optimized.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Для начала считаем данные в массивы `x` и `y` и построим график"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "with open('report-80.xy', 'r') as f:\n",
    "    lines = f.readlines()\n",
    "    x = [float(n.split()[0]) for n in lines]\n",
    "    y = [float(n.split()[1]) for n in lines]\n",
    "    \n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.figure(figsize=(15,5))\n",
    "plt.plot(x, y, color='r')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Если посмотреть на левую часть графика, то видно, что она чуть задрана. Также, пики распространяются не от нуля, а от какого-то базового значения. И это значение не константное, а тоже подвержено какому-то закону. Выводить этот закон аналитически - это отдельная задача. Но можно оценить его численно как локальный минимум в некоторой окрестности. Константу радиуса окрестности выбирал так, чтобы график тренда смотрелся гладким, но при этом не был недооценённым. Именно поэтому левый хвост я сглаживаю с меньшей окрестностью."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "around1 = len(x) // 100\n",
    "around2 = len(x) // 10\n",
    "border = len(x) // 5\n",
    "\n",
    "def get_slice(y, i, around):\n",
    "    return y[max(0, i-around):i+around]\n",
    "\n",
    "trend = np.array(\n",
    "            [min(get_slice(y, i, around1)) for i in range(border)] +\n",
    "            [min(get_slice(y, i, around2)) for i in range(border, len(x))]\n",
    "        )\n",
    "\n",
    "plt.figure(figsize=(15,5))\n",
    "plt.plot(x, trend, color='r')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Вычтем тренд из данных."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from numpy import histogram\n",
    "from random import gauss\n",
    "\n",
    "y_base = np.array(y) - trend\n",
    "\n",
    "plt.figure(figsize=(15,5))\n",
    "plt.plot(x, y_base, color='r')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Опишем процедуру оценки расположения и оценки величин пиков.\n",
    "Пусть есть некоторая функциональная зависимость `y(x)` которая по гипотезе представляет собой сумму Гауссианов.\n",
    "нужно предложить метод оптимизации, который позволит восстановить число этих Гауссианов и их параметры. Это в общем случае называется **обратной задачей**.\n",
    "\n",
    "*Предпосылка*. В статистике есть модель случайного процесса, которая называется [Mixture Model](https://en.wikipedia.org/wiki/Mixture_model) - когда есть популяция, составленная из нескольких различных групп. Параметры каждой их которых можно описать в виде нормального распределения (или распределений, если нас интересует несколько параметров). У нас в начале есть не группы, случайная выборка `Y` для популяции в целом. Например, выборка роста людей вне зависимости от пола. Задача - разделить эти группы на уровне модели, т.е. восстановить параметры нормальных распределений. Часто решение такой задачи осуществляется с помощью [EM-алгоритма](https://ru.wikipedia.org/wiki/EM-%D0%B0%D0%BB%D0%B3%D0%BE%D1%80%D0%B8%D1%82%D0%BC).\n",
    "\n",
    "*Гипотеза*. Процесс анализа хроматограмм фактически сводится к анализу **гистограммы** (т.е. приближения функции распределения) такого случайного процесса.\n",
    "\n",
    "*Метод*. \n",
    "1. По гистограмме сгеренируем достаточно большую случайную выборку, которая будет обладать свойствами, сходными с неизвестным нам процессом.\n",
    "2. К этой выборке применим EM-алгоритм для определения параметров смешанных нормальных распределений.\n",
    "3. Оценим качество модели каким-то подходящим способом. Сейчас я сделал СКО (RMSE), но, кажется, для физхимических процессов лучше применять априорное знание о составе смеси."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "# https://scikit-learn.org/stable/modules/generated/sklearn.mixture.GaussianMixture.html\n",
    "from sklearn.mixture import GaussianMixture as gmm\n",
    "from scipy.stats import norm\n",
    "\n",
    "def process_data(x, y, N_SAMPLES=200000, N_PEAKS=10, CONV_ERROR=1e-5):\n",
    "    # нарисуем y\n",
    "    print(\"Исходные данные\")\n",
    "    plt.figure(figsize=(15,1))\n",
    "    plt.plot(x, y, color='r')\n",
    "    plt.show()\n",
    "\n",
    "    # почитаем полезные величины\n",
    "    min_x = min(x)\n",
    "    width_x = max(x) - min(x)\n",
    "    weight_y = sum(y)\n",
    "   \n",
    "    # отнормируем данные, чтобы интеграл по ним был равен 1.\n",
    "    probs = np.array(y) / weight_y\n",
    "      \n",
    "    # нарисуем распределение\n",
    "    print(\"Распределение на основе исходных данных\")\n",
    "    plt.figure(figsize=(15,1))\n",
    "    plt.plot(x, probs, color='r')\n",
    "    plt.show()\n",
    "    \n",
    "    # сгенерируем достаточно большую выборку и проверим, что её гистограмма похожа на оригинальное распределение\n",
    "    samples = np.random.choice(len(probs), size=N_SAMPLES, p=probs)\n",
    "    print(\"Гистограмма сгенерированной выборки\")\n",
    "    plt.figure(figsize=(15,1))\n",
    "    plt.hist(samples, bins=len(probs))\n",
    "    plt.show()\n",
    "    \n",
    "    # обучим модель\n",
    "    model = gmm(\n",
    "        n_components=N_PEAKS,      # число гауссиан\n",
    "        covariance_type=\"diag\",    # какие ковариации считать. Тут - только диагональные, которые, по сути, дисперсия.\n",
    "        init_params=\"kmeans\",      # как будут проинициализированы исходные значения модели.\n",
    "        tol=CONV_ERROR,            # параметр сходимости метода\n",
    "        max_iter=200               # максимум итераций\n",
    "    )\n",
    "    model.fit(samples.reshape((-1, 1)))\n",
    "    print(\"Модель сошлась за {} итераций\".format(model.n_iter_))\n",
    "    \n",
    "    # соберём все результаты в тройки (μ, σ, w)\n",
    "    result = list(\n",
    "            zip(\n",
    "                model.means_.T[0],                           # μ - матожидание\n",
    "                [v ** .5 for v in model.covariances_.T[0]],  # σ - корень из дисперсии\n",
    "                model.weights_,                              # w - коэффициент пропорциональности\n",
    "            )\n",
    "        )\n",
    "\n",
    "    # приведём эти данные к масштабу исходных данных\n",
    "    result_original = [\n",
    "        (\n",
    "            μ * width_x / len(x) + min_x, \n",
    "            σ * width_x / len(x), \n",
    "            w * width_x / len(x) * weight_y # новый коэффициент пропорциональности фактически равен площади под кривой\n",
    "        )\n",
    "        for (μ, σ, w) in result\n",
    "    ]\n",
    "    \n",
    "    # отсортируем по площади\n",
    "    result_original.sort(reverse=True, key=lambda x: x[2])\n",
    "    \n",
    "    # построим график по модели\n",
    "    y_predicted = [0 for _ in x]\n",
    "    for μ, σ, w in result_original:\n",
    "        for i in range(len(y_predicted)):\n",
    "            y_predicted[i] += norm.pdf(x[i], loc=μ, scale=σ) * w\n",
    "    \n",
    "    print(\"Данные, полученные от модели\")\n",
    "    plt.figure(figsize=(15,2))\n",
    "    plt.plot(x, y)\n",
    "    plt.plot(x, y_predicted)\n",
    "    plt.show()\n",
    "    \n",
    "    return result_original"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Применим сначала к красивому кусочку."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ищем 6 компонентов\n",
      "Исходные данные\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x72 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Распределение на основе исходных данных\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x72 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Гистограмма сгенерированной выборки\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x72 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Модель сошлась за 37 итераций\n",
      "Данные, полученные от модели\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x144 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(43.3915544362856, 0.05089330468059562, 3448.2234955514605)\n",
      "(43.55520949428404, 0.04266846841378224, 812.6037847214013)\n",
      "(42.82898904408771, 0.046534172824533586, 510.8699510869362)\n",
      "(43.64544807236264, 0.8857910002978131, 476.6293226640239)\n",
      "(43.10706101837869, 0.04906673876608332, 353.47994722582297)\n",
      "(45.647323477770406, 0.22757572223783853, 76.36397874629039)\n",
      "Ищем 10 компонентов\n",
      "Исходные данные\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x72 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Распределение на основе исходных данных\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x72 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Гистограмма сгенерированной выборки\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x72 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Модель сошлась за 60 итераций\n",
      "Данные, полученные от модели\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x144 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(43.40594516541748, 0.03766162137173356, 1947.1167705372472)\n",
      "(43.36102004088411, 0.050912620469578763, 1408.5803572725881)\n",
      "(43.545098320482246, 0.048991057943106424, 950.807517852344)\n",
      "(42.8297598623491, 0.04685000380599711, 503.49942501966075)\n",
      "(43.1041082175233, 0.05171747196035608, 383.5238115604638)\n",
      "(43.740776442592, 0.2944531375755815, 152.73054763722666)\n",
      "(42.59974983139729, 0.327370856254493, 130.61858930535405)\n",
      "(44.701446947291956, 0.2928020590250831, 98.77508459319665)\n",
      "(45.672614120239835, 0.205132966275676, 73.3538275013956)\n",
      "(45.174000066336106, 0.31507791011681063, 29.16454872132851)\n"
     ]
    }
   ],
   "source": [
    "# из-за случайности в инициализации весов в общем случае решение не устойчивое\n",
    "print(\"Ищем 6 компонентов\")\n",
    "res = process_data(x[1600:1800], y_base[1600:1800], N_PEAKS=6)\n",
    "print(*res, sep='\\n')\n",
    "print(\"Ищем 10 компонентов\")\n",
    "res = process_data(x[1600:1800], y_base[1600:1800], N_PEAKS=10)\n",
    "print(*res, sep='\\n')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Видно, что при увеличении числа гауссианов модель начинает превращать один пик в \"стопку\" из нескольких очень близких. Поэтому выбор изначального числа пиков должен быть обоснован. Есть два предложения:\n",
    "1. Делать сразу заведомо большее число пиков, а затем их кластеризовать и склеивать автоматически.\n",
    "2. Вычислять пачку локальных максимумов, превосходящих какой-то заданный порог (сообразный эксперименту) и применять метод уже исходя из предпосчитанного числа пиков.\n",
    "\n",
    "Ну а теперь на всех данных."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ищем 100 компонентов\n",
      "Исходные данные\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x72 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Распределение на основе исходных данных\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x72 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Гистограмма сгенерированной выборки\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x72 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Модель сошлась за 12 итераций\n",
      "Данные, полученные от модели\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x144 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(43.38699538412445, 0.0487288716393932, 3300.498460635456)\n",
      "(43.539509158236086, 0.05804675256118325, 1032.2018361423727)\n",
      "(42.82555443320082, 0.04741527398489642, 515.975401764612)\n",
      "(79.6263817557616, 0.07988620439544417, 412.75705591774033)\n",
      "(43.107703388331046, 0.057631251545255884, 394.4678487070145)\n",
      "(36.25046714233618, 0.10932926442742098, 367.5445555327597)\n",
      "(79.85742481554733, 0.07968783253190112, 333.5430084630095)\n",
      "(90.13334680652015, 0.11242084036438114, 300.4859754020603)\n",
      "(63.104980684967, 0.0970031191305584, 276.97767340792603)\n",
      "(74.27419244507088, 0.12887831738960479, 258.8383096449169)\n",
      "(47.65994060374045, 0.1294365204991269, 221.56401779723947)\n",
      "(48.35982012548176, 0.05974389385734986, 212.7690272998354)\n",
      "(35.113282373799265, 0.04238484447498821, 202.34213204099083)\n",
      "(62.87188653006325, 0.09802562313222418, 201.4725445486924)\n",
      "(89.56724183026195, 0.12233284975667355, 192.86933254817046)\n",
      "(31.764569112632383, 0.0844715696949515, 188.00746405942672)\n",
      "(50.54957566481957, 0.19305309263385315, 176.60259006428748)\n",
      "(72.59097156883143, 0.29470922223410656, 171.67448871654236)\n",
      "(34.41352902446779, 0.0955117904996431, 169.24360350851944)\n",
      "(44.254546306704185, 0.6996374111979474, 167.95583134023323)\n",
      "(35.84439621111498, 0.07001410737626945, 167.41762034225798)\n",
      "(75.6792383349643, 0.3149908754562273, 161.90810213272528)\n",
      "(56.57648357470734, 0.14385400321000466, 161.37567074064415)\n",
      "(34.160831021708475, 1.4199789171542052, 147.83784948653945)\n",
      "(73.61386683921278, 0.1808438555103355, 144.15222975086783)\n",
      "(37.99882487927003, 0.20896396288865193, 140.54284666125702)\n",
      "(37.4728062853358, 0.36775769144868586, 138.82221398793632)\n",
      "(80.61055946274676, 0.39928808364719565, 135.20753267493083)\n",
      "(67.95028790108486, 0.20201632190300564, 130.72602031946198)\n",
      "(79.15994187972282, 0.13261361580146694, 129.79702728047036)\n",
      "(95.38561428022662, 0.12717222654564736, 124.38854990601311)\n",
      "(48.236880158163544, 0.0662301756264249, 114.48982673359934)\n",
      "(52.84705424978361, 0.05938106351385037, 111.9283368500506)\n",
      "(54.31437663239771, 1.5177945725255775, 104.78348768029655)\n",
      "(76.17944448355253, 0.18800880129322187, 104.73644473388329)\n",
      "(78.1284877659095, 0.3437939318471032, 103.65621960188282)\n",
      "(52.23203163039193, 0.22578210609697308, 101.97544325852996)\n",
      "(64.49964126884915, 0.47753740529915545, 100.97320702631148)\n",
      "(72.18707402088391, 0.23170872873140325, 96.48048904404371)\n",
      "(99.22295303170793, 0.25489890445985924, 96.45901095680844)\n",
      "(86.20252171921675, 0.17641740009043894, 93.3381011656479)\n",
      "(62.460815258300684, 0.2080617313590988, 90.08052717318785)\n",
      "(91.69129084379476, 0.2532537638321693, 87.47894702659667)\n",
      "(95.84150303043675, 0.104717207081116, 84.52461594030895)\n",
      "(46.85026741446846, 0.49689218644704763, 78.3557471897186)\n",
      "(81.48020410127462, 0.4190821437339022, 76.1410491308603)\n",
      "(45.87754292862043, 0.34563114097635705, 74.29218838869171)\n",
      "(42.5117179331899, 0.36713786977087653, 73.79929443750406)\n",
      "(69.02842977153813, 0.24120378281111943, 72.8968182127113)\n",
      "(57.74694342213678, 0.33466731593213633, 70.74500130291702)\n",
      "(92.82867062328127, 0.3017203288741273, 68.32578984845763)\n",
      "(57.045459214955656, 0.07822407069760913, 64.98147286496014)\n",
      "(88.52184908869913, 0.7452511628098178, 63.822375070490764)\n",
      "(98.40644201307333, 0.3162161664501259, 63.6051603617977)\n",
      "(93.7405852553425, 0.20705390417490238, 63.31094310253208)\n",
      "(10.588934308284578, 0.3565764075607109, 62.85862948771377)\n",
      "(77.0444241210755, 0.17154619414039676, 61.91510334014067)\n",
      "(61.1184629753558, 0.46256780884355486, 61.83762347530226)\n",
      "(11.578367558036671, 0.420070867317484, 61.397171482725426)\n",
      "(31.678980626154598, 0.32285210267863645, 55.96244368433295)\n",
      "(90.94474051662729, 0.4613715045654589, 52.78547101408536)\n",
      "(12.636786155273816, 0.4108461043055707, 52.69103838667846)\n",
      "(49.123307712496754, 0.6818931604883129, 51.8250150857723)\n",
      "(51.12599646266254, 0.235376317129672, 50.777530828264034)\n",
      "(14.547886776573055, 0.32886122160444653, 49.351650765838315)\n",
      "(24.780787998452873, 0.5679842181549896, 48.360742978305105)\n",
      "(66.66626786169044, 0.4860699708308057, 47.86771104064406)\n",
      "(65.86309003677493, 0.16073787188942504, 47.72811000463577)\n",
      "(96.47419243561752, 0.22584983685747367, 46.45517084085385)\n",
      "(26.810280307005375, 0.6366857416538011, 45.94408277415544)\n",
      "(15.612680191612146, 0.32782957055537215, 42.51326007209194)\n",
      "(59.63856997081325, 0.2383717411962383, 42.136952874711604)\n",
      "(85.23493988864278, 0.24465817473020596, 41.19034499506368)\n",
      "(97.46837814159576, 0.43373100259752867, 39.84177584936212)\n",
      "(86.9376003668615, 0.21399417042459795, 39.165441946105354)\n",
      "(94.75172099789056, 0.28123019131817417, 36.554013121297615)\n",
      "(38.715872344515255, 0.06959414506402932, 36.3585929102803)\n",
      "(13.684153721960747, 0.3594873523682392, 35.451318923630204)\n",
      "(28.400452642458188, 0.329968265709113, 35.394918008017754)\n",
      "(19.553191398668602, 0.4512140155297047, 34.801923153901775)\n",
      "(41.48644413157915, 0.3294304671969992, 33.48512000683023)\n",
      "(70.12970376619216, 0.364861615140418, 33.00281537675462)\n",
      "(23.058755119840345, 0.5465915984980678, 32.41346464258841)\n",
      "(16.71013769287395, 0.3951465700004391, 32.29652650322466)\n",
      "(82.57316914750528, 0.25821805970698264, 32.10366434455906)\n",
      "(21.820644930516103, 0.5185043951886629, 30.93490944855719)\n",
      "(71.26729600094744, 0.31390431474052644, 30.698842837039155)\n",
      "(39.59505064938482, 0.39711444242892213, 29.207403908104286)\n",
      "(84.18692434456457, 0.3950353326783068, 28.958741903522622)\n",
      "(17.609247587901166, 0.3955900756806707, 27.105337569788073)\n",
      "(18.325209341860774, 0.3360802835862788, 26.851757944953636)\n",
      "(20.662246695299434, 0.49632962238214035, 26.066120604222185)\n",
      "(64.93115421002443, 0.06219930207586016, 25.231063828039474)\n",
      "(76.64748478087385, 0.3027353539313606, 25.138611683582916)\n",
      "(30.554993516459614, 0.29922235192009505, 24.464655374264918)\n",
      "(40.60889139409645, 0.3193992164374411, 20.319806968539872)\n",
      "(58.9678296579787, 0.29334742478840165, 18.947726425085325)\n",
      "(29.49221757027401, 0.45463473078385364, 18.12179012800284)\n",
      "(65.34931129324366, 0.05385261074255143, 17.836815161658297)\n",
      "(83.08993029717, 0.19224616914823617, 16.522482173580183)\n"
     ]
    }
   ],
   "source": [
    "print(\"Ищем 100 компонентов\")\n",
    "res = process_data(x, y_base, N_PEAKS=100, N_SAMPLES=5000000, CONV_ERROR=1e-3)\n",
    "print(*res, sep='\\n')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}