{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "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": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def calulate_density(travel):\n",
    "    return 2710/(6293-travel)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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": 5,
   "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": 6,
   "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": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<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",
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       "    <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",
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       "    <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": 7,
     "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": 8,
   "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": 9,
   "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": 10,
   "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": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 2000x600 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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1ioiIiIiIiIiIiJQc69bBzTfb8kMPwbXXOluPiIiIiIiUTgpYieRbgTtYnThxgooVKxITE0NsbCxxcXEAxMXFkZGR4fcCRUREREREREREREqM48fhyivh2DHo3Rv++lenKxIRERERkdJKASuRfCtwByuADz74gPj4eLKzs/nf//5H5cqVOXbsmL9rExERERERERERESk5PB649VZYvRpq1oRvvoHISKerEhERERGR0ipUA1azZsGoUfDyyxAb63Q1IkAhAlaJiYl88MEHAFSvXp1PP/30tK+JiIiIiIiIiIiIyFm8/jqMHGmhqlGjoFo1pysSEREREZHSzBuw2rwZcnIgPNzZerzuvRdWrIAmTeDuu52uRgQoRMBq69atAShDREREREREREREpASbPh3+9CdbfuMN6NbN0XJERERERESoXRuioiAzE3bsgHr1nK4I9u2zcBVYJysFrCREhDldgIiIiIiIiIiIiEiJtnMnXHutXRF+001wzz1OVyQiIiIiImIdqxo0sOVQGRM4ZUru8qxZztUhcoZ8d7B6+OGH83W/119/vdDFiIiIiIiIiIiIiJQoJ0/C1VfD/v3Qpg289x64XE5XJSIiIiIiYpKSYO1aC1gNGOB0NTBpUu7y9u3WWatOHefqETkl3wGrpUuXnvfrx48fZ8mSJQpYiYiIiIiIiIiIiHg99BDMmwcJCfDddxAX53RFIiIiIiIiuZKS7DYUOlh5PDB5si1HR9sFK7Nnw3XXOVuXCAUIWE2bNu28X1+2bBkdOnQockEiIiIiIiIiIiIiJcLHH8O771rHqs8/zx29ISIiIiIiEipCKWC1bp2NWI+OhptvhvffV8BKQkaYv1bkUltrEREREREREREREbN0Kdx1ly0/8wxcfLGz9YiIiIiIiJxNKAWsvOMBe/XKHVc4a5Zz9Yjk4beAlYiIiIiIiIiIiIgAhw/DVVdBRgYMGwZPPeV0RSIiIiIiImfnDVht2gRut7O1eMcDDhgAPXrY8ooVcPSoczWJnJLvEYHbt28/79d3795d5GJEREREREREREREirWcHLjxRtiyxUYCfvophOk6VxERERERCVF160JEhF0gsns31K7tTB1ZWTBtmi0PHAg1a0L9+va31bx5MGiQM3WJnJLvgFW9evXOOwbQ4/FoTKCIiIiIiIiIiIiUbs89B+PHQ2wsfPcdVKjgdEUiIiIiIiLnFhEB9erZiMCNG50LWC1cCMeOQaVK0Latfa5nTwtYzZqlgJU4Lt8Bq6VLl57362vXruWGG24ockEiIiIiIiIiIiIixdKPP8ILL9jy++9DmzbO1iMiIiIiIpIfSUm5Aau+fZ2pYdIku73ootwuwD17WlfgWbOcqUkkj3wHrNroYICIiIiIiIiIiIjI2W3cCL/5jS3fd1/usoiIiIiISKhLSrLbjRudq8EbsBo4MPdzPXrY7fz5NkIwMjL4dYmcEuZ0ASIiIiIiIiIiIiLFWloaXHklpKZC9+7w9787XZGIiIiIiEj+OR2wOnoU5s2z5QEDcj/frJmNXT9xApYtc6Q0ES8FrEREREREREREREQKy+OBO++EX36BatVg5EiIinK6KhERERERkfxzOmA1Ywbk5Fgd9erlfj4sLLeLlcYEisPyPSLwyiuvPO/XU1JSilqLiIiIiIiIiIiISPHy1lvwxRcQHg7ffAM1azpdkYiIiIiISMHkDVh5POByBXf7kyfbbd7uVV49esCPP8Ls2fDQQ8GtSySPfAesypcvf8Gv//a3vy1yQSIiIiIiIiIiIiLFwqxZ8H//Z8t/+xv07u1sPSIiIiIiIoVRr551i0pLg337oHr14G5/0iS7HTjw11/r2dNuZ81yJvwlckq+A1YfffRRIOsQERERERERERERKT727IGrr4bsbLjuOnjgAacrEhERERERKZzoaEhMhK1brYtVMANWO3fCmjUW8OrX79df79jRxrDv2webNuV22xIJsjCnCxAREREREREREREpVjIz4ZprYO9eaNkSPvxQV1GLiIiIiEjxlndMYDBNmWK3HTtChQq//npMjH0NbEygiEMUsBIRERERERERERHJr5077arqWbOgXDn47jsoU8bpqkRERERERIrGqYDV+cYDeuUdEyjiEEcDVi+//DKdOnWibNmyVK1alcsvv5x169addh+Px8Ozzz5LzZo1iY2NpW/fvqxatcqhikVERERERERERKTUmjgR2rWDOXOgfHn49lto1MjpqkRERERERIrO+7dNMANWHg9MnmzLAwac+349etitAlbiIEcDVjNmzODee+9l3rx5TJo0iezsbAYNGkRaWprvPq+++iqvv/46b7/9NgsXLqR69eoMHDiQY8eOOVi5iIiIiIiIiIiIlBo5OfDsszBkCBw8aCGrJUvOfwJARERERESkOHGig9XKlbBvH8TFQbdu575f9+52u3at/U0m4gBHA1bjx4/nlltuoUWLFrRp04aPPvqI7du3s3jxYsC6V/3jH//giSee4Morr6Rly5Z8/PHHnDhxgi+++MLJ0kVERERERERERKQ0OHAALr4YnnvOrq6+807rYNWggdOViYiIiIiI+E/egJXHE5xtescD9u4N0dHnvl/lytCsmS3PmRP4ukTOIsLpAvJKTU0FoGLFigBs2bKFvXv3MmjQIN99oqOj6dOnD3PmzOH3v//9r9Zx8uRJTp486fv30aNHAcjKyiIrKyuQ5Yck789cGn92ERGRUKf3aRERkdCm92oRcc2dS/gNN+DatQtPbCw5b7+N56ab7It6bRBxlN6nRUREQpveq4uhOnWIcLlwpaaStXevhZoCLHzSJMKAnP79cV/gsRLerRtha9aQM2MG7qFDA16blA4FeY0KmYCVx+Ph4YcfpmfPnrRs2RKAvXv3AlCtWrXT7lutWjW2bdt21vW8/PLLPPfcc7/6/MSJE4mLi/Nz1cXHJG/yU0REREKO3qdFRERCm96rRUohj4cGY8fS4uOPceXkcKxWLRY++ijHKlWCn392ujoRyUPv0yIiIqFN79XFy8BKlYg7eJC5n37KkSZNArqtsKwshk6bRhgwIyqKYxf4W6tOfDztgZSff2ZW794BrU1KjxMnTuT7viETsLrvvvtYsWIFs2bN+tXXXC7Xaf/2eDy/+pzXn//8Zx5++GHfv48ePUqdOnUYNGgQ5cqV82/RxUBWVhaTJk1i4MCBREZGOl2OiIiI5KH3aRERkdCm92oprVwTJuDauBH38OFQu7bT5QTf0aOE33EHYd9/D4B7xAhi/v1vepUt63BhIpKX3qdFRERCm96ri6fwli1h+nS6V62K5+KLA7otV3IyESdP4qlWjV533w3nyID4NGkCb75JxU2buLhfP4iNDWh9Ujp4p+LlR0gErO6//37GjBlDcnIytfMctKlevTpgnaxq1Kjh+/z+/ft/1dXKKzo6muizzOaMjIws1S/cpf3nFxERCWV6nxYREQlteq+WUmXUKLjmGvB4CH/4YejdG268EUaMgAoVnK4u8FassJ91wwaIjITXXyfs3nsJu9CBfhFxjN6nRUREQpveq4uZRo1g+nQitm61v4kCado0AFwDBhAZFXXh+zdpAtWq4dq3j8jly6FXr8DWJ6VCQV6fwgJYxwV5PB7uu+8+vvvuO6ZOnUr9+vVP+3r9+vWpXr36aW0DMzMzmTFjBt27dw92uSIiIiIiIiIiIiXX9OkWpvJ4oGFDu50xA+68E6pXhyuugJEjIT3d6UoD46OPoEsXC1fVqQMzZ8J99134KmoREREREZGSIinJbjduDPy2vDmQAQPyd3+XC3r2tOXZswNTk8h5OBqwuvfee/nss8/44osvKFu2LHv37mXv3r2knzpI43K5ePDBB3nppZf4/vvvWblyJbfccgtxcXHccMMNTpYuIiIiIiIiIiJScixbBsOHQ2YmXHklrFsH27bBK69Aq1b2+dGjrbtV9erwu9/B5MmQk+N05UWXng633Qa33goZGTBkCCxdamErERERERGR0iRYAasjR2DRIlseODD/3+cNWM2a5f+aRC7A0YDVu+++S2pqKn379qVGjRq+j6+//tp3n0cffZQHH3yQe+65h44dO7Jr1y4mTpxI2bJlHaxcRERERERERESkhNi82UJFR49Cnz7w+ecQHg6JifCnP9nYvBUr4LHH7HNHj8L//mcHwWvXhocesgPjHo/TP0nBbdgA3brBf/8LYWHw4ovw009QqZLTlYmIiIiIiARfsAJW06aB2w3NmkGtWvn/vh497Hb2bPt+kSByfETg2T5uueUW331cLhfPPvsse/bsISMjgxkzZtCyZUvnihYRERERERERESkp9u2DQYPstk0b+OEHiIn59f1atYKXX4YtWyA5GX7/e6hYEfbuhX/8Azp1gqZN4fnngzNKwh+++w46doTly6FKFZg4EZ54woJWIiIiIiIipVHDhnZ76JB1mQqUyZPtNr/jAb3atoW4OEhJgTVr/F2VyHnpaIGIiIiIiIiIiEhpdOwYXHwxbNoE9evDuHFQvvz5vycsDHr1gvfegz17YMwYuPZaiI2F9evhmWegUSMbr/fmmxbcCjVZWfDww3DVVdaNq2dPGwl40UVOVyYiIiIiIuKsMmWgRg1b3rQpcNuZNMluCzIeECAyErp2tWWNCZQgU8BKRERERERERESktDl5Eq64ApYsse5NEybkHkTPr6gouPRS+OorC1J98gkMHmwhrAUL4IEHoGZN+9wnn1igy2k7d0LfvvDGG/bvRx6BqVMLNpJCRERERESkJAv0mMCtW23d4eE2pr6g8o4JFAkiBaxERERERERERERKE7cbbr4ZpkyB+HjrXNWoUdHWWbYs3HQTjB8Pu3fDP/8JnTvbtiZOtO1VqwbXXQdjx0Jmpn9+loKYNAnatYM5c6BcOfj+e3jtNbsCWkRERERERIw3YLVhQ2DW7x0P2LWr/W1WUD172q06WEmQRThdgIiIiIiIiIiIiASJx2Odpb7+2oJF330HHTr4dxvVqsEf/mAfGzfCF1/A55/bCMGvv7aPChUs7FS/PjRocPptlSrgcvmvnpwcePFFeO45+/nbtoVRo6BhQ/9tQ0REREREpKQIdAerwo4H9Ora1Tonb9liF/jUrOm/2kTOQwErERERERERERGR0uLll+Htt235k08Kf0A7v5KS4Omn4amnbBzh55/bSME9e2w039mUKXN64OrM5TJl8r/9gwfhxhutixbAHXdYd63Y2KL/bCIiIiIiIiVRIANWbrd1UwYYMKBw6yhXDlq3hmXLbEzg1Vf7rTyR81HASkREREREREREpKgyMyEqyukqzu/DD+GJJ2z5n/+0cX3B4nJZp6wOHWws38KFNm5i82a76njLFlvetQvS0mDlSvs4m6pVzx6+atAAateGiFOHPOfOhWuugZ07LVD17rs2qlBERERERETOLZABq2XL4NAhGzPfuXPh19Ozp61r1iwFrCRoFLASEREREREREREpik8+sc5Il14KH3xg4+9CzZgx8Pvf2/Kf/2zj+5wSHm4jHbp2/fXXTp6EbdtyA1dn3qakwP799jFv3q+/PyICEhPtY9YsyM6Gxo1tJGCrVgH/0URERERERIo97zj1/fvh6FHrGOUv3vGAffva2PrC6tnTujPPmuWXskTyQwErERERERERERGRwpo3z8JVmZnw7beweDF8/XXRrsT1t5kz4dprbRTDrbfCX/7idEXnFh1tgajGjc/+9ZSUc4evtm61/4fNm+0D7ErmDz/07wkBERERERGRkqx8eahSBQ4cgE2boF07/6178mS7Leq4+h497HbZMjh2zDpiiQSYAlYiIiIiIiIiIiKFsWcPXHmlhXoGDrQDz5s324Hev/4VHnrIRuM56Zdf4LLLICPDOmz9+9/O11QUCQl2cP9sB/jdbvs/8QasqlaFIUOK988rIiIiIiLihKQkC1ht3Oi/gFV6ul0ABDBgQNHWVbs21K1rHZDnzy/6+kTyIczpAkRERERERERERIqdkyfhqqss0NOihXWvWrLEOiZlZ8P//R8MHw6HDztX47ZtFjBKSbHQ11df2Qi9kiosDGrVgl694OabYehQhatEREREREQKIynJbjdu9N86Z8+2v6Vr1YKmTYu+vp497VZjAiVIFLASEREREREREREpqD/8AebOtY5Ko0fbOILy5W084L/+ZaPuxo6Ftm3tfsF28CAMHgy7d1sAbMwYiIsLfh0iIiIiIiJS/AQiYDVpkt0OHOifi2G8YwJnzy76ukTyQQErERERERERERGRgvj3v+H9961j0ldf5R54BjtIfPfdMG8eNGoEO3ZA797w2ms2wi4Y0tJg2DBYtw7q1IHx46FixeBsW0RERERERIq/QAas/DXOz9vBau5c6yQtEmAKWImIiIiIiIiIiOTXrFlw//22/NJL1iXqbNq2hcWL4frr7UDvo4/CpZdaZ6lAysqCESNgwQILVU2cCLVrB3abIiIiIiIiUrL4O2B18CAsXWrL/gpYtWhhnaTT0mD5cv+sU+Q8FLASERERERERERHJj507LbyUlQXXXGOhqfMpWxY+/9y6XcXEwM8/W/Bq1qzA1Od2w623WsequDjbXtOmgdmWiIiIiIiIlFzegNXu3RZgKqopU+y2VSuoVq3o6wPrKt29uy1rTKAEgQJWIiIiIiIiIiIiF5KRAVdeCfv2QevW8N//2jjAC3G54I47YP58aNIEdu2Cvn3h5Zf9PzLw0Ufhs88gPBxGjYIuXfy7fhERERERESkdKlaEChVsefPmoq9v8mS7HTiw6OvKyzsmMFAXMonkoYCViIiIiIiIiIjI+Xg8cPfdsHChHWQePRrKlCnYOlq3hkWL4De/gZwcePxxuPhi2L/fPzX+7W/w97/b8n//C0OH+me9IiIiIiIiUjr5a0ygxwOTJtlyIANWHo9/1y1yBgWsREREREREREREzuedd+B//7PxA998A/XrF2498fHwySfwn/9AbCxMmGAjA2fMKFp9n3wCf/yjLb/2Gvz2t0Vbn4iIiIiIiIi/AlYbN8K2bRAVBb16Fb2uvDp1gshI2LMHtm7177pFzqCAlYiIiIiIiIiIyLlMnw4PPmjLr70GF11UtPW5XHDrrdYNq1kzOwjcvz+88IJ1tiqon3+29QE88oh9iIiIiIiIiBSVvwJW3vGA3bsXvBv0hcTGQocOtqwxgRJgCliJiIiIiIiIiIiczfbtcPXVFny68UZ46CH/rbtFCwtZ3XILuN3w9NMweDDs25f/dcybByNGWH033QR//av/6hMREREREZHSzV8BK+94wAEDiraec8k7JlAkgBSwEhEREREREREROdOJE3D55XDwILRvDx98YN2n/KlMGfjoI/j4Y4iLgylToE0bmDr1wt+7Zg0MGwbp6TB0qI0dDNOhPhEREREREfETfwSscnJy/8YdOLDoNZ1Njx52O3t2YNYvcoqOuoiIiIiIiIiIiOTl8cCdd8LSpVClCnz/vY0dCJTf/hYWLYKWLa2D1YAB8Mwz5x4ZuHOndbs6fBg6d4aRIyEyMnD1iYiIiIiISOnjDVjt2AEZGYVbx6JFkJoKCQm5o/z8zRuwWrXK/k4WCRAFrERERERERERERPL6xz/g888hPBy++QYSEwO/zWbNYP58uP12C3g9/7wFrfbsOf1+hw9buGrHDmjSBH76yTphiYiIiIiIiPhTlSpQtqz9jbplS+HW4R0P2L+//Y0dCFWqQOPGtjxnTmC2IYICViIiIiIiIiIiIrkmT4ZHHrHlN96Avn2Dt+24OBtF+PnnEB8P06dD27a5B6RPnIBLL4XVq6FWLZgwASpXDl59IiIiIiIiUnq4XEUfEzh5st0GajygV8+edqsxgRJACliJiIiIiIiIiIiAXZF77bXgdsMtt8B99zlTxw03wOLF0KYN7N9vHauefNJqmzPHRiuMHw916zpTn4iIiIiIiJQORQlYHT+e21FqwAD/1XQ23oDVrFmB3Y6UagpYiYiIiIiIiIiIpKXB5ZfbCL5OneDdd+1qXac0bgxz58Jdd9k4hr/8BX78EWJiYOxYaNnSudpERERERESkdChKwGrmTMjKgnr1oGFDv5b1Kz162O3ChXDyZGC3JaWWAlYiIiIiIiIiIlK6eTxw662wYgVUqwbffWdBJqfFxlrQ66uvoGxZCA+Hr7/OvTJXREREREREJJCKErDyjrsfODDwFzA1agRVqli4avHiwG5LSi0FrEREREREREREpHR77TX45huIiIBRo6B2bacrOt2118KmTbB+PVx2mdPViIiIiIiISGnhj4BVoMcDggW4NCZQAkwBKxERERERERERKb3Gj4fHHrPlt94K3e5QVapAgwZOVyEiIiIiIiKliTdgtXUrZGbm//v27oWVKy341L9/QEr7Fe+YwNmzg7M9KXUUsBIRERERERERkcDxeGDbNvsINRs3wvXXW4133AG//73TFYmIiIiIiIiEjho1bHy9212wv+snT7bbdu2gcuXA1HYm7wVTs2dbvSJ+poCViIiIiIiIiIgUnccDu3bBxInwxhtw223QtSuUKwf16tnH8OGwbJnDhZ5y7BhcfjmkpEC3bta9yuVyuioRERERERGR0OFyFW5MoDdgNXCg/2s6l3btLAx26BCsWxe87UqpEeF0ASIiIiIiIiIiUszs32+t/letsg/vckrK2e8fEWFXj44ZYx9XXQXPPgstWwaz6lweD9xyi9VcowZ8+y1ERztTi4iIiIiIiEgoS0qCX37Jf8DK44FJk2w5mAGrqCjo3BlmzLAuVs2aBW/bUiooYCUiIiIiIiIiImd3+PCvQ1QrV8LBg2e/f1gYNGoELVpYeMp726gRbNoEzz0HX39tgabvvoNrrrGgVdOmQf2xeOkl235UlN3WqBHc7YuIiIiIiIgUFwXtYLVmDezeDTEx0KNH4Oo6m549LWA1axbcfntwty0lngJWIiIiIiIiIiKl3dGjsHr16SGqVatgz56z39/lggYNLECVN0zVpIkdQD2bpk3hyy/hiScsaDVqlIWtRo6EG26Ap5+2IFag/fgjPPWULb/zjo0xFBEREREREZGzK2jAyjsesFevcx8jCJSePe121qzgbldKBQWsRERERERERERKE48H1q6F0aNh5kwLU+3Yce77JybmBqi8YapmzSAurnDbb9nSQlXLl8Mzz8APP8Bnn1n46re/hSeftPBWIKxbBzfeaL+De+7R1awiIiIiIiIiF1LQgJV3POCAAYGp53y6dbOLwjZtgr17oXr14NcgJZYCViIiIiIiIiIiJV1ODsyfb6GqH36A9et/fZ+aNU/vRtWiBTRvDuXKBaamNm2snsWLLWj100/w0Ufw6afwu99Zp6u6df23vdRUGD7cunX16gVvvOG/dYuIiIiIiIiUVN6A1ZYtkJ0NEeeJmWRlwfTptjxwYMBL+5Xy5aFVK1ixAmbPhquuCn4NUmIpYCUiIiIiIiIiUhJlZMCUKRZiGjMG9u/P/VpUFFx0EVx8MbRta2GqChWcqbNDBxvbN2+eBa0mToQPPoD//Q/uuAMefxxq1SraNtxuuOkm62BVu7Z10IqK8kv5IiIiIiIiIiVa7doQHQ0nT1oH7Pr1z33f+fPh+HGoXNkurHJCz54WsJo1SwEr8aswpwsQERERERERERE/OXzYOkCNGGEHMy+5BD780MJV5cvDDTfAN9/AwYPw889w33124NGpcFVeXbvChAk2trB/f7vq9V//goYN4YEHrLV/YT3/PIwdaweEv/sOqlXzX90iIiIiIiIiJVlYGDRoYMsXGhPoHQ940UX2fU7o0cNuZ892ZvtSYqmDlYiIH7ndblwuFy6Xy+lSgicz02YZR0Y6XUmJ5PF48Hg8hDm1EyoiIiIlisfjAShd+6ulwbZtNvbvhx9gxgwbB+hVuzZcfrmNxuvTp3jst/fsaZ23pk+Hp5+2wNWbb1pXq3vugUcfhapV87++0aPhueds+f33oVOnQFQtIqWIx+MhIyMjoNtwuVzExMQEdBtSNNqvCg16PoaOjIwM3/MiUGJiYvScu4CTJ0/idrudLkNKgZL0fNTrVz4lJcGaNRawGjjw3PtCkyfbrRPjAb169rTbJUsgLQ3KlHGuFilRFLASEfGTbdu28fnnn9OmTRuGDRvmdDn+4/HY1e2bN+d+bNqUu7xzJ4SHQ+PGNlakRQto2dJuk5LOP4dZzsvj8TB69GjWrFnDzTffTK2ijkW5kOnTLc3/0EMQFxfYbYmIiEjQHT58mP/85z/Url2b6667LngH9qZNs65EQ4ZAr1627yhF4/HA8uUWqBo9GpYtO/3rrVpZqOryy6FdO7sgojjq29cCY1OmwFNP2QjBv/8d3n0X7r8f/vhHqFTp/OtYvdpGA4J1wfrtbwNetoiUbB6Ph88//5xNmzYFfFtdunRhyJAhAd+OFJzH4+GTTz7h8OHD3HHHHcTHxztdUqnk8Xj45ptvWLt2bcC31bFjx5J1zNfPxowZw9KlSwO+nebNm3P11VcHfDvF1ZQpU5g1a5bTZUgpkZSUxA033FDsQ0MTJkxg3rx5Ad9Ow4YNufHGG4v37yspyW43buTAgQP897//JSkpiavyjuBLTbURgQADBgS/Rq/ERKhTx8YZzp9vXbJF/EBnvUVE/CAjI4Pvv/+erKwsFi1aRKtWrUhMTHS6rPw7edKuej8zPOX9OH78/N+fnW0nLlavhpEjcz8fFQVNm54eumrZ0mYzqyPTBS1YsIAVK1YA8PPPP3P77bcHbud75Ei4/nrrdjBvHnz/vcJxIiIiJcz48eM5ceIE69evZ8mSJXTo0CHwG123zkbUnTgBf/2rjWUbMQKuucbatStslX/Z2dbJyRuq2rYt92thYXZ1prdTlbdtf0ngctlB2YsusqDeU0/BokX2eHrnHXjwQXj44bOPOExJsd/H8eMW1nrttSAXLyIl0dq1a4MSrgKYP38+rVu3pmbNmkHZnuTfL7/8wtatWwFITk7m4osvdragUmrTpk1BCVcBLFq0iA4dOlC9evWgbK842b17d1DCVQCrV69my5Yt1K9fPyjbK04OHz7MnDlznC5DSpGNGzeycuVKWrVq5XQphXbgwAHme8NAAbZp0yZWrFhBmzZtgrK9gDgVsPJs3MjPP/9MRkYGK1eupEOHDtSrV8/u4+2q3agR1K3rXK1gx52++soaCyhgJX6iM6ciIn4wbtw4UlNTT/v3HXfcETpj3fJ2oTpbgGrnTrvPubhcUKuWnahp0AAaNsxdrl8fsrJg1SpYuTL3dvVqa7u5YoV95BUbC82b/7rjVWJi8b263s92797NxIkTAWuv6j1Q0b59e/9v7Kuv4De/yR0l8+OPcPfdNj5F/x8iIiIlwvr169mwYYPv35MmTaJRo0aUK1cucBs9eRKuu87CVUlJcOgQ7NtnoZh33oEaNXLDVt27K4B/NmlpFioaPRp++gkOH879WmwsDB5sAaJLLoHKlR0rMyhcLuuCNniw7a8+/bR17nrxRXjrLQtZPfAAlC9v98/JgRtusNEFiYnwzTfFYzyiiIQ0t9vN1KlTAejZsyf9+vUL2LZ++OEHVqxYwc8//8xtt91WvLsdlDDZ2dlMmzbN9+/FixfTrVs3Kpwt7CsB4/F4mDJlCgBdu3ZlYADHEH333XesWrWKKVOmcOONNwZsO8XV5FOjoFq3bs3w4cMDtp3x48ezcOFCJk+eHNgLUYupadOm4Xa7SUpK4vrrr3e6HCnhZs2axbRp05g0aRJNmjQhKirK6ZIKZerUqXg8Hpo2bRrQ7nizZ89m6tSpTJ48maZNmxIdHR2wbQXUqYDVyVWr2JrnXNXkyZNz91cnTbJPOtm9yqtnTzv/pc5+4kcKWImIFNGqVatYsWIFLpeLa6+9ltGjR7N3714WL15Mp06dgleItwvV2QJU+elCVabMr8NT3o+6dSEm5vzfX7u2nezwcrutnjODV2vWQHo6LF5sH3mVLZsbvMrb8apGjVIV9MnIyGDkyJG43W6aNm1KYmIiEydOZMqUKTRr1ozY2Fj/bezzz21MitsNt9wCl14KV18NH35o/6fPPOO/bYmIiIgjsrOzGT9+PADdunVj27Zt7N69m59//plrr702cCcmHn3UAjCVK9sVjJUr26i3b76xbpl79lgw5q23oGZN2we55hro2rV0h63274exYy1UNWmS7ed7Va5s+2uXX24HK0vjWGeXy34Hw4bZ7+iZZ+zvjGeegX/8Ax55xMYHvvIKjBtnQbTRo6FKFYcLF5GSYNmyZRw8eJDY2Fh69OgR0AvrBgwYwNq1a9m1axfLly+nbdu2AduWFMzixYtJSUkhPj6eypUrs3XrVqZNm8aVV17pdGmlysqVK9m7dy/R0dH06tUroM/H/v37s2bNGjZu3MjWrVtzu3QImzZtYsuWLYSHh9OvX7+A/j/07t2bZcuWsXv3btasWUPz5s0Dtq3iZs+ePaxcuRKAiy66KHQu/JYSq3v37ixbtowjR46QnJzMgFAI0xTQjh07WLt2LS6Xi/79+wf0edOtWzeWLVvG4cOHmTFjBoMGDQrYtgLqVMAqYts2cLvp2Lkzy5cvZ9euXaxdu5ZmzZrlBqwCGHzOt5497XbuXLsIS13UxQ8UsBLnpafbVaY33QS9ejldjUiBHDt2jJ9++gmwKxebNGlC3759GT9+PNOmTaNFixbE+fOkR06OhaVWrbKPvGGq/HahOluAqkEDO+Hgz5NrYWHW3ap+fbui/syfIW/oatUqGx9z7JjNQj6zJWtCQm7gKm/4qmpV/9UbIjweD2PGjCElJYWEhASGDx9OZGQkS5cu5cCBA0yfPp2hQ4f6Z2Mffwy/+509bm6/Hf79b/t/e+cd62D17LN2svOOO/yzPRF/+vZbqF7d2vyKeOXkwKefQqdO9j4hIgDMnTuXI0eOEB8fT58+fUhJSeH9999n3bp1gTsxMXYsvPmmLf/vf7ZPATB0qH289x5Mnmxhq9GjYfdu+Oc/7aN27dywVZcupSNon55u3UNHjoQ5c07fr2/QwAJVl19unb50QNCEhcGVV9rvZdQo23ddswaeeAL+9jc4csTu9+GH0K6dk5WKSAmRlZXF9OnTAejVqxcxF7oQrYjKli1L7969mTx5sq/bQaC3KRd28uRJkpOTAejbty81a9bk/fff55dffqFbt27UqFHD4QpLh5ycHF8Xse7du/v3+OtZVKxYkfbt27No0aLTu3SUcnm7iHXs2JGEhISAbi8+Pp7u3bszY8YMpk6dSpMmTQjXvjGA7/+hVatWGmMpQREREcHgwYP56quvmDt3Lu3ataNSpUpOl5VvHo/H132vbdu2VAnwBTkREREMGTKEL774gvnz59O+fXsqF8cu1ImJuMPDicjOphYwaNAg4uLiSE5OZsqUKTSJiyNs3Tr7ez2AnV7zrWVLKFcOjh6FX34BXbAgfqCAlTjvpZfggw+sxf+SJXayVKQY8Hg8/PDDD6Snp1OjRg369OkDQKdOnViyZAn79+9n2rRpDBs2rOArz9v9KW8Iac0ayMg49/eVKXPuAFV+ulAFQ3i4zV5u1AiuuCL381lZsGFD7s/q/bk3bICUFGvheWYbzypVfh26atECKlYM6o/kTwsWLGDNmjWEhYUxYsQI38HTIUOG8Omnn7Jw4ULat29PtWrVirah//zHglMeD/z+9/Cvf+V2irjrLti1y8at3HWXvS5femkRfzIRP/r8cxtrGR1tgcziPLde/Ovvf4c//QmqVbP3kWJ0YEckUI4ePcrMmTMBGDhwINHR0VSrVo0ePXowc+ZMfv75Z+rXr+/fDpm7dlmIG+DBB63T0Jmio+3zw4ZZh6aJEy1s9cMPduHAG2/YR2JibtiqU6eSF7byeCw0/Mgjtv/v1bGjjf67/HLbvy1pP7c/hYXZ4+Oqq+Drry1o5R2H+cgjNiZQRMQPFixYwLFjxyhfvnzQOpZ37dqVpUuXcujQIWbMmMHgvJ3DxRFz5szhxIkTVKpUiXbt2hEWFkbLli1ZuXIlU6ZM4Te/+Y3TJZYKixcv9l1A0LVr16Bss0+fPr/u0lHKrVq1ij179hAVFUXv3r2Dss1u3bqxcOFCDh06xNKlS+nYsWNQthvKNm/ezKZNmwgLCwvo6FqRMzVu3JikpCQ2btzIhAkTuKEY/e21YcMGtm/fTkREBH379g3KNhs1akTjxo1Zv34948eP58Ybbyx2Yd0jx47hTkig0qFDDKhXj8jISLp3786iRYs4dOgQ2//7X+qBHb8JcOg2X8LDoVs3mDDBzi8qYCV+oB6R4rzHHrMDxnv2wHXXQXa20xWJ5MvChQvZtGkTERERXHHFFb6rZcLCwnwdhhYvXszevXvPvRKPx04gTZhgJ4VvvRU6d7ZEdYMGFmp57DH47DNYutTCVbGx0L69dX174QULGsydC/v2WQeo5ctt5Mrf/w733msdApo0CY1w1flERtp4wGuugeees6vQ166FtDQbLfP55/DnP8Nll1mIzOWCAwdg+vTcjku9e9vJ9Jo1YdAgeOghu2J93jxLqIe43bt3M3HiRMCS/7Vq1fJ9rUGDBjRr1gyPx8P48ePxnK9b2YW8/751rPJ47DHy7ru/HsPz/PN2YtTthmuvtd+hSCjYssWe72An5K+99sIjUKV0WLcOnn7alvftgz/8wdl6RELEpEmTyMrKIjExkVatWvk+37t3bypXrkxaWppv/8MvcnLgxhvh0CHrGvTKKxf+nuho2+/99FMbjzd6tIVi4uNh+3bbr+3SxTqjPvooLFp0/s6txcXy5XZF59VXW7iqTh3r+rVjByxcCE8+aRcRFLMDro4JD7fHzerV9vfT66/Dyy87XZWIlBDp6enMOnXRV9++fYmICM51y+Hh4QwZMgSwgNeBAweCsl05u+PHjzN37lyA00YJeceieUelSWBlZmb6uoj16dOHqKiooGw3b5hrypQpuN3uoGw3VOXk5DB16lQgOF3EvKKjo31hrhkzZpCVlRWU7YaqM7uIVahQweGKpDRxuVwMGTKEsLAwNmzYwPr1650uKV/cbrfvedO5c2fKlSsXtG0PHjyY8PBwNm3axLp164K2XX+ZMGECh0+9ztQ99frrHdULkD52rN0xFMYDennHBJ7ZwEGkkBSwEueVKWNX7JYtCzNmWDt/kRB38OBBJp2aIzxgwIBftQ+tV68eLVq0wOPxMG7cODxuN+zdC1Om2EmTO++0sVYVKtiJlCFD7Orqjz6ykylpaRAVBa1bw/XXWyeh0aPtSuxjx2DxYvjkEzvpcsMN0LWrjcsriSdfYmKsO80NN1jHux9+gI0bLVCxaJGNnPnjHy1Ilpho37Nnj815/sc/rEtTt25Qvrx18br4Yrv/xx/b96elOfnT+WRkZDBy5EjcbjdNmzalc+fOv7rPoEGDiIiIYOvWraxevbpwG/rXv6xjFdh41rfeOvvjxuWykYFDh9rImksusfCCiJOys+2k/bFj9rpXq5Y9Lu+7z+nKxGk5ORZSPnnSAh1hYfDFFxY4FinFtm7dysqVK3G5XAwdOvS0KyMjIiK49FSHymXLlrF582b/bPSll+zvuvh46yYUHV2w74+Jsc5Nn39uYavvvrMLccqUsRDSa6/ZlZANG9qFCEuWFL+w1YED1iW0fXv7XcXGWteltWvh/vttRKIUXkSE7S889JAti4j4wezZs8nIyKBq1aq0bt06qNtOSkqiSZMmuN3uol9wJUWSnJxMVlYWNWvWPK17UcWKFenQoQNgwRv9HwXW3LlzSUtLo2LFirQL8hjgHj16EBsby6FDh1i2bFlQtx1qlixZwpEjRyhTpgzdunUL6rY7dOhAQkICx48fZ14pvyh09erV7N69O6hdxETyqlSpki98On78eLKLQRONX375hf379xMTE0NPb/gmSCpWrOh7zZwwYUKxColu3LiRdevWceTUxADXpk2+r3Xs2JHyZctSxxuyGzDAiRLPrkcPu501q/gdP5KQpICVhIYmTSxYAvDqqzoZJiEtJyeH7777juzsbBo0aPDrIMyhQ5CczLBt27hk3Dj6PfccOZUqQY0atlPxwAM2FnPOHEhNtSutmzWDESPsxMrIkTYKMC3Nrmr/4gsLHg4fDklJdn+BuDjo0AFuvtleN37+2U66paZaR68PP7SRNAMHWkcrsA4I48bB3/4Gt9xiJ+fKlrUTdMOHw+OP28m85cvPP4rRzzweD2PGjCElJYWEhASGDx9+1tawCQkJvh3+iRMnkpmZWbANvfmmdawC+L//s7E75wvlRUba47FTJ3tcDxli4TURp7z4oj2/y5WDL7+018ewMAtMfvqp09WJk955x95X4+MtkPzoo/b5u+6CgwcdLU3EKW63m3HjxgF2AqL6WUaxJyYm+kZq/Pjjj0U/sDdrlu3PgoW6GzUq2vpiY22k9JdfWthq1CjrdhoXZx0N//pX2x9s1Mg6nRY2gB4sWVl2AUCjRhZk93YKXbsWnnnGfi4REQk5R48eZf78+cDpXYuCydvtYPPmzaxduzbo2xc4fPgwixcvBuxiyzOP2/Tu3ZvIyEh27drFmjVrnCixVEhLS2POnDmAPR/Dg3ycNG+XjunTpxerE+P+lJmZyYwZMwB77Aeri5hXRESEbxTe7NmzSU9PD+r2Q0XeLmLdunWjTJkyDlckpVXv3r2Jj4/nyJEjvk6PoSo7O5tp06YBuaHZYOvVqxdly5YlJSXF954W6nJychg/fjwAZdu3t09u2OD7ekREBENr1yY+LY3MqCjSQ2kUX+fOdvHVrl12jlCkiBSwktBx1VV2wh8s+JDnhVkklCQnJ7Nnzx5iYmJOD8KMGWNj/SpXhj59iH3kETrMn0+9bduISEnB43JZQOryyy0w9cUXsGKFBalWr7YgyzPPWNCqaVNdbV1Y5cpZZ5vbbrMA0cSJtuN0+DDMnGnj8O67D/r2hSpVLLG+ebP9/738MvzmNzaHuUwZ6NMHTu1sB9KCBQtYs2YNYWFhjBgxgpjzjHPs3r07CQkJHD161DeeIF9ef93CfQB/+pN1f8hPx7MyZeDHH+2xu3WrdQArBuMWpQSaPdvGogK89x7Uq2djQZ95xj53991QTNpQi59t2WLBCrDXtsREC3i0aGGBDHU4k1C3Y4ftc9x/vwVu/GTRokXs37+f2NhY38mHsxkwYADlypXjyJEjvoOMhXL4sHUcdbttlPVNNxV+XWcTF2d/M379tT23v/nG9ptjY2HTJhtF2KKF/S6/+goKGkQPtPHjrTvtQw/ZBQHt2kFystXq7cIqIiIhafr06WRnZ5OYmEjjxo0dqaFChQp0794dKH7dDkqKadOm4Xa7adiwIfXr1//V1+Pj430dKaZOnVrqx8cFSnJyMpmZmdSoUYPmzZs7UkOnTp0oX748x44d84UvS5t58+aRlpZGhQoVfN3bgq1Vq1ZUq1aNkydPMnPmTEdqcNrSpUs5fPgwcXFxQe8iJpJXdHQ0A0+NhJs5cyapqakOV3RuixYtIjU1lbJly9KlSxdHaoiKimLQoEEAzJo1i5SUFEfqKIh58+Zx6NAhypQpQ8PBg+2TGzeedp9Gp8JLW+vWZdaCBcEu8dzKlLEO4qAxgeIXClhJaHn5ZejVy07eX3llyIzuEvHasWOH7w+2Sy65xGYzp6dbV6Dhw+0kL9goumHDcD/yCBN/8xv+feedTP3hBwsOfv+9dWG5/npo1argY1OkcCpUsFnLd91lY/GmTbOTc/v2wdSp9rnf/97uU6GCnRxMTob+/a3zWIAOmOzevZuJEycCNgKwVq1a571/ZGQkg0/twM6ZM4fDhw9feCOvvpobYH3iCXutLcg4yapV7aRg1aqwbJmd3Ay1k5ZSsqWm2qgf70n766/P/doTT0C/frbPcO21Qe0+JyHA44Hbb4cTJyw4e+ed9vnoaBshGx5uYYxRo5ysUuTcjhyxcbzJyfD22zbyzg/S0tJ8Yal+/foRd57OSNHR0QwbNgywA2a7d+8u+Aa9z8UdOyyU/c47hao738qUgauvtgsU9u+35/nw4dbVMDnZ3ifq1LH3iG3bAlvLhaxfb6OWhw61TlVVqlg324UL7W9fEREJaQcPHvSNATtb16Jg6tWrF+XKlSM1NZXZs2c7VkdptGfPHlauXAnARRdddM77de/enbi4OA4dOsTSpUuDVV6pceTIERYtWgQ4+3ws7d2T0tLSfK9BTnQR83K5XL7n44IFC0I60BEIZ3YRi9Y5BnFYq1atSExMJCsri0mTJjldzlnlDWT26dOHyMhIx2pp0aIFdevWJTs7O2R/X17Hjh0jOTkZsPffKG/AeePG00buhU2eDMCWBg1YsGABR0PpQn3vmEDtQ4sfKGAloSUy0g6OV6sGK1daEELzUCVEZGZm8v333+PxeGjdujUtWrSwDlQdO9oIFICHH7YgwNat8OOPhL32GnWffJK9NWsyZ9kyDmpMUeipWtXCGffdZ11xZs60cXibN8M999jr0pQp1hXrssvs/9xPMjIyGDlyJG63m6ZNm/563OQ5NGnShAYNGpCTk8OECRPOf+eXXrKOVWCdfl54oWDhKq+GDeGnn+yE5uTJcOutfu2yIXJe99xjJ8gbNLAAQl7h4fDZZ9Y9cNmy3NFwUjp8+KGFZGNjbTnvuJaOHXPDKvfcAwcOOFOjyLlkZFhn01WrLNwN1oXtgw+KvOqpU6eSkZFB9erV83VFeePGjWnZsqVvbHFOTk7BNvjuu3YRQWSkdWQqW7aQlRdCfLyNDRw92t4rnn7aRnPv32/7QfXrw6WX2jjpgv5cRZGaCo88Ai1b2j5URIQF3jdssDCaxn6LiBQLU6dOxePx0KRJE+rUqeNoLZGRkb5uB7Nnzy4W3Q5KiilTpgDQsmVLatSocc77aXxcYE2fPh23202DBg1o0KCBo7W0atWKqlWrkpGRUbAO8yXAzJkzyczMpHr16nZ83EFJSUnUrVuXnJwcpk+f7mgtwTZ//nyOHz9OQkKCb+y7iJNcLhdDhw7F5XKxatUqtm7d6nRJvzJnzhxOnDhBpUqVaNeunaO15P19rV69ms2bNztaz/lMnjyZzMxMateuTZs2bazBRHi4NZ/Ys8fudPKkXfAGnOjRg+zs7NB6Xe7Z025L2Xu2BIYCVhJ6atSwcQ/eE6b//rfTFYkA1oL9yJEjlCtXjqFDhsCbb9rs3tWrLRQ4YQL8/e82oi6Pxo0bk5SUhNvtZsKECXgUGgx9LpedjHvnHes6cMstdtJ+7Fho08a6IhRxFJn3BGZKSgoJCQmnj5u8YHm28x0WFsb69evZcK6Rqs8/b50bwIJVzz5buHCVV8eO1gUmIgI+/zx3JJdIIH32mY1UDQ+3x90Zr7EA1KwJn3xiy2+9ZSfZpeTbudPCCwB/+YsFQc/01FMWbjhwwLpNioSKnBwbS5ycbK9rM2bY+zTYyNNTV/0Vxu7du1myZAmAb38hP4YMGUJsbCz79u1jzpw5+d/gihV2kQHAX/8KDo0IAaB2bXjuOQtajRoFF11kF+z8+CMMG2bdtV55xcJXgZKTA//5DzRubH8bZGXZtleuhL/9DcqXD9y2RUTEr3bu3MmaNWtwuVz079/f6XIAaN68OfXq1SM7O/vCF1yJX2zZsoVNmzYRFhZ23rHLXh07diQhIYHjx4+X2vFxgbBv3z5WnLroccCAAQ5XA2FhYad1TwqpLh0BlJKSEhJdxLxcLpfv8bB8+XIOlJILq06cOOHrItavXz/HuoiJnCnvRV7jxo0LqXG5x48fZ+7cuYB138vvsZJAqlatmi8gOX78+IJf7BYE27dv973/egNhREVZyApyxwTOmWOBq+rV6XjzzQAsW7YsdF6XvR2sVq4EXaQgReT8q4fI2fTubQfnAR54AEJpVquUSuvWrfOdqLqyZ09irrrKHpsnT9oJkxUr4NRVhGdyuVwMGTKEsLAwNm7cyPoiBnMkyOrVg48+su4S11xjn/vqK2jeHG67rdBjZxYsWMCaNWsICwtjxIgRxMTEFOj7K1eu7JsRPn78eLKzs3O/6PFY94ZnnrF/v/wyPPlkoer8lSFDrEsM2OjBN9/0z3pLsowM64iWnGzdMzT+Nv+8neTAHs9du577vkOH5oZtbr0VTs18lxLK47GxrkeP2uPiD384+/2io+Hjjy2gN3KkhfhFnObxwIMPwrff2kGpH36wsdFPP22hq5wcGDHCQvwFXrWHcePGAbnt+fOrTJkyvjHEM2bMyF/n1bQ0uO462ye++GL7uUJBZKSNNJ482cbyPfSQdQnbutUC4rVrww032JWL/rz4YdYsuwDj9tstxNWkib33//ijLYuISLHh8XiYfCrw3KZNG6pWrepwRSZvt4O1a9eyadMmp0sq0fI+Djp06EDFihUv+D15x8fNmjWr1I2PCxRvF7EWLVqct4tYMDVq1IjExMTQ69IRQNOnTycnJ4f69es73kXMq3bt2jRt2hSPx+N7nJR0s2bN4uTJk1SrVo1WrVo5XY7Iafr160dsbCz79+9n4cKFTpfjk5ycTFZWFrVq1aJZs2ZOl+PTr18/4uLiOHDgQEj9vgDcbrfvGFO7du2oWbNm7heTkuzWG7DyjjkcMIA6iYm+1+WpU6cGseLzqFbNavZ44FTQTqSwFLCS0PXww3DllZCZaScYNFpNHJKWlsbYsWMBuCQigrqXXALjxtlJ27fftq5GFzjQVqlSJbp16wZYJ6zTwjBSPDRtaiNMly6FSy6xk5///S80agT33w979+Z7Vbt27WLixIkADBo0iFq1ahWqpD59+hAfH8/hw4eZN2+efdLjsTDVCy/Yv197LXdElr/cfLON3AE7kVqaAwvHj9uJ2ylTLMTxl79Y55FLL4V27aBKFRtd1rAh9Oljgcw2bTTrOz+ysy1ocOyYXWGSn45pf/mLndg+csQ6zem1tuT6/HMLLURF2Wvx+a7WbN8eHn/clu+5B/btC06NIufy6qu5404//RT69rVll8tCzL162Xi5YcMK/Hhdvnw5O3fuJCoqioEDBxa4tNatW9OwYUNycnL48ccfL9x59cEHYc0a60L8v/8VrVNmoDRpAq+/Drt2WWi+c2frKvXll/a7bt3axn0XpevA9u32vtOrFyxZYl2qXn8dfvnFAsAiIlLsbNy4kW3bthEeHk5f73t1iKhatSqdO3cGQrfbQUmxZs0adu/eTWRkJL17987397Vq1Ypq1apx8uRJZs6cGcAKS4etW7eyYcMGwsLCQqabHJzePSmkunQEyL59+1i+fDkAF110kePdq/Ly1rNu3Tp27NjhdDkBlZqayoJTDQlCoYuYyJni4uJ8r9XTp08nLQQuNj58+DCLFy8GQu95Exsbe9rv6/jx4w5XlGvJkiXs3buX6OhoX9dGnzMDVt5O7KeORfXv3993QUDIvC5rTKD4iQJWErpcLjsA3qgR7NgBN95ogQaRIPJ4PIwdO5aMlBQuT06mw5NP2tXoLVvCwoU2biifO2O9e/embNmyHDlypGBjVyS0tG1robo5c6B/fztB9/bb0KAB/OlPcOjQeb89IyODUaNG4Xa7adq0qe+gaGFER0f7DuQkJydzNDXVavCGn954I7ejj7899pg9/j0euOkmG21Ukng8FtL55RcLVH74oY1uuv126+LVsiUkJEDZstCsGQwYYKMkn3wS3nvPOlUsW5YbDo6JsT86qlaFTZvsBOwf/2jdreTsXnjBriYpV87GBEZEXPh7oqLshHm5cvYc9XZxk5Jl3z7rIgn2f5yfq86efNJCFIcOWchK43rFKZ9+mht8fuON3O6YXtHR8P339p6xdStcfrm1WM+HjIwMX4cF735nQblcLi655BIiIyPZtm2b7wDkWX39tb0/ulz2Ol2lSoG3F1SxsfZePX8+LFpknUhjY609/L332rjZu+6CUyeN8uXECRtJ2LSpdTh1ueDOO22U9EMPWSctEREpdvJ2QencuTPlQ3C8a9++fYmLi+PgwYMaQxcgbrfb13WhW7duxMfH5/t7XS7XaePjUlNTA1JjaZC3i1j79u3z1UUsmOrUqUOTJk1Cq0tHgHh/vubNmxf6YtFAqVy5Mm3btgVg8uTJF75QpBjzdhGrV68eDRs2dLockbNq37491atXJyMjgxkhcN5g2rRpuN1ukpKSqFevntPl/Eq7du2oUaMGJ0+eDJlOfCdOnPC97vfr148yZcqcfoe8AavDh+04C8Cp/Z8qVarQpk0bwLpQhsTrsndMoC5+lyJSwEpCW7ly8N13EBcHEyfC8887XZGUMkuXLuXQzJnc8eGHtPH+kXzffTa2soDtd/N2Epg1a5YOrhR33bpZ16IpU2w8VXq6daRo0MBOtJ2lC4LH4+GHH34gJSWFhIQEhg8fXuSrJVq3bk3t2rXJysxk329+Yx2rAN56K7Bjelwu+Oc/czsNDh9uYaTiIj3dOkyMGWNdK554wjpzXXSRdbqIj4eKFS2QcfHFcMcd9v/6n//AhAk2MtL7HC5XzkZGDhoEv/sdPPUU/Pvf8NNPdpL20CE7Abthg51w/d3vLNzxt78R0bkzCd6rPCTXrFnw4ou2/N57Nqozvxo0yB1j+fLLuVfPSMlx3332h3u7dhZUzI+oKOsyFxFh+5Zffx3YGkXOZuJEG2EKFoA+1/t0pUr2HlKhAsybZ6Egt/uCq58xYwZpaWlUqlSJrucbqXoBCQkJvqsnJ0+ezNGzdXbassWCRGAdBkOok0C+dOhg7xW7d9v+TNOmNu7w3/+2MH337haGO1cQ2uOx15GmTS2AnZ5uY+6XLLF1hMgYKRERKZxffvmFffv2ER0dTU/vle4hJiYmxnfB1YwZMzh27JjDFZU8S5cu5dChQ8TFxdG9e/cCf39SUhJ169YlJyen1IyPC4S1a9eya9cuIiMj6dOnj9PlnJW3e9LatWvZuXOn0+UExLZt21i/fj0ulyukuojl1bdvXyIiIti+fTsbNmxwupyA2L9/f8h2ERPJKywsjKGnujkvW7aMEydOOFbLnj17WLlyJcCvuzCFiLCwMC6++GLAfl+7du1yuCILpaWnp1O1alU6der06zvkDVhNm2bHSZo3hzwB3L59+xIeHs62bdvYGArnQLz79fPn2zktkUJSwEpCX8uW8P77tvz88zYORiQIDh86xL7nnuPO99+n2r59ULmydS566y272r0QWrZsSWJiIllZWUzyziSW4q1/f+uUM3asjX47etROtDVoAH/722ldJxYsWMDatWsJCwvj6quvJiYmpsibd7lcXDx0KEPGjaPRjz/aJ9991wIIgRYebh0reva0sNHQodZxMJRlZ1tgp25dO7k6fLh1rXjpJfjkE5g61UJQ3j/6KlWy/9dhw+D3v7euSh99ZCfJV6+2nzs11QJXEybYqLLnn7eTzhdfbAGtihVzO92VL2/3GTsWqlfHtXYtvR59lLBnn9VOvVdKio0GdLutO9r11xd8HVdfbf9fHo+tSyPhSo5Ro+wjIsKeSwXpDtO2rXWyAnveF2C0q0iRLVkCV11l70M33AB//ev579+4sXWyioy0UbxPPXXeux84cMA3JmLIkCGEn29sZj507tyZWrVqcfLkSX7++efTrzTMyrLX5qNHLXD+7LNF2pajEhLgD3+w9/Rp06yjWESEdVD87W+hdm3CHnuMuD17cr9nyRILU113ne33JCba/9H06fY6IyIixVpOTg7Tpk0DoEePHsTFxTlc0bm1bduWWrVqkZmZGTLdDkqKrKwsXyiqV69eREdHF3gdecfHLV++vMSPjwuEvF3EunbtWqAuYsGUt0tHSeyedGYXsUqVKjlc0dmVK1fONylgypQpuPNxkUpxM3XqVDweD82aNaN27dpOlyNyXomJibQ61aRg586djr02eveRWrVqRfXq1R2pIT9q167tey/51XGYINu7d6+vo/nQoUMJCztLnCRvwMp7rvPUfo9X+fLlT3tddvz9sUkTO9+TkWHHdkQKSQErKR5uvNHGuYCdKN2yxdl6pMRz79/PsQEDGDpmDJHZ2XgGDoQVK+CSS4q0XpfLxdChQ3G5XKxatYoteiyXDC6XPTaWLLFuBk2aWNeiP/4RGjaEf/2LXVu2MHHiRAAGDRpEzZo1/bNtt5saL75IlwUL8ADTb7wRt7ejRDDExloXqObNYdcuG5935Ejwtp9fHo8FdNu0gbvvhgMHbGe6Y0cbv3TfffDKKxYYmz7d/jA4ccJG/C1bZiP/3nvPwhm33GKzxJs1s+5VhXHJJbByJe5rriHM7Sb8pZegS5fi1QUsEDwee7/fts1Cim+/Xfh1vfGGhbT37bOT5MXhwNq+fRa8uO02e17lcyxYqXHokAWjwEasFSbI8Pjj9n2HD9soMKf/sJfSYfNmCyEfP26dEj/6CM52cOpMffrkduR76SX43//OejePx8P48eNxu900adKEJO9BriIICwvj0ksvJSwsjHXr1rF69ercLz79tF3tV748fPFFyRiD53JB3762H7djh3VRTEyEQ4cIf/11Bt59N+HDhtk+QMeO1mkxNtZC1WvXWrBXV4+LiJQIixYtIiUlhfj4eLp06eJ0OeflPcYEFuDZEeoXPBUj8+fP5/jx4yQkJNCxY8dCr6d27do0bdq0VIyPC4Rly5Zx8OBBYmNj6eEd6xOiQq5Lhx+tW7eOnTt3EhEREbJdxLx69uxJTEwM+/fv55cSdoxt+/btrFu3LqS7iImcaeDAgURFRXHixAlHnpNbtmxh06ZNhIWF0a9fv6Bvv6AGDBhAVFQUu3fvZtmyZY7U4PF4GDduHB6PhxYtWpx7pGL9+nYc5NgxuxgW7JzJGbxB9X379jn/uuxyaUyg+IUCVlJ8vP66nXw+cgRGjDj3uAaRopoyhazmzam7bBk54eGkv/girvHjoUYNv6y+evXqdOjQAcB3MkxKiLAw63ywcqV1VqlbF/bsgXvvJb5jR1otXkzzxo19qf0ic7stLPTuu3hcLsaNGMGMRo18VxcETYUKMH68tX9dvdq6QoXSa/SKFTB4sHWhWr3aglVvvWX/NwsXWoeQt96CP/3JAr19+lgwrpCd6vKtUiVyPvuMhX/8I55KlSzI1aGDjbXLzg7stkPVZ5/Bl19ad7TPPy98gA3s/+/rr+124kQb4RnKVq60/Zwvv7TXj+HDrXPiFVfYaLtDh5yu0HkPPQT791ug09uJqqAiIy2kEhkJP/xg4RCRQDpwwMLH+/dbuO+772xkZX799re5j/c777QQ8BnWrl3L5s2bCQ8PZ/DgwX4pG6BatWq+sUjjxo0jPT3dxq56u299+GHBRrgWF9Wr2+jgzZthzBjcQ4bgcbkImzTJXo89HgvDrltnncUCvb8gIiJBc/LkSZKTkwHo06cPUQV5z3ZIrVq1aHvqwoNx48bpGJMfpKenM2vWLAD69etHREREkdbXv39/3/g4heDyzx9dxIIp5Lp0+MmZXcTKli3rcEXnFxsb6/sbZtq0aWSXkONrebuItWvXjsqVKztckUj+lC1b1heQnTZtGhlBPGeQ93nTsWNHKlSoELRtF1Z8fDx9+/YFrCNiMH9fXitXrmT79u1ERkYy8CyBKZ+YGKhTx5YPHbJu4GcJ4eZ9XZ46darzr8veMYGn9vVECkMBKyk+oqNh5Eg7Mb5kiY1yEPGnzEz405/wDBxI9KFDHKhcmU2ff07sE0/kr8tAAfTv35/Y2Fj279/PwoUL/bpuCQEREfC738H69Xjefpv0ChUof/gwl//wA1c9+yyukSOL3k0nJwfuuMNGqIaF4fr4Yyr/8Y+A7agGfa55nTowbpx1s5g504JKOTnBreFMe/fa76hdO2tTGxUFjzxi3anuuy9kOm7s7tGD7GXL4LLLbOzS44/bjv66dU6XFlybN+d2J3rmGejatejrbN48twvWk0/ayKdQNH48dO9unbuSkuzxmZhoXdRGj7aOKVWrWneVf/yjdHby/Okn+PRTez/+739tv7Cw2rTJHbd2//0WthQJhLQ061i4YYOFrn/+uXDB0eeeg2uvtfeIK6887f0hKyuLCRMmANC9e3e/HzDs1asXlStXJi0tjenffGOjWz0eG8M6YoRftxVywsPh0kvJGTOGye++S84jj9j+zaxZFgL2HkgUEZESY+7cuZw4cYKKFSvSrl07p8vJtwEDBhAdHc2ePXtYunSp0+UUezNnzuTkyZNUq1bNN9qoKKpUqeILwZXE8XGBsmDBAo4dO0b58uXp1KmT0+XkS0h16fAT73jL4tBFzKtz586ULVuW1NRUFi1a5HQ5frF+/Xp27NhRLLqIiZypc+fOREdHk5aWxowZM4K23dWrV7N7926ioqLo3bt30LZbVJ07d6Zy5cqcOHHCFzQOlszMTCadGvfXs2dPypcvf/5vyNtBvWtXOEcIt0uXLqHzuuwNWM2erckGUmgKWEnxUqeOdXZwueCDD2y8hog/rF9vJ7dffRWXx8PiDh1I/vvfaXTNNQHZXGxsrK+V77Rp00hLSwvIdsRhUVEs6NiR1+++m0mDBuFOSCBs/Xo7Sdq+vY2dK8xOXE4O3HqrhQzCwix0cNNNdOzYkWrVqpGRkeFM6/lWrSwMEhVlHToeeMCZndQTJ+CFF2wH/8MPLcx29dWwZg289hokJAS/pgupVs1+dx9/bCG1+fOt08k//lE8RtsVVVaWnbQ+dsz+yHn8cf+t+3e/g+uvt+fNddeF3gjLt9+27mrHjtlVPvPmWUe1rVstUP7MMxYIcrthxgzr4tSggX3umWfsPiX9j8HUVAtzgP38/hjV8thj9jp85Iitu6T/DiX4srPt/X7BAqhYESZMKHw31LAw67zWrZs9ZocNsxG2wOzZs0lNTaVcuXL06tXLf/WfEhERwaWXXgpuN0kvvmjh5RYtbAxrKXKienXcL71knRaLyYklEREpmLS0NOaeuiCjf//+hIeHO1xR/pUpU8bX7WDq1KnWdVIKJTU1lQULFgBw0UUX4fLTCOC+ffsSERHB9u3b2bBhg1/WWZL5u4tYsOQNIZWE7kl5u4h5R+8VB5GRkb7XxOTkZEc6wPiT2+1mypQpgIUUyhWl27uIA8LDw6lVqxZg4dkDBw4EfJs5OTm+8yPdunWjTJkyAd+mv4SHhzNkyBDAfl/79+8P2raTk5M5duwYFSpUoHv37hf+hrwBq/N0u4qMjPSFQ71Bdse0b28X7h44YBdEihSCAlZS/AwcCM8/b8v33GMjlUQKy+OxkEq7drB4MZlly/L1tdcy/frrGTpihN8OpJxN+/btqV69OidPnvT9kSQly65du5g4cSLZUVGUe/55wrZtg2eftST/8uVw6aV2kq4gYajsbBsX9Mkn1lnhyy9tTA0QFhbG0KFDAVi8eDF7nOjK0revBb5cLnjnndwxQsHgdtvvpXFjePpp6xzSpYtdjfDNNxZKCWUul/3frlwJgwbZmMWHHoJ+/ay7U0n2wgsWLCpf3k5e+/NkhssF771nYx+3b4fbbguNME12tnVPuv9+e+zecouNMqxUyb7uctl707PP2r7O5s0WuOvb18IWK1bY/lCHDtYZ5/77YcoUC6uVNI8+Crt22R/t3n3Aoso7KnDsWHvcifiLxwN33WWd12JjLVDdpEnR1hkTY2Mt69eHTZvg8stJ2beP2bNnAzBo0CAiA9SZMTExkWt27qTRxo1kR0aS9dlnGosnIiIlTnJyMpmZmdSsWZPmzZs7XU6BderUiSpVqnDixAmmTZvmdDnF1vTp08nJyaFu3bok5T1pWETlypU7bXycRjme3+zZs8nIyKBq1ap+6SIWTF27diU+Pp6UlBQWL17sdDlFsnDhQo4ePXra47e4aNu2LZUrVyY9PZ05c+Y4XU6RrFixggMHDhATE1NsuoiJnKlcuXI0atQIt9vN+PHjA97NcenSpRw+fJi4uDi6desW0G0FQsOGDWnatCkej4dx48YFpfvloUOHfBcbDB48OH/h5rz7SgMGnPeu7dq1o1KlSpw4ccLZ1+XoaPC+p2lMoBSSAlZSPD3+OFx8sZ18vuqq0OtGIcXDkSPWWeC22+DECdK7duXt229nbbNmDB8+nLi4uIBuPm8YZunSpezatSug25PgysjIYNSoUbjdbpo1a2YHIsqVs44zW7ZYYCA21kaWXXSRfcybd/6VZmfDb34DX3xhYwi//hrO6LJWt25dWrZsCRC0ne9fueaa3M4Wf/6zhZ4Cbfp06NQJbr7Zghh161r4bO5c605XnNSubSPj3nsPypSB5GRo3Rr+/e/QCAb528yZ8Je/2PJ779n/nb+VK2fPl8hI+P57ePdd/2+jII4etZGQ3vGFL79sYd+oqHN/T/361hVu2jTYv9+6nV1xBcTFwY4dtq4BA2yU4G9+A6NGWVes4m7KFBuFCvCf/9jP6y+tWlmADWz09O7d/lu3lG7PPmuP17Aw+Oor6zzlD1WqWGirfHmYPZvUq64iOyuLevXqBfZE8MKFND31Xj5+8GCmBfHqSRERkWA4cuSIb1yJP7sWBVN4eLjvGNOiRYvYt2+fwxUVPwcOHGD58uWAjV309+PA2wFo//79JWZ8XCAcPXqU+fPnA/Z8DAsrXqew8nbpSE5OdrZLRxFkZGQwc+ZMILcDW3ESFhbmmx4xb948jh8/7nBFhZOdne0Lzfbs2ZNYXegixdiAAQMIDw9n8+bNrF27NmDbycrK8o0i7N27N9HR0QHbViB5Q05bt25l9erVAd2Wx+Nh/PjxuN1ukpKSaNy4cf6+0RuwKls2N7R0Dnlfl+fOnevs67J3TKACVlJIxWvvVMTLOxKrXj3r6HDzzaVjhJL4T3KyjVcaORIiIsh64QXeu+oqjpUvT8eOHf16ldr5JCYm0rp1a8DBMIz4ncfj4YcffiAlJYWEhAQuu+yy0w/MVapknZ02bYL77rPQx9SpdgL20kutu9WZsrJszJk3JDJqlAVMz2LgwIFERkayY8cO5w7aPfAA/PGPtnzbbTYaKRDWr4fLL7cuT0uWWJDmlVdg7VobB1cMD4wDVvfvf29dinr3tm5cd90FQ4bAzp1OV+c/KSkWBnK7rXvXddcFblsdOsCrr9ryww871wFz2zbrXDdunIUsR42ycXUFeaxWqmS/r+++szFhY8bY86xKFfudfv65jcWsXNlGiX3wgY31Km7S0uCOO2z5nnvsueBvjz4KHTva7+3OO0tmiFGC6/33czutvfuuhSn9qVkz+PZbPBER1J09m74zZjB06NDAnQg+ehSuuw5XdjbHBg9mcYcOzJs3TxcGiIhIiTJt2jTcbjcNGzakQah3Pj6P+vXr07x586B2OyhJpkyZgsfjoWnTptSuXdvv6y9p4+MCZfr06WRnZ5OYmEijRo2cLqdQQqZLRxF4u4hVqVKFNm3aOF1OoTRt2pRatWqdFrYobrxdxMqWLVvsuoiJnCnv2LkJEyaQFaAu/N5QZUJCAh06dAjINoIhISHBt98wceJEMjMzA7at9evXs3HjRsLCwhgyZEj+jzENGmTnK154wRoCXECzZs18r8vJyclFrLoIvN0AT3WFFykoBayk+KpYEb791tr5jR0b3DFUUnxlZcFTT1kYY8cOSErCM3s2PzRtytG0NCpVqsSgQYOCWtKAAQOIiopi165dLNPIyxJhwYIFrF27lrCwMK6++mpiYmLOfscaNeCtt2zW86232li0H3+Etm0taLJund0vM9O6rY0aZR1uvvsOhg8/5/bLlStHr169AJg0aZJzV8u98grceKN13rrqKvBna/RDh6zjTIsWNjIpPNwCGBs3wp/+ZKOUSoIGDaxj0Rtv2M80cSK0bGldwYr7wXKPx/7Ptm+3n9PbzSmQHnjAQownT9pzKthXysyfb2MrV66E6tUt7HuOoGS+xcbaz/Thh7Bnj3UEe+QRG4mYmQk//2zBoZo1rZvbX/+a+9oS6p54wjr+JSba60kgRETYqMCoKOsM9PHHgdmOlA5jxsDdd9vy00/bcy8Acvr2ZdqpDpZ9pk+n6sSJAdkOHo/9PJs3Q2IiZb/8kpatWuHxeBg7diw5OTmB2a6IiEgQ7d2713dh0kUXXeRwNUU3aNAgIiIi2LZtG6tWrXK6nGJjx44drFu3DpfL5euuEAhdunShbNmypKam+rqmSa6DBw/6jo0GootYsISHh4dOl45COHbsGPNOddkvjl3EvFwuFwNOjaxasmQJhw8fdriigjmzi1igRsKLBFOvXr0oV64cqampzA5AuOXEiRO+9fbr16/Ydd87U48ePShfvjxHjx5lVoC6LWVnZzPh1MXx3bp1o1KlSvn/5jJl7CLiBx7I191dLpdvf3vx4sXOvS57J56sX2+TIkQKqHjuGYl4tW8P77xjy08+aWNkRM5l82bo1QtefNG6pdxyCyxZwi8xMaxatYqwsDCuuOKKoP+xUrZsWXqf6soxZcoUMjIygrp98a9du3Yx8dSJzkGDBlGzZs0Lf1PdujZKaPXq3A4+X38NzZtb8Oqqq2ysWXS03V5yyQVX2a1bNypWrMjx48eduxogLMzGng0YYJ1oLr7YunYVxcmT8Pe/W/vZt96y8NawYfDLL/Z+UKWKf2oPJWFh8OCD1nGpSxdITbXOjVdcAcV57MNnn9kYx/BwG3tZtmzgt+lywUcfQa1a9gfUffcFfpte33wDffva/1mbNrBggXVO8qfwcGtx/NprFtxcudLGL3bqZEGJuXOtW1bTptYF57HHbDRpKHYBnT0b3nzTlj/4ILCPjxYt4LnnbPnBB0tWlzgJnrlz7T3c7baOct7xkwGwYMECZjZuzPy+fe0Tt91m4Up/+/hje30OD7fX6woVGDJkCLGxsezbt6/YXo0vIiKS15RTxxJbtmxJjRo1HK6m6MqXL0/PU2NPAt3toKTweDxMnjwZgLZt21IlgMcV8o6PmzlzZrEdHxcoU6dOxePx0KRJE+rUqeN0OUXSrFkzatas6XyXjkLwdhGrU6dO/sdEhah69eqRlJSE2+1m6tSpTpdTIHPmzCE9PZ3KlSvTtm1bp8sR8YvIyEhfg4PZs2eTkpLi1/XPmjWLkydPUq1aNVq1auXXdTshMjKSwYMHA/aacOTIEb9vw7ve+Ph430X7gVS/fn0aNmyI2+32jUANugoV7CJ2UBcrKRQFrKT4u+02CyC43TY+SyfF5Gw++8y6As2fD+XLw1dfwUcfkep28/PPPwM2j7lWrVqOlNe1a1cqVapEWlpasW1ZLJCens6oUaNwu900a9as4K2bGze2E5jLl9tIIbfbwiA//mjdi8aMsZBSPkRERPh2vufNm8fBgwcL+uP4R1SUdRts29auBhgypHBXBXg8NtKzeXPrzpOSYiGVyZPt99Osmb8rDz1Nmthc8JdesjGRP/xgwZCRI52urOA2b4Z777XlZ5+14FiwVKpkz7OwMAsPfPppYLfn8Viw99prISPDApIzZ0KgDxa7XPb4ePxxC3Pt3An/+hcMHmyPn7VrrZtVt24WOLvvPusMFwrS023fzuOB3/3O2k0H2iOPQOfOFmC8447i3yFOgmvdOntup6db6Pe99wI2ovb48eNMnz4dgMjXXrMQdmamhW43bvTfhtaty32dfu4539V9ZcqU8e1fzJgxw7n9CxERET/YsmWLbxxKv379nC7Hb3r06EFCQgLHjh3zdT+Rc9uwYQPbt28nIiKCvt4AewCVhPFxgbBz507WrFkT8C5iwZK3e5KjXToK6ODBgyxduhQo3l3E8vJ2S1m1ahW7d+92uJr8ydtFrH///sW2i5jI2TRv3px69eqd1jnJH1JTU1mwYAFgz/uS8PoFNu60QYMG5OTk+PX3BfY783bGGjhwINHR0X5d/7l4X5dXrlzJnj17grLNX9GYQCkCvStLyfD223by/sABuPpqO8kgAnai9De/gZtugmPHrLPH8uVw7bV4PB5Gjx7NyZMnqVWrVlDS2ecSHh7OkCFDAOtKcODAAcdqkcLxeDyMGTOGlJQUEhISuOyyywq/E9+6tYVn5s2zYEGNGhYiKmDIoHHjxjRq1Ai328348ePxOBUYKFfOWsXWq2cnfy+5xDpa5df8+fbcveYaC+bUqGEdvxYvhhIwwqFAIiLgz3+GRYssYHbokP1err8+dMIxF5KVZaMjjx2zroJ//nPwa+jVK7e7zN13B25k3smT1m3sqafs3w89BKNHB6db15lq1bKfdfx421/68kvrtlO2LOzdax3g2rWDUDjA/9xz1mGsRg3rWBcM3lGB0dH2O/roo+BsV4q/PXssuHj4sIX0vv7aHk8BMnnyZDIzM6lZsybtOnSwkbGdO9t7wLBhVkdRnTxprw8nTthY7cceO+3LrVu3pmHDhuTk5DB27Fjn9i9ERESKwOPx+LpXtW/fnooVKzpckf/kveBq7ty5xSbY4QS32+17HHTu3Jly5coFfJthYWHFenxcIOTtItamTRuqVq3qcEX+ERJdOgpo2rRpeDweGjduTGJiotPl+EX16tV9nWymFJMJKDNmzCArK4vatWvTtGlTp8sR8SuXy8XQoUNxuVysXbuWTUWdeHHK9OnTycnJoW7duiQlJfllnaHA5XIxZMgQwsLCWLduHRv9eHHdpEmTyMrKIjExMagdv2rUqOH86/Kpjq8EaPSilGwKWEnJEBtrHVISEiyQ8MgjTlckoWDuXAveff65jTZ5/nmYNs3GsWFdfbZu3UpkZCRXXHGF41eCJCUl0aRJE9xuN+PGjdPJqmJm/vz5rF27lrCwMK6++mpiYmKKvtIuXWDCBNi9u9BBoiFDhhAeHs6mTZtYF6gQSX5Ur26hhUqVYOFCCwVlZZ3/e7ZuteBQ164W+oiLg2eeseDFrbfa87q0at3auhI9+aT9Hr76ytrajh3rdGUX9sIL9l5dvrx1F3Tq//Hxxy04kJZmQQJ/j2c9eNDGY376qf2M774Lr78eGo/b8uXtZ/7ySwtb/fQTNGoEO3ZA797w6qvOjQ1ctAj+9jdbfu89a9kcLM2a2eMTLAy3Y0fwti3F09Gj1lly2zZ7Dv34I5QpE7DN7dixg+XLlwP4DkYSF2eh7MREe3+88sqiX2zy6KM2lrZy5bO+TrtcLi655BIiIyPZvn07ixcvLtr2REREHLBmzRp27dp12si2kqRJkya+QPT48eOdLidk/fLLL+zfv5+YmBjfaMVgaNasGbVq1SIrK0ud7IGNGzeybds2wsPDg9JFLJhCoktHPu3atYvVq1cDuXWXFP369SMsLIzNmzezefNmp8s5r0OHDrFkyRKgZHXhEcmratWqdOrUCYDx48eTk5NTpPXt37/fd7ykpHTfy6tKlSq+aSn++H0BbN26lVWrVp0WeAsm7+vypk2b2LJlS1C3DeQGrBYvtgsMRQpAASspORo0yB3z89ZbdtJQSqecHDtB2quXBTTq1YPkZOsgcqqjwP79+33J6MGDB1OpUiXn6s1j8ODBhIeHs2XLFtasWeN0OZJPu3btYtKkSQAMGjSImjVrOlxRrooVK9KtWzcAJkyYQNaFQk2B1KSJnXyOjYWff4a77jr7GK7UVOuW0bSpBYdcLrjlFjtx/OyzEB8f7MpDU1SUvdbNnWvBkL17bbTkrbfa7zAUzZwJf/mLLf/73xYIcEp4uAUHqlSxIMEf/+i/da9da8HAWbMszDRunD3eQ1F0tAVEFi+2QGNODvzpT9ZpLtijvzIz7fGbk2MBsMsuC+72AR5+2P7vjh6F22/XqEA5t8xMCzMtWwbVqlmIuEqVgG3OG8AHaNu2LbVr1879YvXqFpQsWxZmzIA77yz8Y3fsWHjzTVv+3//gHPs0CQkJvs4LkyZN4ujRo4XbnoiIiAPcbjdTp04FoFu3bsSXwL8x83Y72LBhA+vXr3e6pJCTnZ3t6yrUo0cPYmNjg7btvOPjlixZUqq7jOXtJte5c2fKly/vcEX+VaNGDVq2bAmEdvekktpFzKtChQp07NgRsK7AoXxhs7eLWKNGjahXr57T5YgETL9+/YiLi+PgwYO+0X6FNXXqVDweD82aNTv9eEkJ0rdvX8qUKcOhQ4d8I0QLK+8xpg4dOlC9enV/lFggFSpUoEOHDoBDr8t169oxr+xsawggUgAKWEnJcskl8MQTtnz77bBqlbP1SPBt324dSZ5+2k7Q3nCDnfjq3t13l+zsbL777jtycnJo1KgR7du3d67eM1SoUIEep2b/Tpw40dkwjORLeno6o0aNwu1206xZM9+VBKGkV69elC1blpSUFOY4Pf6ra1cbnxQWBv/9r3Wk8srOhn/9C5KS4K9/tRFF/ftb8OOjj2zEmfxap06wZIl1b3S57HfVqhWcOjAWMlJSbGSr2w2//S1ce63TFdkfUR9/bMtvvw3ff1/0dU6ZAt26waZNUL++dV8bOLDo6w20smWt4+P770NMjIXC2ra1UFywvPwy/PKLdc3xBjyCLTzcQiUxMTBxInz4oTN1SGhzu+F3v7Pne3y8hYYbNAjoJpcuXcqePXuIjo4++9XkLVvCyJH2GP74Y3jppYJvZOdO+7nAurgNG3beu3fu3JlatWqRmZnJTz/9FNInKURERPJaunQphw4dIi4uju55jheVNJUrV6Zr166AXXCVnZ3tcEWhZdGiRaSmplK2bFm6dOkS9O3Xq1ePpKSkYjU+LhB++eUX9u3bR3R0dFC7iAWT41068mHTpk1s3bq1RHYR8+rduzdRUVHs2bPH16kr1OzevZtVp85plbQuYiJniomJ8T3Op0+fXuiRudu3b2fdunW4XC7fhWAlUXR0tC+cnZyczLFjxwq9roULF7J//35iY2Pp16+fv0ossN69exMZGcnu3buD33DC5dKYQCk0Bayk5HnuORuldeIEXHUVFOFNRoqRPXus40bLlnYyuGxZ62j2+efWPSSPadOmsW/fPuLi4rjssstCrl1oz549KV++PKmpqczSG3tI83g8jBkzhpSUFCpUqBCSjyeAqKgoBg0aBMCsWbNISUlxtqBLL7XRX2AdmN57zzpvtG4N995rXXOaNIExYywk1K6ds/UWBzEx8Npr1q2vQQMbbTZwoI1iDIWDRh4P3H23hWAbNLAwU6gYOjR3tPCtt9qor8L64AMYMsTCZD16wPz50Ly5X8oMCpcL7rjD6m7SBHbtstDySy8FfmTgL7/kdjd7++2AdgK6oCZN4MUXbfn//q9ojwkpmR57DL74wjqjfvstBDisn56e7uuy0bdv33N32Rg8OPf19cknLdCcXzk5FoI9dMh+npdfvuC3hIWFcdlllxEWFsb69etD9iSFiIhIXnlHsvXq1Yvo6GiHKwqs3r17Ex8fz+HDh5k7d67T5YSMjIwMkpOTAejTpw+RkZGO1FGcxscFwpldxOLi4hyuKDAqVqzobJeOC8jbRaxTp04kJCQ4W1CAlClTxtflf+rUqX4ZseVv3v+H1q1bU61aNYerEQm8du3aUbNmTTIzM31d9Aoi7+tX27ZtqVy5sr9LDClt2rShdu3ahf59AaSlpTF9+nQA+vfv7+h7b3x8vLOvywpYSSEpYCUlT3i4jQesXRvWrbOTpSH2R4v40fr1djK4Xj149VUL1HXtCkuX2kmiM2zbts3XwefSSy8NyTbwkZGRvjDM7NmzOXLkiMMVybnMnz+ftWvXEh4ezogRI4iJiXG6pHNq0aIFdevWJTs72zfO0FF33JHbveruu60D4Zo1UKmSnRz+5RcLYoVgYC2k9ewJy5fDPffYv0eOtODpjTfae6JTPv3Uxj2Gh1sooWxZ52o5m7/8BTp3tmDUDTdAQbsH5uRYSOvOO60T2w03WDjQyZBQUbRuDYsWwU032c/2xBMWRNu/PzDby862/bWsLBg+3IKBTnvwQet+eeyYRgXK6f75Twu0gnViPLXPFkjTpk3jxIkTVKlShU6dOp3/znfdZaMuAW6+2bro5cdf/mLjBePj7fU6nyebq1at6us0MG7cOE6cOJG/7YmIiDhk/vz5HDt2jPLly/vGRZVk0dHRDDzVUXfmzJka63vKnDlzSE9Pp1KlSrRz8KKu6tWr06pVK4BCnygtzhYvXkxKSgrx8fGOdBELJke7dFzAypUr2bt3L9HR0fTq1cvpcgKqW7duxMXFcfjwYZYuXep0OafZtGkTmzdvJiwsrMR2ERM5k8vlYujQoQAsX76cHTt2FOj7N2zYwPbt24mIiCgVz5u8v68VK1awffv2Aq9jypQpZGRkUL169ZCY7tO9e3fi4uI4dOhQ8F+XT00TYu5cOwYukk8KWEnJVKWKnVSOjIRRo+Af/3C6IvG3hQthxAho2tTG92Rm2pvh2LEwezY0bPirb8nIyOD7U+Of2rZtS9OmTYNddb41a9aM+vXrk5OTw8SJE50uR85i165dvqDSoEGDqFmzpsMVnZ9359vlcrF69Wo2b97sdEkWsLr9dluOioI//hE2brQuVg5dPVoixMfDO+/AihVw5ZUWDPniC+ukdPPNNroumDZtsv9TgGefhVA8aBoVZYGCcuUsjJB3dOWFpKVZx8y//93+/dxz8Nln1lWsOIuPtzFj//0vxMbauLy2beHUFU5+9frrFuhKSLAxoaEQrAwPt3GbMTEWlnv/facrklDwzTc2Og/glVcshBhg+/btY9GiRQAMHTqU8PDwC3/Tq6/CZZfZqN3LL4cLvefPnGmvXWDPwUaNClRjr169qFy5MmlpaaER4hYRETmH9PR0X6fwfv36ERER4XBFwdGqVSvq1KlDVlaW3quB48ePM2/ePMA6SIWFOXuKxDs+bvPmzaFxrCZITp48eVoXsaioKIcrCqwzu3S4A90lOp9ycnJ8XcS8J7lLsujoaPr06QPAjBkzyMzMdLgic2YXsQoVKjhckUjw1K5dm7Zt2wJ24VZ+Xx/dbrfvedOlSxfKlSsXqBJDSs2aNX3BqIL8vsDGkHpDTEOHDnV8Hwjsdbl3796AvS5nFfTC56Jo3dqOgaemwqnxrCL54fwzRyRQuna1E3ZgJ+1nznS2Hik6jwcmTbIRkJ0720gWj8c638ycaW0cL7kEzrFTMH78eFJTU0lISGDIkCFBLr5gXC4XQ4YMweVysXbtWjYFOxAh55Wens6oUaNwu900a9bswt0kQkS1atV8V+iOHz/e+VbYLpeNBxw92rorvfqqBSzEP1q1stfJJUusG5jbDZ98YuPPbr8dtm4NfA1ZWdY96/hx6NUL/vznwG+zsOrXt8AuWHAiPycedu2yn+uHH6zby5dfwtNPh0ZAyB9cLvjd7yzU3Ly5jeO96CJ4/nn/XdWzbp39zgDeeANCKazauHHumLRHHgnOc0ZC1/TpFqjyeOD+++HRRwO+SY/Hw7hx4/B4PDRv3pz69evn7xu93QLbt4cDB2DYMOvQdzaHD9vrtNttP18hQmMRERFcdtllACxbtkz7rSIiErJmzZrFyZMnqVq1qq9rUGmQt9vBypUr2VrK92u9J+9q1aoVEhdfVqhQwXesZsqUKSE3Pi5Q5s6dy4kTJ6hYsaKjXcSCydEuHeewePFijhw5Qnx8PF27dnW6nKDo0KEDFSpU4Pjx48yfP9/pcgBYtWoVe/bsISoqqsR3ERM5m4suuojo6Gj27NmT79fHFStWsH//fmJiYujh7URUSvTv35+YmBj27t3LkiVL8vU9Ho+Hn3/+GbAxpImJiYEssUA6dOhAQkLCaSH4oIiIgFPhZ40JlIJQwEpKtnvvheuvt5OA114Le/c6XZEURk4OfP01dOhgY1imTrU3vptusjFiY8fmzso9h9WrV7N8+XJcLhdXXHEF0fkce+KkqlWr0rlzZ8CS6I6HYQSwHdExY8aQkpJChQoVuOyyy3AVozBFv379iIuL48CBAyxcuNDpcuwk8PDhNuZTAqNdOxgzBhYssDFvOTnwn/9Yh5K77oICtl4ukOefh/nzoXx56+qUn84rTrr6avj97y1AcdNNsG/fue+7ZImFfZcutc6Z06bBddcFr9ZgatHCHj+/+52FMJ55BgYPLvp+ldsNt91mXXYGD7YOa6HmD3+wfYzjx63WELnKWILI44EpU6wTVGamdVB9442gBClXrVrFtm3biIiI8I32ybcyZWwfuXZtWLvW6j7zKkCPxx7XO3ZAUpJ1PyykOnXq+ALnP/74Y8hcCS4iIuKVmprqO5EeCl2Lgq1GjRp06NABsAuuQqV7TrAdPnzYdyJywIABIXM8p3fv3kRFRbF7925Wr17tdDkBd/z4cebOnQvYSeJ8dWktAfKO4Js+fXpwu3ScRWZmpq+LmPcxWBqEh4fTr18/AGbPnu34mPOcnBymTp0KWAivTJkyjtYj4oT4+HjfiL+pU6eSnp5+3vtnZ2cz/VSX/Z49exIbGxvgCkNLmTJlTvt95ed1bPny5ezatYuoqCgGDBgQ4AoLJiIiwrnXZW84b+JEyM4O3nalWCtdf0lK6eNywQcf2InBPXvsxKdeIIuPjAzrbtOkif3fLV0KcXHwwAM2cuqTT6Blywuu5tixY/z4448A9OjRI6SS2RfSt29f35VNoXJFTWk3f/581q5dS3h4OCNGjCCmmI0Bi42NpX///oAdzElLS3O4IgmaTp3g559tjOqAAfZ++O9/20n1+++H3bv9u72ZM+Gll2z53/+G4vLa+8Yb9t6yb5+FrM524mH0aOtctXt3bvjIe7VLSVWmjI0L/OQTey+eMsVGBp5qxV0o77xjj8f4eHuMhMjJjdOEhdmowNhYC3i/957TFUmwbNsGL7xgncwGDLB24b17w6efBiUsmpmZ6Rvh07NnTxIK0+GxZk348Ud7jk2ZAnffbaEqr3fftdezyEgbk1q2bJFqvuiiiyhXrhwpKSm+MSMiIiKhYvr06eTk5JCYmEijAo7DLSm83Q7yjiAubbyj2ZKSkqgXQhd6lSlT5rTxcSX9Isvk5GQyMzOpWbMmzZs3d7qcoOrYsaMzXTrOYu7cuaSlpVGxYkXfuKnSomXLllSvXp2TJ08y0+HJJ0uWLOHIkSOnvQ6IlEadOnWiSpUqnDhx4oLHFBYuXEhqaiply5b1NSkobTp16kTVqlVJT0+/4O8rIyODyZMnAxaoLVvE4z+B0KpVK6pVq8bJkyd9I72Dwts18IcfoE4dm4i1cmXwti/FkgJWUvKVKWMjkuLjYcYMePxxpyuSC0lJsZE89erZiaBNm6BiRXj2Wdi+Hf7xj3yfqPd2G0pPT6d69eq+VHdxERMT40uTz5gxg2PHjjlcUem2a9cu38nOQYMGUTOURlkVQLt27ahRowYnT5707VhLKdK9u42/mzED+vSxjixvvw0NG8JDD52/a1N+paTAb35j4aSbb7YuksVFbKx1TYyNtd/Tq6/mfs3jgddegyuvhBMnrOvS7NmlqwPbTTfB4sW5IbSBA62jVUFPAGzZAo89Zsuvvgp16/q/Vn9JSrKxkWBj4TZvdrYeCZy0NAsR9u9vz+unn4aNG+3viNtvt4MtQQpWz5w5k6NHj5KQkED37t0Lv6I2bSw8FRZm3Qtfe80+v2IFPPywLf/1r9Yptoiio6O55JJLAAuk79q1q8jrFBER8YcDBw6wfPlyILS6FgVbXFyc74KradOmOd61Jdh2797NqlWrAAuGh5pu3boRFxfH4cOHQ2Z8XCAcOXKExYsXA6Xz+RgREeE7Pj179uwLdmkJlLS0NObMmQNYt/vS0kXMy+Vy+V4HvEENJ2RmZjJjxgygdHUREzmb8PBwhgwZAsCiRYvYd45j1BkZGb5gZN++fYmMjAxajaEkLCzMNwJ68eLF7D3PpIEZM2aQlpZGpUqVQnYcbN7X5QULFgTvdblvXztGXbmyTWv429+gVSvo2BHeegsOHgxOHVKsKGAlpUOTJtZ9AOykwnffOVuPnN3u3XbiMjHRgnD79lli+J//tGDVM89ApUoFWuWiRYvYuHEj4eHhXHnllcXyj8W2bdtSq1YtMjMzmVKUTiFSJOnp6YwcORK3202zZs18Y3CKo7w738uWLdMJ0NKqd28bazdlioWuMjIswFq/vr0WHzhQuPV6PDZ6cPt2C2299ZZfyw6K5s0tdAbw5JMwZ44F0e64w343Ho+NIf7xRxt/WNo0bWpdu+64w34Xzz9vHX7y2wXN47HvPXHCQn6//31g6/WH++6z50xaGtx6q0YFliRuN0yfbiMwq1WzUOi0adZRrX9/C1zt3WtdcQvTRaoQDh8+7BuZMnjw4KIfLBw2zF7fAf70J+vCdd11Np7z4ovhwQeLtv48GjVqRKtWrXwXOZT07gsiIlI8TJ06FY/HQ9OmTalTp47T5TiqQ4cOVKtWjYyMjFJ3jMn787Zq1Yrq1as7XM2vRUdH07t3b8BOhJbUkcvTpk3D7XbTsGFD6tev73Q5jsjbpcOp7kkzZ84kMzOTGjVq0KJFC0dqcFrDhg2pV68eOTk5vlFjwTZv3jzS0tKoUKGCb4yrSGnWoEEDmjVrhsfjYdy4cXjyduE+Zc6cOaSnp1O5cmXatm0b/CJDSL169WjRosV5f18HDhxgwYIFAAwZMiSkz5EmJSVRt27d4L4uh4VZw49du6zL+xVXQESEXWD8hz9Yd/grr4QxY8Dh0b4SOhSwktJjxIjcq7RvuQXWr3e0HMlj/Xo70Vq/vgXgjh2zkUuffGLdq/7wB+tEVkAHDx5k4sSJgF0RVaVKFX9XHhQul8sXhlm+fDk7duxwuKLSx+Px8MMPP5CamkqFChW47LLLiv0VdnXq1KFNmzYA/Pzzz2fd+ZZSwBsgmDULJkyAzp0hPd1ei+vXhyeegMOHC7bOTz6xDlDh4fD550UeOeWY3/0ObrjBOjNdfz0MGWLdX8LC4M03LYAVEeF0lc6JjYX337f/4/h4C6i0bWvz6i/kP/+xYF9sLHz4of1OQ513VGBcnHV/+9e/nK5IimrTJgvvN2wI/frB//5nAbqkJBsNuGWLPU5vuqlQ+6FFMWHCBHJycmjYsCFNmjTxz0rvv98+AH77W1izBmrUsJ/bz/s0gwcPJjY2lv379zN79my/rltERKSgduzYwdq1a3G5XL7uTaVZ3guulixZwm5/j4oPUZs3b2bz5s2EhYXRr18/p8s5p7zj4+bPn+90OX63d+9efvnlFyA0u4gFS1hYmDNdOk5JSUnxjQktjV3EvFwul29yxPLly9m/f39Qt3/ixAnf30ulsYuYyLkMGjSIiIgItm3b5us86XXs2DHfeNX+/fsTVhyOKQbYoEGDiIyMZPv27aw8Y7Sdx+Nh/PjxuN1umjRpQlJSkkNV5s+Zr8sHCnsBeGFERcHw4dagZc8eO/7fvr2Fqr7/3r5Wq5ZNADnVGVdKL73ySOnyyivQs6cFeK66yk6iiHMWLrTgW9OmdoI1M9P+f8aOtbElN90EhbxiPycnh++//57s7GwaNGhAly5d/Fx8cNWqVcuXxh83bhxudc4Iqvnz57Nu3TrCw8MZMWIEMUEaDxRoAwYMICoqit27d7Ns2TKnyxEnuVwwaBDMm2ddmdq3t/fIl16yMVnPPGNj/y5k40br9APw3HNQnF97XS54910LX2zfbh1t4uPtahVvSEEshLZ4sY0hO3DAgmhPPAHZ2We//86d8H//Z8svvmhhluKiQYPckZF//CNcfrmNLx49GrZutc5cEtqOHYP//te6kSUlWfe1rVuhXDkL+8+aZcH/J590bGzlhg0bWL9+PWFhYQwZMsS/Jztef906VoG9xn32GQTgAoQyZcr42vonJydzUO3URUTEIR6Ph8mTJwPQpk2bYnvhnb/VrVuXVq1aAZyz20FJkvdx0LFjRypUqOBwRecWHh7uC4DNnj27xI1x9HYRa9myJTVq1HC4Gmc50qXjlGnTppGTk0ODBg1o0KBBULcdamrVquXrljN16tSgbtvbRax69eq0bNkyqNsWCWUJCQn07NkTgIkTJ57W0TE5OZmsrCxq165N06ZNnSoxpJQrV45evXoBMGnSpNN+X2vXrmXz5s2Eh4czePBgp0osEO//rcfjca7bauXKdvx/8WI7V/1//2dd7w8csA7xbdvaxxtvQJDDuRIaFLCS0iUyEr75xl4IV660EUYl/CBCyPF4rLtF//7WKeXbb+1zl15qJ7VmzoRLLilyN4vk5GR2795NTEwMw4cPLxFX4gwYMIDo6Gj27NnD0qVLnS6n1Ni1axeTJk0C7GqAmjVrOlyR/8THx9OnTx8AJk+eTEZGhsMVieNcLhsltWiRXZnRurUFEp5/3jpavfgiHD169u/NyoIbb4Tjxy288Nhjwa09EMqVs25c8fE2vnb2bPv9yOkaN7Zwnne/6qWX7H1+587T7+fxwN1322OoSxd44AFn6i2Ku++GoUNtpOYPP1iQ8Ior7PlRoYKNPHzgAet2tWSJjWATZ+XkwOTJFtyvVg1uu832N73B0i++sBGA778PPXr4vZtTQWRnZzN+/HgAunTpQuXKlf27gYgI+Oor6w772Wf2PA2QVq1akZSURE5ODmPHji3xJ25FRCQ0bdiwge3btxMeHk7fvn2dLiekDBgwgMjISHbu3MmKFSucLiegVq9ezZ49e4iKivKN4AtloTA+LhC2bNnCxo0bQ76LWLA41aVj3759vud8ae4illf//v1xuVysW7eO7du3B/R2Q28AABosSURBVGWbKSkpLFy4ELD/h5Jw7kLEn7p3705CQgLHjh3zvRcePnyYJUuWAHrenKlbt25UqFCBY8eOkZycDEBWVhYTJkwA7PcZygHzMznxunxOrVrB3/5mx7l//NGahkRFWRerhx+2rlaXXWadr0roeGf5tWIRsPrXv/5F/fr1iYmJoUOHDiXqDwtxQI0aFrIKD7cTC++953RFpUN2tp3Q6dABBg+2TiARETai5JdfrCNIjx5+2dTOnTt9rxPDhg2jXLlyflmv08qUKeM7IDhlyhTS09OdLagUSE9PZ+TIkbjdbpo3b06nTp2cLsnvvCdwT5w4EfQr5iSEuVzWnWfpUhg5Epo3tw5WTz1lQZJXXrEgVV7PPQcLFkBCAnz6qb3PlgQdOlgHq40bLXAmZxcTYx2/vv7axkLOnGlX8owbl3ufL76wP0SjoqyLUHF8jISFWafNadPsiqVbbrGfMzISUlMhOdlaSN96qz124uPtcfPb38Lf/24j59TNJzjWr7duavXrw8CBtt+fng5NmsDLL9vzesIEGwEaG+t0tQDMmzePw4cPnxaA9ruyZeGf/7TucwHkcrkYNmyYr029dwSJiIhIsLjdbt9V7507d6Z8+fIOVxRaypUr5wsbTZo0iZMl9MKAnJwcX1eabt26USbIo58Lw+Vy+YIvCxcuDPr4uEDI24Wiffv2VKxY0eGKQoMTXTq822nRokWJuoi0KCpXrky7du0AuwA1GBeHTJ8+nZycHOrVq0fDhg0Dvj2R4iYyMtLXcWnu3LkcPnyYqVOn4na7SUpKol69es4WGGIiIiJ8ncTnzp3LoUOHmD17Nqmpqad1uCouqlSp4pvoM2XKlNC4aC8iwi68HjnSRgi+8441EcnOtmPFV11l+QNv56tQqFkCJuQDVl9//TUPPvggTzzxBEuXLqVXr14MHTrU+cSiFG+9e9vJYbAuAwsWOFtPSZaebiG2Jk3sBNbSpRAX9//t3XuQlfV9P/D3Xrgt9+sSAomYeAUsgXWMxCgRg41KYmyiiVZjbRMRTYM2atLmYpgKE9vEjLGuIWOT1JTRMfXCTHHiQi2NkipV1iQK4lgjd0EUlpvA7p7fH6vb7E+P8cJyzi6v1wzD7PN8D8/nAHM+5zzn/Xyetr/3Z59Nfvaz5ACOwN23b1/uueeeFAqFTJgwoduN1z3++OMzfPjw7NmzJw8++GCpy+nWCoVC7rvvvmzfvj2DBw/OjBkzuuVVEVVVVe1vvh999NFsNtKUP1RZ2XZVxm9+0xaOOfLI5KWXkq9/ve12ad/7XrJ7d1uoZO7ctsf86Edt0566k8GD3/Etaw85557bNrlp0qRk69a225Fde22yfn3b1Jwk+da32kJ7XVVVVTJ16v9Nqlqxoi1w+MQTbe9rrrwy+djH2v7fNDe3Bclvvz356leT005ruyXbmDFtEzu/8Y3kF79oC/C5/e+7t21b2ySqKVPa3nvOnZusXdsW/Jw5s23S2sqVbRP2Ro8udbUdNDU1tV9l+NrU0q5u0KBB7V8OLl68OE3FJiACQCf47W9/m82bN6dXr15d7kulg+XDH/5whgwZkl27dmXp0qWlLqdTrFixIi+99FJqampy4oknlrqct+y1L49Lcfu4zrBy5cqsX78+PXr06LwLCbqoP5zSsXbt2k491vPPP59nnnnGFLE3cMopp6S6ujpr167N6tWrO/VYL7zwQp544okkbZ/9uuP5ZjgQjjrqqHzgAx9IS0tL7rrrrjz55JNJTN8r5sgjj8wRRxyR1tbW3HvvvXn44YeTtN2VpUcXPK89derUVFdXZ82aNXnmmWdKXU5HQ4Yks2YljzySPPVU27nvUaPavje5+eakru7/Jl9t3FjqaukEFYWyiP0Vd8IJJ2TSpEmpr69v33bMMcfk7LPPzrx58/7o45uamjJw4MD2lOahZv/+/Vm0aFHOOOOMsn0BbWpqyvr16w/+gQuFvHf27AxoaMj+kSPzwrXXvuvb0tFRz2efzZB//ddUb92aJGkeNCgvX3BBXr7ggrQMGtQpx3zqqafyu9/9LgMGDMjMmTPTp0wmEhxIzz33XP7lX/4lFRUVOeuss7rlcywH69aty7Jly1JVVZVLLrmk21/Vdeedd2bVqlV53/velw9/+MOlLueQ0dzcnMcffzyTJk1KdXV1qcv545qbM2DRogz/p39Kz1dP/DUPHZpCZWV6bNmSbWefnY2vBa04pFXs25cRN9yQIQsWJElaampStXt3Xjn66Dx3552HRmCtUEj1pk3pvWpVeq9alV6v/t6zyEnzlpqa7D3qqLxy9NHZe9RRaelCo7tLrWLv3vT7z/9M/8WLU/nqOO5CZWV2nXRStn3qU9l56qkplHlgqbGxMatXr86YMWPyF3/xF93mJHtra2t+8pOfZN26dTn88MNTV1dX6pLeti7XqwF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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": 12,
   "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": 13,
   "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[13]\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[12]\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": {
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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": []
  }
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