Lugovtsov/3.3.4-semiconductor-hall-effect/processing.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "f1de9529-db33-4104-8a94-407330ffcb99",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# %load ../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": 24,
   "id": "657cc2c7-920b-46e0-a7a8-ef65146ddf4e",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "I_names = np.array([13, 24, 35, 46, 57, 68, 79, 90, 101])\n",
    "I_values = I_names * 1.5 / 155# ток в мА"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "61022dfd-06b7-43cb-ac1f-2128f46ff7ee",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "colors = ['tab:blue', 'tab:orange', 'tab:green', 'tab:red', 'tab:purple', 'tab:brown', 'tab:pink', 'tab:gray', 'tab:olive', 'tab:cyan']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cab0ae39-aeb0-425a-a38a-8491552135fe",
   "metadata": {
    "tags": []
   },
   "source": [
    "### График зависимости индукции от силы тока в обмотках магнита $B = f(I_\\text{М})$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "4c853de8-c61e-4ce0-b796-ffaa6e697b12",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "df = pd.read_csv(rf'data/induction_on_amperage.csv')\n",
    "\n",
    "B = np.array(df['Ind[mT]'])\n",
    "I_m = np.array(df['I[mA]'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "576974ec-5d4d-42c1-9280-7a7f9b74a6be",
   "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(I_m, B, xerr=5+0.003*I_m, yerr=0.5+0.003*B, lw=1, ls='', marker='.', markersize=8, label=r\"$B = f(I_\\text{М})$\", ds='default')\n",
    "    \n",
    "plt.xlabel(r\"$I_\\text{М}$, мА\")\n",
    "plt.ylabel(r\"$B$, мТл\")\n",
    "\n",
    "plt.legend()\n",
    "plt.savefig(rf\"images/BonI.png\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c9e18b07-8656-49b4-bf8e-1c2371bee3e5",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Семейство характеристик $\\varepsilon_\\text{х} = f(B)$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "fadcfd44-216a-4449-9e75-398c8d3c740c",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def B_approx(x):\n",
    "    for i, xi in enumerate(I_m):\n",
    "        if xi >= x:\n",
    "            x1 = I_m[i - 1]\n",
    "            x2 = I_m[i]\n",
    "            y1 = B[i - 1]\n",
    "            y2 = B[i]\n",
    "            return y1 + (y2-y1)/(x2-x1)*(x-x1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "e6741806-03fb-487f-8366-bbb6c825c49b",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def line(x, arg1, arg2):\n",
    "    return x * arg1 + arg2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "08c7f31f-1c40-4904-b619-91c227f7a6b3",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 900x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dE_dB = []\n",
    "\n",
    "for i, I_name in enumerate(I_names):\n",
    "    df = pd.read_csv(rf\"data/VoA_{I_name}.csv\")\n",
    "    \n",
    "    U_ = df[\"U_34[mV]\"]\n",
    "    U0 = U_[0]\n",
    "    U = 1000*(U0 - U_)\n",
    "    \n",
    "    I_ = df[\"I_m[mA]\"]\n",
    "    B_ = np.array([B_approx(x) for x in I_], dtype=np.float64)\n",
    "    \n",
    "    popt, pcov = curve_fit(line, B_, U)\n",
    "    \n",
    "    # print(f\"k = {popt[0]:.4f} \\t \")\n",
    "    dE_dB.append(popt[0]/1000)\n",
    "    \n",
    "    plt.errorbar(B_, U, xerr=1+0.003*B_, yerr=1+0.003*U, ls='', marker='.', markersize=4, label=r\"$I_{||} = \"+rf\"{I_values[i]:.2f}\\pm{1.5/155:.2f}$ мА\", ds='default', color=colors[i])\n",
    "    plt.plot(B_, line(B_, popt[0], popt[1]), color=colors[i], lw=1)\n",
    "\n",
    "dE_dB = np.array(dE_dB) * 1000\n",
    "    \n",
    "plt.xlabel(r\"$B$, мТл\")\n",
    "plt.ylabel(r\"$\\varepsilon_x$, мкВ\")\n",
    "\n",
    "plt.legend(frameon=True)\n",
    "plt.savefig(rf\"images/UonB.png\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ed4c8024-e86f-4672-bd64-1d5d8e596592",
   "metadata": {},
   "source": [
    "### Зависимость $k = f(I)$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "0d76d070-47ce-4f4f-9342-566a1107abab",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAygAAAIYCAYAAACG+cjoAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy88F64QAAAACXBIWXMAABcSAAAXEgFnn9JSAACFz0lEQVR4nOzdfXyT9b0//leaJtwEaNqiiLf0qiKC3KVlzol6JgnezhtI7M7Od9/9prbR3Zyd6WjoHAJmCglz7uwcNxPcztn5bucMEvBmOpWkOnXOG5qA5UZQehW8QQTbXgUCpWmS3x+cZC1N0qRJeiXp6/l49PEgV67rc70LF+3nnffnRhGJRCIgIiIiIiLKAyVyB0BERERERBTFBIWIiIiIiPIGExQiIiIiIsobTFCIiIiIiChvMEEhIiIiIqK8USp3AJRYOBxGT08PSktLoVAo5A6HiIiIiChjkUgEfX19GDt2LEpKBtdLmKDksZ6eHmg0GrnDICIiIiLKukAggPHjxw86XlAJiiiKsNls0Gq1sddNTU3Q6XRpt+X1euFwOCBJEkRRhCAIsNlsCdtK997ZiLW09PQ/TyAQgEqlSv2bSyAYDEKj0WStvWKLB8i/mBjP0PItJsYztHyLKd/iAfIvJsYztHyLifEMLd9iyrd4gNzFFG032tc9U8EkKH6/HyaTCR6PB4IgAAAkSUJNTQ1sNhuMRmPKbdntdgCAy+WKHTObzaipqUFjYyNsNltG985WrNFhXSqVKqsPRbbby1S+xQPkX0yMZ2j5FhPjGVq+xZRv8QD5FxPjGVq+xcR4hpZvMeVbPEDuYko0haFgJsmbTCaYzeZYhx8AtFotbDYb6uvrIUlSSu2IooiOjg40NjYOOO5wOKDT6WC32+H1ejO6d7ZizbaSkhKsXLky7lg/OeRbPPko3/6O8i0eID9jyif5+PeTbzHlWzz5KN/+jvItHiA/Y8on+fj3k28x5Vs8clJEIpGI3EEMxev1wmAwwOfzxR0ipVAoYLPZBiUd8VgsFixYsCBuFcPtdsNkMsFoNMaqK+neO5uxBoNBqNVq9Pb25l0mXaz4d06Z4jNE2cDniDLFZ4iyIVfP0VDtFkSK5vF4AGBARaI/rVaLDRs2pNRWdPiV0+kc9F40ofD7/cO+dzZjJSIiIiIabQoiQYkOuYpOOD+TIAgDkopkDAYDtFotamtrB70Xb+hVuvfOZqw08lhepUzxGaJs4HNEmeIzRNkg13NUEEO8ysvLAQBdXV1x36+pqYHf70em34rT6YTZbB4wxCvde2cz1mj5K97KCSUlJVAqlal9Y0REREREMgiFQgiHwwOORVfxKughXqlOKs908nk0KWlqahr2vXMRq0ajgVqtHvBltVpTvp6IiIiISA5Wq3VQP3aoff4KIkFJVWdn57Cv9Xq98Hq9aGxsHNa+KuneO53zA4EAent7B3ytWLEi3RCJiIiIiEbUihUrBvVjA4FA0msKYh+URPM5skWSpNjqXWfugZLuvXMRaz6uh01ERERENBSlUpn2tISCqKBUVFRk9bwz1dfXQ6/XD9i4cbj3znWsRERERETFrCASFK1Wm3TORvS94VQvLBYLAMRNToZz71zGSkRERERU7AoiQYkuCZyo4y+K4rDmjTidToiiOCg5EUVx2PfOVaxERERERKNBQSQoZrMZwMDEISqaCNTV1aXVptfrhcfjiVs56T8PJd175yJWIiIiIqLRoiASFJ1OB0EQ4u7AvnHjRmi1WjQ0NAw4LklSbNPEM0WrJomGdWVy7+HESkREREREpxXERo0A4Pf7sWjRIvh8PgiCAOB0ElJTUwOXyzVo2JTBYIDX64XD4RiQEEiShKqqqlgbZxJFEQ0NDQOqKOneO93zE4lu1JhoExsiIiIiokIzVB+3IJYZBk5XJnw+HywWCwRBQGVlJbZu3QqHwxG3w28wGNDS0hKbExJlMpkgSRL8fn/Cey1YsCCje6d7PhERERERnVYwFZTRiBUUIiIiIio2RVNBGc2CwSAAoKSkJO2NboiIiIiI8kEoFEI4HI71bRMpiEnyo51Go4FarYbVapU7FCIiIiKiYbFarVCr1dBoNEnP4xCvPBYtfwUCAahUKlZQiIiIiGhIJ3r7MPOhlwEAux++HuPV+TFoqn8FRaPRcIhXIVOpVJyDQkREREQFTalUpvRhO4d4EaXJYDCguroa5eXlsNvtcodDREREVFSYoBClyePxwGw2Q5IkLhtNRERElGVMUIiGoa2tDQCg1+tljoSIiIiouDBBIRoGr9fL6gkRERHlpcCpvtiff/NG+4DXhYAJClGaJEmCKIqsnhAREZHsTvT2Dfg6cqwHd/zqb7H3H/N8gCW//huOHOsZcF4+4ypeRGnyer0AgLq6urjvu91uOBwOtLS0QJIkCIIAs9mMxsbGkQyTiIiIRoHocsLJ7D10DAseaR5wbP/am3MVUsaYoBAl4ff7sWbNGgiCAEmSYDabsWHDBgAYNMRLkiSYTCbodDpYLBYIgoCKigps3LgRNpsNgiDAaDTK8W0QERERFQxu1JjHohs1JtrEhnLL7XbDYrHA5/NBq9UCACwWC5xOJ2pra+HxeAacbzabYbPZYucSERER5dpB6QRsL+3Fs9sPAgDKxpWi++TgIVwPGKbj7qurYq/l3LxxqD4u56AQxeH1emEymeByuQYkHAaDAZIkwWAwDLpGq9UyOSEiIqIR8+KOz3Drv/8Nz24/CIUCuHthFbz3X4tLz5k44LzLpk7CXQurMF5dGvvKZ/kdHQE4nWUCQElJSUq7b1LmzGYz9Hr9oGFcoigCiL+8sCRJ8Pv9EEURoihCEATodDoIgjAiMRMREdHocOTYKax8bif+vOMQAODisyfAtnQOai4qBwD8/u4vxeacPGCYjrsWVkEzRv5ufygUQjgcjvVtE2EFpQBoNBqo1WpYrVa5QxkV7HY7RFGE2Wwe9J7H44FWq427xLDBYEB9fT0cDkfsXEmSch0uERERjRKRSASb/Z/A8Phr+POOQ1CWKPC9r16MF/55YSw5ATAgGbn76vxITgDAarVCrVZDo9EkPS8/oqWkAoEAVCoVSkqYT46E6CT4eFUSr9cb97jf78fWrVvh8/lyHh8RERGNPgelk3jw6R14de8RAMCscyfBbpyDWeeWyRxZ6lasWIEHH3wQwWAwaZLCBKUAqFSqgpokf6K3L7bk3e6Hr8/7cY5n8vv9EARh0HwSURQTzj9xOByxygkRERFRtoTDEfzP1o+w5s97cPxUH9TKEvxAfwkarhGgUhbWh9dKpTKl6QqF1XMkGiHxhnBF9z+JVlDsdjsaGxtjE+rTYbfbAQBbt26FzWaD1+uFJEloa2tjokNEREQAgAMdAVg2teJtsRMAoLtQC7txDi4+e2LS68arS/N6n5OhFFbaRTQCdDpd3LkjDocDWq02Num9ra0t9l508nw8fr9/wGu73Q69Xo/GxkYYDAYYDIbYhHqn05mdb4KIiIgKVigcwVNviLj+F6/jbbET41RKrLhlJlz3fmXI5KQYsIJCdIampibU19cPOGaxWFBbWxtLXPx+f2yol16vjy0/rNPpUFtbC+B00rJhwwZUVlYOqMh0dHTEXkfb0+v1EEURLpcrx99dfvH7/XGrVYV2DyIiomz58PNjaNzUim0fSQCAr1RXYu2SObiwcry8gY0gbtSYxwp1o8ZCn4MCAE6nEz6fD9XV1ejo6EBTUxO0Wi1MJhMEQUBlZSUaGxtj50uShDVr1sDtdg9YYripqSlp59hkMqGiomJUDutyOp2QJGnA32MuSJKE+vr6UZf8ERFRYQmGwnjyL234t1f2oTcUxsQxpfjxzZfh6wsugEKhkDu8rBqqj8sEJY8xQSl+5eXlWL9+PYxGo9yhZEwURVgsltgQuGhSF4/X64XNZoPH40nYntlshiiKaGlpiR2rra2FIAhpJ3RerxcOh4NJChER5aWdn3aj0d2K3Z8dBQBcN+NsPHLH5ZhaNk7myHKDO8kT5ZnoPBO/3w9JkgYsWxydPF9ooqubmc1m1NXVwel0Ys2aNQnPNZvNQyYLDocDHo8nNrR
      "text/plain": [
       "<Figure size 900x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "popt, pcov = curve_fit(line, I_values, dE_dB)\n",
    "\n",
    "k = popt[0]\n",
    "\n",
    "plt.errorbar(I_values, dE_dB, xerr=0.01, yerr=3.15e-03, color=colors[0], lw=1, ls='', marker='.', markersize=4, label=r\"$\\dfrac{d\\varepsilon_x}{dB} = f(I_{||})$\", ds='default')\n",
    "plt.plot(I_values, line(I_values, popt[0], popt[1]), color=colors[0], lw=1)\n",
    "\n",
    "plt.xlabel(r\"$I_\\text{М}$, мА\")\n",
    "plt.ylabel(r\"$d\\varepsilon_x/dB$, мВ/Тл\")\n",
    "\n",
    "plt.legend()\n",
    "plt.savefig(rf\"images/KonI.png\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "2d140d8b-b9fb-48b7-88fd-ccd862fc794f",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "h = 2.2e-3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "deb90002-f303-4b6b-ad90-e3c83d3ab41b",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0004318927697950297"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "R_h = k*h\n",
    "R_h"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "8731ed11-0b5c-4304-9ab3-eb563dbe33e3",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "q = 1.6e-19"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "048587bd-6a59-449c-a65c-86f810f3ce4c",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.44711846020626e+22"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "n = 1 / (R_h * q)\n",
    "n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "8f61c6e8-356b-4a4c-b797-dd5e8d0331eb",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "a = 2.2e-3\n",
    "L35 = 3e-3\n",
    "l = 2.5e-3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "1f153f00-d44c-41e5-8bab-ad09bd8e5896",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "274.0977615349475"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sigma = 1 * L35/(1.99 * a * l)\n",
    "sigma"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "cf5b7366-16f9-434f-969f-74b61fed23ca",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1183.8084142394603"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b = sigma * R_h\n",
    "b*10000"
   ]
  }
 ],
 "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
}