{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "0d3597ca",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import cantera as ct\n",
    "\n",
    "from matplotlib import pyplot as plt\n",
    "%matplotlib widget"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "0425d561",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Korean Fonts\n",
    "import matplotlib as mpl\n",
    "\n",
    "plt.rc('font', family='NanumBarunGothic') # For Windows\n",
    "mpl.rcParams['axes.unicode_minus'] = False\n",
    "mpl.rcParams['figure.figsize'] = (3.6, 3)\n",
    "mpl.rcParams['figure.dpi'] = 92 # pixel per inch of 24'' 1080p monitor"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d185caa7",
   "metadata": {},
   "source": [
    "# 상대 습도 공식"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "be0e455e-3bfb-4b48-a1ed-cbde1bb5e682",
   "metadata": {},
   "outputs": [],
   "source": [
    "water = ct.Water()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "566dd07e-62ac-47f4-b132-00087f43033f",
   "metadata": {},
   "source": [
    "## 수분 부피 비율 (상대 습도, 온도, 압력)\n",
    "$$\n",
    "X_{\\mathrm{H_2 O}} = \\frac{\\mathrm{RH} p_{\\mathrm{sat}} \\left( T \\right)} {p}\n",
    "$$\n",
    "\n",
    "- $\\mathrm{RH}$ : Relative Humidity, 상대 습도\n",
    "- $p_{\\mathrm{sat}}$ : Saturation Vapor Pressure, 포화 수증기압\n",
    "- $p$ : Atmospheric Pressure, 대기압"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8efd5ca3",
   "metadata": {},
   "source": [
    "### 포화수증기압 곡선"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "587b3f1a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Temperature, ${}^\\\\circ C$')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "3b1599fed6be4add9c8d192cbfaa8b89",
       "version_major": 2,
       "version_minor": 0
      },
      "image/png": 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",
      "text/html": [
       "\n",
       "            <div style=\"display: inline-block;\">\n",
       "                <div class=\"jupyter-widgets widget-label\" style=\"text-align: center;\">\n",
       "                    Figure\n",
       "                </div>\n",
       "                <img 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' width=331.2/>\n",
       "            </div>\n",
       "        "
      ],
      "text/plain": [
       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def p_sat (T):\n",
    "    water.TP = T, None\n",
    "    return water.P_sat\n",
    "\n",
    "T_range = np.linspace(4,90) + 273.15\n",
    "\n",
    "Psat_range = np.array([p_sat(T) for T in T_range])\n",
    "\n",
    "plt.figure()\n",
    "plt.plot(T_range - 273.15, Psat_range)\n",
    "plt.xlim(0, 100)\n",
    "plt.ylabel(\"Saturation Vapor Pressure, Pa\")\n",
    "plt.xlabel(r\"Temperature, ${}^\\circ C$\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "50aec579",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Temperature, ${}^\\\\circ C$')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "75635ea408cc4760b82d7ff5949a3baa",
       "version_major": 2,
       "version_minor": 0
      },
      "image/png": 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",
      "text/html": [
       "\n",
       "            <div style=\"display: inline-block;\">\n",
       "                <div class=\"jupyter-widgets widget-label\" style=\"text-align: center;\">\n",
       "                    Figure\n",
       "                </div>\n",
       "                <img 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bjdbthsNuj1eq/n6vV62Gw2n2MAOh0/m9vthsvl8jyI6JdbtmEnrFVHEW8cgCduGs3PKc9Rm0VV33//PSIjIyFJErZu3Yp77rkHR48ehRACKpV3tqpUKrjdbp9jADodP1t2djaWLl36iyZGRD95v7QGq76uxIBQNXLuGo++IfxI61y12bOMjIwEAKjValx55ZXIzs7Gf/7zH2i1Wtjtdq/n2u12aLVan2MAOh0/W1ZWFiRJgiRJaGlpOf/ZERF2Hz6Oh9aUAQCem5HI877PU6frLFtbW6HRaJCYmIjS0lLPdofDgYqKClgsFlgsFlRWVqKpqckzbrVaYbFYAMBnbbtNqVQIDg72PIjo/Bw72Yr7//kdTjpdmHu1GVMvNXZeRO0TP7Nt2zaRl5cnHA6HEEKIkpISMWTIEJGXlye2bNkidDqdKCsrE06nU8yfP18kJyd7alNSUkRGRoZwOp2ivLxcREREiE2bNgkhRKe1vkiSJAAISZL8ej4RnSa53OL//WOrGLx4nbjrtW+F5HLL3dJ5UUoGeIVlbW2tuOeee4TJZBKRkZFizJgxYtWqVZ7x3NxcER0dLQYOHCimTp0qqqurPWM1NTUiNTVVaDQaYTKZRE5OjtcP8lXri1JeKKKe5umNO8XgxevEr57eJOyOFrnbOW9KyYA26yyVRilrrIh6kg3ba5HxVjH69gnGuxlXYOSgtssBewqlZADPDSfqZXYfPu653/czt1h6dFAqCcOSqBexO5z47Rs/HtC5yozfJPo+Q478x7Ak6iVaXW7Me6sIVY0ncdVwPf5v6gi5W+pVGJZEvYAQAo+9X45v9zfCrO+Pl2eOhTqY/7y7El9Nol7gn9/+gH9trcLAvn3wj3smYmBfXkmoqzEsiXq4ryrqsXTtDgSrgvDqneN4hk43YVgS9WD7604g460iuNwCf/rNKFwZFyF3S70Ww5Koh2p0ODF7VSGaTkm467JY3H35ELlb6tUYlkQ90KlWF+5/4ztUNpw+8v2n31wqd0u9HsOSqIdxuwUe/E8ZvvvBjnjjAPz1Dh75vhD4ChP1MC9+sgfvl9ZAPyAU/5g1EQN4D50LgmFJ1IO8891BvPzpXvTtE4zX75kIk6av3C0FDIYlUQ/xVUU9Hn53O4KCgJduH4OE6IFytxRQGJZEPcD3h45h7j+/g+QWeOSGUUjhRXwvOIYlkcIdbDyJe1cVwuF04bfJl+C+X10id0sBiWFJpGB2hxP3rNyGuuMtSEuMwsPXj5S7pYDFsCRSqFOtLtyXV4j9dQ5cMVSHZ2+1QKXi7WvlwrAkUiDJ5cYf/mVF8Y/3+f7b3eMRquadAuTUYVi63W5MmjQJQ4YM8WzLz89HXFwcDAYD0tLSUFdX5xlrbGxEeno6DAYDzGYz8vLyvL6fr1oi+onbLbB4zXZ8svMITJq+yJudhHCupZRdh2H56quvIiwszPN1WVkZ5s2bh4KCAtTW1iImJgazZ8/2jM+dOxdarRa1tbXYsGEDFi1ahMLCQr9qieg0IQSe3LATa4qroesfgn/el4TI8LDOC6nbtXvDspqaGlx++eXIz8/HzJkzUVlZiczMTDidTuTk5AAAGhoaEBkZiaqqKoSEhMBoNKK6uhpG4+klDQsWLEBzczNyc3N91kZF+b7svVJuVkR0Ifx18148++FuXBSqRv79l2G0iWsplZIB7e5Zzp8/H0uWLMGgQYM824qKipCUlOT5WqfTwWw2w2q1oqSkBCaTyROUAJCUlITi4uJOa4notDe//QHPfrgboWoVXrtnAoNSYdqE5fr163HkyBHcf//9XtttNhv0er3XNr1eD5vN5nOss9r2uN1uuFwuz4Oot3u/tAaPvvc9glVBeOWOcbjMrJO7JTqLV1g6HA4sXLgQK1asQFCQ9xIFIQRUKu9sValUcLvdPsc6q21PdnY21Go11Go1QkNDz29mRD3ER+WHsfDtEggBPJ1uwXWjIuVuidrhlWDZ2dmYOXMmRo5su/BVq9XCbrd7bbPb7dBqtT7HOqttT1ZWFiRJgiRJaGlpOfdZEfUQn++pwx/+ZYXLLfD4jZfilvHRcrdEHfAKS6vViuXLl0Oj0UCj0SAhIQFVVVXQaDRobGxEaWmp57kOhwMVFRWwWCywWCyorKxEU1OT1/eyWCwAgMTExA5r221KpUJwcLDnQdQbfbOvAfe/8R2cLjeWTIvnlc6VTviwefNmMXjwYCGEEFu2bBE6nU6UlZUJp9Mp5s+fL5KTkz3PTUlJERkZGcLpdIry8nIREREhNm3a5FetL5IkCQBCkiS/nk/UE3xX2ShGPrpRDF68Tiz/eI/c7SiaUjLA7zN4rrjiCixbtgzTpk2DXq/H7t27sXr1as/4qlWrsH//fhgMBqSkpODxxx/HlClT/KolCiTbq49h1sptOOl04XdXD8X8a+Pkbon80O46SyVRyhoroq7w/aFjuOPv36LplIRZVwzBY78Z1eZgKnlTSgbw3HCiC+T7Q8dw52tb0XRKwt2XDWZQ9jAMS6IL4ExQHmtuxV2XxSL7xksZlD0Mw5Kom5XXHMNd/zgdlHdOikV22mgGZQ/EsCTqRturT+9RHj3ZijsmxeLxG0fzmpQ9lFruBoh6q6If7Ji1chuOn5Jwx6RYPMGg7NEYlkTdYOv+Bsz+8b459145BFnTeTCnp2NYEnWxLyvq8Ns3vsOpVjd+d/VQLE4dwaDsBRiWRF1o084jmPdWMZySGwt+PQyZ1w5jUPYSDEuiLvJeySH8779LIbkFFqfGY941Q+VuiboQw5KoC/zz2x+Q9d73EAJYmnYp7rliiNwtURdjWBL9AkII/HXzXjz30R4Eq4Lw/IxE3DTWJHdb1A0YlkTnSQiBZRt24u9fHkCoWoW/3jEOv+aFe3sthiXReWh1ufHQmu1YU1yNi0LVeO2eCbwVRC/HsCQ6R44WCRlvFePzPXXQ9Q/BqnuTkBDNm4v1dgxLonNQf6IFs1cVoqz6GGK1/fDG7CQMiegvd1t0ATAsifz0Q4MD97y+DZUNJ5FgGojXZ02EfgBvqBcoGJZEfig9eBT35RWi/oQTycMikHPXeFwUyn8+gYS/baJOfFh+GJn5VpxqdePmsSY8nW5BiJoX7Ao0bX7jK1euxNixY6HT6RATE4OFCxdCkiQAQH5+PuLi4mAwGJCWloa6ujpPXWNjI9LT02EwGGA2m5GXl+f1fX3VEimREAKvfbkfv3uzCKda3Zh/7TC8MCORQRmg2vzWv/jiC7zxxhuor6+H1WrFZ599hr/85S8oKyvDvHnzUFBQgNraWsTExGD27Nmeurlz50Kr1aK2thYbNmzAokWLUFhYCACd1hIpjeRy47H3y/HE+p1Qq4Lw3K2JWHTdcJ7nHcA6vWHZokWLcPz4cfTr1w9OpxM5OTkAgIaGBkRGRqKqqgohISEwGo2orq6G0WgEACxYsADNzc3Izc1FZmZmh7VRUVE+G1TKzYoocBw/1YrM/BJ8usuGAWFq5N49HlcMjZC7rYCllAzw+X6iuLgYBQUFSE9PR1FREZKSkjxjOp0OZrMZVqsVJSUlMJlMnqAEgKSkJBQXFwOAz1oiJalqOIn0nK/x6S4boi/ui/9mXMGgJAA+wlKj0WDChAm47777kJqaCpvNBr1e7/UcvV4Pm83mcwxAp+Nnc7vdcLlcngfRhfDt/gbc+NevsOfICSQN0eK931+JOMMAudsihegwLO12O0pKSvDmm28iJycHQgioVN5PV6lUcLvdPscAdDp+tuzsbKjVaqjVaoSGch0bdb/V26pw12tbYT/ZitsmxODNOZOgu4h/e/STDsMyKCgIFosFDz74IF577TVotVrY7Xav59jtdmi1Wp9jADodP1tWVhYkSYIkSWhpaTmviRH5o9Xlxp/eL8fD726HWwg8csNIPJWewCPe1IbXX0R7b3n79OmD4OBgJCYmorS01LPd4XCgoqICFosFFosFlZWVaGpq8oxbrVZYLBYA8FnbblMqFYKDgz0Pou5Qf6IFd762Fau+rsSAUDX+MWsi5iSbecSb2id+xmq1ihkzZoj9+/cLIYSorq4WCQkJ4rnnnhNbtmwROp1OlJWVCafTKebPny+Sk5M9tSkpKSIjI0M4nU5RXl4uIiIixKZNm4QQotNaXyRJEgCEJEl+PZ/IH6UH7eKyZZ+IwYvXiWuf/0zssx2XuyXqgFIywCssJUkSzz77rBg1apQwGAzCbDaLpUuXeprMzc0V0dHRYuDAgWLq1KmiurraU1tTUyNSU1OFRqMRJpNJ5OTkeP0gX7W+KOWFot7jne8OimF/3CAGL14nfptXKJqanXK3RD4oJQM6XWcpN6WssaKer0VyIXvtDry1tQpBQcCiXw/H7yfH8V7eCqeUDOC54RQQDjaexO//VYyy6mMID1Nj+e1jMCWeVzUn/zEsqdfbvMuGBW+X4FhzK0abwvHqHeMRq+snd1vUwzAsqdeSXG4s/6QCr2zeCwCYmRSDx35zKcL68OMcOncMS+qVao81I3N1CbZVNiKsjwpP3JSAW8ZHy90W9WAMS+p1Nu08ggfeKYX9ZCviDBfhlTvGIt4YLndb1MMxLKnXcEpuPPPBLrz21QEAwIwJ0fhT2qXoF8I/c/rl+FdEvcK+uhNYkF+C7YeOoX9IMJ68OQE3jTXJ3Rb1IgxL6tGEEMgvPIjstTvQ3OrCpVHheOWOcbiEd1ykLsawpB6r0eHEQ2vK8NGOIwgKAuZebcb/XjeCF8GgbsGwpB7p8z11+L93SmE73gJjeBheuC2RF+mlbsWwpB7F0SLhyQ078a+tVQCAaQlGLLs5AZp+ITJ3Rr0dw5J6jG0HGvHAO6WoajyJAWFqLE27FDePNfGSanRBMCxJ8U61uvDCx3vw9y/3QwggeVgEnk63IErTV+7WKIAwLEnRvqtsxIP/KcP+egf69gnGkmnxuOuywdybpAuOYUmKdNIp4ZkPdiPvm0oIASRdosUz6RYM4ZIgkgnDkhTn6731WPxuGQ42NqNfSDAeuj4ed00azOtOkqwYlqQYjQ4nnly/E2uKqwEAv4qLwJ//JwExWl5OjeTHsCTZCSHwX+shPLF+JxodTgzs2wdLpsVjxoQYfjZJisGwJFlV1jvwSMH3+GpvPQDgxjFReOSGUdAP4D27SVnanBe2ceNGTJw4EZGRkYiNjUVWVpZnLD8/H3FxcTAYDEhLS0NdXZ1nrLGxEenp6TAYDDCbzcjLy/P6vr5qKfCcWQ6UsvwLfLW3HjHavsibnYSXbh/LoCRlOvsOZpMmTRKFhYVCCCEOHTokBg0aJN577z1RWloqNBqN2L59u5AkSWRkZIjp06d76m655RYxZ84cIUmS2Llzp9BqtWLbtm1CCNFprS9KubMbdZ1NOw+L5Kc/FYMXrxNxS9aLpzbuFCdb+Pul9iklA9rc3VGSJKjVP707nzFjBhISElBfXw+n04mcnBwAQENDAyIjI1FVVYWQkBAYjUZUV1fDaDQCABYsWIDm5mbk5uYiMzOzw9qoqCifYa6UO7vRL3ew8SQeX7cDH+04AgC4YqgO2TeORpzhIpk7IyVTSga0eRv+86CUJAlFRUWIj49HUVERkpKSPGM6nQ5msxlWqxUlJSUwmUyeoASApKQkFBcXA4DPWur9TjolPPfhblz7wuf4aMcRGAaE4i8zx+KtOZMYlNRj+DzAs2zZMoSHh+Pmm2/GH//4R+j1eq9xvV4Pm82G0NDQDscAwGaz+Rw/m9vtxpkdXpfLdW4zIsUQQuD90hr8ecMuHG46hT7BQbj/KjP+vylxGBDWR+72iM5Jh2H5ySef4KWXXsI333wDtVoNIQRUKu8dUZVK5Qm2jsYAdDp+tuzsbCxduvS8JkTKYK2y44n1O1H0gx0AMHmEHo9OHwWznnuS1DO1G5bl5eWYOXMmVq9ejeHDhwMAtFot7Ha71/Psdju0Wi3CwsI6HOustj1ZWVl49NFHAZzeswwN5dHRnuJg40k88+FurC2tAQBcEtEfWdNHYXK8QebOiH6ZNp9Z1tbW4oYbbsBTTz2FlJQUz/bExESUlpZ6vnY4HKioqIDFYoHFYkFlZSWampo841arFRaLpdPadptSqRAcHOx5kPI1nWrFnzfuxLUvfI61pTXQ9OuDrOmj8OGCqxiU1Dv8/ND4iRMnxLhx48QjjzzS5rD5li1bhE6nE2VlZcLpdIr58+eL5ORkz3hKSorIyMgQTqdTlJeXi4iICLFp0ya/an1RyrIBal+zUxJ//2KfSFz6oWcp0ONry8VRh1Pu1qiXUEoGeIVlQUGBACAiIyO9HnPmzBFCCJGbmyuio6PFwIEDxdSpU0V1dbWntqamRqSmpgqNRiNMJpPIycnx+kG+an1RygtF3iSXW7zz3UFxxZ83icGL14nBi9eJjDeLRGX9Cblbo15GKRnQZp2l0ihljRWdJoTAxzuO4PmP9mD3keMATq+XfOj6eFiiNfI2R72SUjKA54aTX4QQ+KKiHs9/tBtl1ccAAJdGhWNxajySh0XwghfU6zEsqVPf7GvACx/vRmHl6RUNQ/X9sfC64Zg2ehCvMUkBg2FJ7RJC4Jt9DVi+qQLbDjQCAGK1/bDg18Nw4xgTghmSFGAYluRFCIEvK+rxl00V+O7HBeUmTV/8YUocbhkfjT7BbVabEQUEhiUBANxugU92HsGrn+1DycGjAE7vSf5hchxuHmdiSFLAY1gGuFaXG2tLa5Dz2T5U2E4AOH3Wze8nx+HGMVEMSaIfMSwDlKNFwjvfHcTfvzyAQ0ebAQAjB4Uj45qhmJYwiJ9JEp2FYRlgbMdPIe/rSrz5bRWONbcCAJKGaDFv8lBcM1zPJUBEHWBYBojdh4/j9a8O4L/WQ3C63AgKAqZeGonfJpsxYUj7FzQhop8wLHsxt1vg0102vL7lAL7e1wAACFWrcOekWMxJNuOSiP4yd0jUczAse6Fjza1YU1SNvG8q8UPDSQCAfkAo7r5sMO6cFAvdRbzkHdG5Ylj2Ijtrm/DGNz+gwHoIza2nrzBviR6I2VdegmkJgxCi5pFtovPFsOzhTrW68GH5Ybz57Q+e0xH7BAfhxjFR+H+XD8a42It50IaoCzAse6i9tuNYve0g1hRX4+jJ00e1Bw0Mw52TYnHbxFjee5uoizEse5CTTgkbtx/G24UHsa3y9PnaQUHANSP0mJkUi2vjDVBzETlRt2BYKpwQAtaDR/HOdwextrQWJ1okAIBhQChumxiD2ybGIPrifjJ3SdT7MSwVquZoMwpKDuHd4kPY++NpiGpVEFJGReLWCTGYPELPvUiiC4hhqSCOFgkffH8Y71qr8fW+Bpy5hn2c4SLcNiEGN4018bNIIpm0u2tSVFQEo9GI22+/3Wt7fn4+4uLiYDAYkJaWhrq6Os9YY2Mj0tPTYTAYYDabkZeX53dtIGuRXPh4xxH84V/FGP/Ex/jfd0qxZW8DNH374J7LB+O931+Jjxdehd9eZWZQEsmozT141q9fjwceeABxcXHo378/8vPzAQBlZWW4+uqr8eWXX2LkyJGYP38+qqqqsHbtWgDArbfeCo1Gg7/97W+oqKjAlVdeiQ8++AATJ07stNYXpdx/oytJLje2HmjE2tIabNhei6ZTpz+HDFGrMGWEAf8zzoRrRhi4LpIIysmANmF55MgRaLVaPPnkk9i1a5cnLDMzM+F0OpGTkwMAaGhoQGRkJKqqqhASEgKj0Yjq6moYjUYAwIIFC9Dc3Izc3FyftVFRUT4bVMoL9Uu53AJbDzRgfVktPvj+MBocTgCAKgi4Mi4CaYlRmDraiPCwPjJ3SqQsSsmANp9ZRkZGtvvEoqIi3HfffZ6vdTodzGYzrFYrQkNDYTKZPEEJAElJSXjxxRc7re0sLHsyp+TGt/sbsPH7w/h4x2HUn3B6xiYOuRjTEgbhBssgGAaEydglEfnD7wM8NpsNer3ea5ter4fNZkNoaGiHY53VtsftduPMDq/L5fK3RUU46ZTwxZ56fFR+GJ/sPOJ5iw0A4wdfjBsSBmFawiAYBzIgiXoSv8NSCAGVyvszNJVK5Qm2jsY6q21PdnY2li5d6m9rsrMdP4VNO234eMcRfLW3Hk7p9LxUQafvqZ062oiUUUYGJFEP5ndYarVa2O12r212ux1arRZhYWEdjnVW256srCw8+uijAE7vWYaGKusosNstUF7ThE932fDpbhvKqo96lvmEqlX49UgDrhsVietGGaHtHyJvs0TUJfwOy8TERJSWluLOO+8EADgcDlRUVMBisSAsLAyVlZVoampCeHg4AMBqtcJisXRa256z90KV4FhzK77eW4/Pdtdh824bbMdbPGO6/iG4dqQBvx4ZieRhevQN6bkHooiofX6H5axZs5CWloa7774b8fHxWLJkCSZNmoShQ4cCAKZMmYKHH34Yy5cvR0VFBVasWIG3337br1olcrkFymuO4Ys9dfh8Tx2Kq47C5f5p4cBoUzimjDBgcrwBidEaqHjPGqJeze+wvOKKK7Bs2TJMmzYNx48fx2WXXYbVq1d7xletWoXZs2fDYDCgf//+ePzxxzFlyhS/apWiquEkvtpbj6/21uHrfQ2eq/kAQHiYGsnD9LhqeASuGWFAZDg/fyQKJG3WWSrNhVhj5ZTcmLr8Cxyod3i2qYIAS7QGycMicM0IPRKjNTwXm0gGil1nGYhC1CqEh6kxRNcPvxoWgV/F6XG5WYeB/bhAnIhO457ljxwtEvqH8v8OIqVRyp4l31f+iEFJRL4wLImI/MCwJCLyA8OSiMgPDEsiIj8wLImI/MCwJCLyg+LXy/TU61oSUdc4829f7iXhig9LSTp98VylXaaNiC4sSZKgVssXWYoPyzMr9k+dOiXrCyWnM9f0bGlp6dH3ITpfnH9gz1+SJISFhck+d8Wnz5lrW6rVatlfLLkFBwcH9GvA+Qf2/OW+zi0P8BAR+UHxYRkUFITHHnsMQUGBe3HdQH8NOH/OXwnzV/xVh4iIlEDxe5ZERErAsCQi8gPDkojID4oOy8bGRqSnp8NgMMBsNiMvL0/uli6IoqIiGI1G3H777V7b8/PzERcXB4PBgLS0NNTV1cnUYffZuHEjJk6ciMjISMTGxiIrK8szFgjzX7lyJcaOHQudToeYmBgsXLjQc2JGIMz/DLfbjUmTJmHIkCGebbLPXyjYLbfcIubMmSMkSRI7d+4UWq1WbNu2Te62utW6detEfHy8mD59urjttts820tLS4VGoxHbt28XkiSJjIwMMX36dBk77R6TJk0ShYWFQgghDh06JAYNGiTee++9gJn/rFmzRFlZmXC73aKurk6MGTNGPP/88wEz/zNefvllcdVVV4nBgwcLIZTx96/Yo+H19fUwGo2orq6G0WgEACxYsADNzc3Izc2Vubvuc+TIEWi1Wjz55JPYtWsX8vPzAQCZmZlwOp3IyckBADQ0NCAyMhJVVVWIioqSs+UudfYpbTNmzEBCQgLq6+sDYv5nW7RoEY4fP45+/foFzPxrampw+eWXIz8/HzNnzkRlZaUi/v4V+za8pKQEJpPJE5QAkJSUhOLiYhm76n6RkZHo06ftXSWLioqQlJTk+Vqn08FsNsNqtV7I9rrdz4NSkiQUFRUhPj4+YOb/c8XFxSgoKEB6enpAzX/+/PlYsmQJBg0a5NmmhPkrNixtNhv0er3XNr1eD5vNJlNH8grE12PZsmUIDw/HzTffHHDz12g0mDBhAu677z6kpqYGzPzXr1+PI0eO4P777/faroT5K/bccCFEm3NBVSoV3G63TB3JK9Bej08++QQvvfQSvvnmG6jV6oCbv91ux/bt23HbbbdBq9UGxPwdDgcWLlyI9957r83ZOkqYv2L3LLVaLex2u9c2u90OrVYrU0fyCqTXo7y8HDNnzsTq1asxfPhwAIE1f+D0KX4WiwUPPvggXnvttYCYf3Z2NmbOnImRI0e2GVPC/BUblhaLBZWVlWhqavJss1qtsFgsMnYln8TERJSWlnq+djgcqKio6HWvR21tLW644QY89dRTSElJ8WwPhPm3d4HrPn36IDg4OCDmb7VasXz5cmg0Gmg0GiQkJKCqqgoajQaNjY3yz/+CHns/RykpKSIjI0M4nU5RXl4uIiIixKZNm+Ru64J47LHHvJYObdmyReh0OlFWViacTqeYP3++SE5OlrHDrnfixAkxbtw48cgjj7QZC4T5W61WMWPGDLF//34hhBDV1dUiISFBPPfccwEx/7Nt3rzZs3RICfNXdFjW1NSI1NRUodFohMlkEjk5OXK3dMGcHZZCCJGbmyuio6PFwIEDxdSpU0V1dbVM3XWPgoICAUBERkZ6PebMmSOE6P3zlyRJPPvss2LUqFHCYDAIs9ksli5dKiRJEkL0/vmf7edhKYT881fsOksiIiVR7GeWRERKwrAkIvIDw5KIyA8MSyIiPzAsiYj8wLAkIvIDw5KIyA8MSyIiPzAsiYj8wLAkIvLD/w8F8ZVmV+HwawAAAABJRU5ErkJggg==' width=331.2/>\n",
       "            </div>\n",
       "        "
      ],
      "text/plain": [
       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "T_range = np.linspace(4,40) + 273.15\n",
    "\n",
    "Psat_range = np.array([p_sat(T) for T in T_range])\n",
    "\n",
    "plt.figure()\n",
    "plt.plot(T_range - 273.15, Psat_range)\n",
    "plt.xlim(0, 40)\n",
    "plt.ylabel(\"Saturation Vapor Pressure, Pa\")\n",
    "plt.xlabel(r\"Temperature, ${}^\\circ C$\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "8c54e04c",
   "metadata": {},
   "outputs": [],
   "source": [
    "def X_water (rh, T, P=ct.one_atm):\n",
    "    \"\"\"\n",
    "    Mole(Volume) fraction of water(H2O)\n",
    "    rh: Relative humidity in fraction\n",
    "    T: gas temperature (absolute)\n",
    "    P: gas pressure (absolute)\n",
    "    \"\"\"\n",
    "    water.TP = T, P\n",
    "    return rh * water.P_sat / P"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f83fec5c",
   "metadata": {},
   "source": [
    "${30}^\\circ C$ 기온에서 포화수증기압일 때 수분 비율 (부피 %)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "408ba5de",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.04181267022743699"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_water (1, 30 + 273.15)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "87d99ee2",
   "metadata": {},
   "source": [
    "## 수분 질량 비율"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4fa93d7a",
   "metadata": {},
   "source": [
    "혼합기체의 몰분율(부피분율) - 질량분율 변환\n",
    "\n",
    "$$\n",
    "Y_{\\mathrm{H_2O}} = \\frac{ W_{\\mathrm{H_20}} X_{\\mathrm{H_20}} }{W_{\\mathrm{mean}}} = \\frac{ W_{\\mathrm{H_20}} X_{\\mathrm{H_20}} }{\\sum_i{ W_{i} X_{i} }}\n",
    "$$\n",
    "\n",
    "배경가스(공기, LDG) 의 평균 분자량 $ W_{\\mathrm{bg}} $ 일 때\n",
    "\n",
    "$$\n",
    "Y_{\\mathrm{H_2O}} = \\frac{ W_{\\mathrm{H_20}} X_{\\mathrm{H_20}} }{ W_{\\mathrm{bg}} \\left( 1 - X_{\\mathrm{H_20}} \\right) + W_{\\mathrm{H_20}} X_{\\mathrm{H_20}} }\n",
    "$$\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "76e6375f",
   "metadata": {},
   "outputs": [],
   "source": [
    "def Y_water (X_H2O, W_bg):\n",
    "    W_H2O = water.molecular_weights[0]\n",
    "    # LDG_dict[\"H2O\"] = X_H2O / (1 - X_H2O) # (1-X_H2O) divided to have X_H2O after normalize\n",
    "    return X_H2O * W_H2O / (X_H2O * W_H2O + W_bg * (1 - X_H2O))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "20422631",
   "metadata": {},
   "source": [
    "## 상대 습도 from 기체 수분 함량(질량)\n",
    "\n",
    "$$\n",
    "\\frac{\\mathrm{moisture}, (g/l)}{ \\rho_{\\mathrm{sat. vapor}}(T)}\n",
    "$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "edc85180-df3d-4057-ac27-08f1f5f78e12",
   "metadata": {},
   "outputs": [],
   "source": [
    "def prop_from_water_mass (Tgas, mass_g):\n",
    "\n",
    "    water.TP = Tgas, ct.one_atm\n",
    "\n",
    "    # 수분 비율 (상대 습도 100%)\n",
    "    vapor_volume_percent_max = water.P_sat / ct.one_atm\n",
    "\n",
    "    water.TPQ = Tgas, water.P_sat, 1.\n",
    "\n",
    "    # 상대습도(%) = 수분량 / 상대습도100%수분량\n",
    "    rh_percent = 100 * mass_g / (water.density * 1000)\n",
    "\n",
    "    # 수분비율(부피, %) = 상대습도 100% 수분 부피비 * 상대습도\n",
    "    vapor_volume_percent = 100 * (water.P_sat / ct.one_atm) * (mass_g / (water.density * 1000))\n",
    "    \n",
    "    return rh_percent, vapor_volume_percent, vapor_volume_percent_max"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "93d3a439",
   "metadata": {},
   "source": [
    "## 상대 습도 from 수분 부피 비율\n",
    "\n",
    "$$\n",
    "\\mathrm{RH} = \\frac {X_{\\mathrm{H_2O}} }{p_{\\mathrm{sat}} / p}\n",
    "$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "bc890402-7c41-49d8-93c7-24821fbf49c1",
   "metadata": {},
   "outputs": [],
   "source": [
    "def prop_from_water_ratio (Tgas, ratio):\n",
    "\n",
    "    water.TP = Tgas, ct.one_atm\n",
    "\n",
    "    # 수분 비율 (상대 습도 100%)\n",
    "    vapor_volume_percent_max = water.P_sat / ct.one_atm\n",
    "\n",
    "    water.TPQ = Tgas, water.P_sat, 1.\n",
    "\n",
    "    # 상대습도(%) = 수분 비율 / 상대습도100% 수분 비율\n",
    "    rh_percent = 100 * ratio / vapor_volume_percent_max\n",
    "    \n",
    "    # 수분량 = 상대습도(%) * 상대습도100%수분량\n",
    "    mass_g = (water.density * 1000) * rh_percent / 100\n",
    "    \n",
    "    return rh_percent, mass_g, water.density * 1000"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "85a9a35f",
   "metadata": {},
   "source": [
    "## 수증기 분압 (온도, 상대습도)\n",
    "$$\n",
    "p_{\\mathrm{H_2O}} = \\mathrm{RH} p_{\\mathrm{sat}} \\left( T \\right)\n",
    "$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "bcf49111",
   "metadata": {},
   "outputs": [],
   "source": [
    "def vp_from_T_rh (Tgas, rh):\n",
    "    '''\n",
    "    Vapor Pressure from Temperature and Relative Humidity\n",
    "    Tgas : Gas Temperature\n",
    "    rh : Relative Humidity in fraction 0 <= rh <= 1\n",
    "    '''\n",
    "\n",
    "    water.TP = Tgas, ct.one_atm\n",
    "\n",
    "    # 포화수증기압\n",
    "    p_sat = water.P_sat\n",
    "\n",
    "    # 수증기 분압 = 상대습도 * 포화수증기압\n",
    "    vp =  p_sat * min(rh, 1.0)\n",
    "    \n",
    "    return vp"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3ee6d7d5",
   "metadata": {},
   "source": [
    "# LDG 수분 측정 Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "939a48cc",
   "metadata": {},
   "outputs": [],
   "source": [
    "cnames = list(map(str.strip, open('220922/220922-115225_Converted.csv').readlines()[42].split(sep=',')))\n",
    "cnames[1] = 'time'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "98ca4247",
   "metadata": {},
   "outputs": [],
   "source": [
    "def read_humidity_aps_csv (filename, trim=0):\n",
    "    header_size = 44\n",
    "    \n",
    "    ldg_raw = pd.read_csv(filename, skiprows=header_size, names=cnames, skipfooter=trim)\n",
    "    ldg_raw['timestamp'] = ldg_raw.apply(lambda row: pd.Timestamp(row.time + '.' + str(row.ms).strip()), axis=1)\n",
    "    ldg_raw['T'] = ldg_raw.apply(lambda row: (220./4.)*(row.CH2-1.) -40. + 273.15, axis=1)\n",
    "    ldg_raw['RH'] = ldg_raw.apply(lambda row: 25.*(row.CH1-1.), axis=1)\n",
    "    ldg_raw['vp'] = ldg_raw.apply(lambda row: vp_from_T_rh(row['T'], row.RH / 100.), axis=1)\n",
    "    \n",
    "    return ldg_raw"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "fe710faa",
   "metadata": {},
   "outputs": [],
   "source": [
    "def read_humidity_logger_csv (filename, trim=0):\n",
    "    header_size = 46\n",
    "    \n",
    "    ldg_raw = pd.read_csv(filename, skiprows=header_size, names=cnames, skipfooter=trim)\n",
    "    ldg_raw['timestamp'] = ldg_raw.apply(lambda row: pd.Timestamp(row.time + '.' + str(row.ms).strip()), axis=1)\n",
    "    ldg_raw['T'] = ldg_raw.apply(lambda row: (220./4.)*(float(row.CH2.replace('+',''))-1.) -40. + 273.15, axis=1)\n",
    "    ldg_raw['RH'] = ldg_raw.apply(lambda row: 25.*(float(row.CH1.replace('+',''))-1.), axis=1)\n",
    "    ldg_raw['vp'] = ldg_raw.apply(lambda row: vp_from_T_rh(row['T'], row.RH / 100.), axis=1)\n",
    "    \n",
    "    return ldg_raw"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "fec9d1c6",
   "metadata": {},
   "outputs": [],
   "source": [
    "def read_two_humidity_logger_csv (filename, trim=0):\n",
    "    header_size = 46\n",
    "    \n",
    "    ldg_raw = pd.read_csv(filename, skiprows=header_size, names=cnames, skipfooter=trim)\n",
    "    ldg_raw['timestamp'] = ldg_raw.apply(lambda row: pd.Timestamp(row.time + '.' + str(row.ms).strip()), axis=1)\n",
    "    \n",
    "    ldg_raw['T1'] = ldg_raw.apply(lambda row: (220./4.)*(float(row.CH2.replace('+',''))-1.) -40. + 273.15, axis=1)\n",
    "    ldg_raw['RH1'] = ldg_raw.apply(lambda row: 25.*(float(row.CH1.replace('+',''))-1.), axis=1)\n",
    "    ldg_raw['vp1'] = ldg_raw.apply(lambda row: vp_from_T_rh(row.T1, row.RH1 / 100.), axis=1)\n",
    "    \n",
    "    ldg_raw['T2'] = ldg_raw.apply(lambda row: (220./4.)*(float(row.CH5.replace('+',''))-1.) -40. + 273.15, axis=1)\n",
    "    ldg_raw['RH2'] = ldg_raw.apply(lambda row: 25.*(float(row.CH4.replace('+',''))-1.), axis=1)\n",
    "    ldg_raw['vp2'] = ldg_raw.apply(lambda row: vp_from_T_rh(row.T2, row.RH2 / 100.), axis=1)\n",
    "    \n",
    "    return ldg_raw"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "5604f316",
   "metadata": {},
   "outputs": [],
   "source": [
    "def read_two_humidity_logger_csv_v1026 (filename, trim=0):\n",
    "    import re\n",
    "    \n",
    "    upper_limit = re.compile(r'\\++')\n",
    "    \n",
    "    header_size = 46\n",
    "    \n",
    "    ldg_raw = pd.read_csv(filename, skiprows=header_size, names=cnames, skipfooter=trim)\n",
    "    ldg_raw['timestamp'] = ldg_raw.apply(lambda row: pd.Timestamp(row.time + '.' + str(row.ms).strip()), axis=1)\n",
    "    \n",
    "    ldg_raw['T1'] = ldg_raw.apply(lambda row: float(row.CH2) + 273.15, axis=1)\n",
    "    ldg_raw['RH1'] = ldg_raw.apply(lambda row: (float(upper_limit.sub(\"100\", row.CH1[1:]))-1.), axis=1)\n",
    "    ldg_raw['vp1'] = ldg_raw.apply(lambda row: vp_from_T_rh(row.T1, row.RH1 / 100.), axis=1)\n",
    "    \n",
    "    ldg_raw['T2'] = ldg_raw.apply(lambda row: float(row.CH5) + 273.15, axis=1)\n",
    "    try:\n",
    "        ldg_raw['RH2'] = ldg_raw.apply(lambda row: (float(upper_limit.sub(\"100\", row.CH4[1:]))-1.), axis=1)\n",
    "    except TypeError:\n",
    "        ldg_raw['RH2'] = ldg_raw.CH4\n",
    "    ldg_raw['vp2'] = ldg_raw.apply(lambda row: vp_from_T_rh(row.T2, row.RH2 / 100.), axis=1)\n",
    "    \n",
    "    ldg_raw['Tamb'] = ldg_raw.apply(lambda row: float(row.CH7.replace('+','')) + 273.15, axis=1)\n",
    "\n",
    "    return ldg_raw"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "1c12b1ed",
   "metadata": {},
   "outputs": [],
   "source": [
    "def read_two_humidity_logger_csv_v1122 (filename, trim=0):\n",
    "    import re\n",
    "    from io import StringIO\n",
    "    \n",
    "    upper_limit = re.compile(r'\\++')\n",
    "    trailing_ws = re.compile(r'([+-])\\s+')\n",
    "    \n",
    "    header_size = 46\n",
    "    \n",
    "    with open(filename) as gl840_file:\n",
    "        raw_str = gl840_file.read()\n",
    "        raw_str = upper_limit.sub(r'100', raw_str)\n",
    "        raw_str = trailing_ws.sub(r'\\1', raw_str)\n",
    "    \n",
    "    ldg_raw = pd.read_csv(StringIO(raw_str), skiprows=header_size, names=cnames, skipfooter=trim, encoding='cp949')\n",
    "    ldg_raw['timestamp'] = ldg_raw.apply(lambda row: pd.Timestamp(row.time + '.' + str(row.ms).strip()), axis=1)\n",
    "    \n",
    "    try:\n",
    "        ldg_raw['RH1'] = ldg_raw.apply(lambda row: float(upper_limit.sub(\"100\", row.CH1[1:])), axis=1)\n",
    "    except TypeError:\n",
    "        ldg_raw['RH1'] = ldg_raw.CH1\n",
    "    ldg_raw['T1'] = ldg_raw.CH2 + 273.15\n",
    "    ldg_raw['lpm1'] = ldg_raw.CH3\n",
    "    ldg_raw['vp1'] = ldg_raw.apply(lambda row: vp_from_T_rh(row.T1, row.RH1 / 100.), axis=1)\n",
    "    \n",
    "    try:\n",
    "        ldg_raw['RH2'] = ldg_raw.apply(lambda row: float(upper_limit.sub(\"100\", row.CH4[1:])), axis=1)\n",
    "    except TypeError:\n",
    "        ldg_raw['RH2'] = ldg_raw.CH4\n",
    "    ldg_raw['T2'] = ldg_raw.CH5 + 273.15\n",
    "    ldg_raw['lpm2'] = ldg_raw.CH6\n",
    "    ldg_raw['vp2'] = ldg_raw.apply(lambda row: vp_from_T_rh(row.T2, row.RH2 / 100.), axis=1)\n",
    "    \n",
    "    ldg_raw['Tamb'] = ldg_raw.apply(lambda row: float(row.CH7.replace(' ','')) + 273.15, axis=1)\n",
    "\n",
    "    return ldg_raw"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "a4d1e41b",
   "metadata": {},
   "outputs": [],
   "source": [
    "def read_two_humidity_logger_csv_v1122 (filename, trim=0):\n",
    "    import re\n",
    "    from io import StringIO\n",
    "    \n",
    "    upper_limit = re.compile(r'\\+\\++')\n",
    "    trailing_ws = re.compile(r'([+-])\\s+')\n",
    "    \n",
    "    header_size = 46\n",
    "    \n",
    "    with open(filename) as gl840_file:\n",
    "        raw_str = gl840_file.read()\n",
    "        raw_str = upper_limit.sub(r'100', raw_str)\n",
    "        raw_str = trailing_ws.sub(r'\\1', raw_str)\n",
    "    \n",
    "    ldg_raw = pd.read_csv(StringIO(raw_str), skiprows=header_size, names=cnames, skipfooter=trim, encoding='cp949', parse_dates=['time'])\n",
    "    ldg_raw['timestamp'] = ldg_raw.time + ldg_raw.apply(lambda row: pd.Timedelta(row.ms, 'ms'), axis=1)\n",
    "    \n",
    "    ldg_raw['RH1'] = ldg_raw.CH1\n",
    "    ldg_raw['T1'] = ldg_raw.CH2 + 273.15\n",
    "    ldg_raw['lpm1'] = ldg_raw.CH3\n",
    "    ldg_raw['vp1'] = ldg_raw.apply(lambda row: vp_from_T_rh(row.T1, row.RH1 / 100.), axis=1)\n",
    "    \n",
    "    ldg_raw['RH2'] = ldg_raw.CH4\n",
    "    ldg_raw['T2'] = ldg_raw.CH5 + 273.15\n",
    "    ldg_raw['lpm2'] = ldg_raw.CH6\n",
    "    ldg_raw['vp2'] = ldg_raw.apply(lambda row: vp_from_T_rh(row.T2, row.RH2 / 100.), axis=1)\n",
    "    \n",
    "    ldg_raw['Tamb'] = ldg_raw.CH7 + 273.15\n",
    "\n",
    "    return ldg_raw"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "68d59660",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\RIST\\AppData\\Local\\Temp\\ipykernel_24420\\276399028.py:4: ParserWarning: Falling back to the 'python' engine because the 'c' engine does not support skipfooter; you can avoid this warning by specifying engine='python'.\n",
      "  ldg_raw = pd.read_csv(filename, skiprows=header_size, names=cnames, skipfooter=trim)\n"
     ]
    }
   ],
   "source": [
    "ldg_station_raw = read_humidity_aps_csv('./220922/220922-115225_Converted.csv', )\n",
    "ldg_pp_raw = read_humidity_aps_csv('./220922/220922-150245_Converted.csv', trim=500)\n",
    "\n",
    "ldg_station_raw_0926 = read_humidity_logger_csv('./220926/220926-102931.CSV')\n",
    "\n",
    "ldg_pp_0926 = read_two_humidity_logger_csv('./220926/220926-143350.CSV')\n",
    "ldg_station_0926 = read_two_humidity_logger_csv('./220926/220926-160114.CSV')\n",
    "\n",
    "ldg_pp_1013 = read_two_humidity_logger_csv('./221013/221013-100208.CSV')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "cc718bfa",
   "metadata": {},
   "outputs": [],
   "source": [
    "ldg_pp_1019 = read_two_humidity_logger_csv('./221019/221019-145514.CSV')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "b0abe6cc",
   "metadata": {},
   "outputs": [],
   "source": [
    "ldg_pp_1026 = read_two_humidity_logger_csv_v1026('./221026/221026-111532.CSV')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "a9fdd208",
   "metadata": {},
   "outputs": [],
   "source": [
    "ldg_pp_1103 = read_two_humidity_logger_csv_v1026('./221103/221103-102930.CSV')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "79d811aa",
   "metadata": {},
   "outputs": [],
   "source": [
    "ldg_pp_1122a = read_two_humidity_logger_csv_v1122('./221122/221122-103316.CSV')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "df169096",
   "metadata": {},
   "outputs": [],
   "source": [
    "ldg_pp_1122b = read_two_humidity_logger_csv_v1122('./221122/221122-103325.CSV')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "5c17d5d6",
   "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>No.</th>\n",
       "      <th>time</th>\n",
       "      <th>ms</th>\n",
       "      <th>CH1</th>\n",
       "      <th>CH2</th>\n",
       "      <th>CH3</th>\n",
       "      <th>CH4</th>\n",
       "      <th>CH5</th>\n",
       "      <th>CH6</th>\n",
       "      <th>CH7</th>\n",
       "      <th>...</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>RH1</th>\n",
       "      <th>T1</th>\n",
       "      <th>lpm1</th>\n",
       "      <th>vp1</th>\n",
       "      <th>RH2</th>\n",
       "      <th>T2</th>\n",
       "      <th>lpm2</th>\n",
       "      <th>vp2</th>\n",
       "      <th>Tamb</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2022-11-22 10:33:26</td>\n",
       "      <td>0</td>\n",
       "      <td>100</td>\n",
       "      <td>18.081</td>\n",
       "      <td>1.692</td>\n",
       "      <td>50.725</td>\n",
       "      <td>18.188</td>\n",
       "      <td>0.602</td>\n",
       "      <td>17.4</td>\n",
       "      <td>...</td>\n",
       "      <td>2022-11-22 10:33:26</td>\n",
       "      <td>100</td>\n",
       "      <td>291.231</td>\n",
       "      <td>1.692</td>\n",
       "      <td>2070.228022</td>\n",
       "      <td>50.725</td>\n",
       "      <td>291.338</td>\n",
       "      <td>0.602</td>\n",
       "      <td>1057.208928</td>\n",
       "      <td>290.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2022-11-22 10:33:36</td>\n",
       "      <td>0</td>\n",
       "      <td>100</td>\n",
       "      <td>18.069</td>\n",
       "      <td>1.691</td>\n",
       "      <td>50.656</td>\n",
       "      <td>18.188</td>\n",
       "      <td>0.602</td>\n",
       "      <td>17.4</td>\n",
       "      <td>...</td>\n",
       "      <td>2022-11-22 10:33:36</td>\n",
       "      <td>100</td>\n",
       "      <td>291.219</td>\n",
       "      <td>1.691</td>\n",
       "      <td>2068.666536</td>\n",
       "      <td>50.656</td>\n",
       "      <td>291.338</td>\n",
       "      <td>0.602</td>\n",
       "      <td>1055.770832</td>\n",
       "      <td>290.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>2022-11-22 10:33:46</td>\n",
       "      <td>0</td>\n",
       "      <td>100</td>\n",
       "      <td>18.069</td>\n",
       "      <td>1.691</td>\n",
       "      <td>50.575</td>\n",
       "      <td>18.194</td>\n",
       "      <td>0.602</td>\n",
       "      <td>17.4</td>\n",
       "      <td>...</td>\n",
       "      <td>2022-11-22 10:33:46</td>\n",
       "      <td>100</td>\n",
       "      <td>291.219</td>\n",
       "      <td>1.691</td>\n",
       "      <td>2068.666536</td>\n",
       "      <td>50.575</td>\n",
       "      <td>291.344</td>\n",
       "      <td>0.602</td>\n",
       "      <td>1054.480023</td>\n",
       "      <td>290.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>2022-11-22 10:33:56</td>\n",
       "      <td>0</td>\n",
       "      <td>100</td>\n",
       "      <td>18.081</td>\n",
       "      <td>1.692</td>\n",
       "      <td>50.550</td>\n",
       "      <td>18.194</td>\n",
       "      <td>0.602</td>\n",
       "      <td>17.3</td>\n",
       "      <td>...</td>\n",
       "      <td>2022-11-22 10:33:56</td>\n",
       "      <td>100</td>\n",
       "      <td>291.231</td>\n",
       "      <td>1.692</td>\n",
       "      <td>2070.228022</td>\n",
       "      <td>50.550</td>\n",
       "      <td>291.344</td>\n",
       "      <td>0.602</td>\n",
       "      <td>1053.958778</td>\n",
       "      <td>290.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>2022-11-22 10:34:06</td>\n",
       "      <td>0</td>\n",
       "      <td>100</td>\n",
       "      <td>18.081</td>\n",
       "      <td>1.691</td>\n",
       "      <td>50.519</td>\n",
       "      <td>18.181</td>\n",
       "      <td>0.602</td>\n",
       "      <td>17.3</td>\n",
       "      <td>...</td>\n",
       "      <td>2022-11-22 10:34:06</td>\n",
       "      <td>100</td>\n",
       "      <td>291.231</td>\n",
       "      <td>1.691</td>\n",
       "      <td>2070.228022</td>\n",
       "      <td>50.519</td>\n",
       "      <td>291.331</td>\n",
       "      <td>0.602</td>\n",
       "      <td>1052.452538</td>\n",
       "      <td>290.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16770</th>\n",
       "      <td>16771</td>\n",
       "      <td>2022-11-24 09:08:26</td>\n",
       "      <td>0</td>\n",
       "      <td>100</td>\n",
       "      <td>14.225</td>\n",
       "      <td>1.671</td>\n",
       "      <td>51.569</td>\n",
       "      <td>14.400</td>\n",
       "      <td>0.439</td>\n",
       "      <td>12.6</td>\n",
       "      <td>...</td>\n",
       "      <td>2022-11-24 09:08:26</td>\n",
       "      <td>100</td>\n",
       "      <td>287.375</td>\n",
       "      <td>1.671</td>\n",
       "      <td>1618.536231</td>\n",
       "      <td>51.569</td>\n",
       "      <td>287.550</td>\n",
       "      <td>0.439</td>\n",
       "      <td>844.175629</td>\n",
       "      <td>285.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16771</th>\n",
       "      <td>16772</td>\n",
       "      <td>2022-11-24 09:08:36</td>\n",
       "      <td>0</td>\n",
       "      <td>100</td>\n",
       "      <td>14.231</td>\n",
       "      <td>1.671</td>\n",
       "      <td>51.569</td>\n",
       "      <td>14.400</td>\n",
       "      <td>0.439</td>\n",
       "      <td>12.6</td>\n",
       "      <td>...</td>\n",
       "      <td>2022-11-24 09:08:36</td>\n",
       "      <td>100</td>\n",
       "      <td>287.381</td>\n",
       "      <td>1.671</td>\n",
       "      <td>1619.165646</td>\n",
       "      <td>51.569</td>\n",
       "      <td>287.550</td>\n",
       "      <td>0.439</td>\n",
       "      <td>844.175629</td>\n",
       "      <td>285.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16772</th>\n",
       "      <td>16773</td>\n",
       "      <td>2022-11-24 09:08:46</td>\n",
       "      <td>0</td>\n",
       "      <td>100</td>\n",
       "      <td>14.231</td>\n",
       "      <td>1.671</td>\n",
       "      <td>51.550</td>\n",
       "      <td>14.400</td>\n",
       "      <td>0.439</td>\n",
       "      <td>12.7</td>\n",
       "      <td>...</td>\n",
       "      <td>2022-11-24 09:08:46</td>\n",
       "      <td>100</td>\n",
       "      <td>287.381</td>\n",
       "      <td>1.671</td>\n",
       "      <td>1619.165646</td>\n",
       "      <td>51.550</td>\n",
       "      <td>287.550</td>\n",
       "      <td>0.439</td>\n",
       "      <td>843.864603</td>\n",
       "      <td>285.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16773</th>\n",
       "      <td>16774</td>\n",
       "      <td>2022-11-24 09:08:56</td>\n",
       "      <td>0</td>\n",
       "      <td>100</td>\n",
       "      <td>14.231</td>\n",
       "      <td>1.671</td>\n",
       "      <td>51.550</td>\n",
       "      <td>14.400</td>\n",
       "      <td>0.439</td>\n",
       "      <td>12.7</td>\n",
       "      <td>...</td>\n",
       "      <td>2022-11-24 09:08:56</td>\n",
       "      <td>100</td>\n",
       "      <td>287.381</td>\n",
       "      <td>1.671</td>\n",
       "      <td>1619.165646</td>\n",
       "      <td>51.550</td>\n",
       "      <td>287.550</td>\n",
       "      <td>0.439</td>\n",
       "      <td>843.864603</td>\n",
       "      <td>285.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16774</th>\n",
       "      <td>16775</td>\n",
       "      <td>2022-11-24 09:09:06</td>\n",
       "      <td>0</td>\n",
       "      <td>100</td>\n",
       "      <td>14.238</td>\n",
       "      <td>1.672</td>\n",
       "      <td>51.581</td>\n",
       "      <td>14.419</td>\n",
       "      <td>0.440</td>\n",
       "      <td>12.6</td>\n",
       "      <td>...</td>\n",
       "      <td>2022-11-24 09:09:06</td>\n",
       "      <td>100</td>\n",
       "      <td>287.388</td>\n",
       "      <td>1.672</td>\n",
       "      <td>1619.900236</td>\n",
       "      <td>51.581</td>\n",
       "      <td>287.569</td>\n",
       "      <td>0.440</td>\n",
       "      <td>845.410823</td>\n",
       "      <td>285.75</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>16775 rows × 36 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         No.                time  ms  CH1     CH2    CH3     CH4     CH5  \\\n",
       "0          1 2022-11-22 10:33:26   0  100  18.081  1.692  50.725  18.188   \n",
       "1          2 2022-11-22 10:33:36   0  100  18.069  1.691  50.656  18.188   \n",
       "2          3 2022-11-22 10:33:46   0  100  18.069  1.691  50.575  18.194   \n",
       "3          4 2022-11-22 10:33:56   0  100  18.081  1.692  50.550  18.194   \n",
       "4          5 2022-11-22 10:34:06   0  100  18.081  1.691  50.519  18.181   \n",
       "...      ...                 ...  ..  ...     ...    ...     ...     ...   \n",
       "16770  16771 2022-11-24 09:08:26   0  100  14.225  1.671  51.569  14.400   \n",
       "16771  16772 2022-11-24 09:08:36   0  100  14.231  1.671  51.569  14.400   \n",
       "16772  16773 2022-11-24 09:08:46   0  100  14.231  1.671  51.550  14.400   \n",
       "16773  16774 2022-11-24 09:08:56   0  100  14.231  1.671  51.550  14.400   \n",
       "16774  16775 2022-11-24 09:09:06   0  100  14.238  1.672  51.581  14.419   \n",
       "\n",
       "         CH6   CH7  ...           timestamp  RH1       T1   lpm1          vp1  \\\n",
       "0      0.602  17.4  ... 2022-11-22 10:33:26  100  291.231  1.692  2070.228022   \n",
       "1      0.602  17.4  ... 2022-11-22 10:33:36  100  291.219  1.691  2068.666536   \n",
       "2      0.602  17.4  ... 2022-11-22 10:33:46  100  291.219  1.691  2068.666536   \n",
       "3      0.602  17.3  ... 2022-11-22 10:33:56  100  291.231  1.692  2070.228022   \n",
       "4      0.602  17.3  ... 2022-11-22 10:34:06  100  291.231  1.691  2070.228022   \n",
       "...      ...   ...  ...                 ...  ...      ...    ...          ...   \n",
       "16770  0.439  12.6  ... 2022-11-24 09:08:26  100  287.375  1.671  1618.536231   \n",
       "16771  0.439  12.6  ... 2022-11-24 09:08:36  100  287.381  1.671  1619.165646   \n",
       "16772  0.439  12.7  ... 2022-11-24 09:08:46  100  287.381  1.671  1619.165646   \n",
       "16773  0.439  12.7  ... 2022-11-24 09:08:56  100  287.381  1.671  1619.165646   \n",
       "16774  0.440  12.6  ... 2022-11-24 09:09:06  100  287.388  1.672  1619.900236   \n",
       "\n",
       "          RH2       T2   lpm2          vp2    Tamb  \n",
       "0      50.725  291.338  0.602  1057.208928  290.55  \n",
       "1      50.656  291.338  0.602  1055.770832  290.55  \n",
       "2      50.575  291.344  0.602  1054.480023  290.55  \n",
       "3      50.550  291.344  0.602  1053.958778  290.45  \n",
       "4      50.519  291.331  0.602  1052.452538  290.45  \n",
       "...       ...      ...    ...          ...     ...  \n",
       "16770  51.569  287.550  0.439   844.175629  285.75  \n",
       "16771  51.569  287.550  0.439   844.175629  285.75  \n",
       "16772  51.550  287.550  0.439   843.864603  285.85  \n",
       "16773  51.550  287.550  0.439   843.864603  285.85  \n",
       "16774  51.581  287.569  0.440   845.410823  285.75  \n",
       "\n",
       "[16775 rows x 36 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ldg_pp_1122a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "66d863fa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.001"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "float('-0.001')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "500e1f54",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>No.</th>\n",
       "      <th>time</th>\n",
       "      <th>ms</th>\n",
       "      <th>CH1</th>\n",
       "      <th>CH2</th>\n",
       "      <th>CH3</th>\n",
       "      <th>CH4</th>\n",
       "      <th>CH5</th>\n",
       "      <th>CH6</th>\n",
       "      <th>CH7</th>\n",
       "      <th>...</th>\n",
       "      <th>CH18</th>\n",
       "      <th>CH19</th>\n",
       "      <th>CH20</th>\n",
       "      <th>Alarm1</th>\n",
       "      <th>Alarm2</th>\n",
       "      <th>AlarmOut</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>T</th>\n",
       "      <th>RH</th>\n",
       "      <th>vp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2022-09-22 11:52:25</td>\n",
       "      <td>0</td>\n",
       "      <td>4.770</td>\n",
       "      <td>2.197</td>\n",
       "      <td>1.579</td>\n",
       "      <td>0.005</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.001</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>LLLLLLLLLL</td>\n",
       "      <td>LLLLLLLLLL</td>\n",
       "      <td>LLLLLLLLLLLLLL</td>\n",
       "      <td>2022-09-22 11:52:25.000</td>\n",
       "      <td>298.985</td>\n",
       "      <td>94.250</td>\n",
       "      <td>3132.015631</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2022-09-22 11:52:25</td>\n",
       "      <td>200</td>\n",
       "      <td>4.770</td>\n",
       "      <td>2.197</td>\n",
       "      <td>1.579</td>\n",
       "      <td>0.006</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.001</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>0.01</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>LLLLLLLLLL</td>\n",
       "      <td>LLLLLLLLLL</td>\n",
       "      <td>LLLLLLLLLLLLLL</td>\n",
       "      <td>2022-09-22 11:52:25.200</td>\n",
       "      <td>298.985</td>\n",
       "      <td>94.250</td>\n",
       "      <td>3132.015631</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>2022-09-22 11:52:25</td>\n",
       "      <td>400</td>\n",
       "      <td>4.770</td>\n",
       "      <td>2.198</td>\n",
       "      <td>1.579</td>\n",
       "      <td>0.006</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.001</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>LLLLLLLLLL</td>\n",
       "      <td>LLLLLLLLLL</td>\n",
       "      <td>LLLLLLLLLLLLLL</td>\n",
       "      <td>2022-09-22 11:52:25.400</td>\n",
       "      <td>299.040</td>\n",
       "      <td>94.250</td>\n",
       "      <td>3142.234511</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>2022-09-22 11:52:25</td>\n",
       "      <td>600</td>\n",
       "      <td>4.771</td>\n",
       "      <td>2.198</td>\n",
       "      <td>1.579</td>\n",
       "      <td>0.007</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.001</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>LLLLLLLLLL</td>\n",
       "      <td>LLLLLLLLLL</td>\n",
       "      <td>LLLLLLLLLLLLLL</td>\n",
       "      <td>2022-09-22 11:52:25.600</td>\n",
       "      <td>299.040</td>\n",
       "      <td>94.275</td>\n",
       "      <td>3143.067995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>2022-09-22 11:52:25</td>\n",
       "      <td>800</td>\n",
       "      <td>4.771</td>\n",
       "      <td>2.197</td>\n",
       "      <td>1.579</td>\n",
       "      <td>0.007</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.001</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>LLLLLLLLLL</td>\n",
       "      <td>LLLLLLLLLL</td>\n",
       "      <td>LLLLLLLLLLLLLL</td>\n",
       "      <td>2022-09-22 11:52:25.800</td>\n",
       "      <td>298.985</td>\n",
       "      <td>94.275</td>\n",
       "      <td>3132.846404</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33953</th>\n",
       "      <td>33954</td>\n",
       "      <td>2022-09-22 13:45:35</td>\n",
       "      <td>600</td>\n",
       "      <td>4.908</td>\n",
       "      <td>2.275</td>\n",
       "      <td>1.187</td>\n",
       "      <td>0.006</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.001</td>\n",
       "      <td>...</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.01</td>\n",
       "      <td>LLLLLLLLLL</td>\n",
       "      <td>LLLLLLLLLL</td>\n",
       "      <td>LLLLLLLLLLLLLL</td>\n",
       "      <td>2022-09-22 13:45:35.600</td>\n",
       "      <td>303.275</td>\n",
       "      <td>97.700</td>\n",
       "      <td>4169.019855</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33954</th>\n",
       "      <td>33955</td>\n",
       "      <td>2022-09-22 13:45:35</td>\n",
       "      <td>800</td>\n",
       "      <td>4.908</td>\n",
       "      <td>2.275</td>\n",
       "      <td>1.187</td>\n",
       "      <td>0.006</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.001</td>\n",
       "      <td>...</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.01</td>\n",
       "      <td>LLLLLLLLLL</td>\n",
       "      <td>LLLLLLLLLL</td>\n",
       "      <td>LLLLLLLLLLLLLL</td>\n",
       "      <td>2022-09-22 13:45:35.800</td>\n",
       "      <td>303.275</td>\n",
       "      <td>97.700</td>\n",
       "      <td>4169.019855</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33955</th>\n",
       "      <td>33956</td>\n",
       "      <td>2022-09-22 13:45:36</td>\n",
       "      <td>0</td>\n",
       "      <td>4.908</td>\n",
       "      <td>2.275</td>\n",
       "      <td>1.187</td>\n",
       "      <td>0.006</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.001</td>\n",
       "      <td>...</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.01</td>\n",
       "      <td>LLLLLLLLLL</td>\n",
       "      <td>LLLLLLLLLL</td>\n",
       "      <td>LLLLLLLLLLLLLL</td>\n",
       "      <td>2022-09-22 13:45:36.000</td>\n",
       "      <td>303.275</td>\n",
       "      <td>97.700</td>\n",
       "      <td>4169.019855</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33956</th>\n",
       "      <td>33957</td>\n",
       "      <td>2022-09-22 13:45:36</td>\n",
       "      <td>200</td>\n",
       "      <td>4.908</td>\n",
       "      <td>2.275</td>\n",
       "      <td>1.187</td>\n",
       "      <td>0.006</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.001</td>\n",
       "      <td>...</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.01</td>\n",
       "      <td>LLLLLLLLLL</td>\n",
       "      <td>LLLLLLLLLL</td>\n",
       "      <td>LLLLLLLLLLLLLL</td>\n",
       "      <td>2022-09-22 13:45:36.200</td>\n",
       "      <td>303.275</td>\n",
       "      <td>97.700</td>\n",
       "      <td>4169.019855</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33957</th>\n",
       "      <td>33958</td>\n",
       "      <td>2022-09-22 13:45:36</td>\n",
       "      <td>400</td>\n",
       "      <td>4.908</td>\n",
       "      <td>2.275</td>\n",
       "      <td>1.187</td>\n",
       "      <td>0.006</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.001</td>\n",
       "      <td>...</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.01</td>\n",
       "      <td>LLLLLLLLLL</td>\n",
       "      <td>LLLLLLLLLL</td>\n",
       "      <td>LLLLLLLLLLLLLL</td>\n",
       "      <td>2022-09-22 13:45:36.400</td>\n",
       "      <td>303.275</td>\n",
       "      <td>97.700</td>\n",
       "      <td>4169.019855</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>33958 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         No.                 time   ms    CH1    CH2    CH3    CH4    CH5  \\\n",
       "0          1  2022-09-22 11:52:25    0  4.770  2.197  1.579  0.005  0.001   \n",
       "1          2  2022-09-22 11:52:25  200  4.770  2.197  1.579  0.006  0.001   \n",
       "2          3  2022-09-22 11:52:25  400  4.770  2.198  1.579  0.006  0.001   \n",
       "3          4  2022-09-22 11:52:25  600  4.771  2.198  1.579  0.007  0.001   \n",
       "4          5  2022-09-22 11:52:25  800  4.771  2.197  1.579  0.007  0.002   \n",
       "...      ...                  ...  ...    ...    ...    ...    ...    ...   \n",
       "33953  33954  2022-09-22 13:45:35  600  4.908  2.275  1.187  0.006  0.001   \n",
       "33954  33955  2022-09-22 13:45:35  800  4.908  2.275  1.187  0.006  0.001   \n",
       "33955  33956  2022-09-22 13:45:36    0  4.908  2.275  1.187  0.006  0.001   \n",
       "33956  33957  2022-09-22 13:45:36  200  4.908  2.275  1.187  0.006  0.001   \n",
       "33957  33958  2022-09-22 13:45:36  400  4.908  2.275  1.187  0.006  0.001   \n",
       "\n",
       "         CH6    CH7  ...  CH18  CH19  CH20      Alarm1      Alarm2  \\\n",
       "0      0.000  0.001  ... -0.01  0.01  0.00  LLLLLLLLLL  LLLLLLLLLL   \n",
       "1      0.000  0.001  ... -0.01  0.01 -0.01  LLLLLLLLLL  LLLLLLLLLL   \n",
       "2      0.000  0.001  ... -0.01  0.00  0.00  LLLLLLLLLL  LLLLLLLLLL   \n",
       "3      0.001  0.001  ... -0.01  0.00  0.00  LLLLLLLLLL  LLLLLLLLLL   \n",
       "4      0.000  0.001  ... -0.01  0.00 -0.01  LLLLLLLLLL  LLLLLLLLLL   \n",
       "...      ...    ...  ...   ...   ...   ...         ...         ...   \n",
       "33953  0.001  0.001  ...  0.01  0.00  0.01  LLLLLLLLLL  LLLLLLLLLL   \n",
       "33954  0.002  0.001  ...  0.01  0.01  0.01  LLLLLLLLLL  LLLLLLLLLL   \n",
       "33955  0.001  0.001  ...  0.01  0.00  0.01  LLLLLLLLLL  LLLLLLLLLL   \n",
       "33956  0.002  0.001  ...  0.01  0.00  0.01  LLLLLLLLLL  LLLLLLLLLL   \n",
       "33957  0.002  0.001  ...  0.01  0.00  0.01  LLLLLLLLLL  LLLLLLLLLL   \n",
       "\n",
       "             AlarmOut               timestamp        T      RH           vp  \n",
       "0      LLLLLLLLLLLLLL 2022-09-22 11:52:25.000  298.985  94.250  3132.015631  \n",
       "1      LLLLLLLLLLLLLL 2022-09-22 11:52:25.200  298.985  94.250  3132.015631  \n",
       "2      LLLLLLLLLLLLLL 2022-09-22 11:52:25.400  299.040  94.250  3142.234511  \n",
       "3      LLLLLLLLLLLLLL 2022-09-22 11:52:25.600  299.040  94.275  3143.067995  \n",
       "4      LLLLLLLLLLLLLL 2022-09-22 11:52:25.800  298.985  94.275  3132.846404  \n",
       "...               ...                     ...      ...     ...          ...  \n",
       "33953  LLLLLLLLLLLLLL 2022-09-22 13:45:35.600  303.275  97.700  4169.019855  \n",
       "33954  LLLLLLLLLLLLLL 2022-09-22 13:45:35.800  303.275  97.700  4169.019855  \n",
       "33955  LLLLLLLLLLLLLL 2022-09-22 13:45:36.000  303.275  97.700  4169.019855  \n",
       "33956  LLLLLLLLLLLLLL 2022-09-22 13:45:36.200  303.275  97.700  4169.019855  \n",
       "33957  LLLLLLLLLLLLLL 2022-09-22 13:45:36.400  303.275  97.700  4169.019855  \n",
       "\n",
       "[33958 rows x 30 columns]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ldg_station_raw"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "573f079c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>No.</th>\n",
       "      <th>time</th>\n",
       "      <th>ms</th>\n",
       "      <th>CH1</th>\n",
       "      <th>CH2</th>\n",
       "      <th>CH3</th>\n",
       "      <th>CH4</th>\n",
       "      <th>CH5</th>\n",
       "      <th>CH6</th>\n",
       "      <th>CH7</th>\n",
       "      <th>...</th>\n",
       "      <th>CH18</th>\n",
       "      <th>CH19</th>\n",
       "      <th>CH20</th>\n",
       "      <th>Alarm1</th>\n",
       "      <th>Alarm2</th>\n",
       "      <th>AlarmOut</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>T</th>\n",
       "      <th>RH</th>\n",
       "      <th>vp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2022-09-22 15:02:45</td>\n",
       "      <td>0</td>\n",
       "      <td>3.91</td>\n",
       "      <td>2.30</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.01</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>LLLLLLLLLL</td>\n",
       "      <td>LLLLLLLLLL</td>\n",
       "      <td>LLLLLLLLLLLLLL</td>\n",
       "      <td>2022-09-22 15:02:45.000</td>\n",
       "      <td>304.65</td>\n",
       "      <td>72.75</td>\n",
       "      <td>3357.719782</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2022-09-22 15:02:45</td>\n",
       "      <td>200</td>\n",
       "      <td>3.91</td>\n",
       "      <td>2.31</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>LLLLLLLLLL</td>\n",
       "      <td>LLLLLLLLLL</td>\n",
       "      <td>LLLLLLLLLLLLLL</td>\n",
       "      <td>2022-09-22 15:02:45.200</td>\n",
       "      <td>305.20</td>\n",
       "      <td>72.75</td>\n",
       "      <td>3463.981638</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>2022-09-22 15:02:45</td>\n",
       "      <td>400</td>\n",
       "      <td>3.91</td>\n",
       "      <td>2.31</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>LLLLLLLLLL</td>\n",
       "      <td>LLLLLLLLLL</td>\n",
       "      <td>LLLLLLLLLLLLLL</td>\n",
       "      <td>2022-09-22 15:02:45.400</td>\n",
       "      <td>305.20</td>\n",
       "      <td>72.75</td>\n",
       "      <td>3463.981638</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>2022-09-22 15:02:45</td>\n",
       "      <td>600</td>\n",
       "      <td>3.91</td>\n",
       "      <td>2.30</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>LLLLLLLLLL</td>\n",
       "      <td>LLLLLLLLLL</td>\n",
       "      <td>LLLLLLLLLLLLLL</td>\n",
       "      <td>2022-09-22 15:02:45.600</td>\n",
       "      <td>304.65</td>\n",
       "      <td>72.75</td>\n",
       "      <td>3357.719782</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>2022-09-22 15:02:45</td>\n",
       "      <td>800</td>\n",
       "      <td>3.91</td>\n",
       "      <td>2.30</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>0.01</td>\n",
       "      <td>LLLLLLLLLL</td>\n",
       "      <td>LLLLLLLLLL</td>\n",
       "      <td>LLLLLLLLLLLLLL</td>\n",
       "      <td>2022-09-22 15:02:45.800</td>\n",
       "      <td>304.65</td>\n",
       "      <td>72.75</td>\n",
       "      <td>3357.719782</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20565</th>\n",
       "      <td>20566</td>\n",
       "      <td>2022-09-22 16:11:18</td>\n",
       "      <td>0</td>\n",
       "      <td>4.33</td>\n",
       "      <td>2.25</td>\n",
       "      <td>1.01</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>0.01</td>\n",
       "      <td>...</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.01</td>\n",
       "      <td>LLLLLLLLLL</td>\n",
       "      <td>LLLLLLLLLL</td>\n",
       "      <td>LLLLLLLLLLLLLL</td>\n",
       "      <td>2022-09-22 16:11:18.000</td>\n",
       "      <td>301.90</td>\n",
       "      <td>83.25</td>\n",
       "      <td>3281.698205</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20566</th>\n",
       "      <td>20567</td>\n",
       "      <td>2022-09-22 16:11:18</td>\n",
       "      <td>200</td>\n",
       "      <td>4.33</td>\n",
       "      <td>2.25</td>\n",
       "      <td>1.01</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>0.01</td>\n",
       "      <td>...</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.01</td>\n",
       "      <td>LLLLLLLLLL</td>\n",
       "      <td>LLLLLLLLLL</td>\n",
       "      <td>LLLLLLLLLLLLLL</td>\n",
       "      <td>2022-09-22 16:11:18.200</td>\n",
       "      <td>301.90</td>\n",
       "      <td>83.25</td>\n",
       "      <td>3281.698205</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20567</th>\n",
       "      <td>20568</td>\n",
       "      <td>2022-09-22 16:11:18</td>\n",
       "      <td>400</td>\n",
       "      <td>4.33</td>\n",
       "      <td>2.25</td>\n",
       "      <td>1.01</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>0.01</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.01</td>\n",
       "      <td>LLLLLLLLLL</td>\n",
       "      <td>LLLLLLLLLL</td>\n",
       "      <td>LLLLLLLLLLLLLL</td>\n",
       "      <td>2022-09-22 16:11:18.400</td>\n",
       "      <td>301.90</td>\n",
       "      <td>83.25</td>\n",
       "      <td>3281.698205</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20568</th>\n",
       "      <td>20569</td>\n",
       "      <td>2022-09-22 16:11:18</td>\n",
       "      <td>600</td>\n",
       "      <td>4.32</td>\n",
       "      <td>2.25</td>\n",
       "      <td>1.00</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>LLLLLLLLLL</td>\n",
       "      <td>LLLLLLLLLL</td>\n",
       "      <td>LLLLLLLLLLLLLL</td>\n",
       "      <td>2022-09-22 16:11:18.600</td>\n",
       "      <td>301.90</td>\n",
       "      <td>83.00</td>\n",
       "      <td>3271.843255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20569</th>\n",
       "      <td>20570</td>\n",
       "      <td>2022-09-22 16:11:18</td>\n",
       "      <td>800</td>\n",
       "      <td>4.32</td>\n",
       "      <td>2.25</td>\n",
       "      <td>1.00</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>LLLLLLLLLL</td>\n",
       "      <td>LLLLLLLLLL</td>\n",
       "      <td>LLLLLLLLLLLLLL</td>\n",
       "      <td>2022-09-22 16:11:18.800</td>\n",
       "      <td>301.90</td>\n",
       "      <td>83.00</td>\n",
       "      <td>3271.843255</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>20570 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         No.                 time   ms   CH1   CH2   CH3   CH4   CH5   CH6  \\\n",
       "0          1  2022-09-22 15:02:45    0  3.91  2.30  1.00  0.01  0.01  0.00   \n",
       "1          2  2022-09-22 15:02:45  200  3.91  2.31  1.00  0.01  0.01  0.00   \n",
       "2          3  2022-09-22 15:02:45  400  3.91  2.31  1.00  0.01  0.01  0.00   \n",
       "3          4  2022-09-22 15:02:45  600  3.91  2.30  1.00  0.01  0.01  0.01   \n",
       "4          5  2022-09-22 15:02:45  800  3.91  2.30  1.00  0.01  0.01  0.01   \n",
       "...      ...                  ...  ...   ...   ...   ...   ...   ...   ...   \n",
       "20565  20566  2022-09-22 16:11:18    0  4.33  2.25  1.01 -0.01 -0.01 -0.01   \n",
       "20566  20567  2022-09-22 16:11:18  200  4.33  2.25  1.01 -0.01  0.00 -0.01   \n",
       "20567  20568  2022-09-22 16:11:18  400  4.33  2.25  1.01 -0.01  0.00 -0.01   \n",
       "20568  20569  2022-09-22 16:11:18  600  4.32  2.25  1.00 -0.01 -0.01 -0.01   \n",
       "20569  20570  2022-09-22 16:11:18  800  4.32  2.25  1.00 -0.01 -0.01 -0.01   \n",
       "\n",
       "        CH7  ...  CH18  CH19  CH20      Alarm1      Alarm2        AlarmOut  \\\n",
       "0      0.01  ... -0.01 -0.01  0.00  LLLLLLLLLL  LLLLLLLLLL  LLLLLLLLLLLLLL   \n",
       "1      0.00  ... -0.01 -0.01  0.00  LLLLLLLLLL  LLLLLLLLLL  LLLLLLLLLLLLLL   \n",
       "2      0.00  ... -0.01 -0.01  0.00  LLLLLLLLLL  LLLLLLLLLL  LLLLLLLLLLLLLL   \n",
       "3      0.00  ...  0.00 -0.01  0.00  LLLLLLLLLL  LLLLLLLLLL  LLLLLLLLLLLLLL   \n",
       "4      0.00  ...  0.00 -0.01  0.01  LLLLLLLLLL  LLLLLLLLLL  LLLLLLLLLLLLLL   \n",
       "...     ...  ...   ...   ...   ...         ...         ...             ...   \n",
       "20565  0.01  ...  0.01  0.01  0.01  LLLLLLLLLL  LLLLLLLLLL  LLLLLLLLLLLLLL   \n",
       "20566  0.01  ...  0.01  0.01  0.01  LLLLLLLLLL  LLLLLLLLLL  LLLLLLLLLLLLLL   \n",
       "20567  0.01  ...  0.00  0.01  0.01  LLLLLLLLLL  LLLLLLLLLL  LLLLLLLLLLLLLL   \n",
       "20568 -0.01  ...  0.00  0.00  0.00  LLLLLLLLLL  LLLLLLLLLL  LLLLLLLLLLLLLL   \n",
       "20569 -0.01  ...  0.00  0.01  0.00  LLLLLLLLLL  LLLLLLLLLL  LLLLLLLLLLLLLL   \n",
       "\n",
       "                    timestamp       T     RH           vp  \n",
       "0     2022-09-22 15:02:45.000  304.65  72.75  3357.719782  \n",
       "1     2022-09-22 15:02:45.200  305.20  72.75  3463.981638  \n",
       "2     2022-09-22 15:02:45.400  305.20  72.75  3463.981638  \n",
       "3     2022-09-22 15:02:45.600  304.65  72.75  3357.719782  \n",
       "4     2022-09-22 15:02:45.800  304.65  72.75  3357.719782  \n",
       "...                       ...     ...    ...          ...  \n",
       "20565 2022-09-22 16:11:18.000  301.90  83.25  3281.698205  \n",
       "20566 2022-09-22 16:11:18.200  301.90  83.25  3281.698205  \n",
       "20567 2022-09-22 16:11:18.400  301.90  83.25  3281.698205  \n",
       "20568 2022-09-22 16:11:18.600  301.90  83.00  3271.843255  \n",
       "20569 2022-09-22 16:11:18.800  301.90  83.00  3271.843255  \n",
       "\n",
       "[20570 rows x 30 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ldg_pp_raw"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "016ce853",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "        vertical-align: middle;\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>No.</th>\n",
       "      <th>time</th>\n",
       "      <th>ms</th>\n",
       "      <th>CH1</th>\n",
       "      <th>CH2</th>\n",
       "      <th>CH3</th>\n",
       "      <th>CH4</th>\n",
       "      <th>CH5</th>\n",
       "      <th>CH6</th>\n",
       "      <th>CH7</th>\n",
       "      <th>...</th>\n",
       "      <th>CH18</th>\n",
       "      <th>CH19</th>\n",
       "      <th>CH20</th>\n",
       "      <th>Alarm1</th>\n",
       "      <th>Alarm2</th>\n",
       "      <th>AlarmOut</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>T</th>\n",
       "      <th>RH</th>\n",
       "      <th>vp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2022/09/26 10:29:32</td>\n",
       "      <td>0</td>\n",
       "      <td>+ 4.987</td>\n",
       "      <td>+ 2.129</td>\n",
       "      <td>+ 1.007</td>\n",
       "      <td>+ 2.138</td>\n",
       "      <td>+ 0.008</td>\n",
       "      <td>+ 0.001</td>\n",
       "      <td>+ 0.001</td>\n",
       "      <td>...</td>\n",
       "      <td>+  0.00</td>\n",
       "      <td>+  0.00</td>\n",
       "      <td>+  0.00</td>\n",
       "      <td>LLLLLLLLLL</td>\n",
       "      <td>LLLLLLLLLL</td>\n",
       "      <td>LLLL</td>\n",
       "      <td>2022-09-26 10:29:32</td>\n",
       "      <td>295.245</td>\n",
       "      <td>99.675</td>\n",
       "      <td>2645.612154</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2022/09/26 10:29:33</td>\n",
       "      <td>0</td>\n",
       "      <td>+ 4.987</td>\n",
       "      <td>+ 2.129</td>\n",
       "      <td>+ 1.007</td>\n",
       "      <td>+ 2.138</td>\n",
       "      <td>+ 0.008</td>\n",
       "      <td>+ 0.001</td>\n",
       "      <td>+ 0.001</td>\n",
       "      <td>...</td>\n",
       "      <td>+  0.00</td>\n",
       "      <td>+  0.00</td>\n",
       "      <td>+  0.00</td>\n",
       "      <td>LLLLLLLLLL</td>\n",
       "      <td>LLLLLLLLLL</td>\n",
       "      <td>LLLL</td>\n",
       "      <td>2022-09-26 10:29:33</td>\n",
       "      <td>295.245</td>\n",
       "      <td>99.675</td>\n",
       "      <td>2645.612154</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>2022/09/26 10:29:34</td>\n",
       "      <td>0</td>\n",
       "      <td>+ 4.987</td>\n",
       "      <td>+ 2.129</td>\n",
       "      <td>+ 1.007</td>\n",
       "      <td>+ 2.138</td>\n",
       "      <td>+ 0.007</td>\n",
       "      <td>+ 0.002</td>\n",
       "      <td>+ 0.001</td>\n",
       "      <td>...</td>\n",
       "      <td>+  0.00</td>\n",
       "      <td>-  0.01</td>\n",
       "      <td>+  0.00</td>\n",
       "      <td>LLLLLLLLLL</td>\n",
       "      <td>LLLLLLLLLL</td>\n",
       "      <td>LLLL</td>\n",
       "      <td>2022-09-26 10:29:34</td>\n",
       "      <td>295.245</td>\n",
       "      <td>99.675</td>\n",
       "      <td>2645.612154</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>2022/09/26 10:29:35</td>\n",
       "      <td>0</td>\n",
       "      <td>+ 4.987</td>\n",
       "      <td>+ 2.129</td>\n",
       "      <td>+ 1.007</td>\n",
       "      <td>+ 2.138</td>\n",
       "      <td>+ 0.007</td>\n",
       "      <td>+ 0.002</td>\n",
       "      <td>+ 0.001</td>\n",
       "      <td>...</td>\n",
       "      <td>-  0.01</td>\n",
       "      <td>+  0.00</td>\n",
       "      <td>-  0.01</td>\n",
       "      <td>LLLLLLLLLL</td>\n",
       "      <td>LLLLLLLLLL</td>\n",
       "      <td>LLLL</td>\n",
       "      <td>2022-09-26 10:29:35</td>\n",
       "      <td>295.245</td>\n",
       "      <td>99.675</td>\n",
       "      <td>2645.612154</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>2022/09/26 10:29:36</td>\n",
       "      <td>0</td>\n",
       "      <td>+ 4.987</td>\n",
       "      <td>+ 2.129</td>\n",
       "      <td>+ 1.007</td>\n",
       "      <td>+ 2.138</td>\n",
       "      <td>+ 0.007</td>\n",
       "      <td>+ 0.002</td>\n",
       "      <td>+ 0.002</td>\n",
       "      <td>...</td>\n",
       "      <td>+  0.00</td>\n",
       "      <td>+  0.00</td>\n",
       "      <td>-  0.01</td>\n",
       "      <td>LLLLLLLLLL</td>\n",
       "      <td>LLLLLLLLLL</td>\n",
       "      <td>LLLL</td>\n",
       "      <td>2022-09-26 10:29:36</td>\n",
       "      <td>295.245</td>\n",
       "      <td>99.675</td>\n",
       "      <td>2645.612154</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5103</th>\n",
       "      <td>5104</td>\n",
       "      <td>2022/09/26 11:54:35</td>\n",
       "      <td>0</td>\n",
       "      <td>+ 4.996</td>\n",
       "      <td>+ 2.159</td>\n",
       "      <td>+ 1.009</td>\n",
       "      <td>+ 2.169</td>\n",
       "      <td>+ 0.006</td>\n",
       "      <td>+ 0.001</td>\n",
       "      <td>+ 0.001</td>\n",
       "      <td>...</td>\n",
       "      <td>-  0.01</td>\n",
       "      <td>+  0.00</td>\n",
       "      <td>+  0.00</td>\n",
       "      <td>LLLLLLLLLL</td>\n",
       "      <td>LLLLLLLLLL</td>\n",
       "      <td>LLLL</td>\n",
       "      <td>2022-09-26 11:54:35</td>\n",
       "      <td>296.895</td>\n",
       "      <td>99.900</td>\n",
       "      <td>2930.281587</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5104</th>\n",
       "      <td>5105</td>\n",
       "      <td>2022/09/26 11:54:36</td>\n",
       "      <td>0</td>\n",
       "      <td>+ 4.995</td>\n",
       "      <td>+ 2.159</td>\n",
       "      <td>+ 1.009</td>\n",
       "      <td>+ 2.170</td>\n",
       "      <td>+ 0.006</td>\n",
       "      <td>+ 0.001</td>\n",
       "      <td>+ 0.001</td>\n",
       "      <td>...</td>\n",
       "      <td>+  0.00</td>\n",
       "      <td>+  0.00</td>\n",
       "      <td>+  0.00</td>\n",
       "      <td>LLLLLLLLLL</td>\n",
       "      <td>LLLLLLLLLL</td>\n",
       "      <td>LLLL</td>\n",
       "      <td>2022-09-26 11:54:36</td>\n",
       "      <td>296.895</td>\n",
       "      <td>99.875</td>\n",
       "      <td>2929.548283</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5105</th>\n",
       "      <td>5106</td>\n",
       "      <td>2022/09/26 11:54:37</td>\n",
       "      <td>0</td>\n",
       "      <td>+ 4.995</td>\n",
       "      <td>+ 2.159</td>\n",
       "      <td>+ 1.009</td>\n",
       "      <td>+ 2.170</td>\n",
       "      <td>+ 0.006</td>\n",
       "      <td>+ 0.001</td>\n",
       "      <td>+ 0.001</td>\n",
       "      <td>...</td>\n",
       "      <td>+  0.00</td>\n",
       "      <td>+  0.00</td>\n",
       "      <td>+  0.00</td>\n",
       "      <td>LLLLLLLLLL</td>\n",
       "      <td>LLLLLLLLLL</td>\n",
       "      <td>LLLL</td>\n",
       "      <td>2022-09-26 11:54:37</td>\n",
       "      <td>296.895</td>\n",
       "      <td>99.875</td>\n",
       "      <td>2929.548283</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5106</th>\n",
       "      <td>5107</td>\n",
       "      <td>2022/09/26 11:54:38</td>\n",
       "      <td>0</td>\n",
       "      <td>+ 4.994</td>\n",
       "      <td>+ 2.160</td>\n",
       "      <td>+ 1.009</td>\n",
       "      <td>+ 2.170</td>\n",
       "      <td>+ 0.006</td>\n",
       "      <td>+ 0.001</td>\n",
       "      <td>+ 0.001</td>\n",
       "      <td>...</td>\n",
       "      <td>+  0.00</td>\n",
       "      <td>+  0.00</td>\n",
       "      <td>+  0.00</td>\n",
       "      <td>LLLLLLLLLL</td>\n",
       "      <td>LLLLLLLLLL</td>\n",
       "      <td>LLLL</td>\n",
       "      <td>2022-09-26 11:54:38</td>\n",
       "      <td>296.950</td>\n",
       "      <td>99.850</td>\n",
       "      <td>2938.524485</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5107</th>\n",
       "      <td>5108</td>\n",
       "      <td>2022/09/26 11:54:39</td>\n",
       "      <td>0</td>\n",
       "      <td>+ 4.994</td>\n",
       "      <td>+ 2.159</td>\n",
       "      <td>+ 1.009</td>\n",
       "      <td>+ 2.170</td>\n",
       "      <td>+ 0.006</td>\n",
       "      <td>+ 0.001</td>\n",
       "      <td>+ 0.001</td>\n",
       "      <td>...</td>\n",
       "      <td>+  0.00</td>\n",
       "      <td>+  0.00</td>\n",
       "      <td>+  0.00</td>\n",
       "      <td>LLLLLLLLLL</td>\n",
       "      <td>LLLLLLLLLL</td>\n",
       "      <td>LLLL</td>\n",
       "      <td>2022-09-26 11:54:39</td>\n",
       "      <td>296.895</td>\n",
       "      <td>99.850</td>\n",
       "      <td>2928.814979</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5108 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       No.                 time  ms      CH1      CH2      CH3      CH4  \\\n",
       "0        1  2022/09/26 10:29:32   0  + 4.987  + 2.129  + 1.007  + 2.138   \n",
       "1        2  2022/09/26 10:29:33   0  + 4.987  + 2.129  + 1.007  + 2.138   \n",
       "2        3  2022/09/26 10:29:34   0  + 4.987  + 2.129  + 1.007  + 2.138   \n",
       "3        4  2022/09/26 10:29:35   0  + 4.987  + 2.129  + 1.007  + 2.138   \n",
       "4        5  2022/09/26 10:29:36   0  + 4.987  + 2.129  + 1.007  + 2.138   \n",
       "...    ...                  ...  ..      ...      ...      ...      ...   \n",
       "5103  5104  2022/09/26 11:54:35   0  + 4.996  + 2.159  + 1.009  + 2.169   \n",
       "5104  5105  2022/09/26 11:54:36   0  + 4.995  + 2.159  + 1.009  + 2.170   \n",
       "5105  5106  2022/09/26 11:54:37   0  + 4.995  + 2.159  + 1.009  + 2.170   \n",
       "5106  5107  2022/09/26 11:54:38   0  + 4.994  + 2.160  + 1.009  + 2.170   \n",
       "5107  5108  2022/09/26 11:54:39   0  + 4.994  + 2.159  + 1.009  + 2.170   \n",
       "\n",
       "          CH5      CH6      CH7  ...     CH18     CH19     CH20      Alarm1  \\\n",
       "0     + 0.008  + 0.001  + 0.001  ...  +  0.00  +  0.00  +  0.00  LLLLLLLLLL   \n",
       "1     + 0.008  + 0.001  + 0.001  ...  +  0.00  +  0.00  +  0.00  LLLLLLLLLL   \n",
       "2     + 0.007  + 0.002  + 0.001  ...  +  0.00  -  0.01  +  0.00  LLLLLLLLLL   \n",
       "3     + 0.007  + 0.002  + 0.001  ...  -  0.01  +  0.00  -  0.01  LLLLLLLLLL   \n",
       "4     + 0.007  + 0.002  + 0.002  ...  +  0.00  +  0.00  -  0.01  LLLLLLLLLL   \n",
       "...       ...      ...      ...  ...      ...      ...      ...         ...   \n",
       "5103  + 0.006  + 0.001  + 0.001  ...  -  0.01  +  0.00  +  0.00  LLLLLLLLLL   \n",
       "5104  + 0.006  + 0.001  + 0.001  ...  +  0.00  +  0.00  +  0.00  LLLLLLLLLL   \n",
       "5105  + 0.006  + 0.001  + 0.001  ...  +  0.00  +  0.00  +  0.00  LLLLLLLLLL   \n",
       "5106  + 0.006  + 0.001  + 0.001  ...  +  0.00  +  0.00  +  0.00  LLLLLLLLLL   \n",
       "5107  + 0.006  + 0.001  + 0.001  ...  +  0.00  +  0.00  +  0.00  LLLLLLLLLL   \n",
       "\n",
       "          Alarm2 AlarmOut           timestamp        T      RH           vp  \n",
       "0     LLLLLLLLLL     LLLL 2022-09-26 10:29:32  295.245  99.675  2645.612154  \n",
       "1     LLLLLLLLLL     LLLL 2022-09-26 10:29:33  295.245  99.675  2645.612154  \n",
       "2     LLLLLLLLLL     LLLL 2022-09-26 10:29:34  295.245  99.675  2645.612154  \n",
       "3     LLLLLLLLLL     LLLL 2022-09-26 10:29:35  295.245  99.675  2645.612154  \n",
       "4     LLLLLLLLLL     LLLL 2022-09-26 10:29:36  295.245  99.675  2645.612154  \n",
       "...          ...      ...                 ...      ...     ...          ...  \n",
       "5103  LLLLLLLLLL     LLLL 2022-09-26 11:54:35  296.895  99.900  2930.281587  \n",
       "5104  LLLLLLLLLL     LLLL 2022-09-26 11:54:36  296.895  99.875  2929.548283  \n",
       "5105  LLLLLLLLLL     LLLL 2022-09-26 11:54:37  296.895  99.875  2929.548283  \n",
       "5106  LLLLLLLLLL     LLLL 2022-09-26 11:54:38  296.950  99.850  2938.524485  \n",
       "5107  LLLLLLLLLL     LLLL 2022-09-26 11:54:39  296.895  99.850  2928.814979  \n",
       "\n",
       "[5108 rows x 30 columns]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ldg_station_raw_0926"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "e056fd02",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>No.</th>\n",
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       "      <th>ms</th>\n",
       "      <th>CH1</th>\n",
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       "      <th>CH7</th>\n",
       "      <th>...</th>\n",
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       "      <th>Alarm2</th>\n",
       "      <th>AlarmOut</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>T1</th>\n",
       "      <th>RH1</th>\n",
       "      <th>vp1</th>\n",
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       "      <td>LLLLLLLLLL</td>\n",
       "      <td>LLLLLLLLLL</td>\n",
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       "      <td>2217.317510</td>\n",
       "      <td>292.550</td>\n",
       "      <td>35.350</td>\n",
       "      <td>794.769171</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2022/10/13 10:02:10</td>\n",
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       "      <td>+ 2.082</td>\n",
       "      <td>+ 1.323</td>\n",
       "      <td>+ 2.411</td>\n",
       "      <td>+ 2.081</td>\n",
       "      <td>+ 1.289</td>\n",
       "      <td>+ 0.007</td>\n",
       "      <td>...</td>\n",
       "      <td>LLLLLLLLLL</td>\n",
       "      <td>LLLLLLLLLL</td>\n",
       "      <td>LLLL</td>\n",
       "      <td>2022-10-13 10:02:10</td>\n",
       "      <td>292.66</td>\n",
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       "      <td>292.605</td>\n",
       "      <td>35.275</td>\n",
       "      <td>795.801694</td>\n",
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       "      <th>2</th>\n",
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       "      <td>+ 2.080</td>\n",
       "      <td>+ 1.289</td>\n",
       "      <td>+ 0.007</td>\n",
       "      <td>...</td>\n",
       "      <td>LLLLLLLLLL</td>\n",
       "      <td>LLLLLLLLLL</td>\n",
       "      <td>LLLL</td>\n",
       "      <td>2022-10-13 10:02:11</td>\n",
       "      <td>292.66</td>\n",
       "      <td>97.925</td>\n",
       "      <td>2216.751579</td>\n",
       "      <td>292.550</td>\n",
       "      <td>35.275</td>\n",
       "      <td>793.082957</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>2022/10/13 10:02:12</td>\n",
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       "      <td>+ 2.082</td>\n",
       "      <td>+ 1.323</td>\n",
       "      <td>+ 2.410</td>\n",
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       "      <td>...</td>\n",
       "      <td>LLLLLLLLLL</td>\n",
       "      <td>LLLLLLLLLL</td>\n",
       "      <td>LLLL</td>\n",
       "      <td>2022-10-13 10:02:12</td>\n",
       "      <td>292.66</td>\n",
       "      <td>97.925</td>\n",
       "      <td>2216.751579</td>\n",
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       "      <td>35.250</td>\n",
       "      <td>792.520885</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>2022/10/13 10:02:13</td>\n",
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       "      <td>LLLLLLLLLL</td>\n",
       "      <td>LLLLLLLLLL</td>\n",
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       "      <th>21251</th>\n",
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       "      <td>771.323885</td>\n",
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       "    <tr>\n",
       "      <th>21252</th>\n",
       "      <td>21253</td>\n",
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       "      <td>LLLLLLLLLL</td>\n",
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       "      <th>21254</th>\n",
       "      <td>21255</td>\n",
       "      <td>2022/10/13 15:56:23</td>\n",
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       "      <td>+ 4.814</td>\n",
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       "      <td>+ 2.189</td>\n",
       "      <td>+ 1.002</td>\n",
       "      <td>+ 0.004</td>\n",
       "      <td>...</td>\n",
       "      <td>LLLLLLLLLL</td>\n",
       "      <td>LLLLLLLLLL</td>\n",
       "      <td>LLLL</td>\n",
       "      <td>2022-10-13 15:56:23</td>\n",
       "      <td>298.38</td>\n",
       "      <td>95.350</td>\n",
       "      <td>3056.770065</td>\n",
       "      <td>298.545</td>\n",
       "      <td>23.825</td>\n",
       "      <td>771.323885</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21255</th>\n",
       "      <td>21256</td>\n",
       "      <td>2022/10/13 15:56:24</td>\n",
       "      <td>0</td>\n",
       "      <td>+ 4.814</td>\n",
       "      <td>+ 2.186</td>\n",
       "      <td>+ 1.002</td>\n",
       "      <td>+ 1.953</td>\n",
       "      <td>+ 2.189</td>\n",
       "      <td>+ 1.002</td>\n",
       "      <td>+ 0.004</td>\n",
       "      <td>...</td>\n",
       "      <td>LLLLLLLLLL</td>\n",
       "      <td>LLLLLLLLLL</td>\n",
       "      <td>LLLL</td>\n",
       "      <td>2022-10-13 15:56:24</td>\n",
       "      <td>298.38</td>\n",
       "      <td>95.350</td>\n",
       "      <td>3056.770065</td>\n",
       "      <td>298.545</td>\n",
       "      <td>23.825</td>\n",
       "      <td>771.323885</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>21256 rows × 33 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         No.                 time  ms      CH1      CH2      CH3      CH4  \\\n",
       "0          1  2022/10/13 10:02:09   0  + 4.918  + 2.082  + 1.323  + 2.414   \n",
       "1          2  2022/10/13 10:02:10   0  + 4.918  + 2.082  + 1.323  + 2.411   \n",
       "2          3  2022/10/13 10:02:11   0  + 4.917  + 2.082  + 1.323  + 2.411   \n",
       "3          4  2022/10/13 10:02:12   0  + 4.917  + 2.082  + 1.323  + 2.410   \n",
       "4          5  2022/10/13 10:02:13   0  + 4.917  + 2.082  + 1.323  + 2.410   \n",
       "...      ...                  ...  ..      ...      ...      ...      ...   \n",
       "21251  21252  2022/10/13 15:56:20   0  + 4.815  + 2.186  + 1.002  + 1.953   \n",
       "21252  21253  2022/10/13 15:56:21   0  + 4.815  + 2.186  + 1.002  + 1.953   \n",
       "21253  21254  2022/10/13 15:56:22   0  + 4.815  + 2.186  + 1.002  + 1.953   \n",
       "21254  21255  2022/10/13 15:56:23   0  + 4.814  + 2.186  + 1.002  + 1.953   \n",
       "21255  21256  2022/10/13 15:56:24   0  + 4.814  + 2.186  + 1.002  + 1.953   \n",
       "\n",
       "           CH5      CH6      CH7  ...      Alarm1      Alarm2 AlarmOut  \\\n",
       "0      + 2.080  + 1.289  + 0.007  ...  LLLLLLLLLL  LLLLLLLLLL     LLLL   \n",
       "1      + 2.081  + 1.289  + 0.007  ...  LLLLLLLLLL  LLLLLLLLLL     LLLL   \n",
       "2      + 2.080  + 1.289  + 0.007  ...  LLLLLLLLLL  LLLLLLLLLL     LLLL   \n",
       "3      + 2.080  + 1.289  + 0.006  ...  LLLLLLLLLL  LLLLLLLLLL     LLLL   \n",
       "4      + 2.080  + 1.289  + 0.006  ...  LLLLLLLLLL  LLLLLLLLLL     LLLL   \n",
       "...        ...      ...      ...  ...         ...         ...      ...   \n",
       "21251  + 2.189  + 1.002  + 0.004  ...  LLLLLLLLLL  LLLLLLLLLL     LLLL   \n",
       "21252  + 2.189  + 1.002  + 0.004  ...  LLLLLLLLLL  LLLLLLLLLL     LLLL   \n",
       "21253  + 2.189  + 1.002  + 0.004  ...  LLLLLLLLLL  LLLLLLLLLL     LLLL   \n",
       "21254  + 2.189  + 1.002  + 0.004  ...  LLLLLLLLLL  LLLLLLLLLL     LLLL   \n",
       "21255  + 2.189  + 1.002  + 0.004  ...  LLLLLLLLLL  LLLLLLLLLL     LLLL   \n",
       "\n",
       "                timestamp      T1     RH1          vp1       T2     RH2  \\\n",
       "0     2022-10-13 10:02:09  292.66  97.950  2217.317510  292.550  35.350   \n",
       "1     2022-10-13 10:02:10  292.66  97.950  2217.317510  292.605  35.275   \n",
       "2     2022-10-13 10:02:11  292.66  97.925  2216.751579  292.550  35.275   \n",
       "3     2022-10-13 10:02:12  292.66  97.925  2216.751579  292.550  35.250   \n",
       "4     2022-10-13 10:02:13  292.66  97.925  2216.751579  292.550  35.250   \n",
       "...                   ...     ...     ...          ...      ...     ...   \n",
       "21251 2022-10-13 15:56:20  298.38  95.375  3057.571525  298.545  23.825   \n",
       "21252 2022-10-13 15:56:21  298.38  95.375  3057.571525  298.545  23.825   \n",
       "21253 2022-10-13 15:56:22  298.38  95.375  3057.571525  298.545  23.825   \n",
       "21254 2022-10-13 15:56:23  298.38  95.350  3056.770065  298.545  23.825   \n",
       "21255 2022-10-13 15:56:24  298.38  95.350  3056.770065  298.545  23.825   \n",
       "\n",
       "              vp2  \n",
       "0      794.769171  \n",
       "1      795.801694  \n",
       "2      793.082957  \n",
       "3      792.520885  \n",
       "4      792.520885  \n",
       "...           ...  \n",
       "21251  771.323885  \n",
       "21252  771.323885  \n",
       "21253  771.323885  \n",
       "21254  771.323885  \n",
       "21255  771.323885  \n",
       "\n",
       "[21256 rows x 33 columns]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ldg_pp_1013"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "9fa201ae",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>No.</th>\n",
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       "      <th>CH1</th>\n",
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       "      <th>CH7</th>\n",
       "      <th>...</th>\n",
       "      <th>Alarm2</th>\n",
       "      <th>AlarmOut</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>T1</th>\n",
       "      <th>RH1</th>\n",
       "      <th>vp1</th>\n",
       "      <th>T2</th>\n",
       "      <th>RH2</th>\n",
       "      <th>vp2</th>\n",
       "      <th>Tamb</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2022/10/26 11:15:42</td>\n",
       "      <td>0</td>\n",
       "      <td>+++++++</td>\n",
       "      <td>17.044</td>\n",
       "      <td>+ 5.234</td>\n",
       "      <td>36.731</td>\n",
       "      <td>17.050</td>\n",
       "      <td>+ 3.691</td>\n",
       "      <td>+  20.1</td>\n",
       "      <td>...</td>\n",
       "      <td>LLLLLLLLLL</td>\n",
       "      <td>LLLL</td>\n",
       "      <td>2022-10-26 11:15:42</td>\n",
       "      <td>290.194</td>\n",
       "      <td>99.0</td>\n",
       "      <td>1919.649103</td>\n",
       "      <td>290.200</td>\n",
       "      <td>36.731</td>\n",
       "      <td>712.499514</td>\n",
       "      <td>293.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2022/10/26 11:15:52</td>\n",
       "      <td>0</td>\n",
       "      <td>+++++++</td>\n",
       "      <td>17.056</td>\n",
       "      <td>+ 5.235</td>\n",
       "      <td>36.706</td>\n",
       "      <td>17.050</td>\n",
       "      <td>+ 3.691</td>\n",
       "      <td>+  20.5</td>\n",
       "      <td>...</td>\n",
       "      <td>LLLLLLLLLL</td>\n",
       "      <td>LLLL</td>\n",
       "      <td>2022-10-26 11:15:52</td>\n",
       "      <td>290.206</td>\n",
       "      <td>99.0</td>\n",
       "      <td>1921.109731</td>\n",
       "      <td>290.200</td>\n",
       "      <td>36.706</td>\n",
       "      <td>712.014570</td>\n",
       "      <td>293.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>2022/10/26 11:16:02</td>\n",
       "      <td>0</td>\n",
       "      <td>+++++++</td>\n",
       "      <td>17.056</td>\n",
       "      <td>+ 5.234</td>\n",
       "      <td>36.681</td>\n",
       "      <td>17.044</td>\n",
       "      <td>+ 3.691</td>\n",
       "      <td>+  20.0</td>\n",
       "      <td>...</td>\n",
       "      <td>LLLLLLLLLL</td>\n",
       "      <td>LLLL</td>\n",
       "      <td>2022-10-26 11:16:02</td>\n",
       "      <td>290.206</td>\n",
       "      <td>99.0</td>\n",
       "      <td>1921.109731</td>\n",
       "      <td>290.194</td>\n",
       "      <td>36.681</td>\n",
       "      <td>711.259078</td>\n",
       "      <td>293.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>2022/10/26 11:16:12</td>\n",
       "      <td>0</td>\n",
       "      <td>+++++++</td>\n",
       "      <td>17.056</td>\n",
       "      <td>+ 5.234</td>\n",
       "      <td>36.706</td>\n",
       "      <td>17.056</td>\n",
       "      <td>+ 3.691</td>\n",
       "      <td>+  19.8</td>\n",
       "      <td>...</td>\n",
       "      <td>LLLLLLLLLL</td>\n",
       "      <td>LLLL</td>\n",
       "      <td>2022-10-26 11:16:12</td>\n",
       "      <td>290.206</td>\n",
       "      <td>99.0</td>\n",
       "      <td>1921.109731</td>\n",
       "      <td>290.206</td>\n",
       "      <td>36.706</td>\n",
       "      <td>712.285392</td>\n",
       "      <td>292.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>2022/10/26 11:16:22</td>\n",
       "      <td>0</td>\n",
       "      <td>+++++++</td>\n",
       "      <td>17.063</td>\n",
       "      <td>+ 5.234</td>\n",
       "      <td>36.650</td>\n",
       "      <td>17.063</td>\n",
       "      <td>+ 3.691</td>\n",
       "      <td>+  19.5</td>\n",
       "      <td>...</td>\n",
       "      <td>LLLLLLLLLL</td>\n",
       "      <td>LLLL</td>\n",
       "      <td>2022-10-26 11:16:22</td>\n",
       "      <td>290.213</td>\n",
       "      <td>99.0</td>\n",
       "      <td>1921.962213</td>\n",
       "      <td>290.213</td>\n",
       "      <td>36.650</td>\n",
       "      <td>711.514294</td>\n",
       "      <td>292.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17110</th>\n",
       "      <td>17111</td>\n",
       "      <td>2022/10/28 10:47:22</td>\n",
       "      <td>0</td>\n",
       "      <td>+++++++</td>\n",
       "      <td>20.431</td>\n",
       "      <td>+10.030</td>\n",
       "      <td>30.325</td>\n",
       "      <td>19.763</td>\n",
       "      <td>+ 4.251</td>\n",
       "      <td>+  18.7</td>\n",
       "      <td>...</td>\n",
       "      <td>LLLLLLLLLL</td>\n",
       "      <td>LLLL</td>\n",
       "      <td>2022-10-28 10:47:22</td>\n",
       "      <td>293.581</td>\n",
       "      <td>99.0</td>\n",
       "      <td>2372.696223</td>\n",
       "      <td>292.913</td>\n",
       "      <td>30.325</td>\n",
       "      <td>697.348572</td>\n",
       "      <td>291.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17111</th>\n",
       "      <td>17112</td>\n",
       "      <td>2022/10/28 10:47:32</td>\n",
       "      <td>0</td>\n",
       "      <td>+++++++</td>\n",
       "      <td>20.431</td>\n",
       "      <td>+10.031</td>\n",
       "      <td>30.338</td>\n",
       "      <td>19.763</td>\n",
       "      <td>+ 4.251</td>\n",
       "      <td>+  18.7</td>\n",
       "      <td>...</td>\n",
       "      <td>LLLLLLLLLL</td>\n",
       "      <td>LLLL</td>\n",
       "      <td>2022-10-28 10:47:32</td>\n",
       "      <td>293.581</td>\n",
       "      <td>99.0</td>\n",
       "      <td>2372.696223</td>\n",
       "      <td>292.913</td>\n",
       "      <td>30.338</td>\n",
       "      <td>697.647518</td>\n",
       "      <td>291.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17112</th>\n",
       "      <td>17113</td>\n",
       "      <td>2022/10/28 10:47:42</td>\n",
       "      <td>0</td>\n",
       "      <td>+++++++</td>\n",
       "      <td>20.431</td>\n",
       "      <td>+10.031</td>\n",
       "      <td>30.338</td>\n",
       "      <td>19.763</td>\n",
       "      <td>+ 4.252</td>\n",
       "      <td>+  18.7</td>\n",
       "      <td>...</td>\n",
       "      <td>LLLLLLLLLL</td>\n",
       "      <td>LLLL</td>\n",
       "      <td>2022-10-28 10:47:42</td>\n",
       "      <td>293.581</td>\n",
       "      <td>99.0</td>\n",
       "      <td>2372.696223</td>\n",
       "      <td>292.913</td>\n",
       "      <td>30.338</td>\n",
       "      <td>697.647518</td>\n",
       "      <td>291.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17113</th>\n",
       "      <td>17114</td>\n",
       "      <td>2022/10/28 10:47:52</td>\n",
       "      <td>0</td>\n",
       "      <td>+++++++</td>\n",
       "      <td>20.431</td>\n",
       "      <td>+10.030</td>\n",
       "      <td>30.313</td>\n",
       "      <td>19.769</td>\n",
       "      <td>+ 4.251</td>\n",
       "      <td>+  18.7</td>\n",
       "      <td>...</td>\n",
       "      <td>LLLLLLLLLL</td>\n",
       "      <td>LLLL</td>\n",
       "      <td>2022-10-28 10:47:52</td>\n",
       "      <td>293.581</td>\n",
       "      <td>99.0</td>\n",
       "      <td>2372.696223</td>\n",
       "      <td>292.919</td>\n",
       "      <td>30.313</td>\n",
       "      <td>697.332228</td>\n",
       "      <td>291.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17114</th>\n",
       "      <td>17115</td>\n",
       "      <td>2022/10/28 10:48:02</td>\n",
       "      <td>0</td>\n",
       "      <td>+++++++</td>\n",
       "      <td>20.438</td>\n",
       "      <td>+10.031</td>\n",
       "      <td>30.275</td>\n",
       "      <td>19.769</td>\n",
       "      <td>+ 4.251</td>\n",
       "      <td>+  18.8</td>\n",
       "      <td>...</td>\n",
       "      <td>LLLLLLLLLL</td>\n",
       "      <td>LLLL</td>\n",
       "      <td>2022-10-28 10:48:02</td>\n",
       "      <td>293.588</td>\n",
       "      <td>99.0</td>\n",
       "      <td>2373.721861</td>\n",
       "      <td>292.919</td>\n",
       "      <td>30.275</td>\n",
       "      <td>696.458061</td>\n",
       "      <td>291.95</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>17115 rows × 34 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         No.                 time  ms      CH1     CH2      CH3     CH4  \\\n",
       "0          1  2022/10/26 11:15:42   0  +++++++  17.044  + 5.234  36.731   \n",
       "1          2  2022/10/26 11:15:52   0  +++++++  17.056  + 5.235  36.706   \n",
       "2          3  2022/10/26 11:16:02   0  +++++++  17.056  + 5.234  36.681   \n",
       "3          4  2022/10/26 11:16:12   0  +++++++  17.056  + 5.234  36.706   \n",
       "4          5  2022/10/26 11:16:22   0  +++++++  17.063  + 5.234  36.650   \n",
       "...      ...                  ...  ..      ...     ...      ...     ...   \n",
       "17110  17111  2022/10/28 10:47:22   0  +++++++  20.431  +10.030  30.325   \n",
       "17111  17112  2022/10/28 10:47:32   0  +++++++  20.431  +10.031  30.338   \n",
       "17112  17113  2022/10/28 10:47:42   0  +++++++  20.431  +10.031  30.338   \n",
       "17113  17114  2022/10/28 10:47:52   0  +++++++  20.431  +10.030  30.313   \n",
       "17114  17115  2022/10/28 10:48:02   0  +++++++  20.438  +10.031  30.275   \n",
       "\n",
       "          CH5      CH6      CH7  ...      Alarm2 AlarmOut           timestamp  \\\n",
       "0      17.050  + 3.691  +  20.1  ...  LLLLLLLLLL     LLLL 2022-10-26 11:15:42   \n",
       "1      17.050  + 3.691  +  20.5  ...  LLLLLLLLLL     LLLL 2022-10-26 11:15:52   \n",
       "2      17.044  + 3.691  +  20.0  ...  LLLLLLLLLL     LLLL 2022-10-26 11:16:02   \n",
       "3      17.056  + 3.691  +  19.8  ...  LLLLLLLLLL     LLLL 2022-10-26 11:16:12   \n",
       "4      17.063  + 3.691  +  19.5  ...  LLLLLLLLLL     LLLL 2022-10-26 11:16:22   \n",
       "...       ...      ...      ...  ...         ...      ...                 ...   \n",
       "17110  19.763  + 4.251  +  18.7  ...  LLLLLLLLLL     LLLL 2022-10-28 10:47:22   \n",
       "17111  19.763  + 4.251  +  18.7  ...  LLLLLLLLLL     LLLL 2022-10-28 10:47:32   \n",
       "17112  19.763  + 4.252  +  18.7  ...  LLLLLLLLLL     LLLL 2022-10-28 10:47:42   \n",
       "17113  19.769  + 4.251  +  18.7  ...  LLLLLLLLLL     LLLL 2022-10-28 10:47:52   \n",
       "17114  19.769  + 4.251  +  18.8  ...  LLLLLLLLLL     LLLL 2022-10-28 10:48:02   \n",
       "\n",
       "            T1   RH1          vp1       T2     RH2         vp2    Tamb  \n",
       "0      290.194  99.0  1919.649103  290.200  36.731  712.499514  293.25  \n",
       "1      290.206  99.0  1921.109731  290.200  36.706  712.014570  293.65  \n",
       "2      290.206  99.0  1921.109731  290.194  36.681  711.259078  293.15  \n",
       "3      290.206  99.0  1921.109731  290.206  36.706  712.285392  292.95  \n",
       "4      290.213  99.0  1921.962213  290.213  36.650  711.514294  292.65  \n",
       "...        ...   ...          ...      ...     ...         ...     ...  \n",
       "17110  293.581  99.0  2372.696223  292.913  30.325  697.348572  291.85  \n",
       "17111  293.581  99.0  2372.696223  292.913  30.338  697.647518  291.85  \n",
       "17112  293.581  99.0  2372.696223  292.913  30.338  697.647518  291.85  \n",
       "17113  293.581  99.0  2372.696223  292.919  30.313  697.332228  291.85  \n",
       "17114  293.588  99.0  2373.721861  292.919  30.275  696.458061  291.95  \n",
       "\n",
       "[17115 rows x 34 columns]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ldg_pp_1026"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "79e4d4e0",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>No.</th>\n",
       "      <th>time</th>\n",
       "      <th>ms</th>\n",
       "      <th>CH1</th>\n",
       "      <th>CH2</th>\n",
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       "      <th>CH7</th>\n",
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       "      <th>Alarm2</th>\n",
       "      <th>AlarmOut</th>\n",
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       "      <th>T1</th>\n",
       "      <th>RH1</th>\n",
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       "      <th>Tamb</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <td>2022/11/03 10:29:40</td>\n",
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       "      <td>288.938</td>\n",
       "      <td>44.388</td>\n",
       "      <td>794.523446</td>\n",
       "      <td>289.85</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2022/11/03 10:29:50</td>\n",
       "      <td>0</td>\n",
       "      <td>+++++++</td>\n",
       "      <td>15.669</td>\n",
       "      <td>+ 4.368</td>\n",
       "      <td>44.250</td>\n",
       "      <td>15.788</td>\n",
       "      <td>+ 3.064</td>\n",
       "      <td>+  16.7</td>\n",
       "      <td>...</td>\n",
       "      <td>LLLLLLLLLL</td>\n",
       "      <td>LLLL</td>\n",
       "      <td>2022-11-03 10:29:50</td>\n",
       "      <td>288.819</td>\n",
       "      <td>99.0</td>\n",
       "      <td>1758.598163</td>\n",
       "      <td>288.938</td>\n",
       "      <td>44.250</td>\n",
       "      <td>792.053314</td>\n",
       "      <td>289.85</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>2022/11/03 10:30:00</td>\n",
       "      <td>0</td>\n",
       "      <td>+++++++</td>\n",
       "      <td>15.681</td>\n",
       "      <td>+ 4.368</td>\n",
       "      <td>44.119</td>\n",
       "      <td>15.800</td>\n",
       "      <td>+ 3.063</td>\n",
       "      <td>+  16.8</td>\n",
       "      <td>...</td>\n",
       "      <td>LLLLLLLLLL</td>\n",
       "      <td>LLLL</td>\n",
       "      <td>2022-11-03 10:30:00</td>\n",
       "      <td>288.831</td>\n",
       "      <td>99.0</td>\n",
       "      <td>1759.950724</td>\n",
       "      <td>288.950</td>\n",
       "      <td>44.119</td>\n",
       "      <td>790.315287</td>\n",
       "      <td>289.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>2022/11/03 10:30:10</td>\n",
       "      <td>0</td>\n",
       "      <td>+++++++</td>\n",
       "      <td>15.694</td>\n",
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       "      <td>44.044</td>\n",
       "      <td>15.813</td>\n",
       "      <td>+ 3.064</td>\n",
       "      <td>+  16.7</td>\n",
       "      <td>...</td>\n",
       "      <td>LLLLLLLLLL</td>\n",
       "      <td>LLLL</td>\n",
       "      <td>2022-11-03 10:30:10</td>\n",
       "      <td>288.844</td>\n",
       "      <td>99.0</td>\n",
       "      <td>1761.417028</td>\n",
       "      <td>288.963</td>\n",
       "      <td>44.044</td>\n",
       "      <td>789.628511</td>\n",
       "      <td>289.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>2022/11/03 10:30:20</td>\n",
       "      <td>0</td>\n",
       "      <td>+++++++</td>\n",
       "      <td>15.706</td>\n",
       "      <td>+ 4.368</td>\n",
       "      <td>43.913</td>\n",
       "      <td>15.813</td>\n",
       "      <td>+ 3.064</td>\n",
       "      <td>+  16.6</td>\n",
       "      <td>...</td>\n",
       "      <td>LLLLLLLLLL</td>\n",
       "      <td>LLLL</td>\n",
       "      <td>2022-11-03 10:30:20</td>\n",
       "      <td>288.856</td>\n",
       "      <td>99.0</td>\n",
       "      <td>1762.771492</td>\n",
       "      <td>288.963</td>\n",
       "      <td>43.913</td>\n",
       "      <td>787.279920</td>\n",
       "      <td>289.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8648</th>\n",
       "      <td>8649</td>\n",
       "      <td>2022/11/04 10:31:00</td>\n",
       "      <td>0</td>\n",
       "      <td>+++++++</td>\n",
       "      <td>13.088</td>\n",
       "      <td>+ 7.448</td>\n",
       "      <td>44.381</td>\n",
       "      <td>13.219</td>\n",
       "      <td>+ 0.927</td>\n",
       "      <td>+  12.4</td>\n",
       "      <td>...</td>\n",
       "      <td>LLLLLLLLLL</td>\n",
       "      <td>LLLL</td>\n",
       "      <td>2022-11-04 10:31:00</td>\n",
       "      <td>286.238</td>\n",
       "      <td>99.0</td>\n",
       "      <td>1488.041004</td>\n",
       "      <td>286.369</td>\n",
       "      <td>44.381</td>\n",
       "      <td>672.813732</td>\n",
       "      <td>285.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8649</th>\n",
       "      <td>8650</td>\n",
       "      <td>2022/11/04 10:31:10</td>\n",
       "      <td>0</td>\n",
       "      <td>+++++++</td>\n",
       "      <td>13.088</td>\n",
       "      <td>+ 7.023</td>\n",
       "      <td>44.313</td>\n",
       "      <td>13.225</td>\n",
       "      <td>+ 0.766</td>\n",
       "      <td>+  12.2</td>\n",
       "      <td>...</td>\n",
       "      <td>LLLLLLLLLL</td>\n",
       "      <td>LLLL</td>\n",
       "      <td>2022-11-04 10:31:10</td>\n",
       "      <td>286.238</td>\n",
       "      <td>99.0</td>\n",
       "      <td>1488.041004</td>\n",
       "      <td>286.375</td>\n",
       "      <td>44.313</td>\n",
       "      <td>672.046179</td>\n",
       "      <td>285.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8650</th>\n",
       "      <td>8651</td>\n",
       "      <td>2022/11/04 10:31:20</td>\n",
       "      <td>0</td>\n",
       "      <td>+++++++</td>\n",
       "      <td>13.081</td>\n",
       "      <td>+ 7.021</td>\n",
       "      <td>44.281</td>\n",
       "      <td>13.213</td>\n",
       "      <td>+ 0.605</td>\n",
       "      <td>+  12.1</td>\n",
       "      <td>...</td>\n",
       "      <td>LLLLLLLLLL</td>\n",
       "      <td>LLLL</td>\n",
       "      <td>2022-11-04 10:31:20</td>\n",
       "      <td>286.231</td>\n",
       "      <td>99.0</td>\n",
       "      <td>1487.360060</td>\n",
       "      <td>286.363</td>\n",
       "      <td>44.281</td>\n",
       "      <td>671.034693</td>\n",
       "      <td>285.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8651</th>\n",
       "      <td>8652</td>\n",
       "      <td>2022/11/04 10:31:30</td>\n",
       "      <td>0</td>\n",
       "      <td>+++++++</td>\n",
       "      <td>13.075</td>\n",
       "      <td>+ 7.021</td>\n",
       "      <td>44.313</td>\n",
       "      <td>13.200</td>\n",
       "      <td>+ 0.605</td>\n",
       "      <td>+  12.2</td>\n",
       "      <td>...</td>\n",
       "      <td>LLLLLLLLLL</td>\n",
       "      <td>LLLL</td>\n",
       "      <td>2022-11-04 10:31:30</td>\n",
       "      <td>286.225</td>\n",
       "      <td>99.0</td>\n",
       "      <td>1486.776613</td>\n",
       "      <td>286.350</td>\n",
       "      <td>44.313</td>\n",
       "      <td>670.949593</td>\n",
       "      <td>285.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8652</th>\n",
       "      <td>8653</td>\n",
       "      <td>2022/11/04 10:31:40</td>\n",
       "      <td>0</td>\n",
       "      <td>+++++++</td>\n",
       "      <td>13.081</td>\n",
       "      <td>+ 7.021</td>\n",
       "      <td>44.300</td>\n",
       "      <td>13.194</td>\n",
       "      <td>+ 0.604</td>\n",
       "      <td>+  12.2</td>\n",
       "      <td>...</td>\n",
       "      <td>LLLLLLLLLL</td>\n",
       "      <td>LLLL</td>\n",
       "      <td>2022-11-04 10:31:40</td>\n",
       "      <td>286.231</td>\n",
       "      <td>99.0</td>\n",
       "      <td>1487.360060</td>\n",
       "      <td>286.344</td>\n",
       "      <td>44.300</td>\n",
       "      <td>670.489889</td>\n",
       "      <td>285.35</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8653 rows × 34 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       No.                 time  ms      CH1     CH2      CH3     CH4     CH5  \\\n",
       "0        1  2022/11/03 10:29:40   0  +++++++  15.681  + 4.368  44.388  15.788   \n",
       "1        2  2022/11/03 10:29:50   0  +++++++  15.669  + 4.368  44.250  15.788   \n",
       "2        3  2022/11/03 10:30:00   0  +++++++  15.681  + 4.368  44.119  15.800   \n",
       "3        4  2022/11/03 10:30:10   0  +++++++  15.694  + 4.368  44.044  15.813   \n",
       "4        5  2022/11/03 10:30:20   0  +++++++  15.706  + 4.368  43.913  15.813   \n",
       "...    ...                  ...  ..      ...     ...      ...     ...     ...   \n",
       "8648  8649  2022/11/04 10:31:00   0  +++++++  13.088  + 7.448  44.381  13.219   \n",
       "8649  8650  2022/11/04 10:31:10   0  +++++++  13.088  + 7.023  44.313  13.225   \n",
       "8650  8651  2022/11/04 10:31:20   0  +++++++  13.081  + 7.021  44.281  13.213   \n",
       "8651  8652  2022/11/04 10:31:30   0  +++++++  13.075  + 7.021  44.313  13.200   \n",
       "8652  8653  2022/11/04 10:31:40   0  +++++++  13.081  + 7.021  44.300  13.194   \n",
       "\n",
       "          CH6      CH7  ...      Alarm2 AlarmOut           timestamp       T1  \\\n",
       "0     + 3.064  +  16.7  ...  LLLLLLLLLL     LLLL 2022-11-03 10:29:40  288.831   \n",
       "1     + 3.064  +  16.7  ...  LLLLLLLLLL     LLLL 2022-11-03 10:29:50  288.819   \n",
       "2     + 3.063  +  16.8  ...  LLLLLLLLLL     LLLL 2022-11-03 10:30:00  288.831   \n",
       "3     + 3.064  +  16.7  ...  LLLLLLLLLL     LLLL 2022-11-03 10:30:10  288.844   \n",
       "4     + 3.064  +  16.6  ...  LLLLLLLLLL     LLLL 2022-11-03 10:30:20  288.856   \n",
       "...       ...      ...  ...         ...      ...                 ...      ...   \n",
       "8648  + 0.927  +  12.4  ...  LLLLLLLLLL     LLLL 2022-11-04 10:31:00  286.238   \n",
       "8649  + 0.766  +  12.2  ...  LLLLLLLLLL     LLLL 2022-11-04 10:31:10  286.238   \n",
       "8650  + 0.605  +  12.1  ...  LLLLLLLLLL     LLLL 2022-11-04 10:31:20  286.231   \n",
       "8651  + 0.605  +  12.2  ...  LLLLLLLLLL     LLLL 2022-11-04 10:31:30  286.225   \n",
       "8652  + 0.604  +  12.2  ...  LLLLLLLLLL     LLLL 2022-11-04 10:31:40  286.231   \n",
       "\n",
       "       RH1          vp1       T2     RH2         vp2    Tamb  \n",
       "0     99.0  1759.950724  288.938  44.388  794.523446  289.85  \n",
       "1     99.0  1758.598163  288.938  44.250  792.053314  289.85  \n",
       "2     99.0  1759.950724  288.950  44.119  790.315287  289.95  \n",
       "3     99.0  1761.417028  288.963  44.044  789.628511  289.85  \n",
       "4     99.0  1762.771492  288.963  43.913  787.279920  289.75  \n",
       "...    ...          ...      ...     ...         ...     ...  \n",
       "8648  99.0  1488.041004  286.369  44.381  672.813732  285.55  \n",
       "8649  99.0  1488.041004  286.375  44.313  672.046179  285.35  \n",
       "8650  99.0  1487.360060  286.363  44.281  671.034693  285.25  \n",
       "8651  99.0  1486.776613  286.350  44.313  670.949593  285.35  \n",
       "8652  99.0  1487.360060  286.344  44.300  670.489889  285.35  \n",
       "\n",
       "[8653 rows x 34 columns]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ldg_pp_1103"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "6c06b479",
   "metadata": {},
   "outputs": [],
   "source": [
    "gas_pressure = 400 # mmH2O g"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "90264e99",
   "metadata": {},
   "outputs": [],
   "source": [
    "fig_dim = (90/25.4, 120/25.4)\n",
    "fig_dpi = 96"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "199681e9",
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_convert (axe, twin, ldg_station_raw):\n",
    "    axe.plot(ldg_station_raw.timestamp, ldg_station_raw.RH, ':')\n",
    "    twin.plot(ldg_station_raw.timestamp, ldg_station_raw['T']-273.15)\n",
    "    # twin.plot(ldg_station_raw.timestamp, (ldg_station_raw.CH3 - 1) * 25.)\n",
    "    axe.set_ylim(70, 100)\n",
    "    twin.set_ylim(0, 50)\n",
    "    axe.tick_params(axis='x', labelrotation = 45)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "059ba979",
   "metadata": {},
   "source": [
    "##  부스터 후단 데이터 (22-09-22 12:00~ )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "76d35b1b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b11eeecdceeb408aa495afae2ec2de6c",
       "version_major": 2,
       "version_minor": 0
      },
      "image/png": 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",
      "text/html": [
       "\n",
       "            <div style=\"display: inline-block;\">\n",
       "                <div class=\"jupyter-widgets widget-label\" style=\"text-align: center;\">\n",
       "                    Figure\n",
       "                </div>\n",
       "                <img 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bcmInIvL7zwAhQKBc6dOwcASE9Px9ixY6HT6dCzZ09s2bKlSfNr84H65bFs/DfxInJLKxuemIjcxtGjR5GYmGh7X11djSlTpmDSpEnIy8vD559/jvnz5+PIkfpPV7ieNhmohiozur68C11f3oXV06Nw36AQ3NqV1+ITUY2qqio8++yzeOedd2zD9uzZA4PBgIULF0KhUCAqKgqPPfYY1q5d2+j5tslAvVTncRfenh745++jeECKiGyWL1+OqVOnYsCAAbZhcXFxGD58ODw8rtxjeOTIkYiLi2v0fNvkUf4egT744509kJClx23d/HlFFJEbMBqNMBqNtvcqlQpqdf0VqePHj2Pbtm1ISEiwG56XlwedTmc3TKfTIS8vr9FtaJNrqAqFAgsnReLTeSOh8WyTfzOI6CphYWHQaDS2V3R0dL1pzGYznn76aaxZswbt2tmf+SOEgFJpH4lKpRJWqxWNxbQhojYhOzvb7gbTKlX9eFu9ejUGDRqEcePG1Run1WqRkZFhN0yv1zfpptUMVCJqE7y9veHt7X3DaeLj47Fnzx5s27bNbvjAgQMRGBiIqx9gEh8fjyFDhjS6DW1yk5+I6Fo+++wzlJaW4vLly7YXACQlJeHEiROoqqrCqlWrYLFYkJCQgJiYGMybN6/R82egEhEB0Gg02LVrF7Zu3QqtVosHHngAK1euxKRJkxo9D27yE5Fbq7uZHxUVhaNHjzZ7XlxDJSKShIFKRCQJA5WISBIGKhGRJAxUIiJJGKhERJIwUImIJGGgEhFJwkAlIpKEgUpEJInUQE1PT8eUKVPQuXNndO7cGYsWLYLFYrGNa8nDr4iIXJ20QDUYDJg4cSJGjhyJCxcuIDU1FcePH8dbb70l5eFXRESuTlqgHjlyBOXl5ViyZAlUKhX8/PywcuVKfPDBB1IefkVE5OqkBarRaIRKpYJCceX5TZWVlUhPT8fBgwdb/PArIiJXJy1QR48ejcrKSrz99tswmUy4ePGi7Zkuly9fbvLDr0wmk+2hW1c/fIuIyBVJC1StVovdu3dj27Zt6NKlC6ZPn47Zs2dDqVTCbDY3+eFX0dHRdg/cCgsLk9VUIiKHkHqUf9SoUThw4AAuXryIQ4cOwdfXF+Hh4QgKCoJer7ebtqGHXy1evBgVFRW2V3Z2tsymEhFJ59DzUN977z08+OCDGDp0aL1nYDf08Cu1Wm176FZjHr5FRNTapAbqL7/8AiEEqqursXLlSsTFxWHRokWYOnVqix9+RUTk6qQG6ooVK6DT6RAREYGjR4/i4MGDCAwMlPLwKyIiV6cQVz+I2kUVFxdDq9WiqKjohvteici9NDUbNm7ciDVr1iAnJweenp6YMGECVq9ejY4dOyI9PR3z5s1DamoqOnTogGXLluGxxx5rdFt4LT8RuZXvvvsOmzdvRl5eHlJSUpCTk4MXX3xRyhWdDFQiciuffvopBgwYAADw9fXF5MmTkZycLOWKTgYqEbkUq1XAZLHCGXsj09LSEBsbi1mzZiEuLq7FV3SqHNFIIqKG/Hpejw8OZyIjvxxpuWX1xu9dMAY9db6Nnt/VV1SqVCqo1errTh8cHIy8vDzMnTsX8+fPxzPPPNPkKzqvxkAlIoeoMltwrrACr+1IRvx5ParN178yUoarr6ZcunQpli1bdt3pc3Nzcfr0aTz11FOYM2cOlEplk6/ovBoDlYiaxWoVKCivwpp9Gfjwp/PS5rv8/lsw89YIeCgVDU9cR3Z2tt1RfpWq4Xjr1asXVq5ciTFjxuAvf/kLsrKy7MY3dEXn1RioRFSPEAIWq8Anv2TBKoB1+8+gfTsVMvLLpcz//VlD0T3Qp0mb9A1pzBWVFovFbh8pAHh6esLDwwO33XYbtm/fbjeuoSs6r8ZAJXJD1WYrjl+4jBVfpyJbb8SwLp0Qd06PwvKq63+otGk13nxoIO6LCkE7tUfDEzvJoUOHsGHDBixbtgw9evSAXq/HkiVLMH36dNx7771YsGABVq1ahRdeeAGJiYmIiYnBxx9/3Oj5M1CJ2oBKU82jhiqqLRi18ntUmuz3+2k8PVBRbcGA0I4wWaz1DgJ9fTK30bW0Pp7QtvfE3Du6Y0zvQAR1aNfyDjjJqFGj8NNPP+G+++5DYWEhvL29cc8992DFihW2KzqfffZZLF++HB07dmzyFZ28UoqoFdVuWpssAqfzy5CeW4bYw+eQeqnxq4NKBWCV/C1ePKUv7h0UgqAOXnY3jXdFrpQNXEMlcoAzBeX48VQBXtuRgu4BPjhbaHBYrcaEaVgnb4zvF4Qege3RVesD33Yq9O3cAZ4qnoouEwOVqAGVJgsuXjbCUGWBl1qJH08V4PvUfPx0tggeSgUm9Q/GrhOXrvt52WF6azd/9A/pgMxCA34XqcOkW4LhoVCg2mKFv48nvFSus8/S3TBQyS3UblpXmq2wWAQulRqxM/ES9BXV+CY5F2oPJTxVSpwvqmjSfC1WccMwrSvc3xsXimtOPO/csR2CO7bDnNu7w8fLA2GdvKUe8abWwUClFhNC2DY7rdfZJf99ah7iz+nRTu2BIkM1+oV0wLHzemw7lgN/H088M6Y71h04g8sVJoT6eSPncv1niN3dNwi/ni+GvsLkyO402Z19ArE/vcD2/s+/64mRPQIwonvN/jxX3wdJ8vCg1E3EahXILDJArVRCrVJgf3oBAtp7wVvtga9PXsLXJ3NRbKjGwLCOCO+kwcHTBYh+YADUHjVf6H/tPX3NS/yA+qHQlnmqlI26akftocCoHgF46vZuiAr3g6+XCsomnmxOjudK2cBAbYJqsxVqDwVO5ZWjnVqJN/eko6euPcxWKzILDQju4I2oCD8knNdj05Fz9T7vpVKi6qov8oODQxF/Xo+s4ppNzXsGdsbOpMZtQt4MVEoFzI04aqL18USRofqG0zwyNAw/ZxbbflYAMPO2CHh6KHHkTCEi/DW4XGHCvDHd8Vl8NtLzSlFeacb04eGICvNDtcWKMb0C0cnHs8X9ItfhCtlQq00G6i+Zxfi/H07j4OlCJ7XOdXUP9EEvXXtYrMDe1Ovf5MG3nQplleZ6w4d16YT48zUPWLy7bxA8VQqoPZSIDO6AbgEadGinRlmVGe3UHvD0UCKskzc6eKuhUAC+Xipu7pLDuVKgtsl9qIczClstTBuzplXXmw8NxOn8MsQczMSk/sH4XaQOC79Iso1/YlRX9A7yxageWvx4ugBTBnRGe6+axealUsIqAAXATVEiF9AmA3VAaEeM7R2IA6eu7BOce0c3KJUK/Hy2GB281ag2WxAZ3AEqpQIaTw9cKqmEAJCRX447+wSisLwK/j5eGNcnEH4aT3T0VsNksUKlVMDzt033ThrPJt/A4XoWT+1n+//04eHXnKZrgE+9YR7MUSKX0SYD9e5+Qbi7X5BDa/AEFyK6Gi+TICKShIFKRCQJA5WISBIGKhGRJAxUIiJJGKhERJIwUImIJGGgEhFJwkAlIpKEgUpEJAkDlYhIEgYqEbmVH374AXfccQcCAwMRGhqKmTNnoqioCACQnp6OsWPHQqfToWfPntiyZUuT5s1AJSK3snjxYixfvhz5+fk4ffo0CgsLsWjRIlRXV2PKlCmYNGkS8vLy8Pnnn2P+/Pk4cuRIo+fNQCUit3Lw4EHceeedUCgU0Gg0uP/++5GQkIA9e/bAYDBg4cKFUCgUiIqKwmOPPYa1a9c2et4MVCJyKyqV/V1Lf/rpJ/Tr1w9xcXEYPnw4PDyuPIZ75MiRiIuLa/S8GahE1CYYjUa7l8nU8NNxd+3ahe3bt2PJkiXIy8uDTqezG6/T6ZCXd/1HB12NgUpEbUJYWBg0Go3tFR0dfcPp09LSMGvWLGzatAm9e/eGEAJKpX0kKpVKWK0NPyG3Vpu8Yz8RuZ/s7Gy7h/RdvWl/9bQTJ07EK6+8goceeggAoNVqkZGRYTedXq9v0oP/uIZKRG2Ct7e33UutVl9zuuLiYkycOBEzZ87Eiy++aBs+dOhQJCQk2E0bHx+PIUOGNLoNDFQichsGgwFTp07FiBEjsGLFCrtxU6dORVVVFVatWgWLxYKEhATExMRg3rx5jZ4/N/mJyG3s3bsXR48exZkzZxAcHGwbrlKpkJ2djV27duHZZ5/F8uXL0bFjR6xcuRKTJk1q9PwVQgjhiIbLVlxcDK1Wi6Kioibt0yCits2VskHqJn/tJV0hISEIDw/H9OnTce7cOQAtv6SLiMjVSQvU7Oxs3HPPPXj88ceRk5OD9PR0+Pr6Ytq0aVIu6SIicnXSAjUxMRFeXl6YM2eO7ZKuP/3pT0hKSsLXX3/d4ku6iIhcnbRAHTVqFHx9ffH222+juroaxcXFeOuttzB16lTEx8e3+JIuIiJXJy1QO3XqhAMHDmDNmjVo3749AgICcPnyZWzdurVZl3SZTKZ6l5IREbkyaYFaXl6OyZMnY/r06bh8+TIKCgrg6+uLGTNmNOuSrujoaLvLyMLCwmQ1lYjIIaQF6tatWyGEwOuvvw6NRgOtVovY2Fjs2bMHZ86cgV6vt5u+oUu6Fi9ejIqKCtsrOztbVlOJiBxCWqCWlJTAx8fHbpiXlxfUajWefPLJJl/SpVar611KRkTkyqQF6sSJE5GSkoK1a9fCarWisrISCxYsgEajwV133dXiS7qIiFydtEDt27cvduzYgY8++gghISHo1q0b0tLS8O233yIkJAS7du3C1q1bodVq8cADDzT5ki4iIlfHS0+J6KbmStnAu00REUnCQCUikoSBSkQkCQOViEgSBioRkSQMVCIiSRioRESSMFCJiCRhoBIRScJAJSKShIFKRG4nIyMDffr0QWRkpN3wlj5MlIFKRG5l3759mDBhAoYNG2Y3XMbDRBmoRORW+vfvj+TkZIwfP95u+J49e1r8MFEGKhG5FZ1Od80b1sfFxbX4YaIqKS0kImplVz/MU6VSQa1WN/rzzXmY6NW4hkpEbUJYWJjdgz2jo6Ob9PnmPEz0alxDJaI2ITs72+4G0ypV0+JNq9UiIyPDblhDDxO9GtdQiahNuPqhnk3Z3AeAoUOHNvlholdjoBIRAZg6dWqLHybKQCUiAqDRaFr8MFE+pI+IbmqulA1cQyUikoSBSkQkCQOViEgSBioRkSQMVCIiSRioRESSMFCJiCRhoBIRScJAJSKShIFKRCQJA5WISBIGKhGRJAxUIiJJGKhERJIwUImIJGGgEhFJwkAlIpKEgUpEJAkDlYhIEgYqEZEkDFQiIkmkBWpMTAyCg4PtXjqdDgqFAgCQnp6OsWPHQqfToWfPntiyZYus0kREjVZWVobZs2cjKCgIISEhWLhwIaxWq5R5SwvUuXPnIjc31+61du1aDBo0CNXV1ZgyZQomTZqEvLw8fP7555g/fz6OHDkiqzwRUaM899xzKCkpwfnz53HixAl8++23ePPNN6XMWyGEEFLmdA2jR4/G3Llz4e/vj3nz5iEnJwceHh4AgPnz56OkpKTRa6qu9OxtInIdTckGvV6PwMBAJCYmon///gCArVu3YsGCBbhw4UKL2+Kwfai//PILMjIyMGPGDMTFxWH48OG2MAWAkSNHIi4uzlHliYjqSUhIgEajsYUpUJNF2dnZuHTpUovnr2rxHK5j9erVeO655+Dl5YW8vDzodDq78TqdDnl5edf9vMlkgtlstr03GAwAav4aERHVqs0Eg8EAb29v23CVSgW1Wm03bV5eHgIDA+2G1WZTXl4eOnfu3KK2OCRQL1y4gJ07d+Ltt98GAAghoFTarwwrlcob7giOjo7Ga6+9Vm94r1695DaWiNqEiIgIu/dLly7FsmXL7IZdL4sASDkw5ZBAfeedd/Dwww8jKCgIAKDVapGRkWE3jV6vv+H+jsWLF+Oll16yvTebzcjJyUFAQEC9H4gjGI1GhIWFITs72+6vnjO0Zm3W57K/2epbrVYUFhYiNDQUKtWVSKv7/1parRZ6vd5uWO17GcdmpAdqeXk5NmzYgH379tmGDR06FJ999pnddPHx8RgyZMh156NWq+utrkdGRspt7A0YjUYANT/k1vhStVZt1ueyvxnrBwQENGq6wYMHo7i4GFlZWbY12vj4eGi12npruM0hfVUvNjYWgwYNQlRUlG3Y1KlTUVVVhVWrVsFisSAhIQExMTGYN2+e7PJERNcVFBSEadOmYcGCBaioqEBhYSGWLFmCOXPmSNnylRqoVqsV77zzDl544QW74RqNBrt27cLWrVuh1WrxwAMPYOXKlZg0aZLM8kREDdq4cSMUCgXCwsIQGRmJ0aNHY/ny5VLmLXWTX6lU1ttXWisqKgpHjx6VWc6hVCoVli5des39MG25Nutz2bf1+v7+/ti6datD5u3QE/uJiNwJb45CRCQJA5WISBIGKhGRJAxUIiJJGKhERJIwUImIJGGgEhFJwkAlIpKEgUpEJAkDlYhIEgYqEZEkDFQiIkkYqEREkjBQiYgkYaASEUnCQCUikoSBSkQkCQOViEgSBioRkSRODdQnn3wSfn5+CA4Otr2ef/55ZzaBiMhhnPpow9LSUsTGxuLBBx90ZlkiIqdw6hpqaWkp/Pz8nFmSiMhpGKhERJIohBDCWcX69u2LkpISmM1mBAQEYMKECViyZAkCAgLqTWsymWA2m23vrVYrysvL4evrC4VC4awmE5GLE0KgsrISfn5+UCpb+Ti7cKLc3FxhtVqF1WoVycnJYtKkSWLYsGHCYrHUm3bp0qUCAF988cVXo15FRUXOjLNrcuoa6tUuXryI0NBQJCUlYcCAAXbjrl5DraioQEBAAIqKiuDt7e3sphKRizIajdBqtTAYDNBoNK3aFqce5b9aZWUlACAkJKTeOLVaDbVaXW+4t7c3A5WI6nGFXYFO2+Fw8uRJrF27FiUlJQCAnJwczJ07FzNmzIBWq3VWM4iIHMZpgRoSEoKkpCQMHDgQQUFBGDduHEaOHIkNGzY4qwlERA7VqvtQm8JoNEKj0aCiooKb/ERk40rZwGv5iYgkYaASEUnCQCUikoSBSkQkCQOViEgSBioRkSQMVCIiSRioRESSMFCJiCRhoBIRScJAJSKShIFKRCQJA5WISBIGKhGRJAxUIiJJGKhERJIwUImIJGGgEhFJwkAlIpKEgUpEJAkDlYhIklYL1BdeeAEKhQLnzp1rrSYQEUnVKoF69OhRJCYmtkZpIiKHcXqgVlVV4dlnn8U777zj7NJERA7l9EBdvnw5pk6digEDBji7NBGRQ6mcWez48ePYtm0bEhISGpzWZDLBbDbb3huNRkc2jYioxZy2hmo2m/H0009jzZo1aNeuXYPTR0dHQ6PR2F5ardYJrSSi1vTCp8fQ9eVd6PvKHhirLa3dnCZTCCGEMwq9+eabSEtLQ2xs7JXiCgUyMzPRtWvXetNfaw1Vq9WioqIC3t7ezmgyETlY6qVS/CfuAjYdOVdv3H+fH42BYX4NzsNoNEKj0bhENjhtkz8+Ph579uzBtm3b7IYPHDgQM2bMwPr16+2Gq9VqqNVqZzWPiJzotR3J+ODwueuO7xPki24BPs5rkCROW0O9ZvEbrKFezZX+ChFR01SZLfj1vB4zY35ucNqEV8bD38ez0fN2pWxw6kEpInIff/7kGP6beLHB6T6eextG9QhwQoscr1UDtRVXjolIssxCA3YmXsSq7041OO2n80ZgWJdOUHm0ravfuYZKRM1SabLg0OlCzPkwvtGf+fKPozA4opMDW9W6GKhE1CgWq8D8jxKwNzUPZmvjti4zV0yBQqFwcMtcBwOViK5JCIHNR87hs/hspFwqbdRn/t/EPnhoSBiCOzZ8rnlbxEAlIhurVeD+tYeRlF3SqOnvjwrBqulR8FC6z1rojTBQidyU1Srwhw0/46ezRY3+zCdzRyAy2BedmnBakzthoBK5kZzLRhSVV+G+dw83+jM/LfodOnfkud+NwUAlamO6vryrRZ/X+Xrh5/+5y60OJsnCQCVycUII/DfxIqxC4MX/1L8x+2MjIrDlaFaz5t2hnQqbn7q1TZ/K5EwMVCIXYLEKfJeSi/d/PIuErMtN+mxjwvShIWEI8PXEo8Mj0FWr4dqngzBQiZzEZLHiy2M5WPh5ktT5Pn17N5wvqsDRs0VYML437uqrQ3gnDZQ88u50DFQiByirNGH7sRy88lWylPlN7B+E58f1gn97T4T68QCRq2KgEklgtljx1ren8N6BM82ex/FXxwMA/DQ8JelmxUAlaoISowkfHjnXqBuAXO31+29BtwAf+Pt4om/nDtAbquGnUXN/ZhvCQCW6Ss5lI+5atR+VJmuL57XzT7fjltCO1xzHk+PbHgYquaXEC5ex8VBmo+7X2ZAnRnWFocqMaVGhGNlDy8sw3RgDlW4qMT+eRfTuVNv7x0d2Qbi/BjsSLyLxt+vPHx0ejk/jLjik/vFXx0OpVMDTQwmVUtHm7udJLdOqj0BpCld6zAE1jRAC//dDBv4TdwF+GjXyy6owKMwPe1PzWrtp1/XeY0NxZ59AtFN7tHZTqAGulA1cQyUANTcLziquQFpuGTLyy5FysRR39ApAqJ83FArUrBUK4GyhoUnz1fl6Ib+syvY+57IRAFotTCODffHRnNugVCigUAAaTxVUSgXP2SQpGKhtgNUqsPCLJHz+a3a9cSsfHAD1b5ulf91a/7LFG5ERenXDtLEGhfsh8cLlesN3PH87ege3R91tqto1SItVcN8ltTpu8rugz3/Nxv70fCSc1+NiSWVrN6fJRnT3x/Cu/nj01giolApYhUBReTXKKs24JbQDLFYBk0VAQEDn6543IiZ5XCkbuIbaArV/i2pPr8kvq4TFKvDB4XP499HzjZqHv48nig3VDmsjADwwOBRCCGw/fv0j2i/e3Rshfu2gr6jG6bxy3NlHh9BO3uipa48SowkpF0txd19ds8+Z5O3fyB04dQ1148aNWLNmDXJycuDp6YkJEyZg9erV6Njx2ufp1eWsv0JCCCgUClSaLDhbYICAQHmlGamXSvH+j2dRaKhGtbnl5yfKlvDKeHT0VnOzl9yO266hfvfdd9i8eTMGDBiAsrIyPPLII3jxxRcRGxvrzGag0mTBvrR8PPdRglPrXsuhl8bhbIEBs2N/wb9+H4XbewXAWG3BBX0FugX4cM2O6CbSqvtQ3377bXz88cf4+eefG5y2JX+Fki+WYMXuNBzKKGxuUxt0a1d//HFcD9zWTYtqsxVeaiVPuSFyArddQ60rLS0NsbGxmDt3rkPr7EvLx5Ob4pr0mcERfki9VIqJ/YPx2n39ofFUwVPV+BO4vT0ZpETuqFUCNTg4GHl5eZg7dy7mz59/zWlMJhPMZrPtvdFobPT8j5wpxMyY66/1/vHOHhje1R939gnkjSmISJpWuW4uNzcXp06dQmpqKubMmXPNaaKjo6HRaGwvrVbb6PnvS8uvN2ztH4bg3MqpOLdyKhZOisS4yOYfsSYiupZW3Yd6+PBhjBkzBkajEZ6e9nfeudYaqlarbdR+km+Tc/GnT46h6rej8Q8OCcXq6VHS209Erc8t96FaLBZ4eNjvW/T09Kw3rJZarYZarW5WrQn9g5H++uRmfZaIqLmctsl/6NAhzJo1C2fO1NzRXK/XY8mSJZg+fXq9tVMiopuR0wJ11KhR6N+/P+677z4EBQVh8ODB6NWrF9atW+esJhARORSv5Seim5orZQPvjktEJAkDlYhIEgYqEZEkDFQiIkkYqEREkjBQiYgkYaASEUnCQCUikoSBSkQkCQOViEgSBioRkSQMVCIiSRioRESSMFCJiCRhoBIRScJAJSKShIFKRCQJA5WISBIGKhGRJAxUIiJJGKhERJI4NVB/+OEH3HHHHQgMDERoaChmzpyJoqIiZzaBiMhhnBqoixcvxvLly5Gfn4/Tp0+jsLAQixYtcmYTiIgcRuXMYgcPHoRKVVNSo9Hg/vvvR2xsrDObQETkME5dQ60N01o//fQT+vXr58wmEBE5jFPXUOvatWsXtm/fjl9//fWa400mE8xms+290Wh0VtOIiJqlVY7yp6WlYdasWdi0aRN69+59zWmio6Oh0WhsL61W6+RWEhE1jUIIIZxZMDs7G6NHj8YLL7yAF1988brTXWsNVavVoqKiAt7e3s5oKhHdBIxGIzQajUtkg1M3+YuLizFx4kTMnDnzhmEKAGq1Gmq12kktIyJqOadt8hsMBkydOhUjRozAihUrnFWWiMhpnLaGunfvXhw9ehRnzpxBcHDwlQaoVMjOznZWM4iIHMbp+1Cby5X2kxCR63ClbOC1/EREkjBQiYgkYaASEUnCQCUikoSBSkQkCQOViEgSBioRkSQMVCIiSRioRESSMFCJiCRhoBIRScJAJSKShIFKRCQJA5WISBIGKhGRJAxUIiJJGKhERJIwUImIJGGgEhFJwkAlIpLE6YGakZGBPn36IDIy0tmliYgcyqmBum/fPkyYMAHDhg1zZlkiIqdwaqD2798fycnJGD9+vDPLEhE5hcqZxXQ6nTPLERE5lVMDtSlMJhPMZrPtvdFobMXWEBE1zGWP8kdHR0Oj0dheWq22tZtERHRDLhuoixcvRkVFhe1VVFTU2k0iIrohl93kV6vVUKvVrd0MIqJGc9k1VCKimw0DlYhIEoUQQrR2IxrDaDRCo9GgoqIC3t7erd0cInIRrpQNXEMlIpKEgUpEJAkDlYhIEgYqEZEkDFQiIkkYqEREkjBQiYgkYaASEUnCQCUikoSBSkQkCQOViEgSBioRkSQMVCIiSRioRESSMFCJiCRhoBIRScJAJSKShIFKRCQJA5WISBIGKhGRJAxUIiJJnBqoZWVlmD17NoKCghASEoKFCxfCarU6swlERA6jcmax5557DmVlZTh//jwMBgPuuusu+Pv74+WXX3ZmM4iIHEIhhBDOKKTX6xEYGIjExET0798fALB161YsWLAAFy5caPDzrvTsbSJyHa6UDU7b5E9ISIBGo7GFKQCMHDkS2dnZuHTpkrOaQUTkME7b5M/Ly0NgYKDdMJ1OZxvXuXNnu3Emkwlms9n2vqKiAkDNXyMiolq1meCkje0bclqgCiGgVNqvENe+v9aBqejoaLz22mv1hmu1Wsc0kIhuapWVldBoNK3aBqftQ92zZw8ee+wxFBYW2oYVFBRAp9MhMzMTXbt2tZv+6jVUq9WK8vJy+Pr6QqFQOLy9RqMRWq0WRUVFTt8v05q1WZ/L/marL4RAZWUl/Pz86q20OZvT1lAHDx6M4uJiZGVlISIiAgAQHx8PrVZre1+XWq2GWq22G+bj4+OUttbl7e3daju6W7M263PZ30z1W3vNtJbT4jwoKAjTpk3DggULUFFRgcLCQixZsgRz5sxp9b8qREQyODXJNm7cCIVCgbCwMERGRmL06NFYvny5M5tAROQwTj2x39/fH1u3bnVmyWZTqVRYunQpVCqn/ohavTbrc9m7c/2WctpBKSKito47L4mIJGGgEhFJwkBtQ+qet+tu3LnvgHv335X6zkCVxGw2w2KxtFr9lJQUvPvuuzAYDE6v7c59B9y7/+7c92thoEpw8uRJzJkzBxMnTkR0dDS+/vprp9ZPTk7GmDFj0L59e6df/ODOfQfcu//u3PfrEtQip06dEmFhYWL16tXirbfeEo8//ri45ZZbxD/+8Q+n1D9x4oSIiIgQ69atsw2zWq1Oqe3OfRfCvfvvzn2/EQZqC7311ltizpw5tvcXL14U69evF/7+/uLvf/+7Q2tnZWWJgIAAERsbK4S48guVnZ0tzp8/L1JSUhxa3537LoR799+d+34jN+fZsy6koKAAxcXFAGpu0tC5c2c8/vjj0Gg0+Nvf/gZ/f3/Mnz/fIbU9PDzg4eGB8vJyAIBCocA777yD3bt34/Tp0wCAJ554Aq+88opD6rdm35VKZav2HeCyd+dlf12tFuVtxIEDB0RQUJDYsWOH3fDy8nLxz3/+U9x9990iMzNTel2TySSEECItLU34+fmJDRs2iE2bNokuXbqIAwcOiH379okPP/xQREZGip9//ll6fSGE2L9/vwgKChI7d+60G+7IvlssFtv/U1NTW63vQnDZu/Oyvx4GahNlZ2eLb775Rhw5ckScP39eCCHE888/Lx544AFx6NAhu2mTkpJEeHi42L9/v7T6FRUVtv9XV1fb6qhUKhEQEGBrkxBC5OfniwEDBojt27dLqX3u3DmxZcsWsWXLFpGamiqEuNL3w4cP203riL6fOnVK3HPPPeLixYu2YYmJiU7puxBc9u687BuLgdoEiYmJIjw8XIwbN04MGTJEdOvWTezdu1fExcWJhx9+WMyYMaPeL9HkyZPFN998I6V+WlqaGDRokN0vSu0XKz4+XiQlJQkhhKisrLSNf+ihh8T333/f4tpJSUmiW7duYtq0aWLUqFFizJgx4vLly+LQoUPi4YcfFo8++qg4cOCA3Wdk9l2Imv12CoVCjBs3TuTl5dmGx8XFObTvQnDZu/OybwoGaiPp9XoxYsQIsXHjRiGEEGfOnBGvvfaa8PDwEJs3bxbffvutmDlzprjzzjvFpk2bRGFhoXj33XeFTqez++vZEqtXrxZdunQRffv2FV9++aVteN1NobrWr18vIiMjRXZ2dovqZmZmih49eoj3339fCCHE4cOHxbBhw8S5c+eEEEIcPXpU/P73vxdjx451WN+FqAm1559/Xtx1110iMjLS7otVe2Ci9l9ZfReCy96dl31TMVAbqbS0VIwdO1YcP35cCHFl4cXExAgPDw/xySefiMzMTBEdHS06deokxo4dKyIjI0VCQoK0NsyZM0fMnTtXvPzyy6JXr152X6y6kpKSxBtvvCFCQ0Nt7W0uq9UqYmNjxYIFC+yGjxkzRqxfv16sXLlSpKeni/z8fIf23WKxiISEBDF58mQhhBATJkwQ/fv3t20CFhUVCSFqTqdZsWKFlL7X4rJ332XfVAzURrBareLixYuif//+4rPPPhNC1Gxu1X6x1q5dK1QqlYiLixNCCJGXlydyc3NFYWGhtDaYTCaxe/duYbFYRF5enli4cGG9L5bVahXZ2dniww8/FHPmzBEnT56UUnvfvn3i9ddft71fsmSJ8PT0FI8//rj43e9+JxQKhTh48KAQwjF9r2vWrFm2NY+xY8eKwYMHi88//1xMmjRJ/Pzzz2LTpk1S+85l777LvjkYqE2wdOlS4efnJ9LT04UQ9l+s559/XkyfPl2Ul5c77ATjkpIS2/8zMzPFSy+9dM21lerqamE0Gltcr24/6m5avv322+LUqVO29wsXLhSjR48Wer2+xTWvp6qqSlgsFvHII4+IVatW2YYPHjxYKBQKERMTI4So2Y8mo+9X47Kv4Y7LvikYqI2QmJho+6WaMWOGiIqKsv1SVVVVCSGEWLNmjZg6dapD6p84caLefiIh7L9YX331lUNqJyUl2WrWnq5Tq7bvn3/+uZg8ebJDwuTkyZN28/30009FdHS0EEKIQ4cOCX9/fxEVFSUiIiLs9qvJwmXvvsu+OXgtfwOSk5MxceJE/OlPfwIAvPTSS+jXrx/uv/9+pKSkwNPTEwBQXV0NDw8PVFZWSn0+eHJyMsaPH287SbruE1+7du2KZ599Fo888gieeuop7N69W1rd2toTJkyw1a69i3rtY79rH6KYl5cHLy8vGI1G6X2/++677U4Qb9++PZKSkvDll19i2rRp2LRpE44dO4ZBgwahrKxMWu3a+lz27rnsm61189z1FBQUiHPnzomsrCzb+7/85S9i8+bNtmlSUlLEnDlzhJeXl5g1a5aYPXu28PPzE8eOHXNY/X//+99209X9y52RkSGWLVsmMjIynFK7Vm5urnjvvfdEaGioSExMbFHtxtbPyckRd911l/Dx8RH/+c9/WlyzrqKiItvli3XrO2vZX6++M5Z9Y2vXkr3sG1PfkcteFgZqHSdOnBADBw4U48aNEx06dBBffPGFEML+hOq6/v3vf4vly5eLpUuX2k52dmb92vMQhai/Sebo2ps3bxb333+/3dFvZ9Vft26d7RxHq9UqZXPzxIkTYvjw4eLuu+8WXl5etn2Tdc9trMsRy74p9WUv+6bUdsSyb2x9Ryx7mRiov0lPTxedO3cW69evF1lZWWL58uXC19dXnD592m66U6dOiejo6Oue/+fo+qdPnxZvvPGG1F+k5tQuLy8XmZmZtlNWnFE/LS1N/POf/7Tru4yfQ2pqqggJCRHr168XOTk54tVXXxWDBw8W5eXldjXS0tIcsuwbW//UqVPSl31zastc9o2tn5qa6pBlLxsD9TevvvqqWLp0qe19cXGxuO2228QPP/wghKhZeBaLRbz11luiXbt2tjvdtFb9Dz74oFVqe3l52W0Ct0Z9mT/7qqoq8cwzz4g33njDNmzfvn1i+vTp4uLFiyI5Odk2fNWqVdKXfXPqy1r2Ta0te9k3p77s751sPCj1G4PBgKioKNv7Tp06wcfHBwkJCQBqdsYrlUo88cQTiImJwZNPPtmq9Z944olWqb1hwwbMnj1bWu3m1Jf5s7darejUqRPGjRtnG7Znzx788MMPePTRRxEVFYVFixYBAGbPno3333+/1evLWvZNrR0TEyN12TenvuzvnXStneiuou6VHbX7px555BGxbNmy635G5qZfa9Z3574LIezun3nq1Clx1113iePHjwuDwSC+++474evrW+8gSFup7859dwSFEBLPdbiJ6PV6CCHg7+9vN1wIYTs95W9/+xuqq6vxzjvvAAA2bNgAlUqF2bNnQ6ls2cp9a9Z3577fqH6tiooKaDQaVFVVwcvLC0888QTat2+Pd999t0V1XaG+O/fdGdxykz8rKwsDBgzAmjVrkJ+fbzeu7rl+arUaRUVFAID33nsPCxcuxODBg1v8hW7N+u7c94bq165btGvXDkII27mW3t7eCAsLa1FdV6jvzn13FrcM1MzMTCiVSnz00UfYtGkTCgoK7MbXPsVRo9GgS5cu+PLLLxEdHY39+/dj0KBBN3V9d+57Q/VrA12pVEKhUECpVCI2Nha7d+/GAw880OLarV3fnfvuLG75CJTvv/8ec+fORXh4OF599VUoFAo88cQTCAwMBHBl4YaGhmLOnDno3r07/vvf/2LgwIE3fX137ntj6tfudnjuueegUCjw/fff46uvvkKfPn1u+vru3HencdbOWldy/Phx211r1q1bJyIiIsT//u//ivz8fLvpvvrqK+Hp6Wl3+sbNXt+d+96U+t99953Yvn277b6fbaG+O/fdWdwyUIWwPyk4JibmugtXxsnLrlbfnfvelPqO0pr13bnvzuBWgXr1lRV1T7+IiYkR4eHh4h//+IfIzc1tc/Xdue/uXt+d++5sbTpQ9Xq9yM7OtruX5NULt+77jRs3Ch8fH/H2229LOdetNeu7c9/dvb479721tdlATUpKErfeeqvo37+/mDZt2g3vTlN34W7evNnuBro3Y3137ru713fnvruCNhmoKSkpIjw8XKxfv14cP35czJ49Wzz44IOirKzsup+RffVHa9V35767e3137ruraHOBajKZxIIFC8TKlSttw86cOSMCAgLqPeq2rdV35767e3137rsraXMn9qtUKgghEBERAQCoqqpC9+7dMWDAAFRWVrbp+u7cd3ev7859dyVtLlAB4Omnn8aMGTMAAF5eXgAAHx8fpKamtvn67tx3d6/vzn13FW3iSqmMjAx89NFH0Gg0GDRoECZMmACg5jLG2kvZ2rVrB71eb/vMRx99hLKyMjz77LM3dX137ru713fnvruqmz5QT5w4gcmTJ2P8+PEoKCjAli1bUFVVhXvvvRceHh62my506dIFPj4+AGputhEdHY1vvvnmpq7vzn139/ru3HeX5vzdtvLk5eWJW265RaxZs0YIIcTZs2fF3LlzxSuvvCKEsD8t46WXXhL/+te/RGxsrAgPD5fyLJzWrO/OfXf3+u7cd1d3UwdqcnKymDlzpt2w1atXi759+9Z7wNf//M//CC8vL9GtWzcpT6hs7fru3Hd3r+/OfXd1N/VBKR8fH3To0AHAleeFDx8+HN7e3rbniNcaMGAAwsLCsHPnTrvHbdys9d257+5e35377vJaO9FlqT1BOCcnRwwaNEgUFhbaNj3OnDkjqqurRU5OTpus7859d/f67tx3V3RTr6HWpVQqIYSAwWBAfn4+qqqqoFAosG7dOgwdOhQGgwEhISFtsr47993d67tz313R/wd6B/M7nRydIwAAAABJRU5ErkJggg==' width=340.15748031496065/>\n",
       "            </div>\n",
       "        "
      ],
      "text/plain": [
       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, (axe1, axe2) = plt.subplots(2, 1, figsize=fig_dim, sharex=True, dpi=fig_dpi)\n",
    "twin = axe1.twinx()\n",
    "\n",
    "plot_convert(axe1, twin, ldg_station_raw)\n",
    "axe2.plot(ldg_station_raw.timestamp, 100*ldg_station_raw.vp / (ct.one_atm + gas_pressure / 1000. * 997 * 9.80665))\n",
    "axe2.set_ylim(ymin=0, ymax=5)\n",
    "axe2.tick_params(axis='x', labelrotation = 45)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "69b2df79",
   "metadata": {},
   "source": [
    "##  9보일러 전단 데이터 (22-09-22 15:00~ )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "ddc35389",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "ea680cafbc5b4746a2da199ca9cbcb07",
       "version_major": 2,
       "version_minor": 0
      },
      "image/png": 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",
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       "\n",
       "            <div style=\"display: inline-block;\">\n",
       "                <div class=\"jupyter-widgets widget-label\" style=\"text-align: center;\">\n",
       "                    Figure\n",
       "                </div>\n",
       "                <img 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' width=340.15748031496065/>\n",
       "            </div>\n",
       "        "
      ],
      "text/plain": [
       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, (axe1, axe2) = plt.subplots(2, 1, figsize=fig_dim, sharex=True, dpi=fig_dpi)\n",
    "twin = axe1.twinx()\n",
    "\n",
    "plot_convert(axe1, twin, ldg_pp_raw)\n",
    "axe2.plot(ldg_pp_raw.timestamp, 100*ldg_pp_raw.vp / (ct.one_atm + 0.4 * 997 * 9.80665))\n",
    "axe2.set_ylim(ymin=0, ymax=5)\n",
    "axe2.tick_params(axis='x', labelrotation = 45)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2fd8d3e0",
   "metadata": {},
   "source": [
    "##  부스터 후단 데이터 (22-09-26 10:30~ )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "840a5dce",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "365e8b9c42fc4eea9920ff212bcf4e99",
       "version_major": 2,
       "version_minor": 0
      },
      "image/png": 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",
      "text/html": [
       "\n",
       "            <div style=\"display: inline-block;\">\n",
       "                <div class=\"jupyter-widgets widget-label\" style=\"text-align: center;\">\n",
       "                    Figure\n",
       "                </div>\n",
       "                <img 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fxST3TWxR4qEbULdZ+sNBqN9dap/cD7c/n5+UGv12Po0KGNfqKzLgYqEV02tm3bhqlTp+LIkSMAanqg8+bNw+TJk3HHHXc0+4lOBioRXTZGjBiBPn364M4770RUVBQGDhyI+Ph4rFixQskTnXxSiojaNF/KBvZQiYgUYaASESnCQCUiUoSBSkSkCAOViEgRBioRkSIMVCIiRRioRESKMFCJiBRhoBIRKcJAJSJShIFKRKQIA5WISBF+Yj/RZcTlElQ6aj5k2ajXocRmh0FXv191srACFdVOFFVUwyVAaaUdBeXVqKh24vVvDyM6JABPjOru8T3Wkiq8sSkTAHBLbwssIQGwO1xY+2PWedsy5IqO9eYVVtiRmVOGIVd0xJrEofA36M/znb6LH99HpJDLJdA0wOkS5JZVweEUdAoNwNG8chTZ7Ag0ng0Ilwi2ZOTiREEFekYH42RBBbqEmxBm8oMG4MDpEqzadtRj+4nXd4NOp+F0cSX2ZRfjaF45ekUHIyokAABwqsiGwzllAIDIYH/kllYBAMJMRgQY9Mgtq4LT1Sb+5PH+o9fhhvjIS67nS9nAHiq1Ky6X4ERBBepGRlp2Mb7efwa394uBpgEiggOnShASaESQvwG5pVX4+39P4ExJJUICDCipdCDAqIMGDTa7EzdeFYkO/npUVDvx3aFcuAS4MjLIo0ZRRU0vriW9XSdgASD9TCnSz5TWm18bprVtAy48CmhIgGcUBBj16BQagLTsYgiA83W7xl0TDXMHP/f0meIqbDxYM0LoL0dcgbhwE4wGHZZvykRBeTW6R3aAtaQSJn89fnNTPLqag+pvFMD61FO4qXcUru8RccH2+ir2UKlRqhxO2KqdcEnN13pNgwDYdjgP/buE4URBOQ6eLkVsx0DYqp2I6OCP0yWVKK204+//PYGsQhuGX2nG6F6R+OaAFQ6XoHtkB48aB06VIDOnDNXOmtEm/Q06XBUVjMM5pai01x+B0s9w9pS12tHwESq9Ra/T3L3CXtHB0GkaAOBoXjls9vpjHA3tFo7OYYHYfiQP1pIqj/k3xEfAqNehrMoBa0klKu0u3NYnGib/mp5vld2JM8WVCDP5oW9sKE4XVaLa6UTvTiEAAA0aAo162F0uFNvs6BwWiABj2zqtrsuXsoGB6kPO/VWUV9f8oRl0Ghw//zFmF9rgEkFsx0Acy6twB06tYls1/r3nFK7t2hFVDhcOWUsR29GEiA7+KLJV48CpEhw8XYLenUJgDjrbszicU4YdR/IBAMOu9PzZ5pdVI6+s5o9aUNvT8W0mPz0swf7uaQFwPL8CANDRZERksD+iQgKw9XAewkxGDO0WjrBAP3y57zRKKh0IMxnd+3nv4FiEBBrRLzbUfa1x/6lijOgegejQgHq1A4w6lNgciDOb0MGfJ4De4EvZ0C4DdXtmHr4/ko/9p4qxKSPXPf/6HhHYlpkHnQaEBhoxvm8n6DQNwQEG2OxOfJh8EncO6IydP+Ujr7QKlQ4nHrguzuMPI6+sCh/u8rzIPuzKcAT5GVDpcGJ7Zk0wje4ZCZ2mwSWC7CIbiirsKKm0u3tYoYFGFNvOhlNHkxGFbSCs6oro4O8O3Loswf7oFhEEm92J1Kxi9/yoEH9M6BuDf/xwAuP7dsLQOiFeVFGNIznluCIiCHtOFuKxhO4w6DSIAN//lI++nUNR7XChQ4ABXcNNCAn0HN3S36CD9nMvkNo/BmoTNOaH9n8bD+H/Nh72Usu8L8hPj0A/AzqHBUCnOxscheXVsDsFA+LCkFtahVNFNvSKDkZksD8qqp3Y//Op9IND4xBvOXua7RTg091ZGHJFOEZ0j0CQv+cpYFy4CSa/mn8qep0G/59Psdv6qSK1D74UqO3ynGRE9wjk/HyT4VzDrgzH0bxyGHQ69IoOhrmDHwKMekSFBKDYZsdhayniwk3YdbwQ+0+VIOGqSPSwdED4OafH5VUO7DpWiMgQf0R28IdBp6Fzx0DEdjSh2GbH/lPFCAkwom/nUPf3VDtdcLoEep2G//6Uj36xYbgyMgihgUYcL6hAj5+vIVY5nPA36OF0CbqEm6DXacgtrULkOaevLeXR67u1eA2i9q5d9lCJ6PLR2Gz49ttvMX/+fKSnp8PPzw8JCQl4/fXXYTabkZGRgRkzZuDgwYMICQnBggUL8NBDDzW4LXxSioguK3PnzsXChQuRk5ODw4cPIy8vD88++yyqq6sxfvx4jB07FlarFR999BFmzpyJHTt2NHjbDFQiuqxs3boVo0aNgqZpMJlMmDhxIlJSUrBhwwaUl5djzpw50DQNAwYMwEMPPYTly5c3eNsMVCK6rBgMnreOvv/+e1x99dVITk7GkCFDoNefvdk6fPhwJCcnN3jbDFQiahdsNpvHy26/9NsQ169fj3/961+YN28erFYrLBaLx3KLxQKr1drgNjBQiahdiI2Nhclkcr+SkpIuun56ejqmTp2Kd999F1dddRVEBLo6HxSj0+ngcjX86bt2+bYpIrr8ZGVledzlr3tqX3fd2267DX/84x9xzz33AADMZjMyMzM91issLGzUu4rYQyWidiEwMNDjZTQaz7teQUEBbrvtNkyZMgVPP/20e/7gwYORkpLise6uXbswaNCgBreBgUpEl43y8nJMmDABw4YNw4svvuixbMKECaiqqsIrr7wCp9OJlJQUrFy5EjNmzGjw9nnKT0SXjY0bN2Lnzp04cuQIoqOj3fMNBgOysrKwfv16PP7441i4cCFCQ0OxaNEijB07tsHb55NSRNSm+VI2KD3l//bbb3HDDTcgJiYGXbp0weTJk3Hs2DEAQEZGBhISEmCxWNCjRw+sWbNGZWkiolanLFCzsrJw++2345FHHkF2djYyMjIQHByMu+66S8kjXUREvk5ZoO7duxf+/v5ITEx0P9L15JNPIjU1FV9++WWzH+kiIvJ1ygJ1xIgRCA4OxtKlS1FdXY2CggIsXrwYEyZMwK5du5r9SBcRka9TFqgdO3bEli1bsGzZMnTo0AEREREoKirC2rVrm/RIl91ur/coGRGRL1MWqGVlZRg3bhwmT56MoqIi5ObmIjg4GA888ECTHulKSkryeIwsNjZWVVOJiFqEskBdu3YtRAR//vOfYTKZYDabsXr1amzYsAFHjhxBYWGhx/qXeqRr7ty5qKiocL+ysrIuuC4RkS9QFqjFxcUICvIcZ9vf3x9GoxHTpk1r9CNdRqOx3qNkRES+TFmg3nbbbThw4ACWL18Ol8uFyspKzJ49GyaTCTfffHOzH+kiIvJ1ygK1d+/e+Pzzz/G3v/0NMTEx6NatG9LT0/H1118jJiYG69evx9q1a2E2m3H33Xc3+pEuIiJfx0dPiahN86Vs4KdNEREpwkAlIlKEgUpEpAgDlYhIEQYqEZEiDFQiIkUYqEREijBQiYgUYaASESnCQCUiUoSBSkSXnczMTPTs2RO9evXymN/cwUQZqER0Wdm0aRPGjBmDa6+91mO+isFEGahEdFnp06cP9u/fj1tvvdVj/oYNG5o9mCgDlYguKxaL5bwfWJ+cnNzswUQNSlpIRNTK6g7maTAYYDQaG/z9TRlMtC72UImoXYiNjfUY2DMpKalR39+UwUTrYg+ViNqFrKwsjw+YNhgaF29msxmZmZke8y41mGhd7KESUbtQd1DPxpzuA8DgwYMbPZhoXQxUIiIAEyZMaPZgogxUIiIAJpOp2YOJcpA+ImrTfCkb2EMlIlKEgUpEpAgDlYhIEQYqEZEiDFQiIkUYqEREijBQiYgUYaASESnCQCUiUoSBSkSkCAOViEgRBioRkSIMVCIiRRioRESKMFCJiBRhoBIRKcJAJSJShIFKRKQIA5WISBEGKhGRIgxUIiJFlAXqypUrER0d7fGyWCzQNA0AkJGRgYSEBFgsFvTo0QNr1qxRVZqIqMFKS0vx8MMPIyoqCjExMZgzZw5cLpeSbSsL1OnTp+PMmTMer+XLl6N///6orq7G+PHjMXbsWFitVnz00UeYOXMmduzYoao8EVGDPPHEEyguLsbx48eRlpaGr7/+Gi+99JKSbWsiIkq2dB4jR47E9OnTER4ejhkzZiA7Oxt6vR4AMHPmTBQXFze4p+pLY28Tke9oTDYUFhYiMjISe/fuRZ8+fQAAa9euxezZs3Hy5Mlmt6XFrqH+8MMPyMzMxAMPPIDk5GQMGTLEHaYAMHz4cCQnJ7dUeSKielJSUmAymdxhCtRkUVZWFk6fPt3s7RuavYULWLJkCZ544gn4+/vDarXCYrF4LLdYLLBarRf8frvdDofD4Z4uLy8HUPPfiIioVm0mlJeXIzAw0D3fYDDAaDR6rGu1WhEZGekxrzabrFYrOnXq1Ky2tEignjx5EuvWrcPSpUsBACICnc6zM6zT6S56ITgpKQnPP/98vfnx8fFqG0tE7UJcXJzH9Pz587FgwQKPeRfKIgBKbky1SKC+9tpr+MUvfoGoqCgAgNlsRmZmpsc6hYWFF73eMXfuXDzzzDPuaYfDgezsbERERNT7gVyIzWZDbGwssrKyPP5ztRTWa9v1WqMm6zWfy+VCXl4eOnfuDIPhbKSd+3Uts9mMwsJCj3m10yruzSgP1LKyMrz99tvYtGmTe97gwYPx4Ycfeqy3a9cuDBo06ILbMRqN9brrvXr1alRbbDYbgJoflLcOHtZru/VaoybrqREREdGg9QYOHIiCggKcOHHC3aPdtWsXzGZzvR5uUyi/KbV69Wr0798fAwYMcM+bMGECqqqq8Morr8DpdCIlJQUrV67EjBkzVJcnIrqgqKgo3HXXXZg9ezYqKiqQl5eHefPmITExscFnvhejNFBdLhdee+01PPXUUx7zTSYT1q9fj7Vr18JsNuPuu+/GokWLMHbsWJXliYguadWqVdA0DbGxsejVqxdGjhyJhQsXKtm20lN+nU5X71pprQEDBmDnzp0qy12SwWDA/Pnzz3sthfVYzxdqsp73hYeHY+3atS2y7RZ9Yz8R0eWEH45CRKQIA5WISBEGKhGRIgxUIiJFGKhERIowUImIFGGgEhEpwkAlIlKEgUpEpAgDlYhIEQYqEZEiDFQiIkUYqEREijBQiYgUYaASESnCQCUiUoSBSkSkCAOViEgRBioRkSJeDdRp06YhLCwM0dHR7tesWbO82QQiohbj1aEIS0pKsHr1akyaNMmbZYmIvMKrPdSSkhKEhYV5syQRkdcwUImIFNFERLxVrHfv3iguLobD4UBERATGjBmDefPmISIiot66drsdDofDPe1yuVBWVobg4GBomuatJhORjxMRVFZWIiwsDDpdK99nFy86c+aMuFwucblcsn//fhk7dqxce+214nQ66607f/58AcAXX3zx1aBXfn6+N+PsvLzaQ63r1KlT6Ny5M1JTU9G3b1+PZXV7qBUVFYiIiEB+fj4CAwO93VQi8lE2mw1msxnl5eUwmUyt2hav3uWvq7KyEgAQExNTb5nRaITRaKw3PzAwkIFKRPX4wqVAr11w2LdvH5YvX47i4mIAQHZ2NqZPn44HHngAZrPZW80gImoxXgvUmJgYpKamol+/foiKisLo0aMxfPhwvP32295qAhFRi2rVa6iNYbPZYDKZUFFRwVN+InLzpWzgs/xERIowUImIFGGgEhEpwkAlIlKEgUpEpAgDlYhIEQYqEZEiDFQiIkUYqEREijBQiYgUYaASESnCQCUiUoSBSkSkCAOViEgRBioRkSIMVCIiRRioRESKMFCJiBRhoBIRKcJAJSJShIFKRKRIqwXqU089BU3TcOzYsdZqAhGRUq0SqDt37sTevXtbozQRUYvxeqBWVVXh8ccfx2uvvebt0kRELcrrgbpw4UJMmDABffv29XZpIqIWZfBmsT179uCTTz5BSkrKJde12+1wOBzuaZvN1pJNIyJqNq/1UB0OBx599FEsW7YMAQEBl1w/KSkJJpPJ/TKbzV5oJRFR03ktUJcsWYL+/ftj9OjRDVp/7ty5qKiocL/y8/NbuIVERM3jtUDdtWsXPvroI4SFhblfANCvXz889thj9dY3Go0IDAz0eBER+TJNRKTVimsajh49iiuuuOKS69psNphMJlRUVDBcicjNl7KBT0oRESni1bv8dbVi55iISDn2UImIFGGgEhEpwkAlIlKEgUpEpAgDlYhIEQYqEZEiDFQiIkUYqEREijBQiYgUYaASESnCQCUiUoSBSkSkCAOViEgRBioRkSIMVCIiRRioRESKMFCJiBRhoBIRKcJAJSJShIFKRKQIA5WISBGvBuqqVaswaNAgREVFoUuXLnj00UdRXFzszSYQEbUYrwbqN998g/feew9WqxUHDhxAdnY2nn76aW82gYioxXg1UD/44AP07dsXABAcHIxx48Zh//793mwCEVGLabVrqOnp6Vi9ejWmTp3aWk0gIlKqVQI1OjoavXv3xtChQzFz5szzrmO322Gz2TxeRES+rFUC9cyZMzh06BAOHjyIxMTE866TlJQEk8nkfpnNZi+3koiocTQRkdYqvn37dtx4442w2Wzw8/PzWGa32+FwONzTNpsNZrMZFRUVCAwM9HZTichH2Ww2mEwmn8gGg7cKOZ1O6PV6j3l+fn715tUyGo0wGo3eaBoRkRJeO+Xftm0bpk6diiNHjgAACgsLMW/ePEyePLle75SIqC3yWqCOGDECffr0wZ133omoqCgMHDgQ8fHxWLFihbeaQETUolr1Gmpj+NJ1EiJquLSsYjhF4G/QobzKgeRjhYgO9UdEB39o0KBpgPbzuhXVTrz45UHMHN0DkwbFNmj7vpQNXruGSkStz+F0ocLuhF7T4HAKbHYndDoAAtT2rKodLuw5WQSXCI7klAGahqyCCuzNKsKR3HL3tu7sHwOddnbb3/+UD2tJlXs6wKhDpd3VpHbO/nAvBnftiK7moCZ9f2thoBL5qEq7E+tST+N0kQ3hHfxgDvJDVEgANE3DrmMF+CQlGwdOl+BXI7sh0E8HlwArNh8577b6dwmDTgN2nyhS1r7P9p66RPsvHqbXdu0IP4MOtefIAkFWoQ1ZhTXvObc728TJswee8hM1kt3pQmF5NTRNg16nQacBOp2GnJJK7MsuwbbMPIQH+WF96mk4XC48OLQrlnxzCABwe79OEAAigsPWMhzOKTtvjTCTEUUVdq/tU0QHf49T75zSKvTvEobUrCJcKCF+OeIK9O8S6p62OwTfpuegV6dg9IkJxXXdwuFv0EHTasJRA+AUQZCfAfpzu7bN5EvZwECly8Ihaym2Hc7DgLgwGHQaqh0u/PdoAaJCAlBWacfp4kr85bufLvj94UF+iOzgjyJbtcdprbdZgv0RHRqA1Kyzn9I25uoo9IsNhaZp+CLtNPafKkFHkxGTr+2C0ioHSmx2TBt5BQBABDieX4HyagdKKx24vkcE+ncJa52dUcSXsoGBSq2myuFEXll1vfkFZdX4cNdJVDtc+Oeuk1hwx9XwM+hRUF6FxV8fgp9BB6dL4HTVHLq39LYAP9/c+OFoAZyumt5QbU+wvNrZIu03B/nBKQKXS+ASoKzKcd71HriuC/7xw0kAwNL7B0Cn1bTV6RJsSs/BIWsZ+sWGYv+pEvSLDcX9Q+IQ2zEQLhH4G/UIMOjcvWGqz5eygYFKDSJSE2A/5ZXj4OkS5JZWITzID+FBfigor8bsD/fi4eFd8d2hXBzLrwAA+Bl0uKmnBelnStzzatV+X2uJ6ODnDvNru3aETqehsLwah3PK0CcmBPtPlUDTgLsHdEZ8VDBG94qESE0I5pdXY3DXjujgz1sQvsCXsoGB2o5UO1xIP1OCIH8DnC6BwykI8tdDg4YTBRXYc7IQn+7OxnXdzOgeWXP3VNM0fH8kDxsP5gAARveMBFDbuwO2HMqFn0GHakfT7tY2RExogMf0qeJKj+n+XcJwdadg5JZWudtZa8gVHfHo9VdC0/DztT5Bp9BAXBER5L4mqGkaNABGvQ56nYZKuxNBDMN2w5eygUdVE7hcAp1Og4ig2umCQXf2+Yja/08CwCUCEeDg6RLklFYhLNCIzNwyGPU16xv1GtLPlGJAbBgEwK//lgIAGBgXhu6RHeB0CT7dnY1hV4ajo8kPX+47AwDQ6zRYgv1R9wSwbhBdyLlvfalrU0ZuvXm1YarXae7T7Fo3xEegqMKOtOz6Iy/0iw3FqJ4WWIsroWnAB8knoddpeOmefujfJQyFFdW4JiYUgX7nf/y4pTBMqaW0yx7qv/dk45OUbGw5VD8czhUcYECQnwHxUR2QW1qF9DOliA4JwJmSs8HUMyoYUaEBEBG4RLA9M1/J/rSks721Gl3CA3G6qBKOc8LwVyO7QVAT+JV2Jz5IPglLsD9+dX03xFs6/Ny7q+nixVs6ICY0ELqfr+HV/CzAa3rkE3yph9ouA3XxVxl4Y1Oml1p2YUa9Bg0aqp3nP102B/khv7wafWJCEBMWiG8OWN3LxvaJRkSwH9bsPIEb4iNwS+8obDxoRXCAAcO7R8BPr2FkjwhommeohQUaEeRvQLHNDk0DTEY9DHqOxUjtFwO1CRrzQ/sptwx//f443t1xzGN+4vXdsC0zD13NJnQKDYTJryZsBnftiEq7E1sO5WJot3CUVDpwsqACZVUOjOhuRgd/A3SaBp2mweFyYV92MTp3DERUcAAC/PQw+enRKzoEBeXVOGwtRVdzEKLrXBckopbBQG0CX/qhEZHv8KVs4LkgEZEiDFQiIkUYqEREijBQiYgUYaASESnCQCUiUoSBSkSkCAOViEgRBioRkSJeDdRvv/0WN9xwAyIjI9G5c2dMmTIF+fm+/2EjREQN4dVAnTt3LhYuXIicnBwcPnwYeXl5ePbZZ73ZBCKiFuPVD4bcunUrDIaakiaTCRMnTsTq1au92QQiohbj1R5qbZjW+v7773H11Vd7swlERC2m1T66fP369fjXv/6FH3/88bzL7XY7HI6zg57ZbDZvNY2IqEla5S5/eno6pk6dinfffRdXXXXVeddJSkqCyWRyv8xms5dbSUTUOF7/PNSsrCyMHDkSTz31FJ5++ukLrne+HqrZbPaJzzwkIt/hS5+H6tVT/oKCAtx2222YMmXKRcMUAIxGI4xGo5daRkTUfF475S8vL8eECRMwbNgwvPjii94qS0TkNV7roW7cuBE7d+7EkSNHEB0dfbYBBgOysrK81QwiohbDMaWIqE3zpWzgs/xERIowUImIFGGgEhEpwkAlIlKEgUpEpAgDlYhIEQYqEZEiDFQiIkUYqEREijBQiYgUYaASESnCQCUiUoSBSkSkCAOViEgRBioRkSIMVCIiRRioRESKMFCJiBRhoBIRKcJAJSJSxOuBmpmZiZ49e6JXr17eLk1E1KK8GqibNm3CmDFjcO2113qzLBGRV3g1UPv06YP9+/fj1ltv9WZZIiKvMHizmMVi8WY5IiKv8mqgNobdbofD4XBP22y2VmwNEdGl+exd/qSkJJhMJvfLbDa3dpOIiC7KZwN17ty5qKiocL/y8/Nbu0lERBfls6f8RqMRRqOxtZtBRNRgPttDJSJqaxioRESKaCIird2IhrDZbDCZTKioqEBgYGBrN4eIfIQvZQN7qEREijBQiYgUYaASESnCQCUiUoSBSkSkCAOViEgRBioRkSIMVCIiRRioRESKMFCJiBRhoBIRKcJAJSJShIFKRKQIA5WISBEGKhGRIgxUIiJFGKhERIowUImIFGGgEhEpwkAlIlKEgUpEpIhXA7W0tBQPP/wwoqKiEBMTgzlz5sDlcnmzCURELcbgzWJPPPEESktLcfz4cZSXl+Pmm29GeHg4/vCHP3izGURELUITEfFGocLCQkRGRmLv3r3o06cPAGDt2rWYPXs2Tp48ecnv96Wxt4nId/hSNnjtlD8lJQUmk8kdpgAwfPhwZGVl4fTp095qBhFRi/HaKb/VakVkZKTHPIvF4l7WqVMnj2V2ux0Oh8M9XVFRAaDmvxERUa3aTPDSyfZFeS1QRQQ6nWeHuHb6fDemkpKS8Pzzz9ebbzabW6aBRNSmVVZWwmQytWobvHYNdcOGDXjooYeQl5fnnpebmwuLxYKjR4/iiiuu8Fi/bg/V5XKhrKwMwcHB0DStQTVtNhvMZjPy8/O9cm2F9dp2vdaoyXrNJyKorKxEWFhYvU6bt3mthzpw4EAUFBTgxIkTiIuLAwDs2rULZrPZPX0uo9EIo9HoMS8oKKhJtQMDA716sZr12na91qjJes3T2j3TWl6L86ioKNx1112YPXs2KioqkJeXh3nz5iExMbHV/6sQEang1SRbtWoVNE1DbGwsevXqhZEjR2LhwoXebAIRUYvx6hv7w8PDsXbtWq/VMxgMmD9/PgwG7+wm67Xteq1Rk/XaF6/dlCIiau948ZKISBEGKhGRIgxUIiJFGKjncDgccDqdrNdG67VGTdZr2/VUuzxuvTXAvn37sHjxYmRlZWH06NEYNGgQxo0bx3ptpF5r1GS9tl2vJbCHCuDw4cMYN24c+vfvj3HjxuHw4cOYM2cOFi9ezHptoF5r1GS9tl2vxQjJ4sWLJTEx0T196tQp+ctf/iLh4eHypz/9ifV8vF5r1GS9tl2vpfCUHzUf0lJQUACg5oMWOnXqhEceeQQmkwm/+93vEB4ejpkzZ7Kej9YDgLy8vHa9j6yn/phpEa0W5T5k8+bNEhUVJevWrfOYX1ZWJq+++qrccsstcvTo0WbVcDqd7q+3bt3a4vWys7Pl1KlT7nrR0dEtWu9cW7ZskaioKPn8889bvJ7L5RIR7++jN44Zb9e7nI7RlnJZBuqxY8dkzZo1smbNGjl48KCIiMyaNUvuvvtu2b59u8e6qamp0qVLF9m8eXOT6x06dEhuv/1298GTn58vv/nNb1qsXmpqqvTt29e9jcLCwhatl5WVJV999ZXs2LFDjh8/LiJnf57btm1TXk9EpKKiwv21w+Fo8X08evSovP322/K3v/1NMjIyRERk5syZMnHixBapx2NUbT1vuexuSqWlpWH06NFYu3Ytli9fjsceewzFxcW4//77odfr8frrr+O7775zr9+3b19cc801qKqqanLNzz77DOvXr8eDDz6I3NxchIeH4xe/+AV0Oh3eeOMNpfX27duHO+64A9OnT0dCQgIAICwsDPfccw90Oh1ef/11bNmyRVm91NRUDB8+HIsWLcKsWbMwatQo/Oc//8EjjzwCvV6PZcuWKa0HABkZGRg+fDj+/e9/AwD0ej3CwsJw7733Qq/XK/+ZpqWlYdSoUfj000/xxhtvYNq0acjKysK0adNarB6PUbXHjNe0dqJ709GjR6V79+7y1ltviYjI9u3b5dprr5Vjx46JiMjOnTvlvvvuk4SEBHn33XclLy9P3njjDbFYLO6eV1Ps3btXZs2aJTfffLNcddVVkpubKyIiX331ldx///0yevRoWbVqVbPrHTx4UKKiomTlypXnXb5792554IEHZPTo0Ur2r7CwUIYNGyarVq0SEZEjR47I888/L3q9Xt577z35+uuvZcqUKTJq1CilP88lS5ZI165dpXfv3vLpp596LNu4caNMmTJF2T6ePHlSevTo4T5mfvjhB7njjjtk9erVIiLK95HHqNpj1Nsum0B1uVyyevVqmT17tsf8G2+8Uf7yl7/IokWLJCMjQ3JyciQpKUk6duwoCQkJ0qtXL0lJSWlyXafTKSkpKTJu3DgRERkzZoz07t1bzpw5IyI1B+zrr78uZrNZRo0a1eR6VVVV8stf/lLuuusuj/mff/65JCUlyX333SfffPONpKWlyQsvvKBk/0pKSiQhIUH27NkjImevZ65cuVL0er384x//kKNHjyr9eYqIJCYmyvTp0+UPf/iDxMfH1wvV3bt3y6JFiyQ8PLzZNbds2SJPPPGEx7xf/epXMn78ePf0nj17lNTjMar+GPW2yyZQRUQ2bdokf/7zn93T8+bNEz8/P3nkkUfkpptuEk3TZOvWrSIiYrVa5cyZM5KXl6ek9tSpUyUrK0tERBISEqR///7y8ccfy5133imFhYVK6v3nP/+RiRMnyjvvvCMiIi+//LJcc8018thjj8kvfvEL0TTNfdG/ufVcLpecOnVK+vTpIx9++KGIiFRXV7tDdfny5WIwGCQ5OVlJvVp2u12++OILcTqdYrVaZc6cOecNVRGR3NzcZtfcuHGj++08drtdREQ++OADdyjU7q9IzT6ePn26WfV4jKo7RlvDZRGo5x70597JXLp0qRw6dMg9PWfOHBk5cqQUFhYqq11VVSVOp1PuvfdeeeWVV9zzBw4cKJqmXfDUpzHO3aePP/5YRo0aJffff7/Ex8fL4cOH3cuSkpJk6NChSvdv/vz5EhYW5r5Rc26ozpo1SyZPnixlZWUev4PmKi4udn999OhReeaZZ+qFam34NdW57a3dVu28TZs2yfDhw6WyslIcDoeIiLs3p6Iej1G1x6g3XRY3pTRNQ1pamnvk1drB/37zm98gPj4e1dXVAIDrrrsOISEhCA0NbXbN/fv3Q0Tg5+cHnU6He+65B5WVlQCA7du34/jx4+jfvz+SkpKQk5PTrFo6nQ5paWlwuVyYNGkSEhMTkZGRgfXr16NHjx7u/evevTsiIyMRFhbW3N1DamoqXC4XFixYgHHjxuG+++7D4cOHYTQaYbfbAQC9e/dGeXk5goKCGjyw4sXs27cPIoKQkBD3kMFXXHEFHn/8cUyaNAlz5szBZ599BgDN/kDjc48Zg8EAl8sFTdMgIrDb7cjPz4emae6bUqNGjUJlZWWThzLmMar+GG0N7TpQaw/u/fv3Y8yYMe43Btf+sdUOX107GKDVaoW/vz9sNluT/jDOrXfLLbd4vBG5Q4cOSE1Nxaeffoq77roL7777Lnbv3o2+ffuitLRUyf79+te/BgA8+OCD2LhxI+Lj4+F0OuHn5wcAOHPmDMxmM6qqqpo1hvmBAwdw22234cknnwQAPPPMM7j66qsxceJEHDhwwF2vuroaer2+WUFzbs1bb73V/TM9N6BrQ/Xee+/Fr371K3zxxRfNqlVb79xjpnbcM03TYLFYEBQUBD8/P7z11ltYsmQJ1qxZg4CAgCb/46hbr6WO0XPreeMYPbeeN4/RVuP1PrEXFBcXS1VVlRQUFIhIzXvqnn76aXnvvffOu/6ZM2fkzTfflM6dO8vevXtbpF52drbcfPPNEhQUJP/85z+bsFeNq1fXypUrJTY2VtLS0ppVW0SkoKBAnnrqKY96Bw4ckMTERPH395epU6fKww8/LGFhYbJ79+5m1zu35vvvv+8x/9xT5czMTFmwYIFkZma2WD0RkRMnTsiYMWNkwYIF0rlzZ/dNuZaqJ9L8Y7Qh9VQeow2pV5fKY7S1tLtATU1NlREjRsiYMWMkPj5eli9fLlVVVRdc/7333pOJEyd63K1uqXorVqyQr776SkRqgqAp1xUbu3/btm2TWbNmSd++fZsUbgcPHpQVK1a4p2uvGV6o5vvvvy8LFy6U+fPnu9+Q3tI1q6ur3V835dppY+vt3r1bNE2TmJgYSU1NbfF6zT1GG1NPxTHa2P1r7jHqS9pVoJ48eVJiYmJkxYoVkpKSIitWrBCLxSIzZ86UH3/80b3eoUOH5IUXXhCXyyVlZWVy9OhRyc/Pb7F6Bw4ckFdffdXj4GzKgdqU/ausrJQdO3bIyZMnG1XL5XJJVVWVDB8+XDRNk0WLFl2w7YcOHZKkpCSPGw9N0Ziahw8fdu+jN+vV7uOTTz4p+/bta/F6zTlGG1MvPT292cdoU/evqceoL2pXgfr55597vD9QpOZtGiNGjJAZM2a4/7svXrxY/P39L3qK3BL1at8M7q167777brPqiYhMnDhR/vjHP4rRaJQFCxbUW+5yuWTx4sUSEBDQ7P1rSs3at994u57NZmvxeiqO0cbWU/E7bEw9FceoL2lXgbp582bp06eP+20mtf8Vv/vuOxkyZIjMmTNHRERycnLkr3/9K+tdhMvlktzcXLnvvvtERGTdunViNBrl+eefr7duXl7eRa+N+WpN1mvb9XxRuwrU9PR0ueaaa2TZsmUiUvPet9rQ+eqrr8TPz899fahWc05T23O92u+rrKx0z/v000/r/YHUXh9rbr3WqMl6bbueL2rTgVpQUFDvutJbb70lOp1O/v3vf4tIzS+r9hc4adIkmT9/Pus1ol5d5/sDaQ5v12S9tl3P17XZQD1+/Lh07txZ/vSnP4nVavVYtmDBAvHz83M/EllrypQp8vLLL7NeI+vV9emnn4rJZHJfYmgqb9dkvbZdry1os4G6efNm6dKli/Ts2VP+93//V3JycjyWL1q0SAICAmT+/PnyzjvvyBtvvCERERGSnp7Oek2oV9cHH3wgkZGR7k8lags1Wa9t12sLNJG2+DgC8Nxzz8FoNKJLly547rnn8OSTT+KXv/wlIiMj3et8/PHH+Pzzz3Hs2DEEBgbihRdewMCBA1mvifXqKisrQ4cOHZpUrzVqsl7brtcmtHaiN9WePXvcn4yzYsUKiYuLO+9/ycrKSnG5XFJeXs56Cuqp5O2arNe267UFbTZQRTzfLLxy5crz/kLr3lFkvebVU83bNVmvbdfzdW0iUC/2aOW5b7lYuXKldOnSRV5++eVmfZwa66mt1xo1Wa9t12urfD5Q09LS5Le//e1FH/M797/kqlWrJCgoSJYuXdqk97exntp6rVGT9dp2vbbMpwN13759Eh0dLYsWLZKSkhKPZXWfDT53+r333vP4UF7Wa516rVGT9dp2vbbOZwO1srJS7rjjDnn11Vfd80pKSjw+yVvlExesp/4Jlva+j6zXvp96aormfax5C3I4HMjNzcWkSZMgIpg4cSKKioqgaRr69euH1157DXq9Hk6nE3q9HsDZDwFmvdavdznsI+upP2baOp/e+4iICABAYmIiwsLC8OKLL2Ly5Mn49ttvcccddwCA+xfJer5XrzVqsl7brtfmtVrfuAHuuusuefDBB+X3v/+9VFRUiEjNdZrk5GQZPHhwvUcvWc+36rVGTdZr2/XaOp/poWZmZuL555/H4sWLsW7dOgDA4sWLkZOTg08++cR9KiEiuPrqqxEaGooTJ06wno/Uuxz2kfXUHzPtjU8EalpaGkaNGoVjx45h8+bNmDt3LtatW4fu3bvjvvvuQ0lJCV544QUANddoTCYT+vTpA39/fwBnB6tjvdapdznsI+upP2baJW93ieuyWq0en/H5008/SWJiosybN09Eaj4s+eWXX5brr79epk+fLtu3b5eXXnpJoqKimvS2DNZTW+9y2EfWU3/MtFetHqj79++XKVOmeMxbsmSJ9OrVyz3MRHFxsWzfvl0SEhJk7NixMnr06CaPNMl6auu1Rk3Wa9v12rNWD9Rjx47J448/LiJn38O2detWGTRokDgcjnpvHrbb7e6L46zX+vVaoybrte167VmrB+q5an+Z2dnZ0r9/f8nLy3P/MltirG7WU6+97yPr0cX4xE2pWjqdDiKC8vJy5OTkoKqqCpqm4c0330RCQgIKCgqUXvxmPbX1WqMm67Xteu3N/wcLBRkTnJ3kAAAAAABJRU5ErkJggg==' width=340.15748031496065/>\n",
       "            </div>\n",
       "        "
      ],
      "text/plain": [
       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, (axe1, axe2) = plt.subplots(2, 1, figsize=fig_dim, sharex=True, dpi=fig_dpi)\n",
    "twin = axe1.twinx()\n",
    "\n",
    "plot_convert(axe1, twin, ldg_station_raw_0926)\n",
    "axe2.plot(ldg_station_raw_0926.timestamp, 100*ldg_station_raw_0926.vp / (ct.one_atm + 0.4 * 997 * 9.80665))\n",
    "axe2.set_ylim(ymin=0, ymax=5)\n",
    "axe2.tick_params(axis='x', labelrotation = 45)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0811f247",
   "metadata": {},
   "source": [
    "##  9보일러 전단 데이터 (22-09-26 14:30~ )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "73b4b0aa",
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_convert_two (axe, twin, axe2, ldg_station_raw):\n",
    "    axe.plot(ldg_station_raw.timestamp, ldg_station_raw.RH1, ':', label=\"상대습도 %\")\n",
    "    axe.plot(ldg_station_raw.timestamp, ldg_station_raw.RH2, ':')\n",
    "    twin.plot(ldg_station_raw.timestamp, ldg_station_raw.T1 - 273.15, label=\"온도 'C\")\n",
    "    twin.plot(ldg_station_raw.timestamp, ldg_station_raw.T2 - 273.15)\n",
    "    # twin.plot(ldg_station_raw.timestamp, (ldg_station_raw.CH3 - 1) * 25.)\n",
    "    axe.set_ylim(0, 100)\n",
    "    twin.set_ylim(0, 50)\n",
    "    axe.tick_params(axis='x', labelrotation = 45)\n",
    "    \n",
    "    axe2.plot(ldg_station_raw.timestamp, 100*ldg_station_raw.vp1 / (ct.one_atm + 0.4 * 997 * 9.80665), label='수분량 %')\n",
    "    axe2.plot(ldg_station_raw.timestamp, 100*ldg_station_raw.vp2 / (ct.one_atm + 0.4 * 997 * 9.80665))\n",
    "    axe2.set_ylim(ymin=0, ymax=5)\n",
    "    axe2.tick_params(axis='x', labelrotation = 45)\n",
    "    \n",
    "    axe.legend(loc='lower left')\n",
    "    twin.legend(loc='lower right')\n",
    "    axe2.legend()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "88d45629",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d95a7b9afd924e82bd8dd9f9d4436eb6",
       "version_major": 2,
       "version_minor": 0
      },
      "image/png": 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",
      "text/html": [
       "\n",
       "            <div style=\"display: inline-block;\">\n",
       "                <div class=\"jupyter-widgets widget-label\" style=\"text-align: center;\">\n",
       "                    Figure\n",
       "                </div>\n",
       "                <img 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' width=340.15748031496065/>\n",
       "            </div>\n",
       "        "
      ],
      "text/plain": [
       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, (axe1, axe2) = plt.subplots(2, 1, figsize=fig_dim, sharex=True, dpi=fig_dpi)\n",
    "twin = axe1.twinx()\n",
    "\n",
    "plot_convert_two(axe1, twin, axe2, ldg_pp_0926)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b3361c08",
   "metadata": {},
   "source": [
    "##  9보일러 전단 데이터 (22-10-13 10:00~ )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "f00a50bf",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
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8IeipvozElA3UQyWEVAsbRenHXXs5FY2NejrZ4K1ufrCS146dWRSohBCLqe9si0VDWlu6DMHUjq8FQggRAQpUQggRCAUqIYQIhAKVEEIEQoFKCCECoUAlhBCBUKASQohAKFAJIUQgFKiEECIQClRCCBEIBSohhAiEApUQQgQiaKCeOHECzz//PLy9vdGgQQOMGDECsbGxAIDo6Gj06tUL7u7uaNKkCbZt2ybkqgkhpMo++ugjSCQSwXJKsEBNSEjAyy+/jLfeeguJiYmIjo6Gg4MDhgwZgsLCQgwaNAgDBgxAcnIydu/ejenTp+PcuXNCrZ4QQqrk/PnziIiIMN4XJKeYQA4cOMCcnZ1N2i5fvswAsL179zIPDw+m0+mM06ZNm8befPPNSi8/LS2NAWBpaWlClUwIqQWeJhs0Gg1r27Ytu3r1KgPAYmJi2L59+545pwTroXbv3h0ODg749ttvUVhYiPT0dKxYsQKDBw9GWFgYgoKCIJMVnXC2W7duuHjxolCrJ4SQSlu8eDEGDx6MwMCiS81cvHjxmXNKsECtV68eTp8+jdWrV8Pe3h6urq7IzMzErl27kJycDHd300suu7u7Izk5uczlabVaqNVqkxshhJTlybzQarWlznflyhXs2bMH8+bNM2l/mpx6kmCBmpubi4EDB2LEiBHIzMzEo0eP4ODggNGjR4MxBqnUdFVSqRQcx5WxNCA4OBhKpdJ48/HxEapUQkgt5OPjY5IZwcHBJebR6XSYOHEiVq9eDRsbG5NpT5NTTxLsEii7du0CYwxLliwBACiVSmzatAn16tVD9+7dS1w8KyMjo9wLas2ZMwezZs0y3k9PT6dQJYSUKSEhwSRT5PKS8fb111+jbdu26N27d4lpKpUKd+7cMWmrKKeeJFigZmVlwc7OzqTN2toaCoUCEyZMwIIFC0ymhYWFoUOHDmUuT6FQQKFQGO/b2tqWOS8hhNja2laYE2FhYQgJCcGePXtM2tu0aQM3NzewJy4CXVFOPUmwTf7+/fvj5s2bWLNmDTiOg0ajwYwZM6BUKvHSSy+hoKAAK1euhF6vR3h4ODZs2IApU6YItXpCCKnQzp07kZ2djczMTOMNAK5evYpr1649c04JFqgBAQHYv38/fv31V3h7e6Nhw4aIiorC0aNH4e3tjYMHD2LXrl1QqVQYNmwYli9fjgEDBgi1ekIIeSZKpfKZc0rCnuzjilR6ejpUKhXS0tKqNKZBCKndxJQN9Ft+QggRCAUqIYQIhAKVEEIEQoFKCCECoUAlhBCBUKASQohAKFAJIUQgFKiEECIQClRCCBEIBSohhAiEApUQQgRCgUoIIQKhQCWEEIFQoBJCiEAoUAkhRCAUqIQQIhAKVEIIEQgFKiGECIQClRBCBEKBSgghAhE8UK9du4Z+/frB09MTbm5u+OCDDwAA0dHR6NWrF9zd3dGkSRNs27ZN6FUTQkiFNm7ciA4dOsDDwwMNGjTAxIkTkZWVBeDZc0rQQL1z5w769euHt99+G0lJSXjw4AHGjh2LwsJCDBo0CAMGDEBycjJ2796N6dOn49y5c0KunhBCKnTs2DH8/PPPSE5Oxs2bN5GYmIiPP/5YmJxiApo0aRJbtWpVifZ9+/YxDw8PptPpjG3Tpk1jb775ZqWXnZaWxgCwtLQ0IUolhNQSz5oN33zzDevcubMgOSVYD1Wr1eL3339H48aN0blzZ3h4eGDUqFFITU3FxYsXERQUBJlMZpy/W7duuHjxolCrJ4SQKouKisKmTZswbtw4QXJKsEC9f/8+dDod1qxZg3379uHWrVvQ6XQYOXIkkpOT4e7ubjK/u7s7kpOTy1yeVquFWq02uRFCSFmezAutVlvu/J6enggICECXLl0wffr0p8qpJwkWqA8fPoRarcbSpUvh5eUFJycnfPnllzhx4gQYY5BKTVcllUrBcVyZywsODoZSqTTefHx8hCqVEFIL+fj4mGRGcHBwufM/fPgQt27dQmRkJCZNmvRUOfUkwQLV0dEREokEbdq0Mbb5+/tDIpHg3r17yMjIMJk/IyMDLi4uZS5vzpw5yM/PN94SEhKEKpUQUgslJCSYZMacOXMqfEzTpk2xfPlybNmyBQ4ODlXOqScJFqhNmjSBvb097t69a2y7c+cOGGOYMmUKwsPDTeYPCwtDhw4dylyeQqGAra2tyY0QQsryZF4oFIoS8+j1+hJtVlZWkMlk6NKlS5Vz6kmCBaqNjQ1Gjx6Njz76CDk5OcjJycGnn36KkSNH4pVXXkFBQQFWrlwJvV6P8PBwbNiwAVOmTBFq9YQQUqGzZ89i3Lhxxo5fRkYG5s6dixEjRgiTU091nEEZcnNz2TvvvMNUKhWrX78+mzx5MsvOzmaMMXb58mXWpUsX5uTkxHx9fdn69eurtGw6bIoQUpqqZENhYSFbtmwZa9myJXN3d2d+fn5s+vTpguWUhDHGquWrQGDp6elQqVRIS0ur0pgGIaR2E1M20G/5CSFEIBSohBAiEApUQggRCAUqIYQIhAKVEEIEQoFKCCECoUAlhBCByC1dACGklinr0Pa0u0D8OUBXAGjVgLU94NocyEni7zv5AI1eMH2MRFLt5QqJApUQUiUPU1Kgiw2F5+mZkOc9rLb1sGn/QuLeotqWXx0oUAmpi/RaIC8V0GkAKzuA0wNXfwd7cBm6zEQoHlxEqMMAdMsJQZp1A6gK7hsf6mmmEkPvpaG7e8XziQkFKiFiwtjjm54POTzefNZrgeQbQOwZoDAPaD4IkMmBs6uAyP1gCiUk2nzjYjIdmsI553aJxecwWyigg42k9JMvSwAYztHULScEAEzCtCzZzBaRzA9O1lKsyeuNNDhCzazRpP0L6NGsKBULdRyu3M8EAChkUmw5F1vq8jr51cNvnbpWuF6xod/yEyIUxoCMGCApArD3BLR5QPJNIPEScOc4UJgLAIhv+jY8k47BKjfRwgWX7099N3hIMnBf2QpB+WeQbeOF++4voXnfCWjUwAcSqTj2aYspG6iHSggAFOYD+alA3iPgxBLA2Q+48hugL+B3lNw7ZZw1VeoKVy71qVfle3tLlebXMEWpPcp7nCcaSUsfw9yu643R8pMAgKXa0XhFFoqf9f1xWt8WAGAt0eKlVj7QwAqZnC3q2VnjuaausJHL0Mm/HpyVVnj18bK6FFtuYJUqr3uoh0pqDo4DCrL4TeHUW0DMGUCdDjg1ABy9gOt7+E1iDX+NdZ13EOQPxHchyGP6jnCU5KGLNApH9J0QwTWGUqJBL2kEQrlWOMe1hATAXeaNeOaOYe19EJmUjaiHOQCAga090belB34OjUOBVo9P+zWHp5MNGrvZw9ZKVv7KayExZQMFKqlejPGbuhmxQHYSwDjAzg3s4gZIIrabzJrv0BDKnBjL1FmOSM4XAdJ4AMBf+vaI4BpjhmI3AGCudgKcvJvhp/te0IEPMw4StPGph4iELKjsrDCtdxN09KuH1t6OAACpRAKptGYdDiRmYsoGClRSOVmJQNw/QOptIP0ucPs431sEUGjjCivN028Cm0M6s4eLhB/D3KZ7CfHMHVIw3GNe+JcLgAQMw7q2gKuTPcJi09HIzR5N3O3hp1Kikas9HG3lsFXIIKlhx0XWBWLKBhpDrYvUGYAmC1zETmTbN4Tt2WWwziy6FlisojH8tXfLWYApocI0jGuGTtJbAIC/9YGIhReCJFHIgw026QbgJvODDnI8Yk54KdAXmflaONjIceV+Jjr61UO/lp7o0sgFuRod3B1s4KQsuqZQ8T+zsYJUS0hJFKi1AWPQ6/UoVOfCVg5EXT4D13+XwzXrerkPkwJwLqW9KmFa3A3OD5nMHlYSLRykWqwtHAhv3ybw8vTC/PMMjjZyvNbRB6+09UZabiHa+DjBw9HG+PhOxZbV8/GttGkVcnqq8gl5ZhSoIqfPfojroSHQqbPR8co8JDBXZDJ7tJbGmswnA2C4Luyz/rZkmXY0Ziu2I4JrhPN+70HZ5DksPnwbMwe2grWVAhwDfF2UaOphD1d7a1jJpJBKJWj1xHK+Lfb/8UOfsShCagAaQzWzQh2HyKRspCQ/wIGTZyHLuIsR8tPoKo3EUX1HuEhyjJu9QvlbH4iesmuIqj8cEXHp+FY3HKNeaI/W/h5o5uEABxsFnGxLXnKXkJpATNlAgSqwAp0euRodTkY/QmrqI2Rc2oXZhasFXccFrjk6S6NxTt8SD+Xe0DZ/FYFN/ADPttBxOtgqFGjq5SzoOgkRKzFlQ7Vt8n/00Uf49ttvERMTA39/f0RHR2PKlCmIjIyEo6MjFi5ciLFja+7uAT3HcPTGQ0QkZOH8jduon34BE+Qh6CS9BWsArz/FMuPghQuKILyh/RNn5N2hbjEMXm37ILBpI5P5Oj/+t/uzPglC6qATJ05gwYIFiIqKgpWVFXr16oXvv/8eKpXqmXOqWgL1/PnziIiIMN4vLCzEoEGDMGnSJJw6dQoRERHo1asXGjVqhO7dxR8LhToO2y/EY+mfl/Gy9Dw+U/wGN0k2BgIYaJjJquLlZFl7wakgCfqu0yHr/j5g587/Hvsxv8c3AHhe2KdACHlszpw5WLZsGXr16gW1Wo2hQ4di9uzZ+OGHH545pwTf5C8oKECXLl3wyy+/oE2bNoiJicHVq1cxZcoUJCYmQibjD36ePn06srKysG3btkot1xzdesYYkrMLcCLyIXYfPIxuXDhekoWjg/ROpZdRaOsOhTYbkh4fAd2mAzaO1VIrIYRX1WzQ6XSQy4s6MmvWrMGmTZswf/78Z84pwXuoixcvxuDBgxEYWPSr34sXLyIoKMhYJAB069YNixcvFnr1lcYYw8NsDf66cg+3z+7G+ILtUEmy4SnJwxgAY2QAKvgVH7NxhkTpArxzFLB3A1CpjiohxIKKhykAhIaGomXLloLklKCBeuXKFezZswfh4eEm7cnJyXB3Nz2xobu7O5KTk8tcllarhU6nM95Xq9VPXRdjDIU6PY5duAqPI++io+Q2pBIGLxQ7yLsyJ86ZfALw7mA8izj9ZoYQ8VCr1SY5IZfLoVCUf/TKwYMHsXfvXly6dAkrVqyock49SbBA1el0mDhxIlavXg0bGxuTaYwxSJ841ZdUKgXHcWUuLzg4GIsWLXqqWqIf5uDX/YcxNOErdJDcggSANYCXgQqDM6vBi3BsNxSSVkMBa8cadwkGQuoqHx8fk/sLFizAwoULy5w/KioK48aNw5YtW9CsWbOnyqknCRaoX3/9Ndq2bYvevXuXmKZSqXDnjuk4ZEZGRrnjHXPmzMGsWbOM99PT00u8YGU5t28DPk9aVG4XMtvWBzL/7rDr8xng0sgYnPQjG0JqpoSEBJNMeXLT/sl5+/fvj3nz5uG1114D8HQ59STBAjUsLAwhISHYs2ePSXubNm3g5uaGJ/d9hYWFoUOHDmUuT6FQmHTXbW1ty5z3SUF2JbvojwZvhlu7gYCCXw7tKiKkdrG1ta1UTqSnp6N///4YM2YMPv74Y2N7x44dsXPnTpN5K8qpElg1AsBiYmJYXl4e8/b2ZitWrGA6nY5dunSJ1atXjx0+fLjSy0pLS2MAWFpaWuUe8CCCMY57ysoJITVFVbIhNzeXde3alb3zzjslpgmRU2YJVMYYu3z5MuvSpQtzcnJivr6+bP369VVaVpUDlRBSJ1QlG/bu3csAMDc3N+bh4WG81a9fnzH27DlFPz0lhNRoYsoGcVxlixBCaoHaGajqTGDPu8Dqrvx1iAghxAxqZ6DmpwHXdwOZccDDiIrnJ4QQAdTOE0yrGgOvbQSkcsC7Pd926yiQfg/o+Jbx0ClCCBFS7QxUAGg11PT+lV+Bm3uB1Gjg5VWWqIgQUsvVzk3+0vh2A6wcgAFf8PfjzwN7pwNhmy1bFyGk1qg7gdr1PeB/CYD88fmgsh8AV7bxPVfDjqvCfP468oQQ8hTqTqA+SSLhx1dbvAwYTogQ+gPwXXvgerGfzzLG3+hoAUJIBWrvGGpFWg3jb8Wl3AQyYoCCnKK27zvwO7MmnQB8OvLB+l1bQFcIdHoHeOHxCVzOfc//2/0/5qmfECI6dbeHWpphPwKT/uKPBDDIfQRIpIBna/6+RALkpvBnqAp8fOWojDjgxBLg3/VFj8t+ACx0ApbWL2oryAGCvYF1zwOFedX/fAghZlV3e6ilkVsBPp1M2z6N5gNVbs3fl0iA/4TzgWloS78LNOoN1PPn73McEPIZAAl/6JZex187KukqoM0DfIIAhZKf9+Y+YOd4oElfYOxuvk1XCEhl/I0QUmNQoFbEyq5km1N9/mbQ+EX+ZiCVAkPXAd3+A+SnFl2Iz9YZCHgVcA8omleT/XhaPf5fvQ7YOgSIP8eH9Pi9Qj4bQkg1okCtLlZKoEGQaZtHK2DkL6Zt7ccCLQYD8sdXOZBIAdnj88AWD+ndE/le8YifAfvHl2nYPprvxQ5exV/TSq8FDs4AWr8O+D9HPVxCzIwC1dIkEkBZ7Aw5Uinw1p+AVg1IHgeiJov/UQKnKxpmAID0GOBRJDD8J/5+zkMgfCtwdRe/wyz2b2DK3/wy1ZnAtuGAazNgyOqisM1+AOgKAJeG/H2OA/Iejxs/vvAgIaRyaKeUWClsi46ZtXbkx23H7OT/b9B5MmDtBMT8zd9Xqvh/n58BnF/N7yxj+qJlPLgM3D5WFKYcB6zvBXzXDshP59vi/gFWNuPHdQ1SbwNfNQEOflJ0+JgmCzj3A/DgStF8ukL+iIiKdrhxHD8vp6/qq0KIqFEPtSaQSIB6fvytuKCJ/M3ASgkszOJ7qnZu/NEImmzATgVwWuC5GaZjv6nRQF4K/3/DMIM6g//X0DtlDEi7y/daISk6ZjfyAHB0DuDeEpgW+nh5t4B1Pfj/TwgB/Lrxjz/3PeDcoOgwtUOfAGGb+P/Pz+CXmXoHuLQZaDsK8Hx8CXKOA1Ju8DvwVI2L6s5P58ec6QKKRGQoUGsjB0+g0wTTNrk18NI80zb3AD6Ai2v5qmmbRAKomvA/2ZXJ+ZCTSvnxYNfmgFe7onkfRfH/tn69KLgTLgLH5vHBawjUtqOBm3/yP6zQqfkdf/pC/ocVKTeBsXv49ealAOueAxy8gU8i+cdm3ge+ac2POc99fO0wXQGwdyp/lrE3tvBhm/0A+OM9QJsPvPZT0REYxxfyXw5DVvP39Tpg80Ag4QLwYQQ/H8cBO8cBjV7gA97aoSqvftUYjgAhtQK9k6Rirk34W3He7YD3L5i2Bb5edGyugZUd0Pld/rhdA58gYEYkoE4vOopCpwF8uwMOXkU9T4mUv1983fpC/l9ZsbFkuTWQfJMfTzb0XmXWQMxpfrqTb9G8kfuBtDvAgOV8UMrkRUMUhjFrMH7aoU/5Wr3b8cMT+z/kx61HbSs6KiNsE5CdBLQbXfQcH90CHoQDHq2Ljl9OjwHiQ/nn07g333Nf9zw/RPL6RqDZAP55X9vNfzkM/hroMI5/7JXt/JdV21FFR4jcvwik3QYa9jLd6mAMYFzRsE5+OqDJBOw9il5rTg8UZPPzGsbvOT1w+yj/RdWwJ+3QfEo0hkqql0crYNCX/LkUDCQSfnzYwbOorX4H4J3DwNA1RW327sAnUcBb+4vaVI35HvSn0UVtjAEvzgFG/soHBwDYOAHDNwD9gosCmuP4432bDeB7tQbdpvM/QTYcPaHJ4v/fbACAx+d2kMqA5OtA3Fkg/t+ix8afB/7+Eki+UdR2dQfwx7tA3Lmitrh/+KC8fbTo58zdpvPHJd/Yy9eoKwBOBvNfGp6BRePVkfuBf77hw9fg+uPgvXeyqO3abmCRMxD+c1Fb+M/8z6n/+bao7fZR4At/YNfbRW2FucD2UfxNUiwWdo7nf6CS8/Dxa5MNnFwGfN/J9BeFl7YA59eZtsWd44eLqjJW/tdifqtEk1XxvCJEPVRSMxU/p61EAgS8YjpdJgfajDBtk0qBgctLLqv9m/zNQOkC9P285HxD1/Gh5uhV1Fa/ExB7lh8WMXBpDDTpwwcjY3x9Tj5A88F8WEkk/K1xb2BWHP/jD4Cfd8gaIDuR73UbxqvbjuR/9uzWomgdXm35wLcrdiTGneP8v+n3itbLOL43bVvsSBIre/5fG6diT04CNO3PtxUfm765z3RehS3flnYHkBd7Dy5s4L9wWgzme/e6An4oBQBmRBW9Zvum8731V74FXJvyY/b73ud3fI7cBiRFAA+v8QFuUl/NQBfpI6S2SI/hD61z9is6QuRZxfzNDwPU78j30hPCgNNfAD6dgR4fFB3G99tIICMWmHgMsHHke6bbR/Hj2p/eLhpCWN+TD833zvK9cMaA/5vE97jH/h/fM907HXj1u5JfiGU9bTFlQ5Wv2VqOv/76iz333HPM1dWVeXt7s9GjR7PU1FTGGGNRUVGsZ8+ezM3NjTVu3Jj98ssvVVo2XUaakFogOZKx6COMqbOK2lLvMHZgBmMZcfx9vb5Ki3yabLh9+zZr1qwZa968uUn7s+aUoIHatWtXdvLkScZxHMvLy2N9+/ZlkydPZgUFBaxRo0Zs6dKljOM4dvnyZebo6Mj++eefSi+bApUQUpqqZsOJEydYw4YN2ZgxY0wCVYicEnSn1JkzZ/DCCy9AIpFAqVRi6NChCA8PR0hICPLy8jBz5kxIJBK0a9cOY8eOxZo1aypeKCGECKhVq1a4ceMG+vbta9IuRE4JGqhyuek+rtDQULRs2RIXL15EUFAQZLKiQzG6deuGixcvCrl6QgipkLu7O2xtS16oU4icqra9/AcPHsTevXtx6dIlrFixAu7u7ibT3d3dkZycXObjtVotdDqd8b5ara5wnRzHQavVPn3RxGwUCgWk0tp71B59FoVlZWUFSQW/jFOr1SY5IZfLoVAoKr2O5OTkKufUk6olUKOiojBu3Dhs2bIFzZo1A2OsxB+PVCoFV85lRYKDg7Fo0aJKrY8xhpSUFKSnpz9T3cS8XFxc4O7uXuEfSk1Cn8XqIZPJ0LBhw3ID0sfHx+T+ggULsHDhwkqv42ly6kmCB2pCQgL69++PefPm4bXXXgMAqFQq3Llzx2S+jIyMcg9xmDNnDmbNmmW8n56eXuIFMzB8gD08PKBUKmvVH2htxBhDfn6+8Zvfw8PDwhUJhz6LwuM4Dg8ePEBSUhIaNGhQ5muakJBgkilPDkFW5Gly6kmCBmp6ejr69++PMWPG4OOPPza2d+zYETt37jSZNywsDB06dChzWQqFwuTbqLQxD4B/sQ0fYIsfg0YqzcaGP/9rcnIy3NzcasXmP30Wq4+7uzsSEhLAcZzJGGdxtra2ZeZEZTxNTj1JsE9xXl4eBg8ejK5du2LZsmUm0wYPHoyCggKsXLkSer0e4eHh2LBhA6ZMmfLM6zWMUymVymdeFjEvw3tWW8Ya6bNYfQydq+L7VYQmRE4JFqjHjx/H+fPnsX//fnh6ehpvPj4+UCqVOHjwIHbt2gWVSoVhw4Zh+fLlGDBggFCrp02rGqi2vme19XlZkjleUyFyqsb/9LSgoAD37t1Do0aNYG1tXc4SiNjUtveutj0foTVo0ABfffUVRo0aVeXHlvfaiumnpzV/4IoQ8kyys7Mxd+5ctGrVCk5OTnB2dkbbtm0RHByMgoKCihdQSa6urnBzM72szvbt29G9e3e4ubnBx8cH7du3x7p16wRbp7lRoFrQu+++i/fee8+kbcuWLXjuuecqfOzChQvx9ttvAwDGjh1b7uEhnTp1wsqVK5+l1FJxHIf33nsPXl5e6Nu3Lx49emSc1rt3b3z77bflPJqIxahRo/D3339j27ZtSE1NRXJyMtauXYsdO3Zg0qRJJeZ3dXWFRCIp9fb666+Xsgaem5ubSaB+9tlnmDdvHpYsWYLk5GQkJCRg/fr1OHr0aLU8T3OgQLWgjIyMEgcSP+n48eOQSCSQy+XGW1Xs3LkTly5dQmZmZpnzXL9+vcw/EIlEgh9++KHUx/3555+Ijo5GYmIiOnfujOXLlxvbExMTMW3atCrVSsyP4zgcP34c8+bNQ/v27aFQKGBtbY3u3bvj008/RUhISKmPO3PmDBh/LhCT2+7du8tc19GjR9GmTRsA/N7zlStXYs+ePXjxxReNR3l07ty53GWIHQWqhej1epw9exZ6fekn39XpdMYDiuvXrw+dTme8VUZKSgo+/PBDfPrppzhy5AjCwsLQp08fHD9+vNQDlWUyWal/IIwxvP/++6WuIzo6Gt27d4dUKkXPnj0RFRUFnU6HmTNnYsWKFVX6lQqxDKlUihdffBHBwcEICwtDfn4+8vLycPbsWXz99dd45ZVXKl7IU/j111/x6quvGgP2yZpqqlp9gmn/zw4CAGKXDza2TdxyEX9FpeCn8Z3QpyV/QPlv/8bjf39cw+jODbBsOP8GJ2dr0GXpX3B3sMaFOX2Mj3/5+zO4npiN/e8/h0Cfpz8B7q5du1BYWIgNGzbgzTffRMuWLY3T/vnnHygUCgwZMqTMMCuLWq3GoEGDcOPGDbz11lu4evUqnJ2d0a9fP+zbtw9LlizBm2++iR07dqB3795PXT8AtG3bFsuWLUNhYSH++usvtGvXDuvWrYO3tzdeffXVZ1p2bWH4DJpT8c97ZezcuRNffPEFJk2ahMjISEilUrRq1QpjxozBRx99VC01RkdHo3PnztWybEuq1YEqVrdu3cJ7772HFStWwNraGoMGDcKRI0fQvHlzAECPHj1w9uxZAPwmf1m2bt2Kbdu2geM4zJ8/HwB/cPOmTZvQoEGDEsMDQ4YMwZAhQ5CTkwN7e/tnfh4DBgxAaGgoOnbsiHbt2iE4OBhBQUHYunUrhg4ditTUVMycOZPCVeQcHR0RHByM4OBgjBo1Cv7+/sbhm7I8//zzpbZ37NgRYWFhFa6T47hKnZ+jxqn0if4srKxzHmo0Gnbz5k2m0WgsVFnVHDx4kKlUKvbpp58a27755hvm4uLCTp06xTZv3sx69OhhnHbs2DFWv379EstZsGABe+uttxhjjL355ptswYIF7OHDh0wmk1X6tmjRIsYYY9euXWPgL55U6u2TTz6p1HP773//y9555x32+uuvs+3bt7PU1FTm5+dnPMn4k2rae1eRmvZ8WrVqVeIzIZFImEQiKdH+4osvlrqM1157jS1YsKDK654+fTobPnx4pecv77UV07mSqYdqZjExMVi7di3eeOMNY9uHH36I3r17o3nz5oiJiXnqZXt4eECn0yE2NhZDhw7FlStXSsyzdetWXL58GatWrTK2tW7dGqzY4ciurq7YvXs3XnjhhUqvOzY2Fps2bcK1a9cQEBCArVu3wtbWFp06dcK5c+eqbSyOPL3r168D4E8iYjhaw1zeeOMN9OvXD3FxcfDz8zOZlpeXBzs7O7PVIqSaO/pbQ02fPh1vvPEGNBoNFi9ejDZt2qBevXp44YUX0KVLF5w9exYzZ84s8TidToekpCRcvHgRP//8cylLLqLRaBAREVHqtJSUlGcK7bLMnj0bH330Eby8vEx2tEkkkmr9uSB5ditXrqzSKeqE0KtXL4wfPx7Dhw/HpUuXjO3//PMPunXrZtZahEQ9VAuZOnUqIiMjsWHDBmMP8dq1a3j//fdhbW1tHHeUSqV4+PAh7Ozs4OjoCH9/f/Tu3btSvxcvbZxUq9Vi4MCBgj6Xf//9F+fOncOmTZsAAN27d8eff/6JPn36IDQ0FN98842g6yOW0bFjR9y4caNE+4EDB0qMuc6dOxdz584td3k//vgj1q1bh4kTJyIxMRH29vZo0KCBcX9AjWTpMYfKqi1jqAZ+fn5sz549Jdq3bt3KWrRoUeHjSxtDNYiMjGQymazUx3311VdsyJAhjDHGUlNTmbW1daVvp0+fLnWZPXr0MLmYWWRkJGvXrh3z8fFhP/zwQ5nPoaa+d2Wpqc/Hzs6u3DH0+/fvW7pEGkMl5evbty++/PJL+Pj4oFWrVsYe6qpVq575cCaAP861rBNKDBkyBAB//keNRvPM6zIckWDQokULXL58+ZmXS8wjNzfX0iXUGhSoFrJ69WqsXLkSU6ZMQVxcHACgYcOGGDNmDD744IMKH1/8p6bbtm0zmdaiRQuTnUyEEPOgQLUQKysrzJ49G7Nnz7Z0KYQQgdBefkIIEQgFKiGECKTWBCqNGdY8tfU9q63Py5Jqymta4wPVcEaj/Px8C1dCqsrwntWWs1LRZ7H6GK7XVdXTV5qbuKurBKlUChcXF+MvPejSveLHil1G2sXFpUafrq04+ixWD47jkJKSAjs7O9F/Vmp8oAIwnqTZ3D+fI8/GxcWlwhNs1zT0WaweMpkMvr6+ov+CqhWBKpFI4OHhATc3t1pzSeLaTqFQiL638TTosyg8iUQChUIh+jAFzByoOTk5mD59Oo4cOQKZTIaxY8di+fLlgv1hSaVSutokEQX6LIpXdeaQWQN16tSpyMnJQVxcHPLy8vDSSy/BxcUFn332mTnLIITUYdWZQxJmpuMRMjIy4ObmhoiICLRq1QoAfxmQGTNm4P79+xU+XkzX3iaEiEdVsuFZc6giZhvECg8Ph1KpND4JAOjWrRsSEhKQlJRkrjIIIXVYdeeQ2Tb5k5OTTa7JDZjuEX3ybOFardbkxMR5eXkA+G8jQggxMGRCXl4ebG1tje1yubzEMc5VzaGqMlugMsZKDPoa7pd2WePg4GAsWrSoRHvTpk2rp0BCSI3m6+trcn/BggUmZ2UDqp5DVWW2QFWpVMjIyDBpM9wvbdxjzpw5mDVrlvG+TqdDYmIiXF1dy90bp1ar4ePjg4SEBJNvKzERe41irw8Qf41irw+oPTVyHIfU1FTUr1/f5JdUpf2qqqo5VFVmC9T27dsjPT0d8fHxxm+SsLAwqFSqEt8sAH+c4pPd9RYtWlS4HsOlaV1cXET9IQHEW6PY6wPEX6PY6wNqV42urq6VWl5Vc6iqzLZTysPDA0OGDMGMGTOQn5+P1NRUzJ07F5MmTaqVB3gTQsSnunPIrEm2ceNGSCQS+Pj4oEWLFujRowcWL15szhIIIXVcdeaQWQ/sd3Fxwa5du6p1HXK5HAsWLBD1WWnEXqPY6wPEX6PY6wPqbo3VmUNmO7CfEEJqOxq8JIQQgVCgEkKIQChQCSFEIBSohBAiEApUQggRCAUqIYQIhAKVEEIEQoFKCCECoUAlhBCBUKASQohAKFAJIUQgFKiEECIQClRCCBEIBSohhAiEApUQQgRCgUoIIQKhQCWEEIFQoBJCiEAoUAkhRCBmDdQJEybA2dkZnp6extv7779vzhIIIaTamPVyh9nZ2di0aROGDx9uztUSQohZmLWHmp2dDWdnZ3OukhBCzIYClRBCBCJhjDFzrSwgIABZWVnQ6XRwdXVFv379MHfuXLi6upaYV6vVQqfTGe9zHIfc3Fw4ODhAIpGYq2RCiMgxxqDRaODs7Ayp1ML72ZkZPXz4kHEcxziOYzdu3GADBgxgnTp1Ynq9vsS8CxYsYADoRje60a1St7S0NHPGWanM2kN90oMHD1C/fn1cvXoVgYGBJtOe7KHm5+fD1dUVaWlpsLW1NXephBCRUqvVUKlUyMvLg1KptGgtZt3L/ySNRgMA8Pb2LjFNoVBAoVCUaLe1taVAJYSUIIahQLMNOFy/fh1r1qxBVlYWACAxMRGTJ0/G6NGjoVKpzFUGIYRUG7MFqre3N65evYo2bdrAw8MDvXv3Rrdu3fDTTz+ZqwRCCKlWFh1DrQq1Wg2lUon8/Hza5CeEGIkpGyw6hkpITcJxHLRaraXLqJOsrKxEMUZaEQpUQirAGENKSgrS09MtXUqdJZPJ0LBhw1J3VIsJBSohFTCEqYeHB5RKZY3oKdUmHMfhwYMHSEpKQoMGDUT9+lOgElIOjuOMYeri4mLpcuosd3d3JCQkgOM4yGQyS5dTJjofKiHlMIyZWvqA8brOsKlf/Mc+YkSBSkgliHkzsy6oKa8/BSohhAiEApWQWm7lypUICgoqc3pubi4kEgmuX79eYtrcuXPx+uuvG++vWbMG9vb2xputrS18fHyM0xcuXIixY8eWua5FixbBx8cHXbp0wZ07d4ztb7/9Nj7++OOqPjXRoUAlpBYrKCjA5s2bUa9evQrn7dixI2xsbExuy5cvN5ln2rRpyM3NNd4OHz5cYjm//fYb5HI5Pv/8c5P2K1euYPv27bh9+zYmTZqEWbNmGdsPHjyI+fPnP8MzFQfay09ILZWQkIC3334b9evXR2ZmJsaMGYPvvvuu1PMPA8CNGzfQpEkTk7a5c+ciKirKeD86OhqhoaHG+8WnGYwfPx5btmwp0R4dHY2OHTvC1tYWPXv2xDfffAMA+PTTTzFv3rxKhb7YUQ+VkFrm0qVLmDVrFgIDA9G2bVvs378fp0+fho+PD5o3b453330XISEhT7XskydPYubMmQgJCUFISAhiY2MxZMiQSj02MDAQly5dQk5ODv766y+0a9cOBw4cwP379zF16tSnqkdsqIdKSBX5f3bQ7OuMXT640vPu378fer0eFy9eNOlxfvnll5gxYwa2b9+Ohw8fGtslEgns7OzQrl27Upc3bNgwk/stW7bEjh07SsxnOB3n1q1bsW3bNsyfP99kM75ly5b44IMP8Pzzz8PPzw/fffcdBg4ciIULF2LSpEm4desWJk6ciEmTJlX6uYoNBSohtczChQvLnObp6Vli54+dnR1yc3Mrvfzbt2/j/fffB8dx0Gg0ePTokXEH08iRIzFmzBhs27at1MdOmzYN06ZNA8Dv4PLy8kJ4eDhatGiB9evXIygoCN27d0fLli0rXY+YUKASUkVV6S2a2/Dhw3Ho0KFKzTtu3Dhs377d2LM04DgOAEpcn+mtt97ClClTMG7cOACAjY0NnJycUK9ePbRs2RKBgYH44osvKrXu7OxsLF68GCEhIXjrrbewfft22NjYoH///vjrr78oUAkhlrdnz54SbQsXLsT169exe/fuEtM2bNgAjUaDdevWYerUqbC2tjbuiCptfgDo0qULMjIysHLlSvzyyy9ISEiAUqlEkyZN4OTkhD59+lRY57JlyzB48GC0bdsWer3e2C6RSET/a6jy0E4pQuq43NxcfPzxx1Cr1ZV+TP/+/XHv3j1s27YN8fHxuHLlCj799FNcuHChwuXEx8fjp59+wpIlSwAA3bt3x59//gmNRoOQkBD06NHjmZ6PJVGgEkJMtGnTBs8991yZ09PS0nDx4kWsWLECLVu2hI2NDZydnTFgwABMmjQJJ0+eLHf5s2fPxocffghPT08A/MH+hw4dQsOGDfHqq6+ic+fOgj4fc6JNfkIIAJQ4DrT4zquYmBj4+/sDAFQqFbp3746ZM2di7ty5aNiwITQaDUJDQ7Fx40b897//LXc9v/76q8l9T09PnD59WpgnYWF0CRRCylFQUIB79+6hUaNGsLa2tnQ5opGVlYXvvvsOhw4dQmJiImxtbdG0aVNMmDABr732muDrK+99EFM2UKASUg4KVHGoKYFKY6iEECIQiwXqRx99BIlEgtjYWEuVQAghgrJIoJ4/fx4RERGWWDUhhFQbswdqQUEB3nvvPXz33XfmXjUhT62G7GqotWrK62/2QF28eDEGDx6MwMBAc6+akCozXMsoPz/fwpXUbYZre8nl4j7S06zVXblyBXv27EF4eHiF82q1WpOfoFXlVxyECEUqlcLFxQXJyckAQJeRtgCO45CSkgI7O7sS5xcQG7MFqk6nw8SJE7F69WrY2NhUOH9wcDAWLVpkhsoIKZ+7uzsAGEOVmJ9MJoOvr6/ov8zMdhzql19+iaioKGzatKlo5RKJyS8wiiuth6pSqURxrBmpmziOM256EvORSCRQKBRlhqmYjkM1W6COGDECISEhJl32rKwsODg4YPTo0Vi/fn25jxfTi0YIEQ8xZYNFfylVXg/1SWJ60Qgh4iGmbBD3CC8hhNQgFj0GoaYcW0YIIZVBPVRCCBEIBSohhAiEApUQQgRCgUoIIQKhQCWEEIFQoBJCiEAoUAkhRCAUqIQQIhAKVEIIEQgFKiGECIQClRBCBEKBSgghAqFAJYQQgVCgEkKIQChQCSFEIBSohBAiEApUQggRCAUqIYQIhAKVEEIEQoFKCCECoUAlhBCBmDVQN27ciA4dOsDDwwMNGjTAxIkTkZWVZc4SCCGk2pg1UI8dO4aff/4ZycnJuHnzJhITE/Hxxx+bswRCCKk2Zg3UHTt2IDAwEADg4OCAgQMH4saNG+YsgRBCqo3FxlCjoqKwadMmjBs3zlIlEEKIoCwSqJ6enggICECXLl0wffr0UufRarVQq9UmN0IIETOLBOrDhw9x69YtREZGYtKkSaXOExwcDKVSabypVCozV0kIIVUjYYwxS638n3/+Qc+ePaFWq2FlZWUyTavVQqfTGe+r1WqoVCrk5+fD1tbW3KUSQszkZFQKopNz8F6vxpWaX61WQ6lUiiIb5OZakV6vh0wmM2mzsrIq0WagUCigUCjMURohpJJuJ+dg+JpzUFrLcOrT3rC1Kv3v92l99n9XsePifQDA4EAvNHBRCrr86ma2QD179ix++uknLFy4EI0bN0ZGRgbmzp2LESNGlOidEkIs7/StR7iTkou3u/sDAHp9dRIJGfy+jJwCHQLmh5R4zB/TuiMttxANXJTwUykRmZQNK7kUdlZyyKQS5BXqEBabgQ6+9eDlZAMbhcwklA1hCgDpeYUUqGXp3r07QkND8eqrryI1NRW2trZ4+eWXsWzZMnOVQEidpC7Uw0YhhUQiKXc+Pcfwz51UjN90waR98YGblV7XsDXnqlxfI1c7fNS3GRqq7IxtQ9p5o20D5yovy9IsOoZaFWIaJyFErHR6DnsuJ2Lm7qtlztO/lQfWvNkRUglwKzkXXx2Jxr8xacjR6Mp8jIGjjRyHPnwe9Z1t8c3x2/j2r9uVqsvXRQk9x5CYWbmjda4u7AdHm8oN+YkpGyhQCanhYlPz8MKKU7CWS1Gg4wRb7rej2uGlAA9EJWVjy7lYvNnFD90al3+0DWMM4fGZWHLwJpq5O2DuywFwKCUYGWNIzytExyXHS11O7PLBla5TTNlAgUqImTDGcDE2A/P2XkePJq7o0USFVt5OkEgABxs5lFbyEvPHpuXj1/Nx+OlsjLFdLpXA1d4aD7M1Fa5z8dDW8Fcpsf70PZy9k1ruvJOea4hO/i4Y0Nrz6Z7gU3qQqUZ8ej7UhXp0a6yCjaJqO7rElA0UqIRUA8YYlodEYf3pe2Zd7xevBaKZhwPaNXAuc8z0yI2H+OnMPSwdFggHGwVc7a0gl9XcE8+JKRsoUAl5QrZGi8w8LZyUCjhYy5GRX4gPd1xBoY7Dhdh0S5cHAHinR0Ns+ofvtf4ysTOeb+pm4YosR0zZQIFK6gzGGPQcQ0pOAfQcw6gfz5vsJJk5oDm+DImutvUf/bgn7K3lcLGzMm7WpuUWYGdYAr4IiYK1XAo/lRK3knONj9n4Vie8FOBRbTXVBmLKBgpUUmPp9PwOmOKbqzo9hwlbLgIAOvm5YNXxW9W2fm8nG/Rp6YEgfxd8ERJlPEYTAJ5v6oqFr7ZCQ5UdpNLyD1ciz0ZM2UCBSixGp+cQcuMhXOys4P/4GMS/IpPhaKtAUpYGAV6OeOuJYyKr2+jOvuA4hkAfJ7g5WEMCoEsjFZxs6Vd7YiWmbDDbgf2k9tPqOYzb+C/O30vH/wa1wNJDURat59W23vgz4oFJ28/vdEa2WotmHg5o5mFf4cHuhFQF9VBJmR7lFMBKJkVyjgb9Vv2NNzr64NC1JNhayTG+mx/a+zpj49kYnIp+ZLaaOvu7QCIBXmzhjox8LeysZFh5jN+sHxXUADP6NoOTUgFrubC/MSfiJaZsoECtA7R6DvkFejgpTTdbs/K1+P7EbUQ+zEZGnhY3k7Lx3/7N8dURYXfM9AnwwPHIZON9Bxs5cjQ6NHG3x7yXWyLAywEp2QVo7GYPqRRIyS6As1IBOys5jT+SCokpGyhQawnGGDgG5BXq0Gbh0VLnCazvhGuJ1XdRxKHtvLH8tTawllf8u3FChCKmbKAx1Brsl/NxmLf3eqXnr2qYfj6kFYZ38IG9dcmPiUarh5VMSj1IQoqhQBUpxhhuJmXj6I3kSp+Aoiw/je8EF3srXEvIwoI/iy6KuGpkWwxs7QU9x2BXSmiWp6o/DySkLqBNfjN7kKnG7xfvQ2VvhQ1n7uF+On/sYj2lAhn5WgCAnZUMeYX6Ki23e2MVbiXnoI2PMwYHeuG1jj6C106IGIkpG6iHKjCNVg+FTIrw+AycuZ2K7yrZuzSEKYBSw9RKLkVhsTMJtfVxwt7pPWiskhARoUB9RvfT87H29F389m+8YMv0cLRGcnYBfn6nM4L865U4CxEhRJzoL7UKGGNYefQWfjh5p0qP6+zvggux6Xi3VyO0b1APPZu5UkgSUgvRX3UFkrM1WHEkGrsuJVRq/tGdG6CBixLju/mXunecEFJ70V98MXkFOuwMu49cjc7465vyuNhZYfvkrmjsZlejzydJCBFGnQ3Uk9EpWHvqLi7EVP78li29HPHTW53g7VxzjzIghFSfWhmohvNe3kzKxpKDkdDqOdxPVyMtrwBVPUjsz/d7oI2Pc7XUSQipXcwaqCdOnMCCBQsQFRUFKysr9OrVC99//z1UqvIv/FVVq0/ewYqjlT8PppuDNd7o6AOtnsPUF5rAxc5K0HoIIXWDWQ/s79atG5YtW4ZevXpBrVZj6NCh8Pf3x48//ljhY6ty8O4r358t92eW47r64T8vNuHPd0nHcRJSo9XZA/vPnDkDuZxfpVKpxNChQ7Fp0ybB1/N/U7vjcnwGWtV3oj3thBCzMWvaGMLUIDQ0FC1bthR8PVZyKbo0EnYYgRBCKmKx7tvBgwexd+9eXLp0qdTpWq0WOp3OeF+tVpc6HyGEiIVFDp6MiorCuHHjsGXLFjRr1qzUeYKDg6FUKo03oXdcEUKI0Mx+tqmEhAT06NEDH330ET7++OMy5yuth6pSqUQx8EwIEY86u1MqPT0d/fv3x5gxY8oNUwBQKBRQKOhKk4SQmsNsm/x5eXkYPHgwunbtimXLlplrtYQQYjZm66EeP34c58+fx927d+Hp6VlUgFyOhITKnXiEEELEjM7YTwip0cSUDXSKJEIIEQgFKiGECIQClRBCBEKBSgghAqFAJYQQgVCgEkKIQChQCSFEIBSohBAiEApUQggRCAUqIYQIhAKVEEIEQoFKCCECoUAlhBCBUKASQohAKFAJIUQgFKiEECIQClRCCBEIBSohhAiEApUQQgRCgUoIIQIxe6DeuXMHzZs3R4sWLcy9akIIqVZmDdSTJ0+iX79+6NSpkzlXSwghZmHWQG3VqhVu3LiBvn37mnO1hBBiFnJzrszd3d2cqyOEELMya6BWhVarhU6nM95Xq9UWrIYQQiom2r38wcHBUCqVxptKpbJ0SYQQUi7RBuqcOXOQn59vvKWlpVm6JEIIKZdoN/kVCgUUCoWlyyCEkEoTbQ+VEEJqGgpUQggRiIQxxixdRGWo1WoolUrk5+fD1tbW0uUQQkRCTNlAPVRCCBGIaHdKPZO/PgfOrATktkD7sUDjFwGfIMDezdKVVT+OAzgtILe2dCWkpmAM0GsBbT7/r1IFSKWm0wFAIuH/5ThAXwgU5PCftYxYvt3OHSjMARR2gMIG0GQBVnaAjTN/k1ai/3b3JHD3L6DFy/zfrFQm4BOtfrVzk3/Ly0DsmZLt1o580DAOaDYQsHcH0u7wb3yjFwDXpoCdG+Ddnp9PrwNykvhbQQ5QkA3kPAQy7xd9kNLuAo7eAKcHVI35ZV/5lV+fRyBgpwLy0wFdAf+B5HT8OgFA1QSQygGJjP/gSGX84zPigA7jAcf6gEwOPIoGGnTha7Otx3+Y9YWATsMvR2bF36Qy4PAsIPU2MOk4/3zT7wI2Tvz8+Wn887iyHfAMBJoNAKyU/Do5Hf98dIWAwhbIjOM/3K2G8etQ2PL1KFX8/AXZ/PrkNvxzBwO0aiD5OuDSGFCnA67N+dfJsT4/n8IWuP8vcP8CcGIJwPTAoBWAlT2gzeNfD2df0z9QpueXr8ni/5VIHv9hS/hpBTn866zNBzSZgEQKHPgYGPgFYO/Bv75SOd8ulfM1FmQDDy4Dt48CzQcChfn86ySRAk4NgH/XAzkPgBfn8Y/RFQCnlwMDvwT8n+Nfa4mUb5fK+dfVxhGQKvjXISkCkCn4IMpP428OnkBiOB8wchsgPxWI3M+/7pos08+p33P8508iLXquEgmQfBPIiufnaTuanx57ln+vACDgFX6ZANC0P6BT869Z3D98W4MufI2FOXyNAGDlwN9/kr0nkPuw/L+zqpJI+c+63AZw9OLf9wfhZc//0TX+81ABMW3y185AzU0Bzn4D3DvJh6E6wyw1ElLnKF35LwcAqOfPdzacG/BfXHmP263tS35pVMQzEBj2I+DRssJZxRSotXOT394dGLDUtI3j+B5MbjIQfZj/lizMe9zLvAPcP1/28jwD+Z6Zwo7vcWbGA24B/Le80oX/5s1PB9q8wX8DZz8Aog4AL87l7+en8R8MQ0+U0/M9EE7P91DY4385ju9p/f0V/80ss+J7YA8u8+0yq6Kentz6cU9Jwm+m6QuLelo6DT/coXv8c125Ld97Tr5u+ry82vHzSuX8v2l3+GU26cv3hDgOcG8BQAKkRAJ5j/g/DomM72lJJHwvrSCXrynvEf8aO9bne/IujYDUaNN1ujTie85JV/j7vt35555wAVAo+edp4FgfsHXh1yO3Bqwd+F4f4wAwIC8NSLvNbyFoskp+cboFFHttdY97uDJ+3oLHf+DtxvI1Rx0AAl7lX/fQH/hpjV/i18X0QMzfjz9bnnyvPiuB7/nLbfnXys696D1Jv8vP6+DFb1U4ePG9xMJcfn7XJoBfD/61azaAn9e2Hv95klvzzxvM9Lkyxr+fBz4Gnv+U/zzqC4DkG/xrnR4DNOoFRB7g63ljC7+ZLVPwn8eY0/zzUyj5GrPu88/VtSnfU5Q9PuZbq+E/C4V5fL356fxnV27Dt2uy+ZqcfYuGACpDr+U/R4zjA9iw1ZZys+g57v8QeO5jIGgS4ORT+WWLSO3soT6r3Ef8h8fKjg9M8mwY4/94CvP4LyRCBEQ9VLGrCzuvzEki4XdSKGwsXQkh1YoOmyKEEIFQoBJCiEAoUAkhRCAUqIQQIhAKVEIIEQgFKiGECIQClRBCBEKBSgghAqFAJYQQgVCgEkKIQChQCSFEIBSohBAiEApUQggRCAUqIYQIxKyBmpOTg/Hjx8PDwwPe3t6YOXMmOI4zZwmEEFJtzHo+1KlTpyInJwdxcXHIy8vDSy+9BBcXF3z22WfmLIMQQqqF2c7Yn5GRATc3N0RERKBVq1YAgF27dmHGjBm4f/9+hY8X01m5CSHiIaZsMNsmf3h4OJRKpTFMAaBbt25ISEhAUlKSucoghJBqY7ZN/uTkZLi5mV5axN3d3TjNy8vLZJpWq4VOpzPez8/nL96mVquruVJCSE1iyAQxXB7PbIHKGINUatohNtwvbcdUcHAwFi1aVKJdpaKLvBFCStJoNFAqlRatwWxjqCEhIRg7dixSU1ONbY8ePYK7uztiYmLg7+9vMv+TPVSO45CbmwsHBwdIyrl8rVqthkqlQlpamsXHU8oi9hrFXh8g/hrFXh9Qe2pkjEGj0cDZ2blEp83czNZDbd++PdLT0xEfHw9fX18AQFhYGFQqlfF+cQqFAgqFwqTNzs6u0uuztbUV7YfEQOw1ir0+QPw1ir0+oHbUaOmeqYHZ4tzDwwNDhgzBjBkzkJ+fj9TUVMydOxeTJk2y+LcKIYQIwaxJtnHjRkgkEvj4+KBFixbo0aMHFi9ebM4SCCGk2pj1wH4XFxfs2rWrWtchl8uxYMECyOVmfWpVIvYaxV4fIP4axV4fQDVWB7PtlCKEkNqOBi8JIUQgFKiEECIQClQiuJowiiT2GuksbDUTBSoRjCEEyvvhhaUZglSsNWZkZADgf0Uo1lCNiYnBlStXLF1GmeLi4nDx4kUUFhaafd0UqABu3bqFdevWYd68eYiIiEBaWpqlSzKRlpaGlJQUS5dRrqioKMyePRuTJ0/GiRMnkJuba+mSSoiOjsaiRYswdepUHDlyBHl5eZYuyURMTAw6d+6MBQsWABBnqF6/fh1t27ZFbGyspUsp1fXr1xEUFISwsDBkZ2cDMO/WSJ0P1Bs3bqB3796Ijo7G8ePH8eGHH+I///kPoqOjLV0aAOD+/fvo0KEDVq5cWanTHFrCtWvX0LdvX9jY2CAuLg5z587F2bNnAYhn0/r69evo378/OI5DTEwM5s+fj/j4eEuXZSI7OxtqtRqnT5/GrFmzAIgrVK9fv46XX34ZX3zxBYYOHQpAPO8vADx48ADDhg3DvHnzMHXqVLi6ugIw89YIq8N0Oh17/fXX2dKlS41te/bsYWPGjGGdO3dmN27csGB1vNDQUObl5cV69+7NFi1axO7fv2/pkkykpqayoKAgtnbtWmPbqFGjWP/+/S1YlakHDx6wwMBA9uOPPxrbOnXqxH766ScLVmVKr9ez2NhY1rNnT7Z582Y2aNAg9r///c84neM4C1bH2M2bN5mbmxvbuXOnSXtBQQHT6XRMrVZbqLIiJ0+eZK+99hpjjP/b/vrrr9mcOXPY5s2bWUxMjFlqqPM91MzMTDRs2NB4f9iwYZgxYwaaNWuG9957D3fv3rVgdcCZM2cwbtw4jBw5EocOHcKmTZtE1VPNyspCkyZNMG7cOOj1egDA5MmTkZ6eDsaYKHowcXFxCAoKwuTJk401+vr64vbt2xgyZAi+/vpri2/CSqVS+Pn5ISAgAH5+fhg/fjzOnz+P2bNnA+CHfQybsJYQFhaG1NRU9O3b19i2detWTJ8+HT169MCUKVNw8+ZNi9UHALm5ucb3NygoCH///TciIiKwfft2jBw5Ejdu3Kj2Gup0oMpkMgQEBGDz5s3IzMw0tnfs2BHTpk2Dh4cHNm7caHyTLKFXr1745JNP8O6772LMmDE4dOgQNm/eLJpQ5TgOL7zwAuzs7CCTyQAArq6uiI+PR3JysskZwyzFzc0Nr7zyCgD+Pd+5cyf++OMPNG/eHCqVCn///TeWL19uPOeuJXAcB8YYsrOzcfv2bbz++uuYOHEirl27hpEjR6Jt27bIysqy2BfUuHHjsHDhQnTr1g25ubnYsGEDFi1ahO7du6Nv377Q6XSYP3++RcfOnZyccOXKFWzevBk9e/bEH3/8gf379+OLL75AQEAAFixYUP31maUfLCKGTSetVssYY+zYsWOsf//+bMWKFSwnJ8dk3m+++YYFBgay/Px8i9XHGL85aPDDDz+wLl26sEWLFrH4+Hiz1VXck5ufhvuGOuPj41mLFi1YamqqcZ7Q0FD26NEji9VooNfr2eXLl1lSUpKxbevWraxly5YmbdXNUJ9OpzNp379/P5szZ47x/pw5c5itrS179dVXzVabgaHGwsJCY9vnn3/O7O3tWcOGDVlsbKyx/cCBA6x169bswYMHZq+v+Gs4b9481rt3bzZ37lzGWNFnct++fax169YsMTGxWmuqUz3U6OhozJw5E5mZmcbfBvfp0wedO3fGgQMH8OuvvyIrK8s4/9ixY2FtbW08lMXc9Rl6xsV3TEyfPh3jxo3DoUOH8PPPPyMuLs4stZVWI8D3rAyD/oazhtWrVw8KhQJarRYAsHbtWkydOtVsh7GUVqOBVCpFu3bt4OnpaTzT+/Dhw+Ho6Gi2q0EUr08mk5nUp1Qq8ccffwDgh3tWr16Nd999F+np6fj888/NUt+TNSoUCuNn8YMPPsDMmTPx559/ws/Pz/iaderUCY6OjhapTyaTGbeEunfvDo1Gg2PHjiE7O9v4mRw0aBAcHByq/8iOao1rkRk4cCDz9PRkEydOZBkZGSbTZsyYwfr06cPmzZtn/JZdvXo1a9WqVYl5zVlf8d5p8f+vWbOGNW/enH3xxRcmvVlL18gYY2lpaczLy4ulpKSwDRs2sAYNGrDw8HDR1PhkvevWrWPt27dn6enpFqvP0MvS6/Xs3XffZTt37mQqlYrt2bOHMcbY7t27TXqElqjR0CPUaDQm9xnjP4/PP/88y8rKslh9Bhs2bGC9evVi//nPf1hCQoKxLTAwkKWlpVVrXXXq5CgDBgyAn58f4uPj4enpiVWrVsHZ2dk4/euvv8bZs2dx4sQJ9O3bF2fOnMHhw4fRvn17i9bHcZzJ5WIM///pp5/Qp0+fElc7sHSNarUa/fv3R6tWrXD48GHs27cPbdu2FVWNAHDp0iWcOnUKq1atwsGDB81WY0X19ejRA6Ghodi5cydef/11AIBerzeOUYuhRoOMjAxs27YNy5cvx+HDh9GmTRuL1mewfft2HD16FHv27EGfPn1w8eJF7Nu3r/r/lqs1rkUkPj6eLV26lOn1evb777+zAQMGsLfffrvEt1t2djbbs2cPO3bsGLt3755o6iuvhyXGGtu0acM8PT3Z1atXRVWjoSf4+++/s2nTprHRo0eza9euiaY+xhjLyspiFy5cYIzxvUBzHzJV2dfw/PnzbMmSJaxnz54sIiJCNPUVd+TIEXbhwgUWFxdnltrqTKAyxlhKSgpjjD92btu2bSXeCEsf61dRfZYK0uIqqtHwx/bVV1+x6OhoUdZoUFBQYNYdjlWtjzHLfSYrW2NiYmK1b0Y/TX1P7uwzlzq1yV8cx3HYvn07tm3bZrLJYO5Nq6rW9+QmlyWVVaOYiP11LF6fh4cHvvnmG1HVB4j/fRbTe1zrA5UxVuKnZ4Y2wxuxY8cOWFtbY+PGjXBycqL6qMY6V19NqFHs9QFmvgSKOdy9exd79uyBWq3Gc889hxdffLHEPBKJBIwxSKVSjB49GhqNBiEhIcjLy6v2N0Hs9VGNdaO+mlCj2OsrldkGF8zg6tWrzM/Pj02YMIENGzaMeXh4lHu4TvED0s1xuIfY66Ma60Z9NaFGsddXlloTqAkJCSwgIICtX7+eMcZYUlIS69OnDztz5ky5jzPXoL/Y62OMahSC2OtjTPw1ir2+8ohj1FsA169fR9++fTFlyhQAgKenJziOw+HDhzFhwgRs27at1MeZ69ReYq+Paqwb9dWEGsVeX3lqTaD6+PjA19fXeP/bb79FaGgoAMDGxgbjx4/H119/banyRF8fQDUKQez1AeKvUez1lcvSXeTqsn//fpOf6u3YsYN5eXkZf4pmaWKvjzGqUQhir48x8dco9vqKq7GBeuvWLfbrr7+ygoICY1tpvyrRaDSM4zh248YN1rt3b7MNWIu9PqqxbtRXE2oUe31VUeMOm2KMQaPR4M0330RYWBjy8/PxzjvvQCqVmoyhGA7qtba2BgCEhoZCp9NV++UkxF4f1Vg36qsJNYq9vqdiwTB/JmPHjmVTpkxhEomErV69usyfZV6+fJmtXr2aeXt7sytXrlB9VGOdqq8m1Cj2+qqixgWqXq9nMTExbNCgQYwxxjZu3FjqG8FxHDt27BgbOHAgGzZsmNlO0iH2+qjGulFfTahR7PU9jRoXqIZxleIntVi7dm2Z325paWklzsRfl+ujGutGfTWhRrHX9zRqXKAWV3zQurw3wlLEXh9jVKMQxF4fY+KvUez1VVaNDlTGTE9pt3btWmZlZcVWrlwpmjdC7PUxRjUKQez1MSb+GsVeX2XUuEA1nOew+GU/in+7rVq1irm4uJjtchZPEnt9jFGNQhB7fYyJv0ax1/c0alSgGt6Ae/fusXfeecfkKprF3whzXQPqSWKvjzGqUQhir48x8dco9vqeVo356anhxM/x8fHo168ffHx84OrqapxuOI0XAIuctkvs9VGNdaO+mlCj2Ot7JhYM81Kp1eoSe/IMv6CIjY1lzZo1Y8uWLTNOM/cZZsReH2NUY12ojzHx1yj2+qqDqAI1IiKC9evXj3Xq1ImNGzeOfffdd8ZpiYmJzNvbmwUHBxvbzD1YLfb6qMa6UV9NqFHs9VUX0QTq3bt3ma+vL1u7di07ceIEmzlzJuvSpQsbOXIkY4yx6Oho9ttvvxnnN/cbIPb6qMa6UV9NqFHs9VUn0QTq7t272YgRI4z38/Ly2PHjx1lAQAAbOnSosZ3jOIu8AWKvjzGqsS7Ux5j4axR7fdVJNDulUlJScPPmTeN9pVKJF198EZs3b8adO3cwefJkAPyAtSWuBin2+qjGulFfTahR7PVVK0snuuHwiUePHrFmzZqx5cuXm0zXarXsjz/+YL1792YXLlyg+kpBNdb++hgTf41ir88cLPb1kJmZCQCQyWTQ6XRwdHTEpEmTcOrUKfz666/G+eRyOXr06IHExERERERQfVRjnaqvJtQo9vrMySKBevfuXbRu3Rrz588HwL/QVlZWGDFiBPz8/LBjxw5s2bLFOL+bmxu6du0KhUJB9VGNdaa+mlCj2OszO0t0iw8cOMBat27NAgMD2fz5802m3b59m33wwQesZ8+e7IMPPmARERFsxYoVrF69euz27dtUH9VYZ+qrCTWKvT5zs0igzps3j40cOZL9+OOPLCAgoMQbkZSUxHbt2sU6dOjAevXqxbp06cIuX75M9VGNdaq+mlCj2Oszt/8HqE9Tq17Lv2oAAAAASUVORK5CYII=",
      "text/html": [
       "\n",
       "            <div style=\"display: inline-block;\">\n",
       "                <div class=\"jupyter-widgets widget-label\" style=\"text-align: center;\">\n",
       "                    Figure\n",
       "                </div>\n",
       "                <img 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' width=340.15748031496065/>\n",
       "            </div>\n",
       "        "
      ],
      "text/plain": [
       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, (axe1, axe2) = plt.subplots(2, 1, figsize=fig_dim, sharex=True, dpi=fig_dpi)\n",
    "twin = axe1.twinx()\n",
    "\n",
    "plot_convert_two(axe1, twin, axe2, ldg_pp_1013)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4bc37594",
   "metadata": {},
   "source": [
    "##  9보일러 전단 데이터 (22-10-19 15:00~ )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "8683d6fa",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x246dc1c4cd0>"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "05872e1f2e5a43afa3d3574eb64bf214",
       "version_major": 2,
       "version_minor": 0
      },
      "image/png": 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",
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       "\n",
       "            <div style=\"display: inline-block;\">\n",
       "                <div class=\"jupyter-widgets widget-label\" style=\"text-align: center;\">\n",
       "                    Figure\n",
       "                </div>\n",
       "                <img 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' width=340.15748031496065/>\n",
       "            </div>\n",
       "        "
      ],
      "text/plain": [
       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, (axe1, axe2) = plt.subplots(2, 1, figsize=fig_dim, sharex=True, dpi=fig_dpi)\n",
    "twin = axe1.twinx()\n",
    "\n",
    "plot_convert_two(axe1, twin, axe2, ldg_pp_1019)\n",
    "axe2.set_xlim(pd.Timestamp(\"2022.10.19-15:00\"), pd.Timestamp(\"2022.10.21\"))\n",
    "axe1.legend()\n",
    "axe2.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "2cc55e94",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b19a1109dd074bb3a57621dbd4e43f6d",
       "version_major": 2,
       "version_minor": 0
      },
      "image/png": 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",
      "text/html": [
       "\n",
       "            <div style=\"display: inline-block;\">\n",
       "                <div class=\"jupyter-widgets widget-label\" style=\"text-align: center;\">\n",
       "                    Figure\n",
       "                </div>\n",
       "                <img 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' width=331.2/>\n",
       "            </div>\n",
       "        "
      ],
      "text/plain": [
       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "plt.plot(ldg_pp_1019.timestamp, ldg_pp_1019.CH3.apply(lambda row: (float(row[1:]))))\n",
    "plt.plot(ldg_pp_1019.timestamp, ldg_pp_1019.CH6.apply(lambda row: (float(row[1:]))))\n",
    "plt.gca().tick_params(axis='x', labelrotation = 45)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8993dcdb",
   "metadata": {},
   "source": [
    "##  9보일러 전단 데이터 (22-10-26 15:00~ )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "a0cc8692",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x246dadd20d0>"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b9a47bc202ea43118c55a49916d999d8",
       "version_major": 2,
       "version_minor": 0
      },
      "image/png": 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",
      "text/html": [
       "\n",
       "            <div style=\"display: inline-block;\">\n",
       "                <div class=\"jupyter-widgets widget-label\" style=\"text-align: center;\">\n",
       "                    Figure\n",
       "                </div>\n",
       "                <img 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' width=340.15748031496065/>\n",
       "            </div>\n",
       "        "
      ],
      "text/plain": [
       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, (axe1, axe2) = plt.subplots(2, 1, figsize=fig_dim, sharex=True, dpi=fig_dpi)\n",
    "twin = axe1.twinx()\n",
    "\n",
    "plot_convert_two(axe1, twin, axe2, ldg_pp_1026)\n",
    "axe2.set_xlim(pd.Timestamp(\"2022.10.26-10:30\"), pd.Timestamp(\"2022.10.28-11:00\"))\n",
    "axe1.legend()\n",
    "axe2.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "86b0758a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "958db3f530e84f8b99f7ce0eeafc3208",
       "version_major": 2,
       "version_minor": 0
      },
      "image/png": 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",
      "text/html": [
       "\n",
       "            <div style=\"display: inline-block;\">\n",
       "                <div class=\"jupyter-widgets widget-label\" style=\"text-align: center;\">\n",
       "                    Figure\n",
       "                </div>\n",
       "                <img 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' width=331.2/>\n",
       "            </div>\n",
       "        "
      ],
      "text/plain": [
       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "plt.plot(ldg_pp_1026.timestamp, ldg_pp_1026.CH3.apply(lambda row: (float(row[1:]))))\n",
    "plt.plot(ldg_pp_1026.timestamp, ldg_pp_1026.CH6.apply(lambda row: (float(row[1:]))))\n",
    "plt.gca().tick_params(axis='x', labelrotation = 45)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1fea9115",
   "metadata": {},
   "source": [
    "##  9보일러 전단 데이터 (22-11-03 15:00~ )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "60d26b0a",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x246db4e79a0>"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "8c3ffda066e9409ab2be2d7ad72cb15d",
       "version_major": 2,
       "version_minor": 0
      },
      "image/png": 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",
      "text/html": [
       "\n",
       "            <div style=\"display: inline-block;\">\n",
       "                <div class=\"jupyter-widgets widget-label\" style=\"text-align: center;\">\n",
       "                    Figure\n",
       "                </div>\n",
       "                <img 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' width=340.15748031496065/>\n",
       "            </div>\n",
       "        "
      ],
      "text/plain": [
       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, (axe1, axe2) = plt.subplots(2, 1, figsize=fig_dim, sharex=True, dpi=fig_dpi)\n",
    "twin = axe1.twinx()\n",
    "\n",
    "plot_convert_two(axe1, twin, axe2, ldg_pp_1103)\n",
    "axe2.set_xlim(pd.Timestamp(\"2022.11.03-10:30\"), pd.Timestamp(\"2022.11.04-10:30\"))\n",
    "axe1.legend()\n",
    "axe2.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "acf5e688",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "52f72e6e68d045649469d101b4d883e3",
       "version_major": 2,
       "version_minor": 0
      },
      "image/png": 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",
      "text/html": [
       "\n",
       "            <div style=\"display: inline-block;\">\n",
       "                <div class=\"jupyter-widgets widget-label\" style=\"text-align: center;\">\n",
       "                    Figure\n",
       "                </div>\n",
       "                <img 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' width=331.2/>\n",
       "            </div>\n",
       "        "
      ],
      "text/plain": [
       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "plt.plot(ldg_pp_1103.timestamp, ldg_pp_1103.CH3.apply(lambda row: (float(row[1:]))))\n",
    "plt.plot(ldg_pp_1103.timestamp, ldg_pp_1103.CH6.apply(lambda row: (float(row[1:]))))\n",
    "plt.gca().tick_params(axis='x', labelrotation = 45)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "96ed7977",
   "metadata": {},
   "source": [
    "##  9보일러 전단 데이터 (22-11-22 10:00~ )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "cd0ae4a8",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'weather11' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Input \u001b[1;32mIn [48]\u001b[0m, in \u001b[0;36m<cell line: 7>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      4\u001b[0m plot_convert_two(axe1, twin, axe2, ldg_pp_1122a)\n\u001b[0;32m      5\u001b[0m \u001b[38;5;66;03m#axe2.set_xlim(pd.Timestamp(\"2022.11.03-10:30\"), pd.Timestamp(\"2022.11.04-10:30\"))\u001b[39;00m\n\u001b[1;32m----> 7\u001b[0m axe1\u001b[38;5;241m.\u001b[39mplot(\u001b[43mweather11\u001b[49m\u001b[38;5;241m.\u001b[39mtime, weather11\u001b[38;5;241m.\u001b[39mrh, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m:\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[0;32m      9\u001b[0m twin\u001b[38;5;241m.\u001b[39mplot(ldg_pp_1122a\u001b[38;5;241m.\u001b[39mtimestamp, ldg_pp_1122a\u001b[38;5;241m.\u001b[39mTamb \u001b[38;5;241m-\u001b[39m \u001b[38;5;241m273.15\u001b[39m)\n\u001b[0;32m     10\u001b[0m twin\u001b[38;5;241m.\u001b[39mplot(weather11\u001b[38;5;241m.\u001b[39mtime, weather11\u001b[38;5;241m.\u001b[39mtemperature)\n",
      "\u001b[1;31mNameError\u001b[0m: name 'weather11' is not defined"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "dbc5ef88f04546b3a4a1784ad5c594d2",
       "version_major": 2,
       "version_minor": 0
      },
      "image/png": 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",
      "text/html": [
       "\n",
       "            <div style=\"display: inline-block;\">\n",
       "                <div class=\"jupyter-widgets widget-label\" style=\"text-align: center;\">\n",
       "                    Figure\n",
       "                </div>\n",
       "                <img 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' width=340.15748031496065/>\n",
       "            </div>\n",
       "        "
      ],
      "text/plain": [
       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, (axe1, axe2) = plt.subplots(2, 1, figsize=fig_dim, sharex=True, dpi=fig_dpi)\n",
    "twin = axe1.twinx()\n",
    "\n",
    "plot_convert_two(axe1, twin, axe2, ldg_pp_1122a)\n",
    "#axe2.set_xlim(pd.Timestamp(\"2022.11.03-10:30\"), pd.Timestamp(\"2022.11.04-10:30\"))\n",
    "\n",
    "axe1.plot(weather11.time, weather11.rh, ':')\n",
    "\n",
    "twin.plot(ldg_pp_1122a.timestamp, ldg_pp_1122a.Tamb - 273.15)\n",
    "twin.plot(weather11.time, weather11.temperature)\n",
    "\n",
    "axe2.set_xlim(ldg_pp_1122a.timestamp.iloc[0], ldg_pp_1122a.timestamp.iloc[-1])\n",
    "\n",
    "axe1.legend()\n",
    "axe2.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "44a167eb",
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "fig, (axe1, axe2) = plt.subplots(2, 1, figsize=fig_dim, sharex=True, dpi=fig_dpi)\n",
    "twin = axe1.twinx()\n",
    "\n",
    "plot_convert_two(axe1, twin, axe2, ldg_pp_1122b)\n",
    "# axe2.set_xlim(pd.Timestamp(\"2022.11.03-10:30\"), pd.Timestamp(\"2022.11.04-10:30\"))\n",
    "\n",
    "axe1.plot(weather11.time, weather11.rh, ':')\n",
    "\n",
    "twin.plot(weather11.time, weather11.temperature)\n",
    "twin.plot(ldg_pp_1122a.timestamp, ldg_pp_1122a.Tamb - 273.15)\n",
    "\n",
    "axe2.set_xlim(ldg_pp_1122b.timestamp.iloc[0], ldg_pp_1122b.timestamp.iloc[-1])\n",
    "\n",
    "# axe1.legend()\n",
    "# axe2.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "42fd8510",
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.figure()\n",
    "plt.plot(ldg_pp_1122a.timestamp, ldg_pp_1122a.lpm1)\n",
    "plt.plot(ldg_pp_1122a.timestamp, ldg_pp_1122a.lpm2)\n",
    "plt.gca().tick_params(axis='x', labelrotation = 45)\n",
    "\n",
    "# plt.twinx()\n",
    "plt.plot(ldg_pp_1122b.timestamp, ldg_pp_1122b.lpm1, ':')\n",
    "plt.plot(ldg_pp_1122b.timestamp, ldg_pp_1122b.lpm2, ':')\n",
    "'''\n",
    "plt.plot(ldg_pp_1122b.timestamp, ldg_pp_1122b.lpm2 + np.interp(ldg_pp_1122b.timestamp, ldg_pp_1122a.timestamp, ldg_pp_1122a.lpm1), ':')\n",
    "plt.plot(ldg_pp_1122b.timestamp, ldg_pp_1122b.lpm1 - ldg_pp_1122b.lpm2, ':')'''\n",
    "# plt.gca().tick_params(axis='x', labelrotation = 45)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "619e130a",
   "metadata": {},
   "outputs": [],
   "source": [
    "ldg_pp_1019[\"H2O %\"] = 100*ldg_pp_1019.vp1 / (ct.one_atm + 0.4 * 997 * 9.80665)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d548edd8",
   "metadata": {},
   "outputs": [],
   "source": [
    "ldg_pp_1019[[\"timestamp\", \"T1\", \"RH1\", \"vp1\", \"H2O %\"]].to_csv('./221019/vapor1019.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "589c354a",
   "metadata": {},
   "source": [
    "# 압력 Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ed635a3e",
   "metadata": {},
   "outputs": [],
   "source": [
    "def mmAq2Pa (p):\n",
    "    return p * 9.80638"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "13e193d4",
   "metadata": {},
   "outputs": [],
   "source": [
    "pressure0922 = pd.read_csv(\"./220922/Pressure/LDG_pressure_220922.csv\", sep='\\t', parse_dates=['time'], )\n",
    "pressure0922['p_boiler_SI'] = pressure0922.apply(lambda row: mmAq2Pa(row.Boiler), axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "32a95518",
   "metadata": {},
   "outputs": [],
   "source": [
    "pressure0922"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "24f6b1cd",
   "metadata": {},
   "outputs": [],
   "source": [
    "fig, axe = plt.subplots()\n",
    "axe.plot(pressure0922.time, pressure0922.p_boiler_SI)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d7dc3f1e",
   "metadata": {},
   "source": [
    "# 3제강 LDG 홀더 인입 유량 Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fc70b7af",
   "metadata": {},
   "outputs": [],
   "source": [
    "flowrate0922 = pd.read_csv(\"./220922/Pressure/LDG_flowrate_220922.csv\", sep='\\t', parse_dates=['time'], )\n",
    "#flowrate0922['p_boiler_SI'] = pressure0922.apply(lambda row: mmAq2Pa(row.Boiler), axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7f3f7788",
   "metadata": {},
   "outputs": [],
   "source": [
    "fig, axe = plt.subplots()\n",
    "axe.plot(flowrate0922.time, flowrate0922.converter1+flowrate0922.converter2)\n",
    "axe.tick_params(axis='x', labelrotation = 45)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0d1e10e4",
   "metadata": {},
   "source": [
    "# LDG 발열량 계산 방법"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "052c4ab6",
   "metadata": {},
   "source": [
    "## 기상데이터 - 공기 수분량"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a2517c6f",
   "metadata": {},
   "outputs": [],
   "source": [
    "weather09 = pd.read_csv('./220922/Weather/202209-weather-GY-second.csv', sep='\\t', parse_dates=['time'])\n",
    "#display(weather0922[weather0922.isna().any(axis=1)])\n",
    "weather09.temperature.interpolate(inplace=True)\n",
    "weather09.precipitation.interpolate(inplace=True)\n",
    "weather09['vp'] = weather09.apply(lambda row: vp_from_T_rh(row.temperature + 273.15, row.rh / 100.), axis=1)\n",
    "weather09['XH2O'] = weather09.apply(lambda row: row.vp/100/row.atm_sea, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b9c44503",
   "metadata": {},
   "outputs": [],
   "source": [
    "weather09"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9041b52d",
   "metadata": {},
   "outputs": [],
   "source": [
    "weather10 = pd.read_csv('./202210-weather-GY-minute.csv', sep='\\t', parse_dates=['time'])\n",
    "#display(weather0922[weather0922.isna().any(axis=1)])\n",
    "weather10.temperature.interpolate(inplace=True)\n",
    "weather10.precipitation.interpolate(inplace=True)\n",
    "weather10['vp'] = weather10.apply(lambda row: vp_from_T_rh(row.temperature + 273.15, row.rh / 100.), axis=1)\n",
    "weather10['XH2O'] = weather10.apply(lambda row: row.vp/100/row.atm_sea, axis=1)\n",
    "weather10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4fad1e5e",
   "metadata": {},
   "outputs": [],
   "source": [
    "weather11 = pd.read_csv('./202211-weather-GY-minute.csv', sep='\\t', parse_dates=['time'])\n",
    "#display(weather0922[weather0922.isna().any(axis=1)])\n",
    "weather11.temperature.interpolate(inplace=True)\n",
    "weather11.precipitation.interpolate(inplace=True)\n",
    "weather11['vp'] = weather11.apply(lambda row: vp_from_T_rh(row.temperature + 273.15, row.rh / 100.), axis=1)\n",
    "weather11['XH2O'] = weather11.apply(lambda row: row.vp/100/row.atm_sea, axis=1)\n",
    "weather11"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0a3c6e1c",
   "metadata": {},
   "outputs": [],
   "source": [
    "raw_column_names = '지점,일시,기온(°C),누적강수량(mm),풍향(deg),풍속(m/s),현지기압(hPa),해면기압(hPa),습도(%),일사(MJ/m^2),일조(Sec)'.split(',')\n",
    "eng_column_names = 'location time\ttemperature\tprecipitation\twind_direction\twind_speed\tatm_post\tatm_sea\trh\tradiation\tsunshine'.split()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8ee20a6c",
   "metadata": {},
   "outputs": [],
   "source": [
    "{x:y for x, y in zip(raw_column_names, eng_column_names)}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6d37c125",
   "metadata": {},
   "outputs": [],
   "source": [
    "weather11 = pd.read_csv('./SURFACE_ASOS_266_MI_2022-11_2022-11_2022.csv', parse_dates=['일시'], encoding='cp949')\n",
    "weather11.rename(columns={x:y for x, y in zip(raw_column_names, eng_column_names)}, inplace=True)\n",
    "weather11.drop(columns='location', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8c150831",
   "metadata": {},
   "outputs": [],
   "source": [
    "weather12 = pd.read_csv('./SURFACE_ASOS_266_MI_2022-12_2022-12_2022.csv', parse_dates=['일시'], encoding='cp949')\n",
    "weather12.rename(columns={x:y for x, y in zip(raw_column_names, eng_column_names)}, inplace=True)\n",
    "weather12.drop(columns='location', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d617dd43",
   "metadata": {},
   "outputs": [],
   "source": [
    "weather11.wind_speed.isna().any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4ee425af",
   "metadata": {},
   "outputs": [],
   "source": [
    "weather11.temperature.size - 30 * 24 * 60"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "60d37252",
   "metadata": {},
   "outputs": [],
   "source": [
    "weather12.temperature.size - 25 * 24 * 60"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9879e33d",
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.vlines(weather11.time, 0, 1, linewidth=0.01)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1330e7df",
   "metadata": {},
   "source": [
    "### 대기 상대 습도"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "63d2e889",
   "metadata": {},
   "outputs": [],
   "source": [
    "fig, axe = plt.subplots()\n",
    "axe.plot(weather09.time, weather09.rh)\n",
    "axe.set_xlim(ldg_pp_raw.timestamp.iloc[0], ldg_pp_raw.timestamp.iloc[-1])\n",
    "axe.tick_params(axis='x', labelrotation = 45)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "981f8e83",
   "metadata": {},
   "outputs": [],
   "source": [
    "fig, axe = plt.subplots()\n",
    "axe.plot(weather10.time, weather10.rh)\n",
    "axe.set_xlim(ldg_pp_1013.timestamp.iloc[0], ldg_pp_1013.timestamp.iloc[-1])\n",
    "axe.tick_params(axis='x', labelrotation = 45)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "40830101",
   "metadata": {},
   "outputs": [],
   "source": [
    "fig, axe = plt.subplots()\n",
    "axe.plot(weather10.time, weather10.rh)\n",
    "axe.set_xlim(ldg_pp_1019.timestamp.iloc[0], ldg_pp_1019.timestamp.iloc[-1])\n",
    "axe.tick_params(axis='x', labelrotation = 45)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7ea27051",
   "metadata": {},
   "outputs": [],
   "source": [
    "fig, axe = plt.subplots()\n",
    "axe.plot(weather10.time, weather10.rh)\n",
    "axe.set_xlim(ldg_pp_1026.timestamp.iloc[0], ldg_pp_1026.timestamp.iloc[-1])\n",
    "axe.tick_params(axis='x', labelrotation = 45)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "49ae67ba",
   "metadata": {},
   "outputs": [],
   "source": [
    "fig, axe = plt.subplots()\n",
    "axe.plot(weather11.time, weather11.rh)\n",
    "axe.set_xlim(ldg_pp_1103.timestamp.iloc[0], ldg_pp_1103.timestamp.iloc[-1])\n",
    "axe.tick_params(axis='x', labelrotation = 45)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7e74b90a",
   "metadata": {},
   "source": [
    "### 대기 온도"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "834ac5cc",
   "metadata": {},
   "outputs": [],
   "source": [
    "fig, axe = plt.subplots()\n",
    "axe.plot(weather09.time, weather09.temperature, '-o')\n",
    "axe.set_xlim(ldg_pp_raw.timestamp.iloc[0], ldg_pp_raw.timestamp.iloc[-1])\n",
    "axe.tick_params(axis='x', labelrotation = 45)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0f829d1f",
   "metadata": {},
   "outputs": [],
   "source": [
    "fig, axe = plt.subplots()\n",
    "axe.plot(weather10.time, weather10.temperature)\n",
    "axe.set_xlim(ldg_pp_1013.timestamp.iloc[0], ldg_pp_1013.timestamp.iloc[-1])\n",
    "axe.tick_params(axis='x', labelrotation = 45)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a11babab",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "fig, axe = plt.subplots()\n",
    "axe.plot(weather10.time, weather10.temperature)\n",
    "axe.set_xlim(ldg_pp_1019.timestamp.iloc[0], ldg_pp_1019.timestamp.iloc[-1])\n",
    "axe.tick_params(axis='x', labelrotation = 45)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cd06afe4",
   "metadata": {},
   "outputs": [],
   "source": [
    "fig, axe = plt.subplots(figsize=(12, 4))\n",
    "\n",
    "axe.plot(weather09.time, weather09.temperature)\n",
    "axe.plot(weather10.time, weather10.temperature)\n",
    "axe.plot(weather11.time, weather11.temperature)\n",
    "axe.plot(weather12.time, weather12.temperature)\n",
    "axe.tick_params(axis='x', labelrotation = 45)\n",
    "\n",
    "axe.axvline(np.datetime64('2022-09-22'))\n",
    "axe.axvline(np.datetime64('2022-09-26'))\n",
    "axe.axvline(np.datetime64('2022-10-13'))\n",
    "axe.axvline(np.datetime64('2022-10-19'))\n",
    "axe.axvline(np.datetime64('2022-10-26'))\n",
    "axe.axvline(np.datetime64('2022-11-03'))\n",
    "axe.axvline(np.datetime64('2022-11-22'))\n",
    "\n",
    "axe.axvspan(ldg_pp_1026.timestamp.iloc[0], ldg_pp_1026.timestamp.iloc[-1])\n",
    "\n",
    "axe.set_xlim(\n",
    "    np.datetime64('2022-09-16'),\n",
    "    np.datetime64('2022-12-01'),)\n",
    "\n",
    "fig.suptitle(\"9, 10, 11, 12 월 광양 기온\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6031b210",
   "metadata": {},
   "outputs": [],
   "source": [
    "fig, axes = plt.subplots(1, 6, figsize=(12, 4), sharey='row')\n",
    "\n",
    "axe = axes.ravel()[0]\n",
    "axe.plot(weather09.time, weather09.temperature, '-o')\n",
    "axe.set_xlim(ldg_pp_raw.timestamp.iloc[0], ldg_pp_raw.timestamp.iloc[-1])\n",
    "axe.tick_params(axis='x', labelrotation = 45)\n",
    "\n",
    "axe = axes.ravel()[1]\n",
    "axe.plot(weather09.time, weather09.temperature, '-o')\n",
    "axe.set_xlim(ldg_pp_0926.timestamp.iloc[0], ldg_pp_0926.timestamp.iloc[-1])\n",
    "axe.tick_params(axis='x', labelrotation = 45)\n",
    "\n",
    "axe = axes.ravel()[2]\n",
    "axe.plot(weather10.time, weather10.temperature)\n",
    "axe.set_xlim(ldg_pp_1013.timestamp.iloc[0], ldg_pp_1013.timestamp.iloc[-1])\n",
    "axe.tick_params(axis='x', labelrotation = 45)\n",
    "\n",
    "axe = axes.ravel()[3]\n",
    "axe.plot(weather10.time, weather10.temperature)\n",
    "axe.set_xlim(ldg_pp_1019.timestamp.iloc[0], ldg_pp_1019.timestamp.iloc[-1])\n",
    "axe.tick_params(axis='x', labelrotation = 45)\n",
    "\n",
    "axe = axes.ravel()[4]\n",
    "axe.plot(weather10.time, weather10.temperature)\n",
    "axe.set_xlim(ldg_pp_1026.timestamp.iloc[0], ldg_pp_1026.timestamp.iloc[-1])\n",
    "axe.tick_params(axis='x', labelrotation = 45)\n",
    "\n",
    "axe = axes.ravel()[5]\n",
    "axe.plot(weather11.time, weather11.temperature)\n",
    "axe.set_xlim(ldg_pp_1103.timestamp.iloc[0], ldg_pp_1103.timestamp.iloc[-1])\n",
    "axe.tick_params(axis='x', labelrotation = 45)\n",
    "\n",
    "# fig.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f702ecba",
   "metadata": {},
   "outputs": [],
   "source": [
    "fig, axe = plt.subplots()\n",
    "axe.plot(weather10.time, weather10.temperature, label=\"광양시기상관측소\")\n",
    "axe.plot(ldg_pp_1026.timestamp, ldg_pp_1026.Tamb - 273.15, label=\"#9수봉변\")\n",
    "axe.set_xlim(ldg_pp_1026.timestamp.iloc[0], ldg_pp_1026.timestamp.iloc[-1])\n",
    "axe.tick_params(axis='x', labelrotation = 45)\n",
    "axe.legend(frameon=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bd1708e6",
   "metadata": {},
   "outputs": [],
   "source": [
    "fig, axe = plt.subplots()\n",
    "axe.plot(weather11.time, weather11.temperature, label=\"광양시기상관측소\")\n",
    "axe.plot(ldg_pp_1103.timestamp, ldg_pp_1103.Tamb - 273.15, label=\"#9수봉변\")\n",
    "axe.set_xlim(ldg_pp_1103.timestamp.iloc[0], ldg_pp_1103.timestamp.iloc[-1])\n",
    "axe.tick_params(axis='x', labelrotation = 45)\n",
    "axe.legend(frameon=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c0d67e40",
   "metadata": {},
   "source": [
    "### 대기 수분량 (부피 비율)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "120b4798",
   "metadata": {},
   "outputs": [],
   "source": [
    "fig, axe = plt.subplots()\n",
    "axe.plot(weather09.time, weather09.XH2O, '-o')\n",
    "axe.set_xlim(ldg_pp_raw.timestamp.iloc[0], ldg_pp_raw.timestamp.iloc[-1])\n",
    "axe.tick_params(axis='x', labelrotation = 45)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1d9f1269",
   "metadata": {},
   "outputs": [],
   "source": [
    "fig, axe = plt.subplots()\n",
    "axe.plot(weather10.time, weather10.XH2O)\n",
    "axe.set_xlim(ldg_pp_1013.timestamp.iloc[0], ldg_pp_1013.timestamp.iloc[-1])\n",
    "axe.tick_params(axis='x', labelrotation = 45)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "32da81f5",
   "metadata": {},
   "outputs": [],
   "source": [
    "fig, axe = plt.subplots()\n",
    "axe.plot(weather10.time, weather10.XH2O)\n",
    "axe.set_xlim(ldg_pp_1019.timestamp.iloc[0], ldg_pp_1019.timestamp.iloc[-1])\n",
    "axe.tick_params(axis='x', labelrotation = 45)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cf20df4e",
   "metadata": {},
   "source": [
    "### 대기압(해수면)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5fbfb827",
   "metadata": {},
   "outputs": [],
   "source": [
    "fig, axe = plt.subplots()\n",
    "axe.plot(weather09.time, weather09.atm_sea * 100, '-o')\n",
    "axe.set_xlim(ldg_pp_raw.timestamp.iloc[0], ldg_pp_raw.timestamp.iloc[-1])\n",
    "axe.tick_params(axis='x', labelrotation = 45)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "59c5b0a2",
   "metadata": {},
   "outputs": [],
   "source": [
    "fig, axe = plt.subplots()\n",
    "axe.plot(weather10.time, weather10.atm_sea * 100)\n",
    "axe.set_xlim(ldg_pp_1013.timestamp.iloc[0], ldg_pp_1013.timestamp.iloc[-1])\n",
    "axe.tick_params(axis='x', labelrotation = 45)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "673f9aad",
   "metadata": {},
   "outputs": [],
   "source": [
    "fig, axe = plt.subplots()\n",
    "axe.plot(weather10.time, weather10.atm_sea * 100)\n",
    "axe.set_xlim(ldg_pp_1019.timestamp.iloc[0], ldg_pp_1019.timestamp.iloc[-1])\n",
    "axe.tick_params(axis='x', labelrotation = 45)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1dad10da",
   "metadata": {},
   "outputs": [],
   "source": [
    "fig, axe = plt.subplots()\n",
    "axe.plot(weather09.time, weather09.atm_sea * 100, '-o')\n",
    "axe.set_xlim(pd.Timestamp(\"22-09-22 10:00:00\"), pd.Timestamp(\"22-09-22 22:00:00\"))\n",
    "axe.tick_params(axis='x', labelrotation = 45)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cffec2f9",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "710fa93d",
   "metadata": {},
   "outputs": [],
   "source": [
    "components = \"CO2\tN2\tCO\tH2\tO2\".split()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7a9a2b05",
   "metadata": {},
   "outputs": [],
   "source": [
    "raw_array = np.array('''\\\n",
    "16.56 \t27.21 \t55.53 \t0.49 \t0.21 \t1686.00\n",
    "15.48 \t30.60 \t52.94 \t0.63 \t0.35 \t1612.00\n",
    "18.79 \t33.18 \t47.18 \t0.48 \t0.36 \t1434.00\n",
    "15.49 \t31.89 \t51.76 \t0.53 \t0.33 \t1573.00\n",
    "18.49 \t34.21 \t46.43 \t0.63 \t0.24 \t1415.00\n",
    "13.65 \t25.77 \t59.58 \t0.77 \t0.22 \t1815.00\n",
    "16.99 \t31.78 \t49.77 \t1.23 \t0.24 \t1531.00\n",
    "14.28 \t23.85 \t60.21 \t1.27 \t0.39 \t1847.00\n",
    "12.25 \t22.05 \t63.99 \t1.34 \t0.38 \t1963.00\n",
    "15.41 \t29.06 \t53.90 \t1.27 \t0.36 \t1657.00\n",
    "16.00 \t31.37 \t51.35 \t0.94 \t0.34 \t1572.00\n",
    "17.65 \t32.70 \t48.61 \t0.72 \t0.32 \t1484.00\n",
    "18.66 \t35.03 \t45.32 \t0.63 \t0.36 \t1382.00\n",
    "14.87 \t27.54 \t56.60 \t0.56 \t0.43 \t1719.00\n",
    "15.69 \t28.18 \t55.08 \t0.65 \t0.39 \t1677.00\n",
    "14.05 \t27.17 \t57.95 \t0.53 \t0.31 \t1760.00\n",
    "16.56 \t35.68 \t46.51 \t0.88 \t0.37 \t1424.00\n",
    "17.93 \t36.32 \t44.47 \t1.02 \t0.27 \t1366.00\n",
    "17.80 \t33.39 \t47.38 \t0.96 \t0.47 \t1453.00\n",
    "17.05 \t34.60 \t47.43 \t0.59 \t0.33 \t1444.00\n",
    "17.04 \t31.96 \t50.12 \t0.49 \t0.39 \t1523.00\n",
    "13.39 \t24.66 \t61.07 \t0.52 \t0.36 \t1853.49\n",
    "'''.split(), dtype=np.double).reshape((-1,6))\n",
    "\n",
    "fractions = raw_array[:,:-1]\n",
    "\n",
    "fractions[:,2] += 100. - fractions.sum(axis=1)\n",
    "\n",
    "fractions /= 100.\n",
    "\n",
    "HV_CV_Lower = raw_array[:,-1]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ffb96702",
   "metadata": {},
   "source": [
    "## 이론 공연비"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9d2fd74d",
   "metadata": {},
   "outputs": [],
   "source": [
    "(fractions[:,2:4].sum(axis=1) / 2. - fractions[:, components.index('O2')]) * ( 1 + 3.773)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1d6d94a7",
   "metadata": {},
   "outputs": [],
   "source": [
    "gas = ct.Solution('gri30.xml')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a3884f2c",
   "metadata": {},
   "outputs": [],
   "source": [
    "'''for frac in fractions:\n",
    "    gas.Y = {x: y for x, y in zip(components, frac)} \n",
    "    print({elem: gas.elemental_mole_fraction(elem) for elem in gas.element_names})\n",
    "    print(sum([gas.elemental_mole_fraction(elem) for elem in gas.element_names]))'''"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9309e9b9",
   "metadata": {},
   "source": [
    "## 생성 엔탈피, MJ / kmol (298.15 K, 1 atm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "50884b08",
   "metadata": {},
   "outputs": [],
   "source": [
    "{comp: gas.species(comp).thermo.h(273.15+25) / 1e6 for comp in '''\n",
    "O2\n",
    "N2\n",
    "H2\n",
    "C\n",
    "CO2\n",
    "H2O\n",
    "CO\n",
    "CH4\n",
    "C3H8'''.split()}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3c86d089",
   "metadata": {},
   "outputs": [],
   "source": [
    "h_dict = {comp: gas.species(comp).thermo.h(273.15+25) for comp in components}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "41dd9344",
   "metadata": {},
   "source": [
    "### CO 발열량"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "812ba6eb",
   "metadata": {},
   "outputs": [],
   "source": [
    "dhCO = gas.species(\"CO\").thermo.h(273.15+25) - gas.species(\"CO2\").thermo.h(273.15+25) # J / kmol\n",
    "gas.TPX = 273.15, ct.one_atm, \"CO:1\"\n",
    "kmol_in_Nm3_CO = 1000. / (22.4 * 1000) # 1 000 mol / 22.4 x 1000 L\n",
    "dhCO * kmol_in_Nm3_CO / 4184"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b9da5c3b",
   "metadata": {},
   "source": [
    "### LDG 저위 발열량 - 정압, 정적"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "16aa0dfb",
   "metadata": {},
   "outputs": [],
   "source": [
    "def hf(species_name):\n",
    "    gas.TPX = 273.15 + 25, ct.one_atm, \"{}:1\".format(species_name)\n",
    "    return gas.enthalpy_mole\n",
    "\n",
    "def uf(species_name):\n",
    "    gas.TPX = 273.15 + 25, ct.one_atm, \"{}:1\".format(species_name)\n",
    "    return gas.int_energy_mole\n",
    "\n",
    "hO2 = hf(\"O2\")\n",
    "hCO = hf(\"CO\")\n",
    "hH2 = hf(\"H2\")\n",
    "hCO2 = hf(\"CO2\")\n",
    "hH2Og = hf(\"H2O\")\n",
    "\n",
    "uO2 = uf(\"O2\")\n",
    "uCO = uf(\"CO\")\n",
    "uH2 = uf(\"H2\")\n",
    "uCO2 = uf(\"CO2\")\n",
    "uH2Og = uf(\"H2O\")\n",
    "\n",
    "water.TP = 273.15 + 25, ct.one_atm\n",
    "hH2Ol = water.enthalpy_mole\n",
    "\n",
    "dhCO = hCO + hO2/2. - hCO2\n",
    "\n",
    "dhH2l = hH2 + hO2/2. - hH2Ol\n",
    "dhH2g = hH2 + hO2/2. - hH2Og\n",
    "\n",
    "duCO = uCO + uO2/2. - uCO2\n",
    "duH2g = uH2 + uO2/2. - uH2Og\n",
    "\n",
    "HV_CPl = []\n",
    "HV_CVl = []\n",
    "for frac in fractions:\n",
    "    gas.TPX = 273.15, ct.one_atm, {x: y for x, y in zip(components, frac)}\n",
    "    kmol_in_Nm3 = gas.density / gas.mean_molecular_weight\n",
    "    kmol_in_kg = 1./gas.mean_molecular_weight\n",
    "    '''print(\"LDG\", {x: y for x, y in zip(components, frac)})\n",
    "    print(\n",
    "        (\n",
    "            gas.mole_fraction_dict()[\"CO\"] * dhCO\n",
    "            + gas.mole_fraction_dict()[\"H2\"] * dhH2g\n",
    "        ) / 4184 * kmol_in_Nm3\n",
    "    )''' # J / kmol\n",
    "    HV_CPl.append((gas.mole_fraction_dict()[\"CO\"] * dhCO + gas.mole_fraction_dict()[\"H2\"] * dhH2g)/4184*kmol_in_Nm3) # J / kmol\n",
    "    HV_CVl.append((gas.mole_fraction_dict()[\"CO\"] * duCO + gas.mole_fraction_dict()[\"H2\"] * duH2g)/4184*kmol_in_Nm3) # J / kmol\n",
    "    # print((gas.mole_fraction_dict()[\"CO\"] * dhCO + gas.mole_fraction_dict()[\"H2\"] * dhH2)/gas.mean_molecular_weight/4184) # J / kg"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "986f5fc2",
   "metadata": {},
   "source": [
    "### 정압 발열량과 질량 분석기 발열량 비교"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7079e99f",
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.figure()\n",
    "plt.plot(sorted(HV_CV_Lower), sorted(HV_CV_Lower), ':')\n",
    "plt.plot(HV_CV_Lower, HV_CPl, 'x')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "543f98dc",
   "metadata": {},
   "source": [
    "### 정적 발열량과 질량 분석기 발열량 비교"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6b02caaf",
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.figure()\n",
    "plt.plot(sorted(HV_CV_Lower), sorted(HV_CV_Lower), ':')\n",
    "plt.plot(HV_CV_Lower, HV_CVl, 'x')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "12e74081",
   "metadata": {},
   "source": [
    "### 분석기 열량 기준 이론 정압, 정적 발열량 차이"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "76c19f15",
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.stem(np.arange(len(HV_CPl)), HV_CPl- HV_CV_Lower)\n",
    "plt.stem(np.arange(len(HV_CPl)), HV_CVl- HV_CV_Lower, ':')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "01bb418b",
   "metadata": {},
   "source": [
    "## LDG 조성 Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d023d019",
   "metadata": {},
   "outputs": [],
   "source": [
    "LDG_comps = \"H2\tCO\tN2\tO2\tAR\tCO2\".split()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d6a19fe9",
   "metadata": {},
   "outputs": [],
   "source": [
    "X0922_0000 = pd.read_csv('./220922/Composition/LDG_composition_220922-0000.csv', sep='\\t', parse_dates=['Time'])\n",
    "X0922_1126 = pd.read_csv('./220922/Composition/LDG_composition_220922-1126.csv', sep='\\t', parse_dates=['Time'])\n",
    "X0922_1317 = pd.read_csv('./220922/Composition/LDG_composition_220922-1317.csv', sep='\\t', parse_dates=['Time'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "467ac168",
   "metadata": {},
   "outputs": [],
   "source": [
    "X0922 = pd.concat([X0922_0000, X0922_1126, X0922_1317])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e3d3c90f",
   "metadata": {},
   "source": [
    "### NOVA IR 연료분석기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3322b2c5",
   "metadata": {},
   "outputs": [],
   "source": [
    "X1013_raw = pd.read_csv('./221013/13_10_2022_11_34_44.csv', parse_dates=[['Date', 'Time']])\n",
    "X1013_raw['CO %'] = X1013_raw['CO ppm'] / 10000\n",
    "X1013_raw['HV kcal/Nm3'] = (X1013_raw['CO ppm'] / 1000000 * dhCO * (kmol_in_Nm3/4184) \n",
    "                            + X1013_raw['H2 %'] / 100 * dhH2g * (kmol_in_Nm3/4184))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d9478620",
   "metadata": {},
   "outputs": [],
   "source": [
    "X1013_raw"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c586397e",
   "metadata": {},
   "outputs": [],
   "source": [
    "X1019_raw = pd.read_csv('./221019/19_10_2022_13_50_25.csv', parse_dates=[['Date', 'Time']], )\n",
    "X1019_raw"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ae46697f",
   "metadata": {},
   "outputs": [],
   "source": [
    "X1026_raw = pd.read_csv('./221026/26_10_2022_10_58_31.csv', parse_dates=[['Date', 'Time']], )\n",
    "X1026_raw"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f722824e",
   "metadata": {},
   "outputs": [],
   "source": [
    "X1103_raw = pd.read_csv('./221103/03_11_2022_09_21_02.csv', parse_dates=[['Date', 'Time']], dayfirst=True,)\n",
    "X1103_raw"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9d995b8a",
   "metadata": {},
   "outputs": [],
   "source": [
    "X1019_raw['CO %'] = X1019_raw['CO ppm'] / 10000\n",
    "X1019_raw['HV kcal/Nm3'] = (X1019_raw['CO ppm'] / 1000000 * dhCO * (kmol_in_Nm3/4184) \n",
    "                            + X1019_raw['H2 %'] / 100 * dhH2g * (kmol_in_Nm3/4184))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c290c4b4",
   "metadata": {},
   "outputs": [],
   "source": [
    "X1026_raw['CO %'] = X1026_raw['CO ppm'] / 10000\n",
    "X1026_raw['HV kcal/Nm3'] = (X1026_raw['CO ppm'] / 1000000 * dhCO * (kmol_in_Nm3/4184) \n",
    "                            + X1026_raw['H2 %'] / 100 * dhH2g * (kmol_in_Nm3/4184))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0de6bab5",
   "metadata": {},
   "outputs": [],
   "source": [
    "X1103_raw['CO %'] = X1103_raw['CO ppm'] / 10000\n",
    "X1103_raw['HV kcal/Nm3'] = (X1103_raw['CO ppm'] / 1000000 * dhCO * (kmol_in_Nm3/4184) \n",
    "                            + X1103_raw['H2 %'] / 100 * dhH2g * (kmol_in_Nm3/4184))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "67835067",
   "metadata": {},
   "outputs": [],
   "source": [
    "X1019_raw[['Date_Time', 'O2 %', 'CO2 %', 'CO %', 'H2 %', 'N2 %', 'HV kcal/Nm3']].to_csv('./221019/X1019.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c9520176",
   "metadata": {},
   "outputs": [],
   "source": [
    "X1019_raw.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1c842b80",
   "metadata": {},
   "outputs": [],
   "source": [
    "X1019_raw['CxHy %'].max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "56df52a6",
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.figure()\n",
    "plt.plot(X1019_raw['Date_Time'], X1019_raw['O2 %'],)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8270ac11",
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.figure(figsize=(10,4))\n",
    "plt.plot(X1013_raw.Date_Time, X1013_raw['HV kcal/Nm3'])\n",
    "\n",
    "plt.plot(X0922.Time + (pd.Timestamp('2022-10-13')-pd.Timestamp('2022-09-22')), X0922[\"LDG CV Lower\"], ':')\n",
    "plt.grid()\n",
    "plt.xlim(xmin=pd.Timestamp('2022-10-13 08:00:00'))\n",
    "plt.xlim(xmax=pd.Timestamp('2022-10-13 15:50:00'))\n",
    "plt.ylim(ymin=1000)\n",
    "plt.gca().tick_params(axis='x', labelrotation = 45)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dca99a97",
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.figure(figsize=(14,4))\n",
    "plt.plot(X1019_raw.Date_Time, X1019_raw['HV kcal/Nm3'], label='10-19 열량 kcal/Nm3')\n",
    "plt.plot(X1019_raw.Date_Time - pd.Timedelta(1, 'day'), X1019_raw['HV kcal/Nm3'], label='10-20 열량 kcal/Nm3')\n",
    "plt.plot(X1019_raw.Date_Time - pd.Timedelta(2, 'day'), X1019_raw['HV kcal/Nm3'], label='10-21 열량 kcal/Nm3')\n",
    "\n",
    "#plt.plot(X0922.Time + (pd.Timestamp('2022-10-13')-pd.Timestamp('2022-09-22')), X0922[\"LDG CV Lower\"], ':')\n",
    "plt.grid()\n",
    "plt.xlim(xmin=pd.Timestamp('2022-10-19 00:00:00'))\n",
    "plt.xlim(xmax=pd.Timestamp('2022-10-20 00:00:00'))\n",
    "#plt.ylim(ymin=800)\n",
    "plt.ylim(800, 2000)\n",
    "plt.gca().tick_params(axis='x', labelrotation = 45)\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "840c4fa4",
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "plt.figure(figsize=(14,4))\n",
    "plt.plot(X1026_raw.Date_Time, X1026_raw['HV kcal/Nm3'], label='10-26 열량 kcal/Nm3')\n",
    "plt.plot(X1026_raw.Date_Time - pd.Timedelta(1, 'day'), X1026_raw['HV kcal/Nm3'], label='10-27 열량 kcal/Nm3')\n",
    "plt.plot(X1026_raw.Date_Time - pd.Timedelta(2, 'day'), X1026_raw['HV kcal/Nm3'], label='10-28 열량 kcal/Nm3')\n",
    "\n",
    "#plt.plot(X0922.Time + (pd.Timestamp('2022-10-13')-pd.Timestamp('2022-09-22')), X0922[\"LDG CV Lower\"], ':')\n",
    "plt.grid()\n",
    "plt.xlim(xmin=pd.Timestamp('2022-10-26 00:00:00'))\n",
    "plt.xlim(xmax=pd.Timestamp('2022-10-27 00:00:00'))\n",
    "#plt.ylim(ymin=800)\n",
    "plt.ylim(1000, 2200)\n",
    "plt.gca().tick_params(axis='x', labelrotation = 45)\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7171ce7d",
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "plt.figure(figsize=(14,4))\n",
    "plt.plot(X1103_raw.Date_Time, X1103_raw['HV kcal/Nm3'], label='11-03 열량 kcal/Nm3')\n",
    "plt.plot(X1103_raw.Date_Time - pd.Timedelta(1, 'day'), X1103_raw['HV kcal/Nm3'], label='11-04 열량 kcal/Nm3')\n",
    "#plt.plot(X0922.Time + (pd.Timestamp('2022-10-13')-pd.Timestamp('2022-09-22')), X0922[\"LDG CV Lower\"], ':')\n",
    "plt.grid()\n",
    "plt.xlim(xmin=pd.Timestamp('2022-11-03 00:00:00'))\n",
    "plt.xlim(xmax=pd.Timestamp('2022-11-04 00:00:00'))\n",
    "#plt.ylim(ymin=800)\n",
    "plt.ylim(1000, 2200)\n",
    "plt.gca().tick_params(axis='x', labelrotation = 45)\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "70fa0eef",
   "metadata": {},
   "source": [
    "### 이론 공연비 $\\phi$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7439ef2c",
   "metadata": {},
   "outputs": [],
   "source": [
    "X0922['phi'] = X0922.apply(lambda row: (1 + 3.773) * ((row.CO + row.H2) / 2 - row.O2) / 100., axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5609c6f4",
   "metadata": {},
   "source": [
    "### 발열량"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b97ef702",
   "metadata": {},
   "outputs": [],
   "source": [
    "def LDG_Heating_Value (X_dict, XH2O=None, debug=False):\n",
    "    '''\n",
    "    Lower Heating Value of LDG in kcal/Nm3\n",
    "    with or without vapor content\n",
    "    X_dict : LDG composition in volume\n",
    "    XH2O : mole fraction of H2O\n",
    "    '''\n",
    "    gas.TPX = 298.15, ct.one_atm, X_dict\n",
    "    \n",
    "    if XH2O:\n",
    "        X_normalized = gas.mole_fraction_dict()\n",
    "        X_normalized[\"H2O\"] = XH2O / (1-XH2O)\n",
    "        gas.TPX = 298.15, ct.one_atm, X_normalized\n",
    "        if debug : print(gas.X[gas.species_index(\"H2O\")])\n",
    "        \n",
    "    return (gas.mole_fraction_dict()[\"CO\"] * dhCO + gas.mole_fraction_dict()[\"H2\"] * dhH2g) * (kmol_in_Nm3/4184)\n",
    "\n",
    "LDG_Heating_Value(dict(zip(components, fractions[0])), 0.04, True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "71af4327",
   "metadata": {},
   "source": [
    "#### 질량분석기 발열량에 대한 생성 엔탈피 변화 발열량 오차율"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "962f2c0b",
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "plt.figure()\n",
    "plt.plot(X0922.Time, (X0922.apply(lambda row: LDG_Heating_Value(dict(zip(LDG_comps, row[1:7]))), axis=1) - X0922[\"LDG CV Lower\"])/X0922[\"LDG CV Lower\"]*100)\n",
    "plt.ylim(-0.3, 0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7f46d7de",
   "metadata": {},
   "source": [
    "    gas.TPX = 273.15, ct.one_atm, {x: y for x, y in zip(components, frac)}\n",
    "    kmol_in_Nm3 = gas.density / gas.mean_molecular_weight\n",
    "    kmol_in_kg = 1./gas.mean_molecular_weight\n",
    "    '''print(\"LDG\", {x: y for x, y in zip(components, frac)})\n",
    "    print(\n",
    "        (\n",
    "            gas.mole_fraction_dict()[\"CO\"] * dhCO\n",
    "            + gas.mole_fraction_dict()[\"H2\"] * dhH2g\n",
    "        ) / 4184 * kmol_in_Nm3\n",
    "    )''' # J / kmol\n",
    "    HV_CPl.append((gas.mole_fraction_dict()[\"CO\"] * dhCO + gas.mole_fraction_dict()[\"H2\"] * dhH2g)/4184*kmol_in_Nm3) # J / kmol\n",
    "    HV_CVl.append((gas.mole_fraction_dict()[\"CO\"] * duCO + gas.mole_fraction_dict()[\"H2\"] * duH2g)/4184*kmol_in_Nm3) # J / kmol"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "88b10d3a",
   "metadata": {},
   "source": [
    "### LDG 건식 발열량 변화 22-09-22 15:00~ (발전소 앞 수분 측정 시기)\n",
    "홀더 인입 유량과 함께 (우측)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "10ce314e",
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "plt.figure()\n",
    "plt.plot(X0922.Time, X0922.apply(lambda row: LDG_Heating_Value(dict(zip(LDG_comps, row[1:7]))), axis=1))\n",
    "plt.plot(X0922.Time, X0922[\"LDG CV Lower\"], 'x', markevery=3e-2)\n",
    "plt.xlim(ldg_pp_raw.timestamp.iloc[0], ldg_pp_raw.timestamp.iloc[-1])\n",
    "plt.gca().tick_params(axis='x', labelrotation = 45) \n",
    "\n",
    "twin = plt.gca().twinx()\n",
    "\n",
    "twin.plot(flowrate0922.time, flowrate0922.converter1)\n",
    "twin.plot(flowrate0922.time, flowrate0922.converter2)\n",
    "twin.set_ylim(ymin=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "720d5d3b",
   "metadata": {},
   "source": [
    "#### 측정 시기에서(22-09-22 15:00~) 발열량과 공연비"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7d39e42b",
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.figure()\n",
    "# plt.plot(X0922.Time, X0922.apply(lambda row: LDG_Heating_Value(dict(zip(LDG_comps, row[1:7]))), axis=1))\n",
    "plt.plot(X0922.Time, X0922[\"LDG CV Lower\"], '-', markevery=3e-2)\n",
    "plt.xlim(ldg_pp_raw.timestamp.iloc[0], ldg_pp_raw.timestamp.iloc[-1])\n",
    "plt.ylim(ymin=0)\n",
    "plt.gca().tick_params(axis='x', labelrotation = 45)\n",
    "\n",
    "twin = plt.gca().twinx()\n",
    "\n",
    "twin.plot(X0922.Time, X0922[\"phi\"], '--')\n",
    "twin.set_ylim(ymin=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dd8ee2d3",
   "metadata": {},
   "source": [
    "### 수분 함량 4% 에서 발열량 변화"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c92c7cea",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "plt.figure()\n",
    "plt.plot(X0922.Time, X0922.apply(lambda row: LDG_Heating_Value(dict(zip(LDG_comps, row[1:7])), 0.04), axis=1))\n",
    "plt.plot(X0922.Time, X0922[\"LDG CV Lower\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "90b57e8d",
   "metadata": {},
   "source": [
    "#### 수분 측정 시기에서 (22-09-22 15:00~ )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c319e806",
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "plt.figure()\n",
    "plt.plot(X0922.Time, X0922.apply(lambda row: LDG_Heating_Value(dict(zip(LDG_comps, row[1:7])), 0.04), axis=1))\n",
    "plt.plot(X0922.Time, X0922[\"LDG CV Lower\"], '--', markevery=3e-2)\n",
    "plt.xlim(ldg_pp_raw.timestamp.iloc[0], ldg_pp_raw.timestamp.iloc[-1])\n",
    "plt.gca().tick_params(axis='x', labelrotation = 45)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cbee74f6",
   "metadata": {},
   "outputs": [],
   "source": [
    "time_boiler_0922_15s = pressure0922.time[pressure0922.time.between(ldg_pp_raw.timestamp.iloc[0], ldg_pp_raw.timestamp.iloc[-1])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "07e1c9d1",
   "metadata": {},
   "outputs": [],
   "source": [
    "LHV_dry = np.interp(time_boiler_0922_15s, X0922.Time, X0922[\"LDG CV Lower\"])\n",
    "\n",
    "#VP = np.interp(time_boiler_0922_15s, ldg_pp_raw.timestamp, ldg_pp_raw.vp)\n",
    "\n",
    "VP = np.array([ldg_pp_raw.vp[ldg_pp_raw.timestamp.between(\n",
    "    t15s - pd.Timedelta(15/2, 'sec'), \n",
    "    t15s + pd.Timedelta(15/2, 'sec'))].mean()\n",
    "for t15s in time_boiler_0922_15s])\n",
    "\n",
    "LDG_p_gauge = pressure0922.p_boiler_SI[time_boiler_0922_15s.index]\n",
    "\n",
    "ATM_p_sea = np.interp(time_boiler_0922_15s, weather09.time, weather09.atm_sea * 100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0e0c8ce9",
   "metadata": {},
   "outputs": [],
   "source": [
    "time_boiler_0922_mass_anlzr = X0922.Time[X0922.Time.between(ldg_pp_raw.timestamp.iloc[0], ldg_pp_raw.timestamp.iloc[-1])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f1c685b2",
   "metadata": {},
   "outputs": [],
   "source": [
    "LHV_dry = np.interp(time_boiler_0922_mass_anlzr, X0922.Time, X0922[\"LDG CV Lower\"])\n",
    "\n",
    "phi_dry = np.interp(time_boiler_0922_mass_anlzr, X0922.Time, X0922.phi)\n",
    "#VP = np.interp(time_boiler_0922_15s, ldg_pp_raw.timestamp, ldg_pp_raw.vp)\n",
    "\n",
    "LDG_VP = np.array([ldg_pp_raw.vp[ldg_pp_raw.timestamp.between(\n",
    "    t15s - pd.Timedelta(15/2, 'sec'), \n",
    "    t15s + pd.Timedelta(15/2, 'sec'))].mean()\n",
    "for t15s in time_boiler_0922_mass_anlzr])\n",
    "\n",
    "LDG_p_gauge = np.interp(time_boiler_0922_mass_anlzr, pressure0922.time, pressure0922.p_boiler_SI)\n",
    "\n",
    "ATM_p_sea = np.interp(time_boiler_0922_mass_anlzr, weather09.time, weather09.atm_sea * 100)\n",
    "\n",
    "ATM_XH2O = np.interp(time_boiler_0922_mass_anlzr, weather09.time, weather09.XH2O)\n",
    "\n",
    "LDG_XH2O = LDG_VP / (ATM_p_sea + LDG_p_gauge)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d6cd0b37",
   "metadata": {},
   "source": [
    "### 측정된 LDG 내 수분량에서 발열량"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2e57f19a",
   "metadata": {},
   "source": [
    "#### LDG 및 대기 중 수분량 (부피 %)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9b09ca50",
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.figure()\n",
    "plt.plot(time_boiler_0922_mass_anlzr, LDG_XH2O)\n",
    "plt.plot(time_boiler_0922_mass_anlzr, ATM_XH2O)\n",
    "plt.ylim(ymin=0, ymax=0.05)\n",
    "plt.gca().tick_params(axis='x', labelrotation = 45) "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "db8bf243",
   "metadata": {},
   "source": [
    "#### 건조 발열량과 수분 포함 발열량 kcal/Nm3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "93f100db",
   "metadata": {},
   "outputs": [],
   "source": [
    "fig, axe = plt.subplots()\n",
    "\n",
    "axe.plot(time_boiler_0922_mass_anlzr, LHV_dry, label='건조 저위발열량')\n",
    "axe.plot(time_boiler_0922_mass_anlzr, LHV_dry*(1-LDG_VP / (ATM_p_sea + LDG_p_gauge)), label='수분 포함 저위발열량')\n",
    "axe.tick_params(axis='x', labelrotation = 45) \n",
    "axe.set_ylabel(\"kcal/Nm3\")\n",
    "twin = axe.twinx()\n",
    "twin.plot(time_boiler_0922_mass_anlzr, 100*(LHV_dry*(1-LDG_VP / (ATM_p_sea + LDG_p_gauge))-LHV_dry)/LHV_dry, '--', label='변화율')\n",
    "twin.set_ylim(ymin=-6, ymax=0.0)\n",
    "twin.set_ylabel(\"%\")\n",
    "\n",
    "\n",
    "hl, ll = axe.get_legend_handles_labels()\n",
    "hr, lr = twin.get_legend_handles_labels()\n",
    "\n",
    "\n",
    "\n",
    "fig.legend(hl+hr, ll+lr, ncol=2,\n",
    "          bbox_to_anchor=(0.5, -0.1), loc='upper center')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8019c604",
   "metadata": {},
   "source": [
    "#### 건조 LDG + 공기 이론 공연비와 수분 포함 이론 공연비"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5f330012",
   "metadata": {},
   "outputs": [],
   "source": [
    "fig, axe = plt.subplots()\n",
    "\n",
    "axe.plot(time_boiler_0922_mass_anlzr, phi_dry, label='건조 LDG 이론 공연비')\n",
    "axe.plot(time_boiler_0922_mass_anlzr, phi_dry*(1-LDG_XH2O)/(1-ATM_XH2O), label='수분 포함 LDG 이론 공연비')\n",
    "axe.tick_params(axis='x', labelrotation = 45) \n",
    "axe.set_ylabel(\"이론 공연비\")\n",
    "twin = axe.twinx()\n",
    "twin.plot(time_boiler_0922_mass_anlzr, 100*(phi_dry*(1-LDG_XH2O)/(1-ATM_XH2O)-phi_dry)/phi_dry,'--', label='변화율')\n",
    "# twin.set_ylim(ymin=-3, ymax=0.0)\n",
    "twin.set_ylabel(\"%\")\n",
    "\n",
    "hl, ll = axe.get_legend_handles_labels()\n",
    "hr, lr = twin.get_legend_handles_labels()\n",
    "\n",
    "fig.legend(hl+hr, ll+lr, ncol=2,\n",
    "          bbox_to_anchor=(0.5, -0.1), loc='upper center')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a5853c37",
   "metadata": {},
   "source": [
    "#### 수분 4%에서 발열량 변화율"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d78b9365",
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "plt.figure()\n",
    "plt.plot(X0922.Time, (X0922.apply(lambda row: LDG_Heating_Value(dict(zip(LDG_comps, row[1:7])), 0.04), axis=1) - X0922[\"LDG CV Lower\"])/X0922[\"LDG CV Lower\"]*100)\n",
    "plt.xlim(ldg_pp_raw.timestamp.iloc[0], ldg_pp_raw.timestamp.iloc[-1])\n",
    "plt.ylim(-5, -3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f60fa4f0",
   "metadata": {},
   "outputs": [],
   "source": [
    "(dhH2g * 0.6282/100. + dhCO * 62.6294/100.) / 4184"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f075548b",
   "metadata": {},
   "outputs": [],
   "source": [
    "testX = dict (zip(LDG_comps, np.array(\"0.6282\t62.6294\t21.5912\t0.0038\t0.8488\t14.2986\t\".split(), dtype=np.double)/100.))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "41df8920",
   "metadata": {},
   "outputs": [],
   "source": [
    "sum(testX.values())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d819ac28",
   "metadata": {},
   "outputs": [],
   "source": [
    "weather09[weather09.time.between(ldg_pp_raw.timestamp.iloc[0], ldg_pp_raw.timestamp.iloc[-1])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "75703e31",
   "metadata": {},
   "outputs": [],
   "source": [
    "weather09[30930:30935]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "75b99be2",
   "metadata": {},
   "outputs": [],
   "source": [
    "ldg_pp_raw.timestamp.iloc[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "674e25e8",
   "metadata": {},
   "outputs": [],
   "source": [
    "ldg_pp_raw.timestamp.iloc[-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a3903587",
   "metadata": {},
   "outputs": [],
   "source": [
    "weather09.time[0]"
   ]
  }
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