Lugovtsov/4.3.2-light-difraction-liquid-ultrasonic/lab.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "80b69570-3c97-4533-a6e0-f5e254110784",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "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": "markdown",
   "id": "287d428d-047c-4f87-a3d2-dd87b14ed429",
   "metadata": {},
   "source": [
    "При увеличении частоты УЗ волн расстояние между полосами увеличивается"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d957de11-49fd-40a5-8788-54bde84a02dd",
   "metadata": {},
   "source": [
    "## Первый способ"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "212f0889-b035-48c6-a902-5e3696c27e61",
   "metadata": {
    "tags": []
   },
   "source": [
    "**Расстояние между наиболее чёткими дифракционными картинами**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "8c92872b-c1dc-4c49-abdb-5a0542e1312c",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1568.356"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Lambda = 50+38-14\n",
    "Lambda = Lambda * 10 * 2 # в микронах удвоенное +- 2 микрона\n",
    "\n",
    "Lambda = Lambda * 1e-6 # длина УЗ волны в м\n",
    "nu = 1.0597e6 # частота в Гц\n",
    "\n",
    "v = Lambda * nu # Скорость звука в воде по формуле (5)\n",
    "v"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "ecfa5265-d5d0-4194-94e1-82b48d7689b4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1480"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Lambda = 50+38-14\n",
    "Lambda = Lambda * 10 * 2 # в микронах удвоенное +- 2 микрона\n",
    "Lambda"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d3f7247d-7b4c-4c6e-8ec5-88d9becaf144",
   "metadata": {},
   "source": [
    "## Второй способ"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c7011dfa-0db8-4cff-bbb8-cc4e42836911",
   "metadata": {},
   "source": [
    "**Положения максимумов на разных частотах** в делениях винта (цена -- 4 мкм, погр 4 мкм)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "eae1dcdf-7f12-4cc0-b47c-950502dbd13b",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_array = np.array([\n",
    "    (1.059, 3, [-106, -69, -34, 2, 36, 72, 108]),\n",
    "    # (2.898, 2, [-194, -96, 1, 101, 199]),\n",
    "    (1.210, 2, [-81, -40, 0, 42, 81]),\n",
    "    # (2.951, 1, [-98, 0, 99]),\n",
    "    (0.902, 2, [-58, -29, 1, 30, 61]),\n",
    "    (0.294, 1, [-10, 0, 9]),\n",
    "    (0.439, 2, [-29, -14, 0, 16, 30]),\n",
    "    (0.669, 2, [-47, -24, 0, 23, 46])\n",
    "],\n",
    "dtype=[('frequency', 'f4'), ('count', 'i4'), ('data', 'O')])\n",
    "x_array.sort(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "e4bb6e63-0945-4fb8-86e2-a122a27342b5",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 900x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 900x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "k1 = []\n",
    "for x in x_array:\n",
    "    count = x['count']\n",
    "    m = np.linspace(-count, count, count * 2 + 1, dtype=np.int32)\n",
    "    y = 4 * np.array(x['data'])\n",
    "    y = y - y[count]\n",
    "    k1.append(np.mean(y[count+1:] / m[count+1:]))\n",
    "    \n",
    "    plt.errorbar(m, y, yerr=4, label=f'${x[\"frequency\"]:.2f}$ MHz', marker='.')\n",
    "    \n",
    "plt.xlabel('Номер максимума', fontsize='small')\n",
    "plt.ylabel('Расстояние до центрального максимума, мкм', fontsize='small')\n",
    "plt.legend(frameon=True, fontsize='x-small')\n",
    "plt.savefig(r'images/r_on_m.svg')\n",
    "plt.show()\n",
    "\n",
    "k2 = np.mean(k1 / x_array['frequency'])\n",
    "plt.errorbar(x_array['frequency'], k1, marker='.', ls='', ms=6)\n",
    "plt.plot(x_array['frequency'], k2 * x_array['frequency'], color='C0', lw=1)\n",
    "plt.ylabel(r'$\\Delta x_m / \\Delta m$', fontsize='small')\n",
    "plt.xlabel(r'Частота, МГц', fontsize='small')\n",
    "plt.savefig(r'images/deltax_on_nu.svg')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4a240cce-bfaf-4f8b-becf-17e1f7724fba",
   "metadata": {},
   "source": [
    "Полоса пропускания красного фильтра 6400+-200 ангстрем"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "921a119b-62b2-45bb-9982-c8d80e12ff31",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1440.463516686639"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f = 30e-2\n",
    "lamb = 640e-9\n",
    "v = f * lamb / k2 * 1e12 # по формулам 5, 6\n",
    "v"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "34ee029f-b068-404b-8547-c5ac67ead220",
   "metadata": {},
   "source": [
    "## Третий способ"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "332667c3-7164-4702-a533-18f74ca7f67a",
   "metadata": {},
   "source": [
    "**Длина волны по методу тёмного поля от частоты**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "943094f5-a196-4dfe-823b-cdae8f375a30",
   "metadata": {},
   "outputs": [],
   "source": [
    "s = '''lambda[del]/2,nu[MHz]\n",
    "27.33,0.599\n",
    "22.00,0.708\n",
    "17.60,0.901\n",
    "15.33,1.023\n",
    "13.14,1.243\n",
    "5.53,2.889\n",
    "5.33,2.980'''\n",
    "s = s.split('\\n')\n",
    "\n",
    "df = pd.DataFrame([list(map(float, i.split(','))) for i in s[1:]], columns=s[0].split(','))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "fafead29-8ce6-420f-aa62-fa24afac5c29",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1484.112292358804"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "k = 2 / 43 # Цена деления окулярной шкалы микроскопа, мм\n",
    "\n",
    "Lambda_temn = df[df.columns[0]]*2 * k * 1e-3 # длина волны по методу темн поля\n",
    "nu_temn = df[df.columns[1]] * 1e6 # частота\n",
    "\n",
    "v = np.mean(nu_temn * Lambda_temn)\n",
    "v"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2f54defb-0072-4cdf-a30b-05351cfd5197",
   "metadata": {},
   "source": [
    "## Дополнительно"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5eed1189-92d8-4317-9b40-e0a9ea938af2",
   "metadata": {},
   "source": [
    "При перемещении проволоки стало ничего не видно"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "17885155-3769-426f-8be1-62ad3275d077",
   "metadata": {},
   "source": [
    "Второе явление обяснить"
   ]
  }
 ],
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