Lugovtsov/5.2.1-frank-herz-experiment/Untitled.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "4f99854c-1d5d-405a-99c4-90bc90e0a863",
   "metadata": {},
   "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",
    "})\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "20cb3c10-6356-4c6d-bb4b-13f19fa677ed",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 900x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df = pd.read_csv(\"data.csv\")\n",
    "c = df.columns\n",
    "U = df[c[0]][:2000]\n",
    "I = df[c[1]][:2000]\n",
    "plt.plot(U, I, lw=0.2)\n",
    "\n",
    "plt.savefig(\"5v_plot.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b01fad92-ddc1-47d4-8b78-e533e4b8fb70",
   "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
}