Lugovtsov/4.2.1-hewton-rings/measurements.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "5cff7367",
   "metadata": {},
   "source": [
    "# Кольца Ньютона"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "e16deb44-1094-4981-b3b2-9170816472e9",
   "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': 175.0,\n",
    "    'font.size': 12.0,\n",
    "})\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "848f05eb-e21e-4fc0-8429-bed4cdcad3ee",
   "metadata": {},
   "source": [
    "## Стандартный подход к обработке"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "18f7eaab-03eb-44de-ac72-ad4c1908b33a",
   "metadata": {},
   "source": [
    "### Калибровка шкалы"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "b7d28cdc-bfae-4af9-8fb3-11509f9ffab5",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.4353070925719702"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lamb = 577e-3 # мкм\n",
    "\n",
    "l = 700 # +-10\n",
    "dels = 96 + 700 - 76 # +-1\n",
    "k_mean = l / dels\n",
    "k_err = np.sqrt(10**2 * (1/dels)**2 + (-l/(dels**2))**2)\n",
    "k = (k_mean, k_err) # цена деления в микронах\n",
    "\n",
    "k_err / k_mean * 100 # %ошибки"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "52600d36-efe5-460f-acb2-6538c76b0361",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Расстояния от центра до колец"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "06aec23a-bfc1-4a32-a4bf-2abb6158f070",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 1050x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "center_pos = 482 # +- 1\n",
    "dark_dot = (482 - 435) * k[0]\n",
    "\n",
    "dist = np.genfromtxt('iii-data.csv', skip_header=True) - center_pos\n",
    "dist_si = (-dist)[::-1] * k[0]\n",
    "dark_distances = dist_si[1::2]\n",
    "light_distances = dist_si[::2]\n",
    "m = np.arange(0, len(dist) / 2)\n",
    "\n",
    "# dist_si**2 = (dist * k)**2\n",
    "err = lambda dist : np.sqrt((k[0]**2 * 2*dist*1)**2 + (dist**2 * 2*k[0]*k[1])**2)\n",
    "plt.errorbar(m+1, dark_distances**2, yerr=err(dark_distances), label='Тёмные кольца')\n",
    "plt.errorbar(m, light_distances**2, yerr=err(light_distances), label='Светлые кольца')\n",
    "plt.scatter(0, dark_dot**2, marker='o', label='Граница тёмного пятна')\n",
    "\n",
    "plt.ylabel('Квадрат расстояния до центра, $\\\\text{мкм}^2$')\n",
    "plt.xlabel('Порядковый номер m')\n",
    "plt.legend()\n",
    "\n",
    "plt.savefig(r'images/dark-light.svg')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2adad890-f5df-4a1f-ac51-10126ec6f233",
   "metadata": {},
   "source": [
    "**Тёмный проходит через (0,0)**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "718516ca-f89a-490e-aa08-92635336ae3e",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Рассчёт кривизны"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "0ca07e96-9b37-4615-b120-f95e610f525a",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 1050x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def line1(x, k):\n",
    "    return k*x\n",
    "\n",
    "([lR1], [[lR1_err]]) = curve_fit(line1, m+1, dark_distances**2)\n",
    "\n",
    "plt.fill_between(m+1, line1(m+1, lR1+lR1_err), line1(m+1, lR1-lR1_err), alpha=.15, edgecolor='none', label='Случайная ошибка')\n",
    "plt.plot(m+1, line1(m+1, lR1), alpha=.65, label=r'Аппроксимация $r=\\sqrt{m\\lambda R}$', color='C0')\n",
    "plt.errorbar(m+1, dark_distances**2, yerr=err(dark_distances), label='Эксперимент', marker='.', linestyle='none', color='C0')\n",
    "\n",
    "plt.ylabel('Квадрат расстояния до центра, $\\\\text{мкм}^2$')\n",
    "plt.xlabel('Порядковый номер m')\n",
    "plt.legend()\n",
    "\n",
    "plt.savefig(r'images/dark-approx.svg')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "2d3a2c1c-4e35-4c57-8cc7-f1ae559df7d2",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R = 11.9 +- 0.8 мм\n"
     ]
    }
   ],
   "source": [
    "R = lR1 / lamb\n",
    "R_err = np.sqrt((1/lamb)**2 * lR1_err**2)\n",
    "print(f'R = {R*1e-3:.3} +- {R_err*1e-3:.1} мм')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "144b2708-3133-445c-bfe6-71ea94b27e70",
   "metadata": {},
   "source": [
    "## Обработка фото колец"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "08497bc8-6df7-4b62-a710-7525c402bed0",
   "metadata": {},
   "source": [
    "### Расчёт кривизны"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "471e57c5-197e-4f07-96f6-b9f7ba61e6fc",
   "metadata": {},
   "source": [
    "Импорт, кроп, удаление шумов медианным фильтром, перевод в чб фото"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "7e97f19c-7447-493b-bb0d-a9de3771a056",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 1050x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import imageio.v2 as im\n",
    "from skimage.color import rgb2gray\n",
    "from scipy import ndimage\n",
    "from scipy.signal import find_peaks\n",
    "\n",
    "face = im.imread('images/rings2.png')\n",
    "face = face[170:2000,780:815]\n",
    "\n",
    "face_intensity = np.fliplr(np.swapaxes(rgb2gray(face), 0, 1))\n",
    "face_intensity = ndimage.median_filter(face_intensity, 4)\n",
    "\n",
    "plt.imshow(face_intensity)\n",
    "plt.axis('off')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f9fa0024-759c-4e76-9912-17c2a46813c5",
   "metadata": {},
   "source": [
    "Усреднение интенсивности пикселей по вертикали и поиск максимумов"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "bc5404a3-1d9e-400d-bf6e-2cbd11fa73e1",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 1050x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "array([  20,   43,   68,   95,  123,  152,  182,  213,  250,  285,  332,\n",
       "        379,  435,  516,  776,  865,  925,  970, 1012, 1054, 1085, 1118,\n",
       "       1146, 1176, 1202, 1227, 1255, 1278, 1300, 1324, 1344, 1365, 1387,\n",
       "       1406, 1426, 1445, 1464, 1482, 1499, 1518, 1534, 1552, 1569, 1586,\n",
       "       1601, 1618, 1634, 1648, 1665, 1681, 1695])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "intensity = np.mean(face_intensity, axis=0)\n",
    "\n",
    "peaks, _ = find_peaks(intensity, distance=10, prominence=0.03)\n",
    "lows, _ = find_peaks(-intensity, distance=10, prominence=0.03)\n",
    "\n",
    "plt.plot(intensity)\n",
    "plt.plot(peaks, intensity[peaks], \"x\")\n",
    "plt.plot(lows, intensity[lows], \"x\")\n",
    "plt.show()\n",
    "peaks"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "06b6d86d-1b38-4f92-9a63-d0330ed68214",
   "metadata": {},
   "source": [
    "Выбираем лучшую половину и нормируемся по центру **(между двумя светлыми участками)**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "05115497-10d8-42f0-b110-d92d5a927078",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 1050x175 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "half_peaks = peaks[13:]\n",
    "half_lows = lows[13:]\n",
    "half_lows[0] = (half_peaks[1] + half_peaks[0]) / 2\n",
    "half_peaks = half_peaks[1:]\n",
    "\n",
    "# нормировка\n",
    "real_dist = 2*(482-424)*k[0] # мкм\n",
    "idx_dist = 786 - 526\n",
    "step = real_dist / idx_dist\n",
    "start = half_lows[0]\n",
    "half_peaks = (half_peaks - start) * step\n",
    "half_lows = (half_lows - start) * step\n",
    "\n",
    "plt.figure(figsize=(6,1))\n",
    "plt.scatter(half_peaks, np.ones(len(half_peaks)), s=4)\n",
    "plt.scatter(half_lows, np.ones(len(half_lows)), s=1)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "071fdd2b-4a7f-472e-b008-f2062ecb9c14",
   "metadata": {},
   "source": [
    "Квадрат расстояния в зависимости от пор. номера для светлых и тёмных"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "2b31d75b-c7bf-4c9b-99c2-d1ec456974a9",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 1050x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "peaks_sqr = half_peaks**2\n",
    "lows_sqr = half_lows**2\n",
    "m_peaks = np.arange(0, len(half_peaks))\n",
    "m_lows = np.arange(0, len(half_lows))\n",
    "\n",
    "plt.plot(m_lows, lows_sqr, label='Тёмные кольца (по фото)', marker='.')\n",
    "plt.plot(m_peaks, peaks_sqr, label='Светлые кольца (по фото)', marker='.')\n",
    "\n",
    "plt.ylabel('Квадрат расстояния до центра, $\\\\text{мкм}^2$')\n",
    "plt.xlabel('Порядковый номер m')\n",
    "plt.legend()\n",
    "\n",
    "plt.savefig(r'images/dark-light-from-photo.svg')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "51b7efd4-86e9-4b51-b25e-e66af3374cf0",
   "metadata": {},
   "source": [
    "Аппроксимация прямой"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "b0c96924-2496-4418-a2cb-87b48d806f16",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 1050x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "([lR1], [[lR1_err]]) = curve_fit(line1, m_lows, lows_sqr)\n",
    "\n",
    "plt.fill_between(m_lows, line1(m_lows, lR1+lR1_err), line1(m_lows, lR1-lR1_err), alpha=.15, edgecolor='none', label='Случайная ошибка')\n",
    "plt.plot(m_lows, line1(m_lows, lR1), alpha=.65, label=r'Аппроксимация $r=\\sqrt{m\\lambda R}$', color='C0')\n",
    "plt.errorbar(m_lows, lows_sqr, label='Анализ фото эксперимента', marker='.', linestyle='none', color='C0')\n",
    "\n",
    "plt.ylabel('Квадрат расстояния до центра, $\\\\text{мкм}^2$')\n",
    "plt.xlabel('Порядковый номер m')\n",
    "plt.legend()\n",
    "\n",
    "plt.savefig(r'images/dark-approx-photo.svg')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "074ea32d-9071-445e-8f10-a20fccc8fc67",
   "metadata": {},
   "source": [
    "Расчёт кривизны и погрешности"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "5cf756f7-bfc5-422c-9d23-ec525ee4c45d",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R = 9.75 +- 0.06 мм\n"
     ]
    }
   ],
   "source": [
    "R = lR1 / lamb\n",
    "R_err = np.sqrt((1/lamb)**2 * lR1_err**2)\n",
    "print(f'R = {R*1e-3:.3} +- {R_err*1e-3:.1} мм')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "846a4de2-93a9-4f5b-8780-42c74c75b45a",
   "metadata": {},
   "source": [
    "## Биения"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "da401148-6fee-42c2-b359-2ed82281f486",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 1050x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "face = im.imread('images/bieniya.png')\n",
    "face = face[705:735,:]\n",
    "\n",
    "face_intensity = rgb2gray(face)\n",
    "face_intensity = ndimage.median_filter(face_intensity, 4)\n",
    "\n",
    "plt.imshow(face_intensity)\n",
    "plt.axis('off')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "412e0b45-c42a-4077-90ce-c2aff9900023",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 1050x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "intensity = np.mean(face_intensity, axis=0)\n",
    "x = np.linspace(0, 1000, len(intensity))\n",
    "plt.plot(x, intensity*(1e-6*(x-400)**2+1))\n",
    "# plt.plot(x, 1e-11*(x-400)**4+1)\n",
    "plt.xlabel('Радиус в безразмерных координатах')\n",
    "plt.ylabel('Интенсивность')\n",
    "\n",
    "plt.savefig(r'images/bieniya_plot.svg')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "6694fc26-4fc5-497d-b7ec-0412cebc8bb3",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 1575x350 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "wl1 = 577 # нм\n",
    "wl2 = 546 # нм\n",
    "nu = lambda wl : (3e8 * 1e9 / wl)\n",
    "\n",
    "t = np.linspace(0, 7e-14, 1000) - 1.2e-14\n",
    "w1 = np.sin(2*np.pi*nu(wl1)*t)\n",
    "w2 = np.sin(2*np.pi*nu(wl2)*t)\n",
    "\n",
    "plt.figure(figsize=(9,2))\n",
    "plt.plot(t, w1, alpha=.2, color='yellow', label='Первая волна')\n",
    "plt.plot(t, w2, alpha=.2, color='green', label='Вторая волна')\n",
    "plt.plot(t, (w1 + w2)/2, label='Сумма')\n",
    "plt.axis('off')\n",
    "\n",
    "plt.legend(frameon=True)\n",
    "plt.savefig('images/bieniya_scheme.svg')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "19a36a5f-d7ad-4720-9d5c-cbaecaf59636",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Разность длин волн, полученная при наблюдении биений"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "1e23db39-56a0-4a1d-b2bf-8d77f6d7e8d8",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "33.94117647058823"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "delta = 1/17 * lamb * 1e3 # нм\n",
    "delta"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5b220cdb-e764-443e-9f18-834132439e24",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true,
    "tags": []
   },
   "source": [
    "## Лабораторный журнал"
   ]
  },
  {
   "cell_type": "raw",
   "id": "87504042-278d-43b9-82d3-e10518bd63f1",
   "metadata": {
    "tags": []
   },
   "source": [
    "81 (темн)\n",
    "84 (светл)\n",
    "89\n",
    "94\n",
    "98\n",
    "\n",
    "104\n",
    "108\n",
    "113\n",
    "119\n",
    "124\n",
    "128\n",
    "132\n",
    "137\n",
    "144\n",
    "154\n",
    "160\n",
    "165\n",
    "171\n",
    "177\n",
    "183\n",
    "189\n",
    "195\n",
    "\n",
    "202\n",
    "209\n",
    "214\n",
    "222\n",
    "229\n",
    "236\n",
    "243\n",
    "252\n",
    "259\n",
    "268\n",
    "276\n",
    "285\n",
    "295\n",
    "\n",
    "303\n",
    "313\n",
    "324\n",
    "336\n",
    "351\n",
    "364\n",
    "378\n",
    "399 (темн)\n",
    "\n",
    "424 (светл) -- последнее кольцо\n",
    "далее тёмное пятно радиусом 435\n",
    "центр -- 482"
   ]
  },
  {
   "cell_type": "raw",
   "id": "a9da40b4-e2b7-4943-9f76-098686d1824c",
   "metadata": {
    "tags": []
   },
   "source": [
    "14 полос между системами"
   ]
  }
 ],
 "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": "3.10.10"
  },
  "varInspector": {
   "cols": {
    "lenName": 16,
    "lenType": 16,
    "lenVar": 40
   },
   "kernels_config": {
    "python": {
     "delete_cmd_postfix": "",
     "delete_cmd_prefix": "del ",
     "library": "var_list.py",
     "varRefreshCmd": "print(var_dic_list())"
    },
    "r": {
     "delete_cmd_postfix": ") ",
     "delete_cmd_prefix": "rm(",
     "library": "var_list.r",
     "varRefreshCmd": "cat(var_dic_list()) "
    }
   },
   "types_to_exclude": [
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