SVM_ITCLAB.ipynb

{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Import Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-06-10T04:18:53.619663Z",
     "start_time": "2023-06-10T04:18:53.608499400Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From C:\\Users\\INDY\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.11_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python311\\site-packages\\keras\\src\\losses.py:2976: The name tf.losses.sparse_softmax_cross_entropy is deprecated. Please use tf.compat.v1.losses.sparse_softmax_cross_entropy instead.\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\INDY\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.11_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python311\\site-packages\\tqdm\\auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
      "  from .autonotebook import tqdm as notebook_tqdm\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import random\n",
    "import time\n",
    "from tqdm import tqdm # Progress bar\n",
    "\n",
    "# For scaling, feature selection\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.feature_selection import SelectKBest, f_regression\n",
    "from sklearn.model_selection import train_test_split \n",
    "\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense\n",
    "\n",
    "# For TCLab\n",
    "import tclab\n",
    "\n",
    "# For itclab\n",
    "import itclab"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Initialize TCLab Emulator with 100x speedup for data collection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-06-10T04:18:54.777968200Z",
     "start_time": "2023-06-10T04:18:54.777445200Z"
    }
   },
   "outputs": [],
   "source": [
    "speedup = 100\n",
    "TCLab = tclab.setup(connected=False, speedup = speedup)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### PID controller"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-06-10T04:18:55.863871500Z",
     "start_time": "2023-06-10T04:18:55.861528500Z"
    }
   },
   "outputs": [],
   "source": [
    "# PID Parameters\n",
    "Kc   = 6.0\n",
    "tauI = 75.0 # sec\n",
    "tauD = 0.0  # sec\n",
    "\n",
    "#-----------------------------------------\n",
    "# PID Controller\n",
    "#-----------------------------------------\n",
    "\n",
    "def pid(sp,pv,pv_last,ierr,dt):\n",
    "    # PID coefficients in terms of tuning parameters\n",
    "    KP = Kc\n",
    "    KI = Kc / tauI\n",
    "    KD = Kc * tauD\n",
    "    \n",
    "    # ubias for controller (initial heater)\n",
    "    op0 = 0 \n",
    "    \n",
    "    # upper and lower bounds on heater level\n",
    "    ophi = 100\n",
    "    oplo = 0\n",
    "    \n",
    "    # calculate the error\n",
    "    error = sp - pv\n",
    "    \n",
    "    # calculate the integral error\n",
    "    ierr = ierr + KI * error * dt\n",
    "    \n",
    "    # calculate the measurement derivative\n",
    "    if dt>=1e-8:\n",
    "        dpv = (pv - pv_last) / dt\n",
    "    else:\n",
    "        dpv = 0.0;\n",
    "    \n",
    "    # calculate the PID output\n",
    "    P = KP * error\n",
    "    I = ierr\n",
    "    D = -KD * dpv\n",
    "    op = op0 + P + I + D\n",
    "    \n",
    "    # implement anti-reset windup\n",
    "    if op < oplo or op > ophi:\n",
    "        I = I - KI * error * dt\n",
    "        # clip output\n",
    "        op = max(oplo,min(ophi,op))\n",
    "        \n",
    "    # return the controller output and PID terms\n",
    "    return [op,P,I,D]"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Generate data for training SVM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-06-10T05:57:40.147436300Z",
     "start_time": "2023-06-10T05:57:39.998237200Z"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TCLab version 1.0.0\n",
      "Simulated TCLab\n",
      "TCLab Model disconnected successfully.\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "##### Set up run parameters #####\n",
    "# Run time in minutes\n",
    "run_time = 90.0\n",
    "\n",
    "# Number of cycles\n",
    "loops = int(60.0*run_time)\n",
    "\n",
    "# arrays for storing data\n",
    "T1 = np.zeros(loops) # measured T (degC)\n",
    "Q1 = np.zeros(loops) # Heater values\n",
    "tm = np.zeros(loops) # Time\n",
    "\n",
    "# Temperature set point (degC)\n",
    "with TCLab() as lab:\n",
    "    Tsp1 = np.ones(loops) * lab.T1\n",
    "\n",
    "# vary temperature setpoint\n",
    "end = 30 # leave 1st 30 seconds of temp set point as room temp\n",
    "while end <= loops:\n",
    "    start = end\n",
    "    # keep new temp set point value for anywhere from 4 to 10 min\n",
    "    end += random.randint(240,600)\n",
    "    Tsp1[start:end] = random.randint(30,70)\n",
    "\n",
    "# Plot\n",
    "plt.plot(Tsp1)\n",
    "plt.xlabel('Time',size=14)\n",
    "plt.ylabel(r'Temp SP ($^oC$)',size=14)\n",
    "plt.xticks(size=12)\n",
    "plt.yticks(size=12)\n",
    "plt.savefig('SP_profile.png');"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Collection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-06-10T04:20:34.123104800Z",
     "start_time": "2023-06-10T04:19:02.764391600Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TCLab version 1.0.0\n",
      "Simulated TCLab\n",
      "Temperature 1: 20.95 °C\n",
      "Temperature 2: 20.95 °C\n",
      "   0.0  20.95  20.95   0.00   0.00   0.00  -0.00   0.00\n",
      " 100.1  42.00  37.71  60.99  25.75  35.24  -0.00 943.77\n",
      " 201.8  42.00  42.54  32.84  -3.26  36.10  -0.00 1049.80\n",
      " 301.1  42.00  41.90  35.87   0.61  35.26  -0.00 1070.95\n",
      " 401.1  42.00  42.22  34.00  -1.33  35.32  -0.00 1084.54\n",
      " 506.0  39.00  38.68  29.11   1.94  27.16  -0.00 1197.06\n",
      " 608.1  39.00  39.00  30.05   0.01  30.04  -0.00 1232.36\n",
      " 708.1  39.00  39.00  30.11   0.01  30.10  -0.00 1233.17\n",
      " 808.1  49.00  43.19  86.53  34.87  51.66  -0.00 1504.67\n",
      " 908.0  49.00  50.60  43.13  -9.61  52.74  -0.00 1689.14\n",
      "1008.1  49.00  48.99  47.11   0.06  47.05  -0.00 1760.42\n",
      "1108.1  45.00  46.09  29.49  -6.53  36.03  -0.00 1899.19\n",
      "1208.0  45.00  44.48  41.98   3.14  38.84  -0.00 1953.43\n",
      "1308.0  45.00  45.12  39.51  -0.73  40.24  -0.00 1976.25\n",
      "1408.0  45.00  44.80  41.47   1.20  40.27  -0.00 1991.50\n",
      "1508.1  45.00  45.12  39.41  -0.73  40.15  -0.00 2006.28\n",
      "1608.1  33.00  40.61   0.00 -45.66  40.31   0.00 2476.16\n",
      "1708.1  33.00  33.20  20.54  -1.18  21.73   0.00 2772.06\n",
      "1808.0  33.00  33.20  19.41  -1.18  20.59  -0.00 2791.28\n",
      "1908.1  33.00  32.87  21.19   0.75  20.44  -0.00 2807.04\n",
      "2008.0  51.00  34.49 100.00  99.08  20.32  -0.00 3065.60\n",
      "2111.0  51.00  51.25  51.39  -1.47  52.86  -0.00 3712.39\n",
      "2211.1  51.00  50.92  50.48   0.46  50.02  -0.00 3749.45\n",
      "2311.1  51.00  50.92  50.94   0.46  50.48  -0.00 3758.13\n",
      "2411.0  56.00  51.57  82.58  26.59  55.99  -0.00 3839.86\n",
      "2511.1  56.00  56.72  58.28  -4.35  62.63  -0.00 3979.74\n",
      "2611.1  56.00  56.08  58.43  -0.48  58.91  -0.00 4026.11\n",
      "2711.1  56.00  56.08  58.02  -0.48  58.50  -0.00 4035.59\n",
      "2811.1  56.00  56.08  58.09  -0.48  58.58  -0.00 4048.14\n",
      "2911.1  56.00  56.08  58.02  -0.48  58.50  -0.00 4059.72\n",
      "3011.1  69.00  66.39  88.50  15.64  72.87  -0.00 4855.31\n",
      "3111.1  69.00  68.97  79.60   0.17  79.43  -0.00 4937.37\n",
      "3211.1  69.00  68.97  80.11   0.17  79.94  -0.00 4943.70\n",
      "3311.1  52.00  52.53  44.66  -3.21  47.87  -0.00 5733.69\n",
      "3411.1  52.00  51.89  52.55   0.66  51.89  -0.00 5787.82\n",
      "3511.1  52.00  51.89  52.75   0.66  52.10   0.00 5802.74\n",
      "3611.1  51.00  51.89  45.28  -5.34  50.63  -0.00 5832.52\n",
      "3711.1  51.00  50.92  49.84   0.46  49.38  -0.00 5857.14\n",
      "3811.1  51.00  50.92  50.69   0.46  50.23  -0.00 5867.70\n",
      "3911.1  37.00  45.77   0.00 -52.60  50.54  -0.00 6406.46\n",
      "4013.0  37.00  37.06  27.29  -0.39  27.67  -0.00 6703.62\n",
      "4113.0  37.00  37.06  26.75  -0.39  27.13  -0.00 6710.40\n",
      "4214.0  48.00  45.44  72.92  15.33  57.59  -0.00 7089.62\n",
      "4314.0  48.00  49.31  41.36  -7.87  49.24  -0.00 7235.18\n",
      "4414.0  48.00  48.02  45.33  -0.14  45.47  -0.00 7282.25\n",
      "4515.1  48.00  48.02  45.15  -0.14  45.29  -0.00 7284.52\n",
      "4615.1  48.00  48.02  45.04  -0.14  45.18  -0.00 7287.62\n",
      "4715.0  67.00  53.82 100.00  79.06  45.16  -0.00 8054.67\n",
      "4815.0  67.00  65.43  81.62   9.44  72.18  -0.00 8656.11\n",
      "4915.0  67.00  66.72  77.96   1.70  76.25  -0.00 8707.36\n",
      "5015.1  61.00  64.78  42.39 -22.69  65.09   0.00 8858.58\n",
      "5115.0  61.00  59.95  69.63   6.31  63.32  -0.00 8979.21\n",
      "5215.0  61.00  60.91  67.33   0.51  66.82  -0.00 9022.87\n",
      "5315.0  49.00  50.60  31.35  -9.61  40.96  -0.00 9513.73\n",
      "TCLab Model disconnected successfully.\n"
     ]
    }
   ],
   "source": [
    "# Data collection\n",
    "with TCLab() as lab:\n",
    "    # Find current T1, T2\n",
    "    print('Temperature 1: {0:0.2f} °C'.format(lab.T1))\n",
    "    print('Temperature 2: {0:0.2f} °C'.format(lab.T2))\n",
    "\n",
    "    # Integral error\n",
    "    ierr = 0.0\n",
    "    # Integral absolute error\n",
    "    iae = 0.0\n",
    "    \n",
    "    prev_time = 0\n",
    "    for i,t in enumerate(tclab.clock(loops-1)):\n",
    "        tm[i] = t\n",
    "        dt = t - prev_time\n",
    "        \n",
    "        # Read temperatures in Kelvin \n",
    "        T1[i] = lab.T1\n",
    "\n",
    "        # Integral absolute error\n",
    "        iae += np.abs(Tsp1[i]-T1[i])\n",
    "\n",
    "        # Calculate PID output\n",
    "        [Q1[i],P,ierr,D] = pid(Tsp1[i],T1[i],T1[i-1],ierr,dt)\n",
    "        \n",
    "        # Calculate MPC output (optionally replace PID)\n",
    "\n",
    "        # Write heater output (0-100)\n",
    "        lab.Q1(Q1[i])\n",
    "\n",
    "        # Print line of data\n",
    "        if i%100==0:\n",
    "            print(('{:6.1f} {:6.2f} {:6.2f} ' + \\\n",
    "                  '{:6.2f} {:6.2f} {:6.2f} {:6.2f} {:6.2f}').format( \\\n",
    "                      tm[i],Tsp1[i],T1[i], \\\n",
    "                      Q1[i],P,ierr,D,iae))\n",
    "        prev_time = t"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-06-10T05:54:07.653310200Z",
     "start_time": "2023-06-10T05:54:07.403735200Z"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df = pd.DataFrame()\n",
    "df['Q1'] = Q1[:i]\n",
    "df['Q1'].fillna(0,inplace=True)\n",
    "df['T1'] = T1[:i]\n",
    "df['Tsp'] = Tsp1[:i]\n",
    "\n",
    "# Plot\n",
    "plt.plot(df['Q1'],'b-',label='$Q_1$ (%)')\n",
    "plt.plot(df['T1'],'r-',label='$T_1$ $(^oC)$')\n",
    "plt.plot(df['Tsp'],'k-',label='SP $(^oC)$')\n",
    "plt.legend()\n",
    "plt.savefig('PID_train.png');"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Feature selection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-06-10T04:20:34.613154Z",
     "start_time": "2023-06-10T04:20:34.609218800Z"
    }
   },
   "outputs": [],
   "source": [
    "# Create new feature: setpoint error\n",
    "df['err'] = df['Tsp'] - df['T1']\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-06-10T04:20:34.640882200Z",
     "start_time": "2023-06-10T04:20:34.613154Z"
    }
   },
   "outputs": [],
   "source": [
    "df.to_csv('PID_train_data.csv',index=False)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Load dataset, Scale data, Split into training and test sets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-06-10T04:20:34.704377400Z",
     "start_time": "2023-06-10T04:20:34.640882200Z"
    }
   },
   "outputs": [],
   "source": [
    "df = pd.read_csv('PID_train_data.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-06-10T04:25:16.663898700Z",
     "start_time": "2023-06-10T04:25:16.632103800Z"
    }
   },
   "outputs": [],
   "source": [
    "X = df[['Tsp','err']].values\n",
    "y = df[['Q1']].values\n",
    "# Scale data\n",
    "s_x = MinMaxScaler()\n",
    "Xs = s_x.fit_transform(X)\n",
    "\n",
    "s_y = MinMaxScaler()\n",
    "ys = s_y.fit_transform(y)\n",
    "\n",
    "# Split into train and test\n",
    "Xtrain, Xtest, ytrain, ytest = train_test_split(Xs,ys,test_size=0.15,shuffle=False)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Model Creation"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### SVM Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-06-10T04:25:11.406461200Z",
     "start_time": "2023-06-10T04:20:34.673033700Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\INDY\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.11_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python311\\site-packages\\sklearn\\utils\\validation.py:1143: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SVR(C=100, gamma=0.1)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SVR</label><div class=\"sk-toggleable__content\"><pre>SVR(C=100, gamma=0.1)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "SVR(C=100, gamma=0.1)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# import svm regression\n",
    "from sklearn.svm import SVR\n",
    "\n",
    "model = SVR(kernel='rbf', C=100, gamma=0.1, epsilon=.1)\n",
    "model.fit(Xtrain,ytrain)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Deep Learning Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-06-10T04:04:49.777738600Z",
     "start_time": "2023-06-10T04:04:49.730121700Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From C:\\Users\\INDY\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.11_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python311\\site-packages\\keras\\src\\backend.py:873: The name tf.get_default_graph is deprecated. Please use tf.compat.v1.get_default_graph instead.\n",
      "\n",
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " dense (Dense)               (None, 2)                 6         \n",
      "                                                                 \n",
      " dense_1 (Dense)             (None, 3)                 9         \n",
      "                                                                 \n",
      " dense_2 (Dense)             (None, 3)                 12        \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 27 (108.00 Byte)\n",
      "Trainable params: 27 (108.00 Byte)\n",
      "Non-trainable params: 0 (0.00 Byte)\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "# Inisialisasi konstruktor\n",
    "model_dl = Sequential()\n",
    "# Tambahkan lapisan masukan\n",
    "model_dl.add(Dense(2, activation='sigmoid', input_shape=(2,)))\n",
    "# Tambahkan satu lapisan tersembunyi\n",
    "model_dl.add(Dense(3, activation='sigmoid'))\n",
    "# Tambahkan lapisan keluaran\n",
    "model_dl.add(Dense(3, activation='sigmoid'))\n",
    "\n",
    "# Data Latih.\n",
    "X = np.array([\n",
    "    [1, 1],\n",
    "    [0.4, 1.2],\n",
    "    [1.2, 0.1],\n",
    "    [1, 0.1]\n",
    "])\n",
    "\n",
    "# Label untuk Data Latih.\n",
    "y = np.array([\n",
    "    [0.25, 4.31, 0.20],\n",
    "    [0.2, 4.1, 0.1],\n",
    "    [0.1, 4.0, 0],\n",
    "    [0.1, 4.0, 0]\n",
    "])\n",
    "\n",
    "# Ringkasan model\n",
    "model_dl.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-06-10T04:04:53.618229900Z",
     "start_time": "2023-06-10T04:04:51.834225700Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From C:\\Users\\INDY\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.11_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python311\\site-packages\\keras\\src\\optimizers\\__init__.py:309: The name tf.train.Optimizer is deprecated. Please use tf.compat.v1.train.Optimizer instead.\n",
      "\n",
      "Epoch 1/100\n",
      "WARNING:tensorflow:From C:\\Users\\INDY\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.11_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python311\\site-packages\\keras\\src\\utils\\tf_utils.py:492: The name tf.ragged.RaggedTensorValue is deprecated. Please use tf.compat.v1.ragged.RaggedTensorValue instead.\n",
      "\n",
      "WARNING:tensorflow:From C:\\Users\\INDY\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.11_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python311\\site-packages\\keras\\src\\engine\\base_layer_utils.py:384: The name tf.executing_eagerly_outside_functions is deprecated. Please use tf.compat.v1.executing_eagerly_outside_functions instead.\n",
      "\n",
      "4/4 [==============================] - 1s 4ms/step - loss: 1.3423 - accuracy: 0.0000e+00\n",
      "Epoch 2/100\n",
      "4/4 [==============================] - 0s 541us/step - loss: 1.3252 - accuracy: 0.0000e+00\n",
      "Epoch 3/100\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 1.3082 - accuracy: 0.0000e+00\n",
      "Epoch 4/100\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 1.2915 - accuracy: 0.0000e+00\n",
      "Epoch 5/100\n",
      "4/4 [==============================] - 0s 0s/step - loss: 1.2748 - accuracy: 0.0000e+00\n",
      "Epoch 6/100\n",
      "4/4 [==============================] - 0s 535us/step - loss: 1.2580 - accuracy: 0.0000e+00\n",
      "Epoch 7/100\n",
      "4/4 [==============================] - 0s 3ms/step - loss: 1.2415 - accuracy: 0.0000e+00\n",
      "Epoch 8/100\n",
      "4/4 [==============================] - 0s 4ms/step - loss: 1.2253 - accuracy: 0.0000e+00\n",
      "Epoch 9/100\n",
      "4/4 [==============================] - 0s 5ms/step - loss: 1.2089 - accuracy: 0.0000e+00\n",
      "Epoch 10/100\n",
      "4/4 [==============================] - 0s 0s/step - loss: 1.1927 - accuracy: 0.0000e+00\n",
      "Epoch 11/100\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 1.1766 - accuracy: 0.0000e+00\n",
      "Epoch 12/100\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 1.1608 - accuracy: 0.0000e+00\n",
      "Epoch 13/100\n",
      "4/4 [==============================] - 0s 4ms/step - loss: 1.1450 - accuracy: 0.0000e+00\n",
      "Epoch 14/100\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 1.1293 - accuracy: 0.0000e+00\n",
      "Epoch 15/100\n",
      "4/4 [==============================] - 0s 0s/step - loss: 1.1137 - accuracy: 0.0000e+00\n",
      "Epoch 16/100\n",
      "4/4 [==============================] - 0s 3ms/step - loss: 1.0982 - accuracy: 0.0000e+00\n",
      "Epoch 17/100\n",
      "4/4 [==============================] - 0s 4ms/step - loss: 1.0828 - accuracy: 0.0000e+00\n",
      "Epoch 18/100\n",
      "4/4 [==============================] - 0s 4ms/step - loss: 1.0676 - accuracy: 0.0000e+00\n",
      "Epoch 19/100\n",
      "4/4 [==============================] - 0s 171us/step - loss: 1.0524 - accuracy: 0.0000e+00\n",
      "Epoch 20/100\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 1.0374 - accuracy: 0.0000e+00\n",
      "Epoch 21/100\n",
      "4/4 [==============================] - 0s 3ms/step - loss: 1.0224 - accuracy: 0.0000e+00\n",
      "Epoch 22/100\n",
      "4/4 [==============================] - 0s 3ms/step - loss: 1.0075 - accuracy: 0.0000e+00\n",
      "Epoch 23/100\n",
      "4/4 [==============================] - 0s 4ms/step - loss: 0.9928 - accuracy: 0.2500\n",
      "Epoch 24/100\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 0.9781 - accuracy: 0.5000\n",
      "Epoch 25/100\n",
      "4/4 [==============================] - 0s 0s/step - loss: 0.9635 - accuracy: 0.5000 \n",
      "Epoch 26/100\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 0.9490 - accuracy: 0.7500\n",
      "Epoch 27/100\n",
      "4/4 [==============================] - 0s 4ms/step - loss: 0.9345 - accuracy: 1.0000\n",
      "Epoch 28/100\n",
      "4/4 [==============================] - 0s 3ms/step - loss: 0.9202 - accuracy: 1.0000\n",
      "Epoch 29/100\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 0.9059 - accuracy: 1.0000\n",
      "Epoch 30/100\n",
      "4/4 [==============================] - 0s 3ms/step - loss: 0.8917 - accuracy: 1.0000\n",
      "Epoch 31/100\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 0.8775 - accuracy: 1.0000\n",
      "Epoch 32/100\n",
      "4/4 [==============================] - 0s 3ms/step - loss: 0.8635 - accuracy: 1.0000\n",
      "Epoch 33/100\n",
      "4/4 [==============================] - 0s 3ms/step - loss: 0.8494 - accuracy: 1.0000\n",
      "Epoch 34/100\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 0.8354 - accuracy: 1.0000\n",
      "Epoch 35/100\n",
      "4/4 [==============================] - 0s 6ms/step - loss: 0.8215 - accuracy: 1.0000\n",
      "Epoch 36/100\n",
      "4/4 [==============================] - 0s 3ms/step - loss: 0.8076 - accuracy: 1.0000\n",
      "Epoch 37/100\n",
      "4/4 [==============================] - 0s 0s/step - loss: 0.7938 - accuracy: 1.0000\n",
      "Epoch 38/100\n",
      "4/4 [==============================] - 0s 0s/step - loss: 0.7800 - accuracy: 1.0000\n",
      "Epoch 39/100\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 0.7662 - accuracy: 1.0000\n",
      "Epoch 40/100\n",
      "4/4 [==============================] - 0s 3ms/step - loss: 0.7525 - accuracy: 1.0000\n",
      "Epoch 41/100\n",
      "4/4 [==============================] - 0s 0s/step - loss: 0.7389 - accuracy: 1.0000\n",
      "Epoch 42/100\n",
      "4/4 [==============================] - 0s 4ms/step - loss: 0.7252 - accuracy: 1.0000\n",
      "Epoch 43/100\n",
      "4/4 [==============================] - 0s 4ms/step - loss: 0.7116 - accuracy: 1.0000\n",
      "Epoch 44/100\n",
      "4/4 [==============================] - 0s 3ms/step - loss: 0.6979 - accuracy: 1.0000\n",
      "Epoch 45/100\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 0.6844 - accuracy: 1.0000\n",
      "Epoch 46/100\n",
      "4/4 [==============================] - 0s 4ms/step - loss: 0.6709 - accuracy: 1.0000\n",
      "Epoch 47/100\n",
      "4/4 [==============================] - 0s 5ms/step - loss: 0.6574 - accuracy: 1.0000\n",
      "Epoch 48/100\n",
      "4/4 [==============================] - 0s 0s/step - loss: 0.6439 - accuracy: 1.0000\n",
      "Epoch 49/100\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 0.6304 - accuracy: 1.0000\n",
      "Epoch 50/100\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 0.6169 - accuracy: 1.0000\n",
      "Epoch 51/100\n",
      "4/4 [==============================] - 0s 0s/step - loss: 0.6035 - accuracy: 1.0000\n",
      "Epoch 52/100\n",
      "4/4 [==============================] - 0s 0s/step - loss: 0.5901 - accuracy: 1.0000\n",
      "Epoch 53/100\n",
      "4/4 [==============================] - 0s 3ms/step - loss: 0.5767 - accuracy: 1.0000\n",
      "Epoch 54/100\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.5633 - accuracy: 1.0000\n",
      "Epoch 55/100\n",
      "4/4 [==============================] - 0s 0s/step - loss: 0.5499 - accuracy: 1.0000\n",
      "Epoch 56/100\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 0.5365 - accuracy: 1.0000\n",
      "Epoch 57/100\n",
      "4/4 [==============================] - 0s 4ms/step - loss: 0.5233 - accuracy: 1.0000\n",
      "Epoch 58/100\n",
      "4/4 [==============================] - 0s 4ms/step - loss: 0.5099 - accuracy: 1.0000\n",
      "Epoch 59/100\n",
      "4/4 [==============================] - 0s 0s/step - loss: 0.4966 - accuracy: 1.0000\n",
      "Epoch 60/100\n",
      "4/4 [==============================] - 0s 929us/step - loss: 0.4833 - accuracy: 1.0000\n",
      "Epoch 61/100\n",
      "4/4 [==============================] - 0s 4ms/step - loss: 0.4700 - accuracy: 1.0000\n",
      "Epoch 62/100\n",
      "4/4 [==============================] - 0s 4ms/step - loss: 0.4567 - accuracy: 1.0000\n",
      "Epoch 63/100\n",
      "4/4 [==============================] - 0s 4ms/step - loss: 0.4435 - accuracy: 1.0000\n",
      "Epoch 64/100\n",
      "4/4 [==============================] - 0s 836us/step - loss: 0.4301 - accuracy: 1.0000\n",
      "Epoch 65/100\n",
      "4/4 [==============================] - 0s 5ms/step - loss: 0.4169 - accuracy: 1.0000\n",
      "Epoch 66/100\n",
      "4/4 [==============================] - 0s 3ms/step - loss: 0.4037 - accuracy: 1.0000\n",
      "Epoch 67/100\n",
      "4/4 [==============================] - 0s 0s/step - loss: 0.3904 - accuracy: 1.0000\n",
      "Epoch 68/100\n",
      "4/4 [==============================] - 0s 12ms/step - loss: 0.3773 - accuracy: 1.0000\n",
      "Epoch 69/100\n",
      "4/4 [==============================] - 0s 6ms/step - loss: 0.3640 - accuracy: 1.0000\n",
      "Epoch 70/100\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.3508 - accuracy: 1.0000\n",
      "Epoch 71/100\n",
      "4/4 [==============================] - 0s 5ms/step - loss: 0.3375 - accuracy: 1.0000\n",
      "Epoch 72/100\n",
      "4/4 [==============================] - 0s 3ms/step - loss: 0.3243 - accuracy: 1.0000\n",
      "Epoch 73/100\n",
      "4/4 [==============================] - 0s 0s/step - loss: 0.3112 - accuracy: 1.0000\n",
      "Epoch 74/100\n",
      "4/4 [==============================] - 0s 3ms/step - loss: 0.2979 - accuracy: 1.0000\n",
      "Epoch 75/100\n",
      "4/4 [==============================] - 0s 4ms/step - loss: 0.2848 - accuracy: 1.0000\n",
      "Epoch 76/100\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 0.2716 - accuracy: 1.0000\n",
      "Epoch 77/100\n",
      "4/4 [==============================] - 0s 0s/step - loss: 0.2584 - accuracy: 1.0000\n",
      "Epoch 78/100\n",
      "4/4 [==============================] - 0s 3ms/step - loss: 0.2452 - accuracy: 1.0000\n",
      "Epoch 79/100\n",
      "4/4 [==============================] - 0s 4ms/step - loss: 0.2320 - accuracy: 1.0000\n",
      "Epoch 80/100\n",
      "4/4 [==============================] - 0s 3ms/step - loss: 0.2189 - accuracy: 1.0000\n",
      "Epoch 81/100\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 0.2057 - accuracy: 1.0000\n",
      "Epoch 82/100\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 0.1925 - accuracy: 1.0000\n",
      "Epoch 83/100\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 0.1793 - accuracy: 1.0000\n",
      "Epoch 84/100\n",
      "4/4 [==============================] - 0s 0s/step - loss: 0.1662 - accuracy: 1.0000\n",
      "Epoch 85/100\n",
      "4/4 [==============================] - 0s 812us/step - loss: 0.1530 - accuracy: 1.0000\n",
      "Epoch 86/100\n",
      "4/4 [==============================] - 0s 5ms/step - loss: 0.1398 - accuracy: 1.0000\n",
      "Epoch 87/100\n",
      "4/4 [==============================] - 0s 3ms/step - loss: 0.1267 - accuracy: 1.0000\n",
      "Epoch 88/100\n",
      "4/4 [==============================] - 0s 3ms/step - loss: 0.1134 - accuracy: 1.0000\n",
      "Epoch 89/100\n",
      "4/4 [==============================] - 0s 4ms/step - loss: 0.1003 - accuracy: 1.0000\n",
      "Epoch 90/100\n",
      "4/4 [==============================] - 0s 3ms/step - loss: 0.0872 - accuracy: 1.0000\n",
      "Epoch 91/100\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 0.0740 - accuracy: 1.0000\n",
      "Epoch 92/100\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 0.0608 - accuracy: 1.0000\n",
      "Epoch 93/100\n",
      "4/4 [==============================] - 0s 6ms/step - loss: 0.0476 - accuracy: 1.0000\n",
      "Epoch 94/100\n",
      "4/4 [==============================] - 0s 5ms/step - loss: 0.0345 - accuracy: 1.0000\n",
      "Epoch 95/100\n",
      "4/4 [==============================] - 0s 3ms/step - loss: 0.0213 - accuracy: 1.0000\n",
      "Epoch 96/100\n",
      "4/4 [==============================] - 0s 4ms/step - loss: 0.0081 - accuracy: 1.0000\n",
      "Epoch 97/100\n",
      "4/4 [==============================] - 0s 970us/step - loss: -0.0050 - accuracy: 1.0000\n",
      "Epoch 98/100\n",
      "4/4 [==============================] - 0s 4ms/step - loss: -0.0182 - accuracy: 1.0000\n",
      "Epoch 99/100\n",
      "4/4 [==============================] - 0s 3ms/step - loss: -0.0314 - accuracy: 1.0000\n",
      "Epoch 100/100\n",
      "4/4 [==============================] - 0s 3ms/step - loss: -0.0445 - accuracy: 1.0000\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.src.callbacks.History at 0x224350dd910>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_dl.compile(loss='binary_crossentropy',\n",
    "              optimizer='adam',\n",
    "              metrics=['accuracy'])\n",
    "\n",
    "model_dl.fit(X, y,epochs=100, batch_size=1, verbose=1)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### PID Deep Learning Controller"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-06-10T04:05:07.021750100Z",
     "start_time": "2023-06-10T04:05:07.010021400Z"
    }
   },
   "outputs": [],
   "source": [
    "def pid_dl(sp,pv,pv_last,ierr,dt):\n",
    "\n",
    "    # calculate the error\n",
    "    error = sp-pv\n",
    "    d_error = sp-pv_last\n",
    "    delta_error = (error - d_error)\n",
    "\n",
    "    outDL = model_dl.predict(np.array([[error,delta_error]]))\n",
    "\n",
    "    Kc = outDL[0,0]\n",
    "    tauI = outDL[0,1]\n",
    "    tauD = outDL[0,2]\n",
    "\n",
    "    # Parameters in terms of PID coefficients\n",
    "    KP = Kc\n",
    "    KI = Kc/tauI\n",
    "    KD = Kc*tauD\n",
    "    # ubias for controller (initial heater)\n",
    "    op0 = 0\n",
    "    # upper and lower bounds on heater level\n",
    "    ophi = 100\n",
    "    oplo = 0\n",
    "\n",
    "    # calculate the integral error\n",
    "    ierr = ierr + KI * error * dt\n",
    "    # calculate the measurement derivative\n",
    "    dpv = (pv - pv_last) / dt\n",
    "    # calculate the PID output\n",
    "    P = KP * error\n",
    "    I = ierr\n",
    "    D = -KD * dpv\n",
    "    op = op0 + P + I + D\n",
    "    # implement anti-reset windup\n",
    "    if op < oplo or op > ophi:\n",
    "        I = I - KI * error * dt\n",
    "        # clip output\n",
    "        op = max(oplo,min(ophi,op))\n",
    "    # return the controller output and PID terms\n",
    "    return [op,P,I,D]"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### SVM PID Controller"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-06-10T04:25:26.665635100Z",
     "start_time": "2023-06-10T04:25:26.664080600Z"
    }
   },
   "outputs": [],
   "source": [
    "def svm(T1_m, Tsp_m):\n",
    "    # Calculate error (necessary feature for SVM input)\n",
    "    err = Tsp_m - T1_m\n",
    "    # Format data for SVM input\n",
    "    X = np.array([[Tsp_m,err]])\n",
    "    Xs = s_x.transform(X)\n",
    "    # Predict Q for controller and unscale\n",
    "    Q1c_s = model.predict(Xs)\n",
    "    # make q_1c_s back from minmaxscaler\n",
    "    \n",
    "    Q_1 = s_y.inverse_transform([Q1c_s])\n",
    "    Q1c = np.clip(Q_1,0.0,100.0)\n",
    "    return Q1c"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Generate SP data for test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-06-10T04:25:29.744603200Z",
     "start_time": "2023-06-10T04:25:29.655905800Z"
    }
   },
   "outputs": [
    {
     "data": {
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HP5bT6ezq8gAAQA/kMMaY7i6is3w+n9xut7xeL9ezAADQQ4Ty/s1vCQEAAOsRWAAAgPUILAAAwHoEFgAAYD0CCwAAsB6BBQAAWI/AAgAArEdgAQAA1iOwAAAA6xFYAACA9QgsAADAegQWAABgPQILAACwHoEFAABYj8ACAACsR2ABAADWI7AAAADrEVgAAID1CCwAAMB6BBYAAGA9AgsAALAegQUAAFiPwAIAAKxHYAEAANYjsAAAAOsRWAAAgPUILAAAwHoEFgAAYD0CCwAAsB6BBQAAWI/AAgAArNepwLJ27Vo5HA4tXbo0MHb69GktXLhQsbGxioqK0syZM1VXV3fBeYwxWrVqlZKSktSvXz/l5OTo8OHDnSkNAAD0Ih0OLKWlpdqwYYMyMjKCxpctW6b33ntPb731lnbt2qVjx47prrvuuuBcP/3pT/XCCy9o/fr12rt3r/r376/Jkyfr9OnTHS0PAAD0IhEd2ampqUl5eXl6+eWX9dRTTwXGvV6vfvGLX+j111/XrbfeKknauHGjrrnmGu3Zs0fXX3/9d+Yyxui5557Tj370I82YMUOS9K//+q9KSEjQO++8o3vvvbcjJXYJY4z+eLal254fAACb9OsTLofD0S3P3aHAsnDhQk2bNk05OTlBgaWsrExnz55VTk5OYCw9PV2pqakqKSlpM7BUV1fL4/EE7eN2u5WZmamSkpI2A4vf75ff7w889vl8HWnjov54tkUjV23/XuYGAKCn+eIfJuuKyA5Fh04L+VmLiop04MABlZaWfmedx+NRZGSkYmJigsYTEhLk8XjanO/ceEJCQrv3KSgo0Jo1a0ItHQAA9FAhBZaamhotWbJEH3zwgfr27ft91XRR+fn5Wr58eeCxz+dTSkpKlz9Pvz7h+uIfJnf5vAAA9ET9+oR323OHFFjKyspUX1+vcePGBcZaWlq0e/durVu3Ttu3b9eZM2fU0NAQdJalrq5OiYmJbc55bryurk5JSUlB+4wdO7bNfZxOp5xOZyild4jD4ei2U18AAOD/hPQtoezsbFVUVKi8vDywTJgwQXl5eYF/9+nTR8XFxYF9KisrdfToUWVlZbU559ChQ5WYmBi0j8/n0969e8+7DwAAuLyEdPogOjpao0ePDhrr37+/YmNjA+Pz5s3T8uXLNXDgQLlcLi1evFhZWVlBF9ymp6eroKBAubm5gfu4PPXUUxo+fLiGDh2qJ554QsnJybrzzjs73yEAAOjxuvzzjmeffVZhYWGaOXOm/H6/Jk+erJdeeilom8rKSnm93sDjRx55RM3NzXrwwQfV0NCgG2+8Udu2bevW62QAAIA9HMYY091FdJbP55Pb7ZbX65XL5erucgAAQDuE8v7NbwkBAADrEVgAAID1CCwAAMB6BBYAAGA9AgsAALAegQUAAFiPwAIAAKxHYAEAANYjsAAAAOsRWAAAgPUILAAAwHoEFgAAYD0CCwAAsB6BBQAAWI/AAgAArEdgAQAA1iOwAAAA6xFYAACA9QgsAADAegQWAABgPQILAACwHoEFAABYj8ACAACsR2ABAADWI7AAAADrEVgAAID1CCwAAMB6BBYAAGA9AgsAALAegQUAAFiPwAIAAKwXUmApLCxURkaGXC6XXC6XsrKytHXr1sD6qqoq5ebmKj4+Xi6XS3fffbfq6uouOOeTTz4ph8MRtKSnp3esGwAA0CuFFFgGDRqktWvXqqysTPv379ett96qGTNm6PPPP1dzc7MmTZokh8OhHTt26JNPPtGZM2c0ffp0tba2XnDeUaNGqba2NrB8/PHHnWoKAAD0LhGhbDx9+vSgxz/5yU9UWFioPXv26A9/+IO+/PJLHTx4UC6XS5L06quvasCAAdqxY4dycnLOX0REhBITEztQPgAAuBx0+BqWlpYWFRUVqbm5WVlZWfL7/XI4HHI6nYFt+vbtq7CwsIueMTl8+LCSk5M1bNgw5eXl6ejRoxfc3u/3y+fzBS0AAKD3CjmwVFRUKCoqSk6nU/Pnz9fmzZs1cuRIXX/99erfv78effRRnTp1Ss3NzVqxYoVaWlpUW1t73vkyMzO1adMmbdu2TYWFhaqurtZNN92kxsbG8+5TUFAgt9sdWFJSUkJtAwAA9CAOY4wJZYczZ87o6NGj8nq9evvtt/XKK69o165dGjlypN5//30tWLBA1dXVCgsL06xZs/TFF19o4sSJKiwsbNf8DQ0NGjx4sJ555hnNmzevzW38fr/8fn/gsc/nU0pKirxeb+DjKAAAYDefzye3292u9++QrmGRpMjISKWlpUmSxo8fr9LSUj3//PPasGGDJk2apKqqKh0/flwRERGKiYlRYmKihg0b1u75Y2JidPXVV+vIkSPn3cbpdAZ99AQAAHq3Tt+HpbW1NehshyTFxcUpJiZGO3bsUH19ve644452z9fU1KSqqiolJSV1tjQAANBLhHSGJT8/X1OnTlVqaqoaGxv1+uuva+fOndq+fbskaePGjbrmmmsUHx+vkpISLVmyRMuWLdOIESMCc2RnZys3N1eLFi2SJK1YsULTp0/X4MGDdezYMa1evVrh4eGaNWtWF7YJAAB6spACS319vWbPnq3a2lq53W5lZGRo+/btuu222yRJlZWVys/P18mTJzVkyBA9/vjjWrZsWdAc5z4yOuerr77SrFmzdOLECcXHx+vGG2/Unj17FB8f3wXtAQCA3iDki25tFMpFOwAAwA6hvH/zW0IAAMB6BBYAAGA9AgsAALAegQUAAFiPwAIAAKxHYAEAANYjsAAAAOsRWAAAgPUILAAAwHoEFgAAYD0CCwAAsB6BBQAAWI/AAgAArEdgAQAA1iOwAAAA6xFYAACA9QgsAADAegQWAABgPQILAACwHoEFAABYj8ACAACsR2ABAADWI7AAAADrEVgAAID1CCwAAMB6BBYAAGA9AgsAALAegQUAAFiPwAIAAKxHYAEAANYjsAAAAOuFFFgKCwuVkZEhl8sll8ulrKwsbd26NbC+qqpKubm5io+Pl8vl0t133626urqLzvviiy9qyJAh6tu3rzIzM7Vv377QOwEAAL1WSIFl0KBBWrt2rcrKyrR//37deuutmjFjhj7//HM1Nzdr0qRJcjgc2rFjhz755BOdOXNG06dPV2tr63nnfOONN7R8+XKtXr1aBw4c0JgxYzR58mTV19d3ujkAANA7OIwxpjMTDBw4UE8//bRSUlI0depU/e///q9cLpckyev1asCAAXr//feVk5PT5v6ZmZm67rrrtG7dOklSa2urUlJStHjxYq1cubJdNfh8Prndbnm93sBzAwAAu4Xy/t3ha1haWlpUVFSk5uZmZWVlye/3y+FwyOl0Brbp27evwsLC9PHHH7c5x5kzZ1RWVhYUZsLCwpSTk6OSkpLzPrff75fP5wtaAABA7xVyYKmoqFBUVJScTqfmz5+vzZs3a+TIkbr++uvVv39/Pfroozp16pSam5u1YsUKtbS0qLa2ts25jh8/rpaWFiUkJASNJyQkyOPxnLeGgoICud3uwJKSkhJqGwAAoAcJObCMGDFC5eXl2rt3rxYsWKA5c+boiy++UHx8vN566y299957ioqKktvtVkNDg8aNG6ewsK79MlJ+fr68Xm9gqamp6dL5AQCAXSJC3SEyMlJpaWmSpPHjx6u0tFTPP/+8NmzYoEmTJqmqqkrHjx9XRESEYmJilJiYqGHDhrU5V1xcnMLDw7/zTaK6ujolJiaetwan0xn00RMAAOjdOn3qo7W1VX6/P2gsLi5OMTEx2rFjh+rr63XHHXe0uW9kZKTGjx+v4uLioPmKi4uVlZXV2dIAAEAvEdIZlvz8fE2dOlWpqalqbGzU66+/rp07d2r79u2SpI0bN+qaa65RfHy8SkpKtGTJEi1btkwjRowIzJGdna3c3FwtWrRIkrR8+XLNmTNHEyZM0MSJE/Xcc8+publZc+fO7cI2AQBATxZSYKmvr9fs2bNVW1srt9utjIwMbd++XbfddpskqbKyUvn5+Tp58qSGDBmixx9/XMuWLQua49xHRufcc889+vrrr7Vq1Sp5PB6NHTtW27Zt+86FuAAA4PLV6fuw2ID7sAAA0PNckvuwAAAAXCoEFgAAYD0CCwAAsB6BBQAAWI/AAgAArEdgAQAA1iOwAAAA6xFYAACA9QgsAADAegQWAABgPQILAACwHoEFAABYj8ACAACsR2ABAADWI7AAAADrEVgAAID1CCwAAMB6BBYAAGA9AgsAALAegQUAAFiPwAIAAKxHYAEAANYjsAAAAOsRWAAAgPUILAAAwHoEFgAAYD0CCwAAsB6BBQAAWI/AAgAArEdgAQAA1iOwAAAA64UUWAoLC5WRkSGXyyWXy6WsrCxt3bo1sN7j8ei+++5TYmKi+vfvr3HjxunXv/71Bed88skn5XA4gpb09PSOdQMAAHqliFA2HjRokNauXavhw4fLGKNXX31VM2bM0MGDBzVq1CjNnj1bDQ0NevfddxUXF6fXX39dd999t/bv368f/OAH55131KhR+vDDD/+vqIiQygIAAL1cSGdYpk+frttvv13Dhw/X1VdfrZ/85CeKiorSnj17JEmffvqpFi9erIkTJ2rYsGH60Y9+pJiYGJWVlV1w3oiICCUmJgaWuLi4jncEAAB6nQ5fw9LS0qKioiI1NzcrKytLknTDDTfojTfe0MmTJ9Xa2qqioiKdPn1at9xyywXnOnz4sJKTkzVs2DDl5eXp6NGjF9ze7/fL5/MFLQAAoPcKObBUVFQoKipKTqdT8+fP1+bNmzVy5EhJ0ptvvqmzZ88qNjZWTqdTDz30kDZv3qy0tLTzzpeZmalNmzZp27ZtKiwsVHV1tW666SY1Njaed5+CggK53e7AkpKSEmobAACgB3EYY0woO5w5c0ZHjx6V1+vV22+/rVdeeUW7du3SyJEjtXjxYu3bt0//+I//qLi4OL3zzjt69tln9dFHH+naa69t1/wNDQ0aPHiwnnnmGc2bN6/Nbfx+v/x+f+Cxz+dTSkqKvF6vXC5XKO0AAIBu4vP55Ha72/X+HXJg+VM5OTm66qqr9MgjjygtLU2HDh3SqFGjgtanpaVp/fr17Z7zuuuuU05OjgoKCtq1fSgNAwAAO4Ty/t3p+7C0trbK7/fr1KlT304YFjxleHi4Wltb2z1fU1OTqqqqlJSU1NnSAABALxFSYMnPz9fu3bv15ZdfqqKiQvn5+dq5c6fy8vKUnp6utLQ0PfTQQ9q3b5+qqqr0z//8z/rggw905513BubIzs7WunXrAo9XrFihXbt26csvv9Snn36q3NxchYeHa9asWV3WJAAA6NlCuuFJfX29Zs+erdraWrndbmVkZGj79u267bbbJElbtmzRypUrNX36dDU1NSktLU2vvvqqbr/99sAcVVVVOn78eODxV199pVmzZunEiROKj4/XjTfeqD179ig+Pr6LWgQAAD1dp69hsQHXsAAA0PNc0mtYAAAAvm8EFgAAYD0CCwAAsB6BBQAAWI/AAgAArEdgAQAA1iOwAAAA6xFYAACA9QgsAADAegQWAABgPQILAACwHoEFAABYj8ACAACsR2ABAADWI7AAAADrEVgAAID1CCwAAMB6BBYAAGA9AgsAALAegQUAAFiPwAIAAKxHYAEAANYjsAAAAOsRWAAAgPUILAAAwHoEFgAAYD0CCwAAsB6BBQAAWI/AAgAArEdgAQAA1iOwAAAA64UUWAoLC5WRkSGXyyWXy6WsrCxt3bo1sN7j8ei+++5TYmKi+vfvr3HjxunXv/71Red98cUXNWTIEPXt21eZmZnat29f6J0AAIBeK6TAMmjQIK1du1ZlZWXav3+/br31Vs2YMUOff/65JGn27NmqrKzUu+++q4qKCt111126++67dfDgwfPO+cYbb2j58uVavXq1Dhw4oDFjxmjy5Mmqr6/vXGcAAKDXcBhjTGcmGDhwoJ5++mnNmzdPUVFRKiws1H333RdYHxsbq3/6p3/S3/7t37a5f2Zmpq677jqtW7dOktTa2qqUlBQtXrxYK1eubFcNPp9PbrdbXq9XLperM+0AAIBLJJT37w5fw9LS0qKioiI1NzcrKytLknTDDTfojTfe0MmTJ9Xa2qqioiKdPn1at9xyS5tznDlzRmVlZcrJyfm/gsLClJOTo5KSkvM+t9/vl8/nC1oAAEDvFXJgqaioUFRUlJxOp+bPn6/Nmzdr5MiRkqQ333xTZ8+eVWxsrJxOpx566CFt3rxZaWlpbc51/PhxtbS0KCEhIWg8ISFBHo/nvDUUFBTI7XYHlpSUlFDbAAAAPUjIgWXEiBEqLy/X3r17tWDBAs2ZM0dffPGFJOmJJ55QQ0ODPvzwQ+3fv1/Lly/X3XffrYqKii4tOj8/X16vN7DU1NR06fwAAMAuEaHuEBkZGThjMn78eJWWlur555/XI488onXr1unQoUMaNWqUJGnMmDH66KOP9OKLL2r9+vXfmSsuLk7h4eGqq6sLGq+rq1NiYuJ5a3A6nXI6naGWDgAAeqhO34eltbVVfr9fp06d+nbCsOApw8PD1dra2ua+kZGRGj9+vIqLi4PmKy4uDlwXAwAAENIZlvz8fE2dOlWpqalqbGzU66+/rp07d2r79u1KT09XWlqaHnroIf3sZz9TbGys3nnnHX3wwQf63e9+F5gjOztbubm5WrRokSRp+fLlmjNnjiZMmKCJEyfqueeeU3Nzs+bOndu1nQIAgB4rpMBSX1+v2bNnq7a2Vm63WxkZGdq+fbtuu+02SdKWLVu0cuVKTZ8+XU1NTUpLS9Orr76q22+/PTBHVVWVjh8/Hnh8zz336Ouvv9aqVavk8Xg0duxYbdu27TsX4gIAgMtXp+/DYgPuwwIAQM9zSe7DAgAAcKkQWAAAgPUILAAAwHoEFgAAYD0CCwAAsB6BBQAAWI/AAgAArEdgAQAA1iOwAAAA64X8a802OnezXp/P182VAACA9jr3vt2em+73isDS2NgoSUpJSenmSgAAQKgaGxvldrsvuE2v+C2h1tZWHTt2TNHR0XI4HF06t8/nU0pKimpqai673ymi98uv98u1b4ne6Z3eu4MxRo2NjUpOTlZY2IWvUukVZ1jCwsI0aNCg7/U5XC7XZfdiPofeL7/eL9e+JXqn98tPd/d+sTMr53DRLQAAsB6BBQAAWI/AchFOp1OrV6+W0+ns7lIuOXq//Hq/XPuW6J3e6d12veKiWwAA0LtxhgUAAFiPwAIAAKxHYAEAANYjsAAAAOsRWC7ixRdf1JAhQ9S3b19lZmZq37593V1Sl3ryySflcDiClvT09MD606dPa+HChYqNjVVUVJRmzpypurq6bqy443bv3q3p06crOTlZDodD77zzTtB6Y4xWrVqlpKQk9evXTzk5OTp8+HDQNidPnlReXp5cLpdiYmI0b948NTU1XcIuOuZivd9///3feR1MmTIlaJue2HtBQYGuu+46RUdH68orr9Sdd96pysrKoG3a8xo/evSopk2bpiuuuEJXXnmlfvjDH+qbb765lK2ErD2933LLLd857vPnzw/apif2XlhYqIyMjMAN0bKysrR169bA+t56zKWL996jj7nBeRUVFZnIyEjzy1/+0nz++efmgQceMDExMaaurq67S+syq1evNqNGjTK1tbWB5euvvw6snz9/vklJSTHFxcVm//795vrrrzc33HBDN1bccVu2bDGPP/64+c1vfmMkmc2bNwetX7t2rXG73eadd94x//Ef/2HuuOMOM3ToUPPHP/4xsM2UKVPMmDFjzJ49e8xHH31k0tLSzKxZsy5xJ6G7WO9z5swxU6ZMCXodnDx5Mmibntj75MmTzcaNG82hQ4dMeXm5uf32201qaqppamoKbHOx1/g333xjRo8ebXJycszBgwfNli1bTFxcnMnPz++OltqtPb3/xV/8hXnggQeCjrvX6w2s76m9v/vuu+bf//3fzX//93+byspK89hjj5k+ffqYQ4cOGWN67zE35uK99+RjTmC5gIkTJ5qFCxcGHre0tJjk5GRTUFDQjVV1rdWrV5sxY8a0ua6hocH06dPHvPXWW4Gx//zP/zSSTElJySWq8Pvxp2/ara2tJjEx0Tz99NOBsYaGBuN0Os2vfvUrY4wxX3zxhZFkSktLA9ts3brVOBwO84c//OGS1d5Z5wssM2bMOO8+vaX3+vp6I8ns2rXLGNO+1/iWLVtMWFiY8Xg8gW0KCwuNy+Uyfr//0jbQCX/auzHfvnktWbLkvPv0lt6NMWbAgAHmlVdeuayO+TnnejemZx9zPhI6jzNnzqisrEw5OTmBsbCwMOXk5KikpKQbK+t6hw8fVnJysoYNG6a8vDwdPXpUklRWVqazZ88G/Q3S09OVmpra6/4G1dXV8ng8Qb263W5lZmYGei0pKVFMTIwmTJgQ2CYnJ0dhYWHau3fvJa+5q+3cuVNXXnmlRowYoQULFujEiROBdb2ld6/XK0kaOHCgpPa9xktKSnTttdcqISEhsM3kyZPl8/n0+eefX8LqO+dPez/ntddeU1xcnEaPHq38/HydOnUqsK439N7S0qKioiI1NzcrKyvrsjrmf9r7OT31mPeKHz/8Phw/flwtLS1BB02SEhIS9F//9V/dVFXXy8zM1KZNmzRixAjV1tZqzZo1uummm3To0CF5PB5FRkYqJiYmaJ+EhAR5PJ7uKfh7cq6fto73uXUej0dXXnll0PqIiAgNHDiwx/89pkyZorvuuktDhw5VVVWVHnvsMU2dOlUlJSUKDw/vFb23trZq6dKl+vM//3ONHj1aktr1Gvd4PG2+Ls6t6wna6l2S/uqv/kqDBw9WcnKyPvvsMz366KOqrKzUb37zG0k9u/eKigplZWXp9OnTioqK0ubNmzVy5EiVl5f3+mN+vt6lnn3MCSyXualTpwb+nZGRoczMTA0ePFhvvvmm+vXr142V4VK69957A/++9tprlZGRoauuuko7d+5UdnZ2N1bWdRYuXKhDhw7p448/7u5SLrnz9f7ggw8G/n3ttdcqKSlJ2dnZqqqq0lVXXXWpy+xSI0aMUHl5ubxer95++23NmTNHu3bt6u6yLonz9T5y5Mgefcz5SOg84uLiFB4e/p0rx+vq6pSYmNhNVX3/YmJidPXVV+vIkSNKTEzUmTNn1NDQELRNb/wbnOvnQsc7MTFR9fX1Qeu/+eYbnTx5stf9PYYNG6a4uDgdOXJEUs/vfdGiRfrd736n3//+9xo0aFBgvD2v8cTExDZfF+fW2e58vbclMzNTkoKOe0/tPTIyUmlpaRo/frwKCgo0ZswYPf/885fFMT9f723pScecwHIekZGRGj9+vIqLiwNjra2tKi4uDvossLdpampSVVWVkpKSNH78ePXp0yfob1BZWamjR4/2ur/B0KFDlZiYGNSrz+fT3r17A71mZWWpoaFBZWVlgW127Nih1tbWwH/63uKrr77SiRMnlJSUJKnn9m6M0aJFi7R582bt2LFDQ4cODVrfntd4VlaWKioqggLbBx98IJfLFTjNbqOL9d6W8vJySQo67j2x97a0trbK7/f36mN+Pud6b0uPOubdesmv5YqKiozT6TSbNm0yX3zxhXnwwQdNTExM0NXTPd3DDz9sdu7caaqrq80nn3xicnJyTFxcnKmvrzfGfPv1v9TUVLNjxw6zf/9+k5WVZbKysrq56o5pbGw0Bw8eNAcPHjSSzDPPPGMOHjxo/ud//scY8+3XmmNiYsxvf/tb89lnn5kZM2a0+bXmH/zgB2bv3r3m448/NsOHD7f+q73GXLj3xsZGs2LFClNSUmKqq6vNhx9+aMaNG2eGDx9uTp8+HZijJ/a+YMEC43a7zc6dO4O+xnnq1KnANhd7jZ/7muekSZNMeXm52bZtm4mPj7fia54XcrHejxw5Yv7hH/7B7N+/31RXV5vf/va3ZtiwYebmm28OzNFTe1+5cqXZtWuXqa6uNp999plZuXKlcTgc5v333zfG9N5jbsyFe+/px5zAchH/8i//YlJTU01kZKSZOHGi2bNnT3eX1KXuuecek5SUZCIjI82f/dmfmXvuucccOXIksP6Pf/yj+bu/+zszYMAAc8UVV5jc3FxTW1vbjRV33O9//3sj6TvLnDlzjDHffrX5iSeeMAkJCcbpdJrs7GxTWVkZNMeJEyfMrFmzTFRUlHG5XGbu3LmmsbGxG7oJzYV6P3XqlJk0aZKJj483ffr0MYMHDzYPPPDAd4J5T+y9rZ4lmY0bNwa2ac9r/MsvvzRTp041/fr1M3Fxcebhhx82Z8+evcTdhOZivR89etTcfPPNZuDAgcbpdJq0tDTzwx/+MOieHMb0zN7/5m/+xgwePNhERkaa+Ph4k52dHQgrxvTeY27MhXvv6cfcYYwxl+58DgAAQOi4hgUAAFiPwAIAAKxHYAEAANYjsAAAAOsRWAAAgPUILAAAwHoEFgAAYD0CCwAAsB6BBQAAWI/AAgAArEdgAQAA1iOwAAAA6/0/8PT2BKediY4AAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Run time in minutes\n",
    "run_time = 6.0\n",
    "\n",
    "# Number of cycles\n",
    "loops = int(60.0 * run_time)\n",
    "\n",
    "# Arrays for storing data\n",
    "T1 = np.zeros(loops)  # Measured T (degC)\n",
    "tm = np.zeros(loops)  # Time\n",
    "Tsp = np.zeros(loops)\n",
    "# Set Tsp to 40°C for 5 minutes\n",
    "Tsp[:300] = 40.0\n",
    "\n",
    "# Leave the rest as the initial value (room temperature)\n",
    "Tsp[300:] = Tsp[0]\n",
    "\n",
    "plt.plot(Tsp)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-06-10T04:25:32.112003700Z",
     "start_time": "2023-06-10T04:25:32.096228400Z"
    }
   },
   "outputs": [],
   "source": [
    "def step_info(resp, t, set_point):\n",
    "    \"\"\"\n",
    "    Calculates overshoot, settling time, and rising time of a step response.\n",
    "\n",
    "    Parameters:\n",
    "    - resp: numpy array, step response values\n",
    "    - t: numpy array, time vector corresponding to the step response\n",
    "    - set_point: float, the desired set point of the system\n",
    "    \"\"\"\n",
    "\n",
    "    # Calculate overshoot\n",
    "    max_resp = np.max(resp)\n",
    "    overshoot = ((max_resp - set_point) / set_point) * 100\n",
    "\n",
    "    # Calculate settling time\n",
    "    within_range = np.where((resp >= set_point * 0.98) & (resp <= set_point * 1.02))[0]\n",
    "    if len(within_range) > 0:\n",
    "        settling_time = t[within_range[-1]] - t[within_range[0]]\n",
    "    else:\n",
    "        settling_time = np.inf\n",
    "\n",
    "    # Calculate rising time\n",
    "    first_crossing = np.where(resp >= set_point * 0.1)[0][0]\n",
    "    last_crossing = np.where(resp >= set_point * 0.9)[0][0]\n",
    "    rising_time = t[last_crossing] - t[first_crossing]\n",
    "\n",
    "    # Print the results\n",
    "    print(\"Overshoot: {:.2f}%\".format(overshoot))\n",
    "    print(\"Settling Time: {:.2f} seconds\".format(settling_time))\n",
    "    print(\"Rising Time: {:.2f} seconds\".format(rising_time))\n",
    "    return overshoot, settling_time, rising_time"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Testing With itclab"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-06-10T04:51:12.610524700Z",
     "start_time": "2023-06-10T04:51:09.562381300Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Opening connection\n",
      "iTCLab connected via Arduino on port COM3\n",
      "26.08\n",
      "Arduino disconnected successfully\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = itclab.iTCLab()\n",
    "a.LED(0)\n",
    "print(a.T1)\n",
    "a.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-06-10T04:57:15.349639500Z",
     "start_time": "2023-06-10T04:51:14.860968400Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Opening connection\n",
      "iTCLab connected via Arduino on port COM3\n",
      "LED On\n",
      "   1.0  40.00  27.73  74.59 \n",
      "   2.0  40.00  27.71  75.70 \n",
      "   3.0  40.00  27.82  76.00 \n",
      "   4.0  40.00  27.67  77.88 \n",
      "   5.0  40.00  27.71  78.62 \n",
      "   6.0  40.00  27.67  79.84 \n",
      "   7.0  40.00  27.69  80.70 \n",
      "   8.0  40.00  27.72  81.50 \n",
      "   9.0  40.00  27.67  82.78 \n",
      "   9.9  40.00  27.77  83.15 \n",
      "  10.9  40.00  27.90  83.33 \n",
      "  11.9  40.00  27.84  84.66 \n",
      "  12.9  40.00  27.83  85.69 \n",
      "  13.9  40.00  28.00  85.62 \n",
      "  14.9  40.00  28.03  86.39 \n",
      "  15.9  40.00  28.09  86.98 \n",
      "  16.9  40.00  28.08  87.98 \n",
      "  17.9  40.00  28.17  88.38 \n",
      "  18.9  40.00  28.21  89.07 \n",
      "  19.9  40.00  28.29  89.52 \n",
      "  20.9  40.00  28.34  90.15 \n",
      "  21.9  40.00  28.40  90.71 \n",
      "  22.8  40.00  28.45  91.33 \n",
      "  23.8  40.00  28.42  92.43 \n",
      "  24.8  40.00  28.57  92.44 \n",
      "  25.8  40.00  28.71  92.50 \n",
      "  26.8  40.00  28.77  93.03 \n",
      "  27.8  40.00  28.86  93.38 \n",
      "  28.8  40.00  28.95  93.72 \n",
      "  29.8  40.00  28.89  94.96 \n",
      "  30.8  40.00  29.01  95.11 \n",
      "  31.8  40.00  29.30  94.22 \n",
      "  32.8  40.00  29.37  94.65 \n",
      "  33.8  40.00  29.46  94.95 \n",
      "  34.8  40.00  29.46  95.79 \n",
      "  35.8  40.00  29.60  95.78 \n",
      "  36.8  40.00  29.72  95.88 \n",
      "  37.8  40.00  29.95  95.30 \n",
      "  38.8  40.00  30.05  95.49 \n",
      "  39.8  40.00  30.24  95.13 \n",
      "  40.8  40.00  30.32  95.42 \n",
      "  41.7  40.00  30.42  95.58 \n",
      "  42.7  40.00  30.45  96.16 \n",
      "  43.7  40.00  30.65  95.70 \n",
      "  44.7  40.00  30.77  95.72 \n",
      "  45.7  40.00  30.91  95.60 \n",
      "  46.7  40.00  31.09  95.23 \n",
      "  47.7  40.00  31.22  95.15 \n",
      "  48.7  40.00  31.27  95.54 \n",
      "  49.7  40.00  31.33  95.87 \n",
      "  50.7  40.00  31.60  94.91 \n",
      "  51.7  40.00  31.67  95.16 \n",
      "  52.7  40.00  31.77  95.21 \n",
      "  53.7  40.00  31.80  95.69 \n",
      "  54.7  40.00  31.74  96.70 \n",
      "  55.7  40.00  31.98  95.90 \n",
      "  56.7  40.00  32.06  96.05 \n",
      "  57.6  40.00  32.27  95.40 \n",
      "  58.6  40.00  32.49  94.68 \n",
      "  59.6  40.00  32.58  94.73 \n",
      "  60.6  40.00  32.64  94.96 \n",
      "  61.6  40.00  32.72  95.05 \n",
      "  62.6  40.00  32.66  96.00 \n",
      "  63.6  40.00  32.87  95.31 \n",
      "  64.6  40.00  32.97  95.26 \n",
      "  65.6  40.00  32.93  96.07 \n",
      "  66.6  40.00  33.23  94.81 \n",
      "  67.6  40.00  33.41  94.25 \n",
      "  68.6  40.00  33.53  94.04 \n",
      "  69.6  40.00  33.56  94.38 \n",
      "  70.6  40.00  33.68  94.16 \n",
      "  71.6  40.00  33.81  93.87 \n",
      "  72.5  40.00  33.93  93.63 \n",
      "  73.5  40.00  34.14  92.83 \n",
      "  74.5  40.00  34.30  92.33 \n",
      "  75.5  40.00  34.38  92.29 \n",
      "  76.5  40.00  34.46  92.25 \n",
      "  77.5  40.00  34.53  92.27 \n",
      "  78.5  40.00  34.49  92.94 \n",
      "  79.5  40.00  34.63  92.53 \n",
      "  80.5  40.00  34.77  92.10 \n",
      "  81.5  40.00  34.84  92.09 \n",
      "  82.5  40.00  35.08  91.04 \n",
      "  83.5  40.00  35.26  90.34 \n",
      "  84.5  40.00  35.46  89.50 \n",
      "  85.4  40.00  35.59  89.07 \n",
      "  86.4  40.00  35.70  88.76 \n",
      "  87.4  40.00  36.02  87.15 \n",
      "  88.4  40.00  36.24  86.13 \n",
      "  89.4  40.00  36.48  84.97 \n",
      "  90.4  40.00  36.60  84.52 \n",
      "  91.4  40.00  36.71  84.12 \n",
      "  92.4  40.00  36.88  83.35 \n",
      "  93.4  40.00  36.86  83.72 \n",
      "  94.4  40.00  37.13  82.33 \n",
      "  95.4  40.00  37.36  81.16 \n",
      "  96.4  40.00  37.58  80.03 \n",
      "  97.4  40.00  37.65  79.80 \n",
      "  98.4  40.00  37.88  78.59 \n",
      "  99.4  40.00  38.00  78.02 \n",
      " 100.4  40.00  38.13  77.39 \n",
      " 101.3  40.00  38.11  77.66 \n",
      " 102.3  40.00  38.09  77.93 \n",
      " 103.3  40.00  38.15  77.72 \n",
      " 104.3  40.00  38.28  77.08 \n",
      " 105.3  40.00  38.49  75.94 \n",
      " 106.3  40.00  38.61  75.33 \n",
      " 107.3  40.00  38.51  76.05 \n",
      " 108.3  40.00  38.49  76.29 \n",
      " 109.3  40.00  38.47  76.53 \n",
      " 110.3  40.00  38.45  76.77 \n",
      " 111.3  40.00  38.59  76.04 \n",
      " 112.3  40.00  38.64  75.85 \n",
      " 113.3  40.00  38.72  75.47 \n",
      " 114.2  40.00  38.74  75.45 \n",
      " 115.2  40.00  38.65  76.10 \n",
      " 116.2  40.00  38.61  76.45 \n",
      " 117.2  40.00  38.71  75.95 \n",
      " 118.2  40.00  38.85  75.20 \n",
      " 119.2  40.00  39.11  73.71 \n",
      " 120.2  40.00  39.16  73.48 \n",
      " 121.2  40.00  39.13  73.73 \n",
      " 122.2  40.00  39.10  73.98 \n",
      " 123.2  40.00  39.21  73.38 \n",
      " 124.2  40.00  39.29  72.96 \n",
      " 125.2  40.00  39.11  74.11 \n",
      " 126.2  40.00  39.21  73.57 \n",
      " 127.2  40.00  39.19  73.76 \n",
      " 128.2  40.00  39.09  74.43 \n",
      " 129.2  40.00  39.12  74.32 \n",
      " 130.1  40.00  39.05  74.82 \n",
      " 131.1  40.00  39.08  74.71 \n",
      " 132.1  40.00  39.10  74.66 \n",
      " 133.1  40.00  39.11  74.67 \n",
      " 134.1  40.00  39.06  75.05 \n",
      " 135.1  40.00  39.43  72.87 \n",
      " 136.1  40.00  39.57  72.07 \n",
      " 137.1  40.00  39.63  71.74 \n",
      " 138.1  40.00  39.81  70.67 \n",
      " 139.1  40.00  39.83  70.56 \n",
      " 140.1  40.00  39.99  69.61 \n",
      " 141.1  40.00  39.97  69.73 \n",
      " 142.1  40.00  40.04  69.30 \n",
      " 143.1  40.00  40.32  67.60 \n",
      " 144.1  40.00  40.60  65.87 \n",
      " 145.1  40.00  40.75  64.91 \n",
      " 146.0  40.00  40.58  65.89 \n",
      " 147.0  40.00  40.64  65.47 \n",
      " 148.0  40.00  40.77  64.63 \n",
      " 149.0  40.00  40.97  63.36 \n",
      " 150.0  40.00  40.82  64.19 \n",
      " 151.0  40.00  40.80  64.25 \n",
      " 152.0  40.00  41.13  62.18 \n",
      " 153.0  40.00  41.29  61.12 \n",
      " 154.0  40.00  41.26  61.20 \n",
      " 155.0  40.00  41.53  59.45 \n",
      " 156.0  40.00  41.69  58.36 \n",
      " 157.0  40.00  41.80  57.56 \n",
      " 158.0  40.00  41.92  56.68 \n",
      " 159.0  40.00  42.05  55.74 \n",
      " 160.0  40.00  41.99  55.94 \n",
      " 160.9  40.00  42.09  55.18 \n",
      " 161.9  40.00  41.97  55.74 \n",
      " 162.9  40.00  41.88  56.13 \n",
      " 163.9  40.00  42.08  54.77 \n",
      " 164.9  40.00  42.09  54.54 \n",
      " 165.9  40.00  42.00  54.92 \n",
      " 166.9  40.00  42.14  53.91 \n",
      " 167.9  40.00  42.44  51.92 \n",
      " 168.9  40.00  42.57  50.93 \n",
      " 169.9  40.00  42.40  51.76 \n",
      " 170.9  40.00  41.95  54.30 \n",
      " 171.9  40.00  41.92  54.33 \n",
      " 172.9  40.00  42.02  53.57 \n",
      " 173.9  40.00  41.95  53.84 \n",
      " 174.9  40.00  42.21  52.10 \n",
      " 175.9  40.00  42.05  52.90 \n",
      " 176.9  40.00  42.04  52.80 \n",
      " 177.8  40.00  41.98  53.00 \n",
      "Arduino disconnected successfully\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = itclab.iTCLab()\n",
    "print('LED On')\n",
    "a.LED(100)\n",
    "\n",
    "run_times =3\n",
    "loops = int(60.0*run_times)\n",
    "tm = np.zeros(loops)\n",
    "T1 = np.zeros(loops)\n",
    "Tsp = np.zeros(loops)\n",
    "Q1 = np.zeros(loops)\n",
    "# Set point to 40 all the time\n",
    "Tsp[:] = 40\n",
    "\n",
    "start_time = time.time()\n",
    "prev_time = start_time\n",
    "ierr = 0.0\n",
    "try:\n",
    "    for i in range(1,loops):\n",
    "        # Sleep time\n",
    "        sleep_max = 1.0\n",
    "        sleep = sleep_max - (time.time() - prev_time)\n",
    "        if sleep>=0.01:\n",
    "            time.sleep(sleep-0.01)\n",
    "        else:\n",
    "            time.sleep(0.01)\n",
    "\n",
    "        # Record time and change in time\n",
    "        t = time.time()\n",
    "        dt = t - prev_time\n",
    "        prev_time = t\n",
    "        tm[i] = t - start_time\n",
    "\n",
    "        # Read temperatures in C\n",
    "        T1[i] = a.T2\n",
    "\n",
    "        [Q1[i],P,ierr,D] = pid(Tsp[i],T1[i],T1[i-1],ierr,dt)\n",
    "        a.Q2(Q1[i])\n",
    "        a.Q1(0.0)\n",
    "        print(('{:6.1f} {:6.2f} {:6.2f} ' + \\\n",
    "               '{:6.2f} ').format( \\\n",
    "            tm[i],Tsp[i],T1[i], \\\n",
    "            Q1[i]))\n",
    "        # \n",
    "\n",
    "except KeyboardInterrupt:\n",
    "    # Disconnect from Arduino\n",
    "    a.Q1(0)\n",
    "    a.Q2(0)\n",
    "    print('Shutting down')\n",
    "    a.close()\n",
    "\n",
    "\n",
    "# Make sure serial connection still closes when there's an error\n",
    "except:\n",
    "    # Disconnect from Arduino\n",
    "    a.Q1(0)\n",
    "    a.Q2(0)\n",
    "    print('Error: Shutting down')\n",
    "    a.close()\n",
    "\n",
    "    raise\n",
    "\n",
    "a.Q1(0.0)\n",
    "a.Q2(0.0)\n",
    "a.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-06-10T04:57:15.356046900Z",
     "start_time": "2023-06-10T04:57:15.349639500Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Arduino disconnected successfully\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-06-10T04:57:15.361991200Z",
     "start_time": "2023-06-10T04:57:15.356046900Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Overshoot: 6.42%\n",
      "Settling Time: 27.82 seconds\n",
      "Rising Time: 86.45 seconds\n"
     ]
    }
   ],
   "source": [
    "[overshoot, settling_time, rise_time] = step_info(T1, tm, Tsp[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-06-10T04:58:19.424855500Z",
     "start_time": "2023-06-10T04:58:18.124974400Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,4))\n",
    "plt.plot(Tsp[:i],'k-',label='SP $(^oC)$')\n",
    "plt.plot(T1[:i],'r-',label='$T_1$ $(^oC)$')\n",
    "plt.plot(Q1[:i],'g-',label='$Q_{PID}$ (%)')\n",
    "# Add overshoot label\n",
    "plt.text(0.2, -0.2, 'Overshoot: {:.2f} %'.format(overshoot), ha='right', va='center', transform=plt.gca().transAxes, fontsize=12)\n",
    "# add set point label\n",
    "plt.text(0.2, -0.3, 'Set Point: {:.2f} °C'.format(Tsp[0]), ha='right', va='center', transform=plt.gca().transAxes, fontsize=12)\n",
    "\n",
    "\n",
    "# Add rising time annotation\n",
    "plt.axvline(x=rise_time, color='c', linestyle='--', label='Rising Time: {:.2f} seconds'.format(rise_time))\n",
    "# add settling time annotation\n",
    "plt.axvline(x=settling_time, color='m', linestyle='--', label='Settling Time: {:.2f} seconds'.format(settling_time))\n",
    "\n",
    "plt.legend(fontsize=14, bbox_to_anchor=(1.04, 0.5), loc=\"center left\", borderaxespad=0)\n",
    "plt.ylim((0,100))\n",
    "plt.xlabel('Time (s)',size=14)\n",
    "plt.xticks(size=12)\n",
    "plt.yticks(size=12)\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-06-10T04:57:15.552883500Z",
     "start_time": "2023-06-10T04:57:15.544154200Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Arduino disconnected successfully\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-06-09T17:11:15.361067200Z",
     "start_time": "2023-06-09T17:07:15.350286400Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n",
      "2\n",
      "3\n",
      "4\n"
     ]
    }
   ],
   "source": [
    "# stop for 3 minutes code\n",
    "for i in range(1,3):\n",
    "    print(i)\n",
    "    time.sleep(60)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-06-10T05:04:40.173616500Z",
     "start_time": "2023-06-10T05:04:37.128268600Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Opening connection\n",
      "iTCLab connected via Arduino on port COM5\n",
      "30.29\n",
      "Arduino disconnected successfully\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = itclab.iTCLab()\n",
    "a.LED(0)\n",
    "print(a.T1)\n",
    "a.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-06-10T05:10:42.579391400Z",
     "start_time": "2023-06-10T05:04:41.371680900Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Opening connection\n",
      "iTCLab connected via Arduino on port COM3\n",
      "LED On\n",
      "1/1 [==============================] - 0s 204ms/step\n",
      "   1.0  40.00  27.83   6.91 \n",
      "1/1 [==============================] - 0s 26ms/step\n",
      "   2.0  40.00  27.81  14.28 \n",
      "1/1 [==============================] - 0s 20ms/step\n",
      "   3.0  40.00  27.80  19.74 \n",
      "1/1 [==============================] - 0s 22ms/step\n",
      "   4.0  40.00  27.88  25.10 \n",
      "1/1 [==============================] - 0s 21ms/step\n",
      "   5.0  40.00  27.74  30.64 \n",
      "1/1 [==============================] - 0s 18ms/step\n",
      "   6.0  40.00  27.81  36.04 \n",
      "1/1 [==============================] - 0s 20ms/step\n",
      "   7.0  40.00  27.80  41.48 \n",
      "1/1 [==============================] - 0s 21ms/step\n",
      "   7.9  40.00  27.84  46.88 \n",
      "1/1 [==============================] - 0s 21ms/step\n",
      "   8.9  40.00  27.72  52.45 \n",
      "1/1 [==============================] - 0s 29ms/step\n",
      "   9.9  40.00  27.86  57.84 \n",
      "1/1 [==============================] - 0s 20ms/step\n",
      "  10.9  40.00  27.78  63.35 \n",
      "1/1 [==============================] - 0s 20ms/step\n",
      "  11.9  40.00  27.82  68.75 \n",
      "1/1 [==============================] - 0s 22ms/step\n",
      "  12.9  40.00  27.73  74.25 \n",
      "1/1 [==============================] - 0s 20ms/step\n",
      "  13.9  40.00  27.88  79.62 \n",
      "1/1 [==============================] - 0s 21ms/step\n",
      "  14.9  40.00  27.96  84.98 \n",
      "1/1 [==============================] - 0s 21ms/step\n",
      "  15.9  40.00  27.97  90.39 \n",
      "1/1 [==============================] - 0s 18ms/step\n",
      "  16.9  40.00  28.06  95.70 \n",
      "1/1 [==============================] - 0s 25ms/step\n",
      "  17.9  40.00  28.00 100.00 \n",
      "1/1 [==============================] - 0s 20ms/step\n",
      "  18.9  40.00  28.03 100.00 \n",
      "1/1 [==============================] - 0s 20ms/step\n",
      "  19.9  40.00  28.15 100.00 \n",
      "1/1 [==============================] - 0s 24ms/step\n",
      "  20.9  40.00  28.23 100.00 \n",
      "1/1 [==============================] - 0s 20ms/step\n",
      "  21.9  40.00  28.31 100.00 \n",
      "1/1 [==============================] - 0s 19ms/step\n",
      "  22.9  40.00  28.37 100.00 \n",
      "1/1 [==============================] - 0s 25ms/step\n",
      "  23.9  40.00  28.47 100.00 \n",
      "1/1 [==============================] - 0s 20ms/step\n",
      "  24.9  40.00  28.52 100.00 \n",
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      "  25.8  40.00  28.38 100.00 \n",
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      "  27.8  40.00  28.74 100.00 \n",
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      "  28.8  40.00  29.05 100.00 \n",
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      "  34.8  40.00  29.53  99.98 \n",
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      " 100.4  40.00  36.76 100.00 \n",
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      " 101.4  40.00  36.83 100.00 \n",
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      " 102.4  40.00  37.52 100.00 \n",
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      " 103.4  40.00  37.13 100.00 \n",
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      " 104.3  40.00  37.09 100.00 \n",
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      " 105.3  40.00  37.15 100.00 \n",
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      " 106.3  40.00  37.25 100.00 \n",
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      " 107.3  40.00  37.12 100.00 \n",
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      " 108.3  40.00  37.11 100.00 \n",
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      " 109.3  40.00  36.99 100.00 \n",
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      " 110.3  40.00  37.41 100.00 \n",
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      " 111.3  40.00  37.56 100.00 \n",
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      " 112.3  40.00  37.67 100.00 \n",
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      " 113.3  40.00  37.83 100.00 \n",
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      " 114.3  40.00  37.94 100.00 \n",
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      " 115.3  40.00  38.19 100.00 \n",
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      " 116.3  40.00  38.25 100.00 \n",
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      " 117.2  40.00  38.26 100.00 \n",
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      " 118.2  40.00  38.18 100.00 \n",
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      " 119.2  40.00  38.43  99.96 \n",
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      " 120.2  40.00  38.60 100.00 \n",
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      " 121.2  40.00  38.80 100.00 \n",
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      " 122.2  40.00  38.93 100.00 \n",
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      " 123.2  40.00  39.09 100.00 \n",
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      " 126.2  40.00  39.34 100.00 \n",
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      " 127.2  40.00  39.49  99.90 \n",
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      " 128.2  40.00  39.56 100.00 \n",
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      " 129.2  40.00  39.51 100.00 \n",
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      " 130.2  40.00  39.41 100.00 \n",
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      " 131.1  40.00  39.48 100.00 \n",
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      " 133.1  40.00  39.45 100.00 \n",
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      " 134.1  40.00  39.37 100.00 \n",
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      " 135.1  40.00  39.33 100.00 \n",
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      " 136.1  40.00  39.27 100.00 \n",
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      " 137.1  40.00  39.36 100.00 \n",
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      " 138.1  40.00  39.69  99.97 \n",
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      " 139.1  40.00  39.65 100.00 \n",
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      " 140.1  40.00  39.57 100.00 \n",
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      " 141.1  40.00  39.59 100.00 \n",
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      " 142.1  40.00  39.25 100.00 \n",
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      " 143.1  40.00  39.24 100.00 \n",
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      " 144.1  40.00  39.40 100.00 \n",
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      " 145.0  40.00  39.48 100.00 \n",
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      " 146.0  40.00  39.59 100.00 \n",
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      " 147.0  40.00  39.68 100.00 \n",
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      " 148.0  40.00  39.72 100.00 \n",
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      " 149.0  40.00  39.89  99.97 \n",
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      " 150.0  40.00  39.89 100.00 \n",
      "1/1 [==============================] - 0s 28ms/step\n",
      " 151.0  40.00  40.13  99.85 \n",
      "1/1 [==============================] - 0s 21ms/step\n",
      " 152.0  40.00  40.38  99.61 \n",
      "1/1 [==============================] - 0s 20ms/step\n",
      " 153.0  40.00  40.66  99.23 \n",
      "1/1 [==============================] - 0s 17ms/step\n",
      " 154.0  40.00  40.73  98.90 \n",
      "1/1 [==============================] - 0s 20ms/step\n",
      " 155.0  40.00  40.84  98.50 \n",
      "1/1 [==============================] - 0s 29ms/step\n",
      " 156.0  40.00  41.08  97.94 \n",
      "1/1 [==============================] - 0s 28ms/step\n",
      " 157.0  40.00  41.12  97.45 \n",
      "1/1 [==============================] - 0s 25ms/step\n",
      " 158.0  40.00  40.94  97.09 \n",
      "1/1 [==============================] - 0s 19ms/step\n",
      " 159.0  40.00  40.91  96.68 \n",
      "1/1 [==============================] - 0s 21ms/step\n",
      " 160.0  40.00  41.28  95.99 \n",
      "1/1 [==============================] - 0s 20ms/step\n",
      " 160.9  40.00  41.35  95.39 \n",
      "1/1 [==============================] - 0s 19ms/step\n",
      " 161.9  40.00  41.44  94.72 \n",
      "1/1 [==============================] - 0s 18ms/step\n",
      " 162.9  40.00  41.28  94.21 \n",
      "1/1 [==============================] - 0s 21ms/step\n",
      " 163.9  40.00  41.39  93.55 \n",
      "1/1 [==============================] - 0s 19ms/step\n",
      " 164.9  40.00  41.46  92.88 \n",
      "1/1 [==============================] - 0s 24ms/step\n",
      " 165.9  40.00  41.47  92.23 \n",
      "1/1 [==============================] - 0s 18ms/step\n",
      " 166.9  40.00  42.04  91.13 \n",
      "1/1 [==============================] - 0s 21ms/step\n",
      " 167.9  40.00  41.59  90.60 \n",
      "1/1 [==============================] - 0s 31ms/step\n",
      " 168.9  40.00  41.41  90.01 \n",
      "1/1 [==============================] - 0s 24ms/step\n",
      " 169.9  40.00  41.63  89.19 \n",
      "1/1 [==============================] - 0s 20ms/step\n",
      " 170.9  40.00  41.95  88.23 \n",
      "1/1 [==============================] - 0s 20ms/step\n",
      " 171.9  40.00  42.00  87.34 \n",
      "1/1 [==============================] - 0s 21ms/step\n",
      " 172.9  40.00  42.35  86.18 \n",
      "1/1 [==============================] - 0s 19ms/step\n",
      " 173.9  40.00  42.47  85.06 \n",
      "1/1 [==============================] - 0s 21ms/step\n",
      " 174.9  40.00  42.61  83.85 \n",
      "1/1 [==============================] - 0s 20ms/step\n",
      " 175.9  40.00  42.67  82.64 \n",
      "1/1 [==============================] - 0s 20ms/step\n",
      " 176.8  40.00  42.83  81.33 \n",
      "1/1 [==============================] - 0s 26ms/step\n",
      " 177.8  40.00  43.08  79.89 \n",
      "Arduino disconnected successfully\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = itclab.iTCLab()\n",
    "print('LED On')\n",
    "a.LED(100)\n",
    "run_times = 3\n",
    "loops = int(60.0*run_times)\n",
    "tm = np.zeros(loops)\n",
    "T1 = np.zeros(loops)\n",
    "Tsp = np.zeros(loops)\n",
    "Q1 = np.zeros(loops)\n",
    "# Set point to 40 all the time\n",
    "Tsp[:] = 40\n",
    "\n",
    "start_time = time.time()\n",
    "prev_time = start_time\n",
    "ierr = 0.0\n",
    "try:\n",
    "    for i in range(1,loops):\n",
    "        # Sleep time\n",
    "        sleep_max = 1.0\n",
    "        sleep = sleep_max - (time.time() - prev_time)\n",
    "        if sleep>=0.01:\n",
    "            time.sleep(sleep-0.01)\n",
    "        else:\n",
    "            time.sleep(0.01)\n",
    "\n",
    "        # Record time and change in time\n",
    "        t = time.time()\n",
    "        dt = t - prev_time\n",
    "        prev_time = t\n",
    "        tm[i] = t - start_time\n",
    "\n",
    "        # Read temperatures in C\n",
    "        T1[i] = a.T2\n",
    "\n",
    "        [Q1[i],P,ierr,D] = pid_dl(Tsp[i],T1[i],T1[i-1],ierr,dt)\n",
    "\n",
    "        a.Q2(Q1[i])\n",
    "        a.Q1(0.0)\n",
    "        print(('{:6.1f} {:6.2f} {:6.2f} ' + \\\n",
    "               '{:6.2f} ').format( \\\n",
    "            tm[i],Tsp[i],T1[i], \\\n",
    "            Q1[i]))\n",
    "\n",
    "except KeyboardInterrupt:\n",
    "    # Disconnect from Arduino\n",
    "    a.Q1(0)\n",
    "    a.Q2(0)\n",
    "    print('Shutting down')\n",
    "    a.close()\n",
    "\n",
    "\n",
    "# Make sure serial connection still closes when there's an error\n",
    "except:\n",
    "    # Disconnect from Arduino\n",
    "    a.Q1(0)\n",
    "    a.Q2(0)\n",
    "    print('Error: Shutting down')\n",
    "    a.close()\n",
    "\n",
    "    raise\n",
    "\n",
    "a.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-06-10T05:10:45.616684100Z",
     "start_time": "2023-06-10T05:10:42.579391400Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Arduino disconnected successfully\n",
      "Opening connection\n",
      "iTCLab connected via Arduino on port COM3\n",
      "Arduino disconnected successfully\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a.close()\n",
    "a = itclab.iTCLab()\n",
    "a.LED(0)\n",
    "a.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-06-10T05:10:45.622986800Z",
     "start_time": "2023-06-10T05:10:45.616684100Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Arduino disconnected successfully\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-06-10T05:10:45.631038400Z",
     "start_time": "2023-06-10T05:10:45.622986800Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Overshoot: 7.70%\n",
      "Settling Time: 28.79 seconds\n",
      "Rising Time: 89.45 seconds\n"
     ]
    }
   ],
   "source": [
    "[overshoot, settling_time, rise_time] = step_info(T1, tm, Tsp[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-06-10T05:10:45.794422800Z",
     "start_time": "2023-06-10T05:10:45.631038400Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1000x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,4))\n",
    "plt.plot(Tsp[:i],'k-',label='SP $(^oC)$')\n",
    "plt.plot(T1[:i],'r-',label='$T_1$ $(^oC)$')\n",
    "plt.plot(Q1[:i],'g-',label='$Q_{DL}$ (%)')\n",
    "# Add overshoot label\n",
    "plt.text(0.2, -0.2, 'Overshoot: {:.2f} %'.format(overshoot), ha='right', va='center', transform=plt.gca().transAxes, fontsize=12)\n",
    "# add set point label\n",
    "plt.text(0.2, -0.3, 'Set Point: {:.2f} °C'.format(Tsp[0]), ha='right', va='center', transform=plt.gca().transAxes, fontsize=12)\n",
    "\n",
    "\n",
    "# Add rising time annotation\n",
    "plt.axvline(x=rise_time, color='c', linestyle='--', label='Rising Time: {:.2f} seconds'.format(rise_time))\n",
    "# add settling time annotation\n",
    "plt.axvline(x=settling_time, color='m', linestyle='--', label='Settling Time: {:.2f} seconds'.format(settling_time))\n",
    "\n",
    "plt.legend(fontsize=14, bbox_to_anchor=(1.04, 0.5), loc=\"center left\", borderaxespad=0)\n",
    "plt.ylim((0,100))\n",
    "plt.xlabel('Time (s)',size=14)\n",
    "plt.xticks(size=12)\n",
    "plt.yticks(size=12)\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-06-10T05:44:29.471058600Z",
     "start_time": "2023-06-10T05:44:26.430629Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Arduino disconnected successfully\n",
      "Opening connection\n",
      "iTCLab connected via Arduino on port COM5\n",
      "30.4\n",
      "Arduino disconnected successfully\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 151,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a.close()\n",
    "a = itclab.iTCLab()\n",
    "print(a.T1)\n",
    "a.LED(0)\n",
    "a.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-06-10T05:44:07.182709100Z",
     "start_time": "2023-06-10T05:42:07.182323200Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n",
      "2\n"
     ]
    }
   ],
   "source": [
    "# stop for 3 minutes code\n",
    "for i in range(1,3):\n",
    "    print(i)\n",
    "    time.sleep(60)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-06-10T05:50:41.323558400Z",
     "start_time": "2023-06-10T05:44:39.801704Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Opening connection\n",
      "iTCLab connected via Arduino on port COM3\n",
      "LED On\n",
      "   1.0  40.00  25.97  86.39 \n",
      "   2.0  40.00  25.96  86.42 \n",
      "   3.0  40.00  25.91  86.58 \n",
      "   4.0  40.00  26.03  86.20 \n",
      "   5.0  40.00  26.06  86.10 \n",
      "   6.0  40.00  25.99  86.32 \n",
      "   7.0  40.00  26.57  84.46 \n",
      "   8.0  40.00  26.11  85.94 \n",
      "   9.0  40.00  26.15  85.81 \n",
      "  10.0  40.00  26.18  85.72 \n",
      "  11.0  40.00  26.27  85.43 \n",
      "  11.9  40.00  26.22  85.59 \n",
      "  12.9  40.00  26.27  85.43 \n",
      "  13.9  40.00  26.33  85.24 \n",
      "  14.9  40.00  26.37  85.11 \n",
      "  15.9  40.00  26.45  84.85 \n",
      "  16.9  40.00  26.41  84.98 \n",
      "  17.9  40.00  26.50  84.69 \n",
      "  18.9  40.00  26.66  84.17 \n",
      "  19.9  40.00  26.74  83.91 \n",
      "  20.9  40.00  26.67  84.14 \n",
      "  21.9  40.00  26.89  83.43 \n",
      "  22.9  40.00  27.07  82.84 \n",
      "  23.9  40.00  27.04  82.94 \n",
      "  24.9  40.00  27.18  82.48 \n",
      "  25.9  40.00  27.27  82.19 \n",
      "  26.9  40.00  27.37  81.86 \n",
      "  27.9  40.00  27.42  81.70 \n",
      "  28.9  40.00  27.59  81.14 \n",
      "  29.9  40.00  27.68  80.84 \n",
      "  30.9  40.00  27.68  80.84 \n",
      "  31.9  40.00  27.88  80.18 \n",
      "  32.9  40.00  27.93  80.02 \n",
      "  33.9  40.00  27.96  79.92 \n",
      "  34.9  40.00  28.19  79.15 \n",
      "  35.9  40.00  28.33  78.69 \n",
      "  36.9  40.00  28.39  78.48 \n",
      "  37.9  40.00  28.45  78.28 \n",
      "  38.8  40.00  28.57  77.88 \n",
      "  39.8  40.00  28.61  77.75 \n",
      "  40.8  40.00  28.71  77.41 \n",
      "  41.8  40.00  28.74  77.31 \n",
      "  42.8  40.00  29.04  76.30 \n",
      "  43.8  40.00  29.14  75.96 \n",
      "  44.8  40.00  29.18  75.83 \n",
      "  45.8  40.00  29.37  75.18 \n",
      "  46.8  40.00  29.40  75.08 \n",
      "  47.8  40.00  29.53  74.64 \n",
      "  48.8  40.00  29.77  73.82 \n",
      "  49.8  40.00  29.80  73.72 \n",
      "  50.8  40.00  29.95  73.21 \n",
      "  51.8  40.00  30.16  72.49 \n",
      "  52.8  40.00  30.19  72.38 \n",
      "  53.8  40.00  30.31  71.97 \n",
      "  54.8  40.00  30.48  71.39 \n",
      "  55.8  40.00  30.62  70.90 \n",
      "  56.8  40.00  30.71  70.59 \n",
      "  57.8  40.00  30.74  70.49 \n",
      "  58.8  40.00  30.88  70.00 \n",
      "  59.8  40.00  31.11  69.20 \n",
      "  60.8  40.00  31.17  69.00 \n",
      "  61.8  40.00  31.49  67.88 \n",
      "  62.8  40.00  31.47  67.95 \n",
      "  63.8  40.00  31.50  67.85 \n",
      "  64.8  40.00  31.51  67.81 \n",
      "  65.8  40.00  31.62  67.43 \n",
      "  66.8  40.00  31.85  66.62 \n",
      "  67.8  40.00  32.04  65.95 \n",
      "  68.8  40.00  32.20  65.39 \n",
      "  69.7  40.00  32.21  65.36 \n",
      "  70.7  40.00  32.40  64.69 \n",
      "  71.7  40.00  32.61  63.94 \n",
      "  72.7  40.00  32.86  63.06 \n",
      "  73.7  40.00  32.83  63.17 \n",
      "  74.7  40.00  32.89  62.95 \n",
      "  75.7  40.00  33.15  62.03 \n",
      "  76.7  40.00  33.43  61.03 \n",
      "  77.7  40.00  33.48  60.86 \n",
      "  78.7  40.00  33.56  60.57 \n",
      "  79.7  40.00  33.70  60.07 \n",
      "  80.7  40.00  33.78  59.78 \n",
      "  81.7  40.00  33.79  59.75 \n",
      "  82.7  40.00  33.66  60.21 \n",
      "  83.7  40.00  33.94  59.21 \n",
      "  84.7  40.00  34.15  58.46 \n",
      "  85.7  40.00  34.85  55.95 \n",
      "  86.7  40.00  34.58  56.92 \n",
      "  87.7  40.00  34.56  56.99 \n",
      "  88.7  40.00  34.53  57.10 \n",
      "  89.6  40.00  34.61  56.81 \n",
      "  90.6  40.00  34.96  55.55 \n",
      "  91.6  40.00  35.01  55.37 \n",
      "  92.6  40.00  35.24  54.54 \n",
      "  93.6  40.00  35.45  53.78 \n",
      "  94.6  40.00  35.38  54.04 \n",
      "  95.6  40.00  35.51  53.57 \n",
      "  96.6  40.00  35.43  53.86 \n",
      "  97.6  40.00  35.60  53.24 \n",
      "  98.6  40.00  35.83  52.41 \n",
      "  99.6  40.00  35.94  52.01 \n",
      " 100.6  40.00  35.92  52.08 \n",
      " 101.6  40.00  35.91  52.12 \n",
      " 102.6  40.00  36.06  51.58 \n",
      " 103.6  40.00  36.27  50.81 \n",
      " 104.6  40.00  36.55  49.80 \n",
      " 105.6  40.00  36.66  49.40 \n",
      " 106.6  40.00  36.86  48.67 \n",
      " 107.6  40.00  37.02  48.09 \n",
      " 108.6  40.00  37.20  47.44 \n",
      " 109.6  40.00  37.47  46.45 \n",
      " 110.6  40.00  37.55  46.16 \n",
      " 111.6  40.00  37.61  45.94 \n",
      " 112.6  40.00  37.64  45.83 \n",
      " 113.6  40.00  37.61  45.94 \n",
      " 114.6  40.00  37.59  46.02 \n",
      " 115.6  40.00  37.75  45.43 \n",
      " 116.5  40.00  37.91  44.85 \n",
      " 117.5  40.00  38.04  44.38 \n",
      " 118.5  40.00  38.75  41.79 \n",
      " 119.5  40.00  38.39  43.10 \n",
      " 120.5  40.00  38.47  42.81 \n",
      " 121.5  40.00  38.69  42.01 \n",
      " 122.5  40.00  38.83  41.50 \n",
      " 123.5  40.00  38.85  41.42 \n",
      " 124.5  40.00  38.69  42.01 \n",
      " 125.5  40.00  38.88  41.31 \n",
      " 126.5  40.00  39.01  40.84 \n",
      " 127.5  40.00  39.15  40.33 \n",
      " 128.5  40.00  39.12  40.44 \n",
      " 129.5  40.00  39.13  40.40 \n",
      " 130.5  40.00  39.18  40.22 \n",
      " 131.5  40.00  39.11  40.48 \n",
      " 132.5  40.00  39.05  40.69 \n",
      " 133.5  40.00  38.88  41.31 \n",
      " 134.5  40.00  39.02  40.80 \n",
      " 135.5  40.00  39.10  40.51 \n",
      " 136.5  40.00  39.07  40.62 \n",
      " 137.5  40.00  39.20  40.15 \n",
      " 138.5  40.00  39.23  40.04 \n",
      " 139.5  40.00  39.29  39.82 \n",
      " 140.5  40.00  39.57  38.80 \n",
      " 141.4  40.00  39.75  38.14 \n",
      " 142.4  40.00  39.75  38.14 \n",
      " 143.4  40.00  39.69  38.36 \n",
      " 144.4  40.00  39.75  38.14 \n",
      " 145.4  40.00  39.97  37.34 \n",
      " 146.4  40.00  40.22  36.43 \n",
      " 147.4  40.00  40.40  35.77 \n",
      " 148.4  40.00  40.42  35.70 \n",
      " 149.4  40.00  40.34  35.99 \n",
      " 150.4  40.00  40.48  35.48 \n",
      " 151.4  40.00  40.54  35.26 \n",
      " 152.4  40.00  40.68  34.76 \n",
      " 153.4  40.00  40.71  34.65 \n",
      " 154.4  40.00  40.56  35.19 \n",
      " 155.4  40.00  40.74  34.54 \n",
      " 156.4  40.00  40.62  34.97 \n",
      " 157.4  40.00  40.58  35.12 \n",
      " 158.3  40.00  40.66  34.83 \n",
      " 159.3  40.00  40.65  34.86 \n",
      " 160.3  40.00  40.71  34.65 \n",
      " 161.4  40.00  40.73  34.57 \n",
      " 162.3  40.00  40.88  34.03 \n",
      " 163.3  40.00  40.80  34.32 \n",
      " 164.3  40.00  40.76  34.46 \n",
      " 165.3  40.00  41.20  32.86 \n",
      " 166.3  40.00  41.49  31.81 \n",
      " 167.3  40.00  41.62  31.34 \n",
      " 168.3  40.00  41.86  30.47 \n",
      " 169.3  40.00  42.10  29.60 \n",
      " 170.3  40.00  42.01  29.93 \n",
      " 171.3  40.00  42.11  29.56 \n",
      " 172.3  40.00  42.27  28.99 \n",
      " 173.3  40.00  42.56  27.94 \n",
      " 174.3  40.00  42.61  27.76 \n",
      " 175.3  40.00  42.72  27.36 \n",
      " 176.3  40.00  42.61  27.76 \n",
      " 177.3  40.00  42.62  27.72 \n",
      " 178.2  40.00  42.89  26.75 \n",
      "Arduino disconnected successfully\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = itclab.iTCLab()\n",
    "print('LED On')\n",
    "a.LED(100)\n",
    "run_times = 3\n",
    "loops = int(60.0*run_times)\n",
    "tm = np.zeros(loops)\n",
    "T1 = np.zeros(loops)\n",
    "Tsp = np.zeros(loops)\n",
    "Q1 = np.zeros(loops)\n",
    "# Set point to 40 all the time\n",
    "Tsp[:] = 40\n",
    "\n",
    "start_time = time.time()\n",
    "prev_time = start_time\n",
    "ierr = 0.0\n",
    "try:\n",
    "    for i in range(1,loops):\n",
    "        # Sleep time\n",
    "        sleep_max = 1.0\n",
    "        sleep = sleep_max - (time.time() - prev_time)\n",
    "        if sleep>=0.01:\n",
    "            time.sleep(sleep-0.01)\n",
    "        else:\n",
    "            time.sleep(0.01)\n",
    "\n",
    "        # Record time and change in time\n",
    "        t = time.time()\n",
    "        dt = t - prev_time\n",
    "        prev_time = t\n",
    "        tm[i] = t - start_time\n",
    "\n",
    "        # Read temperatures in C\n",
    "        T1[i] = a.T2\n",
    "        Q1[i] = svm(T1[i],Tsp[i])\n",
    "        a.Q2(Q1[i])\n",
    "        a.Q1(0.0)\n",
    "        print(('{:6.1f} {:6.2f} {:6.2f} ' + \\\n",
    "               '{:6.2f} ').format( \\\n",
    "            tm[i],Tsp[i],T1[i], \\\n",
    "            Q1[i]))\n",
    "\n",
    "except KeyboardInterrupt:\n",
    "    # Disconnect from Arduino\n",
    "    a.Q1(0)\n",
    "    a.Q2(0)\n",
    "    print('Shutting down')\n",
    "    a.close()\n",
    "\n",
    "\n",
    "# Make sure serial connection still closes when there's an error\n",
    "except:\n",
    "    # Disconnect from Arduino\n",
    "    a.Q1(0)\n",
    "    a.Q2(0)\n",
    "    print('Error: Shutting down')\n",
    "    a.close()\n",
    "\n",
    "    raise\n",
    "\n",
    "a.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-06-10T05:50:44.883920800Z",
     "start_time": "2023-06-10T05:50:44.880646100Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Arduino disconnected successfully\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-06-10T05:50:46.032607400Z",
     "start_time": "2023-06-10T05:50:46.016617Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Overshoot: 7.23%\n",
      "Settling Time: 26.87 seconds\n",
      "Rising Time: 101.59 seconds\n"
     ]
    }
   ],
   "source": [
    "[overshoot, settling_time, rise_time] = step_info(T1, tm, Tsp[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-06-10T05:53:13.778464600Z",
     "start_time": "2023-06-10T05:53:13.615736Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,4))\n",
    "plt.plot(Tsp[:i],'k-',label='SP $(^oC)$')\n",
    "plt.plot(T1[:i],'r-',label='$T_1$ $(^oC)$')\n",
    "plt.plot(Q1[:i],'g-',label='$Q_{SVM}$ (%)')\n",
    "# Add overshoot label\n",
    "plt.text(0.2, -0.2, 'Overshoot: {:.2f} %'.format(overshoot), ha='right', va='center', transform=plt.gca().transAxes, fontsize=12)\n",
    "# add set point label\n",
    "plt.text(0.2, -0.3, 'Set Point: {:.2f} °C'.format(Tsp[0]), ha='right', va='center', transform=plt.gca().transAxes, fontsize=12)\n",
    "\n",
    "\n",
    "# Add rising time annotation\n",
    "plt.axvline(x=rise_time, color='c', linestyle='--', label='Rising Time: {:.2f} seconds'.format(rise_time))\n",
    "# add settling time annotation\n",
    "plt.axvline(x=settling_time, color='m', linestyle='--', label='Settling Time: {:.2f} seconds'.format(settling_time))\n",
    "\n",
    "plt.legend(fontsize=14, bbox_to_anchor=(1.04, 0.5), loc=\"center left\", borderaxespad=0)\n",
    "plt.ylim((0,100))\n",
    "plt.xlabel('Time (s)',size=14)\n",
    "plt.xticks(size=12)\n",
    "plt.yticks(size=12)\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "# save model svm\n",
    "import pickle\n",
    "filename = 'svm_model.pkl'\n",
    "pickle.dump(model, open(filename, 'wb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "# save x and y scaler\n",
    "filename = 'scaler_x.pkl'\n",
    "pickle.dump(s_x, open(filename, 'wb'))\n",
    "filename = 'scaler_y.pkl'\n",
    "pickle.dump(s_y, open(filename, 'wb'))"
   ]
  }
 ],
 "metadata": {
  "hide_input": false,
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}