{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "65a1d145-551e-436d-977f-0b9e15aa66a3",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# %load /home/glebi/git/experiment-automation/processing_tools.py\n",
    "import numpy as np\n",
    "from scipy.optimize import curve_fit\n",
    "import pandas as pd\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib\n",
    "import scienceplots\n",
    "\n",
    "plt.style.use(['science', 'russian-font'])\n",
    "\n",
    "matplotlib.rcParams.update({\n",
    "    'figure.figsize': [6, 4],\n",
    "    'savefig.facecolor': 'white',\n",
    "    'figure.dpi': 150.0,\n",
    "    'font.size': 12.0,\n",
    "})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "027f3055-bd02-4cf0-9f6a-8ca35e6b36c8",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"data.csv\")\n",
    "cols = df.columns\n",
    "\n",
    "U = df[cols[0]] # +- 0.01 mV\n",
    "I = df[cols[1]] # +- 0.001 mA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "1a1b6e09-f30a-4e32-b1c3-1097d9d0295c",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 900x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.errorbar(U, I, xerr=.01, yerr=.001, fmt=\".\")\n",
    "plt.xlabel(\"Напряжение, мВ\")\n",
    "plt.ylabel(\"Сила тока, мА\")\n",
    "\n",
    "im = plt.imread(\"vah-osc.jpg\")\n",
    "# plt.imshow(im)\n",
    "\n",
    "plt.grid()\n",
    "plt.savefig(\"vah.png\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "048c8b91-5acd-4705-9989-a543d212c68c",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "xi = 170.3\n",
      "En_max = 124.3\n"
     ]
    }
   ],
   "source": [
    "Up = 46e-3\n",
    "Ip = 4.8e-3\n",
    "Uv = 340.6e-3\n",
    "Iv = 0.5e-3\n",
    "e = 1.6e-19\n",
    "\n",
    "xi = Uv/2\n",
    "En_max = xi - Up\n",
    "print(f\"xi = {xi*1e3:.1f}\\nEn_max = {En_max*1e3:.1f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a4878798-dbe1-47f5-adae-cc6a2e58bf27",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "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": "3.10.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}