{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import sklearn\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def calulate_density(travel):\n",
    "    return 2710/(6293-travel)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def filter_data(df):\n",
    "    # filter data\n",
    "    df['P.Zeit'] = pd.to_timedelta(df['P.Zeit'])\n",
    "\n",
    "    df=df[df['Pyrometer'] >460].copy()\n",
    "    df=df[df['AV Force'] > 55].copy()\n",
    "\n",
    "    minPistonTravel = df['Abs. Piston Trav'].min()\n",
    "    df['TravelRelative'] = ((df['Abs. Piston Trav'] - minPistonTravel)*1000).astype(int)\n",
    "    df['TravelRelativeCorrected'] = np.maximum.accumulate(df['TravelRelative'])\n",
    "    minTemperature = df['Pyrometer'].min()\n",
    "    df['TravelRelativeCorrected'] = df['TravelRelativeCorrected'].rolling(window=10).mean()\n",
    "    df.loc[pd.isnull(df['TravelRelativeCorrected']), 'TravelRelativeCorrected'] =  0\n",
    "    df['TravelRelativeTempCorrected']=df['TravelRelativeCorrected']+(df['Pyrometer']-minTemperature)*1.241\n",
    "    #df['TravelRelativeCorrected'] = df['TravelRelativeCorrected'].ewm(span=10, adjust=False).mean()\n",
    "    df.loc[pd.isnull(df['TravelRelativeCorrected']), 'TravelRelativeCorrected'] =  0\n",
    "\n",
    "    #df['TravelDelta'] = df['TravelDelta'].rolling(window=60).mean()\n",
    "    #df.loc[pd.isnull(df['TravelDelta']), 'TravelDelta'] =  0\n",
    "\n",
    "    #df.loc[(df['TravelDelta']<0), 'TravelDelta'] =  0\n",
    "    #df['TravelDelta'] = df['TravelDelta'].astype(int)\n",
    "    df['TravelDeltaOriginal'] = df['Abs. Piston Trav'] - df['Abs. Piston Trav'].shift(1)\n",
    "    #df = df.drop(columns=['Abs. Piston Trav'])\n",
    "    \n",
    "    df['seconds'] = df['P.Zeit'].dt.total_seconds()\n",
    "    df['seconds'] = df['seconds'].astype(int)\n",
    "    minSeconds = df['seconds'].min()\n",
    "    df['seconds'] = (df['seconds'] - minSeconds+1)\n",
    "    df = df.drop(columns=['P.Zeit'])\n",
    "    \n",
    "    df['Heating'] = (df['Heating power']*10).astype(int)\n",
    "    df = df.drop(columns=['Heating power'])\n",
    "    \n",
    "    df = df.iloc[::10]\n",
    "\n",
    "    df['TravelDelta'] = df['TravelRelativeTempCorrected'] - df['TravelRelativeTempCorrected'].shift(1)\n",
    "    df['TravelDelta2'] = df['TravelRelativeCorrected'] - df['TravelRelativeCorrected'].shift(1)\n",
    "    df.loc[pd.isnull(df['TravelDelta']), 'TravelDelta'] =  0\n",
    "\n",
    "    df['TravelRelativeCorrectedShifted'] = df['TravelRelativeCorrected'].shift(-1)\n",
    "    df['TravelRelativeCorrectedShifted'] = df['TravelRelativeCorrectedShifted'].fillna(0)\n",
    "    df['TravelRelativeTempCorrectedShifted'] = df['TravelRelativeTempCorrected'].shift(-1)\n",
    "    #df['TravelRelativeTempCorrectedShifted'] = df['TravelRelativeTempCorrectedShifted'].fillna(df['TravelRelativeTempCorrectedShifted'].iloc[-2])\n",
    "    df['PyrometerShifted'] = df['Pyrometer'].shift(-1)\n",
    "    df['Density'] =  calulate_density(df['TravelRelativeTempCorrected'])\n",
    "    #df['PyrometerShifted'] = df['PyrometerShifted'].fillna(df['PyrometerShifted'].iloc[-2])\n",
    "\n",
    "    df = df.drop(df.index[-1])\n",
    "\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def filter_dataEmpty(df):\n",
    "    # filter data\n",
    "    df['seconds'] = df['No.']\n",
    "    df = df.drop(columns=['No.'])\n",
    "    df['Pyrometer'] = df['AV Pyro top']\n",
    "    df = df.drop(columns=['AV Pyro top'])\n",
    "\n",
    "    df=df[df['Pyrometer'] >460].copy()\n",
    "    df=df[df['AV Force'] >15].copy()\n",
    "\n",
    "    minPistonTravel = df['AV Abs. Piston T'].min()\n",
    "    df['TravelRelative'] = ((df['AV Abs. Piston T'] - minPistonTravel)*1000).astype(int)\n",
    "    # maxPistonTravel = df['TravelRelative'].max()\n",
    "    # df['TravelRelative'] = -(df['TravelRelative']-maxPistonTravel)\n",
    "    #df['TravelRelativeCorrected'] = np.maximum.accumulate(df['TravelRelative'])\n",
    "    df['TravelRelativeCorrected'] = df['TravelRelative']\n",
    "    df['TravelRelativeCorrected'] = df['TravelRelativeCorrected'].rolling(window=60).mean()\n",
    "    #df['TravelRelativeCorrected'] = df['TravelRelativeCorrected'].ewm(span=10, adjust=False).mean()\n",
    "    df['TravelDelta'] = df['TravelRelativeCorrected'] - df['TravelRelativeCorrected'].shift(1)\n",
    "    df.loc[pd.isnull(df['TravelDelta']), 'TravelDelta'] =  0\n",
    "\n",
    "    #df['TravelDelta'] = df['TravelDelta'].rolling(window=60).mean()\n",
    "    #df.loc[pd.isnull(df['TravelDelta']), 'TravelDelta'] =  0\n",
    "\n",
    "    #df.loc[(df['TravelDelta']<0), 'TravelDelta'] =  0\n",
    "    #df['TravelDelta'] = df['TravelDelta'].astype(int)\n",
    "    df = df.drop(columns=['AV Abs. Piston T'])\n",
    "    \n",
    "    \n",
    "    df['Heating'] = (df['AV Heating Power']*10).astype(int)\n",
    "    df = df.drop(columns=['AV Heating Power'])\n",
    "    \n",
    "\n",
    "\n",
    "\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "data1000 = pd.read_csv('data/160508-1021-1000,0min,56kN.csv',sep = ';', skiprows=[1],decimal=',',\n",
    "                    usecols=['P.Zeit','MTC1','MTC2','Pyrometer','AV Abs. Press. 2','yPower','AV Force','AV Speed','I RMS','Pulse Time','Pause Time','U RMS','Heating power','Rel. Piston Trav','Abs. Piston Trav']) \n",
    "                   # usecols=['P.Zeit','Pyrometer','AV Force','Heating power','Abs. Piston Trav']) \n",
    "                    #usecols=['P.Zeit','Pyrometer','Heating power','Abs. Piston Trav']) \n",
    "data900 = pd.read_csv('data/160508-1022-900,0min,56kN.csv',sep = ';', skiprows=[1],decimal=',',\n",
    "                    usecols=['P.Zeit','MTC1','MTC2','Pyrometer','AV Abs. Press. 2','yPower','AV Force','AV Speed','I RMS','Pulse Time','Pause Time','U RMS','Heating power','Rel. Piston Trav','Abs. Piston Trav']) \n",
    "data1350 = pd.read_csv('data/200508-1023-1350,0min,56kN.csv',sep = ';', skiprows=[1],decimal=',',\n",
    "                    usecols=['P.Zeit','MTC1','MTC2','Pyrometer','AV Abs. Press. 2','yPower','AV Force','AV Speed','I RMS','Pulse Time','Pause Time','U RMS','Heating power','Rel. Piston Trav','Abs. Piston Trav']) \n",
    "data1200 = pd.read_csv('data/200508-1024-1200,0min,56kN.csv',sep = ';', skiprows=[1],decimal=',',\n",
    "                    usecols=['P.Zeit','MTC1','MTC2','Pyrometer','AV Abs. Press. 2','yPower','AV Force','AV Speed','I RMS','Pulse Time','Pause Time','U RMS','Heating power','Rel. Piston Trav','Abs. Piston Trav']) \n",
    "dataN1200 = pd.read_csv('data/050608-1037-1200,0min,70kN.csv',sep = ';', skiprows=[1],decimal=',',\n",
    "                    usecols=['P.Zeit','MTC1','MTC2','Pyrometer','AV Abs. Press. 2','yPower','AV Force','AV Speed','I RMS','Pulse Time','Pause Time','U RMS','Heating power','Rel. Piston Trav','Abs. Piston Trav']) \n",
    "dataN1100 = pd.read_csv('data/290508-1033-1100,0min,70kN.csv',sep = ';', skiprows=[1],decimal=',',\n",
    "                    usecols=['P.Zeit','MTC1','MTC2','Pyrometer','AV Abs. Press. 2','yPower','AV Force','AV Speed','I RMS','Pulse Time','Pause Time','U RMS','Heating power','Rel. Piston Trav','Abs. Piston Trav']) \n",
    "dataEmpty1= pd.read_csv('data/fct20-082 graphite 4f 1800 (100) 16kN 5 min d20.csv',sep = ';', skiprows=[1],decimal=',',\n",
    "                    usecols=['No.','AV Pyro top','AV Force','AV Abs. Piston T','AV Heating Power','U RMS','I RMS']) \n",
    "dataEmpty2= pd.read_csv('data/fct20-083 graphite for fct20-075 1800 (100) 16kN 5 min d20.csv',sep = ';', skiprows=[1],decimal=',',\n",
    "                    usecols=['No.','AV Pyro top','AV Force','AV Abs. Piston T','AV Heating Power','U RMS','I RMS']) \n",
    "\n",
    "data1000 = filter_data(data1000)\n",
    "data900 = filter_data(data900)\n",
    "data1350 = filter_data(data1350)\n",
    "data1200 = filter_data(data1200)\n",
    "dataN1200 = filter_data(dataN1200)\n",
    "dataN1100 = filter_data(dataN1100)\n",
    "\n",
    "dataN1200['TravelRelativeTempCorrected'] = dataN1200['TravelRelativeTempCorrected']/2\n",
    "dataN1200['TravelRelativeTempCorrectedShifted'] = dataN1200['TravelRelativeTempCorrectedShifted']/2\n",
    "#dataN1200['TravelRelativeTempCorrected'] = dataN1200['TravelRelativeTempCorrected']/2\n",
    "#dataN1200['TravelRelativeTempCorrectedShifted'] = dataN1200['TravelRelativeTempCorrectedShifted']/2\n",
    "\n",
    "dataEmpty1 = filter_dataEmpty(dataEmpty1)\n",
    "dataEmpty2 = filter_dataEmpty(dataEmpty2)\n",
    "\n",
    "\n",
    "\n",
    "#dataEmpty1.describe()\n",
    "#dataN1200.dtypes\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.microsoft.datawrangler.viewer.v0+json": {
       "columns": [
        {
         "name": "index",
         "rawType": "int64",
         "type": "integer"
        },
        {
         "name": "MTC1",
         "rawType": "int64",
         "type": "integer"
        },
        {
         "name": "MTC2",
         "rawType": "int64",
         "type": "integer"
        },
        {
         "name": "Pyrometer",
         "rawType": "int64",
         "type": "integer"
        },
        {
         "name": "AV Abs. Press. 2",
         "rawType": "int64",
         "type": "integer"
        },
        {
         "name": "yPower",
         "rawType": "int64",
         "type": "integer"
        },
        {
         "name": "AV Force",
         "rawType": "int64",
         "type": "integer"
        },
        {
         "name": "AV Speed",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "I RMS",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "Pulse Time",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "Pause Time",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "U RMS",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "Rel. Piston Trav",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "Abs. Piston Trav",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "TravelRelative",
         "rawType": "int64",
         "type": "integer"
        },
        {
         "name": "TravelRelativeCorrected",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "TravelRelativeTempCorrected",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "TravelDeltaOriginal",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "seconds",
         "rawType": "int64",
         "type": "integer"
        },
        {
         "name": "Heating",
         "rawType": "int64",
         "type": "integer"
        },
        {
         "name": "TravelDelta",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "TravelDelta2",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "TravelRelativeCorrectedShifted",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "TravelRelativeTempCorrectedShifted",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "PyrometerShifted",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "Density",
         "rawType": "float64",
         "type": "float"
        }
       ],
       "ref": "437667e5-b70c-4841-ac2b-edb4712085f9",
       "rows": [
        [
         "432",
         "104",
         "84",
         "462",
         "853",
         "30",
         "56",
         "0.03",
         "1.47",
         "20.0",
         "5.0",
         "4.65",
         "4.33",
         "39.57",
         "0",
         "0.0",
         "0.0",
         null,
         "1",
         "68",
         "0.0",
         null,
         "9.0",
         "23.892000000000003",
         "474.0",
         "0.4306372159542349"
        ],
        [
         "442",
         "108",
         "86",
         "474",
         "965",
         "29",
         "56",
         "0.1",
         "1.39",
         "20.0",
         "5.0",
         "4.51",
         "4.34",
         "39.58",
         "9",
         "9.0",
         "23.892000000000003",
         "0.0",
         "11",
         "63",
         "23.892000000000003",
         "9.0",
         "17.8",
         "43.861000000000004",
         "483.0",
         "0.4322784038813815"
        ],
        [
         "452",
         "111",
         "87",
         "483",
         "957",
         "29",
         "56",
         "0.08",
         "1.43",
         "20.0",
         "5.0",
         "4.5",
         "4.35",
         "39.59",
         "20",
         "17.8",
         "43.861000000000004",
         "0.0",
         "21",
         "64",
         "19.969",
         "8.8",
         "28.9",
         "64.88900000000001",
         "491.0",
         "0.4336597409659155"
        ],
        [
         "462",
         "114",
         "89",
         "491",
         "968",
         "28",
         "57",
         "0.09",
         "1.38",
         "20.0",
         "5.0",
         "4.48",
         "4.37",
         "39.61",
         "39",
         "28.9",
         "64.88900000000001",
         "0.00999999999999801",
         "31",
         "62",
         "21.028000000000006",
         "11.099999999999998",
         "44.0",
         "89.917",
         "499.0",
         "0.43512390835680353"
        ],
        [
         "472",
         "116",
         "90",
         "499",
         "984",
         "29",
         "56",
         "0.11",
         "1.41",
         "20.0",
         "5.0",
         "4.48",
         "4.38",
         "39.62",
         "49",
         "44.0",
         "89.917",
         "0.0",
         "41",
         "63",
         "25.02799999999999",
         "15.100000000000001",
         "63.9",
         "118.504",
         "506.0",
         "0.43687953232287885"
        ],
        [
         "482",
         "118",
         "91",
         "506",
         "999",
         "29",
         "56",
         "0.1",
         "1.42",
         "20.0",
         "5.0",
         "4.56",
         "4.4",
         "39.64",
         "70",
         "63.9",
         "118.504",
         "0.0",
         "51",
         "65",
         "28.587000000000003",
         "19.9",
         "77.2",
         "140.491",
         "513.0",
         "0.438902219711536"
        ],
        [
         "492",
         "120",
         "92",
         "513",
         "1003",
         "29",
         "56",
         "0.03",
         "1.42",
         "20.0",
         "5.0",
         "4.52",
         "4.41",
         "39.65",
         "79",
         "77.2",
         "140.491",
         "0.0",
         "61",
         "64",
         "21.98700000000001",
         "13.300000000000004",
         "89.2",
         "162.419",
         "521.0",
         "0.44047070877913386"
        ],
        [
         "502",
         "122",
         "93",
         "521",
         "1002",
         "29",
         "56",
         "0.1",
         "1.44",
         "20.0",
         "5.0",
         "4.55",
         "4.43",
         "39.67",
         "100",
         "89.2",
         "162.419",
         "0.0",
         "71",
         "65",
         "21.927999999999997",
         "12.0",
         "101.8",
         "184.947",
         "529.0",
         "0.4420461943166561"
        ],
        [
         "512",
         "124",
         "94",
         "529",
         "1002",
         "30",
         "56",
         "0.03",
         "1.47",
         "20.0",
         "5.0",
         "4.57",
         "4.44",
         "39.68",
         "109",
         "101.8",
         "184.947",
         "0.0",
         "81",
         "67",
         "22.52799999999999",
         "12.599999999999994",
         "113.0",
         "206.075",
         "537.0",
         "0.4436765692766582"
        ],
        [
         "522",
         "126",
         "95",
         "537",
         "1002",
         "29",
         "56",
         "0.08",
         "1.46",
         "20.0",
         "5.0",
         "4.59",
         "4.45",
         "39.69",
         "119",
         "113.0",
         "206.075",
         "0.0",
         "91",
         "67",
         "21.127999999999986",
         "11.200000000000003",
         "129.9",
         "234.144",
         "546.0",
         "0.4452165913002049"
        ],
        [
         "533",
         "127",
         "96",
         "546",
         "1002",
         "30",
         "56",
         "0.08",
         "1.48",
         "20.0",
         "5.0",
         "4.57",
         "4.47",
         "39.71",
         "140",
         "129.9",
         "234.144",
         "0.00999999999999801",
         "102",
         "68",
         "28.069000000000017",
         "16.900000000000006",
         "146.3",
         "260.47200000000004",
         "554.0",
         "0.44727915632918164"
        ],
        [
         "543",
         "129",
         "97",
         "554",
         "1002",
         "30",
         "57",
         "0.09",
         "1.49",
         "20.0",
         "5.0",
         "4.56",
         "4.48",
         "39.72",
         "149",
         "146.3",
         "260.47200000000004",
         "0.0",
         "112",
         "68",
         "26.32800000000003",
         "16.400000000000006",
         "157.0",
         "281.1",
         "562.0",
         "0.44923123440123275"
        ],
        [
         "554",
         "131",
         "98",
         "562",
         "1003",
         "30",
         "56",
         "0.04",
         "1.5",
         "20.0",
         "5.0",
         "4.59",
         "4.49",
         "39.73",
         "159",
         "157.0",
         "281.1",
         "0.0",
         "123",
         "69",
         "20.627999999999986",
         "10.699999999999989",
         "170.5",
         "304.528",
         "570.0",
         "0.4507726342753539"
        ],
        [
         "564",
         "133",
         "98",
         "570",
         "1003",
         "30",
         "56",
         "0.13",
         "1.5",
         "20.0",
         "5.0",
         "4.57",
         "4.51",
         "39.75",
         "179",
         "170.5",
         "304.528",
         "0.0",
         "133",
         "69",
         "23.427999999999997",
         "13.5",
         "191.3",
         "336.497",
         "579.0",
         "0.4525361394359029"
        ],
        [
         "576",
         "135",
         "100",
         "579",
         "1003",
         "31",
         "56",
         "0.11",
         "1.53",
         "20.0",
         "5.0",
         "4.57",
         "4.53",
         "39.77",
         "200",
         "191.3",
         "336.497",
         "0.0",
         "145",
         "70",
         "31.968999999999994",
         "20.80000000000001",
         "208.0",
         "361.884",
         "586.0",
         "0.45496493496267865"
        ],
        [
         "586",
         "137",
         "101",
         "586",
         "1004",
         "31",
         "56",
         "0.1",
         "1.54",
         "20.0",
         "5.0",
         "4.61",
         "4.54",
         "39.78",
         "210",
         "208.0",
         "361.884",
         "0.0",
         "155",
         "71",
         "25.387",
         "16.69999999999999",
         "222.0",
         "388.294",
         "596.0",
         "0.4569123247631643"
        ],
        [
         "597",
         "138",
         "102",
         "596",
         "1004",
         "31",
         "56",
         "0.13",
         "1.55",
         "20.0",
         "5.0",
         "4.6",
         "4.56",
         "39.8",
         "229",
         "222.0",
         "388.294",
         "0.0",
         "166",
         "71",
         "26.409999999999968",
         "14.0",
         "233.4",
         "408.38100000000003",
         "603.0",
         "0.4589559581797976"
        ],
        [
         "608",
         "140",
         "103",
         "603",
         "1005",
         "31",
         "56",
         "0.09",
         "1.57",
         "20.0",
         "5.0",
         "4.62",
         "4.57",
         "39.81",
         "240",
         "233.4",
         "408.38100000000003",
         "0.0",
         "177",
         "72",
         "20.087000000000046",
         "11.400000000000006",
         "251.8",
         "435.468",
         "610.0",
         "0.4605225928815443"
        ],
        [
         "618",
         "142",
         "104",
         "610",
         "1005",
         "33",
         "56",
         "0.02",
         "1.64",
         "20.0",
         "5.0",
         "4.69",
         "4.59",
         "39.83",
         "259",
         "251.8",
         "435.468",
         "0.0",
         "187",
         "77",
         "27.08699999999999",
         "18.400000000000006",
         "260.1",
         "453.696",
         "618.0",
         "0.4626521886692211"
        ],
        [
         "628",
         "144",
         "105",
         "618",
         "1005",
         "34",
         "56",
         "0.02",
         "1.7",
         "20.0",
         "5.0",
         "4.78",
         "4.6",
         "39.84",
         "270",
         "260.1",
         "453.696",
         "0.010000000000005116",
         "197",
         "81",
         "18.22800000000001",
         "8.300000000000011",
         "270.0",
         "471.04200000000003",
         "624.0",
         "0.4640964060100313"
        ],
        [
         "638",
         "146",
         "107",
         "624",
         "1006",
         "37",
         "56",
         "0.01",
         "1.82",
         "20.0",
         "5.0",
         "4.9",
         "4.6",
         "39.84",
         "270",
         "270.0",
         "471.04200000000003",
         "0.0",
         "207",
         "89",
         "17.346000000000004",
         "9.899999999999977",
         "277.0",
         "496.65700000000004",
         "639.0",
         "0.46547913949224645"
        ],
        [
         "648",
         "149",
         "108",
         "639",
         "1002",
         "33",
         "56",
         "0.11",
         "1.96",
         "20.0",
         "5.0",
         "5.04",
         "4.61",
         "39.85",
         "280",
         "277.0",
         "496.65700000000004",
         "0.0",
         "217",
         "99",
         "25.61500000000001",
         "7.0",
         "289.2",
         "505.134",
         "636.0",
         "0.467536168925821"
        ],
        [
         "658",
         "152",
         "111",
         "636",
         "1003",
         "44",
         "56",
         "0.08",
         "2.06",
         "20.0",
         "5.0",
         "5.11",
         "4.63",
         "39.87",
         "299",
         "289.2",
         "505.134",
         "0.0",
         "227",
         "105",
         "8.476999999999975",
         "12.199999999999989",
         "300.1",
         "533.408",
         "650.0",
         "0.46822092978655694"
        ],
        [
         "668",
         "157",
         "113",
         "650",
         "1004",
         "42",
         "56",
         "0.0",
         "1.9",
         "20.0",
         "5.0",
         "4.92",
         "4.64",
         "39.88",
         "310",
         "300.1",
         "533.408",
         "0.010000000000005116",
         "237",
         "94",
         "28.274",
         "10.900000000000034",
         "330.3",
         "574.777",
         "659.0",
         "0.4705194395714141"
        ],
        [
         "679",
         "164",
         "118",
         "659",
         "1001",
         "47",
         "56",
         "-0.01",
         "2.48",
         "20.0",
         "5.0",
         "5.43",
         "4.68",
         "39.92",
         "350",
         "330.3",
         "574.777",
         "0.010000000000005116",
         "248",
         "135",
         "41.36900000000003",
         "30.19999999999999",
         "451.6",
         "702.282",
         "664.0",
         "0.4739234548914934"
        ],
        [
         "690",
         "172",
         "124",
         "664",
         "1004",
         "38",
         "56",
         "0.81",
         "2.14",
         "20.0",
         "5.0",
         "5.15",
         "4.85",
         "40.09",
         "520",
         "451.6",
         "702.282",
         "0.0",
         "259",
         "110",
         "127.505",
         "121.30000000000001",
         "538.4",
         "828.794",
         "696.0",
         "0.484732014743008"
        ],
        [
         "701",
         "177",
         "129",
         "696",
         "1005",
         "22",
         "56",
         "0.04",
         "1.56",
         "20.0",
         "5.0",
         "4.49",
         "4.89",
         "40.13",
         "560",
         "538.4",
         "828.794",
         "0.010000000000005116",
         "270",
         "70",
         "126.51199999999994",
         "86.79999999999995",
         "659.3",
         "962.104",
         "706.0",
         "0.4959549475257704"
        ],
        [
         "712",
         "181",
         "133",
         "706",
         "1002",
         "0",
         "56",
         "0.69",
         "0.03",
         "20.0",
         "5.0",
         "0.0",
         "5.05",
         "40.29",
         "719",
         "659.3",
         "962.104",
         "0.00999999999999801",
         "281",
         "0",
         "133.31000000000006",
         "120.89999999999998",
         "765.6",
         "1021.2460000000001",
         "668.0",
         "0.5083573192949178"
        ],
        [
         "723",
         "181",
         "132",
         "668",
         "1003",
         "35",
         "56",
         "0.14",
         "1.43",
         "20.0",
         "5.0",
         "4.43",
         "5.12",
         "40.36",
         "789",
         "765.6",
         "1021.2460000000001",
         "0.0",
         "292",
         "63",
         "59.14200000000005",
         "106.30000000000007",
         "789.0",
         "1083.117",
         "699.0",
         "0.5140604057017835"
        ],
        [
         "733",
         "179",
         "131",
         "699",
         "1003",
         "30",
         "56",
         "-0.05",
         "1.56",
         "20.0",
         "5.0",
         "4.54",
         "5.1",
         "40.34",
         "770",
         "789.0",
         "1083.117",
         "0.0",
         "302",
         "71",
         "61.87099999999987",
         "23.399999999999977",
         "789.0",
         "1099.25",
         "712.0",
         "0.5201652321174967"
        ],
        [
         "744",
         "177",
         "130",
         "712",
         "1004",
         "27",
         "57",
         "0.01",
         "1.43",
         "20.0",
         "5.0",
         "4.37",
         "5.1",
         "40.34",
         "770",
         "789.0",
         "1099.25",
         "0.0",
         "313",
         "63",
         "16.133000000000038",
         "0.0",
         "789.0",
         "1100.491",
         "713.0",
         "0.5217809867629363"
        ],
        [
         "755",
         "177",
         "129",
         "713",
         "1004",
         "34",
         "57",
         "0.05",
         "1.72",
         "20.0",
         "5.0",
         "4.43",
         "5.11",
         "40.35",
         "780",
         "789.0",
         "1100.491",
         "0.0",
         "324",
         "76",
         "1.2409999999999854",
         "0.0",
         "789.0",
         "1105.455",
         "717.0",
         "0.52190569144897"
        ],
        [
         "765",
         "177",
         "129",
         "717",
         "1004",
         "37",
         "56",
         "0.11",
         "1.65",
         "20.0",
         "5.0",
         "4.68",
         "5.12",
         "40.36",
         "789",
         "789.0",
         "1105.455",
         "0.0",
         "334",
         "77",
         "4.963999999999942",
         "0.0",
         "791.0",
         "1128.5520000000001",
         "734.0",
         "0.5224051068472659"
        ],
        [
         "776",
         "177",
         "129",
         "734",
         "1004",
         "36",
         "56",
         "0.06",
         "1.82",
         "20.0",
         "5.0",
         "4.81",
         "5.13",
         "40.37",
         "799",
         "791.0",
         "1128.5520000000001",
         "0.0",
         "345",
         "87",
         "23.097000000000207",
         "2.0",
         "799.0",
         "1146.48",
         "742.0",
         "0.5247414631728309"
        ],
        [
         "786",
         "177",
         "128",
         "742",
         "1005",
         "32",
         "56",
         "0.01",
         "1.69",
         "20.0",
         "5.0",
         "4.92",
         "5.13",
         "40.37",
         "799",
         "799.0",
         "1146.48",
         "0.0",
         "355",
         "83",
         "17.927999999999884",
         "8.0",
         "799.0",
         "1162.613",
         "755.0",
         "0.5265694100090935"
        ],
        [
         "797",
         "177",
         "128",
         "755",
         "1005",
         "29",
         "56",
         "0.0",
         "1.72",
         "20.0",
         "5.0",
         "4.69",
         "5.13",
         "40.37",
         "799",
         "799.0",
         "1162.613",
         "0.0",
         "366",
         "81",
         "16.133000000000038",
         "0.0",
         "799.0",
         "1171.3",
         "762.0",
         "0.5282252586403327"
        ],
        [
         "807",
         "178",
         "128",
         "762",
         "1005",
         "29",
         "56",
         "-0.01",
         "1.65",
         "20.0",
         "5.0",
         "4.63",
         "5.13",
         "40.37",
         "799",
         "799.0",
         "1171.3",
         "0.0",
         "376",
         "76",
         "8.686999999999898",
         "0.0",
         "800.1",
         "1178.605",
         "767.0",
         "0.5291211902298065"
        ],
        [
         "817",
         "178",
         "128",
         "767",
         "1005",
         "32",
         "56",
         "0.01",
         "1.66",
         "20.0",
         "5.0",
         "4.62",
         "5.14",
         "40.38",
         "810",
         "800.1",
         "1178.605",
         "0.010000000000005116",
         "386",
         "77",
         "7.305000000000064",
         "1.1000000000000227",
         "810.0",
         "1197.192",
         "774.0",
         "0.5298769453669495"
        ],
        [
         "828",
         "179",
         "129",
         "774",
         "1005",
         "34",
         "57",
         "0.01",
         "1.77",
         "20.0",
         "5.0",
         "4.72",
         "5.14",
         "40.38",
         "810",
         "810.0",
         "1197.192",
         "0.0",
         "397",
         "83",
         "18.58699999999999",
         "9.899999999999977",
         "810.0",
         "1203.397",
         "779.0",
         "0.5318096757177665"
        ],
        [
         "838",
         "180",
         "129",
         "779",
         "1005",
         "36",
         "56",
         "0.0",
         "1.86",
         "20.0",
         "5.0",
         "4.87",
         "5.14",
         "40.38",
         "810",
         "810.0",
         "1203.397",
         "0.0",
         "407",
         "91",
         "6.204999999999927",
         "0.0",
         "810.0",
         "1218.289",
         "791.0",
         "0.5324580325813232"
        ],
        [
         "848",
         "181",
         "130",
         "791",
         "1002",
         "33",
         "56",
         "0.0",
         "1.74",
         "20.0",
         "5.0",
         "4.73",
         "5.14",
         "40.38",
         "810",
         "810.0",
         "1218.289",
         "0.0",
         "417",
         "82",
         "14.892000000000053",
         "0.0",
         "810.0",
         "1217.048",
         "790.0",
         "0.5340205580179836"
        ],
        [
         "858",
         "182",
         "130",
         "790",
         "1003",
         "43",
         "56",
         "-0.02",
         "2.26",
         "20.0",
         "5.0",
         "5.23",
         "5.14",
         "40.38",
         "810",
         "810.0",
         "1217.048",
         "0.0",
         "427",
         "118",
         "-1.2409999999999854",
         "0.0",
         "810.0",
         "1239.386",
         "808.0",
         "0.533889997383742"
        ],
        [
         "869",
         "184",
         "132",
         "808",
         "1004",
         "37",
         "56",
         "0.0",
         "2.1",
         "20.0",
         "5.0",
         "5.04",
         "5.13",
         "40.37",
         "799",
         "810.0",
         "1239.386",
         "0.0",
         "438",
         "106",
         "22.337999999999965",
         "0.0",
         "810.0",
         "1256.76",
         "822.0",
         "0.5362498995768178"
        ],
        [
         "879",
         "186",
         "133",
         "822",
         "1004",
         "31",
         "56",
         "0.0",
         "1.66",
         "20.0",
         "5.0",
         "4.61",
         "5.14",
         "40.38",
         "810",
         "810.0",
         "1256.76",
         "0.0",
         "448",
         "76",
         "17.374000000000024",
         "0.0",
         "813.9",
         "1260.66",
         "822.0",
         "0.5380998522707416"
        ],
        [
         "889",
         "189",
         "135",
         "822",
         "1005",
         "35",
         "56",
         "0.14",
         "1.76",
         "20.0",
         "5.0",
         "4.69",
         "5.16",
         "40.4",
         "829",
         "813.9",
         "1260.66",
         "0.00999999999999801",
         "458",
         "82",
         "3.900000000000091",
         "3.8999999999999773",
         "839.3",
         "1298.47",
         "832.0",
         "0.5385168728663008"
        ],
        [
         "899",
         "190",
         "136",
         "832",
         "1005",
         "35",
         "56",
         "0.19",
         "1.83",
         "20.0",
         "5.0",
         "4.77",
         "5.18",
         "40.42",
         "850",
         "839.3",
         "1298.47",
         "0.0",
         "468",
         "87",
         "37.809999999999945",
         "25.399999999999977",
         "861.2",
         "1332.7800000000002",
         "842.0",
         "0.5425935973955508"
        ],
        [
         "909",
         "191",
         "137",
         "842",
         "1005",
         "35",
         "57",
         "0.12",
         "1.87",
         "20.0",
         "5.0",
         "4.83",
         "5.2",
         "40.44",
         "869",
         "861.2",
         "1332.7800000000002",
         "0.0",
         "478",
         "90",
         "34.31000000000017",
         "21.90000000000009",
         "881.8",
         "1363.308",
         "850.0",
         "0.5463467346206419"
        ],
        [
         "919",
         "192",
         "138",
         "850",
         "1005",
         "33",
         "56",
         "0.16",
         "1.79",
         "20.0",
         "5.0",
         "4.7",
         "5.22",
         "40.46",
         "890",
         "881.8",
         "1363.308",
         "0.0",
         "488",
         "84",
         "30.527999999999793",
         "20.59999999999991",
         "898.1",
         "1385.813",
         "855.0",
         "0.5497300845570068"
        ],
        [
         "930",
         "193",
         "139",
         "855",
         "1003",
         "41",
         "56",
         "0.01",
         "2.34",
         "20.0",
         "5.0",
         "5.26",
         "5.23",
         "40.47",
         "899",
         "898.1",
         "1385.813",
         "0.0",
         "499",
         "123",
         "22.50500000000011",
         "16.300000000000068",
         "899.0",
         "1400.364",
         "866.0",
         "0.5522512184679329"
        ],
        [
         "941",
         "195",
         "140",
         "866",
         "1004",
         "40",
         "56",
         "0.06",
         "1.9",
         "20.0",
         "5.0",
         "4.95",
         "5.23",
         "40.47",
         "899",
         "899.0",
         "1400.364",
         "0.0",
         "510",
         "94",
         "14.55099999999993",
         "0.8999999999999773",
         "926.4",
         "1442.656",
         "878.0",
         "0.553893647514346"
        ]
       ],
       "shape": {
        "columns": 25,
        "rows": 78
       }
      },
      "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>MTC1</th>\n",
       "      <th>MTC2</th>\n",
       "      <th>Pyrometer</th>\n",
       "      <th>AV Abs. Press. 2</th>\n",
       "      <th>yPower</th>\n",
       "      <th>AV Force</th>\n",
       "      <th>AV Speed</th>\n",
       "      <th>I RMS</th>\n",
       "      <th>Pulse Time</th>\n",
       "      <th>Pause Time</th>\n",
       "      <th>...</th>\n",
       "      <th>TravelRelativeTempCorrected</th>\n",
       "      <th>TravelDeltaOriginal</th>\n",
       "      <th>seconds</th>\n",
       "      <th>Heating</th>\n",
       "      <th>TravelDelta</th>\n",
       "      <th>TravelDelta2</th>\n",
       "      <th>TravelRelativeCorrectedShifted</th>\n",
       "      <th>TravelRelativeTempCorrectedShifted</th>\n",
       "      <th>PyrometerShifted</th>\n",
       "      <th>Density</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>432</th>\n",
       "      <td>104</td>\n",
       "      <td>84</td>\n",
       "      <td>462</td>\n",
       "      <td>853</td>\n",
       "      <td>30</td>\n",
       "      <td>56</td>\n",
       "      <td>0.03</td>\n",
       "      <td>1.47</td>\n",
       "      <td>20.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>68</td>\n",
       "      <td>0.000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.0</td>\n",
       "      <td>23.892</td>\n",
       "      <td>474.0</td>\n",
       "      <td>0.430637</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>442</th>\n",
       "      <td>108</td>\n",
       "      <td>86</td>\n",
       "      <td>474</td>\n",
       "      <td>965</td>\n",
       "      <td>29</td>\n",
       "      <td>56</td>\n",
       "      <td>0.10</td>\n",
       "      <td>1.39</td>\n",
       "      <td>20.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>...</td>\n",
       "      <td>23.892</td>\n",
       "      <td>0.00</td>\n",
       "      <td>11</td>\n",
       "      <td>63</td>\n",
       "      <td>23.892</td>\n",
       "      <td>9.0</td>\n",
       "      <td>17.8</td>\n",
       "      <td>43.861</td>\n",
       "      <td>483.0</td>\n",
       "      <td>0.432278</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>452</th>\n",
       "      <td>111</td>\n",
       "      <td>87</td>\n",
       "      <td>483</td>\n",
       "      <td>957</td>\n",
       "      <td>29</td>\n",
       "      <td>56</td>\n",
       "      <td>0.08</td>\n",
       "      <td>1.43</td>\n",
       "      <td>20.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>...</td>\n",
       "      <td>43.861</td>\n",
       "      <td>0.00</td>\n",
       "      <td>21</td>\n",
       "      <td>64</td>\n",
       "      <td>19.969</td>\n",
       "      <td>8.8</td>\n",
       "      <td>28.9</td>\n",
       "      <td>64.889</td>\n",
       "      <td>491.0</td>\n",
       "      <td>0.433660</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>462</th>\n",
       "      <td>114</td>\n",
       "      <td>89</td>\n",
       "      <td>491</td>\n",
       "      <td>968</td>\n",
       "      <td>28</td>\n",
       "      <td>57</td>\n",
       "      <td>0.09</td>\n",
       "      <td>1.38</td>\n",
       "      <td>20.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>...</td>\n",
       "      <td>64.889</td>\n",
       "      <td>0.01</td>\n",
       "      <td>31</td>\n",
       "      <td>62</td>\n",
       "      <td>21.028</td>\n",
       "      <td>11.1</td>\n",
       "      <td>44.0</td>\n",
       "      <td>89.917</td>\n",
       "      <td>499.0</td>\n",
       "      <td>0.435124</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>472</th>\n",
       "      <td>116</td>\n",
       "      <td>90</td>\n",
       "      <td>499</td>\n",
       "      <td>984</td>\n",
       "      <td>29</td>\n",
       "      <td>56</td>\n",
       "      <td>0.11</td>\n",
       "      <td>1.41</td>\n",
       "      <td>20.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>...</td>\n",
       "      <td>89.917</td>\n",
       "      <td>0.00</td>\n",
       "      <td>41</td>\n",
       "      <td>63</td>\n",
       "      <td>25.028</td>\n",
       "      <td>15.1</td>\n",
       "      <td>63.9</td>\n",
       "      <td>118.504</td>\n",
       "      <td>506.0</td>\n",
       "      <td>0.436880</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>1195</th>\n",
       "      <td>329</td>\n",
       "      <td>238</td>\n",
       "      <td>1065</td>\n",
       "      <td>1006</td>\n",
       "      <td>70</td>\n",
       "      <td>56</td>\n",
       "      <td>0.44</td>\n",
       "      <td>3.91</td>\n",
       "      <td>20.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>...</td>\n",
       "      <td>3873.823</td>\n",
       "      <td>0.01</td>\n",
       "      <td>764</td>\n",
       "      <td>249</td>\n",
       "      <td>116.093</td>\n",
       "      <td>25.5</td>\n",
       "      <td>3286.6</td>\n",
       "      <td>4195.012</td>\n",
       "      <td>1194.0</td>\n",
       "      <td>1.120216</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1207</th>\n",
       "      <td>348</td>\n",
       "      <td>253</td>\n",
       "      <td>1194</td>\n",
       "      <td>1005</td>\n",
       "      <td>0</td>\n",
       "      <td>56</td>\n",
       "      <td>2.64</td>\n",
       "      <td>0.07</td>\n",
       "      <td>20.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>...</td>\n",
       "      <td>4195.012</td>\n",
       "      <td>0.04</td>\n",
       "      <td>776</td>\n",
       "      <td>0</td>\n",
       "      <td>321.189</td>\n",
       "      <td>161.1</td>\n",
       "      <td>3667.4</td>\n",
       "      <td>4615.524</td>\n",
       "      <td>1226.0</td>\n",
       "      <td>1.291714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1217</th>\n",
       "      <td>357</td>\n",
       "      <td>261</td>\n",
       "      <td>1226</td>\n",
       "      <td>1007</td>\n",
       "      <td>0</td>\n",
       "      <td>56</td>\n",
       "      <td>1.75</td>\n",
       "      <td>0.04</td>\n",
       "      <td>20.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>...</td>\n",
       "      <td>4615.524</td>\n",
       "      <td>0.03</td>\n",
       "      <td>786</td>\n",
       "      <td>0</td>\n",
       "      <td>420.512</td>\n",
       "      <td>380.8</td>\n",
       "      <td>3908.5</td>\n",
       "      <td>4950.940</td>\n",
       "      <td>1302.0</td>\n",
       "      <td>1.615522</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1227</th>\n",
       "      <td>358</td>\n",
       "      <td>263</td>\n",
       "      <td>1302</td>\n",
       "      <td>1002</td>\n",
       "      <td>0</td>\n",
       "      <td>56</td>\n",
       "      <td>1.09</td>\n",
       "      <td>0.04</td>\n",
       "      <td>20.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>...</td>\n",
       "      <td>4950.940</td>\n",
       "      <td>0.02</td>\n",
       "      <td>796</td>\n",
       "      <td>0</td>\n",
       "      <td>335.416</td>\n",
       "      <td>241.1</td>\n",
       "      <td>4065.5</td>\n",
       "      <td>5151.375</td>\n",
       "      <td>1337.0</td>\n",
       "      <td>2.019284</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1237</th>\n",
       "      <td>355</td>\n",
       "      <td>262</td>\n",
       "      <td>1337</td>\n",
       "      <td>1001</td>\n",
       "      <td>0</td>\n",
       "      <td>56</td>\n",
       "      <td>0.73</td>\n",
       "      <td>0.03</td>\n",
       "      <td>20.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>...</td>\n",
       "      <td>5151.375</td>\n",
       "      <td>0.01</td>\n",
       "      <td>806</td>\n",
       "      <td>0</td>\n",
       "      <td>200.435</td>\n",
       "      <td>157.0</td>\n",
       "      <td>4185.6</td>\n",
       "      <td>5282.644</td>\n",
       "      <td>1346.0</td>\n",
       "      <td>2.373809</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>78 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      MTC1  MTC2  Pyrometer  AV Abs. Press. 2  yPower  AV Force  AV Speed  \\\n",
       "432    104    84        462               853      30        56      0.03   \n",
       "442    108    86        474               965      29        56      0.10   \n",
       "452    111    87        483               957      29        56      0.08   \n",
       "462    114    89        491               968      28        57      0.09   \n",
       "472    116    90        499               984      29        56      0.11   \n",
       "...    ...   ...        ...               ...     ...       ...       ...   \n",
       "1195   329   238       1065              1006      70        56      0.44   \n",
       "1207   348   253       1194              1005       0        56      2.64   \n",
       "1217   357   261       1226              1007       0        56      1.75   \n",
       "1227   358   263       1302              1002       0        56      1.09   \n",
       "1237   355   262       1337              1001       0        56      0.73   \n",
       "\n",
       "      I RMS  Pulse Time  Pause Time  ...  TravelRelativeTempCorrected  \\\n",
       "432    1.47        20.0         5.0  ...                        0.000   \n",
       "442    1.39        20.0         5.0  ...                       23.892   \n",
       "452    1.43        20.0         5.0  ...                       43.861   \n",
       "462    1.38        20.0         5.0  ...                       64.889   \n",
       "472    1.41        20.0         5.0  ...                       89.917   \n",
       "...     ...         ...         ...  ...                          ...   \n",
       "1195   3.91        20.0         5.0  ...                     3873.823   \n",
       "1207   0.07        20.0         5.0  ...                     4195.012   \n",
       "1217   0.04        20.0         5.0  ...                     4615.524   \n",
       "1227   0.04        20.0         5.0  ...                     4950.940   \n",
       "1237   0.03        20.0         5.0  ...                     5151.375   \n",
       "\n",
       "      TravelDeltaOriginal  seconds  Heating  TravelDelta  TravelDelta2  \\\n",
       "432                   NaN        1       68        0.000           NaN   \n",
       "442                  0.00       11       63       23.892           9.0   \n",
       "452                  0.00       21       64       19.969           8.8   \n",
       "462                  0.01       31       62       21.028          11.1   \n",
       "472                  0.00       41       63       25.028          15.1   \n",
       "...                   ...      ...      ...          ...           ...   \n",
       "1195                 0.01      764      249      116.093          25.5   \n",
       "1207                 0.04      776        0      321.189         161.1   \n",
       "1217                 0.03      786        0      420.512         380.8   \n",
       "1227                 0.02      796        0      335.416         241.1   \n",
       "1237                 0.01      806        0      200.435         157.0   \n",
       "\n",
       "      TravelRelativeCorrectedShifted  TravelRelativeTempCorrectedShifted  \\\n",
       "432                              9.0                              23.892   \n",
       "442                             17.8                              43.861   \n",
       "452                             28.9                              64.889   \n",
       "462                             44.0                              89.917   \n",
       "472                             63.9                             118.504   \n",
       "...                              ...                                 ...   \n",
       "1195                          3286.6                            4195.012   \n",
       "1207                          3667.4                            4615.524   \n",
       "1217                          3908.5                            4950.940   \n",
       "1227                          4065.5                            5151.375   \n",
       "1237                          4185.6                            5282.644   \n",
       "\n",
       "      PyrometerShifted   Density  \n",
       "432              474.0  0.430637  \n",
       "442              483.0  0.432278  \n",
       "452              491.0  0.433660  \n",
       "462              499.0  0.435124  \n",
       "472              506.0  0.436880  \n",
       "...                ...       ...  \n",
       "1195            1194.0  1.120216  \n",
       "1207            1226.0  1.291714  \n",
       "1217            1302.0  1.615522  \n",
       "1227            1337.0  2.019284  \n",
       "1237            1346.0  2.373809  \n",
       "\n",
       "[78 rows x 25 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "#dataEmpty1.describe()\n",
    "selectedData=dataEmpty2[dataEmpty2['Pyrometer'] >1200].copy()\n",
    "selectedData=selectedData[selectedData['Pyrometer'] <1210].copy()\n",
    "dataN1100.head(10000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1000x600 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 6))\n",
    "fig, ax1 = plt.subplots( figsize=(10, 6))\n",
    "ax1.plot(dataEmpty1['Pyrometer'], dataEmpty1['TravelRelative'], color='orange') \n",
    "ax1.plot(dataEmpty2['Pyrometer'], dataEmpty2['TravelRelative'], color='red') \n",
    "\n",
    "minTemperature = data1000['Pyrometer'].min()\n",
    "data1000['TravelRelative2']=data1000['TravelRelative']+(data1000['Pyrometer']-minTemperature)*1.241\n",
    "# sc = ax1.plot(data1000['Pyrometer'], data1000['TravelRelative'], color='green') \n",
    "# sc = ax1.plot(data1000['Pyrometer'], data1000['TravelRelative2'], color='blue') \n",
    "\n",
    "\n",
    "#minTemperature = data1200['Pyrometer'].min()\n",
    "#data1200['TravelRelative2']=data1200['TravelRelative']+(data1200['Pyrometer']-minTemperature)*1.241\n",
    "#sc = ax1.plot(data1200['Pyrometer'], data1200['TravelRelative'], color='green') \n",
    "#sc = ax1.plot(data1200['Pyrometer'], data1200['TravelRelative2'], color='blue') \n",
    "\n",
    "#sc = plt.scatter(setToPlot['seconds'], setToPlot['Pyrometer'],color='red')  \n",
    "#sc = plt.scatter(setToPlot['seconds'], setToPlot['Heating'], color='green') \n",
    "# Add color bar to show the color scale\n",
    "ax1.set_ylabel('Хід поршня, μм')\n",
    "#ax1.set_ylim(400, 1000)\n",
    "#ax1.set_xlim(300, 1400)\n",
    "#ax1.set_title('Спікання без порошку, визначення коєфіцієнта термічного розширення')\n",
    "ax1.set_xlabel('Температура, °C')\n",
    "ax1.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 4000x600 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 2000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(40, 6))\n",
    "fig, ax1 = plt.subplots( figsize=(20, 6))\n",
    "#sc = ax1.plot(data900['seconds'], data900['Pyrometer'], color='red') \n",
    "#sc = ax1.plot(data1000['seconds'], data1000['Pyrometer'], color='red') \n",
    "#sc = ax1.plot(data1000['seconds'], data1000['Density'], color='green')\n",
    "#sc = ax1.plot(data1200['seconds'], data1200['Density'], color='red')\n",
    "#sc = ax1.plot(data1000['Density'], data1000['TravelDelta'], color='green')\n",
    "#sc = ax1.plot(data900['Density'], data900['TravelDelta'], color='black')\n",
    "sc = ax1.plot(data1200['Density'], data1200['TravelDelta'], color='red')\n",
    "#sc = ax1.plot(data1000['seconds'], data1000['TravelRelativeTempCorrected'], color='blue') \n",
    "#sc = ax1.plot(data1200['seconds'], data1200['Pyrometer'], color='green') \n",
    "#sc = ax1.plot(data1350['seconds'], data1350['Pyrometer'], color='black') \n",
    "#sc = ax1.plot(dataN1200['seconds'], dataN1200['Pyrometer'], color='purple') \n",
    "#sc = ax1.plot(dataN1100['seconds'], dataN1100['Pyrometer'], color='orange') \n",
    "#sc = plt.scatter(setToPlot['seconds'], setToPlot['Pyrometer'],color='red')  \n",
    "#sc = plt.scatter(setToPlot['seconds'], setToPlot['Heating'], color='green') \n",
    "# Add color bar to show the color scale\n",
    "ax1.set_ylabel('Швидкість ущільнення')\n",
    "#ax1.set_ylim(400, 1000)\n",
    "#ax1.set_xlim(300, 1400)\n",
    "#ax1.set_title('title')\n",
    "ax1.set_xlabel('Відносна щільність')\n",
    "ax1.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 4000x600 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 4000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(40, 6))\n",
    "fig, ax1 = plt.subplots( figsize=(40, 6))\n",
    "sc = ax1.plot(data900['seconds'], data900['AV Force'], color='red') \n",
    "sc = ax1.plot(data1000['seconds'], data1000['AV Force'], color='blue') \n",
    "sc = ax1.plot(data1200['seconds'], data1200['AV Force'], color='green') \n",
    "sc = ax1.plot(data1350['seconds'], data1350['AV Force'], color='black') \n",
    "sc = ax1.plot(dataN1200['seconds'], dataN1200['AV Force'], color='purple') \n",
    "sc = ax1.plot(dataN1100['seconds'], dataN1100['AV Force'], color='orange') \n",
    "#sc = plt.scatter(setToPlot['seconds'], setToPlot['Pyrometer'],color='red')  \n",
    "#sc = plt.scatter(setToPlot['seconds'], setToPlot['Heating'], color='green') \n",
    "# Add color bar to show the color scale\n",
    "ax1.set_ylabel('AV Force')\n",
    "#ax1.set_ylim(400, 1000)\n",
    "#ax1.set_xlim(300, 1400)\n",
    "ax1.set_title('title')\n",
    "ax1.set_xlabel('seconds')\n",
    "ax1.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 2000x600 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 3000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "plt.figure(figsize=(20, 6))\n",
    "fig, ax1 = plt.subplots( figsize=(30, 6))\n",
    "#sc = ax1.plot(data900['seconds'], data900['TravelDelta'], color='red')  \n",
    "#sc = ax1.plot(data1000['seconds'], data1000['TravelDelta'], color='green') \n",
    "sc = ax1.plot(data1200['seconds'], data1200['TravelDeltaOriginal']*1000, color='gray') \n",
    "sc = ax1.plot(data1200['seconds'], data1200['TravelDelta2'], color='red') \n",
    "\n",
    "##sc = ax1.plot(data1200['seconds'], data1200['TravelDelta'], color='green') \n",
    "#sc = ax1.plot(dataN1200['seconds'], dataN1200['TravelDelta'], color='purple') \n",
    "#sc = ax1.plot(dataN1100['seconds'], dataN1100['TravelDelta'], color='orange') \n",
    "#sc = ax1.plot(data1350['seconds'], data1350['TravelDelta'], color='black') \n",
    "#sc = plt.scatter(setToPlot['seconds'], setToPlot['Pyrometer'],color='red')  \n",
    "#sc = plt.scatter(setToPlot['seconds'], setToPlot['Heating'], color='green') \n",
    "# Add color bar to show the color scale\n",
    "ax1.set_ylabel('Швидкість ходу поршня, μm/с')\n",
    "#ax1.set_ylim(400, 1000)\n",
    "#ax1.set_xlim(300, 1400)\n",
    "ax1.set_title('Швидкість ходу поршня до і після усереднення')\n",
    "ax1.set_xlabel('час, с')\n",
    "ax1.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_data_Travel(data, title):\n",
    "    setToPlot = data.copy()\n",
    "    #setToPlot = setToPlot[(setToPlot['seconds'] > 0) & (setToPlot['seconds'] <650)].copy()\n",
    "    #setToPlot = setToPlot[(setToPlot['Heating'] > 0)].copy()\n",
    "\n",
    "    #setToPlot = setToPlot[(setToPlot['Pyrometer'] < 900)].copy()\n",
    "    #setToPlot = setToPlot[(setToPlot['seconds'] < 850)].copy()\n",
    "    plt.figure(figsize=(40, 6))\n",
    "    fig, ax1 = plt.subplots( figsize=(40, 6))\n",
    "    sc2 = ax1.plot(setToPlot['seconds'], setToPlot['TravelRelative'], color='red')\n",
    "    sc2 = ax1.plot(setToPlot['seconds'], setToPlot['TravelRelative'], color='red')\n",
    "    TravelDeltaOriginal\n",
    "    #sc2 = ax1.plot(setToPlot['seconds'], setToPlot['TravelRelativeCorrected'], color='green')\n",
    "    #sc2 = ax1.plot(setToPlot['seconds'], setToPlot['TravelRelativeCorrectedShifted'], color='blue')\n",
    "    sc2 = ax1.plot(setToPlot['seconds'], setToPlot['TravelRelativeTempCorrected'], color='orange')\n",
    "\n",
    "    \n",
    "    ax1.set_ylabel('TrevelRelative')\n",
    "    #ax1.set_ylim(0, 800)\n",
    "    #ax1.set_ylim(0, 4000)\n",
    "    ax1.set_title(title)\n",
    "    ax1.set_xlabel('seconds')\n",
    "    ax1.grid(True)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'TravelDeltaOriginal' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[31m---------------------------------------------------------------------------\u001b[39m",
      "\u001b[31mNameError\u001b[39m                                 Traceback (most recent call last)",
      "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[17]\u001b[39m\u001b[32m, line 1\u001b[39m\n\u001b[32m----> \u001b[39m\u001b[32m1\u001b[39m \u001b[43mplot_data_Travel\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdata900\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[33;43m'\u001b[39;49m\u001b[33;43m900\u001b[39;49m\u001b[33;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[32m      2\u001b[39m plot_data_Travel(data1000, \u001b[33m'\u001b[39m\u001b[33m1000\u001b[39m\u001b[33m'\u001b[39m)\n\u001b[32m      3\u001b[39m plot_data_Travel(data1200, \u001b[33m'\u001b[39m\u001b[33m1200\u001b[39m\u001b[33m'\u001b[39m)\n",
      "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[16]\u001b[39m\u001b[32m, line 12\u001b[39m, in \u001b[36mplot_data_Travel\u001b[39m\u001b[34m(data, title)\u001b[39m\n\u001b[32m     10\u001b[39m sc2 = ax1.plot(setToPlot[\u001b[33m'\u001b[39m\u001b[33mseconds\u001b[39m\u001b[33m'\u001b[39m], setToPlot[\u001b[33m'\u001b[39m\u001b[33mTravelRelative\u001b[39m\u001b[33m'\u001b[39m], color=\u001b[33m'\u001b[39m\u001b[33mred\u001b[39m\u001b[33m'\u001b[39m)\n\u001b[32m     11\u001b[39m sc2 = ax1.plot(setToPlot[\u001b[33m'\u001b[39m\u001b[33mseconds\u001b[39m\u001b[33m'\u001b[39m], setToPlot[\u001b[33m'\u001b[39m\u001b[33mTravelRelative\u001b[39m\u001b[33m'\u001b[39m], color=\u001b[33m'\u001b[39m\u001b[33mred\u001b[39m\u001b[33m'\u001b[39m)\n\u001b[32m---> \u001b[39m\u001b[32m12\u001b[39m \u001b[43mTravelDeltaOriginal\u001b[49m\n\u001b[32m     13\u001b[39m \u001b[38;5;66;03m#sc2 = ax1.plot(setToPlot['seconds'], setToPlot['TravelRelativeCorrected'], color='green')\u001b[39;00m\n\u001b[32m     14\u001b[39m \u001b[38;5;66;03m#sc2 = ax1.plot(setToPlot['seconds'], setToPlot['TravelRelativeCorrectedShifted'], color='blue')\u001b[39;00m\n\u001b[32m     15\u001b[39m sc2 = ax1.plot(setToPlot[\u001b[33m'\u001b[39m\u001b[33mseconds\u001b[39m\u001b[33m'\u001b[39m], setToPlot[\u001b[33m'\u001b[39m\u001b[33mTravelRelativeTempCorrected\u001b[39m\u001b[33m'\u001b[39m], color=\u001b[33m'\u001b[39m\u001b[33morange\u001b[39m\u001b[33m'\u001b[39m)\n",
      "\u001b[31mNameError\u001b[39m: name 'TravelDeltaOriginal' is not defined"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 4000x600 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 4000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_data_Travel(data900, '900')\n",
    "plot_data_Travel(data1000, '1000')\n",
    "plot_data_Travel(data1200, '1200')\n",
    "plot_data_Travel(dataN1200, 'N1200')\n",
    "#plot_data_Travel(dataN1100, 'N1100')\n",
    "#plot_data_Travel(data1350, '1350')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_data_TravelDelta(data, title):\n",
    "    setToPlot = data.copy()\n",
    "    #setToPlot = setToPlot[(setToPlot['seconds'] > 0) & (setToPlot['seconds'] <650)].copy()\n",
    "    #setToPlot = setToPlot[(setToPlot['Heating'] > 0)].copy()\n",
    "\n",
    "    #setToPlot = setToPlot[(setToPlot['Pyrometer'] < 900)].copy()\n",
    "    #setToPlot = setToPlot[(setToPlot['seconds'] < 850)].copy()\n",
    "\n",
    "    # setToPlot['Heating'] = (setToPlot['Heating']*10).astype(int)\n",
    "    plt.figure(figsize=(40, 6))\n",
    "    fig, ax1 = plt.subplots( figsize=(40, 6))\n",
    "    sc = ax1.plot(setToPlot['Pyrometer'], setToPlot['TravelDelta'], color='red')  \n",
    "    #sc = ax1.plot(setToPlot['seconds'], setToPlot['TravelRelativeTempCorrectedShifted'], color='red')  \n",
    "    #sc = ax1.plot(setToPlot['seconds'], setToPlot['TravelDelta'], color='green')  \n",
    "    #sc = ax1.plot(setToPlot['Pyrometer'], setToPlot['AV Force']*10, color='blue')  \n",
    "    # sc = ax1.plot(setToPlot['seconds'], setToPlot['Heating']) \n",
    "    #sc = plt.scatter(setToPlot['seconds'], setToPlot['Pyrometer'],color='red')  \n",
    "    #sc = plt.scatter(setToPlot['seconds'], setToPlot['Heating'], color='green') \n",
    "    # ax2 = ax1.twinx()\n",
    "    # sc2 = ax2.plot(setToPlot['seconds'], setToPlot['TravelDelta'], color='green')\n",
    "    # Add color bar to show the color scale\n",
    "    # ax2.set_ylim(0, 10)\n",
    "    # ax2.set_ylabel('TravelDelta')\n",
    "    ax1.set_ylabel('seconds, TravelDelta')\n",
    "    # ax1.set_ylim(400, 1000)\n",
    "    # ax1.set_xlim(300, 1400)\n",
    "    # ax2.set_xlim(300, 1400)\n",
    "    ax1.set_title(title)\n",
    "    ax1.set_xlabel('seconds')\n",
    "    ax1.grid(True)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "setToPlot = data1000\n",
    "#setToPlot = setToPlot[(setToPlot['seconds'] > 550) & (setToPlot['seconds'] <650)].copy()\n",
    " \n",
    "\n",
    "plt.figure(figsize=(40, 6))\n",
    "#sc = plt.scatter(setToPlot['seconds'], setToPlot['TravelDelta'], c=setToPlot['Pyrometer'], cmap='viridis')  \n",
    "sc = plt.plot(setToPlot['seconds'], setToPlot['TravelDelta'])  \n",
    "\n",
    "# Add color bar to show the color scale\n",
    "#plt.colorbar(sc, label='Temperature')\n",
    "#plt.title('-')\n",
    "plt.xlabel('seconds')\n",
    "plt.ylabel('TravelDelta')\n",
    "plt.grid(True)\n",
    "plt.show()\n",
    "\n",
    "#setToPlot.head(50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plot_data_TravelDelta(data900, '900')\n",
    "plot_data_TravelDelta(data1000, '1000')\n",
    "plot_data_TravelDelta(data1200, '1200')\n",
    "plot_data_TravelDelta(dataN1200, 'N1200')\n",
    "plot_data_TravelDelta(data1350, '1350')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "#data1000.corr()\n",
    "selected_columns = ['Pyrometer', '', 'column3']\n",
    "new_df = data1000[selected_columns].copy()\n",
    "corr_matrix = new_df.corr()\n",
    "\n",
    "# Побудова теплової карти\n",
    "sns.heatmap(corr_matrix, annot=True, cmap='coolwarm', fmt=\".2f\")\n",
    "plt.title(\"Кореляційна матриця\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.gaussian_process import GaussianProcessRegressor\n",
    "from sklearn.gaussian_process.kernels import RBF, Matern, RationalQuadratic, ExpSineSquared, DotProduct, ConstantKernel as C\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import mean_squared_error\n",
    "\n",
    "\n",
    "# Select the relevant columns\n",
    "X = pd.concat( [data900,data1000,data1200], axis=0)\n",
    "y = pd.concat( [data900,data1000,data1200], axis=0)\n",
    "\n",
    "X = X[['seconds','Heating', 'Pyrometer', 'AV Force']]\n",
    "y = y['TravelRelativeCorrected']\n",
    "\n",
    "\n",
    "# Split the data into training and testing sets\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
    "\n",
    "# Define the kernel for the GPR model\n",
    "kernels = [\n",
    "    C(1.0, (1e-4, 1e1)) * RBF(length_scale=1.0),\n",
    "    C(1.0, (1e-4, 1e1)) * Matern(length_scale=1.0, nu=1.5),\n",
    "    C(1.0, (1e-4, 1e1)) * RationalQuadratic(length_scale=1.0, alpha=0.1),\n",
    "    C(1.0, (1e-4, 1e1)) * ExpSineSquared(length_scale=1.0, periodicity=3.0),\n",
    "    C(1.0, (1e-4, 1e1)) * DotProduct(sigma_0=1.0)\n",
    "]\n",
    "kernel = kernels[0]\n",
    "\n",
    "# Create and train the GPR model\n",
    "gpr = GaussianProcessRegressor(kernel=kernel, n_restarts_optimizer=10, alpha=1e-2)\n",
    "gpr.fit(X_train, y_train)\n",
    "\n",
    "# Make predictions\n",
    "y_pred = gpr.predict(X_test)\n",
    "\n",
    "# Evaluate the model\n",
    "mse = mean_squared_error(y_test, y_pred)\n",
    "print(f'Mean Squared Error: {mse}')\n",
    "\n",
    "# Print the kernel parameters\n",
    "print(f'Kernel parameters: {gpr.kernel_}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.gaussian_process import GaussianProcessRegressor\n",
    "from sklearn.gaussian_process.kernels import RBF, Matern, RationalQuadratic, ExpSineSquared, DotProduct, ConstantKernel as C\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import mean_squared_error\n",
    "\n",
    "# data1000 \n",
    "# data900 \n",
    "# data1350 -- bad data\n",
    "# data1200 \n",
    "# dataN1200 -- Different rate?\n",
    "# dataN1100 -- bad data?\n",
    "\n",
    "# data1000 = data1000.sort_values(by=['seconds'])\n",
    "# data900 = data900.sort_values(by=['seconds'])\n",
    "# data1200 = data1200.sort_values(by=['seconds'])\n",
    "# data1350 = data1350.sort_values(by=['seconds'])\n",
    "# dataN1200 = dataN1200.sort_values(by=['seconds'])\n",
    "# dataN1100 = dataN1100.sort_values(by=['seconds'])\n",
    "\n",
    "# Select the relevant columns\n",
    "#D = pd.concat( [data900, data1000, data1200, data1350,dataN1200,dataN1100], axis=0)\n",
    "D = pd.concat( [data900, data1000, data1200,dataN1200], axis=0)\n",
    "#D = pd.concat( [data900, data1000,dataN1200], axis=0)\n",
    "#D = pd.concat( [data900, data1000, data1200], axis=0)\n",
    "\n",
    "#TravelRelativeCorrectedShifted\n",
    "\n",
    "#X_train = X[['seconds','Heating', 'Pyrometer', 'AV Force']]\n",
    "X_train = D[['seconds','TravelRelativeTempCorrected', 'Pyrometer','PyrometerShifted']]\n",
    "#X_train = X[['seconds','Heating', 'Pyrometer']]\n",
    "y_train = D['TravelRelativeTempCorrectedShifted']\n",
    "\n",
    " # Define the kernel for the GPR model\n",
    "kernels = [\n",
    "    C(1.0, (1e-4, 1e9)) * RBF(length_scale=1.0),\n",
    "    C(1.0, (1e-4, 1e9)) * Matern(length_scale=1.0, nu=1.5),\n",
    "    C(1.0, (1e-4, 1e9)) * RationalQuadratic(length_scale=1.0, alpha=0.1),\n",
    "    C(1.0, (1e-4, 1e9)) * ExpSineSquared(length_scale=1.0, periodicity=3.0),\n",
    "    C(1.0, (1e-4, 1e9)) * DotProduct(sigma_0=1.0)\n",
    "]\n",
    "kernel = kernels[4]\n",
    "\n",
    "# Create and train the GPR model\n",
    "gpr = GaussianProcessRegressor(kernel=kernel, n_restarts_optimizer=100, alpha=1e-3)\n",
    "gpr.fit(X_train, y_train)\n",
    "\n",
    "# Make predictions\n",
    "\n",
    "#X_test = data1000[['seconds','Heating', 'Pyrometer', 'AV Force']]\n",
    "X_test = dataN1200[['seconds','TravelRelativeTempCorrected', 'Pyrometer','PyrometerShifted']]\n",
    "#X_test = data1000[['seconds','Heating', 'Pyrometer']]\n",
    "y_test = dataN1200['TravelRelativeTempCorrectedShifted']\n",
    "\n",
    "y_pred = gpr.predict(X_test)\n",
    "\n",
    "\n",
    "# Evaluate the model\n",
    "mse = mean_squared_error(y_test, y_pred)\n",
    "print(f'Mean Squared Error: {mse}')\n",
    "\n",
    "# Print the kernel parameters\n",
    "print(f'Kernel parameters: {gpr.kernel_}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.figure(figsize=(15, 6))\n",
    "fig, ax1 = plt.subplots( figsize=(15, 6))\n",
    "\n",
    "sc = ax1.plot(data1200['seconds'], data1200['TravelRelativeTempCorrectedShifted'], color='grey') \n",
    "#sc = ax1.plot(data1350['seconds'], data1350['TravelRelativeTempCorrectedShifted'], color='grey') \n",
    "#sc = ax1.plot(data1000['seconds'], data1000['TravelRelativeTempCorrectedShifted'], color='grey') \n",
    "sc = ax1.plot(data900['seconds'], data900['TravelRelativeTempCorrectedShifted'], color='grey') \n",
    "sc = ax1.plot(dataN1200['seconds'], dataN1200['TravelRelativeTempCorrectedShifted'], color='green') \n",
    "sc = ax1.plot(data1000['seconds'], data1000['TravelRelativeTempCorrectedShifted'], color='grey') \n",
    "sc = ax1.plot(X_test['seconds'], y_pred, color='red') \n",
    "#sc = plt.scatter(setToPlot['seconds'], setToPlot['Pyrometer'],color='red')  \n",
    "#sc = plt.scatter(setToPlot['seconds'], setToPlot['Heating'], color='green') \n",
    "# Add color bar to show the color scale\n",
    "#ax1.set_ylabel('Pyrometer, Heating')\n",
    "#ax1.set_ylim(400, 1000)\n",
    "#ax1.set_xlim(300, 1400)\n",
    "ax1.set_title('Поріваняння передбаченого моделлю ходу поршня з реальними даними')\n",
    "ax1.set_xlabel('час, с')\n",
    "ax1.set_ylabel('хід поршня, μм')\n",
    "ax1.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "PredictionSet = data1000.head(1)\n",
    "PredictionSet = PredictionSet[['seconds','TravelRelativeTempCorrected', 'Pyrometer','PyrometerShifted']]\n",
    "PredictionSet = PredictionSet.reset_index(drop=True)\n",
    "time = PredictionSet['seconds'].iloc[0]\n",
    "\n",
    "newRegime = PredictionSet.copy()\n",
    "\n",
    "heating = 1\n",
    "\n",
    "print(PredictionSet)\n",
    "while time < 4000 and ( heating == 1 or PredictionSet['PyrometerShifted'].iloc[0] > 1000) :\n",
    "     TravelRelativeTempCorrectedPredicted = gpr.predict(PredictionSet, return_std=True); \n",
    "     #print(TravelRelativeTempCorrectedPredicted[0][0])\n",
    "     if PredictionSet['PyrometerShifted'].iloc[0] > 1200:\n",
    "           heating = 0\n",
    "     time = time + 10\n",
    "     PredictionSet.loc[0,'seconds'] = time\n",
    "     PredictionSet.loc[0,'TravelRelativeTempCorrected'] = TravelRelativeTempCorrectedPredicted[0][0]\n",
    "     PredictionSet.loc[0,'Pyrometer'] = PredictionSet['PyrometerShifted'].iloc[0]\n",
    "     if heating:\n",
    "          PredictionSet.loc[0,'PyrometerShifted'] = PredictionSet['PyrometerShifted'].iloc[0] + 8\n",
    "     else:\n",
    "          PredictionSet.loc[0,'PyrometerShifted'] = PredictionSet['PyrometerShifted'].iloc[0] -30\n",
    "     #print(PredictionSet)\n",
    "     newRegime = pd.concat([newRegime, PredictionSet], ignore_index=True)\n",
    "     newRegime.at[newRegime.index[-1],'STD'] = TravelRelativeTempCorrectedPredicted[1][0]\n",
    "#newRegime.head(100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def calulate_TargetDensificationRateValue(value):\n",
    "    if value < 0.40:\n",
    "        return 1\n",
    "    elif 0.40 <= value < 0.70:\n",
    "        # Лінійне зростання від 0 до 110\n",
    "        return ((value - 0.40) / (0.70 - 0.40) * 110)+1\n",
    "\n",
    "    elif 0.70 <= value < 0.85:\n",
    "        return 50\n",
    "    elif 0.85 <= value < 0.97:\n",
    "        # Лінійне зменшення від 50 до 0\n",
    "        return (0.97 - value) / (0.97 - 0.85) * 50\n",
    "    else:\n",
    "        return 0\n",
    "\n",
    "def calulate_TargetDensificationRate(column):\n",
    "    result = []\n",
    "    for value in column:\n",
    "        result.append(calulate_TargetDensificationRateValue(value))\n",
    "    return pd.Series(result, index=column.index)\n",
    "\n",
    "def find_closest_index(column, target_value):\n",
    "    squared_diff = (column - target_value) ** 2\n",
    "    return squared_diff.idxmin()\n",
    "\n",
    "#calulate_TargetDensificationRate(0.45)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "PredictionSet = data1000.head(1)\n",
    "PredictionSet = PredictionSet[['seconds','TravelRelativeTempCorrected', 'Pyrometer','PyrometerShifted']]\n",
    "PredictionSet = PredictionSet.reset_index(drop=True)\n",
    "time = PredictionSet['seconds'].iloc[0]\n",
    "numberOfOptions = 60\n",
    "newRegime = PredictionSet.copy()\n",
    "for i in range(0, numberOfOptions):\n",
    "     PredictionSet = pd.concat([newRegime]*numberOfOptions, ignore_index=True)\n",
    "heating = 1\n",
    "#print(newRegime)\n",
    "\n",
    "density=0\n",
    "time = time + 10\n",
    "for i in range(0, numberOfOptions):\n",
    "     PredictionSet.loc[i,'seconds'] = time\n",
    "     PredictionSet.loc[i,'PyrometerShifted'] = PredictionSet['PyrometerShifted'].iloc[i] + i-5\n",
    "\n",
    "#print(PredictionSet)\n",
    "while time < 1000 and (density<0.96 ) :\n",
    "     \n",
    "     TravelRelativeTempCorrectedPredicted = gpr.predict(PredictionSet, return_std=True); \n",
    "     #print(TravelRelativeTempCorrectedPredicted[0][0])\n",
    "     #select best prediction\n",
    "     \n",
    "     densificationRate = ( TravelRelativeTempCorrectedPredicted[0]-PredictionSet['TravelRelativeTempCorrected'])/10/1000\n",
    "     density = calulate_density(PredictionSet['TravelRelativeTempCorrected'].iloc[0])\n",
    "     targetRate = calulate_TargetDensificationRateValue(density)\n",
    "     predictedRates = pd.Series(TravelRelativeTempCorrectedPredicted[0]-PredictionSet.loc[0,'TravelRelativeTempCorrected'])\n",
    "     #print(predictedRates)\n",
    "     bestIndex = find_closest_index(predictedRates, targetRate)\n",
    "     #print(targetRate)\n",
    "     #print(bestIndex)\n",
    "     time = time + 10\n",
    "     for i in range(0, numberOfOptions):\n",
    "          PredictionSet.loc[i,'seconds'] = time\n",
    "          PredictionSet.loc[i,'TravelRelativeTempCorrected'] = TravelRelativeTempCorrectedPredicted[0][bestIndex]\n",
    "          PredictionSet.loc[i,'Pyrometer'] = PredictionSet['PyrometerShifted'].iloc[bestIndex]\n",
    "          if heating:\n",
    "               PredictionSet.loc[i,'PyrometerShifted'] = PredictionSet['PyrometerShifted'].iloc[bestIndex] + i-20\n",
    "          else:\n",
    "               PredictionSet.loc[i,'PyrometerShifted'] = PredictionSet['PyrometerShifted'].iloc[bestIndex] -30\n",
    "     #print(PredictionSet)\n",
    "     bestPrediction = PredictionSet.iloc[bestIndex]\n",
    "     bestPrediction = pd.DataFrame([bestPrediction])\n",
    "     #print(bestPrediction)\n",
    "     newRegime = pd.concat([newRegime, bestPrediction], ignore_index=True)\n",
    "     #print(newRegime)\n",
    "     newRegime.at[newRegime.index[-1],'STD'] = TravelRelativeTempCorrectedPredicted[1][bestIndex]\n",
    "newRegime.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.figure(figsize=(15, 6))\n",
    "fig, ax1 = plt.subplots( figsize=(15, 6))\n",
    "#heating rate\n",
    "sc = ax1.plot(data1200['seconds'], data1200['PyrometerShifted'] - data1200['Pyrometer'], color='gray') \n",
    "sc = ax1.plot(data900['seconds'], data900['PyrometerShifted'] - data900['Pyrometer'], color='gray') \n",
    "sc = ax1.plot(data1000['seconds'], data1000['PyrometerShifted'] - data1000['Pyrometer'], color='gray') \n",
    "sc = ax1.plot(dataN1200['seconds'], dataN1200['PyrometerShifted'] - dataN1200['Pyrometer'], color='gray') \n",
    "sc = ax1.plot(newRegime['seconds'], newRegime['PyrometerShifted'] - newRegime['Pyrometer'], color='red') \n",
    "ax1.set_title('Швидкість нагріву в віртуальному експерименті в порівнянні з реальним даними')\n",
    "ax1.set_xlabel('час в секундах')\n",
    "ax1.set_ylabel('Швидкість °C/c')\n",
    "ax1.set_ylim(-20, 40 )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.figure(figsize=(15, 6))\n",
    "fig, ax1 = plt.subplots( figsize=(15, 6))\n",
    "\n",
    "#densification rate\n",
    "sc = ax1.plot( calulate_density(data1200['TravelRelativeTempCorrected']), data1200['TravelRelativeTempCorrected'] - data1200['TravelRelativeTempCorrected'].shift(1), color='grey')\n",
    "sc = ax1.plot( calulate_density(data1000['TravelRelativeTempCorrected']), data1000['TravelRelativeTempCorrected'] - data1000['TravelRelativeTempCorrected'].shift(1), color='grey')\n",
    "sc = ax1.plot( calulate_density(data900['TravelRelativeTempCorrected']), data900['TravelRelativeTempCorrected'] - data900['TravelRelativeTempCorrected'].shift(1), color='grey')\n",
    "sc = ax1.plot( calulate_density(dataN1200['TravelRelativeTempCorrected']), dataN1200['TravelRelativeTempCorrected'] - dataN1200['TravelRelativeTempCorrected'].shift(1), color='grey')\n",
    "sc = ax1.plot( calulate_density(newRegime['TravelRelativeTempCorrected']), newRegime['TravelRelativeTempCorrected'] - newRegime['TravelRelativeTempCorrected'].shift(1), color='red')\n",
    "sc = ax1.plot( calulate_density(newRegime['TravelRelativeTempCorrected']), calulate_TargetDensificationRate(calulate_density(newRegime['TravelRelativeTempCorrected'])), color='green')\n",
    "\n",
    "\n",
    "mse = ((newRegime['TravelRelativeTempCorrected'] - newRegime['TravelRelativeTempCorrected'].shift(1) - calulate_TargetDensificationRate(calulate_density(newRegime['TravelRelativeTempCorrected']))) ** 2).mean()\n",
    "rmse = mse ** 0.5\n",
    "print(f'RMSE: {rmse}')\n",
    "meanValue = calulate_TargetDensificationRate(calulate_density(newRegime['TravelRelativeTempCorrected'])).mean()\n",
    "\n",
    "\n",
    "difAbs = ((newRegime['TravelRelativeTempCorrected'] - newRegime['TravelRelativeTempCorrected'].shift(1) - calulate_TargetDensificationRate(calulate_density(newRegime['TravelRelativeTempCorrected']))) ** 2)** 0.5\n",
    "difInPercent = (difAbs / calulate_TargetDensificationRate(calulate_density(newRegime['TravelRelativeTempCorrected']))) * 100\n",
    "print(f'RMSE in percent: {difInPercent.mean()}%')\n",
    "# sc = ax1.plot(data1000['seconds'], data1000['TravelRelativeTempCorrected'], color='grey') \n",
    "# sc = ax1.plot(newRegime['seconds'], newRegime['TravelRelativeTempCorrected'], color='green') \n",
    "#sc = ax1.plot(newRegime['seconds'], newRegime['STD'], color='green') \n",
    "\n",
    "#densification rate\n",
    "#sc = ax1.plot(data1200['seconds'], data1200['TravelRelativeTempCorrectedShifted']-data1200['TravelRelativeTempCorrected'], color='grey') \n",
    "#sc = ax1.plot(data1000['seconds'], data1000['TravelRelativeTempCorrectedShifted']-data1000['TravelRelativeTempCorrected'], color='grey') \n",
    "#sc = ax1.plot(newRegime['seconds'], newRegime['TravelRelativeTempCorrected'] - newRegime['TravelRelativeTempCorrected'].shift(1), color='green') \n",
    "\n",
    "\n",
    "#sc = ax1.plot(data1200['seconds'], data1200['PyrometerShifted'] - data1200['Pyrometer'].shift(1), color='orange') \n",
    "#sc = ax1.plot(data1350['seconds'], data1350['TravelRelativeTempCorrectedShifted'], color='grey') \n",
    "#sc = ax1.plot(data900['seconds'], data900['TravelRelativeTempCorrectedShifted']-data900['TravelRelativeTempCorrected'], color='grey') \n",
    "#sc = ax1.plot(dataN1200['seconds'], dataN1200['TravelRelativeTempCorrectedShifted']-dataN1200['TravelRelativeTempCorrected'], color='grey') \n",
    "#sc = ax1.plot(dataN1100['seconds'], dataN1100['TravelRelativeTempCorrectedShifted'], color='grey') \n",
    "#sc = ax1.plot(data1000['seconds'], data1000['TravelRelativeTempCorrectedShifted']-data1000['TravelRelativeTempCorrected'], color='grey') \n",
    "#sc = ax1.plot(newRegime['seconds'], newRegime['PyrometerShifted'] - newRegime['Pyrometer'].shift(1), color='red') \n",
    "# df['TravelDelta'] = df['TravelRelativeTempCorrected'] - df['TravelRelativeTempCorrected'].shift(1)\n",
    " \n",
    "\n",
    "#ax1.set_title('title')\n",
    "ax1.set_xlabel('Щільність')\n",
    "ax1.set_ylabel('Швидкість ущільнення')\n",
    "ax1.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.figure(figsize=(15, 6))\n",
    "fig, ax1 = plt.subplots( figsize=(15, 6))\n",
    "\n",
    "#Temperature\n",
    "sc = ax1.plot(data1200['seconds'], data1200['Pyrometer'], color='grey')\n",
    "sc = ax1.plot(data1000['seconds'], data1000['Pyrometer'], color='grey')\n",
    "sc = ax1.plot(data900['seconds'], data900['Pyrometer'], color='grey')\n",
    "sc = ax1.plot(dataN1200['seconds'], dataN1200['Pyrometer'], color='grey')\n",
    "sc = ax1.plot(newRegime['seconds'], newRegime['Pyrometer'], color='red')\n",
    "\n",
    "ax1.set_title('Порівняння температури з єксперементальними даними')\n",
    "ax1.set_xlabel('Час, с')\n",
    "ax1.set_ylabel('Температура °C')\n",
    "ax1.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.figure(figsize=(15, 6))\n",
    "fig, ax1 = plt.subplots( figsize=(15, 6))\n",
    "\n",
    "sc = ax1.plot(data1200['seconds'], data1200['TravelRelativeTempCorrectedShifted'], color='grey') \n",
    "#sc = ax1.plot(data1350['seconds'], data1350['TravelRelativeTempCorrectedShifted'], color='grey') \n",
    "sc = ax1.plot(data1000['seconds'], data1000['TravelRelativeTempCorrectedShifted'], color='grey') \n",
    "sc = ax1.plot(data900['seconds'], data900['TravelRelativeTempCorrectedShifted'], color='grey') \n",
    "sc = ax1.plot(dataN1200['seconds'], dataN1200['TravelRelativeTempCorrectedShifted'], color='grey') \n",
    "#sc = ax1.plot(dataN1100['seconds'], dataN1100['TravelRelativeTempCorrectedShifted'], color='grey') \n",
    "sc = ax1.plot(data1000['seconds'], data1000['TravelRelativeTempCorrectedShifted'], color='grey') \n",
    "sc = ax1.plot(newRegime['seconds'], newRegime['TravelRelativeTempCorrected'], color='red') \n",
    " \n",
    "ax1.set_title('Порівняння ходу поршня з єкспериментальними даними')\n",
    "ax1.set_xlabel('час, с')\n",
    "ax1.set_ylabel('хід поршня, μм')\n",
    "ax1.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.figure(figsize=(40, 6))\n",
    "fig, ax1 = plt.subplots( figsize=(40, 6))\n",
    "\n",
    "sc = ax1.plot(data1200['TravelRelativeTempCorrectedShifted'], data1200['Pyrometer'], color='grey') \n",
    "#sc = ax1.plot(data1350['Pyrometer'], data1350['Pyrometer'], color='grey') \n",
    "sc = ax1.plot(data1000['TravelRelativeTempCorrectedShifted'], data1000['Pyrometer'], color='grey') \n",
    "sc = ax1.plot(data900['TravelRelativeTempCorrectedShifted'], data900['Pyrometer'], color='grey') \n",
    "sc = ax1.plot(dataN1200['TravelRelativeTempCorrectedShifted'], dataN1200['Pyrometer'], color='grey') \n",
    "sc = ax1.plot(data1000['TravelRelativeTempCorrectedShifted'], data1000['Pyrometer'], color='grey') \n",
    "sc = ax1.plot(newRegime['TravelRelativeTempCorrected'], newRegime['Pyrometer'], color='red') \n",
    " \n",
    "\n",
    "ax1.set_title('title')\n",
    "ax1.set_xlabel('Microns')\n",
    "ax1.set_ylabel('Pyrometer')\n",
    "ax1.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.figure(figsize=(40, 6))\n",
    "fig, ax1 = plt.subplots( figsize=(40, 6))\n",
    "\n",
    "sc = ax1.plot(data1200['seconds'], data1200['Pyrometer'], color='grey') \n",
    "#sc = ax1.plot(data1350['seconds'], data1350['Pyrometer'], color='grey') \n",
    "sc = ax1.plot(data1000['seconds'], data1000['Pyrometer'], color='grey') \n",
    "sc = ax1.plot(data900['seconds'], data900['Pyrometer'], color='grey') \n",
    "sc = ax1.plot(dataN1200['seconds'], dataN1200['Pyrometer'], color='grey') \n",
    "sc = ax1.plot(data1000['seconds'], data1000['Pyrometer'], color='grey') \n",
    "#sc = ax1.plot(dataN1100['seconds'], dataN1100['Pyrometer'], color='grey') \n",
    "sc = ax1.plot(newRegime['seconds'], newRegime['Pyrometer'], color='red') \n",
    " \n",
    "\n",
    "ax1.set_title('title')\n",
    "ax1.set_xlabel('seconds')\n",
    "ax1.set_ylabel('Pyrometer')\n",
    "ax1.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "SPS",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.13.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}