Lugovtsov/6.11.5-tunnel-diode/plots.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 17,
   "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",
    "})\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "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": 51,
   "id": "1a1b6e09-f30a-4e32-b1c3-1097d9d0295c",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f526dba2d70>]"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 900x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(U, I, \"x\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "048c8b91-5acd-4705-9989-a543d212c68c",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "xi = 4.26e+18\n",
      "En_max = 4.26e+18\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 = 2*Uv/e\n",
    "En_max = xi - Up*e\n",
    "print(f\"xi = {xi:.2e}\\nEn_max = {En_max:.2e}\")"
   ]
  },
  {
   "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
}