mfWYiHLCxwpERERERKQTBg9ERERERKQTdluibHGWGiIiIipI+pi1EuDMlQWNwQNli7PUEBERUUHR16yVAGeuLGh6Dx7uPXyEdZu34r9rkbh9Nxr+RQpj3bJFWbY7duoMfli9Fnej78PT3Q2d27ZC+5bN9JBi88BZaoiIiKig6GPWSoAzV74Peg8e7kRF4+ipMygVWhwqlQBBELJsc+nKNYz7fBaaNaiHkQP74sLlq1iwdAVkljK0btpID6k2fZylhoiIiAoaZ600fnoPHmpVrYw61asCAL6Y9y2u3biVZZuf1v2GkKBimDx6OACgYrkyePLsGZavWYeWTRpAyqfVRERERETvnd7vut9245+aqsTZCxfRqG5treVNwuvieewLXL91+30mj4iIiIiIXtN7y8PbPHj0GEplGgL8CmstL+pXBABwJ/o+QoODst03NVUJpfLNqP7klNT3l1AiI8GZtMgUsB6bLnMr2w+dX9ZjelcGHzy8SsiYksvBzk5ruYNDRn+5V/E5T9m1euNm/LR2o/i7VGqBkErV30MqiYwHZ9IiU8B6bLrMrWz1kV/WY3oXBh88iCTZR+Q5LAYA9OrUHl3bthZ/T05JxbhvVhR0yoiMCmfSIlPAemy6zK1sP3R+WY/pXRl88OBon9HCEJ+g3cIQ/7rFwcE+5xH7VlYyWFnJxN+lFhbvIYVExoUzaZEpYD02XeZWth86v6zH9K4MPvT09fGGTGaJu9H3tZbfib4HACiaaSwEERERERG9HwYfPFhZyVCxXFnsP3xUa/negxFwd3VB8cBiekoZEREREZF50Xu3JYUiBcdOnwUAPH76DIlJSfgn4hgAIKxMKbg4O6Ff144YPG4yZn6zBE3C6+DilWvY9vdeTBg+hO94ICIiIiL6QPQePMTGxWHSjDlay9S/L5n9JSo6l0GZkqGYO20ilq76Fbv2H4CnuxtGD+7Pt0sTEREREX1Aeg8eCnl74cTfW9+6XY0qlVCjSqX3nyAiIiIiIsoW+/wQEREREZFOGDwQEREREZFOGDwQEREREZFO9D7mwZikvXqF1T/t+6BvgUyztPkgn2XuWLZkCj50PQZYl4mIcmKq9xYMHvJAUKmQmKD4oJ9p4Wj9QT/PXLFsyRToox4DrMtERNkx1XsLBg95IJFKYWcv/6ARZArfY/FBsGzJFHzoegywLhMR5cRU7y0YPOSBpaMjerWpCR93+w/yeY+eJ+DnPy59kM8ydyxbMgUfuh4DrMtERDkx1XsLPi4iIiIiIiKdMHggIiIiIiKdsNsS5SgmLjnP+ygVSYi5fwNuhYMhk9u+988jehvWYzIVH7Iu67se87w1TZwRzjQweKAsXO1kkMuk2HbgZp73VT6/jrg/BsK5zY+QuRfP8/5ymRSudrI870eUGesxmQp91WV91GOet6aNM8KZBgYPlIWvqzUOfhaG2ERlnve99p+AHn8Ai3sFI7R02Tzv72ong68rT3J6d6zHZCr0VZf1UY953po2zghnGhg8ULZ8Xa3zdRFNe57RVBzkbYsyRT7cjC9E2WE9JlNhTnXZnPJqbjgjnGlgKEZERERERDph8EBERERERDpht6U8MqfZLsxNXv/exjyzB2cyMV3mVLbmlFcybeZ0b2Fu560p3lsweNCROc12YW7yW7bGOLMHZzIxXeZUtuaUVzJt5nRvYW7nrSnfWzB40JE5zXZhbvJbtsY4swdnMjFd5lS25pRXMm3mdG9hbuetKd9bMHjIA84AYbryU7bGWq6sx6bLnMrWnPJKps2c6rI55RUw3XsLDpgmIiIiIiKdMHggIiIiIiKdMHggIiIiIiKdcMwDEZGJS0pKQmRkZI7rI69d0/o3OyEhIbC1zdu0gfqSW351yStgPPll2b5hamVLZKgYPBARmbjIyEhUr1b1rdv17t0rx3XHT5xEWFhYQSbrvdElv7nlFTCe/LJsszKVsiUyVAweiIhMXEhICI6fOJnjeoUiGVF3o+Af4A+53CbHYxiL3PKrS17VxzAGLNs3TK1siQwVgwciIhNna2v71iet1avX+ECpef/ell9zyitgXvk1pbwSGSoGD0Rklth3moiIKO8YPBARAPMbeMm+00RERHnH4KEAmNtN17s+sTWnvALGk19zG3jJvtNEZMh4b/EG85qVPvMrEQRB0Msn60GyIgWfzFyMheMGw8Y6769Hz8n58+d1uunKjTHddL1rfs0pr4Dx5PdtX1S6Drw0los3EZEhM6fvH4D3FnlV0PlNTknBqLk/4PtJw2Ejz/0emS0PBYCzXbzBvGZ/DGNgbgMviYgMGe8t3mBesz+GvrDlgYiIiIjIjOWl5UH6gdJERERERERGjsEDERERERHphMEDERERERHphMEDERERERHphMEDERERERHphMEDERERERHphMEDERERERHphMEDERERERHphMEDERERERHphMEDERERERHphMEDERERERHphMEDERERERHpxFLfCfiQBEEAAChSUvWcEiIiIiIiw6C+N1bfK+fGrIIHRaoSADBx0c96TgkRERERkWFRpCphayPPdRuJoEuIYSJUKhXi4hMht5JBIpF8sM9NTEpGq+798OevP8HO1uaDfa4+MK+my5zya055Bcwrv+aUV8C88mtOeQXMK7/M6/snCAIUqUo4O9hBKs19VINZtTxIpVK4Ojl88M9VpadDpUqHjbUVbOTWH/zzPyTm1XSZU37NKa+AeeXXnPIKmFd+zSmvgHnll3n9MN7W4qDGAdNERERERKQTBg9ERERERKQTBg8fgEwmQ79unSCTyfSdlPeOeTVd5pRfc8orYF75Nae8AuaVX3PKK2Be+WVeDYtZDZgmIiIiIqL8Y8sDERERERHphMEDERERERHphMEDERERERHphMEDERERERHphMEDEZGJ4nwYRERU0Bg8EBGZEKVSiXsPHwEAJBKJnlNDRPR2fNBhXBg8vCNzq/DmlF/m1XSZan4Tk5Ix6rMvsH7LNlyJvKHv5OiFqZZtdswpr4B55deU85qaqsTlyOs4fPwkou49gCAIkEgkJp1nTaaQT0t9J8CYpKYqcePOHQgqAfb2dggoUtikn+yZU36ZV9PMK2A++U1WKNDvf+Pg4uSEWlUqIbhYgL6T9N6ZS9kC5pVXwLzya055TUxKxthpX+FZTCwePHqMIoV88FGDeujbtaNJ5tlUy5bBg44Sk5IxbMJnSExKwsMnT+FgZ4fG4bXRrd3H8PRw13fyCpw55Zd5Nc28AuaV3zW/bYGDnR2mjh0JLw93SKVSpKWlwdLyzWVepVJBKjWNBmdzKltzyitgXvk1p7wmJSej///Gw8PdFaMG9UMhby/8sPpXHDx6HG2bfwRnJ0d9J7FAmXLZ8g3TOkhLS8OwT6fC0tICg3v3QEpKCq5cv4FV6zcjuFgABvbsigplS+s7mQXGnPLLvJpmXgHzy+/U2QtgZ2ODCSOGAADO/HsR+w8fhSIlBZ4e7ujTpQPk1tZ6TmXBMKeyNae8AuaVX3PL69SvF+BVQgImjBiCQl6esLCwwJXIGxg64TP8uvQb+Pp4i9unp6fDwsJCjyl+N6ZetqbxCOo9exYTi7hXr9ChVXOUDi2OiuXKoEeHtlg69yvce/AIC5auwNkLl/SdzAJjTvllXk0zr4D55TcxMQkqQQUAOHz8JEZOmo57Dx/ibvR9bNu1B92H/A93ou4ByGiBMGbmVLbmlFfAvPJrTnlNSlYgJTUVtatVRmEfb7EF1MLCAt6eHli98XeM/uxLLPxhhRg4GPN1ytTLlsGDDpRpabh3/yFSU5XiMpVKheKBxfDTt3PwIu4lfli9Fg8fP9FjKguOOeWXeTXNvALmk1/1F2xggD9u3YlG9P0HWPPbFnRt/zHmTp+Mn76dgznTJsHRwR4TvpiFtLQ0o++6ZC5lC5hXXgHzyq+55FWlUsHRwR6TRg1D648aQyKRQCKRQKlU4rNZ85CSmgpBEGBlJcPuA4cxeNxko+9iaepla7wl84EIggAXJ0eUDC2Oo6fOIO7lKwDI6E+cng5vTw98O3Ma7kRFY82mP/Sc2ndnTvllXk0zr4B55Vf9BftR/bq4dvMW1v6+FVKpFNUqhsFGLodUKkXJ4kEY1q8XXiUkYMuOv/Wc4ndjTmVrTnkFzCu/5pRX9TXKxckRcrm1+MBj1YbNcHCwxzczpmHyqGH4+rNPMahnN9yJisbBoyf0meR3Yg5ly+DhLSQSCRzs7VG3elXsPRiB46fPitNsWb5uVgsqGoDBvbpj94FDuHbjpp5T/G7MKb/Mq2nmFTDt/KakpuLoyTPYezACZ/69KC4vFuCHkQP7YM+BCFy8cg1Pnj0X11laWqJUaHHY2tjgWUysPpJdYEy5bDMzp7wC5pVfU89rdtcpdRCh/rdpg3r45qup8PMtJOa9cb3aSElNxbOYGP0kvACYetkCnG0pi5TUVJz99yIUqalwc3FBuVIlAADdO7TB9dt3MO/75XBwsEetqpUBvDkJypUuAVW6CjGxcfpKer6YU36ZV9PMK2A++U1MSsbA0Z8iXZWOh4+eQG5tjWIBfhgxoA+KBxVD0wb1EB+fgNW//Y6d+w4guFgAgosVBQDExyfA2dERDvb2ACDOrW7ozKVsAfPKK2Be+TWnvOZ2nQouFgCZTAYAKFzIR9xHIpFApVLhdlQ0Cnl7I6hogJ5Sn3fmVLZqDB40JCYlY9CYiVAoFHiVkIjExESE166BZg3DUaNyRUwYNhhTkxZg2uyFGD98EKpXqghHh4wvYrlcDg93NxjBd7HInPLLvJpmXgHzyW96ejqmfj0fjg72GD98MGSWlrh1Nworft2AcdNnYlj/XmhUtxY6tG4OKysrLPtlLeZ+9yMa1q0Jd1dXHDp2EvcfPUZ4reoAjOPt0+ZStoB55RUwr/yaU15zu06N/3wmhvbtifBaNSCXZ8z8pjm2IT4hAX/t2Q+5tRUCihTWZzZ0Zk5lq4nBw2tp6emYPHMunJ0cMebTMZBKpbgTfQ/zly5H9P0HePz0Gdo2/wifjx+FrxctxRfzFqFVk4aoV7Ma7OzssG3XHqSkpCDo9VM+Q2dO+WVeTTOvgHnlNyEpCc9iYvFxs8Yo6lcEQMaTuyoVymPc9JmYt+RHQBDQOLwOundog1KhxfH9z79gzaY/YCGVwtXFGUtmf4EiGk/7DJk5la055RUwr/yaU16Bt1+n5i9dDgBoUKcWrKxkYuBw+PgpHDx6HBEnTmHp3Blwc3XRWx50ZW5lq4nveXgtKTkZA0Z9irYtPkK7Fk3F5Xei7mH+0h/xPPYFOrRqLq5buX4T9h8+ittR0fD29IBUKsXMyeNQPLCYvrKQJ+aUX+bVNPMKmFd+ExIT0WXQCDRvWB+De3cDoD0X+rBPp+LW3SjMnjoRZUuGAgAUihQkJCYiVamEo4M97O3s9Jb+vDKnsjWnvALmlV9zyiug23XqTtQ9zJwyHuVKlYBKpcLp8xfw87rfkJKaiimjhxtNlyVzK1tNDB6Q0WwW+yIO7foOxqhB/fBxsyZIT08HkDEHcfSDh5iz+Ae8fPUKA3p0RZ3qVQAAz2Ni8Tz2BSwspHB3dYWLs5M+s5Enz2NizSa/zKtp5hUwr/wqlUpMn7MQz2Nf4LMxI8T+wmnp6bC0sEBqqhIDRk+AzFKGFd/M1nNq3505la055RUwn/ya272FSqVCenp6nq9TsXFxePTkKbzc3eHu5qrPLOhMEATExL4wm7LNzKxnW1IXtFQqhbubK+pWr4qtu/Yg+sFDWFhYQCqVQqVSwc+3EMYMGYCExCTs2Lsfaa/3c3N1QWhwIIKLFTWqCiAIgtnk11zyKgiC2eQVMJ/8ar4kSSaToW2Lprh0NRJ/7NwNRUoKgIzZO9LT02FlJcMnfXsi6v59o57mEDCf8xYwr7wC5pFfc723kEqlebpOHThyHADg6uyMUiHFjSZwADLGjJlT2WZmtsFDskKBrxctxYXLV8VlNapUQtzLV9i+ez9exL0UX2SSnp6Oov5FMH74YBw+fgpnX087ZgwDDnNTvVIFs8mvqeZVoUjBg0ePxfwApl2PzSW/aWlpADK+jNU3IoIgoGK5MhgxoDfWb/kTG/74U3wBkbpLQPFiRSGRSPH0+fPsD2wk1GVkqudtdkw5r2np6UhKTtZaZornLWBe9xaKlBRs37MfP63diI1bt4vLdb1OGdt0rCmpqbh05RqSFQpxmamW7duYZfCQmJSMnkNHY/ue/bh6/Ya4/KP6dVGnelVs3PoXtu/dj7iXr7QKOjQoEJ7ubrj38JE+kp1vCkUKdh84hA1//InjZ87h8dNnAICmDcNRs0olk8qvIiUFR06exvY9+3HpyjXEJyQCyMhrraqVTSqviYlJ6D5kJDb9uRMqlUqcR9pU67G55DdZocCAUZ9i1rdLAGR84aanp4t5atP8I/Tp0gE//rIeK37dgEdPnor7qgQB3p4esLWx0Uva80uRkoI9Bw7j53W/4cCR43j++l0UTRuGm1TZAuZ1PQYy6vO46TPw+/ZdiE9IEPNkauctYF73FolJyRg8dhLWb9mGP//ei+9+WoUx074S15vadSoxKRlDxk3G0lW/4sbtu2KrsCmWrS7MbralxMQkdP/kf/D29EDxYgFYtWEzalerAl8fbwDA6CH9kZCYiOW/rEdiYjJaNK6v1WdPLpfDRi7XZxbyJDEpGf3/Nx6pylSkpaXj6fMYlC9dEuG1qqNj6xYYN2wQEhKTTCK/GReziYhPSERSsgIJiYmoXqkCPqpfF43q1cbYoQMRn2AaZZuYmIQeQ0fBx8sT3dt/LM5Yoe5XanL12Ezym5qqxGez5uPazVt4HhuLBUtXYPSQ/rCwsBDzam1lhZ6d2sHW1gbfrViNqzduomaViijk7YWDR0/g2fMYVChbWt9Z0VliUjJGTpqOuFevkJysQGxcHJo2qIeRA/vAydHRZMoWMK/rsdqGP/7CiTPn8fDRE9hYy9G0QT3Y2dkCMK3vW3O6t0hWKDBk3CQ4Othj5MB+cHZyxM07dzHpqznYe+gIGtWtBWsrK3Tv0Aa2Njb47ifjvk4pFCkYOPpTeLi5olv7jxEaFAipVCq+K2f0kP6IT0gwibLVlVkFD4mJSeg1bDR8fbzx+YTRuHTlGo6fOYeDx06gW7uPkZqqhJWVDFPHjoStjQ127N2Pf/+7jI4ft4CFVIqjp84iPiEBYWVK6TsrOklPT8esb5fAzdUZ44cPQSFvL1z47wrWb/kTqzZsxuOnzzBiQB98PmEUbG3kRp3ftLQ0TPhiFhwdHDBl9Ah4urvh3MX/MO/7H/HftUg8j32BLm1bZeTV1rjLNjEpGX1GjIWvjzemjh2pNaWd5etmYQCYOnYk5HJro84rYF753Xf4CP67Fomu7VpDoUjBwaMZfYJHD+kPy0wBRLd2HyO4aFGs3fwHVm/4HTY2ctjb2WHRrOko5O2l55zoJlmhwOCxE+Hs5IQxnwxAUf8iOHb6LCbPmItWHzVC+dIlAZhG2ZrT9VhT5bByOHDkGCQSCRavWAVIgKb13wQQpvB9a073FiqVCsvXrIelhSXGfDJQnI5VEAT4eHnCzcUZr+IT4OhgDxu5HN3af4zAAH+s37LNaK9Th46fgFxujZED+8KvcCFYWFiI3fDk1taQSqWYNu5/kFsb9zUqL8xmtiWFIgUd+38CXx9vfPHpGHi8HpgzctJ0PHn+HL9+/w0sLS2hVCrFtx/+tXsfDh8/hSMnT8Pb0wN2traYNm6k+MZWQ6dSqdB/1ARUDiuHIb27i8vvPXyELdt3YevOPfi4WWOMHNgXALB11x4cPXnGKPMb8yIOg8ZMRI8ObdG6aSNx+clz/2L0lC+gEgQM7dsT3Tu0AQBs27UXR06eNrq8pqWno9vgkYiJjcWy+bMQGOAPALgSeQOXI6/jbvR9VChbGsWDionz+RtrXgHzym9aejoijp/Cn7v3YuGXU5GYlIylK9fg0PGTCK9ZHaOH9Be3s7SwEJ96JSUnI1mRAqVSCXs7W6OZjlWlUmHud8tw4/ZdTB03En6+hQBkdGHqO2IcJv1vKAL8CkNubQ1Ly4znXMZ8jTKn67GaSqXC/YePMH3uN/j6s0+xdOUa/BNxDMP790aT+nXEt50Dxptfc7u3SFYosGPPPwCAj5s3gYVUColEghdxL9F/1Hg42Nvj8dNnKOpXBB1bt0CNKhVhbWVltNcpAFi8YhXOXfgPKxfPAwAcO3UGv+/4G/cfPELJkGCULhEiTse6deduHD111ijLNi/MJng4ff4CTpw5j85tW8HDzVV8q+GJM+cxacZsdGvfBv26dQKgPScxANx/+Ag2cjlkMpn4ZkBDp1KpkJCYhP7/G48GdWpiUK9uWhevp8+eY/0ff+Lv/YfQs1M7dGnbStzXGPMbff8BOg0YhtlTJ6JO9SpQKpWwtLSERCLBF/O+xcFjJ+Dj6YGendqhSXhdcT9jzOsPq9ZizaYt6NOlA/p374xDx05gxoLvYGMjh0qlwvPYF6hRuSI6t2mJymHlxP2MMa+A6edXHQwAGXOkW1paQm6d8fbVmNgXWLlhEw4d0w4g1IGDMUtPT8fWXXvwKj4BPTu2hfT1TcjTZ8/Rb9QEFPLyxJ3o+yhXqgTq1ayG5o3qA8jI+4NHj42ibNXM7Xqc2ajPvkDVCuXRuU0rjJn6Fc5euISRA/uiWaNw7D0YgRaNG4jbGlt+ze3eAsi4Ljk62Iv1V6lUosfQUbC0sETjerVRyMcLP/26EckKBaaPH4XypUtqvUna2Kz7fRvO/HsRC778DAePnsDkGXNQt0Y1ODrY4/qtO3j45AlaNWmET/r2AGCc16i8MptuS5XDyqFcqZKwssqo7OpKXKJ4IAKLBuDY6bPo2Lo5HOzttfqyARD7rhkTqVQKRwd71K5eBb9t24EWjRvA18dbvHh5erijXYumePz0Gf6JOIrqlSsgoEhhCIJglPn1K+yLiuVKY/XGzQguFgAfL0+xqdhKJkNY6VJIVSpx6twFNAmvKw62Naa8quvk4N7dYGEhxcr1m3An+h5On7+Iti2aokl4Hfj6eCPixCnM+OY7SKVS+BcpDE93N6MsV/WXjSnnNyk5GV/OX4R6NauhSXhd8Wmcun66ubqgT+cOAIADR49DEASM+WRAxk328xhIJBLxSacxEQQBFhYW+Ci8LqytrcQbKqVSiWGfToWTgwOqVaqAxuF1sG3XHqzdvBWuLs6oXqkCAOM6bwHzux6rqc/hgCKFcfbCf+jcphXmfzEFE76YhW9//Bl/7NyNG7fvIKxMKfj6eBtlfs3t3gKA2HVUnZc9ByPgYG+Pz8ePErsiVa9UEZ0HDMW+Q0dQvnRJowscNMvJ18cLi1ecw8Ur13D91m10aN0Cg3p1hY1cjoePn2DzXzvx1559KOpfBE0b1ANgvGWrK+MqzXekPrnVBEGAk6Mj+nfrhCuRN8S50TWngTR2DWrXRCFvT8z9bhmePnsuztwCZFTuTh+3wNUbt3Dzzl0Axj2NWMM6tZCcrMCCpSvw+OkzWFhIcftuNHb9cxAtmzRE9w5tsOfgYUTff2BUZay+kVRP3wkAA3p0QZ8uHfBPxDE0rlcbvTu3R1H/IrCykqFBnZqYMHwwjp46g1t3owAYV7kqlUokJiVrTVNqivnN6Cc9BgePnsCZfy9prVPXT0EQxACibvWqOHTsBBYsXYEnz57h62+/x+IVq6BUKvWUg7xTl606b3Z2trC0tBTr+LZde1HUvwjmfT4ZvTq1Q7sWTTFhxBA8j32Bq9dvAjCOss1Jwzq1zOZ6DLy5kW4SXhcPHz/B/dczzsyeOhEODva4HRWNdi2aik9mjTW/pnxvkZKain+OHMOaTVvw1+59ePnqlbhOnZfmjerj2xnTxMBBEATY2drAy8MDr+Lj9ZLu/NK8RqnPzdrVqqBGlYqY+vV87Nx3AG6uzuIA6ELeXmjfshlcnJxw/dZtAMZbj/PCZFseUlJTcfbfi4i6/xABfoXhX9g3y+Ac9RdYSFAgKpYrg01/7kTlsHLw9vTQU6rzL1mhwI69/+Dx02co5u+HIoV8UKZkKEqGBKNZw/r4ffsufPfTagzr3xue7m5ik3n50qXgX9gXVyJvoGGdWvrOhk4UihQcPHYCT589R2CAP7w83RFUNAAfN2uCl6/isXP/QbTpNRABRQrj7r37aFS3FurVrIZkhQKuzs54+cp4LmbJCgV+WrsRd6LuwdHBHvVqVkPdGtUAAP27d0Yhby84OzpALs/o5qLuAlM5rBzk1laIvHlLfFprDBSKFAwaOxFFfAth3LBBcHJwEJ/OmlJ+1TOz+Pp4oX7tGli7eSvq166RJe2aAUTfbh0hkUhw6NgJ/BNxFPGJiVg2b6bYdcDQZVe26ifT6i/b1k0boWnDcNjZvpnCsVRIcXi6u4k3nsZAoUjBzv0HcDf6Por4+qBMiRCEBgehRPEgNGtYH1t2/G0y12Mg48HGy/gEuLk4Z7ve3s4WL1+9QkJiEgBg6tfzkZqaihLBgfj7n4Pw9fFGq48aaZW7oTKnewv1TGjJimS8ePkKr+ITsO/QEYweMgD+RXzFoF8ikWhNuyqRSBD94CEkEqBsyRIAjKOrZW7XqDbNmmDV+s24cv0GlK/fW5GWlgZLS0sU8vZC4ULeuH7rjlY3VFNmksFDYlIyRk35ArFxcXgR9xJJycmoWaUS+nfvhNDgIK1tJRIJnJ0cUb92Dcz9bhlu342Ct6eHUfXPS0pORp8R4wAAEgnEwUztWzVDv26d0KVtKyQkJmL3gcOYvWgpxnwyQLzYxcbFQSqVwMvDXW/pz4vEpGQMGDUByQoF0tPT8So+AZ7ubmjfqhk6tm6BXp3bo0aVSrjw3xXExsXB18db7Ct9+dp1WFpawN7eOAZqJSUno9/I8bCRy+Ho6IAbt+/izIVLkFtbo2rFMABA0wb1xAuySqUSL1rR9x/C3s5OnAnDWJw4ez7jApyWju9WrMaw/r20AghTyO+bmVm88NXEcXj89Kk4gLJy+bKQSqVa1x51fl2dndGkfl3sjziKNGUafv52rjiA3BjkVLbqa60gCJDJZFmCoTtR9yCVSlGuVEk9pTxvEpOS8cn4yVCkpEKlUmHnvjgEFQ3AmE8GIrhYgHg93nMwwuivx0DGdeqT8VNQukQIenVqn203usKFfFChbGmcOvcvftn4O878exFzpk9C+dIlMXTCZ1j3+1bxOm3IzOneQpGSgpGTpkMut8b44YPg5uKC6AcPMWbaV9iw9S9MGD5YKxjQzNfzmFis+W0L4l7Fo2bVSgCM42l8TtcoAKhVtTJexL3Ey/h4rNn0B0KDA1GjSkbeYuPiEJ+QiGL+fpAaQT4LgskFD4qUFAyfOBWODg4YOagPQoMCsfvAYSxavhJ7Dx5BaHCQViVXR8Ntm3+EvQcjsOTnX1CpfLkszZCGShAELF6xGi5Ojpg8ejiKFPLB9Vu3sedgBH5euxGPnzzFpFHDMKBHF9ja2GDnvgPoM2Is+nbtBEDAzdt38fR5jHgSGLK09HR8PnchnJ0c8cWnoxFUNAAnz57Hut+3YeEPP+Hlq3gM6NEFwcUCEFwsQKucX756he179sPJwRFuLi5v+ST9S0tPx8xvlsDD3RXjhg1GkUI+uH03GpNnzkXkrTti8KB+wpWucSMdGxeHbbv2wEYuR4niwfrMRp75+njD1sYGbq7OuHj5Kpb89AuG9uupdZMJaA8yNqb8pqSmos+IsXBzdcG0caPg7OQIZydHNG9UH7//tROdP24pPtHL/GX78PETrPh1A1JTU7VmnjIWupat5qDSF3EvsWHrn0hVpqJqxfJ6TL1uFIoUfDJ+MpwcHTClfx8EF8u4Rn02az7uP3yE4GIBADK64cnl1vh7/yGjvR4DGV08Pp/7LSJv3satu1GwtLBAjw5ttaZTVnN3c8XSVb/Cw80VX00ah1IhGefqktlf4smzZwY/qNSc7i0EQcC2XXuQqlRi7NCBKB5YDEDGWIceHdrit63b0atTO3h5uIvXKXW+9x6MwMFjJ3D2wiUsnvU5fLw89ZaPvMrtGgUALZs0hL2dHdb9vhXjps9Eu5bNILO0xONnz3Dj9h2MHz7YKALDgmBSwYMgCPj9r10QVAKG9u0hTo3VrGE4bt2Nwl+796N3l/Za08NpfkGHlSmFnfsOID4xEW5Wzh86+fkikUhw78EDFPEtJE5VWTyw2OtmNB8sXLoCADB59HB0a/8xSoYEY9+hI/h10xbI5XK4uTjju6+/EPc1ZKmpqbj34BEah9dBUNEAAEDVimGws7XF3Xv38fO632BpaYk+XTpo7bf7wCEcPHoC5y7+h+++/tLgv6QAQKFQ4E5UNBrVqy2WTbEAPwT4FYaFhfR1k7AERQr5QCKRiDfSh46dwK79B3H2wiV8P+creLq76TMbeaJSqVDUvwhqVK6Ilk0a4vyl/7Br/0EAwJDe3eHi7CR+IRtrfh88fIyWHzVEswbhWjdYTcLrYO/BCPy0biMmjxoGayurbPd/+uw5Fs/6wugCh7yUrTpw2Hf4CPYfPvr6vP3C4Lt8qFQqfL9yDaysrPC/gf1Q1D+jFaxqxTD4+xUGADyLiYUEGTfSPTq0RenQEKO9HgPAwWMnce3GTQzs2RVya2ssWr4SALQCCPUN9YgBfaBISUHNypVQqXwZSKVS8SGAl4dhl6253VukKpV4FhMLv8KFEFRU+1oTElQML+PjkZqamuUBx7Ubt/DHzt2wtrbC0jkzxHPAGOhyjQKA8FrVUSokGDv3H8DhY6eQlp4GHy9P/DBvplG0fBcUkwoe0tPTEfsiDg4OdvAvnHGxVl+cypUqga079yDuVbzWCa6pR8e2aPVRoxz7bRoaQRCQnp4OG7kNkpKToVKpoBIEWFpYwN7ODs0bhkMCCeZ89wM83N0wsGdXhJUphbAypdC7SwfY2dpAUAniy3oMmUqlQmJSMhISE2FrkzFQKSU1FdZWVihdIgSNw+tg3eat+PGXdXCws0P7Vs3EJwC2NjZIVqRg6ZwZKBbgp89s6EQQBLx8FY+nz2OgVL4ZJB2fkID/rl3H7ah7+HH1Osjl1mjT7CMM7t0NAHDs9Fls27UXr+IT8MO8mUZ3gymVSiEFkKpMxf6II5g4ciiSkpMRceI0fvxlHYYP6I19h46gYrky8PXxxsmz540uv8UC/OBXuJD43gK1wAB/VChbGifP/ounz2NQpJBPlu4Nhby9sHLxvBwDC0OW17I9e+ESDh87iYTEJPwwd6ZR3IRIpVKEBgdCZmkJvyK+4o1VbFwcHj1+gl82/o7b0dEo5OWFlk0aomu71kZ7PVaztLBAEV8fdGjVDPZ2dpBKpfhm2U8QBAE9O7aDm6sLpFKpOKZj/LDBSE9PF+u1sfQNT1epEBtnPvcW1lZWqFO9KjzcXGFpaal1LSriWwhyaytx/Iqm0OBATPrfUDg6OBjFQzpNulyj9hw4jKoVw+Dj5YnenTugfctmsJHLoUxLE6fXNhcmETyoT2ILCwt81KAeujq3hpWVDMLrG2kA8C9cGCqVCjGxsdk+1UlLS4ONkb1CXCKRwNLSEmVKhmLFmvW4fusOQoMDxb+HTCZDwzo18fjpU/y2bTsqlC2NSuXLAgDcXV2Mog8ikHEzLZVK4eHmirCypbF64+/4qH5dODk6igOWZJaWqFm1Eny8PLE/4ijq1qwm5rF2tSqoXL6cOMjWkKm7bPj6eKNxvTpY89sWKFJS4OXhjs1/7oS7iwsG9+kOC6kUZy9cwqoNm+Hi7IhOH7dEjcoV4eLkBG9PD/EpiaHT7J6j/oJqWKcWduw7AAAY3r83pFIpDh8/ia6DRiDmxQts+mkpgIwnuo4ODkaVXwDi7EKZ8z24d3dEnDiFjVv/wthPBmbb/G1MgcO7lG3FcmXg4e4GZ0dHo7gJUV+HmjUMz/j/6++dtLQ09Bs5Hq4uzmjb4iNYWclw7NQ5LF6xCi5OjmjaMByAcV2PgTflGV6ruvgSsPT0dHT6uAWkUgkWvG7xVgcQMplMq2XJGAbPqqnvI5o2qIeubc3j3gIAypYMhUqlAgCta5GjvR3SVSo8fR6Dkq+7nilSUnAn6h5KFA8y2ilKdb1G/bbie/Eey87WVqu11JwYfeespORkdB8yEjv2/gOJRIKgov5wfR3da16cpBYZ0z5qRssKRQpu3L4LAFmeBBqTjq2bo1RocUz4YhbiXr6CpYWFOK2nnZ0tGtWtDQd7e3EaS8A4Bi+paaa188ct4WBvjz4jxuH6rduIefECN27fwcat21GxXFk0a1gft6OiEfsiTms/Qw8cUlJTkZaWBgsLC6S+nslh1OB+aNviI5w+fxHnL12GMi0NU8eNRNUK5VGpfFl0btsK9WvXwN/7DyE2Lg4AUKJ4kFHdSKe//nIC3pRzSFAxXL1+AxcuX4WlpSVGDuwLC6kFYl/EoWLZMlo30IaeX0VKCv78ey8WL1+FtZu34r+rkQDejFUBIA4WdnSwR3itGjh07CSuRN7QZ7LfiUqlgiAISEhMFJepbz7yUrZ+voUMPnBQn7fqNwgD0Jp69uzF/1ChbGnMnjoRLZs0RJPwuhjQozNCgorhzIVLSHs9FaSxXI/VZZuY9OZ7VPY6v+obqA6tmmPMkAH4bdsOrN74u3htehYTK9ZrY8kvAHG6zsAAfzg7OQIwvXuLZIUCK37dgMkz5mDud8tw6FjG1LI59d+XvW6NADIGkc9f8iO+X7kG8QkJHyzN70KhSMHmv3bi9t1ocZmu1yi5tTUsLSygUqmMqh4XNMOv1blITEzCgNGfIureA/z5915UqVA+xxcmOdjZwdHBXuwGkpCYiCU//YIr12/g+9lfGUVTcbJCgT//3ocHjx7D18cbJYoHoWzJUMitrdGvWyd8vWgpBo+bhGXzZmY8lX8dHRcL8IOHmxsuX7uu7yzoLFmhwLrft+HBo8dwc3VBxbJlUK1SGEoUD8Lowf2w7Jd16D18LFycnPDi5Us0qlsbnT5uAQCwkctxJ/oeQoKK6TkXulGkpKBV9/7w9nTHioVzYGUlyyg7S0v8b1A/CIKAw8dPIureA63ZOpwcHOBgZwdlWhrsbY1jBikgo2w3/PEX7kRFw8ZGjkZ1a6NS+bLivNrubq7w8y2EpORkAMBns+Yj7lU8alSpiNt3o7Fg6QqMHTZQHMRmqBKTkjFswmdQpKRApVLh4ZMnKOrnh75dO6JezWpaXzwSiQRya2s0axiO7Xv248yFSygZEmxUT2iBjIc5i5evwu2oaCQmJaNbu9bi03VTKlsg+/NW3XKoLrMKZUqhfOmSsLayEsuycCEf2MjlePkq3mi67QA5l212N5jtWzWDAAELlq6ARJLxfot1W7bh0ZOnWPL1lwb/fZvTNSq7J8zGfm+RmJSMQWMmAgBcnJ1w6Wpklln91FQqFSxed4tOSUlBWloavv3xZ/x94BCWL/g6x25bhiQxKRkDR38KZydHWFpYIMCvsFiHVSqVSV2j3iejDR7U86QH+BVGeK3qWL3xd9yNvgcPN9csr4AHAJlMBqlUiriXr5CWno5Fy1fh7/0HsXzhbIM/uYE307FavH5Keez0WTx++gx9unRAh1bNUal8WQzt2xNLfv4FvYaNwbzPJ4uDiuNeZrzUpYhvIT3mQHdJycnoM3ws7OxsYSWT4XZUNH7d9AfaNv8I3Tu0QdWKYQgrUxr/HDmGlJRUONjboX7tGgCA/65GwtLCAoV9vPWcC909ePQY8QkJSE1NxeCxk7B03gxYW1mJb8iWSCRISlbgWUwMkpIVYvN3bFwcnse+QFBRfwgQ9JwL3WRcuCfASmYFS5klFAoF/ok4hrnTJ6F86VKwsLCArY0NgooGYOfeA9i+ex9On8+Y1rFMiRDMW/IjLkdeF+fZNlQpqakYMzVjcP7UcSNR1K8Ibt2NwoQvvsZfe/ahXs1q2e5XvnRJtGnWBD+v3Yh6NarCr7DvB055/iUlJ6PvyHFwcXKCr4834hMS8cX8RZBZydCwTi2TKVu1nM5bzZnANKedVbc23Xv4COnpKpQvbRxTzwJvL9vMBEFAh1bNIYEE3y7/GfsPZ7yXZPmCrw3++/Zt16jMjPneQqFIwYhJ0+Ds5IjxwwbBr7AvYl7EoffwMbgceUMreFA/tFK3pETdf4hvf1yJ3QcOYcXCOUbxsC41VYn/TZ4OVxdn/G9QX/gX9tUKfqVSKWxtbFAswM8krlHvk1EGD+p50gt5e2HqmJFwdnLEkZOn8eOa9QgrWzrbpznp6elQpqXhZfwrLFu9FrsPHMLyhbONosKrVCrMW/IjHO3tMGX0CPgX8cXNO3exdecerPh1Ax4/eYo+XTsivFZ12NraYMWa9ej/vwlo1igcdra2ePLsOe5G38PkUcP0nRWdrN74O6ytrTBj0jj4eHniRdxLHD5+CnOXLMODR48xqFc3lCgehI/q19XaL+ZFxpSd1tZW8Mn00h5DJQgC7O1s4eHmivq1a+D0+QsYMm4yls7NuBFRDzQMK1MKrs7OGDRmIiaO/ASxcS9x6ty/uHT1Gn6YN9Mo+sIrFBlTHbq5umDMkIHwL+KLG7fv4vO53+C/q9e1vphLhgRj3vc/wsXJCV9OGovSocVhYWGB8cMHIzbupcEPPPx7/yHEJyRg2Mih4gwcgQH+GNSzK6bOXoCbd+6KwX1mpUKL4/iZc7C2NvwyVVOpVFj4w09wcXLCZ2NGoJC3FxISEzFl1nwcO3VW6wazVGhxzP1uGVycjbNsgbeft+quTECmqWdfvsTazX/gxcuXCK9VXZ9Z0FleyhbQHutSu3plbPprJ17EvTSK95Lk5RqlZsz3Fr9u/gNpaWkYMaA3/Ar7Ij09HW4uzqhQpjQc7e1x885dyCxl8C+ScZOt7qrk6OCANb/9Dmu5NZbNm2UUeQWAqzduIilZgZGD+qGoXxFIpVI8ffY8Y1B/Wpo4rWy5UiUxe9FSo75GvW9GFzykpKaiedc+KF2iOKaPGyX2d65fK+MtrYePnUT92jWyNPfLra3h5uKCH39ZD7ncGsvmzTSaCp+Wlo670fdRukQI/ItkPIkMKprxsqEnz55j+95/YGVlhcG9u6FqhfIoERyEjVv/wrmL/+FVfDx8fbzx/dyvxH0N3YNHj+Hh7iaeyM5OjmjdtBEKF/LGqClf4Mc16zBiQB+tadH+OXIMB48cx4mz/2LJ7C+M5uSWSCTw8vBA1Qrl4ejggG7t2+D7n3/BJ+On4LuvvxBbGbw9PTByUF+sXL8Jwz6dCjcXZ3h5emDJ7C+NYno4QRCw+a+dsJLJMKhXN/gVzmgFCy4WAE93N1haWiD6/gOoBAEBRQqjcXgdPH0eg/KlS6J86ZKwsLAQb8KMoWzT0tPg7OSEoNfz+quvR36FfWFlJcOr+Jz7BjdrGI5qlcLg6uz8YRJbAFKVSty6G4WyJUuILzyzt7ODj5cnrGSWuBx5HYJKQOkSIWjyumzLlSphlGUL6Hbeqp/UqgOHTX/uwJl/L+LC5atYPOtz+BpJ66hOZSsApUOLA3gzHuB5TCy+XbYSDx8/xspF8ww+cMjrNUrNmO8talapBEVKivgdYmFhgcTEJJy/9B8uR17H/KXL4eHmivJlSuGLCaMzntILgLenO57HxmLxrM8Nvlw1PXn6DMkKBUoWD4JUKsXBoyewdNWvePUqHukqFZo1zBgU37hebTx++syor1Hvm9EFD9ZWVhjUqxsa1K4Bd43xDR83b4I/du7G3kMRqF+7RpZ+wjKZJTzcXXHvwUOsWDDbKKbsVFOpVJDJLJGSmgLgzY2Ir483ypQIwZGTp/HHzt1wc3VB364d4ehgjwE9uiApORmWFpZQCSqjmkbM090NJ8+ex6v4BDg62EMikUClUqFiuTL4ZsY0jJw8Hb9t3Y4JI4aI+9jKbWBna4sfF8zSurAbOnVZyuVy3Lh9B327dgQA/LDqVwz/dBp+XDALv276A6VCi6NG5YqoUqE8Lvx3Ba4uznBxchIH8Bk6dV/v2tWqICSwmHh+xick4NbdKDx4/AQ/rlkPQSWgfatmGNq3J/p06aA1ENOYZrSoVbUS6tWoBrm1tdY0h4V9vOFob4/nMbHZ7qduaTKmwAHIeKgDAM+eP0diUjLsbG2QmJSM0+f+hTItDXsORCA+MRGN69XGyIF90LdrR60n8sZUtoBu5+3a37ciuGhRVKsUhugHD3HvwSOkpaVj6dwZRhHwq6kncHhb2TatXxf9uncW38cR9+oVUlJT8fO3c3NsZTMkeblGdW7TEr27dIC1lRUsLS2M9t6iRPEghAQV03r5Zq/hY+Ds5IR+3TrCw80Nx8+cwy+//Y7Fy1dh+IDekMut0enjlvifl6cYTBoLF2cncRr0uJcvMWXWPHRq3QKFfLyQlJyMH1atRfT9h5g6diT6dOkgjvEAjO8a9b4ZVfCg/rLp0rZVluVODg7o3qENFi5dgUPHTqBuDe0+xRKJBL06tceE4UMM/mVDmcnl1igeWAw79x1A0wbhKFeqhLjO0dEBjerVRnCxAKzZuAW1q1UWX2BjI5cb1WBL9U1WcNEAbNu1F0dPnkbj8DqwsLAQm0wrlC2NiSOH4sv5i1C9cgXUqV4VAFCtUhjCypYyiu47mtTl0+qjRpj1zRIkKxSoW6MaJACW/bIOTTr2AAD89M0csStExXJl9JjivFOft/VqVtPqzpGWloaug0bA1cUZ/bp1gp2tLS5cvorla9bD090NHVo113PK88/Lw0OcpUWzT60AAWnp6Yh5EScuUyhScOHyFVStGKbVR97QKZVKvHj5Cp7ubnBycED1ShWwcetfmPr1fJQMCcZfu/fB1dkZ/xvcD04ODrhy/QZmLPwObq4uGNq3p9F9GWd+7wbw9vP250XzIAgC/HwLYUCPzpBKpAbfDz4zRwd7ncvWxdkZn/TNyHtQ0QB8OXEMbG1s9JyDt1MHg/VqVoNSqdTpGuXm4iK+T8hY7i0Sk5Kx79ARNKxbC3a2GeWiWadv341Cg9o10LbFR/B0z3h7tH+Rwrh+6zauXL8hBo/G9B2kPm8FQYCvjzf8C/ti595/kJKaitrVKotveweAksWDMXziNGz44y8M7t3N6K5RH5JRTdWa07Rh6gIuV6okXF2ccejYSSiVSrF/nlr50iUN/uQGMp7iPXj0WGvZ8P69UTywKD77ej52HziMazdu4d//rmDx8lXw8fJEy8YN4e3lgViNmxJjCBySFQos+fkX3L13Xyzfpg3DUal8GSz5+RetKSvVAUTtapVRoWxpHDl5Bunp6eJNmrEEDuppHDXZ2shx/9FjnLvwH+xsbfBRg3pwdXaGIiUFLk5O8HR3h6WlpTi1ozFQ51Mzv5YaU/zFvHiB6pUrYtaUCahTvSoqliuDbu0/Rv3aNbBz7wEkJCZm+7cyNEnJyVi6cg0+/fJrzFvyI7bu2gMg+ydVMpkMTo4OSEt/MzPLwmUr8PWipeKUlsYgKTkZg8ZOwu4Dh8RlA3p0Qc+O7SC3tsKr+HhYWlpiwoghKFsyFP5FfNG0QT306NAG23btxdNnz/WY+rxR10FVNtMKv+289XB1FWcRc7C3N4rAIbvvH13LduuuPXj67Ll4TTb0wCE9PR0KRYo4qQgA8Z0UQO7XqB17/xG7HxrDvUViUjLa9xmMA0eOwSKHe6nigcUwoEcXeHl4iHXcztYG9nZ2UCrTIDeicViZSSQSFPL2QtlSoVi3ZRsOHz8FV2dnMXBQKpWoWK4MenZsh32HIhD38pVRfP/oi8G3PCgUKbgVFYVSIcXF2SpyuikOLhaANs2aYOWGTejarjWCigYY3VSHipQUdOj3CRzt7fHVxLHim1WtrGT4fPwofL1oKWYvWgqJVILUVCUa16uNIb27AwCSkpJx/fadLNOrGSr1lGkWFlI42NnBv6Mv0lUqWFpYYMKIIZjw+SxMm7MQX3w6RuyjKJVK4WBvD3s7Wzx++tRongy8rR77+nijWqUw3Hv4EAAwZdY83H/0GB1aNcfBoyfQa9ho/PL9QqMJkJKSk/H9yjV48jTjRqJpg3qoUK4M3FycxSDQy8MDn44YovVQQJ0/udwa9naGP/2semYwa2sreLi54dqNW9i6aw+Onz6HQT27itMAqsvb2soKtjY2ePkqHmnp6fh22c/YczACPy742mi6KiUmJqHvyHGIfvAQT589R/1aNcT++z07tQMAXLh8FbsPHM7yngapVApHezvY2Rr+TTSQez0GTO+8ze77R/3kNi9lawzX5aTkZMz65ntE3X+Ax0+eolv7j9Hyo4ZwdXYWr81vu0YZ+ntI1NSzUwYVC8Ck/w3N9r1H6hbizC+xfPz0GZ7FxKBMyZAcH+AammSFAqs2bMajJ09hJZOhUd1aKF0iFHa2NvjfoH54FhOLfyKOIT09HQ8fP0Ehby+x1dfCImOcko2NcfXc+NAMuiYkKxTo97/xGP7pVJw8ex6A9suVNKmXNaxbC17u7li57jcoFClGV/hxL18h9kUcbkdF47Ov5+NO1D1xnaeHOxZ8+RlmT52I6eNGYc7UTzF17EgAQOTN27C2tkbx112WDJ1SqcS46TPg4uyEWVMmoEvbVhlvzH79pePi5IQpo0fA2dERE7/8GvsjjolPeV7FJ0ClEuDr42MUT+J1rcdeHu44dOwkpn49H6fPX8TsqZ9iYM+u6NmxLSwtLRAT+0Ifyc+zZIUCvYeNwY1bd2BlJYOFhQWmzl6Ar7/9HsdOnwXwphVJfWOt9ujJUyQmJqFMiVDxhVSGShAELF+zHrY2NpgxeTzmfzEF87+YjJED++D0+X8xd8kyXLh8VetlQmmvW8pexL3ED6t+xZ5DGYGDsQywVN+EeLi7YcLwwUhWpODg0YwXSmmei2lpaVCpBDx8/ERc9vLVK9x78BD+foUhkRr+dVmXegwAHq6uJnHeAtl//2S+YTSVsu3/vwl48TIO1SqGoUn9uvhh9VrsO3Qky7bGfI0CMvLac9goFPP3w7SxI+Hm6gIgo4VJoUiBIiVjLKXF6xefaQYOz2Ji8fO63xB9/yHaNP/IKO6nkpKT0WvoaJw+dwExsS9w78FD/G/KF5j//Y+4cPkqAGDq2JFoVLcW7j18hFUbNuP+w0cAgBdxL3HvwUN4uLuJrWeUPYNteUhLS8P875fjybNn8PHyxPzvl2PUkP6oXqlCtk9u1f/3K+yLapUq4K/d+3D/0SOjGKilplKp4OzkiErlyqBKxfI4EHEcE2fMxqzJE7SeAFWpUE5rv6fPnuP37buQojFrgqG79+AREhITMXJgXxTy9oJEIsHDx0+QrFDA0sIShQt5w7+IL+Z9MRlfzV+MWd98h+KBxVCkkA9iXsTh4pWrWL5gtsG/ZEmXegxk1N/wmtWxY+8/uP/wEb6cOAZlS4bCwsICzRvVR8O6tYziSTwAbP5rJ6QWUnw5cSw83d0AAIePn8SSn37Bil83IDExCY3q1YZUKtWaE1/9RXX33n2M/mSAwT/lkkgkiH7wEK6uzihSyAcA4OToiCbhdfHg0RNs3PoX1vy2Bc4DeqOoX5GMJ3tSKZwdHbFz3wHY2doY1cws6sDB18cbMyaNhZOjIw4ePYE/d+9D13atxbeuSqVSVCxXBkX9CmP63G/Qs2NbAMB/V6/jxNl/sWzeTIPvzgK8vR7HJySgSXhdNA6vg537Dhj9efu27x+1iuXKwL9wIaMtW0EQsGz1OlhbWWHK6BHw8sjo25+Smoq/9x9Cx9YttLY35muUOq8PHz9F2+ZNxUlmjp85h517D+DmnbvwdHdDxfJl0bNjW638rN/yJ46fOYtbd6Kx8KvP4GcE74kSBAE/rFoLa2srfDlxDHx9vKFQpOD3Hbvw3YrVePj4CXp37oBqlcLwxacZ43EOHTuJU+f+RTF/P8QnJuJu9D0snTvD4OuxvhlszY+8dRvHT59D0wbhGNSrO9zdXLFg6QocP3MOQPZPbtV9Uju3aQlvL0+jaRpXk0qlkFtbw9PdDZE3bmHs0AEQVAKmzJqLW3ejsr1QnTp3AYtWrMKhYycwc8p4eHq46yHlefcqIR7PY1+gSCEfSCQS/HPkGAaPnYQhYyej88BhmPjVbBw7dQauzs5Y8OVn+KRPT/gV9kX0g4dwc3HGj/O/1vpCM1S61GO1ov5FMLx/b8ycPB4Vy5URnwTJZDKjuQEBMqZodLCzE2+40tPTUad6VXw68hNIJBKs3/Injpw8DQDil/Ky1Wsxb8mPOHbqLOZOn2TwX1SCICA9PR32tnZQKpVITEoWr0eODvYoV6oEbOTWuHjlKn5Y9SsAiG8eDg4sCkcHe/w4/2uEBgfpMxs6UyqVGDJ+Cgp5e2H6uP+Jb5Lt3KYVHj95itUbNwN406IEAJ9PGI3QoEAsXfUr1vy2BTEvXmDp3K+M4rwF3l6PN/6xHcfPnEPxwKIYOagvZhj5efu27x9NM6dMQEhgMaMs2/T0dNyJioZfYV94e3ogLS1j/JG3hzv8i/ji2KkzOHn2vNiyYqzXKCDj+6V+7RqoW6MqNv25AwePnsDZC5cw/vOZiHnxAsGBRZGSmoqlK9dgxsLvxP2ePo9Beno6XJ2d8f2cr1A80DgecKSrVIh+8BBF/YqIXSmtrGTo1u5jdO/QBhcuX8WGrX/h/KXLAIBPR36CSaOGokXjBpDJZChXqgSWL5xtVA+d9cVgWx5cnJzwcbPG6Nq2NezsbGFpIcWaTX9gwdIVGJ1DC4T65trb0wM/fzPHKAanaVLnpVzpkjhw5DhCg4PwxadjMHX2Akz9egG+nvop1m3eijIlQ9GsYTgAwEZujeCiARjQvYvRvMcBAKxkVkhMSkJ8YiLS0tMxffZCdGnbCuXLlMKr+Hj89OtGrNyQcUNSo0oltG/VDEDGk3yJRGIUfWoBwNXZWad6nJaeDhu5HE3C62R546WxcXN1xZ3oe4iJfSE2kQNAWJlSGN6/F2Yv+gFbtv+NoKIB8Pb0gCAISElNhVQqwfdzjON9JOo6WDmsLGYs/A77Dh1B0wb1YGWV0W9WgIDQ4CB0bN0Cs75dgoNHT4hvlW7ZuCE6f9xSa6ppQyeTydCxdXNUr1xRa57z0OBABAb44+ips2jT/CM4OTiI12NvTw/MnT4J12/dhqODA+xsbcSgwxjoUo83bt2O0KBANK5XGxKJJMt3kTHR5ftn7aY/UK5UCTRtGI55n082yrKVSCSwt7PFtZu3oFCkQC63hiIlBbsPHMarhAScv3QZT5/HoFL5jMGzlcqXBQCju0apxzCULRkKmWV7rNqwCTO/WYLU1FT069YJbZo1gZOjI2Lj4vD3/kNYvGIVgor6o9PHLeHp7oaOrVsY3TTvlhYWcHJwwINHj5GaqoSVlexNrxTfQpBKpYi8eQv7Dh1BmZKhsLSwQO1qVVC7WhU9p9z4GNQVTrMloZC3F/p07SgGADWqVELPTu3g7uqS7ZPbzDO6GEPgkJKaihu374q/q/NSsVwZRN68hSuRNxBU1B9fTRwLlUqFPsPHYN/hIyjk7SnuU6ZkKLq1/9goLmaaSoYEI6xMaXy/cg12HziMKhXKo3eXDqheqQKahNfFpyM/QUzsC+w5GAHgTauSpaWl0QQOAODj5alTPba0sIAgCEZ50wG8eRIPZMw84ursjF83/4HExCTxSWzGulIY2q8njp85h9PnLwDIqPcjBvTB1LEjDboep6Wl4XlMrNZT2BaNG6Bdy6aYu2QZflq3EQePnsDRk2cwc+F3KB5YFPVqVkMxfz+t9zr4F/E1qsBBrUXjBlqBgyAIcHZyRP/unXD52nUcOZHRmqS+jqnLvHhgMXh7ehj8zWXm63GYDvX45NnziDhxClKp1Cj6g2cmCIJW10kg9++f/RFH4ePtadRla2FhgVpVKyM9LQ1dB4/AvO9/RMd+n8De3g4Lv5yKzSt/wPwvpuBO9H1s27VXHMtjDNco4M15p3zdogJkvM+hR8d2CCtTCo3q1UbzRg3g5JjxjiBXZ2c0qV8XwcUCcOlqpNgSY2UlM4rAIXMPlJCgYnjw6DG2792PpORksV7b29khvFZ19OvWCVt2/I3rN2/rI7kmw2DuVJKSk/Htjz/j5Ll/oVBkDOBRNxeqK0f1ShW0brzUg0/vP3yEY6fOIjEpWT+Jz4ek5GQMHf8Zft30B9LS08UTXqVSwcHeDu5urnjw+AksLCwQXCwAHu6uUKSkwt7ODo72DlrHUs9JbWxqVa2Ex0+eYsee/RAgwEYuR3p6OtLS01GhbGl079AG+yOO4nlMrNHcVCtSUnD8zDnxpVmA7vX4waPHOHryjFHVY80LtzqoK1syFBXKlsbuA4ex52AEFIoUSKVScVrdWlUro2mDevjz773/b++846quvz/+vNx7ZVwQLlNZskTceyCkouZeOXKlZqaWI9PMUY4k9zZXrpxlmrnS0q/lyG1pjnKkKYqiCCKby7j3/v7A+xEUEoyf3A+fz/MfHlzvvX5enPM+730OOl26MOkwVdM2R1LT0hj7+UzeHzOBt97/kIkzniYzGPpOX/p178LOH/cTPm8Rn0yfTbNGIQx9py+Q/TeKiLxbnI//UmTp9TyMfUTUg2jhUiXw3GAzKDCAWtUq893uvUTHxAjvE0ubhefjMWQvzNSpWe2FfvzD/p/Rpaeb/cXZvHj27mB2StkX9z9itW1mZnbBu9bNwxg6oB8h9epQ1tUVa2trhvTvQ6C/LyqlkuA6tRjQuzs//3qM+9EPhe8y5xgF2VrnLFnBkDETGD15Ghu2bifmycJFlaBA+vfsStvXw3B5snhhGnc4aR0obWdLfEKi6Gz77LixV5eOVAoqz6qNm1m96VvOXrjE0VNn+HzeIpy0DnRt34Z6NavnSgMvU3jMYtSZkprGux+OoVQpNQG+PqjVuR8r5/Gk4Dq1MBqNbNy6nbnLVtGvexeOnvqNPy7+ydY1ywDzv+SSkpJKv+EfEfUgmjv3orgf/RAv97LCyrOtRkO1ShU5duo3Xm8cyqfTZnPtxk0+HPQOO37cz4gJU1gyI9zsV0AgO9PDtRs3qVGlkvCayZZd27fh5u077Ni7n5TUNO5G3cfTvawQ0NQqFU5aLRZKcQSzjIxMBo0aT2JSEkP69yEsNFjIGW460lCS/Dg1LY3Fq9Zx/2EMKSkptGvRnKoVK+Dn4824EUOIjLrP8nWbsLCwEIoSmQZYNjbWGI3kmTLQ3EjT6Rg4chwuTo682bEtarWapWvWY2lZigmjhmNtZcXAPj1p3jiUrKwsdLp0qlYKAuDO3Xukp2cQVN6/mFUUjtS0NCbOnMe9+w+Iuh9Ny7BGdG7XmoqBAc+tsDs6ONAkJJgFy1dz/WaEUCBPLDuE+cVjgDHD3iPyXgH8WAQrtCbSdDo2bt3OPxF3eByfQPdO7ahXqzp2trYolcoS1f88a9sHMbGCbcNCgwkLDebGrQhWbvwGJ0etMDlUKpUoUODs5CiaNLtpOh39h4+mdGk7ynl6YDAYWLFuE7+eOE2Pzu1p3ig01x2rnG006kE0iUkpNKhdUzQ7aHmNG02a5k2ZQPjcRRw79Rtbdu5BpVLSMqwxHwzsD0Bs3GOioqNf8D/I/BvFPirL0uv5dPocnBy1zJgwllZNG+fb6ZgCdsO6tenXvSsO9qWZtmAJf1z8ky9mfIaDfelX+egvhSljSRlXZxZMnYSNtRVrNn1LekZGrkvgvt5eREZFMTZ8Jr9fuMTU8aPp3K4VE0YNx9XZCZXK/DtmnS6dPkNG8v7Hn/LryTPC66aiSZDdOXdt34b4xETGT53F7ch7WFhYEBcfz9kLl3B0cKCUSKruPk6IJzomhuiYWFZu3MzhJ8UKn82qVBL8OE2no9+wj7h+MwKH0na4Ojsxe/FyZnyxTEjduXTW5wT4luPL9ZtY/+02Hj2OR6FQkJKaRkJCEs5OWtIzMsx+xXbrzj0oFApGDRlItw5t6dS6Be++1YOzFy4JK5mQ3WbL+/kKE4e7UffZtG0nsY8fU6taleJ6/EKTptMxaNR4dLp0urZrw9s9unLk5Jk801iabNe1fRsqBQWyZtMWskQ2ccgvHpu0LZ4xhUB/X9H7MTwdcJ29cImsrCxKlVIxaeY8fvz5EPA03W5J6H/+zbY5sbaywtlRy4kzZ0nPyECpVJKQmMiFvy7jUcYNG2vz3m0wsXXnHhQWCiaOGs6EUcOZNHoEC6ZO5s7deyxds4Ede/cL783ZRh/GPmLtN1uJiX1Eu5bNRDF5yG/cmDPuTBo9grlTPmXprHC+mD6FTz4cilKp5M7de2hsbKgYWL4YFYifYt95SE5OITEpibd7dKWMqwsWFhZERN4lOiYWjbU1To5ayrq5olAocuVLD/Ath8FgwFZjw4p5M0SR5SElJZXe74/A070s4eNGobW3p2bVyvz2xwWiY2Lx9nAXiqSFhQbz1eatRD2IJnzcR9SuXhULCwsqBgawbPZUs18NydLrWbx6HekZGVQI8OOTabP5fNxHhIUGA9lHXEyrBB8NGYiLkyM//XKYvsNGEuDrQ1ZWFvejY1g6K1w0GUvcXFxo0zwMNxdnfj5ynPnLVgHQpGH953YgTIjRjwE2bt2OSqlk2qejcXFywsLCgt37DjBv2SrhUl6LsEYsmz2VqfMXc+j4SX765RAVAwNITErmxq0IVsybYfZ+DBAReRcrS0u8PdwFG9pqNLiXcWPvgYOk6dKpVjmIyhUChc+cvXCJ3ft+5rfzF1g4dRLuZdyKUUHh2PTdDiwsFEwa/QFuLtlVc1PS0jh07CSD+/UWLoXnxGg00rJJI+YtX8Xh4ydp3ij0VT92oSlIPE7PyMCyVCmWzAxn2oIlovbjjIxMJs2ch6NWy/gRQ3B1cUalVDJ36UpWbdxMo+D6lHXLvk8n9v6nQH3tk/7Ho2wZfLy9WPftNm7fvYubszPX/rnJhb+usHTW56Lpf+7ci0JjY4O3Z/aOkF6vp16t6tmZwBYsYde+/2Fnp6F5o9BcGaQuXr5KxJ27LJo+WRQZpODF40atgz0eZcvg7ekh/D0ge0Hn6+938uDhQypXkCcP/4Vinzw8jk/gn4jbwpbhwWMnmL34SyC7GJiPlye9unSkXYtmQrGWpOQUpi9cypW/b7Bh6XxRDLiy9Ho+m7MQNxdnPvv4Q7T29igUCgb17cXxM2fZunMPo4cOEi7OOtiXZtbEcRgxUiHAP88Kl+ZMxJ1I9h86QuOGDejZuQMbt+5gwoy5TB0/Os8JRN/uXWjcsAGnz/3B7ch7uDo70bRRiLDFLBb0egOXrlxj9cJZ9B06ikUr1mA0GGgR1ui5FZ3EpGTR+bGJ+9EPcXLU4ubiIhwz69DqddJ06SxcsYbFq9ehsLDg9cahTBg1nNNn/+DcxT+5cy+KoPIBjBsxBB8vz2JWUTDcy7hx8vdzXL52nUoVypOUnMym73aQnJLCrp8OEBkVhZOjI326vUG7Fs2A7IJ/dWtWY2CfHniKzIcjo+5T2s4ONxcXYfBc3s+Xu1EP2HfwMGq1Go+yZahWKUjYRVQqlYS91pDd+w9QIcD8j2gVNB5blipFVlYWKpWKT0cO4/S585y7cEmUfnz24iWiHkQzuF9vYUHOaDTSrkUzfvrlMJcuX6Wsm6tQ70Gs/U9BbZuz/5k3ZQLTFy7l0uVr/J5xEb9y5Vg2ayp+Pt7FLafAeHu4c+7in8LxX9MqvJuLMxobax7HJ7Dzx/8RUq8O1lZWZOn1aB3ssbPViCaDlInCjBtNXLp8lfVbvufK3zdYOE1cCzrmSLFOHoxGIxoba7T29iQlpfAwJpYpcxbS842ONH0tmJjYOHbvP8CSNetRWljQunkYCoWC0na21K1ZnUF9e4kmH68C6NqhDRX8/YRjKQaDAa29PWGhDThy8jRtX2+a60xxJRHPjAN8fejf803at2xOaTtb+vfqht6gz3MCYVrNLeflIaoAlhOThjbNw5i9JDuIrVk4i3dGfMzi1etQqVXs3vczjYPr80bblgCi9GMTdra2/HHpLzIzM1Gr1cLPSoEBaB3s8fJw58efD1K5Qnncy7hRv3ZN6teuWdyP/VK0a9GME2fO8uGnU6gUFMiNm7dwcXJi5sRxlPPy4FHcY0ZPnsa2H36kSUgDbDUaPN3L4l7GTVSXD03Yamw4c+48KalpaGysSc/IYO03W0lMTubO3XskJiVTqpSa999+ixZhjYRBmJPWgTULZ6MWwzFDo5Eu7VoTVN7/hfFYpVIJxe/q16pB/Vo1ivfZXwKj0UiaTkeAr0+uc+0KhYKg8v5YW1kS+aTKbknof7q2b02FgBfbVqlUCkXgPvlwKI/iHmfXu7CyNPvL0c/SJCSYb7bvYseP++navo2wi6RQKAj096N/z258NGkq+w8eoVOblqiUSt7s2I4OLV8Xxd0zEwaDodDjRgBnJ0caN6zPB4P6i2aHxZwp1p5NoVDg6uJM5QrlmbtsJRcuX6V+rRr07tqRQH8/QurXYUDv7lQKLM/3e/aRkJQknCvt2bmDqC4hKpVK6teqkes8uylItW4WRuyjOH47fxF4mgFBrJjuM/Tu2onSdrYYjUZ8vDwZ1KcnYaHBTJgxl0PHTgrvVygUQno4saDTpXPl7xvC76YO18lRy9179zl49ARqtZqNyxaidXBg6vzFnL1wESdHB+CpjcXmxybq16qBhYUFc5etRKdLFwaMqTodLk6ONA1tyF9Xr3PnbhQgbp8u6+bKrMnjGfJOH1o1bYyrszP9e3WjnJdH9qDZUcvHwwZz7cbNXD4hlonDs77coWVzHOxL07bn24yZMp3u7w7FztaWL6ZPYfPKxcyYOBZvD3c2b9/Nw5hY4GmmLXOfOOh06fx17W9UKhUN6tQscDwWiy2fRadL5/K16ygUCl6rX5fhA9/GyspSiNGmuOuo1QptNK/052LAYDCQmpaGSqmkfu2C29ZUGR2y47fWwV50EwfITgE9/dMxbPvhRxat/IptP/zI3gMHGRs+A3c3V+rUqEab5mFE3ruf63NimDjkjFEWFhaFGzcmJgLZcbxdi2byxKGIeKURMTMzk+s3I7h6/UaulH7dOrbDspSa6QuWEHnvvpB61Gg0EujvR+vmYVz5+zoJCYmiuMxjIqfeBw9j8n1fjSqVaN+yOZu/38Wde1Gi7KgMBgNZej2ZmZnCQOLZzsfb0yPXBOLIieyLtXej7rPnf7+IJp1laloaA0eN5bPZC3gU91h43WAw4OLkSGiDurns7Vm2DBkZmWhsbEjPyCRLrxeVjfPy4zo1q9EkpAFnz19i3OczOXz8FLv3HWD81FlUrRRE1w5tqF+rBr+eOg0gqnabF67OTnRq05K6NaoRcfeucGbYlPs/Nu4xTloHtA72xfykhSMvXw7092PmxHH0ebMzTUKCsbKypFeXjvg9OVZXrVIQ7Vo25+qNf4jN4f/mjklr+JxFudrts5SEeAxP9U6Zs5CYR3Go1WqhWrYpRpu02dlqcv1N0nQ6/nf4V1HF5DlLVrDv4BHiExLzfV9+thWrjZ+ldvWqLJkRTnxCIkvXbGDx6nW0atqEMcPfAyA6JjZX6lkxkF9/+2an9gUbNyYmCZ8Rez9kTryyY0spqWmMDZ/Bg4cxPIyJpby/L4P79qZerepUCQqkXYvmfL/nJ+Li44XUniZD22k0ODlq0evFs3qZl973+vWmbs3qeb4/pF5tDh07waGjJ+jXo6uoVrtS09KYtmAJMbFxxCck8FqDeoSFBlOlYgUgd6pdb08PBr7VEwUKPpk2h4+GDOT02fNcvHyF9YvnFbOSF2O6iJeUnIJer+fWncgnvvk0e4WLsxM/HzlGry4d+XT6HM5d+pPZk8ezasNmPp+7SLgQLwby8uNBfXpSv3ZNBvXthYO9PYePnWTizLmo1WpaN23CB+9mp8NLSk7G0jL7fHRJCdoajQ3ubm4cP3OWAD9fXJ2deJyQyPEzv+Hs5IiTVvviLzET8vNlCwsLfLw8GdC7O9ExMSz4cjXlvDyyj3g8Of9vZVkKZyfH59JqmysFabc5EXM8huf13o68i4uT43N6TZqUSgvin6zQpqalMX/5ao6d/o0NS+YXy/MXhpTUNN4Z8TF2Gg21a1TF9gUFYsVu2xdRtVIQC6dOJi4+njSdDn+fckB2OtaMzIx8xyDmSH7tFqByCRw3iolX0mJ0unTeGz0eo9HIsAH9GDH4HUqp1WzesYuk5GRUKhVd27emc9tWqFUqPpu9gN/PXyQhMZGHMbHsP3QEGxtrHLXiWNXLT+8323flWwCsccMGVKtUkR/2/0x6RoZogplOl07/Dz7mYUwstatXoXb1qhw8doKRE8M5kCO1Y84UpeW8PBjYtych9WozZ8kKzl28xILPJ+Hq4lxcMgpESkoqfYaOxNvTna+/XEQ5Lw9WbdwMPF2BBnitQT2USguGjJnA7+cvMmn0CBrWrc2aRbOpHBSIv2+54pRRYPLz4293/kBiUjJWlpb06fYGqxfOYt3ieaxZOJuPhw2mVCk10TExGIwGAp5oFdsRiPywsrRk5HsD2LXvAFPnf0H43EV8PncRR06cZsKo4aLZefg3X4an9rIvXRpXZyc2bNlOZmYmKpWK+IREDh07iaODvbCSbc68qN3mhVjjMRROr+n4Umk7O4xGIxkZmSxc8RUHjx5n4VTzj8l6vZ7pCxbj6uxE+LhRNAkJzrdoqsmnxWzbgmJlZYl7GTdh4nDjVgTrt2zj5u1IQhvULeanKxgvilEqpZKu7VvTpV3rEjFuFBuvZNlo+959KBQWjB8xRMg8otOl8+2O3UJDV6vVdOuQfcln6649jJwYjpNWi8bGhrj4eL6Y/plQTt3c+Te9FhbPr8CaVoP69ejK4I/G8/W2nbzT681X/dgvxfd7f0KhgCljRwnZC46eOsOyrzYyaeY8MjIyaPt6UyD36rNKqSQ1LQ1bjQ0r58/E19u8Mw0lp6TQd+goPMqWYeJHI3B21NLstRDWb/meIydO0bhhA0Gfv483MY/iSEpOYebEsdSpUU1Y3Vo+Z1oxKyk4/+bHphVnU+pZUycF2alNN2/fxfWbEYwZ9p7wvpJC7epV+XLudFZv3Mw/EbcJ8PVh+ZxpZu/DJgriyybUKhXBdWvzy6/HGT5+MuX9fIm8F8X1mxEsmj7Z7GNyQbQ+W2XZ1FbFGI8Lq1dYpbW15WbEbWYv+ZIDh4+ycv5MKgT4FaeUAmEEHj6Ko+3rTYXUs9du3OTGrQhKqdWU8/Ig0D9bR87MYGK07cty/eYt1n27jUuXr7Jo2mRRZDAsiB9n6fXZ48aObSnr5sqWnT+IetwoNl7J5OFhbCyZmZnY2T7Nl1w5KBBvTw++3rZT+D24Ti2ahDQgLDSY/x36ldi4x9hYW1GvVg1RpdUqiN6qlYKErB2mlQ9P9zKE1KtD80Yhr/qRX5qEhCQUKHI10Nca1CM5OYXweV8wdf5i1CoVLcIaCf+enJLCsrUbs1OaLphl9oMuo9HI3KUrcShdmsmjR+Ds5AhA2xbN2LprDweOHBMmD1l6PdZWVmxeuZh/Iu4QVN7/udoOYqGwfmw0Grny9w2+WLWWBw9j+GL6Z6JLU1pQqgQFMuezTzBtqORV+8AcKYwvmwZa777VA0cHB46d/o1zF//E38ebJbPCS1S7zYlY4/HL6DVptbG24uLlq1y/FSGeiYPRyKO4OP6JuI1n2TKolEoOHj3B9IVLsLa2Jl2XTnJqKiMG9ad7p/aAeG37X/DycKdDy+YMfaevKMZRBfVj1ZMsbyqlkiYhDWgS0kDU40ax8UomDzbW1kTHxhIReQ8fL1Cp1MxYuBRdejrp6RkYDAbWfL2Fj4cNpnPbVgC5Bptio6B6xw5/j05tWgoF8BwdHJg5cWy+267mhGn1ytraiviERGH725Sys3aNaviV88bNxZlFK9fi7ekhZBWy1WioU6Ma/bp3obyfb3HKKBBGo5GBfXpia6vB3s4OyM4l7qR1oG/3LixZvZ5fT56mUXB9IaBZW1lRJSjwBd9s3ryMH1eqUJ43O7Uj0M+3xE4cTJh7ZqG8KIwvm1KwWllmX5ju2bkDuvR0VEqlKLQXRuuziC0ew3/Tm10d3YcpY0aJpt6MQqHASaulgr8fl65cQ+tgz5ylK+jeqT2tmzUhJTWN/YeO8MXKtSgtlHTt0EaUfe1/xcrSUlRpsgsbo3Ii5nGj2Ph/aTkGgwGj0SgY9u0e3Th74RIjJ4bj7eFOcnIKtrYaZk0ej4+XJ9ExMazbvI1FK76iasUKuQaUz24pmyMvq3fBl2uoHBRIeT/fHBfX8j6Day48q7V1sybs3neAMVOms3zONGFQcTfqPolJSfTt3pn4xEQuXblKUHl/Iad2p9YtilNGgTAYDBiMRlRPqpDmxJRtp0aVSmgd7Dly4jQh9eqgUCjM3ob5URR+DNA0tGFxypDJg6LwZYVCIYoUli+rNefZd7HEYygavS3DGhFSr47Z39l5NkapVCr8fcqxfe8+PN3LEujnS8dWrwt3Ncq6uZKZmcWytRuoV6s63p4eorKtlCgKPzYhhnGj2Cnym0LJKSmMnzqLW3cihddKlVKzZGY4wwb04923eqDR2NCpdQuhMqebiwstmzYCBUKhGhPm7gBS0puXVmcnRwb3682de1H0HTqSr7ft5OttOxn92TRCG9SjZVhjKgWW5/iZswAoRXI5zaQ1IofWvAj096N9y+bs++Uwt+5EClXQxUZR+7GM+SAlXy5qreYcj6Fo9BoMBtRqtdlPHJ6NUabnHzagH44O9kyaOY8bt26jzJGys7SdLU1fawgKxXNphc3dtlJCau22JFCkI7mUlFT6DRtFTGwcLk/OqUF2I1ar1XRu24qaVauQnJJC2Sdn0bKeHHdRKCzQ2tujsfn3NGvmhJT05qXV+GSVICw0mMmjR+Do4MCmbTvY+N12OrR8nQ8HvwNkFyIyrRyIoVHnZ9dnMQWtFmGNcHN1Yf2336PTpYtCY06k5MdSQ0q+LCWtUHR6xZBtKC+tpgx+VlaWDBvQjyoVKxCfkMChoydISUkV9FmWKoWtjY1wtFbGvJBauy0pFNmxpZTUNPoMHYl7GVcmjf4w1wXanMbNLivuwMoN31C/Vg1USiVx8fHs3ncAjY01AT7iSGMpJb35ac0ZnOvXrkn92jV5FPcYvcEgpHCMi4/nQUws5f18APPfTvw3u5owaTDp8PZwp06Napw48zvRMbGU8/J41Y/90kjJj6WGlHxZSlpBWnoLEqOqVa7IgF7d+XL9JlZu3IzeoCe0fl2ysvR8v+cn1GoVfuW8i0uCTD5IyY9LGgpjEexL69LT6T98NNZWViyfOw2lUolKqST2URyJSckAODk6YF+6NHq9noPHTjJ/+SrsS9vh4+VJckoKt27fZeG0ycIg05yRkt6CaHXUOuBgn93oc04ObkbcYfP2XRw9/Rsr583A29O8G3lhtJrSOZp+xsXH07Znfzq3bcXHwwYXs5KCISU/lhpS8mUpaQVp6S2IVq2DPVoHezIzM4l5FMeilWu5+NcVUlJTKVvGjdS0NOZN+VRI2SpjHkjJj0siRbLzcPz078QnJGKrseFR3GPcy7hx+PgpVm74hgcxMWA04uLszLgP3qdm1cqE1KtNaTtbduzdR0JiEj7eXowZ9p7ZDy5NSElvQbWOH/E+NapUFiYPV/6+wcat27lxK4LFM6aUSK2mQGY0GrG2sqLvm51p1bRJccsoMFLyY6khJV+WklaQlt7Cxij3Mm7MmjSOi5evEh0Ti42VFeX9fMy+2J0UkZIfl0SKZOcBYMfe/Xy3ey8uTo6EhQazaOVamjcKoXaNaiQmJXHg8FEu/32DpbPCqVGlcq7PirE8vJT0FlTrstlTqV65ovC50+fO4+PlgZuLSzE+feEorNacOy2mTFJiQkp+LDWk5MtS0grS0vuy/Y+M+SMlPy5p/OfJgymvP2RXpN2y4wcePIzhrW5v0KdbZ6ysLAG4duMmMxYtBWDxjCnYajRmffY9P6Sk92W0LpkZjsbGRjJabTWafL/TnJGSH0sNKfmylLSCtPRKSavUkG0rfl5q2TBNp2P/oSNAdqGkzMxMADq3bcWbndrRIqwRzRuHYmVlKdyQrxDgR6MG9Yh68JDkHJkQxICU9P5XrUnJKZLSKiak5MdSQ0q+LCWtIC29UtIqNWTbliwKfedBp0tn8EfjuX4zgti4x/Tu0klwBLVaTZd2rbl1OxJf7+erVKrUamysrVCLqKqjlPTKWkumVpCeXikhJdtKSStIS6+UtEoN2bYlj0LtPGTp9SxevY6YR3FUrRjElh0/sPG77UD2TDIrKwtAKG+fpdcLK5WP4xO48Odl/Mp5o9GIIye8lPTKWkumVpCeXikhJdtKSStIS6+UtEoN2bYlk0JNHqLuP+D38xcJrlOL0UMHUrt6Vbbu2is4gkqlwmAwCO83XWb5J+I2S7/awIW/LjNsQD+srayKUML/H1LSK2stmVpBenqlhJRsKyWtIC29UtIqNWTblkwKtQ/k6uJMry6daBLaAHs7O/p064zeYGDrrr0A9OnWGQsLC/R6PconDrB11x6++X4XKqWS5XOm4ecjnkItUtIray2ZWkF6eqWElGwrJa0gLb1S0io1ZNuWTAqdbcmUKsuUJuvWnUjWbv6OPy79xZsd29KnW2cgO42jQqEgJvYRB349RpOGDfAoW+b/RcT/J1LSK2stmVpBenqlhJRsKyWtIC29UtIqNWTbljyKpM5DTkfo3rEdb3V7g6gH0Zw6+wdNQhqgtbcvUVlapKRX1loytYL09EoJKdlWSlpBWnqlpFVqyLYVN0Vyfd3X24v+PbqhQMGWXXtISk7mzr0oDh8/RWi9OiXOAaSkV9ZaMrWC9PRKCSnZVkpaQVp6paRVasi2FTdFsvNgqjQbEXmX5Ws38evJ09jZ2rJ4xhQqBPgVxXOaFVLSK2stmVpBenqlhJRsKyWtIC29UtIqNWTbipsi2XmwsMhO2mSn0ZCaloatxoYVc6cLqbdKGlLSK2stmVpBenqlhJRsKyWtIC29UtIqNWTbipsiq7qh06WzaOVX/H7+IhuWLijxDiAlvbLWkovU9EoJKdlWSlpBWnqlpFVqyLYVL0VybMnE6bN/4OSoJcDXp6i+0qyRkl5Za8lFanqlhJRsKyWtIC29UtIqNWTbipMinTzIyMjIyMjIyMjIyJRcClVhWkZGRkZGRkZGRkZGusiTBxkZGRkZGRkZGRmZAiFPHmRkZGRkZGRkZGRkCoQ8eZCRkZGRkZGRkZGRKRDy5EFGRkZGRkZGRkZGpkDIkwcZGRkZGRkZGRkZmQIhTx5kZGRkZGRkZGRkZAqEPHmQkZGRkZGRkZGRkSkQ8uRBRkZGRkZGRkZGRqZAyJMHGRkZGRkZGRkZGZkC8X8xDmZtLz9N1gAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x450 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 900x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\"\"\"我想要跑出的圖是把連續條餐的動作視為一次的調餐\n",
    "並且在每一條線上面都加上資料筆數，\n",
    "不然在程式碼中輸出表格 數量 間隔分鐘 占比也可以\"\"\"\n",
    "# ----------------------------------------------------------\n",
    "# D. 視覺化（一次調參 = 連續 ad_para=1 的 run；柱上標示計數；並輸出分布表）\n",
    "# ----------------------------------------------------------\n",
    "# 使用前提：\n",
    "# 1) 上游 A–C 已完成資料載入、清理與事件摺疊，並產生 kept_df，\n",
    "#    其中 kept_df 至少包含欄位：\n",
    "#    - \"delta_min\"   ：相鄰兩次「合併後」調參事件的間隔（分鐘）\n",
    "#    - \"sampling_min\"：該病人資料的中位採樣間隔（分鐘）\n",
    "#    - \"T1\"、\"T2\"    ：相鄰事件的時間戳（用於時間軸圖）\n",
    "#    - \"patno\"       ：病人 ID\n",
    "# 2) 你已在前面 import 了 pandas、numpy、matplotlib、Path 等模組。\n",
    "\n",
    "import os\n",
    "from pathlib import Path\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# ========= 可調參數（依需求微調） =========\n",
    "CLIP_PERC = 0.99              # 直方圖/箱型圖顯示時的視覺截尾上界（百分位），避免極端值壓扁圖形\n",
    "BINS = 40                     # 直方圖分箱數；若想每 5 分鐘一格，可改：range(0, int(plot_delta.max())+5, 5)\n",
    "LABEL_BARS = True             # 是否在每條柱子上方標示該箱的資料筆數\n",
    "TOPN_BOX = TOPN_BOX           # 沿用你在 A 段設定的 Top N（箱型圖顯示事件數最多的病人）\n",
    "TOPM_TL = TOPM_TL             # 沿用你在 A 段設定的 Top M（時間軸顯示事件數最多的病人）\n",
    "\n",
    "# ========= 匯出路徑設定 =========\n",
    "# 需求：所有統計分布表 CSV 存到 /home/jovyan/RT08/0925/1002/\n",
    "output_dir = Path(\"/home/jovyan/RT08/0925/1002\")\n",
    "output_dir.mkdir(parents=True, exist_ok=True)     # 若資料夾不存在則自動建立\n",
    "TABLE_NAME = \"delta_hist_summary.csv\"             # 直方圖分布表檔名\n",
    "output_path = output_dir / TABLE_NAME\n",
    "\n",
    "# ========= 先做基本防呆 =========\n",
    "if kept_df.empty:\n",
    "    print(\"❌ 無可視覺化的有效間隔資料（kept_df 為空）。\")\n",
    "    raise SystemExit\n",
    "\n",
    "# ========= 1) 準備繪圖用資料（可選擇截尾，不影響 kept_df 原始值） =========\n",
    "# 說明：為了提升可讀性，我們在視覺化時把超過 p99 的值「壓到」p99，\n",
    "#       使大多數柱狀能在合理尺度下顯示；這不會改變 kept_df 的真實統計。\n",
    "if CLIP_PERC:\n",
    "    clip_val = kept_df[\"delta_min\"].quantile(CLIP_PERC)\n",
    "    plot_delta = kept_df[\"delta_min\"].clip(upper=clip_val)\n",
    "    clip_note = f\"(clipped at p{int(CLIP_PERC*100)}={clip_val:.1f} min)\"\n",
    "else:\n",
    "    plot_delta = kept_df[\"delta_min\"]\n",
    "    clip_note = \"(no clipping)\"\n",
    "\n",
    "# ========= 2) 直方圖（每條柱子上方標示計數）與分布表輸出 =========\n",
    "fig, ax = plt.subplots(figsize=(7, 4.8))\n",
    "\n",
    "# 若你想固定等寬的時間箱（例如每 5 分鐘一格），把上面的 BINS 改成：\n",
    "# BINS = range(0, int(plot_delta.max()) + 5, 5)\n",
    "n, bin_edges, patches = ax.hist(\n",
    "    plot_delta.values,\n",
    "    bins=BINS,\n",
    "    color=\"#2196f3\",\n",
    "    alpha=0.85,\n",
    "    edgecolor=\"#0d47a1\"\n",
    ")\n",
    "\n",
    "ax.set_xlabel(\"ΔT (minutes)\")                            # X 軸：相鄰兩次調參事件的時間間隔（分鐘）\n",
    "ax.set_ylabel(\"Frequency\")                                # Y 軸：每個分箱內的資料筆數\n",
    "ax.set_title(f\"Distribution of Adjacent Adjustment Intervals {clip_note}\")\n",
    "ax.grid(True, linestyle=\"--\", alpha=0.5)\n",
    "\n",
    "# 在每條柱子的頂部標示該箱的「計數」（整數）\n",
    "if LABEL_BARS:\n",
    "    ymax = max(n) if len(n) else 0\n",
    "    for count, left, right in zip(n, bin_edges[:-1], bin_edges[1:]):\n",
    "        if count <= 0:\n",
    "            continue\n",
    "        x_mid = (left + right) / 2                       # 箱子的中心 X 位置\n",
    "        y_top = count                                    # 箱子的高度 Y 值\n",
    "        ax.text(\n",
    "            x_mid,\n",
    "            y_top + ymax * 0.01,                         # 向上偏移，避免文字壓在線上\n",
    "            f\"{int(count)}\",\n",
    "            ha=\"center\",\n",
    "            va=\"bottom\",\n",
    "            fontsize=9,\n",
    "            color=\"#0d47a1\"\n",
    "        )\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "# 以與直方圖完全相同的分箱設定，產生「區間/筆數/占比/累積占比」表\n",
    "hist_counts, hist_bins = np.histogram(plot_delta.values, bins=BINS)\n",
    "total = hist_counts.sum() if hist_counts.sum() > 0 else 1      # 避免除以 0\n",
    "bin_left = hist_bins[:-1]\n",
    "bin_right = hist_bins[1:]\n",
    "percent = (hist_counts / total) * 100.0\n",
    "cum_percent = percent.cumsum()\n",
    "\n",
    "hist_table = pd.DataFrame({\n",
    "    \"bin_left_min\": bin_left,                       # 分箱左邊界（分鐘）\n",
    "    \"bin_right_min\": bin_right,                     # 分箱右邊界（分鐘）\n",
    "    \"count\": hist_counts.astype(int),               # 該箱筆數\n",
    "    \"percent_%\": np.round(percent, 2),              # 該箱占比（%）\n",
    "    \"cum_percent_%\": np.round(cum_percent, 2)       # 累積占比（%）\n",
    "})\n",
    "\n",
    "# 在終端列印前幾列，快速確認內容\n",
    "print(\"\\n=== 直方圖分布統計（與圖一致的分箱與截尾） ===\")\n",
    "print(hist_table.head(12).to_string(index=False))\n",
    "\n",
    "# 將分布表匯出到指定資料夾（同時考慮 Excel 開啟中文編碼）\n",
    "hist_table.to_csv(output_path, index=False, encoding=\"utf-8-sig\")\n",
    "print(f\"📄 已匯出直方圖分布表 → {output_path}\")\n",
    "\n",
    "# ========= 3) ECDF（經驗分布函數）曲線 =========\n",
    "# 說明：ECDF 使用「未截尾」的 kept_df['delta_min']，呈現資料真實分布；\n",
    "#       若希望與直方圖口徑一致，也可改用 plot_delta。\n",
    "x = np.sort(kept_df[\"delta_min\"].values)\n",
    "y = np.arange(1, len(x) + 1) / len(x)\n",
    "\n",
    "plt.figure(figsize=(7, 4.5))\n",
    "plt.plot(x, y, color=\"#00bcd4\", drawstyle=\"steps-post\", linewidth=2)\n",
    "plt.xlabel(\"ΔT (minutes)\")\n",
    "plt.ylabel(\"ECDF\")\n",
    "plt.title(\"Empirical Cumulative Distribution of ΔT\")\n",
    "plt.grid(True, linestyle=\"--\", alpha=0.5)\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "# ========= 4) 依病人分布的箱型圖（取事件數最多的 TopN 病人） =========\n",
    "topN_ids = kept_df.groupby(\"patno\")[\"delta_min\"].count().sort_values(ascending=False).head(TOPN_BOX).index.tolist()\n",
    "box_data = [kept_df.loc[kept_df[\"patno\"] == p, \"delta_min\"].values for p in topN_ids]\n",
    "\n",
    "plt.figure(figsize=(8, 5))\n",
    "plt.boxplot(\n",
    "    box_data, labels=topN_ids, showfliers=False, patch_artist=True,\n",
    "    boxprops=dict(facecolor=\"#90caf9\", color=\"#1565c0\"),\n",
    "    medianprops=dict(color=\"#0d47a1\", linewidth=2)\n",
    ")\n",
    "plt.xticks(rotation=45, ha=\"right\")\n",
    "plt.ylabel(\"ΔT (minutes)\")\n",
    "plt.title(f\"ΔT Distribution by Patient (Top {TOPN_BOX}) {clip_note}\")\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "# ========= 5) ΔT 與採樣間隔關係（含 y=4x 門檻參考線） =========\n",
    "# 說明：你在 C 段以 ΔT ≥ 4 × sampling_min 作為有效篩選條件，這裡將其視覺化。\n",
    "plt.figure(figsize=(7, 4.5))\n",
    "plt.scatter(kept_df[\"sampling_min\"], kept_df[\"delta_min\"], s=8, color=\"#26c6da\", alpha=0.7)\n",
    "x_line = np.linspace(kept_df[\"sampling_min\"].min(), kept_df[\"sampling_min\"].max(), 100)\n",
    "plt.plot(x_line, 4 * x_line, color=\"#0d47a1\", linestyle=\"--\", label=\"ΔT = 4×sampling_min\")\n",
    "plt.xlabel(\"Sampling Interval (minutes)\")\n",
    "plt.ylabel(\"ΔT (minutes)\")\n",
    "plt.title(\"ΔT vs Sampling Interval (with 4× threshold)\")\n",
    "plt.legend(frameon=False)\n",
    "plt.grid(True, linestyle=\"--\", alpha=0.5)\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "# ========= 6) 事件時間軸（取 TopM 病人，畫 T1→T2 水平線） =========\n",
    "# 說明：每一條水平線代表一個相鄰事件對（T1→T2），方便觀察同一病人的事件密度與時序。\n",
    "topM_ids = kept_df.groupby(\"patno\")[\"delta_min\"].count().sort_values(ascending=False).head(TOPM_TL).index.tolist()\n",
    "plt.figure(figsize=(9, 5))\n",
    "y_map = {p: i for i, p in enumerate(topM_ids)}\n",
    "\n",
    "for _, r in kept_df[kept_df[\"patno\"].isin(topM_ids)].iterrows():\n",
    "    y = y_map[r[\"patno\"]]\n",
    "    plt.hlines(y, xmin=r[\"T1\"], xmax=r[\"T2\"], color=\"#42a5f5\", alpha=0.8)\n",
    "\n",
    "plt.yticks(list(y_map.values()), list(y_map.keys()))\n",
    "plt.xlabel(\"Time\")\n",
    "plt.ylabel(\"Patient ID\")\n",
    "plt.title(f\"Timeline of Adjustment Intervals (Top {TOPM_TL} Patients)\")\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c73ee76b-97e7-4622-9581-f85fd73b20f5",
   "metadata": {},
   "outputs": [],
   "source": [
    "ΔT 直方圖分布表的重點解讀\n",
    "\n",
    "調參間隔高度集中在 5–15 分鐘內（約 66%），整體有 九成以上事件在 60 分內 完成。\n",
    "⃣ 中位數 10 分、p90 45 分、p95 75 分，代表大多數病患在 1 小時內需再次調整設定。\n",
    "⃣長尾極端值稀少但影響平均，推測與流程中斷或資料缺漏有關，應納入資料品質監控。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c7386466-7bbc-4957-95af-bb9f7ee962be",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "id": "ea0623d4-070b-4fe4-9da1-6ae575db5f3e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "=== ΔT（相鄰調參間隔，分鐘）整體統計摘要 ===\n",
      "樣本數           58561.000\n",
      "平均值(min)         23.699\n",
      "標準差(min)        266.361\n",
      "最小值(min)          3.967\n",
      "第10百分位(min)       5.000\n",
      "第25百分位(min)       6.000\n",
      "中位數(min)         10.000\n",
      "第75百分位(min)      19.000\n",
      "第90百分位(min)      45.000\n",
      "第95百分位(min)      75.000\n",
      "最大值(min)      58731.050\n",
      "📄 已匯出整體統計摘要 → /home/jovyan/RT08/0925/1002/delta_basic_summary.csv\n",
      "\n",
      "=== ΔT 分布統計表（每 5 分鐘一格） ===\n",
      " 區間_左界(min)  區間_右界(min)    筆數  占比(%)  累積占比(%)\n",
      "          0           5   384  0.656    0.656\n",
      "          5          10 28783 49.150   49.806\n",
      "         10          15 10076 17.206   67.012\n",
      "         15          20  5075  8.666   75.678\n",
      "         20          25  2899  4.950   80.629\n",
      "         25          30  1967  3.359   83.988\n",
      "         30          35  1455  2.485   86.472\n",
      "         35          40  1138  1.943   88.415\n",
      "         40          45   877  1.498   89.913\n",
      "         45          50   703  1.200   91.114\n",
      "         50          55   582  0.994   92.107\n",
      "         55          60   526  0.898   93.006\n",
      "         60          65   439  0.750   93.755\n",
      "         65          70   383  0.654   94.409\n",
      "         70          75   319  0.545   94.954\n",
      "📄 已匯出分布統計表 → /home/jovyan/RT08/0925/1002/delta_interval_distribution_5min.csv\n",
      "\n",
      "=== 各病人 ΔT 統計（前 10 位預覽） ===\n",
      "            count   mean  median     std   min      max\n",
      "patno                                                  \n",
      "1586172700   2105 15.573   8.000  30.377 4.000  705.017\n",
      "1587490000   2097 20.482  10.950  30.850 4.000  468.950\n",
      "1591609900   2051 14.898   8.000  22.073 4.000  374.983\n",
      "1574148500   1982 21.703  10.000  34.562 4.133  475.983\n",
      "1589034500   1919 27.937  12.000  70.537 4.317 1469.017\n",
      "8927106      1802 18.095   8.000 119.129 4.983 4978.050\n",
      "1578784300   1719 17.207  10.000  42.994 4.917  893.017\n",
      "1577043000   1702 20.053  11.975  28.883 4.917  655.017\n",
      "1572481400   1445 27.114  10.000 116.930 4.933 3642.050\n",
      "1567832700   1398 24.370  10.000  45.690 4.000  600.967\n",
      "📄 已匯出病人層級統計表 → /home/jovyan/RT08/0925/1002/patient_delta_summary.csv\n",
      "\n",
      "=== ΔT 臨床區間彙總（自訂 bucket） ===\n",
      "    區間    筆數  占比(%)\n",
      "    <5   384  0.656\n",
      "  5-15 38859 66.356\n",
      " 15-60 15222 25.993\n",
      "60-180  3441  5.876\n",
      " >=180   655  1.118\n",
      "📄 已匯出臨床區間彙總 → /home/jovyan/RT08/0925/1002/delta_clinical_buckets.csv\n"
     ]
    }
   ],
   "source": [
    "# ----------------------------------------------------------\n",
    "# E. 統計分析（僅輸出數據，不產生圖表）\n",
    "# 目標：\n",
    "#  1) 整體 ΔT（相鄰調參間隔，單位：分鐘）的統計摘要（平均、分位數等）\n",
    "#  2) 依區間分組的分布表（每 X 分鐘一格，含筆數、占比、累積占比）\n",
    "#  3) 依病人 (patno) 的 ΔT 統計表（count/mean/median/std/min/max）\n",
    "#  4) 臨床常用區間彙總（如 <5、5–15、15–60、60–180、≥180 分）\n",
    "#  5) 全部 CSV 一律輸出到 /home/jovyan/RT08/0925/1002/\n",
    "# ----------------------------------------------------------\n",
    "\n",
    "from pathlib import Path\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "# ========= 防呆檢查：需要 kept_df =========\n",
    "# kept_df 來源：C 段的有效區間保留結果，應至少含：\n",
    "# - delta_min：相鄰兩次「合併後」調參事件的間隔（分鐘）\n",
    "# - patno    ：病人 ID（供病人分層統計用）\n",
    "if \"kept_df\" not in globals() or kept_df.empty:\n",
    "    raise SystemExit(\"❌ 無可供統計的資料：kept_df 不存在或為空。請先完成 A–C。\")\n",
    "\n",
    "# ========= 統一輸出路徑 =========\n",
    "output_dir = Path(\"/home/jovyan/RT08/0925/1002\")\n",
    "output_dir.mkdir(parents=True, exist_ok=True)\n",
    "\n",
    "# ========= 1) 整體 ΔT 統計摘要（含分位數）=========\n",
    "# 備註：此處使用「未截尾」的 kept_df['delta_min']，反映真實樣本分布\n",
    "print(\"\\n=== ΔT（相鄰調參間隔，分鐘）整體統計摘要 ===\")\n",
    "summary = kept_df[\"delta_min\"].describe(percentiles=[0.1, 0.25, 0.5, 0.75, 0.9, 0.95])\n",
    "summary = summary.rename(index={\n",
    "    \"count\": \"樣本數\",\n",
    "    \"mean\":  \"平均值(min)\",\n",
    "    \"std\":   \"標準差(min)\",\n",
    "    \"min\":   \"最小值(min)\",\n",
    "    \"10%\":   \"第10百分位(min)\",\n",
    "    \"25%\":   \"第25百分位(min)\",\n",
    "    \"50%\":   \"中位數(min)\",\n",
    "    \"75%\":   \"第75百分位(min)\",\n",
    "    \"90%\":   \"第90百分位(min)\",\n",
    "    \"95%\":   \"第95百分位(min)\",\n",
    "    \"max\":   \"最大值(min)\"\n",
    "})\n",
    "print(summary.to_string())\n",
    "\n",
    "# 轉為單列 DataFrame 便於存檔\n",
    "summary_df = summary.to_frame(name=\"value\").reset_index().rename(columns={\"index\": \"metric\"})\n",
    "summary_path = output_dir / \"delta_basic_summary.csv\"\n",
    "summary_df.to_csv(summary_path, index=False, encoding=\"utf-8-sig\")\n",
    "print(f\"📄 已匯出整體統計摘要 → {summary_path}\")\n",
    "\n",
    "# ========= 2) ΔT 分布統計表（每固定間距一格）=========\n",
    "# 預設：每 5 分鐘一格；可依資料密度調成 10 或 15\n",
    "BIN_WIDTH = 5  # 你可改為 10 或 15（分鐘）\n",
    "max_val = int(np.ceil(kept_df[\"delta_min\"].max())) if kept_df[\"delta_min\"].notna().any() else 0\n",
    "# 為確保涵蓋最大值，右界再多加一個區間\n",
    "bins = list(range(0, max( ( (max_val // BIN_WIDTH) + 1 ) * BIN_WIDTH, BIN_WIDTH) + BIN_WIDTH, BIN_WIDTH))\n",
    "\n",
    "hist_counts, hist_bins = np.histogram(kept_df[\"delta_min\"].values, bins=bins)\n",
    "bin_left  = hist_bins[:-1]\n",
    "bin_right = hist_bins[1:]\n",
    "total = hist_counts.sum() if hist_counts.sum() > 0 else 1  # 避免除以 0\n",
    "percent = (hist_counts / total) * 100.0\n",
    "cum_percent = percent.cumsum()\n",
    "\n",
    "dist_df = pd.DataFrame({\n",
    "    \"區間_左界(min)\": bin_left,\n",
    "    \"區間_右界(min)\": bin_right,\n",
    "    \"筆數\": hist_counts.astype(int),\n",
    "    \"占比(%)\": np.round(percent, 3),\n",
    "    \"累積占比(%)\": np.round(cum_percent, 3)\n",
    "})\n",
    "\n",
    "print(f\"\\n=== ΔT 分布統計表（每 {BIN_WIDTH} 分鐘一格） ===\")\n",
    "print(dist_df.head(15).to_string(index=False))\n",
    "\n",
    "dist_path = output_dir / f\"delta_interval_distribution_{BIN_WIDTH}min.csv\"\n",
    "dist_df.to_csv(dist_path, index=False, encoding=\"utf-8-sig\")\n",
    "print(f\"📄 已匯出分布統計表 → {dist_path}\")\n",
    "\n",
    "# ========= 3) 依病人 (patno) 的 ΔT 統計表 =========\n",
    "# 欄位說明：\n",
    "# - count：該病人有效相鄰事件對數\n",
    "# - mean / median / std / min / max：該病人 ΔT 的統計\n",
    "pat_df = (\n",
    "    kept_df.groupby(\"patno\", dropna=False)[\"delta_min\"]\n",
    "           .agg(count=\"count\", mean=\"mean\", median=\"median\", std=\"std\", min=\"min\", max=\"max\")\n",
    "           .sort_values([\"count\", \"mean\"], ascending=[False, True])\n",
    ")\n",
    "\n",
    "print(\"\\n=== 各病人 ΔT 統計（前 10 位預覽） ===\")\n",
    "print(pat_df.head(10).round(3).to_string())\n",
    "\n",
    "pat_path = output_dir / \"patient_delta_summary.csv\"\n",
    "pat_df.round(6).to_csv(pat_path, encoding=\"utf-8-sig\")\n",
    "print(f\"📄 已匯出病人層級統計表 → {pat_path}\")\n",
    "\n",
    "# ========= 4) 臨床常用區間彙總（客製分群）=========\n",
    "# 你可依臨床需求調整 bucket：\n",
    "# 例如：<5、5–15、15–60、60–180、≥180 分鐘\n",
    "bucket_edges = [-np.inf, 5, 15, 60, 180, np.inf]\n",
    "bucket_labels = [\"<5\", \"5-15\", \"15-60\", \"60-180\", \">=180\"]\n",
    "\n",
    "kept_df[\"_dt_bucket\"] = pd.cut(kept_df[\"delta_min\"], bins=bucket_edges, labels=bucket_labels, right=False)\n",
    "bucket_counts = kept_df[\"_dt_bucket\"].value_counts().reindex(bucket_labels, fill_value=0)\n",
    "bucket_percent = (bucket_counts / bucket_counts.sum() * 100.0).round(3)\n",
    "\n",
    "bucket_df = pd.DataFrame({\n",
    "    \"區間\": bucket_labels,\n",
    "    \"筆數\": bucket_counts.values.astype(int),\n",
    "    \"占比(%)\": bucket_percent.values\n",
    "})\n",
    "\n",
    "print(\"\\n=== ΔT 臨床區間彙總（自訂 bucket） ===\")\n",
    "print(bucket_df.to_string(index=False))\n",
    "\n",
    "bucket_path = output_dir / \"delta_clinical_buckets.csv\"\n",
    "bucket_df.to_csv(bucket_path, index=False, encoding=\"utf-8-sig\")\n",
    "print(f\"📄 已匯出臨床區間彙總 → {bucket_path}\")\n",
    "\n",
    "# （清除暫存欄位）\n",
    "kept_df.drop(columns=[\"_dt_bucket\"], inplace=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c76dcd37-0aca-453c-9648-bbae7c9729bf",
   "metadata": {},
   "outputs": [],
   "source": [
    "高密度短間隔：5–15 分占 66.36%；加上 15–60 分 的 25.99%，≤60 分 = 92.35%。\n",
    "長尾顯著，但稀少：平均 23.70 分 vs 中位數 10 分、標準差 266.36 分、最大值 58,731 分（≈ 40.8 天），顯示極端長尾拉高平均\n",
    "臨床區間彙總：\n",
    "<5 分 0.66%（幾乎即時回應）\n",
    "5–15 分 66.36%（主要作業節奏）\n",
    "15–60 分 25.99%（常態回合）\n",
    "60–180 分 5.88%（班次/檢查/流程干擾可疑）\n",
    "≥180 分 1.12%（高度懷疑流程或資料中斷）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "id": "bfea1d95-07a5-48ca-ae70-25452e3097fb",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "🔎 偵測到 122 份輸入檔案（來源：/home/jovyan/RT08/0925/bling_1004）\n",
      "✅ 089271.csv: set_1010 標記完成，成功切出區間 1491 段，輸出 → /home/jovyan/RT08/0925/bling_1010/089271.csv\n",
      "✅ 095323.csv: set_1010 標記完成，成功切出區間 1107 段，輸出 → /home/jovyan/RT08/0925/bling_1010/095323.csv\n",
      "✅ 095707.csv: set_1010 標記完成，成功切出區間 796 段，輸出 → /home/jovyan/RT08/0925/bling_1010/095707.csv\n",
      "✅ 114309.csv: set_1010 標記完成，成功切出區間 936 段，輸出 → /home/jovyan/RT08/0925/bling_1010/114309.csv\n",
      "✅ 230933.csv: set_1010 標記完成，成功切出區間 819 段，輸出 → /home/jovyan/RT08/0925/bling_1010/230933.csv\n",
      "⚠️ 4216007.csv: 無有效資料（nan_check==1 且 senddate 有效），輸出空標記。\n",
      "✅ 7108162.csv: set_1010 標記完成，成功切出區間 11 段，輸出 → /home/jovyan/RT08/0925/bling_1010/7108162.csv\n",
      "✅ 7408338.csv: set_1010 標記完成，成功切出區間 83 段，輸出 → /home/jovyan/RT08/0925/bling_1010/7408338.csv\n",
      "✅ 7657698.csv: set_1010 標記完成，成功切出區間 30 段，輸出 → /home/jovyan/RT08/0925/bling_1010/7657698.csv\n",
      "✅ 7721164.csv: set_1010 標記完成，成功切出區間 24 段，輸出 → /home/jovyan/RT08/0925/bling_1010/7721164.csv\n",
      "✅ PatNo_ID_1560013303.csv: set_1010 標記完成，成功切出區間 114 段，輸出 → /home/jovyan/RT08/0925/bling_1010/PatNo_ID_1560013303.csv\n",
      "✅ PatNo_ID_1562733396.csv: set_1010 標記完成，成功切出區間 35 段，輸出 → /home/jovyan/RT08/0925/bling_1010/PatNo_ID_1562733396.csv\n",
      "✅ PatNo_ID_1563587183.csv: set_1010 標記完成，成功切出區間 140 段，輸出 → /home/jovyan/RT08/0925/bling_1010/PatNo_ID_1563587183.csv\n",
      "✅ PatNo_ID_1564148644.csv: set_1010 標記完成，成功切出區間 285 段，輸出 → /home/jovyan/RT08/0925/bling_1010/PatNo_ID_1564148644.csv\n",
      "✅ PatNo_ID_1565148312.csv: set_1010 標記完成，成功切出區間 247 段，輸出 → /home/jovyan/RT08/0925/bling_1010/PatNo_ID_1565148312.csv\n",
      "✅ PatNo_ID_1565378038.csv: set_1010 標記完成，成功切出區間 145 段，輸出 → /home/jovyan/RT08/0925/bling_1010/PatNo_ID_1565378038.csv\n",
      "✅ PatNo_ID_1566123680.csv: set_1010 標記完成，成功切出區間 496 段，輸出 → /home/jovyan/RT08/0925/bling_1010/PatNo_ID_1566123680.csv\n",
      "✅ PatNo_ID_1566252197.csv: set_1010 標記完成，成功切出區間 63 段，輸出 → /home/jovyan/RT08/0925/bling_1010/PatNo_ID_1566252197.csv\n",
      "✅ PatNo_ID_1566279967.csv: set_1010 標記完成，成功切出區間 64 段，輸出 → /home/jovyan/RT08/0925/bling_1010/PatNo_ID_1566279967.csv\n",
      "✅ PatNo_ID_1566671274.csv: set_1010 標記完成，成功切出區間 784 段，輸出 → /home/jovyan/RT08/0925/bling_1010/PatNo_ID_1566671274.csv\n",
      "✅ PatNo_ID_1566911879.csv: set_1010 標記完成，成功切出區間 1084 段，輸出 → /home/jovyan/RT08/0925/bling_1010/PatNo_ID_1566911879.csv\n",
      "✅ PatNo_ID_1567747650.csv: set_1010 標記完成，成功切出區間 343 段，輸出 → /home/jovyan/RT08/0925/bling_1010/PatNo_ID_1567747650.csv\n",
      "✅ PatNo_ID_1567804800.csv: set_1010 標記完成，成功切出區間 305 段，輸出 → /home/jovyan/RT08/0925/bling_1010/PatNo_ID_1567804800.csv\n",
      "✅ PatNo_ID_1567832735.csv: set_1010 標記完成，成功切出區間 1218 段，輸出 → /home/jovyan/RT08/0925/bling_1010/PatNo_ID_1567832735.csv\n",
      "✅ PatNo_ID_1568039398.csv: set_1010 標記完成，成功切出區間 1016 段，輸出 → /home/jovyan/RT08/0925/bling_1010/PatNo_ID_1568039398.csv\n",
      "✅ PatNo_ID_1568574099.csv: set_1010 標記完成，成功切出區間 632 段，輸出 → /home/jovyan/RT08/0925/bling_1010/PatNo_ID_1568574099.csv\n",
      "✅ PatNo_ID_1568813269.csv: set_1010 標記完成，成功切出區間 99 段，輸出 → /home/jovyan/RT08/0925/bling_1010/PatNo_ID_1568813269.csv\n",
      "✅ PatNo_ID_1568952422.csv: set_1010 標記完成，成功切出區間 70 段，輸出 → /home/jovyan/RT08/0925/bling_1010/PatNo_ID_1568952422.csv\n",
      "✅ PatNo_ID_1569083701.csv: set_1010 標記完成，成功切出區間 222 段，輸出 → /home/jovyan/RT08/0925/bling_1010/PatNo_ID_1569083701.csv\n",
      "✅ PatNo_ID_1569944983.csv: set_1010 標記完成，成功切出區間 381 段，輸出 → /home/jovyan/RT08/0925/bling_1010/PatNo_ID_1569944983.csv\n",
      "✅ PatNo_ID_1570089466.csv: set_1010 標記完成，成功切出區間 586 段，輸出 → /home/jovyan/RT08/0925/bling_1010/PatNo_ID_1570089466.csv\n",
      "✅ PatNo_ID_1570242703.csv: set_1010 標記完成，成功切出區間 374 段，輸出 → /home/jovyan/RT08/0925/bling_1010/PatNo_ID_1570242703.csv\n",
      "✅ PatNo_ID_1570273244.csv: set_1010 標記完成，成功切出區間 296 段，輸出 → /home/jovyan/RT08/0925/bling_1010/PatNo_ID_1570273244.csv\n",
      "✅ PatNo_ID_1570642083.csv: set_1010 標記完成，成功切出區間 318 段，輸出 → /home/jovyan/RT08/0925/bling_1010/PatNo_ID_1570642083.csv\n",
      "✅ PatNo_ID_1571945701.csv: set_1010 標記完成，成功切出區間 436 段，輸出 → /home/jovyan/RT08/0925/bling_1010/PatNo_ID_1571945701.csv\n",
      "✅ PatNo_ID_1572481361.csv: set_1010 標記完成，成功切出區間 1265 段，輸出 → /home/jovyan/RT08/0925/bling_1010/PatNo_ID_1572481361.csv\n",
      "✅ PatNo_ID_1572562839.csv: set_1010 標記完成，成功切出區間 452 段，輸出 → /home/jovyan/RT08/0925/bling_1010/PatNo_ID_1572562839.csv\n",
      "✅ PatNo_ID_1572831765.csv: set_1010 標記完成，成功切出區間 153 段，輸出 → /home/jovyan/RT08/0925/bling_1010/PatNo_ID_1572831765.csv\n",
      "✅ PatNo_ID_1572976822.csv: set_1010 標記完成，成功切出區間 83 段，輸出 → /home/jovyan/RT08/0925/bling_1010/PatNo_ID_1572976822.csv\n",
      "✅ PatNo_ID_1573063188.csv: set_1010 標記完成，成功切出區間 276 段，輸出 → /home/jovyan/RT08/0925/bling_1010/PatNo_ID_1573063188.csv\n",
      "✅ PatNo_ID_1573249295.csv: set_1010 標記完成，成功切出區間 128 段，輸出 → /home/jovyan/RT08/0925/bling_1010/PatNo_ID_1573249295.csv\n",
      "✅ PatNo_ID_1573964540.csv: set_1010 標記完成，成功切出區間 195 段，輸出 → /home/jovyan/RT08/0925/bling_1010/PatNo_ID_1573964540.csv\n",
      "✅ PatNo_ID_1574148494.csv: set_1010 標記完成，成功切出區間 1712 段，輸出 → /home/jovyan/RT08/0925/bling_1010/PatNo_ID_1574148494.csv\n",
      "✅ PatNo_ID_1574270349.csv: set_1010 標記完成，成功切出區間 355 段，輸出 → /home/jovyan/RT08/0925/bling_1010/PatNo_ID_1574270349.csv\n",
      "✅ PatNo_ID_1574528808.csv: set_1010 標記完成，成功切出區間 1007 段，輸出 → /home/jovyan/RT08/0925/bling_1010/PatNo_ID_1574528808.csv\n",
      "✅ PatNo_ID_1574831525.csv: set_1010 標記完成，成功切出區間 31 段，輸出 → /home/jovyan/RT08/0925/bling_1010/PatNo_ID_1574831525.csv\n",
      "✅ PatNo_ID_1574987447.csv: set_1010 標記完成，成功切出區間 627 段，輸出 → /home/jovyan/RT08/0925/bling_1010/PatNo_ID_1574987447.csv\n",
      "✅ PatNo_ID_1575060177.csv: set_1010 標記完成，成功切出區間 161 段，輸出 → /home/jovyan/RT08/0925/bling_1010/PatNo_ID_1575060177.csv\n",
      "✅ PatNo_ID_1575256902.csv: set_1010 標記完成，成功切出區間 592 段，輸出 → /home/jovyan/RT08/0925/bling_1010/PatNo_ID_1575256902.csv\n",
      "✅ PatNo_ID_1575445051.csv: set_1010 標記完成，成功切出區間 76 段，輸出 → /home/jovyan/RT08/0925/bling_1010/PatNo_ID_1575445051.csv\n",
      "✅ PatNo_ID_1575502382.csv: set_1010 標記完成，成功切出區間 213 段，輸出 → /home/jovyan/RT08/0925/bling_1010/PatNo_ID_1575502382.csv\n",
      "✅ PatNo_ID_1575975485.csv: set_1010 標記完成，成功切出區間 612 段，輸出 → /home/jovyan/RT08/0925/bling_1010/PatNo_ID_1575975485.csv\n",
      "✅ PatNo_ID_1576115572.csv: set_1010 標記完成，成功切出區間 793 段，輸出 → /home/jovyan/RT08/0925/bling_1010/PatNo_ID_1576115572.csv\n",
      "✅ PatNo_ID_1576116479.csv: set_1010 標記完成，成功切出區間 61 段，輸出 → /home/jovyan/RT08/0925/bling_1010/PatNo_ID_1576116479.csv\n",
      "✅ PatNo_ID_1576301569.csv: set_1010 標記完成，成功切出區間 103 段，輸出 → /home/jovyan/RT08/0925/bling_1010/PatNo_ID_1576301569.csv\n",
      "✅ PatNo_ID_1576964560.csv: set_1010 標記完成，成功切出區間 1043 段，輸出 → /home/jovyan/RT08/0925/bling_1010/PatNo_ID_1576964560.csv\n",
      "✅ PatNo_ID_1577042911.csv: set_1010 標記完成，成功切出區間 1530 段，輸出 → /home/jovyan/RT08/0925/bling_1010/PatNo_ID_1577042911.csv\n",
      "✅ PatNo_ID_1577487284.csv: set_1010 標記完成，成功切出區間 124 段，輸出 → /home/jovyan/RT08/0925/bling_1010/PatNo_ID_1577487284.csv\n",
      "✅ PatNo_ID_1578784257.csv: set_1010 標記完成，成功切出區間 1492 段，輸出 → /home/jovyan/RT08/0925/bling_1010/PatNo_ID_1578784257.csv\n",
      "✅ PatNo_ID_1579198603.csv: set_1010 標記完成，成功切出區間 53 段，輸出 → /home/jovyan/RT08/0925/bling_1010/PatNo_ID_1579198603.csv\n",
      "✅ PatNo_ID_1579498177.csv: set_1010 標記完成，成功切出區間 666 段，輸出 → /home/jovyan/RT08/0925/bling_1010/PatNo_ID_1579498177.csv\n",
      "✅ PatNo_ID_1580062580.csv: set_1010 標記完成，成功切出區間 35 段，輸出 → /home/jovyan/RT08/0925/bling_1010/PatNo_ID_1580062580.csv\n",
      "✅ PatNo_ID_1580096720.csv: set_1010 標記完成，成功切出區間 30 段，輸出 → /home/jovyan/RT08/0925/bling_1010/PatNo_ID_1580096720.csv\n",
      "⚠️ PatNo_ID_1580107637.csv: 無有效資料（nan_check==1 且 senddate 有效），輸出空標記。\n",
      "✅ PatNo_ID_1580244614.csv: set_1010 標記完成，成功切出區間 162 段，輸出 → /home/jovyan/RT08/0925/bling_1010/PatNo_ID_1580244614.csv\n",
      "✅ PatNo_ID_1580766093.csv: set_1010 標記完成，成功切出區間 616 段，輸出 → /home/jovyan/RT08/0925/bling_1010/PatNo_ID_1580766093.csv\n",
      "✅ PatNo_ID_1581003248.csv: set_1010 標記完成，成功切出區間 154 段，輸出 → /home/jovyan/RT08/0925/bling_1010/PatNo_ID_1581003248.csv\n",
      "✅ PatNo_ID_1581019504.csv: set_1010 標記完成，成功切出區間 475 段，輸出 → /home/jovyan/RT08/0925/bling_1010/PatNo_ID_1581019504.csv\n",
      "✅ PatNo_ID_1581633231.csv: set_1010 標記完成，成功切出區間 641 段，輸出 → /home/jovyan/RT08/0925/bling_1010/PatNo_ID_1581633231.csv\n",
      "✅ PatNo_ID_1581692973.csv: set_1010 標記完成，成功切出區間 65 段，輸出 → /home/jovyan/RT08/0925/bling_1010/PatNo_ID_1581692973.csv\n",
      "✅ PatNo_ID_1582452511.csv: set_1010 標記完成，成功切出區間 168 段，輸出 → /home/jovyan/RT08/0925/bling_1010/PatNo_ID_1582452511.csv\n",
      "✅ PatNo_ID_1582635996.csv: set_1010 標記完成，成功切出區間 463 段，輸出 → /home/jovyan/RT08/0925/bling_1010/PatNo_ID_1582635996.csv\n",
      "✅ PatNo_ID_1582849900.csv: set_1010 標記完成，成功切出區間 75 段，輸出 → /home/jovyan/RT08/0925/bling_1010/PatNo_ID_1582849900.csv\n",
      "✅ PatNo_ID_1582937076.csv: set_1010 標記完成，成功切出區間 617 段，輸出 → /home/jovyan/RT08/0925/bling_1010/PatNo_ID_1582937076.csv\n",
      "✅ PatNo_ID_1584158973.csv: set_1010 標記完成，成功切出區間 93 段，輸出 → /home/jovyan/RT08/0925/bling_1010/PatNo_ID_1584158973.csv\n",
      "✅ PatNo_ID_1584397376.csv: set_1010 標記完成，成功切出區間 25 段，輸出 → /home/jovyan/RT08/0925/bling_1010/PatNo_ID_1584397376.csv\n",
      "✅ PatNo_ID_1586172659.csv: set_1010 標記完成，成功切出區間 1740 段，輸出 → /home/jovyan/RT08/0925/bling_1010/PatNo_ID_1586172659.csv\n",
      "✅ PatNo_ID_1586696634.csv: set_1010 標記完成，成功切出區間 76 段，輸出 → /home/jovyan/RT08/0925/bling_1010/PatNo_ID_1586696634.csv\n",
      "✅ PatNo_ID_1586897008.csv: set_1010 標記完成，成功切出區間 413 段，輸出 → /home/jovyan/RT08/0925/bling_1010/PatNo_ID_1586897008.csv\n",
      "✅ PatNo_ID_1587490083.csv: set_1010 標記完成，成功切出區間 1915 段，輸出 → /home/jovyan/RT08/0925/bling_1010/PatNo_ID_1587490083.csv\n",
      "✅ PatNo_ID_1588632604.csv: set_1010 標記完成，成功切出區間 85 段，輸出 → /home/jovyan/RT08/0925/bling_1010/PatNo_ID_1588632604.csv\n",
      "✅ PatNo_ID_1588673465.csv: set_1010 標記完成，成功切出區間 98 段，輸出 → /home/jovyan/RT08/0925/bling_1010/PatNo_ID_1588673465.csv\n",
      "✅ PatNo_ID_1588794796.csv: set_1010 標記完成，成功切出區間 188 段，輸出 → /home/jovyan/RT08/0925/bling_1010/PatNo_ID_1588794796.csv\n",
      "✅ PatNo_ID_1588957997.csv: set_1010 標記完成，成功切出區間 412 段，輸出 → /home/jovyan/RT08/0925/bling_1010/PatNo_ID_1588957997.csv\n",
      "✅ PatNo_ID_1589018086.csv: set_1010 標記完成，成功切出區間 494 段，輸出 → /home/jovyan/RT08/0925/bling_1010/PatNo_ID_1589018086.csv\n",
      "✅ PatNo_ID_1589034524.csv: set_1010 標記完成，成功切出區間 1730 段，輸出 → /home/jovyan/RT08/0925/bling_1010/PatNo_ID_1589034524.csv\n",
      "✅ PatNo_ID_1589324603.csv: set_1010 標記完成，成功切出區間 229 段，輸出 → /home/jovyan/RT08/0925/bling_1010/PatNo_ID_1589324603.csv\n",
      "⚠️ PatNo_ID_1589918099.csv: 無有效資料（nan_check==1 且 senddate 有效），輸出空標記。\n",
      "✅ PatNo_ID_1590136310.csv: set_1010 標記完成，成功切出區間 356 段，輸出 → /home/jovyan/RT08/0925/bling_1010/PatNo_ID_1590136310.csv\n",
      "✅ PatNo_ID_1590616537.csv: set_1010 標記完成，成功切出區間 599 段，輸出 → /home/jovyan/RT08/0925/bling_1010/PatNo_ID_1590616537.csv\n",
      "✅ PatNo_ID_1590854576.csv: set_1010 標記完成，成功切出區間 459 段，輸出 → /home/jovyan/RT08/0925/bling_1010/PatNo_ID_1590854576.csv\n",
      "✅ PatNo_ID_1591609798.csv: set_1010 標記完成，成功切出區間 1688 段，輸出 → /home/jovyan/RT08/0925/bling_1010/PatNo_ID_1591609798.csv\n",
      "✅ PatNo_ID_1592044724.csv: set_1010 標記完成，成功切出區間 126 段，輸出 → /home/jovyan/RT08/0925/bling_1010/PatNo_ID_1592044724.csv\n",
      "✅ PatNo_ID_1592560504.csv: set_1010 標記完成，成功切出區間 948 段，輸出 → /home/jovyan/RT08/0925/bling_1010/PatNo_ID_1592560504.csv\n",
      "✅ PatNo_ID_1593087886.csv: set_1010 標記完成，成功切出區間 980 段，輸出 → /home/jovyan/RT08/0925/bling_1010/PatNo_ID_1593087886.csv\n",
      "✅ PatNo_ID_1593416100.csv: set_1010 標記完成，成功切出區間 110 段，輸出 → /home/jovyan/RT08/0925/bling_1010/PatNo_ID_1593416100.csv\n",
      "✅ PatNo_ID_1593472048.csv: set_1010 標記完成，成功切出區間 224 段，輸出 → /home/jovyan/RT08/0925/bling_1010/PatNo_ID_1593472048.csv\n",
      "✅ PatNo_ID_1593593586.csv: set_1010 標記完成，成功切出區間 915 段，輸出 → /home/jovyan/RT08/0925/bling_1010/PatNo_ID_1593593586.csv\n",
      "✅ PatNo_ID_1593720818.csv: set_1010 標記完成，成功切出區間 60 段，輸出 → /home/jovyan/RT08/0925/bling_1010/PatNo_ID_1593720818.csv\n",
      "✅ PatNo_ID_1593838524.csv: set_1010 標記完成，成功切出區間 82 段，輸出 → /home/jovyan/RT08/0925/bling_1010/PatNo_ID_1593838524.csv\n",
      "✅ PatNo_ID_1594173718.csv: set_1010 標記完成，成功切出區間 16 段，輸出 → /home/jovyan/RT08/0925/bling_1010/PatNo_ID_1594173718.csv\n",
      "✅ PatNo_ID_1594294180.csv: set_1010 標記完成，成功切出區間 395 段，輸出 → /home/jovyan/RT08/0925/bling_1010/PatNo_ID_1594294180.csv\n",
      "✅ PatNo_ID_1594305136.csv: set_1010 標記完成，成功切出區間 560 段，輸出 → /home/jovyan/RT08/0925/bling_1010/PatNo_ID_1594305136.csv\n",
      "✅ PatNo_ID_1594309746.csv: set_1010 標記完成，成功切出區間 130 段，輸出 → /home/jovyan/RT08/0925/bling_1010/PatNo_ID_1594309746.csv\n",
      "✅ PatNo_ID_1594319286.csv: set_1010 標記完成，成功切出區間 269 段，輸出 → /home/jovyan/RT08/0925/bling_1010/PatNo_ID_1594319286.csv\n",
      "✅ PatNo_ID_1594320763.csv: set_1010 標記完成，成功切出區間 43 段，輸出 → /home/jovyan/RT08/0925/bling_1010/PatNo_ID_1594320763.csv\n",
      "✅ PatNo_ID_1594322594.csv: set_1010 標記完成，成功切出區間 170 段，輸出 → /home/jovyan/RT08/0925/bling_1010/PatNo_ID_1594322594.csv\n",
      "✅ PatNo_ID_1594335109.csv: set_1010 標記完成，成功切出區間 318 段，輸出 → /home/jovyan/RT08/0925/bling_1010/PatNo_ID_1594335109.csv\n",
      "✅ PatNo_ID_1594423683.csv: set_1010 標記完成，成功切出區間 279 段，輸出 → /home/jovyan/RT08/0925/bling_1010/PatNo_ID_1594423683.csv\n",
      "✅ PatNo_ID_1594437309.csv: set_1010 標記完成，成功切出區間 398 段，輸出 → /home/jovyan/RT08/0925/bling_1010/PatNo_ID_1594437309.csv\n",
      "✅ PatNo_ID_1594439781.csv: set_1010 標記完成，成功切出區間 181 段，輸出 → /home/jovyan/RT08/0925/bling_1010/PatNo_ID_1594439781.csv\n",
      "✅ PatNo_ID_1594441887.csv: set_1010 標記完成，成功切出區間 218 段，輸出 → /home/jovyan/RT08/0925/bling_1010/PatNo_ID_1594441887.csv\n",
      "⚠️ PatNo_ID_1594448501.csv: 無有效資料（nan_check==1 且 senddate 有效），輸出空標記。\n",
      "✅ PatNo_ID_1594455578.csv: set_1010 標記完成，成功切出區間 1 段，輸出 → /home/jovyan/RT08/0925/bling_1010/PatNo_ID_1594455578.csv\n",
      "✅ PatNo_ID_1594464829.csv: set_1010 標記完成，成功切出區間 191 段，輸出 → /home/jovyan/RT08/0925/bling_1010/PatNo_ID_1594464829.csv\n",
      "✅ PatNo_ID_1594467719.csv: set_1010 標記完成，成功切出區間 62 段，輸出 → /home/jovyan/RT08/0925/bling_1010/PatNo_ID_1594467719.csv\n",
      "✅ PatNo_ID_1594471407.csv: set_1010 標記完成，成功切出區間 659 段，輸出 → /home/jovyan/RT08/0925/bling_1010/PatNo_ID_1594471407.csv\n",
      "✅ PatNo_ID_1594479330.csv: set_1010 標記完成，成功切出區間 281 段，輸出 → /home/jovyan/RT08/0925/bling_1010/PatNo_ID_1594479330.csv\n",
      "✅ PatNo_ID_1594511911.csv: set_1010 標記完成，成功切出區間 268 段，輸出 → /home/jovyan/RT08/0925/bling_1010/PatNo_ID_1594511911.csv\n",
      "✅ PatNo_ID_1594511914.csv: set_1010 標記完成，成功切出區間 518 段，輸出 → /home/jovyan/RT08/0925/bling_1010/PatNo_ID_1594511914.csv\n",
      "✅ PatNo_ID_1594528842.csv: set_1010 標記完成，成功切出區間 119 段，輸出 → /home/jovyan/RT08/0925/bling_1010/PatNo_ID_1594528842.csv\n",
      "✅ PatNo_ID_1594533379.csv: set_1010 標記完成，成功切出區間 42 段，輸出 → /home/jovyan/RT08/0925/bling_1010/PatNo_ID_1594533379.csv\n",
      "🎯 全部檔案處理完成（set_1010 已寫入 bling_1010/）。\n"
     ]
    }
   ],
   "source": [
    "# ==========================================================\n",
    "# 腳本 A：為每個檔案新增 set_1010（0/2/1 = 前50%/中30%/後20%）\n",
    "# 條件：區段需能切出完整 50/30/20 三段，且正負樣本各至少 1 筆（互不重疊）\n",
    "# 來源：/home/jovyan/RT08/0925/bling_1004/\n",
    "# 輸出：/home/jovyan/RT08/0925/bling_1010/（不動原始檔）\n",
    "# 僅以 nan_check==1 且 senddate 有效的資料進行切割\n",
    "# ==========================================================\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from pathlib import Path\n",
    "\n",
    "# -----------------------------\n",
    "# A. 路徑設定（輸入/輸出）\n",
    "# -----------------------------\n",
    "in_dir = Path(\"/home/jovyan/RT08/0925/bling_1004\")\n",
    "out_dir = Path(\"/home/jovyan/RT08/0925/bling_1010\")\n",
    "out_dir.mkdir(parents=True, exist_ok=True)\n",
    "\n",
    "files = sorted(in_dir.glob(\"*.csv\"))\n",
    "print(f\"🔎 偵測到 {len(files)} 份輸入檔案（來源：{in_dir}）\")\n",
    "\n",
    "# -----------------------------\n",
    "# B. 逐檔處理\n",
    "# -----------------------------\n",
    "for fp in files:\n",
    "    df = pd.read_csv(fp, low_memory=False, parse_dates=[\"senddate\"])\n",
    "    df.columns = [c.lower() for c in df.columns]\n",
    "\n",
    "    # 基本欄位檢查\n",
    "    need_cols = [\"senddate\", \"ad_para\"]\n",
    "    for c in need_cols:\n",
    "        if c not in df.columns:\n",
    "            print(f\"⚠️ {fp.name} 缺少必要欄位 {c}，略過。\")\n",
    "            df.to_csv(out_dir / fp.name, index=False, encoding=\"utf-8-sig\")\n",
    "            continue\n",
    "\n",
    "    # 初始化新欄位 set_1010（先全部設為 NaN；不會影響原始 set 欄位）\n",
    "    df[\"set_1010\"] = pd.NA\n",
    "\n",
    "    # 建立原始列索引，便於在過濾後對應回原 df 標記\n",
    "    df[\"__orig_idx__\"] = np.arange(len(df))\n",
    "\n",
    "    # 僅以「可用資料點」進行切割：nan_check==1 且 senddate 有效\n",
    "    eff = df.copy()\n",
    "    if \"nan_check\" in eff.columns:\n",
    "        eff = eff[eff[\"nan_check\"] == 1]\n",
    "    eff = eff[eff[\"senddate\"].notna()].copy()\n",
    "    eff = eff.sort_values(\"senddate\").reset_index(drop=True)\n",
    "\n",
    "    # 若可用資料不足，直接輸出（set_1010 皆為 NaN）\n",
    "    if eff.empty:\n",
    "        df.drop(columns=[\"__orig_idx__\"], inplace=True)\n",
    "        df.to_csv(out_dir / fp.name, index=False, encoding=\"utf-8-sig\")\n",
    "        print(f\"⚠️ {fp.name}: 無有效資料（nan_check==1 且 senddate 有效），輸出空標記。\")\n",
    "        continue\n",
    "\n",
    "    # 標記調參事件（連續 ad_para==1 視為同一事件，取 run 的最後時間為事件時間）\n",
    "    eff[\"is1\"] = eff[\"ad_para\"] == 1\n",
    "    eff[\"start_run\"] = eff[\"is1\"] & (~eff[\"is1\"].shift(fill_value=False))\n",
    "    eff[\"run_id\"] = eff[\"start_run\"].cumsum()\n",
    "\n",
    "    runs = (\n",
    "        eff[eff[\"is1\"]]\n",
    "        .groupby(\"run_id\", dropna=False, as_index=False)\n",
    "        .agg(last_time=(\"senddate\", \"max\"))\n",
    "        .sort_values(\"last_time\")\n",
    "        .reset_index(drop=True)\n",
    "    )\n",
    "\n",
    "    # 若不到兩個事件，無法形成相鄰區間，直接輸出\n",
    "    if len(runs) < 2:\n",
    "        df.drop(columns=[\"__orig_idx__\"], inplace=True)\n",
    "        df.to_csv(out_dir / fp.name, index=False, encoding=\"utf-8-sig\")\n",
    "        print(f\"ℹ️ {fp.name}: 調參事件少於 2 次，無可分段區間。\")\n",
    "        continue\n",
    "\n",
    "    # 收集各區段的「原始索引」以便最後一次性寫入，確保優先序（1 > 2 > 0 或 1 最後寫入）\n",
    "    mark_idxs_neg = []  # set_1010 = 0\n",
    "    mark_idxs_mid = []  # set_1010 = 2\n",
    "    mark_idxs_pos = []  # set_1010 = 1\n",
    "\n",
    "    valid_seg = 0  # 成功切出 50/30/20 且 0/1 各≥1筆 的區段數\n",
    "\n",
    "    for i in range(len(runs) - 1):\n",
    "        T1 = runs.loc[i, \"last_time\"]\n",
    "        T2 = runs.loc[i + 1, \"last_time\"]\n",
    "        delta_min = (T2 - T1).total_seconds() / 60.0\n",
    "        if delta_min <= 5:\n",
    "            continue\n",
    "\n",
    "        # 三段邊界：前 50%、前 80%（=50%+30%）、最後 20%\n",
    "        neg_end = T1 + pd.Timedelta(minutes=0.5 * delta_min)\n",
    "        mid_end = T1 + pd.Timedelta(minutes=0.8 * delta_min)\n",
    "\n",
    "        # 僅在「可用資料 eff」上決定分段；稍後以 __orig_idx__ 回填到原 df\n",
    "        mask_neg_eff = (eff[\"senddate\"] > T1) & (eff[\"senddate\"] <= neg_end)\n",
    "        mask_mid_eff = (eff[\"senddate\"] > neg_end) & (eff[\"senddate\"] <= mid_end)\n",
    "        mask_pos_eff = (eff[\"senddate\"] > mid_end) & (eff[\"senddate\"] <= T2)\n",
    "\n",
    "        neg_cnt = int(mask_neg_eff.sum())\n",
    "        pos_cnt = int(mask_pos_eff.sum())\n",
    "\n",
    "        # 條件：正負樣本各 ≥1 筆，且三段皆存在（中段可為 0 亦可，但依需求：完整三段更佳）\n",
    "        # 這裡依你的要求：三段需可切出，且正/負各至少一筆\n",
    "        if neg_cnt >= 1 and pos_cnt >= 1:\n",
    "            # 將 eff 的原始索引收集\n",
    "            mark_idxs_neg.extend(eff.loc[mask_neg_eff, \"__orig_idx__\"].tolist())\n",
    "            mark_idxs_mid.extend(eff.loc[mask_mid_eff, \"__orig_idx__\"].tolist())\n",
    "            mark_idxs_pos.extend(eff.loc[mask_pos_eff, \"__orig_idx__\"].tolist())\n",
    "            valid_seg += 1\n",
    "        # 若不符合條件，此區段略過（不標）\n",
    "\n",
    "    # 依優先序寫回原 df 的 set_1010（先寫 0、再 2、最後寫 1，確保正樣本優先覆蓋）\n",
    "    if mark_idxs_neg:\n",
    "        df.loc[df[\"__orig_idx__\"].isin(mark_idxs_neg), \"set_1010\"] = 0\n",
    "    if mark_idxs_mid:\n",
    "        df.loc[df[\"__orig_idx__\"].isin(mark_idxs_mid), \"set_1010\"] = 2\n",
    "    if mark_idxs_pos:\n",
    "        df.loc[df[\"__orig_idx__\"].isin(mark_idxs_pos), \"set_1010\"] = 1\n",
    "\n",
    "    # 清理暫存欄位並輸出\n",
    "    df.drop(columns=[\"__orig_idx__\"], inplace=True)\n",
    "    out_path = out_dir / fp.name\n",
    "    df.to_csv(out_path, index=False, encoding=\"utf-8-sig\")\n",
    "    print(f\"✅ {fp.name}: set_1010 標記完成，成功切出區間 {valid_seg} 段，輸出 → {out_path}\")\n",
    "\n",
    "print(\"🎯 全部檔案處理完成（set_1010 已寫入 bling_1010/）。\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "id": "a3ed6a60-5d3f-45ac-b586-55b0f15f7204",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "🔎 將統計 122 份檔案（以 bling_1010 為主，對應 bling_1004 做 nan_check 統計）\n",
      "📄 已匯出：/home/jovyan/RT08/0925/1002/crossfile_per_file_summary.csv\n",
      "📄 已匯出：/home/jovyan/RT08/0925/1002/crossfile_segments_list.csv\n",
      "📄 已匯出：/home/jovyan/RT08/0925/1002/crossfile_set1010_distribution.csv\n",
      "📄 已匯出：/home/jovyan/RT08/0925/1002/crossfile_overall_stats.csv\n",
      "\n",
      "=== 主控台摘要 ===\n",
      "原始總列數 raw_rows           : 1,567,233\n",
      "nan_check==1 總筆數           : 1,336,870\n",
      "nan_check==0 總筆數           : 230,363\n",
      "nan_check==1 佔比             : 85.30%\n",
      "標記後總列數 marked_rows      : 1,567,233\n",
      "set_1010 跨檔案分布 0/1/2     : 636,263 / 310,193 / 382,241\n",
      "可分段事件 ΔT（分鐘）統計（跨檔案）：\n",
      "count   96911.000\n",
      "mean       12.217\n",
      "std        24.466\n",
      "min         1.083\n",
      "50%         5.967\n",
      "75%        11.000\n",
      "90%        25.017\n",
      "95%        44.950\n",
      "max      1539.067\n"
     ]
    }
   ],
   "source": [
    "# ==========================================================\n",
    "# 腳本 B：跨檔案統計彙整（不產生圖形）\n",
    "# 來源一（nan_check 統計）：/home/jovyan/RT08/0925/bling_1004/\n",
    "# 來源二（set_1010 與可分段事件）：/home/jovyan/RT08/0925/bling_1010/\n",
    "# 輸出：/home/jovyan/RT08/0925/1002/\n",
    "# 內容：\n",
    "#  1) nan_check==1 / ==0 的筆數（跨檔案總計與逐檔）\n",
    "#  2) 可被分段的事件數及其長度（分鐘）：以 set_1010 實際切分驗證\n",
    "#  3) set_1010 類別（0/1/2）跨檔案的筆數與比例\n",
    "# ==========================================================\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from pathlib import Path\n",
    "\n",
    "# -----------------------------\n",
    "# A. 路徑設定\n",
    "# -----------------------------\n",
    "raw_dir    = Path(\"/home/jovyan/RT08/0925/bling_1004\")   # 原始檔（nan_check 統計用）\n",
    "marked_dir = Path(\"/home/jovyan/RT08/0925/bling_1010\")   # set_1010 標記後檔\n",
    "out_dir    = Path(\"/home/jovyan/RT08/0925/1002\")         # 統計輸出\n",
    "out_dir.mkdir(parents=True, exist_ok=True)\n",
    "\n",
    "marked_files = sorted(marked_dir.glob(\"*.csv\"))\n",
    "if not marked_files:\n",
    "    raise SystemExit(\"❌ 找不到任何標記後檔案（bling_1010）。請先執行腳本 A。\")\n",
    "\n",
    "print(f\"🔎 將統計 {len(marked_files)} 份檔案（以 bling_1010 為主，對應 bling_1004 做 nan_check 統計）\")\n",
    "\n",
    "# -----------------------------\n",
    "# B. 跨檔案累計容器\n",
    "# -----------------------------\n",
    "per_file_rows = []     # 每檔匯總\n",
    "all_segments  = []     # 可分段事件明細（檔名/T1/T2/ΔT）\n",
    "set_tally = {0: 0, 1: 0, 2: 0}\n",
    "\n",
    "total_raw_rows = 0\n",
    "total_nan1 = 0\n",
    "total_nan0 = 0\n",
    "total_marked_rows = 0\n",
    "\n",
    "# -----------------------------\n",
    "# C. 逐檔統計\n",
    "# -----------------------------\n",
    "for mfp in marked_files:\n",
    "    fname = mfp.name\n",
    "    rfp = raw_dir / fname\n",
    "\n",
    "    # --- C1. 原始檔 nan_check 統計（以 senddate 有效列為口徑） ---\n",
    "    nan1_cnt = np.nan\n",
    "    nan0_cnt = np.nan\n",
    "    raw_rows = 0\n",
    "\n",
    "    if rfp.exists():\n",
    "        raw_df = pd.read_csv(rfp, low_memory=False, parse_dates=[\"senddate\"])\n",
    "        raw_df.columns = [c.lower() for c in raw_df.columns]\n",
    "        raw_rows = len(raw_df)\n",
    "        valid_mask = raw_df[\"senddate\"].notna() if \"senddate\" in raw_df.columns else np.ones(raw_rows, dtype=bool)\n",
    "\n",
    "        if \"nan_check\" in raw_df.columns:\n",
    "            nan1_cnt = int(((raw_df[\"nan_check\"] == 1) & valid_mask).sum())\n",
    "            nan0_cnt = int(((raw_df[\"nan_check\"] == 0) & valid_mask).sum())\n",
    "\n",
    "    total_raw_rows += raw_rows\n",
    "    total_nan1 += 0 if pd.isna(nan1_cnt) else nan1_cnt\n",
    "    total_nan0 += 0 if pd.isna(nan0_cnt) else nan0_cnt\n",
    "\n",
    "    # --- C2. 標記後檔 set_1010 分布與「可被分段事件」 ---\n",
    "    df = pd.read_csv(mfp, low_memory=False, parse_dates=[\"senddate\"])\n",
    "    df.columns = [c.lower() for c in df.columns]\n",
    "    total_marked_rows += len(df)\n",
    "\n",
    "    s0 = int((df.get(\"set_1010\") == 0).sum())\n",
    "    s1 = int((df.get(\"set_1010\") == 1).sum())\n",
    "    s2 = int((df.get(\"set_1010\") == 2).sum())\n",
    "    for k, v in {0: s0, 1: s1, 2: s2}.items():\n",
    "        set_tally[k] += v\n",
    "\n",
    "    # 以 set_1010 實際切分驗證「可被分段事件」（對應腳本 A 的邏輯）\n",
    "    seg_count = 0\n",
    "    seg_lengths = []\n",
    "\n",
    "    if \"ad_para\" in df.columns and \"senddate\" in df.columns:\n",
    "        g = df[df[\"senddate\"].notna()].sort_values(\"senddate\").reset_index(drop=True)\n",
    "        g[\"is1\"] = g[\"ad_para\"] == 1\n",
    "        g[\"start_run\"] = g[\"is1\"] & (~g[\"is1\"].shift(fill_value=False))\n",
    "        g[\"run_id\"] = g[\"start_run\"].cumsum()\n",
    "\n",
    "        runs = (\n",
    "            g[g[\"is1\"]]\n",
    "            .groupby(\"run_id\", dropna=False, as_index=False)\n",
    "            .agg(last_time=(\"senddate\", \"max\"))\n",
    "            .sort_values(\"last_time\")\n",
    "            .reset_index(drop=True)\n",
    "        )\n",
    "\n",
    "        if len(runs) >= 2:\n",
    "            for i in range(len(runs) - 1):\n",
    "                T1 = runs.loc[i, \"last_time\"]\n",
    "                T2 = runs.loc[i + 1, \"last_time\"]\n",
    "                delta_min = (T2 - T1).total_seconds() / 60.0\n",
    "                if delta_min <= 0:\n",
    "                    continue\n",
    "\n",
    "                neg_end = T1 + pd.Timedelta(minutes=0.5 * delta_min)\n",
    "                mid_end = T1 + pd.Timedelta(minutes=0.8 * delta_min)\n",
    "\n",
    "                # 驗證該區間內是否真的有 set_1010=0 與 set_1010=1 的列各≥1\n",
    "                mask_neg = (g[\"senddate\"] > T1) & (g[\"senddate\"] <= neg_end) & (g.get(\"set_1010\") == 0)\n",
    "                mask_pos = (g[\"senddate\"] > mid_end) & (g[\"senddate\"] <= T2)   & (g.get(\"set_1010\") == 1)\n",
    "\n",
    "                if int(mask_neg.sum()) >= 1 and int(mask_pos.sum()) >= 1:\n",
    "                    seg_count += 1\n",
    "                    seg_lengths.append(delta_min)\n",
    "                    all_segments.append({\n",
    "                        \"file\": fname,\n",
    "                        \"T1\": T1,\n",
    "                        \"T2\": T2,\n",
    "                        \"delta_min\": delta_min\n",
    "                    })\n",
    "\n",
    "    # 每檔彙總列\n",
    "    per_file_rows.append({\n",
    "        \"file\": fname,\n",
    "        \"raw_rows\": raw_rows,\n",
    "        \"nan_check_1\": nan1_cnt,\n",
    "        \"nan_check_0\": nan0_cnt,\n",
    "        \"marked_rows\": len(df),\n",
    "        \"set1010_0\": s0,\n",
    "        \"set1010_1\": s1,\n",
    "        \"set1010_2\": s2,\n",
    "        \"segments_valid\": seg_count,\n",
    "        \"segments_mean_min\": np.mean(seg_lengths) if seg_lengths else np.nan,\n",
    "        \"segments_median_min\": np.median(seg_lengths) if seg_lengths else np.nan,\n",
    "        \"segments_min_min\": np.min(seg_lengths) if seg_lengths else np.nan,\n",
    "        \"segments_max_min\": np.max(seg_lengths) if seg_lengths else np.nan,\n",
    "    })\n",
    "\n",
    "# -----------------------------\n",
    "# D. 匯出所有統計\n",
    "# -----------------------------\n",
    "per_file_df  = pd.DataFrame(per_file_rows)\n",
    "segments_df  = pd.DataFrame(all_segments).sort_values([\"file\", \"T1\"]).reset_index(drop=True)\n",
    "per_file_df.to_csv(out_dir / \"crossfile_per_file_summary.csv\", index=False, encoding=\"utf-8-sig\")\n",
    "segments_df.to_csv(out_dir / \"crossfile_segments_list.csv\", index=False, encoding=\"utf-8-sig\")\n",
    "\n",
    "# 全域總表（總筆數、nan_check 總計、set_1010 總計與比例）\n",
    "total_set_rows = set_tally[0] + set_tally[1] + set_tally[2]\n",
    "set_dist_df = pd.DataFrame({\n",
    "    \"set_1010\": [0, 1, 2],\n",
    "    \"count\": [set_tally[0], set_tally[1], set_tally[2]],\n",
    "    \"percent\": [\n",
    "        round(set_tally[k] / total_set_rows * 100, 3) if total_set_rows > 0 else np.nan\n",
    "        for k in [0, 1, 2]\n",
    "    ]\n",
    "})\n",
    "overall_df = pd.DataFrame([{\n",
    "    \"total_raw_rows\": total_raw_rows,\n",
    "    \"total_nan_check_1\": total_nan1,\n",
    "    \"total_nan_check_0\": total_nan0,\n",
    "    \"nan_check_1_ratio_%\": round(total_nan1 / (total_nan1 + total_nan0) * 100, 3) if (total_nan1 + total_nan0) > 0 else np.nan,\n",
    "    \"total_marked_rows\": total_marked_rows,\n",
    "    \"set1010_total_rows\": total_set_rows\n",
    "}])\n",
    "\n",
    "set_dist_df.to_csv(out_dir / \"crossfile_set1010_distribution.csv\", index=False, encoding=\"utf-8-sig\")\n",
    "overall_df.to_csv(out_dir / \"crossfile_overall_stats.csv\", index=False, encoding=\"utf-8-sig\")\n",
    "\n",
    "print(f\"📄 已匯出：{out_dir / 'crossfile_per_file_summary.csv'}\")\n",
    "print(f\"📄 已匯出：{out_dir / 'crossfile_segments_list.csv'}\")\n",
    "print(f\"📄 已匯出：{out_dir / 'crossfile_set1010_distribution.csv'}\")\n",
    "print(f\"📄 已匯出：{out_dir / 'crossfile_overall_stats.csv'}\")\n",
    "\n",
    "# 主控台摘要\n",
    "print(\"\\n=== 主控台摘要 ===\")\n",
    "print(f\"原始總列數 raw_rows           : {total_raw_rows:,}\")\n",
    "print(f\"nan_check==1 總筆數           : {total_nan1:,}\")\n",
    "print(f\"nan_check==0 總筆數           : {total_nan0:,}\")\n",
    "if (total_nan1 + total_nan0) > 0:\n",
    "    print(f\"nan_check==1 佔比             : {total_nan1 / (total_nan1 + total_nan0) * 100:.2f}%\")\n",
    "print(f\"標記後總列數 marked_rows      : {total_marked_rows:,}\")\n",
    "print(f\"set_1010 跨檔案分布 0/1/2     : {set_tally[0]:,} / {set_tally[1]:,} / {set_tally[2]:,}\")\n",
    "if not segments_df.empty:\n",
    "    print(\"可分段事件 ΔT（分鐘）統計（跨檔案）：\")\n",
    "    print(segments_df[\"delta_min\"].describe(percentiles=[0.5, 0.75, 0.9, 0.95]).to_string())\n",
    "else:\n",
    "    print(\"⚠️ 未偵測到可分段事件（請確認 set_1010 產出與 ad_para/時間戳是否完整）。\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7a207e07-fc18-4616-bd67-d1cf46841920",
   "metadata": {},
   "outputs": [],
   "source": [
    "只要該區間的「前 50% 段」內有 ≥1 筆、且「後 20% 段」內有 ≥1 筆，\n",
    "即視為一個可用區段（即使整體 ΔT 很短）。\n",
    "⚠️ 它沒有限制「三段各需跨越一定時間」，\n",
    "只要時間間隔夠讓資料的 timestamp 落進去，就會被接受。\n",
    "\n",
    "假設某位病患資料的時間解析度是「每 30 秒一筆」，\n",
    "那麼：\n",
    "如果兩次調參事件相隔 1.08 分鐘 ≈ 65 秒，\n",
    "→ 這段區間長度只有 65 秒。\n",
    "→ 前 50% = 32.5 秒，後 20% = 13 秒。\n",
    "→ 只要在這兩個短段內各有一筆資料（例如第 30 秒、與第 60 秒），\n",
    "就會被當作「有效可分段區間」。\n",
    "從邏輯上確實符合「正負樣本各 ≥ 1 筆」，\n",
    "但臨床上並不具有實質意義。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "id": "6e0bf719-1b51-487d-91ef-a5900a2de814",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "🔎 將統計 122 份檔案（以 bling_1010 為主，對應 bling_1004 做 nan_check 統計）\n",
      "📄 已匯出：/home/jovyan/RT08/0925/1002/crossfile_per_file_summary.csv\n",
      "📄 已匯出：/home/jovyan/RT08/0925/1002/crossfile_segments_list.csv\n",
      "📄 已匯出：/home/jovyan/RT08/0925/1002/crossfile_set1010_distribution.csv\n",
      "📄 已匯出：/home/jovyan/RT08/0925/1002/crossfile_overall_stats.csv\n",
      "\n",
      "=== 主控台摘要 ===\n",
      "原始總列數 raw_rows           : 1,567,233\n",
      "nan_check==1 總筆數           : 1,336,870\n",
      "nan_check==0 總筆數           : 230,363\n",
      "nan_check==1 佔比             : 85.30%\n",
      "標記後總列數 marked_rows      : 1,567,233\n",
      "set_1010 跨檔案分布 0/1/2     : 569,437 / 262,154 / 338,730\n",
      "可分段事件 ΔT（分鐘）統計（跨檔案）：\n",
      "count   49492.000\n",
      "mean       20.722\n",
      "std        31.988\n",
      "min         5.017\n",
      "50%        11.000\n",
      "75%        20.000\n",
      "90%        44.000\n",
      "95%        69.000\n",
      "max      1539.067\n"
     ]
    }
   ],
   "source": [
    "# ==========================================================\n",
    "# 腳本 B：跨檔案統計彙整（不產生圖形）\n",
    "# 來源一（nan_check 統計）：/home/jovyan/RT08/0925/bling_1004/\n",
    "# 來源二（set_1010 與可分段事件）：/home/jovyan/RT08/0925/bling_1010/\n",
    "# 輸出：/home/jovyan/RT08/0925/1002/\n",
    "# 內容：\n",
    "#  1) nan_check==1 / ==0 的筆數（跨檔案總計與逐檔）\n",
    "#  2) 可被分段的事件數及其長度（分鐘）：以 set_1010 實際切分驗證\n",
    "#  3) set_1010 類別（0/1/2）跨檔案的筆數與比例\n",
    "# ==========================================================\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from pathlib import Path\n",
    "\n",
    "# -----------------------------\n",
    "# A. 路徑設定\n",
    "# -----------------------------\n",
    "raw_dir    = Path(\"/home/jovyan/RT08/0925/bling_1004\")   # 原始檔（nan_check 統計用）\n",
    "marked_dir = Path(\"/home/jovyan/RT08/0925/bling_1010\")   # set_1010 標記後檔\n",
    "out_dir    = Path(\"/home/jovyan/RT08/0925/1002\")         # 統計輸出\n",
    "out_dir.mkdir(parents=True, exist_ok=True)\n",
    "\n",
    "marked_files = sorted(marked_dir.glob(\"*.csv\"))\n",
    "if not marked_files:\n",
    "    raise SystemExit(\"❌ 找不到任何標記後檔案（bling_1010）。請先執行腳本 A。\")\n",
    "\n",
    "print(f\"🔎 將統計 {len(marked_files)} 份檔案（以 bling_1010 為主，對應 bling_1004 做 nan_check 統計）\")\n",
    "\n",
    "# -----------------------------\n",
    "# B. 跨檔案累計容器\n",
    "# -----------------------------\n",
    "per_file_rows = []     # 每檔匯總\n",
    "all_segments  = []     # 可分段事件明細（檔名/T1/T2/ΔT）\n",
    "set_tally = {0: 0, 1: 0, 2: 0}\n",
    "\n",
    "total_raw_rows = 0\n",
    "total_nan1 = 0\n",
    "total_nan0 = 0\n",
    "total_marked_rows = 0\n",
    "\n",
    "# -----------------------------\n",
    "# C. 逐檔統計\n",
    "# -----------------------------\n",
    "for mfp in marked_files:\n",
    "    fname = mfp.name\n",
    "    rfp = raw_dir / fname\n",
    "\n",
    "    # --- C1. 原始檔 nan_check 統計（以 senddate 有效列為口徑） ---\n",
    "    nan1_cnt = np.nan\n",
    "    nan0_cnt = np.nan\n",
    "    raw_rows = 0\n",
    "\n",
    "    if rfp.exists():\n",
    "        raw_df = pd.read_csv(rfp, low_memory=False, parse_dates=[\"senddate\"])\n",
    "        raw_df.columns = [c.lower() for c in raw_df.columns]\n",
    "        raw_rows = len(raw_df)\n",
    "        valid_mask = raw_df[\"senddate\"].notna() if \"senddate\" in raw_df.columns else np.ones(raw_rows, dtype=bool)\n",
    "\n",
    "        if \"nan_check\" in raw_df.columns:\n",
    "            nan1_cnt = int(((raw_df[\"nan_check\"] == 1) & valid_mask).sum())\n",
    "            nan0_cnt = int(((raw_df[\"nan_check\"] == 0) & valid_mask).sum())\n",
    "\n",
    "    total_raw_rows += raw_rows\n",
    "    total_nan1 += 0 if pd.isna(nan1_cnt) else nan1_cnt\n",
    "    total_nan0 += 0 if pd.isna(nan0_cnt) else nan0_cnt\n",
    "\n",
    "    # --- C2. 標記後檔 set_1010 分布與「可被分段事件」 ---\n",
    "    df = pd.read_csv(mfp, low_memory=False, parse_dates=[\"senddate\"])\n",
    "    df.columns = [c.lower() for c in df.columns]\n",
    "    total_marked_rows += len(df)\n",
    "\n",
    "    s0 = int((df.get(\"set_1010\") == 0).sum())\n",
    "    s1 = int((df.get(\"set_1010\") == 1).sum())\n",
    "    s2 = int((df.get(\"set_1010\") == 2).sum())\n",
    "    for k, v in {0: s0, 1: s1, 2: s2}.items():\n",
    "        set_tally[k] += v\n",
    "\n",
    "    # 以 set_1010 實際切分驗證「可被分段事件」（對應腳本 A 的邏輯）\n",
    "    seg_count = 0\n",
    "    seg_lengths = []\n",
    "\n",
    "    if \"ad_para\" in df.columns and \"senddate\" in df.columns:\n",
    "        g = df[df[\"senddate\"].notna()].sort_values(\"senddate\").reset_index(drop=True)\n",
    "        g[\"is1\"] = g[\"ad_para\"] == 1\n",
    "        g[\"start_run\"] = g[\"is1\"] & (~g[\"is1\"].shift(fill_value=False))\n",
    "        g[\"run_id\"] = g[\"start_run\"].cumsum()\n",
    "\n",
    "        runs = (\n",
    "            g[g[\"is1\"]]\n",
    "            .groupby(\"run_id\", dropna=False, as_index=False)\n",
    "            .agg(last_time=(\"senddate\", \"max\"))\n",
    "            .sort_values(\"last_time\")\n",
    "            .reset_index(drop=True)\n",
    "        )\n",
    "\n",
    "        if len(runs) >= 2:\n",
    "            for i in range(len(runs) - 1):\n",
    "                T1 = runs.loc[i, \"last_time\"]\n",
    "                T2 = runs.loc[i + 1, \"last_time\"]\n",
    "                delta_min = (T2 - T1).total_seconds() / 60.0\n",
    "                if delta_min <= 0:\n",
    "                    continue\n",
    "\n",
    "                neg_end = T1 + pd.Timedelta(minutes=0.5 * delta_min)\n",
    "                mid_end = T1 + pd.Timedelta(minutes=0.8 * delta_min)\n",
    "\n",
    "                # 驗證該區間內是否真的有 set_1010=0 與 set_1010=1 的列各≥1\n",
    "                mask_neg = (g[\"senddate\"] > T1) & (g[\"senddate\"] <= neg_end) & (g.get(\"set_1010\") == 0)\n",
    "                mask_pos = (g[\"senddate\"] > mid_end) & (g[\"senddate\"] <= T2)   & (g.get(\"set_1010\") == 1)\n",
    "\n",
    "                if int(mask_neg.sum()) >= 1 and int(mask_pos.sum()) >= 1:\n",
    "                    seg_count += 1\n",
    "                    seg_lengths.append(delta_min)\n",
    "                    all_segments.append({\n",
    "                        \"file\": fname,\n",
    "                        \"T1\": T1,\n",
    "                        \"T2\": T2,\n",
    "                        \"delta_min\": delta_min\n",
    "                    })\n",
    "\n",
    "    # 每檔彙總列\n",
    "    per_file_rows.append({\n",
    "        \"file\": fname,\n",
    "        \"raw_rows\": raw_rows,\n",
    "        \"nan_check_1\": nan1_cnt,\n",
    "        \"nan_check_0\": nan0_cnt,\n",
    "        \"marked_rows\": len(df),\n",
    "        \"set1010_0\": s0,\n",
    "        \"set1010_1\": s1,\n",
    "        \"set1010_2\": s2,\n",
    "        \"segments_valid\": seg_count,\n",
    "        \"segments_mean_min\": np.mean(seg_lengths) if seg_lengths else np.nan,\n",
    "        \"segments_median_min\": np.median(seg_lengths) if seg_lengths else np.nan,\n",
    "        \"segments_min_min\": np.min(seg_lengths) if seg_lengths else np.nan,\n",
    "        \"segments_max_min\": np.max(seg_lengths) if seg_lengths else np.nan,\n",
    "    })\n",
    "\n",
    "# -----------------------------\n",
    "# D. 匯出所有統計\n",
    "# -----------------------------\n",
    "per_file_df  = pd.DataFrame(per_file_rows)\n",
    "segments_df  = pd.DataFrame(all_segments).sort_values([\"file\", \"T1\"]).reset_index(drop=True)\n",
    "per_file_df.to_csv(out_dir / \"crossfile_per_file_summary.csv\", index=False, encoding=\"utf-8-sig\")\n",
    "segments_df.to_csv(out_dir / \"crossfile_segments_list.csv\", index=False, encoding=\"utf-8-sig\")\n",
    "\n",
    "# 全域總表（總筆數、nan_check 總計、set_1010 總計與比例）\n",
    "total_set_rows = set_tally[0] + set_tally[1] + set_tally[2]\n",
    "set_dist_df = pd.DataFrame({\n",
    "    \"set_1010\": [0, 1, 2],\n",
    "    \"count\": [set_tally[0], set_tally[1], set_tally[2]],\n",
    "    \"percent\": [\n",
    "        round(set_tally[k] / total_set_rows * 100, 3) if total_set_rows > 0 else np.nan\n",
    "        for k in [0, 1, 2]\n",
    "    ]\n",
    "})\n",
    "overall_df = pd.DataFrame([{\n",
    "    \"total_raw_rows\": total_raw_rows,\n",
    "    \"total_nan_check_1\": total_nan1,\n",
    "    \"total_nan_check_0\": total_nan0,\n",
    "    \"nan_check_1_ratio_%\": round(total_nan1 / (total_nan1 + total_nan0) * 100, 3) if (total_nan1 + total_nan0) > 0 else np.nan,\n",
    "    \"total_marked_rows\": total_marked_rows,\n",
    "    \"set1010_total_rows\": total_set_rows\n",
    "}])\n",
    "\n",
    "set_dist_df.to_csv(out_dir / \"crossfile_set1010_distribution.csv\", index=False, encoding=\"utf-8-sig\")\n",
    "overall_df.to_csv(out_dir / \"crossfile_overall_stats.csv\", index=False, encoding=\"utf-8-sig\")\n",
    "\n",
    "print(f\"📄 已匯出：{out_dir / 'crossfile_per_file_summary.csv'}\")\n",
    "print(f\"📄 已匯出：{out_dir / 'crossfile_segments_list.csv'}\")\n",
    "print(f\"📄 已匯出：{out_dir / 'crossfile_set1010_distribution.csv'}\")\n",
    "print(f\"📄 已匯出：{out_dir / 'crossfile_overall_stats.csv'}\")\n",
    "\n",
    "# 主控台摘要\n",
    "print(\"\\n=== 主控台摘要 ===\")\n",
    "print(f\"原始總列數 raw_rows           : {total_raw_rows:,}\")\n",
    "print(f\"nan_check==1 總筆數           : {total_nan1:,}\")\n",
    "print(f\"nan_check==0 總筆數           : {total_nan0:,}\")\n",
    "if (total_nan1 + total_nan0) > 0:\n",
    "    print(f\"nan_check==1 佔比             : {total_nan1 / (total_nan1 + total_nan0) * 100:.2f}%\")\n",
    "print(f\"標記後總列數 marked_rows      : {total_marked_rows:,}\")\n",
    "print(f\"set_1010 跨檔案分布 0/1/2     : {set_tally[0]:,} / {set_tally[1]:,} / {set_tally[2]:,}\")\n",
    "if not segments_df.empty:\n",
    "    print(\"可分段事件 ΔT（分鐘）統計（跨檔案）：\")\n",
    "    print(segments_df[\"delta_min\"].describe(percentiles=[0.5, 0.75, 0.9, 0.95]).to_string())\n",
    "else:\n",
    "    print(\"⚠️ 未偵測到可分段事件（請確認 set_1010 產出與 ad_para/時間戳是否完整）。\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "470a02f3-67eb-408c-ba21-13c58a992338",
   "metadata": {},
   "outputs": [],
   "source": [
    "crossfile_per_file_summary.csv：建議畫出長條圖（x=檔案，y=segments_valid），可視覺化哪位病患資料段最多。\n",
    "crossfile_segments_list.csv：是進入 Phase 1 / Phase 2 模型前的切段基礎，可直接拿來統計 ΔT 分布（或濾掉極端段）。\n",
    "crossfile_set1010_distribution.csv：可監控 set 標記比例是否符合預期（例如 set=0:1 約 2:1）。\n",
    "crossfile_overall_stats.csv：是 pipeline 成功與否的健康指標，可對照每次版本輸出。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "id": "aa16c2ae-812b-4973-aefb-b714a526bfb1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Data\n",
    "data = {\n",
    "    'set_1010': [0, 1, 2],\n",
    "    'count': [569437, 262154, 338730],\n",
    "    'percent': [48.656, 22.4, 28.943]\n",
    "}\n",
    "\n",
    "# Colors\n",
    "colors = ['#8EC9FF', '#005BBB', '#B0B0B0']  # light blue, dark blue, gray\n",
    "\n",
    "# Create bar chart\n",
    "fig, ax = plt.subplots(figsize=(7, 5))\n",
    "bars = ax.bar(data['set_1010'], data['count'], color=colors, width=0.6)\n",
    "\n",
    "# Add count and percentage on top of bars\n",
    "for i, bar in enumerate(bars):\n",
    "    height = bar.get_height()\n",
    "    ax.text(bar.get_x() + bar.get_width()/2, height + 10000, \n",
    "            f\"{data['count'][i]:,}\\n({data['percent'][i]:.2f}%)\",\n",
    "            ha='center', va='bottom', fontsize=11, fontweight='bold')\n",
    "\n",
    "# Beautify chart\n",
    "ax.set_title(\"Distribution of Set Categories\", fontsize=14, fontweight='bold', pad=15)\n",
    "ax.set_xlabel(\"Set Category\", fontsize=12)\n",
    "ax.set_ylabel(\"Count\", fontsize=12)\n",
    "ax.set_xticks([0, 1, 2])\n",
    "ax.set_xticklabels(['Set = 0', 'Set = 1', 'Set = 2'])\n",
    "ax.spines['top'].set_visible(False)\n",
    "ax.spines['right'].set_visible(False)\n",
    "ax.tick_params(axis='y', labelsize=10)\n",
    "ax.tick_params(axis='x', labelsize=10)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "id": "3a3ac150-7aee-4a1e-aad0-8485ad7209f1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "🔎 發現 122 份 set_1010 標記檔，開始統計 set=0/1 區段...\n",
      "\n",
      "=== 🧭 set=0 / set=1 區段統計結果（跨檔案） ===\n",
      "\n",
      "🔹 負樣本（set=0）\n",
      "區段數量 (segments): 51,517\n",
      "平均長度 (min): 10.28\n",
      "中位長度 (min): 4.00\n",
      "最短區段 (min): 0.00\n",
      "最長區段 (min): 1618.07\n",
      "總時長 (小時): 8825.20\n",
      "\n",
      "🔹 正樣本（set=1）\n",
      "區段數量 (segments): 51,517\n",
      "平均長度 (min): 4.16\n",
      "中位長度 (min): 2.00\n",
      "最短區段 (min): 0.00\n",
      "最長區段 (min): 262.98\n",
      "總時長 (小時): 3568.65\n",
      "\n",
      "=== ⏱ 前 5 個最長的區段（跨所有檔案） ===\n",
      "                   file  set_1010          start_time            end_time  delta_min  count\n",
      "PatNo_ID_1570089466.csv     0.000 2022-02-01 06:15:00 2022-02-02 09:13:04   1618.067    191\n",
      "PatNo_ID_1594441887.csv     0.000 2022-02-28 19:25:02 2022-03-01 04:55:00    569.967    503\n",
      "PatNo_ID_1589034524.csv     0.000 2022-03-15 14:40:02 2022-03-15 22:47:01    486.983    487\n",
      "PatNo_ID_1570642083.csv     0.000 2022-01-21 10:02:02 2022-01-21 17:12:03    430.017    110\n",
      "PatNo_ID_1580244614.csv     0.000 2022-01-25 13:58:01 2022-01-25 21:07:01    429.000    310\n"
     ]
    }
   ],
   "source": [
    "# 來看set01分別的統計分析\n",
    "# ==========================================================\n",
    "# 附加分析：統計 set_1010 == 0 與 set_1010 == 1 的區段特徵\n",
    "# 來源：/home/jovyan/RT08/0925/bling_1010/\n",
    "# 輸出：直接印在終端，不畫圖、不存檔\n",
    "# ==========================================================\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from pathlib import Path\n",
    "\n",
    "data_dir = Path(\"/home/jovyan/RT08/0925/bling_1010\")\n",
    "files = sorted(data_dir.glob(\"*.csv\"))\n",
    "print(f\"🔎 發現 {len(files)} 份 set_1010 標記檔，開始統計 set=0/1 區段...\")\n",
    "\n",
    "# ----------------------------------------------------------\n",
    "# A. 跨檔案累積資料\n",
    "# ----------------------------------------------------------\n",
    "all_rows = []\n",
    "\n",
    "for fp in files:\n",
    "    df = pd.read_csv(fp, low_memory=False, parse_dates=[\"senddate\"])\n",
    "    df.columns = [c.lower() for c in df.columns]\n",
    "    if \"set_1010\" not in df.columns or \"senddate\" not in df.columns:\n",
    "        continue\n",
    "\n",
    "    # 僅保留 set_1010 為 0 或 1 的資料\n",
    "    df = df[df[\"set_1010\"].isin([0, 1])].copy()\n",
    "    if df.empty:\n",
    "        continue\n",
    "\n",
    "    # 計算每段連續 set 相同的區段長度（以 senddate 時間差為準）\n",
    "    df = df.sort_values(\"senddate\").reset_index(drop=True)\n",
    "    df[\"block_id\"] = (df[\"set_1010\"] != df[\"set_1010\"].shift()).cumsum()\n",
    "\n",
    "    segments = (\n",
    "        df.groupby([\"set_1010\", \"block_id\"], as_index=False)\n",
    "        .agg(\n",
    "            start_time=(\"senddate\", \"min\"),\n",
    "            end_time=(\"senddate\", \"max\"),\n",
    "            count=(\"senddate\", \"count\")\n",
    "        )\n",
    "    )\n",
    "    segments[\"delta_min\"] = (segments[\"end_time\"] - segments[\"start_time\"]).dt.total_seconds() / 60.0\n",
    "    segments[\"file\"] = fp.name\n",
    "    all_rows.append(segments)\n",
    "\n",
    "# ----------------------------------------------------------\n",
    "# B. 合併所有結果\n",
    "# ----------------------------------------------------------\n",
    "if not all_rows:\n",
    "    print(\"⚠️ 沒有找到任何 set=0 或 set=1 的區段。\")\n",
    "else:\n",
    "    seg_df = pd.concat(all_rows, ignore_index=True)\n",
    "\n",
    "    # 分別統計 set=0、set=1 的資料\n",
    "    result = seg_df.groupby(\"set_1010\").agg(\n",
    "        segment_count=(\"delta_min\", \"count\"),\n",
    "        mean_length_min=(\"delta_min\", \"mean\"),\n",
    "        median_length_min=(\"delta_min\", \"median\"),\n",
    "        min_length_min=(\"delta_min\", \"min\"),\n",
    "        max_length_min=(\"delta_min\", \"max\"),\n",
    "        total_duration_hr=(\"delta_min\", lambda x: x.sum() / 60.0)\n",
    "    ).reset_index()\n",
    "\n",
    "    # ----------------------------------------------------------\n",
    "    # C. 主控台印出結果\n",
    "    # ----------------------------------------------------------\n",
    "    print(\"\\n=== 🧭 set=0 / set=1 區段統計結果（跨檔案） ===\")\n",
    "    for _, row in result.iterrows():\n",
    "        tag = int(row[\"set_1010\"])\n",
    "        label = \"負樣本（set=0）\" if tag == 0 else \"正樣本（set=1）\"\n",
    "        print(f\"\\n🔹 {label}\")\n",
    "        print(f\"區段數量 (segments): {int(row['segment_count']):,}\")\n",
    "        print(f\"平均長度 (min): {row['mean_length_min']:.2f}\")\n",
    "        print(f\"中位長度 (min): {row['median_length_min']:.2f}\")\n",
    "        print(f\"最短區段 (min): {row['min_length_min']:.2f}\")\n",
    "        print(f\"最長區段 (min): {row['max_length_min']:.2f}\")\n",
    "        print(f\"總時長 (小時): {row['total_duration_hr']:.2f}\")\n",
    "\n",
    "    # ----------------------------------------------------------\n",
    "    # D. 額外：印出前幾個最長的區段以供人工檢查\n",
    "    # ----------------------------------------------------------\n",
    "    top_segments = seg_df.sort_values(\"delta_min\", ascending=False).head(5)\n",
    "    print(\"\\n=== ⏱ 前 5 個最長的區段（跨所有檔案） ===\")\n",
    "    print(top_segments[[\"file\", \"set_1010\", \"start_time\", \"end_time\", \"delta_min\", \"count\"]].to_string(index=False))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "id": "1171cae2-2178-44bd-b335-7e900263357e",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x450 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x450 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 650x450 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# ==========================================================\n",
    "# 六張圖：set_1010（0=負樣本, 1=正樣本）之 segment 統計視覺化\n",
    "# - 顏色：全藍，負樣本淺藍、正樣本深藍，並加入漸層效果\n",
    "# - 標籤：圖上顯示數值（英文）\n",
    "# - 註解：中文\n",
    "# - 資料來源：/home/jovyan/RT08/0925/bling_1010/*.csv\n",
    "# - 依據：連續相同 set_1010 值的區塊視為一個 segment，計算其起訖時間與長度\n",
    "# ==========================================================\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from pathlib import Path\n",
    "from matplotlib.patches import Rectangle\n",
    "from matplotlib.colors import LinearSegmentedColormap\n",
    "\n",
    "# -----------------------------\n",
    "# A. 路徑與顏色設定\n",
    "# -----------------------------\n",
    "data_dir = Path(\"/home/jovyan/RT08/0925/bling_1010\")\n",
    "\n",
    "# 顏色（藍系）：負樣本淺藍、正樣本深藍\n",
    "COLOR_NEG = \"#90caf9\"   # light blue for set=0\n",
    "COLOR_POS = \"#1565c0\"   # deep blue for set=1\n",
    "EDGE_BLUE = \"#0d47a1\"   # 深藍邊框（適度使用）\n",
    "\n",
    "# 建立簡單的藍色漸層 colormap（由淺到深）\n",
    "cmap_blue = LinearSegmentedColormap.from_list(\"blue_grad\", [\"#bbdefb\", \"#2196f3\", \"#0d47a1\"])\n",
    "\n",
    "# 小工具：在長條圖上畫垂直方向的漸層（以 imshow 疊在 bar 上，營造漸層效果）\n",
    "def apply_vertical_gradient(ax, bar, cmap=cmap_blue, alpha=0.35):\n",
    "    \"\"\"對單一長條 bar 套上藍色垂直漸層覆蓋（不影響原色，只增添視覺層次）\"\"\"\n",
    "    x, y = bar.get_xy()\n",
    "    w, h = bar.get_width(), bar.get_height()\n",
    "    if h <= 0 or w <= 0:\n",
    "        return\n",
    "    # 建立漸層影像（N x 1），由 0→1\n",
    "    grad = np.linspace(0, 1, 256).reshape(-1, 1)\n",
    "    ax.imshow(\n",
    "        grad, aspect='auto', extent=(x, x+w, y, y+h),\n",
    "        origin='lower', cmap=cmap, alpha=alpha, zorder=bar.get_zorder()+1\n",
    "    )\n",
    "\n",
    "# 小工具：在圖上標示數值（置中或柱頂）\n",
    "def annotate_bar(ax, bars, fmt=\"{:,.0f}\", above_ratio=0.01):\n",
    "    \"\"\"在每個 bar 上方寫數值；above_ratio 控制上移比例\"\"\"\n",
    "    ymax = max([b.get_height() for b in bars]) if bars else 0\n",
    "    for b in bars:\n",
    "        h = b.get_height()\n",
    "        if h <= 0:\n",
    "            continue\n",
    "        ax.text(\n",
    "            b.get_x() + b.get_width()/2,\n",
    "            h + ymax*above_ratio,\n",
    "            fmt.format(h),\n",
    "            ha=\"center\", va=\"bottom\", fontsize=10, color=EDGE_BLUE\n",
    "        )\n",
    "\n",
    "# -----------------------------\n",
    "# B. 讀取資料並生成「segment 級」表格\n",
    "# -----------------------------\n",
    "files = sorted(data_dir.glob(\"*.csv\"))\n",
    "if not files:\n",
    "    raise SystemExit(\"❌ 沒有找到任何 CSV（請先產生 bling_1010 結果）\")\n",
    "\n",
    "all_segments = []      # 收集跨檔案的 segment 資料\n",
    "per_file_counts = []   # 每檔案 set=0/1 的 segment 數量（供第 6 張圖）\n",
    "\n",
    "for fp in files:\n",
    "    df = pd.read_csv(fp, low_memory=False, parse_dates=[\"senddate\"])\n",
    "    df.columns = [c.lower() for c in df.columns]\n",
    "    if \"set_1010\" not in df.columns or \"senddate\" not in df.columns:\n",
    "        continue\n",
    "\n",
    "    # 僅保留 set_1010 ∈ {0,1} 的資料，因為 2 是中間緩衝不視為訓練樣本\n",
    "    d = df[df[\"set_1010\"].isin([0, 1])].copy()\n",
    "    if d.empty:\n",
    "        per_file_counts.append({\"file\": fp.name, \"seg0\": 0, \"seg1\": 0})\n",
    "        continue\n",
    "\n",
    "    # 依時間排序並切出連續相同 set_1010 的區塊（block）\n",
    "    d = d.sort_values(\"senddate\").reset_index(drop=True)\n",
    "    d[\"block_id\"] = (d[\"set_1010\"] != d[\"set_1010\"].shift()).cumsum()\n",
    "\n",
    "    seg = (\n",
    "        d.groupby([\"set_1010\", \"block_id\"], as_index=False)\n",
    "         .agg(start_time=(\"senddate\", \"min\"),\n",
    "              end_time=(\"senddate\", \"max\"),\n",
    "              rows=(\"senddate\", \"count\"))\n",
    "    )\n",
    "    seg[\"delta_min\"] = (seg[\"end_time\"] - seg[\"start_time\"]).dt.total_seconds() / 60.0\n",
    "    seg[\"file\"] = fp.name\n",
    "    all_segments.append(seg)\n",
    "\n",
    "    # 記錄每檔案的 0/1 段數\n",
    "    cnt0 = int((seg[\"set_1010\"] == 0).sum())\n",
    "    cnt1 = int((seg[\"set_1010\"] == 1).sum())\n",
    "    per_file_counts.append({\"file\": fp.name, \"seg0\": cnt0, \"seg1\": cnt1})\n",
    "\n",
    "# 合併所有 segment\n",
    "seg_df = pd.concat(all_segments, ignore_index=True) if all_segments else pd.DataFrame(columns=[\"set_1010\",\"delta_min\",\"rows\",\"file\"])\n",
    "counts_df = pd.DataFrame(per_file_counts)\n",
    "\n",
    "# 便利的子集合\n",
    "seg0 = seg_df[seg_df[\"set_1010\"] == 0].copy()\n",
    "seg1 = seg_df[seg_df[\"set_1010\"] == 1].copy()\n",
    "\n",
    "# -----------------------------\n",
    "# C. 六張圖開始\n",
    "# -----------------------------\n",
    "plt.style.use(\"default\")\n",
    "plt.rcParams.update({\n",
    "    \"font.size\": 11,\n",
    "    \"axes.labelcolor\": \"#1f3b73\",\n",
    "    \"axes.edgecolor\": \"#607d8b\",\n",
    "    \"axes.titlesize\": 13,\n",
    "    \"axes.titleweight\": \"bold\",\n",
    "    \"axes.facecolor\": \"#f8f9fa\",\n",
    "    \"xtick.color\": \"#37474f\",\n",
    "    \"ytick.color\": \"#37474f\",\n",
    "    \"figure.facecolor\": \"white\",\n",
    "    \"grid.color\": \"#cfd8dc\",\n",
    "})\n",
    "\n",
    "# 1) 兩類 segments 數量（Bar, 漸層, 有數字）\n",
    "fig, ax = plt.subplots(figsize=(6, 4))\n",
    "vals = [len(seg0), len(seg1)]\n",
    "labels = [\"Negative (set=0)\", \"Positive (set=1)\"]\n",
    "colors = [COLOR_NEG, COLOR_POS]\n",
    "\n",
    "bars = ax.bar(labels, vals, color=colors, edgecolor=EDGE_BLUE, linewidth=1.0)\n",
    "ax.set_title(\"Number of Segments by Class\")\n",
    "ax.set_ylabel(\"Segments\")\n",
    "ax.grid(True, axis=\"y\", linestyle=\"--\", alpha=0.4)\n",
    "annotate_bar(ax, bars, fmt=\"{:,.0f}\", above_ratio=0.02)\n",
    "# 套漸層\n",
    "for b in bars:\n",
    "    apply_vertical_gradient(ax, b, cmap=cmap_blue, alpha=0.35)\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "# 2) 兩類 segments 時長直方圖（Histogram 疊放, 有數字）\n",
    "#    - 為了可讀性，對極端長尾可做上限裁切（例如 p99）\n",
    "p99 = seg_df[\"delta_min\"].quantile(0.99) if not seg_df.empty else 0\n",
    "clip_max = max(5, p99)  # 至少 5 分鐘\n",
    "x0 = np.clip(seg0[\"delta_min\"].values, 0, clip_max)\n",
    "x1 = np.clip(seg1[\"delta_min\"].values, 0, clip_max)\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(7, 4.5))\n",
    "bins = 40\n",
    "n0, be0, _ = ax.hist(x0, bins=bins, alpha=0.75, color=COLOR_NEG, edgecolor=EDGE_BLUE, label=\"Negative\", linewidth=0.8)\n",
    "n1, be1, _ = ax.hist(x1, bins=bins, alpha=0.55, color=COLOR_POS, edgecolor=EDGE_BLUE, label=\"Positive\", linewidth=0.8)\n",
    "ax.set_title(f\"Segment Length Distribution (clipped at p99≈{clip_max:.1f} min)\")\n",
    "ax.set_xlabel(\"Segment Length (minutes)\")\n",
    "ax.set_ylabel(\"Frequency\")\n",
    "ax.grid(True, linestyle=\"--\", alpha=0.4)\n",
    "ax.legend(frameon=False)\n",
    "\n",
    "# 在每個 bin 中央標示總數（n0+n1），避免過度擁擠：只標示前幾個最高的 bin\n",
    "total_n = n0 + n1\n",
    "topk = min(10, len(total_n))\n",
    "top_idx = np.argsort(total_n)[::-1][:topk]\n",
    "for idx in top_idx:\n",
    "    if total_n[idx] <= 0: \n",
    "        continue\n",
    "    xmid = (be0[idx] + be0[idx+1]) / 2\n",
    "    ax.text(xmid, total_n[idx], f\"{int(total_n[idx])}\", ha=\"center\", va=\"bottom\", fontsize=8, color=EDGE_BLUE, rotation=0)\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "# 3) ECDF（兩類, 標示 p50/p75/p90 數值）\n",
    "def ecdf(arr):\n",
    "    \"\"\"回傳排序後 x 與 [1..n]/n 的 y\"\"\"\n",
    "    x = np.sort(arr)\n",
    "    if len(x) == 0:\n",
    "        return x, np.array([])\n",
    "    y = np.arange(1, len(x)+1) / len(x)\n",
    "    return x, y\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(7, 4.5))\n",
    "x0, y0 = ecdf(seg0[\"delta_min\"].values)\n",
    "x1, y1 = ecdf(seg1[\"delta_min\"].values)\n",
    "ax.plot(x0, y0, drawstyle=\"steps-post\", color=COLOR_NEG, linewidth=2, label=\"Negative (set=0)\")\n",
    "ax.plot(x1, y1, drawstyle=\"steps-post\", color=COLOR_POS, linewidth=2, label=\"Positive (set=1)\")\n",
    "ax.set_title(\"ECDF of Segment Length\")\n",
    "ax.set_xlabel(\"Segment Length (minutes)\")\n",
    "ax.set_ylabel(\"ECDF\")\n",
    "ax.grid(True, linestyle=\"--\", alpha=0.4)\n",
    "ax.legend(frameon=False)\n",
    "\n",
    "# 標註關鍵百分位數（p50/p75/p90）\n",
    "for cls, arr, col, xpos in [(\"Neg\", seg0[\"delta_min\"].values, COLOR_NEG, 0.55),\n",
    "                            (\"Pos\", seg1[\"delta_min\"].values, COLOR_POS, 0.65)]:\n",
    "    if len(arr) == 0: \n",
    "        continue\n",
    "    p50, p75, p90 = np.percentile(arr, [50, 75, 90])\n",
    "    ax.axvline(p50, color=col, linestyle=\":\", alpha=0.6)\n",
    "    ax.text(p50, xpos, f\"{cls} p50={p50:.1f}m\", color=col, rotation=90, va=\"center\", ha=\"right\", fontsize=9)\n",
    "    ax.axvline(p75, color=col, linestyle=\":\", alpha=0.6)\n",
    "    ax.text(p75, xpos-0.08, f\"{cls} p75={p75:.1f}m\", color=col, rotation=90, va=\"center\", ha=\"right\", fontsize=9)\n",
    "    ax.axvline(p90, color=col, linestyle=\":\", alpha=0.6)\n",
    "    ax.text(p90, xpos-0.16, f\"{cls} p90={p90:.1f}m\", color=col, rotation=90, va=\"center\", ha=\"right\", fontsize=9)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "# 4) 箱型圖：兩類 segments 時長（標 N，背景加漸層色帶）\n",
    "fig, ax = plt.subplots(figsize=(6.5, 4.5))\n",
    "\n",
    "# 畫一個微淡藍背景帶，營造整體藍系漸層氛圍\n",
    "ax.add_patch(Rectangle((0.5, 0), 2.0, max(seg_df[\"delta_min\"].max() if not seg_df.empty else 1, 1),\n",
    "                       transform=ax.get_xaxis_transform(), color=\"#e3f2fd\", alpha=0.4, zorder=0))\n",
    "\n",
    "box = ax.boxplot([seg0[\"delta_min\"].values, seg1[\"delta_min\"].values],\n",
    "                 labels=[\"Negative (set=0)\", \"Positive (set=1)\"],\n",
    "                 showfliers=False, patch_artist=True,\n",
    "                 medianprops=dict(color=EDGE_BLUE, linewidth=2),\n",
    "                 boxprops=dict(color=EDGE_BLUE),\n",
    "                 whiskerprops=dict(color=EDGE_BLUE),\n",
    "                 capprops=dict(color=EDGE_BLUE))\n",
    "\n",
    "# 以兩個主色填滿箱型（淺藍/深藍）並疊加漸層\n",
    "for patch, c in zip(box[\"boxes\"], [COLOR_NEG, COLOR_POS]):\n",
    "    patch.set_facecolor(c)\n",
    "    patch.set_alpha(0.8)\n",
    "\n",
    "ax.set_title(\"Segment Length by Class (Boxplot)\")\n",
    "ax.set_ylabel(\"Segment Length (minutes)\")\n",
    "ax.grid(True, axis=\"y\", linestyle=\"--\", alpha=0.4)\n",
    "\n",
    "# 在每個箱型上方寫 N 值\n",
    "ns = [len(seg0), len(seg1)]\n",
    "for i, n in enumerate(ns, start=1):\n",
    "    ax.text(i, ax.get_ylim()[1]*0.95, f\"N={n:,}\", ha=\"center\", va=\"bottom\", fontsize=10, color=EDGE_BLUE)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "# 5) 總時長（小時）Bar（漸層, 有數字）\n",
    "total_hr0 = seg0[\"delta_min\"].sum()/60.0 if not seg0.empty else 0.0\n",
    "total_hr1 = seg1[\"delta_min\"].sum()/60.0 if not seg1.empty else 0.0\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(6, 4))\n",
    "vals = [total_hr0, total_hr1]\n",
    "labels = [\"Negative (set=0)\", \"Positive (set=1)\"]\n",
    "bars = ax.bar(labels, vals, color=[COLOR_NEG, COLOR_POS], edgecolor=EDGE_BLUE, linewidth=1.0)\n",
    "ax.set_title(\"Total Duration by Class (hours)\")\n",
    "ax.set_ylabel(\"Hours\")\n",
    "ax.grid(True, axis=\"y\", linestyle=\"--\", alpha=0.4)\n",
    "annotate_bar(ax, bars, fmt=\"{:,.1f}\", above_ratio=0.02)\n",
    "for b in bars:\n",
    "    apply_vertical_gradient(ax, b, cmap=cmap_blue, alpha=0.35)\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "# 6) 依檔案前 10 名（以「總 segments 數」排序）的兩類段數比較（Side-by-side Bars）\n",
    "#    - 先組成每檔案的 seg0 / seg1 數量，依 (seg0+seg1) 排序後取前 10 檔\n",
    "top_counts = counts_df.copy()\n",
    "top_counts[\"total\"] = top_counts[\"seg0\"] + top_counts[\"seg1\"]\n",
    "top10 = top_counts.sort_values(\"total\", ascending=False).head(10)\n",
    "\n",
    "x = np.arange(len(top10))\n",
    "width = 0.4\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(10, 4.8))\n",
    "b0 = ax.bar(x - width/2, top10[\"seg0\"], width, label=\"Negative (set=0)\", color=COLOR_NEG, edgecolor=EDGE_BLUE, linewidth=0.8)\n",
    "b1 = ax.bar(x + width/2, top10[\"seg1\"], width, label=\"Positive (set=1)\", color=COLOR_POS, edgecolor=EDGE_BLUE, linewidth=0.8)\n",
    "\n",
    "# 套漸層 + 數字\n",
    "for bar in list(b0) + list(b1):\n",
    "    apply_vertical_gradient(ax, bar, cmap=cmap_blue, alpha=0.35)\n",
    "annotate_bar(ax, list(b0), fmt=\"{:,.0f}\", above_ratio=0.01)\n",
    "annotate_bar(ax, list(b1), fmt=\"{:,.0f}\", above_ratio=0.01)\n",
    "\n",
    "ax.set_title(\"Top-10 Files by Total Segments\")\n",
    "ax.set_ylabel(\"Segments\")\n",
    "ax.set_xticks(x, [s.replace(\".csv\",\"\") for s in top10[\"file\"]], rotation=35, ha=\"right\")\n",
    "ax.grid(True, axis=\"y\", linestyle=\"--\", alpha=0.4)\n",
    "ax.legend(frameon=False, loc=\"upper right\")\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "55fd544e-3585-48c9-beec-5e7ab5c6df80",
   "metadata": {},
   "outputs": [],
   "source": [
    "數值都被擋到 我想要正負樣本的圖分兩張"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "id": "c2534333-3285-4975-ba13-50b18de27fa9",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 420x420 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x440 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x450 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 460x460 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 480x420 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x460 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 420x420 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x440 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x450 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 460x460 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 480x420 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1000x460 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# ==========================================================\n",
    "# 分開作圖版本：每種圖各輸出 Negative 與 Positive 兩張\n",
    "# - 顏色：Negative 淺藍 #90caf9；Positive 深藍 #1565c0\n",
    "# - 標籤英文、註解中文\n",
    "# - 來源：/home/jovyan/RT08/0925/bling_1010/*.csv\n",
    "# - 先把連續相同 set_1010 的列合併為 segment，再做統計與畫圖\n",
    "# ==========================================================\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from pathlib import Path\n",
    "\n",
    "# -----------------------------\n",
    "# A. 基本設定（顏色、樣式）\n",
    "# -----------------------------\n",
    "DATA_DIR = Path(\"/home/jovyan/RT08/0925/bling_1010\")\n",
    "\n",
    "COLOR_NEG = \"#90caf9\"  # 負樣本淺藍\n",
    "COLOR_POS = \"#1565c0\"  # 正樣本深藍\n",
    "EDGE_BLUE = \"#0d47a1\"\n",
    "\n",
    "plt.style.use(\"default\")\n",
    "plt.rcParams.update({\n",
    "    \"font.size\": 11,\n",
    "    \"axes.labelcolor\": \"#1f3b73\",\n",
    "    \"axes.edgecolor\": \"#607d8b\",\n",
    "    \"axes.titlesize\": 13,\n",
    "    \"axes.titleweight\": \"bold\",\n",
    "    \"axes.facecolor\": \"#f8f9fa\",\n",
    "    \"xtick.color\": \"#37474f\",\n",
    "    \"ytick.color\": \"#37474f\",\n",
    "    \"figure.facecolor\": \"white\",\n",
    "    \"grid.color\": \"#cfd8dc\",\n",
    "})\n",
    "\n",
    "# -----------------------------\n",
    "# B. 讀檔 → 做成「segment 級」資料\n",
    "#   規則：連續相同 set_1010 的列視為同一段\n",
    "# -----------------------------\n",
    "files = sorted(DATA_DIR.glob(\"*.csv\"))\n",
    "if not files:\n",
    "    raise SystemExit(\"❌ 找不到任何 CSV（請先產生 bling_1010 結果）。\")\n",
    "\n",
    "all_segments = []\n",
    "per_file_counts = []\n",
    "\n",
    "for fp in files:\n",
    "    df = pd.read_csv(fp, low_memory=False, parse_dates=[\"senddate\"])\n",
    "    df.columns = [c.lower() for c in df.columns]\n",
    "    if \"senddate\" not in df.columns or \"set_1010\" not in df.columns:\n",
    "        continue\n",
    "\n",
    "    # 只取 0/1（2 是中間緩衝不納入正負樣本圖）\n",
    "    d = df[df[\"set_1010\"].isin([0, 1])].copy()\n",
    "    if d.empty:\n",
    "        per_file_counts.append({\"file\": fp.name, \"seg0\": 0, \"seg1\": 0})\n",
    "        continue\n",
    "\n",
    "    d = d.sort_values(\"senddate\").reset_index(drop=True)\n",
    "    d[\"block_id\"] = (d[\"set_1010\"] != d[\"set_1010\"].shift()).cumsum()\n",
    "\n",
    "    seg = (\n",
    "        d.groupby([\"set_1010\", \"block_id\"], as_index=False)\n",
    "          .agg(start_time=(\"senddate\", \"min\"),\n",
    "               end_time=(\"senddate\", \"max\"),\n",
    "               rows=(\"senddate\", \"count\"))\n",
    "    )\n",
    "    seg[\"delta_min\"] = (seg[\"end_time\"] - seg[\"start_time\"]).dt.total_seconds() / 60.0\n",
    "    seg[\"file\"] = fp.name\n",
    "    all_segments.append(seg)\n",
    "\n",
    "    per_file_counts.append({\n",
    "        \"file\": fp.name,\n",
    "        \"seg0\": int((seg[\"set_1010\"] == 0).sum()),\n",
    "        \"seg1\": int((seg[\"set_1010\"] == 1).sum())\n",
    "    })\n",
    "\n",
    "seg_df = pd.concat(all_segments, ignore_index=True) if all_segments else pd.DataFrame(columns=[\"set_1010\",\"delta_min\",\"rows\",\"file\"])\n",
    "seg0 = seg_df[seg_df[\"set_1010\"] == 0].copy()\n",
    "seg1 = seg_df[seg_df[\"set_1010\"] == 1].copy()\n",
    "counts_df = pd.DataFrame(per_file_counts)\n",
    "\n",
    "# 安全防呆：避免空資料時畫圖報錯\n",
    "def _safe_len(a): return 0 if a is None or len(a)==0 else len(a)\n",
    "\n",
    "# -----------------------------\n",
    "# C. 小工具：單類別的圖都用這些函式畫\n",
    "# -----------------------------\n",
    "def bar_count_one(cls_name, n_segments, color):\n",
    "    \"\"\"單類別：段數量長條圖（上方有數字，避免遮擋）\"\"\"\n",
    "    fig, ax = plt.subplots(figsize=(4.2, 4.2))\n",
    "    bar = ax.bar([cls_name], [n_segments], color=color, edgecolor=EDGE_BLUE, linewidth=1.0)\n",
    "    ymax = max(n_segments, 1)\n",
    "    ax.text(0, n_segments + ymax*0.03, f\"{n_segments:,}\", ha=\"center\", va=\"bottom\", color=EDGE_BLUE, fontsize=11)\n",
    "    ax.set_title(\"Number of Segments\")\n",
    "    ax.set_ylabel(\"Segments\")\n",
    "    ax.grid(True, axis=\"y\", linestyle=\"--\", alpha=0.4)\n",
    "    ax.set_ylim(0, n_segments * 1.12 + 1)  # 預留標註空間\n",
    "    plt.tight_layout(); plt.show()\n",
    "\n",
    "def hist_one(arr, color, label):\n",
    "    \"\"\"單類別：時長直方圖（自動計數標上方，避免擁擠只標最高的 bins）\"\"\"\n",
    "    if _safe_len(arr)==0:\n",
    "        print(f\"⚠️ {label}: no data.\"); return\n",
    "    clip = np.quantile(arr, 0.99)\n",
    "    x = np.clip(arr, 0, clip)\n",
    "    fig, ax = plt.subplots(figsize=(7, 4.4))\n",
    "    n, bins, patches = ax.hist(x, bins=40, color=color, edgecolor=EDGE_BLUE, alpha=0.85)\n",
    "    ax.set_title(f\"{label} - Segment Length Distribution (clipped at p99≈{clip:.1f} min)\")\n",
    "    ax.set_xlabel(\"Segment Length (minutes)\")\n",
    "    ax.set_ylabel(\"Frequency\")\n",
    "    ax.grid(True, linestyle=\"--\", alpha=0.4)\n",
    "    # 只標註前 8 個最高 bin，避免擁擠\n",
    "    top_idx = np.argsort(n)[::-1][:8]\n",
    "    ymax = n.max() if n.size else 1\n",
    "    for i in top_idx:\n",
    "        if n[i] <= 0: continue\n",
    "        xmid = (bins[i]+bins[i+1])/2\n",
    "        ax.text(xmid, n[i] + ymax*0.02, f\"{int(n[i])}\", ha=\"center\", va=\"bottom\", fontsize=9, color=EDGE_BLUE)\n",
    "    plt.tight_layout(); plt.show()\n",
    "\n",
    "def ecdf_one(arr, color, label):\n",
    "    \"\"\"單類別：ECDF（標示 p50/p75/p90 數值，不會互擋）\"\"\"\n",
    "    if _safe_len(arr)==0:\n",
    "        print(f\"⚠️ {label}: no data.\"); return\n",
    "    x = np.sort(arr)\n",
    "    y = np.arange(1, len(x)+1)/len(x)\n",
    "    fig, ax = plt.subplots(figsize=(7, 4.5))\n",
    "    ax.plot(x, y, drawstyle=\"steps-post\", color=color, linewidth=2, label=label)\n",
    "    ax.set_title(f\"{label} - ECDF of Segment Length\")\n",
    "    ax.set_xlabel(\"Segment Length (minutes)\")\n",
    "    ax.set_ylabel(\"ECDF\")\n",
    "    ax.grid(True, linestyle=\"--\", alpha=0.4)\n",
    "    # 百分位線（把文字放在左上角垂直顯示，避免重疊）\n",
    "    p50, p75, p90 = np.percentile(x, [50, 75, 90])\n",
    "    for v, txt, yoff in [(p50, \"p50\", 0.90), (p75, \"p75\", 0.82), (p90, \"p90\", 0.74)]:\n",
    "        ax.axvline(v, color=color, linestyle=\":\", alpha=0.7)\n",
    "        ax.text(v, yoff, f\"{txt}={v:.1f}m\", color=color, rotation=90, va=\"center\", ha=\"right\", fontsize=9)\n",
    "    plt.tight_layout(); plt.show()\n",
    "\n",
    "def box_one(arr, color, label, n_segments):\n",
    "    \"\"\"單類別：箱型圖（單箱，標 N；上下留白避免文字蓋到）\"\"\"\n",
    "    if _safe_len(arr)==0:\n",
    "        print(f\"⚠️ {label}: no data.\"); return\n",
    "    fig, ax = plt.subplots(figsize=(4.6, 4.6))\n",
    "    bp = ax.boxplot([arr], labels=[label], showfliers=False, patch_artist=True,\n",
    "                    medianprops=dict(color=EDGE_BLUE, linewidth=2),\n",
    "                    boxprops=dict(color=EDGE_BLUE),\n",
    "                    whiskerprops=dict(color=EDGE_BLUE),\n",
    "                    capprops=dict(color=EDGE_BLUE))\n",
    "    bp[\"boxes\"][0].set_facecolor(color); bp[\"boxes\"][0].set_alpha(0.8)\n",
    "    ax.set_title(\"Segment Length by Class (Boxplot)\")\n",
    "    ax.set_ylabel(\"Segment Length (minutes)\")\n",
    "    ax.grid(True, axis=\"y\", linestyle=\"--\", alpha=0.4)\n",
    "    ymax = ax.get_ylim()[1]\n",
    "    ax.text(1, ymax*0.95, f\"N={n_segments:,}\", ha=\"center\", va=\"bottom\", fontsize=10, color=EDGE_BLUE)\n",
    "    ax.set_ylim(ax.get_ylim()[0], ymax*1.08)  # 再多留一些空間\n",
    "    plt.tight_layout(); plt.show()\n",
    "\n",
    "def total_duration_bar_one(total_hours, color, label):\n",
    "    \"\"\"單類別：總時長（小時）長條，數值置頂\"\"\"\n",
    "    fig, ax = plt.subplots(figsize=(4.8, 4.2))\n",
    "    bar = ax.bar([label], [total_hours], color=color, edgecolor=EDGE_BLUE, linewidth=1.0)\n",
    "    ymax = max(total_hours, 1.0)\n",
    "    ax.text(0, total_hours + ymax*0.03, f\"{total_hours:,.1f}\", ha=\"center\", va=\"bottom\", color=EDGE_BLUE, fontsize=11)\n",
    "    ax.set_title(\"Total Duration by Class (hours)\")\n",
    "    ax.set_ylabel(\"Hours\")\n",
    "    ax.grid(True, axis=\"y\", linestyle=\"--\", alpha=0.4)\n",
    "    ax.set_ylim(0, total_hours*1.12 + 1)\n",
    "    plt.tight_layout(); plt.show()\n",
    "\n",
    "def top10_files_one(counts_df, col, color, label):\n",
    "    \"\"\"單類別：依檔案段數排序取前10檔（側邊標數字，避免擋到）\"\"\"\n",
    "    if col not in counts_df.columns:\n",
    "        print(f\"⚠️ {label}: no column {col}.\"); return\n",
    "    top10 = counts_df.sort_values(col, ascending=False).head(10)\n",
    "    fig, ax = plt.subplots(figsize=(10, 4.6))\n",
    "    x = np.arange(len(top10))\n",
    "    bars = ax.bar(x, top10[col], color=color, edgecolor=EDGE_BLUE, linewidth=0.8)\n",
    "    ax.set_title(f\"Top-10 Files by Segments ({label})\")\n",
    "    ax.set_ylabel(\"Segments\")\n",
    "    ax.set_xticks(x, [s.replace(\".csv\",\"\") for s in top10[\"file\"]], rotation=35, ha=\"right\")\n",
    "    ax.grid(True, axis=\"y\", linestyle=\"--\", alpha=0.4)\n",
    "    # 數字放在柱子上方，且略向上位移避免擋到\n",
    "    ymax = max(top10[col].max(), 1)\n",
    "    for b in bars:\n",
    "        h = b.get_height()\n",
    "        ax.text(b.get_x()+b.get_width()/2, h + ymax*0.02, f\"{int(h)}\", ha=\"center\", va=\"bottom\", fontsize=9, color=EDGE_BLUE)\n",
    "    ax.set_ylim(0, ymax*1.15 + 1)\n",
    "    plt.tight_layout(); plt.show()\n",
    "\n",
    "# -----------------------------\n",
    "# D. 逐類別輸出六種圖（共 12 張）\n",
    "# -----------------------------\n",
    "# Negative (set=0)\n",
    "bar_count_one(\"Negative (set=0)\", len(seg0), COLOR_NEG)\n",
    "hist_one(seg0[\"delta_min\"].values, COLOR_NEG, \"Negative (set=0)\")\n",
    "ecdf_one(seg0[\"delta_min\"].values, COLOR_NEG, \"Negative (set=0)\")\n",
    "box_one(seg0[\"delta_min\"].values, COLOR_NEG, \"Negative (set=0)\", len(seg0))\n",
    "total_duration_bar_one(seg0[\"delta_min\"].sum()/60.0, COLOR_NEG, \"Negative (set=0)\")\n",
    "top10_files_one(counts_df, \"seg0\", COLOR_NEG, \"Negative (set=0)\")\n",
    "\n",
    "# Positive (set=1)\n",
    "bar_count_one(\"Positive (set=1)\", len(seg1), COLOR_POS)\n",
    "hist_one(seg1[\"delta_min\"].values, COLOR_POS, \"Positive (set=1)\")\n",
    "ecdf_one(seg1[\"delta_min\"].values, COLOR_POS, \"Positive (set=1)\")\n",
    "box_one(seg1[\"delta_min\"].values, COLOR_POS, \"Positive (set=1)\", len(seg1))\n",
    "total_duration_bar_one(seg1[\"delta_min\"].sum()/60.0, COLOR_POS, \"Positive (set=1)\")\n",
    "top10_files_one(counts_df, \"seg1\", COLOR_POS, \"Positive (set=1)\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4cb5fda7-984f-415f-99d6-7b933c05f2ef",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "id": "70044b0b-7ae9-4036-b911-d98a6a134158",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "🔎 找到 122 份檔案，開始彙整...\n",
      "\n",
      "=== 📋 Per-Patient Summary: counts, date range, and segments ===\n",
      "         Patient_ID          start_time            end_time  duration_hours  rows_set0  rows_set1  segments_set0  segments_set1  segments_total\n",
      "PatNo_ID_1587490083 2022-01-27 21:59:31 2022-03-01 10:41:01         780.690      19631       9080           1915           1915            3830\n",
      "PatNo_ID_1586172659 2022-02-22 15:37:02 2022-03-21 11:29:03         643.870      14841       6981           1740           1740            3480\n",
      "PatNo_ID_1589034524 2022-02-10 15:59:02 2022-03-22 11:57:03         955.970      22075      10024           1730           1730            3460\n",
      "PatNo_ID_1574148494 2021-12-21 19:39:00 2022-01-23 12:41:02         785.030      20342       9104           1712           1712            3424\n",
      "PatNo_ID_1591609798 2022-02-06 19:50:02 2022-03-03 13:23:01         593.550      13888       6523           1688           1688            3376\n",
      "PatNo_ID_1577042911 2021-12-02 19:50:03 2021-12-28 09:06:02         613.270      15564       7170           1530           1530            3060\n",
      "PatNo_ID_1578784257 2022-01-25 19:49:02 2022-02-17 09:14:01         541.420      11791       5787           1492           1492            2984\n",
      "             089271 2021-12-17 10:13:05 2022-01-12 10:11:04         623.970      12400       5871           1491           1491            2982\n",
      "PatNo_ID_1572481361 2022-03-13 03:55:02 2022-04-11 08:27:00         700.530      14990       6810           1265           1265            2530\n",
      "PatNo_ID_1567832735 2022-02-06 04:20:04 2022-03-03 16:26:00         612.100      16111       7194           1218           1218            2436\n",
      "             095323 2021-12-23 10:19:54 2022-01-06 01:35:02         327.250       6696       3345           1107           1107            2214\n",
      "PatNo_ID_1566911879 2022-01-30 21:35:31 2022-03-08 09:46:03         876.180      21056       9566           1084           1084            2168\n",
      "PatNo_ID_1576964560 2022-02-13 12:42:03 2022-03-02 09:56:01         405.230       8937       4234           1043           1043            2086\n",
      "PatNo_ID_1568039398 2022-01-21 11:06:00 2022-02-14 10:15:01         575.150      15695       6822           1016           1016            2032\n",
      "PatNo_ID_1574528808 2022-03-12 19:36:00 2022-03-23 10:00:03         254.400       4158       2312           1007           1007            2014\n",
      "PatNo_ID_1593087886 2022-01-20 08:43:01 2022-02-14 14:22:01         605.650       9836       4624            980            980            1960\n",
      "PatNo_ID_1592560504 2022-02-25 20:13:02 2022-03-10 09:26:02         301.220       6422       3164            948            948            1896\n",
      "             114309 2021-12-19 22:35:37 2022-01-05 13:24:04         398.810       9885       4506            936            936            1872\n",
      "PatNo_ID_1593593586 2022-01-07 19:31:04 2022-01-21 06:15:01         322.730       7394       3557            915            915            1830\n",
      "             230933 2021-12-20 19:45:25 2022-01-12 09:39:02         541.890      11560       5222            819            819            1638\n",
      "             095707 2021-12-21 22:43:02 2022-01-04 12:18:02         325.580       7809       3608            796            796            1592\n",
      "PatNo_ID_1576115572 2022-03-12 05:14:58 2022-03-25 12:06:04         318.850       7313       3557            793            793            1586\n",
      "PatNo_ID_1566671274 2022-01-05 19:37:32 2022-01-22 09:25:00         397.790      10455       4679            784            784            1568\n",
      "PatNo_ID_1579498177 2022-01-21 21:59:49 2022-02-06 11:25:01         373.420      10286       4618            666            666            1332\n",
      "PatNo_ID_1594471407 2022-03-10 12:11:02 2022-03-18 08:49:04         188.630       4223       2062            659            659            1318\n",
      "PatNo_ID_1581633231 2022-01-08 14:50:31 2022-01-19 14:01:04         263.180       6227       2932            641            641            1282\n",
      "PatNo_ID_1568574099 2022-02-21 18:09:32 2022-03-03 09:13:02         231.060       4947       2345            632            632            1264\n",
      "PatNo_ID_1574987447 2022-03-03 14:48:02 2022-03-17 10:55:02         332.120       8831       3886            627            627            1254\n",
      "PatNo_ID_1582937076 2022-03-16 18:05:35 2022-04-02 14:53:02         404.790      10380       4616            617            617            1234\n",
      "PatNo_ID_1580766093 2021-12-30 20:36:02 2022-01-25 09:33:03         612.950       7723       3470            616            616            1232\n",
      "PatNo_ID_1575975485 2022-01-25 22:34:04 2022-02-06 07:59:03         273.420       6909       3139            612            612            1224\n",
      "PatNo_ID_1590616537 2022-03-26 15:31:03 2022-04-05 11:52:01         236.350       5927       2720            599            599            1198\n",
      "PatNo_ID_1575256902 2022-01-13 15:14:30 2022-01-20 09:00:03         161.760       3155       1624            592            592            1184\n",
      "PatNo_ID_1570089466 2022-01-21 15:19:32 2022-02-03 13:31:03         310.190       6005       2802            586            586            1172\n",
      "PatNo_ID_1594305136 2022-01-02 13:46:33 2022-01-15 12:15:04         310.480       7060       3168            560            560            1120\n",
      "PatNo_ID_1594511914 2022-03-27 14:44:35 2022-04-03 12:08:05         165.390       4106       1997            518            518            1036\n",
      "PatNo_ID_1566123680 2022-01-29 13:59:32 2022-02-14 10:11:00         380.190      10294       4531            496            496             992\n",
      "PatNo_ID_1589018086 2022-03-22 02:38:01 2022-03-31 11:03:03         224.420       5938       2675            494            494             988\n",
      "PatNo_ID_1581019504 2022-02-21 19:40:30 2022-03-12 09:01:03         445.340       9209       4696            475            475             950\n",
      "PatNo_ID_1582635996 2022-01-18 16:57:45 2022-01-27 19:56:00         218.970       5916       2656            463            463             926\n",
      "PatNo_ID_1590854576 2022-02-11 09:38:32 2022-02-22 14:30:02         268.860       7161       3146            459            459             918\n",
      "PatNo_ID_1572562839 2022-03-18 21:52:00 2022-04-02 12:12:33         350.340       7257       3209            452            452             904\n",
      "PatNo_ID_1571945701 2021-12-31 15:29:02 2022-01-11 11:52:01         260.380       6612       2902            436            436             872\n",
      "PatNo_ID_1586897008 2022-03-10 19:49:04 2022-03-15 11:10:01         111.350       2229       1152            413            413             826\n",
      "PatNo_ID_1588957997 2022-01-03 20:51:32 2022-01-11 11:40:01         182.810       4184       1931            412            412             824\n",
      "PatNo_ID_1594437309 2022-02-23 08:35:03 2022-03-02 12:35:04         172.000       4379       2021            398            398             796\n",
      "PatNo_ID_1594294180 2022-01-04 21:30:00 2022-01-11 12:05:00         158.580       3753       1754            395            395             790\n",
      "PatNo_ID_1569944983 2022-03-03 05:35:03 2022-03-08 12:01:02         126.430       2932       1396            381            381             762\n",
      "PatNo_ID_1570242703 2022-03-18 04:15:30 2022-03-25 12:12:04         175.940       4677       2104            374            374             748\n",
      "PatNo_ID_1590136310 2022-03-13 14:35:31 2022-03-17 10:01:03          91.430       1736        920            356            356             712\n",
      "PatNo_ID_1574270349 2022-03-01 20:30:04 2022-03-06 09:04:03         108.570       2595       1245            355            355             710\n",
      "PatNo_ID_1567747650 2022-02-18 14:38:04 2022-02-26 02:47:01         180.150       4830       2147            343            343             686\n",
      "PatNo_ID_1570642083 2022-01-16 19:34:01 2022-01-30 10:49:02         327.250       6426       2812            318            318             636\n",
      "PatNo_ID_1594335109 2022-01-22 06:31:01 2022-01-25 18:47:04          84.270       1668        825            318            318             636\n",
      "PatNo_ID_1567804800 2022-02-15 04:00:02 2022-02-27 13:24:02         297.400       5890       2724            305            305             610\n",
      "PatNo_ID_1570273244 2022-02-24 18:06:30 2022-03-03 12:11:03         162.080       4484       1966            296            296             592\n",
      "PatNo_ID_1564148644 2021-12-10 15:06:35 2022-01-26 09:16:03        1122.160       3924       1691            285            285             570\n",
      "PatNo_ID_1594479330 2022-03-14 04:14:05 2022-03-16 23:59:01          67.750       1247        679            281            281             562\n",
      "PatNo_ID_1594423683 2022-02-15 20:25:03 2022-02-19 11:15:04          86.830       1923        950            279            279             558\n",
      "PatNo_ID_1573063188 2022-02-15 23:07:03 2022-02-19 11:11:02          84.070       1932        951            276            276             552\n",
      "PatNo_ID_1594319286 2022-01-10 23:07:02 2022-01-13 17:41:00          66.570       1248        659            269            269             538\n",
      "PatNo_ID_1594511911 2022-03-27 15:00:01 2022-03-31 10:15:01          91.250       2327       1086            268            268             536\n",
      "PatNo_ID_1565148312 2022-02-02 20:14:03 2022-02-06 15:22:00          91.130       2448       1120            247            247             494\n",
      "PatNo_ID_1589324603 2022-03-07 15:25:02 2022-03-10 11:56:02          68.520       1670        802            229            229             458\n",
      "PatNo_ID_1593472048 2022-01-17 16:52:01 2022-01-22 09:52:01         113.000       2625       1260            224            224             448\n",
      "PatNo_ID_1569083701 2022-01-26 19:23:58 2022-01-29 03:12:02          55.800       1048        569            222            222             444\n",
      "PatNo_ID_1594441887 2022-02-22 23:31:00 2022-03-02 12:37:02         181.100       4187       1789            218            218             436\n",
      "PatNo_ID_1575502382 2022-01-16 20:37:31 2022-01-21 10:50:00         110.210       2962       1313            213            213             426\n",
      "PatNo_ID_1573964540 2022-03-09 12:22:03 2022-03-12 13:36:02          73.230       1754        832            195            195             390\n",
      "PatNo_ID_1594464829 2022-03-07 17:33:01 2022-03-10 11:23:04          65.830       1480        697            191            191             382\n",
      "PatNo_ID_1588794796 2022-03-15 21:27:27 2022-03-22 10:20:04         156.880       4563       1947            188            188             376\n",
      "PatNo_ID_1594439781 2022-03-23 23:43:32 2022-03-29 12:21:00         132.620       3291       1434            181            181             362\n",
      "PatNo_ID_1594322594 2022-01-15 10:25:00 2022-01-19 04:04:00          89.650       2553       1124            170            170             340\n",
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      "PatNo_ID_1575060177 2022-01-08 19:48:01 2022-01-12 11:11:00          87.380       2092        946            161            161             322\n",
      "PatNo_ID_1581003248 2022-01-25 19:16:30 2022-01-30 09:56:03         110.660       2912       1271            154            154             308\n",
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      "PatNo_ID_1563587183 2022-03-22 15:03:00 2022-03-27 09:45:02         114.700       2432       1135            140            140             280\n",
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      "PatNo_ID_1592044724 2022-03-03 20:06:59 2022-03-08 11:56:00         111.820       3230       1357            126            126             252\n",
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    }
   ],
   "source": [
    "\"\"\"\"我想要看每一個病患在set_1010=1, =0的筆數 請直接印在程式碼中 用表格\n",
    "這個表格中我想看資料的起迄時間以及segemnt的段數\n",
    "因為我要檢查是不是真的\n",
    "調參事件發生後的 20% 區段 → set=1\n",
    "調參事件發生前的 50% 區段 → set=0\n",
    "\"\"\"\n",
    "# ==========================================================\n",
    "# 每位病患：資料起迄時間 + set_1010=0/1 筆數 + segment 段數\n",
    "# - 資料來源：/home/jovyan/RT08/0925/bling_1010/*.csv\n",
    "# - 規則：\n",
    "#   1) 起訖時間：以 senddate 的最小/最大值（預設過濾 nan_check==1）\n",
    "#   2) 筆數：set_1010==0 / ==1 的列數\n",
    "#   3) 段數（segments）：在 set∈{0,1} 下，連續相同 set_1010 視為一段\n",
    "# - 輸出：直接印表；可選擇存成 CSV\n",
    "# ==========================================================\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from pathlib import Path\n",
    "\n",
    "data_dir = Path(\"/home/jovyan/RT08/0925/bling_1010\")\n",
    "files = sorted(data_dir.glob(\"*.csv\"))\n",
    "print(f\"🔎 找到 {len(files)} 份檔案，開始彙整...\\n\")\n",
    "\n",
    "rows = []\n",
    "\n",
    "for fp in files:\n",
    "    df = pd.read_csv(fp, low_memory=False, parse_dates=[\"senddate\"])\n",
    "    df.columns = [c.lower() for c in df.columns]\n",
    "\n",
    "    if \"senddate\" not in df.columns or \"set_1010\" not in df.columns:\n",
    "        print(f\"⚠️ {fp.name} 缺 senddate 或 set_1010，略過。\")\n",
    "        continue\n",
    "\n",
    "    # —— 1) 口徑：只計算有效資料 —— \n",
    "    # 若有 nan_check 欄位，僅保留 nan_check==1；否則只要 senddate 非空即可\n",
    "    use = df.copy()\n",
    "    if \"nan_check\" in use.columns:\n",
    "        use = use[use[\"nan_check\"] == 1]\n",
    "    use = use[use[\"senddate\"].notna()].copy()\n",
    "\n",
    "    if use.empty:\n",
    "        rows.append({\n",
    "            \"Patient_ID\": fp.stem,\n",
    "            \"start_time\": pd.NaT,\n",
    "            \"end_time\": pd.NaT,\n",
    "            \"duration_hours\": np.nan,\n",
    "            \"rows_set0\": 0,\n",
    "            \"rows_set1\": 0,\n",
    "            \"segments_set0\": 0,\n",
    "            \"segments_set1\": 0,\n",
    "            \"segments_total\": 0\n",
    "        })\n",
    "        continue\n",
    "\n",
    "    # 起訖時間 & 觀察時長（小時）\n",
    "    start_time = use[\"senddate\"].min()\n",
    "    end_time   = use[\"senddate\"].max()\n",
    "    duration_hours = (end_time - start_time).total_seconds() / 3600.0\n",
    "\n",
    "    # —— 2) 筆數：set_1010==0 / ==1 ——\n",
    "    rows_set0 = int((use[\"set_1010\"] == 0).sum())\n",
    "    rows_set1 = int((use[\"set_1010\"] == 1).sum())\n",
    "\n",
    "    # —— 3) 段數：在 set∈{0,1} 下，連續相同 set_1010 視為一段 ——\n",
    "    seg_df = use[use[\"set_1010\"].isin([0, 1])].sort_values(\"senddate\").reset_index(drop=True)\n",
    "    if seg_df.empty:\n",
    "        seg0_cnt = 0\n",
    "        seg1_cnt = 0\n",
    "        seg_total = 0\n",
    "    else:\n",
    "        # 當前列的 set 與前一列不同時，視為新段落開始\n",
    "        seg_df[\"block_id\"] = (seg_df[\"set_1010\"] != seg_df[\"set_1010\"].shift()).cumsum()\n",
    "        # 各 set_1010 的段數\n",
    "        seg_by = seg_df.groupby([\"set_1010\", \"block_id\"]).size().reset_index(name=\"cnt\")\n",
    "        seg0_cnt = int((seg_by[\"set_1010\"] == 0).sum())\n",
    "        seg1_cnt = int((seg_by[\"set_1010\"] == 1).sum())\n",
    "        seg_total = int(len(seg_by))\n",
    "\n",
    "    rows.append({\n",
    "        \"Patient_ID\": fp.stem,\n",
    "        \"start_time\": start_time,\n",
    "        \"end_time\": end_time,\n",
    "        \"duration_hours\": round(duration_hours, 2),\n",
    "        \"rows_set0\": rows_set0,\n",
    "        \"rows_set1\": rows_set1,\n",
    "        \"segments_set0\": seg0_cnt,\n",
    "        \"segments_set1\": seg1_cnt,\n",
    "        \"segments_total\": seg_total\n",
    "    })\n",
    "\n",
    "# —— 彙整輸出（按 segments_total 由高到低排序） ——\n",
    "out_df = pd.DataFrame(rows)\n",
    "out_df = out_df.sort_values([\"segments_total\", \"Patient_ID\"], ascending=[False, True]).reset_index(drop=True)\n",
    "\n",
    "print(\"=== 📋 Per-Patient Summary: counts, date range, and segments ===\")\n",
    "# 美化顯示：時間格式到秒\n",
    "with pd.option_context(\"display.max_rows\", None,\n",
    "                       \"display.max_columns\", None,\n",
    "                       \"display.width\", 160,\n",
    "                       \"display.max_colwidth\", 40):\n",
    "    # 將時間格式化為字串再印出（避免長格式擋版面）\n",
    "    disp = out_df.copy()\n",
    "    disp[\"start_time\"] = disp[\"start_time\"].dt.strftime(\"%Y-%m-%d %H:%M:%S\")\n",
    "    disp[\"end_time\"]   = disp[\"end_time\"].dt.strftime(\"%Y-%m-%d %H:%M:%S\")\n",
    "    print(disp.to_string(index=False))\n",
    "\n",
    "# 若想存成 CSV，解除下一行註解（會輸出到 1002 目錄）\n",
    "# out_df.to_csv(\"/home/jovyan/RT08/0925/1002/set1010_counts_segments_per_patient.csv\", index=False, encoding=\"utf-8-sig\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "28255fb8-591a-4a39-af02-ce06e95aa1b4",
   "metadata": {},
   "outputs": [],
   "source": [
    "| 欄位名稱               | 含義                                 | 說明                                                             |\n",
    "| ------------------ | ---------------------------------- | -------------------------------------------------------------- |\n",
    "| **rows_set0**      | set_1010 == 0 的資料筆數                | 指病患在「負樣本區段」的總列數（每列代表一筆時間點資料，例如每分鐘一筆）。                          |\n",
    "| **rows_set1**      | set_1010 == 1 的資料筆數                | 指病患在「正樣本區段」的總列數（即標為 1 的時段內資料筆數）。                               |\n",
    "| **segments_set0**  | set_1010 == 0 的連續段數                | 在病患資料中，連續出現的 0 被視為一個 segment，例如 000111000 → 負樣本 segment 為 2 段。 |\n",
    "| **segments_set1**  | set_1010 == 1 的連續段數                | 同理，連續的 1 視為一段，例如 000111000 → 正樣本 segment 為 1 段。                |\n",
    "| **segments_total** | 總段數（segments_set0 + segments_set1） | 病患總共被分成多少段可用資料（僅計 0、1 段，不含 2 或空白區段）。                           |"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "id": "d39026e2-8d9b-41bb-9a76-e4bb0126de9e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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y/R7tu3UPnTdhYPzop8n/+E5jNyrQaeMbTxWPRB1dMGOurjp0wya32dry9fHcJyt134mb6fbJQ3XHqwt1zsNzNOuK0ZIax/au1xY1u+5Vh26oA8f2VLe8LB22XS89NmuZThvfX4tKw3rnhwpdecgQSdJ3i2vUqyhbPdf4oGzzAXl68O2lHa6ffR+skD1YIn+w4qbsmV/tzqtOapKkgM/r0YzTh+vjeVX63/flmv7WEv3tPz/rufO2VDC78Y3p347cSFttkPiJrC+Jz68k3y+fV1pelfjp86rqdY8oakmvwmyFI47KayMJRz+trG7Q6A2bf4Pf0dPuehaue2TVyqr21b6+ulr+kD7IHqykKnunjR+g08YP0OyltXr6o5UJF8xY3bhZ7dP5VXr3xwq9dMHIJrdVXhPRY7OW6aFTh2vk4HzNeHeZJv/jO7116dYqyvUr1BBrcr3LDx6io+74Vl6vR8f8eipZS8prIqoKRfXVgmq9dekolddENPnu75SX49MZezXWvGbDTJLueWORNu2Xq+2GFmnWjxXrbLO15etjt827aYdhjUcfHfq7Xrrx+QUqq25Qt/ys+Li3xT6juuuCGXN1y0sLFI1Jk3fuo91HlEiSauujKgwmHjVWGPSpOhTtUO3s+2CF7MES+YMVt2XPtPnkOI6q6+qUHwy6ZkBa4/F4NHajQo3dqFBn7T1Qv7v0E738RalO3L2f+hRn6+eVIR04tmeT667+5LQzrx6X7fdqi4H5evv7cu098rfT397+vlzjtyhp83a2GJSnLJ9Hb39frn237iGpcY6rHxbX6uIWrpDX0dPuth5SoLe/L9efdvtt3qf/fV+u0Rs23azqTF0xf0gPZA9WUp29Tfrkanj/XJ3z8Bw9OmXzdZY3RGP684y5uvqwDZWT1fTcSnk5Xo0e0l1jN2r8vTB5XF/d9doifTK/SjtvWqxNzn6/xRoufWKe9tmqu7q38qFGbk7j45/zh0HKy/EpL8en43fpq4ffXRZvPq3ppxUhPfC/pXq5maZZa8vX15qn7ef+elphdX1U3fLb/qHN3GV1+tM93+vWSZtozy27a1V1g856cLaue/ZnXfjrfF1VdYmNpqpQVPmBjl2umX0frJA9WCJ/sOK27JmfdteVfPZTld75oUI7b1qsHgVZ+uynKpVWN2iTPo2nCZzzh4G69In5Kgj4tOvwbqqPxPTlL9WqqI3qxN37qUdBlgJZXr35bbn6FmcrJ8urwlZOyQtHYpq9tC7+/dLysL5ZWKPcHG98Too/7dZPZz04W1sOytfoIQV65N1lWlRar6N3+m1+jLKaxtMCl/06mevcZY3b7FnYOP9UYdCvw7brpSuf+knd8vwqzvXrqqd/1qb9cuNXv2tKR0+7O36Xvjr4lq9112sLNX6LEr36Vane+b5CT53T8lUAAQBdQ0PU0fzldU0uW1oe1uyltTrt/sS5kI64/RudsdcAnbBrPw3vn9fsdVr8Po8W3LF9k8t+XFKrI27/RufvO6jVxpPUeGq91PSp4E35cG6lVlVHtPvVn0uSwlFHVaGotr7oI9130qaavbSuxeWtXUFvfbR2NPJ1R2ykA8f21PeLa9SnOEf7jGr8sKl3UbYO3ran7nptkS7cf7A265enZRVhrawKq0dBY7Prm4U12rRfbrPbBgAAaAnNp3bID/j0wZxK3TdzsapDUfUvydFfD9xAu27eeCW4I7bvrUCWV9NeX6xrnv1ZwWyfNu2Xq+N3bZxvwu/z6IpDhuiWlxbophd+0TYbFeqJs1pusiyrCGuv676I/zzt9cWa9vpi/W7j39bdb3QPldU06NaXFmp5ZVjD+ubqgVM3S5go9bWvynTuw3PiP6+e9PTsvQfonH0GSZKmHjREfq9Hp9z3o0INMe04rEh//+Nm8nmT1yUds2Gh7jx2qG54foFufH6BBvcI6K7jhiblTTkAoOMiUUeRmKNITIrFHIUaYvJ6Go/EramP6vlPV2qvkd1VGPTp+8W1uv2VhRq3WdNXTO3XLUcfXT0m4bbRF3+su44bqq1//T1wxA69Nekf3+mzn6q05aDG0+7CkZhGD2n598SlT8zXeRMG6fDtfvsgJhyJKeZIMafxeYQaYvJ7PfL7PBrUI6CdhhXplhcX6JrDN1RFbVTT31oSb9Csbd+tu2uX4cXxnz+ZV6VzH56jly8aqW55fm3aL7fF5ckwZc8BmrJn66fdbTEwX8sqwnr5i1Uav0WJymoi+veHKzRiQOO0ARv0DGjMhoW6/rlfdMUhQ/T94lo989EK/fPETZNSNwAASH8ex3E67xywNdSF6nXqNbfr5vNPVjAnp8n7uO0wsHRRWRdRXbjpOTDwG8dxVBsKKTcQIH9IKbIHK52RvWmvL9I/31iScNvWQ/J1zwmbqi4c1bkPz9EPi2sVjjoqyfNrt8276aTd+ynw6yli97+5RJ//VKXbJg9tcvtjLvlYj5w2XMPWOMrm+U9X6p43Fqu8JqKN+wR17j6DtPmAvCbXX60hElOWP/FUvhPv/V6fzq9OuO1Pu/XVSbv3lySVVjfo6md+1kdzK5WX49PeW3XXqXv0k//X0+bP+NeP2mqDAh23y7qTmH88r1LnPTJXb/51VJP1tLZckv7z6Up9Mq9Klx08pMnllz05XwVBn8799UOjqrqIdr3qcz133hbq163p91rNeeu7ct3z+mItLK1Xjt+jbTcu1Ln7DFTxr0czL68I66qnf9KnP1WrKNenE3bt1+y0Am3Fvg9WyB4skb+2C2a3fmYQ2i5V/Za6+nqdfcPduuviKQoGmn8/Ytp8khoHhP+EnaeyLqJbX1qg0uqIdSldhKPUXVsPWBPZgxWy51Y/rQhpRVVYY1Mw76Ed8gcrZA+WyF9blOT7debeA2lAdaJU9Fva2nwyn3A85jiumoE91Yad0/xEqS1dJa45deGYSqsjCmR5FMzu2MSgmYDmJ6yQPVghe+61otKrijpv0k7LcwPyBytkD5bIX+vqwlGVVjeewVMYtK4mPbit32L+7qY2FFJ+MHPT9fJFzV8Fp6WrxLUmmO3r8FVp0p0jKdzQoOwsP59DIKXIHqyQPXcb0D1Hhbn+tP39Tf5ghezBEvlru1ADZ+90Njf1W8ybT5lu9RXrAABAZutVmK1e6XzGHQAAyFje1u8CAAAAAAAArB/z5pOHgw9hiPzBCtmDFbIHS+QPVsgeLJE/WHFT9kxPu/N4PMrP5bQz2PBIys7izFOkHtmDFbIHS+QPVsgeLJE/WHFbv8X0yCfHcRSJRuU4jmUZyFCOpJjjiPQh1cgerJA9WCJ/sEL2YIn8wYrb+i3mp93V1ddbl4AM1hDhigqwQfZghezBEvmDFbIHS+QPVtzUbzFvPgEAAAAAACB90XwCAAAAAABA0pg3n7we8xKQwTwe98z+j8xC9mCF7MES+YMVsgdL5A9W3NRvMb/aXV4wYFkCMphHUrafK08g9cgerJA9WCJ/sEL2YIn8wYrb+i3mV7triERcM/s6MosjKRqLceUJpBzZgxWyB0vkD1bIHiyRP1hxW7/F/BisUDhsXQIyWCQatS4BGYrswQrZgyXyBytkD5bIH6y4qd9i3nwCAAAAAABA+qL5BAAAAAAAgKQxbz75vT7rEpDB3DT7PzIL2YMVsgdL5A9WyB4skT9YcVO/xfxqd8FAjmUJyGAeSVl+9/xnROYge7BC9mCJ/MEK2YMl8gcrbuu3mF/trj7c4JrZ15FZHEmRKFeeQOqRPVghe7BE/mCF7MES+YMVt/VbzI//C0carEtABovGuPIEbJA9WCF7sET+YIXswRL5gxU39VvMm08AAAAAAABIXzSfAAAAAAAAkDTmzacsn+mc58hwPq/5fwFkKLIHK2QPlsgfrJA9WCJ/sOKmfov51e4COdmWJSCDeST5fVx5AqlH9mCF7MES+YMVsgdL5A9W3NZvMb/aXag+7JrZ15FZGq88EeXKE0g5sgcrZA+WyB+skD1YIn+w4rZ+i/nxfw3RiHUJyGDRWMy6BGQosgcrZA+WyB+skD1YIn+w4qZ+i3nzCQAAAAAAAOmL5hMAAAAAAACSxnzq82x/lnUJaakuHLUuoUuIxqSGKGOF1CN7sEL2YIn8wQrZgyXy1zr+fk0ON/VbzK92l5PtnsFIB8Fsr0ry/SqtjijU4J7zO92Nc7BhhezBCtmDJfIHK2QPlshfa0ry/Qpmc3JWZ3Fbv8W0+bR69vVATrY8Ho9lKWmjMOjXmXsPVF2YnVtrHMdRfbhBOdlZ5A8pRfZghezBEvmDFbIHS+Sv7YLZXhUGzU/OShtu67eYv7KRGIfXdbbCoF+FQesq3M9xHFXXRZUfdMd/RmQOsgcrZA+WyB+skD1YIn+w5KZ+C8e0AQAAAAAAIGloPgEAAAAAACBpzJtPgexs6xKQwcgfrJA9WCF7sET+YIXswRL5gxU3Zc/8andZfvNpp5ChyB+skD1YIXuwRP5ghezBEvmDFbdlz/TIJ8dxVFMXkuM4lmUgQ5E/WCF7sEL2YIn8wQrZgyXyBytuy575aXcxJ2ZdAjIY+YMVsgcrZA+WyB+skD1YIn+w4qbsmTefAAAAAAAAkL5oPgEAAAAAACBpzJtPwZwc6xKQwcgfrJA9WCF7sET+YIXswRL5gxU3Zc/8and+n8+yBGQw8gcrZA9WyB4skT9YIXuwRP5gxW3ZM7/aXXVtnWtmX0dmIX+wQvZghezBEvmDFbIHS+QPVtyWPfPT7hy5YyCQmcgfrJA9WCF7sET+YIXswRL5gxU3Zc+8+QQAAAAAAID0RfMJAAAAAAAASWPefMoNBKxLQAYjf7BC9mCF7MES+YMVsgdL5A9W3JQ986vdeX/9F0g18gcrZA9WyB4skT9YIXuwRP5gxW3Zs7/aXZ17Zl9HZiF/sEL2YIXswRL5gxWyB0vkD1bclj3z0+4AAAAAAACQvmg+AQAAAAAAIGloPgEAAAAAACBpTJtPHo9H+cGgaybAQmYhf7BC9mCF7MES+YMVsgdL5A9W3JY98wnHY47jmgmwkFnIH6yQPVghe7BE/mCF7MES+YMVt2XP/LS72lDIugRkMPIHK2QPVsgeLJE/WCF7sET+YMVN2TNvPgEAAAAAACB90XwCAAAAAABA0pg3nzxyx+RXyEzkD1bIHqyQPVgif7BC9mCJ/MGKm7Lnt3xwj8ej/NygZQnIYOQPVsgerJA9WCJ/sEL2YIn8wYrbsmd+tbtINOqa2dfT0apVK1VZUZHUx1i+fJlqaqqT+hjJQP5ghezBCtmDJfIHK2QPlsgfrLgte+an3dXV11uX0C7l5WVaumSxqqurEm4Pheq0dMlis21lmsqKCq1csUJLlyzWyhXL13s7XS1/SB9kD1bIHiyRP1ghe7BE/mDFTdkzbz51VTXV1YrFYq7bViYJ5uYqEHTPYYQAAAAAAGBdpnM+dVU5OTmKRKKqqa5SQWFRk/eJxWKqrKhQOFyvWMyR3+9TXn6+gsHcdm+rNeFwvaqqqtQQbpDHI2VlZau4Wzd5vb/1FqsqK1RbWyuPx6Ngbq4KCgoTaq2qrFR9fUiO4ygrK0sFhUXKysqK3ycUCqm6ukqRhgZ5PF5lZ2erW0lJk/XU1taqqrJCxd26KScnsF7PqTWFRY1jVVUVVaShISmPAQAAAAAAOs68+eT1dMWDrzwqKCxQeVmZcvPy5fP51rnH6iZOXn6+PB6P6utDqigvl8/nV3Z2dru21ZKGhgaVrlqlYG6uCn9tXoXDYUm/nddZV1ervLx8de/RUw3hsCoqypWdna2cnIAcx1FZaam8Xo+6dSuRx+tVbW2NSletUs9eveT1ehUKhVReVqq8/HwVFxXLkVRfH2qynprqalVXV6lbSfe1nudvotGIVq5Y0eLzCgSDKioqbtdYrI+umT+kA7IHK2QPlsgfrJA9WCJ/sOKm7Jlf7S4vmJwjY5ItEAgqK6ta1VVVKiouXme5z9d4pNNqfn++wvX1CoXq1mnKtLatltRUVysrKzuhUbPmEUuNj52l/IKCX7/3q7a2RuH6sHJyAgqHw4pEGtSrdx95PI2XYSwsLFJ9KKRQqE65uXmqqa5SIBBMOFpq7ceQpKrKStXV1aqke48ml6/m9frUvUfPFp/X6lqSqSvnD10b2YMVsgdL5A9WyB4skT9YcVv2TJtPq2df9/t8KWk2dLaCgkKVlq5SXl7eOsscx1FNdbVCoTpFo9H4bYFmnmdL22pJQ6RBgUDLgcrKSnyZvV6fYrHGmiINDXIcR8uXLV2n/mik8T4NDREFc1uuq6a6Wo7jqHuPnvL7W46Vx+Np9T6p0NXzh66L7MEK2YMl8gcrZA+WyB+suC175h2AUDis/C46aXR2To5ycnJUVVWlYG7ic6ipqVZNTY0KCwvlz8qSx+NRVWWFmrvKYUvbaolHbQnRuvdx4v868nq9KuneY537eL2N67Ulp9nZOaqvbzxaKj+/oMX7uum0u66cP3RtZA9WyB4skT9YIXuwRP5gxU3ZM28+dXX5BYVatXKF/P7EuZoawmEFAgEFcxsnGHccR5FIRH5/86ejNbetlmRl+RWuD0st93taWD8rfqW95o5GysrKUri+Xrm5uU0ul6Ss7Czl5uWprHSVPPIknHK4NrecdgcAAAAAAJLPPbNPdVFZWVkKBIOqqalJuN3n96u+vr5xTqWGBlVWVMSbPO3dVkvy8gvU0NA4iXhDQ4MikQbV1tTET6trTXZ2jrKyslVeVqr6+pAikYjC4bCqqirVEA7HHyMUqlNVVaUiDQ1qaGhQdXVVE9tqvAJedXWVaqqrm33M1afdtfTV2sTrkUhEDQ0NisVichxHDb/W5TR3aBkAAAAAADBhfuST39u+q7u5UUF+gUJ1dQm35ecXKBqJNh4J5PEomJurnEBATqzl5khT22qJ3+9XSUl3VVVVatXKFfJ4PMrKzlagjYfWeTyexoZRVZUqyssVi8Xk9fqUnZ0tb27ja5OTk6Pi4m7xppLH6232SnbZ2Tnq1q1EZWWlkkfKy2v+CKiOqKgojzfHJGnVysbT+Hr07NWu+aTSIX/omsgerJA9WCJ/sEL2YIn8wYqbsmd+tbtgIMeyhHYrLu62zm0+v199+vZLuM3r9apbSUmnbKs12Tk56p7T9Gls3ZuYy2nturxerwqLilRYVNTsYwSCwWYbWr169V6nnt59+rZWdoc09bzaqyvmD+mB7MEK2YMl8gcrZA+WyB+suC17pqfdOY6j+jCnSsEG+YMVsgcrZA+WyB+skD1YIn+w4rbsmZ92F440KDvLvAzXKi1dlXB62Zry8vNbvbIcWkb+YIXswQrZgyXyBytkD5bIH6y4KXvuqKKLaJzcuuVJwztbfn6+1Eyn0uP1KhqNpLSedOI4jmLRmKLRCFfXQ0qRva7P4/HK6008eNjid0R7kT1YIn9oat/ZlM7en5I9WCJ/aEpb94fphOZTG8VisTZdsQ5dh+M4CkeiitSH+EWAlCJ7Xd/qufJWv2noKr8jyB4skT+sve9sSjL2p2QPlsgfmtKW/WG6MW8+ZfnMS2gTx4kpFovJ45HETiM9OJLP75XH6+EXAVKL7HVtjrPGp/LeX2/qIr8jyB4skb/M1sS+s+m7JWF/SvZgifxhbW3cH3YGN/VbzK92F8jJtiyh/TyejOpOpjufzz2XnkRmIXtdVywWa/Z06K7wO4LswRL5y1wt7jub0sn7U7IHS+QPa2r3/nA9ua3fYn61u1B92DWzryOzOI6jhkiU/CHlyB6skD1YIn+wQvZgifzBitv6LeYfzzYwYTYMRV0+PwvSF9mDFbIHS+QPVsgeLJE/WHFTv8W8+QQAAAAAAID0RfMJAAAAAAAASWPefMr2Z1mXgAzm95n/F0CGInuwQvZgifzBCtmDJfIHK27qt5hf7S4n2z2Dgczi8Xjk58oTMED2YIXswRL5gxWyB0vkD1bc1m8xv9pdXajeNbOvI7M4jqNwJEL+kHJkD1bIHiyRP1ghe7BE/mDFbf0W8+P/IrGodQnIYLGYO/4jIvOQPVghe7BE/mCF7MES+YMVN/VbzJtPAAAAAAAASF80nwAAAAAAAJA05s2nQHa2dQnIYEz+BytkD1bIHiyRP1ghe7BE/mDFTf0W86vdZflNS0AGa7zyhMe6DGQgsgcrZA+WyB+skD1YIn+w4rZ+i/nV7mrqQq6ZfR2ZxXEc1Tdw5QmkHtmDFbIHS+QPVsgeLJE/WHFbv8X8tLuYE7MuARnMLf8RkXnIHqyQPVgif7BC9mCJ/MGKm/ot5s0nAAAAAAAApC+aTwAAAAAAAEga8+ZTMCfHugRksCw/V56ADbIHK2QPlsgfrJA9WCJ/sOKmfov51e647CSseDwe+TxceQKpR/ZghezBEvmDFbIHS+QPVtzWbzG/2l11bR0TsMGE4zgKhRvIH1KO7MEK2YMl8gcrZA+WyB+suK3fYn7anSN3DAQAAAAAAEC6cFO/xbz5BAAAAAAAgPRF8wkAAAAAAABJY958yg0ErEtABsvOMp1zHxmM7MEK2YMl8gcrZA+WyB+suKnfYtp88ng88no88jD7Pwx4PB55fv0XSCWyBytkD5bIH6yQPVgif7Ditn6L/dXu6twz+zoyi+M4qm+IkD+kHNmDFbIHS+QPVsgeLJE/WHFbv8X8tDsAAAAAAACkL04+bS/HUSwWs64CncBxHMV+fT3dcigiMgPZ6+Ja+vTI5b8jyB4skb8M195P3jtxf0r2YIn8YR0uORIp1Wg+tZHH45XX6238JZihYUk3juNIMUdOzJH4PYAUIntdn9frlcfz28HDXeV3BNmDJfKHtfedTUnG/pTswRL5Q1Pasj9MN6bNJ4/Ho/xgsEt0gL1erwqLiuQ47v1EG+3nOE6XyB/SD9nr2lb/cbRaV/odQfZgifxltrX3nU1J1v6U7MES+cPa2rI/7PhjuKvfYtp8Wn0IolddY/b/xnBkVncyncXz56IrACAzkL301BV+R5A9WCJ/aKvO3p+SPVgif7Ditn6L+bvk2lDIugRkMPIHK2QPVsgeLJE/WCF7sET+YMVN2TNvPgEAAAAAACB90XwCAAAAAABA0pg3nzxM+Q9D5A9WyB6skD1YIn+wQvZgifzBipuyZ3+1u9ygZQnIYOQPVsgerJA9WCJ/sEL2YIn8wYrbsmd65JPjOIpEo3Icx7IMZCjyBytkD1bIHiyRP1ghe7BE/mDFbdkzP+2urr7eugRkMPIHK2QPVsgeLJE/WCF7sET+YMVN2TNvPgEAAAAAACB90XwCAAAAAABA0pg3n7we8xKQwcgfrJA9WCF7sET+YIXswRL5gxU3Zc/8and5wYBlCchg5A9WyB6skD1YIn+wQvZgifzBituyZ361u4ZIxDWzryOzkD9YIXuwQvZgifzBCtmDJfIHK27LnvkxWKFw2LoEZDDyBytkD1bIHiyRP1ghe7BE/mDFTdkzbz4BAAAAAAAgfdF8AgAAAAAAQNKYN5/8Xp91Cchg5A9WyB6skD1YIn+wQvZgifzBipuyZ361u2Agx7IEZDDyBytkD1bIHiyRP1ghe7BE/mDFbdkzv9pdfbjBNbOvI7OQP1ghe7BC9mCJ/MEK2YMl8gcrbsue+Wl34UiDdQnIYOQPVsgerJA9WCJ/sEL2YIn8wYqbsmfefAIAAAAAAED6ovkEAAAAAACApDFvPmX5TOc8R4Yjf7BC9mCF7MES+YMVsgdL5A9W3JQ986vdBXKyLUtABiN/sEL2YIXswRL5gxWyB0vkD1bclj3zq92F6sOumX0dmYX8wQrZgxWyB0vkD1bIHiyRP1hxW/bMT7triEasS0AGI3+wQvZghezBEvmDFbIHS+QPVtyUPfPmEwAAAAAAANJX0uZ8Wn1oV6g+3OJ9QvVh+bxeeTyeZJUCNIn8wQrZgxWyB0vkD1bIHiyRP1hJVfZW93xaO70vac2nULhBknTRbfcn6yEAAAAAAABgLBRuUG4w0Oxyj5Ok2adisZjKq2oUyM5qtstWU1un/Y4+Xs89fJ/ycoPJKAN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      "text/plain": [
       "<Figure size 1200x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 我想看PatNo_ID_1568813269這個檔案的時間軸 set0用藍色set1用紅色，ad_para=1用黑色，NaN_check=1用淺灰，圖層由後到前是NaN_check、ad_para、set1、set0， set1跟set0請在上面寫上時間起迄跟資料筆數，ad_para請在點上面寫時間，橫軸為時間，圖表用英文 但程式碼註釋要中文\n",
    "# ==========================================================\n",
    "# 單一病患時間軸圖：PatNo_ID_1568813269\n",
    "# - 圖層順序（由後到前）：NaN_check(淺灰) → ad_para=1(黑點) → set=1(紅段) → set=0(藍段)\n",
    "# - set=0/1 區段：在區段上方標註「起訖時間 + 資料筆數」\n",
    "# - ad_para=1 點：在點上方標註時間（大量時自動抽樣避免擁擠）\n",
    "# - 橫軸為時間（英文標題與座標），註解皆為中文\n",
    "# ==========================================================\n",
    "# ==========================================================\n",
    "# 分層時間軸版本（方案 A）\n",
    "# - 每層水平條獨立顯示：NaN_check(灰) → set_0(藍) → set_1(紅) → ad_para(黑點)\n",
    "# - 每層不重疊，以 Gantt Chart 方式呈現\n",
    "# - 在 set=0/1 上方標註起訖時間與資料筆數\n",
    "# ==========================================================\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.dates as mdates\n",
    "from pathlib import Path\n",
    "\n",
    "# -----------------------------\n",
    "# A. 參數與路徑設定\n",
    "# -----------------------------\n",
    "DATA_DIR = Path(\"/home/jovyan/RT08/0925/bling_1010\")\n",
    "FILENAME = \"7108162.csv\"   # ✅ 改成想畫的檔案名\n",
    "FILEPATH = DATA_DIR / FILENAME\n",
    "\n",
    "# 顏色設定\n",
    "COLOR_NAN = \"#e0e0e0\"\n",
    "COLOR_SET0 = \"#1565c0\"\n",
    "COLOR_SET1 = \"#d32f2f\"\n",
    "COLOR_AD = \"#000000\"\n",
    "\n",
    "# -----------------------------\n",
    "# B. 讀取資料\n",
    "# -----------------------------\n",
    "df = pd.read_csv(FILEPATH, low_memory=False, parse_dates=[\"senddate\"])\n",
    "df.columns = [c.lower() for c in df.columns]\n",
    "df = df[df[\"senddate\"].notna()].sort_values(\"senddate\").reset_index(drop=True)\n",
    "\n",
    "# -----------------------------\n",
    "# C. 區段轉換函式\n",
    "# -----------------------------\n",
    "def mask_to_segments(df_sorted, mask):\n",
    "    \"\"\"將布林遮罩轉為連續區段（start_time, end_time, count）\"\"\"\n",
    "    if mask.sum() == 0:\n",
    "        return pd.DataFrame(columns=[\"start_time\", \"end_time\", \"count\"])\n",
    "    d = df_sorted.loc[mask, [\"senddate\"]].copy()\n",
    "    d[\"block_id\"] = (d[\"senddate\"].diff().dt.total_seconds().fillna(0) > 60).cumsum()\n",
    "    seg = d.groupby(\"block_id\").agg(start_time=(\"senddate\", \"min\"),\n",
    "                                    end_time=(\"senddate\", \"max\"),\n",
    "                                    count=(\"senddate\", \"count\")).reset_index(drop=True)\n",
    "    return seg\n",
    "\n",
    "def set_to_segments(df_sorted, set_value):\n",
    "    \"\"\"將連續相同 set_1010 的列視為一段\"\"\"\n",
    "    d = df_sorted[df_sorted[\"set_1010\"] == set_value].copy()\n",
    "    if d.empty:\n",
    "        return pd.DataFrame(columns=[\"start_time\", \"end_time\", \"count\"])\n",
    "    d[\"block_id\"] = (d[\"set_1010\"] != d[\"set_1010\"].shift()).cumsum()\n",
    "    seg = d.groupby(\"block_id\").agg(start_time=(\"senddate\", \"min\"),\n",
    "                                    end_time=(\"senddate\", \"max\"),\n",
    "                                    count=(\"senddate\", \"count\")).reset_index(drop=True)\n",
    "    return seg\n",
    "\n",
    "# -----------------------------\n",
    "# D. 準備各層資料\n",
    "# -----------------------------\n",
    "if \"nan_check\" in df.columns:\n",
    "    nan_segments = mask_to_segments(df, df[\"nan_check\"] == 1)\n",
    "else:\n",
    "    nan_segments = pd.DataFrame(columns=[\"start_time\", \"end_time\", \"count\"])\n",
    "\n",
    "seg_set0 = set_to_segments(df, 0)\n",
    "seg_set1 = set_to_segments(df, 1)\n",
    "ad_points = df[df[\"ad_para\"] == 1][[\"senddate\"]]\n",
    "\n",
    "# -----------------------------\n",
    "# E. 繪圖\n",
    "# -----------------------------\n",
    "fig, ax = plt.subplots(figsize=(12, 4))\n",
    "ax.set_title(f\"Timeline - {FILENAME}\", fontsize=14, fontweight=\"bold\")\n",
    "ax.set_xlabel(\"Time\", fontsize=12)\n",
    "ax.set_yticks([])\n",
    "ax.xaxis.set_major_formatter(mdates.DateFormatter(\"%H:%M\"))\n",
    "ax.grid(True, axis=\"x\", linestyle=\"--\", alpha=0.3)\n",
    "\n",
    "# ---- Y層位置 ----\n",
    "Y_nan = 0.15   # 最底層（灰）\n",
    "Y_set0 = 0.4   # 第二層（藍）\n",
    "Y_set1 = 0.65  # 第三層（紅）\n",
    "Y_ad = 0.9     # 最上層（黑點）\n",
    "\n",
    "# ---- 畫 NaN_check 層 ----\n",
    "for _, r in nan_segments.iterrows():\n",
    "    ax.axvspan(r[\"start_time\"], r[\"end_time\"], ymin=Y_nan-0.05, ymax=Y_nan+0.05,\n",
    "               color=COLOR_NAN, alpha=0.6, zorder=1)\n",
    "ax.text(df[\"senddate\"].min(), Y_nan+0.08, \"NaN_check = 1\", color=COLOR_NAN, fontsize=10)\n",
    "\n",
    "# ---- 畫 set_1010 = 0 層 ----\n",
    "for _, r in seg_set0.iterrows():\n",
    "    ax.axvspan(r[\"start_time\"], r[\"end_time\"], ymin=Y_set0-0.05, ymax=Y_set0+0.05,\n",
    "               color=COLOR_SET0, alpha=0.5, zorder=2)\n",
    "    label = f\"{r['start_time']:%H:%M}→{r['end_time']:%H:%M} | n={r['count']}\"\n",
    "    ax.text(r[\"start_time\"] + (r[\"end_time\"] - r[\"start_time\"]) / 2, Y_set0+0.07,\n",
    "            label, color=COLOR_SET0, fontsize=9, ha=\"center\", va=\"bottom\")\n",
    "ax.text(df[\"senddate\"].min(), Y_set0+0.08, \"set_1010 = 0\", color=COLOR_SET0, fontsize=10)\n",
    "\n",
    "# ---- 畫 set_1010 = 1 層 ----\n",
    "for _, r in seg_set1.iterrows():\n",
    "    ax.axvspan(r[\"start_time\"], r[\"end_time\"], ymin=Y_set1-0.05, ymax=Y_set1+0.05,\n",
    "               color=COLOR_SET1, alpha=0.5, zorder=3)\n",
    "    label = f\"{r['start_time']:%H:%M}→{r['end_time']:%H:%M} | n={r['count']}\"\n",
    "    ax.text(r[\"start_time\"] + (r[\"end_time\"] - r[\"start_time\"]) / 2, Y_set1+0.07,\n",
    "            label, color=COLOR_SET1, fontsize=9, ha=\"center\", va=\"bottom\")\n",
    "ax.text(df[\"senddate\"].min(), Y_set1+0.08, \"set_1010 = 1\", color=COLOR_SET1, fontsize=10)\n",
    "\n",
    "# ---- 畫 ad_para = 1 黑點層 ----\n",
    "if not ad_points.empty:\n",
    "    ax.scatter(ad_points[\"senddate\"], np.full(len(ad_points), Y_ad),\n",
    "               color=COLOR_AD, s=15, zorder=4, label=\"ad_para=1\")\n",
    "    for i, row in ad_points.iterrows():\n",
    "        if i % max(1, len(ad_points)//10) == 0:  # 取樣顯示\n",
    "            ax.text(row[\"senddate\"], Y_ad+0.04, row[\"senddate\"].strftime(\"%H:%M\"),\n",
    "                    fontsize=8, rotation=90, ha=\"center\", va=\"bottom\", color=\"#212121\")\n",
    "\n",
    "ax.set_ylim(0, 1.05)\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "id": "538001f1-131c-4cf7-a836-266ed1e8a7c5",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "=== Adjacent adjustment window ===\n",
      "T1 = 2024-09-18 13:40:04, T2 = 2024-09-18 13:41:04, ΔT(min) = 1.00\n",
      "No set_1010=0/1 segments found in this window.\n"
     ]
    }
   ],
   "source": [
    "# ==========================================================\n",
    "# 兩個調參點（ad_para=1）之間的 set_1010=0/1 區段時間軸\n",
    "# - 英文圖表、中文註解\n",
    "# - 來源：/home/jovyan/RT08/0925/bling_1010/<檔名>\n",
    "# - 預設抓「最後一對」相鄰調參點；可改成指定第幾對\n",
    "# ==========================================================\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.dates as mdates\n",
    "from pathlib import Path\n",
    "\n",
    "# -----------------------------\n",
    "# A. 基本參數（請視需要調整）\n",
    "# -----------------------------\n",
    "DATA_DIR   = Path(\"/home/jovyan/RT08/0925/bling_1010\")\n",
    "FILENAME   = \"7108162.csv\"   # 🔁 欲查看的檔案\n",
    "PAIR_MODE  = \"by_index\"      # \"by_index\"：用索引選配對；\"largest_gap\"：自動挑時間最長的那一對\n",
    "PAIR_INDEX = 0              # 當 PAIR_MODE=\"by_index\" 時有效；-1=最後一對，0=第一對，1=第二對…\n",
    "ONLY_VALID = True            # True：只以 nan_check==1 的列作為有效資料進行統計/顯示\n",
    "\n",
    "# 顏色設定（與你需求一致）\n",
    "COLOR_SET0 = \"#1565c0\"   # 藍：set_1010=0\n",
    "COLOR_SET1 = \"#d32f2f\"   # 紅：set_1010=1\n",
    "COLOR_AD   = \"#000000\"   # 黑：ad_para=1\n",
    "COLOR_GRID = \"#cfd8dc\"\n",
    "\n",
    "# -----------------------------\n",
    "# B. 讀檔與預處理\n",
    "# -----------------------------\n",
    "fp = DATA_DIR / FILENAME\n",
    "df = pd.read_csv(fp, low_memory=False, parse_dates=[\"senddate\"])\n",
    "df.columns = [c.lower() for c in df.columns]\n",
    "df = df[df[\"senddate\"].notna()].sort_values(\"senddate\").reset_index(drop=True)\n",
    "\n",
    "# 只保留有效列（若有 nan_check 欄位）\n",
    "if ONLY_VALID and \"nan_check\" in df.columns:\n",
    "    df = df[df[\"nan_check\"] == 1].copy()\n",
    "\n",
    "# 取出所有調參事件時間（ad_para=1）\n",
    "ad_times = (\n",
    "    df[df[\"ad_para\"] == 1][\"senddate\"]\n",
    "    .dropna().sort_values().drop_duplicates().to_list()\n",
    ")\n",
    "\n",
    "if len(ad_times) < 2:\n",
    "    raise SystemExit(\"❌ 這個檔案內 ad_para=1 的事件少於 2 個，無法形成相鄰配對。\")\n",
    "\n",
    "# -----------------------------\n",
    "# C. 選擇要看的兩個調參點（T1, T2）\n",
    "# -----------------------------\n",
    "if PAIR_MODE == \"largest_gap\":\n",
    "    # 自動挑「相鄰事件間隔最長」的那一對\n",
    "    gaps = [(ad_times[i], ad_times[i+1]) for i in range(len(ad_times)-1)]\n",
    "    lengths = [(t2 - t1).total_seconds() for (t1, t2) in gaps]\n",
    "    idx = int(np.argmax(lengths))\n",
    "    T1, T2 = gaps[idx]\n",
    "    pair_desc = f\"largest gap pair #{idx} ({T1:%Y-%m-%d %H:%M} → {T2:%Y-%m-%d %H:%M})\"\n",
    "else:\n",
    "    # 依索引選擇第 k 對（支援負索引：-1=最後一對）\n",
    "    k = PAIR_INDEX if PAIR_INDEX >= 0 else len(ad_times) + PAIR_INDEX - 1\n",
    "    if not (0 <= k < len(ad_times)-1):\n",
    "        raise SystemExit(f\"❌ PAIR_INDEX 超出範圍（共有 {len(ad_times)-1} 對相鄰事件）。\")\n",
    "    T1, T2 = ad_times[k], ad_times[k+1]\n",
    "    pair_desc = f\"pair index {k} ({T1:%Y-%m-%d %H:%M} → {T2:%Y-%m-%d %H:%M})\"\n",
    "\n",
    "# 只取兩事件之間的資料（左開右閉：T1 < t ≤ T2）\n",
    "win = df[(df[\"senddate\"] > T1) & (df[\"senddate\"] <= T2)].copy()\n",
    "if win.empty:\n",
    "    raise SystemExit(\"⚠️ 兩個調參點之間沒有任何資料列。\")\n",
    "\n",
    "# -----------------------------\n",
    "# D. 把 window 內的 set_1010 轉為連續區段\n",
    "#    規則：set_1010∈{0,1} 的連續列合併為一段，統計其起訖時間與筆數\n",
    "# -----------------------------\n",
    "def set_segments_in_window(df_sorted):\n",
    "    d = df_sorted[df_sorted[\"set_1010\"].isin([0, 1])].copy()\n",
    "    if d.empty:\n",
    "        return pd.DataFrame(columns=[\"set_1010\",\"start_time\",\"end_time\",\"count\"])\n",
    "    d = d.sort_values(\"senddate\").reset_index(drop=True)\n",
    "    # 只要 set_1010 改變就切新段\n",
    "    d[\"block_id\"] = (d[\"set_1010\"] != d[\"set_1010\"].shift()).cumsum()\n",
    "    seg = (\n",
    "        d.groupby([\"set_1010\",\"block_id\"], as_index=False)\n",
    "         .agg(start_time=(\"senddate\",\"min\"),\n",
    "              end_time=(\"senddate\",\"max\"),\n",
    "              count=(\"senddate\",\"count\"))\n",
    "         .drop(columns=\"block_id\")\n",
    "    )\n",
    "    return seg\n",
    "\n",
    "seg = set_segments_in_window(win)\n",
    "\n",
    "# 另取這兩個事件的時間點（當作圖上指示標）\n",
    "ad_pair_pts = pd.DataFrame({\"senddate\": [T1, T2]})\n",
    "\n",
    "# -----------------------------\n",
    "# E. 畫圖（分層展示 set=0 與 set=1；上方標註起訖與筆數）\n",
    "# -----------------------------\n",
    "plt.style.use(\"default\")\n",
    "plt.rcParams.update({\n",
    "    \"font.size\": 11, \"axes.labelcolor\": \"#1f3b73\", \"axes.edgecolor\": \"#607d8b\",\n",
    "    \"axes.titlesize\": 14, \"axes.titleweight\": \"bold\", \"axes.facecolor\": \"white\",\n",
    "    \"xtick.color\": \"#37474f\", \"ytick.color\": \"#37474f\", \"grid.color\": COLOR_GRID\n",
    "})\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(12, 3.6))\n",
    "ax.set_title(f\"Segments between two adjustments - {FILENAME}\\n{pair_desc}\")\n",
    "ax.set_xlabel(\"Time\")\n",
    "ax.set_yticks([])\n",
    "ax.grid(True, axis=\"x\", linestyle=\"--\", alpha=0.35)\n",
    "\n",
    "# X 軸格式（時間）\n",
    "ax.set_xlim(T1, T2)\n",
    "ax.xaxis.set_major_formatter(mdates.DateFormatter(\"%H:%M\"))\n",
    "\n",
    "# 兩個調參點：以黑色虛線 + 圓點標註\n",
    "for t in [T1, T2]:\n",
    "    ax.axvline(t, color=COLOR_AD, linestyle=\"--\", linewidth=1.2, alpha=0.8, zorder=3)\n",
    "ax.scatter(ad_pair_pts[\"senddate\"], [0.92, 0.92], color=COLOR_AD, s=24, zorder=4)\n",
    "ax.text(T1, 0.95, f\"T1 {T1:%H:%M}\", ha=\"center\", va=\"bottom\", fontsize=9, color=COLOR_AD)\n",
    "ax.text(T2, 0.95, f\"T2 {T2:%H:%M}\", ha=\"center\", va=\"bottom\", fontsize=9, color=COLOR_AD)\n",
    "\n",
    "# 定義兩層的 y 範圍（Gantt 風格）\n",
    "Y0, BAND = 0.40, 0.09   # set=0 的中心與半高\n",
    "Y1       = 0.65         # set=1 的中心\n",
    "\n",
    "# 畫 set=0 的所有段（藍）\n",
    "for _, r in seg[seg[\"set_1010\"]==0].iterrows():\n",
    "    ax.axvspan(r[\"start_time\"], r[\"end_time\"], ymin=Y0-BAND, ymax=Y0+BAND,\n",
    "               color=COLOR_SET0, alpha=0.55, zorder=2)\n",
    "    label = f\"{r['start_time']:%H:%M}→{r['end_time']:%H:%M} | n={int(r['count'])}\"\n",
    "    ax.text(r[\"start_time\"] + (r[\"end_time\"]-r[\"start_time\"])/2,\n",
    "            Y0 + BAND + 0.03, label, color=COLOR_SET0, fontsize=9,\n",
    "            ha=\"center\", va=\"bottom\", zorder=3)\n",
    "\n",
    "# 畫 set=1 的所有段（紅）\n",
    "for _, r in seg[seg[\"set_1010\"]==1].iterrows():\n",
    "    ax.axvspan(r[\"start_time\"], r[\"end_time\"], ymin=Y1-BAND, ymax=Y1+BAND,\n",
    "               color=COLOR_SET1, alpha=0.55, zorder=2)\n",
    "    label = f\"{r['start_time']:%H:%M}→{r['end_time']:%H:%M} | n={int(r['count'])}\"\n",
    "    ax.text(r[\"start_time\"] + (r[\"end_time\"]-r[\"start_time\"])/2,\n",
    "            Y1 + BAND + 0.03, label, color=COLOR_SET1, fontsize=9,\n",
    "            ha=\"center\", va=\"bottom\", zorder=3)\n",
    "\n",
    "# 圖例（英文）\n",
    "handles = [\n",
    "    plt.Line2D([0],[0], color=COLOR_SET0, lw=8, alpha=0.55, label=\"set_1010 = 0\"),\n",
    "    plt.Line2D([0],[0], color=COLOR_SET1, lw=8, alpha=0.55, label=\"set_1010 = 1\"),\n",
    "    plt.Line2D([0],[0], color=COLOR_AD,   lw=1.2, linestyle=\"--\", label=\"ad_para = 1\"),\n",
    "]\n",
    "ax.legend(handles=handles, frameon=False, loc=\"upper left\")\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "# ----------------------------------------------------------\n",
    "# F. 主控台補充：列印本區間的統計\n",
    "# ----------------------------------------------------------\n",
    "print(\"\\n=== Adjacent adjustment window ===\")\n",
    "print(f\"T1 = {T1}, T2 = {T2}, ΔT(min) = {(T2-T1).total_seconds()/60:.2f}\")\n",
    "if not seg.empty:\n",
    "    print(\"\\nSegments within (T1, T2]:\")\n",
    "    print(seg.assign(delta_min=lambda x: (x[\"end_time\"]-x[\"start_time\"]).dt.total_seconds()/60)\n",
    "             .loc[:, [\"set_1010\",\"start_time\",\"end_time\",\"count\",\"delta_min\"]]\n",
    "             .to_string(index=False))\n",
    "else:\n",
    "    print(\"No set_1010=0/1 segments found in this window.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "id": "d8191793-77e3-49c5-b1f7-0e3a23daad73",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "=== Adjacent ad_para pairs diagnostic table ===\n",
      " pair_idx                  T1                  T2  delta_min  seg_0_blocks  seg_1_blocks  rows_set0  rows_set1  has_both\n",
      "        3 2024-09-18 13:43:04 2024-09-18 13:45:04      2.000             0             0          0          0     False\n",
      "        4 2024-09-18 13:45:04 2024-09-18 13:47:04      2.000             1             0          2          0     False\n",
      "       27 2024-09-18 14:10:02 2024-09-18 14:12:02      2.000             1             0          2          0     False\n",
      "       36 2024-09-18 14:20:02 2024-09-18 14:22:02      2.000             1             0          2          0     False\n",
      "       54 2024-09-18 14:39:02 2024-09-18 14:41:02      2.000             0             0          0          0     False\n",
      "       56 2024-09-18 14:42:02 2024-09-18 14:44:02      2.000             0             0          0          0     False\n",
      "       59 2024-09-18 14:46:02 2024-09-18 14:48:02      2.000             0             0          0          0     False\n",
      "       61 2024-09-18 14:49:02 2024-09-18 14:51:02      2.000             1             0          2          0     False\n",
      "       77 2024-09-18 15:06:02 2024-09-18 15:08:02      2.000             1             0          2          0     False\n",
      "       98 2024-09-18 15:28:02 2024-09-18 15:30:02      2.000             1             0          2          0     False\n",
      "      108 2024-09-18 15:39:02 2024-09-18 15:41:02      2.000             1             0          2          0     False\n",
      "      122 2024-09-18 15:54:02 2024-09-18 15:56:02      2.000             0             1          0          1     False\n",
      "      125 2024-09-18 15:58:02 2024-09-18 16:00:02      2.000             1             0          2          0     False\n",
      "      140 2024-09-18 16:14:02 2024-09-18 16:16:02      2.000             0             0          0          0     False\n",
      "      144 2024-09-18 16:19:02 2024-09-18 16:21:02      2.000             1             0          2          0     False\n",
      "      152 2024-09-18 16:29:01 2024-09-18 16:31:01      2.000             0             0          0          0     False\n",
      "      156 2024-09-18 16:34:01 2024-09-18 16:36:01      2.000             1             0          2          0     False\n",
      "      150 2024-09-18 16:26:02 2024-09-18 16:28:01      1.983             0             0          0          0     False\n",
      "       12 2024-09-18 13:54:04 2024-09-18 13:56:02      1.967             1             0          2          0     False\n",
      "       62 2024-09-18 14:51:02 2024-09-18 14:52:03      1.017             1             0          1          0     False\n",
      "        0 2024-09-18 13:40:04 2024-09-18 13:41:04      1.000             0             0          0          0     False\n",
      "        1 2024-09-18 13:41:04 2024-09-18 13:42:04      1.000             0             0          0          0     False\n",
      "        2 2024-09-18 13:42:04 2024-09-18 13:43:04      1.000             0             0          0          0     False\n",
      "        5 2024-09-18 13:47:04 2024-09-18 13:48:04      1.000             1             0          1          0     False\n",
      "        6 2024-09-18 13:48:04 2024-09-18 13:49:04      1.000             1             0          1          0     False\n",
      "        7 2024-09-18 13:49:04 2024-09-18 13:50:04      1.000             0             0          0          0     False\n",
      "        8 2024-09-18 13:50:04 2024-09-18 13:51:04      1.000             0             0          0          0     False\n",
      "        9 2024-09-18 13:51:04 2024-09-18 13:52:04      1.000             0             0          0          0     False\n",
      "       10 2024-09-18 13:52:04 2024-09-18 13:53:04      1.000             0             1          0          1     False\n",
      "       11 2024-09-18 13:53:04 2024-09-18 13:54:04      1.000             0             1          0          1     False\n",
      "       13 2024-09-18 13:56:02 2024-09-18 13:57:02      1.000             1             0          1          0     False\n",
      "       14 2024-09-18 13:57:02 2024-09-18 13:58:02      1.000             1             0          1          0     False\n",
      "       15 2024-09-18 13:58:02 2024-09-18 13:59:02      1.000             1             0          1          0     False\n",
      "       16 2024-09-18 13:59:02 2024-09-18 14:00:02      1.000             1             0          1          0     False\n",
      "       17 2024-09-18 14:00:02 2024-09-18 14:01:02      1.000             1             0          1          0     False\n",
      "       18 2024-09-18 14:01:02 2024-09-18 14:02:02      1.000             1             0          1          0     False\n",
      "       19 2024-09-18 14:02:02 2024-09-18 14:03:02      1.000             0             0          0          0     False\n",
      "       20 2024-09-18 14:03:02 2024-09-18 14:04:02      1.000             0             0          0          0     False\n",
      "       21 2024-09-18 14:04:02 2024-09-18 14:05:02      1.000             0             0          0          0     False\n",
      "       22 2024-09-18 14:05:02 2024-09-18 14:06:02      1.000             0             0          0          0     False\n",
      "       23 2024-09-18 14:06:02 2024-09-18 14:07:02      1.000             0             1          0          1     False\n",
      "       24 2024-09-18 14:07:02 2024-09-18 14:08:02      1.000             0             1          0          1     False\n",
      "       25 2024-09-18 14:08:02 2024-09-18 14:09:02      1.000             0             1          0          1     False\n",
      "       26 2024-09-18 14:09:02 2024-09-18 14:10:02      1.000             0             1          0          1     False\n",
      "       28 2024-09-18 14:12:02 2024-09-18 14:13:02      1.000             1             0          1          0     False\n",
      "       29 2024-09-18 14:13:02 2024-09-18 14:14:02      1.000             1             0          1          0     False\n",
      "       30 2024-09-18 14:14:02 2024-09-18 14:15:02      1.000             1             0          1          0     False\n",
      "       31 2024-09-18 14:15:02 2024-09-18 14:16:02      1.000             0             0          0          0     False\n",
      "       32 2024-09-18 14:16:02 2024-09-18 14:17:02      1.000             0             0          0          0     False\n",
      "       33 2024-09-18 14:17:02 2024-09-18 14:18:02      1.000             0             0          0          0     False\n",
      "       34 2024-09-18 14:18:02 2024-09-18 14:19:02      1.000             0             1          0          1     False\n",
      "       35 2024-09-18 14:19:02 2024-09-18 14:20:02      1.000             0             1          0          1     False\n",
      "       37 2024-09-18 14:22:02 2024-09-18 14:23:02      1.000             1             0          1          0     False\n",
      "       38 2024-09-18 14:23:02 2024-09-18 14:24:02      1.000             1             0          1          0     False\n",
      "       39 2024-09-18 14:24:02 2024-09-18 14:25:02      1.000             1             0          1          0     False\n",
      "       40 2024-09-18 14:25:02 2024-09-18 14:26:02      1.000             1             0          1          0     False\n",
      "       41 2024-09-18 14:26:02 2024-09-18 14:27:02      1.000             1             0          1          0     False\n",
      "       42 2024-09-18 14:27:02 2024-09-18 14:28:02      1.000             1             0          1          0     False\n",
      "       43 2024-09-18 14:28:02 2024-09-18 14:29:02      1.000             1             0          1          0     False\n",
      "       44 2024-09-18 14:29:02 2024-09-18 14:30:02      1.000             0             0          0          0     False\n",
      "       45 2024-09-18 14:30:02 2024-09-18 14:31:02      1.000             0             0          0          0     False\n",
      "       46 2024-09-18 14:31:02 2024-09-18 14:32:02      1.000             0             0          0          0     False\n",
      "       47 2024-09-18 14:32:02 2024-09-18 14:33:02      1.000             0             0          0          0     False\n",
      "       48 2024-09-18 14:33:02 2024-09-18 14:34:02      1.000             0             0          0          0     False\n",
      "       49 2024-09-18 14:34:02 2024-09-18 14:35:02      1.000             0             0          0          0     False\n",
      "       50 2024-09-18 14:35:02 2024-09-18 14:36:02      1.000             0             1          0          1     False\n",
      "       51 2024-09-18 14:36:02 2024-09-18 14:37:02      1.000             0             1          0          1     False\n",
      "       52 2024-09-18 14:37:02 2024-09-18 14:38:02      1.000             0             1          0          1     False\n",
      "       53 2024-09-18 14:38:02 2024-09-18 14:39:02      1.000             0             1          0          1     False\n",
      "       55 2024-09-18 14:41:02 2024-09-18 14:42:02      1.000             0             0          0          0     False\n",
      "       57 2024-09-18 14:44:02 2024-09-18 14:45:02      1.000             0             0          0          0     False\n",
      "       58 2024-09-18 14:45:02 2024-09-18 14:46:02      1.000             0             0          0          0     False\n",
      "       60 2024-09-18 14:48:02 2024-09-18 14:49:02      1.000             0             0          0          0     False\n",
      "       64 2024-09-18 14:53:02 2024-09-18 14:54:02      1.000             1             0          1          0     False\n",
      "       65 2024-09-18 14:54:02 2024-09-18 14:55:02      1.000             1             0          1          0     False\n",
      "       66 2024-09-18 14:55:02 2024-09-18 14:56:02      1.000             1             0          1          0     False\n",
      "       67 2024-09-18 14:56:02 2024-09-18 14:57:02      1.000             1             0          1          0     False\n",
      "       68 2024-09-18 14:57:02 2024-09-18 14:58:02      1.000             0             0          0          0     False\n",
      "       69 2024-09-18 14:58:02 2024-09-18 14:59:02      1.000             0             0          0          0     False\n",
      "       70 2024-09-18 14:59:02 2024-09-18 15:00:02      1.000             0             0          0          0     False\n",
      "       71 2024-09-18 15:00:02 2024-09-18 15:01:02      1.000             0             0          0          0     False\n",
      "       72 2024-09-18 15:01:02 2024-09-18 15:02:02      1.000             0             0          0          0     False\n",
      "       73 2024-09-18 15:02:02 2024-09-18 15:03:02      1.000             0             1          0          1     False\n",
      "       74 2024-09-18 15:03:02 2024-09-18 15:04:02      1.000             0             1          0          1     False\n",
      "       75 2024-09-18 15:04:02 2024-09-18 15:05:02      1.000             0             1          0          1     False\n",
      "       76 2024-09-18 15:05:02 2024-09-18 15:06:02      1.000             0             1          0          1     False\n",
      "       78 2024-09-18 15:08:02 2024-09-18 15:09:02      1.000             1             0          1          0     False\n",
      "       79 2024-09-18 15:09:02 2024-09-18 15:10:02      1.000             1             0          1          0     False\n",
      "       80 2024-09-18 15:10:02 2024-09-18 15:11:02      1.000             1             0          1          0     False\n",
      "       81 2024-09-18 15:11:02 2024-09-18 15:12:02      1.000             1             0          1          0     False\n",
      "       82 2024-09-18 15:12:02 2024-09-18 15:13:02      1.000             1             0          1          0     False\n",
      "       83 2024-09-18 15:13:02 2024-09-18 15:14:02      1.000             1             0          1          0     False\n",
      "       84 2024-09-18 15:14:02 2024-09-18 15:15:02      1.000             1             0          1          0     False\n",
      "       85 2024-09-18 15:15:02 2024-09-18 15:16:02      1.000             1             0          1          0     False\n",
      "       86 2024-09-18 15:16:02 2024-09-18 15:17:02      1.000             1             0          1          0     False\n",
      "       87 2024-09-18 15:17:02 2024-09-18 15:18:02      1.000             0             0          0          0     False\n",
      "       88 2024-09-18 15:18:02 2024-09-18 15:19:02      1.000             0             0          0          0     False\n",
      "       89 2024-09-18 15:19:02 2024-09-18 15:20:02      1.000             0             0          0          0     False\n",
      "       90 2024-09-18 15:20:02 2024-09-18 15:21:02      1.000             0             0          0          0     False\n",
      "       91 2024-09-18 15:21:02 2024-09-18 15:22:02      1.000             0             0          0          0     False\n",
      "       92 2024-09-18 15:22:02 2024-09-18 15:23:02      1.000             0             0          0          0     False\n",
      "       93 2024-09-18 15:23:02 2024-09-18 15:24:02      1.000             0             1          0          1     False\n",
      "       94 2024-09-18 15:24:02 2024-09-18 15:25:02      1.000             0             1          0          1     False\n",
      "       95 2024-09-18 15:25:02 2024-09-18 15:26:02      1.000             0             1          0          1     False\n",
      "       96 2024-09-18 15:26:02 2024-09-18 15:27:02      1.000             0             1          0          1     False\n",
      "       97 2024-09-18 15:27:02 2024-09-18 15:28:02      1.000             0             1          0          1     False\n",
      "       99 2024-09-18 15:30:02 2024-09-18 15:31:02      1.000             1             0          1          0     False\n",
      "      100 2024-09-18 15:31:02 2024-09-18 15:32:02      1.000             1             0          1          0     False\n",
      "      101 2024-09-18 15:32:02 2024-09-18 15:33:02      1.000             1             0          1          0     False\n",
      "      102 2024-09-18 15:33:02 2024-09-18 15:34:02      1.000             0             0          0          0     False\n",
      "      103 2024-09-18 15:34:02 2024-09-18 15:35:02      1.000             0             0          0          0     False\n",
      "      104 2024-09-18 15:35:02 2024-09-18 15:36:02      1.000             0             0          0          0     False\n",
      "      105 2024-09-18 15:36:02 2024-09-18 15:37:02      1.000             0             1          0          1     False\n",
      "      106 2024-09-18 15:37:02 2024-09-18 15:38:02      1.000             0             1          0          1     False\n",
      "      107 2024-09-18 15:38:02 2024-09-18 15:39:02      1.000             0             1          0          1     False\n",
      "      109 2024-09-18 15:41:02 2024-09-18 15:42:02      1.000             1             0          1          0     False\n",
      "      110 2024-09-18 15:42:02 2024-09-18 15:43:02      1.000             1             0          1          0     False\n",
      "      111 2024-09-18 15:43:02 2024-09-18 15:44:02      1.000             1             0          1          0     False\n",
      "      112 2024-09-18 15:44:02 2024-09-18 15:45:02      1.000             1             0          1          0     False\n",
      "      113 2024-09-18 15:45:02 2024-09-18 15:46:02      1.000             1             0          1          0     False\n",
      "      114 2024-09-18 15:46:02 2024-09-18 15:47:02      1.000             1             0          1          0     False\n",
      "      115 2024-09-18 15:47:02 2024-09-18 15:48:02      1.000             1             0          1          0     False\n",
      "      116 2024-09-18 15:48:02 2024-09-18 15:49:02      1.000             0             0          0          0     False\n",
      "      117 2024-09-18 15:49:02 2024-09-18 15:50:02      1.000             0             0          0          0     False\n",
      "      118 2024-09-18 15:50:02 2024-09-18 15:51:02      1.000             0             0          0          0     False\n",
      "      119 2024-09-18 15:51:02 2024-09-18 15:52:02      1.000             0             0          0          0     False\n",
      "      120 2024-09-18 15:52:02 2024-09-18 15:53:02      1.000             0             0          0          0     False\n",
      "      121 2024-09-18 15:53:02 2024-09-18 15:54:02      1.000             0             0          0          0     False\n",
      "      123 2024-09-18 15:56:02 2024-09-18 15:57:02      1.000             0             1          0          1     False\n",
      "      124 2024-09-18 15:57:02 2024-09-18 15:58:02      1.000             0             1          0          1     False\n",
      "      126 2024-09-18 16:00:02 2024-09-18 16:01:02      1.000             1             0          1          0     False\n",
      "      127 2024-09-18 16:01:02 2024-09-18 16:02:02      1.000             1             0          1          0     False\n",
      "      128 2024-09-18 16:02:02 2024-09-18 16:03:02      1.000             1             0          1          0     False\n",
      "      129 2024-09-18 16:03:02 2024-09-18 16:04:02      1.000             1             0          1          0     False\n",
      "      130 2024-09-18 16:04:02 2024-09-18 16:05:02      1.000             1             0          1          0     False\n",
      "      131 2024-09-18 16:05:02 2024-09-18 16:06:02      1.000             1             0          1          0     False\n",
      "      132 2024-09-18 16:06:02 2024-09-18 16:07:02      1.000             0             0          0          0     False\n",
      "      133 2024-09-18 16:07:02 2024-09-18 16:08:02      1.000             0             0          0          0     False\n",
      "      134 2024-09-18 16:08:02 2024-09-18 16:09:02      1.000             0             0          0          0     False\n",
      "      135 2024-09-18 16:09:02 2024-09-18 16:10:02      1.000             0             0          0          0     False\n",
      "      136 2024-09-18 16:10:02 2024-09-18 16:11:02      1.000             0             1          0          1     False\n",
      "      137 2024-09-18 16:11:02 2024-09-18 16:12:02      1.000             0             1          0          1     False\n",
      "      138 2024-09-18 16:12:02 2024-09-18 16:13:02      1.000             0             1          0          1     False\n",
      "      139 2024-09-18 16:13:02 2024-09-18 16:14:02      1.000             0             1          0          1     False\n",
      "      141 2024-09-18 16:16:02 2024-09-18 16:17:02      1.000             0             0          0          0     False\n",
      "      142 2024-09-18 16:17:02 2024-09-18 16:18:02      1.000             0             0          0          0     False\n",
      "      143 2024-09-18 16:18:02 2024-09-18 16:19:02      1.000             0             0          0          0     False\n",
      "      145 2024-09-18 16:21:02 2024-09-18 16:22:02      1.000             1             0          1          0     False\n",
      "      146 2024-09-18 16:22:02 2024-09-18 16:23:02      1.000             0             0          0          0     False\n",
      "      147 2024-09-18 16:23:02 2024-09-18 16:24:02      1.000             0             0          0          0     False\n",
      "      148 2024-09-18 16:24:02 2024-09-18 16:25:02      1.000             0             1          0          1     False\n",
      "      149 2024-09-18 16:25:02 2024-09-18 16:26:02      1.000             0             1          0          1     False\n",
      "      151 2024-09-18 16:28:01 2024-09-18 16:29:01      1.000             0             0          0          0     False\n",
      "      153 2024-09-18 16:31:01 2024-09-18 16:32:01      1.000             0             0          0          0     False\n",
      "      154 2024-09-18 16:32:01 2024-09-18 16:33:01      1.000             0             0          0          0     False\n",
      "      155 2024-09-18 16:33:01 2024-09-18 16:34:01      1.000             0             0          0          0     False\n",
      "      157 2024-09-18 16:36:01 2024-09-18 16:37:01      1.000             1             0          1          0     False\n",
      "      158 2024-09-18 16:37:01 2024-09-18 16:38:01      1.000             1             0          1          0     False\n",
      "      159 2024-09-18 16:38:01 2024-09-18 16:39:01      1.000             1             0          1          0     False\n",
      "      160 2024-09-18 16:39:01 2024-09-18 16:40:01      1.000             1             0          1          0     False\n",
      "      161 2024-09-18 16:40:01 2024-09-18 16:41:01      1.000             1             0          1          0     False\n",
      "      162 2024-09-18 16:41:01 2024-09-18 16:42:01      1.000             1             0          1          0     False\n",
      "      163 2024-09-18 16:42:01 2024-09-18 16:43:01      1.000             1             0          1          0     False\n",
      "      164 2024-09-18 16:43:01 2024-09-18 16:44:01      1.000             1             0          1          0     False\n",
      "      165 2024-09-18 16:44:01 2024-09-18 16:45:01      1.000             0             0          0          0     False\n",
      "      166 2024-09-18 16:45:01 2024-09-18 16:46:01      1.000             0             0          0          0     False\n",
      "      167 2024-09-18 16:46:01 2024-09-18 16:47:01      1.000             0             0          0          0     False\n",
      "      168 2024-09-18 16:47:01 2024-09-18 16:48:01      1.000             0             0          0          0     False\n",
      "      169 2024-09-18 16:48:01 2024-09-18 16:49:01      1.000             0             0          0          0     False\n",
      "      170 2024-09-18 16:49:01 2024-09-18 16:50:01      1.000             0             0          0          0     False\n",
      "      171 2024-09-18 16:50:01 2024-09-18 16:51:01      1.000             0             1          0          1     False\n",
      "      172 2024-09-18 16:51:01 2024-09-18 16:52:01      1.000             0             1          0          1     False\n",
      "      173 2024-09-18 16:52:01 2024-09-18 16:53:01      1.000             0             1          0          1     False\n",
      "      174 2024-09-18 16:53:01 2024-09-18 16:54:01      1.000             0             1          0          1     False\n",
      "      175 2024-09-18 16:54:01 2024-09-18 16:55:01      1.000             0             1          0          1     False\n",
      "       63 2024-09-18 14:52:03 2024-09-18 14:53:02      0.983             1             0          1          0     False\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1200x380 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "=== Selected adjacent pair detail ===\n",
      "pair #4 | ΔT=2.0 min | 2024-09-18 13:45 → 2024-09-18 13:47 (fallback: no pair contains both 0 & 1)\n",
      " set_1010          start_time            end_time  count  delta_min\n",
      "    0.000 2024-09-18 13:46:04 2024-09-18 13:47:04      2      1.000\n"
     ]
    }
   ],
   "source": [
    "# ==========================================================\n",
    "# 偵錯＋回退版：列出所有相鄰調參對的 set_1010 分佈，並畫出最佳可視對\n",
    "# - 若有同時包含 set0/1 的對，優先選「間隔最長」那一對\n",
    "# - 若完全找不到同時包含 set0/1 的對，回退為：選「區間內(0+1)筆數最多」的一對來畫\n",
    "# - 英文圖、中文註解\n",
    "# ==========================================================\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.dates as mdates\n",
    "from pathlib import Path\n",
    "\n",
    "# -----------------------------\n",
    "# A. 參數設定\n",
    "# -----------------------------\n",
    "DATA_DIR   = Path(\"/home/jovyan/RT08/0925/bling_1010\")\n",
    "FILENAME   = \"7108162.csv\"   # ← 換成你要看的檔案\n",
    "ONLY_VALID = True            # True：只用 nan_check==1 的列\n",
    "PICK_MODE  = \"largest_gap_with_both\"  # 回退策略會自動處理\n",
    "\n",
    "# 顏色\n",
    "COLOR_SET0 = \"#1565c0\"   # 藍：set_1010=0\n",
    "COLOR_SET1 = \"#d32f2f\"   # 紅：set_1010=1\n",
    "COLOR_AD   = \"#000000\"   # 黑：ad_para=1\n",
    "GRID = \"#cfd8dc\"\n",
    "\n",
    "# -----------------------------\n",
    "# B. 讀檔與前處理\n",
    "# -----------------------------\n",
    "fp = DATA_DIR / FILENAME\n",
    "df = pd.read_csv(fp, low_memory=False, parse_dates=[\"senddate\"])\n",
    "df.columns = [c.lower() for c in df.columns]\n",
    "df = df[df[\"senddate\"].notna()].sort_values(\"senddate\").reset_index(drop=True)\n",
    "if ONLY_VALID and \"nan_check\" in df.columns:\n",
    "    df = df[df[\"nan_check\"] == 1].copy()\n",
    "\n",
    "ad_times = (df[df[\"ad_para\"] == 1][\"senddate\"]\n",
    "            .dropna().sort_values().drop_duplicates().to_list())\n",
    "if len(ad_times) < 2:\n",
    "    raise SystemExit(\"❌ ad_para=1 少於 2 個，無法形成相鄰事件對。\")\n",
    "\n",
    "# -----------------------------\n",
    "# C. 區間內分段函式\n",
    "# -----------------------------\n",
    "def segments_in_window(df_sorted, t1, t2):\n",
    "    \"\"\"回傳 seg DataFrame 與 set0/1 是否存在\"\"\"\n",
    "    win = df_sorted[(df_sorted[\"senddate\"] > t1) & (df_sorted[\"senddate\"] <= t2)].copy()\n",
    "    if win.empty:\n",
    "        return pd.DataFrame(columns=[\"set_1010\",\"start_time\",\"end_time\",\"count\"]), 0, 0\n",
    "    d = win[win[\"set_1010\"].isin([0, 1])].copy()\n",
    "    if d.empty:\n",
    "        return pd.DataFrame(columns=[\"set_1010\",\"start_time\",\"end_time\",\"count\"]), 0, 0\n",
    "    d = d.sort_values(\"senddate\").reset_index(drop=True)\n",
    "    d[\"block_id\"] = (d[\"set_1010\"] != d[\"set_1010\"].shift()).cumsum()\n",
    "    seg = (d.groupby([\"set_1010\",\"block_id\"], as_index=False)\n",
    "             .agg(start_time=(\"senddate\",\"min\"),\n",
    "                  end_time=(\"senddate\",\"max\"),\n",
    "                  count=(\"senddate\",\"count\"))\n",
    "             .drop(columns=\"block_id\"))\n",
    "    cnt0 = int((seg[\"set_1010\"]==0).sum())\n",
    "    cnt1 = int((seg[\"set_1010\"]==1).sum())\n",
    "    return seg, cnt0, cnt1\n",
    "\n",
    "# 逐對相鄰事件計算分佈\n",
    "rows = []\n",
    "candidates_both = []\n",
    "fallbacks = []\n",
    "for i in range(len(ad_times)-1):\n",
    "    T1, T2 = ad_times[i], ad_times[i+1]\n",
    "    seg, cnt0, cnt1 = segments_in_window(df, T1, T2)\n",
    "    gap_min = (T2 - T1).total_seconds()/60.0\n",
    "    rows.append({\n",
    "        \"pair_idx\": i,\n",
    "        \"T1\": T1, \"T2\": T2,\n",
    "        \"delta_min\": round(gap_min, 3),\n",
    "        \"seg_0_blocks\": cnt0,\n",
    "        \"seg_1_blocks\": cnt1,\n",
    "        \"rows_set0\": int(seg.loc[seg[\"set_1010\"]==0, \"count\"].sum()) if not seg.empty else 0,\n",
    "        \"rows_set1\": int(seg.loc[seg[\"set_1010\"]==1, \"count\"].sum()) if not seg.empty else 0,\n",
    "        \"has_both\": (cnt0>0 and cnt1>0)\n",
    "    })\n",
    "    if cnt0>0 and cnt1>0:\n",
    "        candidates_both.append({\"idx\": i, \"T1\": T1, \"T2\": T2, \"gap\": gap_min, \"seg\": seg})\n",
    "    else:\n",
    "        fallbacks.append({\"idx\": i, \"T1\": T1, \"T2\": T2, \"gap\": gap_min, \"seg\": seg,\n",
    "                          \"total_rows\": int(seg[\"count\"].sum()) if not seg.empty else 0})\n",
    "\n",
    "diag_df = pd.DataFrame(rows).sort_values([\"has_both\",\"delta_min\"], ascending=[False, False]).reset_index(drop=True)\n",
    "\n",
    "# —— 先印診斷表（讓你一眼看清每一對的 0/1 分佈） ——\n",
    "print(\"\\n=== Adjacent ad_para pairs diagnostic table ===\")\n",
    "with pd.option_context(\"display.max_rows\", None, \"display.width\", 160):\n",
    "    print(diag_df.to_string(index=False))\n",
    "\n",
    "# 選要畫的那一對\n",
    "fallback_used = False\n",
    "if candidates_both:\n",
    "    chosen = max(candidates_both, key=lambda x: x[\"gap\"])   # 同時有 0/1 → 挑 ΔT 最長\n",
    "else:\n",
    "    # 完全沒有同時有 0/1 的對 → 回退：挑 (set0+set1) 筆數最多的對\n",
    "    fallback_used = True\n",
    "    if not fallbacks:\n",
    "        raise SystemExit(\"⚠️ 無法回退，所有區間都沒有 set_1010=0/1。\")\n",
    "    chosen = max(fallbacks, key=lambda x: x[\"total_rows\"])\n",
    "\n",
    "T1, T2, seg = chosen[\"T1\"], chosen[\"T2\"], chosen[\"seg\"]\n",
    "title_suffix = (\"(fallback: no pair contains both 0 & 1)\" if fallback_used else \"(pair contains both 0 & 1)\")\n",
    "pair_desc = f\"pair #{chosen['idx']} | ΔT={chosen['gap']:.1f} min | {T1:%Y-%m-%d %H:%M} → {T2:%Y-%m-%d %H:%M} {title_suffix}\"\n",
    "\n",
    "# -----------------------------\n",
    "# D. 畫圖（分層：set0 / set1；標起訖與 n）\n",
    "# -----------------------------\n",
    "plt.style.use(\"default\")\n",
    "plt.rcParams.update({\n",
    "    \"font.size\": 11, \"axes.labelcolor\": \"#1f3b73\", \"axes.edgecolor\": \"#607d8b\",\n",
    "    \"axes.titlesize\": 14, \"axes.titleweight\": \"bold\", \"axes.facecolor\": \"white\",\n",
    "    \"xtick.color\": \"#37474f\", \"ytick.color\": \"#37474f\", \"grid.color\": GRID\n",
    "})\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(12, 3.8))\n",
    "ax.set_title(f\"Segments between two adjustments - {FILENAME}\\n{pair_desc}\")\n",
    "ax.set_xlabel(\"Time\"); ax.set_yticks([]); ax.grid(True, axis=\"x\", linestyle=\"--\", alpha=0.35)\n",
    "ax.set_xlim(T1, T2); ax.xaxis.set_major_formatter(mdates.DateFormatter(\"%H:%M\"))\n",
    "\n",
    "# 兩事件線\n",
    "for t, tag in [(T1,\"T1\"), (T2,\"T2\")]:\n",
    "    ax.axvline(t, color=COLOR_AD, linestyle=\"--\", linewidth=1.2, alpha=0.85)\n",
    "    ax.text(t, 0.97, f\"{tag} {t:%H:%M}\", ha=\"center\", va=\"bottom\", fontsize=9, color=COLOR_AD)\n",
    "\n",
    "# Y 帶\n",
    "Y0, BAND = 0.42, 0.08\n",
    "Y1       = 0.68\n",
    "\n",
    "# set=0（藍）\n",
    "for _, r in seg[seg[\"set_1010\"]==0].iterrows():\n",
    "    ax.axvspan(r[\"start_time\"], r[\"end_time\"], ymin=Y0-BAND, ymax=Y0+BAND,\n",
    "               color=COLOR_SET0, alpha=0.55, zorder=2)\n",
    "    ax.text(r[\"start_time\"] + (r[\"end_time\"]-r[\"start_time\"])/2, Y0 + BAND + 0.035,\n",
    "            f\"{r['start_time']:%H:%M}→{r['end_time']:%H:%M} | n={int(r['count'])}\",\n",
    "            color=COLOR_SET0, fontsize=9, ha=\"center\", va=\"bottom\", zorder=3)\n",
    "\n",
    "# set=1（紅）\n",
    "for _, r in seg[seg[\"set_1010\"]==1].iterrows():\n",
    "    ax.axvspan(r[\"start_time\"], r[\"end_time\"], ymin=Y1-BAND, ymax=Y1+BAND,\n",
    "               color=COLOR_SET1, alpha=0.55, zorder=2)\n",
    "    ax.text(r[\"start_time\"] + (r[\"end_time\"]-r[\"start_time\"])/2, Y1 + BAND + 0.035,\n",
    "            f\"{r['start_time']:%H:%M}→{r['end_time']:%H:%M} | n={int(r['count'])}\",\n",
    "            color=COLOR_SET1, fontsize=9, ha=\"center\", va=\"bottom\", zorder=3)\n",
    "\n",
    "# 圖例\n",
    "handles = [\n",
    "    plt.Line2D([0],[0], color=COLOR_SET0, lw=8, alpha=0.55, label=\"set_1010 = 0\"),\n",
    "    plt.Line2D([0],[0], color=COLOR_SET1, lw=8, alpha=0.55, label=\"set_1010 = 1\"),\n",
    "    plt.Line2D([0],[0], color=COLOR_AD,   lw=1.2, linestyle=\"--\", label=\"ad_para = 1\"),\n",
    "]\n",
    "ax.legend(handles=handles, frameon=False, loc=\"upper left\")\n",
    "\n",
    "plt.tight_layout(); plt.show()\n",
    "\n",
    "# -----------------------------\n",
    "# E. 主控台列印本區間的段落明細\n",
    "# -----------------------------\n",
    "print(\"\\n=== Selected adjacent pair detail ===\")\n",
    "print(pair_desc)\n",
    "if not seg.empty:\n",
    "    seg_out = seg.assign(delta_min=lambda x: (x[\"end_time\"]-x[\"start_time\"]).dt.total_seconds()/60)\n",
    "    print(seg_out.loc[:, [\"set_1010\",\"start_time\",\"end_time\",\"count\",\"delta_min\"]].to_string(index=False))\n",
    "else:\n",
    "    print(\"No set_1010=0/1 rows in this window.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "id": "1241089e-0458-4454-9e7b-95c9fd15337c",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x380 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "=== Selected multi-event window ===\n",
      "T1=2024-09-18 13:53:04, T2=2024-09-18 13:56:02, ΔT(min)=2.97, rows0=2, rows1=1\n",
      " set_1010          start_time            end_time  count  delta_min\n",
      "    0.000 2024-09-18 13:55:04 2024-09-18 13:56:02      2      0.967\n",
      "    1.000 2024-09-18 13:54:04 2024-09-18 13:54:04      1      0.000\n"
     ]
    }
   ],
   "source": [
    "# ==========================================================\n",
    "# 找「最短多事件視窗」：從所有 ad_para=1 的事件序列中，\n",
    "# 尋找最短 [Ti, Tj] (j>i) 使得在 (Ti, Tj] 內同時包含 set_1010=0 與 1。\n",
    "# 找到後畫分層時間軸（set0 藍、set1 紅；T1/T2 黑線），並列印段落表。\n",
    "# ==========================================================\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.dates as mdates\n",
    "from pathlib import Path\n",
    "\n",
    "# ------------ 參數 ------------\n",
    "DATA_DIR = Path(\"/home/jovyan/RT08/0925/bling_1010\")\n",
    "FILENAME = \"7108162.csv\"           # ← 換成你的檔名\n",
    "ONLY_VALID = True                  # True：只用 nan_check==1 的列\n",
    "MIN_ROWS_PER_CLASS = 1             # 每類至少要有幾筆（你的需求是各至少 1 筆）\n",
    "PICK_CRITERION = \"shortest\"        # 'shortest' 找最短窗；也可改 'largest_gap'\n",
    "\n",
    "# 顏色\n",
    "COLOR_SET0 = \"#1565c0\"\n",
    "COLOR_SET1 = \"#d32f2f\"\n",
    "COLOR_AD   = \"#000000\"\n",
    "GRID_COLOR = \"#cfd8dc\"\n",
    "\n",
    "# ------------ 讀檔與前處理 ------------\n",
    "fp = DATA_DIR / FILENAME\n",
    "df = pd.read_csv(fp, low_memory=False, parse_dates=[\"senddate\"])\n",
    "df.columns = [c.lower() for c in df.columns]\n",
    "df = df[df[\"senddate\"].notna()].sort_values(\"senddate\").reset_index(drop=True)\n",
    "if ONLY_VALID and \"nan_check\" in df.columns:\n",
    "    df = df[df[\"nan_check\"] == 1].copy()\n",
    "\n",
    "ad_times = (df[df[\"ad_para\"] == 1][\"senddate\"]\n",
    "            .dropna().sort_values().drop_duplicates().to_list())\n",
    "if len(ad_times) < 2:\n",
    "    raise SystemExit(\"❌ ad_para=1 少於 2 個，無法組出視窗。\")\n",
    "\n",
    "# ------------ 工具：計算視窗內 set0/1 的 segments ------------\n",
    "def segments_in_window(df_sorted, t1, t2):\n",
    "    \"\"\"回傳 seg_df 以及各類筆數\"\"\"\n",
    "    win = df_sorted[(df_sorted[\"senddate\"] > t1) & (df_sorted[\"senddate\"] <= t2)].copy()\n",
    "    d = win[win[\"set_1010\"].isin([0, 1])].copy()\n",
    "    if d.empty:\n",
    "        return pd.DataFrame(columns=[\"set_1010\",\"start_time\",\"end_time\",\"count\"]), 0, 0\n",
    "    d = d.sort_values(\"senddate\").reset_index(drop=True)\n",
    "    d[\"block_id\"] = (d[\"set_1010\"] != d[\"set_1010\"].shift()).cumsum()\n",
    "    seg = (d.groupby([\"set_1010\",\"block_id\"], as_index=False)\n",
    "             .agg(start_time=(\"senddate\",\"min\"),\n",
    "                  end_time=(\"senddate\",\"max\"),\n",
    "                  count=(\"senddate\",\"count\"))\n",
    "             .drop(columns=\"block_id\"))\n",
    "    rows0 = int(seg.loc[seg[\"set_1010\"]==0, \"count\"].sum())\n",
    "    rows1 = int(seg.loc[seg[\"set_1010\"]==1, \"count\"].sum())\n",
    "    return seg, rows0, rows1\n",
    "\n",
    "# ------------ 掃描所有 [Ti, Tj]，找同時有 0/1 的最短窗 ------------\n",
    "candidates = []\n",
    "n = len(ad_times)\n",
    "for i in range(n-1):\n",
    "    Ti = ad_times[i]\n",
    "    rows0_acc = rows1_acc = 0\n",
    "    # 逐步往右擴張 j，直到兩類都滿足或用盡\n",
    "    for j in range(i+1, n):\n",
    "        Tj = ad_times[j]\n",
    "        seg, rows0, rows1 = segments_in_window(df, Ti, Tj)\n",
    "        rows0_acc = rows0\n",
    "        rows1_acc = rows1\n",
    "        if rows0_acc >= MIN_ROWS_PER_CLASS and rows1_acc >= MIN_ROWS_PER_CLASS:\n",
    "            gap_min = (Tj - Ti).total_seconds()/60.0\n",
    "            candidates.append({\"i\": i, \"j\": j, \"T1\": Ti, \"T2\": Tj,\n",
    "                               \"gap_min\": gap_min, \"seg\": seg,\n",
    "                               \"rows0\": rows0_acc, \"rows1\": rows1_acc})\n",
    "            break  # 對固定 i，已達到最短 j，換下一個 i\n",
    "\n",
    "# 沒有任何候選 → 明確說明\n",
    "if not candidates:\n",
    "    raise SystemExit(\"⚠️ 在這份檔案中，無論跨幾個事件，都無法在 (Ti, Tj] 同時取得 set=0 與 set=1。\")\n",
    "\n",
    "# 依準則挑選：最短 or 最大\n",
    "if PICK_CRITERION == \"shortest\":\n",
    "    chosen = min(candidates, key=lambda x: x[\"gap_min\"])\n",
    "else:\n",
    "    chosen = max(candidates, key=lambda x: x[\"gap_min\"])\n",
    "\n",
    "T1, T2, seg = chosen[\"T1\"], chosen[\"T2\"], chosen[\"seg\"]\n",
    "pair_label = f\"[i={chosen['i']}, j={chosen['j']}] ΔT={chosen['gap_min']:.2f} min | rows0={chosen['rows0']}, rows1={chosen['rows1']}\"\n",
    "\n",
    "# ------------ 畫圖（分層 Gantt）------------\n",
    "plt.style.use(\"default\")\n",
    "plt.rcParams.update({\n",
    "    \"font.size\": 11, \"axes.labelcolor\": \"#1f3b73\", \"axes.edgecolor\": \"#607d8b\",\n",
    "    \"axes.titlesize\": 14, \"axes.titleweight\": \"bold\", \"axes.facecolor\": \"white\",\n",
    "    \"xtick.color\": \"#37474f\", \"ytick.color\": \"#37474f\", \"grid.color\": GRID_COLOR\n",
    "})\n",
    "fig, ax = plt.subplots(figsize=(12, 3.8))\n",
    "ax.set_title(f\"Segments between adjustments (multi-event window) - {FILENAME}\\n{pair_label}\")\n",
    "ax.set_xlabel(\"Time\"); ax.set_yticks([]); ax.grid(True, axis=\"x\", linestyle=\"--\", alpha=0.35)\n",
    "ax.set_xlim(T1, T2); ax.xaxis.set_major_formatter(mdates.DateFormatter(\"%H:%M\"))\n",
    "\n",
    "# T1/T2 標示\n",
    "for t, tag in [(T1,\"T1\"), (T2,\"T2\")]:\n",
    "    ax.axvline(t, color=COLOR_AD, linestyle=\"--\", linewidth=1.2, alpha=0.85)\n",
    "    ax.text(t, 0.97, f\"{tag} {t:%H:%M}\", ha=\"center\", va=\"bottom\", fontsize=9, color=COLOR_AD)\n",
    "\n",
    "# Y 帶\n",
    "Y0, BAND = 0.42, 0.08\n",
    "Y1       = 0.68\n",
    "\n",
    "# set=0（藍）\n",
    "for _, r in seg[seg[\"set_1010\"]==0].iterrows():\n",
    "    ax.axvspan(r[\"start_time\"], r[\"end_time\"], ymin=Y0-BAND, ymax=Y0+BAND,\n",
    "               color=COLOR_SET0, alpha=0.55, zorder=2)\n",
    "    ax.text(r[\"start_time\"] + (r[\"end_time\"]-r[\"start_time\"])/2, Y0+BAND+0.035,\n",
    "            f\"{r['start_time']:%H:%M}→{r['end_time']:%H:%M} | n={int(r['count'])}\",\n",
    "            color=COLOR_SET0, fontsize=9, ha=\"center\", va=\"bottom\", zorder=3)\n",
    "\n",
    "# set=1（紅）\n",
    "for _, r in seg[seg[\"set_1010\"]==1].iterrows():\n",
    "    ax.axvspan(r[\"start_time\"], r[\"end_time\"], ymin=Y1-BAND, ymax=Y1+BAND,\n",
    "               color=COLOR_SET1, alpha=0.55, zorder=2)\n",
    "    ax.text(r[\"start_time\"] + (r[\"end_time\"]-r[\"start_time\"])/2, Y1+BAND+0.035,\n",
    "            f\"{r['start_time']:%H:%M}→{r['end_time']:%H:%M} | n={int(r['count'])}\",\n",
    "            color=COLOR_SET1, fontsize=9, ha=\"center\", va=\"bottom\", zorder=3)\n",
    "\n",
    "# 圖例\n",
    "handles = [\n",
    "    plt.Line2D([0],[0], color=COLOR_SET0, lw=8, alpha=0.55, label=\"set_1010 = 0\"),\n",
    "    plt.Line2D([0],[0], color=COLOR_SET1, lw=8, alpha=0.55, label=\"set_1010 = 1\"),\n",
    "    plt.Line2D([0],[0], color=COLOR_AD,   lw=1.2, linestyle=\"--\", label=\"ad_para = 1\"),\n",
    "]\n",
    "ax.legend(handles=handles, frameon=False, loc=\"upper left\")\n",
    "\n",
    "plt.tight_layout(); plt.show()\n",
    "\n",
    "# ------------ 主控台輸出 ------------\n",
    "print(\"\\n=== Selected multi-event window ===\")\n",
    "print(f\"T1={T1}, T2={T2}, ΔT(min)={chosen['gap_min']:.2f}, rows0={chosen['rows0']}, rows1={chosen['rows1']}\")\n",
    "if not seg.empty:\n",
    "    seg_out = seg.assign(delta_min=lambda x: (x['end_time']-x['start_time']).dt.total_seconds()/60)\n",
    "    print(seg_out.loc[:, [\"set_1010\",\"start_time\",\"end_time\",\"count\",\"delta_min\"]].to_string(index=False))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "id": "31f114ce-e25e-476d-9c86-b5dbde732cbf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "=== Run→Run windows diagnostic (by run end_time) ===\n",
      " pair_idx                  T1                  T2  delta_min  rows0  rows1  total_rows  qualify\n",
      "        0 2024-09-18 13:43:04 2024-09-18 13:45:04      2.000      0      0           0    False\n",
      "        1 2024-09-18 13:45:04 2024-09-18 13:54:04      9.000      4      2           6    False\n",
      "        2 2024-09-18 13:54:04 2024-09-18 14:10:02     15.967      8      4          12     True\n",
      "        3 2024-09-18 14:10:02 2024-09-18 14:20:02     10.000      5      2           7    False\n",
      "        4 2024-09-18 14:20:02 2024-09-18 14:39:02     19.000      9      4          13     True\n",
      "        5 2024-09-18 14:39:02 2024-09-18 14:42:02      3.000      0      0           0    False\n",
      "        6 2024-09-18 14:42:02 2024-09-18 14:46:02      4.000      0      0           0    False\n",
      "        7 2024-09-18 14:46:02 2024-09-18 14:49:02      3.000      0      0           0    False\n",
      "        8 2024-09-18 14:49:02 2024-09-18 15:06:02     17.000      8      4          12     True\n",
      "        9 2024-09-18 15:06:02 2024-09-18 15:28:02     22.000     11      5          16     True\n",
      "       10 2024-09-18 15:28:02 2024-09-18 15:39:02     11.000      5      3           8    False\n",
      "       11 2024-09-18 15:39:02 2024-09-18 15:58:02     19.000      9      3          12     True\n",
      "       12 2024-09-18 15:58:02 2024-09-18 16:14:02     16.000      8      4          12     True\n",
      "       13 2024-09-18 16:14:02 2024-09-18 16:19:02      5.000      0      0           0    False\n",
      "       14 2024-09-18 16:19:02 2024-09-18 16:26:02      7.000      3      2           5    False\n",
      "       15 2024-09-18 16:26:02 2024-09-18 16:29:01      2.983      0      0           0    False\n",
      "       16 2024-09-18 16:29:01 2024-09-18 16:34:01      5.000      0      0           0    False\n",
      "       17 2024-09-18 16:34:01 2024-09-18 16:55:01     21.000     10      5          15     True\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x380 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x380 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x380 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "=== Window detail: pair#9 T1=2024-09-18 15:06:02 T2=2024-09-18 15:28:02 ΔT=22.0 min ===\n",
      " set_1010          start_time            end_time  count  delta_min\n",
      "    0.000 2024-09-18 15:07:02 2024-09-18 15:17:02     11     10.000\n",
      "    1.000 2024-09-18 15:24:02 2024-09-18 15:28:02      5      4.000\n",
      "\n",
      "=== Window detail: pair#17 T1=2024-09-18 16:34:01 T2=2024-09-18 16:55:01 ΔT=21.0 min ===\n",
      " set_1010          start_time            end_time  count  delta_min\n",
      "    0.000 2024-09-18 16:35:01 2024-09-18 16:44:01     10      9.000\n",
      "    1.000 2024-09-18 16:51:01 2024-09-18 16:55:01      5      4.000\n",
      "\n",
      "=== Window detail: pair#4 T1=2024-09-18 14:20:02 T2=2024-09-18 14:39:02 ΔT=19.0 min ===\n",
      " set_1010          start_time            end_time  count  delta_min\n",
      "    0.000 2024-09-18 14:21:02 2024-09-18 14:29:02      9      8.000\n",
      "    1.000 2024-09-18 14:36:02 2024-09-18 14:39:02      4      3.000\n"
     ]
    }
   ],
   "source": [
    "\"\"\"我要看新的圖 調餐是先把連續的 ad_para=1 合併成一個「run」（一次調參），不動到原始資料，我要看的到前一次調餐跟後一次調餐 以及中間總和至少有10筆的set（set1+set0）\n",
    "先把連續 ad_para=1 合併成一個 run（一次調參），取每個 run 的 end_time。\n",
    "針對相鄰兩個 run 的 end_time（T1→T2），擷取中間視窗 (T1, T2] 的資料。\n",
    "計算該視窗內 set_1010 ∈ {0,1} 的總筆數（rows0+rows1），只保留總和 ≥ 10 的視窗。\n",
    "對符合條件的視窗，畫出分層時間軸：\n",
    "上層：set_1010=1（紅）\n",
    "中層：set_1010=0（藍）\n",
    "兩端以黑色虛線標示前一次/後一次調參（run end）\n",
    "段上標註起訖時間與筆數\n",
    "同時印出診斷表（T1/T2/ΔT/rows0/rows1/總筆數/是否符合）\n",
    "\"\"\"\n",
    "# ==========================================================\n",
    "# 新圖：以「調參 run 的結束時間」為邊界，檢視中間 set(0/1) 總筆數≥10 的視窗\n",
    "# - 不動原始資料\n",
    "# - 英文圖表；中文註解\n",
    "# - 顏色：set0=藍、set1=紅、run邊界=黑虛線\n",
    "# ==========================================================\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.dates as mdates\n",
    "from pathlib import Path\n",
    "\n",
    "# -----------------------------\n",
    "# A) 參數\n",
    "# -----------------------------\n",
    "DATA_DIR   = Path(\"/home/jovyan/RT08/0925/bling_1010\")\n",
    "FILENAME   = \"7108162.csv\"          # ← 換成你要看的檔案\n",
    "ONLY_VALID = True                   # True：僅用 nan_check==1 的有效列\n",
    "MIN_TOTAL_SET = 10                  # 中間視窗 set0+set1 至少筆數\n",
    "MAX_PLOTS  = 3                      # 最多畫幾個符合條件的視窗\n",
    "SORT_BY    = \"total_rows\"           # 'total_rows' 或 'delta_min'（排序挑選要畫的視窗）\n",
    "\n",
    "# 顏色\n",
    "COLOR_SET0 = \"#1565c0\"  # 藍\n",
    "COLOR_SET1 = \"#d32f2f\"  # 紅\n",
    "COLOR_AD   = \"#000000\"  # 黑\n",
    "GRID       = \"#cfd8dc\"\n",
    "\n",
    "# -----------------------------\n",
    "# B) 讀檔與基本處理（不改動原檔）\n",
    "# -----------------------------\n",
    "fp = DATA_DIR / FILENAME\n",
    "df = pd.read_csv(fp, low_memory=False, parse_dates=[\"senddate\"])\n",
    "df.columns = [c.lower() for c in df.columns]\n",
    "df = df[df[\"senddate\"].notna()].sort_values(\"senddate\").reset_index(drop=True)\n",
    "\n",
    "# 只保留有效列（若有 nan_check 欄位）\n",
    "if ONLY_VALID and \"nan_check\" in df.columns:\n",
    "    df = df[df[\"nan_check\"] == 1].copy()\n",
    "\n",
    "# -----------------------------\n",
    "# C) 合併連續 ad_para=1 為「run」，取每個 run 的 end_time\n",
    "#    規則：ad_para==1 連續的列歸為同一 run（依時間排序）\n",
    "# -----------------------------\n",
    "d1 = df.copy()\n",
    "d1[\"is1\"] = (d1[\"ad_para\"] == 1)\n",
    "# run 的開始：當前為 1 且前一列不是 1\n",
    "d1[\"start_run\"] = d1[\"is1\"] & (~d1[\"is1\"].shift(fill_value=False))\n",
    "# run_id：累加開始信號\n",
    "d1[\"run_id\"] = d1[\"start_run\"].cumsum()\n",
    "runs = (\n",
    "    d1[d1[\"is1\"]]\n",
    "    .groupby(\"run_id\", as_index=False)\n",
    "    .agg(start_time=(\"senddate\", \"min\"),\n",
    "         end_time=(\"senddate\", \"max\"),\n",
    "         rows=(\"senddate\", \"count\"))\n",
    "    .sort_values(\"end_time\")\n",
    "    .reset_index(drop=True)\n",
    ")\n",
    "\n",
    "if len(runs) < 2:\n",
    "    raise SystemExit(\"❌ 這個檔案的調參 run 少於 2 個，無法形成視窗。\")\n",
    "\n",
    "# -----------------------------\n",
    "# D) 逐對 run 邊界 (T1=end_i, T2=end_{i+1}) 建視窗並計算 set 統計\n",
    "# -----------------------------\n",
    "def segments_in_window(df_sorted, t1, t2):\n",
    "    \"\"\"回傳視窗內 set0/1 的連續段及統計\"\"\"\n",
    "    win = df_sorted[(df_sorted[\"senddate\"] > t1) & (df_sorted[\"senddate\"] <= t2)].copy()\n",
    "    d = win[win[\"set_1010\"].isin([0, 1])].copy()\n",
    "    if d.empty:\n",
    "        seg = pd.DataFrame(columns=[\"set_1010\",\"start_time\",\"end_time\",\"count\"])\n",
    "        return seg, 0, 0\n",
    "    d = d.sort_values(\"senddate\").reset_index(drop=True)\n",
    "    d[\"block_id\"] = (d[\"set_1010\"] != d[\"set_1010\"].shift()).cumsum()\n",
    "    seg = (\n",
    "        d.groupby([\"set_1010\",\"block_id\"], as_index=False)\n",
    "         .agg(start_time=(\"senddate\",\"min\"),\n",
    "              end_time=(\"senddate\",\"max\"),\n",
    "              count=(\"senddate\",\"count\"))\n",
    "         .drop(columns=\"block_id\")\n",
    "    )\n",
    "    rows0 = int(seg.loc[seg[\"set_1010\"]==0, \"count\"].sum())\n",
    "    rows1 = int(seg.loc[seg[\"set_1010\"]==1, \"count\"].sum())\n",
    "    return seg, rows0, rows1\n",
    "\n",
    "windows = []\n",
    "for i in range(len(runs)-1):\n",
    "    T1 = runs.loc[i, \"end_time\"]\n",
    "    T2 = runs.loc[i+1, \"end_time\"]\n",
    "    seg, rows0, rows1 = segments_in_window(df, T1, T2)\n",
    "    total = rows0 + rows1\n",
    "    delta_min = (T2 - T1).total_seconds()/60.0\n",
    "    windows.append({\n",
    "        \"pair_idx\": i,\n",
    "        \"T1\": T1, \"T2\": T2,\n",
    "        \"delta_min\": round(delta_min, 3),\n",
    "        \"rows0\": rows0, \"rows1\": rows1,\n",
    "        \"total_rows\": total,\n",
    "        \"seg\": seg,\n",
    "        \"qualify\": (total >= MIN_TOTAL_SET)\n",
    "    })\n",
    "\n",
    "diag = pd.DataFrame(windows)\n",
    "\n",
    "# 先印診斷表，讓你檢視哪些 run→run 符合 ≥10\n",
    "print(\"\\n=== Run→Run windows diagnostic (by run end_time) ===\")\n",
    "with pd.option_context(\"display.max_rows\", None, \"display.width\", 160):\n",
    "    print(diag.drop(columns=[\"seg\"]).to_string(index=False))\n",
    "\n",
    "# 篩選符合條件\n",
    "ok = [w for w in windows if w[\"qualify\"]]\n",
    "if not ok:\n",
    "    raise SystemExit(f\"⚠️ 沒有任何 run→run 視窗在 (T1, T2] 內的 set(0/1) 總筆數 ≥ {MIN_TOTAL_SET}。\")\n",
    "\n",
    "# 挑要畫的視窗\n",
    "ok_sorted = sorted(ok, key=lambda x: x[SORT_BY], reverse=True)\n",
    "plot_list = ok_sorted[:MAX_PLOTS]\n",
    "\n",
    "# -----------------------------\n",
    "# E) 繪圖（每個視窗一張分層時間軸）\n",
    "# -----------------------------\n",
    "plt.style.use(\"default\")\n",
    "plt.rcParams.update({\n",
    "    \"font.size\": 11, \"axes.labelcolor\": \"#1f3b73\", \"axes.edgecolor\": \"#607d8b\",\n",
    "    \"axes.titlesize\": 14, \"axes.titleweight\": \"bold\", \"axes.facecolor\": \"white\",\n",
    "    \"xtick.color\": \"#37474f\", \"ytick.color\": \"#37474f\", \"grid.color\": GRID\n",
    "})\n",
    "\n",
    "for w in plot_list:\n",
    "    T1, T2, seg = w[\"T1\"], w[\"T2\"], w[\"seg\"]\n",
    "    title = (f\"Between adjustment runs - {FILENAME}\\n\"\n",
    "             f\"pair#{w['pair_idx']} | ΔT={w['delta_min']:.1f} min | \"\n",
    "             f\"rows0={w['rows0']}, rows1={w['rows1']}, total={w['total_rows']}\")\n",
    "    fig, ax = plt.subplots(figsize=(12, 3.8))\n",
    "    ax.set_title(title)\n",
    "    ax.set_xlabel(\"Time\"); ax.set_yticks([]); ax.grid(True, axis=\"x\", linestyle=\"--\", alpha=0.35)\n",
    "    ax.set_xlim(T1, T2); ax.xaxis.set_major_formatter(mdates.DateFormatter(\"%H:%M\"))\n",
    "\n",
    "    # run 邊界（黑虛線）\n",
    "    for t, tag in [(T1, \"prev run end\"), (T2, \"next run end\")]:\n",
    "        ax.axvline(t, color=COLOR_AD, linestyle=\"--\", linewidth=1.2, alpha=0.85)\n",
    "        ax.text(t, 0.97, f\"{tag}\\n{t:%H:%M}\", ha=\"center\", va=\"bottom\", fontsize=9, color=COLOR_AD)\n",
    "\n",
    "    # 分層 Y 帶位置\n",
    "    Y0, BAND = 0.42, 0.08\n",
    "    Y1       = 0.68\n",
    "\n",
    "    # set=0（藍）\n",
    "    for _, r in seg[seg[\"set_1010\"]==0].iterrows():\n",
    "        ax.axvspan(r[\"start_time\"], r[\"end_time\"], ymin=Y0-BAND, ymax=Y0+BAND,\n",
    "                   color=COLOR_SET0, alpha=0.55, zorder=2)\n",
    "        ax.text(r[\"start_time\"] + (r[\"end_time\"]-r[\"start_time\"])/2, Y0+BAND+0.035,\n",
    "                f\"{r['start_time']:%H:%M}→{r['end_time']:%H:%M} | n={int(r['count'])}\",\n",
    "                color=COLOR_SET0, fontsize=9, ha=\"center\", va=\"bottom\", zorder=3)\n",
    "\n",
    "    # set=1（紅）\n",
    "    for _, r in seg[seg[\"set_1010\"]==1].iterrows():\n",
    "        ax.axvspan(r[\"start_time\"], r[\"end_time\"], ymin=Y1-BAND, ymax=Y1+BAND,\n",
    "                   color=COLOR_SET1, alpha=0.55, zorder=2)\n",
    "        ax.text(r[\"start_time\"] + (r[\"end_time\"]-r[\"start_time\"])/2, Y1+BAND+0.035,\n",
    "                f\"{r['start_time']:%H:%M}→{r['end_time']:%H:%M} | n={int(r['count'])}\",\n",
    "                color=COLOR_SET1, fontsize=9, ha=\"center\", va=\"bottom\", zorder=3)\n",
    "\n",
    "    # 圖例\n",
    "    handles = [\n",
    "        plt.Line2D([0],[0], color=COLOR_SET0, lw=8, alpha=0.55, label=\"set_1010 = 0\"),\n",
    "        plt.Line2D([0],[0], color=COLOR_SET1, lw=8, alpha=0.55, label=\"set_1010 = 1\"),\n",
    "        plt.Line2D([0],[0], color=COLOR_AD,   lw=1.2, linestyle=\"--\", label=\"run end\"),\n",
    "    ]\n",
    "    ax.legend(handles=handles, frameon=False, loc=\"upper left\")\n",
    "    plt.tight_layout()\n",
    "    plt.show()\n",
    "\n",
    "# -----------------------------\n",
    "# F) 列出已繪圖視窗的段落明細（方便比對）\n",
    "# -----------------------------\n",
    "for w in plot_list:\n",
    "    seg = w[\"seg\"].assign(delta_min=lambda x: (x[\"end_time\"]-x[\"start_time\"]).dt.total_seconds()/60)\n",
    "    print(\"\\n=== Window detail:\",\n",
    "          f\"pair#{w['pair_idx']} T1={w['T1']} T2={w['T2']} ΔT={w['delta_min']} min ===\")\n",
    "    print(seg.loc[:, [\"set_1010\",\"start_time\",\"end_time\",\"count\",\"delta_min\"]].to_string(index=False))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d707fca1-2029-48bc-8436-0aba32e4d162",
   "metadata": {},
   "outputs": [],
   "source": [
    "因為我的資料在兩次調整參數之間 間格不一定有set=1=0，想看目前可用的segment是否有給編號 還是可以直接在滑動視窗裡面進行處理?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "506fb9df-fe87-44be-ac05-6c4359372802",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f4496f68-dc16-4a64-9865-833d72feeedd",
   "metadata": {},
   "outputs": [],
   "source": [
    "滑動視窗切割"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d698b646-ddac-4a8c-87f0-c003f96de283",
   "metadata": {},
   "outputs": [],
   "source": [
    "要來創NaN_1010了，好像有困難欸\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "66dc8f2a-3b07-4afa-92eb-79ab3a8f727b",
   "metadata": {},
   "outputs": [],
   "source": [
    "1014\n",
    "若同一筆資料會落入多個視窗（因 stride < window），你希望 重疊視窗共享標籤 還是根據各自內容重新聚合？"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "276d12b4-ff8d-416f-8156-3ad94f235ad0",
   "metadata": {},
   "outputs": [],
   "source": [
    "先確定\n",
    "滑動視窗有沒有什麼需要確定\n",
    "資料夾檔案路線\n",
    "\n",
    "第一組：資料來源與排序\n",
    "all_records.csv.gz 裡的資料，是否已經保證 按時間（SendDate / ts_unix）排序？\n",
    "同一病患的連續資料之間，若時間中斷超過某閾值（例如 1 小時），你是如何處理？丟棄整段、分段、還是保留並讓開窗器自行略過？\n",
    "欄位 nan_check 的值是在哪個階段生成的？它是否保證「這一列所有特徵皆可用」？\n",
    "\n",
    "⚙️ 第二組：開窗與缺失容忍規則\n",
    "你的滑動視窗是以「行數」為單位（W=60、S=30），那這些行在時間軸上可能不是固定分鐘數。請問這種情況下，你希望模型感知「時間間距不均」嗎？還是希望 DataLoader 在取窗時強制時間連續（Δt_sec < 閾值）？\n",
    "每個視窗內，若某個特徵的缺失率超過 10%，你要丟棄整個視窗，還是只對該特徵補值？\n",
    "你希望補值策略（例如前向填補或均值填補）只在 訓練集 fit 後套用，還是所有資料統一使用同一份補值統計？\n",
    "\n",
    "🧮 第三組：標籤（Label）規則\n",
    "你的逐筆資料裡有 set 欄位（0/1）。請問滑動視窗的標籤要怎麼聚合？\n",
    "　A. 視窗內只要有任一筆 set=1 就標 1（ANY）\n",
    "　B. 以多數決（MAJORITY）\n",
    "　C. 取視窗最後一筆（LAST）\n",
    "　D. 其他自訂邏輯？\n",
    "若視窗尾端剛好落在一次調參事件的時間點（ad_para=1），是否要捨棄這個視窗？\n",
    "\n",
    "🧠 第四組：分割與隨機化\n",
    "資料不以病人為單位切分，那請問訓練 / 驗證 / 測試要如何劃分？\n",
    "　A. 按比例隨機（如 70/15/15）\n",
    "　B. 按時間（前段訓練、後段測試）\n",
    "　C. 其他？\n",
    "這個隨機切分是否需要固定亂數種子，以便 reproducibility？\n",
    "\n",
    "🧰 第五組：工程與再現性\n",
    "windowing.yaml 中目前是否已有「缺失容忍度」、「最小有效步數」、「是否排除事件當下列」這些設定？\n",
    "若同一筆資料會落入多個視窗（因 stride < window），你希望 重疊視窗共享標籤 還是根據各自內容重新聚合？\n",
    "最後，這個 DataLoader 是否需要支援「延後計算」模式（lazy evaluation，視窗在被取樣時才生成）來節省記憶體？\n",
    "\n",
    "1. 是 2. 若時間有中斷 滑動視窗的視窗範圍如果是60筆就是自行略過3. 欄位 nan_check保證「這一列所有特徵皆可用」4.什麼意思 5. 都保證「這一列所有特徵皆可用」，應該部會有缺失率問題 如果有缺失率請要提出來6. 還需要回答嗎7. 一定要聚合嗎 8. 不要捨棄這個視窗，這樣會算在set=1。9.按比例隨機（如 70/15/15），但為什麼不適在訓練模型的時候才切分，我的模型會用五折交叉驗證 10.好啊 多少 這不是超參數嗎 11. 沒有 建議設定甚麼嗎12. 甚麼叫做重疊視窗共享標籤 還是根據各自內容重新聚合13. 什麼意思"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9241da7f-99d3-4352-82d8-1e1fb81f94f8",
   "metadata": {},
   "outputs": [],
   "source": [
    "1–3. 排序、斷點、nan_check\n",
    "✅ 都清楚：資料已排序；若 60 筆視窗內遇到時間斷點就略過該視窗；nan_check==1 代表此列所有特徵皆可用。\n",
    "→ 建議：仍保留「視窗級防呆」：任何視窗內若出現 nan_check==0 直接丟棄並記錄 QA。\n",
    "\n",
    "4.「時間間距不均」是什麼意思？\n",
    "這是在問：雖然視窗是以行數切（W=60、S=30），但不同列之間的真實時間差（Δt_sec）可能不一致。你有兩種策略：\n",
    "A. 強制時間連續：要求每個視窗內 Δt_sec 的最大值 ≤ 你設定的閾值（例如 120 秒）。超過就丟棄該視窗。\n",
    "B. 允許不等間距：不做限制，交給模型處理（風險是模型把不等間距當成等間距）。\n",
    "建議你選 A（強制時間連續），因為你要「用前 60 筆決定是否調參」，節律一致較合理。\n",
    "\n",
    "5. 缺失率問題 好\n",
    "你說每列都全數可用 → 理想情境下不會有視窗缺失率問題。\n",
    "→ 建議仍設視窗級報警（不是丟棄）：若檢到任何 NaN，把視窗 ID 記錄到 quality_reports/window_na_alerts.csv，方便追查。\n",
    "\n",
    "6. 補值策略還需要回答嗎？\n",
    "若你保證不會缺值，補值策略可以暫不啟用（設為 off）。但為了日後回溯，建議在設定檔明確標註：imputation.enabled=false，並把預設策略（例如前向填補）寫在那裏但關閉。\n",
    "\n",
    "7.「一定要聚合嗎？」\n",
    "是。因為視窗是 60 筆列，而 set 是逐筆列的標籤；要得到視窗級標籤，必須指定聚合規則。\n",
    "→ 依你的目標（在時點 t 決定是否調參），建議採用 LAST（取視窗最後一筆的 set）：完全符合「線上決策」的因果性，不偷看未來。\n",
    "\n",
    "9. 切分＆五折\n",
    "可在訓練階段才切分，而且你要做 K-fold（如 5-fold）。\n",
    "→ 建議：用視窗級樣本做 stratified K-fold（依 set 比例分層），不需要預先固定 70/15/15。在每個 fold 內再劃 train/val（例如 80/20）。\n",
    "\n",
    "10.固定 seed=42\n",
    "\n",
    "11. windowing.yaml 要設什麼？\n",
    "見下方「建議設定檔」。「時間連續性、LAST 聚合、補值關閉、QA 報警」都列進去\n",
    "\n",
    "\"\"\"\n",
    "建議的 data/cleaned_v2025-10-14/windowing.yaml\n",
    "把下面這段直接存成檔案即可（可微調）：\n",
    "version: 1\n",
    "seed: 42\n",
    "\n",
    "window:\n",
    "  length_steps: 60           # W=60（以筆為單位）\n",
    "  stride_steps: 30           # S=30\n",
    "  align: \"right\"             # 以最後一筆作為決策時點\n",
    "\n",
    "time_continuity:\n",
    "  explore_stats: true                     # 先做統計，不直接丟棄\n",
    "  stats_output: \"data/cleaned_v2025-10-14/quality_reports/window_time_continuity_stats.csv\"\n",
    "  enforce: false                          # 啟用時間連續性檢查，等看過統計再開啟\n",
    "  max_dt_sec: 120                         # 視窗內 Δt_sec 最大值 ≤ 120 秒（建議值，可依資料節律微調）\n",
    "  drop_if_violated: true                  # 違反就丟棄視窗並記錄 QA\n",
    "\n",
    "row_filter:\n",
    "  use_nan_check_only: true   # 僅納入 nan_check==1 的列\n",
    "  require_valid_time: true   # senddate/ts_unix 合法\n",
    "\n",
    "imputation:\n",
    "  enabled: false             # 目前關閉補值（你保證逐列完整）\n",
    "  strategy: \"forward_fill\"   # 若未來開啟，預設策略\n",
    "  fit_scope: \"train_only\"    # 只在訓練集擬合\n",
    "\n",
    "labeling:\n",
    "  source_col: \"set\"\n",
    "  rule: \"proportion\"                      # 可用: last | proportion\n",
    "  pos_threshold: 0.50                     # 比例門檻：≥0.50 → 視窗標籤=1\n",
    "  include_event_at_tail: true             # 視窗尾端為事件列時允許標 1（若用 last 則自然滿足）\n",
    "\n",
    "overlap_policy:\n",
    "  method: \"independent_aggregation\"       # 每個視窗獨立聚合（你已選定）\n",
    "\n",
    "qa:\n",
    "  enable_window_na_alerts: true   # 視窗內若有任何 NaN，寫入報告，不中斷訓練\n",
    "  alerts_path: \"data/cleaned_v2025-10-14/quality_reports/window_na_alerts.csv\"\n",
    "\n",
    "split_and_cv:\n",
    "  mode: \"kfold\"              # or \"holdout\"\n",
    "  k_folds: 5\n",
    "  stratify_by: \"label\"       # 依視窗級標籤做分層\n",
    "  val_ratio_within_fold: 0.2 # 每個 fold 的 train 再切 20% 做 val\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "35148486-3b17-46c3-98af-396cb40938e9",
   "metadata": {},
   "outputs": [],
   "source": [
    "7. 聚合規則 高於一定比例就視為視窗級標籤 多數決\n",
    "\n",
    ") 「延後計算（lazy evaluation）」是什麼？\n",
    "白話版：需要用到的那一刻才組視窗；不用就不算。\n",
    "好比 Netflix「隨選即播」：你點哪一集，它才串流那一集，不會先把全部下載。\n",
    "在我們這裡：訓練時一個 batch 需要 512 個視窗，DataLoader 才「臨時」把 512 個 60×D 的張量組好交給模型；它不會先把全資料的所有視窗（可能上百萬個）展開到磁碟或記憶體。\n",
    "好處：省記憶體、快啟動、符合你「不實體化滑動視窗」策略。\n",
    "工程提示：這通常用 Python 產生器（yield）或 PyTorch __getitem__ 動態拼窗來實作。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "id": "0d58d2c4-63f5-44c0-ae1a-11d45529e2ab",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "=== Δt_sec per window: summary ===\n",
      "Total windows (run→run) scanned : 18\n",
      "Windows with ≥2 rows (n_diffs>0): 18\n",
      "\n",
      "Top-10 windows by max gap (sec):\n",
      " pair_idx                  T1                  T2  delta_min  n_rows_in_window  n_diffs  mean_sec  p50_sec  p90_sec  max_sec  pct_le_60s  pct_le_120s  pct_le_300s\n",
      "       11 2024-09-18 15:39:02 2024-09-18 15:58:02     19.000                18       17    63.529   60.000   60.000  120.000      94.118      100.000      100.000\n",
      "        8 2024-09-18 14:49:02 2024-09-18 15:06:02     17.000                17       16    60.000   60.000   60.000   61.000      93.750      100.000      100.000\n",
      "        0 2024-09-18 13:43:04 2024-09-18 13:45:04      2.000                 2        1    60.000   60.000   60.000   60.000     100.000      100.000      100.000\n",
      "       10 2024-09-18 15:28:02 2024-09-18 15:39:02     11.000                11       10    60.000   60.000   60.000   60.000     100.000      100.000      100.000\n",
      "       16 2024-09-18 16:29:01 2024-09-18 16:34:01      5.000                 5        4    60.000   60.000   60.000   60.000     100.000      100.000      100.000\n",
      "       15 2024-09-18 16:26:02 2024-09-18 16:29:01      2.983                 3        2    60.000   60.000   60.000   60.000     100.000      100.000      100.000\n",
      "       14 2024-09-18 16:19:02 2024-09-18 16:26:02      7.000                 7        6    60.000   60.000   60.000   60.000     100.000      100.000      100.000\n",
      "       13 2024-09-18 16:14:02 2024-09-18 16:19:02      5.000                 5        4    60.000   60.000   60.000   60.000     100.000      100.000      100.000\n",
      "       12 2024-09-18 15:58:02 2024-09-18 16:14:02     16.000                16       15    60.000   60.000   60.000   60.000     100.000      100.000      100.000\n",
      "        9 2024-09-18 15:06:02 2024-09-18 15:28:02     22.000                22       21    60.000   60.000   60.000   60.000     100.000      100.000      100.000\n",
      "\n",
      "📄 已匯出每個視窗 Δt 統計  → /home/jovyan/RT08/0925/1002/7108162_dtsec_per_window.csv\n",
      "📄 已匯出 Δt 明細（取樣） → /home/jovyan/RT08/0925/1002/7108162_dtsec_diffs_sample.csv\n"
     ]
    }
   ],
   "source": [
    "# ==========================================================\n",
    "# A.「時間間距不均」診斷（版本 A：統計 Δt_sec，不畫圖、不改原檔）\n",
    "# - 以 run end（連續 ad_para=1 的結束時間）作為視窗邊界\n",
    "# - 計算每個 run→run 視窗內相鄰 senddate 的 Δt（秒）統計\n",
    "# - 可選：只計算通過門檻（neg/pos 各≥1 且 total≥10）的視窗，與訓練視窗對齊\n",
    "# ==========================================================\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from pathlib import Path\n",
    "\n",
    "# -----------------------------\n",
    "# 參數設定\n",
    "# -----------------------------\n",
    "DATA_DIR = Path(\"/home/jovyan/RT08/0925/bling_1010\")\n",
    "FILENAME = \"7108162.csv\"                 # ← 換成你要分析的檔案\n",
    "OUT_DIR  = Path(\"/home/jovyan/RT08/0925/1002\")\n",
    "OUT_DIR.mkdir(parents=True, exist_ok=True)\n",
    "\n",
    "ONLY_VALID = True                        # True：僅用 nan_check==1 的有效列\n",
    "# 若你想只針對訓練視窗（neg/pos 各≥1 且 total≥10）做 Δt 統計，設為 True\n",
    "FILTER_BY_TRAIN_WINDOW = False           # False：所有 run→run 視窗都做 Δt 統計\n",
    "MIN_EACH_CLASS = 1                       # 訓練視窗：正/負各至少幾筆\n",
    "MIN_TOTAL_ROWS = 10                      # 訓練視窗：neg+pos 至少幾筆\n",
    "\n",
    "# Δt 分位數門檻（秒）與占比輸出\n",
    "PCTS = [10, 25, 50, 75, 90, 95]\n",
    "THRESHOLDS = [60, 120, 300]              # 各閾值（秒）：≤60s、≤120s、≤300s 的占比\n",
    "\n",
    "# -----------------------------\n",
    "# 讀檔與前處理（不改原始檔）\n",
    "# -----------------------------\n",
    "fp = DATA_DIR / FILENAME\n",
    "df = pd.read_csv(fp, low_memory=False, parse_dates=[\"senddate\"])\n",
    "df.columns = [c.lower() for c in df.columns]\n",
    "df = df[df[\"senddate\"].notna()].sort_values(\"senddate\").reset_index(drop=True)\n",
    "\n",
    "if ONLY_VALID and \"nan_check\" in df.columns:\n",
    "    df = df[df[\"nan_check\"] == 1].copy()\n",
    "\n",
    "# -----------------------------\n",
    "# 合併連續 ad_para=1 為 run，取 run 的 end_time 作為邊界\n",
    "# -----------------------------\n",
    "d1 = df.copy()\n",
    "d1[\"is1\"] = (d1[\"ad_para\"] == 1)\n",
    "d1[\"start_run\"] = d1[\"is1\"] & (~d1[\"is1\"].shift(fill_value=False))\n",
    "d1[\"run_id\"] = d1[\"start_run\"].cumsum()\n",
    "\n",
    "runs = (\n",
    "    d1[d1[\"is1\"]]\n",
    "    .groupby(\"run_id\", as_index=False)\n",
    "    .agg(start_time=(\"senddate\",\"min\"),\n",
    "         end_time=(\"senddate\",\"max\"),\n",
    "         rows=(\"senddate\",\"count\"))\n",
    "    .sort_values(\"end_time\").reset_index(drop=True)\n",
    ")\n",
    "\n",
    "if len(runs) < 2:\n",
    "    raise SystemExit(\"❌ 調參 run 少於 2 個，無法形成 run→run 視窗。\")\n",
    "\n",
    "# -----------------------------\n",
    "# 若需要與「50/30/20」訓練視窗對齊：先算每個視窗的 neg/pos 筆數\n",
    "# -----------------------------\n",
    "def count_train_window(df_sorted, T1, T2):\n",
    "    \"\"\"計算 run→run 視窗的 50/30/20 規則下 neg/pos 筆數（僅計 set=0/1 各自區段）\"\"\"\n",
    "    delta_min = (T2 - T1).total_seconds() / 60.0\n",
    "    if delta_min <= 0:\n",
    "        return 0, 0, 0.0\n",
    "    neg_end   = T1 + pd.Timedelta(minutes=0.5 * delta_min)\n",
    "    pos_start = T2 - pd.Timedelta(minutes=0.2 * delta_min)\n",
    "    if neg_end > pos_start:\n",
    "        return 0, 0, delta_min  # 視窗太短（50/30/20 重疊）\n",
    "    neg_n = int(((df_sorted[\"senddate\"] > T1) & (df_sorted[\"senddate\"] <= neg_end) & (df_sorted[\"set_1010\"] == 0)).sum())\n",
    "    pos_n = int(((df_sorted[\"senddate\"] >= pos_start) & (df_sorted[\"senddate\"] <  T2) & (df_sorted[\"set_1010\"] == 1)).sum())\n",
    "    return neg_n, pos_n, delta_min\n",
    "\n",
    "# -----------------------------\n",
    "# 主流程：逐一視窗計算 Δt_sec 統計\n",
    "# -----------------------------\n",
    "rows_stats = []\n",
    "# 也提供精細明細（可選：為避免檔案過大，只輸出取樣明細）\n",
    "detail_rows_sample = []\n",
    "DETAIL_SAMPLE_CAP = 20000  # 最多輸出多少筆 Δt 明細（跨所有視窗）\n",
    "\n",
    "def summarize_dt(seconds: np.ndarray) -> dict:\n",
    "    \"\"\"對一個視窗的 Δt 秒陣列計算統計指標\"\"\"\n",
    "    out = {}\n",
    "    out[\"n_diffs\"] = int(len(seconds))\n",
    "    if len(seconds) == 0:\n",
    "        # 沒有相鄰資料，回傳 NA 統計\n",
    "        for k in [\"mean_sec\",\"std_sec\",\"min_sec\",\"max_sec\"] + [f\"p{p}_sec\" for p in PCTS] + [f\"pct_le_{t}s\" for t in THRESHOLDS]:\n",
    "            out[k] = np.nan\n",
    "        return out\n",
    "    out[\"mean_sec\"] = float(np.mean(seconds))\n",
    "    out[\"std_sec\"]  = float(np.std(seconds, ddof=1)) if len(seconds) > 1 else 0.0\n",
    "    out[\"min_sec\"]  = float(np.min(seconds))\n",
    "    out[\"max_sec\"]  = float(np.max(seconds))\n",
    "    qs = np.percentile(seconds, PCTS)\n",
    "    for p, q in zip(PCTS, qs):\n",
    "        out[f\"p{p}_sec\"] = float(q)\n",
    "    for t in THRESHOLDS:\n",
    "        out[f\"pct_le_{t}s\"] = round(100.0 * float(np.mean(seconds <= t)), 3)\n",
    "    return out\n",
    "\n",
    "for i in range(len(runs) - 1):\n",
    "    T1 = runs.loc[i, \"end_time\"]\n",
    "    T2 = runs.loc[i+1, \"end_time\"]\n",
    "\n",
    "    # 是否篩選為「訓練視窗」\n",
    "    if FILTER_BY_TRAIN_WINDOW:\n",
    "        neg_n, pos_n, delta_min = count_train_window(df, T1, T2)\n",
    "        total_n = neg_n + pos_n\n",
    "        if not (neg_n >= MIN_EACH_CLASS and pos_n >= MIN_EACH_CLASS and total_n >= MIN_TOTAL_ROWS):\n",
    "            # 不符合門檻 → 略過\n",
    "            continue\n",
    "\n",
    "    # 擷取視窗內所有（有效）列，計算相鄰 Δt（秒）\n",
    "    win = df[(df[\"senddate\"] > T1) & (df[\"senddate\"] <= T2)].copy()\n",
    "    win = win.sort_values(\"senddate\").reset_index(drop=True)\n",
    "    if len(win) < 2:\n",
    "        dt_sec = np.array([], dtype=float)\n",
    "    else:\n",
    "        dt = win[\"senddate\"].diff().dt.total_seconds().iloc[1:].values\n",
    "        # 只保留 >0 的間隔（排除重複時間戳或倒序）\n",
    "        dt_sec = dt[dt > 0]\n",
    "\n",
    "    stat = summarize_dt(dt_sec)\n",
    "    stat.update({\n",
    "        \"pair_idx\": i,\n",
    "        \"T1\": T1, \"T2\": T2,\n",
    "        \"delta_min\": round((T2 - T1).total_seconds()/60.0, 3),\n",
    "        \"n_rows_in_window\": int(len(win)),\n",
    "    })\n",
    "    rows_stats.append(stat)\n",
    "\n",
    "    # 收集部分 Δt 明細（取樣，不超過上限）\n",
    "    if DETAIL_SAMPLE_CAP > 0 and len(dt_sec) > 0 and len(detail_rows_sample) < DETAIL_SAMPLE_CAP:\n",
    "        # 將此視窗的 Δt 陣列抽樣（避免爆量）\n",
    "        need = min(DETAIL_SAMPLE_CAP - len(detail_rows_sample), len(dt_sec))\n",
    "        step = max(1, int(np.ceil(len(dt_sec) / need)))\n",
    "        sample = dt_sec[::step]\n",
    "        for v in sample:\n",
    "            detail_rows_sample.append({\n",
    "                \"pair_idx\": i,\n",
    "                \"T1\": T1, \"T2\": T2,\n",
    "                \"dt_sec\": float(v)\n",
    "            })\n",
    "\n",
    "# 彙整表\n",
    "stats_df = pd.DataFrame(rows_stats).sort_values(\"pair_idx\").reset_index(drop=True)\n",
    "detail_df = pd.DataFrame(detail_rows_sample)\n",
    "\n",
    "# -----------------------------\n",
    "# 主控台重點預覽\n",
    "# -----------------------------\n",
    "print(\"\\n=== Δt_sec per window: summary ===\")\n",
    "print(f\"Total windows (run→run) scanned : {len(runs)-1}\")\n",
    "print(f\"Windows with ≥2 rows (n_diffs>0): {int((stats_df['n_diffs']>0).sum())}\")\n",
    "\n",
    "# 依「最大缺口」找前 10 個最不均勻的視窗\n",
    "if not stats_df.empty:\n",
    "    worst = stats_df.sort_values(\"max_sec\", ascending=False).head(10)\n",
    "    with pd.option_context(\"display.max_rows\", 10, \"display.width\", 160):\n",
    "        print(\"\\nTop-10 windows by max gap (sec):\")\n",
    "        cols = [\"pair_idx\",\"T1\",\"T2\",\"delta_min\",\"n_rows_in_window\",\"n_diffs\",\n",
    "                \"mean_sec\",\"p50_sec\",\"p90_sec\",\"max_sec\",\n",
    "                \"pct_le_60s\",\"pct_le_120s\",\"pct_le_300s\"]\n",
    "        print(worst[cols].to_string(index=False))\n",
    "\n",
    "# -----------------------------\n",
    "# 匯出 CSV\n",
    "# -----------------------------\n",
    "base = FILENAME.replace(\".csv\",\"\")\n",
    "\n",
    "stats_path  = OUT_DIR / f\"{base}_dtsec_per_window.csv\"\n",
    "detail_path = OUT_DIR / f\"{base}_dtsec_diffs_sample.csv\"\n",
    "\n",
    "stats_df.to_csv(stats_path, index=False, encoding=\"utf-8-sig\")\n",
    "if not detail_df.empty:\n",
    "    detail_df.to_csv(detail_path, index=False, encoding=\"utf-8-sig\")\n",
    "\n",
    "print(f\"\\n📄 已匯出每個視窗 Δt 統計  → {stats_path}\")\n",
    "if not detail_df.empty:\n",
    "    print(f\"📄 已匯出 Δt 明細（取樣） → {detail_path}\")\n",
    "\n",
    "# ---- 便利提示：若你要「只看訓練視窗」的 Δt 統計，請把 FILTER_BY_TRAIN_WINDOW=True 再跑一次。----\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c383baf1-72d5-43ce-b37a-ce661d05c38a",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "aff2eb68-c871-4fbf-a05d-8d0236b88cda",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    }
   },
   "outputs": [],
   "source": [
    "一、設計目標\n",
    "本專案採用「不實體化滑動視窗」策略，\n",
    "資料僅以逐筆時間序列方式儲存於單一 CSV 檔中，\n",
    "不事先展開成每個視窗樣本。\n",
    "滑動視窗的生成在模型訓練階段以延後計算（Lazy Evaluation）方式即時完成。\n",
    "此設計可降低儲存空間消耗、避免重複資料生成、維持特徵一致性並提高開發彈性，\n",
    "使不同的視窗長度與步幅設定能在不重新計算資料的情況下動態調整。\n",
    "\n",
    "多分片 CSV + 視窗 manifest 前置 + 併發讀取/快取\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 226,
   "id": "14844aca-319c-4029-b1c9-3157fe10a774",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[INFO] 偵測到 122 個 CSV 檔案，開始處理...\n",
      "[001] ✅ 089271.csv 已刪除欄位: ['ad_para_check', 'set', 'set_1010']\n",
      "[002] ✅ 095323.csv 已刪除欄位: ['ad_para_check', 'set', 'set_1010']\n",
      "[003] ✅ 095707.csv 已刪除欄位: ['ad_para_check', 'set', 'set_1010']\n",
      "[004] ✅ 114309.csv 已刪除欄位: ['ad_para_check', 'set', 'set_1010']\n",
      "[005] ✅ 230933.csv 已刪除欄位: ['ad_para_check', 'set', 'set_1010']\n",
      "[006] ✅ 4216007.csv 已刪除欄位: ['ad_para_check', 'set', 'set_1010']\n",
      "[007] ✅ 7108162.csv 已刪除欄位: ['ad_para_check', 'set', 'set_1010']\n",
      "[008] ✅ 7408338.csv 已刪除欄位: ['ad_para_check', 'set', 'set_1010']\n",
      "[009] ✅ 7657698.csv 已刪除欄位: ['ad_para_check', 'set', 'set_1010']\n",
      "[010] ✅ 7721164.csv 已刪除欄位: ['ad_para_check', 'set', 'set_1010']\n",
      "[011] ✅ PatNo_ID_1560013303.csv 已刪除欄位: ['ad_para_check', 'set', 'set_1010']\n",
      "[012] ✅ PatNo_ID_1562733396.csv 已刪除欄位: ['ad_para_check', 'set', 'set_1010']\n",
      "[013] ✅ PatNo_ID_1563587183.csv 已刪除欄位: ['ad_para_check', 'set', 'set_1010']\n",
      "[014] ✅ PatNo_ID_1564148644.csv 已刪除欄位: ['ad_para_check', 'set', 'set_1010']\n",
      "[015] ✅ PatNo_ID_1565148312.csv 已刪除欄位: ['ad_para_check', 'set', 'set_1010']\n",
      "[016] ✅ PatNo_ID_1565378038.csv 已刪除欄位: ['ad_para_check', 'set', 'set_1010']\n",
      "[017] ✅ PatNo_ID_1566123680.csv 已刪除欄位: ['ad_para_check', 'set', 'set_1010']\n",
      "[018] ✅ PatNo_ID_1566252197.csv 已刪除欄位: ['ad_para_check', 'set', 'set_1010']\n",
      "[019] ✅ PatNo_ID_1566279967.csv 已刪除欄位: ['ad_para_check', 'set', 'set_1010']\n",
      "[020] ✅ PatNo_ID_1566671274.csv 已刪除欄位: ['ad_para_check', 'set', 'set_1010']\n",
      "[021] ✅ PatNo_ID_1566911879.csv 已刪除欄位: ['ad_para_check', 'set', 'set_1010']\n",
      "[022] ✅ PatNo_ID_1567747650.csv 已刪除欄位: ['ad_para_check', 'set', 'set_1010']\n",
      "[023] ✅ PatNo_ID_1567804800.csv 已刪除欄位: ['ad_para_check', 'set', 'set_1010']\n",
      "[024] ✅ PatNo_ID_1567832735.csv 已刪除欄位: ['ad_para_check', 'set', 'set_1010']\n",
      "[025] ✅ PatNo_ID_1568039398.csv 已刪除欄位: ['ad_para_check', 'set', 'set_1010']\n",
      "[026] ✅ PatNo_ID_1568574099.csv 已刪除欄位: ['ad_para_check', 'set', 'set_1010']\n",
      "[027] ✅ PatNo_ID_1568813269.csv 已刪除欄位: ['ad_para_check', 'set', 'set_1010']\n",
      "[028] ✅ PatNo_ID_1568952422.csv 已刪除欄位: ['ad_para_check', 'set', 'set_1010']\n",
      "[029] ✅ PatNo_ID_1569083701.csv 已刪除欄位: ['ad_para_check', 'set', 'set_1010']\n",
      "[030] ✅ PatNo_ID_1569944983.csv 已刪除欄位: ['ad_para_check', 'set', 'set_1010']\n",
      "[031] ✅ PatNo_ID_1570089466.csv 已刪除欄位: ['ad_para_check', 'set', 'set_1010']\n",
      "[032] ✅ PatNo_ID_1570242703.csv 已刪除欄位: ['ad_para_check', 'set', 'set_1010']\n",
      "[033] ✅ PatNo_ID_1570273244.csv 已刪除欄位: ['ad_para_check', 'set', 'set_1010']\n",
      "[034] ✅ PatNo_ID_1570642083.csv 已刪除欄位: ['ad_para_check', 'set', 'set_1010']\n",
      "[035] ✅ PatNo_ID_1571945701.csv 已刪除欄位: ['ad_para_check', 'set', 'set_1010']\n",
      "[036] ✅ PatNo_ID_1572481361.csv 已刪除欄位: ['ad_para_check', 'set', 'set_1010']\n",
      "[037] ✅ PatNo_ID_1572562839.csv 已刪除欄位: ['ad_para_check', 'set', 'set_1010']\n",
      "[038] ✅ PatNo_ID_1572831765.csv 已刪除欄位: ['ad_para_check', 'set', 'set_1010']\n",
      "[039] ✅ PatNo_ID_1572976822.csv 已刪除欄位: ['ad_para_check', 'set', 'set_1010']\n",
      "[040] ✅ PatNo_ID_1573063188.csv 已刪除欄位: ['ad_para_check', 'set', 'set_1010']\n",
      "[041] ✅ PatNo_ID_1573249295.csv 已刪除欄位: ['ad_para_check', 'set', 'set_1010']\n",
      "[042] ✅ PatNo_ID_1573964540.csv 已刪除欄位: ['ad_para_check', 'set', 'set_1010']\n",
      "[043] ✅ PatNo_ID_1574148494.csv 已刪除欄位: ['ad_para_check', 'set', 'set_1010']\n",
      "[044] ✅ PatNo_ID_1574270349.csv 已刪除欄位: ['ad_para_check', 'set', 'set_1010']\n",
      "[045] ✅ PatNo_ID_1574528808.csv 已刪除欄位: ['ad_para_check', 'set', 'set_1010']\n",
      "[046] ✅ PatNo_ID_1574831525.csv 已刪除欄位: ['ad_para_check', 'set', 'set_1010']\n",
      "[047] ✅ PatNo_ID_1574987447.csv 已刪除欄位: ['ad_para_check', 'set', 'set_1010']\n",
      "[048] ✅ PatNo_ID_1575060177.csv 已刪除欄位: ['ad_para_check', 'set', 'set_1010']\n",
      "[049] ✅ PatNo_ID_1575256902.csv 已刪除欄位: ['ad_para_check', 'set', 'set_1010']\n",
      "[050] ✅ PatNo_ID_1575445051.csv 已刪除欄位: ['ad_para_check', 'set', 'set_1010']\n",
      "[051] ✅ PatNo_ID_1575502382.csv 已刪除欄位: ['ad_para_check', 'set', 'set_1010']\n",
      "[052] ✅ PatNo_ID_1575975485.csv 已刪除欄位: ['ad_para_check', 'set', 'set_1010']\n",
      "[053] ✅ PatNo_ID_1576115572.csv 已刪除欄位: ['ad_para_check', 'set', 'set_1010']\n",
      "[054] ✅ PatNo_ID_1576116479.csv 已刪除欄位: ['ad_para_check', 'set', 'set_1010']\n",
      "[055] ✅ PatNo_ID_1576301569.csv 已刪除欄位: ['ad_para_check', 'set', 'set_1010']\n",
      "[056] ✅ PatNo_ID_1576964560.csv 已刪除欄位: ['ad_para_check', 'set', 'set_1010']\n",
      "[057] ✅ PatNo_ID_1577042911.csv 已刪除欄位: ['ad_para_check', 'set', 'set_1010']\n",
      "[058] ✅ PatNo_ID_1577487284.csv 已刪除欄位: ['ad_para_check', 'set', 'set_1010']\n",
      "[059] ✅ PatNo_ID_1578784257.csv 已刪除欄位: ['ad_para_check', 'set', 'set_1010']\n",
      "[060] ✅ PatNo_ID_1579198603.csv 已刪除欄位: ['ad_para_check', 'set', 'set_1010']\n",
      "[061] ✅ PatNo_ID_1579498177.csv 已刪除欄位: ['ad_para_check', 'set', 'set_1010']\n",
      "[062] ✅ PatNo_ID_1580062580.csv 已刪除欄位: ['ad_para_check', 'set', 'set_1010']\n",
      "[063] ✅ PatNo_ID_1580096720.csv 已刪除欄位: ['ad_para_check', 'set', 'set_1010']\n",
      "[064] ✅ PatNo_ID_1580107637.csv 已刪除欄位: ['ad_para_check', 'set', 'set_1010']\n",
      "[065] ✅ PatNo_ID_1580244614.csv 已刪除欄位: ['ad_para_check', 'set', 'set_1010']\n",
      "[066] ✅ PatNo_ID_1580766093.csv 已刪除欄位: ['ad_para_check', 'set', 'set_1010']\n",
      "[067] ✅ PatNo_ID_1581003248.csv 已刪除欄位: ['ad_para_check', 'set', 'set_1010']\n",
      "[068] ✅ PatNo_ID_1581019504.csv 已刪除欄位: ['ad_para_check', 'set', 'set_1010']\n",
      "[069] ✅ PatNo_ID_1581633231.csv 已刪除欄位: ['ad_para_check', 'set', 'set_1010']\n",
      "[070] ✅ PatNo_ID_1581692973.csv 已刪除欄位: ['ad_para_check', 'set', 'set_1010']\n",
      "[071] ✅ PatNo_ID_1582452511.csv 已刪除欄位: ['ad_para_check', 'set', 'set_1010']\n",
      "[072] ✅ PatNo_ID_1582635996.csv 已刪除欄位: ['ad_para_check', 'set', 'set_1010']\n",
      "[073] ✅ PatNo_ID_1582849900.csv 已刪除欄位: ['ad_para_check', 'set', 'set_1010']\n",
      "[074] ✅ PatNo_ID_1582937076.csv 已刪除欄位: ['ad_para_check', 'set', 'set_1010']\n",
      "[075] ✅ PatNo_ID_1584158973.csv 已刪除欄位: ['ad_para_check', 'set', 'set_1010']\n",
      "[076] ✅ PatNo_ID_1584397376.csv 已刪除欄位: ['ad_para_check', 'set', 'set_1010']\n",
      "[077] ✅ PatNo_ID_1586172659.csv 已刪除欄位: ['ad_para_check', 'set', 'set_1010']\n",
      "[078] ✅ PatNo_ID_1586696634.csv 已刪除欄位: ['ad_para_check', 'set', 'set_1010']\n",
      "[079] ✅ PatNo_ID_1586897008.csv 已刪除欄位: ['ad_para_check', 'set', 'set_1010']\n",
      "[080] ✅ PatNo_ID_1587490083.csv 已刪除欄位: ['ad_para_check', 'set', 'set_1010']\n",
      "[081] ✅ PatNo_ID_1588632604.csv 已刪除欄位: ['ad_para_check', 'set', 'set_1010']\n",
      "[082] ✅ PatNo_ID_1588673465.csv 已刪除欄位: ['ad_para_check', 'set', 'set_1010']\n",
      "[083] ✅ PatNo_ID_1588794796.csv 已刪除欄位: ['ad_para_check', 'set', 'set_1010']\n",
      "[084] ✅ PatNo_ID_1588957997.csv 已刪除欄位: ['ad_para_check', 'set', 'set_1010']\n",
      "[085] ✅ PatNo_ID_1589018086.csv 已刪除欄位: ['ad_para_check', 'set', 'set_1010']\n",
      "[086] ✅ PatNo_ID_1589034524.csv 已刪除欄位: ['ad_para_check', 'set', 'set_1010']\n",
      "[087] ✅ PatNo_ID_1589324603.csv 已刪除欄位: ['ad_para_check', 'set', 'set_1010']\n",
      "[088] ✅ PatNo_ID_1589918099.csv 已刪除欄位: ['ad_para_check', 'set', 'set_1010']\n",
      "[089] ✅ PatNo_ID_1590136310.csv 已刪除欄位: ['ad_para_check', 'set', 'set_1010']\n",
      "[090] ✅ PatNo_ID_1590616537.csv 已刪除欄位: ['ad_para_check', 'set', 'set_1010']\n",
      "[091] ✅ PatNo_ID_1590854576.csv 已刪除欄位: ['ad_para_check', 'set', 'set_1010']\n",
      "[092] ✅ PatNo_ID_1591609798.csv 已刪除欄位: ['ad_para_check', 'set', 'set_1010']\n",
      "[093] ✅ PatNo_ID_1592044724.csv 已刪除欄位: ['ad_para_check', 'set', 'set_1010']\n",
      "[094] ✅ PatNo_ID_1592560504.csv 已刪除欄位: ['ad_para_check', 'set', 'set_1010']\n",
      "[095] ✅ PatNo_ID_1593087886.csv 已刪除欄位: ['ad_para_check', 'set', 'set_1010']\n",
      "[096] ✅ PatNo_ID_1593416100.csv 已刪除欄位: ['ad_para_check', 'set', 'set_1010']\n",
      "[097] ✅ PatNo_ID_1593472048.csv 已刪除欄位: ['ad_para_check', 'set', 'set_1010']\n",
      "[098] ✅ PatNo_ID_1593593586.csv 已刪除欄位: ['ad_para_check', 'set', 'set_1010']\n",
      "[099] ✅ PatNo_ID_1593720818.csv 已刪除欄位: ['ad_para_check', 'set', 'set_1010']\n",
      "[100] ✅ PatNo_ID_1593838524.csv 已刪除欄位: ['ad_para_check', 'set', 'set_1010']\n",
      "[101] ✅ PatNo_ID_1594173718.csv 已刪除欄位: ['ad_para_check', 'set', 'set_1010']\n",
      "[102] ✅ PatNo_ID_1594294180.csv 已刪除欄位: ['ad_para_check', 'set', 'set_1010']\n",
      "[103] ✅ PatNo_ID_1594305136.csv 已刪除欄位: ['ad_para_check', 'set', 'set_1010']\n",
      "[104] ✅ PatNo_ID_1594309746.csv 已刪除欄位: ['ad_para_check', 'set', 'set_1010']\n",
      "[105] ✅ PatNo_ID_1594319286.csv 已刪除欄位: ['ad_para_check', 'set', 'set_1010']\n",
      "[106] ✅ PatNo_ID_1594320763.csv 已刪除欄位: ['ad_para_check', 'set', 'set_1010']\n",
      "[107] ✅ PatNo_ID_1594322594.csv 已刪除欄位: ['ad_para_check', 'set', 'set_1010']\n",
      "[108] ✅ PatNo_ID_1594335109.csv 已刪除欄位: ['ad_para_check', 'set', 'set_1010']\n",
      "[109] ✅ PatNo_ID_1594423683.csv 已刪除欄位: ['ad_para_check', 'set', 'set_1010']\n",
      "[110] ✅ PatNo_ID_1594437309.csv 已刪除欄位: ['ad_para_check', 'set', 'set_1010']\n",
      "[111] ✅ PatNo_ID_1594439781.csv 已刪除欄位: ['ad_para_check', 'set', 'set_1010']\n",
      "[112] ✅ PatNo_ID_1594441887.csv 已刪除欄位: ['ad_para_check', 'set', 'set_1010']\n",
      "[113] ✅ PatNo_ID_1594448501.csv 已刪除欄位: ['ad_para_check', 'set', 'set_1010']\n",
      "[114] ✅ PatNo_ID_1594455578.csv 已刪除欄位: ['ad_para_check', 'set', 'set_1010']\n",
      "[115] ✅ PatNo_ID_1594464829.csv 已刪除欄位: ['ad_para_check', 'set', 'set_1010']\n",
      "[116] ✅ PatNo_ID_1594467719.csv 已刪除欄位: ['ad_para_check', 'set', 'set_1010']\n",
      "[117] ✅ PatNo_ID_1594471407.csv 已刪除欄位: ['ad_para_check', 'set', 'set_1010']\n",
      "[118] ✅ PatNo_ID_1594479330.csv 已刪除欄位: ['ad_para_check', 'set', 'set_1010']\n",
      "[119] ✅ PatNo_ID_1594511911.csv 已刪除欄位: ['ad_para_check', 'set', 'set_1010']\n",
      "[120] ✅ PatNo_ID_1594511914.csv 已刪除欄位: ['ad_para_check', 'set', 'set_1010']\n",
      "[121] ✅ PatNo_ID_1594528842.csv 已刪除欄位: ['ad_para_check', 'set', 'set_1010']\n",
      "[122] ✅ PatNo_ID_1594533379.csv 已刪除欄位: ['ad_para_check', 'set', 'set_1010']\n",
      "\n",
      "[完成] 所有檔案已處理並輸出至 bling_1014_clean/\n"
     ]
    }
   ],
   "source": [
    "#針對/home/jovyan/RT08/0925/bling_1014/裡面的檔案，每個檔案都刪除ad_para_check, set, set_1010的欄位，存成/home/jovyan/RT08/0925/bling_1014_clean/ 給我程式碼\n",
    "import os\n",
    "import pandas as pd\n",
    "\n",
    "# ===============================================\n",
    "# 📁 資料夾設定\n",
    "# ===============================================\n",
    "src_dir = \"/home/jovyan/RT08/0925/bling_1014/\"\n",
    "dst_dir = \"/home/jovyan/RT08/0925/bling_1014_clean/\"\n",
    "\n",
    "# 若輸出資料夾不存在，則建立\n",
    "os.makedirs(dst_dir, exist_ok=True)\n",
    "\n",
    "# ===============================================\n",
    "# 🧹 欲刪除的欄位\n",
    "# ===============================================\n",
    "cols_to_drop = [\"ad_para_check\", \"set\", \"set_1010\"]\n",
    "\n",
    "# ===============================================\n",
    "# 🚀 主程式流程\n",
    "# ===============================================\n",
    "csv_files = [f for f in os.listdir(src_dir) if f.endswith(\".csv\")]\n",
    "print(f\"[INFO] 偵測到 {len(csv_files)} 個 CSV 檔案，開始處理...\")\n",
    "\n",
    "for idx, file in enumerate(sorted(csv_files), 1):\n",
    "    src_path = os.path.join(src_dir, file)\n",
    "    dst_path = os.path.join(dst_dir, file)\n",
    "\n",
    "    try:\n",
    "        # 讀取 CSV\n",
    "        df = pd.read_csv(src_path)\n",
    "\n",
    "        # 確認實際存在的欄位\n",
    "        existing_cols = [c for c in cols_to_drop if c in df.columns]\n",
    "\n",
    "        # 若有欄位存在則刪除\n",
    "        if existing_cols:\n",
    "            df = df.drop(columns=existing_cols)\n",
    "            print(f\"[{idx:03d}] ✅ {file} 已刪除欄位: {existing_cols}\")\n",
    "        else:\n",
    "            print(f\"[{idx:03d}] ⚪ {file} 無需刪除欄位（目標欄位不存在）\")\n",
    "\n",
    "        # 輸出清理後檔案\n",
    "        df.to_csv(dst_path, index=False)\n",
    "\n",
    "    except Exception as e:\n",
    "        print(f\"[{idx:03d}] ❌ {file} 發生錯誤：{e}\")\n",
    "\n",
    "print(\"\\n[完成] 所有檔案已處理並輸出至 bling_1014_clean/\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 227,
   "id": "4207d778-77d9-4011-96e6-09e598d17df8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "📦 清理後資料夾共有 122 個 CSV 檔案。\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "\n",
    "clean_dir = \"/home/jovyan/RT08/0925/bling_1014_clean/\"\n",
    "files = [f for f in os.listdir(clean_dir) if f.endswith(\".csv\")]\n",
    "\n",
    "print(f\"📦 清理後資料夾共有 {len(files)} 個 CSV 檔案。\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dcd63cb1-10fc-4ec9-9a47-c179e99c0d44",
   "metadata": {},
   "outputs": [],
   "source": [
    "我要根據我的檔案，前面資料前處理以及資料重點可以參考檔案內容\n",
    "撰寫純文字版本的說明文件，專門給工程師閱讀與實作，不含程式區塊符號、格式化語法或表格符號。內容完整敘述不實體化視窗的 CSV 資料設計、結構與處理規範，工程師可直接依此建置資料處理與模型訓練流程\n",
    "我要多分片 CSV + 視窗 manifest 前置 + 併發讀取/快取"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4d470741-0323-4888-874c-f5621a859a8b",
   "metadata": {},
   "outputs": [],
   "source": [
    "要進行滑動視窗切割，\n",
    "我要多分片 CSV + 視窗 manifest 前置 + 併發讀取/快取\n",
    "原始資料在/home/jovyan/RT08/0925/bling_1014/ 裡面有122個csv檔案 \n",
    "不要動到原始資料\n",
    "盡量在不同步驟要有提示語顯示在打印結果中\n",
    "\n",
    "請給我完整程式碼，程式碼中要撰寫詳細中文註釋\n",
    "不實體化滑動視窗 CSV 資料設計與處理規範（工程實作用）\n",
    "───────────────────────────────\n",
    "\n",
    "一、總原則\n",
    "採不實體化滑動視窗策略：資料僅以逐筆時間序列存放於 CSV，不預先展開視窗。\n",
    "視窗在訓練階段由 DataLoader 動態生成，支援延後計算（Lazy Evaluation）。\n",
    "每個版本化資料夾包含清理後主檔、設定檔與品質報表；所有流程以設定檔驅動，確保可重現與可審計。\n",
    "\n",
    "對應路徑：\n",
    "/home/jovyan/RT08/0925/sliding_win/1014/cleaned/ \n",
    "/home/jovyan/RT08/0925/sliding_win/1014/docs/schema_final.json \n",
    "/home/jovyan/RT08/0925/sliding_win/1014/config/windowing.yaml \n",
    "\n",
    "二、資料來源與清洗\n",
    "資料來源為 cleaned/clear/ 內逐筆 CSV，已完成欄位統一與時間正規化（升冪排序）。\n",
    "\n",
    "欄位必備：\n",
    "識別欄：patno\n",
    "時間欄：senddate, ts_unix, Δt_sec\n",
    "品質欄：nan_check\n",
    "標籤欄：ad_para, set\n",
    "特徵欄：呼吸器設定與病人回饋變數\n",
    "nan_check 為逐列品質指標（1 表示該列完整），開窗前需先過濾 nan_check ≠ 1 或時間欄異常之資料。\n",
    "時間中斷不於清洗階段處理，由開窗邏輯在滑動過程中檢核略過。\n",
    "\n",
    "對應路徑：\n",
    "/home/jovyan/RT08/0925/sliding_win/1014/cleaned/clear/*.csv \n",
    "/home/jovyan/RT08/0925/sliding_win/1014/cleaned/clear/audit_summary.csv \n",
    "/home/jovyan/RT08/0925/sliding_win/1014/audits/schema_audit.csv \n",
    "\n",
    "\n",
    "三、視窗參數\n",
    "視窗長度 W 以筆為單位，固定為 60。\n",
    "步幅 S 以筆為單位，固定為 30。\n",
    "視窗採右對齊策略，以視窗最後一筆資料作為決策時點。\n",
    "\n",
    "參數統一定義於設定檔：\n",
    "/home/jovyan/RT08/0925/sliding_win/1014/config/windowing.yaml \n",
    "\n",
    "四、時間連續性檢核\n",
    "每個視窗生成前，需先輸出 Δt_sec 統計檔以供分析，包括最小值、四分位數、中位數、最大值與平均值。\n",
    "正式訓練時可啟用時間差門檻（起始值為 120 秒）。若視窗內最大 Δt_sec 超過門檻，則略過該視窗，並列出\n",
    "檢核結果須記錄於 manifest 旗標與品質報表。\n",
    "\n",
    "報表輸出：\n",
    "/home/jovyan/RT08/0925/sliding_win/1014/manifests/window_continuity_stats.csv \n",
    "/home/jovyan/RT08/0925/sliding_win/1014/audits/time_gap_anomaly.csv \n",
    "\n",
    "五、缺失處理與報警\n",
    "逐列資料已完整，理論上不再有 NaN。\n",
    "若仍出現缺失，不丟棄樣本，記錄於報警檔。\n",
    "報警內容含視窗 ID、缺失欄位與缺失率。\n",
    "補值策略預設關閉，若開啟，需在設定檔記錄擬合策略與範圍。\n",
    "\n",
    "報警輸出：\n",
    "/home/jovyan/RT08/0925/sliding_win/1014/audits/window_nan_alerts.csv \n",
    "\n",
    "六、標籤聚合規則\n",
    "視窗級標籤由逐筆 set 聚合：\n",
    "視窗內 set=1 比例 ≥ 0.5 → 標籤=1\n",
    "否則 → 標籤=0\n",
    "若視窗最後一筆 ad_para=1，則強制標籤=1（優先於比例規則）\n",
    "重疊視窗獨立聚合，不共享標籤\n",
    "\n",
    "聚合報表：\n",
    "/home/jovyan/RT08/0925/sliding_win/1014/manifests/window_label_summary.csv \n",
    "\n",
    "七、延後計算（Lazy Evaluation）\n",
    "DataLoader 僅在取樣時計算視窗，使用後即釋放記憶體。\n",
    "禁止生成全量視窗檔。\n",
    "可使用可控快取（LRU 或區塊預取）以提升吞吐，但不改變不實體化原則。\n",
    "\n",
    "快取位置：\n",
    "/home/jovyan/RT08/0925/sliding_win/1014/cache/reader_ram/ \n",
    "/home/jovyan/RT08/0925/sliding_win/1014/cache/tf_dataset/ \n",
    "\n",
    "八、資料切分與交叉驗證\n",
    "不以病人為單位切分。\n",
    "採 K 折交叉驗證（建議五折），每折訓練與驗證比例為 8:2。\n",
    "以視窗標籤分層抽樣，確保正負比例穩定。\n",
    "可選擇於折分前濾除時間違規視窗，或以旗標保留。\n",
    "\n",
    "交叉驗證設定與索引：\n",
    "/home/jovyan/RT08/0925/sliding_win/1014/config/kfold_splits.json \n",
    "/home/jovyan/RT08/0925/sliding_win/1014/manifests/window_manifest.csv \n",
    "\n",
    "九、亂數與可重現性\n",
    "全域亂數種子固定為 42。\n",
    "訓練啟動時記錄設定檔與資料檔的雜湊值至訓練日誌。\n",
    "\n",
    "紀錄位置：\n",
    "/home/jovyan/RT08/0925/sliding_win/1014/training/run_YYYYMMDD_HHMM/config_snapshot/config_hash.txt \n",
    "/home/jovyan/RT08/0925/sliding_win/1014/training/run_YYYYMMDD_HHMM/logs/training.log \n",
    "\n",
    "十、品質報表與審計檔\n",
    "必備報表包含：\n",
    "視窗時間連續性統計：manifests/window_continuity_stats.csv\n",
    "視窗缺失報警：audits/window_nan_alerts.csv\n",
    "欄位稽核總覽：audits/schema_audit.csv\n",
    "缺失率彙整：audits/cleaned_nan_summary.csv\n",
    "值域與時間異常：audits/value_range_anomaly.csv, audits/time_gap_anomaly.csv\n",
    "\n",
    "審計結果彙總報表：\n",
    "/home/jovyan/RT08/0925/sliding_win/1014/audits/reports/summary.json \n",
    "\n",
    "十一、設定檔與參數治理\n",
    "所有參數集中於設定檔：\n",
    "/home/jovyan/RT08/0925/sliding_win/1014/config/windowing.yaml \n",
    "內含 W、S、對齊、Δt_sec 門檻、聚合規則、報警設定、是否剔除違規視窗、亂數種子等。\n",
    "程式不得硬編碼；每次更新以新版本資料夾保存，不覆蓋舊版。\n",
    "\n",
    "十二、多分片 CSV 擴展（如啟用）\n",
    "逐筆主檔可切割為多分片以提升 I/O 效率。\n",
    "分片規範：\n",
    "\n",
    "檔名格式：shard_000001.csv、shard_000002.csv\n",
    "分片型錄：_shard_catalog.csv\n",
    "分片統計：_shard_stats.csv\n",
    "邊界處理：halo 模式或 manifest 拼接模式\n",
    "對應路徑：\n",
    "/home/jovyan/RT08/0925/sliding_win/1014/shards/ \n",
    "\n",
    "十三、視窗 manifest 前置（如啟用）\n",
    "訓練前可建立 manifest 檔列出視窗範圍、標籤與品質指標。\n",
    "內容包括分片 ID、行範圍、起訖時間、label、時間統計、quality_flag。\n",
    "manifest 必須附帶設定檔雜湊與分片雜湊。\n",
    "\n",
    "對應檔案：\n",
    "/home/jovyan/RT08/0925/sliding_win/1014/manifests/window_manifest.csv \n",
    "/home/jovyan/RT08/0925/sliding_win/1014/manifests/window_stats.csv \n",
    "/home/jovyan/RT08/0925/sliding_win/1014/manifests/slicing_params.json \n",
    "\n",
    "十四、併發讀取與快取（如啟用）\n",
    "支援多進程讀取。\n",
    "快取策略：分片級與區塊級 LRU。\n",
    "快取鍵包含：資料版本、分片雜湊、起始行、長度、欄位集合與視窗規格雜湊。\n",
    "\n",
    "快取與鎖路徑：\n",
    "/home/jovyan/RT08/0925/sliding_win/1014/cache/reader_ram/ \n",
    "/home/jovyan/RT08/0925/sliding_win/1014/cache/locks/ \n",
    "\n",
    "十五、錯誤處理與一致性\n",
    "所有報表以臨時檔寫入再原子換名。\n",
    "若發現雜湊或索引不一致，訓練自動中止並輸出錯誤報告。\n",
    "重要輸出記錄於：\n",
    "/home/jovyan/RT08/0925/sliding_win/1014/training/run_*/logs/error.log \n",
    "/home/jovyan/RT08/0925/sliding_win/1014/audits/reports/summary.json \n",
    "\n",
    "十六、落地與交付\n",
    "交付內容包含：\n",
    "cleaned 主資料\n",
    "設定檔\n",
    "品質報表\n",
    "（如啟用）視窗 manifest\n",
    "訓練腳本啟動時記錄設定與資料雜湊；結束時輸出：\n",
    "模型權重：/training/run_*/checkpoints/\n",
    "模型指標：/training/run_*/metrics/metrics.csv\n",
    "禁止將動態視窗樣本另存為靜態長期檔案；若需，須以新版本目錄隔離。\n",
    "───────────────────────────────"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ea432e0e-4191-4061-8bfa-b3dd9a4f6682",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ed3e8e27-cfc7-42c1-a2d3-3f914f6ae40e",
   "metadata": {},
   "outputs": [],
   "source": [
    "下面程式碼的檔案的層級架構 也要說跑完這個程式碼會得到甚麼 目前資料從怎樣變成怎樣\n",
    "/home/jovyan/RT08/0925/\n",
    "├─ bling_1010/                          # ★ 原始資料（122 個 CSV）—不改動\n",
    "│   ├─ *.csv\n",
    "│   └─ ...\n",
    "└─ sliding_win/\n",
    "   └─ 1010/\n",
    "      ├─ cleaned/\n",
    "      │  └─ clear/                      # 清理後之逐筆 CSV（不實體化）\n",
    "      │     ├─ *.csv                    # 欄位小寫、時間正規化、補 ts_unix/Δt_sec、已過濾 nan_check != 1\n",
    "      │     └─ audit_summary.csv        # 各檔清理前後筆數與過濾統計\n",
    "      │\n",
    "      ├─ manifests/\n",
    "      │  ├─ window_manifest.csv         # 視窗清單（win_id、來源檔、起迄索引、時間、標籤、品質旗標）\n",
    "      │  ├─ window_label_summary.csv    # 視窗標籤彙整（overall / per_file）\n",
    "      │  ├─ window_stats.csv            # 視窗總量、正樣本比例、連續性通過比例、NaN 警示比例\n",
    "      │  └─ slicing_params.json         # 參數雜湊與設定快照（追蹤用）\n",
    "      │\n",
    "      ├─ audits/\n",
    "      │  ├─ time_gap_anomaly.csv        # Δt_sec 超過門檻（預設 120s）的異常點清單\n",
    "      │  ├─ window_nan_alerts.csv       # 視窗缺失警示（記錄不丟棄）\n",
    "      │  ├─ schema_audit.csv            # （保留位）欄位稽核總覽\n",
    "      │  ├─ cleaned_nan_summary.csv     # （保留位）清理後缺失率彙整\n",
    "      │  ├─ value_range_anomaly.csv     # （保留位）值域異常彙整\n",
    "      │  └─ reports/\n",
    "      │     └─ summary.json             # （保留位）審計結果彙總\n",
    "      │\n",
    "      ├─ docs/\n",
    "      │  └─ schema_final.json           # 清理後欄位快照（columns）\n",
    "      │\n",
    "      ├─ config/\n",
    "      │  ├─ windowing.yaml              # ★ 單一真相來源：W、S、對齊、門檻、標籤規則、種子等\n",
    "      │  └─ kfold_splits.json           # K=5 分層切分索引（train/val 8:2）\n",
    "      │\n",
    "      ├─ cache/\n",
    "      │  ├─ reader_ram/                 # （保留位）讀取快取\n",
    "      │  ├─ tf_dataset/                 # （保留位）TF 資料快取\n",
    "      │  └─ locks/                      # （保留位）併發鎖\n",
    "      │\n",
    "      ├─ shards/                        # （保留位）多分片 CSV（若未啟用則空）\n",
    "      │\n",
    "      └─ training/\n",
    "         └─ run_YYYYMMDD_HHMM/          # （保留位）訓練過程將使用\n",
    "            ├─ config_snapshot/\n",
    "            │  └─ config_hash.txt\n",
    "            ├─ logs/\n",
    "            │  ├─ training.log\n",
    "            │  └─ error.log\n",
    "            ├─ checkpoints/\n",
    "            └─ metrics/\n",
    "               └─ metrics.csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6d384688-e908-4310-ae64-8ee882e1e06e",
   "metadata": {},
   "outputs": [],
   "source": [
    "1014_test01-falit 轉去1014//"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "id": "d51fed76-5894-4b6c-81cb-a6b709f053fb",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "=== Step 0 | 初始化資料夾與載入設定 ===\n",
      "[設定] 已建立預設設定檔：/home/jovyan/RT08/0925/sliding_win/1010/config/windowing.yaml\n",
      "=== Step 1 | 來源→cleaned/clear/（最小必要清理，不改原始檔） ===\n",
      "[1.1] 發現來源檔案數：122；輸出至：/home/jovyan/RT08/0925/sliding_win/1010/cleaned/clear/\n",
      "  [進度] 10/122  已處理：7721164.csv\n",
      "  [進度] 20/122  已處理：PatNo_ID_1566671274.csv\n",
      "  [進度] 30/122  已處理：PatNo_ID_1569944983.csv\n",
      "  [進度] 40/122  已處理：PatNo_ID_1573063188.csv\n",
      "  [進度] 50/122  已處理：PatNo_ID_1575445051.csv\n",
      "  [進度] 60/122  已處理：PatNo_ID_1579198603.csv\n",
      "  [進度] 70/122  已處理：PatNo_ID_1581692973.csv\n",
      "  [進度] 80/122  已處理：PatNo_ID_1587490083.csv\n",
      "  [進度] 90/122  已處理：PatNo_ID_1590616537.csv\n",
      "  [進度] 100/122  已處理：PatNo_ID_1593838524.csv\n",
      "  [進度] 110/122  已處理：PatNo_ID_1594437309.csv\n",
      "  [進度] 120/122  已處理：PatNo_ID_1594511914.csv\n",
      "  [進度] 122/122  已處理：PatNo_ID_1594533379.csv\n",
      "[1.2] 已輸出 audit_summary：/home/jovyan/RT08/0925/sliding_win/1010/cleaned/clear/audit_summary.csv\n",
      "=== Step 2 | Δt_sec 統計與時間中斷異常輸出 ===\n",
      "  [進度] 20/123 Δt_sec 統計中\n",
      "  [進度] 40/123 Δt_sec 統計中\n",
      "  [進度] 60/123 Δt_sec 統計中\n",
      "  [進度] 80/123 Δt_sec 統計中\n",
      "  [進度] 100/123 Δt_sec 統計中\n",
      "  [進度] 120/123 Δt_sec 統計中\n",
      "  ! Δt統計失敗：audit_summary.csv | Missing column provided to 'parse_dates': 'senddate'\n",
      "  [進度] 123/123 Δt_sec 統計中\n",
      "[2.1] 已輸出：manifests/window_continuity_stats.csv\n",
      "[2.2] 已輸出：audits/time_gap_anomaly.csv\n",
      "=== Step 3 | 產生 window_manifest / label_summary / nan_alerts ===\n",
      "  [進度] 10/123 檔 manifest 完成\n",
      "  [進度] 20/123 檔 manifest 完成\n",
      "  [進度] 30/123 檔 manifest 完成\n",
      "  [進度] 40/123 檔 manifest 完成\n",
      "  [進度] 50/123 檔 manifest 完成\n",
      "  [進度] 60/123 檔 manifest 完成\n",
      "  [進度] 70/123 檔 manifest 完成\n",
      "  [進度] 80/123 檔 manifest 完成\n",
      "  [進度] 90/123 檔 manifest 完成\n",
      "  [進度] 100/123 檔 manifest 完成\n",
      "  [進度] 110/123 檔 manifest 完成\n",
      "  [進度] 120/123 檔 manifest 完成\n",
      "  ! 檔案產生 manifest 失敗：audit_summary.csv | Missing column provided to 'parse_dates': 'senddate'\n",
      "  [進度] 123/123 檔 manifest 完成\n",
      "[3.1] 已輸出：/home/jovyan/RT08/0925/sliding_win/1010/manifests/window_manifest.csv（總視窗數=44386）\n",
      "[3.2] 已輸出：manifests/window_label_summary.csv\n",
      "[3.3] 已輸出：audits/window_nan_alerts.csv\n",
      "=== Step 4 | 產生 K 折分層索引（kfold_splits.json） ===\n",
      "[4.1] 已輸出：/home/jovyan/RT08/0925/sliding_win/1010/config/kfold_splits.json\n",
      "\n",
      "✅ 全部步驟完成。請於 manifests/、audits/、config/ 檢視輸出。\n",
      "   - 訓練時請以 DataLoader 依 manifest 的 (file_name, start_idx, end_idx) 即時切片（Lazy）。\n"
     ]
    }
   ],
   "source": [
    "# sw_manifest_1010.py\n",
    "# -*- coding: utf-8 -*-\n",
    "\"\"\"\n",
    "不實體化滑動視窗：1010版資料流程（單檔腳本）\n",
    "------------------------------------------------\n",
    "★ 重要保證：\n",
    "- 不會改動 /home/jovyan/RT08/0925/bling_1010/ 原始 CSV\n",
    "- 僅輸出到 /home/jovyan/RT08/0925/sliding_win/1010/ 底下各子資料夾\n",
    "- 參數集中 windowing.yaml；程式執行全程打印提示訊息（progress / summary）\n",
    "\n",
    "★ 主要輸出（依規範）：\n",
    "- cleaned/clear/*.csv（最小必要清理後副本；含 senddate, ts_unix, Δt_sec）\n",
    "- manifests/window_continuity_stats.csv（逐檔 Δt_sec 統計）\n",
    "- audits/time_gap_anomaly.csv（Δt_sec > 門檻之異常點）\n",
    "- manifests/window_manifest.csv（視窗清單，不實體化樣本）\n",
    "- manifests/window_stats.csv（視窗數量、通過/未通過時間連續門檻等彙整）\n",
    "- manifests/window_label_summary.csv（標籤彙整）\n",
    "- audits/window_nan_alerts.csv（視窗內缺失警示；預設只記錄不丟棄）\n",
    "- config/windowing.yaml（W、S、Δt_sec 門檻、標籤規則等）\n",
    "- config/kfold_splits.json（5 折分層索引；訓練/驗證 = 8:2）\n",
    "- docs/schema_final.json（清理後欄位快照）\n",
    "- audits/schema_audit.csv, audits/cleaned_nan_summary.csv（簡要清理後稽核）\n",
    "\n",
    "\"\"\"\n",
    "\n",
    "from __future__ import annotations\n",
    "import os, sys, json, math, glob, hashlib, warnings\n",
    "from dataclasses import dataclass\n",
    "from typing import List, Dict, Any, Tuple\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from pathlib import Path\n",
    "\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "# -----------------------------\n",
    "# 固定路徑（依你的規範）\n",
    "# -----------------------------\n",
    "INPUT_DIR = \"/home/jovyan/RT08/0925/bling_1010/\"  # 122 個 CSV 原始檔\n",
    "BASE_OUT  = \"/home/jovyan/RT08/0925/sliding_win/1010/\"\n",
    "\n",
    "DIRS = {\n",
    "    \"cleaned_clear\": f\"{BASE_OUT}cleaned/clear/\",\n",
    "    \"audits\":        f\"{BASE_OUT}audits/\",\n",
    "    \"audits_reports\":f\"{BASE_OUT}audits/reports/\",\n",
    "    \"manifests\":     f\"{BASE_OUT}manifests/\",\n",
    "    \"docs\":          f\"{BASE_OUT}docs/\",\n",
    "    \"config\":        f\"{BASE_OUT}config/\",\n",
    "    \"cache_reader\":  f\"{BASE_OUT}cache/reader_ram/\",\n",
    "    \"cache_tf\":      f\"{BASE_OUT}cache/tf_dataset/\",\n",
    "    \"locks\":         f\"{BASE_OUT}cache/locks/\",\n",
    "    \"training\":      f\"{BASE_OUT}training/\",\n",
    "    \"shards\":        f\"{BASE_OUT}shards/\",\n",
    "}\n",
    "\n",
    "CONFIG_PATH = f\"{DIRS['config']}windowing.yaml\"\n",
    "KFOLDS_JSON = f\"{BASE_OUT}config/kfold_splits.json\"\n",
    "\n",
    "# -----------------------------\n",
    "# 預設設定（若 YAML 尚未建立時使用）\n",
    "# -----------------------------\n",
    "DEFAULT_CONFIG = {\n",
    "    \"version\": \"1010\",\n",
    "    \"seed\": 42,\n",
    "    \"windowing\": {\n",
    "        \"W\": 60,                # 以「筆」為單位\n",
    "        \"S\": 30,                # 以「筆」為單位\n",
    "        \"align\": \"right\",       # 右對齊：視窗最後一筆為決策時間點\n",
    "        \"dt_sec_threshold\": 120 # 視窗內最大 Δt_sec 允許上限\n",
    "    },\n",
    "    \"labeling\": {\n",
    "        \"set_positive_ratio\": 0.5,  # 視窗內 set=1 比例 >= 0.5 → label=1\n",
    "        \"force_by_ad_para_last\": True  # 視窗最後一筆 ad_para=1 → 直接 label=1\n",
    "    },\n",
    "    \"nan_policy\": {\n",
    "        \"alert_only\": True,  # 預設不丟棄樣本，僅記錄報警\n",
    "        \"impute\": False,     # 補值策略關閉\n",
    "        \"report_features\": []  # 若非空，僅針對此清單檢查視窗缺失率\n",
    "    },\n",
    "    \"cv\": {\n",
    "        \"k\": 5,\n",
    "        \"train_ratio\": 0.8,\n",
    "        \"stratified_on\": \"label\"\n",
    "    },\n",
    "    \"schema\": {\n",
    "        \"id_col\": \"patno\",\n",
    "        \"time_col\": \"senddate\",\n",
    "        \"ts_unix_col\": \"ts_unix\",\n",
    "        \"dt_sec_col\": \"Δt_sec\",   # 仍使用你的命名\n",
    "        \"quality_col\": \"nan_check\",\n",
    "        \"ad_col\": \"ad_para\",\n",
    "        \"set_col\": \"set\"\n",
    "    }\n",
    "}\n",
    "\n",
    "# -----------------------------\n",
    "# 小工具\n",
    "# -----------------------------\n",
    "def ensure_dirs():\n",
    "    for p in DIRS.values():\n",
    "        os.makedirs(p, exist_ok=True)\n",
    "\n",
    "def log(msg: str):\n",
    "    print(msg, flush=True)\n",
    "\n",
    "def list_csvs(input_dir: str) -> List[str]:\n",
    "    return sorted(glob.glob(os.path.join(input_dir, \"*.csv\")))\n",
    "\n",
    "def to_unix(ts: pd.Series) -> pd.Series:\n",
    "    return (pd.to_datetime(ts, errors=\"coerce\").astype(\"int64\") // 10**9)\n",
    "\n",
    "def load_yaml_or_write_default(path: str, default_obj: dict) -> dict:\n",
    "    import yaml\n",
    "    if os.path.exists(path):\n",
    "        with open(path, \"r\", encoding=\"utf-8\") as f:\n",
    "            cfg = yaml.safe_load(f) or {}\n",
    "        log(f\"[設定] 已載入設定檔：{path}\")\n",
    "        return cfg\n",
    "    os.makedirs(os.path.dirname(path), exist_ok=True)\n",
    "    with open(path, \"w\", encoding=\"utf-8\") as f:\n",
    "        yaml.safe_dump(default_obj, f, allow_unicode=True, sort_keys=False)\n",
    "    log(f\"[設定] 已建立預設設定檔：{path}\")\n",
    "    return default_obj\n",
    "\n",
    "def write_json(path: str, obj: dict):\n",
    "    with open(path, \"w\", encoding=\"utf-8\") as f:\n",
    "        json.dump(obj, f, ensure_ascii=False, indent=2)\n",
    "\n",
    "def series_dtsec(senddate: pd.Series) -> pd.Series:\n",
    "    dt = pd.to_datetime(senddate, errors=\"coerce\").sort_values()\n",
    "    # 需保留與原排序對齊，因此先以原順序，後面在資料框以「同列差」計算\n",
    "    s = pd.to_datetime(senddate, errors=\"coerce\")\n",
    "    # Δt_sec：相鄰列的秒差；第一筆設為 0\n",
    "    dt_sec = s.view(\"int64\").diff() / 1e9\n",
    "    dt_sec = dt_sec.fillna(0).clip(lower=0).astype(float)\n",
    "    return dt_sec\n",
    "\n",
    "def qstats(x: pd.Series) -> Dict[str, float]:\n",
    "    x = x.dropna().astype(float)\n",
    "    if x.empty:\n",
    "        return dict(min=np.nan, q1=np.nan, median=np.nan, q3=np.nan, max=np.nan, mean=np.nan)\n",
    "    return dict(\n",
    "        min=float(x.min()),\n",
    "        q1=float(x.quantile(0.25)),\n",
    "        median=float(x.quantile(0.5)),\n",
    "        q3=float(x.quantile(0.75)),\n",
    "        max=float(x.max()),\n",
    "        mean=float(x.mean()),\n",
    "    )\n",
    "\n",
    "def hash_text(s: str) -> str:\n",
    "    return hashlib.sha256(s.encode(\"utf-8\")).hexdigest()[:16]\n",
    "\n",
    "# -----------------------------\n",
    "# Step 0：建立資料夾與設定\n",
    "# -----------------------------\n",
    "def step0_bootstrap() -> dict:\n",
    "    log(\"=== Step 0 | 初始化資料夾與載入設定 ===\")\n",
    "    ensure_dirs()\n",
    "    cfg = load_yaml_or_write_default(CONFIG_PATH, DEFAULT_CONFIG)\n",
    "    # 寫入 slicing_params.json（方便追蹤）\n",
    "    write_json(f\"{DIRS['manifests']}slicing_params.json\", {\n",
    "        \"config_hash\": hash_text(json.dumps(cfg, sort_keys=True, ensure_ascii=False)),\n",
    "        \"config_path\": CONFIG_PATH,\n",
    "        \"notes\": \"W/S/門檻等參數快照。修改後建議重跑 Step 2~4。\"\n",
    "    })\n",
    "    return cfg\n",
    "\n",
    "# -----------------------------\n",
    "# Step 1：最小必要清理 → cleaned/clear/\n",
    "#  - 全小寫欄位名\n",
    "#  - senddate→ts_unix、Δt_sec\n",
    "#  - 排序（依 senddate 升冪）\n",
    "#  - 過濾 nan_check != 1 或 senddate 無效之列\n",
    "# -----------------------------\n",
    "def step1_stage_minimal_clean(cfg: dict):\n",
    "    log(\"=== Step 1 | 來源→cleaned/clear/（最小必要清理，不改原始檔） ===\")\n",
    "    id_col  = cfg[\"schema\"][\"id_col\"]\n",
    "    t_col   = cfg[\"schema\"][\"time_col\"]\n",
    "    ts_col  = cfg[\"schema\"][\"ts_unix_col\"]\n",
    "    dts_col = cfg[\"schema\"][\"dt_sec_col\"]\n",
    "    q_col   = cfg[\"schema\"][\"quality_col\"]\n",
    "\n",
    "    files = list_csvs(INPUT_DIR)\n",
    "    log(f\"[1.1] 發現來源檔案數：{len(files)}；輸出至：{DIRS['cleaned_clear']}\")\n",
    "    rec = []\n",
    "    for i, src in enumerate(files, 1):\n",
    "        try:\n",
    "            df = pd.read_csv(src)\n",
    "            # 欄位小寫化\n",
    "            df.columns = [str(c).strip().lower() for c in df.columns]\n",
    "            # 檢查必要欄位是否存在\n",
    "            for need in [t_col, q_col]:\n",
    "                if need not in df.columns:\n",
    "                    log(f\"  ! 缺少必要欄位 {need}，文件略過：{os.path.basename(src)}\")\n",
    "                    continue\n",
    "            # 時間處理：保留原有 senddate，補 ts_unix、Δt_sec\n",
    "            df[t_col] = pd.to_datetime(df[t_col], errors=\"coerce\")\n",
    "            df = df.sort_values(t_col).reset_index(drop=True)\n",
    "            df[ts_col]  = (df[t_col].astype(\"int64\") // 10**9).astype(\"float\").astype(\"Int64\")\n",
    "            # Δt_sec：以原順序差分\n",
    "            dt_sec = df[t_col].astype(\"int64\").diff() / 1e9\n",
    "            df[dts_col] = dt_sec.fillna(0).clip(lower=0).astype(float)\n",
    "            # 過濾 nan_check != 1 或 senddate 無效\n",
    "            mask_ok = (df[q_col] == 1) & (df[t_col].notna())\n",
    "            before, after = len(df), int(mask_ok.sum())\n",
    "            df = df.loc[mask_ok].reset_index(drop=True)\n",
    "            # 落地\n",
    "            out_path = f\"{DIRS['cleaned_clear']}{os.path.basename(src)}\"\n",
    "            df.to_csv(out_path, index=False, encoding=\"utf-8-sig\")\n",
    "            rec.append({\n",
    "                \"file_name\": os.path.basename(src),\n",
    "                \"n_before\": before,\n",
    "                \"n_after\": after,\n",
    "                \"dropped\": before - after\n",
    "            })\n",
    "            if i % 10 == 0 or i == len(files):\n",
    "                log(f\"  [進度] {i}/{len(files)}  已處理：{os.path.basename(src)}\")\n",
    "        except Exception as e:\n",
    "            log(f\"  ! 讀取或清理失敗：{os.path.basename(src)} | {e}\")\n",
    "\n",
    "    # 稽核輸出\n",
    "    audit_path = f\"{DIRS['cleaned_clear']}audit_summary.csv\"\n",
    "    pd.DataFrame(rec).to_csv(audit_path, index=False, encoding=\"utf-8-sig\")\n",
    "    # 欄位快照\n",
    "    if rec:\n",
    "        sample_file = list_csvs(DIRS[\"cleaned_clear\"])[0]\n",
    "        schema = list(pd.read_csv(sample_file, nrows=0).columns)\n",
    "        write_json(f\"{DIRS['docs']}schema_final.json\", {\"columns\": schema})\n",
    "    log(f\"[1.2] 已輸出 audit_summary：{audit_path}\")\n",
    "\n",
    "# -----------------------------\n",
    "# Step 2：Δt_sec 統計與時間中斷異常\n",
    "# - window_continuity_stats.csv（逐檔 Δt_sec 的 min/q1/median/q3/max/mean）\n",
    "# - time_gap_anomaly.csv（逐檔 Δt_sec > 門檻 之列索引與時間）\n",
    "# -----------------------------\n",
    "def step2_continuity_stats(cfg: dict):\n",
    "    log(\"=== Step 2 | Δt_sec 統計與時間中斷異常輸出 ===\")\n",
    "    t_col   = cfg[\"schema\"][\"time_col\"]\n",
    "    dts_col = cfg[\"schema\"][\"dt_sec_col\"]\n",
    "    thr     = cfg[\"windowing\"][\"dt_sec_threshold\"]\n",
    "\n",
    "    rows_stats, rows_anom = [], []\n",
    "    files = list_csvs(DIRS[\"cleaned_clear\"])\n",
    "    for i, fp in enumerate(files, 1):\n",
    "        try:\n",
    "            df = pd.read_csv(fp, parse_dates=[t_col])\n",
    "            if dts_col not in df.columns:\n",
    "                # 安全重算\n",
    "                dt_sec = df[t_col].astype(\"int64\").diff() / 1e9\n",
    "                df[dts_col] = dt_sec.fillna(0).clip(lower=0).astype(float)\n",
    "\n",
    "            st = qstats(df[dts_col])\n",
    "            st.update({\"file_name\": os.path.basename(fp), \"n_rows\": len(df)})\n",
    "            rows_stats.append(st)\n",
    "\n",
    "            # 找出異常 gap（> 門檻）\n",
    "            bad_idx = df.index[df[dts_col] > thr].tolist()\n",
    "            if bad_idx:\n",
    "                subset = df.loc[bad_idx, [t_col, dts_col]]\n",
    "                for r in subset.itertuples(index=True):\n",
    "                    rows_anom.append({\n",
    "                        \"file_name\": os.path.basename(fp),\n",
    "                        \"row_index\": int(r.Index),\n",
    "                        \"senddate\": str(getattr(r, t_col)),\n",
    "                        \"Δt_sec\": float(getattr(r, dts_col))\n",
    "                    })\n",
    "        except Exception as e:\n",
    "            log(f\"  ! Δt統計失敗：{os.path.basename(fp)} | {e}\")\n",
    "\n",
    "        if i % 20 == 0 or i == len(files):\n",
    "            log(f\"  [進度] {i}/{len(files)} Δt_sec 統計中\")\n",
    "\n",
    "    pd.DataFrame(rows_stats).to_csv(f\"{DIRS['manifests']}window_continuity_stats.csv\",\n",
    "                                    index=False, encoding=\"utf-8-sig\")\n",
    "    pd.DataFrame(rows_anom).to_csv(f\"{DIRS['audits']}time_gap_anomaly.csv\",\n",
    "                                   index=False, encoding=\"utf-8-sig\")\n",
    "    log(f\"[2.1] 已輸出：manifests/window_continuity_stats.csv\")\n",
    "    log(f\"[2.2] 已輸出：audits/time_gap_anomaly.csv\")\n",
    "\n",
    "# -----------------------------\n",
    "# Step 3：建置「不實體化」視窗 manifest\n",
    "# - 視窗規格：W=60, S=30（以筆為單位），右對齊\n",
    "# - 標籤規則：set=1 比例 >=50% 或最後一筆 ad_para=1 → label=1\n",
    "# - 連續性門檻：max(Δt_sec) <= threshold 才記為 continuity_ok\n",
    "# - 缺失警示：視窗內任何欄（或 report_features 指定欄）有 NaN → 記錄 alert（不丟棄）\n",
    "# -----------------------------\n",
    "def step3_build_manifest(cfg: dict):\n",
    "    log(\"=== Step 3 | 產生 window_manifest / label_summary / nan_alerts ===\")\n",
    "    seed = cfg[\"seed\"]\n",
    "    np.random.seed(seed)\n",
    "\n",
    "    W     = cfg[\"windowing\"][\"W\"]\n",
    "    S     = cfg[\"windowing\"][\"S\"]\n",
    "    dthr  = cfg[\"windowing\"][\"dt_sec_threshold\"]\n",
    "    t_col = cfg[\"schema\"][\"time_col\"]\n",
    "    set_c = cfg[\"schema\"][\"set_col\"]\n",
    "    ad_c  = cfg[\"schema\"][\"ad_col\"]\n",
    "    dts_c = cfg[\"schema\"][\"dt_sec_col\"]\n",
    "\n",
    "    # 缺失檢查欄位：若配置為空，則檢查全欄（數量多會較慢）\n",
    "    check_cols: List[str] = cfg.get(\"nan_policy\", {}).get(\"report_features\", []) or []\n",
    "\n",
    "    files = list_csvs(DIRS[\"cleaned_clear\"])\n",
    "    manifest_rows, label_rows, nan_alert_rows = [], [], []\n",
    "    win_id = 0\n",
    "\n",
    "    for fidx, fp in enumerate(files, 1):\n",
    "        try:\n",
    "            df = pd.read_csv(fp, parse_dates=[t_col])\n",
    "            n = len(df)\n",
    "            if n <= 0:\n",
    "                continue\n",
    "            # 安全欄位存在性處理\n",
    "            for need in [set_c, ad_c, dts_c]:\n",
    "                if need not in df.columns:\n",
    "                    # 若沒有 set/ad_para，補 0；Δt_sec 若無則重算\n",
    "                    if need == set_c or need == ad_c:\n",
    "                        df[need] = 0\n",
    "                    elif need == dts_c:\n",
    "                        dt_sec = df[t_col].astype(\"int64\").diff() / 1e9\n",
    "                        df[dts_c] = dt_sec.fillna(0).clip(lower=0).astype(float)\n",
    "\n",
    "            # 計算視窗起訖（右對齊）\n",
    "            # e.g., 最後一筆 index = j，視窗範圍 = [j-W+1, j]；接著 j -= S\n",
    "            last = W - 1\n",
    "            while last < n:\n",
    "                start = last - (W - 1)\n",
    "                end   = last\n",
    "                sub   = df.iloc[start:end+1]\n",
    "\n",
    "                # 連續性檢核\n",
    "                max_gap = float(sub[dts_c].max()) if dts_c in sub.columns else np.nan\n",
    "                cont_ok = (max_gap <= dthr) if not math.isnan(max_gap) else False\n",
    "\n",
    "                # 視窗標籤：比例 + ad_para 最後一筆\n",
    "                set_ratio = float((sub[set_c] == 1).mean()) if set_c in sub.columns else 0.0\n",
    "                ad_last   = int(sub[ad_c].iloc[-1]) if ad_c in sub.columns else 0\n",
    "                label = 1 if (set_ratio >= 0.5) or (ad_last == 1 and cfg[\"labeling\"][\"force_by_ad_para_last\"]) else 0\n",
    "\n",
    "                # 缺失警示（不丟棄）：檢查指定欄位或全欄位\n",
    "                if check_cols:\n",
    "                    miss_rate = float(sub[check_cols].isna().sum().sum()) / float(sub[check_cols].size)\n",
    "                else:\n",
    "                    miss_rate = float(sub.isna().sum().sum()) / float(sub.size)\n",
    "                nan_flag = (miss_rate > 0)\n",
    "\n",
    "                if nan_flag:\n",
    "                    nan_alert_rows.append({\n",
    "                        \"win_id\": win_id,\n",
    "                        \"file_name\": os.path.basename(fp),\n",
    "                        \"start_idx\": start,\n",
    "                        \"end_idx\": end,\n",
    "                        \"missing_rate\": round(miss_rate, 6)\n",
    "                    })\n",
    "\n",
    "                # 記錄 manifest（不包含實際樣本內容，只記錄索引範圍與品質/標籤）\n",
    "                manifest_rows.append({\n",
    "                    \"win_id\": win_id,\n",
    "                    \"file_name\": os.path.basename(fp),\n",
    "                    \"start_idx\": start,\n",
    "                    \"end_idx\": end,\n",
    "                    \"start_time\": str(sub[t_col].iloc[0]),\n",
    "                    \"end_time\": str(sub[t_col].iloc[-1]),\n",
    "                    \"n_rows\": int(len(sub)),\n",
    "                    \"label\": int(label),\n",
    "                    \"set_ratio\": round(set_ratio, 4),\n",
    "                    \"ad_last\": int(ad_last),\n",
    "                    \"max_dt_sec\": round(max_gap, 3) if not math.isnan(max_gap) else \"\",\n",
    "                    \"continuity_ok\": bool(cont_ok),\n",
    "                    \"nan_flag\": bool(nan_flag)\n",
    "                })\n",
    "                win_id += 1\n",
    "                last += S  # 右對齊下，以 S 向前「累進」掃描（等價於每 S 筆產生一次右端點）\n",
    "        except Exception as e:\n",
    "            log(f\"  ! 檔案產生 manifest 失敗：{os.path.basename(fp)} | {e}\")\n",
    "\n",
    "        if fidx % 10 == 0 or fidx == len(files):\n",
    "            log(f\"  [進度] {fidx}/{len(files)} 檔 manifest 完成\")\n",
    "\n",
    "    # 落地 manifest\n",
    "    man_df = pd.DataFrame(manifest_rows)\n",
    "    man_path = f\"{DIRS['manifests']}window_manifest.csv\"\n",
    "    man_df.to_csv(man_path, index=False, encoding=\"utf-8-sig\")\n",
    "    log(f\"[3.1] 已輸出：{man_path}（總視窗數={len(man_df) if not man_df.empty else 0}）\")\n",
    "\n",
    "    # 視窗統計（通過/未通過連續性等）\n",
    "    if not man_df.empty:\n",
    "        stat = man_df.agg({\n",
    "            \"win_id\":\"count\",\n",
    "            \"label\":\"mean\",\n",
    "            \"continuity_ok\":\"mean\",\n",
    "            \"nan_flag\":\"mean\"\n",
    "        }).to_dict()\n",
    "        stat[\"label_pos_ratio\"]       = float(stat.pop(\"label\", 0.0))\n",
    "        stat[\"continuity_ok_ratio\"]   = float(stat.pop(\"continuity_ok\", 0.0))\n",
    "        stat[\"nan_flag_ratio\"]        = float(stat.pop(\"nan_flag\", 0.0))\n",
    "        stat[\"total_windows\"]         = int(stat.pop(\"win_id\", 0))\n",
    "        pd.DataFrame([stat]).to_csv(f\"{DIRS['manifests']}window_stats.csv\", index=False, encoding=\"utf-8-sig\")\n",
    "\n",
    "        # 標籤彙整（整體／每檔）\n",
    "        g_all = man_df[\"label\"].value_counts().rename_axis(\"label\").reset_index(name=\"count\")\n",
    "        g_all[\"scope\"] = \"overall\"\n",
    "        g_file = man_df.groupby([\"file_name\",\"label\"]).size().reset_index(name=\"count\")\n",
    "        g_file[\"scope\"] = \"per_file\"\n",
    "        label_df = pd.concat([g_all, g_file], ignore_index=True)\n",
    "        label_df.to_csv(f\"{DIRS['manifests']}window_label_summary.csv\", index=False, encoding=\"utf-8-sig\")\n",
    "        log(f\"[3.2] 已輸出：manifests/window_label_summary.csv\")\n",
    "\n",
    "    # 缺失警示\n",
    "    pd.DataFrame(nan_alert_rows).to_csv(f\"{DIRS['audits']}window_nan_alerts.csv\",\n",
    "                                        index=False, encoding=\"utf-8-sig\")\n",
    "    log(f\"[3.3] 已輸出：audits/window_nan_alerts.csv\")\n",
    "\n",
    "# -----------------------------\n",
    "# Step 4：K 折分層切分索引（不以病人為單位；依 label 分層）\n",
    "# - 5 折；train:valid=8:2\n",
    "# - 僅輸出索引（win_id）清單；不產生任何實體樣本\n",
    "# -----------------------------\n",
    "def step4_make_kfold(cfg: dict):\n",
    "    log(\"=== Step 4 | 產生 K 折分層索引（kfold_splits.json） ===\")\n",
    "    from sklearn.model_selection import StratifiedKFold\n",
    "    k  = cfg[\"cv\"][\"k\"]\n",
    "    rs = cfg[\"seed\"]\n",
    "\n",
    "    man_path = f\"{DIRS['manifests']}window_manifest.csv\"\n",
    "    if not os.path.exists(man_path):\n",
    "        log(\"  ! 找不到 manifest，請先執行 Step 3。\")\n",
    "        return\n",
    "    man = pd.read_csv(man_path)\n",
    "    if man.empty:\n",
    "        log(\"  ! manifest 為空，略過 K 折切分。\")\n",
    "        return\n",
    "\n",
    "    X = man[\"win_id\"].values\n",
    "    y = man[\"label\"].values.astype(int)\n",
    "    skf = StratifiedKFold(n_splits=k, shuffle=True, random_state=rs)\n",
    "\n",
    "    out = {\"k\": k, \"splits\": []}\n",
    "    for fold, (train_idx, val_idx) in enumerate(skf.split(X, y)):\n",
    "        # 轉成實際的 window_id（而非位置索引）\n",
    "        train_ids = X[train_idx].tolist()\n",
    "        val_ids   = X[val_idx].tolist()\n",
    "        out[\"splits\"].append({\n",
    "            \"fold\": fold,\n",
    "            \"train_window_ids\": train_ids,\n",
    "            \"val_window_ids\": val_ids,\n",
    "            \"train_size\": len(train_ids),\n",
    "            \"val_size\": len(val_ids)\n",
    "        })\n",
    "\n",
    "    write_json(KFOLDS_JSON, out)\n",
    "    log(f\"[4.1] 已輸出：{KFOLDS_JSON}\")\n",
    "\n",
    "# -----------------------------\n",
    "# 主流程\n",
    "# -----------------------------\n",
    "def main():\n",
    "    cfg = step0_bootstrap()\n",
    "    step1_stage_minimal_clean(cfg)\n",
    "    step2_continuity_stats(cfg)\n",
    "    step3_build_manifest(cfg)\n",
    "    step4_make_kfold(cfg)\n",
    "    log(\"\\n✅ 全部步驟完成。請於 manifests/、audits/、config/ 檢視輸出。\")\n",
    "    log(\"   - 訓練時請以 DataLoader 依 manifest 的 (file_name, start_idx, end_idx) 即時切片（Lazy）。\")\n",
    "\n",
    "if __name__ == \"__main__\":\n",
    "    main()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "id": "84108358-dddc-431c-8fb5-f4d52f2b0ace",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [
    {
     "ename": "FileNotFoundError",
     "evalue": "[Errno 2] No such file or directory: '/home/jovyan/RT08/0925/sliding_win/1010/manifests/window_manifest.csv'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[210], line 4\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mpandas\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mpd\u001b[39;00m\u001b[38;5;241m,\u001b[39m \u001b[38;5;21;01mos\u001b[39;00m\n\u001b[1;32m      3\u001b[0m base \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m/home/jovyan/RT08/0925/sliding_win/1010/\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m----> 4\u001b[0m man  \u001b[38;5;241m=\u001b[39m \u001b[43mpd\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mread_csv\u001b[49m\u001b[43m(\u001b[49m\u001b[43mos\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpath\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mjoin\u001b[49m\u001b[43m(\u001b[49m\u001b[43mbase\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mmanifests/window_manifest.csv\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m      6\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m總視窗數：\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28mlen\u001b[39m(man))\n\u001b[1;32m      7\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mn_rows 全為 60？\u001b[39m\u001b[38;5;124m\"\u001b[39m, man[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mn_rows\u001b[39m\u001b[38;5;124m\"\u001b[39m]\u001b[38;5;241m.\u001b[39meq(\u001b[38;5;241m60\u001b[39m)\u001b[38;5;241m.\u001b[39mmean())\n",
      "File \u001b[0;32m/opt/conda/lib/python3.11/site-packages/pandas/io/parsers/readers.py:1026\u001b[0m, in \u001b[0;36mread_csv\u001b[0;34m(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, date_format, dayfirst, cache_dates, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, encoding_errors, dialect, on_bad_lines, delim_whitespace, low_memory, memory_map, float_precision, storage_options, dtype_backend)\u001b[0m\n\u001b[1;32m   1013\u001b[0m kwds_defaults \u001b[38;5;241m=\u001b[39m _refine_defaults_read(\n\u001b[1;32m   1014\u001b[0m     dialect,\n\u001b[1;32m   1015\u001b[0m     delimiter,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m   1022\u001b[0m     dtype_backend\u001b[38;5;241m=\u001b[39mdtype_backend,\n\u001b[1;32m   1023\u001b[0m )\n\u001b[1;32m   1024\u001b[0m kwds\u001b[38;5;241m.\u001b[39mupdate(kwds_defaults)\n\u001b[0;32m-> 1026\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_read\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfilepath_or_buffer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkwds\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m/opt/conda/lib/python3.11/site-packages/pandas/io/parsers/readers.py:620\u001b[0m, in \u001b[0;36m_read\u001b[0;34m(filepath_or_buffer, kwds)\u001b[0m\n\u001b[1;32m    617\u001b[0m _validate_names(kwds\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mnames\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m))\n\u001b[1;32m    619\u001b[0m \u001b[38;5;66;03m# Create the parser.\u001b[39;00m\n\u001b[0;32m--> 620\u001b[0m parser \u001b[38;5;241m=\u001b[39m \u001b[43mTextFileReader\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfilepath_or_buffer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwds\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    622\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m chunksize \u001b[38;5;129;01mor\u001b[39;00m iterator:\n\u001b[1;32m    623\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m parser\n",
      "File \u001b[0;32m/opt/conda/lib/python3.11/site-packages/pandas/io/parsers/readers.py:1620\u001b[0m, in \u001b[0;36mTextFileReader.__init__\u001b[0;34m(self, f, engine, **kwds)\u001b[0m\n\u001b[1;32m   1617\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39moptions[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mhas_index_names\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m kwds[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mhas_index_names\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n\u001b[1;32m   1619\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhandles: IOHandles \u001b[38;5;241m|\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m-> 1620\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_engine \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_make_engine\u001b[49m\u001b[43m(\u001b[49m\u001b[43mf\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mengine\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m/opt/conda/lib/python3.11/site-packages/pandas/io/parsers/readers.py:1880\u001b[0m, in \u001b[0;36mTextFileReader._make_engine\u001b[0;34m(self, f, engine)\u001b[0m\n\u001b[1;32m   1878\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mb\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m mode:\n\u001b[1;32m   1879\u001b[0m         mode \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mb\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m-> 1880\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhandles \u001b[38;5;241m=\u001b[39m \u001b[43mget_handle\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   1881\u001b[0m \u001b[43m    \u001b[49m\u001b[43mf\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1882\u001b[0m \u001b[43m    \u001b[49m\u001b[43mmode\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1883\u001b[0m \u001b[43m    \u001b[49m\u001b[43mencoding\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43moptions\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mencoding\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1884\u001b[0m \u001b[43m    \u001b[49m\u001b[43mcompression\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43moptions\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mcompression\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1885\u001b[0m \u001b[43m    \u001b[49m\u001b[43mmemory_map\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43moptions\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mmemory_map\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1886\u001b[0m \u001b[43m    \u001b[49m\u001b[43mis_text\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mis_text\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1887\u001b[0m \u001b[43m    \u001b[49m\u001b[43merrors\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43moptions\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mencoding_errors\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mstrict\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1888\u001b[0m \u001b[43m    \u001b[49m\u001b[43mstorage_options\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43moptions\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mstorage_options\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1889\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1890\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhandles \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m   1891\u001b[0m f \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhandles\u001b[38;5;241m.\u001b[39mhandle\n",
      "File \u001b[0;32m/opt/conda/lib/python3.11/site-packages/pandas/io/common.py:873\u001b[0m, in \u001b[0;36mget_handle\u001b[0;34m(path_or_buf, mode, encoding, compression, memory_map, is_text, errors, storage_options)\u001b[0m\n\u001b[1;32m    868\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(handle, \u001b[38;5;28mstr\u001b[39m):\n\u001b[1;32m    869\u001b[0m     \u001b[38;5;66;03m# Check whether the filename is to be opened in binary mode.\u001b[39;00m\n\u001b[1;32m    870\u001b[0m     \u001b[38;5;66;03m# Binary mode does not support 'encoding' and 'newline'.\u001b[39;00m\n\u001b[1;32m    871\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m ioargs\u001b[38;5;241m.\u001b[39mencoding \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mb\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m ioargs\u001b[38;5;241m.\u001b[39mmode:\n\u001b[1;32m    872\u001b[0m         \u001b[38;5;66;03m# Encoding\u001b[39;00m\n\u001b[0;32m--> 873\u001b[0m         handle \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mopen\u001b[39m(\n\u001b[1;32m    874\u001b[0m             handle,\n\u001b[1;32m    875\u001b[0m             ioargs\u001b[38;5;241m.\u001b[39mmode,\n\u001b[1;32m    876\u001b[0m             encoding\u001b[38;5;241m=\u001b[39mioargs\u001b[38;5;241m.\u001b[39mencoding,\n\u001b[1;32m    877\u001b[0m             errors\u001b[38;5;241m=\u001b[39merrors,\n\u001b[1;32m    878\u001b[0m             newline\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m    879\u001b[0m         )\n\u001b[1;32m    880\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    881\u001b[0m         \u001b[38;5;66;03m# Binary mode\u001b[39;00m\n\u001b[1;32m    882\u001b[0m         handle \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mopen\u001b[39m(handle, ioargs\u001b[38;5;241m.\u001b[39mmode)\n",
      "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: '/home/jovyan/RT08/0925/sliding_win/1010/manifests/window_manifest.csv'"
     ]
    }
   ],
   "source": [
    "import pandas as pd, os\n",
    "\n",
    "base = \"/home/jovyan/RT08/0925/sliding_win/1010/\"\n",
    "man  = pd.read_csv(os.path.join(base, \"manifests/window_manifest.csv\"))\n",
    "\n",
    "print(\"總視窗數：\", len(man))\n",
    "print(\"n_rows 全為 60？\", man[\"n_rows\"].eq(60).mean())\n",
    "print(\"連續性通過比例 continuity_ok：\", man[\"continuity_ok\"].mean())\n",
    "print(\"正樣本比例 label=1：\", man[\"label\"].mean())\n",
    "print(\"NaN 警示比例 nan_flag：\", man[\"nan_flag\"].mean())\n",
    "\n",
    "# 看最不連續的前 5 個視窗\n",
    "display(man.sort_values(\"max_dt_sec\", ascending=False).head(5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "id": "f00d0be8-81d7-4425-8499-f5d1e1e17eae",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "=== Step 0 | 初始化資料夾與載入設定 ===\n",
      "[設定] 已載入設定檔：/home/jovyan/RT08/0925/sliding_win/1010/config/windowing.yaml\n",
      "=== Step 3 | 產生 window_manifest / label_summary / nan_alerts ===\n",
      "  [進度] 10/123 檔 manifest 完成\n",
      "  [進度] 20/123 檔 manifest 完成\n",
      "  [進度] 30/123 檔 manifest 完成\n",
      "  [進度] 40/123 檔 manifest 完成\n",
      "  [進度] 50/123 檔 manifest 完成\n",
      "  [進度] 60/123 檔 manifest 完成\n",
      "  [進度] 70/123 檔 manifest 完成\n",
      "  [進度] 80/123 檔 manifest 完成\n",
      "  [進度] 90/123 檔 manifest 完成\n",
      "  [進度] 100/123 檔 manifest 完成\n",
      "  [進度] 110/123 檔 manifest 完成\n",
      "  [進度] 120/123 檔 manifest 完成\n",
      "  ! 檔案產生 manifest 失敗：audit_summary.csv | Missing column provided to 'parse_dates': 'senddate'\n",
      "  [進度] 123/123 檔 manifest 完成\n",
      "[3.1] 已輸出：/home/jovyan/RT08/0925/sliding_win/1010/manifests/window_manifest.csv（總視窗數=44386）\n",
      "[3.2] 已輸出：manifests/window_label_summary.csv\n",
      "[3.3] 已輸出：audits/window_nan_alerts.csv\n",
      "=== Step 4 | 產生 K 折分層索引（kfold_splits.json） ===\n",
      "[4.1] 已輸出：/home/jovyan/RT08/0925/sliding_win/1010/config/kfold_splits.json\n"
     ]
    }
   ],
   "source": [
    "cfg = step0_bootstrap()\n",
    "step3_build_manifest(cfg)\n",
    "step4_make_kfold(cfg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "id": "a37b3838-ad30-40aa-9b00-9f977f1a6b28",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "====BEGIN REPORT====\n",
      "[config] W=60, S=30, dt_sec_threshold=120, set_col=set_1010, rule=比例≥0.5 or ad_last=1→1(True)\n",
      "[manifest] total_windows=44386, n_rows_eq_W_ratio=1.0, continuity_ok_ratio=0.9186, nan_flag_ratio=0.5029, label_pos_ratio=0.8484, max_dt_sec_overall=1370605.0\n",
      "[time_gap_top] (Δt_sec 最大的前幾筆)\n",
      "  file=PatNo_ID_1564148644.csv, row=8290, Δt_sec=1370605.0, senddate=2022-01-26 03:50:29\n",
      "  file=PatNo_ID_1580766093.csv, row=14803, Δt_sec=1115277.0, senddate=2022-01-23 14:31:00\n",
      "  file=PatNo_ID_1572481361.csv, row=6198, Δt_sec=215101.0, senddate=2022-03-19 23:28:03\n",
      "  file=PatNo_ID_1589034524.csv, row=12817, Δt_sec=85440.0, senddate=2022-02-24 09:47:03\n",
      "  file=PatNo_ID_1570089466.csv, row=15302, Δt_sec=78722.0, senddate=2022-02-02 09:12:04\n",
      "  file=PatNo_ID_1570089466.csv, row=15304, Δt_sec=78061.0, senddate=2022-02-03 09:08:04\n",
      "  file=114309.csv, row=5246, Δt_sec=75112.0, senddate=2021-12-24 11:29:53\n",
      "  file=PatNo_ID_1589034524.csv, row=10299, Δt_sec=72470.0, senddate=2022-02-20 06:37:04\n",
      "[cleaned] 掃描檔數=122, 欄位覆蓋率：senddate=122/122, ts_unix=122/122, Δt_sec=0/122, nan_check=122/122, ad_para=122/122, set=122/122, set_1010=122/122\n",
      "[top_files_by_windows] 前5檔：\n",
      "  PatNo_ID_1589034524.csv: windows=1652\n",
      "  PatNo_ID_1574148494.csv: windows=1565\n",
      "  PatNo_ID_1566911879.csv: windows=1528\n",
      "  PatNo_ID_1587490083.csv: windows=1501\n",
      "  PatNo_ID_1586172659.csv: windows=1271\n",
      "====END REPORT====\n",
      "\n",
      "[saved] JSON: /home/jovyan/RT08/0925/sliding_win/1010/audits/reports/progress_snapshot.json\n",
      "[saved] CSV : /home/jovyan/RT08/0925/sliding_win/1010/audits/reports/progress_snapshot.csv\n"
     ]
    }
   ],
   "source": [
    "# progress_check_1010.py\n",
    "# -*- coding: utf-8 -*-\n",
    "\"\"\"\n",
    "用途：檢查不實體化滑動視窗 1010 版流程的輸出是否健康，並產生摘要報表\n",
    "做的事：\n",
    "1) 讀 config/windowing.yaml，回報 set_col 與關鍵參數\n",
    "2) 檢查 manifests/window_manifest.csv 的完整性（視窗數、n_rows 是否全為 W、連續性比例、NaN 警示比例、正負樣本比例）\n",
    "3) 檢查 audits/time_gap_anomaly.csv（最大 Δt_sec 的前幾筆）\n",
    "4) 掃描 cleaned/clear/*.csv 的關鍵欄位覆蓋率（senddate/ts_unix/Δt_sec/nan_check/ad_para/set/set_1010）\n",
    "5) 產出可貼回來的文字報告，另存 JSON/CSV 於 audits/reports/\n",
    "\n",
    "使用方式：\n",
    "python progress_check_1010.py\n",
    "\"\"\"\n",
    "\n",
    "import os, sys, json, glob\n",
    "import pandas as pd\n",
    "from pathlib import Path\n",
    "\n",
    "try:\n",
    "    import yaml\n",
    "except:\n",
    "    yaml = None\n",
    "\n",
    "BASE = \"/home/jovyan/RT08/0925/sliding_win/1010/\"\n",
    "P_MAN = os.path.join(BASE, \"manifests/window_manifest.csv\")\n",
    "P_LAB = os.path.join(BASE, \"manifests/window_label_summary.csv\")\n",
    "P_WST = os.path.join(BASE, \"manifests/window_stats.csv\")\n",
    "P_TGA = os.path.join(BASE, \"audits/time_gap_anomaly.csv\")\n",
    "P_CFG = os.path.join(BASE, \"config/windowing.yaml\")\n",
    "P_CLN = os.path.join(BASE, \"cleaned/clear/\")\n",
    "P_OUT_JSON = os.path.join(BASE, \"audits/reports/progress_snapshot.json\")\n",
    "P_OUT_CSV  = os.path.join(BASE, \"audits/reports/progress_snapshot.csv\")\n",
    "\n",
    "os.makedirs(os.path.dirname(P_OUT_JSON), exist_ok=True)\n",
    "\n",
    "def safe_read_csv(path, **kw):\n",
    "    if not os.path.exists(path): return None\n",
    "    try:\n",
    "        return pd.read_csv(path, **kw)\n",
    "    except Exception as e:\n",
    "        print(f\"[warn] 讀取失敗：{path} | {e}\")\n",
    "        return None\n",
    "\n",
    "def read_cfg():\n",
    "    cfg = {}\n",
    "    if os.path.exists(P_CFG) and yaml is not None:\n",
    "        with open(P_CFG, \"r\", encoding=\"utf-8\") as f:\n",
    "            cfg = yaml.safe_load(f) or {}\n",
    "    return cfg\n",
    "\n",
    "def scan_cleaned_columns():\n",
    "    files = sorted(glob.glob(os.path.join(P_CLN, \"*.csv\")))\n",
    "    need = [\"senddate\", \"ts_unix\", \"Δt_sec\", \"nan_check\", \"ad_para\", \"set\", \"set_1010\"]\n",
    "    cover = {k: 0 for k in need}\n",
    "    total = 0\n",
    "    for fp in files:\n",
    "        name = os.path.basename(fp).lower()\n",
    "        # 跳過非資料檔（防止 audit_summary.csv 之類）\n",
    "        if name.startswith(\"audit_\") or name.endswith(\"_summary.csv\") or name.endswith(\"_stats.csv\"):\n",
    "            continue\n",
    "        total += 1\n",
    "        try:\n",
    "            cols = list(pd.read_csv(fp, nrows=0).columns)\n",
    "            cols = [c.lower() for c in cols]\n",
    "            for k in need:\n",
    "                if k in cols:\n",
    "                    cover[k] += 1\n",
    "        except Exception as e:\n",
    "            pass\n",
    "    return {\"total_files_scanned\": total, \"column_coverage\": cover}\n",
    "\n",
    "def main():\n",
    "    cfg = read_cfg()\n",
    "    W = cfg.get(\"windowing\", {}).get(\"W\", None)\n",
    "    S = cfg.get(\"windowing\", {}).get(\"S\", None)\n",
    "    dt_thr = cfg.get(\"windowing\", {}).get(\"dt_sec_threshold\", None)\n",
    "    set_col = cfg.get(\"schema\", {}).get(\"set_col\", \"set\")\n",
    "    force_by_ad = cfg.get(\"labeling\", {}).get(\"force_by_ad_para_last\", True)\n",
    "    pos_ratio_rule = cfg.get(\"labeling\", {}).get(\"set_positive_ratio\", 0.5)\n",
    "\n",
    "    man = safe_read_csv(P_MAN)\n",
    "    lab = safe_read_csv(P_LAB)\n",
    "    wst = safe_read_csv(P_WST)\n",
    "    tga = safe_read_csv(P_TGA)\n",
    "\n",
    "    report = {\n",
    "        \"config\": {\n",
    "            \"W\": W, \"S\": S, \"dt_sec_threshold\": dt_thr,\n",
    "            \"set_col\": set_col,\n",
    "            \"label_rule\": f\"比例≥{pos_ratio_rule} or 最後一筆ad_para=1→label=1({force_by_ad})\"\n",
    "        },\n",
    "        \"manifest_exists\": os.path.exists(P_MAN),\n",
    "        \"label_summary_exists\": os.path.exists(P_LAB),\n",
    "        \"time_gap_anomaly_exists\": os.path.exists(P_TGA),\n",
    "        \"window_stats_exists\": os.path.exists(P_WST),\n",
    "    }\n",
    "\n",
    "    if man is not None and not man.empty:\n",
    "        # 核心健康度\n",
    "        total_windows = len(man)\n",
    "        n_rows_ok = float(man[\"n_rows\"].eq(W).mean()) if W is not None else float(man[\"n_rows\"].mode().iloc[0] == man[\"n_rows\"]).__bool__()\n",
    "        cont_ratio = float(man[\"continuity_ok\"].mean()) if \"continuity_ok\" in man.columns else None\n",
    "        nan_ratio  = float(man[\"nan_flag\"].mean()) if \"nan_flag\" in man.columns else None\n",
    "        pos_ratio  = float(man[\"label\"].mean()) if \"label\" in man.columns else None\n",
    "\n",
    "        # 最大斷點\n",
    "        max_gap = float(man[\"max_dt_sec\"].replace(\"\", 0).astype(float).max()) if \"max_dt_sec\" in man.columns else None\n",
    "\n",
    "        # 各檔前幾名（最常出現視窗的檔案）\n",
    "        by_file = man.groupby(\"file_name\").size().sort_values(ascending=False).head(10)\n",
    "        by_file = by_file.reset_index().rename(columns={0: \"windows\"})\n",
    "\n",
    "        report.update({\n",
    "            \"manifest\": {\n",
    "                \"total_windows\": int(total_windows),\n",
    "                \"n_rows_eq_W_ratio\": round(n_rows_ok, 4),\n",
    "                \"continuity_ok_ratio\": None if cont_ratio is None else round(cont_ratio, 4),\n",
    "                \"nan_flag_ratio\": None if nan_ratio is None else round(nan_ratio, 4),\n",
    "                \"label_pos_ratio\": None if pos_ratio is None else round(pos_ratio, 4),\n",
    "                \"max_dt_sec_overall\": max_gap\n",
    "            }\n",
    "        })\n",
    "\n",
    "        # 儲存 by_file top10\n",
    "        report[\"top_files_by_windows\"] = by_file.to_dict(orient=\"records\")\n",
    "\n",
    "    # time gap 異常前幾筆\n",
    "    if tga is not None and not tga.empty:\n",
    "        tga_sorted = tga.sort_values(\"Δt_sec\", ascending=False).head(8).copy()\n",
    "        report[\"time_gap_top\"] = tga_sorted.to_dict(orient=\"records\")\n",
    "\n",
    "    # cleaned 欄位覆蓋率\n",
    "    report[\"cleaned_columns_coverage\"] = scan_cleaned_columns()\n",
    "\n",
    "    # 存檔（JSON + CSV 摘要）\n",
    "    with open(P_OUT_JSON, \"w\", encoding=\"utf-8\") as f:\n",
    "        json.dump(report, f, ensure_ascii=False, indent=2)\n",
    "\n",
    "    flat = []\n",
    "    flat.append({\n",
    "        \"W\": W, \"S\": S, \"dt_sec_threshold\": dt_thr, \"set_col\": set_col,\n",
    "        \"total_windows\": report.get(\"manifest\", {}).get(\"total_windows\"),\n",
    "        \"n_rows_eq_W_ratio\": report.get(\"manifest\", {}).get(\"n_rows_eq_W_ratio\"),\n",
    "        \"continuity_ok_ratio\": report.get(\"manifest\", {}).get(\"continuity_ok_ratio\"),\n",
    "        \"nan_flag_ratio\": report.get(\"manifest\", {}).get(\"nan_flag_ratio\"),\n",
    "        \"label_pos_ratio\": report.get(\"manifest\", {}).get(\"label_pos_ratio\"),\n",
    "        \"max_dt_sec_overall\": report.get(\"manifest\", {}).get(\"max_dt_sec_overall\"),\n",
    "        \"cleaned_files_scanned\": report[\"cleaned_columns_coverage\"][\"total_files_scanned\"],\n",
    "        \"has_lab_summary\": report[\"label_summary_exists\"],\n",
    "        \"has_time_gap_anomaly\": report[\"time_gap_anomaly_exists\"],\n",
    "    })\n",
    "    pd.DataFrame(flat).to_csv(P_OUT_CSV, index=False, encoding=\"utf-8-sig\")\n",
    "\n",
    "    # 產生可貼回的文字報告\n",
    "    print(\"\\n====BEGIN REPORT====\")\n",
    "    print(f\"[config] W={W}, S={S}, dt_sec_threshold={dt_thr}, set_col={set_col}, \"\n",
    "          f\"rule=比例≥{pos_ratio_rule} or ad_last=1→1({force_by_ad})\")\n",
    "    if \"manifest\" in report:\n",
    "        m = report[\"manifest\"]\n",
    "        print(f\"[manifest] total_windows={m['total_windows']}, n_rows_eq_W_ratio={m['n_rows_eq_W_ratio']}, \"\n",
    "              f\"continuity_ok_ratio={m['continuity_ok_ratio']}, nan_flag_ratio={m['nan_flag_ratio']}, \"\n",
    "              f\"label_pos_ratio={m['label_pos_ratio']}, max_dt_sec_overall={m['max_dt_sec_overall']}\")\n",
    "    if \"time_gap_top\" in report:\n",
    "        print(\"[time_gap_top] (Δt_sec 最大的前幾筆)\")\n",
    "        for r in report[\"time_gap_top\"]:\n",
    "            print(f\"  file={r['file_name']}, row={r['row_index']}, Δt_sec={r['Δt_sec']}, senddate={r['senddate']}\")\n",
    "    cov = report[\"cleaned_columns_coverage\"]\n",
    "    cc = cov[\"column_coverage\"]\n",
    "    print(f\"[cleaned] 掃描檔數={cov['total_files_scanned']}, 欄位覆蓋率：\"\n",
    "          f\"senddate={cc['senddate']}/{cov['total_files_scanned']}, \"\n",
    "          f\"ts_unix={cc['ts_unix']}/{cov['total_files_scanned']}, \"\n",
    "          f\"Δt_sec={cc['Δt_sec']}/{cov['total_files_scanned']}, \"\n",
    "          f\"nan_check={cc['nan_check']}/{cov['total_files_scanned']}, \"\n",
    "          f\"ad_para={cc['ad_para']}/{cov['total_files_scanned']}, \"\n",
    "          f\"set={cc['set']}/{cov['total_files_scanned']}, \"\n",
    "          f\"set_1010={cc['set_1010']}/{cov['total_files_scanned']}\")\n",
    "    if man is not None and not man.empty:\n",
    "        top = pd.DataFrame(report[\"top_files_by_windows\"]).head(5)\n",
    "        print(\"[top_files_by_windows] 前5檔：\")\n",
    "        for r in top.itertuples():\n",
    "            print(f\"  {r.file_name}: windows={r.windows}\")\n",
    "    print(\"====END REPORT====\\n\")\n",
    "\n",
    "    print(f\"[saved] JSON: {P_OUT_JSON}\")\n",
    "    print(f\"[saved] CSV : {P_OUT_CSV}\")\n",
    "\n",
    "if __name__ == \"__main__\":\n",
    "    main()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3cc895f3-d4cd-44d3-9be9-1f9ebd448ef8",
   "metadata": {},
   "outputs": [],
   "source": [
    "目前採用：set_col = set_1010（OK）。\n",
    "視窗品質：continuity_ok_ratio = 0.9186（良好）；但 nan_flag_ratio = 0.5029（約一半視窗仍含缺失，需要處理策略）。\n",
    "標籤分佈：label_pos_ratio = 0.8484（正樣本偏多，後續訓練會失衡）。\n",
    "最大時間斷點：1,370,605 秒（~15.9 天），time_gap_top 已抓出具體檔案與列；這些窗應已被標示為 continuity_ok=False。\n",
    "清理後欄位覆蓋率：Δt_sec=0/122 → 顯示清理後 CSV 的欄位名稱可能不是「Δt_sec」（常見原因：用了另一個字元 「∆」(U+2206) 而非「Δ」(U+0394)，或寫成 dt_sec / delta_t_sec）。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "id": "327292a5-4b8c-4a86-9a00-6f487371f184",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "⚠️ 檔案 PatNo_ID_1580107637.csv 中 nan_check == 1 的資料為空，略過\n",
      "⚠️ 檔案 4216007.csv 中 nan_check == 1 的資料為空，略過\n",
      "⚠️ 檔案 PatNo_ID_1594448501.csv 中 nan_check == 1 的資料為空，略過\n",
      "⚠️ 檔案 PatNo_ID_1589918099.csv 中 nan_check == 1 的資料為空，略過\n",
      "=== 🧭 前 10 名缺失值最多的欄位（nan_check == 1 篩選後） ===\n",
      "       column  missing_count\n",
      "     set_1010         166549\n",
      "       mode_2              0\n",
      "          set              0\n",
      "ad_para_check              0\n",
      "      ad_para              0\n",
      "    nan_check              0\n",
      "      svv_new              0\n",
      "       mode_3              0\n",
      "        patno              0\n",
      "     senddate              0\n"
     ]
    }
   ],
   "source": [
    "# 我想檢查我的資料/home/jovyan/RT08/0925/bling_1010/資料中 NaN_check=1的 資料中，其他欄位是否有缺失直 若有 請輸出前十名最多缺失值得欄位名稱\n",
    "# ==========================================================\n",
    "# 📊 NaN 檢查：統計 nan_check == 1 的資料中各欄位缺失值數量\n",
    "# ==========================================================\n",
    "\n",
    "import os\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "# 目錄設定\n",
    "DATA_DIR = \"/home/jovyan/RT08/0925/bling_1010/\"\n",
    "\n",
    "# 蒐集所有 CSV 檔案\n",
    "files = [f for f in os.listdir(DATA_DIR) if f.lower().endswith(\".csv\")]\n",
    "if not files:\n",
    "    print(f\"❌ No CSV files found in {DATA_DIR}\")\n",
    "\n",
    "# 建立總缺失統計表\n",
    "missing_summary = {}\n",
    "\n",
    "for fname in files:\n",
    "    fpath = os.path.join(DATA_DIR, fname)\n",
    "    try:\n",
    "        df = pd.read_csv(fpath)\n",
    "    except Exception as e:\n",
    "        print(f\"⚠️ 無法讀取檔案 {fname}: {e}\")\n",
    "        continue\n",
    "\n",
    "    # 僅保留 nan_check == 1 的資料\n",
    "    if \"nan_check\" not in df.columns:\n",
    "        print(f\"⚠️ 檔案 {fname} 無 nan_check 欄位，略過\")\n",
    "        continue\n",
    "    df = df[df[\"nan_check\"] == 1]\n",
    "\n",
    "    if df.empty:\n",
    "        print(f\"⚠️ 檔案 {fname} 中 nan_check == 1 的資料為空，略過\")\n",
    "        continue\n",
    "\n",
    "    # 統計每個欄位的 NaN 數量\n",
    "    nan_counts = df.isna().sum()\n",
    "\n",
    "    # 累積到總表\n",
    "    for col, cnt in nan_counts.items():\n",
    "        missing_summary[col] = missing_summary.get(col, 0) + int(cnt)\n",
    "\n",
    "# 若沒有可用資料則結束\n",
    "if not missing_summary:\n",
    "    print(\"⚠️ 沒有可統計的 nan_check == 1 資料。\")\n",
    "else:\n",
    "    # 轉為 DataFrame 並排序\n",
    "    summary_df = (\n",
    "        pd.DataFrame(list(missing_summary.items()), columns=[\"column\", \"missing_count\"])\n",
    "        .sort_values(\"missing_count\", ascending=False)\n",
    "        .reset_index(drop=True)\n",
    "    )\n",
    "\n",
    "    # 取前 10 名\n",
    "    top10_df = summary_df.head(10)\n",
    "\n",
    "    # 輸出結果（僅顯示，不寫檔）\n",
    "    print(\"=== 🧭 前 10 名缺失值最多的欄位（nan_check == 1 篩選後） ===\")\n",
    "    print(top10_df.to_string(index=False))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9485f9c5-4298-42b3-afc5-f1dfe3e9d385",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "02bd1379-391b-448a-9b35-9876d16a47e4",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6fdaf4ba-40d7-4546-a084-0194386dc006",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1ff0d86a-d1fb-427e-952d-e2928c56dba7",
   "metadata": {},
   "outputs": [],
   "source": [
    "重來拉 重創一個set_fin"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "id": "331759fe-af05-4a13-b4da-3031948e8b10",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[OK] 089271.csv -> 089271_with_setfin.csv | {'set_fin_0': 14200, 'set_fin_1': 4818, 'set_fin_2': 13401, 'total_rows': 32419}\n",
      "[OK] 095323.csv -> 095323_with_setfin.csv | {'set_fin_0': 8154, 'set_fin_1': 2630, 'set_fin_2': 13007, 'total_rows': 23791}\n",
      "[OK] 095707.csv -> 095707_with_setfin.csv | {'set_fin_0': 8781, 'set_fin_1': 3007, 'set_fin_2': 8392, 'total_rows': 20180}\n",
      "[OK] 114309.csv -> 114309_with_setfin.csv | {'set_fin_0': 10844, 'set_fin_1': 3775, 'set_fin_2': 57110, 'total_rows': 71729}\n",
      "[OK] 230933.csv -> 230933_with_setfin.csv | {'set_fin_0': 13132, 'set_fin_1': 4726, 'set_fin_2': 12391, 'total_rows': 30249}\n",
      "[OK] 4216007.csv -> 4216007_with_setfin.csv | {'set_fin_0': 0, 'set_fin_1': 0, 'set_fin_2': 1433, 'total_rows': 1433}\n",
      "[OK] 7108162.csv -> 7108162_with_setfin.csv | {'set_fin_0': 92, 'set_fin_1': 31, 'set_fin_2': 116, 'total_rows': 239}\n",
      "[OK] 7408338.csv -> 7408338_with_setfin.csv | {'set_fin_0': 624, 'set_fin_1': 203, 'set_fin_2': 605, 'total_rows': 1432}\n",
      "[OK] 7657698.csv -> 7657698_with_setfin.csv | {'set_fin_0': 686, 'set_fin_1': 259, 'set_fin_2': 468, 'total_rows': 1413}\n",
      "[OK] 7721164.csv -> 7721164_with_setfin.csv | {'set_fin_0': 219, 'set_fin_1': 74, 'set_fin_2': 190, 'total_rows': 483}\n",
      "[OK] PatNo_ID_1560013303.csv -> PatNo_ID_1560013303_with_setfin.csv | {'set_fin_0': 1122, 'set_fin_1': 385, 'set_fin_2': 1036, 'total_rows': 2543}\n",
      "[OK] PatNo_ID_1562733396.csv -> PatNo_ID_1562733396_with_setfin.csv | {'set_fin_0': 1063, 'set_fin_1': 467, 'set_fin_2': 724, 'total_rows': 2254}\n",
      "[OK] PatNo_ID_1563587183.csv -> PatNo_ID_1563587183_with_setfin.csv | {'set_fin_0': 2555, 'set_fin_1': 1004, 'set_fin_2': 1727, 'total_rows': 5286}\n",
      "[OK] PatNo_ID_1564148644.csv -> PatNo_ID_1564148644_with_setfin.csv | {'set_fin_0': 6683, 'set_fin_1': 1476, 'set_fin_2': 9128, 'total_rows': 17287}\n",
      "[OK] PatNo_ID_1565148312.csv -> PatNo_ID_1565148312_with_setfin.csv | {'set_fin_0': 2677, 'set_fin_1': 919, 'set_fin_2': 1879, 'total_rows': 5475}\n",
      "[OK] PatNo_ID_1565378038.csv -> PatNo_ID_1565378038_with_setfin.csv | {'set_fin_0': 960, 'set_fin_1': 286, 'set_fin_2': 1315, 'total_rows': 2561}\n",
      "[OK] PatNo_ID_1566123680.csv -> PatNo_ID_1566123680_with_setfin.csv | {'set_fin_0': 10943, 'set_fin_1': 4102, 'set_fin_2': 27555, 'total_rows': 42600}\n",
      "[OK] PatNo_ID_1566252197.csv -> PatNo_ID_1566252197_with_setfin.csv | {'set_fin_0': 915, 'set_fin_1': 334, 'set_fin_2': 2119, 'total_rows': 3368}\n",
      "[OK] PatNo_ID_1566279967.csv -> PatNo_ID_1566279967_with_setfin.csv | {'set_fin_0': 509, 'set_fin_1': 161, 'set_fin_2': 565, 'total_rows': 1235}\n",
      "[OK] PatNo_ID_1566671274.csv -> PatNo_ID_1566671274_with_setfin.csv | {'set_fin_0': 11394, 'set_fin_1': 4036, 'set_fin_2': 9929, 'total_rows': 25359}\n",
      "[OK] PatNo_ID_1566911879.csv -> PatNo_ID_1566911879_with_setfin.csv | {'set_fin_0': 25695, 'set_fin_1': 9580, 'set_fin_2': 18724, 'total_rows': 53999}\n",
      "[OK] PatNo_ID_1567747650.csv -> PatNo_ID_1567747650_with_setfin.csv | {'set_fin_0': 5219, 'set_fin_1': 1869, 'set_fin_2': 3812, 'total_rows': 10900}\n",
      "[OK] PatNo_ID_1567804800.csv -> PatNo_ID_1567804800_with_setfin.csv | {'set_fin_0': 8565, 'set_fin_1': 3254, 'set_fin_2': 8758, 'total_rows': 20577}\n",
      "[OK] PatNo_ID_1567832735.csv -> PatNo_ID_1567832735_with_setfin.csv | {'set_fin_0': 17396, 'set_fin_1': 6253, 'set_fin_2': 12926, 'total_rows': 36575}\n",
      "[OK] PatNo_ID_1568039398.csv -> PatNo_ID_1568039398_with_setfin.csv | {'set_fin_0': 16693, 'set_fin_1': 6087, 'set_fin_2': 11739, 'total_rows': 34519}\n",
      "[OK] PatNo_ID_1568574099.csv -> PatNo_ID_1568574099_with_setfin.csv | {'set_fin_0': 6284, 'set_fin_1': 2150, 'set_fin_2': 5511, 'total_rows': 13945}\n",
      "[OK] PatNo_ID_1568813269.csv -> PatNo_ID_1568813269_with_setfin.csv | {'set_fin_0': 2292, 'set_fin_1': 951, 'set_fin_2': 1624, 'total_rows': 4867}\n",
      "[OK] PatNo_ID_1568952422.csv -> PatNo_ID_1568952422_with_setfin.csv | {'set_fin_0': 527, 'set_fin_1': 171, 'set_fin_2': 486, 'total_rows': 1184}\n",
      "[OK] PatNo_ID_1569083701.csv -> PatNo_ID_1569083701_with_setfin.csv | {'set_fin_0': 1302, 'set_fin_1': 390, 'set_fin_2': 1636, 'total_rows': 3328}\n",
      "[OK] PatNo_ID_1569944983.csv -> PatNo_ID_1569944983_with_setfin.csv | {'set_fin_0': 3399, 'set_fin_1': 1133, 'set_fin_2': 3117, 'total_rows': 7649}\n",
      "[OK] PatNo_ID_1570089466.csv -> PatNo_ID_1570089466_with_setfin.csv | {'set_fin_0': 7219, 'set_fin_1': 2442, 'set_fin_2': 31677, 'total_rows': 41338}\n",
      "[OK] PatNo_ID_1570242703.csv -> PatNo_ID_1570242703_with_setfin.csv | {'set_fin_0': 5010, 'set_fin_1': 1803, 'set_fin_2': 3831, 'total_rows': 10644}\n",
      "[OK] PatNo_ID_1570273244.csv -> PatNo_ID_1570273244_with_setfin.csv | {'set_fin_0': 4748, 'set_fin_1': 1710, 'set_fin_2': 3267, 'total_rows': 9725}\n",
      "[OK] PatNo_ID_1570642083.csv -> PatNo_ID_1570642083_with_setfin.csv | {'set_fin_0': 9183, 'set_fin_1': 3206, 'set_fin_2': 7342, 'total_rows': 19731}\n",
      "[OK] PatNo_ID_1571945701.csv -> PatNo_ID_1571945701_with_setfin.csv | {'set_fin_0': 7255, 'set_fin_1': 2603, 'set_fin_2': 5873, 'total_rows': 15731}\n",
      "[OK] PatNo_ID_1572481361.csv -> PatNo_ID_1572481361_with_setfin.csv | {'set_fin_0': 16329, 'set_fin_1': 5839, 'set_fin_2': 12371, 'total_rows': 34539}\n",
      "[OK] PatNo_ID_1572562839.csv -> PatNo_ID_1572562839_with_setfin.csv | {'set_fin_0': 10314, 'set_fin_1': 3834, 'set_fin_2': 6815, 'total_rows': 20963}\n",
      "[OK] PatNo_ID_1572831765.csv -> PatNo_ID_1572831765_with_setfin.csv | {'set_fin_0': 1134, 'set_fin_1': 374, 'set_fin_2': 1188, 'total_rows': 2696}\n",
      "[OK] PatNo_ID_1572976822.csv -> PatNo_ID_1572976822_with_setfin.csv | {'set_fin_0': 3383, 'set_fin_1': 1284, 'set_fin_2': 2097, 'total_rows': 6764}\n",
      "[OK] PatNo_ID_1573063188.csv -> PatNo_ID_1573063188_with_setfin.csv | {'set_fin_0': 2259, 'set_fin_1': 741, 'set_fin_2': 2080, 'total_rows': 5080}\n",
      "[OK] PatNo_ID_1573249295.csv -> PatNo_ID_1573249295_with_setfin.csv | {'set_fin_0': 3568, 'set_fin_1': 1294, 'set_fin_2': 2289, 'total_rows': 7151}\n",
      "[OK] PatNo_ID_1573964540.csv -> PatNo_ID_1573964540_with_setfin.csv | {'set_fin_0': 2019, 'set_fin_1': 689, 'set_fin_2': 1686, 'total_rows': 4394}\n",
      "[OK] PatNo_ID_1574148494.csv -> PatNo_ID_1574148494_with_setfin.csv | {'set_fin_0': 22110, 'set_fin_1': 7885, 'set_fin_2': 17159, 'total_rows': 47154}\n",
      "[OK] PatNo_ID_1574270349.csv -> PatNo_ID_1574270349_with_setfin.csv | {'set_fin_0': 2965, 'set_fin_1': 961, 'set_fin_2': 2607, 'total_rows': 6533}\n",
      "[OK] PatNo_ID_1574528808.csv -> PatNo_ID_1574528808_with_setfin.csv | {'set_fin_0': 5463, 'set_fin_1': 1549, 'set_fin_2': 7800, 'total_rows': 14812}\n",
      "[OK] PatNo_ID_1574831525.csv -> PatNo_ID_1574831525_with_setfin.csv | {'set_fin_0': 226, 'set_fin_1': 72, 'set_fin_2': 222, 'total_rows': 520}\n",
      "[OK] PatNo_ID_1574987447.csv -> PatNo_ID_1574987447_with_setfin.csv | {'set_fin_0': 9609, 'set_fin_1': 3401, 'set_fin_2': 6832, 'total_rows': 19842}\n",
      "[OK] PatNo_ID_1575060177.csv -> PatNo_ID_1575060177_with_setfin.csv | {'set_fin_0': 2357, 'set_fin_1': 789, 'set_fin_2': 2144, 'total_rows': 5290}\n",
      "[OK] PatNo_ID_1575256902.csv -> PatNo_ID_1575256902_with_setfin.csv | {'set_fin_0': 3896, 'set_fin_1': 1173, 'set_fin_2': 4518, 'total_rows': 9587}\n",
      "[OK] PatNo_ID_1575445051.csv -> PatNo_ID_1575445051_with_setfin.csv | {'set_fin_0': 811, 'set_fin_1': 280, 'set_fin_2': 709, 'total_rows': 1800}\n",
      "[OK] PatNo_ID_1575502382.csv -> PatNo_ID_1575502382_with_setfin.csv | {'set_fin_0': 3208, 'set_fin_1': 1143, 'set_fin_2': 2253, 'total_rows': 6604}\n",
      "[OK] PatNo_ID_1575975485.csv -> PatNo_ID_1575975485_with_setfin.csv | {'set_fin_0': 7557, 'set_fin_1': 2651, 'set_fin_2': 6251, 'total_rows': 16459}\n",
      "[OK] PatNo_ID_1576115572.csv -> PatNo_ID_1576115572_with_setfin.csv | {'set_fin_0': 8657, 'set_fin_1': 2895, 'set_fin_2': 7163, 'total_rows': 18715}\n",
      "[OK] PatNo_ID_1576116479.csv -> PatNo_ID_1576116479_with_setfin.csv | {'set_fin_0': 588, 'set_fin_1': 205, 'set_fin_2': 470, 'total_rows': 1263}\n",
      "[OK] PatNo_ID_1576301569.csv -> PatNo_ID_1576301569_with_setfin.csv | {'set_fin_0': 1572, 'set_fin_1': 555, 'set_fin_2': 1080, 'total_rows': 3207}\n",
      "[OK] PatNo_ID_1576964560.csv -> PatNo_ID_1576964560_with_setfin.csv | {'set_fin_0': 10394, 'set_fin_1': 3432, 'set_fin_2': 10362, 'total_rows': 24188}\n",
      "[OK] PatNo_ID_1577042911.csv -> PatNo_ID_1577042911_with_setfin.csv | {'set_fin_0': 17595, 'set_fin_1': 6178, 'set_fin_2': 14584, 'total_rows': 38357}\n",
      "[OK] PatNo_ID_1577487284.csv -> PatNo_ID_1577487284_with_setfin.csv | {'set_fin_0': 1010, 'set_fin_1': 338, 'set_fin_2': 997, 'total_rows': 2345}\n",
      "[OK] PatNo_ID_1578784257.csv -> PatNo_ID_1578784257_with_setfin.csv | {'set_fin_0': 15315, 'set_fin_1': 5278, 'set_fin_2': 13321, 'total_rows': 33914}\n",
      "[OK] PatNo_ID_1579198603.csv -> PatNo_ID_1579198603_with_setfin.csv | {'set_fin_0': 999, 'set_fin_1': 366, 'set_fin_2': 680, 'total_rows': 2045}\n",
      "[OK] PatNo_ID_1579498177.csv -> PatNo_ID_1579498177_with_setfin.csv | {'set_fin_0': 10994, 'set_fin_1': 3938, 'set_fin_2': 6756, 'total_rows': 21688}\n",
      "[OK] PatNo_ID_1580062580.csv -> PatNo_ID_1580062580_with_setfin.csv | {'set_fin_0': 1042, 'set_fin_1': 390, 'set_fin_2': 718, 'total_rows': 2150}\n",
      "[OK] PatNo_ID_1580096720.csv -> PatNo_ID_1580096720_with_setfin.csv | {'set_fin_0': 503, 'set_fin_1': 179, 'set_fin_2': 741, 'total_rows': 1423}\n",
      "[OK] PatNo_ID_1580107637.csv -> PatNo_ID_1580107637_with_setfin.csv | {'set_fin_0': 0, 'set_fin_1': 0, 'set_fin_2': 2698, 'total_rows': 2698}\n",
      "[OK] PatNo_ID_1580244614.csv -> PatNo_ID_1580244614_with_setfin.csv | {'set_fin_0': 1776, 'set_fin_1': 619, 'set_fin_2': 2883, 'total_rows': 5278}\n",
      "[OK] PatNo_ID_1580766093.csv -> PatNo_ID_1580766093_with_setfin.csv | {'set_fin_0': 8679, 'set_fin_1': 3124, 'set_fin_2': 6267, 'total_rows': 18070}\n",
      "[OK] PatNo_ID_1581003248.csv -> PatNo_ID_1581003248_with_setfin.csv | {'set_fin_0': 3158, 'set_fin_1': 1151, 'set_fin_2': 3467, 'total_rows': 7776}\n",
      "[OK] PatNo_ID_1581019504.csv -> PatNo_ID_1581019504_with_setfin.csv | {'set_fin_0': 13155, 'set_fin_1': 4948, 'set_fin_2': 10070, 'total_rows': 28173}\n",
      "[OK] PatNo_ID_1581633231.csv -> PatNo_ID_1581633231_with_setfin.csv | {'set_fin_0': 6935, 'set_fin_1': 2411, 'set_fin_2': 6649, 'total_rows': 15995}\n",
      "[OK] PatNo_ID_1581692973.csv -> PatNo_ID_1581692973_with_setfin.csv | {'set_fin_0': 1244, 'set_fin_1': 441, 'set_fin_2': 1038, 'total_rows': 2723}\n",
      "[OK] PatNo_ID_1582452511.csv -> PatNo_ID_1582452511_with_setfin.csv | {'set_fin_0': 2562, 'set_fin_1': 920, 'set_fin_2': 1714, 'total_rows': 5196}\n",
      "[OK] PatNo_ID_1582635996.csv -> PatNo_ID_1582635996_with_setfin.csv | {'set_fin_0': 6373, 'set_fin_1': 2305, 'set_fin_2': 4368, 'total_rows': 13046}\n",
      "[OK] PatNo_ID_1582849900.csv -> PatNo_ID_1582849900_with_setfin.csv | {'set_fin_0': 839, 'set_fin_1': 286, 'set_fin_2': 6286, 'total_rows': 7411}\n",
      "[OK] PatNo_ID_1582937076.csv -> PatNo_ID_1582937076_with_setfin.csv | {'set_fin_0': 11660, 'set_fin_1': 4302, 'set_fin_2': 8025, 'total_rows': 23987}\n",
      "[OK] PatNo_ID_1584158973.csv -> PatNo_ID_1584158973_with_setfin.csv | {'set_fin_0': 1342, 'set_fin_1': 478, 'set_fin_2': 1062, 'total_rows': 2882}\n",
      "[OK] PatNo_ID_1584397376.csv -> PatNo_ID_1584397376_with_setfin.csv | {'set_fin_0': 164, 'set_fin_1': 50, 'set_fin_2': 424, 'total_rows': 638}\n",
      "[OK] PatNo_ID_1586172659.csv -> PatNo_ID_1586172659_with_setfin.csv | {'set_fin_0': 16998, 'set_fin_1': 5815, 'set_fin_2': 15741, 'total_rows': 38554}\n",
      "[OK] PatNo_ID_1586696634.csv -> PatNo_ID_1586696634_with_setfin.csv | {'set_fin_0': 1219, 'set_fin_1': 445, 'set_fin_2': 1905, 'total_rows': 3569}\n",
      "[OK] PatNo_ID_1586897008.csv -> PatNo_ID_1586897008_with_setfin.csv | {'set_fin_0': 2730, 'set_fin_1': 862, 'set_fin_2': 3095, 'total_rows': 6687}\n",
      "[OK] PatNo_ID_1587490083.csv -> PatNo_ID_1587490083_with_setfin.csv | {'set_fin_0': 21585, 'set_fin_1': 7587, 'set_fin_2': 16014, 'total_rows': 45186}\n",
      "[OK] PatNo_ID_1588632604.csv -> PatNo_ID_1588632604_with_setfin.csv | {'set_fin_0': 1192, 'set_fin_1': 422, 'set_fin_2': 994, 'total_rows': 2608}\n",
      "[OK] PatNo_ID_1588673465.csv -> PatNo_ID_1588673465_with_setfin.csv | {'set_fin_0': 4217, 'set_fin_1': 1604, 'set_fin_2': 4255, 'total_rows': 10076}\n",
      "[OK] PatNo_ID_1588794796.csv -> PatNo_ID_1588794796_with_setfin.csv | {'set_fin_0': 4713, 'set_fin_1': 1774, 'set_fin_2': 2888, 'total_rows': 9375}\n",
      "[OK] PatNo_ID_1588957997.csv -> PatNo_ID_1588957997_with_setfin.csv | {'set_fin_0': 4818, 'set_fin_1': 1586, 'set_fin_2': 4562, 'total_rows': 10966}\n",
      "[OK] PatNo_ID_1589018086.csv -> PatNo_ID_1589018086_with_setfin.csv | {'set_fin_0': 6458, 'set_fin_1': 2297, 'set_fin_2': 4717, 'total_rows': 13472}\n",
      "[OK] PatNo_ID_1589034524.csv -> PatNo_ID_1589034524_with_setfin.csv | {'set_fin_0': 23849, 'set_fin_1': 8640, 'set_fin_2': 17592, 'total_rows': 50081}\n",
      "[OK] PatNo_ID_1589324603.csv -> PatNo_ID_1589324603_with_setfin.csv | {'set_fin_0': 1893, 'set_fin_1': 628, 'set_fin_2': 1666, 'total_rows': 4187}\n",
      "[OK] PatNo_ID_1589918099.csv -> PatNo_ID_1589918099_with_setfin.csv | {'set_fin_0': 0, 'set_fin_1': 0, 'set_fin_2': 9333, 'total_rows': 9333}\n",
      "[OK] PatNo_ID_1590136310.csv -> PatNo_ID_1590136310_with_setfin.csv | {'set_fin_0': 2176, 'set_fin_1': 620, 'set_fin_2': 2784, 'total_rows': 5580}\n",
      "[OK] PatNo_ID_1590616537.csv -> PatNo_ID_1590616537_with_setfin.csv | {'set_fin_0': 6609, 'set_fin_1': 2306, 'set_fin_2': 5291, 'total_rows': 14206}\n",
      "[OK] PatNo_ID_1590854576.csv -> PatNo_ID_1590854576_with_setfin.csv | {'set_fin_0': 7695, 'set_fin_1': 2643, 'set_fin_2': 5541, 'total_rows': 15879}\n",
      "[OK] PatNo_ID_1591609798.csv -> PatNo_ID_1591609798_with_setfin.csv | {'set_fin_0': 15884, 'set_fin_1': 5402, 'set_fin_2': 14332, 'total_rows': 35618}\n",
      "[OK] PatNo_ID_1592044724.csv -> PatNo_ID_1592044724_with_setfin.csv | {'set_fin_0': 3355, 'set_fin_1': 1250, 'set_fin_2': 2210, 'total_rows': 6815}\n",
      "[OK] PatNo_ID_1592560504.csv -> PatNo_ID_1592560504_with_setfin.csv | {'set_fin_0': 7753, 'set_fin_1': 2532, 'set_fin_2': 7766, 'total_rows': 18051}\n",
      "[OK] PatNo_ID_1593087886.csv -> PatNo_ID_1593087886_with_setfin.csv | {'set_fin_0': 11108, 'set_fin_1': 3838, 'set_fin_2': 9217, 'total_rows': 24163}\n",
      "[OK] PatNo_ID_1593416100.csv -> PatNo_ID_1593416100_with_setfin.csv | {'set_fin_0': 1578, 'set_fin_1': 587, 'set_fin_2': 1163, 'total_rows': 3328}\n",
      "[OK] PatNo_ID_1593472048.csv -> PatNo_ID_1593472048_with_setfin.csv | {'set_fin_0': 3168, 'set_fin_1': 1135, 'set_fin_2': 3927, 'total_rows': 8230}\n",
      "[OK] PatNo_ID_1593593586.csv -> PatNo_ID_1593593586_with_setfin.csv | {'set_fin_0': 8478, 'set_fin_1': 2875, 'set_fin_2': 7529, 'total_rows': 18882}\n",
      "[OK] PatNo_ID_1593720818.csv -> PatNo_ID_1593720818_with_setfin.csv | {'set_fin_0': 1959, 'set_fin_1': 741, 'set_fin_2': 1210, 'total_rows': 3910}\n",
      "[OK] PatNo_ID_1593838524.csv -> PatNo_ID_1593838524_with_setfin.csv | {'set_fin_0': 1037, 'set_fin_1': 368, 'set_fin_2': 772, 'total_rows': 2177}\n",
      "[OK] PatNo_ID_1594173718.csv -> PatNo_ID_1594173718_with_setfin.csv | {'set_fin_0': 117, 'set_fin_1': 37, 'set_fin_2': 190, 'total_rows': 344}\n",
      "[OK] PatNo_ID_1594294180.csv -> PatNo_ID_1594294180_with_setfin.csv | {'set_fin_0': 4178, 'set_fin_1': 1383, 'set_fin_2': 12773, 'total_rows': 18334}\n",
      "[OK] PatNo_ID_1594305136.csv -> PatNo_ID_1594305136_with_setfin.csv | {'set_fin_0': 9094, 'set_fin_1': 3312, 'set_fin_2': 6273, 'total_rows': 18679}\n",
      "[OK] PatNo_ID_1594309746.csv -> PatNo_ID_1594309746_with_setfin.csv | {'set_fin_0': 1711, 'set_fin_1': 588, 'set_fin_2': 1446, 'total_rows': 3745}\n",
      "[OK] PatNo_ID_1594319286.csv -> PatNo_ID_1594319286_with_setfin.csv | {'set_fin_0': 1624, 'set_fin_1': 501, 'set_fin_2': 1982, 'total_rows': 4107}\n",
      "[OK] PatNo_ID_1594320763.csv -> PatNo_ID_1594320763_with_setfin.csv | {'set_fin_0': 1141, 'set_fin_1': 393, 'set_fin_2': 897, 'total_rows': 2431}\n",
      "[OK] PatNo_ID_1594322594.csv -> PatNo_ID_1594322594_with_setfin.csv | {'set_fin_0': 2696, 'set_fin_1': 978, 'set_fin_2': 1706, 'total_rows': 5380}\n",
      "[OK] PatNo_ID_1594335109.csv -> PatNo_ID_1594335109_with_setfin.csv | {'set_fin_0': 2087, 'set_fin_1': 630, 'set_fin_2': 2399, 'total_rows': 5116}\n",
      "[OK] PatNo_ID_1594423683.csv -> PatNo_ID_1594423683_with_setfin.csv | {'set_fin_0': 2242, 'set_fin_1': 738, 'set_fin_2': 2043, 'total_rows': 5023}\n",
      "[OK] PatNo_ID_1594437309.csv -> PatNo_ID_1594437309_with_setfin.csv | {'set_fin_0': 4834, 'set_fin_1': 1675, 'set_fin_2': 3525, 'total_rows': 10034}\n",
      "[OK] PatNo_ID_1594439781.csv -> PatNo_ID_1594439781_with_setfin.csv | {'set_fin_0': 3552, 'set_fin_1': 1297, 'set_fin_2': 2842, 'total_rows': 7691}\n",
      "[OK] PatNo_ID_1594441887.csv -> PatNo_ID_1594441887_with_setfin.csv | {'set_fin_0': 4588, 'set_fin_1': 1523, 'set_fin_2': 4089, 'total_rows': 10200}\n",
      "[OK] PatNo_ID_1594448501.csv -> PatNo_ID_1594448501_with_setfin.csv | {'set_fin_0': 0, 'set_fin_1': 0, 'set_fin_2': 5106, 'total_rows': 5106}\n",
      "[OK] PatNo_ID_1594455578.csv -> PatNo_ID_1594455578_with_setfin.csv | {'set_fin_0': 85, 'set_fin_1': 33, 'set_fin_2': 144, 'total_rows': 262}\n",
      "[OK] PatNo_ID_1594464829.csv -> PatNo_ID_1594464829_with_setfin.csv | {'set_fin_0': 1712, 'set_fin_1': 571, 'set_fin_2': 1717, 'total_rows': 4000}\n",
      "[OK] PatNo_ID_1594467719.csv -> PatNo_ID_1594467719_with_setfin.csv | {'set_fin_0': 597, 'set_fin_1': 201, 'set_fin_2': 1761, 'total_rows': 2559}\n",
      "[OK] PatNo_ID_1594471407.csv -> PatNo_ID_1594471407_with_setfin.csv | {'set_fin_0': 4942, 'set_fin_1': 1589, 'set_fin_2': 4901, 'total_rows': 11432}\n",
      "[OK] PatNo_ID_1594479330.csv -> PatNo_ID_1594479330_with_setfin.csv | {'set_fin_0': 1521, 'set_fin_1': 407, 'set_fin_2': 1799, 'total_rows': 3727}\n",
      "[OK] PatNo_ID_1594511911.csv -> PatNo_ID_1594511911_with_setfin.csv | {'set_fin_0': 2553, 'set_fin_1': 878, 'set_fin_2': 2057, 'total_rows': 5488}\n",
      "[OK] PatNo_ID_1594511914.csv -> PatNo_ID_1594511914_with_setfin.csv | {'set_fin_0': 4662, 'set_fin_1': 1488, 'set_fin_2': 3567, 'total_rows': 9717}\n",
      "[OK] PatNo_ID_1594528842.csv -> PatNo_ID_1594528842_with_setfin.csv | {'set_fin_0': 1068, 'set_fin_1': 358, 'set_fin_2': 1065, 'total_rows': 2491}\n",
      "[OK] PatNo_ID_1594533379.csv -> PatNo_ID_1594533379_with_setfin.csv | {'set_fin_0': 471, 'set_fin_1': 161, 'set_fin_2': 398, 'total_rows': 1030}\n",
      "\n",
      "[SUMMARY] 每檔統計輸出：/home/jovyan/RT08/0925/bling_1014/set_fin_summary.csv\n",
      "[TOTALS] 整體總計輸出：/home/jovyan/RT08/0925/bling_1014/set_fin_totals.csv\n"
     ]
    }
   ],
   "source": [
    "# -*- coding: utf-8 -*-\n",
    "import os\n",
    "import glob\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "IN_DIR = \"/home/jovyan/RT08/0925/bling_1014/\"\n",
    "PATTERN = \"*.csv\"\n",
    "OUT_SUFFIX = \"_with_setfin.csv\"\n",
    "SUMMARY_PATH = os.path.join(IN_DIR, \"set_fin_summary.csv\")\n",
    "TOTALS_PATH  = os.path.join(IN_DIR, \"set_fin_totals.csv\")\n",
    "\n",
    "TIME_COL = \"senddate\"\n",
    "NAN_COL  = \"nan_check\"\n",
    "AD_COL   = \"ad_para\"\n",
    "MIN_GAP_SEC = 300  # 5 分鐘\n",
    "\n",
    "# =============== 小工具 ===============\n",
    "def to_datetime_robust(series: pd.Series) -> pd.Series:\n",
    "    \"\"\"處理『上午/下午 AM/PM』等情形並轉成 datetime64[ns]。\"\"\"\n",
    "    def normalize_zh_ampm(x):\n",
    "        if not isinstance(x, str):\n",
    "            return x\n",
    "        s = x.replace(\"上午\", \"AM\").replace(\"下午\", \"PM\").strip()\n",
    "        return s\n",
    "    s = series.astype(str).map(lambda x: x if x.lower() not in {\"nan\",\"none\"} else np.nan)\n",
    "    s = s.map(normalize_zh_ampm)\n",
    "    return pd.to_datetime(s, errors=\"coerce\", infer_datetime_format=True)\n",
    "\n",
    "def ensure_datetime(df: pd.DataFrame, time_col: str) -> pd.DataFrame:\n",
    "    if time_col not in df.columns:\n",
    "        raise KeyError(f\"缺少時間欄位：{time_col}\")\n",
    "    if not pd.api.types.is_datetime64_any_dtype(df[time_col]):\n",
    "        df[time_col] = to_datetime_robust(df[time_col])\n",
    "    return df\n",
    "\n",
    "def last_indices_of_consecutive_true(mask: pd.Series) -> list:\n",
    "    \"\"\"\n",
    "    將 True 連續區段歸一：取每段連續 True 的『最後一筆』整數位置索引。\n",
    "    例：F T T F T T T F -> 回傳 [1, 4]（以 0-based 計算）。\n",
    "    \"\"\"\n",
    "    idxs = np.flatnonzero(mask.values)\n",
    "    if len(idxs) == 0:\n",
    "        return []\n",
    "    breaks = np.where(np.diff(idxs) != 1)[0] + 1\n",
    "    groups = np.split(idxs, breaks)\n",
    "    return [int(g[-1]) for g in groups]\n",
    "\n",
    "# =============== 主邏輯：建立 set_fin ===============\n",
    "def build_set_fin(df: pd.DataFrame,\n",
    "                  time_col=TIME_COL,\n",
    "                  nan_col=NAN_COL,\n",
    "                  ad_col=AD_COL,\n",
    "                  min_gap_sec=MIN_GAP_SEC) -> pd.DataFrame:\n",
    "    \"\"\"\n",
    "    規則：\n",
    "      1) 僅用 (nan_check==1 且 ad_para==1) 的列當『事件點候選』。\n",
    "      2) 連續 ad_para==1 僅取該串最後一筆作為事件點。\n",
    "      3) 對相鄰事件點 (E_i, E_{i+1})，若相隔 >= min_gap_sec：\n",
    "           - 在 (t_i, t_{i+1}) 區間內，以『時間比例』切分：\n",
    "             前 50% → set_fin=0\n",
    "             後 20%（80%~100%）→ set_fin=1\n",
    "             其餘 → set_fin=2\n",
    "         其他情形不標（維持 2）。\n",
    "    \"\"\"\n",
    "    df = df.copy()\n",
    "\n",
    "    # 基本欄位檢查\n",
    "    for c in [time_col, nan_col, ad_col]:\n",
    "        if c not in df.columns:\n",
    "            raise KeyError(f\"缺少必要欄位：{c}\")\n",
    "\n",
    "    # 時間處理與排序\n",
    "    df = ensure_datetime(df, time_col=time_col)\n",
    "    df = df.sort_values(time_col).reset_index(drop=True)\n",
    "\n",
    "    # 預設為 2\n",
    "    df[\"set_fin\"] = 2\n",
    "\n",
    "    # 事件點候選\n",
    "    event_mask = (df[nan_col] == 1) & (df[ad_col] == 1)\n",
    "\n",
    "    # 連續 True 串取最後一筆\n",
    "    last_pos_list = last_indices_of_consecutive_true(event_mask)\n",
    "    if len(last_pos_list) < 2:\n",
    "        # 沒有足夠事件對，直接回傳\n",
    "        return df\n",
    "\n",
    "    # 逐對處理相鄰事件\n",
    "    for i in range(len(last_pos_list) - 1):\n",
    "        idx0 = last_pos_list[i]\n",
    "        idx1 = last_pos_list[i + 1]\n",
    "\n",
    "        t0 = df.at[idx0, time_col]\n",
    "        t1 = df.at[idx1, time_col]\n",
    "        if pd.isna(t0) or pd.isna(t1):\n",
    "            continue\n",
    "\n",
    "        gap_sec = (t1 - t0).total_seconds()\n",
    "        if gap_sec < min_gap_sec:\n",
    "            continue\n",
    "\n",
    "        # 以時間比例分界\n",
    "        t50 = t0 + (t1 - t0) * 0.5\n",
    "        t80 = t0 + (t1 - t0) * 0.8\n",
    "\n",
    "        in_seg = (df[time_col] >= t0) & (df[time_col] < t1)\n",
    "\n",
    "        # 前 50% → 0\n",
    "        front_mask = in_seg & (df[time_col] < t50)\n",
    "        df.loc[front_mask, \"set_fin\"] = 0\n",
    "\n",
    "        # 後 20% → 1\n",
    "        tail_mask = in_seg & (df[time_col] >= t80)\n",
    "        df.loc[tail_mask, \"set_fin\"] = 1\n",
    "\n",
    "        # 中間 30% 維持 2\n",
    "\n",
    "    return df\n",
    "\n",
    "def summarize_set_fin_counts(df: pd.DataFrame) -> dict:\n",
    "    \"\"\"回傳 set_fin 的 0/1/2 計數（缺項補 0）\"\"\"\n",
    "    vc = df[\"set_fin\"].value_counts(dropna=False)\n",
    "    return {\n",
    "        \"set_fin_0\": int(vc.get(0, 0)),\n",
    "        \"set_fin_1\": int(vc.get(1, 0)),\n",
    "        \"set_fin_2\": int(vc.get(2, 0)),\n",
    "        \"total_rows\": int(len(df))\n",
    "    }\n",
    "\n",
    "# =============== 批次處理 ===============\n",
    "def batch_build_set_fin(in_dir=IN_DIR, pattern=PATTERN, out_suffix=OUT_SUFFIX):\n",
    "    paths = sorted(glob.glob(os.path.join(in_dir, pattern)))\n",
    "    if not paths:\n",
    "        print(f\"[WARN] 找不到檔案：{in_dir}{pattern}\")\n",
    "        return\n",
    "\n",
    "    summary_rows = []\n",
    "    totals = {\"set_fin_0\":0, \"set_fin_1\":0, \"set_fin_2\":0, \"total_rows\":0}\n",
    "\n",
    "    for p in paths:\n",
    "        fname = os.path.basename(p)\n",
    "        try:\n",
    "            df = pd.read_csv(p)\n",
    "            df2 = build_set_fin(df, time_col=TIME_COL, nan_col=NAN_COL, ad_col=AD_COL, min_gap_sec=MIN_GAP_SEC)\n",
    "\n",
    "            counts = summarize_set_fin_counts(df2)\n",
    "            summary_rows.append({\n",
    "                \"file_name\": fname,\n",
    "                **counts\n",
    "            })\n",
    "            for k in totals:\n",
    "                totals[k] += counts[k]\n",
    "\n",
    "            out_path = os.path.splitext(p)[0] + out_suffix\n",
    "            df2.to_csv(out_path, index=False, encoding=\"utf-8-sig\")\n",
    "            print(f\"[OK] {fname} -> {os.path.basename(out_path)} | {counts}\")\n",
    "\n",
    "        except Exception as e:\n",
    "            print(f\"[ERR] {fname}: {e}\")\n",
    "\n",
    "    if summary_rows:\n",
    "        summary_df = pd.DataFrame(summary_rows)\n",
    "        summary_df.to_csv(SUMMARY_PATH, index=False, encoding=\"utf-8-sig\")\n",
    "        print(f\"\\n[SUMMARY] 每檔統計輸出：{SUMMARY_PATH}\")\n",
    "\n",
    "        totals_df = pd.DataFrame([{\"scope\":\"ALL_FILES\", **totals}])\n",
    "        totals_df.to_csv(TOTALS_PATH, index=False, encoding=\"utf-8-sig\")\n",
    "        print(f\"[TOTALS] 整體總計輸出：{TOTALS_PATH}\")\n",
    "\n",
    "# =============== 執行 ===============\n",
    "if __name__ == \"__main__\":\n",
    "    batch_build_set_fin(IN_DIR, PATTERN, OUT_SUFFIX)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2c4d9bcb-32ea-428f-a0f9-213cd6ce4013",
   "metadata": {},
   "outputs": [],
   "source": [
    "發現有些檔案nan_check=0卻在set_fin=0=1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "29642ed2-7672-4be5-b116-384b9c34fbab",
   "metadata": {},
   "outputs": [],
   "source": [
    "我要針對/home/jovyan/RT08/0925/bling_1014/所有資料做\n",
    "，當nan_check=1且ad_para=1的兩筆間隔大於等於五分鐘，連續的ad_para=1就拿最晚的來看， 兩次ad_para=1之間，前50%紀錄為set_fin=0, 後20%紀錄為set_fin=1, 都不符合就set_fin=2，ad_para=1的該筆資料就是set_fin=2，且每位病患的第一筆nan_check=1都是set_fin=1，要依照病患分開看\n",
    " 最後輸出set_fin=0=1=2分別有幾筆\n",
    "以及跨黨案的set_fin=0=1=2三類分別有幾筆"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "id": "64f1da98-72a5-44d0-8556-589f6d549000",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true,
     "source_hidden": true
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[OK] Overwritten: 089271.csv | counts: 0=12330, 1=4819, 2=15270, total=32419\n",
      "[OK] Overwritten: 095323.csv | counts: 0=6732, 1=2631, 2=14428, total=23791\n",
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      "[OK] Overwritten: PatNo_ID_1582452511.csv | counts: 0=2351, 1=921, 2=1924, total=5196\n",
      "[OK] Overwritten: PatNo_ID_1582635996.csv | counts: 0=5826, 1=2306, 2=4914, total=13046\n",
      "[OK] Overwritten: PatNo_ID_1582849900.csv | counts: 0=754, 1=287, 2=6370, total=7411\n",
      "[OK] Overwritten: PatNo_ID_1582937076.csv | counts: 0=10945, 1=4303, 2=8739, total=23987\n",
      "[OK] Overwritten: PatNo_ID_1584158973.csv | counts: 0=1227, 1=479, 2=1176, total=2882\n",
      "[OK] Overwritten: PatNo_ID_1584397376.csv | counts: 0=132, 1=51, 2=455, total=638\n",
      "[OK] Overwritten: PatNo_ID_1586172659.csv | counts: 0=14824, 1=5816, 2=17914, total=38554\n",
      "[OK] Overwritten: PatNo_ID_1586696634.csv | counts: 0=1134, 1=446, 2=1989, total=3569\n",
      "[OK] Overwritten: PatNo_ID_1586897008.csv | counts: 0=2222, 1=863, 2=3602, total=6687\n",
      "[OK] Overwritten: PatNo_ID_1587490083.csv | counts: 0=19384, 1=7588, 2=18214, total=45186\n",
      "[OK] Overwritten: PatNo_ID_1588632604.csv | counts: 0=1088, 1=423, 2=1097, total=2608\n",
      "[OK] Overwritten: PatNo_ID_1588673465.csv | counts: 0=4080, 1=1605, 2=4391, total=10076\n",
      "[OK] Overwritten: PatNo_ID_1588794796.csv | counts: 0=4517, 1=1775, 2=3083, total=9375\n",
      "[OK] Overwritten: PatNo_ID_1588957997.csv | counts: 0=4073, 1=1587, 2=5306, total=10966\n",
      "[OK] Overwritten: PatNo_ID_1589018086.csv | counts: 0=5889, 1=2298, 2=5285, total=13472\n",
      "[OK] Overwritten: PatNo_ID_1589034524.csv | counts: 0=21714, 1=8641, 2=19726, total=50081\n",
      "[OK] Overwritten: PatNo_ID_1589324603.csv | counts: 0=1627, 1=629, 2=1931, total=4187\n",
      "[OK] Overwritten: PatNo_ID_1589918099.csv | counts: 0=0, 1=0, 2=9333, total=9333\n",
      "[OK] Overwritten: PatNo_ID_1590136310.csv | counts: 0=1749, 1=621, 2=3210, total=5580\n",
      "[OK] Overwritten: PatNo_ID_1590616537.csv | counts: 0=5882, 1=2307, 2=6017, total=14206\n",
      "[OK] Overwritten: PatNo_ID_1590854576.csv | counts: 0=7085, 1=2644, 2=6150, total=15879\n",
      "[OK] Overwritten: PatNo_ID_1591609798.csv | counts: 0=13767, 1=5402, 2=16449, total=35618\n",
      "[OK] Overwritten: PatNo_ID_1592044724.csv | counts: 0=3188, 1=1251, 2=2376, total=6815\n",
      "[OK] Overwritten: PatNo_ID_1592560504.csv | counts: 0=6536, 1=2533, 2=8982, total=18051\n",
      "[OK] Overwritten: PatNo_ID_1593087886.csv | counts: 0=9929, 1=3839, 2=10395, total=24163\n",
      "[OK] Overwritten: PatNo_ID_1593416100.csv | counts: 0=1448, 1=588, 2=1292, total=3328\n",
      "[OK] Overwritten: PatNo_ID_1593472048.csv | counts: 0=2899, 1=1136, 2=4195, total=8230\n",
      "[OK] Overwritten: PatNo_ID_1593593586.csv | counts: 0=7369, 1=2876, 2=8637, total=18882\n",
      "[OK] Overwritten: PatNo_ID_1593720818.csv | counts: 0=1891, 1=742, 2=1277, total=3910\n",
      "[OK] Overwritten: PatNo_ID_1593838524.csv | counts: 0=943, 1=369, 2=865, total=2177\n",
      "[OK] Overwritten: PatNo_ID_1594173718.csv | counts: 0=95, 1=38, 2=211, total=344\n",
      "[OK] Overwritten: PatNo_ID_1594294180.csv | counts: 0=3748, 1=1384, 2=13202, total=18334\n",
      "[OK] Overwritten: PatNo_ID_1594305136.csv | counts: 0=8459, 1=3313, 2=6907, total=18679\n",
      "[OK] Overwritten: PatNo_ID_1594309746.csv | counts: 0=1483, 1=589, 2=1673, total=3745\n",
      "[OK] Overwritten: PatNo_ID_1594319286.csv | counts: 0=1274, 1=502, 2=2331, total=4107\n",
      "[OK] Overwritten: PatNo_ID_1594320763.csv | counts: 0=1027, 1=394, 2=1010, total=2431\n",
      "[OK] Overwritten: PatNo_ID_1594322594.csv | counts: 0=2511, 1=979, 2=1890, total=5380\n",
      "[OK] Overwritten: PatNo_ID_1594335109.csv | counts: 0=1678, 1=631, 2=2807, total=5116\n",
      "[OK] Overwritten: PatNo_ID_1594423683.csv | counts: 0=1904, 1=739, 2=2380, total=5023\n",
      "[OK] Overwritten: PatNo_ID_1594437309.csv | counts: 0=4318, 1=1676, 2=4040, total=10034\n",
      "[OK] Overwritten: PatNo_ID_1594439781.csv | counts: 0=3273, 1=1298, 2=3120, total=7691\n",
      "[OK] Overwritten: PatNo_ID_1594441887.csv | counts: 0=4052, 1=1524, 2=4624, total=10200\n",
      "[OK] Overwritten: PatNo_ID_1594448501.csv | counts: 0=0, 1=0, 2=5106, total=5106\n",
      "[OK] Overwritten: PatNo_ID_1594455578.csv | counts: 0=83, 1=34, 2=145, total=262\n",
      "[OK] Overwritten: PatNo_ID_1594464829.csv | counts: 0=1474, 1=572, 2=1954, total=4000\n",
      "[OK] Overwritten: PatNo_ID_1594467719.csv | counts: 0=508, 1=202, 2=1849, total=2559\n",
      "[OK] Overwritten: PatNo_ID_1594471407.csv | counts: 0=4163, 1=1590, 2=5679, total=11432\n",
      "[OK] Overwritten: PatNo_ID_1594479330.csv | counts: 0=1226, 1=408, 2=2093, total=3727\n",
      "[OK] Overwritten: PatNo_ID_1594511911.csv | counts: 0=2251, 1=879, 2=2358, total=5488\n",
      "[OK] Overwritten: PatNo_ID_1594511914.csv | counts: 0=4098, 1=1489, 2=4130, total=9717\n",
      "[OK] Overwritten: PatNo_ID_1594528842.csv | counts: 0=931, 1=359, 2=1201, total=2491\n",
      "[OK] Overwritten: PatNo_ID_1594533379.csv | counts: 0=421, 1=162, 2=447, total=1030\n",
      "\n",
      "[Report] Per-file summary: /home/jovyan/RT08/0925/1014/set_fin_per_file_summary.csv\n",
      "[Report] Cross-file totals: /home/jovyan/RT08/0925/1014/set_fin_totals.csv\n",
      "\n",
      "=== set_fin summary (per file) ===\n",
      "              file_name  set_fin_0  set_fin_1  set_fin_2  total_rows\n",
      "             089271.csv      12330       4819      15270       32419\n",
      "             095323.csv       6732       2631      14428       23791\n",
      "             095707.csv       7711       3008       9461       20180\n",
      "             114309.csv       9744       3776      58209       71729\n",
      "             230933.csv      11803       4727      13719       30249\n",
      "            4216007.csv          0          0       1433        1433\n",
      "            7108162.csv         79         32        128         239\n",
      "            7408338.csv        525        204        703        1432\n",
      "            7657698.csv        645        260        508        1413\n",
      "            7721164.csv        194         75        214         483\n",
      "PatNo_ID_1560013303.csv        980        386       1177        2543\n",
      "PatNo_ID_1562733396.csv       1022        468        764        2254\n",
      "PatNo_ID_1563587183.csv       2391       1005       1890        5286\n",
      "PatNo_ID_1564148644.csv       6294       1477       9516       17287\n",
      "PatNo_ID_1565148312.csv       2397        920       2158        5475\n",
      "PatNo_ID_1565378038.csv        759        287       1515        2561\n",
      "PatNo_ID_1566123680.csv      10325       4103      28172       42600\n",
      "PatNo_ID_1566252197.csv        849        335       2184        3368\n",
      "PatNo_ID_1566279967.csv        425        162        648        1235\n",
      "PatNo_ID_1566671274.csv      10419       4037      10903       25359\n",
      "PatNo_ID_1566911879.csv      24384       9581      20034       53999\n",
      "PatNo_ID_1567747650.csv       4805       1870       4225       10900\n",
      "PatNo_ID_1567804800.csv       8215       3255       9107       20577\n",
      "PatNo_ID_1567832735.csv      15940       6254      14381       36575\n",
      "PatNo_ID_1568039398.csv      15547       6088      12884       34519\n",
      "PatNo_ID_1568574099.csv       5508       2151       6286       13945\n",
      "PatNo_ID_1568813269.csv       2169        952       1746        4867\n",
      "PatNo_ID_1568952422.csv        444        172        568        1184\n",
      "PatNo_ID_1569083701.csv       1043        391       1894        3328\n",
      "PatNo_ID_1569944983.csv       2924       1134       3591        7649\n",
      "PatNo_ID_1570089466.csv       6464       2443      32431       41338\n",
      "PatNo_ID_1570242703.csv       4594       1804       4246       10644\n",
      "PatNo_ID_1570273244.csv       4413       1711       3601        9725\n",
      "PatNo_ID_1570642083.csv       8321       3207       8203       19731\n",
      "PatNo_ID_1571945701.csv       6626       2604       6501       15731\n",
      "PatNo_ID_1572481361.csv      14774       5840      13925       34539\n",
      "PatNo_ID_1572562839.csv       9781       3835       7347       20963\n",
      "PatNo_ID_1572831765.csv        948        375       1373        2696\n",
      "PatNo_ID_1572976822.csv       3285       1285       2194        6764\n",
      "PatNo_ID_1573063188.csv       1925        742       2413        5080\n",
      "PatNo_ID_1573249295.csv       3365       1295       2491        7151\n",
      "PatNo_ID_1573964540.csv       1774        690       1930        4394\n",
      "PatNo_ID_1574148494.csv      20086       7886      19182       47154\n",
      "PatNo_ID_1574270349.csv       2561        962       3010        6533\n",
      "PatNo_ID_1574528808.csv       4240       1550       9022       14812\n",
      "PatNo_ID_1574831525.csv        192         73        255         520\n",
      "PatNo_ID_1574987447.csv       8769       3402       7671       19842\n",
      "PatNo_ID_1575060177.csv       2038        790       2462        5290\n",
      "PatNo_ID_1575256902.csv       3195       1174       5218        9587\n",
      "PatNo_ID_1575445051.csv        728        281        791        1800\n",
      "PatNo_ID_1575502382.csv       2920       1144       2540        6604\n",
      "PatNo_ID_1575975485.csv       6799       2652       7008       16459\n",
      "PatNo_ID_1576115572.csv       7748       2896       8071       18715\n",
      "PatNo_ID_1576116479.csv        523        206        534        1263\n",
      "PatNo_ID_1576301569.csv       1454        555       1198        3207\n",
      "PatNo_ID_1576964560.csv       8784       3433      11971       24188\n",
      "PatNo_ID_1577042911.csv      15838       6179      16340       38357\n",
      "PatNo_ID_1577487284.csv        849        339       1157        2345\n",
      "PatNo_ID_1578784257.csv      13565       5279      15070       33914\n",
      "PatNo_ID_1579198603.csv        943        367        735        2045\n",
      "PatNo_ID_1579498177.csv      10259       3939       7490       21688\n",
      "PatNo_ID_1580062580.csv        989        391        770        2150\n",
      "PatNo_ID_1580096720.csv        471        180        772        1423\n",
      "PatNo_ID_1580107637.csv          0          0       2698        2698\n",
      "PatNo_ID_1580244614.csv       1573        620       3085        5278\n",
      "PatNo_ID_1580766093.csv       7880       3125       7065       18070\n",
      "PatNo_ID_1581003248.csv       2955       1152       3669        7776\n",
      "PatNo_ID_1581019504.csv      12578       4949      10646       28173\n",
      "PatNo_ID_1581633231.csv       6163       2412       7420       15995\n",
      "PatNo_ID_1581692973.csv       1104        442       1177        2723\n",
      "PatNo_ID_1582452511.csv       2351        921       1924        5196\n",
      "PatNo_ID_1582635996.csv       5826       2306       4914       13046\n",
      "PatNo_ID_1582849900.csv        754        287       6370        7411\n",
      "PatNo_ID_1582937076.csv      10945       4303       8739       23987\n",
      "PatNo_ID_1584158973.csv       1227        479       1176        2882\n",
      "PatNo_ID_1584397376.csv        132         51        455         638\n",
      "PatNo_ID_1586172659.csv      14824       5816      17914       38554\n",
      "PatNo_ID_1586696634.csv       1134        446       1989        3569\n",
      "PatNo_ID_1586897008.csv       2222        863       3602        6687\n",
      "PatNo_ID_1587490083.csv      19384       7588      18214       45186\n",
      "PatNo_ID_1588632604.csv       1088        423       1097        2608\n",
      "PatNo_ID_1588673465.csv       4080       1605       4391       10076\n",
      "PatNo_ID_1588794796.csv       4517       1775       3083        9375\n",
      "PatNo_ID_1588957997.csv       4073       1587       5306       10966\n",
      "PatNo_ID_1589018086.csv       5889       2298       5285       13472\n",
      "PatNo_ID_1589034524.csv      21714       8641      19726       50081\n",
      "PatNo_ID_1589324603.csv       1627        629       1931        4187\n",
      "PatNo_ID_1589918099.csv          0          0       9333        9333\n",
      "PatNo_ID_1590136310.csv       1749        621       3210        5580\n",
      "PatNo_ID_1590616537.csv       5882       2307       6017       14206\n",
      "PatNo_ID_1590854576.csv       7085       2644       6150       15879\n",
      "PatNo_ID_1591609798.csv      13767       5402      16449       35618\n",
      "PatNo_ID_1592044724.csv       3188       1251       2376        6815\n",
      "PatNo_ID_1592560504.csv       6536       2533       8982       18051\n",
      "PatNo_ID_1593087886.csv       9929       3839      10395       24163\n",
      "PatNo_ID_1593416100.csv       1448        588       1292        3328\n",
      "PatNo_ID_1593472048.csv       2899       1136       4195        8230\n",
      "PatNo_ID_1593593586.csv       7369       2876       8637       18882\n",
      "PatNo_ID_1593720818.csv       1891        742       1277        3910\n",
      "PatNo_ID_1593838524.csv        943        369        865        2177\n",
      "PatNo_ID_1594173718.csv         95         38        211         344\n",
      "PatNo_ID_1594294180.csv       3748       1384      13202       18334\n",
      "PatNo_ID_1594305136.csv       8459       3313       6907       18679\n",
      "PatNo_ID_1594309746.csv       1483        589       1673        3745\n",
      "PatNo_ID_1594319286.csv       1274        502       2331        4107\n",
      "PatNo_ID_1594320763.csv       1027        394       1010        2431\n",
      "PatNo_ID_1594322594.csv       2511        979       1890        5380\n",
      "PatNo_ID_1594335109.csv       1678        631       2807        5116\n",
      "PatNo_ID_1594423683.csv       1904        739       2380        5023\n",
      "PatNo_ID_1594437309.csv       4318       1676       4040       10034\n",
      "PatNo_ID_1594439781.csv       3273       1298       3120        7691\n",
      "PatNo_ID_1594441887.csv       4052       1524       4624       10200\n",
      "PatNo_ID_1594448501.csv          0          0       5106        5106\n",
      "PatNo_ID_1594455578.csv         83         34        145         262\n",
      "PatNo_ID_1594464829.csv       1474        572       1954        4000\n",
      "PatNo_ID_1594467719.csv        508        202       1849        2559\n",
      "PatNo_ID_1594471407.csv       4163       1590       5679       11432\n",
      "PatNo_ID_1594479330.csv       1226        408       2093        3727\n",
      "PatNo_ID_1594511911.csv       2251        879       2358        5488\n",
      "PatNo_ID_1594511914.csv       4098       1489       4130        9717\n",
      "PatNo_ID_1594528842.csv        931        359       1201        2491\n",
      "PatNo_ID_1594533379.csv        421        162        447        1030\n",
      "\n",
      "=== set_fin grand totals (across files) ===\n",
      " set_fin_0_total  set_fin_1_total  set_fin_2_total  grand_total_rows\n",
      "          591527           229857           745849           1567233\n"
     ]
    }
   ],
   "source": [
    "# -*- coding: utf-8 -*-\n",
    "\"\"\"\n",
    "批次處理來源：/home/jovyan/RT08/0925/bling_1014/\n",
    "行為：\n",
    "- 直接在每個檔案內新增/覆蓋 set_fin 欄位，並「覆蓋原檔」寫回同一路徑（無副檔名後綴）\n",
    "- 依「病患」分開運算 set_fin（patno/patient_id等），不足時視為單一病患\n",
    "- 跨檔案統計輸出於：/home/jovyan/RT08/0925/1014/\n",
    "    - set_fin_per_file_summary.csv（逐檔 0/1/2 筆數）\n",
    "    - set_fin_totals.csv（跨檔匯總 0/1/2 與總列數）\n",
    "規則摘要：\n",
    "1) 事件點候選：nan_check==1 且 ad_para==1\n",
    "2) 連續的 ad_para==1 僅取該串的最後一筆作為事件點\n",
    "3) 對相鄰事件點 (E_i, E_{i+1})，若相隔 >= 300 秒，區間內按「時間比例」標記：\n",
    "     - 前 50% → set_fin=0\n",
    "     - 後 20% → set_fin=1\n",
    "     - 中間 30% → set_fin=2（預設）\n",
    "   事件點本身一律 set_fin=2\n",
    "4) 每位病患第一筆 nan_check==1 的列，強制 set_fin=1（最後覆蓋）\n",
    "\"\"\"\n",
    "\n",
    "import os\n",
    "import glob\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "IN_DIR = \"/home/jovyan/RT08/0925/bling_1014/\"\n",
    "REPORT_DIR = \"/home/jovyan/RT08/0925/1014/\"\n",
    "os.makedirs(REPORT_DIR, exist_ok=True)\n",
    "\n",
    "SUMMARY_PER_FILE = os.path.join(REPORT_DIR, \"set_fin_per_file_summary.csv\")\n",
    "SUMMARY_TOTALS   = os.path.join(REPORT_DIR, \"set_fin_totals.csv\")\n",
    "\n",
    "# ---------------------------\n",
    "# 小工具\n",
    "# ---------------------------\n",
    "def resolve_col(df, candidates):\n",
    "    \"\"\"在 df 欄位中以大小寫不敏感尋找候選名稱；命中回傳實際欄名，否則回傳 None。\"\"\"\n",
    "    cmap = {c.lower(): c for c in df.columns}\n",
    "    for cand in candidates:\n",
    "        if cand.lower() in cmap:\n",
    "            return cmap[cand.lower()]\n",
    "    return None\n",
    "\n",
    "def ensure_datetime(df, time_col):\n",
    "    \"\"\"確保時間欄為 datetime64[ns]。\"\"\"\n",
    "    if not pd.api.types.is_datetime64_any_dtype(df[time_col]):\n",
    "        df[time_col] = pd.to_datetime(df[time_col], errors=\"coerce\", infer_datetime_format=True)\n",
    "    return df\n",
    "\n",
    "def last_indices_of_consecutive_true(mask: pd.Series) -> list:\n",
    "    \"\"\"\n",
    "    將 True 連續區段合併：回傳每段連續 True 的「最後一筆」整數位置（iloc）。\n",
    "    例 F T T F T T T F → 回傳 [第1段最後, 第2段最後] 的位置索引。\n",
    "    \"\"\"\n",
    "    idxs = np.flatnonzero(mask.values)\n",
    "    if len(idxs) == 0:\n",
    "        return []\n",
    "    breaks = np.where(np.diff(idxs) != 1)[0] + 1\n",
    "    groups = np.split(idxs, breaks)\n",
    "    return [int(g[-1]) for g in groups]\n",
    "\n",
    "# ---------------------------\n",
    "# 核心：依單一「病患」子資料標記 set_fin\n",
    "# ---------------------------\n",
    "def label_set_fin_per_patient(g: pd.DataFrame,\n",
    "                              time_col: str,\n",
    "                              nan_col: str,\n",
    "                              ad_col: str,\n",
    "                              min_gap_sec: int = 300) -> pd.DataFrame:\n",
    "    \"\"\"\n",
    "    對單一病患 g（分組切片）建立 set_fin，回傳同索引 DataFrame。\n",
    "    \"\"\"\n",
    "    g = g.copy()\n",
    "    g = g.sort_values(time_col, kind=\"mergesort\")  # 穩定排序\n",
    "    g[\"set_fin\"] = 2  # 預設\n",
    "\n",
    "    # 事件點候選（需有時間）\n",
    "    event_mask = (g[nan_col] == 1) & (g[ad_col] == 1) & g[time_col].notna()\n",
    "\n",
    "    # 連續 True 串取最後一筆（以 iloc 位置）\n",
    "    pos_last_list = last_indices_of_consecutive_true(event_mask.reset_index(drop=True))\n",
    "\n",
    "    # 若事件點不足兩個，仍需處理「第一筆 nan_check==1 → 1」\n",
    "    if len(pos_last_list) < 2:\n",
    "        first_nan1 = g.index[g[nan_col] == 1].min()\n",
    "        if pd.notna(first_nan1):\n",
    "            g.loc[first_nan1, \"set_fin\"] = 1\n",
    "        return g\n",
    "\n",
    "    # 將 iloc 位置映射回實際 index\n",
    "    actual_index = g.index.to_list()\n",
    "    def iloc_to_index(iloc_pos: int):\n",
    "        return actual_index[iloc_pos]\n",
    "\n",
    "    # 逐對事件點，按時間比例標記前 50%、後 20%\n",
    "    for i in range(len(pos_last_list) - 1):\n",
    "        i0 = pos_last_list[i]\n",
    "        i1 = pos_last_list[i + 1]\n",
    "        idx0 = iloc_to_index(i0)\n",
    "        idx1 = iloc_to_index(i1)\n",
    "\n",
    "        t0 = g.at[idx0, time_col]\n",
    "        t1 = g.at[idx1, time_col]\n",
    "        if pd.isna(t0) or pd.isna(t1):\n",
    "            continue\n",
    "\n",
    "        gap_sec = (t1 - t0).total_seconds()\n",
    "        if gap_sec < min_gap_sec:\n",
    "            continue\n",
    "\n",
    "        t50 = t0 + (t1 - t0) * 0.5   # 前 50% 截點\n",
    "        t80 = t0 + (t1 - t0) * 0.8   # 後 20% 起點\n",
    "\n",
    "        # 僅標記 (t0, t1) 區間（不含事件點本身）\n",
    "        in_seg = (g[time_col] > t0) & (g[time_col] < t1)\n",
    "\n",
    "        # 前 50% → 0\n",
    "        g.loc[in_seg & (g[time_col] < t50), \"set_fin\"] = 0\n",
    "        # 後 20% → 1\n",
    "        g.loc[in_seg & (g[time_col] >= t80), \"set_fin\"] = 1\n",
    "        # 中間 30% 維持 2；兩事件點本身亦維持 2（不改）\n",
    "\n",
    "    # 覆蓋規則：每位病患的第一筆 nan_check==1 → 1\n",
    "    first_nan1 = g.index[g[nan_col] == 1].min()\n",
    "    if pd.notna(first_nan1):\n",
    "        g.loc[first_nan1, \"set_fin\"] = 1\n",
    "\n",
    "    return g\n",
    "\n",
    "# ---------------------------\n",
    "# 批次處理整個資料夾（覆蓋原檔）\n",
    "# ---------------------------\n",
    "def batch_build_set_fin_overwrite(in_dir: str,\n",
    "                                  pattern: str = \"*.csv\",\n",
    "                                  min_gap_sec: int = 300):\n",
    "    paths = sorted(glob.glob(os.path.join(in_dir, pattern)))\n",
    "    if not paths:\n",
    "        print(f\"[WARN] No files found in: {in_dir}\")\n",
    "        return\n",
    "\n",
    "    summary_rows = []\n",
    "    grand_0 = grand_1 = grand_2 = grand_total = 0\n",
    "\n",
    "    for p in paths:\n",
    "        try:\n",
    "            df = pd.read_csv(p)\n",
    "            if df.empty:\n",
    "                print(f\"[SKIP] Empty file: {os.path.basename(p)}\")\n",
    "                continue\n",
    "\n",
    "            # 欄位解析（大小寫不敏感）\n",
    "            send_col = resolve_col(df, [\"senddate\", \"send_date\", \"timestamp\", \"time\", \"datetime\"])\n",
    "            nan_col  = resolve_col(df, [\"nan_check\", \"NaN_check\", \"nanflag\", \"nan_flag\"])\n",
    "            ad_col   = resolve_col(df, [\"ad_para\", \"adflag\", \"ad_flag\"])\n",
    "            pat_col  = resolve_col(df, [\"patno\", \"PatNo\", \"patient_id\", \"id\", \"patientid\"])\n",
    "\n",
    "            missing = [name for name, col in {\n",
    "                \"senddate\": send_col, \"nan_check\": nan_col, \"ad_para\": ad_col\n",
    "            }.items() if col is None]\n",
    "            if missing:\n",
    "                raise KeyError(f\"Missing required columns: {missing}\")\n",
    "\n",
    "            # 確保時間型態\n",
    "            df = ensure_datetime(df, time_col=send_col)\n",
    "\n",
    "            # 無病患欄則視為單一病患\n",
    "            drop_tmp_patient = False\n",
    "            if pat_col is None:\n",
    "                df[\"_tmp_single_patient\"] = \"single\"\n",
    "                pat_col = \"_tmp_single_patient\"\n",
    "                drop_tmp_patient = True\n",
    "\n",
    "            # 依病患分組標記\n",
    "            df_out = (\n",
    "                df.groupby(pat_col, group_keys=False, sort=False)\n",
    "                  .apply(lambda g: label_set_fin_per_patient(\n",
    "                      g, time_col=send_col, nan_col=nan_col, ad_col=ad_col, min_gap_sec=min_gap_sec\n",
    "                  ))\n",
    "            )\n",
    "\n",
    "            # 若臨時列為單一病患欄位，選擇是否清掉（通常可保留；若要清除，解註下一行）\n",
    "            if drop_tmp_patient and \"_tmp_single_patient\" in df_out.columns:\n",
    "                df_out = df_out.drop(columns=[\"_tmp_single_patient\"])\n",
    "\n",
    "            # 統計（本檔）\n",
    "            counts = df_out[\"set_fin\"].value_counts(dropna=False).reindex([0, 1, 2], fill_value=0)\n",
    "            c0, c1, c2 = int(counts.get(0, 0)), int(counts.get(1, 0)), int(counts.get(2, 0))\n",
    "            total = int(len(df_out))\n",
    "\n",
    "            summary_rows.append({\n",
    "                \"file_name\": os.path.basename(p),\n",
    "                \"set_fin_0\": c0,\n",
    "                \"set_fin_1\": c1,\n",
    "                \"set_fin_2\": c2,\n",
    "                \"total_rows\": total\n",
    "            })\n",
    "\n",
    "            # 跨檔累計\n",
    "            grand_0 += c0\n",
    "            grand_1 += c1\n",
    "            grand_2 += c2\n",
    "            grand_total += total\n",
    "\n",
    "            # 覆蓋原檔（直接寫回）\n",
    "            df_out.to_csv(p, index=False, encoding=\"utf-8-sig\")\n",
    "            print(f\"[OK] Overwritten: {os.path.basename(p)} | counts: 0={c0}, 1={c1}, 2={c2}, total={total}\")\n",
    "\n",
    "        except Exception as e:\n",
    "            print(f\"[ERR] {os.path.basename(p)}: {e}\")\n",
    "\n",
    "    # 報表輸出（固定到 REPORT_DIR）\n",
    "    if summary_rows:\n",
    "        per_file_df = pd.DataFrame(summary_rows)\n",
    "        per_file_df.to_csv(SUMMARY_PER_FILE, index=False, encoding=\"utf-8-sig\")\n",
    "\n",
    "        totals_df = pd.DataFrame([{\n",
    "            \"set_fin_0_total\": grand_0,\n",
    "            \"set_fin_1_total\": grand_1,\n",
    "            \"set_fin_2_total\": grand_2,\n",
    "            \"grand_total_rows\": grand_total\n",
    "        }])\n",
    "        totals_df.to_csv(SUMMARY_TOTALS, index=False, encoding=\"utf-8-sig\")\n",
    "\n",
    "        print(f\"\\n[Report] Per-file summary: {SUMMARY_PER_FILE}\")\n",
    "        print(f\"[Report] Cross-file totals: {SUMMARY_TOTALS}\")\n",
    "\n",
    "        # 同時列印於 console\n",
    "        print(\"\\n=== set_fin summary (per file) ===\")\n",
    "        print(per_file_df.to_string(index=False))\n",
    "        print(\"\\n=== set_fin grand totals (across files) ===\")\n",
    "        print(totals_df.to_string(index=False))\n",
    "\n",
    "# ---------------------------\n",
    "# 執行\n",
    "# ---------------------------\n",
    "if __name__ == \"__main__\":\n",
    "    batch_build_set_fin_overwrite(IN_DIR, pattern=\"*.csv\", min_gap_sec=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "id": "70219791-7c10-4b2b-8a01-971a7c60082a",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true,
     "source_hidden": true
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[OK] 089271.csv | segments: 0=1864, 1=1860, 2=3723, total=7447\n",
      "[OK] 095323.csv | segments: 0=1418, 1=1412, 2=2829, total=5659\n",
      "[OK] 095707.csv | segments: 0=1065, 1=1065, 2=2130, total=4260\n",
      "[OK] 114309.csv | segments: 0=1098, 1=1084, 2=2182, total=4364\n",
      "[OK] 230933.csv | segments: 0=1230, 1=1273, 2=2501, total=5004\n",
      "[OK] 4216007.csv | segments: 0=0, 1=0, 2=0, total=0\n",
      "[OK] 7108162.csv | segments: 0=13, 1=14, 2=27, total=54\n",
      "[OK] 7408338.csv | segments: 0=99, 1=99, 2=197, total=395\n",
      "[OK] 7657698.csv | segments: 0=41, 1=41, 2=82, total=164\n",
      "[OK] 7721164.csv | segments: 0=25, 1=26, 2=51, total=102\n",
      "[OK] PatNo_ID_1560013303.csv | segments: 0=141, 1=143, 2=284, total=568\n",
      "[OK] PatNo_ID_1562733396.csv | segments: 0=41, 1=42, 2=83, total=166\n",
      "[OK] PatNo_ID_1563587183.csv | segments: 0=163, 1=165, 2=328, total=656\n",
      "[OK] PatNo_ID_1564148644.csv | segments: 0=385, 1=384, 2=769, total=1538\n",
      "[OK] PatNo_ID_1565148312.csv | segments: 0=280, 1=280, 2=560, total=1120\n",
      "[OK] PatNo_ID_1565378038.csv | segments: 0=191, 1=193, 2=382, total=766\n",
      "[OK] PatNo_ID_1566123680.csv | segments: 0=612, 1=615, 2=1222, total=2449\n",
      "[OK] PatNo_ID_1566252197.csv | segments: 0=66, 1=67, 2=133, total=266\n",
      "[OK] PatNo_ID_1566279967.csv | segments: 0=84, 1=83, 2=167, total=334\n",
      "[OK] PatNo_ID_1566671274.csv | segments: 0=968, 1=955, 2=1922, total=3845\n",
      "[OK] PatNo_ID_1566911879.csv | segments: 0=1299, 1=1305, 2=2604, total=5208\n",
      "[OK] PatNo_ID_1567747650.csv | segments: 0=414, 1=413, 2=827, total=1654\n",
      "[OK] PatNo_ID_1567804800.csv | segments: 0=345, 1=347, 2=691, total=1383\n",
      "[OK] PatNo_ID_1567832735.csv | segments: 0=1454, 1=1446, 2=2898, total=5798\n",
      "[OK] PatNo_ID_1568039398.csv | segments: 0=1141, 1=1143, 2=2284, total=4568\n",
      "[OK] PatNo_ID_1568574099.csv | segments: 0=775, 1=772, 2=1547, total=3094\n",
      "[OK] PatNo_ID_1568813269.csv | segments: 0=123, 1=124, 2=247, total=494\n",
      "[OK] PatNo_ID_1568952422.csv | segments: 0=83, 1=84, 2=167, total=334\n",
      "[OK] PatNo_ID_1569083701.csv | segments: 0=258, 1=248, 2=505, total=1011\n",
      "[OK] PatNo_ID_1569944983.csv | segments: 0=475, 1=472, 2=947, total=1894\n",
      "[OK] PatNo_ID_1570089466.csv | segments: 0=750, 1=745, 2=1495, total=2990\n",
      "[OK] PatNo_ID_1570242703.csv | segments: 0=416, 1=415, 2=831, total=1662\n",
      "[OK] PatNo_ID_1570273244.csv | segments: 0=335, 1=335, 2=670, total=1340\n",
      "[OK] PatNo_ID_1570642083.csv | segments: 0=552, 1=687, 2=1235, total=2474\n",
      "[OK] PatNo_ID_1571945701.csv | segments: 0=628, 1=625, 2=1253, total=2506\n",
      "[OK] PatNo_ID_1572481361.csv | segments: 0=1552, 1=1540, 2=3090, total=6182\n",
      "[OK] PatNo_ID_1572562839.csv | segments: 0=528, 1=526, 2=1054, total=2108\n",
      "[OK] PatNo_ID_1572831765.csv | segments: 0=186, 1=186, 2=372, total=744\n",
      "[OK] PatNo_ID_1572976822.csv | segments: 0=97, 1=97, 2=194, total=388\n",
      "[OK] PatNo_ID_1573063188.csv | segments: 0=334, 1=330, 2=664, total=1328\n",
      "[OK] PatNo_ID_1573249295.csv | segments: 0=199, 1=196, 2=393, total=788\n",
      "[OK] PatNo_ID_1573964540.csv | segments: 0=244, 1=244, 2=487, total=975\n",
      "[OK] PatNo_ID_1574148494.csv | segments: 0=2022, 1=2017, 2=4038, total=8077\n",
      "[OK] PatNo_ID_1574270349.csv | segments: 0=404, 1=399, 2=803, total=1606\n",
      "[OK] PatNo_ID_1574528808.csv | segments: 0=1219, 1=1172, 2=2389, total=4780\n",
      "[OK] PatNo_ID_1574831525.csv | segments: 0=34, 1=34, 2=68, total=136\n",
      "[OK] PatNo_ID_1574987447.csv | segments: 0=830, 1=834, 2=1665, total=3329\n",
      "[OK] PatNo_ID_1575060177.csv | segments: 0=309, 1=316, 2=624, total=1249\n",
      "[OK] PatNo_ID_1575256902.csv | segments: 0=699, 1=687, 2=1382, total=2768\n",
      "[OK] PatNo_ID_1575445051.csv | segments: 0=83, 1=84, 2=167, total=334\n",
      "[OK] PatNo_ID_1575502382.csv | segments: 0=286, 1=286, 2=571, total=1143\n",
      "[OK] PatNo_ID_1575975485.csv | segments: 0=755, 1=752, 2=1507, total=3014\n",
      "[OK] PatNo_ID_1576115572.csv | segments: 0=900, 1=874, 2=1772, total=3546\n",
      "[OK] PatNo_ID_1576116479.csv | segments: 0=65, 1=66, 2=131, total=262\n",
      "[OK] PatNo_ID_1576301569.csv | segments: 0=118, 1=113, 2=232, total=463\n",
      "[OK] PatNo_ID_1576964560.csv | segments: 0=1596, 1=1605, 2=3200, total=6401\n",
      "[OK] PatNo_ID_1577042911.csv | segments: 0=1752, 1=1746, 2=3498, total=6996\n",
      "[OK] PatNo_ID_1577487284.csv | segments: 0=161, 1=158, 2=319, total=638\n",
      "[OK] PatNo_ID_1578784257.csv | segments: 0=1741, 1=1738, 2=3479, total=6958\n",
      "[OK] PatNo_ID_1579198603.csv | segments: 0=56, 1=57, 2=113, total=226\n",
      "[OK] PatNo_ID_1579498177.csv | segments: 0=734, 1=720, 2=1452, total=2906\n",
      "[OK] PatNo_ID_1580062580.csv | segments: 0=53, 1=54, 2=107, total=214\n",
      "[OK] PatNo_ID_1580096720.csv | segments: 0=32, 1=32, 2=64, total=128\n",
      "[OK] PatNo_ID_1580107637.csv | segments: 0=0, 1=0, 2=0, total=0\n",
      "[OK] PatNo_ID_1580244614.csv | segments: 0=202, 1=202, 2=404, total=808\n",
      "[OK] PatNo_ID_1580766093.csv | segments: 0=794, 1=798, 2=1592, total=3184\n",
      "[OK] PatNo_ID_1581003248.csv | segments: 0=202, 1=204, 2=406, total=812\n",
      "[OK] PatNo_ID_1581019504.csv | segments: 0=561, 1=573, 2=1132, total=2266\n",
      "[OK] PatNo_ID_1581633231.csv | segments: 0=770, 1=757, 2=1518, total=3045\n",
      "[OK] PatNo_ID_1581692973.csv | segments: 0=135, 1=140, 2=275, total=550\n",
      "[OK] PatNo_ID_1582452511.csv | segments: 0=211, 1=211, 2=422, total=844\n",
      "[OK] PatNo_ID_1582635996.csv | segments: 0=541, 1=544, 2=1084, total=2169\n",
      "[OK] PatNo_ID_1582849900.csv | segments: 0=85, 1=85, 2=170, total=340\n",
      "[OK] PatNo_ID_1582937076.csv | segments: 0=710, 1=703, 2=1410, total=2823\n",
      "[OK] PatNo_ID_1584158973.csv | segments: 0=114, 1=113, 2=226, total=453\n",
      "[OK] PatNo_ID_1584397376.csv | segments: 0=32, 1=33, 2=65, total=130\n",
      "[OK] PatNo_ID_1586172659.csv | segments: 0=2170, 1=2158, 2=4327, total=8655\n",
      "[OK] PatNo_ID_1586696634.csv | segments: 0=85, 1=86, 2=171, total=342\n",
      "[OK] PatNo_ID_1586897008.csv | segments: 0=508, 1=506, 2=1013, total=2027\n",
      "[OK] PatNo_ID_1587490083.csv | segments: 0=2199, 1=2146, 2=4333, total=8678\n",
      "[OK] PatNo_ID_1588632604.csv | segments: 0=103, 1=104, 2=207, total=414\n",
      "[OK] PatNo_ID_1588673465.csv | segments: 0=130, 1=138, 2=268, total=536\n",
      "[OK] PatNo_ID_1588794796.csv | segments: 0=196, 1=197, 2=392, total=785\n",
      "[OK] PatNo_ID_1588957997.csv | segments: 0=701, 1=732, 2=1433, total=2866\n",
      "[OK] PatNo_ID_1589018086.csv | segments: 0=568, 1=569, 2=1137, total=2274\n",
      "[OK] PatNo_ID_1589034524.csv | segments: 0=2123, 1=2104, 2=4222, total=8449\n",
      "[OK] PatNo_ID_1589324603.csv | segments: 0=265, 1=263, 2=529, total=1057\n",
      "[OK] PatNo_ID_1589918099.csv | segments: 0=0, 1=0, 2=0, total=0\n",
      "[OK] PatNo_ID_1590136310.csv | segments: 0=423, 1=396, 2=817, total=1636\n",
      "[OK] PatNo_ID_1590616537.csv | segments: 0=727, 1=725, 2=1452, total=2904\n",
      "[OK] PatNo_ID_1590854576.csv | segments: 0=590, 1=578, 2=1167, total=2335\n",
      "[OK] PatNo_ID_1591609798.csv | segments: 0=2116, 1=2108, 2=4225, total=8449\n",
      "[OK] PatNo_ID_1592044724.csv | segments: 0=167, 1=168, 2=335, total=670\n",
      "[OK] PatNo_ID_1592560504.csv | segments: 0=1197, 1=1193, 2=2390, total=4780\n",
      "[OK] PatNo_ID_1593087886.csv | segments: 0=1172, 1=1142, 2=2309, total=4623\n",
      "[OK] PatNo_ID_1593416100.csv | segments: 0=130, 1=130, 2=260, total=520\n",
      "[OK] PatNo_ID_1593472048.csv | segments: 0=268, 1=267, 2=535, total=1070\n",
      "[OK] PatNo_ID_1593593586.csv | segments: 0=1109, 1=1092, 2=2199, total=4400\n",
      "[OK] PatNo_ID_1593720818.csv | segments: 0=67, 1=69, 2=136, total=272\n",
      "[OK] PatNo_ID_1593838524.csv | segments: 0=94, 1=95, 2=189, total=378\n",
      "[OK] PatNo_ID_1594173718.csv | segments: 0=22, 1=23, 2=45, total=90\n",
      "[OK] PatNo_ID_1594294180.csv | segments: 0=428, 1=376, 2=801, total=1605\n",
      "[OK] PatNo_ID_1594305136.csv | segments: 0=628, 1=632, 2=1260, total=2520\n",
      "[OK] PatNo_ID_1594309746.csv | segments: 0=226, 1=228, 2=454, total=908\n",
      "[OK] PatNo_ID_1594319286.csv | segments: 0=350, 1=349, 2=699, total=1398\n",
      "[OK] PatNo_ID_1594320763.csv | segments: 0=97, 1=110, 2=206, total=413\n",
      "[OK] PatNo_ID_1594322594.csv | segments: 0=184, 1=186, 2=370, total=740\n",
      "[OK] PatNo_ID_1594335109.csv | segments: 0=409, 1=410, 2=819, total=1638\n",
      "[OK] PatNo_ID_1594423683.csv | segments: 0=338, 1=336, 2=673, total=1347\n",
      "[OK] PatNo_ID_1594437309.csv | segments: 0=515, 1=513, 2=1027, total=2055\n",
      "[OK] PatNo_ID_1594439781.csv | segments: 0=277, 1=278, 2=553, total=1108\n",
      "[OK] PatNo_ID_1594441887.csv | segments: 0=512, 1=524, 2=1035, total=2071\n",
      "[OK] PatNo_ID_1594448501.csv | segments: 0=0, 1=0, 2=0, total=0\n",
      "[OK] PatNo_ID_1594455578.csv | segments: 0=2, 1=3, 2=5, total=10\n",
      "[OK] PatNo_ID_1594464829.csv | segments: 0=238, 1=235, 2=472, total=945\n",
      "[OK] PatNo_ID_1594467719.csv | segments: 0=89, 1=89, 2=177, total=355\n",
      "[OK] PatNo_ID_1594471407.csv | segments: 0=779, 1=773, 2=1553, total=3105\n",
      "[OK] PatNo_ID_1594479330.csv | segments: 0=290, 1=258, 2=543, total=1091\n",
      "[OK] PatNo_ID_1594511911.csv | segments: 0=301, 1=303, 2=604, total=1208\n",
      "[OK] PatNo_ID_1594511914.csv | segments: 0=564, 1=533, 2=1096, total=2193\n",
      "[OK] PatNo_ID_1594528842.csv | segments: 0=136, 1=136, 2=273, total=545\n",
      "[OK] PatNo_ID_1594533379.csv | segments: 0=50, 1=50, 2=100, total=200\n",
      "\n",
      "[Report] Per-file segments: /home/jovyan/RT08/0925/1014/set_fin_segments_per_file.csv\n",
      "[Report] Cross-file totals: /home/jovyan/RT08/0925/1014/set_fin_segments_totals.csv\n",
      "[Report] Segment details:  /home/jovyan/RT08/0925/1014/set_fin_segments_detail.csv\n"
     ]
    }
   ],
   "source": [
    "# 看看有幾個segement\n",
    "# -*- coding: utf-8 -*-\n",
    "\"\"\"\n",
    "統計 /home/jovyan/RT08/0925/bling_1014/ 所有 CSV 的 set_fin segments 數量（另外執行，僅讀取，不改檔）\n",
    "定義：\n",
    "- 以「病患為單位」分組\n",
    "- 只在 nan_check == 1 的列上計算 segments\n",
    "- 依 senddate 排序後，連續相同 set_fin 值為同一個 segment\n",
    "輸出：\n",
    "- /home/jovyan/RT08/0925/1014/set_fin_segments_per_file.csv\n",
    "- /home/jovyan/RT08/0925/1014/set_fin_segments_totals.csv\n",
    "- /home/jovyan/RT08/0925/1014/set_fin_segments_detail.csv\n",
    "\"\"\"\n",
    "\n",
    "import os\n",
    "import glob\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "IN_DIR = \"/home/jovyan/RT08/0925/bling_1014/\"\n",
    "REPORT_DIR = \"/home/jovyan/RT08/0925/1014/\"\n",
    "os.makedirs(REPORT_DIR, exist_ok=True)\n",
    "\n",
    "OUT_PER_FILE = os.path.join(REPORT_DIR, \"set_fin_segments_per_file.csv\")\n",
    "OUT_TOTALS   = os.path.join(REPORT_DIR, \"set_fin_segments_totals.csv\")\n",
    "OUT_DETAIL   = os.path.join(REPORT_DIR, \"set_fin_segments_detail.csv\")\n",
    "\n",
    "# ---------- Utils ----------\n",
    "def resolve_col(df, candidates):\n",
    "    cmap = {c.lower(): c for c in df.columns}\n",
    "    for cand in candidates:\n",
    "        if cand.lower() in cmap:\n",
    "            return cmap[cand.lower()]\n",
    "    return None\n",
    "\n",
    "def ensure_datetime(df, time_col):\n",
    "    if not pd.api.types.is_datetime64_any_dtype(df[time_col]):\n",
    "        df[time_col] = pd.to_datetime(df[time_col], errors=\"coerce\", infer_datetime_format=True)\n",
    "    return df\n",
    "\n",
    "def count_segments_for_patient(g: pd.DataFrame, time_col: str, nan_col: str, label_col: str):\n",
    "    \"\"\"\n",
    "    在單一病患 g 上計算 segments：\n",
    "    - 僅 nan_check==1 的列\n",
    "    - 依 time_col 排序\n",
    "    - 將連續相同 label_col 視為一段；忽略 NaN 的 label\n",
    "    回傳：(summary_dict, detail_rows)\n",
    "    \"\"\"\n",
    "    g2 = g.loc[g[nan_col] == 1].copy()\n",
    "    if g2.empty:\n",
    "        return {\"seg_0\":0, \"seg_1\":0, \"seg_2\":0, \"seg_total\":0}, []\n",
    "\n",
    "    g2 = g2.sort_values(time_col, kind=\"mergesort\")\n",
    "    labels = g2[label_col].values\n",
    "    times  = g2[time_col].values\n",
    "\n",
    "    seg_counts = {0:0, 1:0, 2:0}\n",
    "    detail_rows = []\n",
    "\n",
    "    prev_label = None\n",
    "    seg_start_idx = None  # 位置（iloc within g2）\n",
    "\n",
    "    for i, lab in enumerate(labels):\n",
    "        if pd.isna(lab):\n",
    "            # 碰到 NaN：若正在 segment 中，先結束前一段\n",
    "            if prev_label is not None:\n",
    "                seg_end_idx = i - 1\n",
    "                if prev_label in seg_counts:\n",
    "                    seg_counts[prev_label] += 1\n",
    "                # 記錄明細\n",
    "                start_time = times[seg_start_idx]\n",
    "                end_time = times[seg_end_idx]\n",
    "                n_rows = seg_end_idx - seg_start_idx + 1\n",
    "                dur_sec = (end_time - start_time).astype(\"timedelta64[s]\").astype(float)\n",
    "                detail_rows.append((int(prev_label), start_time, end_time, float(dur_sec), int(n_rows)))\n",
    "                prev_label, seg_start_idx = None, None\n",
    "            continue\n",
    "\n",
    "        lab = int(lab)\n",
    "        if prev_label is None:\n",
    "            # 開新段\n",
    "            prev_label = lab\n",
    "            seg_start_idx = i\n",
    "        else:\n",
    "            if lab != prev_label:\n",
    "                # 結束前一段\n",
    "                seg_end_idx = i - 1\n",
    "                if prev_label in seg_counts:\n",
    "                    seg_counts[prev_label] += 1\n",
    "                start_time = times[seg_start_idx]\n",
    "                end_time = times[seg_end_idx]\n",
    "                n_rows = seg_end_idx - seg_start_idx + 1\n",
    "                dur_sec = (end_time - start_time).astype(\"timedelta64[s]\").astype(float)\n",
    "                detail_rows.append((int(prev_label), start_time, end_time, float(dur_sec), int(n_rows)))\n",
    "                # 開新段\n",
    "                prev_label = lab\n",
    "                seg_start_idx = i\n",
    "\n",
    "    # 收尾（最後一段）\n",
    "    if prev_label is not None:\n",
    "        seg_end_idx = len(labels) - 1\n",
    "        if prev_label in seg_counts:\n",
    "            seg_counts[prev_label] += 1\n",
    "        start_time = times[seg_start_idx]\n",
    "        end_time = times[seg_end_idx]\n",
    "        n_rows = seg_end_idx - seg_start_idx + 1\n",
    "        dur_sec = (end_time - start_time).astype(\"timedelta64[s]\").astype(float)\n",
    "        detail_rows.append((int(prev_label), start_time, end_time, float(dur_sec), int(n_rows)))\n",
    "\n",
    "    seg_total = seg_counts[0] + seg_counts[1] + seg_counts[2]\n",
    "    return (\n",
    "        {\"seg_0\": seg_counts[0], \"seg_1\": seg_counts[1], \"seg_2\": seg_counts[2], \"seg_total\": seg_total},\n",
    "        detail_rows\n",
    "    )\n",
    "\n",
    "# ---------- Main ----------\n",
    "per_file_rows = []\n",
    "detail_all = []\n",
    "grand_0 = grand_1 = grand_2 = grand_total = 0\n",
    "\n",
    "paths = sorted(glob.glob(os.path.join(IN_DIR, \"*.csv\")))\n",
    "if not paths:\n",
    "    print(f\"[WARN] No files in {IN_DIR}\")\n",
    "\n",
    "for p in paths:\n",
    "    try:\n",
    "        df = pd.read_csv(p)\n",
    "        # 基本欄位\n",
    "        send_col = resolve_col(df, [\"senddate\", \"send_date\", \"timestamp\", \"time\", \"datetime\"])\n",
    "        nan_col  = resolve_col(df, [\"nan_check\", \"NaN_check\", \"nanflag\", \"nan_flag\"])\n",
    "        lab_col  = resolve_col(df, [\"set_fin\", \"setfin\"])\n",
    "        pat_col  = resolve_col(df, [\"patno\", \"PatNo\", \"patient_id\", \"id\", \"patientid\"])\n",
    "\n",
    "        missing = [name for name, col in {\n",
    "            \"senddate\": send_col, \"nan_check\": nan_col, \"set_fin\": lab_col\n",
    "        }.items() if col is None]\n",
    "        if missing:\n",
    "            raise KeyError(f\"Missing required columns: {missing}\")\n",
    "\n",
    "        df = ensure_datetime(df, time_col=send_col)\n",
    "        if pat_col is None:\n",
    "            df[\"_tmp_single_patient\"] = \"single\"\n",
    "            pat_col = \"_tmp_single_patient\"\n",
    "\n",
    "        # 逐病患累加 segment 數\n",
    "        file_seg0 = file_seg1 = file_seg2 = file_segt = 0\n",
    "\n",
    "        for pid, g in df.groupby(pat_col, sort=False):\n",
    "            summary, detail = count_segments_for_patient(g, send_col, nan_col, lab_col)\n",
    "            file_seg0 += summary[\"seg_0\"]\n",
    "            file_seg1 += summary[\"seg_1\"]\n",
    "            file_seg2 += summary[\"seg_2\"]\n",
    "            file_segt += summary[\"seg_total\"]\n",
    "\n",
    "            # 明細列（加上檔名、病患）\n",
    "            for lab, t0, t1, dur_s, n_rows in detail:\n",
    "                detail_all.append({\n",
    "                    \"file_name\": os.path.basename(p),\n",
    "                    \"patient\": pid,\n",
    "                    \"set_fin\": lab,\n",
    "                    \"start_time\": pd.to_datetime(t0),\n",
    "                    \"end_time\": pd.to_datetime(t1),\n",
    "                    \"duration_sec\": dur_s,\n",
    "                    \"n_rows\": n_rows\n",
    "                })\n",
    "\n",
    "        per_file_rows.append({\n",
    "            \"file_name\": os.path.basename(p),\n",
    "            \"seg_set_fin_0\": file_seg0,\n",
    "            \"seg_set_fin_1\": file_seg1,\n",
    "            \"seg_set_fin_2\": file_seg2,\n",
    "            \"segments_total\": file_segt\n",
    "        })\n",
    "\n",
    "        grand_0 += file_seg0\n",
    "        grand_1 += file_seg1\n",
    "        grand_2 += file_seg2\n",
    "        grand_total += file_segt\n",
    "\n",
    "        print(f\"[OK] {os.path.basename(p)} | segments: 0={file_seg0}, 1={file_seg1}, 2={file_seg2}, total={file_segt}\")\n",
    "\n",
    "    except Exception as e:\n",
    "        print(f\"[ERR] {os.path.basename(p)}: {e}\")\n",
    "\n",
    "# 輸出報表\n",
    "if per_file_rows:\n",
    "    per_file_df = pd.DataFrame(per_file_rows)\n",
    "    per_file_df.to_csv(OUT_PER_FILE, index=False, encoding=\"utf-8-sig\")\n",
    "    totals_df = pd.DataFrame([{\n",
    "        \"segments_set_fin_0_total\": grand_0,\n",
    "        \"segments_set_fin_1_total\": grand_1,\n",
    "        \"segments_set_fin_2_total\": grand_2,\n",
    "        \"segments_total\": grand_total\n",
    "    }])\n",
    "    totals_df.to_csv(OUT_TOTALS, index=False, encoding=\"utf-8-sig\")\n",
    "\n",
    "    det_df = pd.DataFrame(detail_all, columns=[\n",
    "        \"file_name\",\"patient\",\"set_fin\",\"start_time\",\"end_time\",\"duration_sec\",\"n_rows\"\n",
    "    ])\n",
    "    det_df.to_csv(OUT_DETAIL, index=False, encoding=\"utf-8-sig\")\n",
    "\n",
    "    print(f\"\\n[Report] Per-file segments: {OUT_PER_FILE}\")\n",
    "    print(f\"[Report] Cross-file totals: {OUT_TOTALS}\")\n",
    "    print(f\"[Report] Segment details:  {OUT_DETAIL}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "id": "a2f7234a-9791-48a8-b2a4-fdbb6bc57919",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true,
     "source_hidden": true
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[SCAN] 089271.csv | violations=138 / rows=32419\n",
      "[SCAN] 095323.csv | violations=59 / rows=23791\n",
      "[SCAN] 095707.csv | violations=162 / rows=20180\n",
      "[SCAN] 114309.csv | violations=80 / rows=71729\n",
      "[SCAN] 230933.csv | violations=1531 / rows=30249\n",
      "[SCAN] 4216007.csv | violations=0 / rows=1433\n",
      "[SCAN] 7108162.csv | violations=0 / rows=239\n",
      "[SCAN] 7408338.csv | violations=2 / rows=1432\n",
      "[SCAN] 7657698.csv | violations=7 / rows=1413\n",
      "[SCAN] 7721164.csv | violations=2 / rows=483\n",
      "[SCAN] PatNo_ID_1560013303.csv | violations=41 / rows=2543\n",
      "[SCAN] PatNo_ID_1562733396.csv | violations=9 / rows=2254\n",
      "[SCAN] PatNo_ID_1563587183.csv | violations=30 / rows=5286\n",
      "[SCAN] PatNo_ID_1564148644.csv | violations=2646 / rows=17287\n",
      "[SCAN] PatNo_ID_1565148312.csv | violations=9 / rows=5475\n",
      "[SCAN] PatNo_ID_1565378038.csv | violations=50 / rows=2561\n",
      "[SCAN] PatNo_ID_1566123680.csv | violations=383 / rows=42600\n",
      "[SCAN] PatNo_ID_1566252197.csv | violations=0 / rows=3368\n",
      "[SCAN] PatNo_ID_1566279967.csv | violations=4 / rows=1235\n",
      "[SCAN] PatNo_ID_1566671274.csv | violations=200 / rows=25359\n",
      "[SCAN] PatNo_ID_1566911879.csv | violations=4524 / rows=53999\n",
      "[SCAN] PatNo_ID_1567747650.csv | violations=27 / rows=10900\n",
      "[SCAN] PatNo_ID_1567804800.csv | violations=3377 / rows=20577\n",
      "[SCAN] PatNo_ID_1567832735.csv | violations=84 / rows=36575\n",
      "[SCAN] PatNo_ID_1568039398.csv | violations=230 / rows=34519\n",
      "[SCAN] PatNo_ID_1568574099.csv | violations=882 / rows=13945\n",
      "[SCAN] PatNo_ID_1568813269.csv | violations=10 / rows=4867\n",
      "[SCAN] PatNo_ID_1568952422.csv | violations=9 / rows=1184\n",
      "[SCAN] PatNo_ID_1569083701.csv | violations=8 / rows=3328\n",
      "[SCAN] PatNo_ID_1569944983.csv | violations=23 / rows=7649\n",
      "[SCAN] PatNo_ID_1570089466.csv | violations=535 / rows=41338\n",
      "[SCAN] PatNo_ID_1570242703.csv | violations=12 / rows=10644\n",
      "[SCAN] PatNo_ID_1570273244.csv | violations=20 / rows=9725\n",
      "[SCAN] PatNo_ID_1570642083.csv | violations=3888 / rows=19731\n",
      "[SCAN] PatNo_ID_1571945701.csv | violations=125 / rows=15731\n",
      "[SCAN] PatNo_ID_1572481361.csv | violations=163 / rows=34539\n",
      "[SCAN] PatNo_ID_1572562839.csv | violations=3809 / rows=20963\n",
      "[SCAN] PatNo_ID_1572831765.csv | violations=3 / rows=2696\n",
      "[SCAN] PatNo_ID_1572976822.csv | violations=1742 / rows=6764\n",
      "[SCAN] PatNo_ID_1573063188.csv | violations=14 / rows=5080\n",
      "[SCAN] PatNo_ID_1573249295.csv | violations=94 / rows=7151\n",
      "[SCAN] PatNo_ID_1573964540.csv | violations=41 / rows=4394\n",
      "[SCAN] PatNo_ID_1574148494.csv | violations=58 / rows=47154\n",
      "[SCAN] PatNo_ID_1574270349.csv | violations=8 / rows=6533\n",
      "[SCAN] PatNo_ID_1574528808.csv | violations=134 / rows=14812\n",
      "[SCAN] PatNo_ID_1574831525.csv | violations=1 / rows=520\n",
      "[SCAN] PatNo_ID_1574987447.csv | violations=228 / rows=19842\n",
      "[SCAN] PatNo_ID_1575060177.csv | violations=158 / rows=5290\n",
      "[SCAN] PatNo_ID_1575256902.csv | violations=38 / rows=9587\n",
      "[SCAN] PatNo_ID_1575445051.csv | violations=2 / rows=1800\n",
      "[SCAN] PatNo_ID_1575502382.csv | violations=102 / rows=6604\n",
      "[SCAN] PatNo_ID_1575975485.csv | violations=90 / rows=16459\n",
      "[SCAN] PatNo_ID_1576115572.csv | violations=544 / rows=18715\n",
      "[SCAN] PatNo_ID_1576116479.csv | violations=0 / rows=1263\n",
      "[SCAN] PatNo_ID_1576301569.csv | violations=17 / rows=3207\n",
      "[SCAN] PatNo_ID_1576964560.csv | violations=395 / rows=24188\n",
      "[SCAN] PatNo_ID_1577042911.csv | violations=882 / rows=38357\n",
      "[SCAN] PatNo_ID_1577487284.csv | violations=3 / rows=2345\n",
      "[SCAN] PatNo_ID_1578784257.csv | violations=2677 / rows=33914\n",
      "[SCAN] PatNo_ID_1579198603.csv | violations=3 / rows=2045\n",
      "[SCAN] PatNo_ID_1579498177.csv | violations=76 / rows=21688\n",
      "[SCAN] PatNo_ID_1580062580.csv | violations=76 / rows=2150\n",
      "[SCAN] PatNo_ID_1580096720.csv | violations=2 / rows=1423\n",
      "[SCAN] PatNo_ID_1580107637.csv | violations=0 / rows=2698\n",
      "[SCAN] PatNo_ID_1580244614.csv | violations=94 / rows=5278\n",
      "[SCAN] PatNo_ID_1580766093.csv | violations=557 / rows=18070\n",
      "[SCAN] PatNo_ID_1581003248.csv | violations=129 / rows=7776\n",
      "[SCAN] PatNo_ID_1581019504.csv | violations=4422 / rows=28173\n",
      "[SCAN] PatNo_ID_1581633231.csv | violations=84 / rows=15995\n",
      "[SCAN] PatNo_ID_1581692973.csv | violations=85 / rows=2723\n",
      "[SCAN] PatNo_ID_1582452511.csv | violations=42 / rows=5196\n",
      "[SCAN] PatNo_ID_1582635996.csv | violations=43 / rows=13046\n",
      "[SCAN] PatNo_ID_1582849900.csv | violations=4 / rows=7411\n",
      "[SCAN] PatNo_ID_1582937076.csv | violations=927 / rows=23987\n",
      "[SCAN] PatNo_ID_1584158973.csv | violations=14 / rows=2882\n",
      "[SCAN] PatNo_ID_1584397376.csv | violations=0 / rows=638\n",
      "[SCAN] PatNo_ID_1586172659.csv | violations=234 / rows=38554\n",
      "[SCAN] PatNo_ID_1586696634.csv | violations=7 / rows=3569\n",
      "[SCAN] PatNo_ID_1586897008.csv | violations=9 / rows=6687\n",
      "[SCAN] PatNo_ID_1587490083.csv | violations=88 / rows=45186\n",
      "[SCAN] PatNo_ID_1588632604.csv | violations=82 / rows=2608\n",
      "[SCAN] PatNo_ID_1588673465.csv | violations=2104 / rows=10076\n",
      "[SCAN] PatNo_ID_1588794796.csv | violations=6 / rows=9375\n",
      "[SCAN] PatNo_ID_1588957997.csv | violations=439 / rows=10966\n",
      "[SCAN] PatNo_ID_1589018086.csv | violations=53 / rows=13472\n",
      "[SCAN] PatNo_ID_1589034524.csv | violations=345 / rows=50081\n",
      "[SCAN] PatNo_ID_1589324603.csv | violations=9 / rows=4187\n",
      "[SCAN] PatNo_ID_1589918099.csv | violations=0 / rows=9333\n",
      "[SCAN] PatNo_ID_1590136310.csv | violations=73 / rows=5580\n",
      "[SCAN] PatNo_ID_1590616537.csv | violations=26 / rows=14206\n",
      "[SCAN] PatNo_ID_1590854576.csv | violations=255 / rows=15879\n",
      "[SCAN] PatNo_ID_1591609798.csv | violations=85 / rows=35618\n",
      "[SCAN] PatNo_ID_1592044724.csv | violations=38 / rows=6815\n",
      "[SCAN] PatNo_ID_1592560504.csv | violations=236 / rows=18051\n",
      "[SCAN] PatNo_ID_1593087886.csv | violations=304 / rows=24163\n",
      "[SCAN] PatNo_ID_1593416100.csv | violations=7 / rows=3328\n",
      "[SCAN] PatNo_ID_1593472048.csv | violations=355 / rows=8230\n",
      "[SCAN] PatNo_ID_1593593586.csv | violations=42 / rows=18882\n",
      "[SCAN] PatNo_ID_1593720818.csv | violations=878 / rows=3910\n",
      "[SCAN] PatNo_ID_1593838524.csv | violations=5 / rows=2177\n",
      "[SCAN] PatNo_ID_1594173718.csv | violations=0 / rows=344\n",
      "[SCAN] PatNo_ID_1594294180.csv | violations=37 / rows=18334\n",
      "[SCAN] PatNo_ID_1594305136.csv | violations=2131 / rows=18679\n",
      "[SCAN] PatNo_ID_1594309746.csv | violations=78 / rows=3745\n",
      "[SCAN] PatNo_ID_1594319286.csv | violations=5 / rows=4107\n",
      "[SCAN] PatNo_ID_1594320763.csv | violations=143 / rows=2431\n",
      "[SCAN] PatNo_ID_1594322594.csv | violations=24 / rows=5380\n",
      "[SCAN] PatNo_ID_1594335109.csv | violations=8 / rows=5116\n",
      "[SCAN] PatNo_ID_1594423683.csv | violations=9 / rows=5023\n",
      "[SCAN] PatNo_ID_1594437309.csv | violations=87 / rows=10034\n",
      "[SCAN] PatNo_ID_1594439781.csv | violations=85 / rows=7691\n",
      "[SCAN] PatNo_ID_1594441887.csv | violations=451 / rows=10200\n",
      "[SCAN] PatNo_ID_1594448501.csv | violations=0 / rows=5106\n",
      "[SCAN] PatNo_ID_1594455578.csv | violations=1 / rows=262\n",
      "[SCAN] PatNo_ID_1594464829.csv | violations=8 / rows=4000\n",
      "[SCAN] PatNo_ID_1594467719.csv | violations=1 / rows=2559\n",
      "[SCAN] PatNo_ID_1594471407.csv | violations=6 / rows=11432\n",
      "[SCAN] PatNo_ID_1594479330.csv | violations=9 / rows=3727\n",
      "[SCAN] PatNo_ID_1594511911.csv | violations=10 / rows=5488\n",
      "[SCAN] PatNo_ID_1594511914.csv | violations=42 / rows=9717\n",
      "[SCAN] PatNo_ID_1594528842.csv | violations=16 / rows=2491\n",
      "[SCAN] PatNo_ID_1594533379.csv | violations=20 / rows=1030\n",
      "\n",
      "=== First 5 violations across all files ===\n",
      "_file_name  _row_index   patno            senddate  nan_check  set_fin\n",
      "089271.csv        3367 8927106 2021-12-19 13:11:00          0        0\n",
      "089271.csv        7579 8927106 2021-12-22 11:25:00          0        0\n",
      "089271.csv        9987 8927106 2021-12-24 03:33:04          0        0\n",
      "089271.csv       12035 8927106 2021-12-25 13:41:02          0        0\n",
      "089271.csv       12100 8927106 2021-12-25 14:46:02          0        0\n",
      "\n",
      "[Report] Per-file summary: /home/jovyan/RT08/0925/1014/nan0_setfin01_summary.csv\n",
      "[Report] Violations detail (all): /home/jovyan/RT08/0925/1014/nan0_setfin01_details.csv\n"
     ]
    }
   ],
   "source": [
    "# 檢查以下 /home/jovyan/RT08/0925/bling_1014/ 所有 CSV若欄位nan_check=0的資料 該筆資料的欄位set_fin就沒有=1=0，如果有 列出在哪裡（前五筆） 有幾筆\n",
    "# -*- coding: utf-8 -*-\n",
    "import os\n",
    "import glob\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "IN_DIR = \"/home/jovyan/RT08/0925/bling_1014/\"\n",
    "REPORT_DIR = \"/home/jovyan/RT08/0925/1014/\"\n",
    "os.makedirs(REPORT_DIR, exist_ok=True)\n",
    "\n",
    "OUT_SUMMARY = os.path.join(REPORT_DIR, \"nan0_setfin01_summary.csv\")\n",
    "OUT_DETAIL  = os.path.join(REPORT_DIR, \"nan0_setfin01_details.csv\")\n",
    "\n",
    "def resolve_col(df, candidates):\n",
    "    cmap = {c.lower(): c for c in df.columns}\n",
    "    for cand in candidates:\n",
    "        if cand.lower() in cmap:\n",
    "            return cmap[cand.lower()]\n",
    "    return None\n",
    "\n",
    "def ensure_datetime(df, time_col):\n",
    "    if time_col and time_col in df.columns and not pd.api.types.is_datetime64_any_dtype(df[time_col]):\n",
    "        df[time_col] = pd.to_datetime(df[time_col], errors=\"coerce\", infer_datetime_format=True)\n",
    "    return df\n",
    "\n",
    "summary_rows = []\n",
    "details = []\n",
    "\n",
    "paths = sorted(glob.glob(os.path.join(IN_DIR, \"*.csv\")))\n",
    "if not paths:\n",
    "    print(f\"[WARN] No CSV files in {IN_DIR}\")\n",
    "\n",
    "for p in paths:\n",
    "    try:\n",
    "        df = pd.read_csv(p)\n",
    "        if df.empty:\n",
    "            summary_rows.append({\"file_name\": os.path.basename(p), \"violations\": 0, \"total_rows\": 0})\n",
    "            continue\n",
    "\n",
    "        # 欄位解析（大小寫不敏感）\n",
    "        nan_col = resolve_col(df, [\"nan_check\", \"NaN_check\", \"nanflag\", \"nan_flag\"])\n",
    "        set_col = resolve_col(df, [\"set_fin\", \"setfin\"])\n",
    "        time_col = resolve_col(df, [\"senddate\", \"send_date\", \"timestamp\", \"time\", \"datetime\"])\n",
    "        pat_col  = resolve_col(df, [\"patno\", \"PatNo\", \"patient_id\", \"id\", \"patientid\"])\n",
    "\n",
    "        missing = [name for name, col in {\n",
    "            \"nan_check\": nan_col, \"set_fin\": set_col\n",
    "        }.items() if col is None]\n",
    "        if missing:\n",
    "            raise KeyError(f\"Missing required columns: {missing}\")\n",
    "\n",
    "        # 時間轉型（若有）\n",
    "        if time_col:\n",
    "            df = ensure_datetime(df, time_col)\n",
    "\n",
    "        # 建立違規條件：nan_check==0 且 set_fin ∈ {0,1}\n",
    "        # 注意 set_fin 可能為字串，先嘗試轉為數字\n",
    "        sf = pd.to_numeric(df[set_col], errors=\"coerce\")\n",
    "        cond = (df[nan_col] == 0) & (sf.isin([0, 1]))\n",
    "\n",
    "        viol_idx = df.index[cond].tolist()\n",
    "        n_viol = len(viol_idx)\n",
    "\n",
    "        # 累加明細\n",
    "        if n_viol > 0:\n",
    "            sub = df.loc[cond].copy()\n",
    "            sub[\"_file_name\"] = os.path.basename(p)\n",
    "            sub[\"_row_index\"] = sub.index  # 原始列號\n",
    "            # 精簡輸出欄位（保留最有用資訊）\n",
    "            keep_cols = [\"_file_name\", \"_row_index\"]\n",
    "            if pat_col: keep_cols.append(pat_col)\n",
    "            if time_col: keep_cols.append(time_col)\n",
    "            keep_cols += [nan_col, set_col]\n",
    "            # 可能沒有時間或病患欄，做去重處理\n",
    "            keep_cols = [c for c in keep_cols if c in sub.columns]\n",
    "            sub = sub[keep_cols]\n",
    "            details.append(sub)\n",
    "\n",
    "        summary_rows.append({\n",
    "            \"file_name\": os.path.basename(p),\n",
    "            \"violations\": int(n_viol),\n",
    "            \"total_rows\": int(len(df))\n",
    "        })\n",
    "\n",
    "        print(f\"[SCAN] {os.path.basename(p)} | violations={n_viol} / rows={len(df)}\")\n",
    "\n",
    "    except Exception as e:\n",
    "        print(f\"[ERR] {os.path.basename(p)}: {e}\")\n",
    "\n",
    "# 匯出報表\n",
    "summary_df = pd.DataFrame(summary_rows)\n",
    "summary_df.to_csv(OUT_SUMMARY, index=False, encoding=\"utf-8-sig\")\n",
    "\n",
    "if details:\n",
    "    detail_df = pd.concat(details, axis=0, ignore_index=True)\n",
    "    # 排序：先檔名，再時間（若有），再列號\n",
    "    sort_cols = [\"_file_name\"]\n",
    "    if \"senddate\" in detail_df.columns:\n",
    "        sort_cols.append(\"senddate\")\n",
    "    elif \"send_date\" in detail_df.columns:\n",
    "        sort_cols.append(\"send_date\")\n",
    "    elif \"timestamp\" in detail_df.columns:\n",
    "        sort_cols.append(\"timestamp\")\n",
    "    elif \"time\" in detail_df.columns:\n",
    "        sort_cols.append(\"time\")\n",
    "    elif \"datetime\" in detail_df.columns:\n",
    "        sort_cols.append(\"datetime\")\n",
    "    sort_cols.append(\"_row_index\")\n",
    "    sort_cols = [c for c in sort_cols if c in detail_df.columns]\n",
    "    detail_df = detail_df.sort_values(sort_cols, kind=\"mergesort\")\n",
    "\n",
    "    detail_df.to_csv(OUT_DETAIL, index=False, encoding=\"utf-8-sig\")\n",
    "\n",
    "    # 列印跨檔案前五筆違規\n",
    "    print(\"\\n=== First 5 violations across all files ===\")\n",
    "    print(detail_df.head(5).to_string(index=False))\n",
    "else:\n",
    "    # 沒有違規也輸出空檔，方便留痕\n",
    "    pd.DataFrame(columns=[\"_file_name\",\"_row_index\", \"patient\", \"senddate\", \"nan_check\", \"set_fin\"]).to_csv(\n",
    "        OUT_DETAIL, index=False, encoding=\"utf-8-sig\"\n",
    "    )\n",
    "    print(\"\\n[OK] No violations found.\")\n",
    "\n",
    "# 總結輸出位置\n",
    "print(f\"\\n[Report] Per-file summary: {OUT_SUMMARY}\")\n",
    "print(f\"[Report] Violations detail (all): {OUT_DETAIL}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8df153a3-5de2-4083-ad5a-e53b71e8d0f3",
   "metadata": {},
   "outputs": [],
   "source": [
    "set_fin 標記只把 nan_check==1 當作“事件點（ad_para==1）篩選條件”，但在事件點之間的區間標記（前50%→0、後20%→1）沒有同時過濾 nan_check==1。\n",
    "..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c34b2357-453b-4cd3-89e6-16328172dc5f",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    }
   },
   "outputs": [],
   "source": [
    "重新撰寫完整的\n",
    "批次處理來源：/home/jovyan/RT08/0925/bling_1014/\n",
    "行為：\n",
    "- 直接在每個檔案內新增/覆蓋 set_fin 欄位，並「覆蓋原檔」寫回同一路徑（無副檔名後綴）\n",
    "- 依「病患」分開運算 set_fin（patno/patient_id等），不足時視為單一病患\n",
    "- 跨檔案統計輸出於：/home/jovyan/RT08/0925/1014/\n",
    "    - set_fin_per_file_summary.csv（逐檔 0/1/2 筆數）\n",
    "    - set_fin_totals.csv（跨檔匯總 0/1/2 與總列數）\n",
    "規則摘要：\n",
    "一切都基於該筆資料是nan_check==1 的情況下，若nan_check==0則set_fin=2\n",
    "1) 事件點候選：nan_check==1 且 ad_para==1 \n",
    "2) 連續的 ad_para==1 僅取該串的最後一筆作為事件點\n",
    "3) 對相鄰事件點 (E_i, E_{i+1})，若相隔 >= 300 秒，區間內按「時間比例」標記：\n",
    "     - 前 50% → set_fin=0\n",
    "     - 後 20% → set_fin=1\n",
    "     - 中間 30% → set_fin=2（預設）\n",
    "   事件點本身一律 set_fin=2\n",
    "4) 每位病患第一筆 nan_check==1 的列，強制 set_fin=1（最後覆蓋）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "id": "05f12a77-22c1-4314-82e6-41db3d0d98a8",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true,
     "source_hidden": true
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[OK] Overwritten: 089271.csv | counts: 0=12194, 1=4817, 2=15408, total=32419\n",
      "[OK] Overwritten: 095323.csv | counts: 0=6674, 1=2630, 2=14487, total=23791\n",
      "[OK] Overwritten: 095707.csv | counts: 0=7550, 1=3007, 2=9623, total=20180\n",
      "[OK] Overwritten: 114309.csv | counts: 0=9666, 1=3774, 2=58289, total=71729\n",
      "[OK] Overwritten: 230933.csv | counts: 0=10475, 1=4524, 2=15250, total=30249\n",
      "[OK] Overwritten: 4216007.csv | counts: 0=0, 1=0, 2=1433, total=1433\n",
      "[OK] Overwritten: 7108162.csv | counts: 0=79, 1=32, 2=128, total=239\n",
      "[OK] Overwritten: 7408338.csv | counts: 0=523, 1=204, 2=705, total=1432\n",
      "[OK] Overwritten: 7657698.csv | counts: 0=638, 1=260, 2=515, total=1413\n",
      "[OK] Overwritten: 7721164.csv | counts: 0=192, 1=75, 2=216, total=483\n",
      "[OK] Overwritten: PatNo_ID_1560013303.csv | counts: 0=949, 1=376, 2=1218, total=2543\n",
      "[OK] Overwritten: PatNo_ID_1562733396.csv | counts: 0=1013, 1=468, 2=773, total=2254\n",
      "[OK] Overwritten: PatNo_ID_1563587183.csv | counts: 0=2362, 1=1004, 2=1920, total=5286\n",
      "[OK] Overwritten: PatNo_ID_1564148644.csv | counts: 0=3648, 1=1477, 2=12162, total=17287\n",
      "[OK] Overwritten: PatNo_ID_1565148312.csv | counts: 0=2388, 1=920, 2=2167, total=5475\n",
      "[OK] Overwritten: PatNo_ID_1565378038.csv | counts: 0=710, 1=286, 2=1565, total=2561\n",
      "[OK] Overwritten: PatNo_ID_1566123680.csv | counts: 0=9963, 1=4082, 2=28555, total=42600\n",
      "[OK] Overwritten: PatNo_ID_1566252197.csv | counts: 0=849, 1=335, 2=2184, total=3368\n",
      "[OK] Overwritten: PatNo_ID_1566279967.csv | counts: 0=421, 1=162, 2=652, total=1235\n",
      "[OK] Overwritten: PatNo_ID_1566671274.csv | counts: 0=10233, 1=4023, 2=11103, total=25359\n",
      "[OK] Overwritten: PatNo_ID_1566911879.csv | counts: 0=20715, 1=8726, 2=24558, total=53999\n",
      "[OK] Overwritten: PatNo_ID_1567747650.csv | counts: 0=4778, 1=1870, 2=4252, total=10900\n",
      "[OK] Overwritten: PatNo_ID_1567804800.csv | counts: 0=5707, 1=2386, 2=12484, total=20577\n",
      "[OK] Overwritten: PatNo_ID_1567832735.csv | counts: 0=15856, 1=6254, 2=14465, total=36575\n",
      "[OK] Overwritten: PatNo_ID_1568039398.csv | counts: 0=15317, 1=6088, 2=13114, total=34519\n",
      "[OK] Overwritten: PatNo_ID_1568574099.csv | counts: 0=4867, 1=1910, 2=7168, total=13945\n",
      "[OK] Overwritten: PatNo_ID_1568813269.csv | counts: 0=2159, 1=952, 2=1756, total=4867\n",
      "[OK] Overwritten: PatNo_ID_1568952422.csv | counts: 0=435, 1=172, 2=577, total=1184\n",
      "[OK] Overwritten: PatNo_ID_1569083701.csv | counts: 0=1035, 1=391, 2=1902, total=3328\n",
      "[OK] Overwritten: PatNo_ID_1569944983.csv | counts: 0=2901, 1=1134, 2=3614, total=7649\n",
      "[OK] Overwritten: PatNo_ID_1570089466.csv | counts: 0=5936, 1=2436, 2=32966, total=41338\n",
      "[OK] Overwritten: PatNo_ID_1570242703.csv | counts: 0=4582, 1=1804, 2=4258, total=10644\n",
      "[OK] Overwritten: PatNo_ID_1570273244.csv | counts: 0=4393, 1=1711, 2=3621, total=9725\n",
      "[OK] Overwritten: PatNo_ID_1570642083.csv | counts: 0=5245, 1=2395, 2=12091, total=19731\n",
      "[OK] Overwritten: PatNo_ID_1571945701.csv | counts: 0=6501, 1=2604, 2=6626, total=15731\n",
      "[OK] Overwritten: PatNo_ID_1572481361.csv | counts: 0=14611, 1=5840, 2=14088, total=34539\n",
      "[OK] Overwritten: PatNo_ID_1572562839.csv | counts: 0=7036, 1=2771, 2=11156, total=20963\n",
      "[OK] Overwritten: PatNo_ID_1572831765.csv | counts: 0=945, 1=375, 2=1376, total=2696\n",
      "[OK] Overwritten: PatNo_ID_1572976822.csv | counts: 0=2032, 1=796, 2=3936, total=6764\n",
      "[OK] Overwritten: PatNo_ID_1573063188.csv | counts: 0=1911, 1=742, 2=2427, total=5080\n",
      "[OK] Overwritten: PatNo_ID_1573249295.csv | counts: 0=3271, 1=1295, 2=2585, total=7151\n",
      "[OK] Overwritten: PatNo_ID_1573964540.csv | counts: 0=1740, 1=683, 2=1971, total=4394\n",
      "[OK] Overwritten: PatNo_ID_1574148494.csv | counts: 0=20030, 1=7884, 2=19240, total=47154\n",
      "[OK] Overwritten: PatNo_ID_1574270349.csv | counts: 0=2553, 1=962, 2=3018, total=6533\n",
      "[OK] Overwritten: PatNo_ID_1574528808.csv | counts: 0=4118, 1=1538, 2=9156, total=14812\n",
      "[OK] Overwritten: PatNo_ID_1574831525.csv | counts: 0=191, 1=73, 2=256, total=520\n",
      "[OK] Overwritten: PatNo_ID_1574987447.csv | counts: 0=8541, 1=3402, 2=7899, total=19842\n",
      "[OK] Overwritten: PatNo_ID_1575060177.csv | counts: 0=1882, 1=788, 2=2620, total=5290\n",
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      "[OK] Overwritten: PatNo_ID_1575502382.csv | counts: 0=2826, 1=1136, 2=2642, total=6604\n",
      "[OK] Overwritten: PatNo_ID_1575975485.csv | counts: 0=6709, 1=2652, 2=7098, total=16459\n",
      "[OK] Overwritten: PatNo_ID_1576115572.csv | counts: 0=7244, 1=2856, 2=8615, total=18715\n",
      "[OK] Overwritten: PatNo_ID_1576116479.csv | counts: 0=523, 1=206, 2=534, total=1263\n",
      "[OK] Overwritten: PatNo_ID_1576301569.csv | counts: 0=1437, 1=555, 2=1215, total=3207\n",
      "[OK] Overwritten: PatNo_ID_1576964560.csv | counts: 0=8390, 1=3432, 2=12366, total=24188\n",
      "[OK] Overwritten: PatNo_ID_1577042911.csv | counts: 0=15113, 1=6022, 2=17222, total=38357\n",
      "[OK] Overwritten: PatNo_ID_1577487284.csv | counts: 0=846, 1=339, 2=1160, total=2345\n",
      "[OK] Overwritten: PatNo_ID_1578784257.csv | counts: 0=11471, 1=4696, 2=17747, total=33914\n",
      "[OK] Overwritten: PatNo_ID_1579198603.csv | counts: 0=940, 1=367, 2=738, total=2045\n",
      "[OK] Overwritten: PatNo_ID_1579498177.csv | counts: 0=10184, 1=3938, 2=7566, total=21688\n",
      "[OK] Overwritten: PatNo_ID_1580062580.csv | counts: 0=913, 1=391, 2=846, total=2150\n",
      "[OK] Overwritten: PatNo_ID_1580096720.csv | counts: 0=469, 1=180, 2=774, total=1423\n",
      "[OK] Overwritten: PatNo_ID_1580107637.csv | counts: 0=0, 1=0, 2=2698, total=2698\n",
      "[OK] Overwritten: PatNo_ID_1580244614.csv | counts: 0=1501, 1=598, 2=3179, total=5278\n",
      "[OK] Overwritten: PatNo_ID_1580766093.csv | counts: 0=7394, 1=3054, 2=7622, total=18070\n",
      "[OK] Overwritten: PatNo_ID_1581003248.csv | counts: 0=2826, 1=1152, 2=3798, total=7776\n",
      "[OK] Overwritten: PatNo_ID_1581019504.csv | counts: 0=8779, 1=4326, 2=15068, total=28173\n",
      "[OK] Overwritten: PatNo_ID_1581633231.csv | counts: 0=6079, 1=2412, 2=7504, total=15995\n",
      "[OK] Overwritten: PatNo_ID_1581692973.csv | counts: 0=1019, 1=442, 2=1262, total=2723\n",
      "[OK] Overwritten: PatNo_ID_1582452511.csv | counts: 0=2309, 1=921, 2=1966, total=5196\n",
      "[OK] Overwritten: PatNo_ID_1582635996.csv | counts: 0=5787, 1=2302, 2=4957, total=13046\n",
      "[OK] Overwritten: PatNo_ID_1582849900.csv | counts: 0=750, 1=287, 2=6374, total=7411\n",
      "[OK] Overwritten: PatNo_ID_1582937076.csv | counts: 0=10200, 1=4121, 2=9666, total=23987\n",
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      "[OK] Overwritten: PatNo_ID_1587490083.csv | counts: 0=19296, 1=7588, 2=18302, total=45186\n",
      "[OK] Overwritten: PatNo_ID_1588632604.csv | counts: 0=1017, 1=412, 2=1179, total=2608\n",
      "[OK] Overwritten: PatNo_ID_1588673465.csv | counts: 0=2432, 1=1149, 2=6495, total=10076\n",
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      "[OK] Overwritten: PatNo_ID_1589018086.csv | counts: 0=5836, 1=2298, 2=5338, total=13472\n",
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      "[OK] Overwritten: PatNo_ID_1589918099.csv | counts: 0=0, 1=0, 2=9333, total=9333\n",
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      "[OK] Overwritten: PatNo_ID_1593087886.csv | counts: 0=9655, 1=3809, 2=10699, total=24163\n",
      "[OK] Overwritten: PatNo_ID_1593416100.csv | counts: 0=1441, 1=588, 2=1299, total=3328\n",
      "[OK] Overwritten: PatNo_ID_1593472048.csv | counts: 0=2586, 1=1094, 2=4550, total=8230\n",
      "[OK] Overwritten: PatNo_ID_1593593586.csv | counts: 0=7327, 1=2876, 2=8679, total=18882\n",
      "[OK] Overwritten: PatNo_ID_1593720818.csv | counts: 0=1231, 1=524, 2=2155, total=3910\n",
      "[OK] Overwritten: PatNo_ID_1593838524.csv | counts: 0=938, 1=369, 2=870, total=2177\n",
      "[OK] Overwritten: PatNo_ID_1594173718.csv | counts: 0=95, 1=38, 2=211, total=344\n",
      "[OK] Overwritten: PatNo_ID_1594294180.csv | counts: 0=3711, 1=1384, 2=13239, total=18334\n",
      "[OK] Overwritten: PatNo_ID_1594305136.csv | counts: 0=6879, 1=2762, 2=9038, total=18679\n",
      "[OK] Overwritten: PatNo_ID_1594309746.csv | counts: 0=1405, 1=589, 2=1751, total=3745\n",
      "[OK] Overwritten: PatNo_ID_1594319286.csv | counts: 0=1271, 1=500, 2=2336, total=4107\n",
      "[OK] Overwritten: PatNo_ID_1594320763.csv | counts: 0=891, 1=387, 2=1153, total=2431\n",
      "[OK] Overwritten: PatNo_ID_1594322594.csv | counts: 0=2487, 1=979, 2=1914, total=5380\n",
      "[OK] Overwritten: PatNo_ID_1594335109.csv | counts: 0=1670, 1=631, 2=2815, total=5116\n",
      "[OK] Overwritten: PatNo_ID_1594423683.csv | counts: 0=1895, 1=739, 2=2389, total=5023\n",
      "[OK] Overwritten: PatNo_ID_1594437309.csv | counts: 0=4232, 1=1675, 2=4127, total=10034\n",
      "[OK] Overwritten: PatNo_ID_1594439781.csv | counts: 0=3189, 1=1297, 2=3205, total=7691\n",
      "[OK] Overwritten: PatNo_ID_1594441887.csv | counts: 0=3602, 1=1523, 2=5075, total=10200\n",
      "[OK] Overwritten: PatNo_ID_1594448501.csv | counts: 0=0, 1=0, 2=5106, total=5106\n",
      "[OK] Overwritten: PatNo_ID_1594455578.csv | counts: 0=82, 1=34, 2=146, total=262\n",
      "[OK] Overwritten: PatNo_ID_1594464829.csv | counts: 0=1466, 1=572, 2=1962, total=4000\n",
      "[OK] Overwritten: PatNo_ID_1594467719.csv | counts: 0=507, 1=202, 2=1850, total=2559\n",
      "[OK] Overwritten: PatNo_ID_1594471407.csv | counts: 0=4160, 1=1587, 2=5685, total=11432\n",
      "[OK] Overwritten: PatNo_ID_1594479330.csv | counts: 0=1218, 1=407, 2=2102, total=3727\n",
      "[OK] Overwritten: PatNo_ID_1594511911.csv | counts: 0=2241, 1=879, 2=2368, total=5488\n",
      "[OK] Overwritten: PatNo_ID_1594511914.csv | counts: 0=4056, 1=1489, 2=4172, total=9717\n",
      "[OK] Overwritten: PatNo_ID_1594528842.csv | counts: 0=916, 1=358, 2=1217, total=2491\n",
      "[OK] Overwritten: PatNo_ID_1594533379.csv | counts: 0=401, 1=162, 2=467, total=1030\n",
      "\n",
      "[Report] Per-file summary: /home/jovyan/RT08/0925/1014/set_fin_per_file_summary.csv\n",
      "[Report] Cross-file totals: /home/jovyan/RT08/0925/1014/set_fin_totals.csv\n",
      "\n",
      "=== set_fin summary (per file) ===\n",
      "              file_name  set_fin_0  set_fin_1  set_fin_2  total_rows\n",
      "             089271.csv      12194       4817      15408       32419\n",
      "             095323.csv       6674       2630      14487       23791\n",
      "             095707.csv       7550       3007       9623       20180\n",
      "             114309.csv       9666       3774      58289       71729\n",
      "             230933.csv      10475       4524      15250       30249\n",
      "            4216007.csv          0          0       1433        1433\n",
      "            7108162.csv         79         32        128         239\n",
      "            7408338.csv        523        204        705        1432\n",
      "            7657698.csv        638        260        515        1413\n",
      "            7721164.csv        192         75        216         483\n",
      "PatNo_ID_1560013303.csv        949        376       1218        2543\n",
      "PatNo_ID_1562733396.csv       1013        468        773        2254\n",
      "PatNo_ID_1563587183.csv       2362       1004       1920        5286\n",
      "PatNo_ID_1564148644.csv       3648       1477      12162       17287\n",
      "PatNo_ID_1565148312.csv       2388        920       2167        5475\n",
      "PatNo_ID_1565378038.csv        710        286       1565        2561\n",
      "PatNo_ID_1566123680.csv       9963       4082      28555       42600\n",
      "PatNo_ID_1566252197.csv        849        335       2184        3368\n",
      "PatNo_ID_1566279967.csv        421        162        652        1235\n",
      "PatNo_ID_1566671274.csv      10233       4023      11103       25359\n",
      "PatNo_ID_1566911879.csv      20715       8726      24558       53999\n",
      "PatNo_ID_1567747650.csv       4778       1870       4252       10900\n",
      "PatNo_ID_1567804800.csv       5707       2386      12484       20577\n",
      "PatNo_ID_1567832735.csv      15856       6254      14465       36575\n",
      "PatNo_ID_1568039398.csv      15317       6088      13114       34519\n",
      "PatNo_ID_1568574099.csv       4867       1910       7168       13945\n",
      "PatNo_ID_1568813269.csv       2159        952       1756        4867\n",
      "PatNo_ID_1568952422.csv        435        172        577        1184\n",
      "PatNo_ID_1569083701.csv       1035        391       1902        3328\n",
      "PatNo_ID_1569944983.csv       2901       1134       3614        7649\n",
      "PatNo_ID_1570089466.csv       5936       2436      32966       41338\n",
      "PatNo_ID_1570242703.csv       4582       1804       4258       10644\n",
      "PatNo_ID_1570273244.csv       4393       1711       3621        9725\n",
      "PatNo_ID_1570642083.csv       5245       2395      12091       19731\n",
      "PatNo_ID_1571945701.csv       6501       2604       6626       15731\n",
      "PatNo_ID_1572481361.csv      14611       5840      14088       34539\n",
      "PatNo_ID_1572562839.csv       7036       2771      11156       20963\n",
      "PatNo_ID_1572831765.csv        945        375       1376        2696\n",
      "PatNo_ID_1572976822.csv       2032        796       3936        6764\n",
      "PatNo_ID_1573063188.csv       1911        742       2427        5080\n",
      "PatNo_ID_1573249295.csv       3271       1295       2585        7151\n",
      "PatNo_ID_1573964540.csv       1740        683       1971        4394\n",
      "PatNo_ID_1574148494.csv      20030       7884      19240       47154\n",
      "PatNo_ID_1574270349.csv       2553        962       3018        6533\n",
      "PatNo_ID_1574528808.csv       4118       1538       9156       14812\n",
      "PatNo_ID_1574831525.csv        191         73        256         520\n",
      "PatNo_ID_1574987447.csv       8541       3402       7899       19842\n",
      "PatNo_ID_1575060177.csv       1882        788       2620        5290\n",
      "PatNo_ID_1575256902.csv       3157       1174       5256        9587\n",
      "PatNo_ID_1575445051.csv        726        281        793        1800\n",
      "PatNo_ID_1575502382.csv       2826       1136       2642        6604\n",
      "PatNo_ID_1575975485.csv       6709       2652       7098       16459\n",
      "PatNo_ID_1576115572.csv       7244       2856       8615       18715\n",
      "PatNo_ID_1576116479.csv        523        206        534        1263\n",
      "PatNo_ID_1576301569.csv       1437        555       1215        3207\n",
      "PatNo_ID_1576964560.csv       8390       3432      12366       24188\n",
      "PatNo_ID_1577042911.csv      15113       6022      17222       38357\n",
      "PatNo_ID_1577487284.csv        846        339       1160        2345\n",
      "PatNo_ID_1578784257.csv      11471       4696      17747       33914\n",
      "PatNo_ID_1579198603.csv        940        367        738        2045\n",
      "PatNo_ID_1579498177.csv      10184       3938       7566       21688\n",
      "PatNo_ID_1580062580.csv        913        391        846        2150\n",
      "PatNo_ID_1580096720.csv        469        180        774        1423\n",
      "PatNo_ID_1580107637.csv          0          0       2698        2698\n",
      "PatNo_ID_1580244614.csv       1501        598       3179        5278\n",
      "PatNo_ID_1580766093.csv       7394       3054       7622       18070\n",
      "PatNo_ID_1581003248.csv       2826       1152       3798        7776\n",
      "PatNo_ID_1581019504.csv       8779       4326      15068       28173\n",
      "PatNo_ID_1581633231.csv       6079       2412       7504       15995\n",
      "PatNo_ID_1581692973.csv       1019        442       1262        2723\n",
      "PatNo_ID_1582452511.csv       2309        921       1966        5196\n",
      "PatNo_ID_1582635996.csv       5787       2302       4957       13046\n",
      "PatNo_ID_1582849900.csv        750        287       6374        7411\n",
      "PatNo_ID_1582937076.csv      10200       4121       9666       23987\n",
      "PatNo_ID_1584158973.csv       1213        479       1190        2882\n",
      "PatNo_ID_1584397376.csv        132         51        455         638\n",
      "PatNo_ID_1586172659.csv      14607       5799      18148       38554\n",
      "PatNo_ID_1586696634.csv       1127        446       1996        3569\n",
      "PatNo_ID_1586897008.csv       2213        863       3611        6687\n",
      "PatNo_ID_1587490083.csv      19296       7588      18302       45186\n",
      "PatNo_ID_1588632604.csv       1017        412       1179        2608\n",
      "PatNo_ID_1588673465.csv       2432       1149       6495       10076\n",
      "PatNo_ID_1588794796.csv       4511       1775       3089        9375\n",
      "PatNo_ID_1588957997.csv       3655       1566       5745       10966\n",
      "PatNo_ID_1589018086.csv       5836       2298       5338       13472\n",
      "PatNo_ID_1589034524.csv      21377       8633      20071       50081\n",
      "PatNo_ID_1589324603.csv       1618        629       1940        4187\n",
      "PatNo_ID_1589918099.csv          0          0       9333        9333\n",
      "PatNo_ID_1590136310.csv       1678        619       3283        5580\n",
      "PatNo_ID_1590616537.csv       5856       2307       6043       14206\n",
      "PatNo_ID_1590854576.csv       6836       2638       6405       15879\n",
      "PatNo_ID_1591609798.csv      13683       5401      16534       35618\n",
      "PatNo_ID_1592044724.csv       3150       1251       2414        6815\n",
      "PatNo_ID_1592560504.csv       6339       2494       9218       18051\n",
      "PatNo_ID_1593087886.csv       9655       3809      10699       24163\n",
      "PatNo_ID_1593416100.csv       1441        588       1299        3328\n",
      "PatNo_ID_1593472048.csv       2586       1094       4550        8230\n",
      "PatNo_ID_1593593586.csv       7327       2876       8679       18882\n",
      "PatNo_ID_1593720818.csv       1231        524       2155        3910\n",
      "PatNo_ID_1593838524.csv        938        369        870        2177\n",
      "PatNo_ID_1594173718.csv         95         38        211         344\n",
      "PatNo_ID_1594294180.csv       3711       1384      13239       18334\n",
      "PatNo_ID_1594305136.csv       6879       2762       9038       18679\n",
      "PatNo_ID_1594309746.csv       1405        589       1751        3745\n",
      "PatNo_ID_1594319286.csv       1271        500       2336        4107\n",
      "PatNo_ID_1594320763.csv        891        387       1153        2431\n",
      "PatNo_ID_1594322594.csv       2487        979       1914        5380\n",
      "PatNo_ID_1594335109.csv       1670        631       2815        5116\n",
      "PatNo_ID_1594423683.csv       1895        739       2389        5023\n",
      "PatNo_ID_1594437309.csv       4232       1675       4127       10034\n",
      "PatNo_ID_1594439781.csv       3189       1297       3205        7691\n",
      "PatNo_ID_1594441887.csv       3602       1523       5075       10200\n",
      "PatNo_ID_1594448501.csv          0          0       5106        5106\n",
      "PatNo_ID_1594455578.csv         82         34        146         262\n",
      "PatNo_ID_1594464829.csv       1466        572       1962        4000\n",
      "PatNo_ID_1594467719.csv        507        202       1850        2559\n",
      "PatNo_ID_1594471407.csv       4160       1587       5685       11432\n",
      "PatNo_ID_1594479330.csv       1218        407       2102        3727\n",
      "PatNo_ID_1594511911.csv       2241        879       2368        5488\n",
      "PatNo_ID_1594511914.csv       4056       1489       4172        9717\n",
      "PatNo_ID_1594528842.csv        916        358       1217        2491\n",
      "PatNo_ID_1594533379.csv        401        162        467        1030\n",
      "\n",
      "=== set_fin grand totals (across files) ===\n",
      " set_fin_0_total  set_fin_1_total  set_fin_2_total  grand_total_rows\n",
      "          553535           222130           791568           1567233\n"
     ]
    }
   ],
   "source": [
    "# -*- coding: utf-8 -*-\n",
    "\"\"\"\n",
    "批次處理來源：/home/jovyan/RT08/0925/bling_1014/\n",
    "行為：\n",
    "- 直接在每個檔案內新增/覆蓋 set_fin 欄位，並「覆蓋原檔」寫回同一路徑（無副檔名後綴）\n",
    "- 依「病患」分開運算 set_fin（patno/patient_id 等），若無則視為單一病患\n",
    "- 跨檔案統計輸出於：/home/jovyan/RT08/0925/1014/\n",
    "    - set_fin_per_file_summary.csv（逐檔 0/1/2 筆數）\n",
    "    - set_fin_totals.csv（跨檔匯總 0/1/2 與總列數）\n",
    "\n",
    "規則（所有標記均以 nan_check==1 為前提；若 nan_check!=1 則 set_fin=2）：\n",
    "1) 事件點候選：nan_check==1 且 ad_para==1\n",
    "2) 連續的 ad_para==1 僅取該串最後一筆作為事件點\n",
    "3) 對相鄰事件點 (E_i, E_{i+1})，若相隔 >= 300 秒，區間內按「時間比例」標記：\n",
    "     - 前 50% → set_fin=0（僅標記 nan_check==1 的列）\n",
    "     - 後 20% → set_fin=1（僅標記 nan_check==1 的列）\n",
    "     - 中間 30% → set_fin=2（預設）\n",
    "   事件點本身一律 set_fin=2\n",
    "4) 每位病患第一筆 nan_check==1 的列，強制 set_fin=1（最後覆蓋）\n",
    "\"\"\"\n",
    "\n",
    "import os\n",
    "import glob\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "IN_DIR = \"/home/jovyan/RT08/0925/bling_1014/\"\n",
    "REPORT_DIR = \"/home/jovyan/RT08/0925/1014/\"\n",
    "os.makedirs(REPORT_DIR, exist_ok=True)\n",
    "\n",
    "SUMMARY_PER_FILE = os.path.join(REPORT_DIR, \"set_fin_per_file_summary.csv\")\n",
    "SUMMARY_TOTALS   = os.path.join(REPORT_DIR, \"set_fin_totals.csv\")\n",
    "\n",
    "# ---------------------------\n",
    "# Utils\n",
    "# ---------------------------\n",
    "def resolve_col(df, candidates):\n",
    "    \"\"\"大小寫不敏感對齊欄名，命中回傳實際欄名，否則回傳 None。\"\"\"\n",
    "    cmap = {c.lower(): c for c in df.columns}\n",
    "    for cand in candidates:\n",
    "        if cand.lower() in cmap:\n",
    "            return cmap[cand.lower()]\n",
    "    return None\n",
    "\n",
    "def ensure_datetime(df, time_col):\n",
    "    \"\"\"確保時間欄為 datetime64[ns]。\"\"\"\n",
    "    if not pd.api.types.is_datetime64_any_dtype(df[time_col]):\n",
    "        df[time_col] = pd.to_datetime(df[time_col], errors=\"coerce\", infer_datetime_format=True)\n",
    "    return df\n",
    "\n",
    "def last_indices_of_consecutive_true(mask: pd.Series) -> list:\n",
    "    \"\"\"將 True 連續區段合併，回傳每段最後一筆的 iloc 位置索引。\"\"\"\n",
    "    idxs = np.flatnonzero(mask.values)\n",
    "    if len(idxs) == 0:\n",
    "        return []\n",
    "    breaks = np.where(np.diff(idxs) != 1)[0] + 1\n",
    "    groups = np.split(idxs, breaks)\n",
    "    return [int(g[-1]) for g in groups]\n",
    "\n",
    "# ---------------------------\n",
    "# Core: per-patient labeling\n",
    "# ---------------------------\n",
    "def label_set_fin_per_patient(g: pd.DataFrame,\n",
    "                              time_col: str,\n",
    "                              nan_col: str,\n",
    "                              ad_col: str,\n",
    "                              min_gap_sec: int = 300) -> pd.DataFrame:\n",
    "    \"\"\"\n",
    "    對單一病患 g 依規則建立 set_fin。\n",
    "    - 預設 set_fin=2\n",
    "    - 所有標記（0/1）皆僅在 nan_check==1 的列上落筆\n",
    "    - 事件點本身=2\n",
    "    - 每位病患第一筆 nan_check==1 的列，最後覆蓋為 1\n",
    "    - 最後保險：nan_check!=1 的列一律 set_fin=2\n",
    "    \"\"\"\n",
    "    g = g.copy()\n",
    "    g = g.sort_values(time_col, kind=\"mergesort\")\n",
    "    g[\"set_fin\"] = 2  # 預設\n",
    "\n",
    "    # 事件點候選（需 nan_check==1 且 ad_para==1 且時間有效）\n",
    "    event_mask = (g[nan_col] == 1) & (g[ad_col] == 1) & g[time_col].notna()\n",
    "\n",
    "    # 連續 True 串只取最後一筆作為事件點\n",
    "    pos_last_list = last_indices_of_consecutive_true(event_mask.reset_index(drop=True))\n",
    "\n",
    "    # 若事件點不足兩個，仍需處理「第一筆 nan_check==1 → 1」與保險閘\n",
    "    if len(pos_last_list) < 2:\n",
    "        first_nan1 = g.index[g[nan_col] == 1].min()\n",
    "        if pd.notna(first_nan1):\n",
    "            g.loc[first_nan1, \"set_fin\"] = 1\n",
    "        # 保險：非 nan_check==1 的列，一律 2\n",
    "        g.loc[g[nan_col] != 1, \"set_fin\"] = 2\n",
    "        return g\n",
    "\n",
    "    # iloc → index 對應\n",
    "    actual_index = g.index.to_list()\n",
    "    def iloc_to_index(iloc_pos: int):\n",
    "        return actual_index[iloc_pos]\n",
    "\n",
    "    # 逐對事件點，按時間比例標記前 50%、後 20%（僅 nan_check==1）\n",
    "    for i in range(len(pos_last_list) - 1):\n",
    "        i0 = pos_last_list[i]\n",
    "        i1 = pos_last_list[i + 1]\n",
    "        idx0 = iloc_to_index(i0)\n",
    "        idx1 = iloc_to_index(i1)\n",
    "\n",
    "        t0 = g.at[idx0, time_col]\n",
    "        t1 = g.at[idx1, time_col]\n",
    "        if pd.isna(t0) or pd.isna(t1):\n",
    "            continue\n",
    "\n",
    "        gap_sec = (t1 - t0).total_seconds()\n",
    "        if gap_sec < min_gap_sec:\n",
    "            continue\n",
    "\n",
    "        t50 = t0 + (t1 - t0) * 0.5\n",
    "        t80 = t0 + (t1 - t0) * 0.8\n",
    "\n",
    "        # 僅在 (t0, t1) 區間內分配；且僅標記 nan_check==1 的列\n",
    "        in_seg = (g[time_col] > t0) & (g[time_col] < t1) & (g[nan_col] == 1)\n",
    "\n",
    "        # 前 50% → 0\n",
    "        g.loc[in_seg & (g[time_col] < t50), \"set_fin\"] = 0\n",
    "        # 後 20% → 1\n",
    "        g.loc[in_seg & (g[time_col] >= t80), \"set_fin\"] = 1\n",
    "        # 中間 30% 維持 2；事件點本身亦維持 2\n",
    "\n",
    "    # 覆蓋規則：每位病患第一筆 nan_check==1 → 1\n",
    "    first_nan1 = g.index[g[nan_col] == 1].min()\n",
    "    if pd.notna(first_nan1):\n",
    "        g.loc[first_nan1, \"set_fin\"] = 1\n",
    "\n",
    "    # 保險：非 nan_check==1 的列一律回退為 2\n",
    "    g.loc[g[nan_col] != 1, \"set_fin\"] = 2\n",
    "\n",
    "    return g\n",
    "\n",
    "# ---------------------------\n",
    "# Batch overwrite processing\n",
    "# ---------------------------\n",
    "def batch_build_set_fin_overwrite(in_dir: str,\n",
    "                                  pattern: str = \"*.csv\",\n",
    "                                  min_gap_sec: int = 300):\n",
    "    paths = sorted(glob.glob(os.path.join(in_dir, pattern)))\n",
    "    if not paths:\n",
    "        print(f\"[WARN] No files found in: {in_dir}\")\n",
    "        return\n",
    "\n",
    "    summary_rows = []\n",
    "    grand_0 = grand_1 = grand_2 = grand_total = 0\n",
    "\n",
    "    for p in paths:\n",
    "        try:\n",
    "            df = pd.read_csv(p)\n",
    "            if df.empty:\n",
    "                print(f\"[SKIP] Empty file: {os.path.basename(p)}\")\n",
    "                summary_rows.append({\n",
    "                    \"file_name\": os.path.basename(p),\n",
    "                    \"set_fin_0\": 0, \"set_fin_1\": 0, \"set_fin_2\": 0, \"total_rows\": 0\n",
    "                })\n",
    "                continue\n",
    "\n",
    "            # 欄位解析\n",
    "            send_col = resolve_col(df, [\"senddate\", \"send_date\", \"timestamp\", \"time\", \"datetime\"])\n",
    "            nan_col  = resolve_col(df, [\"nan_check\", \"NaN_check\", \"nanflag\", \"nan_flag\"])\n",
    "            ad_col   = resolve_col(df, [\"ad_para\", \"adflag\", \"ad_flag\"])\n",
    "            pat_col  = resolve_col(df, [\"patno\", \"PatNo\", \"patient_id\", \"id\", \"patientid\"])\n",
    "\n",
    "            missing = [name for name, col in {\n",
    "                \"senddate\": send_col, \"nan_check\": nan_col, \"ad_para\": ad_col\n",
    "            }.items() if col is None]\n",
    "            if missing:\n",
    "                raise KeyError(f\"Missing required columns: {missing}\")\n",
    "\n",
    "            df = ensure_datetime(df, time_col=send_col)\n",
    "\n",
    "            # 無病患欄 → 單一病患\n",
    "            drop_tmp_patient = False\n",
    "            if pat_col is None:\n",
    "                df[\"_tmp_single_patient\"] = \"single\"\n",
    "                pat_col = \"_tmp_single_patient\"\n",
    "                drop_tmp_patient = True\n",
    "\n",
    "            # 依病患分組標記\n",
    "            df_out = (\n",
    "                df.groupby(pat_col, group_keys=False, sort=False)\n",
    "                  .apply(lambda g: label_set_fin_per_patient(\n",
    "                      g, time_col=send_col, nan_col=nan_col, ad_col=ad_col, min_gap_sec=min_gap_sec\n",
    "                  ))\n",
    "            )\n",
    "\n",
    "            # 清掉臨時欄（若有）\n",
    "            if drop_tmp_patient and \"_tmp_single_patient\" in df_out.columns:\n",
    "                df_out = df_out.drop(columns=[\"_tmp_single_patient\"])\n",
    "\n",
    "            # 統計本檔\n",
    "            sf = pd.to_numeric(df_out[\"set_fin\"], errors=\"coerce\")\n",
    "            counts = sf.value_counts(dropna=False).reindex([0, 1, 2], fill_value=0)\n",
    "            c0, c1, c2 = int(counts.get(0, 0)), int(counts.get(1, 0)), int(counts.get(2, 0))\n",
    "            total = int(len(df_out))\n",
    "\n",
    "            summary_rows.append({\n",
    "                \"file_name\": os.path.basename(p),\n",
    "                \"set_fin_0\": c0,\n",
    "                \"set_fin_1\": c1,\n",
    "                \"set_fin_2\": c2,\n",
    "                \"total_rows\": total\n",
    "            })\n",
    "\n",
    "            # 跨檔累計\n",
    "            grand_0 += c0\n",
    "            grand_1 += c1\n",
    "            grand_2 += c2\n",
    "            grand_total += total\n",
    "\n",
    "            # 覆蓋原檔\n",
    "            df_out.to_csv(p, index=False, encoding=\"utf-8-sig\")\n",
    "            print(f\"[OK] Overwritten: {os.path.basename(p)} | counts: 0={c0}, 1={c1}, 2={c2}, total={total}\")\n",
    "\n",
    "        except Exception as e:\n",
    "            print(f\"[ERR] {os.path.basename(p)}: {e}\")\n",
    "\n",
    "    # 報表輸出\n",
    "    if summary_rows:\n",
    "        per_file_df = pd.DataFrame(summary_rows)\n",
    "        per_file_df.to_csv(SUMMARY_PER_FILE, index=False, encoding=\"utf-8-sig\")\n",
    "\n",
    "        totals_df = pd.DataFrame([{\n",
    "            \"set_fin_0_total\": grand_0,\n",
    "            \"set_fin_1_total\": grand_1,\n",
    "            \"set_fin_2_total\": grand_2,\n",
    "            \"grand_total_rows\": grand_total\n",
    "        }])\n",
    "        totals_df.to_csv(SUMMARY_TOTALS, index=False, encoding=\"utf-8-sig\")\n",
    "\n",
    "        print(f\"\\n[Report] Per-file summary: {SUMMARY_PER_FILE}\")\n",
    "        print(f\"[Report] Cross-file totals: {SUMMARY_TOTALS}\")\n",
    "\n",
    "        print(\"\\n=== set_fin summary (per file) ===\")\n",
    "        print(per_file_df.to_string(index=False))\n",
    "        print(\"\\n=== set_fin grand totals (across files) ===\")\n",
    "        print(totals_df.to_string(index=False))\n",
    "\n",
    "# ---------------------------\n",
    "# Run\n",
    "# ---------------------------\n",
    "if __name__ == \"__main__\":\n",
    "    batch_build_set_fin_overwrite(IN_DIR, pattern=\"*.csv\", min_gap_sec=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "id": "6342e36c-02c7-4ada-a2b9-221067ac05ea",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true,
     "source_hidden": true
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[OK] 089271.csv | seg0=1864, seg1=1860, total01=3724\n",
      "[OK] 095323.csv | seg0=1418, seg1=1412, total01=2830\n",
      "[OK] 095707.csv | seg0=1065, seg1=1065, total01=2130\n",
      "[OK] 114309.csv | seg0=1098, seg1=1084, total01=2182\n",
      "[OK] 230933.csv | seg0=1230, seg1=1273, total01=2503\n",
      "[OK] 4216007.csv | seg0=0, seg1=0, total01=0\n",
      "[OK] 7108162.csv | seg0=13, seg1=14, total01=27\n",
      "[OK] 7408338.csv | seg0=99, seg1=99, total01=198\n",
      "[OK] 7657698.csv | seg0=41, seg1=41, total01=82\n",
      "[OK] 7721164.csv | seg0=25, seg1=26, total01=51\n",
      "[OK] PatNo_ID_1560013303.csv | seg0=141, seg1=143, total01=284\n",
      "[OK] PatNo_ID_1562733396.csv | seg0=41, seg1=42, total01=83\n",
      "[OK] PatNo_ID_1563587183.csv | seg0=163, seg1=165, total01=328\n",
      "[OK] PatNo_ID_1564148644.csv | seg0=385, seg1=384, total01=769\n",
      "[OK] PatNo_ID_1565148312.csv | seg0=280, seg1=280, total01=560\n",
      "[OK] PatNo_ID_1565378038.csv | seg0=191, seg1=193, total01=384\n",
      "[OK] PatNo_ID_1566123680.csv | seg0=612, seg1=615, total01=1227\n",
      "[OK] PatNo_ID_1566252197.csv | seg0=66, seg1=67, total01=133\n",
      "[OK] PatNo_ID_1566279967.csv | seg0=84, seg1=83, total01=167\n",
      "[OK] PatNo_ID_1566671274.csv | seg0=968, seg1=955, total01=1923\n",
      "[OK] PatNo_ID_1566911879.csv | seg0=1299, seg1=1305, total01=2604\n",
      "[OK] PatNo_ID_1567747650.csv | seg0=414, seg1=413, total01=827\n",
      "[OK] PatNo_ID_1567804800.csv | seg0=345, seg1=347, total01=692\n",
      "[OK] PatNo_ID_1567832735.csv | seg0=1454, seg1=1446, total01=2900\n",
      "[OK] PatNo_ID_1568039398.csv | seg0=1141, seg1=1143, total01=2284\n",
      "[OK] PatNo_ID_1568574099.csv | seg0=775, seg1=772, total01=1547\n",
      "[OK] PatNo_ID_1568813269.csv | seg0=123, seg1=124, total01=247\n",
      "[OK] PatNo_ID_1568952422.csv | seg0=83, seg1=84, total01=167\n",
      "[OK] PatNo_ID_1569083701.csv | seg0=258, seg1=248, total01=506\n",
      "[OK] PatNo_ID_1569944983.csv | seg0=475, seg1=472, total01=947\n",
      "[OK] PatNo_ID_1570089466.csv | seg0=750, seg1=745, total01=1495\n",
      "[OK] PatNo_ID_1570242703.csv | seg0=416, seg1=415, total01=831\n",
      "[OK] PatNo_ID_1570273244.csv | seg0=335, seg1=335, total01=670\n",
      "[OK] PatNo_ID_1570642083.csv | seg0=552, seg1=687, total01=1239\n",
      "[OK] PatNo_ID_1571945701.csv | seg0=628, seg1=625, total01=1253\n",
      "[OK] PatNo_ID_1572481361.csv | seg0=1552, seg1=1540, total01=3092\n",
      "[OK] PatNo_ID_1572562839.csv | seg0=528, seg1=526, total01=1054\n",
      "[OK] PatNo_ID_1572831765.csv | seg0=186, seg1=186, total01=372\n",
      "[OK] PatNo_ID_1572976822.csv | seg0=97, seg1=97, total01=194\n",
      "[OK] PatNo_ID_1573063188.csv | seg0=334, seg1=330, total01=664\n",
      "[OK] PatNo_ID_1573249295.csv | seg0=199, seg1=196, total01=395\n",
      "[OK] PatNo_ID_1573964540.csv | seg0=244, seg1=244, total01=488\n",
      "[OK] PatNo_ID_1574148494.csv | seg0=2022, seg1=2017, total01=4039\n",
      "[OK] PatNo_ID_1574270349.csv | seg0=404, seg1=399, total01=803\n",
      "[OK] PatNo_ID_1574528808.csv | seg0=1219, seg1=1172, total01=2391\n",
      "[OK] PatNo_ID_1574831525.csv | seg0=34, seg1=34, total01=68\n",
      "[OK] PatNo_ID_1574987447.csv | seg0=830, seg1=834, total01=1664\n",
      "[OK] PatNo_ID_1575060177.csv | seg0=309, seg1=316, total01=625\n",
      "[OK] PatNo_ID_1575256902.csv | seg0=699, seg1=687, total01=1386\n",
      "[OK] PatNo_ID_1575445051.csv | seg0=83, seg1=84, total01=167\n",
      "[OK] PatNo_ID_1575502382.csv | seg0=286, seg1=286, total01=572\n",
      "[OK] PatNo_ID_1575975485.csv | seg0=755, seg1=752, total01=1507\n",
      "[OK] PatNo_ID_1576115572.csv | seg0=900, seg1=874, total01=1774\n",
      "[OK] PatNo_ID_1576116479.csv | seg0=65, seg1=66, total01=131\n",
      "[OK] PatNo_ID_1576301569.csv | seg0=118, seg1=113, total01=231\n",
      "[OK] PatNo_ID_1576964560.csv | seg0=1596, seg1=1605, total01=3201\n",
      "[OK] PatNo_ID_1577042911.csv | seg0=1752, seg1=1746, total01=3498\n",
      "[OK] PatNo_ID_1577487284.csv | seg0=161, seg1=158, total01=319\n",
      "[OK] PatNo_ID_1578784257.csv | seg0=1741, seg1=1738, total01=3479\n",
      "[OK] PatNo_ID_1579198603.csv | seg0=56, seg1=57, total01=113\n",
      "[OK] PatNo_ID_1579498177.csv | seg0=734, seg1=720, total01=1454\n",
      "[OK] PatNo_ID_1580062580.csv | seg0=53, seg1=54, total01=107\n",
      "[OK] PatNo_ID_1580096720.csv | seg0=32, seg1=32, total01=64\n",
      "[OK] PatNo_ID_1580107637.csv | seg0=0, seg1=0, total01=0\n",
      "[OK] PatNo_ID_1580244614.csv | seg0=202, seg1=202, total01=404\n",
      "[OK] PatNo_ID_1580766093.csv | seg0=794, seg1=798, total01=1592\n",
      "[OK] PatNo_ID_1581003248.csv | seg0=202, seg1=204, total01=406\n",
      "[OK] PatNo_ID_1581019504.csv | seg0=561, seg1=573, total01=1134\n",
      "[OK] PatNo_ID_1581633231.csv | seg0=770, seg1=757, total01=1527\n",
      "[OK] PatNo_ID_1581692973.csv | seg0=135, seg1=140, total01=275\n",
      "[OK] PatNo_ID_1582452511.csv | seg0=211, seg1=211, total01=422\n",
      "[OK] PatNo_ID_1582635996.csv | seg0=541, seg1=544, total01=1085\n",
      "[OK] PatNo_ID_1582849900.csv | seg0=85, seg1=85, total01=170\n",
      "[OK] PatNo_ID_1582937076.csv | seg0=710, seg1=703, total01=1413\n",
      "[OK] PatNo_ID_1584158973.csv | seg0=114, seg1=113, total01=227\n",
      "[OK] PatNo_ID_1584397376.csv | seg0=32, seg1=33, total01=65\n",
      "[OK] PatNo_ID_1586172659.csv | seg0=2170, seg1=2158, total01=4328\n",
      "[OK] PatNo_ID_1586696634.csv | seg0=85, seg1=86, total01=171\n",
      "[OK] PatNo_ID_1586897008.csv | seg0=508, seg1=506, total01=1014\n",
      "[OK] PatNo_ID_1587490083.csv | seg0=2199, seg1=2146, total01=4345\n",
      "[OK] PatNo_ID_1588632604.csv | seg0=103, seg1=104, total01=207\n",
      "[OK] PatNo_ID_1588673465.csv | seg0=130, seg1=138, total01=268\n",
      "[OK] PatNo_ID_1588794796.csv | seg0=196, seg1=197, total01=393\n",
      "[OK] PatNo_ID_1588957997.csv | seg0=701, seg1=732, total01=1433\n",
      "[OK] PatNo_ID_1589018086.csv | seg0=568, seg1=569, total01=1137\n",
      "[OK] PatNo_ID_1589034524.csv | seg0=2123, seg1=2104, total01=4227\n",
      "[OK] PatNo_ID_1589324603.csv | seg0=265, seg1=263, total01=528\n",
      "[OK] PatNo_ID_1589918099.csv | seg0=0, seg1=0, total01=0\n",
      "[OK] PatNo_ID_1590136310.csv | seg0=423, seg1=396, total01=819\n",
      "[OK] PatNo_ID_1590616537.csv | seg0=727, seg1=725, total01=1452\n",
      "[OK] PatNo_ID_1590854576.csv | seg0=590, seg1=578, total01=1168\n",
      "[OK] PatNo_ID_1591609798.csv | seg0=2116, seg1=2108, total01=4224\n",
      "[OK] PatNo_ID_1592044724.csv | seg0=167, seg1=168, total01=335\n",
      "[OK] PatNo_ID_1592560504.csv | seg0=1197, seg1=1193, total01=2390\n",
      "[OK] PatNo_ID_1593087886.csv | seg0=1172, seg1=1142, total01=2314\n",
      "[OK] PatNo_ID_1593416100.csv | seg0=130, seg1=130, total01=260\n",
      "[OK] PatNo_ID_1593472048.csv | seg0=268, seg1=267, total01=535\n",
      "[OK] PatNo_ID_1593593586.csv | seg0=1109, seg1=1092, total01=2201\n",
      "[OK] PatNo_ID_1593720818.csv | seg0=67, seg1=69, total01=136\n",
      "[OK] PatNo_ID_1593838524.csv | seg0=94, seg1=95, total01=189\n",
      "[OK] PatNo_ID_1594173718.csv | seg0=22, seg1=23, total01=45\n",
      "[OK] PatNo_ID_1594294180.csv | seg0=428, seg1=376, total01=804\n",
      "[OK] PatNo_ID_1594305136.csv | seg0=628, seg1=632, total01=1260\n",
      "[OK] PatNo_ID_1594309746.csv | seg0=226, seg1=228, total01=454\n",
      "[OK] PatNo_ID_1594319286.csv | seg0=350, seg1=349, total01=699\n",
      "[OK] PatNo_ID_1594320763.csv | seg0=97, seg1=110, total01=207\n",
      "[OK] PatNo_ID_1594322594.csv | seg0=184, seg1=186, total01=370\n",
      "[OK] PatNo_ID_1594335109.csv | seg0=409, seg1=410, total01=819\n",
      "[OK] PatNo_ID_1594423683.csv | seg0=338, seg1=336, total01=674\n",
      "[OK] PatNo_ID_1594437309.csv | seg0=515, seg1=513, total01=1028\n",
      "[OK] PatNo_ID_1594439781.csv | seg0=277, seg1=278, total01=555\n",
      "[OK] PatNo_ID_1594441887.csv | seg0=512, seg1=524, total01=1036\n",
      "[OK] PatNo_ID_1594448501.csv | seg0=0, seg1=0, total01=0\n",
      "[OK] PatNo_ID_1594455578.csv | seg0=2, seg1=3, total01=5\n",
      "[OK] PatNo_ID_1594464829.csv | seg0=238, seg1=235, total01=473\n",
      "[OK] PatNo_ID_1594467719.csv | seg0=89, seg1=89, total01=178\n",
      "[OK] PatNo_ID_1594471407.csv | seg0=779, seg1=773, total01=1552\n",
      "[OK] PatNo_ID_1594479330.csv | seg0=290, seg1=258, total01=548\n",
      "[OK] PatNo_ID_1594511911.csv | seg0=301, seg1=303, total01=604\n",
      "[OK] PatNo_ID_1594511914.csv | seg0=564, seg1=533, total01=1097\n",
      "[OK] PatNo_ID_1594528842.csv | seg0=136, seg1=136, total01=272\n",
      "[OK] PatNo_ID_1594533379.csv | seg0=50, seg1=50, total01=100\n",
      "\n",
      "[Report] Per-file: /home/jovyan/RT08/0925/1014/set_fin01_segments_per_file.csv\n",
      "[Report] Totals : /home/jovyan/RT08/0925/1014/set_fin01_segments_totals.csv\n"
     ]
    }
   ],
   "source": [
    "# 我要看set_fin=0, =1分別segmen數量\n",
    "# -*- coding: utf-8 -*-\n",
    "\"\"\"\n",
    "統計 bling_1014 目錄內所有 CSV 的 set_fin=0 與 set_fin=1 的 segment 數量（不改動檔案內容）\n",
    "定義：\n",
    "- 以「病患」為單位\n",
    "- 只在 nan_check==1 的列上計算\n",
    "- 依 senddate 排序後，連續相同 set_fin 視為一段\n",
    "- 僅統計 set_fin ∈ {0,1}\n",
    "輸出：\n",
    "- /home/jovyan/RT08/0925/1014/set_fin01_segments_per_file.csv\n",
    "- /home/jovyan/RT08/0925/1014/set_fin01_segments_totals.csv\n",
    "\"\"\"\n",
    "\n",
    "import os\n",
    "import glob\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "IN_DIR = \"/home/jovyan/RT08/0925/bling_1014/\"\n",
    "REPORT_DIR = \"/home/jovyan/RT08/0925/1014/\"\n",
    "os.makedirs(REPORT_DIR, exist_ok=True)\n",
    "\n",
    "OUT_PER_FILE = os.path.join(REPORT_DIR, \"set_fin01_segments_per_file.csv\")\n",
    "OUT_TOTALS   = os.path.join(REPORT_DIR, \"set_fin01_segments_totals.csv\")\n",
    "\n",
    "# ---------- Utils ----------\n",
    "def resolve_col(df, candidates):\n",
    "    cmap = {c.lower(): c for c in df.columns}\n",
    "    for cand in candidates:\n",
    "        if cand.lower() in cmap:\n",
    "            return cmap[cand.lower()]\n",
    "    return None\n",
    "\n",
    "def ensure_datetime(df, time_col):\n",
    "    if not pd.api.types.is_datetime64_any_dtype(df[time_col]):\n",
    "        df[time_col] = pd.to_datetime(df[time_col], errors=\"coerce\", infer_datetime_format=True)\n",
    "    return df\n",
    "\n",
    "def count_segments_01_for_patient(g: pd.DataFrame, time_col: str, nan_col: str, label_col: str):\n",
    "    \"\"\"\n",
    "    在單一病患切片上，計算 set_fin==0 與 set_fin==1 的 segment 數量（僅 nan_check==1）。\n",
    "    回傳 dict: {\"seg_0\": x, \"seg_1\": y, \"seg01_total\": x+y}\n",
    "    \"\"\"\n",
    "    g2 = g.loc[g[nan_col] == 1].copy()\n",
    "    if g2.empty or label_col not in g2.columns:\n",
    "        return {\"seg_0\": 0, \"seg_1\": 0, \"seg01_total\": 0}\n",
    "\n",
    "    g2 = g2.sort_values(time_col, kind=\"mergesort\")\n",
    "    labs = pd.to_numeric(g2[label_col], errors=\"coerce\").values\n",
    "\n",
    "    seg0 = seg1 = 0\n",
    "    prev = None\n",
    "    in_run = False\n",
    "\n",
    "    for lab in labs:\n",
    "        if np.isnan(lab) or lab not in (0, 1):\n",
    "            # 非 0/1 視為打斷\n",
    "            if in_run and prev in (0, 1):\n",
    "                if prev == 0: seg0 += 1\n",
    "                else: seg1 += 1\n",
    "            prev = None\n",
    "            in_run = False\n",
    "            continue\n",
    "\n",
    "        lab = int(lab)\n",
    "        if not in_run:\n",
    "            # 開新段\n",
    "            prev = lab\n",
    "            in_run = True\n",
    "        else:\n",
    "            if lab != prev:\n",
    "                # 結束前一段\n",
    "                if prev == 0: seg0 += 1\n",
    "                else: seg1 += 1\n",
    "                # 開新段\n",
    "                prev = lab\n",
    "                in_run = True\n",
    "\n",
    "    # 收尾：最後一段\n",
    "    if in_run and prev in (0, 1):\n",
    "        if prev == 0: seg0 += 1\n",
    "        else: seg1 += 1\n",
    "\n",
    "    return {\"seg_0\": seg0, \"seg_1\": seg1, \"seg01_total\": seg0 + seg1}\n",
    "\n",
    "# ---------- Main ----------\n",
    "per_file_rows = []\n",
    "grand_0 = grand_1 = grand_total = 0\n",
    "\n",
    "paths = sorted(glob.glob(os.path.join(IN_DIR, \"*.csv\")))\n",
    "if not paths:\n",
    "    print(f\"[WARN] No CSV files in {IN_DIR}\")\n",
    "\n",
    "for p in paths:\n",
    "    try:\n",
    "        df = pd.read_csv(p)\n",
    "        if df.empty:\n",
    "            per_file_rows.append({\n",
    "                \"file_name\": os.path.basename(p),\n",
    "                \"seg_set_fin_0\": 0,\n",
    "                \"seg_set_fin_1\": 0,\n",
    "                \"segments_01_total\": 0\n",
    "            })\n",
    "            print(f\"[SKIP] Empty file: {os.path.basename(p)}\")\n",
    "            continue\n",
    "\n",
    "        send_col = resolve_col(df, [\"senddate\", \"send_date\", \"timestamp\", \"time\", \"datetime\"])\n",
    "        nan_col  = resolve_col(df, [\"nan_check\", \"NaN_check\", \"nanflag\", \"nan_flag\"])\n",
    "        lab_col  = resolve_col(df, [\"set_fin\", \"setfin\"])\n",
    "        pat_col  = resolve_col(df, [\"patno\", \"PatNo\", \"patient_id\", \"id\", \"patientid\"])\n",
    "\n",
    "        missing = [name for name, col in {\n",
    "            \"senddate\": send_col, \"nan_check\": nan_col, \"set_fin\": lab_col\n",
    "        }.items() if col is None]\n",
    "        if missing:\n",
    "            raise KeyError(f\"Missing required columns: {missing}\")\n",
    "\n",
    "        df = ensure_datetime(df, time_col=send_col)\n",
    "        if pat_col is None:\n",
    "            df[\"_tmp_single_patient\"] = \"single\"\n",
    "            pat_col = \"_tmp_single_patient\"\n",
    "\n",
    "        file_seg0 = file_seg1 = 0\n",
    "\n",
    "        for _, g in df.groupby(pat_col, sort=False):\n",
    "            ret = count_segments_01_for_patient(g, send_col, nan_col, lab_col)\n",
    "            file_seg0 += ret[\"seg_0\"]\n",
    "            file_seg1 += ret[\"seg_1\"]\n",
    "\n",
    "        file_total = file_seg0 + file_seg1\n",
    "        per_file_rows.append({\n",
    "            \"file_name\": os.path.basename(p),\n",
    "            \"seg_set_fin_0\": file_seg0,\n",
    "            \"seg_set_fin_1\": file_seg1,\n",
    "            \"segments_01_total\": file_total\n",
    "        })\n",
    "\n",
    "        grand_0 += file_seg0\n",
    "        grand_1 += file_seg1\n",
    "        grand_total += file_total\n",
    "\n",
    "        print(f\"[OK] {os.path.basename(p)} | seg0={file_seg0}, seg1={file_seg1}, total01={file_total}\")\n",
    "\n",
    "    except Exception as e:\n",
    "        print(f\"[ERR] {os.path.basename(p)}: {e}\")\n",
    "\n",
    "# 輸出\n",
    "per_file_df = pd.DataFrame(per_file_rows)\n",
    "per_file_df.to_csv(OUT_PER_FILE, index=False, encoding=\"utf-8-sig\")\n",
    "\n",
    "totals_df = pd.DataFrame([{\n",
    "    \"segments_set_fin_0_total\": grand_0,\n",
    "    \"segments_set_fin_1_total\": grand_1,\n",
    "    \"segments_01_total\": grand_total\n",
    "}])\n",
    "totals_df.to_csv(OUT_TOTALS, index=False, encoding=\"utf-8-sig\")\n",
    "\n",
    "print(f\"\\n[Report] Per-file: {OUT_PER_FILE}\")\n",
    "print(f\"[Report] Totals : {OUT_TOTALS}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "57789d09-a42f-4867-82a0-8514b447eba8",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    }
   },
   "outputs": [],
   "source": [
    "# 檢查以下 /home/jovyan/RT08/0925/bling_1014/ 所有 CSV若欄位nan_check=0的資料 該筆資料的欄位set_fin就沒有=1=0，如果有 列出在哪裡（前五筆） 有幾筆\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "312c9389-f5b5-46e4-b16f-77eb28eef5ea",
   "metadata": {},
   "outputs": [],
   "source": [
    "整合 重新撰寫完整的\n",
    "批次處理來源：/home/jovyan/RT08/0925/bling_1014/這裡總共有122粉檔案\n",
    "行為：\n",
    "- 直接在每個檔案內新增/覆蓋 set_fin 欄位，並「覆蓋原檔」寫回同一路徑（無副檔名後綴）\n",
    "- 依「病患」分開運算 set_fin（patno/patient_id等），不足時視為單一病患\n",
    "- 跨檔案統計輸出於：/home/jovyan/RT08/0925/1014/\n",
    "    - set_fin_per_file_summary.csv（逐檔 0/1/2 筆數）\n",
    "    - set_fin_totals.csv（跨檔匯總 0/1/2 與總列數）\n",
    "規則摘要：\n",
    "一切都基於該筆資料是nan_check==1 的情況下，若nan_check==0則set_fin=2\n",
    "1) 事件點候選：nan_check==1 且 ad_para==1 \n",
    "2) 連續的 ad_para==1 僅取該串的最後一筆作為事件點\n",
    "3) 對相鄰事件點 (E_i, E_{i+1})，若相隔 >= 300 秒，區間內按「時間比例」標記：\n",
    "     - 前 50% → set_fin=0\n",
    "     - 後 20% → set_fin=1\n",
    "     - 中間 30% → set_fin=2（預設）\n",
    "   事件點本身一律 set_fin=2\n",
    "4) 每位病患第一筆 nan_check==1 的列，強制 set_fin=1（最後覆蓋）\n",
    "\n",
    "我要看set_fin=0, =1分別segmen數量\n",
    "檢查所有 CSV若欄位nan_check=0的資料 該筆資料的欄位set_fin就沒有=1=0，如果有 列出在哪裡（前五筆） 有幾筆"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "id": "a01bc025-9036-4125-a774-52b3a369adfe",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[INFO] 準備處理 CSV：122 檔（預期 122）\n",
      "[OK] 089271.csv: rows=32419, 0=14064, 1=4817, 2=13538, seg0=1933, seg1=1860, viol=0, err=\n",
      "[OK] 095323.csv: rows=23791, 0=8096, 1=2630, 2=13065, seg0=1467, seg1=1412, viol=0, err=\n",
      "[OK] 095707.csv: rows=20180, 0=8620, 1=3007, 2=8553, seg0=1200, seg1=1066, viol=0, err=\n",
      "[OK] 114309.csv: rows=71729, 0=10766, 1=3774, 2=57189, seg0=1154, seg1=1084, viol=0, err=\n",
      "[OK] 230933.csv: rows=30249, 0=11804, 1=4524, 2=13921, seg0=1648, seg1=1273, viol=0, err=\n",
      "[OK] 4216007.csv: rows=1433, 0=0, 1=0, 2=1433, seg0=0, seg1=0, viol=0, err=\n",
      "[OK] 7108162.csv: rows=239, 0=92, 1=32, 2=115, seg0=13, seg1=14, viol=0, err=\n",
      "[OK] 7408338.csv: rows=1432, 0=622, 1=204, 2=606, seg0=101, seg1=99, viol=0, err=\n",
      "[OK] 7657698.csv: rows=1413, 0=679, 1=260, 2=474, seg0=48, seg1=41, viol=0, err=\n",
      "[OK] 7721164.csv: rows=483, 0=217, 1=75, 2=191, seg0=27, seg1=26, viol=0, err=\n",
      "[OK] PatNo_ID_1560013303.csv: rows=2543, 0=1091, 1=376, 2=1076, seg0=145, seg1=143, viol=0, err=\n",
      "[OK] PatNo_ID_1562733396.csv: rows=2254, 0=1054, 1=468, 2=732, seg0=47, seg1=42, viol=0, err=\n",
      "[OK] PatNo_ID_1563587183.csv: rows=5286, 0=2526, 1=1004, 2=1756, seg0=189, seg1=165, viol=0, err=\n",
      "[OK] PatNo_ID_1564148644.csv: rows=17287, 0=4037, 1=1477, 2=11773, seg0=483, seg1=384, viol=0, err=\n",
      "[OK] PatNo_ID_1565148312.csv: rows=5475, 0=2668, 1=920, 2=1887, seg0=289, seg1=280, viol=0, err=\n",
      "[OK] PatNo_ID_1565378038.csv: rows=2561, 0=911, 1=286, 2=1364, seg0=217, seg1=193, viol=0, err=\n",
      "[OK] PatNo_ID_1566123680.csv: rows=42600, 0=10581, 1=4082, 2=27937, seg0=722, seg1=615, viol=0, err=\n",
      "[OK] PatNo_ID_1566252197.csv: rows=3368, 0=915, 1=335, 2=2118, seg0=66, seg1=67, viol=0, err=\n",
      "[OK] PatNo_ID_1566279967.csv: rows=1235, 0=505, 1=162, 2=568, seg0=88, seg1=83, viol=0, err=\n",
      "[OK] PatNo_ID_1566671274.csv: rows=25359, 0=11208, 1=4023, 2=10128, seg0=1077, seg1=955, viol=0, err=\n",
      "[OK] PatNo_ID_1566911879.csv: rows=53999, 0=22026, 1=8726, 2=23247, seg0=1392, seg1=1305, viol=0, err=\n",
      "[OK] PatNo_ID_1567747650.csv: rows=10900, 0=5192, 1=1870, 2=3838, seg0=440, seg1=413, viol=0, err=\n",
      "[OK] PatNo_ID_1567804800.csv: rows=20577, 0=6057, 1=2386, 2=12134, seg0=372, seg1=347, viol=0, err=\n",
      "[OK] PatNo_ID_1567832735.csv: rows=36575, 0=17312, 1=6254, 2=13009, seg0=1519, seg1=1446, viol=0, err=\n",
      "[OK] PatNo_ID_1568039398.csv: rows=34519, 0=16463, 1=6088, 2=11968, seg0=1162, seg1=1143, viol=0, err=\n",
      "[OK] PatNo_ID_1568574099.csv: rows=13945, 0=5643, 1=1910, 2=6392, seg0=792, seg1=772, viol=0, err=\n",
      "[OK] PatNo_ID_1568813269.csv: rows=4867, 0=2282, 1=952, 2=1633, seg0=133, seg1=124, viol=0, err=\n",
      "[OK] PatNo_ID_1568952422.csv: rows=1184, 0=518, 1=172, 2=494, seg0=89, seg1=84, viol=0, err=\n",
      "[OK] PatNo_ID_1569083701.csv: rows=3328, 0=1294, 1=391, 2=1643, seg0=264, seg1=248, viol=0, err=\n",
      "[OK] PatNo_ID_1569944983.csv: rows=7649, 0=3376, 1=1134, 2=3139, seg0=496, seg1=472, viol=0, err=\n",
      "[OK] PatNo_ID_1570089466.csv: rows=41338, 0=6691, 1=2436, 2=32211, seg0=775, seg1=745, viol=0, err=\n",
      "[OK] PatNo_ID_1570242703.csv: rows=10644, 0=4998, 1=1804, 2=3842, seg0=426, seg1=415, viol=0, err=\n",
      "[OK] PatNo_ID_1570273244.csv: rows=9725, 0=4728, 1=1711, 2=3286, seg0=354, seg1=335, viol=0, err=\n",
      "[OK] PatNo_ID_1570642083.csv: rows=19731, 0=6107, 1=2395, 2=11229, seg0=1143, seg1=687, viol=0, err=\n",
      "[OK] PatNo_ID_1571945701.csv: rows=15731, 0=7130, 1=2604, 2=5997, seg0=734, seg1=625, viol=0, err=\n",
      "[OK] PatNo_ID_1572481361.csv: rows=34539, 0=16166, 1=5840, 2=12533, seg0=1668, seg1=1540, viol=0, err=\n",
      "[OK] PatNo_ID_1572562839.csv: rows=20963, 0=7569, 1=2771, 2=10623, seg0=576, seg1=526, viol=0, err=\n",
      "[OK] PatNo_ID_1572831765.csv: rows=2696, 0=1131, 1=375, 2=1190, seg0=189, seg1=186, viol=0, err=\n",
      "[OK] PatNo_ID_1572976822.csv: rows=6764, 0=2130, 1=796, 2=3838, seg0=111, seg1=97, viol=0, err=\n",
      "[OK] PatNo_ID_1573063188.csv: rows=5080, 0=2245, 1=742, 2=2093, seg0=348, seg1=330, viol=0, err=\n",
      "[OK] PatNo_ID_1573249295.csv: rows=7151, 0=3474, 1=1295, 2=2382, seg0=273, seg1=196, viol=0, err=\n",
      "[OK] PatNo_ID_1573964540.csv: rows=4394, 0=1985, 1=683, 2=1726, seg0=252, seg1=244, viol=0, err=\n",
      "[OK] PatNo_ID_1574148494.csv: rows=47154, 0=22054, 1=7884, 2=17216, seg0=2074, seg1=2017, viol=0, err=\n",
      "[OK] PatNo_ID_1574270349.csv: rows=6533, 0=2957, 1=962, 2=2614, seg0=412, seg1=399, viol=0, err=\n",
      "[OK] PatNo_ID_1574528808.csv: rows=14812, 0=5341, 1=1538, 2=7933, seg0=1319, seg1=1174, viol=0, err=\n",
      "[OK] PatNo_ID_1574831525.csv: rows=520, 0=225, 1=73, 2=222, seg0=35, seg1=34, viol=0, err=\n",
      "[OK] PatNo_ID_1574987447.csv: rows=19842, 0=9381, 1=3402, 2=7059, seg0=971, seg1=834, viol=0, err=\n",
      "[OK] PatNo_ID_1575060177.csv: rows=5290, 0=2201, 1=788, 2=2301, seg0=440, seg1=316, viol=0, err=\n",
      "[OK] PatNo_ID_1575256902.csv: rows=9587, 0=3858, 1=1174, 2=4555, seg0=708, seg1=687, viol=0, err=\n",
      "[OK] PatNo_ID_1575445051.csv: rows=1800, 0=809, 1=281, 2=710, seg0=85, seg1=84, viol=0, err=\n",
      "[OK] PatNo_ID_1575502382.csv: rows=6604, 0=3114, 1=1136, 2=2354, seg0=343, seg1=286, viol=0, err=\n",
      "[OK] PatNo_ID_1575975485.csv: rows=16459, 0=7467, 1=2652, 2=6340, seg0=823, seg1=752, viol=0, err=\n",
      "[OK] PatNo_ID_1576115572.csv: rows=18715, 0=8153, 1=2856, 2=7706, seg0=948, seg1=874, viol=0, err=\n",
      "[OK] PatNo_ID_1576116479.csv: rows=1263, 0=588, 1=206, 2=469, seg0=65, seg1=66, viol=0, err=\n",
      "[OK] PatNo_ID_1576301569.csv: rows=3207, 0=1555, 1=556, 2=1096, seg0=132, seg1=114, viol=0, err=\n",
      "[OK] PatNo_ID_1576964560.csv: rows=24188, 0=10000, 1=3432, 2=10756, seg0=1938, seg1=1605, viol=0, err=\n",
      "[OK] PatNo_ID_1577042911.csv: rows=38357, 0=16870, 1=6022, 2=15465, seg0=1813, seg1=1746, viol=0, err=\n",
      "[OK] PatNo_ID_1577487284.csv: rows=2345, 0=1007, 1=339, 2=999, seg0=164, seg1=158, viol=0, err=\n",
      "[OK] PatNo_ID_1578784257.csv: rows=33914, 0=13221, 1=4696, 2=15997, seg0=1777, seg1=1738, viol=0, err=\n",
      "[OK] PatNo_ID_1579198603.csv: rows=2045, 0=996, 1=367, 2=682, seg0=58, seg1=57, viol=0, err=\n",
      "[OK] PatNo_ID_1579498177.csv: rows=21688, 0=10919, 1=3938, 2=6831, seg0=801, seg1=720, viol=0, err=\n",
      "[OK] PatNo_ID_1580062580.csv: rows=2150, 0=966, 1=391, 2=793, seg0=71, seg1=54, viol=0, err=\n",
      "[OK] PatNo_ID_1580096720.csv: rows=1423, 0=501, 1=180, 2=742, seg0=34, seg1=32, viol=0, err=\n",
      "[OK] PatNo_ID_1580107637.csv: rows=2698, 0=0, 1=0, 2=2698, seg0=0, seg1=0, viol=0, err=\n",
      "[OK] PatNo_ID_1580244614.csv: rows=5278, 0=1704, 1=598, 2=2976, seg0=216, seg1=202, viol=0, err=\n",
      "[OK] PatNo_ID_1580766093.csv: rows=18070, 0=8193, 1=3054, 2=6823, seg0=894, seg1=798, viol=0, err=\n",
      "[OK] PatNo_ID_1581003248.csv: rows=7776, 0=3029, 1=1152, 2=3595, seg0=229, seg1=204, viol=0, err=\n",
      "[OK] PatNo_ID_1581019504.csv: rows=28173, 0=9356, 1=4326, 2=14491, seg0=623, seg1=573, viol=0, err=\n",
      "[OK] PatNo_ID_1581633231.csv: rows=15995, 0=6851, 1=2412, 2=6732, seg0=831, seg1=757, viol=0, err=\n",
      "[OK] PatNo_ID_1581692973.csv: rows=2723, 0=1159, 1=442, 2=1122, seg0=207, seg1=140, viol=0, err=\n",
      "[OK] PatNo_ID_1582452511.csv: rows=5196, 0=2520, 1=921, 2=1755, seg0=249, seg1=211, viol=0, err=\n",
      "[OK] PatNo_ID_1582635996.csv: rows=13046, 0=6334, 1=2302, 2=4410, seg0=567, seg1=544, viol=0, err=\n",
      "[OK] PatNo_ID_1582849900.csv: rows=7411, 0=835, 1=287, 2=6289, seg0=88, seg1=85, viol=0, err=\n",
      "[OK] PatNo_ID_1582937076.csv: rows=23987, 0=10915, 1=4121, 2=8951, seg0=749, seg1=703, viol=0, err=\n",
      "[OK] PatNo_ID_1584158973.csv: rows=2882, 0=1328, 1=479, 2=1075, seg0=120, seg1=113, viol=0, err=\n",
      "[OK] PatNo_ID_1584397376.csv: rows=638, 0=164, 1=51, 2=423, seg0=32, seg1=33, viol=0, err=\n",
      "[OK] PatNo_ID_1586172659.csv: rows=38554, 0=16781, 1=5799, 2=15974, seg0=2252, seg1=2158, viol=0, err=\n",
      "[OK] PatNo_ID_1586696634.csv: rows=3569, 0=1212, 1=446, 2=1911, seg0=92, seg1=86, viol=0, err=\n",
      "[OK] PatNo_ID_1586897008.csv: rows=6687, 0=2721, 1=863, 2=3103, seg0=515, seg1=506, viol=0, err=\n",
      "[OK] PatNo_ID_1587490083.csv: rows=45186, 0=21497, 1=7588, 2=16101, seg0=2279, seg1=2146, viol=0, err=\n",
      "[OK] PatNo_ID_1588632604.csv: rows=2608, 0=1121, 1=412, 2=1075, seg0=110, seg1=104, viol=0, err=\n",
      "[OK] PatNo_ID_1588673465.csv: rows=10076, 0=2569, 1=1149, 2=6358, seg0=171, seg1=138, viol=0, err=\n",
      "[OK] PatNo_ID_1588794796.csv: rows=9375, 0=4707, 1=1775, 2=2893, seg0=202, seg1=197, viol=0, err=\n",
      "[OK] PatNo_ID_1588957997.csv: rows=10966, 0=4400, 1=1566, 2=5000, seg0=972, seg1=732, viol=0, err=\n",
      "[OK] PatNo_ID_1589018086.csv: rows=13472, 0=6405, 1=2298, 2=4769, seg0=583, seg1=569, viol=0, err=\n",
      "[OK] PatNo_ID_1589034524.csv: rows=50081, 0=23512, 1=8633, 2=17936, seg0=2346, seg1=2104, viol=0, err=\n",
      "[OK] PatNo_ID_1589324603.csv: rows=4187, 0=1884, 1=629, 2=1674, seg0=270, seg1=263, viol=0, err=\n",
      "[OK] PatNo_ID_1589918099.csv: rows=9333, 0=0, 1=0, 2=9333, seg0=0, seg1=0, viol=0, err=\n",
      "[OK] PatNo_ID_1590136310.csv: rows=5580, 0=2105, 1=619, 2=2856, seg0=483, seg1=396, viol=0, err=\n",
      "[OK] PatNo_ID_1590616537.csv: rows=14206, 0=6583, 1=2307, 2=5316, seg0=748, seg1=725, viol=0, err=\n",
      "[OK] PatNo_ID_1590854576.csv: rows=15879, 0=7446, 1=2638, 2=5795, seg0=755, seg1=578, viol=0, err=\n",
      "[OK] PatNo_ID_1591609798.csv: rows=35618, 0=15800, 1=5402, 2=14416, seg0=2196, seg1=2109, viol=0, err=\n",
      "[OK] PatNo_ID_1592044724.csv: rows=6815, 0=3317, 1=1251, 2=2247, seg0=201, seg1=168, viol=0, err=\n",
      "[OK] PatNo_ID_1592560504.csv: rows=18051, 0=7556, 1=2494, 2=8001, seg0=1269, seg1=1193, viol=0, err=\n",
      "[OK] PatNo_ID_1593087886.csv: rows=24163, 0=10834, 1=3809, 2=9520, seg0=1257, seg1=1142, viol=0, err=\n",
      "[OK] PatNo_ID_1593416100.csv: rows=3328, 0=1571, 1=588, 2=1169, seg0=137, seg1=130, viol=0, err=\n",
      "[OK] PatNo_ID_1593472048.csv: rows=8230, 0=2855, 1=1094, 2=4281, seg0=275, seg1=267, viol=0, err=\n",
      "[OK] PatNo_ID_1593593586.csv: rows=18882, 0=8436, 1=2876, 2=7570, seg0=1148, seg1=1092, viol=0, err=\n",
      "[OK] PatNo_ID_1593720818.csv: rows=3910, 0=1299, 1=524, 2=2087, seg0=74, seg1=69, viol=0, err=\n",
      "[OK] PatNo_ID_1593838524.csv: rows=2177, 0=1032, 1=369, 2=776, seg0=99, seg1=95, viol=0, err=\n",
      "[OK] PatNo_ID_1594173718.csv: rows=344, 0=117, 1=38, 2=189, seg0=22, seg1=23, viol=0, err=\n",
      "[OK] PatNo_ID_1594294180.csv: rows=18334, 0=4141, 1=1384, 2=12809, seg0=455, seg1=376, viol=0, err=\n",
      "[OK] PatNo_ID_1594305136.csv: rows=18679, 0=7514, 1=2762, 2=8403, seg0=651, seg1=632, viol=0, err=\n",
      "[OK] PatNo_ID_1594309746.csv: rows=3745, 0=1633, 1=589, 2=1523, seg0=297, seg1=228, viol=0, err=\n",
      "[OK] PatNo_ID_1594319286.csv: rows=4107, 0=1621, 1=500, 2=1986, seg0=353, seg1=349, viol=0, err=\n",
      "[OK] PatNo_ID_1594320763.csv: rows=2431, 0=1005, 1=387, 2=1039, seg0=171, seg1=110, viol=0, err=\n",
      "[OK] PatNo_ID_1594322594.csv: rows=5380, 0=2672, 1=979, 2=1729, seg0=195, seg1=186, viol=0, err=\n",
      "[OK] PatNo_ID_1594335109.csv: rows=5116, 0=2079, 1=631, 2=2406, seg0=416, seg1=410, viol=0, err=\n",
      "[OK] PatNo_ID_1594423683.csv: rows=5023, 0=2233, 1=739, 2=2051, seg0=345, seg1=336, viol=0, err=\n",
      "[OK] PatNo_ID_1594437309.csv: rows=10034, 0=4748, 1=1675, 2=3611, seg0=599, seg1=513, viol=0, err=\n",
      "[OK] PatNo_ID_1594439781.csv: rows=7691, 0=3468, 1=1297, 2=2926, seg0=348, seg1=278, viol=0, err=\n",
      "[OK] PatNo_ID_1594441887.csv: rows=10200, 0=4138, 1=1523, 2=4539, seg0=808, seg1=524, viol=0, err=\n",
      "[OK] PatNo_ID_1594448501.csv: rows=5106, 0=0, 1=0, 2=5106, seg0=0, seg1=0, viol=0, err=\n",
      "[OK] PatNo_ID_1594455578.csv: rows=262, 0=84, 1=34, 2=144, seg0=3, seg1=3, viol=0, err=\n",
      "[OK] PatNo_ID_1594464829.csv: rows=4000, 0=1704, 1=572, 2=1724, seg0=245, seg1=235, viol=0, err=\n",
      "[OK] PatNo_ID_1594467719.csv: rows=2559, 0=596, 1=202, 2=1761, seg0=89, seg1=89, viol=0, err=\n",
      "[OK] PatNo_ID_1594471407.csv: rows=11432, 0=4939, 1=1587, 2=4906, seg0=781, seg1=776, viol=0, err=\n",
      "[OK] PatNo_ID_1594479330.csv: rows=3727, 0=1513, 1=407, 2=1807, seg0=297, seg1=258, viol=0, err=\n",
      "[OK] PatNo_ID_1594511911.csv: rows=5488, 0=2543, 1=879, 2=2066, seg0=308, seg1=303, viol=0, err=\n",
      "[OK] PatNo_ID_1594511914.csv: rows=9717, 0=4620, 1=1489, 2=3608, seg0=598, seg1=533, viol=0, err=\n",
      "[OK] PatNo_ID_1594528842.csv: rows=2491, 0=1053, 1=358, 2=1080, seg0=138, seg1=136, viol=0, err=\n",
      "[OK] PatNo_ID_1594533379.csv: rows=1030, 0=451, 1=162, 2=417, seg0=52, seg1=50, viol=0, err=\n",
      "\n",
      "=== 完成 ===\n",
      "- 逐檔摘要：/home/jovyan/RT08/0925/1014/set_fin_per_file_summary.csv\n",
      "- 跨檔總表：/home/jovyan/RT08/0925/1014/set_fin_totals.csv\n",
      "- 段數總覽：/home/jovyan/RT08/0925/1014/set_fin_segments_overview.csv\n"
     ]
    }
   ],
   "source": [
    "# -*- coding: utf-8 -*-\n",
    "import os, glob\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "IN_DIR  = \"/home/jovyan/RT08/0925/bling_1014/\"\n",
    "OUT_DIR = \"/home/jovyan/RT08/0925/1014/\"  # 摘要輸出資料夾\n",
    "os.makedirs(OUT_DIR, exist_ok=True)\n",
    "\n",
    "TIME_COL_CANDIDATES    = [\"senddate\", \"SendDate\", \"datetime\", \"time\", \"DateTime\"]\n",
    "PATIENT_ID_CANDIDATES  = [\"patno\", \"patient_id\", \"patientid\", \"id\", \"PatNo\", \"Patient_ID\"]\n",
    "NAN_COL                = \"nan_check\"\n",
    "AD_COL                 = \"ad_para\"\n",
    "MIN_GAP_SEC            = 300   # 5 分鐘\n",
    "EXPECTED_FILES         = 122   # 用於顯示與交叉檢核（不影響邏輯）\n",
    "SUMMARY_PER_FILE_NAME  = os.path.join(OUT_DIR, \"set_fin_per_file_summary.csv\")\n",
    "SUMMARY_TOTALS_NAME    = os.path.join(OUT_DIR, \"set_fin_totals.csv\")\n",
    "VIOLATION_SAMPLE_MAX   = 5     # 列出前五筆違規樣本\n",
    "\n",
    "# -----------------------------\n",
    "# 小工具\n",
    "# -----------------------------\n",
    "def ensure_datetime(df: pd.DataFrame, time_col: str) -> pd.DataFrame:\n",
    "    if time_col not in df.columns:\n",
    "        raise KeyError(f\"找不到時間欄位（嘗試 {TIME_COL_CANDIDATES}）\")\n",
    "    if not pd.api.types.is_datetime64_any_dtype(df[time_col]):\n",
    "        # 嘗試修正中文 AM/PM\n",
    "        def zh_ampm_fix(x):\n",
    "            if not isinstance(x, str): return x\n",
    "            return (x.replace(\"上午\", \" AM \")\n",
    "                     .replace(\"下午\", \" PM \")\n",
    "                     .replace(\"早上\", \" AM \")\n",
    "                     .replace(\"中午\", \" PM \")\n",
    "                     .replace(\"晚上\", \" PM \"))\n",
    "        df[time_col] = pd.to_datetime(df[time_col].map(zh_ampm_fix), errors=\"coerce\", infer_datetime_format=True)\n",
    "    return df\n",
    "\n",
    "def find_first_existing(col_list, df_cols):\n",
    "    for c in col_list:\n",
    "        if c in df_cols:\n",
    "            return c\n",
    "    return None\n",
    "\n",
    "def collapse_consecutive_true_last(mask: pd.Series) -> list:\n",
    "    \"\"\"回傳每段連續 True 的最後一個整數位置索引。\"\"\"\n",
    "    idxs = np.flatnonzero(mask.values)\n",
    "    if len(idxs) == 0:\n",
    "        return []\n",
    "    breaks = np.where(np.diff(idxs) != 1)[0] + 1\n",
    "    groups = np.split(idxs, breaks)\n",
    "    return [int(g[-1]) for g in groups]\n",
    "\n",
    "def count_segments(values: pd.Series, target: int) -> int:\n",
    "    \"\"\"計算 set_fin==target 的連續區段數量。NaN 不視為 target。\"\"\"\n",
    "    v = (values == target).astype(int).values\n",
    "    if len(v) == 0: return 0\n",
    "    # 由 0 -> 1 的跳變次數\n",
    "    starts = (v[1:] == 1) & (v[:-1] == 0)\n",
    "    return int(starts.sum() + (1 if v[0] == 1 else 0))\n",
    "\n",
    "# -----------------------------\n",
    "# 核心：每位病患運算 set_fin\n",
    "# -----------------------------\n",
    "def compute_set_fin_per_patient(pdf: pd.DataFrame, time_col: str) -> pd.DataFrame:\n",
    "    \"\"\"\n",
    "    規則：\n",
    "      - 預設 set_fin=2\n",
    "      - 若 nan_check==0 -> set_fin=2（強制覆蓋）\n",
    "      - 事件點候選：nan_check==1 & ad_para==1，連續 ad_para==1 取該串最後一筆\n",
    "      - 對相鄰事件點 (E_i, E_{i+1})，若時間差 >= 300 秒：\n",
    "           前 50% -> set_fin=0\n",
    "           後 20% -> set_fin=1\n",
    "           中間 30% -> 2\n",
    "        （事件點本身一律 2）\n",
    "      - 每位病患第一筆 nan_check==1 的列，最後覆蓋 set_fin=1\n",
    "    \"\"\"\n",
    "    df = pdf.copy()\n",
    "    # 安全欄位檢查\n",
    "    for c in [NAN_COL, AD_COL, time_col]:\n",
    "        if c not in df.columns:\n",
    "            # 缺欄位時，維持 set_fin=2 返回\n",
    "            df[\"set_fin\"] = 2\n",
    "            return df\n",
    "\n",
    "    df = df.sort_values(time_col).reset_index(drop=True)\n",
    "    df[\"set_fin\"] = 2\n",
    "\n",
    "    # 僅 nan_check==1 的列才會被事件/區間標記\n",
    "    valid_mask = (df[NAN_COL] == 1)\n",
    "\n",
    "    # 事件點候選：nan_check==1 & ad_para==1\n",
    "    event_mask = valid_mask & (df[AD_COL] == 1)\n",
    "    # 連續 True 串取最後一筆\n",
    "    last_idxs = collapse_consecutive_true_last(event_mask)\n",
    "\n",
    "    # 逐對事件點標記（時間比例）\n",
    "    if len(last_idxs) >= 2:\n",
    "        # 確保時間可用\n",
    "        if df[time_col].isna().all():\n",
    "            pass  # 無法分段，維持 2\n",
    "        else:\n",
    "            for i in range(len(last_idxs) - 1):\n",
    "                i0, i1 = last_idxs[i], last_idxs[i+1]\n",
    "                t0, t1 = df.at[i0, time_col], df.at[i1, time_col]\n",
    "                if pd.isna(t0) or pd.isna(t1): \n",
    "                    continue\n",
    "                gap = (t1 - t0).total_seconds()\n",
    "                if gap < MIN_GAP_SEC:\n",
    "                    continue\n",
    "                # 時間比例界點\n",
    "                t50 = t0 + (t1 - t0) * 0.5\n",
    "                t80 = t0 + (t1 - t0) * 0.8\n",
    "\n",
    "                in_span = (df[time_col] >= t0) & (df[time_col] < t1) & valid_mask\n",
    "                front = in_span & (df[time_col] <  t50)\n",
    "                tail  = in_span & (df[time_col] >= t80)\n",
    "\n",
    "                # 標記，事件點本身維持 2，不處理\n",
    "                df.loc[front, \"set_fin\"] = 0\n",
    "                df.loc[tail,  \"set_fin\"] = 1\n",
    "\n",
    "    # 每位病患第一筆 nan_check==1 的列，強制 set_fin=1（最後覆蓋）\n",
    "    first_valid_idx = df.index[valid_mask].min() if valid_mask.any() else None\n",
    "    if first_valid_idx is not None and pd.notna(first_valid_idx):\n",
    "        df.at[first_valid_idx, \"set_fin\"] = 1\n",
    "\n",
    "    # nan_check==0 一律 set_fin=2（最終覆蓋，避免違規）\n",
    "    df.loc[~valid_mask, \"set_fin\"] = 2\n",
    "\n",
    "    return df\n",
    "\n",
    "# -----------------------------\n",
    "# 檔案層級處理\n",
    "# -----------------------------\n",
    "def process_file(path: str) -> dict:\n",
    "    base = os.path.basename(path)\n",
    "    try:\n",
    "        df = pd.read_csv(path)\n",
    "    except Exception as e:\n",
    "        return {\"file_name\": base, \"error\": f\"read_csv failed: {e}\"}\n",
    "\n",
    "    # 找時間欄\n",
    "    time_col = find_first_existing(TIME_COL_CANDIDATES, df.columns)\n",
    "    if time_col is None:\n",
    "        # 沒時間欄位，整檔 set_fin=2 覆蓋寫回\n",
    "        df[\"set_fin\"] = 2\n",
    "        df.to_csv(path, index=False, encoding=\"utf-8-sig\")\n",
    "        return {\n",
    "            \"file_name\": base, \"set_fin_0\": 0, \"set_fin_1\": 0, \"set_fin_2\": int(len(df)),\n",
    "            \"segments_0\": 0, \"segments_1\": 0,\n",
    "            \"total_rows\": int(len(df)), \"error\": \"no_time_column\"\n",
    "        }\n",
    "\n",
    "    # 確保時間型別\n",
    "    df = ensure_datetime(df, time_col)\n",
    "\n",
    "    # 找病患欄位\n",
    "    pid_col = find_first_existing(PATIENT_ID_CANDIDATES, df.columns)\n",
    "    if pid_col is None:\n",
    "        # 視為單一病患\n",
    "        df[\"_tmp_single_patient_id\"] = \"ONE\"\n",
    "        pid_col = \"_tmp_single_patient_id\"\n",
    "\n",
    "    # 分病患計算\n",
    "    out_parts = []\n",
    "    for pid, g in df.groupby(pid_col, sort=False):\n",
    "        gg = compute_set_fin_per_patient(g, time_col=time_col)\n",
    "        out_parts.append(gg)\n",
    "    df2 = pd.concat(out_parts, axis=0).sort_index()\n",
    "\n",
    "    # 違規稽核：nan_check==0 但 set_fin ∈ {0,1}\n",
    "    viol_mask = (df2.get(NAN_COL, 0) == 0) & (df2[\"set_fin\"].isin([0, 1]))\n",
    "    viol_count = int(viol_mask.sum())\n",
    "    viol_sample = df2.loc[viol_mask].head(VIOLATION_SAMPLE_MAX).copy()\n",
    "    viol_sample[\"__file_name\"] = base\n",
    "    # 計算段數（以最終 set_fin）\n",
    "    seg0 = count_segments(df2[\"set_fin\"], 0)\n",
    "    seg1 = count_segments(df2[\"set_fin\"], 1)\n",
    "\n",
    "    # 覆蓋原檔\n",
    "    # 若有臨時病患欄，回寫前移除\n",
    "    if \"_tmp_single_patient_id\" in df2.columns:\n",
    "        df2 = df2.drop(columns=[\"_tmp_single_patient_id\"])\n",
    "    df2.to_csv(path, index=False, encoding=\"utf-8-sig\")\n",
    "\n",
    "    # 計數\n",
    "    vc = df2[\"set_fin\"].value_counts(dropna=False).reindex([0,1,2], fill_value=0)\n",
    "\n",
    "    return {\n",
    "        \"file_name\": base,\n",
    "        \"set_fin_0\": int(vc.get(0, 0)),\n",
    "        \"set_fin_1\": int(vc.get(1, 0)),\n",
    "        \"set_fin_2\": int(vc.get(2, 0)),\n",
    "        \"segments_0\": int(seg0),\n",
    "        \"segments_1\": int(seg1),\n",
    "        \"total_rows\": int(len(df2)),\n",
    "        \"violations_nan0_setfin01\": viol_count,\n",
    "        \"violation_sample\": viol_sample,  # DataFrame（前 5 筆）\n",
    "        \"error\": \"\"\n",
    "    }\n",
    "\n",
    "# -----------------------------\n",
    "# 批次主程式\n",
    "# -----------------------------\n",
    "def main():\n",
    "    paths = sorted(glob.glob(os.path.join(IN_DIR, \"*.csv\")))\n",
    "    print(f\"[INFO] 準備處理 CSV：{len(paths)} 檔（預期 {EXPECTED_FILES}）\")\n",
    "\n",
    "    per_file_rows = []\n",
    "    viol_samples = []  # 收集所有檔的前 5 筆違規樣本（各檔各取最多 5）\n",
    "\n",
    "    for p in paths:\n",
    "        res = process_file(p)\n",
    "        per_file_rows.append({\n",
    "            k: v for k, v in res.items()\n",
    "            if k in [\"file_name\", \"set_fin_0\", \"set_fin_1\", \"set_fin_2\",\n",
    "                     \"segments_0\", \"segments_1\", \"total_rows\", \"violations_nan0_setfin01\", \"error\"]\n",
    "        })\n",
    "        if isinstance(res.get(\"violation_sample\"), pd.DataFrame) and not res[\"violation_sample\"].empty:\n",
    "            viol_samples.append(res[\"violation_sample\"])\n",
    "\n",
    "        # 進度列印\n",
    "        print(f\"[OK] {res['file_name']}: rows={res.get('total_rows', '-')}, \"\n",
    "              f\"0={res.get('set_fin_0', 0)}, 1={res.get('set_fin_1', 0)}, 2={res.get('set_fin_2', 0)}, \"\n",
    "              f\"seg0={res.get('segments_0', 0)}, seg1={res.get('segments_1', 0)}, \"\n",
    "              f\"viol={res.get('violations_nan0_setfin01', 0)}, err={res.get('error','')}\")\n",
    "\n",
    "    # 逐檔摘要\n",
    "    per_df = pd.DataFrame(per_file_rows)\n",
    "    per_df.to_csv(SUMMARY_PER_FILE_NAME, index=False, encoding=\"utf-8-sig\")\n",
    "\n",
    "    # 總表（跨檔匯總）\n",
    "    totals = {\n",
    "        \"set_fin_0\": int(per_df[\"set_fin_0\"].sum()),\n",
    "        \"set_fin_1\": int(per_df[\"set_fin_1\"].sum()),\n",
    "        \"set_fin_2\": int(per_df[\"set_fin_2\"].sum()),\n",
    "        \"segments_0\": int(per_df[\"segments_0\"].sum()),\n",
    "        \"segments_1\": int(per_df[\"segments_1\"].sum()),\n",
    "        \"total_rows\": int(per_df[\"total_rows\"].sum()),\n",
    "        \"files_processed\": int(len(per_df)),\n",
    "        \"files_expected\": int(EXPECTED_FILES),\n",
    "        \"violations_nan0_setfin01_total\": int(per_df[\"violations_nan0_setfin01\"].sum())\n",
    "    }\n",
    "    totals_df = pd.DataFrame([totals])\n",
    "    totals_df.to_csv(SUMMARY_TOTALS_NAME, index=False, encoding=\"utf-8-sig\")\n",
    "\n",
    "    # 違規樣本匯出（前 5 筆/檔；合併後再列出前 5 筆整體）\n",
    "    if viol_samples:\n",
    "        viol_all = pd.concat(viol_samples, axis=0, ignore_index=True)\n",
    "        viol_all_path = os.path.join(OUT_DIR, \"nan0_setfin01_violations_samples.csv\")\n",
    "        viol_all.to_csv(viol_all_path, index=False, encoding=\"utf-8-sig\")\n",
    "\n",
    "        # 整體前 5 筆\n",
    "        viol_top5_path = os.path.join(OUT_DIR, \"nan0_setfin01_violations_top5.csv\")\n",
    "        viol_all.head(5).to_csv(viol_top5_path, index=False, encoding=\"utf-8-sig\")\n",
    "\n",
    "    # 額外：輸出 set_fin=0 與 set_fin=1 的段數（segments）總覽（跨檔）\n",
    "    seg_overview_path = os.path.join(OUT_DIR, \"set_fin_segments_overview.csv\")\n",
    "    per_df[[\"file_name\", \"segments_0\", \"segments_1\"]].to_csv(seg_overview_path, index=False, encoding=\"utf-8-sig\")\n",
    "\n",
    "    print(\"\\n=== 完成 ===\")\n",
    "    print(f\"- 逐檔摘要：{SUMMARY_PER_FILE_NAME}\")\n",
    "    print(f\"- 跨檔總表：{SUMMARY_TOTALS_NAME}\")\n",
    "    print(f\"- 段數總覽：{seg_overview_path}\")\n",
    "    if viol_samples:\n",
    "        print(f\"- 違規樣本：{os.path.join(OUT_DIR, 'nan0_setfin01_violations_samples.csv')}（各檔前 5 筆）\")\n",
    "        print(f\"- 違規整體前 5 筆：{os.path.join(OUT_DIR, 'nan0_setfin01_violations_top5.csv')}\")\n",
    "\n",
    "if __name__ == \"__main__\":\n",
    "    main()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "id": "ae70915f-de4c-4cd0-abf1-bda5e84f9bf8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "== 跨檔案 set_fin 總筆數 ==\n",
      "set_fin=0: 618059\n",
      "set_fin=1: 222132\n",
      "set_fin=2: 727042\n",
      "total_rows: 1567233\n",
      "\n",
      "== 違規檢查 ==\n",
      "沒有發現 nan_check==0 且 set_fin∈{0,1} 的筆數（count=0），無前五筆可列。\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd, glob, os\n",
    "\n",
    "in_dir = \"/home/jovyan/RT08/0925/bling_1014/\"\n",
    "paths = sorted(glob.glob(os.path.join(in_dir, \"*.csv\")))\n",
    "\n",
    "viol_rows = []\n",
    "sum0 = sum1 = sum2 = 0\n",
    "for p in paths:\n",
    "    df = pd.read_csv(p)\n",
    "    if \"set_fin\" in df.columns:\n",
    "        vc = df[\"set_fin\"].value_counts(dropna=False)\n",
    "        sum0 += int(vc.get(0, 0))\n",
    "        sum1 += int(vc.get(1, 0))\n",
    "        sum2 += int(vc.get(2, 0))\n",
    "    # 稽核：nan_check==0 卻出現 set_fin in {0,1}\n",
    "    if {\"nan_check\",\"set_fin\"}.issubset(df.columns):\n",
    "        bad = df[(df[\"nan_check\"]==0) & (df[\"set_fin\"].isin([0,1]))].copy()\n",
    "        if not bad.empty:\n",
    "            bad[\"__file_name\"] = os.path.basename(p)\n",
    "            viol_rows.append(bad)\n",
    "\n",
    "print(\"== 跨檔案 set_fin 總筆數 ==\")\n",
    "print(\"set_fin=0:\", sum0)\n",
    "print(\"set_fin=1:\", sum1)\n",
    "print(\"set_fin=2:\", sum2)\n",
    "print(\"total_rows:\", sum0+sum1+sum2)\n",
    "\n",
    "if viol_rows:\n",
    "    bad_all = pd.concat(viol_rows, ignore_index=True)\n",
    "    print(\"\\n== 違規筆數（nan_check==0 且 set_fin∈{0,1}）==\")\n",
    "    print(\"count:\", len(bad_all))\n",
    "    print(\"\\n前五筆：\")\n",
    "    print(bad_all.head(5))\n",
    "else:\n",
    "    print(\"\\n== 違規檢查 ==\")\n",
    "    print(\"沒有發現 nan_check==0 且 set_fin∈{0,1} 的筆數（count=0），無前五筆可列。\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "id": "3aea5635-31c3-4d1b-b25f-f8ffbb8530d6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "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",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>file_name</th>\n",
       "      <th>set_fin_0</th>\n",
       "      <th>set_fin_1</th>\n",
       "      <th>set_fin_2</th>\n",
       "      <th>segments_0</th>\n",
       "      <th>segments_1</th>\n",
       "      <th>total_rows</th>\n",
       "      <th>violations_nan0_setfin01</th>\n",
       "      <th>error</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>PatNo_ID_1586172659.csv</td>\n",
       "      <td>16781</td>\n",
       "      <td>5799</td>\n",
       "      <td>15974</td>\n",
       "      <td>2252</td>\n",
       "      <td>2158</td>\n",
       "      <td>38554</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>PatNo_ID_1587490083.csv</td>\n",
       "      <td>21497</td>\n",
       "      <td>7588</td>\n",
       "      <td>16101</td>\n",
       "      <td>2279</td>\n",
       "      <td>2146</td>\n",
       "      <td>45186</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>PatNo_ID_1591609798.csv</td>\n",
       "      <td>15800</td>\n",
       "      <td>5402</td>\n",
       "      <td>14416</td>\n",
       "      <td>2196</td>\n",
       "      <td>2109</td>\n",
       "      <td>35618</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>PatNo_ID_1589034524.csv</td>\n",
       "      <td>23512</td>\n",
       "      <td>8633</td>\n",
       "      <td>17936</td>\n",
       "      <td>2346</td>\n",
       "      <td>2104</td>\n",
       "      <td>50081</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>PatNo_ID_1574148494.csv</td>\n",
       "      <td>22054</td>\n",
       "      <td>7884</td>\n",
       "      <td>17216</td>\n",
       "      <td>2074</td>\n",
       "      <td>2017</td>\n",
       "      <td>47154</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>089271.csv</td>\n",
       "      <td>14064</td>\n",
       "      <td>4817</td>\n",
       "      <td>13538</td>\n",
       "      <td>1933</td>\n",
       "      <td>1860</td>\n",
       "      <td>32419</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>PatNo_ID_1577042911.csv</td>\n",
       "      <td>16870</td>\n",
       "      <td>6022</td>\n",
       "      <td>15465</td>\n",
       "      <td>1813</td>\n",
       "      <td>1746</td>\n",
       "      <td>38357</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>PatNo_ID_1578784257.csv</td>\n",
       "      <td>13221</td>\n",
       "      <td>4696</td>\n",
       "      <td>15997</td>\n",
       "      <td>1777</td>\n",
       "      <td>1738</td>\n",
       "      <td>33914</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>PatNo_ID_1576964560.csv</td>\n",
       "      <td>10000</td>\n",
       "      <td>3432</td>\n",
       "      <td>10756</td>\n",
       "      <td>1938</td>\n",
       "      <td>1605</td>\n",
       "      <td>24188</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>PatNo_ID_1572481361.csv</td>\n",
       "      <td>16166</td>\n",
       "      <td>5840</td>\n",
       "      <td>12533</td>\n",
       "      <td>1668</td>\n",
       "      <td>1540</td>\n",
       "      <td>34539</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  file_name  set_fin_0  set_fin_1  set_fin_2  segments_0  segments_1  total_rows  violations_nan0_setfin01  error\n",
       "76  PatNo_ID_1586172659.csv      16781       5799      15974        2252        2158       38554                         0    NaN\n",
       "79  PatNo_ID_1587490083.csv      21497       7588      16101        2279        2146       45186                         0    NaN\n",
       "91  PatNo_ID_1591609798.csv      15800       5402      14416        2196        2109       35618                         0    NaN\n",
       "85  PatNo_ID_1589034524.csv      23512       8633      17936        2346        2104       50081                         0    NaN\n",
       "42  PatNo_ID_1574148494.csv      22054       7884      17216        2074        2017       47154                         0    NaN\n",
       "0                089271.csv      14064       4817      13538        1933        1860       32419                         0    NaN\n",
       "56  PatNo_ID_1577042911.csv      16870       6022      15465        1813        1746       38357                         0    NaN\n",
       "58  PatNo_ID_1578784257.csv      13221       4696      15997        1777        1738       33914                         0    NaN\n",
       "55  PatNo_ID_1576964560.csv      10000       3432      10756        1938        1605       24188                         0    NaN\n",
       "35  PatNo_ID_1572481361.csv      16166       5840      12533        1668        1540       34539                         0    NaN"
      ]
     },
     "execution_count": 224,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#看段數分檔排行（例如前 10 名 set_fin=1 段數最多）\n",
    "per = pd.read_csv(\"/home/jovyan/RT08/0925/1014/set_fin_per_file_summary.csv\")\n",
    "per.sort_values(\"segments_1\", ascending=False).head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3cda8440-f91d-46f1-a9dd-2a8958431df2",
   "metadata": {},
   "outputs": [],
   "source": [
    "以上在  c  創/bling_1014/ set_fin"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e93ee025-3500-4027-9ef1-f9bba3a41b4a",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7f9160bf-cd04-4a68-8575-7f35473b3df3",
   "metadata": {},
   "outputs": [],
   "source": [
    "okkk 我分完set=012了 我想看跨檔案的數量統計 以及segment數量"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5c740b5f-33e7-4e58-a407-18ad646a9070",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "354dc739-4656-4764-8acf-7e5c0229768f",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "875a4365-a943-4a9c-981a-4090c5d31908",
   "metadata": {},
   "outputs": [],
   "source": [
    "用「不實體化」思維把整個滑動視窗流程一次到位，且不會動到原始資料（/home/jovyan/RT08/0925/bling_1014/）。\n",
    "自動建立版本化資料夾、輸出設定檔、清理副本、做視窗連續性統計、標籤聚合摘要、視窗 manifest（可選）、K 折索引等；每個步驟都加中文提示，方便 console 監控。\n",
    "\n",
    "參數：W=60、S=30、右對齊、Δt_sec 門檻預設 120 秒、比例規則≥0.5 且 ad_para 最末筆=1 強制為 1。\n",
    "路徑皆依 1014 版路徑規劃，且所有輸出皆寫在 /home/jovyan/RT08/0925/sliding_win/1014/ 底下。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "291ed5ec-41a8-4458-bbe6-65f63a02927e",
   "metadata": {},
   "outputs": [],
   "source": [
    "我要進行滑動視窗切割，\n",
    "我要多分片 CSV + 視窗 manifest 前置 + 併發讀取/快取\n",
    "原始資料在/home/jovyan/RT08/0925/bling_1014/ 裡面有122個csv檔案 \n",
    "不要動到原始資料\n",
    "盡量在不同步驟要有提示語顯示在打印結果中\n",
    "\n",
    "請給我完整程式碼，程式碼中要撰寫詳細中文註釋\n",
    "不實體化滑動視窗 CSV 資料設計與處理規範（工程實作用）\n",
    "───────────────────────────────\n",
    "\n",
    "一、總原則\n",
    "採不實體化滑動視窗策略：資料僅以逐筆時間序列存放於 CSV，不預先展開視窗。\n",
    "視窗在訓練階段由 DataLoader 動態生成，支援延後計算（Lazy Evaluation）。\n",
    "每個版本化資料夾包含清理後主檔、設定檔與品質報表；所有流程以設定檔驅動，確保可重現與可審計。\n",
    "\n",
    "對應路徑：\n",
    "/home/jovyan/RT08/0925/sliding_win/1014/cleaned/ \n",
    "/home/jovyan/RT08/0925/sliding_win/1014/docs/schema_final.json \n",
    "/home/jovyan/RT08/0925/sliding_win/1014/config/windowing.yaml \n",
    "\n",
    "二、資料來源與清洗\n",
    "資料來源為 cleaned/clear/ 內逐筆 CSV，已完成欄位統一與時間正規化（升冪排序）。\n",
    "\n",
    "欄位必備：\n",
    "識別欄：patno\n",
    "時間欄：senddate, ts_unix, Δt_sec\n",
    "品質欄：nan_check\n",
    "標籤欄：ad_para, set\n",
    "特徵欄：呼吸器設定與病人回饋變數\n",
    "nan_check 為逐列品質指標（1 表示該列完整），開窗前需先過濾 nan_check ≠ 1 或時間欄異常之資料。\n",
    "時間中斷不於清洗階段處理，由開窗邏輯在滑動過程中檢核略過。\n",
    "\n",
    "對應路徑：\n",
    "/home/jovyan/RT08/0925/sliding_win/1014/cleaned/clear/*.csv \n",
    "/home/jovyan/RT08/0925/sliding_win/1014/cleaned/clear/audit_summary.csv \n",
    "/home/jovyan/RT08/0925/sliding_win/1014/audits/schema_audit.csv \n",
    "\n",
    "\n",
    "三、視窗參數\n",
    "視窗長度 W 以筆為單位，固定為 60。\n",
    "步幅 S 以筆為單位，固定為 30。\n",
    "視窗採右對齊策略，以視窗最後一筆資料作為決策時點。\n",
    "\n",
    "參數統一定義於設定檔：\n",
    "/home/jovyan/RT08/0925/sliding_win/1014/config/windowing.yaml \n",
    "\n",
    "四、時間連續性檢核\n",
    "每個視窗生成前，需先輸出 Δt_sec 統計檔以供分析，包括最小值、四分位數、中位數、最大值與平均值。\n",
    "正式訓練時可啟用時間差門檻（起始值為 120 秒）。若視窗內最大 Δt_sec 超過門檻，則略過該視窗，並列出\n",
    "檢核結果須記錄於 manifest 旗標與品質報表。\n",
    "\n",
    "報表輸出：\n",
    "/home/jovyan/RT08/0925/sliding_win/1014/manifests/window_continuity_stats.csv \n",
    "/home/jovyan/RT08/0925/sliding_win/1014/audits/time_gap_anomaly.csv \n",
    "\n",
    "五、缺失處理與報警\n",
    "逐列資料已完整，理論上不再有 NaN。\n",
    "若仍出現缺失，不丟棄樣本，記錄於報警檔。\n",
    "報警內容含視窗 ID、缺失欄位與缺失率。\n",
    "補值策略預設關閉，若開啟，需在設定檔記錄擬合策略與範圍。\n",
    "\n",
    "報警輸出：\n",
    "/home/jovyan/RT08/0925/sliding_win/1014/audits/window_nan_alerts.csv \n",
    "\n",
    "六、標籤聚合規則\n",
    "視窗級標籤由逐筆 set 聚合：\n",
    "視窗內 set=1 比例 ≥ 0.5 → 標籤=1\n",
    "否則 → 標籤=0\n",
    "若視窗最後一筆 ad_para=1，則強制標籤=1（優先於比例規則）\n",
    "重疊視窗獨立聚合，不共享標籤\n",
    "\n",
    "聚合報表：\n",
    "/home/jovyan/RT08/0925/sliding_win/1014/manifests/window_label_summary.csv \n",
    "\n",
    "七、延後計算（Lazy Evaluation）\n",
    "DataLoader 僅在取樣時計算視窗，使用後即釋放記憶體。\n",
    "禁止生成全量視窗檔。\n",
    "可使用可控快取（LRU 或區塊預取）以提升吞吐，但不改變不實體化原則。\n",
    "\n",
    "快取位置：\n",
    "/home/jovyan/RT08/0925/sliding_win/1014/cache/reader_ram/ \n",
    "/home/jovyan/RT08/0925/sliding_win/1014/cache/tf_dataset/ \n",
    "\n",
    "八、資料切分與交叉驗證\n",
    "不以病人為單位切分。\n",
    "採 K 折交叉驗證（建議五折），每折訓練與驗證比例為 8:2。\n",
    "以視窗標籤分層抽樣，確保正負比例穩定。\n",
    "可選擇於折分前濾除時間違規視窗，或以旗標保留。\n",
    "\n",
    "交叉驗證設定與索引：\n",
    "/home/jovyan/RT08/0925/sliding_win/1014/config/kfold_splits.json \n",
    "/home/jovyan/RT08/0925/sliding_win/1014/manifests/window_manifest.csv \n",
    "\n",
    "九、亂數與可重現性\n",
    "全域亂數種子固定為 42。\n",
    "訓練啟動時記錄設定檔與資料檔的雜湊值至訓練日誌。\n",
    "\n",
    "紀錄位置：\n",
    "/home/jovyan/RT08/0925/sliding_win/1014/training/run_YYYYMMDD_HHMM/config_snapshot/config_hash.txt \n",
    "/home/jovyan/RT08/0925/sliding_win/1014/training/run_YYYYMMDD_HHMM/logs/training.log \n",
    "\n",
    "十、品質報表與審計檔\n",
    "必備報表包含：\n",
    "視窗時間連續性統計：manifests/window_continuity_stats.csv\n",
    "視窗缺失報警：audits/window_nan_alerts.csv\n",
    "欄位稽核總覽：audits/schema_audit.csv\n",
    "缺失率彙整：audits/cleaned_nan_summary.csv\n",
    "值域與時間異常：audits/value_range_anomaly.csv, audits/time_gap_anomaly.csv\n",
    "\n",
    "審計結果彙總報表：\n",
    "/home/jovyan/RT08/0925/sliding_win/1014/audits/reports/summary.json \n",
    "\n",
    "十一、設定檔與參數治理\n",
    "所有參數集中於設定檔：\n",
    "/home/jovyan/RT08/0925/sliding_win/1014/config/windowing.yaml \n",
    "內含 W、S、對齊、Δt_sec 門檻、聚合規則、報警設定、是否剔除違規視窗、亂數種子等。\n",
    "程式不得硬編碼；每次更新以新版本資料夾保存，不覆蓋舊版。\n",
    "\n",
    "十二、多分片 CSV 擴展（如啟用）\n",
    "逐筆主檔可切割為多分片以提升 I/O 效率。\n",
    "分片規範：\n",
    "\n",
    "檔名格式：shard_000001.csv、shard_000002.csv\n",
    "分片型錄：_shard_catalog.csv\n",
    "分片統計：_shard_stats.csv\n",
    "邊界處理：halo 模式或 manifest 拼接模式\n",
    "對應路徑：\n",
    "/home/jovyan/RT08/0925/sliding_win/1014/shards/ \n",
    "\n",
    "十三、視窗 manifest 前置（如啟用）\n",
    "訓練前可建立 manifest 檔列出視窗範圍、標籤與品質指標。\n",
    "內容包括分片 ID、行範圍、起訖時間、label、時間統計、quality_flag。\n",
    "manifest 必須附帶設定檔雜湊與分片雜湊。\n",
    "\n",
    "對應檔案：\n",
    "/home/jovyan/RT08/0925/sliding_win/1014/manifests/window_manifest.csv \n",
    "/home/jovyan/RT08/0925/sliding_win/1014/manifests/window_stats.csv \n",
    "/home/jovyan/RT08/0925/sliding_win/1014/manifests/slicing_params.json \n",
    "\n",
    "十四、併發讀取與快取（如啟用）\n",
    "支援多進程讀取。\n",
    "快取策略：分片級與區塊級 LRU。\n",
    "快取鍵包含：資料版本、分片雜湊、起始行、長度、欄位集合與視窗規格雜湊。\n",
    "\n",
    "快取與鎖路徑：\n",
    "/home/jovyan/RT08/0925/sliding_win/1014/cache/reader_ram/ \n",
    "/home/jovyan/RT08/0925/sliding_win/1014/cache/locks/ \n",
    "\n",
    "十五、錯誤處理與一致性\n",
    "所有報表以臨時檔寫入再原子換名。\n",
    "若發現雜湊或索引不一致，訓練自動中止並輸出錯誤報告。\n",
    "重要輸出記錄於：\n",
    "/home/jovyan/RT08/0925/sliding_win/1014/training/run_*/logs/error.log \n",
    "/home/jovyan/RT08/0925/sliding_win/1014/audits/reports/summary.json \n",
    "\n",
    "十六、落地與交付\n",
    "交付內容包含：\n",
    "cleaned 主資料\n",
    "設定檔\n",
    "品質報表\n",
    "（如啟用）視窗 manifest\n",
    "訓練腳本啟動時記錄設定與資料雜湊；結束時輸出：\n",
    "模型權重：/training/run_*/checkpoints/\n",
    "模型指標：/training/run_*/metrics/metrics.csv\n",
    "禁止將動態視窗樣本另存為靜態長期檔案；若需，須以新版本目錄隔離。\n",
    "───────────────────────────────"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5b1cbae5-3700-44d6-bc92-1fab2f49ce5b",
   "metadata": {},
   "outputs": [],
   "source": [
    "下面是取視窗最後一筆的標籤當作是視窗級標籤 但我要改成 多數決"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 228,
   "id": "8e5003b2-9c92-4ecc-84ce-cb20767e4008",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "==[初始化]==\n",
      "[設定] 已寫入設定檔：/home/jovyan/RT08/0925/sliding_win/1014/config/windowing.yaml\n",
      "\n",
      "==[步驟一] 清洗逐筆 CSV（不動原始）==\n",
      "[清洗] 發現檔案數：122（預期 122）\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "清洗中: 100%|██████████| 122/122 [00:02<00:00, 44.34it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[清洗] 已完成；稽核已輸出：audit_summary.csv / schema_audit.csv / cleaned_nan_summary.csv / time_gap_anomaly.csv（若有）\n",
      "[清洗] 已輸出 Δt_sec 統計：manifests/window_continuity_stats.csv\n",
      "\n",
      "==[步驟二] 多分片 CSV（可選）==\n",
      "[分片] 開始建立分片（rows_per_shard=500,000）…\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "分片中: 100%|██████████| 122/122 [00:10<00:00, 11.29it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[分片] 完成：_shard_catalog.csv / _shard_stats.csv\n",
      "\n",
      "==[步驟三] 產生視窗 manifest（不實體化樣本）==\n",
      "[Manifest] 產生視窗 manifest（不實體化樣本）…\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "掃描來源: 100%|██████████| 3/3 [00:07<00:00,  2.64s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Manifest] 已輸出：/home/jovyan/RT08/0925/sliding_win/1014/manifests/window_manifest.csv（total_windows=44,558）\n",
      "[Manifest] 統計輸出：window_stats.csv / window_label_summary.csv；缺失報警（若有）：window_nan_alerts.csv\n",
      "[設定] 已寫入設定檔：/home/jovyan/RT08/0925/sliding_win/1014/config/windowing.yaml\n",
      "[Manifest] 已寫入 slicing_params.json（含 config/shards 雜湊）\n",
      "\n",
      "==[步驟四] K 折分層交叉驗證索引==\n",
      "[KFold] 生成 5 折分層索引（label 分層；seed=42）…\n",
      "[KFold] 已輸出：config/kfold_splits.json\n",
      "\n",
      "✅ 全部完成。重點輸出：\n",
      " - Cleaned：/home/jovyan/RT08/0925/sliding_win/1014/cleaned/clear/\n",
      " - Shards：/home/jovyan/RT08/0925/sliding_win/1014/shards/（_shard_catalog.csv, _shard_stats.csv）\n",
      " - Manifests：/home/jovyan/RT08/0925/sliding_win/1014/manifests/（window_manifest.csv, window_stats.csv, window_label_summary.csv）\n",
      " - Audits：/home/jovyan/RT08/0925/sliding_win/1014/audits/（schema_audit.csv, cleaned_nan_summary.csv, time_gap_anomaly.csv）\n",
      " - Config：/home/jovyan/RT08/0925/sliding_win/1014/config/（windowing.yaml, kfold_splits.json）\n",
      " - Docs：/home/jovyan/RT08/0925/sliding_win/1014/docs/（schema_final.json）\n",
      " - Cache：/home/jovyan/RT08/0925/sliding_win/1014/cache/reader_ram/, /home/jovyan/RT08/0925/sliding_win/1014/cache/tf_dataset/, /home/jovyan/RT08/0925/sliding_win/1014/cache/locks/\n",
      " - Training：/home/jovyan/RT08/0925/sliding_win/1014/training//*/config_snapshot/config_hash.txt, logs/training.log\n",
      "請接著在 DataLoader 端依 manifest 動態取窗（Lazy Evaluation），嚴禁另存成全量靜態視窗檔。\n"
     ]
    }
   ],
   "source": [
    "# -*- coding: utf-8 -*-\n",
    "\"\"\"\n",
    "不實體化滑動視窗 - 版本 1014（工程實作用，含多分片、manifest 前置、併發讀取/快取）\n",
    "================================================================================\n",
    "⚠️ 不會修改原始資料：/home/jovyan/RT08/0925/bling_1014/（122 個 CSV）\n",
    "所有輸出/中繼/報表皆落在：/home/jovyan/RT08/0925/sliding_win/1014/\n",
    "\n",
    "功能總覽（對應您的規範）：\n",
    "1) 目錄初始化與設定檔落地（windowing.yaml、schema_final.json）\n",
    "2) 原始逐筆 CSV → cleaned/clear/（保持逐筆，不展開視窗）\n",
    "   - 基本清洗：欄位正規化、senddate 解析、補 ts_unix/Δt_sec（若缺）\n",
    "   - 品質過濾：先過濾 nan_check != 1 或時間欄異常\n",
    "   - 稽核：schema_audit.csv、cleaned_nan_summary.csv、time_gap_anomaly.csv 等\n",
    "3) Δt_sec 統計輸出（window_continuity_stats.csv）\n",
    "4) 多分片 CSV（shards/）與分片型錄/統計（_shard_catalog.csv、_shard_stats.csv）\n",
    "5) 視窗 manifest 前置（window_manifest.csv, window_stats.csv, window_label_summary.csv）\n",
    "   - W=60 筆、S=30 筆、右對齊\n",
    "   - 連續性檢核：max(Δt_sec) > 門檻（預設 120 秒）→ 視窗標示/略過\n",
    "   - 標籤聚合：比例規則（set_fin==1 比例≥0.5 ⇒ 1；否則 0）；ad_para 最後一筆=1 → 強制 1\n",
    "6) 併發讀取與快取：多進程處理檔案 + LRU 記憶體快取（reader_ram/）\n",
    "7) K 折分層交叉驗證（kfold_splits.json；固定亂數 42；不以病人分割）\n",
    "8) 訓練執行前快照/雜湊記錄（config_hash.txt 等），品質報表彙總 summary.json\n",
    "9) 報警：window_nan_alerts.csv（若有殘留缺失）\n",
    "\n",
    "使用方式：\n",
    "- 直接執行本檔（Python 3.9+，需 pandas/numpy/pyyaml/tqdm）\n",
    "- 可在下方「使用者參數區」調整 W/S/門檻/分片大小/併發數等\n",
    "\"\"\"\n",
    "\n",
    "import os\n",
    "import re\n",
    "import json\n",
    "import math\n",
    "import glob\n",
    "import time\n",
    "import hashlib\n",
    "import warnings\n",
    "from typing import List, Dict, Any, Tuple\n",
    "from dataclasses import dataclass, asdict\n",
    "from functools import lru_cache\n",
    "from multiprocessing import Pool, cpu_count\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from tqdm import tqdm\n",
    "import yaml\n",
    "\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "# =========================\n",
    "# 使用者參數區（依需要可調）\n",
    "# =========================\n",
    "SRC_DIR = \"/home/jovyan/RT08/0925/bling_1014/\"     # 原始逐筆 CSV（❗不修改）\n",
    "OUT_ROOT = \"/home/jovyan/RT08/0925/sliding_win/1014/\"\n",
    "\n",
    "# 版本化資料夾層級\n",
    "DIR_CLEANED = os.path.join(OUT_ROOT, \"cleaned/clear/\")\n",
    "DIR_DOCS    = os.path.join(OUT_ROOT, \"docs/\")\n",
    "DIR_CFG     = os.path.join(OUT_ROOT, \"config/\")\n",
    "DIR_AUDITS  = os.path.join(OUT_ROOT, \"audits/\")\n",
    "DIR_AUDITS_RPT = os.path.join(DIR_AUDITS, \"reports/\")\n",
    "DIR_MAN     = os.path.join(OUT_ROOT, \"manifests/\")\n",
    "DIR_CACHE   = os.path.join(OUT_ROOT, \"cache/reader_ram/\")\n",
    "DIR_CACHE_TF= os.path.join(OUT_ROOT, \"cache/tf_dataset/\")\n",
    "DIR_LOCKS   = os.path.join(OUT_ROOT, \"cache/locks/\")\n",
    "DIR_SHARDS  = os.path.join(OUT_ROOT, \"shards/\")\n",
    "DIR_TRAIN   = os.path.join(OUT_ROOT, \"training/\")\n",
    "\n",
    "# 檔名與設定\n",
    "EXPECTED_FILES = 122\n",
    "SEED = 42\n",
    "\n",
    "# 視窗參數（以「筆」為單位）\n",
    "W = 60\n",
    "S = 30\n",
    "RIGHT_ALIGN = True\n",
    "\n",
    "# 時間連續性門檻（秒）：max(Δt_sec) 超過則標示/略過\n",
    "DT_SEC_THRESHOLD = 120.0\n",
    "\n",
    "# 多分片設定\n",
    "ENABLE_SHARDING = True\n",
    "SHARD_ROWS = 500_000  # 每片最多筆數（依硬體與訓練吞吐需調整）\n",
    "\n",
    "# 併發處理\n",
    "N_WORKERS = max(1, min(cpu_count() - 1, 8))  # 避免炸滿機器\n",
    "\n",
    "# 必備欄位（清洗後應存在）\n",
    "REQUIRED_ID_COL   = \"patno\"\n",
    "REQUIRED_TIME_COL = \"senddate\"\n",
    "REQUIRED_TS_COL   = \"ts_unix\"\n",
    "REQUIRED_DT_COL   = \"Δt_sec\"   # 保留您習慣的字樣\n",
    "REQUIRED_QC_COL   = \"nan_check\"\n",
    "REQUIRED_Y1_COL   = \"ad_para\"\n",
    "REQUIRED_Y2_COL   = \"set_fin\"\n",
    "\n",
    "# =========================\n",
    "# 工具：目錄與雜湊\n",
    "# =========================\n",
    "def ensure_dirs():\n",
    "    for d in [DIR_CLEANED, DIR_DOCS, DIR_CFG, DIR_AUDITS, DIR_AUDITS_RPT,\n",
    "              DIR_MAN, DIR_CACHE, DIR_CACHE_TF, DIR_LOCKS, DIR_SHARDS, DIR_TRAIN]:\n",
    "        os.makedirs(d, exist_ok=True)\n",
    "\n",
    "def sha256_text(s: str) -> str:\n",
    "    return hashlib.sha256(s.encode(\"utf-8\")).hexdigest()\n",
    "\n",
    "def sha256_file(path: str, block_size: int = 1 << 20) -> str:\n",
    "    h = hashlib.sha256()\n",
    "    with open(path, \"rb\") as f:\n",
    "        while True:\n",
    "            b = f.read(block_size)\n",
    "            if not b: break\n",
    "            h.update(b)\n",
    "    return h.hexdigest()\n",
    "\n",
    "# =========================\n",
    "# 設定檔與 schema 落地\n",
    "# =========================\n",
    "def write_windowing_yaml():\n",
    "    cfg = {\n",
    "        \"version\": \"1014\",\n",
    "        \"seed\": SEED,\n",
    "        \"window\": {\n",
    "            \"W\": W,\n",
    "            \"S\": S,\n",
    "            \"align\": \"right\" if RIGHT_ALIGN else \"left\"\n",
    "        },\n",
    "        \"continuity\": {\n",
    "            \"dt_sec_threshold\": DT_SEC_THRESHOLD,\n",
    "            \"enforce\": True\n",
    "        },\n",
    "        \"labeling\": {\n",
    "            \"rule\": \"ratio>=0.5 set_fin==1 OR last_ad_para==1 -> 1 else 0\",\n",
    "            \"ratio_threshold\": 0.5,\n",
    "            \"force_by_last_ad_para\": True\n",
    "        },\n",
    "        \"sharding\": {\n",
    "            \"enabled\": ENABLE_SHARDING,\n",
    "            \"rows_per_shard\": SHARD_ROWS,\n",
    "            \"name_pattern\": \"shard_%06d.csv\"\n",
    "        },\n",
    "        \"lazy_evaluation\": True,\n",
    "        \"drop_static_windows\": False,\n",
    "        \"notes\": \"do not materialize windows; manifest-only\"\n",
    "    }\n",
    "    os.makedirs(DIR_CFG, exist_ok=True)\n",
    "    path = os.path.join(DIR_CFG, \"windowing.yaml\")\n",
    "    with open(path, \"w\", encoding=\"utf-8\") as f:\n",
    "        yaml.safe_dump(cfg, f, allow_unicode=True, sort_keys=False)\n",
    "    print(f\"[設定] 已寫入設定檔：{path}\")\n",
    "    return path, cfg\n",
    "\n",
    "def write_schema_json(final_cols: List[str]):\n",
    "    os.makedirs(DIR_DOCS, exist_ok=True)\n",
    "    path = os.path.join(DIR_DOCS, \"schema_final.json\")\n",
    "    with open(path, \"w\", encoding=\"utf-8\") as f:\n",
    "        json.dump({\"final_columns\": final_cols}, f, ensure_ascii=False, indent=2)\n",
    "    print(f\"[設定] 已寫入 schema：{path}\")\n",
    "    return path\n",
    "\n",
    "# =========================\n",
    "# 讀檔/清洗輔助\n",
    "# =========================\n",
    "def list_csv_files(src: str) -> List[str]:\n",
    "    files = sorted(glob.glob(os.path.join(src, \"*.csv\")))\n",
    "    return files\n",
    "\n",
    "def normalize_col(name: str) -> str:\n",
    "    s = str(name).strip()\n",
    "    s = re.sub(r\"\\s+\", \"_\", s)\n",
    "    return s\n",
    "\n",
    "def to_datetime_safe(series: pd.Series) -> pd.Series:\n",
    "    # 支援「上午/下午」「AM/PM」，最大限度解析成 datetime\n",
    "    s = series.astype(str)\n",
    "    s = s.str.replace(\"上午\", \"AM\").str.replace(\"下午\", \"PM\")\n",
    "    s = s.str.replace(\"早上\", \"AM\").str.replace(\"晚上\", \"PM\").str.replace(\"中午\", \"PM\")\n",
    "    dt = pd.to_datetime(s, errors=\"coerce\", infer_datetime_format=True)\n",
    "    return dt\n",
    "\n",
    "def add_ts_unix_and_dtsec(df: pd.DataFrame, id_col: str) -> pd.DataFrame:\n",
    "    # 若缺 ts_unix：以 senddate 轉換為 epoch 秒；若 senddate 缺失無法轉換則為 NaN\n",
    "    if REQUIRED_TS_COL not in df.columns:\n",
    "        df[REQUIRED_TS_COL] = np.floor(pd.to_datetime(df[REQUIRED_TIME_COL], errors=\"coerce\").astype(\"int64\") / 1e9)\n",
    "\n",
    "    # 若缺 Δt_sec：依同檔（多數情形同患者）時間排序後做 diff\n",
    "    if REQUIRED_DT_COL not in df.columns:\n",
    "        df = df.sort_values(by=[REQUIRED_TIME_COL]).reset_index(drop=True)\n",
    "        # 若單檔包含多個 patno，則以 patno 分組計算 Δt_sec（較穩）\n",
    "        if id_col in df.columns:\n",
    "            df[REQUIRED_DT_COL] = df.groupby(id_col)[REQUIRED_TIME_COL].diff().dt.total_seconds()\n",
    "        else:\n",
    "            df[REQUIRED_DT_COL] = df[REQUIRED_TIME_COL].diff().dt.total_seconds()\n",
    "        df[REQUIRED_DT_COL] = df[REQUIRED_DT_COL].fillna(0.0)\n",
    "    return df\n",
    "\n",
    "def basic_clean_one(path: str) -> Dict[str, Any]:\n",
    "    \"\"\"\n",
    "    單檔清洗：\n",
    "    - 讀取 CSV（僅使用 pandas 內建，避免外部依賴）\n",
    "    - 欄位名稱正規化（保留原名做 mapping ）\n",
    "    - 解析 senddate；補 ts_unix/Δt_sec\n",
    "    - 過濾 nan_check != 1 或 senddate 無效\n",
    "    - 回傳清洗後寫檔路徑與稽核資訊\n",
    "    \"\"\"\n",
    "    base = os.path.basename(path)\n",
    "    out_path = os.path.join(DIR_CLEANED, base)\n",
    "\n",
    "    raw = pd.read_csv(path)\n",
    "    orig_cols = list(raw.columns)\n",
    "    rename_map = {c: normalize_col(c) for c in orig_cols}\n",
    "    df = raw.rename(columns=rename_map)\n",
    "\n",
    "    # 檢查必要欄位存在（若缺，先創空欄再處理）\n",
    "    for c in [REQUIRED_ID_COL, REQUIRED_TIME_COL, REQUIRED_QC_COL, REQUIRED_Y1_COL, REQUIRED_Y2_COL]:\n",
    "        if c not in df.columns:\n",
    "            df[c] = np.nan\n",
    "\n",
    "    # 時間解析\n",
    "    df[REQUIRED_TIME_COL] = to_datetime_safe(df[REQUIRED_TIME_COL])\n",
    "\n",
    "    # 補 ts_unix/Δt_sec\n",
    "    df = add_ts_unix_and_dtsec(df, REQUIRED_ID_COL)\n",
    "\n",
    "    # 先過濾 nan_check != 1 或 時間欄異常\n",
    "    # 若 nan_check 非數值，轉為數值後比對\n",
    "    qc_num = pd.to_numeric(df[REQUIRED_QC_COL], errors=\"coerce\")\n",
    "    valid_mask = (qc_num == 1) & (df[REQUIRED_TIME_COL].notna())\n",
    "    df_clean = df.loc[valid_mask].copy()\n",
    "\n",
    "    # 寫檔\n",
    "    df_clean.to_csv(out_path, index=False, encoding=\"utf-8-sig\")\n",
    "\n",
    "    # 簡要稽核\n",
    "    audit = {\n",
    "        \"file_name\": base,\n",
    "        \"src_path\": path,\n",
    "        \"out_path\": out_path,\n",
    "        \"rows_in\": int(len(raw)),\n",
    "        \"rows_out\": int(len(df_clean)),\n",
    "        \"nan_rows_dropped\": int((~valid_mask).sum()),\n",
    "        \"has_ts_unix\": REQUIRED_TS_COL in df_clean.columns,\n",
    "        \"has_dt_sec\": REQUIRED_DT_COL in df_clean.columns,\n",
    "        \"min_dt_sec\": float(df_clean[REQUIRED_DT_COL].min()) if REQUIRED_DT_COL in df_clean.columns else np.nan,\n",
    "        \"p50_dt_sec\": float(df_clean[REQUIRED_DT_COL].median()) if REQUIRED_DT_COL in df_clean.columns else np.nan,\n",
    "        \"p90_dt_sec\": float(df_clean[REQUIRED_DT_COL].quantile(0.9)) if REQUIRED_DT_COL in df_clean.columns else np.nan,\n",
    "        \"max_dt_sec\": float(df_clean[REQUIRED_DT_COL].max()) if REQUIRED_DT_COL in df_clean.columns else np.nan,\n",
    "        \"total_nan_after\": int(df_clean.isna().sum().sum()),\n",
    "    }\n",
    "    return audit\n",
    "\n",
    "# =========================\n",
    "# 併發清洗（不動原始檔）\n",
    "# =========================\n",
    "def step_clean_all():\n",
    "    files = list_csv_files(SRC_DIR)\n",
    "    print(f\"[清洗] 發現檔案數：{len(files)}（預期 {EXPECTED_FILES}）\")\n",
    "    os.makedirs(DIR_CLEANED, exist_ok=True)\n",
    "\n",
    "    audits = []\n",
    "    with Pool(processes=N_WORKERS) as pool:\n",
    "        for res in tqdm(pool.imap_unordered(basic_clean_one, files), total=len(files), desc=\"清洗中\"):\n",
    "            audits.append(res)\n",
    "\n",
    "    audit_df = pd.DataFrame(audits).sort_values(\"file_name\")\n",
    "    # 稽核輸出\n",
    "    audit_df.to_csv(os.path.join(DIR_CLEANED, \"../audit_summary.csv\"), index=False, encoding=\"utf-8-sig\")\n",
    "\n",
    "    # 欄位稽核（schema）\n",
    "    schema_rows = []\n",
    "    for a in audits:\n",
    "        cols = list(pd.read_csv(a[\"out_path\"], nrows=0).columns)\n",
    "        # 檢查命名規範：小寫/底線/唯一\n",
    "        all_lower = all(c == c.lower() for c in cols)\n",
    "        snake_ok  = all(bool(re.fullmatch(r\"[a-z0-9_]+\", c)) for c in cols)\n",
    "        uniq_ok   = (len(cols) == len(set(cols)))\n",
    "        schema_rows.append({\n",
    "            \"file_name\": a[\"file_name\"],\n",
    "            \"n_cols\": len(cols),\n",
    "            \"all_lowercase\": all_lower,\n",
    "            \"snake_case_only\": snake_ok,\n",
    "            \"unique_names\": uniq_ok\n",
    "        })\n",
    "    pd.DataFrame(schema_rows).to_csv(os.path.join(DIR_AUDITS, \"schema_audit.csv\"), index=False, encoding=\"utf-8-sig\")\n",
    "\n",
    "    # 缺失彙整（清洗後理論上極少）\n",
    "    nan_rows = []\n",
    "    for a in audits:\n",
    "        df = pd.read_csv(a[\"out_path\"])\n",
    "        nan_rows.append({\n",
    "            \"file_name\": a[\"file_name\"],\n",
    "            \"nan_cells\": int(df.isna().sum().sum()),\n",
    "            \"rows\": int(len(df))\n",
    "        })\n",
    "    pd.DataFrame(nan_rows).to_csv(os.path.join(DIR_AUDITS, \"cleaned_nan_summary.csv\"), index=False, encoding=\"utf-8-sig\")\n",
    "\n",
    "    # 時間間隔異常（> 門檻）\n",
    "    gap_rows = []\n",
    "    for a in audits:\n",
    "        df = pd.read_csv(a[\"out_path\"])\n",
    "        if REQUIRED_DT_COL in df.columns:\n",
    "            bad = df[df[REQUIRED_DT_COL] > DT_SEC_THRESHOLD]\n",
    "            if not bad.empty:\n",
    "                tmp = bad[[REQUIRED_TIME_COL, REQUIRED_DT_COL]].copy()\n",
    "                tmp.insert(0, \"file_name\", a[\"file_name\"])\n",
    "                gap_rows.append(tmp)\n",
    "    if gap_rows:\n",
    "        pd.concat(gap_rows, ignore_index=True).to_csv(os.path.join(DIR_AUDITS, \"time_gap_anomaly.csv\"),\n",
    "                                                     index=False, encoding=\"utf-8-sig\")\n",
    "    print(\"[清洗] 已完成；稽核已輸出：audit_summary.csv / schema_audit.csv / cleaned_nan_summary.csv / time_gap_anomaly.csv（若有）\")\n",
    "\n",
    "    # Δt_sec 統計\n",
    "    stat_rows = []\n",
    "    for a in audits:\n",
    "        df = pd.read_csv(a[\"out_path\"])\n",
    "        if REQUIRED_DT_COL in df.columns and not df.empty:\n",
    "            s = df[REQUIRED_DT_COL]\n",
    "            stat_rows.append({\n",
    "                \"file_name\": a[\"file_name\"],\n",
    "                \"count\": int(s.count()),\n",
    "                \"min\": float(s.min()),\n",
    "                \"q1\": float(s.quantile(0.25)),\n",
    "                \"median\": float(s.median()),\n",
    "                \"q3\": float(s.quantile(0.75)),\n",
    "                \"mean\": float(s.mean()),\n",
    "                \"max\": float(s.max())\n",
    "            })\n",
    "    pd.DataFrame(stat_rows).to_csv(os.path.join(DIR_MAN, \"window_continuity_stats.csv\"), index=False, encoding=\"utf-8-sig\")\n",
    "    print(\"[清洗] 已輸出 Δt_sec 統計：manifests/window_continuity_stats.csv\")\n",
    "\n",
    "    # 回傳「清洗後檔案路徑」供後續流程使用\n",
    "    return [os.path.join(DIR_CLEANED, os.path.basename(p)) for p in files if os.path.exists(os.path.join(DIR_CLEANED, os.path.basename(p)))]\n",
    "\n",
    "# =========================\n",
    "# 多分片（Sharding）\n",
    "# =========================\n",
    "def step_build_shards(cleaned_paths: List[str]) -> Tuple[List[str], pd.DataFrame]:\n",
    "    if not ENABLE_SHARDING:\n",
    "        print(\"[分片] 未啟用；略過。\")\n",
    "        return [], pd.DataFrame()\n",
    "\n",
    "    print(f\"[分片] 開始建立分片（rows_per_shard={SHARD_ROWS:,}）…\")\n",
    "    os.makedirs(DIR_SHARDS, exist_ok=True)\n",
    "\n",
    "    shard_idx = 1\n",
    "    shard_paths = []\n",
    "    cat_rows = []   # 分片型錄\n",
    "    stat_rows = []\n",
    "\n",
    "    cur_rows = 0\n",
    "    cur_buf = []\n",
    "\n",
    "    def flush_shard():\n",
    "        nonlocal shard_idx, shard_paths, cur_buf, cur_rows\n",
    "        if not cur_buf: return\n",
    "        shard_df = pd.concat(cur_buf, ignore_index=True)\n",
    "        shard_name = f\"shard_{shard_idx:06d}.csv\"\n",
    "        shard_path = os.path.join(DIR_SHARDS, shard_name)\n",
    "        shard_df.to_csv(shard_path, index=False, encoding=\"utf-8-sig\")\n",
    "        shard_paths.append(shard_path)\n",
    "        # 統計\n",
    "        stat_rows.append({\n",
    "            \"shard\": shard_name,\n",
    "            \"rows\": int(len(shard_df)),\n",
    "            \"hash\": sha256_file(shard_path)\n",
    "        })\n",
    "        # 型錄\n",
    "        cat_rows.append({\n",
    "            \"shard\": shard_name,\n",
    "            \"rows\": int(len(shard_df)),\n",
    "            \"path\": shard_path\n",
    "        })\n",
    "        # 重置\n",
    "        shard_idx += 1\n",
    "        cur_buf = []\n",
    "        cur_rows = 0\n",
    "\n",
    "    for p in tqdm(cleaned_paths, desc=\"分片中\"):\n",
    "        df = pd.read_csv(p)\n",
    "        # 僅保留清洗後需要的欄位集合（保守起見保留全欄）\n",
    "        if cur_rows + len(df) > SHARD_ROWS and cur_rows > 0:\n",
    "            flush_shard()\n",
    "        cur_buf.append(df)\n",
    "        cur_rows += len(df)\n",
    "        # 若單檔已超過上限，直接 flush\n",
    "        while cur_rows >= SHARD_ROWS:\n",
    "            # 切一塊滿片\n",
    "            need = SHARD_ROWS\n",
    "            acc = pd.concat(cur_buf, ignore_index=True)\n",
    "            full = acc.iloc[:need].copy()\n",
    "            # 剩下留回緩衝\n",
    "            left = acc.iloc[need:].copy()\n",
    "            shard_name = f\"shard_{shard_idx:06d}.csv\"\n",
    "            shard_path = os.path.join(DIR_SHARDS, shard_name)\n",
    "            full.to_csv(shard_path, index=False, encoding=\"utf-8-sig\")\n",
    "            shard_paths.append(shard_path)\n",
    "            stat_rows.append({\"shard\": shard_name, \"rows\": int(len(full)), \"hash\": sha256_file(shard_path)})\n",
    "            cat_rows.append({\"shard\": shard_name, \"rows\": int(len(full)), \"path\": shard_path})\n",
    "            shard_idx += 1\n",
    "            cur_buf = [left] if not left.empty else []\n",
    "            cur_rows = len(left)\n",
    "\n",
    "    flush_shard()\n",
    "    # 輸出型錄與統計\n",
    "    cat_df = pd.DataFrame(cat_rows)\n",
    "    stat_df = pd.DataFrame(stat_rows)\n",
    "    cat_df.to_csv(os.path.join(DIR_SHARDS, \"_shard_catalog.csv\"), index=False, encoding=\"utf-8-sig\")\n",
    "    stat_df.to_csv(os.path.join(DIR_SHARDS, \"_shard_stats.csv\"), index=False, encoding=\"utf-8-sig\")\n",
    "    print(\"[分片] 完成：_shard_catalog.csv / _shard_stats.csv\")\n",
    "    return shard_paths, stat_df\n",
    "\n",
    "# =========================\n",
    "# 視窗切割（不實體化：只產生 manifest）\n",
    "# =========================\n",
    "@dataclass\n",
    "class WindowRow:\n",
    "    window_id: str\n",
    "    source: str        # 來源檔（分片或 cleaned 檔）\n",
    "    start_idx: int     # 在來源檔中的起始索引（含）\n",
    "    end_idx: int       # 在來源檔中的結束索引（含）\n",
    "    start_time: str\n",
    "    end_time: str\n",
    "    max_dt_sec: float\n",
    "    continuity_ok: bool\n",
    "    label: int\n",
    "    pos_ratio_setfin1: float\n",
    "    last_ad_para: float\n",
    "    patno_first: str\n",
    "\n",
    "def label_by_rules(sub: pd.DataFrame) -> Tuple[int, float, float]:\n",
    "    # 規則：set_fin==1 比例≥0.5 → 1；否則 0；若最後一筆 ad_para=1 → 強制 1\n",
    "    setfin = pd.to_numeric(sub[REQUIRED_Y2_COL], errors=\"coerce\")\n",
    "    adpara = pd.to_numeric(sub[REQUIRED_Y1_COL], errors=\"coerce\").fillna(0.0)\n",
    "    ratio = float((setfin == 1).mean()) if len(sub) > 0 else 0.0\n",
    "    last_ap = float(adpara.iloc[-1]) if len(sub) > 0 else 0.0\n",
    "    y = 1 if (ratio >= 0.5) or (last_ap == 1.0) else 0\n",
    "    return y, ratio, last_ap\n",
    "\n",
    "def scan_one_source_for_manifest(source_csv: str) -> Tuple[List[WindowRow], Dict[str, Any], pd.DataFrame]:\n",
    "    \"\"\"\n",
    "    從單一來源檔（可為 cleaned 檔或 shard 檔）產生視窗 manifest 記錄（不寫出子視窗）。\n",
    "    回傳：(window_rows, stats_summary, nan_alert_df)\n",
    "    \"\"\"\n",
    "    df = pd.read_csv(source_csv)\n",
    "    if df.empty:\n",
    "        return [], {\"source\": os.path.basename(source_csv), \"total_rows\": 0, \"total_windows\": 0}, pd.DataFrame()\n",
    "\n",
    "    # 以時間排序，右對齊視窗以「最後一筆」為決策點\n",
    "    df = df.sort_values(REQUIRED_TIME_COL).reset_index(drop=True)\n",
    "\n",
    "    # 缺失報警（理論上清洗後應極少，仍保守檢查）\n",
    "    nan_alert = df.isna().mean()\n",
    "    nan_alert = nan_alert[nan_alert > 0.0]\n",
    "    nan_df = pd.DataFrame({\"column\": nan_alert.index, \"nan_ratio\": nan_alert.values})\n",
    "    if not nan_df.empty:\n",
    "        nan_df.insert(0, \"source\", os.path.basename(source_csv))\n",
    "\n",
    "    rows = []\n",
    "    n = len(df)\n",
    "    # 以「筆」為單位切割：W=60、S=30\n",
    "    starts = list(range(0, max(0, n - W + 1), S))\n",
    "    for si in starts:\n",
    "        ei = si + W - 1\n",
    "        sub = df.iloc[si:ei+1]\n",
    "        # 連續性檢核：視窗內 Δt_sec 最大值\n",
    "        if REQUIRED_DT_COL in sub.columns:\n",
    "            max_dt = float(sub[REQUIRED_DT_COL].max())\n",
    "        else:\n",
    "            max_dt = np.nan\n",
    "        cont_ok = (not np.isnan(max_dt)) and (max_dt <= DT_SEC_THRESHOLD)\n",
    "\n",
    "        # 標籤聚合\n",
    "        y, ratio, last_ap = label_by_rules(sub)\n",
    "\n",
    "        # 視窗 ID（可用來源檔名 + 索引範圍）\n",
    "        win_id = f\"{os.path.basename(source_csv)}::{si}-{ei}\"\n",
    "        rows.append(WindowRow(\n",
    "            window_id=win_id,\n",
    "            source=os.path.basename(source_csv),\n",
    "            start_idx=si,\n",
    "            end_idx=ei,\n",
    "            start_time=str(sub[REQUIRED_TIME_COL].iloc[0]) if len(sub) else \"\",\n",
    "            end_time=str(sub[REQUIRED_TIME_COL].iloc[-1]) if len(sub) else \"\",\n",
    "            max_dt_sec=float(max_dt) if not np.isnan(max_dt) else np.nan,\n",
    "            continuity_ok=bool(cont_ok),\n",
    "            label=int(y),\n",
    "            pos_ratio_setfin1=float(ratio),\n",
    "            last_ad_para=float(last_ap),\n",
    "            patno_first=str(sub[REQUIRED_ID_COL].iloc[0]) if REQUIRED_ID_COL in sub.columns and len(sub) else \"\"\n",
    "        ))\n",
    "\n",
    "    stats = {\n",
    "        \"source\": os.path.basename(source_csv),\n",
    "        \"total_rows\": int(n),\n",
    "        \"total_windows\": int(len(rows)),\n",
    "        \"n_cont_ok\": int(sum(r.continuity_ok for r in rows)),\n",
    "        \"n_label_pos\": int(sum(r.label == 1 for r in rows)),\n",
    "        \"max_dt_overall\": float(df[REQUIRED_DT_COL].max()) if REQUIRED_DT_COL in df.columns and not df.empty else np.nan\n",
    "    }\n",
    "    return rows, stats, nan_df\n",
    "\n",
    "def step_build_manifest(cleaned_paths: List[str], shard_paths: List[str]):\n",
    "    print(\"[Manifest] 產生視窗 manifest（不實體化樣本）…\")\n",
    "    sources = shard_paths if ENABLE_SHARDING and shard_paths else cleaned_paths\n",
    "\n",
    "    all_rows: List[WindowRow] = []\n",
    "    all_stats = []\n",
    "    nan_alerts = []\n",
    "\n",
    "    with Pool(processes=N_WORKERS) as pool:\n",
    "        for rows, stats, nan_df in tqdm(pool.imap_unordered(scan_one_source_for_manifest, sources),\n",
    "                                        total=len(sources), desc=\"掃描來源\"):\n",
    "            all_rows.extend(rows)\n",
    "            all_stats.append(stats)\n",
    "            if nan_df is not None and not nan_df.empty:\n",
    "                nan_alerts.append(nan_df)\n",
    "\n",
    "    # 寫 window_manifest.csv\n",
    "    man_df = pd.DataFrame([asdict(r) for r in all_rows])\n",
    "    man_path = os.path.join(DIR_MAN, \"window_manifest.csv\")\n",
    "    man_df.to_csv(man_path, index=False, encoding=\"utf-8-sig\")\n",
    "    print(f\"[Manifest] 已輸出：{man_path}（total_windows={len(man_df):,}）\")\n",
    "\n",
    "    # continuity/label 統計\n",
    "    stats_df = pd.DataFrame(all_stats)\n",
    "    stats_path = os.path.join(DIR_MAN, \"window_stats.csv\")\n",
    "    stats_df.to_csv(stats_path, index=False, encoding=\"utf-8-sig\")\n",
    "\n",
    "    # 視窗標籤彙整\n",
    "    label_summary = man_df.groupby(\"label\").size().rename(\"count\").reset_index()\n",
    "    label_path = os.path.join(DIR_MAN, \"window_label_summary.csv\")\n",
    "    label_summary.to_csv(label_path, index=False, encoding=\"utf-8-sig\")\n",
    "\n",
    "    # 缺失報警\n",
    "    if nan_alerts:\n",
    "        pd.concat(nan_alerts, ignore_index=True).to_csv(os.path.join(DIR_AUDITS, \"window_nan_alerts.csv\"),\n",
    "                                                        index=False, encoding=\"utf-8-sig\")\n",
    "    print(\"[Manifest] 統計輸出：window_stats.csv / window_label_summary.csv；缺失報警（若有）：window_nan_alerts.csv\")\n",
    "\n",
    "    # 參數快照（含雜湊）\n",
    "    cfg_path, cfg_obj = write_windowing_yaml()\n",
    "    cfg_hash = sha256_file(cfg_path)\n",
    "    # 分片雜湊（若有）\n",
    "    shard_hashes = []\n",
    "    if shard_paths:\n",
    "        for p in shard_paths:\n",
    "            shard_hashes.append({\"file\": os.path.basename(p), \"hash\": sha256_file(p)})\n",
    "    snap = {\n",
    "        \"config_hash\": cfg_hash,\n",
    "        \"shards\": shard_hashes,\n",
    "        \"generated_at\": time.strftime(\"%Y-%m-%d %H:%M:%S\")\n",
    "    }\n",
    "    with open(os.path.join(DIR_MAN, \"slicing_params.json\"), \"w\", encoding=\"utf-8\") as f:\n",
    "        json.dump(snap, f, ensure_ascii=False, indent=2)\n",
    "    print(\"[Manifest] 已寫入 slicing_params.json（含 config/shards 雜湊）\")\n",
    "\n",
    "# =========================\n",
    "# K 折交叉驗證（分層）\n",
    "# =========================\n",
    "def step_make_kfold(manifest_csv: str, k: int = 5):\n",
    "    print(f\"[KFold] 生成 {k} 折分層索引（label 分層；seed={SEED}）…\")\n",
    "    df = pd.read_csv(manifest_csv)\n",
    "    if df.empty:\n",
    "        print(\"[KFold] manifest 為空，略過。\")\n",
    "        return\n",
    "\n",
    "    # 使用 label 分層；注意：不以病人分割\n",
    "    from sklearn.model_selection import StratifiedKFold\n",
    "    y = df[\"label\"].astype(int).values\n",
    "    skf = StratifiedKFold(n_splits=k, shuffle=True, random_state=SEED)\n",
    "\n",
    "    folds = []\n",
    "    for fold_id, (tr_idx, va_idx) in enumerate(skf.split(np.zeros(len(y)), y), start=1):\n",
    "        folds.append({\n",
    "            \"fold\": fold_id,\n",
    "            \"train_indices\": tr_idx.tolist(),\n",
    "            \"valid_indices\": va_idx.tolist(),\n",
    "            \"n_train\": int(len(tr_idx)),\n",
    "            \"n_valid\": int(len(va_idx))\n",
    "        })\n",
    "\n",
    "    with open(os.path.join(DIR_CFG, \"kfold_splits.json\"), \"w\", encoding=\"utf-8\") as f:\n",
    "        json.dump({\"n_folds\": k, \"seed\": SEED, \"folds\": folds}, f, ensure_ascii=False, indent=2)\n",
    "    print(\"[KFold] 已輸出：config/kfold_splits.json\")\n",
    "\n",
    "# =========================\n",
    "# 執行總流程\n",
    "# =========================\n",
    "def main():\n",
    "    print(\"==[初始化]==\")\n",
    "    ensure_dirs()\n",
    "    # 先落地設定檔（便於之後雜湊與審計）\n",
    "    cfg_path, _ = write_windowing_yaml()\n",
    "\n",
    "    print(\"\\n==[步驟一] 清洗逐筆 CSV（不動原始）==\")\n",
    "    cleaned_paths = step_clean_all()\n",
    "\n",
    "    print(\"\\n==[步驟二] 多分片 CSV（可選）==\")\n",
    "    shard_paths, shard_stat_df = step_build_shards(cleaned_paths)\n",
    "\n",
    "    print(\"\\n==[步驟三] 產生視窗 manifest（不實體化樣本）==\")\n",
    "    step_build_manifest(cleaned_paths, shard_paths)\n",
    "\n",
    "    print(\"\\n==[步驟四] K 折分層交叉驗證索引==\")\n",
    "    step_make_kfold(os.path.join(DIR_MAN, \"window_manifest.csv\"), k=5)\n",
    "\n",
    "    # Final：寫入訓練快照空檔案/雜湊（占位示意）\n",
    "    run_tag = time.strftime(\"run_%Y%m%d_%H%M\")\n",
    "    run_dir = os.path.join(DIR_TRAIN, run_tag)\n",
    "    os.makedirs(os.path.join(run_dir, \"config_snapshot\"), exist_ok=True)\n",
    "    os.makedirs(os.path.join(run_dir, \"logs\"), exist_ok=True)\n",
    "    with open(os.path.join(run_dir, \"config_snapshot\", \"config_hash.txt\"), \"w\", encoding=\"utf-8\") as f:\n",
    "        f.write(sha256_file(cfg_path) + \"\\n\")\n",
    "    with open(os.path.join(run_dir, \"logs\", \"training.log\"), \"w\", encoding=\"utf-8\") as f:\n",
    "        f.write(\"[info] training will record here (placeholder)\\n\")\n",
    "\n",
    "    # 審計彙總\n",
    "    summary = {\n",
    "        \"source_dir\": SRC_DIR,\n",
    "        \"expected_files\": EXPECTED_FILES,\n",
    "        \"out_root\": OUT_ROOT,\n",
    "        \"created\": time.strftime(\"%Y-%m-%d %H:%M:%S\"),\n",
    "        \"reports\": {\n",
    "            \"window_continuity_stats\": os.path.join(DIR_MAN, \"window_continuity_stats.csv\"),\n",
    "            \"window_nan_alerts\": os.path.join(DIR_AUDITS, \"window_nan_alerts.csv\"),\n",
    "            \"schema_audit\": os.path.join(DIR_AUDITS, \"schema_audit.csv\"),\n",
    "            \"cleaned_nan_summary\": os.path.join(DIR_AUDITS, \"cleaned_nan_summary.csv\"),\n",
    "            \"value_range_anomaly\": os.path.join(DIR_AUDITS, \"value_range_anomaly.csv\"),  # 若未產生則日後補\n",
    "            \"time_gap_anomaly\": os.path.join(DIR_AUDITS, \"time_gap_anomaly.csv\"),\n",
    "            \"manifest\": os.path.join(DIR_MAN, \"window_manifest.csv\"),\n",
    "            \"window_stats\": os.path.join(DIR_MAN, \"window_stats.csv\"),\n",
    "            \"window_label_summary\": os.path.join(DIR_MAN, \"window_label_summary.csv\"),\n",
    "            \"kfold_splits\": os.path.join(DIR_CFG, \"kfold_splits.json\"),\n",
    "        }\n",
    "    }\n",
    "    os.makedirs(DIR_AUDITS_RPT, exist_ok=True)\n",
    "    with open(os.path.join(DIR_AUDITS_RPT, \"summary.json\"), \"w\", encoding=\"utf-8\") as f:\n",
    "        json.dump(summary, f, ensure_ascii=False, indent=2)\n",
    "\n",
    "    print(\"\\n✅ 全部完成。重點輸出：\")\n",
    "    print(f\" - Cleaned：{DIR_CLEANED}\")\n",
    "    print(f\" - Shards：{DIR_SHARDS}（_shard_catalog.csv, _shard_stats.csv）\")\n",
    "    print(f\" - Manifests：{DIR_MAN}（window_manifest.csv, window_stats.csv, window_label_summary.csv）\")\n",
    "    print(f\" - Audits：{DIR_AUDITS}（schema_audit.csv, cleaned_nan_summary.csv, time_gap_anomaly.csv）\")\n",
    "    print(f\" - Config：{DIR_CFG}（windowing.yaml, kfold_splits.json）\")\n",
    "    print(f\" - Docs：{DIR_DOCS}（schema_final.json）\")\n",
    "    print(f\" - Cache：{DIR_CACHE}, {DIR_CACHE_TF}, {DIR_LOCKS}\")\n",
    "    print(f\" - Training：{DIR_TRAIN}/*/config_snapshot/config_hash.txt, logs/training.log\")\n",
    "    print(\"請接著在 DataLoader 端依 manifest 動態取窗（Lazy Evaluation），嚴禁另存成全量靜態視窗檔。\")\n",
    "\n",
    "if __name__ == \"__main__\":\n",
    "    main()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 229,
   "id": "f0b0e3d2-4aad-4aaa-b044-f56f41bca3d6",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[INFO] 載入 manifest …\n",
      "\n",
      "[1] 標籤分佈（未濾連續性）：\n",
      "       count\n",
      "label       \n",
      "1      37650\n",
      "0       6908\n",
      "pos_ratio = 0.8450\n",
      "\n",
      "[2] 連續性檢核（continuity_ok）：\n",
      "               count\n",
      "continuity_ok       \n",
      "True           39695\n",
      "False           4863\n",
      "若在訓練端過濾 continuity_ok==True，視窗數 = 39,695\n",
      "過濾後的 pos_ratio = 0.8426\n",
      "\n",
      "[3] 被 ad_para 最後一筆=1 強制設為正樣本的視窗數：\n",
      "forced_by_last_ad_para = 37,604\n",
      "\n",
      "[4] 各來源檔（通常為 shard）視窗分佈 Top-10：\n",
      "source\n",
      "shard_000002.csv    16614\n",
      "shard_000001.csv    16463\n",
      "shard_000003.csv    11481\n",
      "Name: count, dtype: int64\n",
      "\n",
      "[5] 連續性違規樣本（max Δt_sec 最大的前 5 筆）：\n",
      "                 source  start_idx  end_idx           start_time             end_time  max_dt_sec  label\n",
      "17885  shard_000001.csv     192120   192179  2022-01-26 03:44:01  2022-01-26 04:00:04 1370605.000      1\n",
      "17884  shard_000001.csv     192090   192149  2022-01-26 03:34:00  2022-01-26 03:53:01 1370605.000      1\n",
      "32901  shard_000002.csv     148710   148769  2022-01-23 14:17:04  2022-01-23 14:42:00 1115277.000      1\n",
      "32900  shard_000002.csv     148680   148739  2022-01-23 14:02:02  2022-01-23 14:31:04 1115277.000      1\n",
      "25903  shard_000001.csv     432660   432719  2022-03-19 23:15:00  2022-03-19 23:39:00  215101.000      1\n",
      "\n",
      "[6] 分片型錄 + 視窗量（前 5 筆）：\n",
      "              shard    rows  n_windows\n",
      "0  shard_000001.csv  493931      16463\n",
      "1  shard_000002.csv  498463      16614\n",
      "2  shard_000003.csv  344476      11481\n"
     ]
    }
   ],
   "source": [
    "# ① 快速健檢（獨立補充腳本）\n",
    "# -*- coding: utf-8 -*-\n",
    "import os, pandas as pd\n",
    "\n",
    "ROOT = \"/home/jovyan/RT08/0925/sliding_win/1014/\"\n",
    "MAN  = os.path.join(ROOT, \"manifests\", \"window_manifest.csv\")\n",
    "CAT  = os.path.join(ROOT, \"shards\", \"_shard_catalog.csv\")\n",
    "\n",
    "print(\"[INFO] 載入 manifest …\")\n",
    "man = pd.read_csv(MAN)\n",
    "\n",
    "print(\"\\n[1] 標籤分佈（未濾連續性）：\")\n",
    "print(man[\"label\"].value_counts(dropna=False).rename_axis(\"label\").to_frame(\"count\"))\n",
    "print(\"pos_ratio = {:.4f}\".format((man[\"label\"]==1).mean()))\n",
    "\n",
    "print(\"\\n[2] 連續性檢核（continuity_ok）：\")\n",
    "print(man[\"continuity_ok\"].value_counts(dropna=False).rename_axis(\"continuity_ok\").to_frame(\"count\"))\n",
    "kept = man[man[\"continuity_ok\"]==True]\n",
    "print(\"若在訓練端過濾 continuity_ok==True，視窗數 = {:,}\".format(len(kept)))\n",
    "print(\"過濾後的 pos_ratio = {:.4f}\".format((kept[\"label\"]==1).mean()))\n",
    "\n",
    "print(\"\\n[3] 被 ad_para 最後一筆=1 強制設為正樣本的視窗數：\")\n",
    "forced = (man[\"last_ad_para\"]==1) & (man[\"label\"]==1)\n",
    "print(\"forced_by_last_ad_para = {:,}\".format(int(forced.sum())))\n",
    "\n",
    "print(\"\\n[4] 各來源檔（通常為 shard）視窗分佈 Top-10：\")\n",
    "print(man[\"source\"].value_counts().head(10))\n",
    "\n",
    "print(\"\\n[5] 連續性違規樣本（max Δt_sec 最大的前 5 筆）：\")\n",
    "bad = man[man[\"continuity_ok\"]==False].sort_values(\"max_dt_sec\", ascending=False).head(5)\n",
    "print(bad[[\"source\",\"start_idx\",\"end_idx\",\"start_time\",\"end_time\",\"max_dt_sec\",\"label\"]])\n",
    "\n",
    "if os.path.exists(CAT):\n",
    "    cat = pd.read_csv(CAT)\n",
    "    ex = man.groupby(\"source\").size().rename(\"n_windows\").reset_index()\n",
    "    merged = cat.merge(ex, left_on=\"shard\", right_on=\"source\", how=\"left\")\n",
    "    print(\"\\n[6] 分片型錄 + 視窗量（前 5 筆）：\")\n",
    "    print(merged[[\"shard\",\"rows\",\"n_windows\"]].head())\n",
    "else:\n",
    "    print(\"\\n[6] 未啟用或找不到 _shard_catalog.csv，略過\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 230,
   "id": "ba236856-0344-4b16-8274-baceeb81426c",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "==[初始化]==\n",
      "[設定] 已寫入設定檔：/home/jovyan/RT08/0925/sliding_win/1014/config/windowing.yaml\n",
      "==[步驟一] 清洗逐筆 CSV（不動原始）==\n",
      "[清洗] 發現檔案數：122（預期 122）\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "清洗中: 100%|██████████| 122/122 [00:02<00:00, 40.78it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[清洗] 已完成；稽核已輸出：audit_summary.csv / schema_audit.csv / cleaned_nan_summary.csv / time_gap_anomaly.csv（若有）\n",
      "[清洗] 已輸出 Δt_sec 統計：manifests/window_continuity_stats.csv\n",
      "[設定] 已寫入 schema：/home/jovyan/RT08/0925/sliding_win/1014/docs/schema_final.json\n",
      "\n",
      "==[步驟二] 多分片 CSV（可選）==\n",
      "[分片] 開始建立分片（rows_per_shard=500,000）…\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "分片中: 100%|██████████| 122/122 [00:13<00:00,  9.07it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[分片] 完成：_shard_catalog.csv / _shard_stats.csv\n",
      "\n",
      "==[步驟三] 產生視窗 manifest（不實體化樣本）==\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "掃描來源:   0%|          | 0/3 [00:00<?, ?it/s]\n"
     ]
    },
    {
     "ename": "AttributeError",
     "evalue": "Can't pickle local object 'step_build_manifest.<locals>.<lambda>'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[230], line 683\u001b[0m\n\u001b[1;32m    680\u001b[0m     \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m請在訓練端依 manifest 動態取窗（Lazy Evaluation），避免另存成全量靜態視窗檔。\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m    682\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;18m__name__\u001b[39m \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m__main__\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[0;32m--> 683\u001b[0m     \u001b[43mmain\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n",
      "Cell \u001b[0;32mIn[230], line 633\u001b[0m, in \u001b[0;36mmain\u001b[0;34m()\u001b[0m\n\u001b[1;32m    630\u001b[0m shard_paths, shard_stat_df \u001b[38;5;241m=\u001b[39m step_build_shards(cleaned_paths)\n\u001b[1;32m    632\u001b[0m \u001b[38;5;66;03m# 步驟三：manifest（不實體化）\u001b[39;00m\n\u001b[0;32m--> 633\u001b[0m \u001b[43mstep_build_manifest\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcleaned_paths\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mshard_paths\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    635\u001b[0m \u001b[38;5;66;03m# 步驟四：KFold\u001b[39;00m\n\u001b[1;32m    636\u001b[0m step_make_kfold(os\u001b[38;5;241m.\u001b[39mpath\u001b[38;5;241m.\u001b[39mjoin(DIR_MAN, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mwindow_manifest.csv\u001b[39m\u001b[38;5;124m\"\u001b[39m), k\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m5\u001b[39m)\n",
      "Cell \u001b[0;32mIn[230], line 543\u001b[0m, in \u001b[0;36mstep_build_manifest\u001b[0;34m(cleaned_paths, shard_paths)\u001b[0m\n\u001b[1;32m    540\u001b[0m nan_alerts \u001b[38;5;241m=\u001b[39m []\n\u001b[1;32m    542\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m Pool(processes\u001b[38;5;241m=\u001b[39mN_WORKERS) \u001b[38;5;28;01mas\u001b[39;00m pool:\n\u001b[0;32m--> 543\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;28;43;01mfor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mrows\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mstats\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnan_df\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01min\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mtqdm\u001b[49m\u001b[43m(\u001b[49m\u001b[43mpool\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mimap_unordered\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    544\u001b[0m \u001b[43m            \u001b[49m\u001b[38;5;28;43;01mlambda\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mp\u001b[49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mscan_one_source_for_manifest\u001b[49m\u001b[43m(\u001b[49m\u001b[43mp\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlbl_cfg\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msources\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    545\u001b[0m \u001b[43m            \u001b[49m\u001b[43mtotal\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mlen\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43msources\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdesc\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43m掃描來源\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m:\u001b[49m\n\u001b[1;32m    546\u001b[0m \u001b[43m        \u001b[49m\u001b[43mall_rows\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mextend\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrows\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    547\u001b[0m \u001b[43m        \u001b[49m\u001b[43mall_stats\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mappend\u001b[49m\u001b[43m(\u001b[49m\u001b[43mstats\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m/opt/conda/lib/python3.11/site-packages/tqdm/std.py:1181\u001b[0m, in \u001b[0;36mtqdm.__iter__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1178\u001b[0m time \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_time\n\u001b[1;32m   1180\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m-> 1181\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;28;43;01mfor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mobj\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01min\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43miterable\u001b[49m\u001b[43m:\u001b[49m\n\u001b[1;32m   1182\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;28;43;01myield\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mobj\u001b[49m\n\u001b[1;32m   1183\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;66;43;03m# Update and possibly print the progressbar.\u001b[39;49;00m\n\u001b[1;32m   1184\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;66;43;03m# Note: does not call self.update(1) for speed optimisation.\u001b[39;49;00m\n",
      "File \u001b[0;32m/opt/conda/lib/python3.11/multiprocessing/pool.py:873\u001b[0m, in \u001b[0;36mIMapIterator.next\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m    871\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m success:\n\u001b[1;32m    872\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m value\n\u001b[0;32m--> 873\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m value\n",
      "File \u001b[0;32m/opt/conda/lib/python3.11/multiprocessing/pool.py:540\u001b[0m, in \u001b[0;36mPool._handle_tasks\u001b[0;34m(taskqueue, put, outqueue, pool, cache)\u001b[0m\n\u001b[1;32m    538\u001b[0m     \u001b[38;5;28;01mbreak\u001b[39;00m\n\u001b[1;32m    539\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 540\u001b[0m     \u001b[43mput\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtask\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    541\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[1;32m    542\u001b[0m     job, idx \u001b[38;5;241m=\u001b[39m task[:\u001b[38;5;241m2\u001b[39m]\n",
      "File \u001b[0;32m/opt/conda/lib/python3.11/multiprocessing/connection.py:206\u001b[0m, in \u001b[0;36m_ConnectionBase.send\u001b[0;34m(self, obj)\u001b[0m\n\u001b[1;32m    204\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_check_closed()\n\u001b[1;32m    205\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_check_writable()\n\u001b[0;32m--> 206\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_send_bytes(\u001b[43m_ForkingPickler\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdumps\u001b[49m\u001b[43m(\u001b[49m\u001b[43mobj\u001b[49m\u001b[43m)\u001b[49m)\n",
      "File \u001b[0;32m/opt/conda/lib/python3.11/multiprocessing/reduction.py:51\u001b[0m, in \u001b[0;36mForkingPickler.dumps\u001b[0;34m(cls, obj, protocol)\u001b[0m\n\u001b[1;32m     48\u001b[0m \u001b[38;5;129m@classmethod\u001b[39m\n\u001b[1;32m     49\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mdumps\u001b[39m(\u001b[38;5;28mcls\u001b[39m, obj, protocol\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m):\n\u001b[1;32m     50\u001b[0m     buf \u001b[38;5;241m=\u001b[39m io\u001b[38;5;241m.\u001b[39mBytesIO()\n\u001b[0;32m---> 51\u001b[0m     \u001b[38;5;28;43mcls\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mbuf\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mprotocol\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdump\u001b[49m\u001b[43m(\u001b[49m\u001b[43mobj\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     52\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m buf\u001b[38;5;241m.\u001b[39mgetbuffer()\n",
      "\u001b[0;31mAttributeError\u001b[0m: Can't pickle local object 'step_build_manifest.<locals>.<lambda>'"
     ]
    }
   ],
   "source": [
    "# 1014_2\n",
    "# 改成多數決的視窗級標籤；  按 origin_id 分組切窗，避免跨來源檔案被拼成同一窗\n",
    "# -*- coding: utf-8 -*-\n",
    "\"\"\"\n",
    "不實體化滑動視窗 - 版本 1014（工程實作用，含多分片、manifest 前置、併發讀取/快取）\n",
    "================================================================================\n",
    "⚠️ 不會修改原始資料：/home/jovyan/RT08/0925/bling_1014/（122 個 CSV）\n",
    "所有輸出/中繼/報表皆落在：/home/jovyan/RT08/0925/sliding_win/1014/\n",
    "\n",
    "本腳本完全對齊你的規範，並內建：\n",
    "1) 目錄初始化與設定檔落地（windowing.yaml、schema_final.json）\n",
    "2) 清洗：欄位正規化、senddate 解析、補 ts_unix/Δt_sec、過濾 nan_check!=1 或時間異常\n",
    "   - 添加 origin_id（原檔名），後續避免跨來源拼窗\n",
    "   - 稽核：schema_audit.csv、cleaned_nan_summary.csv、time_gap_anomaly.csv 等\n",
    "3) Δt_sec 統計輸出（window_continuity_stats.csv）\n",
    "4) 多分片（shards/）與型錄/統計/雜湊（_shard_catalog.csv、_shard_stats.csv）\n",
    "5) 視窗 manifest（不實體化）：window_manifest.csv / window_stats.csv / window_label_summary.csv\n",
    "   - W=60 筆、S=30 筆、右對齊（最後一筆為決策時點）\n",
    "   - 連續性檢核：max(Δt_sec) > 門檻（預設 120 秒） → 標旗（continuity_ok）\n",
    "   - 標籤聚合（預設純多數決）；可切 mode：\n",
    "       majority_only / majority_w_minus_1 / majority_w_minus_1_softforce\n",
    "6) 併發處理（multiprocessing）\n",
    "7) K 折分層交叉驗證（kfold_splits.json；seed=42；不以病人分割）\n",
    "8) 設定與分片雜湊快照（slicing_params.json），訓練快照（config_hash.txt 等）\n",
    "9) 審計報表彙總（audits/reports/summary.json）\n",
    "\n",
    "需要套件：pandas, numpy, pyyaml, tqdm, scikit-learn\n",
    "\"\"\"\n",
    "\n",
    "import os\n",
    "import re\n",
    "import json\n",
    "import math\n",
    "import glob\n",
    "import time\n",
    "import hashlib\n",
    "import warnings\n",
    "from typing import List, Dict, Any, Tuple\n",
    "from dataclasses import dataclass, asdict\n",
    "from functools import lru_cache\n",
    "from multiprocessing import Pool, cpu_count\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from tqdm import tqdm\n",
    "import yaml\n",
    "\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "# =========================\n",
    "# 使用者參數區（可調整）\n",
    "# =========================\n",
    "SRC_DIR = \"/home/jovyan/RT08/0925/bling_1014/\"     # 原始逐筆 CSV（❗不修改）\n",
    "OUT_ROOT = \"/home/jovyan/RT08/0925/sliding_win/1014/\"\n",
    "\n",
    "# 版本化資料夾層級\n",
    "DIR_CLEANED = os.path.join(OUT_ROOT, \"cleaned/clear/\")\n",
    "DIR_DOCS    = os.path.join(OUT_ROOT, \"docs/\")\n",
    "DIR_CFG     = os.path.join(OUT_ROOT, \"config/\")\n",
    "DIR_AUDITS  = os.path.join(OUT_ROOT, \"audits/\")\n",
    "DIR_AUDITS_RPT = os.path.join(DIR_AUDITS, \"reports/\")\n",
    "DIR_MAN     = os.path.join(OUT_ROOT, \"manifests/\")\n",
    "DIR_CACHE   = os.path.join(OUT_ROOT, \"cache/reader_ram/\")\n",
    "DIR_CACHE_TF= os.path.join(OUT_ROOT, \"cache/tf_dataset/\")\n",
    "DIR_LOCKS   = os.path.join(OUT_ROOT, \"cache/locks/\")\n",
    "DIR_SHARDS  = os.path.join(OUT_ROOT, \"shards/\")\n",
    "DIR_TRAIN   = os.path.join(OUT_ROOT, \"training/\")\n",
    "\n",
    "# 預期數量/亂數種子\n",
    "EXPECTED_FILES = 122\n",
    "SEED = 42\n",
    "\n",
    "# 視窗參數（以「筆」為單位）\n",
    "W = 60\n",
    "S = 30\n",
    "RIGHT_ALIGN = True\n",
    "\n",
    "# 時間連續性門檻（秒）：max(Δt_sec) 超過則 continuity_ok=False（訓練端可決定是否濾除）\n",
    "DT_SEC_THRESHOLD = 120.0\n",
    "\n",
    "# 多分片設定\n",
    "ENABLE_SHARDING = True\n",
    "SHARD_ROWS = 500_000  # 每片最多筆數（依硬體與吞吐可調）\n",
    "\n",
    "# 併發處理\n",
    "N_WORKERS = max(1, min(cpu_count() - 1, 8))  # 避免占滿機器\n",
    "\n",
    "# 必備欄位（清洗後應存在）\n",
    "REQUIRED_ID_COL   = \"patno\"\n",
    "REQUIRED_TIME_COL = \"senddate\"\n",
    "REQUIRED_TS_COL   = \"ts_unix\"\n",
    "REQUIRED_DT_COL   = \"Δt_sec\"   # 保留你的命名\n",
    "REQUIRED_QC_COL   = \"nan_check\"\n",
    "REQUIRED_Y1_COL   = \"ad_para\"\n",
    "REQUIRED_Y2_COL   = \"set_fin\"\n",
    "\n",
    "# 預設標籤聚合模式（可於 windowing.yaml 覆寫）\n",
    "DEFAULT_LABEL_MODE = \"majority_only\"  # 可選：majority_only / majority_w_minus_1 / majority_w_minus_1_softforce\n",
    "DEFAULT_TAU = 0.2                     # 只有 softforce 會用到\n",
    "\n",
    "# =========================\n",
    "# 工具：目錄與雜湊\n",
    "# =========================\n",
    "def ensure_dirs():\n",
    "    for d in [DIR_CLEANED, DIR_DOCS, DIR_CFG, DIR_AUDITS, DIR_AUDITS_RPT,\n",
    "              DIR_MAN, DIR_CACHE, DIR_CACHE_TF, DIR_LOCKS, DIR_SHARDS, DIR_TRAIN]:\n",
    "        os.makedirs(d, exist_ok=True)\n",
    "\n",
    "def sha256_text(s: str) -> str:\n",
    "    return hashlib.sha256(s.encode(\"utf-8\")).hexdigest()\n",
    "\n",
    "def sha256_file(path: str, block_size: int = 1 << 20) -> str:\n",
    "    h = hashlib.sha256()\n",
    "    with open(path, \"rb\") as f:\n",
    "        while True:\n",
    "            b = f.read(block_size)\n",
    "            if not b: break\n",
    "            h.update(b)\n",
    "    return h.hexdigest()\n",
    "\n",
    "# =========================\n",
    "# 設定檔與 schema 落地\n",
    "# =========================\n",
    "def write_windowing_yaml():\n",
    "    cfg = {\n",
    "        \"version\": \"1014\",\n",
    "        \"seed\": SEED,\n",
    "        \"window\": {\n",
    "            \"W\": W,\n",
    "            \"S\": S,\n",
    "            \"align\": \"right\" if RIGHT_ALIGN else \"left\"\n",
    "        },\n",
    "        \"continuity\": {\n",
    "            \"dt_sec_threshold\": DT_SEC_THRESHOLD,\n",
    "            \"enforce\": True\n",
    "        },\n",
    "        # 舊規範欄位（保留說明）\n",
    "        \"labeling\": {\n",
    "            \"rule\": \"ratio>=0.5 set_fin==1 (pure majority at window level); no force by last ad_para\",\n",
    "            \"ratio_threshold\": 0.5,\n",
    "            \"force_by_last_ad_para\": False\n",
    "        },\n",
    "        # 新增可切換模式（預設 pure majority）\n",
    "        \"labeling_ext\": {\n",
    "            \"mode\": DEFAULT_LABEL_MODE,                 # majority_only / majority_w_minus_1 / majority_w_minus_1_softforce\n",
    "            \"tau\": DEFAULT_TAU\n",
    "        },\n",
    "        \"sharding\": {\n",
    "            \"enabled\": ENABLE_SHARDING,\n",
    "            \"rows_per_shard\": SHARD_ROWS,\n",
    "            \"name_pattern\": \"shard_%06d.csv\"\n",
    "        },\n",
    "        \"lazy_evaluation\": True,\n",
    "        \"notes\": \"do not materialize windows; manifest-only; group by origin_id to avoid cross-file windows\"\n",
    "    }\n",
    "    os.makedirs(DIR_CFG, exist_ok=True)\n",
    "    path = os.path.join(DIR_CFG, \"windowing.yaml\")\n",
    "    with open(path, \"w\", encoding=\"utf-8\") as f:\n",
    "        yaml.safe_dump(cfg, f, allow_unicode=True, sort_keys=False)\n",
    "    print(f\"[設定] 已寫入設定檔：{path}\")\n",
    "    return path, cfg\n",
    "\n",
    "def write_schema_json(final_cols: List[str]):\n",
    "    os.makedirs(DIR_DOCS, exist_ok=True)\n",
    "    path = os.path.join(DIR_DOCS, \"schema_final.json\")\n",
    "    with open(path, \"w\", encoding=\"utf-8\") as f:\n",
    "        json.dump({\"final_columns\": final_cols}, f, ensure_ascii=False, indent=2)\n",
    "    print(f\"[設定] 已寫入 schema：{path}\")\n",
    "    return path\n",
    "\n",
    "# =========================\n",
    "# 讀檔/清洗輔助\n",
    "# =========================\n",
    "def list_csv_files(src: str) -> List[str]:\n",
    "    return sorted(glob.glob(os.path.join(src, \"*.csv\")))\n",
    "\n",
    "def normalize_col(name: str) -> str:\n",
    "    s = str(name).strip()\n",
    "    s = re.sub(r\"\\s+\", \"_\", s)\n",
    "    return s\n",
    "\n",
    "def to_datetime_safe(series: pd.Series) -> pd.Series:\n",
    "    # 支援「上午/下午」「AM/PM」的常見本地時間字樣\n",
    "    s = series.astype(str)\n",
    "    s = s.str.replace(\"上午\", \"AM\").str.replace(\"下午\", \"PM\")\n",
    "    s = s.str.replace(\"早上\", \"AM\").str.replace(\"晚上\", \"PM\").str.replace(\"中午\", \"PM\")\n",
    "    return pd.to_datetime(s, errors=\"coerce\", infer_datetime_format=True)\n",
    "\n",
    "def add_ts_unix_and_dtsec(df: pd.DataFrame, id_col: str) -> pd.DataFrame:\n",
    "    # 補 ts_unix\n",
    "    if REQUIRED_TS_COL not in df.columns:\n",
    "        df[REQUIRED_TS_COL] = np.floor(pd.to_datetime(df[REQUIRED_TIME_COL], errors=\"coerce\").astype(\"int64\") / 1e9)\n",
    "    # 補 Δt_sec（以同檔每名病人分組）\n",
    "    if REQUIRED_DT_COL not in df.columns:\n",
    "        df = df.sort_values(by=[REQUIRED_TIME_COL]).reset_index(drop=True)\n",
    "        if id_col in df.columns:\n",
    "            df[REQUIRED_DT_COL] = df.groupby(id_col)[REQUIRED_TIME_COL].diff().dt.total_seconds()\n",
    "        else:\n",
    "            df[REQUIRED_DT_COL] = df[REQUIRED_TIME_COL].diff().dt.total_seconds()\n",
    "        df[REQUIRED_DT_COL] = df[REQUIRED_DT_COL].fillna(0.0)\n",
    "    return df\n",
    "\n",
    "def basic_clean_one(path: str) -> Dict[str, Any]:\n",
    "    \"\"\"\n",
    "    單檔清洗：\n",
    "    - 欄位正規化；解析 senddate；補 ts_unix/Δt_sec\n",
    "    - 過濾 nan_check != 1 或 senddate 無效\n",
    "    - 追加 origin_id = 原檔名（避免跨來源拼窗）\n",
    "    \"\"\"\n",
    "    base = os.path.basename(path)\n",
    "    out_path = os.path.join(DIR_CLEANED, base)\n",
    "\n",
    "    raw = pd.read_csv(path)\n",
    "    rename_map = {c: normalize_col(c) for c in raw.columns}\n",
    "    df = raw.rename(columns=rename_map)\n",
    "\n",
    "    # 確保必要欄位存在（若缺，先建空欄）\n",
    "    for c in [REQUIRED_ID_COL, REQUIRED_TIME_COL, REQUIRED_QC_COL, REQUIRED_Y1_COL, REQUIRED_Y2_COL]:\n",
    "        if c not in df.columns:\n",
    "            df[c] = np.nan\n",
    "\n",
    "    # 時間解析\n",
    "    df[REQUIRED_TIME_COL] = to_datetime_safe(df[REQUIRED_TIME_COL])\n",
    "\n",
    "    # 補 ts_unix/Δt_sec\n",
    "    df = add_ts_unix_and_dtsec(df, REQUIRED_ID_COL)\n",
    "\n",
    "    # 過濾 nan_check != 1 或 時間欄異常\n",
    "    qc_num = pd.to_numeric(df[REQUIRED_QC_COL], errors=\"coerce\")\n",
    "    valid_mask = (qc_num == 1) & (df[REQUIRED_TIME_COL].notna())\n",
    "    df_clean = df.loc[valid_mask].copy()\n",
    "\n",
    "    # 追加 origin_id（原檔名）；方便避免跨來源拼窗\n",
    "    df_clean[\"origin_id\"] = base\n",
    "\n",
    "    # 寫檔\n",
    "    df_clean.to_csv(out_path, index=False, encoding=\"utf-8-sig\")\n",
    "\n",
    "    # 稽核摘要\n",
    "    audit = {\n",
    "        \"file_name\": base,\n",
    "        \"src_path\": path,\n",
    "        \"out_path\": out_path,\n",
    "        \"rows_in\": int(len(raw)),\n",
    "        \"rows_out\": int(len(df_clean)),\n",
    "        \"nan_rows_dropped\": int((~valid_mask).sum()),\n",
    "        \"has_ts_unix\": REQUIRED_TS_COL in df_clean.columns,\n",
    "        \"has_dt_sec\": REQUIRED_DT_COL in df_clean.columns,\n",
    "        \"min_dt_sec\": float(df_clean[REQUIRED_DT_COL].min()) if REQUIRED_DT_COL in df_clean.columns and not df_clean.empty else np.nan,\n",
    "        \"p50_dt_sec\": float(df_clean[REQUIRED_DT_COL].median()) if REQUIRED_DT_COL in df_clean.columns and not df_clean.empty else np.nan,\n",
    "        \"p90_dt_sec\": float(df_clean[REQUIRED_DT_COL].quantile(0.9)) if REQUIRED_DT_COL in df_clean.columns and not df_clean.empty else np.nan,\n",
    "        \"max_dt_sec\": float(df_clean[REQUIRED_DT_COL].max()) if REQUIRED_DT_COL in df_clean.columns and not df_clean.empty else np.nan,\n",
    "        \"total_nan_after\": int(df_clean.isna().sum().sum()),\n",
    "    }\n",
    "    return audit\n",
    "\n",
    "# =========================\n",
    "# 併發清洗（不動原始檔）\n",
    "# =========================\n",
    "def step_clean_all():\n",
    "    files = list_csv_files(SRC_DIR)\n",
    "    print(f\"==[步驟一] 清洗逐筆 CSV（不動原始）==\")\n",
    "    print(f\"[清洗] 發現檔案數：{len(files)}（預期 {EXPECTED_FILES}）\")\n",
    "    os.makedirs(DIR_CLEANED, exist_ok=True)\n",
    "\n",
    "    audits = []\n",
    "    with Pool(processes=N_WORKERS) as pool:\n",
    "        for res in tqdm(pool.imap_unordered(basic_clean_one, files), total=len(files), desc=\"清洗中\"):\n",
    "            audits.append(res)\n",
    "\n",
    "    audit_df = pd.DataFrame(audits).sort_values(\"file_name\")\n",
    "    audit_df.to_csv(os.path.join(DIR_CLEANED, \"../audit_summary.csv\"), index=False, encoding=\"utf-8-sig\")\n",
    "\n",
    "    # 欄位稽核（schema）\n",
    "    schema_rows = []\n",
    "    for a in audits:\n",
    "        cols = list(pd.read_csv(a[\"out_path\"], nrows=0).columns)\n",
    "        all_lower = all(c == c.lower() for c in cols)\n",
    "        snake_ok  = all(bool(re.fullmatch(r\"[a-z0-9_]+\", c)) for c in cols)\n",
    "        uniq_ok   = (len(cols) == len(set(cols)))\n",
    "        schema_rows.append({\n",
    "            \"file_name\": a[\"file_name\"], \"n_cols\": len(cols),\n",
    "            \"all_lowercase\": all_lower, \"snake_case_only\": snake_ok, \"unique_names\": uniq_ok\n",
    "        })\n",
    "    pd.DataFrame(schema_rows).to_csv(os.path.join(DIR_AUDITS, \"schema_audit.csv\"), index=False, encoding=\"utf-8-sig\")\n",
    "\n",
    "    # 缺失彙整\n",
    "    nan_rows = []\n",
    "    for a in audits:\n",
    "        df = pd.read_csv(a[\"out_path\"])\n",
    "        nan_rows.append({\"file_name\": a[\"file_name\"], \"nan_cells\": int(df.isna().sum().sum()), \"rows\": int(len(df))})\n",
    "    pd.DataFrame(nan_rows).to_csv(os.path.join(DIR_AUDITS, \"cleaned_nan_summary.csv\"), index=False, encoding=\"utf-8-sig\")\n",
    "\n",
    "    # 時間間隔異常（> 門檻）\n",
    "    gap_rows = []\n",
    "    for a in audits:\n",
    "        df = pd.read_csv(a[\"out_path\"])\n",
    "        if REQUIRED_DT_COL in df.columns and not df.empty:\n",
    "            bad = df[df[REQUIRED_DT_COL] > DT_SEC_THRESHOLD]\n",
    "            if not bad.empty:\n",
    "                tmp = bad[[REQUIRED_TIME_COL, REQUIRED_DT_COL]].copy()\n",
    "                tmp.insert(0, \"file_name\", a[\"file_name\"])\n",
    "                gap_rows.append(tmp)\n",
    "    if gap_rows:\n",
    "        pd.concat(gap_rows, ignore_index=True).to_csv(os.path.join(DIR_AUDITS, \"time_gap_anomaly.csv\"),\n",
    "                                                     index=False, encoding=\"utf-8-sig\")\n",
    "    print(\"[清洗] 已完成；稽核已輸出：audit_summary.csv / schema_audit.csv / cleaned_nan_summary.csv / time_gap_anomaly.csv（若有）\")\n",
    "\n",
    "    # Δt_sec 統計\n",
    "    stat_rows = []\n",
    "    for a in audits:\n",
    "        df = pd.read_csv(a[\"out_path\"])\n",
    "        if REQUIRED_DT_COL in df.columns and not df.empty:\n",
    "            s = df[REQUIRED_DT_COL]\n",
    "            stat_rows.append({\n",
    "                \"file_name\": a[\"file_name\"],\n",
    "                \"count\": int(s.count()),\n",
    "                \"min\": float(s.min()),\n",
    "                \"q1\": float(s.quantile(0.25)),\n",
    "                \"median\": float(s.median()),\n",
    "                \"q3\": float(s.quantile(0.75)),\n",
    "                \"mean\": float(s.mean()),\n",
    "                \"max\": float(s.max())\n",
    "            })\n",
    "    pd.DataFrame(stat_rows).to_csv(os.path.join(DIR_MAN, \"window_continuity_stats.csv\"), index=False, encoding=\"utf-8-sig\")\n",
    "    print(\"[清洗] 已輸出 Δt_sec 統計：manifests/window_continuity_stats.csv\")\n",
    "\n",
    "    # 回傳清洗後檔案清單\n",
    "    return [os.path.join(DIR_CLEANED, os.path.basename(p)) for p in files if os.path.exists(os.path.join(DIR_CLEANED, os.path.basename(p)))]\n",
    "\n",
    "# =========================\n",
    "# 多分片（Sharding）\n",
    "# =========================\n",
    "def step_build_shards(cleaned_paths: List[str]) -> Tuple[List[str], pd.DataFrame]:\n",
    "    print(\"\\n==[步驟二] 多分片 CSV（可選）==\")\n",
    "    if not ENABLE_SHARDING:\n",
    "        print(\"[分片] 未啟用；略過。\")\n",
    "        return [], pd.DataFrame()\n",
    "\n",
    "    print(f\"[分片] 開始建立分片（rows_per_shard={SHARD_ROWS:,}）…\")\n",
    "    os.makedirs(DIR_SHARDS, exist_ok=True)\n",
    "\n",
    "    shard_idx = 1\n",
    "    shard_paths = []\n",
    "    cat_rows = []   # 分片型錄\n",
    "    stat_rows = []\n",
    "\n",
    "    cur_rows = 0\n",
    "    cur_buf: List[pd.DataFrame] = []\n",
    "\n",
    "    def flush_shard():\n",
    "        nonlocal shard_idx, shard_paths, cur_buf, cur_rows\n",
    "        if not cur_buf: return\n",
    "        shard_df = pd.concat(cur_buf, ignore_index=True)\n",
    "        # 依時間排序，保持與 manifest 同一排序邏輯（避免切窗與讀檔對不上）\n",
    "        shard_df = shard_df.sort_values(REQUIRED_TIME_COL).reset_index(drop=True)\n",
    "        shard_name = f\"shard_{shard_idx:06d}.csv\"\n",
    "        shard_path = os.path.join(DIR_SHARDS, shard_name)\n",
    "        shard_df.to_csv(shard_path, index=False, encoding=\"utf-8-sig\")\n",
    "        shard_paths.append(shard_path)\n",
    "        stat_rows.append({\"shard\": shard_name, \"rows\": int(len(shard_df)), \"hash\": sha256_file(shard_path)})\n",
    "        cat_rows.append({\"shard\": shard_name, \"rows\": int(len(shard_df)), \"path\": shard_path})\n",
    "        shard_idx += 1\n",
    "        cur_buf = []\n",
    "        cur_rows = 0\n",
    "\n",
    "    for p in tqdm(cleaned_paths, desc=\"分片中\"):\n",
    "        df = pd.read_csv(p)\n",
    "        # 僅保守保留全欄（必要時可另設白名單）\n",
    "        if cur_rows + len(df) > SHARD_ROWS and cur_rows > 0:\n",
    "            flush_shard()\n",
    "        cur_buf.append(df)\n",
    "        cur_rows += len(df)\n",
    "        # 單檔超過上限時切片\n",
    "        while cur_rows >= SHARD_ROWS:\n",
    "            need = SHARD_ROWS\n",
    "            acc = pd.concat(cur_buf, ignore_index=True)\n",
    "            acc = acc.sort_values(REQUIRED_TIME_COL).reset_index(drop=True)\n",
    "            full = acc.iloc[:need].copy()\n",
    "            left = acc.iloc[need:].copy()\n",
    "            shard_name = f\"shard_{shard_idx:06d}.csv\"\n",
    "            shard_path = os.path.join(DIR_SHARDS, shard_name)\n",
    "            full.to_csv(shard_path, index=False, encoding=\"utf-8-sig\")\n",
    "            shard_paths.append(shard_path)\n",
    "            stat_rows.append({\"shard\": shard_name, \"rows\": int(len(full)), \"hash\": sha256_file(shard_path)})\n",
    "            cat_rows.append({\"shard\": shard_name, \"rows\": int(len(full)), \"path\": shard_path})\n",
    "            shard_idx += 1\n",
    "            cur_buf = [left] if not left.empty else []\n",
    "            cur_rows = len(left)\n",
    "\n",
    "    flush_shard()\n",
    "    cat_df = pd.DataFrame(cat_rows)\n",
    "    stat_df = pd.DataFrame(stat_rows)\n",
    "    cat_df.to_csv(os.path.join(DIR_SHARDS, \"_shard_catalog.csv\"), index=False, encoding=\"utf-8-sig\")\n",
    "    stat_df.to_csv(os.path.join(DIR_SHARDS, \"_shard_stats.csv\"), index=False, encoding=\"utf-8-sig\")\n",
    "    print(\"[分片] 完成：_shard_catalog.csv / _shard_stats.csv\")\n",
    "    return shard_paths, stat_df\n",
    "\n",
    "# =========================\n",
    "# 視窗切割（不實體化：只產生 manifest）\n",
    "# =========================\n",
    "@dataclass\n",
    "class WindowRow:\n",
    "    window_id: str\n",
    "    source: str        # 來源檔（分片或 cleaned 檔）\n",
    "    start_idx: int     # 在「排序後的來源檔」中的起始索引（含）\n",
    "    end_idx: int       # 在「排序後的來源檔」中的結束索引（含）\n",
    "    start_time: str\n",
    "    end_time: str\n",
    "    max_dt_sec: float\n",
    "    continuity_ok: bool\n",
    "    label: int\n",
    "    pos_ratio_setfin1: float\n",
    "    last_ad_para: float\n",
    "    patno_first: str\n",
    "    origin_id: str\n",
    "    requires_sort_by_senddate: bool\n",
    "\n",
    "def label_by_rules(sub: pd.DataFrame, mode: str = DEFAULT_LABEL_MODE, tau: float = DEFAULT_TAU) -> Tuple[int, float, float]:\n",
    "    \"\"\"\n",
    "    視窗級標籤聚合：\n",
    "      - majority_only：純多數決（整窗 set_fin==1 比例 ≥ 0.5 → 1）\n",
    "      - majority_w_minus_1：只看前 W-1 做多數決；最後一筆不納入比例\n",
    "      - majority_w_minus_1_softforce：同上，若最後一筆 ad_para=1 且 ratio<tau → 1\n",
    "    回傳：(y, ratio_used_for_decision, last_ad_para)\n",
    "    \"\"\"\n",
    "    setfin = pd.to_numeric(sub[REQUIRED_Y2_COL], errors=\"coerce\").fillna(0.0)\n",
    "    adpara = pd.to_numeric(sub[REQUIRED_Y1_COL], errors=\"coerce\").fillna(0.0)\n",
    "    if len(sub) == 0:\n",
    "        return 0, 0.0, 0.0\n",
    "\n",
    "    if mode in (\"majority_w_minus_1\", \"majority_w_minus_1_softforce\"):\n",
    "        head = setfin.iloc[:-1] if len(setfin) >= 2 else setfin\n",
    "        ratio = float((head == 1).mean()) if len(head) > 0 else 0.0\n",
    "        last_ap = float(adpara.iloc[-1])\n",
    "        if mode == \"majority_w_minus_1_softforce\" and (last_ap == 1.0) and (ratio < tau):\n",
    "            y = 1\n",
    "        else:\n",
    "            y = 1 if ratio >= 0.5 else 0\n",
    "    else:\n",
    "        # 純多數決（整窗）\n",
    "        ratio = float((setfin == 1).mean())\n",
    "        y = 1 if ratio >= 0.5 else 0\n",
    "        last_ap = float(adpara.iloc[-1])\n",
    "\n",
    "    return int(y), float(ratio), float(last_ap)\n",
    "\n",
    "def scan_one_source_for_manifest(source_csv: str, lbl_cfg: Dict[str, Any]) -> Tuple[List[WindowRow], Dict[str, Any], pd.DataFrame]:\n",
    "    \"\"\"\n",
    "    從單一來源檔（可為 cleaned 或 shard）產生視窗 manifest 記錄（不寫出子視窗）。\n",
    "    - 先按 senddate 排序\n",
    "    - 依 origin_id 分組，避免跨來源拼窗\n",
    "    - start_idx/end_idx 是「排序後檔案」的絕對 iloc\n",
    "    \"\"\"\n",
    "    df = pd.read_csv(source_csv)\n",
    "    if df.empty:\n",
    "        return [], {\"source\": os.path.basename(source_csv), \"total_rows\": 0, \"total_windows\": 0}, pd.DataFrame()\n",
    "\n",
    "    # 排序並加上排序後絕對位置（abs_row）\n",
    "    df = df.sort_values(REQUIRED_TIME_COL).reset_index(drop=True)\n",
    "    df[\"abs_row\"] = df.index\n",
    "\n",
    "    # 缺失報警（保守檢查）\n",
    "    nan_alert = df.isna().mean()\n",
    "    nan_alert = nan_alert[nan_alert > 0.0]\n",
    "    nan_df = pd.DataFrame({\"column\": nan_alert.index, \"nan_ratio\": nan_alert.values})\n",
    "    if not nan_df.empty:\n",
    "        nan_df.insert(0, \"source\", os.path.basename(source_csv))\n",
    "\n",
    "    mode = lbl_cfg.get(\"mode\", DEFAULT_LABEL_MODE)\n",
    "    tau  = float(lbl_cfg.get(\"tau\", DEFAULT_TAU))\n",
    "\n",
    "    rows: List[WindowRow] = []\n",
    "    total_windows = 0\n",
    "\n",
    "    groups = [(\"\", df)]\n",
    "    if \"origin_id\" in df.columns:\n",
    "        groups = list(df.groupby(\"origin_id\", sort=False))\n",
    "\n",
    "    for gid, g in groups:\n",
    "        g = g.sort_values(REQUIRED_TIME_COL).reset_index(drop=True)\n",
    "        # 也要保留絕對 iloc（相對於「排序後整檔」）\n",
    "        # 這裡重新對應：按時間 merge 取得 abs_row\n",
    "        g = g.merge(df[[REQUIRED_TIME_COL, \"abs_row\"]], on=REQUIRED_TIME_COL, how=\"left\", suffixes=(\"\",\"\"))\n",
    "        n = len(g)\n",
    "        starts = list(range(0, max(0, n - W + 1), S))\n",
    "        for si in starts:\n",
    "            ei = si + W - 1\n",
    "            sub = g.iloc[si:ei+1]\n",
    "            # 取窗口對應的檔內絕對位置\n",
    "            start_abs = int(sub[\"abs_row\"].iloc[0])\n",
    "            end_abs   = int(sub[\"abs_row\"].iloc[-1])\n",
    "\n",
    "            # 連續性檢核\n",
    "            max_dt = float(sub[REQUIRED_DT_COL].max()) if REQUIRED_DT_COL in sub.columns else np.nan\n",
    "            cont_ok = (not np.isnan(max_dt)) and (max_dt <= DT_SEC_THRESHOLD)\n",
    "\n",
    "            # 標籤聚合\n",
    "            y, ratio, last_ap = label_by_rules(sub, mode=mode, tau=tau)\n",
    "\n",
    "            win_id = f\"{os.path.basename(source_csv)}::{gid}::{start_abs}-{end_abs}\"\n",
    "            rows.append(WindowRow(\n",
    "                window_id=win_id,\n",
    "                source=os.path.basename(source_csv),\n",
    "                start_idx=start_abs,\n",
    "                end_idx=end_abs,\n",
    "                start_time=str(sub[REQUIRED_TIME_COL].iloc[0]),\n",
    "                end_time=str(sub[REQUIRED_TIME_COL].iloc[-1]),\n",
    "                max_dt_sec=float(max_dt) if not np.isnan(max_dt) else np.nan,\n",
    "                continuity_ok=bool(cont_ok),\n",
    "                label=int(y),\n",
    "                pos_ratio_setfin1=float(ratio),\n",
    "                last_ad_para=float(last_ap),\n",
    "                patno_first=str(sub[REQUIRED_ID_COL].iloc[0]) if REQUIRED_ID_COL in sub.columns else \"\",\n",
    "                origin_id=str(gid),\n",
    "                requires_sort_by_senddate=True\n",
    "            ))\n",
    "            total_windows += 1\n",
    "\n",
    "    stats = {\n",
    "        \"source\": os.path.basename(source_csv),\n",
    "        \"total_rows\": int(len(df)),\n",
    "        \"total_windows\": int(total_windows),\n",
    "        \"n_cont_ok\": int(sum(r.continuity_ok for r in rows)),\n",
    "        \"n_label_pos\": int(sum(r.label == 1 for r in rows)),\n",
    "        \"max_dt_overall\": float(df[REQUIRED_DT_COL].max()) if REQUIRED_DT_COL in df.columns and not df.empty else np.nan\n",
    "    }\n",
    "    return rows, stats, nan_df\n",
    "\n",
    "def step_build_manifest(cleaned_paths: List[str], shard_paths: List[str]):\n",
    "    print(\"\\n==[步驟三] 產生視窗 manifest（不實體化樣本）==\")\n",
    "    sources = shard_paths if ENABLE_SHARDING and shard_paths else cleaned_paths\n",
    "\n",
    "    # 讀取設定檔（取得 labeling 模式）\n",
    "    with open(os.path.join(DIR_CFG, \"windowing.yaml\"), \"r\", encoding=\"utf-8\") as f:\n",
    "        cfg_yaml = yaml.safe_load(f)\n",
    "    lbl_cfg = cfg_yaml.get(\"labeling_ext\", {\"mode\": DEFAULT_LABEL_MODE, \"tau\": DEFAULT_TAU})\n",
    "\n",
    "    all_rows: List[WindowRow] = []\n",
    "    all_stats = []\n",
    "    nan_alerts = []\n",
    "\n",
    "    with Pool(processes=N_WORKERS) as pool:\n",
    "        for rows, stats, nan_df in tqdm(pool.imap_unordered(\n",
    "                lambda p: scan_one_source_for_manifest(p, lbl_cfg), sources),\n",
    "                total=len(sources), desc=\"掃描來源\"):\n",
    "            all_rows.extend(rows)\n",
    "            all_stats.append(stats)\n",
    "            if nan_df is not None and not nan_df.empty:\n",
    "                nan_alerts.append(nan_df)\n",
    "\n",
    "    # 輸出 manifest\n",
    "    man_df = pd.DataFrame([asdict(r) for r in all_rows])\n",
    "    man_path = os.path.join(DIR_MAN, \"window_manifest.csv\")\n",
    "    man_df.to_csv(man_path, index=False, encoding=\"utf-8-sig\")\n",
    "    print(f\"[Manifest] 已輸出：{man_path}（total_windows={len(man_df):,}）\")\n",
    "\n",
    "    # continuity/label 統計\n",
    "    stats_df = pd.DataFrame(all_stats)\n",
    "    stats_path = os.path.join(DIR_MAN, \"window_stats.csv\")\n",
    "    stats_df.to_csv(stats_path, index=False, encoding=\"utf-8-sig\")\n",
    "\n",
    "    # 標籤彙整\n",
    "    label_summary = man_df.groupby(\"label\").size().rename(\"count\").reset_index()\n",
    "    label_path = os.path.join(DIR_MAN, \"window_label_summary.csv\")\n",
    "    label_summary.to_csv(label_path, index=False, encoding=\"utf-8-sig\")\n",
    "\n",
    "    # 缺失報警（若有）\n",
    "    if nan_alerts:\n",
    "        pd.concat(nan_alerts, ignore_index=True).to_csv(os.path.join(DIR_AUDITS, \"window_nan_alerts.csv\"),\n",
    "                                                        index=False, encoding=\"utf-8-sig\")\n",
    "    print(\"[Manifest] 統計輸出：window_stats.csv / window_label_summary.csv；缺失報警（若有）：window_nan_alerts.csv\")\n",
    "\n",
    "    # 參數與分片雜湊快照\n",
    "    cfg_path = os.path.join(DIR_CFG, \"windowing.yaml\")\n",
    "    cfg_hash = sha256_file(cfg_path)\n",
    "    shard_hashes = []\n",
    "    if shard_paths:\n",
    "        for p in shard_paths:\n",
    "            shard_hashes.append({\"file\": os.path.basename(p), \"hash\": sha256_file(p)})\n",
    "    snap = {\"config_hash\": cfg_hash, \"shards\": shard_hashes, \"generated_at\": time.strftime(\"%Y-%m-%d %H:%M:%S\")}\n",
    "    with open(os.path.join(DIR_MAN, \"slicing_params.json\"), \"w\", encoding=\"utf-8\") as f:\n",
    "        json.dump(snap, f, ensure_ascii=False, indent=2)\n",
    "    print(\"[Manifest] 已寫入 slicing_params.json（含 config/shards 雜湊）\")\n",
    "\n",
    "# =========================\n",
    "# K 折交叉驗證（分層）\n",
    "# =========================\n",
    "def step_make_kfold(manifest_csv: str, k: int = 5):\n",
    "    print(\"\\n==[步驟四] K 折分層交叉驗證索引==\")\n",
    "    df = pd.read_csv(manifest_csv)\n",
    "    if df.empty:\n",
    "        print(\"[KFold] manifest 為空，略過。\")\n",
    "        return\n",
    "\n",
    "    from sklearn.model_selection import StratifiedKFold\n",
    "    y = df[\"label\"].astype(int).values\n",
    "    skf = StratifiedKFold(n_splits=k, shuffle=True, random_state=SEED)\n",
    "\n",
    "    folds = []\n",
    "    for fold_id, (tr_idx, va_idx) in enumerate(skf.split(np.zeros(len(y)), y), start=1):\n",
    "        folds.append({\n",
    "            \"fold\": fold_id,\n",
    "            \"train_indices\": tr_idx.tolist(),\n",
    "            \"valid_indices\": va_idx.tolist(),\n",
    "            \"n_train\": int(len(tr_idx)),\n",
    "            \"n_valid\": int(len(va_idx))\n",
    "        })\n",
    "\n",
    "    with open(os.path.join(DIR_CFG, \"kfold_splits.json\"), \"w\", encoding=\"utf-8\") as f:\n",
    "        json.dump({\"n_folds\": k, \"seed\": SEED, \"folds\": folds}, f, ensure_ascii=False, indent=2)\n",
    "    print(\"[KFold] 已輸出：config/kfold_splits.json\")\n",
    "\n",
    "# =========================\n",
    "# 執行總流程\n",
    "# =========================\n",
    "def main():\n",
    "    print(\"==[初始化]==\")\n",
    "    ensure_dirs()\n",
    "    cfg_path, _cfg_obj = write_windowing_yaml()\n",
    "\n",
    "    # 步驟一：清洗\n",
    "    cleaned_paths = step_clean_all()\n",
    "\n",
    "    # schema_final.json（依第一個 cleaned 檔記錄欄位）\n",
    "    if cleaned_paths:\n",
    "        sample_cols = list(pd.read_csv(cleaned_paths[0], nrows=0).columns)\n",
    "        write_schema_json(sample_cols)\n",
    "\n",
    "    # 步驟二：多分片\n",
    "    shard_paths, shard_stat_df = step_build_shards(cleaned_paths)\n",
    "\n",
    "    # 步驟三：manifest（不實體化）\n",
    "    step_build_manifest(cleaned_paths, shard_paths)\n",
    "\n",
    "    # 步驟四：KFold\n",
    "    step_make_kfold(os.path.join(DIR_MAN, \"window_manifest.csv\"), k=5)\n",
    "\n",
    "    # 訓練快照（占位）\n",
    "    run_tag = time.strftime(\"run_%Y%m%d_%H%M\")\n",
    "    run_dir = os.path.join(DIR_TRAIN, run_tag)\n",
    "    os.makedirs(os.path.join(run_dir, \"config_snapshot\"), exist_ok=True)\n",
    "    os.makedirs(os.path.join(run_dir, \"logs\"), exist_ok=True)\n",
    "    with open(os.path.join(run_dir, \"config_snapshot\", \"config_hash.txt\"), \"w\", encoding=\"utf-8\") as f:\n",
    "        f.write(sha256_file(cfg_path) + \"\\n\")\n",
    "    with open(os.path.join(run_dir, \"logs\", \"training.log\"), \"w\", encoding=\"utf-8\") as f:\n",
    "        f.write(\"[info] training will record here (placeholder)\\n\")\n",
    "\n",
    "    # 審計彙總\n",
    "    summary = {\n",
    "        \"source_dir\": SRC_DIR,\n",
    "        \"expected_files\": EXPECTED_FILES,\n",
    "        \"out_root\": OUT_ROOT,\n",
    "        \"created\": time.strftime(\"%Y-%m-%d %H:%M:%S\"),\n",
    "        \"reports\": {\n",
    "            \"window_continuity_stats\": os.path.join(DIR_MAN, \"window_continuity_stats.csv\"),\n",
    "            \"window_nan_alerts\": os.path.join(DIR_AUDITS, \"window_nan_alerts.csv\"),\n",
    "            \"schema_audit\": os.path.join(DIR_AUDITS, \"schema_audit.csv\"),\n",
    "            \"cleaned_nan_summary\": os.path.join(DIR_AUDITS, \"cleaned_nan_summary.csv\"),\n",
    "            \"value_range_anomaly\": os.path.join(DIR_AUDITS, \"value_range_anomaly.csv\"),\n",
    "            \"time_gap_anomaly\": os.path.join(DIR_AUDITS, \"time_gap_anomaly.csv\"),\n",
    "            \"manifest\": os.path.join(DIR_MAN, \"window_manifest.csv\"),\n",
    "            \"window_stats\": os.path.join(DIR_MAN, \"window_stats.csv\"),\n",
    "            \"window_label_summary\": os.path.join(DIR_MAN, \"window_label_summary.csv\"),\n",
    "            \"kfold_splits\": os.path.join(DIR_CFG, \"kfold_splits.json\"),\n",
    "        }\n",
    "    }\n",
    "    os.makedirs(DIR_AUDITS_RPT, exist_ok=True)\n",
    "    with open(os.path.join(DIR_AUDITS_RPT, \"summary.json\"), \"w\", encoding=\"utf-8\") as f:\n",
    "        json.dump(summary, f, ensure_ascii=False, indent=2)\n",
    "\n",
    "    print(\"\\n✅ 全部完成。重點輸出：\")\n",
    "    print(f\" - Cleaned：{DIR_CLEANED}\")\n",
    "    print(f\" - Shards：{DIR_SHARDS}（_shard_catalog.csv, _shard_stats.csv）\")\n",
    "    print(f\" - Manifests：{DIR_MAN}（window_manifest.csv, window_stats.csv, window_label_summary.csv, slicing_params.json）\")\n",
    "    print(f\" - Audits：{DIR_AUDITS}（schema_audit.csv, cleaned_nan_summary.csv, time_gap_anomaly.csv）\")\n",
    "    print(f\" - Config：{DIR_CFG}（windowing.yaml, kfold_splits.json）\")\n",
    "    print(f\" - Docs：{DIR_DOCS}（schema_final.json）\")\n",
    "    print(f\" - Cache：{DIR_CACHE}, {DIR_CACHE_TF}, {DIR_LOCKS}\")\n",
    "    print(f\" - Training：{DIR_TRAIN}/*/config_snapshot/config_hash.txt, logs/training.log\")\n",
    "    print(\"請在訓練端依 manifest 動態取窗（Lazy Evaluation），避免另存成全量靜態視窗檔。\")\n",
    "\n",
    "if __name__ == \"__main__\":\n",
    "    main()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c7d03d8e-4215-488c-b1f6-f10d601b3409",
   "metadata": {},
   "outputs": [],
   "source": [
    "結果因為是因為 multiprocessing 在把工作丟到子進程時，\n",
    "需要把任務函式序列化（pickle）。\n",
    "在 step_build_manifest 裡用到匿名函式 lambda，\n",
    "而 lambda（以及巢狀在函式內的本地函式）是不可 pickling 的，\n",
    "所以整個 pool 掛掉"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 231,
   "id": "d99f567c-f69a-4868-907b-b3d6113c4327",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "=[初始化]==\n",
      "[設定] 已寫入設定檔：/home/jovyan/RT08/0925/sliding_win/1014/config/windowing.yaml\n",
      "==[步驟一] 清洗逐筆 CSV（不動原始）==\n",
      "[清洗] 發現檔案數：122（預期 122）\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "清洗中: 100%|██████████| 122/122 [00:03<00:00, 39.80it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[清洗] 已完成；稽核已輸出：audit_summary.csv / schema_audit.csv / cleaned_nan_summary.csv / time_gap_anomaly.csv（若有）\n",
      "[清洗] 已輸出 Δt_sec 統計：manifests/window_continuity_stats.csv\n",
      "[設定] 已寫入 schema：/home/jovyan/RT08/0925/sliding_win/1014/docs/schema_final.json\n",
      "\n",
      "==[步驟二] 多分片 CSV（可選）==\n",
      "[分片] 開始建立分片（rows_per_shard=500,000）…\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "分片中: 100%|██████████| 122/122 [00:13<00:00,  9.06it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[分片] 完成：_shard_catalog.csv / _shard_stats.csv\n",
      "\n",
      "==[步驟三] 產生視窗 manifest（不實體化樣本）==\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "掃描來源: 100%|██████████| 3/3 [00:09<00:00,  3.16s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Manifest] 已輸出：/home/jovyan/RT08/0925/sliding_win/1014/manifests/window_manifest.csv（total_windows=44,386）\n",
      "[Manifest] 統計輸出：window_stats.csv / window_label_summary.csv；缺失報警（若有）：window_nan_alerts.csv\n",
      "[Manifest] 已寫入 slicing_params.json（含 config/shards 雜湊）\n",
      "\n",
      "==[步驟四] K 折分層交叉驗證索引==\n",
      "[KFold] 已輸出：config/kfold_splits.json\n",
      "\n",
      "✅ 全部完成。重點輸出：\n",
      " - Cleaned：/home/jovyan/RT08/0925/sliding_win/1014/cleaned/clear/\n",
      " - Shards：/home/jovyan/RT08/0925/sliding_win/1014/shards/（_shard_catalog.csv, _shard_stats.csv）\n",
      " - Manifests：/home/jovyan/RT08/0925/sliding_win/1014/manifests/（window_manifest.csv, window_stats.csv, window_label_summary.csv, slicing_params.json）\n",
      " - Audits：/home/jovyan/RT08/0925/sliding_win/1014/audits/（schema_audit.csv, cleaned_nan_summary.csv, time_gap_anomaly.csv）\n",
      " - Config：/home/jovyan/RT08/0925/sliding_win/1014/config/（windowing.yaml, kfold_splits.json）\n",
      " - Docs：/home/jovyan/RT08/0925/sliding_win/1014/docs/（schema_final.json）\n",
      " - Cache：/home/jovyan/RT08/0925/sliding_win/1014/cache/reader_ram/, /home/jovyan/RT08/0925/sliding_win/1014/cache/tf_dataset/, /home/jovyan/RT08/0925/sliding_win/1014/cache/locks/\n",
      " - Training：/home/jovyan/RT08/0925/sliding_win/1014/training//*/config_snapshot/config_hash.txt, logs/training.log\n",
      "請在訓練端依 manifest 動態取窗（Lazy Evaluation），避免另存成全量靜態視窗檔。\n"
     ]
    }
   ],
   "source": [
    "# -*- coding: utf-8 -*-\n",
    "\"\"\"\n",
    "不實體化滑動視窗 - 版本 1014（工程實作用，含多分片、manifest 前置、併發讀取/快取）\n",
    "================================================================================\n",
    "⚠️ 不會修改原始資料：/home/jovyan/RT08/0925/bling_1014/（122 個 CSV）\n",
    "所有輸出/中繼/報表皆落在：/home/jovyan/RT08/0925/sliding_win/1014/\n",
    "\n",
    "本腳本完全對齊需求，內建：\n",
    "1) 目錄初始化與設定檔落地（windowing.yaml、schema_final.json）\n",
    "2) 清洗：欄位正規化、senddate 解析、補 ts_unix/Δt_sec、過濾 nan_check!=1 或時間異常\n",
    "   - 添加 origin_id（原檔名），後續避免跨來源拼窗\n",
    "   - 稽核：schema_audit.csv、cleaned_nan_summary.csv、time_gap_anomaly.csv 等\n",
    "3) Δt_sec 統計輸出（manifests/window_continuity_stats.csv）\n",
    "4) 多分片（shards/）與型錄/統計/雜湊（_shard_catalog.csv、_shard_stats.csv）\n",
    "5) 視窗 manifest（不實體化）：window_manifest.csv / window_stats.csv / window_label_summary.csv\n",
    "   - W=60 筆、S=30 筆、右對齊（最後一筆為決策時點）\n",
    "   - 連續性檢核：max(Δt_sec) > 門檻（預設 120 秒） → continuity_ok=False\n",
    "   - 標籤聚合（預設純多數決）；可切 mode：\n",
    "       majority_only / majority_w_minus_1 / majority_w_minus_1_softforce\n",
    "6) 併發處理（multiprocessing；已修正 lambda/pickling 問題，並提供單進程 fallback）\n",
    "7) K 折分層交叉驗證（kfold_splits.json；seed=42；不以病人分割）\n",
    "8) 設定與分片雜湊快照（slicing_params.json），訓練快照（config_hash.txt 等）\n",
    "9) 審計報表彙總（audits/reports/summary.json）\n",
    "\n",
    "需要套件：pandas, numpy, pyyaml, tqdm, scikit-learn\n",
    "\"\"\"\n",
    "\n",
    "import os\n",
    "import re\n",
    "import json\n",
    "import glob\n",
    "import time\n",
    "import hashlib\n",
    "import warnings\n",
    "from typing import List, Dict, Any, Tuple\n",
    "from dataclasses import dataclass, asdict\n",
    "from multiprocessing import Pool, cpu_count\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from tqdm import tqdm\n",
    "import yaml\n",
    "\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "# =========================\n",
    "# 使用者參數區（可調整）\n",
    "# =========================\n",
    "SRC_DIR = \"/home/jovyan/RT08/0925/bling_1014/\"     # 原始逐筆 CSV（❗不修改）\n",
    "OUT_ROOT = \"/home/jovyan/RT08/0925/sliding_win/1014/\"\n",
    "\n",
    "# 版本化資料夾層級\n",
    "DIR_CLEANED = os.path.join(OUT_ROOT, \"cleaned/clear/\")\n",
    "DIR_DOCS    = os.path.join(OUT_ROOT, \"docs/\")\n",
    "DIR_CFG     = os.path.join(OUT_ROOT, \"config/\")\n",
    "DIR_AUDITS  = os.path.join(OUT_ROOT, \"audits/\")\n",
    "DIR_AUDITS_RPT = os.path.join(DIR_AUDITS, \"reports/\")\n",
    "DIR_MAN     = os.path.join(OUT_ROOT, \"manifests/\")\n",
    "DIR_CACHE   = os.path.join(OUT_ROOT, \"cache/reader_ram/\")\n",
    "DIR_CACHE_TF= os.path.join(OUT_ROOT, \"cache/tf_dataset/\")\n",
    "DIR_LOCKS   = os.path.join(OUT_ROOT, \"cache/locks/\")\n",
    "DIR_SHARDS  = os.path.join(OUT_ROOT, \"shards/\")\n",
    "DIR_TRAIN   = os.path.join(OUT_ROOT, \"training/\")\n",
    "\n",
    "# 預期數量/亂數種子\n",
    "EXPECTED_FILES = 122\n",
    "SEED = 42\n",
    "\n",
    "# 視窗參數（以「筆」為單位）\n",
    "W = 60\n",
    "S = 30\n",
    "RIGHT_ALIGN = True\n",
    "\n",
    "# 時間連續性門檻（秒）：max(Δt_sec) 超過則 continuity_ok=False（訓練端可決定是否濾除）\n",
    "DT_SEC_THRESHOLD = 120.0\n",
    "\n",
    "# 多分片設定\n",
    "ENABLE_SHARDING = True\n",
    "SHARD_ROWS = 500_000  # 每片最多筆數（依硬體與吞吐可調）\n",
    "\n",
    "# 併發處理\n",
    "N_WORKERS = max(1, min(cpu_count() - 1, 8))  # 避免占滿機器\n",
    "\n",
    "# 必備欄位（清洗後應存在）\n",
    "REQUIRED_ID_COL   = \"patno\"\n",
    "REQUIRED_TIME_COL = \"senddate\"\n",
    "REQUIRED_TS_COL   = \"ts_unix\"\n",
    "REQUIRED_DT_COL   = \"Δt_sec\"   # 保留你的命名\n",
    "REQUIRED_QC_COL   = \"nan_check\"\n",
    "REQUIRED_Y1_COL   = \"ad_para\"\n",
    "REQUIRED_Y2_COL   = \"set_fin\"\n",
    "\n",
    "# 預設標籤聚合模式（可於 windowing.yaml 覆寫）\n",
    "DEFAULT_LABEL_MODE = \"majority_only\"  # 可選：majority_only / majority_w_minus_1 / majority_w_minus_1_softforce\n",
    "DEFAULT_TAU = 0.2                     # 只有 softforce 會用到\n",
    "\n",
    "# =========================\n",
    "# 工具：目錄與雜湊\n",
    "# =========================\n",
    "def ensure_dirs():\n",
    "    for d in [DIR_CLEANED, DIR_DOCS, DIR_CFG, DIR_AUDITS, DIR_AUDITS_RPT,\n",
    "              DIR_MAN, DIR_CACHE, DIR_CACHE_TF, DIR_LOCKS, DIR_SHARDS, DIR_TRAIN]:\n",
    "        os.makedirs(d, exist_ok=True)\n",
    "\n",
    "def sha256_file(path: str, block_size: int = 1 << 20) -> str:\n",
    "    h = hashlib.sha256()\n",
    "    with open(path, \"rb\") as f:\n",
    "        while True:\n",
    "            b = f.read(block_size)\n",
    "            if not b: break\n",
    "            h.update(b)\n",
    "    return h.hexdigest()\n",
    "\n",
    "# =========================\n",
    "# 設定檔與 schema 落地\n",
    "# =========================\n",
    "def write_windowing_yaml():\n",
    "    cfg = {\n",
    "        \"version\": \"1014\",\n",
    "        \"seed\": SEED,\n",
    "        \"window\": {\"W\": W, \"S\": S, \"align\": \"right\" if RIGHT_ALIGN else \"left\"},\n",
    "        \"continuity\": {\"dt_sec_threshold\": DT_SEC_THRESHOLD, \"enforce\": True},\n",
    "        \"labeling\": {\n",
    "            \"rule\": \"ratio>=0.5 set_fin==1 (pure majority at window level); no force by last ad_para\",\n",
    "            \"ratio_threshold\": 0.5,\n",
    "            \"force_by_last_ad_para\": False\n",
    "        },\n",
    "        \"labeling_ext\": {\n",
    "            \"mode\": DEFAULT_LABEL_MODE,   # majority_only / majority_w_minus_1 / majority_w_minus_1_softforce\n",
    "            \"tau\": DEFAULT_TAU\n",
    "        },\n",
    "        \"sharding\": {\n",
    "            \"enabled\": ENABLE_SHARDING,\n",
    "            \"rows_per_shard\": SHARD_ROWS,\n",
    "            \"name_pattern\": \"shard_%06d.csv\"\n",
    "        },\n",
    "        \"lazy_evaluation\": True,\n",
    "        \"notes\": \"do not materialize windows; manifest-only; group by origin_id to avoid cross-file windows\"\n",
    "    }\n",
    "    os.makedirs(DIR_CFG, exist_ok=True)\n",
    "    path = os.path.join(DIR_CFG, \"windowing.yaml\")\n",
    "    with open(path, \"w\", encoding=\"utf-8\") as f:\n",
    "        yaml.safe_dump(cfg, f, allow_unicode=True, sort_keys=False)\n",
    "    print(f\"[設定] 已寫入設定檔：{path}\")\n",
    "    return path, cfg\n",
    "\n",
    "def write_schema_json(final_cols: List[str]):\n",
    "    os.makedirs(DIR_DOCS, exist_ok=True)\n",
    "    path = os.path.join(DIR_DOCS, \"schema_final.json\")\n",
    "    with open(path, \"w\", encoding=\"utf-8\") as f:\n",
    "        json.dump({\"final_columns\": final_cols}, f, ensure_ascii=False, indent=2)\n",
    "    print(f\"[設定] 已寫入 schema：{path}\")\n",
    "    return path\n",
    "\n",
    "# =========================\n",
    "# 讀檔/清洗輔助\n",
    "# =========================\n",
    "def list_csv_files(src: str) -> List[str]:\n",
    "    return sorted(glob.glob(os.path.join(src, \"*.csv\")))\n",
    "\n",
    "def normalize_col(name: str) -> str:\n",
    "    s = str(name).strip()\n",
    "    s = re.sub(r\"\\s+\", \"_\", s)\n",
    "    return s\n",
    "\n",
    "def to_datetime_safe(series: pd.Series) -> pd.Series:\n",
    "    # 支援「上午/下午」「AM/PM」常見字樣\n",
    "    s = series.astype(str)\n",
    "    s = s.str.replace(\"上午\", \"AM\").str.replace(\"下午\", \"PM\")\n",
    "    s = s.str.replace(\"早上\", \"AM\").str.replace(\"晚上\", \"PM\").str.replace(\"中午\", \"PM\")\n",
    "    return pd.to_datetime(s, errors=\"coerce\", infer_datetime_format=True)\n",
    "\n",
    "def add_ts_unix_and_dtsec(df: pd.DataFrame, id_col: str) -> pd.DataFrame:\n",
    "    # 補 ts_unix\n",
    "    if REQUIRED_TS_COL not in df.columns:\n",
    "        df[REQUIRED_TS_COL] = np.floor(pd.to_datetime(df[REQUIRED_TIME_COL], errors=\"coerce\").astype(\"int64\") / 1e9)\n",
    "    # 補 Δt_sec（以同檔每名病人分組）\n",
    "    if REQUIRED_DT_COL not in df.columns:\n",
    "        df = df.sort_values(by=[REQUIRED_TIME_COL]).reset_index(drop=True)\n",
    "        if id_col in df.columns:\n",
    "            df[REQUIRED_DT_COL] = df.groupby(id_col)[REQUIRED_TIME_COL].diff().dt.total_seconds()\n",
    "        else:\n",
    "            df[REQUIRED_DT_COL] = df[REQUIRED_TIME_COL].diff().dt.total_seconds()\n",
    "        df[REQUIRED_DT_COL] = df[REQUIRED_DT_COL].fillna(0.0)\n",
    "    return df\n",
    "\n",
    "def basic_clean_one(path: str) -> Dict[str, Any]:\n",
    "    \"\"\"\n",
    "    單檔清洗：\n",
    "    - 欄位正規化；解析 senddate；補 ts_unix/Δt_sec\n",
    "    - 過濾 nan_check != 1 或 senddate 無效\n",
    "    - 追加 origin_id = 原檔名（避免跨來源拼窗）\n",
    "    \"\"\"\n",
    "    base = os.path.basename(path)\n",
    "    out_path = os.path.join(DIR_CLEANED, base)\n",
    "\n",
    "    raw = pd.read_csv(path)\n",
    "    rename_map = {c: normalize_col(c) for c in raw.columns}\n",
    "    df = raw.rename(columns=rename_map)\n",
    "\n",
    "    # 確保必要欄位存在（若缺，先建空欄）\n",
    "    for c in [REQUIRED_ID_COL, REQUIRED_TIME_COL, REQUIRED_QC_COL, REQUIRED_Y1_COL, REQUIRED_Y2_COL]:\n",
    "        if c not in df.columns:\n",
    "            df[c] = np.nan\n",
    "\n",
    "    # 時間解析\n",
    "    df[REQUIRED_TIME_COL] = to_datetime_safe(df[REQUIRED_TIME_COL])\n",
    "\n",
    "    # 補 ts_unix/Δt_sec\n",
    "    df = add_ts_unix_and_dtsec(df, REQUIRED_ID_COL)\n",
    "\n",
    "    # 過濾 nan_check != 1 或 時間欄異常\n",
    "    qc_num = pd.to_numeric(df[REQUIRED_QC_COL], errors=\"coerce\")\n",
    "    valid_mask = (qc_num == 1) & (df[REQUIRED_TIME_COL].notna())\n",
    "    df_clean = df.loc[valid_mask].copy()\n",
    "\n",
    "    # 追加 origin_id（原檔名）；避免跨來源拼窗\n",
    "    df_clean[\"origin_id\"] = base\n",
    "\n",
    "    # 寫檔\n",
    "    df_clean.to_csv(out_path, index=False, encoding=\"utf-8-sig\")\n",
    "\n",
    "    # 稽核摘要\n",
    "    audit = {\n",
    "        \"file_name\": base,\n",
    "        \"src_path\": path,\n",
    "        \"out_path\": out_path,\n",
    "        \"rows_in\": int(len(raw)),\n",
    "        \"rows_out\": int(len(df_clean)),\n",
    "        \"nan_rows_dropped\": int((~valid_mask).sum()),\n",
    "        \"has_ts_unix\": REQUIRED_TS_COL in df_clean.columns,\n",
    "        \"has_dt_sec\": REQUIRED_DT_COL in df_clean.columns,\n",
    "        \"min_dt_sec\": float(df_clean[REQUIRED_DT_COL].min()) if REQUIRED_DT_COL in df_clean.columns and not df_clean.empty else np.nan,\n",
    "        \"p50_dt_sec\": float(df_clean[REQUIRED_DT_COL].median()) if REQUIRED_DT_COL in df_clean.columns and not df_clean.empty else np.nan,\n",
    "        \"p90_dt_sec\": float(df_clean[REQUIRED_DT_COL].quantile(0.9)) if REQUIRED_DT_COL in df_clean.columns and not df_clean.empty else np.nan,\n",
    "        \"max_dt_sec\": float(df_clean[REQUIRED_DT_COL].max()) if REQUIRED_DT_COL in df_clean.columns and not df_clean.empty else np.nan,\n",
    "        \"total_nan_after\": int(df_clean.isna().sum().sum()),\n",
    "    }\n",
    "    return audit\n",
    "\n",
    "# =========================\n",
    "# 併發清洗（不動原始檔）\n",
    "# =========================\n",
    "def step_clean_all():\n",
    "    files = list_csv_files(SRC_DIR)\n",
    "    print(\"==[步驟一] 清洗逐筆 CSV（不動原始）==\")\n",
    "    print(f\"[清洗] 發現檔案數：{len(files)}（預期 {EXPECTED_FILES}）\")\n",
    "    os.makedirs(DIR_CLEANED, exist_ok=True)\n",
    "\n",
    "    audits = []\n",
    "    with Pool(processes=N_WORKERS) as pool:\n",
    "        for res in tqdm(pool.imap_unordered(basic_clean_one, files), total=len(files), desc=\"清洗中\"):\n",
    "            audits.append(res)\n",
    "\n",
    "    audit_df = pd.DataFrame(audits).sort_values(\"file_name\")\n",
    "    audit_df.to_csv(os.path.join(DIR_CLEANED, \"../audit_summary.csv\"), index=False, encoding=\"utf-8-sig\")\n",
    "\n",
    "    # 欄位稽核（schema）\n",
    "    schema_rows = []\n",
    "    for a in audits:\n",
    "        cols = list(pd.read_csv(a[\"out_path\"], nrows=0).columns)\n",
    "        all_lower = all(c == c.lower() for c in cols)\n",
    "        snake_ok  = all(bool(re.fullmatch(r\"[a-z0-9_]+\", c)) for c in cols)\n",
    "        uniq_ok   = (len(cols) == len(set(cols)))\n",
    "        schema_rows.append({\n",
    "            \"file_name\": a[\"file_name\"], \"n_cols\": len(cols),\n",
    "            \"all_lowercase\": all_lower, \"snake_case_only\": snake_ok, \"unique_names\": uniq_ok\n",
    "        })\n",
    "    pd.DataFrame(schema_rows).to_csv(os.path.join(DIR_AUDITS, \"schema_audit.csv\"), index=False, encoding=\"utf-8-sig\")\n",
    "\n",
    "    # 缺失彙整\n",
    "    nan_rows = []\n",
    "    for a in audits:\n",
    "        df = pd.read_csv(a[\"out_path\"])\n",
    "        nan_rows.append({\"file_name\": a[\"file_name\"], \"nan_cells\": int(df.isna().sum().sum()), \"rows\": int(len(df))})\n",
    "    pd.DataFrame(nan_rows).to_csv(os.path.join(DIR_AUDITS, \"cleaned_nan_summary.csv\"), index=False, encoding=\"utf-8-sig\")\n",
    "\n",
    "    # 時間間隔異常（> 門檻）\n",
    "    gap_rows = []\n",
    "    for a in audits:\n",
    "        df = pd.read_csv(a[\"out_path\"])\n",
    "        if REQUIRED_DT_COL in df.columns and not df.empty:\n",
    "            bad = df[df[REQUIRED_DT_COL] > DT_SEC_THRESHOLD]\n",
    "            if not bad.empty:\n",
    "                tmp = bad[[REQUIRED_TIME_COL, REQUIRED_DT_COL]].copy()\n",
    "                tmp.insert(0, \"file_name\", a[\"file_name\"])\n",
    "                gap_rows.append(tmp)\n",
    "    if gap_rows:\n",
    "        pd.concat(gap_rows, ignore_index=True).to_csv(os.path.join(DIR_AUDITS, \"time_gap_anomaly.csv\"),\n",
    "                                                     index=False, encoding=\"utf-8-sig\")\n",
    "    print(\"[清洗] 已完成；稽核已輸出：audit_summary.csv / schema_audit.csv / cleaned_nan_summary.csv / time_gap_anomaly.csv（若有）\")\n",
    "\n",
    "    # Δt_sec 統計\n",
    "    stat_rows = []\n",
    "    for a in audits:\n",
    "        df = pd.read_csv(a[\"out_path\"])\n",
    "        if REQUIRED_DT_COL in df.columns and not df.empty:\n",
    "            s = df[REQUIRED_DT_COL]\n",
    "            stat_rows.append({\n",
    "                \"file_name\": a[\"file_name\"],\n",
    "                \"count\": int(s.count()),\n",
    "                \"min\": float(s.min()),\n",
    "                \"q1\": float(s.quantile(0.25)),\n",
    "                \"median\": float(s.median()),\n",
    "                \"q3\": float(s.quantile(0.75)),\n",
    "                \"mean\": float(s.mean()),\n",
    "                \"max\": float(s.max())\n",
    "            })\n",
    "    pd.DataFrame(stat_rows).to_csv(os.path.join(DIR_MAN, \"window_continuity_stats.csv\"), index=False, encoding=\"utf-8-sig\")\n",
    "    print(\"[清洗] 已輸出 Δt_sec 統計：manifests/window_continuity_stats.csv\")\n",
    "\n",
    "    # 回傳清洗後檔案清單\n",
    "    return [os.path.join(DIR_CLEANED, os.path.basename(p)) for p in files if os.path.exists(os.path.join(DIR_CLEANED, os.path.basename(p)))]\n",
    "\n",
    "# =========================\n",
    "# 多分片（Sharding）\n",
    "# =========================\n",
    "def step_build_shards(cleaned_paths: List[str]) -> Tuple[List[str], pd.DataFrame]:\n",
    "    print(\"\\n==[步驟二] 多分片 CSV（可選）==\")\n",
    "    if not ENABLE_SHARDING:\n",
    "        print(\"[分片] 未啟用；略過。\")\n",
    "        return [], pd.DataFrame()\n",
    "\n",
    "    print(f\"[分片] 開始建立分片（rows_per_shard={SHARD_ROWS:,}）…\")\n",
    "    os.makedirs(DIR_SHARDS, exist_ok=True)\n",
    "\n",
    "    shard_idx = 1\n",
    "    shard_paths = []\n",
    "    cat_rows = []   # 分片型錄\n",
    "    stat_rows = []\n",
    "\n",
    "    cur_rows = 0\n",
    "    cur_buf: List[pd.DataFrame] = []\n",
    "\n",
    "    def flush_shard():\n",
    "        nonlocal shard_idx, shard_paths, cur_buf, cur_rows\n",
    "        if not cur_buf: return\n",
    "        shard_df = pd.concat(cur_buf, ignore_index=True)\n",
    "        # 依時間排序，保持與 manifest 同一排序邏輯（避免切窗與讀檔對不上）\n",
    "        shard_df = shard_df.sort_values(REQUIRED_TIME_COL).reset_index(drop=True)\n",
    "        shard_name = f\"shard_{shard_idx:06d}.csv\"\n",
    "        shard_path = os.path.join(DIR_SHARDS, shard_name)\n",
    "        shard_df.to_csv(shard_path, index=False, encoding=\"utf-8-sig\")\n",
    "        shard_paths.append(shard_path)\n",
    "        stat_rows.append({\"shard\": shard_name, \"rows\": int(len(shard_df)), \"hash\": sha256_file(shard_path)})\n",
    "        cat_rows.append({\"shard\": shard_name, \"rows\": int(len(shard_df)), \"path\": shard_path})\n",
    "        shard_idx += 1\n",
    "        cur_buf = []\n",
    "        cur_rows = 0\n",
    "\n",
    "    for p in tqdm(cleaned_paths, desc=\"分片中\"):\n",
    "        df = pd.read_csv(p)\n",
    "        if cur_rows + len(df) > SHARD_ROWS and cur_rows > 0:\n",
    "            flush_shard()\n",
    "        cur_buf.append(df)\n",
    "        cur_rows += len(df)\n",
    "        while cur_rows >= SHARD_ROWS:\n",
    "            need = SHARD_ROWS\n",
    "            acc = pd.concat(cur_buf, ignore_index=True)\n",
    "            acc = acc.sort_values(REQUIRED_TIME_COL).reset_index(drop=True)\n",
    "            full = acc.iloc[:need].copy()\n",
    "            left = acc.iloc[need:].copy()\n",
    "            shard_name = f\"shard_{shard_idx:06d}.csv\"\n",
    "            shard_path = os.path.join(DIR_SHARDS, shard_name)\n",
    "            full.to_csv(shard_path, index=False, encoding=\"utf-8-sig\")\n",
    "            shard_paths.append(shard_path)\n",
    "            stat_rows.append({\"shard\": shard_name, \"rows\": int(len(full)), \"hash\": sha256_file(shard_path)})\n",
    "            cat_rows.append({\"shard\": shard_name, \"rows\": int(len(full)), \"path\": shard_path})\n",
    "            shard_idx += 1\n",
    "            cur_buf = [left] if not left.empty else []\n",
    "            cur_rows = len(left)\n",
    "\n",
    "    flush_shard()\n",
    "    cat_df = pd.DataFrame(cat_rows)\n",
    "    stat_df = pd.DataFrame(stat_rows)\n",
    "    cat_df.to_csv(os.path.join(DIR_SHARDS, \"_shard_catalog.csv\"), index=False, encoding=\"utf-8-sig\")\n",
    "    stat_df.to_csv(os.path.join(DIR_SHARDS, \"_shard_stats.csv\"), index=False, encoding=\"utf-8-sig\")\n",
    "    print(\"[分片] 完成：_shard_catalog.csv / _shard_stats.csv\")\n",
    "    return shard_paths, stat_df\n",
    "\n",
    "# =========================\n",
    "# 視窗切割（不實體化：只產生 manifest）\n",
    "# =========================\n",
    "@dataclass\n",
    "class WindowRow:\n",
    "    window_id: str\n",
    "    source: str        # 來源檔（分片或 cleaned 檔）\n",
    "    start_idx: int     # 在「排序後的來源檔」中的起始索引（含）\n",
    "    end_idx: int       # 在「排序後的來源檔」中的結束索引（含）\n",
    "    start_time: str\n",
    "    end_time: str\n",
    "    max_dt_sec: float\n",
    "    continuity_ok: bool\n",
    "    label: int\n",
    "    pos_ratio_setfin1: float\n",
    "    last_ad_para: float\n",
    "    patno_first: str\n",
    "    origin_id: str\n",
    "    requires_sort_by_senddate: bool\n",
    "\n",
    "def label_by_rules(sub: pd.DataFrame, mode: str = DEFAULT_LABEL_MODE, tau: float = DEFAULT_TAU) -> Tuple[int, float, float]:\n",
    "    \"\"\"\n",
    "    視窗級標籤聚合：\n",
    "      - majority_only：純多數決（整窗 set_fin==1 比例 ≥ 0.5 → 1）\n",
    "      - majority_w_minus_1：只看前 W-1 做多數決；最後一筆不納入比例\n",
    "      - majority_w_minus_1_softforce：同上，若最後一筆 ad_para=1 且 ratio<tau → 1\n",
    "    回傳：(y, ratio_used_for_decision, last_ad_para)\n",
    "    \"\"\"\n",
    "    setfin = pd.to_numeric(sub[REQUIRED_Y2_COL], errors=\"coerce\").fillna(0.0)\n",
    "    adpara = pd.to_numeric(sub[REQUIRED_Y1_COL], errors=\"coerce\").fillna(0.0)\n",
    "    if len(sub) == 0:\n",
    "        return 0, 0.0, 0.0\n",
    "\n",
    "    if mode in (\"majority_w_minus_1\", \"majority_w_minus_1_softforce\"):\n",
    "        head = setfin.iloc[:-1] if len(setfin) >= 2 else setfin\n",
    "        ratio = float((head == 1).mean()) if len(head) > 0 else 0.0\n",
    "        last_ap = float(adpara.iloc[-1])\n",
    "        if mode == \"majority_w_minus_1_softforce\" and (last_ap == 1.0) and (ratio < tau):\n",
    "            y = 1\n",
    "        else:\n",
    "            y = 1 if ratio >= 0.5 else 0\n",
    "    else:\n",
    "        # 純多數決（整窗）\n",
    "        ratio = float((setfin == 1).mean())\n",
    "        y = 1 if ratio >= 0.5 else 0\n",
    "        last_ap = float(adpara.iloc[-1])\n",
    "\n",
    "    return int(y), float(ratio), float(last_ap)\n",
    "\n",
    "# --- multiprocessing 安全的頂層 wrapper（可被 pickle）---\n",
    "def _scan_wrapper(args):\n",
    "    \"\"\"args: (source_csv, lbl_cfg)\"\"\"\n",
    "    source_csv, lbl_cfg = args\n",
    "    return scan_one_source_for_manifest(source_csv, lbl_cfg)\n",
    "\n",
    "def scan_one_source_for_manifest(source_csv: str, lbl_cfg: Dict[str, Any]) -> Tuple[List[WindowRow], Dict[str, Any], pd.DataFrame]:\n",
    "    \"\"\"\n",
    "    從單一來源檔（可為 cleaned 或 shard）產生視窗 manifest 記錄（不寫出子視窗）。\n",
    "    - 先按 senddate 排序並建立 abs_row（整檔絕對 iloc）\n",
    "    - 依 origin_id 分組，避免跨來源拼窗\n",
    "    - start_idx/end_idx 為「排序後整檔」的絕對 iloc（訓練端需以相同排序切片）\n",
    "    \"\"\"\n",
    "    df = pd.read_csv(source_csv)\n",
    "    if df.empty:\n",
    "        return [], {\"source\": os.path.basename(source_csv), \"total_rows\": 0, \"total_windows\": 0}, pd.DataFrame()\n",
    "\n",
    "    # 排序並加上排序後絕對位置（abs_row）\n",
    "    df = df.sort_values(REQUIRED_TIME_COL).reset_index(drop=True)\n",
    "    df[\"abs_row\"] = df.index\n",
    "\n",
    "    # 缺失報警（保守檢查）\n",
    "    nan_alert = df.isna().mean()\n",
    "    nan_alert = nan_alert[nan_alert > 0.0]\n",
    "    nan_df = pd.DataFrame({\"column\": nan_alert.index, \"nan_ratio\": nan_alert.values})\n",
    "    if not nan_df.empty:\n",
    "        nan_df.insert(0, \"source\", os.path.basename(source_csv))\n",
    "\n",
    "    mode = lbl_cfg.get(\"mode\", DEFAULT_LABEL_MODE)\n",
    "    tau  = float(lbl_cfg.get(\"tau\", DEFAULT_TAU))\n",
    "\n",
    "    rows: List[WindowRow] = []\n",
    "    total_windows = 0\n",
    "\n",
    "    groups = [(\"\", df)]\n",
    "    if \"origin_id\" in df.columns:\n",
    "        groups = list(df.groupby(\"origin_id\", sort=False))\n",
    "\n",
    "    for gid, g in groups:\n",
    "        g = g.sort_values(REQUIRED_TIME_COL).reset_index(drop=True)\n",
    "        n = len(g)\n",
    "        starts = list(range(0, max(0, n - W + 1), S))\n",
    "        for si in starts:\n",
    "            ei = si + W - 1\n",
    "            sub = g.iloc[si:ei+1]\n",
    "\n",
    "            # 取窗口對應的檔內絕對位置（直接用 abs_row）\n",
    "            start_abs = int(sub[\"abs_row\"].iloc[0])\n",
    "            end_abs   = int(sub[\"abs_row\"].iloc[-1])\n",
    "\n",
    "            # 連續性檢核\n",
    "            max_dt = float(sub[REQUIRED_DT_COL].max()) if REQUIRED_DT_COL in sub.columns else np.nan\n",
    "            cont_ok = (not np.isnan(max_dt)) and (max_dt <= DT_SEC_THRESHOLD)\n",
    "\n",
    "            # 標籤聚合\n",
    "            y, ratio, last_ap = label_by_rules(sub, mode=mode, tau=tau)\n",
    "\n",
    "            win_id = f\"{os.path.basename(source_csv)}::{gid}::{start_abs}-{end_abs}\"\n",
    "            rows.append(WindowRow(\n",
    "                window_id=win_id,\n",
    "                source=os.path.basename(source_csv),\n",
    "                start_idx=start_abs,\n",
    "                end_idx=end_abs,\n",
    "                start_time=str(sub[REQUIRED_TIME_COL].iloc[0]),\n",
    "                end_time=str(sub[REQUIRED_TIME_COL].iloc[-1]),\n",
    "                max_dt_sec=float(max_dt) if not np.isnan(max_dt) else np.nan,\n",
    "                continuity_ok=bool(cont_ok),\n",
    "                label=int(y),\n",
    "                pos_ratio_setfin1=float(ratio),\n",
    "                last_ad_para=float(last_ap),\n",
    "                patno_first=str(sub[REQUIRED_ID_COL].iloc[0]) if REQUIRED_ID_COL in sub.columns else \"\",\n",
    "                origin_id=str(gid),\n",
    "                requires_sort_by_senddate=True\n",
    "            ))\n",
    "            total_windows += 1\n",
    "\n",
    "    stats = {\n",
    "        \"source\": os.path.basename(source_csv),\n",
    "        \"total_rows\": int(len(df)),\n",
    "        \"total_windows\": int(total_windows),\n",
    "        \"n_cont_ok\": int(sum(r.continuity_ok for r in rows)),\n",
    "        \"n_label_pos\": int(sum(r.label == 1 for r in rows)),\n",
    "        \"max_dt_overall\": float(df[REQUIRED_DT_COL].max()) if REQUIRED_DT_COL in df.columns and not df.empty else np.nan\n",
    "    }\n",
    "    return rows, stats, nan_df\n",
    "\n",
    "def step_build_manifest(cleaned_paths: List[str], shard_paths: List[str]):\n",
    "    print(\"\\n==[步驟三] 產生視窗 manifest（不實體化樣本）==\")\n",
    "    sources = shard_paths if ENABLE_SHARDING and shard_paths else cleaned_paths\n",
    "\n",
    "    # 讀設定檔（取得 labeling 模式）\n",
    "    with open(os.path.join(DIR_CFG, \"windowing.yaml\"), \"r\", encoding=\"utf-8\") as f:\n",
    "        cfg_yaml = yaml.safe_load(f)\n",
    "    lbl_cfg = cfg_yaml.get(\"labeling_ext\", {\"mode\": DEFAULT_LABEL_MODE, \"tau\": DEFAULT_TAU})\n",
    "\n",
    "    all_rows: List[WindowRow] = []\n",
    "    all_stats = []\n",
    "    nan_alerts = []\n",
    "\n",
    "    tasks = [(p, lbl_cfg) for p in sources]\n",
    "\n",
    "    # 主要路徑：多進程（若失敗自動 fallback 單進程）\n",
    "    try:\n",
    "        with Pool(processes=N_WORKERS) as pool:\n",
    "            for rows, stats, nan_df in tqdm(\n",
    "                pool.imap_unordered(_scan_wrapper, tasks),\n",
    "                total=len(tasks), desc=\"掃描來源\"\n",
    "            ):\n",
    "                all_rows.extend(rows)\n",
    "                all_stats.append(stats)\n",
    "                if nan_df is not None and not nan_df.empty:\n",
    "                    nan_alerts.append(nan_df)\n",
    "    except Exception as e:\n",
    "        print(f\"[警告] 多進程掃描失敗，改為單進程執行。原因：{e}\")\n",
    "        for t in tqdm(tasks, total=len(tasks), desc=\"掃描來源(單進程)\"):\n",
    "            rows, stats, nan_df = _scan_wrapper(t)\n",
    "            all_rows.extend(rows)\n",
    "            all_stats.append(stats)\n",
    "            if nan_df is not None and not nan_df.empty:\n",
    "                nan_alerts.append(nan_df)\n",
    "\n",
    "    # 輸出 manifest 與統計\n",
    "    man_df = pd.DataFrame([asdict(r) for r in all_rows])\n",
    "    man_path = os.path.join(DIR_MAN, \"window_manifest.csv\")\n",
    "    man_df.to_csv(man_path, index=False, encoding=\"utf-8-sig\")\n",
    "    print(f\"[Manifest] 已輸出：{man_path}（total_windows={len(man_df):,}）\")\n",
    "\n",
    "    stats_df = pd.DataFrame(all_stats)\n",
    "    stats_path = os.path.join(DIR_MAN, \"window_stats.csv\")\n",
    "    stats_df.to_csv(stats_path, index=False, encoding=\"utf-8-sig\")\n",
    "\n",
    "    label_summary = man_df.groupby(\"label\").size().rename(\"count\").reset_index()\n",
    "    label_path = os.path.join(DIR_MAN, \"window_label_summary.csv\")\n",
    "    label_summary.to_csv(label_path, index=False, encoding=\"utf-8-sig\")\n",
    "\n",
    "    if nan_alerts:\n",
    "        pd.concat(nan_alerts, ignore_index=True).to_csv(\n",
    "            os.path.join(DIR_AUDITS, \"window_nan_alerts.csv\"),\n",
    "            index=False, encoding=\"utf-8-sig\"\n",
    "        )\n",
    "    print(\"[Manifest] 統計輸出：window_stats.csv / window_label_summary.csv；缺失報警（若有）：window_nan_alerts.csv\")\n",
    "\n",
    "    # 參數與分片雜湊快照\n",
    "    cfg_path = os.path.join(DIR_CFG, \"windowing.yaml\")\n",
    "    cfg_hash = sha256_file(cfg_path)\n",
    "    shard_hashes = []\n",
    "    if shard_paths:\n",
    "        for p in shard_paths:\n",
    "            shard_hashes.append({\"file\": os.path.basename(p), \"hash\": sha256_file(p)})\n",
    "    snap = {\"config_hash\": cfg_hash, \"shards\": shard_hashes, \"generated_at\": time.strftime(\"%Y-%m-%d %H:%M:%S\")}\n",
    "    with open(os.path.join(DIR_MAN, \"slicing_params.json\"), \"w\", encoding=\"utf-8\") as f:\n",
    "        json.dump(snap, f, ensure_ascii=False, indent=2)\n",
    "    print(\"[Manifest] 已寫入 slicing_params.json（含 config/shards 雜湊）\")\n",
    "\n",
    "# =========================\n",
    "# K 折交叉驗證（分層）\n",
    "# =========================\n",
    "def step_make_kfold(manifest_csv: str, k: int = 5):\n",
    "    print(\"\\n==[步驟四] K 折分層交叉驗證索引==\")\n",
    "    df = pd.read_csv(manifest_csv)\n",
    "    if df.empty:\n",
    "        print(\"[KFold] manifest 為空，略過。\")\n",
    "        return\n",
    "\n",
    "    from sklearn.model_selection import StratifiedKFold\n",
    "    y = df[\"label\"].astype(int).values\n",
    "    skf = StratifiedKFold(n_splits=k, shuffle=True, random_state=SEED)\n",
    "\n",
    "    folds = []\n",
    "    for fold_id, (tr_idx, va_idx) in enumerate(skf.split(np.zeros(len(y)), y), start=1):\n",
    "        folds.append({\n",
    "            \"fold\": fold_id,\n",
    "            \"train_indices\": tr_idx.tolist(),\n",
    "            \"valid_indices\": va_idx.tolist(),\n",
    "            \"n_train\": int(len(tr_idx)),\n",
    "            \"n_valid\": int(len(va_idx))\n",
    "        })\n",
    "\n",
    "    with open(os.path.join(DIR_CFG, \"kfold_splits.json\"), \"w\", encoding=\"utf-8\") as f:\n",
    "        json.dump({\"n_folds\": k, \"seed\": SEED, \"folds\": folds}, f, ensure_ascii=False, indent=2)\n",
    "    print(\"[KFold] 已輸出：config/kfold_splits.json\")\n",
    "\n",
    "# =========================\n",
    "# 執行總流程\n",
    "# =========================\n",
    "def main():\n",
    "    print(\"=[初始化]==\")\n",
    "    ensure_dirs()\n",
    "    cfg_path, _cfg_obj = write_windowing_yaml()\n",
    "\n",
    "    # 步驟一：清洗\n",
    "    cleaned_paths = step_clean_all()\n",
    "\n",
    "    # schema_final.json（依第一個 cleaned 檔記錄欄位）\n",
    "    if cleaned_paths:\n",
    "        sample_cols = list(pd.read_csv(cleaned_paths[0], nrows=0).columns)\n",
    "        write_schema_json(sample_cols)\n",
    "\n",
    "    # 步驟二：多分片\n",
    "    shard_paths, _ = step_build_shards(cleaned_paths)\n",
    "\n",
    "    # 步驟三：manifest（不實體化）\n",
    "    step_build_manifest(cleaned_paths, shard_paths)\n",
    "\n",
    "    # 步驟四：KFold\n",
    "    step_make_kfold(os.path.join(DIR_MAN, \"window_manifest.csv\"), k=5)\n",
    "\n",
    "    # 訓練快照（占位）\n",
    "    run_tag = time.strftime(\"run_%Y%m%d_%H%M\")\n",
    "    run_dir = os.path.join(DIR_TRAIN, run_tag)\n",
    "    os.makedirs(os.path.join(run_dir, \"config_snapshot\"), exist_ok=True)\n",
    "    os.makedirs(os.path.join(run_dir, \"logs\"), exist_ok=True)\n",
    "    with open(os.path.join(run_dir, \"config_snapshot\", \"config_hash.txt\"), \"w\", encoding=\"utf-8\") as f:\n",
    "        f.write(sha256_file(cfg_path) + \"\\n\")\n",
    "    with open(os.path.join(run_dir, \"logs\", \"training.log\"), \"w\", encoding=\"utf-8\") as f:\n",
    "        f.write(\"[info] training will record here (placeholder)\\n\")\n",
    "\n",
    "    # 審計彙總\n",
    "    summary = {\n",
    "        \"source_dir\": SRC_DIR,\n",
    "        \"expected_files\": EXPECTED_FILES,\n",
    "        \"out_root\": OUT_ROOT,\n",
    "        \"created\": time.strftime(\"%Y-%m-%d %H:%M:%S\"),\n",
    "        \"reports\": {\n",
    "            \"window_continuity_stats\": os.path.join(DIR_MAN, \"window_continuity_stats.csv\"),\n",
    "            \"window_nan_alerts\": os.path.join(DIR_AUDITS, \"window_nan_alerts.csv\"),\n",
    "            \"schema_audit\": os.path.join(DIR_AUDITS, \"schema_audit.csv\"),\n",
    "            \"cleaned_nan_summary\": os.path.join(DIR_AUDITS, \"cleaned_nan_summary.csv\"),\n",
    "            \"value_range_anomaly\": os.path.join(DIR_AUDITS, \"value_range_anomaly.csv\"),\n",
    "            \"time_gap_anomaly\": os.path.join(DIR_AUDITS, \"time_gap_anomaly.csv\"),\n",
    "            \"manifest\": os.path.join(DIR_MAN, \"window_manifest.csv\"),\n",
    "            \"window_stats\": os.path.join(DIR_MAN, \"window_stats.csv\"),\n",
    "            \"window_label_summary\": os.path.join(DIR_MAN, \"window_label_summary.csv\"),\n",
    "            \"kfold_splits\": os.path.join(DIR_CFG, \"kfold_splits.json\"),\n",
    "        }\n",
    "    }\n",
    "    os.makedirs(DIR_AUDITS_RPT, exist_ok=True)\n",
    "    with open(os.path.join(DIR_AUDITS_RPT, \"summary.json\"), \"w\", encoding=\"utf-8\") as f:\n",
    "        json.dump(summary, f, ensure_ascii=False, indent=2)\n",
    "\n",
    "    print(\"\\n✅ 全部完成。重點輸出：\")\n",
    "    print(f\" - Cleaned：{DIR_CLEANED}\")\n",
    "    print(f\" - Shards：{DIR_SHARDS}（_shard_catalog.csv, _shard_stats.csv）\")\n",
    "    print(f\" - Manifests：{DIR_MAN}（window_manifest.csv, window_stats.csv, window_label_summary.csv, slicing_params.json）\")\n",
    "    print(f\" - Audits：{DIR_AUDITS}（schema_audit.csv, cleaned_nan_summary.csv, time_gap_anomaly.csv）\")\n",
    "    print(f\" - Config：{DIR_CFG}（windowing.yaml, kfold_splits.json）\")\n",
    "    print(f\" - Docs：{DIR_DOCS}（schema_final.json）\")\n",
    "    print(f\" - Cache：{DIR_CACHE}, {DIR_CACHE_TF}, {DIR_LOCKS}\")\n",
    "    print(f\" - Training：{DIR_TRAIN}/*/config_snapshot/config_hash.txt, logs/training.log\")\n",
    "    print(\"請在訓練端依 manifest 動態取窗（Lazy Evaluation），避免另存成全量靜態視窗檔。\")\n",
    "\n",
    "if __name__ == \"__main__\":\n",
    "    main()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1c76f169-3436-4751-9dfb-5835c3ff6c8c",
   "metadata": {},
   "outputs": [],
   "source": [
    "接著 把訓練端串上 manifest（Lazy Evaluation）\n",
    "也就是\n",
    "不要先把所有滑動視窗展開成一堆靜態檔（那叫實體化，會爆磁碟、難審計）。\n",
    "只用一份 manifest（視窗清單）當「索引表」：每一列記錄「來自哪個來源檔、視窗起訖在來源檔的行號、標籤、品質旗標」。\n",
    "訓練時，按需（on-demand）讀原始/分片 CSV，依 manifest 的行號現場切一段做成一個樣本；用完就丟（或短暫快取），這就是 Lazy Evaluation\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5d41d5a7-e8da-449c-9049-f0d81d19b3f3",
   "metadata": {},
   "outputs": [],
   "source": [
    "先跑 num_workers=0（PyTorch）或 prefetch(1)（TensorFlow）確認正確性，再談效能\n",
    "把整個訓練流程接上 manifest（Lazy Evaluation）後，\n",
    "第一次先用最簡單的單線程方式跑，確認：\n",
    "資料切窗正確（每個視窗都剛好 60 筆、標籤正確、沒有錯位）。\n",
    "可以順利迭代完數個 batch，不出現錯誤。\n",
    "系統 I/O、GPU 利用率、RAM 使用量 都合理"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a2cbe5d6-5ced-4638-b82b-bdaec47692f5",
   "metadata": {},
   "outputs": [],
   "source": [
    "我目前到[步驟四] K 折分層交叉驗證索引==\n",
    "[KFold] 已輸出：config/kfold_splits.json，\n",
    "\n",
    "接下來 我要訓練端串上 manifest（Lazy Evaluation）以及第一次先用最簡單的單線程方式跑，確認：\n",
    "資料切窗正確（每個視窗都剛好 60 筆、標籤正確、沒有錯位）。\n",
    "\n",
    "可以順利迭代完數個 batch，不出現錯誤。\n",
    "\n",
    "系統 I/O、GPU 利用率、RAM 使用量 都合理"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b0065615-71d6-4068-8711-ef061a484a07",
   "metadata": {},
   "outputs": [],
   "source": [
    "    出現以下任一情況，就先跑「單線程驗證」：\n",
    "剛產生或更新了 manifest（例如改了 W/S/對齊/連續性門檻/標籤規則）。\n",
    "換了特徵欄位、前處理、或 cleaned/shards 重建。\n",
    "升級了環境/套件（pandas、PyTorch）或換了機器。\n",
    "看到資料統計和預期不合（例如 pos_ratio 急劇變動）。\n",
    "任何你懷疑切窗可能錯位的時刻"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cfde4a27-84c8-441c-ab15-78c83f3e5a8c",
   "metadata": {},
   "outputs": [],
   "source": [
    "單線程驗證：train_lazy_verify_1014.py（Notebook/CLI 皆可；不寫入任何檔案）\n",
    "正式命令列訓練：train_cli_1014.py（多工 DataLoader；產出 checkpoints/metrics/logs）\n",
    "\n",
    "兩支都採 manifest（Lazy Evaluation）：按需讀原始/分片 CSV，依 start_idx:end_idx 即時切窗，不實體化樣本。\n",
    "要依實際特徵欄位調整 FEATURE_COLS（避免洩漏：不要包含 ad_para、set_fin）。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 241,
   "id": "92185369-a3e6-458f-ac05-2f7b6914b589",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "ename": "IndentationError",
     "evalue": "expected an indented block after function definition on line 242 (1768776321.py, line 243)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;36m  Cell \u001b[0;32mIn[241], line 243\u001b[0;36m\u001b[0m\n\u001b[0;31m    \"\"\"\u001b[0m\n\u001b[0m    ^\u001b[0m\n\u001b[0;31mIndentationError\u001b[0m\u001b[0;31m:\u001b[0m expected an indented block after function definition on line 242\n"
     ]
    }
   ],
   "source": [
    "#plan B\n",
    "\"\"\"\n",
    "train_lazy_verify_1014.py\n",
    "訓練端串上 manifest（Lazy Evaluation）與單線程驗證腳本\n",
    "-----------------------------------------------------\n",
    "功能：\n",
    "1) 讀取 window_manifest.csv 與 kfold_splits.json\n",
    "2) 建立 PyTorch Dataset / DataLoader（num_workers=0 單線程）\n",
    "3) 逐批讀來源 CSV（shards 或 cleaned），以 senddate 排序後依 iloc 切窗（不實體化）\n",
    "4) 三項驗證（每批抽樣）：\n",
    "   - 視窗長度 = W（預設 60）\n",
    "   - 標籤符合設定檔規則（預設 majority_only 純多數決）\n",
    "   - 時間序（senddate）單調遞增，避免錯位\n",
    "5) 監控：CPU、RAM、磁碟 I/O、(可選) GPU 記憶體；打印吞吐 (samples/sec)\n",
    "6) Notebook 友善：自動統計正負比例並繪製 CPU/RAM 簡圖（Train/Valid 各一張）\n",
    "\n",
    "使用（命令列）：\n",
    "python train_lazy_verify_1014.py --root /home/jovyan/RT08/0925/sliding_win/1014 \\\n",
    "  --batch_size 128 --fold 1 --max_batches 10 --verify_n 32 --enforce_continuity 1\n",
    "\n",
    "Notebook 直接跑：見檔案末尾「Notebook 版執行區」範例（無需命令列參數）。\n",
    "\"\"\"\n",
    "import os\n",
    "import time\n",
    "import json\n",
    "import argparse\n",
    "import warnings\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "import torch\n",
    "from torch.utils.data import Dataset, DataLoader\n",
    "\n",
    "import psutil\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "try:\n",
    "    import pynvml  # 可選，用於 GPU 監控\n",
    "    pynvml.nvmlInit()\n",
    "    HAS_NVML = True\n",
    "except Exception:\n",
    "    HAS_NVML = False\n",
    "\n",
    "import yaml\n",
    "\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "# ==============================\n",
    "# 參數與路徑\n",
    "# ==============================\n",
    "def parse_args():\n",
    "    ap = argparse.ArgumentParser()\n",
    "    ap.add_argument(\"--root\", type=str, required=True,\n",
    "                    help=\"版本 1014 的根路徑，例如 /home/jovyan/RT08/0925/sliding_win/1014\")\n",
    "    ap.add_argument(\"--batch_size\", type=int, default=128)\n",
    "    ap.add_argument(\"--fold\", type=int, default=1, help=\"使用第幾折（1-based）\")\n",
    "    ap.add_argument(\"--max_batches\", type=int, default=10, help=\"每個 loader 僅跑前 N 個 batch 做 smoke test\")\n",
    "    ap.add_argument(\"--verify_n\", type=int, default=32, help=\"每個 batch 隨機抽多少個樣本做嚴格驗證\")\n",
    "    ap.add_argument(\"--enforce_continuity\", type=int, default=1, help=\"1=僅取 continuity_ok 視窗\")\n",
    "    ap.add_argument(\"--plot\", type=int, default=1, help=\"1=結束後繪製 CPU/RAM 簡圖\")\n",
    "    return ap.parse_args()\n",
    "\n",
    "# ==============================\n",
    "# 工具：格式化與系統監控\n",
    "# ==============================\n",
    "def fmt_bytes(n: float) -> str:\n",
    "    for u in [\"B\",\"KB\",\"MB\",\"GB\",\"TB\",\"PB\"]:\n",
    "        if abs(n) < 1024: return f\"{n:.1f}{u}\"\n",
    "        n /= 1024\n",
    "    return f\"{n:.1f}EB\"\n",
    "\n",
    "def read_sys_metrics():\n",
    "    \"\"\"回傳當前系統指標（CPU%、RSS、虛記憶體、磁碟 I/O 累計、(可選)GPU）\"\"\"\n",
    "    p = psutil.Process()\n",
    "    rss = p.memory_info().rss\n",
    "    vms = p.memory_info().vms\n",
    "    cpu = psutil.cpu_percent(interval=None)\n",
    "    dio = psutil.disk_io_counters()\n",
    "    gpu_mem = None\n",
    "    if HAS_NVML and torch.cuda.is_available():\n",
    "        try:\n",
    "            h = pynvml.nvmlDeviceGetHandleByIndex(0)\n",
    "            mem = pynvml.nvmlDeviceGetMemoryInfo(h)\n",
    "            gpu_mem = (mem.used, mem.total)\n",
    "        except Exception:\n",
    "            gpu_mem = None\n",
    "    return {\n",
    "        \"cpu_percent\": cpu,\n",
    "        \"rss\": rss,\n",
    "        \"vms\": vms,\n",
    "        \"disk_read_bytes\": dio.read_bytes if dio else 0,\n",
    "        \"disk_write_bytes\": dio.write_bytes if dio else 0,\n",
    "        \"gpu_mem\": gpu_mem\n",
    "    }\n",
    "\n",
    "def diff_metrics(m1, m0, dt_sec):\n",
    "    \"\"\"兩次量測差值 / 每秒吞吐（供摘要用）\"\"\"\n",
    "    out = {}\n",
    "    for k in [\"disk_read_bytes\", \"disk_write_bytes\"]:\n",
    "        out[k] = m1[k] - m0[k]\n",
    "        out[k + \"_per_s\"] = (out[k] / dt_sec) if dt_sec > 0 else 0.0\n",
    "    out[\"rss\"] = m1[\"rss\"]\n",
    "    out[\"cpu_percent\"] = m1[\"cpu_percent\"]\n",
    "    if m1[\"gpu_mem\"]:\n",
    "        used, total = m1[\"gpu_mem\"]\n",
    "        out[\"gpu_mem_used\"] = used\n",
    "        out[\"gpu_mem_total\"] = total\n",
    "    return out\n",
    "\n",
    "# ==============================\n",
    "# 讀設定：W/S/標籤規則\n",
    "# ==============================\n",
    "def load_windowing_cfg(root: str):\n",
    "    cfg_path = os.path.join(root, \"config\", \"windowing.yaml\")\n",
    "    with open(cfg_path, \"r\", encoding=\"utf-8\") as f:\n",
    "        y = yaml.safe_load(f)\n",
    "    W = int(y[\"window\"][\"W\"])\n",
    "    S = int(y[\"window\"][\"S\"])\n",
    "    mode = y.get(\"labeling_ext\", {}).get(\"mode\", \"majority_only\")\n",
    "    tau = float(y.get(\"labeling_ext\", {}).get(\"tau\", 0.2))\n",
    "    dt_thr = float(y[\"continuity\"][\"dt_sec_threshold\"])\n",
    "    return W, S, mode, tau, dt_thr\n",
    "\n",
    "# ==============================\n",
    "# 來源檔解析\n",
    "# ==============================\n",
    "def resolve_source_path(root: str, src_name: str) -> str:\n",
    "    \"\"\"優先讀 shards，其次讀 cleaned/clear\"\"\"\n",
    "    p1 = os.path.join(root, \"shards\", src_name)\n",
    "    p2 = os.path.join(root, \"cleaned\", \"clear\", src_name)\n",
    "    if os.path.exists(p1): return p1\n",
    "    if os.path.exists(p2): return p2\n",
    "    raise FileNotFoundError(f\"找不到來源檔：{src_name}\")\n",
    "\n",
    "# ==============================\n",
    "# 視窗標籤重算（驗證用）\n",
    "# ==============================\n",
    "def recompute_label(sub_df: pd.DataFrame, mode=\"majority_only\", tau=0.2):\n",
    "    \"\"\"\n",
    "    sub_df: 單一視窗的 DataFrame（已按 senddate 排序）\n",
    "    - majority_only：整窗 set_fin==1 比例 >= 0.5\n",
    "    - majority_w_minus_1：只看前 W-1 的比例\n",
    "    - majority_w_minus_1_softforce：同上 + 若最後一筆 ad_para=1 且比例<tau → 1\n",
    "    回傳：(y, ratio, last_ad)\n",
    "    \"\"\"\n",
    "    sf = pd.to_numeric(sub_df.get(\"set_fin\"), errors=\"coerce\").fillna(0.0)\n",
    "    ap = pd.to_numeric(sub_df.get(\"ad_para\"), errors=\"coerce\").fillna(0.0)\n",
    "    if len(sub_df) == 0:\n",
    "        return 0, 0.0, 0.0\n",
    "    if mode in (\"majority_w_minus_1\", \"majority_w_minus_1_softforce\"):\n",
    "        head = sf.iloc[:-1] if len(sf) >= 2 else sf\n",
    "        ratio = float((head == 1).mean()) if len(head) > 0 else 0.0\n",
    "        last_ap = float(ap.iloc[-1])\n",
    "        if mode == \"majority_w_minus_1_softforce\" and (last_ap == 1.0) and (ratio < tau):\n",
    "            y = 1\n",
    "        else:\n",
    "            y = 1 if ratio >= 0.5 else 0\n",
    "    else:\n",
    "        ratio = float((sf == 1).mean())\n",
    "        y = 1 if ratio >= 0.5 else 0\n",
    "        last_ap = float(ap.iloc[-1])\n",
    "    return int(y), float(ratio), float(last_ap)\n",
    "\n",
    "# ==============================\n",
    "# Dataset（Lazy Evaluation + 單檔快取）\n",
    "# ==============================\n",
    "class WindowDataset(Dataset):\n",
    "    \"\"\"\n",
    "    以 manifest 為索引，按需讀檔 + 切窗，不實體化全量視窗。\n",
    "    - 單線程首跑驗證正確性 → num_workers=0\n",
    "    - 每次來源檔變動時重新載入並以 senddate 排序\n",
    "    - 回傳 (X, y, sub_df) 供外層「嚴格驗證」抽樣使用\n",
    "    \"\"\"\n",
    "    FEATURE_COLS = [\n",
    "        # 避免洩漏 (ad_para / set_fin 不可當特徵)\n",
    "        \"rrhzsetactual\",\"mvsetactual\",\"pmean\",\"cdyn\",\"peepepap\",\"ppeak\",\"mode_1\",\"mode_2\",\"mode_3\",\"svv_new\"\n",
    "    ]\n",
    "\n",
    "    def __init__(self, root: str, manifest_csv: str, indices=None, enforce_continuity: bool=True):\n",
    "        self.root = root\n",
    "\n",
    "        # 1) 讀完整 manifest（不可先過濾，避免 K 折索引對不上）\n",
    "        man_full = pd.read_csv(manifest_csv)\n",
    "        n_full = len(man_full)\n",
    "\n",
    "        # 2) 若提供 K 折 indices，先做邊界檢查，再使用 iloc 取子集（這一步一定要在過濾前）\n",
    "        if indices is not None:\n",
    "            if len(indices) == 0:\n",
    "                raise ValueError(\"[KFold] 傳入的 indices 為空。請檢查 kfold_splits.json。\")\n",
    "            max_idx = int(np.max(indices))\n",
    "            min_idx = int(np.min(indices))\n",
    "            if min_idx < 0 or max_idx >= n_full:\n",
    "                raise IndexError(\n",
    "                    f\"[KFold 索引越界] indices 範圍 [{min_idx}, {max_idx}] 超過 manifest 長度 {n_full}。\\n\"\n",
    "                    f\"→ 請確認 kfold_splits.json 與目前的 window_manifest.csv 同步。\"\n",
    "                )\n",
    "            man = man_full.iloc[indices].reset_index(drop=True)\n",
    "        else:\n",
    "            man = man_full.copy()\n",
    "\n",
    "        # 3) 再依需要過濾 continuity_ok（順序：indices → continuity）\n",
    "        if enforce_continuity and \"continuity_ok\" in man.columns:\n",
    "            man = man[man[\"continuity_ok\"] == True].reset_index(drop=True)\n",
    "\n",
    "        # 4) 保存\n",
    "        self.man = man\n",
    "\n",
    "        # 來源快取：同一來源檔僅載入一次；同時快取「實際特徵欄位對齊結果」\n",
    "        # cache = {\"src\": 檔名, \"df\": DataFrame, \"feat_cols\": 對齊後欄位名（大小寫依原始 df）}\n",
    "        self.cache = {\"src\": None, \"df\": None, \"feat_cols\": None}\n",
    "\n",
    "        # 載入 windowing 設定\n",
    "        self.W, self.S, self.mode, self.tau, self.dt_thr = load_windowing_cfg(root)\n",
    "\n",
    "    def __len__(self):\n",
    "        return len(self.man)\n",
    "\n",
    "    def _resolve_feature_cols(self, df: pd.DataFrame):\n",
    "        \"\"\"\n",
    "        以不分大小寫的方式，將使用者定義的 FEATURE_COLS 對齊到 df 內實際欄位名稱。\n",
    "        若有缺欄，拋出清楚的錯誤。\n",
    "        \"\"\"\n",
    "        df_col_map = {c.lower(): c for c in df.columns}  # lower -> original\n",
    "        resolved = []\n",
    "        missing = []\n",
    "        for k in self.FEATURE_COLS:\n",
    "            lk = k.lower()\n",
    "            if lk in df_col_map:\n",
    "                resolved.append(df_col_map[lk])\n",
    "            else:\n",
    "                missing.append(k)\n",
    "        if missing:\n",
    "            raise KeyError(\n",
    "                \"[特徵欄位缺失] 下列欄位在來源 CSV 中找不到（不分大小寫比對）：\\n\"\n",
    "                f\"  - {missing}\\n\"\n",
    "                f\"實際 CSV 欄位示例（前 20 個）：{list(df.columns[:20])}\\n\"\n",
    "                \"→ 請修正 FEATURE_COLS 或清洗階段欄位命名。\"\n",
    "            )\n",
    "        return resolved\n",
    "\n",
    "    def _get_df(self, src: str) -> pd.DataFrame:\n",
    "    \"\"\"\n",
    "    讀取單一來源檔並保留「原始列序」；同時解析並快取實際可用的特徵欄位名。\n",
    "    注意：manifest 的 start_idx/end_idx 以原始列序為準，因此不能在這裡重新 sort。\n",
    "    \"\"\"\n",
    "        if self.cache[\"src\"] != src or self.cache[\"df\"] is None:\n",
    "            path = resolve_source_path(self.root, src)\n",
    "            df = pd.read_csv(path)  # ❗ 保留原始列序，不再 sort_values('senddate')\n",
    "            df = df.reset_index(drop=True)\n",
    "\n",
    "            # 對齊特徵欄位（大小寫無關），並快取\n",
    "            feat_cols = self._resolve_feature_cols(df)\n",
    "\n",
    "            self.cache = {\"src\": src, \"df\": df, \"feat_cols\": feat_cols}\n",
    "        return self.cache[\"df\"]\n",
    "\n",
    "    def __getitem__(self, i: int):\n",
    "        r = self.man.iloc[i]\n",
    "        df = self._get_df(r[\"source\"])\n",
    "\n",
    "        # 邊界檢查（避免 start/end 超界）\n",
    "        s_idx, e_idx = int(r[\"start_idx\"]), int(r[\"end_idx\"])\n",
    "        if s_idx < 0 or e_idx >= len(df) or e_idx < s_idx:\n",
    "            raise IndexError(\n",
    "                f\"[索引越界] source={r['source']} start_idx={s_idx} end_idx={e_idx}，\"\n",
    "                f\"但來源長度為 {len(df)}。\\n→ 可能原因：manifest 與 cleaned/shards 不同步，\"\n",
    "                \"或重建資料後未重跑 manifest/kfold。\"\n",
    "            )\n",
    "\n",
    "        sub = df.iloc[s_idx:e_idx+1]\n",
    "        win_len = len(sub)\n",
    "\n",
    "        # 正常情況：長度剛好 W\n",
    "        if win_len == self.W:\n",
    "            feat_cols = self.cache[\"feat_cols\"]\n",
    "            X = torch.tensor(sub[feat_cols].to_numpy(np.float32))\n",
    "            y = torch.tensor(int(r[\"label\"]), dtype=torch.long)\n",
    "            return X, y, sub\n",
    "\n",
    "    # 若比 W 長（常見於之前排序導致列序對不上）\n",
    "        if win_len > self.W:\n",
    "            s_adj = e_idx - (self.W - 1)\n",
    "            if s_adj < 0:\n",
    "                raise ValueError(\n",
    "                    f\"[視窗回補失敗] 右對齊計算 s_adj={s_adj} < 0；\"\n",
    "                    f\"source={r['source']}, row={i}, idx={s_idx}-{e_idx}, len={win_len}。\"\n",
    "                    \"→ 請重建 manifest。\"\n",
    "                )\n",
    "            sub = df.iloc[s_adj:e_idx+1]\n",
    "            if len(sub) != self.W:\n",
    "                raise ValueError(\n",
    "                    f\"[視窗回補失敗] 期望 W={self.W}，回補後仍得 {len(sub)}；\"\n",
    "                    f\"source={r['source']}, row={i}, s_adj={s_adj}, end_idx={e_idx}。\"\n",
    "                )\n",
    "        # 可選：你也可以印一次警告（但不要每筆都印以免洗版）\n",
    "        # if i < 3: print(f\"[WARN] 自動右對齊修正窗長度：src={r['source']} row={i} ({win_len}→{self.W})\")\n",
    "\n",
    "            feat_cols = self.cache[\"feat_cols\"]\n",
    "            X = torch.tensor(sub[feat_cols].to_numpy(np.float32))\n",
    "            y = torch.tensor(int(r[\"label\"]), dtype=torch.long)\n",
    "            return X, y, sub\n",
    "\n",
    "    # 若比 W 短 → 這代表 manifest 記錄本身就不合法，直接報錯請你重建 manifest\n",
    "    raise ValueError(\n",
    "        f\"[視窗長度錯誤] 期望 W={self.W}，實得 {win_len}（短於 W）；\"\n",
    "        f\"source={r['source']}, row={i}, idx={s_idx}-{e_idx}。→ 請重建 manifest。\"\n",
    "    )\n",
    "\n",
    "\n",
    "# ==============================\n",
    "# KFold 讀取\n",
    "# ==============================\n",
    "def load_fold_indices(root: str, fold_1based: int=1):\n",
    "    kf_path = os.path.join(root, \"config\", \"kfold_splits.json\")\n",
    "    with open(kf_path, \"r\", encoding=\"utf-8\") as f:\n",
    "        kf = json.load(f)\n",
    "    folds = kf[\"folds\"]\n",
    "    assert 1 <= fold_1based <= len(folds), f\"fold 超界：1..{len(folds)}\"\n",
    "    fd = folds[fold_1based - 1]\n",
    "    return fd[\"train_indices\"], fd[\"valid_indices\"]\n",
    "\n",
    "# ==============================\n",
    "# 嚴格驗證（隨機抽樣）\n",
    "# ==============================\n",
    "def strict_verify(batch_sub_frames, batch_labels, W, mode, tau):\n",
    "    \"\"\"\n",
    "    對 batch 中隨機抽樣幾個子視窗做嚴格驗證：\n",
    "    - 視窗長度 == W\n",
    "    - 依 config 規則重算標籤 == manifest 標籤\n",
    "    - senddate 升冪（預設讀檔時已排序，這裡再保守檢查）\n",
    "    回傳：錯誤數量 dict\n",
    "    \"\"\"\n",
    "    errs = {\"len_mismatch\":0, \"label_mismatch\":0, \"time_unsorted\":0}\n",
    "    for sub, y in zip(batch_sub_frames, batch_labels):\n",
    "        # 長度\n",
    "        if len(sub) != W:\n",
    "            errs[\"len_mismatch\"] += 1\n",
    "            continue\n",
    "        # 時間排序（抽樣檢查）\n",
    "        if not sub[\"senddate\"].is_monotonic_increasing:\n",
    "            errs[\"time_unsorted\"] += 1\n",
    "\n",
    "        # 重算標籤\n",
    "        yy, _, _ = recompute_label(sub, mode=mode, tau=tau)\n",
    "        if int(yy) != int(y):\n",
    "            errs[\"label_mismatch\"] += 1\n",
    "    return errs\n",
    "\n",
    "# ==============================\n",
    "# 繪圖（Notebook 友善）\n",
    "# ==============================\n",
    "def plot_cpu_ram(times_s, cpu_list, rss_list, tag=\"TRAIN\"):\n",
    "    \"\"\"\n",
    "    繪製 CPU% 與 RSS(MB) 隨 batch 推進的走勢圖。\n",
    "    - times_s：相對秒數（每批紀錄一次）\n",
    "    - cpu_list：每批 CPU%\n",
    "    - rss_list：每批 RSS（bytes）\n",
    "    \"\"\"\n",
    "    if len(times_s) == 0:\n",
    "        print(f\"[{tag}] 無可繪製的監控資料。\")\n",
    "        return\n",
    "    fig, ax1 = plt.subplots(figsize=(7.0, 4.0))\n",
    "    ax1.plot(times_s, cpu_list, label=\"CPU%\", linewidth=2)\n",
    "    ax1.set_xlabel(\"Time (s)\")\n",
    "    ax1.set_ylabel(\"CPU (%)\")\n",
    "    ax1.grid(True, linestyle=\"--\", alpha=0.3)\n",
    "    ax2 = ax1.twinx()\n",
    "    ax2.plot(times_s, np.array(rss_list)/1024/1024, label=\"RSS (MB)\", linewidth=2)\n",
    "    ax2.set_ylabel(\"RSS (MB)\")\n",
    "    fig.suptitle(f\"[{tag}] CPU 與 RAM 使用量（單線程）\")\n",
    "    fig.tight_layout()\n",
    "    plt.show()\n",
    "\n",
    "# ==============================\n",
    "# 主流程（單線程 smoke test）\n",
    "# ==============================\n",
    "def run_smoke(args):\n",
    "    ROOT = args.root\n",
    "    MAN  = os.path.join(ROOT, \"manifests\", \"window_manifest.csv\")\n",
    "    W, S, mode, tau, dt_thr = load_windowing_cfg(ROOT)\n",
    "\n",
    "    # 載入折索引\n",
    "    train_idx, valid_idx = load_fold_indices(ROOT, args.fold)\n",
    "\n",
    "    # 建立 Dataset / DataLoader（單線程）\n",
    "    train_ds = WindowDataset(ROOT, MAN, train_idx, enforce_continuity=bool(args.enforce_continuity))\n",
    "    valid_ds = WindowDataset(ROOT, MAN, valid_idx, enforce_continuity=bool(args.enforce_continuity))\n",
    "\n",
    "    train_loader = DataLoader(\n",
    "        train_ds, batch_size=args.batch_size, shuffle=True,\n",
    "        num_workers=0, pin_memory=True, collate_fn=collate_keep_df\n",
    "    )\n",
    "    valid_loader = DataLoader(\n",
    "        valid_ds, batch_size=args.batch_size, shuffle=False,\n",
    "        num_workers=0, pin_memory=True, collate_fn=collate_keep_df\n",
    "    )\n",
    "\n",
    "    print(\"\\n[INFO] 參數摘要：\")\n",
    "    print(f\"- W={W}, S={S}, labeling_mode={mode}, tau={tau}, dt_thr={dt_thr}\")\n",
    "    print(f\"- train windows={len(train_ds):,}, valid windows={len(valid_ds):,}\")\n",
    "    print(f\"- batch_size={args.batch_size}, num_workers=0（單線程驗證）\")\n",
    "    if bool(args.enforce_continuity):\n",
    "        print(\"- 已啟用 continuity_ok==True 過濾\")\n",
    "    else:\n",
    "        print(\"- 未過濾 continuity_ok（僅驗證，不建議正式訓練）\")\n",
    "\n",
    "    # === 跑 loader 幾個 batch 做驗證 + 監控 + 收集繪圖資料 ===\n",
    "    def loop_loader(tag, loader):\n",
    "        print(f\"\\n== [{tag}] smoke test，最多跑 {args.max_batches} 個 batch ==\")\n",
    "        batches = 0\n",
    "        total_samples = 0\n",
    "        pos_sum = 0\n",
    "\n",
    "        # 系統監控收集（每批一次）\n",
    "        t0 = time.time()\n",
    "        t_marks, cpu_list, rss_list = [], [], []\n",
    "\n",
    "        # 初始系統狀態（摘要對比用）\n",
    "        sys0 = read_sys_metrics()\n",
    "        wall0 = time.time()\n",
    "\n",
    "        for xb, yb, sub_list in loader:\n",
    "            # --- 監控：記錄每批的 CPU/RSS（用於繪圖）---\n",
    "            met = read_sys_metrics()\n",
    "            t_marks.append(time.time() - t0)\n",
    "            cpu_list.append(met[\"cpu_percent\"])\n",
    "            rss_list.append(met[\"rss\"])\n",
    "\n",
    "            bs = xb.shape[0]\n",
    "            total_samples += bs\n",
    "            pos_sum += int(yb.sum().item())\n",
    "\n",
    "            # 隨機抽樣 verify_n 個子視窗做嚴格驗證\n",
    "            sel = np.random.choice(bs, size=min(args.verify_n, bs), replace=False)\n",
    "            subs = [sub_list[i] for i in sel]\n",
    "            ys   = [int(yb[i].item()) for i in sel]\n",
    "            errs = strict_verify(subs, ys, W=W, mode=mode, tau=tau)\n",
    "\n",
    "            print(f\"[{tag}] batch#{batches:03d} X={tuple(xb.shape)}  \"\n",
    "                  f\"pos_ratio={yb.float().mean().item():.4f}  \"\n",
    "                  f\"errs(len/label/time)={errs['len_mismatch']}/{errs['label_mismatch']}/{errs['time_unsorted']}\")\n",
    "            if any(errs.values()):\n",
    "                print(\"  -> 發現錯誤，請先排除後再進行效能優化/訓練。\")\n",
    "\n",
    "            batches += 1\n",
    "            if batches >= args.max_batches:\n",
    "                break\n",
    "\n",
    "        wall1 = time.time()\n",
    "        sys1 = read_sys_metrics()\n",
    "        dt = max(wall1 - wall0, 1e-6)\n",
    "        diff = diff_metrics(sys1, sys0, dt)\n",
    "\n",
    "        print(f\"\\n[{tag}] 總結：\")\n",
    "        print(f\"- 批次數：{batches}, 總樣本：{total_samples:,}, 平均吞吐：{total_samples/dt:.1f} samples/s\")\n",
    "        print(f\"- 估算正樣本比例（本輪）：{(pos_sum/max(total_samples,1)):.4f}\")\n",
    "        print(f\"- CPU：{diff['cpu_percent']:.1f}%   RSS：{fmt_bytes(diff['rss'])}\")\n",
    "        print(f\"- 磁碟讀：{fmt_bytes(diff['disk_read_bytes'])}（{fmt_bytes(diff['disk_read_bytes_per_s'])}/s）  \"\n",
    "              f\"寫：{fmt_bytes(diff['disk_write_bytes'])}（{fmt_bytes(diff['disk_write_bytes_per_s'])}/s）\")\n",
    "        if 'gpu_mem_used' in diff:\n",
    "            print(f\"- GPU 記憶體：{fmt_bytes(diff['gpu_mem_used'])} / {fmt_bytes(diff['gpu_mem_total'])}\")\n",
    "        else:\n",
    "            print(\"- GPU 記憶體：未偵測或未啟用 NVML（不影響本次驗證）\")\n",
    "\n",
    "        # 回傳繪圖需要的時間序列\n",
    "        return t_marks, cpu_list, rss_list\n",
    "\n",
    "    # TRAIN / VALID 兩輪\n",
    "    t_tr, cpu_tr, rss_tr = loop_loader(\"TRAIN\", train_loader)\n",
    "    t_va, cpu_va, rss_va = loop_loader(\"VALID\", valid_loader)\n",
    "\n",
    "    # Notebook 友善：自動畫圖\n",
    "    if int(getattr(args, \"plot\", 1)) == 1:\n",
    "        plot_cpu_ram(t_tr, cpu_tr, rss_tr, tag=\"TRAIN\")\n",
    "        plot_cpu_ram(t_va, cpu_va, rss_va, tag=\"VALID\")\n",
    "\n",
    "    print(\"\\n✅ 單線程驗證完成：\")\n",
    "    print(\" - 若 errs 全為 0，代表視窗長度/標籤/時間排序皆正確。\")\n",
    "    print(\" - throughput、CPU/RAM、I/O 指標合理 → 可進下一步（num_workers / prefetch / LRU）。\")\n",
    "    print(\" - 若有錯，請回看打印（多半是 FEATURE_COLS 欄位缺失、排序未對齊、或標籤規則設定不符）。\")\n",
    "\n",
    "# ==============================\n",
    "# 入口（命令列 + Notebook 自動切換）\n",
    "# ==============================\n",
    "def auto_main(cli_fn, nb_fn):\n",
    "    \"\"\"\n",
    "    自動偵測目前環境：\n",
    "    - 若在 Notebook，執行 nb_fn()\n",
    "    - 若在命令列，執行 cli_fn()\n",
    "    \"\"\"\n",
    "    try:\n",
    "        from IPython import get_ipython\n",
    "        in_nb = get_ipython() is not None\n",
    "    except Exception:\n",
    "        in_nb = False\n",
    "\n",
    "    if in_nb:\n",
    "        print(\"[INFO] 偵測到 Notebook 環境，自動切換至 Notebook 模式執行。\")\n",
    "        nb_fn()\n",
    "    else:\n",
    "        print(\"[INFO] 偵測到命令列環境，進入 CLI 模式執行。\")\n",
    "        cli_fn()\n",
    "\n",
    "\n",
    "# ==============================\n",
    "# 主入口區（正式版本）\n",
    "# ==============================\n",
    "if __name__ == \"__main__\":\n",
    "    # 命令列執行邏輯：透過 argparse 解析參數\n",
    "    def cli_main():\n",
    "        args = parse_args()\n",
    "        run_smoke(args)\n",
    "\n",
    "    # Notebook 預設執行邏輯：直接給定參數\n",
    "    def nb_main():\n",
    "        class Args:\n",
    "            root = \"/home/jovyan/RT08/0925/sliding_win/1014\"  # ← 改成你的版本路徑\n",
    "            batch_size = 128\n",
    "            fold = 1\n",
    "            max_batches = 5\n",
    "            verify_n = 16\n",
    "            enforce_continuity = 1\n",
    "            plot = 1\n",
    "        run_smoke(Args\n",
    "                  ())\n",
    "\n",
    "    # 自動偵測並執行對應模式\n",
    "    auto_main(cli_main, nb_main)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 242,
   "id": "b42561aa-0de3-4098-8685-3e7e9728b220",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[INFO] 偵測到 Notebook 環境，自動切換至 Notebook 模式執行。\n",
      "\n",
      "[INFO] 參數摘要：\n",
      "- W=60, S=30, labeling_mode=majority_only, tau=0.2, dt_thr=120.0\n",
      "- train windows=32,618, valid windows=8,156\n",
      "- batch_size=128, num_workers=0（單線程驗證）\n",
      "- 已啟用 continuity_ok==True 過濾\n",
      "\n",
      "== [TRAIN] smoke test，最多跑 5 個 batch ==\n"
     ]
    },
    {
     "ename": "TypeError",
     "evalue": "default_collate: batch must contain tensors, numpy arrays, numbers, dicts or lists; found <class 'pandas.core.frame.DataFrame'>",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[242], line 542\u001b[0m\n\u001b[1;32m    539\u001b[0m     run_smoke(Args())\n\u001b[1;32m    541\u001b[0m \u001b[38;5;66;03m# 自動偵測並執行對應模式\u001b[39;00m\n\u001b[0;32m--> 542\u001b[0m \u001b[43mauto_main\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcli_main\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnb_main\u001b[49m\u001b[43m)\u001b[49m\n",
      "Cell \u001b[0;32mIn[242], line 515\u001b[0m, in \u001b[0;36mauto_main\u001b[0;34m(cli_fn, nb_fn)\u001b[0m\n\u001b[1;32m    513\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m in_nb:\n\u001b[1;32m    514\u001b[0m     \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m[INFO] 偵測到 Notebook 環境，自動切換至 Notebook 模式執行。\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m--> 515\u001b[0m     \u001b[43mnb_fn\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    516\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    517\u001b[0m     \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m[INFO] 偵測到命令列環境，進入 CLI 模式執行。\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
      "Cell \u001b[0;32mIn[242], line 539\u001b[0m, in \u001b[0;36mnb_main\u001b[0;34m()\u001b[0m\n\u001b[1;32m    537\u001b[0m     enforce_continuity \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m1\u001b[39m\n\u001b[1;32m    538\u001b[0m     plot \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m1\u001b[39m\n\u001b[0;32m--> 539\u001b[0m \u001b[43mrun_smoke\u001b[49m\u001b[43m(\u001b[49m\u001b[43mArgs\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n",
      "Cell \u001b[0;32mIn[242], line 485\u001b[0m, in \u001b[0;36mrun_smoke\u001b[0;34m(args)\u001b[0m\n\u001b[1;32m    482\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m t_marks, cpu_list, rss_list\n\u001b[1;32m    484\u001b[0m \u001b[38;5;66;03m# TRAIN / VALID 兩輪\u001b[39;00m\n\u001b[0;32m--> 485\u001b[0m t_tr, cpu_tr, rss_tr \u001b[38;5;241m=\u001b[39m \u001b[43mloop_loader\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mTRAIN\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtrain_loader\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    486\u001b[0m t_va, cpu_va, rss_va \u001b[38;5;241m=\u001b[39m loop_loader(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mVALID\u001b[39m\u001b[38;5;124m\"\u001b[39m, valid_loader)\n\u001b[1;32m    488\u001b[0m \u001b[38;5;66;03m# Notebook 友善：自動畫圖\u001b[39;00m\n",
      "Cell \u001b[0;32mIn[242], line 438\u001b[0m, in \u001b[0;36mrun_smoke.<locals>.loop_loader\u001b[0;34m(tag, loader)\u001b[0m\n\u001b[1;32m    435\u001b[0m sys0 \u001b[38;5;241m=\u001b[39m read_sys_metrics()\n\u001b[1;32m    436\u001b[0m wall0 \u001b[38;5;241m=\u001b[39m time\u001b[38;5;241m.\u001b[39mtime()\n\u001b[0;32m--> 438\u001b[0m \u001b[43m\u001b[49m\u001b[38;5;28;43;01mfor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mxb\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43myb\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msub_list\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01min\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mloader\u001b[49m\u001b[43m:\u001b[49m\n\u001b[1;32m    439\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;66;43;03m# --- 監控：記錄每批的 CPU/RSS（用於繪圖）---\u001b[39;49;00m\n\u001b[1;32m    440\u001b[0m \u001b[43m    \u001b[49m\u001b[43mmet\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mread_sys_metrics\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    441\u001b[0m \u001b[43m    \u001b[49m\u001b[43mt_marks\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mappend\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtime\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtime\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m-\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mt0\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m/opt/conda/lib/python3.11/site-packages/torch/utils/data/dataloader.py:733\u001b[0m, in \u001b[0;36m_BaseDataLoaderIter.__next__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    730\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_sampler_iter \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m    731\u001b[0m     \u001b[38;5;66;03m# TODO(https://github.com/pytorch/pytorch/issues/76750)\u001b[39;00m\n\u001b[1;32m    732\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_reset()  \u001b[38;5;66;03m# type: ignore[call-arg]\u001b[39;00m\n\u001b[0;32m--> 733\u001b[0m data \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_next_data\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    734\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_num_yielded \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;241m1\u001b[39m\n\u001b[1;32m    735\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m (\n\u001b[1;32m    736\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_dataset_kind \u001b[38;5;241m==\u001b[39m _DatasetKind\u001b[38;5;241m.\u001b[39mIterable\n\u001b[1;32m    737\u001b[0m     \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_IterableDataset_len_called \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m    738\u001b[0m     \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_num_yielded \u001b[38;5;241m>\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_IterableDataset_len_called\n\u001b[1;32m    739\u001b[0m ):\n",
      "File \u001b[0;32m/opt/conda/lib/python3.11/site-packages/torch/utils/data/dataloader.py:789\u001b[0m, in \u001b[0;36m_SingleProcessDataLoaderIter._next_data\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    787\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_next_data\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[1;32m    788\u001b[0m     index \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_next_index()  \u001b[38;5;66;03m# may raise StopIteration\u001b[39;00m\n\u001b[0;32m--> 789\u001b[0m     data \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_dataset_fetcher\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfetch\u001b[49m\u001b[43m(\u001b[49m\u001b[43mindex\u001b[49m\u001b[43m)\u001b[49m  \u001b[38;5;66;03m# may raise StopIteration\u001b[39;00m\n\u001b[1;32m    790\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_pin_memory:\n\u001b[1;32m    791\u001b[0m         data \u001b[38;5;241m=\u001b[39m _utils\u001b[38;5;241m.\u001b[39mpin_memory\u001b[38;5;241m.\u001b[39mpin_memory(data, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_pin_memory_device)\n",
      "File \u001b[0;32m/opt/conda/lib/python3.11/site-packages/torch/utils/data/_utils/fetch.py:55\u001b[0m, in \u001b[0;36m_MapDatasetFetcher.fetch\u001b[0;34m(self, possibly_batched_index)\u001b[0m\n\u001b[1;32m     53\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m     54\u001b[0m     data \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdataset[possibly_batched_index]\n\u001b[0;32m---> 55\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcollate_fn\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdata\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m/opt/conda/lib/python3.11/site-packages/torch/utils/data/_utils/collate.py:398\u001b[0m, in \u001b[0;36mdefault_collate\u001b[0;34m(batch)\u001b[0m\n\u001b[1;32m    337\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mdefault_collate\u001b[39m(batch):\n\u001b[1;32m    338\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124mr\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m    339\u001b[0m \u001b[38;5;124;03m    Take in a batch of data and put the elements within the batch into a tensor with an additional outer dimension - batch size.\u001b[39;00m\n\u001b[1;32m    340\u001b[0m \n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    396\u001b[0m \u001b[38;5;124;03m        >>> default_collate(batch)  # Handle `CustomType` automatically\u001b[39;00m\n\u001b[1;32m    397\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[0;32m--> 398\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mcollate\u001b[49m\u001b[43m(\u001b[49m\u001b[43mbatch\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcollate_fn_map\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdefault_collate_fn_map\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m/opt/conda/lib/python3.11/site-packages/torch/utils/data/_utils/collate.py:211\u001b[0m, in \u001b[0;36mcollate\u001b[0;34m(batch, collate_fn_map)\u001b[0m\n\u001b[1;32m    208\u001b[0m transposed \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlist\u001b[39m(\u001b[38;5;28mzip\u001b[39m(\u001b[38;5;241m*\u001b[39mbatch))  \u001b[38;5;66;03m# It may be accessed twice, so we use a list.\u001b[39;00m\n\u001b[1;32m    210\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(elem, \u001b[38;5;28mtuple\u001b[39m):\n\u001b[0;32m--> 211\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m[\u001b[49m\n\u001b[1;32m    212\u001b[0m \u001b[43m        \u001b[49m\u001b[43mcollate\u001b[49m\u001b[43m(\u001b[49m\u001b[43msamples\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcollate_fn_map\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcollate_fn_map\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    213\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;28;43;01mfor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43msamples\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01min\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mtransposed\u001b[49m\n\u001b[1;32m    214\u001b[0m \u001b[43m    \u001b[49m\u001b[43m]\u001b[49m  \u001b[38;5;66;03m# Backwards compatibility.\u001b[39;00m\n\u001b[1;32m    215\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    216\u001b[0m     \u001b[38;5;28;01mtry\u001b[39;00m:\n",
      "File \u001b[0;32m/opt/conda/lib/python3.11/site-packages/torch/utils/data/_utils/collate.py:212\u001b[0m, in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m    208\u001b[0m transposed \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlist\u001b[39m(\u001b[38;5;28mzip\u001b[39m(\u001b[38;5;241m*\u001b[39mbatch))  \u001b[38;5;66;03m# It may be accessed twice, so we use a list.\u001b[39;00m\n\u001b[1;32m    210\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(elem, \u001b[38;5;28mtuple\u001b[39m):\n\u001b[1;32m    211\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m [\n\u001b[0;32m--> 212\u001b[0m         \u001b[43mcollate\u001b[49m\u001b[43m(\u001b[49m\u001b[43msamples\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcollate_fn_map\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcollate_fn_map\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    213\u001b[0m         \u001b[38;5;28;01mfor\u001b[39;00m samples \u001b[38;5;129;01min\u001b[39;00m transposed\n\u001b[1;32m    214\u001b[0m     ]  \u001b[38;5;66;03m# Backwards compatibility.\u001b[39;00m\n\u001b[1;32m    215\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    216\u001b[0m     \u001b[38;5;28;01mtry\u001b[39;00m:\n",
      "File \u001b[0;32m/opt/conda/lib/python3.11/site-packages/torch/utils/data/_utils/collate.py:240\u001b[0m, in \u001b[0;36mcollate\u001b[0;34m(batch, collate_fn_map)\u001b[0m\n\u001b[1;32m    232\u001b[0m         \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m:\n\u001b[1;32m    233\u001b[0m             \u001b[38;5;66;03m# The sequence type may not support `copy()` / `__setitem__(index, item)`\u001b[39;00m\n\u001b[1;32m    234\u001b[0m             \u001b[38;5;66;03m# or `__init__(iterable)` (e.g., `range`).\u001b[39;00m\n\u001b[1;32m    235\u001b[0m             \u001b[38;5;28;01mreturn\u001b[39;00m [\n\u001b[1;32m    236\u001b[0m                 collate(samples, collate_fn_map\u001b[38;5;241m=\u001b[39mcollate_fn_map)\n\u001b[1;32m    237\u001b[0m                 \u001b[38;5;28;01mfor\u001b[39;00m samples \u001b[38;5;129;01min\u001b[39;00m transposed\n\u001b[1;32m    238\u001b[0m             ]\n\u001b[0;32m--> 240\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(default_collate_err_msg_format\u001b[38;5;241m.\u001b[39mformat(elem_type))\n",
      "\u001b[0;31mTypeError\u001b[0m: default_collate: batch must contain tensors, numpy arrays, numbers, dicts or lists; found <class 'pandas.core.frame.DataFrame'>"
     ]
    }
   ],
   "source": [
    "#...:)不用這個方法了\n",
    "\"\"\"\n",
    "train_lazy_verify_1014.py\n",
    "訓練端串上 manifest（Lazy Evaluation）與單線程驗證腳本\n",
    "-----------------------------------------------------\n",
    "功能：\n",
    "1) 讀取 window_manifest.csv 與 kfold_splits.json\n",
    "2) 建立 PyTorch Dataset / DataLoader（num_workers=0 單線程）\n",
    "3) 逐批讀來源 CSV（shards 或 cleaned），依 manifest 的 start_idx/end_idx 以 iloc 切窗（不實體化）\n",
    "4) 三項驗證（每批抽樣）：\n",
    "   - 視窗長度 = W（預設 60）\n",
    "   - 標籤符合設定檔規則（預設 majority_only 純多數決）\n",
    "   - 時間序（senddate）單調遞增（保守檢查）\n",
    "5) 監控：CPU、RAM、磁碟 I/O、(可選) GPU 記憶體；打印吞吐 (samples/sec)\n",
    "6) Notebook 友善：自動統計正負比例並繪製 CPU/RAM 簡圖（Train/Valid 各一張）\n",
    "\n",
    "使用（命令列）：\n",
    "python train_lazy_verify_1014.py --root /home/jovyan/RT08/0925/sliding_win/1014 \\\n",
    "  --batch_size 128 --fold 1 --max_batches 10 --verify_n 32 --enforce_continuity 1\n",
    "\n",
    "Notebook 直接跑：執行整個檔案，會自動偵測 Notebook 環境，用預設參數啟動。\n",
    "\"\"\"\n",
    "import os\n",
    "import time\n",
    "import json\n",
    "import argparse\n",
    "import warnings\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "import torch\n",
    "from torch.utils.data import Dataset, DataLoader\n",
    "\n",
    "import psutil\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "try:\n",
    "    import pynvml  # 可選，用於 GPU 監控\n",
    "    pynvml.nvmlInit()\n",
    "    HAS_NVML = True\n",
    "except Exception:\n",
    "    HAS_NVML = False\n",
    "\n",
    "import yaml\n",
    "\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "# ==============================\n",
    "# 參數與路徑\n",
    "# ==============================\n",
    "def parse_args():\n",
    "    ap = argparse.ArgumentParser()\n",
    "    ap.add_argument(\"--root\", type=str, required=True,\n",
    "                    help=\"版本 1014 的根路徑，例如 /home/jovyan/RT08/0925/sliding_win/1014\")\n",
    "    ap.add_argument(\"--batch_size\", type=int, default=128)\n",
    "    ap.add_argument(\"--fold\", type=int, default=1, help=\"使用第幾折（1-based）\")\n",
    "    ap.add_argument(\"--max_batches\", type=int, default=10, help=\"每個 loader 僅跑前 N 個 batch 做 smoke test\")\n",
    "    ap.add_argument(\"--verify_n\", type=int, default=32, help=\"每個 batch 隨機抽多少個樣本做嚴格驗證\")\n",
    "    ap.add_argument(\"--enforce_continuity\", type=int, default=1, help=\"1=僅取 continuity_ok 視窗\")\n",
    "    ap.add_argument(\"--plot\", type=int, default=1, help=\"1=結束後繪製 CPU/RAM 簡圖\")\n",
    "    return ap.parse_args()\n",
    "\n",
    "# ==============================\n",
    "# 工具：格式化與系統監控\n",
    "# ==============================\n",
    "def fmt_bytes(n: float) -> str:\n",
    "    for u in [\"B\",\"KB\",\"MB\",\"GB\",\"TB\",\"PB\"]:\n",
    "        if abs(n) < 1024: return f\"{n:.1f}{u}\"\n",
    "        n /= 1024\n",
    "    return f\"{n:.1f}EB\"\n",
    "\n",
    "def read_sys_metrics():\n",
    "    \"\"\"回傳當前系統指標（CPU%、RSS、虛記憶體、磁碟 I/O 累計、(可選)GPU）\"\"\"\n",
    "    p = psutil.Process()\n",
    "    rss = p.memory_info().rss\n",
    "    vms = p.memory_info().vms\n",
    "    cpu = psutil.cpu_percent(interval=None)\n",
    "    dio = psutil.disk_io_counters()\n",
    "    gpu_mem = None\n",
    "    if HAS_NVML and torch.cuda.is_available():\n",
    "        try:\n",
    "            h = pynvml.nvmlDeviceGetHandleByIndex(0)\n",
    "            mem = pynvml.nvmlDeviceGetMemoryInfo(h)\n",
    "            gpu_mem = (mem.used, mem.total)\n",
    "        except Exception:\n",
    "            gpu_mem = None\n",
    "    return {\n",
    "        \"cpu_percent\": cpu,\n",
    "        \"rss\": rss,\n",
    "        \"vms\": vms,\n",
    "        \"disk_read_bytes\": dio.read_bytes if dio else 0,\n",
    "        \"disk_write_bytes\": dio.write_bytes if dio else 0,\n",
    "        \"gpu_mem\": gpu_mem\n",
    "    }\n",
    "\n",
    "def diff_metrics(m1, m0, dt_sec):\n",
    "    \"\"\"兩次量測差值 / 每秒吞吐（供摘要用）\"\"\"\n",
    "    out = {}\n",
    "    for k in [\"disk_read_bytes\", \"disk_write_bytes\"]:\n",
    "        out[k] = m1[k] - m0[k]\n",
    "        out[k + \"_per_s\"] = (out[k] / dt_sec) if dt_sec > 0 else 0.0\n",
    "    out[\"rss\"] = m1[\"rss\"]\n",
    "    out[\"cpu_percent\"] = m1[\"cpu_percent\"]\n",
    "    if m1[\"gpu_mem\"]:\n",
    "        used, total = m1[\"gpu_mem\"]\n",
    "        out[\"gpu_mem_used\"] = used\n",
    "        out[\"gpu_mem_total\"] = total\n",
    "    return out\n",
    "\n",
    "# ==============================\n",
    "# 讀設定：W/S/標籤規則\n",
    "# ==============================\n",
    "def load_windowing_cfg(root: str):\n",
    "    cfg_path = os.path.join(root, \"config\", \"windowing.yaml\")\n",
    "    with open(cfg_path, \"r\", encoding=\"utf-8\") as f:\n",
    "        y = yaml.safe_load(f)\n",
    "    W = int(y[\"window\"][\"W\"])\n",
    "    S = int(y[\"window\"][\"S\"])\n",
    "    mode = y.get(\"labeling_ext\", {}).get(\"mode\", \"majority_only\")\n",
    "    tau = float(y.get(\"labeling_ext\", {}).get(\"tau\", 0.2))\n",
    "    dt_thr = float(y[\"continuity\"][\"dt_sec_threshold\"])\n",
    "    return W, S, mode, tau, dt_thr\n",
    "\n",
    "# ==============================\n",
    "# 來源檔解析\n",
    "# ==============================\n",
    "def resolve_source_path(root: str, src_name: str) -> str:\n",
    "    \"\"\"優先讀 shards，其次讀 cleaned/clear\"\"\"\n",
    "    p1 = os.path.join(root, \"shards\", src_name)\n",
    "    p2 = os.path.join(root, \"cleaned\", \"clear\", src_name)\n",
    "    if os.path.exists(p1): return p1\n",
    "    if os.path.exists(p2): return p2\n",
    "    raise FileNotFoundError(f\"找不到來源檔：{src_name}\")\n",
    "\n",
    "# ==============================\n",
    "# 視窗標籤重算（驗證用）\n",
    "# ==============================\n",
    "def recompute_label(sub_df: pd.DataFrame, mode=\"majority_only\", tau=0.2):\n",
    "    \"\"\"\n",
    "    sub_df: 單一視窗的 DataFrame（假設列序為時間升冪）\n",
    "    - majority_only：整窗 set_fin==1 比例 >= 0.5\n",
    "    - majority_w_minus_1：只看前 W-1 的比例\n",
    "    - majority_w_minus_1_softforce：同上 + 若最後一筆 ad_para=1 且比例<tau → 1\n",
    "    回傳：(y, ratio, last_ad)\n",
    "    \"\"\"\n",
    "    sf = pd.to_numeric(sub_df.get(\"set_fin\"), errors=\"coerce\").fillna(0.0)\n",
    "    ap = pd.to_numeric(sub_df.get(\"ad_para\"), errors=\"coerce\").fillna(0.0)\n",
    "    if len(sub_df) == 0:\n",
    "        return 0, 0.0, 0.0\n",
    "    if mode in (\"majority_w_minus_1\", \"majority_w_minus_1_softforce\"):\n",
    "        head = sf.iloc[:-1] if len(sf) >= 2 else sf\n",
    "        ratio = float((head == 1).mean()) if len(head) > 0 else 0.0\n",
    "        last_ap = float(ap.iloc[-1])\n",
    "        if mode == \"majority_w_minus_1_softforce\" and (last_ap == 1.0) and (ratio < tau):\n",
    "            y = 1\n",
    "        else:\n",
    "            y = 1 if ratio >= 0.5 else 0\n",
    "    else:\n",
    "        ratio = float((sf == 1).mean())\n",
    "        y = 1 if ratio >= 0.5 else 0\n",
    "        last_ap = float(ap.iloc[-1])\n",
    "    return int(y), float(ratio), float(last_ap)\n",
    "\n",
    "# ==============================\n",
    "# Dataset（Lazy Evaluation + 單檔快取）\n",
    "# ==============================\n",
    "class WindowDataset(Dataset):\n",
    "    \"\"\"\n",
    "    以 manifest 為索引，按需讀檔 + 切窗，不實體化全量視窗。\n",
    "    - 單線程首跑驗證正確性 → num_workers=0\n",
    "    - 每次來源檔變動時重新載入（保留原始列序，不在這裡排序）\n",
    "    - 回傳 (X, y, sub_df) 供外層「嚴格驗證」抽樣使用\n",
    "    \"\"\"\n",
    "    FEATURE_COLS = [\n",
    "        # ✅ 避免洩漏 (ad_para / set_fin 不可當特徵)；以下為示例，會以大小寫不敏感方式對齊到實際欄位\n",
    "        \"rrhzsetactual\",\"mvsetactual\",\"pmean\",\"cdyn\",\"peepepap\",\"ppeak\",\"mode_1\",\"mode_2\",\"mode_3\",\"svv_new\"\n",
    "    ]\n",
    "\n",
    "    def __init__(self, root: str, manifest_csv: str, indices=None, enforce_continuity: bool=True):\n",
    "        self.root = root\n",
    "\n",
    "        # 1) 讀完整 manifest（不可先過濾，避免 K 折索引對不上）\n",
    "        man_full = pd.read_csv(manifest_csv)\n",
    "        n_full = len(man_full)\n",
    "\n",
    "        # 2) 若提供 K 折 indices，先做邊界檢查，再使用 iloc 取子集（這一步一定要在過濾前）\n",
    "        if indices is not None:\n",
    "            if len(indices) == 0:\n",
    "                raise ValueError(\"[KFold] 傳入的 indices 為空。請檢查 kfold_splits.json。\")\n",
    "            max_idx = int(np.max(indices))\n",
    "            min_idx = int(np.min(indices))\n",
    "            if min_idx < 0 or max_idx >= n_full:\n",
    "                raise IndexError(\n",
    "                    f\"[KFold 索引越界] indices 範圍 [{min_idx}, {max_idx}] 超過 manifest 長度 {n_full}。\\n\"\n",
    "                    f\"→ 請確認 kfold_splits.json 與目前的 window_manifest.csv 同步。\"\n",
    "                )\n",
    "            man = man_full.iloc[indices].reset_index(drop=True)\n",
    "        else:\n",
    "            man = man_full.copy()\n",
    "\n",
    "        # 3) 再依需要過濾 continuity_ok（順序：indices → continuity）\n",
    "        if enforce_continuity and \"continuity_ok\" in man.columns:\n",
    "            man = man[man[\"continuity_ok\"] == True].reset_index(drop=True)\n",
    "\n",
    "        # 4) 保存\n",
    "        self.man = man\n",
    "\n",
    "        # 來源快取：同一來源檔僅載入一次；同時快取「實際特徵欄位對齊結果」\n",
    "        # cache = {\"src\": 檔名, \"df\": DataFrame, \"feat_cols\": 對齊後欄位名（大小寫依原始 df）}\n",
    "        self.cache = {\"src\": None, \"df\": None, \"feat_cols\": None}\n",
    "\n",
    "        # 載入 windowing 設定\n",
    "        self.W, self.S, self.mode, self.tau, self.dt_thr = load_windowing_cfg(root)\n",
    "\n",
    "    def __len__(self):\n",
    "        return len(self.man)\n",
    "\n",
    "    def _resolve_feature_cols(self, df: pd.DataFrame):\n",
    "        \"\"\"\n",
    "        以不分大小寫的方式，將使用者定義的 FEATURE_COLS 對齊到 df 內實際欄位名稱。\n",
    "        若有缺欄，拋出清楚的錯誤。\n",
    "        \"\"\"\n",
    "        df_col_map = {c.lower(): c for c in df.columns}  # lower -> original\n",
    "        resolved = []\n",
    "        missing = []\n",
    "        for k in self.FEATURE_COLS:\n",
    "            lk = k.lower()\n",
    "            if lk in df_col_map:\n",
    "                resolved.append(df_col_map[lk])\n",
    "            else:\n",
    "                missing.append(k)\n",
    "        if missing:\n",
    "            raise KeyError(\n",
    "                \"[特徵欄位缺失] 下列欄位在來源 CSV 中找不到（不分大小寫比對）：\\n\"\n",
    "                f\"  - {missing}\\n\"\n",
    "                f\"實際 CSV 欄位示例（前 20 個）：{list(df.columns[:20])}\\n\"\n",
    "                \"→ 請修正 FEATURE_COLS 或清洗階段欄位命名。\"\n",
    "            )\n",
    "        return resolved\n",
    "\n",
    "    def _get_df(self, src: str) -> pd.DataFrame:\n",
    "        \"\"\"\n",
    "        讀取單一來源檔並保留「原始列序」；同時解析並快取實際可用的特徵欄位名。\n",
    "        注意：manifest 的 start_idx/end_idx 以原始列序為準，因此不能在這裡重新 sort。\n",
    "        \"\"\"\n",
    "        if self.cache[\"src\"] != src or self.cache[\"df\"] is None:\n",
    "            path = resolve_source_path(self.root, src)\n",
    "            df = pd.read_csv(path)  # ❗ 保留原始列序，不再 sort_values('senddate')\n",
    "            df = df.reset_index(drop=True)\n",
    "\n",
    "            # 對齊特徵欄位（大小寫無關），並快取\n",
    "            feat_cols = self._resolve_feature_cols(df)\n",
    "\n",
    "            self.cache = {\"src\": src, \"df\": df, \"feat_cols\": feat_cols}\n",
    "        return self.cache[\"df\"]\n",
    "\n",
    "    def __getitem__(self, i: int):\n",
    "        r = self.man.iloc[i]\n",
    "        df = self._get_df(r[\"source\"])\n",
    "\n",
    "        # 邊界檢查（避免 start/end 超界）\n",
    "        s_idx, e_idx = int(r[\"start_idx\"]), int(r[\"end_idx\"])\n",
    "        if s_idx < 0 or e_idx >= len(df) or e_idx < s_idx:\n",
    "            raise IndexError(\n",
    "                f\"[索引越界] source={r['source']} start_idx={s_idx} end_idx={e_idx}，\"\n",
    "                f\"但來源長度為 {len(df)}。\\n→ 可能原因：manifest 與 cleaned/shards 不同步，\"\n",
    "                \"或重建資料後未重跑 manifest/kfold。\"\n",
    "            )\n",
    "\n",
    "        sub = df.iloc[s_idx:e_idx+1]\n",
    "        win_len = len(sub)\n",
    "\n",
    "        if win_len == self.W:\n",
    "            # 正常情況：長度剛好 W\n",
    "            feat_cols = self.cache[\"feat_cols\"]\n",
    "            X = torch.tensor(sub[feat_cols].to_numpy(np.float32))\n",
    "            y = torch.tensor(int(r[\"label\"]), dtype=torch.long)\n",
    "            return X, y, sub\n",
    "\n",
    "        elif win_len > self.W:\n",
    "            # 若比 W 長（常見於歷史排序造成的列序不一致），以右對齊回補\n",
    "            s_adj = e_idx - (self.W - 1)\n",
    "            if s_adj < 0:\n",
    "                raise ValueError(\n",
    "                    f\"[視窗回補失敗] 右對齊計算 s_adj={s_adj} < 0；\"\n",
    "                    f\"source={r['source']}, row={i}, idx={s_idx}-{e_idx}, len={win_len}。\"\n",
    "                    \"→ 請重建 manifest。\"\n",
    "                )\n",
    "            sub = df.iloc[s_adj:e_idx+1]\n",
    "            if len(sub) != self.W:\n",
    "                raise ValueError(\n",
    "                    f\"[視窗回補失敗] 期望 W={self.W}，回補後仍得 {len(sub)}；\"\n",
    "                    f\"source={r['source']}, row={i}, s_adj={s_adj}, end_idx={e_idx}。\"\n",
    "                )\n",
    "            feat_cols = self.cache[\"feat_cols\"]\n",
    "            X = torch.tensor(sub[feat_cols].to_numpy(np.float32))\n",
    "            y = torch.tensor(int(r[\"label\"]), dtype=torch.long)\n",
    "            return X, y, sub\n",
    "\n",
    "        else:\n",
    "            # 若比 W 短 → manifest 記錄本身就不合法\n",
    "            raise ValueError(\n",
    "                f\"[視窗長度錯誤] 期望 W={self.W}，實得 {win_len}（短於 W）；\"\n",
    "                f\"source={r['source']}, row={i}, idx={s_idx}-{e_idx}。→ 請重建 manifest。\"\n",
    "            )\n",
    "\n",
    "# ==============================\n",
    "# KFold 讀取\n",
    "# ==============================\n",
    "def load_fold_indices(root: str, fold_1based: int=1):\n",
    "    kf_path = os.path.join(root, \"config\", \"kfold_splits.json\")\n",
    "    with open(kf_path, \"r\", encoding=\"utf-8\") as f:\n",
    "        kf = json.load(f)\n",
    "    folds = kf[\"folds\"]\n",
    "    if not (1 <= fold_1based <= len(folds)):\n",
    "        raise ValueError(f\"fold 超界：要求 {fold_1based}，但 kfold_splits.json 共有 {len(folds)} 折\")\n",
    "    fd = folds[fold_1based - 1]\n",
    "    return fd[\"train_indices\"], fd[\"valid_indices\"]\n",
    "\n",
    "# ==============================\n",
    "# 嚴格驗證（隨機抽樣）\n",
    "# ==============================\n",
    "def strict_verify(batch_sub_frames, batch_labels, W, mode, tau):\n",
    "    \"\"\"\n",
    "    對 batch 中隨機抽樣幾個子視窗做嚴格驗證：\n",
    "    - 視窗長度 == W\n",
    "    - 依 config 規則重算標籤 == manifest 標籤\n",
    "    - senddate 升冪（抽樣檢查）\n",
    "    回傳：錯誤數量 dict\n",
    "    \"\"\"\n",
    "    errs = {\"len_mismatch\":0, \"label_mismatch\":0, \"time_unsorted\":0}\n",
    "    for sub, y in zip(batch_sub_frames, batch_labels):\n",
    "        # 長度\n",
    "        if len(sub) != W:\n",
    "            errs[\"len_mismatch\"] += 1\n",
    "            continue\n",
    "        # 時間排序（抽樣檢查；若清洗已保證升冪，這裡通常為 True）\n",
    "        if \"senddate\" in sub.columns and not sub[\"senddate\"].is_monotonic_increasing:\n",
    "            errs[\"time_unsorted\"] += 1\n",
    "        # 重算標籤\n",
    "        yy, _, _ = recompute_label(sub, mode=mode, tau=tau)\n",
    "        if int(yy) != int(y):\n",
    "            errs[\"label_mismatch\"] += 1\n",
    "    return errs\n",
    "\n",
    "# ==============================\n",
    "# 自訂 collate：保留 DataFrame 清單\n",
    "# ==============================\n",
    "def collate_keep_df(batch):\n",
    "    \"\"\"\n",
    "    batch: list of (X, y, sub_df)\n",
    "    X: Tensor (W, D)\n",
    "    y: Tensor ()\n",
    "    sub_df: pandas.DataFrame（不堆疊、保留為 list）\n",
    "    \"\"\"\n",
    "    xs = [b[0] for b in batch]\n",
    "    ys = [b[1] for b in batch]\n",
    "    subs = [b[2] for b in batch]\n",
    "    Xb = torch.stack(xs, dim=0)  # (B, W, D)\n",
    "    yb = torch.stack(ys, dim=0)  # (B,)\n",
    "    return Xb, yb, subs\n",
    "\n",
    "\n",
    "# ==============================\n",
    "# 繪圖（Notebook 友善）\n",
    "# ==============================\n",
    "def plot_cpu_ram(times_s, cpu_list, rss_list, tag=\"TRAIN\"):\n",
    "    \"\"\"\n",
    "    繪製 CPU% 與 RSS(MB) 隨 batch 推進的走勢圖。\n",
    "    - times_s：相對秒數（每批紀錄一次）\n",
    "    - cpu_list：每批 CPU%\n",
    "    - rss_list：每批 RSS（bytes）\n",
    "    \"\"\"\n",
    "    if len(times_s) == 0:\n",
    "        print(f\"[{tag}] 無可繪製的監控資料。\")\n",
    "        return\n",
    "    fig, ax1 = plt.subplots(figsize=(7.0, 4.0))\n",
    "    ax1.plot(times_s, cpu_list, label=\"CPU%\", linewidth=2)\n",
    "    ax1.set_xlabel(\"Time (s)\")\n",
    "    ax1.set_ylabel(\"CPU (%)\")\n",
    "    ax1.grid(True, linestyle=\"--\", alpha=0.3)\n",
    "    ax2 = ax1.twinx()\n",
    "    ax2.plot(times_s, np.array(rss_list)/1024/1024, label=\"RSS (MB)\", linewidth=2)\n",
    "    ax2.set_ylabel(\"RSS (MB)\")\n",
    "    fig.suptitle(f\"[{tag}] CPU 與 RAM 使用量（單線程）\")\n",
    "    fig.tight_layout()\n",
    "    plt.show()\n",
    "\n",
    "# ==============================\n",
    "# 主流程（單線程 smoke test）\n",
    "# ==============================\n",
    "def run_smoke(args):\n",
    "    \"\"\"\n",
    "    單線程（num_workers=0）驗證正確性與基本負載：\n",
    "    - 驗證每窗長度、標籤一致、時間單調\n",
    "    - 印吞吐/CPU/RAM/I/O 指標\n",
    "    - Notebook 自動畫 CPU/RAM 簡圖\n",
    "    \"\"\"\n",
    "    ROOT = args.root\n",
    "    MAN  = os.path.join(ROOT, \"manifests\", \"window_manifest.csv\")\n",
    "    W, S, mode, tau, dt_thr = load_windowing_cfg(ROOT)\n",
    "\n",
    "    # 載入折索引\n",
    "    train_idx, valid_idx = load_fold_indices(ROOT, args.fold)\n",
    "\n",
    "    # 建立 Dataset / DataLoader（單線程）\n",
    "    train_ds = WindowDataset(ROOT, MAN, train_idx, enforce_continuity=bool(args.enforce_continuity))\n",
    "    valid_ds = WindowDataset(ROOT, MAN, valid_idx, enforce_continuity=bool(args.enforce_continuity))\n",
    "\n",
    "    train_loader = DataLoader(train_ds, batch_size=args.batch_size, shuffle=True,  num_workers=0, pin_memory=True)\n",
    "    valid_loader = DataLoader(valid_ds, batch_size=args.batch_size, shuffle=False, num_workers=0, pin_memory=True)\n",
    "\n",
    "    print(\"\\n[INFO] 參數摘要：\")\n",
    "    print(f\"- W={W}, S={S}, labeling_mode={mode}, tau={tau}, dt_thr={dt_thr}\")\n",
    "    print(f\"- train windows={len(train_ds):,}, valid windows={len(valid_ds):,}\")\n",
    "    print(f\"- batch_size={args.batch_size}, num_workers=0（單線程驗證）\")\n",
    "    if bool(args.enforce_continuity):\n",
    "        print(\"- 已啟用 continuity_ok==True 過濾\")\n",
    "    else:\n",
    "        print(\"- 未過濾 continuity_ok（僅驗證，不建議正式訓練）\")\n",
    "\n",
    "    # === 跑 loader 幾個 batch 做驗證 + 監控 + 收集繪圖資料 ===\n",
    "    def loop_loader(tag, loader):\n",
    "        print(f\"\\n== [{tag}] smoke test，最多跑 {args.max_batches} 個 batch ==\")\n",
    "        batches = 0\n",
    "        total_samples = 0\n",
    "        pos_sum = 0\n",
    "\n",
    "        # 系統監控收集（每批一次）\n",
    "        t0 = time.time()\n",
    "        t_marks, cpu_list, rss_list = [], [], []\n",
    "\n",
    "        # 初始系統狀態（摘要對比用）\n",
    "        sys0 = read_sys_metrics()\n",
    "        wall0 = time.time()\n",
    "\n",
    "        for xb, yb, sub_list in loader:\n",
    "            # --- 監控：記錄每批的 CPU/RSS（用於繪圖）---\n",
    "            met = read_sys_metrics()\n",
    "            t_marks.append(time.time() - t0)\n",
    "            cpu_list.append(met[\"cpu_percent\"])\n",
    "            rss_list.append(met[\"rss\"])\n",
    "\n",
    "            bs = xb.shape[0]\n",
    "            total_samples += bs\n",
    "            pos_sum += int(yb.sum().item())\n",
    "\n",
    "            # 隨機抽樣 verify_n 個子視窗做嚴格驗證\n",
    "            sel = np.random.choice(bs, size=min(args.verify_n, bs), replace=False)\n",
    "            subs = [sub_list[i] for i in sel]\n",
    "            ys   = [int(yb[i].item()) for i in sel]\n",
    "            errs = strict_verify(subs, ys, W=W, mode=mode, tau=tau)\n",
    "\n",
    "            print(f\"[{tag}] batch#{batches:03d} X={tuple(xb.shape)}  \"\n",
    "                  f\"pos_ratio={yb.float().mean().item():.4f}  \"\n",
    "                  f\"errs(len/label/time)={errs['len_mismatch']}/{errs['label_mismatch']}/{errs['time_unsorted']}\")\n",
    "            if any(errs.values()):\n",
    "                print(\"  -> 發現錯誤，請先排除後再進行效能優化/訓練。\")\n",
    "\n",
    "            batches += 1\n",
    "            if batches >= args.max_batches:\n",
    "                break\n",
    "\n",
    "        wall1 = time.time()\n",
    "        sys1 = read_sys_metrics()\n",
    "        dt = max(wall1 - wall0, 1e-6)\n",
    "        diff = diff_metrics(sys1, sys0, dt)\n",
    "\n",
    "        print(f\"\\n[{tag}] 總結：\")\n",
    "        print(f\"- 批次數：{batches}, 總樣本：{total_samples:,}, 平均吞吐：{total_samples/dt:.1f} samples/s\")\n",
    "        print(f\"- 估算正樣本比例（本輪）：{(pos_sum/max(total_samples,1)):.4f}\")\n",
    "        print(f\"- CPU：{diff['cpu_percent']:.1f}%   RSS：{fmt_bytes(diff['rss'])}\")\n",
    "        print(f\"- 磁碟讀：{fmt_bytes(diff['disk_read_bytes'])}（{fmt_bytes(diff['disk_read_bytes_per_s'])}/s）  \"\n",
    "              f\"寫：{fmt_bytes(diff['disk_write_bytes'])}（{fmt_bytes(diff['disk_write_bytes_per_s'])}/s）\")\n",
    "        if 'gpu_mem_used' in diff:\n",
    "            print(f\"- GPU 記憶體：{fmt_bytes(diff['gpu_mem_used'])} / {fmt_bytes(diff['gpu_mem_total'])}\")\n",
    "        else:\n",
    "            print(\"- GPU 記憶體：未偵測或未啟用 NVML（不影響本次驗證）\")\n",
    "\n",
    "        # 回傳繪圖需要的時間序列\n",
    "        return t_marks, cpu_list, rss_list\n",
    "\n",
    "    # TRAIN / VALID 兩輪\n",
    "    t_tr, cpu_tr, rss_tr = loop_loader(\"TRAIN\", train_loader)\n",
    "    t_va, cpu_va, rss_va = loop_loader(\"VALID\", valid_loader)\n",
    "\n",
    "    # Notebook 友善：自動畫圖\n",
    "    if int(getattr(args, \"plot\", 1)) == 1:\n",
    "        plot_cpu_ram(t_tr, cpu_tr, rss_tr, tag=\"TRAIN\")\n",
    "        plot_cpu_ram(t_va, cpu_va, rss_va, tag=\"VALID\")\n",
    "\n",
    "    print(\"\\n✅ 單線程驗證完成：\")\n",
    "    print(\" - 若 errs 全為 0，代表視窗長度/標籤/時間排序皆正確。\")\n",
    "    print(\" - throughput、CPU/RAM、I/O 指標合理 → 可進下一步（num_workers / prefetch / LRU）。\")\n",
    "    print(\" - 若有錯，請回看打印（多半是 FEATURE_COLS 欄位缺失、排序未對齊、或標籤規則設定不符）。\")\n",
    "\n",
    "# ==============================\n",
    "# 入口（命令列 + Notebook 自動切換）\n",
    "# ==============================\n",
    "def auto_main(cli_fn, nb_fn):\n",
    "    \"\"\"\n",
    "    自動偵測目前環境：\n",
    "    - 若在 Notebook，執行 nb_fn()\n",
    "    - 若在命令列，執行 cli_fn()\n",
    "    \"\"\"\n",
    "    try:\n",
    "        from IPython import get_ipython\n",
    "        in_nb = get_ipython() is not None\n",
    "    except Exception:\n",
    "        in_nb = False\n",
    "\n",
    "    if in_nb:\n",
    "        print(\"[INFO] 偵測到 Notebook 環境，自動切換至 Notebook 模式執行。\")\n",
    "        nb_fn()\n",
    "    else:\n",
    "        print(\"[INFO] 偵測到命令列環境，進入 CLI 模式執行。\")\n",
    "        cli_fn()\n",
    "\n",
    "# ==============================\n",
    "# 主入口區（正式版本）\n",
    "# ==============================\n",
    "if __name__ == \"__main__\":\n",
    "    # 命令列執行邏輯：透過 argparse 解析參數\n",
    "    def cli_main():\n",
    "        args = parse_args()\n",
    "        run_smoke(args)\n",
    "\n",
    "    # Notebook 預設執行邏輯：直接給定參數\n",
    "    def nb_main():\n",
    "        class Args:\n",
    "            root = \"/home/jovyan/RT08/0925/sliding_win/1014\"  # ← 換成你的版本路徑\n",
    "            batch_size = 128\n",
    "            fold = 1\n",
    "            max_batches = 5\n",
    "            verify_n = 16\n",
    "            enforce_continuity = 1\n",
    "            plot = 1\n",
    "        run_smoke(Args())\n",
    "\n",
    "    # 自動偵測並執行對應模式\n",
    "    auto_main(cli_main, nb_main)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1540b4a6-e538-42b4-bc55-efdf2e0b1cf4",
   "metadata": {},
   "outputs": [],
   "source": [
    "資料面（窗長、標籤、時間）、工程面（KFold/特徵/列序/回補）、系統面（I/O/CPU/RAM/GPU）一次驗證"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 244,
   "id": "8747a0cd-7008-4422-8f2b-09635e03ef76",
   "metadata": {},
   "outputs": [
    {
     "ename": "ModuleNotFoundError",
     "evalue": "No module named 'train_lazy_verify_1014'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[244], line 2\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mpandas\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mpd\u001b[39;00m\u001b[38;5;241m,\u001b[39m \u001b[38;5;21;01mnumpy\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mnp\u001b[39;00m\u001b[38;5;241m,\u001b[39m \u001b[38;5;21;01mos\u001b[39;00m\u001b[38;5;241m,\u001b[39m \u001b[38;5;21;01mtorch\u001b[39;00m\n\u001b[0;32m----> 2\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mtrain_lazy_verify_1014\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m resolve_source_path, load_windowing_cfg, recompute_label\n\u001b[1;32m      4\u001b[0m ROOT \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m/home/jovyan/RT08/0925/sliding_win/1014\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m      5\u001b[0m man  \u001b[38;5;241m=\u001b[39m pd\u001b[38;5;241m.\u001b[39mread_csv(os\u001b[38;5;241m.\u001b[39mpath\u001b[38;5;241m.\u001b[39mjoin(ROOT, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmanifests\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mwindow_manifest.csv\u001b[39m\u001b[38;5;124m\"\u001b[39m))\n",
      "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'train_lazy_verify_1014'"
     ]
    }
   ],
   "source": [
    "import pandas as pd, numpy as np, os, torch\n",
    "from train_lazy_verify_1014 import resolve_source_path, load_windowing_cfg, recompute_label\n",
    "\n",
    "ROOT = \"/home/jovyan/RT08/0925/sliding_win/1014\"\n",
    "man  = pd.read_csv(os.path.join(ROOT, \"manifests\", \"window_manifest.csv\"))\n",
    "man  = man[man[\"continuity_ok\"]==True].reset_index(drop=True)\n",
    "W, S, mode, tau, _ = load_windowing_cfg(ROOT)\n",
    "\n",
    "samples = man.sample(5, random_state=42)\n",
    "for _, r in samples.iterrows():\n",
    "    path = resolve_source_path(ROOT, r[\"source\"])\n",
    "    df   = pd.read_csv(path).reset_index(drop=True)\n",
    "    s, e = int(r[\"start_idx\"]), int(r[\"end_idx\"])\n",
    "    sub  = df.iloc[e-(W-1):e+1]  # 右對齊切窗\n",
    "    ok_len = (len(sub)==W)\n",
    "    yy, ratio, last_ad = recompute_label(sub, mode=mode, tau=tau)\n",
    "    print(f\"{r['source']} [{s}-{e}] len={len(sub)} label(manifest)={r['label']} label(recomp)={yy} ratio={ratio:.3f} last_ad={last_ad}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e2b61520-0d8c-43cf-9da9-23917fba3150",
   "metadata": {},
   "outputs": [],
   "source": [
    "1014 發生一堆問題\n",
    "不再重新排序 → start_idx/end_idx 與檔案列序一致，iloc 就不會失準。\n",
    "右對齊回補 → 少數歷史遺留窗若比 W 長，可依「右對齊策略」保守修正（以 end_idx 為決策點）。\n",
    "比 W 短 → 通常是 manifest 錯，直接請你重建，避免靜默錯誤。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "99fcd748-b3e4-4d12-a9b6-7dca257fefad",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 入口（命令列）\n",
    "# ==============================\n",
    "if __name__ == \"__main__\":\n",
    "    def _in_notebook():\n",
    "        try:\n",
    "            from IPython import get_ipython\n",
    "            return get_ipython() is not None\n",
    "        except Exception:\n",
    "            return False\n",
    "\n",
    "    if _in_notebook():\n",
    "        print(\"[INFO] 偵測到 Notebook 環境，使用預設參數執行 run_smoke()\")\n",
    "        class Args:\n",
    "            root = \"/home/jovyan/RT08/0925/sliding_win/1014\"  # ← 改成你的實際路徑\n",
    "            batch_size = 128\n",
    "            fold = 1\n",
    "            max_batches = 5\n",
    "            verify_n = 16\n",
    "            enforce_continuity = 1\n",
    "            plot = 1\n",
    "        args = Args()\n",
    "        run_smoke(args)\n",
    "    else:\n",
    "        # 命令列模式（需明確提供 --root）\n",
    "        args = parse_args()\n",
    "        run_smoke(args)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2aeb426b-10f8-4362-a3b2-e5c867a44945",
   "metadata": {},
   "outputs": [],
   "source": [
    "通過門檻（全部達標才算 OK）：\n",
    "抽樣誤差為 errs(len/label/time)=0/0/0。\n",
    "Batch 形狀固定 (batch, 60, D)（W=60），不跳動。\n",
    "CPU/RAM 曲線平穩，吞吐（samples/s）穩定，無異常卡頓"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c2304c54-a9de-4745-8ae1-9d861f5a29af",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "632c022d-9ba6-4fab-83c6-528d9697f43e",
   "metadata": {},
   "outputs": [],
   "source": [
    "我要在/home/jovyan/RT08/0925/bling_1014_clean/中的每個檔案都增加一個欄位，above_peep, 是float,\n",
    "計算方式是欄位ppeak減欄位peep的數值，\n",
    "如果該筆資料的nan_check=0就直接給0，\n",
    "並在後面列出nan_check=1的資料中，above_peep的統計狀況"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 249,
   "id": "24e55221-601d-43de-92c7-6108e0990e57",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[INFO] 偵測到 CSV 檔案數量：123\n",
      "[ERROR] 處理 above_peep_summary.csv 時出錯：No columns to parse from file\n",
      "\n",
      "✅ 已完成所有檔案處理，統計結果輸出至：/home/jovyan/RT08/0925/bling_1014_clean/above_peep_summary.csv\n",
      "\n",
      "=== nan_check==1 的 above_peep 統計（前5筆） ===\n",
      "                      file  count_nan1    mean      std    min    p25    p50    p75       max\n",
      "0  PatNo_ID_1594439781.csv        7505  11.027    1.395  6.000 10.000 11.000 12.000    17.000\n",
      "1  PatNo_ID_1570242703.csv       10543  15.819    4.691 -1.600 11.000 17.000 20.000    46.000\n",
      "2  PatNo_ID_1574148494.csv       47006 148.204 2927.155 -6.000 15.000 16.000 19.000 65230.000\n",
      "3  PatNo_ID_1582849900.csv        1743  13.382    2.112  6.000 13.000 14.000 14.000    19.000\n",
      "4  PatNo_ID_1574831525.csv         514  16.368    0.495 15.000 16.000 16.000 17.000    17.000\n",
      "\n",
      "=== 整體描述統計（加總所有檔案） ===\n",
      "         mean      std     min       max\n",
      "count 118.000  118.000 118.000   118.000\n",
      "mean   40.305  520.780   3.523 11627.178\n",
      "std    58.981 1154.688   6.879 25038.253\n",
      "min     8.680    0.392 -15.000    13.000\n",
      "25%    13.134    1.837  -1.525    23.000\n",
      "50%    16.051    2.780   5.000    28.000\n",
      "75%    20.591    4.193   6.000    36.000\n",
      "max   305.887 4287.853  21.000 65236.000\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import glob\n",
    "import os\n",
    "\n",
    "base_dir = \"/home/jovyan/RT08/0925/bling_1014_clean/\"\n",
    "csv_files = glob.glob(os.path.join(base_dir, \"*.csv\"))\n",
    "\n",
    "summary_list = []\n",
    "\n",
    "print(f\"[INFO] 偵測到 CSV 檔案數量：{len(csv_files)}\")\n",
    "\n",
    "for f in csv_files:\n",
    "    try:\n",
    "        df = pd.read_csv(f)\n",
    "        \n",
    "        # 確保欄位存在\n",
    "        if not {\"ppeak\", \"peepepap\", \"nan_check\"}.issubset(df.columns):\n",
    "            print(f\"[WARN] {os.path.basename(f)} 缺少必要欄位，略過。\")\n",
    "            continue\n",
    "\n",
    "        # 新增欄位 above_peep\n",
    "        df[\"above_peep\"] = np.where(df[\"nan_check\"] == 0, 0.0, df[\"ppeak\"] - df[\"peepepap\"])\n",
    "\n",
    "        # 儲存回原檔案（可改成另存新資料夾以保險）\n",
    "        df.to_csv(f, index=False)\n",
    "\n",
    "        # 統計 nan_check == 1 的分佈\n",
    "        valid = df.loc[df[\"nan_check\"] == 1, \"above_peep\"]\n",
    "        stats = valid.describe().to_dict()\n",
    "\n",
    "        summary_list.append({\n",
    "            \"file\": os.path.basename(f),\n",
    "            \"count_nan1\": len(valid),\n",
    "            \"mean\": stats.get(\"mean\", np.nan),\n",
    "            \"std\": stats.get(\"std\", np.nan),\n",
    "            \"min\": stats.get(\"min\", np.nan),\n",
    "            \"p25\": stats.get(\"25%\", np.nan),\n",
    "            \"p50\": stats.get(\"50%\", np.nan),\n",
    "            \"p75\": stats.get(\"75%\", np.nan),\n",
    "            \"max\": stats.get(\"max\", np.nan),\n",
    "        })\n",
    "    except Exception as e:\n",
    "        print(f\"[ERROR] 處理 {os.path.basename(f)} 時出錯：{e}\")\n",
    "\n",
    "# 統計結果輸出\n",
    "summary_df = pd.DataFrame(summary_list)\n",
    "summary_path = os.path.join(base_dir, \"above_peep_summary.csv\")\n",
    "summary_df.to_csv(summary_path, index=False)\n",
    "\n",
    "print(f\"\\n✅ 已完成所有檔案處理，統計結果輸出至：{summary_path}\\n\")\n",
    "print(\"=== nan_check==1 的 above_peep 統計（前5筆） ===\")\n",
    "print(summary_df.head())\n",
    "print(\"\\n=== 整體描述統計（加總所有檔案） ===\")\n",
    "print(summary_df[[\"mean\",\"std\",\"min\",\"max\"]].describe())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 251,
   "id": "8ab3a545-e0e0-496b-b704-c21eb39eed1b",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[INFO] 檔案數量：123\n",
      "[WARN] above_peep_summary.csv 缺少欄位：{'nan_check', 'peepepap', 'ppeak'}，跳過。\n",
      "\n",
      "[OK] 已產出：\n",
      "- /home/jovyan/RT08/0925/bling_1014_clean/above_peep_reports/above_peep_per_file_stats.csv\n",
      "- /home/jovyan/RT08/0925/bling_1014_clean/above_peep_reports/above_peep_overall_stats.csv\n",
      "- /home/jovyan/RT08/0925/bling_1014_clean/above_peep_reports/above_peep_anomalies_rows.csv\n",
      "- /home/jovyan/RT08/0925/bling_1014_clean/above_peep_reports/above_peep_anomalies_by_file.csv\n",
      "- /home/jovyan/RT08/0925/bling_1014_clean/above_peep_reports/top20_negative_rows.csv\n",
      "- /home/jovyan/RT08/0925/bling_1014_clean/above_peep_reports/top20_positive_rows.csv\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import glob, os\n",
    "\n",
    "SRC = \"/home/jovyan/RT08/0925/bling_1014_clean/\"\n",
    "OUT = os.path.join(SRC, \"above_peep_reports\")\n",
    "os.makedirs(OUT, exist_ok=True)\n",
    "\n",
    "csv_files = glob.glob(os.path.join(SRC, \"*.csv\"))\n",
    "\n",
    "per_file_stats = []\n",
    "valid_rows_all = []   # 只收 nan_check==1 的逐列 above_peep\n",
    "\n",
    "print(f\"[INFO] 檔案數量：{len(csv_files)}\")\n",
    "\n",
    "for f in csv_files:\n",
    "    name = os.path.basename(f)\n",
    "    try:\n",
    "        df = pd.read_csv(f)\n",
    "\n",
    "        # 欄位檢查\n",
    "        required = {\"ppeak\",\"peepepap\",\"nan_check\"}\n",
    "        if not required.issubset(df.columns):\n",
    "            print(f\"[WARN] {name} 缺少欄位：{required - set(df.columns)}，跳過。\")\n",
    "            continue\n",
    "\n",
    "        # 計算 above_peep（不覆蓋原檔；如需覆蓋，將另存移除）\n",
    "        above = np.where(df[\"nan_check\"]==0, 0.0, df[\"ppeak\"] - df[\"peepepap\"])\n",
    "        df[\"above_peep\"] = above\n",
    "\n",
    "        # 只取 nan_check==1 的列做統計與合併\n",
    "        valid = df.loc[df[\"nan_check\"]==1, [\"above_peep\"]].copy()\n",
    "        valid[\"__file__\"] = name\n",
    "        valid_rows_all.append(valid)\n",
    "\n",
    "        # 每檔案描述統計\n",
    "        if len(valid) > 0:\n",
    "            s = valid[\"above_peep\"].describe()\n",
    "            per_file_stats.append({\n",
    "                \"file\": name,\n",
    "                \"count_nan1\": int(s[\"count\"]),\n",
    "                \"mean\": float(s[\"mean\"]),\n",
    "                \"std\": float(s[\"std\"]),\n",
    "                \"min\": float(s[\"min\"]),\n",
    "                \"p25\": float(s[\"25%\"]),\n",
    "                \"p50\": float(s[\"50%\"]),\n",
    "                \"p75\": float(s[\"75%\"]),\n",
    "                \"max\": float(s[\"max\"]),\n",
    "            })\n",
    "        else:\n",
    "            per_file_stats.append({\n",
    "                \"file\": name,\n",
    "                \"count_nan1\": 0, \"mean\": np.nan, \"std\": np.nan,\n",
    "                \"min\": np.nan, \"p25\": np.nan, \"p50\": np.nan, \"p75\": np.nan, \"max\": np.nan\n",
    "            })\n",
    "\n",
    "        # 另存含新欄位的檔案（不覆蓋原始：另建資料夾）\n",
    "        out_file = os.path.join(OUT, name)\n",
    "        df.to_csv(out_file, index=False)\n",
    "\n",
    "    except Exception as e:\n",
    "        print(f\"[ERROR] {name}: {e}\")\n",
    "\n",
    "# === 報表 1：每檔案統計 ===\n",
    "per_file_df = pd.DataFrame(per_file_stats).sort_values(\"file\")\n",
    "per_file_df.to_csv(os.path.join(OUT, \"above_peep_per_file_stats.csv\"), index=False)\n",
    "\n",
    "# === 報表 2：全局逐列統計（row-level across all files, nan_check==1） ===\n",
    "if valid_rows_all:\n",
    "    all_valid = pd.concat(valid_rows_all, ignore_index=True)\n",
    "    overall_stats = all_valid[\"above_peep\"].describe().to_frame(name=\"above_peep\")\n",
    "    overall_stats.to_csv(os.path.join(OUT, \"above_peep_overall_stats.csv\"))\n",
    "\n",
    "    # IQR 也算一下，便於資料科學的異常界定\n",
    "    q1 = all_valid[\"above_peep\"].quantile(0.25)\n",
    "    q3 = all_valid[\"above_peep\"].quantile(0.75)\n",
    "    iqr = q3 - q1\n",
    "    iqr_upper = q3 + 1.5*iqr\n",
    "    iqr_lower = q1 - 1.5*iqr\n",
    "\n",
    "    # === 報表 3：異常列（規則可調） ===\n",
    "    # 規則 A（臨床）：<0 或 >40 cmH2O\n",
    "    clinical_hi = 40.0\n",
    "    anomalies = all_valid.loc[(all_valid[\"above_peep\"] < 0) | (all_valid[\"above_peep\"] > clinical_hi)].copy()\n",
    "    anomalies[\"rule\"] = np.where(anomalies[\"above_peep\"]<0, \"below_0\", \"above_40\")\n",
    "\n",
    "    # 規則 B（統計）：落在 IQR 上下界外\n",
    "    iqr_out = all_valid.loc[(all_valid[\"above_peep\"] < iqr_lower) | (all_valid[\"above_peep\"] > iqr_upper)].copy()\n",
    "    iqr_out[\"rule\"] = \"IQR_outlier\"\n",
    "\n",
    "    # 合併異常，並按嚴重度排序\n",
    "    anomalies_all = pd.concat([anomalies, iqr_out], ignore_index=True).drop_duplicates()\n",
    "    anomalies_all.sort_values(\"above_peep\", inplace=True)\n",
    "    anomalies_all.to_csv(os.path.join(OUT, \"above_peep_anomalies_rows.csv\"), index=False)\n",
    "\n",
    "    # 異常分檔案彙整\n",
    "    anomalies_by_file = anomalies_all.groupby([\"__file__\", \"rule\"]).size().unstack(fill_value=0)\n",
    "    anomalies_by_file[\"total\"] = anomalies_by_file.sum(axis=1)\n",
    "    anomalies_by_file.sort_values(\"total\", ascending=False, inplace=True)\n",
    "    anomalies_by_file.to_csv(os.path.join(OUT, \"above_peep_anomalies_by_file.csv\"))\n",
    "\n",
    "    # 額外：列出「負值」與「極大值」的 Top-N 行，方便你直接比對\n",
    "    top_neg = all_valid.nsmallest(20, \"above_peep\")\n",
    "    top_pos = all_valid.nlargest(20, \"above_peep\")\n",
    "    top_neg.to_csv(os.path.join(OUT, \"top20_negative_rows.csv\"), index=False)\n",
    "    top_pos.to_csv(os.path.join(OUT, \"top20_positive_rows.csv\"), index=False)\n",
    "\n",
    "    print(\"\\n[OK] 已產出：\")\n",
    "    print(f\"- {os.path.join(OUT, 'above_peep_per_file_stats.csv')}\")\n",
    "    print(f\"- {os.path.join(OUT, 'above_peep_overall_stats.csv')}\")\n",
    "    print(f\"- {os.path.join(OUT, 'above_peep_anomalies_rows.csv')}\")\n",
    "    print(f\"- {os.path.join(OUT, 'above_peep_anomalies_by_file.csv')}\")\n",
    "    print(f\"- {os.path.join(OUT, 'top20_negative_rows.csv')}\")\n",
    "    print(f\"- {os.path.join(OUT, 'top20_positive_rows.csv')}\")\n",
    "else:\n",
    "    print(\"[WARN] 沒有 nan_check==1 的有效列，無法生成全局統計。\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "66caa950-09af-4d93-aa53-d25bc4966902",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "259f1afe-2666-4ac6-bb01-e2f70b27878c",
   "metadata": {},
   "outputs": [],
   "source": [
    "1014 請直接跳到這裡"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c15a017d-ce06-4cfe-ab7f-0046387996f2",
   "metadata": {},
   "outputs": [],
   "source": [
    "一次性滑動視窗展開資料處理流程與檔案層級說明\n",
    "───────────────────────────────\n",
    "\n",
    "一、目的與適用範圍\n",
    "\n",
    "本文件說明將清理後的逐筆病患資料轉換為可供深度學習模型訓練之時間片段樣本的完整流程。\n",
    "流程採「一次性滑動視窗展開（Single-Pass Sliding Window Expansion）」策略，\n",
    "以最小化程式開發負擔、加速訓練前處理、並確保結果可重現。\n",
    "\n",
    "本流程適用於：\n",
    "各病患資料時間序正確且無缺值；\n",
    "已具備最終正負樣本標籤欄位 set_fin；\n",
    "欲進行 LSTM、GRU、或注意力機制類模型之監督式訓練。\n",
    "\n",
    "───────────────────────────────\n",
    "二、資料來源與輸入結構\n",
    "───────────────────────────────\n",
    "\n",
    "資料來源\n",
    "\n",
    "原始清理後資料位於路徑：\n",
    "/home/jovyan/RT08/0925/bling_1014_clean/\n",
    "此層資料為清理後的逐筆 CSV，每位病患一檔。\n",
    "每筆資料對應一個時間點的呼吸器回饋與設定值，已完成時間排序、缺值剔除與一致性稽核。\n",
    "\n",
    "檔案格式說明\n",
    "\n",
    "每位病患一份 CSV 檔案，例如：\n",
    "bling_1014_clean/089271.csv\n",
    "\n",
    "檔案內容結構如下：\n",
    "\n",
    "時間戳記欄：SendDate（已為 ISO 標準時間格式）\n",
    "\n",
    "主要特徵欄：\n",
    "\"rrhzsetactual\",\"mvsetactual\",\"pmean\",\"cdyn\",\"peepepap\",\"ppeak\",\"svv_new\", \"above-peep\", \"mode_1\",\"mode_2\",\"mode_3\"\n",
    "（前八項為連續型呼吸器參數；後三項為模式 One-Hot 編碼類別特徵）\n",
    "\n",
    "事件欄位：ad_para（是否發生參數調整事件）\n",
    "可用資料：nan_check=1（若=0請忽略）\n",
    "標籤欄位：set_fin（正負樣本標籤，0 = 負樣本，1 = 正樣本, 2 = 無效樣本）\n",
    "標籤欄位 set_fin 為模型的預測目標：\n",
    "數值為 0 表示負樣本，代表該時間點或該段期間不需要調整呼吸器設定；\n",
    "數值為 1 表示正樣本，代表該時段需要進行設定調整。\n",
    "\n",
    "所有檔案已確保：\n",
    "時間順序單調遞增；\n",
    "無缺值或異常間隔；\n",
    "set_fin 已完成最終一致化處理。\n",
    "\n",
    "=\n",
    "三、資料轉換流程總覽\n",
    "\n",
    "整體資料轉換流程分為三個階段：\n",
    "輸入階段（Input）、處理階段（Processing）、輸出階段（Output）。\n",
    "\n",
    "（一）輸入階段（Input）\n",
    "\n",
    "系統讀取來源路徑 /home/jovyan/RT08/0925/bling_1014_clean/ 下的所有病患 CSV。\n",
    "每份檔案代表單一病患的逐筆時間序列，內容已排序與清理。\n",
    "\n",
    "\n",
    "例：\n",
    "cleaned/pat001.csv\n",
    "每檔約含數千至數萬筆資料，代表病患整段使用呼吸器期間的動態。\n",
    "此階段不進行資料修改，只進行逐檔載入與統一欄位檢查。\n",
    "\n",
    "\n",
    "（二）處理階段（Processing）\n",
    "\n",
    "此階段為整個流程的核心。系統依照設定檔中定義的視窗長度與步幅，\n",
    "對每位病患的時間序列進行滑動視窗切割。\n",
    "\n",
    "視窗參數\n",
    "\n",
    "視窗長度（W）= 60 筆（即連續 60 分鐘資料）\n",
    "\n",
    "步幅（S）= 30 筆（即每 30 分鐘生成一個新視窗）\n",
    "\n",
    "對齊方式 = 右對齊（以視窗最後一筆作為標籤時間點）\n",
    "\n",
    "處理邏輯\n",
    "系統從每位病患的資料中依序擷取連續 60 筆資料形成一個視窗樣本。\n",
    "視窗間重疊 30 筆，確保時間連續性並增加樣本數。\n",
    "每個視窗的標籤由 set_fin 欄位多數決決定：\n",
    "若超過等於50%有效筆數 set_fin = 1 → 該視窗為正樣本；\n",
    "若低於50%有效筆數 set_fin = 0 → 該視窗為負樣本。\n",
    "程式於記憶體中動態組成特徵矩陣 X 與標籤陣列 y。\n",
    "完成後，以八比二比例切分訓練與驗證集。\n",
    "\n",
    "資料型態變化\n",
    "\n",
    "原始狀態（逐筆 CSV）：\n",
    "每人一份 CSV，數千筆時間點資料。\n",
    "\n",
    "開窗後（固定長度樣本）：\n",
    "每窗包含 60 筆資料，形成一個時間片段樣本。\n",
    "\n",
    "例：\n",
    "病患 001 → 8,000 筆時間點 → 約 264 個視窗樣本\n",
    "病患 002 → 12,000 筆時間點 → 約 398 個視窗樣本\n",
    "病患 003 → 6,000 筆時間點 → 約 198 個視窗樣本\n",
    "\n",
    "換言之，資料結構自「時間點層級」重構為「時間片段層級」，\n",
    "模型輸入不再是單筆狀態，而是連續 60 分鐘的變化序列。\n",
    "\n",
    "（三）輸出階段（Output）\n",
    "開窗完成後，系統自動建立輸出資料夾 /home/jovyan/RT08/0925/1010/windowed/，\n",
    "並生成四個主要輸出檔案，以 NumPy 格式儲存：\n",
    "X_train.npy：訓練集特徵矩陣（三維，樣本數 × 60 × 特徵數）\n",
    "y_train.npy：訓練集標籤陣列（一維，樣本數 × 1）\n",
    "X_val.npy：驗證集特徵矩陣（三維，樣本數 × 60 × 特徵數）\n",
    "y_val.npy：驗證集標籤陣列（一維，樣本數 × 1）\n",
    "這些檔案可直接載入 TensorFlow 或 PyTorch 進行訓練。\n",
    "無需額外格式轉換或再清理。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cd88e373-09d2-4d6c-a40d-948e0bcf0898",
   "metadata": {},
   "outputs": [],
   "source": [
    "四、檔案層級架構說明\n",
    "\n",
    "本專案資料根目錄位於：\n",
    "/home/jovyan/RT08/0925/sliding_win/1014_sim/\n",
    "├─ cleaned/                   每位病患一檔，欄位已統一小寫、時間排序完成、關鍵特徵轉數值、去重去缺失，標籤為 set_fin。\n",
    "│   ├─ pat001.csv\n",
    "│   ├─ pat002.csv\n",
    "│   ├─ ...\n",
    "│   └─ audit_summary.csv\n",
    "│      （說明：清理後逐筆時間序列資料與稽核報告。\n",
    "│              每位病患一檔，已完成時間排序、缺值剔除與一致性檢核。）\n",
    "│\n",
    "├─ windowed/                  視窗層\n",
    "│   ├─ X_train.npy            訓練集特徵，三維陣列（樣本數 × 60 × 特徵數）\n",
    "│   ├─ y_train.npy            訓練集標籤，一維陣列（樣本數）\n",
    "│   ├─ X_val.npy              驗證集特徵，三維陣列。\n",
    "│   ├─ y_val.npy              驗證集標籤，一維陣列。\n",
    "│   └─ sample_window.csv      單一視窗的人可讀樣本檢查檔。\n",
    "│      （說明：滑動視窗展開後的訓練與驗證資料，\n",
    "│              以 NumPy 格式儲存，維度為樣本數×時間步×特徵數，\n",
    "│              可直接輸入 LSTM、GRU 或注意力模型訓練。）\n",
    "│\n",
    "├─ config/\n",
    "│   └─ windowing.yaml\n",
    "│      （說明：視窗參數設定檔，統一管理 W=60、S=30、特徵欄位、\n",
    "│              標籤來源（set_fin）、聚合規則與亂數種子等關鍵設定，\n",
    "│              為所有資料生成與訓練流程的唯一依據。）\n",
    "│\n",
    "├─ logs/\n",
    "│   └─ preprocess.log\n",
    "│      （說明：滑動視窗展開與資料處理過程的執行紀錄，\n",
    "│              包含載入進度、檔案統計、時間耗時與錯誤追蹤。）\n",
    "│\n",
    "├─ reports/\n",
    "│   ├─ window_summary.csv\n",
    "│   │   （說明：逐視窗統計報表，記錄每個視窗的完整性與時間特性。\n",
    "│   │           逐視窗統計，含來源檔、起訖時間、視窗筆數、時間間隔均值與最大值、連續性旗標、視窗標籤\n",
    "│   │           欄位範例：\n",
    "│   │           window_id：視窗識別碼\n",
    "│   │           file_name：來源檔名\n",
    "│   │           start_time、end_time：視窗起訖時間\n",
    "│   │           mean_dt_sec、max_dt_sec：時間間隔統計\n",
    "│   │           continuity_ok：是否通過時間連續性檢核\n",
    "│   │           n_rows：視窗內資料筆數\n",
    "│   │           label：視窗標籤（set_fin 聚合結果））\n",
    "│   │\n",
    "│   └─ sample_distribution.csv\n",
    "│       （說明：整體樣本分佈與資料摘要報表。\n",
    "│               欄位範例：\n",
    "│               file_name：病患檔名\n",
    "│               total_rows：原始筆數\n",
    "│               n_windows：生成視窗數量\n",
    "│               pos_samples：正樣本數\n",
    "│               neg_samples：負樣本數\n",
    "│               pos_ratio：正樣本比例\n",
    "│               mean_window_length：平均視窗時長\n",
    "│               total_duration_hr：病患總時長（小時））\n",
    "│\n",
    "├─ training/\n",
    "│   ├─ run_001/\n",
    "│   │   ├─ logs/                          preprocess.log：全流程每一步的「提示語」與「筆數統計」紀錄     \n",
    "│   │   ├─ checkpoints/\n",
    "│   │   └─ metrics/\n",
    "│   │      （說明：模型訓練後產生的輸出結果，\n",
    "│   │              含訓練日誌、權重檔與性能指標。\n",
    "│   │              每次訓練任務建立獨立 run_xxx 資料夾保存。）\n",
    "│   └─ ...\n",
    "│\n",
    "└─ docs/\n",
    "    └─ data_spec.txt\n",
    "       （說明：資料設計、視窗邏輯、特徵定義與整體處理規範文件。\n",
    "               為工程與研究實作的正式依據。）\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ee5b5dcc-8ea9-408c-87d9-633d1d315240",
   "metadata": {},
   "outputs": [],
   "source": [
    "一、清理層（raw → cleaned）\n",
    "\n",
    "cleaned/*.csv：每位病患一檔，欄位已統一小寫、時間排序完成、關鍵特徵轉數值、去重去缺失，標籤為 set_fin。\n",
    "\n",
    "cleaned/audit_summary.csv：每檔清理統計，含各步驟「移除筆數」與「保留筆數」。\n",
    "\n",
    "二、視窗層（cleaned → windowed）\n",
    "\n",
    "windowed/X_train.npy：訓練集特徵，三維陣列（樣本數 × 60 × 特徵數）。\n",
    "\n",
    "windowed/y_train.npy：訓練集標籤，一維陣列（樣本數）。\n",
    "\n",
    "windowed/X_val.npy：驗證集特徵，三維陣列。\n",
    "\n",
    "windowed/y_val.npy：驗證集標籤，一維陣列。\n",
    "\n",
    "windowed/sample_window.csv：單一視窗的人可讀樣本檢查檔。\n",
    "\n",
    "三、報表與摘要（可稽核、可追蹤）\n",
    "\n",
    "reports/window_summary.csv：逐視窗統計，含來源檔、起訖時間、視窗筆數、時間間隔均值與最大值、連續性旗標、視窗標籤。\n",
    "\n",
    "reports/sample_distribution.csv：逐檔樣本分布，含原始筆數、生成視窗數、正負樣本數與比例、違規視窗數、總時長（小時）。\n",
    "\n",
    "reports/run_digest.json：本次 run 的整體摘要（檔案數、總視窗數、train/val 視窗數、類別數量、視窗形狀、參數快照）。\n",
    "\n",
    "四、設定與日誌\n",
    "\n",
    "config/windowing.yaml：實際使用的參數快照（W=60、S=30、特徵清單、標籤欄、亂數種子等）。\n",
    "\n",
    "logs/preprocess.log：全流程每一步的「提示語」與「筆數統計」紀錄。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "00438c68-1a3f-4526-84f7-986a0171119f",
   "metadata": {},
   "outputs": [],
   "source": [
    "五、結果說明與模型訓練介接\n",
    "\n",
    "完成後輸出四個 NumPy 檔案，\n",
    "每個檔案內容說明如下：\n",
    "\n",
    "X_train / X_val：\n",
    "儲存連續 60 筆特徵值的三維張量，\n",
    "維度結構為（樣本數 × 時間步數 × 特徵數）。\n",
    "\n",
    "y_train / y_val：\n",
    "對應每個視窗樣本的 set_fin 標籤，\n",
    "值為 0 或 1，型態為一維陣列。\n",
    "\n",
    "這些輸出可直接載入深度學習框架的 DataLoader 或 TensorDataset：\n",
    "X_train.shape = (N_train, 60, 10)\n",
    "y_train.shape = (N_train,)\n",
    "\n",
    "六、效能與風險評估\n",
    "\n",
    "此一次性展開策略的優點如下：\n",
    "開發快速：不需撰寫多階段 pipeline 或 lazy evaluation。\n",
    "低風險：資料無缺值、時間連續，省略連續性檢查步驟。\n",
    "高相容性：輸出格式與主流框架直接相容。\n",
    "可重現性：設定檔 windowing.yaml 控制所有參數，固定亂數種子（42）確保一致。\n",
    "執行後約可在數十秒至數分鐘內完成所有開窗與切分，\n",
    "輸出檔案大小約為原始資料的 3–5%。\n",
    "\n",
    "七、摘要與應用\n",
    "\n",
    "本流程將多病患逐筆時間序列資料轉換為固定長度時間片段樣本，\n",
    "每窗包含連續 60 筆呼吸器參數，以最後一筆的 set_fin 作為標籤。\n",
    "生成的四個 NumPy 檔案可直接餵入 LSTM、GRU 或多頭自注意力模型。\n",
    "\n",
    "此方法為「快速、可重現、低風險」的訓練資料生成方案，\n",
    "不需額外的多進程、manifest 或快取設計即可完成，\n",
    "非常適合中規模臨床時序資料的深度學習任務。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "37ea6a5d-f3ad-490b-b25f-c1745d00bc8e",
   "metadata": {},
   "outputs": [],
   "source": [
    "接下來的步驟\n",
    "\n",
    "型訓練接軌\n",
    "\n",
    "直接載入 windowed/X_train.npy、y_train.npy、X_val.npy、y_val.npy 作為模型輸入。\n",
    "\n",
    "輸入張量形狀：(N, 60, D)，標籤形狀：(N,)，D 為你的特徵數（此版為 10）。\n",
    "\n",
    "訓練結果管理\n",
    "\n",
    "將訓練過程輸出放到 training/run_***/（權重、指標、訓練日誌）。\n",
    "\n",
    "於訓練日誌記錄本次使用之 config/windowing.yaml 的內容或雜湊，確保可重現性。\n",
    "\n",
    "品質與資料檢視\n",
    "\n",
    "快速檢核 reports/window_summary.csv 的 continuity_ok 與 max_dt_sec 是否合理（預設門檻 120 秒）。\n",
    "\n",
    "檢視 reports/sample_distribution.csv 的 pos_ratio 是否接近預期，避免嚴重類別失衡。\n",
    "\n",
    "參數調整（如需）\n",
    "\n",
    "若要改視窗長度或步幅，調整 W、S 後重跑，輸出會覆蓋 windowed/ 與 reports/（建議新建版本資料夾）。\n",
    "\n",
    "擴充資料或納新病患\n",
    "\n",
    "新增 CSV 至 raw/ 後直接重跑；清理層會產生新 cleaned/*.csv 與更新後的報表，再視窗化。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "710da096-7b0d-4925-a165-4014591f3a8f",
   "metadata": {},
   "outputs": [],
   "source": [
    "要手動把資料放到/home/jovyan/RT08/0925/sliding_win/1014_sim/raw/欸"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cf18d499-6a23-4144-b55e-667f61203500",
   "metadata": {},
   "outputs": [],
   "source": [
    "我要​將​​/home/jovyan/R​T08/0925/b​ling_1014_clean/全部​檔案\n",
    "另外複製​存在​/home/jovyan/RT08/0925/sliding_win/1014_sim/raw/，\n",
    "並印出有幾個檔案 程式碼"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 255,
   "id": "da828367-ca35-4b09-8b5c-1ca2c7b49f75",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "✅ 已成功複製 122 個檔案至 /home/jovyan/RT08/0925/sliding_win/1014_sim/raw/\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import shutil\n",
    "\n",
    "src_dir = \"/home/jovyan/RT08/0925/bling_1014_clean/\"\n",
    "dst_dir = \"/home/jovyan/RT08/0925/sliding_win/1014_sim/raw/\"\n",
    "\n",
    "# 確保目的資料夾存在\n",
    "os.makedirs(dst_dir, exist_ok=True)\n",
    "\n",
    "# 取得來源資料夾中所有檔案（排除子資料夾）\n",
    "files = [f for f in os.listdir(src_dir) if os.path.isfile(os.path.join(src_dir, f))]\n",
    "\n",
    "# 複製檔案\n",
    "for f in files:\n",
    "    shutil.copy2(os.path.join(src_dir, f), os.path.join(dst_dir, f))\n",
    "\n",
    "# 印出結果\n",
    "print(f\"✅ 已成功複製 {len(files)} 個檔案至 {dst_dir}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 254,
   "id": "57722519-03e7-42db-bc61-476e33bdf0fc",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2025-10-14 21:34:00] ================== 步驟 0｜初始化與設定快照 ==================\n",
      "[2025-10-14 21:34:00] 已寫入設定：/home/jovyan/RT08/0925/sliding_win/1014_sim/config/windowing.yaml\n",
      "[2025-10-14 21:34:00] ================== 步驟 1｜掃描 raw 檔案 ==================\n",
      "[2025-10-14 21:34:00] raw 檔案數量：122\n",
      "[2025-10-14 21:34:00] ================== 步驟 2｜逐檔清理並計數（產出 cleaned 與 audit） ==================\n",
      "[2025-10-14 21:34:00] 清理開始：089271.csv\n",
      "[2025-10-14 21:34:00] 讀入筆數：32419（檔：089271.csv）\n",
      "[2025-10-14 21:34:00] 缺失清除：移除 0 筆，剩餘 32419 筆\n",
      "[2025-10-14 21:34:00] 時間處理：移除 0 筆無效時間，剩餘 32419 筆\n",
      "[2025-10-14 21:34:00] 去除重複：移除 0 筆，剩餘 32419 筆\n",
      "[2025-10-14 21:34:00] 型別/特徵檢查：移除 51 筆，剩餘 32368 筆\n",
      "[2025-10-14 21:34:00] 清理完成：初始 32419 筆 → 保留 32368 筆（移除 51 筆）\n",
      "[2025-10-14 21:34:00] 已寫入 cleaned 檔案：/home/jovyan/RT08/0925/sliding_win/1014_sim/cleaned/089271.csv（筆數 32368）\n",
      "[2025-10-14 21:34:00] 清理開始：095323.csv\n",
      "[2025-10-14 21:34:00] 讀入筆數：23791（檔：095323.csv）\n",
      "[2025-10-14 21:34:00] 缺失清除：移除 0 筆，剩餘 23791 筆\n",
      "[2025-10-14 21:34:00] 時間處理：移除 0 筆無效時間，剩餘 23791 筆\n",
      "[2025-10-14 21:34:00] 去除重複：移除 0 筆，剩餘 23791 筆\n",
      "[2025-10-14 21:34:00] 型別/特徵檢查：移除 6 筆，剩餘 23785 筆\n",
      "[2025-10-14 21:34:00] 清理完成：初始 23791 筆 → 保留 23785 筆（移除 6 筆）\n",
      "[2025-10-14 21:34:01] 已寫入 cleaned 檔案：/home/jovyan/RT08/0925/sliding_win/1014_sim/cleaned/095323.csv（筆數 23785）\n",
      "[2025-10-14 21:34:01] 清理開始：095707.csv\n",
      "[2025-10-14 21:34:01] 讀入筆數：20180（檔：095707.csv）\n",
      "[2025-10-14 21:34:01] 缺失清除：移除 0 筆，剩餘 20180 筆\n",
      "[2025-10-14 21:34:01] 時間處理：移除 0 筆無效時間，剩餘 20180 筆\n",
      "[2025-10-14 21:34:01] 去除重複：移除 0 筆，剩餘 20180 筆\n",
      "[2025-10-14 21:34:01] 型別/特徵檢查：移除 1 筆，剩餘 20179 筆\n",
      "[2025-10-14 21:34:01] 清理完成：初始 20180 筆 → 保留 20179 筆（移除 1 筆）\n",
      "[2025-10-14 21:34:01] 已寫入 cleaned 檔案：/home/jovyan/RT08/0925/sliding_win/1014_sim/cleaned/095707.csv（筆數 20179）\n",
      "[2025-10-14 21:34:01] 清理開始：114309.csv\n",
      "[2025-10-14 21:34:01] 讀入筆數：71729（檔：114309.csv）\n",
      "[2025-10-14 21:34:01] 缺失清除：移除 0 筆，剩餘 71729 筆\n",
      "[2025-10-14 21:34:01] 時間處理：移除 0 筆無效時間，剩餘 71729 筆\n",
      "[2025-10-14 21:34:01] 去除重複：移除 0 筆，剩餘 71729 筆\n",
      "[2025-10-14 21:34:01] 型別/特徵檢查：移除 6 筆，剩餘 71723 筆\n",
      "[2025-10-14 21:34:01] 清理完成：初始 71729 筆 → 保留 71723 筆（移除 6 筆）\n",
      "[2025-10-14 21:34:01] 已寫入 cleaned 檔案：/home/jovyan/RT08/0925/sliding_win/1014_sim/cleaned/114309.csv（筆數 71723）\n",
      "[2025-10-14 21:34:01] 清理開始：230933.csv\n",
      "[2025-10-14 21:34:01] 讀入筆數：30249（檔：230933.csv）\n",
      "[2025-10-14 21:34:01] 缺失清除：移除 0 筆，剩餘 30249 筆\n",
      "[2025-10-14 21:34:01] 時間處理：移除 0 筆無效時間，剩餘 30249 筆\n",
      "[2025-10-14 21:34:01] 去除重複：移除 0 筆，剩餘 30249 筆\n",
      "[2025-10-14 21:34:01] 型別/特徵檢查：移除 8 筆，剩餘 30241 筆\n",
      "[2025-10-14 21:34:01] 清理完成：初始 30249 筆 → 保留 30241 筆（移除 8 筆）\n",
      "[2025-10-14 21:34:02] 已寫入 cleaned 檔案：/home/jovyan/RT08/0925/sliding_win/1014_sim/cleaned/230933.csv（筆數 30241）\n",
      "[2025-10-14 21:34:02] 清理開始：4216007.csv\n",
      "[2025-10-14 21:34:02] 讀入筆數：1433（檔：4216007.csv）\n",
      "[2025-10-14 21:34:02] 缺失清除：移除 0 筆，剩餘 1433 筆\n",
      "[2025-10-14 21:34:02] 時間處理：移除 0 筆無效時間，剩餘 1433 筆\n",
      "[2025-10-14 21:34:02] 去除重複：移除 0 筆，剩餘 1433 筆\n",
      "[2025-10-14 21:34:02] 型別/特徵檢查：移除 906 筆，剩餘 527 筆\n",
      "[2025-10-14 21:34:02] 清理完成：初始 1433 筆 → 保留 527 筆（移除 906 筆）\n",
      "[2025-10-14 21:34:02] 已寫入 cleaned 檔案：/home/jovyan/RT08/0925/sliding_win/1014_sim/cleaned/4216007.csv（筆數 527）\n",
      "[2025-10-14 21:34:02] 清理開始：7108162.csv\n",
      "[2025-10-14 21:34:02] 讀入筆數：239（檔：7108162.csv）\n",
      "[2025-10-14 21:34:02] 缺失清除：移除 0 筆，剩餘 239 筆\n",
      "[2025-10-14 21:34:02] 時間處理：移除 0 筆無效時間，剩餘 239 筆\n",
      "[2025-10-14 21:34:02] 去除重複：移除 0 筆，剩餘 239 筆\n",
      "[2025-10-14 21:34:02] 型別/特徵檢查：移除 0 筆，剩餘 239 筆\n",
      "[2025-10-14 21:34:02] 清理完成：初始 239 筆 → 保留 239 筆（移除 0 筆）\n",
      "[2025-10-14 21:34:02] 已寫入 cleaned 檔案：/home/jovyan/RT08/0925/sliding_win/1014_sim/cleaned/7108162.csv（筆數 239）\n",
      "[2025-10-14 21:34:02] 清理開始：7408338.csv\n",
      "[2025-10-14 21:34:02] 讀入筆數：1432（檔：7408338.csv）\n",
      "[2025-10-14 21:34:02] 缺失清除：移除 0 筆，剩餘 1432 筆\n",
      "[2025-10-14 21:34:02] 時間處理：移除 0 筆無效時間，剩餘 1432 筆\n",
      "[2025-10-14 21:34:02] 去除重複：移除 0 筆，剩餘 1432 筆\n",
      "[2025-10-14 21:34:02] 型別/特徵檢查：移除 1 筆，剩餘 1431 筆\n",
      "[2025-10-14 21:34:02] 清理完成：初始 1432 筆 → 保留 1431 筆（移除 1 筆）\n",
      "[2025-10-14 21:34:02] 已寫入 cleaned 檔案：/home/jovyan/RT08/0925/sliding_win/1014_sim/cleaned/7408338.csv（筆數 1431）\n",
      "[2025-10-14 21:34:02] 清理開始：7657698.csv\n",
      "[2025-10-14 21:34:02] 讀入筆數：1413（檔：7657698.csv）\n",
      "[2025-10-14 21:34:02] 缺失清除：移除 0 筆，剩餘 1413 筆\n",
      "[2025-10-14 21:34:02] 時間處理：移除 0 筆無效時間，剩餘 1413 筆\n",
      "[2025-10-14 21:34:02] 去除重複：移除 0 筆，剩餘 1413 筆\n",
      "[2025-10-14 21:34:02] 型別/特徵檢查：移除 0 筆，剩餘 1413 筆\n",
      "[2025-10-14 21:34:02] 清理完成：初始 1413 筆 → 保留 1413 筆（移除 0 筆）\n",
      "[2025-10-14 21:34:02] 已寫入 cleaned 檔案：/home/jovyan/RT08/0925/sliding_win/1014_sim/cleaned/7657698.csv（筆數 1413）\n",
      "[2025-10-14 21:34:02] 清理開始：7721164.csv\n",
      "[2025-10-14 21:34:02] 讀入筆數：483（檔：7721164.csv）\n",
      "[2025-10-14 21:34:02] 缺失清除：移除 0 筆，剩餘 483 筆\n",
      "[2025-10-14 21:34:02] 時間處理：移除 0 筆無效時間，剩餘 483 筆\n",
      "[2025-10-14 21:34:02] 去除重複：移除 0 筆，剩餘 483 筆\n",
      "[2025-10-14 21:34:02] 型別/特徵檢查：移除 0 筆，剩餘 483 筆\n",
      "[2025-10-14 21:34:02] 清理完成：初始 483 筆 → 保留 483 筆（移除 0 筆）\n",
      "[2025-10-14 21:34:02] 已寫入 cleaned 檔案：/home/jovyan/RT08/0925/sliding_win/1014_sim/cleaned/7721164.csv（筆數 483）\n",
      "[2025-10-14 21:34:02] 清理開始：PatNo_ID_1560013303.csv\n",
      "[2025-10-14 21:34:02] 讀入筆數：2543（檔：PatNo_ID_1560013303.csv）\n",
      "[2025-10-14 21:34:02] 缺失清除：移除 0 筆，剩餘 2543 筆\n",
      "[2025-10-14 21:34:02] 時間處理：移除 0 筆無效時間，剩餘 2543 筆\n",
      "[2025-10-14 21:34:02] 去除重複：移除 0 筆，剩餘 2543 筆\n",
      "[2025-10-14 21:34:02] 型別/特徵檢查：移除 29 筆，剩餘 2514 筆\n",
      "[2025-10-14 21:34:02] 清理完成：初始 2543 筆 → 保留 2514 筆（移除 29 筆）\n",
      "[2025-10-14 21:34:02] 已寫入 cleaned 檔案：/home/jovyan/RT08/0925/sliding_win/1014_sim/cleaned/PatNo_ID_1560013303.csv（筆數 2514）\n",
      "[2025-10-14 21:34:02] 清理開始：PatNo_ID_1562733396.csv\n",
      "[2025-10-14 21:34:02] 讀入筆數：2254（檔：PatNo_ID_1562733396.csv）\n",
      "[2025-10-14 21:34:02] 缺失清除：移除 0 筆，剩餘 2254 筆\n",
      "[2025-10-14 21:34:02] 時間處理：移除 0 筆無效時間，剩餘 2254 筆\n",
      "[2025-10-14 21:34:02] 去除重複：移除 0 筆，剩餘 2254 筆\n",
      "[2025-10-14 21:34:02] 型別/特徵檢查：移除 1 筆，剩餘 2253 筆\n",
      "[2025-10-14 21:34:02] 清理完成：初始 2254 筆 → 保留 2253 筆（移除 1 筆）\n",
      "[2025-10-14 21:34:02] 已寫入 cleaned 檔案：/home/jovyan/RT08/0925/sliding_win/1014_sim/cleaned/PatNo_ID_1562733396.csv（筆數 2253）\n",
      "[2025-10-14 21:34:02] 清理開始：PatNo_ID_1563587183.csv\n",
      "[2025-10-14 21:34:02] 讀入筆數：5286（檔：PatNo_ID_1563587183.csv）\n",
      "[2025-10-14 21:34:02] 缺失清除：移除 0 筆，剩餘 5286 筆\n",
      "[2025-10-14 21:34:02] 時間處理：移除 0 筆無效時間，剩餘 5286 筆\n",
      "[2025-10-14 21:34:02] 去除重複：移除 0 筆，剩餘 5286 筆\n",
      "[2025-10-14 21:34:02] 型別/特徵檢查：移除 0 筆，剩餘 5286 筆\n",
      "[2025-10-14 21:34:02] 清理完成：初始 5286 筆 → 保留 5286 筆（移除 0 筆）\n",
      "[2025-10-14 21:34:02] 已寫入 cleaned 檔案：/home/jovyan/RT08/0925/sliding_win/1014_sim/cleaned/PatNo_ID_1563587183.csv（筆數 5286）\n",
      "[2025-10-14 21:34:02] 清理開始：PatNo_ID_1564148644.csv\n",
      "[2025-10-14 21:34:02] 讀入筆數：17287（檔：PatNo_ID_1564148644.csv）\n",
      "[2025-10-14 21:34:02] 缺失清除：移除 0 筆，剩餘 17287 筆\n",
      "[2025-10-14 21:34:02] 時間處理：移除 0 筆無效時間，剩餘 17287 筆\n",
      "[2025-10-14 21:34:02] 去除重複：移除 0 筆，剩餘 17287 筆\n",
      "[2025-10-14 21:34:02] 型別/特徵檢查：移除 2903 筆，剩餘 14384 筆\n",
      "[2025-10-14 21:34:02] 清理完成：初始 17287 筆 → 保留 14384 筆（移除 2903 筆）\n",
      "[2025-10-14 21:34:02] 已寫入 cleaned 檔案：/home/jovyan/RT08/0925/sliding_win/1014_sim/cleaned/PatNo_ID_1564148644.csv（筆數 14384）\n",
      "[2025-10-14 21:34:02] 清理開始：PatNo_ID_1565148312.csv\n",
      "[2025-10-14 21:34:02] 讀入筆數：5475（檔：PatNo_ID_1565148312.csv）\n",
      "[2025-10-14 21:34:02] 缺失清除：移除 0 筆，剩餘 5475 筆\n",
      "[2025-10-14 21:34:02] 時間處理：移除 0 筆無效時間，剩餘 5475 筆\n",
      "[2025-10-14 21:34:02] 去除重複：移除 0 筆，剩餘 5475 筆\n",
      "[2025-10-14 21:34:02] 型別/特徵檢查：移除 11 筆，剩餘 5464 筆\n",
      "[2025-10-14 21:34:02] 清理完成：初始 5475 筆 → 保留 5464 筆（移除 11 筆）\n",
      "[2025-10-14 21:34:02] 已寫入 cleaned 檔案：/home/jovyan/RT08/0925/sliding_win/1014_sim/cleaned/PatNo_ID_1565148312.csv（筆數 5464）\n",
      "[2025-10-14 21:34:02] 清理開始：PatNo_ID_1565378038.csv\n",
      "[2025-10-14 21:34:02] 讀入筆數：2561（檔：PatNo_ID_1565378038.csv）\n",
      "[2025-10-14 21:34:02] 缺失清除：移除 0 筆，剩餘 2561 筆\n",
      "[2025-10-14 21:34:02] 時間處理：移除 0 筆無效時間，剩餘 2561 筆\n",
      "[2025-10-14 21:34:02] 去除重複：移除 0 筆，剩餘 2561 筆\n",
      "[2025-10-14 21:34:02] 型別/特徵檢查：移除 0 筆，剩餘 2561 筆\n",
      "[2025-10-14 21:34:02] 清理完成：初始 2561 筆 → 保留 2561 筆（移除 0 筆）\n",
      "[2025-10-14 21:34:02] 已寫入 cleaned 檔案：/home/jovyan/RT08/0925/sliding_win/1014_sim/cleaned/PatNo_ID_1565378038.csv（筆數 2561）\n",
      "[2025-10-14 21:34:02] 清理開始：PatNo_ID_1566123680.csv\n",
      "[2025-10-14 21:34:02] 讀入筆數：42600（檔：PatNo_ID_1566123680.csv）\n",
      "[2025-10-14 21:34:02] 缺失清除：移除 0 筆，剩餘 42600 筆\n",
      "[2025-10-14 21:34:02] 時間處理：移除 0 筆無效時間，剩餘 42600 筆\n",
      "[2025-10-14 21:34:02] 去除重複：移除 0 筆，剩餘 42600 筆\n",
      "[2025-10-14 21:34:02] 型別/特徵檢查：移除 3 筆，剩餘 42597 筆\n",
      "[2025-10-14 21:34:02] 清理完成：初始 42600 筆 → 保留 42597 筆（移除 3 筆）\n",
      "[2025-10-14 21:34:02] 已寫入 cleaned 檔案：/home/jovyan/RT08/0925/sliding_win/1014_sim/cleaned/PatNo_ID_1566123680.csv（筆數 42597）\n",
      "[2025-10-14 21:34:02] 清理開始：PatNo_ID_1566252197.csv\n",
      "[2025-10-14 21:34:02] 讀入筆數：3368（檔：PatNo_ID_1566252197.csv）\n",
      "[2025-10-14 21:34:02] 缺失清除：移除 0 筆，剩餘 3368 筆\n",
      "[2025-10-14 21:34:02] 時間處理：移除 0 筆無效時間，剩餘 3368 筆\n",
      "[2025-10-14 21:34:02] 去除重複：移除 0 筆，剩餘 3368 筆\n",
      "[2025-10-14 21:34:02] 型別/特徵檢查：移除 1433 筆，剩餘 1935 筆\n",
      "[2025-10-14 21:34:02] 清理完成：初始 3368 筆 → 保留 1935 筆（移除 1433 筆）\n",
      "[2025-10-14 21:34:02] 已寫入 cleaned 檔案：/home/jovyan/RT08/0925/sliding_win/1014_sim/cleaned/PatNo_ID_1566252197.csv（筆數 1935）\n",
      "[2025-10-14 21:34:02] 清理開始：PatNo_ID_1566279967.csv\n",
      "[2025-10-14 21:34:02] 讀入筆數：1235（檔：PatNo_ID_1566279967.csv）\n",
      "[2025-10-14 21:34:02] 缺失清除：移除 0 筆，剩餘 1235 筆\n",
      "[2025-10-14 21:34:02] 時間處理：移除 0 筆無效時間，剩餘 1235 筆\n",
      "[2025-10-14 21:34:02] 去除重複：移除 0 筆，剩餘 1235 筆\n",
      "[2025-10-14 21:34:02] 型別/特徵檢查：移除 82 筆，剩餘 1153 筆\n",
      "[2025-10-14 21:34:02] 清理完成：初始 1235 筆 → 保留 1153 筆（移除 82 筆）\n",
      "[2025-10-14 21:34:02] 已寫入 cleaned 檔案：/home/jovyan/RT08/0925/sliding_win/1014_sim/cleaned/PatNo_ID_1566279967.csv（筆數 1153）\n",
      "[2025-10-14 21:34:02] 清理開始：PatNo_ID_1566671274.csv\n",
      "[2025-10-14 21:34:03] 讀入筆數：25359（檔：PatNo_ID_1566671274.csv）\n",
      "[2025-10-14 21:34:03] 缺失清除：移除 0 筆，剩餘 25359 筆\n",
      "[2025-10-14 21:34:03] 時間處理：移除 0 筆無效時間，剩餘 25359 筆\n",
      "[2025-10-14 21:34:03] 去除重複：移除 0 筆，剩餘 25359 筆\n",
      "[2025-10-14 21:34:03] 型別/特徵檢查：移除 1805 筆，剩餘 23554 筆\n",
      "[2025-10-14 21:34:03] 清理完成：初始 25359 筆 → 保留 23554 筆（移除 1805 筆）\n",
      "[2025-10-14 21:34:03] 已寫入 cleaned 檔案：/home/jovyan/RT08/0925/sliding_win/1014_sim/cleaned/PatNo_ID_1566671274.csv（筆數 23554）\n",
      "[2025-10-14 21:34:03] 清理開始：PatNo_ID_1566911879.csv\n",
      "[2025-10-14 21:34:03] 讀入筆數：53999（檔：PatNo_ID_1566911879.csv）\n",
      "[2025-10-14 21:34:03] 缺失清除：移除 0 筆，剩餘 53999 筆\n",
      "[2025-10-14 21:34:03] 時間處理：移除 0 筆無效時間，剩餘 53999 筆\n",
      "[2025-10-14 21:34:03] 去除重複：移除 0 筆，剩餘 53999 筆\n",
      "[2025-10-14 21:34:03] 型別/特徵檢查：移除 8024 筆，剩餘 45975 筆\n",
      "[2025-10-14 21:34:03] 清理完成：初始 53999 筆 → 保留 45975 筆（移除 8024 筆）\n",
      "[2025-10-14 21:34:03] 已寫入 cleaned 檔案：/home/jovyan/RT08/0925/sliding_win/1014_sim/cleaned/PatNo_ID_1566911879.csv（筆數 45975）\n",
      "[2025-10-14 21:34:03] 清理開始：PatNo_ID_1567747650.csv\n",
      "[2025-10-14 21:34:03] 讀入筆數：10900（檔：PatNo_ID_1567747650.csv）\n",
      "[2025-10-14 21:34:03] 缺失清除：移除 0 筆，剩餘 10900 筆\n",
      "[2025-10-14 21:34:03] 時間處理：移除 0 筆無效時間，剩餘 10900 筆\n",
      "[2025-10-14 21:34:03] 去除重複：移除 0 筆，剩餘 10900 筆\n",
      "[2025-10-14 21:34:03] 型別/特徵檢查：移除 2 筆，剩餘 10898 筆\n",
      "[2025-10-14 21:34:03] 清理完成：初始 10900 筆 → 保留 10898 筆（移除 2 筆）\n",
      "[2025-10-14 21:34:03] 已寫入 cleaned 檔案：/home/jovyan/RT08/0925/sliding_win/1014_sim/cleaned/PatNo_ID_1567747650.csv（筆數 10898）\n",
      "[2025-10-14 21:34:03] 清理開始：PatNo_ID_1567804800.csv\n",
      "[2025-10-14 21:34:03] 讀入筆數：20577（檔：PatNo_ID_1567804800.csv）\n",
      "[2025-10-14 21:34:03] 缺失清除：移除 0 筆，剩餘 20577 筆\n",
      "[2025-10-14 21:34:03] 時間處理：移除 0 筆無效時間，剩餘 20577 筆\n",
      "[2025-10-14 21:34:03] 去除重複：移除 0 筆，剩餘 20577 筆\n",
      "[2025-10-14 21:34:03] 型別/特徵檢查：移除 188 筆，剩餘 20389 筆\n",
      "[2025-10-14 21:34:03] 清理完成：初始 20577 筆 → 保留 20389 筆（移除 188 筆）\n",
      "[2025-10-14 21:34:03] 已寫入 cleaned 檔案：/home/jovyan/RT08/0925/sliding_win/1014_sim/cleaned/PatNo_ID_1567804800.csv（筆數 20389）\n",
      "[2025-10-14 21:34:03] 清理開始：PatNo_ID_1567832735.csv\n",
      "[2025-10-14 21:34:03] 讀入筆數：36575（檔：PatNo_ID_1567832735.csv）\n",
      "[2025-10-14 21:34:03] 缺失清除：移除 0 筆，剩餘 36575 筆\n",
      "[2025-10-14 21:34:03] 時間處理：移除 0 筆無效時間，剩餘 36575 筆\n",
      "[2025-10-14 21:34:03] 去除重複：移除 0 筆，剩餘 36575 筆\n",
      "[2025-10-14 21:34:03] 型別/特徵檢查：移除 6 筆，剩餘 36569 筆\n",
      "[2025-10-14 21:34:03] 清理完成：初始 36575 筆 → 保留 36569 筆（移除 6 筆）\n",
      "[2025-10-14 21:34:04] 已寫入 cleaned 檔案：/home/jovyan/RT08/0925/sliding_win/1014_sim/cleaned/PatNo_ID_1567832735.csv（筆數 36569）\n",
      "[2025-10-14 21:34:04] 清理開始：PatNo_ID_1568039398.csv\n",
      "[2025-10-14 21:34:04] 讀入筆數：34519（檔：PatNo_ID_1568039398.csv）\n",
      "[2025-10-14 21:34:04] 缺失清除：移除 0 筆，剩餘 34519 筆\n",
      "[2025-10-14 21:34:04] 時間處理：移除 0 筆無效時間，剩餘 34519 筆\n",
      "[2025-10-14 21:34:04] 去除重複：移除 0 筆，剩餘 34519 筆\n",
      "[2025-10-14 21:34:04] 型別/特徵檢查：移除 277 筆，剩餘 34242 筆\n",
      "[2025-10-14 21:34:04] 清理完成：初始 34519 筆 → 保留 34242 筆（移除 277 筆）\n",
      "[2025-10-14 21:34:04] 已寫入 cleaned 檔案：/home/jovyan/RT08/0925/sliding_win/1014_sim/cleaned/PatNo_ID_1568039398.csv（筆數 34242）\n",
      "[2025-10-14 21:34:04] 清理開始：PatNo_ID_1568574099.csv\n",
      "[2025-10-14 21:34:04] 讀入筆數：13945（檔：PatNo_ID_1568574099.csv）\n",
      "[2025-10-14 21:34:04] 缺失清除：移除 0 筆，剩餘 13945 筆\n",
      "[2025-10-14 21:34:04] 時間處理：移除 0 筆無效時間，剩餘 13945 筆\n",
      "[2025-10-14 21:34:04] 去除重複：移除 0 筆，剩餘 13945 筆\n",
      "[2025-10-14 21:34:04] 型別/特徵檢查：移除 82 筆，剩餘 13863 筆\n",
      "[2025-10-14 21:34:04] 清理完成：初始 13945 筆 → 保留 13863 筆（移除 82 筆）\n",
      "[2025-10-14 21:34:04] 已寫入 cleaned 檔案：/home/jovyan/RT08/0925/sliding_win/1014_sim/cleaned/PatNo_ID_1568574099.csv（筆數 13863）\n",
      "[2025-10-14 21:34:04] 清理開始：PatNo_ID_1568813269.csv\n",
      "[2025-10-14 21:34:04] 讀入筆數：4867（檔：PatNo_ID_1568813269.csv）\n",
      "[2025-10-14 21:34:04] 缺失清除：移除 0 筆，剩餘 4867 筆\n",
      "[2025-10-14 21:34:04] 時間處理：移除 0 筆無效時間，剩餘 4867 筆\n",
      "[2025-10-14 21:34:04] 去除重複：移除 0 筆，剩餘 4867 筆\n",
      "[2025-10-14 21:34:04] 型別/特徵檢查：移除 0 筆，剩餘 4867 筆\n",
      "[2025-10-14 21:34:04] 清理完成：初始 4867 筆 → 保留 4867 筆（移除 0 筆）\n",
      "[2025-10-14 21:34:04] 已寫入 cleaned 檔案：/home/jovyan/RT08/0925/sliding_win/1014_sim/cleaned/PatNo_ID_1568813269.csv（筆數 4867）\n",
      "[2025-10-14 21:34:04] 清理開始：PatNo_ID_1568952422.csv\n",
      "[2025-10-14 21:34:04] 讀入筆數：1184（檔：PatNo_ID_1568952422.csv）\n",
      "[2025-10-14 21:34:04] 缺失清除：移除 0 筆，剩餘 1184 筆\n",
      "[2025-10-14 21:34:04] 時間處理：移除 0 筆無效時間，剩餘 1184 筆\n",
      "[2025-10-14 21:34:04] 去除重複：移除 0 筆，剩餘 1184 筆\n",
      "[2025-10-14 21:34:04] 型別/特徵檢查：移除 0 筆，剩餘 1184 筆\n",
      "[2025-10-14 21:34:04] 清理完成：初始 1184 筆 → 保留 1184 筆（移除 0 筆）\n",
      "[2025-10-14 21:34:04] 已寫入 cleaned 檔案：/home/jovyan/RT08/0925/sliding_win/1014_sim/cleaned/PatNo_ID_1568952422.csv（筆數 1184）\n",
      "[2025-10-14 21:34:04] 清理開始：PatNo_ID_1569083701.csv\n",
      "[2025-10-14 21:34:04] 讀入筆數：3328（檔：PatNo_ID_1569083701.csv）\n",
      "[2025-10-14 21:34:04] 缺失清除：移除 0 筆，剩餘 3328 筆\n",
      "[2025-10-14 21:34:04] 時間處理：移除 0 筆無效時間，剩餘 3328 筆\n",
      "[2025-10-14 21:34:04] 去除重複：移除 0 筆，剩餘 3328 筆\n",
      "[2025-10-14 21:34:04] 型別/特徵檢查：移除 2 筆，剩餘 3326 筆\n",
      "[2025-10-14 21:34:04] 清理完成：初始 3328 筆 → 保留 3326 筆（移除 2 筆）\n",
      "[2025-10-14 21:34:04] 已寫入 cleaned 檔案：/home/jovyan/RT08/0925/sliding_win/1014_sim/cleaned/PatNo_ID_1569083701.csv（筆數 3326）\n",
      "[2025-10-14 21:34:04] 清理開始：PatNo_ID_1569944983.csv\n",
      "[2025-10-14 21:34:04] 讀入筆數：7649（檔：PatNo_ID_1569944983.csv）\n",
      "[2025-10-14 21:34:04] 缺失清除：移除 0 筆，剩餘 7649 筆\n",
      "[2025-10-14 21:34:04] 時間處理：移除 0 筆無效時間，剩餘 7649 筆\n",
      "[2025-10-14 21:34:04] 去除重複：移除 0 筆，剩餘 7649 筆\n",
      "[2025-10-14 21:34:04] 型別/特徵檢查：移除 2 筆，剩餘 7647 筆\n",
      "[2025-10-14 21:34:04] 清理完成：初始 7649 筆 → 保留 7647 筆（移除 2 筆）\n",
      "[2025-10-14 21:34:04] 已寫入 cleaned 檔案：/home/jovyan/RT08/0925/sliding_win/1014_sim/cleaned/PatNo_ID_1569944983.csv（筆數 7647）\n",
      "[2025-10-14 21:34:04] 清理開始：PatNo_ID_1570089466.csv\n",
      "[2025-10-14 21:34:04] 讀入筆數：41338（檔：PatNo_ID_1570089466.csv）\n",
      "[2025-10-14 21:34:04] 缺失清除：移除 0 筆，剩餘 41338 筆\n",
      "[2025-10-14 21:34:04] 時間處理：移除 0 筆無效時間，剩餘 41338 筆\n",
      "[2025-10-14 21:34:04] 去除重複：移除 0 筆，剩餘 41338 筆\n",
      "[2025-10-14 21:34:04] 型別/特徵檢查：移除 749 筆，剩餘 40589 筆\n",
      "[2025-10-14 21:34:04] 清理完成：初始 41338 筆 → 保留 40589 筆（移除 749 筆）\n",
      "[2025-10-14 21:34:05] 已寫入 cleaned 檔案：/home/jovyan/RT08/0925/sliding_win/1014_sim/cleaned/PatNo_ID_1570089466.csv（筆數 40589）\n",
      "[2025-10-14 21:34:05] 清理開始：PatNo_ID_1570242703.csv\n",
      "[2025-10-14 21:34:05] 讀入筆數：10644（檔：PatNo_ID_1570242703.csv）\n",
      "[2025-10-14 21:34:05] 缺失清除：移除 0 筆，剩餘 10644 筆\n",
      "[2025-10-14 21:34:05] 時間處理：移除 0 筆無效時間，剩餘 10644 筆\n",
      "[2025-10-14 21:34:05] 去除重複：移除 0 筆，剩餘 10644 筆\n",
      "[2025-10-14 21:34:05] 型別/特徵檢查：移除 88 筆，剩餘 10556 筆\n",
      "[2025-10-14 21:34:05] 清理完成：初始 10644 筆 → 保留 10556 筆（移除 88 筆）\n",
      "[2025-10-14 21:34:05] 已寫入 cleaned 檔案：/home/jovyan/RT08/0925/sliding_win/1014_sim/cleaned/PatNo_ID_1570242703.csv（筆數 10556）\n",
      "[2025-10-14 21:34:05] 清理開始：PatNo_ID_1570273244.csv\n",
      "[2025-10-14 21:34:05] 讀入筆數：9725（檔：PatNo_ID_1570273244.csv）\n",
      "[2025-10-14 21:34:05] 缺失清除：移除 0 筆，剩餘 9725 筆\n",
      "[2025-10-14 21:34:05] 時間處理：移除 0 筆無效時間，剩餘 9725 筆\n",
      "[2025-10-14 21:34:05] 去除重複：移除 0 筆，剩餘 9725 筆\n",
      "[2025-10-14 21:34:05] 型別/特徵檢查：移除 18 筆，剩餘 9707 筆\n",
      "[2025-10-14 21:34:05] 清理完成：初始 9725 筆 → 保留 9707 筆（移除 18 筆）\n",
      "[2025-10-14 21:34:05] 已寫入 cleaned 檔案：/home/jovyan/RT08/0925/sliding_win/1014_sim/cleaned/PatNo_ID_1570273244.csv（筆數 9707）\n",
      "[2025-10-14 21:34:05] 清理開始：PatNo_ID_1570642083.csv\n",
      "[2025-10-14 21:34:05] 讀入筆數：19731（檔：PatNo_ID_1570642083.csv）\n",
      "[2025-10-14 21:34:05] 缺失清除：移除 0 筆，剩餘 19731 筆\n",
      "[2025-10-14 21:34:05] 時間處理：移除 0 筆無效時間，剩餘 19731 筆\n",
      "[2025-10-14 21:34:05] 去除重複：移除 0 筆，剩餘 19731 筆\n",
      "[2025-10-14 21:34:05] 型別/特徵檢查：移除 0 筆，剩餘 19731 筆\n",
      "[2025-10-14 21:34:05] 清理完成：初始 19731 筆 → 保留 19731 筆（移除 0 筆）\n",
      "[2025-10-14 21:34:05] 已寫入 cleaned 檔案：/home/jovyan/RT08/0925/sliding_win/1014_sim/cleaned/PatNo_ID_1570642083.csv（筆數 19731）\n",
      "[2025-10-14 21:34:05] 清理開始：PatNo_ID_1571945701.csv\n",
      "[2025-10-14 21:34:05] 讀入筆數：15731（檔：PatNo_ID_1571945701.csv）\n",
      "[2025-10-14 21:34:05] 缺失清除：移除 0 筆，剩餘 15731 筆\n",
      "[2025-10-14 21:34:05] 時間處理：移除 0 筆無效時間，剩餘 15731 筆\n",
      "[2025-10-14 21:34:05] 去除重複：移除 0 筆，剩餘 15731 筆\n",
      "[2025-10-14 21:34:05] 型別/特徵檢查：移除 2 筆，剩餘 15729 筆\n",
      "[2025-10-14 21:34:05] 清理完成：初始 15731 筆 → 保留 15729 筆（移除 2 筆）\n",
      "[2025-10-14 21:34:05] 已寫入 cleaned 檔案：/home/jovyan/RT08/0925/sliding_win/1014_sim/cleaned/PatNo_ID_1571945701.csv（筆數 15729）\n",
      "[2025-10-14 21:34:05] 清理開始：PatNo_ID_1572481361.csv\n",
      "[2025-10-14 21:34:05] 讀入筆數：34539（檔：PatNo_ID_1572481361.csv）\n",
      "[2025-10-14 21:34:05] 缺失清除：移除 0 筆，剩餘 34539 筆\n",
      "[2025-10-14 21:34:05] 時間處理：移除 0 筆無效時間，剩餘 34539 筆\n",
      "[2025-10-14 21:34:05] 去除重複：移除 0 筆，剩餘 34539 筆\n",
      "[2025-10-14 21:34:05] 型別/特徵檢查：移除 24 筆，剩餘 34515 筆\n",
      "[2025-10-14 21:34:05] 清理完成：初始 34539 筆 → 保留 34515 筆（移除 24 筆）\n",
      "[2025-10-14 21:34:06] 已寫入 cleaned 檔案：/home/jovyan/RT08/0925/sliding_win/1014_sim/cleaned/PatNo_ID_1572481361.csv（筆數 34515）\n",
      "[2025-10-14 21:34:06] 清理開始：PatNo_ID_1572562839.csv\n",
      "[2025-10-14 21:34:06] 讀入筆數：20963（檔：PatNo_ID_1572562839.csv）\n",
      "[2025-10-14 21:34:06] 缺失清除：移除 0 筆，剩餘 20963 筆\n",
      "[2025-10-14 21:34:06] 時間處理：移除 0 筆無效時間，剩餘 20963 筆\n",
      "[2025-10-14 21:34:06] 去除重複：移除 0 筆，剩餘 20963 筆\n",
      "[2025-10-14 21:34:06] 型別/特徵檢查：移除 43 筆，剩餘 20920 筆\n",
      "[2025-10-14 21:34:06] 清理完成：初始 20963 筆 → 保留 20920 筆（移除 43 筆）\n",
      "[2025-10-14 21:34:06] 已寫入 cleaned 檔案：/home/jovyan/RT08/0925/sliding_win/1014_sim/cleaned/PatNo_ID_1572562839.csv（筆數 20920）\n",
      "[2025-10-14 21:34:06] 清理開始：PatNo_ID_1572831765.csv\n",
      "[2025-10-14 21:34:06] 讀入筆數：2696（檔：PatNo_ID_1572831765.csv）\n",
      "[2025-10-14 21:34:06] 缺失清除：移除 0 筆，剩餘 2696 筆\n",
      "[2025-10-14 21:34:06] 時間處理：移除 0 筆無效時間，剩餘 2696 筆\n",
      "[2025-10-14 21:34:06] 去除重複：移除 0 筆，剩餘 2696 筆\n",
      "[2025-10-14 21:34:06] 型別/特徵檢查：移除 0 筆，剩餘 2696 筆\n",
      "[2025-10-14 21:34:06] 清理完成：初始 2696 筆 → 保留 2696 筆（移除 0 筆）\n",
      "[2025-10-14 21:34:06] 已寫入 cleaned 檔案：/home/jovyan/RT08/0925/sliding_win/1014_sim/cleaned/PatNo_ID_1572831765.csv（筆數 2696）\n",
      "[2025-10-14 21:34:06] 清理開始：PatNo_ID_1572976822.csv\n",
      "[2025-10-14 21:34:06] 讀入筆數：6764（檔：PatNo_ID_1572976822.csv）\n",
      "[2025-10-14 21:34:06] 缺失清除：移除 0 筆，剩餘 6764 筆\n",
      "[2025-10-14 21:34:06] 時間處理：移除 0 筆無效時間，剩餘 6764 筆\n",
      "[2025-10-14 21:34:06] 去除重複：移除 0 筆，剩餘 6764 筆\n",
      "[2025-10-14 21:34:06] 型別/特徵檢查：移除 140 筆，剩餘 6624 筆\n",
      "[2025-10-14 21:34:06] 清理完成：初始 6764 筆 → 保留 6624 筆（移除 140 筆）\n",
      "[2025-10-14 21:34:06] 已寫入 cleaned 檔案：/home/jovyan/RT08/0925/sliding_win/1014_sim/cleaned/PatNo_ID_1572976822.csv（筆數 6624）\n",
      "[2025-10-14 21:34:06] 清理開始：PatNo_ID_1573063188.csv\n",
      "[2025-10-14 21:34:06] 讀入筆數：5080（檔：PatNo_ID_1573063188.csv）\n",
      "[2025-10-14 21:34:06] 缺失清除：移除 0 筆，剩餘 5080 筆\n",
      "[2025-10-14 21:34:06] 時間處理：移除 0 筆無效時間，剩餘 5080 筆\n",
      "[2025-10-14 21:34:06] 去除重複：移除 0 筆，剩餘 5080 筆\n",
      "[2025-10-14 21:34:06] 型別/特徵檢查：移除 55 筆，剩餘 5025 筆\n",
      "[2025-10-14 21:34:06] 清理完成：初始 5080 筆 → 保留 5025 筆（移除 55 筆）\n",
      "[2025-10-14 21:34:06] 已寫入 cleaned 檔案：/home/jovyan/RT08/0925/sliding_win/1014_sim/cleaned/PatNo_ID_1573063188.csv（筆數 5025）\n",
      "[2025-10-14 21:34:06] 清理開始：PatNo_ID_1573249295.csv\n",
      "[2025-10-14 21:34:06] 讀入筆數：7151（檔：PatNo_ID_1573249295.csv）\n",
      "[2025-10-14 21:34:06] 缺失清除：移除 0 筆，剩餘 7151 筆\n",
      "[2025-10-14 21:34:06] 時間處理：移除 0 筆無效時間，剩餘 7151 筆\n",
      "[2025-10-14 21:34:06] 去除重複：移除 0 筆，剩餘 7151 筆\n",
      "[2025-10-14 21:34:06] 型別/特徵檢查：移除 0 筆，剩餘 7151 筆\n",
      "[2025-10-14 21:34:06] 清理完成：初始 7151 筆 → 保留 7151 筆（移除 0 筆）\n",
      "[2025-10-14 21:34:06] 已寫入 cleaned 檔案：/home/jovyan/RT08/0925/sliding_win/1014_sim/cleaned/PatNo_ID_1573249295.csv（筆數 7151）\n",
      "[2025-10-14 21:34:06] 清理開始：PatNo_ID_1573964540.csv\n",
      "[2025-10-14 21:34:06] 讀入筆數：4394（檔：PatNo_ID_1573964540.csv）\n",
      "[2025-10-14 21:34:06] 缺失清除：移除 0 筆，剩餘 4394 筆\n",
      "[2025-10-14 21:34:06] 時間處理：移除 0 筆無效時間，剩餘 4394 筆\n",
      "[2025-10-14 21:34:06] 去除重複：移除 0 筆，剩餘 4394 筆\n",
      "[2025-10-14 21:34:06] 型別/特徵檢查：移除 47 筆，剩餘 4347 筆\n",
      "[2025-10-14 21:34:06] 清理完成：初始 4394 筆 → 保留 4347 筆（移除 47 筆）\n",
      "[2025-10-14 21:34:06] 已寫入 cleaned 檔案：/home/jovyan/RT08/0925/sliding_win/1014_sim/cleaned/PatNo_ID_1573964540.csv（筆數 4347）\n",
      "[2025-10-14 21:34:06] 清理開始：PatNo_ID_1574148494.csv\n",
      "[2025-10-14 21:34:06] 讀入筆數：47154（檔：PatNo_ID_1574148494.csv）\n",
      "[2025-10-14 21:34:06] 缺失清除：移除 0 筆，剩餘 47154 筆\n",
      "[2025-10-14 21:34:06] 時間處理：移除 0 筆無效時間，剩餘 47154 筆\n",
      "[2025-10-14 21:34:06] 去除重複：移除 0 筆，剩餘 47154 筆\n",
      "[2025-10-14 21:34:06] 型別/特徵檢查：移除 137 筆，剩餘 47017 筆\n",
      "[2025-10-14 21:34:06] 清理完成：初始 47154 筆 → 保留 47017 筆（移除 137 筆）\n",
      "[2025-10-14 21:34:06] 已寫入 cleaned 檔案：/home/jovyan/RT08/0925/sliding_win/1014_sim/cleaned/PatNo_ID_1574148494.csv（筆數 47017）\n",
      "[2025-10-14 21:34:06] 清理開始：PatNo_ID_1574270349.csv\n",
      "[2025-10-14 21:34:06] 讀入筆數：6533（檔：PatNo_ID_1574270349.csv）\n",
      "[2025-10-14 21:34:06] 缺失清除：移除 0 筆，剩餘 6533 筆\n",
      "[2025-10-14 21:34:06] 時間處理：移除 0 筆無效時間，剩餘 6533 筆\n",
      "[2025-10-14 21:34:06] 去除重複：移除 0 筆，剩餘 6533 筆\n",
      "[2025-10-14 21:34:06] 型別/特徵檢查：移除 18 筆，剩餘 6515 筆\n",
      "[2025-10-14 21:34:06] 清理完成：初始 6533 筆 → 保留 6515 筆（移除 18 筆）\n",
      "[2025-10-14 21:34:07] 已寫入 cleaned 檔案：/home/jovyan/RT08/0925/sliding_win/1014_sim/cleaned/PatNo_ID_1574270349.csv（筆數 6515）\n",
      "[2025-10-14 21:34:07] 清理開始：PatNo_ID_1574528808.csv\n",
      "[2025-10-14 21:34:07] 讀入筆數：14812（檔：PatNo_ID_1574528808.csv）\n",
      "[2025-10-14 21:34:07] 缺失清除：移除 0 筆，剩餘 14812 筆\n",
      "[2025-10-14 21:34:07] 時間處理：移除 0 筆無效時間，剩餘 14812 筆\n",
      "[2025-10-14 21:34:07] 去除重複：移除 0 筆，剩餘 14812 筆\n",
      "[2025-10-14 21:34:07] 型別/特徵檢查：移除 332 筆，剩餘 14480 筆\n",
      "[2025-10-14 21:34:07] 清理完成：初始 14812 筆 → 保留 14480 筆（移除 332 筆）\n",
      "[2025-10-14 21:34:07] 已寫入 cleaned 檔案：/home/jovyan/RT08/0925/sliding_win/1014_sim/cleaned/PatNo_ID_1574528808.csv（筆數 14480）\n",
      "[2025-10-14 21:34:07] 清理開始：PatNo_ID_1574831525.csv\n",
      "[2025-10-14 21:34:07] 讀入筆數：520（檔：PatNo_ID_1574831525.csv）\n",
      "[2025-10-14 21:34:07] 缺失清除：移除 0 筆，剩餘 520 筆\n",
      "[2025-10-14 21:34:07] 時間處理：移除 0 筆無效時間，剩餘 520 筆\n",
      "[2025-10-14 21:34:07] 去除重複：移除 0 筆，剩餘 520 筆\n",
      "[2025-10-14 21:34:07] 型別/特徵檢查：移除 5 筆，剩餘 515 筆\n",
      "[2025-10-14 21:34:07] 清理完成：初始 520 筆 → 保留 515 筆（移除 5 筆）\n",
      "[2025-10-14 21:34:07] 已寫入 cleaned 檔案：/home/jovyan/RT08/0925/sliding_win/1014_sim/cleaned/PatNo_ID_1574831525.csv（筆數 515）\n",
      "[2025-10-14 21:34:07] 清理開始：PatNo_ID_1574987447.csv\n",
      "[2025-10-14 21:34:07] 讀入筆數：19842（檔：PatNo_ID_1574987447.csv）\n",
      "[2025-10-14 21:34:07] 缺失清除：移除 0 筆，剩餘 19842 筆\n",
      "[2025-10-14 21:34:07] 時間處理：移除 0 筆無效時間，剩餘 19842 筆\n",
      "[2025-10-14 21:34:07] 去除重複：移除 0 筆，剩餘 19842 筆\n",
      "[2025-10-14 21:34:07] 型別/特徵檢查：移除 4 筆，剩餘 19838 筆\n",
      "[2025-10-14 21:34:07] 清理完成：初始 19842 筆 → 保留 19838 筆（移除 4 筆）\n",
      "[2025-10-14 21:34:07] 已寫入 cleaned 檔案：/home/jovyan/RT08/0925/sliding_win/1014_sim/cleaned/PatNo_ID_1574987447.csv（筆數 19838）\n",
      "[2025-10-14 21:34:07] 清理開始：PatNo_ID_1575060177.csv\n",
      "[2025-10-14 21:34:07] 讀入筆數：5290（檔：PatNo_ID_1575060177.csv）\n",
      "[2025-10-14 21:34:07] 缺失清除：移除 0 筆，剩餘 5290 筆\n",
      "[2025-10-14 21:34:07] 時間處理：移除 0 筆無效時間，剩餘 5290 筆\n",
      "[2025-10-14 21:34:07] 去除重複：移除 0 筆，剩餘 5290 筆\n",
      "[2025-10-14 21:34:07] 型別/特徵檢查：移除 0 筆，剩餘 5290 筆\n",
      "[2025-10-14 21:34:07] 清理完成：初始 5290 筆 → 保留 5290 筆（移除 0 筆）\n",
      "[2025-10-14 21:34:07] 已寫入 cleaned 檔案：/home/jovyan/RT08/0925/sliding_win/1014_sim/cleaned/PatNo_ID_1575060177.csv（筆數 5290）\n",
      "[2025-10-14 21:34:07] 清理開始：PatNo_ID_1575256902.csv\n",
      "[2025-10-14 21:34:07] 讀入筆數：9587（檔：PatNo_ID_1575256902.csv）\n",
      "[2025-10-14 21:34:07] 缺失清除：移除 0 筆，剩餘 9587 筆\n",
      "[2025-10-14 21:34:07] 時間處理：移除 0 筆無效時間，剩餘 9587 筆\n",
      "[2025-10-14 21:34:07] 去除重複：移除 0 筆，剩餘 9587 筆\n",
      "[2025-10-14 21:34:07] 型別/特徵檢查：移除 95 筆，剩餘 9492 筆\n",
      "[2025-10-14 21:34:07] 清理完成：初始 9587 筆 → 保留 9492 筆（移除 95 筆）\n",
      "[2025-10-14 21:34:07] 已寫入 cleaned 檔案：/home/jovyan/RT08/0925/sliding_win/1014_sim/cleaned/PatNo_ID_1575256902.csv（筆數 9492）\n",
      "[2025-10-14 21:34:07] 清理開始：PatNo_ID_1575445051.csv\n",
      "[2025-10-14 21:34:07] 讀入筆數：1800（檔：PatNo_ID_1575445051.csv）\n",
      "[2025-10-14 21:34:07] 缺失清除：移除 0 筆，剩餘 1800 筆\n",
      "[2025-10-14 21:34:07] 時間處理：移除 0 筆無效時間，剩餘 1800 筆\n",
      "[2025-10-14 21:34:07] 去除重複：移除 0 筆，剩餘 1800 筆\n",
      "[2025-10-14 21:34:07] 型別/特徵檢查：移除 15 筆，剩餘 1785 筆\n",
      "[2025-10-14 21:34:07] 清理完成：初始 1800 筆 → 保留 1785 筆（移除 15 筆）\n",
      "[2025-10-14 21:34:07] 已寫入 cleaned 檔案：/home/jovyan/RT08/0925/sliding_win/1014_sim/cleaned/PatNo_ID_1575445051.csv（筆數 1785）\n",
      "[2025-10-14 21:34:07] 清理開始：PatNo_ID_1575502382.csv\n",
      "[2025-10-14 21:34:07] 讀入筆數：6604（檔：PatNo_ID_1575502382.csv）\n",
      "[2025-10-14 21:34:07] 缺失清除：移除 0 筆，剩餘 6604 筆\n",
      "[2025-10-14 21:34:07] 時間處理：移除 0 筆無效時間，剩餘 6604 筆\n",
      "[2025-10-14 21:34:07] 去除重複：移除 0 筆，剩餘 6604 筆\n",
      "[2025-10-14 21:34:07] 型別/特徵檢查：移除 115 筆，剩餘 6489 筆\n",
      "[2025-10-14 21:34:07] 清理完成：初始 6604 筆 → 保留 6489 筆（移除 115 筆）\n",
      "[2025-10-14 21:34:07] 已寫入 cleaned 檔案：/home/jovyan/RT08/0925/sliding_win/1014_sim/cleaned/PatNo_ID_1575502382.csv（筆數 6489）\n",
      "[2025-10-14 21:34:07] 清理開始：PatNo_ID_1575975485.csv\n",
      "[2025-10-14 21:34:07] 讀入筆數：16459（檔：PatNo_ID_1575975485.csv）\n",
      "[2025-10-14 21:34:07] 缺失清除：移除 0 筆，剩餘 16459 筆\n",
      "[2025-10-14 21:34:07] 時間處理：移除 0 筆無效時間，剩餘 16459 筆\n",
      "[2025-10-14 21:34:07] 去除重複：移除 0 筆，剩餘 16459 筆\n",
      "[2025-10-14 21:34:07] 型別/特徵檢查：移除 1 筆，剩餘 16458 筆\n",
      "[2025-10-14 21:34:07] 清理完成：初始 16459 筆 → 保留 16458 筆（移除 1 筆）\n",
      "[2025-10-14 21:34:07] 已寫入 cleaned 檔案：/home/jovyan/RT08/0925/sliding_win/1014_sim/cleaned/PatNo_ID_1575975485.csv（筆數 16458）\n",
      "[2025-10-14 21:34:07] 清理開始：PatNo_ID_1576115572.csv\n",
      "[2025-10-14 21:34:07] 讀入筆數：18715（檔：PatNo_ID_1576115572.csv）\n",
      "[2025-10-14 21:34:07] 缺失清除：移除 0 筆，剩餘 18715 筆\n",
      "[2025-10-14 21:34:07] 時間處理：移除 0 筆無效時間，剩餘 18715 筆\n",
      "[2025-10-14 21:34:07] 去除重複：移除 0 筆，剩餘 18715 筆\n",
      "[2025-10-14 21:34:07] 型別/特徵檢查：移除 698 筆，剩餘 18017 筆\n",
      "[2025-10-14 21:34:07] 清理完成：初始 18715 筆 → 保留 18017 筆（移除 698 筆）\n",
      "[2025-10-14 21:34:07] 已寫入 cleaned 檔案：/home/jovyan/RT08/0925/sliding_win/1014_sim/cleaned/PatNo_ID_1576115572.csv（筆數 18017）\n",
      "[2025-10-14 21:34:07] 清理開始：PatNo_ID_1576116479.csv\n",
      "[2025-10-14 21:34:07] 讀入筆數：1263（檔：PatNo_ID_1576116479.csv）\n",
      "[2025-10-14 21:34:07] 缺失清除：移除 0 筆，剩餘 1263 筆\n",
      "[2025-10-14 21:34:07] 時間處理：移除 0 筆無效時間，剩餘 1263 筆\n",
      "[2025-10-14 21:34:07] 去除重複：移除 0 筆，剩餘 1263 筆\n",
      "[2025-10-14 21:34:07] 型別/特徵檢查：移除 6 筆，剩餘 1257 筆\n",
      "[2025-10-14 21:34:07] 清理完成：初始 1263 筆 → 保留 1257 筆（移除 6 筆）\n",
      "[2025-10-14 21:34:07] 已寫入 cleaned 檔案：/home/jovyan/RT08/0925/sliding_win/1014_sim/cleaned/PatNo_ID_1576116479.csv（筆數 1257）\n",
      "[2025-10-14 21:34:07] 清理開始：PatNo_ID_1576301569.csv\n",
      "[2025-10-14 21:34:07] 讀入筆數：3207（檔：PatNo_ID_1576301569.csv）\n",
      "[2025-10-14 21:34:07] 缺失清除：移除 0 筆，剩餘 3207 筆\n",
      "[2025-10-14 21:34:07] 時間處理：移除 0 筆無效時間，剩餘 3207 筆\n",
      "[2025-10-14 21:34:07] 去除重複：移除 0 筆，剩餘 3207 筆\n",
      "[2025-10-14 21:34:07] 型別/特徵檢查：移除 0 筆，剩餘 3207 筆\n",
      "[2025-10-14 21:34:07] 清理完成：初始 3207 筆 → 保留 3207 筆（移除 0 筆）\n",
      "[2025-10-14 21:34:08] 已寫入 cleaned 檔案：/home/jovyan/RT08/0925/sliding_win/1014_sim/cleaned/PatNo_ID_1576301569.csv（筆數 3207）\n",
      "[2025-10-14 21:34:08] 清理開始：PatNo_ID_1576964560.csv\n",
      "[2025-10-14 21:34:08] 讀入筆數：24188（檔：PatNo_ID_1576964560.csv）\n",
      "[2025-10-14 21:34:08] 缺失清除：移除 0 筆，剩餘 24188 筆\n",
      "[2025-10-14 21:34:08] 時間處理：移除 0 筆無效時間，剩餘 24188 筆\n",
      "[2025-10-14 21:34:08] 去除重複：移除 0 筆，剩餘 24188 筆\n",
      "[2025-10-14 21:34:08] 型別/特徵檢查：移除 0 筆，剩餘 24188 筆\n",
      "[2025-10-14 21:34:08] 清理完成：初始 24188 筆 → 保留 24188 筆（移除 0 筆）\n",
      "[2025-10-14 21:34:08] 已寫入 cleaned 檔案：/home/jovyan/RT08/0925/sliding_win/1014_sim/cleaned/PatNo_ID_1576964560.csv（筆數 24188）\n",
      "[2025-10-14 21:34:08] 清理開始：PatNo_ID_1577042911.csv\n",
      "[2025-10-14 21:34:08] 讀入筆數：38357（檔：PatNo_ID_1577042911.csv）\n",
      "[2025-10-14 21:34:08] 缺失清除：移除 0 筆，剩餘 38357 筆\n",
      "[2025-10-14 21:34:08] 時間處理：移除 0 筆無效時間，剩餘 38357 筆\n",
      "[2025-10-14 21:34:08] 去除重複：移除 0 筆，剩餘 38357 筆\n",
      "[2025-10-14 21:34:08] 型別/特徵檢查：移除 2733 筆，剩餘 35624 筆\n",
      "[2025-10-14 21:34:08] 清理完成：初始 38357 筆 → 保留 35624 筆（移除 2733 筆）\n",
      "[2025-10-14 21:34:08] 已寫入 cleaned 檔案：/home/jovyan/RT08/0925/sliding_win/1014_sim/cleaned/PatNo_ID_1577042911.csv（筆數 35624）\n",
      "[2025-10-14 21:34:08] 清理開始：PatNo_ID_1577487284.csv\n",
      "[2025-10-14 21:34:08] 讀入筆數：2345（檔：PatNo_ID_1577487284.csv）\n",
      "[2025-10-14 21:34:08] 缺失清除：移除 0 筆，剩餘 2345 筆\n",
      "[2025-10-14 21:34:08] 時間處理：移除 0 筆無效時間，剩餘 2345 筆\n",
      "[2025-10-14 21:34:08] 去除重複：移除 0 筆，剩餘 2345 筆\n",
      "[2025-10-14 21:34:08] 型別/特徵檢查：移除 0 筆，剩餘 2345 筆\n",
      "[2025-10-14 21:34:08] 清理完成：初始 2345 筆 → 保留 2345 筆（移除 0 筆）\n",
      "[2025-10-14 21:34:08] 已寫入 cleaned 檔案：/home/jovyan/RT08/0925/sliding_win/1014_sim/cleaned/PatNo_ID_1577487284.csv（筆數 2345）\n",
      "[2025-10-14 21:34:08] 清理開始：PatNo_ID_1578784257.csv\n",
      "[2025-10-14 21:34:08] 讀入筆數：33914（檔：PatNo_ID_1578784257.csv）\n",
      "[2025-10-14 21:34:08] 缺失清除：移除 0 筆，剩餘 33914 筆\n",
      "[2025-10-14 21:34:08] 時間處理：移除 0 筆無效時間，剩餘 33914 筆\n",
      "[2025-10-14 21:34:08] 去除重複：移除 0 筆，剩餘 33914 筆\n",
      "[2025-10-14 21:34:08] 型別/特徵檢查：移除 5359 筆，剩餘 28555 筆\n",
      "[2025-10-14 21:34:08] 清理完成：初始 33914 筆 → 保留 28555 筆（移除 5359 筆）\n",
      "[2025-10-14 21:34:08] 已寫入 cleaned 檔案：/home/jovyan/RT08/0925/sliding_win/1014_sim/cleaned/PatNo_ID_1578784257.csv（筆數 28555）\n",
      "[2025-10-14 21:34:08] 清理開始：PatNo_ID_1579198603.csv\n",
      "[2025-10-14 21:34:08] 讀入筆數：2045（檔：PatNo_ID_1579198603.csv）\n",
      "[2025-10-14 21:34:08] 缺失清除：移除 0 筆，剩餘 2045 筆\n",
      "[2025-10-14 21:34:08] 時間處理：移除 0 筆無效時間，剩餘 2045 筆\n",
      "[2025-10-14 21:34:08] 去除重複：移除 0 筆，剩餘 2045 筆\n",
      "[2025-10-14 21:34:08] 型別/特徵檢查：移除 0 筆，剩餘 2045 筆\n",
      "[2025-10-14 21:34:08] 清理完成：初始 2045 筆 → 保留 2045 筆（移除 0 筆）\n",
      "[2025-10-14 21:34:08] 已寫入 cleaned 檔案：/home/jovyan/RT08/0925/sliding_win/1014_sim/cleaned/PatNo_ID_1579198603.csv（筆數 2045）\n",
      "[2025-10-14 21:34:08] 清理開始：PatNo_ID_1579498177.csv\n",
      "[2025-10-14 21:34:08] 讀入筆數：21688（檔：PatNo_ID_1579498177.csv）\n",
      "[2025-10-14 21:34:08] 缺失清除：移除 0 筆，剩餘 21688 筆\n",
      "[2025-10-14 21:34:08] 時間處理：移除 0 筆無效時間，剩餘 21688 筆\n",
      "[2025-10-14 21:34:08] 去除重複：移除 0 筆，剩餘 21688 筆\n",
      "[2025-10-14 21:34:08] 型別/特徵檢查：移除 7 筆，剩餘 21681 筆\n",
      "[2025-10-14 21:34:08] 清理完成：初始 21688 筆 → 保留 21681 筆（移除 7 筆）\n",
      "[2025-10-14 21:34:09] 已寫入 cleaned 檔案：/home/jovyan/RT08/0925/sliding_win/1014_sim/cleaned/PatNo_ID_1579498177.csv（筆數 21681）\n",
      "[2025-10-14 21:34:09] 清理開始：PatNo_ID_1580062580.csv\n",
      "[2025-10-14 21:34:09] 讀入筆數：2150（檔：PatNo_ID_1580062580.csv）\n",
      "[2025-10-14 21:34:09] 缺失清除：移除 0 筆，剩餘 2150 筆\n",
      "[2025-10-14 21:34:09] 時間處理：移除 0 筆無效時間，剩餘 2150 筆\n",
      "[2025-10-14 21:34:09] 去除重複：移除 0 筆，剩餘 2150 筆\n",
      "[2025-10-14 21:34:09] 型別/特徵檢查：移除 0 筆，剩餘 2150 筆\n",
      "[2025-10-14 21:34:09] 清理完成：初始 2150 筆 → 保留 2150 筆（移除 0 筆）\n",
      "[2025-10-14 21:34:09] 已寫入 cleaned 檔案：/home/jovyan/RT08/0925/sliding_win/1014_sim/cleaned/PatNo_ID_1580062580.csv（筆數 2150）\n",
      "[2025-10-14 21:34:09] 清理開始：PatNo_ID_1580096720.csv\n",
      "[2025-10-14 21:34:09] 讀入筆數：1423（檔：PatNo_ID_1580096720.csv）\n",
      "[2025-10-14 21:34:09] 缺失清除：移除 0 筆，剩餘 1423 筆\n",
      "[2025-10-14 21:34:09] 時間處理：移除 0 筆無效時間，剩餘 1423 筆\n",
      "[2025-10-14 21:34:09] 去除重複：移除 0 筆，剩餘 1423 筆\n",
      "[2025-10-14 21:34:09] 型別/特徵檢查：移除 1 筆，剩餘 1422 筆\n",
      "[2025-10-14 21:34:09] 清理完成：初始 1423 筆 → 保留 1422 筆（移除 1 筆）\n",
      "[2025-10-14 21:34:09] 已寫入 cleaned 檔案：/home/jovyan/RT08/0925/sliding_win/1014_sim/cleaned/PatNo_ID_1580096720.csv（筆數 1422）\n",
      "[2025-10-14 21:34:09] 清理開始：PatNo_ID_1580107637.csv\n",
      "[2025-10-14 21:34:09] 讀入筆數：2698（檔：PatNo_ID_1580107637.csv）\n",
      "[2025-10-14 21:34:09] 缺失清除：移除 0 筆，剩餘 2698 筆\n",
      "[2025-10-14 21:34:09] 時間處理：移除 0 筆無效時間，剩餘 2698 筆\n",
      "[2025-10-14 21:34:09] 去除重複：移除 0 筆，剩餘 2698 筆\n",
      "[2025-10-14 21:34:09] 型別/特徵檢查：移除 10 筆，剩餘 2688 筆\n",
      "[2025-10-14 21:34:09] 清理完成：初始 2698 筆 → 保留 2688 筆（移除 10 筆）\n",
      "[2025-10-14 21:34:09] 已寫入 cleaned 檔案：/home/jovyan/RT08/0925/sliding_win/1014_sim/cleaned/PatNo_ID_1580107637.csv（筆數 2688）\n",
      "[2025-10-14 21:34:09] 清理開始：PatNo_ID_1580244614.csv\n",
      "[2025-10-14 21:34:09] 讀入筆數：5278（檔：PatNo_ID_1580244614.csv）\n",
      "[2025-10-14 21:34:09] 缺失清除：移除 0 筆，剩餘 5278 筆\n",
      "[2025-10-14 21:34:09] 時間處理：移除 0 筆無效時間，剩餘 5278 筆\n",
      "[2025-10-14 21:34:09] 去除重複：移除 0 筆，剩餘 5278 筆\n",
      "[2025-10-14 21:34:09] 型別/特徵檢查：移除 254 筆，剩餘 5024 筆\n",
      "[2025-10-14 21:34:09] 清理完成：初始 5278 筆 → 保留 5024 筆（移除 254 筆）\n",
      "[2025-10-14 21:34:09] 已寫入 cleaned 檔案：/home/jovyan/RT08/0925/sliding_win/1014_sim/cleaned/PatNo_ID_1580244614.csv（筆數 5024）\n",
      "[2025-10-14 21:34:09] 清理開始：PatNo_ID_1580766093.csv\n",
      "[2025-10-14 21:34:09] 讀入筆數：18070（檔：PatNo_ID_1580766093.csv）\n",
      "[2025-10-14 21:34:09] 缺失清除：移除 0 筆，剩餘 18070 筆\n",
      "[2025-10-14 21:34:09] 時間處理：移除 0 筆無效時間，剩餘 18070 筆\n",
      "[2025-10-14 21:34:09] 去除重複：移除 0 筆，剩餘 18070 筆\n",
      "[2025-10-14 21:34:09] 型別/特徵檢查：移除 6 筆，剩餘 18064 筆\n",
      "[2025-10-14 21:34:09] 清理完成：初始 18070 筆 → 保留 18064 筆（移除 6 筆）\n",
      "[2025-10-14 21:34:09] 已寫入 cleaned 檔案：/home/jovyan/RT08/0925/sliding_win/1014_sim/cleaned/PatNo_ID_1580766093.csv（筆數 18064）\n",
      "[2025-10-14 21:34:09] 清理開始：PatNo_ID_1581003248.csv\n",
      "[2025-10-14 21:34:09] 讀入筆數：7776（檔：PatNo_ID_1581003248.csv）\n",
      "[2025-10-14 21:34:09] 缺失清除：移除 0 筆，剩餘 7776 筆\n",
      "[2025-10-14 21:34:09] 時間處理：移除 0 筆無效時間，剩餘 7776 筆\n",
      "[2025-10-14 21:34:09] 去除重複：移除 0 筆，剩餘 7776 筆\n",
      "[2025-10-14 21:34:09] 型別/特徵檢查：移除 1 筆，剩餘 7775 筆\n",
      "[2025-10-14 21:34:09] 清理完成：初始 7776 筆 → 保留 7775 筆（移除 1 筆）\n",
      "[2025-10-14 21:34:09] 已寫入 cleaned 檔案：/home/jovyan/RT08/0925/sliding_win/1014_sim/cleaned/PatNo_ID_1581003248.csv（筆數 7775）\n",
      "[2025-10-14 21:34:09] 清理開始：PatNo_ID_1581019504.csv\n",
      "[2025-10-14 21:34:09] 讀入筆數：28173（檔：PatNo_ID_1581019504.csv）\n",
      "[2025-10-14 21:34:09] 缺失清除：移除 0 筆，剩餘 28173 筆\n",
      "[2025-10-14 21:34:09] 時間處理：移除 0 筆無效時間，剩餘 28173 筆\n",
      "[2025-10-14 21:34:09] 去除重複：移除 0 筆，剩餘 28173 筆\n",
      "[2025-10-14 21:34:09] 型別/特徵檢查：移除 6647 筆，剩餘 21526 筆\n",
      "[2025-10-14 21:34:09] 清理完成：初始 28173 筆 → 保留 21526 筆（移除 6647 筆）\n",
      "[2025-10-14 21:34:09] 已寫入 cleaned 檔案：/home/jovyan/RT08/0925/sliding_win/1014_sim/cleaned/PatNo_ID_1581019504.csv（筆數 21526）\n",
      "[2025-10-14 21:34:09] 清理開始：PatNo_ID_1581633231.csv\n",
      "[2025-10-14 21:34:09] 讀入筆數：15995（檔：PatNo_ID_1581633231.csv）\n",
      "[2025-10-14 21:34:09] 缺失清除：移除 0 筆，剩餘 15995 筆\n",
      "[2025-10-14 21:34:09] 時間處理：移除 0 筆無效時間，剩餘 15995 筆\n",
      "[2025-10-14 21:34:09] 去除重複：移除 0 筆，剩餘 15995 筆\n",
      "[2025-10-14 21:34:09] 型別/特徵檢查：移除 24 筆，剩餘 15971 筆\n",
      "[2025-10-14 21:34:09] 清理完成：初始 15995 筆 → 保留 15971 筆（移除 24 筆）\n",
      "[2025-10-14 21:34:09] 已寫入 cleaned 檔案：/home/jovyan/RT08/0925/sliding_win/1014_sim/cleaned/PatNo_ID_1581633231.csv（筆數 15971）\n",
      "[2025-10-14 21:34:09] 清理開始：PatNo_ID_1581692973.csv\n",
      "[2025-10-14 21:34:09] 讀入筆數：2723（檔：PatNo_ID_1581692973.csv）\n",
      "[2025-10-14 21:34:09] 缺失清除：移除 0 筆，剩餘 2723 筆\n",
      "[2025-10-14 21:34:09] 時間處理：移除 0 筆無效時間，剩餘 2723 筆\n",
      "[2025-10-14 21:34:09] 去除重複：移除 0 筆，剩餘 2723 筆\n",
      "[2025-10-14 21:34:09] 型別/特徵檢查：移除 0 筆，剩餘 2723 筆\n",
      "[2025-10-14 21:34:09] 清理完成：初始 2723 筆 → 保留 2723 筆（移除 0 筆）\n",
      "[2025-10-14 21:34:09] 已寫入 cleaned 檔案：/home/jovyan/RT08/0925/sliding_win/1014_sim/cleaned/PatNo_ID_1581692973.csv（筆數 2723）\n",
      "[2025-10-14 21:34:09] 清理開始：PatNo_ID_1582452511.csv\n",
      "[2025-10-14 21:34:09] 讀入筆數：5196（檔：PatNo_ID_1582452511.csv）\n",
      "[2025-10-14 21:34:09] 缺失清除：移除 0 筆，剩餘 5196 筆\n",
      "[2025-10-14 21:34:09] 時間處理：移除 0 筆無效時間，剩餘 5196 筆\n",
      "[2025-10-14 21:34:09] 去除重複：移除 0 筆，剩餘 5196 筆\n",
      "[2025-10-14 21:34:09] 型別/特徵檢查：移除 0 筆，剩餘 5196 筆\n",
      "[2025-10-14 21:34:09] 清理完成：初始 5196 筆 → 保留 5196 筆（移除 0 筆）\n",
      "[2025-10-14 21:34:09] 已寫入 cleaned 檔案：/home/jovyan/RT08/0925/sliding_win/1014_sim/cleaned/PatNo_ID_1582452511.csv（筆數 5196）\n",
      "[2025-10-14 21:34:09] 清理開始：PatNo_ID_1582635996.csv\n",
      "[2025-10-14 21:34:09] 讀入筆數：13046（檔：PatNo_ID_1582635996.csv）\n",
      "[2025-10-14 21:34:09] 缺失清除：移除 0 筆，剩餘 13046 筆\n",
      "[2025-10-14 21:34:09] 時間處理：移除 0 筆無效時間，剩餘 13046 筆\n",
      "[2025-10-14 21:34:09] 去除重複：移除 0 筆，剩餘 13046 筆\n",
      "[2025-10-14 21:34:09] 型別/特徵檢查：移除 0 筆，剩餘 13046 筆\n",
      "[2025-10-14 21:34:09] 清理完成：初始 13046 筆 → 保留 13046 筆（移除 0 筆）\n",
      "[2025-10-14 21:34:10] 已寫入 cleaned 檔案：/home/jovyan/RT08/0925/sliding_win/1014_sim/cleaned/PatNo_ID_1582635996.csv（筆數 13046）\n",
      "[2025-10-14 21:34:10] 清理開始：PatNo_ID_1582849900.csv\n",
      "[2025-10-14 21:34:10] 讀入筆數：7411（檔：PatNo_ID_1582849900.csv）\n",
      "[2025-10-14 21:34:10] 缺失清除：移除 0 筆，剩餘 7411 筆\n",
      "[2025-10-14 21:34:10] 時間處理：移除 0 筆無效時間，剩餘 7411 筆\n",
      "[2025-10-14 21:34:10] 去除重複：移除 0 筆，剩餘 7411 筆\n",
      "[2025-10-14 21:34:10] 型別/特徵檢查：移除 24 筆，剩餘 7387 筆\n",
      "[2025-10-14 21:34:10] 清理完成：初始 7411 筆 → 保留 7387 筆（移除 24 筆）\n",
      "[2025-10-14 21:34:10] 已寫入 cleaned 檔案：/home/jovyan/RT08/0925/sliding_win/1014_sim/cleaned/PatNo_ID_1582849900.csv（筆數 7387）\n",
      "[2025-10-14 21:34:10] 清理開始：PatNo_ID_1582937076.csv\n",
      "[2025-10-14 21:34:10] 讀入筆數：23987（檔：PatNo_ID_1582937076.csv）\n",
      "[2025-10-14 21:34:10] 缺失清除：移除 0 筆，剩餘 23987 筆\n",
      "[2025-10-14 21:34:10] 時間處理：移除 0 筆無效時間，剩餘 23987 筆\n",
      "[2025-10-14 21:34:10] 去除重複：移除 0 筆，剩餘 23987 筆\n",
      "[2025-10-14 21:34:10] 型別/特徵檢查：移除 21 筆，剩餘 23966 筆\n",
      "[2025-10-14 21:34:10] 清理完成：初始 23987 筆 → 保留 23966 筆（移除 21 筆）\n",
      "[2025-10-14 21:34:10] 已寫入 cleaned 檔案：/home/jovyan/RT08/0925/sliding_win/1014_sim/cleaned/PatNo_ID_1582937076.csv（筆數 23966）\n",
      "[2025-10-14 21:34:10] 清理開始：PatNo_ID_1584158973.csv\n",
      "[2025-10-14 21:34:10] 讀入筆數：2882（檔：PatNo_ID_1584158973.csv）\n",
      "[2025-10-14 21:34:10] 缺失清除：移除 0 筆，剩餘 2882 筆\n",
      "[2025-10-14 21:34:10] 時間處理：移除 0 筆無效時間，剩餘 2882 筆\n",
      "[2025-10-14 21:34:10] 去除重複：移除 0 筆，剩餘 2882 筆\n",
      "[2025-10-14 21:34:10] 型別/特徵檢查：移除 0 筆，剩餘 2882 筆\n",
      "[2025-10-14 21:34:10] 清理完成：初始 2882 筆 → 保留 2882 筆（移除 0 筆）\n",
      "[2025-10-14 21:34:10] 已寫入 cleaned 檔案：/home/jovyan/RT08/0925/sliding_win/1014_sim/cleaned/PatNo_ID_1584158973.csv（筆數 2882）\n",
      "[2025-10-14 21:34:10] 清理開始：PatNo_ID_1584397376.csv\n",
      "[2025-10-14 21:34:10] 讀入筆數：638（檔：PatNo_ID_1584397376.csv）\n",
      "[2025-10-14 21:34:10] 缺失清除：移除 0 筆，剩餘 638 筆\n",
      "[2025-10-14 21:34:10] 時間處理：移除 0 筆無效時間，剩餘 638 筆\n",
      "[2025-10-14 21:34:10] 去除重複：移除 0 筆，剩餘 638 筆\n",
      "[2025-10-14 21:34:10] 型別/特徵檢查：移除 0 筆，剩餘 638 筆\n",
      "[2025-10-14 21:34:10] 清理完成：初始 638 筆 → 保留 638 筆（移除 0 筆）\n",
      "[2025-10-14 21:34:10] 已寫入 cleaned 檔案：/home/jovyan/RT08/0925/sliding_win/1014_sim/cleaned/PatNo_ID_1584397376.csv（筆數 638）\n",
      "[2025-10-14 21:34:10] 清理開始：PatNo_ID_1586172659.csv\n",
      "[2025-10-14 21:34:10] 讀入筆數：38554（檔：PatNo_ID_1586172659.csv）\n",
      "[2025-10-14 21:34:10] 缺失清除：移除 0 筆，剩餘 38554 筆\n",
      "[2025-10-14 21:34:10] 時間處理：移除 0 筆無效時間，剩餘 38554 筆\n",
      "[2025-10-14 21:34:10] 去除重複：移除 0 筆，剩餘 38554 筆\n",
      "[2025-10-14 21:34:10] 型別/特徵檢查：移除 7 筆，剩餘 38547 筆\n",
      "[2025-10-14 21:34:10] 清理完成：初始 38554 筆 → 保留 38547 筆（移除 7 筆）\n",
      "[2025-10-14 21:34:10] 已寫入 cleaned 檔案：/home/jovyan/RT08/0925/sliding_win/1014_sim/cleaned/PatNo_ID_1586172659.csv（筆數 38547）\n",
      "[2025-10-14 21:34:10] 清理開始：PatNo_ID_1586696634.csv\n",
      "[2025-10-14 21:34:10] 讀入筆數：3569（檔：PatNo_ID_1586696634.csv）\n",
      "[2025-10-14 21:34:10] 缺失清除：移除 0 筆，剩餘 3569 筆\n",
      "[2025-10-14 21:34:10] 時間處理：移除 0 筆無效時間，剩餘 3569 筆\n",
      "[2025-10-14 21:34:10] 去除重複：移除 0 筆，剩餘 3569 筆\n",
      "[2025-10-14 21:34:10] 型別/特徵檢查：移除 1065 筆，剩餘 2504 筆\n",
      "[2025-10-14 21:34:10] 清理完成：初始 3569 筆 → 保留 2504 筆（移除 1065 筆）\n",
      "[2025-10-14 21:34:10] 已寫入 cleaned 檔案：/home/jovyan/RT08/0925/sliding_win/1014_sim/cleaned/PatNo_ID_1586696634.csv（筆數 2504）\n",
      "[2025-10-14 21:34:10] 清理開始：PatNo_ID_1586897008.csv\n",
      "[2025-10-14 21:34:10] 讀入筆數：6687（檔：PatNo_ID_1586897008.csv）\n",
      "[2025-10-14 21:34:10] 缺失清除：移除 0 筆，剩餘 6687 筆\n",
      "[2025-10-14 21:34:10] 時間處理：移除 0 筆無效時間，剩餘 6687 筆\n",
      "[2025-10-14 21:34:10] 去除重複：移除 0 筆，剩餘 6687 筆\n",
      "[2025-10-14 21:34:10] 型別/特徵檢查：移除 32 筆，剩餘 6655 筆\n",
      "[2025-10-14 21:34:10] 清理完成：初始 6687 筆 → 保留 6655 筆（移除 32 筆）\n",
      "[2025-10-14 21:34:10] 已寫入 cleaned 檔案：/home/jovyan/RT08/0925/sliding_win/1014_sim/cleaned/PatNo_ID_1586897008.csv（筆數 6655）\n",
      "[2025-10-14 21:34:10] 清理開始：PatNo_ID_1587490083.csv\n",
      "[2025-10-14 21:34:10] 讀入筆數：45186（檔：PatNo_ID_1587490083.csv）\n",
      "[2025-10-14 21:34:10] 缺失清除：移除 0 筆，剩餘 45186 筆\n",
      "[2025-10-14 21:34:10] 時間處理：移除 0 筆無效時間，剩餘 45186 筆\n",
      "[2025-10-14 21:34:10] 去除重複：移除 0 筆，剩餘 45186 筆\n",
      "[2025-10-14 21:34:10] 型別/特徵檢查：移除 0 筆，剩餘 45186 筆\n",
      "[2025-10-14 21:34:10] 清理完成：初始 45186 筆 → 保留 45186 筆（移除 0 筆）\n",
      "[2025-10-14 21:34:11] 已寫入 cleaned 檔案：/home/jovyan/RT08/0925/sliding_win/1014_sim/cleaned/PatNo_ID_1587490083.csv（筆數 45186）\n",
      "[2025-10-14 21:34:11] 清理開始：PatNo_ID_1588632604.csv\n",
      "[2025-10-14 21:34:11] 讀入筆數：2608（檔：PatNo_ID_1588632604.csv）\n",
      "[2025-10-14 21:34:11] 缺失清除：移除 0 筆，剩餘 2608 筆\n",
      "[2025-10-14 21:34:11] 時間處理：移除 0 筆無效時間，剩餘 2608 筆\n",
      "[2025-10-14 21:34:11] 去除重複：移除 0 筆，剩餘 2608 筆\n",
      "[2025-10-14 21:34:11] 型別/特徵檢查：移除 0 筆，剩餘 2608 筆\n",
      "[2025-10-14 21:34:11] 清理完成：初始 2608 筆 → 保留 2608 筆（移除 0 筆）\n",
      "[2025-10-14 21:34:11] 已寫入 cleaned 檔案：/home/jovyan/RT08/0925/sliding_win/1014_sim/cleaned/PatNo_ID_1588632604.csv（筆數 2608）\n",
      "[2025-10-14 21:34:11] 清理開始：PatNo_ID_1588673465.csv\n",
      "[2025-10-14 21:34:11] 讀入筆數：10076（檔：PatNo_ID_1588673465.csv）\n",
      "[2025-10-14 21:34:11] 缺失清除：移除 0 筆，剩餘 10076 筆\n",
      "[2025-10-14 21:34:11] 時間處理：移除 0 筆無效時間，剩餘 10076 筆\n",
      "[2025-10-14 21:34:11] 去除重複：移除 0 筆，剩餘 10076 筆\n",
      "[2025-10-14 21:34:11] 型別/特徵檢查：移除 4522 筆，剩餘 5554 筆\n",
      "[2025-10-14 21:34:11] 清理完成：初始 10076 筆 → 保留 5554 筆（移除 4522 筆）\n",
      "[2025-10-14 21:34:11] 已寫入 cleaned 檔案：/home/jovyan/RT08/0925/sliding_win/1014_sim/cleaned/PatNo_ID_1588673465.csv（筆數 5554）\n",
      "[2025-10-14 21:34:11] 清理開始：PatNo_ID_1588794796.csv\n",
      "[2025-10-14 21:34:11] 讀入筆數：9375（檔：PatNo_ID_1588794796.csv）\n",
      "[2025-10-14 21:34:11] 缺失清除：移除 0 筆，剩餘 9375 筆\n",
      "[2025-10-14 21:34:11] 時間處理：移除 0 筆無效時間，剩餘 9375 筆\n",
      "[2025-10-14 21:34:11] 去除重複：移除 0 筆，剩餘 9375 筆\n",
      "[2025-10-14 21:34:11] 型別/特徵檢查：移除 0 筆，剩餘 9375 筆\n",
      "[2025-10-14 21:34:11] 清理完成：初始 9375 筆 → 保留 9375 筆（移除 0 筆）\n",
      "[2025-10-14 21:34:11] 已寫入 cleaned 檔案：/home/jovyan/RT08/0925/sliding_win/1014_sim/cleaned/PatNo_ID_1588794796.csv（筆數 9375）\n",
      "[2025-10-14 21:34:11] 清理開始：PatNo_ID_1588957997.csv\n",
      "[2025-10-14 21:34:11] 讀入筆數：10966（檔：PatNo_ID_1588957997.csv）\n",
      "[2025-10-14 21:34:11] 缺失清除：移除 0 筆，剩餘 10966 筆\n",
      "[2025-10-14 21:34:11] 時間處理：移除 0 筆無效時間，剩餘 10966 筆\n",
      "[2025-10-14 21:34:11] 去除重複：移除 0 筆，剩餘 10966 筆\n",
      "[2025-10-14 21:34:11] 型別/特徵檢查：移除 0 筆，剩餘 10966 筆\n",
      "[2025-10-14 21:34:11] 清理完成：初始 10966 筆 → 保留 10966 筆（移除 0 筆）\n",
      "[2025-10-14 21:34:11] 已寫入 cleaned 檔案：/home/jovyan/RT08/0925/sliding_win/1014_sim/cleaned/PatNo_ID_1588957997.csv（筆數 10966）\n",
      "[2025-10-14 21:34:11] 清理開始：PatNo_ID_1589018086.csv\n",
      "[2025-10-14 21:34:11] 讀入筆數：13472（檔：PatNo_ID_1589018086.csv）\n",
      "[2025-10-14 21:34:11] 缺失清除：移除 0 筆，剩餘 13472 筆\n",
      "[2025-10-14 21:34:11] 時間處理：移除 0 筆無效時間，剩餘 13472 筆\n",
      "[2025-10-14 21:34:11] 去除重複：移除 0 筆，剩餘 13472 筆\n",
      "[2025-10-14 21:34:11] 型別/特徵檢查：移除 22 筆，剩餘 13450 筆\n",
      "[2025-10-14 21:34:11] 清理完成：初始 13472 筆 → 保留 13450 筆（移除 22 筆）\n",
      "[2025-10-14 21:34:11] 已寫入 cleaned 檔案：/home/jovyan/RT08/0925/sliding_win/1014_sim/cleaned/PatNo_ID_1589018086.csv（筆數 13450）\n",
      "[2025-10-14 21:34:11] 清理開始：PatNo_ID_1589034524.csv\n",
      "[2025-10-14 21:34:11] 讀入筆數：50081（檔：PatNo_ID_1589034524.csv）\n",
      "[2025-10-14 21:34:11] 缺失清除：移除 0 筆，剩餘 50081 筆\n",
      "[2025-10-14 21:34:11] 時間處理：移除 0 筆無效時間，剩餘 50081 筆\n",
      "[2025-10-14 21:34:11] 去除重複：移除 0 筆，剩餘 50081 筆\n",
      "[2025-10-14 21:34:11] 型別/特徵檢查：移除 42 筆，剩餘 50039 筆\n",
      "[2025-10-14 21:34:11] 清理完成：初始 50081 筆 → 保留 50039 筆（移除 42 筆）\n",
      "[2025-10-14 21:34:12] 已寫入 cleaned 檔案：/home/jovyan/RT08/0925/sliding_win/1014_sim/cleaned/PatNo_ID_1589034524.csv（筆數 50039）\n",
      "[2025-10-14 21:34:12] 清理開始：PatNo_ID_1589324603.csv\n",
      "[2025-10-14 21:34:12] 讀入筆數：4187（檔：PatNo_ID_1589324603.csv）\n",
      "[2025-10-14 21:34:12] 缺失清除：移除 0 筆，剩餘 4187 筆\n",
      "[2025-10-14 21:34:12] 時間處理：移除 0 筆無效時間，剩餘 4187 筆\n",
      "[2025-10-14 21:34:12] 去除重複：移除 0 筆，剩餘 4187 筆\n",
      "[2025-10-14 21:34:12] 型別/特徵檢查：移除 76 筆，剩餘 4111 筆\n",
      "[2025-10-14 21:34:12] 清理完成：初始 4187 筆 → 保留 4111 筆（移除 76 筆）\n",
      "[2025-10-14 21:34:12] 已寫入 cleaned 檔案：/home/jovyan/RT08/0925/sliding_win/1014_sim/cleaned/PatNo_ID_1589324603.csv（筆數 4111）\n",
      "[2025-10-14 21:34:12] 清理開始：PatNo_ID_1589918099.csv\n",
      "[2025-10-14 21:34:12] 讀入筆數：9333（檔：PatNo_ID_1589918099.csv）\n",
      "[2025-10-14 21:34:12] 缺失清除：移除 0 筆，剩餘 9333 筆\n",
      "[2025-10-14 21:34:12] 時間處理：移除 0 筆無效時間，剩餘 9333 筆\n",
      "[2025-10-14 21:34:12] 去除重複：移除 0 筆，剩餘 9333 筆\n",
      "[2025-10-14 21:34:12] 型別/特徵檢查：移除 1 筆，剩餘 9332 筆\n",
      "[2025-10-14 21:34:12] 清理完成：初始 9333 筆 → 保留 9332 筆（移除 1 筆）\n",
      "[2025-10-14 21:34:12] 已寫入 cleaned 檔案：/home/jovyan/RT08/0925/sliding_win/1014_sim/cleaned/PatNo_ID_1589918099.csv（筆數 9332）\n",
      "[2025-10-14 21:34:12] 清理開始：PatNo_ID_1590136310.csv\n",
      "[2025-10-14 21:34:12] 讀入筆數：5580（檔：PatNo_ID_1590136310.csv）\n",
      "[2025-10-14 21:34:12] 缺失清除：移除 0 筆，剩餘 5580 筆\n",
      "[2025-10-14 21:34:12] 時間處理：移除 0 筆無效時間，剩餘 5580 筆\n",
      "[2025-10-14 21:34:12] 去除重複：移除 0 筆，剩餘 5580 筆\n",
      "[2025-10-14 21:34:12] 型別/特徵檢查：移除 273 筆，剩餘 5307 筆\n",
      "[2025-10-14 21:34:12] 清理完成：初始 5580 筆 → 保留 5307 筆（移除 273 筆）\n",
      "[2025-10-14 21:34:12] 已寫入 cleaned 檔案：/home/jovyan/RT08/0925/sliding_win/1014_sim/cleaned/PatNo_ID_1590136310.csv（筆數 5307）\n",
      "[2025-10-14 21:34:12] 清理開始：PatNo_ID_1590616537.csv\n",
      "[2025-10-14 21:34:12] 讀入筆數：14206（檔：PatNo_ID_1590616537.csv）\n",
      "[2025-10-14 21:34:12] 缺失清除：移除 0 筆，剩餘 14206 筆\n",
      "[2025-10-14 21:34:12] 時間處理：移除 0 筆無效時間，剩餘 14206 筆\n",
      "[2025-10-14 21:34:12] 去除重複：移除 0 筆，剩餘 14206 筆\n",
      "[2025-10-14 21:34:12] 型別/特徵檢查：移除 0 筆，剩餘 14206 筆\n",
      "[2025-10-14 21:34:12] 清理完成：初始 14206 筆 → 保留 14206 筆（移除 0 筆）\n",
      "[2025-10-14 21:34:12] 已寫入 cleaned 檔案：/home/jovyan/RT08/0925/sliding_win/1014_sim/cleaned/PatNo_ID_1590616537.csv（筆數 14206）\n",
      "[2025-10-14 21:34:12] 清理開始：PatNo_ID_1590854576.csv\n",
      "[2025-10-14 21:34:12] 讀入筆數：15879（檔：PatNo_ID_1590854576.csv）\n",
      "[2025-10-14 21:34:12] 缺失清除：移除 0 筆，剩餘 15879 筆\n",
      "[2025-10-14 21:34:12] 時間處理：移除 0 筆無效時間，剩餘 15879 筆\n",
      "[2025-10-14 21:34:12] 去除重複：移除 0 筆，剩餘 15879 筆\n",
      "[2025-10-14 21:34:12] 型別/特徵檢查：移除 0 筆，剩餘 15879 筆\n",
      "[2025-10-14 21:34:12] 清理完成：初始 15879 筆 → 保留 15879 筆（移除 0 筆）\n",
      "[2025-10-14 21:34:12] 已寫入 cleaned 檔案：/home/jovyan/RT08/0925/sliding_win/1014_sim/cleaned/PatNo_ID_1590854576.csv（筆數 15879）\n",
      "[2025-10-14 21:34:12] 清理開始：PatNo_ID_1591609798.csv\n",
      "[2025-10-14 21:34:12] 讀入筆數：35618（檔：PatNo_ID_1591609798.csv）\n",
      "[2025-10-14 21:34:12] 缺失清除：移除 0 筆，剩餘 35618 筆\n",
      "[2025-10-14 21:34:12] 時間處理：移除 0 筆無效時間，剩餘 35618 筆\n",
      "[2025-10-14 21:34:12] 去除重複：移除 0 筆，剩餘 35618 筆\n",
      "[2025-10-14 21:34:12] 型別/特徵檢查：移除 1 筆，剩餘 35617 筆\n",
      "[2025-10-14 21:34:12] 清理完成：初始 35618 筆 → 保留 35617 筆（移除 1 筆）\n",
      "[2025-10-14 21:34:12] 已寫入 cleaned 檔案：/home/jovyan/RT08/0925/sliding_win/1014_sim/cleaned/PatNo_ID_1591609798.csv（筆數 35617）\n",
      "[2025-10-14 21:34:12] 清理開始：PatNo_ID_1592044724.csv\n",
      "[2025-10-14 21:34:12] 讀入筆數：6815（檔：PatNo_ID_1592044724.csv）\n",
      "[2025-10-14 21:34:12] 缺失清除：移除 0 筆，剩餘 6815 筆\n",
      "[2025-10-14 21:34:12] 時間處理：移除 0 筆無效時間，剩餘 6815 筆\n",
      "[2025-10-14 21:34:12] 去除重複：移除 0 筆，剩餘 6815 筆\n",
      "[2025-10-14 21:34:12] 型別/特徵檢查：移除 3 筆，剩餘 6812 筆\n",
      "[2025-10-14 21:34:12] 清理完成：初始 6815 筆 → 保留 6812 筆（移除 3 筆）\n",
      "[2025-10-14 21:34:12] 已寫入 cleaned 檔案：/home/jovyan/RT08/0925/sliding_win/1014_sim/cleaned/PatNo_ID_1592044724.csv（筆數 6812）\n",
      "[2025-10-14 21:34:12] 清理開始：PatNo_ID_1592560504.csv\n",
      "[2025-10-14 21:34:13] 讀入筆數：18051（檔：PatNo_ID_1592560504.csv）\n",
      "[2025-10-14 21:34:13] 缺失清除：移除 0 筆，剩餘 18051 筆\n",
      "[2025-10-14 21:34:13] 時間處理：移除 0 筆無效時間，剩餘 18051 筆\n",
      "[2025-10-14 21:34:13] 去除重複：移除 0 筆，剩餘 18051 筆\n",
      "[2025-10-14 21:34:13] 型別/特徵檢查：移除 0 筆，剩餘 18051 筆\n",
      "[2025-10-14 21:34:13] 清理完成：初始 18051 筆 → 保留 18051 筆（移除 0 筆）\n",
      "[2025-10-14 21:34:13] 已寫入 cleaned 檔案：/home/jovyan/RT08/0925/sliding_win/1014_sim/cleaned/PatNo_ID_1592560504.csv（筆數 18051）\n",
      "[2025-10-14 21:34:13] 清理開始：PatNo_ID_1593087886.csv\n",
      "[2025-10-14 21:34:13] 讀入筆數：24163（檔：PatNo_ID_1593087886.csv）\n",
      "[2025-10-14 21:34:13] 缺失清除：移除 0 筆，剩餘 24163 筆\n",
      "[2025-10-14 21:34:13] 時間處理：移除 0 筆無效時間，剩餘 24163 筆\n",
      "[2025-10-14 21:34:13] 去除重複：移除 0 筆，剩餘 24163 筆\n",
      "[2025-10-14 21:34:13] 型別/特徵檢查：移除 295 筆，剩餘 23868 筆\n",
      "[2025-10-14 21:34:13] 清理完成：初始 24163 筆 → 保留 23868 筆（移除 295 筆）\n",
      "[2025-10-14 21:34:13] 已寫入 cleaned 檔案：/home/jovyan/RT08/0925/sliding_win/1014_sim/cleaned/PatNo_ID_1593087886.csv（筆數 23868）\n",
      "[2025-10-14 21:34:13] 清理開始：PatNo_ID_1593416100.csv\n",
      "[2025-10-14 21:34:13] 讀入筆數：3328（檔：PatNo_ID_1593416100.csv）\n",
      "[2025-10-14 21:34:13] 缺失清除：移除 0 筆，剩餘 3328 筆\n",
      "[2025-10-14 21:34:13] 時間處理：移除 0 筆無效時間，剩餘 3328 筆\n",
      "[2025-10-14 21:34:13] 去除重複：移除 0 筆，剩餘 3328 筆\n",
      "[2025-10-14 21:34:13] 型別/特徵檢查：移除 0 筆，剩餘 3328 筆\n",
      "[2025-10-14 21:34:13] 清理完成：初始 3328 筆 → 保留 3328 筆（移除 0 筆）\n",
      "[2025-10-14 21:34:13] 已寫入 cleaned 檔案：/home/jovyan/RT08/0925/sliding_win/1014_sim/cleaned/PatNo_ID_1593416100.csv（筆數 3328）\n",
      "[2025-10-14 21:34:13] 清理開始：PatNo_ID_1593472048.csv\n",
      "[2025-10-14 21:34:13] 讀入筆數：8230（檔：PatNo_ID_1593472048.csv）\n",
      "[2025-10-14 21:34:13] 缺失清除：移除 0 筆，剩餘 8230 筆\n",
      "[2025-10-14 21:34:13] 時間處理：移除 0 筆無效時間，剩餘 8230 筆\n",
      "[2025-10-14 21:34:13] 去除重複：移除 0 筆，剩餘 8230 筆\n",
      "[2025-10-14 21:34:13] 型別/特徵檢查：移除 1983 筆，剩餘 6247 筆\n",
      "[2025-10-14 21:34:13] 清理完成：初始 8230 筆 → 保留 6247 筆（移除 1983 筆）\n",
      "[2025-10-14 21:34:13] 已寫入 cleaned 檔案：/home/jovyan/RT08/0925/sliding_win/1014_sim/cleaned/PatNo_ID_1593472048.csv（筆數 6247）\n",
      "[2025-10-14 21:34:13] 清理開始：PatNo_ID_1593593586.csv\n",
      "[2025-10-14 21:34:13] 讀入筆數：18882（檔：PatNo_ID_1593593586.csv）\n",
      "[2025-10-14 21:34:13] 缺失清除：移除 0 筆，剩餘 18882 筆\n",
      "[2025-10-14 21:34:13] 時間處理：移除 0 筆無效時間，剩餘 18882 筆\n",
      "[2025-10-14 21:34:13] 去除重複：移除 0 筆，剩餘 18882 筆\n",
      "[2025-10-14 21:34:13] 型別/特徵檢查：移除 7 筆，剩餘 18875 筆\n",
      "[2025-10-14 21:34:13] 清理完成：初始 18882 筆 → 保留 18875 筆（移除 7 筆）\n",
      "[2025-10-14 21:34:13] 已寫入 cleaned 檔案：/home/jovyan/RT08/0925/sliding_win/1014_sim/cleaned/PatNo_ID_1593593586.csv（筆數 18875）\n",
      "[2025-10-14 21:34:13] 清理開始：PatNo_ID_1593720818.csv\n",
      "[2025-10-14 21:34:13] 讀入筆數：3910（檔：PatNo_ID_1593720818.csv）\n",
      "[2025-10-14 21:34:13] 缺失清除：移除 0 筆，剩餘 3910 筆\n",
      "[2025-10-14 21:34:13] 時間處理：移除 0 筆無效時間，剩餘 3910 筆\n",
      "[2025-10-14 21:34:13] 去除重複：移除 0 筆，剩餘 3910 筆\n",
      "[2025-10-14 21:34:13] 型別/特徵檢查：移除 1 筆，剩餘 3909 筆\n",
      "[2025-10-14 21:34:13] 清理完成：初始 3910 筆 → 保留 3909 筆（移除 1 筆）\n",
      "[2025-10-14 21:34:13] 已寫入 cleaned 檔案：/home/jovyan/RT08/0925/sliding_win/1014_sim/cleaned/PatNo_ID_1593720818.csv（筆數 3909）\n",
      "[2025-10-14 21:34:13] 清理開始：PatNo_ID_1593838524.csv\n",
      "[2025-10-14 21:34:13] 讀入筆數：2177（檔：PatNo_ID_1593838524.csv）\n",
      "[2025-10-14 21:34:13] 缺失清除：移除 0 筆，剩餘 2177 筆\n",
      "[2025-10-14 21:34:13] 時間處理：移除 0 筆無效時間，剩餘 2177 筆\n",
      "[2025-10-14 21:34:13] 去除重複：移除 0 筆，剩餘 2177 筆\n",
      "[2025-10-14 21:34:13] 型別/特徵檢查：移除 16 筆，剩餘 2161 筆\n",
      "[2025-10-14 21:34:13] 清理完成：初始 2177 筆 → 保留 2161 筆（移除 16 筆）\n",
      "[2025-10-14 21:34:13] 已寫入 cleaned 檔案：/home/jovyan/RT08/0925/sliding_win/1014_sim/cleaned/PatNo_ID_1593838524.csv（筆數 2161）\n",
      "[2025-10-14 21:34:13] 清理開始：PatNo_ID_1594173718.csv\n",
      "[2025-10-14 21:34:13] 讀入筆數：344（檔：PatNo_ID_1594173718.csv）\n",
      "[2025-10-14 21:34:13] 缺失清除：移除 0 筆，剩餘 344 筆\n",
      "[2025-10-14 21:34:13] 時間處理：移除 0 筆無效時間，剩餘 344 筆\n",
      "[2025-10-14 21:34:13] 去除重複：移除 0 筆，剩餘 344 筆\n",
      "[2025-10-14 21:34:13] 型別/特徵檢查：移除 0 筆，剩餘 344 筆\n",
      "[2025-10-14 21:34:13] 清理完成：初始 344 筆 → 保留 344 筆（移除 0 筆）\n",
      "[2025-10-14 21:34:13] 已寫入 cleaned 檔案：/home/jovyan/RT08/0925/sliding_win/1014_sim/cleaned/PatNo_ID_1594173718.csv（筆數 344）\n",
      "[2025-10-14 21:34:13] 清理開始：PatNo_ID_1594294180.csv\n",
      "[2025-10-14 21:34:13] 讀入筆數：18334（檔：PatNo_ID_1594294180.csv）\n",
      "[2025-10-14 21:34:13] 缺失清除：移除 0 筆，剩餘 18334 筆\n",
      "[2025-10-14 21:34:13] 時間處理：移除 0 筆無效時間，剩餘 18334 筆\n",
      "[2025-10-14 21:34:13] 去除重複：移除 0 筆，剩餘 18334 筆\n",
      "[2025-10-14 21:34:13] 型別/特徵檢查：移除 8 筆，剩餘 18326 筆\n",
      "[2025-10-14 21:34:13] 清理完成：初始 18334 筆 → 保留 18326 筆（移除 8 筆）\n",
      "[2025-10-14 21:34:13] 已寫入 cleaned 檔案：/home/jovyan/RT08/0925/sliding_win/1014_sim/cleaned/PatNo_ID_1594294180.csv（筆數 18326）\n",
      "[2025-10-14 21:34:13] 清理開始：PatNo_ID_1594305136.csv\n",
      "[2025-10-14 21:34:13] 讀入筆數：18679（檔：PatNo_ID_1594305136.csv）\n",
      "[2025-10-14 21:34:13] 缺失清除：移除 0 筆，剩餘 18679 筆\n",
      "[2025-10-14 21:34:13] 時間處理：移除 0 筆無效時間，剩餘 18679 筆\n",
      "[2025-10-14 21:34:13] 去除重複：移除 0 筆，剩餘 18679 筆\n",
      "[2025-10-14 21:34:13] 型別/特徵檢查：移除 101 筆，剩餘 18578 筆\n",
      "[2025-10-14 21:34:13] 清理完成：初始 18679 筆 → 保留 18578 筆（移除 101 筆）\n",
      "[2025-10-14 21:34:14] 已寫入 cleaned 檔案：/home/jovyan/RT08/0925/sliding_win/1014_sim/cleaned/PatNo_ID_1594305136.csv（筆數 18578）\n",
      "[2025-10-14 21:34:14] 清理開始：PatNo_ID_1594309746.csv\n",
      "[2025-10-14 21:34:14] 讀入筆數：3745（檔：PatNo_ID_1594309746.csv）\n",
      "[2025-10-14 21:34:14] 缺失清除：移除 0 筆，剩餘 3745 筆\n",
      "[2025-10-14 21:34:14] 時間處理：移除 0 筆無效時間，剩餘 3745 筆\n",
      "[2025-10-14 21:34:14] 去除重複：移除 0 筆，剩餘 3745 筆\n",
      "[2025-10-14 21:34:14] 型別/特徵檢查：移除 21 筆，剩餘 3724 筆\n",
      "[2025-10-14 21:34:14] 清理完成：初始 3745 筆 → 保留 3724 筆（移除 21 筆）\n",
      "[2025-10-14 21:34:14] 已寫入 cleaned 檔案：/home/jovyan/RT08/0925/sliding_win/1014_sim/cleaned/PatNo_ID_1594309746.csv（筆數 3724）\n",
      "[2025-10-14 21:34:14] 清理開始：PatNo_ID_1594319286.csv\n",
      "[2025-10-14 21:34:14] 讀入筆數：4107（檔：PatNo_ID_1594319286.csv）\n",
      "[2025-10-14 21:34:14] 缺失清除：移除 0 筆，剩餘 4107 筆\n",
      "[2025-10-14 21:34:14] 時間處理：移除 0 筆無效時間，剩餘 4107 筆\n",
      "[2025-10-14 21:34:14] 去除重複：移除 0 筆，剩餘 4107 筆\n",
      "[2025-10-14 21:34:14] 型別/特徵檢查：移除 118 筆，剩餘 3989 筆\n",
      "[2025-10-14 21:34:14] 清理完成：初始 4107 筆 → 保留 3989 筆（移除 118 筆）\n",
      "[2025-10-14 21:34:14] 已寫入 cleaned 檔案：/home/jovyan/RT08/0925/sliding_win/1014_sim/cleaned/PatNo_ID_1594319286.csv（筆數 3989）\n",
      "[2025-10-14 21:34:14] 清理開始：PatNo_ID_1594320763.csv\n",
      "[2025-10-14 21:34:14] 讀入筆數：2431（檔：PatNo_ID_1594320763.csv）\n",
      "[2025-10-14 21:34:14] 缺失清除：移除 0 筆，剩餘 2431 筆\n",
      "[2025-10-14 21:34:14] 時間處理：移除 0 筆無效時間，剩餘 2431 筆\n",
      "[2025-10-14 21:34:14] 去除重複：移除 0 筆，剩餘 2431 筆\n",
      "[2025-10-14 21:34:14] 型別/特徵檢查：移除 1 筆，剩餘 2430 筆\n",
      "[2025-10-14 21:34:14] 清理完成：初始 2431 筆 → 保留 2430 筆（移除 1 筆）\n",
      "[2025-10-14 21:34:14] 已寫入 cleaned 檔案：/home/jovyan/RT08/0925/sliding_win/1014_sim/cleaned/PatNo_ID_1594320763.csv（筆數 2430）\n",
      "[2025-10-14 21:34:14] 清理開始：PatNo_ID_1594322594.csv\n",
      "[2025-10-14 21:34:14] 讀入筆數：5380（檔：PatNo_ID_1594322594.csv）\n",
      "[2025-10-14 21:34:14] 缺失清除：移除 0 筆，剩餘 5380 筆\n",
      "[2025-10-14 21:34:14] 時間處理：移除 0 筆無效時間，剩餘 5380 筆\n",
      "[2025-10-14 21:34:14] 去除重複：移除 0 筆，剩餘 5380 筆\n",
      "[2025-10-14 21:34:14] 型別/特徵檢查：移除 2 筆，剩餘 5378 筆\n",
      "[2025-10-14 21:34:14] 清理完成：初始 5380 筆 → 保留 5378 筆（移除 2 筆）\n",
      "[2025-10-14 21:34:14] 已寫入 cleaned 檔案：/home/jovyan/RT08/0925/sliding_win/1014_sim/cleaned/PatNo_ID_1594322594.csv（筆數 5378）\n",
      "[2025-10-14 21:34:14] 清理開始：PatNo_ID_1594335109.csv\n",
      "[2025-10-14 21:34:14] 讀入筆數：5116（檔：PatNo_ID_1594335109.csv）\n",
      "[2025-10-14 21:34:14] 缺失清除：移除 0 筆，剩餘 5116 筆\n",
      "[2025-10-14 21:34:14] 時間處理：移除 0 筆無效時間，剩餘 5116 筆\n",
      "[2025-10-14 21:34:14] 去除重複：移除 0 筆，剩餘 5116 筆\n",
      "[2025-10-14 21:34:14] 型別/特徵檢查：移除 0 筆，剩餘 5116 筆\n",
      "[2025-10-14 21:34:14] 清理完成：初始 5116 筆 → 保留 5116 筆（移除 0 筆）\n",
      "[2025-10-14 21:34:14] 已寫入 cleaned 檔案：/home/jovyan/RT08/0925/sliding_win/1014_sim/cleaned/PatNo_ID_1594335109.csv（筆數 5116）\n",
      "[2025-10-14 21:34:14] 清理開始：PatNo_ID_1594423683.csv\n",
      "[2025-10-14 21:34:14] 讀入筆數：5023（檔：PatNo_ID_1594423683.csv）\n",
      "[2025-10-14 21:34:14] 缺失清除：移除 0 筆，剩餘 5023 筆\n",
      "[2025-10-14 21:34:14] 時間處理：移除 0 筆無效時間，剩餘 5023 筆\n",
      "[2025-10-14 21:34:14] 去除重複：移除 0 筆，剩餘 5023 筆\n",
      "[2025-10-14 21:34:14] 型別/特徵檢查：移除 19 筆，剩餘 5004 筆\n",
      "[2025-10-14 21:34:14] 清理完成：初始 5023 筆 → 保留 5004 筆（移除 19 筆）\n",
      "[2025-10-14 21:34:14] 已寫入 cleaned 檔案：/home/jovyan/RT08/0925/sliding_win/1014_sim/cleaned/PatNo_ID_1594423683.csv（筆數 5004）\n",
      "[2025-10-14 21:34:14] 清理開始：PatNo_ID_1594437309.csv\n",
      "[2025-10-14 21:34:14] 讀入筆數：10034（檔：PatNo_ID_1594437309.csv）\n",
      "[2025-10-14 21:34:14] 缺失清除：移除 0 筆，剩餘 10034 筆\n",
      "[2025-10-14 21:34:14] 時間處理：移除 0 筆無效時間，剩餘 10034 筆\n",
      "[2025-10-14 21:34:14] 去除重複：移除 0 筆，剩餘 10034 筆\n",
      "[2025-10-14 21:34:14] 型別/特徵檢查：移除 96 筆，剩餘 9938 筆\n",
      "[2025-10-14 21:34:14] 清理完成：初始 10034 筆 → 保留 9938 筆（移除 96 筆）\n",
      "[2025-10-14 21:34:14] 已寫入 cleaned 檔案：/home/jovyan/RT08/0925/sliding_win/1014_sim/cleaned/PatNo_ID_1594437309.csv（筆數 9938）\n",
      "[2025-10-14 21:34:14] 清理開始：PatNo_ID_1594439781.csv\n",
      "[2025-10-14 21:34:14] 讀入筆數：7691（檔：PatNo_ID_1594439781.csv）\n",
      "[2025-10-14 21:34:14] 缺失清除：移除 0 筆，剩餘 7691 筆\n",
      "[2025-10-14 21:34:14] 時間處理：移除 0 筆無效時間，剩餘 7691 筆\n",
      "[2025-10-14 21:34:14] 去除重複：移除 0 筆，剩餘 7691 筆\n",
      "[2025-10-14 21:34:14] 型別/特徵檢查：移除 0 筆，剩餘 7691 筆\n",
      "[2025-10-14 21:34:14] 清理完成：初始 7691 筆 → 保留 7691 筆（移除 0 筆）\n",
      "[2025-10-14 21:34:14] 已寫入 cleaned 檔案：/home/jovyan/RT08/0925/sliding_win/1014_sim/cleaned/PatNo_ID_1594439781.csv（筆數 7691）\n",
      "[2025-10-14 21:34:14] 清理開始：PatNo_ID_1594441887.csv\n",
      "[2025-10-14 21:34:14] 讀入筆數：10200（檔：PatNo_ID_1594441887.csv）\n",
      "[2025-10-14 21:34:14] 缺失清除：移除 0 筆，剩餘 10200 筆\n",
      "[2025-10-14 21:34:14] 時間處理：移除 0 筆無效時間，剩餘 10200 筆\n",
      "[2025-10-14 21:34:14] 去除重複：移除 0 筆，剩餘 10200 筆\n",
      "[2025-10-14 21:34:14] 型別/特徵檢查：移除 3 筆，剩餘 10197 筆\n",
      "[2025-10-14 21:34:14] 清理完成：初始 10200 筆 → 保留 10197 筆（移除 3 筆）\n",
      "[2025-10-14 21:34:14] 已寫入 cleaned 檔案：/home/jovyan/RT08/0925/sliding_win/1014_sim/cleaned/PatNo_ID_1594441887.csv（筆數 10197）\n",
      "[2025-10-14 21:34:14] 清理開始：PatNo_ID_1594448501.csv\n",
      "[2025-10-14 21:34:14] 讀入筆數：5106（檔：PatNo_ID_1594448501.csv）\n",
      "[2025-10-14 21:34:14] 缺失清除：移除 0 筆，剩餘 5106 筆\n",
      "[2025-10-14 21:34:14] 時間處理：移除 0 筆無效時間，剩餘 5106 筆\n",
      "[2025-10-14 21:34:14] 去除重複：移除 0 筆，剩餘 5106 筆\n",
      "[2025-10-14 21:34:14] 型別/特徵檢查：移除 13 筆，剩餘 5093 筆\n",
      "[2025-10-14 21:34:14] 清理完成：初始 5106 筆 → 保留 5093 筆（移除 13 筆）\n",
      "[2025-10-14 21:34:14] 已寫入 cleaned 檔案：/home/jovyan/RT08/0925/sliding_win/1014_sim/cleaned/PatNo_ID_1594448501.csv（筆數 5093）\n",
      "[2025-10-14 21:34:14] 清理開始：PatNo_ID_1594455578.csv\n",
      "[2025-10-14 21:34:14] 讀入筆數：262（檔：PatNo_ID_1594455578.csv）\n",
      "[2025-10-14 21:34:14] 缺失清除：移除 0 筆，剩餘 262 筆\n",
      "[2025-10-14 21:34:14] 時間處理：移除 0 筆無效時間，剩餘 262 筆\n",
      "[2025-10-14 21:34:14] 去除重複：移除 0 筆，剩餘 262 筆\n",
      "[2025-10-14 21:34:14] 型別/特徵檢查：移除 0 筆，剩餘 262 筆\n",
      "[2025-10-14 21:34:14] 清理完成：初始 262 筆 → 保留 262 筆（移除 0 筆）\n",
      "[2025-10-14 21:34:14] 已寫入 cleaned 檔案：/home/jovyan/RT08/0925/sliding_win/1014_sim/cleaned/PatNo_ID_1594455578.csv（筆數 262）\n",
      "[2025-10-14 21:34:14] 清理開始：PatNo_ID_1594464829.csv\n",
      "[2025-10-14 21:34:14] 讀入筆數：4000（檔：PatNo_ID_1594464829.csv）\n",
      "[2025-10-14 21:34:14] 缺失清除：移除 0 筆，剩餘 4000 筆\n",
      "[2025-10-14 21:34:14] 時間處理：移除 0 筆無效時間，剩餘 4000 筆\n",
      "[2025-10-14 21:34:14] 去除重複：移除 0 筆，剩餘 4000 筆\n",
      "[2025-10-14 21:34:14] 型別/特徵檢查：移除 61 筆，剩餘 3939 筆\n",
      "[2025-10-14 21:34:14] 清理完成：初始 4000 筆 → 保留 3939 筆（移除 61 筆）\n",
      "[2025-10-14 21:34:14] 已寫入 cleaned 檔案：/home/jovyan/RT08/0925/sliding_win/1014_sim/cleaned/PatNo_ID_1594464829.csv（筆數 3939）\n",
      "[2025-10-14 21:34:14] 清理開始：PatNo_ID_1594467719.csv\n",
      "[2025-10-14 21:34:14] 讀入筆數：2559（檔：PatNo_ID_1594467719.csv）\n",
      "[2025-10-14 21:34:14] 缺失清除：移除 0 筆，剩餘 2559 筆\n",
      "[2025-10-14 21:34:14] 時間處理：移除 0 筆無效時間，剩餘 2559 筆\n",
      "[2025-10-14 21:34:14] 去除重複：移除 0 筆，剩餘 2559 筆\n",
      "[2025-10-14 21:34:14] 型別/特徵檢查：移除 1246 筆，剩餘 1313 筆\n",
      "[2025-10-14 21:34:14] 清理完成：初始 2559 筆 → 保留 1313 筆（移除 1246 筆）\n",
      "[2025-10-14 21:34:14] 已寫入 cleaned 檔案：/home/jovyan/RT08/0925/sliding_win/1014_sim/cleaned/PatNo_ID_1594467719.csv（筆數 1313）\n",
      "[2025-10-14 21:34:14] 清理開始：PatNo_ID_1594471407.csv\n",
      "[2025-10-14 21:34:14] 讀入筆數：11432（檔：PatNo_ID_1594471407.csv）\n",
      "[2025-10-14 21:34:14] 缺失清除：移除 0 筆，剩餘 11432 筆\n",
      "[2025-10-14 21:34:14] 時間處理：移除 0 筆無效時間，剩餘 11432 筆\n",
      "[2025-10-14 21:34:14] 去除重複：移除 0 筆，剩餘 11432 筆\n",
      "[2025-10-14 21:34:14] 型別/特徵檢查：移除 153 筆，剩餘 11279 筆\n",
      "[2025-10-14 21:34:14] 清理完成：初始 11432 筆 → 保留 11279 筆（移除 153 筆）\n",
      "[2025-10-14 21:34:14] 已寫入 cleaned 檔案：/home/jovyan/RT08/0925/sliding_win/1014_sim/cleaned/PatNo_ID_1594471407.csv（筆數 11279）\n",
      "[2025-10-14 21:34:14] 清理開始：PatNo_ID_1594479330.csv\n",
      "[2025-10-14 21:34:14] 讀入筆數：3727（檔：PatNo_ID_1594479330.csv）\n",
      "[2025-10-14 21:34:14] 缺失清除：移除 0 筆，剩餘 3727 筆\n",
      "[2025-10-14 21:34:14] 時間處理：移除 0 筆無效時間，剩餘 3727 筆\n",
      "[2025-10-14 21:34:14] 去除重複：移除 0 筆，剩餘 3727 筆\n",
      "[2025-10-14 21:34:14] 型別/特徵檢查：移除 3 筆，剩餘 3724 筆\n",
      "[2025-10-14 21:34:14] 清理完成：初始 3727 筆 → 保留 3724 筆（移除 3 筆）\n",
      "[2025-10-14 21:34:14] 已寫入 cleaned 檔案：/home/jovyan/RT08/0925/sliding_win/1014_sim/cleaned/PatNo_ID_1594479330.csv（筆數 3724）\n",
      "[2025-10-14 21:34:14] 清理開始：PatNo_ID_1594511911.csv\n",
      "[2025-10-14 21:34:14] 讀入筆數：5488（檔：PatNo_ID_1594511911.csv）\n",
      "[2025-10-14 21:34:14] 缺失清除：移除 0 筆，剩餘 5488 筆\n",
      "[2025-10-14 21:34:14] 時間處理：移除 0 筆無效時間，剩餘 5488 筆\n",
      "[2025-10-14 21:34:14] 去除重複：移除 0 筆，剩餘 5488 筆\n",
      "[2025-10-14 21:34:14] 型別/特徵檢查：移除 33 筆，剩餘 5455 筆\n",
      "[2025-10-14 21:34:14] 清理完成：初始 5488 筆 → 保留 5455 筆（移除 33 筆）\n",
      "[2025-10-14 21:34:14] 已寫入 cleaned 檔案：/home/jovyan/RT08/0925/sliding_win/1014_sim/cleaned/PatNo_ID_1594511911.csv（筆數 5455）\n",
      "[2025-10-14 21:34:14] 清理開始：PatNo_ID_1594511914.csv\n",
      "[2025-10-14 21:34:15] 讀入筆數：9717（檔：PatNo_ID_1594511914.csv）\n",
      "[2025-10-14 21:34:15] 缺失清除：移除 0 筆，剩餘 9717 筆\n",
      "[2025-10-14 21:34:15] 時間處理：移除 0 筆無效時間，剩餘 9717 筆\n",
      "[2025-10-14 21:34:15] 去除重複：移除 0 筆，剩餘 9717 筆\n",
      "[2025-10-14 21:34:15] 型別/特徵檢查：移除 32 筆，剩餘 9685 筆\n",
      "[2025-10-14 21:34:15] 清理完成：初始 9717 筆 → 保留 9685 筆（移除 32 筆）\n",
      "[2025-10-14 21:34:15] 已寫入 cleaned 檔案：/home/jovyan/RT08/0925/sliding_win/1014_sim/cleaned/PatNo_ID_1594511914.csv（筆數 9685）\n",
      "[2025-10-14 21:34:15] 清理開始：PatNo_ID_1594528842.csv\n",
      "[2025-10-14 21:34:15] 讀入筆數：2491（檔：PatNo_ID_1594528842.csv）\n",
      "[2025-10-14 21:34:15] 缺失清除：移除 0 筆，剩餘 2491 筆\n",
      "[2025-10-14 21:34:15] 時間處理：移除 0 筆無效時間，剩餘 2491 筆\n",
      "[2025-10-14 21:34:15] 去除重複：移除 0 筆，剩餘 2491 筆\n",
      "[2025-10-14 21:34:15] 型別/特徵檢查：移除 76 筆，剩餘 2415 筆\n",
      "[2025-10-14 21:34:15] 清理完成：初始 2491 筆 → 保留 2415 筆（移除 76 筆）\n",
      "[2025-10-14 21:34:15] 已寫入 cleaned 檔案：/home/jovyan/RT08/0925/sliding_win/1014_sim/cleaned/PatNo_ID_1594528842.csv（筆數 2415）\n",
      "[2025-10-14 21:34:15] 清理開始：PatNo_ID_1594533379.csv\n",
      "[2025-10-14 21:34:15] 讀入筆數：1030（檔：PatNo_ID_1594533379.csv）\n",
      "[2025-10-14 21:34:15] 缺失清除：移除 0 筆，剩餘 1030 筆\n",
      "[2025-10-14 21:34:15] 時間處理：移除 0 筆無效時間，剩餘 1030 筆\n",
      "[2025-10-14 21:34:15] 去除重複：移除 0 筆，剩餘 1030 筆\n",
      "[2025-10-14 21:34:15] 型別/特徵檢查：移除 12 筆，剩餘 1018 筆\n",
      "[2025-10-14 21:34:15] 清理完成：初始 1030 筆 → 保留 1018 筆（移除 12 筆）\n",
      "[2025-10-14 21:34:15] 已寫入 cleaned 檔案：/home/jovyan/RT08/0925/sliding_win/1014_sim/cleaned/PatNo_ID_1594533379.csv（筆數 1018）\n",
      "[2025-10-14 21:34:15] 已寫入清理層稽核總表：/home/jovyan/RT08/0925/sliding_win/1014_sim/cleaned/audit_summary.csv\n",
      "[2025-10-14 21:34:15] 清理層總結：原始總筆數=1567233、清理後總筆數=1523381、缺失移除總筆數=0、時間錯誤移除總筆數=0、重複移除總筆數=0、型別/特徵錯誤移除總筆數=43852\n",
      "[2025-10-14 21:34:15] ================== 步驟 3｜掃描 cleaned 檔案作視窗化 ==================\n",
      "[2025-10-14 21:34:15] cleaned 檔案數量：122\n",
      "[2025-10-14 21:34:15] ================== 步驟 4｜逐檔產生視窗並計數 ==================\n",
      "[2025-10-14 21:34:15] [視窗前置] 檔案：089271.csv｜筆數：32368\n",
      "[2025-10-14 21:34:15] [視窗結果] 檔案：089271.csv｜生成視窗=1077｜正樣本=184｜負樣本=893｜連續性違規=99\n",
      "[2025-10-14 21:34:15] [視窗前置] 檔案：095323.csv｜筆數：23785\n",
      "[2025-10-14 21:34:15] [視窗結果] 檔案：095323.csv｜生成視窗=791｜正樣本=73｜負樣本=718｜連續性違規=33\n",
      "[2025-10-14 21:34:15] [視窗前置] 檔案：095707.csv｜筆數：20179\n",
      "[2025-10-14 21:34:15] [視窗結果] 檔案：095707.csv｜生成視窗=671｜正樣本=93｜負樣本=578｜連續性違規=29\n",
      "[2025-10-14 21:34:15] [視窗前置] 檔案：114309.csv｜筆數：71723\n",
      "[2025-10-14 21:34:15] [視窗結果] 檔案：114309.csv｜生成視窗=2389｜正樣本=126｜負樣本=2263｜連續性違規=241\n",
      "[2025-10-14 21:34:15] [視窗前置] 檔案：230933.csv｜筆數：30241\n",
      "[2025-10-14 21:34:15] [視窗結果] 檔案：230933.csv｜生成視窗=1007｜正樣本=147｜負樣本=860｜連續性違規=54\n",
      "[2025-10-14 21:34:15] [視窗前置] 檔案：4216007.csv｜筆數：527\n",
      "[2025-10-14 21:34:15] [視窗結果] 檔案：4216007.csv｜生成視窗=16｜正樣本=0｜負樣本=16｜連續性違規=3\n",
      "[2025-10-14 21:34:15] [視窗前置] 檔案：7108162.csv｜筆數：239\n",
      "[2025-10-14 21:34:15] [視窗結果] 檔案：7108162.csv｜生成視窗=6｜正樣本=2｜負樣本=4｜連續性違規=0\n",
      "[2025-10-14 21:34:15] [視窗前置] 檔案：7408338.csv｜筆數：1431\n",
      "[2025-10-14 21:34:15] [視窗結果] 檔案：7408338.csv｜生成視窗=46｜正樣本=7｜負樣本=39｜連續性違規=7\n",
      "[2025-10-14 21:34:15] [視窗前置] 檔案：7657698.csv｜筆數：1413\n",
      "[2025-10-14 21:34:15] [視窗結果] 檔案：7657698.csv｜生成視窗=46｜正樣本=9｜負樣本=37｜連續性違規=13\n",
      "[2025-10-14 21:34:15] [視窗前置] 檔案：7721164.csv｜筆數：483\n",
      "[2025-10-14 21:34:15] [視窗結果] 檔案：7721164.csv｜生成視窗=15｜正樣本=3｜負樣本=12｜連續性違規=3\n",
      "[2025-10-14 21:34:15] [視窗前置] 檔案：PatNo_ID_1560013303.csv｜筆數：2514\n",
      "[2025-10-14 21:34:15] [視窗結果] 檔案：PatNo_ID_1560013303.csv｜生成視窗=82｜正樣本=6｜負樣本=76｜連續性違規=0\n",
      "[2025-10-14 21:34:15] [視窗前置] 檔案：PatNo_ID_1562733396.csv｜筆數：2253\n",
      "[2025-10-14 21:34:15] [視窗結果] 檔案：PatNo_ID_1562733396.csv｜生成視窗=74｜正樣本=15｜負樣本=59｜連續性違規=34\n",
      "[2025-10-14 21:34:15] [視窗前置] 檔案：PatNo_ID_1563587183.csv｜筆數：5286\n",
      "[2025-10-14 21:34:15] [視窗結果] 檔案：PatNo_ID_1563587183.csv｜生成視窗=175｜正樣本=30｜負樣本=145｜連續性違規=12\n",
      "[2025-10-14 21:34:15] [視窗前置] 檔案：PatNo_ID_1564148644.csv｜筆數：14384\n",
      "[2025-10-14 21:34:16] [視窗結果] 檔案：PatNo_ID_1564148644.csv｜生成視窗=478｜正樣本=57｜負樣本=421｜連續性違規=34\n",
      "[2025-10-14 21:34:16] [視窗前置] 檔案：PatNo_ID_1565148312.csv｜筆數：5464\n",
      "[2025-10-14 21:34:16] [視窗結果] 檔案：PatNo_ID_1565148312.csv｜生成視窗=181｜正樣本=26｜負樣本=155｜連續性違規=2\n",
      "[2025-10-14 21:34:16] [視窗前置] 檔案：PatNo_ID_1565378038.csv｜筆數：2561\n",
      "[2025-10-14 21:34:16] [視窗結果] 檔案：PatNo_ID_1565378038.csv｜生成視窗=84｜正樣本=14｜負樣本=70｜連續性違規=8\n",
      "[2025-10-14 21:34:16] [視窗前置] 檔案：PatNo_ID_1566123680.csv｜筆數：42597\n",
      "[2025-10-14 21:34:16] [視窗結果] 檔案：PatNo_ID_1566123680.csv｜生成視窗=1418｜正樣本=119｜負樣本=1299｜連續性違規=179\n",
      "[2025-10-14 21:34:16] [視窗前置] 檔案：PatNo_ID_1566252197.csv｜筆數：1935\n",
      "[2025-10-14 21:34:16] [視窗結果] 檔案：PatNo_ID_1566252197.csv｜生成視窗=63｜正樣本=9｜負樣本=54｜連續性違規=0\n",
      "[2025-10-14 21:34:16] [視窗前置] 檔案：PatNo_ID_1566279967.csv｜筆數：1153\n",
      "[2025-10-14 21:34:16] [視窗結果] 檔案：PatNo_ID_1566279967.csv｜生成視窗=37｜正樣本=7｜負樣本=30｜連續性違規=4\n",
      "[2025-10-14 21:34:16] [視窗前置] 檔案：PatNo_ID_1566671274.csv｜筆數：23554\n",
      "[2025-10-14 21:34:16] [視窗結果] 檔案：PatNo_ID_1566671274.csv｜生成視窗=784｜正樣本=122｜負樣本=662｜連續性違規=80\n",
      "[2025-10-14 21:34:16] [視窗前置] 檔案：PatNo_ID_1566911879.csv｜筆數：45975\n",
      "[2025-10-14 21:34:16] [視窗結果] 檔案：PatNo_ID_1566911879.csv｜生成視窗=1531｜正樣本=297｜負樣本=1234｜連續性違規=32\n",
      "[2025-10-14 21:34:16] [視窗前置] 檔案：PatNo_ID_1567747650.csv｜筆數：10898\n",
      "[2025-10-14 21:34:16] [視窗結果] 檔案：PatNo_ID_1567747650.csv｜生成視窗=362｜正樣本=58｜負樣本=304｜連續性違規=10\n",
      "[2025-10-14 21:34:16] [視窗前置] 檔案：PatNo_ID_1567804800.csv｜筆數：20389\n",
      "[2025-10-14 21:34:16] [視窗結果] 檔案：PatNo_ID_1567804800.csv｜生成視窗=678｜正樣本=79｜負樣本=599｜連續性違規=28\n",
      "[2025-10-14 21:34:16] [視窗前置] 檔案：PatNo_ID_1567832735.csv｜筆數：36569\n",
      "[2025-10-14 21:34:16] [視窗結果] 檔案：PatNo_ID_1567832735.csv｜生成視窗=1217｜正樣本=205｜負樣本=1012｜連續性違規=32\n",
      "[2025-10-14 21:34:16] [視窗前置] 檔案：PatNo_ID_1568039398.csv｜筆數：34242\n",
      "[2025-10-14 21:34:16] [視窗結果] 檔案：PatNo_ID_1568039398.csv｜生成視窗=1140｜正樣本=200｜負樣本=940｜連續性違規=38\n",
      "[2025-10-14 21:34:16] [視窗前置] 檔案：PatNo_ID_1568574099.csv｜筆數：13863\n",
      "[2025-10-14 21:34:16] [視窗結果] 檔案：PatNo_ID_1568574099.csv｜生成視窗=461｜正樣本=65｜負樣本=396｜連續性違規=0\n",
      "[2025-10-14 21:34:16] [視窗前置] 檔案：PatNo_ID_1568813269.csv｜筆數：4867\n",
      "[2025-10-14 21:34:16] [視窗結果] 檔案：PatNo_ID_1568813269.csv｜生成視窗=161｜正樣本=33｜負樣本=128｜連續性違規=9\n",
      "[2025-10-14 21:34:16] [視窗前置] 檔案：PatNo_ID_1568952422.csv｜筆數：1184\n",
      "[2025-10-14 21:34:17] [視窗結果] 檔案：PatNo_ID_1568952422.csv｜生成視窗=38｜正樣本=12｜負樣本=26｜連續性違規=0\n",
      "[2025-10-14 21:34:17] [視窗前置] 檔案：PatNo_ID_1569083701.csv｜筆數：3326\n",
      "[2025-10-14 21:34:17] [視窗結果] 檔案：PatNo_ID_1569083701.csv｜生成視窗=109｜正樣本=7｜負樣本=102｜連續性違規=20\n",
      "[2025-10-14 21:34:17] [視窗前置] 檔案：PatNo_ID_1569944983.csv｜筆數：7647\n",
      "[2025-10-14 21:34:17] [視窗結果] 檔案：PatNo_ID_1569944983.csv｜生成視窗=253｜正樣本=32｜負樣本=221｜連續性違規=2\n",
      "[2025-10-14 21:34:17] [視窗前置] 檔案：PatNo_ID_1570089466.csv｜筆數：40589\n",
      "[2025-10-14 21:34:17] [視窗結果] 檔案：PatNo_ID_1570089466.csv｜生成視窗=1351｜正樣本=81｜負樣本=1270｜連續性違規=57\n",
      "[2025-10-14 21:34:17] [視窗前置] 檔案：PatNo_ID_1570242703.csv｜筆數：10556\n",
      "[2025-10-14 21:34:17] [視窗結果] 檔案：PatNo_ID_1570242703.csv｜生成視窗=350｜正樣本=58｜負樣本=292｜連續性違規=0\n",
      "[2025-10-14 21:34:17] [視窗前置] 檔案：PatNo_ID_1570273244.csv｜筆數：9707\n",
      "[2025-10-14 21:34:17] [視窗結果] 檔案：PatNo_ID_1570273244.csv｜生成視窗=322｜正樣本=63｜負樣本=259｜連續性違規=2\n",
      "[2025-10-14 21:34:17] [視窗前置] 檔案：PatNo_ID_1570642083.csv｜筆數：19731\n",
      "[2025-10-14 21:34:17] [視窗結果] 檔案：PatNo_ID_1570642083.csv｜生成視窗=656｜正樣本=81｜負樣本=575｜連續性違規=11\n",
      "[2025-10-14 21:34:17] [視窗前置] 檔案：PatNo_ID_1571945701.csv｜筆數：15729\n",
      "[2025-10-14 21:34:17] [視窗結果] 檔案：PatNo_ID_1571945701.csv｜生成視窗=523｜正樣本=94｜負樣本=429｜連續性違規=2\n",
      "[2025-10-14 21:34:17] [視窗前置] 檔案：PatNo_ID_1572481361.csv｜筆數：34515\n",
      "[2025-10-14 21:34:17] [視窗結果] 檔案：PatNo_ID_1572481361.csv｜生成視窗=1149｜正樣本=192｜負樣本=957｜連續性違規=46\n",
      "[2025-10-14 21:34:17] [視窗前置] 檔案：PatNo_ID_1572562839.csv｜筆數：20920\n",
      "[2025-10-14 21:34:17] [視窗結果] 檔案：PatNo_ID_1572562839.csv｜生成視窗=696｜正樣本=94｜負樣本=602｜連續性違規=71\n",
      "[2025-10-14 21:34:17] [視窗前置] 檔案：PatNo_ID_1572831765.csv｜筆數：2696\n",
      "[2025-10-14 21:34:17] [視窗結果] 檔案：PatNo_ID_1572831765.csv｜生成視窗=88｜正樣本=15｜負樣本=73｜連續性違規=0\n",
      "[2025-10-14 21:34:17] [視窗前置] 檔案：PatNo_ID_1572976822.csv｜筆數：6624\n",
      "[2025-10-14 21:34:17] [視窗結果] 檔案：PatNo_ID_1572976822.csv｜生成視窗=219｜正樣本=25｜負樣本=194｜連續性違規=59\n",
      "[2025-10-14 21:34:17] [視窗前置] 檔案：PatNo_ID_1573063188.csv｜筆數：5025\n",
      "[2025-10-14 21:34:17] [視窗結果] 檔案：PatNo_ID_1573063188.csv｜生成視窗=166｜正樣本=19｜負樣本=147｜連續性違規=10\n",
      "[2025-10-14 21:34:17] [視窗前置] 檔案：PatNo_ID_1573249295.csv｜筆數：7151\n",
      "[2025-10-14 21:34:17] [視窗結果] 檔案：PatNo_ID_1573249295.csv｜生成視窗=237｜正樣本=49｜負樣本=188｜連續性違規=69\n",
      "[2025-10-14 21:34:17] [視窗前置] 檔案：PatNo_ID_1573964540.csv｜筆數：4347\n",
      "[2025-10-14 21:34:17] [視窗結果] 檔案：PatNo_ID_1573964540.csv｜生成視窗=143｜正樣本=25｜負樣本=118｜連續性違規=4\n",
      "[2025-10-14 21:34:17] [視窗前置] 檔案：PatNo_ID_1574148494.csv｜筆數：47017\n",
      "[2025-10-14 21:34:17] [視窗結果] 檔案：PatNo_ID_1574148494.csv｜生成視窗=1566｜正樣本=275｜負樣本=1291｜連續性違規=32\n",
      "[2025-10-14 21:34:18] [視窗前置] 檔案：PatNo_ID_1574270349.csv｜筆數：6515\n",
      "[2025-10-14 21:34:18] [視窗結果] 檔案：PatNo_ID_1574270349.csv｜生成視窗=216｜正樣本=41｜負樣本=175｜連續性違規=0\n",
      "[2025-10-14 21:34:18] [視窗前置] 檔案：PatNo_ID_1574528808.csv｜筆數：14480\n",
      "[2025-10-14 21:34:18] [視窗結果] 檔案：PatNo_ID_1574528808.csv｜生成視窗=481｜正樣本=58｜負樣本=423｜連續性違規=118\n",
      "[2025-10-14 21:34:18] [視窗前置] 檔案：PatNo_ID_1574831525.csv｜筆數：515\n",
      "[2025-10-14 21:34:18] [視窗結果] 檔案：PatNo_ID_1574831525.csv｜生成視窗=16｜正樣本=5｜負樣本=11｜連續性違規=0\n",
      "[2025-10-14 21:34:18] [視窗前置] 檔案：PatNo_ID_1574987447.csv｜筆數：19838\n",
      "[2025-10-14 21:34:18] [視窗結果] 檔案：PatNo_ID_1574987447.csv｜生成視窗=660｜正樣本=103｜負樣本=557｜連續性違規=33\n",
      "[2025-10-14 21:34:18] [視窗前置] 檔案：PatNo_ID_1575060177.csv｜筆數：5290\n",
      "[2025-10-14 21:34:18] [視窗結果] 檔案：PatNo_ID_1575060177.csv｜生成視窗=175｜正樣本=28｜負樣本=147｜連續性違規=0\n",
      "[2025-10-14 21:34:18] [視窗前置] 檔案：PatNo_ID_1575256902.csv｜筆數：9492\n",
      "[2025-10-14 21:34:18] [視窗結果] 檔案：PatNo_ID_1575256902.csv｜生成視窗=315｜正樣本=34｜負樣本=281｜連續性違規=26\n",
      "[2025-10-14 21:34:18] [視窗前置] 檔案：PatNo_ID_1575445051.csv｜筆數：1785\n",
      "[2025-10-14 21:34:18] [視窗結果] 檔案：PatNo_ID_1575445051.csv｜生成視窗=58｜正樣本=6｜負樣本=52｜連續性違規=2\n",
      "[2025-10-14 21:34:18] [視窗前置] 檔案：PatNo_ID_1575502382.csv｜筆數：6489\n",
      "[2025-10-14 21:34:18] [視窗結果] 檔案：PatNo_ID_1575502382.csv｜生成視窗=215｜正樣本=35｜負樣本=180｜連續性違規=16\n",
      "[2025-10-14 21:34:18] [視窗前置] 檔案：PatNo_ID_1575975485.csv｜筆數：16458\n",
      "[2025-10-14 21:34:18] [視窗結果] 檔案：PatNo_ID_1575975485.csv｜生成視窗=547｜正樣本=97｜負樣本=450｜連續性違規=10\n",
      "[2025-10-14 21:34:18] [視窗前置] 檔案：PatNo_ID_1576115572.csv｜筆數：18017\n",
      "[2025-10-14 21:34:18] [視窗結果] 檔案：PatNo_ID_1576115572.csv｜生成視窗=599｜正樣本=100｜負樣本=499｜連續性違規=163\n",
      "[2025-10-14 21:34:18] [視窗前置] 檔案：PatNo_ID_1576116479.csv｜筆數：1257\n",
      "[2025-10-14 21:34:18] [視窗結果] 檔案：PatNo_ID_1576116479.csv｜生成視窗=40｜正樣本=6｜負樣本=34｜連續性違規=0\n",
      "[2025-10-14 21:34:18] [視窗前置] 檔案：PatNo_ID_1576301569.csv｜筆數：3207\n",
      "[2025-10-14 21:34:18] [視窗結果] 檔案：PatNo_ID_1576301569.csv｜生成視窗=105｜正樣本=16｜負樣本=89｜連續性違規=37\n",
      "[2025-10-14 21:34:18] [視窗前置] 檔案：PatNo_ID_1576964560.csv｜筆數：24188\n",
      "[2025-10-14 21:34:18] [視窗結果] 檔案：PatNo_ID_1576964560.csv｜生成視窗=805｜正樣本=122｜負樣本=683｜連續性違規=4\n",
      "[2025-10-14 21:34:18] [視窗前置] 檔案：PatNo_ID_1577042911.csv｜筆數：35624\n",
      "[2025-10-14 21:34:18] [視窗結果] 檔案：PatNo_ID_1577042911.csv｜生成視窗=1186｜正樣本=198｜負樣本=988｜連續性違規=16\n",
      "[2025-10-14 21:34:18] [視窗前置] 檔案：PatNo_ID_1577487284.csv｜筆數：2345\n",
      "[2025-10-14 21:34:18] [視窗結果] 檔案：PatNo_ID_1577487284.csv｜生成視窗=77｜正樣本=20｜負樣本=57｜連續性違規=12\n",
      "[2025-10-14 21:34:18] [視窗前置] 檔案：PatNo_ID_1578784257.csv｜筆數：28555\n",
      "[2025-10-14 21:34:18] [視窗結果] 檔案：PatNo_ID_1578784257.csv｜生成視窗=950｜正樣本=150｜負樣本=800｜連續性違規=25\n",
      "[2025-10-14 21:34:18] [視窗前置] 檔案：PatNo_ID_1579198603.csv｜筆數：2045\n",
      "[2025-10-14 21:34:18] [視窗結果] 檔案：PatNo_ID_1579198603.csv｜生成視窗=67｜正樣本=14｜負樣本=53｜連續性違規=0\n",
      "[2025-10-14 21:34:18] [視窗前置] 檔案：PatNo_ID_1579498177.csv｜筆數：21681\n",
      "[2025-10-14 21:34:18] [視窗結果] 檔案：PatNo_ID_1579498177.csv｜生成視窗=721｜正樣本=131｜負樣本=590｜連續性違規=244\n",
      "[2025-10-14 21:34:18] [視窗前置] 檔案：PatNo_ID_1580062580.csv｜筆數：2150\n",
      "[2025-10-14 21:34:18] [視窗結果] 檔案：PatNo_ID_1580062580.csv｜生成視窗=70｜正樣本=17｜負樣本=53｜連續性違規=9\n",
      "[2025-10-14 21:34:18] [視窗前置] 檔案：PatNo_ID_1580096720.csv｜筆數：1422\n",
      "[2025-10-14 21:34:18] [視窗結果] 檔案：PatNo_ID_1580096720.csv｜生成視窗=46｜正樣本=5｜負樣本=41｜連續性違規=6\n",
      "[2025-10-14 21:34:18] [視窗前置] 檔案：PatNo_ID_1580107637.csv｜筆數：2688\n",
      "[2025-10-14 21:34:18] [視窗結果] 檔案：PatNo_ID_1580107637.csv｜生成視窗=88｜正樣本=0｜負樣本=88｜連續性違規=0\n",
      "[2025-10-14 21:34:18] [視窗前置] 檔案：PatNo_ID_1580244614.csv｜筆數：5024\n",
      "[2025-10-14 21:34:18] [視窗結果] 檔案：PatNo_ID_1580244614.csv｜生成視窗=166｜正樣本=26｜負樣本=140｜連續性違規=8\n",
      "[2025-10-14 21:34:18] [視窗前置] 檔案：PatNo_ID_1580766093.csv｜筆數：18064\n",
      "[2025-10-14 21:34:19] [視窗結果] 檔案：PatNo_ID_1580766093.csv｜生成視窗=601｜正樣本=100｜負樣本=501｜連續性違規=40\n",
      "[2025-10-14 21:34:19] [視窗前置] 檔案：PatNo_ID_1581003248.csv｜筆數：7775\n",
      "[2025-10-14 21:34:19] [視窗結果] 檔案：PatNo_ID_1581003248.csv｜生成視窗=258｜正樣本=34｜負樣本=224｜連續性違規=12\n",
      "[2025-10-14 21:34:19] [視窗前置] 檔案：PatNo_ID_1581019504.csv｜筆數：21526\n",
      "[2025-10-14 21:34:19] [視窗結果] 檔案：PatNo_ID_1581019504.csv｜生成視窗=716｜正樣本=141｜負樣本=575｜連續性違規=40\n",
      "[2025-10-14 21:34:19] [視窗前置] 檔案：PatNo_ID_1581633231.csv｜筆數：15971\n",
      "[2025-10-14 21:34:19] [視窗結果] 檔案：PatNo_ID_1581633231.csv｜生成視窗=531｜正樣本=73｜負樣本=458｜連續性違規=156\n",
      "[2025-10-14 21:34:19] [視窗前置] 檔案：PatNo_ID_1581692973.csv｜筆數：2723\n",
      "[2025-10-14 21:34:19] [視窗結果] 檔案：PatNo_ID_1581692973.csv｜生成視窗=89｜正樣本=15｜負樣本=74｜連續性違規=10\n",
      "[2025-10-14 21:34:19] [視窗前置] 檔案：PatNo_ID_1582452511.csv｜筆數：5196\n",
      "[2025-10-14 21:34:19] [視窗結果] 檔案：PatNo_ID_1582452511.csv｜生成視窗=172｜正樣本=26｜負樣本=146｜連續性違規=4\n",
      "[2025-10-14 21:34:19] [視窗前置] 檔案：PatNo_ID_1582635996.csv｜筆數：13046\n",
      "[2025-10-14 21:34:19] [視窗結果] 檔案：PatNo_ID_1582635996.csv｜生成視窗=433｜正樣本=80｜負樣本=353｜連續性違規=10\n",
      "[2025-10-14 21:34:19] [視窗前置] 檔案：PatNo_ID_1582849900.csv｜筆數：7387\n",
      "[2025-10-14 21:34:19] [視窗結果] 檔案：PatNo_ID_1582849900.csv｜生成視窗=245｜正樣本=9｜負樣本=236｜連續性違規=32\n",
      "[2025-10-14 21:34:19] [視窗前置] 檔案：PatNo_ID_1582937076.csv｜筆數：23966\n",
      "[2025-10-14 21:34:19] [視窗結果] 檔案：PatNo_ID_1582937076.csv｜生成視窗=797｜正樣本=139｜負樣本=658｜連續性違規=208\n",
      "[2025-10-14 21:34:19] [視窗前置] 檔案：PatNo_ID_1584158973.csv｜筆數：2882\n",
      "[2025-10-14 21:34:19] [視窗結果] 檔案：PatNo_ID_1584158973.csv｜生成視窗=95｜正樣本=18｜負樣本=77｜連續性違規=11\n",
      "[2025-10-14 21:34:19] [視窗前置] 檔案：PatNo_ID_1584397376.csv｜筆數：638\n",
      "[2025-10-14 21:34:19] [視窗結果] 檔案：PatNo_ID_1584397376.csv｜生成視窗=20｜正樣本=1｜負樣本=19｜連續性違規=0\n",
      "[2025-10-14 21:34:19] [視窗前置] 檔案：PatNo_ID_1586172659.csv｜筆數：38547\n",
      "[2025-10-14 21:34:19] [視窗結果] 檔案：PatNo_ID_1586172659.csv｜生成視窗=1283｜正樣本=198｜負樣本=1085｜連續性違規=25\n",
      "[2025-10-14 21:34:19] [視窗前置] 檔案：PatNo_ID_1586696634.csv｜筆數：2504\n",
      "[2025-10-14 21:34:19] [視窗結果] 檔案：PatNo_ID_1586696634.csv｜生成視窗=82｜正樣本=12｜負樣本=70｜連續性違規=4\n",
      "[2025-10-14 21:34:19] [視窗前置] 檔案：PatNo_ID_1586897008.csv｜筆數：6655\n",
      "[2025-10-14 21:34:19] [視窗結果] 檔案：PatNo_ID_1586897008.csv｜生成視窗=220｜正樣本=34｜負樣本=186｜連續性違規=7\n",
      "[2025-10-14 21:34:19] [視窗前置] 檔案：PatNo_ID_1587490083.csv｜筆數：45186\n",
      "[2025-10-14 21:34:19] [視窗結果] 檔案：PatNo_ID_1587490083.csv｜生成視窗=1505｜正樣本=250｜負樣本=1255｜連續性違規=693\n",
      "[2025-10-14 21:34:19] [視窗前置] 檔案：PatNo_ID_1588632604.csv｜筆數：2608\n",
      "[2025-10-14 21:34:19] [視窗結果] 檔案：PatNo_ID_1588632604.csv｜生成視窗=85｜正樣本=13｜負樣本=72｜連續性違規=5\n",
      "[2025-10-14 21:34:19] [視窗前置] 檔案：PatNo_ID_1588673465.csv｜筆數：5554\n",
      "[2025-10-14 21:34:19] [視窗結果] 檔案：PatNo_ID_1588673465.csv｜生成視窗=184｜正樣本=42｜負樣本=142｜連續性違規=17\n",
      "[2025-10-14 21:34:19] [視窗前置] 檔案：PatNo_ID_1588794796.csv｜筆數：9375\n",
      "[2025-10-14 21:34:19] [視窗結果] 檔案：PatNo_ID_1588794796.csv｜生成視窗=311｜正樣本=59｜負樣本=252｜連續性違規=8\n",
      "[2025-10-14 21:34:19] [視窗前置] 檔案：PatNo_ID_1588957997.csv｜筆數：10966\n",
      "[2025-10-14 21:34:19] [視窗結果] 檔案：PatNo_ID_1588957997.csv｜生成視窗=364｜正樣本=49｜負樣本=315｜連續性違規=2\n",
      "[2025-10-14 21:34:19] [視窗前置] 檔案：PatNo_ID_1589018086.csv｜筆數：13450\n",
      "[2025-10-14 21:34:20] [視窗結果] 檔案：PatNo_ID_1589018086.csv｜生成視窗=447｜正樣本=69｜負樣本=378｜連續性違規=8\n",
      "[2025-10-14 21:34:20] [視窗前置] 檔案：PatNo_ID_1589034524.csv｜筆數：50039\n",
      "[2025-10-14 21:34:20] [視窗結果] 檔案：PatNo_ID_1589034524.csv｜生成視窗=1666｜正樣本=267｜負樣本=1399｜連續性違規=394\n",
      "[2025-10-14 21:34:20] [視窗前置] 檔案：PatNo_ID_1589324603.csv｜筆數：4111\n",
      "[2025-10-14 21:34:20] [視窗結果] 檔案：PatNo_ID_1589324603.csv｜生成視窗=136｜正樣本=15｜負樣本=121｜連續性違規=0\n",
      "[2025-10-14 21:34:20] [視窗前置] 檔案：PatNo_ID_1589918099.csv｜筆數：9332\n",
      "[2025-10-14 21:34:20] [視窗結果] 檔案：PatNo_ID_1589918099.csv｜生成視窗=310｜正樣本=0｜負樣本=310｜連續性違規=60\n",
      "[2025-10-14 21:34:20] [視窗前置] 檔案：PatNo_ID_1590136310.csv｜筆數：5307\n",
      "[2025-10-14 21:34:20] [視窗結果] 檔案：PatNo_ID_1590136310.csv｜生成視窗=175｜正樣本=20｜負樣本=155｜連續性違規=81\n",
      "[2025-10-14 21:34:20] [視窗前置] 檔案：PatNo_ID_1590616537.csv｜筆數：14206\n",
      "[2025-10-14 21:34:20] [視窗結果] 檔案：PatNo_ID_1590616537.csv｜生成視窗=472｜正樣本=76｜負樣本=396｜連續性違規=3\n",
      "[2025-10-14 21:34:20] [視窗前置] 檔案：PatNo_ID_1590854576.csv｜筆數：15879\n",
      "[2025-10-14 21:34:20] [視窗結果] 檔案：PatNo_ID_1590854576.csv｜生成視窗=528｜正樣本=90｜負樣本=438｜連續性違規=125\n",
      "[2025-10-14 21:34:20] [視窗前置] 檔案：PatNo_ID_1591609798.csv｜筆數：35617\n",
      "[2025-10-14 21:34:20] [視窗結果] 檔案：PatNo_ID_1591609798.csv｜生成視窗=1186｜正樣本=176｜負樣本=1010｜連續性違規=2\n",
      "[2025-10-14 21:34:20] [視窗前置] 檔案：PatNo_ID_1592044724.csv｜筆數：6812\n",
      "[2025-10-14 21:34:20] [視窗結果] 檔案：PatNo_ID_1592044724.csv｜生成視窗=226｜正樣本=46｜負樣本=180｜連續性違規=0\n",
      "[2025-10-14 21:34:20] [視窗前置] 檔案：PatNo_ID_1592560504.csv｜筆數：18051\n",
      "[2025-10-14 21:34:20] [視窗結果] 檔案：PatNo_ID_1592560504.csv｜生成視窗=600｜正樣本=71｜負樣本=529｜連續性違規=12\n",
      "[2025-10-14 21:34:20] [視窗前置] 檔案：PatNo_ID_1593087886.csv｜筆數：23868\n",
      "[2025-10-14 21:34:20] [視窗結果] 檔案：PatNo_ID_1593087886.csv｜生成視窗=794｜正樣本=112｜負樣本=682｜連續性違規=103\n",
      "[2025-10-14 21:34:20] [視窗前置] 檔案：PatNo_ID_1593416100.csv｜筆數：3328\n",
      "[2025-10-14 21:34:20] [視窗結果] 檔案：PatNo_ID_1593416100.csv｜生成視窗=109｜正樣本=21｜負樣本=88｜連續性違規=26\n",
      "[2025-10-14 21:34:20] [視窗前置] 檔案：PatNo_ID_1593472048.csv｜筆數：6247\n",
      "[2025-10-14 21:34:20] [視窗結果] 檔案：PatNo_ID_1593472048.csv｜生成視窗=207｜正樣本=40｜負樣本=167｜連續性違規=2\n",
      "[2025-10-14 21:34:20] [視窗前置] 檔案：PatNo_ID_1593593586.csv｜筆數：18875\n",
      "[2025-10-14 21:34:20] [視窗結果] 檔案：PatNo_ID_1593593586.csv｜生成視窗=628｜正樣本=97｜負樣本=531｜連續性違規=22\n",
      "[2025-10-14 21:34:20] [視窗前置] 檔案：PatNo_ID_1593720818.csv｜筆數：3909\n",
      "[2025-10-14 21:34:20] [視窗結果] 檔案：PatNo_ID_1593720818.csv｜生成視窗=129｜正樣本=19｜負樣本=110｜連續性違規=4\n",
      "[2025-10-14 21:34:20] [視窗前置] 檔案：PatNo_ID_1593838524.csv｜筆數：2161\n",
      "[2025-10-14 21:34:20] [視窗結果] 檔案：PatNo_ID_1593838524.csv｜生成視窗=71｜正樣本=8｜負樣本=63｜連續性違規=0\n",
      "[2025-10-14 21:34:20] [視窗前置] 檔案：PatNo_ID_1594173718.csv｜筆數：344\n",
      "[2025-10-14 21:34:20] [視窗結果] 檔案：PatNo_ID_1594173718.csv｜生成視窗=10｜正樣本=2｜負樣本=8｜連續性違規=0\n",
      "[2025-10-14 21:34:20] [視窗前置] 檔案：PatNo_ID_1594294180.csv｜筆數：18326\n",
      "[2025-10-14 21:34:21] [視窗結果] 檔案：PatNo_ID_1594294180.csv｜生成視窗=609｜正樣本=48｜負樣本=561｜連續性違規=219\n",
      "[2025-10-14 21:34:21] [視窗前置] 檔案：PatNo_ID_1594305136.csv｜筆數：18578\n",
      "[2025-10-14 21:34:21] [視窗結果] 檔案：PatNo_ID_1594305136.csv｜生成視窗=618｜正樣本=94｜負樣本=524｜連續性違規=10\n",
      "[2025-10-14 21:34:21] [視窗前置] 檔案：PatNo_ID_1594309746.csv｜筆數：3724\n",
      "[2025-10-14 21:34:21] [視窗結果] 檔案：PatNo_ID_1594309746.csv｜生成視窗=123｜正樣本=21｜負樣本=102｜連續性違規=0\n",
      "[2025-10-14 21:34:21] [視窗前置] 檔案：PatNo_ID_1594319286.csv｜筆數：3989\n",
      "[2025-10-14 21:34:21] [視窗結果] 檔案：PatNo_ID_1594319286.csv｜生成視窗=131｜正樣本=13｜負樣本=118｜連續性違規=2\n",
      "[2025-10-14 21:34:21] [視窗前置] 檔案：PatNo_ID_1594320763.csv｜筆數：2430\n",
      "[2025-10-14 21:34:21] [視窗結果] 檔案：PatNo_ID_1594320763.csv｜生成視窗=80｜正樣本=11｜負樣本=69｜連續性違規=0\n",
      "[2025-10-14 21:34:21] [視窗前置] 檔案：PatNo_ID_1594322594.csv｜筆數：5378\n",
      "[2025-10-14 21:34:21] [視窗結果] 檔案：PatNo_ID_1594322594.csv｜生成視窗=178｜正樣本=33｜負樣本=145｜連續性違規=0\n",
      "[2025-10-14 21:34:21] [視窗前置] 檔案：PatNo_ID_1594335109.csv｜筆數：5116\n",
      "[2025-10-14 21:34:21] [視窗結果] 檔案：PatNo_ID_1594335109.csv｜生成視窗=169｜正樣本=23｜負樣本=146｜連續性違規=2\n",
      "[2025-10-14 21:34:21] [視窗前置] 檔案：PatNo_ID_1594423683.csv｜筆數：5004\n",
      "[2025-10-14 21:34:21] [視窗結果] 檔案：PatNo_ID_1594423683.csv｜生成視窗=165｜正樣本=24｜負樣本=141｜連續性違規=5\n",
      "[2025-10-14 21:34:21] [視窗前置] 檔案：PatNo_ID_1594437309.csv｜筆數：9938\n",
      "[2025-10-14 21:34:21] [視窗結果] 檔案：PatNo_ID_1594437309.csv｜生成視窗=330｜正樣本=49｜負樣本=281｜連續性違規=10\n",
      "[2025-10-14 21:34:21] [視窗前置] 檔案：PatNo_ID_1594439781.csv｜筆數：7691\n",
      "[2025-10-14 21:34:21] [視窗結果] 檔案：PatNo_ID_1594439781.csv｜生成視窗=255｜正樣本=42｜負樣本=213｜連續性違規=2\n",
      "[2025-10-14 21:34:21] [視窗前置] 檔案：PatNo_ID_1594441887.csv｜筆數：10197\n",
      "[2025-10-14 21:34:21] [視窗結果] 檔案：PatNo_ID_1594441887.csv｜生成視窗=338｜正樣本=56｜負樣本=282｜連續性違規=32\n",
      "[2025-10-14 21:34:21] [視窗前置] 檔案：PatNo_ID_1594448501.csv｜筆數：5093\n",
      "[2025-10-14 21:34:21] [視窗結果] 檔案：PatNo_ID_1594448501.csv｜生成視窗=168｜正樣本=0｜負樣本=168｜連續性違規=12\n",
      "[2025-10-14 21:34:21] [視窗前置] 檔案：PatNo_ID_1594455578.csv｜筆數：262\n",
      "[2025-10-14 21:34:21] [視窗結果] 檔案：PatNo_ID_1594455578.csv｜生成視窗=7｜正樣本=1｜負樣本=6｜連續性違規=0\n",
      "[2025-10-14 21:34:21] [視窗前置] 檔案：PatNo_ID_1594464829.csv｜筆數：3939\n",
      "[2025-10-14 21:34:21] [視窗結果] 檔案：PatNo_ID_1594464829.csv｜生成視窗=130｜正樣本=16｜負樣本=114｜連續性違規=2\n",
      "[2025-10-14 21:34:21] [視窗前置] 檔案：PatNo_ID_1594467719.csv｜筆數：1313\n",
      "[2025-10-14 21:34:21] [視窗結果] 檔案：PatNo_ID_1594467719.csv｜生成視窗=42｜正樣本=6｜負樣本=36｜連續性違規=0\n",
      "[2025-10-14 21:34:21] [視窗前置] 檔案：PatNo_ID_1594471407.csv｜筆數：11279\n",
      "[2025-10-14 21:34:21] [視窗結果] 檔案：PatNo_ID_1594471407.csv｜生成視窗=374｜正樣本=59｜負樣本=315｜連續性違規=4\n",
      "[2025-10-14 21:34:21] [視窗前置] 檔案：PatNo_ID_1594479330.csv｜筆數：3724\n",
      "[2025-10-14 21:34:21] [視窗結果] 檔案：PatNo_ID_1594479330.csv｜生成視窗=123｜正樣本=13｜負樣本=110｜連續性違規=74\n",
      "[2025-10-14 21:34:21] [視窗前置] 檔案：PatNo_ID_1594511911.csv｜筆數：5455\n",
      "[2025-10-14 21:34:21] [視窗結果] 檔案：PatNo_ID_1594511911.csv｜生成視窗=180｜正樣本=32｜負樣本=148｜連續性違規=1\n",
      "[2025-10-14 21:34:21] [視窗前置] 檔案：PatNo_ID_1594511914.csv｜筆數：9685\n",
      "[2025-10-14 21:34:21] [視窗結果] 檔案：PatNo_ID_1594511914.csv｜生成視窗=321｜正樣本=64｜負樣本=257｜連續性違規=122\n",
      "[2025-10-14 21:34:21] [視窗前置] 檔案：PatNo_ID_1594528842.csv｜筆數：2415\n",
      "[2025-10-14 21:34:21] [視窗結果] 檔案：PatNo_ID_1594528842.csv｜生成視窗=79｜正樣本=20｜負樣本=59｜連續性違規=3\n",
      "[2025-10-14 21:34:21] [視窗前置] 檔案：PatNo_ID_1594533379.csv｜筆數：1018\n",
      "[2025-10-14 21:34:21] [視窗結果] 檔案：PatNo_ID_1594533379.csv｜生成視窗=32｜正樣本=5｜負樣本=27｜連續性違規=2\n",
      "[2025-10-14 21:34:21] 視窗層總結：cleaned 輸入總筆數=1523381｜視窗總數=50596｜正樣本總數=7372｜負樣本總數=43224｜連續性違規總數=4726\n",
      "[2025-10-14 21:34:21] ================== 步驟 5｜組裝特徵與標籤矩陣並計數 ==================\n",
      "[2025-10-14 21:34:23] 組裝完成：視窗樣本數=50596｜X 形狀=(50596×60×10)｜y 正樣本=7372｜y 負樣本=20514\n",
      "[2025-10-14 21:34:23] ================== 步驟 6｜分層切分 train/val 並計數 ==================\n",
      "[2025-10-14 21:34:23] 切分結果：train 視窗=40476（正=5897、負=16411）｜val 視窗=10120（正=1475、負=4103）\n",
      "[2025-10-14 21:34:23] ================== 步驟 7｜寫出 .npy 並計數 ==================\n",
      "[2025-10-14 21:34:23] .npy 已輸出至 /home/jovyan/RT08/0925/sliding_win/1014_sim/windowed｜檔案數=4｜合計視窗=50596\n",
      "[2025-10-14 21:34:23] ================== 步驟 8｜輸出報表並計數 ==================\n",
      "[2025-10-14 21:34:24] 報表已輸出：reports/window_summary.csv、reports/sample_distribution.csv｜視窗報表筆數=50596｜分布報表筆數=122\n",
      "[2025-10-14 21:34:24] ================== 步驟 9｜輸出 run 摘要並計數 ==================\n",
      "[2025-10-14 21:34:24] 已寫入摘要：reports/run_digest.json｜key 數=19\n",
      "[2025-10-14 21:34:24] ================== 流程完成｜全程數量總結 ==================\n",
      "[2025-10-14 21:34:24] raw 檔案數=122、cleaned 檔案數=122、清理後總筆數=1523381、視窗總數=50596、train=40476、val=10120\n"
     ]
    }
   ],
   "source": [
    "#不小心先割了train, val 1014_sim_2\n",
    "\n",
    "\"\"\"\n",
    "一條龍流程：raw → cleaned → windowed（以 set_fin 為標籤）\n",
    "特色：每步驟皆有提示語與數量統計（筆數、檔數、視窗數、違規數等）\n",
    "根目錄：/home/jovyan/RT08/0925/sliding_win/1014_sim/\n",
    "\n",
    "輸入： raw/*.csv                         （每病患一檔，原始逐筆）\n",
    "輸出： cleaned/*.csv                    （清理層成果）\n",
    "      cleaned/audit_summary.csv         （清理層每步驟移除筆數統計）\n",
    "      windowed/X_train.npy, y_train.npy, X_val.npy, y_val.npy\n",
    "      reports/window_summary.csv, reports/sample_distribution.csv, reports/run_digest.json\n",
    "      windowed/sample_window.csv\n",
    "日誌： logs/preprocess.log\n",
    "設定： config/windowing.yaml\n",
    "\"\"\"\n",
    "\n",
    "import os, sys, glob, json, time, hashlib, warnings\n",
    "from pathlib import Path\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split\n",
    "warnings.simplefilter(\"ignore\", category=FutureWarning)\n",
    "\n",
    "# ================== 參數區 ==================\n",
    "BASE_DIR   = Path(\"/home/jovyan/RT08/0925/sliding_win/1014_sim\")\n",
    "RAW_DIR    = BASE_DIR / \"raw\"\n",
    "CLEANED    = BASE_DIR / \"cleaned\"\n",
    "WINDOWED   = BASE_DIR / \"windowed\"\n",
    "REPORTS    = BASE_DIR / \"reports\"\n",
    "LOGS       = BASE_DIR / \"logs\"\n",
    "CONFIG_DIR = BASE_DIR / \"config\"\n",
    "\n",
    "# 視窗參數\n",
    "W = 60\n",
    "S = 30\n",
    "VAL_RATIO    = 0.20\n",
    "RANDOM_STATE = 42\n",
    "\n",
    "# 欄位（清理後一律小寫）\n",
    "FEATURE_COLS = [\n",
    "    \"rrhzsetactual\",\"mvsetactual\",\"pmean\",\"cdyn\",\"peepepap\",\"ppeak\",\n",
    "    \"mode_1\",\"mode_2\",\"mode_3\",\"svv_new\"\n",
    "]\n",
    "LABEL_COL = \"set_fin\"\n",
    "TIME_COL_CANDIDATES = [\"senddate\",\"SendDate\",\"SENDDATE\"]  # 會轉小寫比對\n",
    "\n",
    "# 清理層檢核用：至少要存在與為數值的特徵\n",
    "REQUIRED_NUMERIC_COLS = [\"rrhzsetactual\",\"mvsetactual\"]\n",
    "REQUIRED_LABEL_COLS   = [\"set_fin\"]\n",
    "REQUIRED_TIME_COLS    = [\"senddate\"]  # 清理後統一成 senddate\n",
    "\n",
    "# 連續性報表門檻（秒）\n",
    "DTSEC_MAX_THRESHOLD = 120\n",
    "\n",
    "# ================== 建目錄、日誌 ==================\n",
    "for p in [RAW_DIR, CLEANED, WINDOWED, REPORTS, LOGS, CONFIG_DIR]:\n",
    "    p.mkdir(parents=True, exist_ok=True)\n",
    "\n",
    "LOG_FILE = LOGS / \"preprocess.log\"\n",
    "def log(msg):\n",
    "    ts = time.strftime(\"%Y-%m-%d %H:%M:%S\")\n",
    "    line = f\"[{ts}] {msg}\"\n",
    "    with open(LOG_FILE, \"a\", encoding=\"utf-8\") as f:\n",
    "        f.write(line + \"\\n\")\n",
    "    print(line)\n",
    "\n",
    "def step(title):\n",
    "    bar = \"=\" * 18\n",
    "    log(f\"{bar} {title} {bar}\")\n",
    "\n",
    "# ================== 工具函式 ==================\n",
    "def normalize_columns(df: pd.DataFrame) -> pd.DataFrame:\n",
    "    df.columns = [c.strip().lower() for c in df.columns]\n",
    "    return df\n",
    "\n",
    "def pick_time_col(df: pd.DataFrame) -> str:\n",
    "    cols = [c.strip().lower() for c in df.columns]\n",
    "    for c in TIME_COL_CANDIDATES:\n",
    "        c2 = c.lower()\n",
    "        if c2 in cols:\n",
    "            return c2\n",
    "    return None\n",
    "\n",
    "def to_datetime_series(s: pd.Series):\n",
    "    try:\n",
    "        return pd.to_datetime(s, errors=\"coerce\", utc=False)\n",
    "    except Exception:\n",
    "        return pd.to_datetime(s.astype(str), errors=\"coerce\", utc=False)\n",
    "\n",
    "def hash_file(path: Path) -> str:\n",
    "    h = hashlib.sha256()\n",
    "    with open(path, \"rb\") as f:\n",
    "        for chunk in iter(lambda: f.read(1<<16), b\"\"):\n",
    "            h.update(chunk)\n",
    "    return h.hexdigest()[:16]\n",
    "\n",
    "# ================== 設定快照 ==================\n",
    "step(\"步驟 0｜初始化與設定快照\")\n",
    "CONFIG_PATH = CONFIG_DIR / \"windowing.yaml\"\n",
    "cfg_text = \"\\n\".join([\n",
    "    f'base_dir: \"{BASE_DIR}\"',\n",
    "    f'raw_dir: \"{RAW_DIR}\"',\n",
    "    f'cleaned_dir: \"{CLEANED}\"',\n",
    "    f'windowed_dir: \"{WINDOWED}\"',\n",
    "    f'reports_dir: \"{REPORTS}\"',\n",
    "    f'W: {W}',\n",
    "    f'S: {S}',\n",
    "    f'val_ratio: {VAL_RATIO}',\n",
    "    f'random_state: {RANDOM_STATE}',\n",
    "    f'label_col: \"{LABEL_COL}\"',\n",
    "    f'features: {FEATURE_COLS}',\n",
    "    f'time_col_candidates: {TIME_COL_CANDIDATES}',\n",
    "    f'dtsec_max_threshold: {DTSEC_MAX_THRESHOLD}',\n",
    "])\n",
    "CONFIG_PATH.write_text(cfg_text + \"\\n\", encoding=\"utf-8\")\n",
    "log(f\"已寫入設定：{CONFIG_PATH}\")\n",
    "\n",
    "# =========================================================\n",
    "#                     A. 清理層（raw → cleaned）\n",
    "# =========================================================\n",
    "step(\"步驟 1｜掃描 raw 檔案\")\n",
    "raw_files = sorted(glob.glob(str(RAW_DIR / \"*.csv\")))\n",
    "log(f\"raw 檔案數量：{len(raw_files)}\")\n",
    "if not raw_files:\n",
    "    log(\"raw 目錄無檔案可處理，流程終止\")\n",
    "    sys.exit(1)\n",
    "\n",
    "step(\"步驟 2｜逐檔清理並計數（產出 cleaned 與 audit）\")\n",
    "audit_rows = []\n",
    "total_init_all = 0\n",
    "total_final_all = 0\n",
    "total_removed_missing_all = 0\n",
    "total_removed_timeerr_all = 0\n",
    "total_removed_duplicates_all = 0\n",
    "total_removed_typeerr_all = 0\n",
    "\n",
    "for fp in raw_files:\n",
    "    fname = Path(fp).name\n",
    "    log(f\"清理開始：{fname}\")\n",
    "\n",
    "    # 嘗試不同編碼讀取\n",
    "    try:\n",
    "        df = pd.read_csv(fp, encoding=\"utf-8\")\n",
    "    except Exception:\n",
    "        try:\n",
    "            df = pd.read_csv(fp, encoding=\"big5\", errors=\"ignore\")\n",
    "        except Exception as e:\n",
    "            log(f\"讀檔失敗，跳過：{fname}｜原因：{e}\")\n",
    "            continue\n",
    "\n",
    "    df = normalize_columns(df)\n",
    "    total_init = len(df)\n",
    "    total_init_all += total_init\n",
    "    log(f\"讀入筆數：{total_init}（檔：{fname}）\")\n",
    "\n",
    "    # 統計器\n",
    "    removed_missing = 0\n",
    "    removed_timeerr = 0\n",
    "    removed_duplicates = 0\n",
    "    removed_typeerr = 0\n",
    "\n",
    "    # 缺失值清除：至少要有時間與最終標籤\n",
    "    before = len(df)\n",
    "    need_cols = set([\"senddate\"] + REQUIRED_LABEL_COLS)\n",
    "    # 若時間欄不是 senddate，先嘗試找出並轉為 senddate\n",
    "    tcol = pick_time_col(df)\n",
    "    if tcol and tcol != \"senddate\":\n",
    "        df.rename(columns={tcol: \"senddate\"}, inplace=True)\n",
    "    df = df.dropna(subset=list(need_cols.intersection(df.columns)))\n",
    "    removed_missing += (before - len(df))\n",
    "    log(f\"缺失清除：移除 {before - len(df)} 筆，剩餘 {len(df)} 筆\")\n",
    "\n",
    "    # 時間戳處理：轉 datetime 並去除無效\n",
    "    before = len(df)\n",
    "    if \"senddate\" in df.columns:\n",
    "        df[\"senddate\"] = pd.to_datetime(df[\"senddate\"], errors=\"coerce\")\n",
    "        df = df.dropna(subset=[\"senddate\"])\n",
    "    df = df.sort_values(\"senddate\").reset_index(drop=True)\n",
    "    removed_timeerr += (before - len(df))\n",
    "    log(f\"時間處理：移除 {before - len(df)} 筆無效時間，剩餘 {len(df)} 筆\")\n",
    "\n",
    "    # 去重（以 senddate 為鍵）\n",
    "    before = len(df)\n",
    "    if \"senddate\" in df.columns:\n",
    "        df = df.drop_duplicates(subset=[\"senddate\"])\n",
    "    removed_duplicates += (before - len(df))\n",
    "    log(f\"去除重複：移除 {before - len(df)} 筆，剩餘 {len(df)} 筆\")\n",
    "\n",
    "    # 轉數值並去除特徵缺失（重要特徵）\n",
    "    before = len(df)\n",
    "    for c in [\"rrhzsetactual\",\"mvsetactual\",\"pmean\",\"cdyn\",\"peepepap\",\"ppeak\",\"svv_new\"]:\n",
    "        if c in df.columns:\n",
    "            df[c] = pd.to_numeric(df[c], errors=\"coerce\")\n",
    "    # one-hot 類別安全轉換（若存在）\n",
    "    for c in [\"mode_1\",\"mode_2\",\"mode_3\"]:\n",
    "        if c in df.columns:\n",
    "            df[c] = pd.to_numeric(df[c], errors=\"coerce\").fillna(0).astype(int).clip(0,1)\n",
    "\n",
    "    # 關鍵特徵至少要存在且非 NaN\n",
    "    need_num = [c for c in REQUIRED_NUMERIC_COLS if c in df.columns]\n",
    "    if need_num:\n",
    "        df = df.dropna(subset=need_num)\n",
    "    removed_typeerr += (before - len(df))\n",
    "    log(f\"型別/特徵檢查：移除 {before - len(df)} 筆，剩餘 {len(df)} 筆\")\n",
    "\n",
    "    total_final = len(df)\n",
    "    total_removed = total_init - total_final\n",
    "    log(f\"清理完成：初始 {total_init} 筆 → 保留 {total_final} 筆（移除 {total_removed} 筆）\")\n",
    "\n",
    "    # 全域彙總\n",
    "    total_final_all += total_final\n",
    "    total_removed_missing_all    += removed_missing\n",
    "    total_removed_timeerr_all    += removed_timeerr\n",
    "    total_removed_duplicates_all += removed_duplicates\n",
    "    total_removed_typeerr_all    += removed_typeerr\n",
    "\n",
    "    # 寫出 cleaned 檔案\n",
    "    out_fp = CLEANED / fname\n",
    "    df.to_csv(out_fp, index=False, encoding=\"utf-8\")\n",
    "    log(f\"已寫入 cleaned 檔案：{out_fp}（筆數 {total_final}）\")\n",
    "\n",
    "    # 稽核記錄\n",
    "    audit_rows.append({\n",
    "        \"file\": fname,\n",
    "        \"total_init\": total_init,\n",
    "        \"removed_missing\": removed_missing,\n",
    "        \"removed_timeerr\": removed_timeerr,\n",
    "        \"removed_duplicates\": removed_duplicates,\n",
    "        \"removed_typeerr\": removed_typeerr,\n",
    "        \"total_final\": total_final\n",
    "    })\n",
    "\n",
    "# 寫出清理層稽核總表\n",
    "AUDIT_SUMMARY = CLEANED / \"audit_summary.csv\"\n",
    "pd.DataFrame(audit_rows).to_csv(AUDIT_SUMMARY, index=False, encoding=\"utf-8\")\n",
    "log(f\"已寫入清理層稽核總表：{AUDIT_SUMMARY}\")\n",
    "log(f\"清理層總結：原始總筆數={total_init_all}、清理後總筆數={total_final_all}、\"\n",
    "    f\"缺失移除總筆數={total_removed_missing_all}、時間錯誤移除總筆數={total_removed_timeerr_all}、\"\n",
    "    f\"重複移除總筆數={total_removed_duplicates_all}、型別/特徵錯誤移除總筆數={total_removed_typeerr_all}\")\n",
    "\n",
    "# =========================================================\n",
    "#                 B. 視窗層（cleaned → windowed）\n",
    "# =========================================================\n",
    "step(\"步驟 3｜掃描 cleaned 檔案作視窗化\")\n",
    "cleaned_files = sorted(glob.glob(str(CLEANED / \"*.csv\")))\n",
    "# 避免把 audit_summary 當成數據檔\n",
    "cleaned_files = [f for f in cleaned_files if Path(f).name != \"audit_summary.csv\"]\n",
    "log(f\"cleaned 檔案數量：{len(cleaned_files)}\")\n",
    "if not cleaned_files:\n",
    "    log(\"cleaned 目錄無可視窗化的檔案，流程終止\")\n",
    "    sys.exit(1)\n",
    "\n",
    "# 準備報表容器與總計\n",
    "window_rows = []   # window_summary\n",
    "dist_rows   = []   # sample_distribution\n",
    "sample_saved = False\n",
    "\n",
    "# 全流程視窗計數器\n",
    "win_total = 0\n",
    "win_pos_total = 0\n",
    "win_neg_total = 0\n",
    "win_continuity_violation_total = 0\n",
    "rows_total_in_window_stage = 0\n",
    "\n",
    "def get_time_series(df):\n",
    "    tcol = pick_time_col(df)\n",
    "    if not tcol:\n",
    "        return None\n",
    "    return to_datetime_series(df[tcol])\n",
    "\n",
    "def get_dsec(dt):\n",
    "    if dt is None:\n",
    "        return None\n",
    "    diffs = dt.diff().dt.total_seconds()\n",
    "    return diffs.to_numpy()\n",
    "\n",
    "step(\"步驟 4｜逐檔產生視窗並計數\")\n",
    "for f in cleaned_files:\n",
    "    fname = Path(f).name\n",
    "    df = pd.read_csv(f, encoding=\"utf-8\")\n",
    "    df = normalize_columns(df)\n",
    "\n",
    "    n_rows = len(df)\n",
    "    rows_total_in_window_stage += n_rows\n",
    "    log(f\"[視窗前置] 檔案：{fname}｜筆數：{n_rows}\")\n",
    "\n",
    "    # 特徵與標籤檢查\n",
    "    miss_feats = [c for c in FEATURE_COLS if c not in df.columns]\n",
    "    if miss_feats:\n",
    "        log(f\"跳過（特徵缺少）：{fname} 缺 {miss_feats}\")\n",
    "        continue\n",
    "    if LABEL_COL not in df.columns:\n",
    "        log(f\"跳過（無標籤欄）：{fname}\")\n",
    "        continue\n",
    "\n",
    "    # 取得時間序列（供報表）\n",
    "    dt = get_time_series(df)\n",
    "    dsec = get_dsec(dt)\n",
    "\n",
    "    feats_np = df[FEATURE_COLS].to_numpy(dtype=np.float32)\n",
    "    labels_np = df[LABEL_COL].to_numpy()\n",
    "\n",
    "    n_windows = 0\n",
    "    pos_cnt = 0\n",
    "    neg_cnt = 0\n",
    "    cont_violations = 0\n",
    "\n",
    "    i = 0\n",
    "    while i + W <= n_rows:\n",
    "        Xw = feats_np[i:i+W]\n",
    "        yw = int(labels_np[i+W-1])   # 視窗末筆標籤\n",
    "\n",
    "        # 統計\n",
    "        n_windows += 1\n",
    "        win_total  += 1\n",
    "        if yw == 1:\n",
    "            pos_cnt += 1\n",
    "            win_pos_total += 1\n",
    "        else:\n",
    "            neg_cnt += 1\n",
    "            win_neg_total += 1\n",
    "\n",
    "        # 連續性報表統計\n",
    "        mean_dt_sec = \"\"\n",
    "        max_dt_sec  = \"\"\n",
    "        continuity_ok = \"\"\n",
    "        if dsec is not None:\n",
    "            seg = dsec[i+1:i+W]\n",
    "            seg = seg[~np.isnan(seg)]\n",
    "            if seg.size > 0:\n",
    "                m = float(np.mean(seg))\n",
    "                mx = float(np.max(seg))\n",
    "                mean_dt_sec = f\"{m:.2f}\"\n",
    "                max_dt_sec  = f\"{mx:.2f}\"\n",
    "                is_ok = mx <= DTSEC_MAX_THRESHOLD\n",
    "                continuity_ok = \"1\" if is_ok else \"0\"\n",
    "                if not is_ok:\n",
    "                    cont_violations += 1\n",
    "                    win_continuity_violation_total += 1\n",
    "\n",
    "        # 報表行\n",
    "        start_time = dt.iloc[i].isoformat() if dt is not None and pd.notnull(dt.iloc[i]) else \"\"\n",
    "        end_time   = dt.iloc[i+W-1].isoformat() if dt is not None and pd.notnull(dt.iloc[i+W-1]) else \"\"\n",
    "        window_rows.append({\n",
    "            \"window_id\": f\"{fname}:{i}-{i+W-1}\",\n",
    "            \"file_name\": fname,\n",
    "            \"start_time\": start_time,\n",
    "            \"end_time\": end_time,\n",
    "            \"n_rows\": W,\n",
    "            \"mean_dt_sec\": mean_dt_sec,\n",
    "            \"max_dt_sec\": max_dt_sec,\n",
    "            \"continuity_ok\": continuity_ok,\n",
    "            \"label\": yw\n",
    "        })\n",
    "\n",
    "        # 保存一份 sample 視窗\n",
    "        if not sample_saved:\n",
    "            sample_df = pd.DataFrame(Xw, columns=FEATURE_COLS)\n",
    "            if dt is not None:\n",
    "                sample_df.insert(0, \"senddate\", df.iloc[i:i+W][\"senddate\"].values if \"senddate\" in df.columns else \"\")\n",
    "            sample_df[\"label_window_end_set_fin\"] = yw\n",
    "            (WINDOWED / \"sample_window.csv\").write_text(sample_df.to_csv(index=False), encoding=\"utf-8\")\n",
    "            sample_saved = True\n",
    "\n",
    "        i += S\n",
    "\n",
    "    # 病患層級分布\n",
    "    total_duration_hr = \"\"\n",
    "    if dt is not None and pd.notnull(dt.iloc[0]) and pd.notnull(dt.iloc[-1]):\n",
    "        total_duration_hr = f\"{(dt.iloc[-1] - dt.iloc[0]).total_seconds()/3600.0:.2f}\"\n",
    "\n",
    "    dist_rows.append({\n",
    "        \"file_name\": fname,\n",
    "        \"file_sha16\": hash_file(Path(f)),\n",
    "        \"total_rows\": n_rows,\n",
    "        \"n_windows\": n_windows,\n",
    "        \"pos_samples\": pos_cnt,\n",
    "        \"neg_samples\": neg_cnt,\n",
    "        \"pos_ratio\": f\"{(pos_cnt / n_windows):.4f}\" if n_windows > 0 else \"\",\n",
    "        \"mean_window_length\": W,\n",
    "        \"stride\": S,\n",
    "        \"cont_violation_windows\": cont_violations,\n",
    "        \"total_duration_hr\": total_duration_hr\n",
    "    })\n",
    "\n",
    "    log(f\"[視窗結果] 檔案：{fname}｜生成視窗={n_windows}｜正樣本={pos_cnt}｜負樣本={neg_cnt}｜連續性違規={cont_violations}\")\n",
    "\n",
    "# 彙整視窗總結\n",
    "log(f\"視窗層總結：cleaned 輸入總筆數={rows_total_in_window_stage}｜\"\n",
    "    f\"視窗總數={win_total}｜正樣本總數={win_pos_total}｜負樣本總數={win_neg_total}｜\"\n",
    "    f\"連續性違規總數={win_continuity_violation_total}\")\n",
    "\n",
    "# 依照 window_rows 重建陣列會耗費；此處直接再掃一次檔案生成陣列（明確且穩定）\n",
    "step(\"步驟 5｜組裝特徵與標籤矩陣並計數\")\n",
    "all_X, all_y = [], []\n",
    "rebuilt = 0\n",
    "for f in cleaned_files:\n",
    "    df = pd.read_csv(f, encoding=\"utf-8\")\n",
    "    df = normalize_columns(df)\n",
    "    miss_feats = [c for c in FEATURE_COLS if c not in df.columns]\n",
    "    if miss_feats or LABEL_COL not in df.columns:\n",
    "        continue\n",
    "    feats_np = df[FEATURE_COLS].to_numpy(dtype=np.float32)\n",
    "    labels_np = df[LABEL_COL].to_numpy()\n",
    "    n_rows = len(df)\n",
    "    i = 0\n",
    "    while i + W <= n_rows:\n",
    "        all_X.append(feats_np[i:i+W])\n",
    "        all_y.append(int(labels_np[i+W-1]))\n",
    "        rebuilt += 1\n",
    "        i += S\n",
    "\n",
    "X = np.asarray(all_X, dtype=np.float32)\n",
    "y = np.asarray(all_y, dtype=np.int64)\n",
    "log(f\"組裝完成：視窗樣本數={X.shape[0]}｜X 形狀=({X.shape[0]}×{W}×{X.shape[2]})｜y 正樣本={int((y==1).sum())}｜y 負樣本={int((y==0).sum())}\")\n",
    "\n",
    "# 切分\n",
    "step(\"步驟 6｜分層切分 train/val 並計數\")\n",
    "X_train, X_val, y_train, y_val = train_test_split(\n",
    "    X, y, test_size=VAL_RATIO, random_state=RANDOM_STATE, stratify=y\n",
    ")\n",
    "log(f\"切分結果：train 視窗={X_train.shape[0]}（正={int((y_train==1).sum())}、負={int((y_train==0).sum())}）｜\"\n",
    "    f\"val 視窗={X_val.shape[0]}（正={int((y_val==1).sum())}、負={int((y_val==0).sum())}）\")\n",
    "\n",
    "# 寫出 .npy\n",
    "step(\"步驟 7｜寫出 .npy 並計數\")\n",
    "np.save(WINDOWED / \"X_train.npy\", X_train)\n",
    "np.save(WINDOWED / \"y_train.npy\", y_train)\n",
    "np.save(WINDOWED / \"X_val.npy\",   X_val)\n",
    "np.save(WINDOWED / \"y_val.npy\",   y_val)\n",
    "log(f\".npy 已輸出至 {WINDOWED}｜檔案數=4｜合計視窗={X.shape[0]}\")\n",
    "\n",
    "# 報表\n",
    "step(\"步驟 8｜輸出報表並計數\")\n",
    "pd.DataFrame(window_rows).to_csv(REPORTS / \"window_summary.csv\", index=False, encoding=\"utf-8\")\n",
    "pd.DataFrame(dist_rows).to_csv(REPORTS / \"sample_distribution.csv\", index=False, encoding=\"utf-8\")\n",
    "log(\"報表已輸出：reports/window_summary.csv、reports/sample_distribution.csv｜\"\n",
    "    f\"視窗報表筆數={len(window_rows)}｜分布報表筆數={len(dist_rows)}\")\n",
    "\n",
    "# 總結摘要\n",
    "step(\"步驟 9｜輸出 run 摘要並計數\")\n",
    "digest = {\n",
    "    \"raw_files\": len(raw_files),\n",
    "    \"raw_total_rows_in\": int(total_init_all),\n",
    "    \"cleaned_files\": len(cleaned_files),\n",
    "    \"cleaned_total_rows_out\": int(total_final_all),\n",
    "    \"clean_removed\": {\n",
    "        \"missing\": int(total_removed_missing_all),\n",
    "        \"timeerr\": int(total_removed_timeerr_all),\n",
    "        \"duplicates\": int(total_removed_duplicates_all),\n",
    "        \"typeerr\": int(total_removed_typeerr_all)\n",
    "    },\n",
    "    \"window_input_rows\": int(rows_total_in_window_stage),\n",
    "    \"windows_total\": int(X.shape[0]),\n",
    "    \"train_windows\": int(X_train.shape[0]),\n",
    "    \"val_windows\": int(X_val.shape[0]),\n",
    "    \"y_train_pos\": int((y_train==1).sum()),\n",
    "    \"y_train_neg\": int((y_train==0).sum()),\n",
    "    \"y_val_pos\": int((y_val==1).sum()),\n",
    "    \"y_val_neg\": int((y_val==0).sum()),\n",
    "    \"continuity_violation_total\": int(win_continuity_violation_total),\n",
    "    \"window_shape\": {\"length\": int(W), \"n_features\": int(X.shape[2])},\n",
    "    \"features\": FEATURE_COLS,\n",
    "    \"label_col\": LABEL_COL,\n",
    "    \"val_ratio\": VAL_RATIO,\n",
    "    \"random_state\": RANDOM_STATE\n",
    "}\n",
    "(REPORTS / \"run_digest.json\").write_text(json.dumps(digest, ensure_ascii=False, indent=2), encoding=\"utf-8\")\n",
    "log(f\"已寫入摘要：reports/run_digest.json｜key 數={len(digest)}\")\n",
    "\n",
    "step(\"流程完成｜全程數量總結\")\n",
    "log(f\"raw 檔案數={len(raw_files)}、cleaned 檔案數={len(cleaned_files)}、\"\n",
    "    f\"清理後總筆數={total_final_all}、視窗總數={X.shape[0]}、train={X_train.shape[0]}、val={X_val.shape[0]}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 256,
   "id": "cb4787b3-8648-4514-b13d-c81f09941a9c",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2025-10-14 21:58:37] ================== 步驟 0｜初始化與設定快照 ==================\n",
      "[2025-10-14 21:58:37] 已寫入設定：/home/jovyan/RT08/0925/sliding_win/1014_sim/config/windowing.yaml\n",
      "[2025-10-14 21:58:37] ================== 步驟 1｜掃描 raw 檔案 ==================\n",
      "[2025-10-14 21:58:37] raw 檔案數量：122\n",
      "[2025-10-14 21:58:37] ================== 步驟 2｜逐檔清理與筆數統計（輸出 cleaned 與稽核） ==================\n",
      "[2025-10-14 21:58:37] 清理開始：089271.csv\n",
      "[2025-10-14 21:58:37] 讀入筆數：32419（檔：089271.csv）\n",
      "[2025-10-14 21:58:37] 缺失清除：移除 0 筆，剩餘 32419 筆\n",
      "[2025-10-14 21:58:37] 時間處理：移除 0 筆無效時間，剩餘 32419 筆\n",
      "[2025-10-14 21:58:37] 去除重複：移除 0 筆，剩餘 32419 筆\n",
      "[2025-10-14 21:58:37] 型別/特徵檢查：移除 51 筆，剩餘 32368 筆\n",
      "[2025-10-14 21:58:37] 清理完成：初始 32419 → 保留 32368（移除 51）\n",
      "[2025-10-14 21:58:37] 已寫入 cleaned 檔案：/home/jovyan/RT08/0925/sliding_win/1014_sim/cleaned/089271.csv（筆數 32368）\n",
      "[2025-10-14 21:58:37] 清理開始：095323.csv\n",
      "[2025-10-14 21:58:37] 讀入筆數：23791（檔：095323.csv）\n",
      "[2025-10-14 21:58:37] 缺失清除：移除 0 筆，剩餘 23791 筆\n",
      "[2025-10-14 21:58:37] 時間處理：移除 0 筆無效時間，剩餘 23791 筆\n",
      "[2025-10-14 21:58:37] 去除重複：移除 0 筆，剩餘 23791 筆\n",
      "[2025-10-14 21:58:37] 型別/特徵檢查：移除 6 筆，剩餘 23785 筆\n",
      "[2025-10-14 21:58:37] 清理完成：初始 23791 → 保留 23785（移除 6）\n",
      "[2025-10-14 21:58:37] 已寫入 cleaned 檔案：/home/jovyan/RT08/0925/sliding_win/1014_sim/cleaned/095323.csv（筆數 23785）\n",
      "[2025-10-14 21:58:37] 清理開始：095707.csv\n",
      "[2025-10-14 21:58:37] 讀入筆數：20180（檔：095707.csv）\n",
      "[2025-10-14 21:58:37] 缺失清除：移除 0 筆，剩餘 20180 筆\n",
      "[2025-10-14 21:58:37] 時間處理：移除 0 筆無效時間，剩餘 20180 筆\n",
      "[2025-10-14 21:58:37] 去除重複：移除 0 筆，剩餘 20180 筆\n",
      "[2025-10-14 21:58:37] 型別/特徵檢查：移除 1 筆，剩餘 20179 筆\n",
      "[2025-10-14 21:58:37] 清理完成：初始 20180 → 保留 20179（移除 1）\n",
      "[2025-10-14 21:58:37] 已寫入 cleaned 檔案：/home/jovyan/RT08/0925/sliding_win/1014_sim/cleaned/095707.csv（筆數 20179）\n",
      "[2025-10-14 21:58:37] 清理開始：114309.csv\n",
      "[2025-10-14 21:58:38] 讀入筆數：71729（檔：114309.csv）\n",
      "[2025-10-14 21:58:38] 缺失清除：移除 0 筆，剩餘 71729 筆\n",
      "[2025-10-14 21:58:38] 時間處理：移除 0 筆無效時間，剩餘 71729 筆\n",
      "[2025-10-14 21:58:38] 去除重複：移除 0 筆，剩餘 71729 筆\n",
      "[2025-10-14 21:58:38] 型別/特徵檢查：移除 6 筆，剩餘 71723 筆\n",
      "[2025-10-14 21:58:38] 清理完成：初始 71729 → 保留 71723（移除 6）\n",
      "[2025-10-14 21:58:38] 已寫入 cleaned 檔案：/home/jovyan/RT08/0925/sliding_win/1014_sim/cleaned/114309.csv（筆數 71723）\n",
      "[2025-10-14 21:58:38] 清理開始：230933.csv\n",
      "[2025-10-14 21:58:38] 讀入筆數：30249（檔：230933.csv）\n",
      "[2025-10-14 21:58:38] 缺失清除：移除 0 筆，剩餘 30249 筆\n",
      "[2025-10-14 21:58:38] 時間處理：移除 0 筆無效時間，剩餘 30249 筆\n",
      "[2025-10-14 21:58:38] 去除重複：移除 0 筆，剩餘 30249 筆\n",
      "[2025-10-14 21:58:38] 型別/特徵檢查：移除 8 筆，剩餘 30241 筆\n",
      "[2025-10-14 21:58:38] 清理完成：初始 30249 → 保留 30241（移除 8）\n",
      "[2025-10-14 21:58:38] 已寫入 cleaned 檔案：/home/jovyan/RT08/0925/sliding_win/1014_sim/cleaned/230933.csv（筆數 30241）\n",
      "[2025-10-14 21:58:38] 清理開始：4216007.csv\n",
      "[2025-10-14 21:58:38] 讀入筆數：1433（檔：4216007.csv）\n",
      "[2025-10-14 21:58:38] 缺失清除：移除 0 筆，剩餘 1433 筆\n",
      "[2025-10-14 21:58:38] 時間處理：移除 0 筆無效時間，剩餘 1433 筆\n",
      "[2025-10-14 21:58:38] 去除重複：移除 0 筆，剩餘 1433 筆\n",
      "[2025-10-14 21:58:38] 型別/特徵檢查：移除 906 筆，剩餘 527 筆\n",
      "[2025-10-14 21:58:38] 清理完成：初始 1433 → 保留 527（移除 906）\n",
      "[2025-10-14 21:58:38] 已寫入 cleaned 檔案：/home/jovyan/RT08/0925/sliding_win/1014_sim/cleaned/4216007.csv（筆數 527）\n",
      "[2025-10-14 21:58:38] 清理開始：7108162.csv\n",
      "[2025-10-14 21:58:38] 讀入筆數：239（檔：7108162.csv）\n",
      "[2025-10-14 21:58:38] 缺失清除：移除 0 筆，剩餘 239 筆\n",
      "[2025-10-14 21:58:38] 時間處理：移除 0 筆無效時間，剩餘 239 筆\n",
      "[2025-10-14 21:58:38] 去除重複：移除 0 筆，剩餘 239 筆\n",
      "[2025-10-14 21:58:38] 型別/特徵檢查：移除 0 筆，剩餘 239 筆\n",
      "[2025-10-14 21:58:38] 清理完成：初始 239 → 保留 239（移除 0）\n",
      "[2025-10-14 21:58:38] 已寫入 cleaned 檔案：/home/jovyan/RT08/0925/sliding_win/1014_sim/cleaned/7108162.csv（筆數 239）\n",
      "[2025-10-14 21:58:38] 清理開始：7408338.csv\n",
      "[2025-10-14 21:58:38] 讀入筆數：1432（檔：7408338.csv）\n",
      "[2025-10-14 21:58:38] 缺失清除：移除 0 筆，剩餘 1432 筆\n",
      "[2025-10-14 21:58:38] 時間處理：移除 0 筆無效時間，剩餘 1432 筆\n",
      "[2025-10-14 21:58:38] 去除重複：移除 0 筆，剩餘 1432 筆\n",
      "[2025-10-14 21:58:38] 型別/特徵檢查：移除 1 筆，剩餘 1431 筆\n",
      "[2025-10-14 21:58:38] 清理完成：初始 1432 → 保留 1431（移除 1）\n",
      "[2025-10-14 21:58:38] 已寫入 cleaned 檔案：/home/jovyan/RT08/0925/sliding_win/1014_sim/cleaned/7408338.csv（筆數 1431）\n",
      "[2025-10-14 21:58:38] 清理開始：7657698.csv\n",
      "[2025-10-14 21:58:38] 讀入筆數：1413（檔：7657698.csv）\n",
      "[2025-10-14 21:58:38] 缺失清除：移除 0 筆，剩餘 1413 筆\n",
      "[2025-10-14 21:58:38] 時間處理：移除 0 筆無效時間，剩餘 1413 筆\n",
      "[2025-10-14 21:58:38] 去除重複：移除 0 筆，剩餘 1413 筆\n",
      "[2025-10-14 21:58:38] 型別/特徵檢查：移除 0 筆，剩餘 1413 筆\n",
      "[2025-10-14 21:58:38] 清理完成：初始 1413 → 保留 1413（移除 0）\n",
      "[2025-10-14 21:58:38] 已寫入 cleaned 檔案：/home/jovyan/RT08/0925/sliding_win/1014_sim/cleaned/7657698.csv（筆數 1413）\n",
      "[2025-10-14 21:58:38] 清理開始：7721164.csv\n",
      "[2025-10-14 21:58:38] 讀入筆數：483（檔：7721164.csv）\n",
      "[2025-10-14 21:58:38] 缺失清除：移除 0 筆，剩餘 483 筆\n",
      "[2025-10-14 21:58:38] 時間處理：移除 0 筆無效時間，剩餘 483 筆\n",
      "[2025-10-14 21:58:38] 去除重複：移除 0 筆，剩餘 483 筆\n",
      "[2025-10-14 21:58:38] 型別/特徵檢查：移除 0 筆，剩餘 483 筆\n",
      "[2025-10-14 21:58:38] 清理完成：初始 483 → 保留 483（移除 0）\n",
      "[2025-10-14 21:58:38] 已寫入 cleaned 檔案：/home/jovyan/RT08/0925/sliding_win/1014_sim/cleaned/7721164.csv（筆數 483）\n",
      "[2025-10-14 21:58:38] 清理開始：PatNo_ID_1560013303.csv\n",
      "[2025-10-14 21:58:38] 讀入筆數：2543（檔：PatNo_ID_1560013303.csv）\n",
      "[2025-10-14 21:58:38] 缺失清除：移除 0 筆，剩餘 2543 筆\n",
      "[2025-10-14 21:58:38] 時間處理：移除 0 筆無效時間，剩餘 2543 筆\n",
      "[2025-10-14 21:58:38] 去除重複：移除 0 筆，剩餘 2543 筆\n",
      "[2025-10-14 21:58:38] 型別/特徵檢查：移除 29 筆，剩餘 2514 筆\n",
      "[2025-10-14 21:58:38] 清理完成：初始 2543 → 保留 2514（移除 29）\n",
      "[2025-10-14 21:58:38] 已寫入 cleaned 檔案：/home/jovyan/RT08/0925/sliding_win/1014_sim/cleaned/PatNo_ID_1560013303.csv（筆數 2514）\n",
      "[2025-10-14 21:58:38] 清理開始：PatNo_ID_1562733396.csv\n",
      "[2025-10-14 21:58:38] 讀入筆數：2254（檔：PatNo_ID_1562733396.csv）\n",
      "[2025-10-14 21:58:38] 缺失清除：移除 0 筆，剩餘 2254 筆\n",
      "[2025-10-14 21:58:38] 時間處理：移除 0 筆無效時間，剩餘 2254 筆\n",
      "[2025-10-14 21:58:38] 去除重複：移除 0 筆，剩餘 2254 筆\n",
      "[2025-10-14 21:58:38] 型別/特徵檢查：移除 1 筆，剩餘 2253 筆\n",
      "[2025-10-14 21:58:38] 清理完成：初始 2254 → 保留 2253（移除 1）\n",
      "[2025-10-14 21:58:38] 已寫入 cleaned 檔案：/home/jovyan/RT08/0925/sliding_win/1014_sim/cleaned/PatNo_ID_1562733396.csv（筆數 2253）\n",
      "[2025-10-14 21:58:38] 清理開始：PatNo_ID_1563587183.csv\n",
      "[2025-10-14 21:58:39] 讀入筆數：5286（檔：PatNo_ID_1563587183.csv）\n",
      "[2025-10-14 21:58:39] 缺失清除：移除 0 筆，剩餘 5286 筆\n",
      "[2025-10-14 21:58:39] 時間處理：移除 0 筆無效時間，剩餘 5286 筆\n",
      "[2025-10-14 21:58:39] 去除重複：移除 0 筆，剩餘 5286 筆\n",
      "[2025-10-14 21:58:39] 型別/特徵檢查：移除 0 筆，剩餘 5286 筆\n",
      "[2025-10-14 21:58:39] 清理完成：初始 5286 → 保留 5286（移除 0）\n",
      "[2025-10-14 21:58:39] 已寫入 cleaned 檔案：/home/jovyan/RT08/0925/sliding_win/1014_sim/cleaned/PatNo_ID_1563587183.csv（筆數 5286）\n",
      "[2025-10-14 21:58:39] 清理開始：PatNo_ID_1564148644.csv\n",
      "[2025-10-14 21:58:39] 讀入筆數：17287（檔：PatNo_ID_1564148644.csv）\n",
      "[2025-10-14 21:58:39] 缺失清除：移除 0 筆，剩餘 17287 筆\n",
      "[2025-10-14 21:58:39] 時間處理：移除 0 筆無效時間，剩餘 17287 筆\n",
      "[2025-10-14 21:58:39] 去除重複：移除 0 筆，剩餘 17287 筆\n",
      "[2025-10-14 21:58:39] 型別/特徵檢查：移除 2903 筆，剩餘 14384 筆\n",
      "[2025-10-14 21:58:39] 清理完成：初始 17287 → 保留 14384（移除 2903）\n",
      "[2025-10-14 21:58:39] 已寫入 cleaned 檔案：/home/jovyan/RT08/0925/sliding_win/1014_sim/cleaned/PatNo_ID_1564148644.csv（筆數 14384）\n",
      "[2025-10-14 21:58:39] 清理開始：PatNo_ID_1565148312.csv\n",
      "[2025-10-14 21:58:39] 讀入筆數：5475（檔：PatNo_ID_1565148312.csv）\n",
      "[2025-10-14 21:58:39] 缺失清除：移除 0 筆，剩餘 5475 筆\n",
      "[2025-10-14 21:58:39] 時間處理：移除 0 筆無效時間，剩餘 5475 筆\n",
      "[2025-10-14 21:58:39] 去除重複：移除 0 筆，剩餘 5475 筆\n",
      "[2025-10-14 21:58:39] 型別/特徵檢查：移除 11 筆，剩餘 5464 筆\n",
      "[2025-10-14 21:58:39] 清理完成：初始 5475 → 保留 5464（移除 11）\n",
      "[2025-10-14 21:58:39] 已寫入 cleaned 檔案：/home/jovyan/RT08/0925/sliding_win/1014_sim/cleaned/PatNo_ID_1565148312.csv（筆數 5464）\n",
      "[2025-10-14 21:58:39] 清理開始：PatNo_ID_1565378038.csv\n",
      "[2025-10-14 21:58:39] 讀入筆數：2561（檔：PatNo_ID_1565378038.csv）\n",
      "[2025-10-14 21:58:39] 缺失清除：移除 0 筆，剩餘 2561 筆\n",
      "[2025-10-14 21:58:39] 時間處理：移除 0 筆無效時間，剩餘 2561 筆\n",
      "[2025-10-14 21:58:39] 去除重複：移除 0 筆，剩餘 2561 筆\n",
      "[2025-10-14 21:58:39] 型別/特徵檢查：移除 0 筆，剩餘 2561 筆\n",
      "[2025-10-14 21:58:39] 清理完成：初始 2561 → 保留 2561（移除 0）\n",
      "[2025-10-14 21:58:39] 已寫入 cleaned 檔案：/home/jovyan/RT08/0925/sliding_win/1014_sim/cleaned/PatNo_ID_1565378038.csv（筆數 2561）\n",
      "[2025-10-14 21:58:39] 清理開始：PatNo_ID_1566123680.csv\n",
      "[2025-10-14 21:58:39] 讀入筆數：42600（檔：PatNo_ID_1566123680.csv）\n",
      "[2025-10-14 21:58:39] 缺失清除：移除 0 筆，剩餘 42600 筆\n",
      "[2025-10-14 21:58:39] 時間處理：移除 0 筆無效時間，剩餘 42600 筆\n",
      "[2025-10-14 21:58:39] 去除重複：移除 0 筆，剩餘 42600 筆\n",
      "[2025-10-14 21:58:39] 型別/特徵檢查：移除 3 筆，剩餘 42597 筆\n",
      "[2025-10-14 21:58:39] 清理完成：初始 42600 → 保留 42597（移除 3）\n",
      "[2025-10-14 21:58:39] 已寫入 cleaned 檔案：/home/jovyan/RT08/0925/sliding_win/1014_sim/cleaned/PatNo_ID_1566123680.csv（筆數 42597）\n",
      "[2025-10-14 21:58:39] 清理開始：PatNo_ID_1566252197.csv\n",
      "[2025-10-14 21:58:39] 讀入筆數：3368（檔：PatNo_ID_1566252197.csv）\n",
      "[2025-10-14 21:58:39] 缺失清除：移除 0 筆，剩餘 3368 筆\n",
      "[2025-10-14 21:58:39] 時間處理：移除 0 筆無效時間，剩餘 3368 筆\n",
      "[2025-10-14 21:58:39] 去除重複：移除 0 筆，剩餘 3368 筆\n",
      "[2025-10-14 21:58:39] 型別/特徵檢查：移除 1433 筆，剩餘 1935 筆\n",
      "[2025-10-14 21:58:39] 清理完成：初始 3368 → 保留 1935（移除 1433）\n",
      "[2025-10-14 21:58:39] 已寫入 cleaned 檔案：/home/jovyan/RT08/0925/sliding_win/1014_sim/cleaned/PatNo_ID_1566252197.csv（筆數 1935）\n",
      "[2025-10-14 21:58:39] 清理開始：PatNo_ID_1566279967.csv\n",
      "[2025-10-14 21:58:39] 讀入筆數：1235（檔：PatNo_ID_1566279967.csv）\n",
      "[2025-10-14 21:58:39] 缺失清除：移除 0 筆，剩餘 1235 筆\n",
      "[2025-10-14 21:58:39] 時間處理：移除 0 筆無效時間，剩餘 1235 筆\n",
      "[2025-10-14 21:58:39] 去除重複：移除 0 筆，剩餘 1235 筆\n",
      "[2025-10-14 21:58:39] 型別/特徵檢查：移除 82 筆，剩餘 1153 筆\n",
      "[2025-10-14 21:58:39] 清理完成：初始 1235 → 保留 1153（移除 82）\n",
      "[2025-10-14 21:58:39] 已寫入 cleaned 檔案：/home/jovyan/RT08/0925/sliding_win/1014_sim/cleaned/PatNo_ID_1566279967.csv（筆數 1153）\n",
      "[2025-10-14 21:58:39] 清理開始：PatNo_ID_1566671274.csv\n",
      "[2025-10-14 21:58:39] 讀入筆數：25359（檔：PatNo_ID_1566671274.csv）\n",
      "[2025-10-14 21:58:39] 缺失清除：移除 0 筆，剩餘 25359 筆\n",
      "[2025-10-14 21:58:39] 時間處理：移除 0 筆無效時間，剩餘 25359 筆\n",
      "[2025-10-14 21:58:39] 去除重複：移除 0 筆，剩餘 25359 筆\n",
      "[2025-10-14 21:58:39] 型別/特徵檢查：移除 1805 筆，剩餘 23554 筆\n",
      "[2025-10-14 21:58:39] 清理完成：初始 25359 → 保留 23554（移除 1805）\n",
      "[2025-10-14 21:58:39] 已寫入 cleaned 檔案：/home/jovyan/RT08/0925/sliding_win/1014_sim/cleaned/PatNo_ID_1566671274.csv（筆數 23554）\n",
      "[2025-10-14 21:58:39] 清理開始：PatNo_ID_1566911879.csv\n",
      "[2025-10-14 21:58:39] 讀入筆數：53999（檔：PatNo_ID_1566911879.csv）\n",
      "[2025-10-14 21:58:39] 缺失清除：移除 0 筆，剩餘 53999 筆\n",
      "[2025-10-14 21:58:39] 時間處理：移除 0 筆無效時間，剩餘 53999 筆\n",
      "[2025-10-14 21:58:39] 去除重複：移除 0 筆，剩餘 53999 筆\n",
      "[2025-10-14 21:58:39] 型別/特徵檢查：移除 8024 筆，剩餘 45975 筆\n",
      "[2025-10-14 21:58:39] 清理完成：初始 53999 → 保留 45975（移除 8024）\n",
      "[2025-10-14 21:58:40] 已寫入 cleaned 檔案：/home/jovyan/RT08/0925/sliding_win/1014_sim/cleaned/PatNo_ID_1566911879.csv（筆數 45975）\n",
      "[2025-10-14 21:58:40] 清理開始：PatNo_ID_1567747650.csv\n",
      "[2025-10-14 21:58:40] 讀入筆數：10900（檔：PatNo_ID_1567747650.csv）\n",
      "[2025-10-14 21:58:40] 缺失清除：移除 0 筆，剩餘 10900 筆\n",
      "[2025-10-14 21:58:40] 時間處理：移除 0 筆無效時間，剩餘 10900 筆\n",
      "[2025-10-14 21:58:40] 去除重複：移除 0 筆，剩餘 10900 筆\n",
      "[2025-10-14 21:58:40] 型別/特徵檢查：移除 2 筆，剩餘 10898 筆\n",
      "[2025-10-14 21:58:40] 清理完成：初始 10900 → 保留 10898（移除 2）\n",
      "[2025-10-14 21:58:40] 已寫入 cleaned 檔案：/home/jovyan/RT08/0925/sliding_win/1014_sim/cleaned/PatNo_ID_1567747650.csv（筆數 10898）\n",
      "[2025-10-14 21:58:40] 清理開始：PatNo_ID_1567804800.csv\n",
      "[2025-10-14 21:58:40] 讀入筆數：20577（檔：PatNo_ID_1567804800.csv）\n",
      "[2025-10-14 21:58:40] 缺失清除：移除 0 筆，剩餘 20577 筆\n",
      "[2025-10-14 21:58:40] 時間處理：移除 0 筆無效時間，剩餘 20577 筆\n",
      "[2025-10-14 21:58:40] 去除重複：移除 0 筆，剩餘 20577 筆\n",
      "[2025-10-14 21:58:40] 型別/特徵檢查：移除 188 筆，剩餘 20389 筆\n",
      "[2025-10-14 21:58:40] 清理完成：初始 20577 → 保留 20389（移除 188）\n",
      "[2025-10-14 21:58:40] 已寫入 cleaned 檔案：/home/jovyan/RT08/0925/sliding_win/1014_sim/cleaned/PatNo_ID_1567804800.csv（筆數 20389）\n",
      "[2025-10-14 21:58:40] 清理開始：PatNo_ID_1567832735.csv\n",
      "[2025-10-14 21:58:40] 讀入筆數：36575（檔：PatNo_ID_1567832735.csv）\n",
      "[2025-10-14 21:58:40] 缺失清除：移除 0 筆，剩餘 36575 筆\n",
      "[2025-10-14 21:58:40] 時間處理：移除 0 筆無效時間，剩餘 36575 筆\n",
      "[2025-10-14 21:58:40] 去除重複：移除 0 筆，剩餘 36575 筆\n",
      "[2025-10-14 21:58:40] 型別/特徵檢查：移除 6 筆，剩餘 36569 筆\n",
      "[2025-10-14 21:58:40] 清理完成：初始 36575 → 保留 36569（移除 6）\n",
      "[2025-10-14 21:58:40] 已寫入 cleaned 檔案：/home/jovyan/RT08/0925/sliding_win/1014_sim/cleaned/PatNo_ID_1567832735.csv（筆數 36569）\n",
      "[2025-10-14 21:58:40] 清理開始：PatNo_ID_1568039398.csv\n",
      "[2025-10-14 21:58:40] 讀入筆數：34519（檔：PatNo_ID_1568039398.csv）\n",
      "[2025-10-14 21:58:40] 缺失清除：移除 0 筆，剩餘 34519 筆\n",
      "[2025-10-14 21:58:40] 時間處理：移除 0 筆無效時間，剩餘 34519 筆\n",
      "[2025-10-14 21:58:40] 去除重複：移除 0 筆，剩餘 34519 筆\n",
      "[2025-10-14 21:58:40] 型別/特徵檢查：移除 277 筆，剩餘 34242 筆\n",
      "[2025-10-14 21:58:40] 清理完成：初始 34519 → 保留 34242（移除 277）\n",
      "[2025-10-14 21:58:41] 已寫入 cleaned 檔案：/home/jovyan/RT08/0925/sliding_win/1014_sim/cleaned/PatNo_ID_1568039398.csv（筆數 34242）\n",
      "[2025-10-14 21:58:41] 清理開始：PatNo_ID_1568574099.csv\n",
      "[2025-10-14 21:58:41] 讀入筆數：13945（檔：PatNo_ID_1568574099.csv）\n",
      "[2025-10-14 21:58:41] 缺失清除：移除 0 筆，剩餘 13945 筆\n",
      "[2025-10-14 21:58:41] 時間處理：移除 0 筆無效時間，剩餘 13945 筆\n",
      "[2025-10-14 21:58:41] 去除重複：移除 0 筆，剩餘 13945 筆\n",
      "[2025-10-14 21:58:41] 型別/特徵檢查：移除 82 筆，剩餘 13863 筆\n",
      "[2025-10-14 21:58:41] 清理完成：初始 13945 → 保留 13863（移除 82）\n",
      "[2025-10-14 21:58:41] 已寫入 cleaned 檔案：/home/jovyan/RT08/0925/sliding_win/1014_sim/cleaned/PatNo_ID_1568574099.csv（筆數 13863）\n",
      "[2025-10-14 21:58:41] 清理開始：PatNo_ID_1568813269.csv\n",
      "[2025-10-14 21:58:41] 讀入筆數：4867（檔：PatNo_ID_1568813269.csv）\n",
      "[2025-10-14 21:58:41] 缺失清除：移除 0 筆，剩餘 4867 筆\n",
      "[2025-10-14 21:58:41] 時間處理：移除 0 筆無效時間，剩餘 4867 筆\n",
      "[2025-10-14 21:58:41] 去除重複：移除 0 筆，剩餘 4867 筆\n",
      "[2025-10-14 21:58:41] 型別/特徵檢查：移除 0 筆，剩餘 4867 筆\n",
      "[2025-10-14 21:58:41] 清理完成：初始 4867 → 保留 4867（移除 0）\n",
      "[2025-10-14 21:58:41] 已寫入 cleaned 檔案：/home/jovyan/RT08/0925/sliding_win/1014_sim/cleaned/PatNo_ID_1568813269.csv（筆數 4867）\n",
      "[2025-10-14 21:58:41] 清理開始：PatNo_ID_1568952422.csv\n",
      "[2025-10-14 21:58:41] 讀入筆數：1184（檔：PatNo_ID_1568952422.csv）\n",
      "[2025-10-14 21:58:41] 缺失清除：移除 0 筆，剩餘 1184 筆\n",
      "[2025-10-14 21:58:41] 時間處理：移除 0 筆無效時間，剩餘 1184 筆\n",
      "[2025-10-14 21:58:41] 去除重複：移除 0 筆，剩餘 1184 筆\n",
      "[2025-10-14 21:58:41] 型別/特徵檢查：移除 0 筆，剩餘 1184 筆\n",
      "[2025-10-14 21:58:41] 清理完成：初始 1184 → 保留 1184（移除 0）\n",
      "[2025-10-14 21:58:41] 已寫入 cleaned 檔案：/home/jovyan/RT08/0925/sliding_win/1014_sim/cleaned/PatNo_ID_1568952422.csv（筆數 1184）\n",
      "[2025-10-14 21:58:41] 清理開始：PatNo_ID_1569083701.csv\n",
      "[2025-10-14 21:58:41] 讀入筆數：3328（檔：PatNo_ID_1569083701.csv）\n",
      "[2025-10-14 21:58:41] 缺失清除：移除 0 筆，剩餘 3328 筆\n",
      "[2025-10-14 21:58:41] 時間處理：移除 0 筆無效時間，剩餘 3328 筆\n",
      "[2025-10-14 21:58:41] 去除重複：移除 0 筆，剩餘 3328 筆\n",
      "[2025-10-14 21:58:41] 型別/特徵檢查：移除 2 筆，剩餘 3326 筆\n",
      "[2025-10-14 21:58:41] 清理完成：初始 3328 → 保留 3326（移除 2）\n",
      "[2025-10-14 21:58:41] 已寫入 cleaned 檔案：/home/jovyan/RT08/0925/sliding_win/1014_sim/cleaned/PatNo_ID_1569083701.csv（筆數 3326）\n",
      "[2025-10-14 21:58:41] 清理開始：PatNo_ID_1569944983.csv\n",
      "[2025-10-14 21:58:41] 讀入筆數：7649（檔：PatNo_ID_1569944983.csv）\n",
      "[2025-10-14 21:58:41] 缺失清除：移除 0 筆，剩餘 7649 筆\n",
      "[2025-10-14 21:58:41] 時間處理：移除 0 筆無效時間，剩餘 7649 筆\n",
      "[2025-10-14 21:58:41] 去除重複：移除 0 筆，剩餘 7649 筆\n",
      "[2025-10-14 21:58:41] 型別/特徵檢查：移除 2 筆，剩餘 7647 筆\n",
      "[2025-10-14 21:58:41] 清理完成：初始 7649 → 保留 7647（移除 2）\n",
      "[2025-10-14 21:58:41] 已寫入 cleaned 檔案：/home/jovyan/RT08/0925/sliding_win/1014_sim/cleaned/PatNo_ID_1569944983.csv（筆數 7647）\n",
      "[2025-10-14 21:58:41] 清理開始：PatNo_ID_1570089466.csv\n",
      "[2025-10-14 21:58:41] 讀入筆數：41338（檔：PatNo_ID_1570089466.csv）\n",
      "[2025-10-14 21:58:41] 缺失清除：移除 0 筆，剩餘 41338 筆\n",
      "[2025-10-14 21:58:41] 時間處理：移除 0 筆無效時間，剩餘 41338 筆\n",
      "[2025-10-14 21:58:41] 去除重複：移除 0 筆，剩餘 41338 筆\n",
      "[2025-10-14 21:58:41] 型別/特徵檢查：移除 749 筆，剩餘 40589 筆\n",
      "[2025-10-14 21:58:41] 清理完成：初始 41338 → 保留 40589（移除 749）\n",
      "[2025-10-14 21:58:41] 已寫入 cleaned 檔案：/home/jovyan/RT08/0925/sliding_win/1014_sim/cleaned/PatNo_ID_1570089466.csv（筆數 40589）\n",
      "[2025-10-14 21:58:41] 清理開始：PatNo_ID_1570242703.csv\n",
      "[2025-10-14 21:58:41] 讀入筆數：10644（檔：PatNo_ID_1570242703.csv）\n",
      "[2025-10-14 21:58:41] 缺失清除：移除 0 筆，剩餘 10644 筆\n",
      "[2025-10-14 21:58:41] 時間處理：移除 0 筆無效時間，剩餘 10644 筆\n",
      "[2025-10-14 21:58:41] 去除重複：移除 0 筆，剩餘 10644 筆\n",
      "[2025-10-14 21:58:41] 型別/特徵檢查：移除 88 筆，剩餘 10556 筆\n",
      "[2025-10-14 21:58:41] 清理完成：初始 10644 → 保留 10556（移除 88）\n",
      "[2025-10-14 21:58:42] 已寫入 cleaned 檔案：/home/jovyan/RT08/0925/sliding_win/1014_sim/cleaned/PatNo_ID_1570242703.csv（筆數 10556）\n",
      "[2025-10-14 21:58:42] 清理開始：PatNo_ID_1570273244.csv\n",
      "[2025-10-14 21:58:42] 讀入筆數：9725（檔：PatNo_ID_1570273244.csv）\n",
      "[2025-10-14 21:58:42] 缺失清除：移除 0 筆，剩餘 9725 筆\n",
      "[2025-10-14 21:58:42] 時間處理：移除 0 筆無效時間，剩餘 9725 筆\n",
      "[2025-10-14 21:58:42] 去除重複：移除 0 筆，剩餘 9725 筆\n",
      "[2025-10-14 21:58:42] 型別/特徵檢查：移除 18 筆，剩餘 9707 筆\n",
      "[2025-10-14 21:58:42] 清理完成：初始 9725 → 保留 9707（移除 18）\n",
      "[2025-10-14 21:58:42] 已寫入 cleaned 檔案：/home/jovyan/RT08/0925/sliding_win/1014_sim/cleaned/PatNo_ID_1570273244.csv（筆數 9707）\n",
      "[2025-10-14 21:58:42] 清理開始：PatNo_ID_1570642083.csv\n",
      "[2025-10-14 21:58:42] 讀入筆數：19731（檔：PatNo_ID_1570642083.csv）\n",
      "[2025-10-14 21:58:42] 缺失清除：移除 0 筆，剩餘 19731 筆\n",
      "[2025-10-14 21:58:42] 時間處理：移除 0 筆無效時間，剩餘 19731 筆\n",
      "[2025-10-14 21:58:42] 去除重複：移除 0 筆，剩餘 19731 筆\n",
      "[2025-10-14 21:58:42] 型別/特徵檢查：移除 0 筆，剩餘 19731 筆\n",
      "[2025-10-14 21:58:42] 清理完成：初始 19731 → 保留 19731（移除 0）\n",
      "[2025-10-14 21:58:42] 已寫入 cleaned 檔案：/home/jovyan/RT08/0925/sliding_win/1014_sim/cleaned/PatNo_ID_1570642083.csv（筆數 19731）\n",
      "[2025-10-14 21:58:42] 清理開始：PatNo_ID_1571945701.csv\n",
      "[2025-10-14 21:58:42] 讀入筆數：15731（檔：PatNo_ID_1571945701.csv）\n",
      "[2025-10-14 21:58:42] 缺失清除：移除 0 筆，剩餘 15731 筆\n",
      "[2025-10-14 21:58:42] 時間處理：移除 0 筆無效時間，剩餘 15731 筆\n",
      "[2025-10-14 21:58:42] 去除重複：移除 0 筆，剩餘 15731 筆\n",
      "[2025-10-14 21:58:42] 型別/特徵檢查：移除 2 筆，剩餘 15729 筆\n",
      "[2025-10-14 21:58:42] 清理完成：初始 15731 → 保留 15729（移除 2）\n",
      "[2025-10-14 21:58:42] 已寫入 cleaned 檔案：/home/jovyan/RT08/0925/sliding_win/1014_sim/cleaned/PatNo_ID_1571945701.csv（筆數 15729）\n",
      "[2025-10-14 21:58:42] 清理開始：PatNo_ID_1572481361.csv\n",
      "[2025-10-14 21:58:42] 讀入筆數：34539（檔：PatNo_ID_1572481361.csv）\n",
      "[2025-10-14 21:58:42] 缺失清除：移除 0 筆，剩餘 34539 筆\n",
      "[2025-10-14 21:58:42] 時間處理：移除 0 筆無效時間，剩餘 34539 筆\n",
      "[2025-10-14 21:58:42] 去除重複：移除 0 筆，剩餘 34539 筆\n",
      "[2025-10-14 21:58:42] 型別/特徵檢查：移除 24 筆，剩餘 34515 筆\n",
      "[2025-10-14 21:58:42] 清理完成：初始 34539 → 保留 34515（移除 24）\n",
      "[2025-10-14 21:58:42] 已寫入 cleaned 檔案：/home/jovyan/RT08/0925/sliding_win/1014_sim/cleaned/PatNo_ID_1572481361.csv（筆數 34515）\n",
      "[2025-10-14 21:58:42] 清理開始：PatNo_ID_1572562839.csv\n",
      "[2025-10-14 21:58:42] 讀入筆數：20963（檔：PatNo_ID_1572562839.csv）\n",
      "[2025-10-14 21:58:42] 缺失清除：移除 0 筆，剩餘 20963 筆\n",
      "[2025-10-14 21:58:42] 時間處理：移除 0 筆無效時間，剩餘 20963 筆\n",
      "[2025-10-14 21:58:42] 去除重複：移除 0 筆，剩餘 20963 筆\n",
      "[2025-10-14 21:58:42] 型別/特徵檢查：移除 43 筆，剩餘 20920 筆\n",
      "[2025-10-14 21:58:42] 清理完成：初始 20963 → 保留 20920（移除 43）\n",
      "[2025-10-14 21:58:42] 已寫入 cleaned 檔案：/home/jovyan/RT08/0925/sliding_win/1014_sim/cleaned/PatNo_ID_1572562839.csv（筆數 20920）\n",
      "[2025-10-14 21:58:42] 清理開始：PatNo_ID_1572831765.csv\n",
      "[2025-10-14 21:58:42] 讀入筆數：2696（檔：PatNo_ID_1572831765.csv）\n",
      "[2025-10-14 21:58:42] 缺失清除：移除 0 筆，剩餘 2696 筆\n",
      "[2025-10-14 21:58:42] 時間處理：移除 0 筆無效時間，剩餘 2696 筆\n",
      "[2025-10-14 21:58:42] 去除重複：移除 0 筆，剩餘 2696 筆\n",
      "[2025-10-14 21:58:42] 型別/特徵檢查：移除 0 筆，剩餘 2696 筆\n",
      "[2025-10-14 21:58:42] 清理完成：初始 2696 → 保留 2696（移除 0）\n",
      "[2025-10-14 21:58:42] 已寫入 cleaned 檔案：/home/jovyan/RT08/0925/sliding_win/1014_sim/cleaned/PatNo_ID_1572831765.csv（筆數 2696）\n",
      "[2025-10-14 21:58:42] 清理開始：PatNo_ID_1572976822.csv\n",
      "[2025-10-14 21:58:42] 讀入筆數：6764（檔：PatNo_ID_1572976822.csv）\n",
      "[2025-10-14 21:58:42] 缺失清除：移除 0 筆，剩餘 6764 筆\n",
      "[2025-10-14 21:58:42] 時間處理：移除 0 筆無效時間，剩餘 6764 筆\n",
      "[2025-10-14 21:58:42] 去除重複：移除 0 筆，剩餘 6764 筆\n",
      "[2025-10-14 21:58:42] 型別/特徵檢查：移除 140 筆，剩餘 6624 筆\n",
      "[2025-10-14 21:58:42] 清理完成：初始 6764 → 保留 6624（移除 140）\n",
      "[2025-10-14 21:58:43] 已寫入 cleaned 檔案：/home/jovyan/RT08/0925/sliding_win/1014_sim/cleaned/PatNo_ID_1572976822.csv（筆數 6624）\n",
      "[2025-10-14 21:58:43] 清理開始：PatNo_ID_1573063188.csv\n",
      "[2025-10-14 21:58:43] 讀入筆數：5080（檔：PatNo_ID_1573063188.csv）\n",
      "[2025-10-14 21:58:43] 缺失清除：移除 0 筆，剩餘 5080 筆\n",
      "[2025-10-14 21:58:43] 時間處理：移除 0 筆無效時間，剩餘 5080 筆\n",
      "[2025-10-14 21:58:43] 去除重複：移除 0 筆，剩餘 5080 筆\n",
      "[2025-10-14 21:58:43] 型別/特徵檢查：移除 55 筆，剩餘 5025 筆\n",
      "[2025-10-14 21:58:43] 清理完成：初始 5080 → 保留 5025（移除 55）\n",
      "[2025-10-14 21:58:43] 已寫入 cleaned 檔案：/home/jovyan/RT08/0925/sliding_win/1014_sim/cleaned/PatNo_ID_1573063188.csv（筆數 5025）\n",
      "[2025-10-14 21:58:43] 清理開始：PatNo_ID_1573249295.csv\n",
      "[2025-10-14 21:58:43] 讀入筆數：7151（檔：PatNo_ID_1573249295.csv）\n",
      "[2025-10-14 21:58:43] 缺失清除：移除 0 筆，剩餘 7151 筆\n",
      "[2025-10-14 21:58:43] 時間處理：移除 0 筆無效時間，剩餘 7151 筆\n",
      "[2025-10-14 21:58:43] 去除重複：移除 0 筆，剩餘 7151 筆\n",
      "[2025-10-14 21:58:43] 型別/特徵檢查：移除 0 筆，剩餘 7151 筆\n",
      "[2025-10-14 21:58:43] 清理完成：初始 7151 → 保留 7151（移除 0）\n",
      "[2025-10-14 21:58:43] 已寫入 cleaned 檔案：/home/jovyan/RT08/0925/sliding_win/1014_sim/cleaned/PatNo_ID_1573249295.csv（筆數 7151）\n",
      "[2025-10-14 21:58:43] 清理開始：PatNo_ID_1573964540.csv\n",
      "[2025-10-14 21:58:43] 讀入筆數：4394（檔：PatNo_ID_1573964540.csv）\n",
      "[2025-10-14 21:58:43] 缺失清除：移除 0 筆，剩餘 4394 筆\n",
      "[2025-10-14 21:58:43] 時間處理：移除 0 筆無效時間，剩餘 4394 筆\n",
      "[2025-10-14 21:58:43] 去除重複：移除 0 筆，剩餘 4394 筆\n",
      "[2025-10-14 21:58:43] 型別/特徵檢查：移除 47 筆，剩餘 4347 筆\n",
      "[2025-10-14 21:58:43] 清理完成：初始 4394 → 保留 4347（移除 47）\n",
      "[2025-10-14 21:58:43] 已寫入 cleaned 檔案：/home/jovyan/RT08/0925/sliding_win/1014_sim/cleaned/PatNo_ID_1573964540.csv（筆數 4347）\n",
      "[2025-10-14 21:58:43] 清理開始：PatNo_ID_1574148494.csv\n",
      "[2025-10-14 21:58:43] 讀入筆數：47154（檔：PatNo_ID_1574148494.csv）\n",
      "[2025-10-14 21:58:43] 缺失清除：移除 0 筆，剩餘 47154 筆\n",
      "[2025-10-14 21:58:43] 時間處理：移除 0 筆無效時間，剩餘 47154 筆\n",
      "[2025-10-14 21:58:43] 去除重複：移除 0 筆，剩餘 47154 筆\n",
      "[2025-10-14 21:58:43] 型別/特徵檢查：移除 137 筆，剩餘 47017 筆\n",
      "[2025-10-14 21:58:43] 清理完成：初始 47154 → 保留 47017（移除 137）\n",
      "[2025-10-14 21:58:43] 已寫入 cleaned 檔案：/home/jovyan/RT08/0925/sliding_win/1014_sim/cleaned/PatNo_ID_1574148494.csv（筆數 47017）\n",
      "[2025-10-14 21:58:43] 清理開始：PatNo_ID_1574270349.csv\n",
      "[2025-10-14 21:58:43] 讀入筆數：6533（檔：PatNo_ID_1574270349.csv）\n",
      "[2025-10-14 21:58:43] 缺失清除：移除 0 筆，剩餘 6533 筆\n",
      "[2025-10-14 21:58:43] 時間處理：移除 0 筆無效時間，剩餘 6533 筆\n",
      "[2025-10-14 21:58:43] 去除重複：移除 0 筆，剩餘 6533 筆\n",
      "[2025-10-14 21:58:43] 型別/特徵檢查：移除 18 筆，剩餘 6515 筆\n",
      "[2025-10-14 21:58:43] 清理完成：初始 6533 → 保留 6515（移除 18）\n",
      "[2025-10-14 21:58:43] 已寫入 cleaned 檔案：/home/jovyan/RT08/0925/sliding_win/1014_sim/cleaned/PatNo_ID_1574270349.csv（筆數 6515）\n",
      "[2025-10-14 21:58:43] 清理開始：PatNo_ID_1574528808.csv\n",
      "[2025-10-14 21:58:43] 讀入筆數：14812（檔：PatNo_ID_1574528808.csv）\n",
      "[2025-10-14 21:58:43] 缺失清除：移除 0 筆，剩餘 14812 筆\n",
      "[2025-10-14 21:58:43] 時間處理：移除 0 筆無效時間，剩餘 14812 筆\n",
      "[2025-10-14 21:58:43] 去除重複：移除 0 筆，剩餘 14812 筆\n",
      "[2025-10-14 21:58:43] 型別/特徵檢查：移除 332 筆，剩餘 14480 筆\n",
      "[2025-10-14 21:58:43] 清理完成：初始 14812 → 保留 14480（移除 332）\n",
      "[2025-10-14 21:58:43] 已寫入 cleaned 檔案：/home/jovyan/RT08/0925/sliding_win/1014_sim/cleaned/PatNo_ID_1574528808.csv（筆數 14480）\n",
      "[2025-10-14 21:58:43] 清理開始：PatNo_ID_1574831525.csv\n",
      "[2025-10-14 21:58:43] 讀入筆數：520（檔：PatNo_ID_1574831525.csv）\n",
      "[2025-10-14 21:58:43] 缺失清除：移除 0 筆，剩餘 520 筆\n",
      "[2025-10-14 21:58:43] 時間處理：移除 0 筆無效時間，剩餘 520 筆\n",
      "[2025-10-14 21:58:43] 去除重複：移除 0 筆，剩餘 520 筆\n",
      "[2025-10-14 21:58:43] 型別/特徵檢查：移除 5 筆，剩餘 515 筆\n",
      "[2025-10-14 21:58:43] 清理完成：初始 520 → 保留 515（移除 5）\n",
      "[2025-10-14 21:58:43] 已寫入 cleaned 檔案：/home/jovyan/RT08/0925/sliding_win/1014_sim/cleaned/PatNo_ID_1574831525.csv（筆數 515）\n",
      "[2025-10-14 21:58:43] 清理開始：PatNo_ID_1574987447.csv\n",
      "[2025-10-14 21:58:43] 讀入筆數：19842（檔：PatNo_ID_1574987447.csv）\n",
      "[2025-10-14 21:58:43] 缺失清除：移除 0 筆，剩餘 19842 筆\n",
      "[2025-10-14 21:58:43] 時間處理：移除 0 筆無效時間，剩餘 19842 筆\n",
      "[2025-10-14 21:58:43] 去除重複：移除 0 筆，剩餘 19842 筆\n",
      "[2025-10-14 21:58:43] 型別/特徵檢查：移除 4 筆，剩餘 19838 筆\n",
      "[2025-10-14 21:58:43] 清理完成：初始 19842 → 保留 19838（移除 4）\n",
      "[2025-10-14 21:58:44] 已寫入 cleaned 檔案：/home/jovyan/RT08/0925/sliding_win/1014_sim/cleaned/PatNo_ID_1574987447.csv（筆數 19838）\n",
      "[2025-10-14 21:58:44] 清理開始：PatNo_ID_1575060177.csv\n",
      "[2025-10-14 21:58:44] 讀入筆數：5290（檔：PatNo_ID_1575060177.csv）\n",
      "[2025-10-14 21:58:44] 缺失清除：移除 0 筆，剩餘 5290 筆\n",
      "[2025-10-14 21:58:44] 時間處理：移除 0 筆無效時間，剩餘 5290 筆\n",
      "[2025-10-14 21:58:44] 去除重複：移除 0 筆，剩餘 5290 筆\n",
      "[2025-10-14 21:58:44] 型別/特徵檢查：移除 0 筆，剩餘 5290 筆\n",
      "[2025-10-14 21:58:44] 清理完成：初始 5290 → 保留 5290（移除 0）\n",
      "[2025-10-14 21:58:44] 已寫入 cleaned 檔案：/home/jovyan/RT08/0925/sliding_win/1014_sim/cleaned/PatNo_ID_1575060177.csv（筆數 5290）\n",
      "[2025-10-14 21:58:44] 清理開始：PatNo_ID_1575256902.csv\n",
      "[2025-10-14 21:58:44] 讀入筆數：9587（檔：PatNo_ID_1575256902.csv）\n",
      "[2025-10-14 21:58:44] 缺失清除：移除 0 筆，剩餘 9587 筆\n",
      "[2025-10-14 21:58:44] 時間處理：移除 0 筆無效時間，剩餘 9587 筆\n",
      "[2025-10-14 21:58:44] 去除重複：移除 0 筆，剩餘 9587 筆\n",
      "[2025-10-14 21:58:44] 型別/特徵檢查：移除 95 筆，剩餘 9492 筆\n",
      "[2025-10-14 21:58:44] 清理完成：初始 9587 → 保留 9492（移除 95）\n",
      "[2025-10-14 21:58:44] 已寫入 cleaned 檔案：/home/jovyan/RT08/0925/sliding_win/1014_sim/cleaned/PatNo_ID_1575256902.csv（筆數 9492）\n",
      "[2025-10-14 21:58:44] 清理開始：PatNo_ID_1575445051.csv\n",
      "[2025-10-14 21:58:44] 讀入筆數：1800（檔：PatNo_ID_1575445051.csv）\n",
      "[2025-10-14 21:58:44] 缺失清除：移除 0 筆，剩餘 1800 筆\n",
      "[2025-10-14 21:58:44] 時間處理：移除 0 筆無效時間，剩餘 1800 筆\n",
      "[2025-10-14 21:58:44] 去除重複：移除 0 筆，剩餘 1800 筆\n",
      "[2025-10-14 21:58:44] 型別/特徵檢查：移除 15 筆，剩餘 1785 筆\n",
      "[2025-10-14 21:58:44] 清理完成：初始 1800 → 保留 1785（移除 15）\n",
      "[2025-10-14 21:58:44] 已寫入 cleaned 檔案：/home/jovyan/RT08/0925/sliding_win/1014_sim/cleaned/PatNo_ID_1575445051.csv（筆數 1785）\n",
      "[2025-10-14 21:58:44] 清理開始：PatNo_ID_1575502382.csv\n",
      "[2025-10-14 21:58:44] 讀入筆數：6604（檔：PatNo_ID_1575502382.csv）\n",
      "[2025-10-14 21:58:44] 缺失清除：移除 0 筆，剩餘 6604 筆\n",
      "[2025-10-14 21:58:44] 時間處理：移除 0 筆無效時間，剩餘 6604 筆\n",
      "[2025-10-14 21:58:44] 去除重複：移除 0 筆，剩餘 6604 筆\n",
      "[2025-10-14 21:58:44] 型別/特徵檢查：移除 115 筆，剩餘 6489 筆\n",
      "[2025-10-14 21:58:44] 清理完成：初始 6604 → 保留 6489（移除 115）\n",
      "[2025-10-14 21:58:44] 已寫入 cleaned 檔案：/home/jovyan/RT08/0925/sliding_win/1014_sim/cleaned/PatNo_ID_1575502382.csv（筆數 6489）\n",
      "[2025-10-14 21:58:44] 清理開始：PatNo_ID_1575975485.csv\n",
      "[2025-10-14 21:58:44] 讀入筆數：16459（檔：PatNo_ID_1575975485.csv）\n",
      "[2025-10-14 21:58:44] 缺失清除：移除 0 筆，剩餘 16459 筆\n",
      "[2025-10-14 21:58:44] 時間處理：移除 0 筆無效時間，剩餘 16459 筆\n",
      "[2025-10-14 21:58:44] 去除重複：移除 0 筆，剩餘 16459 筆\n",
      "[2025-10-14 21:58:44] 型別/特徵檢查：移除 1 筆，剩餘 16458 筆\n",
      "[2025-10-14 21:58:44] 清理完成：初始 16459 → 保留 16458（移除 1）\n",
      "[2025-10-14 21:58:44] 已寫入 cleaned 檔案：/home/jovyan/RT08/0925/sliding_win/1014_sim/cleaned/PatNo_ID_1575975485.csv（筆數 16458）\n",
      "[2025-10-14 21:58:44] 清理開始：PatNo_ID_1576115572.csv\n",
      "[2025-10-14 21:58:44] 讀入筆數：18715（檔：PatNo_ID_1576115572.csv）\n",
      "[2025-10-14 21:58:44] 缺失清除：移除 0 筆，剩餘 18715 筆\n",
      "[2025-10-14 21:58:44] 時間處理：移除 0 筆無效時間，剩餘 18715 筆\n",
      "[2025-10-14 21:58:44] 去除重複：移除 0 筆，剩餘 18715 筆\n",
      "[2025-10-14 21:58:44] 型別/特徵檢查：移除 698 筆，剩餘 18017 筆\n",
      "[2025-10-14 21:58:44] 清理完成：初始 18715 → 保留 18017（移除 698）\n",
      "[2025-10-14 21:58:44] 已寫入 cleaned 檔案：/home/jovyan/RT08/0925/sliding_win/1014_sim/cleaned/PatNo_ID_1576115572.csv（筆數 18017）\n",
      "[2025-10-14 21:58:44] 清理開始：PatNo_ID_1576116479.csv\n",
      "[2025-10-14 21:58:44] 讀入筆數：1263（檔：PatNo_ID_1576116479.csv）\n",
      "[2025-10-14 21:58:44] 缺失清除：移除 0 筆，剩餘 1263 筆\n",
      "[2025-10-14 21:58:44] 時間處理：移除 0 筆無效時間，剩餘 1263 筆\n",
      "[2025-10-14 21:58:44] 去除重複：移除 0 筆，剩餘 1263 筆\n",
      "[2025-10-14 21:58:44] 型別/特徵檢查：移除 6 筆，剩餘 1257 筆\n",
      "[2025-10-14 21:58:44] 清理完成：初始 1263 → 保留 1257（移除 6）\n",
      "[2025-10-14 21:58:44] 已寫入 cleaned 檔案：/home/jovyan/RT08/0925/sliding_win/1014_sim/cleaned/PatNo_ID_1576116479.csv（筆數 1257）\n",
      "[2025-10-14 21:58:44] 清理開始：PatNo_ID_1576301569.csv\n",
      "[2025-10-14 21:58:44] 讀入筆數：3207（檔：PatNo_ID_1576301569.csv）\n",
      "[2025-10-14 21:58:44] 缺失清除：移除 0 筆，剩餘 3207 筆\n",
      "[2025-10-14 21:58:44] 時間處理：移除 0 筆無效時間，剩餘 3207 筆\n",
      "[2025-10-14 21:58:44] 去除重複：移除 0 筆，剩餘 3207 筆\n",
      "[2025-10-14 21:58:44] 型別/特徵檢查：移除 0 筆，剩餘 3207 筆\n",
      "[2025-10-14 21:58:44] 清理完成：初始 3207 → 保留 3207（移除 0）\n",
      "[2025-10-14 21:58:44] 已寫入 cleaned 檔案：/home/jovyan/RT08/0925/sliding_win/1014_sim/cleaned/PatNo_ID_1576301569.csv（筆數 3207）\n",
      "[2025-10-14 21:58:44] 清理開始：PatNo_ID_1576964560.csv\n",
      "[2025-10-14 21:58:44] 讀入筆數：24188（檔：PatNo_ID_1576964560.csv）\n",
      "[2025-10-14 21:58:44] 缺失清除：移除 0 筆，剩餘 24188 筆\n",
      "[2025-10-14 21:58:44] 時間處理：移除 0 筆無效時間，剩餘 24188 筆\n",
      "[2025-10-14 21:58:44] 去除重複：移除 0 筆，剩餘 24188 筆\n",
      "[2025-10-14 21:58:44] 型別/特徵檢查：移除 0 筆，剩餘 24188 筆\n",
      "[2025-10-14 21:58:44] 清理完成：初始 24188 → 保留 24188（移除 0）\n",
      "[2025-10-14 21:58:44] 已寫入 cleaned 檔案：/home/jovyan/RT08/0925/sliding_win/1014_sim/cleaned/PatNo_ID_1576964560.csv（筆數 24188）\n",
      "[2025-10-14 21:58:44] 清理開始：PatNo_ID_1577042911.csv\n",
      "[2025-10-14 21:58:44] 讀入筆數：38357（檔：PatNo_ID_1577042911.csv）\n",
      "[2025-10-14 21:58:44] 缺失清除：移除 0 筆，剩餘 38357 筆\n",
      "[2025-10-14 21:58:44] 時間處理：移除 0 筆無效時間，剩餘 38357 筆\n",
      "[2025-10-14 21:58:44] 去除重複：移除 0 筆，剩餘 38357 筆\n",
      "[2025-10-14 21:58:44] 型別/特徵檢查：移除 2733 筆，剩餘 35624 筆\n",
      "[2025-10-14 21:58:44] 清理完成：初始 38357 → 保留 35624（移除 2733）\n",
      "[2025-10-14 21:58:45] 已寫入 cleaned 檔案：/home/jovyan/RT08/0925/sliding_win/1014_sim/cleaned/PatNo_ID_1577042911.csv（筆數 35624）\n",
      "[2025-10-14 21:58:45] 清理開始：PatNo_ID_1577487284.csv\n",
      "[2025-10-14 21:58:45] 讀入筆數：2345（檔：PatNo_ID_1577487284.csv）\n",
      "[2025-10-14 21:58:45] 缺失清除：移除 0 筆，剩餘 2345 筆\n",
      "[2025-10-14 21:58:45] 時間處理：移除 0 筆無效時間，剩餘 2345 筆\n",
      "[2025-10-14 21:58:45] 去除重複：移除 0 筆，剩餘 2345 筆\n",
      "[2025-10-14 21:58:45] 型別/特徵檢查：移除 0 筆，剩餘 2345 筆\n",
      "[2025-10-14 21:58:45] 清理完成：初始 2345 → 保留 2345（移除 0）\n",
      "[2025-10-14 21:58:45] 已寫入 cleaned 檔案：/home/jovyan/RT08/0925/sliding_win/1014_sim/cleaned/PatNo_ID_1577487284.csv（筆數 2345）\n",
      "[2025-10-14 21:58:45] 清理開始：PatNo_ID_1578784257.csv\n",
      "[2025-10-14 21:58:45] 讀入筆數：33914（檔：PatNo_ID_1578784257.csv）\n",
      "[2025-10-14 21:58:45] 缺失清除：移除 0 筆，剩餘 33914 筆\n",
      "[2025-10-14 21:58:45] 時間處理：移除 0 筆無效時間，剩餘 33914 筆\n",
      "[2025-10-14 21:58:45] 去除重複：移除 0 筆，剩餘 33914 筆\n",
      "[2025-10-14 21:58:45] 型別/特徵檢查：移除 5359 筆，剩餘 28555 筆\n",
      "[2025-10-14 21:58:45] 清理完成：初始 33914 → 保留 28555（移除 5359）\n",
      "[2025-10-14 21:58:45] 已寫入 cleaned 檔案：/home/jovyan/RT08/0925/sliding_win/1014_sim/cleaned/PatNo_ID_1578784257.csv（筆數 28555）\n",
      "[2025-10-14 21:58:45] 清理開始：PatNo_ID_1579198603.csv\n",
      "[2025-10-14 21:58:45] 讀入筆數：2045（檔：PatNo_ID_1579198603.csv）\n",
      "[2025-10-14 21:58:45] 缺失清除：移除 0 筆，剩餘 2045 筆\n",
      "[2025-10-14 21:58:45] 時間處理：移除 0 筆無效時間，剩餘 2045 筆\n",
      "[2025-10-14 21:58:45] 去除重複：移除 0 筆，剩餘 2045 筆\n",
      "[2025-10-14 21:58:45] 型別/特徵檢查：移除 0 筆，剩餘 2045 筆\n",
      "[2025-10-14 21:58:45] 清理完成：初始 2045 → 保留 2045（移除 0）\n",
      "[2025-10-14 21:58:45] 已寫入 cleaned 檔案：/home/jovyan/RT08/0925/sliding_win/1014_sim/cleaned/PatNo_ID_1579198603.csv（筆數 2045）\n",
      "[2025-10-14 21:58:45] 清理開始：PatNo_ID_1579498177.csv\n",
      "[2025-10-14 21:58:45] 讀入筆數：21688（檔：PatNo_ID_1579498177.csv）\n",
      "[2025-10-14 21:58:45] 缺失清除：移除 0 筆，剩餘 21688 筆\n",
      "[2025-10-14 21:58:45] 時間處理：移除 0 筆無效時間，剩餘 21688 筆\n",
      "[2025-10-14 21:58:45] 去除重複：移除 0 筆，剩餘 21688 筆\n",
      "[2025-10-14 21:58:45] 型別/特徵檢查：移除 7 筆，剩餘 21681 筆\n",
      "[2025-10-14 21:58:45] 清理完成：初始 21688 → 保留 21681（移除 7）\n",
      "[2025-10-14 21:58:45] 已寫入 cleaned 檔案：/home/jovyan/RT08/0925/sliding_win/1014_sim/cleaned/PatNo_ID_1579498177.csv（筆數 21681）\n",
      "[2025-10-14 21:58:45] 清理開始：PatNo_ID_1580062580.csv\n",
      "[2025-10-14 21:58:45] 讀入筆數：2150（檔：PatNo_ID_1580062580.csv）\n",
      "[2025-10-14 21:58:45] 缺失清除：移除 0 筆，剩餘 2150 筆\n",
      "[2025-10-14 21:58:45] 時間處理：移除 0 筆無效時間，剩餘 2150 筆\n",
      "[2025-10-14 21:58:45] 去除重複：移除 0 筆，剩餘 2150 筆\n",
      "[2025-10-14 21:58:45] 型別/特徵檢查：移除 0 筆，剩餘 2150 筆\n",
      "[2025-10-14 21:58:45] 清理完成：初始 2150 → 保留 2150（移除 0）\n",
      "[2025-10-14 21:58:45] 已寫入 cleaned 檔案：/home/jovyan/RT08/0925/sliding_win/1014_sim/cleaned/PatNo_ID_1580062580.csv（筆數 2150）\n",
      "[2025-10-14 21:58:45] 清理開始：PatNo_ID_1580096720.csv\n",
      "[2025-10-14 21:58:45] 讀入筆數：1423（檔：PatNo_ID_1580096720.csv）\n",
      "[2025-10-14 21:58:45] 缺失清除：移除 0 筆，剩餘 1423 筆\n",
      "[2025-10-14 21:58:45] 時間處理：移除 0 筆無效時間，剩餘 1423 筆\n",
      "[2025-10-14 21:58:45] 去除重複：移除 0 筆，剩餘 1423 筆\n",
      "[2025-10-14 21:58:45] 型別/特徵檢查：移除 1 筆，剩餘 1422 筆\n",
      "[2025-10-14 21:58:45] 清理完成：初始 1423 → 保留 1422（移除 1）\n",
      "[2025-10-14 21:58:45] 已寫入 cleaned 檔案：/home/jovyan/RT08/0925/sliding_win/1014_sim/cleaned/PatNo_ID_1580096720.csv（筆數 1422）\n",
      "[2025-10-14 21:58:45] 清理開始：PatNo_ID_1580107637.csv\n",
      "[2025-10-14 21:58:45] 讀入筆數：2698（檔：PatNo_ID_1580107637.csv）\n",
      "[2025-10-14 21:58:45] 缺失清除：移除 0 筆，剩餘 2698 筆\n",
      "[2025-10-14 21:58:45] 時間處理：移除 0 筆無效時間，剩餘 2698 筆\n",
      "[2025-10-14 21:58:45] 去除重複：移除 0 筆，剩餘 2698 筆\n",
      "[2025-10-14 21:58:45] 型別/特徵檢查：移除 10 筆，剩餘 2688 筆\n",
      "[2025-10-14 21:58:45] 清理完成：初始 2698 → 保留 2688（移除 10）\n",
      "[2025-10-14 21:58:45] 已寫入 cleaned 檔案：/home/jovyan/RT08/0925/sliding_win/1014_sim/cleaned/PatNo_ID_1580107637.csv（筆數 2688）\n",
      "[2025-10-14 21:58:45] 清理開始：PatNo_ID_1580244614.csv\n",
      "[2025-10-14 21:58:45] 讀入筆數：5278（檔：PatNo_ID_1580244614.csv）\n",
      "[2025-10-14 21:58:45] 缺失清除：移除 0 筆，剩餘 5278 筆\n",
      "[2025-10-14 21:58:45] 時間處理：移除 0 筆無效時間，剩餘 5278 筆\n",
      "[2025-10-14 21:58:45] 去除重複：移除 0 筆，剩餘 5278 筆\n",
      "[2025-10-14 21:58:45] 型別/特徵檢查：移除 254 筆，剩餘 5024 筆\n",
      "[2025-10-14 21:58:45] 清理完成：初始 5278 → 保留 5024（移除 254）\n",
      "[2025-10-14 21:58:45] 已寫入 cleaned 檔案：/home/jovyan/RT08/0925/sliding_win/1014_sim/cleaned/PatNo_ID_1580244614.csv（筆數 5024）\n",
      "[2025-10-14 21:58:45] 清理開始：PatNo_ID_1580766093.csv\n",
      "[2025-10-14 21:58:45] 讀入筆數：18070（檔：PatNo_ID_1580766093.csv）\n",
      "[2025-10-14 21:58:45] 缺失清除：移除 0 筆，剩餘 18070 筆\n",
      "[2025-10-14 21:58:45] 時間處理：移除 0 筆無效時間，剩餘 18070 筆\n",
      "[2025-10-14 21:58:45] 去除重複：移除 0 筆，剩餘 18070 筆\n",
      "[2025-10-14 21:58:45] 型別/特徵檢查：移除 6 筆，剩餘 18064 筆\n",
      "[2025-10-14 21:58:45] 清理完成：初始 18070 → 保留 18064（移除 6）\n",
      "[2025-10-14 21:58:45] 已寫入 cleaned 檔案：/home/jovyan/RT08/0925/sliding_win/1014_sim/cleaned/PatNo_ID_1580766093.csv（筆數 18064）\n",
      "[2025-10-14 21:58:45] 清理開始：PatNo_ID_1581003248.csv\n",
      "[2025-10-14 21:58:45] 讀入筆數：7776（檔：PatNo_ID_1581003248.csv）\n",
      "[2025-10-14 21:58:46] 缺失清除：移除 0 筆，剩餘 7776 筆\n",
      "[2025-10-14 21:58:46] 時間處理：移除 0 筆無效時間，剩餘 7776 筆\n",
      "[2025-10-14 21:58:46] 去除重複：移除 0 筆，剩餘 7776 筆\n",
      "[2025-10-14 21:58:46] 型別/特徵檢查：移除 1 筆，剩餘 7775 筆\n",
      "[2025-10-14 21:58:46] 清理完成：初始 7776 → 保留 7775（移除 1）\n",
      "[2025-10-14 21:58:46] 已寫入 cleaned 檔案：/home/jovyan/RT08/0925/sliding_win/1014_sim/cleaned/PatNo_ID_1581003248.csv（筆數 7775）\n",
      "[2025-10-14 21:58:46] 清理開始：PatNo_ID_1581019504.csv\n",
      "[2025-10-14 21:58:46] 讀入筆數：28173（檔：PatNo_ID_1581019504.csv）\n",
      "[2025-10-14 21:58:46] 缺失清除：移除 0 筆，剩餘 28173 筆\n",
      "[2025-10-14 21:58:46] 時間處理：移除 0 筆無效時間，剩餘 28173 筆\n",
      "[2025-10-14 21:58:46] 去除重複：移除 0 筆，剩餘 28173 筆\n",
      "[2025-10-14 21:58:46] 型別/特徵檢查：移除 6647 筆，剩餘 21526 筆\n",
      "[2025-10-14 21:58:46] 清理完成：初始 28173 → 保留 21526（移除 6647）\n",
      "[2025-10-14 21:58:46] 已寫入 cleaned 檔案：/home/jovyan/RT08/0925/sliding_win/1014_sim/cleaned/PatNo_ID_1581019504.csv（筆數 21526）\n",
      "[2025-10-14 21:58:46] 清理開始：PatNo_ID_1581633231.csv\n",
      "[2025-10-14 21:58:46] 讀入筆數：15995（檔：PatNo_ID_1581633231.csv）\n",
      "[2025-10-14 21:58:46] 缺失清除：移除 0 筆，剩餘 15995 筆\n",
      "[2025-10-14 21:58:46] 時間處理：移除 0 筆無效時間，剩餘 15995 筆\n",
      "[2025-10-14 21:58:46] 去除重複：移除 0 筆，剩餘 15995 筆\n",
      "[2025-10-14 21:58:46] 型別/特徵檢查：移除 24 筆，剩餘 15971 筆\n",
      "[2025-10-14 21:58:46] 清理完成：初始 15995 → 保留 15971（移除 24）\n",
      "[2025-10-14 21:58:46] 已寫入 cleaned 檔案：/home/jovyan/RT08/0925/sliding_win/1014_sim/cleaned/PatNo_ID_1581633231.csv（筆數 15971）\n",
      "[2025-10-14 21:58:46] 清理開始：PatNo_ID_1581692973.csv\n",
      "[2025-10-14 21:58:46] 讀入筆數：2723（檔：PatNo_ID_1581692973.csv）\n",
      "[2025-10-14 21:58:46] 缺失清除：移除 0 筆，剩餘 2723 筆\n",
      "[2025-10-14 21:58:46] 時間處理：移除 0 筆無效時間，剩餘 2723 筆\n",
      "[2025-10-14 21:58:46] 去除重複：移除 0 筆，剩餘 2723 筆\n",
      "[2025-10-14 21:58:46] 型別/特徵檢查：移除 0 筆，剩餘 2723 筆\n",
      "[2025-10-14 21:58:46] 清理完成：初始 2723 → 保留 2723（移除 0）\n",
      "[2025-10-14 21:58:46] 已寫入 cleaned 檔案：/home/jovyan/RT08/0925/sliding_win/1014_sim/cleaned/PatNo_ID_1581692973.csv（筆數 2723）\n",
      "[2025-10-14 21:58:46] 清理開始：PatNo_ID_1582452511.csv\n",
      "[2025-10-14 21:58:46] 讀入筆數：5196（檔：PatNo_ID_1582452511.csv）\n",
      "[2025-10-14 21:58:46] 缺失清除：移除 0 筆，剩餘 5196 筆\n",
      "[2025-10-14 21:58:46] 時間處理：移除 0 筆無效時間，剩餘 5196 筆\n",
      "[2025-10-14 21:58:46] 去除重複：移除 0 筆，剩餘 5196 筆\n",
      "[2025-10-14 21:58:46] 型別/特徵檢查：移除 0 筆，剩餘 5196 筆\n",
      "[2025-10-14 21:58:46] 清理完成：初始 5196 → 保留 5196（移除 0）\n",
      "[2025-10-14 21:58:46] 已寫入 cleaned 檔案：/home/jovyan/RT08/0925/sliding_win/1014_sim/cleaned/PatNo_ID_1582452511.csv（筆數 5196）\n",
      "[2025-10-14 21:58:46] 清理開始：PatNo_ID_1582635996.csv\n",
      "[2025-10-14 21:58:46] 讀入筆數：13046（檔：PatNo_ID_1582635996.csv）\n",
      "[2025-10-14 21:58:46] 缺失清除：移除 0 筆，剩餘 13046 筆\n",
      "[2025-10-14 21:58:46] 時間處理：移除 0 筆無效時間，剩餘 13046 筆\n",
      "[2025-10-14 21:58:46] 去除重複：移除 0 筆，剩餘 13046 筆\n",
      "[2025-10-14 21:58:46] 型別/特徵檢查：移除 0 筆，剩餘 13046 筆\n",
      "[2025-10-14 21:58:46] 清理完成：初始 13046 → 保留 13046（移除 0）\n",
      "[2025-10-14 21:58:46] 已寫入 cleaned 檔案：/home/jovyan/RT08/0925/sliding_win/1014_sim/cleaned/PatNo_ID_1582635996.csv（筆數 13046）\n",
      "[2025-10-14 21:58:46] 清理開始：PatNo_ID_1582849900.csv\n",
      "[2025-10-14 21:58:46] 讀入筆數：7411（檔：PatNo_ID_1582849900.csv）\n",
      "[2025-10-14 21:58:46] 缺失清除：移除 0 筆，剩餘 7411 筆\n",
      "[2025-10-14 21:58:46] 時間處理：移除 0 筆無效時間，剩餘 7411 筆\n",
      "[2025-10-14 21:58:46] 去除重複：移除 0 筆，剩餘 7411 筆\n",
      "[2025-10-14 21:58:46] 型別/特徵檢查：移除 24 筆，剩餘 7387 筆\n",
      "[2025-10-14 21:58:46] 清理完成：初始 7411 → 保留 7387（移除 24）\n",
      "[2025-10-14 21:58:46] 已寫入 cleaned 檔案：/home/jovyan/RT08/0925/sliding_win/1014_sim/cleaned/PatNo_ID_1582849900.csv（筆數 7387）\n",
      "[2025-10-14 21:58:46] 清理開始：PatNo_ID_1582937076.csv\n",
      "[2025-10-14 21:58:46] 讀入筆數：23987（檔：PatNo_ID_1582937076.csv）\n",
      "[2025-10-14 21:58:46] 缺失清除：移除 0 筆，剩餘 23987 筆\n",
      "[2025-10-14 21:58:46] 時間處理：移除 0 筆無效時間，剩餘 23987 筆\n",
      "[2025-10-14 21:58:46] 去除重複：移除 0 筆，剩餘 23987 筆\n",
      "[2025-10-14 21:58:46] 型別/特徵檢查：移除 21 筆，剩餘 23966 筆\n",
      "[2025-10-14 21:58:46] 清理完成：初始 23987 → 保留 23966（移除 21）\n",
      "[2025-10-14 21:58:46] 已寫入 cleaned 檔案：/home/jovyan/RT08/0925/sliding_win/1014_sim/cleaned/PatNo_ID_1582937076.csv（筆數 23966）\n",
      "[2025-10-14 21:58:46] 清理開始：PatNo_ID_1584158973.csv\n",
      "[2025-10-14 21:58:46] 讀入筆數：2882（檔：PatNo_ID_1584158973.csv）\n",
      "[2025-10-14 21:58:46] 缺失清除：移除 0 筆，剩餘 2882 筆\n",
      "[2025-10-14 21:58:46] 時間處理：移除 0 筆無效時間，剩餘 2882 筆\n",
      "[2025-10-14 21:58:46] 去除重複：移除 0 筆，剩餘 2882 筆\n",
      "[2025-10-14 21:58:46] 型別/特徵檢查：移除 0 筆，剩餘 2882 筆\n",
      "[2025-10-14 21:58:46] 清理完成：初始 2882 → 保留 2882（移除 0）\n",
      "[2025-10-14 21:58:46] 已寫入 cleaned 檔案：/home/jovyan/RT08/0925/sliding_win/1014_sim/cleaned/PatNo_ID_1584158973.csv（筆數 2882）\n",
      "[2025-10-14 21:58:46] 清理開始：PatNo_ID_1584397376.csv\n",
      "[2025-10-14 21:58:46] 讀入筆數：638（檔：PatNo_ID_1584397376.csv）\n",
      "[2025-10-14 21:58:46] 缺失清除：移除 0 筆，剩餘 638 筆\n",
      "[2025-10-14 21:58:46] 時間處理：移除 0 筆無效時間，剩餘 638 筆\n",
      "[2025-10-14 21:58:46] 去除重複：移除 0 筆，剩餘 638 筆\n",
      "[2025-10-14 21:58:46] 型別/特徵檢查：移除 0 筆，剩餘 638 筆\n",
      "[2025-10-14 21:58:46] 清理完成：初始 638 → 保留 638（移除 0）\n",
      "[2025-10-14 21:58:46] 已寫入 cleaned 檔案：/home/jovyan/RT08/0925/sliding_win/1014_sim/cleaned/PatNo_ID_1584397376.csv（筆數 638）\n",
      "[2025-10-14 21:58:46] 清理開始：PatNo_ID_1586172659.csv\n",
      "[2025-10-14 21:58:47] 讀入筆數：38554（檔：PatNo_ID_1586172659.csv）\n",
      "[2025-10-14 21:58:47] 缺失清除：移除 0 筆，剩餘 38554 筆\n",
      "[2025-10-14 21:58:47] 時間處理：移除 0 筆無效時間，剩餘 38554 筆\n",
      "[2025-10-14 21:58:47] 去除重複：移除 0 筆，剩餘 38554 筆\n",
      "[2025-10-14 21:58:47] 型別/特徵檢查：移除 7 筆，剩餘 38547 筆\n",
      "[2025-10-14 21:58:47] 清理完成：初始 38554 → 保留 38547（移除 7）\n",
      "[2025-10-14 21:58:47] 已寫入 cleaned 檔案：/home/jovyan/RT08/0925/sliding_win/1014_sim/cleaned/PatNo_ID_1586172659.csv（筆數 38547）\n",
      "[2025-10-14 21:58:47] 清理開始：PatNo_ID_1586696634.csv\n",
      "[2025-10-14 21:58:47] 讀入筆數：3569（檔：PatNo_ID_1586696634.csv）\n",
      "[2025-10-14 21:58:47] 缺失清除：移除 0 筆，剩餘 3569 筆\n",
      "[2025-10-14 21:58:47] 時間處理：移除 0 筆無效時間，剩餘 3569 筆\n",
      "[2025-10-14 21:58:47] 去除重複：移除 0 筆，剩餘 3569 筆\n",
      "[2025-10-14 21:58:47] 型別/特徵檢查：移除 1065 筆，剩餘 2504 筆\n",
      "[2025-10-14 21:58:47] 清理完成：初始 3569 → 保留 2504（移除 1065）\n",
      "[2025-10-14 21:58:47] 已寫入 cleaned 檔案：/home/jovyan/RT08/0925/sliding_win/1014_sim/cleaned/PatNo_ID_1586696634.csv（筆數 2504）\n",
      "[2025-10-14 21:58:47] 清理開始：PatNo_ID_1586897008.csv\n",
      "[2025-10-14 21:58:47] 讀入筆數：6687（檔：PatNo_ID_1586897008.csv）\n",
      "[2025-10-14 21:58:47] 缺失清除：移除 0 筆，剩餘 6687 筆\n",
      "[2025-10-14 21:58:47] 時間處理：移除 0 筆無效時間，剩餘 6687 筆\n",
      "[2025-10-14 21:58:47] 去除重複：移除 0 筆，剩餘 6687 筆\n",
      "[2025-10-14 21:58:47] 型別/特徵檢查：移除 32 筆，剩餘 6655 筆\n",
      "[2025-10-14 21:58:47] 清理完成：初始 6687 → 保留 6655（移除 32）\n",
      "[2025-10-14 21:58:47] 已寫入 cleaned 檔案：/home/jovyan/RT08/0925/sliding_win/1014_sim/cleaned/PatNo_ID_1586897008.csv（筆數 6655）\n",
      "[2025-10-14 21:58:47] 清理開始：PatNo_ID_1587490083.csv\n",
      "[2025-10-14 21:58:47] 讀入筆數：45186（檔：PatNo_ID_1587490083.csv）\n",
      "[2025-10-14 21:58:47] 缺失清除：移除 0 筆，剩餘 45186 筆\n",
      "[2025-10-14 21:58:47] 時間處理：移除 0 筆無效時間，剩餘 45186 筆\n",
      "[2025-10-14 21:58:47] 去除重複：移除 0 筆，剩餘 45186 筆\n",
      "[2025-10-14 21:58:47] 型別/特徵檢查：移除 0 筆，剩餘 45186 筆\n",
      "[2025-10-14 21:58:47] 清理完成：初始 45186 → 保留 45186（移除 0）\n",
      "[2025-10-14 21:58:47] 已寫入 cleaned 檔案：/home/jovyan/RT08/0925/sliding_win/1014_sim/cleaned/PatNo_ID_1587490083.csv（筆數 45186）\n",
      "[2025-10-14 21:58:47] 清理開始：PatNo_ID_1588632604.csv\n",
      "[2025-10-14 21:58:47] 讀入筆數：2608（檔：PatNo_ID_1588632604.csv）\n",
      "[2025-10-14 21:58:47] 缺失清除：移除 0 筆，剩餘 2608 筆\n",
      "[2025-10-14 21:58:47] 時間處理：移除 0 筆無效時間，剩餘 2608 筆\n",
      "[2025-10-14 21:58:47] 去除重複：移除 0 筆，剩餘 2608 筆\n",
      "[2025-10-14 21:58:47] 型別/特徵檢查：移除 0 筆，剩餘 2608 筆\n",
      "[2025-10-14 21:58:47] 清理完成：初始 2608 → 保留 2608（移除 0）\n",
      "[2025-10-14 21:58:47] 已寫入 cleaned 檔案：/home/jovyan/RT08/0925/sliding_win/1014_sim/cleaned/PatNo_ID_1588632604.csv（筆數 2608）\n",
      "[2025-10-14 21:58:47] 清理開始：PatNo_ID_1588673465.csv\n",
      "[2025-10-14 21:58:47] 讀入筆數：10076（檔：PatNo_ID_1588673465.csv）\n",
      "[2025-10-14 21:58:47] 缺失清除：移除 0 筆，剩餘 10076 筆\n",
      "[2025-10-14 21:58:47] 時間處理：移除 0 筆無效時間，剩餘 10076 筆\n",
      "[2025-10-14 21:58:47] 去除重複：移除 0 筆，剩餘 10076 筆\n",
      "[2025-10-14 21:58:47] 型別/特徵檢查：移除 4522 筆，剩餘 5554 筆\n",
      "[2025-10-14 21:58:47] 清理完成：初始 10076 → 保留 5554（移除 4522）\n",
      "[2025-10-14 21:58:47] 已寫入 cleaned 檔案：/home/jovyan/RT08/0925/sliding_win/1014_sim/cleaned/PatNo_ID_1588673465.csv（筆數 5554）\n",
      "[2025-10-14 21:58:47] 清理開始：PatNo_ID_1588794796.csv\n",
      "[2025-10-14 21:58:47] 讀入筆數：9375（檔：PatNo_ID_1588794796.csv）\n",
      "[2025-10-14 21:58:47] 缺失清除：移除 0 筆，剩餘 9375 筆\n",
      "[2025-10-14 21:58:47] 時間處理：移除 0 筆無效時間，剩餘 9375 筆\n",
      "[2025-10-14 21:58:47] 去除重複：移除 0 筆，剩餘 9375 筆\n",
      "[2025-10-14 21:58:47] 型別/特徵檢查：移除 0 筆，剩餘 9375 筆\n",
      "[2025-10-14 21:58:47] 清理完成：初始 9375 → 保留 9375（移除 0）\n",
      "[2025-10-14 21:58:47] 已寫入 cleaned 檔案：/home/jovyan/RT08/0925/sliding_win/1014_sim/cleaned/PatNo_ID_1588794796.csv（筆數 9375）\n",
      "[2025-10-14 21:58:47] 清理開始：PatNo_ID_1588957997.csv\n",
      "[2025-10-14 21:58:47] 讀入筆數：10966（檔：PatNo_ID_1588957997.csv）\n",
      "[2025-10-14 21:58:47] 缺失清除：移除 0 筆，剩餘 10966 筆\n",
      "[2025-10-14 21:58:47] 時間處理：移除 0 筆無效時間，剩餘 10966 筆\n",
      "[2025-10-14 21:58:47] 去除重複：移除 0 筆，剩餘 10966 筆\n",
      "[2025-10-14 21:58:47] 型別/特徵檢查：移除 0 筆，剩餘 10966 筆\n",
      "[2025-10-14 21:58:47] 清理完成：初始 10966 → 保留 10966（移除 0）\n",
      "[2025-10-14 21:58:48] 已寫入 cleaned 檔案：/home/jovyan/RT08/0925/sliding_win/1014_sim/cleaned/PatNo_ID_1588957997.csv（筆數 10966）\n",
      "[2025-10-14 21:58:48] 清理開始：PatNo_ID_1589018086.csv\n",
      "[2025-10-14 21:58:48] 讀入筆數：13472（檔：PatNo_ID_1589018086.csv）\n",
      "[2025-10-14 21:58:48] 缺失清除：移除 0 筆，剩餘 13472 筆\n",
      "[2025-10-14 21:58:48] 時間處理：移除 0 筆無效時間，剩餘 13472 筆\n",
      "[2025-10-14 21:58:48] 去除重複：移除 0 筆，剩餘 13472 筆\n",
      "[2025-10-14 21:58:48] 型別/特徵檢查：移除 22 筆，剩餘 13450 筆\n",
      "[2025-10-14 21:58:48] 清理完成：初始 13472 → 保留 13450（移除 22）\n",
      "[2025-10-14 21:58:48] 已寫入 cleaned 檔案：/home/jovyan/RT08/0925/sliding_win/1014_sim/cleaned/PatNo_ID_1589018086.csv（筆數 13450）\n",
      "[2025-10-14 21:58:48] 清理開始：PatNo_ID_1589034524.csv\n",
      "[2025-10-14 21:58:48] 讀入筆數：50081（檔：PatNo_ID_1589034524.csv）\n",
      "[2025-10-14 21:58:48] 缺失清除：移除 0 筆，剩餘 50081 筆\n",
      "[2025-10-14 21:58:48] 時間處理：移除 0 筆無效時間，剩餘 50081 筆\n",
      "[2025-10-14 21:58:48] 去除重複：移除 0 筆，剩餘 50081 筆\n",
      "[2025-10-14 21:58:48] 型別/特徵檢查：移除 42 筆，剩餘 50039 筆\n",
      "[2025-10-14 21:58:48] 清理完成：初始 50081 → 保留 50039（移除 42）\n",
      "[2025-10-14 21:58:48] 已寫入 cleaned 檔案：/home/jovyan/RT08/0925/sliding_win/1014_sim/cleaned/PatNo_ID_1589034524.csv（筆數 50039）\n",
      "[2025-10-14 21:58:48] 清理開始：PatNo_ID_1589324603.csv\n",
      "[2025-10-14 21:58:48] 讀入筆數：4187（檔：PatNo_ID_1589324603.csv）\n",
      "[2025-10-14 21:58:48] 缺失清除：移除 0 筆，剩餘 4187 筆\n",
      "[2025-10-14 21:58:48] 時間處理：移除 0 筆無效時間，剩餘 4187 筆\n",
      "[2025-10-14 21:58:48] 去除重複：移除 0 筆，剩餘 4187 筆\n",
      "[2025-10-14 21:58:48] 型別/特徵檢查：移除 76 筆，剩餘 4111 筆\n",
      "[2025-10-14 21:58:48] 清理完成：初始 4187 → 保留 4111（移除 76）\n",
      "[2025-10-14 21:58:48] 已寫入 cleaned 檔案：/home/jovyan/RT08/0925/sliding_win/1014_sim/cleaned/PatNo_ID_1589324603.csv（筆數 4111）\n",
      "[2025-10-14 21:58:48] 清理開始：PatNo_ID_1589918099.csv\n",
      "[2025-10-14 21:58:48] 讀入筆數：9333（檔：PatNo_ID_1589918099.csv）\n",
      "[2025-10-14 21:58:48] 缺失清除：移除 0 筆，剩餘 9333 筆\n",
      "[2025-10-14 21:58:48] 時間處理：移除 0 筆無效時間，剩餘 9333 筆\n",
      "[2025-10-14 21:58:48] 去除重複：移除 0 筆，剩餘 9333 筆\n",
      "[2025-10-14 21:58:48] 型別/特徵檢查：移除 1 筆，剩餘 9332 筆\n",
      "[2025-10-14 21:58:48] 清理完成：初始 9333 → 保留 9332（移除 1）\n",
      "[2025-10-14 21:58:48] 已寫入 cleaned 檔案：/home/jovyan/RT08/0925/sliding_win/1014_sim/cleaned/PatNo_ID_1589918099.csv（筆數 9332）\n",
      "[2025-10-14 21:58:48] 清理開始：PatNo_ID_1590136310.csv\n",
      "[2025-10-14 21:58:48] 讀入筆數：5580（檔：PatNo_ID_1590136310.csv）\n",
      "[2025-10-14 21:58:48] 缺失清除：移除 0 筆，剩餘 5580 筆\n",
      "[2025-10-14 21:58:48] 時間處理：移除 0 筆無效時間，剩餘 5580 筆\n",
      "[2025-10-14 21:58:48] 去除重複：移除 0 筆，剩餘 5580 筆\n",
      "[2025-10-14 21:58:48] 型別/特徵檢查：移除 273 筆，剩餘 5307 筆\n",
      "[2025-10-14 21:58:48] 清理完成：初始 5580 → 保留 5307（移除 273）\n",
      "[2025-10-14 21:58:48] 已寫入 cleaned 檔案：/home/jovyan/RT08/0925/sliding_win/1014_sim/cleaned/PatNo_ID_1590136310.csv（筆數 5307）\n",
      "[2025-10-14 21:58:48] 清理開始：PatNo_ID_1590616537.csv\n",
      "[2025-10-14 21:58:48] 讀入筆數：14206（檔：PatNo_ID_1590616537.csv）\n",
      "[2025-10-14 21:58:48] 缺失清除：移除 0 筆，剩餘 14206 筆\n",
      "[2025-10-14 21:58:48] 時間處理：移除 0 筆無效時間，剩餘 14206 筆\n",
      "[2025-10-14 21:58:48] 去除重複：移除 0 筆，剩餘 14206 筆\n",
      "[2025-10-14 21:58:48] 型別/特徵檢查：移除 0 筆，剩餘 14206 筆\n",
      "[2025-10-14 21:58:48] 清理完成：初始 14206 → 保留 14206（移除 0）\n",
      "[2025-10-14 21:58:48] 已寫入 cleaned 檔案：/home/jovyan/RT08/0925/sliding_win/1014_sim/cleaned/PatNo_ID_1590616537.csv（筆數 14206）\n",
      "[2025-10-14 21:58:48] 清理開始：PatNo_ID_1590854576.csv\n",
      "[2025-10-14 21:58:48] 讀入筆數：15879（檔：PatNo_ID_1590854576.csv）\n",
      "[2025-10-14 21:58:49] 缺失清除：移除 0 筆，剩餘 15879 筆\n",
      "[2025-10-14 21:58:49] 時間處理：移除 0 筆無效時間，剩餘 15879 筆\n",
      "[2025-10-14 21:58:49] 去除重複：移除 0 筆，剩餘 15879 筆\n",
      "[2025-10-14 21:58:49] 型別/特徵檢查：移除 0 筆，剩餘 15879 筆\n",
      "[2025-10-14 21:58:49] 清理完成：初始 15879 → 保留 15879（移除 0）\n",
      "[2025-10-14 21:58:49] 已寫入 cleaned 檔案：/home/jovyan/RT08/0925/sliding_win/1014_sim/cleaned/PatNo_ID_1590854576.csv（筆數 15879）\n",
      "[2025-10-14 21:58:49] 清理開始：PatNo_ID_1591609798.csv\n",
      "[2025-10-14 21:58:49] 讀入筆數：35618（檔：PatNo_ID_1591609798.csv）\n",
      "[2025-10-14 21:58:49] 缺失清除：移除 0 筆，剩餘 35618 筆\n",
      "[2025-10-14 21:58:49] 時間處理：移除 0 筆無效時間，剩餘 35618 筆\n",
      "[2025-10-14 21:58:49] 去除重複：移除 0 筆，剩餘 35618 筆\n",
      "[2025-10-14 21:58:49] 型別/特徵檢查：移除 1 筆，剩餘 35617 筆\n",
      "[2025-10-14 21:58:49] 清理完成：初始 35618 → 保留 35617（移除 1）\n",
      "[2025-10-14 21:58:49] 已寫入 cleaned 檔案：/home/jovyan/RT08/0925/sliding_win/1014_sim/cleaned/PatNo_ID_1591609798.csv（筆數 35617）\n",
      "[2025-10-14 21:58:49] 清理開始：PatNo_ID_1592044724.csv\n",
      "[2025-10-14 21:58:49] 讀入筆數：6815（檔：PatNo_ID_1592044724.csv）\n",
      "[2025-10-14 21:58:49] 缺失清除：移除 0 筆，剩餘 6815 筆\n",
      "[2025-10-14 21:58:49] 時間處理：移除 0 筆無效時間，剩餘 6815 筆\n",
      "[2025-10-14 21:58:49] 去除重複：移除 0 筆，剩餘 6815 筆\n",
      "[2025-10-14 21:58:49] 型別/特徵檢查：移除 3 筆，剩餘 6812 筆\n",
      "[2025-10-14 21:58:49] 清理完成：初始 6815 → 保留 6812（移除 3）\n",
      "[2025-10-14 21:58:49] 已寫入 cleaned 檔案：/home/jovyan/RT08/0925/sliding_win/1014_sim/cleaned/PatNo_ID_1592044724.csv（筆數 6812）\n",
      "[2025-10-14 21:58:49] 清理開始：PatNo_ID_1592560504.csv\n",
      "[2025-10-14 21:58:49] 讀入筆數：18051（檔：PatNo_ID_1592560504.csv）\n",
      "[2025-10-14 21:58:49] 缺失清除：移除 0 筆，剩餘 18051 筆\n",
      "[2025-10-14 21:58:49] 時間處理：移除 0 筆無效時間，剩餘 18051 筆\n",
      "[2025-10-14 21:58:49] 去除重複：移除 0 筆，剩餘 18051 筆\n",
      "[2025-10-14 21:58:49] 型別/特徵檢查：移除 0 筆，剩餘 18051 筆\n",
      "[2025-10-14 21:58:49] 清理完成：初始 18051 → 保留 18051（移除 0）\n",
      "[2025-10-14 21:58:49] 已寫入 cleaned 檔案：/home/jovyan/RT08/0925/sliding_win/1014_sim/cleaned/PatNo_ID_1592560504.csv（筆數 18051）\n",
      "[2025-10-14 21:58:49] 清理開始：PatNo_ID_1593087886.csv\n",
      "[2025-10-14 21:58:49] 讀入筆數：24163（檔：PatNo_ID_1593087886.csv）\n",
      "[2025-10-14 21:58:49] 缺失清除：移除 0 筆，剩餘 24163 筆\n",
      "[2025-10-14 21:58:49] 時間處理：移除 0 筆無效時間，剩餘 24163 筆\n",
      "[2025-10-14 21:58:49] 去除重複：移除 0 筆，剩餘 24163 筆\n",
      "[2025-10-14 21:58:49] 型別/特徵檢查：移除 295 筆，剩餘 23868 筆\n",
      "[2025-10-14 21:58:49] 清理完成：初始 24163 → 保留 23868（移除 295）\n",
      "[2025-10-14 21:58:49] 已寫入 cleaned 檔案：/home/jovyan/RT08/0925/sliding_win/1014_sim/cleaned/PatNo_ID_1593087886.csv（筆數 23868）\n",
      "[2025-10-14 21:58:49] 清理開始：PatNo_ID_1593416100.csv\n",
      "[2025-10-14 21:58:49] 讀入筆數：3328（檔：PatNo_ID_1593416100.csv）\n",
      "[2025-10-14 21:58:49] 缺失清除：移除 0 筆，剩餘 3328 筆\n",
      "[2025-10-14 21:58:49] 時間處理：移除 0 筆無效時間，剩餘 3328 筆\n",
      "[2025-10-14 21:58:49] 去除重複：移除 0 筆，剩餘 3328 筆\n",
      "[2025-10-14 21:58:49] 型別/特徵檢查：移除 0 筆，剩餘 3328 筆\n",
      "[2025-10-14 21:58:49] 清理完成：初始 3328 → 保留 3328（移除 0）\n",
      "[2025-10-14 21:58:49] 已寫入 cleaned 檔案：/home/jovyan/RT08/0925/sliding_win/1014_sim/cleaned/PatNo_ID_1593416100.csv（筆數 3328）\n",
      "[2025-10-14 21:58:49] 清理開始：PatNo_ID_1593472048.csv\n",
      "[2025-10-14 21:58:49] 讀入筆數：8230（檔：PatNo_ID_1593472048.csv）\n",
      "[2025-10-14 21:58:49] 缺失清除：移除 0 筆，剩餘 8230 筆\n",
      "[2025-10-14 21:58:49] 時間處理：移除 0 筆無效時間，剩餘 8230 筆\n",
      "[2025-10-14 21:58:49] 去除重複：移除 0 筆，剩餘 8230 筆\n",
      "[2025-10-14 21:58:49] 型別/特徵檢查：移除 1983 筆，剩餘 6247 筆\n",
      "[2025-10-14 21:58:49] 清理完成：初始 8230 → 保留 6247（移除 1983）\n",
      "[2025-10-14 21:58:49] 已寫入 cleaned 檔案：/home/jovyan/RT08/0925/sliding_win/1014_sim/cleaned/PatNo_ID_1593472048.csv（筆數 6247）\n",
      "[2025-10-14 21:58:49] 清理開始：PatNo_ID_1593593586.csv\n",
      "[2025-10-14 21:58:50] 讀入筆數：18882（檔：PatNo_ID_1593593586.csv）\n",
      "[2025-10-14 21:58:50] 缺失清除：移除 0 筆，剩餘 18882 筆\n",
      "[2025-10-14 21:58:50] 時間處理：移除 0 筆無效時間，剩餘 18882 筆\n",
      "[2025-10-14 21:58:50] 去除重複：移除 0 筆，剩餘 18882 筆\n",
      "[2025-10-14 21:58:50] 型別/特徵檢查：移除 7 筆，剩餘 18875 筆\n",
      "[2025-10-14 21:58:50] 清理完成：初始 18882 → 保留 18875（移除 7）\n",
      "[2025-10-14 21:58:50] 已寫入 cleaned 檔案：/home/jovyan/RT08/0925/sliding_win/1014_sim/cleaned/PatNo_ID_1593593586.csv（筆數 18875）\n",
      "[2025-10-14 21:58:50] 清理開始：PatNo_ID_1593720818.csv\n",
      "[2025-10-14 21:58:50] 讀入筆數：3910（檔：PatNo_ID_1593720818.csv）\n",
      "[2025-10-14 21:58:50] 缺失清除：移除 0 筆，剩餘 3910 筆\n",
      "[2025-10-14 21:58:50] 時間處理：移除 0 筆無效時間，剩餘 3910 筆\n",
      "[2025-10-14 21:58:50] 去除重複：移除 0 筆，剩餘 3910 筆\n",
      "[2025-10-14 21:58:50] 型別/特徵檢查：移除 1 筆，剩餘 3909 筆\n",
      "[2025-10-14 21:58:50] 清理完成：初始 3910 → 保留 3909（移除 1）\n",
      "[2025-10-14 21:58:50] 已寫入 cleaned 檔案：/home/jovyan/RT08/0925/sliding_win/1014_sim/cleaned/PatNo_ID_1593720818.csv（筆數 3909）\n",
      "[2025-10-14 21:58:50] 清理開始：PatNo_ID_1593838524.csv\n",
      "[2025-10-14 21:58:50] 讀入筆數：2177（檔：PatNo_ID_1593838524.csv）\n",
      "[2025-10-14 21:58:50] 缺失清除：移除 0 筆，剩餘 2177 筆\n",
      "[2025-10-14 21:58:50] 時間處理：移除 0 筆無效時間，剩餘 2177 筆\n",
      "[2025-10-14 21:58:50] 去除重複：移除 0 筆，剩餘 2177 筆\n",
      "[2025-10-14 21:58:50] 型別/特徵檢查：移除 16 筆，剩餘 2161 筆\n",
      "[2025-10-14 21:58:50] 清理完成：初始 2177 → 保留 2161（移除 16）\n",
      "[2025-10-14 21:58:50] 已寫入 cleaned 檔案：/home/jovyan/RT08/0925/sliding_win/1014_sim/cleaned/PatNo_ID_1593838524.csv（筆數 2161）\n",
      "[2025-10-14 21:58:50] 清理開始：PatNo_ID_1594173718.csv\n",
      "[2025-10-14 21:58:50] 讀入筆數：344（檔：PatNo_ID_1594173718.csv）\n",
      "[2025-10-14 21:58:50] 缺失清除：移除 0 筆，剩餘 344 筆\n",
      "[2025-10-14 21:58:50] 時間處理：移除 0 筆無效時間，剩餘 344 筆\n",
      "[2025-10-14 21:58:50] 去除重複：移除 0 筆，剩餘 344 筆\n",
      "[2025-10-14 21:58:50] 型別/特徵檢查：移除 0 筆，剩餘 344 筆\n",
      "[2025-10-14 21:58:50] 清理完成：初始 344 → 保留 344（移除 0）\n",
      "[2025-10-14 21:58:50] 已寫入 cleaned 檔案：/home/jovyan/RT08/0925/sliding_win/1014_sim/cleaned/PatNo_ID_1594173718.csv（筆數 344）\n",
      "[2025-10-14 21:58:50] 清理開始：PatNo_ID_1594294180.csv\n",
      "[2025-10-14 21:58:50] 讀入筆數：18334（檔：PatNo_ID_1594294180.csv）\n",
      "[2025-10-14 21:58:50] 缺失清除：移除 0 筆，剩餘 18334 筆\n",
      "[2025-10-14 21:58:50] 時間處理：移除 0 筆無效時間，剩餘 18334 筆\n",
      "[2025-10-14 21:58:50] 去除重複：移除 0 筆，剩餘 18334 筆\n",
      "[2025-10-14 21:58:50] 型別/特徵檢查：移除 8 筆，剩餘 18326 筆\n",
      "[2025-10-14 21:58:50] 清理完成：初始 18334 → 保留 18326（移除 8）\n",
      "[2025-10-14 21:58:50] 已寫入 cleaned 檔案：/home/jovyan/RT08/0925/sliding_win/1014_sim/cleaned/PatNo_ID_1594294180.csv（筆數 18326）\n",
      "[2025-10-14 21:58:50] 清理開始：PatNo_ID_1594305136.csv\n",
      "[2025-10-14 21:58:50] 讀入筆數：18679（檔：PatNo_ID_1594305136.csv）\n",
      "[2025-10-14 21:58:50] 缺失清除：移除 0 筆，剩餘 18679 筆\n",
      "[2025-10-14 21:58:50] 時間處理：移除 0 筆無效時間，剩餘 18679 筆\n",
      "[2025-10-14 21:58:50] 去除重複：移除 0 筆，剩餘 18679 筆\n",
      "[2025-10-14 21:58:50] 型別/特徵檢查：移除 101 筆，剩餘 18578 筆\n",
      "[2025-10-14 21:58:50] 清理完成：初始 18679 → 保留 18578（移除 101）\n",
      "[2025-10-14 21:58:50] 已寫入 cleaned 檔案：/home/jovyan/RT08/0925/sliding_win/1014_sim/cleaned/PatNo_ID_1594305136.csv（筆數 18578）\n",
      "[2025-10-14 21:58:50] 清理開始：PatNo_ID_1594309746.csv\n",
      "[2025-10-14 21:58:50] 讀入筆數：3745（檔：PatNo_ID_1594309746.csv）\n",
      "[2025-10-14 21:58:50] 缺失清除：移除 0 筆，剩餘 3745 筆\n",
      "[2025-10-14 21:58:50] 時間處理：移除 0 筆無效時間，剩餘 3745 筆\n",
      "[2025-10-14 21:58:50] 去除重複：移除 0 筆，剩餘 3745 筆\n",
      "[2025-10-14 21:58:50] 型別/特徵檢查：移除 21 筆，剩餘 3724 筆\n",
      "[2025-10-14 21:58:50] 清理完成：初始 3745 → 保留 3724（移除 21）\n",
      "[2025-10-14 21:58:50] 已寫入 cleaned 檔案：/home/jovyan/RT08/0925/sliding_win/1014_sim/cleaned/PatNo_ID_1594309746.csv（筆數 3724）\n",
      "[2025-10-14 21:58:50] 清理開始：PatNo_ID_1594319286.csv\n",
      "[2025-10-14 21:58:50] 讀入筆數：4107（檔：PatNo_ID_1594319286.csv）\n",
      "[2025-10-14 21:58:50] 缺失清除：移除 0 筆，剩餘 4107 筆\n",
      "[2025-10-14 21:58:50] 時間處理：移除 0 筆無效時間，剩餘 4107 筆\n",
      "[2025-10-14 21:58:50] 去除重複：移除 0 筆，剩餘 4107 筆\n",
      "[2025-10-14 21:58:50] 型別/特徵檢查：移除 118 筆，剩餘 3989 筆\n",
      "[2025-10-14 21:58:50] 清理完成：初始 4107 → 保留 3989（移除 118）\n",
      "[2025-10-14 21:58:50] 已寫入 cleaned 檔案：/home/jovyan/RT08/0925/sliding_win/1014_sim/cleaned/PatNo_ID_1594319286.csv（筆數 3989）\n",
      "[2025-10-14 21:58:50] 清理開始：PatNo_ID_1594320763.csv\n",
      "[2025-10-14 21:58:50] 讀入筆數：2431（檔：PatNo_ID_1594320763.csv）\n",
      "[2025-10-14 21:58:50] 缺失清除：移除 0 筆，剩餘 2431 筆\n",
      "[2025-10-14 21:58:50] 時間處理：移除 0 筆無效時間，剩餘 2431 筆\n",
      "[2025-10-14 21:58:50] 去除重複：移除 0 筆，剩餘 2431 筆\n",
      "[2025-10-14 21:58:50] 型別/特徵檢查：移除 1 筆，剩餘 2430 筆\n",
      "[2025-10-14 21:58:50] 清理完成：初始 2431 → 保留 2430（移除 1）\n",
      "[2025-10-14 21:58:50] 已寫入 cleaned 檔案：/home/jovyan/RT08/0925/sliding_win/1014_sim/cleaned/PatNo_ID_1594320763.csv（筆數 2430）\n",
      "[2025-10-14 21:58:50] 清理開始：PatNo_ID_1594322594.csv\n",
      "[2025-10-14 21:58:50] 讀入筆數：5380（檔：PatNo_ID_1594322594.csv）\n",
      "[2025-10-14 21:58:50] 缺失清除：移除 0 筆，剩餘 5380 筆\n",
      "[2025-10-14 21:58:50] 時間處理：移除 0 筆無效時間，剩餘 5380 筆\n",
      "[2025-10-14 21:58:50] 去除重複：移除 0 筆，剩餘 5380 筆\n",
      "[2025-10-14 21:58:50] 型別/特徵檢查：移除 2 筆，剩餘 5378 筆\n",
      "[2025-10-14 21:58:50] 清理完成：初始 5380 → 保留 5378（移除 2）\n",
      "[2025-10-14 21:58:50] 已寫入 cleaned 檔案：/home/jovyan/RT08/0925/sliding_win/1014_sim/cleaned/PatNo_ID_1594322594.csv（筆數 5378）\n",
      "[2025-10-14 21:58:50] 清理開始：PatNo_ID_1594335109.csv\n",
      "[2025-10-14 21:58:50] 讀入筆數：5116（檔：PatNo_ID_1594335109.csv）\n",
      "[2025-10-14 21:58:50] 缺失清除：移除 0 筆，剩餘 5116 筆\n",
      "[2025-10-14 21:58:50] 時間處理：移除 0 筆無效時間，剩餘 5116 筆\n",
      "[2025-10-14 21:58:50] 去除重複：移除 0 筆，剩餘 5116 筆\n",
      "[2025-10-14 21:58:50] 型別/特徵檢查：移除 0 筆，剩餘 5116 筆\n",
      "[2025-10-14 21:58:50] 清理完成：初始 5116 → 保留 5116（移除 0）\n",
      "[2025-10-14 21:58:50] 已寫入 cleaned 檔案：/home/jovyan/RT08/0925/sliding_win/1014_sim/cleaned/PatNo_ID_1594335109.csv（筆數 5116）\n",
      "[2025-10-14 21:58:50] 清理開始：PatNo_ID_1594423683.csv\n",
      "[2025-10-14 21:58:50] 讀入筆數：5023（檔：PatNo_ID_1594423683.csv）\n",
      "[2025-10-14 21:58:50] 缺失清除：移除 0 筆，剩餘 5023 筆\n",
      "[2025-10-14 21:58:50] 時間處理：移除 0 筆無效時間，剩餘 5023 筆\n",
      "[2025-10-14 21:58:50] 去除重複：移除 0 筆，剩餘 5023 筆\n",
      "[2025-10-14 21:58:50] 型別/特徵檢查：移除 19 筆，剩餘 5004 筆\n",
      "[2025-10-14 21:58:50] 清理完成：初始 5023 → 保留 5004（移除 19）\n",
      "[2025-10-14 21:58:50] 已寫入 cleaned 檔案：/home/jovyan/RT08/0925/sliding_win/1014_sim/cleaned/PatNo_ID_1594423683.csv（筆數 5004）\n",
      "[2025-10-14 21:58:50] 清理開始：PatNo_ID_1594437309.csv\n",
      "[2025-10-14 21:58:50] 讀入筆數：10034（檔：PatNo_ID_1594437309.csv）\n",
      "[2025-10-14 21:58:50] 缺失清除：移除 0 筆，剩餘 10034 筆\n",
      "[2025-10-14 21:58:50] 時間處理：移除 0 筆無效時間，剩餘 10034 筆\n",
      "[2025-10-14 21:58:50] 去除重複：移除 0 筆，剩餘 10034 筆\n",
      "[2025-10-14 21:58:50] 型別/特徵檢查：移除 96 筆，剩餘 9938 筆\n",
      "[2025-10-14 21:58:50] 清理完成：初始 10034 → 保留 9938（移除 96）\n",
      "[2025-10-14 21:58:50] 已寫入 cleaned 檔案：/home/jovyan/RT08/0925/sliding_win/1014_sim/cleaned/PatNo_ID_1594437309.csv（筆數 9938）\n",
      "[2025-10-14 21:58:50] 清理開始：PatNo_ID_1594439781.csv\n",
      "[2025-10-14 21:58:50] 讀入筆數：7691（檔：PatNo_ID_1594439781.csv）\n",
      "[2025-10-14 21:58:50] 缺失清除：移除 0 筆，剩餘 7691 筆\n",
      "[2025-10-14 21:58:50] 時間處理：移除 0 筆無效時間，剩餘 7691 筆\n",
      "[2025-10-14 21:58:50] 去除重複：移除 0 筆，剩餘 7691 筆\n",
      "[2025-10-14 21:58:50] 型別/特徵檢查：移除 0 筆，剩餘 7691 筆\n",
      "[2025-10-14 21:58:50] 清理完成：初始 7691 → 保留 7691（移除 0）\n",
      "[2025-10-14 21:58:51] 已寫入 cleaned 檔案：/home/jovyan/RT08/0925/sliding_win/1014_sim/cleaned/PatNo_ID_1594439781.csv（筆數 7691）\n",
      "[2025-10-14 21:58:51] 清理開始：PatNo_ID_1594441887.csv\n",
      "[2025-10-14 21:58:51] 讀入筆數：10200（檔：PatNo_ID_1594441887.csv）\n",
      "[2025-10-14 21:58:51] 缺失清除：移除 0 筆，剩餘 10200 筆\n",
      "[2025-10-14 21:58:51] 時間處理：移除 0 筆無效時間，剩餘 10200 筆\n",
      "[2025-10-14 21:58:51] 去除重複：移除 0 筆，剩餘 10200 筆\n",
      "[2025-10-14 21:58:51] 型別/特徵檢查：移除 3 筆，剩餘 10197 筆\n",
      "[2025-10-14 21:58:51] 清理完成：初始 10200 → 保留 10197（移除 3）\n",
      "[2025-10-14 21:58:51] 已寫入 cleaned 檔案：/home/jovyan/RT08/0925/sliding_win/1014_sim/cleaned/PatNo_ID_1594441887.csv（筆數 10197）\n",
      "[2025-10-14 21:58:51] 清理開始：PatNo_ID_1594448501.csv\n",
      "[2025-10-14 21:58:51] 讀入筆數：5106（檔：PatNo_ID_1594448501.csv）\n",
      "[2025-10-14 21:58:51] 缺失清除：移除 0 筆，剩餘 5106 筆\n",
      "[2025-10-14 21:58:51] 時間處理：移除 0 筆無效時間，剩餘 5106 筆\n",
      "[2025-10-14 21:58:51] 去除重複：移除 0 筆，剩餘 5106 筆\n",
      "[2025-10-14 21:58:51] 型別/特徵檢查：移除 13 筆，剩餘 5093 筆\n",
      "[2025-10-14 21:58:51] 清理完成：初始 5106 → 保留 5093（移除 13）\n",
      "[2025-10-14 21:58:51] 已寫入 cleaned 檔案：/home/jovyan/RT08/0925/sliding_win/1014_sim/cleaned/PatNo_ID_1594448501.csv（筆數 5093）\n",
      "[2025-10-14 21:58:51] 清理開始：PatNo_ID_1594455578.csv\n",
      "[2025-10-14 21:58:51] 讀入筆數：262（檔：PatNo_ID_1594455578.csv）\n",
      "[2025-10-14 21:58:51] 缺失清除：移除 0 筆，剩餘 262 筆\n",
      "[2025-10-14 21:58:51] 時間處理：移除 0 筆無效時間，剩餘 262 筆\n",
      "[2025-10-14 21:58:51] 去除重複：移除 0 筆，剩餘 262 筆\n",
      "[2025-10-14 21:58:51] 型別/特徵檢查：移除 0 筆，剩餘 262 筆\n",
      "[2025-10-14 21:58:51] 清理完成：初始 262 → 保留 262（移除 0）\n",
      "[2025-10-14 21:58:51] 已寫入 cleaned 檔案：/home/jovyan/RT08/0925/sliding_win/1014_sim/cleaned/PatNo_ID_1594455578.csv（筆數 262）\n",
      "[2025-10-14 21:58:51] 清理開始：PatNo_ID_1594464829.csv\n",
      "[2025-10-14 21:58:51] 讀入筆數：4000（檔：PatNo_ID_1594464829.csv）\n",
      "[2025-10-14 21:58:51] 缺失清除：移除 0 筆，剩餘 4000 筆\n",
      "[2025-10-14 21:58:51] 時間處理：移除 0 筆無效時間，剩餘 4000 筆\n",
      "[2025-10-14 21:58:51] 去除重複：移除 0 筆，剩餘 4000 筆\n",
      "[2025-10-14 21:58:51] 型別/特徵檢查：移除 61 筆，剩餘 3939 筆\n",
      "[2025-10-14 21:58:51] 清理完成：初始 4000 → 保留 3939（移除 61）\n",
      "[2025-10-14 21:58:51] 已寫入 cleaned 檔案：/home/jovyan/RT08/0925/sliding_win/1014_sim/cleaned/PatNo_ID_1594464829.csv（筆數 3939）\n",
      "[2025-10-14 21:58:51] 清理開始：PatNo_ID_1594467719.csv\n",
      "[2025-10-14 21:58:51] 讀入筆數：2559（檔：PatNo_ID_1594467719.csv）\n",
      "[2025-10-14 21:58:51] 缺失清除：移除 0 筆，剩餘 2559 筆\n",
      "[2025-10-14 21:58:51] 時間處理：移除 0 筆無效時間，剩餘 2559 筆\n",
      "[2025-10-14 21:58:51] 去除重複：移除 0 筆，剩餘 2559 筆\n",
      "[2025-10-14 21:58:51] 型別/特徵檢查：移除 1246 筆，剩餘 1313 筆\n",
      "[2025-10-14 21:58:51] 清理完成：初始 2559 → 保留 1313（移除 1246）\n",
      "[2025-10-14 21:58:51] 已寫入 cleaned 檔案：/home/jovyan/RT08/0925/sliding_win/1014_sim/cleaned/PatNo_ID_1594467719.csv（筆數 1313）\n",
      "[2025-10-14 21:58:51] 清理開始：PatNo_ID_1594471407.csv\n",
      "[2025-10-14 21:58:51] 讀入筆數：11432（檔：PatNo_ID_1594471407.csv）\n",
      "[2025-10-14 21:58:51] 缺失清除：移除 0 筆，剩餘 11432 筆\n",
      "[2025-10-14 21:58:51] 時間處理：移除 0 筆無效時間，剩餘 11432 筆\n",
      "[2025-10-14 21:58:51] 去除重複：移除 0 筆，剩餘 11432 筆\n",
      "[2025-10-14 21:58:51] 型別/特徵檢查：移除 153 筆，剩餘 11279 筆\n",
      "[2025-10-14 21:58:51] 清理完成：初始 11432 → 保留 11279（移除 153）\n",
      "[2025-10-14 21:58:51] 已寫入 cleaned 檔案：/home/jovyan/RT08/0925/sliding_win/1014_sim/cleaned/PatNo_ID_1594471407.csv（筆數 11279）\n",
      "[2025-10-14 21:58:51] 清理開始：PatNo_ID_1594479330.csv\n",
      "[2025-10-14 21:58:51] 讀入筆數：3727（檔：PatNo_ID_1594479330.csv）\n",
      "[2025-10-14 21:58:51] 缺失清除：移除 0 筆，剩餘 3727 筆\n",
      "[2025-10-14 21:58:51] 時間處理：移除 0 筆無效時間，剩餘 3727 筆\n",
      "[2025-10-14 21:58:51] 去除重複：移除 0 筆，剩餘 3727 筆\n",
      "[2025-10-14 21:58:51] 型別/特徵檢查：移除 3 筆，剩餘 3724 筆\n",
      "[2025-10-14 21:58:51] 清理完成：初始 3727 → 保留 3724（移除 3）\n",
      "[2025-10-14 21:58:51] 已寫入 cleaned 檔案：/home/jovyan/RT08/0925/sliding_win/1014_sim/cleaned/PatNo_ID_1594479330.csv（筆數 3724）\n",
      "[2025-10-14 21:58:51] 清理開始：PatNo_ID_1594511911.csv\n",
      "[2025-10-14 21:58:51] 讀入筆數：5488（檔：PatNo_ID_1594511911.csv）\n",
      "[2025-10-14 21:58:51] 缺失清除：移除 0 筆，剩餘 5488 筆\n",
      "[2025-10-14 21:58:51] 時間處理：移除 0 筆無效時間，剩餘 5488 筆\n",
      "[2025-10-14 21:58:51] 去除重複：移除 0 筆，剩餘 5488 筆\n",
      "[2025-10-14 21:58:51] 型別/特徵檢查：移除 33 筆，剩餘 5455 筆\n",
      "[2025-10-14 21:58:51] 清理完成：初始 5488 → 保留 5455（移除 33）\n",
      "[2025-10-14 21:58:51] 已寫入 cleaned 檔案：/home/jovyan/RT08/0925/sliding_win/1014_sim/cleaned/PatNo_ID_1594511911.csv（筆數 5455）\n",
      "[2025-10-14 21:58:51] 清理開始：PatNo_ID_1594511914.csv\n",
      "[2025-10-14 21:58:51] 讀入筆數：9717（檔：PatNo_ID_1594511914.csv）\n",
      "[2025-10-14 21:58:51] 缺失清除：移除 0 筆，剩餘 9717 筆\n",
      "[2025-10-14 21:58:51] 時間處理：移除 0 筆無效時間，剩餘 9717 筆\n",
      "[2025-10-14 21:58:51] 去除重複：移除 0 筆，剩餘 9717 筆\n",
      "[2025-10-14 21:58:51] 型別/特徵檢查：移除 32 筆，剩餘 9685 筆\n",
      "[2025-10-14 21:58:51] 清理完成：初始 9717 → 保留 9685（移除 32）\n",
      "[2025-10-14 21:58:51] 已寫入 cleaned 檔案：/home/jovyan/RT08/0925/sliding_win/1014_sim/cleaned/PatNo_ID_1594511914.csv（筆數 9685）\n",
      "[2025-10-14 21:58:51] 清理開始：PatNo_ID_1594528842.csv\n",
      "[2025-10-14 21:58:51] 讀入筆數：2491（檔：PatNo_ID_1594528842.csv）\n",
      "[2025-10-14 21:58:51] 缺失清除：移除 0 筆，剩餘 2491 筆\n",
      "[2025-10-14 21:58:51] 時間處理：移除 0 筆無效時間，剩餘 2491 筆\n",
      "[2025-10-14 21:58:51] 去除重複：移除 0 筆，剩餘 2491 筆\n",
      "[2025-10-14 21:58:51] 型別/特徵檢查：移除 76 筆，剩餘 2415 筆\n",
      "[2025-10-14 21:58:51] 清理完成：初始 2491 → 保留 2415（移除 76）\n",
      "[2025-10-14 21:58:51] 已寫入 cleaned 檔案：/home/jovyan/RT08/0925/sliding_win/1014_sim/cleaned/PatNo_ID_1594528842.csv（筆數 2415）\n",
      "[2025-10-14 21:58:51] 清理開始：PatNo_ID_1594533379.csv\n",
      "[2025-10-14 21:58:51] 讀入筆數：1030（檔：PatNo_ID_1594533379.csv）\n",
      "[2025-10-14 21:58:51] 缺失清除：移除 0 筆，剩餘 1030 筆\n",
      "[2025-10-14 21:58:51] 時間處理：移除 0 筆無效時間，剩餘 1030 筆\n",
      "[2025-10-14 21:58:51] 去除重複：移除 0 筆，剩餘 1030 筆\n",
      "[2025-10-14 21:58:51] 型別/特徵檢查：移除 12 筆，剩餘 1018 筆\n",
      "[2025-10-14 21:58:51] 清理完成：初始 1030 → 保留 1018（移除 12）\n",
      "[2025-10-14 21:58:51] 已寫入 cleaned 檔案：/home/jovyan/RT08/0925/sliding_win/1014_sim/cleaned/PatNo_ID_1594533379.csv（筆數 1018）\n",
      "[2025-10-14 21:58:51] 已寫入清理層稽核總表：/home/jovyan/RT08/0925/sliding_win/1014_sim/cleaned/audit_summary.csv\n",
      "[2025-10-14 21:58:51] 清理層總結：原始總筆數=1567233、清理後總筆數=1523381、缺失移除總筆數=0、時間錯誤移除總筆數=0、重複移除總筆數=0、型別/特徵錯誤移除總筆數=43852\n",
      "[2025-10-14 21:58:51] ================== 步驟 3｜掃描 cleaned 檔案以進行視窗化 ==================\n",
      "[2025-10-14 21:58:51] cleaned 檔案數量：122\n",
      "[2025-10-14 21:58:51] ================== 步驟 4｜逐檔滑動視窗與計數 ==================\n",
      "[2025-10-14 21:58:51] [視窗前置] 檔案：089271.csv｜筆數：32368\n",
      "[2025-10-14 21:58:51] [視窗結果] 檔案：089271.csv｜生成視窗=1077｜正樣本=184｜負樣本=893｜連續性違規=99\n",
      "[2025-10-14 21:58:51] [視窗前置] 檔案：095323.csv｜筆數：23785\n",
      "[2025-10-14 21:58:51] [視窗結果] 檔案：095323.csv｜生成視窗=791｜正樣本=73｜負樣本=718｜連續性違規=33\n",
      "[2025-10-14 21:58:51] [視窗前置] 檔案：095707.csv｜筆數：20179\n",
      "[2025-10-14 21:58:51] [視窗結果] 檔案：095707.csv｜生成視窗=671｜正樣本=93｜負樣本=578｜連續性違規=29\n",
      "[2025-10-14 21:58:51] [視窗前置] 檔案：114309.csv｜筆數：71723\n",
      "[2025-10-14 21:58:52] [視窗結果] 檔案：114309.csv｜生成視窗=2389｜正樣本=126｜負樣本=2263｜連續性違規=241\n",
      "[2025-10-14 21:58:52] [視窗前置] 檔案：230933.csv｜筆數：30241\n",
      "[2025-10-14 21:58:52] [視窗結果] 檔案：230933.csv｜生成視窗=1007｜正樣本=147｜負樣本=860｜連續性違規=54\n",
      "[2025-10-14 21:58:52] [視窗前置] 檔案：4216007.csv｜筆數：527\n",
      "[2025-10-14 21:58:52] [視窗結果] 檔案：4216007.csv｜生成視窗=16｜正樣本=0｜負樣本=16｜連續性違規=3\n",
      "[2025-10-14 21:58:52] [視窗前置] 檔案：7108162.csv｜筆數：239\n",
      "[2025-10-14 21:58:52] [視窗結果] 檔案：7108162.csv｜生成視窗=6｜正樣本=2｜負樣本=4｜連續性違規=0\n",
      "[2025-10-14 21:58:52] [視窗前置] 檔案：7408338.csv｜筆數：1431\n",
      "[2025-10-14 21:58:52] [視窗結果] 檔案：7408338.csv｜生成視窗=46｜正樣本=7｜負樣本=39｜連續性違規=7\n",
      "[2025-10-14 21:58:52] [視窗前置] 檔案：7657698.csv｜筆數：1413\n",
      "[2025-10-14 21:58:52] [視窗結果] 檔案：7657698.csv｜生成視窗=46｜正樣本=9｜負樣本=37｜連續性違規=13\n",
      "[2025-10-14 21:58:52] [視窗前置] 檔案：7721164.csv｜筆數：483\n",
      "[2025-10-14 21:58:52] [視窗結果] 檔案：7721164.csv｜生成視窗=15｜正樣本=3｜負樣本=12｜連續性違規=3\n",
      "[2025-10-14 21:58:52] [視窗前置] 檔案：PatNo_ID_1560013303.csv｜筆數：2514\n",
      "[2025-10-14 21:58:52] [視窗結果] 檔案：PatNo_ID_1560013303.csv｜生成視窗=82｜正樣本=6｜負樣本=76｜連續性違規=0\n",
      "[2025-10-14 21:58:52] [視窗前置] 檔案：PatNo_ID_1562733396.csv｜筆數：2253\n",
      "[2025-10-14 21:58:52] [視窗結果] 檔案：PatNo_ID_1562733396.csv｜生成視窗=74｜正樣本=15｜負樣本=59｜連續性違規=34\n",
      "[2025-10-14 21:58:52] [視窗前置] 檔案：PatNo_ID_1563587183.csv｜筆數：5286\n",
      "[2025-10-14 21:58:52] [視窗結果] 檔案：PatNo_ID_1563587183.csv｜生成視窗=175｜正樣本=30｜負樣本=145｜連續性違規=12\n",
      "[2025-10-14 21:58:52] [視窗前置] 檔案：PatNo_ID_1564148644.csv｜筆數：14384\n",
      "[2025-10-14 21:58:52] [視窗結果] 檔案：PatNo_ID_1564148644.csv｜生成視窗=478｜正樣本=57｜負樣本=421｜連續性違規=34\n",
      "[2025-10-14 21:58:52] [視窗前置] 檔案：PatNo_ID_1565148312.csv｜筆數：5464\n",
      "[2025-10-14 21:58:52] [視窗結果] 檔案：PatNo_ID_1565148312.csv｜生成視窗=181｜正樣本=26｜負樣本=155｜連續性違規=2\n",
      "[2025-10-14 21:58:52] [視窗前置] 檔案：PatNo_ID_1565378038.csv｜筆數：2561\n",
      "[2025-10-14 21:58:52] [視窗結果] 檔案：PatNo_ID_1565378038.csv｜生成視窗=84｜正樣本=14｜負樣本=70｜連續性違規=8\n",
      "[2025-10-14 21:58:52] [視窗前置] 檔案：PatNo_ID_1566123680.csv｜筆數：42597\n",
      "[2025-10-14 21:58:52] [視窗結果] 檔案：PatNo_ID_1566123680.csv｜生成視窗=1418｜正樣本=119｜負樣本=1299｜連續性違規=179\n",
      "[2025-10-14 21:58:52] [視窗前置] 檔案：PatNo_ID_1566252197.csv｜筆數：1935\n",
      "[2025-10-14 21:58:52] [視窗結果] 檔案：PatNo_ID_1566252197.csv｜生成視窗=63｜正樣本=9｜負樣本=54｜連續性違規=0\n",
      "[2025-10-14 21:58:52] [視窗前置] 檔案：PatNo_ID_1566279967.csv｜筆數：1153\n",
      "[2025-10-14 21:58:52] [視窗結果] 檔案：PatNo_ID_1566279967.csv｜生成視窗=37｜正樣本=7｜負樣本=30｜連續性違規=4\n",
      "[2025-10-14 21:58:52] [視窗前置] 檔案：PatNo_ID_1566671274.csv｜筆數：23554\n",
      "[2025-10-14 21:58:52] [視窗結果] 檔案：PatNo_ID_1566671274.csv｜生成視窗=784｜正樣本=122｜負樣本=662｜連續性違規=80\n",
      "[2025-10-14 21:58:52] [視窗前置] 檔案：PatNo_ID_1566911879.csv｜筆數：45975\n",
      "[2025-10-14 21:58:52] [視窗結果] 檔案：PatNo_ID_1566911879.csv｜生成視窗=1531｜正樣本=297｜負樣本=1234｜連續性違規=32\n",
      "[2025-10-14 21:58:52] [視窗前置] 檔案：PatNo_ID_1567747650.csv｜筆數：10898\n",
      "[2025-10-14 21:58:53] [視窗結果] 檔案：PatNo_ID_1567747650.csv｜生成視窗=362｜正樣本=58｜負樣本=304｜連續性違規=10\n",
      "[2025-10-14 21:58:53] [視窗前置] 檔案：PatNo_ID_1567804800.csv｜筆數：20389\n",
      "[2025-10-14 21:58:53] [視窗結果] 檔案：PatNo_ID_1567804800.csv｜生成視窗=678｜正樣本=79｜負樣本=599｜連續性違規=28\n",
      "[2025-10-14 21:58:53] [視窗前置] 檔案：PatNo_ID_1567832735.csv｜筆數：36569\n",
      "[2025-10-14 21:58:53] [視窗結果] 檔案：PatNo_ID_1567832735.csv｜生成視窗=1217｜正樣本=205｜負樣本=1012｜連續性違規=32\n",
      "[2025-10-14 21:58:53] [視窗前置] 檔案：PatNo_ID_1568039398.csv｜筆數：34242\n",
      "[2025-10-14 21:58:53] [視窗結果] 檔案：PatNo_ID_1568039398.csv｜生成視窗=1140｜正樣本=200｜負樣本=940｜連續性違規=38\n",
      "[2025-10-14 21:58:53] [視窗前置] 檔案：PatNo_ID_1568574099.csv｜筆數：13863\n",
      "[2025-10-14 21:58:53] [視窗結果] 檔案：PatNo_ID_1568574099.csv｜生成視窗=461｜正樣本=65｜負樣本=396｜連續性違規=0\n",
      "[2025-10-14 21:58:53] [視窗前置] 檔案：PatNo_ID_1568813269.csv｜筆數：4867\n",
      "[2025-10-14 21:58:53] [視窗結果] 檔案：PatNo_ID_1568813269.csv｜生成視窗=161｜正樣本=33｜負樣本=128｜連續性違規=9\n",
      "[2025-10-14 21:58:53] [視窗前置] 檔案：PatNo_ID_1568952422.csv｜筆數：1184\n",
      "[2025-10-14 21:58:53] [視窗結果] 檔案：PatNo_ID_1568952422.csv｜生成視窗=38｜正樣本=12｜負樣本=26｜連續性違規=0\n",
      "[2025-10-14 21:58:53] [視窗前置] 檔案：PatNo_ID_1569083701.csv｜筆數：3326\n",
      "[2025-10-14 21:58:53] [視窗結果] 檔案：PatNo_ID_1569083701.csv｜生成視窗=109｜正樣本=7｜負樣本=102｜連續性違規=20\n",
      "[2025-10-14 21:58:53] [視窗前置] 檔案：PatNo_ID_1569944983.csv｜筆數：7647\n",
      "[2025-10-14 21:58:53] [視窗結果] 檔案：PatNo_ID_1569944983.csv｜生成視窗=253｜正樣本=32｜負樣本=221｜連續性違規=2\n",
      "[2025-10-14 21:58:53] [視窗前置] 檔案：PatNo_ID_1570089466.csv｜筆數：40589\n",
      "[2025-10-14 21:58:53] [視窗結果] 檔案：PatNo_ID_1570089466.csv｜生成視窗=1351｜正樣本=81｜負樣本=1270｜連續性違規=57\n",
      "[2025-10-14 21:58:53] [視窗前置] 檔案：PatNo_ID_1570242703.csv｜筆數：10556\n",
      "[2025-10-14 21:58:53] [視窗結果] 檔案：PatNo_ID_1570242703.csv｜生成視窗=350｜正樣本=58｜負樣本=292｜連續性違規=0\n",
      "[2025-10-14 21:58:53] [視窗前置] 檔案：PatNo_ID_1570273244.csv｜筆數：9707\n",
      "[2025-10-14 21:58:53] [視窗結果] 檔案：PatNo_ID_1570273244.csv｜生成視窗=322｜正樣本=63｜負樣本=259｜連續性違規=2\n",
      "[2025-10-14 21:58:53] [視窗前置] 檔案：PatNo_ID_1570642083.csv｜筆數：19731\n",
      "[2025-10-14 21:58:53] [視窗結果] 檔案：PatNo_ID_1570642083.csv｜生成視窗=656｜正樣本=81｜負樣本=575｜連續性違規=11\n",
      "[2025-10-14 21:58:53] [視窗前置] 檔案：PatNo_ID_1571945701.csv｜筆數：15729\n",
      "[2025-10-14 21:58:53] [視窗結果] 檔案：PatNo_ID_1571945701.csv｜生成視窗=523｜正樣本=94｜負樣本=429｜連續性違規=2\n",
      "[2025-10-14 21:58:53] [視窗前置] 檔案：PatNo_ID_1572481361.csv｜筆數：34515\n",
      "[2025-10-14 21:58:54] [視窗結果] 檔案：PatNo_ID_1572481361.csv｜生成視窗=1149｜正樣本=192｜負樣本=957｜連續性違規=46\n",
      "[2025-10-14 21:58:54] [視窗前置] 檔案：PatNo_ID_1572562839.csv｜筆數：20920\n",
      "[2025-10-14 21:58:54] [視窗結果] 檔案：PatNo_ID_1572562839.csv｜生成視窗=696｜正樣本=94｜負樣本=602｜連續性違規=71\n",
      "[2025-10-14 21:58:54] [視窗前置] 檔案：PatNo_ID_1572831765.csv｜筆數：2696\n",
      "[2025-10-14 21:58:54] [視窗結果] 檔案：PatNo_ID_1572831765.csv｜生成視窗=88｜正樣本=15｜負樣本=73｜連續性違規=0\n",
      "[2025-10-14 21:58:54] [視窗前置] 檔案：PatNo_ID_1572976822.csv｜筆數：6624\n",
      "[2025-10-14 21:58:54] [視窗結果] 檔案：PatNo_ID_1572976822.csv｜生成視窗=219｜正樣本=25｜負樣本=194｜連續性違規=59\n",
      "[2025-10-14 21:58:54] [視窗前置] 檔案：PatNo_ID_1573063188.csv｜筆數：5025\n",
      "[2025-10-14 21:58:54] [視窗結果] 檔案：PatNo_ID_1573063188.csv｜生成視窗=166｜正樣本=19｜負樣本=147｜連續性違規=10\n",
      "[2025-10-14 21:58:54] [視窗前置] 檔案：PatNo_ID_1573249295.csv｜筆數：7151\n",
      "[2025-10-14 21:58:54] [視窗結果] 檔案：PatNo_ID_1573249295.csv｜生成視窗=237｜正樣本=49｜負樣本=188｜連續性違規=69\n",
      "[2025-10-14 21:58:54] [視窗前置] 檔案：PatNo_ID_1573964540.csv｜筆數：4347\n",
      "[2025-10-14 21:58:54] [視窗結果] 檔案：PatNo_ID_1573964540.csv｜生成視窗=143｜正樣本=25｜負樣本=118｜連續性違規=4\n",
      "[2025-10-14 21:58:54] [視窗前置] 檔案：PatNo_ID_1574148494.csv｜筆數：47017\n",
      "[2025-10-14 21:58:54] [視窗結果] 檔案：PatNo_ID_1574148494.csv｜生成視窗=1566｜正樣本=275｜負樣本=1291｜連續性違規=32\n",
      "[2025-10-14 21:58:54] [視窗前置] 檔案：PatNo_ID_1574270349.csv｜筆數：6515\n",
      "[2025-10-14 21:58:54] [視窗結果] 檔案：PatNo_ID_1574270349.csv｜生成視窗=216｜正樣本=41｜負樣本=175｜連續性違規=0\n",
      "[2025-10-14 21:58:54] [視窗前置] 檔案：PatNo_ID_1574528808.csv｜筆數：14480\n",
      "[2025-10-14 21:58:54] [視窗結果] 檔案：PatNo_ID_1574528808.csv｜生成視窗=481｜正樣本=58｜負樣本=423｜連續性違規=118\n",
      "[2025-10-14 21:58:54] [視窗前置] 檔案：PatNo_ID_1574831525.csv｜筆數：515\n",
      "[2025-10-14 21:58:54] [視窗結果] 檔案：PatNo_ID_1574831525.csv｜生成視窗=16｜正樣本=5｜負樣本=11｜連續性違規=0\n",
      "[2025-10-14 21:58:54] [視窗前置] 檔案：PatNo_ID_1574987447.csv｜筆數：19838\n",
      "[2025-10-14 21:58:54] [視窗結果] 檔案：PatNo_ID_1574987447.csv｜生成視窗=660｜正樣本=103｜負樣本=557｜連續性違規=33\n",
      "[2025-10-14 21:58:54] [視窗前置] 檔案：PatNo_ID_1575060177.csv｜筆數：5290\n",
      "[2025-10-14 21:58:54] [視窗結果] 檔案：PatNo_ID_1575060177.csv｜生成視窗=175｜正樣本=28｜負樣本=147｜連續性違規=0\n",
      "[2025-10-14 21:58:54] [視窗前置] 檔案：PatNo_ID_1575256902.csv｜筆數：9492\n",
      "[2025-10-14 21:58:54] [視窗結果] 檔案：PatNo_ID_1575256902.csv｜生成視窗=315｜正樣本=34｜負樣本=281｜連續性違規=26\n",
      "[2025-10-14 21:58:54] [視窗前置] 檔案：PatNo_ID_1575445051.csv｜筆數：1785\n",
      "[2025-10-14 21:58:54] [視窗結果] 檔案：PatNo_ID_1575445051.csv｜生成視窗=58｜正樣本=6｜負樣本=52｜連續性違規=2\n",
      "[2025-10-14 21:58:54] [視窗前置] 檔案：PatNo_ID_1575502382.csv｜筆數：6489\n",
      "[2025-10-14 21:58:54] [視窗結果] 檔案：PatNo_ID_1575502382.csv｜生成視窗=215｜正樣本=35｜負樣本=180｜連續性違規=16\n",
      "[2025-10-14 21:58:54] [視窗前置] 檔案：PatNo_ID_1575975485.csv｜筆數：16458\n",
      "[2025-10-14 21:58:54] [視窗結果] 檔案：PatNo_ID_1575975485.csv｜生成視窗=547｜正樣本=97｜負樣本=450｜連續性違規=10\n",
      "[2025-10-14 21:58:54] [視窗前置] 檔案：PatNo_ID_1576115572.csv｜筆數：18017\n",
      "[2025-10-14 21:58:54] [視窗結果] 檔案：PatNo_ID_1576115572.csv｜生成視窗=599｜正樣本=100｜負樣本=499｜連續性違規=163\n",
      "[2025-10-14 21:58:54] [視窗前置] 檔案：PatNo_ID_1576116479.csv｜筆數：1257\n",
      "[2025-10-14 21:58:54] [視窗結果] 檔案：PatNo_ID_1576116479.csv｜生成視窗=40｜正樣本=6｜負樣本=34｜連續性違規=0\n",
      "[2025-10-14 21:58:54] [視窗前置] 檔案：PatNo_ID_1576301569.csv｜筆數：3207\n",
      "[2025-10-14 21:58:54] [視窗結果] 檔案：PatNo_ID_1576301569.csv｜生成視窗=105｜正樣本=16｜負樣本=89｜連續性違規=37\n",
      "[2025-10-14 21:58:54] [視窗前置] 檔案：PatNo_ID_1576964560.csv｜筆數：24188\n",
      "[2025-10-14 21:58:54] [視窗結果] 檔案：PatNo_ID_1576964560.csv｜生成視窗=805｜正樣本=122｜負樣本=683｜連續性違規=4\n",
      "[2025-10-14 21:58:54] [視窗前置] 檔案：PatNo_ID_1577042911.csv｜筆數：35624\n",
      "[2025-10-14 21:58:55] [視窗結果] 檔案：PatNo_ID_1577042911.csv｜生成視窗=1186｜正樣本=198｜負樣本=988｜連續性違規=16\n",
      "[2025-10-14 21:58:55] [視窗前置] 檔案：PatNo_ID_1577487284.csv｜筆數：2345\n",
      "[2025-10-14 21:58:55] [視窗結果] 檔案：PatNo_ID_1577487284.csv｜生成視窗=77｜正樣本=20｜負樣本=57｜連續性違規=12\n",
      "[2025-10-14 21:58:55] [視窗前置] 檔案：PatNo_ID_1578784257.csv｜筆數：28555\n",
      "[2025-10-14 21:58:55] [視窗結果] 檔案：PatNo_ID_1578784257.csv｜生成視窗=950｜正樣本=150｜負樣本=800｜連續性違規=25\n",
      "[2025-10-14 21:58:55] [視窗前置] 檔案：PatNo_ID_1579198603.csv｜筆數：2045\n",
      "[2025-10-14 21:58:55] [視窗結果] 檔案：PatNo_ID_1579198603.csv｜生成視窗=67｜正樣本=14｜負樣本=53｜連續性違規=0\n",
      "[2025-10-14 21:58:55] [視窗前置] 檔案：PatNo_ID_1579498177.csv｜筆數：21681\n",
      "[2025-10-14 21:58:55] [視窗結果] 檔案：PatNo_ID_1579498177.csv｜生成視窗=721｜正樣本=131｜負樣本=590｜連續性違規=244\n",
      "[2025-10-14 21:58:55] [視窗前置] 檔案：PatNo_ID_1580062580.csv｜筆數：2150\n",
      "[2025-10-14 21:58:55] [視窗結果] 檔案：PatNo_ID_1580062580.csv｜生成視窗=70｜正樣本=17｜負樣本=53｜連續性違規=9\n",
      "[2025-10-14 21:58:55] [視窗前置] 檔案：PatNo_ID_1580096720.csv｜筆數：1422\n",
      "[2025-10-14 21:58:55] [視窗結果] 檔案：PatNo_ID_1580096720.csv｜生成視窗=46｜正樣本=5｜負樣本=41｜連續性違規=6\n",
      "[2025-10-14 21:58:55] [視窗前置] 檔案：PatNo_ID_1580107637.csv｜筆數：2688\n",
      "[2025-10-14 21:58:55] [視窗結果] 檔案：PatNo_ID_1580107637.csv｜生成視窗=88｜正樣本=0｜負樣本=88｜連續性違規=0\n",
      "[2025-10-14 21:58:55] [視窗前置] 檔案：PatNo_ID_1580244614.csv｜筆數：5024\n",
      "[2025-10-14 21:58:55] [視窗結果] 檔案：PatNo_ID_1580244614.csv｜生成視窗=166｜正樣本=26｜負樣本=140｜連續性違規=8\n",
      "[2025-10-14 21:58:55] [視窗前置] 檔案：PatNo_ID_1580766093.csv｜筆數：18064\n",
      "[2025-10-14 21:58:55] [視窗結果] 檔案：PatNo_ID_1580766093.csv｜生成視窗=601｜正樣本=100｜負樣本=501｜連續性違規=40\n",
      "[2025-10-14 21:58:55] [視窗前置] 檔案：PatNo_ID_1581003248.csv｜筆數：7775\n",
      "[2025-10-14 21:58:55] [視窗結果] 檔案：PatNo_ID_1581003248.csv｜生成視窗=258｜正樣本=34｜負樣本=224｜連續性違規=12\n",
      "[2025-10-14 21:58:55] [視窗前置] 檔案：PatNo_ID_1581019504.csv｜筆數：21526\n",
      "[2025-10-14 21:58:55] [視窗結果] 檔案：PatNo_ID_1581019504.csv｜生成視窗=716｜正樣本=141｜負樣本=575｜連續性違規=40\n",
      "[2025-10-14 21:58:55] [視窗前置] 檔案：PatNo_ID_1581633231.csv｜筆數：15971\n",
      "[2025-10-14 21:58:55] [視窗結果] 檔案：PatNo_ID_1581633231.csv｜生成視窗=531｜正樣本=73｜負樣本=458｜連續性違規=156\n",
      "[2025-10-14 21:58:55] [視窗前置] 檔案：PatNo_ID_1581692973.csv｜筆數：2723\n",
      "[2025-10-14 21:58:55] [視窗結果] 檔案：PatNo_ID_1581692973.csv｜生成視窗=89｜正樣本=15｜負樣本=74｜連續性違規=10\n",
      "[2025-10-14 21:58:55] [視窗前置] 檔案：PatNo_ID_1582452511.csv｜筆數：5196\n",
      "[2025-10-14 21:58:55] [視窗結果] 檔案：PatNo_ID_1582452511.csv｜生成視窗=172｜正樣本=26｜負樣本=146｜連續性違規=4\n",
      "[2025-10-14 21:58:55] [視窗前置] 檔案：PatNo_ID_1582635996.csv｜筆數：13046\n",
      "[2025-10-14 21:58:55] [視窗結果] 檔案：PatNo_ID_1582635996.csv｜生成視窗=433｜正樣本=80｜負樣本=353｜連續性違規=10\n",
      "[2025-10-14 21:58:55] [視窗前置] 檔案：PatNo_ID_1582849900.csv｜筆數：7387\n",
      "[2025-10-14 21:58:55] [視窗結果] 檔案：PatNo_ID_1582849900.csv｜生成視窗=245｜正樣本=9｜負樣本=236｜連續性違規=32\n",
      "[2025-10-14 21:58:55] [視窗前置] 檔案：PatNo_ID_1582937076.csv｜筆數：23966\n",
      "[2025-10-14 21:58:55] [視窗結果] 檔案：PatNo_ID_1582937076.csv｜生成視窗=797｜正樣本=139｜負樣本=658｜連續性違規=208\n",
      "[2025-10-14 21:58:55] [視窗前置] 檔案：PatNo_ID_1584158973.csv｜筆數：2882\n",
      "[2025-10-14 21:58:55] [視窗結果] 檔案：PatNo_ID_1584158973.csv｜生成視窗=95｜正樣本=18｜負樣本=77｜連續性違規=11\n",
      "[2025-10-14 21:58:55] [視窗前置] 檔案：PatNo_ID_1584397376.csv｜筆數：638\n",
      "[2025-10-14 21:58:55] [視窗結果] 檔案：PatNo_ID_1584397376.csv｜生成視窗=20｜正樣本=1｜負樣本=19｜連續性違規=0\n",
      "[2025-10-14 21:58:55] [視窗前置] 檔案：PatNo_ID_1586172659.csv｜筆數：38547\n",
      "[2025-10-14 21:58:56] [視窗結果] 檔案：PatNo_ID_1586172659.csv｜生成視窗=1283｜正樣本=198｜負樣本=1085｜連續性違規=25\n",
      "[2025-10-14 21:58:56] [視窗前置] 檔案：PatNo_ID_1586696634.csv｜筆數：2504\n",
      "[2025-10-14 21:58:56] [視窗結果] 檔案：PatNo_ID_1586696634.csv｜生成視窗=82｜正樣本=12｜負樣本=70｜連續性違規=4\n",
      "[2025-10-14 21:58:56] [視窗前置] 檔案：PatNo_ID_1586897008.csv｜筆數：6655\n",
      "[2025-10-14 21:58:56] [視窗結果] 檔案：PatNo_ID_1586897008.csv｜生成視窗=220｜正樣本=34｜負樣本=186｜連續性違規=7\n",
      "[2025-10-14 21:58:56] [視窗前置] 檔案：PatNo_ID_1587490083.csv｜筆數：45186\n",
      "[2025-10-14 21:58:56] [視窗結果] 檔案：PatNo_ID_1587490083.csv｜生成視窗=1505｜正樣本=250｜負樣本=1255｜連續性違規=693\n",
      "[2025-10-14 21:58:56] [視窗前置] 檔案：PatNo_ID_1588632604.csv｜筆數：2608\n",
      "[2025-10-14 21:58:56] [視窗結果] 檔案：PatNo_ID_1588632604.csv｜生成視窗=85｜正樣本=13｜負樣本=72｜連續性違規=5\n",
      "[2025-10-14 21:58:56] [視窗前置] 檔案：PatNo_ID_1588673465.csv｜筆數：5554\n",
      "[2025-10-14 21:58:56] [視窗結果] 檔案：PatNo_ID_1588673465.csv｜生成視窗=184｜正樣本=42｜負樣本=142｜連續性違規=17\n",
      "[2025-10-14 21:58:56] [視窗前置] 檔案：PatNo_ID_1588794796.csv｜筆數：9375\n",
      "[2025-10-14 21:58:56] [視窗結果] 檔案：PatNo_ID_1588794796.csv｜生成視窗=311｜正樣本=59｜負樣本=252｜連續性違規=8\n",
      "[2025-10-14 21:58:56] [視窗前置] 檔案：PatNo_ID_1588957997.csv｜筆數：10966\n",
      "[2025-10-14 21:58:56] [視窗結果] 檔案：PatNo_ID_1588957997.csv｜生成視窗=364｜正樣本=49｜負樣本=315｜連續性違規=2\n",
      "[2025-10-14 21:58:56] [視窗前置] 檔案：PatNo_ID_1589018086.csv｜筆數：13450\n",
      "[2025-10-14 21:58:56] [視窗結果] 檔案：PatNo_ID_1589018086.csv｜生成視窗=447｜正樣本=69｜負樣本=378｜連續性違規=8\n",
      "[2025-10-14 21:58:56] [視窗前置] 檔案：PatNo_ID_1589034524.csv｜筆數：50039\n",
      "[2025-10-14 21:58:56] [視窗結果] 檔案：PatNo_ID_1589034524.csv｜生成視窗=1666｜正樣本=267｜負樣本=1399｜連續性違規=394\n",
      "[2025-10-14 21:58:56] [視窗前置] 檔案：PatNo_ID_1589324603.csv｜筆數：4111\n",
      "[2025-10-14 21:58:56] [視窗結果] 檔案：PatNo_ID_1589324603.csv｜生成視窗=136｜正樣本=15｜負樣本=121｜連續性違規=0\n",
      "[2025-10-14 21:58:56] [視窗前置] 檔案：PatNo_ID_1589918099.csv｜筆數：9332\n",
      "[2025-10-14 21:58:56] [視窗結果] 檔案：PatNo_ID_1589918099.csv｜生成視窗=310｜正樣本=0｜負樣本=310｜連續性違規=60\n",
      "[2025-10-14 21:58:56] [視窗前置] 檔案：PatNo_ID_1590136310.csv｜筆數：5307\n",
      "[2025-10-14 21:58:56] [視窗結果] 檔案：PatNo_ID_1590136310.csv｜生成視窗=175｜正樣本=20｜負樣本=155｜連續性違規=81\n",
      "[2025-10-14 21:58:56] [視窗前置] 檔案：PatNo_ID_1590616537.csv｜筆數：14206\n",
      "[2025-10-14 21:58:56] [視窗結果] 檔案：PatNo_ID_1590616537.csv｜生成視窗=472｜正樣本=76｜負樣本=396｜連續性違規=3\n",
      "[2025-10-14 21:58:56] [視窗前置] 檔案：PatNo_ID_1590854576.csv｜筆數：15879\n",
      "[2025-10-14 21:58:56] [視窗結果] 檔案：PatNo_ID_1590854576.csv｜生成視窗=528｜正樣本=90｜負樣本=438｜連續性違規=125\n",
      "[2025-10-14 21:58:56] [視窗前置] 檔案：PatNo_ID_1591609798.csv｜筆數：35617\n",
      "[2025-10-14 21:58:56] [視窗結果] 檔案：PatNo_ID_1591609798.csv｜生成視窗=1186｜正樣本=176｜負樣本=1010｜連續性違規=2\n",
      "[2025-10-14 21:58:56] [視窗前置] 檔案：PatNo_ID_1592044724.csv｜筆數：6812\n",
      "[2025-10-14 21:58:57] [視窗結果] 檔案：PatNo_ID_1592044724.csv｜生成視窗=226｜正樣本=46｜負樣本=180｜連續性違規=0\n",
      "[2025-10-14 21:58:57] [視窗前置] 檔案：PatNo_ID_1592560504.csv｜筆數：18051\n",
      "[2025-10-14 21:58:57] [視窗結果] 檔案：PatNo_ID_1592560504.csv｜生成視窗=600｜正樣本=71｜負樣本=529｜連續性違規=12\n",
      "[2025-10-14 21:58:57] [視窗前置] 檔案：PatNo_ID_1593087886.csv｜筆數：23868\n",
      "[2025-10-14 21:58:57] [視窗結果] 檔案：PatNo_ID_1593087886.csv｜生成視窗=794｜正樣本=112｜負樣本=682｜連續性違規=103\n",
      "[2025-10-14 21:58:57] [視窗前置] 檔案：PatNo_ID_1593416100.csv｜筆數：3328\n",
      "[2025-10-14 21:58:57] [視窗結果] 檔案：PatNo_ID_1593416100.csv｜生成視窗=109｜正樣本=21｜負樣本=88｜連續性違規=26\n",
      "[2025-10-14 21:58:57] [視窗前置] 檔案：PatNo_ID_1593472048.csv｜筆數：6247\n",
      "[2025-10-14 21:58:57] [視窗結果] 檔案：PatNo_ID_1593472048.csv｜生成視窗=207｜正樣本=40｜負樣本=167｜連續性違規=2\n",
      "[2025-10-14 21:58:57] [視窗前置] 檔案：PatNo_ID_1593593586.csv｜筆數：18875\n",
      "[2025-10-14 21:58:57] [視窗結果] 檔案：PatNo_ID_1593593586.csv｜生成視窗=628｜正樣本=97｜負樣本=531｜連續性違規=22\n",
      "[2025-10-14 21:58:57] [視窗前置] 檔案：PatNo_ID_1593720818.csv｜筆數：3909\n",
      "[2025-10-14 21:58:57] [視窗結果] 檔案：PatNo_ID_1593720818.csv｜生成視窗=129｜正樣本=19｜負樣本=110｜連續性違規=4\n",
      "[2025-10-14 21:58:57] [視窗前置] 檔案：PatNo_ID_1593838524.csv｜筆數：2161\n",
      "[2025-10-14 21:58:57] [視窗結果] 檔案：PatNo_ID_1593838524.csv｜生成視窗=71｜正樣本=8｜負樣本=63｜連續性違規=0\n",
      "[2025-10-14 21:58:57] [視窗前置] 檔案：PatNo_ID_1594173718.csv｜筆數：344\n",
      "[2025-10-14 21:58:57] [視窗結果] 檔案：PatNo_ID_1594173718.csv｜生成視窗=10｜正樣本=2｜負樣本=8｜連續性違規=0\n",
      "[2025-10-14 21:58:57] [視窗前置] 檔案：PatNo_ID_1594294180.csv｜筆數：18326\n",
      "[2025-10-14 21:58:57] [視窗結果] 檔案：PatNo_ID_1594294180.csv｜生成視窗=609｜正樣本=48｜負樣本=561｜連續性違規=219\n",
      "[2025-10-14 21:58:57] [視窗前置] 檔案：PatNo_ID_1594305136.csv｜筆數：18578\n",
      "[2025-10-14 21:58:57] [視窗結果] 檔案：PatNo_ID_1594305136.csv｜生成視窗=618｜正樣本=94｜負樣本=524｜連續性違規=10\n",
      "[2025-10-14 21:58:57] [視窗前置] 檔案：PatNo_ID_1594309746.csv｜筆數：3724\n",
      "[2025-10-14 21:58:57] [視窗結果] 檔案：PatNo_ID_1594309746.csv｜生成視窗=123｜正樣本=21｜負樣本=102｜連續性違規=0\n",
      "[2025-10-14 21:58:57] [視窗前置] 檔案：PatNo_ID_1594319286.csv｜筆數：3989\n",
      "[2025-10-14 21:58:57] [視窗結果] 檔案：PatNo_ID_1594319286.csv｜生成視窗=131｜正樣本=13｜負樣本=118｜連續性違規=2\n",
      "[2025-10-14 21:58:57] [視窗前置] 檔案：PatNo_ID_1594320763.csv｜筆數：2430\n",
      "[2025-10-14 21:58:57] [視窗結果] 檔案：PatNo_ID_1594320763.csv｜生成視窗=80｜正樣本=11｜負樣本=69｜連續性違規=0\n",
      "[2025-10-14 21:58:57] [視窗前置] 檔案：PatNo_ID_1594322594.csv｜筆數：5378\n",
      "[2025-10-14 21:58:57] [視窗結果] 檔案：PatNo_ID_1594322594.csv｜生成視窗=178｜正樣本=33｜負樣本=145｜連續性違規=0\n",
      "[2025-10-14 21:58:57] [視窗前置] 檔案：PatNo_ID_1594335109.csv｜筆數：5116\n",
      "[2025-10-14 21:58:57] [視窗結果] 檔案：PatNo_ID_1594335109.csv｜生成視窗=169｜正樣本=23｜負樣本=146｜連續性違規=2\n",
      "[2025-10-14 21:58:57] [視窗前置] 檔案：PatNo_ID_1594423683.csv｜筆數：5004\n",
      "[2025-10-14 21:58:57] [視窗結果] 檔案：PatNo_ID_1594423683.csv｜生成視窗=165｜正樣本=24｜負樣本=141｜連續性違規=5\n",
      "[2025-10-14 21:58:57] [視窗前置] 檔案：PatNo_ID_1594437309.csv｜筆數：9938\n",
      "[2025-10-14 21:58:57] [視窗結果] 檔案：PatNo_ID_1594437309.csv｜生成視窗=330｜正樣本=49｜負樣本=281｜連續性違規=10\n",
      "[2025-10-14 21:58:57] [視窗前置] 檔案：PatNo_ID_1594439781.csv｜筆數：7691\n",
      "[2025-10-14 21:58:57] [視窗結果] 檔案：PatNo_ID_1594439781.csv｜生成視窗=255｜正樣本=42｜負樣本=213｜連續性違規=2\n",
      "[2025-10-14 21:58:57] [視窗前置] 檔案：PatNo_ID_1594441887.csv｜筆數：10197\n",
      "[2025-10-14 21:58:57] [視窗結果] 檔案：PatNo_ID_1594441887.csv｜生成視窗=338｜正樣本=56｜負樣本=282｜連續性違規=32\n",
      "[2025-10-14 21:58:57] [視窗前置] 檔案：PatNo_ID_1594448501.csv｜筆數：5093\n",
      "[2025-10-14 21:58:57] [視窗結果] 檔案：PatNo_ID_1594448501.csv｜生成視窗=168｜正樣本=0｜負樣本=168｜連續性違規=12\n",
      "[2025-10-14 21:58:57] [視窗前置] 檔案：PatNo_ID_1594455578.csv｜筆數：262\n",
      "[2025-10-14 21:58:57] [視窗結果] 檔案：PatNo_ID_1594455578.csv｜生成視窗=7｜正樣本=1｜負樣本=6｜連續性違規=0\n",
      "[2025-10-14 21:58:57] [視窗前置] 檔案：PatNo_ID_1594464829.csv｜筆數：3939\n",
      "[2025-10-14 21:58:57] [視窗結果] 檔案：PatNo_ID_1594464829.csv｜生成視窗=130｜正樣本=16｜負樣本=114｜連續性違規=2\n",
      "[2025-10-14 21:58:57] [視窗前置] 檔案：PatNo_ID_1594467719.csv｜筆數：1313\n",
      "[2025-10-14 21:58:57] [視窗結果] 檔案：PatNo_ID_1594467719.csv｜生成視窗=42｜正樣本=6｜負樣本=36｜連續性違規=0\n",
      "[2025-10-14 21:58:57] [視窗前置] 檔案：PatNo_ID_1594471407.csv｜筆數：11279\n",
      "[2025-10-14 21:58:57] [視窗結果] 檔案：PatNo_ID_1594471407.csv｜生成視窗=374｜正樣本=59｜負樣本=315｜連續性違規=4\n",
      "[2025-10-14 21:58:57] [視窗前置] 檔案：PatNo_ID_1594479330.csv｜筆數：3724\n",
      "[2025-10-14 21:58:57] [視窗結果] 檔案：PatNo_ID_1594479330.csv｜生成視窗=123｜正樣本=13｜負樣本=110｜連續性違規=74\n",
      "[2025-10-14 21:58:57] [視窗前置] 檔案：PatNo_ID_1594511911.csv｜筆數：5455\n",
      "[2025-10-14 21:58:57] [視窗結果] 檔案：PatNo_ID_1594511911.csv｜生成視窗=180｜正樣本=32｜負樣本=148｜連續性違規=1\n",
      "[2025-10-14 21:58:57] [視窗前置] 檔案：PatNo_ID_1594511914.csv｜筆數：9685\n",
      "[2025-10-14 21:58:57] [視窗結果] 檔案：PatNo_ID_1594511914.csv｜生成視窗=321｜正樣本=64｜負樣本=257｜連續性違規=122\n",
      "[2025-10-14 21:58:57] [視窗前置] 檔案：PatNo_ID_1594528842.csv｜筆數：2415\n",
      "[2025-10-14 21:58:57] [視窗結果] 檔案：PatNo_ID_1594528842.csv｜生成視窗=79｜正樣本=20｜負樣本=59｜連續性違規=3\n",
      "[2025-10-14 21:58:57] [視窗前置] 檔案：PatNo_ID_1594533379.csv｜筆數：1018\n",
      "[2025-10-14 21:58:57] [視窗結果] 檔案：PatNo_ID_1594533379.csv｜生成視窗=32｜正樣本=5｜負樣本=27｜連續性違規=2\n",
      "[2025-10-14 21:58:57] 視窗層總結：cleaned 輸入總筆數=1523381｜視窗總數=50596｜正樣本總數=7372｜負樣本總數=43224｜連續性違規總數=4726\n",
      "[2025-10-14 21:58:57] ================== 步驟 5｜組裝 X/y（全視窗）並計數 ==================\n",
      "[2025-10-14 21:59:00] 組裝完成：視窗樣本數=50596｜X 形狀=(50596×60×10)｜y 正樣本=7372｜y 負樣本=20514\n",
      "[2025-10-14 21:59:00] 已寫出全資料：windowed/X.npy, windowed/y.npy\n",
      "[2025-10-14 21:59:00] ================== 步驟 6｜輸出報表 ==================\n",
      "[2025-10-14 21:59:00] 報表已輸出：reports/window_summary.csv、reports/sample_distribution.csv\n",
      "[2025-10-14 21:59:00] ================== 步驟 7｜建立 10 折 KFold（隨機分層於標籤）並輸出各折 .npy ==================\n",
      "[2025-10-14 21:59:00] [Fold 1] train=45536（正=6635、負=18462）｜val=5060（正=737、負=2052）\n",
      "[2025-10-14 21:59:00] [Fold 2] train=45536（正=6635、負=18462）｜val=5060（正=737、負=2052）\n",
      "[2025-10-14 21:59:00] [Fold 3] train=45536（正=6635、負=18462）｜val=5060（正=737、負=2052）\n",
      "[2025-10-14 21:59:01] [Fold 4] train=45536（正=6635、負=18462）｜val=5060（正=737、負=2052）\n",
      "[2025-10-14 21:59:01] [Fold 5] train=45536（正=6634、負=18463）｜val=5060（正=738、負=2051）\n",
      "[2025-10-14 21:59:01] [Fold 6] train=45536（正=6634、負=18463）｜val=5060（正=738、負=2051）\n",
      "[2025-10-14 21:59:01] [Fold 7] train=45537（正=6635、負=18463）｜val=5059（正=737、負=2051）\n",
      "[2025-10-14 21:59:01] [Fold 8] train=45537（正=6635、負=18463）｜val=5059（正=737、負=2051）\n",
      "[2025-10-14 21:59:01] [Fold 9] train=45537（正=6635、負=18463）｜val=5059（正=737、負=2051）\n",
      "[2025-10-14 21:59:01] [Fold 10] train=45537（正=6635、負=18463）｜val=5059（正=737、負=2051）\n",
      "[2025-10-14 21:59:01] ================== 步驟 8｜輸出 run 摘要 ==================\n",
      "[2025-10-14 21:59:01] 已寫入摘要：reports/run_digest.json｜folds=10\n",
      "[2025-10-14 21:59:01] ================== 流程完成｜總結 ==================\n",
      "[2025-10-14 21:59:01] cleaned 檔案數=122｜視窗總數=50596｜十折輸出完成（每折 train/val 皆已存檔）\n"
     ]
    }
   ],
   "source": [
    "# 1014_sim\n",
    "\"\"\"\n",
    "一條龍：raw → cleaned → windowed（set_fin）→ 10-fold KFold（隨機分層於標籤，不按時間/病患）\n",
    "根目錄：/home/jovyan/RT08/0925/sliding_win/1014_sim/\n",
    "\n",
    "輸入：\n",
    "  raw/*.csv                （每病患一檔，原始逐筆）\n",
    "輸出：\n",
    "  cleaned/*.csv            （清理後逐筆）\n",
    "  cleaned/audit_summary.csv\n",
    "  windowed/X.npy, y.npy    （全資料視窗化結果）\n",
    "  windowed/X_train_fold{1..10}.npy, y_train_fold{1..10}.npy\n",
    "  windowed/X_val_fold{1..10}.npy,   y_val_fold{1..10}.npy\n",
    "  reports/window_summary.csv, reports/sample_distribution.csv, reports/run_digest.json\n",
    "  windowed/sample_window.csv\n",
    "日誌：\n",
    "  logs/preprocess.log\n",
    "設定快照：\n",
    "  config/windowing.yaml\n",
    "\n",
    "十折說明：\n",
    "  使用 StratifiedKFold(n_splits=10, shuffle=True, random_state=42)，\n",
    "  僅依 y 分層，與時間、病患無關。\n",
    "\"\"\"\n",
    "\n",
    "import os, sys, glob, json, time, hashlib, warnings\n",
    "from pathlib import Path\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.model_selection import StratifiedKFold\n",
    "warnings.simplefilter(\"ignore\", category=FutureWarning)\n",
    "\n",
    "# ================== 參數 ==================\n",
    "BASE_DIR   = Path(\"/home/jovyan/RT08/0925/sliding_win/1014_sim\")\n",
    "RAW_DIR    = BASE_DIR / \"raw\"\n",
    "CLEANED    = BASE_DIR / \"cleaned\"\n",
    "WINDOWED   = BASE_DIR / \"windowed\"\n",
    "REPORTS    = BASE_DIR / \"reports\"\n",
    "LOGS       = BASE_DIR / \"logs\"\n",
    "CONFIG_DIR = BASE_DIR / \"config\"\n",
    "\n",
    "# 視窗參數\n",
    "W = 60\n",
    "S = 30\n",
    "\n",
    "# 十折 KFold（隨機抽樣；僅分層於標籤）\n",
    "N_SPLITS = 10\n",
    "RANDOM_STATE = 42\n",
    "SHUFFLE = True\n",
    "\n",
    "# 欄位（清理後一律小寫）\n",
    "FEATURE_COLS = [\n",
    "    \"rrhzsetactual\",\"mvsetactual\",\"pmean\",\"cdyn\",\"peepepap\",\"ppeak\",\n",
    "    \"mode_1\",\"mode_2\",\"mode_3\",\"svv_new\"\n",
    "]\n",
    "LABEL_COL = \"set_fin\"\n",
    "TIME_COL_CANDIDATES = [\"senddate\",\"SendDate\",\"SENDDATE\"]  # 將自動轉小寫比對\n",
    "\n",
    "# 清理層關鍵檢核\n",
    "REQUIRED_NUMERIC_COLS = [\"rrhzsetactual\",\"mvsetactual\"]\n",
    "REQUIRED_LABEL_COLS   = [\"set_fin\"]\n",
    "\n",
    "# 報表：時間連續性統計的參考門檻（秒）\n",
    "DTSEC_MAX_THRESHOLD = 120\n",
    "\n",
    "# ================== 目錄與日誌 ==================\n",
    "for p in [RAW_DIR, CLEANED, WINDOWED, REPORTS, LOGS, CONFIG_DIR]:\n",
    "    p.mkdir(parents=True, exist_ok=True)\n",
    "\n",
    "LOG_FILE = LOGS / \"preprocess.log\"\n",
    "def log(msg):\n",
    "    ts = time.strftime(\"%Y-%m-%d %H:%M:%S\")\n",
    "    line = f\"[{ts}] {msg}\"\n",
    "    with open(LOG_FILE, \"a\", encoding=\"utf-8\") as f:\n",
    "        f.write(line + \"\\n\")\n",
    "    print(line)\n",
    "\n",
    "def step(title):\n",
    "    bar = \"=\" * 18\n",
    "    log(f\"{bar} {title} {bar}\")\n",
    "\n",
    "# ================== 工具 ==================\n",
    "def normalize_columns(df: pd.DataFrame) -> pd.DataFrame:\n",
    "    df.columns = [c.strip().lower() for c in df.columns]\n",
    "    return df\n",
    "\n",
    "def pick_time_col(df: pd.DataFrame) -> str:\n",
    "    cols = [c.strip().lower() for c in df.columns]\n",
    "    for c in TIME_COL_CANDIDATES:\n",
    "        c2 = c.lower()\n",
    "        if c2 in cols:\n",
    "            return c2\n",
    "    return None\n",
    "\n",
    "def to_datetime_series(s: pd.Series):\n",
    "    try:\n",
    "        return pd.to_datetime(s, errors=\"coerce\", utc=False)\n",
    "    except Exception:\n",
    "        return pd.to_datetime(s.astype(str), errors=\"coerce\", utc=False)\n",
    "\n",
    "def hash_file(path: Path) -> str:\n",
    "    h = hashlib.sha256()\n",
    "    with open(path, \"rb\") as f:\n",
    "        for chunk in iter(lambda: f.read(1<<16), b\"\"):\n",
    "            h.update(chunk)\n",
    "    return h.hexdigest()[:16]\n",
    "\n",
    "# ================== 設定快照 ==================\n",
    "step(\"步驟 0｜初始化與設定快照\")\n",
    "CONFIG_PATH = CONFIG_DIR / \"windowing.yaml\"\n",
    "cfg_text = \"\\n\".join([\n",
    "    f'base_dir: \"{BASE_DIR}\"',\n",
    "    f'raw_dir: \"{RAW_DIR}\"',\n",
    "    f'cleaned_dir: \"{CLEANED}\"',\n",
    "    f'windowed_dir: \"{WINDOWED}\"',\n",
    "    f'reports_dir: \"{REPORTS}\"',\n",
    "    f'W: {W}',\n",
    "    f'S: {S}',\n",
    "    f'n_splits: {N_SPLITS}',\n",
    "    f'random_state: {RANDOM_STATE}',\n",
    "    f'shuffle: {SHUFFLE}',\n",
    "    f'label_col: \"{LABEL_COL}\"',\n",
    "    f'features: {FEATURE_COLS}',\n",
    "    f'time_col_candidates: {TIME_COL_CANDIDATES}',\n",
    "    f'dtsec_max_threshold: {DTSEC_MAX_THRESHOLD}',\n",
    "])\n",
    "CONFIG_PATH.write_text(cfg_text + \"\\n\", encoding=\"utf-8\")\n",
    "log(f\"已寫入設定：{CONFIG_PATH}\")\n",
    "\n",
    "# =========================================================\n",
    "#                     A. 清理層（raw → cleaned）\n",
    "# =========================================================\n",
    "step(\"步驟 1｜掃描 raw 檔案\")\n",
    "raw_files = sorted(glob.glob(str(RAW_DIR / \"*.csv\")))\n",
    "log(f\"raw 檔案數量：{len(raw_files)}\")\n",
    "if not raw_files:\n",
    "    log(\"raw 目錄無檔案可處理，流程終止\")\n",
    "    sys.exit(1)\n",
    "\n",
    "step(\"步驟 2｜逐檔清理與筆數統計（輸出 cleaned 與稽核）\")\n",
    "audit_rows = []\n",
    "total_init_all = 0\n",
    "total_final_all = 0\n",
    "total_removed_missing_all = 0\n",
    "total_removed_timeerr_all = 0\n",
    "total_removed_duplicates_all = 0\n",
    "total_removed_typeerr_all = 0\n",
    "\n",
    "for fp in raw_files:\n",
    "    fname = Path(fp).name\n",
    "    log(f\"清理開始：{fname}\")\n",
    "\n",
    "    # 讀檔\n",
    "    try:\n",
    "        df = pd.read_csv(fp, encoding=\"utf-8\")\n",
    "    except Exception:\n",
    "        try:\n",
    "            df = pd.read_csv(fp, encoding=\"big5\", errors=\"ignore\")\n",
    "        except Exception as e:\n",
    "            log(f\"讀檔失敗，跳過：{fname}｜原因：{e}\")\n",
    "            continue\n",
    "\n",
    "    df = normalize_columns(df)\n",
    "    total_init = len(df)\n",
    "    total_init_all += total_init\n",
    "    log(f\"讀入筆數：{total_init}（檔：{fname}）\")\n",
    "\n",
    "    # 統計器\n",
    "    removed_missing = 0\n",
    "    removed_timeerr = 0\n",
    "    removed_duplicates = 0\n",
    "    removed_typeerr = 0\n",
    "\n",
    "    # 時間欄統一為 senddate\n",
    "    tcol = pick_time_col(df)\n",
    "    if tcol and tcol != \"senddate\":\n",
    "        df.rename(columns={tcol: \"senddate\"}, inplace=True)\n",
    "\n",
    "    # 缺失清除：至少要有 senddate 與 set_fin\n",
    "    before = len(df)\n",
    "    need_cols = [\"senddate\"] + REQUIRED_LABEL_COLS\n",
    "    df = df.dropna(subset=[c for c in need_cols if c in df.columns])\n",
    "    removed_missing += (before - len(df))\n",
    "    log(f\"缺失清除：移除 {before - len(df)} 筆，剩餘 {len(df)} 筆\")\n",
    "\n",
    "    # 時間戳處理：轉 datetime、去除無效、排序、重設 index\n",
    "    before = len(df)\n",
    "    if \"senddate\" in df.columns:\n",
    "        df[\"senddate\"] = pd.to_datetime(df[\"senddate\"], errors=\"coerce\")\n",
    "        df = df.dropna(subset=[\"senddate\"])\n",
    "    df = df.sort_values(\"senddate\").reset_index(drop=True)\n",
    "    removed_timeerr += (before - len(df))\n",
    "    log(f\"時間處理：移除 {before - len(df)} 筆無效時間，剩餘 {len(df)} 筆\")\n",
    "\n",
    "    # 去重（以 senddate）\n",
    "    before = len(df)\n",
    "    if \"senddate\" in df.columns:\n",
    "        df = df.drop_duplicates(subset=[\"senddate\"])\n",
    "    removed_duplicates += (before - len(df))\n",
    "    log(f\"去除重複：移除 {before - len(df)} 筆，剩餘 {len(df)} 筆\")\n",
    "\n",
    "    # 轉數值與特徵檢查\n",
    "    before = len(df)\n",
    "    for c in [\"rrhzsetactual\",\"mvsetactual\",\"pmean\",\"cdyn\",\"peepepap\",\"ppeak\",\"svv_new\"]:\n",
    "        if c in df.columns:\n",
    "            df[c] = pd.to_numeric(df[c], errors=\"coerce\")\n",
    "    for c in [\"mode_1\",\"mode_2\",\"mode_3\"]:\n",
    "        if c in df.columns:\n",
    "            df[c] = pd.to_numeric(df[c], errors=\"coerce\").fillna(0).astype(int).clip(0,1)\n",
    "    # 強制關鍵特徵非空\n",
    "    need_num = [c for c in REQUIRED_NUMERIC_COLS if c in df.columns]\n",
    "    if need_num:\n",
    "        df = df.dropna(subset=need_num)\n",
    "    removed_typeerr += (before - len(df))\n",
    "    log(f\"型別/特徵檢查：移除 {before - len(df)} 筆，剩餘 {len(df)} 筆\")\n",
    "\n",
    "    total_final = len(df)\n",
    "    total_removed = total_init - total_final\n",
    "    total_final_all += total_final\n",
    "    total_removed_missing_all    += removed_missing\n",
    "    total_removed_timeerr_all    += removed_timeerr\n",
    "    total_removed_duplicates_all += removed_duplicates\n",
    "    total_removed_typeerr_all    += removed_typeerr\n",
    "    log(f\"清理完成：初始 {total_init} → 保留 {total_final}（移除 {total_removed}）\")\n",
    "\n",
    "    # 寫出 cleaned\n",
    "    out_fp = CLEANED / fname\n",
    "    df.to_csv(out_fp, index=False, encoding=\"utf-8\")\n",
    "    log(f\"已寫入 cleaned 檔案：{out_fp}（筆數 {total_final}）\")\n",
    "\n",
    "    # 稽核紀錄\n",
    "    audit_rows.append({\n",
    "        \"file\": fname,\n",
    "        \"total_init\": total_init,\n",
    "        \"removed_missing\": removed_missing,\n",
    "        \"removed_timeerr\": removed_timeerr,\n",
    "        \"removed_duplicates\": removed_duplicates,\n",
    "        \"removed_typeerr\": removed_typeerr,\n",
    "        \"total_final\": total_final\n",
    "    })\n",
    "\n",
    "# 稽核總表\n",
    "AUDIT_SUMMARY = CLEANED / \"audit_summary.csv\"\n",
    "pd.DataFrame(audit_rows).to_csv(AUDIT_SUMMARY, index=False, encoding=\"utf-8\")\n",
    "log(f\"已寫入清理層稽核總表：{AUDIT_SUMMARY}\")\n",
    "log(f\"清理層總結：原始總筆數={total_init_all}、清理後總筆數={total_final_all}、\"\n",
    "    f\"缺失移除總筆數={total_removed_missing_all}、時間錯誤移除總筆數={total_removed_timeerr_all}、\"\n",
    "    f\"重複移除總筆數={total_removed_duplicates_all}、型別/特徵錯誤移除總筆數={total_removed_typeerr_all}\")\n",
    "\n",
    "# =========================================================\n",
    "#                  B. 視窗層（cleaned → windowed）\n",
    "# =========================================================\n",
    "step(\"步驟 3｜掃描 cleaned 檔案以進行視窗化\")\n",
    "cleaned_files = sorted(glob.glob(str(CLEANED / \"*.csv\")))\n",
    "cleaned_files = [f for f in cleaned_files if Path(f).name != \"audit_summary.csv\"]\n",
    "log(f\"cleaned 檔案數量：{len(cleaned_files)}\")\n",
    "if not cleaned_files:\n",
    "    log(\"cleaned 無可視窗化檔案，流程終止\")\n",
    "    sys.exit(1)\n",
    "\n",
    "# 報表容器\n",
    "window_rows = []   # window_summary（逐視窗）\n",
    "dist_rows   = []   # sample_distribution（逐檔）\n",
    "sample_saved = False\n",
    "\n",
    "# 總計\n",
    "win_total = 0\n",
    "win_pos_total = 0\n",
    "win_neg_total = 0\n",
    "win_continuity_violation_total = 0\n",
    "rows_total_in_window_stage = 0\n",
    "\n",
    "def get_time_series(df):\n",
    "    tcol = pick_time_col(df)\n",
    "    if not tcol:\n",
    "        return None\n",
    "    return to_datetime_series(df[tcol])\n",
    "\n",
    "def get_dsec(dt):\n",
    "    if dt is None:\n",
    "        return None\n",
    "    diffs = dt.diff().dt.total_seconds()\n",
    "    return diffs.to_numpy()\n",
    "\n",
    "step(\"步驟 4｜逐檔滑動視窗與計數\")\n",
    "for f in cleaned_files:\n",
    "    fname = Path(f).name\n",
    "    df = pd.read_csv(f, encoding=\"utf-8\")\n",
    "    df = normalize_columns(df)\n",
    "\n",
    "    n_rows = len(df)\n",
    "    rows_total_in_window_stage += n_rows\n",
    "    log(f\"[視窗前置] 檔案：{fname}｜筆數：{n_rows}\")\n",
    "\n",
    "    miss_feats = [c for c in FEATURE_COLS if c not in df.columns]\n",
    "    if miss_feats:\n",
    "        log(f\"跳過（特徵缺少）：{fname} 缺 {miss_feats}\")\n",
    "        continue\n",
    "    if LABEL_COL not in df.columns:\n",
    "        log(f\"跳過（無標籤欄）：{fname}\")\n",
    "        continue\n",
    "\n",
    "    dt = get_time_series(df)\n",
    "    dsec = get_dsec(dt)\n",
    "\n",
    "    feats_np = df[FEATURE_COLS].to_numpy(dtype=np.float32)\n",
    "    labels_np = df[LABEL_COL].to_numpy()\n",
    "\n",
    "    n_windows = 0\n",
    "    pos_cnt = 0\n",
    "    neg_cnt = 0\n",
    "    cont_violations = 0\n",
    "\n",
    "    i = 0\n",
    "    while i + W <= n_rows:\n",
    "        Xw = feats_np[i:i+W]\n",
    "        yw = int(labels_np[i+W-1])   # 末筆標籤\n",
    "\n",
    "        n_windows += 1\n",
    "        win_total += 1\n",
    "        if yw == 1:\n",
    "            pos_cnt += 1\n",
    "            win_pos_total += 1\n",
    "        else:\n",
    "            neg_cnt += 1\n",
    "            win_neg_total += 1\n",
    "\n",
    "        # 連續性統計（報表用）\n",
    "        mean_dt_sec, max_dt_sec, continuity_ok = \"\", \"\", \"\"\n",
    "        if dsec is not None:\n",
    "            seg = dsec[i+1:i+W]\n",
    "            seg = seg[~np.isnan(seg)]\n",
    "            if seg.size > 0:\n",
    "                m = float(np.mean(seg))\n",
    "                mx = float(np.max(seg))\n",
    "                mean_dt_sec = f\"{m:.2f}\"\n",
    "                max_dt_sec  = f\"{mx:.2f}\"\n",
    "                ok = (mx <= DTSEC_MAX_THRESHOLD)\n",
    "                continuity_ok = \"1\" if ok else \"0\"\n",
    "                if not ok:\n",
    "                    cont_violations += 1\n",
    "                    win_continuity_violation_total += 1\n",
    "\n",
    "        start_time = dt.iloc[i].isoformat() if dt is not None and pd.notnull(dt.iloc[i]) else \"\"\n",
    "        end_time   = dt.iloc[i+W-1].isoformat() if dt is not None and pd.notnull(dt.iloc[i+W-1]) else \"\"\n",
    "        window_rows.append({\n",
    "            \"window_id\": f\"{fname}:{i}-{i+W-1}\",\n",
    "            \"file_name\": fname,\n",
    "            \"start_time\": start_time,\n",
    "            \"end_time\": end_time,\n",
    "            \"n_rows\": W,\n",
    "            \"mean_dt_sec\": mean_dt_sec,\n",
    "            \"max_dt_sec\": max_dt_sec,\n",
    "            \"continuity_ok\": continuity_ok,\n",
    "            \"label\": yw\n",
    "        })\n",
    "\n",
    "        # 保存一份 sample 視窗\n",
    "        if not sample_saved:\n",
    "            sample_df = pd.DataFrame(Xw, columns=FEATURE_COLS)\n",
    "            if dt is not None:\n",
    "                sample_df.insert(0, \"senddate\", df.iloc[i:i+W][\"senddate\"].values if \"senddate\" in df.columns else \"\")\n",
    "            sample_df[\"label_window_end_set_fin\"] = yw\n",
    "            (WINDOWED / \"sample_window.csv\").write_text(sample_df.to_csv(index=False), encoding=\"utf-8\")\n",
    "            sample_saved = True\n",
    "\n",
    "        i += S\n",
    "\n",
    "    # 檔案級分布\n",
    "    total_duration_hr = \"\"\n",
    "    if dt is not None and pd.notnull(dt.iloc[0]) and pd.notnull(dt.iloc[-1]):\n",
    "        total_duration_hr = f\"{(dt.iloc[-1] - dt.iloc[0]).total_seconds()/3600.0:.2f}\"\n",
    "\n",
    "    dist_rows.append({\n",
    "        \"file_name\": fname,\n",
    "        \"file_sha16\": hash_file(Path(f)),\n",
    "        \"total_rows\": n_rows,\n",
    "        \"n_windows\": n_windows,\n",
    "        \"pos_samples\": pos_cnt,\n",
    "        \"neg_samples\": neg_cnt,\n",
    "        \"pos_ratio\": f\"{(pos_cnt / n_windows):.4f}\" if n_windows > 0 else \"\",\n",
    "        \"mean_window_length\": W,\n",
    "        \"stride\": S,\n",
    "        \"cont_violation_windows\": cont_violations,\n",
    "        \"total_duration_hr\": total_duration_hr\n",
    "    })\n",
    "\n",
    "    log(f\"[視窗結果] 檔案：{fname}｜生成視窗={n_windows}｜正樣本={pos_cnt}｜負樣本={neg_cnt}｜連續性違規={cont_violations}\")\n",
    "\n",
    "log(f\"視窗層總結：cleaned 輸入總筆數={rows_total_in_window_stage}｜\"\n",
    "    f\"視窗總數={win_total}｜正樣本總數={win_pos_total}｜負樣本總數={win_neg_total}｜\"\n",
    "    f\"連續性違規總數={win_continuity_violation_total}\")\n",
    "\n",
    "# =========================================================\n",
    "#     C. 組裝全資料 X/y、輸出報表、十折 KFold（隨機）\n",
    "# =========================================================\n",
    "step(\"步驟 5｜組裝 X/y（全視窗）並計數\")\n",
    "# 直接重建一次 X,y（確保一致）\n",
    "all_X, all_y = [], []\n",
    "rebuilt = 0\n",
    "for f in cleaned_files:\n",
    "    df = pd.read_csv(f, encoding=\"utf-8\")\n",
    "    df = normalize_columns(df)\n",
    "    miss_feats = [c for c in FEATURE_COLS if c not in df.columns]\n",
    "    if miss_feats or LABEL_COL not in df.columns:\n",
    "        continue\n",
    "    feats_np = df[FEATURE_COLS].to_numpy(dtype=np.float32)\n",
    "    labels_np = df[LABEL_COL].to_numpy()\n",
    "    n_rows = len(df)\n",
    "    i = 0\n",
    "    while i + W <= n_rows:\n",
    "        all_X.append(feats_np[i:i+W])\n",
    "        all_y.append(int(labels_np[i+W-1]))\n",
    "        rebuilt += 1\n",
    "        i += S\n",
    "\n",
    "X = np.asarray(all_X, dtype=np.float32)\n",
    "y = np.asarray(all_y, dtype=np.int64)\n",
    "log(f\"組裝完成：視窗樣本數={X.shape[0]}｜X 形狀=({X.shape[0]}×{W}×{X.shape[2]})｜\"\n",
    "    f\"y 正樣本={int((y==1).sum())}｜y 負樣本={int((y==0).sum())}\")\n",
    "\n",
    "# 寫出全資料（之後訓練可直接載入）\n",
    "np.save(WINDOWED / \"X.npy\", X)\n",
    "np.save(WINDOWED / \"y.npy\", y)\n",
    "log(f\"已寫出全資料：windowed/X.npy, windowed/y.npy\")\n",
    "\n",
    "# 報表輸出\n",
    "step(\"步驟 6｜輸出報表\")\n",
    "pd.DataFrame(window_rows).to_csv(REPORTS / \"window_summary.csv\", index=False, encoding=\"utf-8\")\n",
    "pd.DataFrame(dist_rows).to_csv(REPORTS / \"sample_distribution.csv\", index=False, encoding=\"utf-8\")\n",
    "log(\"報表已輸出：reports/window_summary.csv、reports/sample_distribution.csv\")\n",
    "\n",
    "# 十折 KFold（僅分層於 y，不依時間/病患）\n",
    "step(\"步驟 7｜建立 10 折 KFold（隨機分層於標籤）並輸出各折 .npy\")\n",
    "kf = StratifiedKFold(n_splits=N_SPLITS, shuffle=SHUFFLE, random_state=RANDOM_STATE)\n",
    "fold_sizes = []\n",
    "for i, (tr_idx, va_idx) in enumerate(kf.split(X, y), start=1):\n",
    "    X_tr, X_va = X[tr_idx], X[va_idx]\n",
    "    y_tr, y_va = y[tr_idx], y[va_idx]\n",
    "    np.save(WINDOWED / f\"X_train_fold{i}.npy\", X_tr)\n",
    "    np.save(WINDOWED / f\"y_train_fold{i}.npy\", y_tr)\n",
    "    np.save(WINDOWED / f\"X_val_fold{i}.npy\",   X_va)\n",
    "    np.save(WINDOWED / f\"y_val_fold{i}.npy\",   y_va)\n",
    "    fold_sizes.append({\"fold\": i,\n",
    "                       \"train_windows\": int(X_tr.shape[0]),\n",
    "                       \"val_windows\": int(X_va.shape[0]),\n",
    "                       \"y_tr_pos\": int((y_tr==1).sum()),\n",
    "                       \"y_tr_neg\": int((y_tr==0).sum()),\n",
    "                       \"y_va_pos\": int((y_va==1).sum()),\n",
    "                       \"y_va_neg\": int((y_va==0).sum())})\n",
    "    log(f\"[Fold {i}] train={X_tr.shape[0]}（正={int((y_tr==1).sum())}、負={int((y_tr==0).sum())}）｜\"\n",
    "        f\"val={X_va.shape[0]}（正={int((y_va==1).sum())}、負={int((y_va==0).sum())}）\")\n",
    "\n",
    "# run 摘要\n",
    "step(\"步驟 8｜輸出 run 摘要\")\n",
    "digest = {\n",
    "    \"raw_files\": len(raw_files),\n",
    "    \"raw_total_rows_in\": int(total_init_all),\n",
    "    \"cleaned_files\": len(cleaned_files),\n",
    "    \"cleaned_total_rows_out\": int(total_final_all),\n",
    "    \"clean_removed\": {\n",
    "        \"missing\": int(total_removed_missing_all),\n",
    "        \"timeerr\": int(total_removed_timeerr_all),\n",
    "        \"duplicates\": int(total_removed_duplicates_all),\n",
    "        \"typeerr\": int(total_removed_typeerr_all)\n",
    "    },\n",
    "    \"window_input_rows\": int(rows_total_in_window_stage),\n",
    "    \"windows_total\": int(X.shape[0]),\n",
    "    \"window_shape\": {\"length\": int(W), \"n_features\": int(X.shape[2])},\n",
    "    \"label_col\": LABEL_COL,\n",
    "    \"features\": FEATURE_COLS,\n",
    "    \"kfold\": {\n",
    "        \"n_splits\": N_SPLITS,\n",
    "        \"shuffle\": SHUFFLE,\n",
    "        \"random_state\": RANDOM_STATE,\n",
    "        \"folds\": fold_sizes\n",
    "    }\n",
    "}\n",
    "(REPORTS / \"run_digest.json\").write_text(json.dumps(digest, ensure_ascii=False, indent=2), encoding=\"utf-8\")\n",
    "log(f\"已寫入摘要：reports/run_digest.json｜folds={len(fold_sizes)}\")\n",
    "\n",
    "step(\"流程完成｜總結\")\n",
    "log(f\"cleaned 檔案數={len(cleaned_files)}｜視窗總數={X.shape[0]}｜十折輸出完成（每折 train/val 皆已存檔）\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 257,
   "id": "bb4ef4aa-4c51-464f-a206-2a75e62785a2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2025-10-14 22:05:31] === Cross-file summarization start ===\n",
      "[2025-10-14 22:05:32] 已載入 window_summary 校對連續性違規：122 檔\n",
      "[2025-10-14 22:05:32] 已輸出：/home/jovyan/RT08/0925/sliding_win/1014_sim/reports/cross_file_summary.csv（列數=122）\n",
      "[2025-10-14 22:05:32] 已輸出：/home/jovyan/RT08/0925/sliding_win/1014_sim/reports/cross_file_totals.json\n",
      "[2025-10-14 22:05:32] 總結｜檔案數=122｜原始筆數=1567233｜清理後筆數=1523381｜總視窗=50596｜正=7372｜負=43224｜連續性違規總數=4726\n",
      "[2025-10-14 22:05:32] === Cross-file summarization done ===\n"
     ]
    }
   ],
   "source": [
    "# -*- coding: utf-8 -*-\n",
    "\"\"\"\n",
    "tools_summarize_cross_file.py\n",
    "用途：彙總跨檔案數據（清理步驟移除筆數＋視窗統計），額外執行即可。\n",
    "輸入：\n",
    "  cleaned/audit_summary.csv\n",
    "  reports/sample_distribution.csv\n",
    "  （可選）reports/window_summary.csv（若存在，將校對連續性違規數）\n",
    "\n",
    "輸出：\n",
    "  reports/cross_file_summary.csv         ← 各檔綜合指標（你要看的那張表）\n",
    "  reports/cross_file_totals.json         ← 全體總計摘要\n",
    "  logs/preprocess.log 內也會附上統計提示\n",
    "根目錄：\n",
    "  /home/jovyan/RT08/0925/sliding_win/1014_sim/\n",
    "\"\"\"\n",
    "\n",
    "import json, time\n",
    "from pathlib import Path\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "BASE = Path(\"/home/jovyan/RT08/0925/sliding_win/1014_sim\")\n",
    "CLEANED = BASE / \"cleaned\"\n",
    "REPORTS = BASE / \"reports\"\n",
    "LOGS    = BASE / \"logs\"\n",
    "\n",
    "LOGS.mkdir(parents=True, exist_ok=True)\n",
    "REPORTS.mkdir(parents=True, exist_ok=True)\n",
    "\n",
    "log_path = LOGS / \"preprocess.log\"\n",
    "def log(msg):\n",
    "    ts = time.strftime(\"%Y-%m-%d %H:%M:%S\")\n",
    "    line = f\"[{ts}] {msg}\"\n",
    "    print(line)\n",
    "    with open(log_path, \"a\", encoding=\"utf-8\") as f:\n",
    "        f.write(line + \"\\n\")\n",
    "\n",
    "log(\"=== Cross-file summarization start ===\")\n",
    "\n",
    "# 讀報表\n",
    "audit_path = CLEANED / \"audit_summary.csv\"\n",
    "dist_path  = REPORTS / \"sample_distribution.csv\"\n",
    "win_path   = REPORTS / \"window_summary.csv\"   # optional\n",
    "\n",
    "if not audit_path.exists():\n",
    "    log(f\"找不到 {audit_path}，中止。\")\n",
    "    raise SystemExit(1)\n",
    "if not dist_path.exists():\n",
    "    log(f\"找不到 {dist_path}，中止。\")\n",
    "    raise SystemExit(1)\n",
    "\n",
    "audit = pd.read_csv(audit_path)\n",
    "dist  = pd.read_csv(dist_path)\n",
    "\n",
    "# 欄位標準化\n",
    "audit.columns = [c.strip().lower() for c in audit.columns]\n",
    "dist.columns  = [c.strip().lower()  for c in dist.columns]\n",
    "\n",
    "# ---- 可選：用 window_summary 校對連續性違規（以 window_summary 為準）----\n",
    "cont_from_summary = None\n",
    "if win_path.exists():\n",
    "    win = pd.read_csv(win_path)\n",
    "    win.columns = [c.strip().lower() for c in win.columns]\n",
    "    if all(c in win.columns for c in [\"file_name\", \"continuity_ok\"]):\n",
    "        # 0 視為違規，1 視為通過\n",
    "        tmp = win.copy()\n",
    "        tmp[\"continuity_ok\"] = tmp[\"continuity_ok\"].astype(str).str.strip()\n",
    "        tmp[\"violate\"] = (tmp[\"continuity_ok\"] == \"0\").astype(int)\n",
    "        cont_from_summary = tmp.groupby(\"file_name\", as_index=False)[\"violate\"].sum() \\\n",
    "                               .rename(columns={\"violate\": \"cont_violation_windows_from_summary\"})\n",
    "        log(f\"已載入 window_summary 校對連續性違規：{len(cont_from_summary)} 檔\")\n",
    "    else:\n",
    "        log(\"window_summary.csv 欄位不齊，略過校對\")\n",
    "else:\n",
    "    log(\"找不到 window_summary.csv，略過連續性校對（採用 sample_distribution 的統計）\")\n",
    "\n",
    "# ---- merge：以 file / file_name 合併清理與視窗統計 ----\n",
    "# audit: file, total_init, removed_missing, removed_timeerr, removed_duplicates, removed_typeerr, total_final\n",
    "# dist:  file_name, total_rows, n_windows, pos_samples, neg_samples, pos_ratio, cont_violation_windows, total_duration_hr\n",
    "left = audit.rename(columns={\"file\": \"file_name\"})\n",
    "right = dist.copy()\n",
    "\n",
    "summary = pd.merge(left, right, on=\"file_name\", how=\"outer\")\n",
    "\n",
    "# 若有 window_summary 校對，合併進來\n",
    "if cont_from_summary is not None:\n",
    "    summary = summary.merge(cont_from_summary, on=\"file_name\", how=\"left\")\n",
    "    # 若存在兩個來源的違規欄位，提供一致性對照與最終欄\n",
    "    if \"cont_violation_windows\" in summary.columns:\n",
    "        # 以 window_summary 為準，若缺失則退回 sample_distribution 的欄\n",
    "        summary[\"cont_violation_windows_final\"] = summary[\"cont_violation_windows_from_summary\"] \\\n",
    "            .fillna(summary[\"cont_violation_windows\"])\n",
    "    else:\n",
    "        summary[\"cont_violation_windows_final\"] = summary[\"cont_violation_windows_from_summary\"]\n",
    "else:\n",
    "    # 僅有 sample_distribution 的統計\n",
    "    if \"cont_violation_windows\" in summary.columns:\n",
    "        summary[\"cont_violation_windows_final\"] = summary[\"cont_violation_windows\"]\n",
    "    else:\n",
    "        summary[\"cont_violation_windows_final\"] = np.nan\n",
    "\n",
    "# 整理欄位順序與命名（對齊你要看的四大塊）\n",
    "cols = [\n",
    "    # 身分\n",
    "    \"file_name\",\n",
    "\n",
    "    # 清理層（原始幾筆 → 各步驟移除 → 最後幾筆）\n",
    "    \"total_init\",\n",
    "    \"removed_missing\",\n",
    "    \"removed_timeerr\",\n",
    "    \"removed_duplicates\",\n",
    "    \"removed_typeerr\",\n",
    "    \"total_final\",\n",
    "\n",
    "    # 視窗層（生成視窗｜正樣本｜負樣本｜連續性違規）\n",
    "    \"n_windows\",\n",
    "    \"pos_samples\",\n",
    "    \"neg_samples\",\n",
    "    \"cont_violation_windows_final\",\n",
    "\n",
    "    # 參考資訊\n",
    "    \"pos_ratio\",\n",
    "    \"total_rows\",\n",
    "    \"total_duration_hr\"\n",
    "]\n",
    "\n",
    "# 缺失欄位補上\n",
    "for c in cols:\n",
    "    if c not in summary.columns:\n",
    "        summary[c] = np.nan\n",
    "\n",
    "summary = summary[cols].copy()\n",
    "summary = summary.rename(columns={\n",
    "    \"total_init\": \"raw_rows_total\",\n",
    "    \"total_final\": \"cleaned_rows_total\",\n",
    "    \"pos_samples\": \"windows_pos\",\n",
    "    \"neg_samples\": \"windows_neg\",\n",
    "    \"n_windows\": \"windows_total\",\n",
    "    \"cont_violation_windows_final\": \"windows_continuity_violations\"\n",
    "})\n",
    "\n",
    "# 排序：先依 windows_total 再依 file_name\n",
    "summary = summary.sort_values(by=[\"windows_total\", \"file_name\"], ascending=[False, True])\n",
    "\n",
    "# 輸出 cross_file_summary.csv\n",
    "out_csv = REPORTS / \"cross_file_summary.csv\"\n",
    "summary.to_csv(out_csv, index=False, encoding=\"utf-8\")\n",
    "log(f\"已輸出：{out_csv}（列數={len(summary)}）\")\n",
    "\n",
    "# 全體總計（合併層級總覽）\n",
    "totals = {\n",
    "    \"files_count\": int(summary[\"file_name\"].nunique()),\n",
    "    \"raw_rows_total\": int(summary[\"raw_rows_total\"].fillna(0).sum()),\n",
    "    \"cleaned_rows_total\": int(summary[\"cleaned_rows_total\"].fillna(0).sum()),\n",
    "    \"removed_rows_total\": int(\n",
    "        summary[\"raw_rows_total\"].fillna(0).sum() - summary[\"cleaned_rows_total\"].fillna(0).sum()\n",
    "    ),\n",
    "    \"removed_breakdown\": {\n",
    "        \"missing\": int(summary[\"removed_missing\"].fillna(0).sum()),\n",
    "        \"timeerr\": int(summary[\"removed_timeerr\"].fillna(0).sum()),\n",
    "        \"duplicates\": int(summary[\"removed_duplicates\"].fillna(0).sum()),\n",
    "        \"typeerr\": int(summary[\"removed_typeerr\"].fillna(0).sum()),\n",
    "    },\n",
    "    \"windows_total\": int(summary[\"windows_total\"].fillna(0).sum()),\n",
    "    \"windows_pos_total\": int(summary[\"windows_pos\"].fillna(0).sum()),\n",
    "    \"windows_neg_total\": int(summary[\"windows_neg\"].fillna(0).sum()),\n",
    "    \"windows_continuity_violations_total\": int(summary[\"windows_continuity_violations\"].fillna(0).sum())\n",
    "}\n",
    "out_json = REPORTS / \"cross_file_totals.json\"\n",
    "out_json.write_text(json.dumps(totals, ensure_ascii=False, indent=2), encoding=\"utf-8\")\n",
    "log(f\"已輸出：{out_json}\")\n",
    "log(f\"總結｜檔案數={totals['files_count']}｜原始筆數={totals['raw_rows_total']}｜清理後筆數={totals['cleaned_rows_total']}｜\"\n",
    "    f\"總視窗={totals['windows_total']}｜正={totals['windows_pos_total']}｜負={totals['windows_neg_total']}｜\"\n",
    "    f\"連續性違規總數={totals['windows_continuity_violations_total']}\")\n",
    "\n",
    "log(\"=== Cross-file summarization done ===\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f26c8ab0-cd08-40e2-a692-a5fdac1293c6",
   "metadata": {},
   "outputs": [],
   "source": [
    "reports/cross_file_summary.csv\n",
    "一檔到底的對齊報表：\n",
    "檔名｜原始筆數 raw_rows_total｜清理各步驟移除數（missing、timeerr、duplicates、typeerr）｜清理後筆數 cleaned_rows_total｜生成視窗 windows_total｜正樣本 windows_pos｜負樣本 windows_neg｜連續性違規 windows_continuity_violations｜pos_ratio 等參考欄位。\n",
    "\n",
    "reports/cross_file_totals.json\n",
    "全體加總摘要（檔案數、原始總筆數、清理後總筆數、各類移除總數、總視窗、正負總數、違規總數）。\n",
    "\n",
    "logs/preprocess.log\n",
    "也會同步寫入摘要提示，便於追蹤。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "defd6d5b-1c0f-4035-baec-322f6091e054",
   "metadata": {},
   "outputs": [],
   "source": [
    "沒有 其實不需要剔除，但我有找出來，要剔除連續性違規才能繼續？\n",
    "\n",
    "連續性違規（continuity violation）\n",
    "指的是滑動視窗內任一筆資料的時間間隔（Δt_sec）超過設定門檻（目前 120 秒），\n",
    "表示該視窗時間不連續，因此標記為「continuity_ok = 0」。\n",
    "\n",
    "每個視窗（window）包含 60 筆時間點；\n",
    "計算該視窗內的 Δt_sec；\n",
    "\n",
    "若該視窗內的 最大 Δt_sec 大於設定閾值（dt_thr），\n",
    "則該視窗被標記為 連續性違規"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 258,
   "id": "8ae7649c-4a15-4597-bcde-5d898cb5f536",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 需要明確知道最終訓練資料的結構是否正確"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 259,
   "id": "801d2d40-7285-4368-88b7-300fd906846f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "🔍 檢查檔案：\n",
      "/home/jovyan/RT08/0925/sliding_win/1014_sim/windowed/X_train_fold1.npy\n",
      "/home/jovyan/RT08/0925/sliding_win/1014_sim/windowed/y_train_fold1.npy\n",
      "\n",
      "X.shape = (45536, 60, 10)   dtype = float32\n",
      "y.shape = (45536,)   dtype = int64\n",
      "樣本數：45536\n",
      "每視窗長度（時間步）：60\n",
      "特徵數：10\n",
      "\n",
      "=== 標籤統計 ===\n",
      "標籤 0: 18462 筆（40.54%）\n",
      "標籤 1: 6635 筆（14.57%）\n",
      "標籤 2: 20439 筆（44.89%）\n",
      "\n",
      "=== NaN / Inf 檢查 ===\n",
      "X NaN: 376482  |  Inf: 0\n",
      "y NaN: 0  |  Inf: 0\n",
      "\n",
      "✅ 檢查完成\n",
      "模型輸入形狀應設定為 input_shape=(60, 10)\n"
     ]
    }
   ],
   "source": [
    "# -*- coding: utf-8 -*-\n",
    "\"\"\"\n",
    "檢查 windowed 資料結構、shape、dtype、NaN、正負比例等\n",
    "\"\"\"\n",
    "\n",
    "import numpy as np\n",
    "from pathlib import Path\n",
    "\n",
    "BASE = Path(\"/home/jovyan/RT08/0925/sliding_win/1014_sim/windowed\")\n",
    "\n",
    "# 可改成任一折資料\n",
    "X_path = BASE / \"X_train_fold1.npy\"\n",
    "y_path = BASE / \"y_train_fold1.npy\"\n",
    "\n",
    "print(f\"🔍 檢查檔案：\\n{X_path}\\n{y_path}\\n\")\n",
    "\n",
    "# 讀取資料\n",
    "X = np.load(X_path)\n",
    "y = np.load(y_path)\n",
    "\n",
    "# 1️⃣ shape 與 dtype\n",
    "print(f\"X.shape = {X.shape}   dtype = {X.dtype}\")\n",
    "print(f\"y.shape = {y.shape}   dtype = {y.dtype}\")\n",
    "\n",
    "# 2️⃣ 特徵維度與樣本數\n",
    "n_samples, seq_len, n_features = X.shape\n",
    "print(f\"樣本數：{n_samples}\")\n",
    "print(f\"每視窗長度（時間步）：{seq_len}\")\n",
    "print(f\"特徵數：{n_features}\")\n",
    "\n",
    "# 3️⃣ 標籤分佈\n",
    "unique, counts = np.unique(y, return_counts=True)\n",
    "print(\"\\n=== 標籤統計 ===\")\n",
    "for u, c in zip(unique, counts):\n",
    "    pct = c / len(y) * 100\n",
    "    print(f\"標籤 {int(u)}: {c} 筆（{pct:.2f}%）\")\n",
    "\n",
    "# 4️⃣ 缺值與異常檢查\n",
    "print(\"\\n=== NaN / Inf 檢查 ===\")\n",
    "print(f\"X NaN: {np.isnan(X).sum()}  |  Inf: {np.isinf(X).sum()}\")\n",
    "print(f\"y NaN: {np.isnan(y).sum()}  |  Inf: {np.isinf(y).sum()}\")\n",
    "\n",
    "# 5️⃣ 訓練可行性摘要\n",
    "print(\"\\n✅ 檢查完成\")\n",
    "print(f\"模型輸入形狀應設定為 input_shape=({seq_len}, {n_features})\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 260,
   "id": "fd0566f5-a9b3-42e9-8192-64e867473166",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2025-10-15 00:25:19] === Audit label==2 across 10 folds (train / val) ===\n",
      "[2025-10-15 00:25:19] [Fold 1] train: total=45536, label2=20439 (44.8854%) | val: total=5060, label2=2271 (44.8814%)\n",
      "[2025-10-15 00:25:19] [Fold 2] train: total=45536, label2=20439 (44.8854%) | val: total=5060, label2=2271 (44.8814%)\n",
      "[2025-10-15 00:25:19] [Fold 3] train: total=45536, label2=20439 (44.8854%) | val: total=5060, label2=2271 (44.8814%)\n",
      "[2025-10-15 00:25:19] [Fold 4] train: total=45536, label2=20439 (44.8854%) | val: total=5060, label2=2271 (44.8814%)\n",
      "[2025-10-15 00:25:19] [Fold 5] train: total=45536, label2=20439 (44.8854%) | val: total=5060, label2=2271 (44.8814%)\n",
      "[2025-10-15 00:25:19] [Fold 6] train: total=45536, label2=20439 (44.8854%) | val: total=5060, label2=2271 (44.8814%)\n",
      "[2025-10-15 00:25:19] [Fold 7] train: total=45537, label2=20439 (44.8844%) | val: total=5059, label2=2271 (44.8903%)\n",
      "[2025-10-15 00:25:19] [Fold 8] train: total=45537, label2=20439 (44.8844%) | val: total=5059, label2=2271 (44.8903%)\n",
      "[2025-10-15 00:25:19] [Fold 9] train: total=45537, label2=20439 (44.8844%) | val: total=5059, label2=2271 (44.8903%)\n",
      "[2025-10-15 00:25:19] [Fold 10] train: total=45537, label2=20439 (44.8844%) | val: total=5059, label2=2271 (44.8903%)\n",
      "[2025-10-15 00:25:19] --- 總結 ---\n",
      "[2025-10-15 00:25:19] train: total=455364, label2=204390 (44.8850%)\n",
      "[2025-10-15 00:25:19] val:   total=50596, label2=22710 (44.8850%)\n",
      "[2025-10-15 00:25:19] 已輸出稽核報表：/home/jovyan/RT08/0925/sliding_win/1014_sim/reports/label2_audit.csv\n",
      "[2025-10-15 00:25:19] 已輸出索引檔：/home/jovyan/RT08/0925/sliding_win/1014_sim/reports/label2_indices/fold{k}_{train|val}_label2_idx.npy\n",
      "[2025-10-15 00:25:19] === Done ===\n"
     ]
    }
   ],
   "source": [
    "#標籤是2的是無效資料，要檢查所有 10 折 我要fold1-10都檢查\n",
    "# -*- coding: utf-8 -*-\n",
    "\"\"\"\n",
    "audit_label2_in_folds.py\n",
    "目的：\n",
    "  1) 檢查 10 折中 y==2（無效資料）的數量與比例（train / val 分開）\n",
    "  2) 將每折 y==2 的索引輸出成檔案，便於後續剔除或修補\n",
    "輸入：\n",
    "  /home/jovyan/RT08/0925/sliding_win/1014_sim/windowed/y_train_fold{k}.npy\n",
    "  /home/jovyan/RT08/0925/sliding_win/1014_sim/windowed/y_val_fold{k}.npy\n",
    "輸出：\n",
    "  reports/label2_audit.csv\n",
    "  reports/label2_indices/fold{k}_train_label2_idx.npy\n",
    "  reports/label2_indices/fold{k}_val_label2_idx.npy\n",
    "\"\"\"\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from pathlib import Path\n",
    "import time\n",
    "\n",
    "BASE = Path(\"/home/jovyan/RT08/0925/sliding_win/1014_sim\")\n",
    "WD   = BASE / \"windowed\"\n",
    "REP  = BASE / \"reports\"\n",
    "IDXD = REP / \"label2_indices\"\n",
    "REP.mkdir(parents=True, exist_ok=True)\n",
    "IDXD.mkdir(parents=True, exist_ok=True)\n",
    "\n",
    "def log(msg: str):\n",
    "    ts = time.strftime(\"%Y-%m-%d %H:%M:%S\")\n",
    "    print(f\"[{ts}] {msg}\")\n",
    "\n",
    "rows = []\n",
    "any_label2 = False\n",
    "\n",
    "log(\"=== Audit label==2 across 10 folds (train / val) ===\")\n",
    "for k in range(1, 11):\n",
    "    ytr_path = WD / f\"y_train_fold{k}.npy\"\n",
    "    yva_path = WD / f\"y_val_fold{k}.npy\"\n",
    "\n",
    "    if not ytr_path.exists() or not yva_path.exists():\n",
    "        log(f\"[Fold {k}] 檔案不存在，跳過：{ytr_path.name} 或 {yva_path.name}\")\n",
    "        continue\n",
    "\n",
    "    ytr = np.load(ytr_path)\n",
    "    yva = np.load(yva_path)\n",
    "\n",
    "    n_tr = ytr.shape[0]\n",
    "    n_va = yva.shape[0]\n",
    "\n",
    "    tr_label2_idx = np.where(ytr == 2)[0]\n",
    "    va_label2_idx = np.where(yva == 2)[0]\n",
    "\n",
    "    tr_label2 = int(tr_label2_idx.size)\n",
    "    va_label2 = int(va_label2_idx.size)\n",
    "\n",
    "    any_label2 = any_label2 or tr_label2 > 0 or va_label2 > 0\n",
    "\n",
    "    # 儲存索引，便於後續剔除或追蹤\n",
    "    np.save(IDXD / f\"fold{k}_train_label2_idx.npy\", tr_label2_idx)\n",
    "    np.save(IDXD / f\"fold{k}_val_label2_idx.npy\", va_label2_idx)\n",
    "\n",
    "    rows.append({\n",
    "        \"fold\": k,\n",
    "        \"train_total\": n_tr,\n",
    "        \"train_label2\": tr_label2,\n",
    "        \"train_label2_ratio\": (tr_label2 / n_tr) if n_tr > 0 else 0.0,\n",
    "        \"val_total\": n_va,\n",
    "        \"val_label2\": va_label2,\n",
    "        \"val_label2_ratio\": (va_label2 / n_va) if n_va > 0 else 0.0\n",
    "    })\n",
    "\n",
    "    log(f\"[Fold {k}] train: total={n_tr}, label2={tr_label2} ({tr_label2 / n_tr:.4%}) | \"\n",
    "        f\"val: total={n_va}, label2={va_label2} ({va_label2 / n_va:.4%})\")\n",
    "\n",
    "# 匯總與輸出\n",
    "df = pd.DataFrame(rows).sort_values(\"fold\")\n",
    "out_csv = REP / \"label2_audit.csv\"\n",
    "df.to_csv(out_csv, index=False, encoding=\"utf-8\")\n",
    "\n",
    "tot_tr = int(df[\"train_total\"].sum())\n",
    "tot_va = int(df[\"val_total\"].sum())\n",
    "tot_tr_label2 = int(df[\"train_label2\"].sum())\n",
    "tot_va_label2 = int(df[\"val_label2\"].sum())\n",
    "\n",
    "log(\"--- 總結 ---\")\n",
    "log(f\"train: total={tot_tr}, label2={tot_tr_label2} ({(tot_tr_label2 / tot_tr):.4%})\")\n",
    "log(f\"val:   total={tot_va}, label2={tot_va_label2} ({(tot_va_label2 / tot_va):.4%})\")\n",
    "log(f\"已輸出稽核報表：{out_csv}\")\n",
    "log(f\"已輸出索引檔：{IDXD}/fold{{k}}_{{train|val}}_label2_idx.npy\")\n",
    "log(\"=== Done ===\")\n",
    "\n",
    "# 若想在 CI 報錯：當存在 label==2 時以非零碼結束\n",
    "# import sys\n",
    "# sys.exit(1 if any_label2 else 0)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2b4e8e06-dce4-46b4-8f8f-98d13e08a174",
   "metadata": {},
   "outputs": [],
   "source": [
    "執行下面踢掉set_fin=2的資料\n",
    "/home/jovyan/RT08/0925/sliding_win/1014_sim/\n",
    "├─ windowed/                ← 原始十折資料（含 label=2）\n",
    "│   ├─ X_train_fold1.npy\n",
    "│   ├─ y_train_fold1.npy\n",
    "│   ├─ X_val_fold1.npy\n",
    "│   ├─ y_val_fold1.npy\n",
    "│   └─ ...（共十折）\n",
    "│\n",
    "├─ windowed_clean/          ← 💎 新產生的乾淨版十折資料（已移除 label=2）\n",
    "│   ├─ X_train_fold1.npy\n",
    "│   ├─ y_train_fold1.npy\n",
    "│   ├─ X_val_fold1.npy\n",
    "│   ├─ y_val_fold1.npy\n",
    "│   └─ ...（共十折）\n",
    "│\n",
    "├─ reports/\n",
    "│   ├─ label2_audit.csv               ← 稽核各折 label=2 比例\n",
    "│   ├─ label2_cleaning_summary.csv    ← 剔除後統計（前→刪→後）\n",
    "│   ├─ label2_cleaning_totals.json    ← 全案統計彙整\n",
    "│   └─ label2_indices/                ← label=2 樣本索引檔（各折）\n",
    "│\n",
    "└─ logs/\n",
    "    └─ preprocess.log                 ← 執行紀錄與提示語\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 261,
   "id": "12202533-32dd-450b-b2ff-da37526ca4cf",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2025-10-15 00:31:37] ================== 剔除 label==2，輸出 windowed_clean ==================\n",
      "[2025-10-15 00:31:37] [Fold 1] Train: 45536 → 刪除 20439 → 保留 25097 | Val: 5060 → 刪除 2271 → 保留 2789 | y_train_after={0: 18462, 1: 6635} y_val_after={0: 2052, 1: 737}\n",
      "[2025-10-15 00:31:37] [Fold 2] Train: 45536 → 刪除 20439 → 保留 25097 | Val: 5060 → 刪除 2271 → 保留 2789 | y_train_after={0: 18462, 1: 6635} y_val_after={0: 2052, 1: 737}\n",
      "[2025-10-15 00:31:38] [Fold 3] Train: 45536 → 刪除 20439 → 保留 25097 | Val: 5060 → 刪除 2271 → 保留 2789 | y_train_after={0: 18462, 1: 6635} y_val_after={0: 2052, 1: 737}\n",
      "[2025-10-15 00:31:38] [Fold 4] Train: 45536 → 刪除 20439 → 保留 25097 | Val: 5060 → 刪除 2271 → 保留 2789 | y_train_after={0: 18462, 1: 6635} y_val_after={0: 2052, 1: 737}\n",
      "[2025-10-15 00:31:38] [Fold 5] Train: 45536 → 刪除 20439 → 保留 25097 | Val: 5060 → 刪除 2271 → 保留 2789 | y_train_after={0: 18463, 1: 6634} y_val_after={0: 2051, 1: 738}\n",
      "[2025-10-15 00:31:38] [Fold 6] Train: 45536 → 刪除 20439 → 保留 25097 | Val: 5060 → 刪除 2271 → 保留 2789 | y_train_after={0: 18463, 1: 6634} y_val_after={0: 2051, 1: 738}\n",
      "[2025-10-15 00:31:38] [Fold 7] Train: 45537 → 刪除 20439 → 保留 25098 | Val: 5059 → 刪除 2271 → 保留 2788 | y_train_after={0: 18463, 1: 6635} y_val_after={0: 2051, 1: 737}\n",
      "[2025-10-15 00:31:38] [Fold 8] Train: 45537 → 刪除 20439 → 保留 25098 | Val: 5059 → 刪除 2271 → 保留 2788 | y_train_after={0: 18463, 1: 6635} y_val_after={0: 2051, 1: 737}\n",
      "[2025-10-15 00:31:39] [Fold 9] Train: 45537 → 刪除 20439 → 保留 25098 | Val: 5059 → 刪除 2271 → 保留 2788 | y_train_after={0: 18463, 1: 6635} y_val_after={0: 2051, 1: 737}\n",
      "[2025-10-15 00:31:39] [Fold 10] Train: 45537 → 刪除 20439 → 保留 25098 | Val: 5059 → 刪除 2271 → 保留 2788 | y_train_after={0: 18463, 1: 6635} y_val_after={0: 2051, 1: 737}\n",
      "[2025-10-15 00:31:39] --- 剔除總結 ---\n",
      "[2025-10-15 00:31:39] Train 總筆數：455364 → 刪除 204390 → 保留 250974\n",
      "[2025-10-15 00:31:39] Val   總筆數：50596 → 刪除 22710 → 保留 27886\n",
      "[2025-10-15 00:31:39] 已輸出報表：/home/jovyan/RT08/0925/sliding_win/1014_sim/reports/label2_cleaning_summary.csv\n",
      "[2025-10-15 00:31:39] 已輸出統計：/home/jovyan/RT08/0925/sliding_win/1014_sim/reports/label2_cleaning_totals.json\n",
      "[2025-10-15 00:31:39] ================== 完成 windowed_clean 產生 ==================\n"
     ]
    }
   ],
   "source": [
    "# -*- coding: utf-8 -*-\n",
    "\"\"\"\n",
    "clean_label2_from_folds.py\n",
    "用途：\n",
    "  依照稽核結果剔除 y==2 的樣本，產生乾淨版十折資料到 windowed_clean/\n",
    "  優先使用 reports/label2_indices/ 中的索引；若不存在，現場偵測 y==2。\n",
    "輸入：\n",
    "  /home/jovyan/RT08/0925/sliding_win/1014_sim/windowed/X_train_fold{k}.npy\n",
    "  /home/jovyan/RT08/0925/sliding_win/1014_sim/windowed/y_train_fold{k}.npy\n",
    "  /home/jovyan/RT08/0925/sliding_win/1014_sim/windowed/X_val_fold{k}.npy\n",
    "  /home/jovyan/RT08/0925/sliding_win/1014_sim/windowed/y_val_fold{k}.npy\n",
    "  （可選）/home/jovyan/RT08/0925/sliding_win/1014_sim/reports/label2_indices/fold{k}_train_label2_idx.npy\n",
    "          /home/jovyan/RT08/0925/sliding_win/1014_sim/reports/label2_indices/fold{k}_val_label2_idx.npy\n",
    "輸出：\n",
    "  windowed_clean/X_train_fold{k}.npy, y_train_fold{k}.npy\n",
    "  windowed_clean/X_val_fold{k}.npy,   y_val_fold{k}.npy\n",
    "  reports/label2_cleaning_summary.csv\n",
    "  reports/label2_cleaning_totals.json\n",
    "日誌：\n",
    "  logs/preprocess.log（追加提示語與筆數統計）\n",
    "\"\"\"\n",
    "\n",
    "import json, time\n",
    "from pathlib import Path\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "BASE = Path(\"/home/jovyan/RT08/0925/sliding_win/1014_sim\")\n",
    "WD_IN   = BASE / \"windowed\"\n",
    "WD_OUT  = BASE / \"windowed_clean\"\n",
    "REPORTS = BASE / \"reports\"\n",
    "LOGS    = BASE / \"logs\"\n",
    "IDXDIR  = REPORTS / \"label2_indices\"\n",
    "\n",
    "for p in [WD_OUT, REPORTS, LOGS]:\n",
    "    p.mkdir(parents=True, exist_ok=True)\n",
    "\n",
    "LOG_FILE = LOGS / \"preprocess.log\"\n",
    "\n",
    "def log(msg: str):\n",
    "    ts = time.strftime(\"%Y-%m-%d %H:%M:%S\")\n",
    "    line = f\"[{ts}] {msg}\"\n",
    "    print(line)\n",
    "    with open(LOG_FILE, \"a\", encoding=\"utf-8\") as f:\n",
    "        f.write(line + \"\\n\")\n",
    "\n",
    "def load_idx_if_exists(path: Path, y: np.ndarray):\n",
    "    if path.exists():\n",
    "        idx = np.load(path)\n",
    "        # 保護：過濾越界或非整數\n",
    "        idx = idx.astype(int)\n",
    "        idx = idx[(idx >= 0) & (idx < y.shape[0])]\n",
    "        return idx\n",
    "    # 若無索引檔，現場偵測 y==2\n",
    "    return np.where(y == 2)[0]\n",
    "\n",
    "def label_counts(y: np.ndarray):\n",
    "    u, c = np.unique(y, return_counts=True)\n",
    "    return {int(k): int(v) for k, v in zip(u, c)}\n",
    "\n",
    "def main():\n",
    "    log(\"=\"*18 + \" 剔除 label==2，輸出 windowed_clean \" + \"=\"*18)\n",
    "    rows = []\n",
    "    total_before = {\"train\": 0, \"val\": 0}\n",
    "    total_after  = {\"train\": 0, \"val\": 0}\n",
    "    total_removed= {\"train\": 0, \"val\": 0}\n",
    "\n",
    "    for k in range(1, 11):\n",
    "        # 檔名\n",
    "        Xtr_in = WD_IN / f\"X_train_fold{k}.npy\"\n",
    "        ytr_in = WD_IN / f\"y_train_fold{k}.npy\"\n",
    "        Xva_in = WD_IN / f\"X_val_fold{k}.npy\"\n",
    "        yva_in = WD_IN / f\"y_val_fold{k}.npy\"\n",
    "\n",
    "        if not (Xtr_in.exists() and ytr_in.exists() and Xva_in.exists() and yva_in.exists()):\n",
    "            log(f\"[Fold {k}] 檔案不全，跳過此折\")\n",
    "            continue\n",
    "\n",
    "        # 載入\n",
    "        Xtr = np.load(Xtr_in)\n",
    "        ytr = np.load(ytr_in)\n",
    "        Xva = np.load(Xva_in)\n",
    "        yva = np.load(yva_in)\n",
    "\n",
    "        ntr0, nva0 = ytr.shape[0], yva.shape[0]\n",
    "        total_before[\"train\"] += ntr0\n",
    "        total_before[\"val\"]   += nva0\n",
    "\n",
    "        # 取得要刪除的索引（優先用稽核索引）\n",
    "        tr_idx_path = IDXDIR / f\"fold{k}_train_label2_idx.npy\"\n",
    "        va_idx_path = IDXDIR / f\"fold{k}_val_label2_idx.npy\"\n",
    "        idx_tr_rm = load_idx_if_exists(tr_idx_path, ytr)\n",
    "        idx_va_rm = load_idx_if_exists(va_idx_path, yva)\n",
    "\n",
    "        rm_tr = int(idx_tr_rm.size)\n",
    "        rm_va = int(idx_va_rm.size)\n",
    "\n",
    "        # 建立保留遮罩\n",
    "        keep_tr = np.ones(ntr0, dtype=bool)\n",
    "        keep_va = np.ones(nva0, dtype=bool)\n",
    "        keep_tr[idx_tr_rm] = False\n",
    "        keep_va[idx_va_rm] = False\n",
    "\n",
    "        # 套用遮罩\n",
    "        Xtr_c, ytr_c = Xtr[keep_tr], ytr[keep_tr]\n",
    "        Xva_c, yva_c = Xva[keep_va], yva[keep_va]\n",
    "\n",
    "        # 安全：將殘存的 2 轉為 0（理論上不應存在）\n",
    "        ytr_c = np.where(ytr_c == 2, 0, ytr_c).astype(np.int64)\n",
    "        yva_c = np.where(yva_c == 2, 0, yva_c).astype(np.int64)\n",
    "\n",
    "        ntr1, nva1 = ytr_c.shape[0], yva_c.shape[0]\n",
    "        total_after[\"train\"] += ntr1\n",
    "        total_after[\"val\"]   += nva1\n",
    "        total_removed[\"train\"] += rm_tr\n",
    "        total_removed[\"val\"]   += rm_va\n",
    "\n",
    "        # 統計\n",
    "        stats = {\n",
    "            \"fold\": k,\n",
    "            \"train_before\": ntr0,\n",
    "            \"train_removed_label2\": rm_tr,\n",
    "            \"train_after\": ntr1,\n",
    "            \"train_label_dist_before\": label_counts(ytr),\n",
    "            \"train_label_dist_after\":  label_counts(ytr_c),\n",
    "            \"val_before\": nva0,\n",
    "            \"val_removed_label2\": rm_va,\n",
    "            \"val_after\": nva1,\n",
    "            \"val_label_dist_before\": label_counts(yva),\n",
    "            \"val_label_dist_after\":  label_counts(yva_c),\n",
    "        }\n",
    "\n",
    "        # 寫出\n",
    "        WD_OUT.mkdir(parents=True, exist_ok=True)\n",
    "        np.save(WD_OUT / f\"X_train_fold{k}.npy\", Xtr_c.astype(np.float32))\n",
    "        np.save(WD_OUT / f\"y_train_fold{k}.npy\", ytr_c)\n",
    "        np.save(WD_OUT / f\"X_val_fold{k}.npy\",   Xva_c.astype(np.float32))\n",
    "        np.save(WD_OUT / f\"y_val_fold{k}.npy\",   yva_c)\n",
    "\n",
    "        # 提示語\n",
    "        log(f\"[Fold {k}] Train: {ntr0} → 刪除 {rm_tr} → 保留 {ntr1} | \"\n",
    "            f\"Val: {nva0} → 刪除 {rm_va} → 保留 {nva1} | \"\n",
    "            f\"y_train_after={stats['train_label_dist_after']} y_val_after={stats['val_label_dist_after']}\")\n",
    "\n",
    "        rows.append(stats)\n",
    "\n",
    "    # 匯出彙總報表\n",
    "    df_rows = []\n",
    "    for s in rows:\n",
    "        df_rows.append({\n",
    "            \"fold\": s[\"fold\"],\n",
    "            \"train_before\": s[\"train_before\"],\n",
    "            \"train_removed_label2\": s[\"train_removed_label2\"],\n",
    "            \"train_after\": s[\"train_after\"],\n",
    "            \"train_y0_after\": s[\"train_label_dist_after\"].get(0, 0),\n",
    "            \"train_y1_after\": s[\"train_label_dist_after\"].get(1, 0),\n",
    "            \"val_before\": s[\"val_before\"],\n",
    "            \"val_removed_label2\": s[\"val_removed_label2\"],\n",
    "            \"val_after\": s[\"val_after\"],\n",
    "            \"val_y0_after\": s[\"val_label_dist_after\"].get(0, 0),\n",
    "            \"val_y1_after\": s[\"val_label_dist_after\"].get(1, 0),\n",
    "        })\n",
    "    df = pd.DataFrame(df_rows).sort_values(\"fold\")\n",
    "    out_csv = REPORTS / \"label2_cleaning_summary.csv\"\n",
    "    df.to_csv(out_csv, index=False, encoding=\"utf-8\")\n",
    "\n",
    "    totals = {\n",
    "        \"train_before_total\": int(total_before[\"train\"]),\n",
    "        \"train_removed_label2_total\": int(total_removed[\"train\"]),\n",
    "        \"train_after_total\": int(total_after[\"train\"]),\n",
    "        \"val_before_total\": int(total_before[\"val\"]),\n",
    "        \"val_removed_label2_total\": int(total_removed[\"val\"]),\n",
    "        \"val_after_total\": int(total_after[\"val\"]),\n",
    "        \"folds_processed\": int(len(df_rows)),\n",
    "        \"source_dir\": str(WD_IN),\n",
    "        \"output_dir\": str(WD_OUT),\n",
    "        \"note\": \"label==2 samples removed per fold. Remaining labels are {0,1}.\"\n",
    "    }\n",
    "    out_json = REPORTS / \"label2_cleaning_totals.json\"\n",
    "    out_json.write_text(json.dumps(totals, ensure_ascii=False, indent=2), encoding=\"utf-8\")\n",
    "\n",
    "    # 總結提示\n",
    "    log(\"--- 剔除總結 ---\")\n",
    "    log(f\"Train 總筆數：{totals['train_before_total']} → 刪除 {totals['train_removed_label2_total']} → \"\n",
    "        f\"保留 {totals['train_after_total']}\")\n",
    "    log(f\"Val   總筆數：{totals['val_before_total']} → 刪除 {totals['val_removed_label2_total']} → \"\n",
    "        f\"保留 {totals['val_after_total']}\")\n",
    "    log(f\"已輸出報表：{out_csv}\")\n",
    "    log(f\"已輸出統計：{out_json}\")\n",
    "    log(\"=\"*18 + \" 完成 windowed_clean 產生 \" + \"=\"*18)\n",
    "\n",
    "if __name__ == \"__main__\":\n",
    "    main()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2a844891-4181-44f8-81ab-7c3fca008290",
   "metadata": {},
   "outputs": [],
   "source": [
    "為甚麼部是先針對原始資料做十折交叉的資料分層時就只針對nan_check=1的資料且不納入set_fin=2的資料?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 262,
   "id": "212ac455-9604-4871-bb0b-bda4ee270deb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "=== 📊 視窗稽核結果 ===\n",
      " fold  n_samples  seq_len  n_features unique_lengths  nan_count  inf_count  y_label_0  y_label_1  y_label_2\n",
      "    1      25097       60          10           [60]       2884          0      18462       6635          0\n",
      "    2      25097       60          10           [60]       2842          0      18462       6635          0\n",
      "    3      25097       60          10           [60]       2919          0      18462       6635          0\n",
      "    4      25097       60          10           [60]       2898          0      18462       6635          0\n",
      "    5      25097       60          10           [60]       2846          0      18463       6634          0\n",
      "    6      25097       60          10           [60]       2923          0      18463       6634          0\n",
      "    7      25098       60          10           [60]       2944          0      18463       6635          0\n",
      "    8      25098       60          10           [60]       2940          0      18463       6635          0\n",
      "    9      25098       60          10           [60]       2844          0      18463       6635          0\n",
      "   10      25098       60          10           [60]       2922          0      18463       6635          0\n",
      "\n",
      "=== 📈 總結 ===\n",
      "總樣本數：250,974\n",
      "平均視窗長度：60.0（應為60）\n",
      "含NaN視窗比率：約 0.0192%\n",
      "label=2 總數：0\n"
     ]
    }
   ],
   "source": [
    "# 看看新資料是否 先開窗再刪label=2會維持60\n",
    "import numpy as np\n",
    "import os\n",
    "import pandas as pd\n",
    "\n",
    "BASE_DIR = \"/home/jovyan/RT08/0925/sliding_win/1014_sim/windowed_clean\"\n",
    "folds = [f\"fold{i}\" for i in range(1, 11)]  # 若你的檔案是 fold1 ~ fold10\n",
    "\n",
    "records = []\n",
    "\n",
    "for i in range(1, 11):\n",
    "    x_path = os.path.join(BASE_DIR, f\"X_train_fold{i}.npy\")\n",
    "    y_path = os.path.join(BASE_DIR, f\"y_train_fold{i}.npy\")\n",
    "\n",
    "    if not os.path.exists(x_path):\n",
    "        print(f\"[警告] 找不到 {x_path}\")\n",
    "        continue\n",
    "\n",
    "    X = np.load(x_path)\n",
    "    y = np.load(y_path)\n",
    "\n",
    "    n_samples, seq_len, n_features = X.shape\n",
    "    nan_count = np.isnan(X).sum()\n",
    "    inf_count = np.isinf(X).sum()\n",
    "\n",
    "    # 長度檢查（所有樣本長度應相同）\n",
    "    unique_lengths = np.unique([x.shape[0] for x in X]) if isinstance(X, list) else [seq_len]\n",
    "\n",
    "    records.append({\n",
    "        \"fold\": i,\n",
    "        \"n_samples\": n_samples,\n",
    "        \"seq_len\": seq_len,\n",
    "        \"n_features\": n_features,\n",
    "        \"unique_lengths\": unique_lengths,\n",
    "        \"nan_count\": int(nan_count),\n",
    "        \"inf_count\": int(inf_count),\n",
    "        \"y_label_0\": int((y == 0).sum()),\n",
    "        \"y_label_1\": int((y == 1).sum()),\n",
    "        \"y_label_2\": int((y == 2).sum())\n",
    "    })\n",
    "\n",
    "df = pd.DataFrame(records)\n",
    "print(\"\\n=== 📊 視窗稽核結果 ===\")\n",
    "print(df.to_string(index=False))\n",
    "\n",
    "# 總覽\n",
    "print(\"\\n=== 📈 總結 ===\")\n",
    "print(f\"總樣本數：{df['n_samples'].sum():,}\")\n",
    "print(f\"平均視窗長度：{df['seq_len'].mean():.1f}（應為60）\")\n",
    "print(f\"含NaN視窗比率：約 {df['nan_count'].sum() / (df['n_samples'].sum() * df['n_features'].iloc[0] * 60):.4%}\")\n",
    "print(f\"label=2 總數：{df['y_label_2'].sum():,}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 263,
   "id": "1eb9a556-370d-48e0-bd29-699ab8023ec0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "=== 📊 視窗稽核結果（train/val） ===\n",
      "split  fold         X_shape  y_shape  seq_len  n_features   dtype  nan_count  inf_count    y0   y1  y2\n",
      "train     1 (25097, 60, 10) (25097,)       60          10 float32       2884          0 18462 6635   0\n",
      "train     2 (25097, 60, 10) (25097,)       60          10 float32       2842          0 18462 6635   0\n",
      "train     3 (25097, 60, 10) (25097,)       60          10 float32       2919          0 18462 6635   0\n",
      "train     4 (25097, 60, 10) (25097,)       60          10 float32       2898          0 18462 6635   0\n",
      "train     5 (25097, 60, 10) (25097,)       60          10 float32       2846          0 18463 6634   0\n",
      "train     6 (25097, 60, 10) (25097,)       60          10 float32       2923          0 18463 6634   0\n",
      "train     7 (25098, 60, 10) (25098,)       60          10 float32       2944          0 18463 6635   0\n",
      "train     8 (25098, 60, 10) (25098,)       60          10 float32       2940          0 18463 6635   0\n",
      "train     9 (25098, 60, 10) (25098,)       60          10 float32       2844          0 18463 6635   0\n",
      "train    10 (25098, 60, 10) (25098,)       60          10 float32       2922          0 18463 6635   0\n",
      "  val     1  (2789, 60, 10)  (2789,)       60          10 float32        334          0  2052  737   0\n",
      "  val     2  (2789, 60, 10)  (2789,)       60          10 float32        376          0  2052  737   0\n",
      "  val     3  (2789, 60, 10)  (2789,)       60          10 float32        299          0  2052  737   0\n",
      "  val     4  (2789, 60, 10)  (2789,)       60          10 float32        320          0  2052  737   0\n",
      "  val     5  (2789, 60, 10)  (2789,)       60          10 float32        372          0  2051  738   0\n",
      "  val     6  (2789, 60, 10)  (2789,)       60          10 float32        295          0  2051  738   0\n",
      "  val     7  (2788, 60, 10)  (2788,)       60          10 float32        274          0  2051  737   0\n",
      "  val     8  (2788, 60, 10)  (2788,)       60          10 float32        278          0  2051  737   0\n",
      "  val     9  (2788, 60, 10)  (2788,)       60          10 float32        374          0  2051  737   0\n",
      "  val    10  (2788, 60, 10)  (2788,)       60          10 float32        296          0  2051  737   0\n",
      "\n",
      "=== 📈 總結 ===\n",
      "Train 視窗總數：250,974\n",
      "Val   視窗總數：27,886\n",
      "平均 seq_len（應為 60）：Train=60.0｜Val=60.0\n",
      "dtype 統計：{'float32': 20}\n",
      "NaN 總數：Train=28962｜Val=3218\n",
      "Inf 總數：Train=0｜Val=0\n",
      "label=2 總數（應為 0）：Train=0｜Val=0\n",
      "Train 正負比 ≈ 1:2.78｜Val 正負比 ≈ 1:2.78\n",
      "\n",
      "已輸出報表：/home/jovyan/RT08/0925/sliding_win/1014_sim/reports/windowed_clean_audit.csv\n"
     ]
    }
   ],
   "source": [
    "# 看看新資料是否先開窗再刪 label=2 仍維持 60\n",
    "import numpy as np\n",
    "import os\n",
    "import pandas as pd\n",
    "from pathlib import Path\n",
    "\n",
    "BASE_DIR = Path(\"/home/jovyan/RT08/0925/sliding_win/1014_sim/windowed_clean\")\n",
    "REPORTS = BASE_DIR.parent / \"reports\"\n",
    "REPORTS.mkdir(parents=True, exist_ok=True)\n",
    "\n",
    "def audit_split(split: str):\n",
    "    records = []\n",
    "    for i in range(1, 11):\n",
    "        x_path = BASE_DIR / f\"X_{split}_fold{i}.npy\"\n",
    "        y_path = BASE_DIR / f\"y_{split}_fold{i}.npy\"\n",
    "\n",
    "        if not x_path.exists() or not y_path.exists():\n",
    "            print(f\"[警告] 找不到 {x_path.name} 或 {y_path.name}，略過 fold{i}\")\n",
    "            continue\n",
    "\n",
    "        X = np.load(x_path)\n",
    "        y = np.load(y_path)\n",
    "\n",
    "        n_samples, seq_len, n_features = X.shape\n",
    "        nan_count = int(np.isnan(X).sum())\n",
    "        inf_count = int(np.isinf(X).sum())\n",
    "\n",
    "        records.append({\n",
    "            \"split\": split,\n",
    "            \"fold\": i,\n",
    "            \"X_shape\": f\"({n_samples}, {seq_len}, {n_features})\",\n",
    "            \"y_shape\": f\"({y.shape[0]},)\",\n",
    "            \"seq_len\": seq_len,                 # 應全為 60\n",
    "            \"n_features\": n_features,\n",
    "            \"dtype\": str(X.dtype),              # 應為 float32\n",
    "            \"nan_count\": nan_count,\n",
    "            \"inf_count\": inf_count,\n",
    "            \"y0\": int((y == 0).sum()),\n",
    "            \"y1\": int((y == 1).sum()),\n",
    "            \"y2\": int((y == 2).sum()),         # 在 windowed_clean 應為 0\n",
    "        })\n",
    "    return pd.DataFrame(records)\n",
    "\n",
    "df_tr = audit_split(\"train\")\n",
    "df_va = audit_split(\"val\")\n",
    "df = pd.concat([df_tr, df_va], ignore_index=True)\n",
    "\n",
    "print(\"\\n=== 📊 視窗稽核結果（train/val） ===\")\n",
    "print(df.to_string(index=False))\n",
    "\n",
    "# 彙總與判讀輔助\n",
    "def safe_div(a,b): return (a/b) if b else float('nan')\n",
    "tot_tr = df[df.split==\"train\"]\n",
    "tot_va = df[df.split==\"val\"]\n",
    "\n",
    "print(\"\\n=== 📈 總結 ===\")\n",
    "print(f\"Train 視窗總數：{tot_tr['y0'].sum() + tot_tr['y1'].sum():,}\")\n",
    "print(f\"Val   視窗總數：{tot_va['y0'].sum() + tot_va['y1'].sum():,}\")\n",
    "print(f\"平均 seq_len（應為 60）：Train={tot_tr['seq_len'].mean():.1f}｜Val={tot_va['seq_len'].mean():.1f}\")\n",
    "print(f\"dtype 統計：{df['dtype'].value_counts().to_dict()}\")\n",
    "print(f\"NaN 總數：Train={int(tot_tr['nan_count'].sum())}｜Val={int(tot_va['nan_count'].sum())}\")\n",
    "print(f\"Inf 總數：Train={int(tot_tr['inf_count'].sum())}｜Val={int(tot_va['inf_count'].sum())}\")\n",
    "print(f\"label=2 總數（應為 0）：Train={int(tot_tr['y2'].sum())}｜Val={int(tot_va['y2'].sum())}\")\n",
    "\n",
    "# 正負比例（約 1:2.8～1:3.0 取決於你清理後分佈；原先 1:5.8 是清理前總體）\n",
    "pos_tr = tot_tr['y1'].sum(); neg_tr = tot_tr['y0'].sum()\n",
    "pos_va = tot_va['y1'].sum(); neg_va = tot_va['y0'].sum()\n",
    "print(f\"Train 正負比 ≈ 1:{safe_div(neg_tr, pos_tr):.2f}｜Val 正負比 ≈ 1:{safe_div(neg_va, pos_va):.2f}\")\n",
    "\n",
    "# 存成報表\n",
    "out_csv = REPORTS / \"windowed_clean_audit.csv\"\n",
    "df.to_csv(out_csv, index=False, encoding=\"utf-8\")\n",
    "print(f\"\\n已輸出報表：{out_csv}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "10026ed5-fe4c-45f0-90b2-601ce22c931d",
   "metadata": {},
   "outputs": [],
   "source": [
    "我想針對 windowed_clean進行Focal Loss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 264,
   "id": "83628b70-cb30-46f4-9d60-1c43aa0775c6",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true,
     "source_hidden": true
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2025-10-15 01:55:54.581905: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.\n",
      "2025-10-15 01:55:54.748056: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:485] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n",
      "2025-10-15 01:55:54.830231: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:8454] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n",
      "2025-10-15 01:55:54.854477: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1452] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n",
      "2025-10-15 01:55:54.971093: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n",
      "To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
      "2025-10-15 01:55:56.091973: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2025-10-15 01:55:56] === 十折｜Focal Loss 訓練開始 ===\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2025-10-15 01:55:58.814693: E external/local_xla/xla/stream_executor/cuda/cuda_driver.cc:266] failed call to cuInit: CUDA_ERROR_NO_DEVICE: no CUDA-capable device is detected\n"
     ]
    },
    {
     "ename": "ValueError",
     "evalue": "When using `save_weights_only=True` in `ModelCheckpoint`, the filepath provided must end in `.weights.h5` (Keras weights format). Received: filepath=/home/jovyan/RT08/0925/sliding_win/1014_sim/training_focal/run_fold_01/best.ckpt",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[264], line 223\u001b[0m\n\u001b[1;32m    220\u001b[0m     \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m已輸出彙總到：\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mout_csv\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m    222\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;18m__name__\u001b[39m \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m__main__\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[0;32m--> 223\u001b[0m     \u001b[43mmain\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n",
      "Cell \u001b[0;32mIn[264], line 201\u001b[0m, in \u001b[0;36mmain\u001b[0;34m()\u001b[0m\n\u001b[1;32m    199\u001b[0m all_rows \u001b[38;5;241m=\u001b[39m []\n\u001b[1;32m    200\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m k \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(\u001b[38;5;241m1\u001b[39m, \u001b[38;5;241m11\u001b[39m):\n\u001b[0;32m--> 201\u001b[0m     m \u001b[38;5;241m=\u001b[39m \u001b[43mrun_fold\u001b[49m\u001b[43m(\u001b[49m\u001b[43mk\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    202\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m m \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m    203\u001b[0m         all_rows\u001b[38;5;241m.\u001b[39mappend(m)\n",
      "Cell \u001b[0;32mIn[264], line 156\u001b[0m, in \u001b[0;36mrun_fold\u001b[0;34m(k)\u001b[0m\n\u001b[1;32m    154\u001b[0m fold_dir \u001b[38;5;241m=\u001b[39m OUT \u001b[38;5;241m/\u001b[39m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mrun_fold_\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mk\u001b[38;5;132;01m:\u001b[39;00m\u001b[38;5;124m02d\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    155\u001b[0m fold_dir\u001b[38;5;241m.\u001b[39mmkdir(parents\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m, exist_ok\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n\u001b[0;32m--> 156\u001b[0m ckpt \u001b[38;5;241m=\u001b[39m \u001b[43mcallbacks\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mModelCheckpoint\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    157\u001b[0m \u001b[43m    \u001b[49m\u001b[43mfilepath\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mstr\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mfold_dir\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m/\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mbest.ckpt\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    158\u001b[0m \u001b[43m    \u001b[49m\u001b[43mmonitor\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mval_AUC_PR\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmode\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mmax\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msave_best_only\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msave_weights_only\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m)\u001b[49m\n\u001b[1;32m    159\u001b[0m es \u001b[38;5;241m=\u001b[39m callbacks\u001b[38;5;241m.\u001b[39mEarlyStopping(monitor\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mval_AUC_PR\u001b[39m\u001b[38;5;124m\"\u001b[39m, mode\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmax\u001b[39m\u001b[38;5;124m\"\u001b[39m, patience\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m6\u001b[39m, restore_best_weights\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n\u001b[1;32m    160\u001b[0m rlrop \u001b[38;5;241m=\u001b[39m callbacks\u001b[38;5;241m.\u001b[39mReduceLROnPlateau(monitor\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mval_AUC_PR\u001b[39m\u001b[38;5;124m\"\u001b[39m, mode\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmax\u001b[39m\u001b[38;5;124m\"\u001b[39m, patience\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m3\u001b[39m, factor\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0.5\u001b[39m, min_lr\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1e-5\u001b[39m)\n",
      "File \u001b[0;32m/opt/conda/lib/python3.11/site-packages/keras/src/callbacks/model_checkpoint.py:183\u001b[0m, in \u001b[0;36mModelCheckpoint.__init__\u001b[0;34m(self, filepath, monitor, verbose, save_best_only, save_weights_only, mode, save_freq, initial_value_threshold)\u001b[0m\n\u001b[1;32m    181\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m save_weights_only:\n\u001b[1;32m    182\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfilepath\u001b[38;5;241m.\u001b[39mendswith(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m.weights.h5\u001b[39m\u001b[38;5;124m\"\u001b[39m):\n\u001b[0;32m--> 183\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[1;32m    184\u001b[0m             \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mWhen using `save_weights_only=True` in `ModelCheckpoint`\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    185\u001b[0m             \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m, the filepath provided must end in `.weights.h5` \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    186\u001b[0m             \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m(Keras weights format). Received: \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    187\u001b[0m             \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mfilepath=\u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfilepath\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    188\u001b[0m         )\n\u001b[1;32m    189\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    190\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfilepath\u001b[38;5;241m.\u001b[39mendswith(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m.keras\u001b[39m\u001b[38;5;124m\"\u001b[39m):\n",
      "\u001b[0;31mValueError\u001b[0m: When using `save_weights_only=True` in `ModelCheckpoint`, the filepath provided must end in `.weights.h5` (Keras weights format). Received: filepath=/home/jovyan/RT08/0925/sliding_win/1014_sim/training_focal/run_fold_01/best.ckpt"
     ]
    }
   ],
   "source": [
    "# -*- coding: utf-8 -*-\n",
    "\"\"\"\n",
    "train_kfold_focal.py\n",
    "目的：\n",
    "  讀取 windowed_clean/ 的十折資料，使用 Focal Loss 訓練 Keras 模型（每折自動設定 alpha）\n",
    "  - 每折以訓練集 fit：SimpleImputer(median) + StandardScaler，並套用到 train/val\n",
    "  - Focal Loss 參數：alpha = neg_ratio（每折自動計算）、gamma 預設 2.0（可調）\n",
    "  - 輸出每折最佳權重、metrics、彙總報表\n",
    "路徑：\n",
    "  /home/jovyan/RT08/0925/sliding_win/1014_sim/windowed_clean/\n",
    "  /home/jovyan/RT08/0925/sliding_win/1014_sim/training_focal/\n",
    "  /home/jovyan/RT08/0925/sliding_win/1014_sim/reports/focal_cv_metrics.csv\n",
    "\"\"\"\n",
    "\n",
    "import os, json, time\n",
    "from pathlib import Path\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "from sklearn.impute import SimpleImputer\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "import tensorflow as tf\n",
    "from tensorflow.keras import layers, models, callbacks, metrics as kmetrics, backend as K\n",
    "\n",
    "# ===================== 路徑與參數 =====================\n",
    "BASE = Path(\"/home/jovyan/RT08/0925/sliding_win/1014_sim\")\n",
    "WD   = BASE / \"windowed_clean\"\n",
    "OUT  = BASE / \"training_focal\"\n",
    "REPO = BASE / \"reports\"\n",
    "LOGS = BASE / \"logs\"\n",
    "for p in [OUT, REPO, LOGS]:\n",
    "    p.mkdir(parents=True, exist_ok=True)\n",
    "\n",
    "SEED = 42\n",
    "np.random.seed(SEED)\n",
    "tf.random.set_seed(SEED)\n",
    "\n",
    "EPOCHS = 50\n",
    "BATCH  = 128\n",
    "GAMMA  = 2.0            # Focal Loss gamma\n",
    "LEARN  = 1e-3\n",
    "\n",
    "# ===================== Focal Loss（Binary） =====================\n",
    "def binary_focal_loss(alpha=0.25, gamma=2.0):\n",
    "    \"\"\"\n",
    "    alpha：正類的權重（class 1）；建議用 neg_ratio（每折自動計算）\n",
    "    gamma：focusing 參數，常用 2.0\n",
    "    y_true: (N,1) 或 (N,)\n",
    "    y_pred: sigmoid 機率（非 logit）\n",
    "    \"\"\"\n",
    "    def loss(y_true, y_pred):\n",
    "        y_true = tf.cast(y_true, tf.float32)\n",
    "        y_pred = tf.clip_by_value(y_pred, 1e-7, 1.-1e-7)\n",
    "        pt     = tf.where(tf.equal(y_true, 1.), y_pred, 1.-y_pred)\n",
    "        w      = tf.where(tf.equal(y_true, 1.), alpha, 1.-alpha)\n",
    "        return K.mean(- w * K.pow(1. - pt, gamma) * K.log(pt), axis=0)\n",
    "    return loss\n",
    "\n",
    "# ===================== 簡潔模型（可替換） =====================\n",
    "def build_model(input_shape):\n",
    "    \"\"\"\n",
    "    輕量、穩定、適合 60×D 的序列：Conv1D + BiLSTM + Dense\n",
    "    \"\"\"\n",
    "    inputs = layers.Input(shape=input_shape)\n",
    "    x = layers.Conv1D(64, 3, padding=\"same\")(inputs)\n",
    "    x = layers.BatchNormalization()(x)\n",
    "    x = layers.ReLU()(x)\n",
    "    x = layers.Conv1D(64, 3, padding=\"same\")(x)\n",
    "    x = layers.BatchNormalization()(x)\n",
    "    x = layers.ReLU()(x)\n",
    "    x = layers.Bidirectional(layers.LSTM(64, return_sequences=False))(x)\n",
    "    x = layers.Dropout(0.3)(x)\n",
    "    x = layers.Dense(64, activation=\"relu\")(x)\n",
    "    x = layers.Dropout(0.2)(x)\n",
    "    outputs = layers.Dense(1, activation=\"sigmoid\")(x)\n",
    "    model = models.Model(inputs, outputs)\n",
    "    return model\n",
    "\n",
    "# ===================== 工具函式 =====================\n",
    "def fit_imputer_scaler(X_train, X_val):\n",
    "    \"\"\"\n",
    "    以訓練集 fit：median 補值 + 標準化；避免資料洩漏。\n",
    "    X: (N, T, D) → 展平成 (N, T*D) 後處理，再 reshape 回來\n",
    "    \"\"\"\n",
    "    Ntr, T, D = X_train.shape\n",
    "    Nva = X_val.shape[0]\n",
    "    Xtr2d = X_train.reshape(Ntr, T*D)\n",
    "    Xva2d = X_val.reshape(Nva, T*D)\n",
    "\n",
    "    imputer = SimpleImputer(strategy=\"median\")\n",
    "    scaler  = StandardScaler(with_mean=True, with_std=True)\n",
    "\n",
    "    Xtr_imp = imputer.fit_transform(Xtr2d)\n",
    "    Xtr_scl = scaler.fit_transform(Xtr_imp)\n",
    "\n",
    "    Xva_imp = imputer.transform(Xva2d)\n",
    "    Xva_scl = scaler.transform(Xva_imp)\n",
    "\n",
    "    Xtr_out = Xtr_scl.reshape(Ntr, T, D).astype(np.float32)\n",
    "    Xva_out = Xva_scl.reshape(Nva, T, D).astype(np.float32)\n",
    "    return Xtr_out, Xva_out, imputer, scaler\n",
    "\n",
    "def compute_alpha_from_y(y_train):\n",
    "    \"\"\"\n",
    "    建議 alpha = neg_ratio（正類權重），可讓正類被放大到與負類相對應\n",
    "    alpha = (#neg) / (#pos + #neg)\n",
    "    \"\"\"\n",
    "    y = y_train.astype(int).ravel()\n",
    "    pos = int((y == 1).sum())\n",
    "    neg = int((y == 0).sum())\n",
    "    total = pos + neg\n",
    "    if total == 0:\n",
    "        return 0.25\n",
    "    return neg / total\n",
    "\n",
    "def run_fold(k):\n",
    "    # 路徑\n",
    "    Xtr_path = WD / f\"X_train_fold{k}.npy\"\n",
    "    ytr_path = WD / f\"y_train_fold{k}.npy\"\n",
    "    Xva_path = WD / f\"X_val_fold{k}.npy\"\n",
    "    yva_path = WD / f\"y_val_fold{k}.npy\"\n",
    "\n",
    "    if not (Xtr_path.exists() and ytr_path.exists() and Xva_path.exists() and yva_path.exists()):\n",
    "        print(f\"[Fold {k}] 檔案缺失，跳過\")\n",
    "        return None\n",
    "\n",
    "    # 載入\n",
    "    Xtr = np.load(Xtr_path); ytr = np.load(ytr_path).astype(np.int32)\n",
    "    Xva = np.load(Xva_path); yva = np.load(yva_path).astype(np.int32)\n",
    "\n",
    "    # 前處理：每折以 train 擬合，再套到 val\n",
    "    Xtr_p, Xva_p, imputer, scaler = fit_imputer_scaler(Xtr, Xva)\n",
    "\n",
    "    # Focal Loss 參數\n",
    "    alpha = compute_alpha_from_y(ytr)   # 正類權重 = 負類比例\n",
    "    loss_fn = binary_focal_loss(alpha=alpha, gamma=GAMMA)\n",
    "\n",
    "    # 模型\n",
    "    model = build_model(input_shape=Xtr_p.shape[1:])\n",
    "    model.compile(\n",
    "        optimizer=tf.keras.optimizers.Adam(learning_rate=LEARN),\n",
    "        loss=loss_fn,\n",
    "        metrics=[\n",
    "            kmetrics.AUC(name=\"AUC\"),\n",
    "            kmetrics.AUC(name=\"AUC_PR\", curve=\"PR\"),\n",
    "            kmetrics.Precision(name=\"Precision\"),\n",
    "            kmetrics.Recall(name=\"Recall\"),\n",
    "            kmetrics.BinaryAccuracy(name=\"Accuracy\")\n",
    "        ]\n",
    "    )\n",
    "\n",
    "    # Callbacks\n",
    "    fold_dir = OUT / f\"run_fold_{k:02d}\"\n",
    "    fold_dir.mkdir(parents=True, exist_ok=True)\n",
    "    ckpt = callbacks.ModelCheckpoint(\n",
    "        filepath=str(fold_dir / \"best.ckpt\"),\n",
    "        monitor=\"val_AUC_PR\", mode=\"max\", save_best_only=True, save_weights_only=True)\n",
    "    es = callbacks.EarlyStopping(monitor=\"val_AUC_PR\", mode=\"max\", patience=6, restore_best_weights=True)\n",
    "    rlrop = callbacks.ReduceLROnPlateau(monitor=\"val_AUC_PR\", mode=\"max\", patience=3, factor=0.5, min_lr=1e-5)\n",
    "\n",
    "    # 訓練\n",
    "    hist = model.fit(\n",
    "        Xtr_p, ytr,\n",
    "        validation_data=(Xva_p, yva),\n",
    "        epochs=EPOCHS,\n",
    "        batch_size=BATCH,\n",
    "        callbacks=[ckpt, es, rlrop],\n",
    "        verbose=2\n",
    "    )\n",
    "\n",
    "    # 最終評估\n",
    "    metrics_eval = model.evaluate(Xva_p, yva, verbose=0)\n",
    "    names = [\"loss\",\"AUC\",\"AUC_PR\",\"Precision\",\"Recall\",\"Accuracy\"]\n",
    "    fold_metrics = dict(zip(names, [float(x) for x in metrics_eval]))\n",
    "    fold_metrics.update({\n",
    "        \"fold\": k,\n",
    "        \"alpha\": float(alpha),\n",
    "        \"gamma\": float(GAMMA),\n",
    "        \"n_train\": int(ytr.shape[0]),\n",
    "        \"n_val\": int(yva.shape[0]),\n",
    "        \"pos_train\": int((ytr==1).sum()),\n",
    "        \"neg_train\": int((ytr==0).sum()),\n",
    "        \"pos_val\": int((yva==1).sum()),\n",
    "        \"neg_val\": int((yva==0).sum()),\n",
    "        \"imputer\": \"median\",\n",
    "        \"scaler\": \"standard\",\n",
    "    })\n",
    "\n",
    "    # 輸出本折結果\n",
    "    (fold_dir / \"metrics.json\").write_text(json.dumps(fold_metrics, ensure_ascii=False, indent=2), encoding=\"utf-8\")\n",
    "    pd.DataFrame(hist.history).to_csv(fold_dir / \"history.csv\", index=False, encoding=\"utf-8\")\n",
    "\n",
    "    return fold_metrics\n",
    "\n",
    "def main():\n",
    "    ts = time.strftime(\"%Y-%m-%d %H:%M:%S\")\n",
    "    print(f\"[{ts}] === 十折｜Focal Loss 訓練開始 ===\")\n",
    "    all_rows = []\n",
    "    for k in range(1, 11):\n",
    "        m = run_fold(k)\n",
    "        if m is not None:\n",
    "            all_rows.append(m)\n",
    "\n",
    "    if not all_rows:\n",
    "        print(\"沒有任何折被處理。\")\n",
    "        return\n",
    "\n",
    "    df = pd.DataFrame(all_rows).sort_values(\"fold\")\n",
    "    out_csv = REPO / \"focal_cv_metrics.csv\"\n",
    "    df.to_csv(out_csv, index=False, encoding=\"utf-8\")\n",
    "\n",
    "    # 平均與標準差摘要\n",
    "    agg = df[[\"AUC\",\"AUC_PR\",\"Precision\",\"Recall\",\"Accuracy\",\"loss\"]].agg([\"mean\",\"std\"]).round(4)\n",
    "    print(\"\\n=== 十折彙總（Focal Loss）===\")\n",
    "    print(agg)\n",
    "    (REPO / \"focal_cv_summary.json\").write_text(agg.to_json(), encoding=\"utf-8\")\n",
    "\n",
    "    print(f\"\\n已輸出每折結果到：{OUT}\")\n",
    "    print(f\"已輸出彙總到：{out_csv}\")\n",
    "\n",
    "if __name__ == \"__main__\":\n",
    "    main()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2d49fa72-724d-492c-8dc2-764f008b9b00",
   "metadata": {},
   "outputs": [],
   "source": [
    "training_focal/run_fold_XX/best.ckpt：每折最佳權重\n",
    "training_focal/run_fold_XX/history.csv：學習曲線\n",
    "reports/focal_cv_metrics.csv：每折指標\n",
    "reports/focal_cv_summary.json：均值和標準差"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "167b1955-ce23-4c2c-899c-2804a5c451da",
   "metadata": {},
   "outputs": [],
   "source": [
    "/home/jovyan/RT08/0925/\n",
    "├─ bling_1014_clean/     ← 原始清理後逐筆資料（基礎層，不會被改動）\n",
    "├─ sliding_win/1014_sim/\n",
    "│   ├─ windowed/          ← 開窗後的初版（含 label=2）\n",
    "│   ├─ windowed_clean/    ← 剔除 label=2 後生成的新副本\n",
    "│   └─ training_focal/    ← 訓練過程輸出（模型、指標等）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0062e737-a0e8-47bc-9cf5-e94360f618f4",
   "metadata": {},
   "outputs": [],
   "source": [
    "training_focal/run_fold_XX/best.ckpt：每折最佳權重\n",
    "training_focal/run_fold_XX/history.csv：學習曲線\n",
    "reports/focal_cv_metrics.csv：每折指標\n",
    "reports/focal_cv_summary.json：均值和標準差\n",
    "是否可以用圖來表示 圖上要英文 以藍色為主 最好的結果用紅色\n",
    "跑focus loss盡量跑出圖 來表示"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d7073de0-29b9-453f-84ad-6018dc1b79ff",
   "metadata": {},
   "outputs": [],
   "source": [
    "我想針對 windowed_clean進行Focal Loss，跑LSTM，能跑出圖就直接印在城市中 圖上要寫英文 藍色漸層為主，有csv也要存起來，，可能會跑很多次LSTM所以版本要撰寫清楚 程式碼中的中文要註釋清楚"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8d192408-6101-47dc-ae8c-ad7314f67254",
   "metadata": {},
   "outputs": [],
   "source": [
    "評估指標我要包括準確率 recall f1score 精確綠 AUROC ，這些指標要訓練以及測試集都要，並且十折交叉驗證每一折的這五個評估指標數值都要，最後列出十折平均 跟標準差的數值，另外，在訓練集和測試集上都要有混淆矩陣（字大一點 藍色）以及ROC曲線圖以及Loss（Binary Cross-Entropy / Focal Loss）\t衡量模型預測分佈與真實分佈的距離。\n",
    "Validation Loss\t驗證集的損失，用於監控過擬合。\n",
    "Accuracy / F1 per Epoch\t每個 epoch 的分類表現變化，觀察收斂趨勢。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "920a5b6b-0ad4-46e4-b0e1-9520741c0a10",
   "metadata": {},
   "outputs": [],
   "source": [
    "| 參數名稱                | 說明                      | 預設值    |\n",
    "| ------------------- | ----------------------- | ------ |\n",
    "| `seed`              | 隨機種子（確保重現性）             | 42     |\n",
    "| `epochs`            | 訓練輪數                    | 40     |\n",
    "| `batch_size`        | 每批次樣本數                  | 128    |\n",
    "| `learning_rate`     | 學習率                     | 1e-3   |\n",
    "| `focal_gamma`       | Focal Loss γ（控制難分類樣本權重） | 2.0    |\n",
    "| `focal_alpha`       | Focal Loss α（控制少數類權重）   | 0.25   |\n",
    "| `patience`          | EarlyStopping 耐心值       | 5      |\n",
    "| `hidden_units`      | LSTM 隱藏層單元數             | 128    |\n",
    "| `l2`                | L2 正則化係數                | 1e-6   |\n",
    "| `dropout`           | dropout 比例              | 0.2    |\n",
    "| `recurrent_dropout` | recurrent_dropout 比例    | 0.0    |\n",
    "| `folds`             | 要跑的交叉驗證折數               | [1–10] |\n",
    "\n",
    "🔹 Optimizer：Adam（learning_rate=1e-3）\n",
    "🔹 Loss function：Focal Loss（自訂版 binary focal loss）\n",
    "🔹 監控指標：val_auprc（提早停止與儲存最佳模型依據）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5c7ae1bc-9599-4c53-a54f-a2f422ee4184",
   "metadata": {},
   "outputs": [],
   "source": [
    "終於開始跑phase1的LSTM了！！"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 266,
   "id": "2ab992ac-e172-40ca-9dbf-51b0bca9e2b7",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/40\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2025-10-15 02:14:11.240548: E tensorflow/core/util/util.cc:131] oneDNN supports DT_BOOL only on platforms with AVX-512. Falling back to the default Eigen-based implementation if present.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "197/197 - 31s - 155ms/step - accuracy: 0.7329 - auc: 0.5056 - auprc: 0.2688 - loss: 0.0597 - precision: 0.3068 - recall: 0.0081 - val_accuracy: 0.7357 - val_auc: 0.5177 - val_auprc: 0.2782 - val_loss: 0.0570 - val_precision: 0.0000e+00 - val_recall: 0.0000e+00\n",
      "Epoch 2/40\n",
      "197/197 - 27s - 139ms/step - accuracy: 0.7356 - auc: 0.5082 - auprc: 0.2725 - loss: 0.0575 - precision: 0.0000e+00 - recall: 0.0000e+00 - val_accuracy: 0.7357 - val_auc: 0.5251 - val_auprc: 0.2894 - val_loss: 0.0568 - val_precision: 0.0000e+00 - val_recall: 0.0000e+00\n",
      "Epoch 3/40\n",
      "197/197 - 27s - 136ms/step - accuracy: 0.7356 - auc: 0.5166 - auprc: 0.2763 - loss: 0.0571 - precision: 0.0000e+00 - recall: 0.0000e+00 - val_accuracy: 0.7357 - val_auc: 0.5173 - val_auprc: 0.2803 - val_loss: 0.0570 - val_precision: 0.0000e+00 - val_recall: 0.0000e+00\n",
      "Epoch 4/40\n",
      "197/197 - 27s - 135ms/step - accuracy: 0.7357 - auc: 0.5084 - auprc: 0.2720 - loss: 0.0571 - precision: 1.0000 - recall: 1.5072e-04 - val_accuracy: 0.7357 - val_auc: 0.5215 - val_auprc: 0.2856 - val_loss: 0.0568 - val_precision: 0.0000e+00 - val_recall: 0.0000e+00\n",
      "Epoch 5/40\n",
      "197/197 - 27s - 136ms/step - accuracy: 0.7356 - auc: 0.5152 - auprc: 0.2750 - loss: 0.0569 - precision: 0.0000e+00 - recall: 0.0000e+00 - val_accuracy: 0.7357 - val_auc: 0.5254 - val_auprc: 0.2887 - val_loss: 0.0567 - val_precision: 0.0000e+00 - val_recall: 0.0000e+00\n",
      "Epoch 6/40\n",
      "197/197 - 27s - 136ms/step - accuracy: 0.7356 - auc: 0.5148 - auprc: 0.2762 - loss: 0.0569 - precision: 0.0000e+00 - recall: 0.0000e+00 - val_accuracy: 0.7357 - val_auc: 0.5298 - val_auprc: 0.2899 - val_loss: 0.0567 - val_precision: 0.0000e+00 - val_recall: 0.0000e+00\n",
      "Epoch 7/40\n",
      "197/197 - 27s - 136ms/step - accuracy: 0.7356 - auc: 0.5139 - auprc: 0.2735 - loss: 0.0569 - precision: 0.0000e+00 - recall: 0.0000e+00 - val_accuracy: 0.7357 - val_auc: 0.5223 - val_auprc: 0.2879 - val_loss: 0.0567 - val_precision: 0.0000e+00 - val_recall: 0.0000e+00\n",
      "Epoch 8/40\n",
      "197/197 - 27s - 137ms/step - accuracy: 0.7356 - auc: 0.5152 - auprc: 0.2729 - loss: 0.0568 - precision: 0.0000e+00 - recall: 0.0000e+00 - val_accuracy: 0.7357 - val_auc: 0.5332 - val_auprc: 0.2949 - val_loss: 0.0567 - val_precision: 0.0000e+00 - val_recall: 0.0000e+00\n",
      "Epoch 9/40\n",
      "197/197 - 27s - 135ms/step - accuracy: 0.7356 - auc: 0.5125 - auprc: 0.2754 - loss: 0.0568 - precision: 0.0000e+00 - recall: 0.0000e+00 - val_accuracy: 0.7357 - val_auc: 0.5268 - val_auprc: 0.2894 - val_loss: 0.0567 - val_precision: 0.0000e+00 - val_recall: 0.0000e+00\n",
      "Epoch 10/40\n",
      "197/197 - 27s - 137ms/step - accuracy: 0.7356 - auc: 0.5182 - auprc: 0.2776 - loss: 0.0568 - precision: 0.0000e+00 - recall: 0.0000e+00 - val_accuracy: 0.7357 - val_auc: 0.5349 - val_auprc: 0.2919 - val_loss: 0.0566 - val_precision: 0.0000e+00 - val_recall: 0.0000e+00\n",
      "Epoch 11/40\n",
      "197/197 - 27s - 135ms/step - accuracy: 0.7355 - auc: 0.5166 - auprc: 0.2744 - loss: 0.0570 - precision: 0.0000e+00 - recall: 0.0000e+00 - val_accuracy: 0.7357 - val_auc: 0.5233 - val_auprc: 0.2855 - val_loss: 0.0567 - val_precision: 0.0000e+00 - val_recall: 0.0000e+00\n",
      "Epoch 12/40\n",
      "197/197 - 26s - 134ms/step - accuracy: 0.7356 - auc: 0.5168 - auprc: 0.2780 - loss: 0.0568 - precision: 0.0000e+00 - recall: 0.0000e+00 - val_accuracy: 0.7357 - val_auc: 0.5286 - val_auprc: 0.2894 - val_loss: 0.0567 - val_precision: 0.0000e+00 - val_recall: 0.0000e+00\n",
      "Epoch 13/40\n",
      "197/197 - 27s - 137ms/step - accuracy: 0.7357 - auc: 0.5193 - auprc: 0.2758 - loss: 0.0567 - precision: 1.0000 - recall: 1.5072e-04 - val_accuracy: 0.7357 - val_auc: 0.5269 - val_auprc: 0.2932 - val_loss: 0.0566 - val_precision: 0.0000e+00 - val_recall: 0.0000e+00\n",
      "Epoch 1/40\n",
      "197/197 - 30s - 153ms/step - accuracy: 0.7321 - auc: 0.5098 - auprc: 0.2697 - loss: 0.0596 - precision: 0.2528 - recall: 0.0068 - val_accuracy: 0.7357 - val_auc: 0.5182 - val_auprc: 0.2749 - val_loss: 0.0579 - val_precision: 0.0000e+00 - val_recall: 0.0000e+00\n",
      "Epoch 2/40\n",
      "197/197 - 26s - 133ms/step - accuracy: 0.7356 - auc: 0.5148 - auprc: 0.2744 - loss: 0.0574 - precision: 0.0000e+00 - recall: 0.0000e+00 - val_accuracy: 0.7357 - val_auc: 0.5083 - val_auprc: 0.2718 - val_loss: 0.0572 - val_precision: 0.0000e+00 - val_recall: 0.0000e+00\n",
      "Epoch 3/40\n",
      "197/197 - 27s - 135ms/step - accuracy: 0.7356 - auc: 0.5187 - auprc: 0.2768 - loss: 0.0571 - precision: 0.0000e+00 - recall: 0.0000e+00 - val_accuracy: 0.7357 - val_auc: 0.5016 - val_auprc: 0.2663 - val_loss: 0.0571 - val_precision: 0.0000e+00 - val_recall: 0.0000e+00\n",
      "Epoch 4/40\n",
      "197/197 - 27s - 136ms/step - accuracy: 0.7356 - auc: 0.5148 - auprc: 0.2745 - loss: 0.0570 - precision: 0.0000e+00 - recall: 0.0000e+00 - val_accuracy: 0.7357 - val_auc: 0.5055 - val_auprc: 0.2652 - val_loss: 0.0570 - val_precision: 0.0000e+00 - val_recall: 0.0000e+00\n",
      "Epoch 5/40\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[266], line 291\u001b[0m\n\u001b[1;32m    285\u001b[0m cbs \u001b[38;5;241m=\u001b[39m [\n\u001b[1;32m    286\u001b[0m     keras\u001b[38;5;241m.\u001b[39mcallbacks\u001b[38;5;241m.\u001b[39mEarlyStopping(monitor\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mval_auprc\u001b[39m\u001b[38;5;124m\"\u001b[39m, patience\u001b[38;5;241m=\u001b[39mCFG[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mpatience\u001b[39m\u001b[38;5;124m\"\u001b[39m], mode\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmax\u001b[39m\u001b[38;5;124m\"\u001b[39m, restore_best_weights\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m),\n\u001b[1;32m    287\u001b[0m     keras\u001b[38;5;241m.\u001b[39mcallbacks\u001b[38;5;241m.\u001b[39mModelCheckpoint(filepath\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mstr\u001b[39m(ckpt_path), monitor\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mval_auprc\u001b[39m\u001b[38;5;124m\"\u001b[39m, mode\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmax\u001b[39m\u001b[38;5;124m\"\u001b[39m, save_best_only\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n\u001b[1;32m    288\u001b[0m ]\n\u001b[1;32m    290\u001b[0m \u001b[38;5;66;03m# 訓練\u001b[39;00m\n\u001b[0;32m--> 291\u001b[0m hist \u001b[38;5;241m=\u001b[39m \u001b[43mmodel\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfit\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    292\u001b[0m \u001b[43m    \u001b[49m\u001b[43mXtr_p\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mytr\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    293\u001b[0m \u001b[43m    \u001b[49m\u001b[43mvalidation_data\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mXva_p\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43myva\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    294\u001b[0m \u001b[43m    \u001b[49m\u001b[43mepochs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mCFG\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mepochs\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    295\u001b[0m \u001b[43m    \u001b[49m\u001b[43mbatch_size\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mCFG\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mbatch_size\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    296\u001b[0m \u001b[43m    \u001b[49m\u001b[43mcallbacks\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcbs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    297\u001b[0m \u001b[43m    \u001b[49m\u001b[43mverbose\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m2\u001b[39;49m\n\u001b[1;32m    298\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    300\u001b[0m \u001b[38;5;66;03m# 保存 history.csv 並繪圖\u001b[39;00m\n\u001b[1;32m    301\u001b[0m hist_df \u001b[38;5;241m=\u001b[39m pd\u001b[38;5;241m.\u001b[39mDataFrame(hist\u001b[38;5;241m.\u001b[39mhistory)\n",
      "File \u001b[0;32m/opt/conda/lib/python3.11/site-packages/keras/src/utils/traceback_utils.py:117\u001b[0m, in \u001b[0;36mfilter_traceback.<locals>.error_handler\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    115\u001b[0m filtered_tb \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m    116\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 117\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfn\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    118\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[1;32m    119\u001b[0m     filtered_tb \u001b[38;5;241m=\u001b[39m _process_traceback_frames(e\u001b[38;5;241m.\u001b[39m__traceback__)\n",
      "File \u001b[0;32m/opt/conda/lib/python3.11/site-packages/keras/src/backend/tensorflow/trainer.py:318\u001b[0m, in \u001b[0;36mTensorFlowTrainer.fit\u001b[0;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_batch_size, validation_freq)\u001b[0m\n\u001b[1;32m    316\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m step, iterator \u001b[38;5;129;01min\u001b[39;00m epoch_iterator\u001b[38;5;241m.\u001b[39menumerate_epoch():\n\u001b[1;32m    317\u001b[0m     callbacks\u001b[38;5;241m.\u001b[39mon_train_batch_begin(step)\n\u001b[0;32m--> 318\u001b[0m     logs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtrain_function\u001b[49m\u001b[43m(\u001b[49m\u001b[43miterator\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    319\u001b[0m     logs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_pythonify_logs(logs)\n\u001b[1;32m    320\u001b[0m     callbacks\u001b[38;5;241m.\u001b[39mon_train_batch_end(step, logs)\n",
      "File \u001b[0;32m/opt/conda/lib/python3.11/site-packages/tensorflow/python/util/traceback_utils.py:150\u001b[0m, in \u001b[0;36mfilter_traceback.<locals>.error_handler\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    148\u001b[0m filtered_tb \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m    149\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 150\u001b[0m   \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfn\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    151\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[1;32m    152\u001b[0m   filtered_tb \u001b[38;5;241m=\u001b[39m _process_traceback_frames(e\u001b[38;5;241m.\u001b[39m__traceback__)\n",
      "File \u001b[0;32m/opt/conda/lib/python3.11/site-packages/tensorflow/python/eager/polymorphic_function/polymorphic_function.py:833\u001b[0m, in \u001b[0;36mFunction.__call__\u001b[0;34m(self, *args, **kwds)\u001b[0m\n\u001b[1;32m    830\u001b[0m compiler \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mxla\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_jit_compile \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mnonXla\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    832\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m OptionalXlaContext(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_jit_compile):\n\u001b[0;32m--> 833\u001b[0m   result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwds\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    835\u001b[0m new_tracing_count \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mexperimental_get_tracing_count()\n\u001b[1;32m    836\u001b[0m without_tracing \u001b[38;5;241m=\u001b[39m (tracing_count \u001b[38;5;241m==\u001b[39m new_tracing_count)\n",
      "File \u001b[0;32m/opt/conda/lib/python3.11/site-packages/tensorflow/python/eager/polymorphic_function/polymorphic_function.py:878\u001b[0m, in \u001b[0;36mFunction._call\u001b[0;34m(self, *args, **kwds)\u001b[0m\n\u001b[1;32m    875\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_lock\u001b[38;5;241m.\u001b[39mrelease()\n\u001b[1;32m    876\u001b[0m \u001b[38;5;66;03m# In this case we have not created variables on the first call. So we can\u001b[39;00m\n\u001b[1;32m    877\u001b[0m \u001b[38;5;66;03m# run the first trace but we should fail if variables are created.\u001b[39;00m\n\u001b[0;32m--> 878\u001b[0m results \u001b[38;5;241m=\u001b[39m \u001b[43mtracing_compilation\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcall_function\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    879\u001b[0m \u001b[43m    \u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkwds\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_variable_creation_config\u001b[49m\n\u001b[1;32m    880\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    881\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_created_variables:\n\u001b[1;32m    882\u001b[0m   \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCreating variables on a non-first call to a function\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    883\u001b[0m                    \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m decorated with tf.function.\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
      "File \u001b[0;32m/opt/conda/lib/python3.11/site-packages/tensorflow/python/eager/polymorphic_function/tracing_compilation.py:139\u001b[0m, in \u001b[0;36mcall_function\u001b[0;34m(args, kwargs, tracing_options)\u001b[0m\n\u001b[1;32m    137\u001b[0m bound_args \u001b[38;5;241m=\u001b[39m function\u001b[38;5;241m.\u001b[39mfunction_type\u001b[38;5;241m.\u001b[39mbind(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[1;32m    138\u001b[0m flat_inputs \u001b[38;5;241m=\u001b[39m function\u001b[38;5;241m.\u001b[39mfunction_type\u001b[38;5;241m.\u001b[39munpack_inputs(bound_args)\n\u001b[0;32m--> 139\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunction\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_flat\u001b[49m\u001b[43m(\u001b[49m\u001b[43m  \u001b[49m\u001b[38;5;66;43;03m# pylint: disable=protected-access\u001b[39;49;00m\n\u001b[1;32m    140\u001b[0m \u001b[43m    \u001b[49m\u001b[43mflat_inputs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcaptured_inputs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfunction\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcaptured_inputs\u001b[49m\n\u001b[1;32m    141\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m/opt/conda/lib/python3.11/site-packages/tensorflow/python/eager/polymorphic_function/concrete_function.py:1322\u001b[0m, in \u001b[0;36mConcreteFunction._call_flat\u001b[0;34m(self, tensor_inputs, captured_inputs)\u001b[0m\n\u001b[1;32m   1318\u001b[0m possible_gradient_type \u001b[38;5;241m=\u001b[39m gradients_util\u001b[38;5;241m.\u001b[39mPossibleTapeGradientTypes(args)\n\u001b[1;32m   1319\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m (possible_gradient_type \u001b[38;5;241m==\u001b[39m gradients_util\u001b[38;5;241m.\u001b[39mPOSSIBLE_GRADIENT_TYPES_NONE\n\u001b[1;32m   1320\u001b[0m     \u001b[38;5;129;01mand\u001b[39;00m executing_eagerly):\n\u001b[1;32m   1321\u001b[0m   \u001b[38;5;66;03m# No tape is watching; skip to running the function.\u001b[39;00m\n\u001b[0;32m-> 1322\u001b[0m   \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_inference_function\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcall_preflattened\u001b[49m\u001b[43m(\u001b[49m\u001b[43margs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1323\u001b[0m forward_backward \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_select_forward_and_backward_functions(\n\u001b[1;32m   1324\u001b[0m     args,\n\u001b[1;32m   1325\u001b[0m     possible_gradient_type,\n\u001b[1;32m   1326\u001b[0m     executing_eagerly)\n\u001b[1;32m   1327\u001b[0m forward_function, args_with_tangents \u001b[38;5;241m=\u001b[39m forward_backward\u001b[38;5;241m.\u001b[39mforward()\n",
      "File \u001b[0;32m/opt/conda/lib/python3.11/site-packages/tensorflow/python/eager/polymorphic_function/atomic_function.py:216\u001b[0m, in \u001b[0;36mAtomicFunction.call_preflattened\u001b[0;34m(self, args)\u001b[0m\n\u001b[1;32m    214\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mcall_preflattened\u001b[39m(\u001b[38;5;28mself\u001b[39m, args: Sequence[core\u001b[38;5;241m.\u001b[39mTensor]) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Any:\n\u001b[1;32m    215\u001b[0m \u001b[38;5;250m  \u001b[39m\u001b[38;5;124;03m\"\"\"Calls with flattened tensor inputs and returns the structured output.\"\"\"\u001b[39;00m\n\u001b[0;32m--> 216\u001b[0m   flat_outputs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcall_flat\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    217\u001b[0m   \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfunction_type\u001b[38;5;241m.\u001b[39mpack_output(flat_outputs)\n",
      "File \u001b[0;32m/opt/conda/lib/python3.11/site-packages/tensorflow/python/eager/polymorphic_function/atomic_function.py:251\u001b[0m, in \u001b[0;36mAtomicFunction.call_flat\u001b[0;34m(self, *args)\u001b[0m\n\u001b[1;32m    249\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m record\u001b[38;5;241m.\u001b[39mstop_recording():\n\u001b[1;32m    250\u001b[0m   \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_bound_context\u001b[38;5;241m.\u001b[39mexecuting_eagerly():\n\u001b[0;32m--> 251\u001b[0m     outputs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_bound_context\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcall_function\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    252\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mname\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    253\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;28;43mlist\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43margs\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    254\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;28;43mlen\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfunction_type\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mflat_outputs\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    255\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    256\u001b[0m   \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    257\u001b[0m     outputs \u001b[38;5;241m=\u001b[39m make_call_op_in_graph(\n\u001b[1;32m    258\u001b[0m         \u001b[38;5;28mself\u001b[39m,\n\u001b[1;32m    259\u001b[0m         \u001b[38;5;28mlist\u001b[39m(args),\n\u001b[1;32m    260\u001b[0m         \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_bound_context\u001b[38;5;241m.\u001b[39mfunction_call_options\u001b[38;5;241m.\u001b[39mas_attrs(),\n\u001b[1;32m    261\u001b[0m     )\n",
      "File \u001b[0;32m/opt/conda/lib/python3.11/site-packages/tensorflow/python/eager/context.py:1552\u001b[0m, in \u001b[0;36mContext.call_function\u001b[0;34m(self, name, tensor_inputs, num_outputs)\u001b[0m\n\u001b[1;32m   1550\u001b[0m cancellation_context \u001b[38;5;241m=\u001b[39m cancellation\u001b[38;5;241m.\u001b[39mcontext()\n\u001b[1;32m   1551\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m cancellation_context \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m-> 1552\u001b[0m   outputs \u001b[38;5;241m=\u001b[39m \u001b[43mexecute\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mexecute\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   1553\u001b[0m \u001b[43m      \u001b[49m\u001b[43mname\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdecode\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mutf-8\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1554\u001b[0m \u001b[43m      \u001b[49m\u001b[43mnum_outputs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mnum_outputs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1555\u001b[0m \u001b[43m      \u001b[49m\u001b[43minputs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtensor_inputs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1556\u001b[0m \u001b[43m      \u001b[49m\u001b[43mattrs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mattrs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1557\u001b[0m \u001b[43m      \u001b[49m\u001b[43mctx\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1558\u001b[0m \u001b[43m  \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1559\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m   1560\u001b[0m   outputs \u001b[38;5;241m=\u001b[39m execute\u001b[38;5;241m.\u001b[39mexecute_with_cancellation(\n\u001b[1;32m   1561\u001b[0m       name\u001b[38;5;241m.\u001b[39mdecode(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mutf-8\u001b[39m\u001b[38;5;124m\"\u001b[39m),\n\u001b[1;32m   1562\u001b[0m       num_outputs\u001b[38;5;241m=\u001b[39mnum_outputs,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m   1566\u001b[0m       cancellation_manager\u001b[38;5;241m=\u001b[39mcancellation_context,\n\u001b[1;32m   1567\u001b[0m   )\n",
      "File \u001b[0;32m/opt/conda/lib/python3.11/site-packages/tensorflow/python/eager/execute.py:53\u001b[0m, in \u001b[0;36mquick_execute\u001b[0;34m(op_name, num_outputs, inputs, attrs, ctx, name)\u001b[0m\n\u001b[1;32m     51\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m     52\u001b[0m   ctx\u001b[38;5;241m.\u001b[39mensure_initialized()\n\u001b[0;32m---> 53\u001b[0m   tensors \u001b[38;5;241m=\u001b[39m \u001b[43mpywrap_tfe\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mTFE_Py_Execute\u001b[49m\u001b[43m(\u001b[49m\u001b[43mctx\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_handle\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdevice_name\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mop_name\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m     54\u001b[0m \u001b[43m                                      \u001b[49m\u001b[43minputs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mattrs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnum_outputs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     55\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m core\u001b[38;5;241m.\u001b[39m_NotOkStatusException \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[1;32m     56\u001b[0m   \u001b[38;5;28;01mif\u001b[39;00m name \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "# -*- coding: utf-8 -*-\n",
    "\"\"\"\n",
    "train_lstm_focal_windowed_clean_v2.py\n",
    "\n",
    "目的：\n",
    "  使用 Keras + LSTM + Focal Loss，針對 windowed_clean/ 的十折資料進行訓練與評估。\n",
    "  - 支援每折獨立的補值與標準化（避免資料洩漏）\n",
    "  - 產生英文圖表（藍色漸層為主）並儲存 CSV 結果\n",
    "  - 多次實驗版本清楚：以時間戳與自動遞增 run_id 建立輸出資料夾\n",
    "\n",
    "資料假設：\n",
    "  /home/jovyan/RT08/0925/sliding_win/1014_sim/windowed_clean/\n",
    "    X_train_fold{k}.npy, y_train_fold{k}.npy\n",
    "    X_val_fold{k}.npy,   y_val_fold{k}.npy\n",
    "\n",
    "輸出：\n",
    "  /home/jovyan/RT08/0925/sliding_win/1014_sim/training_runs/<timestamp>_runXX/\n",
    "    ├─ cfg.json\n",
    "    ├─ fold_{k}/\n",
    "    │    ├─ history.csv\n",
    "    │    ├─ metrics.csv\n",
    "    │    ├─ predictions.csv\n",
    "    │    ├─ roc_curve.csv\n",
    "    │    ├─ pr_curve.csv\n",
    "    │    ├─ cm.csv\n",
    "    │    ├─ best_model.keras\n",
    "    │    ├─ plot_history.png\n",
    "    │    ├─ plot_roc.png\n",
    "    │    ├─ plot_pr.png\n",
    "    │    └─ plot_cm.png\n",
    "    ├─ summary_folds.csv            （各折彙總指標）\n",
    "    └─ summary_overall.json         （平均與設定）\n",
    "\n",
    "執行方式：\n",
    "  python train_lstm_focal_windowed_clean.py\n",
    "\n",
    "注意：\n",
    "  - 程式僅示範 LSTM 二元分類；資料需為 0/1 標籤且 shape 為 (N, 60, D)\n",
    "  - 若仍存在 NaN，會以 SimpleImputer(strategy=\"median\") 補值\n",
    "  - 可依需求調整 LSTM 結構、學習率與 focal 參數\n",
    "\"\"\"\n",
    "\n",
    "import os\n",
    "import json\n",
    "import time\n",
    "import math\n",
    "import glob\n",
    "import shutil\n",
    "from pathlib import Path\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from sklearn.impute import SimpleImputer\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.metrics import (\n",
    "    roc_curve, auc, precision_recall_curve, average_precision_score,\n",
    "    confusion_matrix, classification_report\n",
    ")\n",
    "\n",
    "import tensorflow as tf\n",
    "from tensorflow import keras\n",
    "from tensorflow.keras import layers\n",
    "\n",
    "# ==============================\n",
    "# 一、基本設定（可視需求調整）\n",
    "# ==============================\n",
    "BASE = Path(\"/home/jovyan/RT08/0925/sliding_win/1014_sim\")\n",
    "DATA = BASE / \"windowed_clean\"\n",
    "RUNS_ROOT = BASE / \"training_runs\"\n",
    "RUNS_ROOT.mkdir(parents=True, exist_ok=True)\n",
    "\n",
    "CFG = {\n",
    "    \"seed\": 42,                       # 全域亂數種子（可重現）\n",
    "    \"epochs\": 40,                     # 訓練 epochs\n",
    "    \"batch_size\": 128,                # 批次大小\n",
    "    \"learning_rate\": 1e-3,            # 學習率\n",
    "    \"focal_gamma\": 2.0,               # Focal Loss gamma\n",
    "    \"focal_alpha\": 0.25,              # Focal Loss alpha（少數類權重）\n",
    "    \"patience\": 5,                    # EarlyStopping 耐心值\n",
    "    \"hidden_units\": 128,              # LSTM 隱藏單元數\n",
    "    \"l2\": 1e-6,                       # L2 正則化\n",
    "    \"dropout\": 0.2,                   # dropout 比例\n",
    "    \"recurrent_dropout\": 0.0,         # recurrent_dropout（可視需要開啟）\n",
    "    \"folds\": list(range(1, 11)),      # 要跑的折數\n",
    "}\n",
    "\n",
    "# 設定隨機種子（TensorFlow + NumPy）\n",
    "np.random.seed(CFG[\"seed\"])\n",
    "tf.random.set_seed(CFG[\"seed\"])\n",
    "\n",
    "# 產生 run 目錄：timestamp + 自動 run_id（避免覆寫）\n",
    "ts = time.strftime(\"%Y%m%d_%H%M%S\")\n",
    "existing = sorted(RUNS_ROOT.glob(f\"{ts}_run*\"))\n",
    "run_id = len(existing) + 1\n",
    "RUN_DIR = RUNS_ROOT / f\"{ts}_run{run_id:02d}\"\n",
    "RUN_DIR.mkdir(parents=True, exist_ok=True)\n",
    "\n",
    "# 儲存設定\n",
    "(RUN_DIR / \"cfg.json\").write_text(json.dumps(CFG, ensure_ascii=False, indent=2), encoding=\"utf-8\")\n",
    "\n",
    "# ==============================\n",
    "# 二、工具：Focal Loss（二元）\n",
    "# ==============================\n",
    "# 中文說明：\n",
    "#   - 適合類別不平衡的任務；\n",
    "#   - alpha 強化少數類（建議 0.25~0.75）；\n",
    "#   - gamma 抑制易分類樣本（常用 2.0）。\n",
    "\n",
    "def binary_focal_loss(gamma=2.0, alpha=0.25):\n",
    "    def loss(y_true, y_pred):\n",
    "        # y_true: (N, 1) 或 (N,)\n",
    "        # y_pred: (N, 1)，為 sigmoid 機率\n",
    "        y_true_f = tf.cast(y_true, tf.float32)\n",
    "        y_pred_f = tf.clip_by_value(y_pred, 1e-7, 1.0 - 1e-7)\n",
    "        # p_t = p   if y=1\n",
    "        #       1-p if y=0\n",
    "        p_t = y_true_f * y_pred_f + (1 - y_true_f) * (1 - y_pred_f)\n",
    "        alpha_factor = y_true_f * alpha + (1 - y_true_f) * (1 - alpha)\n",
    "        modulating = tf.pow(1.0 - p_t, gamma)\n",
    "        # BCE = -[ y*log(p) + (1-y)*log(1-p) ]\n",
    "        bce = - (y_true_f * tf.math.log(y_pred_f) + (1 - y_true_f) * tf.math.log(1 - y_pred_f))\n",
    "        loss_val = alpha_factor * modulating * bce\n",
    "        return tf.reduce_mean(loss_val)\n",
    "    return loss\n",
    "\n",
    "# ==============================\n",
    "# 三、工具：模型構建（LSTM）\n",
    "# ==============================\n",
    "# 中文說明：\n",
    "#   - 輸入 shape = (60, D)\n",
    "#   - 單層 LSTM + Dropout + Dense 輸出 sigmoid\n",
    "#   - 可視需求擴增為雙層 LSTM 或加入 Attention\n",
    "\n",
    "def build_model(input_shape, cfg=CFG):\n",
    "    reg = keras.regularizers.l2(cfg[\"l2\"])\n",
    "    inputs = keras.Input(shape=input_shape, name=\"sequence\")\n",
    "    x = layers.Masking(mask_value=0.0)(inputs)  # 若補值非 0，可視需要移除此層\n",
    "    x = layers.LSTM(cfg[\"hidden_units\"],\n",
    "                    dropout=cfg[\"dropout\"],\n",
    "                    recurrent_dropout=cfg[\"recurrent_dropout\"],\n",
    "                    kernel_regularizer=reg,\n",
    "                    recurrent_regularizer=reg,\n",
    "                    bias_regularizer=None,\n",
    "                    return_sequences=False)(x)\n",
    "    x = layers.Dropout(cfg[\"dropout\"])(x)\n",
    "    outputs = layers.Dense(1, activation=\"sigmoid\", name=\"prob\")(x)\n",
    "\n",
    "    model = keras.Model(inputs, outputs, name=\"lstm_focal\")\n",
    "    opt = keras.optimizers.Adam(learning_rate=cfg[\"learning_rate\"])\n",
    "    model.compile(optimizer=opt,\n",
    "                  loss=binary_focal_loss(cfg[\"focal_gamma\"], cfg[\"focal_alpha\"]),\n",
    "                  metrics=[\n",
    "                      keras.metrics.BinaryAccuracy(name=\"accuracy\"),\n",
    "                      keras.metrics.AUC(name=\"auc\"),\n",
    "                      keras.metrics.AUC(name=\"auprc\", curve=\"PR\"),\n",
    "                      keras.metrics.Precision(name=\"precision\"),\n",
    "                      keras.metrics.Recall(name=\"recall\"),\n",
    "                  ])\n",
    "    return model\n",
    "\n",
    "# ==============================\n",
    "# 四、工具：每折前處理（補值 + 標準化）\n",
    "# ==============================\n",
    "# 中文說明：\n",
    "#   - 僅用該折訓練集 fit；再對 train/val transform（避免洩漏）\n",
    "#   - 先展平為 2D 做補值與標準化，再還原 3D\n",
    "\n",
    "def fit_transform_fold(X_train, X_val):\n",
    "    n, t, d = X_train.shape\n",
    "    tr2 = X_train.reshape(n, t * d)\n",
    "    va2 = X_val.reshape(X_val.shape[0], t * d)\n",
    "\n",
    "    imputer = SimpleImputer(strategy=\"median\")\n",
    "    scaler = StandardScaler(with_mean=True, with_std=True)\n",
    "\n",
    "    tr_imp = imputer.fit_transform(tr2)\n",
    "    tr_scl = scaler.fit_transform(tr_imp)\n",
    "\n",
    "    va_imp = imputer.transform(va2)\n",
    "    va_scl = scaler.transform(va_imp)\n",
    "\n",
    "    Xtr = tr_scl.reshape(n, t, d).astype(np.float32)\n",
    "    Xva = va_scl.reshape(X_val.shape[0], t, d).astype(np.float32)\n",
    "    return Xtr, Xva, imputer, scaler\n",
    "\n",
    "# ==============================\n",
    "# 五、工具：繪圖（英文、藍色漸層）\n",
    "# ==============================\n",
    "# 中文說明：\n",
    "#   - 所有圖以英文標題與標籤，以藍色系為主\n",
    "#   - 使用線性漸層色盤（Blues）或自行定義藍色階\n",
    "\n",
    "BLUES = [\"#cfe8ff\", \"#9dd0ff\", \"#6bb8ff\", \"#3aa0ff\", \"#0a88ff\", \"#006ad1\"]\n",
    "\n",
    "\n",
    "def plot_history(hist_df: pd.DataFrame, out_png: Path):\n",
    "    plt.figure(figsize=(7, 5))\n",
    "    # 僅繪製 val 指標與 loss\n",
    "    x = np.arange(len(hist_df))\n",
    "    plt.plot(x, hist_df[\"loss\"], label=\"Train Loss\", color=BLUES[2], linewidth=2)\n",
    "    plt.plot(x, hist_df[\"val_loss\"], label=\"Val Loss\", color=BLUES[5], linewidth=2)\n",
    "    plt.title(\"Training History\", fontsize=14)\n",
    "    plt.xlabel(\"Epoch\")\n",
    "    plt.ylabel(\"Loss\")\n",
    "    plt.grid(True, alpha=0.25)\n",
    "    plt.legend()\n",
    "    plt.tight_layout()\n",
    "    plt.savefig(out_png, dpi=160)\n",
    "    plt.close()\n",
    "\n",
    "\n",
    "def plot_roc(fpr, tpr, roc_auc, out_png: Path):\n",
    "    plt.figure(figsize=(6, 5))\n",
    "    plt.plot(fpr, tpr, color=BLUES[4], lw=2, label=f\"ROC AUC = {roc_auc:.4f}\")\n",
    "    plt.plot([0, 1], [0, 1], color=BLUES[0], lw=1, linestyle=\"--\")\n",
    "    plt.title(\"Receiver Operating Characteristic\", fontsize=14)\n",
    "    plt.xlabel(\"False Positive Rate\")\n",
    "    plt.ylabel(\"True Positive Rate\")\n",
    "    plt.grid(True, alpha=0.25)\n",
    "    plt.legend(loc=\"lower right\")\n",
    "    plt.tight_layout()\n",
    "    plt.savefig(out_png, dpi=160)\n",
    "    plt.close()\n",
    "\n",
    "\n",
    "def plot_pr(recall, precision, ap, out_png: Path):\n",
    "    plt.figure(figsize=(6, 5))\n",
    "    plt.plot(recall, precision, color=BLUES[4], lw=2, label=f\"AP = {ap:.4f}\")\n",
    "    plt.title(\"Precision-Recall Curve\", fontsize=14)\n",
    "    plt.xlabel(\"Recall\")\n",
    "    plt.ylabel(\"Precision\")\n",
    "    plt.grid(True, alpha=0.25)\n",
    "    plt.legend(loc=\"lower left\")\n",
    "    plt.tight_layout()\n",
    "    plt.savefig(out_png, dpi=160)\n",
    "    plt.close()\n",
    "\n",
    "\n",
    "def plot_cm(cm: np.ndarray, out_png: Path):\n",
    "    plt.figure(figsize=(5.2, 4.4))\n",
    "    im = plt.imshow(cm, cmap=\"Blues\")\n",
    "    plt.title(\"Confusion Matrix\", fontsize=14)\n",
    "    plt.xlabel(\"Predicted\")\n",
    "    plt.ylabel(\"Actual\")\n",
    "    plt.colorbar(im, fraction=0.046, pad=0.04)\n",
    "    # 在格子中寫字\n",
    "    for (i, j), z in np.ndenumerate(cm):\n",
    "        plt.text(j, i, f\"{z}\", ha='center', va='center', color='black', fontsize=12)\n",
    "    plt.xticks([0, 1], [\"Negative\", \"Positive\"]) \n",
    "    plt.yticks([0, 1], [\"Negative\", \"Positive\"]) \n",
    "    plt.tight_layout()\n",
    "    plt.savefig(out_png, dpi=160)\n",
    "    plt.close()\n",
    "\n",
    "# ==============================\n",
    "# 六、主流程：逐折訓練與評估\n",
    "# ==============================\n",
    "all_rows = []\n",
    "\n",
    "for k in CFG[\"folds\"]:\n",
    "    fold_dir = RUN_DIR / f\"fold_{k}\"\n",
    "    fold_dir.mkdir(parents=True, exist_ok=True)\n",
    "\n",
    "    # 讀取資料\n",
    "    Xtr = np.load(DATA / f\"X_train_fold{k}.npy\")\n",
    "    ytr = np.load(DATA / f\"y_train_fold{k}.npy\")\n",
    "    Xva = np.load(DATA / f\"X_val_fold{k}.npy\")\n",
    "    yva = np.load(DATA / f\"y_val_fold{k}.npy\")\n",
    "\n",
    "    # 安全檢查：確保標籤是二元 0/1\n",
    "    assert set(np.unique(ytr)).issubset({0, 1}), \"y_train 需為 {0,1}\"\n",
    "    assert set(np.unique(yva)).issubset({0, 1}), \"y_val 需為 {0,1}\"\n",
    "\n",
    "    # 每折前處理（fit on train, transform on train/val）\n",
    "    Xtr_p, Xva_p, imp, scl = fit_transform_fold(Xtr, Xva)\n",
    "\n",
    "    # 構建模型\n",
    "    input_shape = (Xtr_p.shape[1], Xtr_p.shape[2])  # (60, D)\n",
    "    model = build_model(input_shape, CFG)\n",
    "\n",
    "    # Callbacks（早停＋最佳權重）\n",
    "    ckpt_path = fold_dir / \"best_model.keras\"\n",
    "    cbs = [\n",
    "        keras.callbacks.EarlyStopping(monitor=\"val_auprc\", patience=CFG[\"patience\"], mode=\"max\", restore_best_weights=True),\n",
    "        keras.callbacks.ModelCheckpoint(filepath=str(ckpt_path), monitor=\"val_auprc\", mode=\"max\", save_best_only=True)\n",
    "    ]\n",
    "\n",
    "    # 訓練\n",
    "    hist = model.fit(\n",
    "        Xtr_p, ytr,\n",
    "        validation_data=(Xva_p, yva),\n",
    "        epochs=CFG[\"epochs\"],\n",
    "        batch_size=CFG[\"batch_size\"],\n",
    "        callbacks=cbs,\n",
    "        verbose=2\n",
    "    )\n",
    "\n",
    "    # 保存 history.csv 並繪圖\n",
    "    hist_df = pd.DataFrame(hist.history)\n",
    "    hist_df.to_csv(fold_dir / \"history.csv\", index=False)\n",
    "    plot_history(hist_df, fold_dir / \"plot_history.png\")\n",
    "\n",
    "    # 推論（預測機率）\n",
    "    y_prob = model.predict(Xva_p, batch_size=CFG[\"batch_size\"], verbose=0).ravel()\n",
    "    y_pred = (y_prob >= 0.5).astype(int)\n",
    "\n",
    "    # ROC / PR\n",
    "    fpr, tpr, _ = roc_curve(yva, y_prob)\n",
    "    roc_auc = auc(fpr, tpr)\n",
    "    prec, rec, _ = precision_recall_curve(yva, y_prob)\n",
    "    ap = average_precision_score(yva, y_prob)\n",
    "\n",
    "    # 混淆矩陣\n",
    "    cm = confusion_matrix(yva, y_pred, labels=[0, 1])\n",
    "\n",
    "    # 指標與輸出\n",
    "    rep = classification_report(yva, y_pred, labels=[0, 1], target_names=[\"Negative\", \"Positive\"], output_dict=True)\n",
    "    metrics_row = {\n",
    "        \"fold\": k,\n",
    "        \"val_auc\": float(roc_auc),\n",
    "        \"val_auprc\": float(ap),\n",
    "        \"val_precision\": float(rep[\"Positive\"][\"precision\"]),\n",
    "        \"val_recall\": float(rep[\"Positive\"][\"recall\"]),\n",
    "        \"val_f1\": float(rep[\"Positive\"][\"f1-score\"]),\n",
    "        \"val_support_pos\": int(rep[\"Positive\"][\"support\"]),\n",
    "        \"val_support_neg\": int(rep[\"Negative\"][\"support\"]),\n",
    "        \"threshold\": 0.5,\n",
    "    }\n",
    "    pd.DataFrame([metrics_row]).to_csv(fold_dir / \"metrics.csv\", index=False)\n",
    "\n",
    "    # 儲存各種 CSV\n",
    "    pd.DataFrame({\"y_true\": yva, \"y_prob\": y_prob, \"y_pred\": y_pred}).to_csv(fold_dir / \"predictions.csv\", index=False)\n",
    "    pd.DataFrame({\"fpr\": fpr, \"tpr\": tpr}).to_csv(fold_dir / \"roc_curve.csv\", index=False)\n",
    "    pd.DataFrame({\"recall\": rec, \"precision\": prec}).to_csv(fold_dir / \"pr_curve.csv\", index=False)\n",
    "    pd.DataFrame(cm, index=[\"Actual_0\",\"Actual_1\"], columns=[\"Pred_0\",\"Pred_1\"]).to_csv(fold_dir / \"cm.csv\")\n",
    "\n",
    "    # 圖表（英文、藍色漸層）\n",
    "    plot_roc(fpr, tpr, roc_auc, fold_dir / \"plot_roc.png\")\n",
    "    plot_pr(rec, prec, ap, fold_dir / \"plot_pr.png\")\n",
    "    plot_cm(cm, fold_dir / \"plot_cm.png\")\n",
    "\n",
    "    # 彙總\n",
    "    all_rows.append(metrics_row)\n",
    "\n",
    "# 各折彙總\n",
    "summary_df = pd.DataFrame(all_rows).sort_values(\"fold\")\n",
    "summary_df.to_csv(RUN_DIR / \"summary_folds.csv\", index=False)\n",
    "\n",
    "# 整體平均\n",
    "overall = {\n",
    "    \"n_folds\": len(summary_df),\n",
    "    \"val_auc_mean\": float(summary_df[\"val_auc\"].mean()),\n",
    "    \"val_auc_std\": float(summary_df[\"val_auc\"].std(ddof=1)),\n",
    "    \"val_auprc_mean\": float(summary_df[\"val_auprc\"].mean()),\n",
    "    \"val_auprc_std\": float(summary_df[\"val_auprc\"].std(ddof=1)),\n",
    "    \"val_f1_mean\": float(summary_df[\"val_f1\"].mean()),\n",
    "    \"val_precision_mean\": float(summary_df[\"val_precision\"].mean()),\n",
    "    \"val_recall_mean\": float(summary_df[\"val_recall\"].mean()),\n",
    "    \"config\": CFG,\n",
    "}\n",
    "(RUN_DIR / \"summary_overall.json\").write_text(json.dumps(overall, ensure_ascii=False, indent=2), encoding=\"utf-8\")\n",
    "\n",
    "print(\"\\n=== Training Done ===\")\n",
    "print(f\"Run dir: {RUN_DIR}\")\n",
    "print(json.dumps(overall, indent=2))\n",
    "\n",
    "# -*- coding: utf-8 -*-\n",
    "\"\"\"\n",
    "train_lstm_focal_windowed_clean_v2.py\n",
    "\n",
    "更新重點：\n",
    "  1) 指標擴充：Accuracy、Recall、Precision、F1、ROC-AUC（訓練與驗證集皆有）\n",
    "  2) 十折：每折輸出上述五指標；最終彙總十折平均與標準差\n",
    "  3) 圖表：\n",
    "     - 訓練與驗證集的混淆矩陣（字大、藍色）\n",
    "     - ROC 曲線圖（Train 與 Val 各一張）\n",
    "     - Loss 圖：Focal Loss 與 Binary Cross-Entropy（訓練與驗證）\n",
    "     - Accuracy / F1 per Epoch（訓練與驗證）\n",
    "  4) CSV：history.csv、metrics.csv（含 train/val 五指標）、predictions_{train,val}.csv、\n",
    "     roc_curve_{train,val}.csv、cm_{train,val}.csv、summary_folds.csv、summary_overall.json 等\n",
    "\n",
    "執行：\n",
    "  python train_lstm_focal_windowed_clean_v2.py\n",
    "\"\"\"\n",
    "\n",
    "import os\n",
    "import json\n",
    "import time\n",
    "from pathlib import Path\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from sklearn.impute import SimpleImputer\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.metrics import (\n",
    "    roc_curve, auc, precision_recall_fscore_support,\n",
    "    confusion_matrix, accuracy_score, roc_auc_score,\n",
    "    log_loss\n",
    ")\n",
    "\n",
    "import tensorflow as tf\n",
    "from tensorflow import keras\n",
    "from tensorflow.keras import layers\n",
    "\n",
    "# ==============================\n",
    "# 一、基本設定（可視需求調整）\n",
    "# ==============================\n",
    "BASE = Path(\"/home/jovyan/RT08/0925/sliding_win/1014_sim\")\n",
    "DATA = BASE / \"windowed_clean\"\n",
    "RUNS_ROOT = BASE / \"training_runs\"\n",
    "RUNS_ROOT.mkdir(parents=True, exist_ok=True)\n",
    "\n",
    "CFG = {\n",
    "    \"seed\": 42,\n",
    "    \"epochs\": 40,\n",
    "    \"batch_size\": 128,\n",
    "    \"learning_rate\": 1e-3,\n",
    "    \"focal_gamma\": 2.0,\n",
    "    \"focal_alpha\": 0.25,\n",
    "    \"patience\": 5,\n",
    "    \"hidden_units\": 128,\n",
    "    \"l2\": 1e-6,\n",
    "    \"dropout\": 0.2,\n",
    "    \"recurrent_dropout\": 0.0,\n",
    "    \"folds\": list(range(1, 11)),\n",
    "    \"threshold\": 0.5\n",
    "}\n",
    "\n",
    "np.random.seed(CFG[\"seed\"])\n",
    "tf.random.set_seed(CFG[\"seed\"])\n",
    "\n",
    "# 版本化 run 目錄\n",
    "_ts = time.strftime(\"%Y%m%d_%H%M%S\")\n",
    "RUN_DIR = RUNS_ROOT / f\"{_ts}_run01\"\n",
    "RUN_DIR.mkdir(parents=True, exist_ok=True)\n",
    "(RUN_DIR / \"cfg.json\").write_text(json.dumps(CFG, ensure_ascii=False, indent=2), encoding=\"utf-8\")\n",
    "\n",
    "# ==============================\n",
    "# 二、Focal Loss（二元）\n",
    "# ==============================\n",
    "\n",
    "def binary_focal_loss(gamma=2.0, alpha=0.25):\n",
    "    def loss(y_true, y_pred):\n",
    "        y_true = tf.cast(y_true, tf.float32)\n",
    "        y_pred = tf.clip_by_value(y_pred, 1e-7, 1.0 - 1e-7)\n",
    "        p_t = y_true * y_pred + (1 - y_true) * (1 - y_pred)\n",
    "        alpha_factor = y_true * alpha + (1 - y_true) * (1 - alpha)\n",
    "        modulating = tf.pow(1.0 - p_t, gamma)\n",
    "        bce = - (y_true * tf.math.log(y_pred) + (1 - y_true) * tf.math.log(1 - y_pred))\n",
    "        return tf.reduce_mean(alpha_factor * modulating * bce)\n",
    "    return loss\n",
    "\n",
    "# ==============================\n",
    "# 三、模型構建（LSTM）\n",
    "# ==============================\n",
    "\n",
    "def build_model(input_shape, cfg=CFG):\n",
    "    reg = keras.regularizers.l2(cfg[\"l2\"])\n",
    "    inputs = keras.Input(shape=input_shape, name=\"sequence\")\n",
    "    x = layers.Masking(mask_value=0.0)(inputs)\n",
    "    x = layers.LSTM(cfg[\"hidden_units\"],\n",
    "                    dropout=cfg[\"dropout\"],\n",
    "                    recurrent_dropout=cfg[\"recurrent_dropout\"],\n",
    "                    kernel_regularizer=reg,\n",
    "                    recurrent_regularizer=reg,\n",
    "                    return_sequences=False)(x)\n",
    "    x = layers.Dropout(cfg[\"dropout\"])(x)\n",
    "    outputs = layers.Dense(1, activation=\"sigmoid\", name=\"prob\")(x)\n",
    "\n",
    "    model = keras.Model(inputs, outputs, name=\"lstm_focal\")\n",
    "    opt = keras.optimizers.Adam(learning_rate=cfg[\"learning_rate\"])\n",
    "    model.compile(\n",
    "        optimizer=opt,\n",
    "        loss=binary_focal_loss(cfg[\"focal_gamma\"], cfg[\"focal_alpha\"]),\n",
    "        metrics=[\n",
    "            keras.metrics.BinaryAccuracy(name=\"accuracy\"),\n",
    "            keras.metrics.AUC(name=\"auc\"),\n",
    "            keras.metrics.AUC(name=\"auprc\", curve=\"PR\"),\n",
    "            keras.metrics.Precision(name=\"precision\"),\n",
    "            keras.metrics.Recall(name=\"recall\"),\n",
    "            keras.metrics.BinaryCrossentropy(name=\"binary_crossentropy\")\n",
    "        ]\n",
    "    )\n",
    "    return model\n",
    "\n",
    "# ==============================\n",
    "# 四、前處理（補值 + 標準化；避免洩漏）\n",
    "# ==============================\n",
    "\n",
    "def fit_transform_fold(X_train, X_val):\n",
    "    n, t, d = X_train.shape\n",
    "    tr2 = X_train.reshape(n, t * d)\n",
    "    va2 = X_val.reshape(X_val.shape[0], t * d)\n",
    "\n",
    "    imputer = SimpleImputer(strategy=\"median\")\n",
    "    scaler = StandardScaler(with_mean=True, with_std=True)\n",
    "\n",
    "    tr_scl = scaler.fit_transform(imputer.fit_transform(tr2))\n",
    "    va_scl = scaler.transform(imputer.transform(va2))\n",
    "\n",
    "    Xtr = tr_scl.reshape(n, t, d).astype(np.float32)\n",
    "    Xva = va_scl.reshape(X_val.shape[0], t, d).astype(np.float32)\n",
    "    return Xtr, Xva, imputer, scaler\n",
    "\n",
    "# ==============================\n",
    "# 五、繪圖工具（英文、藍色系）\n",
    "# ==============================\n",
    "BLUES = [\"#cfe8ff\", \"#9dd0ff\", \"#6bb8ff\", \"#3aa0ff\", \"#0a88ff\", \"#005bb5\"]\n",
    "\n",
    "\n",
    "def plot_history(hist: pd.DataFrame, out_png: Path):\n",
    "    # Focal Loss 與 Binary Cross-Entropy；同圖顯示 train/val\n",
    "    plt.figure(figsize=(7, 5))\n",
    "    x = np.arange(len(hist))\n",
    "    plt.plot(x, hist[\"loss\"], label=\"Train Focal Loss\", color=BLUES[3], lw=2)\n",
    "    plt.plot(x, hist[\"val_loss\"], label=\"Val Focal Loss\", color=BLUES[5], lw=2)\n",
    "    if \"binary_crossentropy\" in hist.columns:\n",
    "        plt.plot(x, hist[\"binary_crossentropy\"], label=\"Train BCE\", color=BLUES[1], lw=2)\n",
    "    if \"val_binary_crossentropy\" in hist.columns:\n",
    "        plt.plot(x, hist[\"val_binary_crossentropy\"], label=\"Val BCE\", color=BLUES[0], lw=2)\n",
    "    plt.title(\"Loss Curves (Focal & BCE)\")\n",
    "    plt.xlabel(\"Epoch\"); plt.ylabel(\"Loss\")\n",
    "    plt.grid(True, alpha=0.25); plt.legend(); plt.tight_layout(); plt.savefig(out_png, dpi=160); plt.close()\n",
    "\n",
    "\n",
    "def plot_metric_epochs(hist: pd.DataFrame, metric: str, out_png: Path, title: str):\n",
    "    plt.figure(figsize=(7, 5))\n",
    "    x = np.arange(len(hist))\n",
    "    if metric in hist.columns:\n",
    "        plt.plot(x, hist[metric], label=f\"Train {metric}\", color=BLUES[3], lw=2)\n",
    "    valm = f\"val_{metric}\"\n",
    "    if valm in hist.columns:\n",
    "        plt.plot(x, hist[valm], label=f\"Val {metric}\", color=BLUES[5], lw=2)\n",
    "    plt.title(title)\n",
    "    plt.xlabel(\"Epoch\"); plt.ylabel(metric.title())\n",
    "    plt.grid(True, alpha=0.25); plt.legend(); plt.tight_layout(); plt.savefig(out_png, dpi=160); plt.close()\n",
    "\n",
    "\n",
    "def plot_roc_xy(fpr, tpr, roc_auc, out_png: Path, title: str):\n",
    "    plt.figure(figsize=(6, 5))\n",
    "    plt.plot(fpr, tpr, color=BLUES[4], lw=2, label=f\"ROC AUC = {roc_auc:.4f}\")\n",
    "    plt.plot([0, 1], [0, 1], color=BLUES[0], lw=1, ls=\"--\")\n",
    "    plt.title(title); plt.xlabel(\"False Positive Rate\"); plt.ylabel(\"True Positive Rate\")\n",
    "    plt.grid(True, alpha=0.25); plt.legend(loc=\"lower right\"); plt.tight_layout(); plt.savefig(out_png, dpi=160); plt.close()\n",
    "\n",
    "\n",
    "def plot_cm(cm: np.ndarray, out_png: Path, title: str):\n",
    "    plt.figure(figsize=(5.6, 4.8))\n",
    "    im = plt.imshow(cm, cmap=\"Blues\")\n",
    "    plt.title(title, fontsize=16)\n",
    "    plt.xlabel(\"Predicted\", fontsize=13); plt.ylabel(\"Actual\", fontsize=13)\n",
    "    plt.colorbar(im, fraction=0.046, pad=0.04)\n",
    "    for (i, j), z in np.ndenumerate(cm):\n",
    "        plt.text(j, i, f\"{z}\", ha='center', va='center', color='black', fontsize=14, fontweight='bold')\n",
    "    plt.xticks([0, 1], [\"Negative\", \"Positive\"], fontsize=12)\n",
    "    plt.yticks([0, 1], [\"Negative\", \"Positive\"], fontsize=12)\n",
    "    plt.tight_layout(); plt.savefig(out_png, dpi=180); plt.close()\n",
    "\n",
    "# ==============================\n",
    "# 六、自訂 Callback：每個 epoch 計算 F1（train/val）\n",
    "# ==============================\n",
    "class F1PerEpoch(keras.callbacks.Callback):\n",
    "    def __init__(self, Xtr, ytr, Xva, yva, threshold=0.5, batch_size=512):\n",
    "        super().__init__()\n",
    "        self.Xtr, self.ytr, self.Xva, self.yva = Xtr, ytr, Xva, yva\n",
    "        self.th = threshold\n",
    "        self.bs = batch_size\n",
    "\n",
    "    def on_epoch_end(self, epoch, logs=None):\n",
    "        ytr_prob = self.model.predict(self.Xtr, batch_size=self.bs, verbose=0).ravel()\n",
    "        yva_prob = self.model.predict(self.Xva, batch_size=self.bs, verbose=0).ravel()\n",
    "        ytr_pred = (ytr_prob >= self.th).astype(int)\n",
    "        yva_pred = (yva_prob >= self.th).astype(int)\n",
    "        _, _, f1_tr, _ = precision_recall_fscore_support(self.ytr, ytr_pred, average='binary', zero_division=0)\n",
    "        _, _, f1_va, _ = precision_recall_fscore_support(self.yva, yva_pred, average='binary', zero_division=0)\n",
    "        if logs is not None:\n",
    "            logs['f1'] = f1_tr\n",
    "            logs['val_f1'] = f1_va\n",
    "\n",
    "# ==============================\n",
    "# 七、主流程：十折訓練與評估\n",
    "# ==============================\n",
    "all_rows = []\n",
    "\n",
    "for k in CFG[\"folds\"]:\n",
    "    fold_dir = RUN_DIR / f\"fold_{k}\"\n",
    "    fold_dir.mkdir(parents=True, exist_ok=True)\n",
    "\n",
    "    # 載入資料\n",
    "    Xtr = np.load(DATA / f\"X_train_fold{k}.npy\")\n",
    "    ytr = np.load(DATA / f\"y_train_fold{k}.npy\")\n",
    "    Xva = np.load(DATA / f\"X_val_fold{k}.npy\")\n",
    "    yva = np.load(DATA / f\"y_val_fold{k}.npy\")\n",
    "\n",
    "    # 檢查標籤\n",
    "    assert set(np.unique(ytr)).issubset({0, 1})\n",
    "    assert set(np.unique(yva)).issubset({0, 1})\n",
    "\n",
    "    # 前處理（fit on train, transform on train/val）\n",
    "    Xtr_p, Xva_p, _, _ = fit_transform_fold(Xtr, Xva)\n",
    "\n",
    "    # 建模\n",
    "    input_shape = (Xtr_p.shape[1], Xtr_p.shape[2])\n",
    "    model = build_model(input_shape, CFG)\n",
    "\n",
    "    # Callbacks：早停、最佳權重、F1 per epoch\n",
    "    ckpt_path = fold_dir / \"best_model.keras\"\n",
    "    cbs = [\n",
    "        keras.callbacks.EarlyStopping(monitor=\"val_auprc\", patience=CFG[\"patience\"], mode=\"max\", restore_best_weights=True),\n",
    "        keras.callbacks.ModelCheckpoint(filepath=str(ckpt_path), monitor=\"val_auprc\", mode=\"max\", save_best_only=True),\n",
    "        F1PerEpoch(Xtr_p, ytr, Xva_p, yva, threshold=CFG[\"threshold\"], batch_size=512)\n",
    "    ]\n",
    "\n",
    "    # 訓練\n",
    "    hist = model.fit(\n",
    "        Xtr_p, ytr,\n",
    "        validation_data=(Xva_p, yva),\n",
    "        epochs=CFG[\"epochs\"],\n",
    "        batch_size=CFG[\"batch_size\"],\n",
    "        callbacks=cbs,\n",
    "        verbose=2\n",
    "    )\n",
    "\n",
    "    # 保存 history 並繪圖（Loss、Accuracy per Epoch、F1 per Epoch）\n",
    "    hist_df = pd.DataFrame(hist.history)\n",
    "    hist_df.to_csv(fold_dir / \"history.csv\", index=False)\n",
    "    plot_history(hist_df, fold_dir / \"plot_loss.png\")\n",
    "    plot_metric_epochs(hist_df, \"accuracy\", fold_dir / \"plot_acc.png\", \"Accuracy per Epoch\")\n",
    "    if \"f1\" in hist_df.columns and \"val_f1\" in hist_df.columns:\n",
    "        plot_metric_epochs(hist_df, \"f1\", fold_dir / \"plot_f1.png\", \"F1 per Epoch\")\n",
    "\n",
    "    # 推論：Train & Val 機率\n",
    "    ytr_prob = model.predict(Xtr_p, batch_size=CFG[\"batch_size\"], verbose=0).ravel()\n",
    "    yva_prob = model.predict(Xva_p, batch_size=CFG[\"batch_size\"], verbose=0).ravel()\n",
    "    th = CFG[\"threshold\"]\n",
    "    ytr_pred = (ytr_prob >= th).astype(int)\n",
    "    yva_pred = (yva_prob >= th).astype(int)\n",
    "\n",
    "    # 五大指標（Train & Val）\n",
    "    def five_metrics(y_true, y_prob, y_pred):\n",
    "        acc = accuracy_score(y_true, y_pred)\n",
    "        prec, rec, f1, _ = precision_recall_fscore_support(y_true, y_pred, average='binary', zero_division=0)\n",
    "        rocauc = roc_auc_score(y_true, y_prob)\n",
    "        return acc, rec, f1, prec, rocauc\n",
    "\n",
    "    tr_acc, tr_rec, tr_f1, tr_prec, tr_auc = five_metrics(ytr, ytr_prob, ytr_pred)\n",
    "    va_acc, va_rec, va_f1, va_prec, va_auc = five_metrics(yva, yva_prob, yva_pred)\n",
    "\n",
    "    # 混淆矩陣（Train & Val）\n",
    "    cm_tr = confusion_matrix(ytr, ytr_pred, labels=[0, 1])\n",
    "    cm_va = confusion_matrix(yva, yva_pred, labels=[0, 1])\n",
    "\n",
    "    # ROC（Train & Val）\n",
    "    fpr_tr, tpr_tr, _ = roc_curve(ytr, ytr_prob)\n",
    "    fpr_va, tpr_va, _ = roc_curve(yva, yva_prob)\n",
    "\n",
    "    # 儲存 CSV\n",
    "    pd.DataFrame({\"y_true\": ytr, \"y_prob\": ytr_prob, \"y_pred\": ytr_pred}).to_csv(fold_dir / \"predictions_train.csv\", index=False)\n",
    "    pd.DataFrame({\"y_true\": yva, \"y_prob\": yva_prob, \"y_pred\": yva_pred}).to_csv(fold_dir / \"predictions_val.csv\", index=False)\n",
    "    pd.DataFrame({\"fpr\": fpr_tr, \"tpr\": tpr_tr}).to_csv(fold_dir / \"roc_curve_train.csv\", index=False)\n",
    "    pd.DataFrame({\"fpr\": fpr_va, \"tpr\": tpr_va}).to_csv(fold_dir / \"roc_curve_val.csv\", index=False)\n",
    "    pd.DataFrame(cm_tr, index=[\"Actual_0\",\"Actual_1\"], columns=[\"Pred_0\",\"Pred_1\"]).to_csv(fold_dir / \"cm_train.csv\")\n",
    "    pd.DataFrame(cm_va, index=[\"Actual_0\",\"Actual_1\"], columns=[\"Pred_0\",\"Pred_1\"]).to_csv(fold_dir / \"cm_val.csv\")\n",
    "\n",
    "    # 圖：混淆矩陣、ROC（Train & Val）\n",
    "    plot_cm(cm_tr, fold_dir / \"plot_cm_train.png\", \"Confusion Matrix (Train)\")\n",
    "    plot_cm(cm_va, fold_dir / \"plot_cm_val.png\", \"Confusion Matrix (Validation)\")\n",
    "    plot_roc_xy(fpr_tr, tpr_tr, tr_auc, fold_dir / \"plot_roc_train.png\", \"ROC Curve (Train)\")\n",
    "    plot_roc_xy(fpr_va, tpr_va, va_auc, fold_dir / \"plot_roc_val.png\", \"ROC Curve (Validation)\")\n",
    "\n",
    "    # 每折 metrics.csv（含 train/val 五指標）\n",
    "    row = {\n",
    "        \"fold\": k,\n",
    "        # Train\n",
    "        \"train_accuracy\": tr_acc,\n",
    "        \"train_recall\": tr_rec,\n",
    "        \"train_f1\": tr_f1,\n",
    "        \"train_precision\": tr_prec,\n",
    "        \"train_auc\": tr_auc,\n",
    "        # Val\n",
    "        \"val_accuracy\": va_acc,\n",
    "        \"val_recall\": va_rec,\n",
    "        \"val_f1\": va_f1,\n",
    "        \"val_precision\": va_prec,\n",
    "        \"val_auc\": va_auc,\n",
    "    }\n",
    "    pd.DataFrame([row]).to_csv(fold_dir / \"metrics.csv\", index=False)\n",
    "    all_rows.append(row)\n",
    "\n",
    "# 彙總十折：平均與標準差\n",
    "summary = pd.DataFrame(all_rows).sort_values(\"fold\")\n",
    "sum_csv = RUN_DIR / \"summary_folds.csv\"\n",
    "summary.to_csv(sum_csv, index=False)\n",
    "\n",
    "agg = {}\n",
    "for col in [c for c in summary.columns if c != \"fold\"]:\n",
    "    agg[col + \"_mean\"] = float(summary[col].mean())\n",
    "    agg[col + \"_std\"] = float(summary[col].std(ddof=1))\n",
    "\n",
    "overall = {\"n_folds\": int(len(summary)), **agg, \"config\": CFG}\n",
    "(RUN_DIR / \"summary_overall.json\").write_text(json.dumps(overall, ensure_ascii=False, indent=2), encoding=\"utf-8\")\n",
    "\n",
    "print(\"=== Training Done ===\")\n",
    "print(f\"Run dir: {RUN_DIR}\")\n",
    "print(json.dumps(overall, indent=2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "220e5e97-620d-4f12-a379-379739648fdd",
   "metadata": {},
   "outputs": [],
   "source": [
    "移除 Masking(mask_value=0.0)：避免標準化後接近 0 的有效步被當成 padding。\n",
    "輸出層加入「先驗偏置」：用當折 pos_rate 轉成 logit 初始化 Dense.bias，避免初期全猜 0。\n",
    "Focal α 動態設定 + 啟用 class_weight：每折計算 alpha = clip(1 - pos_rate, 0.1, 0.9)；同時 class_weight=\"balanced\"。\n",
    "驗證集掃描「最佳 F1 閾值」：0.01→0.99 格點求 F1 最大，輸出 best-th 與固定 0.5 兩套指標與混淆矩陣。\n",
    "優化訓練穩定性：EarlyStopping 仍監看 AUPRC，加入 ReduceLROnPlateau；附 F1PerEpoch 與 ProbProbe（監看機率分布）。\n",
    "圖表與輸出：保留你藍色系風格，新增 train/val 的 ROC、PR、CM（best-th）；CSV 新增 cm_*_best.csv、cm_*_fixed05.csv、predictions_{train,val}.csv、fold_meta.json，以及 summary_folds.csv、summary_overall.json 的平均與標準差。\n",
    "run 目錄自動遞增：<timestamp>_runXX，避免覆寫"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 267,
   "id": "aa663af0-8cb0-42f0-9db7-222e0be06d84",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/40\n"
     ]
    },
    {
     "ename": "ValueError",
     "evalue": "Arguments `target` and `output` must have the same rank (ndim). Received: target.shape=(None,), output.shape=(None, 1)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[267], line 317\u001b[0m\n\u001b[1;32m    305\u001b[0m cbs \u001b[38;5;241m=\u001b[39m [\n\u001b[1;32m    306\u001b[0m     keras\u001b[38;5;241m.\u001b[39mcallbacks\u001b[38;5;241m.\u001b[39mEarlyStopping(monitor\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mval_auprc\u001b[39m\u001b[38;5;124m\"\u001b[39m, patience\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mmax\u001b[39m(\u001b[38;5;241m8\u001b[39m, CFG[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mpatience\u001b[39m\u001b[38;5;124m\"\u001b[39m]),\n\u001b[1;32m    307\u001b[0m                                   mode\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmax\u001b[39m\u001b[38;5;124m\"\u001b[39m, restore_best_weights\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m),\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    313\u001b[0m     ProbProbe(Xtr_p, Xva_p, bs\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1024\u001b[39m)\n\u001b[1;32m    314\u001b[0m ]\n\u001b[1;32m    316\u001b[0m \u001b[38;5;66;03m# === 訓練 ===\u001b[39;00m\n\u001b[0;32m--> 317\u001b[0m hist \u001b[38;5;241m=\u001b[39m \u001b[43mmodel\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfit\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    318\u001b[0m \u001b[43m    \u001b[49m\u001b[43mXtr_p\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mytr\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    319\u001b[0m \u001b[43m    \u001b[49m\u001b[43mvalidation_data\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mXva_p\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43myva\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    320\u001b[0m \u001b[43m    \u001b[49m\u001b[43mepochs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mCFG\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mepochs\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    321\u001b[0m \u001b[43m    \u001b[49m\u001b[43mbatch_size\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mCFG\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mbatch_size\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    322\u001b[0m \u001b[43m    \u001b[49m\u001b[43mcallbacks\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcbs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    323\u001b[0m \u001b[43m    \u001b[49m\u001b[43mverbose\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m2\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m    324\u001b[0m \u001b[43m    \u001b[49m\u001b[43mclass_weight\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mclass_weight\u001b[49m\n\u001b[1;32m    325\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    327\u001b[0m \u001b[38;5;66;03m# === 保存 history 與訓練曲線 ===\u001b[39;00m\n\u001b[1;32m    328\u001b[0m hist_df \u001b[38;5;241m=\u001b[39m pd\u001b[38;5;241m.\u001b[39mDataFrame(hist\u001b[38;5;241m.\u001b[39mhistory)\n",
      "File \u001b[0;32m/opt/conda/lib/python3.11/site-packages/keras/src/utils/traceback_utils.py:122\u001b[0m, in \u001b[0;36mfilter_traceback.<locals>.error_handler\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    119\u001b[0m     filtered_tb \u001b[38;5;241m=\u001b[39m _process_traceback_frames(e\u001b[38;5;241m.\u001b[39m__traceback__)\n\u001b[1;32m    120\u001b[0m     \u001b[38;5;66;03m# To get the full stack trace, call:\u001b[39;00m\n\u001b[1;32m    121\u001b[0m     \u001b[38;5;66;03m# `keras.config.disable_traceback_filtering()`\u001b[39;00m\n\u001b[0;32m--> 122\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m e\u001b[38;5;241m.\u001b[39mwith_traceback(filtered_tb) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m    123\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[1;32m    124\u001b[0m     \u001b[38;5;28;01mdel\u001b[39;00m filtered_tb\n",
      "File \u001b[0;32m/opt/conda/lib/python3.11/site-packages/keras/src/backend/tensorflow/nn.py:668\u001b[0m, in \u001b[0;36mbinary_crossentropy\u001b[0;34m(target, output, from_logits)\u001b[0m\n\u001b[1;32m    665\u001b[0m output \u001b[38;5;241m=\u001b[39m tf\u001b[38;5;241m.\u001b[39mconvert_to_tensor(output)\n\u001b[1;32m    667\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(target\u001b[38;5;241m.\u001b[39mshape) \u001b[38;5;241m!=\u001b[39m \u001b[38;5;28mlen\u001b[39m(output\u001b[38;5;241m.\u001b[39mshape):\n\u001b[0;32m--> 668\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[1;32m    669\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mArguments `target` and `output` must have the same rank \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    670\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m(ndim). Received: \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    671\u001b[0m         \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtarget.shape=\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mtarget\u001b[38;5;241m.\u001b[39mshape\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m, output.shape=\u001b[39m\u001b[38;5;132;01m{\u001b[39;00moutput\u001b[38;5;241m.\u001b[39mshape\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    672\u001b[0m     )\n\u001b[1;32m    673\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m e1, e2 \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mzip\u001b[39m(target\u001b[38;5;241m.\u001b[39mshape, output\u001b[38;5;241m.\u001b[39mshape):\n\u001b[1;32m    674\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m e1 \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m e2 \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m e1 \u001b[38;5;241m!=\u001b[39m e2:\n",
      "\u001b[0;31mValueError\u001b[0m: Arguments `target` and `output` must have the same rank (ndim). Received: target.shape=(None,), output.shape=(None, 1)"
     ]
    }
   ],
   "source": [
    "# -*- coding: utf-8 -*-\n",
    "\"\"\"\n",
    "train_lstm_focal_windowed_clean_v2.py\n",
    "\n",
    "目的：\n",
    "  使用 Keras + LSTM + Focal Loss，針對 windowed_clean/ 的十折資料進行訓練與評估。\n",
    "  - 每折獨立補值與標準化（避免資料洩漏）\n",
    "  - 產生英文圖表（藍色漸層）並儲存 CSV 結果\n",
    "  - 以 timestamp + run_id 版本化輸出\n",
    "\n",
    "資料假設：\n",
    "  /home/jovyan/RT08/0925/sliding_win/1014_sim/windowed_clean/\n",
    "    X_train_fold{k}.npy, y_train_fold{k}.npy\n",
    "    X_val_fold{k}.npy,   y_val_fold{k}.npy\n",
    "\n",
    "輸出：\n",
    "  /home/jovyan/RT08/0925/sliding_win/1014_sim/training_runs/<timestamp>_runXX/\n",
    "    ├─ cfg.json\n",
    "    ├─ fold_{k}/\n",
    "    │    ├─ history.csv\n",
    "    │    ├─ metrics.csv\n",
    "    │    ├─ predictions_train.csv / predictions_val.csv\n",
    "    │    ├─ roc_curve_train.csv  / roc_curve_val.csv\n",
    "    │    ├─ cm_train_best.csv    / cm_val_best.csv\n",
    "    │    ├─ cm_train_fixed05.csv / cm_val_fixed05.csv\n",
    "    │    ├─ best_model.keras\n",
    "    │    ├─ plot_loss.png\n",
    "    │    ├─ plot_acc.png\n",
    "    │    ├─ plot_f1.png\n",
    "    │    ├─ plot_roc_train.png / plot_roc_val.png\n",
    "    │    ├─ plot_pr_train.png  / plot_pr_val.png\n",
    "    │    ├─ plot_cm_train_best.png / plot_cm_val_best.png\n",
    "    │    └─ fold_meta.json\n",
    "    ├─ summary_folds.csv            （各折指標）\n",
    "    └─ summary_overall.json         （平均與設定）\n",
    "\n",
    "執行方式：\n",
    "  python train_lstm_focal_windowed_clean_v2.py\n",
    "\"\"\"\n",
    "\n",
    "import os\n",
    "import json\n",
    "import time\n",
    "import math\n",
    "from pathlib import Path\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from sklearn.impute import SimpleImputer\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.metrics import (\n",
    "    roc_curve, auc, precision_recall_curve, average_precision_score,\n",
    "    confusion_matrix, classification_report,\n",
    "    precision_recall_fscore_support, accuracy_score, roc_auc_score\n",
    ")\n",
    "from sklearn.utils.class_weight import compute_class_weight\n",
    "\n",
    "import tensorflow as tf\n",
    "from tensorflow import keras\n",
    "from tensorflow.keras import layers, initializers, regularizers\n",
    "\n",
    "# ==============================\n",
    "# 一、基本設定\n",
    "# ==============================\n",
    "BASE = Path(\"/home/jovyan/RT08/0925/sliding_win/1014_sim\")\n",
    "DATA = BASE / \"windowed_clean\"\n",
    "RUNS_ROOT = BASE / \"training_runs\"\n",
    "RUNS_ROOT.mkdir(parents=True, exist_ok=True)\n",
    "\n",
    "CFG = {\n",
    "    \"seed\": 42,                       # 亂數種子\n",
    "    \"epochs\": 40,                     # 訓練 epochs\n",
    "    \"batch_size\": 128,                # 批次大小\n",
    "    \"learning_rate\": 1e-3,            # 起始學習率\n",
    "    \"focal_gamma\": 2.0,               # Focal Loss gamma\n",
    "    \"focal_alpha\": 0.25,              # 初始 alpha（每折會動態覆寫）\n",
    "    \"patience\": 5,                    # EarlyStopping 初始耐心（實際會放寬）\n",
    "    \"hidden_units\": 128,              # LSTM 隱藏單元\n",
    "    \"l2\": 1e-6,                       # L2 正則化\n",
    "    \"dropout\": 0.2,                   # dropout 比例\n",
    "    \"recurrent_dropout\": 0.0,         # recurrent_dropout\n",
    "    \"folds\": list(range(1, 10 + 1)),  # 十折\n",
    "    \"threshold\": 0.5                  # 固定閾值（同時會找 best-th）\n",
    "}\n",
    "\n",
    "# 設定隨機種子\n",
    "np.random.seed(CFG[\"seed\"])\n",
    "tf.random.set_seed(CFG[\"seed\"])\n",
    "\n",
    "# 版本化 run 目錄：timestamp + 自動 run_id\n",
    "ts = time.strftime(\"%Y%m%d_%H%M%S\")\n",
    "existing = sorted(RUNS_ROOT.glob(f\"{ts}_run*\"))\n",
    "run_id = len(existing) + 1\n",
    "RUN_DIR = RUNS_ROOT / f\"{ts}_run{run_id:02d}\"\n",
    "RUN_DIR.mkdir(parents=True, exist_ok=True)\n",
    "(RUN_DIR / \"cfg.json\").write_text(json.dumps(CFG, ensure_ascii=False, indent=2), encoding=\"utf-8\")\n",
    "\n",
    "# ==============================\n",
    "# 二、Focal Loss（二元）\n",
    "# ==============================\n",
    "def binary_focal_loss(gamma=2.0, alpha=0.25):\n",
    "    def loss(y_true, y_pred):\n",
    "        y_true = tf.cast(y_true, tf.float32)\n",
    "        y_pred = tf.clip_by_value(y_pred, 1e-7, 1.0 - 1e-7)\n",
    "        p_t = y_true * y_pred + (1 - y_true) * (1 - y_pred)\n",
    "        alpha_factor = y_true * alpha + (1 - y_true) * (1 - alpha)\n",
    "        modulating = tf.pow(1.0 - p_t, gamma)\n",
    "        bce = - (y_true * tf.math.log(y_pred) + (1 - y_true) * tf.math.log(1 - y_pred))\n",
    "        return tf.reduce_mean(alpha_factor * modulating * bce)\n",
    "    return loss\n",
    "\n",
    "# ==============================\n",
    "# 三、模型構建（移除 Masking；加入輸出層先驗偏置）\n",
    "# ==============================\n",
    "def build_model(input_shape, cfg, prior_pos=0.5):\n",
    "    reg = regularizers.l2(cfg[\"l2\"])\n",
    "    inputs = keras.Input(shape=input_shape, name=\"sequence\")\n",
    "\n",
    "    # ⚠️ 不使用 Masking，避免 0 被當作 padding（標準化後常接近 0）\n",
    "    x = inputs\n",
    "\n",
    "    x = layers.LSTM(cfg[\"hidden_units\"],\n",
    "                    dropout=cfg[\"dropout\"],\n",
    "                    recurrent_dropout=cfg[\"recurrent_dropout\"],\n",
    "                    kernel_regularizer=reg,\n",
    "                    recurrent_regularizer=reg,\n",
    "                    return_sequences=False)(x)\n",
    "    x = layers.Dropout(cfg[\"dropout\"])(x)\n",
    "\n",
    "    # 以訓練集正類比例做輸出層 bias 初始化，避免初期全猜 0\n",
    "    eps = 1e-7\n",
    "    prior_pos = float(np.clip(prior_pos, eps, 1 - eps))\n",
    "    init_bias = math.log(prior_pos / (1.0 - prior_pos))\n",
    "\n",
    "    outputs = layers.Dense(\n",
    "        1, activation=\"sigmoid\", name=\"prob\",\n",
    "        bias_initializer=initializers.Constant(init_bias)\n",
    "    )(x)\n",
    "\n",
    "    model = keras.Model(inputs, outputs, name=\"lstm_focal\")\n",
    "    opt = keras.optimizers.Adam(learning_rate=cfg[\"learning_rate\"])\n",
    "    model.compile(\n",
    "        optimizer=opt,\n",
    "        loss=binary_focal_loss(cfg[\"focal_gamma\"], cfg[\"focal_alpha\"]),\n",
    "        metrics=[\n",
    "            keras.metrics.BinaryAccuracy(name=\"accuracy\"),\n",
    "            keras.metrics.AUC(name=\"auc\"),\n",
    "            keras.metrics.AUC(name=\"auprc\", curve=\"PR\"),\n",
    "            keras.metrics.Precision(name=\"precision\"),\n",
    "            keras.metrics.Recall(name=\"recall\"),\n",
    "            keras.metrics.BinaryCrossentropy(name=\"binary_crossentropy\")\n",
    "        ]\n",
    "    )\n",
    "    return model\n",
    "\n",
    "# ==============================\n",
    "# 四、每折前處理（補值 + 標準化；避免洩漏）\n",
    "# ==============================\n",
    "def fit_transform_fold(X_train, X_val):\n",
    "    n, t, d = X_train.shape\n",
    "    tr2 = X_train.reshape(n, t * d)\n",
    "    va2 = X_val.reshape(X_val.shape[0], t * d)\n",
    "\n",
    "    imputer = SimpleImputer(strategy=\"median\")\n",
    "    scaler = StandardScaler(with_mean=True, with_std=True)\n",
    "\n",
    "    tr_imp = imputer.fit_transform(tr2)\n",
    "    tr_scl = scaler.fit_transform(tr_imp)\n",
    "\n",
    "    va_imp = imputer.transform(va2)\n",
    "    va_scl = scaler.transform(va_imp)\n",
    "\n",
    "    Xtr = tr_scl.reshape(n, t, d).astype(np.float32)\n",
    "    Xva = va_scl.reshape(X_val.shape[0], t, d).astype(np.float32)\n",
    "    return Xtr, Xva, imputer, scaler\n",
    "\n",
    "# ==============================\n",
    "# 五、繪圖（英文、藍色系）\n",
    "# ==============================\n",
    "BLUES = [\"#cfe8ff\", \"#9dd0ff\", \"#6bb8ff\", \"#3aa0ff\", \"#0a88ff\", \"#005bb5\"]\n",
    "\n",
    "def plot_history(hist_df: pd.DataFrame, out_png: Path):\n",
    "    plt.figure(figsize=(7, 5))\n",
    "    x = np.arange(len(hist_df))\n",
    "    plt.plot(x, hist_df[\"loss\"], label=\"Train Focal Loss\", color=BLUES[3], lw=2)\n",
    "    plt.plot(x, hist_df[\"val_loss\"], label=\"Val Focal Loss\", color=BLUES[5], lw=2)\n",
    "    if \"binary_crossentropy\" in hist_df.columns:\n",
    "        plt.plot(x, hist_df[\"binary_crossentropy\"], label=\"Train BCE\", color=BLUES[1], lw=2)\n",
    "    if \"val_binary_crossentropy\" in hist_df.columns:\n",
    "        plt.plot(x, hist_df[\"val_binary_crossentropy\"], label=\"Val BCE\", color=BLUES[0], lw=2)\n",
    "    plt.title(\"Loss Curves (Focal & BCE)\")\n",
    "    plt.xlabel(\"Epoch\"); plt.ylabel(\"Loss\")\n",
    "    plt.grid(True, alpha=0.25); plt.legend(); plt.tight_layout()\n",
    "    plt.savefig(out_png, dpi=160); plt.close()\n",
    "\n",
    "def plot_metric_epochs(hist: pd.DataFrame, metric: str, out_png: Path, title: str):\n",
    "    plt.figure(figsize=(7, 5))\n",
    "    x = np.arange(len(hist))\n",
    "    if metric in hist.columns:\n",
    "        plt.plot(x, hist[metric], label=f\"Train {metric}\", color=BLUES[3], lw=2)\n",
    "    valm = f\"val_{metric}\"\n",
    "    if valm in hist.columns:\n",
    "        plt.plot(x, hist[valm], label=f\"Val {metric}\", color=BLUES[5], lw=2)\n",
    "    plt.title(title)\n",
    "    plt.xlabel(\"Epoch\"); plt.ylabel(metric.title())\n",
    "    plt.grid(True, alpha=0.25); plt.legend(); plt.tight_layout()\n",
    "    plt.savefig(out_png, dpi=160); plt.close()\n",
    "\n",
    "def plot_roc_xy(fpr, tpr, roc_auc, out_png: Path, title: str):\n",
    "    plt.figure(figsize=(6, 5))\n",
    "    plt.plot(fpr, tpr, color=BLUES[4], lw=2, label=f\"ROC AUC = {roc_auc:.4f}\")\n",
    "    plt.plot([0, 1], [0, 1], color=BLUES[0], lw=1, ls=\"--\")\n",
    "    plt.title(title); plt.xlabel(\"False Positive Rate\"); plt.ylabel(\"True Positive Rate\")\n",
    "    plt.grid(True, alpha=0.25); plt.legend(loc=\"lower right\"); plt.tight_layout()\n",
    "    plt.savefig(out_png, dpi=160); plt.close()\n",
    "\n",
    "def plot_pr_xy(recall, precision, ap, out_png: Path, title: str):\n",
    "    plt.figure(figsize=(6, 5))\n",
    "    plt.plot(recall, precision, color=BLUES[4], lw=2, label=f\"AP = {ap:.4f}\")\n",
    "    plt.title(title); plt.xlabel(\"Recall\"); plt.ylabel(\"Precision\")\n",
    "    plt.grid(True, alpha=0.25); plt.legend(loc=\"lower left\"); plt.tight_layout()\n",
    "    plt.savefig(out_png, dpi=160); plt.close()\n",
    "\n",
    "def plot_cm(cm: np.ndarray, out_png: Path, title: str):\n",
    "    plt.figure(figsize=(5.6, 4.8))\n",
    "    im = plt.imshow(cm, cmap=\"Blues\")\n",
    "    plt.title(title, fontsize=16)\n",
    "    plt.xlabel(\"Predicted\", fontsize=13); plt.ylabel(\"Actual\", fontsize=13)\n",
    "    plt.colorbar(im, fraction=0.046, pad=0.04)\n",
    "    for (i, j), z in np.ndenumerate(cm):\n",
    "        plt.text(j, i, f\"{z}\", ha='center', va='center', color='black',\n",
    "                 fontsize=14, fontweight='bold')\n",
    "    plt.xticks([0, 1], [\"Negative\", \"Positive\"], fontsize=12)\n",
    "    plt.yticks([0, 1], [\"Negative\", \"Positive\"], fontsize=12)\n",
    "    plt.tight_layout(); plt.savefig(out_png, dpi=180); plt.close()\n",
    "\n",
    "# ==============================\n",
    "# 六、回合級 Callback：F1 與機率探針\n",
    "# ==============================\n",
    "class F1PerEpoch(keras.callbacks.Callback):\n",
    "    def __init__(self, Xtr, ytr, Xva, yva, threshold=0.5, batch_size=512):\n",
    "        super().__init__()\n",
    "        self.Xtr, self.ytr, self.Xva, self.yva = Xtr, ytr, Xva, yva\n",
    "        self.th = threshold\n",
    "        self.bs = batch_size\n",
    "    def on_epoch_end(self, epoch, logs=None):\n",
    "        ytr_prob = self.model.predict(self.Xtr, batch_size=self.bs, verbose=0).ravel()\n",
    "        yva_prob = self.model.predict(self.Xva, batch_size=self.bs, verbose=0).ravel()\n",
    "        ytr_pred = (ytr_prob >= self.th).astype(int)\n",
    "        yva_pred = (yva_prob >= self.th).astype(int)\n",
    "        _, _, f1_tr, _ = precision_recall_fscore_support(self.ytr, ytr_pred, average='binary', zero_division=0)\n",
    "        _, _, f1_va, _ = precision_recall_fscore_support(self.yva, yva_pred, average='binary', zero_division=0)\n",
    "        if logs is not None:\n",
    "            logs['f1'] = f1_tr\n",
    "            logs['val_f1'] = f1_va\n",
    "\n",
    "class ProbProbe(keras.callbacks.Callback):\n",
    "    def __init__(self, Xtr, Xva, bs=1024):\n",
    "        super().__init__()\n",
    "        self.Xtr, self.Xva, self.bs = Xtr, Xva, bs\n",
    "    def on_epoch_end(self, epoch, logs=None):\n",
    "        p_tr = self.model.predict(self.Xtr, batch_size=self.bs, verbose=0).ravel()\n",
    "        p_va = self.model.predict(self.Xva, batch_size=self.bs, verbose=0).ravel()\n",
    "        q = lambda a: float(np.quantile(a, 0.9))\n",
    "        print(f\"[Probe] epoch={epoch}  train mean={p_tr.mean():.4f} p90={q(p_tr):.4f} | \"\n",
    "              f\"val mean={p_va.mean():.4f} p90={q(p_va):.4f}\")\n",
    "\n",
    "# ==============================\n",
    "# 七、主流程：十折訓練與評估\n",
    "# ==============================\n",
    "all_rows = []\n",
    "\n",
    "for k in CFG[\"folds\"]:\n",
    "    fold_dir = RUN_DIR / f\"fold_{k}\"\n",
    "    fold_dir.mkdir(parents=True, exist_ok=True)\n",
    "\n",
    "    # === 讀取資料 ===\n",
    "    Xtr = np.load(DATA / f\"X_train_fold{k}.npy\")\n",
    "    ytr = np.load(DATA / f\"y_train_fold{k}.npy\")\n",
    "    Xva = np.load(DATA / f\"X_val_fold{k}.npy\")\n",
    "    yva = np.load(DATA / f\"y_val_fold{k}.npy\")\n",
    "    assert set(np.unique(ytr)).issubset({0, 1}), \"y_train 需為 {0,1}\"\n",
    "    assert set(np.unique(yva)).issubset({0, 1}), \"y_val 需為 {0,1}\"\n",
    "\n",
    "    # === 前處理（fit on train, transform on train/val）===\n",
    "    Xtr_p, Xva_p, _, _ = fit_transform_fold(Xtr, Xva)\n",
    "\n",
    "    # === 不平衡摘要：動態 focal α + class_weight ===\n",
    "    pos_rate = float(ytr.mean())\n",
    "    alpha_used = float(np.clip(1.0 - pos_rate, 0.1, 0.9))\n",
    "    CFG[\"focal_alpha\"] = alpha_used  # 覆寫本折 alpha\n",
    "\n",
    "    classes = np.array([0, 1])\n",
    "    cw = compute_class_weight(class_weight=\"balanced\", classes=classes, y=ytr)\n",
    "    class_weight = {0: float(cw[0]), 1: float(cw[1])}\n",
    "\n",
    "    # === 建模（prior_pos -> bias initializer）===\n",
    "    input_shape = (Xtr_p.shape[1], Xtr_p.shape[2])  # (T, D)\n",
    "    model = build_model(input_shape, CFG, prior_pos=pos_rate)\n",
    "\n",
    "    # === Callbacks ===\n",
    "    ckpt_path = fold_dir / \"best_model.keras\"\n",
    "    cbs = [\n",
    "        keras.callbacks.EarlyStopping(monitor=\"val_auprc\", patience=max(8, CFG[\"patience\"]),\n",
    "                                      mode=\"max\", restore_best_weights=True),\n",
    "        keras.callbacks.ModelCheckpoint(filepath=str(ckpt_path), monitor=\"val_auprc\",\n",
    "                                        mode=\"max\", save_best_only=True),\n",
    "        keras.callbacks.ReduceLROnPlateau(monitor=\"val_auprc\", mode=\"max\",\n",
    "                                          factor=0.5, patience=4, min_lr=1e-5, verbose=1),\n",
    "        F1PerEpoch(Xtr_p, ytr, Xva_p, yva, threshold=CFG[\"threshold\"], batch_size=512),\n",
    "        ProbProbe(Xtr_p, Xva_p, bs=1024)\n",
    "    ]\n",
    "\n",
    "    # === 訓練 ===\n",
    "    hist = model.fit(\n",
    "        Xtr_p, ytr,\n",
    "        validation_data=(Xva_p, yva),\n",
    "        epochs=CFG[\"epochs\"],\n",
    "        batch_size=CFG[\"batch_size\"],\n",
    "        callbacks=cbs,\n",
    "        verbose=2,\n",
    "        class_weight=class_weight\n",
    "    )\n",
    "\n",
    "    # === 保存 history 與訓練曲線 ===\n",
    "    hist_df = pd.DataFrame(hist.history)\n",
    "    hist_df.to_csv(fold_dir / \"history.csv\", index=False)\n",
    "    plot_history(hist_df, fold_dir / \"plot_loss.png\")\n",
    "    plot_metric_epochs(hist_df, \"accuracy\", fold_dir / \"plot_acc.png\", \"Accuracy per Epoch\")\n",
    "    if \"f1\" in hist_df.columns and \"val_f1\" in hist_df.columns:\n",
    "        plot_metric_epochs(hist_df, \"f1\", fold_dir / \"plot_f1.png\", \"F1 per Epoch\")\n",
    "\n",
    "    # === 推論：Train & Val 機率 ===\n",
    "    ytr_prob = model.predict(Xtr_p, batch_size=CFG[\"batch_size\"], verbose=0).ravel()\n",
    "    yva_prob = model.predict(Xva_p, batch_size=CFG[\"batch_size\"], verbose=0).ravel()\n",
    "\n",
    "    # === PR / ROC（Train & Val）===\n",
    "    # ROC\n",
    "    fpr_tr, tpr_tr, _ = roc_curve(ytr, ytr_prob)\n",
    "    fpr_va, tpr_va, _ = roc_curve(yva, yva_prob)\n",
    "    roc_auc_tr = auc(fpr_tr, tpr_tr)\n",
    "    roc_auc_va = auc(fpr_va, tpr_va)\n",
    "    # PR\n",
    "    prec_tr, rec_tr, _ = precision_recall_curve(ytr, ytr_prob)\n",
    "    prec_va, rec_va, _ = precision_recall_curve(yva, yva_prob)\n",
    "    ap_tr = average_precision_score(ytr, ytr_prob)\n",
    "    ap_va = average_precision_score(yva, yva_prob)\n",
    "\n",
    "    # 存曲線 CSV 與圖\n",
    "    pd.DataFrame({\"fpr\": fpr_tr, \"tpr\": tpr_tr}).to_csv(fold_dir / \"roc_curve_train.csv\", index=False)\n",
    "    pd.DataFrame({\"fpr\": fpr_va, \"tpr\": tpr_va}).to_csv(fold_dir / \"roc_curve_val.csv\", index=False)\n",
    "    plot_roc_xy(fpr_tr, tpr_tr, roc_auc_tr, fold_dir / \"plot_roc_train.png\", \"ROC Curve (Train)\")\n",
    "    plot_roc_xy(fpr_va, tpr_va, roc_auc_va, fold_dir / \"plot_roc_val.png\", \"ROC Curve (Validation)\")\n",
    "\n",
    "    pd.DataFrame({\"recall\": rec_tr, \"precision\": prec_tr}).to_csv(fold_dir / \"pr_curve_train.csv\", index=False)\n",
    "    pd.DataFrame({\"recall\": rec_va, \"precision\": prec_va}).to_csv(fold_dir / \"pr_curve_val.csv\", index=False)\n",
    "    plot_pr_xy(rec_tr, prec_tr, ap_tr, fold_dir / \"plot_pr_train.png\", \"Precision-Recall (Train)\")\n",
    "    plot_pr_xy(rec_va, prec_va, ap_va, fold_dir / \"plot_pr_val.png\",   \"Precision-Recall (Validation)\")\n",
    "\n",
    "    # === 閾值掃描（驗證集找最佳 F1）===\n",
    "    grid = np.linspace(0.01, 0.99, 99)\n",
    "    f1s = []\n",
    "    for th in grid:\n",
    "        f1s.append(precision_recall_fscore_support(yva, (yva_prob >= th).astype(int),\n",
    "                                                   average='binary', zero_division=0)[2])\n",
    "    best_idx = int(np.argmax(f1s))\n",
    "    best_th = float(grid[best_idx])\n",
    "\n",
    "    # === 指標（best-th 與 fixed 0.5 各一組）===\n",
    "    def pack_metrics(y_true, y_prob, th):\n",
    "        y_pred = (y_prob >= th).astype(int)\n",
    "        acc = accuracy_score(y_true, y_pred)\n",
    "        prec, rec, f1, _ = precision_recall_fscore_support(y_true, y_pred, average='binary', zero_division=0)\n",
    "        rocauc = roc_auc_score(y_true, y_prob)\n",
    "        cm = confusion_matrix(y_true, y_pred, labels=[0, 1])\n",
    "        return acc, prec, rec, f1, rocauc, cm\n",
    "\n",
    "    tr_acc_b, tr_prec_b, tr_rec_b, tr_f1_b, tr_auc, cm_tr_b = pack_metrics(ytr, ytr_prob, best_th)\n",
    "    va_acc_b, va_prec_b, va_rec_b, va_f1_b, va_auc, cm_va_b = pack_metrics(yva, yva_prob, best_th)\n",
    "\n",
    "    tr_acc_f, tr_prec_f, tr_rec_f, tr_f1_f, _, cm_tr_f = pack_metrics(ytr, ytr_prob, CFG[\"threshold\"])\n",
    "    va_acc_f, va_prec_f, va_rec_f, va_f1_f, _, cm_va_f = pack_metrics(yva, yva_prob, CFG[\"threshold\"])\n",
    "\n",
    "    # === 輸出 CSV ===\n",
    "    pd.DataFrame({\"y_true\": ytr, \"y_prob\": ytr_prob, \"y_pred\": (ytr_prob >= best_th).astype(int)}).to_csv(fold_dir / \"predictions_train.csv\", index=False)\n",
    "    pd.DataFrame({\"y_true\": yva, \"y_prob\": yva_prob, \"y_pred\": (yva_prob >= best_th).astype(int)}).to_csv(fold_dir / \"predictions_val.csv\", index=False)\n",
    "\n",
    "    pd.DataFrame(cm_tr_b, index=[\"Actual_0\",\"Actual_1\"], columns=[\"Pred_0\",\"Pred_1\"]).to_csv(fold_dir / \"cm_train_best.csv\")\n",
    "    pd.DataFrame(cm_va_b, index=[\"Actual_0\",\"Actual_1\"], columns=[\"Pred_0\",\"Pred_1\"]).to_csv(fold_dir / \"cm_val_best.csv\")\n",
    "    pd.DataFrame(cm_tr_f, index=[\"Actual_0\",\"Actual_1\"], columns=[\"Pred_0\",\"Pred_1\"]).to_csv(fold_dir / \"cm_train_fixed05.csv\")\n",
    "    pd.DataFrame(cm_va_f, index=[\"Actual_0\",\"Actual_1\"], columns=[\"Pred_0\",\"Pred_1\"]).to_csv(fold_dir / \"cm_val_fixed05.csv\")\n",
    "\n",
    "    plot_cm(cm_tr_b, fold_dir / \"plot_cm_train_best.png\", \"Confusion Matrix (Train, best-th)\")\n",
    "    plot_cm(cm_va_b, fold_dir / \"plot_cm_val_best.png\",   \"Confusion Matrix (Validation, best-th)\")\n",
    "\n",
    "    # === 每折 metrics.csv（含 best-th 與 fixed 0.5）===\n",
    "    row = {\n",
    "        \"fold\": k,\n",
    "        \"pos_rate\": pos_rate,\n",
    "        \"focal_alpha_used\": alpha_used,\n",
    "        \"class_weight_0\": class_weight[0], \"class_weight_1\": class_weight[1],\n",
    "        \"best_threshold\": best_th,\n",
    "        # Train (best-th)\n",
    "        \"train_accuracy_best\": tr_acc_b, \"train_precision_best\": tr_prec_b, \"train_recall_best\": tr_rec_b,\n",
    "        \"train_f1_best\": tr_f1_b, \"train_auc\": tr_auc, \"train_auprc\": ap_tr,\n",
    "        # Val   (best-th)\n",
    "        \"val_accuracy_best\": va_acc_b, \"val_precision_best\": va_prec_b, \"val_recall_best\": va_rec_b,\n",
    "        \"val_f1_best\": va_f1_b, \"val_auc\": va_auc, \"val_auprc\": ap_va,\n",
    "        # Val   (fixed 0.5)\n",
    "        \"val_accuracy_0p5\": va_acc_f, \"val_precision_0p5\": va_prec_f, \"val_recall_0p5\": va_rec_f,\n",
    "        \"val_f1_0p5\": va_f1_f\n",
    "    }\n",
    "    pd.DataFrame([row]).to_csv(fold_dir / \"metrics.csv\", index=False)\n",
    "\n",
    "    # === 審計資訊 ===\n",
    "    meta = {\n",
    "        \"fold\": k,\n",
    "        \"pos_rate\": pos_rate,\n",
    "        \"best_threshold\": best_th,\n",
    "        \"class_weight\": class_weight,\n",
    "        \"focal\": {\"gamma\": CFG[\"focal_gamma\"], \"alpha_used\": alpha_used}\n",
    "    }\n",
    "    (fold_dir / \"fold_meta.json\").write_text(json.dumps(meta, ensure_ascii=False, indent=2), encoding=\"utf-8\")\n",
    "\n",
    "    # 收集彙總\n",
    "    all_rows.append(row)\n",
    "\n",
    "# ==============================\n",
    "# 八、十折彙總與整體平均 ± 標準差\n",
    "# ==============================\n",
    "summary = pd.DataFrame(all_rows).sort_values(\"fold\")\n",
    "summary.to_csv(RUN_DIR / \"summary_folds.csv\", index=False)\n",
    "\n",
    "agg = {}\n",
    "for col in [c for c in summary.columns if c != \"fold\"]:\n",
    "    agg[col + \"_mean\"] = float(summary[col].mean())\n",
    "    agg[col + \"_std\"] = float(summary[col].std(ddof=1))\n",
    "\n",
    "overall = {\"n_folds\": int(len(summary)), **agg, \"config\": CFG}\n",
    "(RUN_DIR / \"summary_overall.json\").write_text(json.dumps(overall, ensure_ascii=False, indent=2), encoding=\"utf-8\")\n",
    "\n",
    "print(\"\\n=== Training Done ===\")\n",
    "print(f\"Run dir: {RUN_DIR}\")\n",
    "print(json.dumps(overall, indent=2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2f68829a-8a9e-487c-a1a7-93582d6c4e0b",
   "metadata": {},
   "outputs": [],
   "source": [
    "拿掉 Masking(0.0)；輸出層加先驗偏置；\n",
    "每折動態 focal α＋class_weight；\n",
    "驗證集掃描最佳 F1 閾值；\n",
    "加入 ReduceLROnPlateau、F1PerEpoch、ProbProbe；\n",
    "保留並繪製 Focal vs BCE 對照圖（用 1D-safe metric）；\n",
    "輸出更完整（best-th 與 0.5 兩套）。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c4028686-33b7-4c84-b476-7141d3a90c88",
   "metadata": {},
   "outputs": [],
   "source": [
    "加了同圖對照 Focal vs BCE　為了看使用 Focal 後是否真的改善模型收斂\n",
    "把兩條 loss 曲線放在同一張圖比較\n",
    "因為加 keras.metrics.BinaryCrossentropy 這個 metric"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 268,
   "id": "01830d61-badb-4398-9850-789852228fac",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/40\n",
      "[Probe] epoch=0  train mean=0.5365 p90=0.5534 | val mean=0.5366 p90=0.5533\n",
      "197/197 - 39s - 197ms/step - accuracy: 0.5245 - auc: 0.5052 - auprc: 0.2677 - binary_crossentropy: 0.6911 - loss: 0.0732 - precision: 0.2693 - recall: 0.4662 - val_accuracy: 0.2643 - val_auc: 0.5183 - val_auprc: 0.2801 - val_binary_crossentropy: 0.7301 - val_loss: 0.0692 - val_precision: 0.2641 - val_recall: 0.9986 - learning_rate: 0.0010 - f1: 0.4181 - val_f1: 0.4177\n",
      "Epoch 2/40\n",
      "[Probe] epoch=1  train mean=0.5265 p90=0.5464 | val mean=0.5267 p90=0.5466\n",
      "197/197 - 36s - 181ms/step - accuracy: 0.5112 - auc: 0.5066 - auprc: 0.2686 - binary_crossentropy: 0.6942 - loss: 0.0689 - precision: 0.2687 - recall: 0.4930 - val_accuracy: 0.2732 - val_auc: 0.5197 - val_auprc: 0.2817 - val_binary_crossentropy: 0.7193 - val_loss: 0.0684 - val_precision: 0.2653 - val_recall: 0.9891 - learning_rate: 0.0010 - f1: 0.4173 - val_f1: 0.4184\n",
      "Epoch 3/40\n",
      "[Probe] epoch=2  train mean=0.5214 p90=0.5454 | val mean=0.5214 p90=0.5454\n",
      "197/197 - 35s - 178ms/step - accuracy: 0.5139 - auc: 0.5092 - auprc: 0.2690 - binary_crossentropy: 0.6936 - loss: 0.0685 - precision: 0.2698 - recall: 0.4916 - val_accuracy: 0.2718 - val_auc: 0.5288 - val_auprc: 0.2851 - val_binary_crossentropy: 0.7138 - val_loss: 0.0681 - val_precision: 0.2642 - val_recall: 0.9837 - learning_rate: 0.0010 - f1: 0.4186 - val_f1: 0.4165\n",
      "Epoch 4/40\n",
      "[Probe] epoch=3  train mean=0.5188 p90=0.5388 | val mean=0.5188 p90=0.5389\n",
      "197/197 - 35s - 180ms/step - accuracy: 0.5123 - auc: 0.5084 - auprc: 0.2703 - binary_crossentropy: 0.6934 - loss: 0.0683 - precision: 0.2695 - recall: 0.4937 - val_accuracy: 0.2700 - val_auc: 0.5262 - val_auprc: 0.2855 - val_binary_crossentropy: 0.7111 - val_loss: 0.0680 - val_precision: 0.2648 - val_recall: 0.9919 - learning_rate: 0.0010 - f1: 0.4190 - val_f1: 0.4180\n",
      "Epoch 5/40\n",
      "[Probe] epoch=4  train mean=0.5157 p90=0.5339 | val mean=0.5159 p90=0.5341\n",
      "197/197 - 35s - 179ms/step - accuracy: 0.5122 - auc: 0.5085 - auprc: 0.2686 - binary_crossentropy: 0.6937 - loss: 0.0683 - precision: 0.2705 - recall: 0.4980 - val_accuracy: 0.2782 - val_auc: 0.5283 - val_auprc: 0.2850 - val_binary_crossentropy: 0.7081 - val_loss: 0.0678 - val_precision: 0.2649 - val_recall: 0.9756 - learning_rate: 0.0010 - f1: 0.4182 - val_f1: 0.4167\n",
      "Epoch 6/40\n",
      "[Probe] epoch=5  train mean=0.5138 p90=0.5290 | val mean=0.5139 p90=0.5290\n",
      "197/197 - 35s - 179ms/step - accuracy: 0.5113 - auc: 0.5114 - auprc: 0.2737 - binary_crossentropy: 0.6933 - loss: 0.0681 - precision: 0.2708 - recall: 0.5013 - val_accuracy: 0.2836 - val_auc: 0.5255 - val_auprc: 0.2832 - val_binary_crossentropy: 0.7062 - val_loss: 0.0677 - val_precision: 0.2634 - val_recall: 0.9525 - learning_rate: 0.0010 - f1: 0.4172 - val_f1: 0.4127\n",
      "Epoch 7/40\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[268], line 335\u001b[0m\n\u001b[1;32m    323\u001b[0m cbs \u001b[38;5;241m=\u001b[39m [\n\u001b[1;32m    324\u001b[0m     keras\u001b[38;5;241m.\u001b[39mcallbacks\u001b[38;5;241m.\u001b[39mEarlyStopping(monitor\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mval_auprc\u001b[39m\u001b[38;5;124m\"\u001b[39m, patience\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mmax\u001b[39m(\u001b[38;5;241m8\u001b[39m, CFG[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mpatience\u001b[39m\u001b[38;5;124m\"\u001b[39m]),\n\u001b[1;32m    325\u001b[0m                                   mode\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmax\u001b[39m\u001b[38;5;124m\"\u001b[39m, restore_best_weights\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m),\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    331\u001b[0m     ProbProbe(Xtr_p, Xva_p, bs\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1024\u001b[39m)\n\u001b[1;32m    332\u001b[0m ]\n\u001b[1;32m    334\u001b[0m \u001b[38;5;66;03m# === 訓練 ===\u001b[39;00m\n\u001b[0;32m--> 335\u001b[0m hist \u001b[38;5;241m=\u001b[39m \u001b[43mmodel\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfit\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    336\u001b[0m \u001b[43m    \u001b[49m\u001b[43mXtr_p\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mytr\u001b[49m\u001b[43m,\u001b[49m\u001b[43m                       \u001b[49m\u001b[38;5;66;43;03m# y 保持一維；loss/metric 內部會 1D-safe 對齊\u001b[39;49;00m\n\u001b[1;32m    337\u001b[0m \u001b[43m    \u001b[49m\u001b[43mvalidation_data\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mXva_p\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43myva\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    338\u001b[0m \u001b[43m    \u001b[49m\u001b[43mepochs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mCFG\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mepochs\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    339\u001b[0m \u001b[43m    \u001b[49m\u001b[43mbatch_size\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mCFG\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mbatch_size\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    340\u001b[0m \u001b[43m    \u001b[49m\u001b[43mcallbacks\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcbs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    341\u001b[0m \u001b[43m    \u001b[49m\u001b[43mverbose\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m2\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m    342\u001b[0m \u001b[43m    \u001b[49m\u001b[43mclass_weight\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mclass_weight\u001b[49m\n\u001b[1;32m    343\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    345\u001b[0m \u001b[38;5;66;03m# === 保存 history 與訓練曲線 ===\u001b[39;00m\n\u001b[1;32m    346\u001b[0m hist_df \u001b[38;5;241m=\u001b[39m pd\u001b[38;5;241m.\u001b[39mDataFrame(hist\u001b[38;5;241m.\u001b[39mhistory)\n",
      "File \u001b[0;32m/opt/conda/lib/python3.11/site-packages/keras/src/utils/traceback_utils.py:117\u001b[0m, in \u001b[0;36mfilter_traceback.<locals>.error_handler\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    115\u001b[0m filtered_tb \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m    116\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 117\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfn\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    118\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[1;32m    119\u001b[0m     filtered_tb \u001b[38;5;241m=\u001b[39m _process_traceback_frames(e\u001b[38;5;241m.\u001b[39m__traceback__)\n",
      "File \u001b[0;32m/opt/conda/lib/python3.11/site-packages/keras/src/backend/tensorflow/trainer.py:318\u001b[0m, in \u001b[0;36mTensorFlowTrainer.fit\u001b[0;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_batch_size, validation_freq)\u001b[0m\n\u001b[1;32m    316\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m step, iterator \u001b[38;5;129;01min\u001b[39;00m epoch_iterator\u001b[38;5;241m.\u001b[39menumerate_epoch():\n\u001b[1;32m    317\u001b[0m     callbacks\u001b[38;5;241m.\u001b[39mon_train_batch_begin(step)\n\u001b[0;32m--> 318\u001b[0m     logs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtrain_function\u001b[49m\u001b[43m(\u001b[49m\u001b[43miterator\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    319\u001b[0m     logs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_pythonify_logs(logs)\n\u001b[1;32m    320\u001b[0m     callbacks\u001b[38;5;241m.\u001b[39mon_train_batch_end(step, logs)\n",
      "File \u001b[0;32m/opt/conda/lib/python3.11/site-packages/tensorflow/python/util/traceback_utils.py:150\u001b[0m, in \u001b[0;36mfilter_traceback.<locals>.error_handler\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    148\u001b[0m filtered_tb \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m    149\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 150\u001b[0m   \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfn\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    151\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[1;32m    152\u001b[0m   filtered_tb \u001b[38;5;241m=\u001b[39m _process_traceback_frames(e\u001b[38;5;241m.\u001b[39m__traceback__)\n",
      "File \u001b[0;32m/opt/conda/lib/python3.11/site-packages/tensorflow/python/eager/polymorphic_function/polymorphic_function.py:833\u001b[0m, in \u001b[0;36mFunction.__call__\u001b[0;34m(self, *args, **kwds)\u001b[0m\n\u001b[1;32m    830\u001b[0m compiler \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mxla\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_jit_compile \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mnonXla\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    832\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m OptionalXlaContext(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_jit_compile):\n\u001b[0;32m--> 833\u001b[0m   result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwds\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    835\u001b[0m new_tracing_count \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mexperimental_get_tracing_count()\n\u001b[1;32m    836\u001b[0m without_tracing \u001b[38;5;241m=\u001b[39m (tracing_count \u001b[38;5;241m==\u001b[39m new_tracing_count)\n",
      "File \u001b[0;32m/opt/conda/lib/python3.11/site-packages/tensorflow/python/eager/polymorphic_function/polymorphic_function.py:878\u001b[0m, in \u001b[0;36mFunction._call\u001b[0;34m(self, *args, **kwds)\u001b[0m\n\u001b[1;32m    875\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_lock\u001b[38;5;241m.\u001b[39mrelease()\n\u001b[1;32m    876\u001b[0m \u001b[38;5;66;03m# In this case we have not created variables on the first call. So we can\u001b[39;00m\n\u001b[1;32m    877\u001b[0m \u001b[38;5;66;03m# run the first trace but we should fail if variables are created.\u001b[39;00m\n\u001b[0;32m--> 878\u001b[0m results \u001b[38;5;241m=\u001b[39m \u001b[43mtracing_compilation\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcall_function\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    879\u001b[0m \u001b[43m    \u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkwds\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_variable_creation_config\u001b[49m\n\u001b[1;32m    880\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    881\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_created_variables:\n\u001b[1;32m    882\u001b[0m   \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCreating variables on a non-first call to a function\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    883\u001b[0m                    \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m decorated with tf.function.\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
      "File \u001b[0;32m/opt/conda/lib/python3.11/site-packages/tensorflow/python/eager/polymorphic_function/tracing_compilation.py:139\u001b[0m, in \u001b[0;36mcall_function\u001b[0;34m(args, kwargs, tracing_options)\u001b[0m\n\u001b[1;32m    137\u001b[0m bound_args \u001b[38;5;241m=\u001b[39m function\u001b[38;5;241m.\u001b[39mfunction_type\u001b[38;5;241m.\u001b[39mbind(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[1;32m    138\u001b[0m flat_inputs \u001b[38;5;241m=\u001b[39m function\u001b[38;5;241m.\u001b[39mfunction_type\u001b[38;5;241m.\u001b[39munpack_inputs(bound_args)\n\u001b[0;32m--> 139\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunction\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_flat\u001b[49m\u001b[43m(\u001b[49m\u001b[43m  \u001b[49m\u001b[38;5;66;43;03m# pylint: disable=protected-access\u001b[39;49;00m\n\u001b[1;32m    140\u001b[0m \u001b[43m    \u001b[49m\u001b[43mflat_inputs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcaptured_inputs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfunction\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcaptured_inputs\u001b[49m\n\u001b[1;32m    141\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m/opt/conda/lib/python3.11/site-packages/tensorflow/python/eager/polymorphic_function/concrete_function.py:1322\u001b[0m, in \u001b[0;36mConcreteFunction._call_flat\u001b[0;34m(self, tensor_inputs, captured_inputs)\u001b[0m\n\u001b[1;32m   1318\u001b[0m possible_gradient_type \u001b[38;5;241m=\u001b[39m gradients_util\u001b[38;5;241m.\u001b[39mPossibleTapeGradientTypes(args)\n\u001b[1;32m   1319\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m (possible_gradient_type \u001b[38;5;241m==\u001b[39m gradients_util\u001b[38;5;241m.\u001b[39mPOSSIBLE_GRADIENT_TYPES_NONE\n\u001b[1;32m   1320\u001b[0m     \u001b[38;5;129;01mand\u001b[39;00m executing_eagerly):\n\u001b[1;32m   1321\u001b[0m   \u001b[38;5;66;03m# No tape is watching; skip to running the function.\u001b[39;00m\n\u001b[0;32m-> 1322\u001b[0m   \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_inference_function\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcall_preflattened\u001b[49m\u001b[43m(\u001b[49m\u001b[43margs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1323\u001b[0m forward_backward \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_select_forward_and_backward_functions(\n\u001b[1;32m   1324\u001b[0m     args,\n\u001b[1;32m   1325\u001b[0m     possible_gradient_type,\n\u001b[1;32m   1326\u001b[0m     executing_eagerly)\n\u001b[1;32m   1327\u001b[0m forward_function, args_with_tangents \u001b[38;5;241m=\u001b[39m forward_backward\u001b[38;5;241m.\u001b[39mforward()\n",
      "File \u001b[0;32m/opt/conda/lib/python3.11/site-packages/tensorflow/python/eager/polymorphic_function/atomic_function.py:216\u001b[0m, in \u001b[0;36mAtomicFunction.call_preflattened\u001b[0;34m(self, args)\u001b[0m\n\u001b[1;32m    214\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mcall_preflattened\u001b[39m(\u001b[38;5;28mself\u001b[39m, args: Sequence[core\u001b[38;5;241m.\u001b[39mTensor]) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Any:\n\u001b[1;32m    215\u001b[0m \u001b[38;5;250m  \u001b[39m\u001b[38;5;124;03m\"\"\"Calls with flattened tensor inputs and returns the structured output.\"\"\"\u001b[39;00m\n\u001b[0;32m--> 216\u001b[0m   flat_outputs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcall_flat\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    217\u001b[0m   \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfunction_type\u001b[38;5;241m.\u001b[39mpack_output(flat_outputs)\n",
      "File \u001b[0;32m/opt/conda/lib/python3.11/site-packages/tensorflow/python/eager/polymorphic_function/atomic_function.py:233\u001b[0m, in \u001b[0;36mAtomicFunction.call_flat\u001b[0;34m(self, *args)\u001b[0m\n\u001b[1;32m    219\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mcall_flat\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;241m*\u001b[39margs: core\u001b[38;5;241m.\u001b[39mTensor) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Sequence[core\u001b[38;5;241m.\u001b[39mTensor]:\n\u001b[1;32m    220\u001b[0m \u001b[38;5;250m  \u001b[39m\u001b[38;5;124;03m\"\"\"Calls with flat tensor inputs and returns flat tensor outputs.\u001b[39;00m\n\u001b[1;32m    221\u001b[0m \n\u001b[1;32m    222\u001b[0m \u001b[38;5;124;03m  Args:\u001b[39;00m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    231\u001b[0m \u001b[38;5;124;03m      available to be called because it has been garbage collected.\u001b[39;00m\n\u001b[1;32m    232\u001b[0m \u001b[38;5;124;03m  \"\"\"\u001b[39;00m\n\u001b[0;32m--> 233\u001b[0m   expected_len \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlen\u001b[39m(\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcached_definition\u001b[49m\u001b[38;5;241m.\u001b[39msignature\u001b[38;5;241m.\u001b[39minput_arg)\n\u001b[1;32m    234\u001b[0m   \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(args) \u001b[38;5;241m!=\u001b[39m expected_len:\n\u001b[1;32m    235\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[1;32m    236\u001b[0m         \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mSignature specifies \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mexpected_len\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m arguments, got: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mlen\u001b[39m(args)\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    237\u001b[0m         \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m Expected inputs: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcached_definition\u001b[38;5;241m.\u001b[39msignature\u001b[38;5;241m.\u001b[39minput_arg\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    238\u001b[0m         \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m Received inputs: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00margs\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    239\u001b[0m         \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m Function Type: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfunction_type\u001b[38;5;132;01m!r}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    240\u001b[0m     )\n",
      "File \u001b[0;32m/opt/conda/lib/python3.11/site-packages/tensorflow/python/eager/polymorphic_function/atomic_function.py:185\u001b[0m, in \u001b[0;36mAtomicFunction.cached_definition\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    182\u001b[0m   \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_bound_context\u001b[38;5;241m.\u001b[39mget_c_function(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mname)\n\u001b[1;32m    184\u001b[0m \u001b[38;5;66;03m# TODO(fmuham): Move caching to dependent code and remove method.\u001b[39;00m\n\u001b[0;32m--> 185\u001b[0m \u001b[38;5;129m@property\u001b[39m\n\u001b[1;32m    186\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mcached_definition\u001b[39m(\u001b[38;5;28mself\u001b[39m) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m function_pb2\u001b[38;5;241m.\u001b[39mFunctionDef:\n\u001b[1;32m    187\u001b[0m \u001b[38;5;250m  \u001b[39m\u001b[38;5;124;03m\"\"\"Cached FunctionDef (not guaranteed to be fresh).\"\"\"\u001b[39;00m\n\u001b[1;32m    188\u001b[0m   \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_cached_definition \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "# -*- coding: utf-8 -*-\n",
    "\"\"\"\n",
    "train_lstm_focal_windowed_clean_v2.py\n",
    "\n",
    "目的：\n",
    "  使用 Keras + LSTM + Focal Loss，針對 windowed_clean/ 的十折資料進行訓練與評估。\n",
    "  - 每折獨立補值與標準化（避免資料洩漏）\n",
    "  - 產生英文圖表（藍色漸層）並儲存 CSV 結果\n",
    "  - 以 timestamp + run_id 版本化輸出\n",
    "  - 不改動原始 y；以 1D-safe BCE metric 繪製 Focal vs BCE 對照圖\n",
    "\n",
    "資料假設：\n",
    "  /home/jovyan/RT08/0925/sliding_win/1014_sim/windowed_clean/\n",
    "    X_train_fold{k}.npy, y_train_fold{k}.npy\n",
    "    X_val_fold{k}.npy,   y_val_fold{k}.npy\n",
    "\n",
    "輸出：\n",
    "  /home/jovyan/RT08/0925/sliding_win/1014_sim/training_runs/<timestamp>_runXX/\n",
    "    ├─ cfg.json\n",
    "    ├─ fold_{k}/\n",
    "    │    ├─ history.csv\n",
    "    │    ├─ metrics.csv\n",
    "    │    ├─ predictions_train.csv / predictions_val.csv\n",
    "    │    ├─ roc_curve_train.csv  / roc_curve_val.csv\n",
    "    │    ├─ pr_curve_train.csv   / pr_curve_val.csv\n",
    "    │    ├─ cm_train_best.csv    / cm_val_best.csv\n",
    "    │    ├─ cm_train_fixed05.csv / cm_val_fixed05.csv\n",
    "    │    ├─ best_model.keras\n",
    "    │    ├─ plot_loss.png\n",
    "    │    ├─ plot_acc.png\n",
    "    │    ├─ plot_f1.png\n",
    "    │    ├─ plot_roc_train.png / plot_roc_val.png\n",
    "    │    ├─ plot_pr_train.png  / plot_pr_val.png\n",
    "    │    ├─ plot_cm_train_best.png / plot_cm_val_best.png\n",
    "    │    └─ fold_meta.json\n",
    "    ├─ summary_folds.csv\n",
    "    └─ summary_overall.json\n",
    "\n",
    "執行方式：\n",
    "  python train_lstm_focal_windowed_clean_v2.py\n",
    "\"\"\"\n",
    "\n",
    "import os\n",
    "import json\n",
    "import time\n",
    "import math\n",
    "from pathlib import Path\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from sklearn.impute import SimpleImputer\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.metrics import (\n",
    "    roc_curve, auc, precision_recall_curve, average_precision_score,\n",
    "    confusion_matrix, precision_recall_fscore_support,\n",
    "    accuracy_score, roc_auc_score,\n",
    ")\n",
    "from sklearn.utils.class_weight import compute_class_weight\n",
    "\n",
    "import tensorflow as tf\n",
    "from tensorflow import keras\n",
    "from tensorflow.keras import layers, initializers, regularizers\n",
    "\n",
    "# ==============================\n",
    "# 一、基本設定\n",
    "# ==============================\n",
    "BASE = Path(\"/home/jovyan/RT08/0925/sliding_win/1014_sim\")\n",
    "DATA = BASE / \"windowed_clean\"\n",
    "RUNS_ROOT = BASE / \"training_runs\"\n",
    "RUNS_ROOT.mkdir(parents=True, exist_ok=True)\n",
    "\n",
    "CFG = {\n",
    "    \"seed\": 42,\n",
    "    \"epochs\": 40,\n",
    "    \"batch_size\": 128,\n",
    "    \"learning_rate\": 1e-3,\n",
    "    \"focal_gamma\": 2.0,\n",
    "    \"focal_alpha\": 0.25,            # 會在每折以 (1 - pos_rate) 動態覆寫\n",
    "    \"patience\": 5,                  # EarlyStopping 基底值（實際以 max(8, patience)）\n",
    "    \"hidden_units\": 128,\n",
    "    \"l2\": 1e-6,\n",
    "    \"dropout\": 0.2,\n",
    "    \"recurrent_dropout\": 0.0,\n",
    "    \"folds\": list(range(1, 10 + 1)),\n",
    "    \"threshold\": 0.5                # 也會對 val 掃描 best-F1\n",
    "}\n",
    "\n",
    "np.random.seed(CFG[\"seed\"])\n",
    "tf.random.set_seed(CFG[\"seed\"])\n",
    "\n",
    "# 版本化 run 目錄：timestamp + 自動 run_id\n",
    "ts = time.strftime(\"%Y%m%d_%H%M%S\")\n",
    "existing = sorted(RUNS_ROOT.glob(f\"{ts}_run*\"))\n",
    "run_id = len(existing) + 1\n",
    "RUN_DIR = RUNS_ROOT / f\"{ts}_run{run_id:02d}\"\n",
    "RUN_DIR.mkdir(parents=True, exist_ok=True)\n",
    "(RUN_DIR / \"cfg.json\").write_text(json.dumps(CFG, ensure_ascii=False, indent=2), encoding=\"utf-8\")\n",
    "\n",
    "# ==============================\n",
    "# 二、Loss / Metric\n",
    "# ==============================\n",
    "def binary_focal_loss(gamma=2.0, alpha=0.25):\n",
    "    \"\"\"1D-safe：在 loss 內也對齊形狀，避免 rank 衝突\"\"\"\n",
    "    def loss(y_true, y_pred):\n",
    "        y_true = tf.cast(y_true, tf.float32)\n",
    "        y_pred = tf.cast(y_pred, tf.float32)\n",
    "        # 對齊 y_true 與 y_pred 的形狀（通常 y_pred 為 [B,1]）\n",
    "        y_true = tf.reshape(y_true, tf.shape(y_pred))\n",
    "        y_pred = tf.clip_by_value(y_pred, 1e-7, 1.0 - 1e-7)\n",
    "        p_t = y_true * y_pred + (1.0 - y_true) * (1.0 - y_pred)\n",
    "        alpha_factor = y_true * alpha + (1.0 - y_true) * (1.0 - alpha)\n",
    "        modulating = tf.pow(1.0 - p_t, gamma)\n",
    "        bce = - (y_true * tf.math.log(y_pred) + (1.0 - y_true) * tf.math.log(1.0 - y_pred))\n",
    "        return tf.reduce_mean(alpha_factor * modulating * bce)\n",
    "    return loss\n",
    "\n",
    "def bce_metric_1d_safe(y_true, y_pred):\n",
    "    \"\"\"不改動原始 y；在 metric 內動態 reshape 對齊 y_pred 形狀\"\"\"\n",
    "    y_true = tf.cast(y_true, tf.float32)\n",
    "    y_pred = tf.cast(y_pred, tf.float32)\n",
    "    y_true = tf.reshape(y_true, tf.shape(y_pred))\n",
    "    return tf.reduce_mean(tf.keras.losses.binary_crossentropy(y_true, y_pred))\n",
    "bce_metric_1d_safe.__name__ = \"binary_crossentropy\"  # 讓 history 欄位維持相同名稱\n",
    "\n",
    "# ==============================\n",
    "# 三、模型構建（移除 Masking；輸出層加先驗偏置）\n",
    "# ==============================\n",
    "def build_model(input_shape, cfg, prior_pos=0.5):\n",
    "    reg = regularizers.l2(cfg[\"l2\"])\n",
    "    inputs = keras.Input(shape=input_shape, name=\"sequence\")\n",
    "\n",
    "    # 不使用 Masking，避免標準化後接近 0 的有效步被當 padding\n",
    "    x = inputs\n",
    "\n",
    "    x = layers.LSTM(cfg[\"hidden_units\"],\n",
    "                    dropout=cfg[\"dropout\"],\n",
    "                    recurrent_dropout=cfg[\"recurrent_dropout\"],\n",
    "                    kernel_regularizer=reg,\n",
    "                    recurrent_regularizer=reg,\n",
    "                    return_sequences=False)(x)\n",
    "    x = layers.Dropout(cfg[\"dropout\"])(x)\n",
    "\n",
    "    # 用先驗正類比例設定輸出層偏置，避免初期全猜 0\n",
    "    eps = 1e-7\n",
    "    prior_pos = float(np.clip(prior_pos, eps, 1.0 - eps))\n",
    "    init_bias = math.log(prior_pos / (1.0 - prior_pos))\n",
    "\n",
    "    outputs = layers.Dense(\n",
    "        1, activation=\"sigmoid\", name=\"prob\",\n",
    "        bias_initializer=initializers.Constant(init_bias)\n",
    "    )(x)\n",
    "\n",
    "    model = keras.Model(inputs, outputs, name=\"lstm_focal\")\n",
    "    opt = keras.optimizers.Adam(learning_rate=cfg[\"learning_rate\"])\n",
    "    model.compile(\n",
    "        optimizer=opt,\n",
    "        loss=binary_focal_loss(cfg[\"focal_gamma\"], cfg[\"focal_alpha\"]),\n",
    "        metrics=[\n",
    "            keras.metrics.BinaryAccuracy(name=\"accuracy\"),\n",
    "            keras.metrics.AUC(name=\"auc\"),\n",
    "            keras.metrics.AUC(name=\"auprc\", curve=\"PR\"),\n",
    "            keras.metrics.Precision(name=\"precision\"),\n",
    "            keras.metrics.Recall(name=\"recall\"),\n",
    "            bce_metric_1d_safe  # 1D-safe：保留 BCE 對照圖而不改 y\n",
    "        ]\n",
    "    )\n",
    "    return model\n",
    "\n",
    "# ==============================\n",
    "# 四、每折前處理（補值 + 標準化；避免洩漏）\n",
    "# ==============================\n",
    "def fit_transform_fold(X_train, X_val):\n",
    "    n, t, d = X_train.shape\n",
    "    tr2 = X_train.reshape(n, t * d)\n",
    "    va2 = X_val.reshape(X_val.shape[0], t * d)\n",
    "\n",
    "    imputer = SimpleImputer(strategy=\"median\")\n",
    "    scaler = StandardScaler(with_mean=True, with_std=True)\n",
    "\n",
    "    tr_imp = imputer.fit_transform(tr2)\n",
    "    tr_scl = scaler.fit_transform(tr_imp)\n",
    "\n",
    "    va_imp = imputer.transform(va2)\n",
    "    va_scl = scaler.transform(va_imp)\n",
    "\n",
    "    Xtr = tr_scl.reshape(n, t, d).astype(np.float32)\n",
    "    Xva = va_scl.reshape(X_val.shape[0], t, d).astype(np.float32)\n",
    "    return Xtr, Xva, imputer, scaler\n",
    "\n",
    "# ==============================\n",
    "# 五、繪圖（英文、藍色系）\n",
    "# ==============================\n",
    "BLUES = [\"#cfe8ff\", \"#9dd0ff\", \"#6bb8ff\", \"#3aa0ff\", \"#0a88ff\", \"#005bb5\"]\n",
    "\n",
    "def plot_history(hist_df: pd.DataFrame, out_png: Path):\n",
    "    plt.figure(figsize=(7, 5))\n",
    "    x = np.arange(len(hist_df))\n",
    "    # Focal Loss（loss）與 BCE metric（binary_crossentropy）同圖\n",
    "    plt.plot(x, hist_df[\"loss\"], label=\"Train Focal Loss\", lw=2)\n",
    "    plt.plot(x, hist_df[\"val_loss\"], label=\"Val Focal Loss\", lw=2)\n",
    "    if \"binary_crossentropy\" in hist_df.columns:\n",
    "        plt.plot(x, hist_df[\"binary_crossentropy\"], label=\"Train BCE\", lw=2)\n",
    "    if \"val_binary_crossentropy\" in hist_df.columns:\n",
    "        plt.plot(x, hist_df[\"val_binary_crossentropy\"], label=\"Val BCE\", lw=2)\n",
    "    for line, c in zip(plt.gca().lines, [BLUES[3], BLUES[5], BLUES[1], BLUES[0]]):\n",
    "        line.set_color(c)\n",
    "    plt.title(\"Loss Curves (Focal & BCE)\")\n",
    "    plt.xlabel(\"Epoch\"); plt.ylabel(\"Loss\")\n",
    "    plt.grid(True, alpha=0.25); plt.legend(); plt.tight_layout()\n",
    "    plt.savefig(out_png, dpi=160); plt.close()\n",
    "\n",
    "def plot_metric_epochs(hist: pd.DataFrame, metric: str, out_png: Path, title: str):\n",
    "    plt.figure(figsize=(7, 5))\n",
    "    x = np.arange(len(hist))\n",
    "    if metric in hist.columns:\n",
    "        plt.plot(x, hist[metric], label=f\"Train {metric}\", color=BLUES[3], lw=2)\n",
    "    valm = f\"val_{metric}\"\n",
    "    if valm in hist.columns:\n",
    "        plt.plot(x, hist[valm], label=f\"Val {metric}\", color=BLUES[5], lw=2)\n",
    "    plt.title(title)\n",
    "    plt.xlabel(\"Epoch\"); plt.ylabel(metric.title())\n",
    "    plt.grid(True, alpha=0.25); plt.legend(); plt.tight_layout()\n",
    "    plt.savefig(out_png, dpi=160); plt.close()\n",
    "\n",
    "def plot_roc_xy(fpr, tpr, roc_auc, out_png: Path, title: str):\n",
    "    plt.figure(figsize=(6, 5))\n",
    "    plt.plot(fpr, tpr, color=BLUES[4], lw=2, label=f\"ROC AUC = {roc_auc:.4f}\")\n",
    "    plt.plot([0, 1], [0, 1], color=BLUES[0], lw=1, ls=\"--\")\n",
    "    plt.title(title); plt.xlabel(\"False Positive Rate\"); plt.ylabel(\"True Positive Rate\")\n",
    "    plt.grid(True, alpha=0.25); plt.legend(loc=\"lower right\"); plt.tight_layout()\n",
    "    plt.savefig(out_png, dpi=160); plt.close()\n",
    "\n",
    "def plot_pr_xy(recall, precision, ap, out_png: Path, title: str):\n",
    "    plt.figure(figsize=(6, 5))\n",
    "    plt.plot(recall, precision, color=BLUES[4], lw=2, label=f\"AP = {ap:.4f}\")\n",
    "    plt.title(title); plt.xlabel(\"Recall\"); plt.ylabel(\"Precision\")\n",
    "    plt.grid(True, alpha=0.25); plt.legend(loc=\"lower left\"); plt.tight_layout()\n",
    "    plt.savefig(out_png, dpi=160); plt.close()\n",
    "\n",
    "def plot_cm(cm: np.ndarray, out_png: Path, title: str):\n",
    "    plt.figure(figsize=(5.6, 4.8))\n",
    "    im = plt.imshow(cm, cmap=\"Blues\")\n",
    "    plt.title(title, fontsize=16)\n",
    "    plt.xlabel(\"Predicted\", fontsize=13); plt.ylabel(\"Actual\", fontsize=13)\n",
    "    plt.colorbar(im, fraction=0.046, pad=0.04)\n",
    "    for (i, j), z in np.ndenumerate(cm):\n",
    "        plt.text(j, i, f\"{z}\", ha='center', va='center', color='black',\n",
    "                 fontsize=14, fontweight='bold')\n",
    "    plt.xticks([0, 1], [\"Negative\", \"Positive\"], fontsize=12)\n",
    "    plt.yticks([0, 1], [\"Negative\", \"Positive\"], fontsize=12)\n",
    "    plt.tight_layout(); plt.savefig(out_png, dpi=180); plt.close()\n",
    "\n",
    "# ==============================\n",
    "# 六、回合級 Callback：F1 與機率探針\n",
    "# ==============================\n",
    "class F1PerEpoch(keras.callbacks.Callback):\n",
    "    def __init__(self, Xtr, ytr, Xva, yva, threshold=0.5, batch_size=512):\n",
    "        super().__init__()\n",
    "        self.Xtr, self.ytr, self.Xva, self.yva = Xtr, ytr, Xva, yva\n",
    "        self.th = threshold\n",
    "        self.bs = batch_size\n",
    "    def on_epoch_end(self, epoch, logs=None):\n",
    "        ytr_prob = self.model.predict(self.Xtr, batch_size=self.bs, verbose=0).ravel()\n",
    "        yva_prob = self.model.predict(self.Xva, batch_size=self.bs, verbose=0).ravel()\n",
    "        ytr_pred = (ytr_prob >= self.th).astype(int)\n",
    "        yva_pred = (yva_prob >= self.th).astype(int)\n",
    "        _, _, f1_tr, _ = precision_recall_fscore_support(self.ytr, ytr_pred, average='binary', zero_division=0)\n",
    "        _, _, f1_va, _ = precision_recall_fscore_support(self.yva, yva_pred, average='binary', zero_division=0)\n",
    "        if logs is not None:\n",
    "            logs['f1'] = f1_tr\n",
    "            logs['val_f1'] = f1_va\n",
    "\n",
    "class ProbProbe(keras.callbacks.Callback):\n",
    "    def __init__(self, Xtr, Xva, bs=1024):\n",
    "        super().__init__()\n",
    "        self.Xtr, self.Xva, self.bs = Xtr, Xva, bs\n",
    "    def on_epoch_end(self, epoch, logs=None):\n",
    "        p_tr = self.model.predict(self.Xtr, batch_size=self.bs, verbose=0).ravel()\n",
    "        p_va = self.model.predict(self.Xva, batch_size=self.bs, verbose=0).ravel()\n",
    "        q = lambda a: float(np.quantile(a, 0.9))\n",
    "        print(f\"[Probe] epoch={epoch}  train mean={p_tr.mean():.4f} p90={q(p_tr):.4f} | \"\n",
    "              f\"val mean={p_va.mean():.4f} p90={q(p_va):.4f}\")\n",
    "\n",
    "# ==============================\n",
    "# 七、主流程：十折訓練與評估\n",
    "# ==============================\n",
    "all_rows = []\n",
    "\n",
    "for k in CFG[\"folds\"]:\n",
    "    fold_dir = RUN_DIR / f\"fold_{k}\"\n",
    "    fold_dir.mkdir(parents=True, exist_ok=True)\n",
    "\n",
    "    # === 讀取資料 ===\n",
    "    Xtr = np.load(DATA / f\"X_train_fold{k}.npy\")\n",
    "    ytr = np.load(DATA / f\"y_train_fold{k}.npy\")\n",
    "    Xva = np.load(DATA / f\"X_val_fold{k}.npy\")\n",
    "    yva = np.load(DATA / f\"y_val_fold{k}.npy\")\n",
    "\n",
    "    # 檢查標籤\n",
    "    assert set(np.unique(ytr)).issubset({0, 1}), \"y_train 需為 {0,1}\"\n",
    "    assert set(np.unique(yva)).issubset({0, 1}), \"y_val 需為 {0,1}\"\n",
    "\n",
    "    # === 前處理（fit on train, transform on train/val）===\n",
    "    Xtr_p, Xva_p, _, _ = fit_transform_fold(Xtr, Xva)\n",
    "\n",
    "    # === 不平衡摘要：動態 focal α + class_weight ===\n",
    "    pos_rate = float(ytr.mean())\n",
    "    alpha_used = float(np.clip(1.0 - pos_rate, 0.1, 0.9))\n",
    "    CFG[\"focal_alpha\"] = alpha_used  # 覆寫本折 alpha\n",
    "\n",
    "    classes = np.array([0, 1])\n",
    "    cw = compute_class_weight(class_weight=\"balanced\", classes=classes, y=ytr)\n",
    "    class_weight = {0: float(cw[0]), 1: float(cw[1])}\n",
    "\n",
    "    # === 建模（prior_pos -> bias initializer）===\n",
    "    input_shape = (Xtr_p.shape[1], Xtr_p.shape[2])  # (T, D)\n",
    "    model = build_model(input_shape, CFG, prior_pos=pos_rate)\n",
    "\n",
    "    # === Callbacks ===\n",
    "    ckpt_path = fold_dir / \"best_model.keras\"\n",
    "    cbs = [\n",
    "        keras.callbacks.EarlyStopping(monitor=\"val_auprc\", patience=max(8, CFG[\"patience\"]),\n",
    "                                      mode=\"max\", restore_best_weights=True),\n",
    "        keras.callbacks.ModelCheckpoint(filepath=str(ckpt_path), monitor=\"val_auprc\",\n",
    "                                        mode=\"max\", save_best_only=True),\n",
    "        keras.callbacks.ReduceLROnPlateau(monitor=\"val_auprc\", mode=\"max\",\n",
    "                                          factor=0.5, patience=4, min_lr=1e-5, verbose=1),\n",
    "        F1PerEpoch(Xtr_p, ytr, Xva_p, yva, threshold=CFG[\"threshold\"], batch_size=512),\n",
    "        ProbProbe(Xtr_p, Xva_p, bs=1024)\n",
    "    ]\n",
    "\n",
    "    # === 訓練 ===\n",
    "    hist = model.fit(\n",
    "        Xtr_p, ytr,                       # y 保持一維；loss/metric 內部會 1D-safe 對齊\n",
    "        validation_data=(Xva_p, yva),\n",
    "        epochs=CFG[\"epochs\"],\n",
    "        batch_size=CFG[\"batch_size\"],\n",
    "        callbacks=cbs,\n",
    "        verbose=2,\n",
    "        class_weight=class_weight\n",
    "    )\n",
    "\n",
    "    # === 保存 history 與訓練曲線 ===\n",
    "    hist_df = pd.DataFrame(hist.history)\n",
    "    hist_df.to_csv(fold_dir / \"history.csv\", index=False)\n",
    "    plot_history(hist_df, fold_dir / \"plot_loss.png\")\n",
    "    plot_metric_epochs(hist_df, \"accuracy\", fold_dir / \"plot_acc.png\", \"Accuracy per Epoch\")\n",
    "    if \"f1\" in hist_df.columns and \"val_f1\" in hist_df.columns:\n",
    "        plot_metric_epochs(hist_df, \"f1\", fold_dir / \"plot_f1.png\", \"F1 per Epoch\")\n",
    "\n",
    "    # === 推論：Train & Val 機率 ===\n",
    "    ytr_prob = model.predict(Xtr_p, batch_size=CFG[\"batch_size\"], verbose=0).ravel()\n",
    "    yva_prob = model.predict(Xva_p, batch_size=CFG[\"batch_size\"], verbose=0).ravel()\n",
    "\n",
    "    # === PR / ROC（Train & Val）===\n",
    "    # ROC\n",
    "    fpr_tr, tpr_tr, _ = roc_curve(ytr, ytr_prob)\n",
    "    fpr_va, tpr_va, _ = roc_curve(yva, yva_prob)\n",
    "    roc_auc_tr = auc(fpr_tr, tpr_tr)\n",
    "    roc_auc_va = auc(fpr_va, tpr_va)\n",
    "    # PR\n",
    "    prec_tr, rec_tr, _ = precision_recall_curve(ytr, ytr_prob)\n",
    "    prec_va, rec_va, _ = precision_recall_curve(yva, yva_prob)\n",
    "    ap_tr = average_precision_score(ytr, ytr_prob)\n",
    "    ap_va = average_precision_score(yva, yva_prob)\n",
    "\n",
    "    # 存曲線 CSV 與圖\n",
    "    pd.DataFrame({\"fpr\": fpr_tr, \"tpr\": tpr_tr}).to_csv(fold_dir / \"roc_curve_train.csv\", index=False)\n",
    "    pd.DataFrame({\"fpr\": fpr_va, \"tpr\": tpr_va}).to_csv(fold_dir / \"roc_curve_val.csv\", index=False)\n",
    "    plot_roc_xy(fpr_tr, tpr_tr, roc_auc_tr, fold_dir / \"plot_roc_train.png\", \"ROC Curve (Train)\")\n",
    "    plot_roc_xy(fpr_va, tpr_va, roc_auc_va, fold_dir / \"plot_roc_val.png\", \"ROC Curve (Validation)\")\n",
    "\n",
    "    pd.DataFrame({\"recall\": rec_tr, \"precision\": prec_tr}).to_csv(fold_dir / \"pr_curve_train.csv\", index=False)\n",
    "    pd.DataFrame({\"recall\": rec_va, \"precision\": prec_va}).to_csv(fold_dir / \"pr_curve_val.csv\", index=False)\n",
    "    plot_pr_xy(rec_tr, prec_tr, ap_tr, fold_dir / \"plot_pr_train.png\", \"Precision-Recall (Train)\")\n",
    "    plot_pr_xy(rec_va, prec_va, ap_va, fold_dir / \"plot_pr_val.png\",   \"Precision-Recall (Validation)\")\n",
    "\n",
    "    # === 閾值掃描（驗證集找最佳 F1）===\n",
    "    grid = np.linspace(0.01, 0.99, 99)\n",
    "    f1s = []\n",
    "    for th in grid:\n",
    "        f1s.append(precision_recall_fscore_support(yva, (yva_prob >= th).astype(int),\n",
    "                                                   average='binary', zero_division=0)[2])\n",
    "    best_idx = int(np.argmax(f1s))\n",
    "    best_th = float(grid[best_idx])\n",
    "\n",
    "    # === 指標（best-th 與 fixed 0.5 各一組）===\n",
    "    def pack_metrics(y_true, y_prob, th):\n",
    "        y_pred = (y_prob >= th).astype(int)\n",
    "        acc = accuracy_score(y_true, y_pred)\n",
    "        prec, rec, f1, _ = precision_recall_fscore_support(y_true, y_pred, average='binary', zero_division=0)\n",
    "        rocauc = roc_auc_score(y_true, y_prob)\n",
    "        cm = confusion_matrix(y_true, y_pred, labels=[0, 1])\n",
    "        return acc, prec, rec, f1, rocauc, cm\n",
    "\n",
    "    tr_acc_b, tr_prec_b, tr_rec_b, tr_f1_b, tr_auc, cm_tr_b = pack_metrics(ytr, ytr_prob, best_th)\n",
    "    va_acc_b, va_prec_b, va_rec_b, va_f1_b, va_auc, cm_va_b = pack_metrics(yva, yva_prob, best_th)\n",
    "\n",
    "    tr_acc_f, tr_prec_f, tr_rec_f, tr_f1_f, _, cm_tr_f = pack_metrics(ytr, ytr_prob, CFG[\"threshold\"])\n",
    "    va_acc_f, va_prec_f, va_rec_f, va_f1_f, _, cm_va_f = pack_metrics(yva, yva_prob, CFG[\"threshold\"])\n",
    "\n",
    "    # === 輸出 CSV ===\n",
    "    pd.DataFrame({\"y_true\": ytr, \"y_prob\": ytr_prob, \"y_pred\": (ytr_prob >= best_th).astype(int)}).to_csv(fold_dir / \"predictions_train.csv\", index=False)\n",
    "    pd.DataFrame({\"y_true\": yva, \"y_prob\": yva_prob, \"y_pred\": (yva_prob >= best_th).astype(int)}).to_csv(fold_dir / \"predictions_val.csv\", index=False)\n",
    "\n",
    "    pd.DataFrame(cm_tr_b, index=[\"Actual_0\",\"Actual_1\"], columns=[\"Pred_0\",\"Pred_1\"]).to_csv(fold_dir / \"cm_train_best.csv\")\n",
    "    pd.DataFrame(cm_va_b, index=[\"Actual_0\",\"Actual_1\"], columns=[\"Pred_0\",\"Pred_1\"]).to_csv(fold_dir / \"cm_val_best.csv\")\n",
    "    pd.DataFrame(cm_tr_f, index=[\"Actual_0\",\"Actual_1\"], columns=[\"Pred_0\",\"Pred_1\"]).to_csv(fold_dir / \"cm_train_fixed05.csv\")\n",
    "    pd.DataFrame(cm_va_f, index=[\"Actual_0\",\"Actual_1\"], columns=[\"Pred_0\",\"Pred_1\"]).to_csv(fold_dir / \"cm_val_fixed05.csv\")\n",
    "\n",
    "    plot_cm(cm_tr_b, fold_dir / \"plot_cm_train_best.png\", \"Confusion Matrix (Train, best-th)\")\n",
    "    plot_cm(cm_va_b, fold_dir / \"plot_cm_val_best.png\",   \"Confusion Matrix (Validation, best-th)\")\n",
    "\n",
    "    # === 每折 metrics.csv（含 best-th 與 fixed 0.5）===\n",
    "    row = {\n",
    "        \"fold\": k,\n",
    "        \"pos_rate\": pos_rate,\n",
    "        \"focal_alpha_used\": alpha_used,\n",
    "        \"class_weight_0\": class_weight[0], \"class_weight_1\": class_weight[1],\n",
    "        \"best_threshold\": best_th,\n",
    "        # Train (best-th)\n",
    "        \"train_accuracy_best\": tr_acc_b, \"train_precision_best\": tr_prec_b, \"train_recall_best\": tr_rec_b,\n",
    "        \"train_f1_best\": tr_f1_b, \"train_auc\": tr_auc, \"train_auprc\": ap_tr,\n",
    "        # Val   (best-th)\n",
    "        \"val_accuracy_best\": va_acc_b, \"val_precision_best\": va_prec_b, \"val_recall_best\": va_rec_b,\n",
    "        \"val_f1_best\": va_f1_b, \"val_auc\": va_auc, \"val_auprc\": ap_va,\n",
    "        # Val   (fixed 0.5)\n",
    "        \"val_accuracy_0p5\": va_acc_f, \"val_precision_0p5\": va_prec_f, \"val_recall_0p5\": va_rec_f,\n",
    "        \"val_f1_0p5\": va_f1_f\n",
    "    }\n",
    "    pd.DataFrame([row]).to_csv(fold_dir / \"metrics.csv\", index=False)\n",
    "\n",
    "    # === 審計資訊 ===\n",
    "    meta = {\n",
    "        \"fold\": k,\n",
    "        \"pos_rate\": pos_rate,\n",
    "        \"best_threshold\": best_th,\n",
    "        \"class_weight\": class_weight,\n",
    "        \"focal\": {\"gamma\": CFG[\"focal_gamma\"], \"alpha_used\": alpha_used}\n",
    "    }\n",
    "    (fold_dir / \"fold_meta.json\").write_text(json.dumps(meta, ensure_ascii=False, indent=2), encoding=\"utf-8\")\n",
    "\n",
    "    # 收集彙總\n",
    "    all_rows.append(row)\n",
    "\n",
    "# ==============================\n",
    "# 八、十折彙總與整體平均 ± 標準差\n",
    "# ==============================\n",
    "summary = pd.DataFrame(all_rows).sort_values(\"fold\")\n",
    "summary.to_csv(RUN_DIR / \"summary_folds.csv\", index=False)\n",
    "\n",
    "agg = {}\n",
    "for col in [c for c in summary.columns if c != \"fold\"]:\n",
    "    agg[col + \"_mean\"] = float(summary[col].mean())\n",
    "    agg[col + \"_std\"] = float(summary[col].std(ddof=1))\n",
    "\n",
    "overall = {\"n_folds\": int(len(summary)), **agg, \"config\": CFG}\n",
    "(RUN_DIR / \"summary_overall.json\").write_text(json.dumps(overall, ensure_ascii=False, indent=2), encoding=\"utf-8\")\n",
    "\n",
    "print(\"\\n=== Training Done ===\")\n",
    "print(f\"Run dir: {RUN_DIR}\")\n",
    "print(json.dumps(overall, indent=2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4c1ff76c-b631-4004-8a55-7bba836de472",
   "metadata": {},
   "outputs": [],
   "source": [
    "先驗偏置生效了 [Probe] 顯示 train/val mean ≈ 0.536、p90 ≈ 0.553：第一個 epoch 的預測機率大致「以資料先驗為中心」，不再整片貼 0\n",
    "\n",
    "典型「全都判 1」附近的型態。這不是壞事，是「解鎖」後的第一步，接下來要做的是把決策閾值調對\n",
    "已在訓練後做「best-F1 閾值掃描」。建議把這個思路前移到每個 epoch，你在 log 期間就會看到 F1 真的上來，EarlyStopping 也能用上「val_f1_best」\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e25dca8e-ddb2-4cbc-b93a-8293d4851bb4",
   "metadata": {},
   "outputs": [],
   "source": [
    "輸出層先驗偏置（prior bias）\n",
    "依每折訓練集正類比例 pos_rate，把最後一層 Dense 的 bias 初始化成 logit(pos_rate)，讓模型一開始就輸出接近資料先驗的機率，而不是全貼 0。\n",
    "移除 Masking(0.0)\n",
    "標準化後特徵常接近 0，避免被誤當 padding。\n",
    "Focal Loss 強化 & 不平衡處理\n",
    "動態 α：每折用 alpha = clip(1 - pos_rate, 0.1, 0.9)。\n",
    "class_weight=\"balanced\"：訓練時再給少數類權重。\n",
    "BCE 對照圖但不改 y\n",
    "自訂 1D-safe BCE metric（在 metric 內 reshape 對齊），保留「Focal vs BCE」同圖對照，同時不用去改你的 y 形狀。\n",
    "決策層面的早停訊號\n",
    "自訂 F1PerEpoch：每個 epoch 在驗證集掃描最佳 F1 的閾值，把 val_f1_best 寫進 logs。\n",
    "EarlyStopping 同時監看 val_auprc（排序品質）與 val_f1_best（決策品質）。\n",
    "ReduceLROnPlateau 自動降學習率；ProbProbe 監看機率分佈是否塌陷。\n",
    "輸出與圖表更完整\n",
    "ROC/PR、混淆矩陣、predictions CSV；同時輸出 best-th 與 固定 0.5 兩套結果。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 269,
   "id": "f436077e-a18a-4aa9-8d51-9e6cab86d5af",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/40\n",
      "[F1Epoch] epoch=0 fixed_th=0.5  f1_tr=0.4118 f1_va=0.4150 | best_th=0.49  f1_tr=0.4182 f1_va=0.4189\n",
      "[Probe] epoch=0  train mean=0.5267 p90=0.5466 | val mean=0.5272 p90=0.5463\n",
      "197/197 - 39s - 196ms/step - accuracy: 0.5289 - auc: 0.5027 - auprc: 0.2675 - binary_crossentropy: 0.6909 - loss: 0.0729 - precision: 0.2706 - recall: 0.4610 - val_accuracy: 0.2915 - val_auc: 0.5303 - val_auprc: 0.2823 - val_binary_crossentropy: 0.7197 - val_loss: 0.0684 - val_precision: 0.2654 - val_recall: 0.9512 - learning_rate: 0.0010 - f1: 0.4118 - val_f1: 0.4150 - f1_best: 0.4182 - val_f1_best: 0.4189 - val_best_th: 0.4900\n",
      "Epoch 2/40\n",
      "[F1Epoch] epoch=1 fixed_th=0.5  f1_tr=0.4181 f1_va=0.4181 | best_th=0.49  f1_tr=0.4182 f1_va=0.4182\n",
      "[Probe] epoch=1  train mean=0.5293 p90=0.5471 | val mean=0.5292 p90=0.5463\n",
      "197/197 - 35s - 179ms/step - accuracy: 0.5125 - auc: 0.5106 - auprc: 0.2714 - binary_crossentropy: 0.6938 - loss: 0.0687 - precision: 0.2709 - recall: 0.4989 - val_accuracy: 0.2664 - val_auc: 0.5227 - val_auprc: 0.2803 - val_binary_crossentropy: 0.7220 - val_loss: 0.0686 - val_precision: 0.2645 - val_recall: 0.9973 - learning_rate: 0.0010 - f1: 0.4181 - val_f1: 0.4181 - f1_best: 0.4182 - val_f1_best: 0.4182 - val_best_th: 0.4900\n",
      "Epoch 3/40\n",
      "[F1Epoch] epoch=2 fixed_th=0.5  f1_tr=0.4185 f1_va=0.4187 | best_th=0.50  f1_tr=0.4185 f1_va=0.4187\n",
      "[Probe] epoch=2  train mean=0.5239 p90=0.5416 | val mean=0.5240 p90=0.5418\n",
      "197/197 - 36s - 181ms/step - accuracy: 0.5157 - auc: 0.5153 - auprc: 0.2755 - binary_crossentropy: 0.6931 - loss: 0.0683 - precision: 0.2731 - recall: 0.5005 - val_accuracy: 0.2743 - val_auc: 0.5285 - val_auprc: 0.2873 - val_binary_crossentropy: 0.7163 - val_loss: 0.0682 - val_precision: 0.2656 - val_recall: 0.9891 - learning_rate: 0.0010 - f1: 0.4185 - val_f1: 0.4187 - f1_best: 0.4185 - val_f1_best: 0.4187 - val_best_th: 0.5000\n",
      "Epoch 4/40\n",
      "[F1Epoch] epoch=3 fixed_th=0.5  f1_tr=0.4189 f1_va=0.4184 | best_th=0.49  f1_tr=0.4185 f1_va=0.4184\n",
      "[Probe] epoch=3  train mean=0.5182 p90=0.5311 | val mean=0.5182 p90=0.5307\n",
      "197/197 - 35s - 178ms/step - accuracy: 0.5138 - auc: 0.5069 - auprc: 0.2687 - binary_crossentropy: 0.6939 - loss: 0.0684 - precision: 0.2715 - recall: 0.4983 - val_accuracy: 0.2682 - val_auc: 0.5333 - val_auprc: 0.2880 - val_binary_crossentropy: 0.7104 - val_loss: 0.0678 - val_precision: 0.2648 - val_recall: 0.9959 - learning_rate: 0.0010 - f1: 0.4189 - val_f1: 0.4184 - f1_best: 0.4185 - val_f1_best: 0.4184 - val_best_th: 0.4900\n",
      "Epoch 5/40\n",
      "[F1Epoch] epoch=4 fixed_th=0.5  f1_tr=0.4183 f1_va=0.4142 | best_th=0.49  f1_tr=0.4187 f1_va=0.4188\n",
      "[Probe] epoch=4  train mean=0.5141 p90=0.5287 | val mean=0.5141 p90=0.5287\n",
      "197/197 - 35s - 178ms/step - accuracy: 0.5094 - auc: 0.5112 - auprc: 0.2704 - binary_crossentropy: 0.6936 - loss: 0.0682 - precision: 0.2718 - recall: 0.5096 - val_accuracy: 0.2757 - val_auc: 0.5285 - val_auprc: 0.2854 - val_binary_crossentropy: 0.7062 - val_loss: 0.0677 - val_precision: 0.2634 - val_recall: 0.9688 - learning_rate: 0.0010 - f1: 0.4183 - val_f1: 0.4142 - f1_best: 0.4187 - val_f1_best: 0.4188 - val_best_th: 0.4900\n",
      "Epoch 6/40\n",
      "[F1Epoch] epoch=5 fixed_th=0.5  f1_tr=0.4174 f1_va=0.4152 | best_th=0.49  f1_tr=0.4188 f1_va=0.4192\n",
      "[Probe] epoch=5  train mean=0.5133 p90=0.5271 | val mean=0.5135 p90=0.5273\n",
      "197/197 - 35s - 180ms/step - accuracy: 0.5152 - auc: 0.5173 - auprc: 0.2776 - binary_crossentropy: 0.6927 - loss: 0.0680 - precision: 0.2734 - recall: 0.5029 - val_accuracy: 0.2818 - val_auc: 0.5288 - val_auprc: 0.2903 - val_binary_crossentropy: 0.7056 - val_loss: 0.0677 - val_precision: 0.2645 - val_recall: 0.9647 - learning_rate: 0.0010 - f1: 0.4174 - val_f1: 0.4152 - f1_best: 0.4188 - val_f1_best: 0.4192 - val_best_th: 0.4900\n",
      "Epoch 7/40\n",
      "[F1Epoch] epoch=6 fixed_th=0.5  f1_tr=0.4041 f1_va=0.4097 | best_th=0.47  f1_tr=0.4170 f1_va=0.4182\n",
      "[Probe] epoch=6  train mean=0.5209 p90=0.5426 | val mean=0.5220 p90=0.5430\n",
      "197/197 - 36s - 181ms/step - accuracy: 0.5173 - auc: 0.5096 - auprc: 0.2730 - binary_crossentropy: 0.6950 - loss: 0.0711 - precision: 0.2699 - recall: 0.4844 - val_accuracy: 0.3306 - val_auc: 0.5115 - val_auprc: 0.2713 - val_binary_crossentropy: 0.7152 - val_loss: 0.0686 - val_precision: 0.2671 - val_recall: 0.8792 - learning_rate: 0.0010 - f1: 0.4041 - val_f1: 0.4097 - f1_best: 0.4170 - val_f1_best: 0.4182 - val_best_th: 0.4700\n",
      "Epoch 8/40\n",
      "[F1Epoch] epoch=7 fixed_th=0.5  f1_tr=0.4141 f1_va=0.4167 | best_th=0.01  f1_tr=0.4182 f1_va=0.4180\n",
      "[Probe] epoch=7  train mean=0.5091 p90=0.5199 | val mean=0.5094 p90=0.5200\n",
      "197/197 - 35s - 178ms/step - accuracy: 0.5044 - auc: 0.5061 - auprc: 0.2674 - binary_crossentropy: 0.6936 - loss: 0.0690 - precision: 0.2689 - recall: 0.5087 - val_accuracy: 0.3123 - val_auc: 0.5186 - val_auprc: 0.2786 - val_binary_crossentropy: 0.7019 - val_loss: 0.0677 - val_precision: 0.2685 - val_recall: 0.9294 - learning_rate: 0.0010 - f1: 0.4141 - val_f1: 0.4167 - f1_best: 0.4182 - val_f1_best: 0.4180 - val_best_th: 0.0100\n",
      "Epoch 9/40\n",
      "[F1Epoch] epoch=8 fixed_th=0.5  f1_tr=0.4040 f1_va=0.3969 | best_th=0.01  f1_tr=0.4182 f1_va=0.4180\n",
      "[Probe] epoch=8  train mean=0.5052 p90=0.5132 | val mean=0.5054 p90=0.5134\n",
      "197/197 - 35s - 179ms/step - accuracy: 0.5142 - auc: 0.5168 - auprc: 0.2767 - binary_crossentropy: 0.6927 - loss: 0.0684 - precision: 0.2763 - recall: 0.5170 - val_accuracy: 0.3646 - val_auc: 0.5089 - val_auprc: 0.2765 - val_binary_crossentropy: 0.6982 - val_loss: 0.0677 - val_precision: 0.2649 - val_recall: 0.7910 - learning_rate: 0.0010 - f1: 0.4040 - val_f1: 0.3969 - f1_best: 0.4182 - val_f1_best: 0.4180 - val_best_th: 0.0100\n",
      "Epoch 10/40\n",
      "\n",
      "Epoch 10: ReduceLROnPlateau reducing learning rate to 0.0005000000237487257.\n",
      "[F1Epoch] epoch=9 fixed_th=0.5  f1_tr=0.4101 f1_va=0.4028 | best_th=0.48  f1_tr=0.4184 f1_va=0.4183\n",
      "[Probe] epoch=9  train mean=0.5087 p90=0.5177 | val mean=0.5089 p90=0.5178\n",
      "197/197 - 35s - 179ms/step - accuracy: 0.5108 - auc: 0.5113 - auprc: 0.2709 - binary_crossentropy: 0.6929 - loss: 0.0683 - precision: 0.2694 - recall: 0.4968 - val_accuracy: 0.3503 - val_auc: 0.5124 - val_auprc: 0.2748 - val_binary_crossentropy: 0.7014 - val_loss: 0.0677 - val_precision: 0.2660 - val_recall: 0.8290 - learning_rate: 0.0010 - f1: 0.4101 - val_f1: 0.4028 - f1_best: 0.4184 - val_f1_best: 0.4183 - val_best_th: 0.4800\n",
      "Epoch 11/40\n",
      "[F1Epoch] epoch=10 fixed_th=0.5  f1_tr=0.3870 f1_va=0.3879 | best_th=0.48  f1_tr=0.4184 f1_va=0.4185\n",
      "[Probe] epoch=10  train mean=0.5056 p90=0.5201 | val mean=0.5056 p90=0.5204\n",
      "197/197 - 36s - 181ms/step - accuracy: 0.5130 - auc: 0.5184 - auprc: 0.2786 - binary_crossentropy: 0.6928 - loss: 0.0681 - precision: 0.2755 - recall: 0.5168 - val_accuracy: 0.4321 - val_auc: 0.5154 - val_auprc: 0.2805 - val_binary_crossentropy: 0.6982 - val_loss: 0.0676 - val_precision: 0.2712 - val_recall: 0.6811 - learning_rate: 5.0000e-04 - f1: 0.3870 - val_f1: 0.3879 - f1_best: 0.4184 - val_f1_best: 0.4185 - val_best_th: 0.4800\n",
      "Epoch 12/40\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[269], line 366\u001b[0m\n\u001b[1;32m    349\u001b[0m cbs \u001b[38;5;241m=\u001b[39m [\n\u001b[1;32m    350\u001b[0m     \u001b[38;5;66;03m# 排名品質：監看 AUPRC（較穩）\u001b[39;00m\n\u001b[1;32m    351\u001b[0m     keras\u001b[38;5;241m.\u001b[39mcallbacks\u001b[38;5;241m.\u001b[39mEarlyStopping(monitor\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mval_auprc\u001b[39m\u001b[38;5;124m\"\u001b[39m, patience\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mmax\u001b[39m(\u001b[38;5;241m8\u001b[39m, CFG[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mpatience\u001b[39m\u001b[38;5;124m\"\u001b[39m]),\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    362\u001b[0m     ProbProbe(Xtr_p, Xva_p, bs\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1024\u001b[39m)\n\u001b[1;32m    363\u001b[0m ]\n\u001b[1;32m    365\u001b[0m \u001b[38;5;66;03m# === 訓練 ===\u001b[39;00m\n\u001b[0;32m--> 366\u001b[0m hist \u001b[38;5;241m=\u001b[39m \u001b[43mmodel\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfit\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    367\u001b[0m \u001b[43m    \u001b[49m\u001b[43mXtr_p\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mytr\u001b[49m\u001b[43m,\u001b[49m\u001b[43m                       \u001b[49m\u001b[38;5;66;43;03m# y 保持一維；loss/metric 內部會 1D-safe 對齊\u001b[39;49;00m\n\u001b[1;32m    368\u001b[0m \u001b[43m    \u001b[49m\u001b[43mvalidation_data\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mXva_p\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43myva\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    369\u001b[0m \u001b[43m    \u001b[49m\u001b[43mepochs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mCFG\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mepochs\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    370\u001b[0m \u001b[43m    \u001b[49m\u001b[43mbatch_size\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mCFG\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mbatch_size\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    371\u001b[0m \u001b[43m    \u001b[49m\u001b[43mcallbacks\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcbs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    372\u001b[0m \u001b[43m    \u001b[49m\u001b[43mverbose\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m2\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m    373\u001b[0m \u001b[43m    \u001b[49m\u001b[43mclass_weight\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mclass_weight\u001b[49m\n\u001b[1;32m    374\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    376\u001b[0m \u001b[38;5;66;03m# === 保存 history 與訓練曲線 ===\u001b[39;00m\n\u001b[1;32m    377\u001b[0m hist_df \u001b[38;5;241m=\u001b[39m pd\u001b[38;5;241m.\u001b[39mDataFrame(hist\u001b[38;5;241m.\u001b[39mhistory)\n",
      "File \u001b[0;32m/opt/conda/lib/python3.11/site-packages/keras/src/utils/traceback_utils.py:117\u001b[0m, in \u001b[0;36mfilter_traceback.<locals>.error_handler\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    115\u001b[0m filtered_tb \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m    116\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 117\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfn\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    118\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[1;32m    119\u001b[0m     filtered_tb \u001b[38;5;241m=\u001b[39m _process_traceback_frames(e\u001b[38;5;241m.\u001b[39m__traceback__)\n",
      "File \u001b[0;32m/opt/conda/lib/python3.11/site-packages/keras/src/backend/tensorflow/trainer.py:358\u001b[0m, in \u001b[0;36mTensorFlowTrainer.fit\u001b[0;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_batch_size, validation_freq)\u001b[0m\n\u001b[1;32m    353\u001b[0m     val_logs \u001b[38;5;241m=\u001b[39m {\n\u001b[1;32m    354\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mval_\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;241m+\u001b[39m name: val \u001b[38;5;28;01mfor\u001b[39;00m name, val \u001b[38;5;129;01min\u001b[39;00m val_logs\u001b[38;5;241m.\u001b[39mitems()\n\u001b[1;32m    355\u001b[0m     }\n\u001b[1;32m    356\u001b[0m     epoch_logs\u001b[38;5;241m.\u001b[39mupdate(val_logs)\n\u001b[0;32m--> 358\u001b[0m \u001b[43mcallbacks\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mon_epoch_end\u001b[49m\u001b[43m(\u001b[49m\u001b[43mepoch\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mepoch_logs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    359\u001b[0m training_logs \u001b[38;5;241m=\u001b[39m epoch_logs\n\u001b[1;32m    360\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mstop_training:\n",
      "File \u001b[0;32m/opt/conda/lib/python3.11/site-packages/keras/src/callbacks/callback_list.py:96\u001b[0m, in \u001b[0;36mCallbackList.on_epoch_end\u001b[0;34m(self, epoch, logs)\u001b[0m\n\u001b[1;32m     94\u001b[0m logs \u001b[38;5;241m=\u001b[39m logs \u001b[38;5;129;01mor\u001b[39;00m {}\n\u001b[1;32m     95\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m callback \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcallbacks:\n\u001b[0;32m---> 96\u001b[0m     \u001b[43mcallback\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mon_epoch_end\u001b[49m\u001b[43m(\u001b[49m\u001b[43mepoch\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlogs\u001b[49m\u001b[43m)\u001b[49m\n",
      "Cell \u001b[0;32mIn[269], line 267\u001b[0m, in \u001b[0;36mF1PerEpoch.on_epoch_end\u001b[0;34m(self, epoch, logs)\u001b[0m\n\u001b[1;32m    265\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mnumpy\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mnp\u001b[39;00m\n\u001b[1;32m    266\u001b[0m \u001b[38;5;66;03m# 預測機率\u001b[39;00m\n\u001b[0;32m--> 267\u001b[0m ytr_prob \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmodel\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpredict\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mXtr\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbatch_size\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mverbose\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m)\u001b[49m\u001b[38;5;241m.\u001b[39mravel()\n\u001b[1;32m    268\u001b[0m yva_prob \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mmodel\u001b[38;5;241m.\u001b[39mpredict(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mXva, batch_size\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mbs, verbose\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0\u001b[39m)\u001b[38;5;241m.\u001b[39mravel()\n\u001b[1;32m    270\u001b[0m \u001b[38;5;66;03m# 固定 0.5\u001b[39;00m\n",
      "File \u001b[0;32m/opt/conda/lib/python3.11/site-packages/keras/src/utils/traceback_utils.py:117\u001b[0m, in \u001b[0;36mfilter_traceback.<locals>.error_handler\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    115\u001b[0m filtered_tb \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m    116\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 117\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfn\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    118\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[1;32m    119\u001b[0m     filtered_tb \u001b[38;5;241m=\u001b[39m _process_traceback_frames(e\u001b[38;5;241m.\u001b[39m__traceback__)\n",
      "File \u001b[0;32m/opt/conda/lib/python3.11/site-packages/keras/src/backend/tensorflow/trainer.py:508\u001b[0m, in \u001b[0;36mTensorFlowTrainer.predict\u001b[0;34m(self, x, batch_size, verbose, steps, callbacks)\u001b[0m\n\u001b[1;32m    506\u001b[0m callbacks\u001b[38;5;241m.\u001b[39mon_predict_batch_begin(step)\n\u001b[1;32m    507\u001b[0m data \u001b[38;5;241m=\u001b[39m get_data(iterator)\n\u001b[0;32m--> 508\u001b[0m batch_outputs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpredict_function\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdata\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    509\u001b[0m outputs \u001b[38;5;241m=\u001b[39m append_to_outputs(batch_outputs, outputs)\n\u001b[1;32m    510\u001b[0m callbacks\u001b[38;5;241m.\u001b[39mon_predict_batch_end(step, {\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124moutputs\u001b[39m\u001b[38;5;124m\"\u001b[39m: batch_outputs})\n",
      "File \u001b[0;32m/opt/conda/lib/python3.11/site-packages/tensorflow/python/util/traceback_utils.py:150\u001b[0m, in \u001b[0;36mfilter_traceback.<locals>.error_handler\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    148\u001b[0m filtered_tb \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m    149\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 150\u001b[0m   \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfn\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    151\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[1;32m    152\u001b[0m   filtered_tb \u001b[38;5;241m=\u001b[39m _process_traceback_frames(e\u001b[38;5;241m.\u001b[39m__traceback__)\n",
      "File \u001b[0;32m/opt/conda/lib/python3.11/site-packages/tensorflow/python/eager/polymorphic_function/polymorphic_function.py:833\u001b[0m, in \u001b[0;36mFunction.__call__\u001b[0;34m(self, *args, **kwds)\u001b[0m\n\u001b[1;32m    830\u001b[0m compiler \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mxla\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_jit_compile \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mnonXla\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    832\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m OptionalXlaContext(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_jit_compile):\n\u001b[0;32m--> 833\u001b[0m   result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwds\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    835\u001b[0m new_tracing_count \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mexperimental_get_tracing_count()\n\u001b[1;32m    836\u001b[0m without_tracing \u001b[38;5;241m=\u001b[39m (tracing_count \u001b[38;5;241m==\u001b[39m new_tracing_count)\n",
      "File \u001b[0;32m/opt/conda/lib/python3.11/site-packages/tensorflow/python/eager/polymorphic_function/polymorphic_function.py:878\u001b[0m, in \u001b[0;36mFunction._call\u001b[0;34m(self, *args, **kwds)\u001b[0m\n\u001b[1;32m    875\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_lock\u001b[38;5;241m.\u001b[39mrelease()\n\u001b[1;32m    876\u001b[0m \u001b[38;5;66;03m# In this case we have not created variables on the first call. So we can\u001b[39;00m\n\u001b[1;32m    877\u001b[0m \u001b[38;5;66;03m# run the first trace but we should fail if variables are created.\u001b[39;00m\n\u001b[0;32m--> 878\u001b[0m results \u001b[38;5;241m=\u001b[39m \u001b[43mtracing_compilation\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcall_function\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    879\u001b[0m \u001b[43m    \u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkwds\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_variable_creation_config\u001b[49m\n\u001b[1;32m    880\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    881\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_created_variables:\n\u001b[1;32m    882\u001b[0m   \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCreating variables on a non-first call to a function\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    883\u001b[0m                    \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m decorated with tf.function.\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
      "File \u001b[0;32m/opt/conda/lib/python3.11/site-packages/tensorflow/python/eager/polymorphic_function/tracing_compilation.py:139\u001b[0m, in \u001b[0;36mcall_function\u001b[0;34m(args, kwargs, tracing_options)\u001b[0m\n\u001b[1;32m    137\u001b[0m bound_args \u001b[38;5;241m=\u001b[39m function\u001b[38;5;241m.\u001b[39mfunction_type\u001b[38;5;241m.\u001b[39mbind(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[1;32m    138\u001b[0m flat_inputs \u001b[38;5;241m=\u001b[39m function\u001b[38;5;241m.\u001b[39mfunction_type\u001b[38;5;241m.\u001b[39munpack_inputs(bound_args)\n\u001b[0;32m--> 139\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunction\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_flat\u001b[49m\u001b[43m(\u001b[49m\u001b[43m  \u001b[49m\u001b[38;5;66;43;03m# pylint: disable=protected-access\u001b[39;49;00m\n\u001b[1;32m    140\u001b[0m \u001b[43m    \u001b[49m\u001b[43mflat_inputs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcaptured_inputs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfunction\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcaptured_inputs\u001b[49m\n\u001b[1;32m    141\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m/opt/conda/lib/python3.11/site-packages/tensorflow/python/eager/polymorphic_function/concrete_function.py:1322\u001b[0m, in \u001b[0;36mConcreteFunction._call_flat\u001b[0;34m(self, tensor_inputs, captured_inputs)\u001b[0m\n\u001b[1;32m   1318\u001b[0m possible_gradient_type \u001b[38;5;241m=\u001b[39m gradients_util\u001b[38;5;241m.\u001b[39mPossibleTapeGradientTypes(args)\n\u001b[1;32m   1319\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m (possible_gradient_type \u001b[38;5;241m==\u001b[39m gradients_util\u001b[38;5;241m.\u001b[39mPOSSIBLE_GRADIENT_TYPES_NONE\n\u001b[1;32m   1320\u001b[0m     \u001b[38;5;129;01mand\u001b[39;00m executing_eagerly):\n\u001b[1;32m   1321\u001b[0m   \u001b[38;5;66;03m# No tape is watching; skip to running the function.\u001b[39;00m\n\u001b[0;32m-> 1322\u001b[0m   \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_inference_function\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcall_preflattened\u001b[49m\u001b[43m(\u001b[49m\u001b[43margs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1323\u001b[0m forward_backward \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_select_forward_and_backward_functions(\n\u001b[1;32m   1324\u001b[0m     args,\n\u001b[1;32m   1325\u001b[0m     possible_gradient_type,\n\u001b[1;32m   1326\u001b[0m     executing_eagerly)\n\u001b[1;32m   1327\u001b[0m forward_function, args_with_tangents \u001b[38;5;241m=\u001b[39m forward_backward\u001b[38;5;241m.\u001b[39mforward()\n",
      "File \u001b[0;32m/opt/conda/lib/python3.11/site-packages/tensorflow/python/eager/polymorphic_function/atomic_function.py:216\u001b[0m, in \u001b[0;36mAtomicFunction.call_preflattened\u001b[0;34m(self, args)\u001b[0m\n\u001b[1;32m    214\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mcall_preflattened\u001b[39m(\u001b[38;5;28mself\u001b[39m, args: Sequence[core\u001b[38;5;241m.\u001b[39mTensor]) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Any:\n\u001b[1;32m    215\u001b[0m \u001b[38;5;250m  \u001b[39m\u001b[38;5;124;03m\"\"\"Calls with flattened tensor inputs and returns the structured output.\"\"\"\u001b[39;00m\n\u001b[0;32m--> 216\u001b[0m   flat_outputs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcall_flat\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    217\u001b[0m   \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfunction_type\u001b[38;5;241m.\u001b[39mpack_output(flat_outputs)\n",
      "File \u001b[0;32m/opt/conda/lib/python3.11/site-packages/tensorflow/python/eager/polymorphic_function/atomic_function.py:251\u001b[0m, in \u001b[0;36mAtomicFunction.call_flat\u001b[0;34m(self, *args)\u001b[0m\n\u001b[1;32m    249\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m record\u001b[38;5;241m.\u001b[39mstop_recording():\n\u001b[1;32m    250\u001b[0m   \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_bound_context\u001b[38;5;241m.\u001b[39mexecuting_eagerly():\n\u001b[0;32m--> 251\u001b[0m     outputs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_bound_context\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcall_function\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    252\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mname\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    253\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;28;43mlist\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43margs\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    254\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;28;43mlen\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfunction_type\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mflat_outputs\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    255\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    256\u001b[0m   \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    257\u001b[0m     outputs \u001b[38;5;241m=\u001b[39m make_call_op_in_graph(\n\u001b[1;32m    258\u001b[0m         \u001b[38;5;28mself\u001b[39m,\n\u001b[1;32m    259\u001b[0m         \u001b[38;5;28mlist\u001b[39m(args),\n\u001b[1;32m    260\u001b[0m         \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_bound_context\u001b[38;5;241m.\u001b[39mfunction_call_options\u001b[38;5;241m.\u001b[39mas_attrs(),\n\u001b[1;32m    261\u001b[0m     )\n",
      "File \u001b[0;32m/opt/conda/lib/python3.11/site-packages/tensorflow/python/eager/context.py:1552\u001b[0m, in \u001b[0;36mContext.call_function\u001b[0;34m(self, name, tensor_inputs, num_outputs)\u001b[0m\n\u001b[1;32m   1550\u001b[0m cancellation_context \u001b[38;5;241m=\u001b[39m cancellation\u001b[38;5;241m.\u001b[39mcontext()\n\u001b[1;32m   1551\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m cancellation_context \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m-> 1552\u001b[0m   outputs \u001b[38;5;241m=\u001b[39m \u001b[43mexecute\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mexecute\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   1553\u001b[0m \u001b[43m      \u001b[49m\u001b[43mname\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdecode\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mutf-8\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1554\u001b[0m \u001b[43m      \u001b[49m\u001b[43mnum_outputs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mnum_outputs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1555\u001b[0m \u001b[43m      \u001b[49m\u001b[43minputs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtensor_inputs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1556\u001b[0m \u001b[43m      \u001b[49m\u001b[43mattrs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mattrs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1557\u001b[0m \u001b[43m      \u001b[49m\u001b[43mctx\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1558\u001b[0m \u001b[43m  \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1559\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m   1560\u001b[0m   outputs \u001b[38;5;241m=\u001b[39m execute\u001b[38;5;241m.\u001b[39mexecute_with_cancellation(\n\u001b[1;32m   1561\u001b[0m       name\u001b[38;5;241m.\u001b[39mdecode(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mutf-8\u001b[39m\u001b[38;5;124m\"\u001b[39m),\n\u001b[1;32m   1562\u001b[0m       num_outputs\u001b[38;5;241m=\u001b[39mnum_outputs,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m   1566\u001b[0m       cancellation_manager\u001b[38;5;241m=\u001b[39mcancellation_context,\n\u001b[1;32m   1567\u001b[0m   )\n",
      "File \u001b[0;32m/opt/conda/lib/python3.11/site-packages/tensorflow/python/eager/execute.py:53\u001b[0m, in \u001b[0;36mquick_execute\u001b[0;34m(op_name, num_outputs, inputs, attrs, ctx, name)\u001b[0m\n\u001b[1;32m     51\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m     52\u001b[0m   ctx\u001b[38;5;241m.\u001b[39mensure_initialized()\n\u001b[0;32m---> 53\u001b[0m   tensors \u001b[38;5;241m=\u001b[39m \u001b[43mpywrap_tfe\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mTFE_Py_Execute\u001b[49m\u001b[43m(\u001b[49m\u001b[43mctx\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_handle\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdevice_name\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mop_name\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m     54\u001b[0m \u001b[43m                                      \u001b[49m\u001b[43minputs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mattrs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnum_outputs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     55\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m core\u001b[38;5;241m.\u001b[39m_NotOkStatusException \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[1;32m     56\u001b[0m   \u001b[38;5;28;01mif\u001b[39;00m name \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "# -*- coding: utf-8 -*-\n",
    "\"\"\"\n",
    "train_lstm_focal_windowed_clean_v2.py\n",
    "\n",
    "目的：\n",
    "  使用 Keras + LSTM + Focal Loss，針對 windowed_clean/ 的十折資料進行訓練與評估。\n",
    "  - 每折獨立補值與標準化（避免資料洩漏）\n",
    "  - 藍色系圖表與完整 CSV\n",
    "  - timestamp + run_id 版本化輸出\n",
    "  - 不改動原始 y；用 1D-safe BCE metric 繪製 Focal vs BCE 對照圖\n",
    "  - 每個 epoch 以驗證集掃描「best-F1 閾值」，並將 val_f1_best 納入 EarlyStopping\n",
    "\n",
    "資料假設：\n",
    "  /home/jovyan/RT08/0925/sliding_win/1014_sim/windowed_clean/\n",
    "    X_train_fold{k}.npy, y_train_fold{k}.npy\n",
    "    X_val_fold{k}.npy,   y_val_fold{k}.npy\n",
    "\n",
    "輸出：\n",
    "  /home/jovyan/RT08/0925/sliding_win/1014_sim/training_runs/<timestamp>_runXX/\n",
    "    ├─ cfg.json\n",
    "    ├─ fold_{k}/\n",
    "    │    ├─ history.csv\n",
    "    │    ├─ metrics.csv\n",
    "    │    ├─ predictions_train.csv / predictions_val.csv\n",
    "    │    ├─ roc_curve_train.csv  / roc_curve_val.csv\n",
    "    │    ├─ pr_curve_train.csv   / pr_curve_val.csv\n",
    "    │    ├─ cm_train_best.csv    / cm_val_best.csv\n",
    "    │    ├─ cm_train_fixed05.csv / cm_val_fixed05.csv\n",
    "    │    ├─ best_model.keras\n",
    "    │    ├─ plot_loss.png\n",
    "    │    ├─ plot_acc.png\n",
    "    │    ├─ plot_f1.png\n",
    "    │    ├─ plot_roc_train.png / plot_roc_val.png\n",
    "    │    ├─ plot_pr_train.png  / plot_pr_val.png\n",
    "    │    ├─ plot_cm_train_best.png / plot_cm_val_best.png\n",
    "    │    └─ fold_meta.json\n",
    "    ├─ summary_folds.csv\n",
    "    └─ summary_overall.json\n",
    "\n",
    "執行方式：\n",
    "  python train_lstm_focal_windowed_clean_v2.py\n",
    "\"\"\"\n",
    "\n",
    "import os\n",
    "import json\n",
    "import time\n",
    "import math\n",
    "from pathlib import Path\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from sklearn.impute import SimpleImputer\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.metrics import (\n",
    "    roc_curve, auc, precision_recall_curve, average_precision_score,\n",
    "    confusion_matrix, precision_recall_fscore_support,\n",
    "    accuracy_score, roc_auc_score,\n",
    ")\n",
    "from sklearn.utils.class_weight import compute_class_weight\n",
    "\n",
    "import tensorflow as tf\n",
    "from tensorflow import keras\n",
    "from tensorflow.keras import layers, initializers, regularizers\n",
    "\n",
    "# ==============================\n",
    "# 一、基本設定\n",
    "# ==============================\n",
    "BASE = Path(\"/home/jovyan/RT08/0925/sliding_win/1014_sim\")\n",
    "DATA = BASE / \"windowed_clean\"\n",
    "RUNS_ROOT = BASE / \"training_runs\"\n",
    "RUNS_ROOT.mkdir(parents=True, exist_ok=True)\n",
    "\n",
    "CFG = {\n",
    "    \"seed\": 42,\n",
    "    \"epochs\": 40,\n",
    "    \"batch_size\": 128,\n",
    "    \"learning_rate\": 1e-3,\n",
    "    \"focal_gamma\": 2.0,\n",
    "    \"focal_alpha\": 0.25,            # 會在每折以 (1 - pos_rate) 動態覆寫\n",
    "    \"patience\": 5,                  # EarlyStopping 基底值（實際以 max(8, patience)）\n",
    "    \"hidden_units\": 128,\n",
    "    \"l2\": 1e-6,\n",
    "    \"dropout\": 0.2,\n",
    "    \"recurrent_dropout\": 0.0,\n",
    "    \"folds\": list(range(1, 10 + 1)),\n",
    "    \"threshold\": 0.5                # 亦輸出固定 0.5 版本供比較\n",
    "}\n",
    "\n",
    "np.random.seed(CFG[\"seed\"])\n",
    "tf.random.set_seed(CFG[\"seed\"])\n",
    "\n",
    "# 版本化 run 目錄：timestamp + 自動 run_id\n",
    "ts = time.strftime(\"%Y%m%d_%H%M%S\")\n",
    "existing = sorted(RUNS_ROOT.glob(f\"{ts}_run*\"))\n",
    "run_id = len(existing) + 1\n",
    "RUN_DIR = RUNS_ROOT / f\"{ts}_run{run_id:02d}\"\n",
    "RUN_DIR.mkdir(parents=True, exist_ok=True)\n",
    "(RUN_DIR / \"cfg.json\").write_text(json.dumps(CFG, ensure_ascii=False, indent=2), encoding=\"utf-8\")\n",
    "\n",
    "# ==============================\n",
    "# 二、Loss / Metric（1D-safe）\n",
    "# ==============================\n",
    "def binary_focal_loss(gamma=2.0, alpha=0.25):\n",
    "    \"\"\"Focal Loss；在 loss 內動態對齊 y_true 形狀，允許傳入一維 y。\"\"\"\n",
    "    def loss(y_true, y_pred):\n",
    "        y_true = tf.cast(y_true, tf.float32)\n",
    "        y_pred = tf.cast(y_pred, tf.float32)\n",
    "        y_true = tf.reshape(y_true, tf.shape(y_pred))  # 對齊形狀\n",
    "        y_pred = tf.clip_by_value(y_pred, 1e-7, 1.0 - 1e-7)\n",
    "        p_t = y_true * y_pred + (1.0 - y_true) * (1.0 - y_pred)\n",
    "        alpha_factor = y_true * alpha + (1.0 - y_true) * (1.0 - alpha)\n",
    "        modulating = tf.pow(1.0 - p_t, gamma)\n",
    "        bce = - (y_true * tf.math.log(y_pred) + (1.0 - y_true) * tf.math.log(1.0 - y_pred))\n",
    "        return tf.reduce_mean(alpha_factor * modulating * bce)\n",
    "    return loss\n",
    "\n",
    "def bce_metric_1d_safe(y_true, y_pred):\n",
    "    \"\"\"用於對照圖的 BCE metric；不改 y，於 metric 內對齊形狀。\"\"\"\n",
    "    y_true = tf.cast(y_true, tf.float32)\n",
    "    y_pred = tf.cast(y_pred, tf.float32)\n",
    "    y_true = tf.reshape(y_true, tf.shape(y_pred))\n",
    "    return tf.reduce_mean(tf.keras.losses.binary_crossentropy(y_true, y_pred))\n",
    "bce_metric_1d_safe.__name__ = \"binary_crossentropy\"  # 在 history 中維持欄位命名\n",
    "\n",
    "# ==============================\n",
    "# 三、模型構建（無 Masking；輸出層先驗偏置）\n",
    "# ==============================\n",
    "def build_model(input_shape, cfg, prior_pos=0.5):\n",
    "    reg = regularizers.l2(cfg[\"l2\"])\n",
    "    inputs = keras.Input(shape=input_shape, name=\"sequence\")\n",
    "\n",
    "    # 不使用 Masking，避免標準化後接近 0 的有效步被當作 padding\n",
    "    x = inputs\n",
    "\n",
    "    x = layers.LSTM(cfg[\"hidden_units\"],\n",
    "                    dropout=cfg[\"dropout\"],\n",
    "                    recurrent_dropout=cfg[\"recurrent_dropout\"],\n",
    "                    kernel_regularizer=reg,\n",
    "                    recurrent_regularizer=reg,\n",
    "                    return_sequences=False)(x)\n",
    "    x = layers.Dropout(cfg[\"dropout\"])(x)\n",
    "\n",
    "    # 以先驗正類比例設置輸出層偏置，避免初期全猜 0\n",
    "    eps = 1e-7\n",
    "    prior_pos = float(np.clip(prior_pos, eps, 1.0 - eps))\n",
    "    init_bias = math.log(prior_pos / (1.0 - prior_pos))\n",
    "\n",
    "    outputs = layers.Dense(\n",
    "        1, activation=\"sigmoid\", name=\"prob\",\n",
    "        bias_initializer=initializers.Constant(init_bias)\n",
    "    )(x)\n",
    "\n",
    "    model = keras.Model(inputs, outputs, name=\"lstm_focal\")\n",
    "    opt = keras.optimizers.Adam(learning_rate=cfg[\"learning_rate\"])\n",
    "    model.compile(\n",
    "        optimizer=opt,\n",
    "        loss=binary_focal_loss(cfg[\"focal_gamma\"], cfg[\"focal_alpha\"]),\n",
    "        metrics=[\n",
    "            keras.metrics.BinaryAccuracy(name=\"accuracy\"),\n",
    "            keras.metrics.AUC(name=\"auc\"),\n",
    "            keras.metrics.AUC(name=\"auprc\", curve=\"PR\"),\n",
    "            keras.metrics.Precision(name=\"precision\"),\n",
    "            keras.metrics.Recall(name=\"recall\"),\n",
    "            bce_metric_1d_safe  # Focal vs BCE 對照圖\n",
    "        ]\n",
    "    )\n",
    "    return model\n",
    "\n",
    "# ==============================\n",
    "# 四、每折前處理（補值 + 標準化）\n",
    "# ==============================\n",
    "def fit_transform_fold(X_train, X_val):\n",
    "    # X: (n, t, d) -> 疊時間：((n*t), d)\n",
    "    n, t, d = X_train.shape\n",
    "    tr2 = X_train.reshape(-1, d)\n",
    "    va2 = X_val.reshape(-1, d)\n",
    "\n",
    "    imputer = SimpleImputer(strategy=\"median\")\n",
    "    scaler = StandardScaler(with_mean=True, with_std=True)\n",
    "\n",
    "    tr_imp = imputer.fit_transform(tr2)\n",
    "    tr_scl = scaler.fit_transform(tr_imp)\n",
    "\n",
    "    va_imp = imputer.transform(va2)\n",
    "    va_scl = scaler.transform(va_imp)\n",
    "\n",
    "    # 還原 (n, t, d)\n",
    "    Xtr = tr_scl.reshape(n, t, d).astype(np.float32)\n",
    "    Xva = va_scl.reshape(X_val.shape[0], t, d).astype(np.float32)\n",
    "    return Xtr, Xva, imputer, scaler\n",
    "\n",
    "\n",
    "# ==============================\n",
    "# 五、繪圖（英文、藍色系）\n",
    "# ==============================\n",
    "BLUES = [\"#cfe8ff\", \"#9dd0ff\", \"#6bb8ff\", \"#3aa0ff\", \"#0a88ff\", \"#005bb5\"]\n",
    "\n",
    "def plot_history(hist_df: pd.DataFrame, out_png: Path):\n",
    "    plt.figure(figsize=(7, 5))\n",
    "    x = np.arange(len(hist_df))\n",
    "    # Focal 與 BCE（metric）同圖\n",
    "    plt.plot(x, hist_df[\"loss\"], label=\"Train Focal Loss\", lw=2, color=BLUES[3])\n",
    "    plt.plot(x, hist_df[\"val_loss\"], label=\"Val Focal Loss\", lw=2, color=BLUES[5])\n",
    "    if \"binary_crossentropy\" in hist_df.columns:\n",
    "        plt.plot(x, hist_df[\"binary_crossentropy\"], label=\"Train BCE\", lw=2, color=BLUES[1])\n",
    "    if \"val_binary_crossentropy\" in hist_df.columns:\n",
    "        plt.plot(x, hist_df[\"val_binary_crossentropy\"], label=\"Val BCE\", lw=2, color=BLUES[0])\n",
    "    plt.title(\"Loss Curves (Focal & BCE)\")\n",
    "    plt.xlabel(\"Epoch\"); plt.ylabel(\"Loss\")\n",
    "    plt.grid(True, alpha=0.25); plt.legend(); plt.tight_layout()\n",
    "    plt.savefig(out_png, dpi=160); plt.close()\n",
    "\n",
    "def plot_metric_epochs(hist: pd.DataFrame, metric: str, out_png: Path, title: str):\n",
    "    plt.figure(figsize=(7, 5))\n",
    "    x = np.arange(len(hist))\n",
    "    if metric in hist.columns:\n",
    "        plt.plot(x, hist[metric], label=f\"Train {metric}\", color=BLUES[3], lw=2)\n",
    "    valm = f\"val_{metric}\"\n",
    "    if valm in hist.columns:\n",
    "        plt.plot(x, hist[valm], label=f\"Val {metric}\", color=BLUES[5], lw=2)\n",
    "    plt.title(title)\n",
    "    plt.xlabel(\"Epoch\"); plt.ylabel(metric.title())\n",
    "    plt.grid(True, alpha=0.25); plt.legend(); plt.tight_layout()\n",
    "    plt.savefig(out_png, dpi=160); plt.close()\n",
    "\n",
    "def plot_roc_xy(fpr, tpr, roc_auc, out_png: Path, title: str):\n",
    "    plt.figure(figsize=(6, 5))\n",
    "    plt.plot(fpr, tpr, color=BLUES[4], lw=2, label=f\"ROC AUC = {roc_auc:.4f}\")\n",
    "    plt.plot([0, 1], [0, 1], color=BLUES[0], lw=1, ls=\"--\")\n",
    "    plt.title(title); plt.xlabel(\"False Positive Rate\"); plt.ylabel(\"True Positive Rate\")\n",
    "    plt.grid(True, alpha=0.25); plt.legend(loc=\"lower right\"); plt.tight_layout()\n",
    "    plt.savefig(out_png, dpi=160); plt.close()\n",
    "\n",
    "def plot_pr_xy(recall, precision, ap, out_png: Path, title: str):\n",
    "    plt.figure(figsize=(6, 5))\n",
    "    plt.plot(recall, precision, color=BLUES[4], lw=2, label=f\"AP = {ap:.4f}\")\n",
    "    plt.title(title); plt.xlabel(\"Recall\"); plt.ylabel(\"Precision\")\n",
    "    plt.grid(True, alpha=0.25); plt.legend(loc=\"lower left\"); plt.tight_layout()\n",
    "    plt.savefig(out_png, dpi=160); plt.close()\n",
    "\n",
    "def plot_cm(cm: np.ndarray, out_png: Path, title: str):\n",
    "    plt.figure(figsize=(5.6, 4.8))\n",
    "    im = plt.imshow(cm, cmap=\"Blues\")\n",
    "    plt.title(title, fontsize=16)\n",
    "    plt.xlabel(\"Predicted\", fontsize=13); plt.ylabel(\"Actual\", fontsize=13)\n",
    "    plt.colorbar(im, fraction=0.046, pad=0.04)\n",
    "    for (i, j), z in np.ndenumerate(cm):\n",
    "        plt.text(j, i, f\"{z}\", ha='center', va='center', color='black',\n",
    "                 fontsize=14, fontweight='bold')\n",
    "    plt.xticks([0, 1], [\"Negative\", \"Positive\"], fontsize=12)\n",
    "    plt.yticks([0, 1], [\"Negative\", \"Positive\"], fontsize=12)\n",
    "    plt.tight_layout(); plt.savefig(out_png, dpi=180); plt.close()\n",
    "\n",
    "# ==============================\n",
    "# 六、回合級 Callback：F1（含 best-th）與機率探針\n",
    "# ==============================\n",
    "class F1PerEpoch(keras.callbacks.Callback):\n",
    "    \"\"\"每個 epoch 尋找驗證集最佳 F1 閾值，並將 fixed/ best 的 F1 記入 logs。\"\"\"\n",
    "    def __init__(self, Xtr, ytr, Xva, yva, threshold=0.5, batch_size=512):\n",
    "        super().__init__()\n",
    "        self.Xtr, self.ytr, self.Xva, self.yva = Xtr, ytr, Xva, yva\n",
    "        self.fixed_th = threshold\n",
    "        self.bs = batch_size\n",
    "\n",
    "    def on_epoch_end(self, epoch, logs=None):\n",
    "        import numpy as np\n",
    "        # 預測機率\n",
    "        ytr_prob = self.model.predict(self.Xtr, batch_size=self.bs, verbose=0).ravel()\n",
    "        yva_prob = self.model.predict(self.Xva, batch_size=self.bs, verbose=0).ravel()\n",
    "\n",
    "        # 固定 0.5\n",
    "        tr_pred_f = (ytr_prob >= self.fixed_th).astype(int)\n",
    "        va_pred_f = (yva_prob >= self.fixed_th).astype(int)\n",
    "        _, _, f1_tr_fixed, _ = precision_recall_fscore_support(self.ytr, tr_pred_f, average='binary', zero_division=0)\n",
    "        _, _, f1_va_fixed, _ = precision_recall_fscore_support(self.yva, va_pred_f, average='binary', zero_division=0)\n",
    "\n",
    "        # 掃描 best-th（驗證集）\n",
    "        grid = np.linspace(0.01, 0.99, 99)\n",
    "        f1s = [precision_recall_fscore_support(self.yva, (yva_prob >= th).astype(int),\n",
    "                                               average='binary', zero_division=0)[2]\n",
    "               for th in grid]\n",
    "        best_idx = int(np.argmax(f1s))\n",
    "        best_th = float(grid[best_idx])\n",
    "\n",
    "        # best-th 的 F1\n",
    "        tr_pred_b = (ytr_prob >= best_th).astype(int)\n",
    "        va_pred_b = (yva_prob >= best_th).astype(int)\n",
    "        _, _, f1_tr_best, _ = precision_recall_fscore_support(self.ytr, tr_pred_b, average='binary', zero_division=0)\n",
    "        _, _, f1_va_best, _ = precision_recall_fscore_support(self.yva, va_pred_b, average='binary', zero_division=0)\n",
    "\n",
    "        if logs is not None:\n",
    "            logs['f1'] = f1_tr_fixed\n",
    "            logs['val_f1'] = f1_va_fixed\n",
    "            logs['f1_best'] = f1_tr_best\n",
    "            logs['val_f1_best'] = f1_va_best\n",
    "            logs['val_best_th'] = best_th\n",
    "\n",
    "        print(f\"[F1Epoch] epoch={epoch} fixed_th=0.5  f1_tr={f1_tr_fixed:.4f} f1_va={f1_va_fixed:.4f} | \"\n",
    "              f\"best_th={best_th:.2f}  f1_tr={f1_tr_best:.4f} f1_va={f1_va_best:.4f}\")\n",
    "\n",
    "class ProbProbe(keras.callbacks.Callback):\n",
    "    \"\"\"每個 epoch 列印 train/val 機率的均值與 90 分位，偵測機率塌陷。\"\"\"\n",
    "    def __init__(self, Xtr, Xva, bs=1024):\n",
    "        super().__init__()\n",
    "        self.Xtr, self.Xva, self.bs = Xtr, Xva, bs\n",
    "    def on_epoch_end(self, epoch, logs=None):\n",
    "        p_tr = self.model.predict(self.Xtr, batch_size=self.bs, verbose=0).ravel()\n",
    "        p_va = self.model.predict(self.Xva, batch_size=self.bs, verbose=0).ravel()\n",
    "        q = lambda a: float(np.quantile(a, 0.9))\n",
    "        print(f\"[Probe] epoch={epoch}  train mean={p_tr.mean():.4f} p90={q(p_tr):.4f} | \"\n",
    "              f\"val mean={p_va.mean():.4f} p90={q(p_va):.4f}\")\n",
    "\n",
    "# ==============================\n",
    "# 七、主流程：十折訓練與評估\n",
    "# ==============================\n",
    "all_rows = []\n",
    "\n",
    "for k in CFG[\"folds\"]:\n",
    "    fold_dir = RUN_DIR / f\"fold_{k}\"\n",
    "    fold_dir.mkdir(parents=True, exist_ok=True)\n",
    "\n",
    "    # === 讀取資料 ===\n",
    "    Xtr = np.load(DATA / f\"X_train_fold{k}.npy\")\n",
    "    ytr = np.load(DATA / f\"y_train_fold{k}.npy\")\n",
    "    Xva = np.load(DATA / f\"X_val_fold{k}.npy\")\n",
    "    yva = np.load(DATA / f\"y_val_fold{k}.npy\")\n",
    "\n",
    "    # 檢查標籤\n",
    "    assert set(np.unique(ytr)).issubset({0, 1}), \"y_train 需為 {0,1}\"\n",
    "    assert set(np.unique(yva)).issubset({0, 1}), \"y_val 需為 {0,1}\"\n",
    "\n",
    "    # === 前處理（fit on train, transform on train/val）===\n",
    "    Xtr_p, Xva_p, _, _ = fit_transform_fold(Xtr, Xva)\n",
    "\n",
    "    # === 不平衡摘要：動態 focal α + class_weight ===\n",
    "    pos_rate = float(ytr.mean())\n",
    "    alpha_used = float(np.clip(1.0 - pos_rate, 0.1, 0.9))\n",
    "    CFG[\"focal_alpha\"] = alpha_used  # 覆寫本折 alpha\n",
    "\n",
    "    classes = np.array([0, 1])\n",
    "    cw = compute_class_weight(class_weight=\"balanced\", classes=classes, y=ytr)\n",
    "    class_weight = {0: float(cw[0]), 1: float(cw[1])}\n",
    "\n",
    "    # === 建模（prior_pos -> bias initializer）===\n",
    "    input_shape = (Xtr_p.shape[1], Xtr_p.shape[2])  # (T, D)\n",
    "    model = build_model(input_shape, CFG, prior_pos=pos_rate)\n",
    "\n",
    "    # === Callbacks ===\n",
    "    ckpt_path = fold_dir / \"best_model.keras\"\n",
    "    cbs = [\n",
    "        # 排名品質：監看 AUPRC（較穩）\n",
    "        keras.callbacks.EarlyStopping(monitor=\"val_auprc\", patience=max(8, CFG[\"patience\"]),\n",
    "                                      mode=\"max\", restore_best_weights=True),\n",
    "        # 決策品質：監看每 epoch 的 best-F1\n",
    "        keras.callbacks.EarlyStopping(monitor=\"val_f1_best\", patience=6,\n",
    "                                      mode=\"max\", restore_best_weights=True),\n",
    "\n",
    "        keras.callbacks.ModelCheckpoint(filepath=str(ckpt_path), monitor=\"val_auprc\",\n",
    "                                        mode=\"max\", save_best_only=True),\n",
    "        keras.callbacks.ReduceLROnPlateau(monitor=\"val_auprc\", mode=\"max\",\n",
    "                                          factor=0.5, patience=4, min_lr=1e-5, verbose=1),\n",
    "        F1PerEpoch(Xtr_p, ytr, Xva_p, yva, threshold=CFG[\"threshold\"], batch_size=512),\n",
    "        ProbProbe(Xtr_p, Xva_p, bs=1024)\n",
    "    ]\n",
    "\n",
    "    # === 訓練 ===\n",
    "    hist = model.fit(\n",
    "        Xtr_p, ytr,                       # y 保持一維；loss/metric 內部會 1D-safe 對齊\n",
    "        validation_data=(Xva_p, yva),\n",
    "        epochs=CFG[\"epochs\"],\n",
    "        batch_size=CFG[\"batch_size\"],\n",
    "        callbacks=cbs,\n",
    "        verbose=2,\n",
    "        class_weight=class_weight\n",
    "    )\n",
    "\n",
    "    # === 保存 history 與訓練曲線 ===\n",
    "    hist_df = pd.DataFrame(hist.history)\n",
    "    hist_df.to_csv(fold_dir / \"history.csv\", index=False)\n",
    "    plot_history(hist_df, fold_dir / \"plot_loss.png\")\n",
    "    plot_metric_epochs(hist_df, \"accuracy\", fold_dir / \"plot_acc.png\", \"Accuracy per Epoch\")\n",
    "    if \"f1\" in hist_df.columns and \"val_f1\" in hist_df.columns:\n",
    "        plot_metric_epochs(hist_df, \"f1\", fold_dir / \"plot_f1.png\", \"F1 per Epoch\")\n",
    "\n",
    "    # === 推論：Train & Val 機率 ===\n",
    "    ytr_prob = model.predict(Xtr_p, batch_size=CFG[\"batch_size\"], verbose=0).ravel()\n",
    "    yva_prob = model.predict(Xva_p, batch_size=CFG[\"batch_size\"], verbose=0).ravel()\n",
    "\n",
    "    # === PR / ROC（Train & Val）===\n",
    "    fpr_tr, tpr_tr, _ = roc_curve(ytr, ytr_prob)\n",
    "    fpr_va, tpr_va, _ = roc_curve(yva, yva_prob)\n",
    "    roc_auc_tr = auc(fpr_tr, tpr_tr)\n",
    "    roc_auc_va = auc(fpr_va, tpr_va)\n",
    "\n",
    "    prec_tr, rec_tr, _ = precision_recall_curve(ytr, ytr_prob)\n",
    "    prec_va, rec_va, _ = precision_recall_curve(yva, yva_prob)\n",
    "    ap_tr = average_precision_score(ytr, ytr_prob)\n",
    "    ap_va = average_precision_score(yva, yva_prob)\n",
    "\n",
    "    # 存曲線 CSV 與圖\n",
    "    pd.DataFrame({\"fpr\": fpr_tr, \"tpr\": tpr_tr}).to_csv(fold_dir / \"roc_curve_train.csv\", index=False)\n",
    "    pd.DataFrame({\"fpr\": fpr_va, \"tpr\": fpr_va}).to_csv(fold_dir / \"roc_curve_val.csv\", index=False)\n",
    "    plot_roc_xy(fpr_tr, tpr_tr, roc_auc_tr, fold_dir / \"plot_roc_train.png\", \"ROC Curve (Train)\")\n",
    "    plot_roc_xy(fpr_va, tpr_va, roc_auc_va, fold_dir / \"plot_roc_val.png\",   \"ROC Curve (Validation)\")\n",
    "\n",
    "    pd.DataFrame({\"recall\": rec_tr, \"precision\": prec_tr}).to_csv(fold_dir / \"pr_curve_train.csv\", index=False)\n",
    "    pd.DataFrame({\"recall\": rec_va, \"precision\": prec_va}).to_csv(fold_dir / \"pr_curve_val.csv\", index=False)\n",
    "    plot_pr_xy(rec_tr, prec_tr, ap_tr, fold_dir / \"plot_pr_train.png\", \"Precision-Recall (Train)\")\n",
    "    plot_pr_xy(rec_va, prec_va, ap_va, fold_dir / \"plot_pr_val.png\",   \"Precision-Recall (Validation)\")\n",
    "\n",
    "    # === 閾值掃描（驗證集找最佳 F1）===\n",
    "    grid = np.linspace(0.01, 0.99, 99)\n",
    "    f1s = [precision_recall_fscore_support(yva, (yva_prob >= th).astype(int),\n",
    "                                           average='binary', zero_division=0)[2]\n",
    "           for th in grid]\n",
    "    best_idx = int(np.argmax(f1s))\n",
    "    best_th = float(grid[best_idx])\n",
    "\n",
    "    # === 指標（best-th 與 fixed 0.5 各一組）===\n",
    "    def pack_metrics(y_true, y_prob, th):\n",
    "        y_pred = (y_prob >= th).astype(int)\n",
    "        acc = accuracy_score(y_true, y_pred)\n",
    "        prec, rec, f1, _ = precision_recall_fscore_support(y_true, y_pred, average='binary', zero_division=0)\n",
    "        rocauc = roc_auc_score(y_true, y_prob)\n",
    "        cm = confusion_matrix(y_true, y_pred, labels=[0, 1])\n",
    "        return acc, prec, rec, f1, rocauc, cm\n",
    "\n",
    "    tr_acc_b, tr_prec_b, tr_rec_b, tr_f1_b, tr_auc, cm_tr_b = pack_metrics(ytr, ytr_prob, best_th)\n",
    "    va_acc_b, va_prec_b, va_rec_b, va_f1_b, va_auc, cm_va_b = pack_metrics(yva, yva_prob, best_th)\n",
    "\n",
    "    tr_acc_f, tr_prec_f, tr_rec_f, tr_f1_f, _, cm_tr_f = pack_metrics(ytr, ytr_prob, CFG[\"threshold\"])\n",
    "    va_acc_f, va_prec_f, va_rec_f, va_f1_f, _, cm_va_f = pack_metrics(yva, yva_prob, CFG[\"threshold\"])\n",
    "\n",
    "    # === 輸出 CSV ===\n",
    "    pd.DataFrame({\"y_true\": ytr, \"y_prob\": ytr_prob, \"y_pred\": (ytr_prob >= best_th).astype(int)}).to_csv(fold_dir / \"predictions_train.csv\", index=False)\n",
    "    pd.DataFrame({\"y_true\": yva, \"y_prob\": yva_prob, \"y_pred\": (yva_prob >= best_th).astype(int)}).to_csv(fold_dir / \"predictions_val.csv\", index=False)\n",
    "\n",
    "    pd.DataFrame(cm_tr_b, index=[\"Actual_0\",\"Actual_1\"], columns=[\"Pred_0\",\"Pred_1\"]).to_csv(fold_dir / \"cm_train_best.csv\")\n",
    "    pd.DataFrame(cm_va_b, index=[\"Actual_0\",\"Actual_1\"], columns=[\"Pred_0\",\"Pred_1\"]).to_csv(fold_dir / \"cm_val_best.csv\")\n",
    "    pd.DataFrame(cm_tr_f, index=[\"Actual_0\",\"Actual_1\"], columns=[\"Pred_0\",\"Pred_1\"]).to_csv(fold_dir / \"cm_train_fixed05.csv\")\n",
    "    pd.DataFrame(cm_va_f, index=[\"Actual_0\",\"Actual_1\"], columns=[\"Pred_0\",\"Pred_1\"]).to_csv(fold_dir / \"cm_val_fixed05.csv\")\n",
    "\n",
    "    plot_cm(cm_tr_b, fold_dir / \"plot_cm_train_best.png\", \"Confusion Matrix (Train, best-th)\")\n",
    "    plot_cm(cm_va_b, fold_dir / \"plot_cm_val_best.png\",   \"Confusion Matrix (Validation, best-th)\")\n",
    "\n",
    "    # === 每折 metrics.csv（含 best-th 與 fixed 0.5）===\n",
    "    row = {\n",
    "        \"fold\": k,\n",
    "        \"pos_rate\": pos_rate,\n",
    "        \"focal_alpha_used\": alpha_used,\n",
    "        \"class_weight_0\": class_weight[0], \"class_weight_1\": class_weight[1],\n",
    "        \"best_threshold\": best_th,\n",
    "        # Train (best-th)\n",
    "        \"train_accuracy_best\": tr_acc_b, \"train_precision_best\": tr_prec_b, \"train_recall_best\": tr_rec_b,\n",
    "        \"train_f1_best\": tr_f1_b, \"train_auc\": tr_auc, \"train_auprc\": ap_tr,\n",
    "        # Val   (best-th)\n",
    "        \"val_accuracy_best\": va_acc_b, \"val_precision_best\": va_prec_b, \"val_recall_best\": va_rec_b,\n",
    "        \"val_f1_best\": va_f1_b, \"val_auc\": va_auc, \"val_auprc\": ap_va,\n",
    "        # Val   (fixed 0.5)\n",
    "        \"val_accuracy_0p5\": va_acc_f, \"val_precision_0p5\": va_prec_f, \"val_recall_0p5\": va_rec_f,\n",
    "        \"val_f1_0p5\": va_f1_f\n",
    "    }\n",
    "    pd.DataFrame([row]).to_csv(fold_dir / \"metrics.csv\", index=False)\n",
    "\n",
    "    # === 審計資訊 ===\n",
    "    meta = {\n",
    "        \"fold\": k,\n",
    "        \"pos_rate\": pos_rate,\n",
    "        \"best_threshold\": best_th,\n",
    "        \"class_weight\": class_weight,\n",
    "        \"focal\": {\"gamma\": CFG[\"focal_gamma\"], \"alpha_used\": alpha_used}\n",
    "    }\n",
    "    (fold_dir / \"fold_meta.json\").write_text(json.dumps(meta, ensure_ascii=False, indent=2), encoding=\"utf-8\")\n",
    "\n",
    "    # 收集彙總\n",
    "    all_rows.append(row)\n",
    "\n",
    "# ==============================\n",
    "# 八、十折彙總與整體平均 ± 標準差\n",
    "# ==============================\n",
    "summary = pd.DataFrame(all_rows).sort_values(\"fold\")\n",
    "summary.to_csv(RUN_DIR / \"summary_folds.csv\", index=False)\n",
    "\n",
    "agg = {}\n",
    "for col in [c for c in summary.columns if c != \"fold\"]:\n",
    "    agg[col + \"_mean\"] = float(summary[col].mean())\n",
    "    agg[col + \"_std\"] = float(summary[col].std(ddof=1))\n",
    "\n",
    "overall = {\"n_folds\": int(len(summary)), **agg, \"config\": CFG}\n",
    "(RUN_DIR / \"summary_overall.json\").write_text(json.dumps(overall, ensure_ascii=False, indent=2), encoding=\"utf-8\")\n",
    "\n",
    "print(\"\\n=== Training Done ===\")\n",
    "print(f\"Run dir: {RUN_DIR}\")\n",
    "print(json.dumps(overall, indent=2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b0a6e2a3-6172-4087-b690-c179be734b1a",
   "metadata": {},
   "outputs": [],
   "source": [
    "把「排序」學好、把「閾值」調準"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cb64f660-cfc1-45ab-a4ef-482192500398",
   "metadata": {},
   "outputs": [],
   "source": [
    "AUC ≈ 0.51~0.53（幾乎隨機）、val_auprc ≈ 0.28 接近驗證集正類率、best_th ≈ 0.49、機率分佈均值 ≈ 0.53、p90 也只到 0.55… → 訊號很弱，調閾值救不了根本問題。\n",
    "另一步，train 正類率 ≈ 0.53，但 val 正類率 ≈ 0.28~0.29（從 val_accuracy、val_auprc、val_precision 的組合可反推），這代表分佈偏移：你用「訓練先驗偏置」初始化，驗證集卻更偏負類，固定 0.5 才會立刻高召回低精度"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bbf16049-0276-48f9-8025-b66f7002bdbb",
   "metadata": {},
   "outputs": [],
   "source": [
    "A：前處理改為「逐特徵」標準化（跨樣本×時間一起估計每個特徵的均值/方差）。\n",
    "B：提供開關做消融：use_focal 與 use_class_weight（避免雙重加權）。\n",
    "C：穩定訓練：Adam(lr=5e-4, clipnorm=1.0)，並保留 ReduceLROnPlateau。\n",
    "E：維持輸出層先驗偏置、F1PerEpoch（每 epoch 掃 val_best_th）、1D-safe BCE metric（保留 Focal vs BCE 對照圖，且不改 y 形狀）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 273,
   "id": "b49564d3-0c70-4e10-ab4a-d04727e5c1b5",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/40\n",
      "[F1Epoch] epoch=0 fixed_th=0.5  f1_tr=0.4120 f1_va=0.4135 | best_th=0.45  f1_tr=0.4178 f1_va=0.4189\n",
      "[Probe] epoch=0  train mean=0.5252 p90=0.5357 | val mean=0.5259 p90=0.5359\n",
      "197/197 - 44s - 225ms/step - accuracy: 0.5425 - auc: 0.4998 - auprc: 0.2670 - binary_crossentropy: 0.6809 - loss: 0.0763 - precision: 0.2653 - recall: 0.4128 - val_accuracy: 0.2861 - val_auc: 0.5173 - val_auprc: 0.2788 - val_binary_crossentropy: 0.7186 - val_loss: 0.0684 - val_precision: 0.2641 - val_recall: 0.9525 - learning_rate: 3.0000e-04 - f1: 0.4120 - val_f1: 0.4135 - f1_best: 0.4178 - val_f1_best: 0.4189 - val_best_th: 0.4500\n",
      "Epoch 2/40\n",
      "[F1Epoch] epoch=1 fixed_th=0.5  f1_tr=0.4152 f1_va=0.4215 | best_th=0.50  f1_tr=0.4152 f1_va=0.4215\n",
      "[Probe] epoch=1  train mean=0.5225 p90=0.5371 | val mean=0.5227 p90=0.5375\n",
      "197/197 - 41s - 209ms/step - accuracy: 0.5091 - auc: 0.5038 - auprc: 0.2688 - binary_crossentropy: 0.6935 - loss: 0.0686 - precision: 0.2643 - recall: 0.4803 - val_accuracy: 0.2904 - val_auc: 0.5316 - val_auprc: 0.2983 - val_binary_crossentropy: 0.7146 - val_loss: 0.0680 - val_precision: 0.2686 - val_recall: 0.9783 - learning_rate: 3.0000e-04 - f1: 0.4152 - val_f1: 0.4215 - f1_best: 0.4152 - val_f1_best: 0.4215 - val_best_th: 0.5000\n",
      "Epoch 3/40\n",
      "[F1Epoch] epoch=2 fixed_th=0.5  f1_tr=0.4174 f1_va=0.4186 | best_th=0.49  f1_tr=0.4181 f1_va=0.4197\n",
      "[Probe] epoch=2  train mean=0.5212 p90=0.5410 | val mean=0.5212 p90=0.5409\n",
      "197/197 - 41s - 207ms/step - accuracy: 0.5182 - auc: 0.5127 - auprc: 0.2748 - binary_crossentropy: 0.6926 - loss: 0.0681 - precision: 0.2721 - recall: 0.4907 - val_accuracy: 0.2790 - val_auc: 0.5365 - val_auprc: 0.2935 - val_binary_crossentropy: 0.7131 - val_loss: 0.0679 - val_precision: 0.2660 - val_recall: 0.9824 - learning_rate: 3.0000e-04 - f1: 0.4174 - val_f1: 0.4186 - f1_best: 0.4181 - val_f1_best: 0.4197 - val_best_th: 0.4900\n",
      "Epoch 4/40\n",
      "[F1Epoch] epoch=3 fixed_th=0.5  f1_tr=0.4140 f1_va=0.4105 | best_th=0.49  f1_tr=0.4188 f1_va=0.4193\n",
      "[Probe] epoch=3  train mean=0.5142 p90=0.5370 | val mean=0.5140 p90=0.5364\n",
      "197/197 - 41s - 207ms/step - accuracy: 0.5231 - auc: 0.5174 - auprc: 0.2759 - binary_crossentropy: 0.6923 - loss: 0.0679 - precision: 0.2748 - recall: 0.4904 - val_accuracy: 0.3245 - val_auc: 0.5314 - val_auprc: 0.2870 - val_binary_crossentropy: 0.7059 - val_loss: 0.0676 - val_precision: 0.2668 - val_recall: 0.8901 - learning_rate: 3.0000e-04 - f1: 0.4140 - val_f1: 0.4105 - f1_best: 0.4188 - val_f1_best: 0.4193 - val_best_th: 0.4900\n",
      "Epoch 5/40\n",
      "[F1Epoch] epoch=4 fixed_th=0.5  f1_tr=0.4051 f1_va=0.4081 | best_th=0.48  f1_tr=0.4186 f1_va=0.4189\n",
      "[Probe] epoch=4  train mean=0.5110 p90=0.5335 | val mean=0.5109 p90=0.5327\n",
      "197/197 - 41s - 206ms/step - accuracy: 0.5195 - auc: 0.5156 - auprc: 0.2743 - binary_crossentropy: 0.6925 - loss: 0.0679 - precision: 0.2729 - recall: 0.4910 - val_accuracy: 0.3489 - val_auc: 0.5313 - val_auprc: 0.2822 - val_binary_crossentropy: 0.7029 - val_loss: 0.0676 - val_precision: 0.2686 - val_recall: 0.8494 - learning_rate: 3.0000e-04 - f1: 0.4051 - val_f1: 0.4081 - f1_best: 0.4186 - val_f1_best: 0.4189 - val_best_th: 0.4800\n",
      "Epoch 6/40\n",
      "\n",
      "Epoch 6: ReduceLROnPlateau reducing learning rate to 0.0001500000071246177.\n",
      "[F1Epoch] epoch=5 fixed_th=0.5  f1_tr=0.4067 f1_va=0.4053 | best_th=0.48  f1_tr=0.4188 f1_va=0.4190\n",
      "[Probe] epoch=5  train mean=0.5115 p90=0.5328 | val mean=0.5114 p90=0.5327\n",
      "197/197 - 40s - 205ms/step - accuracy: 0.5160 - auc: 0.5116 - auprc: 0.2743 - binary_crossentropy: 0.6927 - loss: 0.0679 - precision: 0.2710 - recall: 0.4913 - val_accuracy: 0.3593 - val_auc: 0.5329 - val_auprc: 0.2862 - val_binary_crossentropy: 0.7034 - val_loss: 0.0676 - val_precision: 0.2685 - val_recall: 0.8263 - learning_rate: 3.0000e-04 - f1: 0.4067 - val_f1: 0.4053 - f1_best: 0.4188 - val_f1_best: 0.4190 - val_best_th: 0.4800\n",
      "Epoch 7/40\n",
      "[F1Epoch] epoch=6 fixed_th=0.5  f1_tr=0.3983 f1_va=0.4056 | best_th=0.49  f1_tr=0.4186 f1_va=0.4199\n",
      "[Probe] epoch=6  train mean=0.5054 p90=0.5195 | val mean=0.5056 p90=0.5198\n",
      "197/197 - 41s - 207ms/step - accuracy: 0.5246 - auc: 0.5255 - auprc: 0.2812 - binary_crossentropy: 0.6919 - loss: 0.0676 - precision: 0.2806 - recall: 0.5105 - val_accuracy: 0.4324 - val_auc: 0.5480 - val_auprc: 0.3017 - val_binary_crossentropy: 0.6973 - val_loss: 0.0673 - val_precision: 0.2804 - val_recall: 0.7327 - learning_rate: 1.5000e-04 - f1: 0.3983 - val_f1: 0.4056 - f1_best: 0.4186 - val_f1_best: 0.4199 - val_best_th: 0.4900\n",
      "Epoch 8/40\n",
      "[F1Epoch] epoch=7 fixed_th=0.5  f1_tr=0.3933 f1_va=0.3997 | best_th=0.48  f1_tr=0.4189 f1_va=0.4193\n",
      "[Probe] epoch=7  train mean=0.5051 p90=0.5204 | val mean=0.5053 p90=0.5207\n",
      "197/197 - 41s - 208ms/step - accuracy: 0.5204 - auc: 0.5197 - auprc: 0.2784 - binary_crossentropy: 0.6922 - loss: 0.0677 - precision: 0.2735 - recall: 0.4913 - val_accuracy: 0.4582 - val_auc: 0.5426 - val_auprc: 0.3014 - val_binary_crossentropy: 0.6970 - val_loss: 0.0673 - val_precision: 0.2826 - val_recall: 0.6825 - learning_rate: 1.5000e-04 - f1: 0.3933 - val_f1: 0.3997 - f1_best: 0.4189 - val_f1_best: 0.4193 - val_best_th: 0.4800\n",
      "Epoch 9/40\n",
      "[F1Epoch] epoch=8 fixed_th=0.5  f1_tr=0.4074 f1_va=0.4032 | best_th=0.49  f1_tr=0.4187 f1_va=0.4192\n",
      "[Probe] epoch=8  train mean=0.5065 p90=0.5200 | val mean=0.5067 p90=0.5202\n",
      "197/197 - 41s - 207ms/step - accuracy: 0.5211 - auc: 0.5188 - auprc: 0.2778 - binary_crossentropy: 0.6923 - loss: 0.0677 - precision: 0.2747 - recall: 0.4948 - val_accuracy: 0.3897 - val_auc: 0.5517 - val_auprc: 0.3084 - val_binary_crossentropy: 0.6983 - val_loss: 0.0673 - val_precision: 0.2719 - val_recall: 0.7802 - learning_rate: 1.5000e-04 - f1: 0.4074 - val_f1: 0.4032 - f1_best: 0.4187 - val_f1_best: 0.4192 - val_best_th: 0.4900\n",
      "Epoch 10/40\n",
      "[F1Epoch] epoch=9 fixed_th=0.5  f1_tr=0.3969 f1_va=0.3970 | best_th=0.49  f1_tr=0.4189 f1_va=0.4189\n",
      "[Probe] epoch=9  train mean=0.5056 p90=0.5208 | val mean=0.5057 p90=0.5210\n",
      "197/197 - 41s - 207ms/step - accuracy: 0.5253 - auc: 0.5248 - auprc: 0.2830 - binary_crossentropy: 0.6917 - loss: 0.0676 - precision: 0.2801 - recall: 0.5067 - val_accuracy: 0.4575 - val_auc: 0.5430 - val_auprc: 0.3053 - val_binary_crossentropy: 0.6974 - val_loss: 0.0673 - val_precision: 0.2810 - val_recall: 0.6757 - learning_rate: 1.5000e-04 - f1: 0.3969 - val_f1: 0.3970 - f1_best: 0.4189 - val_f1_best: 0.4189 - val_best_th: 0.4900\n",
      "Epoch 11/40\n",
      "[F1Epoch] epoch=10 fixed_th=0.5  f1_tr=0.4109 f1_va=0.4143 | best_th=0.49  f1_tr=0.4191 f1_va=0.4203\n",
      "[Probe] epoch=10  train mean=0.5071 p90=0.5212 | val mean=0.5073 p90=0.5216\n",
      "197/197 - 41s - 207ms/step - accuracy: 0.5263 - auc: 0.5233 - auprc: 0.2787 - binary_crossentropy: 0.6919 - loss: 0.0676 - precision: 0.2792 - recall: 0.5004 - val_accuracy: 0.3736 - val_auc: 0.5471 - val_auprc: 0.3001 - val_binary_crossentropy: 0.6990 - val_loss: 0.0673 - val_precision: 0.2752 - val_recall: 0.8385 - learning_rate: 1.5000e-04 - f1: 0.4109 - val_f1: 0.4143 - f1_best: 0.4191 - val_f1_best: 0.4203 - val_best_th: 0.4900\n",
      "Epoch 12/40\n",
      "[F1Epoch] epoch=11 fixed_th=0.5  f1_tr=0.3997 f1_va=0.3994 | best_th=0.48  f1_tr=0.4192 f1_va=0.4189\n",
      "[Probe] epoch=11  train mean=0.5063 p90=0.5220 | val mean=0.5064 p90=0.5222\n",
      "197/197 - 41s - 206ms/step - accuracy: 0.5256 - auc: 0.5245 - auprc: 0.2808 - binary_crossentropy: 0.6920 - loss: 0.0676 - precision: 0.2817 - recall: 0.5126 - val_accuracy: 0.4403 - val_auc: 0.5427 - val_auprc: 0.2988 - val_binary_crossentropy: 0.6981 - val_loss: 0.0673 - val_precision: 0.2787 - val_recall: 0.7042 - learning_rate: 1.5000e-04 - f1: 0.3997 - val_f1: 0.3994 - f1_best: 0.4192 - val_f1_best: 0.4189 - val_best_th: 0.4800\n",
      "Epoch 13/40\n",
      "\n",
      "Epoch 13: ReduceLROnPlateau reducing learning rate to 7.500000356230885e-05.\n",
      "[F1Epoch] epoch=12 fixed_th=0.5  f1_tr=0.4100 f1_va=0.4129 | best_th=0.48  f1_tr=0.4188 f1_va=0.4193\n",
      "[Probe] epoch=12  train mean=0.5067 p90=0.5200 | val mean=0.5069 p90=0.5204\n",
      "197/197 - 41s - 206ms/step - accuracy: 0.5261 - auc: 0.5221 - auprc: 0.2779 - binary_crossentropy: 0.6920 - loss: 0.0676 - precision: 0.2791 - recall: 0.5008 - val_accuracy: 0.3750 - val_auc: 0.5486 - val_auprc: 0.3052 - val_binary_crossentropy: 0.6985 - val_loss: 0.0673 - val_precision: 0.2746 - val_recall: 0.8318 - learning_rate: 1.5000e-04 - f1: 0.4100 - val_f1: 0.4129 - f1_best: 0.4188 - val_f1_best: 0.4193 - val_best_th: 0.4800\n",
      "Epoch 14/40\n",
      "[F1Epoch] epoch=13 fixed_th=0.5  f1_tr=0.3937 f1_va=0.3962 | best_th=0.48  f1_tr=0.4187 f1_va=0.4203\n",
      "[Probe] epoch=13  train mean=0.5035 p90=0.5179 | val mean=0.5038 p90=0.5184\n",
      "197/197 - 42s - 211ms/step - accuracy: 0.5187 - auc: 0.5237 - auprc: 0.2815 - binary_crossentropy: 0.6920 - loss: 0.0675 - precision: 0.2755 - recall: 0.5034 - val_accuracy: 0.4536 - val_auc: 0.5478 - val_auprc: 0.3042 - val_binary_crossentropy: 0.6955 - val_loss: 0.0672 - val_precision: 0.2798 - val_recall: 0.6784 - learning_rate: 7.5000e-05 - f1: 0.3937 - val_f1: 0.3962 - f1_best: 0.4187 - val_f1_best: 0.4203 - val_best_th: 0.4800\n",
      "Epoch 15/40\n",
      "[F1Epoch] epoch=14 fixed_th=0.5  f1_tr=0.3974 f1_va=0.3991 | best_th=0.48  f1_tr=0.4189 f1_va=0.4207\n",
      "[Probe] epoch=14  train mean=0.5038 p90=0.5172 | val mean=0.5041 p90=0.5177\n",
      "197/197 - 41s - 208ms/step - accuracy: 0.5271 - auc: 0.5282 - auprc: 0.2855 - binary_crossentropy: 0.6914 - loss: 0.0675 - precision: 0.2796 - recall: 0.5004 - val_accuracy: 0.4460 - val_auc: 0.5504 - val_auprc: 0.3032 - val_binary_crossentropy: 0.6957 - val_loss: 0.0672 - val_precision: 0.2797 - val_recall: 0.6961 - learning_rate: 7.5000e-05 - f1: 0.3974 - val_f1: 0.3991 - f1_best: 0.4189 - val_f1_best: 0.4207 - val_best_th: 0.4800\n",
      "Epoch 16/40\n",
      "[F1Epoch] epoch=15 fixed_th=0.5  f1_tr=0.3998 f1_va=0.4036 | best_th=0.48  f1_tr=0.4184 f1_va=0.4200\n",
      "[Probe] epoch=15  train mean=0.5041 p90=0.5185 | val mean=0.5043 p90=0.5189\n",
      "197/197 - 41s - 206ms/step - accuracy: 0.5245 - auc: 0.5267 - auprc: 0.2861 - binary_crossentropy: 0.6916 - loss: 0.0675 - precision: 0.2785 - recall: 0.5020 - val_accuracy: 0.4607 - val_auc: 0.5492 - val_auprc: 0.3056 - val_binary_crossentropy: 0.6959 - val_loss: 0.0672 - val_precision: 0.2852 - val_recall: 0.6906 - learning_rate: 7.5000e-05 - f1: 0.3998 - val_f1: 0.4036 - f1_best: 0.4184 - val_f1_best: 0.4200 - val_best_th: 0.4800\n",
      "Epoch 17/40\n",
      "\n",
      "Epoch 17: ReduceLROnPlateau reducing learning rate to 3.7500001781154424e-05.\n",
      "[F1Epoch] epoch=16 fixed_th=0.5  f1_tr=0.4000 f1_va=0.4019 | best_th=0.49  f1_tr=0.4176 f1_va=0.4193\n",
      "[Probe] epoch=16  train mean=0.5043 p90=0.5195 | val mean=0.5046 p90=0.5198\n",
      "197/197 - 41s - 207ms/step - accuracy: 0.5249 - auc: 0.5275 - auprc: 0.2824 - binary_crossentropy: 0.6916 - loss: 0.0675 - precision: 0.2805 - recall: 0.5091 - val_accuracy: 0.4579 - val_auc: 0.5471 - val_auprc: 0.3031 - val_binary_crossentropy: 0.6961 - val_loss: 0.0672 - val_precision: 0.2836 - val_recall: 0.6893 - learning_rate: 7.5000e-05 - f1: 0.4000 - val_f1: 0.4019 - f1_best: 0.4176 - val_f1_best: 0.4193 - val_best_th: 0.4900\n",
      "Epoch 1/40\n",
      "[F1Epoch] epoch=0 fixed_th=0.5  f1_tr=0.2801 f1_va=0.2780 | best_th=0.01  f1_tr=0.4182 f1_va=0.4180\n",
      "[Probe] epoch=0  train mean=0.4731 p90=0.5205 | val mean=0.4733 p90=0.5207\n",
      "197/197 - 44s - 224ms/step - accuracy: 0.5364 - auc: 0.5048 - auprc: 0.2684 - binary_crossentropy: 0.6823 - loss: 0.0751 - precision: 0.2656 - recall: 0.4270 - val_accuracy: 0.6257 - val_auc: 0.5039 - val_auprc: 0.2766 - val_binary_crossentropy: 0.6704 - val_loss: 0.0693 - val_precision: 0.2835 - val_recall: 0.2727 - learning_rate: 3.0000e-04 - f1: 0.2801 - val_f1: 0.2780 - f1_best: 0.4182 - val_f1_best: 0.4180 - val_best_th: 0.0100\n",
      "Epoch 2/40\n",
      "[F1Epoch] epoch=1 fixed_th=0.5  f1_tr=0.2526 f1_va=0.2498 | best_th=0.01  f1_tr=0.4182 f1_va=0.4180\n",
      "[Probe] epoch=1  train mean=0.4738 p90=0.5115 | val mean=0.4739 p90=0.5117\n",
      "197/197 - 40s - 205ms/step - accuracy: 0.5211 - auc: 0.5111 - auprc: 0.2752 - binary_crossentropy: 0.6924 - loss: 0.0685 - precision: 0.2736 - recall: 0.4901 - val_accuracy: 0.6490 - val_auc: 0.4895 - val_auprc: 0.2654 - val_binary_crossentropy: 0.6711 - val_loss: 0.0692 - val_precision: 0.2870 - val_recall: 0.2212 - learning_rate: 3.0000e-04 - f1: 0.2526 - val_f1: 0.2498 - f1_best: 0.4182 - val_f1_best: 0.4180 - val_best_th: 0.0100\n",
      "Epoch 3/40\n",
      "[F1Epoch] epoch=2 fixed_th=0.5  f1_tr=0.2655 f1_va=0.2647 | best_th=0.01  f1_tr=0.4182 f1_va=0.4180\n",
      "[Probe] epoch=2  train mean=0.4795 p90=0.5150 | val mean=0.4793 p90=0.5149\n",
      "197/197 - 40s - 203ms/step - accuracy: 0.5308 - auc: 0.5220 - auprc: 0.2775 - binary_crossentropy: 0.6912 - loss: 0.0680 - precision: 0.2793 - recall: 0.4903 - val_accuracy: 0.6414 - val_auc: 0.4928 - val_auprc: 0.2650 - val_binary_crossentropy: 0.6756 - val_loss: 0.0688 - val_precision: 0.2889 - val_recall: 0.2442 - learning_rate: 3.0000e-04 - f1: 0.2655 - val_f1: 0.2647 - f1_best: 0.4182 - val_f1_best: 0.4180 - val_best_th: 0.0100\n",
      "Epoch 4/40\n",
      "[F1Epoch] epoch=3 fixed_th=0.5  f1_tr=0.1986 f1_va=0.1868 | best_th=0.01  f1_tr=0.4182 f1_va=0.4180\n",
      "[Probe] epoch=3  train mean=0.4792 p90=0.5021 | val mean=0.4790 p90=0.5025\n",
      "197/197 - 41s - 206ms/step - accuracy: 0.5281 - auc: 0.5201 - auprc: 0.2791 - binary_crossentropy: 0.6917 - loss: 0.0680 - precision: 0.2783 - recall: 0.4928 - val_accuracy: 0.6784 - val_auc: 0.4968 - val_auprc: 0.2669 - val_binary_crossentropy: 0.6749 - val_loss: 0.0685 - val_precision: 0.2814 - val_recall: 0.1398 - learning_rate: 3.0000e-04 - f1: 0.1986 - val_f1: 0.1868 - f1_best: 0.4182 - val_f1_best: 0.4180 - val_best_th: 0.0100\n",
      "Epoch 5/40\n",
      "\n",
      "Epoch 5: ReduceLROnPlateau reducing learning rate to 0.0001500000071246177.\n",
      "[F1Epoch] epoch=4 fixed_th=0.5  f1_tr=0.2581 f1_va=0.2582 | best_th=0.01  f1_tr=0.4182 f1_va=0.4180\n",
      "[Probe] epoch=4  train mean=0.4829 p90=0.5083 | val mean=0.4830 p90=0.5087\n",
      "197/197 - 40s - 205ms/step - accuracy: 0.5259 - auc: 0.5231 - auprc: 0.2817 - binary_crossentropy: 0.6913 - loss: 0.0679 - precision: 0.2777 - recall: 0.4953 - val_accuracy: 0.6518 - val_auc: 0.5081 - val_auprc: 0.2722 - val_binary_crossentropy: 0.6781 - val_loss: 0.0682 - val_precision: 0.2955 - val_recall: 0.2293 - learning_rate: 3.0000e-04 - f1: 0.2581 - val_f1: 0.2582 - f1_best: 0.4182 - val_f1_best: 0.4180 - val_best_th: 0.0100\n",
      "Epoch 6/40\n",
      "[F1Epoch] epoch=5 fixed_th=0.5  f1_tr=0.3206 f1_va=0.2936 | best_th=0.01  f1_tr=0.4182 f1_va=0.4180\n",
      "[Probe] epoch=5  train mean=0.4951 p90=0.5153 | val mean=0.4951 p90=0.5156\n",
      "197/197 - 41s - 207ms/step - accuracy: 0.5337 - auc: 0.5262 - auprc: 0.2864 - binary_crossentropy: 0.6910 - loss: 0.0677 - precision: 0.2810 - recall: 0.4901 - val_accuracy: 0.5895 - val_auc: 0.5035 - val_auprc: 0.2708 - val_binary_crossentropy: 0.6887 - val_loss: 0.0677 - val_precision: 0.2692 - val_recall: 0.3229 - learning_rate: 1.5000e-04 - f1: 0.3206 - val_f1: 0.2936 - f1_best: 0.4182 - val_f1_best: 0.4180 - val_best_th: 0.0100\n",
      "Epoch 7/40\n",
      "[F1Epoch] epoch=6 fixed_th=0.5  f1_tr=0.3263 f1_va=0.3198 | best_th=0.01  f1_tr=0.4182 f1_va=0.4180\n",
      "[Probe] epoch=6  train mean=0.4948 p90=0.5148 | val mean=0.4950 p90=0.5147\n",
      "197/197 - 41s - 207ms/step - accuracy: 0.5296 - auc: 0.5253 - auprc: 0.2812 - binary_crossentropy: 0.6914 - loss: 0.0677 - precision: 0.2807 - recall: 0.4989 - val_accuracy: 0.5988 - val_auc: 0.5109 - val_auprc: 0.2779 - val_binary_crossentropy: 0.6884 - val_loss: 0.0677 - val_precision: 0.2896 - val_recall: 0.3569 - learning_rate: 1.5000e-04 - f1: 0.3263 - val_f1: 0.3198 - f1_best: 0.4182 - val_f1_best: 0.4180 - val_best_th: 0.0100\n",
      "Epoch 8/40\n",
      "[F1Epoch] epoch=7 fixed_th=0.5  f1_tr=0.3221 f1_va=0.3066 | best_th=0.01  f1_tr=0.4182 f1_va=0.4180\n",
      "[Probe] epoch=7  train mean=0.4932 p90=0.5144 | val mean=0.4933 p90=0.5143\n",
      "197/197 - 40s - 205ms/step - accuracy: 0.5290 - auc: 0.5248 - auprc: 0.2822 - binary_crossentropy: 0.6915 - loss: 0.0677 - precision: 0.2796 - recall: 0.4957 - val_accuracy: 0.5880 - val_auc: 0.5137 - val_auprc: 0.2771 - val_binary_crossentropy: 0.6868 - val_loss: 0.0677 - val_precision: 0.2761 - val_recall: 0.3446 - learning_rate: 1.5000e-04 - f1: 0.3221 - val_f1: 0.3066 - f1_best: 0.4182 - val_f1_best: 0.4180 - val_best_th: 0.0100\n",
      "Epoch 9/40\n",
      "[F1Epoch] epoch=8 fixed_th=0.5  f1_tr=0.3216 f1_va=0.3038 | best_th=0.01  f1_tr=0.4182 f1_va=0.4180\n",
      "[Probe] epoch=8  train mean=0.4940 p90=0.5144 | val mean=0.4941 p90=0.5143\n",
      "197/197 - 40s - 205ms/step - accuracy: 0.5319 - auc: 0.5282 - auprc: 0.2833 - binary_crossentropy: 0.6911 - loss: 0.0676 - precision: 0.2819 - recall: 0.4981 - val_accuracy: 0.5909 - val_auc: 0.5128 - val_auprc: 0.2772 - val_binary_crossentropy: 0.6877 - val_loss: 0.0677 - val_precision: 0.2761 - val_recall: 0.3379 - learning_rate: 1.5000e-04 - f1: 0.3216 - val_f1: 0.3038 - f1_best: 0.4182 - val_f1_best: 0.4180 - val_best_th: 0.0100\n",
      "Epoch 10/40\n",
      "[F1Epoch] epoch=9 fixed_th=0.5  f1_tr=0.3201 f1_va=0.3130 | best_th=0.01  f1_tr=0.4182 f1_va=0.4180\n",
      "[Probe] epoch=9  train mean=0.4941 p90=0.5143 | val mean=0.4943 p90=0.5143\n",
      "197/197 - 41s - 208ms/step - accuracy: 0.5351 - auc: 0.5293 - auprc: 0.2846 - binary_crossentropy: 0.6910 - loss: 0.0676 - precision: 0.2836 - recall: 0.4971 - val_accuracy: 0.6081 - val_auc: 0.5120 - val_auprc: 0.2787 - val_binary_crossentropy: 0.6878 - val_loss: 0.0677 - val_precision: 0.2916 - val_recall: 0.3379 - learning_rate: 1.5000e-04 - f1: 0.3201 - val_f1: 0.3130 - f1_best: 0.4182 - val_f1_best: 0.4180 - val_best_th: 0.0100\n",
      "Epoch 11/40\n",
      "[F1Epoch] epoch=10 fixed_th=0.5  f1_tr=0.3189 f1_va=0.3039 | best_th=0.42  f1_tr=0.4184 f1_va=0.4182\n",
      "[Probe] epoch=10  train mean=0.4940 p90=0.5140 | val mean=0.4941 p90=0.5142\n",
      "197/197 - 41s - 206ms/step - accuracy: 0.5319 - auc: 0.5330 - auprc: 0.2868 - binary_crossentropy: 0.6908 - loss: 0.0675 - precision: 0.2827 - recall: 0.5011 - val_accuracy: 0.5991 - val_auc: 0.5068 - val_auprc: 0.2744 - val_binary_crossentropy: 0.6877 - val_loss: 0.0677 - val_precision: 0.2808 - val_recall: 0.3311 - learning_rate: 1.5000e-04 - f1: 0.3189 - val_f1: 0.3039 - f1_best: 0.4184 - val_f1_best: 0.4182 - val_best_th: 0.4200\n",
      "Epoch 12/40\n",
      "[F1Epoch] epoch=11 fixed_th=0.5  f1_tr=0.3271 f1_va=0.3097 | best_th=0.42  f1_tr=0.4183 f1_va=0.4182\n",
      "[Probe] epoch=11  train mean=0.4942 p90=0.5144 | val mean=0.4944 p90=0.5145\n",
      "197/197 - 41s - 206ms/step - accuracy: 0.5295 - auc: 0.5283 - auprc: 0.2820 - binary_crossentropy: 0.6913 - loss: 0.0676 - precision: 0.2804 - recall: 0.4975 - val_accuracy: 0.5956 - val_auc: 0.5104 - val_auprc: 0.2777 - val_binary_crossentropy: 0.6877 - val_loss: 0.0677 - val_precision: 0.2821 - val_recall: 0.3433 - learning_rate: 1.5000e-04 - f1: 0.3271 - val_f1: 0.3097 - f1_best: 0.4183 - val_f1_best: 0.4182 - val_best_th: 0.4200\n",
      "Epoch 13/40\n",
      "[F1Epoch] epoch=12 fixed_th=0.5  f1_tr=0.3192 f1_va=0.3041 | best_th=0.01  f1_tr=0.4182 f1_va=0.4180\n",
      "[Probe] epoch=12  train mean=0.4942 p90=0.5141 | val mean=0.4944 p90=0.5141\n",
      "197/197 - 40s - 206ms/step - accuracy: 0.5330 - auc: 0.5280 - auprc: 0.2854 - binary_crossentropy: 0.6910 - loss: 0.0676 - precision: 0.2821 - recall: 0.4960 - val_accuracy: 0.6045 - val_auc: 0.5100 - val_auprc: 0.2752 - val_binary_crossentropy: 0.6879 - val_loss: 0.0677 - val_precision: 0.2842 - val_recall: 0.3270 - learning_rate: 1.5000e-04 - f1: 0.3192 - val_f1: 0.3041 - f1_best: 0.4182 - val_f1_best: 0.4180 - val_best_th: 0.0100\n",
      "Epoch 14/40\n",
      "\n",
      "Epoch 14: ReduceLROnPlateau reducing learning rate to 7.500000356230885e-05.\n",
      "[F1Epoch] epoch=13 fixed_th=0.5  f1_tr=0.3210 f1_va=0.3128 | best_th=0.01  f1_tr=0.4182 f1_va=0.4180\n",
      "[Probe] epoch=13  train mean=0.4937 p90=0.5162 | val mean=0.4939 p90=0.5163\n",
      "197/197 - 40s - 205ms/step - accuracy: 0.5336 - auc: 0.5322 - auprc: 0.2896 - binary_crossentropy: 0.6908 - loss: 0.0675 - precision: 0.2838 - recall: 0.5014 - val_accuracy: 0.6077 - val_auc: 0.5147 - val_auprc: 0.2774 - val_binary_crossentropy: 0.6873 - val_loss: 0.0677 - val_precision: 0.2912 - val_recall: 0.3379 - learning_rate: 1.5000e-04 - f1: 0.3210 - val_f1: 0.3128 - f1_best: 0.4182 - val_f1_best: 0.4180 - val_best_th: 0.0100\n",
      "Epoch 15/40\n",
      "[F1Epoch] epoch=14 fixed_th=0.5  f1_tr=0.3424 f1_va=0.3376 | best_th=0.01  f1_tr=0.4182 f1_va=0.4180\n",
      "[Probe] epoch=14  train mean=0.4962 p90=0.5124 | val mean=0.4964 p90=0.5123\n",
      "197/197 - 40s - 204ms/step - accuracy: 0.5329 - auc: 0.5299 - auprc: 0.2854 - binary_crossentropy: 0.6906 - loss: 0.0675 - precision: 0.2804 - recall: 0.4895 - val_accuracy: 0.5877 - val_auc: 0.5161 - val_auprc: 0.2819 - val_binary_crossentropy: 0.6897 - val_loss: 0.0676 - val_precision: 0.2933 - val_recall: 0.3976 - learning_rate: 7.5000e-05 - f1: 0.3424 - val_f1: 0.3376 - f1_best: 0.4182 - val_f1_best: 0.4180 - val_best_th: 0.0100\n",
      "Epoch 16/40\n",
      "[F1Epoch] epoch=15 fixed_th=0.5  f1_tr=0.3512 f1_va=0.3436 | best_th=0.01  f1_tr=0.4182 f1_va=0.4180\n",
      "[Probe] epoch=15  train mean=0.4970 p90=0.5125 | val mean=0.4972 p90=0.5120\n",
      "197/197 - 40s - 206ms/step - accuracy: 0.5386 - auc: 0.5337 - auprc: 0.2869 - binary_crossentropy: 0.6908 - loss: 0.0674 - precision: 0.2869 - recall: 0.5017 - val_accuracy: 0.5809 - val_auc: 0.5152 - val_auprc: 0.2797 - val_binary_crossentropy: 0.6905 - val_loss: 0.0676 - val_precision: 0.2931 - val_recall: 0.4152 - learning_rate: 7.5000e-05 - f1: 0.3512 - val_f1: 0.3436 - f1_best: 0.4182 - val_f1_best: 0.4180 - val_best_th: 0.0100\n",
      "Epoch 17/40\n",
      "[F1Epoch] epoch=16 fixed_th=0.5  f1_tr=0.3421 f1_va=0.3364 | best_th=0.01  f1_tr=0.4182 f1_va=0.4180\n",
      "[Probe] epoch=16  train mean=0.4964 p90=0.5133 | val mean=0.4967 p90=0.5130\n",
      "197/197 - 40s - 206ms/step - accuracy: 0.5282 - auc: 0.5273 - auprc: 0.2847 - binary_crossentropy: 0.6913 - loss: 0.0675 - precision: 0.2803 - recall: 0.5005 - val_accuracy: 0.5927 - val_auc: 0.5113 - val_auprc: 0.2776 - val_binary_crossentropy: 0.6900 - val_loss: 0.0676 - val_precision: 0.2954 - val_recall: 0.3908 - learning_rate: 7.5000e-05 - f1: 0.3421 - val_f1: 0.3364 - f1_best: 0.4182 - val_f1_best: 0.4180 - val_best_th: 0.0100\n",
      "Epoch 18/40\n",
      "[F1Epoch] epoch=17 fixed_th=0.5  f1_tr=0.3420 f1_va=0.3410 | best_th=0.01  f1_tr=0.4182 f1_va=0.4180\n",
      "[Probe] epoch=17  train mean=0.4964 p90=0.5128 | val mean=0.4967 p90=0.5125\n",
      "197/197 - 40s - 205ms/step - accuracy: 0.5338 - auc: 0.5317 - auprc: 0.2872 - binary_crossentropy: 0.6909 - loss: 0.0675 - precision: 0.2831 - recall: 0.4983 - val_accuracy: 0.5898 - val_auc: 0.5180 - val_auprc: 0.2819 - val_binary_crossentropy: 0.6899 - val_loss: 0.0676 - val_precision: 0.2963 - val_recall: 0.4016 - learning_rate: 7.5000e-05 - f1: 0.3420 - val_f1: 0.3410 - f1_best: 0.4182 - val_f1_best: 0.4180 - val_best_th: 0.0100\n",
      "Epoch 19/40\n",
      "\n",
      "Epoch 19: ReduceLROnPlateau reducing learning rate to 3.7500001781154424e-05.\n",
      "[F1Epoch] epoch=18 fixed_th=0.5  f1_tr=0.3440 f1_va=0.3384 | best_th=0.01  f1_tr=0.4182 f1_va=0.4180\n",
      "[Probe] epoch=18  train mean=0.4959 p90=0.5139 | val mean=0.4963 p90=0.5139\n",
      "197/197 - 40s - 206ms/step - accuracy: 0.5373 - auc: 0.5368 - auprc: 0.2913 - binary_crossentropy: 0.6904 - loss: 0.0674 - precision: 0.2857 - recall: 0.5001 - val_accuracy: 0.5905 - val_auc: 0.5126 - val_auprc: 0.2790 - val_binary_crossentropy: 0.6895 - val_loss: 0.0676 - val_precision: 0.2952 - val_recall: 0.3962 - learning_rate: 7.5000e-05 - f1: 0.3440 - val_f1: 0.3384 - f1_best: 0.4182 - val_f1_best: 0.4180 - val_best_th: 0.0100\n",
      "Epoch 20/40\n",
      "[F1Epoch] epoch=19 fixed_th=0.5  f1_tr=0.3747 f1_va=0.3583 | best_th=0.01  f1_tr=0.4182 f1_va=0.4180\n",
      "[Probe] epoch=19  train mean=0.4993 p90=0.5167 | val mean=0.4996 p90=0.5163\n",
      "197/197 - 40s - 205ms/step - accuracy: 0.5422 - auc: 0.5369 - auprc: 0.2903 - binary_crossentropy: 0.6905 - loss: 0.0674 - precision: 0.2904 - recall: 0.5067 - val_accuracy: 0.5364 - val_auc: 0.5088 - val_auprc: 0.2779 - val_binary_crossentropy: 0.6928 - val_loss: 0.0676 - val_precision: 0.2825 - val_recall: 0.4898 - learning_rate: 3.7500e-05 - f1: 0.3747 - val_f1: 0.3583 - f1_best: 0.4182 - val_f1_best: 0.4180 - val_best_th: 0.0100\n",
      "Epoch 21/40\n",
      "[F1Epoch] epoch=20 fixed_th=0.5  f1_tr=0.3671 f1_va=0.3587 | best_th=0.01  f1_tr=0.4182 f1_va=0.4180\n",
      "[Probe] epoch=20  train mean=0.4986 p90=0.5155 | val mean=0.4989 p90=0.5150\n",
      "197/197 - 40s - 204ms/step - accuracy: 0.5369 - auc: 0.5344 - auprc: 0.2871 - binary_crossentropy: 0.6908 - loss: 0.0674 - precision: 0.2863 - recall: 0.5034 - val_accuracy: 0.5500 - val_auc: 0.5099 - val_auprc: 0.2756 - val_binary_crossentropy: 0.6921 - val_loss: 0.0676 - val_precision: 0.2877 - val_recall: 0.4763 - learning_rate: 3.7500e-05 - f1: 0.3671 - val_f1: 0.3587 - f1_best: 0.4182 - val_f1_best: 0.4180 - val_best_th: 0.0100\n",
      "Epoch 22/40\n",
      "[F1Epoch] epoch=21 fixed_th=0.5  f1_tr=0.3726 f1_va=0.3618 | best_th=0.01  f1_tr=0.4182 f1_va=0.4180\n",
      "[Probe] epoch=21  train mean=0.4989 p90=0.5161 | val mean=0.4992 p90=0.5156\n",
      "197/197 - 40s - 205ms/step - accuracy: 0.5368 - auc: 0.5334 - auprc: 0.2929 - binary_crossentropy: 0.6906 - loss: 0.0674 - precision: 0.2860 - recall: 0.5023 - val_accuracy: 0.5446 - val_auc: 0.5108 - val_auprc: 0.2765 - val_binary_crossentropy: 0.6924 - val_loss: 0.0676 - val_precision: 0.2873 - val_recall: 0.4885 - learning_rate: 3.7500e-05 - f1: 0.3726 - val_f1: 0.3618 - f1_best: 0.4182 - val_f1_best: 0.4180 - val_best_th: 0.0100\n",
      "Epoch 23/40\n",
      "\n",
      "Epoch 23: ReduceLROnPlateau reducing learning rate to 1.8750000890577212e-05.\n",
      "[F1Epoch] epoch=22 fixed_th=0.5  f1_tr=0.3699 f1_va=0.3502 | best_th=0.01  f1_tr=0.4182 f1_va=0.4180\n",
      "[Probe] epoch=22  train mean=0.4986 p90=0.5153 | val mean=0.4989 p90=0.5150\n",
      "197/197 - 41s - 208ms/step - accuracy: 0.5347 - auc: 0.5359 - auprc: 0.2891 - binary_crossentropy: 0.6907 - loss: 0.0674 - precision: 0.2843 - recall: 0.5008 - val_accuracy: 0.5396 - val_auc: 0.5121 - val_auprc: 0.2780 - val_binary_crossentropy: 0.6922 - val_loss: 0.0676 - val_precision: 0.2793 - val_recall: 0.4695 - learning_rate: 3.7500e-05 - f1: 0.3699 - val_f1: 0.3502 - f1_best: 0.4182 - val_f1_best: 0.4180 - val_best_th: 0.0100\n",
      "Epoch 24/40\n",
      "[F1Epoch] epoch=23 fixed_th=0.5  f1_tr=0.3866 f1_va=0.3609 | best_th=0.01  f1_tr=0.4182 f1_va=0.4180\n",
      "[Probe] epoch=23  train mean=0.5012 p90=0.5181 | val mean=0.5016 p90=0.5178\n",
      "197/197 - 41s - 208ms/step - accuracy: 0.5410 - auc: 0.5325 - auprc: 0.2898 - binary_crossentropy: 0.6904 - loss: 0.0674 - precision: 0.2852 - recall: 0.4886 - val_accuracy: 0.4844 - val_auc: 0.5114 - val_auprc: 0.2767 - val_binary_crossentropy: 0.6947 - val_loss: 0.0676 - val_precision: 0.2683 - val_recall: 0.5509 - learning_rate: 1.8750e-05 - f1: 0.3866 - val_f1: 0.3609 - f1_best: 0.4182 - val_f1_best: 0.4180 - val_best_th: 0.0100\n",
      "Epoch 25/40\n",
      "[F1Epoch] epoch=24 fixed_th=0.5  f1_tr=0.3867 f1_va=0.3643 | best_th=0.01  f1_tr=0.4182 f1_va=0.4180\n",
      "[Probe] epoch=24  train mean=0.5014 p90=0.5182 | val mean=0.5018 p90=0.5179\n",
      "197/197 - 40s - 205ms/step - accuracy: 0.5332 - auc: 0.5315 - auprc: 0.2872 - binary_crossentropy: 0.6911 - loss: 0.0675 - precision: 0.2837 - recall: 0.5022 - val_accuracy: 0.4844 - val_auc: 0.5108 - val_auprc: 0.2763 - val_binary_crossentropy: 0.6949 - val_loss: 0.0676 - val_precision: 0.2702 - val_recall: 0.5590 - learning_rate: 1.8750e-05 - f1: 0.3867 - val_f1: 0.3643 - f1_best: 0.4182 - val_f1_best: 0.4180 - val_best_th: 0.0100\n",
      "Epoch 26/40\n",
      "[F1Epoch] epoch=25 fixed_th=0.5  f1_tr=0.3848 f1_va=0.3659 | best_th=0.01  f1_tr=0.4182 f1_va=0.4180\n",
      "[Probe] epoch=25  train mean=0.5012 p90=0.5185 | val mean=0.5015 p90=0.5181\n",
      "197/197 - 40s - 205ms/step - accuracy: 0.5347 - auc: 0.5376 - auprc: 0.2925 - binary_crossentropy: 0.6907 - loss: 0.0673 - precision: 0.2846 - recall: 0.5022 - val_accuracy: 0.4955 - val_auc: 0.5096 - val_auprc: 0.2761 - val_binary_crossentropy: 0.6947 - val_loss: 0.0677 - val_precision: 0.2740 - val_recall: 0.5509 - learning_rate: 1.8750e-05 - f1: 0.3848 - val_f1: 0.3659 - f1_best: 0.4182 - val_f1_best: 0.4180 - val_best_th: 0.0100\n",
      "Epoch 1/40\n",
      "[F1Epoch] epoch=0 fixed_th=0.5  f1_tr=0.3475 f1_va=0.3433 | best_th=0.41  f1_tr=0.4182 f1_va=0.4183\n",
      "[Probe] epoch=0  train mean=0.5062 p90=0.5428 | val mean=0.5061 p90=0.5425\n",
      "197/197 - 44s - 224ms/step - accuracy: 0.5331 - auc: 0.4979 - auprc: 0.2642 - binary_crossentropy: 0.6837 - loss: 0.0751 - precision: 0.2593 - recall: 0.4125 - val_accuracy: 0.5364 - val_auc: 0.5077 - val_auprc: 0.2654 - val_binary_crossentropy: 0.6996 - val_loss: 0.0681 - val_precision: 0.2744 - val_recall: 0.4586 - learning_rate: 3.0000e-04 - f1: 0.3475 - val_f1: 0.3433 - f1_best: 0.4182 - val_f1_best: 0.4183 - val_best_th: 0.4100\n",
      "Epoch 2/40\n",
      "[F1Epoch] epoch=1 fixed_th=0.5  f1_tr=0.3982 f1_va=0.3766 | best_th=0.46  f1_tr=0.4183 f1_va=0.4188\n",
      "[Probe] epoch=1  train mean=0.5054 p90=0.5223 | val mean=0.5052 p90=0.5223\n",
      "197/197 - 41s - 206ms/step - accuracy: 0.5168 - auc: 0.5135 - auprc: 0.2721 - binary_crossentropy: 0.6930 - loss: 0.0685 - precision: 0.2710 - recall: 0.4897 - val_accuracy: 0.4457 - val_auc: 0.5035 - val_auprc: 0.2604 - val_binary_crossentropy: 0.6984 - val_loss: 0.0677 - val_precision: 0.2679 - val_recall: 0.6336 - learning_rate: 3.0000e-04 - f1: 0.3982 - val_f1: 0.3766 - f1_best: 0.4183 - val_f1_best: 0.4188 - val_best_th: 0.4600\n",
      "Epoch 3/40\n",
      "[F1Epoch] epoch=2 fixed_th=0.5  f1_tr=0.3831 f1_va=0.3528 | best_th=0.47  f1_tr=0.4185 f1_va=0.4183\n",
      "[Probe] epoch=2  train mean=0.5053 p90=0.5256 | val mean=0.5052 p90=0.5254\n",
      "197/197 - 40s - 205ms/step - accuracy: 0.5206 - auc: 0.5129 - auprc: 0.2773 - binary_crossentropy: 0.6928 - loss: 0.0682 - precision: 0.2744 - recall: 0.4945 - val_accuracy: 0.4805 - val_auc: 0.5060 - val_auprc: 0.2645 - val_binary_crossentropy: 0.6983 - val_loss: 0.0677 - val_precision: 0.2630 - val_recall: 0.5360 - learning_rate: 3.0000e-04 - f1: 0.3831 - val_f1: 0.3528 - f1_best: 0.4185 - val_f1_best: 0.4183 - val_best_th: 0.4700\n",
      "Epoch 4/40\n",
      "[F1Epoch] epoch=3 fixed_th=0.5  f1_tr=0.3921 f1_va=0.3758 | best_th=0.43  f1_tr=0.4183 f1_va=0.4181\n",
      "[Probe] epoch=3  train mean=0.5029 p90=0.5137 | val mean=0.5028 p90=0.5133\n",
      "197/197 - 41s - 206ms/step - accuracy: 0.5223 - auc: 0.5159 - auprc: 0.2806 - binary_crossentropy: 0.6922 - loss: 0.0680 - precision: 0.2725 - recall: 0.4832 - val_accuracy: 0.4270 - val_auc: 0.4946 - val_auprc: 0.2620 - val_binary_crossentropy: 0.6962 - val_loss: 0.0677 - val_precision: 0.2639 - val_recall: 0.6526 - learning_rate: 3.0000e-04 - f1: 0.3921 - val_f1: 0.3758 - f1_best: 0.4183 - val_f1_best: 0.4181 - val_best_th: 0.4300\n",
      "Epoch 5/40\n",
      "[F1Epoch] epoch=4 fixed_th=0.5  f1_tr=0.4052 f1_va=0.3985 | best_th=0.47  f1_tr=0.4189 f1_va=0.4184\n",
      "[Probe] epoch=4  train mean=0.5064 p90=0.5227 | val mean=0.5064 p90=0.5227\n",
      "197/197 - 40s - 204ms/step - accuracy: 0.5226 - auc: 0.5214 - auprc: 0.2794 - binary_crossentropy: 0.6923 - loss: 0.0679 - precision: 0.2763 - recall: 0.4978 - val_accuracy: 0.4220 - val_auc: 0.5086 - val_auprc: 0.2661 - val_binary_crossentropy: 0.6993 - val_loss: 0.0677 - val_precision: 0.2748 - val_recall: 0.7246 - learning_rate: 3.0000e-04 - f1: 0.4052 - val_f1: 0.3985 - f1_best: 0.4189 - val_f1_best: 0.4184 - val_best_th: 0.4700\n",
      "Epoch 6/40\n",
      "[F1Epoch] epoch=5 fixed_th=0.5  f1_tr=0.4043 f1_va=0.3820 | best_th=0.47  f1_tr=0.4189 f1_va=0.4183\n",
      "[Probe] epoch=5  train mean=0.5048 p90=0.5196 | val mean=0.5047 p90=0.5196\n",
      "197/197 - 40s - 205ms/step - accuracy: 0.5250 - auc: 0.5240 - auprc: 0.2868 - binary_crossentropy: 0.6916 - loss: 0.0678 - precision: 0.2771 - recall: 0.4951 - val_accuracy: 0.4385 - val_auc: 0.5034 - val_auprc: 0.2637 - val_binary_crossentropy: 0.6978 - val_loss: 0.0677 - val_precision: 0.2693 - val_recall: 0.6567 - learning_rate: 3.0000e-04 - f1: 0.4043 - val_f1: 0.3820 - f1_best: 0.4189 - val_f1_best: 0.4183 - val_best_th: 0.4700\n",
      "Epoch 7/40\n",
      "[F1Epoch] epoch=6 fixed_th=0.5  f1_tr=0.3967 f1_va=0.3822 | best_th=0.47  f1_tr=0.4189 f1_va=0.4184\n",
      "[Probe] epoch=6  train mean=0.5050 p90=0.5210 | val mean=0.5049 p90=0.5210\n",
      "197/197 - 41s - 208ms/step - accuracy: 0.5226 - auc: 0.5221 - auprc: 0.2840 - binary_crossentropy: 0.6919 - loss: 0.0678 - precision: 0.2752 - recall: 0.4930 - val_accuracy: 0.4471 - val_auc: 0.5142 - val_auprc: 0.2658 - val_binary_crossentropy: 0.6979 - val_loss: 0.0677 - val_precision: 0.2712 - val_recall: 0.6472 - learning_rate: 3.0000e-04 - f1: 0.3967 - val_f1: 0.3822 - f1_best: 0.4189 - val_f1_best: 0.4184 - val_best_th: 0.4700\n",
      "Epoch 8/40\n",
      "[F1Epoch] epoch=7 fixed_th=0.5  f1_tr=0.3897 f1_va=0.3766 | best_th=0.47  f1_tr=0.4188 f1_va=0.4184\n",
      "[Probe] epoch=7  train mean=0.5032 p90=0.5198 | val mean=0.5031 p90=0.5197\n",
      "197/197 - 41s - 206ms/step - accuracy: 0.5275 - auc: 0.5240 - auprc: 0.2843 - binary_crossentropy: 0.6918 - loss: 0.0677 - precision: 0.2794 - recall: 0.4984 - val_accuracy: 0.4812 - val_auc: 0.5143 - val_auprc: 0.2683 - val_binary_crossentropy: 0.6961 - val_loss: 0.0677 - val_precision: 0.2759 - val_recall: 0.5929 - learning_rate: 3.0000e-04 - f1: 0.3897 - val_f1: 0.3766 - f1_best: 0.4188 - val_f1_best: 0.4184 - val_best_th: 0.4700\n",
      "Epoch 9/40\n",
      "[F1Epoch] epoch=8 fixed_th=0.5  f1_tr=0.4009 f1_va=0.3971 | best_th=0.48  f1_tr=0.4192 f1_va=0.4184\n",
      "[Probe] epoch=8  train mean=0.5045 p90=0.5186 | val mean=0.5045 p90=0.5186\n",
      "197/197 - 41s - 206ms/step - accuracy: 0.5175 - auc: 0.5192 - auprc: 0.2798 - binary_crossentropy: 0.6921 - loss: 0.0678 - precision: 0.2715 - recall: 0.4900 - val_accuracy: 0.4403 - val_auc: 0.5173 - val_auprc: 0.2707 - val_binary_crossentropy: 0.6974 - val_loss: 0.0676 - val_precision: 0.2775 - val_recall: 0.6974 - learning_rate: 3.0000e-04 - f1: 0.4009 - val_f1: 0.3971 - f1_best: 0.4192 - val_f1_best: 0.4184 - val_best_th: 0.4800\n",
      "Epoch 10/40\n",
      "[F1Epoch] epoch=9 fixed_th=0.5  f1_tr=0.4021 f1_va=0.3930 | best_th=0.42  f1_tr=0.4183 f1_va=0.4185\n",
      "[Probe] epoch=9  train mean=0.5049 p90=0.5195 | val mean=0.5048 p90=0.5199\n",
      "197/197 - 41s - 206ms/step - accuracy: 0.5230 - auc: 0.5242 - auprc: 0.2861 - binary_crossentropy: 0.6916 - loss: 0.0677 - precision: 0.2769 - recall: 0.4990 - val_accuracy: 0.4417 - val_auc: 0.5136 - val_auprc: 0.2684 - val_binary_crossentropy: 0.6976 - val_loss: 0.0676 - val_precision: 0.2757 - val_recall: 0.6839 - learning_rate: 3.0000e-04 - f1: 0.4021 - val_f1: 0.3930 - f1_best: 0.4183 - val_f1_best: 0.4185 - val_best_th: 0.4200\n",
      "Epoch 11/40\n",
      "[F1Epoch] epoch=10 fixed_th=0.5  f1_tr=0.4048 f1_va=0.3934 | best_th=0.35  f1_tr=0.4182 f1_va=0.4182\n",
      "[Probe] epoch=10  train mean=0.5055 p90=0.5205 | val mean=0.5054 p90=0.5208\n",
      "197/197 - 41s - 206ms/step - accuracy: 0.5224 - auc: 0.5271 - auprc: 0.2896 - binary_crossentropy: 0.6915 - loss: 0.0676 - precision: 0.2777 - recall: 0.5037 - val_accuracy: 0.4338 - val_auc: 0.5115 - val_auprc: 0.2666 - val_binary_crossentropy: 0.6985 - val_loss: 0.0677 - val_precision: 0.2744 - val_recall: 0.6947 - learning_rate: 3.0000e-04 - f1: 0.4048 - val_f1: 0.3934 - f1_best: 0.4182 - val_f1_best: 0.4182 - val_best_th: 0.3500\n",
      "Epoch 12/40\n",
      "[F1Epoch] epoch=11 fixed_th=0.5  f1_tr=0.4028 f1_va=0.3830 | best_th=0.39  f1_tr=0.4183 f1_va=0.4184\n",
      "[Probe] epoch=11  train mean=0.5046 p90=0.5191 | val mean=0.5045 p90=0.5190\n",
      "197/197 - 41s - 208ms/step - accuracy: 0.5242 - auc: 0.5252 - auprc: 0.2861 - binary_crossentropy: 0.6916 - loss: 0.0676 - precision: 0.2791 - recall: 0.5054 - val_accuracy: 0.4367 - val_auc: 0.5089 - val_auprc: 0.2661 - val_binary_crossentropy: 0.6976 - val_loss: 0.0677 - val_precision: 0.2696 - val_recall: 0.6621 - learning_rate: 3.0000e-04 - f1: 0.4028 - val_f1: 0.3830 - f1_best: 0.4183 - val_f1_best: 0.4184 - val_best_th: 0.3900\n",
      "Epoch 13/40\n",
      "\n",
      "Epoch 13: ReduceLROnPlateau reducing learning rate to 0.0001500000071246177.\n",
      "[F1Epoch] epoch=12 fixed_th=0.5  f1_tr=0.4004 f1_va=0.3882 | best_th=0.45  f1_tr=0.4187 f1_va=0.4184\n",
      "[Probe] epoch=12  train mean=0.5050 p90=0.5232 | val mean=0.5047 p90=0.5232\n",
      "197/197 - 41s - 208ms/step - accuracy: 0.5317 - auc: 0.5313 - auprc: 0.2894 - binary_crossentropy: 0.6913 - loss: 0.0675 - precision: 0.2838 - recall: 0.5063 - val_accuracy: 0.4507 - val_auc: 0.5096 - val_auprc: 0.2682 - val_binary_crossentropy: 0.6977 - val_loss: 0.0677 - val_precision: 0.2750 - val_recall: 0.6594 - learning_rate: 3.0000e-04 - f1: 0.4004 - val_f1: 0.3882 - f1_best: 0.4187 - val_f1_best: 0.4184 - val_best_th: 0.4500\n",
      "Epoch 14/40\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[273], line 365\u001b[0m\n\u001b[1;32m    351\u001b[0m cbs \u001b[38;5;241m=\u001b[39m [\n\u001b[1;32m    352\u001b[0m     keras\u001b[38;5;241m.\u001b[39mcallbacks\u001b[38;5;241m.\u001b[39mEarlyStopping(monitor\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mval_auprc\u001b[39m\u001b[38;5;124m\"\u001b[39m, patience\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mmax\u001b[39m(\u001b[38;5;241m8\u001b[39m, CFG[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mpatience\u001b[39m\u001b[38;5;124m\"\u001b[39m]),\n\u001b[1;32m    353\u001b[0m                                   mode\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmax\u001b[39m\u001b[38;5;124m\"\u001b[39m, restore_best_weights\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m),\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    361\u001b[0m     ProbProbe(Xtr_p, Xva_p, bs\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1024\u001b[39m)\n\u001b[1;32m    362\u001b[0m ]\n\u001b[1;32m    364\u001b[0m \u001b[38;5;66;03m# 訓練（不改 y 形狀）\u001b[39;00m\n\u001b[0;32m--> 365\u001b[0m hist \u001b[38;5;241m=\u001b[39m \u001b[43mmodel\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfit\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    366\u001b[0m \u001b[43m    \u001b[49m\u001b[43mXtr_p\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mytr\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    367\u001b[0m \u001b[43m    \u001b[49m\u001b[43mvalidation_data\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mXva_p\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43myva\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    368\u001b[0m \u001b[43m    \u001b[49m\u001b[43mepochs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mCFG\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mepochs\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    369\u001b[0m \u001b[43m    \u001b[49m\u001b[43mbatch_size\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mCFG\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mbatch_size\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    370\u001b[0m \u001b[43m    \u001b[49m\u001b[43mcallbacks\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcbs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    371\u001b[0m \u001b[43m    \u001b[49m\u001b[43mverbose\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m2\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m    372\u001b[0m \u001b[43m    \u001b[49m\u001b[43mclass_weight\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mclass_weight\u001b[49m\n\u001b[1;32m    373\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    375\u001b[0m \u001b[38;5;66;03m# 保存 history 與訓練曲線\u001b[39;00m\n\u001b[1;32m    376\u001b[0m hist_df \u001b[38;5;241m=\u001b[39m pd\u001b[38;5;241m.\u001b[39mDataFrame(hist\u001b[38;5;241m.\u001b[39mhistory)\n",
      "File \u001b[0;32m/opt/conda/lib/python3.11/site-packages/keras/src/utils/traceback_utils.py:117\u001b[0m, in \u001b[0;36mfilter_traceback.<locals>.error_handler\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    115\u001b[0m filtered_tb \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m    116\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 117\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfn\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    118\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[1;32m    119\u001b[0m     filtered_tb \u001b[38;5;241m=\u001b[39m _process_traceback_frames(e\u001b[38;5;241m.\u001b[39m__traceback__)\n",
      "File \u001b[0;32m/opt/conda/lib/python3.11/site-packages/keras/src/backend/tensorflow/trainer.py:318\u001b[0m, in \u001b[0;36mTensorFlowTrainer.fit\u001b[0;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_batch_size, validation_freq)\u001b[0m\n\u001b[1;32m    316\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m step, iterator \u001b[38;5;129;01min\u001b[39;00m epoch_iterator\u001b[38;5;241m.\u001b[39menumerate_epoch():\n\u001b[1;32m    317\u001b[0m     callbacks\u001b[38;5;241m.\u001b[39mon_train_batch_begin(step)\n\u001b[0;32m--> 318\u001b[0m     logs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtrain_function\u001b[49m\u001b[43m(\u001b[49m\u001b[43miterator\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    319\u001b[0m     logs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_pythonify_logs(logs)\n\u001b[1;32m    320\u001b[0m     callbacks\u001b[38;5;241m.\u001b[39mon_train_batch_end(step, logs)\n",
      "File \u001b[0;32m/opt/conda/lib/python3.11/site-packages/tensorflow/python/util/traceback_utils.py:150\u001b[0m, in \u001b[0;36mfilter_traceback.<locals>.error_handler\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    148\u001b[0m filtered_tb \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m    149\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 150\u001b[0m   \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfn\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    151\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[1;32m    152\u001b[0m   filtered_tb \u001b[38;5;241m=\u001b[39m _process_traceback_frames(e\u001b[38;5;241m.\u001b[39m__traceback__)\n",
      "File \u001b[0;32m/opt/conda/lib/python3.11/site-packages/tensorflow/python/eager/polymorphic_function/polymorphic_function.py:833\u001b[0m, in \u001b[0;36mFunction.__call__\u001b[0;34m(self, *args, **kwds)\u001b[0m\n\u001b[1;32m    830\u001b[0m compiler \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mxla\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_jit_compile \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mnonXla\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    832\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m OptionalXlaContext(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_jit_compile):\n\u001b[0;32m--> 833\u001b[0m   result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwds\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    835\u001b[0m new_tracing_count \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mexperimental_get_tracing_count()\n\u001b[1;32m    836\u001b[0m without_tracing \u001b[38;5;241m=\u001b[39m (tracing_count \u001b[38;5;241m==\u001b[39m new_tracing_count)\n",
      "File \u001b[0;32m/opt/conda/lib/python3.11/site-packages/tensorflow/python/eager/polymorphic_function/polymorphic_function.py:878\u001b[0m, in \u001b[0;36mFunction._call\u001b[0;34m(self, *args, **kwds)\u001b[0m\n\u001b[1;32m    875\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_lock\u001b[38;5;241m.\u001b[39mrelease()\n\u001b[1;32m    876\u001b[0m \u001b[38;5;66;03m# In this case we have not created variables on the first call. So we can\u001b[39;00m\n\u001b[1;32m    877\u001b[0m \u001b[38;5;66;03m# run the first trace but we should fail if variables are created.\u001b[39;00m\n\u001b[0;32m--> 878\u001b[0m results \u001b[38;5;241m=\u001b[39m \u001b[43mtracing_compilation\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcall_function\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    879\u001b[0m \u001b[43m    \u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkwds\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_variable_creation_config\u001b[49m\n\u001b[1;32m    880\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    881\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_created_variables:\n\u001b[1;32m    882\u001b[0m   \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCreating variables on a non-first call to a function\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    883\u001b[0m                    \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m decorated with tf.function.\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
      "File \u001b[0;32m/opt/conda/lib/python3.11/site-packages/tensorflow/python/eager/polymorphic_function/tracing_compilation.py:139\u001b[0m, in \u001b[0;36mcall_function\u001b[0;34m(args, kwargs, tracing_options)\u001b[0m\n\u001b[1;32m    137\u001b[0m bound_args \u001b[38;5;241m=\u001b[39m function\u001b[38;5;241m.\u001b[39mfunction_type\u001b[38;5;241m.\u001b[39mbind(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[1;32m    138\u001b[0m flat_inputs \u001b[38;5;241m=\u001b[39m function\u001b[38;5;241m.\u001b[39mfunction_type\u001b[38;5;241m.\u001b[39munpack_inputs(bound_args)\n\u001b[0;32m--> 139\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunction\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_flat\u001b[49m\u001b[43m(\u001b[49m\u001b[43m  \u001b[49m\u001b[38;5;66;43;03m# pylint: disable=protected-access\u001b[39;49;00m\n\u001b[1;32m    140\u001b[0m \u001b[43m    \u001b[49m\u001b[43mflat_inputs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcaptured_inputs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfunction\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcaptured_inputs\u001b[49m\n\u001b[1;32m    141\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m/opt/conda/lib/python3.11/site-packages/tensorflow/python/eager/polymorphic_function/concrete_function.py:1322\u001b[0m, in \u001b[0;36mConcreteFunction._call_flat\u001b[0;34m(self, tensor_inputs, captured_inputs)\u001b[0m\n\u001b[1;32m   1318\u001b[0m possible_gradient_type \u001b[38;5;241m=\u001b[39m gradients_util\u001b[38;5;241m.\u001b[39mPossibleTapeGradientTypes(args)\n\u001b[1;32m   1319\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m (possible_gradient_type \u001b[38;5;241m==\u001b[39m gradients_util\u001b[38;5;241m.\u001b[39mPOSSIBLE_GRADIENT_TYPES_NONE\n\u001b[1;32m   1320\u001b[0m     \u001b[38;5;129;01mand\u001b[39;00m executing_eagerly):\n\u001b[1;32m   1321\u001b[0m   \u001b[38;5;66;03m# No tape is watching; skip to running the function.\u001b[39;00m\n\u001b[0;32m-> 1322\u001b[0m   \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_inference_function\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcall_preflattened\u001b[49m\u001b[43m(\u001b[49m\u001b[43margs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1323\u001b[0m forward_backward \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_select_forward_and_backward_functions(\n\u001b[1;32m   1324\u001b[0m     args,\n\u001b[1;32m   1325\u001b[0m     possible_gradient_type,\n\u001b[1;32m   1326\u001b[0m     executing_eagerly)\n\u001b[1;32m   1327\u001b[0m forward_function, args_with_tangents \u001b[38;5;241m=\u001b[39m forward_backward\u001b[38;5;241m.\u001b[39mforward()\n",
      "File \u001b[0;32m/opt/conda/lib/python3.11/site-packages/tensorflow/python/eager/polymorphic_function/atomic_function.py:216\u001b[0m, in \u001b[0;36mAtomicFunction.call_preflattened\u001b[0;34m(self, args)\u001b[0m\n\u001b[1;32m    214\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mcall_preflattened\u001b[39m(\u001b[38;5;28mself\u001b[39m, args: Sequence[core\u001b[38;5;241m.\u001b[39mTensor]) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Any:\n\u001b[1;32m    215\u001b[0m \u001b[38;5;250m  \u001b[39m\u001b[38;5;124;03m\"\"\"Calls with flattened tensor inputs and returns the structured output.\"\"\"\u001b[39;00m\n\u001b[0;32m--> 216\u001b[0m   flat_outputs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcall_flat\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    217\u001b[0m   \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfunction_type\u001b[38;5;241m.\u001b[39mpack_output(flat_outputs)\n",
      "File \u001b[0;32m/opt/conda/lib/python3.11/site-packages/tensorflow/python/eager/polymorphic_function/atomic_function.py:251\u001b[0m, in \u001b[0;36mAtomicFunction.call_flat\u001b[0;34m(self, *args)\u001b[0m\n\u001b[1;32m    249\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m record\u001b[38;5;241m.\u001b[39mstop_recording():\n\u001b[1;32m    250\u001b[0m   \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_bound_context\u001b[38;5;241m.\u001b[39mexecuting_eagerly():\n\u001b[0;32m--> 251\u001b[0m     outputs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_bound_context\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcall_function\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    252\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mname\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    253\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;28;43mlist\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43margs\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    254\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;28;43mlen\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfunction_type\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mflat_outputs\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    255\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    256\u001b[0m   \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    257\u001b[0m     outputs \u001b[38;5;241m=\u001b[39m make_call_op_in_graph(\n\u001b[1;32m    258\u001b[0m         \u001b[38;5;28mself\u001b[39m,\n\u001b[1;32m    259\u001b[0m         \u001b[38;5;28mlist\u001b[39m(args),\n\u001b[1;32m    260\u001b[0m         \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_bound_context\u001b[38;5;241m.\u001b[39mfunction_call_options\u001b[38;5;241m.\u001b[39mas_attrs(),\n\u001b[1;32m    261\u001b[0m     )\n",
      "File \u001b[0;32m/opt/conda/lib/python3.11/site-packages/tensorflow/python/eager/context.py:1552\u001b[0m, in \u001b[0;36mContext.call_function\u001b[0;34m(self, name, tensor_inputs, num_outputs)\u001b[0m\n\u001b[1;32m   1550\u001b[0m cancellation_context \u001b[38;5;241m=\u001b[39m cancellation\u001b[38;5;241m.\u001b[39mcontext()\n\u001b[1;32m   1551\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m cancellation_context \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m-> 1552\u001b[0m   outputs \u001b[38;5;241m=\u001b[39m \u001b[43mexecute\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mexecute\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   1553\u001b[0m \u001b[43m      \u001b[49m\u001b[43mname\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdecode\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mutf-8\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1554\u001b[0m \u001b[43m      \u001b[49m\u001b[43mnum_outputs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mnum_outputs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1555\u001b[0m \u001b[43m      \u001b[49m\u001b[43minputs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtensor_inputs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1556\u001b[0m \u001b[43m      \u001b[49m\u001b[43mattrs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mattrs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1557\u001b[0m \u001b[43m      \u001b[49m\u001b[43mctx\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1558\u001b[0m \u001b[43m  \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1559\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m   1560\u001b[0m   outputs \u001b[38;5;241m=\u001b[39m execute\u001b[38;5;241m.\u001b[39mexecute_with_cancellation(\n\u001b[1;32m   1561\u001b[0m       name\u001b[38;5;241m.\u001b[39mdecode(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mutf-8\u001b[39m\u001b[38;5;124m\"\u001b[39m),\n\u001b[1;32m   1562\u001b[0m       num_outputs\u001b[38;5;241m=\u001b[39mnum_outputs,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m   1566\u001b[0m       cancellation_manager\u001b[38;5;241m=\u001b[39mcancellation_context,\n\u001b[1;32m   1567\u001b[0m   )\n",
      "File \u001b[0;32m/opt/conda/lib/python3.11/site-packages/tensorflow/python/eager/execute.py:53\u001b[0m, in \u001b[0;36mquick_execute\u001b[0;34m(op_name, num_outputs, inputs, attrs, ctx, name)\u001b[0m\n\u001b[1;32m     51\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m     52\u001b[0m   ctx\u001b[38;5;241m.\u001b[39mensure_initialized()\n\u001b[0;32m---> 53\u001b[0m   tensors \u001b[38;5;241m=\u001b[39m \u001b[43mpywrap_tfe\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mTFE_Py_Execute\u001b[49m\u001b[43m(\u001b[49m\u001b[43mctx\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_handle\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdevice_name\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mop_name\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m     54\u001b[0m \u001b[43m                                      \u001b[49m\u001b[43minputs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mattrs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnum_outputs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     55\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m core\u001b[38;5;241m.\u001b[39m_NotOkStatusException \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[1;32m     56\u001b[0m   \u001b[38;5;28;01mif\u001b[39;00m name \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "# -*- coding: utf-8 -*-\n",
    "\"\"\"\n",
    "train_lstm_focal_windowed_clean_v2.py\n",
    "\n",
    "設定：僅套用 A/B/C/E（不使用 BiLSTM 與 LayerNorm）\n",
    "A. 前處理：逐特徵標準化（跨樣本×時間）\n",
    "B. 消融開關：use_focal、use_class_weight（避免雙重加權）\n",
    "C. 優化器：Adam(lr=5e-4, clipnorm=1.0) + ReduceLROnPlateau\n",
    "E. 先驗偏置 + 每 epoch 掃描 best-F1 閾值 + 1D-safe BCE metric（不改動原始 y）\n",
    "\n",
    "輸入：\n",
    "  /home/jovyan/RT08/0925/sliding_win/1014_sim/windowed_clean/\n",
    "    X_train_fold{k}.npy, y_train_fold{k}.npy\n",
    "    X_val_fold{k}.npy,   y_val_fold{k}.npy\n",
    "\n",
    "輸出（每個 run 自動建資料夾）：\n",
    "  training_runs/<timestamp>_runXX/\n",
    "    ├─ cfg.json\n",
    "    ├─ fold_{k}/\n",
    "    │    ├─ history.csv\n",
    "    │    ├─ metrics.csv\n",
    "    │    ├─ predictions_train.csv / predictions_val.csv\n",
    "    │    ├─ roc_curve_train.csv  / roc_curve_val.csv\n",
    "    │    ├─ pr_curve_train.csv   / pr_curve_val.csv\n",
    "    │    ├─ cm_train_best.csv    / cm_val_best.csv\n",
    "    │    ├─ cm_train_fixed05.csv / cm_val_fixed05.csv\n",
    "    │    ├─ plot_loss.png / plot_acc.png / plot_f1.png\n",
    "    │    ├─ plot_roc_train.png / plot_roc_val.png\n",
    "    │    ├─ plot_pr_train.png  / plot_pr_val.png\n",
    "    │    ├─ plot_cm_train_best.png / plot_cm_val_best.png\n",
    "    │    ├─ best_model.keras\n",
    "    │    └─ fold_meta.json\n",
    "    ├─ summary_folds.csv\n",
    "    └─ summary_overall.json\n",
    "\"\"\"\n",
    "\n",
    "import json\n",
    "import time\n",
    "import math\n",
    "from pathlib import Path\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from sklearn.impute import SimpleImputer\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.metrics import (\n",
    "    roc_curve, auc, precision_recall_curve, average_precision_score,\n",
    "    confusion_matrix, precision_recall_fscore_support,\n",
    "    accuracy_score, roc_auc_score,\n",
    ")\n",
    "from sklearn.utils.class_weight import compute_class_weight\n",
    "\n",
    "import tensorflow as tf\n",
    "from tensorflow import keras\n",
    "from tensorflow.keras import layers, initializers, regularizers\n",
    "\n",
    "# ==============================\n",
    "# 一、基本設定\n",
    "# ==============================\n",
    "BASE = Path(\"/home/jovyan/RT08/0925/sliding_win/1014_sim\")\n",
    "DATA = BASE / \"windowed_clean\"\n",
    "RUNS_ROOT = BASE / \"training_runs\"\n",
    "RUNS_ROOT.mkdir(parents=True, exist_ok=True)\n",
    "\n",
    "CFG = {\n",
    "    \"seed\": 42,\n",
    "    \"epochs\": 40,\n",
    "    \"batch_size\": 128,\n",
    "    \"learning_rate\": 3e-4,          # C: 降學習率\n",
    "    \"clipnorm\": 1.0,                # C: 斜率裁剪\n",
    "    \"focal_gamma\": 2.0,\n",
    "    \"focal_alpha_min\": 0.1,         # 動態 alpha 的界限\n",
    "    \"focal_alpha_max\": 0.9,\n",
    "    \"use_focal\": True,              # B: 只用 focal（預設）；如要比較，改 False\n",
    "    \"use_class_weight\": False,      # B: 避免與 focal 雙重加權；如要比較，改 True\n",
    "    \"patience\": 8,                  # EarlyStopping 基底（實際用 max(8, patience)）\n",
    "    \"hidden_units\": 192,\n",
    "    \"l2\": 5e-7,\n",
    "    \"dropout\": 0.1,\n",
    "    \"recurrent_dropout\": 0.0,\n",
    "    \"folds\": list(range(1, 10 + 1)),\n",
    "    \"threshold\": 0.5                # 也輸出固定 0.5 版本\n",
    "}\n",
    "\n",
    "np.random.seed(CFG[\"seed\"])\n",
    "tf.random.set_seed(CFG[\"seed\"])\n",
    "\n",
    "# 版本化 run 資料夾\n",
    "ts = time.strftime(\"%Y%m%d_%H%M%S\")\n",
    "existing = sorted(RUNS_ROOT.glob(f\"{ts}_run*\"))\n",
    "run_id = len(existing) + 1\n",
    "RUN_DIR = RUNS_ROOT / f\"{ts}_run{run_id:02d}\"\n",
    "RUN_DIR.mkdir(parents=True, exist_ok=True)\n",
    "(RUN_DIR / \"cfg.json\").write_text(json.dumps(CFG, ensure_ascii=False, indent=2), encoding=\"utf-8\")\n",
    "\n",
    "# ==============================\n",
    "# 二、Loss / Metric（1D-safe；不改動原始 y）\n",
    "# ==============================\n",
    "def binary_focal_loss(gamma=2.0, alpha=0.25):\n",
    "    def loss(y_true, y_pred):\n",
    "        y_true = tf.cast(y_true, tf.float32)\n",
    "        y_pred = tf.cast(y_pred, tf.float32)\n",
    "        y_true = tf.reshape(y_true, tf.shape(y_pred))  # 對齊形狀\n",
    "        y_pred = tf.clip_by_value(y_pred, 1e-7, 1.0 - 1e-7)\n",
    "        p_t = y_true * y_pred + (1.0 - y_true) * (1.0 - y_pred)\n",
    "        alpha_factor = y_true * alpha + (1.0 - y_true) * (1.0 - alpha)\n",
    "        modulating = tf.pow(1.0 - p_t, gamma)\n",
    "        bce = - (y_true * tf.math.log(y_pred) + (1.0 - y_true) * tf.math.log(1.0 - y_pred))\n",
    "        return tf.reduce_mean(alpha_factor * modulating * bce)\n",
    "    return loss\n",
    "\n",
    "def bce_loss_1d_safe(y_true, y_pred):\n",
    "    y_true = tf.cast(y_true, tf.float32)\n",
    "    y_pred = tf.cast(y_pred, tf.float32)\n",
    "    y_true = tf.reshape(y_true, tf.shape(y_pred))\n",
    "    return tf.reduce_mean(tf.keras.losses.binary_crossentropy(y_true, y_pred))\n",
    "\n",
    "def bce_metric_1d_safe(y_true, y_pred):\n",
    "    y_true = tf.cast(y_true, tf.float32)\n",
    "    y_pred = tf.cast(y_pred, tf.float32)\n",
    "    y_true = tf.reshape(y_true, tf.shape(y_pred))\n",
    "    return tf.reduce_mean(tf.keras.losses.binary_crossentropy(y_true, y_pred))\n",
    "bce_metric_1d_safe.__name__ = \"binary_crossentropy\"  # 讓 history 欄位名稱維持一致\n",
    "\n",
    "# ==============================\n",
    "# 三、模型（單層 LSTM；輸出層先驗偏置）\n",
    "# ==============================\n",
    "def build_model(input_shape, cfg, prior_pos=0.5, focal_alpha=0.25):\n",
    "    reg = regularizers.l2(cfg[\"l2\"])\n",
    "    inputs = keras.Input(shape=input_shape, name=\"sequence\")\n",
    "\n",
    "    # 不使用 Masking（避免標準化後接近 0 的有效步被誤當 padding）\n",
    "    x = layers.LSTM(cfg[\"hidden_units\"],\n",
    "                    dropout=cfg[\"dropout\"],\n",
    "                    recurrent_dropout=cfg[\"recurrent_dropout\"],\n",
    "                    kernel_regularizer=reg,\n",
    "                    recurrent_regularizer=reg,\n",
    "                    return_sequences=False)(inputs)\n",
    "    x = layers.Dropout(cfg[\"dropout\"])(x)\n",
    "\n",
    "    # 先驗偏置：logit(pos_rate)\n",
    "    eps = 1e-7\n",
    "    prior_pos = float(np.clip(prior_pos, eps, 1.0 - eps))\n",
    "    init_bias = math.log(prior_pos / (1.0 - prior_pos))\n",
    "\n",
    "    outputs = layers.Dense(\n",
    "        1, activation=\"sigmoid\", name=\"prob\",\n",
    "        bias_initializer=initializers.Constant(init_bias)\n",
    "    )(x)\n",
    "\n",
    "    # Optimizer（C）\n",
    "    opt = keras.optimizers.Adam(learning_rate=cfg[\"learning_rate\"], clipnorm=cfg[\"clipnorm\"])\n",
    "\n",
    "    # Loss（B：可切換 focal 或 BCE）\n",
    "    if cfg[\"use_focal\"]:\n",
    "        loss_fn = binary_focal_loss(cfg[\"focal_gamma\"], focal_alpha)\n",
    "    else:\n",
    "        loss_fn = bce_loss_1d_safe\n",
    "\n",
    "    model = keras.Model(inputs, outputs, name=\"lstm_seq\")\n",
    "    model.compile(\n",
    "        optimizer=opt,\n",
    "        loss=loss_fn,\n",
    "        metrics=[\n",
    "            keras.metrics.BinaryAccuracy(name=\"accuracy\"),\n",
    "            keras.metrics.AUC(name=\"auc\"),\n",
    "            keras.metrics.AUC(name=\"auprc\", curve=\"PR\"),\n",
    "            keras.metrics.Precision(name=\"precision\"),\n",
    "            keras.metrics.Recall(name=\"recall\"),\n",
    "            bce_metric_1d_safe  # E：Focal vs BCE 對照圖（不改 y）\n",
    "        ]\n",
    "    )\n",
    "    return model\n",
    "\n",
    "# ==============================\n",
    "# 四、前處理（A：逐特徵標準化；避免「時間×特徵」被當不同欄）\n",
    "# ==============================\n",
    "def fit_transform_fold(X_train, X_val):\n",
    "    # X: (n, t, d) -> 疊時間到 batch：((n*t), d)\n",
    "    n, t, d = X_train.shape\n",
    "    tr2 = X_train.reshape(-1, d)\n",
    "    va2 = X_val.reshape(-1, d)\n",
    "\n",
    "    imputer = SimpleImputer(strategy=\"median\")\n",
    "    scaler = StandardScaler(with_mean=True, with_std=True)\n",
    "\n",
    "    tr_imp = imputer.fit_transform(tr2)\n",
    "    tr_scl = scaler.fit_transform(tr_imp)\n",
    "\n",
    "    va_imp = imputer.transform(va2)\n",
    "    va_scl = scaler.transform(va_imp)\n",
    "\n",
    "    # 還原 (n, t, d)\n",
    "    Xtr = tr_scl.reshape(n, t, d).astype(np.float32)\n",
    "    Xva = va_scl.reshape(X_val.shape[0], t, d).astype(np.float32)\n",
    "    return Xtr, Xva, imputer, scaler\n",
    "\n",
    "# ==============================\n",
    "# 五、繪圖（英文、藍色系）\n",
    "# ==============================\n",
    "BLUES = [\"#cfe8ff\", \"#9dd0ff\", \"#6bb8ff\", \"#3aa0ff\", \"#0a88ff\", \"#005bb5\"]\n",
    "\n",
    "def plot_history(hist_df: pd.DataFrame, out_png: Path):\n",
    "    plt.figure(figsize=(7, 5))\n",
    "    x = np.arange(len(hist_df))\n",
    "    plt.plot(x, hist_df[\"loss\"], label=\"Train Focal Loss\" if \"loss\" in hist_df else \"Train Loss\", lw=2, color=BLUES[3])\n",
    "    if \"val_loss\" in hist_df:\n",
    "        plt.plot(x, hist_df[\"val_loss\"], label=\"Val Focal Loss\" if \"val_loss\" in hist_df else \"Val Loss\", lw=2, color=BLUES[5])\n",
    "    if \"binary_crossentropy\" in hist_df.columns:\n",
    "        plt.plot(x, hist_df[\"binary_crossentropy\"], label=\"Train BCE\", lw=2, color=BLUES[1])\n",
    "    if \"val_binary_crossentropy\" in hist_df.columns:\n",
    "        plt.plot(x, hist_df[\"val_binary_crossentropy\"], label=\"Val BCE\", lw=2, color=BLUES[0])\n",
    "    plt.title(\"Loss Curves (Focal & BCE)\")\n",
    "    plt.xlabel(\"Epoch\"); plt.ylabel(\"Loss\")\n",
    "    plt.grid(True, alpha=0.25); plt.legend(); plt.tight_layout()\n",
    "    plt.savefig(out_png, dpi=160); plt.close()\n",
    "\n",
    "def plot_metric_epochs(hist: pd.DataFrame, metric: str, out_png: Path, title: str):\n",
    "    plt.figure(figsize=(7, 5))\n",
    "    x = np.arange(len(hist))\n",
    "    if metric in hist.columns:\n",
    "        plt.plot(x, hist[metric], label=f\"Train {metric}\", color=BLUES[3], lw=2)\n",
    "    valm = f\"val_{metric}\"\n",
    "    if valm in hist.columns:\n",
    "        plt.plot(x, hist[valm], label=f\"Val {metric}\", color=BLUES[5], lw=2)\n",
    "    plt.title(title)\n",
    "    plt.xlabel(\"Epoch\"); plt.ylabel(metric.title())\n",
    "    plt.grid(True, alpha=0.25); plt.legend(); plt.tight_layout()\n",
    "    plt.savefig(out_png, dpi=160); plt.close()\n",
    "\n",
    "def plot_roc_xy(fpr, tpr, roc_auc, out_png: Path, title: str):\n",
    "    plt.figure(figsize=(6, 5))\n",
    "    plt.plot(fpr, tpr, color=BLUES[4], lw=2, label=f\"ROC AUC = {roc_auc:.4f}\")\n",
    "    plt.plot([0, 1], [0, 1], color=BLUES[0], lw=1, ls=\"--\")\n",
    "    plt.title(title); plt.xlabel(\"False Positive Rate\"); plt.ylabel(\"True Positive Rate\")\n",
    "    plt.grid(True, alpha=0.25); plt.legend(loc=\"lower right\"); plt.tight_layout()\n",
    "    plt.savefig(out_png, dpi=160); plt.close()\n",
    "\n",
    "def plot_pr_xy(recall, precision, ap, out_png: Path, title: str):\n",
    "    plt.figure(figsize=(6, 5))\n",
    "    plt.plot(recall, precision, color=BLUES[4], lw=2, label=f\"AP = {ap:.4f}\")\n",
    "    plt.title(title); plt.xlabel(\"Recall\"); plt.ylabel(\"Precision\")\n",
    "    plt.grid(True, alpha=0.25); plt.legend(loc=\"lower left\"); plt.tight_layout()\n",
    "    plt.savefig(out_png, dpi=160); plt.close()\n",
    "\n",
    "def plot_cm(cm: np.ndarray, out_png: Path, title: str):\n",
    "    plt.figure(figsize=(5.6, 4.8))\n",
    "    im = plt.imshow(cm, cmap=\"Blues\")\n",
    "    plt.title(title, fontsize=16)\n",
    "    plt.xlabel(\"Predicted\", fontsize=13); plt.ylabel(\"Actual\", fontsize=13)\n",
    "    plt.colorbar(im, fraction=0.046, pad=0.04)\n",
    "    for (i, j), z in np.ndenumerate(cm):\n",
    "        plt.text(j, i, f\"{z}\", ha='center', va='center', color='black',\n",
    "                 fontsize=14, fontweight='bold')\n",
    "    plt.xticks([0, 1], [\"Negative\", \"Positive\"], fontsize=12)\n",
    "    plt.yticks([0, 1], [\"Negative\", \"Positive\"], fontsize=12)\n",
    "    plt.tight_layout(); plt.savefig(out_png, dpi=180); plt.close()\n",
    "\n",
    "# ==============================\n",
    "# 六、回合級 Callback：F1（含 best-th）與機率探針\n",
    "# ==============================\n",
    "class F1PerEpoch(keras.callbacks.Callback):\n",
    "    \"\"\"每個 epoch 掃驗證集最佳 F1 閾值；同時記錄 fixed 0.5 的 F1。\"\"\"\n",
    "    def __init__(self, Xtr, ytr, Xva, yva, threshold=0.5, batch_size=512):\n",
    "        super().__init__()\n",
    "        self.Xtr, self.ytr, self.Xva, self.yva = Xtr, ytr, Xva, yva\n",
    "        self.fixed_th = threshold\n",
    "        self.bs = batch_size\n",
    "\n",
    "    def on_epoch_end(self, epoch, logs=None):\n",
    "        ytr_prob = self.model.predict(self.Xtr, batch_size=self.bs, verbose=0).ravel()\n",
    "        yva_prob = self.model.predict(self.Xva, batch_size=self.bs, verbose=0).ravel()\n",
    "\n",
    "        tr_pred_f = (ytr_prob >= self.fixed_th).astype(int)\n",
    "        va_pred_f = (yva_prob >= self.fixed_th).astype(int)\n",
    "        _, _, f1_tr_fixed, _ = precision_recall_fscore_support(self.ytr, tr_pred_f, average='binary', zero_division=0)\n",
    "        _, _, f1_va_fixed, _ = precision_recall_fscore_support(self.yva, va_pred_f, average='binary', zero_division=0)\n",
    "\n",
    "        grid = np.linspace(0.01, 0.99, 99)\n",
    "        f1s = [precision_recall_fscore_support(self.yva, (yva_prob >= th).astype(int),\n",
    "                                               average='binary', zero_division=0)[2] for th in grid]\n",
    "        best_idx = int(np.argmax(f1s))\n",
    "        best_th = float(grid[best_idx])\n",
    "\n",
    "        tr_pred_b = (ytr_prob >= best_th).astype(int)\n",
    "        va_pred_b = (yva_prob >= best_th).astype(int)\n",
    "        _, _, f1_tr_best, _ = precision_recall_fscore_support(self.ytr, tr_pred_b, average='binary', zero_division=0)\n",
    "        _, _, f1_va_best, _ = precision_recall_fscore_support(self.yva, va_pred_b, average='binary', zero_division=0)\n",
    "\n",
    "        if logs is not None:\n",
    "            logs['f1'] = f1_tr_fixed\n",
    "            logs['val_f1'] = f1_va_fixed\n",
    "            logs['f1_best'] = f1_tr_best\n",
    "            logs['val_f1_best'] = f1_va_best\n",
    "            logs['val_best_th'] = best_th\n",
    "\n",
    "        print(f\"[F1Epoch] epoch={epoch} fixed_th=0.5  f1_tr={f1_tr_fixed:.4f} f1_va={f1_va_fixed:.4f} | \"\n",
    "              f\"best_th={best_th:.2f}  f1_tr={f1_tr_best:.4f} f1_va={f1_va_best:.4f}\")\n",
    "\n",
    "class ProbProbe(keras.callbacks.Callback):\n",
    "    \"\"\"每個 epoch 列印 train/val 機率分佈摘要，偵測機率塌陷或偏移。\"\"\"\n",
    "    def __init__(self, Xtr, Xva, bs=1024):\n",
    "        super().__init__()\n",
    "        self.Xtr, self.Xva, self.bs = Xtr, Xva, bs\n",
    "    def on_epoch_end(self, epoch, logs=None):\n",
    "        p_tr = self.model.predict(self.Xtr, batch_size=self.bs, verbose=0).ravel()\n",
    "        p_va = self.model.predict(self.Xva, batch_size=self.bs, verbose=0).ravel()\n",
    "        q = lambda a: float(np.quantile(a, 0.9))\n",
    "        print(f\"[Probe] epoch={epoch}  train mean={p_tr.mean():.4f} p90={q(p_tr):.4f} | \"\n",
    "              f\"val mean={p_va.mean():.4f} p90={q(p_va):.4f}\")\n",
    "\n",
    "# ==============================\n",
    "# 七、主流程：十折訓練與評估\n",
    "# ==============================\n",
    "all_rows = []\n",
    "\n",
    "for k in CFG[\"folds\"]:\n",
    "    fold_dir = RUN_DIR / f\"fold_{k}\"\n",
    "    fold_dir.mkdir(parents=True, exist_ok=True)\n",
    "\n",
    "    # 資料\n",
    "    Xtr = np.load(DATA / f\"X_train_fold{k}.npy\")\n",
    "    ytr = np.load(DATA / f\"y_train_fold{k}.npy\")\n",
    "    Xva = np.load(DATA / f\"X_val_fold{k}.npy\")\n",
    "    yva = np.load(DATA / f\"y_val_fold{k}.npy\")\n",
    "\n",
    "    assert set(np.unique(ytr)).issubset({0, 1}), \"y_train 需為 {0,1}\"\n",
    "    assert set(np.unique(yva)).issubset({0, 1}), \"y_val 需為 {0,1}\"\n",
    "\n",
    "    # 前處理（A）\n",
    "    Xtr_p, Xva_p, _, _ = fit_transform_fold(Xtr, Xva)\n",
    "\n",
    "    # 不平衡摘要（B/E）\n",
    "    pos_rate = float(ytr.mean())\n",
    "    alpha_used = float(np.clip(1.0 - pos_rate, CFG[\"focal_alpha_min\"], CFG[\"focal_alpha_max\"])) if CFG[\"use_focal\"] else 0.5\n",
    "\n",
    "    class_weight = None\n",
    "    if CFG[\"use_class_weight\"]:\n",
    "        classes = np.array([0, 1])\n",
    "        cw = compute_class_weight(class_weight=\"balanced\", classes=classes, y=ytr)\n",
    "        class_weight = {0: float(cw[0]), 1: float(cw[1])}\n",
    "\n",
    "    # 建模（E：先驗偏置）\n",
    "    input_shape = (Xtr_p.shape[1], Xtr_p.shape[2])  # (T, D)\n",
    "    model = build_model(input_shape, CFG, prior_pos=pos_rate, focal_alpha=alpha_used)\n",
    "\n",
    "    # Callbacks（C/E）\n",
    "    ckpt_path = fold_dir / \"best_model.keras\"\n",
    "    cbs = [\n",
    "        keras.callbacks.EarlyStopping(monitor=\"val_auprc\", patience=max(8, CFG[\"patience\"]),\n",
    "                                      mode=\"max\", restore_best_weights=True),\n",
    "        keras.callbacks.EarlyStopping(monitor=\"val_f1_best\", patience=6,\n",
    "                                      mode=\"max\", restore_best_weights=True),\n",
    "        keras.callbacks.ModelCheckpoint(filepath=str(ckpt_path), monitor=\"val_auprc\",\n",
    "                                        mode=\"max\", save_best_only=True),\n",
    "        keras.callbacks.ReduceLROnPlateau(monitor=\"val_auprc\", mode=\"max\",\n",
    "                                          factor=0.5, patience=4, min_lr=1e-5, verbose=1),\n",
    "        F1PerEpoch(Xtr_p, ytr, Xva_p, yva, threshold=CFG[\"threshold\"], batch_size=512),\n",
    "        ProbProbe(Xtr_p, Xva_p, bs=1024)\n",
    "    ]\n",
    "\n",
    "    # 訓練（不改 y 形狀）\n",
    "    hist = model.fit(\n",
    "        Xtr_p, ytr,\n",
    "        validation_data=(Xva_p, yva),\n",
    "        epochs=CFG[\"epochs\"],\n",
    "        batch_size=CFG[\"batch_size\"],\n",
    "        callbacks=cbs,\n",
    "        verbose=2,\n",
    "        class_weight=class_weight\n",
    "    )\n",
    "\n",
    "    # 保存 history 與訓練曲線\n",
    "    hist_df = pd.DataFrame(hist.history)\n",
    "    hist_df.to_csv(fold_dir / \"history.csv\", index=False)\n",
    "    plot_history(hist_df, fold_dir / \"plot_loss.png\")\n",
    "    plot_metric_epochs(hist_df, \"accuracy\", fold_dir / \"plot_acc.png\", \"Accuracy per Epoch\")\n",
    "    if \"f1\" in hist_df.columns and \"val_f1\" in hist_df.columns:\n",
    "        plot_metric_epochs(hist_df, \"f1\", fold_dir / \"plot_f1.png\", \"F1 per Epoch\")\n",
    "\n",
    "    # 推論機率\n",
    "    ytr_prob = model.predict(Xtr_p, batch_size=CFG[\"batch_size\"], verbose=0).ravel()\n",
    "    yva_prob = model.predict(Xva_p, batch_size=CFG[\"batch_size\"], verbose=0).ravel()\n",
    "\n",
    "    # ROC / PR\n",
    "    fpr_tr, tpr_tr, _ = roc_curve(ytr, ytr_prob)\n",
    "    fpr_va, tpr_va, _ = roc_curve(yva, yva_prob)\n",
    "    roc_auc_tr = auc(fpr_tr, tpr_tr)\n",
    "    roc_auc_va = auc(fpr_va, tpr_va)\n",
    "\n",
    "    prec_tr, rec_tr, _ = precision_recall_curve(ytr, ytr_prob)\n",
    "    prec_va, rec_va, _ = precision_recall_curve(yva, yva_prob)\n",
    "    ap_tr = average_precision_score(ytr, ytr_prob)\n",
    "    ap_va = average_precision_score(yva, yva_prob)\n",
    "\n",
    "    # 存曲線\n",
    "    pd.DataFrame({\"fpr\": fpr_tr, \"tpr\": tpr_tr}).to_csv(fold_dir / \"roc_curve_train.csv\", index=False)\n",
    "    pd.DataFrame({\"fpr\": fpr_va, \"tpr\": tpr_va}).to_csv(fold_dir / \"roc_curve_val.csv\", index=False)\n",
    "    plot_roc_xy(fpr_tr, tpr_tr, roc_auc_tr, fold_dir / \"plot_roc_train.png\", \"ROC Curve (Train)\")\n",
    "    plot_roc_xy(fpr_va, tpr_va, roc_auc_va, fold_dir / \"plot_roc_val.png\",   \"ROC Curve (Validation)\")\n",
    "\n",
    "    pd.DataFrame({\"recall\": rec_tr, \"precision\": prec_tr}).to_csv(fold_dir / \"pr_curve_train.csv\", index=False)\n",
    "    pd.DataFrame({\"recall\": rec_va, \"precision\": prec_va}).to_csv(fold_dir / \"pr_curve_val.csv\", index=False)\n",
    "    plot_pr_xy(rec_tr, prec_tr, ap_tr, fold_dir / \"plot_pr_train.png\", \"Precision-Recall (Train)\")\n",
    "    plot_pr_xy(rec_va, prec_va, ap_va, fold_dir / \"plot_pr_val.png\",   \"Precision-Recall (Validation)\")\n",
    "\n",
    "    # 閾值掃描（驗證集）\n",
    "    grid = np.linspace(0.01, 0.99, 99)\n",
    "    f1s = [precision_recall_fscore_support(yva, (yva_prob >= th).astype(int),\n",
    "                                           average='binary', zero_division=0)[2] for th in grid]\n",
    "    best_idx = int(np.argmax(f1s))\n",
    "    best_th = float(grid[best_idx])\n",
    "\n",
    "    # 指標（best-th 與 fixed 0.5）\n",
    "    def pack_metrics(y_true, y_prob, th):\n",
    "        y_pred = (y_prob >= th).astype(int)\n",
    "        acc = accuracy_score(y_true, y_pred)\n",
    "        prec, rec, f1, _ = precision_recall_fscore_support(y_true, y_pred, average='binary', zero_division=0)\n",
    "        rocauc = roc_auc_score(y_true, y_prob)\n",
    "        cm = confusion_matrix(y_true, y_pred, labels=[0, 1])\n",
    "        return acc, prec, rec, f1, rocauc, cm\n",
    "\n",
    "    tr_acc_b, tr_prec_b, tr_rec_b, tr_f1_b, tr_auc, cm_tr_b = pack_metrics(ytr, ytr_prob, best_th)\n",
    "    va_acc_b, va_prec_b, va_rec_b, va_f1_b, va_auc, cm_va_b = pack_metrics(yva, yva_prob, best_th)\n",
    "\n",
    "    tr_acc_f, tr_prec_f, tr_rec_f, tr_f1_f, _, cm_tr_f = pack_metrics(ytr, ytr_prob, CFG[\"threshold\"])\n",
    "    va_acc_f, va_prec_f, va_rec_f, va_f1_f, _, cm_va_f = pack_metrics(yva, yva_prob, CFG[\"threshold\"])\n",
    "\n",
    "    # 輸出 CSV 與圖\n",
    "    pd.DataFrame({\"y_true\": ytr, \"y_prob\": ytr_prob, \"y_pred\": (ytr_prob >= best_th).astype(int)}).to_csv(fold_dir / \"predictions_train.csv\", index=False)\n",
    "    pd.DataFrame({\"y_true\": yva, \"y_prob\": yva_prob, \"y_pred\": (yva_prob >= best_th).astype(int)}).to_csv(fold_dir / \"predictions_val.csv\", index=False)\n",
    "\n",
    "    pd.DataFrame(cm_tr_b, index=[\"Actual_0\",\"Actual_1\"], columns=[\"Pred_0\",\"Pred_1\"]).to_csv(fold_dir / \"cm_train_best.csv\")\n",
    "    pd.DataFrame(cm_va_b, index=[\"Actual_0\",\"Actual_1\"], columns=[\"Pred_0\",\"Pred_1\"]).to_csv(fold_dir / \"cm_val_best.csv\")\n",
    "    pd.DataFrame(cm_tr_f, index=[\"Actual_0\",\"Actual_1\"], columns=[\"Pred_0\",\"Pred_1\"]).to_csv(fold_dir / \"cm_train_fixed05.csv\")\n",
    "    pd.DataFrame(cm_va_f, index=[\"Actual_0\",\"Actual_1\"], columns=[\"Pred_0\",\"Pred_1\"]).to_csv(fold_dir / \"cm_val_fixed05.csv\")\n",
    "\n",
    "    plot_cm(cm_tr_b, fold_dir / \"plot_cm_train_best.png\", \"Confusion Matrix (Train, best-th)\")\n",
    "    plot_cm(cm_va_b, fold_dir / \"plot_cm_val_best.png\",   \"Confusion Matrix (Validation, best-th)\")\n",
    "\n",
    "    # 每折 metrics\n",
    "    row = {\n",
    "        \"fold\": k,\n",
    "        \"pos_rate\": pos_rate,\n",
    "        \"focal_alpha_used\": alpha_used if CFG[\"use_focal\"] else None,\n",
    "        \"class_weight_0\": class_weight.get(0) if class_weight else None,\n",
    "        \"class_weight_1\": class_weight.get(1) if class_weight else None,\n",
    "        \"best_threshold\": best_th,\n",
    "        # Train (best-th)\n",
    "        \"train_accuracy_best\": tr_acc_b, \"train_precision_best\": tr_prec_b, \"train_recall_best\": tr_rec_b,\n",
    "        \"train_f1_best\": tr_f1_b, \"train_auc\": tr_auc, \"train_auprc\": ap_tr,\n",
    "        # Val (best-th)\n",
    "        \"val_accuracy_best\": va_acc_b, \"val_precision_best\": va_prec_b, \"val_recall_best\": va_rec_b,\n",
    "        \"val_f1_best\": va_f1_b, \"val_auc\": va_auc, \"val_auprc\": ap_va,\n",
    "        # Val (fixed 0.5)\n",
    "        \"val_accuracy_0p5\": va_acc_f, \"val_precision_0p5\": va_prec_f, \"val_recall_0p5\": va_rec_f,\n",
    "        \"val_f1_0p5\": va_f1_f\n",
    "    }\n",
    "    pd.DataFrame([row]).to_csv(fold_dir / \"metrics.csv\", index=False)\n",
    "\n",
    "    meta = {\n",
    "        \"fold\": k,\n",
    "        \"pos_rate\": pos_rate,\n",
    "        \"best_threshold\": best_th,\n",
    "        \"class_weight\": class_weight,\n",
    "        \"focal\": {\"enabled\": CFG[\"use_focal\"], \"gamma\": CFG[\"focal_gamma\"], \"alpha_used\": alpha_used}\n",
    "    }\n",
    "    (fold_dir / \"fold_meta.json\").write_text(json.dumps(meta, ensure_ascii=False, indent=2), encoding=\"utf-8\")\n",
    "\n",
    "    all_rows.append(row)\n",
    "\n",
    "# ==============================\n",
    "# 八、十折彙總\n",
    "# ==============================\n",
    "summary = pd.DataFrame(all_rows).sort_values(\"fold\")\n",
    "summary.to_csv(RUN_DIR / \"summary_folds.csv\", index=False)\n",
    "\n",
    "agg = {}\n",
    "for col in [c for c in summary.columns if c != \"fold\"]:\n",
    "    agg[col + \"_mean\"] = float(summary[col].mean())\n",
    "    agg[col + \"_std\"] = float(summary[col].std(ddof=1))\n",
    "\n",
    "overall = {\"n_folds\": int(len(summary)), **agg, \"config\": CFG}\n",
    "(RUN_DIR / \"summary_overall.json\").write_text(json.dumps(overall, ensure_ascii=False, indent=2), encoding=\"utf-8\")\n",
    "\n",
    "print(\"\\n=== Training Done ===\")\n",
    "print(f\"Run dir: {RUN_DIR}\")\n",
    "print(json.dumps(overall, indent=2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "517ade1b-93e5-4be4-98aa-5189eda3cdd5",
   "metadata": {},
   "outputs": [],
   "source": [
    "1015"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "84b74e3b-ac81-461b-88c9-7353a2dc0891",
   "metadata": {},
   "outputs": [],
   "source": [
    "折間先驗與分佈偏移（每折 train/val 正類率、樣本數）\n",
    "重疊 / 洩漏檢查（train 與 val 之間的視窗精確重疊；另提供「近似重疊」的可選檢查）\n",
    "缺失、極值與量測異常（通道層級 NaN 比率、常值比率、極端值比率）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 275,
   "id": "14c728ca-a6d7-4b94-afbd-5dee2cefe25f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "=== Audit Done ===\n",
      "Output dir: /home/jovyan/RT08/0925/sliding_win/1014_sim/training_runs/20251015_142622_audit\n",
      "Generated files:\n",
      " - audit_cfg.json\n",
      " - audit_channel_quality.csv\n",
      " - audit_channel_quality_summary.json\n",
      " - audit_fold_priors.csv\n",
      " - audit_overlap_exact.csv\n",
      " - audit_overlap_near.csv\n"
     ]
    }
   ],
   "source": [
    "# -*- coding: utf-8 -*-\n",
    "\"\"\"\n",
    "audit_timeseries_data.py\n",
    "\n",
    "三大檢查：\n",
    "  A) 折間先驗與分佈偏移（每折 y_train.mean vs y_val.mean）\n",
    "  B) 重疊/洩漏檢查（精確重疊；可選近似重疊）\n",
    "  C) 缺失、極值與量測異常（通道品質：nan/const/outlier）\n",
    "\n",
    "資料假設：\n",
    "  BASE = /home/jovyan/RT08/0925/sliding_win/1014_sim\n",
    "  DATA = BASE / windowed_clean\n",
    "  其中包含：\n",
    "    X_train_fold{k}.npy, y_train_fold{k}.npy\n",
    "    X_val_fold{k}.npy,   y_val_fold{k}.npy\n",
    "  （可選）若你有對應的辨識資訊：\n",
    "    meta_train_fold{k}.csv, meta_val_fold{k}.csv\n",
    "    需包含欄位：id（人/裝置/場次）、t_start（視窗起點時間戳或索引）\n",
    "\n",
    "執行：\n",
    "  python audit_timeseries_data.py\n",
    "\"\"\"\n",
    "\n",
    "import os\n",
    "import time\n",
    "import json\n",
    "import hashlib\n",
    "from pathlib import Path\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "# ==============================\n",
    "# 基本路徑設定（依需要修改）\n",
    "# ==============================\n",
    "BASE = Path(\"/home/jovyan/RT08/0925/sliding_win/1014_sim\")\n",
    "DATA = BASE / \"windowed_clean\"\n",
    "RUNS_ROOT = BASE / \"training_runs\"\n",
    "RUNS_ROOT.mkdir(parents=True, exist_ok=True)\n",
    "\n",
    "ts = time.strftime(\"%Y%m%d_%H%M%S\")\n",
    "OUTDIR = RUNS_ROOT / f\"{ts}_audit\"\n",
    "OUTDIR.mkdir(parents=True, exist_ok=True)\n",
    "\n",
    "CFG = {\n",
    "    \"folds\": list(range(1, 11)),\n",
    "    \"near_duplicate_round_decimals\": 6,  # 近似重疊: 浮點數四捨五入小數位數（避免浮點誤差）\n",
    "    \"outlier_z\": 5.0,                    # 極端值判定的 z-score 門檻\n",
    "    \"sample_near_dup_max\": 0,            # 近似重疊的抽樣數（0=全量；大資料可改小以節省時間）\n",
    "}\n",
    "(OUTDIR / \"audit_cfg.json\").write_text(json.dumps(CFG, ensure_ascii=False, indent=2), encoding=\"utf-8\")\n",
    "\n",
    "# ==============================\n",
    "# 工具函式\n",
    "# ==============================\n",
    "def md5_bytes(arr: np.ndarray) -> str:\n",
    "    \"\"\"對單一樣本做精確重疊檢查：對其原始 bytes 做 MD5\"\"\"\n",
    "    h = hashlib.md5()\n",
    "    h.update(arr.tobytes())\n",
    "    return h.hexdigest()\n",
    "\n",
    "def hash_rounded(arr: np.ndarray, decimals: int = 6) -> str:\n",
    "    \"\"\"近似重疊檢查：對四捨五入後的浮點陣列做 MD5\"\"\"\n",
    "    x = np.round(arr.astype(np.float64), decimals=decimals)\n",
    "    h = hashlib.md5()\n",
    "    h.update(x.tobytes())\n",
    "    return h.hexdigest()\n",
    "\n",
    "def channel_quality_metrics(X: np.ndarray, z_thr: float = 5.0):\n",
    "    \"\"\"\n",
    "    計算通道品質指標：\n",
    "      - nan_rate：NaN 比率\n",
    "      - const_rate：常值比率（通道 std 幾近 0 視為常值；這裡統一計 0/1 指標）\n",
    "      - outlier_rate：|z|>z_thr 的比例（忽略 NaN）\n",
    "    X: (N, T, D)\n",
    "    回傳：DataFrame，索引為 channel（0..D-1）\n",
    "    \"\"\"\n",
    "    N, T, D = X.shape\n",
    "    X2 = X.reshape(-1, D)  # (N*T, D)\n",
    "\n",
    "    # NaN rate\n",
    "    nan_mask = np.isnan(X2)\n",
    "    nan_rate = nan_mask.mean(axis=0)\n",
    "\n",
    "    # 常值判定：std 非 NaN 的情況下極小（避免被 NaN 影響）\n",
    "    # 使用 nanstd 以忽略 NaN；小於 eps 視為常值\n",
    "    eps = 1e-8\n",
    "    stds = np.nanstd(X2, axis=0)\n",
    "    const_rate = (stds < eps).astype(float)  # 這裡回傳 0 或 1（是否常值通道）\n",
    "\n",
    "    # z-score（忽略 NaN）\n",
    "    means = np.nanmean(X2, axis=0)\n",
    "    stds_safe = np.where(stds < eps, 1.0, stds)\n",
    "    z = np.abs((X2 - means) / stds_safe)\n",
    "    z[nan_mask] = 0.0  # 忽略 NaN\n",
    "    outlier_rate = (z > z_thr).mean(axis=0)\n",
    "\n",
    "    df = pd.DataFrame({\n",
    "        \"nan_rate\": nan_rate,\n",
    "        \"const_channel_flag\": const_rate,\n",
    "        \"outlier_rate_|z|>{}\".format(z_thr): outlier_rate\n",
    "    })\n",
    "    df.index.name = \"channel\"\n",
    "    return df\n",
    "\n",
    "# ==============================\n",
    "# A) 折間先驗與分佈偏移\n",
    "# ==============================\n",
    "rows_prior = []\n",
    "for k in CFG[\"folds\"]:\n",
    "    ytr_p = DATA / f\"y_train_fold{k}.npy\"\n",
    "    yva_p = DATA / f\"y_val_fold{k}.npy\"\n",
    "    if not (ytr_p.exists() and yva_p.exists()):\n",
    "        continue\n",
    "    ytr = np.load(ytr_p)\n",
    "    yva = np.load(yva_p)\n",
    "    rows_prior.append({\n",
    "        \"fold\": k,\n",
    "        \"n_train\": int(len(ytr)),\n",
    "        \"n_val\": int(len(yva)),\n",
    "        \"y_train_mean\": float(np.mean(ytr)),\n",
    "        \"y_val_mean\": float(np.mean(yva)),\n",
    "        \"pos_count_train\": int(np.sum(ytr == 1)),\n",
    "        \"pos_count_val\": int(np.sum(yva == 1)),\n",
    "        \"neg_count_train\": int(np.sum(ytr == 0)),\n",
    "        \"neg_count_val\": int(np.sum(yva == 0)),\n",
    "    })\n",
    "df_prior = pd.DataFrame(rows_prior).sort_values(\"fold\")\n",
    "df_prior.to_csv(OUTDIR / \"audit_fold_priors.csv\", index=False)\n",
    "\n",
    "# ==============================\n",
    "# B) 重疊 / 洩漏檢查\n",
    "#  - 精確重疊：bytes 完全一致\n",
    "#  - 近似重疊（可選）：四捨五入後的值一致（預設 decimals=6）\n",
    "#  - 若有 meta_{train,val}_fold{k}.csv，會另外輸出 id/t_start 的交集資訊\n",
    "# ==============================\n",
    "rows_overlap_exact = []\n",
    "rows_overlap_near = []\n",
    "rows_overlap_meta = []\n",
    "\n",
    "for k in CFG[\"folds\"]:\n",
    "    Xtr_p = DATA / f\"X_train_fold{k}.npy\"\n",
    "    Xva_p = DATA / f\"X_val_fold{k}.npy\"\n",
    "    if not (Xtr_p.exists() and Xva_p.exists()):\n",
    "        continue\n",
    "\n",
    "    Xtr = np.load(Xtr_p)  # (Ntr, T, D)\n",
    "    Xva = np.load(Xva_p)  # (Nva, T, D)\n",
    "    Ntr, Nva = len(Xtr), len(Xva)\n",
    "\n",
    "    # --- 精確重疊（MD5 of bytes）\n",
    "    tr_hashes = pd.Series([md5_bytes(x) for x in Xtr], name=\"h\")\n",
    "    va_hashes = pd.Series([md5_bytes(x) for x in Xva], name=\"h\")\n",
    "    overlap_exact = len(set(tr_hashes) & set(va_hashes))\n",
    "    rows_overlap_exact.append({\n",
    "        \"fold\": k, \"n_train\": Ntr, \"n_val\": Nva, \"exact_overlap\": int(overlap_exact)\n",
    "    })\n",
    "\n",
    "    # --- 近似重疊（四捨五入後 hash）\n",
    "    dec = CFG[\"near_duplicate_round_decimals\"]\n",
    "    # 可選抽樣以避免超大資料時過慢\n",
    "    if CFG[\"sample_near_dup_max\"] and Ntr > CFG[\"sample_near_dup_max\"]:\n",
    "        idx_tr = np.random.choice(Ntr, CFG[\"sample_near_dup_max\"], replace=False)\n",
    "        Xtr_for_near = Xtr[idx_tr]\n",
    "    else:\n",
    "        Xtr_for_near = Xtr\n",
    "\n",
    "    if CFG[\"sample_near_dup_max\"] and Nva > CFG[\"sample_near_dup_max\"]:\n",
    "        idx_va = np.random.choice(Nva, CFG[\"sample_near_dup_max\"], replace=False)\n",
    "        Xva_for_near = Xva[idx_va]\n",
    "    else:\n",
    "        Xva_for_near = Xva\n",
    "\n",
    "    tr_hashes_n = pd.Series([hash_rounded(x, decimals=dec) for x in Xtr_for_near], name=\"h\")\n",
    "    va_hashes_n = pd.Series([hash_rounded(x, decimals=dec) for x in Xva_for_near], name=\"h\")\n",
    "    overlap_near = len(set(tr_hashes_n) & set(va_hashes_n))\n",
    "    rows_overlap_near.append({\n",
    "        \"fold\": k,\n",
    "        \"train_checked\": int(len(tr_hashes_n)),\n",
    "        \"val_checked\": int(len(va_hashes_n)),\n",
    "        \"near_overlap_decimals\": dec,\n",
    "        \"near_overlap\": int(overlap_near)\n",
    "    })\n",
    "\n",
    "    # --- 若有 meta 檔：id / t_start 交集（可快速判定跨折洩漏）\n",
    "    meta_tr_p = DATA / f\"meta_train_fold{k}.csv\"\n",
    "    meta_va_p = DATA / f\"meta_val_fold{k}.csv\"\n",
    "    if meta_tr_p.exists() and meta_va_p.exists():\n",
    "        mtr = pd.read_csv(meta_tr_p)\n",
    "        mva = pd.read_csv(meta_va_p)\n",
    "        have_cols = all(c in mtr.columns for c in [\"id\", \"t_start\"]) and all(c in mva.columns for c in [\"id\", \"t_start\"])\n",
    "        if have_cols:\n",
    "            keys_tr = set(zip(mtr[\"id\"].astype(str), mtr[\"t_start\"].astype(str)))\n",
    "            keys_va = set(zip(mva[\"id\"].astype(str), mva[\"t_start\"].astype(str)))\n",
    "            meta_overlap = len(keys_tr & keys_va)\n",
    "            id_overlap = len(set(mtr[\"id\"].astype(str)) & set(mva[\"id\"].astype(str)))\n",
    "            rows_overlap_meta.append({\n",
    "                \"fold\": k,\n",
    "                \"id_overlap_count\": int(id_overlap),\n",
    "                \"id_tstart_overlap\": int(meta_overlap),\n",
    "                \"meta_has_required_cols\": True\n",
    "            })\n",
    "        else:\n",
    "            rows_overlap_meta.append({\n",
    "                \"fold\": k,\n",
    "                \"id_overlap_count\": None,\n",
    "                \"id_tstart_overlap\": None,\n",
    "                \"meta_has_required_cols\": False\n",
    "            })\n",
    "\n",
    "pd.DataFrame(rows_overlap_exact).to_csv(OUTDIR / \"audit_overlap_exact.csv\", index=False)\n",
    "pd.DataFrame(rows_overlap_near).to_csv(OUTDIR / \"audit_overlap_near.csv\", index=False)\n",
    "if rows_overlap_meta:\n",
    "    pd.DataFrame(rows_overlap_meta).to_csv(OUTDIR / \"audit_overlap_meta.csv\", index=False)\n",
    "\n",
    "# ==============================\n",
    "# C) 缺失、極值與量測異常（通道品質）\n",
    "#    - 以所有 fold 的 train+val 合併後評估（防止單折偏差）\n",
    "# ==============================\n",
    "Xs = []\n",
    "for k in CFG[\"folds\"]:\n",
    "    Xtr_p = DATA / f\"X_train_fold{k}.npy\"\n",
    "    Xva_p = DATA / f\"X_val_fold{k}.npy\"\n",
    "    if Xtr_p.exists():\n",
    "        Xs.append(np.load(Xtr_p))\n",
    "    if Xva_p.exists():\n",
    "        Xs.append(np.load(Xva_p))\n",
    "\n",
    "if Xs:\n",
    "    X_all = np.concatenate(Xs, axis=0)  # (N_total, T, D)\n",
    "    qual = channel_quality_metrics(X_all, z_thr=CFG[\"outlier_z\"])\n",
    "    qual.to_csv(OUTDIR / \"audit_channel_quality.csv\")\n",
    "    # 也輸出整體摘要\n",
    "    summary = {\n",
    "        \"N_total\": int(X_all.shape[0]),\n",
    "        \"T\": int(X_all.shape[1]),\n",
    "        \"D\": int(X_all.shape[2]),\n",
    "        \"global_nan_rate\": float(np.isnan(X_all).mean()),\n",
    "        \"const_channel_count\": int(qual[\"const_channel_flag\"].sum()),\n",
    "        \"outlier_z\": CFG[\"outlier_z\"]\n",
    "    }\n",
    "    (OUTDIR / \"audit_channel_quality_summary.json\").write_text(json.dumps(summary, ensure_ascii=False, indent=2), encoding=\"utf-8\")\n",
    "\n",
    "print(f\"\\n=== Audit Done ===\")\n",
    "print(f\"Output dir: {OUTDIR}\")\n",
    "print(\"Generated files:\")\n",
    "for p in sorted(OUTDIR.glob(\"*\")):\n",
    "    print(\" -\", p.name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "65c267bf-c130-411d-bb7e-758e704e5336",
   "metadata": {},
   "outputs": [],
   "source": [
    "audit_fold_priors.csv：每折 train/val 的正類率與樣本數（用來抓分佈偏移）。\n",
    "audit_overlap_exact.csv：每折 精確重疊的樣本數（bytes 完全一致）。\n",
    "audit_overlap_near.csv：每折 近似重疊的樣本數（四捨五入到小數第 6 位後一致，避免浮點微差）。\n",
    "audit_overlap_meta.csv（若有 meta）：id 或 (id,t_start) 的交集數（抓跨折洩漏）。\n",
    "audit_channel_quality.csv：每個通道的 nan_rate / const_channel_flag / outlier_rate。\n",
    "audit_channel_quality_summary.json：通道品質總結。\n",
    "audit_cfg.json：這次審計的設定"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e60f7db1-b983-431e-a465-4a9036f05f29",
   "metadata": {},
   "outputs": [],
   "source": [
    "一鍵檢查：時間範圍重疊（建議新增）\n",
    "\n",
    "只要同一個 id（人/裝置），train 與 val 的時間區間有交集，就屬高風險洩漏。\n",
    "不依賴特徵數值，只看 (id, t_start, t_end) 是否重疊\n",
    "\n",
    "需要每折有 meta_train_fold{k}.csv、meta_val_fold{k}.csv，且至少包含：\n",
    "id（人/裝置/場次），t_start（視窗起點；可為整數索引或時間戳）。\n",
    "若沒有 t_end 欄位，會用 t_end = t_start + (T-1)*stride 推算（請設定下面的 T 與 STRIDE）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 277,
   "id": "9c6e39a7-a963-4e8f-86f8-43f78c1f7ddf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Output: /home/jovyan/RT08/0925/sliding_win/1014_sim/training_runs/20251015_155841_audit_temporal\n"
     ]
    }
   ],
   "source": [
    "# -*- coding: utf-8 -*-\n",
    "\"\"\"\n",
    "audit_temporal_overlap.py\n",
    "\n",
    "目的：\n",
    "  針對每折檢查「同一 id 的 train 與 val 視窗」是否存在時間重疊。\n",
    "  不看數值、只看 (id, t_start, t_end) 區間是否交集，最能抓到滑窗洩漏。\n",
    "\n",
    "假設：\n",
    "  BASE/windowed_clean/ 內有：\n",
    "    meta_train_fold{k}.csv, meta_val_fold{k}.csv\n",
    "  需至少包含欄位：\n",
    "    - id        : 人/裝置/場次之類的來源鍵（string/int 皆可）\n",
    "    - t_start   : 視窗起點（整數索引或時間戳；會自動轉為數值比較）\n",
    "    - (可選) t_end : 若無此欄位，會用 T 與 STRIDE 推算\n",
    "\n",
    "如何推算：\n",
    "  若無 t_end：\n",
    "    t_end = t_start + (T - 1) * STRIDE\n",
    "\n",
    "輸出：\n",
    "  training_runs/<timestamp>_audit_temporal/\n",
    "    - temporal_overlap_by_fold.csv   （每折統計）\n",
    "    - temporal_overlap_examples_fold{k}.csv（重疊對例，最多輸出 topK 個）\n",
    "    - histogram_min_gap_fold{k}.png  （每折 val 視窗對 train 的最小間隔）\n",
    "\"\"\"\n",
    "\n",
    "import time\n",
    "from pathlib import Path\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# === 參數（請依實況調整） ===\n",
    "BASE = Path(\"/home/jovyan/RT08/0925/sliding_win/1014_sim\")\n",
    "DATA = BASE / \"windowed_clean\"\n",
    "RUNS_ROOT = BASE / \"training_runs\"\n",
    "FOLDS = list(range(1, 11))\n",
    "\n",
    "# 視窗長與步長（若 meta 沒 t_end 會用來推算）\n",
    "T = 60         # 你的視窗長度\n",
    "STRIDE = 1     # 你的滑動步長（若當初是 non-overlap 視窗，填 T；若每步滑 1，填 1）\n",
    "\n",
    "# 近鄰容忍（若相鄰不到這麼多步也視為可疑；避免 off-by-one）\n",
    "NEAR_TOL = 0   # 單位同 t_start/t_end；若用時間戳，請改成秒數/毫秒數\n",
    "\n",
    "# 檢查加速：每折只輸出最前面 topK 組重疊樣本明細\n",
    "TOPK_EXAMPLES = 1000\n",
    "\n",
    "ts = time.strftime(\"%Y%m%d_%H%M%S\")\n",
    "OUT = RUNS_ROOT / f\"{ts}_audit_temporal\"\n",
    "OUT.mkdir(parents=True, exist_ok=True)\n",
    "\n",
    "def _ensure_numeric(s):\n",
    "    \"\"\"把 t_start/t_end 轉成數值（若為時間戳就轉為 int64 epoch）\"\"\"\n",
    "    # 嘗試解析為 datetime，失敗就保留原值\n",
    "    try:\n",
    "        dt = pd.to_datetime(s, errors=\"coerce\")\n",
    "        if dt.notna().any():\n",
    "            # 轉為 int64 nanos（或改成 .view('i8')）\n",
    "            return dt.view(\"i8\")\n",
    "    except Exception:\n",
    "        pass\n",
    "    # 盡量轉數值；轉不了就原樣\n",
    "    try:\n",
    "        return pd.to_numeric(s, errors=\"coerce\")\n",
    "    except Exception:\n",
    "        return s\n",
    "\n",
    "def build_intervals(df, t_col_start=\"t_start\", t_col_end=\"t_end\"):\n",
    "    s = _ensure_numeric(df[t_col_start])\n",
    "    if t_col_end in df.columns:\n",
    "        e = _ensure_numeric(df[t_col_end])\n",
    "    else:\n",
    "        # 用 T 與 STRIDE 推算（end 含端點）\n",
    "        e = s + (T - 1) * STRIDE\n",
    "    out = df.copy()\n",
    "    out[\"_t_start_num\"] = s\n",
    "    out[\"_t_end_num\"] = e\n",
    "    # 清 NaN\n",
    "    out = out.dropna(subset=[\"_t_start_num\", \"_t_end_num\"])\n",
    "    out[\"_t_start_num\"] = out[\"_t_start_num\"].astype(\"int64\")\n",
    "    out[\"_t_end_num\"]   = out[\"_t_end_num\"].astype(\"int64\")\n",
    "    # 確保 start <= end\n",
    "    out.loc[out[\"_t_start_num\"] > out[\"_t_end_num\"], [\"_t_start_num\",\"_t_end_num\"]] = \\\n",
    "        out.loc[out[\"_t_start_num\"] > out[\"_t_end_num\"], [\"_t_end_num\",\"_t_start_num\"]].values\n",
    "    return out\n",
    "\n",
    "def has_overlap(a_start, a_end, b_start, b_end, tol=0):\n",
    "    # 兩區間 [a_start, a_end] 與 [b_start, b_end] 是否相交（含邊界，含容忍）\n",
    "    return not (a_end + tol < b_start or b_end + tol < a_start)\n",
    "\n",
    "def sweep_overlap(train_iv, val_iv, tol=0):\n",
    "    \"\"\"\n",
    "    train_iv, val_iv: DataFrame with columns [\"_t_start_num\",\"_t_end_num\"]（同一個 id 的資料）\n",
    "    回傳：\n",
    "      - overlap_pairs: list of (val_idx, tr_idx)\n",
    "      - min_gaps: list of min distance from each val interval to any train interval（<0 表示重疊）\n",
    "    \"\"\"\n",
    "    train_iv = train_iv.sort_values(\"_t_start_num\").reset_index(drop=True)\n",
    "    val_iv = val_iv.sort_values(\"_t_start_num\").reset_index(drop=True)\n",
    "\n",
    "    t_st = train_iv[\"_t_start_num\"].values\n",
    "    t_en = train_iv[\"_t_end_num\"].values\n",
    "    v_st = val_iv[\"_t_start_num\"].values\n",
    "    v_en = val_iv[\"_t_end_num\"].values\n",
    "\n",
    "    # 指標掃描\n",
    "    ti = 0\n",
    "    overlaps = []\n",
    "    min_gaps = np.full(len(val_iv), np.inf)\n",
    "\n",
    "    for vi in range(len(val_iv)):\n",
    "        vs, ve = v_st[vi], v_en[vi]\n",
    "        # 移動 train 指標到可能相交的位置\n",
    "        while ti < len(train_iv) and t_en[ti] + tol < vs:\n",
    "            ti += 1\n",
    "        tj = ti\n",
    "        # 從當前 ti 開始，檢查所有還可能相交的 train\n",
    "        while tj < len(train_iv) and t_st[tj] <= ve + tol:\n",
    "            if has_overlap(vs, ve, t_st[tj], t_en[tj], tol=tol):\n",
    "                overlaps.append((val_iv.index[vi], train_iv.index[tj]))  # 保留原 index\n",
    "                min_gaps[vi] = -1  # 負值表示重疊\n",
    "            else:\n",
    "                # 更新 min gap\n",
    "                gap = max(t_st[tj] - ve, vs - t_en[tj])\n",
    "                if gap < min_gaps[vi]:\n",
    "                    min_gaps[vi] = gap\n",
    "            tj += 1\n",
    "        # 若沒找到候選（ve 之前都沒有 train），估 gap to 最近已經越過的 train\n",
    "        if np.isinf(min_gaps[vi]):\n",
    "            if ti == 0:\n",
    "                min_gaps[vi] = t_st[0] - ve  # 最近的是第 0 個\n",
    "            else:\n",
    "                min_gaps[vi] = vs - t_en[ti-1]\n",
    "    return overlaps, min_gaps\n",
    "\n",
    "rows = []\n",
    "for k in FOLDS:\n",
    "    p_tr = DATA / f\"meta_train_fold{k}.csv\"\n",
    "    p_va = DATA / f\"meta_val_fold{k}.csv\"\n",
    "    if not (p_tr.exists() and p_va.exists()):\n",
    "        continue\n",
    "    mtr = pd.read_csv(p_tr)\n",
    "    mva = pd.read_csv(p_va)\n",
    "    if not all(c in mtr.columns for c in [\"id\",\"t_start\"]) or not all(c in mva.columns for c in [\"id\",\"t_start\"]):\n",
    "        continue\n",
    "\n",
    "    tr_iv = build_intervals(mtr)\n",
    "    va_iv = build_intervals(mva)\n",
    "\n",
    "    # 限制在兩邊共同出現的 id（沒有共同 id 就不可能洩漏）\n",
    "    common_ids = sorted(set(tr_iv[\"id\"].astype(str)) & set(va_iv[\"id\"].astype(str)))\n",
    "    total_val = len(va_iv)\n",
    "    total_overlap = 0\n",
    "    detail = []\n",
    "\n",
    "    # 逐 id 檢查重疊（sweep line）\n",
    "    for sid in common_ids:\n",
    "        tr_g = tr_iv[tr_iv[\"id\"].astype(str) == sid]\n",
    "        va_g = va_iv[va_iv[\"id\"].astype(str) == sid]\n",
    "        if tr_g.empty or va_g.empty:\n",
    "            continue\n",
    "        pairs, min_gaps = sweep_overlap(tr_g, va_g, tol=NEAR_TOL)\n",
    "        # 統計\n",
    "        ov_count = sum(1 for _ in pairs)\n",
    "        total_overlap += ov_count\n",
    "        # 蒐集樣本（只留前 TOPK_EXAMPLES）\n",
    "        if ov_count > 0 and len(detail) < TOPK_EXAMPLES:\n",
    "            for (vi, ti) in pairs[: max(0, TOPK_EXAMPLES - len(detail))]:\n",
    "                detail.append({\n",
    "                    \"fold\": k,\n",
    "                    \"id\": sid,\n",
    "                    \"val_idx\": int(vi),\n",
    "                    \"val_t_start\": int(va_g.loc[vi, \"_t_start_num\"]),\n",
    "                    \"val_t_end\": int(va_g.loc[vi, \"_t_end_num\"]),\n",
    "                    \"train_idx\": int(ti),\n",
    "                    \"train_t_start\": int(tr_g.loc[ti, \"_t_start_num\"]),\n",
    "                    \"train_t_end\": int(tr_g.loc[ti, \"_t_end_num\"]),\n",
    "                })\n",
    "\n",
    "        # 畫圖：每個 fold 一張，val 視窗對 train 的最小距離分佈\n",
    "        figp = OUT / f\"histogram_min_gap_fold{k}.png\"\n",
    "        # 只畫一次（彙總各 id 的 min_gap）\n",
    "        if not figp.exists():\n",
    "            all_min_gaps = []\n",
    "            # 再跑一遍收集 min gaps\n",
    "            for sid2 in common_ids:\n",
    "                tr_g2 = tr_iv[tr_iv[\"id\"].astype(str) == sid2]\n",
    "                va_g2 = va_iv[va_iv[\"id\"].astype(str) == sid2]\n",
    "                _, mg2 = sweep_overlap(tr_g2, va_g2, tol=NEAR_TOL)\n",
    "                all_min_gaps.extend(mg2.tolist())\n",
    "            all_min_gaps = np.array(all_min_gaps)\n",
    "            plt.figure(figsize=(7,4))\n",
    "            # 負值 = 重疊；0~T 以內 = 近鄰\n",
    "            plt.hist(all_min_gaps, bins=50)\n",
    "            plt.title(f\"Fold {k}: min distance from VAL windows to nearest TRAIN window (id-wise)\")\n",
    "            plt.xlabel(\"min_gap (negative = overlap)\")\n",
    "            plt.ylabel(\"count\")\n",
    "            plt.tight_layout()\n",
    "            plt.savefig(figp, dpi=160)\n",
    "            plt.close()\n",
    "\n",
    "    rows.append({\n",
    "        \"fold\": k,\n",
    "        \"n_val_windows\": int(total_val),\n",
    "        \"n_common_ids\": int(len(common_ids)),\n",
    "        \"n_overlapping_pairs\": int(total_overlap),\n",
    "        \"overlap_rate_over_val\": float(total_overlap / max(total_val, 1)),\n",
    "    })\n",
    "\n",
    "    # 明細\n",
    "    if detail:\n",
    "        pd.DataFrame(detail).to_csv(OUT / f\"temporal_overlap_examples_fold{k}.csv\", index=False)\n",
    "\n",
    "# 每折總表\n",
    "pd.DataFrame(rows).to_csv(OUT / \"temporal_overlap_by_fold.csv\", index=False)\n",
    "\n",
    "print(\"Output:\", OUT)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 279,
   "id": "8971e39d-e48d-4f44-8ef6-50ae8606c5e8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-rw-r--r-- 1 jovyan users 1 Oct 15 14:33 /home/jovyan/RT08/0925/sliding_win/1014_sim/training_runs/20251015_143302_audit_temporal/temporal_overlap_by_fold.csv\n",
      "1 /home/jovyan/RT08/0925/sliding_win/1014_sim/training_runs/20251015_143302_audit_temporal/temporal_overlap_by_fold.csv\n",
      "\n"
     ]
    }
   ],
   "source": [
    "!ls -lh /home/jovyan/RT08/0925/sliding_win/1014_sim/training_runs/20251015_143302_audit_temporal/temporal_overlap_by_fold.csv\n",
    "!wc -l /home/jovyan/RT08/0925/sliding_win/1014_sim/training_runs/20251015_143302_audit_temporal/temporal_overlap_by_fold.csv\n",
    "!head /home/jovyan/RT08/0925/sliding_win/1014_sim/training_runs/20251015_143302_audit_temporal/temporal_overlap_by_fold.csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "df8b4700-2a91-4541-9473-fa7cd387bc0c",
   "metadata": {},
   "outputs": [],
   "source": [
    "驗證集的正類率顯著低於訓練集可以怎麼處理 優缺點\n",
    "我想確認是否存在「同一 id 的 train/val 視窗在時間上重疊」的洩漏"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b682dc46-f0aa-4a27-ba0a-27ae1243062f",
   "metadata": {},
   "outputs": [],
   "source": [
    "驗證集正類率（prevalence）明顯低於訓練集：怎麼處理？\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e8960d94-047f-417f-8318-d60fdbeaf7bf",
   "metadata": {},
   "outputs": [],
   "source": [
    "先檢查目前flod10 train val的資料的正負樣本比例的程式碼 結果請直接印在程式中"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 280,
   "id": "0b44860d-b2cb-4e57-8cc1-a782acba66df",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "=== Train (fold10) ===\n",
      "Total samples : 25,098\n",
      "Positive (1)  : 6,635 (26.44%)\n",
      "Negative (0)  : 18,463 (73.56%)\n",
      "----------------------------------------\n",
      "=== Validation (fold10) ===\n",
      "Total samples : 2,788\n",
      "Positive (1)  : 737 (26.43%)\n",
      "Negative (0)  : 2,051 (73.57%)\n",
      "----------------------------------------\n",
      "Δ Positive rate (val - train): -0.00%\n"
     ]
    }
   ],
   "source": [
    "# -*- coding: utf-8 -*-\n",
    "\"\"\"\n",
    "檢查 fold10 訓練與驗證資料的正負樣本比例\n",
    "假設檔案位於：\n",
    "  /home/jovyan/RT08/0925/sliding_win/1014_sim/windowed_clean/\n",
    "    y_train_fold10.npy\n",
    "    y_val_fold10.npy\n",
    "\"\"\"\n",
    "\n",
    "import numpy as np\n",
    "\n",
    "base = \"/home/jovyan/RT08/0925/sliding_win/1014_sim/windowed_clean\"\n",
    "\n",
    "# 載入 fold10 的標籤\n",
    "y_train = np.load(f\"{base}/y_train_fold10.npy\")\n",
    "y_val   = np.load(f\"{base}/y_val_fold10.npy\")\n",
    "\n",
    "# 確保為一維\n",
    "y_train = y_train.ravel()\n",
    "y_val   = y_val.ravel()\n",
    "\n",
    "# 統計正負樣本數\n",
    "def summarize_ratio(y, name):\n",
    "    n_total = len(y)\n",
    "    n_pos = int(np.sum(y == 1))\n",
    "    n_neg = int(np.sum(y == 0))\n",
    "    pct_pos = 100 * n_pos / n_total\n",
    "    pct_neg = 100 * n_neg / n_total\n",
    "    print(f\"=== {name} ===\")\n",
    "    print(f\"Total samples : {n_total:,}\")\n",
    "    print(f\"Positive (1)  : {n_pos:,} ({pct_pos:.2f}%)\")\n",
    "    print(f\"Negative (0)  : {n_neg:,} ({pct_neg:.2f}%)\")\n",
    "    print(\"-\" * 40)\n",
    "\n",
    "# 印出訓練與驗證集的比例\n",
    "summarize_ratio(y_train, \"Train (fold10)\")\n",
    "summarize_ratio(y_val, \"Validation (fold10)\")\n",
    "\n",
    "# 額外印出 train/val 正類率差異\n",
    "train_pos_rate = np.mean(y_train)\n",
    "val_pos_rate = np.mean(y_val)\n",
    "diff = (val_pos_rate - train_pos_rate) * 100\n",
    "print(f\"Δ Positive rate (val - train): {diff:.2f}%\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 281,
   "id": "5246992c-90d1-4d89-9425-7f1972d05a0e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "📊 十折訓練/驗證集 正負樣本比例檢查\n",
      "\n",
      "\n",
      "===== 🧩 Fold 1 =====\n",
      "=== Train ===\n",
      "Total samples : 25,097\n",
      "Positive (1)  : 6,635 (26.44%)\n",
      "Negative (0)  : 18,462 (73.56%)\n",
      "----------------------------------------\n",
      "=== Validation ===\n",
      "Total samples : 2,789\n",
      "Positive (1)  : 737 (26.43%)\n",
      "Negative (0)  : 2,052 (73.57%)\n",
      "----------------------------------------\n",
      "Δ Positive rate (val - train): -0.01%\n",
      "=============================================\n",
      "\n",
      "===== 🧩 Fold 2 =====\n",
      "=== Train ===\n",
      "Total samples : 25,097\n",
      "Positive (1)  : 6,635 (26.44%)\n",
      "Negative (0)  : 18,462 (73.56%)\n",
      "----------------------------------------\n",
      "=== Validation ===\n",
      "Total samples : 2,789\n",
      "Positive (1)  : 737 (26.43%)\n",
      "Negative (0)  : 2,052 (73.57%)\n",
      "----------------------------------------\n",
      "Δ Positive rate (val - train): -0.01%\n",
      "=============================================\n",
      "\n",
      "===== 🧩 Fold 3 =====\n",
      "=== Train ===\n",
      "Total samples : 25,097\n",
      "Positive (1)  : 6,635 (26.44%)\n",
      "Negative (0)  : 18,462 (73.56%)\n",
      "----------------------------------------\n",
      "=== Validation ===\n",
      "Total samples : 2,789\n",
      "Positive (1)  : 737 (26.43%)\n",
      "Negative (0)  : 2,052 (73.57%)\n",
      "----------------------------------------\n",
      "Δ Positive rate (val - train): -0.01%\n",
      "=============================================\n",
      "\n",
      "===== 🧩 Fold 4 =====\n",
      "=== Train ===\n",
      "Total samples : 25,097\n",
      "Positive (1)  : 6,635 (26.44%)\n",
      "Negative (0)  : 18,462 (73.56%)\n",
      "----------------------------------------\n",
      "=== Validation ===\n",
      "Total samples : 2,789\n",
      "Positive (1)  : 737 (26.43%)\n",
      "Negative (0)  : 2,052 (73.57%)\n",
      "----------------------------------------\n",
      "Δ Positive rate (val - train): -0.01%\n",
      "=============================================\n",
      "\n",
      "===== 🧩 Fold 5 =====\n",
      "=== Train ===\n",
      "Total samples : 25,097\n",
      "Positive (1)  : 6,634 (26.43%)\n",
      "Negative (0)  : 18,463 (73.57%)\n",
      "----------------------------------------\n",
      "=== Validation ===\n",
      "Total samples : 2,789\n",
      "Positive (1)  : 738 (26.46%)\n",
      "Negative (0)  : 2,051 (73.54%)\n",
      "----------------------------------------\n",
      "Δ Positive rate (val - train): 0.03%\n",
      "=============================================\n",
      "\n",
      "===== 🧩 Fold 6 =====\n",
      "=== Train ===\n",
      "Total samples : 25,097\n",
      "Positive (1)  : 6,634 (26.43%)\n",
      "Negative (0)  : 18,463 (73.57%)\n",
      "----------------------------------------\n",
      "=== Validation ===\n",
      "Total samples : 2,789\n",
      "Positive (1)  : 738 (26.46%)\n",
      "Negative (0)  : 2,051 (73.54%)\n",
      "----------------------------------------\n",
      "Δ Positive rate (val - train): 0.03%\n",
      "=============================================\n",
      "\n",
      "===== 🧩 Fold 7 =====\n",
      "=== Train ===\n",
      "Total samples : 25,098\n",
      "Positive (1)  : 6,635 (26.44%)\n",
      "Negative (0)  : 18,463 (73.56%)\n",
      "----------------------------------------\n",
      "=== Validation ===\n",
      "Total samples : 2,788\n",
      "Positive (1)  : 737 (26.43%)\n",
      "Negative (0)  : 2,051 (73.57%)\n",
      "----------------------------------------\n",
      "Δ Positive rate (val - train): -0.00%\n",
      "=============================================\n",
      "\n",
      "===== 🧩 Fold 8 =====\n",
      "=== Train ===\n",
      "Total samples : 25,098\n",
      "Positive (1)  : 6,635 (26.44%)\n",
      "Negative (0)  : 18,463 (73.56%)\n",
      "----------------------------------------\n",
      "=== Validation ===\n",
      "Total samples : 2,788\n",
      "Positive (1)  : 737 (26.43%)\n",
      "Negative (0)  : 2,051 (73.57%)\n",
      "----------------------------------------\n",
      "Δ Positive rate (val - train): -0.00%\n",
      "=============================================\n",
      "\n",
      "===== 🧩 Fold 9 =====\n",
      "=== Train ===\n",
      "Total samples : 25,098\n",
      "Positive (1)  : 6,635 (26.44%)\n",
      "Negative (0)  : 18,463 (73.56%)\n",
      "----------------------------------------\n",
      "=== Validation ===\n",
      "Total samples : 2,788\n",
      "Positive (1)  : 737 (26.43%)\n",
      "Negative (0)  : 2,051 (73.57%)\n",
      "----------------------------------------\n",
      "Δ Positive rate (val - train): -0.00%\n",
      "=============================================\n",
      "\n",
      "===== 🧩 Fold 10 =====\n",
      "=== Train ===\n",
      "Total samples : 25,098\n",
      "Positive (1)  : 6,635 (26.44%)\n",
      "Negative (0)  : 18,463 (73.56%)\n",
      "----------------------------------------\n",
      "=== Validation ===\n",
      "Total samples : 2,788\n",
      "Positive (1)  : 737 (26.43%)\n",
      "Negative (0)  : 2,051 (73.57%)\n",
      "----------------------------------------\n",
      "Δ Positive rate (val - train): -0.00%\n",
      "=============================================\n",
      "\n",
      "=== 📈 Summary across folds ===\n",
      "Train 平均正類率 : 26.44%\n",
      "Val   平均正類率 : 26.44%\n",
      "Δ 平均差 (val - train): -0.00%\n"
     ]
    }
   ],
   "source": [
    "# -*- coding: utf-8 -*-\n",
    "\"\"\"\n",
    "檢查十折 (fold1~fold10) 訓練與驗證資料的正負樣本比例\n",
    "假設資料路徑：\n",
    "  /home/jovyan/RT08/0925/sliding_win/1014_sim/windowed_clean/\n",
    "\"\"\"\n",
    "\n",
    "import numpy as np\n",
    "import os\n",
    "\n",
    "base = \"/home/jovyan/RT08/0925/sliding_win/1014_sim/windowed_clean\"\n",
    "\n",
    "def summarize_ratio(y, name):\n",
    "    y = y.ravel()\n",
    "    n_total = len(y)\n",
    "    n_pos = int(np.sum(y == 1))\n",
    "    n_neg = int(np.sum(y == 0))\n",
    "    pct_pos = 100 * n_pos / n_total\n",
    "    pct_neg = 100 * n_neg / n_total\n",
    "    print(f\"=== {name} ===\")\n",
    "    print(f\"Total samples : {n_total:,}\")\n",
    "    print(f\"Positive (1)  : {n_pos:,} ({pct_pos:.2f}%)\")\n",
    "    print(f\"Negative (0)  : {n_neg:,} ({pct_neg:.2f}%)\")\n",
    "    print(\"-\" * 40)\n",
    "    return pct_pos\n",
    "\n",
    "print(\"📊 十折訓練/驗證集 正負樣本比例檢查\\n\")\n",
    "\n",
    "pos_rates_train = []\n",
    "pos_rates_val = []\n",
    "\n",
    "for k in range(1, 11):\n",
    "    ytr_path = os.path.join(base, f\"y_train_fold{k}.npy\")\n",
    "    yva_path = os.path.join(base, f\"y_val_fold{k}.npy\")\n",
    "    if not os.path.exists(ytr_path) or not os.path.exists(yva_path):\n",
    "        print(f\"⚠️  fold{k} 缺少 y_train 或 y_val 檔案，略過。\")\n",
    "        continue\n",
    "\n",
    "    ytr = np.load(ytr_path)\n",
    "    yva = np.load(yva_path)\n",
    "\n",
    "    print(f\"\\n===== 🧩 Fold {k} =====\")\n",
    "    pct_tr = summarize_ratio(ytr, \"Train\")\n",
    "    pct_va = summarize_ratio(yva, \"Validation\")\n",
    "    diff = pct_va - pct_tr\n",
    "    print(f\"Δ Positive rate (val - train): {diff:.2f}%\")\n",
    "    print(\"=\" * 45)\n",
    "    pos_rates_train.append(pct_tr)\n",
    "    pos_rates_val.append(pct_va)\n",
    "\n",
    "if pos_rates_train and pos_rates_val:\n",
    "    print(\"\\n=== 📈 Summary across folds ===\")\n",
    "    mean_tr = np.mean(pos_rates_train)\n",
    "    mean_va = np.mean(pos_rates_val)\n",
    "    diff_all = mean_va - mean_tr\n",
    "    print(f\"Train 平均正類率 : {mean_tr:.2f}%\")\n",
    "    print(f\"Val   平均正類率 : {mean_va:.2f}%\")\n",
    "    print(f\"Δ 平均差 (val - train): {diff_all:.2f}%\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9a6e8afe-d74e-4a7b-aece-8ca5fe4ba2f8",
   "metadata": {},
   "outputs": [],
   "source": [
    "每一折的訓練集與驗證集都類別平衡（正=負），\n",
    "多出來的樣本作為「測試集」或「保留集」"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a91d5b86-4205-4f6d-b5c7-9a1ebf287ffb",
   "metadata": {},
   "outputs": [],
   "source": [
    "分層平衡再切 (Stratified Fold Balance)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 282,
   "id": "0e0ef1ae-1364-46f6-a2de-8c9a066108e0",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/40\n"
     ]
    },
    {
     "ename": "ValueError",
     "evalue": "Arguments `target` and `output` must have the same rank (ndim). Received: target.shape=(None,), output.shape=(None, 1)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[282], line 318\u001b[0m\n\u001b[1;32m    306\u001b[0m cbs \u001b[38;5;241m=\u001b[39m [\n\u001b[1;32m    307\u001b[0m     F1PerEpoch(Xtr_p, ytr, Xva_p, yva, bs\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1024\u001b[39m),\n\u001b[1;32m    308\u001b[0m     ProbProbe(Xtr_p, Xva_p, bs\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1024\u001b[39m),\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    314\u001b[0m                                       min_lr\u001b[38;5;241m=\u001b[39mCFG[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mreduce_min_lr\u001b[39m\u001b[38;5;124m\"\u001b[39m], verbose\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m)\n\u001b[1;32m    315\u001b[0m ]\n\u001b[1;32m    317\u001b[0m \u001b[38;5;66;03m# 訓練\u001b[39;00m\n\u001b[0;32m--> 318\u001b[0m hist \u001b[38;5;241m=\u001b[39m \u001b[43mmodel\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfit\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    319\u001b[0m \u001b[43m    \u001b[49m\u001b[43mXtr_p\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mytr\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    320\u001b[0m \u001b[43m    \u001b[49m\u001b[43mvalidation_data\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mXva_p\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43myva\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    321\u001b[0m \u001b[43m    \u001b[49m\u001b[43mepochs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mCFG\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mepochs\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    322\u001b[0m \u001b[43m    \u001b[49m\u001b[43mbatch_size\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mCFG\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mbatch_size\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    323\u001b[0m \u001b[43m    \u001b[49m\u001b[43mcallbacks\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcbs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    324\u001b[0m \u001b[43m    \u001b[49m\u001b[43mverbose\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m2\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m    325\u001b[0m \u001b[43m    \u001b[49m\u001b[43mclass_weight\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mclass_weight\u001b[49m\n\u001b[1;32m    326\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    328\u001b[0m \u001b[38;5;66;03m# 保存 history 與學習曲線\u001b[39;00m\n\u001b[1;32m    329\u001b[0m hist_df \u001b[38;5;241m=\u001b[39m pd\u001b[38;5;241m.\u001b[39mDataFrame(hist\u001b[38;5;241m.\u001b[39mhistory)\n",
      "File \u001b[0;32m/opt/conda/lib/python3.11/site-packages/keras/src/utils/traceback_utils.py:122\u001b[0m, in \u001b[0;36mfilter_traceback.<locals>.error_handler\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    119\u001b[0m     filtered_tb \u001b[38;5;241m=\u001b[39m _process_traceback_frames(e\u001b[38;5;241m.\u001b[39m__traceback__)\n\u001b[1;32m    120\u001b[0m     \u001b[38;5;66;03m# To get the full stack trace, call:\u001b[39;00m\n\u001b[1;32m    121\u001b[0m     \u001b[38;5;66;03m# `keras.config.disable_traceback_filtering()`\u001b[39;00m\n\u001b[0;32m--> 122\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m e\u001b[38;5;241m.\u001b[39mwith_traceback(filtered_tb) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m    123\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[1;32m    124\u001b[0m     \u001b[38;5;28;01mdel\u001b[39;00m filtered_tb\n",
      "File \u001b[0;32m/opt/conda/lib/python3.11/site-packages/keras/src/backend/tensorflow/nn.py:668\u001b[0m, in \u001b[0;36mbinary_crossentropy\u001b[0;34m(target, output, from_logits)\u001b[0m\n\u001b[1;32m    665\u001b[0m output \u001b[38;5;241m=\u001b[39m tf\u001b[38;5;241m.\u001b[39mconvert_to_tensor(output)\n\u001b[1;32m    667\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(target\u001b[38;5;241m.\u001b[39mshape) \u001b[38;5;241m!=\u001b[39m \u001b[38;5;28mlen\u001b[39m(output\u001b[38;5;241m.\u001b[39mshape):\n\u001b[0;32m--> 668\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[1;32m    669\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mArguments `target` and `output` must have the same rank \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    670\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m(ndim). Received: \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    671\u001b[0m         \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtarget.shape=\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mtarget\u001b[38;5;241m.\u001b[39mshape\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m, output.shape=\u001b[39m\u001b[38;5;132;01m{\u001b[39;00moutput\u001b[38;5;241m.\u001b[39mshape\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    672\u001b[0m     )\n\u001b[1;32m    673\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m e1, e2 \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mzip\u001b[39m(target\u001b[38;5;241m.\u001b[39mshape, output\u001b[38;5;241m.\u001b[39mshape):\n\u001b[1;32m    674\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m e1 \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m e2 \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m e1 \u001b[38;5;241m!=\u001b[39m e2:\n",
      "\u001b[0;31mValueError\u001b[0m: Arguments `target` and `output` must have the same rank (ndim). Received: target.shape=(None,), output.shape=(None, 1)"
     ]
    }
   ],
   "source": [
    "# -*- coding: utf-8 -*-\n",
    "\"\"\"\n",
    "train_lstm_focal_classweight_cv.py\n",
    "\n",
    "需求對應：\n",
    "- 單層 LSTM(192) + Dropout(0.1) + L2(5e-7)；不使用 Masking / BiLSTM / LayerNorm\n",
    "- Focal Loss + Class Weight；alpha 依每折正類率動態設定\n",
    "- Sigmoid 輸出層 bias = log(p/(1-p))（用訓練集正類率）\n",
    "- 10 折交叉驗證；每折 fit-transform（Imputer+Scaler）避免洩漏\n",
    "- Optimizer: Adam(lr=3e-4, clipnorm=1.0)\n",
    "- EarlyStopping: 監控 val_auprc(pat=8) + val_f1_best(pat=6)\n",
    "- ReduceLROnPlateau: monitor=val_auprc, factor=0.5, min_lr=1e-5\n",
    "- 紀錄/圖表：Loss(Focal vs BCE)、Accuracy、F1 per epoch、ROC/PR(train/val)、CM(固定/最佳閾值)\n",
    "- 每折輸出與總結輸出\n",
    "\n",
    "執行：\n",
    "  python train_lstm_focal_classweight_cv.py\n",
    "\"\"\"\n",
    "\n",
    "import os\n",
    "import json\n",
    "import time\n",
    "from pathlib import Path\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from sklearn.impute import SimpleImputer\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.metrics import (\n",
    "    roc_curve, auc, precision_recall_curve, average_precision_score,\n",
    "    confusion_matrix, precision_recall_fscore_support, accuracy_score, roc_auc_score\n",
    ")\n",
    "\n",
    "import tensorflow as tf\n",
    "from tensorflow import keras\n",
    "from tensorflow.keras import layers\n",
    "\n",
    "# -------------------------\n",
    "# 基本路徑與設定\n",
    "# -------------------------\n",
    "BASE = Path(\"/home/jovyan/RT08/0925/sliding_win/1014_sim\")\n",
    "DATA = BASE / \"windowed_clean\"\n",
    "RUNS_ROOT = BASE / \"training_runs\"\n",
    "RUNS_ROOT.mkdir(parents=True, exist_ok=True)\n",
    "\n",
    "CFG = {\n",
    "    \"seed\": 42,\n",
    "    \"folds\": list(range(1, 11)),\n",
    "    \"epochs\": 40,\n",
    "    \"batch_size\": 128,\n",
    "    \"learning_rate\": 3e-4,\n",
    "    \"clipnorm\": 1.0,\n",
    "    \"l2\": 5e-7,\n",
    "    \"hidden_units\": 192,\n",
    "    \"dropout\": 0.1,\n",
    "    \"recurrent_dropout\": 0.0,\n",
    "    \"patience_auprc\": 8,\n",
    "    \"patience_f1best\": 6,\n",
    "    \"reduce_factor\": 0.5,\n",
    "    # 注意：min_lr 必須 < 初始 lr 才會「往下」生效\n",
    "    \"reduce_min_lr\": 1e-5,\n",
    "    \"threshold_fixed\": 0.5,\n",
    "    \"focal_gamma\": 2.0\n",
    "}\n",
    "\n",
    "np.random.seed(CFG[\"seed\"])\n",
    "tf.random.set_seed(CFG[\"seed\"])\n",
    "\n",
    "# 版本化 run 目錄\n",
    "_ts = time.strftime(\"%Y%m%d_%H%M%S\")\n",
    "existing = sorted(RUNS_ROOT.glob(f\"{_ts}_run*\"))\n",
    "run_id = len(existing) + 1\n",
    "RUN_DIR = RUNS_ROOT / f\"{_ts}_run{run_id:02d}\"\n",
    "RUN_DIR.mkdir(parents=True, exist_ok=True)\n",
    "(RUN_DIR / \"cfg.json\").write_text(json.dumps(CFG, ensure_ascii=False, indent=2), encoding=\"utf-8\")\n",
    "\n",
    "# -------------------------\n",
    "# 視覺元素\n",
    "# -------------------------\n",
    "BLUES = [\"#cfe8ff\", \"#9dd0ff\", \"#6bb8ff\", \"#3aa0ff\", \"#0a88ff\", \"#005bb5\"]\n",
    "\n",
    "def plot_history(hist: pd.DataFrame, out_png: Path):\n",
    "    plt.figure(figsize=(7,5))\n",
    "    x = np.arange(len(hist))\n",
    "    # Focal loss\n",
    "    plt.plot(x, hist[\"loss\"], label=\"Train Focal Loss\", lw=2, color=BLUES[4])\n",
    "    plt.plot(x, hist[\"val_loss\"], label=\"Val Focal Loss\", lw=2, color=BLUES[5])\n",
    "    # BCE (metric)\n",
    "    if \"binary_crossentropy\" in hist.columns:\n",
    "        plt.plot(x, hist[\"binary_crossentropy\"], label=\"Train BCE\", lw=1.8, color=BLUES[2])\n",
    "    if \"val_binary_crossentropy\" in hist.columns:\n",
    "        plt.plot(x, hist[\"val_binary_crossentropy\"], label=\"Val BCE\", lw=1.8, color=BLUES[1])\n",
    "    plt.title(\"Loss Curves (Focal vs BCE)\")\n",
    "    plt.xlabel(\"Epoch\"); plt.ylabel(\"Loss\")\n",
    "    plt.grid(True, alpha=0.25); plt.legend(); plt.tight_layout(); plt.savefig(out_png, dpi=160); plt.close()\n",
    "\n",
    "def plot_metric_epochs(hist: pd.DataFrame, metric: str, out_png: Path, title: str):\n",
    "    plt.figure(figsize=(7,5))\n",
    "    x = np.arange(len(hist))\n",
    "    if metric in hist.columns:\n",
    "        plt.plot(x, hist[metric], label=f\"Train {metric}\", lw=2, color=BLUES[4])\n",
    "    valm = f\"val_{metric}\"\n",
    "    if valm in hist.columns:\n",
    "        plt.plot(x, hist[valm], label=f\"Val {metric}\", lw=2, color=BLUES[5])\n",
    "    plt.title(title); plt.xlabel(\"Epoch\"); plt.ylabel(metric.title())\n",
    "    plt.grid(True, alpha=0.25); plt.legend(); plt.tight_layout(); plt.savefig(out_png, dpi=160); plt.close()\n",
    "\n",
    "def plot_roc_xy(fpr, tpr, roc_auc, out_png: Path, title: str):\n",
    "    plt.figure(figsize=(6,5))\n",
    "    plt.plot(fpr, tpr, lw=2, color=BLUES[4], label=f\"AUC={roc_auc:.4f}\")\n",
    "    plt.plot([0,1],[0,1], lw=1, ls=\"--\", color=BLUES[0])\n",
    "    plt.title(title); plt.xlabel(\"False Positive Rate\"); plt.ylabel(\"True Positive Rate\")\n",
    "    plt.grid(True, alpha=0.25); plt.legend(loc=\"lower right\"); plt.tight_layout(); plt.savefig(out_png, dpi=160); plt.close()\n",
    "\n",
    "def plot_pr_xy(rec, prec, ap, out_png: Path, title: str):\n",
    "    plt.figure(figsize=(6,5))\n",
    "    plt.plot(rec, prec, lw=2, color=BLUES[4], label=f\"AP={ap:.4f}\")\n",
    "    plt.title(title); plt.xlabel(\"Recall\"); plt.ylabel(\"Precision\")\n",
    "    plt.grid(True, alpha=0.25); plt.legend(loc=\"lower left\"); plt.tight_layout(); plt.savefig(out_png, dpi=160); plt.close()\n",
    "\n",
    "def plot_cm(cm: np.ndarray, out_png: Path, title: str):\n",
    "    plt.figure(figsize=(5.6,4.8))\n",
    "    im = plt.imshow(cm, cmap=\"Blues\")\n",
    "    plt.title(title, fontsize=15)\n",
    "    plt.xlabel(\"Predicted\", fontsize=12); plt.ylabel(\"Actual\", fontsize=12)\n",
    "    plt.colorbar(im, fraction=0.046, pad=0.04)\n",
    "    for (i,j), z in np.ndenumerate(cm):\n",
    "        plt.text(j, i, f\"{z}\", ha='center', va='center', color='black', fontsize=12)\n",
    "    plt.xticks([0,1], [\"Negative\",\"Positive\"]); plt.yticks([0,1], [\"Negative\",\"Positive\"])\n",
    "    plt.tight_layout(); plt.savefig(out_png, dpi=170); plt.close()\n",
    "\n",
    "# -------------------------\n",
    "# Focal Loss（二元）\n",
    "# alpha 由每折正類率動態設定；gamma 使用 CFG[\"focal_gamma\"]\n",
    "# -------------------------\n",
    "def binary_focal_loss(gamma=2.0, alpha=0.25):\n",
    "    def loss(y_true, y_pred):\n",
    "        y_true = tf.cast(y_true, tf.float32)\n",
    "        y_pred = tf.clip_by_value(y_pred, 1e-7, 1. - 1e-7)\n",
    "        p_t = y_true * y_pred + (1 - y_true) * (1 - y_pred)\n",
    "        alpha_factor = y_true * alpha + (1 - y_true) * (1 - alpha)\n",
    "        modulating = tf.pow(1. - p_t, gamma)\n",
    "        bce = tf.keras.losses.binary_crossentropy(y_true, y_pred)\n",
    "        return tf.reduce_mean(alpha_factor * modulating * bce)\n",
    "    return loss\n",
    "\n",
    "# -------------------------\n",
    "# 模型構建（無 Masking / 無 BiLSTM / 無 LN）\n",
    "# 輸出層 bias 以先驗 logit 初始化\n",
    "# -------------------------\n",
    "def build_model(input_shape, prior_pos, cfg=CFG):\n",
    "    prior_pos = float(np.clip(prior_pos, 1e-6, 1 - 1e-6))\n",
    "    prior_logit = np.log(prior_pos / (1 - prior_pos))\n",
    "\n",
    "    reg = keras.regularizers.l2(cfg[\"l2\"])\n",
    "    inputs = keras.Input(shape=input_shape, name=\"sequence\")\n",
    "\n",
    "    x = layers.LSTM(\n",
    "        cfg[\"hidden_units\"],\n",
    "        dropout=cfg[\"dropout\"],\n",
    "        recurrent_dropout=cfg[\"recurrent_dropout\"],\n",
    "        kernel_regularizer=reg,\n",
    "        recurrent_regularizer=reg,\n",
    "        return_sequences=False\n",
    "    )(inputs)\n",
    "    x = layers.Dropout(cfg[\"dropout\"])(x)\n",
    "\n",
    "    outputs = layers.Dense(\n",
    "        1, activation=\"sigmoid\", name=\"prob\",\n",
    "        bias_initializer=keras.initializers.Constant(prior_logit)\n",
    "    )(x)\n",
    "\n",
    "    model = keras.Model(inputs, outputs, name=\"lstm_focal_classweight\")\n",
    "    opt = keras.optimizers.Adam(learning_rate=cfg[\"learning_rate\"], clipnorm=cfg[\"clipnorm\"])\n",
    "    model.compile(\n",
    "        optimizer=opt,\n",
    "        loss=binary_focal_loss(gamma=cfg[\"focal_gamma\"], alpha=prior_pos),  # alpha = 正類率（可依需求調整為 1 - pos）\n",
    "        metrics=[\n",
    "            keras.metrics.BinaryAccuracy(name=\"accuracy\"),\n",
    "            keras.metrics.AUC(name=\"auc\"),\n",
    "            keras.metrics.AUC(name=\"auprc\", curve=\"PR\"),\n",
    "            keras.metrics.Precision(name=\"precision\"),\n",
    "            keras.metrics.Recall(name=\"recall\"),\n",
    "            keras.metrics.BinaryCrossentropy(name=\"binary_crossentropy\")\n",
    "        ]\n",
    "    )\n",
    "    return model\n",
    "\n",
    "# -------------------------\n",
    "# 每折前處理（fit on train, transform train/val）\n",
    "# -------------------------\n",
    "def fit_transform_fold(Xtr, Xva):\n",
    "    n, t, d = Xtr.shape\n",
    "    tr2 = Xtr.reshape(n, t * d)\n",
    "    va2 = Xva.reshape(Xva.shape[0], t * d)\n",
    "\n",
    "    imputer = SimpleImputer(strategy=\"median\")\n",
    "    scaler = StandardScaler(with_mean=True, with_std=True)\n",
    "\n",
    "    tr_imp = imputer.fit_transform(tr2)\n",
    "    tr_scl = scaler.fit_transform(tr_imp)\n",
    "\n",
    "    va_imp = imputer.transform(va2)\n",
    "    va_scl = scaler.transform(va_imp)\n",
    "\n",
    "    Xtr_p = tr_scl.reshape(n, t, d).astype(np.float32)\n",
    "    Xva_p = va_scl.reshape(Xva.shape[0], t, d).astype(np.float32)\n",
    "    return Xtr_p, Xva_p, imputer, scaler\n",
    "\n",
    "# -------------------------\n",
    "# 指標工具\n",
    "# -------------------------\n",
    "def sweep_best_f1(y_true, y_prob, step=0.01):\n",
    "    thresholds = np.arange(step, 1.0, step)\n",
    "    best = {\"th\": 0.5, \"f1\": -1.0, \"prec\": 0.0, \"rec\": 0.0, \"acc\": 0.0}\n",
    "    for th in thresholds:\n",
    "        pred = (y_prob >= th).astype(int)\n",
    "        prec, rec, f1, _ = precision_recall_fscore_support(y_true, pred, average='binary', zero_division=0)\n",
    "        acc = accuracy_score(y_true, pred)\n",
    "        if f1 > best[\"f1\"]:\n",
    "            best = {\"th\": float(th), \"f1\": float(f1), \"prec\": float(prec), \"rec\": float(rec), \"acc\": float(acc)}\n",
    "    return best\n",
    "\n",
    "def five_metrics(y_true, y_prob, th):\n",
    "    pred = (y_prob >= th).astype(int)\n",
    "    acc = accuracy_score(y_true, pred)\n",
    "    prec, rec, f1, _ = precision_recall_fscore_support(y_true, pred, average='binary', zero_division=0)\n",
    "    rocauc = roc_auc_score(y_true, y_prob)\n",
    "    ap = average_precision_score(y_true, y_prob)\n",
    "    return {\"acc\":acc, \"prec\":prec, \"rec\":rec, \"f1\":f1, \"auc\":rocauc, \"auprc\":ap}\n",
    "\n",
    "# -------------------------\n",
    "# Callbacks：F1 掃門檻 + 機率分佈探針 + 以 F1best/auprc 早停\n",
    "# -------------------------\n",
    "class F1PerEpoch(keras.callbacks.Callback):\n",
    "    def __init__(self, Xtr, ytr, Xva, yva, bs=1024):\n",
    "        super().__init__()\n",
    "        self.Xtr, self.ytr, self.Xva, self.yva = Xtr, ytr, Xva, yva\n",
    "        self.bs = bs\n",
    "    def on_epoch_end(self, epoch, logs=None):\n",
    "        ytr_prob = self.model.predict(self.Xtr, batch_size=self.bs, verbose=0).ravel()\n",
    "        yva_prob = self.model.predict(self.Xva, batch_size=self.bs, verbose=0).ravel()\n",
    "        best_tr = sweep_best_f1(self.ytr, ytr_prob, step=0.01)\n",
    "        best_va = sweep_best_f1(self.yva, yva_prob, step=0.01)\n",
    "        if logs is not None:\n",
    "            logs[\"f1_best\"] = best_tr[\"f1\"]\n",
    "            logs[\"val_f1_best\"] = best_va[\"f1\"]\n",
    "            logs[\"val_best_th\"] = best_va[\"th\"]\n",
    "        print(f\"[F1Epoch] epoch={epoch} f1_tr_best={best_tr['f1']:.4f} f1_va_best={best_va['f1']:.4f} | val_best_th={best_va['th']:.2f}\")\n",
    "\n",
    "class ProbProbe(keras.callbacks.Callback):\n",
    "    def __init__(self, Xtr, Xva, bs=1024):\n",
    "        super().__init__()\n",
    "        self.Xtr, self.Xva = Xtr, Xva\n",
    "        self.bs = bs\n",
    "    def on_epoch_end(self, epoch, logs=None):\n",
    "        ytr_prob = self.model.predict(self.Xtr, batch_size=self.bs, verbose=0).ravel()\n",
    "        yva_prob = self.model.predict(self.Xva, batch_size=self.bs, verbose=0).ravel()\n",
    "        def stats(v):\n",
    "            return np.mean(v), np.percentile(v, 90)\n",
    "        m_tr, p90_tr = stats(ytr_prob)\n",
    "        m_va, p90_va = stats(yva_prob)\n",
    "        print(f\"[Probe] epoch={epoch} train mean={m_tr:.4f} p90={p90_tr:.4f} | val mean={m_va:.4f} p90={p90_va:.4f}\")\n",
    "\n",
    "class EarlyStopOnKey(keras.callbacks.EarlyStopping):\n",
    "    \"\"\"Keras 內建 EarlyStopping，只是 monitor 換成自訂 log key，例如 'val_f1_best'。\"\"\"\n",
    "    pass\n",
    "\n",
    "# -------------------------\n",
    "# 主流程\n",
    "# -------------------------\n",
    "all_rows = []\n",
    "\n",
    "for k in CFG[\"folds\"]:\n",
    "    fold_dir = RUN_DIR / f\"fold_{k}\"\n",
    "    fold_dir.mkdir(parents=True, exist_ok=True)\n",
    "\n",
    "    # 載入資料\n",
    "    Xtr = np.load(DATA / f\"X_train_fold{k}.npy\")\n",
    "    ytr = np.load(DATA / f\"y_train_fold{k}.npy\").ravel().astype(int)\n",
    "    Xva = np.load(DATA / f\"X_val_fold{k}.npy\")\n",
    "    yva = np.load(DATA / f\"y_val_fold{k}.npy\").ravel().astype(int)\n",
    "\n",
    "    assert set(np.unique(ytr)).issubset({0,1}) and set(np.unique(yva)).issubset({0,1}), \"Labels must be {0,1}\"\n",
    "\n",
    "    # 前處理（fit on train, transform on train/val）\n",
    "    Xtr_p, Xva_p, _, _ = fit_transform_fold(Xtr, Xva)\n",
    "\n",
    "    # 先驗：正類率 + 輸出層 bias\n",
    "    pos_rate = float(np.mean(ytr))\n",
    "    # class_weight（balanced）\n",
    "    n_pos = np.sum(ytr==1); n_neg = np.sum(ytr==0)\n",
    "    total = n_pos + n_neg\n",
    "    # keras 的 class_weight= {class: weight}\n",
    "    class_weight = {\n",
    "        0: total/(2.0*n_neg) if n_neg>0 else 1.0,\n",
    "        1: total/(2.0*n_pos) if n_pos>0 else 1.0\n",
    "    }\n",
    "\n",
    "    # 建模（alpha 使用 pos_rate；若想更強化少數類，也可用 alpha=1-pos_rate）\n",
    "    model = build_model(input_shape=(Xtr_p.shape[1], Xtr_p.shape[2]), prior_pos=pos_rate, cfg=CFG)\n",
    "\n",
    "    # Callbacks\n",
    "    ckpt_path = fold_dir / \"best_model.keras\"\n",
    "    cbs = [\n",
    "        F1PerEpoch(Xtr_p, ytr, Xva_p, yva, bs=1024),\n",
    "        ProbProbe(Xtr_p, Xva_p, bs=1024),\n",
    "        keras.callbacks.ModelCheckpoint(filepath=str(ckpt_path), monitor=\"val_auprc\", mode=\"max\", save_best_only=True),\n",
    "        EarlyStopOnKey(monitor=\"val_auprc\", mode=\"max\", patience=CFG[\"patience_auprc\"], restore_best_weights=True),\n",
    "        EarlyStopOnKey(monitor=\"val_f1_best\", mode=\"max\", patience=CFG[\"patience_f1best\"], restore_best_weights=True),\n",
    "        keras.callbacks.ReduceLROnPlateau(monitor=\"val_auprc\", mode=\"max\",\n",
    "                                          factor=CFG[\"reduce_factor\"], patience=3,\n",
    "                                          min_lr=CFG[\"reduce_min_lr\"], verbose=1)\n",
    "    ]\n",
    "\n",
    "    # 訓練\n",
    "    hist = model.fit(\n",
    "        Xtr_p, ytr,\n",
    "        validation_data=(Xva_p, yva),\n",
    "        epochs=CFG[\"epochs\"],\n",
    "        batch_size=CFG[\"batch_size\"],\n",
    "        callbacks=cbs,\n",
    "        verbose=2,\n",
    "        class_weight=class_weight\n",
    "    )\n",
    "\n",
    "    # 保存 history 與學習曲線\n",
    "    hist_df = pd.DataFrame(hist.history)\n",
    "    hist_df.to_csv(fold_dir / \"history.csv\", index=False)\n",
    "    plot_history(hist_df, fold_dir / \"plot_loss.png\")\n",
    "    plot_metric_epochs(hist_df, \"accuracy\", fold_dir / \"plot_acc.png\", \"Accuracy per Epoch\")\n",
    "    if \"f1_best\" in hist_df.columns and \"val_f1_best\" in hist_df.columns:\n",
    "        plot_metric_epochs(hist_df, \"f1_best\", fold_dir / \"plot_f1.png\", \"F1 (Best-threshold) per Epoch\")\n",
    "\n",
    "    # 推論\n",
    "    ytr_prob = model.predict(Xtr_p, batch_size=CFG[\"batch_size\"], verbose=0).ravel()\n",
    "    yva_prob = model.predict(Xva_p, batch_size=CFG[\"batch_size\"], verbose=0).ravel()\n",
    "\n",
    "    # 固定閾值 & 最佳閾值\n",
    "    th_fixed = CFG[\"threshold_fixed\"]\n",
    "    best_va = sweep_best_f1(yva, yva_prob, step=0.01)\n",
    "    th_best = best_va[\"th\"]\n",
    "\n",
    "    # 五大指標（train/val; fixed/best）\n",
    "    m_tr_fixed = five_metrics(ytr, ytr_prob, th_fixed)\n",
    "    m_va_fixed = five_metrics(yva, yva_prob, th_fixed)\n",
    "    m_tr_best  = five_metrics(ytr, ytr_prob, th_best)\n",
    "    m_va_best  = five_metrics(yva, yva_prob, th_best)\n",
    "\n",
    "    # 混淆矩陣（固定/最佳）\n",
    "    cm_tr_fixed = confusion_matrix(ytr, (ytr_prob>=th_fixed).astype(int), labels=[0,1])\n",
    "    cm_va_fixed = confusion_matrix(yva, (yva_prob>=th_fixed).astype(int), labels=[0,1])\n",
    "    cm_tr_best  = confusion_matrix(ytr, (ytr_prob>=th_best).astype(int), labels=[0,1])\n",
    "    cm_va_best  = confusion_matrix(yva, (yva_prob>=th_best).astype(int), labels=[0,1])\n",
    "\n",
    "    # ROC / PR（Train & Val）\n",
    "    fpr_tr, tpr_tr, _ = roc_curve(ytr, ytr_prob)\n",
    "    fpr_va, tpr_va, _ = roc_curve(yva, yva_prob)\n",
    "    rec_tr, prec_tr, _ = precision_recall_curve(ytr, ytr_prob)\n",
    "    rec_va, prec_va, _ = precision_recall_curve(yva, yva_prob)\n",
    "    ap_tr = average_precision_score(ytr, ytr_prob)\n",
    "    ap_va = average_precision_score(yva, yva_prob)\n",
    "\n",
    "    # 存 CSV\n",
    "    pd.DataFrame({\"y_true\": ytr, \"y_prob\": ytr_prob}).to_csv(fold_dir / \"predictions_train.csv\", index=False)\n",
    "    pd.DataFrame({\"y_true\": yva, \"y_prob\": yva_prob}).to_csv(fold_dir / \"predictions_val.csv\", index=False)\n",
    "    pd.DataFrame({\"fpr\": fpr_tr, \"tpr\": tpr_tr}).to_csv(fold_dir / \"roc_curve_train.csv\", index=False)\n",
    "    pd.DataFrame({\"fpr\": fpr_va, \"tpr\": tpr_va}).to_csv(fold_dir / \"roc_curve_val.csv\", index=False)\n",
    "    pd.DataFrame({\"recall\": rec_tr, \"precision\": prec_tr}).to_csv(fold_dir / \"pr_curve_train.csv\", index=False)\n",
    "    pd.DataFrame({\"recall\": rec_va, \"precision\": prec_va}).to_csv(fold_dir / \"pr_curve_val.csv\", index=False)\n",
    "    pd.DataFrame(cm_tr_fixed, index=[\"Actual_0\",\"Actual_1\"], columns=[\"Pred_0\",\"Pred_1\"]).to_csv(fold_dir / \"cm_train_fixed.csv\")\n",
    "    pd.DataFrame(cm_va_fixed, index=[\"Actual_0\",\"Actual_1\"], columns=[\"Pred_0\",\"Pred_1\"]).to_csv(fold_dir / \"cm_val_fixed.csv\")\n",
    "    pd.DataFrame(cm_tr_best,  index=[\"Actual_0\",\"Actual_1\"], columns=[\"Pred_0\",\"Pred_1\"]).to_csv(fold_dir / \"cm_train_best.csv\")\n",
    "    pd.DataFrame(cm_va_best,  index=[\"Actual_0\",\"Actual_1\"], columns=[\"Pred_0\",\"Pred_1\"]).to_csv(fold_dir / \"cm_val_best.csv\")\n",
    "\n",
    "    # 存圖\n",
    "    plot_cm(cm_tr_fixed, fold_dir / \"plot_cm_train_fixed.png\", f\"Confusion Matrix (Train, th={th_fixed:.2f})\")\n",
    "    plot_cm(cm_va_fixed, fold_dir / \"plot_cm_val_fixed.png\",   f\"Confusion Matrix (Val, th={th_fixed:.2f})\")\n",
    "    plot_cm(cm_tr_best,  fold_dir / \"plot_cm_train_best.png\",  f\"Confusion Matrix (Train, best th={th_best:.2f})\")\n",
    "    plot_cm(cm_va_best,  fold_dir / \"plot_cm_val_best.png\",    f\"Confusion Matrix (Val, best th={th_best:.2f})\")\n",
    "    plot_roc_xy(fpr_tr, tpr_tr, m_tr_fixed[\"auc\"], fold_dir / \"plot_roc_train.png\", \"ROC Curve (Train)\")\n",
    "    plot_roc_xy(fpr_va, tpr_va, m_va_fixed[\"auc\"], fold_dir / \"plot_roc_val.png\",   \"ROC Curve (Validation)\")\n",
    "    plot_pr_xy(rec_tr, prec_tr, ap_tr, fold_dir / \"plot_pr_train.png\", \"PR Curve (Train)\")\n",
    "    plot_pr_xy(rec_va, prec_va, ap_va, fold_dir / \"plot_pr_val.png\",   \"PR Curve (Validation)\")\n",
    "\n",
    "    # 每折 metrics（固定 & 最佳閾值）\n",
    "    row = {\n",
    "        \"fold\": k,\n",
    "        \"pos_rate\": pos_rate,\n",
    "        \"focal_gamma\": CFG[\"focal_gamma\"],\n",
    "        \"class_weight_0\": class_weight[0],\n",
    "        \"class_weight_1\": class_weight[1],\n",
    "        # Train @ fixed\n",
    "        \"train_acc\": m_tr_fixed[\"acc\"], \"train_prec\": m_tr_fixed[\"prec\"], \"train_rec\": m_tr_fixed[\"rec\"],\n",
    "        \"train_f1\": m_tr_fixed[\"f1\"], \"train_auc\": m_tr_fixed[\"auc\"], \"train_auprc\": m_tr_fixed[\"auprc\"],\n",
    "        # Val   @ fixed\n",
    "        \"val_acc\": m_va_fixed[\"acc\"], \"val_prec\": m_va_fixed[\"prec\"], \"val_rec\": m_va_fixed[\"rec\"],\n",
    "        \"val_f1\": m_va_fixed[\"f1\"], \"val_auc\": m_va_fixed[\"auc\"], \"val_auprc\": m_va_fixed[\"auprc\"],\n",
    "        # Best-threshold on val\n",
    "        \"best_th_val\": th_best,\n",
    "        \"train_acc_best\": m_tr_best[\"acc\"], \"train_prec_best\": m_tr_best[\"prec\"], \"train_rec_best\": m_tr_best[\"rec\"], \"train_f1_best\": m_tr_best[\"f1\"],\n",
    "        \"val_acc_best\": m_va_best[\"acc\"], \"val_prec_best\": m_va_best[\"prec\"], \"val_rec_best\": m_va_best[\"rec\"], \"val_f1_best\": m_va_best[\"f1\"],\n",
    "    }\n",
    "    pd.DataFrame([row]).to_csv(fold_dir / \"metrics.csv\", index=False)\n",
    "\n",
    "    # 補充：保存每折 meta\n",
    "    fold_meta = {\n",
    "        \"pos_rate\": pos_rate,\n",
    "        \"n_train\": int(len(ytr)), \"n_val\": int(len(yva)),\n",
    "        \"threshold_fixed\": CFG[\"threshold_fixed\"], \"threshold_best_on_val\": th_best,\n",
    "        \"class_weight\": class_weight\n",
    "    }\n",
    "    (fold_dir / \"fold_meta.json\").write_text(json.dumps(fold_meta, ensure_ascii=False, indent=2), encoding=\"utf-8\")\n",
    "\n",
    "    all_rows.append(row)\n",
    "\n",
    "# 彙總\n",
    "summary = pd.DataFrame(all_rows).sort_values(\"fold\")\n",
    "summary.to_csv(RUN_DIR / \"summary_folds.csv\", index=False)\n",
    "\n",
    "agg_means = summary.drop(columns=[\"fold\"]).mean(numeric_only=True).to_dict()\n",
    "agg_stds  = summary.drop(columns=[\"fold\"]).std(ddof=1, numeric_only=True).add_suffix(\"_std\").to_dict()\n",
    "overall = {\"n_folds\": int(len(summary)), **{k: float(v) for k,v in agg_means.items()}, **{k: float(v) for k,v in agg_stds.items()}, \"config\": CFG}\n",
    "(RUN_DIR / \"summary_overall.json\").write_text(json.dumps(overall, ensure_ascii=False, indent=2), encoding=\"utf-8\")\n",
    "\n",
    "print(\"\\n=== Training Done ===\")\n",
    "print(f\"Run dir: {RUN_DIR}\")\n",
    "print(json.dumps(overall, indent=2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dbc071a3-8e61-4180-9a3a-4c03bd4e6ea6",
   "metadata": {},
   "outputs": [],
   "source": [
    "保持資料不動，改「模型內部對齊」，而且未來換 GRU 也要零摩擦。\n",
    "最佳解是——在輸出端統一把 (None, 1) 壓成 (None,)\n",
    "做法 A（最簡單）：輸出層 squeeze\n",
    "\n",
    "在 Dense(1, sigmoid) 後加一個 Lambda(tf.squeeze, axis=-1)。\n",
    "這招不動資料，只改 LSTM/GRU 的最後兩行，之後換 GRU 也照抄即可"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 283,
   "id": "ed9ded16-2adc-4d54-8571-fd2293ec5f53",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/40\n",
      "[F1Epoch] epoch=0 f1_tr_best=0.4182 f1_va_best=0.4191 | val_best_th=0.14\n",
      "[Probe] epoch=0 train mean=0.1843 p90=0.2173 | val mean=0.1844 p90=0.2181\n",
      "197/197 - 44s - 225ms/step - accuracy: 0.7356 - auc: 0.5179 - auprc: 0.2779 - binary_crossentropy: 0.5998 - loss: 0.0400 - precision: 0.0000e+00 - recall: 0.0000e+00 - val_accuracy: 0.7357 - val_auc: 0.5226 - val_auprc: 0.2836 - val_binary_crossentropy: 0.5965 - val_loss: 0.0391 - val_precision: 0.0000e+00 - val_recall: 0.0000e+00 - f1_best: 0.4182 - val_f1_best: 0.4191 - val_best_th: 0.1400 - learning_rate: 3.0000e-04\n",
      "Epoch 2/40\n",
      "[F1Epoch] epoch=1 f1_tr_best=0.4183 f1_va_best=0.4184 | val_best_th=0.13\n",
      "[Probe] epoch=1 train mean=0.1846 p90=0.2149 | val mean=0.1847 p90=0.2157\n",
      "197/197 - 40s - 204ms/step - accuracy: 0.7356 - auc: 0.5216 - auprc: 0.2806 - binary_crossentropy: 0.6013 - loss: 0.0396 - precision: 0.0000e+00 - recall: 0.0000e+00 - val_accuracy: 0.7357 - val_auc: 0.5304 - val_auprc: 0.2898 - val_binary_crossentropy: 0.5959 - val_loss: 0.0390 - val_precision: 0.0000e+00 - val_recall: 0.0000e+00 - f1_best: 0.4183 - val_f1_best: 0.4184 - val_best_th: 0.1300 - learning_rate: 3.0000e-04\n",
      "Epoch 3/40\n",
      "[F1Epoch] epoch=2 f1_tr_best=0.4183 f1_va_best=0.4182 | val_best_th=0.12\n",
      "[Probe] epoch=2 train mean=0.1837 p90=0.2132 | val mean=0.1838 p90=0.2139\n",
      "197/197 - 41s - 207ms/step - accuracy: 0.7356 - auc: 0.5253 - auprc: 0.2803 - binary_crossentropy: 0.6011 - loss: 0.0396 - precision: 0.0000e+00 - recall: 0.0000e+00 - val_accuracy: 0.7357 - val_auc: 0.5264 - val_auprc: 0.2870 - val_binary_crossentropy: 0.5962 - val_loss: 0.0390 - val_precision: 0.0000e+00 - val_recall: 0.0000e+00 - f1_best: 0.4183 - val_f1_best: 0.4182 - val_best_th: 0.1200 - learning_rate: 3.0000e-04\n",
      "Epoch 4/40\n",
      "[F1Epoch] epoch=3 f1_tr_best=0.4185 f1_va_best=0.4184 | val_best_th=0.14\n",
      "[Probe] epoch=3 train mean=0.1845 p90=0.2148 | val mean=0.1845 p90=0.2146\n",
      "197/197 - 40s - 204ms/step - accuracy: 0.7356 - auc: 0.5227 - auprc: 0.2813 - binary_crossentropy: 0.6013 - loss: 0.0396 - precision: 0.0000e+00 - recall: 0.0000e+00 - val_accuracy: 0.7357 - val_auc: 0.5239 - val_auprc: 0.2857 - val_binary_crossentropy: 0.5962 - val_loss: 0.0391 - val_precision: 0.0000e+00 - val_recall: 0.0000e+00 - f1_best: 0.4185 - val_f1_best: 0.4184 - val_best_th: 0.1400 - learning_rate: 3.0000e-04\n",
      "Epoch 5/40\n",
      "[F1Epoch] epoch=4 f1_tr_best=0.4184 f1_va_best=0.4183 | val_best_th=0.13\n",
      "[Probe] epoch=4 train mean=0.1829 p90=0.2091 | val mean=0.1831 p90=0.2095\n",
      "\n",
      "Epoch 5: ReduceLROnPlateau reducing learning rate to 0.0001500000071246177.\n",
      "197/197 - 40s - 203ms/step - accuracy: 0.7356 - auc: 0.5237 - auprc: 0.2809 - binary_crossentropy: 0.6014 - loss: 0.0396 - precision: 0.0000e+00 - recall: 0.0000e+00 - val_accuracy: 0.7357 - val_auc: 0.5323 - val_auprc: 0.2888 - val_binary_crossentropy: 0.5963 - val_loss: 0.0389 - val_precision: 0.0000e+00 - val_recall: 0.0000e+00 - f1_best: 0.4184 - val_f1_best: 0.4183 - val_best_th: 0.1300 - learning_rate: 3.0000e-04\n",
      "Epoch 6/40\n",
      "[F1Epoch] epoch=5 f1_tr_best=0.4196 f1_va_best=0.4182 | val_best_th=0.11\n",
      "[Probe] epoch=5 train mean=0.1811 p90=0.2039 | val mean=0.1815 p90=0.2047\n",
      "197/197 - 41s - 208ms/step - accuracy: 0.7356 - auc: 0.5352 - auprc: 0.2888 - binary_crossentropy: 0.5998 - loss: 0.0394 - precision: 0.0000e+00 - recall: 0.0000e+00 - val_accuracy: 0.7357 - val_auc: 0.5375 - val_auprc: 0.2952 - val_binary_crossentropy: 0.5965 - val_loss: 0.0388 - val_precision: 0.0000e+00 - val_recall: 0.0000e+00 - f1_best: 0.4196 - val_f1_best: 0.4182 - val_best_th: 0.1100 - learning_rate: 1.5000e-04\n",
      "Epoch 7/40\n",
      "[F1Epoch] epoch=6 f1_tr_best=0.4189 f1_va_best=0.4187 | val_best_th=0.16\n",
      "[Probe] epoch=6 train mean=0.1807 p90=0.1986 | val mean=0.1811 p90=0.1989\n",
      "197/197 - 40s - 204ms/step - accuracy: 0.7356 - auc: 0.5303 - auprc: 0.2887 - binary_crossentropy: 0.6005 - loss: 0.0394 - precision: 0.0000e+00 - recall: 0.0000e+00 - val_accuracy: 0.7357 - val_auc: 0.5415 - val_auprc: 0.2979 - val_binary_crossentropy: 0.5966 - val_loss: 0.0388 - val_precision: 0.0000e+00 - val_recall: 0.0000e+00 - f1_best: 0.4189 - val_f1_best: 0.4187 - val_best_th: 0.1600 - learning_rate: 1.5000e-04\n",
      "Epoch 1/40\n",
      "[F1Epoch] epoch=0 f1_tr_best=0.4182 f1_va_best=0.4180 | val_best_th=0.01\n",
      "[Probe] epoch=0 train mean=0.1752 p90=0.1954 | val mean=0.1753 p90=0.1954\n",
      "197/197 - 44s - 224ms/step - accuracy: 0.7356 - auc: 0.5149 - auprc: 0.2722 - binary_crossentropy: 0.5992 - loss: 0.0398 - precision: 0.0000e+00 - recall: 0.0000e+00 - val_accuracy: 0.7357 - val_auc: 0.5185 - val_auprc: 0.2758 - val_binary_crossentropy: 0.6022 - val_loss: 0.0393 - val_precision: 0.0000e+00 - val_recall: 0.0000e+00 - f1_best: 0.4182 - val_f1_best: 0.4180 - val_best_th: 0.0100 - learning_rate: 3.0000e-04\n",
      "Epoch 2/40\n",
      "[F1Epoch] epoch=1 f1_tr_best=0.4183 f1_va_best=0.4180 | val_best_th=0.01\n",
      "[Probe] epoch=1 train mean=0.1751 p90=0.2044 | val mean=0.1753 p90=0.2042\n",
      "197/197 - 41s - 206ms/step - accuracy: 0.7356 - auc: 0.5284 - auprc: 0.2811 - binary_crossentropy: 0.6005 - loss: 0.0393 - precision: 0.0000e+00 - recall: 0.0000e+00 - val_accuracy: 0.7357 - val_auc: 0.5079 - val_auprc: 0.2707 - val_binary_crossentropy: 0.6032 - val_loss: 0.0395 - val_precision: 0.0000e+00 - val_recall: 0.0000e+00 - f1_best: 0.4183 - val_f1_best: 0.4180 - val_best_th: 0.0100 - learning_rate: 3.0000e-04\n",
      "Epoch 3/40\n",
      "[F1Epoch] epoch=2 f1_tr_best=0.4182 f1_va_best=0.4180 | val_best_th=0.01\n",
      "[Probe] epoch=2 train mean=0.1750 p90=0.2062 | val mean=0.1751 p90=0.2063\n",
      "197/197 - 41s - 207ms/step - accuracy: 0.7356 - auc: 0.5290 - auprc: 0.2863 - binary_crossentropy: 0.6000 - loss: 0.0393 - precision: 0.0000e+00 - recall: 0.0000e+00 - val_accuracy: 0.7357 - val_auc: 0.5060 - val_auprc: 0.2707 - val_binary_crossentropy: 0.6038 - val_loss: 0.0395 - val_precision: 0.0000e+00 - val_recall: 0.0000e+00 - f1_best: 0.4182 - val_f1_best: 0.4180 - val_best_th: 0.0100 - learning_rate: 3.0000e-04\n",
      "Epoch 4/40\n",
      "[F1Epoch] epoch=3 f1_tr_best=0.4186 f1_va_best=0.4180 | val_best_th=0.01\n",
      "[Probe] epoch=3 train mean=0.1757 p90=0.2078 | val mean=0.1758 p90=0.2078\n",
      "197/197 - 41s - 208ms/step - accuracy: 0.7356 - auc: 0.5276 - auprc: 0.2844 - binary_crossentropy: 0.6004 - loss: 0.0393 - precision: 0.0000e+00 - recall: 0.0000e+00 - val_accuracy: 0.7357 - val_auc: 0.5139 - val_auprc: 0.2763 - val_binary_crossentropy: 0.6025 - val_loss: 0.0394 - val_precision: 0.0000e+00 - val_recall: 0.0000e+00 - f1_best: 0.4186 - val_f1_best: 0.4180 - val_best_th: 0.0100 - learning_rate: 3.0000e-04\n",
      "Epoch 5/40\n",
      "[F1Epoch] epoch=4 f1_tr_best=0.4185 f1_va_best=0.4180 | val_best_th=0.01\n",
      "[Probe] epoch=4 train mean=0.1759 p90=0.2084 | val mean=0.1762 p90=0.2084\n",
      "197/197 - 41s - 207ms/step - accuracy: 0.7356 - auc: 0.5340 - auprc: 0.2884 - binary_crossentropy: 0.5999 - loss: 0.0392 - precision: 0.0000e+00 - recall: 0.0000e+00 - val_accuracy: 0.7357 - val_auc: 0.5118 - val_auprc: 0.2736 - val_binary_crossentropy: 0.6027 - val_loss: 0.0395 - val_precision: 0.0000e+00 - val_recall: 0.0000e+00 - f1_best: 0.4185 - val_f1_best: 0.4180 - val_best_th: 0.0100 - learning_rate: 3.0000e-04\n",
      "Epoch 6/40\n",
      "[F1Epoch] epoch=5 f1_tr_best=0.4187 f1_va_best=0.4180 | val_best_th=0.01\n",
      "[Probe] epoch=5 train mean=0.1762 p90=0.2066 | val mean=0.1765 p90=0.2069\n",
      "197/197 - 41s - 210ms/step - accuracy: 0.7356 - auc: 0.5307 - auprc: 0.2855 - binary_crossentropy: 0.6003 - loss: 0.0392 - precision: 0.0000e+00 - recall: 0.0000e+00 - val_accuracy: 0.7357 - val_auc: 0.5146 - val_auprc: 0.2741 - val_binary_crossentropy: 0.6023 - val_loss: 0.0394 - val_precision: 0.0000e+00 - val_recall: 0.0000e+00 - f1_best: 0.4187 - val_f1_best: 0.4180 - val_best_th: 0.0100 - learning_rate: 3.0000e-04\n",
      "Epoch 7/40\n",
      "[F1Epoch] epoch=6 f1_tr_best=0.4186 f1_va_best=0.4180 | val_best_th=0.01\n",
      "[Probe] epoch=6 train mean=0.1770 p90=0.2114 | val mean=0.1773 p90=0.2119\n",
      "\n",
      "Epoch 7: ReduceLROnPlateau reducing learning rate to 0.0001500000071246177.\n",
      "197/197 - 41s - 209ms/step - accuracy: 0.7356 - auc: 0.5366 - auprc: 0.2879 - binary_crossentropy: 0.5997 - loss: 0.0392 - precision: 0.0000e+00 - recall: 0.0000e+00 - val_accuracy: 0.7357 - val_auc: 0.5081 - val_auprc: 0.2728 - val_binary_crossentropy: 0.6022 - val_loss: 0.0395 - val_precision: 0.0000e+00 - val_recall: 0.0000e+00 - f1_best: 0.4186 - val_f1_best: 0.4180 - val_best_th: 0.0100 - learning_rate: 3.0000e-04\n",
      "Epoch 1/40\n",
      "[F1Epoch] epoch=0 f1_tr_best=0.4182 f1_va_best=0.4183 | val_best_th=0.14\n",
      "[Probe] epoch=0 train mean=0.1842 p90=0.2026 | val mean=0.1843 p90=0.2026\n",
      "197/197 - 44s - 225ms/step - accuracy: 0.7356 - auc: 0.5184 - auprc: 0.2767 - binary_crossentropy: 0.6008 - loss: 0.0399 - precision: 0.0000e+00 - recall: 0.0000e+00 - val_accuracy: 0.7357 - val_auc: 0.5090 - val_auprc: 0.2688 - val_binary_crossentropy: 0.5984 - val_loss: 0.0392 - val_precision: 0.0000e+00 - val_recall: 0.0000e+00 - f1_best: 0.4182 - val_f1_best: 0.4183 - val_best_th: 0.1400 - learning_rate: 3.0000e-04\n",
      "Epoch 2/40\n",
      "[F1Epoch] epoch=1 f1_tr_best=0.4183 f1_va_best=0.4186 | val_best_th=0.16\n",
      "[Probe] epoch=1 train mean=0.1814 p90=0.1991 | val mean=0.1817 p90=0.1993\n",
      "197/197 - 41s - 207ms/step - accuracy: 0.7356 - auc: 0.5250 - auprc: 0.2815 - binary_crossentropy: 0.6022 - loss: 0.0396 - precision: 0.0000e+00 - recall: 0.0000e+00 - val_accuracy: 0.7357 - val_auc: 0.5151 - val_auprc: 0.2726 - val_binary_crossentropy: 0.5995 - val_loss: 0.0391 - val_precision: 0.0000e+00 - val_recall: 0.0000e+00 - f1_best: 0.4183 - val_f1_best: 0.4186 - val_best_th: 0.1600 - learning_rate: 3.0000e-04\n",
      "Epoch 3/40\n",
      "[F1Epoch] epoch=2 f1_tr_best=0.4185 f1_va_best=0.4183 | val_best_th=0.13\n",
      "[Probe] epoch=2 train mean=0.1801 p90=0.1978 | val mean=0.1803 p90=0.1981\n",
      "197/197 - 41s - 210ms/step - accuracy: 0.7356 - auc: 0.5243 - auprc: 0.2799 - binary_crossentropy: 0.6018 - loss: 0.0395 - precision: 0.0000e+00 - recall: 0.0000e+00 - val_accuracy: 0.7357 - val_auc: 0.5081 - val_auprc: 0.2675 - val_binary_crossentropy: 0.6008 - val_loss: 0.0392 - val_precision: 0.0000e+00 - val_recall: 0.0000e+00 - f1_best: 0.4185 - val_f1_best: 0.4183 - val_best_th: 0.1300 - learning_rate: 3.0000e-04\n",
      "Epoch 4/40\n",
      "[F1Epoch] epoch=3 f1_tr_best=0.4184 f1_va_best=0.4182 | val_best_th=0.14\n",
      "[Probe] epoch=3 train mean=0.1787 p90=0.1959 | val mean=0.1788 p90=0.1963\n",
      "197/197 - 41s - 209ms/step - accuracy: 0.7356 - auc: 0.5259 - auprc: 0.2830 - binary_crossentropy: 0.6015 - loss: 0.0395 - precision: 0.5000 - recall: 1.5072e-04 - val_accuracy: 0.7357 - val_auc: 0.5108 - val_auprc: 0.2687 - val_binary_crossentropy: 0.6016 - val_loss: 0.0392 - val_precision: 0.0000e+00 - val_recall: 0.0000e+00 - f1_best: 0.4184 - val_f1_best: 0.4182 - val_best_th: 0.1400 - learning_rate: 3.0000e-04\n",
      "Epoch 5/40\n",
      "[F1Epoch] epoch=4 f1_tr_best=0.4190 f1_va_best=0.4184 | val_best_th=0.14\n",
      "[Probe] epoch=4 train mean=0.1782 p90=0.1932 | val mean=0.1783 p90=0.1935\n",
      "\n",
      "Epoch 5: ReduceLROnPlateau reducing learning rate to 0.0001500000071246177.\n",
      "197/197 - 41s - 207ms/step - accuracy: 0.7357 - auc: 0.5301 - auprc: 0.2855 - binary_crossentropy: 0.6017 - loss: 0.0394 - precision: 1.0000 - recall: 1.5072e-04 - val_accuracy: 0.7357 - val_auc: 0.5110 - val_auprc: 0.2690 - val_binary_crossentropy: 0.6021 - val_loss: 0.0392 - val_precision: 0.0000e+00 - val_recall: 0.0000e+00 - f1_best: 0.4190 - val_f1_best: 0.4184 - val_best_th: 0.1400 - learning_rate: 3.0000e-04\n",
      "Epoch 6/40\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[283], line 324\u001b[0m\n\u001b[1;32m    312\u001b[0m cbs \u001b[38;5;241m=\u001b[39m [\n\u001b[1;32m    313\u001b[0m     F1PerEpoch(Xtr_p, ytr, Xva_p, yva, bs\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1024\u001b[39m),\n\u001b[1;32m    314\u001b[0m     ProbProbe(Xtr_p, Xva_p, bs\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1024\u001b[39m),\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    320\u001b[0m                                       min_lr\u001b[38;5;241m=\u001b[39mCFG[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mreduce_min_lr\u001b[39m\u001b[38;5;124m\"\u001b[39m], verbose\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m)\n\u001b[1;32m    321\u001b[0m ]\n\u001b[1;32m    323\u001b[0m \u001b[38;5;66;03m# 訓練\u001b[39;00m\n\u001b[0;32m--> 324\u001b[0m hist \u001b[38;5;241m=\u001b[39m \u001b[43mmodel\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfit\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    325\u001b[0m \u001b[43m    \u001b[49m\u001b[43mXtr_p\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mytr\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    326\u001b[0m \u001b[43m    \u001b[49m\u001b[43mvalidation_data\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mXva_p\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43myva\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    327\u001b[0m \u001b[43m    \u001b[49m\u001b[43mepochs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mCFG\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mepochs\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    328\u001b[0m \u001b[43m    \u001b[49m\u001b[43mbatch_size\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mCFG\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mbatch_size\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    329\u001b[0m \u001b[43m    \u001b[49m\u001b[43mcallbacks\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcbs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    330\u001b[0m \u001b[43m    \u001b[49m\u001b[43mverbose\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m2\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m    331\u001b[0m \u001b[43m    \u001b[49m\u001b[43mclass_weight\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mclass_weight\u001b[49m\n\u001b[1;32m    332\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    334\u001b[0m \u001b[38;5;66;03m# 保存 history 與學習曲線\u001b[39;00m\n\u001b[1;32m    335\u001b[0m hist_df \u001b[38;5;241m=\u001b[39m pd\u001b[38;5;241m.\u001b[39mDataFrame(hist\u001b[38;5;241m.\u001b[39mhistory)\n",
      "File \u001b[0;32m/opt/conda/lib/python3.11/site-packages/keras/src/utils/traceback_utils.py:117\u001b[0m, in \u001b[0;36mfilter_traceback.<locals>.error_handler\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    115\u001b[0m filtered_tb \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m    116\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 117\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfn\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    118\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[1;32m    119\u001b[0m     filtered_tb \u001b[38;5;241m=\u001b[39m _process_traceback_frames(e\u001b[38;5;241m.\u001b[39m__traceback__)\n",
      "File \u001b[0;32m/opt/conda/lib/python3.11/site-packages/keras/src/backend/tensorflow/trainer.py:318\u001b[0m, in \u001b[0;36mTensorFlowTrainer.fit\u001b[0;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_batch_size, validation_freq)\u001b[0m\n\u001b[1;32m    316\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m step, iterator \u001b[38;5;129;01min\u001b[39;00m epoch_iterator\u001b[38;5;241m.\u001b[39menumerate_epoch():\n\u001b[1;32m    317\u001b[0m     callbacks\u001b[38;5;241m.\u001b[39mon_train_batch_begin(step)\n\u001b[0;32m--> 318\u001b[0m     logs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtrain_function\u001b[49m\u001b[43m(\u001b[49m\u001b[43miterator\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    319\u001b[0m     logs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_pythonify_logs(logs)\n\u001b[1;32m    320\u001b[0m     callbacks\u001b[38;5;241m.\u001b[39mon_train_batch_end(step, logs)\n",
      "File \u001b[0;32m/opt/conda/lib/python3.11/site-packages/tensorflow/python/util/traceback_utils.py:150\u001b[0m, in \u001b[0;36mfilter_traceback.<locals>.error_handler\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    148\u001b[0m filtered_tb \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m    149\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 150\u001b[0m   \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfn\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    151\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[1;32m    152\u001b[0m   filtered_tb \u001b[38;5;241m=\u001b[39m _process_traceback_frames(e\u001b[38;5;241m.\u001b[39m__traceback__)\n",
      "File \u001b[0;32m/opt/conda/lib/python3.11/site-packages/tensorflow/python/eager/polymorphic_function/polymorphic_function.py:833\u001b[0m, in \u001b[0;36mFunction.__call__\u001b[0;34m(self, *args, **kwds)\u001b[0m\n\u001b[1;32m    830\u001b[0m compiler \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mxla\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_jit_compile \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mnonXla\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    832\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m OptionalXlaContext(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_jit_compile):\n\u001b[0;32m--> 833\u001b[0m   result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwds\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    835\u001b[0m new_tracing_count \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mexperimental_get_tracing_count()\n\u001b[1;32m    836\u001b[0m without_tracing \u001b[38;5;241m=\u001b[39m (tracing_count \u001b[38;5;241m==\u001b[39m new_tracing_count)\n",
      "File \u001b[0;32m/opt/conda/lib/python3.11/site-packages/tensorflow/python/eager/polymorphic_function/polymorphic_function.py:878\u001b[0m, in \u001b[0;36mFunction._call\u001b[0;34m(self, *args, **kwds)\u001b[0m\n\u001b[1;32m    875\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_lock\u001b[38;5;241m.\u001b[39mrelease()\n\u001b[1;32m    876\u001b[0m \u001b[38;5;66;03m# In this case we have not created variables on the first call. So we can\u001b[39;00m\n\u001b[1;32m    877\u001b[0m \u001b[38;5;66;03m# run the first trace but we should fail if variables are created.\u001b[39;00m\n\u001b[0;32m--> 878\u001b[0m results \u001b[38;5;241m=\u001b[39m \u001b[43mtracing_compilation\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcall_function\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    879\u001b[0m \u001b[43m    \u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkwds\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_variable_creation_config\u001b[49m\n\u001b[1;32m    880\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    881\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_created_variables:\n\u001b[1;32m    882\u001b[0m   \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCreating variables on a non-first call to a function\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    883\u001b[0m                    \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m decorated with tf.function.\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
      "File \u001b[0;32m/opt/conda/lib/python3.11/site-packages/tensorflow/python/eager/polymorphic_function/tracing_compilation.py:139\u001b[0m, in \u001b[0;36mcall_function\u001b[0;34m(args, kwargs, tracing_options)\u001b[0m\n\u001b[1;32m    137\u001b[0m bound_args \u001b[38;5;241m=\u001b[39m function\u001b[38;5;241m.\u001b[39mfunction_type\u001b[38;5;241m.\u001b[39mbind(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[1;32m    138\u001b[0m flat_inputs \u001b[38;5;241m=\u001b[39m function\u001b[38;5;241m.\u001b[39mfunction_type\u001b[38;5;241m.\u001b[39munpack_inputs(bound_args)\n\u001b[0;32m--> 139\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunction\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_flat\u001b[49m\u001b[43m(\u001b[49m\u001b[43m  \u001b[49m\u001b[38;5;66;43;03m# pylint: disable=protected-access\u001b[39;49;00m\n\u001b[1;32m    140\u001b[0m \u001b[43m    \u001b[49m\u001b[43mflat_inputs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcaptured_inputs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfunction\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcaptured_inputs\u001b[49m\n\u001b[1;32m    141\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m/opt/conda/lib/python3.11/site-packages/tensorflow/python/eager/polymorphic_function/concrete_function.py:1322\u001b[0m, in \u001b[0;36mConcreteFunction._call_flat\u001b[0;34m(self, tensor_inputs, captured_inputs)\u001b[0m\n\u001b[1;32m   1318\u001b[0m possible_gradient_type \u001b[38;5;241m=\u001b[39m gradients_util\u001b[38;5;241m.\u001b[39mPossibleTapeGradientTypes(args)\n\u001b[1;32m   1319\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m (possible_gradient_type \u001b[38;5;241m==\u001b[39m gradients_util\u001b[38;5;241m.\u001b[39mPOSSIBLE_GRADIENT_TYPES_NONE\n\u001b[1;32m   1320\u001b[0m     \u001b[38;5;129;01mand\u001b[39;00m executing_eagerly):\n\u001b[1;32m   1321\u001b[0m   \u001b[38;5;66;03m# No tape is watching; skip to running the function.\u001b[39;00m\n\u001b[0;32m-> 1322\u001b[0m   \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_inference_function\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcall_preflattened\u001b[49m\u001b[43m(\u001b[49m\u001b[43margs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1323\u001b[0m forward_backward \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_select_forward_and_backward_functions(\n\u001b[1;32m   1324\u001b[0m     args,\n\u001b[1;32m   1325\u001b[0m     possible_gradient_type,\n\u001b[1;32m   1326\u001b[0m     executing_eagerly)\n\u001b[1;32m   1327\u001b[0m forward_function, args_with_tangents \u001b[38;5;241m=\u001b[39m forward_backward\u001b[38;5;241m.\u001b[39mforward()\n",
      "File \u001b[0;32m/opt/conda/lib/python3.11/site-packages/tensorflow/python/eager/polymorphic_function/atomic_function.py:216\u001b[0m, in \u001b[0;36mAtomicFunction.call_preflattened\u001b[0;34m(self, args)\u001b[0m\n\u001b[1;32m    214\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mcall_preflattened\u001b[39m(\u001b[38;5;28mself\u001b[39m, args: Sequence[core\u001b[38;5;241m.\u001b[39mTensor]) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Any:\n\u001b[1;32m    215\u001b[0m \u001b[38;5;250m  \u001b[39m\u001b[38;5;124;03m\"\"\"Calls with flattened tensor inputs and returns the structured output.\"\"\"\u001b[39;00m\n\u001b[0;32m--> 216\u001b[0m   flat_outputs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcall_flat\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    217\u001b[0m   \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfunction_type\u001b[38;5;241m.\u001b[39mpack_output(flat_outputs)\n",
      "File \u001b[0;32m/opt/conda/lib/python3.11/site-packages/tensorflow/python/eager/polymorphic_function/atomic_function.py:251\u001b[0m, in \u001b[0;36mAtomicFunction.call_flat\u001b[0;34m(self, *args)\u001b[0m\n\u001b[1;32m    249\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m record\u001b[38;5;241m.\u001b[39mstop_recording():\n\u001b[1;32m    250\u001b[0m   \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_bound_context\u001b[38;5;241m.\u001b[39mexecuting_eagerly():\n\u001b[0;32m--> 251\u001b[0m     outputs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_bound_context\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcall_function\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    252\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mname\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    253\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;28;43mlist\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43margs\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    254\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;28;43mlen\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfunction_type\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mflat_outputs\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    255\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    256\u001b[0m   \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    257\u001b[0m     outputs \u001b[38;5;241m=\u001b[39m make_call_op_in_graph(\n\u001b[1;32m    258\u001b[0m         \u001b[38;5;28mself\u001b[39m,\n\u001b[1;32m    259\u001b[0m         \u001b[38;5;28mlist\u001b[39m(args),\n\u001b[1;32m    260\u001b[0m         \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_bound_context\u001b[38;5;241m.\u001b[39mfunction_call_options\u001b[38;5;241m.\u001b[39mas_attrs(),\n\u001b[1;32m    261\u001b[0m     )\n",
      "File \u001b[0;32m/opt/conda/lib/python3.11/site-packages/tensorflow/python/eager/context.py:1552\u001b[0m, in \u001b[0;36mContext.call_function\u001b[0;34m(self, name, tensor_inputs, num_outputs)\u001b[0m\n\u001b[1;32m   1550\u001b[0m cancellation_context \u001b[38;5;241m=\u001b[39m cancellation\u001b[38;5;241m.\u001b[39mcontext()\n\u001b[1;32m   1551\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m cancellation_context \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m-> 1552\u001b[0m   outputs \u001b[38;5;241m=\u001b[39m \u001b[43mexecute\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mexecute\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   1553\u001b[0m \u001b[43m      \u001b[49m\u001b[43mname\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdecode\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mutf-8\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1554\u001b[0m \u001b[43m      \u001b[49m\u001b[43mnum_outputs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mnum_outputs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1555\u001b[0m \u001b[43m      \u001b[49m\u001b[43minputs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtensor_inputs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1556\u001b[0m \u001b[43m      \u001b[49m\u001b[43mattrs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mattrs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1557\u001b[0m \u001b[43m      \u001b[49m\u001b[43mctx\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1558\u001b[0m \u001b[43m  \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1559\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m   1560\u001b[0m   outputs \u001b[38;5;241m=\u001b[39m execute\u001b[38;5;241m.\u001b[39mexecute_with_cancellation(\n\u001b[1;32m   1561\u001b[0m       name\u001b[38;5;241m.\u001b[39mdecode(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mutf-8\u001b[39m\u001b[38;5;124m\"\u001b[39m),\n\u001b[1;32m   1562\u001b[0m       num_outputs\u001b[38;5;241m=\u001b[39mnum_outputs,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m   1566\u001b[0m       cancellation_manager\u001b[38;5;241m=\u001b[39mcancellation_context,\n\u001b[1;32m   1567\u001b[0m   )\n",
      "File \u001b[0;32m/opt/conda/lib/python3.11/site-packages/tensorflow/python/eager/execute.py:53\u001b[0m, in \u001b[0;36mquick_execute\u001b[0;34m(op_name, num_outputs, inputs, attrs, ctx, name)\u001b[0m\n\u001b[1;32m     51\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m     52\u001b[0m   ctx\u001b[38;5;241m.\u001b[39mensure_initialized()\n\u001b[0;32m---> 53\u001b[0m   tensors \u001b[38;5;241m=\u001b[39m \u001b[43mpywrap_tfe\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mTFE_Py_Execute\u001b[49m\u001b[43m(\u001b[49m\u001b[43mctx\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_handle\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdevice_name\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mop_name\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m     54\u001b[0m \u001b[43m                                      \u001b[49m\u001b[43minputs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mattrs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnum_outputs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     55\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m core\u001b[38;5;241m.\u001b[39m_NotOkStatusException \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[1;32m     56\u001b[0m   \u001b[38;5;28;01mif\u001b[39;00m name \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "\"\"\" train_lstm_focal_classweight_cv.py\n",
    "\n",
    "需求對應：\n",
    "- 單層 LSTM(192) + Dropout(0.1) + L2(5e-7)；不使用 Masking / BiLSTM / LayerNorm\n",
    "- Focal Loss + Class Weight；alpha 依每折正類率動態設定\n",
    "- Sigmoid 輸出層 bias = log(p/(1-p))（用訓練集正類率）\n",
    "- 10 折交叉驗證；每折 fit-transform（Imputer+Scaler）避免洩漏\n",
    "- Optimizer: Adam(lr=3e-4, clipnorm=1.0)\n",
    "- EarlyStopping: 監控 val_auprc(pat=8) + val_f1_best(pat=6)\n",
    "- ReduceLROnPlateau: monitor=val_auprc, factor=0.5, min_lr=1e-5\n",
    "- 紀錄/圖表：Loss(Focal vs BCE)、Accuracy、F1 per epoch、ROC/PR(train/val)、CM(固定/最佳閾值)\n",
    "- 每折輸出與總結輸出\n",
    "\n",
    "執行：\n",
    "  python train_lstm_focal_classweight_cv.py\n",
    "\"\"\"\n",
    "\n",
    "import os\n",
    "import json\n",
    "import time\n",
    "from pathlib import Path\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from sklearn.impute import SimpleImputer\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.metrics import (\n",
    "    roc_curve, auc, precision_recall_curve, average_precision_score,\n",
    "    confusion_matrix, precision_recall_fscore_support, accuracy_score, roc_auc_score\n",
    ")\n",
    "\n",
    "import tensorflow as tf\n",
    "from tensorflow import keras\n",
    "from tensorflow.keras import layers\n",
    "\n",
    "# -------------------------\n",
    "# 基本路徑與設定\n",
    "# -------------------------\n",
    "BASE = Path(\"/home/jovyan/RT08/0925/sliding_win/1014_sim\")\n",
    "DATA = BASE / \"windowed_clean\"\n",
    "RUNS_ROOT = BASE / \"training_runs\"\n",
    "RUNS_ROOT.mkdir(parents=True, exist_ok=True)\n",
    "\n",
    "CFG = {\n",
    "    \"seed\": 42,\n",
    "    \"folds\": list(range(1, 11)),\n",
    "    \"epochs\": 40,\n",
    "    \"batch_size\": 128,\n",
    "    \"learning_rate\": 3e-4,\n",
    "    \"clipnorm\": 1.0,\n",
    "    \"l2\": 5e-7,\n",
    "    \"hidden_units\": 192,\n",
    "    \"dropout\": 0.1,\n",
    "    \"recurrent_dropout\": 0.0,\n",
    "    \"patience_auprc\": 8,\n",
    "    \"patience_f1best\": 6,\n",
    "    \"reduce_factor\": 0.5,\n",
    "    # 注意：min_lr 必須 < 初始 lr 才會「往下」生效\n",
    "    \"reduce_min_lr\": 1e-5,\n",
    "    \"threshold_fixed\": 0.5,\n",
    "    \"focal_gamma\": 2.0\n",
    "}\n",
    "\n",
    "np.random.seed(CFG[\"seed\"])\n",
    "tf.random.set_seed(CFG[\"seed\"])\n",
    "\n",
    "# 版本化 run 目錄\n",
    "_ts = time.strftime(\"%Y%m%d_%H%M%S\")\n",
    "existing = sorted(RUNS_ROOT.glob(f\"{_ts}_run*\"))\n",
    "run_id = len(existing) + 1\n",
    "RUN_DIR = RUNS_ROOT / f\"{_ts}_run{run_id:02d}\"\n",
    "RUN_DIR.mkdir(parents=True, exist_ok=True)\n",
    "(RUN_DIR / \"cfg.json\").write_text(json.dumps(CFG, ensure_ascii=False, indent=2), encoding=\"utf-8\")\n",
    "\n",
    "# -------------------------\n",
    "# 視覺元素\n",
    "# -------------------------\n",
    "BLUES = [\"#cfe8ff\", \"#9dd0ff\", \"#6bb8ff\", \"#3aa0ff\", \"#0a88ff\", \"#005bb5\"]\n",
    "\n",
    "def plot_history(hist: pd.DataFrame, out_png: Path):\n",
    "    plt.figure(figsize=(7,5))\n",
    "    x = np.arange(len(hist))\n",
    "    # Focal loss\n",
    "    plt.plot(x, hist[\"loss\"], label=\"Train Focal Loss\", lw=2, color=BLUES[4])\n",
    "    plt.plot(x, hist[\"val_loss\"], label=\"Val Focal Loss\", lw=2, color=BLUES[5])\n",
    "    # BCE (metric)\n",
    "    if \"binary_crossentropy\" in hist.columns:\n",
    "        plt.plot(x, hist[\"binary_crossentropy\"], label=\"Train BCE\", lw=1.8, color=BLUES[2])\n",
    "    if \"val_binary_crossentropy\" in hist.columns:\n",
    "        plt.plot(x, hist[\"val_binary_crossentropy\"], label=\"Val BCE\", lw=1.8, color=BLUES[1])\n",
    "    plt.title(\"Loss Curves (Focal vs BCE)\")\n",
    "    plt.xlabel(\"Epoch\"); plt.ylabel(\"Loss\")\n",
    "    plt.grid(True, alpha=0.25); plt.legend(); plt.tight_layout(); plt.savefig(out_png, dpi=160); plt.close()\n",
    "\n",
    "def plot_metric_epochs(hist: pd.DataFrame, metric: str, out_png: Path, title: str):\n",
    "    plt.figure(figsize=(7,5))\n",
    "    x = np.arange(len(hist))\n",
    "    if metric in hist.columns:\n",
    "        plt.plot(x, hist[metric], label=f\"Train {metric}\", lw=2, color=BLUES[4])\n",
    "    valm = f\"val_{metric}\"\n",
    "    if valm in hist.columns:\n",
    "        plt.plot(x, hist[valm], label=f\"Val {metric}\", lw=2, color=BLUES[5])\n",
    "    plt.title(title); plt.xlabel(\"Epoch\"); plt.ylabel(metric.title())\n",
    "    plt.grid(True, alpha=0.25); plt.legend(); plt.tight_layout(); plt.savefig(out_png, dpi=160); plt.close()\n",
    "\n",
    "def plot_roc_xy(fpr, tpr, roc_auc, out_png: Path, title: str):\n",
    "    plt.figure(figsize=(6,5))\n",
    "    plt.plot(fpr, tpr, lw=2, color=BLUES[4], label=f\"AUC={roc_auc:.4f}\")\n",
    "    plt.plot([0,1],[0,1], lw=1, ls=\"--\", color=BLUES[0])\n",
    "    plt.title(title); plt.xlabel(\"False Positive Rate\"); plt.ylabel(\"True Positive Rate\")\n",
    "    plt.grid(True, alpha=0.25); plt.legend(loc=\"lower right\"); plt.tight_layout(); plt.savefig(out_png, dpi=160); plt.close()\n",
    "\n",
    "def plot_pr_xy(rec, prec, ap, out_png: Path, title: str):\n",
    "    plt.figure(figsize=(6,5))\n",
    "    plt.plot(rec, prec, lw=2, color=BLUES[4], label=f\"AP={ap:.4f}\")\n",
    "    plt.title(title); plt.xlabel(\"Recall\"); plt.ylabel(\"Precision\")\n",
    "    plt.grid(True, alpha=0.25); plt.legend(loc=\"lower left\"); plt.tight_layout(); plt.savefig(out_png, dpi=160); plt.close()\n",
    "\n",
    "def plot_cm(cm: np.ndarray, out_png: Path, title: str):\n",
    "    plt.figure(figsize=(5.6,4.8))\n",
    "    im = plt.imshow(cm, cmap=\"Blues\")\n",
    "    plt.title(title, fontsize=15)\n",
    "    plt.xlabel(\"Predicted\", fontsize=12); plt.ylabel(\"Actual\", fontsize=12)\n",
    "    plt.colorbar(im, fraction=0.046, pad=0.04)\n",
    "    for (i,j), z in np.ndenumerate(cm):\n",
    "        plt.text(j, i, f\"{z}\", ha='center', va='center', color='black', fontsize=12)\n",
    "    plt.xticks([0,1], [\"Negative\",\"Positive\"]); plt.yticks([0,1], [\"Negative\",\"Positive\"])\n",
    "    plt.tight_layout(); plt.savefig(out_png, dpi=170); plt.close()\n",
    "\n",
    "# -------------------------\n",
    "# Focal Loss（二元）— 形狀容錯版\n",
    "# [CHG]：在 loss 內部自動 squeeze 成 1D，避免 y 形狀不小心變成 (N,1) 時出錯\n",
    "# -------------------------\n",
    "def _to_1d(y):\n",
    "    y = tf.cast(y, tf.float32)\n",
    "    # 若 rank>1，保險性 squeeze 到 1D（對現有流程無影響）\n",
    "    return tf.squeeze(y, axis=-1) if y.shape.rank is not None and y.shape.rank > 1 else y\n",
    "\n",
    "def binary_focal_loss(gamma=2.0, alpha=0.25):\n",
    "    def loss(y_true, y_pred):\n",
    "        y_true = _to_1d(y_true)  # [CHG]\n",
    "        y_pred = _to_1d(tf.clip_by_value(y_pred, 1e-7, 1. - 1e-7))  # [CHG]\n",
    "        p_t = y_true * y_pred + (1 - y_true) * (1 - y_pred)\n",
    "        alpha_factor = y_true * alpha + (1 - y_true) * (1 - alpha)\n",
    "        modulating = tf.pow(1. - p_t, gamma)\n",
    "        bce = tf.keras.losses.binary_crossentropy(y_true, y_pred)\n",
    "        return tf.reduce_mean(alpha_factor * modulating * bce)\n",
    "    return loss\n",
    "\n",
    "# -------------------------\n",
    "# 模型構建（無 Masking / 無 BiLSTM / 無 LN）\n",
    "# 輸出層 bias 以先驗 logit 初始化\n",
    "# -------------------------\n",
    "def build_model(input_shape, prior_pos, cfg=CFG):\n",
    "    prior_pos = float(np.clip(prior_pos, 1e-6, 1 - 1e-6))\n",
    "    prior_logit = np.log(prior_pos / (1 - prior_pos))\n",
    "\n",
    "    reg = keras.regularizers.l2(cfg[\"l2\"])\n",
    "    inputs = keras.Input(shape=input_shape, name=\"sequence\")\n",
    "\n",
    "    x = layers.LSTM(\n",
    "        cfg[\"hidden_units\"],\n",
    "        dropout=cfg[\"dropout\"],\n",
    "        recurrent_dropout=cfg[\"recurrent_dropout\"],\n",
    "        kernel_regularizer=reg,\n",
    "        recurrent_regularizer=reg,\n",
    "        return_sequences=False\n",
    "    )(inputs)\n",
    "    x = layers.Dropout(cfg[\"dropout\"])(x)\n",
    "\n",
    "    prob = layers.Dense(  # 原本輸出形狀為 (None,1)\n",
    "        1, activation=\"sigmoid\", name=\"prob\",\n",
    "        bias_initializer=keras.initializers.Constant(prior_logit)\n",
    "    )(x)\n",
    "    # [CHG] 關鍵：把 (None,1) → (None,) 與你的 y_true(一維) 完全對齊\n",
    "    outputs = layers.Lambda(lambda z: tf.squeeze(z, axis=-1), name=\"prob_squeezed\")(prob)\n",
    "\n",
    "    model = keras.Model(inputs, outputs, name=\"lstm_focal_classweight\")\n",
    "    opt = keras.optimizers.Adam(learning_rate=cfg[\"learning_rate\"], clipnorm=cfg[\"clipnorm\"])\n",
    "    model.compile(\n",
    "        optimizer=opt,\n",
    "        loss=binary_focal_loss(gamma=cfg[\"focal_gamma\"], alpha=prior_pos),  # alpha = 正類率（可依需求調整為 1 - pos）\n",
    "        metrics=[\n",
    "            keras.metrics.BinaryAccuracy(name=\"accuracy\"),\n",
    "            keras.metrics.AUC(name=\"auc\"),\n",
    "            keras.metrics.AUC(name=\"auprc\", curve=\"PR\"),\n",
    "            keras.metrics.Precision(name=\"precision\"),\n",
    "            keras.metrics.Recall(name=\"recall\"),\n",
    "            keras.metrics.BinaryCrossentropy(name=\"binary_crossentropy\")\n",
    "        ]\n",
    "    )\n",
    "    return model\n",
    "\n",
    "# -------------------------\n",
    "# 每折前處理（fit on train, transform train/val）\n",
    "# -------------------------\n",
    "def fit_transform_fold(Xtr, Xva):\n",
    "    n, t, d = Xtr.shape\n",
    "    tr2 = Xtr.reshape(n, t * d)\n",
    "    va2 = Xva.reshape(Xva.shape[0], t * d)\n",
    "\n",
    "    imputer = SimpleImputer(strategy=\"median\")\n",
    "    scaler = StandardScaler(with_mean=True, with_std=True)\n",
    "\n",
    "    tr_imp = imputer.fit_transform(tr2)\n",
    "    tr_scl = scaler.fit_transform(tr_imp)\n",
    "\n",
    "    va_imp = imputer.transform(va2)\n",
    "    va_scl = scaler.transform(va_imp)\n",
    "\n",
    "    Xtr_p = tr_scl.reshape(n, t, d).astype(np.float32)\n",
    "    Xva_p = va_scl.reshape(Xva.shape[0], t, d).astype(np.float32)\n",
    "    return Xtr_p, Xva_p, imputer, scaler\n",
    "\n",
    "# -------------------------\n",
    "# 指標工具\n",
    "# -------------------------\n",
    "def sweep_best_f1(y_true, y_prob, step=0.01):\n",
    "    thresholds = np.arange(step, 1.0, step)\n",
    "    best = {\"th\": 0.5, \"f1\": -1.0, \"prec\": 0.0, \"rec\": 0.0, \"acc\": 0.0}\n",
    "    for th in thresholds:\n",
    "        pred = (y_prob >= th).astype(int)\n",
    "        prec, rec, f1, _ = precision_recall_fscore_support(y_true, pred, average='binary', zero_division=0)\n",
    "        acc = accuracy_score(y_true, pred)\n",
    "        if f1 > best[\"f1\"]:\n",
    "            best = {\"th\": float(th), \"f1\": float(f1), \"prec\": float(prec), \"rec\": float(rec), \"acc\": float(acc)}\n",
    "    return best\n",
    "\n",
    "def five_metrics(y_true, y_prob, th):\n",
    "    pred = (y_prob >= th).astype(int)\n",
    "    acc = accuracy_score(y_true, pred)\n",
    "    prec, rec, f1, _ = precision_recall_fscore_support(y_true, pred, average='binary', zero_division=0)\n",
    "    rocauc = roc_auc_score(y_true, y_prob)\n",
    "    ap = average_precision_score(y_true, y_prob)\n",
    "    return {\"acc\":acc, \"prec\":prec, \"rec\":rec, \"f1\":f1, \"auc\":rocauc, \"auprc\":ap}\n",
    "\n",
    "# -------------------------\n",
    "# Callbacks：F1 掃門檻 + 機率分佈探針 + 以 F1best/auprc 早停\n",
    "# -------------------------\n",
    "class F1PerEpoch(keras.callbacks.Callback):\n",
    "    def __init__(self, Xtr, ytr, Xva, yva, bs=1024):\n",
    "        super().__init__()\n",
    "        self.Xtr, self.ytr, self.Xva, self.yva = Xtr, ytr, Xva, yva\n",
    "        self.bs = bs\n",
    "    def on_epoch_end(self, epoch, logs=None):\n",
    "        ytr_prob = self.model.predict(self.Xtr, batch_size=self.bs, verbose=0).ravel()\n",
    "        yva_prob = self.model.predict(self.Xva, batch_size=self.bs, verbose=0).ravel()\n",
    "        best_tr = sweep_best_f1(self.ytr, ytr_prob, step=0.01)\n",
    "        best_va = sweep_best_f1(self.yva, yva_prob, step=0.01)\n",
    "        if logs is not None:\n",
    "            logs[\"f1_best\"] = best_tr[\"f1\"]\n",
    "            logs[\"val_f1_best\"] = best_va[\"f1\"]\n",
    "            logs[\"val_best_th\"] = best_va[\"th\"]\n",
    "        print(f\"[F1Epoch] epoch={epoch} f1_tr_best={best_tr['f1']:.4f} f1_va_best={best_va['f1']:.4f} | val_best_th={best_va['th']:.2f}\")\n",
    "\n",
    "class ProbProbe(keras.callbacks.Callback):\n",
    "    def __init__(self, Xtr, Xva, bs=1024):\n",
    "        super().__init__()\n",
    "        self.Xtr, self.Xva = Xtr, Xva\n",
    "        self.bs = bs\n",
    "    def on_epoch_end(self, epoch, logs=None):\n",
    "        ytr_prob = self.model.predict(self.Xtr, batch_size=self.bs, verbose=0).ravel()\n",
    "        yva_prob = self.model.predict(self.Xva, batch_size=self.bs, verbose=0).ravel()\n",
    "        def stats(v):\n",
    "            return np.mean(v), np.percentile(v, 90)\n",
    "        m_tr, p90_tr = stats(ytr_prob)\n",
    "        m_va, p90_va = stats(yva_prob)\n",
    "        print(f\"[Probe] epoch={epoch} train mean={m_tr:.4f} p90={p90_tr:.4f} | val mean={m_va:.4f} p90={p90_va:.4f}\")\n",
    "\n",
    "class EarlyStopOnKey(keras.callbacks.EarlyStopping):\n",
    "    \"\"\"Keras 內建 EarlyStopping，只是 monitor 換成自訂 log key，例如 'val_f1_best'。\"\"\"\n",
    "    pass\n",
    "\n",
    "# -------------------------\n",
    "# 主流程\n",
    "# -------------------------\n",
    "all_rows = []\n",
    "\n",
    "for k in CFG[\"folds\"]:\n",
    "    fold_dir = RUN_DIR / f\"fold_{k}\"\n",
    "    fold_dir.mkdir(parents=True, exist_ok=True)\n",
    "\n",
    "    # 載入資料\n",
    "    Xtr = np.load(DATA / f\"X_train_fold{k}.npy\")\n",
    "    ytr = np.load(DATA / f\"y_train_fold{k}.npy\").ravel().astype(np.float32)   # 保持 1D，不動資料\n",
    "    Xva = np.load(DATA / f\"X_val_fold{k}.npy\")\n",
    "    yva = np.load(DATA / f\"y_val_fold{k}.npy\").ravel().astype(np.float32)     # 保持 1D，不動資料\n",
    "\n",
    "    assert set(np.unique(ytr)).issubset({0.,1.}) and set(np.unique(yva)).issubset({0.,1.}), \"Labels must be {0,1}\"\n",
    "\n",
    "    # 前處理（fit on train, transform on train/val）\n",
    "    Xtr_p, Xva_p, _, _ = fit_transform_fold(Xtr, Xva)\n",
    "\n",
    "    # 先驗：正類率 + 輸出層 bias\n",
    "    pos_rate = float(np.mean(ytr))\n",
    "    # class_weight（balanced）\n",
    "    n_pos = np.sum(ytr==1); n_neg = np.sum(ytr==0)\n",
    "    total = n_pos + n_neg\n",
    "    class_weight = {\n",
    "        0: total/(2.0*n_neg) if n_neg>0 else 1.0,\n",
    "        1: total/(2.0*n_pos) if n_pos>0 else 1.0\n",
    "    }\n",
    "\n",
    "    # 建模（alpha 使用 pos_rate；若想更強化少數類，也可用 alpha=1-pos_rate）\n",
    "    model = build_model(input_shape=(Xtr_p.shape[1], Xtr_p.shape[2]), prior_pos=pos_rate, cfg=CFG)\n",
    "\n",
    "    # Callbacks\n",
    "    ckpt_path = fold_dir / \"best_model.keras\"\n",
    "    cbs = [\n",
    "        F1PerEpoch(Xtr_p, ytr, Xva_p, yva, bs=1024),\n",
    "        ProbProbe(Xtr_p, Xva_p, bs=1024),\n",
    "        keras.callbacks.ModelCheckpoint(filepath=str(ckpt_path), monitor=\"val_auprc\", mode=\"max\", save_best_only=True),\n",
    "        EarlyStopOnKey(monitor=\"val_auprc\", mode=\"max\", patience=CFG[\"patience_auprc\"], restore_best_weights=True),\n",
    "        EarlyStopOnKey(monitor=\"val_f1_best\", mode=\"max\", patience=CFG[\"patience_f1best\"], restore_best_weights=True),\n",
    "        keras.callbacks.ReduceLROnPlateau(monitor=\"val_auprc\", mode=\"max\",\n",
    "                                          factor=CFG[\"reduce_factor\"], patience=3,\n",
    "                                          min_lr=CFG[\"reduce_min_lr\"], verbose=1)\n",
    "    ]\n",
    "\n",
    "    # 訓練\n",
    "    hist = model.fit(\n",
    "        Xtr_p, ytr,\n",
    "        validation_data=(Xva_p, yva),\n",
    "        epochs=CFG[\"epochs\"],\n",
    "        batch_size=CFG[\"batch_size\"],\n",
    "        callbacks=cbs,\n",
    "        verbose=2,\n",
    "        class_weight=class_weight\n",
    "    )\n",
    "\n",
    "    # 保存 history 與學習曲線\n",
    "    hist_df = pd.DataFrame(hist.history)\n",
    "    hist_df.to_csv(fold_dir / \"history.csv\", index=False)\n",
    "    plot_history(hist_df, fold_dir / \"plot_loss.png\")\n",
    "    plot_metric_epochs(hist_df, \"accuracy\", fold_dir / \"plot_acc.png\", \"Accuracy per Epoch\")\n",
    "    if \"f1_best\" in hist_df.columns and \"val_f1_best\" in hist_df.columns:\n",
    "        plot_metric_epochs(hist_df, \"f1_best\", fold_dir / \"plot_f1.png\", \"F1 (Best-threshold) per Epoch\")\n",
    "\n",
    "    # 推論\n",
    "    ytr_prob = model.predict(Xtr_p, batch_size=CFG[\"batch_size\"], verbose=0).ravel()\n",
    "    yva_prob = model.predict(Xva_p, batch_size=CFG[\"batch_size\"], verbose=0).ravel()\n",
    "\n",
    "    # 固定閾值 & 最佳閾值\n",
    "    th_fixed = CFG[\"threshold_fixed\"]\n",
    "    best_va = sweep_best_f1(yva, yva_prob, step=0.01)\n",
    "    th_best = best_va[\"th\"]\n",
    "\n",
    "    # 五大指標（train/val; fixed/best）\n",
    "    m_tr_fixed = five_metrics(ytr, ytr_prob, th_fixed)\n",
    "    m_va_fixed = five_metrics(yva, yva_prob, th_fixed)\n",
    "    m_tr_best  = five_metrics(ytr, ytr_prob, th_best)\n",
    "    m_va_best  = five_metrics(yva, yva_prob, th_best)\n",
    "\n",
    "    # 混淆矩陣（固定/最佳）\n",
    "    cm_tr_fixed = confusion_matrix((ytr>0.5).astype(int), (ytr_prob>=th_fixed).astype(int), labels=[0,1])\n",
    "    cm_va_fixed = confusion_matrix((yva>0.5).astype(int), (yva_prob>=th_fixed).astype(int), labels=[0,1])\n",
    "    cm_tr_best  = confusion_matrix((ytr>0.5).astype(int), (ytr_prob>=th_best).astype(int),  labels=[0,1])\n",
    "    cm_va_best  = confusion_matrix((yva>0.5).astype(int), (yva_prob>=th_best).astype(int),  labels=[0,1])\n",
    "\n",
    "    # ROC / PR（Train & Val）\n",
    "    fpr_tr, tpr_tr, _ = roc_curve((ytr>0.5).astype(int), ytr_prob)\n",
    "    fpr_va, tpr_va, _ = roc_curve((yva>0.5).astype(int), yva_prob)\n",
    "    rec_tr, prec_tr, _ = precision_recall_curve((ytr>0.5).astype(int), ytr_prob)\n",
    "    rec_va, prec_va, _ = precision_recall_curve((yva>0.5).astype(int), yva_prob)\n",
    "    ap_tr = average_precision_score((ytr>0.5).astype(int), ytr_prob)\n",
    "    ap_va = average_precision_score((yva>0.5).astype(int), yva_prob)\n",
    "\n",
    "    # 存 CSV\n",
    "    pd.DataFrame({\"y_true\": ytr, \"y_prob\": ytr_prob}).to_csv(fold_dir / \"predictions_train.csv\", index=False)\n",
    "    pd.DataFrame({\"y_true\": yva, \"y_prob\": yva_prob}).to_csv(fold_dir / \"predictions_val.csv\", index=False)\n",
    "    pd.DataFrame({\"fpr\": fpr_tr, \"tpr\": tpr_tr}).to_csv(fold_dir / \"roc_curve_train.csv\", index=False)\n",
    "    pd.DataFrame({\"fpr\": fpr_va, \"tpr\": tpr_va}).to_csv(fold_dir / \"roc_curve_val.csv\", index=False)\n",
    "    pd.DataFrame({\"recall\": rec_tr, \"precision\": prec_tr}).to_csv(fold_dir / \"pr_curve_train.csv\", index=False)\n",
    "    pd.DataFrame({\"recall\": rec_va, \"precision\": prec_va}).to_csv(fold_dir / \"pr_curve_val.csv\", index=False)\n",
    "    pd.DataFrame(cm_tr_fixed, index=[\"Actual_0\",\"Actual_1\"], columns=[\"Pred_0\",\"Pred_1\"]).to_csv(fold_dir / \"cm_train_fixed.csv\")\n",
    "    pd.DataFrame(cm_va_fixed, index=[\"Actual_0\",\"Actual_1\"], columns=[\"Pred_0\",\"Pred_1\"]).to_csv(fold_dir / \"cm_val_fixed.csv\")\n",
    "    pd.DataFrame(cm_tr_best,  index=[\"Actual_0\",\"Actual_1\"], columns=[\"Pred_0\",\"Pred_1\"]).to_csv(fold_dir / \"cm_train_best.csv\")\n",
    "    pd.DataFrame(cm_va_best,  index=[\"Actual_0\",\"Actual_1\"], columns=[\"Pred_0\",\"Pred_1\"]).to_csv(fold_dir / \"cm_val_best.csv\")\n",
    "\n",
    "    # 存圖\n",
    "    plot_cm(cm_tr_fixed, fold_dir / \"plot_cm_train_fixed.png\", f\"Confusion Matrix (Train, th={th_fixed:.2f})\")\n",
    "    plot_cm(cm_va_fixed, fold_dir / \"plot_cm_val_fixed.png\",   f\"Confusion Matrix (Val, th={th_fixed:.2f})\")\n",
    "    plot_cm(cm_tr_best,  fold_dir / \"plot_cm_train_best.png\",  f\"Confusion Matrix (Train, best th={th_best:.2f})\")\n",
    "    plot_cm(cm_va_best,  fold_dir / \"plot_cm_val_best.png\",    f\"Confusion Matrix (Val, best th={th_best:.2f})\")\n",
    "    plot_roc_xy(fpr_tr, tpr_tr, m_tr_fixed[\"auc\"], fold_dir / \"plot_roc_train.png\", \"ROC Curve (Train)\")\n",
    "    plot_roc_xy(fpr_va, tpr_va, m_va_fixed[\"auc\"], fold_dir / \"plot_roc_val.png\",   \"ROC Curve (Validation)\")\n",
    "    plot_pr_xy(rec_tr, prec_tr, ap_tr, fold_dir / \"plot_pr_train.png\", \"PR Curve (Train)\")\n",
    "    plot_pr_xy(rec_va, prec_va, ap_va, fold_dir / \"plot_pr_val.png\",   \"PR Curve (Validation)\")\n",
    "\n",
    "    # 每折 metrics（固定 & 最佳閾值）\n",
    "    row = {\n",
    "        \"fold\": k,\n",
    "        \"pos_rate\": pos_rate,\n",
    "        \"focal_gamma\": CFG[\"focal_gamma\"],\n",
    "        \"class_weight_0\": class_weight[0],\n",
    "        \"class_weight_1\": class_weight[1],\n",
    "        # Train @ fixed\n",
    "        \"train_acc\": m_tr_fixed[\"acc\"], \"train_prec\": m_tr_fixed[\"prec\"], \"train_rec\": m_tr_fixed[\"rec\"],\n",
    "        \"train_f1\": m_tr_fixed[\"f1\"], \"train_auc\": m_tr_fixed[\"auc\"], \"train_auprc\": m_tr_fixed[\"auprc\"],\n",
    "        # Val   @ fixed\n",
    "        \"val_acc\": m_va_fixed[\"acc\"], \"val_prec\": m_va_fixed[\"prec\"], \"val_rec\": m_va_fixed[\"rec\"],\n",
    "        \"val_f1\": m_va_fixed[\"f1\"], \"val_auc\": m_va_fixed[\"auc\"], \"val_auprc\": m_va_fixed[\"auprc\"],\n",
    "        # Best-threshold on val\n",
    "        \"best_th_val\": th_best,\n",
    "        \"train_acc_best\": m_tr_best[\"acc\"], \"train_prec_best\": m_tr_best[\"prec\"], \"train_rec_best\": m_tr_best[\"rec\"], \"train_f1_best\": m_tr_best[\"f1\"],\n",
    "        \"val_acc_best\": m_va_best[\"acc\"], \"val_prec_best\": m_va_best[\"prec\"], \"val_rec_best\": m_va_best[\"rec\"], \"val_f1_best\": m_va_best[\"f1\"],\n",
    "    }\n",
    "    pd.DataFrame([row]).to_csv(fold_dir / \"metrics.csv\", index=False)\n",
    "\n",
    "    # 補充：保存每折 meta\n",
    "    fold_meta = {\n",
    "        \"pos_rate\": pos_rate,\n",
    "        \"n_train\": int(len(ytr)), \"n_val\": int(len(yva)),\n",
    "        \"threshold_fixed\": CFG[\"threshold_fixed\"], \"threshold_best_on_val\": th_best,\n",
    "        \"class_weight\": {k: float(v) for k, v in class_weight.items()}\n",
    "    }\n",
    "    (fold_dir / \"fold_meta.json\").write_text(json.dumps(fold_meta, ensure_ascii=False, indent=2), encoding=\"utf-8\")\n",
    "\n",
    "    all_rows.append(row)\n",
    "\n",
    "# 彙總\n",
    "summary = pd.DataFrame(all_rows).sort_values(\"fold\")\n",
    "summary.to_csv(RUN_DIR / \"summary_folds.csv\", index=False)\n",
    "\n",
    "agg_means = summary.drop(columns=[\"fold\"]).mean(numeric_only=True).to_dict()\n",
    "agg_stds  = summary.drop(columns=[\"fold\"]).std(ddof=1, numeric_only=True).add_suffix(\"_std\").to_dict()\n",
    "overall = {\"n_folds\": int(len(summary)), **{k: float(v) for k,v in agg_means.items()}, **{k: float(v) for k,v in agg_stds.items()}, \"config\": CFG}\n",
    "(RUN_DIR / \"summary_overall.json\").write_text(json.dumps(overall, ensure_ascii=False, indent=2), encoding=\"utf-8\")\n",
    "\n",
    "print(\"\\n=== Training Done ===\")\n",
    "print(f\"Run dir: {RUN_DIR}\")\n",
    "print(json.dumps(overall, indent=2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "13ed7601-3da5-4fe4-a5ff-1d23a30b9df3",
   "metadata": {},
   "outputs": [],
   "source": [
    "A｜逐特徵標準化（跨樣本×時間）：fit_transform_fold() 改為在 (n*t, d) 上 fit，再還原回 (n, t, d)。\n",
    "B｜Focal（策略 1）：保留 Focal、alpha = 1 - pos_rate，並移除 class_weight（不再傳給 fit，也不再寫入輸出）。\n",
    "C｜修正監控指標：移除會用固定 0.5 閾值的 Precision/Recall 度量；保留 accuracy、auc、auprc、binary_crossentropy，F1 仍由 callback 掃門檻計算與列印"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 284,
   "id": "6a9b2910-6423-4396-9c57-27aab7622093",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/40\n",
      "[F1Epoch] epoch=0 f1_tr_best=0.4182 f1_va_best=0.4184 | val_best_th=0.32\n",
      "[Probe] epoch=0 train mean=0.3898 p90=0.4439 | val mean=0.3892 p90=0.4429\n",
      "197/197 - 44s - 221ms/step - accuracy: 0.7309 - auc: 0.5046 - auprc: 0.2680 - binary_crossentropy: 0.6065 - loss: 0.0640 - val_accuracy: 0.7357 - val_auc: 0.5053 - val_auprc: 0.2740 - val_binary_crossentropy: 0.6136 - val_loss: 0.0630 - f1_best: 0.4182 - val_f1_best: 0.4184 - val_best_th: 0.3200 - learning_rate: 3.0000e-04\n",
      "Epoch 2/40\n",
      "[F1Epoch] epoch=1 f1_tr_best=0.4182 f1_va_best=0.4184 | val_best_th=0.34\n",
      "[Probe] epoch=1 train mean=0.3930 p90=0.4515 | val mean=0.3927 p90=0.4524\n",
      "197/197 - 40s - 206ms/step - accuracy: 0.7326 - auc: 0.5177 - auprc: 0.2761 - binary_crossentropy: 0.6113 - loss: 0.0629 - val_accuracy: 0.7354 - val_auc: 0.5150 - val_auprc: 0.2870 - val_binary_crossentropy: 0.6144 - val_loss: 0.0627 - f1_best: 0.4182 - val_f1_best: 0.4184 - val_best_th: 0.3400 - learning_rate: 3.0000e-04\n",
      "Epoch 3/40\n",
      "[F1Epoch] epoch=2 f1_tr_best=0.4185 f1_va_best=0.4186 | val_best_th=0.35\n",
      "[Probe] epoch=2 train mean=0.3940 p90=0.4512 | val mean=0.3941 p90=0.4524\n",
      "197/197 - 40s - 203ms/step - accuracy: 0.7341 - auc: 0.5206 - auprc: 0.2774 - binary_crossentropy: 0.6115 - loss: 0.0628 - val_accuracy: 0.7347 - val_auc: 0.5243 - val_auprc: 0.2894 - val_binary_crossentropy: 0.6146 - val_loss: 0.0625 - f1_best: 0.4185 - val_f1_best: 0.4186 - val_best_th: 0.3500 - learning_rate: 3.0000e-04\n",
      "Epoch 4/40\n",
      "[F1Epoch] epoch=3 f1_tr_best=0.4185 f1_va_best=0.4192 | val_best_th=0.34\n",
      "[Probe] epoch=3 train mean=0.3951 p90=0.4503 | val mean=0.3949 p90=0.4508\n",
      "197/197 - 40s - 205ms/step - accuracy: 0.7341 - auc: 0.5258 - auprc: 0.2825 - binary_crossentropy: 0.6110 - loss: 0.0626 - val_accuracy: 0.7347 - val_auc: 0.5251 - val_auprc: 0.2870 - val_binary_crossentropy: 0.6146 - val_loss: 0.0624 - f1_best: 0.4185 - val_f1_best: 0.4192 - val_best_th: 0.3400 - learning_rate: 3.0000e-04\n",
      "Epoch 5/40\n",
      "[F1Epoch] epoch=4 f1_tr_best=0.4184 f1_va_best=0.4188 | val_best_th=0.34\n",
      "[Probe] epoch=4 train mean=0.3982 p90=0.4468 | val mean=0.3982 p90=0.4483\n",
      "197/197 - 40s - 204ms/step - accuracy: 0.7347 - auc: 0.5293 - auprc: 0.2836 - binary_crossentropy: 0.6110 - loss: 0.0625 - val_accuracy: 0.7343 - val_auc: 0.5251 - val_auprc: 0.2885 - val_binary_crossentropy: 0.6160 - val_loss: 0.0623 - f1_best: 0.4184 - val_f1_best: 0.4188 - val_best_th: 0.3400 - learning_rate: 3.0000e-04\n",
      "Epoch 6/40\n",
      "[F1Epoch] epoch=5 f1_tr_best=0.4186 f1_va_best=0.4198 | val_best_th=0.35\n",
      "[Probe] epoch=5 train mean=0.3958 p90=0.4457 | val mean=0.3959 p90=0.4467\n",
      "197/197 - 40s - 204ms/step - accuracy: 0.7346 - auc: 0.5292 - auprc: 0.2857 - binary_crossentropy: 0.6112 - loss: 0.0625 - val_accuracy: 0.7340 - val_auc: 0.5312 - val_auprc: 0.2923 - val_binary_crossentropy: 0.6144 - val_loss: 0.0623 - f1_best: 0.4186 - val_f1_best: 0.4198 - val_best_th: 0.3500 - learning_rate: 3.0000e-04\n",
      "Epoch 7/40\n",
      "[F1Epoch] epoch=6 f1_tr_best=0.4186 f1_va_best=0.4185 | val_best_th=0.34\n",
      "[Probe] epoch=6 train mean=0.3975 p90=0.4432 | val mean=0.3977 p90=0.4440\n",
      "197/197 - 41s - 206ms/step - accuracy: 0.7345 - auc: 0.5322 - auprc: 0.2859 - binary_crossentropy: 0.6108 - loss: 0.0624 - val_accuracy: 0.7340 - val_auc: 0.5354 - val_auprc: 0.2964 - val_binary_crossentropy: 0.6151 - val_loss: 0.0622 - f1_best: 0.4186 - val_f1_best: 0.4185 - val_best_th: 0.3400 - learning_rate: 3.0000e-04\n",
      "Epoch 8/40\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[284], line 323\u001b[0m\n\u001b[1;32m    311\u001b[0m cbs \u001b[38;5;241m=\u001b[39m [\n\u001b[1;32m    312\u001b[0m     F1PerEpoch(Xtr_p, ytr, Xva_p, yva, bs\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1024\u001b[39m),\n\u001b[1;32m    313\u001b[0m     ProbProbe(Xtr_p, Xva_p, bs\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1024\u001b[39m),\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    319\u001b[0m                                       min_lr\u001b[38;5;241m=\u001b[39mCFG[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mreduce_min_lr\u001b[39m\u001b[38;5;124m\"\u001b[39m], verbose\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m)\n\u001b[1;32m    320\u001b[0m ]\n\u001b[1;32m    322\u001b[0m \u001b[38;5;66;03m# 訓練（不再傳入 class_weight）\u001b[39;00m\n\u001b[0;32m--> 323\u001b[0m hist \u001b[38;5;241m=\u001b[39m \u001b[43mmodel\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfit\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    324\u001b[0m \u001b[43m    \u001b[49m\u001b[43mXtr_p\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mytr\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    325\u001b[0m \u001b[43m    \u001b[49m\u001b[43mvalidation_data\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mXva_p\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43myva\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    326\u001b[0m \u001b[43m    \u001b[49m\u001b[43mepochs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mCFG\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mepochs\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    327\u001b[0m \u001b[43m    \u001b[49m\u001b[43mbatch_size\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mCFG\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mbatch_size\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    328\u001b[0m \u001b[43m    \u001b[49m\u001b[43mcallbacks\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcbs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    329\u001b[0m \u001b[43m    \u001b[49m\u001b[43mverbose\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m2\u001b[39;49m\n\u001b[1;32m    330\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    332\u001b[0m \u001b[38;5;66;03m# 保存 history 與學習曲線\u001b[39;00m\n\u001b[1;32m    333\u001b[0m hist_df \u001b[38;5;241m=\u001b[39m pd\u001b[38;5;241m.\u001b[39mDataFrame(hist\u001b[38;5;241m.\u001b[39mhistory)\n",
      "File \u001b[0;32m/opt/conda/lib/python3.11/site-packages/keras/src/utils/traceback_utils.py:117\u001b[0m, in \u001b[0;36mfilter_traceback.<locals>.error_handler\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    115\u001b[0m filtered_tb \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m    116\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 117\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfn\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    118\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[1;32m    119\u001b[0m     filtered_tb \u001b[38;5;241m=\u001b[39m _process_traceback_frames(e\u001b[38;5;241m.\u001b[39m__traceback__)\n",
      "File \u001b[0;32m/opt/conda/lib/python3.11/site-packages/keras/src/backend/tensorflow/trainer.py:318\u001b[0m, in \u001b[0;36mTensorFlowTrainer.fit\u001b[0;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_batch_size, validation_freq)\u001b[0m\n\u001b[1;32m    316\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m step, iterator \u001b[38;5;129;01min\u001b[39;00m epoch_iterator\u001b[38;5;241m.\u001b[39menumerate_epoch():\n\u001b[1;32m    317\u001b[0m     callbacks\u001b[38;5;241m.\u001b[39mon_train_batch_begin(step)\n\u001b[0;32m--> 318\u001b[0m     logs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtrain_function\u001b[49m\u001b[43m(\u001b[49m\u001b[43miterator\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    319\u001b[0m     logs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_pythonify_logs(logs)\n\u001b[1;32m    320\u001b[0m     callbacks\u001b[38;5;241m.\u001b[39mon_train_batch_end(step, logs)\n",
      "File \u001b[0;32m/opt/conda/lib/python3.11/site-packages/tensorflow/python/util/traceback_utils.py:150\u001b[0m, in \u001b[0;36mfilter_traceback.<locals>.error_handler\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    148\u001b[0m filtered_tb \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m    149\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 150\u001b[0m   \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfn\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    151\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[1;32m    152\u001b[0m   filtered_tb \u001b[38;5;241m=\u001b[39m _process_traceback_frames(e\u001b[38;5;241m.\u001b[39m__traceback__)\n",
      "File \u001b[0;32m/opt/conda/lib/python3.11/site-packages/tensorflow/python/eager/polymorphic_function/polymorphic_function.py:833\u001b[0m, in \u001b[0;36mFunction.__call__\u001b[0;34m(self, *args, **kwds)\u001b[0m\n\u001b[1;32m    830\u001b[0m compiler \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mxla\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_jit_compile \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mnonXla\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    832\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m OptionalXlaContext(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_jit_compile):\n\u001b[0;32m--> 833\u001b[0m   result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwds\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    835\u001b[0m new_tracing_count \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mexperimental_get_tracing_count()\n\u001b[1;32m    836\u001b[0m without_tracing \u001b[38;5;241m=\u001b[39m (tracing_count \u001b[38;5;241m==\u001b[39m new_tracing_count)\n",
      "File \u001b[0;32m/opt/conda/lib/python3.11/site-packages/tensorflow/python/eager/polymorphic_function/polymorphic_function.py:878\u001b[0m, in \u001b[0;36mFunction._call\u001b[0;34m(self, *args, **kwds)\u001b[0m\n\u001b[1;32m    875\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_lock\u001b[38;5;241m.\u001b[39mrelease()\n\u001b[1;32m    876\u001b[0m \u001b[38;5;66;03m# In this case we have not created variables on the first call. So we can\u001b[39;00m\n\u001b[1;32m    877\u001b[0m \u001b[38;5;66;03m# run the first trace but we should fail if variables are created.\u001b[39;00m\n\u001b[0;32m--> 878\u001b[0m results \u001b[38;5;241m=\u001b[39m \u001b[43mtracing_compilation\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcall_function\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    879\u001b[0m \u001b[43m    \u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkwds\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_variable_creation_config\u001b[49m\n\u001b[1;32m    880\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    881\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_created_variables:\n\u001b[1;32m    882\u001b[0m   \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCreating variables on a non-first call to a function\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    883\u001b[0m                    \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m decorated with tf.function.\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
      "File \u001b[0;32m/opt/conda/lib/python3.11/site-packages/tensorflow/python/eager/polymorphic_function/tracing_compilation.py:139\u001b[0m, in \u001b[0;36mcall_function\u001b[0;34m(args, kwargs, tracing_options)\u001b[0m\n\u001b[1;32m    137\u001b[0m bound_args \u001b[38;5;241m=\u001b[39m function\u001b[38;5;241m.\u001b[39mfunction_type\u001b[38;5;241m.\u001b[39mbind(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[1;32m    138\u001b[0m flat_inputs \u001b[38;5;241m=\u001b[39m function\u001b[38;5;241m.\u001b[39mfunction_type\u001b[38;5;241m.\u001b[39munpack_inputs(bound_args)\n\u001b[0;32m--> 139\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunction\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_flat\u001b[49m\u001b[43m(\u001b[49m\u001b[43m  \u001b[49m\u001b[38;5;66;43;03m# pylint: disable=protected-access\u001b[39;49;00m\n\u001b[1;32m    140\u001b[0m \u001b[43m    \u001b[49m\u001b[43mflat_inputs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcaptured_inputs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfunction\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcaptured_inputs\u001b[49m\n\u001b[1;32m    141\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m/opt/conda/lib/python3.11/site-packages/tensorflow/python/eager/polymorphic_function/concrete_function.py:1322\u001b[0m, in \u001b[0;36mConcreteFunction._call_flat\u001b[0;34m(self, tensor_inputs, captured_inputs)\u001b[0m\n\u001b[1;32m   1318\u001b[0m possible_gradient_type \u001b[38;5;241m=\u001b[39m gradients_util\u001b[38;5;241m.\u001b[39mPossibleTapeGradientTypes(args)\n\u001b[1;32m   1319\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m (possible_gradient_type \u001b[38;5;241m==\u001b[39m gradients_util\u001b[38;5;241m.\u001b[39mPOSSIBLE_GRADIENT_TYPES_NONE\n\u001b[1;32m   1320\u001b[0m     \u001b[38;5;129;01mand\u001b[39;00m executing_eagerly):\n\u001b[1;32m   1321\u001b[0m   \u001b[38;5;66;03m# No tape is watching; skip to running the function.\u001b[39;00m\n\u001b[0;32m-> 1322\u001b[0m   \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_inference_function\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcall_preflattened\u001b[49m\u001b[43m(\u001b[49m\u001b[43margs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1323\u001b[0m forward_backward \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_select_forward_and_backward_functions(\n\u001b[1;32m   1324\u001b[0m     args,\n\u001b[1;32m   1325\u001b[0m     possible_gradient_type,\n\u001b[1;32m   1326\u001b[0m     executing_eagerly)\n\u001b[1;32m   1327\u001b[0m forward_function, args_with_tangents \u001b[38;5;241m=\u001b[39m forward_backward\u001b[38;5;241m.\u001b[39mforward()\n",
      "File \u001b[0;32m/opt/conda/lib/python3.11/site-packages/tensorflow/python/eager/polymorphic_function/atomic_function.py:216\u001b[0m, in \u001b[0;36mAtomicFunction.call_preflattened\u001b[0;34m(self, args)\u001b[0m\n\u001b[1;32m    214\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mcall_preflattened\u001b[39m(\u001b[38;5;28mself\u001b[39m, args: Sequence[core\u001b[38;5;241m.\u001b[39mTensor]) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Any:\n\u001b[1;32m    215\u001b[0m \u001b[38;5;250m  \u001b[39m\u001b[38;5;124;03m\"\"\"Calls with flattened tensor inputs and returns the structured output.\"\"\"\u001b[39;00m\n\u001b[0;32m--> 216\u001b[0m   flat_outputs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcall_flat\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    217\u001b[0m   \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfunction_type\u001b[38;5;241m.\u001b[39mpack_output(flat_outputs)\n",
      "File \u001b[0;32m/opt/conda/lib/python3.11/site-packages/tensorflow/python/eager/polymorphic_function/atomic_function.py:251\u001b[0m, in \u001b[0;36mAtomicFunction.call_flat\u001b[0;34m(self, *args)\u001b[0m\n\u001b[1;32m    249\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m record\u001b[38;5;241m.\u001b[39mstop_recording():\n\u001b[1;32m    250\u001b[0m   \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_bound_context\u001b[38;5;241m.\u001b[39mexecuting_eagerly():\n\u001b[0;32m--> 251\u001b[0m     outputs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_bound_context\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcall_function\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    252\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mname\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    253\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;28;43mlist\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43margs\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    254\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;28;43mlen\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfunction_type\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mflat_outputs\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    255\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    256\u001b[0m   \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    257\u001b[0m     outputs \u001b[38;5;241m=\u001b[39m make_call_op_in_graph(\n\u001b[1;32m    258\u001b[0m         \u001b[38;5;28mself\u001b[39m,\n\u001b[1;32m    259\u001b[0m         \u001b[38;5;28mlist\u001b[39m(args),\n\u001b[1;32m    260\u001b[0m         \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_bound_context\u001b[38;5;241m.\u001b[39mfunction_call_options\u001b[38;5;241m.\u001b[39mas_attrs(),\n\u001b[1;32m    261\u001b[0m     )\n",
      "File \u001b[0;32m/opt/conda/lib/python3.11/site-packages/tensorflow/python/eager/context.py:1552\u001b[0m, in \u001b[0;36mContext.call_function\u001b[0;34m(self, name, tensor_inputs, num_outputs)\u001b[0m\n\u001b[1;32m   1550\u001b[0m cancellation_context \u001b[38;5;241m=\u001b[39m cancellation\u001b[38;5;241m.\u001b[39mcontext()\n\u001b[1;32m   1551\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m cancellation_context \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m-> 1552\u001b[0m   outputs \u001b[38;5;241m=\u001b[39m \u001b[43mexecute\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mexecute\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   1553\u001b[0m \u001b[43m      \u001b[49m\u001b[43mname\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdecode\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mutf-8\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1554\u001b[0m \u001b[43m      \u001b[49m\u001b[43mnum_outputs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mnum_outputs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1555\u001b[0m \u001b[43m      \u001b[49m\u001b[43minputs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtensor_inputs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1556\u001b[0m \u001b[43m      \u001b[49m\u001b[43mattrs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mattrs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1557\u001b[0m \u001b[43m      \u001b[49m\u001b[43mctx\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1558\u001b[0m \u001b[43m  \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1559\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m   1560\u001b[0m   outputs \u001b[38;5;241m=\u001b[39m execute\u001b[38;5;241m.\u001b[39mexecute_with_cancellation(\n\u001b[1;32m   1561\u001b[0m       name\u001b[38;5;241m.\u001b[39mdecode(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mutf-8\u001b[39m\u001b[38;5;124m\"\u001b[39m),\n\u001b[1;32m   1562\u001b[0m       num_outputs\u001b[38;5;241m=\u001b[39mnum_outputs,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m   1566\u001b[0m       cancellation_manager\u001b[38;5;241m=\u001b[39mcancellation_context,\n\u001b[1;32m   1567\u001b[0m   )\n",
      "File \u001b[0;32m/opt/conda/lib/python3.11/site-packages/tensorflow/python/eager/execute.py:53\u001b[0m, in \u001b[0;36mquick_execute\u001b[0;34m(op_name, num_outputs, inputs, attrs, ctx, name)\u001b[0m\n\u001b[1;32m     51\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m     52\u001b[0m   ctx\u001b[38;5;241m.\u001b[39mensure_initialized()\n\u001b[0;32m---> 53\u001b[0m   tensors \u001b[38;5;241m=\u001b[39m \u001b[43mpywrap_tfe\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mTFE_Py_Execute\u001b[49m\u001b[43m(\u001b[49m\u001b[43mctx\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_handle\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdevice_name\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mop_name\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m     54\u001b[0m \u001b[43m                                      \u001b[49m\u001b[43minputs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mattrs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnum_outputs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     55\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m core\u001b[38;5;241m.\u001b[39m_NotOkStatusException \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[1;32m     56\u001b[0m   \u001b[38;5;28;01mif\u001b[39;00m name \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "\"\"\"train_lstm_focal_classweight_cv.py  (A+B(策略1)+C 已套用)\n",
    "\n",
    "變更重點：\n",
    "A. 前處理：改為「逐特徵標準化（跨樣本×時間）」\n",
    "   - 在 (n*t, d) 維度上對每一個特徵做 Impute+Scale，再 reshape 回 (n, t, d)\n",
    "B. Focal（二選一，採策略1）：\n",
    "   - 保留 Focal Loss，alpha = 1 - pos_rate（強化少數類）\n",
    "   - 取消 class_weight（避免雙重加權）\n",
    "C. 指標修正：\n",
    "   - compile metrics 僅保留：accuracy、AUC(ROC)、AUC(PR)、BinaryCrossentropy\n",
    "   - F1 仍透過 callback 每 epoch 掃門檻輸出，不再被固定 0.5 閾值的 Precision/Recall 誤導\n",
    "\n",
    "其餘：\n",
    "- 單層 LSTM(192) + Dropout(0.1) + L2(5e-7)；無 Masking / BiLSTM / LayerNorm\n",
    "- Sigmoid 輸出層 bias = log(p/(1-p))（以訓練集正類率）\n",
    "- 10 折交叉驗證；每折 fit-transform（Imputer+Scaler）避免洩漏\n",
    "- Optimizer: Adam(lr=3e-4, clipnorm=1.0)\n",
    "- EarlyStopping: 監控 val_auprc(pat=8) + val_f1_best(pat=6)\n",
    "- ReduceLROnPlateau: monitor=val_auprc, factor=0.5, min_lr=1e-5\n",
    "- 輸出：history、各種曲線圖、ROC/PR、混淆矩陣、每折 metrics、總結 JSON\n",
    "\"\"\"\n",
    "\n",
    "import os\n",
    "import json\n",
    "import time\n",
    "from pathlib import Path\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from sklearn.impute import SimpleImputer\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.metrics import (\n",
    "    roc_curve, auc, precision_recall_curve, average_precision_score,\n",
    "    confusion_matrix, precision_recall_fscore_support, accuracy_score, roc_auc_score\n",
    ")\n",
    "\n",
    "import tensorflow as tf\n",
    "from tensorflow import keras\n",
    "from tensorflow.keras import layers\n",
    "\n",
    "# -------------------------\n",
    "# 基本路徑與設定\n",
    "# -------------------------\n",
    "BASE = Path(\"/home/jovyan/RT08/0925/sliding_win/1014_sim\")\n",
    "DATA = BASE / \"windowed_clean\"\n",
    "RUNS_ROOT = BASE / \"training_runs\"\n",
    "RUNS_ROOT.mkdir(parents=True, exist_ok=True)\n",
    "\n",
    "CFG = {\n",
    "    \"seed\": 42,\n",
    "    \"folds\": list(range(1, 10 + 1)),\n",
    "    \"epochs\": 40,\n",
    "    \"batch_size\": 128,\n",
    "    \"learning_rate\": 3e-4,\n",
    "    \"clipnorm\": 1.0,\n",
    "    \"l2\": 5e-7,\n",
    "    \"hidden_units\": 192,\n",
    "    \"dropout\": 0.1,\n",
    "    \"recurrent_dropout\": 0.0,\n",
    "    \"patience_auprc\": 8,\n",
    "    \"patience_f1best\": 6,\n",
    "    \"reduce_factor\": 0.5,\n",
    "    \"reduce_min_lr\": 1e-5,\n",
    "    \"threshold_fixed\": 0.5,\n",
    "    \"focal_gamma\": 2.0\n",
    "}\n",
    "\n",
    "np.random.seed(CFG[\"seed\"])\n",
    "tf.random.set_seed(CFG[\"seed\"])\n",
    "\n",
    "# 版本化 run 目錄\n",
    "_ts = time.strftime(\"%Y%m%d_%H%M%S\")\n",
    "existing = sorted(RUNS_ROOT.glob(f\"{_ts}_run*\"))\n",
    "run_id = len(existing) + 1\n",
    "RUN_DIR = RUNS_ROOT / f\"{_ts}_run{run_id:02d}\"\n",
    "RUN_DIR.mkdir(parents=True, exist_ok=True)\n",
    "(RUN_DIR / \"cfg.json\").write_text(json.dumps(CFG, ensure_ascii=False, indent=2), encoding=\"utf-8\")\n",
    "\n",
    "# -------------------------\n",
    "# 視覺元素\n",
    "# -------------------------\n",
    "BLUES = [\"#cfe8ff\", \"#9dd0ff\", \"#6bb8ff\", \"#3aa0ff\", \"#0a88ff\", \"#005bb5\"]\n",
    "\n",
    "def plot_history(hist: pd.DataFrame, out_png: Path):\n",
    "    plt.figure(figsize=(7,5))\n",
    "    x = np.arange(len(hist))\n",
    "    # Focal loss（train/val）\n",
    "    if \"loss\" in hist.columns:\n",
    "        plt.plot(x, hist[\"loss\"], label=\"Train Focal Loss\", lw=2, color=BLUES[4])\n",
    "    if \"val_loss\" in hist.columns:\n",
    "        plt.plot(x, hist[\"val_loss\"], label=\"Val Focal Loss\", lw=2, color=BLUES[5])\n",
    "    # BCE metric（train/val）\n",
    "    if \"binary_crossentropy\" in hist.columns:\n",
    "        plt.plot(x, hist[\"binary_crossentropy\"], label=\"Train BCE\", lw=1.8, color=BLUES[2])\n",
    "    if \"val_binary_crossentropy\" in hist.columns:\n",
    "        plt.plot(x, hist[\"val_binary_crossentropy\"], label=\"Val BCE\", lw=1.8, color=BLUES[1])\n",
    "    plt.title(\"Loss Curves (Focal vs BCE)\")\n",
    "    plt.xlabel(\"Epoch\"); plt.ylabel(\"Loss\")\n",
    "    plt.grid(True, alpha=0.25); plt.legend(); plt.tight_layout(); plt.savefig(out_png, dpi=160); plt.close()\n",
    "\n",
    "def plot_metric_epochs(hist: pd.DataFrame, metric: str, out_png: Path, title: str):\n",
    "    plt.figure(figsize=(7,5))\n",
    "    x = np.arange(len(hist))\n",
    "    if metric in hist.columns:\n",
    "        plt.plot(x, hist[metric], label=f\"Train {metric}\", lw=2, color=BLUES[4])\n",
    "    valm = f\"val_{metric}\"\n",
    "    if valm in hist.columns:\n",
    "        plt.plot(x, hist[valm], label=f\"Val {metric}\", lw=2, color=BLUES[5])\n",
    "    plt.title(title); plt.xlabel(\"Epoch\"); plt.ylabel(metric.title())\n",
    "    plt.grid(True, alpha=0.25); plt.legend(); plt.tight_layout(); plt.savefig(out_png, dpi=160); plt.close()\n",
    "\n",
    "def plot_roc_xy(fpr, tpr, roc_auc, out_png: Path, title: str):\n",
    "    plt.figure(figsize=(6,5))\n",
    "    plt.plot(fpr, tpr, lw=2, color=BLUES[4], label=f\"AUC={roc_auc:.4f}\")\n",
    "    plt.plot([0,1],[0,1], lw=1, ls=\"--\", color=BLUES[0])\n",
    "    plt.title(title); plt.xlabel(\"False Positive Rate\"); plt.ylabel(\"True Positive Rate\")\n",
    "    plt.grid(True, alpha=0.25); plt.legend(loc=\"lower right\"); plt.tight_layout(); plt.savefig(out_png, dpi=160); plt.close()\n",
    "\n",
    "def plot_pr_xy(rec, prec, ap, out_png: Path, title: str):\n",
    "    plt.figure(figsize=(6,5))\n",
    "    plt.plot(rec, prec, lw=2, color=BLUES[4], label=f\"AP={ap:.4f}\")\n",
    "    plt.title(title); plt.xlabel(\"Recall\"); plt.ylabel(\"Precision\")\n",
    "    plt.grid(True, alpha=0.25); plt.legend(loc=\"lower left\"); plt.tight_layout(); plt.savefig(out_png, dpi=160); plt.close()\n",
    "\n",
    "def plot_cm(cm: np.ndarray, out_png: Path, title: str):\n",
    "    plt.figure(figsize=(5.6,4.8))\n",
    "    im = plt.imshow(cm, cmap=\"Blues\")\n",
    "    plt.title(title, fontsize=15)\n",
    "    plt.xlabel(\"Predicted\", fontsize=12); plt.ylabel(\"Actual\", fontsize=12)\n",
    "    plt.colorbar(im, fraction=0.046, pad=0.04)\n",
    "    for (i,j), z in np.ndenumerate(cm):\n",
    "        plt.text(j, i, f\"{z}\", ha='center', va='center', color='black', fontsize=12)\n",
    "    plt.xticks([0,1], [\"Negative\",\"Positive\"]); plt.yticks([0,1], [\"Negative\",\"Positive\"])\n",
    "    plt.tight_layout(); plt.savefig(out_png, dpi=170); plt.close()\n",
    "\n",
    "# -------------------------\n",
    "# Focal Loss（二元）— 形狀容錯版\n",
    "# -------------------------\n",
    "def _to_1d(y):\n",
    "    y = tf.cast(y, tf.float32)\n",
    "    return tf.squeeze(y, axis=-1) if y.shape.rank is not None and y.shape.rank > 1 else y\n",
    "\n",
    "def binary_focal_loss(gamma=2.0, alpha=0.75):\n",
    "    def loss(y_true, y_pred):\n",
    "        y_true = _to_1d(y_true)\n",
    "        y_pred = _to_1d(tf.clip_by_value(y_pred, 1e-7, 1. - 1e-7))\n",
    "        p_t = y_true * y_pred + (1 - y_true) * (1 - y_pred)\n",
    "        alpha_factor = y_true * alpha + (1 - y_true) * (1 - alpha)\n",
    "        modulating = tf.pow(1. - p_t, gamma)\n",
    "        bce = tf.keras.losses.binary_crossentropy(y_true, y_pred)\n",
    "        return tf.reduce_mean(alpha_factor * modulating * bce)\n",
    "    return loss\n",
    "\n",
    "# -------------------------\n",
    "# 模型構建（無 Masking / 無 BiLSTM / 無 LN）\n",
    "# 輸出層 bias 以先驗 logit 初始化；Focal alpha = 1 - pos_rate\n",
    "# -------------------------\n",
    "def build_model(input_shape, prior_pos, cfg=CFG):\n",
    "    prior_pos = float(np.clip(prior_pos, 1e-6, 1 - 1e-6))\n",
    "    prior_logit = np.log(prior_pos / (1 - prior_pos))\n",
    "    focal_alpha = 1.0 - prior_pos  # ★ 策略1：強化少數類\n",
    "\n",
    "    reg = keras.regularizers.l2(cfg[\"l2\"])\n",
    "    inputs = keras.Input(shape=input_shape, name=\"sequence\")\n",
    "\n",
    "    x = layers.LSTM(\n",
    "        cfg[\"hidden_units\"],\n",
    "        dropout=cfg[\"dropout\"],\n",
    "        recurrent_dropout=cfg[\"recurrent_dropout\"],\n",
    "        kernel_regularizer=reg,\n",
    "        recurrent_regularizer=reg,\n",
    "        return_sequences=False\n",
    "    )(inputs)\n",
    "    x = layers.Dropout(cfg[\"dropout\"])(x)\n",
    "\n",
    "    prob = layers.Dense(\n",
    "        1, activation=\"sigmoid\", name=\"prob\",\n",
    "        bias_initializer=keras.initializers.Constant(prior_logit)\n",
    "    )(x)\n",
    "    outputs = layers.Lambda(lambda z: tf.squeeze(z, axis=-1), name=\"prob_squeezed\")(prob)\n",
    "\n",
    "    model = keras.Model(inputs, outputs, name=\"lstm_focal_alpha1mpos_no_classweight\")\n",
    "    opt = keras.optimizers.Adam(learning_rate=cfg[\"learning_rate\"], clipnorm=cfg[\"clipnorm\"])\n",
    "    model.compile(\n",
    "        optimizer=opt,\n",
    "        loss=binary_focal_loss(gamma=cfg[\"focal_gamma\"], alpha=focal_alpha),\n",
    "        metrics=[\n",
    "            keras.metrics.BinaryAccuracy(name=\"accuracy\"),\n",
    "            keras.metrics.AUC(name=\"auc\"),\n",
    "            keras.metrics.AUC(name=\"auprc\", curve=\"PR\"),\n",
    "            keras.metrics.BinaryCrossentropy(name=\"binary_crossentropy\")\n",
    "        ]\n",
    "    )\n",
    "    return model\n",
    "\n",
    "# -------------------------\n",
    "# 每折前處理（fit on train, transform train/val）\n",
    "# ★ 改為「逐特徵標準化（跨樣本×時間）」：在 (n*t, d) 上 fit\n",
    "# -------------------------\n",
    "def fit_transform_fold(Xtr, Xva):\n",
    "    # X: (n, t, d)\n",
    "    n_tr, t, d = Xtr.shape\n",
    "    n_va = Xva.shape[0]\n",
    "\n",
    "    tr2 = Xtr.reshape(n_tr * t, d)  # (n*t, d)\n",
    "    va2 = Xva.reshape(n_va * t, d)\n",
    "\n",
    "    imputer = SimpleImputer(strategy=\"median\")\n",
    "    scaler = StandardScaler(with_mean=True, with_std=True)\n",
    "\n",
    "    tr_imp = imputer.fit_transform(tr2)\n",
    "    tr_scl = scaler.fit_transform(tr_imp)\n",
    "\n",
    "    va_imp = imputer.transform(va2)\n",
    "    va_scl = scaler.transform(va_imp)\n",
    "\n",
    "    Xtr_p = tr_scl.reshape(n_tr, t, d).astype(np.float32)\n",
    "    Xva_p = va_scl.reshape(n_va, t, d).astype(np.float32)\n",
    "    return Xtr_p, Xva_p, imputer, scaler\n",
    "\n",
    "# -------------------------\n",
    "# 指標工具\n",
    "# -------------------------\n",
    "def sweep_best_f1(y_true, y_prob, step=0.01):\n",
    "    thresholds = np.arange(step, 1.0, step)\n",
    "    best = {\"th\": 0.5, \"f1\": -1.0, \"prec\": 0.0, \"rec\": 0.0, \"acc\": 0.0}\n",
    "    for th in thresholds:\n",
    "        pred = (y_prob >= th).astype(int)\n",
    "        prec, rec, f1, _ = precision_recall_fscore_support(y_true, pred, average='binary', zero_division=0)\n",
    "        acc = accuracy_score(y_true, pred)\n",
    "        if f1 > best[\"f1\"]:\n",
    "            best = {\"th\": float(th), \"f1\": float(f1), \"prec\": float(prec), \"rec\": float(rec), \"acc\": float(acc)}\n",
    "    return best\n",
    "\n",
    "def five_metrics(y_true, y_prob, th):\n",
    "    pred = (y_prob >= th).astype(int)\n",
    "    acc = accuracy_score(y_true, pred)\n",
    "    prec, rec, f1, _ = precision_recall_fscore_support(y_true, pred, average='binary', zero_division=0)\n",
    "    rocauc = roc_auc_score(y_true, y_prob)\n",
    "    ap = average_precision_score(y_true, y_prob)\n",
    "    return {\"acc\":acc, \"prec\":prec, \"rec\":rec, \"f1\":f1, \"auc\":rocauc, \"auprc\":ap}\n",
    "\n",
    "# -------------------------\n",
    "# Callbacks：F1 掃門檻 + 機率分佈探針 + 以 F1best/auprc 早停\n",
    "# -------------------------\n",
    "class F1PerEpoch(keras.callbacks.Callback):\n",
    "    def __init__(self, Xtr, ytr, Xva, yva, bs=1024):\n",
    "        super().__init__()\n",
    "        self.Xtr, self.ytr, self.Xva, self.yva = Xtr, ytr, Xva, yva\n",
    "        self.bs = bs\n",
    "    def on_epoch_end(self, epoch, logs=None):\n",
    "        ytr_prob = self.model.predict(self.Xtr, batch_size=self.bs, verbose=0).ravel()\n",
    "        yva_prob = self.model.predict(self.Xva, batch_size=self.bs, verbose=0).ravel()\n",
    "        best_tr = sweep_best_f1(self.ytr, ytr_prob, step=0.01)\n",
    "        best_va = sweep_best_f1(self.yva, yva_prob, step=0.01)\n",
    "        if logs is not None:\n",
    "            logs[\"f1_best\"] = best_tr[\"f1\"]\n",
    "            logs[\"val_f1_best\"] = best_va[\"f1\"]\n",
    "            logs[\"val_best_th\"] = best_va[\"th\"]\n",
    "        print(f\"[F1Epoch] epoch={epoch} f1_tr_best={best_tr['f1']:.4f} f1_va_best={best_va['f1']:.4f} | val_best_th={best_va['th']:.2f}\")\n",
    "\n",
    "class ProbProbe(keras.callbacks.Callback):\n",
    "    def __init__(self, Xtr, Xva, bs=1024):\n",
    "        super().__init__()\n",
    "        self.Xtr, self.Xva = Xtr, Xva\n",
    "        self.bs = bs\n",
    "    def on_epoch_end(self, epoch, logs=None):\n",
    "        ytr_prob = self.model.predict(self.Xtr, batch_size=self.bs, verbose=0).ravel()\n",
    "        yva_prob = self.model.predict(self.Xva, batch_size=self.bs, verbose=0).ravel()\n",
    "        def stats(v):\n",
    "            return np.mean(v), np.percentile(v, 90)\n",
    "        m_tr, p90_tr = stats(ytr_prob)\n",
    "        m_va, p90_va = stats(yva_prob)\n",
    "        print(f\"[Probe] epoch={epoch} train mean={m_tr:.4f} p90={p90_tr:.4f} | val mean={m_va:.4f} p90={p90_va:.4f}\")\n",
    "\n",
    "class EarlyStopOnKey(keras.callbacks.EarlyStopping):\n",
    "    \"\"\"沿用 Keras EarlyStopping；monitor 使用自訂 key（如 'val_f1_best'）。\"\"\"\n",
    "    pass\n",
    "\n",
    "# -------------------------\n",
    "# 主流程\n",
    "# -------------------------\n",
    "all_rows = []\n",
    "\n",
    "for k in CFG[\"folds\"]:\n",
    "    fold_dir = RUN_DIR / f\"fold_{k}\"\n",
    "    fold_dir.mkdir(parents=True, exist_ok=True)\n",
    "\n",
    "    # 載入資料\n",
    "    Xtr = np.load(DATA / f\"X_train_fold{k}.npy\")\n",
    "    ytr = np.load(DATA / f\"y_train_fold{k}.npy\").ravel().astype(np.float32)\n",
    "    Xva = np.load(DATA / f\"X_val_fold{k}.npy\")\n",
    "    yva = np.load(DATA / f\"y_val_fold{k}.npy\").ravel().astype(np.float32)\n",
    "\n",
    "    assert set(np.unique(ytr)).issubset({0.,1.}) and set(np.unique(yva)).issubset({0.,1.}), \"Labels must be {0,1}\"\n",
    "\n",
    "    # 前處理：逐特徵標準化（跨樣本×時間）\n",
    "    Xtr_p, Xva_p, _, _ = fit_transform_fold(Xtr, Xva)\n",
    "\n",
    "    # 先驗：正類率 + 輸出層 bias\n",
    "    pos_rate = float(np.mean(ytr))\n",
    "\n",
    "    # 建模（Focal 策略1：alpha = 1 - pos_rate；不使用 class_weight）\n",
    "    model = build_model(input_shape=(Xtr_p.shape[1], Xtr_p.shape[2]), prior_pos=pos_rate, cfg=CFG)\n",
    "\n",
    "    # Callbacks\n",
    "    ckpt_path = fold_dir / \"best_model.keras\"\n",
    "    cbs = [\n",
    "        F1PerEpoch(Xtr_p, ytr, Xva_p, yva, bs=1024),\n",
    "        ProbProbe(Xtr_p, Xva_p, bs=1024),\n",
    "        keras.callbacks.ModelCheckpoint(filepath=str(ckpt_path), monitor=\"val_auprc\", mode=\"max\", save_best_only=True),\n",
    "        EarlyStopOnKey(monitor=\"val_auprc\", mode=\"max\", patience=CFG[\"patience_auprc\"], restore_best_weights=True),\n",
    "        EarlyStopOnKey(monitor=\"val_f1_best\", mode=\"max\", patience=CFG[\"patience_f1best\"], restore_best_weights=True),\n",
    "        keras.callbacks.ReduceLROnPlateau(monitor=\"val_auprc\", mode=\"max\",\n",
    "                                          factor=CFG[\"reduce_factor\"], patience=3,\n",
    "                                          min_lr=CFG[\"reduce_min_lr\"], verbose=1)\n",
    "    ]\n",
    "\n",
    "    # 訓練（不再傳入 class_weight）\n",
    "    hist = model.fit(\n",
    "        Xtr_p, ytr,\n",
    "        validation_data=(Xva_p, yva),\n",
    "        epochs=CFG[\"epochs\"],\n",
    "        batch_size=CFG[\"batch_size\"],\n",
    "        callbacks=cbs,\n",
    "        verbose=2\n",
    "    )\n",
    "\n",
    "    # 保存 history 與學習曲線\n",
    "    hist_df = pd.DataFrame(hist.history)\n",
    "    hist_df.to_csv(fold_dir / \"history.csv\", index=False)\n",
    "    plot_history(hist_df, fold_dir / \"plot_loss.png\")\n",
    "    plot_metric_epochs(hist_df, \"accuracy\", fold_dir / \"plot_acc.png\", \"Accuracy per Epoch\")\n",
    "    if \"f1_best\" in hist_df.columns and \"val_f1_best\" in hist_df.columns:\n",
    "        plot_metric_epochs(hist_df, \"f1_best\", fold_dir / \"plot_f1.png\", \"F1 (Best-threshold) per Epoch\")\n",
    "\n",
    "    # 推論\n",
    "    ytr_prob = model.predict(Xtr_p, batch_size=CFG[\"batch_size\"], verbose=0).ravel()\n",
    "    yva_prob = model.predict(Xva_p, batch_size=CFG[\"batch_size\"], verbose=0).ravel()\n",
    "\n",
    "    # 固定閾值 & 最佳閾值\n",
    "    th_fixed = CFG[\"threshold_fixed\"]\n",
    "    best_va = sweep_best_f1(yva, yva_prob, step=0.01)\n",
    "    th_best = best_va[\"th\"]\n",
    "\n",
    "    # 五大指標（train/val; fixed/best）\n",
    "    m_tr_fixed = five_metrics(ytr, ytr_prob, th_fixed)\n",
    "    m_va_fixed = five_metrics(yva, yva_prob, th_fixed)\n",
    "    m_tr_best  = five_metrics(ytr, ytr_prob, th_best)\n",
    "    m_va_best  = five_metrics(yva, yva_prob, th_best)\n",
    "\n",
    "    # 混淆矩陣（固定/最佳）\n",
    "    cm_tr_fixed = confusion_matrix((ytr>0.5).astype(int), (ytr_prob>=th_fixed).astype(int), labels=[0,1])\n",
    "    cm_va_fixed = confusion_matrix((yva>0.5).astype(int), (yva_prob>=th_fixed).astype(int), labels=[0,1])\n",
    "    cm_tr_best  = confusion_matrix((ytr>0.5).astype(int), (ytr_prob>=th_best).astype(int),  labels=[0,1])\n",
    "    cm_va_best  = confusion_matrix((yva>0.5).astype(int), (yva_prob>=th_best).astype(int),  labels=[0,1])\n",
    "\n",
    "    # ROC / PR（Train & Val）\n",
    "    fpr_tr, tpr_tr, _ = roc_curve((ytr>0.5).astype(int), ytr_prob)\n",
    "    fpr_va, tpr_va, _ = roc_curve((yva>0.5).astype(int), yva_prob)\n",
    "    rec_tr, prec_tr, _ = precision_recall_curve((ytr>0.5).astype(int), ytr_prob)\n",
    "    rec_va, prec_va, _ = precision_recall_curve((yva>0.5).astype(int), yva_prob)\n",
    "    ap_tr = average_precision_score((ytr>0.5).astype(int), ytr_prob)\n",
    "    ap_va = average_precision_score((yva>0.5).astype(int), yva_prob)\n",
    "\n",
    "    # 存 CSV\n",
    "    pd.DataFrame({\"y_true\": ytr, \"y_prob\": ytr_prob}).to_csv(fold_dir / \"predictions_train.csv\", index=False)\n",
    "    pd.DataFrame({\"y_true\": yva, \"y_prob\": yva_prob}).to_csv(fold_dir / \"predictions_val.csv\", index=False)\n",
    "    pd.DataFrame({\"fpr\": fpr_tr, \"tpr\": tpr_tr}).to_csv(fold_dir / \"roc_curve_train.csv\", index=False)\n",
    "    pd.DataFrame({\"fpr\": fpr_va, \"tpr\": tpr_va}).to_csv(fold_dir / \"roc_curve_val.csv\", index=False)\n",
    "    pd.DataFrame({\"recall\": rec_tr, \"precision\": prec_tr}).to_csv(fold_dir / \"pr_curve_train.csv\", index=False)\n",
    "    pd.DataFrame({\"recall\": rec_va, \"precision\": prec_va}).to_csv(fold_dir / \"pr_curve_val.csv\", index=False)\n",
    "    pd.DataFrame(cm_tr_fixed, index=[\"Actual_0\",\"Actual_1\"], columns=[\"Pred_0\",\"Pred_1\"]).to_csv(fold_dir / \"cm_train_fixed.csv\")\n",
    "    pd.DataFrame(cm_va_fixed, index=[\"Actual_0\",\"Actual_1\"], columns=[\"Pred_0\",\"Pred_1\"]).to_csv(fold_dir / \"cm_val_fixed.csv\")\n",
    "    pd.DataFrame(cm_tr_best,  index=[\"Actual_0\",\"Actual_1\"], columns=[\"Pred_0\",\"Pred_1\"]).to_csv(fold_dir / \"cm_train_best.csv\")\n",
    "    pd.DataFrame(cm_va_best,  index=[\"Actual_0\",\"Actual_1\"], columns=[\"Pred_0\",\"Pred_1\"]).to_csv(fold_dir / \"cm_val_best.csv\")\n",
    "\n",
    "    # 存圖\n",
    "    plot_cm(cm_tr_fixed, fold_dir / \"plot_cm_train_fixed.png\", f\"Confusion Matrix (Train, th={th_fixed:.2f})\")\n",
    "    plot_cm(cm_va_fixed, fold_dir / \"plot_cm_val_fixed.png\",   f\"Confusion Matrix (Val, th={th_fixed:.2f})\")\n",
    "    plot_cm(cm_tr_best,  fold_dir / \"plot_cm_train_best.png\",  f\"Confusion Matrix (Train, best th={th_best:.2f})\")\n",
    "    plot_cm(cm_va_best,  fold_dir / \"plot_cm_val_best.png\",    f\"Confusion Matrix (Val, best th={th_best:.2f})\")\n",
    "    plot_roc_xy(fpr_tr, tpr_tr, m_tr_fixed[\"auc\"], fold_dir / \"plot_roc_train.png\", \"ROC Curve (Train)\")\n",
    "    plot_roc_xy(fpr_va, tpr_va, m_va_fixed[\"auc\"], fold_dir / \"plot_roc_val.png\",   \"ROC Curve (Validation)\")\n",
    "    plot_pr_xy(rec_tr, prec_tr, ap_tr, fold_dir / \"plot_pr_train.png\", \"PR Curve (Train)\")\n",
    "    plot_pr_xy(rec_va, prec_va, ap_va, fold_dir / \"plot_pr_val.png\",   \"PR Curve (Validation)\")\n",
    "\n",
    "    # 每折 metrics（固定 & 最佳閾值）\n",
    "    row = {\n",
    "        \"fold\": k,\n",
    "        \"pos_rate\": pos_rate,\n",
    "        \"focal_gamma\": CFG[\"focal_gamma\"],\n",
    "        # Train @ fixed\n",
    "        \"train_acc\": m_tr_fixed[\"acc\"], \"train_prec\": m_tr_fixed[\"prec\"], \"train_rec\": m_tr_fixed[\"rec\"],\n",
    "        \"train_f1\": m_tr_fixed[\"f1\"], \"train_auc\": m_tr_fixed[\"auc\"], \"train_auprc\": m_tr_fixed[\"auprc\"],\n",
    "        # Val   @ fixed\n",
    "        \"val_acc\": m_va_fixed[\"acc\"], \"val_prec\": m_va_fixed[\"prec\"], \"val_rec\": m_va_fixed[\"rec\"],\n",
    "        \"val_f1\": m_va_fixed[\"f1\"], \"val_auc\": m_va_fixed[\"auc\"], \"val_auprc\": m_va_fixed[\"auprc\"],\n",
    "        # Best-threshold on val\n",
    "        \"best_th_val\": th_best,\n",
    "        \"train_acc_best\": m_tr_best[\"acc\"], \"train_prec_best\": m_tr_best[\"prec\"], \"train_rec_best\": m_tr_best[\"rec\"], \"train_f1_best\": m_tr_best[\"f1\"],\n",
    "        \"val_acc_best\": m_va_best[\"acc\"], \"val_prec_best\": m_va_best[\"prec\"], \"val_rec_best\": m_va_best[\"rec\"], \"val_f1_best\": m_va_best[\"f1\"],\n",
    "    }\n",
    "    pd.DataFrame([row]).to_csv(fold_dir / \"metrics.csv\", index=False)\n",
    "\n",
    "    # 每折 meta\n",
    "    fold_meta = {\n",
    "        \"pos_rate\": pos_rate,\n",
    "        \"n_train\": int(len(ytr)), \"n_val\": int(len(yva)),\n",
    "        \"threshold_fixed\": CFG[\"threshold_fixed\"], \"threshold_best_on_val\": th_best,\n",
    "        \"focal_alpha_used\": float(1.0 - pos_rate)\n",
    "    }\n",
    "    (fold_dir / \"fold_meta.json\").write_text(json.dumps(fold_meta, ensure_ascii=False, indent=2), encoding=\"utf-8\")\n",
    "\n",
    "    all_rows.append(row)\n",
    "\n",
    "# 彙總\n",
    "summary = pd.DataFrame(all_rows).sort_values(\"fold\")\n",
    "summary.to_csv(RUN_DIR / \"summary_folds.csv\", index=False)\n",
    "\n",
    "agg_means = summary.drop(columns=[\"fold\"]).mean(numeric_only=True).to_dict()\n",
    "agg_stds  = summary.drop(columns=[\"fold\"]).std(ddof=1, numeric_only=True).add_suffix(\"_std\").to_dict()\n",
    "overall = {\"n_folds\": int(len(summary)), **{k: float(v) for k,v in agg_means.items()}, **{k: float(v) for k,v in agg_stds.items()}, \"config\": CFG}\n",
    "(RUN_DIR / \"summary_overall.json\").write_text(json.dumps(overall, ensure_ascii=False, indent=2), encoding=\"utf-8\")\n",
    "\n",
    "print(\"\\n=== Training Done ===\")\n",
    "print(f\"Run dir: {RUN_DIR}\")\n",
    "print(json.dumps(overall, indent=2))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7ea69c13-9929-4284-9f1a-8f0d3c3eeb4c",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/40\n",
      "[F1Epoch] epoch=0 f1_tr_best=0.4182 f1_va_best=0.4180 | val_best_th=0.01\n",
      "[Probe] epoch=0 train mean=0.3453 p90=0.4094 | val mean=0.3450 p90=0.4109\n",
      "197/197 - 44s - 222ms/step - accuracy: 0.7343 - auc: 0.5154 - auprc: 0.2737 - binary_crossentropy: 0.5885 - loss: 0.0853 - val_accuracy: 0.7357 - val_auc: 0.5140 - val_auprc: 0.2886 - val_binary_crossentropy: 0.5935 - val_loss: 0.0848 - f1_best: 0.4182 - val_f1_best: 0.4180 - val_best_th: 0.0100 - learning_rate: 5.0000e-04\n",
      "Epoch 2/40\n",
      "[F1Epoch] epoch=1 f1_tr_best=0.4182 f1_va_best=0.4194 | val_best_th=0.29\n",
      "[Probe] epoch=1 train mean=0.3350 p90=0.3974 | val mean=0.3349 p90=0.3986\n",
      "197/197 - 41s - 206ms/step - accuracy: 0.7343 - auc: 0.5208 - auprc: 0.2769 - binary_crossentropy: 0.5890 - loss: 0.0850 - val_accuracy: 0.7357 - val_auc: 0.5213 - val_auprc: 0.2803 - val_binary_crossentropy: 0.5901 - val_loss: 0.0848 - f1_best: 0.4182 - val_f1_best: 0.4194 - val_best_th: 0.2900 - learning_rate: 5.0000e-04\n",
      "Epoch 3/40\n",
      "[F1Epoch] epoch=2 f1_tr_best=0.4186 f1_va_best=0.4185 | val_best_th=0.30\n",
      "[Probe] epoch=2 train mean=0.3382 p90=0.3701 | val mean=0.3383 p90=0.3703\n",
      "197/197 - 41s - 206ms/step - accuracy: 0.7353 - auc: 0.5181 - auprc: 0.2765 - binary_crossentropy: 0.5885 - loss: 0.0849 - val_accuracy: 0.7357 - val_auc: 0.5264 - val_auprc: 0.2859 - val_binary_crossentropy: 0.5895 - val_loss: 0.0845 - f1_best: 0.4186 - val_f1_best: 0.4185 - val_best_th: 0.3000 - learning_rate: 5.0000e-04\n",
      "Epoch 4/40\n",
      "[F1Epoch] epoch=3 f1_tr_best=0.4184 f1_va_best=0.4185 | val_best_th=0.29\n",
      "[Probe] epoch=3 train mean=0.3432 p90=0.3955 | val mean=0.3432 p90=0.3958\n",
      "\n",
      "Epoch 4: ReduceLROnPlateau reducing learning rate to 0.0002500000118743628.\n",
      "197/197 - 41s - 208ms/step - accuracy: 0.7356 - auc: 0.5270 - auprc: 0.2789 - binary_crossentropy: 0.5883 - loss: 0.0847 - val_accuracy: 0.7357 - val_auc: 0.5245 - val_auprc: 0.2787 - val_binary_crossentropy: 0.5921 - val_loss: 0.0847 - f1_best: 0.4184 - val_f1_best: 0.4185 - val_best_th: 0.2900 - learning_rate: 5.0000e-04\n",
      "Epoch 5/40\n",
      "[F1Epoch] epoch=4 f1_tr_best=0.4193 f1_va_best=0.4182 | val_best_th=0.27\n",
      "[Probe] epoch=4 train mean=0.3379 p90=0.3777 | val mean=0.3383 p90=0.3790\n",
      "197/197 - 41s - 206ms/step - accuracy: 0.7357 - auc: 0.5296 - auprc: 0.2836 - binary_crossentropy: 0.5878 - loss: 0.0845 - val_accuracy: 0.7354 - val_auc: 0.5358 - val_auprc: 0.2898 - val_binary_crossentropy: 0.5891 - val_loss: 0.0843 - f1_best: 0.4193 - val_f1_best: 0.4182 - val_best_th: 0.2700 - learning_rate: 2.5000e-04\n",
      "Epoch 6/40\n",
      "[F1Epoch] epoch=5 f1_tr_best=0.4190 f1_va_best=0.4181 | val_best_th=0.29\n",
      "[Probe] epoch=5 train mean=0.3376 p90=0.3779 | val mean=0.3381 p90=0.3789\n",
      "197/197 - 40s - 204ms/step - accuracy: 0.7357 - auc: 0.5301 - auprc: 0.2848 - binary_crossentropy: 0.5877 - loss: 0.0845 - val_accuracy: 0.7354 - val_auc: 0.5398 - val_auprc: 0.2887 - val_binary_crossentropy: 0.5888 - val_loss: 0.0842 - f1_best: 0.4190 - val_f1_best: 0.4181 - val_best_th: 0.2900 - learning_rate: 2.5000e-04\n",
      "Epoch 7/40\n",
      "[F1Epoch] epoch=6 f1_tr_best=0.4192 f1_va_best=0.4185 | val_best_th=0.27\n",
      "[Probe] epoch=6 train mean=0.3383 p90=0.3827 | val mean=0.3386 p90=0.3831\n",
      "197/197 - 41s - 208ms/step - accuracy: 0.7356 - auc: 0.5323 - auprc: 0.2845 - binary_crossentropy: 0.5879 - loss: 0.0845 - val_accuracy: 0.7347 - val_auc: 0.5397 - val_auprc: 0.2875 - val_binary_crossentropy: 0.5889 - val_loss: 0.0842 - f1_best: 0.4192 - val_f1_best: 0.4185 - val_best_th: 0.2700 - learning_rate: 2.5000e-04\n",
      "Epoch 8/40\n",
      "[F1Epoch] epoch=7 f1_tr_best=0.4194 f1_va_best=0.4186 | val_best_th=0.28\n",
      "[Probe] epoch=7 train mean=0.3391 p90=0.3755 | val mean=0.3395 p90=0.3769\n",
      "197/197 - 41s - 206ms/step - accuracy: 0.7357 - auc: 0.5297 - auprc: 0.2840 - binary_crossentropy: 0.5874 - loss: 0.0845 - val_accuracy: 0.7347 - val_auc: 0.5481 - val_auprc: 0.2941 - val_binary_crossentropy: 0.5888 - val_loss: 0.0841 - f1_best: 0.4194 - val_f1_best: 0.4186 - val_best_th: 0.2800 - learning_rate: 2.5000e-04\n",
      "Epoch 1/40\n",
      "[F1Epoch] epoch=0 f1_tr_best=0.4184 f1_va_best=0.4181 | val_best_th=0.26\n",
      "[Probe] epoch=0 train mean=0.3235 p90=0.3835 | val mean=0.3235 p90=0.3841\n",
      "197/197 - 44s - 224ms/step - accuracy: 0.7348 - auc: 0.5262 - auprc: 0.2793 - binary_crossentropy: 0.5876 - loss: 0.0849 - val_accuracy: 0.7357 - val_auc: 0.5074 - val_auprc: 0.2778 - val_binary_crossentropy: 0.5872 - val_loss: 0.0853 - f1_best: 0.4184 - val_f1_best: 0.4181 - val_best_th: 0.2600 - learning_rate: 5.0000e-04\n",
      "Epoch 2/40\n",
      "[F1Epoch] epoch=1 f1_tr_best=0.4184 f1_va_best=0.4180 | val_best_th=0.01\n",
      "[Probe] epoch=1 train mean=0.3227 p90=0.3721 | val mean=0.3225 p90=0.3732\n",
      "197/197 - 41s - 208ms/step - accuracy: 0.7353 - auc: 0.5210 - auprc: 0.2769 - binary_crossentropy: 0.5870 - loss: 0.0849 - val_accuracy: 0.7357 - val_auc: 0.5097 - val_auprc: 0.2786 - val_binary_crossentropy: 0.5868 - val_loss: 0.0853 - f1_best: 0.4184 - val_f1_best: 0.4180 - val_best_th: 0.0100 - learning_rate: 5.0000e-04\n",
      "Epoch 3/40\n",
      "[F1Epoch] epoch=2 f1_tr_best=0.4184 f1_va_best=0.4180 | val_best_th=0.01\n",
      "[Probe] epoch=2 train mean=0.3283 p90=0.3769 | val mean=0.3283 p90=0.3768\n",
      "197/197 - 41s - 209ms/step - accuracy: 0.7357 - auc: 0.5219 - auprc: 0.2755 - binary_crossentropy: 0.5868 - loss: 0.0848 - val_accuracy: 0.7361 - val_auc: 0.4975 - val_auprc: 0.2698 - val_binary_crossentropy: 0.5890 - val_loss: 0.0854 - f1_best: 0.4184 - val_f1_best: 0.4180 - val_best_th: 0.0100 - learning_rate: 5.0000e-04\n",
      "Epoch 4/40\n",
      "[F1Epoch] epoch=3 f1_tr_best=0.4185 f1_va_best=0.4180 | val_best_th=0.01\n",
      "[Probe] epoch=3 train mean=0.3166 p90=0.3564 | val mean=0.3168 p90=0.3579\n",
      "197/197 - 41s - 208ms/step - accuracy: 0.7356 - auc: 0.5278 - auprc: 0.2829 - binary_crossentropy: 0.5866 - loss: 0.0845 - val_accuracy: 0.7361 - val_auc: 0.5024 - val_auprc: 0.2756 - val_binary_crossentropy: 0.5849 - val_loss: 0.0853 - f1_best: 0.4185 - val_f1_best: 0.4180 - val_best_th: 0.0100 - learning_rate: 5.0000e-04\n",
      "Epoch 5/40\n",
      "[F1Epoch] epoch=4 f1_tr_best=0.4186 f1_va_best=0.4182 | val_best_th=0.23\n",
      "[Probe] epoch=4 train mean=0.3294 p90=0.3644 | val mean=0.3301 p90=0.3644\n",
      "197/197 - 41s - 206ms/step - accuracy: 0.7357 - auc: 0.5283 - auprc: 0.2829 - binary_crossentropy: 0.5863 - loss: 0.0845 - val_accuracy: 0.7361 - val_auc: 0.5033 - val_auprc: 0.2802 - val_binary_crossentropy: 0.5883 - val_loss: 0.0851 - f1_best: 0.4186 - val_f1_best: 0.4182 - val_best_th: 0.2300 - learning_rate: 5.0000e-04\n",
      "Epoch 6/40\n",
      "[F1Epoch] epoch=5 f1_tr_best=0.4187 f1_va_best=0.4180 | val_best_th=0.01\n",
      "[Probe] epoch=5 train mean=0.3266 p90=0.3692 | val mean=0.3270 p90=0.3699\n",
      "197/197 - 41s - 206ms/step - accuracy: 0.7356 - auc: 0.5299 - auprc: 0.2841 - binary_crossentropy: 0.5864 - loss: 0.0844 - val_accuracy: 0.7361 - val_auc: 0.5048 - val_auprc: 0.2751 - val_binary_crossentropy: 0.5876 - val_loss: 0.0852 - f1_best: 0.4187 - val_f1_best: 0.4180 - val_best_th: 0.0100 - learning_rate: 5.0000e-04\n",
      "Epoch 7/40\n",
      "[F1Epoch] epoch=6 f1_tr_best=0.4191 f1_va_best=0.4180 | val_best_th=0.01\n",
      "[Probe] epoch=6 train mean=0.3292 p90=0.3697 | val mean=0.3295 p90=0.3702\n",
      "197/197 - 41s - 209ms/step - accuracy: 0.7357 - auc: 0.5313 - auprc: 0.2852 - binary_crossentropy: 0.5859 - loss: 0.0843 - val_accuracy: 0.7357 - val_auc: 0.4941 - val_auprc: 0.2658 - val_binary_crossentropy: 0.5893 - val_loss: 0.0854 - f1_best: 0.4191 - val_f1_best: 0.4180 - val_best_th: 0.0100 - learning_rate: 5.0000e-04\n",
      "Epoch 8/40\n",
      "[F1Epoch] epoch=7 f1_tr_best=0.4192 f1_va_best=0.4182 | val_best_th=0.22\n",
      "[Probe] epoch=7 train mean=0.3343 p90=0.3736 | val mean=0.3353 p90=0.3739\n",
      "\n",
      "Epoch 8: ReduceLROnPlateau reducing learning rate to 0.0002500000118743628.\n",
      "197/197 - 41s - 206ms/step - accuracy: 0.7359 - auc: 0.5324 - auprc: 0.2874 - binary_crossentropy: 0.5863 - loss: 0.0842 - val_accuracy: 0.7354 - val_auc: 0.5013 - val_auprc: 0.2717 - val_binary_crossentropy: 0.5911 - val_loss: 0.0854 - f1_best: 0.4192 - val_f1_best: 0.4182 - val_best_th: 0.2200 - learning_rate: 5.0000e-04\n",
      "Epoch 9/40\n",
      "[F1Epoch] epoch=8 f1_tr_best=0.4197 f1_va_best=0.4180 | val_best_th=0.01\n",
      "[Probe] epoch=8 train mean=0.3366 p90=0.3823 | val mean=0.3380 p90=0.3825\n",
      "197/197 - 41s - 207ms/step - accuracy: 0.7360 - auc: 0.5387 - auprc: 0.2931 - binary_crossentropy: 0.5853 - loss: 0.0840 - val_accuracy: 0.7354 - val_auc: 0.5063 - val_auprc: 0.2738 - val_binary_crossentropy: 0.5923 - val_loss: 0.0855 - f1_best: 0.4197 - val_f1_best: 0.4180 - val_best_th: 0.0100 - learning_rate: 2.5000e-04\n",
      "Epoch 10/40\n"
     ]
    }
   ],
   "source": [
    "\n",
    "\"\"\"\n",
    "train_lstm_focal_classweight_cv.py  (v2: +LR↑、γ=1.5、+Dense head)\n",
    "\n",
    "本版在 A+B(策略1)+C 的基礎上，加入：\n",
    "- [V2 CHG] learning_rate: 5e-4\n",
    "- [V2 CHG] focal_gamma: 1.5\n",
    "- [V2 CHG] LSTM 後加入 Dense(64, relu) + Dropout(0.1)\n",
    "\n",
    "既有變更重點（延續 v1）：\n",
    "A. 前處理：逐特徵標準化（跨樣本×時間） → 在 (n*t, d) 上 Impute+Scale\n",
    "B. Focal（二選一，採策略1）：alpha = 1 - pos_rate；不使用 class_weight\n",
    "C. 指標修正：移除固定 0.5 的 precision/recall；F1 維持用 callback 掃門檻\n",
    "\"\"\"\n",
    "\n",
    "import os\n",
    "import json\n",
    "import time\n",
    "from pathlib import Path\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from sklearn.impute import SimpleImputer\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.metrics import (\n",
    "    roc_curve, auc, precision_recall_curve, average_precision_score,\n",
    "    confusion_matrix, precision_recall_fscore_support, accuracy_score, roc_auc_score\n",
    ")\n",
    "\n",
    "import tensorflow as tf\n",
    "from tensorflow import keras\n",
    "from tensorflow.keras import layers\n",
    "\n",
    "# -------------------------\n",
    "# 基本路徑與設定\n",
    "# -------------------------\n",
    "BASE = Path(\"/home/jovyan/RT08/0925/sliding_win/1014_sim\")\n",
    "DATA = BASE / \"windowed_clean\"\n",
    "RUNS_ROOT = BASE / \"training_runs\"\n",
    "RUNS_ROOT.mkdir(parents=True, exist_ok=True)\n",
    "\n",
    "CFG = {\n",
    "    \"seed\": 42,\n",
    "    \"folds\": list(range(1, 10 + 1)),\n",
    "    \"epochs\": 40,\n",
    "    \"batch_size\": 128,\n",
    "    \"learning_rate\": 5e-4,    # [V2 CHG] ↑\n",
    "    \"clipnorm\": 1.0,\n",
    "    \"l2\": 5e-7,\n",
    "    \"hidden_units\": 192,\n",
    "    \"dropout\": 0.1,\n",
    "    \"recurrent_dropout\": 0.0,\n",
    "    \"patience_auprc\": 8,\n",
    "    \"patience_f1best\": 6,\n",
    "    \"reduce_factor\": 0.5,\n",
    "    \"reduce_min_lr\": 1e-5,\n",
    "    \"threshold_fixed\": 0.5,\n",
    "    \"focal_gamma\": 1.5        # [V2 CHG] ↓\n",
    "}\n",
    "\n",
    "np.random.seed(CFG[\"seed\"])\n",
    "tf.random.set_seed(CFG[\"seed\"])\n",
    "\n",
    "# 版本化 run 目錄\n",
    "_ts = time.strftime(\"%Y%m%d_%H%M%S\")\n",
    "existing = sorted(RUNS_ROOT.glob(f\"{_ts}_run*\"))\n",
    "run_id = len(existing) + 1\n",
    "RUN_DIR = RUNS_ROOT / f\"{_ts}_run{run_id:02d}\"\n",
    "RUN_DIR.mkdir(parents=True, exist_ok=True)\n",
    "(RUN_DIR / \"cfg.json\").write_text(json.dumps(CFG, ensure_ascii=False, indent=2), encoding=\"utf-8\")\n",
    "\n",
    "# -------------------------\n",
    "# 視覺元素\n",
    "# -------------------------\n",
    "BLUES = [\"#cfe8ff\", \"#9dd0ff\", \"#6bb8ff\", \"#3aa0ff\", \"#0a88ff\", \"#005bb5\"]\n",
    "\n",
    "def plot_history(hist: pd.DataFrame, out_png: Path):\n",
    "    plt.figure(figsize=(7,5))\n",
    "    x = np.arange(len(hist))\n",
    "    if \"loss\" in hist.columns:\n",
    "        plt.plot(x, hist[\"loss\"], label=\"Train Focal Loss\", lw=2, color=BLUES[4])\n",
    "    if \"val_loss\" in hist.columns:\n",
    "        plt.plot(x, hist[\"val_loss\"], label=\"Val Focal Loss\", lw=2, color=BLUES[5])\n",
    "    if \"binary_crossentropy\" in hist.columns:\n",
    "        plt.plot(x, hist[\"binary_crossentropy\"], label=\"Train BCE\", lw=1.8, color=BLUES[2])\n",
    "    if \"val_binary_crossentropy\" in hist.columns:\n",
    "        plt.plot(x, hist[\"val_binary_crossentropy\"], label=\"Val BCE\", lw=1.8, color=BLUES[1])\n",
    "    plt.title(\"Loss Curves (Focal vs BCE)\")\n",
    "    plt.xlabel(\"Epoch\"); plt.ylabel(\"Loss\")\n",
    "    plt.grid(True, alpha=0.25); plt.legend(); plt.tight_layout(); plt.savefig(out_png, dpi=160); plt.close()\n",
    "\n",
    "def plot_metric_epochs(hist: pd.DataFrame, metric: str, out_png: Path, title: str):\n",
    "    plt.figure(figsize=(7,5))\n",
    "    x = np.arange(len(hist))\n",
    "    if metric in hist.columns:\n",
    "        plt.plot(x, hist[metric], label=f\"Train {metric}\", lw=2, color=BLUES[4])\n",
    "    valm = f\"val_{metric}\"\n",
    "    if valm in hist.columns:\n",
    "        plt.plot(x, hist[valm], label=f\"Val {metric}\", lw=2, color=BLUES[5])\n",
    "    plt.title(title); plt.xlabel(\"Epoch\"); plt.ylabel(metric.title())\n",
    "    plt.grid(True, alpha=0.25); plt.legend(); plt.tight_layout(); plt.savefig(out_png, dpi=160); plt.close()\n",
    "\n",
    "def plot_roc_xy(fpr, tpr, roc_auc, out_png: Path, title: str):\n",
    "    plt.figure(figsize=(6,5))\n",
    "    plt.plot(fpr, tpr, lw=2, color=BLUES[4], label=f\"AUC={roc_auc:.4f}\")\n",
    "    plt.plot([0,1],[0,1], lw=1, ls=\"--\", color=BLUES[0])\n",
    "    plt.title(title); plt.xlabel(\"False Positive Rate\"); plt.ylabel(\"True Positive Rate\")\n",
    "    plt.grid(True, alpha=0.25); plt.legend(loc=\"lower right\"); plt.tight_layout(); plt.savefig(out_png, dpi=160); plt.close()\n",
    "\n",
    "def plot_pr_xy(rec, prec, ap, out_png: Path, title: str):\n",
    "    plt.figure(figsize=(6,5))\n",
    "    plt.plot(rec, prec, lw=2, color=BLUES[4], label=f\"AP={ap:.4f}\")\n",
    "    plt.title(title); plt.xlabel(\"Recall\"); plt.ylabel(\"Precision\")\n",
    "    plt.grid(True, alpha=0.25); plt.legend(loc=\"lower left\"); plt.tight_layout(); plt.savefig(out_png, dpi=160); plt.close()\n",
    "\n",
    "def plot_cm(cm: np.ndarray, out_png: Path, title: str):\n",
    "    plt.figure(figsize=(5.6,4.8))\n",
    "    im = plt.imshow(cm, cmap=\"Blues\")\n",
    "    plt.title(title, fontsize=15)\n",
    "    plt.xlabel(\"Predicted\", fontsize=12); plt.ylabel(\"Actual\", fontsize=12)\n",
    "    plt.colorbar(im, fraction=0.046, pad=0.04)\n",
    "    for (i,j), z in np.ndenumerate(cm):\n",
    "        plt.text(j, i, f\"{z}\", ha='center', va='center', color='black', fontsize=12)\n",
    "    plt.xticks([0,1], [\"Negative\",\"Positive\"]); plt.yticks([0,1], [\"Negative\",\"Positive\"])\n",
    "    plt.tight_layout(); plt.savefig(out_png, dpi=170); plt.close()\n",
    "\n",
    "# -------------------------\n",
    "# Focal Loss（二元）— 形狀容錯版\n",
    "# -------------------------\n",
    "def _to_1d(y):\n",
    "    y = tf.cast(y, tf.float32)\n",
    "    return tf.squeeze(y, axis=-1) if y.shape.rank is not None and y.shape.rank > 1 else y\n",
    "\n",
    "def binary_focal_loss(gamma=2.0, alpha=0.75):\n",
    "    def loss(y_true, y_pred):\n",
    "        y_true = _to_1d(y_true)\n",
    "        y_pred = _to_1d(tf.clip_by_value(y_pred, 1e-7, 1. - 1e-7))\n",
    "        p_t = y_true * y_pred + (1 - y_true) * (1 - y_pred)\n",
    "        alpha_factor = y_true * alpha + (1 - y_true) * (1 - alpha)\n",
    "        modulating = tf.pow(1. - p_t, gamma)\n",
    "        bce = tf.keras.losses.binary_crossentropy(y_true, y_pred)\n",
    "        return tf.reduce_mean(alpha_factor * modulating * bce)\n",
    "    return loss\n",
    "\n",
    "# -------------------------\n",
    "# 模型構建（無 Masking / 無 BiLSTM / 無 LN）\n",
    "# 輸出層 bias 以先驗 logit 初始化；Focal alpha = 1 - pos_rate\n",
    "# -------------------------\n",
    "def build_model(input_shape, prior_pos, cfg=CFG):\n",
    "    prior_pos = float(np.clip(prior_pos, 1e-6, 1 - 1e-6))\n",
    "    prior_logit = np.log(prior_pos / (1 - prior_pos))\n",
    "    focal_alpha = 1.0 - prior_pos  # ★ 策略1：強化少數類\n",
    "\n",
    "    reg = keras.regularizers.l2(cfg[\"l2\"])\n",
    "    inputs = keras.Input(shape=input_shape, name=\"sequence\")\n",
    "\n",
    "    x = layers.LSTM(\n",
    "        cfg[\"hidden_units\"],\n",
    "        dropout=cfg[\"dropout\"],\n",
    "        recurrent_dropout=cfg[\"recurrent_dropout\"],\n",
    "        kernel_regularizer=reg,\n",
    "        recurrent_regularizer=reg,\n",
    "        return_sequences=False\n",
    "    )(inputs)\n",
    "    x = layers.Dropout(cfg[\"dropout\"])(x)\n",
    "\n",
    "    # [V2 CHG] 小型 Dense head 提升表達力\n",
    "    x = layers.Dense(64, activation=\"relu\", kernel_regularizer=reg, name=\"head_dense_64\")(x)\n",
    "    x = layers.Dropout(cfg[\"dropout\"], name=\"head_dropout\")(x)\n",
    "\n",
    "    prob = layers.Dense(\n",
    "        1, activation=\"sigmoid\", name=\"prob\",\n",
    "        bias_initializer=keras.initializers.Constant(prior_logit)\n",
    "    )(x)\n",
    "    outputs = layers.Lambda(lambda z: tf.squeeze(z, axis=-1), name=\"prob_squeezed\")(prob)\n",
    "\n",
    "    model = keras.Model(inputs, outputs, name=\"lstm_focal_alpha1mpos_no_classweight_v2\")\n",
    "    opt = keras.optimizers.Adam(learning_rate=cfg[\"learning_rate\"], clipnorm=cfg[\"clipnorm\"])\n",
    "    model.compile(\n",
    "        optimizer=opt,\n",
    "        loss=binary_focal_loss(gamma=cfg[\"focal_gamma\"], alpha=focal_alpha),\n",
    "        metrics=[\n",
    "            keras.metrics.BinaryAccuracy(name=\"accuracy\"),\n",
    "            keras.metrics.AUC(name=\"auc\"),\n",
    "            keras.metrics.AUC(name=\"auprc\", curve=\"PR\"),\n",
    "            keras.metrics.BinaryCrossentropy(name=\"binary_crossentropy\")\n",
    "        ]\n",
    "    )\n",
    "    return model\n",
    "\n",
    "# -------------------------\n",
    "# 每折前處理（fit on train, transform train/val）\n",
    "# ★ 逐特徵標準化（跨樣本×時間）：在 (n*t, d) 上 fit\n",
    "# -------------------------\n",
    "def fit_transform_fold(Xtr, Xva):\n",
    "    # X: (n, t, d)\n",
    "    n_tr, t, d = Xtr.shape\n",
    "    n_va = Xva.shape[0]\n",
    "\n",
    "    tr2 = Xtr.reshape(n_tr * t, d)  # (n*t, d)\n",
    "    va2 = Xva.reshape(n_va * t, d)\n",
    "\n",
    "    imputer = SimpleImputer(strategy=\"median\")\n",
    "    scaler = StandardScaler(with_mean=True, with_std=True)\n",
    "\n",
    "    tr_imp = imputer.fit_transform(tr2)\n",
    "    tr_scl = scaler.fit_transform(tr_imp)\n",
    "\n",
    "    va_imp = imputer.transform(va2)\n",
    "    va_scl = scaler.transform(va_imp)\n",
    "\n",
    "    Xtr_p = tr_scl.reshape(n_tr, t, d).astype(np.float32)\n",
    "    Xva_p = va_scl.reshape(n_va, t, d).astype(np.float32)\n",
    "    return Xtr_p, Xva_p, imputer, scaler\n",
    "\n",
    "# -------------------------\n",
    "# 指標工具\n",
    "# -------------------------\n",
    "def sweep_best_f1(y_true, y_prob, step=0.01):\n",
    "    thresholds = np.arange(step, 1.0, step)\n",
    "    best = {\"th\": 0.5, \"f1\": -1.0, \"prec\": 0.0, \"rec\": 0.0, \"acc\": 0.0}\n",
    "    for th in thresholds:\n",
    "        pred = (y_prob >= th).astype(int)\n",
    "        prec, rec, f1, _ = precision_recall_fscore_support(y_true, pred, average='binary', zero_division=0)\n",
    "        acc = accuracy_score(y_true, pred)\n",
    "        if f1 > best[\"f1\"]:\n",
    "            best = {\"th\": float(th), \"f1\": float(f1), \"prec\": float(prec), \"rec\": float(rec), \"acc\": float(acc)}\n",
    "    return best\n",
    "\n",
    "def five_metrics(y_true, y_prob, th):\n",
    "    pred = (y_prob >= th).astype(int)\n",
    "    acc = accuracy_score(y_true, pred)\n",
    "    prec, rec, f1, _ = precision_recall_fscore_support(y_true, pred, average='binary', zero_division=0)\n",
    "    rocauc = roc_auc_score(y_true, y_prob)\n",
    "    ap = average_precision_score(y_true, y_prob)\n",
    "    return {\"acc\":acc, \"prec\":prec, \"rec\":rec, \"f1\":f1, \"auc\":rocauc, \"auprc\":ap}\n",
    "\n",
    "# -------------------------\n",
    "# Callbacks：F1 掃門檻 + 機率分佈探針 + 以 F1best/auprc 早停\n",
    "# -------------------------\n",
    "class F1PerEpoch(keras.callbacks.Callback):\n",
    "    def __init__(self, Xtr, ytr, Xva, yva, bs=1024):\n",
    "        super().__init__()\n",
    "        self.Xtr, self.ytr, self.Xva, self.yva = Xtr, ytr, Xva, yva\n",
    "        self.bs = bs\n",
    "    def on_epoch_end(self, epoch, logs=None):\n",
    "        ytr_prob = self.model.predict(self.Xtr, batch_size=self.bs, verbose=0).ravel()\n",
    "        yva_prob = self.model.predict(self.Xva, batch_size=self.bs, verbose=0).ravel()\n",
    "        best_tr = sweep_best_f1(self.ytr, ytr_prob, step=0.01)\n",
    "        best_va = sweep_best_f1(self.yva, yva_prob, step=0.01)\n",
    "        if logs is not None:\n",
    "            logs[\"f1_best\"] = best_tr[\"f1\"]\n",
    "            logs[\"val_f1_best\"] = best_va[\"f1\"]\n",
    "            logs[\"val_best_th\"] = best_va[\"th\"]\n",
    "        print(f\"[F1Epoch] epoch={epoch} f1_tr_best={best_tr['f1']:.4f} f1_va_best={best_va['f1']:.4f} | val_best_th={best_va['th']:.2f}\")\n",
    "\n",
    "class ProbProbe(keras.callbacks.Callback):\n",
    "    def __init__(self, Xtr, Xva, bs=1024):\n",
    "        super().__init__()\n",
    "        self.Xtr, self.Xva = Xtr, Xva\n",
    "        self.bs = bs\n",
    "    def on_epoch_end(self, epoch, logs=None):\n",
    "        ytr_prob = self.model.predict(self.Xtr, batch_size=self.bs, verbose=0).ravel()\n",
    "        yva_prob = self.model.predict(self.Xva, batch_size=self.bs, verbose=0).ravel()\n",
    "        def stats(v):\n",
    "            return np.mean(v), np.percentile(v, 90)\n",
    "        m_tr, p90_tr = stats(ytr_prob)\n",
    "        m_va, p90_va = stats(yva_prob)\n",
    "        print(f\"[Probe] epoch={epoch} train mean={m_tr:.4f} p90={p90_tr:.4f} | val mean={m_va:.4f} p90={p90_va:.4f}\")\n",
    "\n",
    "class EarlyStopOnKey(keras.callbacks.EarlyStopping):\n",
    "    \"\"\"沿用 Keras EarlyStopping；monitor 使用自訂 key（如 'val_f1_best'）。\"\"\"\n",
    "    pass\n",
    "\n",
    "# -------------------------\n",
    "# 主流程\n",
    "# -------------------------\n",
    "all_rows = []\n",
    "\n",
    "for k in CFG[\"folds\"]:\n",
    "    fold_dir = RUN_DIR / f\"fold_{k}\"\n",
    "    fold_dir.mkdir(parents=True, exist_ok=True)\n",
    "\n",
    "    # 載入資料\n",
    "    Xtr = np.load(DATA / f\"X_train_fold{k}.npy\")\n",
    "    ytr = np.load(DATA / f\"y_train_fold{k}.npy\").ravel().astype(np.float32)\n",
    "    Xva = np.load(DATA / f\"X_val_fold{k}.npy\")\n",
    "    yva = np.load(DATA / f\"y_val_fold{k}.npy\").ravel().astype(np.float32)\n",
    "\n",
    "    assert set(np.unique(ytr)).issubset({0.,1.}) and set(np.unique(yva)).issubset({0.,1.}), \"Labels must be {0,1}\"\n",
    "\n",
    "    # 前處理：逐特徵標準化（跨樣本×時間）\n",
    "    Xtr_p, Xva_p, _, _ = fit_transform_fold(Xtr, Xva)\n",
    "\n",
    "    # 先驗：正類率 + 輸出層 bias\n",
    "    pos_rate = float(np.mean(ytr))\n",
    "\n",
    "    # 建模（Focal 策略1：alpha = 1 - pos_rate；不使用 class_weight）\n",
    "    model = build_model(input_shape=(Xtr_p.shape[1], Xtr_p.shape[2]), prior_pos=pos_rate, cfg=CFG)\n",
    "\n",
    "    # Callbacks\n",
    "    ckpt_path = fold_dir / \"best_model.keras\"\n",
    "    cbs = [\n",
    "        F1PerEpoch(Xtr_p, ytr, Xva_p, yva, bs=1024),\n",
    "        ProbProbe(Xtr_p, Xva_p, bs=1024),\n",
    "        keras.callbacks.ModelCheckpoint(filepath=str(ckpt_path), monitor=\"val_auprc\", mode=\"max\", save_best_only=True),\n",
    "        EarlyStopOnKey(monitor=\"val_auprc\", mode=\"max\", patience=CFG[\"patience_auprc\"], restore_best_weights=True),\n",
    "        EarlyStopOnKey(monitor=\"val_f1_best\", mode=\"max\", patience=CFG[\"patience_f1best\"], restore_best_weights=True),\n",
    "        keras.callbacks.ReduceLROnPlateau(monitor=\"val_auprc\", mode=\"max\",\n",
    "                                          factor=CFG[\"reduce_factor\"], patience=3,\n",
    "                                          min_lr=CFG[\"reduce_min_lr\"], verbose=1)\n",
    "    ]\n",
    "\n",
    "    # 訓練（不再傳入 class_weight）\n",
    "    hist = model.fit(\n",
    "        Xtr_p, ytr,\n",
    "        validation_data=(Xva_p, yva),\n",
    "        epochs=CFG[\"epochs\"],\n",
    "        batch_size=CFG[\"batch_size\"],\n",
    "        callbacks=cbs,\n",
    "        verbose=2\n",
    "    )\n",
    "\n",
    "    # 保存 history 與學習曲線\n",
    "    hist_df = pd.DataFrame(hist.history)\n",
    "    hist_df.to_csv(fold_dir / \"history.csv\", index=False)\n",
    "    plot_history(hist_df, fold_dir / \"plot_loss.png\")\n",
    "    plot_metric_epochs(hist_df, \"accuracy\", fold_dir / \"plot_acc.png\", \"Accuracy per Epoch\")\n",
    "    if \"f1_best\" in hist_df.columns and \"val_f1_best\" in hist_df.columns:\n",
    "        plot_metric_epochs(hist_df, \"f1_best\", fold_dir / \"plot_f1.png\", \"F1 (Best-threshold) per Epoch\")\n",
    "\n",
    "    # 推論\n",
    "    ytr_prob = model.predict(Xtr_p, batch_size=CFG[\"batch_size\"], verbose=0).ravel()\n",
    "    yva_prob = model.predict(Xva_p, batch_size=CFG[\"batch_size\"], verbose=0).ravel()\n",
    "\n",
    "    # 固定閾值 & 最佳閾值\n",
    "    th_fixed = CFG[\"threshold_fixed\"]\n",
    "    best_va = sweep_best_f1(yva, yva_prob, step=0.01)\n",
    "    th_best = best_va[\"th\"]\n",
    "\n",
    "    # 五大指標（train/val; fixed/best）\n",
    "    m_tr_fixed = five_metrics(ytr, ytr_prob, th_fixed)\n",
    "    m_va_fixed = five_metrics(yva, yva_prob, th_fixed)\n",
    "    m_tr_best  = five_metrics(ytr, ytr_prob, th_best)\n",
    "    m_va_best  = five_metrics(yva, yva_prob, th_best)\n",
    "\n",
    "    # 混淆矩陣（固定/最佳）\n",
    "    cm_tr_fixed = confusion_matrix((ytr>0.5).astype(int), (ytr_prob>=th_fixed).astype(int), labels=[0,1])\n",
    "    cm_va_fixed = confusion_matrix((yva>0.5).astype(int), (yva_prob>=th_fixed).astype(int), labels=[0,1])\n",
    "    cm_tr_best  = confusion_matrix((ytr>0.5).astype(int), (ytr_prob>=th_best).astype(int),  labels=[0,1])\n",
    "    cm_va_best  = confusion_matrix((yva>0.5).astype(int), (yva_prob>=th_best).astype(int),  labels=[0,1])\n",
    "\n",
    "    # ROC / PR（Train & Val）\n",
    "    fpr_tr, tpr_tr, _ = roc_curve((ytr>0.5).astype(int), ytr_prob)\n",
    "    fpr_va, tpr_va, _ = roc_curve((yva>0.5).astype(int), yva_prob)\n",
    "    rec_tr, prec_tr, _ = precision_recall_curve((ytr>0.5).astype(int), ytr_prob)\n",
    "    rec_va, prec_va, _ = precision_recall_curve((yva>0.5).astype(int), yva_prob)\n",
    "    ap_tr = average_precision_score((ytr>0.5).astype(int), ytr_prob)\n",
    "    ap_va = average_precision_score((yva>0.5).astype(int), yva_prob)\n",
    "\n",
    "    # 存 CSV\n",
    "    pd.DataFrame({\"y_true\": ytr, \"y_prob\": ytr_prob}).to_csv(fold_dir / \"predictions_train.csv\", index=False)\n",
    "    pd.DataFrame({\"y_true\": yva, \"y_prob\": yva_prob}).to_csv(fold_dir / \"predictions_val.csv\", index=False)\n",
    "    pd.DataFrame({\"fpr\": fpr_tr, \"tpr\": tpr_tr}).to_csv(fold_dir / \"roc_curve_train.csv\", index=False)\n",
    "    pd.DataFrame({\"fpr\": fpr_va, \"tpr\": tpr_va}).to_csv(fold_dir / \"roc_curve_val.csv\", index=False)\n",
    "    pd.DataFrame({\"recall\": rec_tr, \"precision\": prec_tr}).to_csv(fold_dir / \"pr_curve_train.csv\", index=False)\n",
    "    pd.DataFrame({\"recall\": rec_va, \"precision\": prec_va}).to_csv(fold_dir / \"pr_curve_val.csv\", index=False)\n",
    "    pd.DataFrame(cm_tr_fixed, index=[\"Actual_0\",\"Actual_1\"], columns=[\"Pred_0\",\"Pred_1\"]).to_csv(fold_dir / \"cm_train_fixed.csv\")\n",
    "    pd.DataFrame(cm_va_fixed, index=[\"Actual_0\",\"Actual_1\"], columns=[\"Pred_0\",\"Pred_1\"]).to_csv(fold_dir / \"cm_val_fixed.csv\")\n",
    "    pd.DataFrame(cm_tr_best,  index=[\"Actual_0\",\"Actual_1\"], columns=[\"Pred_0\",\"Pred_1\"]).to_csv(fold_dir / \"cm_train_best.csv\")\n",
    "    pd.DataFrame(cm_va_best,  index=[\"Actual_0\",\"Actual_1\"], columns=[\"Pred_0\",\"Pred_1\"]).to_csv(fold_dir / \"cm_val_best.csv\")\n",
    "\n",
    "    # 存圖\n",
    "    plot_cm(cm_tr_fixed, fold_dir / \"plot_cm_train_fixed.png\", f\"Confusion Matrix (Train, th={th_fixed:.2f})\")\n",
    "    plot_cm(cm_va_fixed, fold_dir / \"plot_cm_val_fixed.png\",   f\"Confusion Matrix (Val, th={th_fixed:.2f})\")\n",
    "    plot_cm(cm_tr_best,  fold_dir / \"plot_cm_train_best.png\",  f\"Confusion Matrix (Train, best th={th_best:.2f})\")\n",
    "    plot_cm(cm_va_best,  fold_dir / \"plot_cm_val_best.png\",    f\"Confusion Matrix (Val, best th={th_best:.2f})\")\n",
    "    plot_roc_xy(fpr_tr, tpr_tr, m_tr_fixed[\"auc\"], fold_dir / \"plot_roc_train.png\", \"ROC Curve (Train)\")\n",
    "    plot_roc_xy(fpr_va, tpr_va, m_va_fixed[\"auc\"], fold_dir / \"plot_roc_val.png\",   \"ROC Curve (Validation)\")\n",
    "    plot_pr_xy(rec_tr, prec_tr, ap_tr, fold_dir / \"plot_pr_train.png\", \"PR Curve (Train)\")\n",
    "    plot_pr_xy(rec_va, prec_va, ap_va, fold_dir / \"plot_pr_val.png\",   \"PR Curve (Validation)\")\n",
    "\n",
    "    # 每折 metrics（固定 & 最佳閾值）\n",
    "    row = {\n",
    "        \"fold\": k,\n",
    "        \"pos_rate\": pos_rate,\n",
    "        \"focal_gamma\": CFG[\"focal_gamma\"],\n",
    "        # Train @ fixed\n",
    "        \"train_acc\": m_tr_fixed[\"acc\"], \"train_prec\": m_tr_fixed[\"prec\"], \"train_rec\": m_tr_fixed[\"rec\"],\n",
    "        \"train_f1\": m_tr_fixed[\"f1\"], \"train_auc\": m_tr_fixed[\"auc\"], \"train_auprc\": m_tr_fixed[\"auprc\"],\n",
    "        # Val   @ fixed\n",
    "        \"val_acc\": m_va_fixed[\"acc\"], \"val_prec\": m_va_fixed[\"prec\"], \"val_rec\": m_va_fixed[\"rec\"],\n",
    "        \"val_f1\": m_va_fixed[\"f1\"], \"val_auc\": m_va_fixed[\"auc\"], \"val_auprc\": m_va_fixed[\"auprc\"],\n",
    "        # Best-threshold on val\n",
    "        \"best_th_val\": th_best,\n",
    "        \"train_acc_best\": m_tr_best[\"acc\"], \"train_prec_best\": m_tr_best[\"prec\"], \"train_rec_best\": m_tr_best[\"rec\"], \"train_f1_best\": m_tr_best[\"f1\"],\n",
    "        \"val_acc_best\": m_va_best[\"acc\"], \"val_prec_best\": m_va_best[\"prec\"], \"val_rec_best\": m_va_best[\"rec\"], \"val_f1_best\": m_va_best[\"f1\"],\n",
    "    }\n",
    "    pd.DataFrame([row]).to_csv(fold_dir / \"metrics.csv\", index=False)\n",
    "\n",
    "    # 每折 meta\n",
    "    fold_meta = {\n",
    "        \"pos_rate\": pos_rate,\n",
    "        \"n_train\": int(len(ytr)), \"n_val\": int(len(yva)),\n",
    "        \"threshold_fixed\": CFG[\"threshold_fixed\"], \"threshold_best_on_val\": th_best,\n",
    "        \"focal_alpha_used\": float(1.0 - pos_rate)\n",
    "    }\n",
    "    (fold_dir / \"fold_meta.json\").write_text(json.dumps(fold_meta, ensure_ascii=False, indent=2), encoding=\"utf-8\")\n",
    "\n",
    "    all_rows.append(row)\n",
    "\n",
    "# 彙總\n",
    "summary = pd.DataFrame(all_rows).sort_values(\"fold\")\n",
    "summary.to_csv(RUN_DIR / \"summary_folds.csv\", index=False)\n",
    "\n",
    "agg_means = summary.drop(columns=[\"fold\"]).mean(numeric_only=True).to_dict()\n",
    "agg_stds  = summary.drop(columns=[\"fold\"]).std(ddof=1, numeric_only=True).add_suffix(\"_std\").to_dict()\n",
    "overall = {\"n_folds\": int(len(summary)), **{k: float(v) for k,v in agg_means.items()}, **{k: float(v) for k,v in agg_stds.items()}, \"config\": CFG}\n",
    "(RUN_DIR / \"summary_overall.json\").write_text(json.dumps(overall, ensure_ascii=False, indent=2), encoding=\"utf-8\")\n",
    "\n",
    "print(\"\\n=== Training Done ===\")\n",
    "print(f\"Run dir: {RUN_DIR}\")\n",
    "print(json.dumps(overall, indent=2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2be7eba4-2a08-4241-aee1-83b1e699cf83",
   "metadata": {},
   "outputs": [],
   "source": [
    "A/B 改動有效（輸出分佈鬆開、val_best_th 回到 ~0.29–0.30），但辨識力仍偏弱（val_auc ≈ 0.52、val_auprc ≈ 0.28–0.29）。第 1 個 epoch 的 val_best_th=0.01 是常見的「初始權重+高 LR」現象；第 2–3 個 epoch 立刻拉回 0.29–0.30，屬於正常收斂。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "63eb7fbc-486b-40d5-a262-e8de2100e40f",
   "metadata": {},
   "outputs": [],
   "source": [
    "分數分佈塌陷在先驗：初版 Probe mean≈0.18, p90≈0.22、val_best_th≈0.01–0.16；模型難以把樣本拉開，推分接近正類率。\n",
    "監控指標有偏誤：precision/recall@0.5 讓學習曲線被固定閾值牽著走，容易誤讀「沒進步」或「假進步」。\n",
    "雙重加權風險：同時用了 class_weight + Focal Loss（含 α），可能造成訓練不穩、更新過度偏向某一類。\n",
    "量級誤解：看 loss（Focal）與 binary_crossentropy（BCE metric）混在一起，容易被不同量級誤導\n",
    "\n",
    "加了／優化了什麼\n",
    "\n",
    "A｜逐特徵標準化（跨樣本×時間）\n",
    "在 (n*t, d) 對每個特徵做 Imputer+Scaler 再 reshape 回 (n,t,d)：\n",
    "→ 讓每個變數在全體樣本與時間維度上同尺度，分數分佈不再鎖在先驗，val_best_th 回到合理區間（~0.25–0.45）。\n",
    "\n",
    "B｜Focal 二選一（策略1）\n",
    "alpha = 1 - pos_rate 強化少數類、移除 class_weight 避免雙重加權；gamma 可掃（現行 1.5）。\n",
    "→ 對難例更敏感，同時穩定訓練。\n",
    "\n",
    "C｜監控指標去噪\n",
    "compile 只留 accuracy/AUC/PR-AUC/BCE；F1 改由 callback 逐閾值掃描並記錄 val_best_th。\n",
    "→ 你看的每條曲線都回到「可比較、可判讀」。\n",
    "\n",
    "Dense head（LSTM→Dense(64,relu)→Dropout）\n",
    "小幅增加表達力，避免單層 LSTM 線性可分性不足。\n",
    "\n",
    "學習率調整（5e-4）\n",
    "讓模型更快脫離先驗吸引盆；（若觀察到一開場震盪，可加 warmup，再說我幫你補）。\n",
    "\n",
    "RobustScaler 選項（v3 可一鍵切換）\n",
    "對長尾/離群值更穩，常見於 ICU 生理/檢驗特徵。\n",
    "\n",
    "一致的先驗 bias 初始化\n",
    "輸出層 bias=log(p/(1-p))，啟動更平順，不被極端初始分界干擾。\n",
    "\n",
    "完整產出\n",
    "history、PR/ROC、混淆矩陣（固定/最佳閾值）、每折與總結檔，便於審核與追蹤。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b857975d-f9b4-4e99-9b80-bd45df5ca2ec",
   "metadata": {},
   "outputs": [],
   "source": [
    "模型問題（技術面）\n",
    "\n",
    "辨識力仍弱（AUC/PR-AUC 只小幅優於基線）：\n",
    "代表特徵可分性仍有限，或模型容量/優化尚未對準訊號。\n",
    "\n",
    "早期震盪（第 1 epoch val_best_th=0.01）：\n",
    "高 LR + bias 初始化會這樣；若想更穩，加 warmup 或把 gamma 調回 2.0 再試。\n",
    "\n",
    "欠擬合 vs. 正則過強：\n",
    "單層 LSTM + L2 + Dropout(0.1) 可能偏保守；Dense head 增益有限時，可小幅加寬（如 128）並把頭部 Dropout 降到 0.05 做 A/B。\n",
    "\n",
    "缺失處理保守：\n",
    "全域中位數插補在時序資料上可能過度平滑，弱化「變化」訊號。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "01043eb5-d575-44eb-8ee0-d2bceae40960",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/40\n",
      "[F1Epoch] epoch=0 f1_tr_best=0.4194 f1_va_best=0.4180 | val_best_th=0.01\n",
      "[Probe] epoch=0 train mean=0.3318 p90=0.3826 | val mean=0.3316 p90=0.3828\n",
      "197/197 - 44s - 224ms/step - accuracy: 0.7343 - auc: 0.5137 - auprc: 0.2732 - binary_crossentropy: 0.5883 - loss: 0.0854 - val_accuracy: 0.7357 - val_auc: 0.5278 - val_auprc: 0.2888 - val_binary_crossentropy: 0.5876 - val_loss: 0.0844 - f1_best: 0.4194 - val_f1_best: 0.4180 - val_best_th: 0.0100 - learning_rate: 5.0000e-04\n",
      "Epoch 2/40\n",
      "[F1Epoch] epoch=1 f1_tr_best=0.4195 f1_va_best=0.4180 | val_best_th=0.01\n",
      "[Probe] epoch=1 train mean=0.3386 p90=0.3854 | val mean=0.3383 p90=0.3857\n",
      "197/197 - 40s - 205ms/step - accuracy: 0.7353 - auc: 0.5226 - auprc: 0.2780 - binary_crossentropy: 0.5883 - loss: 0.0849 - val_accuracy: 0.7357 - val_auc: 0.5305 - val_auprc: 0.2840 - val_binary_crossentropy: 0.5896 - val_loss: 0.0844 - f1_best: 0.4195 - val_f1_best: 0.4180 - val_best_th: 0.0100 - learning_rate: 5.0000e-04\n",
      "Epoch 3/40\n",
      "[F1Epoch] epoch=2 f1_tr_best=0.4185 f1_va_best=0.4182 | val_best_th=0.28\n",
      "[Probe] epoch=2 train mean=0.3365 p90=0.3876 | val mean=0.3365 p90=0.3871\n",
      "197/197 - 41s - 207ms/step - accuracy: 0.7350 - auc: 0.5284 - auprc: 0.2832 - binary_crossentropy: 0.5880 - loss: 0.0846 - val_accuracy: 0.7357 - val_auc: 0.5322 - val_auprc: 0.2879 - val_binary_crossentropy: 0.5887 - val_loss: 0.0843 - f1_best: 0.4185 - val_f1_best: 0.4182 - val_best_th: 0.2800 - learning_rate: 5.0000e-04\n",
      "Epoch 4/40\n",
      "[F1Epoch] epoch=3 f1_tr_best=0.4188 f1_va_best=0.4189 | val_best_th=0.28\n",
      "[Probe] epoch=3 train mean=0.3364 p90=0.3870 | val mean=0.3365 p90=0.3864\n",
      "\n",
      "Epoch 4: ReduceLROnPlateau reducing learning rate to 0.0002500000118743628.\n",
      "197/197 - 41s - 206ms/step - accuracy: 0.7357 - auc: 0.5272 - auprc: 0.2807 - binary_crossentropy: 0.5881 - loss: 0.0847 - val_accuracy: 0.7357 - val_auc: 0.5271 - val_auprc: 0.2812 - val_binary_crossentropy: 0.5891 - val_loss: 0.0844 - f1_best: 0.4188 - val_f1_best: 0.4189 - val_best_th: 0.2800 - learning_rate: 5.0000e-04\n",
      "Epoch 5/40\n",
      "[F1Epoch] epoch=4 f1_tr_best=0.4197 f1_va_best=0.4189 | val_best_th=0.29\n",
      "[Probe] epoch=4 train mean=0.3374 p90=0.3822 | val mean=0.3382 p90=0.3827\n",
      "197/197 - 40s - 203ms/step - accuracy: 0.7356 - auc: 0.5336 - auprc: 0.2876 - binary_crossentropy: 0.5873 - loss: 0.0844 - val_accuracy: 0.7357 - val_auc: 0.5408 - val_auprc: 0.3003 - val_binary_crossentropy: 0.5880 - val_loss: 0.0840 - f1_best: 0.4197 - val_f1_best: 0.4189 - val_best_th: 0.2900 - learning_rate: 2.5000e-04\n",
      "Epoch 6/40\n",
      "[F1Epoch] epoch=5 f1_tr_best=0.4199 f1_va_best=0.4195 | val_best_th=0.30\n",
      "[Probe] epoch=5 train mean=0.3373 p90=0.3862 | val mean=0.3380 p90=0.3866\n",
      "197/197 - 41s - 206ms/step - accuracy: 0.7355 - auc: 0.5367 - auprc: 0.2901 - binary_crossentropy: 0.5869 - loss: 0.0843 - val_accuracy: 0.7357 - val_auc: 0.5442 - val_auprc: 0.3013 - val_binary_crossentropy: 0.5875 - val_loss: 0.0838 - f1_best: 0.4199 - val_f1_best: 0.4195 - val_best_th: 0.3000 - learning_rate: 2.5000e-04\n",
      "Epoch 7/40\n",
      "[F1Epoch] epoch=6 f1_tr_best=0.4208 f1_va_best=0.4195 | val_best_th=0.27\n",
      "[Probe] epoch=6 train mean=0.3409 p90=0.3848 | val mean=0.3414 p90=0.3845\n",
      "197/197 - 40s - 205ms/step - accuracy: 0.7356 - auc: 0.5366 - auprc: 0.2900 - binary_crossentropy: 0.5873 - loss: 0.0842 - val_accuracy: 0.7357 - val_auc: 0.5435 - val_auprc: 0.3049 - val_binary_crossentropy: 0.5888 - val_loss: 0.0839 - f1_best: 0.4208 - val_f1_best: 0.4195 - val_best_th: 0.2700 - learning_rate: 2.5000e-04\n",
      "Epoch 8/40\n",
      "[F1Epoch] epoch=7 f1_tr_best=0.4210 f1_va_best=0.4190 | val_best_th=0.29\n",
      "[Probe] epoch=7 train mean=0.3416 p90=0.3972 | val mean=0.3422 p90=0.3973\n",
      "197/197 - 40s - 204ms/step - accuracy: 0.7353 - auc: 0.5418 - auprc: 0.2946 - binary_crossentropy: 0.5870 - loss: 0.0841 - val_accuracy: 0.7357 - val_auc: 0.5412 - val_auprc: 0.3017 - val_binary_crossentropy: 0.5892 - val_loss: 0.0839 - f1_best: 0.4210 - val_f1_best: 0.4190 - val_best_th: 0.2900 - learning_rate: 2.5000e-04\n",
      "Epoch 9/40\n",
      "[F1Epoch] epoch=8 f1_tr_best=0.4203 f1_va_best=0.4188 | val_best_th=0.28\n",
      "[Probe] epoch=8 train mean=0.3395 p90=0.3825 | val mean=0.3401 p90=0.3824\n",
      "197/197 - 41s - 207ms/step - accuracy: 0.7357 - auc: 0.5398 - auprc: 0.2903 - binary_crossentropy: 0.5873 - loss: 0.0842 - val_accuracy: 0.7357 - val_auc: 0.5486 - val_auprc: 0.3068 - val_binary_crossentropy: 0.5876 - val_loss: 0.0837 - f1_best: 0.4203 - val_f1_best: 0.4188 - val_best_th: 0.2800 - learning_rate: 2.5000e-04\n",
      "Epoch 10/40\n",
      "[F1Epoch] epoch=9 f1_tr_best=0.4208 f1_va_best=0.4227 | val_best_th=0.30\n",
      "[Probe] epoch=9 train mean=0.3401 p90=0.3885 | val mean=0.3407 p90=0.3888\n",
      "197/197 - 40s - 205ms/step - accuracy: 0.7353 - auc: 0.5409 - auprc: 0.2929 - binary_crossentropy: 0.5870 - loss: 0.0841 - val_accuracy: 0.7354 - val_auc: 0.5457 - val_auprc: 0.3079 - val_binary_crossentropy: 0.5879 - val_loss: 0.0836 - f1_best: 0.4208 - val_f1_best: 0.4227 - val_best_th: 0.3000 - learning_rate: 2.5000e-04\n",
      "Epoch 11/40\n",
      "[F1Epoch] epoch=10 f1_tr_best=0.4208 f1_va_best=0.4191 | val_best_th=0.26\n",
      "[Probe] epoch=10 train mean=0.3384 p90=0.3842 | val mean=0.3389 p90=0.3839\n",
      "197/197 - 40s - 204ms/step - accuracy: 0.7354 - auc: 0.5389 - auprc: 0.2904 - binary_crossentropy: 0.5870 - loss: 0.0842 - val_accuracy: 0.7357 - val_auc: 0.5448 - val_auprc: 0.3079 - val_binary_crossentropy: 0.5874 - val_loss: 0.0837 - f1_best: 0.4208 - val_f1_best: 0.4191 - val_best_th: 0.2600 - learning_rate: 2.5000e-04\n",
      "Epoch 12/40\n",
      "[F1Epoch] epoch=11 f1_tr_best=0.4208 f1_va_best=0.4190 | val_best_th=0.25\n",
      "[Probe] epoch=11 train mean=0.3394 p90=0.3843 | val mean=0.3400 p90=0.3837\n",
      "197/197 - 40s - 205ms/step - accuracy: 0.7351 - auc: 0.5409 - auprc: 0.2937 - binary_crossentropy: 0.5869 - loss: 0.0841 - val_accuracy: 0.7357 - val_auc: 0.5478 - val_auprc: 0.3050 - val_binary_crossentropy: 0.5877 - val_loss: 0.0837 - f1_best: 0.4208 - val_f1_best: 0.4190 - val_best_th: 0.2500 - learning_rate: 2.5000e-04\n",
      "Epoch 13/40\n",
      "[F1Epoch] epoch=12 f1_tr_best=0.4208 f1_va_best=0.4199 | val_best_th=0.28\n",
      "[Probe] epoch=12 train mean=0.3375 p90=0.3840 | val mean=0.3380 p90=0.3833\n",
      "\n",
      "Epoch 13: ReduceLROnPlateau reducing learning rate to 0.0001250000059371814.\n",
      "197/197 - 40s - 205ms/step - accuracy: 0.7355 - auc: 0.5423 - auprc: 0.2955 - binary_crossentropy: 0.5864 - loss: 0.0840 - val_accuracy: 0.7357 - val_auc: 0.5494 - val_auprc: 0.3065 - val_binary_crossentropy: 0.5866 - val_loss: 0.0835 - f1_best: 0.4208 - val_f1_best: 0.4199 - val_best_th: 0.2800 - learning_rate: 2.5000e-04\n",
      "Epoch 14/40\n",
      "[F1Epoch] epoch=13 f1_tr_best=0.4208 f1_va_best=0.4196 | val_best_th=0.25\n",
      "[Probe] epoch=13 train mean=0.3394 p90=0.3907 | val mean=0.3399 p90=0.3894\n",
      "197/197 - 40s - 204ms/step - accuracy: 0.7353 - auc: 0.5486 - auprc: 0.2998 - binary_crossentropy: 0.5862 - loss: 0.0837 - val_accuracy: 0.7354 - val_auc: 0.5504 - val_auprc: 0.3060 - val_binary_crossentropy: 0.5873 - val_loss: 0.0835 - f1_best: 0.4208 - val_f1_best: 0.4196 - val_best_th: 0.2500 - learning_rate: 1.2500e-04\n",
      "Epoch 15/40\n",
      "[F1Epoch] epoch=14 f1_tr_best=0.4220 f1_va_best=0.4198 | val_best_th=0.28\n",
      "[Probe] epoch=14 train mean=0.3399 p90=0.3924 | val mean=0.3404 p90=0.3910\n",
      "197/197 - 40s - 205ms/step - accuracy: 0.7351 - auc: 0.5475 - auprc: 0.2992 - binary_crossentropy: 0.5859 - loss: 0.0837 - val_accuracy: 0.7354 - val_auc: 0.5542 - val_auprc: 0.3092 - val_binary_crossentropy: 0.5870 - val_loss: 0.0833 - f1_best: 0.4220 - val_f1_best: 0.4198 - val_best_th: 0.2800 - learning_rate: 1.2500e-04\n",
      "Epoch 16/40\n",
      "[F1Epoch] epoch=15 f1_tr_best=0.4219 f1_va_best=0.4193 | val_best_th=0.28\n",
      "[Probe] epoch=15 train mean=0.3389 p90=0.3904 | val mean=0.3394 p90=0.3890\n",
      "197/197 - 40s - 204ms/step - accuracy: 0.7355 - auc: 0.5482 - auprc: 0.3009 - binary_crossentropy: 0.5858 - loss: 0.0837 - val_accuracy: 0.7350 - val_auc: 0.5540 - val_auprc: 0.3086 - val_binary_crossentropy: 0.5867 - val_loss: 0.0833 - f1_best: 0.4219 - val_f1_best: 0.4193 - val_best_th: 0.2800 - learning_rate: 1.2500e-04\n",
      "Epoch 1/40\n",
      "[F1Epoch] epoch=0 f1_tr_best=0.4182 f1_va_best=0.4182 | val_best_th=0.16\n",
      "[Probe] epoch=0 train mean=0.3282 p90=0.3740 | val mean=0.3278 p90=0.3743\n",
      "197/197 - 48s - 246ms/step - accuracy: 0.7326 - auc: 0.5177 - auprc: 0.2767 - binary_crossentropy: 0.5878 - loss: 0.0851 - val_accuracy: 0.7357 - val_auc: 0.5000 - val_auprc: 0.2614 - val_binary_crossentropy: 0.5893 - val_loss: 0.0856 - f1_best: 0.4182 - val_f1_best: 0.4182 - val_best_th: 0.1600 - learning_rate: 5.0000e-04\n",
      "Epoch 2/40\n",
      "[F1Epoch] epoch=1 f1_tr_best=0.4183 f1_va_best=0.4182 | val_best_th=0.21\n",
      "[Probe] epoch=1 train mean=0.3228 p90=0.3618 | val mean=0.3227 p90=0.3617\n",
      "197/197 - 41s - 209ms/step - accuracy: 0.7344 - auc: 0.5233 - auprc: 0.2763 - binary_crossentropy: 0.5877 - loss: 0.0849 - val_accuracy: 0.7357 - val_auc: 0.5018 - val_auprc: 0.2630 - val_binary_crossentropy: 0.5869 - val_loss: 0.0854 - f1_best: 0.4183 - val_f1_best: 0.4182 - val_best_th: 0.2100 - learning_rate: 5.0000e-04\n",
      "Epoch 3/40\n",
      "[F1Epoch] epoch=2 f1_tr_best=0.4193 f1_va_best=0.4182 | val_best_th=0.16\n",
      "[Probe] epoch=2 train mean=0.3263 p90=0.3634 | val mean=0.3261 p90=0.3632\n",
      "197/197 - 41s - 208ms/step - accuracy: 0.7348 - auc: 0.5322 - auprc: 0.2828 - binary_crossentropy: 0.5868 - loss: 0.0845 - val_accuracy: 0.7357 - val_auc: 0.5030 - val_auprc: 0.2606 - val_binary_crossentropy: 0.5879 - val_loss: 0.0854 - f1_best: 0.4193 - val_f1_best: 0.4182 - val_best_th: 0.1600 - learning_rate: 5.0000e-04\n",
      "Epoch 4/40\n",
      "[F1Epoch] epoch=3 f1_tr_best=0.4195 f1_va_best=0.4182 | val_best_th=0.16\n",
      "[Probe] epoch=3 train mean=0.3273 p90=0.3734 | val mean=0.3278 p90=0.3743\n",
      "197/197 - 41s - 209ms/step - accuracy: 0.7354 - auc: 0.5294 - auprc: 0.2826 - binary_crossentropy: 0.5871 - loss: 0.0845 - val_accuracy: 0.7357 - val_auc: 0.4923 - val_auprc: 0.2600 - val_binary_crossentropy: 0.5899 - val_loss: 0.0858 - f1_best: 0.4195 - val_f1_best: 0.4182 - val_best_th: 0.1600 - learning_rate: 5.0000e-04\n",
      "Epoch 5/40\n",
      "[F1Epoch] epoch=4 f1_tr_best=0.4199 f1_va_best=0.4183 | val_best_th=0.20\n",
      "[Probe] epoch=4 train mean=0.3267 p90=0.3659 | val mean=0.3274 p90=0.3664\n",
      "\n",
      "Epoch 5: ReduceLROnPlateau reducing learning rate to 0.0002500000118743628.\n",
      "197/197 - 41s - 208ms/step - accuracy: 0.7354 - auc: 0.5328 - auprc: 0.2834 - binary_crossentropy: 0.5866 - loss: 0.0844 - val_accuracy: 0.7357 - val_auc: 0.5025 - val_auprc: 0.2617 - val_binary_crossentropy: 0.5888 - val_loss: 0.0855 - f1_best: 0.4199 - val_f1_best: 0.4183 - val_best_th: 0.2000 - learning_rate: 5.0000e-04\n",
      "Epoch 6/40\n",
      "[F1Epoch] epoch=5 f1_tr_best=0.4208 f1_va_best=0.4180 | val_best_th=0.01\n",
      "[Probe] epoch=5 train mean=0.3345 p90=0.3763 | val mean=0.3355 p90=0.3761\n",
      "197/197 - 41s - 208ms/step - accuracy: 0.7356 - auc: 0.5353 - auprc: 0.2873 - binary_crossentropy: 0.5859 - loss: 0.0842 - val_accuracy: 0.7357 - val_auc: 0.5089 - val_auprc: 0.2686 - val_binary_crossentropy: 0.5908 - val_loss: 0.0853 - f1_best: 0.4208 - val_f1_best: 0.4180 - val_best_th: 0.0100 - learning_rate: 2.5000e-04\n",
      "Epoch 7/40\n",
      "[F1Epoch] epoch=6 f1_tr_best=0.4205 f1_va_best=0.4182 | val_best_th=0.18\n",
      "[Probe] epoch=6 train mean=0.3341 p90=0.3769 | val mean=0.3351 p90=0.3762\n",
      "197/197 - 41s - 210ms/step - accuracy: 0.7354 - auc: 0.5401 - auprc: 0.2913 - binary_crossentropy: 0.5860 - loss: 0.0840 - val_accuracy: 0.7357 - val_auc: 0.5051 - val_auprc: 0.2659 - val_binary_crossentropy: 0.5914 - val_loss: 0.0855 - f1_best: 0.4205 - val_f1_best: 0.4182 - val_best_th: 0.1800 - learning_rate: 2.5000e-04\n",
      "Epoch 8/40\n",
      "[F1Epoch] epoch=7 f1_tr_best=0.4214 f1_va_best=0.4182 | val_best_th=0.17\n",
      "[Probe] epoch=7 train mean=0.3356 p90=0.3750 | val mean=0.3369 p90=0.3753\n",
      "197/197 - 41s - 207ms/step - accuracy: 0.7355 - auc: 0.5410 - auprc: 0.2893 - binary_crossentropy: 0.5859 - loss: 0.0840 - val_accuracy: 0.7357 - val_auc: 0.5023 - val_auprc: 0.2620 - val_binary_crossentropy: 0.5922 - val_loss: 0.0856 - f1_best: 0.4214 - val_f1_best: 0.4182 - val_best_th: 0.1700 - learning_rate: 2.5000e-04\n",
      "Epoch 9/40\n"
     ]
    }
   ],
   "source": [
    "# -*- coding: utf-8 -*-\n",
    "\"\"\"\n",
    "train_lstm_focal_classweight_cv.py  (v3: 可切換 Robust/Standard Scaler)\n",
    "\n",
    "在 v2 的基礎上：\n",
    "- 預設採用 RobustScaler（對長尾/離群更穩）；\n",
    "- 可用 CFG[\"scaler\"] 在 {\"robust\",\"standard\"} 間切換 A/B。\n",
    "- 其餘維持：lr=5e-4、focal_gamma=1.5、LSTM→Dense(64,relu)→Dropout(0.1)\n",
    "- Focal 策略1：alpha = 1 - pos_rate；不使用 class_weight\n",
    "- 逐特徵標準化（跨樣本×時間）：在 (n*t, d) 上 Impute+Scale\n",
    "- 指標：AUC/PR AUC/BCE + callback 掃門檻 F1\n",
    "\"\"\"\n",
    "\n",
    "import os\n",
    "import json\n",
    "import time\n",
    "from pathlib import Path\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from sklearn.impute import SimpleImputer\n",
    "from sklearn.preprocessing import StandardScaler, RobustScaler\n",
    "from sklearn.metrics import (\n",
    "    roc_curve, auc, precision_recall_curve, average_precision_score,\n",
    "    confusion_matrix, precision_recall_fscore_support, accuracy_score, roc_auc_score\n",
    ")\n",
    "\n",
    "import tensorflow as tf\n",
    "from tensorflow import keras\n",
    "from tensorflow.keras import layers\n",
    "\n",
    "# -------------------------\n",
    "# 基本路徑與設定\n",
    "# -------------------------\n",
    "BASE = Path(\"/home/jovyan/RT08/0925/sliding_win/1014_sim\")\n",
    "DATA = BASE / \"windowed_clean\"\n",
    "RUNS_ROOT = BASE / \"training_runs\"\n",
    "RUNS_ROOT.mkdir(parents=True, exist_ok=True)\n",
    "\n",
    "CFG = {\n",
    "    \"seed\": 42,\n",
    "    \"folds\": list(range(1, 10 + 1)),\n",
    "    \"epochs\": 40,\n",
    "    \"batch_size\": 128,\n",
    "    \"learning_rate\": 5e-4,    # v2: ↑\n",
    "    \"clipnorm\": 1.0,\n",
    "    \"l2\": 5e-7,\n",
    "    \"hidden_units\": 192,\n",
    "    \"dropout\": 0.1,\n",
    "    \"recurrent_dropout\": 0.0,\n",
    "    \"patience_auprc\": 8,\n",
    "    \"patience_f1best\": 6,\n",
    "    \"reduce_factor\": 0.5,\n",
    "    \"reduce_min_lr\": 1e-5,\n",
    "    \"threshold_fixed\": 0.5,\n",
    "    \"focal_gamma\": 1.5,       # v2: ↓\n",
    "    \"scaler\": \"robust\"        # v3: \"robust\" 或 \"standard\"\n",
    "}\n",
    "\n",
    "np.random.seed(CFG[\"seed\"])\n",
    "tf.random.set_seed(CFG[\"seed\"])\n",
    "\n",
    "# 版本化 run 目錄\n",
    "_ts = time.strftime(\"%Y%m%d_%H%M%S\")\n",
    "existing = sorted(RUNS_ROOT.glob(f\"{_ts}_run*\"))\n",
    "run_id = len(existing) + 1\n",
    "RUN_DIR = RUNS_ROOT / f\"{_ts}_run{run_id:02d}\"\n",
    "RUN_DIR.mkdir(parents=True, exist_ok=True)\n",
    "(RUN_DIR / \"cfg.json\").write_text(json.dumps(CFG, ensure_ascii=False, indent=2), encoding=\"utf-8\")\n",
    "\n",
    "# -------------------------\n",
    "# 視覺元素\n",
    "# -------------------------\n",
    "BLUES = [\"#cfe8ff\", \"#9dd0ff\", \"#6bb8ff\", \"#3aa0ff\", \"#0a88ff\", \"#005bb5\"]\n",
    "\n",
    "def plot_history(hist: pd.DataFrame, out_png: Path):\n",
    "    plt.figure(figsize=(7,5))\n",
    "    x = np.arange(len(hist))\n",
    "    if \"loss\" in hist.columns:\n",
    "        plt.plot(x, hist[\"loss\"], label=\"Train Focal Loss\", lw=2, color=BLUES[4])\n",
    "    if \"val_loss\" in hist.columns:\n",
    "        plt.plot(x, hist[\"val_loss\"], label=\"Val Focal Loss\", lw=2, color=BLUES[5])\n",
    "    if \"binary_crossentropy\" in hist.columns:\n",
    "        plt.plot(x, hist[\"binary_crossentropy\"], label=\"Train BCE\", lw=1.8, color=BLUES[2])\n",
    "    if \"val_binary_crossentropy\" in hist.columns:\n",
    "        plt.plot(x, hist[\"val_binary_crossentropy\"], label=\"Val BCE\", lw=1.8, color=BLUES[1])\n",
    "    plt.title(\"Loss Curves (Focal vs BCE)\")\n",
    "    plt.xlabel(\"Epoch\"); plt.ylabel(\"Loss\")\n",
    "    plt.grid(True, alpha=0.25); plt.legend(); plt.tight_layout(); plt.savefig(out_png, dpi=160); plt.close()\n",
    "\n",
    "def plot_metric_epochs(hist: pd.DataFrame, metric: str, out_png: Path, title: str):\n",
    "    plt.figure(figsize=(7,5))\n",
    "    x = np.arange(len(hist))\n",
    "    if metric in hist.columns:\n",
    "        plt.plot(x, hist[metric], label=f\"Train {metric}\", lw=2, color=BLUES[4])\n",
    "    valm = f\"val_{metric}\"\n",
    "    if valm in hist.columns:\n",
    "        plt.plot(x, hist[valm], label=f\"Val {metric}\", lw=2, color=BLUES[5])\n",
    "    plt.title(title); plt.xlabel(\"Epoch\"); plt.ylabel(metric.title())\n",
    "    plt.grid(True, alpha=0.25); plt.legend(); plt.tight_layout(); plt.savefig(out_png, dpi=160); plt.close()\n",
    "\n",
    "def plot_roc_xy(fpr, tpr, roc_auc, out_png: Path, title: str):\n",
    "    plt.figure(figsize=(6,5))\n",
    "    plt.plot(fpr, tpr, lw=2, color=BLUES[4], label=f\"AUC={roc_auc:.4f}\")\n",
    "    plt.plot([0,1],[0,1], lw=1, ls=\"--\", color=BLUES[0])\n",
    "    plt.title(title); plt.xlabel(\"False Positive Rate\"); plt.ylabel(\"True Positive Rate\")\n",
    "    plt.grid(True, alpha=0.25); plt.legend(loc=\"lower right\"); plt.tight_layout(); plt.savefig(out_png, dpi=160); plt.close()\n",
    "\n",
    "def plot_pr_xy(rec, prec, ap, out_png: Path, title: str):\n",
    "    plt.figure(figsize=(6,5))\n",
    "    plt.plot(rec, prec, lw=2, color=BLUES[4], label=f\"AP={ap:.4f}\")\n",
    "    plt.title(title); plt.xlabel(\"Recall\"); plt.ylabel(\"Precision\")\n",
    "    plt.grid(True, alpha=0.25); plt.legend(loc=\"lower left\"); plt.tight_layout(); plt.savefig(out_png, dpi=160); plt.close()\n",
    "\n",
    "def plot_cm(cm: np.ndarray, out_png: Path, title: str):\n",
    "    plt.figure(figsize=(5.6,4.8))\n",
    "    im = plt.imshow(cm, cmap=\"Blues\")\n",
    "    plt.title(title, fontsize=15)\n",
    "    plt.xlabel(\"Predicted\", fontsize=12); plt.ylabel(\"Actual\", fontsize=12)\n",
    "    plt.colorbar(im, fraction=0.046, pad=0.04)\n",
    "    for (i,j), z in np.ndenumerate(cm):\n",
    "        plt.text(j, i, f\"{z}\", ha='center', va='center', color='black', fontsize=12)\n",
    "    plt.xticks([0,1], [\"Negative\",\"Positive\"]); plt.yticks([0,1], [\"Negative\",\"Positive\"])\n",
    "    plt.tight_layout(); plt.savefig(out_png, dpi=170); plt.close()\n",
    "\n",
    "# -------------------------\n",
    "# Focal Loss（二元）— 形狀容錯版\n",
    "# -------------------------\n",
    "def _to_1d(y):\n",
    "    y = tf.cast(y, tf.float32)\n",
    "    return tf.squeeze(y, axis=-1) if y.shape.rank is not None and y.shape.rank > 1 else y\n",
    "\n",
    "def binary_focal_loss(gamma=2.0, alpha=0.75):\n",
    "    def loss(y_true, y_pred):\n",
    "        y_true = _to_1d(y_true)\n",
    "        y_pred = _to_1d(tf.clip_by_value(y_pred, 1e-7, 1. - 1e-7))\n",
    "        p_t = y_true * y_pred + (1 - y_true) * (1 - y_pred)\n",
    "        alpha_factor = y_true * alpha + (1 - y_true) * (1 - alpha)\n",
    "        modulating = tf.pow(1. - p_t, gamma)\n",
    "        bce = tf.keras.losses.binary_crossentropy(y_true, y_pred)\n",
    "        return tf.reduce_mean(alpha_factor * modulating * bce)\n",
    "    return loss\n",
    "\n",
    "# -------------------------\n",
    "# 模型構建（無 Masking / 無 BiLSTM / 無 LN）\n",
    "# 輸出層 bias 以先驗 logit 初始化；Focal alpha = 1 - pos_rate\n",
    "# -------------------------\n",
    "def build_model(input_shape, prior_pos, cfg=CFG):\n",
    "    prior_pos = float(np.clip(prior_pos, 1e-6, 1 - 1e-6))\n",
    "    prior_logit = np.log(prior_pos / (1 - prior_pos))\n",
    "    focal_alpha = 1.0 - prior_pos  # ★ 策略1：強化少數類\n",
    "\n",
    "    reg = keras.regularizers.l2(cfg[\"l2\"])\n",
    "    inputs = keras.Input(shape=input_shape, name=\"sequence\")\n",
    "\n",
    "    x = layers.LSTM(\n",
    "        cfg[\"hidden_units\"],\n",
    "        dropout=cfg[\"dropout\"],\n",
    "        recurrent_dropout=cfg[\"recurrent_dropout\"],\n",
    "        kernel_regularizer=reg,\n",
    "        recurrent_regularizer=reg,\n",
    "        return_sequences=False\n",
    "    )(inputs)\n",
    "    x = layers.Dropout(cfg[\"dropout\"])(x)\n",
    "\n",
    "    # 小型 Dense head 提升表達力\n",
    "    x = layers.Dense(64, activation=\"relu\", kernel_regularizer=reg, name=\"head_dense_64\")(x)\n",
    "    x = layers.Dropout(cfg[\"dropout\"], name=\"head_dropout\")(x)\n",
    "\n",
    "    prob = layers.Dense(\n",
    "        1, activation=\"sigmoid\", name=\"prob\",\n",
    "        bias_initializer=keras.initializers.Constant(prior_logit)\n",
    "    )(x)\n",
    "    outputs = layers.Lambda(lambda z: tf.squeeze(z, axis=-1), name=\"prob_squeezed\")(prob)\n",
    "\n",
    "    model = keras.Model(inputs, outputs, name=\"lstm_focal_alpha1mpos_no_classweight_v3\")\n",
    "    opt = keras.optimizers.Adam(learning_rate=cfg[\"learning_rate\"], clipnorm=cfg[\"clipnorm\"])\n",
    "    model.compile(\n",
    "        optimizer=opt,\n",
    "        loss=binary_focal_loss(gamma=cfg[\"focal_gamma\"], alpha=focal_alpha),\n",
    "        metrics=[\n",
    "            keras.metrics.BinaryAccuracy(name=\"accuracy\"),\n",
    "            keras.metrics.AUC(name=\"auc\"),\n",
    "            keras.metrics.AUC(name=\"auprc\", curve=\"PR\"),\n",
    "            keras.metrics.BinaryCrossentropy(name=\"binary_crossentropy\")\n",
    "        ]\n",
    "    )\n",
    "    return model\n",
    "\n",
    "# -------------------------\n",
    "# 每折前處理（fit on train, transform train/val）\n",
    "# ★ 逐特徵標準化（跨樣本×時間）：在 (n*t, d) 上 fit；scaler 可切換\n",
    "# -------------------------\n",
    "def fit_transform_fold(Xtr, Xva):\n",
    "    # X: (n, t, d)\n",
    "    n_tr, t, d = Xtr.shape\n",
    "    n_va = Xva.shape[0]\n",
    "\n",
    "    tr2 = Xtr.reshape(n_tr * t, d)  # (n*t, d)\n",
    "    va2 = Xva.reshape(n_va * t, d)\n",
    "\n",
    "    imputer = SimpleImputer(strategy=\"median\")\n",
    "    if CFG.get(\"scaler\", \"robust\") == \"robust\":\n",
    "        scaler = RobustScaler(with_centering=True, with_scaling=True, quantile_range=(25.0, 75.0))\n",
    "    else:\n",
    "        scaler = StandardScaler(with_mean=True, with_std=True)\n",
    "\n",
    "    tr_imp = imputer.fit_transform(tr2)\n",
    "    tr_scl = scaler.fit_transform(tr_imp)\n",
    "\n",
    "    va_imp = imputer.transform(va2)\n",
    "    va_scl = scaler.transform(va_imp)\n",
    "\n",
    "    Xtr_p = tr_scl.reshape(n_tr, t, d).astype(np.float32)\n",
    "    Xva_p = va_scl.reshape(n_va, t, d).astype(np.float32)\n",
    "    return Xtr_p, Xva_p, imputer, scaler\n",
    "\n",
    "# -------------------------\n",
    "# 指標工具\n",
    "# -------------------------\n",
    "def sweep_best_f1(y_true, y_prob, step=0.01):\n",
    "    thresholds = np.arange(step, 1.0, step)\n",
    "    best = {\"th\": 0.5, \"f1\": -1.0, \"prec\": 0.0, \"rec\": 0.0, \"acc\": 0.0}\n",
    "    for th in thresholds:\n",
    "        pred = (y_prob >= th).astype(int)\n",
    "        prec, rec, f1, _ = precision_recall_fscore_support(y_true, pred, average='binary', zero_division=0)\n",
    "        acc = accuracy_score(y_true, pred)\n",
    "        if f1 > best[\"f1\"]:\n",
    "            best = {\"th\": float(th), \"f1\": float(f1), \"prec\": float(prec), \"rec\": float(rec), \"acc\": float(acc)}\n",
    "    return best\n",
    "\n",
    "def five_metrics(y_true, y_prob, th):\n",
    "    pred = (y_prob >= th).astype(int)\n",
    "    acc = accuracy_score(y_true, pred)\n",
    "    prec, rec, f1, _ = precision_recall_fscore_support(y_true, pred, average='binary', zero_division=0)\n",
    "    rocauc = roc_auc_score(y_true, y_prob)\n",
    "    ap = average_precision_score(y_true, y_prob)\n",
    "    return {\"acc\":acc, \"prec\":prec, \"rec\":rec, \"f1\":f1, \"auc\":rocauc, \"auprc\":ap}\n",
    "\n",
    "# -------------------------\n",
    "# Callbacks：F1 掃門檻 + 機率分佈探針 + 以 F1best/auprc 早停\n",
    "# -------------------------\n",
    "class F1PerEpoch(keras.callbacks.Callback):\n",
    "    def __init__(self, Xtr, ytr, Xva, yva, bs=1024):\n",
    "        super().__init__()\n",
    "        self.Xtr, self.ytr, self.Xva, self.yva = Xtr, ytr, Xva, yva\n",
    "        self.bs = bs\n",
    "    def on_epoch_end(self, epoch, logs=None):\n",
    "        ytr_prob = self.model.predict(self.Xtr, batch_size=self.bs, verbose=0).ravel()\n",
    "        yva_prob = self.model.predict(self.Xva, batch_size=self.bs, verbose=0).ravel()\n",
    "        best_tr = sweep_best_f1(self.ytr, ytr_prob, step=0.01)\n",
    "        best_va = sweep_best_f1(self.yva, yva_prob, step=0.01)\n",
    "        if logs is not None:\n",
    "            logs[\"f1_best\"] = best_tr[\"f1\"]\n",
    "            logs[\"val_f1_best\"] = best_va[\"f1\"]\n",
    "            logs[\"val_best_th\"] = best_va[\"th\"]\n",
    "        print(f\"[F1Epoch] epoch={epoch} f1_tr_best={best_tr['f1']:.4f} f1_va_best={best_va['f1']:.4f} | val_best_th={best_va['th']:.2f}\")\n",
    "\n",
    "class ProbProbe(keras.callbacks.Callback):\n",
    "    def __init__(self, Xtr, Xva, bs=1024):\n",
    "        super().__init__()\n",
    "        self.Xtr, self.Xva = Xtr, Xva\n",
    "        self.bs = bs\n",
    "    def on_epoch_end(self, epoch, logs=None):\n",
    "        ytr_prob = self.model.predict(self.Xtr, batch_size=self.bs, verbose=0).ravel()\n",
    "        yva_prob = self.model.predict(self.Xva, batch_size=self.bs, verbose=0).ravel()\n",
    "        def stats(v):\n",
    "            return np.mean(v), np.percentile(v, 90)\n",
    "        m_tr, p90_tr = stats(ytr_prob)\n",
    "        m_va, p90_va = stats(yva_prob)\n",
    "        print(f\"[Probe] epoch={epoch} train mean={m_tr:.4f} p90={p90_tr:.4f} | val mean={m_va:.4f} p90={p90_va:.4f}\")\n",
    "\n",
    "class EarlyStopOnKey(keras.callbacks.EarlyStopping):\n",
    "    \"\"\"沿用 Keras EarlyStopping；monitor 使用自訂 key（如 'val_f1_best'）。\"\"\"\n",
    "    pass\n",
    "\n",
    "# -------------------------\n",
    "# 主流程\n",
    "# -------------------------\n",
    "all_rows = []\n",
    "\n",
    "for k in CFG[\"folds\"]:\n",
    "    fold_dir = RUN_DIR / f\"fold_{k}\"\n",
    "    fold_dir.mkdir(parents=True, exist_ok=True)\n",
    "\n",
    "    # 載入資料\n",
    "    Xtr = np.load(DATA / f\"X_train_fold{k}.npy\")\n",
    "    ytr = np.load(DATA / f\"y_train_fold{k}.npy\").ravel().astype(np.float32)\n",
    "    Xva = np.load(DATA / f\"X_val_fold{k}.npy\")\n",
    "    yva = np.load(DATA / f\"y_val_fold{k}.npy\").ravel().astype(np.float32)\n",
    "\n",
    "    assert set(np.unique(ytr)).issubset({0.,1.}) and set(np.unique(yva)).issubset({0.,1.}), \"Labels must be {0,1}\"\n",
    "\n",
    "    # 前處理：逐特徵標準化（跨樣本×時間）\n",
    "    Xtr_p, Xva_p, _, _ = fit_transform_fold(Xtr, Xva)\n",
    "\n",
    "    # 先驗：正類率 + 輸出層 bias\n",
    "    pos_rate = float(np.mean(ytr))\n",
    "\n",
    "    # 建模（Focal 策略1：alpha = 1 - pos_rate；不使用 class_weight）\n",
    "    model = build_model(input_shape=(Xtr_p.shape[1], Xtr_p.shape[2]), prior_pos=pos_rate, cfg=CFG)\n",
    "\n",
    "    # Callbacks\n",
    "    ckpt_path = fold_dir / \"best_model.keras\"\n",
    "    cbs = [\n",
    "        F1PerEpoch(Xtr_p, ytr, Xva_p, yva, bs=1024),\n",
    "        ProbProbe(Xtr_p, Xva_p, bs=1024),\n",
    "        keras.callbacks.ModelCheckpoint(filepath=str(ckpt_path), monitor=\"val_auprc\", mode=\"max\", save_best_only=True),\n",
    "        EarlyStopOnKey(monitor=\"val_auprc\", mode=\"max\", patience=CFG[\"patience_auprc\"], restore_best_weights=True),\n",
    "        EarlyStopOnKey(monitor=\"val_f1_best\", mode=\"max\", patience=CFG[\"patience_f1best\"], restore_best_weights=True),\n",
    "        keras.callbacks.ReduceLROnPlateau(monitor=\"val_auprc\", mode=\"max\",\n",
    "                                          factor=CFG[\"reduce_factor\"], patience=3,\n",
    "                                          min_lr=CFG[\"reduce_min_lr\"], verbose=1)\n",
    "    ]\n",
    "\n",
    "    # 訓練（不再傳入 class_weight）\n",
    "    hist = model.fit(\n",
    "        Xtr_p, ytr,\n",
    "        validation_data=(Xva_p, yva),\n",
    "        epochs=CFG[\"epochs\"],\n",
    "        batch_size=CFG[\"batch_size\"],\n",
    "        callbacks=cbs,\n",
    "        verbose=2\n",
    "    )\n",
    "\n",
    "    # 保存 history 與學習曲線\n",
    "    hist_df = pd.DataFrame(hist.history)\n",
    "    hist_df.to_csv(fold_dir / \"history.csv\", index=False)\n",
    "    plot_history(hist_df, fold_dir / \"plot_loss.png\")\n",
    "    plot_metric_epochs(hist_df, \"accuracy\", fold_dir / \"plot_acc.png\", \"Accuracy per Epoch\")\n",
    "    if \"f1_best\" in hist_df.columns and \"val_f1_best\" in hist_df.columns:\n",
    "        plot_metric_epochs(hist_df, \"f1_best\", fold_dir / \"plot_f1.png\", \"F1 (Best-threshold) per Epoch\")\n",
    "\n",
    "    # 推論\n",
    "    ytr_prob = model.predict(Xtr_p, batch_size=CFG[\"batch_size\"], verbose=0).ravel()\n",
    "    yva_prob = model.predict(Xva_p, batch_size=CFG[\"batch_size\"], verbose=0).ravel()\n",
    "\n",
    "    # 固定閾值 & 最佳閾值\n",
    "    th_fixed = CFG[\"threshold_fixed\"]\n",
    "    best_va = sweep_best_f1(yva, yva_prob, step=0.01)\n",
    "    th_best = best_va[\"th\"]\n",
    "\n",
    "    # 五大指標（train/val; fixed/best）\n",
    "    m_tr_fixed = five_metrics(ytr, ytr_prob, th_fixed)\n",
    "    m_va_fixed = five_metrics(yva, yva_prob, th_fixed)\n",
    "    m_tr_best  = five_metrics(ytr, ytr_prob, th_best)\n",
    "    m_va_best  = five_metrics(yva, yva_prob, th_best)\n",
    "\n",
    "    # 混淆矩陣（固定/最佳）\n",
    "    cm_tr_fixed = confusion_matrix((ytr>0.5).astype(int), (ytr_prob>=th_fixed).astype(int), labels=[0,1])\n",
    "    cm_va_fixed = confusion_matrix((yva>0.5).astype(int), (yva_prob>=th_fixed).astype(int), labels=[0,1])\n",
    "    cm_tr_best  = confusion_matrix((ytr>0.5).astype(int), (ytr_prob>=th_best).astype(int),  labels=[0,1])\n",
    "    cm_va_best  = confusion_matrix((yva>0.5).astype(int), (yva_prob>=th_best).astype(int),  labels=[0,1])\n",
    "\n",
    "    # ROC / PR（Train & Val）\n",
    "    fpr_tr, tpr_tr, _ = roc_curve((ytr>0.5).astype(int), ytr_prob)\n",
    "    fpr_va, tpr_va, _ = roc_curve((yva>0.5).astype(int), yva_prob)\n",
    "    rec_tr, prec_tr, _ = precision_recall_curve((ytr>0.5).astype(int), ytr_prob)\n",
    "    rec_va, prec_va, _ = precision_recall_curve((yva>0.5).astype(int), yva_prob)\n",
    "    ap_tr = average_precision_score((ytr>0.5).astype(int), ytr_prob)\n",
    "    ap_va = average_precision_score((yva>0.5).astype(int), yva_prob)\n",
    "\n",
    "    # 存 CSV\n",
    "    pd.DataFrame({\"y_true\": ytr, \"y_prob\": ytr_prob}).to_csv(fold_dir / \"predictions_train.csv\", index=False)\n",
    "    pd.DataFrame({\"y_true\": yva, \"y_prob\": yva_prob}).to_csv(fold_dir / \"predictions_val.csv\", index=False)\n",
    "    pd.DataFrame({\"fpr\": fpr_tr, \"tpr\": tpr_tr}).to_csv(fold_dir / \"roc_curve_train.csv\", index=False)\n",
    "    pd.DataFrame({\"fpr\": fpr_va, \"tpr\": tpr_va}).to_csv(fold_dir / \"roc_curve_val.csv\", index=False)\n",
    "    pd.DataFrame({\"recall\": rec_tr, \"precision\": prec_tr}).to_csv(fold_dir / \"pr_curve_train.csv\", index=False)\n",
    "    pd.DataFrame({\"recall\": rec_va, \"precision\": prec_va}).to_csv(fold_dir / \"pr_curve_val.csv\", index=False)\n",
    "    pd.DataFrame(cm_tr_fixed, index=[\"Actual_0\",\"Actual_1\"], columns=[\"Pred_0\",\"Pred_1\"]).to_csv(fold_dir / \"cm_train_fixed.csv\")\n",
    "    pd.DataFrame(cm_va_fixed, index=[\"Actual_0\",\"Actual_1\"], columns=[\"Pred_0\",\"Pred_1\"]).to_csv(fold_dir / \"cm_val_fixed.csv\")\n",
    "    pd.DataFrame(cm_tr_best,  index=[\"Actual_0\",\"Actual_1\"], columns=[\"Pred_0\",\"Pred_1\"]).to_csv(fold_dir / \"cm_train_best.csv\")\n",
    "    pd.DataFrame(cm_va_best,  index=[\"Actual_0\",\"Actual_1\"], columns=[\"Pred_0\",\"Pred_1\"]).to_csv(fold_dir / \"cm_val_best.csv\")\n",
    "\n",
    "    # 存圖\n",
    "    plot_cm(cm_tr_fixed, fold_dir / \"plot_cm_train_fixed.png\", f\"Confusion Matrix (Train, th={th_fixed:.2f})\")\n",
    "    plot_cm(cm_va_fixed, fold_dir / \"plot_cm_val_fixed.png\",   f\"Confusion Matrix (Val, th={th_fixed:.2f})\")\n",
    "    plot_cm(cm_tr_best,  fold_dir / \"plot_cm_train_best.png\",  f\"Confusion Matrix (Train, best th={th_best:.2f})\")\n",
    "    plot_cm(cm_va_best,  fold_dir / \"plot_cm_val_best.png\",    f\"Confusion Matrix (Val, best th={th_best:.2f})\")\n",
    "    plot_roc_xy(fpr_tr, tpr_tr, m_tr_fixed[\"auc\"], fold_dir / \"plot_roc_train.png\", \"ROC Curve (Train)\")\n",
    "    plot_roc_xy(fpr_va, tpr_va, m_va_fixed[\"auc\"], fold_dir / \"plot_roc_val.png\",   \"ROC Curve (Validation)\")\n",
    "    plot_pr_xy(rec_tr, prec_tr, ap_tr, fold_dir / \"plot_pr_train.png\", \"PR Curve (Train)\")\n",
    "    plot_pr_xy(rec_va, prec_va, ap_va, fold_dir / \"plot_pr_val.png\",   \"PR Curve (Validation)\")\n",
    "\n",
    "    # 每折 metrics（固定 & 最佳閾值）\n",
    "    row = {\n",
    "        \"fold\": k,\n",
    "        \"pos_rate\": pos_rate,\n",
    "        \"focal_gamma\": CFG[\"focal_gamma\"],\n",
    "        # Train @ fixed\n",
    "        \"train_acc\": m_tr_fixed[\"acc\"], \"train_prec\": m_tr_fixed[\"prec\"], \"train_rec\": m_tr_fixed[\"rec\"],\n",
    "        \"train_f1\": m_tr_fixed[\"f1\"], \"train_auc\": m_tr_fixed[\"auc\"], \"train_auprc\": m_tr_fixed[\"auprc\"],\n",
    "        # Val   @ fixed\n",
    "        \"val_acc\": m_va_fixed[\"acc\"], \"val_prec\": m_va_fixed[\"prec\"], \"val_rec\": m_va_fixed[\"rec\"],\n",
    "        \"val_f1\": m_va_fixed[\"f1\"], \"val_auc\": m_va_fixed[\"auc\"], \"val_auprc\": m_va_fixed[\"auprc\"],\n",
    "        # Best-threshold on val\n",
    "        \"best_th_val\": th_best,\n",
    "        \"train_acc_best\": m_tr_best[\"acc\"], \"train_prec_best\": m_tr_best[\"prec\"], \"train_rec_best\": m_tr_best[\"rec\"], \"train_f1_best\": m_tr_best[\"f1\"],\n",
    "        \"val_acc_best\": m_va_best[\"acc\"], \"val_prec_best\": m_va_best[\"prec\"], \"val_rec_best\": m_va_best[\"rec\"], \"val_f1_best\": m_va_best[\"f1\"],\n",
    "    }\n",
    "    pd.DataFrame([row]).to_csv(fold_dir / \"metrics.csv\", index=False)\n",
    "\n",
    "    # 每折 meta\n",
    "    fold_meta = {\n",
    "        \"pos_rate\": pos_rate,\n",
    "        \"n_train\": int(len(ytr)), \"n_val\": int(len(yva)),\n",
    "        \"threshold_fixed\": CFG[\"threshold_fixed\"], \"threshold_best_on_val\": th_best,\n",
    "        \"focal_alpha_used\": float(1.0 - pos_rate),\n",
    "        \"scaler\": CFG[\"scaler\"]\n",
    "    }\n",
    "    (fold_dir / \"fold_meta.json\").write_text(json.dumps(fold_meta, ensure_ascii=False, indent=2), encoding=\"utf-8\")\n",
    "\n",
    "    all_rows.append(row)\n",
    "\n",
    "# 彙總\n",
    "summary = pd.DataFrame(all_rows).sort_values(\"fold\")\n",
    "summary.to_csv(RUN_DIR / \"summary_folds.csv\", index=False)\n",
    "\n",
    "agg_means = summary.drop(columns=[\"fold\"]).mean(numeric_only=True).to_dict()\n",
    "agg_stds  = summary.drop(columns=[\"fold\"]).std(ddof=1, numeric_only=True).add_suffix(\"_std\").to_dict()\n",
    "overall = {\"n_folds\": int(len(summary)), **{k: float(v) for k,v in agg_means.items()}, **{k: float(v) for k,v in agg_stds.items()}, \"config\": CFG}\n",
    "(RUN_DIR / \"summary_overall.json\").write_text(json.dumps(overall, ensure_ascii=False, indent=2), encoding=\"utf-8\")\n",
    "\n",
    "print(\"\\n=== Training Done ===\")\n",
    "print(f\"Run dir: {RUN_DIR}\")\n",
    "print(json.dumps(overall, indent=2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "862da96e-5a1c-47e1-a2b8-c2090a1a3332",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e47b6e9d-de76-4b16-8534-092a1b8b3779",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d5ddef3d-1a93-476d-a372-981d27849b8c",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dc057ad4-f911-47d5-a4be-577f1df2b9e4",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d25bab07-3c57-4c76-92dc-bdc8cc4f765a",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4843ef77-5cd1-4c12-9025-482e5216fe68",
   "metadata": {},
   "outputs": [],
   "source": [
    "目前進行甚麼優化  LSTM模型 c問\n",
    "要記錄喔"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fbccfac4-9d32-4e5f-8439-bd13131c9bc6",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8a4bbe4a-2dce-4e81-81bd-0e0e3cef64c5",
   "metadata": {},
   "outputs": [],
   "source": [
    "猶豫\n",
    "逐特徵標準化（z-score）\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d4a49754-7334-49ff-ac82-6837f6172ed6",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dcf6993d-bacb-4aea-a9fe-b8a5c405af0e",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f821316b-baca-4bd5-aa4f-273328be60b6",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f3ca8ff8-f23d-437e-a315-aa5a281f4508",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8610f37b-3fcb-4183-9e72-dffeed4761e2",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 236,
   "id": "1c8e0ac3-1819-4faf-8c7b-43f307394c71",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['patno', 'senddate', 'rrhzsetactual', 'mvsetactual', 'peepepap', 'ppeak', 'cdyn', 'pmean', 'mode_1', 'mode_2', 'mode_3', 'svv_new', 'nan_check', 'ad_para', 'ad_para_check', 'set', 'set_1010', 'set_fin', 'ts_unix', 'Δt_sec', 'origin_id']\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "df = pd.read_csv(\"/home/jovyan/RT08/0925/sliding_win/1014/cleaned/clear/089271.csv\")\n",
    "print(df.columns.tolist())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b6f5f6b0-948a-4910-b7b5-52002ebf959e",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4760eeac-d733-4498-b446-7cacf1126416",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "31150973-cbea-4407-be70-f572c8c5bbb9",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "376df7f5-caba-4fcb-a781-8e818f4e48ee",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6b79afd2-a687-41ec-bb92-6ce2109a6cb2",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e7588d2a-7107-43ea-91ea-b59dd18899fb",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1435b301-0ec6-4f32-a0ee-eda5b479b695",
   "metadata": {},
   "outputs": [],
   "source": [
    "先把連續的 ad_para=1 合併成一個「run」（一次調參）\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6610370b-f27a-4e58-b6f2-436d17f628e3",
   "metadata": {},
   "outputs": [],
   "source": [
    "【目的】\n",
    "本資料設計提供深度學習模型（LSTM、GRU、多頭自注意力）訓練使用，\n",
    "採用「不實體化滑動視窗」策略。\n",
    "資料以 CSV 格式儲存，保留逐筆時間序列，不事先展開視窗；\n",
    "在訓練時動態生成視窗，節省空間並維持靈活性\n",
    "\n",
    "一、整體架構說明\n",
    "所有病患資料集中存放於同一 CSV 檔案，不依病患建立子資料夾。\n",
    "每筆資料代表一個時間點的觀測紀錄（例如每分鐘一筆）。\n",
    "透過欄位 seg_id 表示不同病患或不同連續區段的識別碼，確保訓練時切窗不跨越病患。\n",
    "訓練時以 seg_id 為分組依據，在同一組內以指定長度 L 與步長 STRIDE 動態產生視窗。\n",
    "所有資料應依 patient_id 及 timestamp 遞增排序。\n",
    "\n",
    "二、資料夾與檔案結構\n",
    "/home/jovyan/RT08/0925/1010/\n",
    "└─ v2025-10-13/\n",
    "├─ raw_csv/\n",
    "│ └─ all.csv.gz → 集中式主檔，包含所有病患逐列資料\n",
    "├─ splits/\n",
    "│ ├─ train_ids.txt → 訓練集病患 ID 名單\n",
    "│ ├─ valid_ids.txt → 驗證集病患 ID 名單\n",
    "│ └─ test_ids.txt → 測試集病患 ID 名單\n",
    "├─ metadata/\n",
    "│ ├─ schema.yaml → 欄位定義與單位說明\n",
    "│ └─ README.md → 操作說明與資料說明\n",
    "└─ logs/\n",
    "└─ build_dataset.log → 資料前處理與索引建立日誌\n",
    "\n",
    "三、CSV 欄位設計說明\n",
    "欄位名稱及用途如下：\n",
    "patient_id：病患識別碼，用於分組與資料篩選。\n",
    "timestamp：時間戳記，格式為 ISO 標準（例如 2025-09-01T00:00:00）。\n",
    "seg_id：病患段落代碼，用於區分不同病患或不同連續時段。\n",
    "f1 至 f10：呼吸器相關特徵，共 10 維，為連續型數值變數。\n",
    "\n",
    "所有資料應依 patient_id 及 timestamp 遞增排序，確保時間序列正確。\n",
    "相同 seg_id 表示同一段連續可切窗資料，不可跨越不同 seg_id 切窗。\n",
    "\n",
    "五、訓練階段動態切窗邏輯（需實作）\n",
    "在載入資料後，以 seg_id 為 key 進行分組，確保不同病患或不同段落不會混合。\n",
    "在每一個 seg_id 群組內，依時間順序生成固定長度 L 的滑動視窗。\n",
    "例如：視窗 1 含樣本 0 至 119，視窗 2 含樣本 5 至 124，依 STRIDE 位移。\n",
    "若尾段不足 L 筆資料則捨棄。\n",
    "每個視窗的形狀為 (L, 10)，其中 L 代表時間步長，10 為特徵維度。\n",
    "輸出批次形狀為 (batch_size, L, 10)。\n",
    "\n",
    "八、病患邊界控制\n",
    "seg_id 為資料切窗的主要控制變數。\n",
    "相同 seg_id 代表同一段連續資料，可在內部切窗。\n",
    "不同 seg_id 之間的資料不可連接或跨越。\n",
    "若病患在不同時間住院、或出現資料中斷超過指定秒數（例如 Δt_sec > 600），應重新分配 seg_id。\n",
    "所有 seg_id 必須為整數且連續，不可重複跨病患使用。\n",
    "\n",
    "九、欄位一致性與檢查\n",
    "在前處理階段，需檢查以下項目：\n",
    "每一列都有完整的 f1 至 f10 數值。\n",
    "timestamp 單調遞增。\n",
    "seg_id 與 patient_id 配對關係正確，不重疊。\n",
    "無空白欄位或異常值（例如 NaN、負數、無效範圍）。\n",
    "檔案整體大小控制在合理範圍\n",
    "\n",
    "十、模型訓練階段資料格式\n",
    "輸入張量維度：\n",
    "batch_size × L × 10\n",
    "\n",
    "十二、檔案交付項目\n",
    "\n",
    "主資料檔：dataset/v2025-10-13/raw_csv/all.csv.gz\n",
    "資料分割名單：dataset/v2025-10-13/splits/train_ids.txt、valid_ids.txt、test_ids.txt\n",
    "欄位說明文件：dataset/v2025-10-13/metadata/schema.yaml\n",
    "建置與稽核紀錄：dataset/v2025-10-13/logs/build_dataset.log\n",
    "程式腳本：train_dataset_loader.py（負責動態切窗與資料載入）\n",
    "\n",
    "十三、規格摘要（工程師應遵守）\n",
    "檔案格式：CSV（UTF-8），可壓縮為 gzip。\n",
    "每列代表一筆觀測值。\n",
    "所有病患共用一個檔案，不分資料夾。\n",
    "以 seg_id 控制病患邊界。\n",
    "動態切窗於訓練階段完成，不事先展開視窗。\n",
    "可讀性高，結構簡單，適合長期維護與版本化。\n",
    "調整參數（L、stride、batch_size）時無須重產資料。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0938f4e4-20e6-4b9f-b193-32bf25c39c92",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f812b92d-e09c-430a-a3ea-e70213d8a004",
   "metadata": {},
   "outputs": [],
   "source": [
    "把視窗合格性前置（manifest）、把序列資料序列化（TFRecord/SequenceExample + GZIP 分片）、用 tf.data 的 interleave + AUTOTUNE + prefetch，GPU 會吃得又快又穩，還能「可審計、可回放、可重現」\n",
    "一個壓縮好的『時間序列樣本倉庫』——\n",
    "每筆 SequenceExample 含一整個滑動視窗的特徵矩陣、遮罩、時間資訊與標籤，\n",
    "用 GZIP 壓成固定大小的分片 (shard-xxxxx-of-yyyyy.tfrecord.gz)，"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3fabee82-e82d-46af-b816-1d8276bb8ddb",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c832e073-3d16-4c51-afd1-f21642150328",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a0950b7d-475e-4690-b73b-e67e97640019",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d3cbf3d0-44aa-4792-beb1-a2f2e4b24d3f",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ee9af55b-746a-4264-8c68-4cd09aa7d078",
   "metadata": {},
   "outputs": [],
   "source": [
    "要進行划動視窗之前應該要具備那些條件 資料應該長怎樣"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "45d93c65-9bd1-4069-82ef-b66fffa70026",
   "metadata": {},
   "outputs": [],
   "source": [
    "必備的條件（Checklist）\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fd54b30d-4187-411d-a4f1-e74d4154a8ad",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "235ef322-cf83-404e-8837-d194176ec916",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4154224c-693a-41c5-a4b4-4ba47193f164",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "eccfe4cd-5747-473a-9dbd-d4e8596b78f1",
   "metadata": {},
   "outputs": [],
   "source": [
    "若前面20%的資料大於兩小時，就只取前面兩小時的資料標記1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2af9ee05-3b1a-4dc4-a186-77df3a28bd0f",
   "metadata": {},
   "outputs": [],
   "source": [
    "調參定義 欄位ad_para\n",
    "在 /home/jovyan/RT08/0925/bling_svv_14/ 中為每個 CSV 新增 ad_para 欄位，定義：\n",
    "1) 僅在 NaN_check=1 的列上評估是否有「調參」。\n",
    "2) 以 (patno) 分組，依 senddate 排序建立時序。\n",
    "3) 與上一筆(同為 NaN_check=1)比較：\n",
    "   - 若 rrhzsetactual 或 mvsetactual 或 peepepap 任一數值有變化 → ad_para=1\n",
    "   - 或 mode_1/mode_2/mode_3 任一位元有變化(0↔1) → ad_para=1\n",
    "4) 每位病患的第一筆 NaN_check=1 → ad_para=0\n",
    "5) 若同一病患內出現相同 senddate（重複時間點），印出細節後立刻停止整個流程（不再處理其餘檔案）。\n",
    "\n",
    "注意：\n",
    "- 僅修改/寫回 ad_para 欄位，不改動其他欄位。\n",
    "- 若缺少必要欄位，該檔案 ad_para 一律設 0 並警告\n",
    "\n",
    "分片張量檔（per-patient .npy 或 .npz）\n",
    "以病患或分片為單位存放大矩陣，方便 I/O 與訓練。\n",
    "直接被 DataLoader 載入，餵給 LSTM/GRU 學習。\n",
    "儲存內容通常是：\n",
    "X：形狀 (N, 120, D)，每個視窗的輸入特徵序列。\n",
    "y：形狀 (N,)，對應的標籤（下一分鐘是否調參）。\n",
    "可選 ts（時間戳序列）、window_ids（對齊 meta）、m0（基準向量）\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2b87f10a-d45f-455f-b351-24af131fb7de",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1fa82403-dfe6-4ffb-81a0-b5df3882aaac",
   "metadata": {},
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   "source": [
    "請依照以下需求，幫我完成呼吸器資料 Phase 1 的「滑動視窗切割」與「資料集切分」：\n",
    "\n",
    "## 研究任務背景\n",
    "- 資料來源：ICU 呼吸器紀錄，跨病患總筆數約 100 萬，病患數 ~100 人。\n",
    "- 特徵數：不到 10 維（包含設定值、病患回饋數據）。\n",
    "- Phase 1 任務：預測「下一分鐘是否需要調參」。\n",
    "- 已有欄位：PatNo (病患ID), SendDate (時間戳記), NaN_check (有效標記), ad_para (是否調參標籤)。\n",
    "\n",
    "## 滑動視窗設定\n",
    "- 視窗長度 W = 120 分鐘\n",
    "- 步長 S = 60 分鐘（50% overlap）\n",
    "- 每個視窗輸入：形狀 (120, D)，D = 特徵數\n",
    "- 視窗標籤 y：視窗結束後「下一分鐘」的 ad_para 值 (0/1)\n",
    "\n",
    "## 切窗規則\n",
    "1. 先依 gap 規則切成連續區段（gap > X 分鐘就分段，X 可設定 1/3/5）。\n",
    "2. 在每個區段內，套用 W=120, S=60 的滑動視窗。\n",
    "3. 若區段長度 < 120 → 不產生視窗。\n",
    "4. 每個視窗貼上 window_id（病患ID + segmentID + 起始index）。\n",
    "\n",
    "## 資料集切分規則\n",
    "- **以病患為單位分割**，不可讓同一病患同時出現在 train/val/test。\n",
    "- 建議比例：Train 70% / Val 15% / Test 15%。\n",
    "- 請輸出 split_manifest.json，記錄各 split 對應的病患清單。\n",
    "\n",
    "## 輸出檔案結構\n",
    "請輸出以下檔案與目錄：\n",
    "\n",
    "/bling_phase1_windows/\n",
    "├─ tables/\n",
    "│  └─ windows_meta.parquet\n",
    "│      # 每窗一列，欄位：\n",
    "│      # window_id, patient_id, segment_id, start_time, end_time, next_time,\n",
    "│      # N_points, y_next_adjust, split, X_shape, build_run_id\n",
    "├─ tensors/\n",
    "│  ├─ train/\n",
    "│  │  └─ patient_<ID>.npy   # X: float32 (N,120,D), y: int8 (N,)\n",
    "│  ├─ val/\n",
    "│  │  └─ patient_<ID>.npy\n",
    "│  └─ test/\n",
    "│     └─ patient_<ID>.npy\n",
    "└─ manifest/\n",
    "   ├─ feature_manifest.json   # 特徵名稱、順序、縮放方式\n",
    "   └─ split_manifest.json     # 病患名單對應 train/val/test\n",
    "\n",
    "## 技術要求\n",
    "- 輸入 shape：建議儲存為 (N,120,D)，對 LSTM/GRU 最方便。\n",
    "- X 用 float32，y 用 int8。\n",
    "- 訓練時可用 np.load(mmap_mode=\"r\") 讀取，不需一次載入全部。\n",
    "- 請確保 scaler（均值/標準差）只用 train split 計算，再套用到 val/test。\n",
    "\n",
    "## 輸出需求\n",
    "- 提供完整 Python 程式碼（pandas/numpy 可用，PyTorch DataLoader 可直接讀取）。\n",
    "- 附上如何檢查每位病患能切出幾個視窗，以及全體總視窗數。\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2a68ff81-3d74-4578-9de0-886ae6d96002",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5fe490a4-ee1b-4a66-89de-73a775b26f4a",
   "metadata": {},
   "outputs": [],
   "source": [
    "#確定空值再檔案的紀錄長怎樣 確定沒有了！\n",
    "看一下如果有問題是為什麼 yaaaaaaa"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7c3d4715-2ebd-42e4-8c5f-7d55f5a5d843",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9b16b610-9f90-4de4-980e-0bc30b87a84e",
   "metadata": {},
   "outputs": [],
   "source": [
    "檢查所有檔案的所有欄位空值狀況   yaaaaaaa\n",
    "- 路徑: /home/jovyan/RT08/0925/bling_useable/*.csv\n",
    "- 空值定義: NaN、空字串、'null'、'(null)'\n",
    "- 直接印出結果（不存檔、不修改原始資料）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d104d7ae-f356-4c32-84c3-c0a7b08fb51b",
   "metadata": {},
   "outputs": [],
   "source": [
    "應該要在fin之前先算出特徵變化量並產生時間差的欄位 數值\n",
    "\n",
    "畫完時間軸再刪資料\n",
    "時間軸直接看useable欄位=1藍色點 =0灰色點"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c680f0d9-0430-4e7e-99f6-4af59e98e98f",
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"\n",
    "依據/home/jovyan/RT08/0925/bling_onehot/的所有檔案\n",
    "先印出時間軸。橫軸是時間 縱軸是檔名\n",
    "有時間資料的資料就用淺灰色點表示，\"rrhzsetactual\",\"peepepap\", \"ppeak\"特徵都有數字的資料用藍色的點，而且\"senddate\"有時間，\"patno\"有數字，且\"svv\"有>0的\n",
    "紀錄在圖上 \n",
    "並在最右邊標出該檔案有幾筆資料符合藍點點（佔比%），幾筆資料符合灰點點\n",
    "依據年份印出多張圖\n",
    "\n",
    "** 這樣設定是因為檢查的NaN的熱力圖\n",
    "全部特徵指的是\"rrhzsetactual\",\"peepepap\", \"ppeak\"\n",
    "不包括\"patno\", \"senddate\", \"ventilatormode\", \"sponvt\", \"vti\", \"vte\", \"cdyn\", \"pmean\", \"mvsetactual\"\n",
    "\n",
    "每張圖最多紀錄50個檔案，- PatNo_ID_1594305136.csv: blue=0 (0.0%), gray=0 (0.0%), total_rows_this_year=0這種文字部用輸出在程式碼中程式碼要輸出的是幾年分的有幾張圖\n",
    "\n",
    "\"patno\", \"senddate\", \"ventilatormode\", \"rrhzsetactual\", \"mvsetactual\",\"peepepap\", \"ppeak\", \"cdyn\", \"vti\", \"pmean\", \"vte\", \"sponvt\"\n",
    "\"\"\"\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8115ab8d-ec8c-4a65-9baf-0a892513dadf",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "997d01ed-96d1-4415-a479-06868e99d1b9",
   "metadata": {},
   "outputs": [],
   "source": [
    "將/home/jovyan/RT08/0925/bling_onehot/的所有檔案複製一份到/home/jovyan/RT08/0925/bling_useable/\n",
    "對路徑中每個檔案，刪掉不符合以下規定的資料，最後印出紀錄每個檔案的甚麼欄位不符合有幾筆，並在最後一欄紀錄保留下來的比數佔比\n",
    "規定：\n",
    "\"rrhzsetactual\",\"peepepap\", \"ppeak\"特徵都有數字的資料用藍色的點\n",
    "而且\"senddate\"有時間，\"patno\"有數字\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "96c7c04b-31ee-4256-82a2-299b488f9ceb",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a879ea8e-18c4-4375-ae2b-6922169807cf",
   "metadata": {},
   "outputs": [],
   "source": [
    "並輸出每檔案中欄位是連續性數據的小提琴圖（Violin）也將數據寫在圖上面，\n",
    "以及輸出每檔案的QQ-Plot也將數據寫在圖上面，\n",
    "也輸出所有檔案\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6f38c786-70e9-4e53-bcef-0c92aa4d6707",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "44ad04b6-c4bd-4e68-8dc7-81977edff884",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    }
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   "source": [
    "#發現那四個檔案在搞事\n",
    "import os, io, csv, re\n",
    "import pandas as pd\n",
    "\n",
    "# ---- 小工具：判斷欄位名稱健全度 ----\n",
    "def _is_reasonable_header(cols):\n",
    "    cols = [str(c).strip() for c in cols]\n",
    "    if len(cols) <= 1:\n",
    "        return False\n",
    "    non_empty = sum(1 for c in cols if c and c.lower() != 'nan')\n",
    "    if non_empty / len(cols) < 0.6:\n",
    "        return False\n",
    "    unnamed_ratio = sum(1 for c in cols if str(c).startswith('Unnamed')) / len(cols)\n",
    "    if unnamed_ratio > 0.5:\n",
    "        return False\n",
    "    return True\n",
    "\n",
    "# ---- 嘗試推測 CSV 分隔符 ----\n",
    "def _guess_sep_from_head(text_head, candidates=(',', '\\t', ';', '|', '^')):\n",
    "    lines = [ln for ln in text_head.splitlines() if ln.strip()]\n",
    "    if not lines:\n",
    "        return None\n",
    "    # 計算每行的各分隔符出現次數，挑一致性最高且總量大的\n",
    "    best_sep, best_score = None, -1\n",
    "    for sep in candidates:\n",
    "        counts = [ln.count(sep) for ln in lines[:10]]\n",
    "        if max(counts) == 0:\n",
    "            continue\n",
    "        # 一致性分數：中位數 * (1 / 方差+1)\n",
    "        import statistics as st\n",
    "        med = st.median(counts)\n",
    "        var = st.pvariance(counts) if len(counts) > 1 else 0.0\n",
    "        score = med / (var + 1e-6)\n",
    "        if score > best_score:\n",
    "            best_score, best_sep = score, sep\n",
    "    return best_sep\n",
    "\n",
    "# ---- CSV 韌性讀取 ----\n",
    "def _read_csv_resilient(path, max_header_search=30, sample_bytes=65536):\n",
    "    meta = {\n",
    "        \"file_path\": path, \"kind\": \"csv\", \"read_ok\": False, \"error\": None,\n",
    "        \"encoding\": None, \"sep\": None, \"header_row\": None,\n",
    "        \"n_rows\": None, \"n_cols\": None, \"columns\": None\n",
    "    }\n",
    "    # 讀頭部原文，用於猜 sep 與處理 BOM\n",
    "    with open(path, 'rb') as f:\n",
    "        raw_head = f.read(sample_bytes)\n",
    "    # 嘗試幾種常見編碼（避免依賴 chardet）\n",
    "    encodings = [\"utf-8-sig\", \"utf-8\", \"cp950\", \"big5\", \"latin1\"]\n",
    "    text_head = None\n",
    "    for enc in encodings:\n",
    "        try:\n",
    "            text_head = raw_head.decode(enc, errors='strict')\n",
    "            meta[\"encoding\"] = enc\n",
    "            break\n",
    "        except Exception:\n",
    "            continue\n",
    "    if text_head is None:\n",
    "        # 退而求其次：用寬鬆解碼\n",
    "        enc = \"utf-8\"\n",
    "        text_head = raw_head.decode(enc, errors='ignore')\n",
    "        meta[\"encoding\"] = enc\n",
    "\n",
    "    # 猜分隔符\n",
    "    guessed_sep = _guess_sep_from_head(text_head) or \",\"\n",
    "    meta[\"sep\"] = guessed_sep\n",
    "\n",
    "    # 嘗試不同 header 列（0..max_header_search）\n",
    "    last_err = None\n",
    "    for hdr in range(0, max_header_search + 1):\n",
    "        try:\n",
    "            df_try = pd.read_csv(\n",
    "                path,\n",
    "                header=hdr,\n",
    "                sep=guessed_sep,\n",
    "                engine=\"python\",         # 避開 pyarrow 與 low_memory 衝突\n",
    "                encoding=meta[\"encoding\"],\n",
    "                on_bad_lines=\"skip\"      # 遇壞行跳過，避免卡死\n",
    "            )\n",
    "            # 欄位健檢\n",
    "            if _is_reasonable_header(df_try.columns):\n",
    "                meta.update({\n",
    "                    \"read_ok\": True,\n",
    "                    \"header_row\": hdr,\n",
    "                    \"n_rows\": int(df_try.shape[0]),\n",
    "                    \"n_cols\": int(df_try.shape[1]),\n",
    "                    \"columns\": list(map(str, df_try.columns))\n",
    "                })\n",
    "                return df_try, meta\n",
    "        except Exception as e:\n",
    "            last_err = str(e)\n",
    "            continue\n",
    "\n",
    "    meta[\"error\"] = last_err or \"Failed to find a valid header row within search range.\"\n",
    "    return None, meta\n",
    "\n",
    "# ---- Excel 韌性讀取（取第一個工作表，循環 header 列）----\n",
    "def _read_excel_resilient(path, max_header_search=30):\n",
    "    meta = {\n",
    "        \"file_path\": path, \"kind\": \"excel\", \"read_ok\": False, \"error\": None,\n",
    "        \"sheet_name\": None, \"header_row\": None,\n",
    "        \"n_rows\": None, \"n_cols\": None, \"columns\": None\n",
    "    }\n",
    "    try:\n",
    "        # 先讀 sheet 名\n",
    "        xl = pd.ExcelFile(path)\n",
    "        sheet = xl.sheet_names[0]\n",
    "        meta[\"sheet_name\"] = sheet\n",
    "    except Exception as e:\n",
    "        meta[\"error\"] = f\"Excel open failed: {e}\"\n",
    "        return None, meta\n",
    "\n",
    "    last_err = None\n",
    "    for hdr in range(0, max_header_search + 1):\n",
    "        try:\n",
    "            df_try = pd.read_excel(path, sheet_name=sheet, header=hdr)\n",
    "            if _is_reasonable_header(df_try.columns):\n",
    "                meta.update({\n",
    "                    \"read_ok\": True,\n",
    "                    \"header_row\": hdr,\n",
    "                    \"n_rows\": int(df_try.shape[0]),\n",
    "                    \"n_cols\": int(df_try.shape[1]),\n",
    "                    \"columns\": list(map(str, df_try.columns))\n",
    "                })\n",
    "                return df_try, meta\n",
    "        except Exception as e:\n",
    "            last_err = str(e)\n",
    "            continue\n",
    "\n",
    "    meta[\"error\"] = last_err or \"Failed to find a valid header row within search range.\"\n",
    "    return None, meta\n",
    "\n",
    "# ---- 入口：自動辨識副檔名，走 CSV 或 Excel ----\n",
    "def read_table_resilient(path, max_header_search=30):\n",
    "    ext = os.path.splitext(path)[1].lower()\n",
    "    if ext in [\".csv\", \".tsv\", \".txt\"]:\n",
    "        return _read_csv_resilient(path, max_header_search=max_header_search)\n",
    "    elif ext in [\".xlsx\", \".xls\"]:\n",
    "        return _read_excel_resilient(path, max_header_search=max_header_search)\n",
    "    else:\n",
    "        # 嘗試當 CSV 讀（不少 .log / 無副檔名其實是 CSV）\n",
    "        return _read_csv_resilient(path, max_header_search=max_header_search)"
   ]
  },
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   "execution_count": 22,
   "id": "753bcd79-4745-4334-8209-e6e7b3e48fdb",
   "metadata": {
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    "jupyter": {
     "outputs_hidden": true,
     "source_hidden": true
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'file_path': '/home/jovyan/RT08/0921/095323.csv', 'kind': 'csv', 'read_ok': True, 'error': None, 'encoding': 'cp950', 'sep': ',', 'header_row': 3, 'n_rows': 23791, 'n_cols': 115, 'columns': ['Row Count', 'Id', 'PatNo', 'SendDate', 'Room', 'DongleId', 'VentilatorType', 'O2therapy', 'VentilatorMode', 'VtSet', 'VtSetActual', 'RRHZset', 'RRHZsetActual', 'MVset', 'MVsetActual', 'FlowRate', 'Trise_Sec', 'Ti', 'TL', 'FiO2set', 'mPaw', 'PEEPEPAP', 'Pinsp', 'PCL', 'PCLvolume', 'PSL', 'PSLvolume', 'BaseFlow', 'Senstivity', 'Ppeak', 'Pplat', 'LMV', 'HP', 'NOset', 'NOsetActual', 'Breathing', 'Endo', 'mark', 'Cuff', 'Cough', 'Cdyn', 'RI', 'P01', 'WOB', 'HMV', 'Vti', 'FiO2', 'M_IE', 'M_PEEP', 'Pmean', 'O2_Inlet', 'SponRate', 'VTe', 'TiMonitor', 'SponVe', 'MandVt', 'SponVt', '_100O2', 'PC_Flow_cycle', 'PSVCycle', 'Inspause_Percent', 'PresHigh', 'NPPVPinsp', 'PresLow', 'NPPV_PSV', 'TimeHigh', 'PSVHighTimeEnable', 'THighSync', 'TimeLow', 'TLowSync', 'PSVTmax', 'AssuresVol', 'VolLimit', 'sigh', 'waveform', 'vsync', 'ApneaInterval', 'LowPpeak', 'HighRate', 'RALO', 'Alarm', 'Esense', 'Cstat', 'SC_EtCO2', 'EndTidalCO2', 'VCO2', 'Leak', 'NAVALevel', 'Trigg_EDI', 'EDI_PEAK', 'EDI_MIN', 'AutoPEEP', 'PSPH', 'RSBI', 'ExpResistance', 'HeatingScale', 'HeatingTemp', 'SputumQuantity', 'SputumColor', 'SputumQuality', 'SputumBlood', 'PS_Ristime_Percent', 'PS_Ristime_Sec', 'HighPpeak', 'Ramp', 'Trise_Percent', 'Inspause_Sec', 'HFO Delta P', 'HFO Mean', 'HFO Rate', 'M_HFO Delta P', 'M_HFO Mean', 'LP', 'PS_above_Phigh', 'PS_above_PEEP']}\n"
     ]
    }
   ],
   "source": [
    "df, meta = read_table_resilient(\"/home/jovyan/RT08/0921/095323.csv\")\n",
    "print(meta)\n",
    "# 如果 meta[\"read_ok\"] 為 True，df 就有正確的欄位；否則看 meta[\"error\"] 與 header_row/sep 以修正。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "4cd24457-c108-4353-a80b-9d1b646486cf",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[audit] Total files scanned: 20\n",
      "[audit] read_ok=False files: 0\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import pandas as pd\n",
    "\n",
    "def audit_folder(root, exts=(\".csv\", \".xlsx\", \".xls\"), max_files=None):\n",
    "    records = []\n",
    "    cnt = 0\n",
    "    for dirpath, _, filenames in os.walk(root):\n",
    "        for fn in sorted(filenames):\n",
    "            if max_files and cnt >= max_files:\n",
    "                break\n",
    "            if os.path.splitext(fn)[1].lower() not in exts:\n",
    "                continue\n",
    "            cnt += 1\n",
    "            path = os.path.join(dirpath, fn)\n",
    "            size = os.path.getsize(path)\n",
    "            df, meta = read_table_resilient(path, max_header_search=30)\n",
    "            rec = {\n",
    "                \"file_name\": fn,\n",
    "                \"path\": path,\n",
    "                \"exists\": True,\n",
    "                \"size\": size,\n",
    "                \"kind\": meta.get(\"kind\"),\n",
    "                \"read_ok\": meta.get(\"read_ok\"),\n",
    "                \"error\": meta.get(\"error\"),\n",
    "                \"header_row\": meta.get(\"header_row\"),\n",
    "                \"sep\": meta.get(\"sep\"),\n",
    "                \"n_rows\": meta.get(\"n_rows\"),\n",
    "                \"n_cols\": meta.get(\"n_cols\"),\n",
    "                \"first_5_cols\": \", \".join((meta.get(\"columns\") or [])[:5])\n",
    "            }\n",
    "            records.append(rec)\n",
    "    report = pd.DataFrame(records)\n",
    "    # 顯示讀失敗清單（重點：那些「有欄位被吃掉」的案子會在這裡浮出來）\n",
    "    bad = report[report[\"read_ok\"] == False]\n",
    "    print(f\"[audit] Total files scanned: {len(report)}\")\n",
    "    print(f\"[audit] read_ok=False files: {len(bad)}\")\n",
    "    if not bad.empty:\n",
    "        print(\"[audit] Problem files:\")\n",
    "        for p in bad[\"path\"].tolist():\n",
    "            print(\"  -\", p)\n",
    "    return report\n",
    "\n",
    "# 範例：掃整個資料夾\n",
    "rpt = audit_folder(\"/home/jovyan/RT08/0921\")\n",
    "rpt.to_csv(\"/home/jovyan/RT08/0925/bling_record/4/.csv\", index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "14bccb33-db31-4a0f-b24c-7576193e71e3",
   "metadata": {},
   "outputs": [],
   "source": [
    "\"patno\",\"senddate\",\"ventilatormode\",\"rrhzsetactual\",\"mvsetactual\",\n",
    "\"peepepap\",\"ppeak\",\"cdyn\",\"vti\",\"pmean\",\"vte\",\"sponvt\""
   ]
  }
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