NEandBGD-Video/data-gattering.ipynb

{
 "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",
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      "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"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
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   "name": "python",
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   "pygments_lexer": "ipython3",
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 },
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}