For spectras.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import csv\n",
    "import os\n",
    "from matplotlib import pyplot as plt\n",
    "from sklearn import mixture\n",
    "from scipy.signal import savgol_filter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "def calc(filepath):\n",
    "#     for root, dirs, files in os.walk(filepath):\n",
    "#         for file in files:\n",
    "#             if os.path.splitext(file)[0] == 'Sample Table':\n",
    "#                 df_samples = pd.read_csv(root + '\\\\' + file, engine = 'python', sep = ';')\n",
    "    \n",
    "    new_folder = filepath + r'\\Calculated'\n",
    "    try:\n",
    "        os.makedirs(new_folder)\n",
    "    except:\n",
    "        pass\n",
    "    \n",
    "#     df_result = df_samples\n",
    "    A550 = []\n",
    "    T550 = []\n",
    "    Thickness550 = []\n",
    "#     df_result.drop(columns = [' Description', ' Type', ' CBDep'], inplace = True)\n",
    "#     for item in df_samples['Sample ID']:\n",
    "    for root, dirs, files in os.walk(filepath):\n",
    "        for file in files:\n",
    "            if '.Sample.Raw.csv' in file:\n",
    "                fig,ax = plt.subplots(figsize = (6,6))\n",
    "                df = pd.read_csv(root + '\\\\' + file, engine = 'python', sep = ';')\n",
    "                df.columns = ['nm', 'A']\n",
    "                df.query('nm >= 200 & nm <= 2500', inplace = True)\n",
    "                pd.DataFrame.reset_index(df, drop = True, inplace = True)\n",
    "                q = df['A'].iloc[0]\n",
    "                df.query(\"A >= @q\", inplace = True)\n",
    "                pd.DataFrame.reset_index(df, drop = True, inplace = True)\n",
    "                df.columns = ['Wavelength,nm', 'A,a.u.']\n",
    "                diff_1800 = df['A,a.u.'].iloc[df.index[df['Wavelength,nm'] == 1800].tolist()[0]] - df['A,a.u.'].iloc[df.index[df['Wavelength,nm'] == 1802].tolist()[0]]\n",
    "                for i in range (0, df.index[df['Wavelength,nm'] == 1800].tolist()[0]):\n",
    "                    df['A,a.u.'].iloc[i] += diff_1800\n",
    "                diff_860 = df['A,a.u.'].iloc[df.index[df['Wavelength,nm'] == 860].tolist()[0]] - df['A,a.u.'].iloc[df.index[df['Wavelength,nm'] == 862].tolist()[0]]\n",
    "                for i in range (0, df.index[df['Wavelength,nm'] == 860].tolist()[0]):\n",
    "                    df['A,a.u.'].iloc[i] += diff_860\n",
    "                add = np.array([item/np.max(df['A,a.u.']) for item in df['A,a.u.']])\n",
    "                df['Normalized A, a.u.'] = add\n",
    "                add = np.array([10**(-item)*100 for item in df['A,a.u.']])\n",
    "                df['T,%'] = add\n",
    "                df['A,a.u.'] = savgol_filter(df['A,a.u.'], window_length = 75, polyorder = 5)\n",
    "                A550.append(df['A,a.u.'].iloc[df.index[df['Wavelength,nm'] == 550].tolist()[0]])\n",
    "                T550.append(df['T,%'].iloc[df.index[df['Wavelength,nm'] == 550].tolist()[0]])\n",
    "                Thickness550.append(417*df['A,a.u.'].iloc[df.index[df['Wavelength,nm'] == 550].tolist()[0]])\n",
    "                pd.DataFrame.to_csv(df, path_or_buf = new_folder + '//' + file.replace('Raw', 'Calc'), sep = ';', header = True, index = False)\n",
    "                plt.plot(df['Wavelength,nm'], df['A,a.u.'], label = os.path.splitext(file)[0].replace('.Sample.Raw', ''))\n",
    "                plt.xlabel('\\u03BB, nm')\n",
    "                plt.ylabel('A,a.u.')\n",
    "                plt.title(os.path.splitext(file)[0].replace('.Sample.Raw', ''))\n",
    "                fig.tight_layout(pad = 1)\n",
    "                fig.savefig((new_folder + '//' + os.path.splitext(file)[0].replace('.Sample.Raw', '') + '.png'), format = 'png', dpi = 600)\n",
    "                plt.clf()\n",
    "                plt.close(fig)\n",
    "#     df_result['A550,a.u.'] = A550\n",
    "#     df_result['T550,%'] = T550\n",
    "#     df_result['Thickness'] = Thickness550\n",
    "#     pd.DataFrame.to_csv(df_result, path_or_buf = new_folder + '//' + 'Result.csv', sep = ';', header = True, index = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = calc(r'C:\\Users\\kpebe\\OneDrive\\Рабочий стол\\Optics\\Pristine regimes 1-5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x14cc6d1f388>]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(df['Wavelength,nm'], df['A,a.u.'])\n",
    "# df.index[df['Wavelength,nm'] == 2200]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv(r'C:\\Users\\kpebe\\OneDrive\\Рабочий стол\\Grigoriy\\600C\\Calculated\\\\Pristine 10 min.Sample.Calc.csv', engine = 'python', sep = ';')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.drop(columns = ['Normalized A, a.u.', 'T, %'], inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "estimator = mixture.GaussianMixture(n_components = 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "estimator_bayes = mixture.BayesianGaussianMixture(n_components = 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_labels = np.reshape(np.array(data['Wavelength, nm']), (-1,1))\n",
    "train_data = np.reshape(np.array(data['A, a.u.']), (-1,1))\n",
    "arr = np.transpose(np.vstack((train_labels, train_data)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred = estimator.fit(X = train_data, y = train_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "k = pred.sample(n_samples = 1151)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.02043971],\n",
       "       [0.02590892],\n",
       "       [0.04711531],\n",
       "       ...,\n",
       "       [0.10083384],\n",
       "       [0.0865148 ],\n",
       "       [0.07484596]])"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "k[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = np.linspace(200,2500,2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "Expected 2D array, got 1D array instead:\narray=[ 200. 2500.].\nReshape your data either using array.reshape(-1, 1) if your data has a single feature or array.reshape(1, -1) if it contains a single sample.",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-43-b5619c284c29>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0my\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mexp\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpred\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mscore\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m~\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\mixture\\base.py\u001b[0m in \u001b[0;36mscore\u001b[1;34m(self, X, y)\u001b[0m\n\u001b[0;32m    357\u001b[0m             \u001b[0mLog\u001b[0m \u001b[0mlikelihood\u001b[0m \u001b[0mof\u001b[0m \u001b[0mthe\u001b[0m \u001b[0mGaussian\u001b[0m \u001b[0mmixture\u001b[0m \u001b[0mgiven\u001b[0m \u001b[0mX\u001b[0m\u001b[1;33m.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    358\u001b[0m         \"\"\"\n\u001b[1;32m--> 359\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mscore_samples\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmean\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    360\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    361\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mpredict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mX\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\mixture\\base.py\u001b[0m in \u001b[0;36mscore_samples\u001b[1;34m(self, X)\u001b[0m\n\u001b[0;32m    339\u001b[0m         \"\"\"\n\u001b[0;32m    340\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_check_is_fitted\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 341\u001b[1;33m         \u001b[0mX\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_check_X\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmeans_\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    342\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    343\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mlogsumexp\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_estimate_weighted_log_prob\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\mixture\\base.py\u001b[0m in \u001b[0;36m_check_X\u001b[1;34m(X, n_components, n_features, ensure_min_samples)\u001b[0m\n\u001b[0;32m     53\u001b[0m     \"\"\"\n\u001b[0;32m     54\u001b[0m     X = check_array(X, dtype=[np.float64, np.float32],\n\u001b[1;32m---> 55\u001b[1;33m                     ensure_min_samples=ensure_min_samples)\n\u001b[0m\u001b[0;32m     56\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mn_components\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0mX\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m<\u001b[0m \u001b[0mn_components\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     57\u001b[0m         raise ValueError('Expected n_samples >= n_components '\n",
      "\u001b[1;32m~\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py\u001b[0m in \u001b[0;36mcheck_array\u001b[1;34m(array, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, warn_on_dtype, estimator)\u001b[0m\n\u001b[0;32m    550\u001b[0m                     \u001b[1;34m\"Reshape your data either using array.reshape(-1, 1) if \"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    551\u001b[0m                     \u001b[1;34m\"your data has a single feature or array.reshape(1, -1) \"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 552\u001b[1;33m                     \"if it contains a single sample.\".format(array))\n\u001b[0m\u001b[0;32m    553\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    554\u001b[0m         \u001b[1;31m# in the future np.flexible dtypes will be handled like object dtypes\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mValueError\u001b[0m: Expected 2D array, got 1D array instead:\narray=[ 200. 2500.].\nReshape your data either using array.reshape(-1, 1) if your data has a single feature or array.reshape(1, -1) if it contains a single sample."
     ]
    }
   ],
   "source": [
    "y = np.exp(pred.score(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.random.seed(2)\n",
    "x = np.concatenate([np.random.normal(0,2,2000), np.random.normal(5,5,2000), np.random.normal(3,0.5,600)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-0.83351569 -0.11253365 -4.27239219 ...  3.3388401   2.95246163\n",
      "  2.55136449]\n"
     ]
    }
   ],
   "source": [
    "print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1de690184a8>]"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\kpebe\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\scipy\\optimize\\minpack.py:447: RuntimeWarning: Number of calls to function has reached maxfev = 2200.\n",
      "  warnings.warn(errors[info][0], RuntimeWarning)\n",
      "C:\\Users\\kpebe\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\scipy\\optimize\\minpack.py:447: RuntimeWarning: Number of calls to function has reached maxfev = 2800.\n",
      "  warnings.warn(errors[info][0], RuntimeWarning)\n",
      "C:\\Users\\kpebe\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\scipy\\optimize\\minpack.py:447: RuntimeWarning: Number of calls to function has reached maxfev = 3400.\n",
      "  warnings.warn(errors[info][0], RuntimeWarning)\n",
      "C:\\Users\\kpebe\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\scipy\\optimize\\minpack.py:447: RuntimeWarning: Number of calls to function has reached maxfev = 4000.\n",
      "  warnings.warn(errors[info][0], RuntimeWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 1.07897485e+00  2.34365500e+03 -8.36725252e-01  9.76275123e+02\n",
      "  1.12009514e+03 -1.79201726e-01 -2.49008088e+02  4.34599739e+01\n",
      "  1.09399994e+01  1.07868401e+02  2.03977921e+02 -1.08802123e+01\n",
      " -1.86258701e+01  2.44856904e+02 -2.91682116e-01  1.53174544e+01\n",
      " -5.95546369e+02 -9.59589263e+00  3.92190415e+02  2.04675100e+02\n",
      "  9.09202234e+00  1.66430964e+01]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\kpebe\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\scipy\\optimize\\minpack.py:447: RuntimeWarning: Number of calls to function has reached maxfev = 4600.\n",
      "  warnings.warn(errors[info][0], RuntimeWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from scipy.optimize import leastsq\n",
    "\n",
    "def lorentzian( x, x0, a, gam ):\n",
    "    return a * gam**2 / ( gam**2 + ( x - x0 )**2)\n",
    "\n",
    "def multi_lorentz( x, params ):\n",
    "    off = params[0]\n",
    "    paramsRest = params[1:]\n",
    "    assert not ( len( paramsRest ) % 3 )\n",
    "    return off + sum( [ lorentzian( x, *paramsRest[ i : i+3 ] ) for i in range( 0, len( paramsRest ), 3 ) ] )\n",
    "\n",
    "def res_multi_lorentz( params, xData, yData ):\n",
    "    diff = [ multi_lorentz( x, params ) - y for x, y in zip( xData, yData ) ]\n",
    "    return diff\n",
    "\n",
    "xData = np.array(data['Wavelength, nm'])\n",
    "yData = np.array(data['A, a.u.']) / max(np.array(data['A, a.u.']))\n",
    "\n",
    "generalWidth = 1\n",
    "\n",
    "yDataLoc = yData\n",
    "startValues = [ max( yData ) ]\n",
    "counter = 0\n",
    "\n",
    "while max( yDataLoc ) - min( yDataLoc ) > .1:\n",
    "    counter += 1\n",
    "    if counter > 50: ### max 20 peak...emergency break to avoid infinite loop\n",
    "        break\n",
    "    minP = np.argmin( yDataLoc )\n",
    "    minY = yData[ minP ]\n",
    "    x0 = xData[ minP ]\n",
    "    startValues += [ x0, minY - max( yDataLoc ), generalWidth ]\n",
    "    popt, ier = leastsq( res_multi_lorentz, startValues, args=( xData, yData ) )\n",
    "    yDataLoc = [ y - multi_lorentz( x, popt ) for x,y in zip( xData, yData ) ]\n",
    "\n",
    "print(popt)\n",
    "testData = [ multi_lorentz(x, popt ) for x in xData ]\n",
    "\n",
    "fig = plt.figure()\n",
    "ax = fig.add_subplot( 1, 1, 1 )\n",
    "ax.plot( xData, yData )\n",
    "ax.plot( xData, testData )\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1de41ae5160>]"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(data['Wavelength, nm'][1:], np.diff(data['A, a.u.'], n = 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1de382415f8>]"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(data['Wavelength, nm'][:],x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.signal import savgol_filter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = savgol_filter(data['A, a.u.'], window_length = 51, polyorder = 10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1de381de358>]"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(data['Wavelength, nm'][1:], np.diff(x, n = 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n"
     ]
    }
   ],
   "source": [
    "i = 0\n",
    "for item in np.diff(x, n = 1):\n",
    "    if item == 0:\n",
    "        i+=1\n",
    "print (i)\n",
    "i = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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