{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import time\n",
    "import numpy as np\n",
    "import tclab"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TCLab version 1.0.0\n",
      "Arduino Leonardo connected on port /dev/cu.usbmodem1301 at 115200 baud.\n",
      "TCLab Firmware 2.0.1 Arduino Leonardo/Micro.\n",
      "24.218\n",
      "23.154\n",
      "24.347\n",
      "24.411\n",
      "24.411\n",
      "24.347\n",
      "24.314\n",
      "24.347\n",
      "24.347\n",
      "23.896\n",
      "24.476\n",
      "24.637\n",
      "24.669\n",
      "24.669\n",
      "25.056\n",
      "25.088\n",
      "24.991\n",
      "25.088\n",
      "25.217\n",
      "25.281\n",
      "25.313\n",
      "25.668\n",
      "25.668\n",
      "25.636\n",
      "26.022\n",
      "25.926\n",
      "19.126\n",
      "26.248\n",
      "26.248\n",
      "26.055\n",
      "25.152\n",
      "26.699\n",
      "26.989\n",
      "26.957\n",
      "27.021\n",
      "27.118\n",
      "27.247\n",
      "27.344\n",
      "27.666\n",
      "27.183\n",
      "27.795\n",
      "27.892\n",
      "28.021\n",
      "28.311\n",
      "28.214\n",
      "28.504\n",
      "28.536\n",
      "28.762\n",
      "28.826\n",
      "28.858\n",
      "29.245\n",
      "29.181\n",
      "29.374\n",
      "29.6\n",
      "29.567\n",
      "29.793\n",
      "29.761\n",
      "29.89\n",
      "30.147\n",
      "30.147\n",
      "30.438\n",
      "30.599\n",
      "30.728\n",
      "30.856\n",
      "30.76\n",
      "31.018\n",
      "31.114\n",
      "31.34\n",
      "31.533\n",
      "31.501\n",
      "31.727\n",
      "31.469\n",
      "32.017\n",
      "32.081\n",
      "32.113\n",
      "32.5\n",
      "32.403\n",
      "32.403\n",
      "32.693\n",
      "32.726\n",
      "32.887\n",
      "33.016\n",
      "33.048\n",
      "33.08\n",
      "33.37\n",
      "33.37\n",
      "33.499\n",
      "33.725\n",
      "33.789\n",
      "33.821\n",
      "34.047\n",
      "34.079\n",
      "34.144\n",
      "34.305\n",
      "34.434\n",
      "34.434\n",
      "34.659\n",
      "34.756\n",
      "34.659\n",
      "34.691\n",
      "34.917\n",
      "34.981\n",
      "34.981\n",
      "35.271\n",
      "35.4\n",
      "35.336\n",
      "35.239\n",
      "35.594\n",
      "35.626\n",
      "35.819\n",
      "26.796\n",
      "35.948\n",
      "27.408\n",
      "36.174\n",
      "35.304\n",
      "36.271\n",
      "36.528\n",
      "36.561\n",
      "36.689\n",
      "36.657\n",
      "36.979\n",
      "36.979\n",
      "37.044\n",
      "37.205\n",
      "37.173\n",
      "37.237\n",
      "37.205\n",
      "37.302\n",
      "37.656\n",
      "37.56\n",
      "37.592\n",
      "37.882\n",
      "37.882\n",
      "37.817\n",
      "38.043\n",
      "37.173\n",
      "38.269\n",
      "38.365\n",
      "38.397\n",
      "38.591\n",
      "33.016\n",
      "26.022\n",
      "38.913\n",
      "38.945\n",
      "38.913\n",
      "38.945\n",
      "38.945\n",
      "39.235\n",
      "39.203\n",
      "39.268\n",
      "39.3\n",
      "39.493\n",
      "39.042\n",
      "39.59\n",
      "39.622\n",
      "39.654\n",
      "39.815\n",
      "39.88\n",
      "39.912\n",
      "39.912\n",
      "40.009\n",
      "40.009\n",
      "40.234\n",
      "40.234\n",
      "40.234\n",
      "40.363\n",
      "40.524\n",
      "40.524\n",
      "40.557\n",
      "40.557\n",
      "40.653\n",
      "40.814\n",
      "40.557\n",
      "40.911\n",
      "40.879\n",
      "41.072\n",
      "41.169\n",
      "41.104\n",
      "41.072\n",
      "41.104\n",
      "41.137\n",
      "41.523\n",
      "41.33\n",
      "41.523\n",
      "41.523\n",
      "41.62\n",
      "41.813\n",
      "41.781\n",
      "41.846\n",
      "41.813\n",
      "41.942\n",
      "42.136\n",
      "42.136\n",
      "42.136\n",
      "42.136\n",
      "42.104\n",
      "42.168\n",
      "42.361\n",
      "42.458\n",
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      "42.49\n",
      "42.361\n",
      "42.394\n",
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      "42.716\n",
      "42.748\n",
      "42.813\n",
      "42.651\n",
      "42.813\n",
      "42.748\n",
      "42.941\n",
      "43.103\n",
      "43.135\n",
      "43.103\n",
      "43.038\n",
      "43.135\n",
      "43.264\n",
      "43.425\n",
      "43.328\n",
      "43.328\n",
      "43.457\n",
      "43.457\n",
      "43.521\n",
      "43.683\n",
      "43.779\n",
      "43.683\n",
      "43.683\n",
      "43.715\n",
      "43.973\n",
      "43.94\n",
      "44.102\n",
      "44.005\n",
      "44.005\n",
      "44.005\n",
      "44.23\n",
      "44.359\n",
      "44.424\n",
      "44.392\n",
      "44.327\n",
      "44.327\n",
      "44.424\n",
      "44.521\n",
      "43.779\n",
      "44.682\n",
      "44.714\n",
      "44.649\n",
      "44.649\n",
      "44.746\n",
      "44.778\n",
      "44.907\n",
      "44.972\n",
      "42.2\n",
      "44.939\n",
      "45.036\n",
      "44.907\n",
      "44.327\n",
      "43.876\n",
      "45.004\n",
      "45.197\n",
      "45.294\n",
      "45.358\n",
      "45.326\n",
      "45.229\n",
      "45.358\n",
      "45.101\n",
      "45.423\n",
      "45.391\n",
      "45.713\n",
      "45.681\n",
      "45.616\n",
      "45.713\n",
      "45.616\n",
      "45.713\n",
      "45.713\n",
      "45.713\n",
      "45.745\n",
      "45.648\n",
      "45.971\n",
      "45.938\n",
      "45.938\n",
      "45.938\n",
      "46.067\n",
      "45.971\n",
      "46.035\n",
      "46.132\n",
      "46.196\n",
      "45.938\n",
      "46.164\n",
      "46.261\n",
      "46.261\n",
      "46.229\n",
      "46.261\n",
      "46.229\n",
      "46.229\n",
      "46.357\n",
      "46.551\n",
      "46.519\n",
      "46.551\n",
      "46.583\n",
      "TCLab disconnected successfully.\n"
     ]
    }
   ],
   "source": [
    "n = 300\n",
    "t = np.linspace(0,n-1,n)\n",
    "T1 = np.empty_like(t)\n",
    "with tclab.TCLab() as lab:\n",
    "    lab.Q1(40)\n",
    "    for i in range(n):\n",
    "        T1[i] = lab.T1\n",
    "        print(T1[i])\n",
    "        time.sleep(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.plot(T1, '.r')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "DF = pd.DataFrame(T1)\n",
    "DF.to_csv(\"data.csv\", index=False)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": ".venv",
   "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": "3.12.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}